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Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators. 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Packages produced are intended to be used with AnnotationDbi. 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Package: r-bioc-annotatr Architecture: all Version: 1.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4132 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-annotationhub, r-cran-dplyr, r-bioc-genomicfeatures, r-bioc-genomicranges, r-bioc-seqinfo, r-cran-ggplot2, r-bioc-iranges, r-cran-readr, r-bioc-regioner, r-cran-reshape2, r-cran-rlang, r-bioc-rtracklayer, r-bioc-s4vectors Suggests: r-bioc-genomeinfodb, r-bioc-biocstyle, r-cran-devtools, r-cran-knitr, r-bioc-org.dm.eg.db, r-bioc-org.gg.eg.db, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-org.rn.eg.db, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.dmelanogaster.ucsc.dm6.ensgene, r-bioc-txdb.drerio.ucsc.danrer10.refgene, r-bioc-txdb.drerio.ucsc.danrer11.refgene, r-bioc-txdb.ggallus.ucsc.galgal5.refgene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.mmusculus.ucsc.mm9.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-txdb.mmusculus.ucsc.mm39.knowngene, r-bioc-txdb.rnorvegicus.ucsc.rn4.ensgene, r-bioc-txdb.rnorvegicus.ucsc.rn5.refgene, r-bioc-txdb.rnorvegicus.ucsc.rn6.refgene, r-bioc-txdb.rnorvegicus.ucsc.rn7.refgene Filename: pool/dists/resolute/main/r-bioc-annotatr_1.38.0-1.ca2604.1_all.deb Size: 2973812 MD5sum: 6f8189b711e84c6614b4999446afca97 SHA1: c1a9f6f11a946194a40f6a7ef3af221a41248e10 SHA256: fe9d3acbc08f03030c1737576d649afeb2964ddf79e42e55f0464f9bc466a6d2 SHA512: cfbcf373a284fc11248d212326f150994aa680a85a7a5dae8fc0c071d265a738ef02be9b8cdcb64fd768b5f8471fb2c79b19f719c88e5b37252d6e32d90dbb58 Homepage: https://cran.r-project.org/package=annotatr Description: Bioc Package 'annotatr' (Annotation of Genomic Regions to Genomic Annotations) Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. Package: r-bioc-anvil Architecture: all Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4238 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-bioc-anvilbase, r-cran-futile.logger, r-bioc-gcptools, r-cran-jsonlite, r-cran-httr, r-cran-digest, r-cran-keyring, r-cran-rapiclient, r-cran-yaml, r-cran-tibble, r-cran-shiny, r-cran-dt, r-cran-miniui, r-cran-htmltools, r-bioc-biocbaseutils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-readr, r-bioc-biocstyle, r-cran-devtools, r-bioc-anvilaz, r-bioc-anvilgcp, r-cran-lifecycle Filename: pool/dists/resolute/main/r-bioc-anvil_1.24.0-1.ca2604.1_all.deb Size: 550214 MD5sum: 79767f0a4b20366ab2a0d89b79a7a5ef SHA1: 0c6aba36222b33ba2f935445b34f7e30aef7bfc1 SHA256: c18056686b0d247e8a770478e248028838155587a5654abc4158981af4d8ca3d SHA512: 4cee69d48d640cef72e892ca45d0652220b09983f48445f92b993ceec71d0a043fd75bf764a4d5d60f18aa2f08349a68e45ccfe4b760e6a3fc7cd3b63e2b5b22 Homepage: https://cran.r-project.org/package=AnVIL Description: Bioc Package 'AnVIL' (Bioconductor on the AnVIL compute environment) The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides programatic access to the Dockstore, Leonardo, Rawls, TDR, and Terra RESTful programming interfaces. For platform-specific user-level functionality, see either the AnVILGCP or AnVILAz package. 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Packages that use either GCP or Azure in AnVIL are built on top of AnVILBase. Extension packages will provide methods for interacting with other cloud providers. 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These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. 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Package: r-bioc-arrayqualitymetrics Architecture: all Version: 3.68.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1653 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-affy, r-bioc-affyplm, r-bioc-beadarray, r-bioc-biobase, r-bioc-genefilter, r-cran-gridsvg, r-cran-hmisc, r-cran-hwriter, r-cran-jsonlite, r-cran-lattice, r-cran-latticeextra, r-bioc-limma, r-cran-rcolorbrewer, r-cran-setrng, r-bioc-vsn, r-cran-xml, r-cran-svglite Suggests: r-bioc-allmll, r-bioc-ccl4, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-arrayqualitymetrics_3.68.0-1.ca2604.1_all.deb Size: 470786 MD5sum: b482cdd047f1baafd2e7d6db6958ee45 SHA1: 2ad08bac5e5f1de74f03958c84467441e7d6104a SHA256: a3f716810082f5f3fb9a3814088d8309be67beef5ef98693f05211600358f9a5 SHA512: 63228fe655819c96a1b4f4f69e68572362fa09d24ea335e0bcd61b4515b003f2ba024fa4873b50082ed8e2587d4d16abf7628db2c39abf02094ce8de7881be3b Homepage: https://cran.r-project.org/package=arrayQualityMetrics Description: Bioc Package 'arrayQualityMetrics' (Quality metrics report for microarray data sets) This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. Package: r-bioc-assorthead Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13170 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-assorthead_1.6.1-1.ca2604.1_all.deb Size: 1591232 MD5sum: 3a28aebacbc2a3624e82febf17e1207d SHA1: 59ea8ef80ba93c1f987e3be10d0aa118df19b1ba SHA256: 4fc993037b80348dba8f4cb233aac6d276ac0905429222077b36f4bb67787dc9 SHA512: 1879e9d93f900e1519c5ba7e1fb41fca371b69182d513a44c0c106aff5e57912a072d44aa801302f175726a4b9bfc760162fae34a1928ee96885b87131eaf70b Homepage: https://cran.r-project.org/package=assorthead Description: Bioc Package 'assorthead' (Assorted Header-Only C++ Libraries) Vendors an assortment of useful header-only C++ libraries. Bioconductor packages can use these libraries in their own C++ code by LinkingTo this package without introducing any additional dependencies. The use of a central repository avoids duplicate vendoring of libraries across multiple R packages, and enables better coordination of version updates across cohorts of interdependent C++ libraries. Package: r-bioc-atacseqqc Architecture: all Version: 1.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16468 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-chippeakanno, r-bioc-iranges, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-genomeinfodb, r-bioc-genomicscores, r-bioc-limma, r-bioc-rsamtools, r-cran-randomforest, r-bioc-rtracklayer, r-bioc-motifstack, r-cran-preseqr, r-cran-kernsmooth, r-bioc-edger, r-bioc-biocparallel Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-phastcons100way.ucsc.hg19, r-bioc-motifdb, r-bioc-trackviewer, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-atacseqqc_1.36.0-1.ca2604.1_all.deb Size: 14580760 MD5sum: 40f68617d83ee2d0f7d4ba3b11ddad6f SHA1: da7cb8040e54f429b35deb4d19a390afe6239ffc SHA256: a4eed5cfadea04e73110f46c84da893ab775afb7330fb7c385eb0aedd47cf882 SHA512: 4da846efac0e87df042dd01a46f4aff28c3ba1845fe0f4ee123be88227604869f15f7b50283e7169294b14d06cfb9aa27f9e7e5aaa20e56e1ce2f2ada72e739f Homepage: https://cran.r-project.org/package=ATACseqQC Description: Bioc Package 'ATACseqQC' (ATAC-seq Quality Control) ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. Package: r-bioc-aucell Architecture: all Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3566 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-cran-data.table, r-bioc-gseabase, r-cran-matrix, r-cran-mixtools, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-biocgenerics Suggests: r-bioc-biobase, r-bioc-biocstyle, r-cran-dosnow, r-cran-dynamictreecut, r-cran-dt, r-bioc-geoquery, r-cran-knitr, r-cran-nmf, r-cran-plyr, r-cran-r2html, r-cran-rmarkdown, r-cran-reshape2, r-cran-plotly, r-cran-rtsne, r-cran-testthat, r-cran-zoo Filename: pool/dists/resolute/main/r-bioc-aucell_1.34.0-1.ca2604.1_all.deb Size: 2269304 MD5sum: ad11b99253324b187aca3dbe738dda3e SHA1: 9357c8593792b3197b551c0beefc365e6f398b85 SHA256: 9e5a425078fa69b3e1c475cd5304104038bd7964b82f4ed9196fee125b39c646 SHA512: 1d737294f53cb6b53c30b88dce45fbf735d8dcea529ec1650398febc6f8165e0c977896220e5fcd342d3bdac83d5b200391cece056dd9d1e31717570a56e7f46 Homepage: https://cran.r-project.org/package=AUCell Description: Bioc Package 'AUCell' (AUCell: Analysis of 'gene set' activity in single-cell RNA-seqdata (e.g. identify cells with specific gene signatures)) AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. 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Package: r-bioc-basilisk.utils Architecture: all Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-dir.expiry Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-basilisk.utils_1.24.0-1.ca2604.1_all.deb Size: 246750 MD5sum: 217a7e5157ce034b2b9dfa95c266bfe8 SHA1: 5c560b0e45726e95bdc19cdae2c45d9712025433 SHA256: d70e62bcbbd3031fd333bb7c071ba6a3c2a57fe0961cf9684e2221ad51ad9ce5 SHA512: 54932673137b3f8a00e4dc5914bc160bc2bae15eae198ddec8e621400fab4cc91fab3b06c9253de645d05a50eaa0d3efdf941821fcab44d3f965be7c04685c03 Homepage: https://cran.r-project.org/package=basilisk.utils Description: Bioc Package 'basilisk.utils' (Centralized Conda Installation for Bioconductor Packages) Provides a centralized conda installation for use by other Bioconductor packages. If conda is not already available on the system, it is downloaded and installed from the Miniforge project; otherwise, no action is performed. Historically, this package was used to provide a Python installation for basilisk, hence the name. 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Package: r-bioc-bumphunter Architecture: all Version: 1.54.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-foreach, r-cran-iterators, r-cran-locfit, r-cran-matrixstats, r-bioc-limma, r-cran-dorng, r-bioc-biocgenerics, r-bioc-genomicfeatures, r-bioc-annotationdbi Suggests: r-cran-testthat, r-cran-runit, r-cran-doparallel, r-bioc-genomeinfodb, r-bioc-txdbmaker, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/resolute/main/r-bioc-bumphunter_1.54.0-1.ca2604.1_all.deb Size: 4303620 MD5sum: 6a76d5780194b52e8a09dd0b7983b37e SHA1: cd0ac9e702c90f7b7f89961cfbc74ef6b188c994 SHA256: f9eff09b8ebc7baa204c9c4dd0033fad4a63cafe48ee49f539051af56e934db5 SHA512: bbf7f2e3a34c9b9fdc9b951c2461af52cf7bc6d8d642634af02ece1ed0445f539ad5eecad7a527c863ba4cc5ae8496c6fe0cbef9a86ab960a9cc9248836fe7a8 Homepage: https://cran.r-project.org/package=bumphunter Description: Bioc Package 'bumphunter' (Bump Hunter) Tools for finding bumps in genomic data Package: r-bioc-bumpymatrix Architecture: all Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-s4vectors, r-bioc-iranges Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-bumpymatrix_1.20.0-1.ca2604.1_all.deb Size: 876982 MD5sum: bc0ffda5ad4428f519e94f4413a005ae SHA1: e872a033d3ef04034460039956d6dbbf0d87685c SHA256: 652901faf140d4291192024f9fbfc46b59df2a0f553d26be58e0c839bd87d5b7 SHA512: 3e5aacdeb0bc0d6459a1ddfe3fe271f604ffee255976e7ef255d8f80bf4e17cebde17802648e4939f343caaf9820f13d9b9247af418097e5d1d8a6295c779bad Homepage: https://cran.r-project.org/package=BumpyMatrix Description: Bioc Package 'BumpyMatrix' (Bumpy Matrix of Non-Scalar Objects) Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects. Package: r-bioc-catalyst Architecture: all Version: 1.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-circlize, r-bioc-complexheatmap, r-bioc-consensusclusterplus, r-cran-cowplot, r-cran-dplyr, r-cran-drc, r-bioc-flowcore, r-bioc-flowsom, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-gridextra, r-cran-matrix, r-cran-matrixstats, r-cran-nnls, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rtsne, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-scales, r-bioc-scater Suggests: r-bioc-biocstyle, r-bioc-diffcyt, r-bioc-flowworkspace, r-bioc-ggcyto, r-cran-knitr, r-bioc-opencyto, r-cran-rmarkdown, r-cran-testthat, r-cran-uwot Filename: pool/dists/resolute/main/r-bioc-catalyst_1.36.0-1.ca2604.1_all.deb Size: 12185132 MD5sum: 8b4994124d6fd9915e646f4fdf444ba3 SHA1: 7b3b0fbc29286de8843cc13ecd2b961caf3f74e8 SHA256: 2d5872040a068be06a8e0caf5a6931a63387b1ed09a5297853b7a1763e1460df SHA512: 167ec20e878285929de76bf57b3f4478f4348ccdc88a6fe618f434686d27681f57fb54ec37d9a44fe0cdac5f483c6bdd9d5e1c12188e2493e5f555e4d40ac4f5 Homepage: https://cran.r-project.org/package=CATALYST Description: Bioc Package 'CATALYST' (Cytometry dATa anALYSis Tools) CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively. Package: r-bioc-category Architecture: all Version: 2.78.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1983 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-annotationdbi, r-bioc-biobase, r-cran-matrix, r-bioc-graph, r-bioc-rbgl, r-bioc-gseabase, r-bioc-genefilter, r-bioc-annotate, r-cran-dbi Suggests: r-bioc-ebarrays, r-bioc-all, r-bioc-rgraphviz, r-cran-rcolorbrewer, r-cran-xtable, r-bioc-hgu95av2.db, r-bioc-keggrest, r-bioc-karyoploter, r-bioc-geneplotter, r-bioc-limma, r-cran-lattice, r-cran-runit, r-bioc-org.sc.sgd.db, r-bioc-gostats, r-bioc-go.db Filename: pool/dists/resolute/main/r-bioc-category_2.78.0-1.ca2604.1_all.deb Size: 1357906 MD5sum: 2d437682227555cf4cb4965a7c7d07f0 SHA1: 19eeff3f342a0033172e7fb8acf3844a72f1beef SHA256: 61de39886abdb001d9a0911dfd3ead2ae3d86119040ccefd2ca68067dcdc3bc3 SHA512: f073f4eefdfe574a35567320dbb805349f5f1ccc6f678eb54ad68f62d5bd01012c6fc2af46d3edbaa2b12eedf123c3963e33ecdaf055593f1160ca4a97646918 Homepage: https://cran.r-project.org/package=Category Description: Bioc Package 'Category' (Category Analysis) A collection of tools for performing category (gene set enrichment) analysis. Package: r-bioc-cbioportaldata Architecture: all Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3101 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-anvil, r-bioc-multiassayexperiment, r-bioc-biocbaseutils, r-bioc-biocfilecache, r-cran-digest, r-cran-dplyr, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-httr, r-bioc-iranges, r-cran-readr, r-bioc-raggedexperiment, r-bioc-rtcgatoolbox, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr, r-bioc-tcgautils Suggests: r-bioc-biocstyle, r-cran-jsonlite, r-cran-knitr, r-cran-survival, r-cran-survminer, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-cbioportaldata_2.24.0-1.ca2604.1_all.deb Size: 468894 MD5sum: cda77c04bcad91bc1dbc49fc4ea6312a SHA1: 7645a5aa2721fbae5539c3939ce0b4e4875ecabb SHA256: 0e231f8794cfa87a82c3bbbd7bd3fe5ee1fc89af49720b5936079e4ddf8855a7 SHA512: f1d8330d9fb9d406a28b7b33a034a0b7ebf44b9b7ec3f7442288790c875324d6b9e5e94838245ed0f557f791beed48eb6b1c5fcc79939fb1ff9361aab1a09a1e Homepage: https://cran.r-project.org/package=cBioPortalData Description: Bioc Package 'cBioPortalData' (Exposes and Makes Available Data from the cBioPortal WebResources) The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations. Package: r-bioc-celldex Architecture: all Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1370 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-matrix, r-bioc-experimenthub, r-bioc-annotationhub, r-bioc-annotationdbi, r-bioc-s4vectors, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-gypsum, r-bioc-alabaster.base, r-bioc-alabaster.matrix, r-bioc-alabaster.se, r-cran-dbi, r-cran-rsqlite, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-dt, r-cran-jsonvalidate, r-cran-biocmanager, r-bioc-ensembldb Filename: pool/dists/resolute/main/r-bioc-celldex_1.22.0-1.ca2604.1_all.deb Size: 429784 MD5sum: 3ef654110b4968bf3db01844fe7dc80d SHA1: bb96dccc7491cd17076e543f19c975157a889dbd SHA256: 2374c41de8293c4c42da55be7588475e6df47c4e85513fa15547b53867db3021 SHA512: 8d64250347133ca71be1a10e9056d04da7f0241b4c273c7f76dd3dee7fc1cad972b9fad042e21364e739e3fb53724f34df22b0ce8911e8a125605b1c91d6b295 Homepage: https://cran.r-project.org/package=celldex Description: Bioc Package 'celldex' (Index of Reference Cell Type Datasets) Provides a collection of reference expression datasets with curated cell type labels, for use in procedures like automated annotation of single-cell data or deconvolution of bulk RNA-seq. Package: r-bioc-cghbase Architecture: all Version: 1.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1386 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-marray Filename: pool/dists/resolute/main/r-bioc-cghbase_1.72.0-1.ca2604.1_all.deb Size: 1090226 MD5sum: 1c50821a799794f9e5047f28b38ffd47 SHA1: 4cdf77a7619833c09375af932db8129359defbfd SHA256: f88918a77b030bbcc8c33284cf82bc552b934f4d83cda9d40a0393c2f3381150 SHA512: 5a57e3429a135543a0d400c5c8bd12803acb4a211e30c3080caacbfeba553bdd906e47d98df2483e8b65795e0b15df390db4e8ac0e7093992b3b0f244fcf56d6 Homepage: https://cran.r-project.org/package=CGHbase Description: Bioc Package 'CGHbase' (CGHbase: Base functions and classes for arrayCGH data analysis.) Contains functions and classes that are needed by arrayCGH packages. Package: r-bioc-cghcall Architecture: all Version: 2.74.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-impute, r-bioc-dnacopy, r-bioc-biobase, r-bioc-cghbase, r-cran-snowfall Filename: pool/dists/resolute/main/r-bioc-cghcall_2.74.0-1.ca2604.1_all.deb Size: 540144 MD5sum: 1f27611879f256c989223f94fdadfeb4 SHA1: 64ee30c272a6862c30bb2265d30ead0b8fee6051 SHA256: 594e0338c894a0031623f88c7a5f011129448845cd4f6e9ee6d7c26514e5770f SHA512: 4b8006ec5148378efaa38f0389c047295003e329c1ee9f581b415d1907a443d43c70784bb9a3620a1cfdb774c6b9088519bf40cc974ac2f5d23dd866ffb79f4b Homepage: https://cran.r-project.org/package=CGHcall Description: Bioc Package 'CGHcall' (Calling aberrations for array CGH tumor profiles.) Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. Package: r-bioc-chippeakanno Architecture: all Version: 3.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 28452 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-iranges, r-bioc-genomicranges, r-bioc-s4vectors, r-bioc-annotationdbi, r-bioc-biocgenerics, r-bioc-biostrings, r-bioc-pwalign, r-cran-dbi, r-cran-dplyr, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-rbgl, r-bioc-rsamtools, r-bioc-summarizedexperiment, r-cran-venndiagram, r-bioc-biomart, r-cran-ggplot2, r-bioc-graph, r-bioc-interactionset, r-bioc-keggrest, r-cran-matrixstats, r-bioc-multtest, r-bioc-regioner, r-bioc-rtracklayer, r-bioc-universalmotif, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-data.table, r-cran-scales, r-bioc-ensembldb Suggests: r-bioc-annotationhub, r-bioc-bsgenome, r-bioc-limma, r-bioc-reactome.db, r-cran-biocmanager, r-bioc-biocstyle, r-bioc-bsgenome.ecoli.ncbi.20080805, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-org.ce.eg.db, r-bioc-org.hs.eg.db, r-bioc-bsgenome.celegans.ucsc.ce10, r-bioc-bsgenome.drerio.ucsc.danrer7, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-delayedarray, r-cran-idr, r-cran-seqinr, r-bioc-ensdb.hsapiens.v75, r-bioc-ensdb.hsapiens.v79, r-bioc-ensdb.hsapiens.v86, r-bioc-txdb.hsapiens.ucsc.hg18.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-go.db, r-cran-gplots, r-cran-upsetr, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-testthat, r-bioc-trackviewer, r-bioc-motifstack, r-bioc-organismdbi, r-bioc-biocfilecache Filename: pool/dists/resolute/main/r-bioc-chippeakanno_3.46.0-1.ca2604.1_all.deb Size: 22852190 MD5sum: cec8b53f90772fd6702129de1991cbbe SHA1: a200ac7a94746ef31c034692e731738983193872 SHA256: ebaffcd3b7f8bd950a96134d03dde185cf5f9a08bc9ad9cafc378a74a214e7d0 SHA512: 82c3a523599cdb0d2319ede9db53fc7baf2c8e8197550858925eb63224de51552fd530a5468b2d18d37d8441ec5e53e20352803b19e353db90dd8b48e13d69c7 Homepage: https://cran.r-project.org/package=ChIPpeakAnno Description: Bioc Package 'ChIPpeakAnno' (Batch annotation of the peaks identified from either ChIP-seq,ChIP-chip experiments, or any experiments that result in largenumber of genomic interval data) The package encompasses a range of functions for identifying the closest gene, exon, miRNA, or custom features—such as highly conserved elements and user-supplied transcription factor binding sites. Additionally, users can retrieve sequences around the peaks and obtain enriched Gene Ontology (GO) or Pathway terms. In version 2.0.5 and beyond, new functionalities have been introduced. These include features for identifying peaks associated with bi-directional promoters along with summary statistics (peaksNearBDP), summarizing motif occurrences in peaks (summarizePatternInPeaks), and associating additional identifiers with annotated peaks or enrichedGO (addGeneIDs). The package integrates with various other packages such as biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and stat to enhance its analytical capabilities. Package: r-bioc-chipseeker Architecture: all Version: 1.48.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8083 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-aplot, r-bioc-biocgenerics, r-cran-boot, r-cran-dplyr, r-bioc-enrichplot, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-ggplot2, r-cran-gplots, r-cran-gtools, r-cran-magrittr, r-cran-plotrix, r-cran-rcolorbrewer, r-cran-rlang, r-bioc-rtracklayer, r-bioc-s4vectors, r-cran-scales, r-cran-tibble, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-yulab.utils Suggests: r-bioc-clusterprofiler, r-cran-ggimage, r-cran-ggplotify, r-cran-ggupset, r-cran-ggvenndiagram, r-cran-knitr, r-bioc-org.hs.eg.db, r-cran-prettydoc, r-bioc-reactomepa, r-cran-rmarkdown, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene Filename: pool/dists/resolute/main/r-bioc-chipseeker_1.48.0-1.ca2604.1_all.deb Size: 7478724 MD5sum: cf0c0636044f79cefee30daf2aaa13fc SHA1: 0055b4858ac931d614969b023bc66956309ca57c SHA256: 0d5252fac7416052f132d9eddc01fac5d03359b9787e8edaa8cebf5ed66aa041 SHA512: d387baf42a914c2da11f63e4a05f98c2df241a10ffe7889288ffff9fa9548c930137e2c1b096e1abb10ef4dc451cd5c3fee75a20e0bb6501d63342d46a529343 Homepage: https://cran.r-project.org/package=ChIPseeker Description: Bioc Package 'ChIPseeker' (ChIPseeker for ChIP peak Annotation, Comparison, andVisualization) This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. Package: r-bioc-cicero Architecture: all Version: 1.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1802 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-monocle, r-bioc-gviz, r-cran-assertthat, r-bioc-biobase, r-bioc-biocgenerics, r-cran-data.table, r-cran-dplyr, r-cran-fnn, r-bioc-genomicranges, r-cran-ggplot2, r-cran-glasso, r-cran-igraph, r-bioc-iranges, r-cran-matrix, r-cran-plyr, r-cran-reshape2, r-bioc-s4vectors, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vgam Suggests: r-bioc-annotationdbi, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-rtracklayer, r-cran-testthat, r-cran-vdiffr, r-cran-covr Filename: pool/dists/resolute/main/r-bioc-cicero_1.30.0-1.ca2604.1_all.deb Size: 1125284 MD5sum: 5ffd3293b5fe1c7f25be0e33034cf7c2 SHA1: 56d549beff1b8e8cb199b5ea005eb0e74ec1b4e2 SHA256: 96d240f1d0f4e5a504d4b55add83254e955e313acc03076f4fc2b0d9c76c9dfa SHA512: 091eca1adea19883c66d689ac256b1c54184d782f0721a94c9d275a7f55426e6e532e8b82a2ff0b1c91406783be02340b6466b3d75ed3c0dbf73801643ea9ca0 Homepage: https://cran.r-project.org/package=cicero Description: Bioc Package 'cicero' (Predict cis-co-accessibility from single-cell chromatinaccessibility data) Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. Package: r-bioc-clusterprofiler Architecture: all Version: 4.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1214 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aisdk, r-bioc-annotationdbi, r-cran-dplyr, r-cran-enrichit, r-bioc-enrichplot, r-cran-ggplot2, r-bioc-go.db, r-bioc-gosemsim, r-cran-gson, r-cran-httr, r-cran-igraph, r-cran-jsonlite, r-cran-magrittr, r-cran-plyr, r-bioc-qvalue, r-cran-rlang, r-cran-tidyr, r-cran-yulab.utils Suggests: r-bioc-annotationhub, r-cran-biocmanager, r-bioc-dose, r-cran-ggtangle, r-cran-readr, r-bioc-org.hs.eg.db, r-cran-quarto, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-clusterprofiler_4.20.0-1.ca2604.1_all.deb Size: 788862 MD5sum: d33ba8b725679e03c674cff50aca4469 SHA1: 126c74e13a2e0e946ca1e291e2808888bea5d6db SHA256: 79b1d3232b715d06aa6550a2d64adb7af41b684f7848fe1f0e2377d0e16242be SHA512: 3470d239d941c134fb91029ee67fae73851f65865a9745a8f2ad63ff54527a9e360445931b3ffe664ff3784b0fce2522ee3e1adc9bed9e01a27b06b59423ac23 Homepage: https://cran.r-project.org/package=clusterProfiler Description: Bioc Package 'clusterProfiler' (A Universal Enrichment Tool for Interpreting Omics Data) A universal tool for interpreting functional characteristics of omics data. It supports Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) for both coding and non-coding genomics data of thousands of species. It provides a unified and tidy interface to access, manipulate, and visualize enrichment results. A key capability is the simultaneous analysis and comparison of datasets from multiple treatments or time points. Furthermore, it integrates Large Language Model (LLM) capabilities to provide automated and insightful interpretation of enrichment results. Package: r-bioc-cmapr Architecture: all Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6091 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rhdf5, r-cran-data.table, r-bioc-flowcore, r-bioc-summarizedexperiment, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-cmapr_1.24.0-1.ca2604.1_all.deb Size: 3916378 MD5sum: f555d5b53fb077c65ea2a703635c968f SHA1: 0ccf59221b99986cbe7b70fa0abd5b04d08f4fed SHA256: 5c032f191e5eb6c007dcd13b1904a69193bf1b443f572cc02bf7ad2e0ec3ed5b SHA512: 0ae73886ed17c4cfc19a0b244319ace0aec441dbe7c7f0b1c771f8f279b198689ef4b4de3b97c560dca100faa70febe81ffb8fc3442bc6243b17fb0d5d83ca61 Homepage: https://cran.r-project.org/package=cmapR Description: Bioc Package 'cmapR' (CMap Tools in R) The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. Package: r-bioc-complexheatmap Architecture: all Version: 2.28.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3560 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-cran-getoptlong, r-cran-colorspace, r-cran-clue, r-cran-rcolorbrewer, r-cran-globaloptions, r-cran-png, r-cran-digest, r-bioc-iranges, r-cran-matrixstats, r-cran-foreach, r-cran-doparallel, r-cran-codetools Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-dendsort, r-cran-jpeg, r-cran-tiff, r-cran-fastcluster, r-bioc-enrichedheatmap, r-cran-dendextend, r-cran-grimport, r-cran-grimport2, r-cran-glue, r-bioc-genomicranges, r-cran-gridtext, r-cran-pheatmap, r-cran-gridgraphics, r-cran-gplots, r-cran-rmarkdown, r-cran-cairo, r-cran-magick Filename: pool/dists/resolute/main/r-bioc-complexheatmap_2.28.0-1.ca2604.1_all.deb Size: 3036882 MD5sum: 8c5aadf49d2f08af29ee8c37d6d41e09 SHA1: a0436baec27c6075f55b83ff749dd871b95f19e2 SHA256: c6be80e3676333d88830ce1d7a86d85aaa0d448b30e423f2caa7d5030d1da875 SHA512: 7c7d8898faec84f574ed65152bc7c54a05bccbceba48bfbd7ee694a3520e8653264b05c2bd61bf1f32d623606dfb3db340514152b0311dcd849ed4de42c3c4e0 Homepage: https://cran.r-project.org/package=ComplexHeatmap Description: Bioc Package 'ComplexHeatmap' (Make Complex Heatmaps) Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. Package: r-bioc-compounddb Architecture: all Version: 1.16.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3234 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationfilter, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-chemminer, r-cran-tibble, r-cran-jsonlite, r-cran-dplyr, r-cran-dbi, r-cran-dbplyr, r-cran-rsqlite, r-bioc-biobase, r-bioc-protgenerics, r-cran-xml2, r-bioc-iranges, r-bioc-spectra, r-bioc-mscoreutils, r-bioc-metabocoreutils, r-bioc-biocparallel, r-cran-stringi, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-bioc-msbackendmgf Filename: pool/dists/resolute/main/r-bioc-compounddb_1.16.0-1.ca2604.1_all.deb Size: 1136114 MD5sum: 523f90dc4592edd073d7e7332090a0d3 SHA1: d9c429ec927480a3af5b45ddea997c5f56a02c15 SHA256: d50d4b9d9ce5f2ebde5ec79d2cbc95eaeabf3ef5c7c83632c67b5eafe1ae499e SHA512: 85abad25b43a88fa341b37e812d55f0a06d00a11ec1dcf1bd06d718587c39e4335d2513568e265d2934ece5a19ec9de1767547475312482fac3f9512dd16e722 Homepage: https://cran.r-project.org/package=CompoundDb Description: Bioc Package 'CompoundDb' (Creating and Using (Chemical) Compound Annotation Databases) CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable. Package: r-bioc-consensusclusterplus Architecture: all Version: 1.76.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-all, r-cran-cluster Filename: pool/dists/resolute/main/r-bioc-consensusclusterplus_1.76.0-1.ca2604.1_all.deb Size: 446528 MD5sum: c877c9872106a465ac11e3d0e6ac3c90 SHA1: fe00616cbe71b871369b5271b4b227ec317467c0 SHA256: 8fdcd7f27dca2f689a290c82dcef35c36dc5d8329d49c166f8af5acf24ed3578 SHA512: 87b80bdd3e2ca6d410822d2d494859bc31d91af7244df601faceeb88155574390c355062e6187522835ad0f8712d599dfb3beda16f3d308e419c38a99bfa134e Homepage: https://cran.r-project.org/package=ConsensusClusterPlus Description: Bioc Package 'ConsensusClusterPlus' (ConsensusClusterPlus) algorithm for determining cluster count and membership by stability evidence in unsupervised analysis Package: r-bioc-cordon Architecture: all Version: 1.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3891 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-bioc-biobase, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-data.table Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-cordon_1.30.0-1.ca2604.1_all.deb Size: 2646536 MD5sum: 4625bd0f509f07ffd4f7197d4fe318c3 SHA1: 6aaa6a92b6f2e9c19ba173612dcdbf1216823e86 SHA256: 5fbe1c42be0e58371b64de03cdcdb4f4f2f2a47843adba8ea23ff74ae86db8e2 SHA512: d2da2c4ae622869ba8c467531bda0b9f8e8d7cdc2938aaf3e95e9c6d5f2680199d4f9d26eab03027529df88e26e730adabd5785efc9472637f8f187d1ad78840 Homepage: https://cran.r-project.org/package=coRdon Description: Bioc Package 'coRdon' (Codon Usage Analysis and Prediction of Gene Expressivity) Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. Package: r-bioc-coregx Architecture: all Version: 2.16.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3870 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-summarizedexperiment, r-bioc-biobase, r-bioc-s4vectors, r-bioc-multiassayexperiment, r-bioc-matrixgenerics, r-bioc-piano, r-bioc-biocparallel, r-bioc-bumpymatrix, r-cran-checkmate, r-cran-lsa, r-cran-data.table, r-cran-crayon, r-cran-glue, r-cran-rlang, r-cran-bench Suggests: r-cran-pander, r-cran-markdown, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-knitr, r-cran-formatr, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-coregx_2.16.0-1.ca2604.1_all.deb Size: 2267332 MD5sum: bcab064a3417bc7053e5a8f62f5266af SHA1: 91c0dff214da7ebabe8523f086edf7c96a27c780 SHA256: 54e758d34e15856c5bb04870f3235f23ed34b68e4d1471d550bef7c10559ef6a SHA512: d404b9aad1fb0908f982fa084b0b3f9510f4e56bc5bb8130c88fa7e36ea7291a1da1327368a306297bb7c34c861deb1d5c5bf08c9cbe1146d4c7bf109cc18505 Homepage: https://cran.r-project.org/package=CoreGx Description: Bioc Package 'CoreGx' (Classes and Functions to Serve as the Basis for Other 'Gx'Packages) A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . Package: r-bioc-cqn Architecture: all Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1090 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mclust, r-cran-nor1mix, r-cran-quantreg Suggests: r-cran-scales, r-bioc-edger Filename: pool/dists/resolute/main/r-bioc-cqn_1.58.0-1.ca2604.1_all.deb Size: 992914 MD5sum: 46d9314b635d67f078d82835bd523505 SHA1: f8108af409dc7e18289a0efa9840b247f280ee68 SHA256: bca30f9c3ab627c45dafd7d2d8ec05eae8ea73a7804bc9d1073ca607a7850234 SHA512: dab8e46b208e3b85281f5ec52a331fd508cc4210ce987b397cf33b58ade3774a342d39cfd97b4a9b39f6692539e4eb5332d36d75e6ca0699ff107cbd8eff224e Homepage: https://cran.r-project.org/package=cqn Description: Bioc Package 'cqn' (Conditional quantile normalization) A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. Package: r-bioc-cytomapper Architecture: all Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7667 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-ebimage, r-bioc-singlecellexperiment, r-bioc-spatialexperiment, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-hdf5array, r-bioc-delayedarray, r-cran-rcolorbrewer, r-cran-viridis, r-bioc-summarizedexperiment, r-cran-raster, r-cran-ggplot2, r-cran-ggbeeswarm, r-cran-svgpanzoom, r-cran-svglite, r-cran-shiny, r-cran-shinydashboard, r-cran-matrixstats, r-bioc-rhdf5, r-cran-nnls Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-cowplot, r-cran-testthat, r-cran-shinytest Filename: pool/dists/resolute/main/r-bioc-cytomapper_1.24.0-1.ca2604.1_all.deb Size: 4187768 MD5sum: 6717d97d1ab23d6760f7d6b40d2af3f6 SHA1: 8d5d976aab5899e7712e1d7f0e70c7340179ed55 SHA256: c7c26855f5a62c1e451736c28206a410c8fbfcfdfc3b1d74ec5e44b617338d11 SHA512: d11d76c0d3452956f8eb44216de6f9dd9cd43780f71bdb499cff6506015188956b288fa89fd916958176581d20c752d204787e5720a4194ea355c7ba96f48298 Homepage: https://cran.r-project.org/package=cytomapper Description: Bioc Package 'cytomapper' (Visualization of highly multiplexed imaging data in R) Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. Package: r-bioc-decontam Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-phyloseq Filename: pool/dists/resolute/main/r-bioc-decontam_1.32.0-1.ca2604.1_all.deb Size: 733302 MD5sum: a5e397f8eb4e595a3623996a3980a6a6 SHA1: 1cb38168ca6877de61294a366bf4c9e61a3d69fa SHA256: 7cce0fbf0b355d41277746ecf731886e93210771a0996f705893f1664673aafa SHA512: 24bbcd3a6e3010c32d4cfc84a2c994e5eee9e373f51bc1beb51ddf824400cedb210dfd259545614fe71ad0e904870697c553040292181a85ea83e9a381f8145b Homepage: https://cran.r-project.org/package=decontam Description: Bioc Package 'decontam' (Identify Contaminants in Marker-gene and Metagenomics SequencingData) Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. Package: r-bioc-decoupler Architecture: all Version: 2.17.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8526 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-cran-broom, r-cran-dplyr, r-cran-magrittr, r-cran-matrix, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-glmnet, r-bioc-gsva, r-bioc-viper, r-bioc-fgsea, r-bioc-aucell, r-bioc-summarizedexperiment, r-cran-rpart, r-cran-ranger, r-bioc-biocstyle, r-cran-covr, r-cran-knitr, r-cran-pkgdown, r-cran-refmanager, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sessioninfo, r-cran-pheatmap, r-cran-testthat, r-bioc-omnipathr, r-cran-seurat, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-magick Filename: pool/dists/resolute/main/r-bioc-decoupler_2.17.0-1.ca2604.1_all.deb Size: 3535272 MD5sum: 10e7ff60531680df43ee359bc6011dd3 SHA1: 640fe1c94304f6144039e3902ee5f9d0035971ff SHA256: c9ab22f0125b04aad723afffde6f2cb6e01932e3d27baa3eda819ce1171e902b SHA512: eb31771bb04eaab90d8f61b1966f63072b3788fce3f89ca820ca924e9e64ef9bccac6322e5361e816e7a793ba77362b5a21905efa9f081eac252285f8959feba Homepage: https://cran.r-project.org/package=decoupleR Description: Bioc Package 'decoupleR' (decoupleR: Ensemble of computational methods to infer biologicalactivities from omics data) Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. Package: r-bioc-degreport Architecture: all Version: 1.48.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-cran-broom, r-cran-circlize, r-bioc-complexheatmap, r-cran-cowplot, r-bioc-consensusclusterplus, r-cran-cluster, r-cran-dendextend, r-bioc-deseq2, r-cran-dplyr, r-bioc-edger, r-cran-ggplot2, r-cran-ggdendro, r-cran-ggrepel, r-cran-knitr, r-cran-logging, r-cran-magrittr, r-cran-psych, r-cran-rcolorbrewer, r-cran-reshape, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-stringi, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tidyr, r-cran-tibble Suggests: r-bioc-biocstyle, r-bioc-annotationdbi, r-bioc-limma, r-cran-pheatmap, r-cran-rmarkdown, r-cran-statmod, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-degreport_1.48.0-1.ca2604.1_all.deb Size: 3252718 MD5sum: 800f5d33b4d930059c20bb6a654b3fe5 SHA1: 818dbbf4d66fe2288640b42f299116d7331e7a0c SHA256: 0ac51f3f4b4206a721f5d759fbfee785b79926a0fbc919d70aaefe09b096955f SHA512: 9a84a219e468ce4af583c24ff2fc53b268e21c5e90ef07b1f16d40e318c523157fbc2de8400ce8e99a8fd7c40135099c2c02c08f4ef1be23c7e3e56eed6941f1 Homepage: https://cran.r-project.org/package=DEGreport Description: Bioc Package 'DEGreport' (Report of DEG analysis) Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. Package: r-bioc-delayedarray Architecture: all Version: 0.38.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3692 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-sparsearray Suggests: r-bioc-biocparallel, r-bioc-hdf5array, r-bioc-zarrarray, r-bioc-genefilter, r-bioc-summarizedexperiment, r-bioc-airway, r-cran-lobstr, r-bioc-delayedmatrixstats, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-runit Filename: pool/dists/resolute/main/r-bioc-delayedarray_0.38.1-1.ca2604.1_all.deb Size: 2182014 MD5sum: cc04d980c8fed9a30b772d4175f63af9 SHA1: 87d943b9fc5d44907eac313cfef93e518583f427 SHA256: 1026fe75bbbadbac8d682081a3b6a848dcb419b953546b27fe0880cb098083c8 SHA512: b132095990405e6c83653f1603edae38e3144bb00d89eaa356967d91b3a30233754357538e2a07f62863ab77525ff78bb6b3990e0e89f4281ca9ef1ce1c3721a Homepage: https://cran.r-project.org/package=DelayedArray Description: Bioc Package 'DelayedArray' (A unified framework for working transparently with on-disk andin-memory array-like datasets) Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. Package: r-bioc-delayedmatrixstats Architecture: all Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-delayedarray, r-bioc-sparsematrixstats, r-cran-matrix, r-bioc-s4vectors, r-bioc-iranges, r-bioc-sparsearray Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-microbenchmark, r-cran-profmem, r-bioc-hdf5array, r-cran-matrixstats Filename: pool/dists/resolute/main/r-bioc-delayedmatrixstats_1.34.0-1.ca2604.1_all.deb Size: 704686 MD5sum: 66626360a6fbb9df69a75ab12d35f048 SHA1: 9acc68e70e03de64c478536978fbd61b3c08709f SHA256: 34aceee16a4fb8980575bd6348396b1184e5d93a51e9d77346fbfa3c23b5ea71 SHA512: e6868db9e6cec0a772d94c0efe9e319fb3f7e23a7e842b4990ae48475edd62e7d2b56675f02e5c0cb2181cf15b8d548fbb9765fc8936a05de807b8365cc3f3ec Homepage: https://cran.r-project.org/package=DelayedMatrixStats Description: Bioc Package 'DelayedMatrixStats' (Functions that Apply to Rows and Columns of 'DelayedMatrix'Objects) A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. Package: r-bioc-dep Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-bioc-summarizedexperiment, r-bioc-msnbase, r-bioc-limma, r-bioc-vsn, r-cran-fdrtool, r-cran-ggrepel, r-bioc-complexheatmap, r-cran-rcolorbrewer, r-cran-circlize, r-cran-shiny, r-cran-shinydashboard, r-cran-dt, r-cran-rmarkdown, r-cran-assertthat, r-cran-gridextra, r-cran-imputelcmd, r-cran-cluster Suggests: r-cran-testthat, r-cran-enrichr, r-cran-knitr, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-dep_1.32.0-1.ca2604.1_all.deb Size: 4169422 MD5sum: b1cd91f6e32dea30f1ff8975b563fc9c SHA1: cb60c68fea29e459c5d61b19eee841a0a2802ec6 SHA256: ebe737a3c8dd75d42ea823ff1a35f4b24ce8505daf5f91ca9d5614013bb339f5 SHA512: 85426acc9c7a79cc67eb5859ca6d9e80470dd70eb32e5ed5d832eefde8a5d4ea1d0a0dcb0f0d4bf64cb3b90aff43237996fa3e61af908ded4ac8a67af34601f9 Homepage: https://cran.r-project.org/package=DEP Description: Bioc Package 'DEP' (Differential Enrichment analysis of Proteomics data) This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. Package: r-bioc-depmap Architecture: all Version: 1.26.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-bioc-experimenthub, r-bioc-annotationhub, r-bioc-biocfilecache, r-cran-httr2, r-cran-curl, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-viridis, r-cran-gridextra, r-cran-ggplot2, r-cran-readr, r-cran-stringr, r-cran-tidyverse, r-cran-magick Filename: pool/dists/resolute/main/r-bioc-depmap_1.26.0-1.ca2604.1_all.deb Size: 1507244 MD5sum: 19fd8d625f7c0fc0b05ab328a9638a41 SHA1: 30b7c132cb444ea668ea9814900bf35e32ff58ab SHA256: 42d4a62225bb1e5b21b687554d5b9c1b9841589d701d06407a13b028ee06da51 SHA512: 75fca3834f4e8ddc8043aa35ddf7f9125bf4d41ff883349449e435ca6a7cdc497956ccae2e687eeb6780047c464a2edf91c091a878d606913090545ecce166d2 Homepage: https://cran.r-project.org/package=depmap Description: Bioc Package 'depmap' (Cancer Dependency Map Data Package) The depmap package is a data package that accesses datsets from the Broad Institute DepMap cancer dependency study using ExperimentHub. Datasets from the most current release are available, including RNAI and CRISPR-Cas9 gene knockout screens quantifying the genetic dependency for select cancer cell lines. Additional datasets are also available pertaining to the log copy number of genes for select cell lines, protein expression of cell lines as measured by reverse phase protein lysate microarray (RPPA), 'Transcript Per Million' (TPM) data, as well as supplementary datasets which contain metadata and mutation calls for the other datasets found in the current release. The 19Q3 release adds the drug_dependency dataset, that contains cancer cell line dependency data with respect to drug and drug-candidate compounds. The 20Q2 release adds the proteomic dataset that contains quantitative profiling of proteins via mass spectrometry. This package will be updated on a quarterly basis to incorporate the latest Broad Institute DepMap Public cancer dependency datasets. All data made available in this package was generated by the Broad Institute DepMap for research purposes and not intended for clinical use. This data is distributed under the Creative Commons license (Attribution 4.0 International (CC BY 4.0)). Package: r-bioc-derfinder Architecture: all Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4872 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-annotationdbi, r-bioc-biocparallel, r-bioc-bumphunter, r-bioc-derfinderhelper, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-genomicfiles, r-bioc-genomicranges, r-cran-hmisc, r-bioc-iranges, r-bioc-qvalue, r-bioc-rsamtools, r-bioc-rtracklayer, r-bioc-s4vectors Suggests: r-bioc-biocstyle, r-cran-sessioninfo, r-bioc-derfinderdata, r-bioc-derfinderplot, r-bioc-deseq2, r-cran-ggplot2, r-cran-knitr, r-bioc-limma, r-cran-refmanager, r-cran-rmarkdown, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-covr Filename: pool/dists/resolute/main/r-bioc-derfinder_1.46.0-1.ca2604.1_all.deb Size: 1932946 MD5sum: 8c6fca8c6bed0eddb71fa2731c82f94e SHA1: 6a48008a4589ffb4d772e68509640da9b48f3811 SHA256: 6833224f842f8c80bf95cdd1c69d9b828370cfb81ddacba57b5b49c49775732e SHA512: 1bc79383194c46584f1b5ca76b5c6524b000c4a383c48c3c405a2190c4049a9eee70da101e487d90237b5d2857c073a62d47c9d9ed327147ea17c1491abb201a Homepage: https://cran.r-project.org/package=derfinder Description: Bioc Package 'derfinder' (Annotation-agnostic differential expression analysis of RNA-seqdata at base-pair resolution via the DER Finder approach) This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. Package: r-bioc-derfinderhelper Architecture: all Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-iranges, r-cran-matrix, r-bioc-s4vectors Suggests: r-cran-sessioninfo, r-cran-knitr, r-bioc-biocstyle, r-cran-refmanager, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-bioc-derfinderhelper_1.46.0-1.ca2604.1_all.deb Size: 304024 MD5sum: 6cb8becf831b90fd646c5e93a0763137 SHA1: cd2620c1df1e91837b584ef61297d0dca7d357d3 SHA256: 9b3517043ef5c5c1ce075c1b0e5c8f2c76df7ae9d1be3d0f000ae35b286f7f76 SHA512: f45c14f69816115cbf09e2d9c323ba4c8b4948d74522f821bd8bac185068d9864e8fbc015c48e5035666365ec06896a778db91117d661522e21eda2b886a6b7b Homepage: https://cran.r-project.org/package=derfinderHelper Description: Bioc Package 'derfinderHelper' (derfinder helper package) Helper package for speeding up the derfinder package when using multiple cores. This package is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel. Package: r-bioc-dexseq Architecture: all Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3467 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-iranges, r-bioc-genomicranges, r-bioc-deseq2, r-bioc-annotationdbi, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biomart, r-cran-hwriter, r-cran-stringr, r-bioc-rsamtools, r-cran-statmod, r-bioc-geneplotter, r-bioc-genefilter Suggests: r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-pasilla, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-pasillabamsubset, r-bioc-genomicalignments, r-cran-roxygen2, r-bioc-glmgampoi Filename: pool/dists/resolute/main/r-bioc-dexseq_1.58.0-1.ca2604.1_all.deb Size: 2050514 MD5sum: efee847a237d01009d8898b4c1afb4f4 SHA1: 8a8fa5b95af22671280dd30181dc445dbd3b0dc8 SHA256: 94356e98555d55e8c7c13f780cba44dda0cf84badf9166668f2daeb3c68da781 SHA512: fd7a8d6c5ec6b831347ff7f4979821b75c4cdeee069ab31630309e333451dbf8a2e61e1261959eff1cda9626ca3261f53b655edac38225f0b8a6307a58fab0fa Homepage: https://cran.r-project.org/package=DEXSeq Description: Bioc Package 'DEXSeq' (Inference of differential exon usage in RNA-Seq) The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. Package: r-bioc-diffcoexp Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-wgcna, r-bioc-summarizedexperiment, r-cran-diffcorr, r-cran-psych, r-cran-igraph, r-bioc-biocgenerics Suggests: r-bioc-geoquery, r-cran-runit Filename: pool/dists/resolute/main/r-bioc-diffcoexp_1.32.0-1.ca2604.1_all.deb Size: 327416 MD5sum: 7bb6e8c8a7001f920c79b4a9c58e7020 SHA1: 9d71fe2cbfc769385c5c0e21f3b92500bb6df53d SHA256: 5afb2dc399265e0efab4d8d0573a807c5f5354bd538dfc25145cc7f94ecb0453 SHA512: b44b80f62130d21562e6e3614c3a00d786223c157bd74a4db8af2fdf4347ec36fd75cb8f8889a1f93bae21b960a62d53f543559ce6aa799c6b69736ef1f03a89 Homepage: https://cran.r-project.org/package=diffcoexp Description: Bioc Package 'diffcoexp' (Differential Co-expression Analysis) A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. Package: r-bioc-diffcyt Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-flowcore, r-bioc-flowsom, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-limma, r-bioc-edger, r-cran-lme4, r-cran-multcomp, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-magrittr, r-bioc-complexheatmap, r-cran-circlize Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-hdcytodata, r-bioc-catalyst Filename: pool/dists/resolute/main/r-bioc-diffcyt_1.32.0-1.ca2604.1_all.deb Size: 898188 MD5sum: 5509ccecfd900bb139441d1d7eb3f03a SHA1: 162c1364c0ae64f2eec281692633ccc9d0b66040 SHA256: b5e035d71ab33dbf6e1e73ea22334f13dbfa30386514cbdc90b43a1ddf13b275 SHA512: 007041eb59fd8221fc8a07c334579373d581d847cff63f5988075ccb09b335b0ab3e4fa46d55bbf326f9310f421502f85e65b4d26279cdbc3088dbb4648a15a6 Homepage: https://cran.r-project.org/package=diffcyt Description: Bioc Package 'diffcyt' (Differential discovery in high-dimensional cytometry viahigh-resolution clustering) Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. Package: r-bioc-dir.expiry Architecture: all Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-filelock Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-dir.expiry_1.20.0-1.ca2604.1_all.deb Size: 296996 MD5sum: c354f4e97ee720af00ba81a11177c3f6 SHA1: 0e5d3e58285b523538bf5e3ec964125e252428fb SHA256: f1bbac241c8f4f941114851fd96de82dc42c9e58615a00c7f9826520d2a28c51 SHA512: 2b9433b18dd274c6cddf0bf7670c7781487a49121e7a6efd589355def0e154b697ea2ed11ff018c9a4c2dca29a8a01b56bf908ee11edf112cf569fc0840c2509 Homepage: https://cran.r-project.org/package=dir.expiry Description: Bioc Package 'dir.expiry' (Managing Expiration for Cache Directories) Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages. Package: r-bioc-dittoseq Architecture: all Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3421 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-colorspace, r-cran-gridextra, r-cran-cowplot, r-cran-reshape2, r-cran-pheatmap, r-cran-ggrepel, r-cran-ggridges, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-s4vectors Suggests: r-cran-plotly, r-cran-testthat, r-cran-seurat, r-bioc-deseq2, r-bioc-edger, r-cran-ggplot.multistats, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-scrnaseq, r-cran-ggrastr, r-bioc-complexheatmap, r-bioc-bluster, r-bioc-scater, r-bioc-scran, r-cran-mass Filename: pool/dists/resolute/main/r-bioc-dittoseq_1.24.0-1.ca2604.1_all.deb Size: 1948132 MD5sum: 0617cd5a1eb50caa096859f3d9eaf1c0 SHA1: 765300c7a21f7c281464775bda609acc3c972347 SHA256: 2545e6cefae3f7cc387cf993feb26301f80246034f16666c21033f0a4e868208 SHA512: c6e52ad25b53926cd4bbab8aea29c2df354b8bb232385b7c47f1e7559e7f7892f382c767e942d0b37fceb1ae44503efc069078970122de4eb912a0b7014ef45b Homepage: https://cran.r-project.org/package=dittoSeq Description: Bioc Package 'dittoSeq' (User Friendly Single-Cell and Bulk RNA Sequencing Visualization) A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). Package: r-bioc-dmrcate Architecture: all Version: 3.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1283 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-bsseq, r-bioc-seqinfo, r-bioc-limma, r-bioc-edger, r-bioc-minfi, r-bioc-missmethyl, r-bioc-genomicranges, r-cran-plyr, r-bioc-gviz, r-bioc-iranges, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-biomart Suggests: r-cran-knitr, r-cran-runit, r-bioc-biocgenerics, r-bioc-genomeinfodb, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-illuminahumanmethylationepicanno.ilm10b4.hg19, r-bioc-illuminahumanmethylationepicv2anno.20a1.hg38, r-bioc-flowsorted.blood.epic, r-bioc-tissuetreg, r-bioc-dmrcatedata, r-bioc-epicv2manifest Filename: pool/dists/resolute/main/r-bioc-dmrcate_3.8.0-1.ca2604.1_all.deb Size: 1038228 MD5sum: ff652c0ac2817dfcb357151695e746cc SHA1: c9c08649bd823e662a6cd9e710ab25fc42fe822b SHA256: 789783d822f6bcdebe0b71f7a1192226138dfde790b7da82c000d69343a9fb72 SHA512: 88051d610ec8e1f173d7f05d5b72e76c131b7ffb2412edb3e5993ef9e5206ecc32a68dc40449ce2713ed9ffce603cd05ac6a1dc51b589ccf135e2d57b4394668 Homepage: https://cran.r-project.org/package=DMRcate Description: Bioc Package 'DMRcate' (Methylation array and sequencing spatial analysis methods) De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. Package: r-bioc-dose Architecture: all Version: 4.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5792 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-enrichit, r-cran-ggplot2, r-bioc-gosemsim, r-cran-reshape2, r-cran-yulab.utils Suggests: r-cran-prettydoc, r-bioc-clusterprofiler, r-cran-gson, r-cran-knitr, r-cran-memoise, r-bioc-org.hs.eg.db, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-dose_4.6.0-1.ca2604.1_all.deb Size: 5834422 MD5sum: 9f265dcbf2edb12fa102aa45e3a36b42 SHA1: f7526f2c11e765da3aac454a82bdeab43e0c913e SHA256: 4e35330406c0e3cc9201b01525344effa98d75ec76fb1bf4efaa2ca08835eb01 SHA512: 4ff8fa07669ab0fbecf3397c6701392dc45a7c390d0acbbaced56969f787858d9971c15ef91e73714ba9e25613a4eed7303253cacfafec6667b14b89359bdafb Homepage: https://cran.r-project.org/package=DOSE Description: Bioc Package 'DOSE' (Disease Ontology Semantic and Enrichment analysis) This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. Package: r-bioc-drimseq Architecture: all Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-limma, r-bioc-edger, r-cran-ggplot2, r-cran-reshape2 Suggests: r-bioc-pasillatranscriptexpr, r-bioc-geuvadistranscriptexpr, r-bioc-biocstyle, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-drimseq_1.40.0-1.ca2604.1_all.deb Size: 786146 MD5sum: fe129d7b3cf8637af0911daf281733bb SHA1: c0cad9da44ec5475b04e483ef2fdfd5fa09f2581 SHA256: 686a21e9cd61a001d6900cda298664901b41417a58d676823a96d1a941e9677c SHA512: 5d210f76320d1087717cdbc408ea0df2f9f757c3003e51426c4ccf73543f425a1299435c3ca46f4d07127f666b38d205eb2ce33b96d620832ee95868ae170f96 Homepage: https://cran.r-project.org/package=DRIMSeq Description: Bioc Package 'DRIMSeq' (Differential transcript usage and tuQTL analyses withDirichlet-multinomial model in RNA-seq) The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. Package: r-bioc-dyndoc Architecture: all Version: 1.90.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-bioc-dyndoc_1.90.0-1.ca2604.1_all.deb Size: 217280 MD5sum: 00ed4bcc522ede19b5fe00f3fb4e018e SHA1: 91371518af8dae399696468cd30922d490b223ef SHA256: eb4d68919ced8bd51108fd7d335af1a97fe8abddcfa1ddaf92eeb8d870825944 SHA512: 092b7d39e89789489b85eb9b8a64399854bbade38a02467cf6c9b173cf565955453be06137bccd9d0a5f17cc1ea7b33b63a3b6a90781082ff067b9adc2193bd7 Homepage: https://cran.r-project.org/package=DynDoc Description: Bioc Package 'DynDoc' (Dynamic document tools) A set of functions to create and interact with dynamic documents and vignettes. Package: r-bioc-edaseq Architecture: all Version: 2.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2306 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-shortread, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-aroma.light, r-bioc-rsamtools, r-bioc-biomart, r-bioc-biostrings, r-bioc-annotationdbi, r-bioc-genomicfeatures, r-bioc-genomicranges, r-cran-biocmanager Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-yeastrnaseq, r-bioc-leebamviews, r-bioc-edger, r-cran-kernsmooth, r-cran-testthat, r-bioc-deseq2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-edaseq_2.46.0-1.ca2604.1_all.deb Size: 1399516 MD5sum: 53e5f342fe3b77a11c1aebaf2ce0ad1b SHA1: 03f540d090ac687f415b794ab70c98f41fbf17f1 SHA256: 7d1a47837d39252bed2636b227203a502114b34f61b09a20714467a16f6bbe83 SHA512: 7c4b70249edfae81d79faa52cba8331b6f68778151c3555a8194bb8a9eb6c324968aa4c41cb4d17ad8b4381e233b049a406155fee574dad6027d4a3eaf88b9ef Homepage: https://cran.r-project.org/package=EDASeq Description: Bioc Package 'EDASeq' (Exploratory Data Analysis and Normalization for RNA-Seq) Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). Package: r-bioc-empiricalbrownsmethod Architecture: all Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-empiricalbrownsmethod_1.40.0-1.ca2604.1_all.deb Size: 53682 MD5sum: 85d9b2b3e815bc32b0416b1192fed2c2 SHA1: db39330b851e5c69bd6c1a7b288182e80768dc01 SHA256: edf18c36d75943f9ad39ac13a7e48db59b76fc46a0525b5a6fbdc42ef935c3c0 SHA512: ae0ac79d64e465f34d026310bfdeb3cfc7a23d5afca47c63d1f1eb5f9fe78f6e949809d9601bff231f4639e36723ed91622057338321d44210ea2aa62c6d8c98 Homepage: https://cran.r-project.org/package=EmpiricalBrownsMethod Description: Bioc Package 'EmpiricalBrownsMethod' (Uses Brown's method to combine p-values from dependent tests) Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. Package: r-bioc-enhancedvolcano Architecture: all Version: 1.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7923 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-scales Suggests: r-cran-runit, r-cran-ggrastr, r-bioc-biocgenerics, r-cran-knitr, r-bioc-deseq2, r-bioc-pasilla, r-bioc-airway, r-bioc-org.hs.eg.db, r-cran-gridextra, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-enhancedvolcano_1.30.0-1.ca2604.1_all.deb Size: 5620160 MD5sum: e5227e8e54bc26b9f17d444a3afed4c7 SHA1: 031d41c19b4095722bbc73e3fce3713a5c8a6408 SHA256: eb0a50fda0184e1dd844a6e34e6a7da24b801e112cfed0f80a251bd77f1dc842 SHA512: 9b2cab77a3cb85e727df6cf59452912d6b1b29eedb0d4e6172dc7aef5ab4f908fd94227c9f24dbc16e0d5f1576212d3c353670c1f217dda89f137daa4ece6724 Homepage: https://cran.r-project.org/package=EnhancedVolcano Description: Bioc Package 'EnhancedVolcano' (Publication-ready volcano plots with enhanced colouring andlabeling) Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. Package: r-bioc-enrichmentbrowser Architecture: all Version: 2.42.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2739 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-graph, r-bioc-annotationdbi, r-bioc-biocfilecache, r-cran-biocmanager, r-bioc-gseabase, r-bioc-go.db, r-bioc-keggrest, r-bioc-kegggraph, r-bioc-rgraphviz, r-bioc-s4vectors, r-bioc-spia, r-bioc-edger, r-bioc-graphite, r-cran-hwriter, r-bioc-limma, r-bioc-pathview, r-bioc-safe Suggests: r-bioc-all, r-bioc-biocstyle, r-bioc-complexheatmap, r-bioc-deseq2, r-bioc-reportingtools, r-bioc-airway, r-bioc-biocgraph, r-bioc-hgu95av2.db, r-bioc-geneplotter, r-cran-knitr, r-cran-msigdbr, r-cran-rmarkdown, r-cran-statmod Filename: pool/dists/resolute/main/r-bioc-enrichmentbrowser_2.42.0-1.ca2604.1_all.deb Size: 1478438 MD5sum: 5a97b0c9db75d950c2be7e2f9fb8245d SHA1: ad954841cad3885dfd242cee36d45f2e6967eabb SHA256: 2942c09358b359aca1b614a967ff36c0fbc214bfb9b9ed28373463b5e8b502db SHA512: 5efd2e042b697f6b63fe2e28403c9f8ab05c44b7074876945a05d50cbf0b575caa5c27b3f0401834f7d2ad23956c40b4640439399cd4766e0591dc9e8191c04a Homepage: https://cran.r-project.org/package=EnrichmentBrowser Description: Bioc Package 'EnrichmentBrowser' (Seamless navigation through combined results of set-based andnetwork-based enrichment analysis) The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. Package: r-bioc-enrichplot Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aplot, r-bioc-dose, r-cran-dplyr, r-cran-enrichit, r-cran-ggfun, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggtangle, r-bioc-ggtree, r-bioc-gosemsim, r-cran-igraph, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rlang, r-cran-scatterpie, r-cran-tidydr, r-cran-yulab.utils Suggests: r-bioc-annotationdbi, r-bioc-clusterprofiler, r-cran-europepmc, r-cran-ggarchery, r-cran-ggforce, r-cran-gghoriplot, r-cran-ggplotify, r-cran-ggridges, r-cran-ggstar, r-bioc-ggtreeextra, r-cran-ggupset, r-cran-glue, r-cran-gridextra, r-cran-gson, r-bioc-org.hs.eg.db, r-cran-quarto, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-bioc-enrichplot_1.32.0-1.ca2604.1_all.deb Size: 292528 MD5sum: d76680c8265964d5ed77eb955204fba9 SHA1: e7437e19adf8be453e21f7f5d65cdc369d3d4ac3 SHA256: 1445c64b17d4d227107bffc21b44ecd6806b9e2391243ae86f8835ad6e77186e SHA512: 8d243c428e6407e90dffe2f6ddab76ae3fe4c3ef7eee2c40a4addf38a1ec66e3e2d2a9f07989a27be39b785d5366d43dbd9d764b04b28408afe6ca64fd37935b Homepage: https://cran.r-project.org/package=enrichplot Description: Bioc Package 'enrichplot' (Visualization of Functional Enrichment Result) The 'enrichplot' package provides visualization methods for interpreting functional enrichment results from ORA or GSEA analyses. It is designed to work with the 'clusterProfiler' ecosystem and builds on 'ggplot2' for flexible and extensible graphics. Package: r-bioc-ensdb.hsapiens.v75 Architecture: all Version: 2.99.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-ensembldb Filename: pool/dists/resolute/main/r-bioc-ensdb.hsapiens.v75_2.99.0-1.ca2604.1_all.deb Size: 60264404 MD5sum: 18424c3f4ab17ba9e18cc49546b571ea SHA1: a65b748ebc8bbf235955495789c17e93df9057be SHA256: 8a78144138074c7990fb97af75b1e292abbfbd640e6194a2211a641694edc39a SHA512: 97989f80b312edb1b5a85f8c186d13f1f6a5b54eea663549dc19ed688ec90a2cd8c00ddf56e88338017e84f5d2b10823217f6a3869d1db58599d75a54ae01149 Homepage: https://cran.r-project.org/package=EnsDb.Hsapiens.v75 Description: Bioc Package 'EnsDb.Hsapiens.v75' (Ensembl based annotation package) Exposes an annotation databases generated from Ensembl. Package: r-bioc-ensembldb Architecture: all Version: 2.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8053 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-genomicfeatures, r-bioc-annotationfilter, r-cran-rsqlite, r-cran-dbi, r-bioc-biobase, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-annotationdbi, r-bioc-rtracklayer, r-bioc-s4vectors, r-bioc-rsamtools, r-bioc-iranges, r-bioc-protgenerics, r-bioc-biostrings, r-cran-curl Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-ensdb.hsapiens.v86, r-cran-testthat, r-bioc-bsgenome.hsapiens.ncbi.grch38, r-bioc-ggbio, r-bioc-gviz, r-cran-rmarkdown, r-bioc-annotationhub Filename: pool/dists/resolute/main/r-bioc-ensembldb_2.36.0-1.ca2604.1_all.deb Size: 2468208 MD5sum: 5675df8057bf8ae2dffeb91f91d5dfa8 SHA1: a0c49c35f6e0608447a2001809d15d7b1480c8a3 SHA256: 10735332cc4c5bba1be6c4455aa4373f074090d43e5df7ecc98baf3c38cfc99a SHA512: 9d1cd12ad2c3a90ba82130537467a12b0a0656580d2b5ed6a9cee3f93f9fd73cf5e17d1ad9fb22b31fe09200d81912b38a0673c5f8fc6c66f69ec4bd13b93c21 Homepage: https://cran.r-project.org/package=ensembldb Description: Bioc Package 'ensembldb' (Utilities to create and use Ensembl-based annotation databases) The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. Package: r-bioc-epidish Architecture: all Version: 2.28.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3859 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-e1071, r-cran-quadprog, r-cran-matrixstats, r-cran-stringr, r-cran-locfdr, r-cran-matrix, r-bioc-genefilter Suggests: r-cran-roxygen2, r-bioc-geoquery, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-biobase, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-epidish_2.28.0-1.ca2604.1_all.deb Size: 2141574 MD5sum: 726cb9803930aa42838e562563ef968e SHA1: bac9b749660829cf9b310fffe3bd193757547f7a SHA256: b20037ab700881c861ade487da04ebe90eac719e8cb16d10c79f3e58ee87ba99 SHA512: f1f82f6285c2136dc5dc351f9b61ad28671fb6930b9a974b2e31e9a1c26bb788851b319a7ead91c4a35d1fd738b37cfe0908da83aa18eafc07d3fd1699db18f9 Homepage: https://cran.r-project.org/package=EpiDISH Description: Bioc Package 'EpiDISH' (Epigenetic Dissection of Intra-Sample-Heterogeneity) EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of blood-tissue of any age, from birth to old-age, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. Package: r-bioc-escape Architecture: all Version: 2.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2969 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggdist, r-cran-ggplot2, r-cran-matrix, r-bioc-matrixgenerics, r-bioc-summarizedexperiment Suggests: r-bioc-aucell, r-bioc-biocparallel, r-bioc-biocstyle, r-bioc-delayedmatrixstats, r-cran-dplyr, r-bioc-fgsea, r-bioc-gseabase, r-cran-ggraph, r-cran-ggridges, r-cran-ggpointdensity, r-bioc-gsva, r-cran-hexbin, r-cran-igraph, r-cran-irlba, r-cran-knitr, r-bioc-msigdb, r-cran-patchwork, r-cran-rmarkdown, r-cran-rlang, r-bioc-scran, r-cran-seuratobject, r-cran-seurat, r-bioc-singlecellexperiment, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-bioc-ucell Filename: pool/dists/resolute/main/r-bioc-escape_2.8.0-1.ca2604.1_all.deb Size: 1753872 MD5sum: 957a2502ba370faeb86469a464258487 SHA1: 28e0a20c1a8621837d7c22a16099ee3ad6fc1598 SHA256: fccf467f1377cf5432a30afe0657a63886ac6cc8029a6708586c0cb32bfad641 SHA512: a419b643c1bfaaf2075ec4012dafeaf382a3877dfcb02140a8014b9daacbce7e320ffb077d5a776f00b9c95ccd89f2d9647b87924b47a34aaf963858b147822a Homepage: https://cran.r-project.org/package=escape Description: Bioc Package 'escape' (Easy single cell analysis platform for enrichment) A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells. Alternatively, escape supports use of rank-based GSEA, such as the use of differential gene expression via fgsea. 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ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. 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The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. Package: r-bioc-ggcyto Architecture: all Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7171 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-bioc-flowcore, r-bioc-ncdfflow, r-bioc-flowworkspace, r-cran-plyr, r-cran-scales, r-cran-hexbin, r-cran-data.table, r-cran-rcolorbrewer, r-cran-gridextra, r-cran-rlang Suggests: r-cran-testthat, r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-bioc-flowstats, r-bioc-opencyto, r-bioc-flowviz, r-cran-ggridges, r-cran-vdiffr Filename: pool/dists/resolute/main/r-bioc-ggcyto_1.40.0-1.ca2604.1_all.deb Size: 3230134 MD5sum: 0ee322903dd9a552613e83b0f5bf8667 SHA1: e00d357b87fbbc48f1bfbfee3757704f1c0166f4 SHA256: 4c7675d2f427037752165836069f291133a9d2d44ed14c6e3555d93c53122458 SHA512: 1cdd689e35110dff3ee0cb492f801d3bbd329a5de83ecfa519f68c63c11cd400f39b5c38b2db2449405638a917f1115f46d0859c1645c98aa87dc4d088c6f6b2 Homepage: https://cran.r-project.org/package=ggcyto Description: Bioc Package 'ggcyto' (Visualize Cytometry data with ggplot) With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. Package: r-bioc-ggkegg Architecture: all Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5403 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggraph, r-cran-xml, r-cran-igraph, r-cran-tidygraph, r-bioc-biocfilecache, r-cran-data.table, r-cran-dplyr, r-cran-magick, r-cran-patchwork, r-cran-shadowtext, r-cran-stringr, r-cran-tibble, r-cran-gtable Suggests: r-cran-knitr, r-bioc-clusterprofiler, r-cran-bnlearn, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-annotationdbi, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-ggkegg_1.10.0-1.ca2604.1_all.deb Size: 3881626 MD5sum: 9c28ec6f35c2e06bf9eb9ee63786f9b5 SHA1: b19b693c7ecddd7f9dd803426ee7910c9a63b0a6 SHA256: ad77f882222d76196f0c3e7bb9c5620dd0cdb65a76f57f59fd51730f80a5d977 SHA512: fe68a74f2bd1847880323f837820ef10aff4a1b49e4821b24db80c4fb309160f5a7c62a2feb65dd9ede0b1ceaf5f2fc5797742a36e82907fa9ba151df9079d6c Homepage: https://cran.r-project.org/package=ggkegg Description: Bioc Package 'ggkegg' (Analyzing and visualizing KEGG information using the grammar ofgraphics) This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages. Package: r-bioc-ggmsa Architecture: all Version: 1.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3266 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr, r-cran-aplot, r-cran-rcolorbrewer, r-cran-ggfun, r-cran-ggforce, r-cran-dplyr, r-bioc-r4rna, r-cran-seqmagick, r-bioc-ggtree Suggests: r-bioc-ggtreeextra, r-cran-ape, r-cran-cowplot, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-ggnewscale, r-cran-kableextra, r-cran-gggenes, r-cran-statebins, r-cran-prettydoc, r-cran-testthat, r-cran-yulab.utils Filename: pool/dists/resolute/main/r-bioc-ggmsa_1.18.0-1.ca2604.1_all.deb Size: 2283292 MD5sum: bf38d5a459bd4092fd5cf5c8e332c58d SHA1: c8eb764ce367ee3913761c74c3433b19796a7837 SHA256: 9cb007f5e07fdcbdce0e30c81b388d92d3e249d19945c0fa4a40e6415a3e52b8 SHA512: 156578f041efb069b4ff006b16654a3f42235f9461e8dba1bcc07c6b651ea5ce817c6d2001852dee7dbb4392be0fb52dbefaf65d2db02266b53af9f9a5a57cb2 Homepage: https://cran.r-project.org/package=ggmsa Description: Bioc Package 'ggmsa' (Plot Multiple Sequence Alignment using 'ggplot2') A visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using 'ggplot2'. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as phylogenetic tree Visualized by 'ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations. Package: r-bioc-ggtree Architecture: all Version: 4.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4878 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-aplot, r-cran-cli, r-cran-dplyr, r-cran-ggfun, r-cran-ggiraph, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-tidytree, r-bioc-treeio, r-cran-yulab.utils Suggests: r-cran-emojifont, r-cran-ggimage, r-cran-ggplotify, r-cran-shadowtext, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-igraph, r-cran-testthat, r-cran-tibble, r-cran-glue, r-bioc-biostrings Filename: pool/dists/resolute/main/r-bioc-ggtree_4.2.0-1.ca2604.1_all.deb Size: 1022674 MD5sum: 5526cbd875ddcadca35647f7f7887bcf SHA1: 8588a7c2bc99e5577f9d679822d76484eb40897a SHA256: 41415093907b5a8ea3d9e8f6e8a9196beb4fd27f25fd7aed4d56c2be3e524340 SHA512: b70475df9feea158c88add3553178c5ae9b69ecc73a7d7f823d221009397a17c559fbcee947d60186e96df1fe3a153915de3b19cd9d35e17806275f15f35b407 Homepage: https://cran.r-project.org/package=ggtree Description: Bioc Package 'ggtree' (an R package for visualization of tree and annotation data) 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 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These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by 'ggtree' with the grammar of 'ggplot2'. Package: r-bioc-glimma Architecture: all Version: 2.21.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 38838 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-htmlwidgets, r-bioc-edger, r-bioc-deseq2, r-bioc-limma, r-bioc-summarizedexperiment, r-cran-jsonlite, r-bioc-s4vectors Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-iranges, r-bioc-genomicranges, r-bioc-annotationhub, r-bioc-scrnaseq, r-bioc-scater, r-bioc-scran Filename: pool/dists/resolute/main/r-bioc-glimma_2.21.0-1.ca2604.1_all.deb Size: 5857230 MD5sum: f7c80a2539eec81ffdf155bf55193c58 SHA1: b30b7ee1c0fe149a073e72560b7de67ad713e8ec SHA256: 12de093977e22374a91c8203e7a81f8d2ee7fc1c36a750125f0fb637028f55a2 SHA512: f1c0e526cd9d37c4c0722f09375fce86007d2b4a4a4c596ef2705e9f98ee4e98e58387785e57c7fe75eda9e38c913156015a667a4a10cbd748e649e4edeeddfe Homepage: https://cran.r-project.org/package=Glimma Description: Bioc Package 'Glimma' (Interactive visualizations for gene expression analysis) This package produces interactive visualizations for RNA-seq data analysis, utilizing output from limma, edgeR, or DESeq2. 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Package: r-bioc-glmsparsenet Architecture: all Version: 1.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6097 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biomart, r-cran-checkmate, r-cran-dplyr, r-cran-forcats, r-cran-futile.logger, r-cran-ggplot2, r-cran-glue, r-cran-httr, r-cran-lifecycle, r-cran-readr, r-cran-rlang, r-cran-glmnet, r-cran-matrix, r-bioc-multiassayexperiment, r-bioc-summarizedexperiment, r-cran-survminer, r-bioc-tcgautils Suggests: r-bioc-biocstyle, r-bioc-curatedtcgadata, r-cran-knitr, r-cran-magrittr, r-cran-reshape2, r-cran-proc, r-cran-rmarkdown, r-cran-survival, r-cran-testthat, r-cran-venndiagram, r-cran-withr Filename: pool/dists/resolute/main/r-bioc-glmsparsenet_1.30.0-1.ca2604.1_all.deb Size: 1932930 MD5sum: e6792a5c83f51a0dcf6648a4e93d96e3 SHA1: ac1e4747180a878c059abb88001bb071aac18834 SHA256: 34c7e23c2f070f94197a901ce52364a7796a7430a4e2251c0a0671a1db66127d SHA512: ef4d3c7ad351b876675924082d4fbf0383f2e0309c2ac1bd550c7664e9df59db9b8eddfc6ee4399767cd6e50422e97305be007d60d9aa88f121dcfd3394f7622 Homepage: https://cran.r-project.org/package=glmSparseNet Description: Bioc Package 'glmSparseNet' (Network Centrality Metrics for Elastic-Net Regularized Models) glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". 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This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. 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A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. 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Package: r-bioc-greylistchip Architecture: all Version: 1.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 978 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-bsgenome, r-bioc-rsamtools, r-bioc-rtracklayer, r-cran-mass, r-bioc-seqinfo, r-bioc-summarizedexperiment Suggests: r-bioc-biocstyle, r-bioc-biocgenerics, r-cran-runit, r-bioc-bsgenome.hsapiens.ucsc.hg19 Filename: pool/dists/resolute/main/r-bioc-greylistchip_1.44.0-1.ca2604.1_all.deb Size: 760016 MD5sum: dd54d8e25f12c4f4dce2a15ca4c333af SHA1: a33b5793a433aa090f5e864f9a104699d09a61eb SHA256: f07ede5c3f17a384d1523cc5fd82df89d01e7d957b151ac7ca1699cff6cd6135 SHA512: 82575cf08c5c04d3973a3acda18105b6ba7c1bee15327059dd662681e65032891300565b68bc8aa27017a6255a66a19c9e938bdc683c77817467e80ad9370201 Homepage: https://cran.r-project.org/package=GreyListChIP Description: Bioc Package 'GreyListChIP' (Grey Lists -- Mask Artefact Regions Based on ChIP Inputs) Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. Package: r-bioc-gseabase Architecture: all Version: 1.74.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1869 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-annotate, r-bioc-graph, r-bioc-annotationdbi, r-cran-xml Suggests: r-bioc-hgu95av2.db, r-bioc-go.db, r-bioc-org.hs.eg.db, r-bioc-rgraphviz, r-bioc-reportingtools, r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-runit Filename: pool/dists/resolute/main/r-bioc-gseabase_1.74.0-1.ca2604.1_all.deb Size: 910128 MD5sum: b53437b53b592717aeb1bc3978daf35e SHA1: 7aad6449a2ac921af92e032d4cefebc38c61bd08 SHA256: ac506649236ba101f4bd2494d62678cfc8f28c3f7df30f77215e864d3c0511eb SHA512: 19c3a744a671fb51259074ccba00616a5d0708dd950e1c5538eac0871b11a83f0fbc276dc6a61f00971d72f3615d382185cff4a479fa3f4ad0e5cafe249edab7 Homepage: https://cran.r-project.org/package=GSEABase Description: Bioc Package 'GSEABase' (Gene set enrichment data structures and methods) This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). Package: r-bioc-gsvadata Architecture: all Version: 1.48.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16294 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-gseabase, r-bioc-summarizedexperiment, r-cran-matrix, r-bioc-spatialexperiment Filename: pool/dists/resolute/main/r-bioc-gsvadata_1.48.0-1.ca2604.1_all.deb Size: 16509426 MD5sum: 46f1b95113522795a63e325328fe62c3 SHA1: ee969cdd42462c08f5bb4c1c340457c5ba5c2d42 SHA256: 997e6cd690ec0f472d6097f529649d67797a19bedb1df890a06a4d0ea9b8b414 SHA512: 76f0e3e241aa8e153210ec2acebb83694def00afcae6e8314a7550392c80d87e2fd7faacea14704c6dd6bd301a5660922a80e1b6c0c5fe3b12072edc6f5e5d9b Homepage: https://cran.r-project.org/package=GSVAdata Description: Bioc Package 'GSVAdata' (Data employed in the vignette of the GSVA package) This package stores the data employed in the vignette of the GSVA package. These data belong to the following publications: Cahoy et al. J Neurosci 28:264-278, 2008; Carrel and Willard, Nature, 434:400-404, 2005; Huang et al. PNAS, 104:9758-9763, 2007; Pickrell et al. Nature, 464:768-722, 2010; Skaletsky et al. Nature, 423:825-837; Verhaak et al. Cancer Cell 17:98-110, 2010; Costa et al. FEBS J, 288:2311-2331, 2021. Package: r-bioc-gviz Architecture: all Version: 1.56.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10684 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-xvector, r-bioc-rtracklayer, r-cran-lattice, r-cran-rcolorbrewer, r-bioc-biomart, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-genomicfeatures, r-bioc-ensembldb, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-biovizbase, r-bioc-rsamtools, r-cran-latticeextra, r-cran-matrixstats, r-bioc-genomicalignments, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-biocgenerics, r-cran-digest Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-xml2, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-gviz_1.56.0-1.ca2604.1_all.deb Size: 7134768 MD5sum: f0743607d48d99be2708bdf331dc5f3b SHA1: f5296cbcec721294535d4e9025869dcc3fd37774 SHA256: d1414fc7053cc1f2105ed07072af271a696d4b869163b8da6b3b348df19a480d SHA512: 64de14768ad2e7b36c8f4008b0174a06674afdf9ebcec8d55ca7ad09d13c12f7a0feca30d67566a2555f4594a7aa204d0ae522057cf0e28210da6332ba0183c7 Homepage: https://cran.r-project.org/package=Gviz Description: Bioc Package 'Gviz' (Plotting data and annotation information along genomiccoordinates) Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. Package: r-bioc-gwascat Architecture: all Version: 2.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 37093 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-readr, r-bioc-biostrings, r-bioc-annotationdbi, r-bioc-biocfilecache, r-bioc-snpstats, r-bioc-variantannotation, r-bioc-annotationhub, r-cran-data.table, r-cran-tibble Suggests: r-bioc-do.db, r-cran-dt, r-cran-knitr, r-bioc-rbgl, r-cran-testthat, r-cran-rmarkdown, r-cran-dplyr, r-bioc-gviz, r-bioc-rsamtools, r-bioc-rtracklayer, r-bioc-graph, r-bioc-ggbio, r-bioc-delayedarray, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-org.hs.eg.db, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-gwascat_2.44.0-1.ca2604.1_all.deb Size: 35683440 MD5sum: ee8773d48d09382536e042212edc9b74 SHA1: 9a5e7f642f84343e8b779e274e303d4b48f7c1fb SHA256: 55ec4b0a8aa7ac4077ac1e0884fb9b280ba3b61f9c3febc21136b2a6f3a860b2 SHA512: 261377def8ebe59997c782ce9124f2442a014b61df3dcc4ab9e93244e73c97f7f685aadb560c2e35f1da71b1e3bf4b568da98f024f039234b66cecc52d49e0e1 Homepage: https://cran.r-project.org/package=gwascat Description: Bioc Package 'gwascat' (representing and modeling data in the EMBL-EBI GWAS catalog) Represent and model data in the EMBL-EBI GWAS catalog. 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Package: r-bioc-gypsum Architecture: all Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-filelock, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-cran-digest, r-cran-jsonvalidate, r-cran-dbi, r-cran-rsqlite, r-bioc-s4vectors Filename: pool/dists/resolute/main/r-bioc-gypsum_1.8.0-1.ca2604.1_all.deb Size: 412628 MD5sum: 386686e139f648f32462ecf6187f93a4 SHA1: 9fb8d7cc552748f4795b5f593a42b0470017a959 SHA256: 7d21a495948795f0139054cb5d5cdd2c27dfa3fefd490529d01becd7c0db241b SHA512: d8f05f43a029f7f4cb1ba890306326e70ab2b32cf86cab0fefebd08cc25877a41fe589017a403f077caef2d02166a2cf3791bf763f1c96ab12294ad79b95e140 Homepage: https://cran.r-project.org/package=gypsum Description: Bioc Package 'gypsum' (Interface to the gypsum REST API) Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative and management tasks. Check out the documentation at https://github.com/ArtifactDB/gypsum-worker for more details. Package: r-bioc-hdf5array Architecture: all Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10236 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-h5mread, r-cran-matrix, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-rhdf5 Suggests: r-bioc-biocparallel, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-h5vcdata, r-bioc-experimenthub, r-bioc-tenxbraindata, r-bioc-zellkonverter, r-bioc-genomicfeatures, r-bioc-singlecellexperiment, r-bioc-delayedmatrixstats, r-bioc-genefilter, r-cran-rspectra, r-cran-runit, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-hdf5array_1.40.0-1.ca2604.1_all.deb Size: 8885990 MD5sum: 9fda40c7d93589af6de78c709aeaff63 SHA1: 07781f31fa9edc1ef35810eb12df12fd055fda50 SHA256: 9e909ec3f8a1c410dfbdb5add4656754a62af1517e23068933bb464d2fd807f3 SHA512: 91f8ed5b1b04137bc5dbcd435be35bb2697de9e801cd136501d4e4e2eeddb88f10f9dc04e0c4e64970d672961c799ebb90a1019b6a7b076b5f1947a8b921bca7 Homepage: https://cran.r-project.org/package=HDF5Array Description: Bioc Package 'HDF5Array' (HDF5 datasets as array-like objects in R) The HDF5Array package is an HDF5 backend for DelayedArray objects. It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. Package: r-bioc-hdo.db Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8932 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-annotationdbi, r-cran-dbi Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-hdo.db_1.0.0-1.ca2604.1_all.deb Size: 2203014 MD5sum: 1070801990f8da4c4f6c11c803a3d463 SHA1: 1edf7be01bd05b964efc20cc96ce613c3067d8df SHA256: 463ca4e2d4cd17e4c2ad036bdbba88cd7e255f9cf56e48e8bcb9b7b01883a6ea SHA512: d3d2111c5128fb0a801bc9f0ec49c0b47fe063448a9f9d85b5de0978b97854861fdd37bcc7cb2aff500e894e7c2f50beb36d212a8dad55a8506c828f78585eae Homepage: https://cran.r-project.org/package=HDO.db Description: Bioc Package 'HDO.db' (A set of annotation maps describing the entire Human DiseaseOntology) A set of annotation maps describing the entire Human Disease Ontology assembled using data from DO. 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By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. Package: r-bioc-hgu95a.db Architecture: all Version: 3.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-org.hs.eg.db Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/resolute/main/r-bioc-hgu95a.db_3.13.0-1.ca2604.1_all.deb Size: 482442 MD5sum: 3693ebb37d283e47c7bd2c2712ecb7b9 SHA1: 593d78d10ae49b946d6e6113fca576d68a03daff SHA256: cc5dba336492ad2730b20883bdc82efe0b3c375a35035d2c4f0c382064224290 SHA512: f54d9066e463ea405ecc5c0dd3852788168d01e92f5a1b0b61d588d35c8282a50917c00eef71d673c412ac035184894b1be4007bf21d06231715177edf39c02f Homepage: https://cran.r-project.org/package=hgu95a.db Description: Bioc Package 'hgu95a.db' (Affymetrix Affymetrix HG_U95A Array annotation data (chiphgu95a)) Affymetrix Affymetrix HG_U95A Array annotation data (chip hgu95a) assembled using data from public repositories Package: r-bioc-hiccompare Architecture: all Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4116 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-mgcv, r-bioc-interactionset, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-biocparallel, r-cran-kernsmooth, r-cran-pheatmap, r-cran-gtools, r-bioc-rhdf5 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-multihiccompare Filename: pool/dists/resolute/main/r-bioc-hiccompare_1.34.0-1.ca2604.1_all.deb Size: 3882672 MD5sum: 653d30fd202d518a1d791cdd1f7e176a SHA1: a78eba7da45f82d12d9e25a9e4b51fe01b3371e7 SHA256: e313b791d13f408e02d7b11f3a047cfe6d6cbee535787c37053d3242f6523ca0 SHA512: f0e673aba6462fcd35184c55cde719ae3368ec1853a75ad6c58bc0c2f32df9236d7842da3ade41e4288e344425841871f847caa562b59139fb0d5f0e68bb23e5 Homepage: https://cran.r-project.org/package=HiCcompare Description: Bioc Package 'HiCcompare' (HiCcompare: Joint normalization and comparative analysis ofmultiple Hi-C datasets) HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. 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In this experiment, primary human skeletal muscle myoblasts (HSMM) were expanded under high mitogen conditions (GM) and then differentiated by switching to low-mitogen media (DM). RNA-Seq libraries were sequenced from each of several hundred cells taken over a time-course of serum-induced differentiation. Between 49 and 77 cells were captured at each of four time points (0, 24, 48, 72 hours) following serum switch using the Fluidigm C1 microfluidic system. RNA from each cell was isolated and used to construct mRNA-Seq libraries, which were then sequenced to a depth of ~4 million reads per library, resulting in a complete gene expression profile for each cell. 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The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. Package: r-bioc-karyoploter Architecture: all Version: 1.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3657 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-regioner, r-bioc-genomicranges, r-bioc-iranges, r-bioc-rsamtools, r-cran-memoise, r-bioc-rtracklayer, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-s4vectors, r-bioc-biovizbase, r-cran-digest, r-cran-bezier, r-bioc-genomicfeatures, r-bioc-bamsignals, r-bioc-annotationdbi, r-bioc-variantannotation Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat, r-cran-magrittr, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.hsapiens.ucsc.hg19.masked, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-pasillabamsubset Filename: pool/dists/resolute/main/r-bioc-karyoploter_1.38.0-1.ca2604.1_all.deb Size: 2524030 MD5sum: 4219687ef18618b99c9ddac55cae70df SHA1: aa35550e555b3b2a4a14c26ff00113d4b7405448 SHA256: 83e049949f01b81ca78ff0865254c79be21d349845900c83d78d2d379e8002a4 SHA512: 526b80929df881dfedf7c7df8d10e0f7a9ebb5aff5e644e96d95248c698f98f91383383dba29e8d0492e0c6aaaa32421b4547c4b25fd9e1f350ff58f3e5f83b8 Homepage: https://cran.r-project.org/package=karyoploteR Description: Bioc Package 'karyoploteR' (Plot customizable linear genomes displaying arbitrary data) karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. Package: r-bioc-kegggraph Architecture: all Version: 1.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2351 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-bioc-graph, r-cran-rcurl, r-bioc-rgraphviz Suggests: r-bioc-rbgl, r-cran-testthat, r-cran-rcolorbrewer, r-bioc-org.hs.eg.db, r-bioc-hgu133plus2.db, r-bioc-spia Filename: pool/dists/resolute/main/r-bioc-kegggraph_1.72.0-1.ca2604.1_all.deb Size: 1658436 MD5sum: 58740cdb024ec3a27570a3d3afd5f66b SHA1: 793465024e2370c2e025a50d58cc476ae5ef5c1e SHA256: 8e58a58d958077123491bbd871030b4895b25db10bd8cb07e1da9b451be1afd6 SHA512: ebccec4a9c98d0ab6e3a536a68bda6e286f3bb0add20d044983c8f002d65149b86737db9d876ece1b41d8091427b30afccb8b1e46e0f26ce8dd954f943a20492 Homepage: https://cran.r-project.org/package=KEGGgraph Description: Bioc Package 'KEGGgraph' (KEGGgraph: A graph approach to KEGG PATHWAY in R andBioconductor) KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. Package: r-bioc-keggrest Architecture: all Version: 1.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-png, r-bioc-biostrings Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-bioc-keggrest_1.52.0-1.ca2604.1_all.deb Size: 383498 MD5sum: 68d7a8874f2255758eaba4aff52bd045 SHA1: e5b73e707cb96daab073862a96e560ecea51b399 SHA256: 2a7c426c166dc67af2e663e12509674e86f22a91638c05631321d92ffe116bdd SHA512: 7f5bb8bae69ff54f4b799313fd24268c199737d86abdb77370c6e51245caaf61914e89b550ccc2286a523f8864a1b86733a3fec8706abee9100dab0ca279e4d6 Homepage: https://cran.r-project.org/package=KEGGREST Description: Bioc Package 'KEGGREST' (Client-side REST access to the Kyoto Encyclopedia of Genes andGenomes (KEGG)) A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see ). Note that KEGGREST is based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. Package: r-bioc-lbe Architecture: all Version: 1.80.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-qvalue Filename: pool/dists/resolute/main/r-bioc-lbe_1.80.0-1.ca2604.1_all.deb Size: 615224 MD5sum: 2c4108f3501db81f9d87bb83041fa978 SHA1: c0fecc22a046f7b9d508e2a4f9cf634688990f52 SHA256: 380db90856ce14a0ebafa8d1435e2fabccdef106a0672d17c79055bb453cc82d SHA512: b3e034a5f60df50e2dd9af3de5cb968012479893c169f422f78083c264b60769792c92faf8438975846fd8b0628f35aacdf17b0bd4b6113483843b2d2d8b0638 Homepage: https://cran.r-project.org/package=LBE Description: Bioc Package 'LBE' (Estimation of the false discovery rate) LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. 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It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. Package: r-bioc-m3c Architecture: all Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-dosnow, r-cran-cluster, r-cran-foreach, r-cran-doparallel, r-cran-matrixcalc, r-cran-rtsne, r-cran-corpcor, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-m3c_1.34.0-1.ca2604.1_all.deb Size: 775928 MD5sum: 3bdc2a231c9b2b580809018e080a7006 SHA1: 1cef6e02fb4d01594d8fc7f663b40888c4621102 SHA256: ffc37d87bf523893e408dc0d63abaa57dccb1c8f9fac92a46e371a13464a9d5a SHA512: 1e263b584844de48385d358a7a121aa2a3bc31f9651a67791bb121029081f541ee891ea366846416d5d5001cbf2419b3c1a90e58ce31b7b5ac2281f16fe97e3e Homepage: https://cran.r-project.org/package=M3C Description: Bioc Package 'M3C' (Monte Carlo Reference-based Consensus Clustering) M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. 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Package: r-bioc-matrixgenerics Architecture: all Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixstats Suggests: r-cran-matrix, r-bioc-sparsematrixstats, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-summarizedexperiment, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-matrixgenerics_1.24.0-1.ca2604.1_all.deb Size: 439578 MD5sum: e177ab0f74848b1a79f7170a185632b6 SHA1: da37ec79d5e0d39ecd9075417639e86c86667f18 SHA256: 7b97cfa3f4fe00dae6795e5d24f95eb6a63a9a3575c5218101075cddaf228673 SHA512: 8f5b3d76a854861cce53fb38805b6e1c51aaf5f44e3d3e3b2c2387e5d77942ba1f6bbbfd76a3099060d05b83bc8dbd42bb37c16801f288aaf92be3f8a170be5a Homepage: https://cran.r-project.org/package=MatrixGenerics Description: Bioc Package 'MatrixGenerics' (S4 Generic Summary Statistic Functions that Operate onMatrix-Like Objects) S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. Package: r-bioc-metaboannotation Architecture: all Version: 1.16.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4597 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-mscoreutils, r-bioc-metabocoreutils, r-bioc-protgenerics, r-bioc-s4vectors, r-bioc-spectra, r-bioc-biocparallel, r-bioc-summarizedexperiment, r-bioc-qfeatures, r-bioc-annotationhub, r-bioc-compounddb Suggests: r-cran-testthat, r-cran-knitr, r-bioc-msdatahub, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-plotly, r-cran-shiny, r-cran-shinyjs, r-cran-msentropy, r-cran-dt, r-cran-microbenchmark, r-bioc-mzr Filename: pool/dists/resolute/main/r-bioc-metaboannotation_1.16.0-1.ca2604.1_all.deb Size: 1440678 MD5sum: bce453fd9b97f96a5e7be4806a2870e0 SHA1: 320aa6dd57389e482975369dc3223759e88f7d3f SHA256: 53ee6843adc0112806816e6e64f9def7a8da06f5a26877b19501d29c94d3f352 SHA512: cf0f1c6463c7928b4937785c26c9e5320278362768f680a2a175cdda1f7a96d44043dbccd058eb6ab619a498a6d01205c2f9b81ff22ed3ada0ecba4d26c2e1c9 Homepage: https://cran.r-project.org/package=MetaboAnnotation Description: Bioc Package 'MetaboAnnotation' (Utilities for Annotation of Metabolomics Data) High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data. Package: r-bioc-metabocoreutils Architecture: all Version: 1.20.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2893 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-mscoreutils, r-bioc-biocparallel Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-robustbase Filename: pool/dists/resolute/main/r-bioc-metabocoreutils_1.20.1-1.ca2604.1_all.deb Size: 1356486 MD5sum: a1ea9fcb915aa84c20e86a34f458b2c2 SHA1: c0264d254aa8129f821a8d9fd3604825d1124cae SHA256: 1227c5cd7c0db729d880c82518885a9af64bb5f607f23aa76351d7f91e898aad SHA512: 064a3b2a045fe0365946ea82bc1ca04c57488e6d1d332e8419deec813968441c23aa26d621e459dc2c0123ecd006b696af75819ad20597d0ffff8a075c444a41 Homepage: https://cran.r-project.org/package=MetaboCoreUtils Description: Bioc Package 'MetaboCoreUtils' (Core Utils for Metabolomics Data) MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments. Package: r-bioc-metagenomeseq Architecture: all Version: 1.54.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2642 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-limma, r-cran-glmnet, r-cran-rcolorbrewer, r-cran-matrixstats, r-cran-foreach, r-cran-matrix, r-cran-gplots, r-bioc-wrench Suggests: r-bioc-annotate, r-bioc-biocgenerics, r-bioc-biomformat, r-cran-knitr, r-cran-gss, r-cran-testthat, r-cran-vegan, r-bioc-ihw, r-bioc-sparsearray Filename: pool/dists/resolute/main/r-bioc-metagenomeseq_1.54.0-1.ca2604.1_all.deb Size: 2139248 MD5sum: e421d1810d1f2a59f47202ea375be0dc SHA1: e8746f00a12f75c68e70995ba1cd9348e4f6e9f8 SHA256: 07d07be9d1b2df744d0e7c5bc5a0226b6f5c5146bb6c4512c26f9e7e6266c805 SHA512: 7858e6df934c27d1d653f01157c45ad70fa95929de8551c9edb2be9b7fd5621fbe6cf0ebc5acbdaee39357fef76ea623ee2ae03d518218d2a98263abdf8fe24c Homepage: https://cran.r-project.org/package=metagenomeSeq Description: Bioc Package 'metagenomeSeq' (Statistical analysis for sparse high-throughput sequencing) metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. Package: r-bioc-methylumi Architecture: all Version: 2.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12116 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-scales, r-cran-reshape2, r-cran-ggplot2, r-cran-matrixstats, r-bioc-fdb.infiniummethylation.hg19, r-bioc-minfi, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-lattice, r-bioc-annotate, r-bioc-genefilter, r-bioc-annotationdbi, r-bioc-illuminaio, r-bioc-genomicfeatures Suggests: r-bioc-lumi, r-bioc-limma, r-cran-xtable, r-cran-sqn, r-cran-mass, r-bioc-rtracklayer, r-bioc-biostrings, r-bioc-tcgamethylation450k, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-fdb.infiniummethylation.hg18, r-bioc-homo.sapiens, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-methylumi_2.58.0-1.ca2604.1_all.deb Size: 7411324 MD5sum: 3ef11ab0bb3f59a629631c52d3788f0b SHA1: e750cda89cc6fa87a8b76850d23eb3d16da0f3b9 SHA256: 5197b88142d19ae80201e0c3af6ec5c65afd985a3f6ebd881dcba42669276660 SHA512: 4a29820daa170e3ffbd35b0d234a76cc2a3bfc8c4efe200361d2de34ff6471af67bb9eb0ec8ad098155f228c08ef2e20b77fa1cec84e1691a955a1626bdf8a8e Homepage: https://cran.r-project.org/package=methylumi Description: Bioc Package 'methylumi' (Handle Illumina methylation data) This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. Package: r-bioc-mfuzz Architecture: all Version: 2.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1365 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-e1071, r-bioc-tkwidgets Suggests: r-bioc-marray Filename: pool/dists/resolute/main/r-bioc-mfuzz_2.72.0-1.ca2604.1_all.deb Size: 776808 MD5sum: 21d2b54c8d0d1e762b7a109ef2b80dc3 SHA1: b9c9d557874b7e6c3044e691eaec701c7bcda791 SHA256: e1574345d94cd6aba81cb232aeaa8952c7ceafe250e4d2c86530a61008f79bcb SHA512: 8d22b3c2992e7d2264b02cecc557f30954d825c0df1e5f1f53417f92ee2fe5e7fd1428c567efc45d5693e9ad73819777cf4fd6b059f7f5c4b80a0d5d64f4f733 Homepage: https://cran.r-project.org/package=Mfuzz Description: Bioc Package 'Mfuzz' (Soft clustering of omics time series data) The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface. Package: r-bioc-microbiome Architecture: all Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1530 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-phyloseq, r-cran-ggplot2, r-bioc-biostrings, r-cran-compositions, r-cran-dplyr, r-cran-reshape2, r-cran-rtsne, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-vegan Suggests: r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-cairo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-microbiome_1.34.0-1.ca2604.1_all.deb Size: 1016476 MD5sum: 09b89a892ec82eda22a576786f8bf906 SHA1: c92f6e85e8e224a80b4cc3622b1f1abb35f76ef2 SHA256: cb2fe7c8030226adadb5712b1dc5b498a46da30f3dd2e2b33e8bdf99b4797fbb SHA512: 2cacb749425ef8aed744e6a8b18af6a228493cc31a3d6c8423490d79f9625c737633c6975cc0186f143a6498f7dd9e84c6a3c4e2fb0bda5f4fc70e2cf44f3a06 Homepage: https://cran.r-project.org/package=microbiome Description: Bioc Package 'microbiome' (Microbiome Analytics) Utilities for microbiome analysis. Package: r-bioc-microbiotaprocess Architecture: all Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8131 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-bioc-biostrings, r-cran-ggrepel, r-cran-vegan, r-cran-zoo, r-bioc-ggtree, r-cran-tidytree, r-cran-mass, r-cran-rlang, r-cran-tibble, r-cran-coin, r-cran-ggsignif, r-cran-patchwork, r-cran-ggstar, r-cran-tidyselect, r-bioc-summarizedexperiment, r-cran-foreach, r-bioc-treeio, r-cran-pillar, r-cran-cli, r-cran-plyr, r-cran-dtplyr, r-bioc-ggtreeextra, r-cran-data.table, r-cran-ggfun Suggests: r-cran-rmarkdown, r-cran-prettydoc, r-cran-testthat, r-cran-knitr, r-cran-nlme, r-cran-phangorn, r-bioc-decipher, r-cran-randomforest, r-cran-jsonlite, r-bioc-biomformat, r-cran-scales, r-cran-yaml, r-cran-withr, r-bioc-s4vectors, r-cran-purrr, r-cran-seqmagick, r-cran-glue, r-cran-ggupset, r-cran-ggvenndiagram, r-cran-ggalluvial, r-cran-forcats, r-bioc-phyloseq, r-cran-aplot, r-cran-ggnewscale, r-cran-ggside, r-cran-ggh4x, r-bioc-hopach, r-cran-shadowtext, r-bioc-dirichletmultinomial, r-cran-ggpp, r-cran-biocmanager, r-bioc-rhdf5 Filename: pool/dists/resolute/main/r-bioc-microbiotaprocess_1.24.0-1.ca2604.1_all.deb Size: 5385980 MD5sum: d349d9a62033ae6daa4ba6aeca08cbd3 SHA1: e0440bdc01320d66e351be0a5ea7209979b730a1 SHA256: 34edceb366fefa94510d70bc43f0942dd6b4a930a3b9a2156d9bbfa6663e5223 SHA512: ef1d20bb050e3f3be9315cf42a5618f51331010fe68fb0272b28edec9132d38b315a1497fb4f1c525fa66789ce642013e5306833a99042dd3118837dfad9e839 Homepage: https://cran.r-project.org/package=MicrobiotaProcess Description: Bioc Package 'MicrobiotaProcess' (A comprehensive R package for managing and analyzing microbiomeand other ecological data within the tidy framework) MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework). Package: r-bioc-minfi Architecture: all Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2582 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-bumphunter, r-bioc-s4vectors, r-bioc-seqinfo, r-bioc-biobase, r-bioc-iranges, r-cran-beanplot, r-cran-rcolorbrewer, r-cran-lattice, r-cran-nor1mix, r-bioc-siggenes, r-bioc-limma, r-bioc-preprocesscore, r-bioc-illuminaio, r-bioc-delayedmatrixstats, r-cran-mclust, r-bioc-genefilter, r-cran-nlme, r-cran-reshape, r-cran-mass, r-cran-quadprog, r-cran-data.table, r-bioc-geoquery, r-bioc-delayedarray, r-bioc-hdf5array, r-bioc-biocparallel Suggests: r-bioc-illuminahumanmethylation450kmanifest, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-minfidata, r-bioc-minfidataepic, r-bioc-flowsorted.blood.450k, r-cran-runit, r-cran-digest, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-minfi_1.58.0-1.ca2604.1_all.deb Size: 1341058 MD5sum: 36055efacf5b34eaa899fe4827db74bb SHA1: 7294b4ee61c210dca70447142b9d57a4aab6a434 SHA256: a6e61cd918c9aeb3bbafa4d6ecab76ae96ca87b4dded023d3574af266a1dc7eb SHA512: 7dbc2b63e3a727b84ad2e5d1b89a6704d9ea4bcf19ef1fdf16d39df3e39071d540bbddb8d32bcb3bb88aa0b8025c1e6ad1634c8d7f2fd423710f4b6a68254cfe Homepage: https://cran.r-project.org/package=minfi Description: Bioc Package 'minfi' (Analyze Illumina Infinium DNA methylation arrays) Tools to analyze & visualize Illumina Infinium methylation arrays. Package: r-bioc-missmethyl Architecture: all Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2557 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-illuminahumanmethylationepicanno.ilm10b4.hg19, r-bioc-illuminahumanmethylationepicv2anno.20a1.hg38, r-bioc-annotationdbi, r-cran-biasedurn, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-go.db, r-bioc-illuminahumanmethylation450kmanifest, r-bioc-illuminahumanmethylationepicmanifest, r-bioc-illuminahumanmethylationepicv2manifest, r-bioc-iranges, r-bioc-limma, r-bioc-methylumi, r-bioc-minfi, r-bioc-org.hs.eg.db, r-cran-ruv, r-bioc-s4vectors, r-cran-statmod, r-cran-stringr, r-bioc-summarizedexperiment Suggests: r-bioc-biocstyle, r-bioc-edger, r-cran-knitr, r-bioc-minfidata, r-cran-rmarkdown, r-bioc-tweedeseqcountdata, r-bioc-dmrcate, r-bioc-experimenthub Filename: pool/dists/resolute/main/r-bioc-missmethyl_1.46.0-1.ca2604.1_all.deb Size: 1569894 MD5sum: c3f636a40ad1f68a23736f6e89e7a11d SHA1: 74df590e0c1f47aab460f7c3d0bd12df6ac835b8 SHA256: 4ec4bc758fcfcbab09a68d51cbfe9aa7d83793319e25595e97443091c8155ff5 SHA512: 37902f5c0aab7e52661211090d440f1b5d88bdc86c860a10634ce528f559a58fa264099134f9f2b253dbebe174fa30bf8d5ca66f56c9deabfe160614ea727908 Homepage: https://cran.r-project.org/package=missMethyl Description: Bioc Package 'missMethyl' (Analysing Illumina HumanMethylation BeadChip Data) Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. Package: r-bioc-mixomics Architecture: all Version: 6.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 24943 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-lattice, r-cran-ggplot2, r-cran-igraph, r-cran-ellipse, r-cran-corpcor, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-matrixstats, r-cran-rarpack, r-cran-gridextra, r-cran-ggrepel, r-bioc-biocparallel, r-cran-rgl, r-cran-rlang Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-mime, r-cran-testthat, r-cran-microbenchmark, r-cran-magick, r-cran-vdiffr, r-cran-kableextra, r-cran-devtools Filename: pool/dists/resolute/main/r-bioc-mixomics_6.36.0-1.ca2604.1_all.deb Size: 19468664 MD5sum: 93645f79c702512c22f1e69b0d4a288f SHA1: a5fa2d0302327e6713d46ad474a5e1fea6f52d4a SHA256: 1d29aedcb232a12e80d855dd3fbac68b4ca19173626f54846e18553f77ef11b9 SHA512: d0943b3b680d69bed3a6dbd8bc502f3edbf3c4a498f4021a46ea5a5ab92dc8cb303805602644736f93c59b7abbc0afc73c45ff55727323931f4c70d8123d9935 Homepage: https://cran.r-project.org/package=mixOmics Description: Bioc Package 'mixOmics' (Omics Data Integration Project) Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. Package: r-bioc-mlinterfaces Architecture: all Version: 1.92.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5098 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-annotate, r-cran-cluster, r-cran-gdata, r-cran-pls, r-cran-sfsmisc, r-cran-mass, r-cran-rpart, r-bioc-genefilter, r-cran-fpc, r-cran-ggvis, r-cran-shiny, r-cran-gbm, r-cran-rcolorbrewer, r-cran-hwriter, r-cran-threejs, r-cran-mlbench, r-cran-magrittr, r-bioc-summarizedexperiment Suggests: r-cran-class, r-cran-e1071, r-cran-ipred, r-cran-randomforest, r-bioc-gpls, r-cran-pamr, r-cran-nnet, r-bioc-all, r-bioc-hgu95av2.db, r-cran-som, r-bioc-hu6800.db, r-cran-lattice, r-cran-caret, r-bioc-golubesets, r-cran-ada, r-bioc-keggorthology, r-cran-kernlab, r-cran-mboost, r-cran-party, r-cran-klar, r-bioc-biocstyle, r-cran-knitr, r-cran-testthat, r-bioc-airway Filename: pool/dists/resolute/main/r-bioc-mlinterfaces_1.92.0-1.ca2604.1_all.deb Size: 3024466 MD5sum: 8495c238b481b6b531fd030c236d5f74 SHA1: 655268a9771151009ee4c0e244af016c15cc11ff SHA256: 21396e83350b55785f3119808a7c2e9348f180f2ded3d60b14242c58ac8869c6 SHA512: dcd1b3e2b58294011bd8b9af4e079c8f2fcfa924d99f9127a7d4425b09f680d278f4ac319dfd37d9ec4a6818b9743a8c37b1a0788da0bf473a2d9872576b16b6 Homepage: https://cran.r-project.org/package=MLInterfaces Description: Bioc Package 'MLInterfaces' (Uniform interfaces to R machine learning procedures for data inBioconductor containers) This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. 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This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports. 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Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package. 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The MsExperiment package provides light-weight and flexible containers for MS experiments building on the new MS infrastructure provided by the Spectra, QFeatures and related packages. Along with raw data representations, links to original data files and sample annotations, additional metadata or annotations can also be stored within the MsExperiment container. To guarantee maximum flexibility only minimal constraints are put on the type and content of the data within the containers. Package: r-bioc-msfeatures Architecture: all Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2640 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-protgenerics, r-bioc-mscoreutils, r-bioc-summarizedexperiment Suggests: r-cran-testthat, r-cran-roxygen2, r-bioc-biocstyle, r-cran-pheatmap, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-msfeatures_1.20.0-1.ca2604.1_all.deb Size: 1508136 MD5sum: 08af8dbcdcb2d4b5b54e4867f79f7de5 SHA1: 1de6bc9d4b64a8df4524f1d9aa0fc611881cd451 SHA256: 0464207a0c4e31f9954b501ae4d5d99937757edaf15c63786b7bfdf1e1ab3a86 SHA512: f4ef8a0a0ae31ed86b36558714319a356fbb455ad7f6304ee2740985c6e72357a465a25ef2ade825bd116a409dcce36ba3a8ed2c085f02f6e4cea3ce3284a41e Homepage: https://cran.r-project.org/package=MsFeatures Description: Bioc Package 'MsFeatures' (Functionality for Mass Spectrometry Features) The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This packge hence allows to group features, and its results can be used as an input for the `QFeatures` package which allows to aggregate abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. `xcms`). All functionality of this package is implemented in a modular way which allows combination of different grouping approaches and enables its re-use in other R packages. 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Signatures are stored in GeneSet class objects form the GSEABase package and the entire database is stored in a GeneSetCollection object. These data are then hosted on the ExperimentHub. Data used in this package was obtained from the MSigDB of the Broad Institute. Metadata for each gene set is stored along with the gene set in the GeneSet class object. 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It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. 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MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. Package: r-bioc-multihiccompare Architecture: all Version: 1.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7051 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-bioc-hiccompare, r-bioc-edger, r-bioc-biocparallel, r-cran-qqman, r-cran-pheatmap, r-bioc-genomicranges, r-cran-pbapply, r-bioc-genomeinfodbdata, r-bioc-genomeinfodb, r-cran-aggregation Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-multihiccompare_1.30.0-1.ca2604.1_all.deb Size: 5238342 MD5sum: 4e94ab50f0a2a896dd869f3656ae1e5f SHA1: be5a625e114d24db261b1698251a419318dc2e5c SHA256: aa40ecb7505e70610428a5014b13d39cf3272a1d8c97f9c15d700366860f5278 SHA512: 85b384e00220d4d36497199b936707fa495e6161017911179c8400e5a3259c8d280e83b84b309948701b7355ee6b2e00edbd63385e7f47487ded8cc2a33a5254 Homepage: https://cran.r-project.org/package=multiHiCcompare Description: Bioc Package 'multiHiCcompare' (Normalize and detect differences between Hi-C datasets whenreplicates of each experimental condition are available) multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. Package: r-bioc-mungesumstats Architecture: all Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4315 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-r.utils, r-cran-dplyr, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-iranges, r-cran-ieugwasr, r-bioc-bsgenome, r-bioc-biostrings, r-cran-stringr, r-bioc-variantannotation, r-bioc-rtracklayer, r-cran-rcurl Suggests: r-bioc-snplocs.hsapiens.dbsnp144.grch37, r-bioc-snplocs.hsapiens.dbsnp144.grch38, r-bioc-snplocs.hsapiens.dbsnp155.grch37, r-bioc-snplocs.hsapiens.dbsnp155.grch38, r-bioc-bsgenome.hsapiens.1000genomes.hs37d5, r-bioc-bsgenome.hsapiens.ncbi.grch38, r-bioc-biocgenerics, r-bioc-s4vectors, r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-upsetr, r-bioc-biocstyle, r-cran-covr, r-bioc-rsamtools, r-bioc-matrixgenerics, r-cran-badger, r-bioc-biocparallel, r-bioc-genomicfiles Filename: pool/dists/resolute/main/r-bioc-mungesumstats_1.20.0-1.ca2604.1_all.deb Size: 2410990 MD5sum: d55d76c3fe3807cc09d48fe9255e96dc SHA1: 1bde8dd52b38a52fe546b4eca0d68e010b7908fd SHA256: f5b1210bc728437cc0ae7a27a4985255379c92456b3d7587fe0ad71e5ef7fb11 SHA512: 64edba7d13c8ce22428aac19fc3f41a47a173fb700c65754c059cb27f30b3229ee066b9fb1f4dce2061f0fa4fed7cf7ae32e561c48eb4e610d9e39a3f55bf220 Homepage: https://cran.r-project.org/package=MungeSumstats Description: Bioc Package 'MungeSumstats' (Standardise summary statistics from GWAS) The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences. Package: r-bioc-muscat Architecture: all Version: 1.26.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11292 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-cran-blme, r-bioc-complexheatmap, r-cran-dplyr, r-bioc-edger, r-cran-ggplot2, r-cran-glmmtmb, r-bioc-limma, r-cran-lmertest, r-cran-lme4, r-cran-matrix, r-bioc-matrixgenerics, r-cran-matrixstats, r-cran-progress, r-cran-rlang, r-bioc-s4vectors, r-cran-scales, r-bioc-scater, r-bioc-scuttle, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-variancepartition Suggests: r-bioc-biocstyle, r-bioc-countsimqc, r-bioc-deseq2, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-icobra, r-bioc-ihw, r-cran-knitr, r-cran-patchwork, r-cran-phylogram, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-sctransform, r-cran-statmod, r-bioc-stager, r-cran-testthat, r-cran-tidyr, r-cran-upsetr Filename: pool/dists/resolute/main/r-bioc-muscat_1.26.0-1.ca2604.1_all.deb Size: 7270312 MD5sum: cd2bb11b8624623a022fd2c5044341d9 SHA1: 3e78b41cadedc9f7ef24e95eee48798880beea71 SHA256: be9f05be48df5a3bd2ebaf26d943fcc901204db8620825fccb729b10db8044cc SHA512: c503704bd78a76e89ed72ac7433606412d9ba5d981c62b640df838d8da61ae65004d5eca2e1a64137b84dae4e3770df720437ba3b527b20e629e822dff75d7bc Homepage: https://cran.r-project.org/package=muscat Description: Bioc Package 'muscat' (Multi-sample multi-group scRNA-seq data analysis tools) `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. Package: r-bioc-mutationalpatterns Architecture: all Version: 3.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10139 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-cran-nmf, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-bsgenome, r-bioc-variantannotation, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-magrittr, r-cran-ggplot2, r-cran-pracma, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-biostrings, r-cran-ggdendro, r-cran-cowplot, r-cran-ggalluvial, r-cran-rcolorbrewer Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-biocstyle, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-biomart, r-cran-gridextra, r-bioc-rtracklayer, r-bioc-ccfindr, r-bioc-genomicfeatures, r-bioc-annotationdbi, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-mutationalpatterns_3.22.0-1.ca2604.1_all.deb Size: 6241776 MD5sum: 681489c2da707d8e08bfd367ec2c614e SHA1: a5c3cd82537a80cb906e433de8bdf69838aac600 SHA256: 786d41a47baeae606a28b9a721e7456c0b34ebf1fe099c9fa74da8a0c228c697 SHA512: ea7a5f33d17be5cb92ee45247217a4bc920cd52808414de4a83e28d487c6e5ec5c1a4ae0a23e8b238214eb05db0ab954ffe6206ad7e20115801bb16a33243fa3 Homepage: https://cran.r-project.org/package=MutationalPatterns Description: Bioc Package 'MutationalPatterns' (Comprehensive genome-wide analysis of mutational processes) Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. Package: r-bioc-mygene Architecture: all Version: 1.48.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-cran-httr, r-cran-jsonlite, r-cran-hmisc, r-cran-sqldf, r-cran-plyr, r-bioc-s4vectors Suggests: r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-mygene_1.48.0-1.ca2604.1_all.deb Size: 228196 MD5sum: 91375cac07d9dba34d62536bd26d3922 SHA1: a43530f16fb02bff19381258de0254c178ed7f84 SHA256: 9c09ebe40fd34933e48020145f025e6c99cfcbfff07aa5e8292c2bd737944ef6 SHA512: 1799f8e417653c445517f77c8b17ab23dac753880782c7d8579dd701cd6f8ff97fcb2d79a400cae4028a616df1f0f6742137c31fdbc534b2fb040e8387b10ffc Homepage: https://cran.r-project.org/package=mygene Description: Bioc Package 'mygene' (Access MyGene.Info_ services) MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. Package: r-bioc-mzid Architecture: all Version: 1.50.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3398 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-cran-plyr, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-bioc-protgenerics Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-mzid_1.50.0-1.ca2604.1_all.deb Size: 749322 MD5sum: c893a5417e693e2d76bb48be1bbcb696 SHA1: 73bbfc70ea1d50ed95835247bcf1421f12e633a2 SHA256: b1fc53eaf6828948e5121b0884ba21668044e63dedfe71b77426e39e3c2ea601 SHA512: cfb5aac1481fc916a113acf30426f7765d30b14078432634fcd55bc42695d33f262584df345c4d720872b762f78cd0c8fdceb5d44bef3a726d9c83832e0d502b Homepage: https://cran.r-project.org/package=mzID Description: Bioc Package 'mzID' (An mzIdentML parser for R) A parser for mzIdentML files implemented using the XML package. 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Package: r-bioc-nanostringnctools Architecture: all Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10333 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-s4vectors, r-cran-ggplot2, r-bioc-biocgenerics, r-bioc-biostrings, r-cran-ggbeeswarm, r-cran-ggiraph, r-cran-ggthemes, r-bioc-iranges, r-cran-pheatmap, r-cran-rcolorbrewer Suggests: r-bioc-biovizbase, r-bioc-ggbio, r-cran-runit, r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/resolute/main/r-bioc-nanostringnctools_1.20.0-1.ca2604.1_all.deb Size: 5008510 MD5sum: eff83e59ff5d60ba1deb8f3b236e1a32 SHA1: 7a834ad509d7b448afb2daf40b62ac1f6663962e SHA256: 427dcca1deeb86766741eda35d9aa296a99b61e58b14b7f6d9c645ba3cf326b5 SHA512: a2297e64b02836efb88c137a4d03230c2a633f5bbb7e043d894c8f0cda4660c0ac3fbbd6b73f71af5b31170fbaefa80ac095e46ef07eb3ff1d5a4cc7f59a2fda Homepage: https://cran.r-project.org/package=NanoStringNCTools Description: Bioc Package 'NanoStringNCTools' (NanoString nCounter Tools) Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data. Package: r-bioc-nebulosa Architecture: all Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5273 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-seuratobject, r-cran-ks, r-cran-matrix, r-cran-ggrastr Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-bioc-scater, r-bioc-scran, r-bioc-dropletutils, r-cran-igraph, r-bioc-biocfilecache, r-cran-seurat Filename: pool/dists/resolute/main/r-bioc-nebulosa_1.22.0-1.ca2604.1_all.deb Size: 2792184 MD5sum: fe28ce32fe2a23f3d5225912c30565be SHA1: c844683da80bf95d56819d4c8b888e368b866b4f SHA256: b039b389e7d6a09452be929755c98832358fae868f10c0c07835bb07240c50c3 SHA512: 6058680650d74e4625cffc49f4fd42e70313acdad4586e65bb473ac7e1f9b1cfbfdc750a9dadd8262343097e69ae98a681d0f93a40de12fec2c0fd70cd9a2990 Homepage: https://cran.r-project.org/package=Nebulosa Description: Bioc Package 'Nebulosa' (Single-Cell Data Visualisation Using Kernel Gene-WeightedDensity Estimation) This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. Package: r-bioc-noiseq Architecture: all Version: 2.56.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2718 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-matrix Filename: pool/dists/resolute/main/r-bioc-noiseq_2.56.0-1.ca2604.1_all.deb Size: 2507318 MD5sum: 468cbd64fdfd277206a708ff20a0fd91 SHA1: 03900a8edbc7aa97d359318307b862e0702d5090 SHA256: e8fa1c951cf4c47b7db043ddcfb7ef4ca1b798f5c174b0eea07e80f1b6d26589 SHA512: bbd422f2bf535101fae8fffb3396a55d2a10792edc6b620a70e3e2feb5a71992b01fb34d21a4a14edebc9ca7d58668d46fd554ef61277c1a6c980ba50e62bacc Homepage: https://cran.r-project.org/package=NOISeq Description: Bioc Package 'NOISeq' (Exploratory analysis and differential expression for RNA-seqdata) Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. Package: r-bioc-oligoclasses Architecture: all Version: 1.74.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-iranges, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-affyio, r-cran-foreach, r-cran-biocmanager, r-bioc-s4vectors, r-cran-rsqlite, r-cran-dbi, r-cran-ff Suggests: r-bioc-hapmapsnp5, r-bioc-hapmapsnp6, r-bioc-pd.genomewidesnp.6, r-bioc-pd.genomewidesnp.5, r-bioc-pd.mapping50k.hind240, r-bioc-pd.mapping50k.xba240, r-bioc-pd.mapping250k.sty, r-bioc-pd.mapping250k.nsp, r-bioc-genomewidesnp6crlmm, r-bioc-genomewidesnp5crlmm, r-cran-runit, r-bioc-human370v1ccrlmm, r-bioc-vanillaice, r-bioc-crlmm Filename: pool/dists/resolute/main/r-bioc-oligoclasses_1.74.0-1.ca2604.1_all.deb Size: 1256182 MD5sum: e0e3a802889031442f2bd7f125b812f8 SHA1: 6054c58d590eb5cd1e8988e5995dc29ee3492961 SHA256: 023ea76c4fa76770602c34109e039a8d0b36a89a83518213e73bd85733c42c8c SHA512: cc77f84b203b35b2c641a9b693c6871449a85e92ee6f703b082c24287431bca48c0005408b78df99dc0a21e2b0408b30a639ed4f0018100fd9dd3324ddd7d53d Homepage: https://cran.r-project.org/package=oligoClasses Description: Bioc Package 'oligoClasses' (Classes for high-throughput arrays supported by oligo and crlmm) This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. Package: r-bioc-omnipathr Architecture: all Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8154 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-crayon, r-cran-curl, r-cran-digest, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-igraph, r-cran-jsonlite, r-cran-later, r-cran-logger, r-cran-lubridate, r-cran-magrittr, r-cran-progress, r-cran-purrr, r-cran-rappdirs, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-r.utils, r-cran-rvest, r-cran-sessioninfo, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-withr, r-cran-xml, r-cran-xml2, r-cran-yaml, r-cran-zip Suggests: r-bioc-biocstyle, r-cran-bookdown, r-cran-ggplot2, r-cran-ggraph, r-cran-gprofiler2, r-cran-knitr, r-cran-mlrmbo, r-cran-parallelmap, r-cran-paramhelpers, r-cran-r.matlab, r-bioc-sbmlr, r-cran-sigmajs, r-cran-smoof, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-omnipathr_4.0.0-1.ca2604.1_all.deb Size: 2550072 MD5sum: af15162a73d45524c66ef87fd9df7dc2 SHA1: 6fe6c9e7c309ee88eb90a81cf722301f28dbdc75 SHA256: 8a0669439b358f3eadc0015e1e553ee8c66ef158acf2f38f7aeffda47b99a16a SHA512: c1fbf1d461f411293d7acd22aa1c6657196841cefcb93d93e5495b52ad1067c4da0a3542cc6de1934fd394eda7b74a36929f3218bb1a9f6fa3278938054830c7 Homepage: https://cran.r-project.org/package=OmnipathR Description: Bioc Package 'OmnipathR' (OmniPath web service client and more) A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). Package: r-bioc-org.dr.eg.db Architecture: all Version: 3.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144078 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/resolute/main/r-bioc-org.dr.eg.db_3.22.0-1.ca2604.1_all.deb Size: 24177630 MD5sum: 2ea4d6e870870dfd716eaf01d3de8a3b SHA1: a8a6377db508a0d88ecb2a948587fbfc971255b1 SHA256: b77ca81e9ca855d6105da9d2b6662f87c2509eb588e93ab23786b22297a1099b SHA512: ee26b4373474e39bcd950dae7bb45c6c607aa0d75d5d1680b4bc6d3d025c658f63b2eb8c57bd95cce5f2e9017a57bcab3196fa8a7d5f3af416199f1f9beadba3 Homepage: https://cran.r-project.org/package=org.Dr.eg.db Description: Bioc Package 'org.Dr.eg.db' (Genome wide annotation for Zebrafish) Genome wide annotation for Zebrafish, primarily based on mapping using Entrez Gene identifiers. Package: r-bioc-org.hs.eg.db Architecture: all Version: 3.23.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407999 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/resolute/main/r-bioc-org.hs.eg.db_3.23.1-1.ca2604.1_all.deb Size: 67461810 MD5sum: 9d72f2a59bea164180d4aa4d475d08f5 SHA1: bd4381d4cb5b5960e735c02d37d12dc002a829c1 SHA256: 3921721ce68be44c9531557c6bfee67875dae9a2b39bcf60419682953d3fad0f SHA512: 5486bec8a56beb881a20287948affe24d485834f327aab747b92a41bd69c5c2baaa658e490454579dae9cf9c00af27577befe67305048ad71a07e48ebb1ac8b1 Homepage: https://cran.r-project.org/package=org.Hs.eg.db Description: Bioc Package 'org.Hs.eg.db' (Genome wide annotation for Human) Genome wide annotation for Human, primarily based on mapping using Entrez Gene identifiers. 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The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for Pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the Pedigree object with various criteria, and kinship for the X chromosome. 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Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. 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These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics. Package: r-bioc-protgenerics Architecture: all Version: 1.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-protgenerics_1.44.0-1.ca2604.1_all.deb Size: 253476 MD5sum: ecec6623e9467ce391a3ee933b5b104f SHA1: 934a34e7b4503a6ba6913c2a395bf7f401314ca1 SHA256: c87770d50eef9016fff50b80b1b4d34bf3d6e349f2d5db9cd67ec3f2d9d0c8e8 SHA512: 96d038a94f4ff4000832d5fcad0783feb1e83937597217519d99600f3add49741d1d7175df2e1a266676c4345d14b7928d22f1be11885fd154c3a7995547e61d Homepage: https://cran.r-project.org/package=ProtGenerics Description: Bioc Package 'ProtGenerics' (Generic infrastructure for Bioconductor mass spectrometrypackages) S4 generic functions and classes needed by Bioconductor proteomics packages. 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This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. 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The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. Package: r-bioc-qfeatures Architecture: all Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11926 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-multiassayexperiment, r-bioc-s4vectors, r-bioc-iranges, r-bioc-summarizedexperiment, r-bioc-biocgenerics, r-bioc-protgenerics, r-bioc-annotationfilter, r-cran-lazyeval, r-bioc-biobase, r-bioc-mscoreutils, r-cran-igraph, r-cran-plotly, r-cran-tidyr, r-cran-tidyselect, r-cran-reshape2 Suggests: r-bioc-singlecellexperiment, r-bioc-msdatahub, r-cran-arrow, r-cran-matrix, r-bioc-hdf5array, r-bioc-msdata, r-cran-ggplot2, r-cran-gplots, r-cran-dplyr, r-bioc-limma, r-cran-dt, r-cran-shiny, r-cran-shinydashboard, r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown, r-bioc-vsn, r-bioc-preprocesscore, r-cran-matrixstats, r-cran-imputelcmd, r-bioc-pcamethods, r-bioc-impute, r-cran-norm, r-bioc-complexheatmap Filename: pool/dists/resolute/main/r-bioc-qfeatures_1.22.0-1.ca2604.1_all.deb Size: 4284318 MD5sum: 48038202f0a52079a750cba053fc5359 SHA1: 99301791175e963122f2af7b4edc14ebf42ac80a SHA256: 90b846acca825eed345b3d144d7ad49ac9f58b9892bcd4e81e48c8178d175695 SHA512: 3fb144bc8bd0026780d0b716d43fe075e813d2e04957ee18ddaf6d78817ec43c30c10a5cc7af046409220a831c02ab6860a6a609b2ad36e56df5c7c974219f20 Homepage: https://cran.r-project.org/package=QFeatures Description: Bioc Package 'QFeatures' (Quantitative features for mass spectrometry data) The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format. 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There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. 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The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data. Package: r-bioc-quantro Architecture: all Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4047 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-minfi, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-ggplot2, r-cran-rcolorbrewer Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-quantro_1.46.0-1.ca2604.1_all.deb Size: 3152970 MD5sum: ff76ccf1206617ac529de80ec0274d89 SHA1: bfa1641c4a54e23cd9008fe12e826ecb6ccbf2f5 SHA256: 413bb3a4e61cb30f3e45e322e2747c80769afcf6675699d726a1d98946e992b3 SHA512: c678bc343b602b7049cdec3a042c9f15f718661a44f33279a5b77aaeac89e3b5c3faf4b18a2df5109eb6babdc0df64312760e135cab3e2566e4707ed991a8e40 Homepage: https://cran.r-project.org/package=quantro Description: Bioc Package 'quantro' (A test for when to use quantile normalization) A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. Package: r-bioc-quantsmooth Architecture: all Version: 1.78.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg Filename: pool/dists/resolute/main/r-bioc-quantsmooth_1.78.0-1.ca2604.1_all.deb Size: 430594 MD5sum: f3ec032c9a0f1e04b872a126ac5f93f0 SHA1: 98b84ae6f955c2fde9a2871b01f33a1696d57572 SHA256: 623582ff41046fd9da10d21e7a03adfef26d0e1efb6055f885c21679397ebaab SHA512: c44d329590527348dbe1af464ded1c25b359650d69638d45d99eb4493dd05c54faa9ecc5b235f3270f287de874731da8581f3537a4045d3da492a5dc01785000 Homepage: https://cran.r-project.org/package=quantsmooth Description: Bioc Package 'quantsmooth' (Quantile smoothing and genomic visualization of array data) Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. Package: r-bioc-qusage Architecture: all Version: 2.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10270 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-limma, r-bioc-biobase, r-cran-nlme, r-cran-emmeans, r-cran-fftw Filename: pool/dists/resolute/main/r-bioc-qusage_2.46.0-1.ca2604.1_all.deb Size: 10238622 MD5sum: c58ad14a4d0c6e6dd3bcd1bd140f0771 SHA1: 52001a65765f395b12a8d539710cd405a002b4f8 SHA256: 1936b348f2314f41c8746f38d9a79a11ce10c2dd45de1618f22f33e6e634b9ce SHA512: 0b4ee7f46a3850469ce59ea191fddcc65b801a0018de3ae7005327dd14845115d77de6456b36a29ae9c047c88fc670432028b1132fe95c5d6326f444ca800061 Homepage: https://cran.r-project.org/package=qusage Description: Bioc Package 'qusage' (qusage: Quantitative Set Analysis for Gene Expression) This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) Package: r-bioc-qvalue Architecture: all Version: 2.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2823 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-qvalue_2.44.0-1.ca2604.1_all.deb Size: 2810030 MD5sum: 138bc4acda13b0cb0c1533f5d7bf891a SHA1: 0ac02eb6fc4eed5d75096bbf645ce32c5285a546 SHA256: 58f224a294be9a0aad87ab506b8d65cf8776a8521bfd9ff27b8cb70a3ac12cbd SHA512: 3d8105a2a147ea2a100284230ff5273186824a0a0063d3f305d306dc09084f4a2910a1b3d92bbf9eb280740133d395ea5c6e65a7f036b11ae0ea32e8b3b0f497 Homepage: https://cran.r-project.org/package=qvalue Description: Bioc Package 'qvalue' (Q-value estimation for false discovery rate control) This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. Package: r-bioc-r4rna Architecture: all Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings Filename: pool/dists/resolute/main/r-bioc-r4rna_1.40.0-1.ca2604.1_all.deb Size: 1039326 MD5sum: ef19808071f5741baa2a897e15b2462f SHA1: 312f53177581e15e8e1a478bc85c864017303e10 SHA256: 0dc8f5611d36b381a8dbb75fb0be2e2164efe5215d3808255da28841e5cbeefa SHA512: 9f622fe3cfd1fabf541e574a65670ab6df97a1e55641210a5d92811f8583d5340cb795ae192a07b95e5eabe14b5bbf07278ea065a353f4d5ed585f1b9f07451a Homepage: https://cran.r-project.org/package=R4RNA Description: Bioc Package 'R4RNA' (An R package for RNA visualization and analysis) A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. Package: r-bioc-raggedexperiment Architecture: all Version: 1.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2952 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-biocbaseutils, r-bioc-biocgenerics, r-bioc-seqinfo, r-bioc-iranges, r-cran-matrix, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-summarizedexperiment Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-genomeinfodb, r-bioc-multiassayexperiment Filename: pool/dists/resolute/main/r-bioc-raggedexperiment_1.36.0-1.ca2604.1_all.deb Size: 774654 MD5sum: c95e0c98654671a136becf7f31dbba09 SHA1: 2fecbbfb4d682798c9f22b28144406f0f3260a97 SHA256: 385fc3c241644e642ac526dbb17a3712a35cbcd6d04b13580e7bba58acc9d21e SHA512: 6f72aaed62ce68ccc5fe785aea707bdcc66bae688f96cc3f6f4d0d65f418aa5e63b25c467b3d73ab7d159e350894d9ec0d3dca7e97215ac0e23b83c5943ab498 Homepage: https://cran.r-project.org/package=RaggedExperiment Description: Bioc Package 'RaggedExperiment' (Representation of Sparse Experiments and Assays Across Samples) This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset. Package: r-bioc-rankprod Architecture: all Version: 3.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 959 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rmpfr, r-cran-gmp Filename: pool/dists/resolute/main/r-bioc-rankprod_3.38.0-1.ca2604.1_all.deb Size: 888198 MD5sum: ddb2c8b288fd2ddb12d97d2dd5c80261 SHA1: 17ae1d9a941c2e5ee6099da0353f4e80b68ef9d6 SHA256: fad431c4c3ce304b29fb6713fc65eefe15000869309f367db570a589e2b0d800 SHA512: ce935a583c2244499675aa2b29ce6c0b3a0590464c559305dcf76ca0b5e031cdb6b0e66a162f3da0c6d38b11918ecaad7b7ff83da9b7ce660bc29b17bc40c062 Homepage: https://cran.r-project.org/package=RankProd Description: Bioc Package 'RankProd' (Rank Product method for identifying differentially expressedgenes with application in meta-analysis) Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). 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Package: r-bioc-rcistarget Architecture: all Version: 1.29.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15553 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-aucell, r-bioc-biocgenerics, r-cran-data.table, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-arrow, r-cran-dplyr, r-cran-tibble, r-bioc-gseabase, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-zoo Suggests: r-bioc-biobase, r-bioc-biocstyle, r-bioc-biocparallel, r-cran-doparallel, r-cran-dt, r-cran-foreach, r-cran-gplots, r-bioc-rtracklayer, r-cran-igraph, r-cran-knitr, r-bioc-rcistarget.hg19.motifdbs.cisbponly.500bp, r-cran-rmarkdown, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/resolute/main/r-bioc-rcistarget_1.29.0-1.ca2604.1_all.deb Size: 12915162 MD5sum: 3f8305bc4d79154f789da6bfbc01fab4 SHA1: c9148f7fc537530c3dcb59cb66c6f0067cffe27f SHA256: f391d599ea2d811300ea2aa7cd6dc027f1d111b89c2fc0bf1c2f06c15243dfc6 SHA512: 3ef52456875c240789eb65fd2801ea810c0b82496be93d3c31cd5af570183f3a4abccdc834e8b8740b3264e9df35853aa576c0314157d327fced0248bd333a74 Homepage: https://cran.r-project.org/package=RcisTarget Description: Bioc Package 'RcisTarget' (RcisTarget Identify transcription factor binding motifs enrichedon a list of genes or genomic regions) RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). Package: r-bioc-rcy3 Architecture: all Version: 2.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17763 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-rjsonio, r-cran-xml, r-bioc-biocgenerics, r-bioc-graph, r-cran-fs, r-cran-uuid, r-cran-stringi, r-cran-glue, r-cran-rcurl, r-cran-base64url, r-cran-base64enc, r-cran-irkernel, r-cran-irdisplay, r-cran-rcolorbrewer, r-cran-gplots Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph Filename: pool/dists/resolute/main/r-bioc-rcy3_2.32.0-1.ca2604.1_all.deb Size: 3745388 MD5sum: c67d493f386190b06efbf8a702baf53a SHA1: 39e3ca4346df55bc1bfe10baeb97495cac0ebc83 SHA256: 8a9511ca801c795805cb5c90b469f85a756aa087ec0504d4586336a761691156 SHA512: b562299c8168997bcf022636fcde317c8f72485cfc2d4e71f6d3daa56d7f00429cb7356520539b4cd604833fd1421966881b72a955b43f207ec54c31a9efb591 Homepage: https://cran.r-project.org/package=RCy3 Description: Bioc Package 'RCy3' (Functions to Access and Control Cytoscape) Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function. Package: r-bioc-reactome.db Architecture: all Version: 1.96.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-rsqlite Filename: pool/dists/resolute/main/r-bioc-reactome.db_1.96.0-1.ca2604.1_all.deb Size: 207842638 MD5sum: 7067275fd9c0c2872ee3833ee8a415b6 SHA1: 9845c6e660905576cff9e03bc559e00c956b20f5 SHA256: 9e37e489c74d58a2cc0df6f5d38bfb5ff8fcd989d52883aa2e334180100857e7 SHA512: e077d9e9906c5d858f18cf0c403a0123e4e82e0736e9d9434b83e2ad35e97b79436b5ac557cd2722f4918f324583c3490f0c50accb85a7b60a295ff8350701c7 Homepage: https://cran.r-project.org/package=reactome.db Description: Bioc Package 'reactome.db' (A set of annotation maps for reactome) A set of annotation maps for reactome assembled using data from reactome. This package has been created by a third-party developer, and is not affiliated with the Reactome team. 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The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. 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It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team. 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You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. Package: r-bioc-reder Architecture: all Version: 3.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-scales, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-bioc-biocstyle, r-bioc-treeandleaf Filename: pool/dists/resolute/main/r-bioc-reder_3.8.0-1.ca2604.1_all.deb Size: 3738826 MD5sum: 7a6be6e73bbe2c8d4c7586b062f5f4c8 SHA1: 3f6e37b95e0b3511ab9ffbd959ec01a80bc55fb9 SHA256: 9c9ed212e2c3ceee5a6e323b450de18c836ffb287a5aca25805988c47a9c27ad SHA512: 8e4750f9b67b6307c7602e96659deccba599755d2da0027e297587c10629d4b55b8cdcc5254aa6fe932d76ed93bc2bbcf760f5925670887c4c9f4a129732bec1 Homepage: https://cran.r-project.org/package=RedeR Description: Bioc Package 'RedeR' (Interactive visualization and manipulation of nested networks) RedeR combines an R package with a stand-alone Java application for interactive visualization and manipulation of nested networks. Graph, node, and edge attributes can be configured using either graphical or command-line methods, following igraph syntax rules. Package: r-bioc-regioner Architecture: all Version: 1.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4081 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-cran-memoise, r-bioc-iranges, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-rtracklayer, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-s4vectors Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-bsgenome.hsapiens.ucsc.hg19.masked, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-regioner_1.44.0-1.ca2604.1_all.deb Size: 1756114 MD5sum: 39976c692edd5d0637bea037e0da1293 SHA1: 904f04ea7f3947e6dee5a648c6312da2ddb74350 SHA256: 526275bab1043f2907659eb3322b38d9aab2f228afebd28ad7215498a79b4141 SHA512: fcc6aa624382a036fdce10d74b89b602f80ccce28bcb3fa27189c4280a29e31a036524cc16e23e1c7af797924fe675eb5f56753e1c254a8d7e4a64758b510db1 Homepage: https://cran.r-project.org/package=regioneR Description: Bioc Package 'regioneR' (Association analysis of genomic regions based on permutationtests) regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. Package: r-bioc-reportingtools Architecture: all Version: 2.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2873 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-knitr, r-bioc-biobase, r-cran-hwriter, r-bioc-category, r-bioc-gostats, r-bioc-limma, r-cran-lattice, r-bioc-annotationdbi, r-bioc-edger, r-bioc-annotate, r-bioc-pfam.db, r-bioc-gseabase, r-bioc-biocgenerics, r-cran-xml, r-cran-r.utils, r-bioc-deseq2, r-cran-ggplot2, r-bioc-ggbio, r-bioc-iranges Suggests: r-cran-runit, r-bioc-all, r-bioc-hgu95av2.db, r-bioc-org.mm.eg.db, r-cran-shiny, r-bioc-pasilla, r-bioc-org.sc.sgd.db, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-bioc-reportingtools_2.52.0-1.ca2604.1_all.deb Size: 1696022 MD5sum: 49bdbbd793616b24a75feb18514e533f SHA1: 4a14fa06c61e71d9777c5e75d5cbb2fb7951951e SHA256: af5e3cd08587c647a8392ca3a230885c553ebfed81594d420f2f76cf6e95977e SHA512: 791f7f9ef3347386bbdb62eb5fe15c081ec72021644b057d245c5997fb9b0b3e340e40fa46c673dce99eb7c979acdf5eca6dc50e0136b65edcbf99c217eab568 Homepage: https://cran.r-project.org/package=ReportingTools Description: Bioc Package 'ReportingTools' (Tools for making reports in various formats) The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. Package: r-bioc-residualmatrix Architecture: all Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1517 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-s4vectors, r-bioc-delayedarray Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocsingular Filename: pool/dists/resolute/main/r-bioc-residualmatrix_1.22.0-1.ca2604.1_all.deb Size: 659938 MD5sum: c5d9cf226429e1b6c9ef73edd57b7705 SHA1: e71bd3e6f9cf3f8b4967d127572521855bba40f7 SHA256: 12a3f77347f19182f9e31a3c3624ff1ce216512b4017968dfcc9ed0dad08cde8 SHA512: 0713c6bd5908721d7ba76586cdab4e6d128b3031f775e028d53f0566752b06a0f7792caa0f2c61fe4bf494a89a4bd42f47026741bb65433526e03889cec8623b Homepage: https://cran.r-project.org/package=ResidualMatrix Description: Bioc Package 'ResidualMatrix' (Creating a DelayedMatrix of Regression Residuals) Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements a number of efficient methods for operating on the delayed matrix of residuals, most notably matrix multiplication and calculation of row/column sums or means. Package: r-bioc-rols Architecture: all Version: 3.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1593 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-bioc-biobase Suggests: r-bioc-go.db, r-cran-knitr, r-bioc-biocstyle, r-cran-testthat, r-cran-lubridate, r-cran-dt, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-rols_3.7.1-1.ca2604.1_all.deb Size: 621372 MD5sum: a4467f56b38877b7c37bbba1df1126e3 SHA1: e364cb921f36c7a73b5fd618cdead62e3895d1ae SHA256: 165922e194570357c94a3d07f1a093438a14bb5fb457fa9c150a3f881aceec7b SHA512: b493e8d4777c14f5b5e80a3a02c4f9cbbf85d2ecc153c6828930da8c2fd030974c4174e1a4737c58313f831f55b331c967f6d91f2e0ae5c96aee5d8bf615ff29 Homepage: https://cran.r-project.org/package=rols Description: Bioc Package 'rols' (An R interface to the Ontology Lookup Service) The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. Package: r-bioc-ropls Architecture: all Version: 1.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10814 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-ggplot2, r-cran-plotly, r-bioc-multiassayexperiment, r-bioc-multidataset, r-bioc-summarizedexperiment Suggests: r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr, r-bioc-multtest, r-bioc-omicade4, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-ropls_1.44.0-1.ca2604.1_all.deb Size: 6034574 MD5sum: 259089a37c4017b1cbddd7fff4e24ac9 SHA1: 24166a473a3c12239804485e50a56cc650467159 SHA256: 88720a4794bee5436b7487f687c60b2e0f8450da112d31554baa2fc65ea6ed63 SHA512: 670a3e5c1043a209b4605757fd1e413eeaaa8f274cc60a06887cd83711b1eba61d2bfe68806fd95bfaa1e5c06c803fa9546df68c7f6807e84d6707e815a36694 Homepage: https://cran.r-project.org/package=ropls Description: Bioc Package 'ropls' (PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis andfeature selection of omics data) Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). Package: r-bioc-rrvgo Architecture: all Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2472 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-gosemsim, r-bioc-annotationdbi, r-bioc-go.db, r-cran-pheatmap, r-cran-ggplot2, r-cran-ggrepel, r-cran-treemap, r-cran-tm, r-cran-wordcloud, r-cran-shiny, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-testthat, r-cran-shinydashboard, r-cran-dt, r-cran-plotly, r-cran-heatmaply, r-cran-magrittr, r-bioc-clusterprofiler, r-bioc-dose, r-cran-slam, r-bioc-org.ag.eg.db, r-bioc-org.at.tair.db, r-bioc-org.bt.eg.db, r-bioc-org.ce.eg.db, r-bioc-org.cf.eg.db, r-bioc-org.dm.eg.db, r-bioc-org.dr.eg.db, r-bioc-org.eck12.eg.db, r-bioc-org.ecsakai.eg.db, r-bioc-org.gg.eg.db, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-org.mmu.eg.db, r-bioc-org.pt.eg.db, r-bioc-org.rn.eg.db, r-bioc-org.sc.sgd.db, r-bioc-org.ss.eg.db, r-bioc-org.xl.eg.db Filename: pool/dists/resolute/main/r-bioc-rrvgo_1.24.0-1.ca2604.1_all.deb Size: 1535512 MD5sum: c43df174c13ec61fe40d8331f9ae53a1 SHA1: 23f77d8228687f41201a9215fe85bc7413992254 SHA256: b429af739eab0501d3c0bcd73cb64565b281fe9363616597db7689a811fc07be SHA512: 1139d03374d08e55f6a0e771ae21f1ffec289c0e21eb27d9e3cc6c09874078dd68a55200562288b4608c85fc226fd68e88cce6f8b7a13b83c2e801e929134996 Homepage: https://cran.r-project.org/package=rrvgo Description: Bioc Package 'rrvgo' (Reduce + Visualize GO) Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. Package: r-bioc-rtcga Architecture: all Version: 1.41.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5027 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-cran-rcurl, r-cran-assertthat, r-cran-stringi, r-cran-rvest, r-cran-data.table, r-cran-xml2, r-cran-dplyr, r-cran-purrr, r-cran-survival, r-cran-survminer, r-cran-ggplot2, r-cran-ggthemes, r-cran-viridis, r-cran-knitr, r-cran-scales, r-cran-rmarkdown, r-cran-htmltools Suggests: r-cran-devtools, r-cran-testthat, r-cran-pander, r-bioc-biobase, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-rtcga.rnaseq, r-bioc-rtcga.clinical, r-bioc-rtcga.mutations, r-bioc-rtcga.rppa, r-bioc-rtcga.mrna, r-bioc-rtcga.mirnaseq, r-bioc-rtcga.methylation, r-bioc-rtcga.cnv, r-cran-magrittr, r-cran-tidyr Filename: pool/dists/resolute/main/r-bioc-rtcga_1.41.0-1.ca2604.1_all.deb Size: 1133794 MD5sum: 489bf3d8bb2b0707c762a8133c7e71ba SHA1: 25d24932c39500b84d9a9a018ee538886522a792 SHA256: c70af37d2abdc3936af39339751e64026ba76e6e70b809197bf74911a079e96b SHA512: 6aeb4767bfd6221a60ec74e5939086a2b1cb1a2e49f6d835115bb78a3c25a479876cf24fc2124ef65947a06aa04400249490672d6db088a379710800036f7b5c Homepage: https://cran.r-project.org/package=RTCGA Description: Bioc Package 'RTCGA' (The Cancer Genome Atlas Data Integration) The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. Package: r-bioc-rtcgatoolbox Architecture: all Version: 2.42.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1152 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-cran-data.table, r-bioc-delayedarray, r-bioc-genomicranges, r-bioc-seqinfo, r-cran-httr, r-bioc-raggedexperiment, r-cran-rcurl, r-cran-rjsonio, r-cran-rvest, r-bioc-s4vectors, r-cran-stringr, r-bioc-summarizedexperiment, r-bioc-tcgautils Suggests: r-bioc-biocstyle, r-bioc-homo.sapiens, r-cran-knitr, r-cran-readr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-rtcgatoolbox_2.42.0-1.ca2604.1_all.deb Size: 581598 MD5sum: 6254c761ff7cc6e94296daf379cdc134 SHA1: 7a0601bca0566de3f9938e93d2d7c4a77aca2558 SHA256: 4d4011cddd6f52b1874e459fa2002bd654ac6738406baf82d19559261d8aca06 SHA512: 83d5d49823192430e31cbbd6a62ef785a3692197a6183ca9aa27402899e03b3fcd17dbb98221c98c612c309c06dac61431d646ed778ec929ed58c2a1c5337a94 Homepage: https://cran.r-project.org/package=RTCGAToolbox Description: Bioc Package 'RTCGAToolbox' (A new tool for exporting TCGA Firehose data) Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. Package: r-bioc-ruvseq Architecture: all Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-edaseq, r-bioc-edger, r-cran-mass Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rcolorbrewer, r-bioc-zebrafishrnaseq, r-bioc-deseq2 Filename: pool/dists/resolute/main/r-bioc-ruvseq_1.46.0-1.ca2604.1_all.deb Size: 531408 MD5sum: eb114eadf596b811dc60493a375556ff SHA1: d7c1a0bac66060fc4320c067b3e570283fa19cbb SHA256: e0aff83e630f645df0b28f9f61fc47da35130ff684db88deac69007ef0e79785 SHA512: 7c5fd39517287b82f029743d2ca6c3d6aee7a6f954ff3c87499193435d0458f1ef37ec1d6c76d358b40c6d39a5ce85296712af6bf8bf18152a11aabcad4b43f6 Homepage: https://cran.r-project.org/package=RUVSeq Description: Bioc Package 'RUVSeq' (Remove Unwanted Variation from RNA-Seq Data) This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. Package: r-bioc-rwikipathways Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6942 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-xml, r-cran-rjson, r-cran-data.table, r-cran-rcurl, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-stringr, r-cran-purrr, r-cran-lubridate Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-rwikipathways_1.32.0-1.ca2604.1_all.deb Size: 1950520 MD5sum: d42680b64b2471bff8ed94edafc57bcc SHA1: f55d55111d45b1219705fe44b370ba29b28db973 SHA256: 53649c0b070d59703bafb2862e67a06205bda8b2265ce5d110ee39acec750ce1 SHA512: aac4db0f5222a07688d33df3808de93d4b48b417294f219b51f33fdeef22124df9fd3be17f1ea5b54de189be25ae5097c0a898083952d881075eff9eb0ca9cd6 Homepage: https://cran.r-project.org/package=rWikiPathways Description: Bioc Package 'rWikiPathways' (rWikiPathways - R client library for the WikiPathways API) Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions. 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SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. Package: r-bioc-sangerseqr Architecture: all Version: 1.48.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5097 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-bioc-pwalign, r-cran-stringr, r-cran-shiny Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/resolute/main/r-bioc-sangerseqr_1.48.0-1.ca2604.1_all.deb Size: 3097202 MD5sum: 7f231c4dbde4e1f33efd2f79148349dd SHA1: 660dce2c334a17ac3ebb6ab7c172aa5a102bc1c9 SHA256: 831c0746fee44e6a6943cd0623e61d3cfbc57eacfff7194ba74c99c28b2fb540 SHA512: faa2f44f146d07b9c5974498e7c2bcfb282209d89262ae654091435f098df1f43636c32c9564c6769d6b3f7e41daa57c758c177920f7fbac1613e5f620a28058 Homepage: https://cran.r-project.org/package=sangerseqR Description: Bioc Package 'sangerseqR' (Tools for Sanger Sequencing Data in R) This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. Package: r-bioc-scaledmatrix Architecture: all Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1420 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-s4vectors, r-bioc-delayedarray Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocsingular, r-bioc-delayedmatrixstats Filename: pool/dists/resolute/main/r-bioc-scaledmatrix_1.20.0-1.ca2604.1_all.deb Size: 591248 MD5sum: 9132b7934ce45a47c3f86eb4f1e53230 SHA1: ce67b17558861bd21f89518b66faffc0a567c4d9 SHA256: e09d3966552decabc4175384a26cc4b628c49519e63c3e7bfc9f01bae0757ef0 SHA512: 282e81a266d2bd428ea328f0cc5f2ea77dec51357a49577990277e69ea51fb42e576fc9f12d6ad7b71a74b16c7f8fcfe229378e59827ef8f4db5877b5e7faf74 Homepage: https://cran.r-project.org/package=ScaledMatrix Description: Bioc Package 'ScaledMatrix' (Creating a DelayedMatrix of Scaled and Centered Values) Provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale() function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication. Package: r-bioc-scater Architecture: all Version: 1.40.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6825 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-scuttle, r-cran-ggplot2, r-cran-matrix, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-matrixgenerics, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-beachmat, r-bioc-biocneighbors, r-bioc-biocsingular, r-bioc-biocparallel, r-cran-rlang, r-cran-ggbeeswarm, r-cran-viridis, r-cran-rtsne, r-cran-rcolorbrewer, r-cran-rcppml, r-cran-uwot, r-cran-pheatmap, r-cran-ggrepel, r-cran-ggrastr Suggests: r-bioc-biocstyle, r-bioc-delayedmatrixstats, r-bioc-snifter, r-bioc-densvis, r-cran-cowplot, r-bioc-biomart, r-cran-knitr, r-bioc-scrnaseq, r-cran-robustbase, r-cran-rmarkdown, r-cran-testthat, r-bioc-biobase, r-cran-scattermore Filename: pool/dists/resolute/main/r-bioc-scater_1.40.1-1.ca2604.1_all.deb Size: 4802306 MD5sum: d16fd64eb896102d64880e43190213cb SHA1: 61342e8a974dc85dbe5b345fc0e075c3d30a18ec SHA256: 920d7e6ce8d96fb7bdb2fb178f38afe604f9254130b084b0f4c6d51daa26f7f0 SHA512: c169ca5710ae46cfcab9be442ecc8dfbd4a8be8f8bfe34393bc065b903da0275dea79717a996fbebe157ddd73098b2a16afa001f53e575a1e619f2e5030b07c6 Homepage: https://cran.r-project.org/package=scater Description: Bioc Package 'scater' (Single-Cell Analysis Toolkit for Gene Expression Data in R) A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization. Package: r-bioc-scdblfinder Architecture: all Version: 1.26.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5293 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-igraph, r-cran-matrix, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-biocneighbors, r-bioc-biocsingular, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-scran, r-bioc-scater, r-bioc-scuttle, r-bioc-bluster, r-bioc-delayedarray, r-cran-xgboost, r-cran-mass, r-bioc-iranges, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-rsamtools, r-bioc-rtracklayer Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-scrnaseq, r-cran-circlize, r-bioc-complexheatmap, r-cran-ggplot2, r-cran-dplyr, r-cran-viridislite, r-bioc-mbkmeans Filename: pool/dists/resolute/main/r-bioc-scdblfinder_1.26.0-1.ca2604.1_all.deb Size: 1353628 MD5sum: f14293025c49690fe98c6f62751cdbcc SHA1: 15f1c7df5d22977919c72efb3f7d4a2875cf7957 SHA256: ea37d957bbb80ff9fc07c36f7feda8c931b60c94f1dfda171c3ef7bc440fc3a1 SHA512: 79040a28c00e25b6ff8591d87fd005c1cce25d1910e656edbab224c9eab95ccf469fc67b4f313a498703b3290aa04da8495b4f58c698521d4c85cc2ca88be655 Homepage: https://cran.r-project.org/package=scDblFinder Description: Bioc Package 'scDblFinder' (scDblFinder) The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq. Package: r-bioc-scds Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2072 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-xgboost, r-cran-dplyr, r-cran-proc Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rsvd, r-cran-rtsne, r-bioc-scater, r-cran-cowplot, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-scds_2.0.0-1.ca2604.1_all.deb Size: 1469484 MD5sum: ccf82cd6dacef0217e53087cbf239e22 SHA1: cfdd0b2cc79c6dc5716e47a89d4a3cbe657f65c6 SHA256: 05cf5f835a7b0d1cff647d01267a881c54c52647a4b491484b3c1f90c642ba9f SHA512: c821d9e57df1f2de898b9258e6f3d1606f36d930317b97b7a592ac96cd4bce50edb49d0a9f2eb14f7dfa0db165e49f5978b0357a7eb3a1ecc0fe2f2dc3bd6595 Homepage: https://cran.r-project.org/package=scds Description: Bioc Package 'scds' (In-Silico Annotation of Doublets for Single Cell RNA SequencingData) In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally. Package: r-bioc-scmerge Architecture: all Version: 1.28.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4430 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-bioc-biocsingular, r-bioc-biocneighbors, r-cran-cluster, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-cran-distr, r-cran-igraph, r-bioc-m3drop, r-cran-proxyc, r-cran-ruv, r-cran-cvtools, r-bioc-scater, r-bioc-batchelor, r-bioc-scran, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment Suggests: r-bioc-biocstyle, r-cran-covr, r-bioc-hdf5array, r-cran-knitr, r-cran-matrix, r-cran-rmarkdown, r-cran-scales, r-cran-proxy, r-cran-testthat, r-cran-badger Filename: pool/dists/resolute/main/r-bioc-scmerge_1.28.0-1.ca2604.1_all.deb Size: 2504024 MD5sum: 9532516d15d1e287fd676eae7f9f9eff SHA1: 8e04585d6dc90321f0e92985b0e4acbb64556451 SHA256: 46eefc74b25da9e68e40d24767902cb12189cb1430dcc0c9d5aa6dab8dc4d17f SHA512: f9f6378af80d420d765c0e6c02ebdb35fc21e8fd97a59fe4ef8bcff35b3f1f84ac9a3afc38048b54b370ec92e74b3e2137619c54b50f6b7c1bb8334a21ea70f5 Homepage: https://cran.r-project.org/package=scMerge Description: Bioc Package 'scMerge' (scMerge: Merging multiple batches of scRNA-seq data) Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data. 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Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. 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Package: r-bioc-singlecellexperiment Architecture: all Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4202 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-delayedarray Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-bioc-scrnaseq, r-cran-rtsne Filename: pool/dists/resolute/main/r-bioc-singlecellexperiment_1.34.0-1.ca2604.1_all.deb Size: 1110248 MD5sum: 23c009b46d29831a1151f342afff1ce2 SHA1: b1f63f4f9cac08d14cce3e4682a216d71837947c SHA256: 2a99e1dcefde669eadc1f519a5b49e2fcf266da2d94383090219cf0351307e50 SHA512: a3ec025285203168ad0e7d210b721c587b9474297c388d8effa4c64e1c507977f23c03e44c91fd686fb9988adef80d1acd1bb75a6402d364f5df9be4bd6f52f3 Homepage: https://cran.r-project.org/package=SingleCellExperiment Description: Bioc Package 'SingleCellExperiment' (S4 Classes for Single Cell Data) Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. Package: r-bioc-singlecelltk Architecture: all Version: 2.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7338 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-delayedarray, r-bioc-biobase, r-cran-ape, r-cran-anndata, r-bioc-annotationhub, r-bioc-batchelor, r-bioc-biocparallel, r-bioc-celldex, r-cran-colourpicker, r-cran-colorspace, r-cran-cowplot, r-cran-cluster, r-bioc-complexheatmap, r-cran-data.table, r-bioc-delayedmatrixstats, r-bioc-deseq2, r-cran-dplyr, r-cran-dt, r-bioc-experimenthub, r-bioc-ensembldb, r-cran-fields, r-cran-ggplot2, r-cran-ggplotify, r-cran-ggrepel, r-bioc-ggtree, r-cran-gridextra, r-bioc-gsva, r-bioc-gsvadata, r-cran-igraph, r-cran-kernsmooth, r-bioc-limma, r-bioc-mast, r-cran-matrix, r-cran-matrixstats, r-cran-msigdbr, r-bioc-multtest, r-cran-plotly, r-cran-plyr, r-cran-rocr, r-cran-rtsne, r-bioc-s4vectors, r-bioc-scater, r-bioc-scmerge, r-bioc-scran, r-cran-seurat, r-cran-shiny, r-cran-shinyjs, r-bioc-singler, r-cran-stringr, r-cran-soupx, r-bioc-sva, r-cran-reshape2, r-cran-shinyalert, r-cran-circlize, r-cran-enrichr, r-bioc-celda, r-cran-shinycssloaders, r-bioc-dropletutils, r-bioc-scds, r-cran-reticulate, r-bioc-tximport, r-cran-tidyr, r-bioc-eds, r-cran-withr, r-bioc-gseabase, r-cran-r.utils, r-bioc-zinbwave, r-bioc-scrnaseq, r-bioc-tenxpbmcdata, r-cran-yaml, r-cran-rmarkdown, r-cran-magrittr, r-bioc-scdblfinder, r-cran-metap, r-cran-vam, r-cran-tibble, r-cran-rlang, r-bioc-tscan, r-bioc-trajectoryutils, r-bioc-scuttle, r-bioc-zellkonverter, r-cran-lifecycle Suggests: r-cran-testthat, r-bioc-rsubread, r-bioc-biocstyle, r-cran-knitr, r-cran-lintr, r-cran-spelling, r-bioc-org.mm.eg.db, r-cran-kableextra, r-cran-shinythemes, r-cran-shinybs, r-cran-shinyjqui, r-cran-shinywidgets, r-cran-shinyfiles, r-bioc-biocgenerics, r-cran-rcolorbrewer, r-cran-fastmap, r-cran-harmony, r-cran-seuratobject, r-cran-optparse Filename: pool/dists/resolute/main/r-bioc-singlecelltk_2.22.0-1.ca2604.1_all.deb Size: 4486812 MD5sum: ce9e81ba217441c49203489d17b22628 SHA1: 7fd9297d3e44830a281a60a4c6c4f4dde5d6c0f9 SHA256: dad2a833f9caff72b5e4d3ecdbdcc43a6c738321556c803482419e25bbcdea1b SHA512: 4bba4c30514ee94bb56724d9e57d76a87147f523e1c0d57fb65ed2596efd96d42f5a8216707641cbacedfeb07cd885bb7b302424df279ab2917e16afac186848 Homepage: https://cran.r-project.org/package=singleCellTK Description: Bioc Package 'singleCellTK' (Comprehensive and Interactive Analysis of Single Cell RNA-SeqData) The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk. Package: r-bioc-singscore Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4620 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-bioc-gseabase, r-cran-plotly, r-cran-tidyr, r-cran-plyr, r-cran-magrittr, r-cran-reshape, r-bioc-edger, r-cran-rcolorbrewer, r-bioc-biobase, r-bioc-biocparallel, r-bioc-summarizedexperiment, r-cran-matrixstats, r-cran-reshape2, r-bioc-s4vectors Suggests: r-cran-pkgdown, r-bioc-biocstyle, r-cran-hexbin, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-bioc-singscore_1.32.0-1.ca2604.1_all.deb Size: 3602266 MD5sum: e47262258156edec9d1e6422f2fb3b65 SHA1: 76ea7fed06c9f0a6d9669384251360424c683c68 SHA256: 04865d6ce9a4d656b658454f1c634655ea532ac96ece98f493b444305f2d560a SHA512: 875e3c3d897388e981e6845065d0a1c5f3aed63bac2ad9f45818cd5815a2c5ac19c453b49deff6cd23597118fe703172dbb9a03e4acbdcf855328f0f9283120b Homepage: https://cran.r-project.org/package=singscore Description: Bioc Package 'singscore' (Rank-based single-sample gene set scoring method) A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. Package: r-bioc-slingshot Architecture: all Version: 2.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3673 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-princurve, r-bioc-trajectoryutils, r-cran-igraph, r-cran-matrixstats, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment Suggests: r-bioc-biocgenerics, r-bioc-biocstyle, r-bioc-clusterexperiment, r-bioc-delayedmatrixstats, r-cran-knitr, r-cran-mclust, r-cran-mgcv, r-cran-rcolorbrewer, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat, r-cran-uwot, r-cran-covr Filename: pool/dists/resolute/main/r-bioc-slingshot_2.20.0-1.ca2604.1_all.deb Size: 1988828 MD5sum: 86557d4bb9993280e3f88c2ecb3d21fe SHA1: db17a1ed11045a696760bf17078c88e12921aff8 SHA256: 8432d32386542dca82b6f53b67bf4d9bf9174409b9a97a291c2e61e9a15362b1 SHA512: 726d736809b3bd03f36f38087a86d5f0b081e4391fb3389d28d036993e39de1aaa8fc63e87d629647a0a07f3fa4c36c64b30c2d3f93c512dfee52e1dc2e6b9d0 Homepage: https://cran.r-project.org/package=slingshot Description: Bioc Package 'slingshot' (Tools for ordering single-cell sequencing) Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. Package: r-bioc-spatialexperiment Architecture: all Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7716 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-rjson, r-cran-magick, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-biocgenerics, r-bioc-biocfilecache Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-bioc-bumpymatrix, r-bioc-dropletutils, r-bioc-visiumio Filename: pool/dists/resolute/main/r-bioc-spatialexperiment_1.22.0-1.ca2604.1_all.deb Size: 5180036 MD5sum: afc20731b367e4c0e2fc7f6b245b2f59 SHA1: d77eb25069735910a60b45674209755e46794e7c SHA256: 631631e517fc8d8fd7efe9d6925c53bb7c5132a254e893037dd6dc0772a336c0 SHA512: a826ff3b0e39c81b569ddd56fa4cf07f6e321791d70f9b22278bf2d032a5a0946017f96b28da2491821d1615f5e2fecfca34d36d4caef0c99d59591af0df730f Homepage: https://cran.r-project.org/package=SpatialExperiment Description: Bioc Package 'SpatialExperiment' (S4 Class for Spatially Resolved -omics Data) Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform. Package: r-bioc-spectra Architecture: all Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5399 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-protgenerics, r-bioc-iranges, r-bioc-mscoreutils, r-cran-fs, r-bioc-biocgenerics, r-bioc-metabocoreutils, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-bioc-msdatahub, r-cran-roxygen2, r-bioc-biocstyle, r-bioc-mzr, r-bioc-rhdf5, r-cran-rmarkdown, r-cran-vdiffr, r-cran-msentropy, r-cran-patrick Filename: pool/dists/resolute/main/r-bioc-spectra_1.22.0-1.ca2604.1_all.deb Size: 1851888 MD5sum: 4429eed94fb6d204a72abe183a6c6af3 SHA1: f3bb2fd7aed84887eb29b5036c42c87897602117 SHA256: c75c8e407b6f8d47eca6f1b44d5899185cfa888d673047471b34ff065eb3b915 SHA512: ba94b14047ed1561cca4849758146df896aa735ec56cd0b08439abbd4598b5713156d02da23b06d47c5b4d83ac3db17178284202a8f2fe182cc9acbaa3768336 Homepage: https://cran.r-project.org/package=Spectra Description: Bioc Package 'Spectra' (Spectra Infrastructure for Mass Spectrometry Data) The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. Package: r-bioc-spia Architecture: all Version: 2.64.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4910 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-kegggraph Suggests: r-bioc-graph, r-bioc-rgraphviz, r-bioc-hgu133plus2.db Filename: pool/dists/resolute/main/r-bioc-spia_2.64.0-1.ca2604.1_all.deb Size: 2592684 MD5sum: 941091af9bf1ae1d07520412eb27c336 SHA1: 1a158c7495ede8a0165a038222e9ee041d8b5b6f SHA256: 52ed302effd6ceadd73ef5a8374f756de3dd61d957c124b31f62561f58977691 SHA512: 197b245ed40a8bc69042e2fbcbf8c2cdb57adcab22632ec4ad765b01cead497db9c13c32203e5fe238028bf5e9c0e1a6d692c069b2ff9ce6280c616710b501dd Homepage: https://cran.r-project.org/package=SPIA Description: Bioc Package 'SPIA' (Signaling Pathway Impact Analysis (SPIA) using combined evidenceof pathway over-representation and unusual signalingperturbations) This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. Package: r-bioc-splatter Architecture: all Version: 1.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10254 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-checkmate, r-cran-crayon, r-bioc-edger, r-cran-fitdistrplus, r-cran-lifecycle, r-cran-locfit, r-cran-matrixstats, r-cran-rlang, r-bioc-s4vectors, r-bioc-scrapper, r-bioc-scuttle, r-bioc-summarizedexperiment, r-cran-withr Suggests: r-bioc-basics, r-cran-biocmanager, r-bioc-biocsingular, r-bioc-biocstyle, r-bioc-biostrings, r-cran-covr, r-cran-cowplot, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-ggplot2, r-cran-igraph, r-bioc-iranges, r-cran-knitr, r-cran-limsolve, r-cran-lme4, r-cran-magick, r-bioc-phenopath, r-bioc-preprocesscore, r-cran-progress, r-cran-pscl, r-cran-rmarkdown, r-cran-scales, r-bioc-scater, r-bioc-scdd, r-bioc-scran, r-cran-sparsedc, r-cran-spelling, r-cran-testthat, r-bioc-variantannotation, r-bioc-zinbwave Filename: pool/dists/resolute/main/r-bioc-splatter_1.36.0-1.ca2604.1_all.deb Size: 5942814 MD5sum: f277ea76de57fd8a6b27aab8fc0336f4 SHA1: 527d4bb49b4389e5eae5d3de6eeaa4b0b2d996e7 SHA256: 2c8d72720ceca770bdf2c5398af6553a56d8c450995fd43429a33c9331d6cf14 SHA512: bff02d8294b591ac938b9de1be5abb7e4960e0dca045a2abe955d63c8023f8e3803eb80b317a268ca299a3dc56490aefa2d9152f9b5a8900b4270bf885dc6151 Homepage: https://cran.r-project.org/package=splatter Description: Bioc Package 'splatter' (Simple Simulation of Single-cell RNA Sequencing Data) Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. Package: r-bioc-sradb Architecture: all Version: 1.74.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5067 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rsqlite, r-bioc-graph, r-cran-rcurl, r-cran-r.utils Suggests: r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-bioc-sradb_1.74.0-1.ca2604.1_all.deb Size: 723052 MD5sum: 592a0fd5d226567aa25759ee53484b97 SHA1: e436a5b1096451f370036f2ecfa34d5f9cb69431 SHA256: 4d5e9cb7232b0fa215efb5cd7672d5009bbd58b31f37e0eacaa0c2eb21144055 SHA512: 22d338ed831b9af24755cec4cd453cafbc0a4537f83e400bf890fe1d4abec99a7bd4ea3b7d381a131628ef1480eb18fc79a6391a98fc23a2572c115689bfdf9b Homepage: https://cran.r-project.org/package=SRAdb Description: Bioc Package 'SRAdb' (A compilation of metadata from NCBI SRA and tools) The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. Package: r-bioc-stringdb Architecture: all Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4689 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-png, r-cran-sqldf, r-cran-plyr, r-cran-igraph, r-cran-httr, r-cran-rcolorbrewer, r-cran-gplots, r-cran-hash, r-cran-plotrix Suggests: r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/resolute/main/r-bioc-stringdb_2.24.0-1.ca2604.1_all.deb Size: 4654326 MD5sum: e7902eb29b8d2ed7ba06d257dd423d55 SHA1: 966cb598783f4a99dbb93837f1f96ab58c69c608 SHA256: c331084dd8e85e75abb671591daf58a0c3600f2f2b70919bd097c0ee98b0f075 SHA512: 22cfc6ac108ee82a9a12bebf1915e79dba0020c5a5a787091bec97c181567159fbf6a2cbafffa16d0163b1fd990985b35ceb76c47531ba3a1be01f22d5ead69b Homepage: https://cran.r-project.org/package=STRINGdb Description: Bioc Package 'STRINGdb' (STRINGdb - Protein-Protein Interaction Networks and FunctionalEnrichment Analysis) The STRINGdb package provides an R interface to STRING, a protein-protein interaction database and functional enrichment analysis tool (https://string-db.org). Package: r-bioc-summarizedexperiment Architecture: all Version: 1.42.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3977 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-genomicranges, r-bioc-biobase, r-cran-matrix, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-s4arrays, r-bioc-delayedarray Suggests: r-bioc-genomeinfodb, r-bioc-rhdf5, r-bioc-hdf5array, r-bioc-annotate, r-bioc-annotationdbi, r-bioc-genomicfeatures, r-bioc-sparsearray, r-bioc-singlecellexperiment, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-hgu95av2.db, r-bioc-airway, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-cran-testthat, r-cran-digest Filename: pool/dists/resolute/main/r-bioc-summarizedexperiment_1.42.0-1.ca2604.1_all.deb Size: 1437368 MD5sum: cc85e86eb67ea02ba6e04bfb403c73b5 SHA1: 520edd2f37a12871bc9032e0993c3a2500749a2c SHA256: 6a8c5275d1221a4a9bf6732ad1d127d4288bdcfdd9ae0fdbbfd8e930ae336113 SHA512: 369b6af16ab4dc7fda408a0f611b19c796511ec5ab314af813b8ada90701e73c3ab3b461e7afd3c2dc9f3409865db73cf31475020bd8cd30ad081337610644d4 Homepage: https://cran.r-project.org/package=SummarizedExperiment Description: Bioc Package 'SummarizedExperiment' (A container (S4 class) for matrix-like assays) The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. Package: r-bioc-systempiper Architecture: all Version: 2.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13516 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rsamtools, r-bioc-biostrings, r-bioc-shortread, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-yaml, r-cran-stringr, r-cran-magrittr, r-bioc-s4vectors, r-cran-crayon, r-bioc-biocgenerics, r-cran-htmlwidgets Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-systempiperdata, r-bioc-genomicalignments, r-cran-dplyr, r-cran-testthat, r-cran-rjson, r-bioc-annotate, r-bioc-annotationdbi, r-cran-kableextra, r-bioc-go.db, r-bioc-genomeinfodb, r-cran-dt, r-bioc-rtracklayer, r-bioc-limma, r-bioc-edger, r-bioc-deseq2, r-bioc-iranges, r-cran-batchtools, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-genomeinfodbdata, r-bioc-variantannotation Filename: pool/dists/resolute/main/r-bioc-systempiper_2.18.0-1.ca2604.1_all.deb Size: 6360776 MD5sum: f0b82c2dcb1f5311396e5f20e5bc460c SHA1: 7c31e1f1644b48d8d46634397ae8530ab887274e SHA256: d7baac936d3e4f98031ae726c3c28ba3d0afbe0564b6b3b089de76343c183581 SHA512: f3fb4a9d6050370ac0b6393fc39e84f3923263bc05289be6c338e0be89661e9883848cace388c5e5c02d4ae9dd078d6a183664faa34a4062fc2d8e947c91b57a Homepage: https://cran.r-project.org/package=systemPipeR Description: Bioc Package 'systemPipeR' (systemPipeR: A Multipurpose Workflow Management System forReproducible Data Analysis) systemPipeR is a workflow management environment for reproducible data analysis that integrates R with command-line software. It enables researchers to design, execute, and report complex workflows on local machines and HPC systems. The framework combines R-based analysis with external tools through a Common Workflow Language (CWL) interface, manages workflow dependencies and restart capabilities, and automatically generates reproducible scientific analysis reports. The companion package systemPipeRdata provides ready-to-use workflow templates that simplify workflow setup and customization. Alternatively, workflow templates can be loaded from dedicated GitHub repositories. Package: r-bioc-tcgabiolinks Architecture: all Version: 2.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110005 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-downloader, r-bioc-biomart, r-cran-dplyr, r-cran-tibble, r-bioc-genomicranges, r-cran-xml, r-cran-data.table, r-cran-jsonlite, r-cran-plyr, r-cran-knitr, r-cran-ggplot2, r-cran-stringr, r-bioc-iranges, r-cran-rvest, r-bioc-s4vectors, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-tcgabiolinksgui.data, r-cran-readr, r-cran-tidyr, r-cran-purrr, r-cran-xml2, r-cran-httr Suggests: r-cran-jpeg, r-cran-png, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-devtools, r-bioc-maftools, r-cran-parmigene, r-cran-c3net, r-bioc-minet, r-bioc-biobase, r-bioc-affy, r-cran-testthat, r-bioc-sesame, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-pathview, r-bioc-clusterprofiler, r-cran-seurat, r-bioc-complexheatmap, r-cran-circlize, r-bioc-consensusclusterplus, r-cran-igraph, r-bioc-limma, r-bioc-edger, r-bioc-sva, r-bioc-edaseq, r-cran-survminer, r-bioc-genefilter, r-cran-gridextra, r-cran-survival, r-cran-doparallel, r-cran-ggrepel, r-cran-scales, r-cran-dt Filename: pool/dists/resolute/main/r-bioc-tcgabiolinks_2.40.0-1.ca2604.1_all.deb Size: 34546418 MD5sum: 072e2d0bab6f1688cbddabaa1b45dac7 SHA1: beb2213c6a6aa84eb14811fea7feb16b2733e9f4 SHA256: c488bd809a259d4419b5ec3d38c82901b3c8bf3094c61c7fa5113a9e7ceec1f0 SHA512: e61e525e77734214fc19c1a5d65372c4ca5e76e09588408eb17229c32e5fee57dd4835e69c99849a121070dcb3fc2202be4d887bcd6dfda4913b6e5a346bd8f2 Homepage: https://cran.r-project.org/package=TCGAbiolinks Description: Bioc Package 'TCGAbiolinks' (TCGAbiolinks: An R/Bioconductor package for integrative analysiswith GDC data) The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. Package: r-bioc-tcgabiolinksgui.data Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 23171 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-readr, r-cran-dt Filename: pool/dists/resolute/main/r-bioc-tcgabiolinksgui.data_1.32.0-1.ca2604.1_all.deb Size: 22789828 MD5sum: fa16315ee7fe5b089954d301553df3d9 SHA1: 9a2e132fe14d74c0989ee46453d9c681b12b9229 SHA256: cc7f1a95e046eaa97fdf5eb095eddf809169897da296cff5bf3a164819f4b5d5 SHA512: 9429c2f3dbe5ba45494b1f7495e327fab745feb2fa06bbc9e14e0573c78d817d9202533a167db18e98256c282471320ea19e1959b7fc37189ab13d585b2add86 Homepage: https://cran.r-project.org/package=TCGAbiolinksGUI.data Description: Bioc Package 'TCGAbiolinksGUI.data' (Data for the TCGAbiolinksGUI package) Supporting data for the TCGAbiolinksGUI package. Package: r-bioc-tcgautils Architecture: all Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-biocgenerics, r-bioc-biocbaseutils, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-genomicranges, r-bioc-genomicdatacommons, r-cran-glue, r-bioc-iranges, r-bioc-multiassayexperiment, r-bioc-raggedexperiment, r-cran-rvest, r-bioc-s4vectors, r-bioc-seqinfo, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-xml2 Suggests: r-bioc-annotationhub, r-bioc-bioc.gff, r-bioc-biocfilecache, r-bioc-biocstyle, r-bioc-curatedtcgadata, r-bioc-complexheatmap, r-cran-devtools, r-cran-dplyr, r-cran-httr, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-impute, r-cran-knitr, r-cran-magrittr, r-bioc-mirnameconverter, r-bioc-org.hs.eg.db, r-cran-rcolorbrewer, r-cran-readr, r-cran-rmarkdown, r-bioc-rtcgatoolbox, r-bioc-rtracklayer, r-cran-r.utils, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg18.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/resolute/main/r-bioc-tcgautils_1.32.0-1.ca2604.1_all.deb Size: 517852 MD5sum: 10cd14346a017e46e0d8d83d4a7a9191 SHA1: b0725bfb35aad180bc6618317fbf64fd72dfe58a SHA256: 15ea462fe13372505d36c7b95f969cf7286edb4e1e1217e8decd4bd473d8293f SHA512: 835afed0a9b979aab186cbc699efdc7c15f7c787190f0f70e0c633414756e8717416d1b4bf39407d66bf60728cc2db390374d889aeb6cce0be057702e2934b26 Homepage: https://cran.r-project.org/package=TCGAutils Description: Bioc Package 'TCGAutils' (TCGA utility functions for data management) A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API. Package: r-bioc-tcseq Architecture: all Version: 1.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-edger, r-bioc-biocgenerics, r-cran-reshape2, r-bioc-genomicranges, r-bioc-iranges, r-bioc-summarizedexperiment, r-bioc-genomicalignments, r-bioc-rsamtools, r-cran-e1071, r-cran-cluster, r-cran-ggplot2, r-cran-locfit Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-tcseq_1.36.0-1.ca2604.1_all.deb Size: 843468 MD5sum: 4fb7fa0be13e042529fc32099583eaf6 SHA1: ddfd95870c46cda76e28a106c6781c3f799d7142 SHA256: 6314dc8fbf0a4f2104329084d44da5b7d15a7c30e090f8f407e26c7552ca923e SHA512: b5f9b8ee71df76a8199ef2c0003ab9b71cc93a6b3fad8268a492d0740bdf69e628aaa0d1aeada3012b4ed0f90c232bc069f8be285666a0784a15a85303ec5968 Homepage: https://cran.r-project.org/package=TCseq Description: Bioc Package 'TCseq' (Time course sequencing data analysis) Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of the temporal patterns of time course data. Package: r-bioc-tenxpbmcdata Architecture: all Version: 1.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-hdf5array, r-bioc-annotationhub, r-bioc-experimenthub Suggests: r-cran-rmarkdown, r-cran-knitr, r-bioc-biocstyle, r-cran-snow, r-bioc-biocfilecache, r-bioc-biocparallel Filename: pool/dists/resolute/main/r-bioc-tenxpbmcdata_1.30.0-1.ca2604.1_all.deb Size: 288528 MD5sum: 03724f4e0a6255921c3cb0b41a6744cb SHA1: b1aaf5cae8f58947ba7b4a0c865a0d299d0b988d SHA256: c0093c2d2b84e01d3e323561e6e6e6287494c6b8f1b6bbc62f457ab9af304b64 SHA512: b2abb7b4cc75869ff03482115a77b77d07176ccc4fc2333ca3e7d6b25c1c0b5cdae0c66f442b34db843c84c773c82dcc2f4dad1adaa751632a7e2f03246d2bf2 Homepage: https://cran.r-project.org/package=TENxPBMCData Description: Bioc Package 'TENxPBMCData' (PBMC data from 10X Genomics) Single-cell RNA-seq data for on PBMC cells, generated by 10X Genomics. 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SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis. Package: r-bioc-tkwidgets Architecture: all Version: 1.90.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-widgettools, r-bioc-dyndoc Suggests: r-bioc-biobase, r-bioc-hgu95av2 Filename: pool/dists/resolute/main/r-bioc-tkwidgets_1.90.0-1.ca2604.1_all.deb Size: 654584 MD5sum: 2ead899985c0b63261302b4ce099cd96 SHA1: 406944463fb73937af5129313b2488e84068ffdd SHA256: 76498fac5f337de13197b98897bd6511ef584b760d4f19b3673298b21a692c14 SHA512: d47b43a47f7fa6cc13ebb93dbe2e6388043d302cc6536d8bf2e1abe65dc8c3e993d3f2853480cdb71d962ce8003a855217ecee2f4934f704bdf2f1ba5e2ae161 Homepage: https://cran.r-project.org/package=tkWidgets Description: Bioc Package 'tkWidgets' (R based tk widgets) Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. Package: r-bioc-toast Architecture: all Version: 1.26.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4593 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-epidish, r-bioc-limma, r-cran-nnls, r-cran-quadprog, r-bioc-summarizedexperiment, r-cran-corpcor, r-cran-doparallel, r-cran-ggplot2, r-cran-tidyr, r-cran-ggally Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-matrixstats, r-cran-matrix Filename: pool/dists/resolute/main/r-bioc-toast_1.26.0-1.ca2604.1_all.deb Size: 3930778 MD5sum: f423b060bbaaf61c0185fca5840d7972 SHA1: 2fcf77bda990ccb0bd67a921672445eaf0a3bc1d SHA256: 4a963f50b7dadf701de64cb2b66e913921efa836bbfa780b238e4181572b7a9e SHA512: 061f0ad5b25b6f869629f999db2262828660509bb4f8875f46f18ccb4a2fdcb721587d0aec29dadfc288d4969ad4c414a992b1e549302d88e67108372a51dc2d Homepage: https://cran.r-project.org/package=TOAST Description: Bioc Package 'TOAST' (Tools for the analysis of heterogeneous tissues) This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge. 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Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. Package: r-bioc-trackviewer Architecture: all Version: 1.48.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20480 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-gviz, r-bioc-rsamtools, r-bioc-s4vectors, r-bioc-rtracklayer, r-bioc-biocgenerics, r-cran-scales, r-bioc-iranges, r-bioc-annotationdbi, r-cran-grimport, r-cran-htmlwidgets, r-bioc-interactionset, r-bioc-rhdf5, r-cran-strawr, r-bioc-txdbmaker Suggests: r-bioc-biomart, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-runit, r-bioc-org.hs.eg.db, r-bioc-biocstyle, r-cran-knitr, r-bioc-variantannotation, r-cran-httr, r-cran-htmltools, r-cran-rmarkdown, r-bioc-motifstack Filename: pool/dists/resolute/main/r-bioc-trackviewer_1.48.0-1.ca2604.1_all.deb Size: 9706550 MD5sum: 7e992cb9002b0f5c2019e279b6841f48 SHA1: 1cc6415e2b8fbc7bbfe8b61c3d749ca855ded76e SHA256: 2c1cfda32126ae41b42d1aff202495ac2fbcbee085516e3cba65b1804a86e439 SHA512: ce6e39658d044c83905e96b7629a1a07e45438cdf7746b85a3ed4b90da35c880ed249bb0b50586d2a94583702635c59911a668d896a1b5750b892927fe3c35ad Homepage: https://cran.r-project.org/package=trackViewer Description: Bioc Package 'trackViewer' (A R/Bioconductor package with web interface for drawing elegantinteractive tracks or lollipop plot to facilitate integratedanalysis of multi-omics data) Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. 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Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. Package: r-bioc-trajectoryutils Architecture: all Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-matrix, r-cran-igraph, r-bioc-s4vectors, r-bioc-summarizedexperiment Suggests: r-bioc-biocneighbors, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-biocparallel, r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-trajectoryutils_1.20.0-1.ca2604.1_all.deb Size: 510608 MD5sum: 42228701e685abd27d0f00894f8fbfdb SHA1: 1b53405ee38040d5b16468eab1fb2d77915a5573 SHA256: 3d45191681c07be0fb92e2697eb8bfc37273f0d926e668ad50296ccbdcff3273 SHA512: aae80608fbe154103d25fce71f12b0b7924107d2dcc76fcab28528758ad73fd4386f935856192d68bb49a7ce0db8a3ba17ea863d0de00acc872cf209be28d6da Homepage: https://cran.r-project.org/package=TrajectoryUtils Description: Bioc Package 'TrajectoryUtils' (Single-Cell Trajectory Analysis Utilities) Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results. 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It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. Package: r-bioc-treesummarizedexperiment Architecture: all Version: 2.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3118 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-s4vectors, r-bioc-biostrings, r-bioc-biocgenerics, r-cran-ape, r-cran-rlang, r-cran-dplyr, r-bioc-summarizedexperiment, r-bioc-biocparallel, r-bioc-iranges, r-bioc-treeio Suggests: r-bioc-ggtree, r-cran-ggplot2, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-bioc-treesummarizedexperiment_2.20.0-1.ca2604.1_all.deb Size: 1359632 MD5sum: ec140a0caf1bc152eba66b9451e31e7c SHA1: 825ea9a9f175b64b62fd92d80b9b18143bb85eb4 SHA256: 451eb1e791c95fc05b52238f0b046b4a5ffee171851d3afbe98686816d7e8113 SHA512: f69cd9094a9ad681aad2b2dbe24f4044306bc94b7faa0205f550ddb5a553af23f61940fcfe9c6c760a64d5686a90c6b762c7495800d274224c38b44d4f5f2102 Homepage: https://cran.r-project.org/package=TreeSummarizedExperiment Description: Bioc Package 'TreeSummarizedExperiment' (TreeSummarizedExperiment: a S4 Class for Data with TreeStructures) TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. 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Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. 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These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats. Package: r-bioc-tximeta Architecture: all Version: 1.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-tximport, r-cran-jsonlite, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-annotationdbi, r-cran-dbi, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-ensembldb, r-bioc-biocfilecache, r-bioc-annotationhub, r-bioc-biostrings, r-cran-tibble, r-bioc-seqinfo, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-tximportdata, r-bioc-org.dm.eg.db, r-bioc-deseq2, r-bioc-edger, r-bioc-limma, r-cran-devtools, r-bioc-macrophage Filename: pool/dists/resolute/main/r-bioc-tximeta_1.30.0-1.ca2604.1_all.deb Size: 927530 MD5sum: f4c6b68e81219536a242f8e539d5cce1 SHA1: 192f09f784627de18177c877584cf710e390efdc SHA256: 32124c2c55f6d73c2299a1dfd56c591f1d28af077589af40f73f4bd00dce0ef9 SHA512: f61f2c5f3634cc15dc6a3c3a053db67a9f6d2ec98f5657f136846f09dffc78a87d4825662e7784e0495054b7eea7235f82dc863fcd042611ca594667543a4236 Homepage: https://cran.r-project.org/package=tximeta Description: Bioc Package 'tximeta' (Transcript Quantification Import with Automatic Metadata) Transcript quantification import from Salmon and other quantifiers with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. Package: r-bioc-tximport Architecture: all Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-tximportdata, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-readr, r-cran-arrow, r-bioc-limma, r-bioc-edger, r-bioc-deseq2, r-bioc-rhdf5, r-cran-jsonlite, r-cran-matrixstats, r-cran-matrix, r-bioc-eds Filename: pool/dists/resolute/main/r-bioc-tximport_1.40.0-1.ca2604.1_all.deb Size: 333452 MD5sum: c0712771537080c2a8e0003fe38a263e SHA1: b65c4a2b3d8ba2a6d8ef4e45e92c3533393019ec SHA256: 4bc4f308f5fd89abd09d004fc82f4bf3567aa4f5b436f1c9eb00ec14f82014f8 SHA512: 954062d829f6be848ff7c7bda295f4ae3050d7ec0f3956d61a4f519f89e4b8c0d69f600dd1316aa5c5f74e9df7e0a938d34e4bb0678a4fcfc9950f471bcbf934 Homepage: https://cran.r-project.org/package=tximport Description: Bioc Package 'tximport' (Import and summarize transcript-level estimates for transcript-and gene-level analysis) Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. Package: r-bioc-ucell Architecture: all Version: 2.16.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-matrix, r-bioc-biocparallel, r-bioc-biocneighbors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment Suggests: r-bioc-scater, r-bioc-scrnaseq, r-cran-reshape2, r-cran-patchwork, r-cran-ggplot2, r-bioc-biocstyle, r-cran-seurat, r-cran-seuratobject, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-ucell_2.16.0-1.ca2604.1_all.deb Size: 1520552 MD5sum: b193f97d7415c2c8ea2c865294014701 SHA1: ae930da62ad185eb5fdc6a7487d660c4be043145 SHA256: 94eb94c4135ab8c07f84279046e21430d438b25b8483235ec7e9a024876c1f26 SHA512: 01ad556f230e9906d4482ac87cba2b01472bc42357d817c7f90836134ff5991144a1c0ad5e8c8a56a9580326d7faa9f1a218ecb9073936214dab2e80181f53c9 Homepage: https://cran.r-project.org/package=UCell Description: Bioc Package 'UCell' (Rank-based signature enrichment analysis for single-cell data) UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects. Package: r-bioc-ucsc.utils Architecture: all Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-bioc-s4vectors Suggests: r-cran-dbi, r-cran-rmariadb, r-bioc-genomeinfodb, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-ucsc.utils_1.8.0-1.ca2604.1_all.deb Size: 275258 MD5sum: b7941537dea1870cdec361e7bca2e3f7 SHA1: 4802d5579ed66757ea630c5a6fd82eb23d10bc69 SHA256: 24ede2ae6ed0196eb07bbc8910ab1be1c9fbda32facc15906745e6de7a8f7d25 SHA512: dd06668353ce29bcad50e36ee7d6797a927e1be1dbaafb6d3bbde10d7a07c09b0da6cfb65a0e1284be65f5838a6c28af61f5fcb2cd72d4bc24eddf4352d9957d Homepage: https://cran.r-project.org/package=UCSC.utils Description: Bioc Package 'UCSC.utils' (Low-level utilities to retrieve data from the UCSC GenomeBrowser) A set of low-level utilities to retrieve data from the UCSC Genome Browser. Most functions in the package access the data via the UCSC REST API but some of them query the UCSC MySQL server directly. Note that the primary purpose of the package is to support higher-level functionalities implemented in downstream packages like GenomeInfoDb or txdbmaker. Package: r-bioc-uniprot.ws Architecture: all Version: 2.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-biocfilecache, r-bioc-biocbaseutils, r-bioc-biocgenerics, r-cran-httr2, r-cran-jsonlite, r-cran-progress, r-cran-rjsoncons, r-cran-rlang Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-bioc-uniprot.ws_2.52.0-1.ca2604.1_all.deb Size: 444842 MD5sum: 236ce1580540a47535e9a4110b1c3fe4 SHA1: c17b880ee04538daa255d68204ee87482228c18a SHA256: 709a9e774249be11ad86669efb5ed386d2c52b4ffd2517e5e0da4f390f862b62 SHA512: 15a530c85b0d29accb09c26b1ea32888b951ec98b3fabec7755d80d71a1cd5bd66c3e923d4c9ca72e0dc71ef90ef7a313bb2be17a31650ffc91ec422e05a1654 Homepage: https://cran.r-project.org/package=UniProt.ws Description: Bioc Package 'UniProt.ws' (R Interface to UniProt Web Services) The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. This package provides a collection of functions for retrieving, processing, and re-packaging UniProt web services. The package makes use of UniProt's modernized REST API and allows mapping of identifiers accross different databases. Package: r-bioc-variancepartition Architecture: all Version: 1.42.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8591 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-bioc-limma, r-bioc-biocparallel, r-cran-mass, r-cran-pbkrtest, r-cran-lmertest, r-cran-matrix, r-cran-iterators, r-cran-gplots, r-cran-corpcor, r-cran-reformulas, r-cran-matrixstats, r-cran-rhpcblasctl, r-cran-reshape2, r-cran-gtools, r-cran-remacor, r-cran-fancova, r-cran-aod, r-cran-scales, r-cran-rdpack, r-cran-rlang, r-cran-lme4, r-bioc-biobase Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-bioc-edger, r-cran-dendextend, r-bioc-tximport, r-bioc-tximportdata, r-bioc-ballgown, r-bioc-deseq2, r-cran-runit, r-cran-cowplot, r-cran-rfast, r-bioc-zenith, r-cran-statmod, r-bioc-biocgenerics, r-cran-r2glmm, r-cran-readr Filename: pool/dists/resolute/main/r-bioc-variancepartition_1.42.0-1.ca2604.1_all.deb Size: 3855742 MD5sum: 2ae8b9e5bd6ba7543cb3904999afa0f9 SHA1: 04f363a7256addad5f12aa698362c28d3080ac9d SHA256: f10e2cfcabf0ced57552fdd1e4e160a47a9a8fd104b0e0f95de67748956a5c0f SHA512: 1f2c8f921d176e885718396dd1c5dbfc05d07f33907fde2d3f67aa731264601bf87e479597acc704468f3578b2d32c82798339ea081b66cda15b1ae4bb4899a7 Homepage: https://cran.r-project.org/package=variancePartition Description: Bioc Package 'variancePartition' (Quantify and interpret drivers of variation in multilevel geneexpression experiments) Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. 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Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data", PLoS One. Ultsch. A., Lotsch J. (2015) . 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This package has a set of datasets commonly used in our book. 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This package implements general purpose tools used by ABJ, such as functions for sampling and basic manipulation of Brazilian lawsuits identification number. It also implements functions for text cleaning, such as accentuation removal. 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Under the spike-and-slab method, a probability for each possible model is estimated with the posterior mean, credibility interval, and standard deviation of coefficients and parameters under the most probable model. Package: r-cran-abodoutlier Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-abodoutlier_0.1-1.ca2604.1_all.deb Size: 15564 MD5sum: f05f9da92a99f5f9e86d07d4f2f097d8 SHA1: e32dcb5cd8090ac0afafa578bb128f472500c778 SHA256: a9d84c3a343a594fe007d7cb89e843e01587acbdf800c2bac6d6290ae69de992 SHA512: f541b0a33fdf1bce0202136b877c7f232d8cfbf5da7bd5dfa39a7ffa743d7c8305c7392e496c2a215937d6a0b1ca0d134401611b9e70d4199d7a80dcf16f63b8 Homepage: https://cran.r-project.org/package=abodOutlier Description: CRAN Package 'abodOutlier' (Angle-Based Outlier Detection) Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection. Package: r-cran-abover Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lidr, r-cran-terra, r-cran-sf Suggests: r-cran-covr, r-cran-rstac, r-cran-httr2, r-cran-cli, r-cran-rgl, r-cran-mapview, r-cran-ggplot2, r-cran-whitebox, r-cran-withr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-abover_1.0.0-1.ca2604.1_all.deb Size: 242258 MD5sum: b5d9628e4b7c02f0edd02c5c04dd1fac SHA1: 39ed58bd68951401bf90a11e175cdec01e1ea485 SHA256: 93747e89b716b483491f2f9444e9d8a366badfb0c7aa112aa9528757334319f1 SHA512: c04e2248c973b0039620ce40ef5ae4bc328500bad9806881e89750b94137eed39b40b20cc0c1363806ed1783ae8d30d1de044d210631df8e82f1d6504c5554e5 Homepage: https://cran.r-project.org/package=aboveR Description: CRAN Package 'aboveR' ('LiDAR' Terrain Analysis and Change Detection from Above) Terrain change detection, cut and fill volume estimation, terrain profiling, flood inundation analysis, slope and aspect computation, hillshade generation, contour extraction, reclamation monitoring, erosion analysis, and engineering export (LandXML, STL) from 'LiDAR' (Light Detection and Ranging) point clouds and digital elevation models ('DEMs'). Applications include surface mine reclamation monitoring, sediment pond capacity tracking, highwall safety classification, and erosion channel detection. Built on 'lidR' for point cloud I/O and 'terra' for raster operations. Includes access utilities for 'KyFromAbove' cloud-native elevation data on Amazon Web Services ('AWS') . Methods for terrain change detection and volume estimation follow Li and others (2005) . Package: r-cran-abps Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernlab Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-abps_0.3-1.ca2604.1_all.deb Size: 125836 MD5sum: 94d835089290226e5e12477c773d19bf SHA1: 4fcd0d81e35146c802ec62f7cad58a0eab7a735b SHA256: f52fecdfa67db398205a732af6111247a10c84ed43c483160abc9c0cc5d89c01 SHA512: 1be1ae3281a4669fc7aea6f5fbe289998e2cf1ef5f15c7bbfec3e7b27e05d0dd941496c4aced9d685009589624517e691a16252bf9807d71358b59ff4ba3b428 Homepage: https://cran.r-project.org/package=ABPS Description: CRAN Package 'ABPS' (The Abnormal Blood Profile Score to Detect Blood Doping) An implementation of the Abnormal Blood Profile Score (ABPS, part of the Athlete Biological Passport program of the World Anti-Doping Agency), which combines several blood parameters into a single score in order to detect blood doping (Sottas et al. (2006) ). The package also contains functions to calculate other scores used in anti-doping programs, such as the OFF-score (Gore et al. (2003) ), as well as example data. Package: r-cran-abrsqol Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-abrsqol_1.0.0-1.ca2604.1_all.deb Size: 28564 MD5sum: e0c1d7a6bedf4ce1bbf91a862f8038ee SHA1: ab3836baf18dfa2169c738662e6841eb800dc28c SHA256: 8c2158f1dc70bf01132661b35ca22ea73b5f2cd7c45bffe7c109c560e54e1aed SHA512: 997a600324e55f54ec83eef869b139821cca20f2c1fbe343a40a5d3147302c94fb905e1dae147cafbe325d303b2c68db59622ac73a2590aac198d56e6ba46bf0 Homepage: https://cran.r-project.org/package=ABRSQOL Description: CRAN Package 'ABRSQOL' (Quality-of-Life Solver for "Measuring Quality of Life underSpatial Frictions") This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictions—generated by idiosyncratic tastes and local ties — and trade frictions — generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper. Package: r-cran-abseil Architecture: all Version: 2023.8.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10768 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-abseil_2023.8.2.1-1.ca2604.1_all.deb Size: 1574844 MD5sum: 5de4121552bbc214247309f3d75f46c8 SHA1: 3a243350437fc36c67a699b1d889ea0f20a15b29 SHA256: 30bd4295edca9151f450fa63740192fb5f6d47957e0cb9e834c16dcce6122c85 SHA512: 39206b730fd29843b208064bf465ce48d7f01700274fded07f16846dd3ce88d139f3858b817002b10b88ddfdd310de3bd14796773fd095f16c6b5954d790bbfe Homepage: https://cran.r-project.org/package=abseil Description: CRAN Package 'abseil' ('C++' Header Files from 'Abseil') Wraps the 'Abseil' 'C++' library for use by R packages. Original files are from . Patches are located at . Package: r-cran-absolution Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-alakazam, r-cran-attachment, r-cran-benchmarkme, r-cran-bigassertr, r-cran-bigparallelr, r-cran-bigstatsr, r-bioc-biostrings, r-cran-bs4dash, r-cran-callr, r-cran-colourpicker, r-cran-config, r-cran-dashboardthemes, r-cran-data.table, r-cran-dockerfiler, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-fresh, r-cran-fs, r-cran-ggplot2, r-cran-golem, r-cran-htmlwidgets, r-bioc-iranges, r-cran-iterors, r-cran-knitr, r-cran-peptides, r-cran-plotly, r-bioc-pwalign, r-cran-reactable, r-cran-rlang, r-cran-rmarkdown, r-cran-seqinr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinymanager, r-cran-shinymeta, r-cran-shinythemes, r-cran-shinywidgets, r-cran-sortable, r-cran-stringdist, r-cran-stringr, r-cran-sunburstr, r-cran-umap, r-cran-upsetjs, r-cran-viridis, r-cran-xfun Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-absolution_1.0.1-1.ca2604.1_all.deb Size: 1807400 MD5sum: 246887981d0314846f188e9f79b6740f SHA1: 856497659fff4d5c73084dbb99e6fc865f79e7d4 SHA256: c3eed661d6bb8b7427de4ef7a2bda4db3155e4cfa465acae368fa11679ed548c SHA512: ca41ccbf6abeb1f44e5d04ee678aa5e30308a9012db3db5092e0204348958ff9d24bdd8fe2dc597148604f6c0681890de6da010f4dd41400c119f9f765dfcad6 Homepage: https://cran.r-project.org/package=AbSolution Description: CRAN Package 'AbSolution' (Interactive Feature-Based Analysis of AIRR-Seq Data) An interactive framework for the exploration and analysis of adaptive immune receptor repertoire sequencing (AIRR-seq) data. It enables large-scale computation and integrated analysis of sequence-derived features, including physicochemical properties, amino acid descriptor sets, sequence motifs, compositional patterns, and somatic hypermutation metrics. The application supports multiscale analysis across sequences, clones, and repertoires, with interactive visualizations and statistical feature selection. 'AbSolution' also facilitates reproducible research by enabling structured export of data, code, parameters, and computational environments. See for more details. Package: r-cran-absorber Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-sparsegl, r-cran-fda, r-cran-ggplot2, r-cran-mass, r-cran-irlba Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-absorber_1.0-1.ca2604.1_all.deb Size: 94542 MD5sum: 6892c8ceeb0648d650e8c9657f2de0f6 SHA1: ded8b0cc327742353f014ddfbc9b5f0178dcfdd2 SHA256: 16e33e620e8e0f766e810a64dfcd21619b8aa660d238dcd53b6ec4bc0488a539 SHA512: bcb247508940d3b4c7904fe70127d672948f72e6086a7a78bed1eb067c49d605819ef6481622e7da77f78e02bef0c4547d05735a0c6e7e987cad4ad5c2fbcc6b Homepage: https://cran.r-project.org/package=absorber Description: CRAN Package 'absorber' (Variable Selection in Nonparametric Models using B-Splines) A variable selection method using B-Splines in multivariate nOnparametric Regression models Based on partial dErivatives Regularization (ABSORBER) implements a novel variable selection method in a nonlinear multivariate model using B-splines. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2024), . Package: r-cran-abstr Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4633 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-lwgeom, r-cran-magrittr, r-cran-od, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-dplyr, r-cran-rmarkdown, r-cran-tmap, r-cran-pct, r-cran-foreign Filename: pool/dists/resolute/main/r-cran-abstr_0.4.2-1.ca2604.1_all.deb Size: 3318494 MD5sum: 05533c7720fff7cc06136b02e75d6591 SHA1: a86453d1ac375b92648ffe587c21304345ab9841 SHA256: 8eef1b3cf3d485a26f6b7097a136fb74277ac712c539436044b0d7f466b80be4 SHA512: 29bef9d780bce9765a604cecd20ed9a86525ba00cb55b0401d0c4b8a1ffd6a48ee1775e6d81fe792fe0882d9dde09faf4bc4209dd2791afe1e10d329adeb4614 Homepage: https://cran.r-project.org/package=abstr Description: CRAN Package 'abstr' (R Interface to the A/B Street Transport System SimulationSoftware) Provides functions to convert origin-destination data, represented as straight 'desire lines' in the 'sf' Simple Features class system, into JSON files that can be directly imported into A/B Street , a free and open source tool for simulating urban transport systems and scenarios of change . 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A variety of user defined options and formatting are included. Package: r-cran-absurvtdc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-absurvtdc_0.1.0-1.ca2604.1_all.deb Size: 51086 MD5sum: f9d6ae19065945dc2b54cc62d70405bd SHA1: 28ae671e3f4ac9378424161ec4fd469f861f125f SHA256: f3998a95cf4206a67b85375c30d781fb97d52e554947b15eed5ab4f2cd5bf0e0 SHA512: e75b68280307f745bcd4613825a8e7ce4d8382ca046cce756bc51144c0babd470b3d1508eb50eb4fc1d5f5dc56b88383eee828ada4714619727c15000226d4eb Homepage: https://cran.r-project.org/package=ABSurvTDC Description: CRAN Package 'ABSurvTDC' (Survival Analysis using Time Dependent Covariate for AnimalBreeding) Survival analysis is employed to model the time it takes for events to occur. Survival model examines the relationship between survival and one or more predictors, usually termed covariates in the survival-analysis literature. To this end, Cox-proportional (Cox-PH) hazard rate model introduced in a seminal paper by Cox (1972) , is a broadly applicable and the most widely used method of survival analysis. This package can be used to estimate the effect of fixed and time-dependent covariates and also to compute the survival probabilities of the lactation of dairy animal. This package has been developed using algorithm of Klein and Moeschberger (2003) . 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Package: r-cran-acca Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-acca_0.2-1.ca2604.1_all.deb Size: 55740 MD5sum: 7dae5a80fdcee2c0eac4ba3f005b5094 SHA1: 691fea4cdd32a801d447d294ec33453c8ed04e54 SHA256: 6fb759202292cc073ff7e47577a84aa616e101503dd06a093fb573e22ad7701b SHA512: 8f74110f6076d71aa0b03927b17571ada12dee1cb8c00dacd20191dc4351b27c3fdefb590f2821b91145d541af10835ab97e2c9728f5f5a8bc42b74f0e0be6a0 Homepage: https://cran.r-project.org/package=acca Description: CRAN Package 'acca' (A Canonical Correlation Analysis with Inferential Guaranties) It performs Canonical Correlation Analysis and provides inferential guaranties on the correlation components. The p-values are computed following the resampling method developed in Winkler, A. M., Renaud, O., Smith, S. M., & Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, . Furthermore, it provides plotting tools to visualize the results. Package: r-cran-accelee Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-nnet, r-cran-pautilities, r-cran-randomforest, r-cran-rlang, r-cran-sojourn, r-cran-tidyr, r-cran-tree, r-cran-tworegression Suggests: r-cran-ee.data, r-cran-physactbedrest, r-cran-physicalactivity, r-cran-read.gt3x Filename: pool/dists/resolute/main/r-cran-accelee_0.3.1-1.ca2604.1_all.deb Size: 122794 MD5sum: 61e34cba5aae37edb58603a83af8cfbe SHA1: c5078cc9d43dfdb4aa65ce296c37ea0ed3605186 SHA256: e48cbf69f6b523a4342e072867509c03b8e55b85a29fc2357d293a851b41dcab SHA512: cd1569a33958ea69f79f40f897899af4a5159c9ab794725784595ff3ed3b5b8189d82ab78778c664976d34e9d607613db522d2197fd609522ff1c9f9d636cd2e Homepage: https://cran.r-project.org/package=accelEE Description: CRAN Package 'accelEE' (Predict Energy Expenditure from Accelerometer Data) Simplifies the application of various energy expenditure models. The package is intended as a hub that brings together methods from a variety of other, themed packages such as 'Sojourn' and 'TwoRegression'. Several methods are supported locally as well, including the linear methods of Hildebrand et al. (2014) and the non-linear adaptation by Ellingson et al. (2017) . The package can combine output from different methods and produce standardized output in a range of units. Package: r-cran-accelmissing Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4845 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-pscl Filename: pool/dists/resolute/main/r-cran-accelmissing_2.2-1.ca2604.1_all.deb Size: 4924440 MD5sum: 319ef41a06b4b35ebae977a23251599f SHA1: 921187245fc2f34ebb30a4b020f1e2ef33de51d7 SHA256: bbeb0dafa4a5c2db21cca5894449fc07250718528d5f0e8c8d3e8ffabcf21466 SHA512: 3671ca865a6ec71766271991ddc5261ecc52902630e738ed3d8a35ed5dcd679b6c4a0f3d694a2ae7c606a2dcad1a3cdaba5eb84a5b96543206187e0021eb5a3b Homepage: https://cran.r-project.org/package=accelmissing Description: CRAN Package 'accelmissing' (Missing Value Imputation for Accelerometer Data) We present a statistical method for imputing missing values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputation under the zero-inflated Poisson lognormal model. It also offers several functions to preprocess accelerometer data before imputation. These include detecting wear and non-wear time, selecting valid days and subjects, and generating plots. Package: r-cran-accelstab Architecture: all Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-minpack.lm, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lifecycle Filename: pool/dists/resolute/main/r-cran-accelstab_2.3.2-1.ca2604.1_all.deb Size: 784386 MD5sum: cd5a206e23e1e24209b168b7da7da707 SHA1: a03dc8fbf0c3932082563543e5076f180aba0372 SHA256: 23aed234a027d444ec28ae5f0551a3dc998b55d67f296a2b793066a43fc81aad SHA512: 35a6e01cda55f82494895be2ba72b94b739903a4c3b79154070d1e845ba4077d226062082d7178f596db037a92ece54b95feb695c178632a7d201c8477b2c9aa Homepage: https://cran.r-project.org/package=AccelStab Description: CRAN Package 'AccelStab' (Accelerated Stability Kinetic Modelling) Estimate the Šesták–Berggren kinetic model (degradation model) from experimental data. A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit, allowing for the extrapolation of the degradation of a drug product - both in time and temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate t-distribution, and analytical formulae (the delta method) are available options to calculate the confidence and prediction intervals. The results (modelling, extrapolations and statistical intervals) can be visualised with multiple plots. The examples illustrate the accelerated stability modelling in drugs and vaccines development. Package: r-cran-accept Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyselect, r-cran-dplyr, r-cran-reldist, r-cran-tibble, r-cran-hardhat, r-cran-vctrs, r-cran-vetiver Suggests: r-cran-jsonlite, r-cran-plotly, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-accept_1.0.2-1.ca2604.1_all.deb Size: 737154 MD5sum: ac4f50a702693c3eaf37c1d24a089998 SHA1: e4873ef123bee313ee39f091181833770131ad54 SHA256: 823c3f88539bd57eaa7dc2eaf29b572e71967bc3eeeebe92a0409cacb0822a55 SHA512: 2e2cdbe0511411e437d7d4966f075360e38eef69bbc4fb71bfdccc83d1e78a612944bc73919eb31e67d455b940820053d882226f97c5c1a80119fbc434bf4827 Homepage: https://cran.r-project.org/package=accept Description: CRAN Package 'accept' (The Acute COPD Exacerbation Prediction Tool (ACCEPT)) Allows clinicians to predict the rate and severity of future acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) patients, based on the clinical prediction models published in Adibi et al. (2020) and Safari et al. (2022) . Package: r-cran-acceptancesampling Architecture: all Version: 1.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-acceptancesampling_1.0.11-1.ca2604.1_all.deb Size: 417354 MD5sum: bb5641c216f79a167e5a0331e023d340 SHA1: b5525d8a3e34289e38d24d38c250e4424a3e698b SHA256: ca4a4a29e010d48f450ff7d3d76a060d6e746217b503ed3678b6b99521ea712f SHA512: 3a8be0aef1de29eff767e51645e898c649c251b6f7ab5a2bd0f116d220cbc1b0f802999ddbaaaebf22c7428def55d2206380e732c39ec02fe066262c62a36562 Homepage: https://cran.r-project.org/package=AcceptanceSampling Description: CRAN Package 'AcceptanceSampling' (Creation and Evaluation of Acceptance Sampling Plans) Provides functionality for creating and evaluating acceptance sampling plans. Sampling plans can be single, double or multiple. Package: r-cran-accessibility Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3225 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-hmisc, r-cran-rdpack Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-accessibility_1.5.0-1.ca2604.1_all.deb Size: 2752230 MD5sum: c400e405d5e64292bce94e3e42dce7d5 SHA1: 06ee776caf53c43f71c1920c190a422d835623eb SHA256: 8bab2ce3a7d2fce005574298b091828b9df4b3b8f47d6afd239e3b7d12b43f47 SHA512: c290e51b1da1a8b092326675a32dd684627fa9d9d831232e90e83d5b80282ab37ef64f0f7ffc2f53f5c8225823ae9a6d7c8998e6b7ec142b2d4e6e53ded9281f Homepage: https://cran.r-project.org/package=accessibility Description: CRAN Package 'accessibility' (Transport Accessibility Measures) A set of fast and convenient functions to help conducting accessibility analyses. Given a pre-computed travel cost matrix and a land use dataset (containing for example the location of jobs, healthcare and population), the package allows one to calculate accessibility levels, and accessibility poverty and inequality. The package covers the majority of the most commonly used accessibility measures (such as cumulative opportunities, gravity-based and floating catchment areas methods), some cutting edge measures proposed in the literature (e.g. balancing cost and constrained accessibility) as well as the most frequently used inequality and poverty metrics (such as the Palma ratio, the concentration and Theil indices and the FGT family of measures). 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Accessible 'PDF' files are produced only on a 'Windows' Operating System. One aspect of accessibility is providing a headings structure that is recognised by a screen reader, providing a navigational tool for a blind or partially-sighted person. A key aim is to produce documents of different formats easily from each of a collection of 'R markdown' source files. Input 'R markdown' files are rendered using the render() function from the 'rmarkdown' package . A 'zip' file containing multiple output files can be produced from one function call. A user-supplied template 'Word' document can be used to determine the formatting of an output 'Word' document. Accessible 'PDF' files are produced from 'Word' documents using 'OfficeToPDF' . A convenience function, install_otp() is provided to install this software. The option to print 'HTML' output to (non-accessible) 'PDF' files is also available. Package: r-cran-accessrmd Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-htmltools, r-cran-stringr, r-cran-rlist, r-cran-knitr, r-cran-rcurl Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-markdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-accessrmd_1.0.0-1.ca2604.1_all.deb Size: 71054 MD5sum: 5ed12dc70e9a9464f2f3197ca66834df SHA1: cf8805c371c1492031c20945bedf814635a5bd5a SHA256: 6c1ba77d293f7e76c0102447a149f6d1aa6de5146b69fd8d202e7053d1f025ea SHA512: 08a1a2ecfbc9c2b14a9f3f525392560aa50e941d47dfa3bf7b306e4fc9ba7b4b9e437c01b1e82d455d7584ffd04330bfc310987660c2306fe597b7d2cfbc9aa5 Homepage: https://cran.r-project.org/package=accessrmd Description: CRAN Package 'accessrmd' (Improving the Accessibility of 'rmarkdown' Documents) Provides a simple method to improve the accessibility of 'rmarkdown' documents. The package provides functions for creating or modifying 'rmarkdown' documents, resolving known errors and alerts that result in accessibility issues for screen reader users. Package: r-cran-accrual Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-cran-fgui, r-cran-smpracticals Filename: pool/dists/resolute/main/r-cran-accrual_1.4-1.ca2604.1_all.deb Size: 60200 MD5sum: d64aa34f40a60bedb956788370b7bb14 SHA1: b9718bebadee0cd942a1c483f28895f7e3974f35 SHA256: 626f938e0bfa78c4a1bd0a0827018c7f9205e6def675926796222391716a0d1f SHA512: d77828869374401ec84d2c5994bd0bf87c1b5307beecd6e4b2690782e01c493f620034348c894ecf9f9f2c24ca378f6cafb18888c4a1827575559e5a7c82ba2b Homepage: https://cran.r-project.org/package=accrual Description: CRAN Package 'accrual' (Bayesian Accrual Prediction) Participant recruitment for medical research is challenging. Slow accrual leads to delays in research. Accrual monitoring during the process of recruitment is critical. Researchers need reliable tools to manage the accrual rate. We developed a Bayesian method that integrates the researcher's experience with previous trials and data from the current study, providing reliable predictions on accrual rate for clinical studies. For more details and background on these methodologies, see the publications of Byron, Stephen and Susan (2008) , and Yu et al. (2015) . In this R package, Bayesian accrual prediction functions are presented, which can be easily used by statisticians and clinical researchers. Package: r-cran-accrualplot Architecture: all Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-markdown, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-accrualplot_1.0.10-1.ca2604.1_all.deb Size: 282826 MD5sum: e9051685ff802416386b1b4fdd819fdc SHA1: ac5a777825881746951c41137fbcef4538194255 SHA256: 53b11b474a331712a5db1ca33b2fb0cbcc2738126f8da75e349f432b4801492e SHA512: 5a8be4a612b896941e1660eb9fb28be636bc4b13a8af2a9d3e8f19bc4d3ca17eb29184bbce8986d9d68689fe41d5e28de2edcd02b5444f9d6657429f6ea22700 Homepage: https://cran.r-project.org/package=accrualPlot Description: CRAN Package 'accrualPlot' (Accrual Plots and Predictions for Clinical Trials) Tracking accrual in clinical trials is important for trial success. If accrual is too slow, the trial will take too long and be too expensive. If accrual is much faster than expected, time sensitive tasks such as the writing of statistical analysis plans might need to be rushed. 'accrualPlot' provides functions to aid the tracking of accrual and predict when a trial will reach it's intended sample size. Package: r-cran-accsamplingdesign Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vgam, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-accsamplingdesign_0.0.8-1.ca2604.1_all.deb Size: 309598 MD5sum: 5d6fca90a74cd7d922e19fc50eead53d SHA1: 7201fd2396a6416985dccde517a92dc9add26241 SHA256: 3b34a13ed9332897ffb329061d1559fe786b884ba48eb903e5c793e0de07e4b7 SHA512: a584cc89791ee373084ad8749a1df387764c7309f72d74dd8229fda289332bb0c48844c19db5ea63caa3b7de2400f5eff6604d6f4ab4aeb039d1b51e913183f4 Homepage: https://cran.r-project.org/package=AccSamplingDesign Description: CRAN Package 'AccSamplingDesign' (Acceptance Sampling Plans Design) Provides tools for designing and analyzing Acceptance Sampling plans. Supports both Attributes Sampling (Binomial and Poisson distributions) and Variables Sampling (Normal and Beta distributions), enabling quality control for fractional and compositional data. Uses nonlinear programming for sampling plan optimization, minimizing sample size while controlling producer's and consumer's risks. Operating Characteristic curves are available for plan visualization. Package: r-cran-accsda Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-accsda_1.1.3-1.ca2604.1_all.deb Size: 244938 MD5sum: 066dec3d34e084410e668b668ea8b8f5 SHA1: dc0b5569c4d602fe0d26ac6ca149f889a0eba38a SHA256: 918758148bd2fd73028dcfc0a67efe14b44378133e728dab94ab4e1373befc31 SHA512: ab4b24c940ea2a0c35a2dd7c4bb8934a72750e8f07d2606846bf4057369608cd2621e16dbaf2e4aa795a21c689a60243770b1a5d95fee3d146a4e251e783eeb8 Homepage: https://cran.r-project.org/package=accSDA Description: CRAN Package 'accSDA' (Accelerated Sparse Discriminant Analysis) Implementation of sparse linear discriminant analysis, which is a supervised classification method for multiple classes. Various novel optimization approaches to this problem are implemented including alternating direction method of multipliers ('ADMM'), proximal gradient (PG) and accelerated proximal gradient ('APG') (See Atkins 'et al'. ). Functions for performing cross validation are also supplied along with basic prediction and plotting functions. Sparse zero variance discriminant analysis ('SZVD') is also included in the package (See Ames and Hong, ). See the 'github' wiki for a more extended description. Package: r-cran-accucor Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnls, r-cran-dplyr, r-cran-stringr, r-cran-readxl, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-writexl, r-cran-chnosz Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-accucor_0.3.1-1.ca2604.1_all.deb Size: 730046 MD5sum: 2e097b58ef22cd06ab462bcdca39cd52 SHA1: 79ae956667471ba6957cf9a0509da53c6cb20fd8 SHA256: eb0b4beea1fd2ab635c097f6f4bd498abf5e7607c1085655b5b5431f515911f5 SHA512: b59e1a86e8a389818009c02be0a0d419f41ac509088e486b7603d142cf3f476a3ab99dade5813356d778ac0a717637b82bd43d30094d39a6a74b83867b54a7a6 Homepage: https://cran.r-project.org/package=accucor Description: CRAN Package 'accucor' (Natural Abundance Correction of Mass Spectrometer Data) An isotope natural abundance correction algorithm that is needed especially for high resolution mass spectrometers. Supports correction for 13C, 2H and 15N. Su X, Lu W and Rabinowitz J (2017) . Package: r-cran-accumulate Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-simplermarkdown, r-cran-validate Filename: pool/dists/resolute/main/r-cran-accumulate_1.0.0-1.ca2604.1_all.deb Size: 92694 MD5sum: 18b97834e17817d32ad4de5510e491a6 SHA1: 9950fb5f8fe58c3f54cdb35ed6ee46db0433d888 SHA256: db4c9d838758e6042548a0c55af7fb073976650036d553aee5964208478f145a SHA512: 06ed7ce88b96a0d7ec30fd8717160dc65953bc30846953662802d47a07605992bcf0f6ee0470d8ddddfbba4c55e5f3cbd9c8333853d1c572943b65e2fc84bbcb Homepage: https://cran.r-project.org/package=accumulate Description: CRAN Package 'accumulate' (Split-Apply-Combine with Dynamic Groups) Estimate group aggregates, where one can set user-defined conditions that each group of records must satisfy to be suitable for aggregation. If a group of records is not suitable, it is expanded using a collapsing scheme defined by the user. A paper on this package was published in the Journal of Statistical Software . Package: r-cran-acdcquery Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-httr, r-cran-jsonlite, r-cran-digest Filename: pool/dists/resolute/main/r-cran-acdcquery_1.2.3-1.ca2604.1_all.deb Size: 93432 MD5sum: 628cc4d8856df5f7c03ad0d7073d6d25 SHA1: 783c34f7ee7096a7f56d11a98ca83fbaff805f9b SHA256: d683c81b89144df45366c36b815d791f63f201ce16e5a4a3fabacea74c1d620e SHA512: 26b9fae22b78c10a6bb66426b73687b1a80e5d4578e3b175dfa9445b7e46cc73884ae05478126ba399482e42a4deaa0257ee4ebf37db48494c2b27eab403fd3d Homepage: https://cran.r-project.org/package=acdcquery Description: CRAN Package 'acdcquery' (Query the Attentional Control Data Collection or the TruthEffect Database) Interact with the Attentional Control Data Collection (ACDC) or the Truth Effect Database (TED). Download the databases using download_acdc() or download_ted(), connect to the database via connect_to_db(), set filter arguments via add_argument() and query the database via query_db(). Package: r-cran-acdcr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-acdcr_1.0.0-1.ca2604.1_all.deb Size: 39384 MD5sum: 89f7e5b24c073fb8a575ed8a8ddb4535 SHA1: e7243475fe9a849e89f974123f7dae7bc4c38675 SHA256: 7ddb4189fc9d8fd8c8f9c25039dd58c3ac87f2a6a7f2cddb12b9508c129cfffe SHA512: 75110aac0ffb516916f84754cc4d01a33b7dc65e3be2fd1cf6765083161cccdd9c24f3a81b3fd8745c1b1c7bd0908d2256744aa0be74982b7a11cb150ae2add7 Homepage: https://cran.r-project.org/package=acdcR Description: CRAN Package 'acdcR' (Agro-Climatic Data by County) The functions are designed to calculate the most widely-used county-level variables in agricultural production or agricultural-climatic and weather analyses. To operate some functions in this package needs download of the bulk PRISM raster. See the examples, testing versions and more details from: . Package: r-cran-ace.coco Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-mvtnorm, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ace.coco_0.1-1.ca2604.1_all.deb Size: 18648 MD5sum: b7d998899e4cc389f217ea16387214e2 SHA1: 4499fb57b119d383d25533ca834ba734eeb822ef SHA256: c8646e23e52eaa8442509fda5bcd44f9b04270c559efa3ecb5a659fa06be3539 SHA512: 2b3c557eac3a34bdea38f1f328862b27c21f488bf19a88f2a71cf332183d8a4888ac776dd0c684fe12a79452b9628aa0b7b5042d2eb8e4705a34a447478fb746 Homepage: https://cran.r-project.org/package=ACE.CoCo Description: CRAN Package 'ACE.CoCo' (Analysis of Correlated High-Dimensional Expression (ACE) Data) A function for estimating factor models. Give factor-adjusted statistics, factor-adjusted mean estimation (one-sample test) or factor-adjusted mean difference estimation (two-sample test). Package: r-cran-ace2fastq Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-ace2fastq_0.6.0-1.ca2604.1_all.deb Size: 26688 MD5sum: 47eefcd94405b47e9d9a0a2e0f7a375f SHA1: d720e09da5a38f835f08603989fc39eecef442f4 SHA256: 2680a264852f59f5f5a36608d97a5c0379dfef93f183548f1b5a5a9cff1bf61e SHA512: 86b241e6ae87ba60d4a7c6d01e432394557f85325876fb09940139fe2a7575a7ce97aba3ebcefc839028ab449b62659a430894c00654d4cf9edad2d36f0b3a76 Homepage: https://cran.r-project.org/package=ace2fastq Description: CRAN Package 'ace2fastq' (ACE File to FASTQ Converter) The ACE file format is used in genomics to store contigs from sequencing machines. This tools converts it into FASTQ format. Both formats contain the sequence characters and their corresponding quality information. Unlike the FASTQ file, the ace file stores the quality values numerically. The conversion algorithm uses the standard Sanger formula. The package facilitates insertion into pipelines, and content inspection. Package: r-cran-aceeditor Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11922 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-reactr, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-aceeditor_1.0.1-1.ca2604.1_all.deb Size: 1657698 MD5sum: 9c976aaf7a1619c3f71e112ad055ce32 SHA1: e93b5c5735cf19d55706beafca74d10575dc7a1e SHA256: 5e723738bfd1c73a60a7cb066d7f703b2458f2d77a5916ed0fffdb6a5c476f5d SHA512: 8ca7d51ba7966294c2175e03e2235813cf86da20cc3998e0c8a49d230246f86ade7062a9852b74a82c9fc32307296f1f47ba6bf754007396e085e9d0f2289daa Homepage: https://cran.r-project.org/package=aceEditor Description: CRAN Package 'aceEditor' (The 'Ace' Editor as a HTML Widget) Wraps the 'Ace' editor in a HTML widget. The 'Ace' editor has support for many languages. It can be opened in the viewer pane of 'RStudio', and this provides a second source editor. Package: r-cran-acep Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4062 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringr, r-cran-magrittr Suggests: r-cran-furrr, r-cran-ollamar, r-cran-future, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-reticulate, r-cran-rsyntax, r-cran-spacyr, r-cran-tidygeocoder, r-cran-udpipe, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acep_0.1.1-1.ca2604.1_all.deb Size: 3477372 MD5sum: 0cdb45059ff5961717d40d659c41da49 SHA1: 7b4c9111ac94bb49463939288715254f56c6ed10 SHA256: f18643da6f2a8091028ccb52ec2ae6c5abda8e8cd8d8aab433a853bcfa168223 SHA512: 43454057216f038a3a2b480c522d439e436a82308b6587ef69006301f9f7acb0cb71795d5914aabf5859436798b82f07a5c1561e6efe68550275df76bbe483bc Homepage: https://cran.r-project.org/package=ACEP Description: CRAN Package 'ACEP' (Análisis Computacional de Eventos de Protesta) La librería 'ACEP' contiene funciones específicas para desarrollar análisis computacional de eventos de protesta. Asimismo, contiene bases de datos con colecciones de notas sobre protestas y diccionarios de palabras conflictivas. La colección de diccionarios reune diccionarios de diferentes orígenes. The 'ACEP' library contains specific functions to perform computational analysis of protest events. It also contains a database with collections of notes on protests and dictionaries of conflicting words. Collection of dictionaries that brings together dictionaries from different sources. Package: r-cran-acesearch Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acesearch_1.0.0-1.ca2604.1_all.deb Size: 26456 MD5sum: 581d813ca26dcd4a8fceca74d41007e6 SHA1: 20e107114e901c13733ecc74a8d2a95d469b1c4d SHA256: 9f336a6b5900bce61d2d10eef18171d6d7762e3ab299063c2ce376325f1cc3ac SHA512: 6e30c08f608d9957e868f2b4d0b28a7bf6f61d0fd0c5b1064881e8b1d07664f9b474f45d6e61f85886b214aa63e41f60299d6b4d18b3567f1bbdc14002137308 Homepage: https://cran.r-project.org/package=ACEsearch Description: CRAN Package 'ACEsearch' ('ACE' Search Engine API) 'ACE' (Advanced Cohort Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. This package is designed to interface directly with an instance of 'ACE' search engine and facilitates API queries and data dumps. Prerequisite is a good knowledge of the temporal language to be able to efficiently construct a query. More information available at . Package: r-cran-acesimfit Architecture: all Version: 0.0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-acesimfit_0.0.0.9-1.ca2604.1_all.deb Size: 53184 MD5sum: 9c314e5b1ef27e830411c6c4431a2bc0 SHA1: a2b82982e9aad382f2b8e6fd529be7d3ed3b3219 SHA256: 52d75687e9b6670f93eb1ea3108e6ed0b6e86617c7c5f1a38dc7841c31053156 SHA512: 2c557c96fe9ada970295eaab9cfee193c69266fdde81271c4c356f9275300a7bf79080b777ceeabf9d7a32419f189635bc5896f5e15ddf80a3d36c3eab6aa80d Homepage: https://cran.r-project.org/package=ACEsimFit Description: CRAN Package 'ACEsimFit' (ACE Kin Pair Data Simulations and Model Fitting) A few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004). Package: r-cran-acfmperiod Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acfmperiod_1.1.0-1.ca2604.1_all.deb Size: 432344 MD5sum: ddea4d72666e05552caf64e5a09e0a0f SHA1: bcd9c6a150bb567befd9b4b30d376a13d83cb9f0 SHA256: 8f51f310415f317b43739ed9f6fcec2bab46692eee14c14ea0ad447e21029567 SHA512: 9acc48ee945d1a311e12aa390c1462df249ffa6fa47b86f6d7fc5e36875f661e3320e424904f8f09583e47a01049f9c006b3c743f4f59052d7cb2cde185909be Homepage: https://cran.r-project.org/package=acfMPeriod Description: CRAN Package 'acfMPeriod' (Robust Estimation of the ACF from the M-Periodogram) Non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) . The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) . Package: r-cran-achievegap Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-lme4, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-achievegap_0.1.0-1.ca2604.1_all.deb Size: 142432 MD5sum: 64c5e365a7d1314d3e0da7e8adf35b38 SHA1: 1d46350a55b5b42fa396904f089070ca82e725d2 SHA256: 46ca6a113eb3ec24b84632ed72fd1f384d382b4aa5f1ede9e1c29f452a6d8914 SHA512: 43f8416ee9a6ef9674b081ae35704d6617c6143fc0e281f8ffe116e71c4014e67a8ffcc961d7ede3543ee822d7958d7aafe4c6a438f537b72a2175f6543614a3 Homepage: https://cran.r-project.org/package=achieveGap Description: CRAN Package 'achieveGap' (Modeling Achievement Gap Trajectories with HierarchicalPenalized Splines) Implements a hierarchical penalized spline framework for estimating achievement gap trajectories in longitudinal educational data. The achievement gap between two groups (e.g., low versus high socioeconomic status) is modeled directly as a smooth function of grade while the baseline trajectory is estimated simultaneously within a mixed-effects model. Smoothing parameters are selected using restricted maximum likelihood (REML), and simultaneous confidence bands with correct joint coverage are constructed using posterior simulation. The package also includes functions for simulation-based benchmarking, visualization of gap trajectories, and hypothesis testing for global and grade-specific differences. The modeling framework builds on penalized spline methods (Eilers and Marx, 1996, ) and generalized additive modeling approaches (Wood, 2017, ), with uncertainty quantification following Marra and Wood (2012, ). Package: r-cran-achilles Architecture: all Version: 1.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1564 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-databaseconnector, r-cran-sqlrender, r-cran-dplyr, r-cran-jsonlite, r-cran-parallellogger, r-cran-readr, r-cran-data.table, r-cran-lubridate, r-cran-tseries, r-cran-rlang Suggests: r-cran-dt, r-cran-magrittr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-achilles_1.7.2-1.ca2604.1_all.deb Size: 700638 MD5sum: 2803c3bdaf12041fd946d2da4c568440 SHA1: 145c77810f90f39f8dd6b09709dcf0189a0046cf SHA256: e489d09620bba18f4ace5e31ac51d3e42fcbdc5cad439868a9e66ecb77aeacc9 SHA512: 35c1b232dab5e5aa27f35ff45455e9a5a21dd538729d04ea4dc1a3f05d4dd593b541eba0f0c3e689fe784e95065919eaf24dc47cb6aed42b54c962fba4b03b2a Homepage: https://cran.r-project.org/package=Achilles Description: CRAN Package 'Achilles' (Achilles Data Source Characterization) Automated Characterization of Health Information at Large-Scale Longitudinal Evidence Systems. Creates a descriptive statistics summary for an Observational Medical Outcomes Partnership Common Data Model standardized data source. This package includes functions for executing summary queries on the specified data source and exporting reporting content for use across a variety of Observational Health Data Sciences and Informatics community applications. Package: r-cran-acid Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-hmisc Suggests: r-cran-ineq Filename: pool/dists/resolute/main/r-cran-acid_1.1-1.ca2604.1_all.deb Size: 348866 MD5sum: 9f5ea01295ef9e003bdabc7418da46ea SHA1: 68b2ae9b07fefc010006f61a3ded9c594f8a6316 SHA256: 1bf11431eed219d38848d72e2728f77401776068f8ee260c05d9a0ec26ed171a SHA512: 62359a519cf860aee738bf182480408721493fb12133f16c8e22b5141b7f7e41ddf63ea37a1221cf3b6d857a4dd6e306a8bcf7d21edcf92cc2f0150de4e77014 Homepage: https://cran.r-project.org/package=acid Description: CRAN Package 'acid' (Analysing Conditional Income Distributions) Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level. Package: r-cran-acled.api Architecture: all Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acled.api_1.1.8-1.ca2604.1_all.deb Size: 34038 MD5sum: 8cb45f3be28d98dddac6c01a70e1ead9 SHA1: fba30410f05180412ab0b4a349beda5a68671441 SHA256: 59d0568a0995451f81f25b98d5b9a9022a3ddbb41187c2f45da1e1645860a616 SHA512: 5755a7b86cda5b2f88d1b897d99a34a1aac3ef5461e6eea8b20e7e75c9220fb6d83fce051b99634721bac750df0ed4f9fe123ae2a2a1f338ab11934c1621d4b7 Homepage: https://cran.r-project.org/package=acled.api Description: CRAN Package 'acled.api' (Automated Retrieval of ACLED Conflict Event Data) Access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED) at . The package makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow. It requires a minimal number of dependencies. See the package's README file for a note on replicability when drawing on ACLED data. When using this package, you acknowledge that you have read ACLED's terms and conditions of use, and that you agree with their attribution requirements. Package: r-cran-acledr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-lubridate, r-cran-stringr, r-cran-tidyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-janitor, r-cran-rmarkdown, r-cran-readr, r-cran-kableextra, r-cran-ggplot2, r-cran-covr, r-cran-here, r-cran-secret, r-cran-sf, r-cran-raster, r-cran-forcats, r-cran-igraph, r-cran-sjmisc, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acledr_1.0.1-1.ca2604.1_all.deb Size: 1691108 MD5sum: 28bbfcd7eb80f580ad214c2fa46ff07d SHA1: d8ecaf01a93e6c6eb879c1d7f9773548ab18d9fe SHA256: 1f5148dea28336155ead717071a9eebfc6eeec73e51bccc727f586916f849c15 SHA512: e07b26cf955d76147fef6a864b49932a6d7d7b2e1347a0aa408e5fd7c68f04d5a0bb4edb96b33af8ae36fae419c2962d479c22bd4885ad2b4f5a68bf56b6ae4e Homepage: https://cran.r-project.org/package=acledR Description: CRAN Package 'acledR' (Manipulate ACLED Data) Tools working with data from ACLED (Armed Conflict Location and Event Data). Functions include simplified access to ACLED's API (), methods for keeping local versions of ACLED data up-to-date, and functions for common ACLED data transformations. Package: r-cran-aclhs Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-geor Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aclhs_1.0.1-1.ca2604.1_all.deb Size: 68536 MD5sum: 45e757795ebd452af04e30871ef7ef1b SHA1: 0211e93f4f5b98e78b54ba465248adc87f4eb8d3 SHA256: d6132accb32715169f1b6218ff07667a34cc79c68cf68f415828646c50381b06 SHA512: d34f6d78917eb24c3a0b86fba8656d18850d2c8d2fae27670094df8e339bb637ef3580f6e5790ced1f442901f822ccb53a422d414a8f926e1f02044002d6deb0 Homepage: https://cran.r-project.org/package=aclhs Description: CRAN Package 'aclhs' (Autocorrelated Conditioned Latin Hypercube Sampling) Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hypercube Sampling (cLHS) algorithm that allows sampled data to have similar correlative and statistical features of the original data. Only a properly formatted dataframe needs to be provided to yield subsample indices from the primary function. For more details about the cLHS algorithm, see Minasny and McBratney (2006), . For acLHS, see Le and Vargas (2024) . Package: r-cran-acmgscaler Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-acmgscaler_1.0.0-1.ca2604.1_all.deb Size: 55240 MD5sum: 932e0c8b0ce3b0a1cc7fdd9fed29e1dc SHA1: 0ee0d981ead080c51b11ab06498bf976e546da3e SHA256: 585ed3829870cd2579c2e6d1fbdaef38048afa481d38af8738fe57205551f9a7 SHA512: 7a3d65f14037e5ed21c0158efee7a717acb86f1480c75c10f0e13dccf817dc0f531e8163b2c7f147bd20e6cd7d9e1a148772826b0b8f1717afd76545f111baa3 Homepage: https://cran.r-project.org/package=acmgscaler Description: CRAN Package 'acmgscaler' (Variant Effect Calibration to ACMG/AMP Evidence Strength) Provides a function to calibrate variant effect scores against evidence strength categories defined by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines. The method computes likelihood ratios of pathogenicity via kernel density estimation of pathogenic and benign score distributions, and derives score intervals corresponding to ACMG/AMP evidence levels. This enables researchers and clinical geneticists to interpret functional and computational variant scores in a reproducible and standardised manner. For details, see Badonyi and Marsh (2025) . Package: r-cran-acne Architecture: all Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aroma.affymetrix, r-cran-mass, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.utils, r-cran-matrixstats, r-cran-r.filesets, r-cran-aroma.core Suggests: r-bioc-dnacopy Filename: pool/dists/resolute/main/r-cran-acne_0.9.2-1.ca2604.1_all.deb Size: 131816 MD5sum: 59f470fc2311ee90e30d830b8d737e4a SHA1: 57d582d3fce47336cad530b4e63b7e7a10410dd6 SHA256: 5b999436e4456df940ad43afdbeed331185b2f30430ff0cf5b04dc3742dbbe53 SHA512: a5df4aef48975b213675593dc110ef3c2f824c77bd6db3ad9fb5865ee500522ff0c7607caa14ecd6360d14743c095750254484f917a05eceb471c9a6a1a8662e Homepage: https://cran.r-project.org/package=ACNE Description: CRAN Package 'ACNE' (Affymetrix SNP Probe-Summarization using Non-Negative MatrixFactorization) A summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF). Package: r-cran-acnr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3037 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-r.utils, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acnr_1.0.0-1.ca2604.1_all.deb Size: 2829368 MD5sum: cce6f0321660ad5d39466816c5c0e2c0 SHA1: 2521e1b8d8b204c051294a0f89236ef42b6085e6 SHA256: 9fef1d4308110c727df30d861ad7dd07848c5bfb3db32f126fc6b035e06763ed SHA512: 6640efade2c60297f8ac51ab987b5500ac5d02d1e5824ef75239354c9df2e86020c780d903f55d3897749bad5457befa7159396d92710355a0b996abac5f7e7b Homepage: https://cran.r-project.org/package=acnr Description: CRAN Package 'acnr' (Annotated Copy-Number Regions) Provides SNP array data from different types of copy-number regions. These regions were identified manually by the authors of the package and may be used to generate realistic data sets with known truth. Package: r-cran-acopula Architecture: all Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-acopula_0.9.4-1.ca2604.1_all.deb Size: 620344 MD5sum: 743b89360bfe7bb11c630a8e8d89d1bc SHA1: 055597033f98871c39972abd5f84139467386d8c SHA256: dd9597ba2bf0155cc7091e2e3748103db4a29f6265489deb05de93d7a46eb10b SHA512: 67f4894534c062db54371c078daae28b88dbb821c53adfebe07d768e4474e55921f9707e45bf780e74269e16ee06c0c0031d205bbc8d4894220dd8b12d6021d3 Homepage: https://cran.r-project.org/package=acopula Description: CRAN Package 'acopula' (Modelling Dependence with Multivariate Archimax (or anyUser-Defined Continuous) Copulas) Archimax copulas are mixture of Archimedean and EV copulas. The package provides definitions of several parametric families of generator and dependence function, computes CDF and PDF, estimates parameters, tests for goodness of fit, generates random sample and checks copula properties for custom constructs. In 2-dimensional case explicit formulas for density are used, contrary to higher dimensions when all derivatives are linearly approximated. Several non-archimax families (normal, FGM, Plackett) are provided as well. Package: r-cran-acorn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acorn_0.1.0-1.ca2604.1_all.deb Size: 110252 MD5sum: 63cbd444ff3e9a1a0d85f2c3c924c6d2 SHA1: a07cc8828177e4cb84207ed6c2884a5500b5ac62 SHA256: 954a112be6abda2b2716faa85d89a4f44e45cf2a257012aac86d9adcc4b803bb SHA512: af2a420cc4a89c0dc7e0c969b1e964afb1e9a64e340c9ff45b05a34179e009b7158346a1241a816d7ed3af89702a4751cd5643183dceabbde915981cfb7b7b52 Homepage: https://cran.r-project.org/package=acoRn Description: CRAN Package 'acoRn' (Exclusion-Based Parentage Assignment Using Multilocus GenotypeData) Exclusion-based parentage assignment is essential for studies in biodiversity conservation and breeding programs - Kang Huang, Rui Mi, Derek W Dunn, Tongcheng Wang, Baoguo Li, (2018), . The tool compares multilocus genotype data of potential parents and offspring, identifying likely parentage relationships while accounting for genotyping errors, missing data, and duplicate genotypes. 'acoRn' includes two algorithms: one generates synthetic genotype data based on user-defined parameters, while the other analyzes existing genotype data to identify parentage patterns. The package is versatile, applicable to diverse organisms, and offers clear visual outputs, making it a valuable resource for researchers. Package: r-cran-acp Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tseries, r-cran-quantmod Filename: pool/dists/resolute/main/r-cran-acp_2.1-1.ca2604.1_all.deb Size: 59662 MD5sum: c1ab9ca2d1f5fa0aacf3432a5e42ebc2 SHA1: 78931c3564c34d44be52d04355436d820e30860b SHA256: 983faae3005056a90a7304db76bfee6c7666e5702306e6fecc8c35aaeb0aab51 SHA512: e8b14ec004ab504bc1002500cf85adc4c0586005f63ca51b5c8c72f7813504243f09371c2d54bc8feecdf39c4419692e018d89b6240f750eb29e814f0fb902bc Homepage: https://cran.r-project.org/package=acp Description: CRAN Package 'acp' (Autoregressive Conditional Poisson) Analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003). Package: r-cran-acro Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-admiraldev, r-cran-png Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acro_0.1.7-1.ca2604.1_all.deb Size: 390018 MD5sum: 0342d53e8b091dce60eba43c255a1e95 SHA1: 602edf4094e2846607e74befe065d14dcbe0f4e0 SHA256: 326aaa5fc27d517a574114abb1ea108de7dadf6662fd2e461038664c46fc5fad SHA512: 772c7c711a3ff684014aee0c1bf05138e0eeaee6d14b9b9a0ff4d64dfdf1d85d01a9e488e4890c2ee0a038e4cd0847a8a7667c0d1ae18565887ec5b5b8a77dd9 Homepage: https://cran.r-project.org/package=acro Description: CRAN Package 'acro' (A Tool for Semi-Automating the Statistical Disclosure Control ofResearch Outputs) A Tool for Semi-Automating the Statistical Disclosure Control of Research Outputs. Package: r-cran-acroname Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-r.utils, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-hunspell Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-acroname_0.1.0-1.ca2604.1_all.deb Size: 29606 MD5sum: 0534808c9c1da8adae0884d1b9163d29 SHA1: e901199b2c32161e47329dbc183dde7db6fb2769 SHA256: 77ac5122e18bfbf853b277fdf8a47e1c0af3171781722c5b005f945ac1fbbd13 SHA512: e70ee375c3f67d68588402524ea1601b5fa3ff4e9c69a82f6ab58792a978a1f16358fe31a40b4ab59b0bd7d69b372c3fd47e8b82791fb0f2885808fbef317db0 Homepage: https://cran.r-project.org/package=acroname Description: CRAN Package 'acroname' (Engine for Acronyms and Initialisms) A tool for generating acronyms and initialisms from arbitrary text input. Package: r-cran-acss.data Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-acss.data_1.2-1.ca2604.1_all.deb Size: 14460646 MD5sum: 48a9c8bb1e29941cb571c78ebbe8c8c9 SHA1: 90be8edc01762795058a2fc53a718705eb7095e6 SHA256: 33f3b4265eb6958670addda554cc1de0b510815929a1e1424cc84f430b06a3b6 SHA512: bfd01478cfba90dc01cb818792f605a5552c09a349fadf44edb22eb2fbcde9689f12dd7eb937840aa3b3f480d260f695a59b1aa6dbef2321e4314981ca31866a Homepage: https://cran.r-project.org/package=acss.data Description: CRAN Package 'acss.data' (Data Only: Algorithmic Complexity of Short Strings (Computed viaCoding Theorem Method)) Data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on TMs with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states of the TMs. Package: r-cran-acss Architecture: all Version: 0.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-acss.data, r-cran-zoo Suggests: r-cran-effects, r-cran-lattice, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-acss_0.3-2-1.ca2604.1_all.deb Size: 1208250 MD5sum: 11ca67ba4be6e20269194c021490e3c0 SHA1: de4c2b57224f89e90f6d52253f7d29550fc71e7a SHA256: 4060621eab893429733ffa651b13c6f531cf38bd2d34578ac5e8e556f0d4994d SHA512: 3d7577e2c005e3189eee01233a75be6bed0c54d98a714803b593f8cc6a320676bd7fb985cf7642a4579dd23b7633ece00026c8218e344608901eec34a3d4d465 Homepage: https://cran.r-project.org/package=acss Description: CRAN Package 'acss' (Algorithmic Complexity for Short Strings) Main functionality is to provide the algorithmic complexity for short strings, an approximation of the Kolmogorov Complexity of a short string using the coding theorem method (see ?acss). The database containing the complexity is provided in the data only package acss.data, this package provides functions accessing the data such as prob_random returning the posterior probability that a given string was produced by a random process. In addition, two traditional (but problematic) measures of complexity are also provided: entropy and change complexity. Package: r-cran-acswr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-acswr_1.0-1.ca2604.1_all.deb Size: 246544 MD5sum: bfe3524c806589b91bd6b6c5558f7481 SHA1: 4cd2d5f8ea1d7bee4f503df064f99e3966491907 SHA256: 4ee142312ea00fb700d16a3dc8febb62bc58ff50718340dcac9c5e8d69e69f2c SHA512: 30332c137e217b9824d21be3ebb9ab13d94815be7e1629f674a7603224c05a58529472b8992b2d921429d5e27775227999dfaaaeb3da5c39d8240f3ef975ba92 Homepage: https://cran.r-project.org/package=ACSWR Description: CRAN Package 'ACSWR' (A Companion Package for the Book "A Course in Statistics with R") A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. "A Course in Statistics with R", J. Wiley, ISBN 978-1-119-15272-9. Package: r-cran-act Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4414 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-stringi, r-cran-textutils, r-cran-progress, r-cran-xml, r-cran-xml2, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-act_1.3.1-1.ca2604.1_all.deb Size: 2059468 MD5sum: 99e78d1161e8459ac58ba5ceba9d8fea SHA1: 4f37d7ffbe3cd5cda41ba00ed629476461d667fa SHA256: 716d3233f08fbd698f06b5b7cf6ebb657785952fa3748de776d0412f503f1308 SHA512: 94507b60ecb7b6afac6ccff6a6465f1ef63834508211f590fa1b23bc3cba5c34c2c3639d301bbaacf80102edde2af55d259f69b788e8c03696207e342aa53442 Homepage: https://cran.r-project.org/package=act Description: CRAN Package 'act' (Aligned Corpus Toolkit) The Aligned Corpus Toolkit (act) is designed for linguists that work with time aligned transcription data. It offers functions to import and export various annotation file formats ('ELAN' .eaf, 'EXMARaLDA .exb and 'Praat' .TextGrid files), create print transcripts in the style of conversation analysis, search transcripts (span searches across multiple annotations, search in normalized annotations, make concordances etc.), export and re-import search results (.csv and 'Excel' .xlsx format), create cuts for the search results (print transcripts, audio/video cuts using 'FFmpeg' and video sub titles in 'Subrib title' .srt format), modify the data in a corpus (search/replace, delete, filter etc.), interact with 'Praat' using 'Praat'-scripts, and exchange data with the 'rPraat' package. The package is itself written in R and may be expanded by other users. Package: r-cran-actcr Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-cosinor, r-cran-cosinor2, r-cran-dplyr, r-cran-minpack.lm Filename: pool/dists/resolute/main/r-cran-actcr_0.4.0-1.ca2604.1_all.deb Size: 420672 MD5sum: ffc1c5969662aa80a6a4f9b729dd7e7f SHA1: 273f5a90ed89de62c4b5790da94d1b4e2b21a265 SHA256: fd86505d7e21f2360e24de5adf5f1488e3f4ae4dd1d09f356bbf645c9816c21f SHA512: 2361b7698f8fbfb6dd41aac71352cf539dd50938d3446dc5e036c7d5011bfa5009ae04348aca1602cdbe5625ca29218673ec1de7ef618b9b6c8b8613bfae0f03 Homepage: https://cran.r-project.org/package=ActCR Description: CRAN Package 'ActCR' (Extract Circadian Rhythms Metrics from Actigraphy Data) Circadian rhythms are rhythms that oscillate about every 24 h, which has been observed in multiple physiological processes including core body temperature, hormone secretion, heart rate, blood pressure, and many others. Measuring circadian rhythm with wearables is based on a principle that there is increased movement during wake periods and reduced movement during sleep periods, and has been shown to be reliable and valid. This package can be used to extract nonparametric circadian metrics like intradaily variability (IV), interdaily stability (IS), and relative amplitude (RA); and parametric cosinor model and extended cosinor model coefficient. Details can be found in Junrui Di et al (2019) . Package: r-cran-actel Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1592 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-data.table, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-fasttime, r-cran-ggplot2, r-cran-knitr, r-cran-readr, r-cran-reshape2, r-cran-rmarkdown, r-cran-rsvg, r-cran-scales, r-cran-stringi, r-cran-stringr, r-cran-svglite Suggests: r-cran-gdistance, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-actel_1.4.0-1.ca2604.1_all.deb Size: 1477072 MD5sum: 309aa8d73e7d6968dc5e502c9f08536c SHA1: d567ded47c5d3829a48914b10ee03c84179caf52 SHA256: 96180446823a672aefc8f67267b94d4a6186020f4a86b3d2e1c1948af1cc6e76 SHA512: b2bbe4f7d6c043edd79616473ceaf6644bf82fbb27723b304e8952ee2f0dd441ed780351ff90277b90f8972f14abf58fa3adf7b102e8b2ba5f745182ddcccb0c Homepage: https://cran.r-project.org/package=actel Description: CRAN Package 'actel' (Acoustic Telemetry Data Analysis) Designed for studies where animals tagged with acoustic tags are expected to move through receiver arrays. This package combines the advantages of automatic sorting and checking of animal movements with the possibility for user intervention on tags that deviate from expected behaviour. The three analysis functions (explore(), migration() and residency()) allow the users to analyse their data in a systematic way, making it easy to compare results from different studies. CJS calculations are based on Perry et al. (2012) . Package: r-cran-actfrag Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-accelerometry, r-cran-dplyr, r-cran-ineq, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-actfrag_0.1.2-1.ca2604.1_all.deb Size: 399344 MD5sum: 21ef1a7129a9480a055eb17ed5a97fd7 SHA1: b1dd11178eba3be49146450735b52a8689a672dc SHA256: ec951cbb50c9454b8a7942010ab4896f4d9f15aa724f925f0450a0f65d5c6660 SHA512: 670134ae171bdf7e2288e550cc77cf68091bf0c100611aee6a964017f38d14579cc691a8899aee8026e6cb703c84fe9387cf3b04cfd88a50cca3f3265d3adc37 Homepage: https://cran.r-project.org/package=ActFrag Description: CRAN Package 'ActFrag' (Activity Fragmentation Metrics Extracted from Minute LevelActivity Data) Recent studies haven shown that, on top of total daily active/sedentary volumes, the time accumulation strategies provide more sensitive information. This package provides functions to extract commonly used fragmentation metrics to quantify such time accumulation strategies based on minute level actigraphy-measured activity counts data. Package: r-cran-actfts Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4033 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-plotly, r-cran-reactable, r-cran-tseries, r-cran-xts, r-cran-forecast, r-cran-lifecycle Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-actfts_0.3.0-1.ca2604.1_all.deb Size: 1052908 MD5sum: 9b634526a118268d2dcd5b4d5d825547 SHA1: ee4a68191e1a834e9bc02b6008b80df886913d62 SHA256: 0e3f58802d49a7da57db62bca3d33725de74b8c4b86d39f4c5df1f6d5ccf6cc1 SHA512: 2f131827b091fb8c29776b7ebcb4db359fe0c9ebb5b89d04fb187318ee7ec34caef01057b641a3f20e8030305319dd59c20bbca223240fb27e89a934e632109d Homepage: https://cran.r-project.org/package=actfts Description: CRAN Package 'actfts' (Autocorrelation Tools Featured for Time Series) The 'actfts' package provides tools for performing autocorrelation analysis of time series data. It includes functions to compute and visualize the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Additionally, it performs the Dickey-Fuller, KPSS, and Phillips-Perron unit root tests to assess the stationarity of time series. Theoretical foundations are based on Box and Cox (1964) , Box and Jenkins (1976) , and Box and Pierce (1970) . Statistical methods are also drawn from Kolmogorov (1933) , Kwiatkowski et al. (1992) , and Ljung and Box (1978) . The package integrates functions from 'forecast' (Hyndman & Khandakar, 2008) , 'tseries' (Trapletti & Hornik, 2020) , 'xts' (Ryan & Ulrich, 2020) , and 'stats' (R Core Team, 2023) . Additionally, it provides visualization tools via 'plotly' (Sievert, 2020) and 'reactable' (Glaz, 2023) . The package also incorporates macroeconomic datasets from the U.S. Bureau of Economic Analysis: Disposable Personal Income (DPI) , Gross Domestic Product (GDP) , and Personal Consumption Expenditures (PCEC) . Package: r-cran-actilifecounts Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4860 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsignal, r-cran-pracma, r-cran-ggirread Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-actilifecounts_1.1.1-1.ca2604.1_all.deb Size: 560548 MD5sum: b80f174432f435dba87f34670fe4c615 SHA1: 1ef86315de02ebca9db58ee7b4465dc41bb9f17a SHA256: ee658035eb2be98b6821af5477b67a908c1e37f467ae6067716c8f8e3a345b15 SHA512: 96e8ac79d10909295c32b87c23fadb1fafeb71b0544e89da88bea9e3ef769567621ffbc4e9ad217c33999729e7c62f53669b2bfb285f40ef079ce5db9f77b042 Homepage: https://cran.r-project.org/package=actilifecounts Description: CRAN Package 'actilifecounts' (Generate Activity Counts from Raw Accelerometer Data) A tool to obtain activity counts, originally a translation of the 'python' package 'agcounts' . This tool allows the processing of data from any accelerometer brand, with a more flexible approach to handle different sampling frequencies. Package: r-cran-activanalyzer Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dbplyr, r-cran-dplyr, r-cran-flextable, r-cran-forcats, r-cran-ggplot2, r-cran-golem, r-cran-hms, r-cran-lubridate, r-cran-magrittr, r-cran-modelr, r-cran-patchwork, r-cran-physicalactivity, r-cran-plyr, r-cran-reactable, r-cran-rmarkdown, r-cran-rsqlite, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-stringr, r-cran-tidyr, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-spelling, r-cran-testthat, r-cran-processx, r-cran-globals, r-cran-config, r-cran-tidyselect, r-cran-dbi, r-cran-htmltools, r-cran-officer, r-cran-pkgload, r-cran-scales, r-cran-tibble, r-cran-rlang, r-cran-tinytex, r-cran-shinytest2, r-cran-pkgdown, r-cran-callr Filename: pool/dists/resolute/main/r-cran-activanalyzer_2.1.2-1.ca2604.1_all.deb Size: 2161154 MD5sum: 56bf83ab72ff0009d7db113c75bf051c SHA1: 4c03cd00e9ab754351bea072629bb05f68ac86af SHA256: 284146473a52cddaabf8ba6a782a30df47c729cec57ddbafbd10c6ad48d2814b SHA512: 2f7bdfe0d6853b94520b70f99fdc494e945ac47e983ed64427b8c377d5dc73a03d8bcaade4929c23757abfe7f26a8783ef5ac0d39ebebd7da4ae83877e56810a Homepage: https://cran.r-project.org/package=activAnalyzer Description: CRAN Package 'activAnalyzer' (A 'Shiny' App to Analyze Accelerometer-Measured Daily PhysicalBehavior Data) A tool to analyse 'ActiGraph' accelerometer data and to implement the use of the PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments. Once analysis is completed, the app allows to export results to .csv files and to generate a report of the measurement. All the configured inputs relevant for interpreting the results are recorded in the report. In addition to the existing 'R' packages that are fully integrated with the app, the app uses some functions from the 'actigraph.sleepr' package developed by Petkova (2021) . Package: r-cran-activatr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggmap, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-rlang, r-cran-tibble, r-cran-slider, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-activatr_0.2.1-1.ca2604.1_all.deb Size: 2791590 MD5sum: 50da96f7fd5e86d07f76672d32bf1eb7 SHA1: 4330e1104ca440827d894b77f405c3d78214def3 SHA256: 5296d6ff70653ae18da94b03700ef6fa760b2fb847c1c9374f6cf8cbe9d6d000 SHA512: a9579ea9d612d54248d891b828a94bbaff70cec0ba4d51f2346e50b45d248b3e07a0a580b63f5da356ca3deadf1720df952fdd7edd575e0874c8975641d6e1ae Homepage: https://cran.r-project.org/package=activatr Description: CRAN Package 'activatr' (Utilities for Parsing and Plotting Activities) This contains helpful functions for parsing, managing, plotting, and visualizing activities, most often from GPX (GPS Exchange Format) files recorded by GPS devices. It allows easy parsing of the source files into standard R data formats, along with functions to compute derived data for the activity, and to plot the activity in a variety of ways. Package: r-cran-activedriver Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-activedriver_1.0.0-1.ca2604.1_all.deb Size: 179862 MD5sum: cabbcd5d18ba060ed62dd9b83c2348f3 SHA1: c1ab265b7af1eda3d3eb3bb31eebbb810d54c176 SHA256: 107e26bc1e974270222fa528d4aef5b84f845cf7e26e392b64d8be148e6798e9 SHA512: 555fb63ffb91971b8c68add2a792a6eb25ade19afa92d2f7961652c47480baf2efbd37017e70ce40267788a3f74a176e68f34fef39964b178c2656c51333ea7d Homepage: https://cran.r-project.org/package=ActiveDriver Description: CRAN Package 'ActiveDriver' (Finding Cancer Driver Proteins with Enriched Mutations inPost-Translational Modification Sites) A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 . Package: r-cran-activedriverwgs Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm9, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-activedriverwgs_1.2.1-1.ca2604.1_all.deb Size: 859938 MD5sum: ad3d533d2a1d532c1ae4e31d1ac1122d SHA1: ec643559c1414a4ec1255fb127f9c4efc1944dbd SHA256: becaff29c3329cdb86a34cd11ed115d8e0985e1108f8dd46f2eb9b2d68734c9b SHA512: 1d4d76003f8ed16378711ed8f3d3b9bfab9898a60e40688bcdff2bd2d247b1b99c3758e834f787963da76b101d5c577a7e298e8bd436a889a93362d1898114ac Homepage: https://cran.r-project.org/package=ActiveDriverWGS Description: CRAN Package 'ActiveDriverWGS' (A Driver Discovery Tool for Cancer Whole Genomes) A method for finding enrichments of somatic single nucleotide variants (SNVs) and small insertions-deletions (Indels) in functional elements in the human genome. 'ActiveDriverWGS' detects coding and noncoding cancer driver elements using whole genome sequencing data. The method is part of the publication H. Zhu et al. (2020) "Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks" in Molecular Cell. Package: r-cran-activepathways Architecture: all Version: 2.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3045 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-activepathways_2.0.6-1.ca2604.1_all.deb Size: 1749572 MD5sum: 0cd1a68a9078b853f5d3d924f9f2235e SHA1: d35819f25cac537976537d125848d3c8f85c0d83 SHA256: 514501745f4d0b267a57ef93222b447e25426aad2bbcbcfd75852af74d8f7523 SHA512: ea79f00090c4530177985c4b3c6e191b942b87532e0129e091af9ae08f65a2a87cd16a23955058e695e49e3ecc86c3847d09b7cade4c1ea5bc01374787534949 Homepage: https://cran.r-project.org/package=ActivePathways Description: CRAN Package 'ActivePathways' (Integrative Pathway Enrichment Analysis of Multivariate OmicsData) Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) . Package: r-cran-activity Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply Filename: pool/dists/resolute/main/r-cran-activity_1.3.4-1.ca2604.1_all.deb Size: 258268 MD5sum: 6fa7224ce59a2d731d175f125d248b60 SHA1: 1c97d0375ab0cfa93064abc3dd0a1b1460a606a2 SHA256: 897a1650eb44a10f9f0fba5b117c389cb9840bcea34599024a4f6b24de89996c SHA512: 0cc6e91f59e01e56a2a3b9742e527a7179db286b7509c3ad011464fcc3e4196036577ba030bc94793fa54dc3fa0a58820f3de6c48e9a6b762479c7b134b3c11b Homepage: https://cran.r-project.org/package=activity Description: CRAN Package 'activity' (Animal Activity Statistics) Provides functions to express clock time data relative to anchor points (typically solar); fit kernel density functions to animal activity time data; plot activity distributions; quantify overall levels of activity; statistically compare activity metrics through bootstrapping; evaluate variation in linear variables with time (or other circular variables). Package: r-cran-activitycounts Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seewave, r-cran-signal, r-cran-tibble, r-cran-lubridate, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-activitycounts_0.2.1-1.ca2604.1_all.deb Size: 1011876 MD5sum: 816afe2886a349c04e99ed24564fc5e9 SHA1: fc0cf29c0355a132503969ac4a35fbfa90abf5f8 SHA256: c6d7e1064e3c44111bcfb66abd815617f269f9d0e6066a7422345f67191708dc SHA512: 76b516a42d406a09422ba11fa16bb680384fa09bca2837b95838f7c282fa8312e45d24dddc9c52ce0f21115510fa4e01b729bafaffb3204e37ef08261c308cd3 Homepage: https://cran.r-project.org/package=activityCounts Description: CRAN Package 'activityCounts' (Generate ActiLife Counts) ActiLife software generates activity counts from data collected by Actigraph accelerometers . Actigraph is one of the most common research-grade accelerometers. There is considerable research validating and developing algorithms for human activity using ActiLife counts. Unfortunately, ActiLife counts are proprietary and difficult to implement if researchers use different accelerometer brands. The code creates ActiLife counts from raw acceleration data for different accelerometer brands and it is developed based on the study done by Brond and others (2017) . Package: r-cran-activityindex Architecture: all Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-data.table, r-cran-r.utils Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-activityindex_0.3.7-1.ca2604.1_all.deb Size: 4086356 MD5sum: 309ddeca676ec14da4f3c86a0dbc0b13 SHA1: d2ceaaf93ebd26ffb0f6ab7a84668ca30969f925 SHA256: ecce6635cb18d474472d0d5334ed8e30cf12e69c3637ac474f78d03b88460e3b SHA512: b76350410492797592161a74f721a10d3bf7109c2fcf3adfef73867c9c2da2f885d480818d74e383a03560b169334f2a825cd823f2283c9f36e86c30498cab5d Homepage: https://cran.r-project.org/package=ActivityIndex Description: CRAN Package 'ActivityIndex' (Activity Index Calculation using Raw 'Accelerometry' Data) Reads raw 'accelerometry' from 'GT3X+' data and plain table data to calculate Activity Index from 'Bai et al.' (2016) . The Activity Index refers to the square root of the second-level average variance of the three 'accelerometry' axes. Package: r-cran-activpal Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-lubridate, r-cran-magrittr, r-cran-devtools Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-activpal_0.1.4-1.ca2604.1_all.deb Size: 338536 MD5sum: 528a1a2d276fc5e560eb26711954ee21 SHA1: 6746d8849819c3f44ac2f9486ed704b8c39821ab SHA256: e83493a2029194cef89e27e68740591f1ad1ad250dd9bd7ec1cc0c9f5c0dc3a9 SHA512: 53b3c174942f775e36baa1ec454fbb01d6d99b377aee1edec0995c99ab694e0fc3300c97df72523324a13c2a47305098510b89093eb3b1a454c1300b82eb84b8 Homepage: https://cran.r-project.org/package=activPAL Description: CRAN Package 'activPAL' (Advanced Processing and Chart Generation from activPAL EventsFiles) Contains functions to generate pre-defined summary statistics from activPAL events files . The package also contains functions to produce informative graphics that visualise physical activity behaviour and trends. This includes generating graphs that align physical activity behaviour with additional time based observations described by other data sets, such as sleep diaries and continuous glucose monitoring data. Package: r-cran-actlifer Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-actlifer_1.0.0-1.ca2604.1_all.deb Size: 459744 MD5sum: e93bbbc2ab3493d623da9f8873022ac1 SHA1: 492d2d1e12910973fbb8dcd69afc05a3af842cb8 SHA256: 9e55e2c3cd461d4bfe2f1440c360226e675c0db6a99e43eb5357964559958e97 SHA512: 96a753b05c399c1c660ee00857eb299babb1786b165e3149100711916804e90132196a83fc3b0970a3af760cb42bb12cf009209c6922862084339753110e0889 Homepage: https://cran.r-project.org/package=actLifer Description: CRAN Package 'actLifer' (Creating Actuarial Life Tables) Contains data and functions that can be used to make actuarial life tables. Each function adds a column to the inputted dataset for each intermediate calculation between mortality rate and life expectancy. Users can run any of our functions to complete the life table until that step, or run lifetable() to output a full life table that can be customized to remove optional columns. Methods for creating lifetables are as described in Zedstatistics (2021) . Package: r-cran-actogrammr Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-readr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-actogrammr_0.2.3-1.ca2604.1_all.deb Size: 174518 MD5sum: fc8bdc5e5187e820b2e1948ffefb4a1f SHA1: dacf55cfd8116912929d2b6eb62cbd903e6176b6 SHA256: d732fba4bb378af08e51df7968813e4eed7ad4737feee2a9e5809993aa980242 SHA512: 86d6c96ed1398a0d636bbbee8d09ba8163cb942cf74f50ae2e7436262c4ace0e82092c58c2f92b0c97b76283359f78005f1e79d4878e0aedf92bb97f3f07445c Homepage: https://cran.r-project.org/package=actogrammr Description: CRAN Package 'actogrammr' (Read in Activity Data and Plot Actograms) Read in activity measurements from standard file formats used by circadian rhythm researchers, currently only 'ClockLab' format, and process and plot the data. The central type of plot is the actogram, as first described by in "Activity and distribution of certain wild mice in relation to biotic communities" by MS Johnson (1926) . Package: r-cran-actuare Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cplm, r-cran-statmod, r-cran-nlme, r-cran-lme4, r-cran-magrittr, r-cran-data.table, r-cran-ggplot2, r-cran-reformulas Suggests: r-cran-plyr, r-cran-knitr, r-cran-bookdown, r-cran-insurancedata, r-cran-actuar, r-cran-lattice, r-cran-minqa, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-actuare_1.0.0-1.ca2604.1_all.deb Size: 1486094 MD5sum: dca64795299219b439b6b22ff600916d SHA1: a7b1285e32caaa0ad039fb161bf312da8a1cda0c SHA256: da32fc0c47d132ec0101b095864b17c527a24409ba9bf1d47f9cdbc4b65198b6 SHA512: b3b569eefc7655795d56361ffb68e8fede613b530c7610f57529ae055edf16283ff28785731e0303c2158c28ac3dc131b22d1a13b719526f0ea803a70af92722 Homepage: https://cran.r-project.org/package=actuaRE Description: CRAN Package 'actuaRE' (Handling Single-Level and Hierarchically Structured Risk Factorsusing Credibility and Random Effects Models) Fits random effects models for multi-level/high-cardinality factors using credibility theory (Buhlmann-Straub for single-level, Jewell for hierarchical structures), GLM extensions following Ohlsson (2008) , or Tweedie generalized linear mixed models. Provides functions for model fitting, visualization, and prediction. See Campo, B.D.C. and Antonio, K. (2023) . Package: r-cran-actuarialm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-actuarialm_0.1.0-1.ca2604.1_all.deb Size: 51540 MD5sum: 5d08319a3cf760725a97f61164cd16a2 SHA1: 48ea3b0310c5abfb79f3c80acd760696a8601b55 SHA256: 0dbe91556968e25d44b8c74ec8f113a9b1ffbfd768599690163f8ff55a5b4006 SHA512: c32ec70268a7692ca2cd777cf5e902b76a2cba2cffe144e4486d377231688fc1301552857c3ce5731e3528adb699d6e37ae28471f82b053701611ca6294aede0 Homepage: https://cran.r-project.org/package=ActuarialM Description: CRAN Package 'ActuarialM' (Computation of Actuarial Measures Using Bell G Family) It computes two frequently applied actuarial measures, the expected shortfall and the value at risk. Seven well-known classical distributions in connection to the Bell generalized family are used as follows: Bell-exponential distribution, Bell-extended exponential distribution, Bell-Weibull distribution, Bell-extended Weibull distribution, Bell-Lomax distribution, Bell-Burr-12 distribution, and Bell-Burr-X distribution. Related works include: a) Fayomi, A., Tahir, M. H., Algarni, A., Imran, M., & Jamal, F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. . b) Alsadat, N., Imran, M., Tahir, M. H., Jamal, F., Ahmad, H., & Elgarhy, M. (2023). "Compounded Bell-G class of statistical models with applications to COVID-19 and actuarial data". Open Physics, 21(1), 20220242. . Package: r-cran-actuary Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 994 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dtplyr, r-cran-forcats, r-cran-furrr, r-cran-future, r-cran-gamlss.dist, r-cran-ggplot2, r-cran-ggtext, r-cran-mass, r-cran-pracma, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-yardstick Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-actuary_0.1.2-1.ca2604.1_all.deb Size: 971916 MD5sum: b825bbacb80fd8144b49ba589e1acf88 SHA1: 03d043da09d7910f0b299b2f239fa9ccfd15a3c3 SHA256: 600e0353f7289201209d59e93552788395ce731e6e6aa5a8775d5d55f0201a64 SHA512: 52b0023443e5eec828629b49c4c5c62fcfee5805be667da5d21eb874c1af5be17d6cc6a022cde5de19087070ac74ff7bfa2ab957d8e6b7708d867ad5b9b6f107 Homepage: https://cran.r-project.org/package=actuary Description: CRAN Package 'actuary' (Actuarial Functions and Utilities) Provides actuarial modeling tools for Monte Carlo loss simulations, loss reserving, and reinsurance layer loss calculations. 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Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. Since version 2.0 the function margins() is available to calculate the margins for these classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as a function of the number of iterations. In addition, the ensembles can be pruned using the option 'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies the relative importance measure to take into account the gain of the Gini index given by a variable in each tree and the weights of these trees. 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Package: r-cran-adaplots Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adaplots_0.1.0-1.ca2604.1_all.deb Size: 87568 MD5sum: 70c286c9a1b1f06e7dc91dc7f7a6f085 SHA1: 5a3d5d769f4fc044dfad7c0ac84b2592b0f66244 SHA256: b2479ee569b9d7976c763e517fc99a528d65e406973b5c36c1148269f993765f SHA512: 116ff3d18c5126ead6865b2ea046ceed943b38d7c8478f9ae2f477055939fd1a868b800b1ec9698d77966fa269e4e92e296c2852ccda42b8ae98a16ca133d765 Homepage: https://cran.r-project.org/package=adaplots Description: CRAN Package 'adaplots' (Ada-Plot and Uda-Plot for Assessing Distributional Attributesand Normality) The centralized empirical cumulative average deviation function is utilized to develop both Ada-plot and Uda-plot as alternatives to Ad-plot and Ud-plot introduced by the author. Analogous to Ad-plot, Ada-plot can identify symmetry, skewness, and outliers of the data distribution. The Uda-plot is as exceptional as Ud-plot in assessing normality. The d-value that quantifies the degree of proximity between the Uda-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Extreme values in the data can be eliminated using the 1.5IQR rule to create its robust version if user demands. Full description of the methodology can be found in the article by Wijesuriya (2025a) . Further, the development of Ad-plot and Ud-plot is contained in both article and the 'adplots' R package by Wijesuriya (2025b & 2025c) and . Package: r-cran-adapsamp Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-adapsamp_1.1.1-1.ca2604.1_all.deb Size: 53354 MD5sum: f82d27e1c1683ec7c7916fa328e95a1f SHA1: b008e575683b1326b443836f41edfd6f7be03816 SHA256: b77cbb64ccd0c059918d705557bad20f97c0ded5b8d431b1532e0ab0dcf4e584 SHA512: d6b54715847964035f9b46199dc66f09dcd1ea079defe17642e8e85a8b8b3dad151aecedcba3cf4848dda04d873b510d3a600b4bd48122e72edac21baac8095c Homepage: https://cran.r-project.org/package=AdapSamp Description: CRAN Package 'AdapSamp' (Adaptive Sampling Algorithms) For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, we can use the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm and the adaptive slice sampling algorithm. So we designed an R package mainly including 4 functions: rARS(), rMARS(), rCCARS() and rASS(). These functions can realize sampling based on the algorithms above. 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Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al . 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This makes it easier to find and correct the most important problems first. Package: r-cran-adaptdiag Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-extradistr, r-cran-foreach, r-cran-pbmcapply Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vgam, r-cran-covr Filename: pool/dists/resolute/main/r-cran-adaptdiag_0.1.0-1.ca2604.1_all.deb Size: 194332 MD5sum: a1b8f1f95dde22351bc4d4cfbb6f9fde SHA1: b6223396ea1357384a1011932993e72c2c896d3b SHA256: 5ec17dba9eae7828c8da340b47fd913611654836431487768441db6c0ca18fa6 SHA512: 3f09c8b68cf76097c6814bd1cbcc9262326f1d99affd7b2973e3157152c92acdcce61fbd172d9bf872f02dfbec43e5bdd96ea456986d4de79399ac2e24751c30 Homepage: https://cran.r-project.org/package=adaptDiag Description: CRAN Package 'adaptDiag' (Bayesian Adaptive Designs for Diagnostic Trials) Simulate clinical trials for diagnostic test devices and evaluate the operating characteristics under an adaptive design with futility assessment determined via the posterior predictive probabilities. Package: r-cran-adaptiveboxplot Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-adaptiveboxplot_0.1.1-1.ca2604.1_all.deb Size: 25834 MD5sum: 9f5bf49a77f5e821e7b07b4be2edc3c9 SHA1: 3021f71f08c368edbc48d196addf62e37153e4e5 SHA256: 36dec92c4c41f1e2daf0fb438cb06ad1b46466bf03a1d7edf44162cac834f2c5 SHA512: 7f8418ebeaa59927455549d7c1077f1b379398e21ff4c4d6de44b636e0083e3de5f2e13b6c5b5e57955f0aef5b2c829eb9de6401d1640647fd2d8071526a9cb7 Homepage: https://cran.r-project.org/package=AdaptiveBoxplot Description: CRAN Package 'AdaptiveBoxplot' (FDR(BH) Boxplot and FWER(Holm) Boxplot) Implements a framework for creating boxplots where the whisker lengths are determined by formal multiple testing procedures, making them adaptive to sample size and data characteristics. The function bh_boxplot() generates boxplots that control the False Discovery Rate (FDR) via the Benjamini-Hochberg procedure, and the function holm_boxplot() generates boxplots that control the Family-Wise Error Rate (FWER) via the Holm procedure. The methods are based on the framework in Gang, Lin, and Tong (2025) . Package: r-cran-adaptivegpca Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-ggplot2, r-cran-shiny, r-bioc-phyloseq Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-adaptivegpca_0.1.3-1.ca2604.1_all.deb Size: 1332180 MD5sum: 95052a36c7f09811847cb8bdf0d72eb8 SHA1: 2ee0a12b9dda297b8e746838f0e768a741a09d3b SHA256: 5ecabfc09ee26d4cddd746e6b4e11b887c2cd8f8322915877e57bdebd0e3055e SHA512: cadb06d4bd4d1c879ca2f6a3368f1e54ca367537a4a24ad4b9b4cb900ad820ac6fb7ef9bee29babef20cac9ce3424f26131c1a56317256d99f79aa76f560620e Homepage: https://cran.r-project.org/package=adaptiveGPCA Description: CRAN Package 'adaptiveGPCA' (Adaptive Generalized PCA) Implements adaptive gPCA, as described in: Fukuyama, J. (2017) . The package also includes functionality for applying the method to 'phyloseq' objects so that the method can be easily applied to microbiome data and a 'shiny' app for interactive visualization. Package: r-cran-adaptmcmc Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-matrix, r-cran-ramcmc Filename: pool/dists/resolute/main/r-cran-adaptmcmc_1.5-1.ca2604.1_all.deb Size: 43142 MD5sum: ab69eaaeae0bd92c3043650a733080d6 SHA1: 2c72027ca6452c8c32ac31bb596afee1ff4ff5cd SHA256: d6ded62225d6de7958804121ffd43f28885a125d4af920555aa4e8eac2f59fb2 SHA512: d46337e8aec63563e28fb7661ffdacc2864b958fe828edf801e58a5dedd725d1f7aa4094df1437488f5c3ef0ebdf8f791e9bef0e0ba75a9d6c3dc9a3c5f442e9 Homepage: https://cran.r-project.org/package=adaptMCMC Description: CRAN Package 'adaptMCMC' (Implementation of a Generic Adaptive Monte Carlo Markov ChainSampler) Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate. Package: r-cran-adaptr Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4201 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-covr, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-adaptr_1.5.0-1.ca2604.1_all.deb Size: 3795600 MD5sum: 7a8d839c2e7aace3cb0399da8c255be4 SHA1: 326ed8cd31ce3632fd3606d59160c272a73c6b83 SHA256: 653b1f147549a593c930e1729d8f848f138883c04329752d35fbff57203b42ab SHA512: e8876e0d41a1a2b7fd4a0109bfab4bd3fc10bc2574a5e5dc1bc27bbe726db64801819ad42e533c06040d554b94aa6ee53199eec0c23745a04562b32fe7e7251a Homepage: https://cran.r-project.org/package=adaptr Description: CRAN Package 'adaptr' (Adaptive Trial Simulator) Package that simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation. Developed as part of the INCEPT (Intensive Care Platform Trial) project (), primarily supported by a grant from Sygeforsikringen "danmark" (). 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Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells . Package: r-cran-adaptsmofmri Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-matrix, r-cran-coda, r-cran-mvtnorm, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-adaptsmofmri_1.2-1.ca2604.1_all.deb Size: 1346642 MD5sum: c2c08dd885f9908caff2356b64537a4f SHA1: 820e9ecbc503caccbd0724590e052cc7283757da SHA256: 02725d6a7b4efa79f0c98c4fce1529267e8f89f24da2da082f6fd613098d426e SHA512: 22d3634a66bab67628c4f0b1c61f4edcb7baad11d3c960ea74fc7a7885d3813b0c5eacdfd2812d6b767de046424f21ba990e5359861b6f9e2f42197d877b8c6d Homepage: https://cran.r-project.org/package=adaptsmoFMRI Description: CRAN Package 'adaptsmoFMRI' (Adaptive Smoothing of FMRI Data) Adaptive smoothing functions for estimating the blood oxygenation level dependent (BOLD) effect by using functional Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random fields, for real as well as simulated data. The implemented models make use of efficient Markov Chain Monte Carlo methods. Implemented methods are based on the research developed by A. Brezger, L. Fahrmeir, A. Hennerfeind (2007) . Package: r-cran-adapttest Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-adapttest_1.2-1.ca2604.1_all.deb Size: 130806 MD5sum: e4bba1e31d6bd79e2c74e2d09ee7d056 SHA1: bf22fe62e3c987160b16c4517ee703a438b3ccfd SHA256: d7ed2ce122ca2307d6453ac2c26ec570983beec7ee7f48aaad93964122e2f3f7 SHA512: 858fd6b5a44d368d7e0a689ebb1c07164d8daf65b307667fd70437bb7d04d7fee20604b5fa16f5dd45bebb2db232e894bb379ffa35e251d8cc7803cbbd6afd65 Homepage: https://cran.r-project.org/package=adaptTest Description: CRAN Package 'adaptTest' (Adaptive Two-Stage Tests) The functions defined in this program serve for implementing adaptive two-stage tests. Currently, four tests are included: Bauer and Koehne (1994), Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke, An investigation of two-stage tests, Statistica Sinica 2006. 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DoE approach follows the approach in "Design and Analysis of Experiments" by Douglas C. Montgomery (2019, ISBN:978-1-119-49244-3). The package also provides utilities used in the course "Analysis of Data and Statistics" at the University of Trento, Italy. 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Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) . 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(2023) . 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Package: r-cran-adept Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-dvmisc, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-lubridate, r-cran-reshape2, r-cran-gridextra, r-cran-spelling, r-cran-cluster, r-cran-adeptdata, r-cran-covr Filename: pool/dists/resolute/main/r-cran-adept_1.2-1.ca2604.1_all.deb Size: 3700236 MD5sum: 5fb89daaba44fef85799838f34dc0220 SHA1: cd6c2052811786c84f4e4c513a6eccb3d0e82481 SHA256: 9f036f86b29c13aad0c2b1875d4fd1be9e3809fb7ab507a2736f18ac372e5c83 SHA512: 1c2e8031960b84ef32a6841335a1856ce17c52a6a43aa2d8407ae23e19e74dbed04bc5c60952fbfee9b7703f60cea01476bd4331bb7fa5b6bd2b60dc70153f5d Homepage: https://cran.r-project.org/package=adept Description: CRAN Package 'adept' (Adaptive Empirical Pattern Transformation) Designed for optimal use in performing fast, accurate walking strides segmentation from high-density data collected from a wearable accelerometer worn during continuous walking activity. Package: r-cran-adeptdata Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9609 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-adeptdata_1.1-1.ca2604.1_all.deb Size: 9774482 MD5sum: 91eba4de0f81b3a27c5acc936d7d13ed SHA1: 70dd5384c704c12d6bcc1477be4572f17fa2c71d SHA256: 303cad2fbefd20cdfdb211577b8119b8f01748de41fcd740ab015753b1e77a2e SHA512: 73d73e0c5a62026066689a8ff9ebb106daab833d89d01994a94856e42a078065932c95962a8d863160e473daa8a2a0ebbda97215e090986ae7a1bee4e01546ff Homepage: https://cran.r-project.org/package=adeptdata Description: CRAN Package 'adeptdata' (Accelerometry Data Sets) Created to host raw accelerometry data sets and their derivatives which are used in the corresponding 'adept' package. Package: r-cran-adequacymodel Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-adequacymodel_2.0.0-1.ca2604.1_all.deb Size: 52984 MD5sum: 081d1ee231132db8ea5a10e5f3040188 SHA1: d4d05f37d72e128f73704582127ab909d82aa2ba SHA256: aade1740fbcf31cdb2a65ea3c9b470821a7b1f6e85089cb678d38e2cf4c3aa17 SHA512: 92c8d627a779f3891b774a74834bef71bf084e38b36de91b8e1054adb2659cf9efe65d9e705a423b7676fe57276fa30cd176656e1adb8ffe805e7afb93385da5 Homepage: https://cran.r-project.org/package=AdequacyModel Description: CRAN Package 'AdequacyModel' (Adequacy of Probabilistic Models and General PurposeOptimization) The main application concerns to a new robust optimization package with two major contributions. The first contribution refers to the assessment of the adequacy of probabilistic models through a combination of several statistics, which measure the relative quality of statistical models for a given data set. The second one provides a general purpose optimization method based on meta-heuristics functions for maximizing or minimizing an arbitrary objective function. Package: r-cran-ader Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ader_1.5-1.ca2604.1_all.deb Size: 62524 MD5sum: bf1b9c90ca5ac4c0f0b605ccffeff507 SHA1: b8ed657ce9075ad7c66613843c7a4fafb57e315d SHA256: b5f37f7b1350bb8ee4a54e20474636c14ad22957c504f5dbd613e6ff6ddbb75f SHA512: 7c97c3cb985d4f7db3facf95c0810e5402f65520e2d58148c7fa356ebaa7d664f0e098dd2170bde2463874949b8704a1c617fbcd582fab8b2857d06f41636882 Homepage: https://cran.r-project.org/package=ADER Description: CRAN Package 'ADER' (Data Analysis in Ecology) Data sets used in Cayuela and De la Cruz (2022, ISBN:978-84-8476-833-3). Package: r-cran-adformr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-adformr_0.1.0-1.ca2604.1_all.deb Size: 22902 MD5sum: 05b9b6fecc96cca2c133f46b338f58bd SHA1: 289fd89b7f0688d0609290a126b62c7178e3edfb SHA256: 783505338ee1d992d9525a76c502cbe0c844498c04af9726f25e48558eecf565 SHA512: bda7e5e23cc71e257a0cc4260decbe84102abaa9824a67ba42e1fad2e6400f6c7f56182a14b61063d47090ab6669f934767083a205b873e85c442012bca054d5 Homepage: https://cran.r-project.org/package=adformR Description: CRAN Package 'adformR' (Get Adform Ads Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from Adform Ads using the 'Windsor.ai' API . 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The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the estimation of event duration and time to initiation, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings. It scales from very small datasets stored in flat CSV files to very large databases and from single-thread processing on mid-range consumer laptops to parallel processing on large heterogeneous computing clusters. It exposes a standardized interface allowing it to be used from other programming languages and platforms, such as Python. Package: r-cran-adhererviz Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2929 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adherer, r-cran-data.table, r-cran-manipulate, r-cran-shiny, r-cran-shinywidgets, r-cran-shinyjs, r-cran-v8, r-cran-colourpicker, r-cran-viridislite, r-cran-highlight, r-cran-clipr, r-cran-knitr, r-cran-dbi, r-cran-rmariadb, r-cran-rsqlite Suggests: r-cran-rmarkdown, r-cran-readods, r-cran-readxl, r-cran-haven, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-adhererviz_0.2.2-1.ca2604.1_all.deb Size: 1604562 MD5sum: 48c091c33155f68769b4bc659ec14511 SHA1: 474db268e9cfef0804ee52cd5cc604bc0c6c1714 SHA256: 1ba1206736b99434603264542f17d8c650c63bb2f393556dcd1f7727cfc23dec SHA512: 3336c652d2fbdb0fb830bb94335e49d7d18d150564999198dc8c72cd70b0627591c3434c44faec1a7026f8557b2c9a4bb766a058fc59fc5f4bac3dbc6988cd3d Homepage: https://cran.r-project.org/package=AdhereRViz Description: CRAN Package 'AdhereRViz' (Adherence to Medications) Interactive graphical user interface (GUI) for the package 'AdhereR', allowing the user to access different data sources, to explore the patterns of medication use therein, and the computation of various measures of adherence. It is implemented using Shiny and HTML/CSS/JavaScript. Package: r-cran-adismf Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aiccmodavg, r-cran-ggplot2, r-cran-nls2 Filename: pool/dists/resolute/main/r-cran-adismf_0.1.1-1.ca2604.1_all.deb Size: 39242 MD5sum: 4f38cf98cd202b3d43e6460fd2295dd2 SHA1: b248f636779f7fe62081c3b44103e301ef659fc4 SHA256: 5da00706540f46a5c7df6af61224adc28687e66d7a097ac2231bf474c15ce7e6 SHA512: d6cfeb36c70a5cc6696af38b1ffeea5517c35d46b4c7ec123d3d3e5882ced0cf935c4850409f52d38f648b47f019555a553dcded57cb213f137b5dfe5be27f22 Homepage: https://cran.r-project.org/package=AdIsMF Description: CRAN Package 'AdIsMF' (Adsorption Isotherm Model Fitting) The Langmuir and Freundlich adsorption isotherms are pivotal in characterizing adsorption processes, essential across various scientific disciplines. Proper interpretation of adsorption isotherms involves robust fitting of data to the models, accurate estimation of parameters, and efficiency evaluation of the models, both in linear and non-linear forms. For researchers and practitioners in the fields of chemistry, environmental science, soil science, and engineering, a comprehensive package that satisfies all these requirements would be ideal for accurate and efficient analysis of adsorption data, precise model selection and validation for rigorous scientific inquiry and real-world applications. Details can be found in Langmuir (1918) and Giles (1973) . Package: r-cran-adiv Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-adegraphics, r-cran-ape, r-cran-cluster, r-cran-lpsolve, r-cran-phylobase, r-cran-phytools, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-adiv_2.2.1-1.ca2604.1_all.deb Size: 996970 MD5sum: f63c930437cd108feaa3cf9a31ee35f6 SHA1: 8fc276bac716565ca5972b16ceedf42e250da773 SHA256: f576c02771a010a88c09c47e5ee8a3b653bb27b124cbcbb1d156001bee2c0363 SHA512: 61677803897c1866b1f9291455898987836ffac97775b9678cdb25ce6bb967a7fe906d0ff86adaffadd20478d7681740c6602353f207b0f4f4379d973333c59c Homepage: https://cran.r-project.org/package=adiv Description: CRAN Package 'adiv' (Analysis of Diversity) Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities. Package: r-cran-adjroc Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocit, r-cran-ggplot2, r-cran-boot, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-adjroc_0.3-1.ca2604.1_all.deb Size: 629254 MD5sum: a0ca92f54be3b346873023e39b47e621 SHA1: 69831a0de227dd367ef4924d60bcda6cfc9073c2 SHA256: 396d68796173828bdf896489104f22d2f793ca96efce10cec84b34b0f2199023 SHA512: ce71b65c46e4f1480a583881dc2b75038b87ca611b94c6de82f7c7723e1f39cec6a76343d437e1851efb24d4a052721e532a796f2a9dfc8c0556d53cb549a1ed Homepage: https://cran.r-project.org/package=adjROC Description: CRAN Package 'adjROC' (Computing Sensitivity at a Fix Value of Specificity and ViceVersa as Well as Bootstrap Metrics for ROC Curves) For a binary classification the adjusted sensitivity and specificity are measured for a given fixed threshold. If the threshold for either sensitivity or specificity is not given, the crossing point between the sensitivity and specificity curves are returned. For bootstrap procedures, mean and CI bootstrap values of sensitivity, specificity, crossing point between specificity and specificity as well as AUC and AUCPR can be evaluated. 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Package: r-cran-adjustedcurves Architecture: all Version: 0.11.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1666 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.utils, r-cran-doparallel, r-cran-dorng, r-cran-dplyr, r-cran-foreach, r-cran-rlang, r-cran-survival Suggests: r-cran-mass, r-cran-matching, r-cran-weightit, r-cran-cmprsk, r-cran-eventglm, r-cran-geepack, r-cran-ggplot2, r-cran-knitr, r-cran-mice, r-cran-nnet, r-cran-pammtools, r-cran-pec, r-cran-prodlim, r-cran-riskregression, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-ggpp, r-cran-vdiffr, r-cran-covr, r-cran-data.table, r-cran-numderiv, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-adjustedcurves_0.11.4-1.ca2604.1_all.deb Size: 1273546 MD5sum: f358a1b87b31ac6e84bc22f568ed5ef0 SHA1: e0e30040fe2b66dcf1a8787aba0d941c63d1d222 SHA256: 0130ca5c0d8c6b69295469cfbb23edd3eed84591fb796609efbeb43524fb69be SHA512: cae95b772fdd1c9e1d669a2b6bcbe40885b72bcaf96b684ebfdb0985a3657bd8aefd9b877e69cbb2ea543ad6a14092e2cd3472ff557cb50ccde8159379572e27 Homepage: https://cran.r-project.org/package=adjustedCurves Description: CRAN Package 'adjustedCurves' (Confounder-Adjusted Survival Curves and Cumulative IncidenceFunctions) Estimate and plot confounder-adjusted survival curves using either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values', various forms of 'Inverse Probability of Treatment Weighting', two forms of 'Augmented Inverse Probability of Treatment Weighting', 'Empirical Likelihood Estimation' or 'Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) . Package: r-cran-adklakedata Architecture: all Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-rappdirs, r-cran-tibble Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adklakedata_0.6.4-1.ca2604.1_all.deb Size: 241558 MD5sum: 068f77579fffa5d61abb3751b8ab1242 SHA1: 5fa0a381f5683847f2cc1870ba5087eec808f7de SHA256: 555e407fcea1834f03813f4bd1e4d123ed3004b5ebc9314d25d91193740685c1 SHA512: 5e11a96f084e026d17b538e370e1ea281a857dbed92b63e8844b7142803bb2430fbc742a94c7a77214b2b590d090974370956e5165c79d4db4771f8e6f90780a Homepage: https://cran.r-project.org/package=adklakedata Description: CRAN Package 'adklakedata' (Adirondack Long-Term Lake Data) Package for the access and distribution of long-term lake datasets from lakes in the Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and biological parameters from 28 lakes. Data are from multiple collection organizations and have been harmonized in both time and space for ease of reuse. Package: r-cran-adlp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1193 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-tidyverse, r-cran-synthetic Filename: pool/dists/resolute/main/r-cran-adlp_0.1.0-1.ca2604.1_all.deb Size: 539156 MD5sum: 8f64b9aefa71b6a8143adabb2830dc66 SHA1: bf74ad920c960c688f953f65ef81ec46e0b0b38c SHA256: e946c2bb9c01885579e270250f4bd65efe8295ac6014d86b06e74cd709eb11d0 SHA512: 18a57f9c40402e7dc32af6710a099de890e63c406eb9bceebc79c3e2dee28256b7cf3a2f10beb964707c344da5efc0cf4fa8b5cc7a64f8ef06d347975e1f613b Homepage: https://cran.r-project.org/package=ADLP Description: CRAN Package 'ADLP' (Accident and Development Period Adjusted Linear Pools forActuarial Stochastic Reserving) Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" . Package: r-cran-admiral.test Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3768 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-lintr, r-cran-pkgdown, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-usethis, r-cran-covr Filename: pool/dists/resolute/main/r-cran-admiral.test_0.7.0-1.ca2604.1_all.deb Size: 3255388 MD5sum: fc4fa6e1dd8dfd8e000ae08b0621254d SHA1: c3b38a006c9f68fc78690f7405697a1aac07f08e SHA256: 963c4c08212bdcdeb2c230782940fb7b4c1b4e6949b86fc1388b867e9dc7b999 SHA512: c4a3b081b2c497865cfa3b854f141f48089024afcac789c752306499aacf0156942984991b693700f3166ea8d6fa56e76f6a321770208b90b4af0400ca55d601 Homepage: https://cran.r-project.org/package=admiral.test Description: CRAN Package 'admiral.test' (Test Data for the 'admiral' Package) A set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) derivations inside the 'admiral' package. Package: r-cran-admiral Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4794 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-here, r-cran-htmltools, r-cran-knitr, r-cran-pharmaversesdtm, r-cran-reactable, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-admiral_1.4.1-1.ca2604.1_all.deb Size: 2184940 MD5sum: af47fd3dcf34f295af360ad87557903a SHA1: e2f54942dba80843c8092fdb112996c7359462d7 SHA256: ffc2e7bfc60e205088bc992abec74a6d6feadd92f4795246276033e416ee8473 SHA512: 09c5d4407bd559202e68f7797737077f52141c4313ed63df19063fa6764f465901997397c45e702446e92ea800e72b58d480344ba86f3efac4c047372dd2f0bb Homepage: https://cran.r-project.org/package=admiral Description: CRAN Package 'admiral' (ADaM in R Asset Library) A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). Package: r-cran-admiraldev Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-roxygen2, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-diffdf, r-cran-dt, r-cran-htmltools, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-admiraldev_1.4.0-1.ca2604.1_all.deb Size: 1217848 MD5sum: 29b3b244aae5ca74c459e71ffd5c73c0 SHA1: ab6545599423a7c2d84d1d189f83b3e9d3413a45 SHA256: 08e65c1336a99e58ad9ad09887a1941b57cd0c2307193a41a82b7be0a304d328 SHA512: 66bc06ba84816b13455cd708cafcc49956ac8e9f9ae025085403ca3cb36c9a80451f5d8e76faef7ab8d95982968ee5a3de64e75cd4f6c44a752ec113a43ada48 Homepage: https://cran.r-project.org/package=admiraldev Description: CRAN Package 'admiraldev' (Utility Functions and Development Tools for the Admiral PackageFamily) Utility functions to check data, variables and conditions for functions used in 'admiral' and 'admiral' extension packages. Additional utility helper functions to assist developers with maintaining documentation, testing and general upkeep of 'admiral' and 'admiral' extension packages. Package: r-cran-admiralmetabolic Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-htmltools, r-cran-knitr, r-cran-pharmaversesdtm, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-admiralmetabolic_0.3.0-1.ca2604.1_all.deb Size: 225936 MD5sum: 886a408cf8a143e5fca73e5a9b3f7993 SHA1: ade94aa24db227259a9dc9e9ede343d358e04dfc SHA256: 515297adf21cf4eb562a3e115a860da1f4220aa017fdd93442faef253eabdfd3 SHA512: ce209482e9000d273d0b02361997a3a5a2735e6c4ed0baa397908bee2dc2c46686f38847a51fa1014bf733ea2fbf41c912cb403c7369aba0af0b3b0406ea615a Homepage: https://cran.r-project.org/package=admiralmetabolic Description: CRAN Package 'admiralmetabolic' (Metabolism Extension Package for ADaM in 'R' Asset Library) A toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). The package is an extension package of the 'admiral' package focusing on the metabolism therapeutic area. Package: r-cran-admiralneuro Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-htmltools, r-cran-knitr, r-cran-metatools, r-cran-pharmaversesdtm, r-cran-reactable, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-admiralneuro_0.2.1-1.ca2604.1_all.deb Size: 211316 MD5sum: d348756b4691167c95003f1a26d00ca0 SHA1: ba1252e7f247b1dd9dffe3ce938e43c8dfc4703f SHA256: 2750355185a89e1e5de11b94f610309c5479c29af5a1f74908388f79e042f44a SHA512: cf68c54f07aacdd2f8b5f3215a96aefc11b5b23cc7ca10650210242200323814eb63f8df797fa367adb2307d114f1561fcaad66c35a78c56e7a5827d3575f77c Homepage: https://cran.r-project.org/package=admiralneuro Description: CRAN Package 'admiralneuro' (Neuroscience Extension Package for ADaM in 'R' Asset Library) Programming neuroscience specific Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). This package extends the 'admiral' package. Package: r-cran-admiralonco Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-covr, r-cran-devtools, r-cran-diffdf, r-cran-dt, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-lintr, r-cran-metatools, r-cran-miniui, r-cran-pharmaverseadam, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-admiralonco_1.4.1-1.ca2604.1_all.deb Size: 346348 MD5sum: 84eb2a8eb707bdf5dea21b7f83f1fb51 SHA1: 3cda5161e20eff433d01c71647fe5fea6c2b38da SHA256: 91d50de0ccf0935ef9bb692a9f2fe2cfb0a0f1026761f7369b016933327c4d6d SHA512: c0d059082d38b75d203f437af2f1954471039bc2c05a250598cd0cb358ad28d41708dfbc52a7e8000e72b2ebbcd98c5997282d3378e16424796d1b5456d87f6e Homepage: https://cran.r-project.org/package=admiralonco Description: CRAN Package 'admiralonco' (Oncology Extension Package for ADaM in 'R' Asset Library) Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), ). The package is an extension package of the 'admiral' package. Package: r-cran-admiralophtha Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-devtools, r-cran-diffdf, r-cran-knitr, r-cran-lintr, r-cran-miniui, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-admiralophtha_1.4.0-1.ca2604.1_all.deb Size: 756782 MD5sum: 93d5336dc5224a6f82794a7d07606495 SHA1: c15de440dca49857820565b0e4f1ffb460ac018b SHA256: 1ca54ebaf2830addd2dba868a9510c0f1efb273416c41b35d909ee4b4babc315 SHA512: 7eaae519d0bb5976b25a7a41dbed60b44d18af8ded0941b2c71258cb7049ba0ea1ada7a5a4cbca26ae753c3ff45b4e0228e420da3aca65645ec2875096147d16 Homepage: https://cran.r-project.org/package=admiralophtha Description: CRAN Package 'admiralophtha' (ADaM in R Asset Library - Ophthalmology) Aids the programming of Clinical Data Standards Interchange Consortium (CDISC) compliant Ophthalmology Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). Package: r-cran-admiralpeds Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-knitr, r-cran-lubridate, r-cran-pharmaversesdtm, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-admiralpeds_0.3.0-1.ca2604.1_all.deb Size: 442642 MD5sum: 648b4b89507632720b0cebb61fd63fa8 SHA1: 099805c193cd46be7d514972213fd92f78c28c6b SHA256: 56dface84c037fb7bc063c43a82e50fe80529d8101fd0d811af4dd2adb2d42e2 SHA512: ff386d658c73c5df5be2207f6ce609c2fce22f7af9781fdc3357bdf5743a2a192d062dd850b7670f151f7ca3e168367f3863dc8fd43ffad721abcdad31444455 Homepage: https://cran.r-project.org/package=admiralpeds Description: CRAN Package 'admiralpeds' (Pediatrics Extension Package for ADaM in 'R' Asset Library) A toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). The package is an extension package of the 'admiral' package for pediatric clinical trials. Package: r-cran-admiralvaccine Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-devtools, r-cran-diffdf, r-cran-dt, r-cran-knitr, r-cran-lintr, r-cran-metatools, r-cran-miniui, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-admiralvaccine_0.6.0-1.ca2604.1_all.deb Size: 204972 MD5sum: eb6629765bae9180a8f518c23ac36d5e SHA1: 83c8716e765676290d247cdec2ad1bedb8c086ed SHA256: 57d02e38becc76f25838e140b32bc460405883b62ea096d9135d3b893e54c3ab SHA512: 9147c9c98a153a0d07abd7067c500ceb0eb0793084cb2f1e01024bd62c450e6bfca646fdeab4967b89d1b2ad99bc2ca1ff3d7d82d90af6ae5d63b79a9fc691fd Homepage: https://cran.r-project.org/package=admiralvaccine Description: CRAN Package 'admiralvaccine' (Vaccine Extension Package for ADaM in 'R' Asset Library) Programming vaccine specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. Flat model is followed as per Center for Biologics Evaluation and Research (CBER) guidelines for creating vaccine specific domains. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), ). The package is an extension package of the 'admiral' package. Package: r-cran-admixr Architecture: all Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-rlang Suggests: r-cran-glue, r-cran-ggplot2, r-cran-testthat, r-cran-forcats, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-admixr_0.9.2-1.ca2604.1_all.deb Size: 560928 MD5sum: d5087543bafcd03a089ee2d04dae551a SHA1: 5642fd40bc16b6121ebe065f37af18ab80f23b25 SHA256: 417e3e25370ff75e882ac4c9523b3b365eaddb54db7010574354318bee8a2719 SHA512: 2a712bc4271546b6b5d8a1002ff922237c8ead9884648bfda5ffab8535395a80f14eed2c7b01e2a8036de1ee468012d81dea9e242c0fde467115795426ad7de4 Homepage: https://cran.r-project.org/package=admixr Description: CRAN Package 'admixr' (An Interface for Running 'ADMIXTOOLS' Analyses) An interface for performing all stages of 'ADMIXTOOLS' analyses () entirely from R. Wrapper functions (D, f4, f3, etc.) completely automate the generation of intermediate configuration files, run 'ADMIXTOOLS' programs on the command-line, and parse output files to extract values of interest. This allows users to focus on the analysis itself instead of worrying about low-level technical details. A set of complementary functions for processing and filtering of data in the 'EIGENSTRAT' format is also provided. Package: r-cran-admmdensestsubmatrix Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-admmdensestsubmatrix_0.1.0-1.ca2604.1_all.deb Size: 497126 MD5sum: f97e3997494790722eef5cabeeae19dc SHA1: dbeb23d3b1407bdaa84675e30ffc61a496dfa217 SHA256: 7279c9a62e6ab11b0ab6cccce60d9383c48531b5781a714a8991387468f8c3a3 SHA512: 6acde01b0c1c9704c66f613a19dba17016e971c4ddaf8c523598769e5bacb41741259bee693ce8b50f8b5228bd3f37cdd1fedc8feaedf2cfadf6fc40ed38c2da Homepage: https://cran.r-project.org/package=admmDensestSubmatrix Description: CRAN Package 'admmDensestSubmatrix' (Alternating Direction Method of Multipliers to Solve DenseDubmatrix Problem) Solves the problem of identifying the densest submatrix in a given or sampled binary matrix, Bombina et al. (2019) . Package: r-cran-admtools Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape Suggests: r-cran-fossilsim, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-admtools_0.6.0-1.ca2604.1_all.deb Size: 440360 MD5sum: c5b227a9fa6f93dc35c5ec638bcb443b SHA1: f509bfcfba9178d1d422edab0ac5d50059ec84f4 SHA256: a21b4f5dba4eed2acad83ac768f255cc8df77a669ba355ce35d5822cf730adbe SHA512: 3a2a8e1545be427a31217f2da980f14700eb27dc944b6b66dca68f6e67b9c14533a3d385872eadcd73113ec7eb04860c32e5b1f85a2dccca0893fdd3913eb09e Homepage: https://cran.r-project.org/package=admtools Description: CRAN Package 'admtools' (Estimate and Manipulate Age-Depth Models) Estimate age-depth models from stratigraphic and sedimentological data, and transform data between the time and stratigraphic domain. Package: r-cran-adnuts Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall, r-cran-ellipse, r-cran-rstan, r-cran-r2admb, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-shinystan, r-cran-matrixcalc, r-cran-knitr, r-cran-tmb, r-cran-rmarkdown, r-cran-withr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adnuts_1.1.2-1.ca2604.1_all.deb Size: 778294 MD5sum: ec23a4f7cee6b3eaa60adbe1c457caee SHA1: ebc5d39397ccedcfb82809219bb95382fcf9c8b9 SHA256: 7b39ac0889120ca8ec55cfe5a53d01116ab0a5b22dbbcef81ff72dbea5364731 SHA512: e380d7beba4b1847da729f3b43b2fa17c814b145bd31116858763f852ff62340fbe1b7c3f18c0e7f5411b300c4c4cc415c421cb9237fcf1c763b6e344e23c7f2 Homepage: https://cran.r-project.org/package=adnuts Description: CRAN Package 'adnuts' (No-U-Turn MCMC Sampling for 'ADMB' Models) Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) . Designed for 'AD Model Builder' ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as 'Template Model Builder' models and other special cases. Functionality is similar to 'Stan', and the 'rstan' and 'shinystan' packages are used for diagnostics and inference. Package: r-cran-adobeanalyticsr Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1510 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-jsonlite, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-httr, r-cran-tidyr, r-cran-rlang, r-cran-lubridate, r-cran-ggplot2, r-cran-scales, r-cran-r6, r-cran-jose, r-cran-openssl, r-cran-lifecycle, r-cran-glue, r-cran-vctrs, r-cran-progress, r-cran-memoise, r-cran-httr2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-adobeanalyticsr_0.5.1-1.ca2604.1_all.deb Size: 1235182 MD5sum: 5e14787d34d4dfe2be4d68b196d87074 SHA1: 7abcdde3ee5f51c99b2bdade14cebc82774d39ef SHA256: 4e2e468758e5a3fc4e5ad534f03386fc484b67eb4de942708afbd5ee3ffed1bb SHA512: 5d1f47d29c9d5cb6b5cbcc761a026e9297119e8c06b558b6f0904b949f8d4d23289b4ea8abfdc69b4ee62e876a3c650e12c2e2027fe610a5adeac7c5153aab41 Homepage: https://cran.r-project.org/package=adobeanalyticsr Description: CRAN Package 'adobeanalyticsr' (R Client for 'Adobe Analytics' API 2.0) Connect to the 'Adobe Analytics' API v2.0 which powers 'Analysis Workspace'. The package was developed with the analyst in mind, and it will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. Package: r-cran-adoptr Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1855 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nloptr, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-rpact, r-cran-vdiffr, r-cran-pwr, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-gridextra, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-adoptr_1.1.2-1.ca2604.1_all.deb Size: 905736 MD5sum: cc612ee250cd3dad3c3c0b68e435f075 SHA1: c88363fe45564357417ce5cddba630cd551f6b08 SHA256: e9751a6c76a89c4b5a11f3e2eefc3ea9342b801a96dfa07eec9b3a34403e4a4f SHA512: 645302bf644689e94cf9bb78804b5a0697fec19a264582164bd48a63b0573b934b79216c2de8c15f128844e38b933ba07689e5bb2ebd81c2443b399491c22eb0 Homepage: https://cran.r-project.org/package=adoptr Description: CRAN Package 'adoptr' (Adaptive Optimal Two-Stage Designs) Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) and Kunzmann et al. (2021) for details. Package: r-cran-adp Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-adp_0.1.6-1.ca2604.1_all.deb Size: 25470 MD5sum: 86b73dc90c7e02f46975c60521f5f3b2 SHA1: efa27bcc00bd27ee8e3b97172f3adddf5fcf472d SHA256: 6c57b0c4b542c2609aee89c1249ec56b2946ad9589c353218461b30f7025c6c0 SHA512: 8052d698f4b35e6a0582c6dd21af651c0df31a9c3ad31801f51c9d8818ae7b5a4352241f9db0881052ac148a6dbe0f929c3230a90fe4757949f62335bb044398 Homepage: https://cran.r-project.org/package=ADP Description: CRAN Package 'ADP' (Adoption Probability, Triers and Users Rate of a New Product) Calculate users prevalence of a product based on the prevalence of triers in the population. The measurement of triers is relatively easy. It is just a question of whether a person tried a product even once in his life or not. On the other hand, The measurement of people who also adopt it as part of their life is more complicated since adopting an innovative product is a subjective view of the individual. Mickey Kislev and Shira Kislev developed a formula to calculate the prevalence of a product's users to overcome this difficulty. The current package assists in calculating the users prevalence of a product based on the prevalence of triers in the population. See for: Kislev, M. M., and S. Kislev (2020) . Package: r-cran-adpf Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-adpf_0.0.1-1.ca2604.1_all.deb Size: 37246 MD5sum: f8c92ac26ddf793b997bf0d8dc9584c7 SHA1: 447838ce5bf7742ac6016e5a4a65b0b1326677b9 SHA256: 9f0fcd9da274373c020ce04627ec9d6e757e34bcc2ef6ae792c818ad168cf35b SHA512: 342cdb0e6d340cd63a9a17226be7e8cc11b94795e9e32491bf597a89f58d436b8fbe3fdc5add2088508bb975fe5a35d9dcd97ad6e8db2b899f8a628ec5ecb78a Homepage: https://cran.r-project.org/package=ADPF Description: CRAN Package 'ADPF' (Use Least Squares Polynomial Regression and Statistical Testingto Improve Savitzky-Golay) This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) for more information. Package: r-cran-adplots Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adplots_0.1.0-1.ca2604.1_all.deb Size: 70086 MD5sum: 512c4832893a7adb8d09881687cef028 SHA1: f8c00e7129996d0a4c5331c312ea4bc808260363 SHA256: 1f4d35890b6549ee2924f029d0a490120b9ed56eb9aea86c54f9a38284a09d74 SHA512: f475040fd9421662a3d58204e13fe5a74b18e3e689f17eafc331ac5edfcd8d6477500166c68ef7635d7ad86aeffd5eb7686a9d2b5fe2c5492e13771779949468 Homepage: https://cran.r-project.org/package=adplots Description: CRAN Package 'adplots' (Ad-Plot and Ud-Plot for Visualizing Distributional Propertiesand Normality) The empirical cumulative average deviation function introduced by the author is utilized to develop both Ad- and Ud-plots. The Ad-plot can identify symmetry, skewness, and outliers of the data distribution, including anomalies. The Ud-plot created by slightly modifying Ad-plot is exceptional in assessing normality, outperforming normal QQ-plot, normal PP-plot, and their derivations. The d-value that quantifies the degree of proximity between the Ud-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Full description of this methodology can be found in the article by Wijesuriya (2025) . Package: r-cran-adproclus Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-corrplot, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtools, r-cran-igraph, r-cran-matrixstats, r-cran-multichull, r-cran-nmfn, r-cran-qgraph, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adproclus_2.0.1-1.ca2604.1_all.deb Size: 181636 MD5sum: ef85e5071582cac11287dd8a868b6df9 SHA1: 20d00fdf4f1766148a00d2613030a1aa015d99fa SHA256: eab4833ce2e5ebfb535050118d20e13510dffcd511bca90ad6edf207771a5e72 SHA512: 6d6eac52ee1e125bf9880bde4a9473aab1c0d8d4eee17f79f84c018f3fe799d486ac172cbe0e1c1955e70d05e9ab9cfb4897fc4044c5ba314ffd6a3be22ff9d7 Homepage: https://cran.r-project.org/package=adproclus Description: CRAN Package 'adproclus' (Additive Profile Clustering Algorithms) Obtain overlapping clustering models for object-by-variable data matrices using the Additive Profile Clustering (ADPROCLUS) method. Also contains the low dimensional ADPROCLUS method for simultaneous dimension reduction and overlapping clustering. For reference see Depril, Van Mechelen, Mirkin (2008) and Depril, Van Mechelen, Wilderjans (2012) . Package: r-cran-adrftools Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1797 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-collapse, r-cran-insight, r-cran-ggplot2, r-cran-marginaleffects, r-cran-mvtnorm, r-cran-rlang, r-cran-sandwich Suggests: r-cran-compquadform, r-cran-fwb, r-cran-survey, r-cran-weightit, r-cran-matchthem, r-cran-mice, r-cran-generics, r-cran-dbarts, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-adrftools_0.1.0-1.ca2604.1_all.deb Size: 1277444 MD5sum: 38bd39ee2f62ed07b4adc922f5ecbf25 SHA1: 597018fd48f2e2eb1cae11c1886ad3796743a5b6 SHA256: 0640009fa5b9ffb22e3f975fe909e6f1b16894dc9a04e7658c84a43e4704e3d1 SHA512: e6653dd79a58383b12dd6d2054bd62fa105a2324bce4bc4eee0980733030ea3c9dc8346cddadd0bda0da23e54b469dadd62cf2844b82be12ae6b876d2e65986c Homepage: https://cran.r-project.org/package=adrftools Description: CRAN Package 'adrftools' (Estimating, Visualizing, and Testing Average Dose-ResponseFunctions) Facilitates estimating, visualizing, and testing average dose-response functions (ADRFs) for characterizing the causal effect of a continuous (i.e., non-discrete) treatment or exposure. Includes support for frequentist and Bayesian regression models, analytical and bootstrap inference, and characterization of subgroup effects. Package: r-cran-adsasi Architecture: all Version: 0.9.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/resolute/main/r-cran-adsasi_0.9.0.2-1.ca2604.1_all.deb Size: 60146 MD5sum: 0749150c410e673da763595bab7537a3 SHA1: 4dc929ee12fcf79bc6396545734dc6a3b2c8d8b7 SHA256: 6d70ea3c997df7158977c7a45ab4732f132bfe9c4592ecd9ea609079b463884b SHA512: c3b1da20020429f9970d1db94fb658fa1183d98889c8dcfc92846321b377f513f5e6f45dd60bc58a710baf8567b6b184a13de840de2eee5493303a4ddbab20bc Homepage: https://cran.r-project.org/package=adsasi Description: CRAN Package 'adsasi' (Adaptive Sample Size Simulator) A simulations-first sample size determination package that aims at making sample size formulae obsolete for most easily computable statistical experiments ; the main envisioned use case is clinical trials. The proposed clinical trial must be written by the user in the form of a function that takes as argument a sample size and returns a boolean (for whether or not the trial is a success). The 'adsasi' functions will then use it to find the correct sample size empirically. The unavoidable mis-specification is obviated by trying sample size values close to the right value, the latter being understood as the value that gives the probability of success the user wants (usually 80 or 90% in biostatistics, corresponding to 20 or 10% type II error). Package: r-cran-adsdatahubr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-adsdatahubr_0.1.1-1.ca2604.1_all.deb Size: 28748 MD5sum: 756e9c3e5000308a4b7216796515852b SHA1: e5153c97e5ddf38bde34c754247515d2c111e009 SHA256: 7230748fb244e0eb5e565cce7f8590025a76fa6eec7952e96951189a54aaa9a2 SHA512: 52fa54e975b16bbc8ff30ccacaa58a898d7e4c8755c416da9db3aa2e2bc25c9c75587147eced38eefe473164965d9fd6e3065004a47d4b2af4e3f76b8e88e8dc Homepage: https://cran.r-project.org/package=adsDataHubR Description: CRAN Package 'adsDataHubR' (Google Ads Data Hub API Client) Interact with Google Ads Data Hub API . The functionality allows to fetch customer details, submit queries to ADH. Package: r-cran-adsorpr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-adsorpr_0.1.0-1.ca2604.1_all.deb Size: 218024 MD5sum: 3e3754a9c2bbe4d5d69f7b50f4d322e3 SHA1: 1821321ae50bc2d4fb1cff4ff486bb8c4100c9c6 SHA256: c33dced74ea13817906db2e300ee18b4e129704a4cf9f2697810531d47ed578b SHA512: 3a94b2a9227726caa5623260e08482a261ad6f5a3ba32da79c545ce5256663a2a26c1784591e18acfb3c64ab38f5baec94f2e457cca80df0929f7ede773983a5 Homepage: https://cran.r-project.org/package=AdsorpR Description: CRAN Package 'AdsorpR' (Adsorption Isotherm Models) Model adsorption behavior using classical isotherms, including Langmuir, Freundlich, Brunauer–Emmett–Teller (BET), and Temkin models. The package supports parameter estimation through both linearized and non-linear fitting techniques and generates high-quality plots for model diagnostics. It is intended for environmental scientists, chemists, and researchers working on adsorption phenomena in soils, water treatment, and material sciences. Functions are compatible with base 'R' and 'ggplot2' for visualization. Package: r-cran-adsorptioncmf Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nls2, r-cran-metrics, r-cran-ggplot2, r-cran-boot Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adsorptioncmf_0.1.1-1.ca2604.1_all.deb Size: 105726 MD5sum: f0a2fa68af2b1c39c83b9901b1647ece SHA1: 1a080363f13ec444c8747f683156d4d4e79d25e5 SHA256: 4cc3224a0f1962f47a263a3ddf7a8f70abc5e46b6f289078c5a52aa5df97c3c8 SHA512: c92d6f020d68b7e9cd8d95714433b5ef5a5469c8db39c8786fba39871a4c681a032c72e353a5807540e09dd4c779f60be73e71afae09ee0db7c692f0139af496 Homepage: https://cran.r-project.org/package=adsoRptionCMF Description: CRAN Package 'adsoRptionCMF' (Classical Model Fitting of Adsorption Isotherms) Provides tools for classical parameter estimation of adsorption isotherm models, including both linear and nonlinear forms of the Freundlich, Langmuir, and Temkin isotherms. This package allows users to fit these models to experimental data, providing parameter estimates along with fit statistics such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Error metrics are computed to evaluate model performance, and the package produces model fit plots with bootstrapped 95% confidence intervals. Additionally, it generates residual plots for diagnostic assessment of the models. Researchers and engineers in material science, environmental engineering, and chemical engineering can rigorously analyze adsorption behavior in their systems using this straightforward, non-Bayesian approach. For more details, see Harding (1907) . Package: r-cran-adsorptioncv Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nls2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adsorptioncv_0.1.0-1.ca2604.1_all.deb Size: 68436 MD5sum: 595ca7eb7c9131ca82a1c913bcfc9faf SHA1: da4c1a277ba7692effe1debb3889f9a8d463cedd SHA256: 2f2edc9ad88f8ff4933ccc69064466da2083f0efbc0cc4f4683ae8a499b949d7 SHA512: 8299b13bd7ed6422b04604be516c71c4ef8671007621c21ca148e8c4a445fb5a2d75703b9e2c88ad56cd746a13a2d4354765fd7eb3a162c73d949975ec01a7eb Homepage: https://cran.r-project.org/package=adsoRptionCV Description: CRAN Package 'adsoRptionCV' (Cross-Validation Methods for Adsorption Isotherm Models) Provides cross-validation tools for adsorption isotherm models, supporting both linear and non-linear forms. Current methods cover commonly used isotherms including the Freundlich, Langmuir, and Temkin models. This package implements K-fold and leave-one-out cross-validation (LOOCV) with optional clustering-based fold assignment to preserve underlying data structures during validation. Model predictive performance is assessed using mean squared error (MSE), with optional graphical visualization of fold-wise MSEs to support intuitive evaluation of model accuracy. This package is intended to facilitate rigorous model validation in adsorption studies and aid researchers in selecting robust isotherm models. For more details, see Montgomery et al. (2012) , Lumumba et al. (2024) , and Yates et al. (2022) . Package: r-cran-adsorptionmcmc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-coda Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adsorptionmcmc_0.1.0-1.ca2604.1_all.deb Size: 86646 MD5sum: a2df0ea58e6627fafddb08aec6e74712 SHA1: b1a701a91faec6f104326735a522c65b3d1d291c SHA256: 886c08b9b1b3053e090e9bdc17639a9db5967da21800c5d307fcb61f89d86105 SHA512: bf676ad2a97985eb791c13c276d32836f5bcadc6bc47971535dfa41bce35639ecfd3fff885b52172f738bc1eb4d9b52f236c412b513b28a35956c018dd4da495 Homepage: https://cran.r-project.org/package=adsoRptionMCMC Description: CRAN Package 'adsoRptionMCMC' (Bayesian Estimation of Adsorption Isotherms via MCMC) Provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. This package enables users to fit non-linear and linear adsorption isotherm models—Freundlich, Langmuir, and Temkin—within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) , and Gamerman & Lopes (2006) . Package: r-cran-adtsa Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-adtsa_1.0.1-1.ca2604.1_all.deb Size: 54444 MD5sum: 3b24ee05ecc19a7b7f0769e43834e59b SHA1: 68b0fc356976f31bb03527f2151e03dbc5919756 SHA256: 50768eb5c678eb6e9c7c5021817caab567b4f7f4dc9ec01765c476c10bfcfd39 SHA512: 6040b7daadc0c59449073c183eaa9363a303236a3c27591ed77efea410292e4c6420040ad7dbfa9d831c79c5ed2f074d95a9be58fa44678c5ba5aa0d9c7098e3 Homepage: https://cran.r-project.org/package=ADTSA Description: CRAN Package 'ADTSA' (Time Series Analysis) Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series. 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Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. . In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t). 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Topics include general workflow in 'R' and 'Rstudio', the 'R' environment and 'tidyverse', summarizing data, model fitting, central tendency, visualising data using 'ggplot2', inferential statistics and robust estimation, hypothesis testing, the general linear model, comparing means, repeated measures designs, factorial designs, multilevel models, growth models, and generalized linear models (logistic regression). Package: r-cran-adverbial Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-vctrs Suggests: r-cran-lifecycle, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adverbial_0.2.1-1.ca2604.1_all.deb Size: 41246 MD5sum: 2564801ee7d6e061c8e7f5fd99f0399c SHA1: 529de21f18e3615f3bfe1347269e455c50605475 SHA256: 3fd76f96da8efcd220aee4d493a1e9205f76faaae2c5a5edb9dd9e025890ea86 SHA512: aea5928d9205b34195c13dc3fe720e4be428a094a3174e08e047da799fcf8072b1beafe9c1648f0aabdc7f13b7ef66cbcd24c5399def51f2034f8e69dc801d72 Homepage: https://cran.r-project.org/package=adverbial Description: CRAN Package 'adverbial' (Enhanced Adverbial Functions) Provides new_partialised() and new_composed(), which extend partial() and compose() functions of 'purrr' to make it easier to extract and replace arguments and functions. 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Package: r-cran-adverseevents Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3698 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-tidyverse, r-cran-rio, r-cran-janitor, r-cran-dt, r-cran-skimr, r-cran-ggpubr, r-cran-ggnewscale, r-cran-survival, r-cran-survminer, r-cran-ggplot2, r-cran-shinythemes, r-cran-ggrepel, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-adverseevents_0.0.5-1.ca2604.1_all.deb Size: 2076438 MD5sum: 245509a6e78799cd8dc6389d34903151 SHA1: 226eafc210cdc8197ac0ce1c6bc8d7674dc0bb28 SHA256: 8679b959841bc708b79e4b973e26f1ec502b38467325b725a94f06251128eadf SHA512: b8d72faaacc5b106aaef8b18f3de0906cff620e6aecd9251eab002d48b1a9a9f9d0fe3b9bc85dd0296ec03b27a4d44c15ea9fa6349c0985059977b622187e41d Homepage: https://cran.r-project.org/package=AdverseEvents Description: CRAN Package 'AdverseEvents' ('shiny' Application for Adverse Event Analysis of 'OnCore' Data) An application for analysis of Adverse Events, as described in Chen, et al., (2023) . The required data for the application includes demographics, follow up, adverse event, drug administration and optional tumor measurement data. The app can produce swimmers plots of adverse events, Kaplan-Meier plots and Cox Proportional Hazards model results for the association of adverse event biomarkers and overall survival and progression free survival. The adverse event biomarkers include occurrence of grade 3, low grade (1-2), and treatment related adverse events. Plots and tables of results are downloadable. Package: r-cran-advice Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-advice_1.0-1.ca2604.1_all.deb Size: 44502 MD5sum: 09273a9ef8bb885479401f0d6f932b83 SHA1: b010f89438efe4d2ddb96b13f616298e6f94bb64 SHA256: b4ea5b73b6af18610654d7612674e8cfe6f8a7293b3f22fc9c69671f74b050d0 SHA512: 076e7e7e2bc99c4ed2ee5f802c499ece04efe9ade510a853b231eac660b6299a2ac280115d965f35a174d04d76c63a183fb168ae83b11b4b9052c89f070510b3 Homepage: https://cran.r-project.org/package=ADVICE Description: CRAN Package 'ADVICE' (Automatic Direct Variable Selection via Interrupted CoefficientEstimation) Accurate point and interval estimation methods for multiple linear regression coefficients, under classical normal and independent error assumptions, taking into account variable selection. Package: r-cran-adw Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-cnmap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-adw_0.4.2-1.ca2604.1_all.deb Size: 401662 MD5sum: 7225c136339ee785188e289ea31ad7d5 SHA1: d5a9bb69f8d9bf9f719c187d05bf68f971da742f SHA256: a872d8e537c56623e69881c7eb9943c56c1e03ff784ac3c0bfa5b96826077394 SHA512: 74ff9fdc39cde6e4cdf94ba103e7f5d2beb5f4ca09ddd57d2d94fce8b001509b842c5fdf0bd27518bad03db8ff859f8d90d144c73a0230ca6e0a6802c18d27b2 Homepage: https://cran.r-project.org/package=adw Description: CRAN Package 'adw' (Angular Distance Weighting Interpolation) The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius. In addition to this, we also provide a simple way (Jones and Hulme, 1996) to grid the irregularly-spaced data points onto regular latitude-longitude grids by averaging all stations in grid-boxes. This study was supported by the National Natural Science Foundation of China (NSFC, Grant No. 42205177). Package: r-cran-adwave Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-waveslim Filename: pool/dists/resolute/main/r-cran-adwave_1.4-1.ca2604.1_all.deb Size: 156794 MD5sum: 7129a23634514e5bb93dada526a7a744 SHA1: d2dba634b06ed5bc70fa85a5b7485f8ede421ae7 SHA256: 48db5f5b655bb1a77ec06c51fcfdcd75f5255ad7bdf1e4ef6ce1a4240ce3e82f SHA512: c0d0f7f2daeb16b2e6113898246a6610c1669ba61b33b1103733c5f14faafc230ab936941b868d6e37448b800a263353fe6c2374aed344900cb89af733618b51 Homepage: https://cran.r-project.org/package=adwave Description: CRAN Package 'adwave' (Wavelet Analysis of Genomic Data from Admixed Populations) Implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see J Sanderson, H Sudoyo, TM Karafet, MF Hammer and MP Cox. 2015. Reconstructing past admixture processes from local genomic ancestry using wavelet transformation. Genetics 200:469-481 . 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Currently, there are two methods of data of accessing the API, depending on the type of request. One method uses 'SOAP' requests which require building an 'XML' structure and then sent to the API. These are used for the 'ManagedCustomerService' and the 'TargetingIdeaService'. The second method is by building 'AWQL' queries for the reporting side of the 'Google Adwords' API. Package: r-cran-aebdata Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-readr, r-cran-rvest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aebdata_0.1.5-1.ca2604.1_all.deb Size: 33986 MD5sum: 46dd0466cf35393cfdda21bba5edbd50 SHA1: 33ae0bf6afad4dacdad8a0c1a1762a65151d267b SHA256: bac119c9013b4a7693d0d689f9da30ccebfece73ac4490305e559e08404a2ab7 SHA512: b45d51f7a554fc4d3c028014d7baa4442749e74fedfe2dd425ab2b7e09641ce08580f2ae17af10cd6daa168ba3e43a86ede7d3efa9b0f10835b813765186a452 Homepage: https://cran.r-project.org/package=aebdata Description: CRAN Package 'aebdata' (Access Data from the Atlas do Estado Brasileiro) Facilitates access to the data from the Atlas do Estado Brasileiro (), maintained by the Instituto de Pesquisa Econômica Aplicada (Ipea). 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'aeddo' employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557. Package: r-cran-aedforecasting Architecture: all Version: 0.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-forecast, r-cran-signal Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-aedforecasting_0.20.0-1.ca2604.1_all.deb Size: 194932 MD5sum: ca524d260c57364be6d615a3af493416 SHA1: 1cb16de93e6745e2333f08d09513440005e41d24 SHA256: 29cc454afb372d1ebc82fafa7f417e04891efdaa7d8daf7dd31003ca03f8eaf2 SHA512: f02937cc06c425253a8e6803193ea5596e5ab4cea35de84ebe91bb54de9d40748dbdf635db50b1282b6cddaae9df8ebbe32b22e489cb1c7a744fe8a959631541 Homepage: https://cran.r-project.org/package=AEDForecasting Description: CRAN Package 'AEDForecasting' (Change Point Analysis in ARIMA Forecasting) Package to incorporate change point analysis in ARIMA forecasting. Package: r-cran-aedseo Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-plyr, r-cran-purrr, r-cran-pracma, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-isoweek, r-cran-kableextra, r-cran-knitr, r-cran-mem, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-aedseo_1.1.0-1.ca2604.1_all.deb Size: 862854 MD5sum: f10ad0d81a1eb0c2e594906bfcecd088 SHA1: 24e08e8e4d5cedc283493ff81ee32dc3db8be2e3 SHA256: 44d97d87dabadd3faa2aea1bb43ba42f045dad22b13c246b4b0dfbd24f859834 SHA512: 7d33ea048a4199a40e36d405ac43c3dd830d98e05c11eaa60f7e298f960f3030777ce2e29270870f939e3dea28ff0729a250ef06a852d5b5f73f0a3de3042762 Homepage: https://cran.r-project.org/package=aedseo Description: CRAN Package 'aedseo' (Automated and Early Detection of Seasonal Epidemic Onset andBurden Levels) A powerful tool for automating the early detection of seasonal epidemic onsets in time series data. It offers the ability to estimate growth rates across consecutive time intervals, calculate the sum of cases (SoC) within those intervals, and estimate seasonal onsets within user defined seasons. With use of a disease-specific threshold it also offers the possibility to estimate seasonal onset of epidemics. Additionally it offers the ability to estimate burden levels for seasons based on historical data. It is aimed towards epidemiologists, public health professionals, and researchers seeking to identify and respond to seasonal epidemics in a timely fashion. Package: r-cran-aeenrich Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-bioc-qvalue, r-cran-doparallel, r-cran-tidyr, r-cran-modelr, r-cran-foreach, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aeenrich_1.1.1-1.ca2604.1_all.deb Size: 476008 MD5sum: daeea0a582028f39f5f92c0b46ebce2f SHA1: 730c0c82ea0123ba11a3e4e59ae96cf3f2a4c341 SHA256: a1edc3440961dc9ad787e60cf53dc21c563f1fd21067a2dd14319e8d5bbc237a SHA512: be9879a47828ae461643b063d009645a91b055c776b869aeec7cb20e3edc475e9e45b530762218c76d806a10608606ff1f125aff4c7757c7d71a21eda045c74e Homepage: https://cran.r-project.org/package=AEenrich Description: CRAN Package 'AEenrich' (Adverse Event Enrichment Tests) We extend existing gene enrichment tests to perform adverse event enrichment analysis. Unlike the continuous gene expression data, adverse event data are counts. Therefore, adverse event data has many zeros and ties. We propose two enrichment tests. One is a modified Fisher's exact test based on pre-selected significant adverse events, while the other is based on a modified Kolmogorov-Smirnov statistic. We add Covariate adjustment to improve the analysis."Adverse event enrichment tests using VAERS" Shuoran Li, Lili Zhao (2020) . Package: r-cran-aelab Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2049 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble, r-cran-lubridate, r-cran-dplyr, r-cran-openxlsx, r-cran-readxl, r-cran-ggplot2, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-purrr, r-cran-rlang, r-cran-multcompview, r-cran-fsa, r-cran-rcompanion, r-cran-rnaturalearth, r-cran-sf, r-cran-ggspatial Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rnaturalearthdata Filename: pool/dists/resolute/main/r-cran-aelab_1.1.3-1.ca2604.1_all.deb Size: 1727578 MD5sum: fd3daa8d0c15f4150fe2f715bc4b2982 SHA1: cd9b2def15b9fc749f3fde8a3170e82936bf490e SHA256: 376f5218280df778eef2fca103299a8f43c7e5950cecff77fc882e554014265d SHA512: fc600bbf13c7e790a9603afd63fa1b7e614fb0c6af1032311367d6c8f7f57052b080d751c9a19bb787479d88da66817cc8f18a7c1bff0ff3377d8d60d6c3ee1b Homepage: https://cran.r-project.org/package=aelab Description: CRAN Package 'aelab' (Data Processing for Aquatic Ecology) Facilitate the analysis of data related to aquatic ecology, specifically the establishment of carbon budget. Currently, the package allows the below analysis. (i) the calculation of greenhouse gas flux based on data obtained from trace gas analyzer using the method described in Lin et al. (2024). (ii) the calculation of Dissolved Oxygen (DO) metabolism based on data obtained from dissolved oxygen data logger using the method described in Staehr et al. (2010). Yong et al. (2024) . Staehr et al. (2010) . Package: r-cran-aemo Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-openssl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-aemo_0.4.0-1.ca2604.1_all.deb Size: 236060 MD5sum: 845cfbe8b5caf730b1a60bbf57af7244 SHA1: 8dd82eb86189da64fcd1d03e96aa08cc221353b3 SHA256: b2b08e403ca751254ee60d3aecc384bcde272407f7b0a420d995cf4fd0f87d54 SHA512: cb553d407439deac5553cd1aa28d13751b3eed60b322c5dcbe1a461791d18a8b2ea9bb072b29412156e1346030c78fc32805876438f8a993437f903b68b74d7c Homepage: https://cran.r-project.org/package=aemo Description: CRAN Package 'aemo' (Download Australian Energy Market Operator Data) Fetch Australian Energy Market Operator ('AEMO') public data from 'NEMweb' and the Market Management System Data Model ('MMSDM') historical archive. Provides tidy access to 5-minute and 30-minute wholesale electricity prices, regional demand, dispatch-unit output, interconnector flows, rooftop photovoltaic generation, generator bids, predispatch forecasts, frequency control ancillary services markets, and gas market data across the National Electricity Market ('NEM') regions. Data is published by AEMO under its Copyright Permissions Notice . Package: r-cran-aep Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-aep_0.1.4-1.ca2604.1_all.deb Size: 56272 MD5sum: 760a3c2590229100ee4ae309c1131246 SHA1: fef689517d0d23c50cb754af3857c6488a53a258 SHA256: d3448feac6686046a5964448f00deeeb18aed2660cec85d6a99cd815bcf1db07 SHA512: 37d5b72cf8af4913ce8af89d5f71758f62853197ec7c9a2323ea0d3b6fb31431b5d6fd4f089fe8a431c6059283e692ea32bc4216bc2b24faaf62daf0b922f93c Homepage: https://cran.r-project.org/package=AEP Description: CRAN Package 'AEP' (Statistical Modelling for Asymmetric Exponential PowerDistribution) Developed for Computing the probability density function, cumulative distribution function, random generation, estimating the parameters of asymmetric exponential power distribution, and robust regression analysis with error term that follows asymmetric exponential power distribution. The asymmetric exponential power distribution studied here is a special case of that introduced by Dongming and Zinde-Walsh (2009) . Package: r-cran-aer Architecture: all Version: 1.2-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-lmtest, r-cran-sandwich, r-cran-survival, r-cran-zoo, r-cran-formula Suggests: r-cran-boot, r-cran-dynlm, r-cran-effects, r-cran-fgarch, r-cran-forecast, r-cran-foreign, r-cran-ineq, r-cran-kernsmooth, r-cran-lattice, r-cran-longmemo, r-cran-mass, r-cran-mlogit, r-cran-nlme, r-cran-nnet, r-cran-np, r-cran-plm, r-cran-pscl, r-cran-quantreg, r-cran-rgl, r-cran-rocr, r-cran-rugarch, r-cran-sampleselection, r-cran-scatterplot3d, r-cran-strucchange, r-cran-systemfit, r-cran-truncreg, r-cran-tseries, r-cran-urca, r-cran-vars Filename: pool/dists/resolute/main/r-cran-aer_1.2-16-1.ca2604.1_all.deb Size: 2553948 MD5sum: 7bbf1e1e2802f6215991e00a778b2716 SHA1: 63d2913d0ccbc9674cc725fdea8b2614c7afc962 SHA256: 47d67ab2d3a67f66f8ebf08c6a5552cbf2e2a8e1d04dd8f08dbb10bc67193c1a SHA512: 25bdb12b24b4d393ac2ace3186f9aff5ca266f843ba7b42e820b6e8de25ae419205f1412456bded23c44fc9bae9070f92a4d0d2f67e032ae313b47473f7bcc52 Homepage: https://cran.r-project.org/package=AER Description: CRAN Package 'AER' (Applied Econometrics with R) Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.) Package: r-cran-aerobiology Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1091 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-writexl, r-cran-ggvis, r-cran-lubridate, r-cran-plotly, r-cran-ggplot2, r-cran-tidyr, r-cran-circular, r-cran-scales, r-cran-zoo, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aerobiology_2.0.2-1.ca2604.1_all.deb Size: 677738 MD5sum: cf2a4739cde724a525e3d4bdca53da37 SHA1: 6b519eb0bb5533eb8b409c9376a164c3434712ad SHA256: 24ff4d68cd7183cd6d76bb406d5dfa3f0df864c50d811905ff8b10de6d3f4c6c SHA512: cb8bd942c7394c9adfa21f2fe94a0742b03b34320db5f388aeff799d7378cfe5b0abea83a618e90308413b8e8b80c3c5827ebaee23db6ca6e57f04d4807fe293 Homepage: https://cran.r-project.org/package=AeRobiology Description: CRAN Package 'AeRobiology' (A Computational Tool for Aerobiological Data) Different tools for managing databases of airborne particles, elaborating the main calculations and visualization of results. In a first step, data are checked using tools for quality control and all missing gaps are completed. Then, the main parameters of the pollen season are calculated and represented graphically. Multiple graphical tools are available: pollen calendars, phenological plots, time series, tendencies, interactive plots, abundance plots... Package: r-cran-aeroevapr Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-data.table, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-aeroevapr_0.1.6-1.ca2604.1_all.deb Size: 83272 MD5sum: 59f152641a26a0c39a20889587d38f85 SHA1: 1c4ef74ee60f87a4f21edba367e32e09c2831e97 SHA256: 7420fd7bce78a8c13acc1103c8bf56b0be6b39ac02060065030d0927626a6f66 SHA512: f787e6b5b000937d901c1469927c61dc34aa8c6e0ef0c060b173325b948df69dd7a5dd0d69f96f91ccc64e57a9a3646106949dd95de9f31fef4733c731ac235e Homepage: https://cran.r-project.org/package=AeroEvapR Description: CRAN Package 'AeroEvapR' (Estimating Reservoir Evaporation via Aerodynamic Approach) Developed as an 'R' alternative to the 'AeroEvap' model developed by the Desert Research Institute (DRI) in 'python' which estimates open water evaporation using the aerodynamic mass transfer approach. Package: r-cran-aerosampler Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-stringr, r-cran-tidyselect, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aerosampler_0.3.0-1.ca2604.1_all.deb Size: 171096 MD5sum: 1a14eb372566556dc9e6fe5ef4383b58 SHA1: aed9fbddbd1e3fd9dd115420153ca8069ce619c1 SHA256: 810df5aef76d8d0f80ff8e204e6284afaa24ab9ac7bc9b0e3b667c2afca6cf45 SHA512: f83d07cfb764795f91476f46aa18bff1e288cdba88fbdd5fbe458465bb0030bfe90b53ec4d3e5b9e84868bf5fcfc1840b0c5cd5a6c5c4e56cdb6b099b7dc13ee Homepage: https://cran.r-project.org/package=AeroSampleR Description: CRAN Package 'AeroSampleR' (Estimate Aerosol Particle Collection Through Sample Lines) Estimate ideal efficiencies of aerosol sampling through sample lines. Functions were developed consistent with the approach described in Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), "Hand Calculations for Transport of Radioactive Aerosols through Sampling Systems" Health Phys 106, 5, S78-S87, . Package: r-cran-aesopr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-aesopr_0.1.0-1.ca2604.1_all.deb Size: 169902 MD5sum: b8a74b41500ab7bd40a6f798c95e2dfe SHA1: dc471d0d61fa2851edf708e255e6e959e7c63751 SHA256: eba42d965bac6a4a4b3a838a13158834c408a0924c5545d2f490817bcd8cd781 SHA512: b04cd482f888e5ffd3f4629f06ac49655a58a7b0f2e360e358389518ad511d0eae6059ecd50d3ff887602f4a5a9b15b22be9d171f401e77566d750ab28c5027d Homepage: https://cran.r-project.org/package=aesopR Description: CRAN Package 'aesopR' (Tools for Text Analysis of Aesop's Fables) Provides a tidy text corpus of Aesop's Fables sourced from the Library of Congress, along with analysis-ready datasets for sentiment, emotion, and linguistic analysis of moral storytelling. The package includes both full narrative texts and word-level representations to support exploratory text analysis and teaching workflows. Package: r-cran-af Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-drgee, r-cran-stdreg, r-cran-data.table, r-cran-ivtools Filename: pool/dists/resolute/main/r-cran-af_0.1.5-1.ca2604.1_all.deb Size: 257340 MD5sum: 242ae8beb51e522cef34e8616a9df965 SHA1: c10d9cc43a4e4ca4d1f415e680006acaf973e187 SHA256: 7f85d73f442c79c900c1c1cf5c0702df6f0e6a47a132f758ae0efb8e8b150bcb SHA512: cd2e2ca9b3e9b62ae8dcbc3da94a27b13a175249147342c9206fabc9bfb91c4368468f4cc5304ee455a3154c66952b5f26ffe325d890984b7d269b7b7a687edd Homepage: https://cran.r-project.org/package=AF Description: CRAN Package 'AF' (Model-Based Estimation of Confounder-Adjusted AttributableFractions) Estimates the attributable fraction in different sampling designs adjusted for measured confounders using logistic regression (cross-sectional and case-control designs), conditional logistic regression (matched case-control design), Cox proportional hazard regression (cohort design with time-to- event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis. An exploration of the AF with a genetic exposure can be found in the package 'AFheritability' Dahlqwist E et al. (2019) . Package: r-cran-afc Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-afc_1.4.0-1.ca2604.1_all.deb Size: 108194 MD5sum: 1e3fce93c272ebe3b6e4536a7b247a72 SHA1: 1dc223bb266e57afd41d38cba630eabc4b8ef9df SHA256: abc5645908f7b0df7acb5fbe466f47c2e69d9f9b96b07da68831582a7318da5c SHA512: 347e5ba1c34abb805bdd948c46d7e48b7526aca697f549df3d27015e7c789935514945088411bfce8a51d4b5e220d4b6e2b27affa25e0d33fe45103ff6c7464c Homepage: https://cran.r-project.org/package=afc Description: CRAN Package 'afc' (Generalized Discrimination Score) This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. The Generalized Discrimination Score is a generic forecast verification framework which can be applied to any of the following verification contexts: dichotomous, polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble. A comprehensive description of the Generalized Discrimination Score, including all equations used in this package, is provided by Mason and Weigel (2009) . Package: r-cran-afcharts Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-cli, r-cran-rlang, r-cran-dplyr, r-cran-purrr Suggests: r-cran-ggtext, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-tidyr, r-cran-glue, r-cran-stringr, r-cran-testthat, r-cran-plotly, r-cran-gt, r-cran-svglite, r-cran-ragg, r-cran-gapminder, r-cran-diffviewer, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-afcharts_0.5.1-1.ca2604.1_all.deb Size: 1185584 MD5sum: fae22dc140554035b8bb038430be3877 SHA1: 081ae17fa07e2b6529fccf8e7d4fec68b86cdfb8 SHA256: c81c8690f000c89b95146b46a0b0cbe5f6dbed8c5f72f14514d8c874f0ef8de4 SHA512: c1452f10d98ecb854972b641629b02a37195ecef4ca6dcc1cb0282e7973b49adbd07d4d642dbc36d50f34e1f285ae1d514be493c3e00fa77bd908ca2ee34da34 Homepage: https://cran.r-project.org/package=afcharts Description: CRAN Package 'afcharts' (Produce Charts Following UK Government Analysis FunctionGuidance) Colour palettes and a 'ggplot2' theme to follow the UK Government Analysis Function best practice guidance for producing data visualisations, available at . 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Package: r-cran-afdx Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-desctools, r-cran-kableextra, r-cran-coda, r-cran-rjags, r-cran-ggmcmc, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-afdx_1.1.2-1.ca2604.1_all.deb Size: 323172 MD5sum: fda64644e265562b323b0419adbf203b SHA1: 84df18d6fae2a4d1c452f697c667efd3ce4e7308 SHA256: a0f8292fa0cb5493755efb080e508f46adaa85a72a9c0d8838892e4004469f3a SHA512: c036f799a389cc3d3af1d47686c92190eefaa92efe2c446a43104d1d9c9b5cb3f2dffe675420ad16ee1fad2340c8699e467c09070948ce08c1408324361aa8a1 Homepage: https://cran.r-project.org/package=afdx Description: CRAN Package 'afdx' (Diagnosis Performance Using Attributable Fraction) Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction. Package: r-cran-afex Architecture: all Version: 1.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4740 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-pbkrtest, r-cran-lmertest, r-cran-car, r-cran-reshape2, r-cran-reformulas, r-cran-rlang Suggests: r-cran-emmeans, r-cran-coin, r-cran-xtable, r-cran-plyr, r-cran-optimx, r-cran-nloptr, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-lattice, r-cran-latticeextra, r-cran-multcomp, r-cran-testthat, r-cran-mlmrev, r-cran-dplyr, r-cran-tidyr, r-cran-dfoptim, r-cran-matrix, r-cran-psychtools, r-cran-ggplot2, r-cran-memss, r-cran-effects, r-cran-cardata, r-cran-ggbeeswarm, r-cran-nlme, r-cran-cowplot, r-cran-jtools, r-cran-ggpubr, r-cran-mass, r-cran-glmmtmb, r-cran-brms, r-cran-rstanarm, r-cran-statmod, r-cran-performance, r-cran-see, r-cran-ez, r-cran-ggresidpanel, r-cran-vdiffr, r-cran-glmmadaptive, r-cran-ggthemes Filename: pool/dists/resolute/main/r-cran-afex_1.5-1-1.ca2604.1_all.deb Size: 3582606 MD5sum: d7138feddb7c840cb0da3e76b89ae95d SHA1: a88d3d898955fb5a2f351098d2f979159f1dba3e SHA256: 18951fd31615dd08cf3541cfd198c5590402f6a7074514ac65ab100aad773328 SHA512: 518b3d7b9f60dceb1d9c56fa207fac6e0e5f3635099e79ccf9044638d03cd3aaffe518960e5e1aa840e09b39ba777a239d1553db646a0fbc2624dd07dc9a3dc9 Homepage: https://cran.r-project.org/package=afex Description: CRAN Package 'afex' (Analysis of Factorial Experiments) Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software). Package: r-cran-affect Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-affect_0.1.2-1.ca2604.1_all.deb Size: 41796 MD5sum: 1648513ddebcf9e39192659cf1ac574d SHA1: d2cc564785076fcd4663d217240a16de7d0cef01 SHA256: bd769015bf5ea1204d8a7d378ab41bcc479488b904618ec67842d68c0b0a1b00 SHA512: a651d2373d9a4b9a4790be0d00eb1d82c1f1c111fb6a4475ec9bcb03d0b548d7deb6ced9358cc3c6f792bbb70029401c872abf5a54d50b460fd326a534842209 Homepage: https://cran.r-project.org/package=AFFECT Description: CRAN Package 'AFFECT' (Accelerated Functional Failure Time Model withError-Contaminated Survival Times) We aim to deal with data with measurement error in the response and misclassification censoring status under an AFT model. This package primarily contains three functions, which are used to generate artificial data, correction for error-prone data and estimate the functional covariates for an AFT model. Package: r-cran-affiner Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-artsy, r-cran-ggplot2, r-cran-gridpattern, r-cran-gtable, r-cran-knitr, r-cran-ragg, r-cran-rgl, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-affiner_0.3.1-1.ca2604.1_all.deb Size: 1586114 MD5sum: 7131f95358f3b25739bb264b422ba417 SHA1: a619002786bfd9a28ae269d8a7a9c74925eab66a SHA256: 1b66721e09a2224db67c935109601ccc713e22f488280fa4e960ebdeb3119a80 SHA512: 194e494f81ded2bbc1db559e62964fc3222d34318abcdc443bba0fa26f2562e84b21539af8e3897bb02c78f3058c32596e57e88b33fd97f5e2e894519f788592 Homepage: https://cran.r-project.org/package=affiner Description: CRAN Package 'affiner' (A Finer Way to Render 3D Illustrated Objects in 'grid' UsingAffine Transformations) Dilate, permute, project, reflect, rotate, shear, and translate 2D and 3D points. Supports parallel projections including oblique projections such as the cabinet projection as well as axonometric projections such as the isometric projection. Use 'grid's "affine transformation" feature to render illustrated flat surfaces. Package: r-cran-affinity Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-reproj Suggests: r-cran-rmarkdown, r-cran-covr, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-affinity_0.2.5-1.ca2604.1_all.deb Size: 619076 MD5sum: 2200db20ba2c9afb492ca38d49623562 SHA1: 5f2f9b1501504791819b4b27ba35414030239e8d SHA256: c98d288049d97b571d6ff5aa1fc29f326cb7282591501e69722f63bd9d9fa687 SHA512: 07404e552045473430fba3ce8b455eb384f9c15f8276d42b6978088e2623cc616772a8537aee9c0d0c0f8047bc37f67bf625cb4613403759e78ebc1649de429d Homepage: https://cran.r-project.org/package=affinity Description: CRAN Package 'affinity' (Raster Georeferencing, Grid Affine Transforms, Cell Abstraction) Tools for raster georeferencing, grid affine transforms, and general raster logic. These functions provide converters between raster specifications, world vector, geotransform, 'RasterIO' window, and 'RasterIO window' in 'sf' package list format. There are functions to offset a matrix by padding any of four corners (useful for vectorizing neighbourhood operations), and helper functions to harvesting user clicks on a graphics device to use for simple georeferencing of images. Methods used are available from and . Package: r-cran-affinitymatrix Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expm, r-cran-mass, r-cran-hmisc, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-affinitymatrix_0.1.0-1.ca2604.1_all.deb Size: 125992 MD5sum: 14ae0e25a5008055895a8aeb66bdb609 SHA1: 6d6285188da52b741fbda2265ff66f5c033fc2f6 SHA256: 2bdf1321e8b7ce8830ba01061b7b4a29d655dd01258be7855837975219c0273d SHA512: a88dabaf961b623887b427b328d7ba37047927111b09c3b25bbc277263cd526b0f8eb2a5f5f36b081d6855db6a2be609dac89833d1b1db29dc586aee3af7eed4 Homepage: https://cran.r-project.org/package=affinitymatrix Description: CRAN Package 'affinitymatrix' (Estimation of Affinity Matrix) Tools to study sorting patterns in matching markets and to estimate the affinity matrix of both the bipartite one-to-one matching model without frictions and with Transferable Utility by 'Dupuy' and 'Galichon' (2014) and its 'unipartite' variant by 'Ciscato', 'Galichon' and 'Gousse' (2020) . It also contains all the necessary tools to implement the 'saliency' analysis, to run rank tests of the affinity matrix and to build tables and plots summarizing the findings. Package: r-cran-affluenceindex Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.univar Filename: pool/dists/resolute/main/r-cran-affluenceindex_2.2-1.ca2604.1_all.deb Size: 88322 MD5sum: 2117a51a6b6ce3e79502920ba2541af5 SHA1: 3efd523e76fa99a15cd969f78b0a190ea449f421 SHA256: ed5d19857cf6fdb2f600f4d8bf635cd7a0f80632ca266d3f4d01909198ceaa7c SHA512: 4b0117fe222d22b89be9841d7d3d4bad15bb708e227648055cab2d928f1e8b986db1d6fbe3c6b3d5704eb65617bc355fcdbbace28ab5ce704f16c6380c1584bf Homepage: https://cran.r-project.org/package=affluenceIndex Description: CRAN Package 'affluenceIndex' (Affluence (Richness) Indices) Enables to compute the statistical indices of affluence (richness) with bootstrap errors, and inequality and polarization indices. Moreover, gives the possibility of calculation of affluence line. Some simple errors are fixed and it works with new version of Spatial Statistics packaged. Package: r-cran-afheritability Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape2, r-cran-mvtnorm, r-cran-ggplot2, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-afheritability_0.1.0-1.ca2604.1_all.deb Size: 66010 MD5sum: 5891202aaff497868aa90d5c5292dc92 SHA1: 4f42be2828350edea5666d5fa0d95c9e7ccef1b0 SHA256: 74d77f71e0fb008a00dcb3e82f1453a6d2406b5d99a0d152089784b2fea50137 SHA512: 338fdc85e5a048b764fca204b4a6729a401103961ec2d20d6540031c748897d2907d5e6b0174443902ea22f94d3112ebc8e92e995529ce1e2301f4b2a008e612 Homepage: https://cran.r-project.org/package=AFheritability Description: CRAN Package 'AFheritability' (The Attributable Fraction (AF) Described as a Function ofDisease Heritability, Prevalence and Intervention SpecificFactors) The AFfunction() is a function which returns an estimate of the Attributable Fraction (AF) and a plot of the AF as a function of heritability, disease prevalence, size of target group and intervention effect. Since the AF is a function of several factors, a shiny app is used to better illustrate how the relationship between the AF and heritability depends on several other factors. The app is ran by the function runShinyApp(). For more information see Dahlqwist E et al. (2019) . Package: r-cran-afmpar Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-afmpar_0.2.0-1.ca2604.1_all.deb Size: 16588 MD5sum: 1191cbafb578109c80c0c78da97a80fb SHA1: aa53361cb59698070becc568b178f89b5c9e7531 SHA256: 7913b970eb931adf9ccea581770c16d387f9a674a8c7ff31dc7214dc038d97d8 SHA512: 8758e97fe61a3cac38405e5870b4e094af09946e0ff919da988186133b7566274f2f976441356e8e006e22652f0bed503a3a55839e0f7c4b6e4669af5143a41a Homepage: https://cran.r-project.org/package=afmpar Description: CRAN Package 'afmpar' (Check Validity of Greek AFM and PA Numbers) With the functions in this package you can check the validity of the Greek Tax Identification Number (AFM) and the Greek Personal Number (PA) . The PA is a new universal ID for Greek citizens across all public services and it is to replace older numbers issued by various Greek state agencies. Its format is a 12-character ID consisting of three alphanumeric characters followed by the nine numerical digits of the AFM. Package: r-cran-afmtoolkit Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4823 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-gridextra, r-cran-scales, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-afmtoolkit_1.0.0-1.ca2604.1_all.deb Size: 4634614 MD5sum: ccc5b87b0cc1bc098050d60b86d65505 SHA1: 9a0757b286048bcadde82ccd32219b3064f3440c SHA256: 972bdc2216502229ed27c107430e6830a9227188fd4ba54f6e30cf110244f341 SHA512: b5fcf78d7a2b1a0159d9170dc349c04b73b9386588abb3647c2104fa95c1a047db541b60585c0e687ab61259f3a0b2564837964e9bc2542e673dcc99dbe395bf Homepage: https://cran.r-project.org/package=afmToolkit Description: CRAN Package 'afmToolkit' (Functions for Atomic Force Microscope Force-Distance CurvesAnalysis) Set of functions for analyzing Atomic Force Microscope (AFM) force-distance curves. It allows to obtain the contact and unbinding points, perform the baseline correction, estimate the Young's modulus, fit up to two exponential decay function to a stress-relaxation / creep experiment, obtain adhesion energies. These operations can be done either over a single F-d curve or over a set of F-d curves in batch mode. Package: r-cran-afpt Architecture: all Version: 1.1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-afpt_1.1.0.4-1.ca2604.1_all.deb Size: 427780 MD5sum: 13231492c2fd38b56e883e3a8c2cbc13 SHA1: 4c9ca256bc294f60f911a39dca05dcec68deda62 SHA256: e582f95063a4e1d317e8139ad4257f11d3c0209317781b5462c871457448dafe SHA512: a530e4b31bbafc1531f43419a150b314883cce05d8c8248f0d1fcecd0ed6006d4d916f39f7f26183d993b6734f3ae238194c5c36c90859f9389c75bf9a4c63cf Homepage: https://cran.r-project.org/package=afpt Description: CRAN Package 'afpt' (Tools for Modelling of Animal Flight Performance) Allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model described in Klein Heerenbrink et al. (2015) . Taking inspiration from the program 'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. 'afpt' can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions. Package: r-cran-afr Architecture: all Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-forecast, r-cran-zoo, r-cran-olsrr, r-cran-lmtest, r-cran-nlme, r-cran-ggplot2, r-cran-tseries, r-cran-gridextra, r-cran-rlang, r-cran-xts, r-cran-nortest, r-cran-goftest, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-afr_0.3.8-1.ca2604.1_all.deb Size: 201688 MD5sum: 55fbcd47d4fcaaa55d885cb64eba15f0 SHA1: 4de2db4caaa3ae60bfb8baabb055470a13ac3d3a SHA256: 9c5fd963ec95b7f1455828ff966940998d9b52bceecd21ca1d0515d15ee19d40 SHA512: 11cc65dfe18c2fad77700ee70db4dab375b2e8c54a1bf3b17b6ede6bdd7e36c1474112af11e6de8f06bb7df032eed79c44f11c51ba040daf93a1357cd962a4de Homepage: https://cran.r-project.org/package=AFR Description: CRAN Package 'AFR' (Toolkit for Regression Analysis of Kazakhstan Banking SectorData) Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians. Package: r-cran-africamonitor Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rmysql, r-cran-data.table, r-cran-collapse Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-africamonitor_0.2.4-1.ca2604.1_all.deb Size: 75316 MD5sum: 729a70e6e5f010d929442065221a0b13 SHA1: bf244517f8d8b1ca6d51832e9ed5098d8fa7f95b SHA256: d266b8ace8ab1a3361923f27968306dc8f5bb8d2dc1082d765bf7db5a6453dd3 SHA512: e8b4958ce7fde8afc508100d0603fbc8a2047b1d51f284a0bf5163e46aae260aadcf3f79e57c23d922f15d09058f9cc61d2abd497e755d9e13e8642094ca7bd1 Homepage: https://cran.r-project.org/package=africamonitor Description: CRAN Package 'africamonitor' (Africa Macroeconomic Monitor Database API) An R API providing access to a relational database with macroeconomic data for Africa. The database contains >700 macroeconomic time series from mostly international sources, grouped into 50 macroeconomic and development-related topics. Series are carefully selected on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. The project is part of the 'Kiel Institute Africa Initiative' , which, amongst other things, aims to develop a parsimonious database with highly relevant indicators to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform at . The database is maintained at the Kiel Institute for the World Economy . Package: r-cran-aftables Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx2, r-cran-pillar, r-cran-purrr, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-tidyr, r-cran-rlang, r-cran-yaml, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aftables_2.0.1-1.ca2604.1_all.deb Size: 1083432 MD5sum: 4835b945d6665a422b21d51f1a4a5c85 SHA1: 318c4ca1ff432f6c791ac4e6ea491aaf1082186d SHA256: d0d1bd56260a2e6b75f0f3a5800978eb893d262460b90ead8cff2d01e5c909d7 SHA512: 6ad9ed35277b6fa5ee17715e7ce1d27153005d228372951b30243833f885cf27894933e34c74cbb028622bde9d1efb0c38311b920f365718933049e195ac6ce2 Homepage: https://cran.r-project.org/package=aftables Description: CRAN Package 'aftables' (Create Spreadsheet Publications Following Best Practice) Generate spreadsheet publications that follow best practice guidance from the UK government's Analysis Function, available at , with a focus on accessibility. See also the 'Python' package 'gptables'. Package: r-cran-afthd Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-photobiology, r-cran-r2jags, r-cran-rstpm2, r-cran-survival Filename: pool/dists/resolute/main/r-cran-afthd_1.1.0-1.ca2604.1_all.deb Size: 530704 MD5sum: 66f1ad63a7c5e866e38f5481a80a7bc5 SHA1: a4c9fe4715af5a183ffbc6ca56114b6071485b02 SHA256: 785514d9f92ec72afbef4566badca14f77ade1ee8172813a27bddf00b440831d SHA512: 199959a7f9f28011d077f28036a800b6b17d6da132ab576a6ae9173c9167e5f1202b743ca1bf492be8f974fb17dd86dbb856b31120df914efccfadcc84038d04 Homepage: https://cran.r-project.org/package=afthd Description: CRAN Package 'afthd' (Accelerated Failure Time for High Dimensional Data with MCMC) Functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this 'afthd' package. AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016) . Package: r-cran-aftr2 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-aftr2_0.1.0-1.ca2604.1_all.deb Size: 10476 MD5sum: 10774535650b903cd9b6af8e6f305ec5 SHA1: 20f2ae522e5498d9aa1104b061cf8efc58bf9c71 SHA256: 6524fe44abb9ba21096895e4da66770ab4ee434cc214ac45b7a4560d4b4e7f31 SHA512: 1dc3db465e9acf5aa5452060039a6167abeb4294a79b8ad02bdca875a7699e3f72cecd549dc9530fdd94a3572cce439efb2243cdf94cb46d6653890bc4444365 Homepage: https://cran.r-project.org/package=aftR2 Description: CRAN Package 'aftR2' (R-Squared Measure under Accelerated Failure Time (AFT) Models) Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) . Package: r-cran-ag5tools Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-reticulate, r-cran-fs, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-ag5tools_0.0.3-1.ca2604.1_all.deb Size: 531456 MD5sum: f4b6e8ea7cc8d079b2700c056fe4101a SHA1: 4b6fcde87437394085715799fddc7140f6aea066 SHA256: 3f7235c4f9aefa791abbc8f3429df6bfa208269f9695bb476cfeb5dea23e7f11 SHA512: d61eb70655df7f1657b581c95763800b4fec372ad09a4480b6dc3276c5922f3e01123e9d4fab0d87bb81690f7811ed69f6343495b9f2a4795e1fa079001e8d1f Homepage: https://cran.r-project.org/package=ag5Tools Description: CRAN Package 'ag5Tools' (Toolbox for Downloading and Extracting Copernicus AgERA5 Data) Tools for downloading and extracting data from the Copernicus "Agrometeorological indicators from 1979 to present derived from reanalysis" (AgERA5). Package: r-cran-agd Architecture: all Version: 0.45.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist Filename: pool/dists/resolute/main/r-cran-agd_0.45.0-1.ca2604.1_all.deb Size: 449200 MD5sum: 2174c25f7b46b27dc9a96b7faee10bcd SHA1: 90cdd916ff6340957e2aecdb2f66818365bad62a SHA256: f4eca88c67571881f3e31d25ffbeeba518db4673b3a589023c1cee1e97e5e97e SHA512: 8cb5c76a5b2f8795836cde438eaebd514bca1c7c235a2ec552dce3c73e25c0a7bd2cdcf76cfa2dd89eb6852efb6e99d92c377f0151c4343f12bd2c7f51cca692 Homepage: https://cran.r-project.org/package=AGD Description: CRAN Package 'AGD' (Analysis of Growth Data) Tools for the analysis of growth data: to extract an LMS table from a gamlss object, to calculate the standard deviation scores and its inverse, and to superpose two wormplots from different models. The package contains a some varieties of reference tables, especially for The Netherlands. Package: r-cran-agebanddecomposition Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3085 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-readxl, r-cran-ggplot2, r-cran-dplr, r-cran-patchwork Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-agebanddecomposition_2.0.1-1.ca2604.1_all.deb Size: 1958618 MD5sum: 7119c36a9186dc85c2bc7699b00f72da SHA1: 24a642ff52dab9897be85389b6fa9e67543cebc0 SHA256: e2871304f0911d8c670e26aa39f8044ebe736b4cbaeb6a750d7396fddb8c82f2 SHA512: bd738fb64711bf215439ebf6566992996e50f522a24c6b3691439d5b0a796a3329c26313c4492481f228ed092e5600c7658b77fbdd7f5cc27df7b2b3e7a6aab6 Homepage: https://cran.r-project.org/package=AgeBandDecomposition Description: CRAN Package 'AgeBandDecomposition' (Age Band Decomposition Method for Tree Ring Standardization) Implements the Age Band Decomposition (ABD) method for standardizing tree ring width data while preserving both low and high frequency variability. Unlike traditional detrending approaches that can distort long term growth trends, ABD decomposes ring width series into multiple age classes, detrends each class separately, and then recombines them to create standardized chronologies. This approach improves the detection of growth signals linked to past climatic and environmental factors, making it particularly valuable for dendroecological and dendroclimatological studies. The package provides functions to perform ABD-based standardization, compare results with other common methods (e.g., BAI, C method, RCS), and facilitate the interpretation of growth patterns under current and future climate variability. Package: r-cran-ageg Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ageg_1.0.0-1.ca2604.1_all.deb Size: 14562 MD5sum: 4352348d24adcd05d3f12ef879c60ed8 SHA1: c1f59cb709280eb567e4e689eb7f561e379b6957 SHA256: 7148a5339b4579dece96bee74057fc7409f709dfa826c3b32de45ec04a3b69c7 SHA512: 756d61c8b647dbc982583e5d77a7191f7b17f81b1f258906dc163e2079b30cd7e412c0914ed1d46fa425f020d9b89dcc05237a7b5f647f2f60b26af33f6c0733 Homepage: https://cran.r-project.org/package=ageg Description: CRAN Package 'ageg' (Age Grouping Functions) Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates. Package: r-cran-agena.ai Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson, r-cran-httr, r-cran-openxlsx, r-bioc-rgraphviz Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-agena.ai_1.1.2-1.ca2604.1_all.deb Size: 234784 MD5sum: 859347dcd96f7f5397e2305277b53dc6 SHA1: b3ea335d14e6c8b0913a59486e3ffc446af343f0 SHA256: fb106ce92ec16a9f3eb45c08d02cdbc93a5ae32af42dbcc3210d4f2a7ce6810f SHA512: 90498db45290fabdd38dbc822564e726385fdd789ed3040345ad959fe7864e8e3832b2a209864667adef2ed29a60598f745442a5b670b4e36cea0385f8357523 Homepage: https://cran.r-project.org/package=agena.ai Description: CRAN Package 'agena.ai' (R Wrapper for 'agena.ai' API) An R wrapper for 'agena.ai' which provides users capabilities to work with 'agena.ai' using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to 'agena.ai' cloud or local API for calculations. Note: running calculations requires a valid 'agena.ai' API license (past the initial trial period of the local API). Package: r-cran-agepopdenom Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-glue, r-cran-terra, r-cran-sf, r-cran-here, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-rdhs, r-cran-tmb, r-cran-curl, r-cran-gstat, r-cran-haven, r-cran-crayon, r-cran-purrr, r-cran-countrycode Suggests: r-cran-sp, r-cran-tibble, r-cran-rstudioapi, r-cran-future, r-cran-future.apply, r-cran-pbmcapply, r-cran-knitr, r-cran-httr, r-cran-scales, r-cran-rmarkdown, r-cran-exactextractr, r-cran-pdist, r-cran-numderiv, r-cran-openxlsx2, r-cran-matrixstats, r-cran-testthat, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-agepopdenom_1.2.3-1.ca2604.1_all.deb Size: 683598 MD5sum: e2c53dd2b857359b5a35ac62505c713e SHA1: 1ff60598d314575a174a313961ab547d963b9b0f SHA256: 3197fe496a24243144b6849681c23bc050a4252e5d4fc66298b5fff28f995c73 SHA512: b40d6b05d5577027adc60a4055587793115fd7e8c36498212201518c04b5becb61a6d107d6094ee6afb87c7e4f13cfe8d6d41183b9c6cced3b710c846e0197f2 Homepage: https://cran.r-project.org/package=AgePopDenom Description: CRAN Package 'AgePopDenom' (Model Fine-Scale Age-Structured Population Data usingOpen-Source Data) Automate the modelling of age-structured population data using survey data, grid population estimates and urban-rural extents. Package: r-cran-agetopicmodels Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5040 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtools, r-cran-magrittr, r-cran-proc, r-cran-reshape2, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-agetopicmodels_0.3.0-1.ca2604.1_all.deb Size: 5064306 MD5sum: 7771fdc362b769dc3dc3aa65e051b10f SHA1: dd557eb728b7b28eca5bc57037b6d4c845692340 SHA256: 5e1ef9366c03bb689c86b2ec0572f7738d711c42f5e838deefdb350700d661c0 SHA512: 28009de1cdad775f6c324b7244bb43b76f3924f02807fa8db62801425a713f656350034f5da2ac0f53f00b49fc4450b9eda1b7f7c76596db8c490d4275de337d Homepage: https://cran.r-project.org/package=AgeTopicModels Description: CRAN Package 'AgeTopicModels' (Inferring Age-Dependent Disease Topic from Diagnosis Data) We propose an age-dependent topic modelling (ATM) model, providing a low-rank representation of longitudinal records of hundreds of distinct diseases in large electronic health record data sets. The model assigns to each individual topic weights for several disease topics; each disease topic reflects a set of diseases that tend to co-occur as a function of age, quantified by age-dependent topic loadings for each disease. The model assumes that for each disease diagnosis, a topic is sampled based on the individual’s topic weights (which sum to 1 across topics, for a given individual), and a disease is sampled based on the individual’s age and the age-dependent topic loadings (which sum to 1 across diseases, for a given topic at a given age). The model generalises the Latent Dirichlet Allocation (LDA) model by allowing topic loadings for each topic to vary with age. References: Jiang (2023) . Package: r-cran-ageutils Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-litedown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ageutils_0.1.2-1.ca2604.1_all.deb Size: 54142 MD5sum: 837a1d547ed8860f7dc15bc9c0f385c5 SHA1: c2902995f8c6e319eb094e7797c18563197a7294 SHA256: cb5c33856ad73949ca9231b2b56565d7f3430acae35e656800f64e5c371f7697 SHA512: 1e15f0bf93cdd0ebca7d9202986216c122d850e07a02df04ca0384f3a9660025b02e94ca6a840d0b1990899642be92a602c6d663e389a16b90d0274dea89dd14 Homepage: https://cran.r-project.org/package=ageutils Description: CRAN Package 'ageutils' (Collection of Functions for Working with Age Intervals) Provides a collection of efficient functions for working with individual ages and corresponding intervals. These include functions for conversion from an age to an interval, aggregation of ages with associated counts in to intervals and the splitting of interval counts based on specified age distributions. Package: r-cran-agfh Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-goftest, r-cran-ks, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-agfh_0.2.1-1.ca2604.1_all.deb Size: 213966 MD5sum: a0605d0cd4c246a7da0a826a3981e55f SHA1: 2c80721fd7d06dffdcf51884c75d562bfed47e39 SHA256: affe461581bb4f967143c86d87e65ef8e3180095d5d155d9e031dc151a3e7608 SHA512: 303ed6619b760d2152db8d38df7142ecc5e2313b31f9b1b644c35ba424fe46fc189dc1f267a8d1ed1400dc7d0636372d97daa8d64869c8a43f35b3cdf70b7aa2 Homepage: https://cran.r-project.org/package=agfh Description: CRAN Package 'agfh' (Agnostic Fay-Herriot Model for Small Area Statistics) Implements the Agnostic Fay-Herriot model, an extension of the traditional small area model. In place of normal sampling errors, the sampling error distribution is estimated with a Gaussian process to accommodate a broader class of distributions. This flexibility is most useful in the presence of bounded, multi-modal, or heavily skewed sampling errors. Package: r-cran-agghoo Architecture: all Version: 0.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-class, r-cran-r6, r-cran-rpart, r-cran-fnn Suggests: r-cran-roxygen2, r-cran-mlbench Filename: pool/dists/resolute/main/r-cran-agghoo_0.1-0-1.ca2604.1_all.deb Size: 125182 MD5sum: 8032b3390981d092a57e1d0a848915fc SHA1: ff711c84182f4cbb8a7a7eb8e846f4eb61e1bcde SHA256: 5caeb218db7b2411d878bdb540d22a710f6756a61eedf8dc3dfb71ddd3d53632 SHA512: e39c44b29324c2779fd48c7d579ff296696a5c821c43895bb22236f4736dc7e61dc20e04405d631de096bd34476d70fb17c4c8415f51fe3574a4feced5ca6a30 Homepage: https://cran.r-project.org/package=agghoo Description: CRAN Package 'agghoo' (Aggregated Hold-Out Cross Validation) The 'agghoo' procedure is an alternative to usual cross-validation. Instead of choosing the best model trained on V subsamples, it determines a winner model for each subsample, and then aggregates the V outputs. For the details, see "Aggregated hold-out" by Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle (2021) published in Journal of Machine Learning Research 22(20):1--55. Package: r-cran-aggrecat Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3084 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-gofkernel, r-cran-purrr, r-cran-r2jags, r-cran-coda, r-cran-precrec, r-cran-mathjaxr, r-cran-cli, r-cran-vgam, r-cran-crayon, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-insight, r-cran-desctools, r-cran-mlmetrics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-pointblank, r-cran-janitor, r-cran-qualtrics, r-cran-here, r-cran-readxl, r-cran-readr, r-cran-lubridate, r-cran-forcats, r-cran-ggforce, r-cran-ggpubr, r-cran-ggridges, r-cran-rjags, r-cran-tidybayes, r-cran-tidyverse, r-cran-usethis, r-cran-nlme, r-cran-gt, r-cran-gtextras, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-aggrecat_1.1.0-1.ca2604.1_all.deb Size: 1910770 MD5sum: 7de28519d48b2f61f82a672b73493d73 SHA1: b44bfe1500514c193a89f10294767a0e17719a59 SHA256: 5db3f409b9c939e2ae70163715f556150ddc57b82ad544d31b69c40b4845d8ae SHA512: 79f445f4e7b0f0636678f513e638e57783d06fecc84db918216081ba62314e1912cd3646bf0c734f281c3e7724a02935bb072b43b4fb3c5fdf5be2fc5478b15e Homepage: https://cran.r-project.org/package=aggreCAT Description: CRAN Package 'aggreCAT' (Mathematically Aggregating Expert Judgments) The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. 'aggreCAT' provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) . The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data. Package: r-cran-aggregater Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tibble, r-cran-ncmisc Filename: pool/dists/resolute/main/r-cran-aggregater_0.1.1-1.ca2604.1_all.deb Size: 36710 MD5sum: d17821ef41211223c2c1e1d7aca9b9eb SHA1: 7fc0bc87872cd1551916e6e3ac87b982bbe55966 SHA256: 27634e754090b7acce11ee0569159845295db3865e9e540ba7a7a030cb2d9bf1 SHA512: 142a35d114b0ee2d2b34b69b5845d3814ab740f2dc5186948e5bad07fa8caa5b668781d1cf1f8ffb25f1031260410d46d77c7642b5843ac5b4affb1caa4c97a9 Homepage: https://cran.r-project.org/package=AggregateR Description: CRAN Package 'AggregateR' (Aggregate Numeric, Date and Categorical Variables) Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. There is also support for dummy variables in predictive contexts. Code has been completely re-written in data.table for computational speed. Package: r-cran-aggregation Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-aggregation_1.0.1-1.ca2604.1_all.deb Size: 15756 MD5sum: 692006ea26f11699bbf8b2f72592885c SHA1: ef64fa9f439bb5543c7caf6e5f8eb4ad9e22f6b1 SHA256: 26fab9cb07705d9a10ec08dbc1bd877c51b328c0c45db510fa54e48a59d8dd3e SHA512: 376f209c3a3576934c89834db5950642619f90fec26bdc196de1c2c9d6995063cfbe1066a1635beea58e5ef747dbae8b9af324b5a085efe1d3061c934ab2bc2f Homepage: https://cran.r-project.org/package=aggregation Description: CRAN Package 'aggregation' (p-Value Aggregation Methods) Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, )], and Sidak correction [Sidak, Z (1967, )]. Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), ]. Package: r-cran-aggtrees Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 895 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-broom, r-cran-car, r-cran-caret, r-cran-estimatr, r-cran-grf, r-cran-rpart, r-cran-rpart.plot, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aggtrees_2.1.0-1.ca2604.1_all.deb Size: 663358 MD5sum: d0f1780b3eeb9ca840d56a59a3b2a416 SHA1: 46293fcc6a653c54b8e968f2f41f9ce23e838f3a SHA256: 6be9e08e1ac947c67da3f7d5e3e5ce7c1813dee6c989ba9001e5bc6ec54cadc8 SHA512: 8bf2c3d9890cf4429545592250ffbadc985317af7a9bb1f30d04736d72256bf19883fc28349d0e9d0ba3306156fade3aa741ad56834e5799fa57f4ab2324ecd3 Homepage: https://cran.r-project.org/package=aggTrees Description: CRAN Package 'aggTrees' (Aggregation Trees) Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. 'aggTrees' allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. Please reference the use as Di Francesco (2022) . 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You want to take advantage of the "wisdom of the crowd" and combine these forecasts in some sensible way. This package provides implementations of several strategies, including geometric mean of odds, an extremized aggregate (Neyman, Roughgarden (2021) ), and "high-density trimmed mean" (Powell et al. (2022) ). 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Several methods are implemented considering additive and non-additive models. Package: r-cran-aghq Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvquad, r-cran-matrix, r-cran-rlang, r-cran-polynom, r-cran-numderiv Suggests: r-cran-trustoptim, r-cran-trust, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aghq_0.4.1-1.ca2604.1_all.deb Size: 328478 MD5sum: 54a37c1c581cf7e3d9e95a0320f77d1e SHA1: fb5be27b5d33db5c6ffa17ece8f3d7e500e08435 SHA256: e2c7171e376f3da683a575635a9ff6ac6a6fd6e4cf826dd222d9a1419942ae04 SHA512: c317682d69c10ba0d795ccc498b5848f2e7016dc1b97bed24986e0679cf51056d7d42fa8efb25d5fe6c773243029fa3ccaf0a0884c94e02a9f87362d6b55e4c0 Homepage: https://cran.r-project.org/package=aghq Description: CRAN Package 'aghq' (Adaptive Gauss Hermite Quadrature for Bayesian Inference) Adaptive Gauss Hermite Quadrature for Bayesian inference. The AGHQ method for normalizing posterior distributions and making Bayesian inferences based on them. Functions are provided for doing quadrature and marginal Laplace approximations, and summary methods are provided for making inferences based on the results. See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package" . Package: r-cran-aglm Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-assertthat, r-cran-mathjaxr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-faraway Filename: pool/dists/resolute/main/r-cran-aglm_0.4.1-1.ca2604.1_all.deb Size: 123602 MD5sum: c9c9d84474cdd743b7aa8cfcda7d145d SHA1: dfc1cda36e91a9504f03ba529976eaa8602e376b SHA256: fff582fefcdba5e85ebe901c91966a6ec7e06557b44b38f55bffdce65f2e0626 SHA512: 3f2a566e3bdf0b4a26ad41537e7216d07788fc45714cad4547cfed7f2e6dca2ccd80b81516aa3feb5e28b14613e80cada5b7f648cf829669805896479b42c072 Homepage: https://cran.r-project.org/package=aglm Description: CRAN Package 'aglm' (Accurate Generalized Linear Model) Provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) . Package: r-cran-agpower Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-agpower_0.1.2-1.ca2604.1_all.deb Size: 92424 MD5sum: e92f75d631052f3226ec2c719cc45c52 SHA1: f5ccc0bd8dc8a52d796b4df5ebfae2c9c05c661e SHA256: 4eabcc26b57ed78ce3cd770690a665e29e7fcf8e2c07f7a8cbbea4e490178140 SHA512: 2780fa6694fefe31117093435425f0733478b8c640a7690b51609a7e6030e16e9d9cc1f836cdb1fa2b836068aadbdcf8d5519204bf10166c1da08d69dd0f6670 Homepage: https://cran.r-project.org/package=agpower Description: CRAN Package 'agpower' (Recurrent Event Analysis Planning for Robust Andersen-Gill Model) Power and associated functions useful in prospective planning and monitoring of a clinical trial when a recurrent event endpoint is to be assessed by the robust Andersen-Gill model, see Lin, Wei, Yang, and Ying (2010) . The equations developed in Ingel and Jahn-Eimermacher (2014) and their consequences are employed. Package: r-cran-agpris Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-plyr, r-cran-sp, r-cran-spdep, r-cran-spacetime, r-cran-matrixcalc, r-cran-maxlik Suggests: r-cran-terra Filename: pool/dists/resolute/main/r-cran-agpris_2.0-1.ca2604.1_all.deb Size: 1168180 MD5sum: afb88387e3a353fe0fbb4c6ffc0ee771 SHA1: 460a497b2a49f82b03ce24cc6591508230e4bf72 SHA256: 4a40585c93b2ce5f06032a360ea17e2e37a63bc27fd282aa78e86aa789eb8283 SHA512: 084bbb3d7336d0a0de42ab61d01eb1a06bf1239ad32fc5e1b7adaeecb61e207305c7bf94efa700d8866ed9e9161385405265abac2f82d110baa7ca978b37a74d Homepage: https://cran.r-project.org/package=AGPRIS Description: CRAN Package 'AGPRIS' (AGricultural PRoductivity in Space) Functionalities to simulate space-time data and to estimate dynamic-spatial panel data models. Estimators implemented are the BCML (Elhorst (2010), ), the MML (Elhorst (2010) ) and the INLA Bayesian estimator (Lindgren and Rue, (2015) ; Bivand, Gomez-Rubio and Rue, (2015) ) adapted to panel data. The package contains functions to replicate the analyses of the scientific article entitled "Agricultural Productivity in Space" (Baldoni and Esposti (2021), )). Package: r-cran-agree Architecture: all Version: 0.5-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-miscf, r-cran-lme4, r-cran-r2jags, r-cran-coda Filename: pool/dists/resolute/main/r-cran-agree_0.5-3-1.ca2604.1_all.deb Size: 71986 MD5sum: 98b638f8114cd33f20b8da3e458872aa SHA1: 8efb8815837aca1a2ace8753166a473c1b1c380b SHA256: 4dc2b7569df85c8afb65b03184a6d6ecf6de0fe234c7df42e2163876db6be602 SHA512: f4051dab321fb4fb4c9410cea33b14361b1fb324229140269ea4e839c03845374bba6703083c59bec416008de32f8c6d6431ec9a1d2fa1ad0c2293a115054f28 Homepage: https://cran.r-project.org/package=agRee Description: CRAN Package 'agRee' (Various Methods for Measuring Agreement) Bland-Altman plot and scatter plot with identity line for visualization and point and interval estimates for different metrics related to reproducibility/repeatability/agreement including the concordance correlation coefficient, intraclass correlation coefficient, within-subject coefficient of variation, smallest detectable difference, and mean normalized smallest detectable difference. 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Package: r-cran-agricolae Architecture: all Version: 1.3-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-cluster, r-cran-algdesign Filename: pool/dists/resolute/main/r-cran-agricolae_1.3-7-1.ca2604.1_all.deb Size: 1173650 MD5sum: 53dff90009e4aac325b0d6df737f090c SHA1: 152e6350a50d5d8f1c9dd1d306bd9974d7329035 SHA256: ff744cc0a47ac65801e70092159880507d2450e0a7e1d804a62e8f59001a96d6 SHA512: 2ebd9d2a4a6a18c97aa2bc24e8a073079405344080fbc82ca4f8ab02c82ca3c7b638ab8bd3baf3c42a28d86cdfc870aa466e550918d537a2028e2c3966ffddae Homepage: https://cran.r-project.org/package=agricolae Description: CRAN Package 'agricolae' (Statistical Procedures for Agricultural Research) Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster. Package: r-cran-agricolaeplotr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3604 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-agricolae, r-cran-raster, r-cran-sp, r-cran-fieldhub, r-cran-tibble, r-cran-sf, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-stplanr, r-cran-ggspatial Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet Filename: pool/dists/resolute/main/r-cran-agricolaeplotr_1.0.0-1.ca2604.1_all.deb Size: 2298488 MD5sum: eaf7036b11d112d1f7a6a69d5c2d6e45 SHA1: 71f39269a6b498ae74b8847d9eb6553cf7fb3418 SHA256: 4f6a45f26d512d44b22ad77680150af5f0fe84bf9e6bd1b735991087f29c8355 SHA512: c7f92d89f23d0248c8160e4b3b6c28ef0883ab82f275688b1c354994b06c30706792bf1a4dc8ec2d12e7bd65ed53f99a350f1674dcccf6ec93b9dd8813370187 Homepage: https://cran.r-project.org/package=agricolaeplotr Description: CRAN Package 'agricolaeplotr' (Visualization of Design of Experiments from the 'agricolae'Package) Visualization of Design of Experiments from the 'agricolae' package with 'ggplot2' framework The user provides an experiment design from the 'agricolae' package, calls the corresponding function and will receive a visualization with 'ggplot2' based functions that are specific for each design. As there are many different designs, each design is tested on its type. The output can be modified with standard 'ggplot2' commands or with other packages with 'ggplot2' function extensions. Package: r-cran-agridat Architecture: all Version: 1.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3932 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-aer, r-cran-agricolae, r-cran-betareg, r-cran-broom, r-cran-car, r-cran-coin, r-cran-corrgram, r-cran-desplot, r-cran-dplyr, r-cran-effects, r-cran-emmeans, r-cran-equivalence, r-cran-frf2, r-cran-gam, r-cran-gge, r-cran-ggplot2, r-cran-gnm, r-cran-gstat, r-cran-hh, r-cran-knitr, r-cran-lattice, r-cran-latticeextra, r-cran-lme4, r-cran-lucid, r-cran-mapproj, r-cran-maps, r-cran-mass, r-cran-mcmcglmm, r-cran-metafor, r-cran-mgcv, r-cran-nlme, r-cran-nullabor, r-cran-ordinal, r-cran-pbkrtest, r-cran-pls, r-cran-pscl, r-cran-qicharts, r-cran-qtl, r-cran-reshape2, r-cran-rmarkdown, r-cran-sp, r-cran-spats, r-cran-survival, r-cran-testthat, r-cran-vcd Filename: pool/dists/resolute/main/r-cran-agridat_1.26-1.ca2604.1_all.deb Size: 3304428 MD5sum: 820e27c460b2ed45927f79521c1eec9a SHA1: 9171f71c866679659bf06061f8f8a53254dd1f62 SHA256: 4d1e273076e437ea4911a7d51fda3ffa9a953420274672cf1a79e8d4dd3dbbcf SHA512: b0adef93eebf85906060b956ed5ec5c521bda62179e5e1fbced929b351b273504f7d5746ea49e11054ffca6d1102e99019c0c0f564bc301569a47d1e659efb32 Homepage: https://cran.r-project.org/package=agridat Description: CRAN Package 'agridat' (Agricultural Datasets) Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more. 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Functions cover outlier detection using Interquartile Range (IQR) fence, Z-score, modified Z-score (Hampel identifier), Grubbs test and Dixon Q-test with consensus flagging; missing data pattern analysis and mechanism classification (Missing Completely At Random/Missing At Random/Missing Not At Random (MCAR/MAR/MNAR)) via Little's test; normality testing using Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov, Lilliefors, Pearson chi-square and Jarque-Bera tests; homogeneity of variance via Bartlett, Levene and Fligner-Killeen tests; independence of errors via Durbin-Watson, Breusch-Godfrey and Wald-Wolfowitz runs tests; experimental design validation for Completely Randomised Design (CRD), Randomised Complete Block Design (RCBD), Latin Square Design (LSD) and factorial designs; qualitative variable consistency checks; and automated HyperText Markup Language (HTML) report generation. Designed to align with Findable, Accessible, Interoperable and Reusable (FAIR) data principles. Methods follow Gomez and Gomez (1984, ISBN:978-0471870920) and Montgomery (2017, ISBN:978-1119492443). Package: r-cran-agrifeature Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-agrifeature_1.0.3-1.ca2604.1_all.deb Size: 24830 MD5sum: 713bcee7935610c46df6a326a86aad5a SHA1: 0ce2d65413a13e7cc14a60b8d1245a381d5d924a SHA256: b82c4e561a895e6bdba48ab35d66c355264407dea4aba14385fd352704e5fed5 SHA512: 35abb01af2732a251c672bc44d236049a85b5f1108c698be7a7c261155e177358c326f027dd18e725f582c00994b8b4ddc8d3ebe50b56670017a4bd9c83684db Homepage: https://cran.r-project.org/package=agrifeature Description: CRAN Package 'agrifeature' (Agriculture Image Feature) Functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) . Package: r-cran-agrireg Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-drc, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-broom, r-cran-broom.mixed, r-cran-emmeans, r-cran-nlme, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lmertest, r-cran-car, r-cran-multcomp Filename: pool/dists/resolute/main/r-cran-agrireg_0.1.0-1.ca2604.1_all.deb Size: 197622 MD5sum: 161d89a40cb545811173fe3cde58593f SHA1: 06cb35b1a4b30277e43ec8f4f7857d07f7caaccc SHA256: 046c02d6a1bac3b93ddea6a2b869062ed067f1ddf688896078d9b33a330e74ec SHA512: 44201b2140691e51abcc2fe7843c0f2f5926a7e20dcf84a4568164ec2cba344ca3d8bc0ebc9e283140342ae74bf8e5311fd915295c037fd21f95227e6de9fc31 Homepage: https://cran.r-project.org/package=agriReg Description: CRAN Package 'agriReg' (Linear and Nonlinear Regression for Agricultural Data) Fit, compare, and visualise linear and nonlinear regression models tailored to field-trial and dose-response agricultural data. Provides S3 classes for mixed-effects models (via 'lme4'), nonlinear growth curves (logistic, 'Gompertz', asymptotic, linear-plateau, quadratic), and four/five-parameter log-logistic dose-response models (via 'drc'). Includes automated starting-value heuristics, goodness-of-fit statistics, residual diagnostics, and 'ggplot2'-based visualisation. Methods are based on Bates and Watts (1988, ISBN:9780471816430), Ritz and others (2015) , and Bates and others (2015) . Package: r-cran-agritutorial Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmertest, r-cran-emmeans, r-cran-pbkrtest, r-cran-lattice, r-cran-nlme, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-agritutorial_0.1.5-1.ca2604.1_all.deb Size: 294456 MD5sum: 3c6df21e344cdefab03b742b027a8179 SHA1: 9369626300a0e36ccf55a5f757cf0819009e4b32 SHA256: 0aac247c073d8b9d52c250d8cdac2ff28c2f17ae91b5d8915c09c194b52c1ce5 SHA512: 85393e8d0775dbd36b9cb24ef4abb69349b681414a23da4f0d47d5e27f87a6c0b517ba334fdbded2c72267f33e0146aaff25bc3ea8a07878a6f115b21c326625 Homepage: https://cran.r-project.org/package=agriTutorial Description: CRAN Package 'agriTutorial' (Tutorial Analysis of Some Agricultural Experiments) Example software for the analysis of data from designed experiments, especially agricultural crop experiments. The basics of the analysis of designed experiments are discussed using real examples from agricultural field trials. A range of statistical methods using a range of R statistical packages are exemplified . The experimental data is made available as separate data sets for each example and the R analysis code is made available as example code. The example code can be readily extended, as required. Package: r-cran-agriutilities Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-psych, r-cran-dplyr, r-cran-tidyr, r-cran-lme4, r-cran-matrix, r-cran-ggpubr, r-cran-lmertest, r-cran-data.table, r-cran-magrittr, r-cran-emmeans, r-cran-ggrepel, r-cran-tibble, r-cran-rlang, r-cran-statgensta, r-cran-spats Suggests: r-cran-knitr, r-cran-lattice, r-cran-cluster, r-cran-rmarkdown, r-cran-agridat, r-cran-gt Filename: pool/dists/resolute/main/r-cran-agriutilities_1.2.3-1.ca2604.1_all.deb Size: 1698306 MD5sum: b01f519b67453f0180dad9e1b6880e4b SHA1: ae74672cf39c067b5750d07da972e59c5679317e SHA256: 1547fa242335f2cc4da0056b2bc33d3c9a96919fd8fb47ff0beab7c825f531dd SHA512: 3ec49bb6267d6712b4a80b933a51348e10ccb6e5681d927b468511d46136c28a1353f61c50d53bad7e158b65943f47c8acad02538adcbb565c53671d75e8f0bb Homepage: https://cran.r-project.org/package=agriutilities Description: CRAN Package 'agriutilities' (Utilities for Data Analysis in Agriculture) Utilities designed to make the analysis of field trials easier and more accessible for everyone working in plant breeding. It provides a simple and intuitive interface for conducting single and multi-environmental trial analysis, with minimal coding required. Whether you're a beginner or an experienced user, 'agriutilities' will help you quickly and easily carry out complex analyses with confidence. With built-in functions for fitting Linear Mixed Models, 'agriutilities' is the ideal choice for anyone who wants to save time and focus on interpreting their results. Some of the functions require the R package 'asreml' for the 'ASReml' software, this can be obtained upon purchase from 'VSN' international . Package: r-cran-agriwater Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-agriwater_1.0.2-1.ca2604.1_all.deb Size: 309518 MD5sum: 4b391e3430e168a7663b5ba45ceee759 SHA1: 9b242d4285981c05a7a62daa6be28097a67cd754 SHA256: 441be55f16b50b0f92b46d5569471689527b6eff6c59a93287ced1b92e34c1cd SHA512: 8b994f1dd7ebe63e006b07d599c2e2d8548fefbf8dfe4e4bc97f8c1bc9f3fa691a6959cf33981505e20cd374df872d05ac1f5c86df3fbb3cca15f28896e5fd4a Homepage: https://cran.r-project.org/package=agriwater Description: CRAN Package 'agriwater' (Evapotranspiration and Energy Fluxes Spatial Analysis) Spatial modeling of energy balance and actual evapotranspiration using satellite images and meteorological data. Options of satellite are: Landsat-8 (with and without thermal bands), Sentinel-2 and MODIS. Respectively spatial resolutions are 30, 100, 10 and 250 meters. User can use data from a single meteorological station or a grid of meteorological stations (using any spatial interpolation method). Silva, Teixeira, and Manzione (2019) . Package: r-cran-agrmt Architecture: all Version: 1.42.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-agrmt_1.42.19-1.ca2604.1_all.deb Size: 278202 MD5sum: 14a710a5976434ba591a6d15668f0774 SHA1: cbaf1e863e4d50d370a0352e724d3c8feecd1fa5 SHA256: a1122e7333bba4fefa9bca7685402b50879d0444446a0adc2952eff406c94a28 SHA512: 1207827583d50cdc82bb26a89b9664a81ef1169118a3c0e14348f3ef0e92bc510cacc0893d247ab165a84e4e2b4d5827e746456dbebd5a1efa58ac7d1ee54bcb Homepage: https://cran.r-project.org/package=agrmt Description: CRAN Package 'agrmt' (Calculate Concentration and Dispersion in Ordered Rating Scales) Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes. Package: r-cran-agrobox Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-stringr, r-cran-agricolae, r-cran-pwr, r-cran-rlang, r-cran-openxlsx, r-cran-rstatix, r-cran-multcompview Suggests: r-cran-kableextra, r-cran-magick, r-cran-tinytex, r-cran-cli, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-agrobox_0.3.0-1.ca2604.1_all.deb Size: 102488 MD5sum: 10bb9894cf6c715249b34f44f9a07a5b SHA1: 20dc863791b3db51887459ddaad5e2fb6ccfb491 SHA256: a264b5a5a47fdb6d22d7b18236706785de3245ba03062ac33e3d1350c8ea4f0b SHA512: 29062a910f53c3944e19777cc78b5d266a41264dbd36fc2a8ba4213bfbad13d5af3e176edbfea765839bf1a8adf0c2ea75aa3976bd0a7a91c19c3c59137c9473 Homepage: https://cran.r-project.org/package=agrobox Description: CRAN Package 'agrobox' (Data Visualization and Statistical Tools for AgroindustrialExperiments) Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform one-way and two-way ANOVA with post-hoc tests (Tukey HSD and Duncan MRT), Welch ANOVA for heteroscedastic data, and the Games-Howell post-hoc test as a robust alternative when variance homogeneity fails. Normality of residuals is assessed with the Shapiro-Wilk test and homoscedasticity with the Fligner-Killeen test; the appropriate statistical path is selected automatically based on these diagnostics. Coefficients of variation and statistical power (via one-way ANOVA power analysis) are reported alongside the post-hoc letter display. High-level wrappers allow automated multi-variable analysis with optional clustering by one or two experimental factors, with support for custom level ordering and relabeling. Results are returned as 'ggplot2' boxplots with mean and letter annotations, wide-format summary tables ready for publication or LaTeX rendering, and structured decision summaries for rapid agronomic interpretation. Direct export to Excel spreadsheets and high-resolution image tables is also supported. Functions follow methods widely used in agronomy, field trials, and plant breeding. Key references: Tukey (1949) ; Duncan (1955) ; Welch (1951) ; Games and Howell (1976) ; Shapiro and Wilk (1965) ; Fligner and Killeen (1976) ; Cohen (1988, ISBN:9781138892899); Wickham (2016, ISBN:9783319242750) for 'ggplot2'; see also 'agricolae' and 'rstatix' . Version en espanol: Conjunto de herramientas para el analisis estadistico, visualizacion y generacion de reportes en ensayos agroindustriales y agricolas. Incluye ANOVA univariado y bifactorial con pruebas post-hoc (Tukey HSD y Duncan MRT), ANOVA de Welch para datos heterocedasticos y la prueba post-hoc de Games-Howell como alternativa robusta cuando falla la homogeneidad de varianzas. La normalidad de residuos se evalua con la prueba de Shapiro-Wilk y la homogeneidad de varianzas con la prueba de Fligner-Killeen; la ruta estadistica apropiada se selecciona automaticamente segun estos diagnosticos. Se reportan coeficientes de variacion y potencia estadistica junto con las letras de separacion de medias. Los envoltorios de alto nivel permiten analisis multivariable automatizado con agrupamiento opcional por uno o dos factores experimentales, con soporte para orden y etiquetado personalizado de niveles. Los resultados se devuelven como boxplots con anotaciones de medias y letras, tablas resumen en formato ancho listas para publicacion o renderizado en LaTeX, y resumenes de decision para interpretacion agronomica rapida. Tambien se soporta exportacion directa a Excel e imagenes de alta resolucion para informes tecnicos. Package: r-cran-agror Architecture: all Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1647 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-ggplot2, r-cran-nortest, r-cran-lme4, r-cran-crayon, r-cran-lmtest, r-cran-emmeans, r-cran-multcomp, r-cran-ggrepel, r-cran-mass, r-cran-cowplot, r-cran-multcompview, r-cran-rcolorbrewer, r-cran-drc, r-cran-dunn.test, r-cran-gtools, r-cran-gridextra Suggests: r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-agror_1.3.7-1.ca2604.1_all.deb Size: 1601632 MD5sum: 8be4f9018394cfeb412ebf9e6ff42b98 SHA1: 89a99e92eaba8c8470e05b3161a316b7a547856c SHA256: f027a3a3b07134692344006ec412cec548a9daa4b0728717bcf86dcd6698f524 SHA512: be7b8bade5b795e50848a2bbff4775acd9cc1c97620f79174ea7b7ea980cdd17a2425620fc16dfe27044129d54f781e9461dc7e10852da1f8089afb5191ea0a5 Homepage: https://cran.r-project.org/package=AgroR Description: CRAN Package 'AgroR' (Experimental Statistics and Graphics for Agricultural Sciences) Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), ). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) . Package: r-cran-agroreg Architecture: all Version: 1.2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 717 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-ggplot2, r-cran-boot, r-cran-minpack.lm, r-cran-dplyr, r-cran-rcompanion, r-cran-broom, r-cran-egg, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-agroreg_1.2.11-1.ca2604.1_all.deb Size: 670458 MD5sum: a8e99431e99e70a965426c0ff86ec4ed SHA1: 298929f149549adcb0ae9c01580f8610f35b1aed SHA256: 113ad9869f4a1d786e05e5e4d01e1ff926e7ced8f483476bb823a6d5df09cef8 SHA512: de588e74272b4e2ac9f6fc9c11b1ff874fce7633efd579d6bc24061401519ae40067b122eefb45dbd1fe3920472c7c4f2bbcbbcd491306ccfbb3445262844245 Homepage: https://cran.r-project.org/package=AgroReg Description: CRAN Package 'AgroReg' (Regression Analysis Linear and Nonlinear for Agriculture) Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). ). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically. Package: r-cran-agrostab Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-agrostab_0.1.0-1.ca2604.1_all.deb Size: 92374 MD5sum: fa00359f31d4d30947be87b2a7c19cd7 SHA1: eb25f020371cad22182fc2b97f415a6f0232f21a SHA256: 86e583909500cc5d260e89ef34bfcac73150bc5dc4c12a71e242830083490637 SHA512: 8d25fba4d786333417b7c3ebc6b44d236724031216cd73b25451617aaeceeb2d64b6987ea918cdb1ea44ef14b4f3f9b21f4a2217f09dc91e547910bee79bddf3 Homepage: https://cran.r-project.org/package=agrostab Description: CRAN Package 'agrostab' (Stability Analysis for Agricultural Research) Statistical procedures to perform stability analysis in plant breeding and to identify stable genotypes under diverse environments. It is possible to calculate coefficient of homeostaticity by Khangildin et al. (1979), variance of specific adaptive ability by Kilchevsky&Khotyleva (1989), weighted homeostaticity index by Martynov (1990), steadiness of stability index by Udachin (1990), superiority measure by Lin&Binn (1988) , regression on environmental index by Erberhart&Rassel (1966) , Tai's (1971) stability parameters , stability variance by Shukla (1972) , ecovalence by Wricke (1962), nonparametric stability parameters by Nassar&Huehn (1987) , Francis&Kannenberg's parameters of stability (1978) . Package: r-cran-agrotech Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-xlsx, r-cran-readxl, r-cran-ggrepel, r-cran-nortest, r-cran-lmtest, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-agrotech_1.0.2-1.ca2604.1_all.deb Size: 256504 MD5sum: 966c43df259373650e55c5e165fb720a SHA1: f7096377cea69ff22e5a93008c08c37f7a98f32f SHA256: 2b8724ed9708baf900811011576adc5a5ee6eb8ce646ed6b29ac5494c47256f8 SHA512: 1b1b32d52288d6331e075b8884d35e363ffaf972a0d25724bf248a0b10965acaff56159ace46b0134e4d1e4d7475e2184d45c034f4fe14398237b84c7161bec6 Homepage: https://cran.r-project.org/package=AgroTech Description: CRAN Package 'AgroTech' (Data Analysis of Pesticide Application Technology) In total it has 7 functions, three for calculating machine calibration, which determine application rate (L/ha), nozzle flow (L/min) and amount of product (L or kg) to be added. to the tank with each sprayer filling. Two functions for graphs of the flow distribution of the nozzles (L/min) in the application bar and, of the temporal variability of the meteorological conditions (air temperature, relative humidity of the air and wind speed). Two functions to determine the spray deposit (uL/cm2), through the methodology called spectrophotometry, with the aid of bright blue (Palladini, L.A., Raetano, C.G., Velini, E.D. (2005), ) or metallic markers (Chaim, A., Castro, V.L.S.S., Correles, F.M., Galvão, J.A.H., Cabral, O.M.R., Nicolella, G. (1999), ). The package supports the analysis and representation of information, using a single free software that meets the most diverse areas of activity in application technology. Package: r-cran-ags Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-ags_1.0.1-1.ca2604.1_all.deb Size: 389580 MD5sum: 4e6122a29e0ce0fc744694c6899fe948 SHA1: 7ee300c0aecf26f24c11ff0d78e8f810b1ddf1b0 SHA256: 0be62b75ee646a16d728d4ea3a2865cb53f026f9b8c000ee5a350aeea6c0e932 SHA512: 436b071235701839e9d7b717bf321acef52c4c34d5b65cce097e1bd5fdfcfc54b0204fffd49bfe48ea32d99e1bbb80af253ddf5c4e9f67bd0b6c42f6aec97833 Homepage: https://cran.r-project.org/package=ags Description: CRAN Package 'ags' (Crosswalk Municipality and District Statistics in Germany) Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR). 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The package enables users to specify 'h2o' as an engine for several modeling methods. 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Package: r-cran-ahmbook Architecture: all Version: 0.2.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-unmarked, r-cran-mvtnorm Suggests: r-cran-coda, r-cran-fields, r-cran-raster, r-cran-sp, r-cran-spdep Filename: pool/dists/resolute/main/r-cran-ahmbook_0.2.12-1.ca2604.1_all.deb Size: 2230808 MD5sum: fceb7dcc45d44716d160ab34e18606c5 SHA1: 494248f4c51b032ed882b0561f5fc19cffb9073f SHA256: 44b3dfea5377d38719d80b2c71177abe0a5c3438e8a14e02621747e1b4c178e3 SHA512: 3bcd1315b3140d803dea5615b167e20d517e26a74c44dec371a13b35e98942db01e52728d37de0b3c62da87ec4fe8b0718564f5907e21b7fe09014d2b2dfd825 Homepage: https://cran.r-project.org/package=AHMbook Description: CRAN Package 'AHMbook' (Functions and Data for the Book 'Applied Hierarchical Modelingin Ecology' Vols 1 and 2) Provides functions to simulate data sets from hierarchical ecological models, including all the simulations described in the two volume publication 'Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS' by Marc Kéry and Andy Royle: volume 1 (2016, ISBN: 978-0-12-801378-6) and volume 2 (2021, ISBN: 978-0-12-809585-0), . 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The package consists in two functions allowing to perform the assimilation of observed discharges via the Ensemble Kalman filter or the Particle filter as described in Piazzi et al. (2021) . Package: r-cran-airgriwrm Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1895 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite, r-cran-png, r-cran-rlang, r-cran-zlib Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-airgriwrm_0.7.0-1.ca2604.1_all.deb Size: 1336790 MD5sum: fa2c617bd94ae2cbc1b2b706d8f59feb SHA1: 64598e648686d491cea23ff3b80f7101b3f85365 SHA256: 750dee81c57aef5d649341105d1de174d6a37a192034af237112c38843922e56 SHA512: e39a5c4c52733613bfd3b3cdd407ac6ec9ede10e7ab5a216080f81b78763d1504cc1fb0670cfbbfeab41d20c810f3bcd3cc14552bf1fe107dcca31c4294f27df Homepage: https://cran.r-project.org/package=airGRiwrm Description: CRAN Package 'airGRiwrm' ('airGR' Integrated Water Resource Management) Semi-distributed Precipitation-Runoff Modeling based on 'airGR' package models integrating human infrastructures and their managements. Package: r-cran-airgrteaching Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7093 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dygraphs, r-cran-markdown, r-cran-plotrix, r-cran-shiny, r-cran-shinyjs, r-cran-xts Suggests: r-cran-airgrdatasets, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-htmlwidgets Filename: pool/dists/resolute/main/r-cran-airgrteaching_0.3.5-1.ca2604.1_all.deb Size: 3510588 MD5sum: e6580e95de5107e24efcf1ad5ed68eaf SHA1: 64b1372a45ece90343f8ddc83a3c6a062e16eb2e SHA256: c637fa8cdaf8236690ea1f73d990c2570b8ef6c770c998cbb4e3fca575560149 SHA512: 0e07cff8e0944765a024bd4f88c9ee4169259825a0c79ea300b93e4e78f94df47aa65f80252771d33068e2e157fbd5b9c65648b4be5810ec33ab348c37d35d08 Homepage: https://cran.r-project.org/package=airGRteaching Description: CRAN Package 'airGRteaching' (Teaching Hydrological Modelling with the GR Rainfall-RunoffModels ('Shiny' Interface Included)) Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables. Package: r-cran-airly Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-reshape2, r-cran-tibble Suggests: r-cran-testthat, r-cran-httptest, r-cran-covr Filename: pool/dists/resolute/main/r-cran-airly_0.1.0-1.ca2604.1_all.deb Size: 118068 MD5sum: fa46f1e9980291885fecf99d2a7e632e SHA1: f71a527726dfbc1c98c888a80e4daef6a9994165 SHA256: 868b3f5388c6ac26a59f50ede7d7981c319fdfc8ce10cd3ac1c9105ddf69585d SHA512: 1c7852ed2c827afee9f57f9ee3a1af872a7cf960643febefb729728e9bc7a537e60e38b6d6c2914a9c4b29cf803f79b8c8ac587d8d50e05d6019af1bc6e0f57b Homepage: https://cran.r-project.org/package=aiRly Description: CRAN Package 'aiRly' (R Wrapper for 'Airly' API) Get information about air quality using 'Airly' API through R. Package: r-cran-airmonitor Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dygraphs, r-cran-leaflet, r-cran-lubridate, r-cran-magrittr, r-cran-mazamacoreutils, r-cran-mazamarollutils, r-cran-mazamatimeseries, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyselect, r-cran-xts Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-airmonitor_0.4.3-1.ca2604.1_all.deb Size: 989458 MD5sum: fea710bcaedcf879773e3a5247a7cafd SHA1: bc105b9b48bb367157afac3aaccf796f2242a542 SHA256: 561732aff05832369a288ea29e29aac7fc1bd38fc6ee61fe1e5a5a23af3b9f18 SHA512: e770f02c42adc4e66141d8387d4c138d9f8ab12ed2b41d493fb9d566fd7de643a326aeacb90a15f3cf099e57f81d04d73ab39ecd6c834a0a932adb6688af7f51 Homepage: https://cran.r-project.org/package=AirMonitor Description: CRAN Package 'AirMonitor' (Air Quality Data Analysis) Utilities for working with hourly air quality monitoring data with a focus on small particulates (PM2.5). A compact data model is structured as a list with two dataframes. A 'meta' dataframe contains spatial and measuring device metadata associated with deployments at known locations. A 'data' dataframe contains a 'datetime' column followed by columns of measurements associated with each "device-deployment". Algorithms to calculate NowCast and the associated Air Quality Index (AQI) are defined at the US Environmental Projection Agency AirNow program: . Package: r-cran-airnow Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-lintr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-airnow_0.1.1-1.ca2604.1_all.deb Size: 59644 MD5sum: 3d884d74ab098863716464f3a64fa06a SHA1: dee2a9d89435f6694b854ee100d62ef293436676 SHA256: 4d93332a4beff1dcda70efd146db2aa0d8b9bd75bfaa1973a2b65559087f5d62 SHA512: 8b286da2239fc864415356d36710ec13f2ca54364507850c36acae3815487f85817e803bd16cf87af7d61c4daecf9d5a63ef0a2ba372cc01bb6abd8b7870b08b Homepage: https://cran.r-project.org/package=airnow Description: CRAN Package 'airnow' (Retrieve 'AirNow' Air Quality Observations and Forecasts) Retrieve air quality data via the 'AirNow' API. Package: r-cran-airportproblems Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-plotly Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-airportproblems_0.1.0-1.ca2604.1_all.deb Size: 335040 MD5sum: 85a7fa8a5db4a828de5f78faed4b3a83 SHA1: e947dcec11fa1e5949323684fde6af271f10c3e5 SHA256: 7f37cc0bbb9312a38aa7ce1f513aa58beda6ca53d1559ff5cc97433a6a51a6c7 SHA512: 2402f4b46aaa302930b198ff5fec93f43d0da76135a35d6f1b84df6c6f286c93aa99b0bb0f80d06d17d1fc5867d264fc5c94efbc7f5766646db05b4bdd717f55 Homepage: https://cran.r-project.org/package=AirportProblems Description: CRAN Package 'AirportProblems' (Analysis of Cost Allocation for Airport Problems) Airport problems, introduced by Littlechild and Owen (1973) , are cost allocation problems where agents share the cost of a facility (or service) based on their ordered needs. Valid allocations must satisfy no-subsidy constraints, meaning that no group of agents contributes more than the highest cost of its members (i.e., no agent is allowed to subsidize another). A rule is a mechanism that selects an allocation vector for a given problem. This package computes several rules proposed in the literature, including both standard rules and their variants, such as weighted versions, rules for clones, and rules based on the agents’ hierarchy order. These rules can be applied to various problems of interest, including the allocation of liabilities and the maintenance of irrigation systems, among others. Moreover, the package provides functions for graphical representation, enabling users to visually compare the outcomes produced by each rule, or to display the no-subsidy set. In addition, it includes four datasets illustrating different applications and examples of airport problems. For a more detailed explanation of all concepts, see Thomson (2024) . Package: r-cran-airportr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-airportr_0.1.3-1.ca2604.1_all.deb Size: 370990 MD5sum: 120927e271e3729b8f82a1a4814b9a2f SHA1: 874f9a6127d9873382150683bd2fd29fcf7edaf0 SHA256: f696ae71ca834c6c16fb228f15cd287ae0cba8c3c44999a299120906ee7f2316 SHA512: 12c52f29aea0d8c9124605c9a7cb291c0c2493763ed11ae16182708bacef8c41c4d0063eadfb973e4144c5baf413f1493c945c526b3871c7139df8eabb932b1e Homepage: https://cran.r-project.org/package=airportr Description: CRAN Package 'airportr' (Convenience Tools for Working with Airport Data) Retrieves open source airport data and provides tools to look up information, translate names into codes and vice-verse, as well as some basic calculation functions for measuring distances. Data is licensed under the Open Database License. Package: r-cran-airports Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 817 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-airports_0.1.0-1.ca2604.1_all.deb Size: 791488 MD5sum: b18ef2c273b6b3c1684a74252bb5f3f6 SHA1: 2e0c06d5c812b8734cee074c35c5b6fcf975f838 SHA256: 21216d31e2a3ac2156b2c44e9d44988d5ad27149027c5b34212a5ebab8485aaa SHA512: 661e414622a22d9afa72cc2b4c2bac11f4d1715326d7c6554a86a2e1bb6be673694a848dd89fa54619d31968d872d5036f9dabdc8459818293e94967a5901760 Homepage: https://cran.r-project.org/package=airports Description: CRAN Package 'airports' (Data on Airports) Geographic, use, and property related data on airports. Package: r-cran-airqualityes Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3808 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-airqualityes_1.0.0-1.ca2604.1_all.deb Size: 3810782 MD5sum: d32fa86919349abf1fd9015cac4e4432 SHA1: 713b1f654aaf008333261935e3c41befde8a16d1 SHA256: bd9e064e00f90994ac7a9c7325a1ae2ff7069434b502d4b98c4b13460f024cb4 SHA512: 7ddfc3e6a2b4587ff4647a88a08eedd88dcad0826479171e8b475427d25a8b1cbd0a51d6aa3d960e2ea79eafc2d4864c7df98a2adc5b9ce0f9a721b392ac7767 Homepage: https://cran.r-project.org/package=airqualityES Description: CRAN Package 'airqualityES' (Air Quality Measurements in Spain from 2011 to 2018) These dataset contains daily quality air measurements in Spain over a period of 18 years (from 2001 to 2018). The measurements refer to several pollutants. These data are openly published by the Government of Spain. The datasets were originally spread over a number of files and formats. Here, the same information is contained in simple dataframe for convenience of researches, journalists or general public. See the Spanish Government website for more information. Package: r-cran-airr Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-readr, r-cran-stringi, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-airr_1.6.1-1.ca2604.1_all.deb Size: 565572 MD5sum: ce20a2030384d8646470ffc5e00a4302 SHA1: 8ab540e3bcd057e13d467ccaaa5d3d92ee32b348 SHA256: 55a67a7d4feb65d184eeebbdf21acb0c84b810c55e7051892260ccc0581f8234 SHA512: c9d0fc4b847570e81054faa5928cff1665acc17e8b14ec4d9d82654f6ccaa03ed88039ebe90aec0ffd04f42bb50cacb2d3b45b3171a18aa99b3157f180cf34b3 Homepage: https://cran.r-project.org/package=airr Description: CRAN Package 'airr' (AIRR Data Representation Reference Library) Schema definitions and read, write and validation tools for data formatted in accordance with the AIRR Data Representation schemas defined by the AIRR Community . Package: r-cran-airscreen Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-airscreen_0.1.0-1.ca2604.1_all.deb Size: 61982 MD5sum: a9109972346f552ab5b3747e7b92d31e SHA1: 0e26440abc326ee0c7788e8e48c37b37f03905db SHA256: c1f62ed29efd5cdc4de8cbfebcff3696fe4b0be241ad3b54f5f99f78262c6c0a SHA512: a2ed8a56f36e1d64a5af73307144da9e80c24ae1901e571586884f005fa8971a7a335b7c918a0299ebac9149fb4cc6d7e0bfd307779fc6cdb6859c75f6dea965 Homepage: https://cran.r-project.org/package=AirScreen Description: CRAN Package 'AirScreen' (Feature Screening via Adaptive Iterative Ridge (Air-HOLP andAir-OLS)) Implements two complementary high-dimensional feature screening methods, Adaptive Iterative Ridge High-dimensional Ordinary Least-squares Projection (Air-HOLP, suitable when the number of predictors p is greater than or equal to the sample size n) and Adaptive Iterative Ridge Ordinary Least Squares (Air-OLS, for n greater than p). Also provides helper functions to generate compound-symmetry and AR(1) correlated data, plus a unified Air() front end and a summary method. For methodological details see Joudah, Muller and Zhu (2025) . Package: r-cran-airship Architecture: all Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-dt, r-cran-shinybusy, r-cran-plotly, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-stringr, r-cran-colourpicker, r-cran-shinywidgets, r-cran-shinydashboard, r-cran-scales, r-cran-cairo, r-cran-ggplot2, r-cran-rlang, r-cran-magrittr, r-cran-shinyjs, r-cran-data.table, r-cran-shinyalert, r-cran-vctrs, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggpubr Filename: pool/dists/resolute/main/r-cran-airship_1.4.3-1.ca2604.1_all.deb Size: 1900878 MD5sum: 018eb94b46adf9f3858d2908ff486552 SHA1: 8f2f8ceb42daa9aa965862071c6d60ea0064c30f SHA256: 0e41187bbc21fa5cbf1d06e4ea9b767edde56c3d9ec1d7dcace29c5fbd260077 SHA512: 18cb46922aaa8171e9e6a4271f63aa51fa665903e8dcaa52b5ca058de0b126e7089ed9479dde7227ac4c4cb52cfd73d077ed3ef0833f86391dff4c1f533c0993 Homepage: https://cran.r-project.org/package=airship Description: CRAN Package 'airship' (Visualization of Simulated Datasets with Multiple SimulationInput Dimensions) Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. or the vignettes on 'GitHub'. 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These functions can enhance the quality of title and abstract screenings while reducing the total screening time significantly. In addition, the package includes tools for quality assessment of title and abstract screenings, as described in Vembye, Christensen, Mølgaard, and Schytt (2025) . 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Package: r-cran-aisoph Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-survival Filename: pool/dists/resolute/main/r-cran-aisoph_0.4-1.ca2604.1_all.deb Size: 38188 MD5sum: a19420a51af8f695db7f116719a1c92d SHA1: 4a4b87d4de7d5adc40e7e7beb057c5870440b3de SHA256: 917e2fafda329456b7f1302f453c7fbefe2234b3ce5f52d515d665a9c4c3e552 SHA512: 3049c22966300b38eef02dc88a5fd9afcb64fecc9056f6dc5af800a85b0741e48e00af97d63f64b372f57623d78eb5fe79a7ab00e8d74a4ea0111ef63465f381 Homepage: https://cran.r-project.org/package=aisoph Description: CRAN Package 'aisoph' (Additive Isotonic Proportional Hazards Model) Nonparametric estimation of additive isotonic covariate effects for proportional hazards model. Package: r-cran-aiuq Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 923 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fftwtools, r-cran-supergauss, r-cran-plot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aiuq_0.5.3-1.ca2604.1_all.deb Size: 746228 MD5sum: 9fd2aa56fa193dc3eec8c1a09885fc20 SHA1: f84facfe0632471086ec92b60f6eb906673baefb SHA256: 0ff346b293eb4220d653af530bda254a6677ef49823d9875bebbe6cc4171c69b SHA512: 171935e8cd77b5a4f542dc6f9f34c1e6e607923e1f4423bb275cea6ff29184c420db19397ae4fbd73559d3dfc3f445709ce4e632099ac375533e4d5f053f772d Homepage: https://cran.r-project.org/package=AIUQ Description: CRAN Package 'AIUQ' (Ab Initio Uncertainty Quantification) Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), . 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Package: r-cran-ake Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ake_1.0.2-1.ca2604.1_all.deb Size: 106388 MD5sum: ab6b336846dcdd31d3894a0761398d5d SHA1: eedea44fbf3ad32089f14b9db545188d1d465478 SHA256: 823ab9b906101724af83b4a983ce12063ad0bdda21012e664233d08fde83d18a SHA512: 6c309f894d0d32a266c6aa0df3f4c170dd14de5544c1cfd5695146e040f47f88d715f21d8b0a7f8b6051ba88c82e6a2155ef15f48596ab4ea99e67a53baad380 Homepage: https://cran.r-project.org/package=Ake Description: CRAN Package 'Ake' (Associated Kernel Estimations) Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using associated kernels. The cross-validation technique and the local Bayesian procedure are also implemented for bandwidth selection. Package: r-cran-akiflagger Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-zoo, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-akiflagger_0.3.0-1.ca2604.1_all.deb Size: 42800 MD5sum: 92097e7b24d49278bdfe0c290070b4e8 SHA1: 2c87f56c25ee5e2fd4a8fb11635d6039da5ef9b5 SHA256: 1acc4d11e4961b80d7abab89e40658fefe8cb5e1210f09100eb4d95b656fc505 SHA512: e7c3a3bd88126e667cea9355f13c6ea85a9b73ab6de47fbf7af93dfa128a93a5613bb98ee0b215cb1ac0fa2c73779a1bd03d3e558c03addde1a84407c80f8c97 Homepage: https://cran.r-project.org/package=akiFlagger Description: CRAN Package 'akiFlagger' (Flags Acute Kidney Injury (AKI)) Flagger to detect acute kidney injury (AKI) in a patient dataset. Package: r-cran-akin Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-callr, r-cran-fastmatch, r-cran-listenv, r-cran-matrix, r-cran-rcppalgos, r-cran-data.table, r-cran-rverbalexpressions Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-akin_0.3.3-1.ca2604.1_all.deb Size: 124584 MD5sum: f468e35a8468c11ce36cba6b1550cacc SHA1: 9f8d03860e9f5e75c748334d77b8c42e476e8654 SHA256: f7bb4cb52f7e78cb2ce4cf19e6d6b9cf5b5d27b18ddf3358d06750611e8fa742 SHA512: 9362842d755f65d21956a53a756d56616b514e566b4726ca106e4f85d67fcc713a44113c15bff4a2e17fa6e9780d08de962b0dc9004d424534e13c79df8f2b61 Homepage: https://cran.r-project.org/package=akin Description: CRAN Package 'akin' (Functional Utilities for Data Processing) Covers several areas of data processing: batch-splitting, reading and writing of large data files, data tiling, one-hot encoding and decoding of data tiles, stratified proportional (random or probabilistic) data sampling, data normalization and thresholding, substring location and commonalities inside strings, and location and tabulation of amino acids, modifications or associated monoisotopic masses inside modified peptides. The extractor utility implements code from 'Matrix.utils', Varrichio C (2020), . Package: r-cran-akmbiclust Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-akmbiclust_0.1.0-1.ca2604.1_all.deb Size: 18796 MD5sum: c39488e3556dde1e73997884bcbac822 SHA1: 27a254601257bd60ef6f188a2c081d0daf88f60a SHA256: 406ad6e3bd08a9fcfb5b67e7109d21affbbe88bb91aeec29b412cc8021236ea9 SHA512: 3d34cb87e63a54247607fb8812845eb8e9ce95a35cbea9f1d29b80234d993e061b940259b1ba4ae86fb83b2762086a8ef3363a4d6638b05b199f203a7bf021bf Homepage: https://cran.r-project.org/package=akmbiclust Description: CRAN Package 'akmbiclust' (Alternating K-Means Biclustering) Implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) . Package: r-cran-alabama Architecture: all Version: 2025.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-alabama_2025.1.0-1.ca2604.1_all.deb Size: 72760 MD5sum: fac2813e7c7709442350484ebb170fcc SHA1: 08875f0cc5f9f6a41216b983df2ef0b59f66e1bc SHA256: 04db2f5d6c21776acb2d74c400b146b72578bc8b2fa6d3fbc9681e3719024b7a SHA512: 631d59bd79fd5ffc8859c9d340225dbb7cb1876c597b98541f1360979f7024eecf1f4f79170b846d260fee5580a127a8c76413c067b4bdb9834edebe9ebc612d Homepage: https://cran.r-project.org/package=alabama Description: CRAN Package 'alabama' (Constrained Nonlinear Optimization) Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed. 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The package extends the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) to also include states with only a single district. The package also includes the Japanese 2022 redistricting files from the 47-Prefecture Redistricting Simulations of Miyazaki, Yamada, Yatsuhashi, and Imai (2022) . Package: r-cran-albatross Architecture: all Version: 0.3-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multiway, r-cran-cmls, r-cran-pracma, r-cran-lattice, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-albatross_0.3-9-1.ca2604.1_all.deb Size: 524122 MD5sum: f9a5bf97618e412c2577ebc49c91dd04 SHA1: dfcdf33d0c193a21cd45d20eed8b9073dc385661 SHA256: d11bfd8334fa4dfca9de79ef28d981ff6595aee79bc5bce185d63494a798231b SHA512: 70a4561cc1bb146c465d04f4bd47d4873cc503f6511779b228956d5f20fdbd1a48c74c2671696838766409202c39b0aa245d6db7057b37f99c261eca66404636 Homepage: https://cran.r-project.org/package=albatross Description: CRAN Package 'albatross' (PARAFAC Analysis of Fluorescence Excitation-Emission Matrices) Perform parallel factor analysis (PARAFAC: Hitchcock, 1927) on fluorescence excitation-emission matrices: handle scattering signal and inner filter effect, scale the dataset, fit the model; perform split-half validation or jack-knifing. Modified approaches such as Whittaker interpolation, randomised split-half, and fluorescence and scattering model estimation are also available. The package has a low dependency footprint and has been tested on a wide range of R versions. Package: r-cran-albersdown Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-gt, r-cran-bslib, r-cran-usethis, r-cran-yaml, r-cran-cli, r-cran-testthat, r-cran-viridislite, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-albersdown_1.0.0-1.ca2604.1_all.deb Size: 787134 MD5sum: dd0709f9ca0992db7fd5c6597da36da2 SHA1: 48f84439320543bb737d1468de6b8064f0801393 SHA256: c346abc31cb595940fa5e5607c83a564e39cd491acc699f9cc5ea2cb99e39e27 SHA512: 1f062a474705df5cd71472130934ac1440365a4dab136472a25030071eef4526ecfd99239f421639578f32d6e1cfb1d645b2f0cb553de75e07d46593901ba247 Homepage: https://cran.r-project.org/package=albersdown Description: CRAN Package 'albersdown' (Minimalist Theme and Vignette Kit for 'pkgdown' and R Markdown) Provides a minimalist 'ggplot2' theme, colour scales, and 'pkgdown' template built around a curated colour palette system inspired by Josef Albers' colour theory (Albers (1963, ISBN:978-0-300-17935-4) "Interaction of Color"). Includes helpers to apply consistent theming to 'ggplot2' plots, 'gt' tables, and 'bslib' Bootstrap 5 sites, along with one-command setup functions for adopting the style across an R package. Package: r-cran-albi Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-openxlsx, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-rlang, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-albi_0.1.9-1.ca2604.1_all.deb Size: 190302 MD5sum: fceda54f79021f0b67aea00fa8a7889b SHA1: 7b26ff5ae68b3046f0669a2a676ef310ceed209f SHA256: fbf17138441cf59fece37fe138078ff2c1552793caf930d62318717c4803ac22 SHA512: 0d0371cae8080ace2c8f3e7687b8a37347222bf6455679d0b56360c11553124233000e2aaeb63dbb9026c45b45c5ae0fe5e86269d378214f51bc00cfca3fc4f8 Homepage: https://cran.r-project.org/package=aLBI Description: CRAN Package 'aLBI' (Estimating Length-Based Indicators for Fish Stock) Provides tools for estimating length-based indicators from length frequency data to assess fish stock status and manage fisheries sustainably. Implements methods from Cope and Punt (2009) for data-limited stock assessment and Froese (2004) for detecting overfishing using simple indicators. Key functions include: FrequencyTable(): Calculate the frequency table from the collected and also the extract the length frequency data from the frequency table with the upper length_range. A numeric value specifying the bin width for class intervals. If not provided, the bin width is automatically calculated using Wang (2020) formula. FreqTM(): Creates a frequency distribution table for fish length data across multiple months using a consistent length class structure. The bin width is determined by either a custom value or Wang's formula, applied uniformly across all months. The function dynamically detects and renames columns to 'Month' and 'Length' from the input dataframe. The maximum observed length is included as part of the last class, with the upper bound set to the smallest multiple of the bin width greater than or equal to the maximum length. Months can be converted to dates using a configurable day and year, with dates assigned sequentially in 'day.month.year' format (e.g., 15.01.26). FishPar(): Calculates length-based indicators (LBIs) proposed by Froese (2004) such as the percentage of mature fish (Pmat), percentage of optimal length fish (Popt), percentage of mega spawners (Pmega), and the sum of these as Pobj. This function also estimates confidence intervals for different lengths, visualizes length frequency distributions, and provides data frames containing calculated values. FishSS(): Makes decisions based on input from Cope and Punt (2009) and parameters calculated by FishPar() (e.g., Pobj, Pmat, Popt, LM_ratio) to determine stock status as target spawning biomass (TSB40) and limit spawning biomass (LSB25), and selectivity. LWR(): Fits and visualizes length-weight relationships using linear regression, with options for log-transformation and customizable plotting. Package: r-cran-albopictus Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-albopictus_0.5-1.ca2604.1_all.deb Size: 128718 MD5sum: ace404bdfc1a5fa2b37bd47a2310191c SHA1: 79eb68e5963598145764cf69bca7119371e89c4d SHA256: e5595831534be1a823972eb3526fd9a1b4be6d1cc940d459272f95c569ec9dfe SHA512: 9e7626a4977d8986cac48580687f6a3541e2d2a11872705cf3eb2dd38a776823933d89d5c5d981884d0592ca113985bcb50804b1c803cb9e2098d2e6d03d255a Homepage: https://cran.r-project.org/package=albopictus Description: CRAN Package 'albopictus' (Age-Structured Population Dynamics Model) Implements discrete time deterministic and stochastic age-structured population dynamics models described in Erguler and others (2016) and Erguler and others (2017) . Package: r-cran-alcoholsurv Architecture: all Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sensitivitymv Suggests: r-cran-survival, r-cran-itos, r-cran-coin Filename: pool/dists/resolute/main/r-cran-alcoholsurv_0.7.2-1.ca2604.1_all.deb Size: 122192 MD5sum: 1d96da89b431a57ca95a256f5ab9a063 SHA1: fc704d510fcb392819139250af305d6891d390b1 SHA256: e32e96a629cceebd1a6ec724ead9a6a70b69849f44e4f9760388776a5d9d52f4 SHA512: 2e60a6a0915fdabb16a6587fac1d0b465a2afdf94d1355b75fd8fe5630d17ee9b8c0a96e617748cce1ec9aeced4e6e322354904111b6339a125a007047398b17 Homepage: https://cran.r-project.org/package=alcoholSurv Description: CRAN Package 'alcoholSurv' (Light Daily Alcohol and Longevity) Contains data from an observational study concerning possible effects of light daily alcohol consumption on survival and on HDL cholesterol. It also replicates various simple analyses in Rosenbaum (2025) . Finally, it includes new R code in wgtRankCef() that implements and replicates a new method for constructing evidence factors in observational block designs in Rosenbaum (2026) ). Package: r-cran-ald Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ald_1.3.1-1.ca2604.1_all.deb Size: 44126 MD5sum: 2c7b9a9e801bd0e624d85eae5199575a SHA1: 6ca91d4e8df7e6eb6e409dd501dd2dedfb6d5a9e SHA256: 4c67ae02b8bba1bf8fc63c105d1a7677afb0c6529cf2e2ed27d58089b3973470 SHA512: 3eaa16047e1253ebd43bb0d3f2f2911a003400e5bb54e199243daddd68d1e1fea70549c4b740f4e6dc901e2bcb987a6dd10032ba600a81aa9fee24e0e021a5ff Homepage: https://cran.r-project.org/package=ald Description: CRAN Package 'ald' (The Asymmetric Laplace Distribution) It provides the density, distribution function, quantile function, random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for the three parameter Asymmetric Laplace Distribution defined in Koenker and Machado (1999). This is a special case of the skewed family of distributions available in Galarza et.al. (2017) useful for quantile regression. Package: r-cran-aldex3 Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-lme4, r-cran-lmertest, r-cran-mass, r-cran-nlme, r-cran-abind, r-cran-matrixstats Suggests: r-cran-rbeta2009, r-cran-testthat, r-cran-lmtest, r-cran-sandwich, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aldex3_1.0.2-1.ca2604.1_all.deb Size: 137756 MD5sum: c5962cc0290ecb905c4024062e7edc3f SHA1: a2e693f3f61cef6d60d9458c56e812a0dd077fa0 SHA256: 6cd54e9c17cf61c4ff923a8f8442763ee204d30cd59625d6a4115325df80b8ef SHA512: 301e4e66dff44455d8a6f442a3a1202e1e7a2fc6c631f6c5c4fb258ec810445ec1df475ab6fe64bbf8aefe18b6f82366aad61eb80df3e9c4e7b272201471cd64 Homepage: https://cran.r-project.org/package=ALDEx3 Description: CRAN Package 'ALDEx3' (Linear Models for Sequence Count Data) Provides scalable generalized linear and mixed effects models tailored for sequence count data analysis (e.g., analysis of 16S or RNA-seq data). Uses Dirichlet-multinomial sampling to quantify uncertainty in relative abundance or relative expression conditioned on observed count data. Implements scale models as a generalization of normalizations which account for uncertainty in scale (e.g., total abundances) as described in Nixon et al. (2025) and McGovern et al. (2025) . Package: r-cran-aldqr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hyperbolicdist, r-cran-sn Filename: pool/dists/resolute/main/r-cran-aldqr_1.0-1.ca2604.1_all.deb Size: 40230 MD5sum: 3950d53c67f0edc8ebd984e1d28f43a9 SHA1: a83e0be2f3373663cb1da5a69ade7129e827f884 SHA256: 911dc99f273a962a0a784cc5b3c4e85a68b7087d74b04f616e458d6923c3f5f1 SHA512: da0739925037d768a456d88dc4cb1a9f99a9cc7ab3d0783907c2476079d63d5a9fb51df780782b608d09eb145064303a17dc075bb8dd3dc8472caaf9cae3f277 Homepage: https://cran.r-project.org/package=ALDqr Description: CRAN Package 'ALDqr' (Quantile Regression Using Asymmetric Laplace Distribution) EM algorithm for estimation of parameters and other methods in a quantile regression. Package: r-cran-aldvmm Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-checkmate, r-cran-optimx, r-cran-formula, r-cran-sandwich, r-cran-lmtest Suggests: r-cran-knitr, r-cran-kableextra, r-cran-markdown, r-cran-tinytex, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-bookdown, r-cran-xtable, r-cran-ggplot2, r-cran-scales, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-aldvmm_0.9.0-1.ca2604.1_all.deb Size: 419838 MD5sum: 2e570f74abcefaff7a241376af11c206 SHA1: 09126dde00f9da0a78b5f9df5540ef11cee6bc74 SHA256: 6c31c342fdf00b31f99970a04da8810fefd8534c7a8d6c90a338e8b15b7e18f6 SHA512: b47143f5c405942fb9a3c774df88a7cbd99ff0cd392588687faad074d54e473a80397c3f8fcfcdd7524a44743ae8f591e0fae89f08592679c0f6417b54b7f793 Homepage: https://cran.r-project.org/package=aldvmm Description: CRAN Package 'aldvmm' (Adjusted Limited Dependent Variable Mixture Models) The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) using normal component distributions and a multinomial logit model of probabilities of component membership. Package: r-cran-ale Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-insight, r-cran-patchwork, r-cran-progressr, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-staccuracy, r-cran-stringr, r-cran-tidyr, r-cran-univariateml Suggests: r-cran-knitr, r-cran-mgcv, r-cran-nnet, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-yaimpute Filename: pool/dists/resolute/main/r-cran-ale_0.5.3-1.ca2604.1_all.deb Size: 1557778 MD5sum: de8a939ed505b8499e0c52caf21f4aae SHA1: 6a470650f8368ada4920511d31a02e4a12d50609 SHA256: d79dc0d2eb95a5cc2f94c319197a0ce6b52b43a6c0fb187d147d88fb611ccc5e SHA512: 9ec660c1fcad71a3c25af9f3711f8a40bafef81bc6bb63adbb4a1c4d7f9c3443c970b4876bcc50cabb0fd2cb61783f0e36d2499685882612d8db8fe85c85b487 Homepage: https://cran.r-project.org/package=ale Description: CRAN Package 'ale' (Interpretable Machine Learning and Statistical Inference withAccumulated Local Effects (ALE)) Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package reimplements the algorithms for calculating ALE data and develops highly interpretable visualizations for plotting these ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. . Package: r-cran-alfq Architecture: all Version: 1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-plyr, r-cran-caret, r-cran-seqinr, r-cran-lattice, r-cran-randomforest, r-cran-rocr, r-cran-reshape2, r-cran-bio3d Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-alfq_1.3.6-1.ca2604.1_all.deb Size: 264460 MD5sum: 95a5ef2f880c095c7f8f6172108b94c3 SHA1: f959bf0407336a8fca7180d1adda6d1429f38963 SHA256: 2b757908bac201652b1bda54a16d463901cb9ddd87f057b9f260d34150a09f64 SHA512: 43685e0fcf39b1f4f32664160edc41144acd02eea1181c980c5bf635b493e675a156410c4aa4c481dc0704f9b1446a7b95be2d616be10ac9973a2943f96782f1 Homepage: https://cran.r-project.org/package=aLFQ Description: CRAN Package 'aLFQ' (Estimating Absolute Protein Quantities from Label-Free LC-MS/MSProteomics Data) Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample. Package: r-cran-alfr Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-stringr Suggests: r-cran-devtools, r-cran-httptest, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-remotes, r-cran-spelling, r-cran-fs Filename: pool/dists/resolute/main/r-cran-alfr_1.2.1-1.ca2604.1_all.deb Size: 52416 MD5sum: 686de682d474b336d02ac2c95faeaf8b SHA1: c6c59ee01098c7d05f827f7d3e70fd69e87202de SHA256: 769dc171f5e681bcf7fa8a8fa6d016cffd1728de294fac1e3ea3759f599a0c42 SHA512: 55e2c81fbedead31fc1afddcff5ee52b247604d3bacfecb5ec1fbdd68c251cc9b6a0103b97cfe1d339fce0cdb9f1356629b2b001e9d9b9458ae535c63941aadf Homepage: https://cran.r-project.org/package=alfr Description: CRAN Package 'alfr' (Connectivity to 'Alfresco' Content Management Repositories) Allows you to connect to an 'Alfresco' content management repository and interact with its contents using simple and intuitive functions. You will be able to establish a connection session to the 'Alfresco' repository, read and upload content and manage folder hierarchies. For more details on the 'Alfresco' content management repository see . Package: r-cran-alfred Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-lubridate, r-cran-jsonlite, r-cran-rlang, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-covr Filename: pool/dists/resolute/main/r-cran-alfred_0.2.1-1.ca2604.1_all.deb Size: 19872 MD5sum: d5a5dafa0a9d8cce6df9e128014243e6 SHA1: 9e883c4c89eec0864eb0093d1561b5d990154ebf SHA256: dd829ffb6eadb6cb9863564b2a211a01b2030c679393e157154c29c732e40d1e SHA512: d1ed2a706d79c3b0b8613eca1d1b1515723861bf90aa87b2ebb9ff06b33681779d9795fa55e8a7fd7f64df8889baea66b05fc63b7e2886c277c19a0b4072dd49 Homepage: https://cran.r-project.org/package=alfred Description: CRAN Package 'alfred' (Downloading Time Series from ALFRED Database for VariousVintages) Provides direct access to the ALFRED () and FRED () databases. Its functions return tidy data frames for different releases of the specified time series. Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis. Package: r-cran-algaeclassify Architecture: all Version: 2.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-curl, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-algaeclassify_2.0.6-1.ca2604.1_all.deb Size: 238856 MD5sum: 75b8787ab1ffee4c96edc0267d688fce SHA1: 80e6101d9ef1a8cd49151ed5bcc497c7510a54ae SHA256: a684967f7c4cbeb4b93abba15f40b2c74286c34ea84410c17a2b49df239158f0 SHA512: f819ce436b55d25adad540d903884d3960b23a5f8e486ea0ba19c982edd825666a9bc4a84efc887dc9fc8c6cda721c1b411af57f6405a4f31d1a5450a29afd55 Homepage: https://cran.r-project.org/package=algaeClassify Description: CRAN Package 'algaeClassify' (Tools to Query the 'Algaebase' Online Database, StandardizePhytoplankton Taxonomic Data, and Perform Functional GroupClassifications) Functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 ) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 2.0.0 and later includes new functions for querying the 'algaebase' online taxonomic database (www.algaebase.org), however these functions require a valid API key that must be acquired from the 'algaebase' administrators. Note that none of the 'algaeClassify' authors are affiliated with 'algaebase' in any way. Taxonomic names can also be checked against a variety of taxonomic databases using the 'Global Names Resolver' service via its API (). In addition, currently accepted and outdated synonyms, and higher taxonomy, can be extracted for lists of species from the 'ITIS' database via its JSON web service API. The 'algaeClassify' package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). DISCLAIMER: This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use. Package: r-cran-algebraic.dist Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1246 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-algebraic.dist_1.0.0-1.ca2604.1_all.deb Size: 865510 MD5sum: e08f56832a16f7f78eaf631530d88cb3 SHA1: 3760ad35662167aa922178857172852c722b8578 SHA256: 7004ba9901e17c11da7685fd3c56546c1dd7ea5d2da5b1e12557ff6a2ed61d2c SHA512: 6bcb693def91dcac21abf86f7a6c9576320162db6a359f59e0ce07f20d09990e85ad93546dbaa1ecd3ceefcf0a7a61c945291a2cd2ca4d7649563a6cd0b133a8 Homepage: https://cran.r-project.org/package=algebraic.dist Description: CRAN Package 'algebraic.dist' (Algebra over Probability Distributions) Provides an algebra over probability distributions enabling composition, sampling, and automatic simplification to closed forms. Supports normal, exponential, gamma, Weibull, chi-squared, uniform, beta, log-normal, Poisson, multivariate normal, empirical, and mixture distributions with algebraic operators (addition, subtraction, multiplication, division, power, exp, log, min, max) that automatically simplify when mathematical identities apply. Includes closed-form MVN conditioning (Schur complement), affine transformations, mixture marginals/conditionals (Bayes rule), and limiting distribution builders (CLT, LLN, delta method). Uses S3 classes for distributions and R6 for support objects. Package: r-cran-algebraic.mle Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 923 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algebraic.dist, r-cran-boot, r-cran-mvtnorm, r-cran-mass, r-cran-numderiv Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-tibble, r-cran-cdft Filename: pool/dists/resolute/main/r-cran-algebraic.mle_2.0.2-1.ca2604.1_all.deb Size: 480076 MD5sum: 167aa0303081fc38ac069ed52605ae4c SHA1: 76adabb7c44d1e99c92c21ec4166e7884bcb9f0e SHA256: ff9bf22b6780f03d44dda0424ed89ffb00ff523ab189becd7783279fc04065f0 SHA512: 4f9622306a34810aa8abe96888d58db498f0dd5f7a004b563dd705bf683365aeff6a6c5e81f0e19b685fc55de9949182310311a586f8beaed2f94af22d5a1beb Homepage: https://cran.r-project.org/package=algebraic.mle Description: CRAN Package 'algebraic.mle' (Algebraic Maximum Likelihood Estimators) The maximum likelihood estimator (MLE) is a technology: under regularity conditions, any MLE is asymptotically normal with variance given by the inverse Fisher information. This package exploits that structure by defining an algebra over MLEs. Compose independent estimators into joint MLEs via block-diagonal covariance ('joint'), optimally combine repeated estimates via inverse-variance weighting ('combine'), propagate transformations via the delta method ('rmap'), and bridge to distribution algebra via conversion to normal or multivariate normal objects ('as_dist'). Supports asymptotic ('mle', 'mle_numerical') and bootstrap ('mle_boot') estimators with a unified interface for inference: confidence intervals, standard errors, AIC, Fisher information, and predictive intervals. For background on maximum likelihood estimation, see Casella and Berger (2002, ISBN:978-0534243128). For the delta method and variance estimation, see Lehmann and Casella (1998, ISBN:978-0387985022). Package: r-cran-algeriapis Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-algeriapis_0.1.0-1.ca2604.1_all.deb Size: 304932 MD5sum: 5c49fd4c1b7e2ec370eea5baf8e88544 SHA1: 4803bf5843f7ac0292ef4e1707d2c2d7fa5abf58 SHA256: b51296ffe98d16a1d0b50a1fd19d773838258102215bf9cacf1d08da56ef7a9c SHA512: 20c374b2934cc3cd0794889b752049d18bb01f21b6bf2dc0fbd860a80ac460a405b9444edb215336285cf07973a795484eb002630ad90c8599885dc87446bf8a Homepage: https://cran.r-project.org/package=AlgeriAPIs Description: CRAN Package 'AlgeriAPIs' (Access Algerian Data via Public APIs) Provides functions to access data from public RESTful APIs including 'World Bank API' and 'REST Countries API', retrieving real-time or historical information related to Algeria. The package enables users to query economic indicators and international demographic and geopolitical statistics in a reproducible way. It is designed for researchers, analysts, and developers who require reliable and programmatic access to Algerian data through established APIs. For more information on the APIs, see: 'World Bank API' and 'REST Countries API' . Package: r-cran-algo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-algo_0.1.0-1.ca2604.1_all.deb Size: 17920 MD5sum: b73901a7aa66b0c757b4bb29011b16aa SHA1: ab5f83aac26d2e67af46f18e60c917cf96d3955c SHA256: 33496840e3807cbdb40df01b5d0088d16839649e52760c4b259f369d2b35411b SHA512: 82d3d81844be69a1b4addc1a42d03dfadff4a3ca7c1ec5edf204bf2c978d383db2c67668c8908f9a8d0a1141e8eb8b9aecb63f76770f48ed1996d44081e46a25 Homepage: https://cran.r-project.org/package=algo Description: CRAN Package 'algo' (Implement an Address Search Auto Completion Menu on 'Shiny' TextInputs Using the 'Algolia Places' 'Javascript' Library) Allows the user to implement an address search auto completion menu on 'shiny' text inputs. This is done using the 'Algolia Places' 'JavaScript' library. See . Package: r-cran-algorithmia Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-httr, r-cran-rjson Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-runit Filename: pool/dists/resolute/main/r-cran-algorithmia_0.3.0-1.ca2604.1_all.deb Size: 222286 MD5sum: 7d2bb71f66bc487bf581ad698ced9704 SHA1: fdb7c0d6e350f8d903807c7ee23065f404591757 SHA256: aff1cce48c69c17a77d8ad62860b2f0512a8381b9f17578e6f4c8dcf01a747bf SHA512: 6e145dcdcef12b647a17d49a43c7bcef10524a9cbf851feb91c0cb00c971c78b4075efdd1e1fe0afa0349ef6de802fce39fdd2f0312bdcc8a2ebe399c85bfd10 Homepage: https://cran.r-project.org/package=algorithmia Description: CRAN Package 'algorithmia' (Allows you to Easily Interact with the Algorithmia Platform) The company, Algorithmia, houses the largest marketplace of online algorithms. This package essentially holds a bunch of REST wrappers that make it very easy to call algorithms in the Algorithmia platform and access files and directories in the Algorithmia data API. To learn more about the services they offer and the algorithms in the platform visit . More information for developers can be found at . Package: r-cran-alien Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-helpersmg, r-cran-rlang, r-cran-tidyr Suggests: r-cran-bh, r-cran-knitr, r-cran-purrr, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-alien_1.0.2-1.ca2604.1_all.deb Size: 138708 MD5sum: 4bee4195ff504373beb2afd90d151e21 SHA1: df9821d623952333f959ac2045c48f8e0f703037 SHA256: 24843267e1124b2ecf00f7b6de48d7fd097967bd6bb1b428329c8e57a72c6742 SHA512: a2fe7c5eec8124bbbacf7eb4bf0314d5ba6c21901943aee24e1ae721c32c80e41bfe8424db720ab871c22a0fae9554a6846a44dd115e43e61cff20370ca807c7 Homepage: https://cran.r-project.org/package=alien Description: CRAN Package 'alien' (Estimate Invasive and Alien Species (IAS) Introduction Rates) Easily estimate the introduction rates of alien species given first records data. It specializes in addressing the role of sampling on the pattern of discoveries, thus providing better estimates than using Generalized Linear Models which assume perfect immediate detection of newly introduced species. Package: r-cran-align Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matlab Filename: pool/dists/resolute/main/r-cran-align_0.1.0-1.ca2604.1_all.deb Size: 30014 MD5sum: ca9b286edcdcd5e36cd52daef4b2f246 SHA1: 067974a1fae925e3834f15b2a0f7c36cb1855bee SHA256: 87c1ea997083bf39c89043c0c27d782c79d6e0e891f3e9a0f6b8722575aed0dd SHA512: 4f28eab38741fe08cb76c7d6500dfdfe7efc0e85a755822b519f55a4a06837ce4d6b63654280db7c68c384123608375c5d86628a42596ae51d6093bca0761bbb Homepage: https://cran.r-project.org/package=align Description: CRAN Package 'align' (A Modified DTW Algorithm for Stratigraphic Time Series Alignment) A dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published 'MATLAB' code by Hay et al. (2019) . The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review). Package: r-cran-alignlv Architecture: all Version: 0.1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-mirt, r-cran-lavaan, r-cran-magrittr, r-cran-purrr, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-dorng, r-cran-doparallel, r-cran-foreach, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-alignlv_0.1.0.0-1.ca2604.1_all.deb Size: 91328 MD5sum: ef84d49a1bd600dff00986e8490a429f SHA1: af3f7af92d6e90795d05cefe76a72387b5d9b4aa SHA256: d50c423482f277a410ef4166f8e3b110a2c7115d5c17167468ff4d56d8926fb7 SHA512: ad133397b6b329fab519273a233d14c8a68b9ba3db474bec02167d3fce374ad7f64b79ee6ffb9796b88732bebe1d6481b70e215dbf045ab93a52d4627b9b9dc0 Homepage: https://cran.r-project.org/package=AlignLV Description: CRAN Package 'AlignLV' (Multiple Group Item Response Theory Alignment Helpers for'lavaan' and 'mirt') Allows for multiple group item response theory alignment a la 'Mplus' to be applied to lists of single-group models estimated in 'lavaan' or 'mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments. Package: r-cran-alkahest Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1323 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-matrix, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-alkahest_1.3.0-1.ca2604.1_all.deb Size: 1064498 MD5sum: 4f5f121cf4d65b769913ba9666cb37f8 SHA1: 83b2d68715a94a5d87acad0745c149f605c02498 SHA256: f50301077e3b1008e2483f2e392f727b01413f4df5caadf1e713c02d0d269b86 SHA512: 8688ed08473850f2e8ea7869a2776400fb9a7cbad54a5012353e1928a7ace00bf9d1eeed9189bdb4d8eae57b59fd6ace5717e0b94fb7e83a47184c9c4efa7172 Homepage: https://cran.r-project.org/package=alkahest Description: CRAN Package 'alkahest' (Pre-Processing XY Data from Experimental Methods) A lightweight, dependency-free toolbox for pre-processing XY data from experimental methods (i.e. any signal that can be measured along a continuous variable). This package provides methods for baseline estimation and correction, smoothing, normalization, integration and peaks detection. Baseline correction methods includes polynomial fitting as described in Lieber and Mahadevan-Jansen (2003) , Rolling Ball algorithm after Kneen and Annegarn (1996) , SNIP algorithm after Ryan et al. (1988) , 4S Peak Filling after Liland (2015) and more. Package: r-cran-allcontributors Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1534 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clipr, r-cran-curl, r-cran-gert, r-cran-gh, r-cran-gitcreds, r-cran-magrittr Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-allcontributors_0.2.3-1.ca2604.1_all.deb Size: 481850 MD5sum: ebe7975cf9a75d13b09ab714540e861e SHA1: cbda707b4eba0e3fcf19173709748b75bb1afd49 SHA256: 2a2a25a0989301937b7bdd1abdc07c5e954b9ad0e46aa8cf9decbd74c37bd799 SHA512: aeea2bbd9cb6a2569f449357993ffbffe6c0c8b86617e86d935f2d9a33a7a06d4b10b030935edc918bda1281fb1774f1c1b563b32c4a2a0b7701ca7a943439a4 Homepage: https://cran.r-project.org/package=allcontributors Description: CRAN Package 'allcontributors' (Acknowledge all Contributors to a Project) Acknowledge all contributors to a project via a single function call. The function appends to a 'README' or other specified file(s) a table with names of all individuals who contributed via code or repository issues. The package also includes several additional functions to extract and quantify contributions to any repository. Package: r-cran-allehap Architecture: all Version: 0.9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-allehap_0.9.9-1.ca2604.1_all.deb Size: 580034 MD5sum: 08943f0a553a28b832825ced907ccaad SHA1: 8348cedcdbeb4d711cd64698913bc812bffba730 SHA256: 7f1ac391fc20941fa14f87275db764a7ce7ba39a9df7b940a276f55f0dfe8cb9 SHA512: caf08ae538a7704e866fbd43ebbbc2f36673d1ece84b33d8fd0ffa7ec2c4a42ccfac03a05961d3cdb6af5156e42e079c843493f14494cf8efdd03d1f9b72a629 Homepage: https://cran.r-project.org/package=alleHap Description: CRAN Package 'alleHap' (Allele Imputation and Haplotype Reconstruction from PedigreeDatabases) Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated. Package: r-cran-allelematch Architecture: all Version: 2.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dynamictreecut Filename: pool/dists/resolute/main/r-cran-allelematch_2.5.5-1.ca2604.1_all.deb Size: 212262 MD5sum: 409231524186350519990d37b71d5411 SHA1: 2de8a5ba0ca978c10aabde6621ed0a109f2f2185 SHA256: adbe481195b31a4306cca7643af573ac2ad4680c6a8db2f8f9db749ca7e4feb6 SHA512: 7ae41951b0cdd5b85afd79dab2b0c291bf0c9f38935c281c35c8dd96747d8e2b3815a137ffedc7e6f95064ca5d4e2697378e261ee5601d077ec4fc16ce29433a Homepage: https://cran.r-project.org/package=allelematch Description: CRAN Package 'allelematch' (Identifying Unique Multilocus Genotypes where Genotyping Errorand Missing Data may be Present) Tools for the identification of unique of multilocus genotypes when both genotyping error and missing data may be present; targeted for use with large datasets and databases containing multiple samples of each individual (a common situation in conservation genetics, particularly in non-invasive wildlife sampling applications). Functions explicitly incorporate missing data and can tolerate allele mismatches created by genotyping error. If you use this package, please cite the original publication in Molecular Ecology Resources (Galpern et al., 2012), the details for which can be generated using citation('allelematch'). For a complete vignette, please access via the Data S1 Supplementary documentation and tutorials (PDF) located at . Package: r-cran-alleleretain Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pedigree Filename: pool/dists/resolute/main/r-cran-alleleretain_2.0.2-1.ca2604.1_all.deb Size: 350196 MD5sum: 0ef7bccab479db46ef9cf2670524b241 SHA1: 9972abb953fe0098cc25844c9dd395366f41da9a SHA256: b237c378680b548fc170280ab1f7bc214f958b935de19e0943e5631f083f73c1 SHA512: 1f83da1a2ee49bc59343b933d98ac355898aacd2da70d5da46fcff17d1532d0fae254d5ecf1133124d405950fea40a6219573c721cefcb6b0e12550a318e5c3a Homepage: https://cran.r-project.org/package=AlleleRetain Description: CRAN Package 'AlleleRetain' (Allele Retention, Inbreeding, and Demography) Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations. Package: r-cran-alleleshift Architecture: all Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-biodiversityr, r-cran-adegenet Suggests: r-cran-poppr, r-cran-mgcv, r-cran-dplyr, r-cran-ggplot2, r-cran-ggally, r-cran-ggforce, r-cran-ggrepel, r-cran-ggsci, r-cran-gggibbous, r-cran-gganimate, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-alleleshift_1.1-3-1.ca2604.1_all.deb Size: 573078 MD5sum: 3f3ff869e28d8bc06238c89484fe373e SHA1: 72a1548ddf6607fd11c313fa05b24accf0cfcf78 SHA256: 9fc596e348678523b9a5a0f329e68e731c4a9e2ab25e20746948304ae2ab9326 SHA512: 74e645b54ad5d81fad25c4db6a22c8b383ee3ea0e5a9c3a460cd0467114f9772d87b1a2cbe47f4c380438cb050c0e56a832d2408632a2453a1058450ffd57358 Homepage: https://cran.r-project.org/package=AlleleShift Description: CRAN Package 'AlleleShift' (Predict and Visualize Population-Level Changes in AlleleFrequencies in Response to Climate Change) Methods () are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include 'shift.dot.ggplot()', 'shift.pie.ggplot()', 'shift.moon.ggplot()', 'shift.waffle.ggplot()' and 'shift.surf.ggplot()' that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the 'gganimate' package. Function 'amova.rda()' shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis. Package: r-cran-allestimates Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-ggplot2, r-cran-survival, r-cran-tidyr, r-cran-stringr, r-cran-dplyr Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-allestimates_0.2.3-1.ca2604.1_all.deb Size: 273292 MD5sum: 9f9c6b80d91ce3f33943c9cd24337861 SHA1: 5d27acfcd7cdfde503a377d4b373bdc95e83cd65 SHA256: ee54143621cc0bfa62eb9e99f57e9b3aa1411b0ae7417cbef601721d3cc3d873 SHA512: e226cd8d7bf187ab4902185d367976a114a03b722dc8a70da6487be4bb6452c9fa32071386c9427e9ed47e95db2e3fff24a0d296c7f2c05a5bd089a9c7acf15b Homepage: https://cran.r-project.org/package=allestimates Description: CRAN Package 'allestimates' (Effect Estimates from All Models) Estimates and plots effect estimates from models with all possible combinations of a list of variables. It can be used for assessing treatment effects in clinical trials or risk factors in bio-medical and epidemiological research. Like Stata command 'confall' (Wang Z (2007) ), 'allestimates' calculates and stores all effect estimates, and plots them against p values or Akaike information criterion (AIC) values. It currently has functions for linear regression: all_lm(), logistic and Poisson regression: all_glm(), and Cox proportional hazards regression: all_cox(). Package: r-cran-allmetrics Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-allmetrics_0.2.1-1.ca2604.1_all.deb Size: 12884 MD5sum: cbcd340a8d69e4fbc670539c54553dce SHA1: ed83f1039054043da54b6f98034ae68768a2f4c8 SHA256: 92a97c32cead4e2e9a7719838ce76d0c2ff583e13f1e363e602ee2abe53a396f SHA512: 5184ba3608e311324ac71c2570211b65de589a2599e66e0f82087e68997ec56fae2eb0ba1990418ea2f6b9d3467d30b3fc759528655015ac6a489f9afac7ae3d Homepage: https://cran.r-project.org/package=AllMetrics Description: CRAN Package 'AllMetrics' (Calculating Multiple Performance Metrics of a Prediction Model) Provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models' performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmott’s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) and Garai et al. (2024) . Package: r-cran-allmt Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1588 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltable, r-cran-plyr, r-cran-readxl, r-cran-reshape2, r-cran-rio, r-cran-scales, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble Suggests: r-cran-bibtex, r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-allmt_0.1.1-1.ca2604.1_all.deb Size: 480288 MD5sum: 0371b1a771e2dd7c9cc7b419dec21661 SHA1: cc11074ceb21df51cce2738e8caf677b401d7bc3 SHA256: f44f018f60c888cd5b7054f5f04dd0a9baaa5a852fa92d4a1b7953da9287e31b SHA512: 753a82baebe25f733c54c6f7c7d7763fdbefc313e21918cffb9b5d7889da1144c5b803d5af20649af51da958f55aeb8ee60607ec3dce50a3e9068ce0ef5acf60 Homepage: https://cran.r-project.org/package=allMT Description: CRAN Package 'allMT' (Acute Lymphoblastic Leukemia Maintenance Therapy Analysis) Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level. Package: r-cran-allocation Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpfr Suggests: r-cran-quarto Filename: pool/dists/resolute/main/r-cran-allocation_0.1.0-1.ca2604.1_all.deb Size: 107146 MD5sum: fe0f701dfd5921403d66504ed7ca3e37 SHA1: 3e3b3828ca4e16fdd7390461113b3588ba68215f SHA256: 576dda41f4002d6dc461e08302a9ee274d51f01405bec13de616722cfe8e6cca SHA512: 43bd89d60915a57bb094744f7e097b9ec7768da81566a664386deeda5cfc1814bdb05f04721c157dcf0e22820849dadb3c35d346340a5554e579e26a72b0cf2c Homepage: https://cran.r-project.org/package=allocation Description: CRAN Package 'allocation' (Exact Optimal Allocation Algorithms for Stratified Sampling) Implements several exact methods for allocating optimal sample sizes when designing stratified samples. These methods are discussed in Wright (2012) and Wright (2017) . Package: r-cran-allofus Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1574 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-tidyr, r-cran-magrittr, r-cran-dplyr, r-cran-glue, r-cran-bigrquery, r-cran-purrr, r-cran-dbplyr, r-cran-sessioninfo, r-cran-rlang, r-cran-stringr, r-cran-dbi, r-cran-lifecycle, r-cran-bit64 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-dt, r-cran-googlesheets4, r-cran-tibble, r-cran-forcats, r-cran-gh, r-cran-sqlrender Filename: pool/dists/resolute/main/r-cran-allofus_1.2.0-1.ca2604.1_all.deb Size: 1002922 MD5sum: 08729526283801346edf99caa824539f SHA1: f541cdf9de945dd5383557024299592ccec5ad86 SHA256: 9fb8c1f1c2aabd149c39466dffe5f7e97dfab94dfa5597c6d1d0913c7777b6ff SHA512: 63c4714ae21b94eee88dbd898dabf34ccc83da72101beac888348d159f966a78223770b335b021fa14364dffb67585c064290141952054fbd77bceb75575239d Homepage: https://cran.r-project.org/package=allofus Description: CRAN Package 'allofus' (Interface for 'All of Us' Researcher Workbench) Streamline use of the 'All of Us' Researcher Workbench ()with tools to extract and manipulate data from the 'All of Us' database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of 'All of Us' tools and allowing for cohort creation via 'Atlas'. Improve reproducible and transparent research using 'All of Us'. Package: r-cran-allometry Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-allometry_0.1.1-1.ca2604.1_all.deb Size: 15830 MD5sum: 131fdfe534821eed52eddde6ce9fd61b SHA1: 3e2de4f43c914d8e9be2b822c07739509f366aa0 SHA256: aa2aaa7d618a5baba2adbb8ace2f4c0033a9e52ff980cefc71cabdcd2cd236ec SHA512: 122f969b93695101ab4a0f56570057f533bbc5f1bf5bf1e1a5c080f5f78e0fa6bf8aec43fd336dae0e3ee5d27f1ea5fc58ca3f65c363b59bb0559ff27e4d78c5 Homepage: https://cran.r-project.org/package=allometry Description: CRAN Package 'allometry' (Examples of Datasets on Allometry) Examples of datasets on allometry, the study of the relationship of biological traits to body size. This package contains the dataset of morphological measurement taken from 113 maritime earwigs (Anisolabis maritima) by Matsuzawa and Konuma (2025) . Package: r-cran-allomr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-allomr_0.3.0-1.ca2604.1_all.deb Size: 15480 MD5sum: 1f7417369a968a8bdf09892971fe9de2 SHA1: 736efb55e7a2435ff93773e9623108230e112400 SHA256: ad6363b225d2f104008d5dcf9c097e4af45c4dac7d121e5484aff72de8b7ece0 SHA512: ba1cc1ddd194ea24ec4b8afa638cd4bff428bbd04d55fe63704cf0405ec5d9f93fc29f18295e6b92120dfa91bb888f79814f2eac4c98db04d55ddac909a73c59 Homepage: https://cran.r-project.org/package=allomr Description: CRAN Package 'allomr' (Removing Allometric Effects of Body Size in MorphologicalAnalysis) Implementation of the technique of Lleonart et al. (2000) to scale body measurements that exhibit an allometric growth. This procedure is a theoretical generalization of the technique used by Thorpe (1975) and Thorpe (1976) . Package: r-cran-allspice Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4373 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-allspice_1.0.7-1.ca2604.1_all.deb Size: 1522308 MD5sum: 1cd788a5d237b8f213af6707b3d03042 SHA1: 2ae6ab91b5253eb6b26c00301634869b0f40b1e4 SHA256: d17cb0969b8a11f4f99435ab1ec01f4b213c107eff3118a5ebd889bf88551acb SHA512: d04af356abe86c785d12580453f2112fbeef6ab204c7cca476916367f767549f01a93917d2d8d28207fa4d6812f432fe32608d8227cf74ff8669a1339cb0596a Homepage: https://cran.r-project.org/package=Allspice Description: CRAN Package 'Allspice' (RNA-Seq Profile Classifier) We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, International Journal of Molecular Sciences 23:4574, . The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified. Package: r-cran-allspicer Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-allspicer_0.1.9-1.ca2604.1_all.deb Size: 43542 MD5sum: 1c2127245aa457c6913dd80ceb63b572 SHA1: 235ce2b550ab1a1119b00c3b84c9cf2471b210d5 SHA256: 764106088ed31abc2bf441aa5b063fe1e4296e05ac71e0aa504f288bd50f7927 SHA512: 9b6067bbe529ae65332a33c26d68daec44eb2bcafe9fcb68b456b0c395ba2314f66910f9bc3396a2e772a3bb3a444ed35e87d071b03b69ccee2b07eb17ef6862 Homepage: https://cran.r-project.org/package=ALLSPICER Description: CRAN Package 'ALLSPICER' (ALLelic Spectrum of Pleiotropy Informed Correlated Effects) Provides statistical tools to analyze heterogeneous effects of rare variants within genes that are associated with multiple traits. The package implements methods for assessing pleiotropic effects and identifying allelic heterogeneity, which can be useful in large-scale genetic studies. Methods include likelihood-based statistical tests to assess these effects. For more details, see Lu et al. (2024) . 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Package: r-cran-alone Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-forcats Filename: pool/dists/resolute/main/r-cran-alone_0.7-1.ca2604.1_all.deb Size: 55390 MD5sum: 15a969cc48f9fcf56f252ce1d4d0c595 SHA1: 19b46827cef20110571f67a17d3a825d756c4bc4 SHA256: 0c0388cc17eb59acce76e26984879a070c7e5bfc8ad5b8816a31546daf7ae742 SHA512: 322541e2a65b62dbb155ffff286bb22d7dfec7e225bacd83085eeb88ccb4910b3ea14c849e8052bac4c1f5dffa4286c454ba5716147b94e12d1e41acd7f7a5c3 Homepage: https://cran.r-project.org/package=alone Description: CRAN Package 'alone' (Datasets from the Survival TV Series Alone) A collection of datasets on the Alone survival TV series in tidy format. Included in the package are 4 datasets detailing the survivors, their loadouts, episode details and season information. 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Package: r-cran-alpha.correction.bh Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-alpha.correction.bh_0.1.0-1.ca2604.1_all.deb Size: 19008 MD5sum: 17bc75e5ac8f8923b64d78402854b11e SHA1: 4d4c9945fe2188ddbfdadf27ce66832b932dd63c SHA256: ad6b59659b6c5e6185443e4128624601d8651c5a5608b3d5cba479c05a7e1135 SHA512: 3b06a1ddb67507f8379df25e7c77037156670867c3399898c9af22262cba5237a676d17f99b6ea9c19a229bc724c170b1aaefa64c8e4621ff7b5ab36ccf472ec Homepage: https://cran.r-project.org/package=alpha.correction.bh Description: CRAN Package 'alpha.correction.bh' (Benjamini-Hochberg Alpha Correction) Provides the alpha-adjustment correction from "Benjamini, Y., & Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. 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All you need to do is get a free API key at . Then you can use the R interface to retrieve free equity information. Refer to the Alpha Vantage website for more information. 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The methods target applications in reliability and biomedical survival analysis. The package implements Bayesian estimation for the alpha-mixture methodology introduced in Asadi et al. (2019) . Package: r-cran-alr4 Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-effects Filename: pool/dists/resolute/main/r-cran-alr4_1.0.7-1.ca2604.1_all.deb Size: 642568 MD5sum: fbb44c6d86f56f96db387ac24acd96f1 SHA1: db93d67afc3976e137ed93c7ff234a704b45094a SHA256: 7b02da44ce2ea079d40d32d110f9e458466b5a4950e729639ff45c512c7865fd SHA512: 6c6fa79478d2c4212108c1ad0a5bcf081229766afc3678f7cb260766dc2aae51d0d0206ea2036752b58d050bd7855f85b7c11817396ed158dc8d9689bc3495bb Homepage: https://cran.r-project.org/package=alr4 Description: CRAN Package 'alr4' (Data to Accompany Applied Linear Regression 4th Edition) Datasets to Accompany S. Weisberg (2014), "Applied Linear Regression," 4th edition. Many data files in this package are included in the alr3 package as well, so only one of them should be used. Package: r-cran-als Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnls, r-cran-iso Filename: pool/dists/resolute/main/r-cran-als_0.0.7-1.ca2604.1_all.deb Size: 499782 MD5sum: 977939765e06ea3d51fb76cf87eeb877 SHA1: b85b9c99e3245bddbcfcfeb719b151a972049287 SHA256: db42c2005720f30a8bfcfdaceb45c01e25956bd2a89b630e97ee933cf9f8d91e SHA512: 4341530eeaadc11ffbe1fc4d0d32ff30466eaeed8df014c1475f6c2a68b14f8ff4f73fd4a274710e01633a236aba1175d1e797289eee7636b1c767b9bfb4c00c Homepage: https://cran.r-project.org/package=ALS Description: CRAN Package 'ALS' (Multivariate Curve Resolution Alternating Least Squares(MCR-ALS)) Alternating least squares is often used to resolve components contributing to data with a bilinear structure; the basic technique may be extended to alternating constrained least squares. Commonly applied constraints include unimodality, non-negativity, and normalization of components. Several data matrices may be decomposed simultaneously by assuming that one of the two matrices in the bilinear decomposition is shared between datasets. Package: r-cran-alscpc Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-alscpc_1.0-1.ca2604.1_all.deb Size: 24418 MD5sum: c39742a060018af9f6b9fbc0cf4c2277 SHA1: 264b5fe925f90b5b19f4f9408e75c0c447ae2a7d SHA256: 02ecf03bd09ecab62966c578f36d9368c14d6d76691982b3bdc27abd7ba2a693 SHA512: b2acfc63972f1915ad7f2060338635c30758adc461a9cbe31155999c5012b5b05e8a717dfa0c13e3923a9a0987b4266b1cfe686081a3faac479687e0447813eb Homepage: https://cran.r-project.org/package=ALSCPC Description: CRAN Package 'ALSCPC' (Accelerated line search algorithm for simultaneous orthogonaltransformation of several positive definite symmetric matricesto nearly diagonal form) Using of the accelerated line search algorithm for simultaneously diagonalize a set of symmetric positive definite matrices. Package: r-cran-alsi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-homals Suggests: r-cran-paran, r-cran-readxl, r-cran-openxlsx, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-alsi_0.2.0-1.ca2604.1_all.deb Size: 156710 MD5sum: c38c51fff14bed85044800b2cd53464d SHA1: f7c26b0478aaec3d556a982eaf48b43a3e9f2340 SHA256: c3cb491479bf43f91d26a1bd266e0e555fa6dba099b1c33f7499725c52fa1b45 SHA512: 64a3d9b9137e7bbf2713136be7bf16342a2dad51bb3368ee1e8daee1477bdb24aefa231c35c3f232a4ed2f004c269d1d8c8928fde3edbe56f0ad2cf3debfbe59 Homepage: https://cran.r-project.org/package=alsi Description: CRAN Package 'alsi' (Aggregated Latent Space Index for Binary, Ordinal, andContinuous Data) Provides three stability-validated pipelines for computing an Aggregated Latent Space Index (ALSI): a binary MCA pipeline (alsi_workflow()), an ordinal pipeline using homals alternating least squares optimal scaling (alsi_workflow_ordinal()), and a continuous ipsatized SVD pipeline (calsi_workflow()). 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Package: r-cran-altfuelr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2434 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-altfuelr_0.1.0-1.ca2604.1_all.deb Size: 2410238 MD5sum: b2ef421b549ad92b3492c4248b2b1078 SHA1: 4148596fae4664eb40df40eec1498c22bd4dbff5 SHA256: 6ee92b752babcc0944c4ab240ee8acc81b5cc8c5b171658c1b3a892af92e8845 SHA512: 5982a576c1eeee83f80c340bcfd24eecf15261a19b96d799b157af787dbc15def76be0c785679e6a75a193919fc07f847b9f43f8fab8ff93e21ddf8e1838ccdb Homepage: https://cran.r-project.org/package=altfuelr Description: CRAN Package 'altfuelr' (Provides an Interface to the NREL Alternate Fuels Locator) Provides a number of functions to access the National Energy Research Laboratory Alternate Fuel Locator API . 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Package: r-cran-altmeta Architecture: all Version: 4.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-lme4, r-cran-matrix, r-cran-metafor Filename: pool/dists/resolute/main/r-cran-altmeta_4.3.1-1.ca2604.1_all.deb Size: 554742 MD5sum: ef438f65b7f7b122d63fc37b37f86994 SHA1: 7d054ae1a1f507c717983813ac9bec05deb8a95e SHA256: bd804f1dd4542b5d1de18c3a20e7ecf79291d40835d28baff47a31423e135ac6 SHA512: c315f4dcbef8eb9425499eebb203ba6f160eeb05e963fa8c7ca2d895ca7ef4214f4b468a79b7a87e2532c2a4505198af3ef3f8e4107bdf84489eb7754b239086 Homepage: https://cran.r-project.org/package=altmeta Description: CRAN Package 'altmeta' (Alternative Meta-Analysis Methods) Provides alternative statistical methods for meta-analysis, including: - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences (Chu et al., 2012 ) - tests and measures for between-study heterogeneity (Lin et al., 2017 ; Wang et al., 2022 ; Yu et al., 2025 ); - measures, tests, and visualization tools for publication bias or small-study effects (Lin and Chu, 2018 ; Lin, 2019 ; Lin, 2020 ; Shi et al., 2020 ); - meta-analysis of combining standardized mean differences and odds ratios (Jing et al., 2023 ); - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc. (Reitsma et al., 2005 ; Chu and Cole, 2006 ); - meta-analysis methods for synthesizing proportions (Lin and Chu, 2020 ); - models for multivariate meta-analysis, measures of inconsistency degrees of freedom in Bayesian network meta-analysis, and predictive P-score (Lin and Chu, 2018 ; Lin, 2020 ; Rosenberger et al., 2021 ). Package: r-cran-altopt Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-altopt_0.1.2-1.ca2604.1_all.deb Size: 114450 MD5sum: 094c9d9ba4ce9c8c2ff917b26433e18f SHA1: 2d82c63963403df9daf302dc2a84444b9edac968 SHA256: ba0b011377cb911ae1f11f9d7a8e45e6c8282c3a98f9a20c368d83a0da395069 SHA512: 6238077b8d87d937efc5d2fc20e01a7684513de1d781327724a7352b638683a176598813914fb336dffebf09526a3475db02d23684244e7f60f9e0d17fced1b9 Homepage: https://cran.r-project.org/package=ALTopt Description: CRAN Package 'ALTopt' (Optimal Experimental Designs for Accelerated Life Testing) Creates the optimal (D, U and I) designs for the accelerated life testing with right censoring or interval censoring. It uses generalized linear model (GLM) approach to derive the asymptotic variance-covariance matrix of regression coefficients. The failure time distribution is assumed to follow Weibull distribution with a known shape parameter and log-linear link functions are used to model the relationship between failure time parameters and stress variables. The acceleration model may have multiple stress factors, although most ALTs involve only two or less stress factors. ALTopt package also provides several plotting functions including contour plot, Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space (VDUS) plot. For more details, see Seo and Pan (2015) . 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The package contains functions for downloading raw data files from web URLs (download_data()), processing the raw data files into clean spatial objects (process_covariates()), and extracting values from the spatial data objects at point and polygon locations (calculate_covariates()). These functions call a series of source-specific functions which are tailored to each data sources/datasets particular URL structure, data format, and spatial/temporal resolution. The functions are tested, versioned, and open source and open access. For sum_edc() method details, see Messier, Akita, and Serre (2012) . 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Provides a novel algorithm for solving the sparse principal component analysis problem which provides advantages over existing methods in terms of efficiency and convergence guarantees. Chen, S., Ma, S., Xue, L., & Zou, H. (2020) . Zou, H., Hastie, T., & Tibshirani, R. (2006) . Zou, H., & Xue, L. (2018) . 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Package: r-cran-amim Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-amim_1.0.0-1.ca2604.1_all.deb Size: 43526 MD5sum: 924323455e299a1817562723d7ed65bc SHA1: 9f9b315ec5f36ad6b20b269193a339cf2fb33c3f SHA256: 9b4cdadad30d89e1ccc3c52577b008f8ec23078f0f52e85adc42b302f6a1519f SHA512: 67f6fb0f2121f89bb3f2de04b79b720263de1fe64027558949abdafbee18f3e43c0079c41d7723dfaa8637461a855fcd63569c8e8c6e55e633de55c0d7294927 Homepage: https://cran.r-project.org/package=AMIM Description: CRAN Package 'AMIM' (Compute the Adjusted Market Inefficiency Measure) Fast tool to calculate the Adjusted Market Inefficiency Measure following Tran & Leirvik (2019) . This tool provides rolling window estimates of the Adjusted Market Inefficiency Measure for multiple instruments simultaneously. Package: r-cran-ammibayes Architecture: all Version: 2.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1583 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-distfree.cr, r-cran-coda, r-cran-spam, r-cran-movmf, r-cran-msm, r-cran-bayesplot, r-cran-hmisc Suggests: r-cran-ggpubr, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-ammibayes_2.1-1-1.ca2604.1_all.deb Size: 1470328 MD5sum: b1ecd4ded6ade96e2196767f655a6175 SHA1: 528e1f04e49735e3b75d5ad32d9dc8692d4be211 SHA256: 781f9406c7df4cd3b05bc8ac70683ac7c35758af210865bc2a0687aeca24e9b9 SHA512: daa0257bfca5db2b9324c143a2a49078c6df70afcf6e62f02963bb4b63c6a6a095fee7e8cfe1e729a365554f644386d7aec0f434ba6c7bc5ac132662da7a883a Homepage: https://cran.r-project.org/package=ammiBayes Description: CRAN Package 'ammiBayes' (Bayesian Ammi Model for Continuous Data with or without Additiveand Dominance Effect) Flexible multi-environment trials analysis via MCMC method for Additive Main Effects and Multiplicative Interaction Model (AMMI) for continuous data. Biplot with the averages and regions of confidence can be generated. The chains run in parallel on Linux systems and run serially on Windows. Package: r-cran-ammodels Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1621 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-unmarked Suggests: r-cran-r.rsp, r-cran-shiny, r-cran-shinybs, r-cran-knitr, r-cran-aiccmodavg Filename: pool/dists/resolute/main/r-cran-ammodels_0.1.4-1.ca2604.1_all.deb Size: 1199788 MD5sum: 48eb0f848421d32d24f8a11704f957da SHA1: f8ee5b5ad7a5bf7303f5dd0e0b70ae9f2494fd94 SHA256: b35f93d841d11569e7cea187466433cbc4bc28baa04c6c6605847a1c507f6e0f SHA512: 5937c14f14379069ebd77a33254cf7ed282e70484c73138143d14e5beedb1bd18116ff3b4f39d3182a912820c6fa41eae4e50c07c9665edc8efe2c7581e80d65 Homepage: https://cran.r-project.org/package=AMModels Description: CRAN Package 'AMModels' (Adaptive Management Model Manager) Helps enable adaptive management by codifying knowledge in the form of models generated from numerous analyses and data sets. Facilitates this process by storing all models and data sets in a single object that can be updated and saved, thus tracking changes in knowledge through time. A shiny application called AM Model Manager (modelMgr()) enables the use of these functions via a GUI. Package: r-cran-ammoniaconcentration Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ammoniaconcentration_0.1-1.ca2604.1_all.deb Size: 11294 MD5sum: 426f2dffe5f0360b91d3ac8e896e9ab4 SHA1: 5281411dcaf5aebac096f6d217e082a0e20172e5 SHA256: 8dd6265f36b07ab73b793b4ed8fe9e56d1572a0f122099f6f9f138b313748b3f SHA512: 49f99caa11a66cd18d424624ffa988da4822ca1c0f3f7f3f72581aa9051693cd02a370142dbfc672ece099bfa4eebee75e2ce01e583a647cf631e971bffef2e1 Homepage: https://cran.r-project.org/package=AmmoniaConcentration Description: CRAN Package 'AmmoniaConcentration' (Un-Ionized Ammonia Concentration) Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values. Package: r-cran-amnlfa Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mplusautomation, r-cran-reshape2, r-cran-gridextra, r-cran-stringr, r-cran-plyr, r-cran-devtools, r-cran-dplyr, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-amnlfa_1.1.2-1.ca2604.1_all.deb Size: 235166 MD5sum: 17a0b1851b1fb24b66e55f37c36b537a SHA1: fd1152c5a159bdab021605be8646215f12b21578 SHA256: 06bccffe2839430cc6c3685bc36a2fa3f1f26b225dd5556e646cdccecd416aed SHA512: 8208c7459167c968fc3e69804a87edeb54682d6fd4ea16414813fde432fe57705220947a125fc280bd268de3609138d870f347d8a1cdcdfcb98cfcc43a3a6b87 Homepage: https://cran.r-project.org/package=aMNLFA Description: CRAN Package 'aMNLFA' (Automated Moderated Nonlinear Factor Analysis Using 'M-plus') Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) . This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment. Package: r-cran-amoudsurv Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ahsurv, r-cran-flexsurv, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-amoudsurv_0.1.0-1.ca2604.1_all.deb Size: 194872 MD5sum: 6e7c0d509fd2b374546b7831d9176b3c SHA1: 77b39e92c0bfabdfe9bba7cbb02b8e10296e7eeb SHA256: 1f9078975a5528c0bc25881e993e6d826645c6383fc8f930fbbbe56b7e9bbdfb SHA512: b3207210203d785c5ed04573447a26da9711540d632fc82f3b05686ee69c183ce6d0a034cebdcf2fa512537f9cad9e93e9f267cd5a9c3fe24f9e4f1a6ac10a27 Homepage: https://cran.r-project.org/package=AmoudSurv Description: CRAN Package 'AmoudSurv' (Tractable Parametric Odds-Based Regression Models) Fits tractable fully parametric odds-based regression models for survival data, including proportional odds (PO), accelerated failure time (AFT), accelerated odds (AO), and General Odds (GO) models in overall survival frameworks. Given at least an R function specifying the survivor, hazard rate and cumulative distribution functions, any user-defined parametric distribution can be fitted. We applied and evaluated a minimum of seventeen (17) various baseline distributions that can handle different failure rate shapes for each of the four different proposed odds-based regression models. For more information see Bennet et al., (1983) , and Muse et al., (2022) . Package: r-cran-amp.dm Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-dplyr, r-cran-vctrs, r-cran-forcats, r-cran-readxl, r-cran-haven, r-cran-fs, r-cran-xtable Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-rstudioapi, r-cran-knitr, r-cran-r3port, r-cran-testthat, r-cran-quarto, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-amp.dm_0.2.1-1.ca2604.1_all.deb Size: 653370 MD5sum: c4e6dd904971021861f3e958a2604783 SHA1: 6e0ae11ff717367ccb1560ba46ac745ea39bb44b SHA256: cc6c82d9d2c9b6532f4cae2244a8497a640c5a265f69c94a64d41696a11fa2aa SHA512: 4f3b5975e7bf3ab6e30cbe4f7271ad4c65f77f614c8cff24efe1f2710e6d6732b20d92a6d71d4e91da3bdf7bed9527077255f2968d0d006e4575195f7006c379 Homepage: https://cran.r-project.org/package=amp.dm Description: CRAN Package 'amp.dm' (Data Management Tools for Pharmacometrics) Tools and functions to efficiently create datasets used in pharmacometric analysis. 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Package: r-cran-amp.sim Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-whisker, r-cran-rlang, r-cran-cli, r-cran-lifecycle, r-cran-nmdata Suggests: r-cran-nonmem2rx, r-cran-tidyr, r-cran-shiny, r-cran-shinyjs, r-cran-bslib, r-cran-rstudioapi, r-cran-desolve, r-cran-rxode2, r-cran-mrgsolve, r-cran-r3port, r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-amp.sim_0.1.1-1.ca2604.1_all.deb Size: 860190 MD5sum: f6a7ca8b32c3ba8edbda731b5bbe9309 SHA1: d48c9208267a5f9d2d1b45be51cebe187071ca30 SHA256: 8e90e7d867953ea9eb567053b7bcef9ea658e6438d52effa12e249beff9c42e9 SHA512: c84749fa25b913f3aaedc0677759b9c0ed4ef510cbed68d51777b553c6aff6c51f1d8c3b8bb42dcb7086b2df01a36fd3e28c93c0030af5ae3b4bfaee9d989898 Homepage: https://cran.r-project.org/package=amp.sim Description: CRAN Package 'amp.sim' (Flexible Simulation Utilities for Pharmacometric Modeling) The goal of 'amp.sim' is to transform 'NONMEM' models into R syntax so they can be used for simulations using the 'deSolve', 'nlmixr2' or 'mrgsolve' package. 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Package: r-cran-amp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-superlearner, r-cran-glmnet, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-amp_1.0.0-1.ca2604.1_all.deb Size: 236150 MD5sum: ebca3009da840dbf298f8c6819d506f8 SHA1: 8c979933945a291687e823c838455c96702099f8 SHA256: 9ec91da88531f7b3c38b77e4c36d05674ff21dce0e56249b88aa1d0f8825e9a7 SHA512: 0c1982523186fe326fd932650cbf0d3b8dd2568f252e3a4db35bfb717bdf9fe29ef2a90e55c38021ef654f86328e43e69225c8b6635aa7a0f1eaa1af5256522c Homepage: https://cran.r-project.org/package=amp Description: CRAN Package 'amp' (Statistical Test for the Multivariate Point Null Hypotheses) A testing framework for testing the multivariate point null hypothesis. A testing framework described in Elder et al. (2022) to test the multivariate point null hypothesis. After the user selects a parameter of interest and defines the assumed data generating mechanism, this information should be encoded in functions for the parameter estimator and its corresponding influence curve. Some parameter and data generating mechanism combinations have codings in this package, and are explained in detail in the article. Package: r-cran-ampd Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ampd_0.2-1.ca2604.1_all.deb Size: 16928 MD5sum: b5f3f511b14d3192ffde29a5c206f81d SHA1: e215d6796944bdc3cdfe20b93e86cd04742491db SHA256: 74dcd88ccce3a971c5da2226339010f5c20e85c1e3ff55ff5f4dbcb2171b3668 SHA512: c384fae3053ea1aa9d3a4450d56343b1e99f615a38e81a677d0d905c76cf051e5cf87ce71f49572895f2311bcd7e328082c0debc5106291d985c460c841bab46 Homepage: https://cran.r-project.org/package=ampd Description: CRAN Package 'ampd' (An Algorithm for Automatic Peak Detection in Noisy Periodic andQuasi-Periodic Signals) A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. For further information see . Package: r-cran-ampgram Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biogram, r-cran-devtools, r-cran-pbapply, r-cran-ranger, r-cran-shiny, r-cran-stringi Suggests: r-cran-dt, r-cran-ggplot2, r-cran-pander, r-cran-rmarkdown, r-cran-shinythemes, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-ampgram_1.0-1.ca2604.1_all.deb Size: 86588 MD5sum: 0acbf0cbecb4973adae656646930a843 SHA1: a2263e07d36b70390f9cba69ebf77c0b1ce41c5e SHA256: 8c2ae54cea4298ff8013ad8939977e37580ef587ed902605023a74e2cb54f04a SHA512: aa904a0485f7b80d0e7d5bdd8d075f1ee4b3c7c3d9a2bb628b483ea2e4cadeb64aef03ab15ab23d476310c3b1de721fa3ef70a7f79cfb7ac0b3bb811b29c10fa Homepage: https://cran.r-project.org/package=AmpGram Description: CRAN Package 'AmpGram' (Prediction of Antimicrobial Peptides) Predicts antimicrobial peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The AmpGram model is too large for CRAN and it has to be downloaded separately from the repository: . Package: r-cran-ample Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3943 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-markdown, r-cran-rcolorbrewer, r-cran-r6, r-cran-scales, r-cran-shinyjs, r-cran-ggplot2, r-cran-shinyscreenshot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ample_1.0.2-1.ca2604.1_all.deb Size: 1510318 MD5sum: cee7e5115cc4306fb9691c0073607cc7 SHA1: 1501571108aaca64a38f05ad9830af62165fb998 SHA256: 8e0663bcf6e01f4c5c8a940742f194af99fc6756118931c0a19e6f1a18f519eb SHA512: 0fe0061b726a8dfa07ef478716bf1b15afc871c62fe143e563780526052fbdc10ec98007d3a231b75812f447be28206f00ae0764e07bbb82b6942bc8eb1d5cc7 Homepage: https://cran.r-project.org/package=AMPLE Description: CRAN Package 'AMPLE' (Shiny Apps to Support Capacity Building on Harvest Control Rules) Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management. 'Introduction to HCRs' provides a simple overview to how HCRs work. Users are able to select their own HCR and step through its performance, year by year. Biological variability and estimation uncertainty are introduced. 'Measuring performance' builds on the previous app and introduces the idea of using performance indicators to measure HCR performance. 'Comparing performance' allows multiple HCRs to be created and tested, and their performance compared so that the preferred HCR can be selected. Package: r-cran-ampliconduo Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-ampliconduo_1.1.1-1.ca2604.1_all.deb Size: 565134 MD5sum: 4c93bb1e4f7e9bdeaa6db1dedd09650c SHA1: 553d842496e82a28bd9b95e1941bec37be060bd1 SHA256: 2fbff29128d304f2623d70d6055ef4e104b0198b0221db9db3442adaf8646efa SHA512: ef68fadc0bf2d031fda9302a43cbffbc58f71109919e435b89b025d0b2cf397bb33f6f8043921b16f68f4fb883b9ffe55eb4ed89f90126218b79f42aad6acf1a Homepage: https://cran.r-project.org/package=AmpliconDuo Description: CRAN Package 'AmpliconDuo' (Statistical Analysis of Amplicon Data of the Same Sample toIdentify Artefacts) Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads. Package: r-cran-amr Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5001 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cleaner, r-cran-cli, r-cran-crayon, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-openxlsx, r-cran-parallelly, r-cran-pillar, r-cran-progress, r-cran-readxl, r-cran-recipes, r-cran-rlang, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rvest, r-cran-skimr, r-cran-testthat, r-cran-tibble, r-cran-tidymodels, r-cran-tidyselect, r-cran-tinytest, r-cran-vctrs, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-amr_3.0.1-1.ca2604.1_all.deb Size: 4802662 MD5sum: 02a99baec397a5e0eaa0b8e9f8fe390d SHA1: 6301785b788e24c70cfa00715e0e08764b7f6a8a SHA256: 1db85840ef5ec40515263003c7d87883141f8b2c3dec1cfb13e25cc465017ffc SHA512: 201194dabd431d163d45e78ed5df6db2064c788f22de17722c7416542ed910a4d914cfabe4103b88a053be058fa82d6c5d50a0aad63e47282a9955221f71816b Homepage: https://cran.r-project.org/package=AMR Description: CRAN Package 'AMR' (Antimicrobial Resistance Data Analysis) Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in . Package: r-cran-amregtest Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-allelematch, r-cran-digest, r-cran-remotes, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-amregtest_1.0.5-1.ca2604.1_all.deb Size: 134100 MD5sum: 8976aa98a64654ef2b8f563a0b9723a6 SHA1: 788655bc075f55716893f62634b61b67417f153a SHA256: 257be8ef222adcc0df4d78b5bbd83bcb8dcf141dd2de5e7d0aca811f90635f4f SHA512: aa07bd1d52f9e6390dd83f4f5c02449a701c440d93d8e0541071936122e14a5e518f7ee86b967f0b634f78072a2039405a890514d434f186fb84322946c8f49d Homepage: https://cran.r-project.org/package=amregtest Description: CRAN Package 'amregtest' (Runs Allelematch Regression Tests) Automates regression testing of package 'allelematch'. Over 2500 tests covers all functions in 'allelematch', reproduces the examples from the documentation and includes negative tests. The implementation is based on 'testthat'. Package: r-cran-amscorer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-amscorer_0.1.0-1.ca2604.1_all.deb Size: 69502 MD5sum: 8aabe26b40e98ae40c882ed0c267dc1a SHA1: a42634d995539ab98a98b1880e56f7eb8864093a SHA256: f6e3b5c4e39824fcf5b2aa334bf4b6886bafc4221630de0f9caae9960d9c0c65 SHA512: eb320334ec4882e87b240cff9e9703a05b42cf409fa09b60b5477a3cd002dfea489d12406221431a5476cd3e304428af0c6f03d1473945e13e2c863508e4b1ca Homepage: https://cran.r-project.org/package=amscorer Description: CRAN Package 'amscorer' (Clinical Scores Calculator for Healthcare) Provides functions to compute various clinical scores used in healthcare. These include the Charlson Comorbidity Index (CCI), predicting 10-year survival in patients with multiple comorbidities; the EPICES score, an individual indicator of precariousness considering its multidimensional nature; the MELD score for chronic liver disease severity; the Alternative Fistula Risk Score (a-FRS) for postoperative pancreatic fistula risk; and the Distal Pancreatectomy Fistula Risk Score (D-FRS) for risk following distal pancreatectomy. For detailed methodology, refer to Charlson et al. (1987) , Sass et al. (2006) , Kamath et al. (2001) , Kim et al. (2008) Kim et al. (2021) , Mungroop et al. (2019) , and de Pastena et al. (2023) .. Package: r-cran-amt Architecture: all Version: 0.3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-circular, r-cran-ctmm, r-cran-data.table, r-cran-dplyr, r-cran-fitdistrplus, r-cran-fnn, r-cran-kernsmooth, r-cran-lubridate, r-cran-mass, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-survival, r-cran-terra, r-cran-tibble, r-cran-tidyr Suggests: r-cran-adehabitatlt, r-cran-broom, r-cran-ggplot2, r-cran-ggraph, r-cran-geosphere, r-cran-knitr, r-cran-leaflet, r-cran-movehmm, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-suncalc, r-cran-tidygraph, r-cran-tinytest, r-cran-units Filename: pool/dists/resolute/main/r-cran-amt_0.3.1.0-1.ca2604.1_all.deb Size: 3505590 MD5sum: 016e7d4afb6ef87f93c134c36b32a80a SHA1: 4a13e5a4f0ab4a5888d640558e2226069dc4afc5 SHA256: 6d855ff7f079e4f723c7ecf7d52893c7ad43d712f982a6af3e682791ece3b6a3 SHA512: 7b49b7b1c0fce9c1bd1de62aedb263c9200e7996f8464817d66a5a0ed88202878ae2467541698512a8b2034a3958636ab34a54534cacf297b3d152423b6c9b35 Homepage: https://cran.r-project.org/package=amt Description: CRAN Package 'amt' (Animal Movement Tools) Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions. Package: r-cran-amvenndiagram5 Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1628 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-partitions, r-cran-venn Suggests: r-cran-shiny Filename: pool/dists/resolute/main/r-cran-amvenndiagram5_1.0.0-1.ca2604.1_all.deb Size: 1119620 MD5sum: 6680eda84e0198caf3c1711e2d251ec3 SHA1: 05a3393d4ad4a538460b46d6a805503106f11893 SHA256: 3079ca1b53d6f055e88b2c28850c32f3d95e0d7ef268bab1c00ddca183ce9a18 SHA512: 83d5beb76720c3d72601cb9a94fb2248ed17281a14cf2c1ed6f65c52bbc557a66db265b0e0eef4121ec7d7d1746f7fb21f7ba67cda82d86a236ea9e36b2dc48a Homepage: https://cran.r-project.org/package=amVennDiagram5 Description: CRAN Package 'amVennDiagram5' (Interactive Venn Diagrams) Creates interactive Venn diagrams using the 'amCharts5' library for 'JavaScript'. They can be used directly from the R console, from 'RStudio', in 'shiny' applications, and in 'rmarkdown' documents. Package: r-cran-amylogram Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biogram, r-cran-ranger, r-cran-seqinr, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-amylogram_1.1-1.ca2604.1_all.deb Size: 643714 MD5sum: d2675263c49e76cec8df3d33091356c9 SHA1: f267d07f2794b96ebe0fcb126bd43de5474f57d4 SHA256: 37bc7a007b921c18fe9d72f2a55a3e7eff3ad0072a3620bb72724ad9eb3d4f3b SHA512: aa3f88fc2966ac7412a2827ce7d2059995b68eafb8c672fbe2661c6cfb5c80f2bbdd0e786656f62d7a1f9fe497a17be5980ae1ec5a336373ecb2e0d85a3a82e2 Homepage: https://cran.r-project.org/package=AmyloGram Description: CRAN Package 'AmyloGram' (Prediction of Amyloid Proteins) Predicts amyloid proteins using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. Package: r-cran-anabel Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-kableextra, r-cran-minpack.lm, r-cran-openxlsx, r-cran-progress, r-cran-purrr, r-cran-qpdf, r-cran-reshape2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-anabel_3.0.2-1.ca2604.1_all.deb Size: 795466 MD5sum: df421b0ad225138b1d9f288a6f27be08 SHA1: 178c96461d63db6a553dfa9427db8ac04281250e SHA256: 873fa1fad9877aeada0fc34151637356fe08203a87881b795cff095d3637c33f SHA512: 0951fb9969b5fcad41d805d9174784898a4e7bc20bd333a2280fc7abe8eca8d535199a01d7f761e6b088d5bbcb5e837f911d4c91542dc69b34e081e6352492d0 Homepage: https://cran.r-project.org/package=anabel Description: CRAN Package 'anabel' (Analysis of Binding Events + l) A free software for a fast and easy analysis of 1:1 molecular interaction studies. This package is suitable for a high-throughput data analysis. Both the online app and the package are completely open source. You provide a table of sensogram, tell 'anabel' which method to use, and it takes care of all fitting details. The first two releases of 'anabel' were created and implemented as in (, ). Package: r-cran-anaconda Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-bioc-deseq2, r-cran-ggplot2, r-cran-ggrepel, r-cran-pheatmap, r-cran-lookup, r-cran-plyr, r-cran-data.table, r-cran-rafalib, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-anaconda_0.1.5-1.ca2604.1_all.deb Size: 177798 MD5sum: 2d8283e3005c9203a581f40ad89e044b SHA1: d6f6eb177ca7dce5af4cd87c68fbe7d58c06956c SHA256: 04e69375cbd25a997dbc7294b4abd1c9ef82d8a214ae19aaae145bc347ebccf5 SHA512: d044e48c51a1e770a76657596ba6fc5a951de3fb4f5b7d1c5d8fcff48d3dcfb813637e25140af0e06f0d84e3e06d95e6dc2365ce58a07fe3e4329615d4c6e0ca Homepage: https://cran.r-project.org/package=Anaconda Description: CRAN Package 'Anaconda' (Targeted Differential and Global Enrichment Analysis ofTaxonomic Rank by Shared Asvs) Targeted differential and global enrichment analysis of taxonomic rank by shared ASVs (Amplicon Sequence Variant), for high-throughput eDNA sequencing of fungi, bacteria, and metazoan. Actually works in two steps: I) Targeted differential analysis from QIIME2 data and II) Global analysis by Taxon Mann-Whitney U test analysis from targeted analysis (I) (I) Estimate variance-mean dependence in count/abundance ASVs data from high-throughput sequencing assays and test for differential represented ASVs based on a model using the negative binomial distribution. (II) NCBITaxon_MWU uses continuous measure of significance (such as fold-change or -log(p-value)) to identify NCBITaxon that are significantly enriches with either up- or down-represented ASVs. If the measure is binary (0 or 1) the script will perform a typical 'NCBITaxon enrichment' analysis based Fisher's exact test: it will show NCBITaxon over-represented among the ASVs that have 1 as their measure. On the plot, different fonts are used to indicate significance and color indicates enrichment with either up (red) or down (blue) regulated ASVs. No colors are shown for binary measure analysis. The tree on the plot is hierarchical clustering of NCBITaxon based on shared ASVs. Categories with no branch length between them are subsets of each other. The fraction next to the category name indicates the fraction of 'good' ASVs in it; 'good' ASVs are the ones exceeding the arbitrary absValue cutoff (option in taxon_mwuPlot()). For Fisher's based test, specify absValue=0.5. This value does not affect statistics and is used for plotting only. The original idea was for genes differential expression analysis from Wright et al (2015) ; adapted here for taxonomic analysis. The 'Anaconda' package makes it possible to carry out these analyses by automatically creating several graphs and tables and storing them in specially created subfolders. You will need your QIIME2 pipeline output for each kingdom (eg; Fungi and/or Bacteria and/or Metazoan): i) taxonomy.tsv, ii) taxonomy_RepSeq.tsv, iii) ASV.tsv and iv) SampleSheet_comparison.txt (the latter being created by you). Package: r-cran-anacor Architecture: all Version: 1.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-colorspace, r-cran-fda Filename: pool/dists/resolute/main/r-cran-anacor_1.1-5-1.ca2604.1_all.deb Size: 338424 MD5sum: 6808d918cb0481b7ceb3877eae04b929 SHA1: b05d7fda854bc49da4a842712658facd5cd3fa5b SHA256: f31d0ebcf0f5ab0dcdd2d8f9322d84e8f235755fac7049f6877f6414614f351e SHA512: caba04229fd6dcd5447bbf8924c8c41389bc4ccce456c1df65a512212510c2017a75a40d8a229cfe8e0357ce1eb645eee4531070370e87c6a96b11d7bdefbff4 Homepage: https://cran.r-project.org/package=anacor Description: CRAN Package 'anacor' (Simple and Canonical Correspondence Analysis) Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided. Package: r-cran-analitica Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-ggridges, r-cran-patchwork, r-cran-moments, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect, r-cran-multcompview Suggests: r-cran-car, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-analitica_2.2.0-1.ca2604.1_all.deb Size: 328544 MD5sum: 3ad5bd499055a0ba964a3fe6fec12317 SHA1: d582b1b4ed973fb84a4988bdcf4e973dbee55ac9 SHA256: b3cd871224e1495b7a6a084080445a6c955b76f77320f9a9f3fd7ff7bfa89fcc SHA512: fb1fa70def35cb2c521979fd707c303e379431c73445f7b6aa8ab0b0b4d6095d098345acefb7fff02c750ebf5f3bc82df83fb301678d74686b5bfe3a1372aa9c Homepage: https://cran.r-project.org/package=Analitica Description: CRAN Package 'Analitica' (Exploratory Data Analysis, Group Comparison Tools, and OtherProcedures) Provides a comprehensive set of tools for descriptive statistics, graphical data exploration, outlier detection, homoscedasticity testing, and multiple comparison procedures. Includes manual implementations of Levene's test, Bartlett's test, and the Fligner-Killeen test, as well as post hoc comparison methods such as Tukey, Scheffé, Games-Howell, Brunner-Munzel, and others. This version introduces two new procedures: the Jonckheere-Terpstra trend test and the Jarque-Bera test with Glinskiy's (2024) correction. Designed for use in teaching, applied statistical analysis, and reproducible research. Additionally you can find a post hoc Test Planner, which helps you to make a decision on which procedure is most suitable. Package: r-cran-analogsea Architecture: all Version: 1.0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-yaml Suggests: r-cran-testthat, r-cran-knitr, r-cran-ssh, r-cran-aws.s3, r-cran-arrow Filename: pool/dists/resolute/main/r-cran-analogsea_1.0.7.2-1.ca2604.1_all.deb Size: 372606 MD5sum: 0efa021cfde9082e870842ed510c3079 SHA1: 2a8f520023076b00aea777fbe8ff8c83835b2bcc SHA256: 029d24de3d970ecbf4a9488214c29ee6e97b9d3ea5a76ff55d15df3835e673a6 SHA512: 086cdbec4d12adf20fc3e81a08e27661a3304969fbcac3fd4e20694b6effce0f3c2581a74b74c186e620ef4d57ad2ee0fed3b0f66ee048e12502d2c288f49fb6 Homepage: https://cran.r-project.org/package=analogsea Description: CRAN Package 'analogsea' (Interface to 'DigitalOcean') Provides a set of functions for interacting with the 'DigitalOcean' API , including creating images, destroying them, rebooting, getting details on regions, and available images. Package: r-cran-analyzer Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr Suggests: r-cran-data.table, r-cran-testthat, r-cran-tidyr, r-cran-reshape2, r-cran-rmarkdown, r-cran-mass, r-cran-shiny, r-cran-nnet, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-analyzer_1.0.1-1.ca2604.1_all.deb Size: 430386 MD5sum: 03cb1fccd36830b15c816f58f7dd2892 SHA1: 1c87313df9d47064375223e7b617d7578f0a53b8 SHA256: 6cb58edda1da64edd685d9911da39b3b487415326a9751733b0119dc548b59c0 SHA512: ffc4294a3196b2909db750aa29faa8f8d303310e0ef0c5cef6d2bb60942114765b696b788f22afea2750d611be174c45ea104a24c67b0c7647a97432ec4550eb Homepage: https://cran.r-project.org/package=analyzer Description: CRAN Package 'analyzer' (Data Analysis and Automated R Notebook Generation) Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive. Package: r-cran-ananke Architecture: all Version: 0.2.0-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4036 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aion, r-cran-arkhe Suggests: r-cran-folio, r-cran-fontquiver, r-cran-knitr, r-cran-markdown, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-ananke_0.2.0-1.ca2604.2_all.deb Size: 798634 MD5sum: 30352d150c69abc0261846d1f23e0e00 SHA1: 5a0c81714a243368953486ba55e26d4b03d0ff9f SHA256: ffb5f3388de7041607c8137e7690699b4d0451439bf0e5fe0cf688795fa6e9c5 SHA512: 3014c3173a175a38f5d012f1786d6010d22dca68f8c0bb8bda9fbea92ed5e06c9bc80b0e68ee36019c09b01e5bd499b504c355fa2c8ae3c6f980ad905eeb8eec Homepage: https://cran.r-project.org/package=ananke Description: CRAN Package 'ananke' (Quantitative Chronology in Archaeology) Simple radiocarbon calibration and chronological analysis. This package allows the calibration of radiocarbon ages and modern carbon fraction values using multiple calibration curves. It allows the calculation of highest density region intervals and credible intervals. The package also provides tools for visualising results and estimating statistical summaries. Package: r-cran-ananseseurat Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-patchwork, r-cran-png, r-cran-purrr, r-cran-rlang, r-cran-seurat, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-signac, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ananseseurat_1.2.0-1.ca2604.1_all.deb Size: 380404 MD5sum: 1f1f719d2bdc8bab6a969642c70e8e96 SHA1: d0303a930b5c5a4d819858bdfbe1b70dc58553cb SHA256: e750231ad193e7e701dfcf974811708fed519443242ea4327986c5dddc7779a6 SHA512: 6609edf4d67c7c7ba671395191511283a69aa0ab7dda6a96e6b085a0c9903e9fa0e2becc5c4b3f95ddb8a2dd3278cc84af8fdff0770f3c7b03fad626632f2724 Homepage: https://cran.r-project.org/package=AnanseSeurat Description: CRAN Package 'AnanseSeurat' (Construct ANANSE GRN-Analysis Seurat) Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software 'ANANSE', Xu et al.(2021) . Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' implemented in 'snakemake'. Finally, incorporate results for visualization and interpretation. Package: r-cran-anca Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-formatters, r-cran-flextable, r-cran-ggplot2, r-cran-magrittr, r-cran-officer, r-cran-pknca, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-rlistings, r-cran-scales, r-cran-stringr, r-cran-tern, r-cran-tidyr, r-cran-units Suggests: r-cran-arrow, r-cran-covr, r-cran-dt, r-cran-ggh4x, r-cran-glue, r-cran-haven, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-knitr, r-cran-lintr, r-cran-logger, r-cran-markdown, r-cran-mockery, r-cran-nestcolor, r-cran-openxlsx2, r-cran-pak, r-cran-reactable, r-cran-reactable.extras, r-cran-rmarkdown, r-cran-sass, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinyjqui, r-cran-shinytest2, r-cran-shinywidgets, r-cran-stringi, r-cran-testthat, r-cran-quarto, r-cran-vdiffr, r-cran-withr, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-anca_0.1.0-1.ca2604.1_all.deb Size: 2155684 MD5sum: 36b194272b79e84d68dee3f575e2c9d3 SHA1: f338df81d83b179c3866020d5ab70d7525862ce8 SHA256: a71aba396c6d00d57ce9ba0b5472b0af2e0952fc38e0d7828ac4766ceabdd650 SHA512: af75e8ebd149ee3e4d706a8cadabf5c22e1d603b2625032b28cccf227ee0447c7c8626f50b9c5dc5bf2a257a8f37745c3ece433d3666c8d07a63294525d2c6fb Homepage: https://cran.r-project.org/package=aNCA Description: CRAN Package 'aNCA' ((Pre-)Clinical NCA in a Dynamic Shiny App) An interactive 'shiny' application for performing non-compartmental analysis (NCA) on pre-clinical and clinical pharmacokinetic data. The package builds on 'PKNCA' for core estimators and provides interactive visualizations, CDISC outputs ('ADNCA', 'PP', 'ADPP') and configurable TLGs (tables, listings, and graphs). Typical use cases include exploratory analysis, validation, reporting or teaching/demonstration of NCA methods. Methods and core estimators are described in Denney, Duvvuri, and Buckeridge (2015) "Simple, Automatic Noncompartmental Analysis: The PKNCA R Package" . 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Package: r-cran-ancreg Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tsutils, r-cran-rdpack Suggests: r-cran-pcalg, r-cran-mass, r-cran-igraph, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ancreg_1.0.1-1.ca2604.1_all.deb Size: 122320 MD5sum: 12fd5499b02e7c85fd00b289dd7de8ee SHA1: 3d48c7fcd3dd737f85bd920549d3f6ca37426e2d SHA256: 869788a3e1d31ce63ebe8dd62196f6a441e3be07a8da0c9db733312b96e43628 SHA512: d4410449b9f1be18a99db050285f8b1ceebf012213393ec839cfa5c4988fc6587cd9173c49f6824984ce3b16d7aa64222444a9a0bc069ed703b41d06e93d25a6 Homepage: https://cran.r-project.org/package=AncReg Description: CRAN Package 'AncReg' (Ancestor Regression) Causal discovery in linear structural equation models (Schultheiss, and Bühlmann (2023) ) and vector autoregressive models (Schultheiss, Ulmer, and Bühlmann (2025) ) with explicit error control for false discovery, at least asymptotically. 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Package: r-cran-animation Architecture: all Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick Suggests: r-cran-mass, r-cran-class, r-cran-cairo, r-cran-fimport, r-cran-rgl, r-cran-zoo, r-cran-testit Filename: pool/dists/resolute/main/r-cran-animation_2.8-1.ca2604.1_all.deb Size: 531320 MD5sum: 91742813fa19a6c89a3d3f91af0bdb07 SHA1: 19d47834f0f75dec3f476f7ee65cdcc457a70dcc SHA256: 0c15d4abd5ab36570c609b793bbf27ae603fc3564b69e7130963193047d7302c SHA512: dbfcb51769a2343e45c85e941f47ab474f18286fa688801f4f63beae1b3f2dae67c1b0edeba471a549cc914354f4d32f8043a06fa0549e46a8454fdd7e987d9b Homepage: https://cran.r-project.org/package=animation Description: CRAN Package 'animation' (A Gallery of Animations in Statistics and Utilities to CreateAnimations) Provides functions for animations in statistics, covering topics in probability theory, mathematical statistics, multivariate statistics, non-parametric statistics, sampling survey, linear models, time series, computational statistics, data mining and machine learning. These functions may be helpful in teaching statistics and data analysis. Also provided in this package are a series of functions to save animations to various formats, e.g. Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted into 'Sweave' / 'knitr' easily. Package: r-cran-animbook Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gganimate, r-cran-ggplot2, r-cran-plotly, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-animbook_1.0.1-1.ca2604.1_all.deb Size: 802732 MD5sum: fa9a63ecaf3801c607b21b2d173b3864 SHA1: 318a95696960ab5b56c65ee9ab0be111a27b1b43 SHA256: 37ed41f2fd9995b24f27ef5f1dd4648e2f677ce3defaed19e09142c4519d76a0 SHA512: 54300ae2d9c00489841499705297a7ccb218c188a60932dfb8ffa2213ca5eb2477e5b18c8e4b81eabbd0c2e6fb6a2d3a2840810233b507bc5c0558283ce8f0a7 Homepage: https://cran.r-project.org/package=animbook Description: CRAN Package 'animbook' (Visualizing Changes in Performance Measures and DemographicAffiliations using Animation) Create an interactive visualization to be used for communication purposes. Providing the function for preparing, plotting, and animating the data. Krisanat Anukarnsakulchularp (2023) . Package: r-cran-animejs Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-rlang Suggests: r-cran-htmltools, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-animejs_0.1.0-1.ca2604.1_all.deb Size: 125662 MD5sum: 0d1dea8716d4374c0b6a9a4a6c1cafd7 SHA1: 9acee339b17843819542b7d7d276e9d2f54460f3 SHA256: 4e61746332594a967eebba6cabd3f182e604cef25ea1d95ace92dbf061a2b818 SHA512: cb3bf2a6ccf9a6e274a56f3b70ea6def38f119b53c74c228ff4be301a1badb9d0d8cc6eca9089c329c4d73e7c4290eac3814d48e2b0f0b55be57b77b8933953d Homepage: https://cran.r-project.org/package=animejs Description: CRAN Package 'animejs' (R Bindings to the 'Anime.js' Animation Library) Provides low-level R bindings to the 'Anime.js' library (), enabling the creation of browser-native SVG and HTML animations via the 'htmlwidgets' framework. Package: r-cran-animint2 Architecture: all Version: 2025.10.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4846 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-servr, r-cran-digest, r-cran-rjsonio, r-cran-gtable, r-cran-mass, r-cran-plyr, r-cran-reshape2, r-cran-scales, r-cran-knitr, r-cran-data.table Suggests: r-cran-gert, r-cran-gitcreds, r-cran-gh, r-cran-sp, r-cran-gistr, r-cran-shiny, r-cran-covr, r-cran-rcolorbrewer, r-cran-htmltools, r-cran-rmarkdown, r-cran-testthat, r-cran-xml, r-cran-devtools, r-cran-httr, r-cran-maps, r-cran-ggplot2movies, r-cran-hexbin, r-cran-hmisc, r-cran-lattice, r-cran-mapproj, r-cran-mgcv, r-cran-nlme, r-cran-rpart, r-cran-svglite, r-cran-ggplot2, r-cran-chromote, r-cran-magick Filename: pool/dists/resolute/main/r-cran-animint2_2025.10.17-1.ca2604.1_all.deb Size: 4190428 MD5sum: 5dcd13943c995a57eeb405dcb72f3a2c SHA1: c499f62e0d7c8b69b9a35324f71a080d66047eae SHA256: af3d7be9b6aca9a2ebc41ccf026334e4423776f41562bd7edeb0470a27fdd58f SHA512: d006ba1cbfc9c1f616bee1bbaab8cc4069a236d2960aab8c6aaf3abe984dcec5e1b20c374caab4ac8b9c1cdfa1aa6cad0977738d499042c6a911e995ecb1e5e1 Homepage: https://cran.r-project.org/package=animint2 Description: CRAN Package 'animint2' (Animated Interactive Grammar of Graphics) Functions are provided for defining animated, interactive data visualizations in R code, and rendering on a web page. The 2018 Journal of Computational and Graphical Statistics paper, describes the concepts implemented. Package: r-cran-animl Architecture: all Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-reticulate Filename: pool/dists/resolute/main/r-cran-animl_3.2.0-1.ca2604.1_all.deb Size: 126052 MD5sum: de7f1f55a0e60903e95e4ae8b2be104b SHA1: 51284a9ed2e4bdf2fbb5c7449422b73eaaa235be SHA256: a1ae379fa9f46df3052cead590a142b11d3b44112de1e03c1e8841c748a3b017 SHA512: 01047ef01dad10cb3ef361fbb61d0311d362a214ef09dad7f9a93150cb7c411cf7f3d5d5505cef903112bf982d100c883f9e14a8b25b04200a80d9fa795f1de9 Homepage: https://cran.r-project.org/package=animl Description: CRAN Package 'animl' (A Collection of ML Tools for Conservation Research) Functions required to classify subjects within camera trap field data. The package can handle both images and videos. The authors recommend a two-step approach using Microsoft's 'MegaDector' model and then a second model trained on the classes of interest. 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E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations. 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Package: r-cran-ankir Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-jsonlite, r-cran-dbi, r-cran-rsqlite, r-cran-tibble, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-withr, r-cran-dplyr, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-ankir_0.6.0-1.ca2604.1_all.deb Size: 1226040 MD5sum: a634a06b9004da2a4252102b5a308a77 SHA1: 81eae179c29d19424762774972b0164361b61843 SHA256: 79541465233129958e328528de253911b73ac3af4078ecf8cb03b411c19d53f7 SHA512: b411bfe1e282f5dad79d119a8d03d2716c396eb3942da7d7ff52160101c866bb3b543fe2db309f7f88517598417419fa336b04dcda1fd62319b0ce38c8f8daec Homepage: https://cran.r-project.org/package=ankiR Description: CRAN Package 'ankiR' (Read and Analyze 'Anki' Flashcard Databases) Comprehensive toolkit for reading and analyzing 'Anki' flashcard collection databases. 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Package: r-cran-anndata Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-lifecycle, r-cran-matrix, r-cran-r6, r-cran-reticulate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-anndata_0.8.0-1.ca2604.1_all.deb Size: 229324 MD5sum: befc9388bc71e89c600cdb8dda141e83 SHA1: e07ea862f24082c12eca593e01b3ece02c44c62e SHA256: 715ee7c447b33217611dbad5d608147122f779e03c75a441c94d60640aad7225 SHA512: 427df65d4ee2a28178236643c72c14d296228f99fd2d97052bce57af94fcdd28739da5e4a716a151cc99f0dc0112f61b76fd723e3f0e9181a1248e4c3e5edff0 Homepage: https://cran.r-project.org/package=anndata Description: CRAN Package 'anndata' ('anndata' for R) A 'reticulate' wrapper for the Python package 'anndata'. Provides a scalable way of keeping track of data and learned annotations. Used to read from and write to the h5ad file format. Package: r-cran-annmatrix Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-annmatrix_0.1.2-1.ca2604.1_all.deb Size: 66586 MD5sum: 65f628bc2650f983443c2781ebdf8150 SHA1: f8493da10be2a014ee605c62095bacd676b9741e SHA256: 62e396fd1af6f126e145acc24f2bb6c9e2bf3e3cfe7552527a5e02a6e05b454e SHA512: 80fb8f06ea896f95512055c384322d5fc09d258ef183528dc019c424dd477d70fee04606cf33fe4468212a07dedddd7d4fc5ff3a9c883e1d4dd419a36f43f0c6 Homepage: https://cran.r-project.org/package=annmatrix Description: CRAN Package 'annmatrix' (Annotated Matrix: Matrices with Persistent Row and ColumnAnnotations) Implements persistent row and column annotations for R matrices. The annotations associated with rows and columns are preserved after subsetting, transposition, and various other matrix-specific operations. Intended use case is for storing and manipulating genomic datasets which typically consist of a matrix of measurements (like gene expression values) as well as annotations about rows (i.e. genomic locations) and annotations about columns (i.e. meta-data about collected samples). But 'annmatrix' objects are also expected to be useful in various other contexts. Package: r-cran-annoprobe Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2048 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dt, r-cran-ggpubr, r-cran-pheatmap, r-bioc-biobase, r-cran-xml2, r-cran-httr, r-cran-curl Suggests: r-bioc-limma, r-bioc-geoquery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-annoprobe_0.1.7-1.ca2604.1_all.deb Size: 2059304 MD5sum: 33d079b1d2fa30c3a0f9bf053b9b3426 SHA1: e6d7f14d9db9ed4dac15df8ef6d6bd53d8703294 SHA256: 3fc5290dcaf21e54b1f60c511d8c9ccc324e6b6a976a85b0bb73333fcef78b81 SHA512: 09806c545c2ca52366ea8a40d4a101cc077cf1fa41e3f3d8a622614c7157aea978775c3546894a66566bfa98152e32f1f044bcc1e94be02b746496726aa5cc37 Homepage: https://cran.r-project.org/package=AnnoProbe Description: CRAN Package 'AnnoProbe' (Annotate the Gene Symbols for Probes in Expression Array) We curated 147 of expression array, from 3 species(human,mouse,rat), 3 companies('Affymetrix','Illumina','Agilent'), by aligning the 'Fasta' sequences of all probes of each platform to their corresponding reference genome, and then annotate them to genes. 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Methods used include g:Profiler (Raudvere et al. (2019) ), biomaRt (Durinck et al. (2009) ), and the Open Targets Platform (Koscielny et al. (2017) ). 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Package: r-cran-anocva Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster Suggests: r-cran-mass, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-anocva_0.1.1-1.ca2604.1_all.deb Size: 41488 MD5sum: b72e96d154d21bf53d0c1045804434df SHA1: ad5d93705e151696be3cb158e33b0e0c08b8b8cc SHA256: 57e172202a4e9a590ef95efe176c3d11efdd61155d7b8a3f2280e8d784ca4014 SHA512: a1594c947925645dc0c64875ceca824c38d995c5a63d37f9c78e9110c103fe4a496e52fa05319e336618a26bb4fe6de660b5ab753e6b3334c4415c9ac40de9c2 Homepage: https://cran.r-project.org/package=anocva Description: CRAN Package 'anocva' (A Non-Parametric Statistical Test to Compare ClusteringStructures) Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. Package: r-cran-anofa Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrapply, r-cran-superb, r-cran-rdpack, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-anofa_0.1.3-1.ca2604.1_all.deb Size: 181668 MD5sum: 3511d16da13c51365b2c20517729259d SHA1: 8fac00caefc8d7f9ea544f7ec4d1ecde248dd242 SHA256: 8e78765008f6669d80fd13ba2453cc708f1383194ae7bee594282141f13e5b80 SHA512: 8c29bd9f1757c0c487df46538243601257795379275250b594c28d417bdce7a358fd599e509cb5cdb91e7c050d51da3a4c8926a79a5bba3c59c58af93e3d81fc Homepage: https://cran.r-project.org/package=ANOFA Description: CRAN Package 'ANOFA' (Analyses of Frequency Data) Analyses of frequencies can be performed using an alternative test based on the G statistic. The test has similar type-I error rates and power as the chi-square test. However, it is based on a total statistic that can be decomposed in an additive fashion into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call this set of tools 'ANOFA' (Analysis of Frequency data) to highlight its similarities with ANOVA. This framework also renders plots of frequencies along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models. See Laurencelle and Cousineau (2023) . Package: r-cran-anoint Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-anoint_1.5-1.ca2604.1_all.deb Size: 260358 MD5sum: 4ffe355399d8c8ad197d622ca4a7081d SHA1: b36d154cce6fa2cc53284b69d578bc6d16e7e2d4 SHA256: 6a4386ac8c1a68cd04415d0f9bc97a33759932a7d1288978e3074eddcf93a8e6 SHA512: bb5fc14d57e00ac87f0d0635e9a44e268ebd97ed7fbe409816e056f09f2555ea752998bc7ff6bd6725c3f48eab89be1692570968ab5ab9d0af5a9f724b2228c3 Homepage: https://cran.r-project.org/package=anoint Description: CRAN Package 'anoint' (Analysis of Interactions) The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) . Package: r-cran-anom Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mcpan, r-cran-multcomp, r-cran-nparcomp, r-cran-simcomp Suggests: r-cran-knitr, r-cran-lme4, r-cran-nlme, r-cran-sandwich Filename: pool/dists/resolute/main/r-cran-anom_0.5-1.ca2604.1_all.deb Size: 252056 MD5sum: 50758d10a509c93bba44d8733c74ab22 SHA1: 4b82d4d2d0488792cbcd7b26c6519f7b7443952a SHA256: 1cf037fc24583ffab0c01a4bbd5e9a1617aee473e73d1dffd3fc3e2637defe51 SHA512: dbdf326cb878b6aaf84afae7ddd86dbcd65e5fa0e07da860e630f4b9f00ae013fed97ddf5d879557a18bacb2e53c8d90878c1f2ce7afc5d771e572398b71a012 Homepage: https://cran.r-project.org/package=ANOM Description: CRAN Package 'ANOM' (Analysis of Means) Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with 'multcomp', 'SimComp', 'nparcomp', or 'MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean. Package: r-cran-anomalize Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3697 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-timetk, r-cran-sweep, r-cran-tibbletime, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-tidyverse, r-cran-tidyquant, r-cran-stringr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-anomalize_0.3.0-1.ca2604.1_all.deb Size: 3055058 MD5sum: 898527d407f535265c629789f81d56a5 SHA1: b641fb66d80661c124ed8b15426a7bde81f5bda5 SHA256: bef65c5124bc9d68a5089c269b01a46ffd7479cb2ffe5393275b2d5756cdb21c SHA512: 777efedccb575db1ab491c3cce5fdfd1a79201644ea2de1f5711bf08566c9a552aee0eac8d66ced6eaa014f0ba192f3a08bd75d30694e6e311aba69a6d1e27dd Homepage: https://cran.r-project.org/package=anomalize Description: CRAN Package 'anomalize' (Tidy Anomaly Detection) The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods. Package: r-cran-anomalyscore Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dtw, r-cran-class, r-cran-astsa, r-cran-transport, r-cran-marima, r-cran-tsa, r-cran-rann, r-cran-mass, r-cran-mvlsw Filename: pool/dists/resolute/main/r-cran-anomalyscore_0.1-1.ca2604.1_all.deb Size: 122884 MD5sum: 9bab42ed1c013b895ee6a331e74d2dce SHA1: 80b4ad82b06fe7a31eb94023ba482764f0e35601 SHA256: 03d306d0e05aa6a606cb0be79fc781dd95b28bcfab44b62cdc8a33ab1991e108 SHA512: c0d9e0f9c123e881e7af9bbecd6b017f47d5277d9e7573442b9d6e41cc96613eaf5989099a53458c5afdb93606246f3a81c3cf4ed4457072e6e4e140a7626c5d Homepage: https://cran.r-project.org/package=AnomalyScore Description: CRAN Package 'AnomalyScore' (Anomaly Scoring for Multivariate Time Series) Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided. Package: r-cran-anomo Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-markdown, r-cran-odr Filename: pool/dists/resolute/main/r-cran-anomo_1.3.2-1.ca2604.1_all.deb Size: 64978 MD5sum: 9ec0014759833907770383d1b1081724 SHA1: faac3166b4c4354930c9531cf75dfc70a9c764c5 SHA256: a233e453f390ab83078ce645ac5ea02d8dfa51dfdda8f03f6caa0541e18bf29f SHA512: 70ae7d04316ac46086a6c01411f1458964b428eda5a2d2b16ab01103ad44e0b014e769f66ec5dfcd227244dd526d3136c8df3e78d7f38bc1f6497bc730d1a810 Homepage: https://cran.r-project.org/package=anomo Description: CRAN Package 'anomo' (Analysis of Moderation, Statistical Power, and Optimal Designfor Studies Detecting Difference and Equivalence) Analysis of moderation (ANOMO) method conceptualizes the difference and equivalence tests as a moderation problem to test the difference and equivalence of two estimates (e.g., two means or two effects). Package: r-cran-anopa Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-superb, r-cran-rdpack, r-cran-ggplot2, r-cran-scales, r-cran-rrapply, r-cran-plyr Suggests: r-cran-rmarkdown, r-cran-ggh4x, r-cran-gridextra, r-cran-psych, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-anopa_0.2.3-1.ca2604.1_all.deb Size: 286412 MD5sum: cd6dff032d3690b1b62d17846b68a3b5 SHA1: d58f9ce35a79b5e5ca645741ecd53b011bd669fd SHA256: 29f26d4127eb26e1f1c712ce1bbd7249aff8b3de3ff578db57d5063bc5a69090 SHA512: 0cb1b43f75f095f2d5b76c5f7088bdd95f80e88f11252ef365aa659c3aec8abd3d3f95560836fbf21528b873fac531ec2ff33dda5a4964f2f6b5c51a98dc4054 Homepage: https://cran.r-project.org/package=ANOPA Description: CRAN Package 'ANOPA' (Analyses of Proportions using Anscombe Transform) Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed data. The 'ANOPA' package can analyze proportions obtained from up to four factors. The factors can be within-subject or between-subject or a mix of within- and between-subject. The main, omnibus analysis can be followed by additive decompositions into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For that reason, we call this set of tools 'ANOPA' (Analysis of Proportion using Anscombe transform) to highlight its similarities with ANOVA. The 'ANOPA' framework also allows plots of proportions easy to obtain along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. Only particularity, the 'ANOPA' computes F statistics which have an infinite degree of freedom on the denominator. See Laurencelle and Cousineau (2023) . Package: r-cran-anovaireva Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-dplyr, r-cran-car, r-cran-ggplot2, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-anovaireva_0.1.0-1.ca2604.1_all.deb Size: 88536 MD5sum: a9d46dd8f5e7810d978dcf5f05f1e6b7 SHA1: 068558da2923c9cfa0dc490b84ce3094bf5252fd SHA256: e1800cce1ee305f64f77f5c896340a146a6b2e8aa9873dae148872a3075c696f SHA512: a8dc4af4625a87bda49df0dbf9cf82e924e85b2a14b79b277d6ce9dc668de98a47172b243cd0f3dd2712050bea7f3781ef41c5869c8faa86de809559d343f6d2 Homepage: https://cran.r-project.org/package=ANOVAIREVA Description: CRAN Package 'ANOVAIREVA' (Interactive Document for Working with Analysis of Variance) An interactive document on the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . Package: r-cran-anovashiny2 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-dplyr, r-cran-rhandsontable, r-cran-desctools, r-cran-hh Filename: pool/dists/resolute/main/r-cran-anovashiny2_0.1.0-1.ca2604.1_all.deb Size: 71644 MD5sum: 2bfb42b1a2f360baf2e649b538788394 SHA1: 332d341e2dd79ba7915e8e067478ba3d58770b37 SHA256: 4a5cabefbf0e6477635de20de6b4687ba8c69e38c079665320b4963bcd1b35d3 SHA512: c66c68f93cd9bd6f3aa9b544c83887447b0c934741a484c9d8c0266d73d8e2d1c2764430b75d5cfbd12905f9b579011095f994e1e922ae3d14191bcdd8422009 Homepage: https://cran.r-project.org/package=ANOVAShiny2 Description: CRAN Package 'ANOVAShiny2' (Interactive Document for Working with Analysis of Variance) An interactive document on the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . Package: r-cran-anovashiny Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-dplyr, r-cran-rhandsontable, r-cran-desctools, r-cran-hh Filename: pool/dists/resolute/main/r-cran-anovashiny_0.1.0-1.ca2604.1_all.deb Size: 71532 MD5sum: 0e804a079e3d02753454e5ae48bd52d8 SHA1: c504d339510295c32cfba00198e7afcf03512627 SHA256: 1b163ad2b8b1b71f3ee9a16c6a68c99a995c4e41515b7c33597b84f396307e02 SHA512: ae3ed0186c14ad1e3f3e66f295961332303764315c5c1989f973b7e00949d0a9cf687bdf1b91648235ec492bcb615351ee504f849d51febd102f9dccafdde616 Homepage: https://cran.r-project.org/package=ANOVAShiny Description: CRAN Package 'ANOVAShiny' (Interactive Document for Working with Analysis of Variance) An interactive document on the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . Package: r-cran-anovir Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-anovir_0.1.0-1.ca2604.1_all.deb Size: 401954 MD5sum: d190a5ef0b654a278eecd0ad87e1602f SHA1: 4b64ed52dcf08f957a27e814eaeac7d2e0a5b1f6 SHA256: cebf1371fc0561ffcda89d38ba75701fb33672d5ad384e306b9c2ff2a5e05459 SHA512: c417ae0194da200c8e63f4aad04aab12e519cadb68457c09ea67eede1698812d4b6d62bde6526382932fa3dc599fbfcbc144ef1594e207cf2b7797d4d6d390bd Homepage: https://cran.r-project.org/package=anovir Description: CRAN Package 'anovir' (Analysis of Virulence) Epidemiological population dynamics models traditionally define a pathogen's virulence as the increase in the per capita rate of mortality of infected hosts due to infection. This package provides functions allowing virulence to be estimated by maximum likelihood techniques. The approach is based on the analysis of relative survival comparing survival in matching cohorts of infected vs. uninfected hosts (Agnew 2019) . Package: r-cran-anscombiser Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-datasaurus, r-cran-gganimate, r-cran-ggplot2, r-cran-maps, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-anscombiser_1.1.0-1.ca2604.1_all.deb Size: 192738 MD5sum: 7c40cc329e82a9722ee1ddc3738e1901 SHA1: 2c4a56694390b6f94cc3aa8809117fb2d2827091 SHA256: 47f4a7f1aaceede614da401ec75c625186d65bead1ac89f5591cd45e2a64c6d7 SHA512: fac57e00ab9e71240844c1eecf0d7c7b1a4f501bffc263688497cf086225a4b2b1187660c56316c7024c673fca086fa905ebe4589af0bce6b2368272662e21ea Homepage: https://cran.r-project.org/package=anscombiser Description: CRAN Package 'anscombiser' (Create Datasets with Identical Summary Statistics) Anscombe's quartet are a set of four two-variable datasets that have several common summary statistics but which have very different joint distributions. This becomes apparent when the data are plotted, which illustrates the importance of using graphical displays in Statistics. This package enables the creation of datasets that have identical marginal sample means and sample variances, sample correlation, least squares regression coefficients and coefficient of determination. The user supplies an initial dataset, which is shifted, scaled and rotated in order to achieve target summary statistics. The general shape of the initial dataset is retained. The target statistics can be supplied directly or calculated based on a user-supplied dataset. The 'datasauRus' package provides further examples of datasets that have markedly different scatter plots but share many sample summary statistics. Package: r-cran-ansm5 Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ansm5_1.1.1-1.ca2604.1_all.deb Size: 566724 MD5sum: c749f179229a91d93253fa4fd87aa4dc SHA1: fd7798fd908af3bb8fae988291043850bcf043ac SHA256: d2865ecaf2e79875f11f8ff97b9ea79f625375cc9f655326fc81fd8c9f4bb2db SHA512: 2606abf4d9d6a7fc18c80b48902dc2026df4472acb41ec484b501187bee4c81e0ae77352c77de14d7a3f6bcd05b488199a409d4c89a3e3775895a315195a78ae Homepage: https://cran.r-project.org/package=ANSM5 Description: CRAN Package 'ANSM5' (Functions and Data for the Book "Applied NonparametricStatistical Methods", 5th Edition) Functions and data to accompany the 5th edition of the book "Applied Nonparametric Statistical Methods" (4th edition: Sprent & Smeeton, 2024, ISBN:158488701X), the revisions from the 4th edition including a move from describing the output from a miscellany of statistical software packages to using R. While the output from many of the functions can also be obtained using a range of other R functions, this package provides functions in a unified setting and give output using both p-values and confidence intervals, exemplifying the book's approach of treating p-values as a guide to statistical importance and not an end product in their own right. Please note that in creating the ANSM5 package we do not claim to have produced software which is necessarily the most computationally efficient nor the most comprehensive. Package: r-cran-antangiocool Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 35286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-rjava, r-cran-rweka, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-antangiocool_1.2-1.ca2604.1_all.deb Size: 17349498 MD5sum: 0eeb4db4e202b710a106dc6ac84e39e8 SHA1: 4ef6fc5d5e13a89c6b9d73eb44cb05cb252531b4 SHA256: f76cd3d58fc01e8826363473bda0e2325f5fe27dcbd54b231d2bf8455f197ade SHA512: 998dbe4e64c0d721d67cc6cb006427e2429b35fc819a868e702cd9d84801ac3eb1cc0ff06c9ee61c3445f33e94e6bcd678533cd72b8e932ea9e17ad5f14a5129 Homepage: https://cran.r-project.org/package=AntAngioCOOL Description: CRAN Package 'AntAngioCOOL' (Anti-Angiogenic Peptide Prediction) Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. 'AntAngioCOOL' exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, 'AntAngioCOOL' reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset. Package: r-cran-antareseditobject Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1836 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-antaresread, r-cran-assertthat, r-cran-cli, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-whisker, r-cran-doparallel, r-cran-dofuture, r-cran-memuse, r-cran-progressr, r-cran-pbapply, r-cran-future, r-cran-plyr, r-cran-yaml, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-data.tree Filename: pool/dists/resolute/main/r-cran-antareseditobject_1.0.0-1.ca2604.1_all.deb Size: 926548 MD5sum: 1212d2cd43862d487ce9571b9387ae52 SHA1: c007bf8b3aacc0bae495001dc1d3b47b43abe719 SHA256: 078568c14938eba786c07cdabdde85475515976bdd1bac8808663b4f0f4dd0c9 SHA512: dcc183267433b5976bc46ac503673754d00640c973f142041060a9c77cef35e7d3fc05f120c2e5f5f2dfb5e6d1feac9126010f3d3e252e800d01c0e555c01947 Homepage: https://cran.r-project.org/package=antaresEditObject Description: CRAN Package 'antaresEditObject' (Edit an 'Antares' Simulation) Edit an 'Antares' simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update 'Antares' general & optimization settings. 'Antares' is an open source power system generator, more information available here : . 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This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors. Package: r-cran-antaresread Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4628 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-bit64, r-cran-lubridate, r-cran-plyr, r-cran-stringr, r-cran-stringi, r-cran-shiny, r-cran-pbapply, r-cran-doparallel, r-cran-jsonlite, r-cran-httr, r-cran-memuse, r-cran-purrr, r-cran-lifecycle, r-cran-assertthat, r-cran-arrow Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-antaresread_3.0.0-1.ca2604.1_all.deb Size: 3434846 MD5sum: c51cd6c74101bd1e2ab6ae2e83c39d74 SHA1: b8058b0a7d1d973cb43d3e6e110f13c650eed979 SHA256: 14cbbc9503c7cb827e7209d7a2c694fa7d122d29d75ef28704149261966a0a42 SHA512: a09316b7042344b500deeafe183b02c58e9e756c5d3fac4145954324f0ba0b1d5d2e2a6104f7a9f41274b461129c7c475b695687efca14185689767d28061a92 Homepage: https://cran.r-project.org/package=antaresRead Description: CRAN Package 'antaresRead' (Import, Manipulate and Explore the Results of an 'Antares'Simulation) Import, manipulate and explore results generated by 'Antares', a powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about 'Antares' here : ). Package: r-cran-antaresviz Architecture: all Version: 0.18.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-antaresread, r-cran-antaresprocessing, r-cran-spmaps, r-cran-dygraphs, r-cran-shiny, r-cran-plotly, r-cran-htmltools, r-cran-htmlwidgets, r-cran-manipulatewidget, r-cran-leaflet, r-cran-sp, r-cran-sf, r-cran-webshot, r-cran-data.table, r-cran-lubridate, r-cran-geojsonio, r-cran-leaflet.minicharts, r-cran-assertthat, r-cran-ramcharts, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-visnetwork, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-antaresviz_0.18.3-1.ca2604.1_all.deb Size: 2977534 MD5sum: d206f2962b60cd43095ad9f15c302379 SHA1: 53546738a843f6641b7456a00ab1ac74e0af9dc6 SHA256: 953d9f85b5947303b584dbf5993648ce76850d394c441af43271d855d46f2ff5 SHA512: c2b138df55ec84e9a947e3264bea6657dd65614092afe8481fcaf7478b11234a3d55be62d205d7952388d9a864ec14bf9c2956c1ee5c30456bf2a65001c5260e Homepage: https://cran.r-project.org/package=antaresViz Description: CRAN Package 'antaresViz' (Antares Visualizations) Visualize results generated by Antares, a powerful open source software developed by RTE to simulate and study electric power systems (more information about 'Antares' here: ). 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Package: r-cran-antclassify Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-antclassify_0.2.1-1.ca2604.1_all.deb Size: 106332 MD5sum: 9a9d4726c04c0156b7e7242e119c6343 SHA1: ae068a76a089c1f102bb13aa3b0eae52ccc5261f SHA256: 5606fce88cc2b5fb62368bbe3fa0ac36821008c21563868297616358d7b92c4c SHA512: c0778aebd64a781e6fe38c62b1e029f1b874beda7f42f424092939b4a5082d430cadfebe02a9f3d29d8ab163cee396b5b61fb9d0513beba8e93800a6c69938f4 Homepage: https://cran.r-project.org/package=AntClassify Description: CRAN Package 'AntClassify' (Functional Guilds, Invasion Status, Endemism, and Rarity of Ants) Provides functions for the analysis of ant communities, aiming to standardize workflows in myrmecology. The package automates the assignment of species to functional guilds based on trophic strategies, feeding habits, and foraging behavior, using established classification frameworks (Silva et al., 2015 ; Silvestre et al., 2003 ; Delabie et al., 2000 ), and also includes a novel classification system implemented within the package, developed from ant species occurring in urban environments. It also includes routines to flag exotic species of Brazil (Vieira, 2025, unpublished master's thesis), identify endemic species (Silva et al., 2025 ), and classify species rarity and rarity forms of the Atlantic Forest (Silva et al., 2024 ). The package reduces manual effort and improves reproducibility, supporting research and biodiversity management of Neotropical ant communities. Package: r-cran-antedep Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nloptr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-antedep_0.2.0-1.ca2604.1_all.deb Size: 956722 MD5sum: e8e5b88f2b7b32f8e6e8e76a68921735 SHA1: 11dd30b1724ed14466743be6bf1f754c7c63f617 SHA256: 4cf659c40bd52d9aac14a7a9baa6f4728502ea981aa36e77815e1696f8146088 SHA512: c34b8b36e437c507ee36ee789009a81c793e112926652e122e78104c71b5f685112ff6c673c4a6a26798de21280ce7be760cb10674b7817c3a8073db9a409edc Homepage: https://cran.r-project.org/package=antedep Description: CRAN Package 'antedep' (Antedependence Models for Longitudinal Data) Fitting, simulation, and inference for antedependence models for longitudinal data, as described in Zimmerman and Nunez-Anton (2009, ISBN:9781420011074). Supports integer-valued antedependence (INAD) models for count data with thinning operators (binomial, Poisson, negative binomial) and flexible innovation distributions (Poisson, Bell, negative binomial), categorical antedependence models for discrete-state longitudinal outcomes, and Gaussian antedependence (AD) models for continuous data. Implements maximum likelihood estimation via time-separable optimization and block coordinate descent, with confidence intervals based on Louis' identity and profile likelihood. Package: r-cran-anthro Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-anthro_1.1.0-1.ca2604.1_all.deb Size: 367124 MD5sum: 48b873d0fecab0fee536531541df96ec SHA1: 21e24f4dd19a79c79496fd09320dd747121ba010 SHA256: 8aa32fa00f260171d88c7519793af9f36c44f626956da6d1a72957b9e000fa23 SHA512: 3e9bb0fa9bdc5e8db561eca8f63e20f42926b61fff29cbea3bc36f858d99f652108a4cf939c30b3a972e97ec65181b657b464b8ca55843a55acf2f95dc8420cf Homepage: https://cran.r-project.org/package=anthro Description: CRAN Package 'anthro' (Computation of the WHO Child Growth Standards) Provides WHO Child Growth Standards (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: . 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Package: r-cran-anthropmmd Architecture: all Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-plotrix, r-cran-rlang, r-cran-scatterplot3d, r-cran-shiny, r-cran-smacof Suggests: r-cran-cluster, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-anthropmmd_4.1.0-1.ca2604.1_all.deb Size: 247894 MD5sum: e5f08bac11a0a30b85aaba2c28483aaa SHA1: 2751cd04474310505a03cb846dc76e1532b0da06 SHA256: 01a3546c69665df3528d70836586e0fd62f7acaebf924981f23edaadd321bcd3 SHA512: 66ff23f375e3b328c88040098d50b33e2e73135b003bca1b42f94228f8f8569a61a8fdf04305f47827aa011fac979f2c35e02e1283836c99d6472cbfb13414ed Homepage: https://cran.r-project.org/package=AnthropMMD Description: CRAN Package 'AnthropMMD' (An R Package for the Mean Measure of Divergence (MMD)) Offers a graphical user interface for the calculation of the mean measure of divergence, with facilities for trait selection and graphical representations . Package: r-cran-antibodyforests Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2745 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-bioc-biostrings, r-cran-dplyr, r-cran-gtools, r-cran-igraph, r-cran-magrittr, r-bioc-pwalign, r-cran-rlang, r-cran-scales, r-cran-seqinr, r-cran-stringdist, r-cran-stringr, r-cran-tidyr, r-cran-viridis Suggests: r-cran-alakazam, r-cran-base64enc, r-cran-bio3d, r-cran-combinat, r-cran-devtools, r-cran-dt, r-cran-fpc, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsignif, r-cran-htmltools, r-cran-knitr, r-bioc-msa, r-cran-phangorn, r-cran-pheatmap, r-cran-philentropy, r-cran-peptides, r-cran-rcompadre, r-cran-rmarkdown, r-cran-swiper Filename: pool/dists/resolute/main/r-cran-antibodyforests_1.1.0-1.ca2604.1_all.deb Size: 1790262 MD5sum: e6244e27230757a6b97936ebb029fe8b SHA1: b5002e7656e9027c00df8e4b13d8ccee0e7fdfb3 SHA256: b53725fe3d643b7c17cf212e4b294d4e660dc59501db1a52c4c34ea45be65764 SHA512: 0af98eaf38fb3871be421f52cca26373a873d305b2e911aea8d16628f46db49ef48359f12c9ab6d3a173a2fee4eecd3a2f6c93b4db6a94e46acea8b32d050e26 Homepage: https://cran.r-project.org/package=AntibodyForests Description: CRAN Package 'AntibodyForests' (Delineating Inter- And Intra-Antibody Repertoire Evolution) The generated wealth of immune repertoire sequencing data requires software to investigate and quantify inter- and intra-antibody repertoire evolution to uncover how B cells evolve during immune responses. Here, we present 'AntibodyForests', a software to investigate and quantify inter- and intra-antibody repertoire evolution. Package: r-cran-antibodytiters Architecture: all Version: 0.1.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-desctools Filename: pool/dists/resolute/main/r-cran-antibodytiters_0.1.24-1.ca2604.1_all.deb Size: 216632 MD5sum: b7f9d0c2ca9a803b0037fdc042b1c9d6 SHA1: 0dbbc3281a3b0562a6fa2dbf02253b1167068e9b SHA256: 37cbc94867d27358da5d1f178d1d003e85751fc87f5564b3f49bc7c4e535643e SHA512: 0729ef9557bc500ddfb5769c9c13c6d67244ceb7ce1f3c3cd01c3d4dcafa9df0cecca8af2747949390d5717eb3f809e536bf0af3dc675070a7bb8bdfcfbdeaa6 Homepage: https://cran.r-project.org/package=AntibodyTiters Description: CRAN Package 'AntibodyTiters' (Antibody Titer Analysis of Vaccinated Patients) Visualization of antibody titer scores is valuable for examination of vaccination effects. 'AntibodyTiters' visualizes antibody titers of all or selected patients. This package also produces empty excel files in a specified format, in which users can fill in experimental data for visualization. Excel files with toy data can also be produced, so that users can see how it is visualized before obtaining real data. The data should contain titer scores at pre-vaccination, after-1st shot, after-2nd shot, and at least one additional sampling points. Patients with missing values can be included. The first two sampling points (pre-vaccination and after-1st shot) will be plotted discretely, whereas those following will be plotted on a continuous time scale that starts from the day of second shot. Half-life of titer can also be calculated for each pair of sampling points. Package: r-cran-antitrust Architecture: all Version: 0.99.30-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-numderiv Suggests: r-cran-mass, r-cran-ggplot2, r-cran-knitr, r-cran-bookdown, r-cran-rmarkdown, r-cran-competitiontoolbox Filename: pool/dists/resolute/main/r-cran-antitrust_0.99.30-1.ca2604.1_all.deb Size: 1129250 MD5sum: c0a8a70e7bc769f010eec61ce7c4fa08 SHA1: 9d8838dc983b0e9d6ffad0b89d527ae85394c2d8 SHA256: f5dfdb9d27b8f2775e1a73fc652511b2693e45c70fdb5b6bf21f311072580ca6 SHA512: 64e464ca0c7ddcfb0e0b3be94daf0c65312a42966ba56973f4eb8d141139c333fd263391e8f6d88d230679249a86c7269fdc36b03f35a39adf234b42977d32f8 Homepage: https://cran.r-project.org/package=antitrust Description: CRAN Package 'antitrust' (Tools for Antitrust Practitioners) A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes. Package: r-cran-antsnet Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-viridis, r-cran-ggpubr Suggests: r-cran-shiny, r-cran-cowplot, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-antsnet_1.0.0-1.ca2604.1_all.deb Size: 248750 MD5sum: 6f3e0fa55a762799160dd3880a8f0f7d SHA1: 4a432b336f7deb6cd516043e0c2badd39074c8f1 SHA256: 6fed89d3b9bd4081e2cdc3b1ad1fd893cb395b15b25bffb4439e5080cad9bc15 SHA512: aade2698ca4a4720a05c4cd692aaf21684d0bf0020417a34112271dfa8e9d7d8a02f55505ca5e3164378c271bfcb46b7bb1b10b4fde5c4c7d8741d373cfb79b0 Homepage: https://cran.r-project.org/package=AntsNet Description: CRAN Package 'AntsNet' (Unified Simulation of Isomorphisms Between Ant ColonyIntelligence and Machine Learning) Implements the full suite of simulation, visualization, and analysis tools for exploring the mathematical isomorphisms between ant colony decision-making and three major paradigms of machine learning: random forests (Part I: variance reduction through decorrelation), boosting (Part II: bias reduction through adaptive recruitment), and neural networks (Part III: gradient-based generational learning). Accompanies the trilogy "Isomorphic Functionalities between Ant Colony and Ensemble Learning" (Fokoué, Babbitt, and Levental, 2026, , ). Package: r-cran-anxietysleep Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-lifecycle Filename: pool/dists/resolute/main/r-cran-anxietysleep_0.0.1-1.ca2604.1_all.deb Size: 58940 MD5sum: a1beb85630e1277329a04f18a333d512 SHA1: b4770b79bd0ba218c67036eb5f576f13d94029b4 SHA256: ba97ef12227b9ac49adc1ce644fffbad1a371f07c56641b0ea67670824d2c0dc SHA512: 606ba0a0c665c1228c506b33553b4202cae756fbc897531ab4ccc6c5629751e4bcf9b55f864ad90657b3be5bda6517a4a755ca7ace28c764b16c8771b8f53a69 Homepage: https://cran.r-project.org/package=AnxietySleep Description: CRAN Package 'AnxietySleep' (Sleep Quality and Anxiety in Confinement) Data from the anxiety and confinement study from Alvarado-Aravena et al. (2022) . 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These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod. 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Package: r-cran-aopdata Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-rlang, r-cran-sf Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-units Filename: pool/dists/resolute/main/r-cran-aopdata_1.1.2-1.ca2604.1_all.deb Size: 399894 MD5sum: 3b9516a2581655abbb4935ef1d8182dd SHA1: 22b9065ac7d8b71066598c17f52d1c0537485596 SHA256: 727509aaf67994505e579fcde5026e0da0b6ba52249122656c3f1e8fd4ec5a4b SHA512: 6aba81a49e7f8fddd56e2c77ef2b732e4392e2ac8df840e3c7f857bea0d1b4e5fe3302a16a50ba0ea7fd260a2601cc2d7667c8772eff63f19dfdaff4493faf0b Homepage: https://cran.r-project.org/package=aopdata Description: CRAN Package 'aopdata' (Data from the 'Access to Opportunities Project (AOP)') Download data from the 'Access to Opportunities Project (AOP)'. The 'aopdata' package brings annual estimates of access to employment, health, education and social assistance services by transport mode, as well as data on the spatial distribution of population, jobs, health care, schools and social assistance facilities at a fine spatial resolution for all cities included in the project. More info on the 'AOP' website . Package: r-cran-aoptbdtvc Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-mass Filename: pool/dists/resolute/main/r-cran-aoptbdtvc_0.0.3-1.ca2604.1_all.deb Size: 88730 MD5sum: 65262f9ae750aec5db2edc6e4187ebc6 SHA1: 8fddaa647ded384f0487ded3d74dd2d97ac88ea1 SHA256: 5f1a306c98e567f3d098b0b0105e26989e2625ab6b408ebc3a5bc3a51abfff9a SHA512: 9d0f21d2c4473b4b64522e90c7455acf0916fcd160e60db01b70da0ec40cc4c375a1faba6619abf2a3d4d3f664446bd966ce32f9fbe5f0a7225b03aaddff1a3d Homepage: https://cran.r-project.org/package=Aoptbdtvc Description: CRAN Package 'Aoptbdtvc' (A-Optimal Block Designs for Comparing Test Treatments withControls) A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737. Package: r-cran-aoristic Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-ggplot2, r-cran-lubridate, r-cran-plyr, r-cran-scales, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-aoristic_1.1.1-1.ca2604.1_all.deb Size: 123554 MD5sum: 532e3a8a16c95a53226cf4f222b90e16 SHA1: 4e17ac91f13385c8a8faa691459af4ed2676c85c SHA256: 873dd83eee9242d3d427d9794ffba9c777ca0ab790fe546ff6924a9e6a01ca85 SHA512: 5d2e421bf9542dbf4c798fd35bf9f32392e3b2e954f22ce27858b8ff160cce1aa0ad5f6d82b41f5546977c674435dc24b3615317710bb22e8aea17b7a31e7f00 Homepage: https://cran.r-project.org/package=aoristic Description: CRAN Package 'aoristic' (Generates Aoristic Probability Distributions) It can sometimes be difficult to ascertain when some events (such as property crime) occur because the victim is not present when the crime happens. As a result, police databases often record a 'start' (or 'from') date and time, and an 'end' (or 'to') date and time. The time span between these date/times can be minutes, hours, or sometimes days, hence the term 'Aoristic'. Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. For events with a location describes with either a latitude/longitude, or X,Y coordinate pair, and a start and end date/time, this package generates an aoristic data frame with aoristic weighted probability values for each hour of the week, for each observation. The coordinates are not necessary for the program to calculate aoristic weights; however, they are part of this package because a spatial component has been integral to aoristic analysis from the start. Dummy coordinates can be introduced if the user only has temporal data. Outputs include an aoristic data frame, as well as summary graphs and displays. For more information see: Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, Journal of Quantitative Criminology. 18 (1): 23-43. Note: This package replaces an original 'aoristic' package (version 0.6) by George Kikuchi that has been discontinued with his permission. Package: r-cran-aos Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-htmltools Suggests: r-cran-shiny Filename: pool/dists/resolute/main/r-cran-aos_0.1.0-1.ca2604.1_all.deb Size: 67686 MD5sum: 0782b0a2dd3e8b366b8b481af5f52cbe SHA1: da4c117ade9c816f7e5ae50c2a6c47d1b45c17f8 SHA256: 6d7860289cff680b5ff1d45a5b008076694150d0002f369ec14a9408ff291e69 SHA512: e2e470ba26a1b47a374d5bdf22d0147c961be735588d631895902cd0e06f5f35b878fe5ce144d30d08b7e70837a15875e4b396a535698d67ab387ab7fba6157f Homepage: https://cran.r-project.org/package=aos Description: CRAN Package 'aos' (Animate on Scroll Library for 'shiny') Trigger animation effects on scroll on any HTML element of 'shiny' and 'rmarkdown', such as any text or plot, thanks to the 'AOS' Animate On Scroll jQuery library. 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For more details see - Koleck TA, Dreisbach C, Zhang C, Grayson S, Lor M, Deng Z, Conway A, Higgins PDR, Bakken S (2024) . 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References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) ; Lin & Liao (2008) . 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Format numbers and text consistent with APA style. Create tables that comply with APA style by extending flextable functions. 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Package: r-cran-apatables Architecture: all Version: 2.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-broom, r-cran-dplyr, r-cran-boot, r-cran-tibble, r-cran-mbess Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-apatables_2.0.8-1.ca2604.1_all.deb Size: 310732 MD5sum: d921a53ce62aa450d146263b353d1ea1 SHA1: 4bd399f3dc3ba5777e187dd7dbf59c3e76122af5 SHA256: 9a271d72e287221430dafb248ee250f6ba26a339e2a1afc7b231dc8d36b0f0c4 SHA512: c661533af11d97fcf203f7673d69e1f44e407e190ed52df2fabad6f9ce77b3efa5da1bb8d89176db07bb7a40963c1670cc71f9cac647aef1c1167e8b76248d85 Homepage: https://cran.r-project.org/package=apaTables Description: CRAN Package 'apaTables' (Create American Psychological Association (APA) Style Tables) A common task faced by researchers is the creation of APA style (i.e., American Psychological Association style) tables from statistical output. In R a large number of function calls are often needed to obtain all of the desired information for a single APA style table. As well, the process of manually creating APA style tables in a word processor is prone to transcription errors. This package creates Word files (.doc files) containing APA style tables for several types of analyses. Using this package minimizes transcription errors and reduces the number commands needed by the user. Package: r-cran-apatext Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cocor Suggests: r-cran-apatables Filename: pool/dists/resolute/main/r-cran-apatext_0.1.7-1.ca2604.1_all.deb Size: 52806 MD5sum: a35bfb39db08dc26f32887e5458942d8 SHA1: b87165c5c1d495d73cd848fee2edce52174ab850 SHA256: dd579321e75ce0b9dc645873f432ca7f6417c0dcc3dec27b881a7ef25cdfd774 SHA512: ba93b65802fdab69752afff9b60088edd0e2ea019436353045f2c8b93a17b354437f524b617f76666c7476140d283238f864bcaf2039a4587a582bdf1f2eae1d Homepage: https://cran.r-project.org/package=apaText Description: CRAN Package 'apaText' (Create R Markdown Text for Results in the Style of the AmericanPsychological Association (APA)) Create APA style text from analyses for use within R Markdown documents. Descriptive statistics, confidence intervals, and cell sizes are reported. Package: r-cran-apathe Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-papaja, r-cran-bookdown, r-cran-rmdfiltr, r-cran-rmarkdown, r-cran-assertthat Suggests: r-cran-stringr, r-cran-bibtex, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-apathe_0.1.0-1.ca2604.1_all.deb Size: 86164 MD5sum: fa02f6ec19b082cfbf9559d2679704b7 SHA1: afcb626b8217b3588ee61a4ebe80dde7d77f9fce SHA256: 739472c6ff102b4698de39dea5174297ea56f4c615a1d4fe0422c5e537bce27f SHA512: dda288cccc08e3d00f16b25af07cb06cc5da126a131695c7c2d2fc3ee7f3da8de9a6924037937e25a04e135c9616348c909dfdf243be8eed3a6252d80ea1275f Homepage: https://cran.r-project.org/package=apathe Description: CRAN Package 'apathe' (American Psychological Association Thesis Templates for RMarkdown) Facilitates writing computationally reproducible student theses in PDF format that conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). The package currently provides two R Markdown templates for homework and theses at the Psychology Department of the University of Cologne. The package builds on the package 'papaja' but is tailored to the requirements of student theses and omits features for simplicity. Package: r-cran-apca Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-apca_1.0.0-1.ca2604.1_all.deb Size: 42310 MD5sum: b3932c7a845982901e739925114e9c61 SHA1: 0da9c908c285d139881aef16f365ca184db6a728 SHA256: d0cd1ed7817572ad29982fb43cea20f07de09868b1995ea06351f07a8402b906 SHA512: c87887593b67b8a3d3700927e42815c4c5780e4a588c2859f5ae32473cb69321181a6b98548edcf3c28f52a61fbcc4892dfd986115f8d1e68fe63161bc97b6a0 Homepage: https://cran.r-project.org/package=apca Description: CRAN Package 'apca' (Advanced Principal Component Analysis) Provides nine computational algorithms for dimensionality reduction via Principal Component Analysis (PCA), built using an object-oriented (S3) architecture. The package includes classical and modern methods: Singular Value Decomposition (SVD) based on Eckart and Young (1936) , Power Iteration based on Hotelling (1933) , QR Algorithm based on Francis (1961) , Jacobi Algorithm based on Jacobi (1846) , Arnoldi Iteration based on Arnoldi (1951) , 'NIPALS' based on Wold (1975) , Alternating Least Squares (ALS) based on Kolda and Bader (2009) , Probabilistic PCA (PPCA) with EM Algorithm based on Tipping and Bishop (1999) , and Generalized Hebbian Algorithm (GHA) based on Sanger (1989) . 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'APCalign' uses the Australian Plant Census (APC) and the Australian Plant Name Index (APNI) to align and update plant taxon names to current, accepted standards. 'APCalign' also supplies information about the establishment status (i.e. native or introduced) of plant taxa across different states/territories. 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Package: r-cran-apci Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-data.table, r-cran-ggpubr, r-cran-stringr, r-cran-gee Filename: pool/dists/resolute/main/r-cran-apci_1.0.8-1.ca2604.1_all.deb Size: 1196072 MD5sum: 49f7ebc39a7a1d207668ab76b99fcf6d SHA1: 4cf04d929a8eb9024d822c2fb25ef473eef8fe22 SHA256: 5f64890c126733c53d8ca24d96dc785bd367f504390a9850d5f44f5ed28b38e0 SHA512: 7b708532f5a853b47a792b1e42908e7da407e2587225817f45b6980d82dff88015815faf8f2f1fceb8a6b3ff72fec8e91c9b2cecbc66f7dd3938267b5c6322bd Homepage: https://cran.r-project.org/package=APCI Description: CRAN Package 'APCI' (A New Age-Period-Cohort Model for Describing and InvestigatingInter-Cohort Differences and Life Course Dynamics) It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics. Package: r-cran-apcinteraction Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-pbapply, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-apcinteraction_0.1.0-1.ca2604.1_all.deb Size: 520262 MD5sum: 7ca7f385b660e5a0faa44f0dd3c6a3a0 SHA1: 44f0e4e907f6b780765b669158095a667fd0c9e9 SHA256: 85b781170cffee97e713331b2c0baa112bc228409bcc71108b0d4ca8875eb3df SHA512: 99388d226583d01129d8729fa84cd696e2fa275df3dcae5c0be09e7097f903830032881b389e08c516dbf77f79d6269d34410bc2a73a861c511f063a519c3522 Homepage: https://cran.r-project.org/package=APCinteraction Description: CRAN Package 'APCinteraction' (Nonparametric Interaction Tests in Balanced Two-Way ANOVA Models) Provides novel nonparametric tests, 'APCSSA' and 'APCSSM', for interaction in two-way ANOVA designs with balanced replications using all possible comparisons. These statistics extend previous methods, allow greater flexibility, and demonstrate higher power in detecting interactions for non-normal data. The package includes optimized functions for computing these test statistics, generating interaction plots, and simulating their null distributions. The companion package 'APCinteractionData' is available on 'GitHub' . Methods are described and compared empirically in Tran, Wagaman, Nguyen, Jacobson, and Hartlaub (2024) . 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The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis. Sophisticated functions are available both for descriptive and regression model-based analyses. For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization techniques building on the concept of Lexis diagrams. Model-based analyses build on the separation of the temporal dimensions based on generalized additive models, where a tensor product interaction surface (usually between age and period) is utilized to represent the third dimension (usually cohort) on its diagonal. Such tensor product surfaces can also be estimated while accounting for further covariates in the regression model. See Weigert et al. (2021) for methodological details. 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Package: r-cran-apercu Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-apercu_0.2.5-1.ca2604.1_all.deb Size: 27830 MD5sum: 809372e44193b2d5ed1fa186d93a6d23 SHA1: 9345ca4df49268e108ab845e17f92ec4d9ed5fbe SHA256: c6585577a526b9bf278b6e970ee180aaab3c99ed54ebaea3e29abcf932601c59 SHA512: 1bf6bfa338f568ffdbfd7022588e075e7ff34b92cd799a0d74ece6bf25b671e46b6ecaca9a76b72a214c2e8751c176d1aae94eb7fc67577d0d0a42fea997299e Homepage: https://cran.r-project.org/package=apercu Description: CRAN Package 'apercu' (Quick Look at your Data) The goal is to print an "aperçu", a short view of a vector, a matrix, a data.frame, a list or an array. By default, it prints the first 5 elements of each dimension. By default, the number of columns is equal to the number of lines. 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Package: r-cran-apexcharter Architecture: all Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-rlang, r-cran-ggplot2, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-testthat, r-cran-knitr, r-cran-scales, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-apexcharter_0.4.5-1.ca2604.1_all.deb Size: 636724 MD5sum: 3d99e7cb663206f82518a3d25ca629a8 SHA1: 0af5a395654efd34d235a4d275ef8dffcb6823b8 SHA256: 2611eb7905998485b7d9c8566980db58539c8234e85ebeb5bdbb8fe010799331 SHA512: 881f6dfc078cb1fd0cf3b8149d670171f34ea8c72bbd8f99a867eff2ef6c520582f26f746e7fdf0ccef82ee0c658b3332a2cae030c18ef28de0f7117b47091b4 Homepage: https://cran.r-project.org/package=apexcharter Description: CRAN Package 'apexcharter' (Create Interactive Chart with the JavaScript 'ApexCharts'Library) Provides an 'htmlwidgets' interface to 'apexcharts.js'. 'Apexcharts' is a modern JavaScript charting library to build interactive charts and visualizations with simple API. 'Apexcharts' examples and documentation are available here: . Package: r-cran-apfr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-apfr_1.0.2-1.ca2604.1_all.deb Size: 63074 MD5sum: 5167cb127502995376ed72cab1a542f6 SHA1: 09a277b47d739513dbb53cb489a0c97fdbdf7980 SHA256: 4919d258ad96fea4f33b3c3c0096cd5d18c6f847ee3b633ca2c9eda4370124ea SHA512: eb1cbb34e2247ef4e101885976b8d544a600bc2eaa6b6d9c3cc9f43c5bb0a6c0475681d959a1122771cccd627d83af4eaf90f929c661b010e9e2633f3063c387 Homepage: https://cran.r-project.org/package=APFr Description: CRAN Package 'APFr' (Multiple Testing Approach using Average Power Function (APF) andBayes FDR Robust Estimation) Implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) . Package: r-cran-api2lm Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-api2lm_0.2-1.ca2604.1_all.deb Size: 292202 MD5sum: 9a7276ba9041d4d39630049d26cc5a00 SHA1: 877835b4f91192711ef137e78cb28fc11ffb71d6 SHA256: a0d3e7afe9f5907607f89a2e75043a5dbfdf747d92933175f60390225f2c3149 SHA512: a2ffaf5282d9fde095376b676b42ac404df75e9a79783831fee3c774b1ed8f309e049ef96846a0cf660f6d607cbf2252b2618688c967add45c6d62b549848d4f Homepage: https://cran.r-project.org/package=api2lm Description: CRAN Package 'api2lm' (Functions and Data Sets for the Book "A Progressive Introductionto Linear Models") Simplifies aspects of linear regression analysis, particularly simultaneous inference. 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See . Package: r-cran-aplms Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-mass, r-cran-matrix, r-cran-rlist, r-cran-psych, r-cran-rmutil Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aplms_0.1.0-1.ca2604.1_all.deb Size: 265278 MD5sum: c23797db3fe06efc4d7074b65148164d SHA1: 2e7ec766e586171988857b21b5166260a99332db SHA256: 8233cba4ab9836be0447768c1d5d8430c85437dcfa64fef851af3d868b4a0375 SHA512: 52c5b73817c5850aef9596c0805ae058573a7dc3f92e8961837873aad7458a70280601c08bc5670f4c46948ffb8e67d4c9a298858b359c923b034f78b4e4b46f Homepage: https://cran.r-project.org/package=aplms Description: CRAN Package 'aplms' (Additive Partial Linear Models with Symmetric AutoregressiveErrors) Set of tools for fitting the additive partial linear models with symmetric autoregressive errors of order p, or APLMS-AR(p). 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Package: r-cran-aplore3 Architecture: all Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-mass, r-cran-vcdextra, r-cran-nnet, r-cran-survival, r-cran-proc Filename: pool/dists/resolute/main/r-cran-aplore3_0.9-1.ca2604.1_all.deb Size: 443330 MD5sum: 4cb9e7ee6121b4f867384991e2c4c178 SHA1: 0a7257d916ab9dae6e7f912898f3ddb3a6fe6938 SHA256: 027ebc597982ee98765e075b50ae522e018473d0190229dfc91af949fd70a486 SHA512: cf74124bff537359d3394029477504c517f5be9a24a114b55d8375241c755c58aa094033ba3f0e2deb7c471eeb9e9ab3442f7fe690edc0e2fae0e90c477427cc Homepage: https://cran.r-project.org/package=aplore3 Description: CRAN Package 'aplore3' (Datasets from Hosmer, Lemeshow and Sturdivant, "Applied LogisticRegression" (3rd Ed., 2013)) An unofficial companion to "Applied Logistic Regression" by D.W. Hosmer, S. Lemeshow and R.X. 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Subsequent estimation and inference of causal effect's bounds accounts for both model and sampling uncertainty, and calculates the robustness changepoint value at which bounds go from excluding to including 0. The package also includes a range of diagnostic plots, such as those illustrating models' differential average prediction errors and the posterior distribution of which model is most robust. 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Package: r-cran-apticalc Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2 Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-apticalc_0.1.1-1.ca2604.1_all.deb Size: 30074 MD5sum: b019ce1249b5b691e601c3c0e58de271 SHA1: 3289d7dac5cdc5342f1173ee1e5d5dfcaed4bd77 SHA256: 6e850707c7db5cb4e4b5c9b39636e907cd649e21b47cc60544aacb8b0f7ec274 SHA512: 0ba612915ee0365ce08b8b353f6eb790e29c79b3e593ddc1f41aa2dfb052469ecc369a5393c3dc48a64b70937e45a2fecb009b8c36634e7b66066e5f2f3cd88e Homepage: https://cran.r-project.org/package=APTIcalc Description: CRAN Package 'APTIcalc' (Air Pollution Tolerance Index (APTI) Calculator) It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a 'shiny' app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).. Package: r-cran-aptools Architecture: all Version: 6.8.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-cmprsk Filename: pool/dists/resolute/main/r-cran-aptools_6.8.8-1.ca2604.1_all.deb Size: 75172 MD5sum: c101ac70166e488aa85baae081920fd6 SHA1: 5767de02e528bdded72d9d53b70a0662857f7443 SHA256: 504b43ae8c60158f64762464ee105ec742733359c1aa6e61215499b9db69ca1c SHA512: b3625bb84f1d6363e0e5e05c5f684a9695edeac297e515115c4bc2daccb40336e7bdd842732b83965805ab870980e89a44c37d29d2a5c7397bad513b4e2980f2 Homepage: https://cran.r-project.org/package=APtools Description: CRAN Package 'APtools' (Average Positive Predictive Values (AP) for Binary Outcomes andCensored Event Times) We provide tools to estimate two prediction accuracy metrics, the average positive predictive values (AP) as well as the well-known AUC (the area under the receiver operator characteristic curve) for risk scores. The outcome of interest is either binary or censored event time. Note that for censored event time, our functions' estimates, the AP and the AUC, are time-dependent for pre-specified time interval(s). A function that compares the APs of two risk scores/markers is also included. Optional outputs include positive predictive values and true positive fractions at the specified marker cut-off values, and a plot of the time-dependent AP versus time (available for event time data). Package: r-cran-apyramid Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyselect, r-cran-rlang, r-cran-forcats, r-cran-dplyr, r-cran-scales, r-cran-glue Suggests: r-cran-testthat, r-cran-survey, r-cran-srvyr, r-cran-vdiffr, r-cran-covr, r-cran-outbreaks, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-apyramid_0.1.3-1.ca2604.1_all.deb Size: 809380 MD5sum: 1dffb81e0706cea87df169aeadae8d13 SHA1: 8f8e678fd09c20448996beef76e291637250566b SHA256: 97ed38995dee9ae6bfe9ed9912ba10cf4aa432d99bff3d45fb6dc478a058f122 SHA512: 0c8d762db59e6e60c274d85994a0a60ea030931ac0eb781a6fe4ebf6d9f6e16489d8e4496180c4e5a69e056cb866a506254420dec0fdd015705bb2f71c73e06e Homepage: https://cran.r-project.org/package=apyramid Description: CRAN Package 'apyramid' (Visualize Population Pyramids Aggregated by Age) Provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a 'ggplot' object, allowing the user to further customize the output. This package is part of the 'R4Epis' project . Package: r-cran-aqeval Architecture: all Version: 0.6.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openair, r-cran-dplyr, r-cran-loa, r-cran-ggplot2, r-cran-strucchange, r-cran-segmented, r-cran-mgcv, r-cran-tidyr, r-cran-lubridate, r-cran-purrr, r-cran-ggtext, r-cran-data.table Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-aqeval_0.6.11-1.ca2604.1_all.deb Size: 853982 MD5sum: 6ea4e0f2f43323c65cf0f90fd39a134c SHA1: e6f0e7acdd3bd546ceca057207f46ece902d1373 SHA256: 2f6047b2e721925f0335720728fd04eff2616352a1f7cdfc147016817258b12a SHA512: 755febedb516cf5a3fb765119cf330c7c2a5c12b10dc113a8cc89c3bd7653e9c2a67f29b72bd3de7eba980bcdc51cf9fca4fc4fdf48a91982f428308efafad76 Homepage: https://cran.r-project.org/package=AQEval Description: CRAN Package 'AQEval' (Air Quality Evaluation) Developed for use by those tasked with the routine detection, characterisation and quantification of discrete changes in air quality time-series, such as identifying the impacts of air quality policy interventions. The main functions use signal isolation then break-point/segment (BP/S) methods based on 'strucchange' and 'segmented' methods to detect and quantify change events (Ropkins & Tate, 2021, ; Ropkins et al., 2026, ). Package: r-cran-aqfig Architecture: all Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geor Suggests: r-cran-maps Filename: pool/dists/resolute/main/r-cran-aqfig_0.9-1.ca2604.1_all.deb Size: 53882 MD5sum: 9e9eb3c9fa3999cb70fffb571843fe8d SHA1: 269c9ece26ef0125b7bb3ea1bde0b48ff8ef0589 SHA256: 38f73b8eb86a8fb3435a56cb2448a82ef6f685f726f68c53663dc2ebefb492ef SHA512: 6b4c6e6a35126e2087ce990552a436178f3ca9fdbba99c2c542395e36d0ba11b98cdeebc66833f38350ddbf11b31f009940e1a0d11ee2d6c3a9132c858d44bd9 Homepage: https://cran.r-project.org/package=aqfig Description: CRAN Package 'aqfig' (Display Air Quality Model Output and Monitoring Data) Display air quality model output and monitoring data using scatterplots, grids, and legends. Package: r-cran-aqlschemes Architecture: all Version: 1.7-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-aqlschemes_1.7-2-1.ca2604.1_all.deb Size: 352842 MD5sum: a0ceedd483e6e9e50cfd6e9521276ee5 SHA1: a8a50dbce01735276a27f9ddd55c9fb418da3dc3 SHA256: 18d7b1644655585f38e3bec0ee2ba8fea76900b1b686707b5d59c130ecb35f31 SHA512: 24146fb792a205ac9a39fa5e772ba42b3c78344a3921d431d1d2d5c2e87ab27c6685fe2dc444edde5ec975c6f29e0507dbe3d676e0ecd53b0888c044251b1cba Homepage: https://cran.r-project.org/package=AQLSchemes Description: CRAN Package 'AQLSchemes' (Retrieving Acceptance Sampling Schemes) Functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes were the most used and copied set of standards in the world. They are intended to be used for sampling a stream of lots, and were used in contract agreements between supplier and customer companies. When the US military dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This package is useful because its computer implementation of these tables duplicates that available in other commercial software and subscription online calculators. Package: r-cran-aqp Architecture: all Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-cluster, r-cran-data.table, r-cran-farver, r-cran-digest, r-cran-colorspace, r-cran-ape Suggests: r-cran-mvtnorm, r-cran-soildb, r-cran-sp, r-cran-sf, r-cran-latticeextra, r-cran-tactile, r-cran-compositions, r-cran-markovchain, r-cran-xtable, r-cran-testthat, r-cran-gmedian, r-cran-hmisc, r-cran-tibble, r-cran-rcolorbrewer, r-cran-scales, r-cran-mpspline2, r-cran-soiltexture, r-cran-gower, r-cran-knitr, r-cran-rmarkdown, r-cran-dendextend Filename: pool/dists/resolute/main/r-cran-aqp_2.3.2-1.ca2604.1_all.deb Size: 4927908 MD5sum: 87e840cbdd041bd7e21d9ac939d1e36e SHA1: a6a0007af319f14a780fac4c64fc4b6925d1d0df SHA256: 469e8d046681d80a130c22e55548cb7e03cab194f27408b191a273ccb71a33e9 SHA512: 46ff15db14f6b5fa1dd3aeb341bd0007548fc7435f539444530ee81bf471cade5e02262b9d9acc7bf7c00f63446dcb3fe28493095d80afc31b7ae8d2fdc5c4c5 Homepage: https://cran.r-project.org/package=aqp Description: CRAN Package 'aqp' (Algorithms for Quantitative Pedology) The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks . These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb . Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice. Package: r-cran-aquaanalytix Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-aquaanalytix_0.1.0-1.ca2604.1_all.deb Size: 36056 MD5sum: 5f3b9f401e02fa387b18ed726996a9ca SHA1: 20aa770e540ec7cd0b328860617329d8c6101483 SHA256: ecf705d4f09637a877f1d875409766ecba9efa4463cf8564e7f93ade2c86e134 SHA512: b70d0b0876c1590de5d303c9fc2c354ed6f71ba73674cc1011ff409e5b352e42c498d769d68e2db4a957f6af7ee8a25121b6a3a3b5ec88227da54cc12437f9b3 Homepage: https://cran.r-project.org/package=AquaAnalytix Description: CRAN Package 'AquaAnalytix' (Water Quality Analysis) A varied array of mathematical derivations from various titrimetric and colorimetric methods for analyzing water quality parameters were condensed and integrated for the better physicochemical analysis. It is indispensable for managing any aquatic ecosystem, including aquaculture facilities. By substituting titrant and spectrophotometric absorbance readings, accurate determination of the concentrations of critical parameters such as Dissolved Oxygen, Free Carbon Dioxide, Total Alkalinity, Water Hardness, Hydrogen Sulfide, Total Ammonia Nitrogen, Nitrite, Nitrate, Chlorinity, Salinity, Inorganic Phosphate, and Transparency can be facilitated APHA(2017,ISBN:9780875532875). Package: r-cran-aquabeher Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-sp, r-cran-terra, r-cran-zoo Suggests: r-cran-ggplot2, r-cran-ggrepel, r-cran-knitr, r-cran-learnr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aquabeher_1.4.0-1.ca2604.1_all.deb Size: 3228768 MD5sum: 7d861f3682613c6b8b9006c2ab3115b9 SHA1: 513662e2409851165399ceb543571b3a6f581995 SHA256: e510187ed09ea5f37795f5a61df0bd711e35da6ee6773fbec3dce354cc1a9270 SHA512: 1bdbad0605ad098403b8bf6802317a3cf0fa8426c9328361edaf9939eee85436000a14fd069ee2d964fb5c0f25f92e07e8589596d29191757e927e8f7ca8add9 Homepage: https://cran.r-project.org/package=AquaBEHER Description: CRAN Package 'AquaBEHER' (Estimation and Prediction of Wet Season Calendar and Soil WaterBalance for Agriculture) Computes and integrates daily potential evapotranspiration (PET) and a soil water balance model. It allows users to estimate and predict the wet season calendar, including onset, cessation, and duration, based on an agroclimatic approach for a specified period. This functionality helps in managing agricultural water resources more effectively. For detailed methodologies, users can refer to Allen et al. (1998, ISBN:92-5-104219-5); Allen (2005, ISBN:9780784408056); Doorenbos and Pruitt (1975, ISBN:9251002797); Guo et al. (2016) ; Hargreaves and Samani (1985) ; Priestley and Taylor (1972) . Package: r-cran-aquacultur Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2942 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-lubridate, r-cran-tidyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aquacultur_1.1.1-1.ca2604.1_all.deb Size: 418580 MD5sum: 75d473978072dcd3a1ab79dfb831f777 SHA1: bd5c575231ab73e074c1145642424392d704b327 SHA256: 9da14ae2d71584e58e729f5c5898fe98d8352c0388a0e0567cebc92c57edca4a SHA512: b84048fb9386f3d9b156330fc91f385c3083b56de7a5451f3b857c66c2a102a30890abcb7f94892498ae461f551bf6ea702e31b3b28f53ae57eb45b6487e8143 Homepage: https://cran.r-project.org/package=aquacultuR Description: CRAN Package 'aquacultuR' (A Comprehensive R Tool for Zootechnical Metrics) A collection of functions to compute frequently used metrics for nutrition trials in aquaculture. Implementations include metrics to calculate growth, feed conversion, nutrient use efficiency, and feed digestibility. The package supports reproducible workflows for summarising experimental results and reduces manual calculation errors. For additional information see Machado e Silva, Karthikeyan and Tellbüscher (2025) . Package: r-cran-aquadtree Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-dplyr, r-cran-rlang Suggests: r-cran-sf, r-cran-knitr, r-cran-devtools, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aquadtree_1.0.6-1.ca2604.1_all.deb Size: 4495234 MD5sum: fc8c7829fb7ea9c727b0a3cc141ed38b SHA1: f72db5be7af29b940ce0ee94592ff674589a0060 SHA256: e6456beb82567cc159787437a04f0c7b92f6e093c2069530292e04d1bc0aaf12 SHA512: 970f028944754946344491c78977b3acceded6e48a53e2061d104b07f463309411b3aa4bd6070c29dd74dafb24367bbefb92ae52d7ded8cfeeaa0cebe4d3778d Homepage: https://cran.r-project.org/package=AQuadtree Description: CRAN Package 'AQuadtree' (Confidentiality of Spatial Point Data) Provides an automatic aggregation tool to manage point data privacy, intended to be helpful for the production of official spatial data and for researchers. The package pursues the data accuracy at the smallest possible areas preventing individual information disclosure. The methodology, based on hierarchical geographic data structures performs aggregation and local suppression of point data to ensure privacy as described in Lagonigro, R., Oller, R., Martori J.C. (2017) . The data structures are created following the guidelines for grid datasets from the European Forum for Geography and Statistics. Package: r-cran-aquaenv Architecture: all Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm Suggests: r-cran-desolve Filename: pool/dists/resolute/main/r-cran-aquaenv_1.0-5-1.ca2604.1_all.deb Size: 725292 MD5sum: 63d9d6399460a25e5f6ec530ea074142 SHA1: ffb87a2c112b4386269ce7565f3b4fdf4d22b95f SHA256: 70ee7fd04c30df58dbbe3f95b2d52e448a5a3e9c317baaa4ce6be77a2bfbc13c SHA512: 4d3b28d855aa0f89dd06da7a13835025c850d277d62b1e3f3fe508345a4758aeb84e5307bac8b06a4092b8915b90009c0749fb5a681dd2cb55ffd3f6668ee8c2 Homepage: https://cran.r-project.org/package=AquaEnv Description: CRAN Package 'AquaEnv' (Integrated Development Toolbox for Aquatic Chemical ModelGeneration) Toolbox for the experimental aquatic chemist, focused on acidification and CO2 air-water exchange. It contains all elements to model the pH, the related CO2 air-water exchange, and aquatic acid-base chemistry for an arbitrary marine, estuarine or freshwater system. It contains a suite of tools for sensitivity analysis, visualisation, modelling of chemical batches, and can be used to build dynamic models of aquatic systems. As from version 1.0-4, it also contains functions to calculate the buffer factors. Package: r-cran-aquality Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-aquality_1.4-1.ca2604.1_all.deb Size: 122192 MD5sum: 513b3c1d711f7acdeea051cf21684231 SHA1: 47a376ccbf4d85ed83ddf82fdd4ec395988d6812 SHA256: 229d0af0612e1c35ba894ffba735ae427d186bff3bb9ea41af28d5d42cd810a5 SHA512: 03e24783c8b7d859dfec34795317d8030aca50a10fe8e9e725555438dc1a7bde2a7728c324e6cf328943c5fcffab8d17d32a2e6b06575b7e7debb00c462c681d Homepage: https://cran.r-project.org/package=AQuality Description: CRAN Package 'AQuality' (Water and Measurements Quality) The functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. Validation() is a function that allows to calculate the quality parameters of a technique for the measurement of a chemical component. Package: r-cran-aquaticlifehistory Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-mass, r-cran-readr, r-cran-broom, r-cran-mumin, r-cran-magrittr, r-cran-rlist, r-cran-minpack.lm, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aquaticlifehistory_1.0.6-1.ca2604.1_all.deb Size: 964184 MD5sum: f12ea864a46861ba87ffbe7bc144ec33 SHA1: 3e607ef7af64c2fffac46672fd2219d5d05526ff SHA256: 07ad89e2bbe5310637091f202f5b42d02ede8d97c1b67d1c85c11e84d1756ab8 SHA512: 3289d5279695da4f508e4f7db36eb01609f110595471e2b936056f103d5d64180a74d77aab9637c0ce7aa1aed8b583911e0bb255dc3087aecbe8e08191820a8b Homepage: https://cran.r-project.org/package=AquaticLifeHistory Description: CRAN Package 'AquaticLifeHistory' (Life History Analysis Tools) Estimate aquatic species life history using robust techniques. This package supports users undertaking two types of analysis: 1) Growth from length-at-age data, and 2) maturity analyses for length and/or age data. Maturity analyses are performed using generalised linear model approaches incorporating either a binomial or quasibinomial distribution. Growth modelling is performed using the multimodel approach presented by Smart et al. (2016) "Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future" . Package: r-cran-aquodom Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-httr, r-cran-memoise, r-cran-cachem, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-lubridate, r-cran-readr, r-cran-glue, r-cran-rlang, r-cran-openxlsx Suggests: r-cran-testthat, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-aquodom_0.1.1-1.ca2604.1_all.deb Size: 33592 MD5sum: 819876c060a1ca0bd617a8e7da2d251e SHA1: c9b476623744ade2dac804f89b68f4a097f3dac5 SHA256: 9ac10aade187a32e6d8bec051f72f93b165049aa087e79c76fb7a20e051d678a SHA512: f2ad0622c04e8d57480af60105fc2dea2cf9af6455e3240d881a2037de427af6af850aa9b4eeb4073ec25ebe5df0b10f776e921ad9fcab063f409114c8afcb66 Homepage: https://cran.r-project.org/package=aquodom Description: CRAN Package 'aquodom' (Access to Aquo domaintables from R (Dutch)) The Aquo Standard is the Dutch Standard for the exchange of data in water management. With *aquodom* (short for aquo domaintables) it is easy to exploit the API () to download domaintables of the Aquo Standard and use them in R. Package: r-cran-ar.matrix Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-sparsemvn, r-cran-sp Suggests: r-cran-ggplot2, r-cran-leaflet Filename: pool/dists/resolute/main/r-cran-ar.matrix_0.1.0-1.ca2604.1_all.deb Size: 441884 MD5sum: fc90eec05e8706c2c6150730328fc112 SHA1: cc29658d5d81856d3a1dc522e24d9214883b2ed9 SHA256: e1a1701640083190b1914796efac772e38fa5452ff115d282affa7de1fb2e7b1 SHA512: 820a37cbb04a21fb4fa8514f51410f858159714e83e27fabb785114671afdf595326ad538f2107802ca4bee214cbf8f24b07ab9fc462393bf3eca0aa84b797bc Homepage: https://cran.r-project.org/package=ar.matrix Description: CRAN Package 'ar.matrix' (Simulate Auto Regressive Data from Precision Matricies) Using sparse precision matricies and Choleski factorization simulates data that is auto-regressive. Package: r-cran-ar Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-distrib Filename: pool/dists/resolute/main/r-cran-ar_1.1-1.ca2604.1_all.deb Size: 26892 MD5sum: 9d61badaf46707a13452eb3cd0aa932a SHA1: d5bd9ef29ceb72be9198adc64363077160bd0b2c SHA256: 74933010e4a423fcf3c33f7ecda1ea2c5b018d327f2a89154644ce3c43a65afb SHA512: b9bc0a459f67a5f5662221168a688f6a28c9b750dc54fc439108bde1ab140a29a341c5c6d04a8dbdeacc322f3fde6c0ae646fe5c175bf4d8c91afd1055e33b07 Homepage: https://cran.r-project.org/package=AR Description: CRAN Package 'AR' (Another Look at the Acceptance-Rejection Method) In mathematics, 'rejection sampling' is a basic technique used to generate observations from a distribution. It is also commonly called 'the Acceptance-Rejection method' or 'Accept-Reject algorithm' and is a type of Monte Carlo method. 'Acceptance-Rejection method' is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package 'AR' is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package 'AR' is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method. Package: r-cran-arabic2kansuji Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-arabic2kansuji_0.1.3-1.ca2604.1_all.deb Size: 20864 MD5sum: 2da232106b3104aaffb392cf73d23b4d SHA1: c4bd9f973d6027d6521dcd302136d15bbceeee41 SHA256: 59e2b8bc6cb4788d9e1c7821266a2868d0a36665b705779b0733f4be7093e008 SHA512: 67c469b03db3992da949ec5432548e01669e6a98f0c051c9ae036b63e3283f37612d7d8814a078f34d59887d4007efbab92c3e0dd72d0558f2642494b97cd1fa Homepage: https://cran.r-project.org/package=arabic2kansuji Description: CRAN Package 'arabic2kansuji' (Convert Arabic Numerals to Kansuji) Simple functions to convert given Arabic numerals to Kansuji numerical figures that represent numbers written in Chinese characters. Package: r-cran-arabicstemr Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-arabicstemr_1.3-1.ca2604.1_all.deb Size: 78812 MD5sum: e563b38b53e00cf0f12ca69994396a64 SHA1: b31fcc639c7144ef531f692a7b2966aed6e09063 SHA256: 5a1eee300fe986de0bfd12d882b8d6a1bc05b587c7cc4603d841cf9d808eb5c0 SHA512: c7cd0d9f965804aed8e26e3a762315ad62a8a4ef63131992ac03a187d17fca26d4d367f52c361326a74f5a4ef365afb5561382e6db5656a72e562f87ae37311e Homepage: https://cran.r-project.org/package=arabicStemR Description: CRAN Package 'arabicStemR' (Arabic Stemmer for Text Analysis) Allows users to stem Arabic texts for text analysis. Package: r-cran-arakno Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-httr, r-cran-jsonlite, r-cran-phytools, r-cran-rgbif, r-cran-rworldmap, r-cran-rworldxtra Filename: pool/dists/resolute/main/r-cran-arakno_1.3.2-1.ca2604.1_all.deb Size: 139428 MD5sum: 0ed5e9b64682ca7890e6ce7a72b097cd SHA1: eecbe5c3b24f18bd346c50b0b3cc106e4d938127 SHA256: f9ebfe133089be737fd84bf4f0900d229ad88755f72d8553d61901e1423247fe SHA512: 720ca50e0444c074120a0716b6bf1c76a38cc3755e2bc85751350619e6b24bf3089fd5d1c4284aadf85c203b09163647149a5fb339637bb086ff6aaece4575cd Homepage: https://cran.r-project.org/package=arakno Description: CRAN Package 'arakno' (ARAchnid KNowledge Online) Allows the user to connect with the World Spider Catalogue (WSC; ) and the World Spider Trait (WST; ) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; ), and mapping. Package: r-cran-aramappings Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clarabel, r-cran-cvxr, r-cran-matrix, r-cran-pracma, r-cran-rglpk, r-cran-slam, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-parallelly, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aramappings_0.2.0-1.ca2604.1_all.deb Size: 1107744 MD5sum: cf9f25953fe321e5391d2f9624ecb1ab SHA1: 38b838c7506cc4c6cacc7719c34a137938070b62 SHA256: d98105589ad62d16a0b1a2425bef28448a25260f4b684300983c21083db8f068 SHA512: bf3d54b7d233d28d18c5dbd1a9d925311617f66a8c1d05475f7d9a2f44cfbe06ae673d25433be11af7f62c072db610d914d02937d6a57a8a786479fb4f180b30 Homepage: https://cran.r-project.org/package=aramappings Description: CRAN Package 'aramappings' (Computes Adaptable Radial Axes Mappings) Computes low-dimensional point representations of high-dimensional numerical data according to the data visualization method Adaptable Radial Axes described in: Manuel Rubio-Sánchez, Alberto Sanchez, and Dirk J. Lehmann (2017) "Adaptable radial axes plots for improved multivariate data visualization" . Package: r-cran-ararredux Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ararredux_1.0-1.ca2604.1_all.deb Size: 274888 MD5sum: 564321e5a1b4784cf7e61114cf46e952 SHA1: da28789153aa5ef918ee5914861d2d8dcf3d7436 SHA256: 0a1f2a998e18becd5463c6e626b7a33bce967c2b23f3363630a924996b814cdc SHA512: 1952a19bfa072383f53045db9fa108129973e84ad6d350c700c2f5680d7d2ca0c12f86bd361626156e0617d75b1d685cf7ba8eeb7af38264bb87e41aa1f865e8 Homepage: https://cran.r-project.org/package=ArArRedux Description: CRAN Package 'ArArRedux' (Rigorous Data Reduction and Error Propagation of Ar40 / Ar39Data) Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall]. Package: r-cran-arc Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arules, r-cran-r.utils, r-cran-discretization, r-cran-matrix Suggests: r-cran-qcba Filename: pool/dists/resolute/main/r-cran-arc_1.4.2-1.ca2604.1_all.deb Size: 233708 MD5sum: a01bb5cac38d6d8624427d583e9504b3 SHA1: bf484c39e22613b15b69fa34315f06d635f6e850 SHA256: 3cd9aedc735813333a8492c53b07b38b87086da2347a0f58b438366b60037b89 SHA512: 06287063b60aff9950a750cdfe75f619e61ef586c07d32475a26d7563a5924b4a4a158b84bb183a58013f310a9286f65a5563ace890e9e454381ac4901106685 Homepage: https://cran.r-project.org/package=arc Description: CRAN Package 'arc' (Association Rule Classification) Implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) , contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the 'arules' package. To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 'qCBA' package is suggested. Package: r-cran-arcgeocoder Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-quarto, r-cran-sf, r-cran-testthat, r-cran-tibble, r-cran-tidygeocoder Filename: pool/dists/resolute/main/r-cran-arcgeocoder_0.4.0-1.ca2604.1_all.deb Size: 528140 MD5sum: e98e7276dc8961e8aeb661364b76d27d SHA1: eccd7daf72f89a38a0ffd73f20f2cd43d24e1ea8 SHA256: 8a4a90031a4c97dc5e9b4b7b268e1378fca98da89536cd2dffdf8f982bfce3c2 SHA512: 1e703226991ddfae8480d5b5aaea17540ae83fed05befaae69ec8b6e4eb25ab3103c0db63ff5b4f17bdde13a057a94362f0c0b026451ce970cd261ef325ccbda Homepage: https://cran.r-project.org/package=arcgeocoder Description: CRAN Package 'arcgeocoder' (Geocoding with the 'ArcGIS' REST API Service) Lite interface for finding locations of addresses or businesses around the world using the 'ArcGIS' REST API service . Address text can be converted to location candidates and a location can be converted into an address. No API key required. Package: r-cran-arcgis Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcgisgeocode, r-cran-arcgislayers, r-cran-arcgisplaces, r-cran-arcgisutils, r-cran-cli, r-cran-httr2 Suggests: r-cran-arcpbf, r-cran-calcite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-arcgis_0.2.0-1.ca2604.1_all.deb Size: 15308 MD5sum: b8cecb7a940da1c2f52e65542b0751aa SHA1: 49988e52ec02f97406ffa189cf86b8c1790f8850 SHA256: bb81764f6a73fc09033e68f28e6bd8ec6577e35cca4c4a7357684f41521f7da4 SHA512: 7bc3acdfe169486e75afafc9a5c60c3e03c275c553d8f09949d79484cb43f6921b14c839937e68e73882c085d12ec27c2dcc6324bedb4cc614ca3c7809ced0b5 Homepage: https://cran.r-project.org/package=arcgis Description: CRAN Package 'arcgis' (ArcGIS Location Services Meta-Package) Provides easy installation and loading of core ArcGIS location services packages 'arcgislayers', 'arcgisutils', 'arcgisgeocode', and 'arcgisplaces'. Enabling developers to interact with spatial data and services from 'ArcGIS Online', 'ArcGIS Enterprise', and 'ArcGIS Platform'. Learn more about the 'arcgis' meta-package at . Package: r-cran-arcgislayers Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcgisutils, r-cran-arcpbf, r-cran-cli, r-cran-httr2, r-cran-jsonify, r-cran-lifecycle, r-cran-rcppsimdjson, r-cran-rlang, r-cran-sf, r-cran-terra Suggests: r-cran-testthat, r-cran-vctrs, r-cran-curl, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-arcgislayers_0.6.0-1.ca2604.1_all.deb Size: 272100 MD5sum: 5bd9759c6feef07b3bdf345357f8e912 SHA1: a9bd8ba6c7e2a65cfb8e3d85acb3f71f445ec026 SHA256: fbbece7e1495fe334285739191df4742c3c160f2f0c31e9f0b0ca12a516b5377 SHA512: 1f94b4c20a00111bec4ef3ef7ac7df1ebe9b0ecba77848ba80c00c0f0836447dbf4778096040c5bd41c4a6fe1b27aff27729b006c88299dcde1d02f9dc48ee1d Homepage: https://cran.r-project.org/package=arcgislayers Description: CRAN Package 'arcgislayers' (Harness ArcGIS Data Services) Enables users of 'ArcGIS Enterprise', 'ArcGIS Online', or 'ArcGIS Platform' to read, write, publish, or manage vector and raster data via ArcGIS location services REST API endpoints . Package: r-cran-archaeophases.dataset Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3488 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-archaeophases.dataset_0.2.0-1.ca2604.1_all.deb Size: 3400814 MD5sum: 3a72cca0255ec0d7f292e2cd684849cc SHA1: 9499867719913cfe23d260cf875466b24225650b SHA256: 3faf0a61f57d01c4c5ea94268ba071885c315c599d8327b436d3b9d46b031259 SHA512: 9d67a3dae2cb6d680a8a01e71a088969a8f6e565587ae0738382fb15f28f41905d2221c7df16adb815e71fd69a9e59ed11a28571e1dcbd324915908f16dd8e57 Homepage: https://cran.r-project.org/package=ArchaeoPhases.dataset Description: CRAN Package 'ArchaeoPhases.dataset' (Data Sets for 'ArchaeoPhases' Vignettes) Provides the data sets used to build the 'ArchaeoPhases' vignettes. The data sets were formerly distributed with 'ArchaeoPhases', however they exceed current CRAN policy for package size. Package: r-cran-archaeophases Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aion, r-cran-arkhe Suggests: r-cran-coda, r-cran-fontquiver, r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-archaeophases_2.1.0-1.ca2604.1_all.deb Size: 1923196 MD5sum: 151b83b93bdf88db7fe40f980288b54b SHA1: 559343c465702fa8401896b4355f3709f490b3c1 SHA256: e143ec8db8a5e937d15f3b92309f9a081ebc1f7e1c5a318deb54398066e5dae3 SHA512: 7d9952cf1fb252f5fac6b275098a1edbe0d58f01f2338bf8cd0277d4ff8fdc29f89cec941af1236ba8155f56e2a33e637ee6c4eee0eb069819f5e9d9c80e2c46 Homepage: https://cran.r-project.org/package=ArchaeoPhases Description: CRAN Package 'ArchaeoPhases' (Post-Processing of Markov Chain Monte Carlo Simulations forChronological Modelling) Statistical analysis of archaeological dates and groups of dates. This package allows to post-process Markov Chain Monte Carlo (MCMC) simulations from 'ChronoModel' , 'Oxcal' or 'BCal' . It provides functions for the study of rhythms of the long term from the posterior distribution of a series of dates (tempo and activity plot). It also allows the estimation and visualization of time ranges from the posterior distribution of groups of dates (e.g. duration, transition and hiatus between successive phases) as described in Philippe and Vibet (2020) . Package: r-cran-archdata Architecture: all Version: 1.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ca, r-cran-circular, r-cran-plotrix, r-cran-mass, r-cran-spatstat Filename: pool/dists/resolute/main/r-cran-archdata_1.2-1-1.ca2604.1_all.deb Size: 160454 MD5sum: 01540d85f0774ba0e59aa4d36ed43a62 SHA1: da352b1fac44f7bacb93035c2f1f6b93ad60334b SHA256: 62d003a889beb8ca228560e168ca7d765a853f0e25b4691620ddc2b55d979a5d SHA512: 25b645feb739eb4f2795d0b7645ba39910e691dbdf66f3a075f62381f79a23c8ff9918c76c20960af2b1314661dfba6e47edf54789d3f2b0106fff77bd808b41 Homepage: https://cran.r-project.org/package=archdata Description: CRAN Package 'archdata' (Example Datasets from Archaeological Research) The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology. Package: r-cran-archeofrag.gui Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archeofrag, r-cran-dendextend, r-cran-doparallel, r-cran-dorng, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-igraph, r-cran-shiny, r-cran-shinythemes Suggests: r-bioc-rbgl Filename: pool/dists/resolute/main/r-cran-archeofrag.gui_1.1.4-1.ca2604.1_all.deb Size: 389154 MD5sum: 7daed482ebf10f14fc8d7e15de55b167 SHA1: 471fb835d9e5620075d32c80a7b7ae1a68522f78 SHA256: c6e10f119711bd57c654083ea2cefb117fb768f82663d59241274b91366e75da SHA512: d9c191ecc7b6880edfd2181333f1b4a58f3d78b50fdc3dfd18f65e7e26d02643723892e275b2df89487973dd275c36afa0da1b605345af5193c7ca9fc5ff5610 Homepage: https://cran.r-project.org/package=archeofrag.gui Description: CRAN Package 'archeofrag.gui' (Spatial Analysis in Archaeology from Refitting Fragments (GUI)) A 'Shiny' application to access the functionalities and datasets of the 'archeofrag' package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and exports, anomaly detection in the spatial distribution of refitting connection, exploration of spatial units merging solutions, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the 'openMOLE' model exploration software. A demonstration of the app is available at . Package: r-cran-archeofrag Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-bioc-rbgl, r-cran-knitr, r-cran-covr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-archeofrag_1.2.4-1.ca2604.1_all.deb Size: 610336 MD5sum: af2a2291efe225e12161540483d8d988 SHA1: a5a350ca11eb328fd426bd5e70478a7c1a26d510 SHA256: 6fac99d0c6fb1c0a5ba871d49664bce5894288768fc443217de0bcc5dd86612b SHA512: 92555d141a57f218ce3a95b23eb020bbb08ff705119a43c25b1a55748a0f3177efb3068558910cd52a1838e1be35b65368d9dcd6d5fa04e447bcd762491392b9 Homepage: https://cran.r-project.org/package=archeofrag Description: CRAN Package 'archeofrag' (Spatial Analysis in Archaeology from Refitting Fragments) Methods to analyse spatial units in archaeology from the refitting relationships between fragments of objects scattered in these units (e.g. stratigraphic layers). Graphs are used to model archaeological observations. The package is mainly based on the 'igraph' package for graph analysis. Functions can: 1) create, manipulate, visualise, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. A series of published empirical datasets is included. Documentation about 'archeofrag' is provided by a vignette and by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, ) and Plutniak (2022, Journal of Open Source Software, ). This package is complemented by the 'archeofrag.gui' R package, a companion GUI application available at . Package: r-cran-archeoviz Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-mgcv, r-cran-geometry, r-cran-reshape2, r-cran-svglite, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinythemes, r-cran-knitr Suggests: r-cran-covr, r-cran-seahors, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-archeoviz_1.4.1-1.ca2604.1_all.deb Size: 322348 MD5sum: d5b34abce336af2b25e1a673d225dc66 SHA1: 3a708b1ca40b359a10c101e3b440ce69599483d4 SHA256: 27a4d3e0dc2ac018bdb1267da44ff9472389e9551ea54e7468fb21ddd71f9431 SHA512: 47a46fe5939d6973663aa09920424720a91d56d72b0da8be9890a9d777903770c11e89bd909363316bd4167fefb8dfcef9ce33dba8e13fdb82e74f874357b876 Homepage: https://cran.r-project.org/package=archeoViz Description: CRAN Package 'archeoViz' (Visualisation, Exploration, and Web Communication ofArchaeological Spatial Data) An R 'Shiny' application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. 'archeoViz' can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at . Package: r-cran-archetypal Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-geometry, r-cran-inflection, r-cran-doparallel, r-cran-lpsolve, r-cran-plot3d, r-cran-entropy Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-archetypal_1.3.1-1.ca2604.1_all.deb Size: 3969146 MD5sum: f2421cb1f0f888780950f15bafa5f20c SHA1: 881adf7436e310d8a002bee1d75af405f66275b3 SHA256: 688c378d79a1cb8bec1a9f4b177ed392c25f61fd4aa8db001661da583b177fcd SHA512: 88edb7622735469b9477eff2b7aeabaabb0e5d60d46f98febee844b9133f2889e572fb2fbb3f0398f05a56ce8e99f00c25e85d83eeebb371e01b3184b3c68cc1 Homepage: https://cran.r-project.org/package=archetypal Description: CRAN Package 'archetypal' (Finds the Archetypal Analysis of a Data Frame) Performs archetypal analysis by using Principal Convex Hull Analysis under a full control of all algorithmic parameters. It contains a set of functions for determining the initial solution, the optimal algorithmic parameters and the optimal number of archetypes. Post run tools are also available for the assessment of the derived solution. Morup, M., Hansen, LK (2012) . Hochbaum, DS, Shmoys, DB (1985) . Eddy, WF (1977) . Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) . Christopoulos, DT (2016) . Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., Sunde, U. (2018), . Christopoulos, DT (2015) . Murari, A., Peluso, E., Cianfrani, Gaudio, F., Lungaroni, M., (2019), . Package: r-cran-archetyper Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-here, r-cran-knitr, r-cran-log4r, r-cran-config, r-cran-tidyverse, r-cran-rmarkdown, r-cran-skimr, r-cran-stringr, r-cran-snakecase, r-cran-testthat, r-cran-bannercommenter, r-cran-feather, r-cran-readr, r-cran-ps Suggests: r-cran-rcurl, r-cran-mass, r-cran-broom, r-cran-caret, r-cran-covr, r-cran-gtsummary, r-cran-performance, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-archetyper_0.1.0-1.ca2604.1_all.deb Size: 1047416 MD5sum: a471bc8dca915811923b5783d15e5274 SHA1: a606a4a461dd0909ec40b487f6f6188afaf12959 SHA256: e451cfcf3ca9a36f5f590556029336bd048968be0075ef911f9b03c0f9c4c6df SHA512: 054dbc21621fad7b37fe0d22b4492e7b016cfe2510ffbb6acb3e4e98b8a33efbdf69f0fc4ca6ce1204f1fce8f29eb964544548fa722998f335237a330f201913 Homepage: https://cran.r-project.org/package=archetyper Description: CRAN Package 'archetyper' (An Archetype for Data Mining and Data Science Projects) A project template to support the data science workflow. Package: r-cran-archetypes Architecture: all Version: 2.2-0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltools, r-cran-nnls Suggests: r-cran-mass, r-cran-vcd, r-cran-mlbench, r-cran-ggplot2, r-cran-tsp, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-archetypes_2.2-0.2-1.ca2604.1_all.deb Size: 1190254 MD5sum: 7633990b9993ce14ecc2f1857db3702c SHA1: ba86b49457df13d08bfe53800c350d58a68bbd39 SHA256: 3d373214248da9323b517e5bcf18a3c84347081b453b9a34e2117ad773242f0e SHA512: 457f84b0ef09d876208d5c8771281e25a7673f5f0bba6f8d023f034b6f3e3f1516c270b0f4bff5fe57c742da9cf2718b66d1874313fe5124a9f798e3cc187d35 Homepage: https://cran.r-project.org/package=archetypes Description: CRAN Package 'archetypes' (Archetypal Analysis) The main function archetypes implements a framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm. Package: r-cran-archidart Architecture: all Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-xml, r-cran-geometry, r-cran-sp Suggests: r-cran-rgl, r-cran-tda Filename: pool/dists/resolute/main/r-cran-archidart_3.4-1.ca2604.1_all.deb Size: 1670144 MD5sum: 8dde48dd905163de49db8abc36fe029f SHA1: 62a252fcd4703fbf36521c3eee5f972f1bb72f92 SHA256: 5b8a9c28136738692b23f26ecfc15f0a28f328cdef9fd0a538657b8fdbc2fdce SHA512: 7cebc9c009a7da666cdc55de4b2615ff76fd8242d75aa03799605bf96015a14bc1c860a970a66835f2cd1b4880429c4fcbaff25f422672a0d7c6b47c8878fe4f Homepage: https://cran.r-project.org/package=archiDART Description: CRAN Package 'archiDART' (Plant Root System Architecture Analysis Using DART and RSMLFiles) Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, ), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, ). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, . Package: r-cran-archipelago Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-archipelago_0.1.0-1.ca2604.1_all.deb Size: 967874 MD5sum: fa225a48e5db7dec86b81a8af3170551 SHA1: 0b72381bc1a9b3bc06d2307f8968f2f0ff142e7b SHA256: c7423e5a6ccd3cca642826a8537b017735a365d584c908fc83be34cc4b12447e SHA512: a0988d74b375775522b073b6133741eac322a8a8b5afcc9e4c5c354a676a0553f0ada7bcd255d8668562ade8721ca7fc59673f6c56bcafd7deb7d427a15bd4dc Homepage: https://cran.r-project.org/package=archipelago Description: CRAN Package 'archipelago' (Visualising Variant Set Association Test Results) Provides a graphical method for joint visualisation of Variant Set Association Test (VSAT) results and individual variant association statistics. 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Package: r-cran-archissur Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gpcsign, r-cran-dicekriging, r-cran-kriginv, r-cran-future.apply, r-cran-truncatednormal, r-cran-randtoolbox, r-cran-rgenoud Suggests: r-cran-future, r-cran-dicedesign, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-archissur_0.0.1-1.ca2604.1_all.deb Size: 91110 MD5sum: 65378dcf830b5717049c89ec283d4345 SHA1: b7401f7107a06d88d866706bb4439c6e62df3e6b SHA256: d2e5c320afa71b9db1a46afef819a770c697f2c7d741ce4887c499fc7372df03 SHA512: 80fd346b5465ad4c91d2700bd49cbf05282548d84190b9ac8fbb572dc3f95d90745410866236e016740dbbbe224f6fb198df952384c699793aa47c02a7e1ca22 Homepage: https://cran.r-project.org/package=ARCHISSUR Description: CRAN Package 'ARCHISSUR' (Active Recovery of a Constrained and Hidden Set by StepwiseUncertainty Reduction Strategy) Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025). 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Get an overview of the mementos available from the respective homepage, retrieve the Urls and links of the page and finally scrape the content. The final output is stored in tibbles, which can be then easily used for further analysis. Package: r-cran-archivist Architecture: all Version: 2.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcurl, r-cran-digest, r-cran-httr, r-cran-dbi, r-cran-lubridate, r-cran-rsqlite, r-cran-magrittr, r-cran-flock Suggests: r-cran-shiny, r-cran-dplyr, r-cran-testthat, r-cran-ggplot2, r-cran-devtools, r-cran-knitr, r-cran-gridextra, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-archivist_2.3.9-1.ca2604.1_all.deb Size: 543928 MD5sum: 0573c2a77f118a214e2cd5994fdf99f2 SHA1: 878590626b1bb4db6e0daebdcb20d17843511376 SHA256: ddd6316326b84cbe5efa508f6cb780f5d66e269858db2322a30f9e081ba6607c SHA512: a71068049ed3e043b4403cbe32465ecc89bf752e14608c0dd8f859540c49a6a5ae8eebcacd2d1b34243bcb560f8d8c849807da744f7e4c6434b679054bf6e82f Homepage: https://cran.r-project.org/package=archivist Description: CRAN Package 'archivist' (Tools for Storing, Restoring and Searching for R Objects) Data exploration and modelling is a process in which a lot of data artifacts are produced. Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. The more projects we work with the more artifacts are produced and the harder it is to manage these artifacts. Archivist helps to store and manage artifacts created in R. Archivist allows you to store selected artifacts as a binary files together with their metadata and relations. Archivist allows to share artifacts with others, either through shared folder or github. Archivist allows to look for already created artifacts by using it's class, name, date of the creation or other properties. Makes it easy to restore such artifacts. Archivist allows to check if new artifact is the exact copy that was produced some time ago. That might be useful either for testing or caching. Package: r-cran-arco Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-boot Filename: pool/dists/resolute/main/r-cran-arco_0.3-1-1.ca2604.1_all.deb Size: 92728 MD5sum: 49ac48dd9a0806d4fa2ec214fdd22977 SHA1: 1c7eb0cfce7807cb62d1769d26ea03c8b6906e81 SHA256: 1b14ef6bc2cb5f2488b970ef6eb1885b682f43b8a33f7ac9593f16c3a69643f9 SHA512: 4b9955f6935f1d2100aa4ea01aea88e9602398da452152d154726100c5307dc57a9602540635439328b535bed9313e5f9c0834d27f42d0d10ea2297899e6382a Homepage: https://cran.r-project.org/package=ArCo Description: CRAN Package 'ArCo' (Artificial Counterfactual Package) Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) . Package: r-cran-arcpullr Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1347 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-rlang, r-cran-raster, r-cran-terra, r-cran-purrr, r-cran-dt Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rvest, r-cran-xml2, r-cran-stringr, r-cran-cowplot, r-cran-magick Filename: pool/dists/resolute/main/r-cran-arcpullr_0.3.2-1.ca2604.1_all.deb Size: 909722 MD5sum: 3455c6f2cc5df6b2a31e8a5bcc00f5c0 SHA1: 9e268f38066e6b1398e6a00339bd0a814d5821b1 SHA256: 65b403300f046635c6d945541952b8f894d0d2c0279a8e148a3099a158c02c55 SHA512: 8d13e43a94683d734f97f8ea48d3e3b17de69ba7bd0d5f22a87f2fae4c06d4f2a5ac1e8497cb4e14460cd02f4336629333ed957f389263df6591fccec93cc376 Homepage: https://cran.r-project.org/package=arcpullr Description: CRAN Package 'arcpullr' (Pull Data from an 'ArcGIS REST' API) Functions to efficiently query 'ArcGIS REST' APIs . 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Similar methods are described in Wagenpfeil, Schöpe and Bekhit (2025, ISBN:9783111341972) "Estimation of adjusted relative risks in log-binomial regression using the BSW algorithm". In: Mau, Mukhin, Wang and Xu (Eds.), Biokybernetika. De Gruyter, Berlin, pp. 665–676. 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Package: r-cran-arg Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cli Filename: pool/dists/resolute/main/r-cran-arg_0.1.0-1.ca2604.1_all.deb Size: 171076 MD5sum: 9087699d6f1bb132af812124c9cbe0c4 SHA1: a6116d6a3ebfff5222d9f5903c72e4d0a44dbabe SHA256: cedf33ae08d3590a99d7dff0cdb762b036b685cab0d59fe6648ce294db0c2301 SHA512: a7d849d0cbbe1639561f8c404613762bbe9024b344b00452ba61b7e16824259adc0893adc5ecb89f357596d3fb63d7f85b49a7cf4570890b441a6ed42b26fd84 Homepage: https://cran.r-project.org/package=arg Description: CRAN Package 'arg' (Clean and Simple Argument Checking) Checks function arguments, ideally for use in R packages. Uses a simple interface and produces clean, informative error messages using 'cli'. Package: r-cran-argentinapi Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-lubridate, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-argentinapi_0.2.1-1.ca2604.1_all.deb Size: 962178 MD5sum: 23fc84a1de821a975601413c0a4e679d SHA1: 3cde23b8e40255297fecad87ffef01f94404f1ea SHA256: 2c8d6ebe2b16765439714e7d3196f2035eb9bad7663a6baac302c772028e682d SHA512: db5a8b7b3e85d1eae0f36a3425fdd7685828d6d64dcfb23d64bfcaeff2a9e261a88997f9406ab7f29486ba9a349c2aae6f56bd1e6c1f0d5b414ff78821f6e101 Homepage: https://cran.r-project.org/package=ArgentinAPI Description: CRAN Package 'ArgentinAPI' (Access Argentinian Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including the 'ArgentinaDatos API', 'REST Countries API', and 'World Bank API' related to Argentina's exchange rates, inflation, political figures, holidays, economic indicators, and general country-level statistics. Additionally, the package includes curated datasets related to Argentina, covering topics such as economic indicators, biodiversity, agriculture, human rights, genetic data, and consumer prices. The package supports research and analysis focused on Argentina by integrating open APIs with high-quality datasets from various domains. For more details on the APIs, see: 'ArgentinaDatos API' , 'REST Countries API' , and 'World Bank API' . Package: r-cran-argentum Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-xml2, r-cran-sf, r-cran-readr Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-argentum_1.0.0-1.ca2604.1_all.deb Size: 52818 MD5sum: b9f779dca62d0c34ed5a3e8e52fe53e5 SHA1: c1df9d0919e8f8e4d88ee3f016bb67614f03def0 SHA256: 337f3fb081fef233de613dd26d1b1d383211a895274260c1ec99ee54f3ffbcd9 SHA512: 11c2a997726b09710da043638eaf1b0df664d90fa9c6ae9e8503457087ca3ec20abdfb6cc214475ae641218c86fd161428a6fcc2b8054b4d7495503d946ac0e3 Homepage: https://cran.r-project.org/package=Argentum Description: CRAN Package 'Argentum' (Access and Import WMS and WFS Data from Argentine Organizations) Provides functions to retrieve information from Web Feature Service (WFS) and Web Map Service (WMS) layers from various Argentine organizations and import them into R for further analysis. WFS and WMS are standardized protocols for serving georeferenced map data over the internet. For more information on these services, see and . Package: r-cran-argmincs Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsda, r-cran-glue, r-cran-ldats, r-cran-mass, r-cran-rdpack, r-cran-withr Filename: pool/dists/resolute/main/r-cran-argmincs_1.1.0-1.ca2604.1_all.deb Size: 99366 MD5sum: 0c645b3a400479a82f6ff0488713d861 SHA1: 2e28b832895899b6e7d34e7b3b73de3fd711365a SHA256: dc824a3915db29d54276c0c65ef9a1df000c350baa713bbf7b3edc5b3a5ec97f SHA512: fe8936a1a1509d58f9922a2b8f3fc2e4ec028ec0a51a030cd74b42cd6da3c84308e13c7256bd8ab0ef1f8c8d714adfb671741e7a96cc2d8813c5c46f5945b31c Homepage: https://cran.r-project.org/package=argminCS Description: CRAN Package 'argminCS' (Argmin Inference over a Discrete Candidate Set) Provides methods to construct frequentist confidence sets with valid marginal coverage for identifying the population-level argmin or argmax based on IID data. For instance, given an n by p loss matrix—where n is the sample size and p is the number of models—the CS.argmin() method produces a discrete confidence set that contains the model with the minimal (best) expected risk with desired probability. The argmin.HT() method helps check if a specific model should be included in such a confidence set. The main implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) "Winners with confidence: Discrete argmin inference with an application to model selection". Package: r-cran-argo Architecture: all Version: 3.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-glmnet, r-cran-zoo, r-cran-xml, r-cran-xtable, r-cran-matrix, r-cran-boot Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-argo_3.0.3-1.ca2604.1_all.deb Size: 866074 MD5sum: 65048d86e41383d0816d9da0121cb264 SHA1: 8ea5c7564813b336ac9a54bf48ff6003575fabf0 SHA256: a8f140bea30771bfef41729f20cb76a7285f63ac48a80fa3b1cb7dc8d0a43535 SHA512: 10a5fb18656f4d3403e4bc91e8d09c85d96f1e159d4195d665120a010dfb9e6f84d899de793888c3fc3d1a31777eb1cbeebe848b64fae167d4ad0bafc62118a7 Homepage: https://cran.r-project.org/package=argo Description: CRAN Package 'argo' (Accurate Estimation of Influenza Epidemics using Google SearchData) Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on national level, regional level and state level. 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Functions are provided to (a) download and cache data files, (b) subset data in various ways, (c) handle quality-control flags and (d) plot the results according to oceanographic conventions. A shiny app is provided for easy exploration of datasets. The package is designed to work well with the 'oce' package, providing a wide range of processing capabilities that are particular to oceanographic analysis. See Kelley, Harbin, and Richards (2021) for more on the scientific context and applications. Package: r-cran-argondash Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-argonr Suggests: r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-argondash_0.2.5-1.ca2604.1_all.deb Size: 1839558 MD5sum: d5a025f6a25a58f6a45be72460b40b60 SHA1: 285697bb8a922ab6e4a6fd235f038f30ea262d47 SHA256: 844c6f1b40a4ed7c9da2c2826a4c7d18e719f83479c6fbccb32d90bf4a1cb5c6 SHA512: 4b23c3e2f706b7d550c0bec24ea8c85a1155240ee87aec89eeeba986598cebfe7b5e79ccb655f977729703c4aabac0b590812ad55060aab77c31bc5b87205f48 Homepage: https://cran.r-project.org/package=argonDash Description: CRAN Package 'argonDash' (Argon Shiny Dashboard Template) Create awesome 'Bootstrap 4' dashboards powered by 'Argon'. 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Package: r-cran-argos Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-metrics, r-cran-boot, r-cran-tidyverse, r-cran-magrittr, r-cran-tidyr, r-cran-signal, r-cran-desolve Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-argos_0.1.1-1.ca2604.1_all.deb Size: 80350 MD5sum: b3a9e34ed1bce74283c108212a2ebfbe SHA1: f3ee893aa59569260a13fb51ee8aaaf3926e0b28 SHA256: 520b4321a5d8470cbdc6ceb42ec347f65a23a7f539c1111242cc3118a952540e SHA512: 96502957bcae7e0d4890b624cbb70f49091540b164fd2226bd854324d237c5bca4c0324cb09a008731d2f3742e2b9d0d0534d2b3fc7edfecd7892aa9ae14a587 Homepage: https://cran.r-project.org/package=ARGOS Description: CRAN Package 'ARGOS' (Automatic Regression for Governing Equations (ARGOS)) Comprehensive set of tools for performing system identification of both linear and nonlinear dynamical systems directly from data. The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis. Package: r-cran-argosfilter Architecture: all Version: 0.71-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-argosfilter_0.71-1.ca2604.1_all.deb Size: 56964 MD5sum: b8c46e6fbe75322952a11c19e224c8e8 SHA1: c05e32c7846319b6540f782d6c5be50487d9ed2e SHA256: 3a85e29342fc79729d671d689eae9107927baa25cc8cafd7613ba57984afb123 SHA512: d57945708c91dcacd76be4e83e10eca0eea1acc71a2ad19273ca5b4a10c01785d6ea2baac0c91205fa047e05a79a9aaec471ba8f12458dd8706f7a52fd9fbd48 Homepage: https://cran.r-project.org/package=argosfilter Description: CRAN Package 'argosfilter' (Argos Locations Filter) Filters animal satellite tracking data obtained from the Argos system(), following the algorithm described in Freitas et al (2008) . It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality. 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It contains a function to consistently estimate higher order moments of the population covariance spectral distribution using the spectral of the sample covariance matrix (Bai et al. (2010) ). In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately with a given correlation matrix when the degrees of freedom are large. Package: r-cran-aribrain Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hommel, r-cran-rnifti, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-aribrain_0.2-1.ca2604.1_all.deb Size: 1237646 MD5sum: c9183d9e962324e6a5618255b8325591 SHA1: 7578c8ecf9f3481ee98581d22b89f95247bdc707 SHA256: 0424296686d700956cc8654df733a8dd169079f002cffa9c431dbdb1266d80b5 SHA512: 7eb82efdc1d3b6bfa7247383ae4869e57e3ba9cca3851ae425536f09e8beda8a764316f0e3c643379579f751177f3ec07bf726e5bfce16a0b1184625a74d2c23 Homepage: https://cran.r-project.org/package=ARIbrain Description: CRAN Package 'ARIbrain' (All-Resolution Inference) It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) . Package: r-cran-aridagri Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-emmeans, r-cran-multcomp, r-cran-corrplot, r-cran-factoextra, r-cran-factominer, r-cran-lavaan, r-cran-semplot, r-cran-agricolae, r-cran-car, r-cran-writexl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aridagri_2.0.3-1.ca2604.1_all.deb Size: 221448 MD5sum: eea00140681af90747ff120f655dc011 SHA1: 4f739f77eb6f9f832e440a3adc86bc2dce6b6ece SHA256: c0ba40e6ecbc7e7ad73d1e2ab9101b61b63347e35bf0d60a59e7914d54ce3771 SHA512: 8f96c648bb43d50e85dd90da7f1da97fead4598befdb48da52e379b1390a418cbc375d3f7159703f714c098b0e2accb8d4edab85d4822100f831a8421f22184b Homepage: https://cran.r-project.org/package=aridagri Description: CRAN Package 'aridagri' (Comprehensive Statistical Tools for Agricultural Research) A comprehensive suite of statistical and analytical tools for agricultural research. Includes complete analysis of variance (ANOVA) functions for all experimental designs: Completely Randomized Design (CRD), Randomized Block Design (RBD), Pooled RBD, Split Plot with all variations, Split-Split Plot, Strip Plot, Latin Square, Factorial, Augmented, and Alpha Lattice, with proper error terms and comprehensive Standard Error (SE) and Critical Difference (CD) calculations. Features multiple post-hoc tests: Least Significant Difference (LSD), Duncan Multiple Range Test (DMRT), Tukey Honestly Significant Difference (HSD), Student-Newman-Keuls (SNK), Scheffe, Bonferroni, and Dunnett, along with assumption checking and publication-ready output. Advanced methods include stability analysis using Eberhart-Russell regression, Additive Main Effects and Multiplicative Interaction (AMMI), Finlay-Wilkinson regression, Shukla stability variance, Wricke ecovalence, Coefficient of Variation (CV), and Cultivar Superiority Index as described in Eberhart and Russell (1966) . Thermal indices include Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU), and Heat Use Efficiency (HUE). Crop growth analysis covers Crop Growth Rate (CGR), Relative Growth Rate (RGR), Net Assimilation Rate (NAR), and Leaf Area Index (LAI). Also provides harvest index, yield gap analysis, economic efficiency indices (Benefit-Cost ratio), nutrient use efficiency calculations, correlation matrix, Principal Component Analysis (PCA), path analysis, and Structural Equation Modeling (SEM). Statistical methods follow Gomez and Gomez (1984, ISBN:0471870927) and Panse and Sukhatme (1985, ISBN:8170271169). 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After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) . 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(The name 'arl' is short for 'An R Lisp.') Implemented in pure R with no compiled code. Package: r-cran-arlclustering Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-arules Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-arlclustering_1.0.5-1.ca2604.1_all.deb Size: 800598 MD5sum: b3d86987818319344f091b2ed180b67b SHA1: d87dd31defdff3c2ca8f3eea4147471ac67b8238 SHA256: 25f96597803a90b68bbe116d35465ba37e5eeda6d528a3716828f3ed46775b14 SHA512: 271cc1f1c519ac0bbd3c8a43b13f85df8d333db6c0c2743ab638eb046eb8dbfdab19cafabe20033d668dac2e26142f7c54633380a878d370bf7c6015262b8cfa Homepage: https://cran.r-project.org/package=arlclustering Description: CRAN Package 'arlclustering' (Exploring Social Network Structures Through Friendship-DrivenCommunity Detection with Association Rules Mining) Implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) . Package: r-cran-arm Architecture: all Version: 1.15-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-lme4, r-cran-abind, r-cran-coda, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-arm_1.15-3-1.ca2604.1_all.deb Size: 437596 MD5sum: ca679eb53890cefcb63c98bc66bbf495 SHA1: 414ae3a5a4dc0ec591af547dd58949e02ae00ae0 SHA256: 927ebfd3c5a083ab6dc7e3c7fcb5846072bee41072d66238a1119746a5a3bcf0 SHA512: 88250e71ac4e407da24fa8d4fb8531c74befb5c697654998168389dcf080b83379b473f14d10d54e3a6c105115acf25daecf6937243cad0e1a30f3c7797cfbbd Homepage: https://cran.r-project.org/package=arm Description: CRAN Package 'arm' (Data Analysis Using Regression and Multilevel/HierarchicalModels) Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007. 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This implementation is derived from Vargas Sepulveda and Schneider Malamud (2024) . Package: r-cran-armalstm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rugarch, r-cran-tseries, r-cran-tensorflow, r-cran-keras, r-cran-reticulate Filename: pool/dists/resolute/main/r-cran-armalstm_0.1.0-1.ca2604.1_all.deb Size: 25386 MD5sum: ed7ac8ca76a678fca1b431472c87874d SHA1: 264ba5a5e4c62466b8d5d6f192b7d11a176d8500 SHA256: e58a9763597b934ea0578f3cecb8b5cb1c1a92d3100929b89f7df3d3597ac492 SHA512: 0bfdf2da9c32fb0a3c9657c3789aa6f8f9578011cc46fedaf394ef3f4ad4b7d568367cce97a5a66232762ab0d5713546b6ea156de00b89493a87389b0feafcfa Homepage: https://cran.r-project.org/package=ARMALSTM Description: CRAN Package 'ARMALSTM' (Fitting of Hybrid ARMA-LSTM Models) The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) . 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This package is made to be easy to understand and for financial analysis capabilities. Package: r-cran-aroma.affymetrix Architecture: all Version: 3.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4914 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.utils, r-cran-aroma.core, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.cache, r-cran-r.devices, r-cran-r.filesets, r-cran-aroma.apd, r-cran-mass, r-cran-matrixstats, r-cran-listenv, r-cran-future Suggests: r-cran-dbi, r-cran-rcolorbrewer, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-affxparser, r-bioc-affy, r-bioc-affyplm, r-bioc-aroma.light, r-bioc-gcrma, r-bioc-limma, r-bioc-oligo, r-bioc-oligoclasses, r-bioc-pdinfobuilder, r-bioc-preprocesscore, r-bioc-affymetrixdatatestfiles, r-cran-dchipio Filename: pool/dists/resolute/main/r-cran-aroma.affymetrix_3.2.3-1.ca2604.1_all.deb Size: 3567446 MD5sum: 80de5b256ea6a18702895926aee12dfc SHA1: 40fbfa58ea1c0795ef70024cf8f992f28b4d8e1f SHA256: 6e51baa7549862b68e8c2425aa016f44c9acba433185545d7c6a575ecbeadfed SHA512: 4bc823984785d0b8d227a8041e7a91fd15bb93b66cd1b73bfa6a9854a46bf2b52b5ecd9842ff92524dcc484b1ce3f63c2ebbe29886ce11d5bf3f432ace41fbea Homepage: https://cran.r-project.org/package=aroma.affymetrix Description: CRAN Package 'aroma.affymetrix' (Analysis of Large Affymetrix Microarray Data Sets) A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006. Package: r-cran-aroma.apd Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.utils, r-cran-r.huge Suggests: r-bioc-affxparser Filename: pool/dists/resolute/main/r-cran-aroma.apd_0.7.1-1.ca2604.1_all.deb Size: 118272 MD5sum: 33f2d96277ae3345ae434208c1d50004 SHA1: 6e91987cab38c08f152d69264e7c0811c2e5e639 SHA256: bf0242ec94923c46298ac810685f18186379dfa60b0139c285c3345f6388a129 SHA512: 9571dc9ec43308183a459c881aef224f38be66524e6cf77d8ceb1040052a451a5b797e1e5efa0cf988484bd6328804aca57a37ec4e04c21550bd560c4de31e64 Homepage: https://cran.r-project.org/package=aroma.apd Description: CRAN Package 'aroma.apd' (A Probe-Level Data File Format Used by 'aroma.affymetrix'[deprecated]) DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities. Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets). In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit. This speeds up the read substantially. This package is supporting the Aroma framework and should not be used elsewhere.) 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Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data. Package: r-cran-aroma.core Architecture: all Version: 3.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.utils, r-cran-r.filesets, r-cran-r.devices, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.cache, r-cran-r.rsp, r-cran-matrixstats, r-cran-rcolorbrewer, r-cran-pscbs, r-cran-listenv, r-cran-future, r-cran-biocmanager Suggests: r-cran-kernsmooth, r-cran-png, r-cran-cairo, r-bioc-ebimage, r-bioc-preprocesscore, r-bioc-aroma.light, r-bioc-dnacopy, r-bioc-glad Filename: pool/dists/resolute/main/r-cran-aroma.core_3.3.2-1.ca2604.1_all.deb Size: 1888942 MD5sum: a75815c7aaaf93edde6d3713086c7b4c SHA1: 4ee215c915b5d82bcab6e92d0c2bb13e75f752fb SHA256: c67caf886287b6ec107bf9c731aac91114152037847488fb3305b67280849c66 SHA512: 85bf5eaa3453353cc55da6200c35c772d66e1bfa0393a54654fe5b8bcbbd207605902f8b2359dc57cc439f184baf59783f2dfa54e44bf4ba8d433d0e8354cf00 Homepage: https://cran.r-project.org/package=aroma.core Description: CRAN Package 'aroma.core' (Core Methods and Classes Used by 'aroma.*' Packages Part of theAroma Framework) Core methods and classes used by higher-level 'aroma.*' packages part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'. Package: r-cran-arothron Architecture: all Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5427 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-alphashape3d, r-cran-compositions, r-cran-doparallel, r-cran-foreach, r-cran-geometry, r-cran-morpho, r-cran-rgl, r-cran-rvcg, r-cran-stringr, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-arothron_2.0.5-1.ca2604.1_all.deb Size: 5490650 MD5sum: 74442f4fb564dcde86c70254979d0ec0 SHA1: 4fc0d16b93e06a21a927ce262baa0f5741ec0428 SHA256: 0cddea68a918f2fbad0f3c08679a514587de98651052b5cf3ca57b5b808a1155 SHA512: 199a4907a59c48efd96014a0ee5b8e854f9e80332f287f614e66cf8a4e8435473956ab4695767d5b3890b7caf58f3bc1e47e491cfe58beacedd3fef5e9a36614 Homepage: https://cran.r-project.org/package=Arothron Description: CRAN Package 'Arothron' (Geometric Morphometric Methods and Virtual Anthropology Tools) Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 ). Package: r-cran-arpobservation Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-shiny, r-cran-plyr, r-cran-reshape2, r-cran-dplyr, r-cran-ggplot2, r-cran-viridis, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-arpobservation_1.2.2-1.ca2604.1_all.deb Size: 373688 MD5sum: 5a90957aa5043669b61c07b609e2b11e SHA1: 39361438c8443ddd3f6c0cc46a93c78f55ef2aad SHA256: 864697b6429fc07fc25469014c86a19ee97b2abb9825fdd4d6a425696b77422e SHA512: c5e8a155b1a6fa175d2ec8e25ae348306a300b1f0ac6ec267796f31a47f12684ef051b0501874c00eebbcd6d9f585b4bd8f442397220e6e5e8dc5a4ecc91dea8 Homepage: https://cran.r-project.org/package=ARPobservation Description: CRAN Package 'ARPobservation' (Tools for Simulating Direct Behavioral Observation RecordingProcedures Based on Alternating Renewal Processes) Tools for simulating data generated by direct observation recording. 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It is named after the dadaist Hans Arp, a friend of Rene Magritte. Package: r-cran-arpsdca Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-arpsdca_1.1.1-1.ca2604.1_all.deb Size: 136794 MD5sum: 3efdf3ea085699fc765eb714a935cb06 SHA1: 0fcf9ae316af0914b3cc3e4825dcae8f7aea8245 SHA256: 44423326bb2db611fb685df659d64c5e11288fd3a090be06081daf3728a2393b SHA512: 7903233a8343136efe1e00a274856d649e22ce011bf8aede6ec441e5d2947435c6d2afe3215f90a7cb81ee3005f9fd47bc1b3034b24fe3e02205e763111073b1 Homepage: https://cran.r-project.org/package=aRpsDCA Description: CRAN Package 'aRpsDCA' (Arps Decline Curve Analysis in R) Functions for Arps decline-curve analysis on oil and gas data. 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Package: r-cran-arrg Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ore Suggests: r-cran-tinytest, r-cran-covr Filename: pool/dists/resolute/main/r-cran-arrg_0.1.0-1.ca2604.1_all.deb Size: 34832 MD5sum: 5b9a39b0f598e9d02d93974388178da2 SHA1: d41a7fd15070b64c067e9ae50140c70b967222c8 SHA256: 8676e389713501baa33f76bc0d2093496d90a60a5bd8b9ec727f70caa40668cc SHA512: 63b6bf6a364ca96a9e2233b76da4baef68f71ac3be6f71008d8fb42cb55517ec814ec425f41cef99a66d8a9933b2608b703d97b773a71e96285ca99027fbaff4 Homepage: https://cran.r-project.org/package=arrg Description: CRAN Package 'arrg' (Flexible Argument Parsing for R Scripts) Argument parsing for R scripts, with support for long and short Unix-style options including option clustering, positional arguments including those of variable length, and multiple usage patterns which may take different subsets of options. Package: r-cran-arrowheadr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3655 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bezier, r-cran-purrr Suggests: r-cran-ggarrow, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-arrowheadr_1.0.2-1.ca2604.1_all.deb Size: 1664256 MD5sum: d9b0686e5d408e0d89ff0a4775dfdec6 SHA1: 7b2a0b18404a0d82920408407cae5065c8a14802 SHA256: f9337ccf7a7684861c1f982f25aebe189eff29894d71df852736ae3e88b75a87 SHA512: b16bf962dbd50c4c6133b90cb8609d656bf5ce7d8d8b3c46233b53b5fa817024b816e8e8b3d2edc799748bc67a1c906d636fab884bad19271a684e67a14586dd Homepage: https://cran.r-project.org/package=arrowheadr Description: CRAN Package 'arrowheadr' (Make Custom Arrowheads) The 'ggarrow' package is a 'ggplot2' extension that plots a variety of different arrow segments with many options to customize. The 'arrowheadr' package makes it easy to create custom arrowheads and fins within the parameters that 'ggarrow' functions expect. It has preset arrowheads and a collection of functions to create and transform data for customizing arrows. Package: r-cran-arse Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-arse_1.0.0-1.ca2604.1_all.deb Size: 82232 MD5sum: b3c1be51c7947c49f2c3ae38912b5a81 SHA1: 82a0df854af383c846ba978af533cb3b62630935 SHA256: 1d1d825c5d09cde07a1a0a99edea2b6a7988b4e21c506e0269574c7527cc075e SHA512: 01f90845f2440d18b820370caba15f8ca09132ed932d2812116f3012dfcb165834ef77b98fa8fb0a6fd6cc3251d70fe865f966c539ccc05d8253506f2e6a88f1 Homepage: https://cran.r-project.org/package=arse Description: CRAN Package 'arse' (Area of Resilience to Stress Event) A method for quantifying resilience after a stress event. A set of functions calculate the area of resilience that is created by the departure of baseline 'y' (i.e., robustness) and the time taken 'x' to return to baseline (i.e., rapidity) after a stress event using the Cartesian coordinates of the data. This package has the capability to calculate areas of resilience, growth, and cases in which resilience is not achieved (e.g., diminished performance without return to baseline). Package: r-cran-arsenal Architecture: all Version: 3.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2097 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-broom, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat, r-cran-xtable, r-cran-pander, r-cran-survival, r-cran-coin, r-cran-proc, r-cran-mass, r-cran-rpart, r-cran-yaml, r-cran-geepack Filename: pool/dists/resolute/main/r-cran-arsenal_3.6.3-1.ca2604.1_all.deb Size: 849980 MD5sum: 7125468aae33d3def415bb051bdd09ce SHA1: ecf59c81549a832e5da085210b038b13a09f8e81 SHA256: d5b67204cef349a4e072d3def89ed728e25599d28256e4eabce4615f77d85313 SHA512: b1ed7967fdc78e3379e2390fa76c271cf89f209d40d52d7b193b616d71b4e485f9bfcad7c8ca27c556ac6b9ac2db17e151fe5678a6edbf29c204c0ce3de2ef00 Homepage: https://cran.r-project.org/package=arsenal Description: CRAN Package 'arsenal' (An Arsenal of 'R' Functions for Large-Scale StatisticalSummaries) An Arsenal of 'R' functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in 'R' and 'RStudio' and which use formulas and versatile summary statistics for summary tables and models. The primary functions include tableby(), a Table-1-like summary of multiple variable types 'by' the levels of one or more categorical variables; paired(), a Table-1-like summary of multiple variable types paired across two time points; modelsum(), which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates); freqlist(), a powerful frequency table across many categorical variables; comparedf(), a function for comparing data.frames; and write2(), a function to output tables to a document. Package: r-cran-art Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car Filename: pool/dists/resolute/main/r-cran-art_1.0-1.ca2604.1_all.deb Size: 26160 MD5sum: 6df97182f314673b868b9d8f1579ce3b SHA1: 8ad241ba01afdccbb97a539e6d432cde569b77e0 SHA256: 1d5c981d561c7ddd264ff5373093f2daa4897ae1858e4ea136ae0fd444b113a5 SHA512: 0fee11139d6937f3f392c65b31967866901b65d119d7844ff600076d63d02f2c71bd8b86aa0a74173dfa627c647f3306bc47334b139656288d0f4629389484ec Homepage: https://cran.r-project.org/package=ART Description: CRAN Package 'ART' (Aligned Rank Transform for Nonparametric Factorial Analysis) An implementation of the Aligned Rank Transform technique for factorial analysis (see references below for details) including models with missing terms (unsaturated factorial models). The function first computes a separate aligned ranked response variable for each effect of the user-specified model, and then runs a classic ANOVA on each of the aligned ranked responses. For further details, see Higgins, J. J. and Tashtoush, S. (1994). An aligned rank transform test for interaction. Nonlinear World 1 (2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '11). New York: ACM Press, pp. 143-146. . Package: r-cran-arthistory Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-arthistory_0.1.0-1.ca2604.1_all.deb Size: 255088 MD5sum: 44cd9368a07e93a5c200adaba3f6122b SHA1: 758ab9ed2d75bbbb9f2120a71d16f3f35e0bc7cd SHA256: b7389260054892ac2950479bea851418259662d5c8b0d13b933263aafc865d8e SHA512: cf105fb8a2c57e710af58ef7594329e41abe6997fd5769534c6bf8c0df65178fc36784d728d0b378179883a99742befb98c1364ca5ed170fab93e7e924b2cf61 Homepage: https://cran.r-project.org/package=arthistory Description: CRAN Package 'arthistory' (Art History Textbook Data) Data from Gardner and Janson art history textbooks about both the artists featured in these books as well as their works. See Helen Gardner ("Art through the ages; an introduction to its history and significance," 1926, . Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1980, ISBN: 0155037587). Fred S. Kleiner ("Gardner’s art through the ages: a global history," 2020, ISBN: 9781337630702). Horst de la Croix and Richard G. Tansey ("Gardner's art through the ages," 1986, ISBN: 0155037633). Helen Gardner ("Art through the ages; an introduction to its history and significance," 1936, ). Helen Gardner ("Art through the ages," 1948, ). Helen Gardner, revised under the editorship of Sumner M. Crosby ("Art through the ages," 1959, ). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1975, ISBN: 0155037560). Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2013, ISBN: 9780495915423. Fred S. Kleiner, Christin J. Mamiya, Richard G. Tansey ("Gardner’s art through the ages," 2001, ISBN: 0155083155). Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2016, ISBN: 9781285837840). Fred S. Kleiner, Christin J. Mamiya ("Gardner’s art through the ages," 2005, ISBN: 0534640958). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1970, ISBN: 0155037528). Helen Gardner, Richard G. Tansey, Fred S. Kleiner ("Gardner’s Art through the ages," 1996, ISBN: 0155011413). Helen Gardner, Horst de la Croix, Richard G. Tansey, Diane Kirkpatrick ("Gardner’s Art through the ages," 1991, ISBN: 0155037692). Helen Gardner, Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2009, ISBN: 9780495093077). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Janson’s history of art: the western tradition," 2007, ISBN: 0131934554). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Janson’s history of art: the western tradition," 2011, ISBN: 9780205685172). H. W. Janson, Anthony F. Janson ("History of Art," 2001, ISBN: 0810934469). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1986, ISBN: 013389388). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1977, ISBN: 0810910527). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1969, ). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1963, ). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1991, ISBN: 0810934019). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1995, ISBN: 0810934213). Package: r-cran-artofr Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bannercommenter, r-cran-clipr, r-cran-rstudioapi, r-cran-shiny Suggests: r-cran-rmarkdown, r-cran-miniui, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-artofr_0.4.1-1.ca2604.1_all.deb Size: 48964 MD5sum: 9b4337d91be98c7e85cd2830e4cf3940 SHA1: 87dfc2acbff688fdf7fbbda54cd0333e06d086a3 SHA256: 522b5aa2d9db255c010664a7c073e7dbd3776472dc1434ee697a92cbbb9e4b73 SHA512: f4044f99032848096ff41b7052e93a18586e2ceeff3763b86c3d67d3e3f6d4459c234d176e867ce1c0d7c5be9550c5d2a618224806ebeee22a296d87f7d13a75 Homepage: https://cran.r-project.org/package=ARTofR Description: CRAN Package 'ARTofR' (To Insert Title, Divider, and Block of Comments) For instructions, check . This is a wrapper of 'bannerCommenter', for inserting neat comments, headers and dividers. 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Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art. 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The 'Arvind' distribution is a flexible single-parameter continuous distribution on the positive real line characterised by a polynomial numerator with Gaussian-type decay. The package provides complete distribution functions (darvind(), parvind(), qarvind(), rarvind()), maximum likelihood estimation via fit_arvind_mle(), and five model-fitting routines: Random Walk on Coefficients via fit_rw1(), Time-Varying Coefficient Linear Model via fit_tvlm(), Simulation-Extrapolation via fit_simex(), Mixed-Effects Regression via fit_mixed(), and Regime-Switching Hidden Markov Model via fit_hmm(). Additionally provides Monte Carlo forecasting with prediction intervals via forecast_arvind(), comprehensive goodness-of-fit diagnostics (21 metrics and 25 plots) via diagnostics_arvind() and plot_arvind(), k-fold and rolling-window cross-validation via cv_arvind(), and unified model comparison via summary_arvind(). For more details see Pandey, Singh, Tyagi, and Tyagi (2024) "Modelling climate, COVID-19, and reliability data: A new continuous lifetime model under different methods of estimation", Statistics and Applications, 22(2), . 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This package provides an interface to those shapefiles, as well as methods for converting coordinates to shapefiles. Package: r-cran-ashapesampler Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-alphahull, r-cran-alphashape3d, r-cran-truncnorm, r-cran-rvcg, r-cran-tda, r-cran-doparallel, r-cran-foreach, r-cran-dplyr Suggests: r-cran-knitr, r-cran-testthat, r-cran-rgl, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ashapesampler_1.0.0-1.ca2604.1_all.deb Size: 803930 MD5sum: 4673e3a84e8a74a853ee34965b597afa SHA1: 52c350cf728423477e9dc4d8c8042b58cac64793 SHA256: 36fed9d854d7329a5a45812747a51d64cb1c17f1b930803f5713b462a23a5ab0 SHA512: 33cf0b156d03d64cb59da00342f09b79c323d6d2ad3b5f1bdccd85950e00f5425314ce114374a1f80feb4a4ad2c900fb1e2238282c01f11f931f4bd46ffc482f Homepage: https://cran.r-project.org/package=ashapesampler Description: CRAN Package 'ashapesampler' (Generating Alpha Shapes) Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The 'ashapesampler' package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) . Package: r-cran-asht Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exact2x2, r-cran-exactci, r-cran-bpcp, r-cran-coin, r-cran-perm, r-cran-ssanv Suggests: r-cran-bootstrap Filename: pool/dists/resolute/main/r-cran-asht_1.0.3-1.ca2604.1_all.deb Size: 263246 MD5sum: 46178746949e7af90396fd4b63f0538b SHA1: 75f25b6a89031849a69c87e70f40dfa5d5248344 SHA256: ccead3f47406debef5bbba5adaf5e67441838887d3a88ab7aa38a273a2b99563 SHA512: 33f0f15996abd9d056f1e87e917ce69c4b244b2ad0c84b7c7495aff676198fe3121cea0505c836d93ae9602ee905e0187cf444e41821b036b8c92c1b6c7c14d8 Homepage: https://cran.r-project.org/package=asht Description: CRAN Package 'asht' (Applied Statistical Hypothesis Tests) Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim ], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky ], two-sample melding tests [see Fay, Proschan, and Brittain ], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 ] or Bayer, Fay, and Graubard, 2023 ). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp). Package: r-cran-asioheaders Architecture: all Version: 1.30.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5320 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-asioheaders_1.30.2-1-1.ca2604.1_all.deb Size: 411670 MD5sum: 084bbb9031ab2bec85f44a39bf900ff1 SHA1: c4c18ec236a13870929d0e1679c21809d0f500b2 SHA256: 51a0f81ba77a49f84b1d726c7aad4e7ac4dc20fe783200cebfe5a425efe5c7dc SHA512: 94995695fe291f5057eb07510dcd52a9385b1c7e31326251a565222450a89b3aa42fedf95af63bac1a32da37db6620a0f9f229e43608707f182f1f91a5bfe780 Homepage: https://cran.r-project.org/package=AsioHeaders Description: CRAN Package 'AsioHeaders' ('Asio' C++ Header Files) 'Asio' is a cross-platform C++ library for network and low-level I/O programming that provides developers with a consistent asynchronous model using a modern C++ approach. It is also included in Boost but requires linking when used with Boost. Standalone it can be used header-only (provided a recent compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and released under the 'Boost Software License', Version 1.0. Package: r-cran-askgpt Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-callr, r-cran-dplyr, r-cran-glue, r-cran-rlang, r-cran-httr2, r-cran-rappdirs, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-miniui, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-shinycssloaders, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-askgpt_0.1.3-1.ca2604.1_all.deb Size: 2395934 MD5sum: 9e3912c40885dd226a70996f8e91e2ed SHA1: 2bfe86eacd2bc81f481fbe50b6102a8be7a0ceb9 SHA256: e45ff1f68903d89d07b3465b07a8c1bac490be7a6e2a73abc319356f35b4206d SHA512: 988bb8cb57ab491d77e1f482c4ebeed048bab7a601c289219bb577d3928895cb83e8356ec17888db648a8fc8cf3a78d9f38d20d183b8b9370b55a94f0349d5e9 Homepage: https://cran.r-project.org/package=askgpt Description: CRAN Package 'askgpt' (Asking GPT About R Stuff) A chat package connecting to API endpoints by 'OpenAI' () to answer questions (about R). Package: r-cran-aslib Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-batchtools, r-cran-data.table, r-cran-bbmisc, r-cran-checkmate, r-cran-corrplot, r-cran-ggplot2, r-cran-llama, r-cran-mlr, r-cran-parallelmap, r-cran-paramhelpers, r-cran-plyr, r-cran-reshape2, r-cran-rweka, r-cran-stringr, r-cran-yaml Suggests: r-cran-testthat, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-aslib_0.1.3-1.ca2604.1_all.deb Size: 171140 MD5sum: 6a1e8be6deb0630b9b2fc8037dc7aadb SHA1: 41cb7e6cd87bdd95a44a2de4ec80e3cb4bd1e270 SHA256: f810e42f63701ce96155706e8cdedd3b164b21f7b907543fef6299b65d8cc456 SHA512: a043c6a6f508002a1d98057cfb3bf0b6b7b94e98751148b738f89d812d2f453c8720e5e93ab6610c1a3ca8eeb1fe257bfccc5862ee534c085396efb711378fdc Homepage: https://cran.r-project.org/package=aslib Description: CRAN Package 'aslib' (Interface to the Algorithm Selection Benchmark Library) Provides an interface to the algorithm selection benchmark library at and the 'LLAMA' package () for building algorithm selection models; see Bischl et al. (2016) . Package: r-cran-asm Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-pracma, r-cran-iso, r-cran-mass, r-cran-quantreg Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-asm_0.2.4-1.ca2604.1_all.deb Size: 72532 MD5sum: 5e3e5743976b854971f9dfeae1629655 SHA1: ead58470d38e345fb585050284e53a01b1145a64 SHA256: 346e8e74a769cc5e833aabe3cd86e1ad283368e7cf30f4484aede05fb3569891 SHA512: 62c559408bab8c6bd31a398b16cdcbe98f9441a64b9d7a916fd92379080170dd556a4821df9b5ed787bbee3f7155ce4968efecd670307c42df327b078bcb7413 Homepage: https://cran.r-project.org/package=asm Description: CRAN Package 'asm' (Optimal Convex M-Estimation for Linear Regression via AntitonicScore Matching) Performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) . Package: r-cran-asmbook Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-lattice, r-cran-mass, r-cran-tmb Suggests: r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-asmbook_1.0.2-1.ca2604.1_all.deb Size: 130926 MD5sum: bf06e70726c2315efda939d8eb146b62 SHA1: 6e415a89371f6694e89a51b72929748342ce498d SHA256: cbf4706b3b8cc4862e0b552fb44bd72591ac670dac87e2daba4292a9d51f7d5f SHA512: da63dc177b24d2d8f3b2c3835015aa5df85a31440872e983bd283762b8cc2a83ca9a13d68eedc5321533f95161341b5bebd3fef7d95d73f744ff6b2ed83254c3 Homepage: https://cran.r-project.org/package=ASMbook Description: CRAN Package 'ASMbook' (Functions for the Book "Applied Statistical Modeling forEcologists") Provides functions to accompany the book "Applied Statistical Modeling for Ecologists" by Marc Kéry and Kenneth F. Kellner (2024, ISBN: 9780443137150). Included are functions for simulating and customizing the datasets used for the example models in each chapter, summarizing output from model fitting engines, and running custom Markov Chain Monte Carlo. Package: r-cran-asml Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-ggplot2, r-cran-dalex, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-reshape2, r-cran-polychrome, r-cran-scales, r-cran-rlang Suggests: r-cran-snow Filename: pool/dists/resolute/main/r-cran-asml_1.1.0-1.ca2604.1_all.deb Size: 661146 MD5sum: c1a2af0b9e3ab50b7682e3293d316053 SHA1: 8354c171cfbfaf0523c5b33a8afd1cc81bfbc071 SHA256: 0c57213d42d52e14d4f27d7cfab52d57bdeed3ec0a717d2a0659c6931d108c93 SHA512: 85f34ccef8db1773c45277b77c31f8e2fe99fe7cb761cb46111194b01f9bd049ba5d808b4b3b84a42b9b2062d39a94dfe0bfaa4058c02db40cdef20a43557c4b Homepage: https://cran.r-project.org/package=ASML Description: CRAN Package 'ASML' (Algorithm Portfolio Selection with Machine Learning) A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) ). Package: r-cran-asnipe Architecture: all Version: 1.1.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-ape, r-cran-raster, r-cran-sna Filename: pool/dists/resolute/main/r-cran-asnipe_1.1.17-1.ca2604.1_all.deb Size: 440180 MD5sum: d708858cedb7bd3ed644b822f345fee8 SHA1: 4260a0810c6902548014338cccc2340b7a20cecf SHA256: f0a862e89226cfd315c7dc7bf29255f582c39ceb6ccbc8ce00bb794a07c618c5 SHA512: 3faa01059277f939d120c7d423c3c1fa5da0ea3af3f310526cbd4c452747d28fdf6f84e09f6e16d8748aa57424738e1a688883b697a1154e41023df18d6b26b3 Homepage: https://cran.r-project.org/package=asnipe Description: CRAN Package 'asnipe' (Animal Social Network Inference and Permutations for Ecologists) Implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies and Farine & Whitehead (2015) . In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data. Package: r-cran-aspace Architecture: all Version: 4.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-splancs, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-aspace_4.1.2-1.ca2604.1_all.deb Size: 124964 MD5sum: cdc615ed5f11a5988b95f7881b449837 SHA1: 81bffccca96346dbd2f02d53334ffc37c0d86ce0 SHA256: f1915c04b9dda86f4ec0e345a42bc0aa6b7ffe27621b1b6bc186de4c00e50e71 SHA512: 7120655ebe08bbba3ca7fd0930a7935baea8d1a957917cbf1d852b3667ca48ea68e926a36b9580abada76d480ca71d184f19404cf6620566a22756c7d1399bec Homepage: https://cran.r-project.org/package=aspace Description: CRAN Package 'aspace' (Functions for Estimating Centrographic Statistics) A collection of functions for computing centrographic statistics (e.g., standard distance, standard deviation ellipse, standard deviation box) for observations taken at point locations. Separate plotting functions have been developed for each measure. Users interested in writing results to ESRI shapefiles can do so by using results from 'aspace' functions as inputs to the convert.to.shapefile() and write.shapefile() functions in the 'shapefiles' library. We intend to provide 'terra' integration for geographic data in a future release. The 'aspace' package was originally conceived to aid in the analysis of spatial patterns of travel behaviour (see Buliung and Remmel 2008 ). Package: r-cran-aspect Architecture: all Version: 1.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sem, r-cran-polycor Filename: pool/dists/resolute/main/r-cran-aspect_1.0-7-1.ca2604.1_all.deb Size: 94522 MD5sum: 0ee5f7e6f109ed5dcdf59faf07f0c070 SHA1: 4a3181f1ed7aa1eca288729a614498e3a80fd4d9 SHA256: de0573027b43fc7d84bb58be1aefe6eeec21f62a33357cde3eb42135d9cbd0ee SHA512: 8b1988a5569f6834b2ec9beb10fc86bea3e87bb3d10df44e90ee190dda4a241efc78171eb686762bbfd62c70332b0e84eabe4c4ad64acdff1c2cf481d63d3837 Homepage: https://cran.r-project.org/package=aspect Description: CRAN Package 'aspect' (A General Framework for Multivariate Analysis with OptimalScaling) Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models. Package: r-cran-aspi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aspi_0.2.0-1.ca2604.1_all.deb Size: 76154 MD5sum: 3db0ba086cd1d7cec4630d6702d8ee3d SHA1: 8f5ab559e078de66682c8b0f39644a7fbaed8f9a SHA256: ca713551dd90d1a952b98b193125f81d0985e4103102c80742f1f2f9f55a4509 SHA512: 608b3d2ff2733427c3e7d02da2537da81015a7c9c0acd437d0dc841a1a1dbd2c90ae46834de9632ea4ee94a2d28b93fafe4ea8543c96c5184d78d360506b4320 Homepage: https://cran.r-project.org/package=aspi Description: CRAN Package 'aspi' (Analysis of Symmetry of Parasitic Infections) Tools for the analysis and visualization of bilateral asymmetry in parasitic infections. Package: r-cran-aspu Architecture: all Version: 1.50-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gee, r-cran-mass, r-cran-mvtnorm, r-cran-fields, r-cran-matrixstats Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-aspu_1.50-1.ca2604.1_all.deb Size: 746832 MD5sum: fa5750703057dae496d2d8695390feb4 SHA1: d9bc06dcd2370be67c9b3206905fc0dea557005d SHA256: 69c3cb12561f00b51078c46956f7629e070f89afe41ce697b518dd8c73d6dc05 SHA512: 898b5270d2a55bfc7255c197c59b25309b185f92c585a6b59096f988fe04ab8d7dd164de99a6f1e27ddb8b54e06f55729cbf9fed84ac5699c570e8c5986f52c9 Homepage: https://cran.r-project.org/package=aSPU Description: CRAN Package 'aSPU' (Adaptive Sum of Powered Score Test) R codes for the (adaptive) Sum of Powered Score ('SPU' and 'aSPU') tests, inverse variance weighted Sum of Powered score ('SPUw' and 'aSPUw') tests and gene-based and some pathway based association tests (Pathway based Sum of Powered Score tests ('SPUpath'), adaptive 'SPUpath' ('aSPUpath') test, 'GEEaSPU' test for multiple traits - single 'SNP' (single nucleotide polymorphism) association in generalized estimation equations, 'MTaSPUs' test for multiple traits - single 'SNP' association with Genome Wide Association Studies ('GWAS') summary statistics, Gene-based Association Test that uses an extended 'Simes' procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version of 'GATES' test for pathway-based association testing ('GATES-Simes'). ). The tests can be used with genetic and other data sets with covariates. The response variable is binary or quantitative. Summary; (1) Single trait-'SNP' set association with individual-level data ('aSPU', 'aSPUw', 'aSPUr'), (2) Single trait-'SNP' set association with summary statistics ('aSPUs'), (3) Single trait-pathway association with individual-level data ('aSPUpath'), (4) Single trait-pathway association with summary statistics ('aSPUsPath'), (5) Multiple traits-single 'SNP' association with individual-level data ('GEEaSPU'), (6) Multiple traits- single 'SNP' association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP' set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath'). Package: r-cran-asremlplus Architecture: all Version: 4.4.58-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dae, r-cran-devtools, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-nloptr, r-cran-qqplotr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rlang, r-cran-sticky, r-cran-stringr, r-cran-trycatchlog Suggests: r-cran-emmeans, r-cran-lattice, r-cran-lmertest, r-cran-pbkrtest, r-cran-r.rsp, r-cran-testthat, r-cran-tictoc Filename: pool/dists/resolute/main/r-cran-asremlplus_4.4.58-1.ca2604.1_all.deb Size: 3109002 MD5sum: 2f8ec2a15ee4db19128b4d41ffde44cb SHA1: 58b62704ed42a295b7d08dc1245ccd3c1cfa8688 SHA256: ec4539b3f549444f1478beab0b16bf8d09963df2d557880543e6cf83075fd28f SHA512: 1ec0482e8ca7a9459ef31ff985821163d97c05383e3863708147ef5c8a047ce501d1903496071a5f6bcb50688dba8202beb9e8112355d9556474564619fd32b4 Homepage: https://cran.r-project.org/package=asremlPlus Description: CRAN Package 'asremlPlus' (Augments 'ASReml-R' in Fitting Mixed Models and PackagesGenerally in Exploring Prediction Differences) Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' as 'asreml-R', who will supply a zip file for local installation/updating (see ). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from . 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Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) . 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Package: r-cran-assert Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-assert_1.0.1-1.ca2604.1_all.deb Size: 11612 MD5sum: 6e5441c3b8a89634875363fa04a2f054 SHA1: d67bbc55d6ae1f590e353d796d3a0d3afbb984f3 SHA256: 3cc6255e1f52dd36089ce31a752792e4e5ae150cdb7019e432bf58751266f643 SHA512: 3e22d2564b7e621022fd8e5dd0a6bd1fa450646999f7eee152d3d35c864e1455980f41e1284b8ecd4b97c3d1aa0b936be13387098fe1733d4c2a6e8780f58f52 Homepage: https://cran.r-project.org/package=assert Description: CRAN Package 'assert' (Validate Function Arguments) Lightweight validation tool for checking function arguments and validating data analysis scripts. This is an alternative to stopifnot() from the 'base' package and to assert_that() from the 'assertthat' package. It provides more informative error messages and facilitates debugging. 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Package: r-cran-astsa Architecture: all Version: 2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1409 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-astsa_2.5-1.ca2604.1_all.deb Size: 1371574 MD5sum: 4ff8f0fe697bf11d6679652d30f588a6 SHA1: 6601ac78865c2bc4e8c05a2b5d0454d9045a04fc SHA256: e19c167bb846139e7a6463215c479717e1141512d567fec9a5aed4f57b5ce8bf SHA512: cfe03dff5924a59422d9d4efe185e7b54240f1d09de586c404e83d65aaadc6b1cfd4088449fff9d338d9a8f414223e36a172e4ea2db41845739dee472618a1ad Homepage: https://cran.r-project.org/package=astsa Description: CRAN Package 'astsa' (Applied Statistical Time Series Analysis) Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2025, , and Time Series: A Data Analysis Approach Using R (2nd ed). Chapman-Hall, 2026, . Most scripts are designed to require minimal input to produce aesthetically pleasing output for ease of use in live demonstrations and course work. Package: r-cran-asus Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-asus_1.5.0-1.ca2604.1_all.deb Size: 62462 MD5sum: 07c48127b9eb69904cacf01473f59a33 SHA1: 0e844f76056074a7881c6d7060b7d1386970d6df SHA256: 7deb5620a1ceda41c803cf6ab5c0b905103eeaf9c8198d63498c5c4891b71de6 SHA512: 251cd6a01f5b3038d238e53da0ba21aa23cbff306d38c19bdba9a4e18dda62807e6ddb53087a94ba1b39404f8d464a0c370e9c3015be425525f810bc1dc7064a Homepage: https://cran.r-project.org/package=asus Description: CRAN Package 'asus' (Adaptive SURE Thresholding Using Side Information) Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity. It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data. For more information, please see the paper Adaptive Sparse Estimation with Side Information by Banerjee, Mukherjee and Sun (JASA 2020). Package: r-cran-asyk Architecture: all Version: 1.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-asyk_1.5.6-1.ca2604.1_all.deb Size: 32814 MD5sum: 84124b9711c61ae5a695d45b40783045 SHA1: 48d55f2f19df1d8a2dc84dc703a7b4e0e89a4676 SHA256: 95d51788644baeed6f24cd2544622aa2a11cb8b15d27fd8a1319b540eca7053c SHA512: 2b9de13174d1fc554cccc4c263d40262f57eaa78bb4d76054f54b013fb32cb2e89ae41c82c3b112a41ddee32b295a05a429c415906c48d56e4bcd2bb561cc247 Homepage: https://cran.r-project.org/package=AsyK Description: CRAN Package 'AsyK' (Kernel Density Estimation) A collection of functions related to density estimation by using Chen's (2000) idea. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000), Scaillet (2004) and Khan and Akbar. Package: r-cran-asylum Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4744 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-asylum_1.1.2-1.ca2604.1_all.deb Size: 4229702 MD5sum: dab54cfb77f4a79ef0a28d4cb15e83df SHA1: b3412358b52cd73bae962f20f3d8f50364494688 SHA256: 3dd3a8027c8363e9ade8eefc681717146f2b2588c19c7c966a445d9fde350020 SHA512: 0ffda944ac15f890c7e47c369948fd3390a556e68812b36ab3170ffc1baac73f72b941e41fe5249ca14f07b998c788614360b65e6838048d18fa780e9ba609b6 Homepage: https://cran.r-project.org/package=asylum Description: CRAN Package 'asylum' (Data on Asylum and Resettlement for the UK) Data on Asylum and Resettlement for the UK, provided by the Home Office . Package: r-cran-asymld Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fields Filename: pool/dists/resolute/main/r-cran-asymld_0.1-1.ca2604.1_all.deb Size: 53094 MD5sum: 33ec35e86506fdb56b14d0d31d5fa2ad SHA1: 1861160cdab87144330f87d7291989135706f42e SHA256: db9902571859b54b4d2b257fab03f92ca894870b8d550452b538c3f2f9221f98 SHA512: f217bdbe6a5c664e4f04f29438a9de47577c1b520016e64f6b6ea725ae43600158e0f492c1feaa60503c5631ba88ab3ffb09fda24f3e983e7027239e9198f5af Homepage: https://cran.r-project.org/package=asymLD Description: CRAN Package 'asymLD' (Asymmetric Linkage Disequilibrium (ALD) for Polymorphic GeneticData) Computes asymmetric LD measures (ALD) for multi-allelic genetic data. These measures are identical to the correlation measure (r) for bi-allelic data. Package: r-cran-asymmetricsords Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-asymmetricsords_1.0.0-1.ca2604.1_all.deb Size: 161518 MD5sum: 3692c083a017091b2979a2a8bc60a514 SHA1: 3410a7f251f763c299f364dbef4fe9dce453950a SHA256: 13c41afbeb425fa62f33ca3c080dc0c173d495e8a123fa3d3302bafc84e21c69 SHA512: fea2073e3446d96125a26822e82ef3e9493ca26a9a6c9fdaffffc5d87c1fb66451aaa0f8d919b8a648173c0d4a5ada3b4ec4c418d384de042d9032c0dfb97c28 Homepage: https://cran.r-project.org/package=AsymmetricSORDs Description: CRAN Package 'AsymmetricSORDs' (Asymmetric Second Order Rotatable Designs (AsymmetricSORDs)) Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) ."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) . "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model. Package: r-cran-asymmetry.measures Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sn, r-cran-skewt, r-cran-gamlss.dist Filename: pool/dists/resolute/main/r-cran-asymmetry.measures_0.2-1.ca2604.1_all.deb Size: 145456 MD5sum: cc2dd4534984047c5e2e31d3b4c4be85 SHA1: 861cc5491ae30503cbc734866810735136de2efd SHA256: de3a7e1ee48a7241050d837eca7196f82511f1a1fc8ebc242214a91eadbb3b19 SHA512: a42b02ba534152de524379cae2247a94c2398d14d0576b67ccda54ac208ec28e7cefbafd383ab37c90a6f6cfb752f8e28cffbb0b09200df7160e924cd7142a13 Homepage: https://cran.r-project.org/package=asymmetry.measures Description: CRAN Package 'asymmetry.measures' (Asymmetry Measures for Probability Density Functions) Provides functions and examples for the weak and strong density asymmetry measures in the articles: "A measure of asymmetry", Patil, Patil and Bagkavos (2012) and "A measure of asymmetry based on a new necessary and sufficient condition for symmetry", Patil, Bagkavos and Wood (2014) . The measures provided here are useful for quantifying the asymmetry of the shape of a density of a random variable. The package facilitates implementation of the measures which are applicable in a variety of fields including e.g. probability theory, statistics and economics. Package: r-cran-asymmetry Architecture: all Version: 2.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smacof Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-asymmetry_2.0.6-1.ca2604.1_all.deb Size: 153984 MD5sum: bcb75efb22a7967ff9a8197cbedab60f SHA1: 331724d84c60a4af553cb8177f5d915b16651bba SHA256: a4187c0ece57852bec0a72fc6d4b02fa5391c8e8a5fcd8eaa9cd2148d4add678 SHA512: f646d0dd9904093f3e8b892ad072ffea59cf8cae2b3a461470a3f5c315816c21a4a243dc20e24ef2d74edc1cb899b9774a408e8b42bb7718de3098298b96b553 Homepage: https://cran.r-project.org/package=asymmetry Description: CRAN Package 'asymmetry' (Multidimensional Scaling of Asymmetric Proximities) Multidimensional scaling models and methods for the visualization and analysis of asymmetric proximity data. An asymmetric data matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example of an asymmetric matrix is a student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides algorithms for three multidimensional scaling models, the slide-vector model, a scaling model with unique dimensions and the asymscal model. Furthermore, some other procedures, such as a heat map for skew-symmetric data, and the decomposition of asymmetry are also provided for the exploratory analysis of asymmetric tables. Package: r-cran-asympdiag Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-glmmtmb, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-rlang, r-cran-sandwich, r-cran-survival, r-cran-testthat, r-cran-vctrs, r-cran-withr Filename: pool/dists/resolute/main/r-cran-asympdiag_0.3.2-1.ca2604.1_all.deb Size: 106584 MD5sum: d03afdd636034e1ecd17afbac4a61ac7 SHA1: 716f7f8698b4810ca3c8be183486b8b512fa38ea SHA256: 70b40cfd73341123c13ba76fbaced689b422f6cda09df5e6e9d5b3881bec4d31 SHA512: 5d6c878b6e388fbcab9227e17608c8b28bc99df3695d20103cdb5dd45c7c4ad8e026264f647469e783b4c83d3e057ac0f4e56d074e79b020cf9a71f40aed3bee Homepage: https://cran.r-project.org/package=asympDiag Description: CRAN Package 'asympDiag' (Diagnostic Tools for Asymptotic Theory) Leveraging Monte Carlo simulations, this package provides tools for diagnosing regression models. It implements a parametric bootstrap framework to compute statistics, generates diagnostic envelopes to assess goodness-of-fit, and evaluates type I error control for Wald tests. By simulating data under the assumption that the model is true, it helps to identify model mis-specifications and enhances the reliability of the model inferences. Package: r-cran-asymptest Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-asymptest_0.1.4-1.ca2604.1_all.deb Size: 345706 MD5sum: 4a5658b9b7f7350573d680e16e344ea7 SHA1: 31537662e8b1968ff9b7a2096f2340f1ede68b80 SHA256: a2322b34886046da57d069eb614be968094ec982f19854001e839d578be15175 SHA512: 9e00e5224bd71cd54f66b35a662d2f67d298e2222cbfca8ae9af42d37f801401b975c6b7f37bda31a99cbe81cb4a5ea922b722cae6d8c7a5f921b6bfa6d37035 Homepage: https://cran.r-project.org/package=asympTest Description: CRAN Package 'asympTest' (A Simple R Package for Classical Parametric Statistical Testsand Confidence Intervals in Large Samples) One and two sample mean and variance tests (differences and ratios) are considered. The test statistics are all expressed in the same form as the Student t-test, which facilitates their presentation in the classroom. This contribution also fills the gap of a robust (to non-normality) alternative to the chi-square single variance test for large samples, since no such procedure is implemented in standard statistical software. Package: r-cran-asymptor Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-asymptor_1.1.0-1.ca2604.1_all.deb Size: 135582 MD5sum: 7572f810979bbb252e9b76b325eb4b6b SHA1: ff937313f03c372f6bd3fc3c6eb1319b0c206731 SHA256: 0456bd9019d60451de5a126c75fba4ff7111e96381d9ed86d4c909d8d2d3b481 SHA512: cc7237d84e5ac852cd0a2a25e916ccaf472e6f87ade6c90294001822879cc77f97efd2cb51054e09718cae0ea31e0aebe4d8de585bec741da840d35c11a35981 Homepage: https://cran.r-project.org/package=asymptor Description: CRAN Package 'asymptor' (Estimate Asymptomatic Cases via Capture/Recapture Methods) Estimate the lower and upper bound of asymptomatic cases in an epidemic using the capture/recapture methods from Böhning et al. (2020) and Rocchetti et al. (2020) . Note there is currently some discussion about the validity of the methods implemented in this package. You should read carefully the original articles, alongside this answer from Li et al. (2022) before using this package in your project. 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Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients. Cao, H., Li, J., and Fine, J. P. (2016) . Cao, H., Zeng, D., and Fine, J. P. (2015) . 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These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers. 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It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest. Package: r-cran-autobagging Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2038 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-xgboost, r-cran-e1071, r-cran-rpart, r-cran-abind, r-cran-caret, r-cran-mass, r-cran-entropy, r-cran-lsr, r-cran-corelearn, r-cran-infotheo, r-cran-minerva, r-cran-party Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-autobagging_0.1.0-1.ca2604.1_all.deb Size: 1968340 MD5sum: 3661e2d42d4219f5a0b6f61f44ebc8aa SHA1: d7efe9c00e1c09a877fb29a408dd00cfd39149bc SHA256: 4deedc3e68405faeee4883fca7517897343b243257f0216bf275fce76b236f10 SHA512: 269ef408ed9981a7e8b468a2601ec749e9c4c38dacd8e1cf54073bf0e54a0c9aa1251828731b19de427e65571d8c9756030b8cf66d3fdc067292609bb0606beb Homepage: https://cran.r-project.org/package=autoBagging Description: CRAN Package 'autoBagging' (Learning to Rank Bagging Workflows with Metalearning) A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367. Package: r-cran-autocogs Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-checkmate, r-cran-diptest, r-cran-dplyr, r-cran-ggplot2, r-cran-hexbin, r-cran-mass, r-cran-mclust, r-cran-moments, r-cran-progress, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-autocogs_0.1.5-1.ca2604.1_all.deb Size: 822098 MD5sum: 36fca7e9340c334984b241c991ab59da SHA1: 9a403cf5600dc50a7ad765edfd30bce6b48d8ccb SHA256: 0c3bf850177aaed3ff7f4d26e44a96f58a167285d6fdf660465e5d96ebd960ad SHA512: 29ffb1d00a19611234aeeaa269f94bc2e0bd9406c2ac7ba8feb9705d47a4634be768722286346a1329be6bca7f7f334ffb0a3338783df552db691470355a3413 Homepage: https://cran.r-project.org/package=autocogs Description: CRAN Package 'autocogs' (Automatic Cognostic Summaries) Automatically calculates cognostic groups for plot objects and list column plot objects. Results are returned in a nested data frame. Package: r-cran-autocovariateselection Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-autocovariateselection_1.0.0-1.ca2604.1_all.deb Size: 544720 MD5sum: a6ac3d4b3fb17a56c0fb18b144e76b87 SHA1: 1e37cd3f6888e4098fd32b3cce7190ebf907ae62 SHA256: 7d366f7ba81b4f490da5a415c3f2d59c2ff53fa11cfb404c6dccd38c40bee293 SHA512: 40439de4bbdf52a024df83746df3c430e2c15fb88b2715862b6982a2565389614765e983346cd5f08900770b7e9e13e11ecb014841c03dd1376d808327b8958e Homepage: https://cran.r-project.org/package=autoCovariateSelection Description: CRAN Package 'autoCovariateSelection' (Automated Covariate Selection Using HDPS Algorithm) Contains functions to implement automated covariate selection using methods described in the high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in. The functions in this package help to implement the three major steps of automated covariate selection as described by Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates(). In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is also available for running examples and also for further exploration. The original article where the algorithm is described by Schneeweiss et.al. (2009) . Package: r-cran-autodb Architecture: all Version: 3.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14582 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rlang, r-cran-spelling, r-cran-diagrammer, r-cran-testthat, r-cran-r.utils, r-cran-hedgehog, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-autodb_3.2.4-1.ca2604.1_all.deb Size: 1267886 MD5sum: ed5ffeca707a589645beef789ef93f13 SHA1: d5d527babca689de6ccde0b7ac0d66dcf62c980c SHA256: f6946fe7cb33570c13efae72bb269518e0c728a7e66af6817accfb623636c50d SHA512: d327883ef9142bb429b3072a73f8ebb79d5b86fdb4ad2baec538bc3f71f8f8b4938256e51d4412821013aa4423f57ecfc3ac9ef72f6398394135745caa21b749 Homepage: https://cran.r-project.org/package=autodb Description: CRAN Package 'autodb' (Automatic Database Normalisation for Data Frames) Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx' (), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via 'Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time. Package: r-cran-autodeskr Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3508 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-autodeskr_0.1.5-1.ca2604.1_all.deb Size: 2388866 MD5sum: 1f6551819b8755351d0b45288f7c83d5 SHA1: 4dc764bcbc937ced1a700e5aebdf674929a165b8 SHA256: 6ef50ab113183fc98b02a1408e106bf81f83adcc4f2e7846cebd20d161d96395 SHA512: 7d1733376001929a9f0ecc588db2abf2f1914c589b4dcfd8e9e75bb197ded1c4235868722bc1b3c56788040e377053d4796ca227d46f0087034b468bbc650361 Homepage: https://cran.r-project.org/package=AutoDeskR Description: CRAN Package 'AutoDeskR' (An Interface to the 'AutoDesk' 'API' Platform) An interface to the 'AutoDesk' 'API' Platform including the Authentication 'API' for obtaining authentication to the 'AutoDesk' Forge Platform, Data Management 'API' for managing data across the platform's cloud services, Design Automation 'API' for performing automated tasks on design files in the cloud, Model Derivative 'API' for translating design files into different formats, sending them to the viewer app, and extracting design data, and Viewer for rendering 2D and 3D models. Package: r-cran-autoensemble Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-h2o, r-cran-h2otools, r-cran-curl Filename: pool/dists/resolute/main/r-cran-autoensemble_0.3-1.ca2604.1_all.deb Size: 258470 MD5sum: b9df641a308bf96ee864fe60509a8dc7 SHA1: 7976ca2b77b0e09d6bac6a027f834131aa0b3195 SHA256: 62f1b9fefa0ae02a64d3f9aba32b6267d173d231982762301bbcd9e9314f46f9 SHA512: b5370b222f8f03fa2c432a36e181a67cbf9844b2522faf56a62c7ead57bda5f235d2ef17f6cb318431257a01e4291945e5f9427d7a794f1b2ef33610fa011b0a Homepage: https://cran.r-project.org/package=autoEnsemble Description: CRAN Package 'autoEnsemble' (Automated Stacked Ensemble Classifier for Severe Class Imbalance) A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability. Package: r-cran-autofc Architecture: all Version: 0.2.0.1010-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lavaan, r-cran-mass, r-cran-simdesign, r-cran-thurstonianirt, r-cran-mplusautomation, r-cran-glue, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-autofc_0.2.0.1010-1.ca2604.1_all.deb Size: 168278 MD5sum: 9c13f9c703de5b3098e0531b89fb46f2 SHA1: 0c3933ddffd964eed5746ff0e4d4e14f8508bcdd SHA256: 66391bd7f2101773ff4940f35ac736204963b19eb4ebb23c6e43d58e98f3a9f0 SHA512: 253c466e77e385004307602c8f3a4236c14d20bee4e61d3662fff0342df4fbac53365d587bba86d848dea7674013abc19b477db11708e7421155b7629cccc6ec Homepage: https://cran.r-project.org/package=autoFC Description: CRAN Package 'autoFC' (Automatic Construction of Forced-Choice Tests) Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 ). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 ). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 ). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 ) or minimize item location differences (Cao & Drasgow, 2019 ) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests (Li et al., 2022 ), essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC measures can be constructed either automatically based on user-defined pairing criteria and weights, or based on exact specifications of each block (i.e., blueprint; see Li et al., 2024 ). Users can also generate simulated responses based on the Thurstonian Item Response Theory model (Brown & Maydeu-Olivares, 2011 ) and predict trait scores of simulated/actual respondents based on an estimated model. Package: r-cran-autoflagr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-isotree, r-cran-dbscan, r-cran-dplyr, r-cran-ggplot2, r-cran-proc, r-cran-prroc, r-cran-knitr, r-cran-gt, r-cran-scales, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-pkgdown, r-cran-ggnewscale Filename: pool/dists/resolute/main/r-cran-autoflagr_1.0.0-1.ca2604.1_all.deb Size: 701938 MD5sum: 18ba1875833a8aeb9054d07ba24b26fb SHA1: e99636249c1009f59d2c39fbb4ecf3e29a6347dc SHA256: 8fb98968dc33c179af318aceaa55be0410dad44fabe9304e312269595fa700bf SHA512: 89402279c24a4f496b61d50fb10a8c2c410840f879d407c0d4bf71bc29eea300143f910f154379aaa5abd82d502b287323aff85e94f23211d1fb1ea0928d13df Homepage: https://cran.r-project.org/package=autoFlagR Description: CRAN Package 'autoFlagR' (AI-Driven Anomaly Detection for Data Quality) Automated data quality auditing using unsupervised machine learning. Provides AI-driven anomaly detection for data quality assessment, primarily designed for Electronic Health Records (EHR) data, with benchmarking capabilities for validation and publication. Methods based on: Liu et al. (2008) , Breunig et al. (2000) . Package: r-cran-autogam Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-mgcv, r-cran-purrr, r-cran-rlang, r-cran-staccuracy, r-cran-stringr, r-cran-univariateml Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-autogam_0.1.0-1.ca2604.1_all.deb Size: 111918 MD5sum: 228283dad7250da9566e10de3a43ccbf SHA1: a0bd092925fb46c98ddc4460edcb3928f5bd2272 SHA256: 4d7f90523b8646acd4039825badd3719af673bb9f4f8c77750025c554c8c8e2b SHA512: 0d185771bd767bcaa5401f59ea82ca43087040cc7616108b760378ea726526693d4995698dced6a87a611e0005dc352323f756d9bd75dd40b878ce937ee25265 Homepage: https://cran.r-project.org/package=autogam Description: CRAN Package 'autogam' (Automate the Creation of Generalized Additive Models (GAMs)) This wrapper package for 'mgcv' makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, 'AutoGAM' tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets. Package: r-cran-autogo Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4077 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-dplyr, r-cran-ape, r-bioc-complexheatmap, r-bioc-deseq2, r-cran-dichromat, r-cran-ggplot2, r-cran-ggrepel, r-bioc-gsva, r-cran-msigdbr, r-cran-openxlsx, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-textshape, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rlang, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-autogo_1.0.3-1.ca2604.1_all.deb Size: 3311850 MD5sum: d848d6ed17b310881dc719670acde0d3 SHA1: a8236ee087d6640644080bca370707b8405c5da1 SHA256: 609889cd9493918643e6dd0b0947a8961b8adb67ea38ebfe5800164beed836a6 SHA512: 8dce646ec3ece3cc08c4feb221c2841877a5e4277b18869e2e08386e04489914705c7258ad16ace5ddfb28c917fd2c077f806ebfd3bcaaa9c7045111a6093d31 Homepage: https://cran.r-project.org/package=autoGO Description: CRAN Package 'autoGO' (Auto-GO: Reproducible, Robust and High Quality OntologyEnrichment Visualizations) Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) . The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce. Package: r-cran-autograph Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3973 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manynet, r-cran-dplyr, r-cran-ggdendro, r-cran-ggraph, r-cran-ggplot2, r-cran-igraph, r-cran-patchwork, r-cran-tidygraph Suggests: r-cran-gganimate, r-cran-ggforce, r-cran-gifski, r-cran-graphlayouts, r-cran-netrics, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-autograph_1.0.3-1.ca2604.1_all.deb Size: 3200000 MD5sum: 60b9ae33bf52759bee42dc4d01355c18 SHA1: 1828f8a5bee6e3d501d53bdfbcf38b2cbdd847d9 SHA256: a75bfe6a9e6875a85c994fd983dd0446a574d6c652287ea8a2b2e146dd08d8f6 SHA512: 64d06e0bc1444f49ec877cea86a745f0a1ad2c1a979b14ddf9a08011320afb58aa2b63a3dbc2b27ff4d4073b6af3fd4989dfa498f73ed92bc2574388abb72bf5 Homepage: https://cran.r-project.org/package=autograph Description: CRAN Package 'autograph' (Automatic Plotting and Theming of Many Graphs) Visual exploration and presentation of networks should not be difficult. This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults. It includes 'ggplot2'-based plot methods for many popular network package classes. It also includes some novel layout algorithms, and options for straightforward, consistent themes. Package: r-cran-autoharp Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-rlang, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-lintr, r-cran-igraph, r-cran-xfun Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-autoharp_0.3.2-1.ca2604.1_all.deb Size: 3324530 MD5sum: b92f63c53570ff59d0a547a8553c3dd2 SHA1: 868175071c67eb1ccac5b2ecd3859fe6b6d1cb00 SHA256: 7c01af16f1b692d0146398acbe1a1c971d01aa06726f8bf685fc5212fb7ba346 SHA512: 100a763e26453d8ba6914b6e8d2fd6467b1e99893caf3609fe68ef3e340d9255ab21cd2f868550eb36474a5353c778a03bb7e50c65ccd0130f0c29e5cd050ddb Homepage: https://cran.r-project.org/package=autoharp Description: CRAN Package 'autoharp' (Semi-Automatic Grading of R and Rmd Scripts) A customisable set of tools for assessing and grading R or R-markdown scripts from students. It allows for checking correctness of code output, runtime statistics and static code analysis. The latter feature is made possible by representing R expressions using a tree structure. Package: r-cran-autohrf Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2899 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-gtools, r-cran-lubridate, r-cran-magrittr, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-autohrf_1.1.3-1.ca2604.1_all.deb Size: 1892852 MD5sum: 35901eb47f70f9333149572b5f8307f0 SHA1: d5f893b34cdd8d16c8cdbfab9423c66d06ee1ecb SHA256: cc533d89bf7dcf54c93c5db10195423bb657478e5a5eca94342d500415af5c97 SHA512: bf908c66a151896f663985fb9f98d1fe1936e0d882e04655e73ef68c1bdfab5ce7edfe11b213bea3f068574d1923c0b568d270ad0d1e9d66bb2123fd5c552c35 Homepage: https://cran.r-project.org/package=autohrf Description: CRAN Package 'autohrf' (Automated Generation of Data-Informed GLM Models in Task-BasedfMRI Data Analysis) Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner. Package: r-cran-autoimage Architecture: all Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-fields, r-cran-mapproj, r-cran-ggplot2, r-cran-maps, r-cran-mba Suggests: r-cran-abind, r-cran-testthat, r-cran-spatstat.geom, r-cran-gear, r-cran-broom, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-autoimage_2.2.3-1.ca2604.1_all.deb Size: 2165594 MD5sum: d6ac175b2980c25869efaaedda8713db SHA1: 2db1db9cf89bd071b7daefdc95cf39060b5c908c SHA256: b37115239d2fa2de821031a555f0652dfb8eb7fb163d689c81b4aee5bbfd0a27 SHA512: 1fc5248507f9b12017ddf39b05d5d8bc9eb030b0bce4518ce12b08bc64efc36e52f702c3013b01962ef5701dba45326f4c58cf652d6a64bf80bc7c56f96554c5 Homepage: https://cran.r-project.org/package=autoimage Description: CRAN Package 'autoimage' (Multiple Heat Maps for Projected Coordinates) Functions for displaying multiple images or scatterplots with a color scale, i.e., heat maps, possibly with projected coordinates. The package relies on the base graphics system, so graphics are rendered rapidly. 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Package: r-cran-automagic Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-formatr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-remotes, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-automagic_0.5.1-1.ca2604.1_all.deb Size: 30876 MD5sum: 48811c0b29a1be149719591a852550ef SHA1: fed839fb886245c4db9f96eff799f0e1b5654326 SHA256: 60ff1c5c684de068ab537ab4f941a801c6f52d4a14dbd1bb3ccf52f3a8a31413 SHA512: e9713c018d184fe403f17473750c184de1509eb5a63230644a37288a1aaf468b06f8cfe0e45c043d63bb070f7e978a717a539bb1cebaa41b7911323aae133512 Homepage: https://cran.r-project.org/package=automagic Description: CRAN Package 'automagic' (Automagically Document and Install Packages Necessary to Run RCode) Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. 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Package: r-cran-automl Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-automl_1.3.2-1.ca2604.1_all.deb Size: 418886 MD5sum: eaa7d65ed6d21642692a2d19a30900ed SHA1: 6210228fda385036defc483950bd6742f861c48d SHA256: 14158086355e837148a4c671ac2820227b0c968df082ff33790c9c1f152d7c83 SHA512: 733a5d9546500d71bd52051933112aa4ea1a8f17e9882131f6ce2dbc045c6944dd4a571bd84d2271d77f6ff223dbe9fa985e510197c9c7ad0a467dd1757427ad Homepage: https://cran.r-project.org/package=automl Description: CRAN Package 'automl' (Deep Learning with Metaheuristic) Fits from simple regression to highly customizable deep neural networks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization. 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Package: r-cran-autonewsmd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-git2r, r-cran-quarto, r-cran-r6 Suggests: r-cran-lintr, r-cran-precommit, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-autonewsmd_0.1.0-1.ca2604.1_all.deb Size: 79206 MD5sum: 3b7e5514ef768e33cbd2c346474b836d SHA1: 95f74e0f0e09b21d0e4ef72aec7feed5c198cb84 SHA256: 3d91a55570d19b1e27116b314736317f87f1c8e626b8fe3beee3315270581133 SHA512: 5e5c92129b2f9538631a54290e7e116dbfd75a6e6d98b4d1dd9d61342ada3e3bdc0b450f4d0b7286dc8287b14658d0f985e2162a038681a2bb8db57f11a2f796 Homepage: https://cran.r-project.org/package=autonewsmd Description: CRAN Package 'autonewsmd' (Auto-Generate Changelog using Conventional Commits) Automatically generate a changelog file (NEWS.md / CHANGELOG.md) from the git history using conventional commit messages (). 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Support linear models, generalized linear models and cox-proportional hazard models. Generate publication-ready tables summarizing result of regression analysis and plots. The tables and plots can be exported in "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents. 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Package: r-cran-autoscore Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2473 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tableone, r-cran-proc, r-cran-randomforest, r-cran-ggplot2, r-cran-knitr, r-cran-hmisc, r-cran-car, r-cran-dplyr, r-cran-ordinal, r-cran-survival, r-cran-tidyr, r-cran-plotly, r-cran-magrittr, r-cran-randomforestsrc, r-cran-rlang, r-cran-survauc, r-cran-survminer Suggests: r-cran-rpart, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-autoscore_1.1.0-1.ca2604.1_all.deb Size: 1922746 MD5sum: 254902442f78eef49252cf30be8ef78b SHA1: 7fb46e516d4e59c3663e7b90709a07a547e2bbc3 SHA256: c0709857e4c24adfe6aecce3770ee4d536e63c1fb320d8c20517d8cc63f63499 SHA512: 2abcbe1e7d4e62a00b9c42b850230c1b2be624853264812e2208fbb73776f1f4ac26f2b6e8bfda6aab0b560fffa070ac877c30b85229bf70af66a3f351552da9 Homepage: https://cran.r-project.org/package=AutoScore Description: CRAN Package 'AutoScore' (An Interpretable Machine Learning-Based Automatic Clinical ScoreGenerator) A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies. Package: r-cran-autoscorecard Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-infotheo, r-cran-rocr, r-cran-rpart, r-cran-discretization, r-cran-corrplot, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-autoscorecard_0.3.0-1.ca2604.1_all.deb Size: 702912 MD5sum: ec06eaf40e9facd5ee527025ac38d83b SHA1: c3144d5f5790a6dcf1f4b05fc9e102c0346139a7 SHA256: f34f71ffb007679c97c0a2d8b8425b45d752186e5a2c3748b5d0234193757b98 SHA512: db0edb788b0855c69e643dcafe8f5dd38e9ee0249e0b66cbe22857a208428321df60e6322cfd0d8d0f97383062b37c7d93ce3150035535e98ea3ba5dd140fd73 Homepage: https://cran.r-project.org/package=autoScorecard Description: CRAN Package 'autoScorecard' (Fully Automatic Generation of Scorecards) Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. Including equal frequency binning, equidistant binning, K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, fully automatic generation of scorecards, etc. This package is designed to make scorecard development easier and faster. References include: 1. . 2. Dong-feng Li(Peking University),Class PPT. 3. . 4. . 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Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5). 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Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" . Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . 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This package has no restriction on the number of weeks and weather variables to be taken as input.The details of the method can be seen (i)'Joint effects of weather variables on rice yields' by R. Agrawal, R. C. Jain and M. P. Jha in Mausam, vol. 34, pp. 189-194, 1983,,(ii)'Improved weather indices based Bayesian regression model for forecasting crop yield' by M. Yeasin, K. N. Singh, A. Lama and B. Gurung in Mausam, vol. 72, pp.879-886, 2021,. 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For more information on 'Kinesis', see . Package: r-cran-awr Architecture: all Version: 1.11.189-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Filename: pool/dists/resolute/main/r-cran-awr_1.11.189-1-1.ca2604.1_all.deb Size: 14630 MD5sum: 2f1f12235974396debd4d3bd64568a2a SHA1: a593f435702b36970410930096856cca879c72c0 SHA256: cb49d28222d0f1ea3d64f83c0110bb3371d93380a6fc95df9822f27dbf28bedb SHA512: 779551a58264373f4c1674c60f1de9644cbe5232766490befebb0d799ca7a418ab5589b00c2d94ae229f221802a3d18be436b652c64b956391ca8bcd56c2794b Homepage: https://cran.r-project.org/package=AWR Description: CRAN Package 'AWR' ('AWS' Java 'SDK' for R) Make the compiled Java modules of the Amazon Web Services ('AWS') 'SDK' available to be used in downstream R packages interacting with 'AWS'. See for more information on the 'AWS' 'SDK' for Java. Package: r-cran-aws.alexa Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-aws.signature, r-cran-xml2, r-cran-dplyr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-lintr Filename: pool/dists/resolute/main/r-cran-aws.alexa_0.1.8-1.ca2604.1_all.deb Size: 251322 MD5sum: 4f3021609d3df86bff74a7bff3affe46 SHA1: 7325f046aa5a1f18946cd00ecd085d86fd791ab8 SHA256: 73a5127caf60b2665fddf1dfb19ef37d142576a8f4b95c03644a16116ab955f6 SHA512: ab50db4521e63aa942fefb17f82b050aa4a12d0a1069dcbe9c91c8e51becad2e936244054241f78defb721034fe3682d8215396567b1fe129596106f6fa205de Homepage: https://cran.r-project.org/package=aws.alexa Description: CRAN Package 'aws.alexa' (Client for the Amazon Alexa Web Information Services API) Use the Amazon Alexa Web Information Services API to find information about domains, including the kind of content that they carry, how popular are they---rank and traffic history, sites linking to them, among other things. 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Package: r-cran-aws.transcribe Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aws.transcribe_0.1.3-1.ca2604.1_all.deb Size: 28422 MD5sum: a9d983882d34c76dfc28c2f7f8195734 SHA1: 8e00429d2a0b395a264588507b7198de680372e9 SHA256: 92101cfeff2ed2b04db14a40c1c14554208ca6102c1db7eb1eaaec2090283fdb SHA512: 62a27f70ea3422fa0126e732b1ad0e4b9de480e676bb0d2ce12f7c06660bb0fb9c7eaa7bf8764c67335389e969cc79413de2c59f9a90ad32466a01f7933626b8 Homepage: https://cran.r-project.org/package=aws.transcribe Description: CRAN Package 'aws.transcribe' (Client for 'AWS Transcribe') Client for 'AWS Transcribe' , a cloud transcription service that can convert an audio media file in English and other languages into a text transcript. Package: r-cran-aws.translate Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature Filename: pool/dists/resolute/main/r-cran-aws.translate_0.1.4-1.ca2604.1_all.deb Size: 19684 MD5sum: 0046924c08da07f6dc861bac1eb0bb90 SHA1: 7374ec358c50d055bfa1a12da799e30b1274a1f7 SHA256: a3bc931f152a3c1da8d3f5e63955e64a4f915c6352611049346f400e0520c641 SHA512: 06347c89b4953970b6bc7f9f2faefc9f8f11b2582c8ca0d5f0931522a389579e4c16bedc155faf5e81991c452d71dc03262a9db12a54f76bb27cf6b50086ab89 Homepage: https://cran.r-project.org/package=aws.translate Description: CRAN Package 'aws.translate' (Client for 'AWS Translate') A client for 'AWS Translate' , a machine translation service that will convert a text input in one language into a text output in another language. Package: r-cran-aws.wrfsmn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aws.s3, r-cran-lubridate, r-cran-terra, r-cran-dplyr, r-cran-ggplot2, r-cran-hydrogof, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aws.wrfsmn_0.1.0-1.ca2604.1_all.deb Size: 129414 MD5sum: 48323183dd6d697dce2b923fe267194c SHA1: 69726ada06e1c5d621c83bb00014b75347a1f1ec SHA256: e93050d567a072707eb0b4a8cd152fc5a21b249554a7566ba2326300d8527a3c SHA512: d2fa95e3ac0a0cc8a11722143e76b4e08d73992cd7706bbc5b7026e968d5f98404a7da9cc8366ffaf749879f0638a3c9c1d122f438a6e67180debfdc9d21c5d5 Homepage: https://cran.r-project.org/package=aws.wrfsmn Description: CRAN Package 'aws.wrfsmn' (Data Processing of SMN Hi-Res Weather Forecast from 'AWS') Exploration of Weather Research & Forecasting ('WRF') Model data of Servicio Meteorologico Nacional (SMN) from Amazon Web Services () cloud. The package provides the possibility of data downloading, processing and correction methods. It also has map management and series exploration of available meteorological variables of 'WRF' forecast. Package: r-cran-axisandallies Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-axisandallies_0.1.1-1.ca2604.1_all.deb Size: 38882 MD5sum: 0aff41b765b0fe3af604fc3f40fc01cf SHA1: 0c363cc9c1abc793414b05a662c9d130f155735a SHA256: f2c5c628ff5add8232c3e3ff67636c81a2be4fdbd3ff655be0490b0859510139 SHA512: 75380c84a8d9b9063bb6e39df5937bf240fccf12e67abef30a1484391e8ce3d5078359d387bf242393f5acb0618d2e68afe1eeb6c7ef74756e54f844bf53daca Homepage: https://cran.r-project.org/package=axisandallies Description: CRAN Package 'axisandallies' (Axis and Allies Spring) Simulates battles in the board game Axis and Allies Spring 1942, and calculates your probability of winning a battle. This speeds the game up significantly. Package: r-cran-aziad Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr, r-cran-foreach, r-cran-doparallel, r-cran-qrm, r-cran-corpcor, r-cran-envstats Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-aziad_0.0.3-1.ca2604.1_all.deb Size: 216178 MD5sum: c7b81958d0963104d2556a18032eea15 SHA1: 728c4a3cb8feb586524e6b8f5d5010a6207adc08 SHA256: e1b4f744cb55da0253ba003cb60f8d4877634dd42b2ea32a4abf665198bb5867 SHA512: 08b3ac1866006b2f580e754b589273b7e6b0f430eeaba41a6b9449e401c1bc8677e4d5db92c0771d3b9b8af8152f09ff1af27347bb60045e4cec12aed9fb6698 Homepage: https://cran.r-project.org/package=AZIAD Description: CRAN Package 'AZIAD' (Analyzing Zero-Inflated and Zero-Altered Data) Description: Computes maximum likelihood estimates of general, zero-inflated, and zero-altered models for discrete and continuous distributions. It also performs Kolmogorov-Smirnov (KS) tests and likelihood ratio tests for general, zero-inflated, and zero-altered data. Additionally, it obtains the inverse of the Fisher information matrix and confidence intervals for the parameters of general, zero-inflated, and zero-altered models. The package simulates random deviates from zero-inflated or hurdle models to obtain maximum likelihood estimates. Based on the work of Aldirawi et al. (2022) and Dousti Mousavi et al. (2023) . Package: r-cran-azlogr Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-catools, r-cran-digest, r-cran-httr, r-cran-jsonlite, r-cran-logger Suggests: r-cran-covr, r-cran-knitr, r-cran-mockery, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-azlogr_0.0.6-1.ca2604.1_all.deb Size: 52528 MD5sum: 19b8032d2de54217c3ce93b27e99b76c SHA1: aa3feeff530c8002f833c337122e420fef9902ce SHA256: 809df7bfc555b6cf16e1be0b3d4e0f7f789e1fb14cdd061b7dbf7a1df6c71b73 SHA512: 14aab6dc6cac6aaa34ffe59fdda85297411c87270215f51570848068a31b80c50ba85a75dccf69b8e4c3fca0d9bedc81754d823d90a04a776f25a198c5cf01c9 Homepage: https://cran.r-project.org/package=azlogr Description: CRAN Package 'azlogr' (Logging in 'R' and Post to 'Azure Log Analytics' Workspace) It extends the functionality of 'logger' package. Additional logging metadata can be configured to be collected. Logging messages are displayed on console and optionally they are sent to 'Azure Log Analytics' workspace in real-time. Package: r-cran-azr Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-data.table, r-cran-httpuv, r-cran-clipr, r-cran-processx, r-cran-testthat, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-azr_0.3.4-1.ca2604.1_all.deb Size: 648164 MD5sum: b9f960eddfd7672b2e3d31bea4802518 SHA1: 6c34670d42ee0a5d30c53369eac9e1e8d6794393 SHA256: b349eaf69e0f82f851d4aaafc85af88381e66e54ec3a75a8a7456f5c3219c8f3 SHA512: d75620a334bfc604af3a5093b295ace4fba7da0fb4dc1cacd18bd689c801b38e29d0a3cad715dc456bac3b87f609f32854eb92c4f4e36715bf6229eeb3bc5486 Homepage: https://cran.r-project.org/package=azr Description: CRAN Package 'azr' (Credential Chain for Seamless 'OAuth 2.0' Authentication to'Azure Services') Implements a credential chain for 'Azure OAuth 2.0' authentication based on the package 'httr2''s 'OAuth' framework. Sequentially attempts authentication methods until one succeeds. During development allows interactive browser-based flows ('Device Code' and 'Auth Code' flows) and non-interactive flow ('Client Secret') in batch mode. Package: r-cran-azureappinsights Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rlang, r-cran-assertthat, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-testthat, r-cran-here Filename: pool/dists/resolute/main/r-cran-azureappinsights_0.3.1-1.ca2604.1_all.deb Size: 302858 MD5sum: 42729a71e24c7559a45333e39a67dafc SHA1: 9da2dd831f0d453e519cc869e2b9b218f28375a6 SHA256: 32736837c30f9429acb7ecd796a0e6f11c35bc3f22a228dcac9dd5ecba7cce29 SHA512: bcc30452ecd047ae4d4188470cce761bd81e175a61cc7c1189b7b958c6744620ca7d613910e68e667fb55156f3d3fa28e7f7a2ccac69fc2eefc6386d75c8e14f Homepage: https://cran.r-project.org/package=AzureAppInsights Description: CRAN Package 'AzureAppInsights' (Include Azure Application Insights in Shiny Apps) Imports Azure Application Insights for web pages into Shiny apps via Microsoft's JavaScript snippet. Allows app developers to submit page tracking and submit events. Package: r-cran-azureauth Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-openssl, r-cran-jsonlite, r-cran-jose, r-cran-r6, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httpuv, r-cran-shiny, r-cran-shinyjs, r-cran-azurermr, r-cran-azuregraph Filename: pool/dists/resolute/main/r-cran-azureauth_1.3.4-1.ca2604.1_all.deb Size: 459462 MD5sum: 47e35411b16cf525695a74a768565d7e SHA1: cb259b4fe6d241813ccc46f26451024480b46342 SHA256: c7c351d613777754f4c2174761b0770039927dc72e0c752c10838bc26419fbb2 SHA512: 2bd6ce11eb0c48d6f6aefdbbc88944c7615b22b17709434a0108863e7c4a976040001fb465b1d123174cabcaaa6b88f55e7230540854a190dcf5bfabce9d2f83 Homepage: https://cran.r-project.org/package=AzureAuth Description: CRAN Package 'AzureAuth' (Authentication Services for Azure Active Directory) Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud . Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages. Package: r-cran-azurecognitive Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azureauth, r-cran-azurermr, r-cran-jsonlite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-azurecognitive_1.0.2-1.ca2604.1_all.deb Size: 146974 MD5sum: 2fea2b0386d6d9e20a3d66e826ac4e09 SHA1: 37e79318f7769df7c692ab18ff7e3361513d1404 SHA256: 9968e0568dc7b99c1f121f64e93b00ed2068c97723fd63ba11e18c175a4f8b83 SHA512: 466934acec1b59d009e76f0f224c55c7106594b87e0f94f94ed303bd5b62db8472d16ca10b5ad468f8879765fa9be817512c6dbad7f7d15e902b9da9cc06a400 Homepage: https://cran.r-project.org/package=AzureCognitive Description: CRAN Package 'AzureCognitive' (Interface to Azure Cognitive Services) An interface to Azure Cognitive Services . Both an 'Azure Resource Manager' interface, for deploying Cognitive Services resources, and a client framework are supplied. While 'AzureCognitive' can be called by the end-user, it is meant to provide a foundation for other packages that will support specific services, like Computer Vision, Custom Vision, language translation, and so on. Part of the 'AzureR' family of packages. Package: r-cran-azurecontainers Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azurermr, r-cran-azuregraph, r-cran-openssl, r-cran-httr, r-cran-r6, r-cran-processx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-uuid, r-cran-mass, r-cran-bcrypt, r-cran-randomforest, r-cran-plumber, r-cran-restrserve, r-cran-azurekeyvault Filename: pool/dists/resolute/main/r-cran-azurecontainers_1.3.3-1.ca2604.1_all.deb Size: 475736 MD5sum: 4f57833b0b0e33d292bdaf63fe4aea7d SHA1: 30247a0568f769fdbfe12549e0bf461470f2ae51 SHA256: ba02a5dcc3132fb6443c6935215ce65c16f5f4675e9f01223e3d26c29f30c25e SHA512: 3fa0156d3941bf7550697cabacc2cf43b8df1ace6b135a2fb51925d74434408b8c1a3b84c0d9a861981c69d2bbd66575b430fa9478e834b3a975c3c00aa25c4a Homepage: https://cran.r-project.org/package=AzureContainers Description: CRAN Package 'AzureContainers' (Interface to 'Container Instances', 'Docker Registry' and'Kubernetes' in 'Azure') An interface to container functionality in Microsoft's 'Azure' cloud: . Manage 'Azure Container Instance' (ACI), 'Azure Container Registry' (ACR) and 'Azure Kubernetes Service' (AKS) resources, push and pull images, and deploy services. On the client side, lightweight shells to the 'docker', 'docker-compose', 'kubectl' and 'helm' commandline tools are provided. Part of the 'AzureR' family of packages. Package: r-cran-azurecosmosr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azurermr, r-cran-curl, r-cran-openssl, r-cran-jsonlite, r-cran-httr, r-cran-uuid, r-cran-vctrs Suggests: r-cran-azuretablestor, r-cran-mongolite, r-cran-dbi, r-cran-odbc, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-azurecosmosr_1.0.0-1.ca2604.1_all.deb Size: 223362 MD5sum: f26cceab8fc470747fe4d41c43566dcd SHA1: a8001efa36aa8e211f469a822794526be4eb9c8e SHA256: a7ac00020229a54e49119f0097efed2c686ad3b61942cac8c37917c41abd2839 SHA512: eec0c768c6832090bd167b6b505a9b9cf3dc042211b7529e0f201d2db174a481b5590ae839de94a994ba98b087ba5f4effef4e8610fc645593289168efa52c6f Homepage: https://cran.r-project.org/package=AzureCosmosR Description: CRAN Package 'AzureCosmosR' (Interface to the 'Azure Cosmos DB' 'NoSQL' Database Service) An interface to 'Azure CosmosDB': . On the admin side, 'AzureCosmosR' provides functionality to create and manage 'Cosmos DB' instances in Microsoft's 'Azure' cloud. On the client side, it provides an interface to the 'Cosmos DB' SQL API, letting the user store and query documents and attachments in 'Cosmos DB'. Part of the 'AzureR' family of packages. Package: r-cran-azuregraph Architecture: all Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azureauth, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-curl, r-cran-r6 Suggests: r-cran-azurermr, r-cran-vctrs, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-azuregraph_1.3.5-1.ca2604.1_all.deb Size: 537740 MD5sum: 9a165c7145eb57e6374f8f3a7c3282ba SHA1: 98d232497989bc63145060ba4003cb30b4522d6f SHA256: dce882d3ae1bebf5c674f5ce76fd91b2a441e188de55683b1dd44576b3c5c864 SHA512: f66fb713d32cf2c7f2e13516a6623050705cac2f50480dba2e011122bfe6c5f4bdc503576a81ac39fcf31fe5db45a56ac94065b5533adfa2dcc7a9d80e4b931d Homepage: https://cran.r-project.org/package=AzureGraph Description: CRAN Package 'AzureGraph' (Simple Interface to 'Microsoft Graph') A simple interface to the 'Microsoft Graph' API . 'Graph' is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the 'Azure Active Directory' part, with a view to supporting interoperability of R and 'Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the 'AzureR' family of packages. Package: r-cran-azurekeyvault Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-jose, r-cran-azurermr, r-cran-azuregraph, r-cran-azureauth Suggests: r-cran-azurestor, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-azurekeyvault_1.0.6-1.ca2604.1_all.deb Size: 514730 MD5sum: 930a3bd304123b1b3e338b95a0f63096 SHA1: bac8de8c02fbb73e8572fe7fd64e2de34accb236 SHA256: 08d6e22382c80cb092ba141d628d627e2c1d947b2d47953d220eefc04a0f7618 SHA512: 3315ac72458eecce6e31e7bb5be33ede0d76bed4fe11368e97b3edd420177df09adb70d7e28c492e00942b57d77a6b5627caff8c81238796e9ce6b06b2545487 Homepage: https://cran.r-project.org/package=AzureKeyVault Description: CRAN Package 'AzureKeyVault' (Key and Secret Management in 'Azure') Manage keys, certificates, secrets, and storage accounts in Microsoft's 'Key Vault' service: . Provides facilities to store and retrieve secrets, use keys to encrypt, decrypt, sign and verify data, and manage certificates. Integrates with the 'AzureAuth' package to enable authentication with a certificate, and with the 'openssl' package for importing and exporting cryptographic objects. Part of the 'AzureR' family of packages. Package: r-cran-azurekusto Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-openssl, r-cran-azureauth, r-cran-azurermr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-dbi Suggests: r-cran-bit64, r-cran-knitr, r-cran-testthat, r-cran-azuregraph, r-cran-azurestor, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-azurekusto_1.1.4-1.ca2604.1_all.deb Size: 577188 MD5sum: e9113d5fae39f558ffa7e7cc05b6c054 SHA1: 53eb8daab3bac39d0cd1c577bbc7af8eb4b70406 SHA256: e3055dbb020f354a547ada267cba53df4547a32aff67bd9ef1bc2318b27d2acc SHA512: 5cf647edcba89583ae8a258891db80109ebc003c59cf71f6282adb6a95fa29bec3e9f48f23823a92ebef54e88a0a64fb53c1a00f97836888dc624bc072e90862 Homepage: https://cran.r-project.org/package=AzureKusto Description: CRAN Package 'AzureKusto' (Interface to 'Kusto'/'Azure Data Explorer') An interface to 'Azure Data Explorer', also known as 'Kusto', a fast, distributed data exploration service from Microsoft: . Includes 'DBI' and 'dplyr' interfaces, with the latter modelled after the 'dbplyr' package, whereby queries are translated from R into the native 'KQL' query language and executed lazily. On the admin side, the package extends the object framework provided by 'AzureRMR' to support creation and deletion of databases, and management of database principals. Part of the 'AzureR' family of packages. Package: r-cran-azuremapsr Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-purrr, r-cran-rlist, r-cran-sf, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-azuremapsr_0.0.2-1.ca2604.1_all.deb Size: 50396 MD5sum: 738f325f62f1b59a0a08d9be5436f8f6 SHA1: 47426c6662e5bdc599c0cc045f0681572964d332 SHA256: b47bfebf0141b56908ea357a7e583b664fe93a96967f64bfbbd3d65562194dd1 SHA512: f3521f478c8b7aaac16276d2f1ee53cc1b9b2a0c55fe6908c6e58f916ca1b668dda1f9208c6ee4b727908d80edec091f881084ef1f1c2248676af7758a3a7dfb Homepage: https://cran.r-project.org/package=azuremapsr Description: CRAN Package 'azuremapsr' (Interface to the 'Azure Maps' API) Provides a wrapper for the Microsoft 'Azure Maps' REST APIs , enabling users to access mapping and geospatial services directly from R. This package simplifies authenticating, building, and sending requests for services like route directions. It handles conversions between R objects (such as 'sf' objects) and the GeoJSON+JSON format required by the API, making it easier to integrate 'Azure Maps' into R-based data analysis workflows. Package: r-cran-azuremlsdk Architecture: all Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reticulate, r-cran-plyr, r-cran-dt, r-cran-rstudioapi, r-cran-htmltools, r-cran-servr, r-cran-shiny, r-cran-shinycssloaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-dplyr, r-cran-jsonlite, r-cran-foreach, r-cran-iterators Filename: pool/dists/resolute/main/r-cran-azuremlsdk_1.10.0-1.ca2604.1_all.deb Size: 500140 MD5sum: fcddebaff2bb7755b17add55f9dd7791 SHA1: 2310fa25484e0e4fb9c5ba74def0c0b397d7aec2 SHA256: cef1acae3ab8300de2bb1f937d074c0c8ee660caccd4103a4f6ea19196d0ec83 SHA512: 1f579776c05edda72563d721e679a1901dd571910205749237cf78abd13f6f42e73032700385462875562f2c2b55210465bebd14cb4e0a540504dcb809b67fa6 Homepage: https://cran.r-project.org/package=azuremlsdk Description: CRAN Package 'azuremlsdk' (Interface to the 'Azure Machine Learning' 'SDK') Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: . Package: r-cran-azureqstor Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azurermr, r-cran-azurestor, r-cran-openssl, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-azureqstor_1.0.2-1.ca2604.1_all.deb Size: 165286 MD5sum: 44479a8ca42d94476d9be2c38475edc9 SHA1: a6277c9446b68ad236eea0dc9b9500f6c713fe18 SHA256: f8301727f6c180cfee1e479bf4b5ab06854f46c0f82af499d07cd53c84b4d560 SHA512: fa9aad6d720fa21be7efcc7f104c4b1f9956178efdaf8f7bef86ee189ed3182e3621da673ab9ee07e87cb7f2f3a125132e06240146549bc44af108a0b589cff3 Homepage: https://cran.r-project.org/package=AzureQstor Description: CRAN Package 'AzureQstor' (Interface to 'Azure Queue Storage') An interface to 'Azure Queue Storage'. This is a cloud service for storing large numbers of messages, for example from automated sensors, that can be accessed remotely via authenticated calls using HTTP or HTTPS. Queue storage is often used to create a backlog of work to process asynchronously. Part of the 'AzureR' family of packages. Package: r-cran-azurermr Architecture: all Version: 2.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azuregraph, r-cran-azureauth, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-uuid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httpuv, r-cran-azurestor Filename: pool/dists/resolute/main/r-cran-azurermr_2.4.5-1.ca2604.1_all.deb Size: 464618 MD5sum: b3953b67db1f9428f70ad671c7f38c60 SHA1: 232c8f31b0ec269ac3222ef7b003a57e7cffd602 SHA256: fa8b3a038b92f97f1ec3613102724755a565989de08b657cf0656d63c40e27a0 SHA512: f1a710bb67225b7454a8cbab18f2983155e9f1a9d7f87a55bfcead80fc13d9cdf630c87ab441b4213931d919790d65cd00fe2913872345fa8e411ff6a46af901 Homepage: https://cran.r-project.org/package=AzureRMR Description: CRAN Package 'AzureRMR' (Interface to 'Azure Resource Manager') A lightweight but powerful R interface to the 'Azure Resource Manager' REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting 'Azure' resource groups, resources and templates. While 'AzureRMR' can be used to manage any 'Azure' service, it can also be extended by other packages to provide extra functionality for specific services. Part of the 'AzureR' family of packages. Package: r-cran-azurestor Architecture: all Version: 3.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-mime, r-cran-openssl, r-cran-xml2, r-cran-vctrs, r-cran-azurermr Suggests: r-cran-azureauth, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-testthat, r-cran-processx, r-cran-uuid Filename: pool/dists/resolute/main/r-cran-azurestor_3.7.1-1.ca2604.1_all.deb Size: 469330 MD5sum: 7b664a8fef4ef6b9e4c86675f6e5dee6 SHA1: a12119dd7e40543496525ffa339d864a4c0d8770 SHA256: fabe7192932c131233a480fe75756121edad8639b9703b822b517af63c071e53 SHA512: de7397c1efc08c7120621fc9fb1de4d74df402e8d2a76fc2ce46c6ca41f30f85bc5c23ee7edb01e4f55eb6fb4545adcb6c949d78d5a558aa3088a2ec27a860cd Homepage: https://cran.r-project.org/package=AzureStor Description: CRAN Package 'AzureStor' (Storage Management in 'Azure') Manage storage in Microsoft's 'Azure' cloud: . On the admin side, 'AzureStor' includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages. Package: r-cran-azuretablestor Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azurermr, r-cran-azurestor, r-cran-jsonlite, r-cran-openssl, r-cran-httr, r-cran-uuid, r-cran-vctrs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-azuretablestor_1.0.0-1.ca2604.1_all.deb Size: 113794 MD5sum: 38a373e03869530ef1fc977acc4413b8 SHA1: 8476abd262d109cfbdce48bf4197180f0b8606fc SHA256: 5b54c3f3d77b4bcab51c02fcf3a79055c04ab3e11956b603d540b958a938fd42 SHA512: c4f5b5b63ce1e2e21506881b6e828a6615c7dfd83bcb113ec6cef84bdf5b89d256b5964c3d4cd6cd8090bab950524b2db9ff417ffff9999887808ca29ccd74e6 Homepage: https://cran.r-project.org/package=AzureTableStor Description: CRAN Package 'AzureTableStor' (Interface to the Table Storage Service in 'Azure') An interface to the table storage service in 'Azure': . Supplies functionality for reading and writing data stored in tables, both as part of a storage account and from a 'CosmosDB' database with the table service API. Part of the 'AzureR' family of packages. Package: r-cran-azurevision Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1850 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azurermr, r-cran-azurecognitive, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-azureauth, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-azurevision_1.0.2-1.ca2604.1_all.deb Size: 1548020 MD5sum: 78e2b5079600550ffde543bcff790b81 SHA1: f398d39525ce8b19225403a187a633189ba59b87 SHA256: c1d728113f99d4cf2bf773e423d40bc60eba54e817a734bb4b98b59f597e13fb SHA512: 44dc9ccf36377a4e5923f1335140f4187c604b72321e5abfaec96cc0c45b2298554a445b90af921d9f47a2d0582cf52d1251a2193889b5d03c859e4d9538094f Homepage: https://cran.r-project.org/package=AzureVision Description: CRAN Package 'AzureVision' (Interface to Azure Computer Vision Services) An interface to 'Azure Computer Vision' and 'Azure Custom Vision' , building on the low-level functionality provided by the 'AzureCognitive' package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the 'AzureR' family of packages. Package: r-cran-azurevm Architecture: all Version: 2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-azurermr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-azurekeyvault, r-cran-azurevmmetadata Filename: pool/dists/resolute/main/r-cran-azurevm_2.2.2-1.ca2604.1_all.deb Size: 414598 MD5sum: 2592d3a94bb84ef246304e9a5e45ed73 SHA1: 1cc4f109cd9c370a4e6758fefa7211419f3ca132 SHA256: e40a1142e244d8e813943c8cdaca1edcd1cb01847d12d41c37816f54e3e19c5c SHA512: f236907b98a979f7557c3f1c5241601bc7048677170b74cce202f6e279725bbadf42795dcc13a4bf6d6f8574aa558954a77975960fea2f7d0ad514a2415be734 Homepage: https://cran.r-project.org/package=AzureVM Description: CRAN Package 'AzureVM' (Virtual Machines in 'Azure') Functionality for working with virtual machines (VMs) in Microsoft's 'Azure' cloud: . Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the 'AzureR' family of packages. Package: r-cran-azurevmmetadata Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl, r-cran-httr Suggests: r-cran-azureauth, r-cran-azurevm Filename: pool/dists/resolute/main/r-cran-azurevmmetadata_1.0.1-1.ca2604.1_all.deb Size: 91874 MD5sum: 7a30fd58eda62632c24fbd24845a2253 SHA1: af98c14e1aee7728de1f41427008c0e8ddc4a00b SHA256: 0800855a2e26f72677b4288e44b20ed6d932db8199d47d490bbd2017e5672e99 SHA512: f311b869c4339477a39057eb9c2d320ea4dcccd473e2f1a3ca994249cdc1ec2c021abb07d9f46996f47ee930f6e0acbe2c9a88a4d54516b641029b5fcd8df5e6 Homepage: https://cran.r-project.org/package=AzureVMmetadata Description: CRAN Package 'AzureVMmetadata' (Interface to Azure Virtual Machine Instance Metadata) A simple interface to the instance metadata for a virtual machine running in Microsoft's 'Azure' cloud. This provides information about the VM's configuration, such as its processors, memory, networking, storage, and so on. Part of the 'AzureR' family of packages. Package: r-cran-babebi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-babebi_0.1.0-1.ca2604.1_all.deb Size: 72300 MD5sum: 2c964fccab7242b59c5a70e17e5162d5 SHA1: 18465255a1eb17c553eba2ee1c865b18935cff2a SHA256: c57aef2854ed8047b0cb1c656268c99dc7a2cbc518dba71d86671787d0666aab SHA512: 1ee7960d3ccb91f19f505cdeaa6fa83e3461b1bbe307022d3dffbd5055ef1de29a56f9cd8d1a2bc1e020cd1e979085c9f2b76eb9fa884116249af60556502895 Homepage: https://cran.r-project.org/package=babebi Description: CRAN Package 'babebi' (Bayesian Estimation and Validation for Small-N Designs withRater Bias) Approximate Bayesian inference and Monte Carlo validation for small-N repeated-measures designs with two time points and two raters. The package is intended for applications in which sample size is limited and the observed outcome may be affected by rater-specific bias. User-supplied data are standardised into a common long-format structure. Pre-post effects are analysed using difference scores in a linear model with a rater indicator as covariate. Posterior summaries for the regression coefficients are obtained from a large-sample normal approximation centred at the least-squares estimate with plug-in covariance under a flat improper prior. Evidence for a non-zero pre-post effect, adjusted for rater differences, is summarised using a BIC-based approximation to the Bayes factor for comparison between models with and without the pre-post effect. Monte Carlo validation uses design quantities estimated from the observed data, including sample size, mean pre-post change, and second-rater additive discrepancy, and summarises inferential performance in terms of bias, root mean squared error, credible interval coverage, posterior tail probabilities, and mean Bayes factor values. For background on the BIC approximation and Bayes factors, see Schwarz (1978) and Kass and Raftery (1995) . Package: r-cran-babel Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-edger Suggests: r-cran-r.rsp, r-cran-r.devices, r-cran-r.utils Filename: pool/dists/resolute/main/r-cran-babel_0.3-0-1.ca2604.1_all.deb Size: 208328 MD5sum: 1caa6a2f3b4cd839f87f5c4c49070eac SHA1: 9783f7d664459e2ff918fddb25e905720ed220d8 SHA256: de270285874e94b05bf1d1454f67a04bf5bcdf0de891f43e6002d3eef4e91155 SHA512: 8dade49819092fd32dc96d3cd36ebea0538413d92d235ba62f1f8b3c6198f366983d92cbcb72a1f9416fd7f5cd48a9fa67394b364012f9895a91999b7c74c5d2 Homepage: https://cran.r-project.org/package=babel Description: CRAN Package 'babel' (Ribosome Profiling Data Analysis) Included here are babel routines for identifying unusual ribosome protected fragment counts given mRNA counts. Package: r-cran-babelgene Architecture: all Version: 22.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-babelgene_22.9-1.ca2604.1_all.deb Size: 3652158 MD5sum: f763ed85cf0a6a220c705dc364e5246f SHA1: ce0a93a962be020e0c36e00b103866b5ecaa3422 SHA256: b8734b5ae3d19622982285bd65f8f4ecd115bdc9207dedf3bd8843756539d1bd SHA512: 70a0d922c045a3183fb859b9bbccf8dec1ad909dc362170b5cb42e3355929956720414c1b990433decfd86b7b90c6a1edb5b9705d1512fe7cd1eba772d1cb471 Homepage: https://cran.r-project.org/package=babelgene Description: CRAN Package 'babelgene' (Gene Orthologs for Model Organisms in a Tidy Data Format) Genomic analysis of model organisms frequently requires the use of databases based on human data or making comparisons to patient-derived resources. This requires harmonization of gene names into the same gene space. The 'babelgene' R package converts between human and non-human gene orthologs/homologs. The package integrates orthology assertion predictions sourced from multiple databases as compiled by the HGNC Comparison of Orthology Predictions (HCOP) (Wright et al. 2005 , Eyre et al. 2007 , Seal et al. 2011 ). Package: r-cran-babelwhale Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-fs, r-cran-dynutils, r-cran-processx, r-cran-purrr, r-cran-digest, r-cran-glue Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-babelwhale_1.2.0-1.ca2604.1_all.deb Size: 418714 MD5sum: 8b5d365348234c225d1a5d926f0fa266 SHA1: b92674786b713136e8dec64735909bf37dd3ee2a SHA256: 815af7c811f0b9460a89b7dc62835398ece571ee17da4e9203ddb4c3d449b726 SHA512: a0f8b1c502f0172517c8cbd2effbb6ea28141b1188aa54545ffc81eaf8abb894d0fc3e8eb39f7f540e9f4cbfec8fa063489554e04352a28779f4a5f30df8c00f Homepage: https://cran.r-project.org/package=babelwhale Description: CRAN Package 'babelwhale' (Talking to 'Docker' and 'Singularity' Containers) Provides a unified interface to interact with 'docker' and 'singularity' containers. 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Package: r-cran-babette Architecture: all Version: 2.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beautier, r-cran-beastier, r-cran-mauricer, r-cran-tracerer, r-cran-phangorn, r-cran-rlang, r-cran-stringr, r-cran-xml2 Suggests: r-cran-ape, r-cran-ggplot2, r-cran-knitr, r-cran-lintr, r-cran-nltt, r-cran-rappdirs, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-babette_2.3.4-1.ca2604.1_all.deb Size: 1502356 MD5sum: af0c40a24de67ca70a3dc1e949a434e1 SHA1: 6f7b7fcd0488bedd008594265320938d3cbce6ab SHA256: a004a143e4b999bfda1bc1a99f1ae676fe7675deb2a4cfc4ac5fbfcc88f88b30 SHA512: 1469287a873f7d9b746475481dbcef079acd2c49e13011b1e1892dd4079ca2ca58a589ab3f7f71be5b4dcf6b2da5be800c9b127225b6bdbde665739c2667c634 Homepage: https://cran.r-project.org/package=babette Description: CRAN Package 'babette' (Control 'BEAST2') 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is commonly accompanied by 'BEAUti 2', 'Tracer' and 'DensiTree'. 'babette' provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from 'R'. Package: r-cran-babynames Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-babynames_1.0.1-1.ca2604.1_all.deb Size: 5554028 MD5sum: 386742f11054acc7fc1322af3ac69fd4 SHA1: a3268a8d178bc52904274f41d16fd5ff51066b05 SHA256: a6dd2f2408ac10a290bf7229560b0ec5a17de53127487946ef639f7c92e4c964 SHA512: c0bc379e2b7ee220163df3dc2fe4e0bfae4cee8d5287e2b013cfc3aff99be19f17953e2734cbc211f9b850b271767c312b83c308176a8ff130c2fd718a26685d Homepage: https://cran.r-project.org/package=babynames Description: CRAN Package 'babynames' (US Baby Names 1880-2017) US baby names provided by the SSA. This package contains all names used for at least 5 children of either sex. Package: r-cran-babynamesil Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-babynamesil_0.2.3-1.ca2604.1_all.deb Size: 764544 MD5sum: b988a620c32aee543e827d9d2747ec5f SHA1: 106fb8140f861feb78fced18f418db050b8f7588 SHA256: d97d495ebf6526ca3dfc0d5b43a21c764df9f8115af2a5d84a1992f73fb4a499 SHA512: fdc616ff635d2ad84a0a65178a5d240dcb0925c46da1adae579e96120f6eb17fe635b5d889a7246477ee46ff6b4be237473d654fb08d8bd4b4bc3adf51b750d1 Homepage: https://cran.r-project.org/package=babynamesIL Description: CRAN Package 'babynamesIL' (Israel Baby Names 1949-2024) Israeli baby names provided by Israel's Central Bureau of Statistics (CBS/LAMAS). Contains names used for at least 5 children in a given year, covering sectors "Jewish", "Muslim", "Christian-Arab", and "Druze" from 1949-2024. Legacy 1948 data and archived "Other" sector data are provided as separate datasets. Primary data source: CBS Release 391/2025 . Package: r-cran-babytimer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-janitor, r-cran-lubridate, r-cran-readr, r-cran-snakecase, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-babytimer_0.1.0-1.ca2604.1_all.deb Size: 31232 MD5sum: bfccd9e7e468efcfc080d9897d0d88e1 SHA1: 808549ebb1cabe1591a421e7ee20057f8f30b68c SHA256: 00ac71fa9336a545887d9eae45a831f8767ab70f4ea6b305e761322b3d345ed8 SHA512: ee45691719264635b2d56587b3d9e925614a97f7185299f1c2f695d6d037a1929cf9258236cde52ef1217c1d1eb8296eaeaa78c7938b83e8ff0fc2990fcb06aa Homepage: https://cran.r-project.org/package=babyTimeR Description: CRAN Package 'babyTimeR' (Parse Output from 'BabyTime' Application) 'BabyTime' is an application for tracking infant and toddler care activities like sleeping, eating, etc. This package will take the outputted .zip files and parse it into a usable list object with cleaned data. 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Package: r-cran-bacct Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-bacct_1.0-1.ca2604.1_all.deb Size: 44054 MD5sum: 1dec1987cabeb5a0d70a4a2ee223c333 SHA1: 6dcee38a547ad52e99335e57f4eb3eb1208be3fb SHA256: 470d1b8f7f92d9633907f9956e82ff81a14d4495d7c28e3df03bbfccb054b2f5 SHA512: 3668ad3d1787859b433607b4f2e8b569c266556c96a7698a68fb0fb7a863d98a67f7844c6899907ff1a6ca70c611a559cd3af7ed86b7cd23852cc1ce5ff06997 Homepage: https://cran.r-project.org/package=BACCT Description: CRAN Package 'BACCT' (Bayesian Augmented Control for Clinical Trials) Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. 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Package: r-cran-bacistool Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-bacistool_1.0.0-1.ca2604.1_all.deb Size: 59816 MD5sum: ac159bf50358d4c233d9369d7be0c659 SHA1: 1bafc6508f557a565844beaa0665202bf5c4f333 SHA256: 271ec8912ebaf9e7da34c4d70db185dbcafae340412464086f906fb540a72dda SHA512: b9bd3c690077bfe83f97def7ee52036ad9ce8879ec3f62cf5f7eabbb8291729dcbdb342ce3625d7aa82c7f315ae58bec412256e325f94c3e54b4219fd47d1ae0 Homepage: https://cran.r-project.org/package=bacistool Description: CRAN Package 'bacistool' (Bayesian Classification and Information Sharing (BaCIS) Tool forthe Design of Multi-Group Phase II Clinical Trials) Provides the design of multi-group phase II clinical trials with binary outcomes using the hierarchical Bayesian classification and information sharing (BaCIS) model. Subgroups are classified into two clusters on the basis of their outcomes mimicking the hypothesis testing framework. Subsequently, information sharing takes place within subgroups in the same cluster, rather than across all subgroups. This method can be applied to the design and analysis of multi-group clinical trials with binary outcomes. Reference: Nan Chen and J. Jack Lee (2019) . 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Package: r-cran-bagoft Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-dcov Suggests: r-cran-glmnet, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-bagoft_1.0.0-1.ca2604.1_all.deb Size: 55754 MD5sum: e9bb7b64dcf9228b9f2cb272d4cb176f SHA1: b6390257945d713926d40839611fe2cb81f065e6 SHA256: b92ac6bd7effc25406980d43626dab167d5a77b6a9288b3311b26212b544c73a SHA512: 264f1d58e08e241cc0ce3ad56a37d466979a08db811779631d55cf4298dee12eb27bd3bd6eb46460e53a099bc6c304018f6f08119d16652963f26f6dceff3f7c Homepage: https://cran.r-project.org/package=BAGofT Description: CRAN Package 'BAGofT' (A Binary Regression Adaptive Goodness-of-Fit Test (BAGofT)) The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) . 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Package: r-cran-bagwhiskerplot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bagwhiskerplot_0.1.0-1.ca2604.1_all.deb Size: 220164 MD5sum: 017bddc9229a56f084425c7e9446e96b SHA1: 9fa34bc6e04cc30b2bb93c8a1bbd4dea20879216 SHA256: e4484fc1d42ef407843f38450c39c1bc0c4620707ca7ec8c7e562120b3f731e6 SHA512: 419cc98d5e88d87948182616efec08a70d5287a0af354d24153a9a317f3564502fc36987be07a38b60db01bdd502258ac5be8986bcb72089f489b502c78af799 Homepage: https://cran.r-project.org/package=BagWhiskerPlot Description: CRAN Package 'BagWhiskerPlot' (Bag-and-Whisker Plot) Implementation of the Bag-and-Whisker Plot for bivariate data. Provides a single user-facing function bag_whisker() that wraps the computation and plotting helpers in this package. For more details, please refer to the paper "The Bag-and-Whisker Plot: A New Bagplot for Bivariate Data" by Qin, Gang, Tong and Cui (2025) . Package: r-cran-bagyo Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-lubridate, r-cran-pdftools, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-bagyo_0.2.0-1.ca2604.1_all.deb Size: 1476452 MD5sum: ffc1daf81ea8ab80035855cf2106de22 SHA1: 543749fdcb10be3b29b026b939c98b75582f5d5c SHA256: 15e0f07316f717efb35bdac14d3f5ceb5d901bd5a05d5a8105a35bca18c82e20 SHA512: a5ecb811562aa587dc7b39b876edc50ac949180544238ea887c7bce64276e646075a302cd8d70f1abd961eebef76c2a1ceeab13bbd135104677d03fefd1290db Homepage: https://cran.r-project.org/package=bagyo Description: CRAN Package 'bagyo' (Philippine Tropical Cyclones Data) The Philippines frequently experiences tropical cyclones (called 'bagyo' in the Filipino language) because of its geographical position. These cyclones typically bring heavy rainfall, leading to widespread flooding, as well as strong winds that cause significant damage to human life, crops, and property. Data on cyclones are collected and curated by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration or 'PAGASA' and made available through its website . This package contains Philippine tropical cyclones data in a machine-readable format. It is hoped that this data package provides an interesting and unique dataset for data exploration and visualisation. 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In the motivating Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities of interest sum to one hundred percent, the PERCH scientists frequently refer to them as population etiology pie and individual etiology pie, hence the name of the package. 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Package: r-cran-ballmapper Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-scales, r-cran-networkd3, r-cran-testthat, r-cran-fields, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-ballmapper_0.2.0-1.ca2604.1_all.deb Size: 77162 MD5sum: 3b6ed9fd44308e6170d3f328e4e41045 SHA1: f78706e5fe19ebb8264174fb42f70fa94a31428c SHA256: 37c4e0d3bfda732ffd2acaf9dbfe65506df2a997d13d008859efdb74dd75f69a SHA512: 1407207f0cefd32bd9cd9c0e15e5d35a0de3f9e0043eb6693431e9adafa8dc42d677f834ab23b616d036a8c126cfad10faf93ea242e8cf8f0aaaaae8485121fa Homepage: https://cran.r-project.org/package=BallMapper Description: CRAN Package 'BallMapper' (The Ball Mapper Algorithm) The core algorithm is described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) . Please consult the following youtube video the idea of functionality. Ball Mapper provide a topologically accurate summary of a data in a form of an abstract graph. To create it, please provide the coordinates of points (in the points array), values of a function of interest at those points (can be initialized randomly if you do not have it) and the value epsilon which is the radius of the ball in the Ball Mapper construction. It can be understood as the minimal resolution on which we use to create the model of the data. Package: r-cran-bam Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mice Suggests: r-cran-coda, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-bam_1.0.3-1.ca2604.1_all.deb Size: 214534 MD5sum: da577b7e96e03b6032fa18f692d5fa62 SHA1: 43b65a60e2f3a90a3f6257242918de83bd5c17cb SHA256: 03b865b77102e10ecf4a9dfbcb25e5098715aa6cd6784f7ae71d9970124bb6e8 SHA512: 339b93d1684d4a0dcfdd01534fb773f59943417513be483d30703fa028d817240b0d3bf9ec7873db7e9679f2b56da3f1d62269ca2bd21d53c7e87e0e50ee337e Homepage: https://cran.r-project.org/package=BaM Description: CRAN Package 'BaM' (Functions and Datasets for "Bayesian Methods: A Social andBehavioral Sciences Approach") Functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014) . 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Package: r-cran-bamdit Architecture: all Version: 3.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-r2jags, r-cran-ggplot2, r-cran-ggextra, r-cran-mass, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-bamdit_3.6.0-1.ca2604.1_all.deb Size: 146708 MD5sum: 8e5424b9ecd676f75e670a32aaa5a992 SHA1: c63a93d2dd7c1af676e016eefc9962ff6d3832ca SHA256: 99ebac75a9c56c7556161fd1786d60cdc74baa18e1489fe7e68ad7ad22d29093 SHA512: ba68e5152b7981d45e070325f36edda9724beb11d22a9bb7731098a245d8b93b29c5b12f838439a259c2b3c290716c81212c72fef37dc905b8b5d697b78565bd Homepage: https://cran.r-project.org/package=bamdit Description: CRAN Package 'bamdit' (Bayesian Meta-Analysis of Diagnostic Test Data) Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS). 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The degree of social influence is quantified via the network autocorrelation parameters. In case of a single network, the Bayesian methods of Dittrich, Leenders, and Mulder (2017) and Dittrich, Leenders, and Mulder (2019) are implemented using a normal, flat, or independence Jeffreys prior for the network autocorrelation. In the case of multiple networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) are implemented using a multivariate normal prior for the network autocorrelation parameters. Flat priors are implemented for estimating the coefficients. For Bayesian testing of equality and order-constrained hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) is used with the posterior mean and posterior covariance matrix of the NAM parameters based on flat priors as input. Package: r-cran-bandicoot Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bandicoot_1.0.0-1.ca2604.1_all.deb Size: 152024 MD5sum: 9e32157dfb97988e4fccc5800b84c5c6 SHA1: b80b89af85f0c30f2ec988421bbbcf51914641ea SHA256: 302a09dbf116803abbdc263e7b345eb227b87765f5c7fc55045119dbafd4737c SHA512: c89ed5df7d034a40254aa224d1db96cab04d2e08e4fd5ae97aaaf23b9b524b186f8e061a9118bbb90b2d85d773fe7c51fb6adf6bd04c0b58b3fd03c946d70ce3 Homepage: https://cran.r-project.org/package=bandicoot Description: CRAN Package 'bandicoot' (Light-Weight 'python'-Like Object-Oriented System) A light-weight object-oriented system with 'python'-like syntax which supports multiple inheritances and incorporates a 'python'-like method resolution order. Package: r-cran-bandit Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-gam Filename: pool/dists/resolute/main/r-cran-bandit_0.5.1-1.ca2604.1_all.deb Size: 45892 MD5sum: 598f6eb5c3d5d071b6be62f8d9c2b793 SHA1: ae1e6c8718f96729a9ce808abe684e969cface6d SHA256: eacde9abad6400cc2398aa4493b3f51d13e3f8d652bdf1622174fbf357b40bc7 SHA512: 4b6d747a067bba188d7ba3133ecd1e099286a62cefc56c138859dbf065e66119600eb4c2fae1e5b8794d9c321fb10b6abcf6fa09581f91958bc3055ad6d7fb15 Homepage: https://cran.r-project.org/package=bandit Description: CRAN Package 'bandit' (Functions for Simple a/B Split Test and Multi-Armed BanditAnalysis) A set of functions for doing analysis of A/B split test data and web metrics in general. Package: r-cran-banditsci Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1639 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-mvtnorm, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-banditsci_1.0.0-1.ca2604.1_all.deb Size: 1040000 MD5sum: d48484f1347bead4cebac90843e1ef34 SHA1: deb9b95649ec2f30aa800e1c015826fa9268c5b8 SHA256: a9ad5da85e343bf7212e16e7dc2b93e5533cfcdcba052e56702d8c8e3e6b56e7 SHA512: f97cf0ea2676cc8161d5bd0f5a1ff8cda06e3eab7aab98bd5eb0c77b619043b4e0311820316e0369f02796c0177c3adcf778994ba7bbc497235e1306118d32c7 Homepage: https://cran.r-project.org/package=banditsCI Description: CRAN Package 'banditsCI' (Bandit-Based Experiments and Policy Evaluation) Frequentist inference on adaptively generated data. The methods implemented are based on Zhan et al. (2021) and Hadad et al. (2021) . For illustration, several functions for simulating non-contextual and contextual adaptive experiments using Thompson sampling are also supplied. Package: r-cran-bandsfdp Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bandsfdp_1.1.0-1.ca2604.1_all.deb Size: 54678 MD5sum: 1d9dbc4bc7cb93982e1e6ba21e282dc0 SHA1: 4e56a13da453978a6d514d5d9e91f64a9c3e7ca5 SHA256: 61cc7688024964f041cab7ca10131ca25eebf59a2498b5aeb1eaaa33d72be882 SHA512: d01fd089bb14bd7ff3f0cbae557fbbcd178649ccf349fea5f74e64d2255b3588a401c8cd46075d051ca147806e839e355546820af2a4a996e092f155b9a05d60 Homepage: https://cran.r-project.org/package=bandsfdp Description: CRAN Package 'bandsfdp' (Compute Upper Prediction Bounds on the FDP in Competition-BasedSetups) Implements functions that calculate upper prediction bounds on the false discovery proportion (FDP) in the list of discoveries returned by competition-based setups, implementing Ebadi et al. (2022) . Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression (note this package typically uses the terminology of TDC). Included is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the simultaneous standardized and uniform bands. Requires pre-computed Monte Carlo statistics available at . This data can be downloaded by running the command 'devtools::install_github("uni-Arya/fdpbandsdata")' in R and restarting R after installation. The size of this data is roughly 81Mb. Package: r-cran-banffit Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-crayon, r-cran-stringr, r-cran-fs, r-cran-lubridate, r-cran-tidyr, r-cran-fabr, r-cran-madshapr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-banffit_2.0.0-1.ca2604.1_all.deb Size: 263996 MD5sum: a3ea57c23f9e25b125165e63552b60ff SHA1: 11e1a9c228504042e2900eeebd9f1f0770813b9a SHA256: 446b2df4ca07ae4c13f41fc571402d64be79762e97d84d6670069f1f4cb10b64 SHA512: bc4343e4cf54c7282dbf7cc0e83809f8da67a866cf0ea99e3b283bc3a337d4bd416e9c8183f6c8201838506622aa0e8ced607f2e553035b7a51bf238d2712ef5 Homepage: https://cran.r-project.org/package=banffIT Description: CRAN Package 'banffIT' (Automated Standardized Assignment of the Banff Classification) Assigns standardized diagnoses using the Banff Classification (Category 1 to 6 diagnoses, including Acute and Chronic active T-cell mediated rejection as well as Active, Chronic active, and Chronic antibody mediated rejection). The main function considers a minimal dataset containing biopsies information in a specific format (described by a data dictionary), verifies its content and format (based on the data dictionary), assigns diagnoses, and creates a summary report. The package is developed on the reference guide to the Banff classification of renal allograft pathology Roufosse C, Simmonds N, Clahsen-van Groningen M, et al. A (2018) . The full description of the Banff classification is available at . Package: r-cran-bang Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-rust Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bang_1.0.4-1.ca2604.1_all.deb Size: 312696 MD5sum: 46607e572a6e82a4786b94c44287c6ea SHA1: a3e5143ff46070fd280aedc9cda14aa328950170 SHA256: 2dc89ad894d1ba3734ad12f77c1d218fbb75e74d8a6f1e725dc7377ed37e9380 SHA512: 8af688b212ed514c9ed3ef94191c309568a8e24ba54fddaca1b04e1ccbdf505ce93c4d3fa88b20f33cbd9d1a45c49629a15d4c4c7bba4086e25fe3a20fae8c15 Homepage: https://cran.r-project.org/package=bang Description: CRAN Package 'bang' (Bayesian Analysis, No Gibbs) Provides functions for the Bayesian analysis of some simple commonly-used models, without using Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling. The 'rust' package is used to simulate a random sample from the required posterior distribution, using the generalized ratio-of-uniforms method. See Wakefield, Gelfand and Smith (1991) for details. At the moment three conjugate hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way analysis of variance (ANOVA). Package: r-cran-bangladesh Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tmap, r-cran-sf Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-bangladesh_1.0.0-1.ca2604.1_all.deb Size: 5261924 MD5sum: bd42494c954f951ec35cbcbd868b832d SHA1: 09b24d2a77c8f928391f49d432226f7db92047f4 SHA256: 26ca838160fb4ddb373f55582d0c0655ce6ef17756553df883f4b8fcd1ae3f58 SHA512: 53ed020758656177a2aca30aefd2de3644db1dad627908690b7adda9d3c419a6eda6fe0621ff4c34dc23ed890d3e9eb0c6bd0c6ddbe5cfef210321a3cc764870 Homepage: https://cran.r-project.org/package=bangladesh Description: CRAN Package 'bangladesh' (Provides Ready to Use Shapefiles for Geographical Map ofBangladesh) Usually, it is difficult to plot choropleth maps for Bangladesh in 'R'. The 'bangladesh' package provides ready-to-use shapefiles for different administrative regions of Bangladesh (e.g., Division, District, Upazila, and Union). This package helps users to draw thematic maps of administrative regions of Bangladesh easily as it comes with the 'sf' objects for the boundaries. It also provides functions allowing users to efficiently get specific area maps and center coordinates for regions. Users can also search for a specific area and calculate the centroids of those areas. Package: r-cran-bannercommenter Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-bannercommenter_1.0.0-1.ca2604.1_all.deb Size: 202546 MD5sum: 40c9c1199a8ca1afe719c4a282a41235 SHA1: 16a30cdf2957d8ed38f6440007c15f419beb6c50 SHA256: 28a6dd57aac9ab415920ebd4d3933ab58014bdf743676c4b11bb70364a6dfb81 SHA512: eb9fc3a6751b35f08947567eeed9878904e15186ea22e2cb9cde735d5d1c6e5563760f88d4bc43a64ce72d0775d8dbc9cd63ac5ab4cdb1f5a0536e44536cfa8e Homepage: https://cran.r-project.org/package=bannerCommenter Description: CRAN Package 'bannerCommenter' (Make Banner Comments with a Consistent Format) A convenience package for use while drafting code. It facilitates making stand-out comment lines decorated with bands of characters. The input text strings are converted into R comment lines, suitably formatted. These are then displayed in a console window and, if possible, automatically transferred to a clipboard ready for pasting into an R script. Designed to save time when drafting R scripts that will need to be navigated and maintained by other programmers. 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For more information about the 'BAN' and its API, please see . Package: r-cran-banter Architecture: all Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-randomforest, r-cran-rfpermute, r-cran-rlang, r-cran-swfscmisc, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-banter_0.9.8-1.ca2604.1_all.deb Size: 1843666 MD5sum: f69e721c4929956a97ff7a7343278ee2 SHA1: 596bd16ddd5980b1c699390bc1e1345c0acb3764 SHA256: 87f76ddc4aa96ae8397a5ef87eab4016e0e8284ea5fd73326a84098c9dc3d799 SHA512: adc9ce8cb33ef92f267cb959472f849204f478134d46c8eb0a294f8f8ea41aeccb383227ebd0df04e42fd22d51c48735f1673a22f5a49d3286246c696e5e1363 Homepage: https://cran.r-project.org/package=banter Description: CRAN Package 'banter' (BioAcoustic eveNT classifiER) Create a hierarchical acoustic event species classifier out of multiple call type detectors as described in Rankin et al (2017) . Package: r-cran-banxicor Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rvest, r-cran-stringr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-banxicor_0.9.0-1.ca2604.1_all.deb Size: 24766 MD5sum: 19f991cfafd8bfed6612e3d065ecdf70 SHA1: 83636c6dccb783330cfb8ee3208d5d93a16d6892 SHA256: 1352bd3830af5b1611b2dc3ae6caa16197c00a99aac0e14946bff699f51abc44 SHA512: c69acc7a4e46b161f0f328c48dc61c0bb3349e5ee26ee5b50d3cb943b53c6848125364a425fc1366ac0686f87fa7b3015c9b2f9e52e30ae7d0330a4b3e0b3469 Homepage: https://cran.r-project.org/package=banxicoR Description: CRAN Package 'banxicoR' (Download Data from the Bank of Mexico) Provides functions to scrape IQY calls to Bank of Mexico, downloading and ordering the data conveniently. Package: r-cran-baorista Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-baorista_0.2.1-1.ca2604.1_all.deb Size: 186378 MD5sum: b91d9830c3d0a27cad7b72bdee835cfd SHA1: 9ffd4a4427494d828744a2e24857841069a3ea85 SHA256: 3c491e1f978d24706b2eb7abc0826a6ffd59a5cea8986a48d0c76a36e2365c85 SHA512: 57162ef6b01b991ee4dea12332fc9734e8b30c8b3f38781766fa804806dd1abafc89484bb6774e9b2ffa528d237a4d89615680a94d37edccd72136d1eeac0ad0 Homepage: https://cran.r-project.org/package=baorista Description: CRAN Package 'baorista' (Bayesian Aoristic Analyses) Provides an alternative approach to aoristic analyses for archaeological datasets by fitting Bayesian parametric growth models and non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on time frequency data (Crema (2024)). It handles event typo-chronology based timespans defined by start/end date as well as more complex user-provided vector of probabilities. Package: r-cran-bapred Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 915 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-lme4, r-cran-mass, r-bioc-sva, r-bioc-affyplm, r-cran-fnn, r-cran-fuzzyranktests, r-cran-mnormt, r-bioc-affy, r-bioc-biobase Suggests: r-bioc-arrayexpress Filename: pool/dists/resolute/main/r-cran-bapred_1.1-1.ca2604.1_all.deb Size: 897690 MD5sum: 8fa1be143c553ee47652052f249abfaf SHA1: 784ce89f59092530be8070dcd0d228dfa8d30fe7 SHA256: 21a99db92b9f02fedb6e6c81696d4c22062325be9534a8e6c3388c41ec811949 SHA512: efcd11d3e3bfb9ce0996e808162f6868b402f07216a177643e9b6c89497a79a5b1c616967a8fa5385a648a5a8b3ed54f6350f1c4d328ed450415acc7cdf39cf7 Homepage: https://cran.r-project.org/package=bapred Description: CRAN Package 'bapred' (Batch Effect Removal and Addon Normalization (in PhenotypePrediction using Gene Data)) Various tools dealing with batch effects, in particular enabling the removal of discrepancies between training and test sets in prediction scenarios. Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 2008) is implemented to enable integrating the quantile normalization step into prediction rules. The following batch effect removal methods are implemented: FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. For each of these we provide an additional function which enables a posteriori ('addon') batch effect removal in independent batches ('test data'). Here, the (already batch effect adjusted) training data is not altered. For evaluating the success of batch effect adjustment several metrics are provided. Moreover, the package implements a plot for the visualization of batch effects using principal component analysis. The main functions of the package for batch effect adjustment are ba() and baaddon() which enable batch effect removal and addon batch effect removal, respectively, with one of the seven methods mentioned above. Another important function here is bametric() which is a wrapper function for all implemented methods for evaluating the success of batch effect removal. For (addon) quantile normalization and (addon) RMA normalization the functions qunormtrain(), qunormaddon(), rmatrain() and rmaaddon() can be used. Package: r-cran-baqm Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-ggplot2, r-cran-ggrepel, r-cran-lmtest Suggests: r-cran-leaps, r-cran-shiny, r-cran-testthat, r-cran-utf8, r-cran-vdiffr, r-cran-waldo, r-cran-withr Filename: pool/dists/resolute/main/r-cran-baqm_0.1.4-1.ca2604.1_all.deb Size: 233468 MD5sum: 92e4f8d0cad7f15a2acbdf7f2157d967 SHA1: 31abf84c87e21530a13c375fb99b56d026b35cbd SHA256: 206cb618e90ed71e3c490d21425fa67290c0351191e0af22ee149b2ec4ff5de3 SHA512: 234c82102b46a47ad1e2ec75a225f76fc0da85b62b54e0529c27539948e104b5b0faaf9394ed9d745ac11bb3b960a9d982397b618719e4f0a791db09bc8fd7c1 Homepage: https://cran.r-project.org/package=BAQM Description: CRAN Package 'BAQM' (Babson Analytics and Quantitative Methods Tools) Instructor-developed tools for Analytics and Quantitative Methods (AQM) courses at Babson College. Included are compact descriptive statistics for data frames and lists, expanded reporting and graphics for linear regressions, and formatted reports for best subsets analyses. 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The creation of unique ID codes and printable PDF files can be initiated by standard commands, user prompts, or through a GUI addin for R Studio. Biologically informative codes can be included for hierarchically structured sampling designs. 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Package: r-cran-barrel Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-ggplot2, r-cran-ggrepel, r-cran-vegan, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-barrel_0.1.0-1.ca2604.1_all.deb Size: 2351082 MD5sum: 85760a396147cbfa989641f5a2d25c5f SHA1: 85f9fa60bb2d16e2ad4120fb1c1d9d0debd4748b SHA256: dbe07949e8bfc2e35dd6e35c775dc410f79936dc85bcfc1185f315558c908e96 SHA512: aaaf2c7a5bb6a3ad9f41dc97366a38dddcec53d7e2cc5749b1fad3bf08da5456a28edd07be1915b95b36831cd48d77b066ca47b0a577b932257cc2f109fbc792 Homepage: https://cran.r-project.org/package=barrel Description: CRAN Package 'barrel' (Covariance-Based Ellipses and Annotation Tools for OrdinationPlots) Provides tools to visualize ordination results in 'R' by adding covariance-based ellipses, centroids, vectors, and confidence regions to plots created with 'ggplot2'. The package extends the 'vegan' framework and supports Principal Component Analysis (PCA), Redundancy Analysis (RDA), and Non-metric Multidimensional Scaling (NMDS). Ellipses can represent either group dispersion (standard deviation, SD) or centroid precision (standard error, SE), following Wang et al. (2015) . Robust estimators of covariance are implemented, including the Minimum Covariance Determinant (MCD) method of Hubert et al. (2018) . This approach reduces the influence of outliers. barrel is particularly useful for multivariate ecological datasets, promoting reproducible, publication-quality ordination graphics with minimal effort. Package: r-cran-barrks Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-terra, r-cran-rdpack, r-cran-lubridate, r-cran-readr, r-cran-stringr Suggests: r-cran-devtools, r-cran-geosphere, r-cran-knitr, r-cran-mnormt, r-cran-ncdf4, r-cran-rlang, r-cran-rmarkdown, r-cran-suncalc, r-cran-tidyverse, r-cran-tinytest, r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-barrks_1.1.2-1.ca2604.1_all.deb Size: 2036192 MD5sum: f3053d5658cae046833e44470a1a384d SHA1: 3f720a1ddfe1fb87e8c94512104d585e1a484710 SHA256: 70c62e0ac1e9b67fd604d1cd308850b5fab69b1bbe6e238a7ee50a26ff87cd2d SHA512: 55ad39f9a3a16a210fad2d06e1c140a91a741a2a0a95dcc9924a905955ca3126f5fcd592f4714e571ab375cde2236ad380b89fa5bc70307b1319896727c6f8b2 Homepage: https://cran.r-project.org/package=barrks Description: CRAN Package 'barrks' (Calculate Bark Beetle Phenology Using Different Models) Calculate the bark beetle phenology based on raster data or point-related data. There are multiple models implemented for two bark beetle species. The models can be customized and their submodels (onset of infestation, beetle development, diapause initiation, mortality) can be combined. The following models are available in the package: PHENIPS-Clim (first-time release in this package), PHENIPS (Baier et al. 2007) , RITY (Ogris et al. 2019) , CHAPY (Ogris et al. 2020) , BSO (Jakoby et al. 2019) , Lange et al. (2008) , Jönsson et al. (2011) . The package may be expanded by models for other bark beetle species in the future. Package: r-cran-barry Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-barry_0.2.1-1.ca2604.1_all.deb Size: 93512 MD5sum: d5c09b58b2bf4bf7680a61d2ceb62160 SHA1: 34f48dd1a9294eb7075c7be4b68538f1bb808aad SHA256: 5c5865e6d3d6cd574ec5ce8c15d15f8abfb9e14554e0ba3f52d8325f88a70f7b SHA512: c27cba8c5fdd20ff3dce684e6db79d3f11fabb7e8b0c74a5c5dbaa681de492b257d482fe57a4b2e88928183af87825128f16e76952756b5d9c05f815c4596df5 Homepage: https://cran.r-project.org/package=barry Description: CRAN Package 'barry' (Your Go-to Motif Accountant) Provides the 'C++' header-only library 'barry' for use in R packages. 'barry' is a 'C++' template library for counting sufficient statistics on binary arrays and building discrete exponential-family models. It provides tools for sparse arrays, user-defined count statistics, support set constraints, power set generation, and includes modules for Discrete Exponential Family Models (DEFMs) and network statistics. By placing these headers in this package, we offer an efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. This package follows the same approach as the 'BH' package which provides 'Boost' headers for R packages. Package: r-cran-bartcause Architecture: all Version: 1.0-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbarts Suggests: r-cran-testthat, r-cran-lme4, r-cran-rpart, r-cran-tmle, r-cran-stan4bart Filename: pool/dists/resolute/main/r-cran-bartcause_1.0-10-1.ca2604.1_all.deb Size: 273652 MD5sum: 1f9cf6b8bce5199755141e3d48dc72f9 SHA1: 72e3f8d8b65c99360318b8bbdd88dbfeb5cad433 SHA256: 19851892e9cff9a7f380b0498af72e01f7d7042efd20a452ccb7b1fb372462be SHA512: 2b4361a7181f5f47c7c353fae6509f4285fa5b121e5b99bae6df4d469e7ee6fb74de1ee00b909a8e41fffbf6aad7257765cd71cf213d2e8f411271f1b58394d8 Homepage: https://cran.r-project.org/package=bartCause Description: CRAN Package 'bartCause' (Causal Inference using Bayesian Additive Regression Trees) Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012) ). Package: r-cran-bartmachine Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bartmachinejars, r-cran-rjava, r-cran-randomforest, r-cran-missforest, r-cran-ggplot2, r-cran-checkmate, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-bartmachine_1.4.2-1.ca2604.1_all.deb Size: 1366704 MD5sum: 41e8453eb1181ee861ae481a767508f6 SHA1: a6c08f0e6a4911d2b750d6db6a459ed6896c433a SHA256: e0425480903cc21a42441005f8593500dd173b4fe589424df22020d608749619 SHA512: 774cea184564907410fc509ecfc1c982431f0694a4f96f069dc6ebd69b4c834187855be3e389063f661ee83d5fff5eb7e16ed5743bd4af1a540c71e29a324614 Homepage: https://cran.r-project.org/package=bartMachine Description: CRAN Package 'bartMachine' (Bayesian Additive Regression Trees) An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization. Package: r-cran-bartmachinejars Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9804 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Filename: pool/dists/resolute/main/r-cran-bartmachinejars_1.2.2-1.ca2604.1_all.deb Size: 8728210 MD5sum: c9a435ddaac1e9119a83efceef31eabb SHA1: 668bd04959b4a35eb2597c9cfffc1c75fe3a75bf SHA256: f5565d77aaba0cf4f5f81223514d3651dcd6471b0f57abc602807a42ea1a11ab SHA512: 3f323f71cb6a2335aa45ac796aaca6ce0de50e451aed8383b0d30e3b6cce7ab9f1c433b86b7d4391890315ca978c69914c920127531fb4044d16480f7f679251 Homepage: https://cran.r-project.org/package=bartMachineJARs Description: CRAN Package 'bartMachineJARs' (bartMachine JARs) These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine. Package: r-cran-bartman Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-cowplot, r-cran-dendser, r-cran-dplyr, r-cran-ggiraph, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggraph, r-cran-gtable, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-rrapply, r-cran-scales, r-cran-tidygraph, r-cran-tidyr, r-cran-bart, r-cran-bartmachine, r-cran-dbarts, r-cran-rjava Suggests: r-cran-rocr, r-cran-ggridges, r-cran-ggforce, r-cran-lvplot Filename: pool/dists/resolute/main/r-cran-bartman_0.2.1-1.ca2604.1_all.deb Size: 410588 MD5sum: 6afe1ebe384e3ca9e7e648b123cc49a1 SHA1: b89f0c4a2ae3262d49e05eb75723f0972cb0cb7f SHA256: 3daf8dec41dfce651e0e8b789d33682863a06acadbc47db17515d6f7961f8105 SHA512: 679bb951d5781b2299540be260b320286963b981d2dee3913bf0c2818189642f2f5bbc571cdb81353cfee82fd8f43e7143d13721f09a2667e51651c4c3e89967 Homepage: https://cran.r-project.org/package=bartMan Description: CRAN Package 'bartMan' (Create Visualisations for BART Models) Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 , 'dbarts' (Vincent Dorie 2023) , and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) . 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The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 ). Package: r-cran-base.rms Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-survival, r-cran-do Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-base.rms_1.0-1.ca2604.1_all.deb Size: 31258 MD5sum: 4a1433bd665add36f42607cd64fe8001 SHA1: cfd9cb410787822242cc83283ada1b9ded65483e SHA256: dcef51580792d94aabc22a57630fa4908f87ff56aad7fded893d886584a85a3d SHA512: 3561598e9c3cea91ffe8b48361aeb711d73ee45acf23970103341d2c8808bb46003269a25a3fdd5acf6144cbe1729edc9de4ffd5e57907264e9a2a03219c8bb2 Homepage: https://cran.r-project.org/package=base.rms Description: CRAN Package 'base.rms' (Convert Regression Between Base Function and 'rms' Package) We perform linear, logistic, and cox regression using the base functions lm(), glm(), and coxph() in the R software and the 'survival' package. Likewise, we can use ols(), lrm() and cph() from the 'rms' package for the same functionality. Each of these two sets of commands has a different focus. In many cases, we need to use both sets of commands in the same situation, e.g. we need to filter the full subset model using AIC, and we need to build a visualization graph for the final model. 'base.rms' package can help you to switch between the two sets of commands easily. 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This package provides nowcasting methods based on using empirical delay distributions and uncertainty from past performance. It is also designed to be used as a baseline method for developers of new nowcasting methods. For more details on the performance of the method(s) in this package applied to case studies of COVID-19 and norovirus, see our recent paper at . The package supports standard data frame inputs with reference date, report date, and count columns, as well as the direct use of reporting triangles, and is compatible with 'epinowcast' objects. Alongside an opinionated default workflow, it has a low-level pipe-friendly modular interface, allowing context-specific workflows. It can accommodate a wide spectrum of reporting schedules, including mixed patterns of reference and reporting (daily-weekly, weekly-daily). It also supports sharing delay distributions and uncertainty estimates between strata, as well as custom uncertainty models and delay estimation methods. 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Package: r-cran-basicmcmcplots Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-basicmcmcplots_0.2.7-1.ca2604.1_all.deb Size: 38756 MD5sum: d7af1ebd00e779458af6d720bb7a6101 SHA1: fd035be49f8bee69a21a28d26a88ee55c0574a56 SHA256: b965b7ecb7cba6a3c9d72642b9267d64e64b6b2ba02bb668adb97edf32907831 SHA512: 3ae4bd5a1dd28a44a78c04cb7f671b5b540cf771ae1f5c3175fc3b7b727cfd62f848ed5f55f38125abf6521cd21f334fe9952920a1b11b0b03fc8881e51df66b Homepage: https://cran.r-project.org/package=basicMCMCplots Description: CRAN Package 'basicMCMCplots' (Trace Plots, Density Plots and Chain Comparisons for MCMCSamples) Provides methods for examining posterior MCMC samples from a single chain using trace plots and density plots, and from multiple chains by comparing posterior medians and credible intervals from each chain. These plotting functions have a variety of options, such as figure sizes, legends, parameters to plot, and saving plots to file. Functions interface with the NIMBLE software package, see de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017) . 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Create/manipulate tables row-by-row, column-by-column or cell-by-cell. Use common formatting/styling to output rich tables as 'HTML', 'HTML widgets' or to 'Excel'. Package: r-cran-basifor Architecture: all Version: 0.7.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6018 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-foreign, r-cran-hmisc, r-cran-httr, r-cran-measurements, r-cran-rodbc, r-cran-rvest Suggests: r-cran-odbc Filename: pool/dists/resolute/main/r-cran-basifor_0.7.7-1.ca2604.1_all.deb Size: 5698406 MD5sum: 1129ebf1d0c5c4c8d5fcf05126e29182 SHA1: c5e57c68b5ef7915fdf49368eb34edbadc4aecaa SHA256: 8161d728d01ebf2fb7f7720668e368c9a8a587e728d92cb81501c02f23271625 SHA512: 6281fc2d07091963ac8ca1bf515b6c426f4d98611c7b642c89d43b58d98567894ed4c15650911e672bfd8c451115f7146df6a19d89f62d9ee03d7ffc7648758f Homepage: https://cran.r-project.org/package=basifoR Description: CRAN Package 'basifoR' (Retrieval and Processing of the Spanish National ForestInventory) Fetches, harmonizes, and analyses data from the Spanish National Forest Inventory for reproducible, design-aware forest inventory workflows. Computes tree- and stand-level metrics, applies sampling-based expansion factors, estimates volume, and supports extensible processing for external inventory designs with custom sampling schemes and volume equations. Package: r-cran-basinet Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-bioc-biostrings, r-cran-rweka, r-cran-randomforest, r-cran-rmcfs, r-cran-rjava Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-basinet_0.0.5-1.ca2604.1_all.deb Size: 264392 MD5sum: 07695e129b2861c5e789ced2fe1a1724 SHA1: 3893d15b57fb224cc20a3a2de0bd466a9ce85c8c SHA256: f83a49fbbe6fa24a10b77c1ad05cd36559e878f0a4abc0afca2ba15976008ab4 SHA512: ad12a12b2491159dcccaaba72254b8351bb65b42dc6d225eed10022e6929a91e930568bf1bb0c4ef73b209645372b45e71b246ceaf0f6dfe536bb0749593553d Homepage: https://cran.r-project.org/package=BASiNET Description: CRAN Package 'BASiNET' (Classification of RNA Sequences using Complex Network Theory) It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) , these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) . Package: r-cran-basinetentropy Architecture: all Version: 0.99.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-bioc-biostrings, r-cran-randomforest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-basinetentropy_0.99.6-1.ca2604.1_all.deb Size: 153124 MD5sum: 7fd906ddebff565b585af012ae99397b SHA1: 8c01fbd298d722918aaf1629a91f921ef92f11b4 SHA256: 7a755cfd97c37810b3c6939c5c9645b593279bc1a18c4d1c3eba0eb55307119b SHA512: 40562db19d542e85d99b2a5b0cda8eb2ef2a02a78292cbbb0f624e6aecd3b07a442e1d1f3135962b7d526a05d07cf6c7dd04fbf278a780e757a9bcac87f82b4f Homepage: https://cran.r-project.org/package=BASiNETEntropy Description: CRAN Package 'BASiNETEntropy' (Classification of RNA Sequences using Complex Network andInformation Theory) It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) , these sequences are used to run examples. Package: r-cran-baskepro Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-baskepro_1.1.1-1.ca2604.1_all.deb Size: 22066 MD5sum: 3fabe55f3360888ebc84f732af35cf97 SHA1: ffd2c54dca95067b34db200a83807c511f5ee8a7 SHA256: 41d5c091ef4aae2e4171a9fd07d811ad123217a4fb9f79952eead811c4fae9d8 SHA512: 69df50cc38f09c97883d2e68a49bfabb1f054189c5598af5b9902e03b7e23d78a482824fa4c6a565be702b72cb03996e34f7a39a66ae6804722fd7e73fbf3110 Homepage: https://cran.r-project.org/package=BaSkePro Description: CRAN Package 'BaSkePro' (Bayesian Model to Archaeological Faunal Skeletal Profiles) Tool to perform Bayesian inference of carcass processing/transport strategy and bone attrition from archaeofaunal skeletal profiles characterized by percentages of MAU (Minimum Anatomical Units). The approach is based on a generative model for skeletal profiles that replicates the two phases of formation of any faunal assemblage: initial accumulation as a function of human transport strategies and subsequent attrition.Two parameters define this model: 1) the transport preference (alpha), which can take any value between - 1 (mostly axial contribution) and 1 (mostly appendicular contribution) following strategies constructed as a function of butchering efficiency of different anatomical elements and the results of ethnographic studies, and 2) degree of attrition (beta), which can vary between 0 (no attrition) and 10 (maximum attrition) and relates the survivorship of bone elements to their maximum bone density. Starting from uniform prior probability distribution functions of alpha and beta, a Monte Carlo Markov Chain sampling based on a random walk Metropolis-Hasting algorithm is adopted to derive the posterior probability distribution functions, which are then available for interpretation. During this process, the likelihood of obtaining the observed percentages of MAU given a pair of parameter values is estimated by the inverse of the Chi2 statistic, multiplied by the proportion of elements within a 1 percent of the observed value. See Ana B. Marin-Arroyo, David Ocio (2018).. Package: r-cran-basket Architecture: all Version: 0.10.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1236 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gensa, r-cran-foreach, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-igraph, r-cran-gridextra, r-cran-itertools, r-cran-crayon, r-cran-cli, r-cran-rcolorbrewer, r-cran-tidygraph, r-cran-ggraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-basket_0.10.11-1.ca2604.1_all.deb Size: 577034 MD5sum: 6b2d393d0f5f9c9473eb3c47a04b87e3 SHA1: 437647b5c5d33a58c7d0b92ebdb950fb7a54fe5a SHA256: ac73e168e8d2c45798c3a5c421b1c49dfefdcc31f084f8342441bb3f0d54e370 SHA512: 0aaa87d68837571b5044a36e08ab1e8117b70f68a9bd06778fe008731c481c4edf9097387c29f54eee58da5df14753a9df1fe6d6c3d965e4fd6ec30a39c5d792 Homepage: https://cran.r-project.org/package=basket Description: CRAN Package 'basket' (Basket Trial Analysis) Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring. The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability. References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) ; Hobbs, B.P. and Landin, R. (2018) ; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) ; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) . Package: r-cran-basketballanalyzer Architecture: all Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2837 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-ggrepel, r-cran-gridextra, r-cran-mass, r-cran-directlabels, r-cran-corrplot, r-cran-pbsmapping, r-cran-sp, r-cran-operators, r-cran-stringr, r-cran-ggally, r-cran-statnet.common, r-cran-readr, r-cran-gtools, r-cran-data.table, r-cran-mathjaxr Suggests: r-cran-dendextend, r-cran-ggnetwork, r-cran-ggplotify, r-cran-network Filename: pool/dists/resolute/main/r-cran-basketballanalyzer_0.8.1-1.ca2604.1_all.deb Size: 2797318 MD5sum: ea6cc3c08b7d97839edef98e7223a127 SHA1: 5e30dcf2fddc8f4db7ac886dd76bb94e9f7b21f2 SHA256: 823ecb9b4668de097f0bc4f9f1e56ff53188bf2025434a903041aad69790eea7 SHA512: 8e27e07ebdefc16b052f019cabc87ba5b6570be40405f87ee123c7cb9ac588fef6beffc295a01367e3618bc12e5c79c3fc5e117bb9bd53ac66fbf38615cb27f4 Homepage: https://cran.r-project.org/package=BasketballAnalyzeR Description: CRAN Package 'BasketballAnalyzeR' (Analysis and Visualization of Basketball Data) Contains data and code to accompany the book P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799. Package: r-cran-baskettrial Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-baskettrial_0.1.0-1.ca2604.1_all.deb Size: 46660 MD5sum: e4f75333e163aa5137ac55b9c80ec96a SHA1: d52f435f9159230d227784b0b9978f2d97e41da8 SHA256: 03e8eee47aeaf5535b7d1233cb60937169228d7ea50a29bad564ce9a5cc81079 SHA512: 1eafeeb27eb7b5bec336146ed50dab714774b82fc9f484cfedef4de548857ec07591cbf2ed323f00f610c6a71bdd17c8c878a12b22c036740417afeccea30fef Homepage: https://cran.r-project.org/package=BasketTrial Description: CRAN Package 'BasketTrial' (Bayesian Basket Trial Design and Analysis) Provides tools for Bayesian basket trial design and analysis using a novel three-component local power prior framework with global borrowing control, pairwise similarity assessment and a borrowing threshold. Supports simulation-based evaluation of operating characteristics and comparison with other methods. Applicable to both equal and unequal sample size settings in early-phase oncology trials. For more details see Zhou et al. (2023) . Package: r-cran-baskoptr Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-baskwrap, r-cran-future.apply Suggests: r-cran-optimizr, r-cran-baskexact, r-cran-testthat, r-cran-progressr, r-cran-basksim Filename: pool/dists/resolute/main/r-cran-baskoptr_1.0.4-1.ca2604.1_all.deb Size: 179174 MD5sum: f27997874d5f1b49dd9ae814bb97002c SHA1: 31f5b028faf63304668858e83f937638b8f4d60c SHA256: 241f7c0b678dbf17c4256f5422bdf0fadc4cb53666cf711cad77b7a0a0b946b4 SHA512: 2ea94edde75301c8dce29edb397db2af2351adbe34b8dfa3ec4660177eb9fbaa549d18e1d8700ac642c438177495a3c75be2dc812d612e8267f0ae57eeda6855 Homepage: https://cran.r-project.org/package=baskoptr Description: CRAN Package 'baskoptr' (Utility-Based Optimization for Basket Trial Designs) A unified framework for optimizing basket trial designs. To this end, the package supplies several utility functions and also a function for executing optimization algorithms on basket trial designs. The considered utility functions are discussed in Sauer et al. (2025) . Package: r-cran-basksim Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrangements, r-cran-bhmbasket, r-cran-dofuture, r-cran-extradistr, r-cran-foreach, r-cran-hdinterval, r-cran-progressr, r-cran-purrr Suggests: r-cran-covr, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-basksim_2.2.0-1.ca2604.1_all.deb Size: 281054 MD5sum: 28e8bcef9e1a59be10370c0cb062600b SHA1: 8e4723b378d225e4d26e75e6b1f430563630a15b SHA256: 44b0d7406a801c7cd2d45477997c4725eab09e6d77e5e65133e45cb72b5f69dc SHA512: ebb428681659e60a0881462cde15845b869401de0f5dd2552da2a368a11a658ae97b329bbbdfd803c895e1a6a26bbf8f77645bdd4e0d339ce0fcdea5bc2880cc Homepage: https://cran.r-project.org/package=basksim Description: CRAN Package 'basksim' (Simulation-Based Calculation of Basket Trial OperatingCharacteristics) Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2025) , Schmitt and Baumann (2025) , Fujikawa et al. (2020) , Berry et al. (2020) , and Neuenschwander et al. (2016) . For the latter two designs, the functions are mostly wrappers for functions provided by the package 'bhmbasket'. Package: r-cran-baskwrap Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-basksim, r-cran-baskexact Suggests: r-cran-testthat, r-cran-here, r-cran-reticulate, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-baskwrap_1.0.3-1.ca2604.1_all.deb Size: 242202 MD5sum: e10fbb992d89eba06e99a04e38544d48 SHA1: 1d6c4d7e9aa9abb04e0f0d77d0b677777952cd31 SHA256: 244bdbc265cacfbb2e0a855cc7cfe4697ecaf9863e4d7b7f03628df31f5d2fea SHA512: 8877172b37b6a9853ff602d85cf38b26fff0a7b8c4a07e575d2279c2e2ec7a832ec6c834854eaeaef34c276e43e21cf6eb7604a1ef1c19012487580eab893130 Homepage: https://cran.r-project.org/package=baskwrap Description: CRAN Package 'baskwrap' (Wrapper Package for Several Basket Trial R Packages) A simple interface to switch between two methods for calculating basket trial characteristics, numerical integration ("exact") and Monte Carlo simulation ("simulated") for the basket trial design by Fujikawa et al. 2020 . The exact implementation is from the 'baskexact' package, see Baumann (2024) . The simulated implementation is from the 'basksim' package, which was developed for Baumann et al. (2024) . The package's syntax is compatible with the 'basksim' syntax and easily extendable. Package: r-cran-bass Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1781 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncdist, r-cran-hypergeo Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bass_1.3.1-1.ca2604.1_all.deb Size: 1734398 MD5sum: f9b621530a8055d2b302ba411263e76d SHA1: 67a494cc7f6cd16707f26f86d41730c42acd83ef SHA256: 73c3977e110f90d971c5d6c4ae6aad1e13cb3c8f503c729085e79f158b998761 SHA512: 498ae071b1e0e1f372995c04fd6935cc1f9436be04f95f8f61b97a485c82e1c9f0fd43955a56e8330d2fd6cc37ce0879b08f42c1d70bdfcc7854213fea8289a6 Homepage: https://cran.r-project.org/package=BASS Description: CRAN Package 'BASS' (Bayesian Adaptive Spline Surfaces) Bayesian fitting and sensitivity analysis methods for adaptive spline surfaces described in . Built to handle continuous and categorical inputs as well as functional or scalar output. An extension of the methodology in Denison, Mallick and Smith (1998) . 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It includes tools for data checking, model diagnostics and outputs such as life-tables and plots, as described in Colchero, Jones, and Rebke (2012) and Colchero et al. (2021) . 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Functions available are related to friendly web scraping, data management and visualization. Data were obtained from , and , following the instructions of their respectives robots.txt files, when available. Box score data are available for the three leagues. Play-by-play and spatial shooting data are also available for the Spanish league. Methods for analysis include a population pyramid, 2D plots, circular plots of players' percentiles, plots of players' monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games, maps of nationalities, combinations of lineups, possessions-related variables, timeouts, performance by periods, personal fouls, offensive rebounds and different types of shooting charts. Please see Vinue (2020) and Vinue (2024) . 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Package: r-cran-baycn Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-egg, r-cran-ggplot2, r-cran-igraph, r-cran-mass, r-cran-expm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-baycn_2.0.0-1.ca2604.1_all.deb Size: 3090118 MD5sum: 7dc0b7e48cb0e9a7d9bc1cc137fdf6d7 SHA1: b496cc73fbd66dbf731a0926a58a2e4c449faf00 SHA256: 6b8c996d647d391fa60c74ddbfdf2ad9b4f8c94cad2f7c89a0de7ca030dc0e5b SHA512: d114d121e528c7571a8d987c8e40b286f87e5241f3747fae5bc7f70ff6224b4ebc680632a028ffa520e3dfb5214d0e61f279563ff852a877c0063cad5702d3cb Homepage: https://cran.r-project.org/package=baycn Description: CRAN Package 'baycn' (Bayesian Inference for Causal Networks) An approximate Bayesian method for inferring Directed Acyclic Graphs (DAGs) for continuous, discrete, and mixed data. The algorithm can use the graph inferred by another more efficient graph inference method as input; the input graph may contain false edges or undirected edges but can help reduce the search space to a more manageable size. A Metropolis-Hastings-like sampling algorithm is then used to infer the posterior probabilities of edge direction and edge absence. References: Martin, Patchigolla and Fu (2026) . Package: r-cran-bayefdr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-assertthat, r-cran-cowplot, r-cran-ggextra Suggests: r-cran-testthat, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-bayefdr_0.2.1-1.ca2604.1_all.deb Size: 71968 MD5sum: f426b0cafa1dce58c5bacca256ffe679 SHA1: 72f02aadcc9108fb85dd3f3b426457b6b79ddeec SHA256: c9cb4ef5574ffe5dbd8c58f8beda346d49b11dc8cfef07820b117e36ec7fc886 SHA512: 0e281820da5d79388637efeb8150a4b51ce6757f0ef27dfb7e05ca42d45ab4e3c566c9f25039c42109226313e39b99ca4ed4cc8667cf36073a0101de215a9709 Homepage: https://cran.r-project.org/package=bayefdr Description: CRAN Package 'bayefdr' (Bayesian Estimation and Optimisation of Expected False DiscoveryRate) Implements the Bayesian FDR control described by Newton et al. (2004), . Allows optimisation and visualisation of expected error rates based on tail posterior probability tests. Based on code written by Catalina Vallejos for BASiCS, see Beyond comparisons of means: understanding changes in gene expression at the single-cell level Vallejos et al. (2016) . 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The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (). Package: r-cran-bayesanova Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-coda, r-cran-knitr, r-cran-mass Filename: pool/dists/resolute/main/r-cran-bayesanova_1.6-1.ca2604.1_all.deb Size: 129478 MD5sum: 2e77e175bdd9fa66c8b01526ba43c6e9 SHA1: 97ac290245537222ea371d21de010cfe109eaf81 SHA256: c639cdbcbb95a1b07c8cc3edc7ec6c714401b8416e3fdb026abebe23f13b3186 SHA512: ad115cb9b876b00da8ddd7df6485ab68610b33d6e2f31cb9e527b2f254d9dacc6b062da6c0673cc3a4d4db4fcbf9ce0cc7a4eb5ec82ce5209d78f0d2310e0af6 Homepage: https://cran.r-project.org/package=bayesanova Description: CRAN Package 'bayesanova' (Bayesian Inference in the Analysis of Variance via Markov ChainMonte Carlo in Gaussian Mixture Models) Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) . Package: r-cran-bayesarimax Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-bayesarimax_0.1.1-1.ca2604.1_all.deb Size: 16252 MD5sum: e38d5ec517fdf655350d32b976a15ad0 SHA1: 52b1f7c9d771c4b23c8ab4b7ba369a3b9006e2d3 SHA256: 8547e980733d03ae69cb6868e3e47f2c67ae717007fb4d2b5f9e3e431a52c299 SHA512: 76566741c457ae7007bc515414fd215ade09ac14d532bb6dae79f075019c64a0e0d7a39042757e6eb3aed2852402406db1755093148c43bf795b4da7fc8ca222 Homepage: https://cran.r-project.org/package=BayesARIMAX Description: CRAN Package 'BayesARIMAX' (Bayesian Estimation of ARIMAX Model) The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) . In this package we estimate the ARIMAX model using Bayesian framework. Package: r-cran-bayesat Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayesat_0.1.0-1.ca2604.1_all.deb Size: 69214 MD5sum: 537a857c12f9527551b7720f88fe7522 SHA1: 3b6958ab5201b77253915af023d728cf0743e5e2 SHA256: e3dbce3a596208da6e7e6ec1dc14ccef7fbc981c3f3b3a82e9a6f82ab6032bf5 SHA512: 34330099198db814d7983ef43ce30dbae81b16994cdaad1d9428622a6a645279ff2fab1c98ba9b4cb0f1be578001f58b1daaf4a97414606c7a71be5f24191023 Homepage: https://cran.r-project.org/package=BayesAT Description: CRAN Package 'BayesAT' (Bayesian Adaptive Trial) Bayesian adaptive trial algorithm implements multiple-stage interim analysis. Package includes data generating function, and Bayesian hypothesis testing function. Package: r-cran-bayesbekk Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mts, r-cran-coda, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-bayesbekk_0.1.1-1.ca2604.1_all.deb Size: 18346 MD5sum: 6b44ef7785b2e24969e898f8391c5fd1 SHA1: 2db2531a7d2d512240e40fbe53e73a9e5d9481ab SHA256: 778c555ebaf3e36830efac4db0b06c97a8bd82007f533120709900c78f12c9f4 SHA512: b08f9fbecc83c80a5f034abdc6713747d38e6aa25c2fed8bab9159598681a38f5a64b5d9e0963f4f8bb6301d5d71b70a6e851ae434e37f8d5647691808d24058 Homepage: https://cran.r-project.org/package=BayesBEKK Description: CRAN Package 'BayesBEKK' (Bayesian Estimation of Bivariate Volatility Model) The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) has been used to estimate the bivariate time series data using Bayesian technique. Package: r-cran-bayesbinmix Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-label.switching, r-cran-foreach, r-cran-doparallel, r-cran-coda Filename: pool/dists/resolute/main/r-cran-bayesbinmix_1.4.1-1.ca2604.1_all.deb Size: 155536 MD5sum: 72ebcda04110e4b8b18440699c212f27 SHA1: 758ef8f7bb7712e03b64befcf96695605b02ab19 SHA256: f56d01daec6ed880160f171d56a1469dd51dad0b58dbce53f09f240c8f41d4a9 SHA512: 62ea7f21c882ad5380a4b85d58cbe81796cc0cdc12ac323bdaef19e814df4eab15e8f91b3e62bc2fd5404238afbb2338d1228296ab5c0a58501646cecf4c1738 Homepage: https://cran.r-project.org/package=BayesBinMix Description: CRAN Package 'BayesBinMix' (Bayesian Estimation of Mixtures of Multivariate BernoulliDistributions) Fully Bayesian inference for estimating the number of clusters and related parameters to heterogeneous binary data. Package: r-cran-bayesbio Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-rismed, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesbio_1.0.0-1.ca2604.1_all.deb Size: 51178 MD5sum: 555dbd1a31998a64bb7ef3f2195aab12 SHA1: 751c8f69a66cb484cf304d3778fe8f4fbe129d4f SHA256: 91306b2c399471f171121ee054eec4d8b3afedebafde3b218c4fc367952f8316 SHA512: 4026e2b1490eb9eb0bbb9743e7832967188e9b2973c4aa11f770763873fb5a3f58898b643eeaf615b79b60d2e97c983bdbbafd09b8d40cb39dd70fdf7cbe2b44 Homepage: https://cran.r-project.org/package=bayesbio Description: CRAN Package 'bayesbio' (Miscellaneous Functions for Bioinformatics and BayesianStatistics) A hodgepodge of hopefully helpful functions. Two of these perform shrinkage estimation: one using a simple weighted method where the user can specify the degree of shrinkage required, and one using James-Stein shrinkage estimation for the case of unequal variances. Package: r-cran-bayesboot Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-hdinterval Suggests: r-cran-testthat, r-cran-foreach, r-cran-doparallel, r-cran-boot Filename: pool/dists/resolute/main/r-cran-bayesboot_0.2.3-1.ca2604.1_all.deb Size: 160072 MD5sum: 8871305a7a24a0e65063a4387a7c364c SHA1: 210ca814192ee4619229d14efee45577f32629eb SHA256: 8b25b2b679a437e3449c3d62972f1ce821487ded311f1989ba2d83a3c62d1383 SHA512: 997b5e37b26ecd1801e1f21d66ea0b6c79bb0aef0a5f198e4137a09126e1a6f4d4678c4844b6739864b412c51f682d5b490206a6dd18a6654a984d213a286606 Homepage: https://cran.r-project.org/package=bayesboot Description: CRAN Package 'bayesboot' (An Implementation of Rubin's (1981) Bayesian Bootstrap) Functions for performing the Bayesian bootstrap as introduced by Rubin (1981) and for summarizing the result. The implementation can handle both summary statistics that works on a weighted version of the data and summary statistics that works on a resampled data set. Package: r-cran-bayesbp Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iterators, r-cran-openxlsx Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesbp_1.1-1.ca2604.1_all.deb Size: 115030 MD5sum: aa62a572726c8493b7aed524f9d0b664 SHA1: 253b1d326bb0a373ed781f355f9948e05ca0f330 SHA256: 5b98e9278286d1cf7bd6f1091f00180a114d7a3133da96d78bd65ef8ec4ddc34 SHA512: 9a1e68c9515da787778c6281bc34211fb113130f83d2287fe65d141dceb734b3e93acf91388ac06330350f680645d63c9b46903e1494c0cdc0bc3885a9f28b77 Homepage: https://cran.r-project.org/package=BayesBP Description: CRAN Package 'BayesBP' (Bayesian Estimation using Bernstein Polynomial Fits Rate Matrix) Smoothed lexis diagrams with Bayesian method specifically tailored to cancer incidence data. Providing to calculating slope and constructing credible interval. LC Chien et al. (2015) . LH Chien et al. (2017) . Package: r-cran-bayesbrainmap Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-fmritools, r-cran-fmriscrub, r-cran-foreach, r-cran-matrix, r-cran-matrixstats, r-cran-pesel, r-cran-squarem Suggests: r-cran-ciftitools, r-cran-excursions, r-cran-rnifti, r-cran-oro.nifti, r-cran-gifti, r-cran-ggplot2, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesbrainmap_0.2.0-1.ca2604.1_all.deb Size: 504574 MD5sum: c9605e1f8ce85bd046704f97d5457bc3 SHA1: 843402b5bbf1f9c07d6b00dd86608baf92be43e2 SHA256: 6087ad5dbd24f8713cbc31d0bbeb2deee1e06303177a461fa6932aa57dafde36 SHA512: eb0193244b34d32047d1678cd1ee1daa0c7318bfa7384221837dc5714d7522fe638825e7176a827dec54893bf687fc9ae40a89d11412ff991a4a4ad367f79cd5 Homepage: https://cran.r-project.org/package=BayesBrainMap Description: CRAN Package 'BayesBrainMap' (Estimate Brain Networks and Connectivity with Population-DerivedPriors) Implements Bayesian brain mapping with population-derived priors, including the original model described in Mejia et al. (2020) , the model with spatial priors described in Mejia et al. (2022) , and the model with population-derived priors on functional connectivity described in Mejia et al. (2025) . Population-derived priors are based on templates representing established brain network maps, for example derived from independent component analysis (ICA), parcellations, or other methods.  Model estimation is based on expectation-maximization or variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats. Package: r-cran-bayescace Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1888 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-rdpack, r-cran-forestplot, r-cran-metafor, r-cran-lme4 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-bayescace_1.2.3-1.ca2604.1_all.deb Size: 1817772 MD5sum: d523a5f7d6f9661d1e56a4f1e121bcac SHA1: adcd93bec5e20f32acec29ec475744f388855150 SHA256: d223d6e5c9c483813fe3e91bc6801a80ed0cde07a18ce6907666a4572e7561dd SHA512: 5f0e95e02caf2c53f25da41e3a17364f663658d743921c1a9d4ea8f020f291ac64ada02ca6e07982846276620ef05054be763493641a9c933d419dbab2c4fee5 Homepage: https://cran.r-project.org/package=BayesCACE Description: CRAN Package 'BayesCACE' (Bayesian Model for CACE Analysis) Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) , which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) , with an application example on Epidural Analgesia. Package: r-cran-bayescombo Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-labstats, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayescombo_1.0-1.ca2604.1_all.deb Size: 188924 MD5sum: 6e1129c6e2975cd3881875d4132211a0 SHA1: 5f00a26da13f07e6d1520e5b2fc98ca9d1ce517a SHA256: c24cdf1a18fa0eacc70929f0ac510e91ba7d221b716d241bca3dbc1134001f2f SHA512: 34b01019b3a05290b738c6e7f97c02f8f673d0d2498a593c617873ad4c8c13e01fe45ee0dcd14ca1ee7ce15c3872f6b538697f4543af15b98d42cb3e7996221d Homepage: https://cran.r-project.org/package=BayesCombo Description: CRAN Package 'BayesCombo' (Bayesian Evidence Combination) Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis. Package: r-cran-bayescpclust Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr, r-cran-rcppalgos Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayescpclust_0.1.0-1.ca2604.1_all.deb Size: 96998 MD5sum: e2595a8ffb80cc1dcf4fee060eeb5cff SHA1: 42ca8f8534bd7dedbd80874b424fe5e8bbe929ac SHA256: 73f3e8a36b26b076459f6e9605d13c35d6c8a8aea3d2643a347af5627911d7a5 SHA512: db48704891c47b1dfd3db205c22b598019254f7e12385c48b91fa9719f245f2eef7c21a33a1f1f7973e9a61c8c46e26006df1203eeb20cca23cd11606ee98b8e Homepage: https://cran.r-project.org/package=BayesCPclust Description: CRAN Package 'BayesCPclust' (A Bayesian Approach for Clustering Constant-Wise Change-PointData) A Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" . Package: r-cran-bayescr Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve, r-cran-truncdist, r-cran-mvtnorm, r-cran-mnormt Filename: pool/dists/resolute/main/r-cran-bayescr_2.1-1.ca2604.1_all.deb Size: 97734 MD5sum: ad612502414978c420273421ab08cc98 SHA1: 79842f62278e2561f1955e6789a16d27bb632fac SHA256: b9fe83016fd68c1ed0e30f300b136f3740a41265427d4c76544856ea98378715 SHA512: ef63ed50041f62dcb6743d8f6ecde9f7bf89dd5c761c16abeee6ee56e24a4be90805b9031c5db3884f807d4532898361331a730f69bb0a9bda3c3fa4732c1d18 Homepage: https://cran.r-project.org/package=BayesCR Description: CRAN Package 'BayesCR' (Bayesian Analysis of Censored Regression Models Under ScaleMixture of Skew Normal Distributions) Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables. Package: r-cran-bayesctdesign Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eha, r-cran-ggplot2, r-cran-survival, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-bayesctdesign_0.6.1-1.ca2604.1_all.deb Size: 313544 MD5sum: b63c7028d8c1ef0a5eb6de763b5e94d6 SHA1: 70a731bdc1f1fb660547b9b8acdbc7d9ecc3c58b SHA256: bb2f9e5393e8bd92e1afef17c6ef0ec43479eabdd3006fe4640c5b2e46e0a5ed SHA512: b2f4617689fc027a06579ed7356d8cbded4dfc78d5201af0bc318e29a33de2a078ad19ac03e96e6cea7c0787114d90dd1c16c16c0e762bbecfc95453084c3a22 Homepage: https://cran.r-project.org/package=BayesCTDesign Description: CRAN Package 'BayesCTDesign' (Two Arm Bayesian Clinical Trial Design with and WithoutHistorical Control Data) A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) . Package: r-cran-bayescvi Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mclust, r-cran-ggplot2, r-cran-universalcvi Filename: pool/dists/resolute/main/r-cran-bayescvi_1.0.2-1.ca2604.1_all.deb Size: 459316 MD5sum: 2cee203f2fa7a000d0b9c6bfc4966221 SHA1: 83dc8e06eb8b66da836a53f42a944d6f5e0f43f6 SHA256: a419516bf07768e25d2848035476c49734d17e4649740bb2d6765acebf4ca2bd SHA512: 39ee8f1eeb0507a6d64163e0cb96a4537c96fdc19b238bbe38d1569e5ff4c420f75bfb6fcd2242797bc39fbb30106d5c2028675a4e8b93229defc0b8798385c0 Homepage: https://cran.r-project.org/package=BayesCVI Description: CRAN Package 'BayesCVI' (Bayesian Cluster Validity Index) Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202, 108053, 2025. ) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn, Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI. Package: r-cran-bayesdesign Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bayesdesign_0.1.1-1.ca2604.1_all.deb Size: 35450 MD5sum: 120d42e6545e84103728821396827e5f SHA1: 3cde49987acefc480e6f57bf7f91710b018dd4df SHA256: 1e405690e61881b0aafeecfa7b7b5f608ab258698d0f39b6437f4750c66448ba SHA512: 8dd023902b8de3660ae51182a3065b7f5df77c92f92e4c29ef8d32f0823b8897d57a420fef306010912f249fd20c7ab255125cbb6d6dd6f142d0b4920492d37a Homepage: https://cran.r-project.org/package=BayesDesign Description: CRAN Package 'BayesDesign' (Bayesian Single-Arm Design with Survival Endpoints) The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint. Package: r-cran-bayesdiagnostics Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-checkmate, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-posterior, r-cran-matrixstats, r-cran-bridgesampling, r-cran-rstan, r-cran-tidyr Suggests: r-cran-bayesplot, r-cran-covr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-bayesdiagnostics_0.1.0-1.ca2604.1_all.deb Size: 255696 MD5sum: 9d2a5a946dcdcf9bbb28f44d48e9db16 SHA1: cba2f98a0f1622271d74fc7efa63f8d3de286427 SHA256: 21db95987e0db50deff394f0d98971d168e49908246832d3d9245b6c96279a9b SHA512: 998f352e3d4a5b269076d9a2294ced254b74e92ab20d16ee55358f6614557a4a082fd9c76781e965d262a5316afc0ba85dd10820680ce8ddef85562670e1785f Homepage: https://cran.r-project.org/package=bayesDiagnostics Description: CRAN Package 'bayesDiagnostics' (Comprehensive Bayesian Model Diagnostics and Comparison Tools) Provides comprehensive tools for Bayesian model diagnostics and comparison. Includes prior sensitivity analysis, posterior predictive checks (Gelman et al. (2013) ), advanced model comparison using Pareto-smoothed importance sampling leave-one-out cross-validation (Vehtari et al. (2017) ), convergence diagnostics, and prior elicitation tools. Integrates with 'brms' (Burkner (2017) ), 'rstan', and 'rstanarm' packages for comprehensive Bayesian workflow diagnostics. Package: r-cran-bayesdip Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bayesdip_0.1.1-1.ca2604.1_all.deb Size: 124572 MD5sum: 25c49269c35033375296c18d0fd7337c SHA1: aee3b3bbacb603a39f16fcbd9e8dbea8fd91054c SHA256: dee6ad0c66d782311609617630ec7dda2e73da8fc6be0cb6f441519519d11284 SHA512: b5d2429c8e67d39cea550fd3940938733194ede149440f87a3e8552becfb43252d058171244ba5ab0820c43be4cd303286d1d863088388c250eda883a5073dfd Homepage: https://cran.r-project.org/package=BayesDIP Description: CRAN Package 'BayesDIP' (Bayesian Decreasingly Informative Priors for Early TerminationPhase II Trials) Provide early termination phase II trial designs with a decreasingly informative prior (DIP) or a regular Bayesian prior chosen by the user. The program can determine the minimum planned sample size necessary to achieve the user-specified admissible designs. The program can also perform power and expected sample size calculations for the tests in early termination Phase II trials. See Wang C and Sabo RT (2022) ; Sabo RT (2014) . Package: r-cran-bayesdissolution Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-geor, r-cran-mcmcpack, r-cran-mnormt, r-cran-pscl, r-cran-shiny Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/resolute/main/r-cran-bayesdissolution_0.2.1-1.ca2604.1_all.deb Size: 107174 MD5sum: 8fb446ca38b4d75e1d6e09cbe60213dc SHA1: 3c4eaacfe9c421f608abee7918ae43b5f563ba43 SHA256: 7f38af7e455e57de6380e87eca3a6c6299c5848eadef1aa6118fa2feeaabc068 SHA512: 93ce1915c0056d373d0a62f2ca64fcb154686c26f6c12a5eda9ef3c366663d3799817e1c1686a1522ac294b106953f83fe0f741bb00abf2a03ba9150124524ee Homepage: https://cran.r-project.org/package=BayesDissolution Description: CRAN Package 'BayesDissolution' (Bayesian Models for Dissolution Testing) Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) . However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time. Package: r-cran-bayesertools Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-gt, r-cran-cli, r-cran-rlang, r-cran-rstanarm, r-cran-rstanemax, r-cran-loo, r-cran-tidybayes, r-cran-posterior Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-htmltools, r-cran-digest, r-cran-ggforce, r-cran-xgxr, r-cran-scales, r-cran-readr, r-cran-bayestestr, r-cran-patchwork, r-cran-projpred, r-cran-rsample, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-bayesertools_0.2.5-1.ca2604.1_all.deb Size: 1234606 MD5sum: 839abe6e169a7dbae49f91fd88d2420f SHA1: 0ab8a12794b620123fce7819d40bf691e6bd8584 SHA256: 398e5aa622630940d986a5d714ab720bd1c2040cd4eeb4e6c06ea99ca63abb44 SHA512: c212e0bcb3852e5043e44e7514ac4e35e964a5c78e8ad62662c897b979352b6979a3785e446d0276f7542da02dd3759d97e4f62955d464392d4c7bcaf70a1f1b Homepage: https://cran.r-project.org/package=BayesERtools Description: CRAN Package 'BayesERtools' (Bayesian Exposure-Response Analysis Tools) Suite of tools that facilitate exposure-response analysis using Bayesian methods. The package provides a streamlined workflow for fitting types of models that are commonly used in exposure-response analysis - linear and Emax for continuous endpoints, logistic linear and logistic Emax for binary endpoints, as well as performing simulation and visualization. Learn more about the workflow at . Package: r-cran-bayesestdft Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-bayesestdft_1.0.0-1.ca2604.1_all.deb Size: 877762 MD5sum: 15bc08b4fac222a3c1bc20f7bb1a9f1d SHA1: 3025e01120b612e239990b94b23eba1a04da5edd SHA256: 7edf0df27d177858d834e07acddccb69c48af82abb2c3e34dd07b92f65bfbcae SHA512: 91dc5cdfe2e9bd372ddfa4dc43108ef473c04c624a51857ec0bf6e55db4cb351427f222b2361e043ff788c1f64f0d941e802fbff8f16906db39a0fab893fd48d Homepage: https://cran.r-project.org/package=bayesestdft Description: CRAN Package 'bayesestdft' (Estimating the Degrees of Freedom of the Student'st-Distribution under a Bayesian Framework) A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior. Package: r-cran-bayesfbhborrow Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-survival, r-cran-invgamma, r-cran-mvtnorm, r-cran-checkmate, r-cran-magrittr, r-cran-ggplot2 Suggests: r-cran-tibble, r-cran-readxl, r-cran-testthat, r-cran-rmarkdown, r-cran-ggfortify, r-cran-condsurv Filename: pool/dists/resolute/main/r-cran-bayesfbhborrow_2.0.2-1.ca2604.1_all.deb Size: 248240 MD5sum: 348ddb54389ca7387e60be78635615fc SHA1: 1503475933b5fcee13e7db31aa8a4cf5d462edc1 SHA256: a79fb825f31952ad67ba1bacbbd3864e79be97cd726063f8421b99a25068ef77 SHA512: 5c115b676f1e9f632c546cb5c5ca7d82ce6a540a565c41bf6e2aaedf302e51bc72ef2e61bd94b5f7fc6a3d7ad27b51b26204c8d0310e60e672b7d05eacafd9de Homepage: https://cran.r-project.org/package=BayesFBHborrow Description: CRAN Package 'BayesFBHborrow' (Bayesian Dynamic Borrowing with Flexible Baseline HazardFunction) Allows Bayesian borrowing from a historical dataset for time-to- event data. A flexible baseline hazard function is achieved via a piecewise exponential likelihood with time varying split points and smoothing prior on the historic baseline hazards. The method is described in Scott and Lewin (2024) , and the software paper is in Axillus et al. (2024) . Package: r-cran-bayesfluxr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesfluxr_0.1.3-1.ca2604.1_all.deb Size: 1065746 MD5sum: fe4b2dd2488dde6c81943283979474ba SHA1: 4bfa6930607b2551a98cfe056acd2423a0ba20e9 SHA256: b790260541940ad5e918ba8dadee0f7ac1c5207711cf12bc5e84073b3eee64c1 SHA512: a425e3603f5dc9f3d91946b2c24d1d758553841643555162a8be47b1b8a2965f647fe988b8769dabcff5c0e18649a8bb2aa90b46001324fd6293b8d488c34d2a Homepage: https://cran.r-project.org/package=BayesFluxR Description: CRAN Package 'BayesFluxR' (Implementation of Bayesian Neural Networks) Implementation of 'BayesFlux.jl' for R; It extends the famous 'Flux.jl' machine learning library to Bayesian Neural Networks. The goal is not to have the fastest production ready library, but rather to allow more people to be able to use and research on Bayesian Neural Networks. Package: r-cran-bayesgof Architecture: all Version: 5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-orthopolynom, r-cran-vgam, r-cran-bolstad2, r-cran-nleqslv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayesgof_5.2-1.ca2604.1_all.deb Size: 311710 MD5sum: 642e94c56201d2c5d3a3ca2049d2b1c3 SHA1: 1ea1a3a62ecad7ac0ba5762246d00e01a35ac408 SHA256: 0111cc82905c9d42d2ade5043f917b770718d1f228159c7eb92a76b1d3188b37 SHA512: 23d85cab37c1f446d4ec74edc1ff2542802becaac04f8fbbb82cb3f47e54e14027912a87a6e505175c89e3f7f31e7380c7b7ef6b995de2fc45be36e9b3ddc2c3 Homepage: https://cran.r-project.org/package=BayesGOF Description: CRAN Package 'BayesGOF' (Bayesian Modeling via Frequentist Goodness-of-Fit) A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (). Package: r-cran-bayesgwqs Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesgwqs_0.1.1-1.ca2604.1_all.deb Size: 146362 MD5sum: 8dae8c7f278ce7ec5d3b98253e4545ff SHA1: 91649b91928dc49403338280e086fb47f37b3b3b SHA256: dcc1de206949628b22b61753b9153a6c8065a105add10afa8546abb3a8d42cd5 SHA512: 4396ea912bd7d81b0364118ea74e953682e9c41ced5291b6578dc562ad2f275bd905ef9f9490b2751428a2fa5f94c16c54e3062f48f585b45e5efe158418101f Homepage: https://cran.r-project.org/package=BayesGWQS Description: CRAN Package 'BayesGWQS' (Bayesian Grouped Weighted Quantile Sum Regression) Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) . Package: r-cran-bayesian Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-parsnip, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-devtools, r-cran-future, r-cran-knitr, r-cran-recipes, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstan, r-cran-spelling, r-cran-testthat, r-cran-workflows Filename: pool/dists/resolute/main/r-cran-bayesian_1.0.1-1.ca2604.1_all.deb Size: 127750 MD5sum: 308c564f83afa468998265a507e2239b SHA1: ce9a71fec6ea946cd3e310dc543896b751cde408 SHA256: 1d13588e2a1bb4a4c5bc821d11561220867bacb3e824edb3c38501c42d6b2dcb SHA512: ee61e50fe2e6b0d425f877209bed1effaf8dc9eb9cd98494fde5d77b1a42c1633f2b9d27771940ea2e79846ec89f14644d3b447deaafa0c1d8f373e1a0762b8e Homepage: https://cran.r-project.org/package=bayesian Description: CRAN Package 'bayesian' (Bindings for Bayesian TidyModels) Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) ). The technical details of 'brms' and 'Stan' are described in Bürkner (2017) , Bürkner (2018) , and Carpenter et al. (2017) . Package: r-cran-bayesiandeb Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-posterior, r-cran-rlang, r-cran-cli, r-cran-ggplot2, r-cran-bayesplot, r-cran-desolve Suggests: r-cran-testthat, r-cran-loo, r-cran-digest, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/resolute/main/r-cran-bayesiandeb_0.1.4-1.ca2604.1_all.deb Size: 316404 MD5sum: 031070efa44c250a392ec328e16237b1 SHA1: 2349a8a41f58b2b5b5edf8eab0a647a0b0360b6c SHA256: 406fa629048cdce081048c1066a6d4868d2e792437930070c179e744c5900721 SHA512: 2dd079c7655e0c0940f0b6ee175381746fd078191be9daf402e3af1cb61f5a466e5c8d1c9e51c34ba78ed58b9a9d193a9d98736520cc985ede3a1c18795e3abf Homepage: https://cran.r-project.org/package=BayesianDEB Description: CRAN Package 'BayesianDEB' (Bayesian Dynamic Energy Budget Modelling) Provides a Bayesian framework for Dynamic Energy Budget (DEB) modelling via 'Stan'. Implements the standard DEB model of Kooijman (2010, ) as a state-space model with Hamiltonian Monte Carlo inference (Carpenter et al., 2017, ). Includes individual-level growth models, growth-reproduction models, hierarchical multi-individual models with partial pooling, and toxicokinetic-toxicodynamic (TKTD) models for ecotoxicology following the DEBtox framework (Jager et al., 2006, ). Supports prior specification from biological knowledge, convergence diagnostics (Vehtari et al., 2021, ), posterior predictive checks, derived quantity estimation, and visualisation via 'ggplot2'. Package: r-cran-bayesiandisaggregation Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-foreach, r-cran-doparallel, r-cran-openxlsx, r-cran-rlang, r-cran-tibble, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-readr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesiandisaggregation_0.1.2-1.ca2604.1_all.deb Size: 127168 MD5sum: 4d013b93456e221c90035dc0c4064e10 SHA1: d41388a778d179b3d873b1ee6d4345edcbe306f9 SHA256: c8a6950d3a745be48af41330d4993b6541e4717d1d54bb3877c92c6298f11f87 SHA512: 1265759bb8753f9d784cefb3da8dc78d55a60905b69b5c1b5ecacafb427ceb476ad627e1dceeae237e2fce4e9204c4544c525bf6d4de6e4185d344a29e745358 Homepage: https://cran.r-project.org/package=BayesianDisaggregation Description: CRAN Package 'BayesianDisaggregation' (Bayesian Methods for Economic Data Disaggregation) Implements a novel Bayesian disaggregation framework that combines Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) dimension reduction of prior weight matrices with deterministic Bayesian updating rules. The method provides Markov Chain Monte Carlo (MCMC) free posterior estimation with built-in diagnostic metrics. While based on established PCA (Jolliffe, 2002) and Bayesian principles (Gelman et al., 2013) , the specific integration for economic disaggregation represents an original methodological contribution. Package: r-cran-bayesianfitforecast Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-readxl, r-cran-xlsx, r-cran-loo, r-cran-openxlsx, r-cran-rstan, r-cran-ggplot2, r-cran-stringr, r-cran-gridextra, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesianfitforecast_1.1.0-1.ca2604.1_all.deb Size: 3060626 MD5sum: 3ba0eca16db16035fb03016feb1a465f SHA1: fd849e440035a71892cc92eca053bca2d989a320 SHA256: 9005d2b0ed23b08ce0de5d4ec48f75f73d246d5d7e7c95ecc1657b537b05306e SHA512: a59a226fa599070c8ece3d336153e052f2cb0aac415483836dcbf2e03594ecdc4a596ac14f70e022dddd23f459ba0fe9869081eb83647fc9992dd5620c73b76e Homepage: https://cran.r-project.org/package=BayesianFitForecast Description: CRAN Package 'BayesianFitForecast' (Bayesian Parameter Estimation and Forecasting forEpidemiological Models) Methods for Bayesian parameter estimation and forecasting in epidemiological models. Functions enable model fitting using Bayesian methods and generate forecasts with uncertainty quantification. Implements approaches described in and . Package: r-cran-bayesiangammareg Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-bayesiangammareg_0.1.1-1.ca2604.1_all.deb Size: 66652 MD5sum: 10b8ee31e12395110fa114f174302a0d SHA1: debe53ec1b21738c3b73862066694dbde484a410 SHA256: bb02ad9679644f9ada8cfed742555ed7c496ca8ffda520c2a566cda86ab7be57 SHA512: cc371b0f951b64a2f3952465ff4f79058ade88c50cfeecb2d28973e3b5bfc48ca455f86a89f67494e16986adf9e1fd0028c604d9659ae4ff6f9f7e9c182a6471 Homepage: https://cran.r-project.org/package=Bayesiangammareg Description: CRAN Package 'Bayesiangammareg' (Double Generalized Gamma Regression Models) Fits double generalized Gamma regression models from a Bayesian perspective, where both the mean and shape parameters are modeled simultaneously using flexible link functions. The methodology is based on Cepeda-Cuervo and Urdinola (2012) and extended in Cepeda-Cuervo (2026), 'Double Generalized Linear Models: Likelihood and Bayesian Methods' (ISBN: 9781041169970). The package provides parameter estimation, model fitting, and model comparison tools, including Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Package: r-cran-bayesianglasso Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-mass Filename: pool/dists/resolute/main/r-cran-bayesianglasso_0.2.0-1.ca2604.1_all.deb Size: 16128 MD5sum: 34368dc2f1c13e883647dd0a563d5d96 SHA1: 4b466981e0f8cd260ea80cb321beb4bdb2918b4a SHA256: 760aab77858d40dc60f6132f84d7249f8479a607437d58a92366114126cc5798 SHA512: 110640ce306c1c427131dd72007dba0d07928747965fc4e7475c73b093b4af592a0d7c7f4e53628a6f662229ef6eda6def73fdfc3d7eac85c09faa6ae8004c32 Homepage: https://cran.r-project.org/package=BayesianGLasso Description: CRAN Package 'BayesianGLasso' (Bayesian Graphical Lasso) Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) . Package: r-cran-bayesianinference Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesianinference_0.0.1-1.ca2604.1_all.deb Size: 410152 MD5sum: 3eae44bbcf6c35f621347e48d3079510 SHA1: da70d6be6bfc09b66732b44abeb133cce4bf74b1 SHA256: cc43822c913e76d7c39210565ef76cd46e87c567f83e2969b8539e5393fca558 SHA512: 4d3d3aee17471bea84aaff26d69c3f64af85595764b12bba8f7011e5cfaa39bf9b148944abd89845f706646c1c793e1fa6577616b1f5f473f32dd3020532f5ec Homepage: https://cran.r-project.org/package=BayesianInference Description: CRAN Package 'BayesianInference' (Bayesian Inference) Beta version of 'Bayesian Inference' (BI) using 'python' and BI. It aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU, GPU, or TPU. Package: r-cran-bayesianlaterality Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tmvtnorm, r-cran-rdpack Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesianlaterality_0.1.2-1.ca2604.1_all.deb Size: 29690 MD5sum: 3d416fd6651fffcba170358c37ffecff SHA1: 493d06511178f25fb31771a1cbbd2c0dc9895911 SHA256: fdd83ed01db141a45e80c29ba78d6243b2fe133b6c15fd5d64bf21998171f065 SHA512: 8f74eb5d00dc8bdee3167cfd9e91689ded32257caf86aa243f50960f93694257cabe691097a87d17220fb682528ce15ea5ecbf7ea6edb17364bf38048aca9464 Homepage: https://cran.r-project.org/package=BayesianLaterality Description: CRAN Package 'BayesianLaterality' (Predict Brain Asymmetry Based on Handedness and DichoticListening) Functional differences between the cerebral hemispheres are a fundamental characteristic of the human brain. Researchers interested in studying these differences often infer underlying hemispheric dominance for a certain function (e.g., language) from laterality indices calculated from observed performance or brain activation measures . However, any inference from observed measures to latent (unobserved) classes has to consider the prior probability of class membership in the population. The provided functions implement a Bayesian model for predicting hemispheric dominance from observed laterality indices (Sorensen and Westerhausen, Laterality: Asymmetries of Body, Brain and Cognition, 2020, ). Package: r-cran-bayesianmcpmod Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2321 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dosefinding, r-cran-dplyr, r-cran-ggplot2, r-cran-logistf, r-cran-nloptr, r-cran-rbest, r-cran-tidyr Suggests: r-cran-clindr, r-cran-dofuture, r-cran-future.apply, r-cran-kableextra, r-cran-knitr, r-cran-mcpmodpack, r-cran-reactable, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-bayesianmcpmod_1.3.2-1.ca2604.1_all.deb Size: 1102032 MD5sum: 5b4f57854161e912a429c6000d4b12fb SHA1: 750bb2dea120d682c6ad85f780d4a7cdb62ffd74 SHA256: 0d2bfb1479d28423d4b1b8d7365b63d59e34327f6fd4b543d089b74b4ffd1298 SHA512: 1cf74209d9f397aa5baecdfe639d8f4410f0930670aca8ad3a00a7ace7b859586eba0dc9fe7b7f783dfe8dddb218018872dd3c25db25b7f99e298741dc4f6181 Homepage: https://cran.r-project.org/package=BayesianMCPMod Description: CRAN Package 'BayesianMCPMod' (Simulate, Evaluate, and Analyze Dose Finding Trials withBayesian MCPMod) Bayesian MCPMod (Fleischer et al. (2022) ) is an innovative method that improves the traditional MCPMod by systematically incorporating historical data, such as previous placebo group data. This package offers functions for simulating, analyzing, and evaluating Bayesian MCPMod trials with normally and binary distributed endpoints. It enables the assessment of trial designs incorporating historical data across various true dose-response relationships and sample sizes. Robust mixture prior distributions, such as those derived with the Meta-Analytic-Predictive approach (Schmidli et al. (2014) ), can be specified for each dose group. Resulting mixture posterior distributions are used in the Bayesian Multiple Comparison Procedure and modeling steps. The modeling step also includes a weighted model averaging approach (Pinheiro et al. (2014) ). Estimated dose-response relationships can be bootstrapped and visualized. Package: r-cran-bayesianmediationa Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-r2jags, r-cran-car, r-cran-gplots, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayesianmediationa_1.0.1-1.ca2604.1_all.deb Size: 212652 MD5sum: 06ec7d75be491ea2f3463cc446a84764 SHA1: c406027fea3b021cd800d3b301155cc9482576cc SHA256: ad8b08aae35dce1755c1cad42977f7ba9a9e283651321244d35c7de721165896 SHA512: c0a0e3773a29d9bf9926ac98e1e478b8871bc407562bc1f6e36f4fde39a5771c76eacc9d2c378a9333740a741fe3fe01542b9a8eb3a6141b39ab64ae181b8a23 Homepage: https://cran.r-project.org/package=BayesianMediationA Description: CRAN Package 'BayesianMediationA' (Bayesian Mediation Analysis) We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results. Package: r-cran-bayesiannetwork Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4810 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-heatmaply, r-cran-lattice, r-cran-networkd3, r-cran-plotly, r-cran-rintrojs, r-cran-shiny, r-cran-shinyace, r-cran-shinydashboard, r-cran-shinywidgets Suggests: r-cran-chromote, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shinytest2 Filename: pool/dists/resolute/main/r-cran-bayesiannetwork_0.4-1.ca2604.1_all.deb Size: 2797762 MD5sum: dfda5102fd9eb7835c380dd8d7df1651 SHA1: f93aeee65d3c8198c0c3c09ec0c0245e109d4853 SHA256: abcce58b1c7df2f1e2f0a0bfc2bda28bdafe4dfa0332aac4029274913aaa00ea SHA512: 7b6a29d59a2b0c817d29d2421974573c6fd078365d3e4766d62c6716ac3f363ed4b75df446b96dc6ff64b5f7483495c492e98672621799d0da75c9c856dfa64f Homepage: https://cran.r-project.org/package=BayesianNetwork Description: CRAN Package 'BayesianNetwork' (Bayesian Network Modeling and Analysis) A "Shiny"" web application for creating interactive Bayesian Network models, learning the structure and parameters of Bayesian networks, and utilities for classic network analysis. Package: r-cran-bayesianou Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rstan, r-cran-loo, r-cran-posterior, r-cran-ggplot2, r-cran-tidyr, r-cran-openxlsx, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesianou_0.1.3-1.ca2604.1_all.deb Size: 116290 MD5sum: bc9b848d38145033d8beae0763f455bf SHA1: 8e760bb829bd72677f7e38f4e989369f3ba7267f SHA256: f0645fb5882b6cc235d43f44d487379cd3235bb989192a51f29347faee6e9a90 SHA512: 78cac3225e868992926eb6c5b140cf796a19746c2766f1c252eedeeedb83b603754090175a25b6ec3e35c54d23a78a151f95df385785826fe23d6dd6a872ae5a Homepage: https://cran.r-project.org/package=bayesianOU Description: CRAN Package 'bayesianOU' (Bayesian Nonlinear Ornstein-Uhlenbeck Models with StochasticVolatility) Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift, stochastic volatility, and Student-t innovations. The package implements hierarchical priors for sector-specific parameters and supports parallel MCMC sampling via 'Stan'. Model comparison is performed using Pareto Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation following Vehtari, Gelman, and Gabry (2017) . Prior specifications follow recommendations from Gelman (2006) for scale parameters. Package: r-cran-bayesianpower Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesianpower_0.2.3-1.ca2604.1_all.deb Size: 46510 MD5sum: 2796ab310af1b42d5de7801f92ae9d7f SHA1: cd38521d102663d7bcbc96412ae2ef4883d2cf4e SHA256: 15babd91f6fe06aea8fbe3db66940a1c1dac794efdb6b154e8f5d16739f196cc SHA512: 9018e8752278ac176c76d47d8b75756d56cb757e040b3f9a5b6e7f33a6d95f5c4cd2f4af8ef8f351933afc2107d286d8a6d8c5f01814af40ab058732b003965e Homepage: https://cran.r-project.org/package=BayesianPower Description: CRAN Package 'BayesianPower' (Sample Size and Power for Comparing Inequality ConstrainedHypotheses) A collection of methods to determine the required sample size for the evaluation of inequality constrained hypotheses by means of a Bayes factor. Alternatively, for a given sample size, the unconditional error probabilities or the expected conditional error probabilities can be determined. Additional material on the methods in this package is available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) . Package: r-cran-bayesianqdm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-bayesianqdm_0.1.0-1.ca2604.1_all.deb Size: 744696 MD5sum: 25e6f5dff3a6a90e594f9653dec734a1 SHA1: 95ee32561fe8c217b3e9f6ae3a0d589785c9dc1c SHA256: 7762807db70ac662d72517f45972aaa8a364e94722fcd1ff2c453cc5179f9048 SHA512: cbea167ed42a74c651df3f6377cfd845019fdc777e2495be9646a236258e0506bdefb8575b086c5a366d2ded4041be3a9c4e231631b927f081ce7e3980ffcb7b Homepage: https://cran.r-project.org/package=BayesianQDM Description: CRAN Package 'BayesianQDM' (Bayesian Quantitative Decision-Making Framework for Binary andContinuous Endpoints) Provides comprehensive methods to calculate posterior probabilities, posterior predictive probabilities, and Go/NoGo/Gray decision probabilities for quantitative decision-making under a Bayesian paradigm in clinical trials. The package supports both single and two-endpoint analyses for binary and continuous outcomes, with controlled, uncontrolled, and external designs. For single continuous endpoints, three calculation methods are available: numerical integration (NI), Monte Carlo simulation (MC), and Moment-Matching approximation (MM). For two continuous endpoints, a bivariate Normal-Inverse-Wishart conjugate model is implemented with MC and MM methods. For two binary endpoints, a Dirichlet-multinomial model is implemented. External designs incorporate historical data through power priors using exact conjugate representations (Normal-Inverse-Chi-squared for single continuous, Normal-Inverse-Wishart for two continuous, and Dirichlet for binary endpoints), enabling closed-form posterior computation without Markov chain Monte Carlo (MCMC) sampling. This approach significantly reduces computational burden while preserving complete Bayesian rigor. The package also provides grid-search functions to find optimal Go and NoGo thresholds that satisfy user-specified operating characteristic criteria for all supported endpoint types and study designs. S3 print() and plot() methods are provided for all decision probability classes, enabling formatted display and visualisation of Go/NoGo/Gray operating characteristics across treatment scenarios. See Kang, Yamaguchi, and Han (2026) for the methodological framework. Package: r-cran-bayesianreasoning Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2008 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext, r-cran-gt, r-cran-magrittr, r-cran-png, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-cran-tidyr Suggests: r-cran-curl, r-cran-httr, r-cran-knitr, r-cran-patchwork, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-webshot2 Filename: pool/dists/resolute/main/r-cran-bayesianreasoning_0.4.3-1.ca2604.1_all.deb Size: 1458306 MD5sum: 4fddafe98f0f665e2d6520271da571a0 SHA1: 37c673db6785bf097bd07bd23ea73ebe4ba31f47 SHA256: beaf9245771bba8449949e3634f251b57dd8cce4ccb50a7047d6ad88300b52c5 SHA512: 04103be6a1d2dcf2ae0f7a0ce47be45750e88fb5252efa7810a9bc58cce96656133c3b3a9d9d408bb652b9384f5eccd19cbb4068e3b8e9c6b000f9f7dc5ebc82 Homepage: https://cran.r-project.org/package=BayesianReasoning Description: CRAN Package 'BayesianReasoning' (Plot Positive and Negative Predictive Values for Medical Tests) Functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding . Package: r-cran-bayesiansurpriser Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sf, r-cran-scales, r-cran-rlang, r-cran-cli, r-cran-mass, r-cran-rcolorbrewer Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tibble, r-cran-vdiffr, r-cran-tidycensus, r-cran-tigris, r-cran-cancensus, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-bayesiansurpriser_0.1.0-1.ca2604.1_all.deb Size: 2335832 MD5sum: 3663c93f2f2ae4b309178fc14f3544b7 SHA1: b39ef8cd9396ee3c99083e2a387f9a0b7c91da16 SHA256: d4ace497e85824eceb18349497565b20d68f10f97b38879b3c0706f8d72061fc SHA512: 883818e6f16e576baa883b8323788134054e441d9ad012f51a66cb8ae2298cb7ff0d4944f10118115f5edf14035eaaa359b3667cd0d8f6ceea559277422411e3 Homepage: https://cran.r-project.org/package=bayesiansurpriser Description: CRAN Package 'bayesiansurpriser' (Bayesian Surprise for De-Biasing Thematic Maps) Implements Bayesian Surprise methodology for data visualization, based on Correll and Heer (2017) "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides tools to weight event data relative to spatio-temporal models, highlighting unexpected patterns while de-biasing against known factors like population density or sampling variation. Integrates seamlessly with 'sf' for spatial data and 'ggplot2' for visualization. Supports temporal/streaming data analysis. Package: r-cran-bayesiantreg Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-bayesiantreg_1.0.1-1.ca2604.1_all.deb Size: 102882 MD5sum: ad0cb86d7ed3df0e545c85b372ddc1df SHA1: bd24d8046462cbbeb78a1891c274125e5e16cc11 SHA256: 0bcca7b8fc62ab53d69705175217a79a418db3be8580b51448cb01af47958a38 SHA512: a0f22a9c79c87a69ddc9e8c64ade1e3552fa5eed522169a1e56840e3c680ba4312e1dfcab530879ec878d1ff7b4434e85b37be4d0eb510a72d1d47aef16e5830 Homepage: https://cran.r-project.org/package=Bayesiantreg Description: CRAN Package 'Bayesiantreg' (Bayesian t Regression for Modeling Mean and Scale Parameters) Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated. Package: r-cran-bayesics Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-janitor, r-cran-extradistr, r-cran-mvtnorm, r-cran-matrix, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-patchwork, r-cran-bms, r-cran-cluster, r-cran-dfba, r-cran-tibble, r-cran-survival Suggests: r-cran-rstanarm, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesics_2.1.1-1.ca2604.1_all.deb Size: 557080 MD5sum: 714477400273dbc65a4b2a7a87fbfe9d SHA1: b69a193454f5e4f47397226eb2aaa1e15a4470aa SHA256: b4b464cdf60cf7ebc8537cc2eec715cbcef8c4a070597ebf1609910447959d27 SHA512: 458b0b62c9b15d80dbfdb33ad26136c9301fe9b04325a3a85329f8366910b1e57bbc2e9407975e51ceca817253ab270d2e3ef2506c120cc26fb9c78e13003808 Homepage: https://cran.r-project.org/package=bayesics Description: CRAN Package 'bayesics' (Bayesian Analyses for One- and Two-Sample Inference andRegression Methods) Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) or by Chib's method as given in xxx. Interpretations are from Kass and Raftery (1995) . ROPE bounds are based on discussions in Kruschke (2018) . Methods for determining the number of posterior samples required are described in Doss et al. (2014) . Bayesian model averaging is done in part by Feldkircher and Zeugner (2015) . Methods for contingency table analysis is described in Gunel et al. (1974) . Variational Bayes (VB) methods are described in Salimans and Knowles (2013) . Mediation analysis uses the framework described in Imai et al. (2010) . The loss-likelihood bootstrap used in the non-parametric regression modeling is described in Lyddon et al. (2019) . Non-parametric survival methods are described in Qing et al. (2023) . Methods used for the Bayesian Wilcoxon signed-rank analysis is given in Chechile (2018) and for the Bayesian Wilcoxon rank sum analysis in Chechile (2020) . Correlation analysis methods are carried out by Barch and Chechile (2023) , and described in Lindley and Phillips (1976) and Chechile and Barch (2021) . See also Chechile (2020, ISBN: 9780262044585). Package: r-cran-bayeslca Architecture: all Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-coda, r-cran-fields, r-cran-nlme, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-bayeslca_1.9-1.ca2604.1_all.deb Size: 168248 MD5sum: ba93d73f56b188f9dae50576d46a03af SHA1: bd96950ebaf087fd1a27a8fc93915e9c8ec8ad79 SHA256: 15ef158e33faaf63066a532c6505c41c61bc867a3ecd1c31b49404f2ac844200 SHA512: 2f9998833362f7317647513d77a872eeae23ff55d07f7e26eadcca65004db5f647eda7279668ebdcc2a00599302cb239a0f3383c7ac5c066541d8cf4956f9cd0 Homepage: https://cran.r-project.org/package=BayesLCA Description: CRAN Package 'BayesLCA' (Bayesian Latent Class Analysis) Bayesian Latent Class Analysis using several different methods. 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As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components in the hazard function. Package: r-cran-bayesmlogit Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-bayesmlogit_1.0.1-1.ca2604.1_all.deb Size: 554644 MD5sum: f46d0b7de75899338268235900dcef3d SHA1: a51d82f8b89cfa0aaba36f42c021cbfc459fb2e2 SHA256: 4675167369313115e205d2ccf7c1fd3940eac928f0d1212524abd326ed2160d1 SHA512: 93d7cb0e03ddaf299cc9c43cb95fb3fa31b47f0e4fd84c43f5717e0378e6f850ea70387feaadee4b4f486bf33de0fbdea2ba635276856fcd71bde99c3b6608f5 Homepage: https://cran.r-project.org/package=bayesmlogit Description: CRAN Package 'bayesmlogit' (A Multistate Life Table (MSLT) Methodology Based on BayesianApproach) Create life tables with a Bayesian approach, which can be very useful for modelling a complex health process when considering multiple predisposing factors and multiple coexisting health conditions. Details for this method can be found in: Lynch, Scott, et al., (2022) ; Zang, Emma, et al., (2022) . 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Models supported include numerous well-established approaches introduced in the actuarial and demographic literature, such as the Lee-Carter (1992) , the Cairns-Blake-Dowd (2009) , the Li-Lee (2005) , and the Plat (2009) models. The package is designed to analyse stratified mortality data structured as a 3-dimensional array of dimensions p × A × T (strata × age × year). Stratification can represent factors such as cause of death, country, deprivation level, sex, geographic region, insurance product, marital status, socioeconomic group, or smoking behavior. While the primary focus is on analysing stratified data (p > 1), the package can also handle mortality data that are not stratified (p = 1). Model selection via the Deviance Information Criterion (DIC) is supported. Package: r-cran-bayesmortalityplus Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-progress, r-cran-tidyr, r-cran-scales, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-bayesmortalityplus_1.0.0-1.ca2604.1_all.deb Size: 682522 MD5sum: 39aaef5bfa958806e7b2a4388a7327fa SHA1: 3bd09cf23de6e0caff8a3ffbda71158bbd044096 SHA256: f781a1f25de8c4825f3d624eaf87aa882eb253d613cec6a41f3935fdfda25fcd SHA512: 0d8ca3657fca2f9dacc729df117d44be04e3db47e437c5793407f494c768221cf58e9aaa03f0e09f54567330f3802cb8f844f00bc4f7ff23d1e201ce75387dd7 Homepage: https://cran.r-project.org/package=BayesMortalityPlus Description: CRAN Package 'BayesMortalityPlus' (Bayesian Mortality Modelling) Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) and Dellaportas, Petros, et al. (2001) , and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) ). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) . The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) and Pedroza, C. (2006) . Journal publication available at . 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First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference. 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The posterior for the model parameters, namely the overall mean vector and the between-study covariance matrix, are assessed by constructing Markov chains based on the Metropolis-Hastings algorithms as developed in Bodnar and Bodnar (2021) (). The Metropolis-Hastings algorithm is designed under the assumption of the normal distribution and the t-distribution when the Berger and Bernardo reference prior and the Jeffreys prior are assigned to the model parameters. Convergence properties of the generated Markov chains are investigated by the rank plots and the split hat-R estimate based on the rank normalization, which are proposed in Vehtari et al. (2021) (). Package: r-cran-bayesnec Architecture: all Version: 2.1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-ggplot2, r-cran-formula.tools, r-cran-loo, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-evaluate, r-cran-rlang, r-cran-chk Suggests: r-cran-rstan, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesnec_2.1.3.1-1.ca2604.1_all.deb Size: 4648554 MD5sum: 5a48a7af13533e39c540a3581fc18bfa SHA1: 19863f3092c75749a3ace722c2be0e3a98ab0c8c SHA256: e845e219845cefaf9c553970897768c3262dcf99ed6ef45b640395cfbd1482b2 SHA512: 08566dd828dd514d61b1f7396708d19c9460d1f344620a353c60cc73781eb8a442a6f42d49b8cff7fcb43fd4271f78b2b9365ea34882708773be6efcdfcb0046 Homepage: https://cran.r-project.org/package=bayesnec Description: CRAN Package 'bayesnec' (A Bayesian No-Effect- Concentration (NEC) Algorithm) Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017); Burkner (2018); Carpenter 'et al.' (2017) to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECx' values, but more particularly 'NEC' (see Fox (2010)), 'NSEC' (see Fisher and Fox (2023)), and 'N(S)EC (see Fisher et al. 2023). A full description of this package can be found in Fisher 'et al.' (2024). This package expands and supersedes an original version implemented in 'R2jags' (see Su and Yajima (2020); Fisher et al. (2020)). Package: r-cran-bayesnetbp Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rcolorbrewer, r-cran-fields, r-cran-doby, r-bioc-graph, r-cran-bnlearn Suggests: r-bioc-rgraphviz, r-cran-shiny, r-cran-googlevis, r-cran-cyjshiny, r-cran-qtl, r-cran-qtlnet Filename: pool/dists/resolute/main/r-cran-bayesnetbp_1.6.1-1.ca2604.1_all.deb Size: 285262 MD5sum: 3b46ee21456a2062abb3a5c212cb4e25 SHA1: f54b5def5e81106624305fe91c0381e93d3608ad SHA256: 734ec915dcf5fe2b7e29f07a144a9c0fc6afed888faed9834f670cea6f53a8d7 SHA512: 3895c0d2728af1a857ae1e8986edc4de52e39ca98738cb1d8a8c60602e0a89f7179d3df5b045f05ad8b769ae0758dced35d8ab55d45b07091266877ea79775c9 Homepage: https://cran.r-project.org/package=BayesNetBP Description: CRAN Package 'BayesNetBP' (Bayesian Network Belief Propagation) Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks . For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks . The optional 'cyjShiny' package for running the Shiny app is available at . Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub. Package: r-cran-bayesnsgp Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-fnn, r-cran-matrix, r-cran-statmatch Filename: pool/dists/resolute/main/r-cran-bayesnsgp_0.2.0-1.ca2604.1_all.deb Size: 469006 MD5sum: 7efd75b101641919744efa278fca6c49 SHA1: 10853203e2545cdc68250cff656afdeec8419513 SHA256: 04d7aabeb15549911db38915bd9aa993b98d1bff6e27289f61e665e71730ffed SHA512: e2b716d6c3f7bdd36c8d65a1d9b917f7dad96a7c494345c7b7a27744b6667015539ffd22ebc0ebd81cdd55d818ff8597f20a21159bf5574cfce34ed0c019b0c2 Homepage: https://cran.r-project.org/package=BayesNSGP Description: CRAN Package 'BayesNSGP' (Bayesian Analysis of Non-Stationary Gaussian Process Models) Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) ). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step. Package: r-cran-bayespiecehazselect Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-bayespiecehazselect_1.1.0-1.ca2604.1_all.deb Size: 90302 MD5sum: d93dfb6acb6160e3e662f7d5fc6c0a6c SHA1: 1937dc45356fb41dcbfa672efe89a49267448af2 SHA256: 2310fe223d8c3dca13ad3d0dcd89bf9ce3d464aba21f16152f228a1613dfbb9b SHA512: 29e24a0de716bba533aa628e8afe23b1c32d373d3f1a0b9fbe6f372b1c47add3e2161983da7d778ff210dfd7644be6d87d8117aa3a2c95239e1ea5d05eb9734e Homepage: https://cran.r-project.org/package=BayesPieceHazSelect Description: CRAN Package 'BayesPieceHazSelect' (Variable Selection in a Hierarchical Bayesian Model for a HazardFunction) Fits a piecewise exponential hazard to survival data using a Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive formulation for the spatial dependency in the hazard rates for each piece. This function uses Metropolis- Hastings-Green MCMC to allow the number of split points to vary and also uses Stochastic Search Variable Selection to determine what covariates drive the risk of the event. This function outputs trace plots depicting the number of split points in the hazard and the number of variables included in the hazard. The function saves all posterior quantities to the desired path. Package: r-cran-bayesplay Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gginnards Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-vdiffr, r-cran-ggplot2, r-cran-patrick Filename: pool/dists/resolute/main/r-cran-bayesplay_0.9.3-1.ca2604.1_all.deb Size: 929696 MD5sum: 3f9ab8fcf86ad5277d682bd2e31278e7 SHA1: 052f5c41c83736c8fd86cad8ffade52ebe255ba3 SHA256: 4417f7ba9b1b567bcc42f5115c57ac76da8f99aea29a30976661b6c8e8392d87 SHA512: 2093f9d0bb1a241f9d134256e0ab743d0fb3f7b63e6de7256f08410e3eb82b0025ed0bd94d2e1f3f2051f19970ec530a35e8831b7c7149c42bdf558be7746e4d Homepage: https://cran.r-project.org/package=bayesplay Description: CRAN Package 'bayesplay' (The Bayes Factor Playground) A lightweight modelling syntax for defining likelihoods and priors and for computing Bayes factors for simple one parameter models. It includes functionality for computing and plotting priors, likelihoods, and model predictions. Additional functionality is included for computing and plotting posteriors. Package: r-cran-bayesplot Architecture: all Version: 1.15.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-glue, r-cran-posterior, r-cran-reshape2, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggdist, r-cran-ggfortify, r-cran-gridextra, r-cran-hexbin, r-cran-knitr, r-cran-loo, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-rstan, r-cran-rstanarm, r-cran-rstantools, r-cran-scales, r-cran-shinystan, r-cran-survival, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-bayesplot_1.15.0-1.ca2604.1_all.deb Size: 5650854 MD5sum: d14db9bc56dec2166d28e654c938dff0 SHA1: 8b9245f479b4bacf80519cc70ffd652d9d61c37c SHA256: b020ba322780cb20fb118a741ebd03511d1fdf5d34cc5904a9283ecdb36eb22d SHA512: 4eba9c458fa2ea40596584dc774dece4a7c3f4f72021b9fce577d85550ae6df8f0b98168234f1b001072daee76f5bb39bca226b4597abc981504234391d70731 Homepage: https://cran.r-project.org/package=bayesplot Description: CRAN Package 'bayesplot' (Plotting for Bayesian Models) Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) . The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'. Package: r-cran-bayespm Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-extradistr, r-cran-rmutil, r-cran-invgamma Filename: pool/dists/resolute/main/r-cran-bayespm_0.2.0-1.ca2604.1_all.deb Size: 446650 MD5sum: 8b5b4ab1cc6e43fe6835e971d30c4ae5 SHA1: dfe767372e8661fc07b5da2bc5ed0c80fc6b4720 SHA256: 59ff2f34a1b57a3ce7d5ea8dbe1d6aa4c06499ea10240c9755cbe6e194edd4fc SHA512: fb9818a1c265e6d88143c8438da60ad573330e0ab8b52ada64866204c16c9213de81148492f53a081da950245f1c5587326caf9124c6fdd3d8b464a14af9962b Homepage: https://cran.r-project.org/package=bayespm Description: CRAN Package 'bayespm' (Bayesian Statistical Process Monitoring) The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" , Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" , Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" . Package: r-cran-bayespmtools Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastlogisticregressionwrap, r-cran-logitnorm, r-cran-mc2d, r-cran-mcmapper, r-cran-proc, r-cran-cobs, r-cran-oor, r-cran-quantreg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-bayespmtools_0.0.1-1.ca2604.1_all.deb Size: 201596 MD5sum: b9c7c93ace59699dbc483cdae6fd5244 SHA1: 55a66ae9de7925cf629cc8ee111320e63d21d7e4 SHA256: 9700c8b7f405c89290d77e7aaf81791e2d81c92aaa610d48b21bcaf1c2f48451 SHA512: 9155a914f4e06745e91f93bb28f117469397e75655e35566e9bf3ad016a63459c2c0c643fec1924318862e1ef4f39262b802278dbc276cd9751374962d54facc Homepage: https://cran.r-project.org/package=bayespmtools Description: CRAN Package 'bayespmtools' (Bayesian Sample Size and Precision Considerations for RiskPrediction Models) Performs Bayesian sample size, precision, and value-of-information analysis for external validation of existing multi-variable prediction models using the approach proposed by Sadatsafavi and colleagues (2025) . Package: r-cran-bayespocket Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gigrvg, r-cran-truncnorm, r-cran-progress, r-cran-stochtree, r-cran-softbart, r-cran-pbmcapply Filename: pool/dists/resolute/main/r-cran-bayespocket_0.1.0-1.ca2604.1_all.deb Size: 64040 MD5sum: 684c25270ae3ca033e873f94d8e706c6 SHA1: 868d4106b05d08e768c122d7298cc586b52dcbc5 SHA256: c8aa26ee9ee49fb3d45bc88472ecd509a65065afe75d2fe125b5db5ead945d82 SHA512: 3fd1ff443c17c66303a3db2985f95c06d6b7dc6239b29f25b5e20711101cd901172891ac243b5fa39c98183ada780ba83212bacbd94cf070c1e2f401faeeacbb Homepage: https://cran.r-project.org/package=BayesPocket Description: CRAN Package 'BayesPocket' (Bayesian Causal Inference for Periodontal Diseases inLongitudinal Studies) Implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian framework for estimating causal effects of clinical interventions on bounded continuous outcomes in longitudinal observational studies with irregular visits. The methodology is specifically designed for periodontal disease research, where discrete treatments and continuous disease states (e.g., proportion of periodontal pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. The package integrates a double-censored Tobit likelihood to handle boundary mass at zero and one, subject-specific random effects to capture within-subject correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) to model complex nonlinear interactions without parametric restrictions. Causal identification is established under the potential outcomes framework via the G-computation formula, with key estimands including the Mixed Average Potential Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The package provides functions for model fitting, posterior inference, and causal estimand estimation. Package: r-cran-bayespostest Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1329 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cardata, r-cran-catools, r-cran-coda, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2, r-cran-rlang, r-cran-texreg, r-cran-tidyr, r-cran-hdinterval, r-cran-rocr, r-cran-r2jags, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-covr, r-cran-runjags, r-cran-rstanarm, r-cran-mcmcpack, r-cran-r2winbugs, r-cran-brms Filename: pool/dists/resolute/main/r-cran-bayespostest_0.4.0-1.ca2604.1_all.deb Size: 878232 MD5sum: 4dbd31ed48435a07732a022fccf6f569 SHA1: 82fea81de8de64e256885a9e395d4e963b56472a SHA256: 97e79dd2c6b39309f3dea50e83e1cb54cc635aa5fcebf6a4ef47586b5680e77b SHA512: 8b244221e9a8c41fb188e0e71480a1442e4c0453531bb1def56ef048070419f09a28f467acf0496e3e9e9a353dc05df587cb019f447c69d615edab42d7efef9b Homepage: https://cran.r-project.org/package=BayesPostEst Description: CRAN Package 'BayesPostEst' (Generate Postestimation Quantities for Bayesian MCMC Estimation) An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 ), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) , the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 ), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 ). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'. Package: r-cran-bayesppr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesppr_0.1.0-1.ca2604.1_all.deb Size: 96404 MD5sum: aaf34873230c91ff9ca9c19ab3d7a071 SHA1: 86c2ef4d629a54830c08d0c714f32014d77cf1f7 SHA256: 8d90753996e3e5f07330c56e87f6b66189f6c13b5b960cb153ca081e63cf0d27 SHA512: 13308a89d03af6f01f6ef8508740a8e7a7b927b1b7a2262a0d3cd675879045481df209f08e6c8801fdbc97b5e2ec7e2f34e18cc6ab9134516ace3aaeac1e3973 Homepage: https://cran.r-project.org/package=BayesPPR Description: CRAN Package 'BayesPPR' (Bayesian Projection Pursuit Regression) Bayesian fitting of projection pursuit regression model. Built to handle continuous and categorical inputs and scalar output (Collins et al., 2023 ). Package: r-cran-bayesrecon Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-forecast, r-cran-glarma, r-cran-scoringrules, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesrecon_1.0.1-1.ca2604.1_all.deb Size: 2851716 MD5sum: e832cf7aa06395c5c7a7e20852944253 SHA1: 673a55443eeeb8f5cb6a6481e24330c54af1289c SHA256: 575eb9c17e69be088b8c19f2cd9d3fb06546a33156f7147f842b6badb4809d5f SHA512: a52e5689d3616f013b1c6990b6b4682bcb77605de0790ac67eb9519aba0d18cbb358efab32ef92e691049026651866401f29053f783441f812ddcf580fd85fbf Homepage: https://cran.r-project.org/package=bayesRecon Description: CRAN Package 'bayesRecon' (Probabilistic Reconciliation via Conditioning) Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) , MCMC reconciliation of count time series (Corani et al., 2024) , Bottom-Up Importance Sampling (Zambon et al., 2024) , methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024) , analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) . Package: r-cran-bayesreg Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pgdraw, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-bayesreg_1.3-1.ca2604.1_all.deb Size: 310474 MD5sum: 7d2212a09ae911e92b102305e9861fa0 SHA1: 7bd71b7a0c348e54063fabea3da634e67c7db7ed SHA256: 1dddac9127142284f8ddc7b735de3b9015c47227bf1efbca40343468f81fe177 SHA512: bdf2638da16e70722b75bcc9bf4213c5dfbd1922f9310781aa1b9469c2b91caedf2de1e135c03673d476cf0560bfd01c89de85226a08392b829676d79f337b25 Homepage: https://cran.r-project.org/package=bayesreg Description: CRAN Package 'bayesreg' (Bayesian Regression Models with Global-Local Shrinkage Priors) Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) . Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) . Package: r-cran-bayesrep Architecture: all Version: 0.42.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lamw, r-cran-hypergeo Suggests: r-cran-roxygen2, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-bayesrep_0.42.2-1.ca2604.1_all.deb Size: 126808 MD5sum: 550e6c663ffefe8cc2277af788117c78 SHA1: 9e5d2a591476e403a74edf38e986c770517243ec SHA256: e57889b041784521f24a519af5e289bd599da09197243b0644e54e291bb497be SHA512: 024fbdbea8eca1155907a30b230ceb5fbf7b32bed3622d6c40af10112ae16e7f2323c634048651f243a5c8e600a13cb096b4c9b60ab56aee165c8f1018af681f Homepage: https://cran.r-project.org/package=BayesRep Description: CRAN Package 'BayesRep' (Bayesian Analysis of Replication Studies) Provides tools for the analysis of replication studies using Bayes factors (Pawel and Held, 2022) . Package: r-cran-bayesrepdesign Architecture: all Version: 0.42-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lamw Suggests: r-cran-roxygen2, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-bayesrepdesign_0.42-1.ca2604.1_all.deb Size: 158718 MD5sum: 8d6d2ad6b7da03c493da67bd956af399 SHA1: 328db91842a2328f8656978588f49590af6fed3c SHA256: 781901a523953e046771ca336c730687292a7e7748a9ac1dc6409b6f4239f5cb SHA512: d4567372f2b1778a39821df978a06f1f90f3f4cf630884eb9a7e13c75c0146da6388cc5a22790fcabc54a7d64616a2e08c2e55d57844247add58c1bbc8267f25 Homepage: https://cran.r-project.org/package=BayesRepDesign Description: CRAN Package 'BayesRepDesign' (Bayesian Design of Replication Studies) Provides functionality for determining the sample size of replication studies using Bayesian design approaches in the normal-normal hierarchical model (Pawel et al., 2022) . Package: r-cran-bayesroe Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colourpicker, r-cran-config, r-cran-ggplot2, r-cran-golem, r-cran-scales, r-cran-shiny, r-cran-shinybs Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesroe_0.2-1.ca2604.1_all.deb Size: 76730 MD5sum: ce2fc412dda3ac03ae64fa886f3dd08f SHA1: 70639f163c6706e553163e83726e5e8edf636229 SHA256: 1a2abf795d31d9a80b9bd8a641f7ea32548294a3a84616a3a7b40d9c6c95dfc5 SHA512: d35ef6881884e6fc149fef219ebe5c257536151cfc977f4c547c896668d2a637071871fca596807049f5c89278e9ef755029b2844998f58fdad43a327d508707 Homepage: https://cran.r-project.org/package=bayesROE Description: CRAN Package 'bayesROE' (Bayesian Regions of Evidence) Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (). Besides generic functions, the package also provides an intuitive 'Shiny' application, that can be run in local R environments. Package: r-cran-bayesrs Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-ggplot2, r-cran-metrology, r-cran-reshape, r-cran-coda Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesrs_0.1.3-1.ca2604.1_all.deb Size: 785420 MD5sum: 7cad09f6e3a8bc3c9bd20a6927c8f46b SHA1: 7b9fee9d8a125b91de97484fac252fbb7fb8a6b2 SHA256: 3f5bc564f888919111efc2130721098ff25043505c5c07ed70bea0e8c465a046 SHA512: 623646215abb49e6b7bc1720c9fe15a0e7e251aef9d8ab64132a727b2332afbaca9065c52bd85e88b8173749c1f791d4886ac0bfa379ac7a6f90ea4e0a3663c5 Homepage: https://cran.r-project.org/package=BayesRS Description: CRAN Package 'BayesRS' (Bayes Factors for Hierarchical Linear Models with ContinuousPredictors) Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2017, ). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, ). Package: r-cran-bayesrules Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-janitor, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rstanarm, r-cran-e1071, r-cran-groupdata2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayesrules_0.0.3-1.ca2604.1_all.deb Size: 2147386 MD5sum: 34a7da932f354383dbbaff228da121ef SHA1: 7885d9ef34ded6c29d588f896342a43f6e7caa5f SHA256: 091fdd7d37ef5fbd04a55322d99b2ae0966561e601f2ef1d5c021ee96ab89c10 SHA512: 03646f2748f412e9067973415fe81d7e9984c5030d65b1530d7af24769ae43c33906bb0250d079d2cf65935c69a01219cea9e97f243ed29d76b93b9b38a92ded Homepage: https://cran.r-project.org/package=bayesrules Description: CRAN Package 'bayesrules' (Datasets and Supplemental Functions from Bayes Rules! Book) Provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models. Package: r-cran-bayess5 Architecture: all Version: 1.41-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-snowfall, r-cran-abind, r-cran-splines2 Filename: pool/dists/resolute/main/r-cran-bayess5_1.41-1.ca2604.1_all.deb Size: 158292 MD5sum: b31329a41f784c385b1f01ff3214a913 SHA1: cef6e1798c02b42270a161de226a697cfd2aeb65 SHA256: 383e39f285660851f91feb4c7b89984a10f60f86b3ed082d252e8702571e736e SHA512: 9aafcecdec22182832bb66e792006f9c765c6a952b64b1c16a411c4eb24517a5cc034341ead2844ebfdc7a28ec2a0e393bf5c4ec75c0a74daed5deba7d783c53 Homepage: https://cran.r-project.org/package=BayesS5 Description: CRAN Package 'BayesS5' (Bayesian Variable Selection Using Simplified Shotgun StochasticSearch with Screening (S5)) In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya. Package: r-cran-bayess Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mnormt, r-cran-gplots, r-cran-combinat Filename: pool/dists/resolute/main/r-cran-bayess_1.6-1.ca2604.1_all.deb Size: 321630 MD5sum: d98b36e2bca231ec746729062fafbca6 SHA1: e0d5d792e080fe2e75a9f576b0a87580dad29078 SHA256: e1bcdd5995c932716fc707bc83a7f91f9991c6ed02582005372d82a6a5006057 SHA512: c6c4bb2a5c9bdfd19704a5ceb2d1754f6c49a87639446a9b63c52c33b58d8a78bb53676c4335734e3c1397bc00497e36ec6655cd5a650c7abd882274fc5febff Homepage: https://cran.r-project.org/package=bayess Description: CRAN Package 'bayess' (Bayesian Essentials with R) Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) . Package: r-cran-bayessampling Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 882 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-teachingsampling Filename: pool/dists/resolute/main/r-cran-bayessampling_1.1.0-1.ca2604.1_all.deb Size: 639676 MD5sum: 8c63e8489d17230120c1c306d8044a92 SHA1: dff909505b5784c36e2d1baa5952befd351b1249 SHA256: df16cb1bea5bac54d592c99c18ed6f5644c2ed5a40ce2ce54029bb03289e8dac SHA512: 153ee4021bc8418880f4b446d697d200035c1e3f7e35e54818c969b7d8baebb47df366e14ebac6c05a45fb7cd25e67eee40cecff53130ba8d20021ba7bcf1d64 Homepage: https://cran.r-project.org/package=BayesSampling Description: CRAN Package 'BayesSampling' (Bayes Linear Estimators for Finite Population) Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary information) - BLE_Ratio() - and to categorical data - BLE_Categorical(). The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) . Package: r-cran-bayessim Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1930 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-coda, r-cran-magrittr, r-cran-mass, r-cran-mvtnorm, r-cran-patchwork, r-cran-tidyr, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayessim_1.0.4-1.ca2604.1_all.deb Size: 1541270 MD5sum: 2cc7025675356ed8c8d85f31dc599f29 SHA1: aacbc7349cf688f28d163be773c1ecfef2668baf SHA256: a6d08a128083fd5abe8b07a975273d14361c556ab8dc5c93bea6ae863b97d0a0 SHA512: cbe6e8d0382da0da1289aa02e0c1d94a822b0f428d0ba2d6f7bd9da7512567e359e26449c0e85907b7a2e879043b94f35c1606b86f5b1b08c4b019d5e0ed5d0e Homepage: https://cran.r-project.org/package=BayesSIM Description: CRAN Package 'BayesSIM' (Integrated Interface of Bayesian Single Index Models using'nimble') Provides tools for fitting Bayesian single index models with flexible choices of priors for both the index and the link function. The package implements model estimation and posterior inference using efficient MCMC algorithms built on the 'nimble' framework, allowing users to specify, extend, and simulate models in a unified and reproducible manner. The following methods are implemented in the package: Antoniadis et al. (2004) , Wang (2009) , Choi et al. (2011) , Dhara et al. (2019) , McGee et al. (2023) . Package: r-cran-bayessurvival Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2 Suggests: r-cran-simsurv, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayessurvival_0.2.0-1.ca2604.1_all.deb Size: 339412 MD5sum: 3c50f766c3b73ef41f5013cc7de07c97 SHA1: c43621bd3395b84c357bb193b53894b10773dee5 SHA256: 8e98a03cdb6537439d4eb157e2c593ba575db5b1596e2b4ddfe8b5344d6d58ee SHA512: da796b5fcabba3c661d7218c5e68a03b67d12f3c8d7b6b4e4ccf95d03be6873760d87a8330433518cc170ef5ab44a5e563621a9d3d7bc1fb8e540dced8608a1f Homepage: https://cran.r-project.org/package=BayesSurvival Description: CRAN Package 'BayesSurvival' (Bayesian Survival Analysis for Right Censored Data) Performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure. A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two. The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" . In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification. Package: r-cran-bayest Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-coda, r-cran-mass Filename: pool/dists/resolute/main/r-cran-bayest_1.5-1.ca2604.1_all.deb Size: 35908 MD5sum: 2184b4afd4f389abfd9188710277df41 SHA1: 5e63e86b0415fc8219dba03e850bcff34e147dbd SHA256: 1c83b449641d773cccda1c32ff9207e905c0042b4b4f2cd6de85cb284d9c7674 SHA512: 40af3c34990a7bf10427fe2ead4fe7e8543609c356ba7a5d5ca25c38c8c668507079f11227e960c3da1ebc2ebf6340a962a11069ba6b841e738515923092c8f0 Homepage: https://cran.r-project.org/package=bayest Description: CRAN Package 'bayest' (Effect Size Targeted Bayesian Two-Sample t-Tests via MarkovChain Monte Carlo in Gaussian Mixture Models) Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) . Package: r-cran-bayestestr Architecture: all Version: 0.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-insight, r-cran-datawizard Suggests: r-cran-bayesfactor, r-cran-bayesqr, r-cran-bayesplot, r-cran-betareg, r-cran-bh, r-cran-blavaan, r-cran-bridgesampling, r-cran-brms, r-cran-collapse, r-cran-curl, r-cran-effectsize, r-cran-emmeans, r-cran-gamm4, r-cran-ggdist, r-cran-ggplot2, r-cran-glmmtmb, r-cran-httr2, r-cran-kernsmooth, r-cran-knitr, r-cran-lavaan, r-cran-lme4, r-cran-logspline, r-cran-marginaleffects, r-cran-mass, r-cran-mclust, r-cran-mediation, r-cran-modelbased, r-cran-ordbetareg, r-cran-parameters, r-cran-patchwork, r-cran-performance, r-cran-posterior, r-cran-quadprog, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-rstan, r-cran-rstanarm, r-cran-see, r-cran-testthat, r-cran-tinytable, r-cran-tweedie, r-cran-withr Filename: pool/dists/resolute/main/r-cran-bayestestr_0.18.0-1.ca2604.1_all.deb Size: 1281738 MD5sum: 9b2a7f979144665b69bf678911b0ca25 SHA1: 20e162a57338af4a6512f34eed7b62871c4d56ba SHA256: 80a59f0ac67fa8fa35745a5f9b323c0ae574480b3f02e010d8f13361b9cb25c8 SHA512: 2edd8d19df520fc2d92cb20e74e8435f28668415f2114438244154cd2e1054ffb755b5631ece62a074fcc9b98df72c04f06da873a1885309b932f9f35d6bca4b Homepage: https://cran.r-project.org/package=bayestestR Description: CRAN Package 'bayestestR' (Understand and Describe Bayesian Models and PosteriorDistributions) Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 ) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) . Package: r-cran-bayestools Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2173 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-extradistr, r-cran-mvtnorm, r-cran-coda, r-cran-bridgesampling, r-cran-ggplot2, r-cran-rdpack, r-cran-rlang Suggests: r-cran-scales, r-cran-testthat, r-cran-vdiffr, r-cran-covr, r-cran-knitr, r-cran-rstan, r-cran-rjags, r-cran-runjags, r-cran-lme4, r-cran-bayesfactor, r-cran-robma, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayestools_0.3.0-1.ca2604.1_all.deb Size: 1806554 MD5sum: 7eab9df074f5e64af1d2dced7b446943 SHA1: 9c8d9237ac722d7f9211568198fc069573549327 SHA256: fe8b44ed7343812eb4129f15d50d5e14df9318bb909eaa85ac5cec00ee368277 SHA512: b110fabb34685acc5271e7459290625212cf5855d32f2f30466ed4f5dc7b8b604a0b213a523bc1e5739d956a38d1e56897c667323381e16c598f2e926143a3a7 Homepage: https://cran.r-project.org/package=BayesTools Description: CRAN Package 'BayesTools' (Tools for Bayesian Analyses) Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, , Hoeting et al., 1999, ). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions. Package: r-cran-bayestreeprior Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tgp, r-cran-bayestree, r-cran-bartmachine, r-cran-mass Filename: pool/dists/resolute/main/r-cran-bayestreeprior_1.0.1-1.ca2604.1_all.deb Size: 58360 MD5sum: 571160be41182a1e9738fbee68ae8c46 SHA1: 0a460becd2e9ddd9e525c872082cebfbf5ec1f31 SHA256: d4a02023367b551d4eef64c1da0c789fc78a7c24df2d8b8fabf66df7df1768df SHA512: a185eb72b972f6ade89d355ae1e9a10bbe456886d7bb4dce4d2ea5a88a72b18f6f9a2c4f687e5013cd641f083a5cd277e9dc3188d041d3057724d8db13383c93 Homepage: https://cran.r-project.org/package=BayesTreePrior Description: CRAN Package 'BayesTreePrior' (Bayesian Tree Prior Simulation) Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) . The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016). Package: r-cran-bayestwin Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-coda, r-cran-matrixstats, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-bayestwin_1.0-1.ca2604.1_all.deb Size: 524894 MD5sum: b1827efa232229c6892e27c3c0449a16 SHA1: f595b7e6bd464a03b6a4758820ea23decf35a5ee SHA256: 98ce3c0e670aff16640b9f3328f602791a07e31511a64312b8429635dc9c3b23 SHA512: b3618624bcc8198fe66eb7d8d5f2e2f59180b9c5e886c4358387f8d8a5d4f9a057207e13c97eaa98ee1a098dfe054d6a9250d596a1ef749e05a134c3118c040a Homepage: https://cran.r-project.org/package=BayesTwin Description: CRAN Package 'BayesTwin' (Bayesian Analysis of Item-Level Twin Data) Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) , Molenaar & Dolan (2014) , Schwabe, Jonker & van den Berg (2016) and Schwabe, Boomsma & van den Berg (2016) . Package: r-cran-bayesvl Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-stanheaders, r-cran-coda, r-cran-bnlearn, r-cran-ggplot2, r-cran-bayesplot, r-cran-viridis, r-cran-reshape2 Suggests: r-cran-loo Filename: pool/dists/resolute/main/r-cran-bayesvl_1.0.0-1.ca2604.1_all.deb Size: 559064 MD5sum: 4321a48839267ead80bdf3174ece84f0 SHA1: 38dde8dcb804f139ca506c1dac5ea608c20b5085 SHA256: 3f17529819ed937e641c510d0c949425e28b645df2d3516e346a67a7dbd44085 SHA512: 039d3dc06b26a207cadcfc298520ca82ec297b78379db822a736f62644911e078a6bccd5710e0eac7af0c8d61759a17aac96c89ded055bf91519d15f43fd8889 Homepage: https://cran.r-project.org/package=bayesvl Description: CRAN Package 'bayesvl' (Visually Learning the Graphical Structure of Bayesian Networksand Performing MCMC with 'Stan') Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate Stan code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18). Package: r-cran-bayesvolcano Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hdinterval, r-cran-purrr, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-brms, r-cran-rstan, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayesvolcano_1.0.1-1.ca2604.1_all.deb Size: 1359210 MD5sum: fa056e6d288b9dd006be84093c2b8c61 SHA1: 49ab2a02328951a4647d4ecd9661e669d2ad769f SHA256: f53ed5e7ca79366296596d16d7d242184310114d011f661a5b852a8dc170c782 SHA512: 35bd90ba8fb9358593c530745a8443b6279b22e1e8bb6341ecad82451965e9bc03d5a3a08cb6c73b25a75ef6d6dd419b91825e960c5a3828d0be1d5e239512f0 Homepage: https://cran.r-project.org/package=BayesVolcano Description: CRAN Package 'BayesVolcano' (Creating Volcano Plots from Bayesian Model Posteriors) Bayesian models are used to estimate effect sizes (e.g., gene expression changes, protein abundance differences, drug response effects) while accounting for uncertainty, small sample sizes, and complex experimental designs. However, Bayesian posteriors of models with many parameters are often difficult to interpret at a glance. One way to quickly identify important biological changes based on frequentist analysis are volcano plots (using fold-changes and p-values). Bayesian volcano plots bring together the explicit treatment of uncertainty in Bayesian models and the familiar visualization of volcano plots. Package: r-cran-bayesx Architecture: all Version: 0.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shapefiles, r-cran-sp, r-cran-sf, r-cran-colorspace, r-cran-coda, r-cran-interp Suggests: r-cran-spdep Filename: pool/dists/resolute/main/r-cran-bayesx_0.3-3-1.ca2604.1_all.deb Size: 1292132 MD5sum: 8379ca52683b92047526859d82cb3329 SHA1: ac3e4768df3729e8cfb96748d153c2e0b640a5fd SHA256: 2a1fd3820b7472888cc906cae0e4fcd8ececce734296c2a68c21648369608de9 SHA512: eab9a5be36ca873432a2814b0dcd3586a0c1611951f00684933ebe9a3f682588778da754829be3fe3aaadb26a0a81c8643329111355fab6e342557c0e29e506d Homepage: https://cran.r-project.org/package=BayesX Description: CRAN Package 'BayesX' (R Utilities Accompanying the Software Package BayesX) Functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX. 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Package: r-cran-baylum Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-cli, r-cran-hexbin, r-cran-kernsmooth, r-cran-luminescence, r-cran-rjags, r-cran-runjags, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-baylum_0.3.3-1.ca2604.1_all.deb Size: 2147394 MD5sum: be0f42b7310f4c2b352979e470dd0463 SHA1: d48aa690745379e07a198085c15622482ff3bb11 SHA256: c180a6a6f0b1a940ef5a40208d7ea11a143e30978c8af4bdd3d6e7a87fa6095f SHA512: 9650d5a2a3f8f2be825b7af31c383b64022d0ba04d61509d7719e8b0377ace916520f0677233388d0793ddd4e7b1f39b0447418d4798805affc336d012de38f6 Homepage: https://cran.r-project.org/package=BayLum Description: CRAN Package 'BayLum' (Chronological Bayesian Models Integrating Optically StimulatedLuminescence and Radiocarbon Age Dating) Bayesian analysis of luminescence data and C-14 age estimates. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017) and Combes et al. (2015) . This includes, amongst others, data import, export, application of age models and palaeodose model. Package: r-cran-baymedr Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-pso, r-cran-rms, r-cran-stringr, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-baymedr_0.2-1.ca2604.1_all.deb Size: 233432 MD5sum: 7961fe773c12531d0a3ff9dfc8fd015e SHA1: e1d422e845c309071125c54f839c458949ad3fea SHA256: 285fba20e0fa0d30fce03595f9f86068c15e614d77afe21287b2b7c9e3f6fb16 SHA512: 415f98578ca278b764c8bd7225bb6810d651e282002e44c9c7d61130f68bb8d86fa7404f385f9c5312e32cf2752b98bf9f6d0bf3cf44e9dab3a41a3fc5a95370 Homepage: https://cran.r-project.org/package=baymedr Description: CRAN Package 'baymedr' (Computation of Bayes Factors for Common Biomedical Designs) BAYesian inference for MEDical designs in R. Functions for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority (super_bf) of an experimental group compared to a control group on a continuous outcome measure, as well as functions for simulating survival data and calculating a Bayes factor for Cox proportional hazards models. Bayes factors for these tests can be computed based on raw data or summary statistics. Package: r-cran-baystability Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-mass, r-cran-rstiefel, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-baystability_0.2.0-1.ca2604.1_all.deb Size: 91760 MD5sum: 24dc445d1407a260dd3f577109c40809 SHA1: 4fc30afde5e2ce7b7f04bf693ad0416dce3d59d4 SHA256: 76e93aa22aac4da6583c776fbdb491fbe075edceca2b68ae9c14d2b032632bf1 SHA512: 10705c52d3d84d7978c017955cf60f494f2eea4a0268b00e4a496cd939af9098e289229dae6e8cef0560d7e2d4fdf03f15aaf4cce12238e35512d820021dcd04 Homepage: https://cran.r-project.org/package=baystability Description: CRAN Package 'baystability' (Bayesian Stability Analysis of Genotype by EnvironmentInteraction (GEI)) Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (). Package: r-cran-baystar Architecture: all Version: 0.2-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda Filename: pool/dists/resolute/main/r-cran-baystar_0.2-10-1.ca2604.1_all.deb Size: 68688 MD5sum: 56e0ef6175080a90142369e865593af0 SHA1: 49648df70b54d0521126e6fecb95c50b0efbe466 SHA256: 3a24075f8dd14daf163e40c78e5ce09d20f0ae19077a8b50c4916b4bf992950d SHA512: eef7487f8ccef66d48586b290054279ac38c194f8c0dd31efdc7cc10d116dd8ad6092e1b77d34e1e7a586f7cc1dacf634b0cae35730c2fbaec5758c02af5adb9 Homepage: https://cran.r-project.org/package=BAYSTAR Description: CRAN Package 'BAYSTAR' (On Bayesian Analysis of Threshold Autoregressive Models) Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods. Package: r-cran-baytrends Architecture: all Version: 2.0.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3095 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dataretrieval, r-cran-digest, r-cran-dplyr, r-cran-fitdistrplus, r-cran-lubridate, r-cran-knitr, r-cran-memoise, r-cran-mgcv, r-cran-pander, r-cran-plyr, r-cran-readxl, r-cran-sessioninfo, r-cran-survival, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-imputets, r-cran-markdown, r-cran-nlme, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-baytrends_2.0.14-1.ca2604.1_all.deb Size: 2133380 MD5sum: 520284f35a05dac86dd3de0260f105f0 SHA1: 8ca97cbf179ca031dd59ae1d7169ba06107001b4 SHA256: fa6669af02983d546b4a7ffbbbb36c8da80504e874c9fe4e082c50a64013e998 SHA512: 6c981c541c0559a4c4fe331af0cc557c9bfc63bdd161524d62b813a0b626cfad52eed6e0a9c15b3e00ff5c245f12d47ef6e42eacf47beabc182d584d3e641aff Homepage: https://cran.r-project.org/package=baytrends Description: CRAN Package 'baytrends' (Long Term Water Quality Trend Analysis) Enable users to evaluate long-term trends using a Generalized Additive Modeling (GAM) approach. The model development includes selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporation of hydrologic variability via either a river flow or salinity, the use of an intervention to deal with method or laboratory changes suspected to impact data values, and representation of left- and interval-censored data. The approach has been applied to water quality data in the Chesapeake Bay, a major estuary on the east coast of the United States to provide insights to a range of management- and research-focused questions. Methodology described in Murphy (2019) . Package: r-cran-bb Architecture: all Version: 2026.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog Suggests: r-cran-setrng, r-cran-survival, r-cran-hmisc, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-bb_2026.1.0-1.ca2604.1_all.deb Size: 403600 MD5sum: b4a38af46e065afcf82c141aa671a639 SHA1: a90383ef06b039808032612ba62fb56298fcd888 SHA256: ab1fb89abc06e3365384598ff2b97d54a0c72e940b21a1346fc19cb10e9169be SHA512: 7182fdda39a003429bc7b9cd06cb58f185e86c5b05a916e1309b289b826b5dc52c62042824c774555a4e985c90dad244de8b456aad3046226e558fd41abf935c Homepage: https://cran.r-project.org/package=BB Description: CRAN Package 'BB' (Solving and Optimizing Large-Scale Nonlinear Systems) Barzilai-Borwein spectral methods for solving nonlinear system of equations, and for optimizing nonlinear objective functions subject to simple constraints. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("BB"). Package: r-cran-bbest Architecture: all Version: 0.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-aws, r-cran-ggplot2, r-cran-reshape2, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-bbest_0.1-8-1.ca2604.1_all.deb Size: 956182 MD5sum: 80abdf1b268055343297f3eea3834eb2 SHA1: fe9bb34fd6ee0e5c310afb8c96c497f3fe17fa3a SHA256: d820054c3f07e02a3f603a7ecd3acfbf74e251446f53567128673f59e64ff011 SHA512: 9f7950a8c2f1749ee7a09f133db5b06568e68607acd507673b41a674ba75004d1f2d1c2232f922783fd0ef201c6ce8616e31c63cf8b0fcbdd642211a26c6306e Homepage: https://cran.r-project.org/package=BBEST Description: CRAN Package 'BBEST' (Bayesian Estimation of Incoherent Neutron Scattering Backgrounds) We implemented a Bayesian-statistics approach for subtraction of incoherent scattering from neutron total-scattering data. In this approach, the estimated background signal associated with incoherent scattering maximizes the posterior probability, which combines the likelihood of this signal in reciprocal and real spaces with the prior that favors smooth lines. The description of the corresponding approach could be found at Gagin and Levin (2014) . Package: r-cran-bbi Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-bbi_0.3.0-1.ca2604.1_all.deb Size: 177702 MD5sum: dfb9c74a22582c083d1a4d74d82b1c7e SHA1: 6316e23ec7c98a303baa7afd49553228cac9605c SHA256: 6515aba49610a91f448270060ee59992a9b8839e1c48a3fb3baa6f2ea9f23181 SHA512: b417d3e296d29a3eed616ae70b04fdc0d99db637a72e4581b0c94ab92504636fd4f4a09d0d7800d18ff3a71205a8483eed1c28584e36a7f4f3ed9a335123da11 Homepage: https://cran.r-project.org/package=BBI Description: CRAN Package 'BBI' (Benthic Biotic Indices Calculation from Composition Data) Set of functions to calculate Benthic Biotic Indices from composition data, obtained whether from morphotaxonomic inventories or sequencing data. Based on reference ecological weights publicly available for a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, ). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio. 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This package modifies and extends the 'mle' classes in the 'stats4' package. Package: r-cran-bbnet Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bbnet_1.2.1-1.ca2604.1_all.deb Size: 625412 MD5sum: 4b72b6f07459a5ddbf70d48ab2b2604d SHA1: 41839cde1fb8fc16a88d4417447cd2c80ac36b82 SHA256: 8e3823b0dadab256f58834a15c7eb4ef073ece82b72814b8fe5611ef564c94fb SHA512: 9a943b70cc0ab8bb4c095e13e15599e7a89467a46e6abb8d296615d8765b896d36cc7ead989c8511d917faedec95a21e0f510cdcbdb620dfbc1e98aad671fbb8 Homepage: https://cran.r-project.org/package=bbnet Description: CRAN Package 'bbnet' (Create Simple Predictive Models on Bayesian Belief Networks) A system to build, visualise and evaluate Bayesian belief networks. The methods are described in Stafford et al. (2015) . 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The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) and Barreto-Souza, Mayrink and Simas (2020) . 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See Cameron et al (2008) for application of bootstrap to cluster samples. See Aaron et al (2016) and Aaron et al (2016) for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys. 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Package: r-cran-bcmixed Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-nlme Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bcmixed_0.1.6-1.ca2604.1_all.deb Size: 152008 MD5sum: bdd610e14f4dc494e40c4c7ff7c77b04 SHA1: 7f1d3108ce4fdef3d73f43f2a6c807798467c7ae SHA256: 078e319527542d3119328f485f970fa6c7e0408765a6f2ed0d1b54397f9c3d82 SHA512: 33a8e28e265de1350de0d5719ecdaa65495ed82a6f45939fea4dc3b7c06a1b308ee9acb9bf5f126f63f72ed250b929baf077a7358cd86ee8e86caaeec317a598 Homepage: https://cran.r-project.org/package=bcmixed Description: CRAN Package 'bcmixed' (Mixed Effect Model with the Box-Cox Transformation) Inference on the marginal model of the mixed effect model with the Box-Cox transformation and on the model median differences between treatment groups for longitudinal randomized clinical trials. These statistical methods are proposed by Maruo et al. (2017) . Package: r-cran-bcputility Architecture: all Version: 0.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-sf Suggests: r-cran-blob Filename: pool/dists/resolute/main/r-cran-bcputility_0.4.6-1.ca2604.1_all.deb Size: 267502 MD5sum: 2c09f382ffe27cf3596e852c2431eede SHA1: 005ebb5c2f70cb877e5cae06e29502fb14526e5c SHA256: 35d076b8ed9112ff5eae16b41daa98d3e20f74297480b68a41fc73462f2666af SHA512: e02bc50c1a05b9c1120d4ebd889ec6474880e78d9ff1fa27e402927e355776279d7020234273e160f0a108c5883aa4c1f9406dce04097cacc4806e18017c88de Homepage: https://cran.r-project.org/package=bcputility Description: CRAN Package 'bcputility' (Wrapper for SQL Server bcp Utility) Provides functions to utilize a command line utility that does bulk inserts and exports from SQL Server databases. 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Package: r-cran-bcrm Architecture: all Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-rlang, r-cran-ggplot2, r-cran-knitr Suggests: r-cran-r2winbugs, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-bcrm_0.5.4-1.ca2604.1_all.deb Size: 222280 MD5sum: 2703a0a68d436ec50b6a80fbd040fab9 SHA1: 0299e8746f668d8f193b1a8888e2f4fbb0d801dd SHA256: b1a25ade74358e264de20a53ccd91815f67e50a3f3c85227a34c13b1193ed786 SHA512: 19c58bc2473d3eea5ad61da18fe5333a45d7190e93aeaabd1bfa433820d85756b66f84307633c6668968b3bf5460fa1ef337bb3661ec805ed3906ec40ed388eb Homepage: https://cran.r-project.org/package=bcrm Description: CRAN Package 'bcrm' (Bayesian Continual Reassessment Method for Phase IDose-Escalation Trials) Implements a wide variety of one- and two-parameter Bayesian CRM designs. 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Package: r-cran-bddkr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-writexl, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bddkr_0.1.1-1.ca2604.1_all.deb Size: 30452 MD5sum: b7eb9812dc444d33be86d5137104f11b SHA1: 64e567751b366c0a05a234756947ba3f7e0bb1fe SHA256: e28c03838bf2e79877f836a57b8a1df83fbc86b1ad604e4194e224195b31c265 SHA512: af0a6f068a92cd040213d887dcbe6d9076643c596d5c2486d7dd9690c1ec57bab8bc6aa9e9c1d86b4d9048207d17af402436b107f618e3551b477d9d1696a6ae Homepage: https://cran.r-project.org/package=bddkR Description: CRAN Package 'bddkR' (Gathering Monthly Banking Sector Data from BDDK of Turkey) Fetches monthly financial tables and banking sector data published on the official website of the Banking Regulation and Supervision Agency of Turkey and also enables you to save it as an Excel file. 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Package: r-cran-bde Architecture: all Version: 1.0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-bde_1.0.1.1-1.ca2604.1_all.deb Size: 865626 MD5sum: 2909db5f111e39ffe069c68db99b9e6b SHA1: 3e73a858c0d0305df7a7d500e95ef3ccb1489c85 SHA256: c9df7753ecd6fe544cbea342db6506ae30edfaf7e451bdac16fa458f5b9e2ad9 SHA512: f4d5ec0650118b7285aa0976228114e56fee257c1321e3f09dbba61f724f65bba8861700dcbaaba36c114e32782c3f0f0bf4b37eac645d0b10f45bdf07db8c5d Homepage: https://cran.r-project.org/package=bde Description: CRAN Package 'bde' (Bounded Density Estimation) A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user. 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Package: r-cran-beans Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-beans_0.1.0-1.ca2604.1_all.deb Size: 1394418 MD5sum: 4a1540f0793f9d185d0b86f52ac05f1e SHA1: d806cabea654c80b97646093fff9759bcf020b61 SHA256: fb460149cb7281c1f2f0a289f15cd1fa6c9d08fe055688ca8616e675c1d381a8 SHA512: 5ecc0242f3d1c8e6e5c55fa07a0d3e155dc80aadc0386d1a52c26dbd5362707284e0f5b5620ae8d4ade376b6226e10b0f96a80fc22df9e4db46eeeb408618dc3 Homepage: https://cran.r-project.org/package=beans Description: CRAN Package 'beans' (Data on Dried Beans) These data contain morphological image measurements for dried beans from Koklu and Ozkan (2020) . Package: r-cran-bearishtrader Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bearishtrader_1.0.2-1.ca2604.1_all.deb Size: 188672 MD5sum: 8c7a80187a953ab3cd7367ced7ec8f4c SHA1: 1d0b26ce84b7394c87b2250429a4b8b1aa54c574 SHA256: 820f2bf6ca04058b405a6cd2e99adb296171202fd2a86cb09aff373747cd3a38 SHA512: aa176a14c0e469818bbfce4305e425456d8da1e844e82151b3644508e475880ae4bb9db362cd4a4f59e17a936db7613e80eb7dd63fb6c1be9fb03378a3f4e527 Homepage: https://cran.r-project.org/package=bearishTrader Description: CRAN Package 'bearishTrader' (Trading Strategies for Bearish Outlook) Stock, Options and Futures Trading Strategies for Traders and Investors with Bearish Outlook. The indicators, strategies, calculations, functions and all other discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Juan A. Serur, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 385-453)", 2019, ISBN: 9781119593577). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). Package: r-cran-beast Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-beast_1.2-1.ca2604.1_all.deb Size: 307196 MD5sum: de1d8dcec7dcaeb532d5c1b830b78d4a SHA1: 07641491adde9912b8932aa02e717e7882f513d9 SHA256: 490497c462b55ec6435f7d0877d05a4fa8e20fbc4cfd253c55a2e16383fd2340 SHA512: 680a90c42c9712785ecfd48b60a23f80d624151df7f3ee58b08891c762cf2bddeb2fa9b483f676fd39d36f757d52322dc139a8179107da84eaca93f0910df7cc Homepage: https://cran.r-project.org/package=beast Description: CRAN Package 'beast' (Bayesian Estimation of Change-Points in the Slope ofMultivariate Time-Series) Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2019) for a detailed presentation of the method. Package: r-cran-beastier Architecture: all Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-beautier, r-cran-phangorn, r-cran-rappdirs, r-cran-readr, r-cran-rjava, r-cran-rlang, r-cran-sessioninfo, r-cran-stringr, r-cran-tibble, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tracerer Filename: pool/dists/resolute/main/r-cran-beastier_2.5.2-1.ca2604.1_all.deb Size: 524426 MD5sum: 70e3fac42d7617228c5d358b320aed68 SHA1: d93b953cfa9c4258079f48df844f3d88dad64512 SHA256: 57ba1f6a1006522a4e88e63361bab5f87e075fecef73500161dfe652a191ed38 SHA512: e95dbbc9108f755d8dc3fdc2577bddb4062025a5dbf8b669d27b6656e14da8e2e4cdf8e239c6286a5fa87f0435bfc9a3eb1e02b5561ac7413138bcaae8fced59 Homepage: https://cran.r-project.org/package=beastier Description: CRAN Package 'beastier' (Call 'BEAST2') 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is a command-line tool. This package provides a way to call 'BEAST2' from an 'R' function call. Package: r-cran-beastjar Architecture: all Version: 10.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8814 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-beastjar_10.5.1-1.ca2604.1_all.deb Size: 7957442 MD5sum: b53b29f73c933b39dd8e910b4b673121 SHA1: f0dd5f2cf9b7f632f393b88ef414753f3fa50426 SHA256: 8a19ad34b0b7ecff01b93ebfce0e3c22c61b689e31063315e895ea314c729df2 SHA512: 49447844871e8f13e2f4290ad9201586a8b2b966d055516ecec8cc7a63bc458f02f7db59bc9ac684cecee4a629d2b3d86910cf9089b9512c362417ba0ca492ca Homepage: https://cran.r-project.org/package=BeastJar Description: CRAN Package 'BeastJar' (JAR Dependency for MCMC Using 'BEAST') Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST X' software library of Baele et al (2025) . 'BEAST X' supports auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo sampling for a large variety of composable standard and phylogenetic statistical models using high performance computing. By placing the 'BEAST X' JAR in this package, we offer an efficient distribution system for 'BEAST X' use by other R packages using CRAN. Package: r-cran-beautier Architecture: all Version: 2.6.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4783 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rappdirs, r-cran-purrr, r-cran-rlang, r-cran-seqinr, r-cran-stringr Suggests: r-cran-knitr, r-cran-markdown, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-beautier_2.6.12-1.ca2604.1_all.deb Size: 1714484 MD5sum: 9ec169b13ea8cfcf34941dbc2871b096 SHA1: 794e8a4f13a790ac9c4416593ed45ad7bce803bc SHA256: ea9277b4b4e24b5852bf0cc68037b2bc9bc4fa9ad7f308de8cb9bb5ed7ed31e7 SHA512: a71aaf6ff450a3e24428059cb88999f9ee14b39d112d9c0ea7f62dc5a10ecc752bcf5c44d084d74275df3f09b83cfcd2b78ad417da04f51d3fa3911a75bd1444 Homepage: https://cran.r-project.org/package=beautier Description: CRAN Package 'beautier' ('BEAUti' from R) 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAUti 2' (which is part of 'BEAST2') is a GUI tool that allows users to specify the many possible setups and generates the XML file 'BEAST2' needs to run. This package provides a way to create 'BEAST2' input files without active user input, but using R function calls instead. Package: r-cran-beaver Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-ellipsis, r-cran-fs, r-cran-ggplot2, r-cran-purrr, r-cran-rjags, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-yodel Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-beaver_1.0.0-1.ca2604.1_all.deb Size: 214538 MD5sum: d239c311bb764608fcfbae41e4245008 SHA1: d4aa497cdee4beab8f4b5f6e3f6498a77b047f42 SHA256: 26bb78b247d3a90481f27e4206cff16f912bcf98acecd275945bab453194eaf6 SHA512: cce8e00e38717032a31ac758191a2a023131faaada91e5ed394d816e090769d10831d74e9ab99f818d96c6a4a3b4be906dfe63e957e6b1284146ba0278a81225 Homepage: https://cran.r-project.org/package=beaver Description: CRAN Package 'beaver' (Bayesian Model Averaging of Covariate Adjusted Negative-BinomialDose-Response) Dose-response modeling for negative-binomial distributed data with a variety of dose-response models. Covariate adjustment and Bayesian model averaging is supported. Functions are provided to easily obtain inference on the dose-response relationship and plot the dose-response curve. Package: r-cran-bedassle Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-emdbook Filename: pool/dists/resolute/main/r-cran-bedassle_1.6.1-1.ca2604.1_all.deb Size: 234324 MD5sum: b7bf1fd7b79a3a593dfc132c4ce4d724 SHA1: 1df5620d99b757ea661738e562c990ad2f569ef5 SHA256: 94936942633cf4c1a3b2afdc67fa0e838e6a6655aaaba41f9a51d4f4cb8ba586 SHA512: 27b3221cdaccfff8c7922cea4bb9c6d14a94e0da54ac35082e7a96a50558892c3124cdf768856f00fdced7c4e3f68b90b9dfdef78445150350a7a45ca4c81c8b Homepage: https://cran.r-project.org/package=BEDASSLE Description: CRAN Package 'BEDASSLE' (Quantifies Effects of Geo/Eco Distance on GeneticDifferentiation) Provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy. Package: r-cran-bedr Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-venndiagram, r-cran-data.table, r-cran-r.utils, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bedr_1.1.5-1.ca2604.1_all.deb Size: 967162 MD5sum: ac72c6af81256b4888d28a984c72765e SHA1: 939b690c1937787455d36cc473e4286e6c1e6c7a SHA256: 3f7c998e536f32a8c0828cbb0d455bd1858a4d68ddf28122f261fd60060ad7a4 SHA512: 6a38a919e78d9936909ba9385133e9563353976189287d20fd94849081ca2120cf6cb5a66a2aa01d5258033bc300ba225f4c13ff4052f47702e99856efc93012 Homepage: https://cran.r-project.org/package=bedr Description: CRAN Package 'bedr' (Genomic Region Processing using Tools Such as 'BEDTools','BEDOPS' and 'Tabix') Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'. These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging. bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing. Package: r-cran-bedrockbio Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bedrockbio_1.3.1-1.ca2604.1_all.deb Size: 23872 MD5sum: efc4d5ce7b272cf3a1f47f81fd6b7e98 SHA1: 1fd03b80d1aca5bc645a65b713b2115c8dbffa68 SHA256: edf1df138f18e0a69a22e1eb96f2e70ba966e388e57ce371c37129b340e1ef89 SHA512: 7f2317c66c29c8706a9fc0df584b5ad7ddc28e52a32bbddbd1272a5a34027bdbd856497e934ae06eeb4fbeb9ea490219509b8b0a4d4f6cbdefe32b9987273abe Homepage: https://cran.r-project.org/package=bedrockbio Description: CRAN Package 'bedrockbio' (Open-Access Computational Biology Datasets) Efficiently access the 'Bedrock Bio' library of open-access computational biology datasets. Lazily query datasets backed by 'DuckDB' and 'Apache Iceberg', with support for predicate pushdown and column projection to the cloud storage backend. This enables quick, iterative access to otherwise massive, unwieldy datasets without downloading them in full. See for available datasets and documentation. Package: r-cran-beebdc Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-cran-coordinatecleaner, r-cran-cowplot, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggspatial, r-cran-here, r-cran-igraph, r-cran-lubridate, r-cran-mgsub, r-cran-openxlsx, r-cran-paletteer, r-cran-readr, r-cran-rnaturalearth, r-cran-sf, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-bdc, r-cran-biocmanager, r-cran-classint, r-bioc-complexheatmap, r-cran-countrycode, r-cran-curl, r-cran-devtools, r-cran-emld, r-cran-formatr, r-cran-galah, r-cran-hexbin, r-cran-htmltools, r-cran-htmlwidgets, r-cran-httr, r-cran-inext, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-leaflet, r-cran-magrittr, r-cran-mosaic, r-cran-pkgdown, r-cran-plotly, r-cran-prettydoc, r-cran-purrr, r-cran-r.utils, r-cran-renv, r-cran-rgnparser, r-cran-rlang, r-cran-rmarkdown, r-cran-rmdformats, r-cran-rnaturalearthdata, r-cran-rvest, r-cran-spader, r-cran-taxadb, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-beebdc_1.3.4-1.ca2604.1_all.deb Size: 1024330 MD5sum: 00ec32820ced126c9aa53292c41c824b SHA1: 14fd6aecc1cb0805d3a2b6f8f10b74e6347d3bff SHA256: 89407f42d8da89f1d553ccdf6d2b63ee57361e796761f97e637e39aedd11bb89 SHA512: 23ce6354e9947538fee8f3a0b2e2d5c7212cf678c7e09c50daa23e671c7d5e793017aefd586e0f1fdd4496ce405098422cf2922f76541de533ef013f1d48efc5 Homepage: https://cran.r-project.org/package=BeeBDC Description: CRAN Package 'BeeBDC' (Occurrence Data Cleaning) Flags and checks occurrence data that are in Darwin Core format. The package includes generic functions and data as well as some that are specific to bees. This package is meant to build upon and be complimentary to other excellent occurrence cleaning packages, including 'bdc' and 'CoordinateCleaner'. This package uses datasets from several sources and particularly from the Discover Life Website, created by Ascher and Pickering (2020). For further information, please see the original publication and package website. Publication - Dorey et al. (2023) and package website - Dorey et al. (2023) . Package: r-cran-beeca Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-sandwich Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-marginaleffects, r-cran-margins, r-cran-robincar Filename: pool/dists/resolute/main/r-cran-beeca_0.2.0-1.ca2604.1_all.deb Size: 120700 MD5sum: 61e7032bcadb8532eaaccc78f8d9bd1b SHA1: 941d055c4c8a407412a958953442b2cffe8e04b3 SHA256: 55eb572f033f53f078a6dedee78af8bdca541286fc6d1413465bcb434d8a502c SHA512: 1dab03e59f32778f8702f89613eebf024e90b29a89e74a8b00c07ad80db15a5c861c6ae946dfda0071d2e52de419b3ddb03eddf7feb78373cdf647f2b302435c Homepage: https://cran.r-project.org/package=beeca Description: CRAN Package 'beeca' (Binary Endpoint Estimation with Covariate Adjustment) Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) ). Implements the variance estimators of Ge et al (2011) and Ye et al (2023) . Package: r-cran-beepr Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-audio Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-beepr_2.0-1.ca2604.1_all.deb Size: 1022124 MD5sum: 3d7615005e4338a2603a3154b689c495 SHA1: 64edf6f0f46fbf3141938d78fe6f9cbb31c1cf26 SHA256: b4c31a4be8fa07de67fe9c9f5cfa3d4b2ffebb1b44b4409f3d5a8ff3167410c8 SHA512: d542725335a4b104672db142f4ac37ba18d817dfc88a0c886911d0575bbc1307442ca74c9313887909f980702657e5becdd23eece2e2a36b52fa2d5039aa9316 Homepage: https://cran.r-project.org/package=beepr Description: CRAN Package 'beepr' (Easily Play Notification Sounds on any Platform) The main function of this package is beep(), with the purpose to make it easy to play notification sounds on whatever platform you are on. It is intended to be useful, for example, if you are running a long analysis in the background and want to know when it is ready. Package: r-cran-beezdiscounting Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beezdemand, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-gtools, r-cran-magrittr, r-cran-minpack.lm, r-cran-psych, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-beezdiscounting_0.3.2-1.ca2604.1_all.deb Size: 274286 MD5sum: 4e386a84e38922af92c8af6898b90346 SHA1: 79e715dec013a31c3c13e2cbe79593ca0ff57308 SHA256: e875fd1b362123dc072eeeb35cae738679c0be9fbd909333c97be04b45604075 SHA512: 08082e3b7d2730b769c5fd7db1c711678159c2bbdcd391ad19f66797d40a0bbe6baaebb47b768a3a7be685f73ff37b22df9aeb99f285d5dbae5ce1b4d16e1e25 Homepage: https://cran.r-project.org/package=beezdiscounting Description: CRAN Package 'beezdiscounting' (Behavioral Economic Easy Discounting) Facilitates some of the analyses performed in studies of behavioral economic discounting. The package supports scoring of the 27-Item Monetary Choice Questionnaire (see Kaplan et al., 2016; ), calculating k values (Mazur's simple hyperbolic and exponential) using nonlinear regression, calculating various Area Under the Curve (AUC) measures, plotting regression curves for both fit-to-group and two-stage approaches, checking for unsystematic discounting (Johnson & Bickel, 2008; ) and scoring of the minute discounting task (see Koffarnus & Bickel, 2014; ) using the Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide; ), which is also available as a .qsf file in this package. 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Package: r-cran-behaviorchange Architecture: all Version: 25.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4031 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biasedurn, r-cran-data.tree, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-ggplot2, r-cran-googlesheets4, r-cran-gridextra, r-cran-gtable, r-cran-knitr, r-cran-rmdpartials, r-cran-ufs, r-cran-viridis, r-cran-yum Suggests: r-cran-htmltools, r-cran-kableextra, r-cran-openxlsx, r-cran-patchwork, r-cran-png, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rsvg, r-cran-webshot Filename: pool/dists/resolute/main/r-cran-behaviorchange_25.8.0-1.ca2604.1_all.deb Size: 1795454 MD5sum: 7b7eb134be24b76185e45fa3fe54b019 SHA1: 8e23104f6ff37c46dd20d3166dab18777d875783 SHA256: 5b131a376f2f996707c0a1c3915590a91a0e224f19cf9e8458d3253c7f71188c SHA512: bbe4fee8155c0675a0ab1ee990be70c9d260cf20c92e68b75b4294e173ee743264eab81d751e410947f9719833032d4360df17cf7d9bc0fcd9b75910f9a9264c Homepage: https://cran.r-project.org/package=behaviorchange Description: CRAN Package 'behaviorchange' (Tools for Behavior Change Researchers and Professionals) Contains specialised analyses and visualisation tools for behavior change science. These facilitate conducting determinant studies (for example, using confidence interval-based estimation of relevance, CIBER, or CIBERlite plots, see Crutzen, Noijen & Peters (2017) ), systematically developing, reporting, and analysing interventions (for example, using Acyclic Behavior Change Diagrams), and reporting about intervention effectiveness (for example, using the Numbers Needed for Change, see Gruijters & Peters (2017) ), and computing the required sample size (using the Meaningful Change Definition, see Gruijters & Peters (2020) ). This package is especially useful for researchers in the field of behavior change or health psychology and to behavior change professionals such as intervention developers and prevention workers. Package: r-cran-behavr Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-behavr_0.3.3-1.ca2604.1_all.deb Size: 74254 MD5sum: eb9dbaa0e7e8863352dee01e3d01fbc3 SHA1: 58abeaa1663b43528951a2df7d0a705d5212553b SHA256: 7069cc5807a2afece187d78455edd6d497f7f4ed44e2b26943bdcab2696a3546 SHA512: da5eec19c84902850b66deeea51e54547c2f66ec4f39601073986a1115854144c30c06e6ca186183bcc70116e2ffad20d377c7f34e00374fa6497640eb64671c Homepage: https://cran.r-project.org/package=behavr Description: CRAN Package 'behavr' (Canonical Data Structure for Behavioural Data) Implements an S3 class based on 'data.table' to store and process efficiently ethomics (high-throughput behavioural) data. 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Package: r-cran-belikelihood Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-nlme, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-belikelihood_1.1-1.ca2604.1_all.deb Size: 329046 MD5sum: 3e78854953e2c4d14d02ff4213b98748 SHA1: c8bce94450b26c6da0c83f163947be43561f7207 SHA256: 40eafd93301b36a3ef6232c8374784e1e54179fa532fc6e2061fc9c58e38a9d6 SHA512: 50925b47aaeabe59a508f944508a531dfa11e1ebb20932ad4fc51e70f5aa6e4755dc9a12d6140a69fa1c76ac19a69b7ddcc4c719d575fc08be29d288307c7726 Homepage: https://cran.r-project.org/package=BElikelihood Description: CRAN Package 'BElikelihood' (Likelihood Method for Evaluating Bioequivalence) A likelihood method is implemented to present evidence for evaluating bioequivalence (BE). The functions use bioequivalence data [area under the blood concentration-time curve (AUC) and peak concentration (Cmax)] from various crossover designs commonly used in BE studies including a fully replicated, a partially replicated design, and a conventional 2x2 crossover design. They will calculate the profile likelihoods for the mean difference, total standard deviation ratio, and within subject standard deviation ratio for a test and a reference drug. A plot of a standardized profile likelihood can be generated along with the maximum likelihood estimate and likelihood intervals, which present evidence for bioequivalence. See Liping Du and Leena Choi (2015) . Package: r-cran-bempdata Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2656 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-haven, r-cran-shiny Suggests: r-cran-bslib, r-cran-dt, r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bempdata_0.2.3-1.ca2604.1_all.deb Size: 2335912 MD5sum: a8deb5291aa6849221e957f7c0ca38bc SHA1: 2955b19eeac4ce7e1ae555d7708dcc106556fab8 SHA256: 01fdefda5702ca5a8c45d26a035cffdc547c7ee83f2aa5122863404a86357a5e SHA512: 31310da86edf4ca1ef14fbd782310412625d48d3da5d322afd32ee4c058d662ee26d93695bc2b093f02d68941d641da3e6628741e5f4a8db22f4f9bca2222a3d Homepage: https://cran.r-project.org/package=BEMPdata Description: CRAN Package 'BEMPdata' (Access the Bangladesh Environmental Mobility Panel Dataset) Provides functions to download and work with the Bangladesh Environmental Mobility Panel (BEMP), a household panel survey tracing the impacts of riverbank erosion and flooding on (im)mobility, socio-economic outcomes, and political attitudes along the Jamuna River in Bangladesh (2021-2024). 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Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrected a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved robustness of the error-message producing mechanism. Version 0.3.1 improves the NAMESPACE file of the package. It is the author's intention to distribute any future updates via GitHub. 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Package: r-cran-bestree Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bestree_0.5.2-1.ca2604.1_all.deb Size: 190848 MD5sum: fdb7a17340ef3aaee28afab5abc114ed SHA1: 0b7bcb73059c68c2bb4988ccfc1624994306ef1c SHA256: c369b3121755458ab82cc657b9ca39cba13c605746b26db117cd052d91b1677b SHA512: 4460bc250fb301b6a583ba8e20a00a26f17ea8c740f813db0b4535c8b7f635706789be691274885872a44253f5f8e6204fc3f0f1aed10beb03ad4105b50d1a58 Homepage: https://cran.r-project.org/package=BESTree Description: CRAN Package 'BESTree' (Branch-Exclusive Splits Trees) Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process. Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) . Package: r-cran-bestsdp Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-shinybs, r-cran-shinyjs, r-cran-tidyr, r-cran-dplyr, r-cran-readxl, r-cran-ggplot2, r-cran-rlist, r-cran-dt, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-bestsdp_0.1.2-1.ca2604.1_all.deb Size: 168684 MD5sum: fde15c06ce0e413adfd2d26503fd70fc SHA1: bcbcc20302c646fcce4d5595454be7e7b0157ce7 SHA256: 4ccf9ffb5d3749899b1af72f9538abd71c4e42cbe732d5b18db83e053e765a44 SHA512: db971804b4703ff0262dc26e6b8e5a3f598fd3ec7705e02c7c5dabaddc126e817120ead5bff57d1301cca0f9644ff06be6dab7781c563ecc1a7faa2628917e4f Homepage: https://cran.r-project.org/package=bestSDP Description: CRAN Package 'bestSDP' (Burden Estimate of Common Communicable Diseases in Settlementsof Displaced Populations) Provides a practical tool for estimating the burden of common communicable diseases in settlements of displaced populations. 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Package: r-cran-betaarma Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast Suggests: r-cran-knitr, r-cran-xtable, r-cran-here, r-cran-moments, r-cran-rmarkdown, r-cran-tseries, r-cran-lbfgs, r-cran-ggplot2, r-cran-foreach, r-cran-zoo, r-cran-dplyr, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-betaarma_1.1.0-1.ca2604.1_all.deb Size: 115770 MD5sum: c8c00fee3c8986e5d57355d9e803e65d SHA1: 5407c7c64608b9557988f7db8c0b944d46384f4a SHA256: 6b60625909850299f3984257e7a293cce61036c34748beef44ad88e72091747d SHA512: 009c5e33da32f16ebc56b024008f2a509b01ffc82940ac8e17c114062276e493a241d72422505df91035bfef1eaf0f120eb1ec99125957564537c9058700951f Homepage: https://cran.r-project.org/package=betaARMA Description: CRAN Package 'betaARMA' (Beta Autoregressive Moving Average Models) Fits Beta Autoregressive Moving Average (BARMA) models for time series data distributed in the standard unit interval (0, 1). 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Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. In proton you have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages. This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge? In regression you will test your modeling skills in a series of eight sub-tasks. Try only if ANOVA is your close friend. It's a part of Beta and Bit project. You will find more about the Beta and Bit project at . Package: r-cran-betacal Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-betacal_0.1.0-1.ca2604.1_all.deb Size: 15230 MD5sum: 3717bea87cb5c0d82f717f61e6f274f7 SHA1: 14d7df65db31dc3ef34d8a8890bf2f33522d9b6d SHA256: 0b867dd22c47c13f5fa22001b3156aa18d3cb5df55db71628000e31c326646e9 SHA512: 5dd6edffd2335c1c20ce99622381d055434f076e83e2e8a49f533f9c629742e11940982b14c44db1416f4b2124e2f53b9469e2c65caba2001006b56ed22f0e98 Homepage: https://cran.r-project.org/package=betacal Description: CRAN Package 'betacal' (Beta Calibration) Fit beta calibration models and obtain calibrated probabilities from them. 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(2022) to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility. Package: r-cran-betadanish Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-maxlik, r-cran-survival Suggests: r-cran-readxl, r-cran-flexsurv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-betadanish_0.1.0-1.ca2604.1_all.deb Size: 159818 MD5sum: c61ed8371f8059fa330ed6893c203570 SHA1: c9c355329aaebb8d721027ccdce45de2439d70b3 SHA256: b17da3fc22768cd1f46eb39d5dcc5d94bc2891691a51a255aed6bd61095220f8 SHA512: 6585ca08930769ed17fefce2385e18f13bb35fd2291bb7ad1ad439934bc9a5912ef71d83c07c4bc7324fc709b3e544c3bca86129a60a453ad9700f90ccefbbb7 Homepage: https://cran.r-project.org/package=BetaDanish Description: CRAN Package 'BetaDanish' (The Beta-Danish Distribution for Lifetime Data Analysis) Implements the four-parameter Beta-Danish distribution and its three-parameter submodel for survival and reliability analysis, based on Ahmad and Danish (2025) . Provides functions for density, distribution, quantile, hazard, and random generation. Includes maximum likelihood estimation for complete and right-censored data, goodness-of-fit assessment, comparison with standard lifetime distributions, and publication-quality visualizations. Advanced modules support Accelerated Failure Time (AFT) regression, mixture and promotion-time cure models, and competing risks analysis. 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The package can also be used to generate confidence intervals for differences of standardized regression coefficients and as a general approach to performing the delta method. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) . 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Includes, among other things: - d/p/q/r functions for Four-Parameter Beta distributions and Generalized "Binomial" (continuous) distributions, and d/p/r- functions for Beta- Binomial distributions. - d/p/q/r functions for Two- and Four-Parameter Beta distributions parameterized in terms of their means and variances rather than their shape-parameters. - Moment generating functions for Binomial distributions, Beta-Binomial distributions, and observed value distributions. - Functions for estimating classification accuracy and consistency, making use of the Classical Test-Theory based 'Livingston and Lewis' (L&L) and 'Hanson and Brennan' approaches. A shiny app is available, providing a GUI for the L&L approach when used for binary classifications. For url to the app, see documentation for the LL.CA() function. Livingston and Lewis (1995) . Lord (1965) . Hanson (1991) . 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'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect (Pesigan and Cheung, 2024 ) and the sampling covariance matrix of regression coefficients (Dudgeon, 2017 ) to generate confidence intervals effect sizes in regression. 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Package: r-cran-bfbin2arm Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2157 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vgam, r-cran-dplyr, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bfbin2arm_0.1.2-1.ca2604.1_all.deb Size: 1402300 MD5sum: e42ffdff859781f4a73939b10320f3d0 SHA1: 3a68d88988c4bff08f61511237639d83b4d694b0 SHA256: 53d827e42ff99a4db3dd11c6487bbee92516a932d0aef27be5060194abae1d37 SHA512: 701c4023273a0d45364224d762ffa276fe4ba6ad1041b6a4bf73457194cf62cf6c597ba9e6cef9aaac5c273f3b5ab676ac69c2dd0b6f9d0600591d0d76c73dc7 Homepage: https://cran.r-project.org/package=bfbin2arm Description: CRAN Package 'bfbin2arm' (Bayes Factor Design for Two-Arm Binomial Trials) Design and analysis of one- and two-stage two-arm binomial clinical phase II trials using Bayes factors. 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Package: r-cran-bfboin Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boin, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-bfboin_0.1.1-1.ca2604.1_all.deb Size: 145894 MD5sum: d30d02c1b0903de77fe1170e32619347 SHA1: 93abee6179ca9e1de809cc92e7d9b7ab03a3893a SHA256: aa0ae01885af7680f4a7677c0084198e0b5df68ed1b45906d28c37fd5fc2bd9b SHA512: 5965ad0baef90c964045323c50d7e5ba79c73520b21901425d8ed690997d7e8e21785ab46edf8768ed8e640ce2bd7f6aac6b73c7b6b3590e95ef15d8aa9d7e2c Homepage: https://cran.r-project.org/package=bfboin Description: CRAN Package 'bfboin' (Operating Characteristics for the Bayesian Optimal IntervalDesign with Back Filling) Calculate the operating characteristics of the Bayesian Optimal Interval with Back Filling Design for dose escalation in early-phase oncology trials. Package: r-cran-bfboinet Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-copula, r-cran-dplyr, r-cran-tidyselect, r-cran-magrittr, r-cran-bop2fe, r-cran-boinet, r-cran-boin Filename: pool/dists/resolute/main/r-cran-bfboinet_1.1.0-1.ca2604.1_all.deb Size: 161758 MD5sum: e2e68cfa5a274922502789e40ffca91b SHA1: de3c1203e82908644f56fd14b379a002da5604de SHA256: 95cda2ff6a7a1d2f1b09715dea53cd2d0c487d421ff0c3752dc14ebd978b3c8c SHA512: 98e545723d2966cd68017eee02f0f594b4a8a816e6d85e431eef736b231eeb8e932922598d0ddeb6e897ccea1af38f6c9a2e42851a44018b5698f2e2fc39e7fa Homepage: https://cran.r-project.org/package=bfboinet Description: CRAN Package 'bfboinet' (Backfill Bayesian Optimal Interval Design Using Efficacy andToxicity) Implements the Backfill Bayesian Optimal Interval Design (BF-BOIN-ET), a novel clinical trial methodology for dose optimization that simultaneously consider both efficacy and toxicity outcome as described in (Takeda et al (2025) ). The package has been extended to include a seamless two-stage phase I/II trial design with backfill and joint efficacy and toxicity monitoring as described in (Takeda et al (2026) ). Package: r-cran-bfcluster Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bfcluster_1.0.0-1.ca2604.1_all.deb Size: 16136 MD5sum: 874e66dffc90a560d99212ca9e80d7fe SHA1: 98d3683a0c70841a8797ac9ea6b0787c895adb39 SHA256: 5d881fcc5b68ff284d4a08469facd8bdc1cd69079d703d34c2224233f32222fe SHA512: 45e3cbcc7518caf71e5e11401d008d50cf3d0d0d48ee7032a7847530f4c1d2dbb4b388c208d19d98d74c45bb3d6fe9034c793235ec223f3155fde6ce4485cd53 Homepage: https://cran.r-project.org/package=bfcluster Description: CRAN Package 'bfcluster' (Buttler-Fickel Distance and R2 for Mixed-Scale Cluster Analysis) Implements the distance measure for mixed-scale variables proposed by Buttler and Fickel (1995), based on normalized mean pairwise distances (Gini mean difference), and an R2 statistic to assess clustering quality. Package: r-cran-bff Architecture: all Version: 5.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-gsl, r-cran-rlang, r-cran-dpq Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bsda, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-bff_5.0.0-1.ca2604.1_all.deb Size: 354728 MD5sum: 8d1cee3eee38c991e511bd1d0a6d2036 SHA1: 9721623f3b201bef45f886cfe8e2ad64356a82a9 SHA256: 9b9c6b945ed910c50fa9ed0528909fe52b075fb7e781c9ef7a2c33a80dd43005 SHA512: e5b0fa19aadaee279f6c832d7ae9875a1f9d4d81c872cf670601d93db470ad46c474ef05aad37c461080f2f678dfb62b6150887a9af573169c8b67c0124c52fa Homepage: https://cran.r-project.org/package=BFF Description: CRAN Package 'BFF' (Bayes Factor Functions) Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. However, one drawback of Bayes factors is their dependence on prior specifications that define null and alternative hypotheses. Additionally, there are challenges in their computation. To address these issues, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs express Bayes factors as a function of the prior densities used to define the alternative hypotheses. These prior densities are centered on standardized effects, which serve as indices for the BFF. Therefore, BFFs offer a summary of evidence in favor of alternative hypotheses that correspond to a range of scientifically interesting effect sizes. Such summaries remove the need for arbitrary thresholds to determine "statistical significance." BFFs are available in closed form and can be easily computed from z, t, chi-squared, and F statistics. They depend on hyperparameters "r" and "tau^2", which determine the shape and scale of the prior distributions defining the alternative hypotheses. Plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies. Package: r-cran-bfi Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-devtools, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bfi_3.1.0-1.ca2604.1_all.deb Size: 1237820 MD5sum: 41117e12b87a2b9ddd852e5ac1765824 SHA1: 6d6e386def80516b447cc2b9561a845f9ccb58f1 SHA256: 98afc9ded68957ae782a0cbb521b8cdc467460087b50dbaf47cf32f5838275d5 SHA512: 23903153f55551778fd32453f5b6d9b6ee23b43ac923f0362a7a812e6c577d434b4c9c569d40d4dbe36f7c451eafc23b82e51689c0b2d6a6b4b20f1ab7472356 Homepage: https://cran.r-project.org/package=BFI Description: CRAN Package 'BFI' (Bayesian Federated Inference) The Bayesian Federated Inference ('BFI') method combines inference results obtained from local data sets in the separate centers. In this version of the package, the 'BFI' methodology is programmed for linear, logistic and survival regression models. For GLMs, see Jonker, Pazira and Coolen (2024) ; for survival models, see Pazira, Massa, Weijers, Coolen and Jonker (2025) ; and for heterogeneous populations, see Jonker, Pazira and Coolen (2025) . Package: r-cran-bfm Architecture: all Version: 0.2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-psych Suggests: r-cran-testthat, r-cran-spelling, r-cran-betareg, r-cran-zoib Filename: pool/dists/resolute/main/r-cran-bfm_0.2.11-1.ca2604.1_all.deb Size: 32930 MD5sum: 099c804532f5619e93721645f9cf441f SHA1: 5674a6e81f98bd974842f70fcfd23a000dcca97b SHA256: 3cabc3dffbf9c3d31a03cf251ebee53e1078ee9503f48d0ad3c3b63ba6d98b82 SHA512: 8ef410b2cd65b4f3d98e092d5a735275a35d31b452dc0c551562cedc02367eae4131a2af719f2df9809e69c486d3cca36951118616713cf5273dbe18ebaf2a02 Homepage: https://cran.r-project.org/package=BFM Description: CRAN Package 'BFM' (Beta Factor Model) Provides tools for factor analysis in financial and econometric settings under Beta factor models. It includes functions to simulate factor-model data with Beta-distributed idiosyncratic components (e.g., standard Beta, scaled Beta, and truncated Beta distributions) and to conduct model diagnostic assessments such as likelihood ratio tests for factor number selection and goodness-of-fit tests for Beta distribution assumptions. Estimation routines encompass maximum likelihood estimation for finite-dimensional Beta factor models, regularized Beta factor analysis for high-dimensional datasets, and shrinkage-based estimation for robust Beta factor loading recovery in noisy or incomplete data environments. The package's methodological framework is detailed in Guo G. (2023) . Package: r-cran-bfpwr Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lamw Suggests: r-cran-roxygen2, r-cran-tinytest, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-bfpwr_0.1.6-1.ca2604.1_all.deb Size: 443446 MD5sum: ee10f0d7a0b640b7fdd6cd9d00afeff6 SHA1: a9b1b0a8a9f2917138431f28e3479092cac57200 SHA256: bd81fe004a83a36d10cc976f8ba67e29b109f02515dc438d50d30217684322a8 SHA512: f5b26b7b70281b533032e2c82564769550679656c95c747b3e4f4a190e4b84dc67a19ff3f571a7fceda03c0609497651ec0941a9267b827b388375d983fc5c74 Homepage: https://cran.r-project.org/package=bfpwr Description: CRAN Package 'bfpwr' (Power and Sample Size Calculations for Bayes Factor Analysis) Implements z-test, t-test, and normal moment prior Bayes factors based on summary statistics, along with functionality to perform corresponding power and sample size calculations as described in Pawel and Held (2025) . 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This package contains convenience functions to import ESRI (Environmental Systems Research Institute) shape files using the package 'sf' and to plot them easily and quickly without having to worry too much about the technical details. It contains utilities to combine multiple areas to one single polygon and to find neighbours for single regions. For any point on a map, a special locator can be used to determine to which municipality, district or canton it belongs. Package: r-cran-bfw Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-cran-coda, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mass, r-cran-officer, r-cran-plyr, r-cran-png, r-cran-runjags, r-cran-rvg, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-lavaan, r-cran-psych, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bfw_0.4.2-1.ca2604.1_all.deb Size: 729344 MD5sum: a4c2d646974802f7304b1c9c2d3a65f9 SHA1: 62e338a74e28aad0f5f03ff073accd4faa35045a SHA256: f9eb59de4c1f3e7084af1f9591e5037a4348386cab65bdeffdbaeba12118a15b SHA512: b3db2ca372a75843486cec65c8f26c0c9f325d598c7cdaee12dcb07998a513dd65a19f7471b2515970fc1946496dc3571d6090b5d249301aa985fb8d59f3d16b Homepage: https://cran.r-project.org/package=bfw Description: CRAN Package 'bfw' (Bayesian Framework for Computational Modeling) Derived from the work of Kruschke (2015, ), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, ). 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This new generalization of the well-known GEV (Generalized Extreme Value) distribution is useful for modeling heterogeneous bimodal data from different areas. Package: r-cran-bgeva Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-magic, r-cran-trust Filename: pool/dists/resolute/main/r-cran-bgeva_0.3-1-1.ca2604.1_all.deb Size: 73384 MD5sum: 1396672e1800cfdba89a1e787b793278 SHA1: 277a7800a465a158034a7919cfd6ee11ccef4c25 SHA256: f57ac04219348f96943f09a9c138dd78b5e740b9256912ce096153eaefc63b10 SHA512: f56db44997acce80f5dcba79787f91928c5270ffcc2f09c542fdd5541d6319030c269fcdeaf3ba8bcea9b8d2f64f15e7c2f3c08abb12cf913269190d711c7274 Homepage: https://cran.r-project.org/package=bgeva Description: CRAN Package 'bgeva' (Binary Generalized Extreme Value Additive Models) Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable. Package: r-cran-bgfd Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adequacymodel Filename: pool/dists/resolute/main/r-cran-bgfd_0.1-1.ca2604.1_all.deb Size: 241882 MD5sum: 2ad23ed52cf4f853e1a812b8c4f993a8 SHA1: 2da5b280a6fd49ce059f1478e0363cf30e7859de SHA256: 5d5683c10b9daa6fe82fe0233763786db347a79b9c0f1db0139218bb415a63bc SHA512: e2e73561d5302d0db1fb2216e76cbb712ac181613e5a2ee3c3e16eba0d1972b2760227f8362fe52925b521975e765344a1a36eb30825231e5af2b7a126bc5ca8 Homepage: https://cran.r-project.org/package=BGFD Description: CRAN Package 'BGFD' (Bell-G and Complementary Bell-G Family of Distributions) Evaluates the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the following distributions: Bell exponential, Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk, Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential, complementary Bell extended exponential, complementary Bell Weibull, complementary Bell extended Weibull, complementary Bell-Fisk, complementary Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X distribution. Related work includes: a) Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. . b) Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). "Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities". AIMS Mathematics, 8(3): 6970-7004. . c) Algarni A. (2022). "Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model". Axioms, 11(9): 438. . 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It consists a group of functions that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) and Lopez-Cruz et al. (2015) . Also, it computes genomic predictions based on Bayesian approaches. The prediction function uses an orthogonal transformation of the data and specific priors present by Cuevas et al. (2014) . 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(2021) ], calculation of relatedness coefficients using path-tracing methods [Wright (1922) ; McArdle & McDonald (1984) ], inference of relatedness, pedigree conversion, and simulation of multi-generational family data [Lyu et al. (2025) ]. For a full overview, see [Garrison et al. (2024) ]. For a big data application see [Burt et al. (2025) . 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Package: r-cran-bibliometrix Architecture: all Version: 5.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4588 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bibliometrixdata, r-cran-contentanalysis, r-cran-dimensionsr, r-cran-dplyr, r-cran-ca, r-cran-forcats, r-cran-ggplot2, r-cran-ggrepel, r-cran-httr2, r-cran-igraph, r-cran-jsonlite, r-cran-matrix, r-cran-plotly, r-cran-openalexr, r-cran-openxlsx, r-cran-pubmedr, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rscopus, r-cran-shiny, r-cran-shinycssloaders, r-cran-snowballc, r-cran-stringdist, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidytext, r-cran-visnetwork, r-cran-xml2 Suggests: r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat, r-cran-wordcloud2 Filename: pool/dists/resolute/main/r-cran-bibliometrix_5.4.0-1.ca2604.1_all.deb Size: 2973372 MD5sum: 7621dbf6673ae053588f02fcc45faadb SHA1: 275c774aa2c2ebb3b276e767d98ccd592b6f2018 SHA256: 583fcc8afa62425fe23c7fc6b6e6dd87d70d17da88d027f94b4d9da2a0fe353b SHA512: 9a63071a315c6b9ccda8c4d1e2f640edf4a6500e9ebe4a0a41a7f550562ae62155d7ea36745f3bbbd7751bb85b0c3d83a559643a500d38f309f329764a05d4ab Homepage: https://cran.r-project.org/package=bibliometrix Description: CRAN Package 'bibliometrix' (Comprehensive Science Mapping Analysis) Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) . 'bibliometrix' provides various routines for importing bibliographic data from 'SCOPUS', 'Clarivate Analytics Web of Science' (), 'Digital Science Dimensions' (), 'OpenAlex' (), 'Cochrane Library' (), 'Lens' (), and 'PubMed' () databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis. Package: r-cran-bibliometrixdata Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bibliometrix Filename: pool/dists/resolute/main/r-cran-bibliometrixdata_0.3.0-1.ca2604.1_all.deb Size: 3946452 MD5sum: b3b024d721023f68a166ff7c71dd690f SHA1: 463cac5e4a4cf8e13338aebcf810461dd87bf2ea SHA256: 3d32c7129d866020e66c4eabbddd64e9f60f5601f802afef7fb2fc5e7486a43b SHA512: e4b5d321eb013246e3bebfdd1ac2fd49d9cccdeee13ba2ebaa7dcf2c9298b22eb93a1021d4013b265aa51f6e898e5d5faab4232c77be4286e46edf8eea3a1532 Homepage: https://cran.r-project.org/package=bibliometrixData Description: CRAN Package 'bibliometrixData' (Bibliometrix Example Datasets) It contains some example datasets used in 'bibliometrix'. The data are bibliographic datasets exported from the 'SCOPUS' () and 'Clarivate Analytics Web of Science' () databases. They can be used to test the different features of the package 'bibliometrix' (). Package: r-cran-biblionetwork Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-biblionetwork_0.1.0-1.ca2604.1_all.deb Size: 206066 MD5sum: 09bf36e33a38d4c8bfaa4ede1935810e SHA1: e16ab376cddf28f3d3ed17409a6c47c1e69ab227 SHA256: 2ad8c1fa24251d750792b59f43af3ba4658c6566497c5d154f5ade0fe4f2e165 SHA512: cb150ce1124d6cc091645b274136e29daf2c1c2944ae76b18b061a7fce4b30fdd96cf86b4062136874a6bb2d8fa0046657b2dee692f35bafa7b25377e4b79b89 Homepage: https://cran.r-project.org/package=biblionetwork Description: CRAN Package 'biblionetwork' (Create Different Types of Bibliometric Networks) Functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) ; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) ; Sen, Subir K. and Shymal K. Gan (1983) ; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) ; Zhao, Dangzhi and Strotmann, Andreas (2008) . Package: r-cran-bibliorefer Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bibliorefer_0.1.3-1.ca2604.1_all.deb Size: 402442 MD5sum: ca6d9cd603cf978cac90e31e45933408 SHA1: 8a9b2d917e2465e06722c9fc390d21dc71403895 SHA256: 81ca87b90e2d0602a3ad8a6825f0267807d3dd3ff7bd9576c0ebb88c498d9507 SHA512: 76d2dddf2933991739f3df0b38c24bcf25ae5b93dbb97b8d71d9a60720511b955c31b1be035d354e1611ffdf629a55c3f876299e849435e2d2d9aacf5b88c667 Homepage: https://cran.r-project.org/package=bibliorefer Description: CRAN Package 'bibliorefer' (Generator of Main Scientific References) Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained. The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc. The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity. Massimo Aria, Corrado Cuccurullo. (2017) . Caibo Zhou, Wenyan Song. (2021) . Package: r-cran-biblioverlap Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggvenndiagram, r-cran-magrittr, r-cran-matrix, r-cran-rlang, r-cran-shiny, r-cran-stringdist, r-cran-upsetr, r-cran-uuid Suggests: r-cran-dt, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-biblioverlap_1.0.2-1.ca2604.1_all.deb Size: 527704 MD5sum: fd21b114bcd64b362274c9309054f7f5 SHA1: 4166976a7dfb7a106c2ed4808e99be216524aba4 SHA256: 5982aaadd22e07a5b1d9f57ce76e844bcdb76daabc7543eb05d5fb1687344ed7 SHA512: 877f32b17214926774e9140fae48a882d592b41be5d4ce739f40342a57e8429f15d0b66128322a6ba3c53fdf5221ddf798a9c7ad13b677202db3031aaa4e30f9 Homepage: https://cran.r-project.org/package=biblioverlap Description: CRAN Package 'biblioverlap' (Document-Level Matching Between Bibliographic Datasets) Identifies and visualizes document overlap in any number of bibliographic datasets. This package implements the identification of overlapping documents through the exact match of a unique identifier (e.g. Digital Object Identifier - DOI) and, for records where the identifier is absent, through a score calculated from a set of fields commonly found in bibliographic datasets (Title, Source, Authors and Publication Year). Additionally, it provides functions to visualize the results of the document matching through a Venn diagram and/or UpSet plot, as well as a summary of the matching procedure. Package: r-cran-bibnets Architecture: all Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cograph, r-cran-igraph, r-cran-knitr, r-cran-openalexr, r-cran-rcrossref, r-cran-rmarkdown, r-cran-testthat, r-cran-tidygraph Filename: pool/dists/resolute/main/r-cran-bibnets_0.4.4-1.ca2604.1_all.deb Size: 1325424 MD5sum: 9b164d731cc03056d74054b17f4d6dd9 SHA1: f7335cd4b479d04f9e4e1a8a916d5a356ecf0409 SHA256: f99e618efc525e84b70e3c1afdb2d168f997436652bd5c169f95dbc82ac6f078 SHA512: c88e1f04fcaf14b8e00d069c9b58d00743527270393cf9ca048eb14a16967f7255a023a6da1a683102292f8ae94e415f7606a7307ae74be811d695f05154b617 Homepage: https://cran.r-project.org/package=bibnets Description: CRAN Package 'bibnets' (Importing, Constructing, and Exporting Bibliometric Networks) Imports, constructs, and exports bibliometric networks from scholarly metadata. Reads 'Scopus', 'Web of Science', 'BibTeX', 'RIS', 'OpenAlex', 'Lens.org', 'Dimensions', and 'Crossref' exports. Goes beyond standard co-networks with attention-weighted networks (lead, last, proximity, circular position weights), position-aware counting (harmonic, arithmetic, geometric, golden-ratio), similarity and dissimilarity normalisations, temporal networks with fixed, sliding, and cumulative windows, disparity-filter backbone extraction, historiograph construction, and local citation scoring. Methods described in López-Pernas, Saqr & Apiola (2023) . Package: r-cran-bibplots Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bibplots_0.0.8-1.ca2604.1_all.deb Size: 75070 MD5sum: 521a23838a1d3c78538ef32d9f419dac SHA1: 186847d2d78e7abeac36ca0d6918d739a970aba6 SHA256: d24e6c15e668a9e711066aa7e4a3493e29daf8159066a5f65b9ff7f329ae3680 SHA512: f20b4895b28cbd5b9d486a1cb4d80c622692cacafb85dcb4ba2edf790308d1dd09753e92d34de1629d460044caf0d39539202063b7c8a5704d8a290c4abef6b0 Homepage: https://cran.r-project.org/package=BibPlots Description: CRAN Package 'BibPlots' (Plot Functions for Use in Bibliometrics) Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see ). Further extension to more plot variants is planned. Package: r-cran-bibtex Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bibtex_0.5.2-1.ca2604.1_all.deb Size: 70994 MD5sum: 0386047c84e118c464ede9338dbef284 SHA1: 5974a7578325d66886945a25f4bc4dff9e0e59b1 SHA256: abae4f5f1a74a730d4fae165862a7a9de8ce89fc302c9c60b9acf1696ad7e14c SHA512: 4b3525d9eed745d20138b8c58e5494516aa5346b6998bed98c4b676b7214677315fd4d2f1313c8d3b84f31549a9f33dc6ba356282ddb9a16aa5ad0dd7c4eea61 Homepage: https://cran.r-project.org/package=bibtex Description: CRAN Package 'bibtex' (Bibtex Parser) Utility to parse a bibtex file. Package: r-cran-bicausality Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-bicausality_0.1.4-1.ca2604.1_all.deb Size: 140962 MD5sum: 39c54291a97bfaeb3204545f4128762d SHA1: 635c6e36769c6067ded469dc1961b53db5c6bb37 SHA256: 488b7ceef5ca52d8f0bcc983659349efb367107c2447ded7c4df691def804004 SHA512: 0378e7ff2d4f610a9bce9cbd90c1ef9376125a5145401877f67ab7dabd20ac430f046425b8b36161affc17eee5fa0dcc3f630ab49df8f699afd43aef85feb889 Homepage: https://cran.r-project.org/package=BiCausality Description: CRAN Package 'BiCausality' (Binary Causality Inference Framework) A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) . Package: r-cran-bicorn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bicorn_0.1.0-1.ca2604.1_all.deb Size: 78056 MD5sum: 0311c77d2b48072c77fb5059387220ec SHA1: af6e13ce2bbbfb62c819fcd1b7d8249d76c2cc90 SHA256: 7d7448bc9472fe1c555d84db2ad88ff0d51b60ccf936d0447f4464772f72ab23 SHA512: 3bcf309050ef91954d22423386c013a52f5202580aa1bdd45cc604dde446034b4c89658d3c11371d7cdecb9a45d170966d5f046850190cba6bd7a11617d94d3b Homepage: https://cran.r-project.org/package=BICORN Description: CRAN Package 'BICORN' (Integrative Inference of De Novo Cis-Regulatory Modules) Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor. Package: r-cran-bidask Architecture: all Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-xts, r-cran-zoo, r-cran-dplyr, r-cran-crypto2, r-cran-quantmod, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bidask_2.1.5-1.ca2604.1_all.deb Size: 1163150 MD5sum: 6034f34cf264cb94cbbd6c5b4da30e74 SHA1: 3f73e2803929b9d544d9724a895ae3870574865e SHA256: c217df11a025e1d9480a29a3bc4820ef615774e8acf448132738e714b91f54c2 SHA512: 1f2399516cc6ab321cf1d7cc8e275a9b56ae76f0a4a3fd6fb7e925949b991039ff01d4fc7a3f3207e91cd32cd9efb3e570edaf009d79e3b1242852e443b35c63 Homepage: https://cran.r-project.org/package=bidask Description: CRAN Package 'bidask' (Efficient Estimation of Bid-Ask Spreads from Open, High, Low,and Close Prices) Implements the efficient estimator of bid-ask spreads from open, high, low, and close prices described in Ardia, Guidotti, & Kroencke (JFE, 2024) . It also provides an implementation of the estimators described in Roll (JF, 1984) , Corwin & Schultz (JF, 2012) , and Abdi & Ranaldo (RFS, 2017) . Package: r-cran-bidimregression Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-bidimregression_2.0.1-1.ca2604.1_all.deb Size: 129708 MD5sum: 21ec332303083b1e2679fa17cc5baa7b SHA1: 618a3ce005f5c9590bcd2dbbe734ec89931cbe33 SHA256: 1fbd2c6260ae185a5a17ac2a337977892937a9476a558ad116e1d372f9994ca5 SHA512: a8afef086043e21de9d4624ba966f4d1f4cfb16b488803a6514328095bef5810abac12c570a2127d886773be22788486290aacbc90b5cf7c0f7208e7536227bb Homepage: https://cran.r-project.org/package=BiDimRegression Description: CRAN Package 'BiDimRegression' (Calculates the Bidimensional Regression Between Two 2DConfigurations) Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965). Package: r-cran-bidser Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-data.tree, r-cran-neuroim2, r-cran-tidyselect, r-cran-dplyr, r-cran-assertthat, r-cran-crayon, r-cran-fs, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringdist, r-cran-tibble, r-cran-tidyr, r-cran-httr, r-cran-rio Suggests: r-cran-ggplot2, r-cran-plotly, r-cran-patchwork, r-cran-viridis, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-lintr, r-cran-gluedown, r-cran-rnifti, r-cran-future, r-cran-future.apply Filename: pool/dists/resolute/main/r-cran-bidser_0.2.0-1.ca2604.1_all.deb Size: 514982 MD5sum: 1acf420c774de7a0af8fcb61ca4a0cf6 SHA1: 9e83d5bff4f4a71f6b219f5bc2b64ad21aa1f7e7 SHA256: 801316dd23e19dde36f192a97d4ffd6c376d673634866fee47abbba1845b9cf8 SHA512: 83bc3a188f3d3f5b8d4823acb388d823b72d2a1a1a1c4b5db766b8870dbb42d732665282a62446cafc3cd0e1f410d97d18c498a0eba421a1e01686eab526d031 Homepage: https://cran.r-project.org/package=bidser Description: CRAN Package 'bidser' (Work with 'BIDS' (Brain Imaging Data Structure) Projects) Tools for working with 'BIDS' (Brain Imaging Data Structure) formatted neuroimaging datasets. The package provides functionality for reading and querying 'BIDS'-compliant projects, creating mock 'BIDS' datasets for testing, and extracting preprocessed data from 'fMRIPrep' derivatives. It supports searching and filtering 'BIDS' files by various entities such as subject, session, task, and run to streamline neuroimaging data workflows. See Gorgolewski et al. (2016) for the 'BIDS' specification. Package: r-cran-bidsr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2508 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-fastmap, r-cran-fs, r-cran-jsonlite, r-cran-nanotime, r-cran-s7, r-cran-uuid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bidsr_0.1.1-1.ca2604.1_all.deb Size: 700234 MD5sum: 084ff030bb11521656495eb00e8e495a SHA1: fdf6d92ddf8be1f2f71d8ba93b30a7210df62c8f SHA256: 005fc8a549717a3ea2e5b7b7653f80c610278afa89b2b9660b789be125a9b61e SHA512: c7e8864d1340a0d20134c7fe95f4b88611d73ddcf7395907c331d9eff410d031e642cf05c179e2cd2b19143c4b632a0dbd704db460710de2297dfe20d193b98e Homepage: https://cran.r-project.org/package=bidsr Description: CRAN Package 'bidsr' (A Brain Imaging Data Structure ('BIDS') Parser) Parse and read the files that comply with the brain imaging data structure, or 'BIDS' format, see the publication from Gorgolewski, K., Auer, T., Calhoun, V. et al. (2016) . Provides query functions to extract and check the 'BIDS' entity information (such as subject, session, task, etc.) from the file paths and suffixes according to the specification. The package is developed and used in the reproducible analysis and visualization of intracranial electroencephalography, or 'RAVE', see Magnotti, J. F., Wang, Z., and Beauchamp, M. S. (2020) ; see 'citation("bidsr")' for details and attributions. Package: r-cran-bidux Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dplyr, r-cran-glue, r-cran-janitor, r-cran-jsonlite, r-cran-memoise, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-stringdist, r-cran-tibble Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-otel, r-cran-otelsdk, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-bidux_0.4.0-1.ca2604.1_all.deb Size: 1197832 MD5sum: d9b02d5f8461cba568139d4fc19e23af SHA1: f37338741c4fadfa29d60fe0b180add1ae9696b5 SHA256: dccec8a669cd848ea4fa5baebea9dd22f3be11fbd453d27f7da06a2601b5b1d1 SHA512: bd6a7debfc342101b0b3745c4362f264068094564f2f723d77ed54c0ddf51db5a15585d22aa88be809ef0b0d81f199ef3d2cf651b1d65add1da34ae9f4ad4f92 Homepage: https://cran.r-project.org/package=bidux Description: CRAN Package 'bidux' (Behavioral Insight Design: A Toolkit for Integrating BehavioralScience in UI/UX Design) Provides a framework and toolkit to guide R dashboard developers in implementing the Behavioral Insight Design (BID) framework. The package offers functions for documenting each of the five stages (Interpret, Notice, Anticipate, Structure, and Validate), along with a comprehensive concept dictionary. Works with both 'shiny' applications and 'Quarto' dashboards. Package: r-cran-bien Architecture: all Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpostgresql, r-cran-dbi, r-cran-ape, r-cran-sf, r-cran-terra, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-curl, r-cran-maps Filename: pool/dists/resolute/main/r-cran-bien_1.2.8-1.ca2604.1_all.deb Size: 382982 MD5sum: 6cb6983ded2990bc2ccdd72a4c9a0462 SHA1: 4c216f2da599683ab28d95ebf67699e2e23f4e7d SHA256: 2183b7f6d647377b6482a0025cde0a0bf91ca62eb93ab4ca53ca5420f14df8b4 SHA512: c9381b7e5621a58ddf2e0de2a4be70c58c629e164803f89cd2044e535a07bca811f622ba107fa3b41e8e1556c8011f4d72b9aa312dddffb5208a1d5f0332ea60 Homepage: https://cran.r-project.org/package=BIEN Description: CRAN Package 'BIEN' (Tools for Accessing the Botanical Information and EcologyNetwork Database) Provides Tools for Accessing the Botanical Information and Ecology Network Database. The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See for more Information). This package provides functions that query the BIEN database by constructing and executing optimized SQL queries. Package: r-cran-bifactorindicescalculator Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-lavaan, r-cran-mirt, r-cran-mplusautomation, r-cran-mnormt Suggests: r-cran-testthat, r-cran-psych Filename: pool/dists/resolute/main/r-cran-bifactorindicescalculator_0.2.2-1.ca2604.1_all.deb Size: 226612 MD5sum: 1b7f324eb25e3b90c1297b59ac7b71da SHA1: 2e400c5ea90ffc8a8f3fab569fa3cb96685ebc94 SHA256: 9b45a0a30caeb6c8b4025c4c9a4b984d963ac17eb985a6edb3c0da6302302049 SHA512: 1a642f774368df9415681633cb39a90cc60929348b41c3a0ae4db026cb282cb80e478d4092f9069a3954354609f67e3ef5438906d0b3949c15ce551c099e57df Homepage: https://cran.r-project.org/package=BifactorIndicesCalculator Description: CRAN Package 'BifactorIndicesCalculator' (Bifactor Indices Calculator) The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the 'Excel' based 'Bifactor Indices Calculator' (Dueber, 2017) with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models. Package: r-cran-bifrost Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-future, r-cran-future.apply, r-cran-phytools, r-cran-mvmorph, r-cran-viridis, r-cran-txtplot Suggests: r-cran-hdinterval, r-cran-rcolorbrewer, r-cran-boot, r-cran-classint, r-cran-evd, r-cran-fitdistrplus, r-cran-knitr, r-cran-palaeoverse, r-cran-patchwork, r-cran-pbmcapply, r-cran-phylolm, r-cran-plotly, r-cran-readxl, r-cran-rmarkdown, r-cran-scales, r-cran-scatterplot3d, r-cran-testthat, r-cran-univariateml, r-cran-spelling, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-bifrost_0.1.4-1.ca2604.1_all.deb Size: 1887526 MD5sum: ac9c88384ad951b091d443f9156f350a SHA1: af2207bf118b6bc06f87786302cde74c40e1360e SHA256: e061daa83467e51a89c310fdd4ce59b86b6b43b7645f9191618824cb97649839 SHA512: e506c76144b6e5d27f02947ab8e604af95f15d07c2e88746da69016b16ba89f55586b1e5e4965ce628038a635668a5c09f6d3ea045841ed0107c5cc13e0430ba Homepage: https://cran.r-project.org/package=bifrost Description: CRAN Package 'bifrost' (Branch-Level Inference Framework for Recognizing Optimal Shiftsin Traits) Methods for detecting and visualizing cladogenic shifts in multivariate trait data on phylogenies. Implements penalized-likelihood multivariate generalized least squares models, enabling analyses of high-dimensional trait datasets and large trees via searchOptimalConfiguration(). Includes a greedy step-wise shift-search algorithm following approaches developed in Smith et al. (2023) and Berv et al. (2024) . Methods build on multivariate GLS approaches described in Clavel et al. (2019) and implemented in the mvgls() function from the 'mvMORPH' package. Documentation and vignettes are available at , including worked examples for the jaw-shape dataset. Package: r-cran-bifurcatingr Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1441 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmultivar Suggests: r-cran-igraph, r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bifurcatingr_2.1.0-1.ca2604.1_all.deb Size: 860740 MD5sum: c95abb7aa563d7771a42253768cfc3da SHA1: 125c47a5068ff9b344468fd9822a170c547fa576 SHA256: 23715a524a0a8b18ffe05261865254f21d141a3864cfe040f01f867e5cb2c069 SHA512: 03d86675892f4fd98d4f9b72884a63f5258e824a6de0e3a4869d38139c84ef57edd9b348f0980c3eeda97003fb1a9d51736f77e47af69175828db41e4a049b99 Homepage: https://cran.r-project.org/package=bifurcatingr Description: CRAN Package 'bifurcatingr' (Bifurcating Autoregressive Models) Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) and Elbayoumi and Mostafa (2020) . Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023) . Package: r-cran-bigassertr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-bigassertr_0.2.0-1.ca2604.1_all.deb Size: 34328 MD5sum: f4ddd9334ce59323d85d5c71ad22718d SHA1: 4a898bc1eae692cab0b75301b3de870b34febc2d SHA256: ae7998d7c3995bb2b7d7f98998324a304309a07c4bb924533a5279d5643db573 SHA512: 901857d3c88a5729ec40398f433af57bfc794ef6af174d6cbc143fb71b619953f64451d3032f1e9da3f7a09c4aba45453d60f9006c31c3523efea1390fdd5657 Homepage: https://cran.r-project.org/package=bigassertr Description: CRAN Package 'bigassertr' (Assertion and Message Functions) Enhanced message functions (cat() / message() / warning() / error()) using wrappers around sprintf(). Also, multiple assertion functions (e.g. to check class, length, values, files, arguments, etc.). 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A direct base to base converter is included. Package: r-cran-bigd Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1190 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-vctrs Filename: pool/dists/resolute/main/r-cran-bigd_0.3.1-1.ca2604.1_all.deb Size: 1168752 MD5sum: 75340974eeb834a4d4b91d218aac8d44 SHA1: e961f4ac45060e95260b2d4af37e6270d39a8bd4 SHA256: b765909c9ae08b681c3aebd3c7710306685acd11cdd8b5621883cee1d85671e0 SHA512: 8aaa990fe2894ed4a27431f4b068cc36ccdd77f123fd5d876813885badf3761eaf953bb7906db993a719f8fe7dccf0f1ae2b4dc54e749a31ade302b9178446f8 Homepage: https://cran.r-project.org/package=bigD Description: CRAN Package 'bigD' (Flexibly Format Dates and Times to a Given Locale) Format dates and times flexibly and to whichever locales make sense. Parses dates, times, and date-times in various formats (including string-based ISO 8601 constructions). The formatting syntax gives the user many options for formatting the date and time output in a precise manner. Time zones in the input can be expressed in multiple ways and there are many options for formatting time zones in the output as well. Several of the provided helper functions allow for automatic generation of locale-aware formatting patterns based on date/time skeleton formats and standardized date/time formats with varying specificity. Package: r-cran-bigdatape Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2370 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-httr2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-bigdatape_0.1.0-1.ca2604.1_all.deb Size: 104430 MD5sum: daddd9c1c2fa440c8d488138e4eb9590 SHA1: 021b70534b45984f257d7602f24f510842cde419 SHA256: 5d4ff61e8dae5a3729b5ad47c8581e4362137b6dfdcb9baa9746bb07d1a624eb SHA512: eff49073bf29e96edc79f092efd885678a4b9d8f2f311eeef909f7cbe8f128692f870ef62748e53ffb61f055907a37a21f0c4424f73d5eebd13c12c3acc2c27a Homepage: https://cran.r-project.org/package=BigDataPE Description: CRAN Package 'BigDataPE' (Secure and Intuitive Access to 'BigDataPE' 'API' Datasets) Designed to simplify the process of retrieving datasets from the 'Big Data PE' platform using secure token-based authentication. It provides functions for securely storing, retrieving, and managing tokens associated with specific datasets, as well as fetching and processing data using the 'httr2' package. Package: r-cran-bigdawg Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-haplo.stats Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bigdawg_3.1.0-1.ca2604.1_all.deb Size: 1164132 MD5sum: c6579e94ab298ab940e2e775b19362a8 SHA1: f7eb450e3e8e99a5b4880fee970aca84a3c9e3ca SHA256: 5722e09fd6e60cf18e1227c3a97464089b724220dc544840d07b265d847490cc SHA512: 80607d4f3a0a3a335b3505d732defda6b5f7fcee37db0ee2af13cd5b0e98d171b98ff30fab78ca2c5da3ad05781795184e19d84d92995fb044c3f49ed1a03c27 Homepage: https://cran.r-project.org/package=BIGDAWG Description: CRAN Package 'BIGDAWG' (Case-Cotrol Analysis of Multi-Allelic Loci) Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) . Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) . Package: r-cran-bigdm Architecture: all Version: 0.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4944 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-doparallel, r-cran-fastdummies, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-geos, r-cran-mass, r-cran-matrix, r-cran-parallelly, r-cran-rcolorbrewer, r-cran-rdpack, r-cran-sf, r-cran-spatialreg, r-cran-spdep, r-cran-rlist Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/resolute/main/r-cran-bigdm_0.5.7-1.ca2604.1_all.deb Size: 5015054 MD5sum: 79f3d136447048cb0ec8cd44225fb077 SHA1: f781a51e89c724cbb9d427265bba9270f3c40c17 SHA256: 70a3b770c309b4bce49e5cc904a54e4c8702cd08aa68f08de26aa482e949d443 SHA512: 02ca2f24c975be1c5ba18b55124a00aecec6dd5b323e7f6fa466ce4abcc54259fcfb54f7cc711add1f1015c3c663c5b5e8d432a7d147de49847bc218a4c1ccfe Homepage: https://cran.r-project.org/package=bigDM Description: CRAN Package 'bigDM' (Scalable Bayesian Disease Mapping Models for High-DimensionalData) Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 ; Orozco-Acosta et al., 2023 and Vicente et al., 2023 ). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001). Package: r-cran-bigl Architecture: all Version: 1.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4797 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-minpack.lm, r-cran-numderiv, r-cran-progress, r-cran-plotly, r-cran-robustbase, r-cran-scales, r-cran-nleqslv, r-cran-data.table, r-cran-lifecycle, r-cran-htmlwidgets, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shiny, r-cran-dt Filename: pool/dists/resolute/main/r-cran-bigl_1.9.3-1.ca2604.1_all.deb Size: 1446372 MD5sum: e6e4304a12bbb3a0dcd6527b1a788910 SHA1: 84b44a232535517eaee400d55e87026265dd7e5a SHA256: 61b6a290e34e5d01afd6e6fa5f6227b4e4d621257442bdc5e72b699f8e88e2c6 SHA512: 15979b479437ef6622d432550dc5ac4e2944fa195ffc2004c5534cdd355b9655b4f9261497ae0cadade090cd42e2a6a531123143ef43c1085df995552a8fcf38 Homepage: https://cran.r-project.org/package=BIGL Description: CRAN Package 'BIGL' (Biochemically Intuitive Generalized Loewe Model) Response surface methods for drug synergy analysis. Available methods include generalized and classical Loewe formulations as well as Highest Single Agent methodology. Response surfaces can be plotted in an interactive 3-D plot and formal statistical tests for presence of synergistic effects are available. Implemented methods and tests are described in the article "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism" by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) . Package: r-cran-bigleaf Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-solartime Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bigleaf_0.8.2-1.ca2604.1_all.deb Size: 1027094 MD5sum: d9a0e5b2711edac5d0d6b7cc647181d1 SHA1: 8e71417e97674384f9e70b0389771df2704c64bf SHA256: f9a26fd3d0e8cf6db3a83c2bc461ab6d0c29fb2a34f427a388366d1af020bff6 SHA512: 34300dc2a12bebcd555681ed624373373c19e92b360c0a73e6cace2c34bdde12b77de0ac10f8562b6c8de05d05732c45f44d246bb35e7af4cdbf713aa111bcfd Homepage: https://cran.r-project.org/package=bigleaf Description: CRAN Package 'bigleaf' (Physical and Physiological Ecosystem Properties from EddyCovariance Data) Calculation of physical (e.g. aerodynamic conductance, surface temperature), and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. Calculations assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables. Package: r-cran-bigmatch Architecture: all Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcbalance, r-cran-liqueuer, r-cran-plyr, r-cran-mvnfast Suggests: r-cran-optmatch Filename: pool/dists/resolute/main/r-cran-bigmatch_0.6.4-1.ca2604.1_all.deb Size: 199800 MD5sum: d556cfe25d812e2727667501fd6a0296 SHA1: 52bea23a1d3990250e9663f9ba2aeb517824150a SHA256: 714ec8dbb2c41b70b1b9740bb585e3951c9fa70d6812dba9f6aaee9d6b386b22 SHA512: e6393b1446fb812318d27d92b59d73e9b541fb007783ae4c00a5683c87e1f59ed80dbd32880d07b76449ad8324587899cb4e63e382a58eda29af2cecabbcdd4b Homepage: https://cran.r-project.org/package=bigmatch Description: CRAN Package 'bigmatch' (Making Optimal Matching Size-Scalable Using Optimal Calipers) Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors. Yu, R., Silber, J. H., and Rosenbaum, P. R. (2020). . Package: r-cran-bigmds Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-svd, r-cran-corpcor Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bigmds_3.0.0-1.ca2604.1_all.deb Size: 70244 MD5sum: 77285b2e0050cb31566746a455c622cc SHA1: fdf57128a2aa215338fcd5d5fd21393c5f3e09a4 SHA256: 945d7655d4ae46840f2550e6e9edff5b0e436cfe947e7c2e3c9d5640ef59f0df SHA512: 201a0d6c453d489f25373ccbb0d46979db1c133ad3180037f46547633b57277e179e4e2e790f2ec7424a5e21b2c4f348939d573d04d4cd185974ef8bc390774a Homepage: https://cran.r-project.org/package=bigmds Description: CRAN Package 'bigmds' (Multidimensional Scaling for Big Data) MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021) (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021) (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006). Package: r-cran-bigmemory.sri Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bigmemory.sri_0.1.8-1.ca2604.1_all.deb Size: 12760 MD5sum: de1796a3f4acf1b2f63faecd8d0c210f SHA1: 6fdd29072d112b25c5876f14c817ce501618cb85 SHA256: e46976223269374bb0595ae639e34ddd8ab844316c78ecce816ff5d4be92c4ad SHA512: a7b35103b2b9b0e06d265cb9459939106201668915400e6a0e585ec8af91f478916e9846ff7646d5c2324d506089145b3f414035c8883f820766253b71d3f3a2 Homepage: https://cran.r-project.org/package=bigmemory.sri Description: CRAN Package 'bigmemory.sri' (A Shared Resource Interface for Bigmemory Project Packages) A shared resource interface for the bigmemory and synchronicity packages. Package: r-cran-bigmice Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-rlang, r-cran-sparklyr, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bigmice_1.0.0-1.ca2604.1_all.deb Size: 163434 MD5sum: e87f8bcb48b037e440ebde7024caa83c SHA1: 33c4023cf1712b253adfd19bd13c8cea5e179c79 SHA256: 18bfb83a9f3398852383055a6a94f0f9d7c4274f679107ca42c4036b7ef0ce4b SHA512: 4081e11d45de3990acc356b08e2c9f2c456ad17f31ffe29fe832a57bf9005fdcdf7fc6653c373bb88568239db20c542f827bd6c6701490ab3739e6d188dc83a1 Homepage: https://cran.r-project.org/package=bigMICE Description: CRAN Package 'bigMICE' (Multiple Imputation of Big Data) A computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using 'Apache Spark'. The methodology is described in Morvan et al. (2026) . Package: r-cran-bigparallelr Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-bigassertr, r-cran-doparallel, r-cran-flock, r-cran-parallelly, r-cran-rhpcblasctl Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-bigparallelr_0.3.2-1.ca2604.1_all.deb Size: 42618 MD5sum: 03748e987ade84a2af3f465b35229a6f SHA1: b223a33fe05fbb9459e48fce7f0b7e5c065d17e5 SHA256: 59cdcf21dd7d5ada4bbfd5de08f44968b89ad15ce48a5ea4f3c6eef09a024c0f SHA512: 56f7a9839a7242f13baf569b9d1149d8750b7c4b62d9fd784f36a404d4bc9504ec11cb73074d20ada3a2446ce4cb62db87dca8b0e73a731f85aeb0642220a8b4 Homepage: https://cran.r-project.org/package=bigparallelr Description: CRAN Package 'bigparallelr' (Easy Parallel Tools) Utility functions for easy parallelism in R. Include some reexports from other packages, utility functions for splitting and parallelizing over blocks, and choosing and setting the number of cores used. Package: r-cran-bigr Architecture: all Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2971 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rdpack, r-cran-readr, r-cran-reshape2, r-cran-rlang, r-cran-tidyr, r-cran-vcfr, r-bioc-rsamtools, r-bioc-biostrings, r-bioc-pwalign, r-cran-janitor, r-cran-quadprog, r-cran-tibble, r-cran-stringr, r-cran-data.table Suggests: r-cran-covr, r-cran-ggplot2, r-cran-spelling, r-cran-rmdformats, r-cran-knitr, r-cran-rmarkdown, r-cran-polyrad, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bigr_0.7.2-1.ca2604.1_all.deb Size: 1424562 MD5sum: a5fb4f71a7693015ca24b51ec94a066b SHA1: 95509ba44014497308d917e5cbfc2c42091db43f SHA256: c1aa1d07da110468e9360aad84d87c33311320722d2ef5a40f587c579bdcf9ad SHA512: 697b7e33e1ecc103d41ae2bc044559429df841eddfcac756c3454dc3b351554ee5393d10dea67decae5b16da6acde302219a219bba50a655c6cc94bcc3dc2b6b Homepage: https://cran.r-project.org/package=BIGr Description: CRAN Package 'BIGr' (Breeding Insight Genomics Functions for Polyploid and DiploidSpecies) Functions developed within Breeding Insight to analyze diploid and polyploid breeding and genetic data. 'BIGr' provides the ability to filter variant call format (VCF) files, extract single nucleotide polymorphisms (SNPs) from diversity arrays technology missing allele discovery count (DArT MADC) files, and manipulate genotype data for both diploid and polyploid species. It also serves as the core dependency for the 'BIGapp' 'Shiny' app, which provides a user-friendly interface for performing routine genotype analysis tasks such as dosage calling, filtering, principal component analysis (PCA), genome-wide association studies (GWAS), and genomic prediction. For more details about the included 'breedTools' functions, see Funkhouser et al. (2017) , and the 'updog' output format, see Gerard et al. (2018) . Package: r-cran-bigsimr Architecture: all Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bigsimr_0.12.0-1.ca2604.1_all.deb Size: 22550 MD5sum: df0b28be039db6380765eeb17ad430cd SHA1: 9b09d0f3880a135e0f82442315a9fb9473defd16 SHA256: fea53444f71dbde2be6359f42ebc5439f9802e12f3262db74b2096a35f3c4b26 SHA512: 6f0996b7cfdb0efe2a037cf98091e22e476e93622bb8a3b24ef46a86c0372077f5a335508d2318191bd974edab4725077034b98aedf7fe4ccbb99e378ee02025 Homepage: https://cran.r-project.org/package=bigsimr Description: CRAN Package 'bigsimr' (Fast Generation of High-Dimensional Random Vectors) Simulate multivariate data with arbitrary marginal distributions. 'bigsimr' is a package for simulating high-dimensional multivariate data with a target correlation and arbitrary marginal distributions via Gaussian copula. It utilizes the Julia package 'Bigsimr.jl' for its core routines. Package: r-cran-bigstep Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-magrittr, r-cran-matrixstats, r-cran-r.utils, r-cran-rcppeigen, r-cran-speedglm Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bigstep_1.1.2-1.ca2604.1_all.deb Size: 88568 MD5sum: 185d426674d5a91ef7fac6ff87859b96 SHA1: 98876d2ff626b343e8ffb6dc47ed4b3033cb8d97 SHA256: c41afd75c7ef773264f1adae80401ded45ad2d530257120526f1c541e7ab7422 SHA512: c98d2d46d805ac69b707ccd136d72f1a6fd588b43e757f0bb2b41e743e31520ce074d66007ca8369643ed0ce643c746881ff61c3b0eb304d8762be07645cae58 Homepage: https://cran.r-project.org/package=bigstep Description: CRAN Package 'bigstep' (Stepwise Selection for Large Data Sets) Selecting linear and generalized linear models for large data sets using modified stepwise procedure and modern selection criteria (like modifications of Bayesian Information Criterion). Selection can be performed on data which exceed RAM capacity. Bogdan et al., (2004) . Package: r-cran-bikeshare14 Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3525 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-data.table Filename: pool/dists/resolute/main/r-cran-bikeshare14_0.1.4-1.ca2604.1_all.deb Size: 3578814 MD5sum: 080acc60490aa6a5c57196284143eb48 SHA1: 52c0880f13cd85a00b09422f8a47deba11d1ac3e SHA256: 7529c0b580e8dd8e0ebfafac1d61dfbc5fb30f2c2b9762787472b373eb76399d SHA512: 8eb55ace4f98cbeb92a4b7a7c500b8bf5b319df59b83768451549e55173c263d91ad2f93160468d3cc5ff5fc6a3ed0307b60501c1bbe9c5613bd89f420141782 Homepage: https://cran.r-project.org/package=bikeshare14 Description: CRAN Package 'bikeshare14' (Bay Area Bike Share Trips in 2014) Anonymised Bay Area bike share trip data for the year 2014. Also contains additional metadata on stations and weather. Package: r-cran-bikm1 Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-ade4, r-cran-pracma, r-cran-ggplot2, r-cran-reshape2, r-cran-lpsolve Filename: pool/dists/resolute/main/r-cran-bikm1_1.1.0-1.ca2604.1_all.deb Size: 454770 MD5sum: d0b6674400aadee1706bdd7300ad3021 SHA1: 24c0d02c56d63af76973082a26a953d4b75e1dd6 SHA256: cfbe7533e708f1496bf6a66df622007d183450947e627daf926a7688eb4e28e4 SHA512: 60a50e96654d614d23180e85534bfd1e344e23a2dd29fde79e3e05ea1159dd916be3b7f8e86aba2a0cf6d59b31906f1202f7fc75659dbeed7d04a856bddce18f Homepage: https://cran.r-project.org/package=bikm1 Description: CRAN Package 'bikm1' (Co-Clustering Adjusted Rand Index and Bikm1 Procedure forContingency and Binary Data-Sets) Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) . Package: r-cran-billboard Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-billboard_0.1.0-1.ca2604.1_all.deb Size: 2633016 MD5sum: 8ea3918c57936afa38edbc52e46a145a SHA1: 4b25753686d9b36a05710b8c5eab6cbb595e4fe6 SHA256: 22a37be46d72c4539dcf6e256a19c99d5ea763515d750c8a8f5487a8330c2f5d SHA512: 579c08714e54abb64001ffa3137c9046e2a673ed38c8fc1aa224a969f27d263e7c8466b4c5c6b7aebd4ada3a958cc233499066ee6e7f7102454d3c2ae64ac31d Homepage: https://cran.r-project.org/package=billboard Description: CRAN Package 'billboard' (Contains Data of Billboard Hot 100 Songs) Contains data sets regarding songs on the Billboard Hot 100 list from 1960 to 2016. The data sets include the ranks for the given year, musical features of a lot of the songs and lyrics for several of the songs as well. 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Chart types include line charts, scatterplots, bar/lollipop charts, histogram/density plots, pie/donut charts and gauge charts. All charts are interactive, and a proxy method is implemented to smoothly update a chart without rendering it again in 'shiny' apps. 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Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, rational expectations, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control, by A. Luciani (2022) . Package: r-cran-bimodalindex Architecture: all Version: 1.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oompabase, r-cran-mclust Suggests: r-cran-oompadata Filename: pool/dists/resolute/main/r-cran-bimodalindex_1.1.11-1.ca2604.1_all.deb Size: 231206 MD5sum: 1a8fa1924218026ce523973b4d434351 SHA1: 47836ea9365b1752ee13c4947eb7ffc239cb9df8 SHA256: de6728354957eb42716e101dbb67a232f9c915340ca20e9fc66f59a2bce2d5e4 SHA512: a5685cd70c2b378ce024cb9934f2da3d34170bf7ff99b14837cc09a349aaff0d8037a7a68ff54717e5e89e6a07331dbe21a821f1d4a381715de29b95ef867811 Homepage: https://cran.r-project.org/package=BimodalIndex Description: CRAN Package 'BimodalIndex' (The Bimodality Index) Defines the functions used to compute the bimodal index as defined by Wang et al. (2009) , . 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Supports fixed-mean and random-mean models with maximum likelihood estimation (MLE), generalized linear mixed model (GLMM), and Bayesian Markov chain Monte Carlo (MCMC) implementations. For methodological background, see Albert and Chib (1993) and McCulloch (1994) . Package: r-cran-binancer Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-digest, r-cran-snakecase, r-cran-logger, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-binancer_1.2.0-1.ca2604.1_all.deb Size: 90952 MD5sum: a2e15f6e314c3d4724c16571939b8554 SHA1: 0fabb03c4dca77973971c13a5a14bff513558f11 SHA256: 298c239e47e8ef2150aa2bad80b86ec6cf797e0f942f904de70747ff236a0fd3 SHA512: ba024f8e269a3d93a45b4b30add44aca775b17650849a1be6e241f2eac10069d39a590e341043488a0a49b630283ca07ec291d1667b9d0e8b35bacc121aaf275 Homepage: https://cran.r-project.org/package=binancer Description: CRAN Package 'binancer' (API Client to 'Binance') R client to the 'Binance' Public Rest API for data collection on cryptocurrencies, portfolio management and trading: . Package: r-cran-binarybalancedcut Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-binarybalancedcut_0.2-1.ca2604.1_all.deb Size: 15296 MD5sum: e874dc266043be8beaa7054c7b06a178 SHA1: f424ed93887b40c155609330ef09ef1fc1f9db24 SHA256: 8937b427a7c82ad9bdc100bf0859b76df6e22ee8307fe6f47c07eea27f8a68ee SHA512: 8aa87962f4b8c82e64ffe5a446d50efa19ff9a83d57678272d85f1a4db13fd0c3e3e55460dfefdc3713c74d9eeb37a0dad469552e27d3250e7338c3cc9600f0a Homepage: https://cran.r-project.org/package=BinarybalancedCut Description: CRAN Package 'BinarybalancedCut' (Threshold Cut Point of Probability for a Binary Classifier Model) Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold. Package: r-cran-binaryemvs Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-binaryemvs_0.1-1.ca2604.1_all.deb Size: 32130 MD5sum: 52b1f90837c21ee853511b2cd0f32159 SHA1: e6c106c5c2a36520f152a3001634f03bb09ff0e3 SHA256: d8a8b5f6ae208e1f213ce1916b2ffb2efc4940df72e1ae186282b6112811dc71 SHA512: 39472e819d23c5ded7aed12833f01c429fcc07a12059c1c402b1c016fc1639d65af974f181d6de603fb3d30cc7eb4cf166b427b2b385d85fae05e1da43972f69 Homepage: https://cran.r-project.org/package=BinaryEMVS Description: CRAN Package 'BinaryEMVS' (Variable Selection for Binary Data Using the EM Algorithm) Implements variable selection for high dimensional datasets with a binary response variable using the EM algorithm. Both probit and logit models are supported. Also included is a useful function to generate high dimensional data with correlated variables. Package: r-cran-binaryeppm Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-expm, r-cran-numderiv, r-cran-lmtest Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-binaryeppm_3.0-1.ca2604.1_all.deb Size: 428930 MD5sum: 74400f3b53d5138b917a7049fdb1d149 SHA1: c66c13ea7d690c000bb26f82da4cfaef28d69264 SHA256: 621974d8f7c965ce072a34882c54886ca3446f1bc451254d01cfbcafd53e4ebd SHA512: 1f384fdad55753398af6c30064ac63de78a3b3650eaf444e55753c0627f0c1ace236c64f8206c482ec7893b90d7afaa79ea1e8866cd6345e18dbc57438974d98 Homepage: https://cran.r-project.org/package=BinaryEPPM Description: CRAN Package 'BinaryEPPM' (Mean and Scale-Factor Modeling of Under- And Over-DispersedBinary Data) Under- and over-dispersed binary data are modeled using an extended Poisson process model (EPPM) appropriate for binary data. A feature of the model is that the under-dispersion relative to the binomial distribution only needs to be greater than zero, but the over-dispersion is restricted compared to other distributional models such as the beta and correlated binomials. Because of this, the examples focus on under-dispersed data and how, in combination with the beta or correlated distributions, flexible models can be fitted to data displaying both under- and over-dispersion. Using Generalized Linear Model (GLM) terminology, the functions utilize linear predictors for the probability of success and scale-factor with various link functions for p, and log link for scale-factor, to fit a variety of models relevant to areas such as bioassay. Details of the EPPM are in Faddy and Smith (2012) and Smith and Faddy (2019) . Package: r-cran-binb Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3471 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown, r-cran-knitr, r-cran-codetools Filename: pool/dists/resolute/main/r-cran-binb_0.0.8-1.ca2604.1_all.deb Size: 1813278 MD5sum: c0ab0d32af36b3440736876a5ecad060 SHA1: ce491fb66f263fc6bd953a72a996ce2170318893 SHA256: 5cad0c962c847de4fa03d3d3a11da1ca36e67a7dad6771f4186ae2b30c2a93c4 SHA512: e0868303c9c5dc0c2aa163a6276b6e0207c8d6ee79f5155f18ac58ffcd8ea332389ffb68da4e8a569b27cd2dafe07ef6ce2f40990dd132235a5b660f0dfca557 Homepage: https://cran.r-project.org/package=binb Description: CRAN Package 'binb' ('binb' is not 'Beamer') A collection of 'LaTeX' styles using 'Beamer' customization for pdf-based presentation slides in 'RMarkdown'. 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Package: r-cran-bincor Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-bincor_0.2.1-1.ca2604.1_all.deb Size: 85538 MD5sum: 4b123cc79d68eb724bb977641a0b09e4 SHA1: e80515f57ab6673c84a5ebf4001765b0b38e4082 SHA256: e2e8f2132ea7aa4c49b990818b152c2ce608051eead8ca7a39c598ef83466676 SHA512: c7cb80424fa244b788cf4225a13a5129ee08b97d849af83ebec82e69d5a72eec8f37601129c0a32efa2fa8bc7e3c346cd1b45570b05ccf372a38cd962aeb0ec7 Homepage: https://cran.r-project.org/package=BINCOR Description: CRAN Package 'BINCOR' (Estimate the Correlation Between Two Irregular Time Series) Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series). A description of the method and package is provided in Polanco-Martínez et al. (2019), . Package: r-cran-binda Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-entropy Suggests: r-cran-crossval Filename: pool/dists/resolute/main/r-cran-binda_1.0.4-1.ca2604.1_all.deb Size: 54510 MD5sum: 4ab9eb804de245745851652c0e5c363b SHA1: 2e5d8b3e35e4880e7d625263d48d5cd98596cac5 SHA256: 734b936c1aace8c94bf83d95d67b992487e9241f68f82ddbb010392901a92334 SHA512: 1019ba3eb2ec8ee6fa20551fa4ebd377bc3e659dbc00fd852df739e447ed679da5e6ebb6577b9daefa6291949b6a16512fba933ac6e33a1a1dd29397cc8bb813 Homepage: https://cran.r-project.org/package=binda Description: CRAN Package 'binda' (Multi-Class Discriminant Analysis using Binary Predictors) Implements functions for multi-class discriminant analysis using binary predictors, for corresponding variable selection, and for dichotomizing continuous data. Package: r-cran-bindata Architecture: all Version: 0.9-24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-bindata_0.9-24-1.ca2604.1_all.deb Size: 254974 MD5sum: 7a81cd367749e3172b2315d6e075d31a SHA1: 1ac024169aea1c1a28f45082fdd0d3baca3fa73b SHA256: 2a57d6467b06806852e912d72fcaf7d66c775ec5b144bd24ce9e00f8b3a8b529 SHA512: 0e8dc02420567f701b526c883442a23d1eda39b750b8a34f2561f77e854d4f4825361be3fbcc89afb9c8084c58dec5f19f66c7f5cea2f4d6dfeb12153bac6ea9 Homepage: https://cran.r-project.org/package=bindata Description: CRAN Package 'bindata' (Generation of Artificial Binary Data) Generation of correlated artificial binary data. Package: r-cran-bindr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bindr_0.1.3-1.ca2604.1_all.deb Size: 17192 MD5sum: 0e9e1574de347335d75083733eb07dfe SHA1: e4207f1264c6d19e6280463c4c19fb1d472d87a0 SHA256: 0e2ed3a319a3341b95973dbf73f9c3f08b97807e8f70bd8d494b1bec7d141ad1 SHA512: ded8becced11088ab847db76625c7cf98bc7a56e6486e1d49218c530e39b7ea9b7b8c4cbfc724546e59c301b6111290cbf9abe68574a28510c0003966807ba97 Homepage: https://cran.r-project.org/package=bindr Description: CRAN Package 'bindr' (Parametrized Active Bindings) Provides a simple interface for creating active bindings where the bound function accepts additional arguments. Package: r-cran-binequality Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.cens, r-cran-gamlss.dist, r-cran-survival, r-cran-ineq Filename: pool/dists/resolute/main/r-cran-binequality_1.0.4-1.ca2604.1_all.deb Size: 2508688 MD5sum: 1c9e4439b1e82e4ad211dcd4a2289b3f SHA1: 1e954841e32afb723964af7455d9432e52367f6d SHA256: 247c674a8683f6389051dcdf5e40ab7e1c4a76b0356799e683d93bd83c18f40a SHA512: 21dc1fbada5dec80d4232042e229e93f4ffab3ef82f98870d58abcd66f954b51162c194aa94d5e76a58bbee698e2a7ba0610b033dda760e0db4d269451b57dfe Homepage: https://cran.r-project.org/package=binequality Description: CRAN Package 'binequality' (Methods for Analyzing Binned Income Data) Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states. Package: r-cran-binford Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-binford_0.1.0-1.ca2604.1_all.deb Size: 372946 MD5sum: 56d735fadefe61c675bc6762b38087a8 SHA1: 914b1182adee798c0447282e5f954e601469b993 SHA256: 905e584195bd8e1593537542afbc8c7a038fb4ffc5c89023373999a67e0e95e5 SHA512: c2bf8edba7b3f98502cdf0715ccc2cd4fbf29596aa8ea1147b2ff7faee883231fe383931394d52c66f2c2db71a3b8353c16d3f718902a636bbb65ac3c9086e51 Homepage: https://cran.r-project.org/package=binford Description: CRAN Package 'binford' (Binford's Hunter-Gatherer Data) Binford's hunter-gatherer data includes more than 200 variables coding aspects of hunter-gatherer subsistence, mobility, and social organization for 339 ethnographically documented groups of hunter-gatherers. Package: r-cran-binfunest Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-binfunest_0.1.0-1.ca2604.1_all.deb Size: 130320 MD5sum: ea43c88f69f239a0442cdb7003ef61de SHA1: 4c6fde6811cff82e4a5e728893ee2df0b08f498d SHA256: c6c676d77fa6c4f8fa2180e37dde34a40b76dade5abd45d3f5984193bb132c1a SHA512: 95cf1e3c1235d25be4e598ae294406fc6796c33de9be7586ce357e6eba8857d88ea97f97d266385aa404e5b6484e6b8b67e5c429674d21d5ff786e42de4013f9 Homepage: https://cran.r-project.org/package=binfunest Description: CRAN Package 'binfunest' (Estimates Parameters of Functions Driving Binomial RandomVariables) Provides maximum likelihood estimates of the performance parameters that drive a binomial distribution of observed errors, and takes full advantage of zero error observations. High performance communications systems typically have inherent noise sources and other performance limitations that need to be estimated. Measurements made at high signal to noise ratios typically result in zero errors due to limitation in available measurement time. Package includes theoretical performance functions for common modulation schemes (Proakis, "Digital Communications" (1995, )), polarization shifted QPSK (Agrell & Karlsson (2009, )), and utility functions to work with the performance functions. Package: r-cran-bingadsr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-bingadsr_0.1.0-1.ca2604.1_all.deb Size: 22936 MD5sum: 5fee3f7273b438ed8312d9e7aecb1fcb SHA1: 15d17cb12c43363a2538e00b3cd6c03a6ba5e591 SHA256: 3e4a56ea6b7d0a76289eb83b31e5d5b52a8c8dae91ec2d7ebe73bae654dd7590 SHA512: ef70e06c39491cefc37033c26da9e4d867c9d75375dc86f377488a99e38680f201f6b345459fd126725915685eb85383e60cbd1233914cc49d657e9e6d21a2f6 Homepage: https://cran.r-project.org/package=bingadsR Description: CRAN Package 'bingadsR' (Get Bing Ads Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from bing Ads using the 'Windsor.ai' API . Package: r-cran-bingat Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-network, r-cran-gplots, r-cran-doparallel, r-cran-foreach, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-bingat_1.3-1.ca2604.1_all.deb Size: 125598 MD5sum: 3e4517e1fb6c3c47c1ac40f3d7138efb SHA1: d0b06a1cbed57ce0155c541e3e284bdf71abc692 SHA256: 55a6803c331dc0c9b0a93793ed53957309327642e4d1778b19c3bd5d32f12dd9 SHA512: bb927d2a21c834433aee259952be324525d80821168c4ef9fdfa2143faa9b51e92a347bb1c7ca4b09f90294bea305175f16184ff4d125da69c64e09ae7746430 Homepage: https://cran.r-project.org/package=bingat Description: CRAN Package 'bingat' (Binary Graph Analysis Tools) Tools to analyze binary graph objects. 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For estimating one proportion or the difference of proportions, a number of confidence interval methods are included, which can deal with various different pool sizes. Further, regression methods are implemented for simple pooling and matrix pooling designs. Methods for identification of positive items in group testing designs: Optimal testing configurations can be found for hierarchical and array-based algorithms. Operating characteristics can be calculated for testing configurations across a wide variety of situations. Package: r-cran-bingsd Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1062 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-gsdesign Filename: pool/dists/resolute/main/r-cran-bingsd_1.1-1.ca2604.1_all.deb Size: 1003458 MD5sum: eb80deb18b937205f54027e4f3963a9a SHA1: 1de6c5482c94e9bd4d4b217a93b8ce91797e4edd SHA256: 00d25ffb71757ca867351b3873c4e5657fa803addbd51d242636b599b9c753cc SHA512: 72c1f63957dcd101571b7172e647a841ece5a4f535f90d37b923f25fc351866dc5c7c1a3cf59d2ef9e906dbdee8658bd4da88b5b21c95198cce35ec6da9fe5e2 Homepage: https://cran.r-project.org/package=BinGSD Description: CRAN Package 'BinGSD' (Calculate Boundaries and Conditional Power for Single Arm GroupSequential Test with Binary Endpoint) Consider an at-most-K-stage group sequential design with only an upper bound for the last analysis and non-binding lower bounds.With binary endpoint, two kinds of test can be applied, asymptotic test based on normal distribution and exact test based on binomial distribution. This package supports the computation of boundaries and conditional power for single-arm group sequential test with binary endpoint, via either asymptotic or exact test. The package also provides functions to obtain boundary crossing probabilities given the design. 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Includes functions for model fitting and making prediction under isothermal and dynamic conditions. The methods (algorithms & models) are based on predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)). Package: r-cran-biogsp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2072 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-rann, r-cran-rspectra, r-cran-ggplot2, r-cran-patchwork, r-cran-gridextra, r-cran-viridis, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-biogsp_1.0.0-1.ca2604.1_all.deb Size: 1304820 MD5sum: 4b4e991eb035bb86de4931eb0317913e SHA1: 2f704f7de148fb0c1e2c1dd1fa033d9c86a0e44b SHA256: 142836d51278d72a15ef77e614faa821fc1e81c11303a4814167f4fc8bd3a1d2 SHA512: a1519077358198be234e9791db23e77d3f1d16d0a8ba94f73d424ddb5c8c3ce514c64e1d8f63a131a6767ae5c9ceeae0ae313e8f9b7bbfadeab96cc5ee8688df Homepage: https://cran.r-project.org/package=BioGSP Description: CRAN Package 'BioGSP' (Biological Graph Signal Processing for Spatial Data Analysis) Implementation of Graph Signal Processing (GSP) methods including Spectral Graph Wavelet Transform (SGWT) for analyzing spatial patterns in biological data. Based on Hammond, Vandergheynst, and Gribonval (2011) . Provides tools for multi-scale analysis of biology spatial signals, including forward and inverse transforms, energy analysis, and visualization functions tailored for biological applications. Biological application example is on Stephanie, Yao, Yuzhou (2024) . Package: r-cran-bioinactivation Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-desolve, r-cran-fme, r-cran-lazyeval, r-cran-ggplot2, r-cran-mass, r-cran-rlang, r-cran-purrr Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-bioinactivation_1.3.1-1.ca2604.1_all.deb Size: 285204 MD5sum: b0ac8e16c3f0e280571bb6d8b9265a33 SHA1: 306e03644f6358edbc54d16973b2c266c1aa2b3f SHA256: e204bb45404ac5d1698d60054f6387de68f1efcad7a5dc7995d20876ec89705e SHA512: 5438efc5a3059189acdaa5ec0946e2b323c9a559230a700195495e0ece0a262e5f1ef7f1b20642312ecfbc04c9a7f4abe35966461c1db22d1538b0f739cf46fd Homepage: https://cran.r-project.org/package=bioinactivation Description: CRAN Package 'bioinactivation' (Mathematical Modelling of (Dynamic) Microbial Inactivation) Functions for modelling microbial inactivation under isothermal or dynamic conditions. The calculations are based on several mathematical models broadly used by the scientific community and industry. Functions enable to make predictions for cases where the kinetic parameters are known. It also implements functions for parameter estimation for isothermal and dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian approach to parameter estimation. Package: r-cran-bioinsight Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3912 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-edger, r-bioc-limma, r-cran-knitr, r-cran-wordcloud, r-cran-rcolorbrewer Suggests: r-cran-testthat, r-bioc-biomart Filename: pool/dists/resolute/main/r-cran-bioinsight_0.3.1-1.ca2604.1_all.deb Size: 875536 MD5sum: 6e583c6fbdf1df14d3e81d25f84fdcd0 SHA1: f5cd432a725449c743699a50a5a3ed13e452083f SHA256: 941224ee22ca57010c0081eecbae3f194abfb469697f05839b4dfba80ab70254 SHA512: ef1ff161aabd251b688041a8053d25aa24a39abd1261616e9cc0b0c976da3fd66f785b13cbbfbe80b77641a5f4ff39e09c5c057ac75e5d75a2f83001cbc24a4f Homepage: https://cran.r-project.org/package=BioInsight Description: CRAN Package 'BioInsight' (Filter and Plot RNA Biotypes) Analyze and plot the abundance of different RNA biotypes present in a count matrix, this evaluation can be useful if you want to test different strategies of normalization or analyze a particular biotype in a differential gene expression analysis. Package: r-cran-bioleak Architecture: all Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2889 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-generics, r-bioc-summarizedexperiment, r-cran-hardhat, r-cran-parsnip Suggests: r-bioc-biocparallel, r-cran-splitgraph, r-cran-cli, r-cran-dials, r-cran-fnn, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-glmnet, r-cran-mice, r-cran-missforest, r-cran-pkgload, r-cran-ranger, r-cran-randomforest, r-cran-recipes, r-cran-rann, r-cran-rsample, r-cran-tune, r-cran-vim, r-cran-withr, r-cran-workflows, r-cran-xgboost, r-cran-yardstick, r-cran-proc, r-cran-prroc, r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bioleak_0.3.8-1.ca2604.1_all.deb Size: 1386396 MD5sum: 6362ceba422a1d2bc02ca3b7c925b3b6 SHA1: 17b6f4f05fd44b2cc2fc0773afff887d5cb88adc SHA256: 5525fdeeb6ee5a3403751fbefbc9eeb7c97a161bbab5a99f4724ae5742e22efc SHA512: db84c9aee1ce309f3f8e11c3c51f7bfc02df515b24725817b48a1d88f14ec484f4f10ecaa2e12ff65e266a7a4a03ca56889d45fe1aac24375bb29e852ca274ec Homepage: https://cran.r-project.org/package=bioLeak Description: CRAN Package 'bioLeak' (Leakage-Safe Modeling and Auditing for Genomic and Clinical Data) Prevents and detects information leakage in biomedical machine learning. Provides leakage-resistant split policies (subject-grouped, batch-blocked, study leave-out, time-ordered), guarded preprocessing (train-only imputation, normalization, filtering, feature selection), cross-validated fitting with common learners, permutation-gap auditing, batch and fold association tests, and duplicate detection. Package: r-cran-biolink Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rentrez, r-cran-xml2, r-cran-dbi, r-cran-rmysql, r-cran-glue, r-cran-memoise Suggests: r-cran-testthat, r-cran-lintr, r-cran-httr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-biolink_0.1.8-1.ca2604.1_all.deb Size: 72482 MD5sum: a33c79083d5ab5a389c284d7b26a6d92 SHA1: a270880a5adffcd5270fef714acd617db10b4af2 SHA256: 8a6d85a056b88567b1dc235ab54edfe9d0b2ae3b1e5a8ec9a00212ac321e0252 SHA512: a0fd7bca5a7f009e43c32b492303014ff4796446f4e64a6785b19d60fa518c27605cc6b45c94530aacfb34a04c146b5a57cc0bf7baa08d07b6584b770b1fcce7 Homepage: https://cran.r-project.org/package=biolink Description: CRAN Package 'biolink' (Create Hyperlinks to Biological Databases and Resources) Generate urls and hyperlinks to commonly used biological databases and resources based on standard identifiers. This is primarily useful when writing dynamic reports that reference things like gene symbols in text or tables, allowing you to, for example, convert gene identifiers to hyperlinks pointing to their entry in the 'NCBI' Gene database. Currently supports 'NCBI' Gene, 'PubMed', Gene Ontology, 'KEGG', CRAN and Bioconductor. Package: r-cran-biologicalactivityindices Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-biologicalactivityindices_0.1.0-1.ca2604.1_all.deb Size: 12416 MD5sum: 865ffa703d0a81840d08eb4146cdf783 SHA1: 4e6cc2074a8914fb35174c195c203eea1e141ece SHA256: 5b6f6900cfe20b9416142e43d1a89060a0facd4af5f7ed2753454210fa249703 SHA512: 3abb225cc0b43181f75d5cf5be589338a1333a506034250a2e826a7bf5be37c5590fa2703545fffef27183e8aa24ea3bd195c97e60322149549d93633dfc2226 Homepage: https://cran.r-project.org/package=biologicalActivityIndices Description: CRAN Package 'biologicalActivityIndices' (Biological Activity Indices) Ecological alteration of degraded lands can improve their sustainability by addition of large amount of biomass to soil resulting in improved soil health. Soil biological parameters (such as carbon, nitrogen and phosphorus cycling enzyme activity) are reactive to minute variations in soils [Ghosh et al. (2021) ]. Hence, biological activity index combining Urease, Alkaline Phosphatase, Dehydrogenase (DHA) & Beta-Glucosidase activity will assist in detecting early changes in restored land use systems [Patidar et al. (2023) ]. This package helps to calculate Biological Activity Index (BAI) based on vectors of Land Use System/treatment and control/reference Land Use System containing four values of Urease, Alkaline Phosphatase, DHA & Beta-Glucosidase. (DHA), urease (URE), fluorescein diacetate hydrolysis (FDA) and alkaline phosphatase (ALP) activities are measured in soil samples using triphenyl tetrazolium chloride, urea, fluorescein diacetate and p-nitro phenyl-phosphate as substrates, respectively. Package: r-cran-biom2 Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wgcna, r-cran-mlr3, r-cran-cmplot, r-cran-ggsci, r-cran-rocr, r-cran-caret, r-cran-ggplot2, r-cran-ggpubr, r-cran-viridis, r-cran-ggthemes, r-cran-ggstatsplot, r-cran-htmlwidgets, r-cran-mlr3verse, r-cran-uwot, r-cran-webshot, r-cran-wordcloud2, r-cran-ggforce, r-cran-igraph, r-cran-ggnetwork Filename: pool/dists/resolute/main/r-cran-biom2_1.1.3-1.ca2604.1_all.deb Size: 4171028 MD5sum: b250762dac3514d8f40d31c7b93df50c SHA1: 5c33ded010e7cc1b201fc527de0532217fc3d85c SHA256: 69f6d8927b92b5045a2e73e63d19dc7a0c220a1d241a63010c770f56e8d56bc7 SHA512: ff7ebc28fee5bc2fd345d64e2c4057669189c0a9a7bbb6effa46516131d916636e6b0f04ee00a3a3aae172e8b9e1c7ebea5aff0c5211ddf9977a1f28b0b3152f Homepage: https://cran.r-project.org/package=BioM2 Description: CRAN Package 'BioM2' (Biologically Explainable Machine Learning Framework) Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) .Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) ) methods. Package: r-cran-biomass Architecture: all Version: 2.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-jsonlite, r-cran-proj4, r-cran-data.table, r-cran-rappdirs, r-cran-sf, r-cran-terra, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-testthat, r-cran-vdiffr, r-cran-curl, r-cran-geodata, r-cran-httr2, r-cran-pkgdown, r-cran-dplyr, r-cran-brms, r-cran-bh, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-biomass_2.2.7-1.ca2604.1_all.deb Size: 3599272 MD5sum: fca6e5d0c470187f4e726bb059fa0a15 SHA1: b96bd6893853bc3f2b4fd1de5c6efad2bc73ae6e SHA256: 1d7458ecf5bf1be6a55a4ea7376e005d19e884be810f09bc71705eb2920f2eae SHA512: 1d7ac1a852cb2b90bf442cfa785edf504f373b7d865ba3a4f48ebeddedaf76650f3e2c3963ac36f5b76fcc7ccde6c34b3a49c077684f020a37420c08f45aa569 Homepage: https://cran.r-project.org/package=BIOMASS Description: CRAN Package 'BIOMASS' (Estimating Aboveground Biomass and Its Uncertainty in TropicalForests) Contains functions for estimating above-ground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and correct taxonomy, (2) estimate wood density and its uncertainty, (3) build height-diameter models, (4) manage tree and plot coordinates, (5) estimate above-ground biomass/carbon at stand level with associated uncertainty. To cite ‘BIOMASS’, please use citation(‘BIOMASS’). For more information, see Réjou-Méchain et al. (2017) . Package: r-cran-biometrics Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-nlme Suggests: r-cran-datana, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-biometrics_1.0.4-1.ca2604.1_all.deb Size: 3645958 MD5sum: 5416cff7ec3c00d2d3e396081a3bf4a0 SHA1: b2ae9b931a52de406e1a907f02705bf879188877 SHA256: d6470eaf14565699808cad58f532ac67aa439ff4d1a4c39d5559a05b5a9c32b2 SHA512: 8e95a87f0703e56c3db2670ca3ec8d161849cb3befc17899e9061f102f30072ac6b70cae696a3edd8279bfa07d8b3eac2241d42d4537d2251c2b035782281262 Homepage: https://cran.r-project.org/package=biometrics Description: CRAN Package 'biometrics' (A Package for Biometrics and Modelling) A system of functions and datasets to carry out quantitative analyses in the biological sciences. The package facilitates data management, exploratory analyses, and model assessment. Although it currently focuses on forest ecology, silviculture and decision-making, most of the package functions are applicable across several disciplines, including economics, environmental science, and healthcare. Package: r-cran-biometryassist Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-agricolae, r-cran-askpass, r-cran-curl, r-cran-emmeans, r-cran-ggplot2, r-cran-lattice, r-cran-multcompview, r-cran-pracma, r-cran-patchwork, r-cran-rlang, r-cran-scales, r-cran-stringi, r-cran-xml2 Suggests: r-cran-covr, r-cran-crayon, r-cran-ggspatial, r-cran-knitr, r-cran-matrix, r-cran-mockery, r-cran-openxlsx2, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-biometryassist_1.4.0-1.ca2604.1_all.deb Size: 377360 MD5sum: 4c52bfb818873443ce13d11e67c781c4 SHA1: 90ff2d84eee1b22e798a8a8ccdf0c278392f592b SHA256: a9c3b90ecfe81d58a3be80a967e71687310e7d9a33b71ffa54944a77571947dd SHA512: cb75cdcd155721147e4a405e300614c706a9aa31ded7543c26e6aebdb21924ba2ac1fee4ff6007a47767482ec153050be22210cdca1d795e791488b497a95506 Homepage: https://cran.r-project.org/package=biometryassist Description: CRAN Package 'biometryassist' (Functions to Assist Design and Analysis of Agronomic Experiments) Provides functions to aid in the design and analysis of agronomic and agricultural experiments through easy access to documentation and helper functions, especially for users who are learning these concepts. While not required for most functionality, this package enhances the `asreml` package which provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased as 'asreml-R' from 'VSNi' , who will supply a zip file for local installation/updating (see ). Package: r-cran-biomod2 Architecture: all Version: 4.3-4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3035 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-sp, r-cran-reshape, r-cran-reshape2, r-cran-abind, r-cran-foreach, r-cran-ggplot2, r-cran-gbm, r-cran-rpart, r-cran-mass, r-cran-proc, r-cran-presenceabsence, r-cran-dplyr, r-cran-rlang, r-cran-scales Suggests: r-cran-hmisc, r-cran-gam, r-cran-mgcv, r-cran-earth, r-cran-maxnet, r-cran-mda, r-cran-nnet, r-cran-randomforest, r-cran-xgboost, r-cran-cito, r-cran-torch, r-cran-car, r-cran-caret, r-cran-dismo, r-cran-enmeval, r-cran-doparallel, r-cran-raster, r-cran-ggpubr, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-tidyterra, r-cran-ggtext, r-cran-patchwork, r-cran-kableextra, r-cran-dt, r-cran-r.utils, r-cran-paletteer, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-biomod2_4.3-4-5-1.ca2604.1_all.deb Size: 1936818 MD5sum: 5bf6da59ec51448a9a24b9ed4311b608 SHA1: 3a9c5ec15eef052c518cfe7093bc9021a9f0c332 SHA256: 2217f7573f546818b9871d7592637f89f0b39f2c09b0ca2ee7b6a7f47a18501c SHA512: 4960b430559929259ab42088b954af93ec6e9610b0ca448f15e65c40903771758c49a4837725b9663474eab776026441ca47e1d86051b96d464588643691b20d Homepage: https://cran.r-project.org/package=biomod2 Description: CRAN Package 'biomod2' (Ensemble Platform for Species Distribution Modeling) Functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualization. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualisation tools are also available within the package. Package: r-cran-biomontools Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-maps, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-dataexplorer, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-lazyeval, r-cran-readxl, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-shiny, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyjs, r-cran-shinywidgets, r-cran-writexl, r-cran-shinyalert Filename: pool/dists/resolute/main/r-cran-biomontools_1.2.4-1.ca2604.1_all.deb Size: 1681350 MD5sum: fab3a8da0966bfc757b6994b0ce6c516 SHA1: 2bd238010ff2853186c442d93446b1022e059b7f SHA256: 5b61c2f02557a1db6d827351a4a6e029bd8a7a8ca4acc543e64c94fae5a7cbb7 SHA512: b4c7c9b98f17219ddea3f58b8aac65230fff61df53bd04c5971e00e847460abcec561f9fa0f851569d7b3ffec1a4c6d6c3616645ea2c33a3bdb1c32e14d41324 Homepage: https://cran.r-project.org/package=BioMonTools Description: CRAN Package 'BioMonTools' (Biomonitoring and Bioassessment Calculations) An aid for manipulating data associated with biomonitoring and bioassessment. Calculations include metric calculation, marking of excluded taxa, subsampling, and multimetric index calculation. Targeted communities are benthic macroinvertebrates, fish, periphyton, and coral. As described in the Revised Rapid Bioassessment Protocols (Barbour et al. 1999) . Package: r-cran-biomor Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-recipes, r-cran-themis, r-cran-xgboost, r-cran-magrittr, r-cran-dplyr, r-cran-proc Suggests: r-cran-randomforest, r-cran-testthat, r-cran-prroc, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-yardstick, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-biomor_0.1.1-1.ca2604.1_all.deb Size: 48742 MD5sum: e9568c3f05857cb6cba2037c06456962 SHA1: 7e66b8e3f7eb462665cfa60257f82f1bfdc814cb SHA256: 7c85c37fac7d5f6fa4490e4c683c4390cdcd7cc2c7a502ef3135f50d369ed779 SHA512: d166b8f3193d817c5ba44ba3255d3c740cfbd1130c234f88492f6ce129f259f80a09e369ef36baafd93703f38cdc4eca3e02fe3ff00a5e5b27b2cdd5e8341991 Homepage: https://cran.r-project.org/package=BioMoR Description: CRAN Package 'BioMoR' (Bioinformatics Modeling with Recursion and Autoencoder-BasedEnsemble) Tools for bioinformatics modeling using recursive transformer-inspired architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. Includes utilities for cross-validation, calibration, benchmarking, and threshold optimization in predictive modeling workflows. The methodology builds on ensemble learning (Breiman 2001 ), gradient boosting (Chen and Guestrin 2016 ), autoencoders (Hinton and Salakhutdinov 2006 ), and recursive transformer efficiency approaches such as Mixture-of-Recursions (Bae et al. 2025 ). Package: r-cran-bionetdata Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3273 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bionetdata_1.1-1.ca2604.1_all.deb Size: 3313346 MD5sum: ce1f537043a809e900cae58a647494ca SHA1: 654c76e3a53c88cd8f2ac68fe1305291abe3ea8f SHA256: 9cac1dd12964e501614db2b7a00ca5087260beb38109bd0d69c93aab228c970f SHA512: 87e1fa377fadcd9dff0ebe93f1df7e6bca91e9fe5c10fbfe7599bceaf552d92ab68c11cd07fd0242628d6d895fac71f878195cb4fa51c84a2a55e4159bb2b2ac Homepage: https://cran.r-project.org/package=bionetdata Description: CRAN Package 'bionetdata' (Biological and Chemical Data Networks) Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured. Package: r-cran-biopet Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-proc, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-biopet_0.2.2-1.ca2604.1_all.deb Size: 78692 MD5sum: e7a1557cce0d7b832e5b49396d80d8c9 SHA1: edfa7c4b052c1461f4851e2684ec5b6c00b64583 SHA256: 3a338004dc7f444e2d4bfb4a10d97f14d6c44caeb57253e05309214f021b4c9e SHA512: 250106c1104c79a3c6b3327768db0474b234121a2d727ffdc9e23f28d950beda34aa75210d1474608679d76d8801a4619acb5c1d2aafc4f5455e871a8b3b8e50 Homepage: https://cran.r-project.org/package=BioPET Description: CRAN Package 'BioPET' (Biomarker Prognostic Enrichment Tool) Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) ). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials. Package: r-cran-biopetsurv Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-biopetsurv_0.1.0-1.ca2604.1_all.deb Size: 112906 MD5sum: c6b456398d70ec9b855097225bf24599 SHA1: 823eeab0a99c966c966c0c8c6ed42ea754ae4751 SHA256: f642a96a25f5627293ac36050928e0eae9af4306591d6e76f54bf22d599ea8f8 SHA512: 6d83a8cf8a01e6ef15b62a3a447f0cf817b2eebf5472a30dce779a88fac1dde1bb8a94c4e9a1dd90044591dbe9f46cbbb68b80a679783f4edaf0193f6f715ff7 Homepage: https://cran.r-project.org/package=BioPETsurv Description: CRAN Package 'BioPETsurv' (Biomarker Prognostic Enrichment Tool for Time-to-Event Trial) Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event. A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) ). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages. This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes. Package: r-cran-biopixr Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-imager, r-cran-magick, r-cran-data.table, r-cran-cluster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-kohonen, r-cran-imagerextra, r-cran-gpareto, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-biopixr_1.2.0-1.ca2604.1_all.deb Size: 4065222 MD5sum: 2ca46a7935606b5e8f07861fe4d4dc52 SHA1: 6276072bf51c9e0039985c4d658a0d137aff1388 SHA256: 2fd8ea30a47f0f9c1a1585f54ab641b4eb2c859d2fc4c66668937dfb368cb3ed SHA512: f9a6ba42411af64484fee7983fe1e5fd746da014ef9ef2ed4911a5b1cf8f2b3efdea9f0fafdca8f4a7743d29705c794735cc1648dd4c8d3b1e3c811f28936af9 Homepage: https://cran.r-project.org/package=biopixR Description: CRAN Package 'biopixR' (Extracting Insights from Biological Images) Combines the 'magick' and 'imager' packages to streamline image analysis, focusing on feature extraction and quantification from biological images, especially microparticles. By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) ). Package: r-cran-biopred Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost, r-cran-proc, r-cran-ggplot2, r-cran-propcis, r-cran-survival, r-cran-survminer, r-cran-mgcv, r-cran-onewaytests, r-cran-car Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-biopred_1.0.2-1.ca2604.1_all.deb Size: 796138 MD5sum: 782f718643804fc0d6426e046b4986c8 SHA1: d5fd2e23c5fb93c9aa05abefafc660b625f36184 SHA256: 75b6cf25ddbdcf0bd584764a57a29eaa30f82474c64966c4971868a5b8d5ad3c SHA512: 76014f0ca5821dafee98489aee864dc313ebabb4630886a66bc39b684ee8b40d4df119b492217859eaec6658a721a058866ac58174543e19a2a3153934442fa9 Homepage: https://cran.r-project.org/package=BioPred Description: CRAN Package 'BioPred' (An R Package for Biomarkers Analysis in Precision Medicine) Provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) . Package: r-cran-bioprobability Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bioprobability_1.0-1.ca2604.1_all.deb Size: 34608 MD5sum: 685621b5999ad51ecf2ffd66c6ff57c6 SHA1: 984519ecf26b688a4602780d6244a0b974f96452 SHA256: 9f9e7e811511c5e78a6e2a9266ceb6b0bc8de124f829204f92d905286f0191a7 SHA512: 0896ea7162be59892ad6c6b16cefe385357bf6f97c018f391eca00d3edbf1b414a75adf629c2018fb35e29bebb5c5cb3c6aa1bd6c52f96b03227507b2b999a0e Homepage: https://cran.r-project.org/package=BioProbability Description: CRAN Package 'BioProbability' (Probability in Biostatistics) Several tools for analyzing diagnostic tests and 2x2 contingency tables are provided. In particular, positive and negative predictive values for a diagnostic tests can be calculated from prevalence, sensitivity and specificity values. For contingency tables, relative risk and odds ratio measures are estimated. Furthermore, confidence intervals are provided. Package: r-cran-biorad Architecture: all Version: 0.11.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5538 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-dplyr, r-cran-fields, r-cran-ggplot2, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-lutz, r-cran-raster, r-cran-readr, r-bioc-rhdf5, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stringr, r-cran-suntools, r-cran-tidyr, r-cran-tidyselect, r-cran-viridis, r-cran-viridislite, r-cran-xml2 Suggests: r-cran-getrad, r-cran-ggspatial, r-cran-knitr, r-cran-prettymapr, r-cran-rmarkdown, r-cran-rosm, r-cran-testthat, r-cran-vdiffr, r-cran-vol2birdr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-biorad_0.11.0-1.ca2604.1_all.deb Size: 4795414 MD5sum: 63598d9312db000f8a03c350b2ff53b0 SHA1: 3f607192944161e199fe1131af9f59fa98f90653 SHA256: 542231d48be5b11aa586ac78b009bb505d1d4aad8848e5f21d4d04260c163866 SHA512: b884e0cd04685264005434928e9a7c20dbd27b47a8b6493b771b478c0e74f5595370ace67b14d60160890b9b92ff99bc1200fb94264d18fa0922c111bbcd44d1 Homepage: https://cran.r-project.org/package=bioRad Description: CRAN Package 'bioRad' (Biological Analysis and Visualization of Weather Radar Data) Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" for a software paper describing package and methodologies. Package: r-cran-biorssay Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-biorssay_1.1.0-1.ca2604.1_all.deb Size: 239342 MD5sum: e791ba684f043db400ee397f0926ade0 SHA1: 4f82c4b4e1aba5e5cec8393d0638b89fd606c55c SHA256: a241e4da512b814508ad49bc0934feacbdca9c9a90c82d429f84ff7e9628f63f SHA512: 03924d5ad9b3c9a14c6f94cc05723fc7c8859dd2c9d225d87e58f51baa699f2ddcb97ce5f423e709b332d1ade0e22e3e9fb65fbf4cf74c8124ec0a42c828f2a3 Homepage: https://cran.r-project.org/package=BioRssay Description: CRAN Package 'BioRssay' (Analyze Bioassays and Probit Graphs) A robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations. Package: r-cran-bios2cor Architecture: all Version: 2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bio3d, r-cran-circular, r-cran-bigmemory, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-bios2cor_2.2.2-1.ca2604.1_all.deb Size: 768730 MD5sum: 5cd14cc7fdf254c181776e145422cee5 SHA1: f70a809f2051d0570c4fd8cc7b0a290584f2d62b SHA256: 3b11700dfdc081c187cd60e84c7e7d02847247c4f81e36502bfd8e2ce1480929 SHA512: 578311f2365389b35fc1a55144ddfa65d437c4d8abb04242838f6d7f6ca92e67d3971540310f36062ae1e01c8267eef4b19b9c3d2077043d1d48b782041ca4ef Homepage: https://cran.r-project.org/package=Bios2cor Description: CRAN Package 'Bios2cor' (From Biological Sequences and Simulations to CorrelationAnalysis) Utilities for computation and analysis of correlation/covariation in multiple sequence alignments and in side chain motions during molecular dynamics simulations. Features include the computation of correlation/covariation scores using a variety of scoring functions between either sequence positions in alignments or side chain dihedral angles in molecular dynamics simulations and utilities to analyze the correlation/covariation matrix through a variety of tools including network representation and principal components analysis. In addition, several utility functions are based on the R graphical environment to provide friendly tools for help in data interpretation. Examples of sequence covariation analysis are provided in: (1) Pele J, Moreau M, Abdi H, Rodien P, Castel H, Chabbert M (2014) and (2) Taddese B, Deniaud M, Garnier A, Tiss A, Guissouma H, Abdi H, Henrion D, Chabbert M (2018) . An example of side chain correlated motion analysis is provided in: Taddese B, Garnier A, Abdi H, Henrion D, Chabbert M (2020) . This work was supported by the French National Research Agency (Grant number: ANR-11-BSV2-026) and by GENCI (Grant number: 100567). Package: r-cran-bios2mds Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3032 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amap, r-cran-e1071, r-cran-scales, r-cran-cluster, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-bios2mds_1.2.3-1.ca2604.1_all.deb Size: 2800344 MD5sum: 6864bd90a8128007742ea8223ef86177 SHA1: 36f1d84a4274c2954e1a6265cf6100150f3db247 SHA256: df1bc32ca6ba5916ce89ea3d6039d6d83cde51261d0ac951c9f49753fd3c5d0c SHA512: bf22c006a2e194a8c624ee96bf5d54a872f27f77265e122b0c98cdd84ec171a7183521d4ac64ad7442673cc2069b02a300a907ee5c34b705b6bda31012330cdc Homepage: https://cran.r-project.org/package=bios2mds Description: CRAN Package 'bios2mds' (From Biological Sequences to Multidimensional Scaling) Utilities dedicated to the analysis of biological sequences by metric MultiDimensional Scaling with projection of supplementary data. It contains functions for reading multiple sequence alignment files, calculating distance matrices, performing metric multidimensional scaling and visualizing results. Package: r-cran-biosampler Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-biosampler_1.0.4-1.ca2604.1_all.deb Size: 56534 MD5sum: 9fec460fe944ea66aaff87071e4e265f SHA1: 14f6870e87b26b362e39d35c1e0ecead6a77ed3a SHA256: 7c48210602463381743acc5a879b4affd785692f26f17aa8c82d942786b15396 SHA512: ca08ca6b1184f2c344d23c9c26d231a6d8dfa58297d1c560050260b2bb78f53f6e39aa5b58c53cf9b6a3a58b36217496ee5ddbc73f90c8aa03fdfcd03e919e4a Homepage: https://cran.r-project.org/package=biosampleR Description: CRAN Package 'biosampleR' (Biodiversity Index Calculation and Bootstrap Confidence IntervalEstimation) Provides tools for the calculation of common biodiversity indices from count data. Additionally, it incorporates bootstrapping techniques to generate multiple samples, facilitating the estimation of confidence intervals around these indices. Furthermore, the package allows for the exploration of how variation in these indices changes with differing numbers of sites, making it a useful tool with which to begin an ecological analysis. Methods are based on the following references: Chao et al. (2014) , Chao and Colwell (2022) , Hsieh, Ma,` and Chao (2016) . Package: r-cran-bioseq Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1410 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-tibble, r-cran-ape, r-cran-crayon, r-cran-dplyr, r-cran-pillar, r-cran-stringi, r-cran-stringr, r-cran-stringdist, r-cran-readr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-bioseq_0.1.5-1.ca2604.1_all.deb Size: 748760 MD5sum: 3fb95276933a555fc0e878806338af50 SHA1: 81a761caa022fdb3e8f5b93bf4965de2bdb4ca08 SHA256: 44a4f86e3d87f29994976150b144cdf4c7407e5ea6d7f9a33b0a8a0f254d4b5f SHA512: bd65ce404cb63ad43b980dd70958e702b28bcaf907c62fe9f0c807ac607d1b2f199d3cef402f7334cf647d51f5c4eebd3cc37082e154af968dbcb3c5cdcd39b3 Homepage: https://cran.r-project.org/package=bioseq Description: CRAN Package 'bioseq' (A Toolbox for Manipulating Biological Sequences) Classes and functions to work with biological sequences (DNA, RNA and amino acid sequences). Implements S3 infrastructure to work with biological sequences as described in Keck (2020) . Provides a collection of functions to perform biological conversion among classes (transcription, translation) and basic operations on sequences (detection, selection and replacement based on positions or patterns). The package also provides functions to import and export sequences from and to other package formats. Package: r-cran-biosignalemg Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal Filename: pool/dists/resolute/main/r-cran-biosignalemg_2.1.0-1.ca2604.1_all.deb Size: 481874 MD5sum: 022c40df7ae43667c0015326c4e6a1e0 SHA1: 1af6231bdd340470b9618c5f06d84b5667245e30 SHA256: 128806ee9d17c32ad7f1471bc3216f0cec041e558d2ac88a6f0539dfa58391b5 SHA512: d15b981cf7105ebdc1babbf146e0bff020082cc3a9e56cde77f29925778cbdf55c015d07978571992534279e0b75cc680926b344145a2ca1443d7e4ab1a21a92 Homepage: https://cran.r-project.org/package=biosignalEMG Description: CRAN Package 'biosignalEMG' (Tools for Electromyogram Signals (EMG) Analysis) Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes. Package: r-cran-biosnr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-pracma, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-biosnr_1.0-1.ca2604.1_all.deb Size: 125338 MD5sum: 4f07977de0963eca2f571b201ee7726f SHA1: d4678fa3137d3e92a7ba0d269748961cb75260c2 SHA256: 96ecceb0f93a3ca057d96f2f50beec648c7f73c1647fa867b8261f513fcff6af SHA512: 5a3fa394126fdb027b1d09ba174038ef77c15b52f9309468bc6fa05fad148bb22d1427a530fb56a544f75618c6ad030e8ffac0e0980077005cc8acbf39239734 Homepage: https://cran.r-project.org/package=bioSNR Description: CRAN Package 'bioSNR' (Bioacoustic Basic Operations with Decibels and the Passive SonarEquation) A beginners toolbox to help those in ecology who want to deepen their understanding or utilize Bioacoustics in their work. The package has a number of utilizations from calculating frequency from waveform, performing operations in dB, and determining acoustic range of recorders. The majority of this package is based on key concepts learned from the K. Lisa Yang Center for Conservation Bioacoustics at Cornell University and their associated course: Introduction to Bioacoustics course. More information can be found within the walk through vignettes at . Package: r-cran-biospear Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pkgconfig, r-cran-cobs, r-cran-corpcor, r-cran-devtools, r-cran-glmnet, r-cran-grplasso, r-cran-mass, r-cran-matrix, r-cran-mboost, r-cran-plsrcox, r-cran-proc, r-cran-prroc, r-cran-rcurl, r-cran-survauc, r-cran-survival Filename: pool/dists/resolute/main/r-cran-biospear_1.0.2-1.ca2604.1_all.deb Size: 3295036 MD5sum: 524d1c52946b8b465645c11b8144e042 SHA1: 04e4605f5c9b5abb91edef5be6683d29abed5367 SHA256: 63beb22f9a73231e9f699f3dec472cc052d9e13657061c427541545f854cf978 SHA512: 0484768deedfe80a8e5aa5676e3264cf81835c2117fead792b4f84f8cd4914734297c09489819867c1db120152b6eb688e9289b24de22e820a2bf5b2d745a868 Homepage: https://cran.r-project.org/package=biospear Description: CRAN Package 'biospear' (Biomarker Selection in Penalized Regression Models) Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) ), the adaptive lasso (Zou H (2006) ), the stability selection (Meinshausen N et al. (2010) ), some extensions of the lasso (Ternes et al. (2016) ), some methods for the interaction setting (Ternes N et al. (2016) ), or others. A function generating simulated survival data set is also provided. Package: r-cran-biostat3 Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mass Suggests: r-cran-car, r-cran-bshazard, r-cran-rstpm2, r-cran-epi, r-cran-dplyr, r-cran-ggplot2, r-cran-muhaz Filename: pool/dists/resolute/main/r-cran-biostat3_0.2.3-1.ca2604.1_all.deb Size: 2802374 MD5sum: 3900a4d5fc661cfb7b76f373d7f2434b SHA1: 52ce2a4ab42df45ed0e5b3f5523109139dc17644 SHA256: 88fd36425099b386cfb9496c31571ac60f85846ed85410654149ee077f6a16ff SHA512: 7d695ab75bd2a8dcfdba4c32b427202d4df2ed1872935d0d60c2f4aa8bc06dcc0c6534cd136784395564279cdf819b9b0b3ccb97f3875f6a3834a7d7c1704a6d Homepage: https://cran.r-project.org/package=biostat3 Description: CRAN Package 'biostat3' (Utility Functions, Datasets and Extended Examples for SurvivalAnalysis) Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R". 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Bertrand and M. Maumy-Bertrand (2022, ISBN:978-2100782826 Dunod, fourth edition). 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The methods implemented in this package are based on the works: Connor (1987) Fleiss, Levin, & Paik (2013, ISBN:978-1-118-62561-3) Levin & Chen (1999) McNemar (1947) . 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The package integrates thermographic data import (FLIR, raw, CSV), automated region of interest (ROI) segmentation based on 'EBImage' (Pau et al., 2010 ), interactive ROI refinement, and high-throughput batch processing. 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Package: r-cran-biotimer Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-broom, r-cran-vegan, r-cran-dggridr, r-cran-checkmate, r-cran-lifecycle Suggests: r-cran-maps, r-cran-quarto, r-cran-knitr, r-cran-testthat, r-cran-vdiffr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-biotimer_0.3.2-1.ca2604.1_all.deb Size: 754294 MD5sum: cfebdc2411a453d8f0b2355b7c7cecf5 SHA1: d5d04340c324301713d3e0a3451d42f8bab421bb SHA256: 2e4b77185aae0c017bbcaef53e3cd0412a662938a1871bdf78b8a5352415b5cc SHA512: 400b70708357c1bbad4694a7a10b07fb089fbc230e20a3fbe7e67db23046f98c9af74c884eeb411c400253dba5aa47a02231f404f4d337f3d5232e0ec8c83d28 Homepage: https://cran.r-project.org/package=BioTIMEr Description: CRAN Package 'BioTIMEr' (Tools to Use and Explore the 'BioTIME' Database) The 'BioTIME' database was first published in 2018 and inspired ideas, questions, project and research article. 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Package: r-cran-biotools Architecture: all Version: 4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-boot Suggests: r-cran-soilphysics, r-cran-tkrplot, r-cran-rpanel, r-cran-lattice, r-cran-spatialepi, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-biotools_4.3-1.ca2604.1_all.deb Size: 248692 MD5sum: d93748c1296f1fd352a96c77aa8ae463 SHA1: b143cc123793c28dc46fb95ff6f452fc55e7b0a5 SHA256: b097963814c430ddc00e46ba683994768e223498bd272ef7d761ef01bc8f803b SHA512: 3cf8573383b8b864046b21ffbd35803ed1bea1c1c677dbf9024d56f5a9d5d41d0debe6f18d3e82da2d12aa617350a145e9d59e614635867df418e862cecb28d8 Homepage: https://cran.r-project.org/package=biotools Description: CRAN Package 'biotools' (Tools for Biometry and Applied Statistics in AgriculturalScience) Tools designed to perform and evaluate cluster analysis (including Tocher's algorithm), discriminant analysis and path analysis (standard and under collinearity), as well as some useful miscellaneous tools for dealing with sample size and optimum plot size calculations. A test for seed sample heterogeneity is now available. Mantel's permutation test can be found in this package. A new approach for calculating its power is implemented. biotools also contains tests for genetic covariance components. Heuristic approaches for performing non-parametric spatial predictions of generic response variables and spatial gene diversity are implemented. Package: r-cran-biotrajectory Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4933 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-av, r-cran-imager, r-cran-png, r-cran-tiff, r-cran-jpeg, r-cran-mass, r-cran-dplyr, r-cran-rpanel Filename: pool/dists/resolute/main/r-cran-biotrajectory_1.1.0-1.ca2604.1_all.deb Size: 4170960 MD5sum: 9dcf8bd032ae3f65ec1581dbb305fb8e SHA1: 6161c60d950020b95919455ace01076db523b1ad SHA256: ef194aeffe7801f6b06b1a47fd4089a20a3c67c14d2678fc46d4b44c284250ce SHA512: cbc1afc2e0d82ff534ce5746513b1b99777fe011ee9564d56265ea1378fdd9d5034a427b8b956360540f0484eec5d3dd4d78c4e6ff7242cf1acf896f3e1b8664 Homepage: https://cran.r-project.org/package=BioTrajectory Description: CRAN Package 'BioTrajectory' (Image Processing Tools for Barnes Maze Experiments) Tools to process the information obtained from experiments conducted in the Barnes Maze. These tools enable the detection of trajectories generated by subjects during trials, as well as the acquisition of precise coordinates and relevant statistical data regarding the results. Through this approach, it aims to facilitate the analysis and interpretation of observed behaviors, thereby contributing to a deeper understanding of learning and memory processes in such experiments. 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Implements a complete workflow including data import, quality control, differential expression analysis, co-expression network analysis, pathway enrichment, and multi-gene biomarker discovery. Differential expression uses the empirical Bayes moderated t-statistic of Smyth (2004) . Gene set enrichment analysis follows Subramanian et al. (2005) . Multi-gene biomarker selection uses the LASSO method of Tibshirani (1996) . Effect sizes are computed as Cohen's d following Cohen (1988). Package: r-cran-biovenn Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biomart, r-cran-plotrix, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-biovenn_1.1.3-1.ca2604.1_all.deb Size: 93172 MD5sum: e58a4225cb6e1914c82db2cd46b4c87e SHA1: fcb4c64c93e08d8196f448b9e580e5535df13916 SHA256: e5cd2ee9df6b0b3d84991d8d8d5c0eb9f41693a234758e513925fd3b4f0272c4 SHA512: 61805d5428002f780d3709289df2a1a2abd0bc95a17a9f25d491d3b9107125714000e5eec1c58e77e31e289083c2aadbb8ec05ee64ea22b5f7471b7334da91a1 Homepage: https://cran.r-project.org/package=BioVenn Description: CRAN Package 'BioVenn' (Create Area-Proportional Venn Diagrams from Biological Lists) Creates an area-proportional Venn diagram of 2 or 3 circles. 'BioVenn' is the only R package that can automatically generate an accurate area-proportional Venn diagram by having only lists of (biological) identifiers as input. Also offers the option to map Entrez and/or Affymetrix IDs to Ensembl IDs. In SVG mode, text and numbers can be dragged and dropped. Based on the BioVenn web interface available at . Hulsen (2021) . 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At the same time, We have developed a geom layer, geom_rrect(), that can generate rounded rectangles. No external references are used in the development of this package. Package: r-cran-bioworldr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2801 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bioworldr_0.1.0-1.ca2604.1_all.deb Size: 1479568 MD5sum: 3a00d34ae3b4836e910a7745a7cb4f8a SHA1: 4ec63d52a22fd61f8921e0089d0b52dce64bd85b SHA256: c6dc7ccb58d6dd26d7066acdea07a447dff6c158eff29eadcd286c2736fbdfcf SHA512: b188ad164a47de0cd0e50aba0d673aa8455a05aece9b3e21534aff8bd666c99a18c5341c645b531a4c0dd3754c7bc5b54e301f1b26fcae3264068cff07b77672 Homepage: https://cran.r-project.org/package=BioWorldR Description: CRAN Package 'BioWorldR' (A Curated Collection of Biodiversity and Species Datasets andUtilities) Provides a curated collection of biodiversity and species-related datasets (birds, plants, reptiles, turtles, mammals, bees, marine data and related biological measurements), together with small utilities to load and explore them. The package gathers data sourced from public repositories (including Kaggle and well-known ecological/biological R packages) and standardizes access for researchers, educators, and data analysts working on biodiversity, biogeography, ecology and comparative biology. It aims to simplify reproducible workflows by packaging commonly used example datasets and metadata so they can be easily inspected, visualized, and used for teaching, testing, and prototyping analyses. Package: r-cran-bipartited3 Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-r2d3, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-bipartite, r-cran-vegan, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bipartited3_0.3.2-1.ca2604.1_all.deb Size: 190436 MD5sum: b4d75738b24a8f4c6ff51474990577ed SHA1: 55ca45b9144da0de260500f86847fddccfa105b5 SHA256: 7eba3b8aae00bf7b8953452df6099935ff756e2f851deeb4917ba32a426b7a48 SHA512: edf2f8d06cf260c9e6bb4548f1f4556d246b6ced2eca76313396575e00cd1faff31e062b86542ab47bf1ee042c9fa842057fb2ffa5a9a5729b2b5e4d67a1eb1d Homepage: https://cran.r-project.org/package=bipartiteD3 Description: CRAN Package 'bipartiteD3' (Interactive Bipartite Graphs) Generates interactive bipartite graphs using the D3 library. Designed for use with the 'bipartite' analysis package. Includes open source 'viz-js' library Adapted from examples at (released under GPL-3). Package: r-cran-bipd Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-mvtnorm, r-cran-dplyr Suggests: r-cran-dclone, r-cran-r2winbugs, r-cran-mice, r-cran-micemd, r-cran-miceadds, r-cran-mitools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bipd_0.3-1.ca2604.1_all.deb Size: 126784 MD5sum: 6eeb6701f8de9db4aaf4877897493ef1 SHA1: f5d9b06c713980adfc1138335cd221712c69e2e1 SHA256: 6c60094f327808b34d7f77517eec4e44657145c036d8ce9c650dadc57b36ac07 SHA512: 631372d70f7f3a9d76839bdcc281e2a27bdd6187af11f7f9d989f935145be4ba43ab6a52bdaebecc833068f98d08926c8d07a9e843dabc1fcc9c9998c201a8f0 Homepage: https://cran.r-project.org/package=bipd Description: CRAN Package 'bipd' (Bayesian Individual Patient Data Meta-Analysis using 'JAGS') We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) . This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages. Package: r-cran-bipl5 Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-crayon, r-cran-htmlwidgets, r-cran-knitr, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-bipl5_1.0.2-1.ca2604.1_all.deb Size: 273906 MD5sum: 0b02db2253e5186c8da0b75d8a118346 SHA1: e8bf24907be2e7c0418eb2a97352e19fab6fd91c SHA256: 574d79729c1eac12406cef9fa414222534f21433bf797539c997e874fd767c23 SHA512: f6e92fa595a9aa1bbc84f148ed751df87217e7cc6fd502259fb1cb6ae62b297640fd39b9db46fd215fd005c9ece6cb9f98c934b67ed915b52fd1ef134249890b Homepage: https://cran.r-project.org/package=bipl5 Description: CRAN Package 'bipl5' (Construct Reactive Calibrated Axes Biplots) A modern view on the principal component analysis biplot with calibrated axes. Create principal component analysis biplots rendered in HTML with significant reactivity embedded within the plot. Furthermore, the traditional biplot view is enhanced by translated axes with inter-class kernel densities superimposed. For more information on biplots, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0). Package: r-cran-biplotbootgui Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-dendroextras, r-cran-mass, r-cran-matlib, r-cran-rgl, r-cran-shapes, r-cran-tcltk2, r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-biplotbootgui_1.3-1.ca2604.1_all.deb Size: 292702 MD5sum: 8e0a1b8b4a63d4a853377eb6f8d7af80 SHA1: 7031a92cde56586cf9235f8a90d173583d324954 SHA256: 7d142257b71eb24cb822504b9c8882be2b95ce969bc224b90795ef2bd9964cac SHA512: b65511a1fe656a5ebb5cd24cb8e9f3e4bc73a6cb3cd556af2e69cf0e624082c1e55d64a0927db7743df38f51afabdbaa04bc3f4c8c049483e289aed7596dbc0c Homepage: https://cran.r-project.org/package=biplotbootGUI Description: CRAN Package 'biplotbootGUI' (Bootstrap on Classical Biplots and Clustering Disjoint Biplot) A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology. Package: r-cran-biplotml Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-optimx, r-cran-rspectra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-pracma, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-biplotml_1.1.1-1.ca2604.1_all.deb Size: 129436 MD5sum: 5365de144e16c32517e81f042b69cadc SHA1: 4be513e91f9321e623f8ce7fe18caf1caf4682aa SHA256: 605313fb2a6e1717386de89c080423429e0498fac7d57afcfe0034ad8b84d555 SHA512: b7ff1b519fe1ca5e8c79bc7ceb1936d236f567a126ea6a352de4aa503d43d2c211ea492a7f019496ec0bc43a33f447a5acbc8388f5927a091995a8736cd6c447 Homepage: https://cran.r-project.org/package=BiplotML Description: CRAN Package 'BiplotML' (Logistic Biplot Estimation Using Machine Learning Algorithms) Implements methods for fitting logistic biplot models to multivariate binary data. The logistic biplot represents individuals as points and binary variables as directed vectors in a low-dimensional subspace; the orthogonal projection of each individual onto a variable vector approximates the expected probability that the corresponding characteristic is present. Available fitting methods include conjugate gradient algorithms, a coordinate descent Majorization-Minimization (MM) algorithm, and a block coordinate descent algorithm based on data projection that supports matrices with missing values and allows new individuals to be projected as supplementary rows without refitting the model. A cross-validation procedure is provided to select the number of latent dimensions k. References: Babativa-Marquez and Vicente-Villardon (2021) ; Vicente-Villardon and Galindo (2006, ISBN:9780470973196). Package: r-cran-birankr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-birankr_1.0.1-1.ca2604.1_all.deb Size: 67702 MD5sum: e50b1879cd20c293a537b99faf49ca25 SHA1: cdf35a44287b49753c880823aed779c6f9448e5b SHA256: ac41465af95c2128716611bdde51f346713a0c3d72e8c3c2382b43f4e8001c80 SHA512: c27e985d2d303a7838ca673451a3bc2186a8fe14bd6cdb3d8d89afa9cd809cd8bad304d6879a02e1f3557c08b6c724da0648d29e488b949cc8d06b14fcdf6c7a Homepage: https://cran.r-project.org/package=birankr Description: CRAN Package 'birankr' (Ranking Nodes in Bipartite and Weighted Networks) Highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks). Includes methods for estimating HITS, CoHITS, BGRM, and BiRank with implementation primarily inspired by He et al. (2016) . Also provides easy-to-use tools for efficiently estimating PageRank in one-mode graphs, incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or dgCMatrix). Package: r-cran-birdcolors Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-birdcolors_1.0.1-1.ca2604.1_all.deb Size: 29436 MD5sum: d70c1d5cee106b156ec607fb73d6a814 SHA1: ad162b4dd7dab9f2023d5dd2b8e3f2ca05be1c53 SHA256: e93104026a926cab98056839c6cb660bf4bdac1ab35fba6f52a46e172bd302c0 SHA512: 12459f62030ac3f2192ce9520a8fb8779b3a4151335627bad9f1e92bdb4ab5cadba16490ed61932b7735966714b9f519b9c2a461ee9a055eaba8e3ce7d56bbdd Homepage: https://cran.r-project.org/package=birdcolors Description: CRAN Package 'birdcolors' (Create Palettes from the Colors of the World's Birds) Create attractive palettes based on the colors of the world's birds. Palettes are composed of 2 to 9 colors, with options to expand palettes via interpolation. Compatible with the package 'ggplot2' and base R graphics. Package: r-cran-birddog Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3650 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggraph, r-cran-ggplot2, r-cran-plotly, r-cran-igraph, r-cran-tidygraph, r-cran-tidyr, r-cran-tibble, r-cran-matrix, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-glue, r-cran-openalexr, r-cran-rcolorbrewer, r-cran-scales, r-cran-stringr Suggests: r-cran-benchmarkme, r-cran-knitr, r-cran-rmarkdown, r-cran-cli, r-cran-gghoriplot, r-cran-ggrepel, r-cran-ggthemes, r-cran-janitor, r-cran-gt, r-cran-testthat, r-cran-tictoc, r-cran-viridis, r-cran-zoo, r-cran-stm, r-cran-tidytext, r-cran-udpipe Filename: pool/dists/resolute/main/r-cran-birddog_1.0.4-1.ca2604.1_all.deb Size: 2436736 MD5sum: 36988f13aec79187ca10e9882dfeca1f SHA1: f5f6b56fd30f2239f991af59c2b8c6f567242993 SHA256: 0f5f0ee2613dedb2d8747a8ba67e664c99586b7c8f0e5ea5e1113e47d2de6cfa SHA512: 07ff4e5997e352b0f8c54a32d743c964f1480c2f3573d56c9420f051574d3ab4ad60782ad604f2fff49ae8f49abe48dc9466ce1ee9d1659af151b07e9f34f4ef Homepage: https://cran.r-project.org/package=birddog Description: CRAN Package 'birddog' (Sniffing Emergence and Trajectories in Academic Papers andPatents) Provides a unified set of methods to detect scientific emergence and technological trajectories in academic papers and patents. The package combines citation network analysis with community detection and attribute extraction, also applying natural language processing (NLP) and structural topic modeling (STM) to uncover the contents of research communities. It implements metrics and visualizations of community trajectories, including novelty indicators, citation cycle time, and main path analysis, allowing researchers to map and interpret the dynamics of emerging knowledge fields. Applications of the method include: Souza et al. (2022) , Souza et al. (2022) , Matos et al. (2023) , Maria et al. (2023) , Biazatti et al. (2024) , Felizardo et al. (2025) , and Miranda et al. (2025) . Package: r-cran-birdnetr Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-arrow, r-cran-curl, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-birdnetr_0.3.2-1.ca2604.1_all.deb Size: 574206 MD5sum: d4be18e8b29a47653c4861a9ba273da0 SHA1: 190e2e6d448574067b88a882313b73ba9b45bd43 SHA256: 3aed48ed68412a12d29c9f479fedeff7b772ae9d1c371c3193fdbf2c0cbd0179 SHA512: e32dc84ad4a33a5bbefef8f207c2889777ca2dd55aafaf26e5954aa49a884f30eb154130093fca995f6d8a11680a0ee6f7f6ec42f339dbf263ac983df5a06bb9 Homepage: https://cran.r-project.org/package=birdnetR Description: CRAN Package 'birdnetR' (Deep Learning for Automated (Bird) Sound Identification) Use 'BirdNET', a state-of-the-art deep learning classifier, to automatically identify (bird) sounds. Analyze bioacoustic datasets without any computer science background using a pre-trained model or a custom trained classifier. Predict bird species occurrence based on location and week of the year. Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) . Package: r-cran-birdring Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-ks, r-cran-lazydata, r-cran-raster Filename: pool/dists/resolute/main/r-cran-birdring_1.6-1.ca2604.1_all.deb Size: 582758 MD5sum: 57a14fc50eb3eaa70d953a2855b04e0c SHA1: ba06c334f2db74be663e60461b0f0b8b364264a6 SHA256: 874c9db0f3faeaefe3d791ffaea14400d8fbe2df03202b1ea6ac1a238a9674c4 SHA512: e744968a992467857714639387ac947078b9fb1b3bf1044353c2b8ca6ff75da6676126fcfeedfed28ae5d3e22ed212fd9bb01bb15186b9008b8747c92def1683 Homepage: https://cran.r-project.org/package=birdring Description: CRAN Package 'birdring' (Methods to Analyse Ring Re-Encounter Data) R functions to read EURING data and analyse re-encounter data of birds marked by metal rings. For a tutorial, go to . Package: r-cran-birdscanr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 892 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-suntools, r-cran-modi, r-cran-reshape2, r-cran-rodbc, r-cran-rpostgresql, r-cran-rlang, r-cran-rstudioapi, r-cran-sp, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-birdscanr_0.3.0-1.ca2604.1_all.deb Size: 694312 MD5sum: 17c6053b004456d6c1b236d40e05f852 SHA1: 7627e2066eec355ab183b24ac947ae164d772b6d SHA256: c28860b1fdcc4057eebfed79c583bf13fb73b1793dafb4e62a9230af0faf4613 SHA512: ea6c7d56565ecc176ac11fe2d553d037eb1fbd26f4fa7a5b5f8cf7dfc79f237609b6de8c2e06a956978e493dd216331dc9317f1be5890f990a81d9c81151b14d Homepage: https://cran.r-project.org/package=birdscanR Description: CRAN Package 'birdscanR' (Migration Traffic Rate Calculation Package for 'Birdscan MR1'Radars) Extract data from 'Birdscan MR1' 'SQL' vertical-looking radar databases, filter, and process them to Migration Traffic Rates (#objects per hour and km) or density (#objects per km3) of, for example birds, and insects. Object classifications in the 'Birdscan MR1' databases are based on the dataset of Haest et al. (2021) ). Migration Traffic Rates and densities can be calculated separately for different height bins (with a height resolution of choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day, day/night, the full time period of observation, and anything in between). Two plotting functions are also included to explore the data in the 'SQL' databases and the resulting Migration Traffic Rate results. For details on the Migration Traffic Rate calculation procedures, see Schmid et al. (2019) . Package: r-cran-birk Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-birk_2.1.2-1.ca2604.1_all.deb Size: 47518 MD5sum: bdf1064dc9d4d45465866f71a3f77830 SHA1: 35f6ba07bb4cbdca8ccec23422757687837219ec SHA256: 5d897275bf3048c4dff918b7dc14d187f2d7bc55e1f0773779a6ff8c49b66c5b SHA512: 2fefe7663f45852a527c474c207f0b78e6e3c72ec22a348cb0b635779e2e883e1021e995f1740f8963948387816ea5d30b6d450bce70ab0623151fba6f788eb1 Homepage: https://cran.r-project.org/package=birk Description: CRAN Package 'birk' (MA Birk's Functions) Collection of tools to make R more convenient. Includes tools to summarize data using statistics not available with base R and manipulate objects for analyses. Package: r-cran-birtr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-birtr_1.0.0-1.ca2604.1_all.deb Size: 56758 MD5sum: 11b8470111855b2beea06c05360853ac SHA1: 3cb464fe6de87dbd32195a690acb9161083a317d SHA256: 97dde5359b7cc8bb1cb381e6cb647695a02b00236f74709f5f2a6e5680a0a967 SHA512: 1cbdd364b29f0cbf468437547df125d3f778358f69aca05fe516d156443d89ab8b5216cb6a8f76e6776eab09639234bdb3ba198519b75c4512dbb80e9eb6e0e6 Homepage: https://cran.r-project.org/package=birtr Description: CRAN Package 'birtr' (The R Package for "The Basics of Item Response Theory Using R") R functions for "The Basics of Item Response Theory Using R" by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch(). For example, iccplot() plots an item characteristic curve under the two-parameter logistic model. 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Package: r-cran-biscale Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-cowplot, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-biscale_1.1.0-1.ca2604.1_all.deb Size: 2586444 MD5sum: b4abc76493137c0df429b7df1267ef7c SHA1: 314aae928de4fd20e714acb67c9339e0bdd872c5 SHA256: 2044764b25f627ecc60c207a0d7569c5063b9b318196419bdba14b5d3aeb08e7 SHA512: b3075b10a4302c12869f01a743eecdad9351756239f53cf37c729e371c7e7f91377b2981f023e4cbbfe903f180ca82b3294747cc65ebf0a9a0758155d5267e53 Homepage: https://cran.r-project.org/package=biscale Description: CRAN Package 'biscale' (Tools and Palettes for Bivariate Thematic Mapping) Provides a 'ggplot2' centric approach to bivariate mapping. 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Package: r-cran-bisdata Architecture: all Version: 0.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-zoo Filename: pool/dists/resolute/main/r-cran-bisdata_0.2-3-1.ca2604.1_all.deb Size: 22054 MD5sum: d8e9ed37a4788937c623cb7510291516 SHA1: 968416ae55b545de09b4430969f09251f13cab01 SHA256: de431b485bd869a39f9f31efdf0f52a8db7703d8cf36d99630ba60f01dc14979 SHA512: bd5428cf672bda0afabcc0e6cd817c98ed5f7b2d745297252c11214fb4fcdc4e8692ac05d6faaf9b8ade7e5f76a247cd204150ffc15d3bc0ad5af98192a19e07 Homepage: https://cran.r-project.org/package=BISdata Description: CRAN Package 'BISdata' (Download Data from the Bank for International Settlements (BIS)) Functions for downloading data from the Bank for International Settlements (BIS; ) in Basel. Supported are only full datasets in (typically) CSV format. 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Package: r-cran-bispdep Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdata, r-cran-sf, r-cran-spdep, r-cran-spatialreg, r-cran-combinat, r-cran-boot, r-cran-ggplot2, r-cran-sp, r-cran-rcolorbrewer Suggests: r-cran-spam Filename: pool/dists/resolute/main/r-cran-bispdep_1.0-2-1.ca2604.1_all.deb Size: 184874 MD5sum: 9731fe44e64209831f72541aa27c1389 SHA1: 838d4822edca50a1adf1add1f4c1e7218c1e3d4a SHA256: 5df0f1a201eb5d0611800ccb737b092639d025f56476088fb861c1f904e7eab4 SHA512: 62440ee60299c6b25197ac2393ca0e3467092e9ddf8f1ab71a554ebf913bfcb4278e0a356c4c39d37f0f869f8d4934dc7330e7fe3085ea5e931c708fa508dd17 Homepage: https://cran.r-project.org/package=bispdep Description: CRAN Package 'bispdep' (Statistical Tools for Bivariate Spatial Dependence Analysis) A collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established. Package: r-cran-bisrna Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-bioc-ihw, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bisrna_0.2.2-1.ca2604.1_all.deb Size: 54572 MD5sum: ed168375dd484adfeed210d28397d611 SHA1: 0cb848e4266068cdd0da251f4179291c0ed26a7b SHA256: bfae5276654c20e070541dc7b65f6cbfa926a9d8415f22c4bf75ef75f483ac87 SHA512: e1ba8d78cb56d4ca5a1bb9a0876f4a9d7844bbf5bd99feaa57376aa1664dea531bd156c70e69cb78c99bd82865d6e6cb4dc7e74ac017f7d7e2c3bc7db4e01421 Homepage: https://cran.r-project.org/package=BisRNA Description: CRAN Package 'BisRNA' (Analysis of RNA Cytosine-5 Methylation) Bisulfite-treated RNA non-conversion in a set of samples is analysed as follows : each sample's non-conversion distribution is identified to a Poisson distribution. P-values adjusted for multiple testing are calculated in each sample. 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Package: r-cran-bivpois Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-bivpois_1.2-1.ca2604.1_all.deb Size: 56074 MD5sum: 89dc4e76d00a0bc3304a47c9a11b2398 SHA1: 269183a6d52860eface99ebe4f050614883261af SHA256: fbee9b3366d9637bddd52382dbe4fb5ec524f006dbcc831cca24ce33e9f651f1 SHA512: 2735bc64aebfd847eb382ac7a59133482083717836665b2a1bf47112d28b2dd63e11e0250afc2d90d4348720b3cdeff84b4811e07889031189a2078fd7d7b5a0 Homepage: https://cran.r-project.org/package=bivpois Description: CRAN Package 'bivpois' (Bivariate Poisson Distribution) Maximum likelihood estimation, random values generation, density computation and other functions for the bivariate Poisson distribution. References include: Kawamura K. (1984). "Direct calculation of maximum likelihood estimator for the bivariate Poisson distribution". Kodai Mathematical Journal, 7(2): 211--221. . Kocherlakota S. and Kocherlakota K. (1992). "Bivariate discrete distributions". CRC Press. . Karlis D. and Ntzoufras I. (2003). "Analysis of sports data by using bivariate Poisson models". Journal of the Royal Statistical Society: Series D (The Statistician), 52(3): 381--393. . 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The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) , B.G. Francq, B. Govaerts (2014) , B.G. Francq, B. Govaerts (2014) , B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, . 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Package: r-cran-blob Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-vctrs Suggests: r-cran-covr, r-cran-crayon, r-cran-pillar, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-blob_1.3.0-1.ca2604.1_all.deb Size: 50714 MD5sum: 4b8adc2b0ecab514a7ca3bfc99a6e9af SHA1: e02ed08ac137d7a05d2ace348712b355aba93ffd SHA256: ca527b7bcb428bf5eee3380e05ecab281426423bb2615e41eb2fe834d09adf47 SHA512: 07287d2baef97d569552ad1211c0b3cc84b048b669e97392d592295a7b3b8cf0ecb0faeb3283deed7f08b8b8e143acacc468203c276b947dbd13a8ed4eae84d9 Homepage: https://cran.r-project.org/package=blob Description: CRAN Package 'blob' (A Simple S3 Class for Representing Vectors of Binary Data('BLOBS')) R's raw vector is useful for storing a single binary object. What if you want to put a vector of them in a data frame? The 'blob' package provides the blob object, a list of raw vectors, suitable for use as a column in data frame. Package: r-cran-blockcov Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rdpack, r-cran-bbmisc, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-blockcov_0.1.1-1.ca2604.1_all.deb Size: 163024 MD5sum: a7c614b04c1552e6de9b46bdc4bb2af6 SHA1: 895155bf50155248866232ff04c2e8081158c40d SHA256: d48d89c36b0b11eeac11e25133a0d300641630b4788a439acecf6dd0223cb8da SHA512: 31eb70cd08129e762325ddfd4d229d7c7317c202f862c98c9340a130e5f322c4d0dd9584598a96bd5c22545bfe2937e27f5cb8c8fd38dad656b9be8dd30af76e Homepage: https://cran.r-project.org/package=BlockCov Description: CRAN Package 'BlockCov' (Estimation of Large Block Covariance Matrices) Computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of samples with respect to the dimension of the matrix. The method is described in the paper Perrot-Dockès et al. (2019) . Package: r-cran-blockedff Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-blockedff_0.1.0-1.ca2604.1_all.deb Size: 70952 MD5sum: 6ecbd3d5e2e793b9bc21afdd788e2dfa SHA1: 3eb1135bc6237c46e9227456797c8708151357e3 SHA256: a0a4cea4c6d15a002146d5eb9ac2e423e2b898a25bb9e89c7f9a03bc39d404ad SHA512: 588be3d0d54dbe920e21f908c0e9bd65f98858cb4fbbb3672323af647fa6243baa40fcd4c7befd9e31691fd5cfae5bb7653c707f24ddce602b6ac33b06bca162 Homepage: https://cran.r-project.org/package=blockedFF Description: CRAN Package 'blockedFF' (Generation of Blocked Fractional Factorial Designs (Two-Leveland Three-Level)) Provides computational tools to generate efficient blocked and unblocked fractional factorial designs for two-level and three-level factors using the generalized Minimum Aberration (MA) criterion and related optimization algorithms. Methodological foundations include the general theory of minimum aberration as described by Cheng and Tang (2005) , and the catalogue of three-level regular fractional factorial designs developed by Xu (2005) . The main functions dol2() and dol3() generate blocked two-level and three-level fractional factorial designs, respectively, using beam search, optimization-based ranking, confounding assessment, and structured output suitable for complete factorial situations. Package: r-cran-blocking Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-text2vec, r-cran-tokenizers, r-cran-rcpphnsw, r-cran-rcppannoy, r-cran-mlpack, r-cran-rnndescent, r-cran-igraph, r-cran-data.table, r-cran-readr, r-cran-matrix Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-reclin2 Filename: pool/dists/resolute/main/r-cran-blocking_1.0.2-1.ca2604.1_all.deb Size: 2326966 MD5sum: ae8428b80b215042046cb7d1d4a14299 SHA1: 36fe5e87d4a68731097194ddab70d736ffc57f98 SHA256: a2463caaa0bab389f28f2497dbdaae0dec8bb442d44d151f12147694146f42ab SHA512: c9d21f1e99c6e7dacdcb4263e8b75e90c2b3d8b12435595985820f6cc99ae45ab6778d7aca1089cc249358bc9aae81e4923c790c7ebb987e207285979af9072e Homepage: https://cran.r-project.org/package=blocking Description: CRAN Package 'blocking' (Various Blocking Methods for Entity Resolution) The goal of 'blocking' is to provide blocking methods for record linkage and deduplication using approximate nearest neighbour (ANN) algorithms and graph techniques. It supports multiple ANN implementations via 'rnndescent', 'RcppHNSW', 'RcppAnnoy', and 'mlpack' packages, and provides integration with the 'reclin2' package. The package generates shingles from character strings and similarity vectors for record comparison, and includes evaluation metrics for assessing blocking performance including false positive rate (FPR) and false negative rate (FNR) estimates. For details see: Papadakis et al. (2020) , Steorts et al. (2014) , Dasylva and Goussanou (2021) , Dasylva and Goussanou (2022) . Package: r-cran-blocklength Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tseries Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-blocklength_0.2.2-1.ca2604.1_all.deb Size: 126272 MD5sum: 9465fa6e8cce5c8c4e237a564980946d SHA1: bd758d3ffa3a960e8e4a242385d2ef827ca1c292 SHA256: 828b827149d62ee9a0be9d3975ddd34c81fbad0483d4699ee31fbe3b6ae4a008 SHA512: 7647b02de4e8c2f32049a12f089ff550c4dd04367e52eb73fd79bb38245f3dfa7fecf7f49cf35990af1fe999138988bf84dcda7f3d69592c93e26d6cd7a419ec Homepage: https://cran.r-project.org/package=blocklength Description: CRAN Package 'blocklength' (Select an Optimal Block-Length to Bootstrap Dependent Data(Block Bootstrap)) A set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) subsampling-based cross-validation method, the Politis and White (2004) Spectral Density Plug-in method, including the Patton, Politis, and White (2009) correction, and the Lahiri, Furukawa, and Lee (2007) nonparametric plug-in method, with a corresponding set of S3 plot methods. Package: r-cran-blockmatrix Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-blockmatrix_1.0-1.ca2604.1_all.deb Size: 65716 MD5sum: 7a09441d682d41e6e0b1ed376f861002 SHA1: c4789006a5a8380ab03b8986dc8d8e80d287da6c SHA256: 856989cdaab8e84e198906ff4306af72d87bc103c721d275e7bc8cf0d4dd87fd SHA512: c6032eaec4b43c8523d76e6f371aa1bd7a89025920994465e98d33c43aae358cb2dc7a2a9ac8157a2177f3b212541394fe8a602b9986e906c2c652a18193048f Homepage: https://cran.r-project.org/package=blockmatrix Description: CRAN Package 'blockmatrix' (blockmatrix: Tools to solve algebraic systems with partitionedmatrices) Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed. Package: r-cran-blockmodelinggui Architecture: all Version: 1.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockmodeling, r-cran-shiny, r-cran-dt, r-cran-htmlwidgets, r-cran-igraph, r-cran-intergraph, r-cran-network, r-cran-shinybusy, r-cran-shinyjs, r-cran-shinythemes, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-blockmodelinggui_1.8.4-1.ca2604.1_all.deb Size: 46768 MD5sum: 609aea056d7f7999b62fb10f938e53f0 SHA1: b9f1f2bef21aaca50044eaf5dac38ecb93d616e5 SHA256: 1d6e98fae4400b844e69d07197a64d9ad81cb34328954b4873e2085f904283de SHA512: 4d1a21c1c82d77aa01c5bfc457955c3d9f87505884f565ed99872f73f5748acba4da27f229dbf4347f3b165c8ebee6a73386ee8892dbe60a48c5ff9d2a49faef Homepage: https://cran.r-project.org/package=BlockmodelingGUI Description: CRAN Package 'BlockmodelingGUI' (GUI for the Generalised Blockmodeling of Valued Networks) This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-blockr.core Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1088 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-bsicons, r-cran-jsonlite, r-cran-vctrs, r-cran-generics, r-cran-rlang, r-cran-htmltools, r-cran-evaluate, r-cran-shinyfiles, r-cran-digest, r-cran-cli, r-cran-glue Suggests: r-cran-testthat, r-cran-memuse, r-cran-withr, r-cran-shinytest2, r-cran-roxy.shinylive, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-scoutbar, r-cran-thematic, r-cran-ids, r-cran-ellmer Filename: pool/dists/resolute/main/r-cran-blockr.core_0.1.2-1.ca2604.1_all.deb Size: 687546 MD5sum: 6960872a28c1678721cf12e03cc4b382 SHA1: d1a41baf9b89d4ab654d9cf8f9db2a3024c5436e SHA256: a2a7b40dd77677eb4d9a7087542c8e46ea7432862dda15d7f3f18bae1ba45506 SHA512: ec03b8933113195b17e4b0cdd47d4f4fd5b5462cbd16e36c5140c6d633791ce99d5f508bf01d369b60d772636ff62ef2c8f588e918c3ccda7a9f020447a552f0 Homepage: https://cran.r-project.org/package=blockr.core Description: CRAN Package 'blockr.core' (Graphical Web-Framework for Data Manipulation and Visualization) A framework for data manipulation and visualization using a web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks. Package: r-cran-blockr.dag Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2910 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockr.core, r-cran-blockr.dock, r-cran-shiny, r-cran-g6r, r-cran-jsonlite, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxy.shinylive, r-cran-webshot2, r-cran-quarto, r-cran-cyclocomp, r-cran-shinytest2, r-cran-colorspace Filename: pool/dists/resolute/main/r-cran-blockr.dag_0.1.2-1.ca2604.1_all.deb Size: 797958 MD5sum: 0c5f278c146fbe289ffc65cd9b9698f9 SHA1: b60bf9c396ae1cd82b758ca10bf91a4bdc877387 SHA256: af26085408f5114cbb78901efa514a8c051ee2286488a7037e738a8b9a8bab75 SHA512: 12b99827022b1551acfaaa1743adcbda36b64d9c6dc34509eae93bc14902c9e112490d97e4aca483891bd74e7b039caa6fa00ef94e44cecb88839ecefc46e78b Homepage: https://cran.r-project.org/package=blockr.dag Description: CRAN Package 'blockr.dag' (A Directed Acyclic Graph Extension for 'blockr') Building on the docking layout manager provided by 'blockr.dock', this provides an extension that allows for visualizing and manipulating a 'blockr' board using a DAG-based user interface powered by the 'g6R' graph visualisation HTML widget. 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Supports CSV, Excel, Parquet, RDS, and other formats through a graphical interface without writing code directly. Includes file browser integration and configurable import/export options. 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Underlying data manipulation and visualization is possible using a web-based point and click user interface. Package: r-cran-blockrand Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-blockrand_1.5-1.ca2604.1_all.deb Size: 34270 MD5sum: 99b9b57284008f33c9cdc2be725ac905 SHA1: a17b31bff04bf1c93ec6628286aa9319772462dd SHA256: 25b8c05f93e4f9802d6d5761c847c655128e76e1de3c90013cf28a1cdaff7016 SHA512: 83d0dbd6f6ad89d6107ec5b5982d7da67344c84fb493adbe69715e4731a840020e41923b032b59424168cbd88b5562e513cd8d3fd8f7817b1331b312a8e6cb1a Homepage: https://cran.r-project.org/package=blockrand Description: CRAN Package 'blockrand' (Randomization for Block Random Clinical Trials) Create randomizations for block random clinical trials. Can also produce a pdf file of randomization cards. Package: r-cran-blocksdesign Architecture: all Version: 4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-polynomf Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-blocksdesign_4.9-1.ca2604.1_all.deb Size: 484850 MD5sum: b259c4f34dcfa9a756494f64ac5e499e SHA1: f72923f3c04d39d6268e7a511a2fe98529b1d0e0 SHA256: 9286bc9c189d1d3238f35fb6c7fd5a7b6e0e17dd2dd7c1f125be4d608bd73ec9 SHA512: 1095b79f09d37071c315cbdc7c8b8fcf496be707a4f1440495861cdbff86defd45afb66a924f5d4bd9bec543ad9d79f29fb0b6491b69011ebea73de6f508d02f Homepage: https://cran.r-project.org/package=blocksdesign Description: CRAN Package 'blocksdesign' (Nested and Crossed Block Designs for Factorial and UnstructuredTreatment Sets) Constructs treatment and block designs for linear treatment models with crossed or nested block factors. The treatment design can be any feasible linear model and the block design can be any feasible combination of crossed or nested block factors. The block design is a sum of one or more block factors and the block design is optimized sequentially with the levels of each successive block factor optimized conditional on all previously optimized block factors. D-optimality is used throughout except for square or rectangular lattice block designs which are constructed algebraically using mutually orthogonal Latin squares. Crossed block designs with interaction effects are optimized using a weighting scheme which allows for differential weighting of first and second-order block effects. Outputs include a table showing the allocation of treatments to blocks and tables showing the achieved D-efficiency factors for each block and treatment design. Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020) . 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Entire groups are returned allowing for a single 'observation' to span multiple rows of the dataframe. 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Package: r-cran-blrm Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-rjags, r-cran-mvtnorm, r-cran-openxlsx, r-cran-reshape2, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-blrm_1.0-2-1.ca2604.1_all.deb Size: 135836 MD5sum: 9773975010fcf710e2b445aec3a6cc3a SHA1: dc0bf70f1c93c96626ba02011f1425adcd3f42b4 SHA256: 898c343052595bf83f340daaf30ba95046bce1f1d175baa3109fe538d8d420f0 SHA512: acf318d46b5ceee9105608981e53842233df078a61669a6df45ded124c30c97d46320946437a39ba5bca4c3d95271d7425edb75cc7f5a1fd98d035c33af41573 Homepage: https://cran.r-project.org/package=blrm Description: CRAN Package 'blrm' (Dose Escalation Design in Phase I Oncology Trial Using BayesianLogistic Regression Modeling) Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial. Package: r-cran-blrpm Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6 Filename: pool/dists/resolute/main/r-cran-blrpm_1.0-1.ca2604.1_all.deb Size: 98172 MD5sum: 2a635629a718e6e2da33d7b0da271c31 SHA1: 6a0f6d71f42bdc5affd114665344b6e6c4ee3a03 SHA256: f4f6dd71c521042b586eb03da7793b61360db728826d7f7bbc34ddbe456debf9 SHA512: 9eda08ffb8e81016370d738f34ef1f55aacd7d0642cd3053ab85ecbe839c8d48993bcbcd5280ca868f27c3bdf5dbbfcf4e8395cab3bdaac2ebf928268487e2d6 Homepage: https://cran.r-project.org/package=BLRPM Description: CRAN Package 'BLRPM' (Stochastic Rainfall Generator Bartlett-Lewis Rectangular PulseModel) Due to a limited availability of observed high-resolution precipitation records with adequate length, simulations with stochastic precipitation models are used to generate series for subsequent studies [e.g. Khaliq and Cunmae, 1996, , Vandenberghe et al., 2011, ]. 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Runtime examples are provided in the package function as well as at . Package: r-cran-blsbandit Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-jsonlite, r-cran-plotly, r-cran-rsqlite, r-cran-shiny, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-blsbandit_0.1-1.ca2604.1_all.deb Size: 48994 MD5sum: 58c90de4dd60dfcbe8f0e98454f8bb41 SHA1: 63531d98159ed6c9dfb73bf576e4a4c73aebad15 SHA256: cf1a195757f97b9d5127c2f31aa829cc4b50c1f9ada074913b0e0b09a57ff4dd SHA512: 360dbe5f0d0734a5881705f758aa0bd623a18ebb87337f9295fad8ff284aa7f4cf1185fca2c0a1e9adb1f7a6804371b79ef1c4effc599271941bf2d8d00da964 Homepage: https://cran.r-project.org/package=blsBandit Description: CRAN Package 'blsBandit' (Data Viewer for Bureau of Labor Statistics Data) Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) . 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The functions in this package utilize flat files produced by the Bureau of Labor Statistics, which contain full series history. These files include employment, unemployment, wages, prices, industry and occupational data at a national, state, and sub-state level, depending on the series. Individual functions are included for those programs which have data available at the state level. The core functions provide direct access to the Current Employment Statistics (CES) , Local Area Unemployment Statistics (LAUS) , Occupational Employment and Wage Statistics (OEWS) and Alternative Measures of Labor Underutilization (SALT) data produced by the Bureau of Labor Statistics. 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Examples may be checked inside the demonstration files. 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Functions include the computation of trip distances of given trip data. It can also map the location of stations within a given radius and calculate the distance to nearby stations. Data is from . Package: r-cran-bluecarbon Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 791 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bluecarbon_0.1.1-1.ca2604.1_all.deb Size: 601184 MD5sum: 7dd30accf56cce74021a50ca4be6bf2a SHA1: 96a110419ac40364c93049e95954fc95c2f7e46c SHA256: 0186c3850bc59e1775390bccc14704da36cda0b4bc7f1cae882b531829b59bc3 SHA512: 6b99ba043b4d521912555a8f5532c07e9081914c497d0d7bc24e974bc0a9a798ffb3194026f37444aab6ac4ab4d2ad91be31cb1e576dd290ef6a251e0c6024f0 Homepage: https://cran.r-project.org/package=BlueCarbon Description: CRAN Package 'BlueCarbon' (Estimation of Organic Carbon Stocks and Sequestration Rates fromSoil Core Data) Tools to estimate soil organic carbon stocks and sequestration rates in blue carbon ecosystems. 'BlueCarbon' contains functions to estimate and correct for core compaction, estimate sample thickness, estimate organic carbon content from organic matter content, estimate organic carbon stocks and sequestration rates, and visualize the error of carbon stock extrapolation. Package: r-cran-blythstillcasellaci Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-blythstillcasellaci_1.1.0-1.ca2604.1_all.deb Size: 39576 MD5sum: 7ae255cdf3660f6b55f3f78879ea70ca SHA1: 20671f1f844e48369e9bada7c7d4e919a497f6b3 SHA256: 17845c48192accd23e5c28e0a690a4423d100daa57c7e25a0692a5f303918fc0 SHA512: 4cd9220e92cab396e0b34f907de5efbf86276948e585c3bd4cbb79726f83879a68e304557e4d8e6e88f528003437ce0bc46aed2b03d4a57e017a3617ac21fa67 Homepage: https://cran.r-project.org/package=BlythStillCasellaCI Description: CRAN Package 'BlythStillCasellaCI' (Blyth-Still-Casella Exact Binomial Confidence Intervals) Computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986) . Package: r-cran-bmabart Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bart, r-cran-survival, r-cran-gplots, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bmabart_2.0-1.ca2604.1_all.deb Size: 263156 MD5sum: 0689ee44134060bec47c1051e151a6ba SHA1: 97dbcae146361e26dfefb2562a629c7d572ed531 SHA256: 376d51d2176d1739822681dc4dfecfaf0a1cb444f148debb40d8a3ed71f22b1f SHA512: 4338c023143d4ea67bc96d85050a2880ee6585ce2c5cd4f2fad26ab8215f54e3667227e976ce83b1af0a128c252cf1b364ab9169ee558740105a893be617808b Homepage: https://cran.r-project.org/package=bmabart Description: CRAN Package 'bmabart' (Bayesian Mediation Analysis Using BART) Used for Bayesian mediation analysis based on Bayesian additive Regression Trees (BART). The analysis method is described in Yu and Li (2025) "Mediation Analysis with Bayesian Additive Regression Trees", submitted for publication. Package: r-cran-bmass Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bmass_1.0.3-1.ca2604.1_all.deb Size: 114636 MD5sum: 2e0b8670c3ee6f24aee101bcd6a30195 SHA1: 31cd36edd667417be9c8dfc0096469a46f001989 SHA256: 4c6bd9782d03a06342c6a805f9e68d10b2f40a9615c80a43b19080c3418feaba SHA512: 5134ab1165262a9e7a1eb1a174eee5d68d38bb68c2392365b84766cb53f45a7b107e47afae499bcf2879cc0f262572c3ad1f28b818727d7d9727184fde78e061 Homepage: https://cran.r-project.org/package=bmass Description: CRAN Package 'bmass' (Bayesian Multivariate Analysis of Summary Statistics) Multivariate tool for analyzing genome-wide association study results in the form of univariate summary statistics. The goal of 'bmass' is to comprehensively test all possible multivariate models given the phenotypes and datasets provided. Multivariate models are determined by assigning each phenotype to being either Unassociated (U), Directly associated (D) or Indirectly associated (I) with the genetic variant of interest. Test results for each model are presented in the form of Bayes factors, thereby allowing direct comparisons between models. The underlying framework implemented here is based on the modeling developed in "A Unified Framework for Association Analysis with Multiple Related Phenotypes", M. Stephens (2013) . Package: r-cran-bmco Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3722 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-coda, r-cran-mcmcpack, r-cran-msm, r-cran-pgdraw, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-refmanager Filename: pool/dists/resolute/main/r-cran-bmco_0.1.0-1.ca2604.1_all.deb Size: 3440104 MD5sum: 9261be76f9e23d3507a3b10d1de57df1 SHA1: 89aaaf98a5817f8bcace1aea39ce96005de4acd6 SHA256: f671b83ecb875d1b4a6263338df0d915f95027171d5703c6c4ac689790a6f2fe SHA512: 848e8ac35ceac343f1d837f5fd8f1318ed3ca0cf2a0c22b57df15c357484dc875cee1d3b2affe07d31915d8ce64ac735ed8d140b532ea6c636c8f59cc3fc9fa9 Homepage: https://cran.r-project.org/package=bmco Description: CRAN Package 'bmco' (Bayesian Analysis for Multivariate Categorical Outcomes) Provides Bayesian methods for comparing groups on multiple binary outcomes. Includes basic tests using multivariate Bernoulli distributions, subgroup analysis via generalized linear models, and multilevel models for clustered data. For statistical underpinnings, see Kavelaars, Mulder, and Kaptein (2020) , Kavelaars, Mulder, and Kaptein (2024) , and Kavelaars, Mulder, and Kaptein (2023) . An interactive shiny app to perform sample size computations is available. Package: r-cran-bmconcor Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4229 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bmconcor_2.0.0-1.ca2604.1_all.deb Size: 657418 MD5sum: c052a5be4a8c21b6c8be85bc8a8873ac SHA1: e23cf6bca2e23ff02ebfa537e7827fe4ba864713 SHA256: f3ea328c075b091411341b89491f2988f73577363be3959791644178d59174c2 SHA512: 560c93bc4b61b1d192d26d00419e309a35d8f1d95924d202c3a26d97b71b05f73c9515c7c2be8463f5e5d0187da74692a5f47c0bf4c08e6e64203f8a6af34f1a Homepage: https://cran.r-project.org/package=BMconcor Description: CRAN Package 'BMconcor' (CONCOR for Structural- And Regular-Equivalence Blockmodeling) The four functions svdcp() ('cp' for column partitioned), svdbip() or svdbip2() ('bip' for bipartitioned), and svdbips() ('s' for a simultaneous optimization of a set of 'r' solutions), correspond to a singular value decomposition (SVD) by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets x_i and y_j of variables and amount to estimate a link between x_i and y_j for the pair (x_i, y_j) relatively to the links associated to all the other pairs. These methods were first presented in: Lafosse R. & Hanafi M.,(1997) and Hanafi M. & Lafosse, R. (2001) . Package: r-cran-bmem Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amelia, r-cran-mass, r-cran-snowfall, r-cran-lavaan, r-cran-sem Filename: pool/dists/resolute/main/r-cran-bmem_2.2-1.ca2604.1_all.deb Size: 950350 MD5sum: b7ef638719bd5f757c4c30b9e4b00b83 SHA1: a4a7a8c7d6978f982ba3e50333fd272dbe853615 SHA256: 84b7f6e4b5164b4eb538585ef0cd5533196a865be12c3a4498ff42ede4b0c774 SHA512: eebbad9b75c31c78358c8cea07df0f786018204dce2f6e81f8e1cac264c5fdb2ebab7c9b79fd423b9987c4998b7943cebe6e7be84de7a2afc709e3e8e8021e81 Homepage: https://cran.r-project.org/package=bmem Description: CRAN Package 'bmem' (Mediation Analysis with Missing Data Using Bootstrap) Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) . Zhang (2014) . Package: r-cran-bmemapping Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bmemapping_1.2.2-1.ca2604.1_all.deb Size: 109094 MD5sum: f5be55a63f491de8bbf478f920f2963d SHA1: c3575e7c6cf05839f73e74c6a01060ea57ee4e81 SHA256: 8df7f2d361020e30c333529e160d991f776988a55add6da2a5b27694a06f73f4 SHA512: 19edd837d2455e7c3a4a10aa018063ebb777b8385a2e84033f0acd6467dc4cd2cf59a5074a7e1fbeffbd66ceaf322fef01a74ec638ef55c749c5771c2cab7770 Homepage: https://cran.r-project.org/package=BMEmapping Description: CRAN Package 'BMEmapping' (Spatial Interpolation using Bayesian Maximum Entropy (BME)) Provides an accessible and robust implementation of core BME methodologies for spatial prediction. It enables the systematic integration of heterogeneous data sources including both hard data (precise measurements) and soft interval data (bounded or uncertain observations) while incorporating prior knowledge and supporting variogram-based spatial modeling. The BME methodology is described in Christakos (1990) and Serre and Christakos (1999) . Package: r-cran-bmemlavaan Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amelia, r-cran-mass, r-cran-snowfall, r-cran-rsem, r-cran-lavaan, r-cran-sem Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-bmemlavaan_0.7-1.ca2604.1_all.deb Size: 514144 MD5sum: c6d587306d2d3665a4951621eb6f53cd SHA1: 1130648574d5aaa60811afd4105e775671263881 SHA256: d83eec40f117ad2007abc03829a026667b58cf35abb17a1b13832495330738ed SHA512: 08735cf92d366fe37fa577b4b0f6a94817cd71d007388b45c2b06f35a3688c216589a68f10c9d36842d2ab7a7fad57f5371e7daa78d4f3f605a53640a9b16b72 Homepage: https://cran.r-project.org/package=bmemLavaan Description: CRAN Package 'bmemLavaan' (Mediation Analysis with Missing Data and Non-Normal Data) Methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, ), Zhang (2014, ), and Yuan and Zhang (2012, ). Package: r-cran-bmet Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-bmet_0.1.0-1.ca2604.1_all.deb Size: 23280 MD5sum: 890117f5a32d5b6d7148c5598de1f3c6 SHA1: 56c20a388302c8fe6b72317a0e5f391f6ed6aec4 SHA256: 2003dc6c8eee15412b7bbb2eaf362ea277a31c64f29741b48a4fbe8ec066c7d5 SHA512: 6b2778b41e64aaca1642d6c7eec7621706865b2db542a66309fe37d28049e9bec14d889275a9460ffab0e038507f106834d8230a885a71dd236dee624cfb3050 Homepage: https://cran.r-project.org/package=bmet Description: CRAN Package 'bmet' (Bayesian Multigroup Equivalence Testing) Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) , but more models and equivalence testing features may be added over time. Package: r-cran-bmiselect Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvnfast, r-cran-gigrvg, r-cran-mass, r-cran-rfast, r-cran-foreach, r-cran-doparallel, r-cran-arm, r-cran-mice, r-cran-abind, r-cran-stringr, r-cran-posterior Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bmiselect_1.0.3-1.ca2604.1_all.deb Size: 193188 MD5sum: 0096ddea20f401dbef519d27fba8bbf0 SHA1: b657ca570387b43a6e26d331ba697450e15e90ee SHA256: 8d5fe8218fca21800a7c8d3e7eec0c80ac2818911979a0212d3a200a4f34ed10 SHA512: 6d74f145a0248410430a5b83c99f8940fee6e17eb94b92dd9fe3d855db800b7526d34bccdd58b1bdd43c688c7fdafe9c9cadbe5f1d62ac5286702ceb84abe7c3 Homepage: https://cran.r-project.org/package=BMIselect Description: CRAN Package 'BMIselect' (Bayesian MI-LASSO for Variable Selection on Multiply-ImputedDatasets) Provides a suite of Bayesian MI-LASSO for variable selection methods for multiply-imputed datasets. The package includes four Bayesian MI-LASSO models using shrinkage (Multi-Laplace, Horseshoe, ARD) and Spike-and-Slab (Spike-and-Laplace) priors, along with tools for model fitting via MCMC, four-step projection predictive variable selection, and hyperparameter calibration. Methods are suitable for both continuous and binary covariates under missing-at-random or missing-completely-at-random assumptions. See Zou, J., Wang, S. and Chen, Q. (2025), Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data. ArXiv, 2211.00114. for more details. We also provide the frequentist`s MI-LASSO function. Package: r-cran-bml Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-r2jags, r-cran-ggplot2, r-cran-ggmcmc, r-cran-coda, r-cran-patchwork, r-cran-tibble, r-cran-purrr, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-bml_0.9.0-1.ca2604.1_all.deb Size: 576028 MD5sum: 56456123089a439e3609b901777e2e1c SHA1: 56d0e860862338505d94e48c9985f40aed97ef61 SHA256: 2448455e3e11def6ca7df3d33656afae6f5d1598184e26a7f62f747d4c66ca8d SHA512: ac55e950871f2a526021bee28d551ec409e10df308133effc8da15ce19a77c8584919ee94c6b03bf6aee65552d077ebfb9e3dc2718caef8c4cb8557917942ee0 Homepage: https://cran.r-project.org/package=bml Description: CRAN Package 'bml' (Bayesian Multiple-Membership Multilevel Models withParameterizable Weight Functions) Implements Bayesian multiple-membership multilevel models with parameterizable weight functions via 'JAGS' to model how lower-level units jointly shape higher-level outcomes (micro-macro link) across a range of outcome types (e.g., linear, logit, and survival models). Supports estimation and comparison of alternative aggregation mechanisms, allows weight matrices to be endogenized through parameters and covariates, and accommodates complex dependence structures that extend beyond traditional multilevel frameworks. For details, see Rosche (2026) "A Multilevel Model for Coalition Governments. Uncovering Party-Level Dependencies Within and Between Governments" . 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The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2023) . Package: r-cran-bmp Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pixmap, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bmp_0.3.1-1.ca2604.1_all.deb Size: 108552 MD5sum: d516c2708b410ac826d55def0b050281 SHA1: 70402a597594a108bed859dfbe9044a90f34b98d SHA256: b192b032132a7dd08ab4b32b6016704f0e9040b64946b0cd5e9bee41b7dd917c SHA512: e1125654bbd9f70587d6108341926a3e4323da4de438aa88930b4b9badd0007e1430be9a9a42560ab1961a27f749a6c573743966c39233c685e6f9658486579d Homepage: https://cran.r-project.org/package=bmp Description: CRAN Package 'bmp' (Read Windows Bitmap (BMP) Images) Reads Windows BMP format images. Currently limited to 8 bit greyscale images and 24,32 bit (A)RGB images. Pure R implementation without external dependencies. Package: r-cran-bmrmm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-logofgamma, r-cran-mcmcpack, r-cran-multicool, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-bmrmm_1.0.1-1.ca2604.1_all.deb Size: 233718 MD5sum: e798712f1750e934d4ea9ee73148ee4b SHA1: 78501a38ec55e7e64a5be1336f269b9dd4bc9285 SHA256: 6e87698104384fbe47fd71371dcbdc680ea48e0a416b789215a7286ec4825949 SHA512: 96fa0fab8786e78c56b004877bf4a954554ad3a4ad8ad2ac276a0b269c44fef265670f5e58209e548755e3c7376f4026fa7eeb97a280596410f938f09c3aab6f Homepage: https://cran.r-project.org/package=BMRMM Description: CRAN Package 'BMRMM' (An Implementation of the Bayesian Markov (Renewal) Mixed Models) The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) and Yutong Wu et al., (2022) . Package: r-cran-bms Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3106 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bms_0.3.5-1.ca2604.1_all.deb Size: 2914726 MD5sum: a265015862a56a452bfa7a8b831e341b SHA1: f856f822ca9cd76633fda9fe68c593df2e5e6bdf SHA256: 74d22993140068cc81fb0b6864297d4f834218065f554454950daa3ece93aafe SHA512: f02793c592f35fcd21dc010dc5e7ea90187cc59ce06a9803d757ce12875506cb1e32e5d20d2ef28515e9dbb1e22b049bc1900224d12e1c43e90d79801af183d2 Homepage: https://cran.r-project.org/package=BMS Description: CRAN Package 'BMS' (Bayesian Model Averaging Library) Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods. Package: r-cran-bmscstan Architecture: all Version: 1.2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2, r-cran-bayesplot, r-cran-loo, r-cran-logspline, r-cran-laplacesdemon Suggests: r-cran-reshape2, r-cran-gridextra, r-cran-bridgesampling, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-bmscstan_1.2.1.0-1.ca2604.1_all.deb Size: 305922 MD5sum: 3b66760b7ea5136a03b99cf5d0716779 SHA1: dab9af9a32b850dad2c6f84b61f15291b4342bab SHA256: 5b83411a67ff80f99c8b4c02e5173dcaaaf596a98ec4a19de732067e73a71f64 SHA512: f9595968ef05b68efbc29b8b332d729871ef910c4e230ec3dd625d63aa131510a5bc0001226e9c4a3d0f81521946aa79f51efa67f094274ea8402c98c5fc11df Homepage: https://cran.r-project.org/package=bmscstan Description: CRAN Package 'bmscstan' (Bayesian Multilevel Single Case Models using 'Stan') Analyse single case analyses against a control group. Its purpose is to provide a flexible, with good power and low first type error approach that can manage at the same time controls' and patient's data. The use of Bayesian statistics allows to test both the alternative and null hypothesis. Scandola, M., & Romano, D. (2020, August 3). Scandola, M., & Romano, D. (2021). . Package: r-cran-bmt Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partitions, r-cran-fitdistrplus Filename: pool/dists/resolute/main/r-cran-bmt_0.1.3-1.ca2604.1_all.deb Size: 338390 MD5sum: ff9fe2a66877e6a584cfdb9696a7d55f SHA1: f4fbe14834fb700c75f2af70eb52587674d2829f SHA256: 83d8639db072c1e4846588b041a9e22458a6021a8d6d80f2f04b123ac3730327 SHA512: 25d6c4fc00de19451a3562c9d99d443aa205d44764ff166e045c092d4b316685beabe437267420b3dcbb9997df9533d98486ec7fb33ea5a4fa775b0dff9f103f Homepage: https://cran.r-project.org/package=BMT Description: CRAN Package 'BMT' (The BMT Distribution) Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) . Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included. Package: r-cran-bndovb Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 650 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-np, r-cran-pracma, r-cran-mass, r-cran-dplyr, r-cran-factormodel, r-cran-nnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bndovb_1.1-1.ca2604.1_all.deb Size: 603322 MD5sum: bb20d67bae09accf42c7d360531c2365 SHA1: 0546bbc4be992a18c6e6a75a365a85817b47aaa5 SHA256: 3d32871e69034b39b3c2a55c44b61c9417d5fede1300e8b4c99ae3008cee99b8 SHA512: fc5510ecd0b33b88be5231b576d0f1a129cde43a1f1c95ac38ca69bb334390352bc649053cdd01c0d18020aea362196fc6b7107b57838d5a2a6d3a076b231c3d Homepage: https://cran.r-project.org/package=bndovb Description: CRAN Package 'bndovb' (Bounding Omitted Variable Bias Using Auxiliary Data) Functions to implement a Hwang(2021) estimator, which bounds an omitted variable bias using auxiliary data. Package: r-cran-bnma Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-ggplot2, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bnma_1.6.1-1.ca2604.1_all.deb Size: 381720 MD5sum: 8d6b27fa1b656333621d9ee97c07313d SHA1: b025909f37f6fc1701595507f40f2c25b842b1f0 SHA256: f0b8b928e7218e8bdb6e0f86030267c515e0d9e0161b49d8fc916d465ca75c81 SHA512: 99d0244064da977afb8e88ca46081584ac24746e16690cb75f2481d93f66d635815765be827023deb37112b4d2bb24c30649044d1ebb2407a14783918eccf231 Homepage: https://cran.r-project.org/package=bnma Description: CRAN Package 'bnma' (Bayesian Network Meta-Analysis using 'JAGS') Network meta-analyses using Bayesian framework following Dias et al. (2013) . Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results. Package: r-cran-bnmonitor Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 672 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-dplyr, r-cran-ggplot2, r-cran-grain, r-cran-grbase, r-cran-igraph, r-cran-purrr, r-cran-qgraph, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bnmonitor_0.2.2-1.ca2604.1_all.deb Size: 470152 MD5sum: 31e8f05c73cfdffcc5a973292da0a505 SHA1: 3010e83ed7fbe3305fe8a295f759baab597fab77 SHA256: 39c3284687453c69ba77d42fed9718736ef1f52e5ab6b46d70ab246da144388e SHA512: 47c4693ac77c6df4673524178ad2d17e761e93d7b856428580b4ed80ecc6bfa6b67b97a2dec5f4c5fbb9710af6c889b0e3b2dcdcdddb81f2bdfb42b7c579541d Homepage: https://cran.r-project.org/package=bnmonitor Description: CRAN Package 'bnmonitor' (An Implementation of Sensitivity Analysis in Bayesian Networks) An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) . Package: r-cran-bnns Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bh, r-cran-proc, r-cran-rcppeigen, r-cran-rstan Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mlbench, r-cran-ranger, r-cran-rmarkdown, r-cran-rsample, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bnns_0.1.2-1.ca2604.1_all.deb Size: 182960 MD5sum: 43c02dee2fc8efa210662b28397f9f81 SHA1: 85516360e968012c8219cf47228325e4604602e7 SHA256: 222a10c24a341a0a4fffb4e617a5caed949694bcd48bf30ca255e78ef2eefda7 SHA512: 8d875852621ee586937d9919e42a9987faddd7ccb9c9ae3fe758b928b9a83ffe83d39f5f43c4deb577d4ad6c8520efa3897f563ee551d9b200a51dbabeb993aa Homepage: https://cran.r-project.org/package=bnns Description: CRAN Package 'bnns' (Bayesian Neural Network with 'Stan') Offers a flexible formula-based interface for building and training Bayesian Neural Networks powered by 'Stan'. The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) . Package: r-cran-bnpa Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-fastdummies, r-cran-lavaan, r-bioc-rgraphviz, r-cran-semplot, r-cran-xlsx Filename: pool/dists/resolute/main/r-cran-bnpa_0.3.0-1.ca2604.1_all.deb Size: 128488 MD5sum: bde605bc30d93f275fc51c054c430403 SHA1: 430ae328bfb9b2f259c5471776d323978eec3e52 SHA256: 88fa59743c7c8f778309248716f0993c892f2d8ace8b5ca6bc3928b12ea18f53 SHA512: 5d4f43b086e7c75063a9bc887ff3d7166a143d65befa8a8523d00378d2ff147816fd9909cfccb49274ff15671aa6f377283ac80209ebbb9cd06940a38970928b Homepage: https://cran.r-project.org/package=bnpa Description: CRAN Package 'bnpa' (Bayesian Networks & Path Analysis) This project aims to enable the method of Path Analysis to infer causalities from data. For this we propose a hybrid approach, which uses Bayesian network structure learning algorithms from data to create the input file for creation of a PA model. The process is performed in a semi-automatic way by our intermediate algorithm, allowing novice researchers to create and evaluate their own PA models from a data set. The references used for this project are: Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. . Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. . Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. . Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. . Package: r-cran-bnpmtp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bnpmtp_1.0.0-1.ca2604.1_all.deb Size: 33398 MD5sum: d1015dfe9bda6cc757a86909703ed4f1 SHA1: a57212961bd0a1af6cd92f621330719b11008370 SHA256: 0bab99e449546c74bf1f8a3caca9e62571cb078708b40724078e9299b12cee5e SHA512: 4fd9ff481ae233df0aa6b709186f924ef0a784248a15019064a7da859e72d4b80296b8e81d3ab23e72170cfd0a65f24ddf0fc6e146abea25f2af5132418f7187 Homepage: https://cran.r-project.org/package=bnpMTP Description: CRAN Package 'bnpMTP' (Bayesian Nonparametric Sensitivity Analysis of Multiple TestingProcedures for p Values) Bayesian Nonparametric sensitivity analysis of multiple testing procedures for p values with arbitrary dependencies, based on the Dirichlet process prior distribution. Package: r-cran-bnpsd Architecture: all Version: 1.3.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-nnls Suggests: r-cran-popkin, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-bnpsd_1.3.13-1.ca2604.1_all.deb Size: 407688 MD5sum: b7979dcd04774b46d8f5de9ed1d0202e SHA1: c01e68cf19a735c7f621fc29c2d8368d5f1b6fd7 SHA256: 3dabcb90a28cad39af05ea58fb55e190d4ba52115d4a67f70a719e5a30b1711b SHA512: ca80d11cc6cf3858e119c1fa03ad59434c4d4e9efb8ae0fb7f1cc0658d467e81fa5e4e1351971703c5a5888cd489d263d3fe6039ce843cb831d09022c057ded7 Homepage: https://cran.r-project.org/package=bnpsd Description: CRAN Package 'bnpsd' (Simulate Genotypes from the BN-PSD Admixture Model) The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions. The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters. The BN-PSD model can be used to yield complex population structures. This simulation approach is now extended to subpopulations related by a tree. Method described in Ochoa and Storey (2021) . Package: r-cran-bnptsclust Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass Filename: pool/dists/resolute/main/r-cran-bnptsclust_2.0-1.ca2604.1_all.deb Size: 2103422 MD5sum: a920d4071278c86573ed3221ba5cdfc3 SHA1: 0e68c073305df538499f23ba8004fe3b755190bb SHA256: a77ee1b46f9d610f265a06ab244b2942a55c3aebdc46fe3932a68f4d7855573e SHA512: af26ce26739fef7fb75018daeb96aac9a583ad0f485ed65961b0de0c61d014675a6c0c246f9aa5df5e3c992370aa927ef9c92c992ee4fc2287332faba0d82f4f Homepage: https://cran.r-project.org/package=BNPTSclust Description: CRAN Package 'BNPTSclust' (A Bayesian Nonparametric Algorithm for Time Series Clustering) Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014). Package: r-cran-bnrep Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2553 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-dplyr, r-cran-dt, r-bioc-rgraphviz, r-cran-qgraph, r-cran-shiny, r-cran-shinyjs, r-cran-shinythemes Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-scales, r-cran-stringr, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-bnrep_0.0.6-1.ca2604.1_all.deb Size: 2247424 MD5sum: 19345a16eb84e0adc492a35dd3b0fea5 SHA1: afe9d3edcb636601cd7acc35e145af4c28f728dc SHA256: 6b6df6d9a11eb48de29f73ee33125b82b3b26dda56cf7ff76dcc655c484e5eb7 SHA512: 06d3f1696a93a4573eccea864896f8d109aba128a0ff830b67176d93c66e543237a4c296396cf4a8d72a0f8b36b0d89831a67e1ca0b6bf47cde74bcaa3b4846f Homepage: https://cran.r-project.org/package=bnRep Description: CRAN Package 'bnRep' (A Repository of Bayesian Networks from the Academic Literature) A collection of Bayesian networks (discrete, Gaussian, and conditional linear Gaussian) collated from recent academic literature. The 'bnRep_summary' object provides an overview of the Bayesian networks in the repository and the package documentation includes details about the variables in each network. A Shiny app to explore the repository can be launched with 'bnRep_app()' and is available online at . Reference: 'M. Leonelli' (2025) . Package: r-cran-bnrich Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-corpcor, r-cran-glmnet, r-bioc-graph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bnrich_0.1.1-1.ca2604.1_all.deb Size: 916852 MD5sum: 3b52efc2a51e9f61d5c1303f70a81858 SHA1: bd7f4cfdf9f0ba7bce3f002b3539dc7869ae4be9 SHA256: e044c7324ee0b54302be058a60d689dc812ea0d38f87c3ba71bd5ae8a7896475 SHA512: 637562d5b083d6983b447154c3569708391baf3601355b43da2ed3178409167693691d7c2fda33f3dc3bcb627dea50c90173dcfa3bccd0ef8d5d0b24596310c0 Homepage: https://cran.r-project.org/package=BNrich Description: CRAN Package 'BNrich' (Pathway Enrichment Analysis Based on Bayesian Network) Maleknia et al. (2020) . A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters. Package: r-cran-bnviewer Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-visnetwork, r-cran-bnlearn, r-cran-igraph, r-cran-shiny, r-cran-assertthat, r-cran-caret, r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-bnviewer_0.1.6-1.ca2604.1_all.deb Size: 66100 MD5sum: afd00189245e9fb23c9e44c6b763cd22 SHA1: 7e1307d1fee13e2e0c79b0626753d123d6e63de5 SHA256: 7a38cdde6f74c036d47692e3c5586515721c444b4713616bb2d298e577f95ea8 SHA512: 33d18ff0d6b4077e35be2359850dd3fc5088adcc9188ff87740712160188204ae353cc77bd13c9da838f2eb09ae954faac43e3b645b1c47940f1c8890db97340 Homepage: https://cran.r-project.org/package=bnviewer Description: CRAN Package 'bnviewer' (Bayesian Networks Interactive Visualization and ExplainableArtificial Intelligence) Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The 'bnviewer' is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages. 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Package: r-cran-bodenmiller Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7322 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-cytofan, r-cran-dplyr, r-cran-reshape2, r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bodenmiller_0.1.1-1.ca2604.1_all.deb Size: 7406080 MD5sum: ff4df06208d7ece6e82a97db1e91ffa2 SHA1: 89bf4d67872775e0dfd09f7a04c79f0c0a31cf85 SHA256: 21b24d9a652f460e930b1c52e3d97c84b86d7056afcfb37f9ae2fd69f47e6243 SHA512: 51f13846057219832fc07ca2942c7cbca5e9de7b794629a6564363171a78d47ad2e62d099125ff0e09b6bf9803ecbe80c351f418d68f1b8478f7bb7a38b0a031 Homepage: https://cran.r-project.org/package=bodenmiller Description: CRAN Package 'bodenmiller' (Profiling of Peripheral Blood Mononuclear Cells using CyTOF) This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods. 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Body composition is a term that describes the relative proportions of fat, bone, and muscle mass in the human body. Following the collection of skinfold measurements, regression analysis (a statistical procedure used to predict a dependent variable based on one or more independent or predictor variables) is used to estimate total percent body fat in humans. . 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Functionality requires installing the data packages 'adiposerefdata' and 'musclerefdata'. For more information on the underlying research, please visit our website which also includes a graphical interface. The models and underlying data are described in Marquardt JP et al.(planned publication 2025; reserved doi 10.1097/RLI.0000000000001104), "Subcutaneous and Visceral adipose tissue Reference Values from Framingham Heart Study Thoracic and Abdominal CT", *Investigative Radiology* and Tonnesen PE et al. (2023), "Muscle Reference Values from Thoracic and Abdominal CT for Sarcopenia Assessment [column] The Framingham Heart Study", *Investigative Radiology*, . Package: r-cran-boe Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-scales, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-boe_0.2.0-1.ca2604.1_all.deb Size: 135420 MD5sum: a66fc666fd3292e4d81ef3062cb0fbd9 SHA1: 5c066969a7c139637b871436af66e9c459b54a48 SHA256: a9e1a86950f03e4a766fe39d223dc95a2107e6a6806aa9cc11e26520fb9b5eab SHA512: 27213d7632da226a8465b6abc4d4b2beebeb94f08f7351f1757ddce1813f1d460e8c0104f1063365289bb05ffa1c5d72800e17026a84008bc9863ce6ebeaeda7 Homepage: https://cran.r-project.org/package=boe Description: CRAN Package 'boe' (Download Data from the 'Bank of England' Statistical Database) Provides functions to download and tidy statistical data published by the 'Bank of England' . Covers Bank Rate, 'SONIA', gilt yields, exchange rates, mortgage rates, mortgage approvals, consumer credit, and money supply. Series are fetched from the 'Bank of England Interactive Statistical Database' using its CSV endpoint. Data is cached locally between sessions. Package: r-cran-boggy Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-boggy_0.0.1-1.ca2604.1_all.deb Size: 88288 MD5sum: 07a20464f9caf50e6c7a075a5e977847 SHA1: bb38098c636fc32512e5e48b84145ef171edf910 SHA256: 6d8449a8d529d8287e851d56f16c36c39d02c8683827b0c4ed1477c4c08d5153 SHA512: 1f1af34db274b954ca17e4b1ccdbdbaa656efeb22c068c39ee19c9239dcae80ef79f942962493a0c742ce62c5bee264c88a254e0ff4e03b14e025109f5add735 Homepage: https://cran.r-project.org/package=boggy Description: CRAN Package 'boggy' (Real-Time PCR Data Sets by Boggy et al. (2010)) Real-time quantitative polymerase chain reaction (qPCR) data sets by Boggy et al. (2008) . This package provides a dilution series for one PCR target: a random sequence that minimizes secondary structure and off-target primer binding. The data set is a six-point, ten-fold dilution series. For each concentration there are two replicates. Each amplification curve is 40 cycles long. Original raw data file: . Package: r-cran-boilerpiper Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1730 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Suggests: r-cran-rcurl Filename: pool/dists/resolute/main/r-cran-boilerpiper_1.3.2-1.ca2604.1_all.deb Size: 1549012 MD5sum: c3ff48b61647bf19efc4e12bd5d1cfbc SHA1: dd1b092e230ecfb240a2e30276a9246cfd312e17 SHA256: 106d3910616d45028a022d6cd214d88ff07a6b8f11038e6f021361bdf7fccd79 SHA512: a1eb34bc2691b835bc6f773127e14f31e2b6a73b776feae2f2f3ceedb06899cba321f408af19c5cca91a6e5c331c43192d8e5d07da9f74b62d7862a34734e58d Homepage: https://cran.r-project.org/package=boilerpipeR Description: CRAN Package 'boilerpipeR' (Interface to the Boilerpipe Java Library) Generic Extraction of main text content from HTML files; removal of ads, sidebars and headers using the boilerpipe Java library. 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Package: r-cran-boin Architecture: all Version: 2.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso Filename: pool/dists/resolute/main/r-cran-boin_2.7.2-1.ca2604.1_all.deb Size: 201870 MD5sum: b8a8d358a914feb04cb2d666edad5db6 SHA1: 7cf26c4a594d3fdec108d5b5f1da0b1e1f0bdeb3 SHA256: 8a4a6464e15cd7005341cd52e87c162000ecc537d28068f871c3830bb40bd94b SHA512: ca6c6dc3d1d33de31e99bb2991e08b86f8e01be2f85d5a42760d30b8e61b04c479ed548bcaf5ba072ed8d8e6b8754f62882ee16b5f2d78a5b78135e13c17b1d7 Homepage: https://cran.r-project.org/package=BOIN Description: CRAN Package 'BOIN' (Bayesian Optimal INterval (BOIN) Design for Single-Agent andDrug- Combination Phase I Clinical Trials) The Bayesian optimal interval (BOIN) design is a novel phase I clinical trial design for finding the maximum tolerated dose (MTD). It can be used to design both single-agent and drug-combination trials. The BOIN design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. The prominent advantage of the BOIN design is that it achieves simplicity and superior performance at the same time. The BOIN design is algorithm-based and can be implemented in a simple way similar to the traditional 3+3 design. The BOIN design yields an average performance that is comparable to that of the continual reassessment method (CRM, one of the best model-based designs) in terms of selecting the MTD, but has a substantially lower risk of assigning patients to subtherapeutic or overly toxic doses. For tutorial, please check Yan et al. (2020) . Package: r-cran-boinet Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 900 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-mfp, r-cran-copula, r-cran-gt, r-cran-tibble, r-cran-shiny, r-cran-shinydashboard, r-cran-dt, r-cran-plotly, r-cran-ggplot2, r-cran-rhandsontable, r-cran-shinybs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-boinet_1.5.0-1.ca2604.1_all.deb Size: 302104 MD5sum: cd5bbe33c402c31a48b04e21a4da944b SHA1: 2bc201c01e42f5f3527c13e7d83bcb733ec7cdb1 SHA256: e8bc3710651117d07827a98f01c3cebab6dad3b3b17ed7f91cdbe8bfc6fc799d SHA512: dab93943b474c14db562653759fbb132426949bd3ffa3f9967a5f85ed0e38c661fd5213ffce9b6a0701a408088036e7598cc3696940cff71d07249e79ebbb692 Homepage: https://cran.r-project.org/package=boinet Description: CRAN Package 'boinet' (Conduct Simulation Study of Bayesian Optimal Interval Designwith BOIN-ET Family) Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application. 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Package: r-cran-boostmtree Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforestsrc, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-boostmtree_2.0.0-1.ca2604.1_all.deb Size: 536700 MD5sum: 7f46c8a465a663cc1403f49fc2eb342d SHA1: 0493e3b6675da9fd69416479e6acfebd5dc6a8f9 SHA256: 3482af8cb972c5d464a61b0d70f14323c263602a4b66f7a74286c70620379218 SHA512: b8ec00874546b7028ed93d58cb028eb85790a55d4ea07817a75a0a443906d97dd6152edcb9268b17ba0a3d8daf106defca1297520b40d878e7ee5927161f8215 Homepage: https://cran.r-project.org/package=boostmtree Description: CRAN Package 'boostmtree' (Boosted Multivariate Trees for Longitudinal Data) Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal. 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Package: r-cran-boostrq Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-stabs, r-cran-quantreg, r-cran-checkmate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-boostrq_1.0.0-1.ca2604.1_all.deb Size: 76566 MD5sum: 236bf952f00942b3c79dcabc05dc4c91 SHA1: 9abe7a3eb4b1855e33db31ca4698acef924be3dd SHA256: 2113647c72f5ae0981ace3773fdf7681dfaf3271912ed10d1d185dc97cd46fa4 SHA512: ae25bd82346af6114c37f9f902ec0158ed5bf7dacc35ef75a72e6c05c7bfcac0b8a0902f3570463842c40e2d5af9d113460e6d8704a1407c896fb670eab5d568 Homepage: https://cran.r-project.org/package=boostrq Description: CRAN Package 'boostrq' (Boosting Regression Quantiles) Boosting Regression Quantiles is a component-wise boosting algorithm, that embeds all boosting steps in the well-established framework of quantile regression. 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Package: r-cran-boot.heterogeneity Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 865 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metafor, r-cran-pbmcapply, r-cran-hsaur3, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-boot.heterogeneity_1.1.5-1.ca2604.1_all.deb Size: 301216 MD5sum: 839c5a914f1f2801dc768c2946c758de SHA1: e812a5be59b0e017110931a42122cb952631c13a SHA256: 805644ee7179de16f508e28e4dfd43018a74e02f76ceb3dd269b1960c77b89d2 SHA512: 2ad235a02626c58347e820f3a41e652f6d6587a76bcf17726fcd70775d35a5f8764df87d3d054c5eba65a83c66966b175d86420ed3c6ec5e22746b1aa26c5d88 Homepage: https://cran.r-project.org/package=boot.heterogeneity Description: CRAN Package 'boot.heterogeneity' (A Bootstrap-Based Heterogeneity Test for Meta-Analysis) Implements a bootstrap-based heterogeneity test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (or) in meta-analysis studies. Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihoods, so installation of R package 'metafor' is required. This approach refers to the studies of Anscombe (1956) , Haldane (1940) , Hedges (1981) , Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) , Viechtbauer (2010) , and Zuckerman (1994, ISBN:978-0521432009). Package: r-cran-boot.pval Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-rdpack, r-cran-car, r-cran-lme4, r-cran-survival, r-cran-rms, r-cran-gt, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-boot.pval_0.7.0-1.ca2604.1_all.deb Size: 320254 MD5sum: 4c0cde2fad66b01222968c9650f5f476 SHA1: fcd98963a2d096b71fa6af817dd7118c392bc4f7 SHA256: c30155aa0fccc5aa2dec0b6352ba7a9deb52df3dea13b58b0318b89d6c713641 SHA512: 4912d6762935fe359d5731ee63208d7a3b6e9cec5980065a47a5b29eb52fbd6cc0d0fa61f0dd07dc46b37ad0b76a1db838ed224c8941bda02e38458548a69ed0 Homepage: https://cran.r-project.org/package=boot.pval Description: CRAN Package 'boot.pval' (Bootstrap p-Values) Computation of bootstrap p-values through inversion of confidence intervals, including convenience functions for regression models and tests of location. Package: r-cran-boot Architecture: all Version: 1.3-32-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-survival Filename: pool/dists/resolute/main/r-cran-boot_1.3-32-1.ca2604.1_all.deb Size: 633908 MD5sum: ad635ba913f8930ea18fa05170a175e1 SHA1: 46d28dc9e794d8c27ed8e807e264835c63e97f0e SHA256: 3d00bef2dd9672942cd831dcef0542c048f1a1694e2c6aae1c904bf72d1d6be6 SHA512: cee158a0549dbc43d212f1bdf8918d47f6cac81805045b703d3dadefea02e0a6ef31c446d619c0b4a37498dbbd95224730704247dc3ca29f957e27ae2b33fb95 Homepage: https://cran.r-project.org/package=boot Description: CRAN Package 'boot' (Bootstrap Functions) Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S. Package: r-cran-bootcluster Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-mclust, r-cran-flexclust, r-cran-fpc, r-cran-dplyr, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-ggplot2, r-cran-gridextra, r-cran-intergraph, r-cran-ggally, r-cran-network, r-cran-kernlab, r-cran-sna, r-cran-progress Filename: pool/dists/resolute/main/r-cran-bootcluster_0.4.3-1.ca2604.1_all.deb Size: 195392 MD5sum: cebe76f9a2c11ce3ce5b947449caa40f SHA1: 86548950b027703c2e4adade10fa47d11f643ff7 SHA256: 71ac62454df0f86228f10bc0057f74b840655dfe987bca148b3466c35e25040c SHA512: f8ffacb7c7795ca95608df325e0714b1c6d38a94586ecb7e96991b1c0eda610f0b57f6bebc4bf4a1db669b71012d9fa7dcf708b80d1650f542d75527e71b9517 Homepage: https://cran.r-project.org/package=bootcluster Description: CRAN Package 'bootcluster' (Bootstrapping Estimates of Clustering Stability) Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016). Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) . Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) . Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method. Package: r-cran-bootcomb Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-hdinterval Filename: pool/dists/resolute/main/r-cran-bootcomb_1.1.2-1.ca2604.1_all.deb Size: 102184 MD5sum: 89c2f9c7c1d33ea1c3a7ef71c2394d36 SHA1: 54c4af1dbc2319fd2fc6bfbbae2c5bf8738fb3a3 SHA256: 20547a694a9c4407d7d93b6409c0f69ee4dfe02a1035a85615141379f9d1a9cc SHA512: 18dfcab31092798ed6651ee3e48f687e73a78fa08ac24f2606b0b4adc67375df4f1b4014815d088bee16058649922cbeb1e73278572cd1ada22c861bc4cb21aa Homepage: https://cran.r-project.org/package=bootComb Description: CRAN Package 'bootComb' (Combine Parameter Estimates via Parametric Bootstrap) Propagate uncertainty from several estimates when combining these estimates via a function. This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter. Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage. Gaussian copulas are used for when parameters are assumed to be dependent / correlated. References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020) for an example of combining conditional prevalences. Package: r-cran-bootes Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bootes_1.3.1-1.ca2604.1_all.deb Size: 55388 MD5sum: 7a335bd465dd9131a1469835b5b28a7f SHA1: 8aaa0e1e811e94174f30f7d1d92f481238f11f7d SHA256: 7c9d9f2e519040c87bb01b3bb33043f4f568080c723f8c089a88ea61f22665e8 SHA512: 92cf330b55489ad59d9193f59f5382d1fa0e5e4742f7f85c2016abc5f94435ecf2f64ca9753c1d1f382860decdcd87a9bd5a7c0a66eb426f1ba1fc59d7b8c3d8 Homepage: https://cran.r-project.org/package=bootES Description: CRAN Package 'bootES' (Bootstrap Confidence Intervals on Effect Sizes) Calculate robust measures of effect sizes using the bootstrap. Package: r-cran-bootf2 Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-mass, r-cran-readxl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bootf2_0.4.1-1.ca2604.1_all.deb Size: 1074760 MD5sum: 412b9c4663117aea1c16d987520336c1 SHA1: 650f40d78f50f4f18b23a2978f3105829a2ac1a2 SHA256: a904398cec33b2b161058e351d1064c134b26af8a82ce536a7fcf85cfceff435 SHA512: db0349e0a6649d409e286b33690bc38a0fafbfbcffcfa1dee26be31acc7978bc6e666f69afaa8a269bd5467fec6fdbcdc6c5888fea0ca2aefafeb88f0fcce2d7 Homepage: https://cran.r-project.org/package=bootf2 Description: CRAN Package 'bootf2' (Simulation and Comparison of Dissolution Profiles) Compare dissolution profiles with confidence interval of similarity factor f2 using bootstrap methodology as described in the literature, such as Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997, ISBN:9780521573917), and Shah et al. (1998) . The package can also be used to simulate dissolution profiles based on mathematical modelling and multivariate normal distribution. 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It allows to perform rigorous statistical tests to check if a chosen model family is correct based on the marked empirical process. The implemented algorithms are described in (Dikta and Scheer (2021) ) and can be applied to generalized linear models without any further implementation effort. As far as certain linearity conditions are fulfilled the resampling scheme are also applicable beyond generalized linear models. This is reflected in the software architecture which allows to reuse the resampling scheme by implementing only certain interfaces for models that are not supported natively by the package. 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From paper by Ghashti, J.S., Andrews, J.L. Thompson, J.R.J., Epp, J. and H.S. Kochar (2025), "A bootstrap augmented k-means algorithm for fuzzy partitions" (Submitted). 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Package: r-cran-bootmlm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-lme4, r-cran-matrix, r-cran-numderiv Suggests: r-cran-nlme, r-cran-testthat, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-haven, r-cran-msm, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-bootmlm_0.1.1-1.ca2604.1_all.deb Size: 140916 MD5sum: 4b9356e709274df8c0cb8fcd30669f0e SHA1: 0a811fdc460792ba80cb36b62e6893027536d4fc SHA256: 254304786b843e502a3873861a65d962a32fa553c3b887d308f329887c148c91 SHA512: 00245c5f85c872d70e168011ceff52bfd6d2e6761be415911bb8fc588fda3db06d3d15ed6afd35a0e443b525cbdf02343b513ef94278dec49b84cb90c19899cb Homepage: https://cran.r-project.org/package=bootmlm Description: CRAN Package 'bootmlm' (Bootstrap Resampling for Multilevel Models) Functions for bootstrapping with multilevel data and models (and mixed-effect models). It implements multiple bootstrap methods under the parametric, residual, and case bootstrap categories, as discussed in Van der Leeden, Meijer, and Busing (2008) and Carpenter, Goldstein, and Rasbash (2003) . Currently it supports fitted objects from the 'lme4' package. Package: r-cran-bootmrmr Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bootmrmr_0.1-1.ca2604.1_all.deb Size: 130354 MD5sum: 2092d690a4ce35708e16bf09ba588afc SHA1: 3f80bb11c5691b2230ef0eda385aa1ebb6a8364a SHA256: 8b5081712d10f5d82071bcaa504a9540e6152258ea060e02822d1112354e471c SHA512: 234c21389b2da8faa307064be012a8698bcc5058d7d5e6bad1db878e7a549407731fac3981d904c9ef6ea6c76eb926cb91178628bac7111fafb8758f77492bd4 Homepage: https://cran.r-project.org/package=BootMRMR Description: CRAN Package 'BootMRMR' (Bootstrap-MRMR Technique for Informative Gene Selection) Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications). Package: r-cran-bootnet Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-isingfit, r-cran-qgraph, r-cran-mantar, r-cran-dplyr, r-cran-tidyr, r-cran-gtools, r-cran-corpcor, r-cran-isingsampler, r-cran-mvtnorm, r-cran-abind, r-cran-matrix, r-cran-snow, r-cran-mgm, r-cran-networktoolbox, r-cran-pbapply, r-cran-networktools, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-glasso, r-cran-bdgraph, r-cran-graphicalvar, r-cran-relaimpo, r-cran-lavaan, r-cran-psychtools Filename: pool/dists/resolute/main/r-cran-bootnet_1.8-1.ca2604.1_all.deb Size: 334128 MD5sum: d1e563fac54efd4385427711d90a4dfe SHA1: cfbc601be6356d6e5f3f622a75b4dce29330d4ce SHA256: 5bf83f561998d7b05c6ee3b6b614ab9c6cc7b17a91a05760b7d17848723077fb SHA512: ec9bc79b9399d388f66a91b36e0754261f8dd34a4f1f8e28ca9848686b8b950f2bf66b78997cc0baf5f3ecf3aa6842cfdd7dce5270d6378075f9fc079b7aa382 Homepage: https://cran.r-project.org/package=bootnet Description: CRAN Package 'bootnet' (Bootstrap Methods for Various Network Estimation Routines) Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices . 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Package: r-cran-bootpls Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-doparallel, r-cran-foreach, r-cran-plsrglm, r-cran-pls, r-cran-spls, r-cran-bipartite, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-markdown, r-cran-plsdof, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-bootpls_1.1.0-1.ca2604.1_all.deb Size: 217112 MD5sum: d44b2ef91c031a99f0313663a5c22b78 SHA1: 1ee4703691b85729821bdc9fac89b2a696cbe0dd SHA256: 810676f0fa4ac35ec10fedff2e6cb5fdc0fc6c603fe249513c016d9e4915b215 SHA512: 91379c6a5108938d3b8ed285f817ef23d30149a9307cfa79451cf156cc85f77b185e4743e7be2e0ea99b25752620110aa9afc6288eee7d91cbcced10e019df35 Homepage: https://cran.r-project.org/package=bootPLS Description: CRAN Package 'bootPLS' (Bootstrap Hyperparameter Selection for PLS Models and Extensions) Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', ) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', ) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', ). Package: r-cran-bootpr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bootpr_1.0-1.ca2604.1_all.deb Size: 153922 MD5sum: 8d76c2308e37e2fb0b094a9b420cb18c SHA1: 24728ce00291ed72807385c446cbf82b0f223521 SHA256: 107448d2c708c4fdd5b9f87486848fd7ba61bc0e9bd77faf08a543600f6d9f63 SHA512: c87335eb1eec72e96a690230e8721f708bc5b0e12446a10cf4501ff3ccd92975f76f5533e93f9e9f9dd7528614ec3c636ad3077b242b4f02dd1c5e9a23ec2a80 Homepage: https://cran.r-project.org/package=BootPR Description: CRAN Package 'BootPR' (Bootstrap Prediction Intervals and Bias-Corrected Forecasting) Contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series. Package: r-cran-bootstatespace Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-simstatespace, r-cran-dynr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bootstatespace_1.0.3-1.ca2604.1_all.deb Size: 169208 MD5sum: d4a65bf5ed616d9765e1c51393d72256 SHA1: a02b37278d4b54c6fb4008f1beac6ebcd64a2976 SHA256: 07cc78f311a66cd907b2f3d9dc1d5e27fb260bf018a7f0507ddcc231d46f7da1 SHA512: 8b11fc8d6547848cfa86c31b9de6d2ebf4c1fbf7fae3c9082e35b633105736964b9ad0e4e6e0586be7d1f1cdd835c9a1ae61273b06063e7000b18ed53a52cb50 Homepage: https://cran.r-project.org/package=bootStateSpace Description: CRAN Package 'bootStateSpace' (Bootstrap for State Space Models) Provides a streamlined and user-friendly framework for bootstrapping in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. The parametric bootstrap implemented here was developed and applied in Pesigan, Russell, and Chow (2025) . Package: r-cran-bootstepaic Architecture: all Version: 1.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-bootstepaic_1.4-0-1.ca2604.1_all.deb Size: 23106 MD5sum: 78058920fc48e3016bedbd755d160965 SHA1: f2305a5c4173cb045c867e28e5d8adbc117b2434 SHA256: 94c2eb68662b243a355caa2622d338407589e18eb8fb9aef5477fa7a94dd5638 SHA512: 3c9c71f6f599340e64064d8a0015a0de26e661ce2f7de0d5e1cffe6a32d4138c6e69c7cc27d89b25cbddab6d41b98a281185f7ecc86b3566aa1fc4613122e851 Homepage: https://cran.r-project.org/package=bootStepAIC Description: CRAN Package 'bootStepAIC' (Bootstrap stepAIC) Model selection by bootstrapping the stepAIC() procedure. Package: r-cran-bootstrapfp Architecture: all Version: 0.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sampling Filename: pool/dists/resolute/main/r-cran-bootstrapfp_0.4.6-1.ca2604.1_all.deb Size: 66864 MD5sum: e9fe8a8e70bcda98c5fd28843373c6eb SHA1: 6025cb66076b0a7416553155cad71af5aecbbee7 SHA256: 12bd6b38a6cf082e19dfb7a2fe9216e28c936df06fdbd532f9b80f1d20e09962 SHA512: 590e45b0c4c33db08fa10b4ce28b7ba92da7210ceae8e328f843e7ef9e040f85e32bfa84673d12810f58c98886290d420ba2b65e09af15e932c741284c6050fa Homepage: https://cran.r-project.org/package=bootstrapFP Description: CRAN Package 'bootstrapFP' (Bootstrap Algorithms for Finite Population Inference) Finite Population bootstrap algorithms to estimate the variance of the Horvitz-Thompson estimator for single-stage sampling. For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) . Package: r-cran-bootstrapqtl Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixeqtl, r-cran-foreach, r-cran-data.table Suggests: r-cran-domc, r-cran-doparallel, r-bioc-qvalue, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bootstrapqtl_1.0.5-1.ca2604.1_all.deb Size: 49500 MD5sum: 2d25cbd1803c5e50cb2636f356c5b5f8 SHA1: 3470b56b791ee45713ed0146df76e0faa24547dc SHA256: b65252c73c8efa644feb815fd6830a486d1266cced61af35c84bf503908bd242 SHA512: 044e4686955c594c3847e84f188886e78ee6ae9ab71e0b433e4acb8da89637dcb26ec443864758b7ddd3ddefd11117965c617be392cac7e2a4e10985bed75874 Homepage: https://cran.r-project.org/package=BootstrapQTL Description: CRAN Package 'BootstrapQTL' (Bootstrap cis-QTL Method that Corrects for the Winner's Curse) Identifies genome-related molecular traits with significant evidence of genetic regulation and performs a bootstrap procedure to correct estimated effect sizes for over-estimation present in cis-QTL mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 2018 . Package: r-cran-bootstraptests Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bootstraptests_0.1.0-1.ca2604.1_all.deb Size: 136398 MD5sum: 2de135229448eb19f870c5308a79f527 SHA1: 9b1f45954d82f647b3f98c75e8d294b65b35bc20 SHA256: f40651e3bf769aed9e6d5f1a8da3a61ecbe648c5d65d41718d2093ba60b80a65 SHA512: 9ac268a4e51a65c25a815b4c0e172865d07c5f65ac28b8d2420bd9c00ec7f3d0b0cd9308aebec8531e99f5163d615d84233dca8c04ac3be759b1f5148530ef77 Homepage: https://cran.r-project.org/package=BootstrapTests Description: CRAN Package 'BootstrapTests' (Bootstrap-Based Hypothesis Testing using Different ResamplingSchemes) Perform bootstrap-based hypothesis testing procedures on three statistical problems. In particular, it covers independence testing, testing the slope in a linear regression setting, and goodness-of-fit testing, following (Derumigny, Galanis, Schipper and Van der Vaart, 2025) . Package: r-cran-bootsurv Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-bootsurv_0.0.1-1.ca2604.1_all.deb Size: 553376 MD5sum: 89a3fe254de942ff985894e2d3bef575 SHA1: aa63c613de4d53175152c95649e9d1261682ea09 SHA256: 3a378bb2366b1911dcdaf5e77bdb6dbc8d263ca40a638ed89cbcf96a1820ee6f SHA512: 45e5ae232e6c94150a5edf7fe149b748fe36bb765d316aa7fda6a310036d4490dd13c46827672c01879ceabf9b3dbbfa83e705c536da50e799b30cf8c07c26b0 Homepage: https://cran.r-project.org/package=bootsurv Description: CRAN Package 'bootsurv' (Bootstrap Methods for Complete Survey Data) Bootstrap resampling methods have been widely studied in the context of survey data. This package implements various bootstrap resampling techniques tailored for survey data, with a focus on stratified simple random sampling and stratified two-stage cluster sampling. It provides tools for precise and consistent bootstrap variance estimation for population totals, means, and quartiles. Additionally, it enables easy generation of bootstrap samples for in-depth analysis. Package: r-cran-bootsvd Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ff Filename: pool/dists/resolute/main/r-cran-bootsvd_1.2-1.ca2604.1_all.deb Size: 149956 MD5sum: 03cf1872ee1578943e6d9579d84551ee SHA1: d7c09ef9daecc6e052e27f775ac8ad550fb83909 SHA256: bfcca75ccd92f2052e53a5fdb9551845dd94ff94722dbcdae240fac1f47c9a1f SHA512: 618c98f8c6582a43378fbcd5705453aacf3ba00f1783a250f64b6079d2eb26ab349164d794b66eb73d92e3f779a74fe76468fb8eb4fdfcd2e095ceca21f833d5 Homepage: https://cran.r-project.org/package=bootSVD Description: CRAN Package 'bootSVD' (Fast, Exact Bootstrap Principal Component Analysis for HighDimensional Data) Implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in (see also ). For data matrices that are too large to operate on in memory, users can input objects with class 'ff' (see the 'ff' package), where the actual data is stored on disk. In response, this package will implement a block matrix algebra procedure for calculating the principal components (PCs) and bootstrap PCs. Depending on options set by the user, the 'parallel' package can be used to parallelize the calculation of the bootstrap PCs. Package: r-cran-bootwar Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2043 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mmcards, r-cran-npboottprm, r-cran-shiny, r-cran-shinyjs, r-cran-shinythemes Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bootwar_0.2.1-1.ca2604.1_all.deb Size: 1767864 MD5sum: bab1df0e5d93988c1477c22bfed647c7 SHA1: 2d9566bb7e15b568d73c6a59f0b69c36b7ee23a8 SHA256: 2ed119914d0ade2c48523e2e9053ac24bc3278d2e75430bee095582394b335de SHA512: 6c5719d5518f1563834ae8f662829e2e698ca297048b0178955b427a81aa2a6ccb9092af43c7dae6b3eb39a2720cf24c45ca901813005ac2834751e9143a2c5f Homepage: https://cran.r-project.org/package=bootwar Description: CRAN Package 'bootwar' (Nonparametric Bootstrap Test with Pooled Resampling Card Game) The card game War is simple in its rules but can be lengthy. In another domain, the nonparametric bootstrap test with pooled resampling (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) , is optimal for comparing paired or unpaired means in non-normal data, especially for small sample size studies. However, many researchers are unfamiliar with these methods. The 'bootwar' package bridges this gap by enabling users to grasp the concepts of nbpr via Boot War, a variation of the card game War designed for small samples. The package provides functions like score_keeper() and play_round() to streamline gameplay and scoring. Once a predetermined number of rounds concludes, users can employ the analyze_game() function to derive game results. This function leverages the 'npboottprm' package's nonparboot() to report nbpr results and, for comparative analysis, also reports results from the 'stats' package's t.test() function. Additionally, 'bootwar' features an interactive 'shiny' web application, bootwar(). This offers a user-centric interface to experience Boot War, enhancing understanding of nbpr methods across various distributions, sample sizes, number of bootstrap resamples, and confidence intervals. Package: r-cran-bootwptos Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh, r-cran-tseries Filename: pool/dists/resolute/main/r-cran-bootwptos_1.2.1-1.ca2604.1_all.deb Size: 56600 MD5sum: c11f2e8a643e080c7f18ed579ce5b849 SHA1: c9209f9140999d7468c563503c3c1e5fd45bbfed SHA256: 3e475df4be21fee3f0fef63d9c4e4757ddd802a7dee1749962e0fe8ec6564c91 SHA512: 6e466d02aee09974f1513217d3907fbac2ed268684181e084e433c8d32056ae6b59c1c7b25379a9361b4b26764eeeccf443de49fe2889fd8e1941a1cb61c81fc Homepage: https://cran.r-project.org/package=BootWPTOS Description: CRAN Package 'BootWPTOS' (Test Stationarity using Bootstrap Wavelet Packet Tests) Provides significance tests for second-order stationarity for time series using bootstrap wavelet packet tests. Provides functionality to visualize the time series with the results of the hypothesis tests superimposed. The methodology is described in Cardinali, A and Nason, G P (2016) "Practical powerful wavelet packet tests for second-order stationarity." Applied and Computational Harmonic Analysis, 44, 558-585 . Package: r-cran-bop2fe Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-patchwork, r-cran-ggplot2 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-bop2fe_1.0.3-1.ca2604.1_all.deb Size: 606400 MD5sum: 0edd16dcd93b8c293bab4cc2dafff8ba SHA1: 41e14a9afeb87ae23638da47ff8b1a19d9b42d8f SHA256: 648e508e4b171d8569172f39465268a39011f04c93e4bc1fdf3193cb7cd584c1 SHA512: 9fb0660c40599b418c9057d3b59c4e6b1f9d12982b0977746574d3d30eae7f9c4570954869f80c5d41cce0b63c85f559b2a091bb7e907c911642b5546a492c3a Homepage: https://cran.r-project.org/package=BOP2FE Description: CRAN Package 'BOP2FE' (Bayesian Optimal Phase II Design with Futility and EfficacyStopping Boundaries) Bayesian optimal design with futility and efficacy stopping boundaries (BOP2-FE) is a novel statistical framework for single-arm Phase II clinical trials. It enables early termination for efficacy when interim data are promising, while explicitly controlling Type I and Type II error rates. The design supports a variety of endpoint structures, including single binary endpoints, nested endpoints, co-primary endpoints, and joint monitoring of efficacy and toxicity. The package provides tools for enumerating stopping boundaries prior to trial initiation and for conducting simulation studies to evaluate the design’s operating characteristics. Users can flexibly specify design parameters to suit their specific applications. For methodological details, refer to Xu et al. (2025) . Package: r-cran-boptbd Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-boptbd_1.0.7-1.ca2604.1_all.deb Size: 84122 MD5sum: df9931f1b43138ea4b8c77be9b5a0810 SHA1: 03735e59162fb2d2fb8a4d66ba1d92836729f8bb SHA256: 07c7dffd37a84f2d38dc83ed809867acee87c205209bad5663b17e2332ceb796 SHA512: f6d9f6fc729c78a45c3c2669e34f10cdf48a7879dd88ac4497e76bc991fab9d71e045ece47b0de35fd8cbc0ae2e01ab7211be9a22a95b81f8d81f17052f27b1e Homepage: https://cran.r-project.org/package=Boptbd Description: CRAN Package 'Boptbd' (Bayesian Optimal Block Designs) Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) and Gemechu, Debusho and Haines (2025) where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly. Package: r-cran-bor Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bor_0.1.0-1.ca2604.1_all.deb Size: 22256 MD5sum: e9f5245aa94da1737fcec65773c92f4a SHA1: ea7ce883e57d75be45dc51c4f946afbeeb3bb48d SHA256: 1c3115e0b64618e19905c42fab02bd35fa36252701a0362adbdda0b5046c03ee SHA512: 168edca8ec41dc3a4b1fb8d96a4939d226c2dfe1ecb99250597e4af46681c1403d9c95e00eecf3582a64818a44f331746daead93d8b3e22eddd95de745c8f567 Homepage: https://cran.r-project.org/package=bor Description: CRAN Package 'bor' (Transforming Behavioral Observation Records into Data Matrices) Transforms focal observations' data, where different types of social interactions can be recorded by multiple observers, into asymmetric data matrices. Each cell in these matrices provides counts on the number of times a specific type of social interaction was initiated by the row subject and directed to the column subject. Package: r-cran-boral Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-abind, r-cran-corpcor, r-cran-fishmod, r-cran-lifecycle, r-cran-mass, r-cran-mvtnorm, r-cran-r2jags, r-cran-reshape2 Suggests: r-cran-mvabund, r-cran-corrplot Filename: pool/dists/resolute/main/r-cran-boral_2.0.3-1.ca2604.1_all.deb Size: 584906 MD5sum: 7fe7d2d0842b252e78fde1c9bea2b50f SHA1: bda0bab24b0623e906b04e67101226871e9ac80e SHA256: e2137eb9740da6d0f88f64252fe1f833573823181e3b25eca40702a6934e2c13 SHA512: a731cc84fba2d89ab399cb66fa3b844905fd8fa9f9959245d3eb1e701f9807ea7097c33c6d124d440b8325972ff4c53a237d4fc6607892eb152b3e97a29cff6f Homepage: https://cran.r-project.org/package=boral Description: CRAN Package 'boral' (Bayesian Ordination and Regression AnaLysis) Bayesian approaches for analyzing multivariate data in ecology. 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Package: r-cran-boruta Architecture: all Version: 10.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fru Suggests: r-cran-rferns, r-cran-randomforest, r-cran-ranger, r-cran-survival Filename: pool/dists/resolute/main/r-cran-boruta_10.0.0-1.ca2604.1_all.deb Size: 415984 MD5sum: e2289b9a0890d9a4e982608c234d488b SHA1: b94cb8833d155c73bf7c280a6602496838713613 SHA256: 19a5b89670e5a316e0c52a10eacc94f43d56c0afff6eef7c1dfa99c6f978de8d SHA512: 9d74abdf0da8ae7229e611ab7460ad66ddc2434081ac5aa416571f621c0c9b91f1193343dedaaab2f503d55a2d68f1a90a49fc96ba4dcd1864982bdaf62c07f2 Homepage: https://cran.r-project.org/package=Boruta Description: CRAN Package 'Boruta' (Wrapper Algorithm for All Relevant Feature Selection) An all relevant feature selection wrapper algorithm. 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Package: r-cran-bosfr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bosfr_0.1.0-1.ca2604.1_all.deb Size: 54792 MD5sum: a2af20622d49073ad6645fe7d9a558bb SHA1: a9201ccadd0134c2272b648c3891055aea74204f SHA256: f3d64b6e5d55d35a031f46aca21fef1ae3b32973d1511b4f4b1886acf08675c1 SHA512: 19f792a8de88358f8c2b9c65aba3bca46d28dad382ae3514d8cf4bdaef5272d0adf1abf51907048d65a0a4b989e2e67ba89bf77d22fe142fd3e6aaf620002f58 Homepage: https://cran.r-project.org/package=bosfr Description: CRAN Package 'bosfr' (Computes Exact Bounds of Spearman's Footrule with Missing Data) Computes exact bounds of Spearman's footrule in the presence of missing data, and performs independence test based on the bounds with controlled Type I error regardless of the values of missing data. Suitable only for distinct, univariate data where no ties is allowed. Package: r-cran-bossa Architecture: all Version: 3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2274 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rsqlite, r-cran-jsonlite, r-cran-phangorn, r-cran-plotrix Suggests: r-cran-prettydoc, r-cran-knitr, r-cran-rmarkdown, r-cran-xml, r-cran-rentrez, r-cran-httr Filename: pool/dists/resolute/main/r-cran-bossa_3.7-1.ca2604.1_all.deb Size: 1163278 MD5sum: 0e451adbaddf06f3165de9433cfe3fb4 SHA1: 701064a7f0eee618a89d23409293e4fe0767a521 SHA256: 6d5b2c5b42b9ab8391da841bed2415844d9f1842df16cb5676a210b152dc6643 SHA512: 31ccfb019bff00d68fc36d1fa7d8111658961c19795f642e6ff3d13dffc8de69d460a446fe355b86ec0f25676da22f95c54fb765a214d8c21464c5df7998168e Homepage: https://cran.r-project.org/package=BoSSA Description: CRAN Package 'BoSSA' (A Bunch of Structure and Sequence Analysis) Reads and plots phylogenetic placements. Package: r-cran-bossr Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-survival Filename: pool/dists/resolute/main/r-cran-bossr_1.0.4-1.ca2604.1_all.deb Size: 25918 MD5sum: d1f37b6a5b964702c48bef33d2315c74 SHA1: b2f6983cda71c5076cbda89da0b410f6063480ef SHA256: baf7e01c66c21cb029c1bceb3be7ea0ad5665a806894893c42959be9b05ad966 SHA512: 9f58e57d610a51fbf6ba733e10418189cdd600b2411d0790d08b9346812c32c6d64fa7b6b45908e2b58f7892a99392e56e6ec91d30dd167f570bef7892a59253 Homepage: https://cran.r-project.org/package=bossR Description: CRAN Package 'bossR' (Biomarker Optimal Segmentation System) The Biomarker Optimal Segmentation System R package, 'bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. 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Package: r-cran-botor Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-checkmate, r-cran-logger, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr, r-cran-digest Filename: pool/dists/resolute/main/r-cran-botor_0.4.1-1.ca2604.1_all.deb Size: 142194 MD5sum: 8d872309bc208c917709d0e6dd7dec72 SHA1: 13a00e95802fd5c81c9c8d19db1e7d7fc7111e6e SHA256: 0e7ffed12fd56a5d79056f41ec036a2844478711de8e23201a35e6391a803846 SHA512: e978cf3ceffee9d9913ae12a60a8eabe31290ca83dd903b484e72d1c43912f5ea899d8d80b7ad1e3ec50f15271325fc4e2861fc5c52060e27d8c9976e846a83b Homepage: https://cran.r-project.org/package=botor Description: CRAN Package 'botor' ('AWS Python SDK' ('boto3') for R) Fork-safe, raw access to the 'Amazon Web Services' ('AWS') 'SDK' via the 'boto3' 'Python' module, and convenient helper functions to query the 'Simple Storage Service' ('S3') and 'Key Management Service' ('KMS'), partial support for 'IAM', the 'Systems Manager Parameter Store' and 'Secrets Manager'. Package: r-cran-boundarystats Architecture: all Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-gstat, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-igraph, r-cran-fields, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-boundarystats_2.3.0-1.ca2604.1_all.deb Size: 189482 MD5sum: 1ede1a3d1d4a7c7ccd5efe50d5167720 SHA1: effe099d0ccd9901db696509b6d88f0b275f1492 SHA256: cb374517a16db349723305e185ae843a1b7fb007a9545424c8b0d9323e8e52e0 SHA512: 18e4d0c294bc14762ef2ef82093b74c649b66d4f3f42da24de29e875d526f66f6829d94029d008090687dcd596f56e2f16147b1b314f262ed21a292690ae78c1 Homepage: https://cran.r-project.org/package=BoundaryStats Description: CRAN Package 'BoundaryStats' (Boundary Overlap Statistics) Analysis workflow for finding geographic boundaries of ecological or landscape traits and comparing the placement of geographic boundaries of two traits. If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) . Package: r-cran-boundedgeworth Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expint Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-boundedgeworth_0.1.3-1.ca2604.1_all.deb Size: 79970 MD5sum: 9efbfb5fdf9a43f9bb5799760d5163c5 SHA1: d01add5c40b3489a5cca4698e5c7d3871af55029 SHA256: eab3a97176c2665eb0030958c29aa3daf59e0e810cb40a69716ff95d441b64a8 SHA512: 3254b53200a3803d612ca04fefc66a2fdd4ed38b616bf7918d71891a1a7cfb66626b1b06c83cff515fc8164f167d4a23496140247dbff485cdac3da5a75065eb Homepage: https://cran.r-project.org/package=BoundEdgeworth Description: CRAN Package 'BoundEdgeworth' (Bound on the Error of the First-Order Edgeworth Expansion) Computes uniform bounds on the distance between the cumulative distribution function of a standardized sum of random variables and its first-order Edgeworth expansion, following the article Derumigny, Girard, Guyonvarch (2023) . Package: r-cran-boundedur Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-boundedur_1.0.1-1.ca2604.1_all.deb Size: 49306 MD5sum: ef428fd4868ebcf171639b8d13521dfe SHA1: 71e40af5d30ff91d2c015999a2fd2426523a05de SHA256: 0f94462de966b07fe7125905089bbcf4a576e468d48c06b821a9216a3c0da402 SHA512: 70b3d22b9f62f1cadfd101c773632b522ede327fbfc6fca1318e3e89c921a19afcaff576f6c283792ea2a96252a3175b6956b8a4094c43e93f785a4a235006f5 Homepage: https://cran.r-project.org/package=boundedur Description: CRAN Package 'boundedur' (Unit Root Tests for Bounded Time Series) Implements unit root tests for bounded time series following Cavaliere and Xu (2014) . Standard unit root tests (ADF, Phillips-Perron) have non-standard limiting distributions when the time series is bounded. This package provides modified ADF and M-type tests (MZ-alpha, MZ-t, MSB) with p-values computed via Monte Carlo simulation of bounded Brownian motion. Supports one-sided (lower bound only) and two-sided bounds, with automatic lag selection using the MAIC criterion of Ng and Perron (2001) . Package: r-cran-boundingbox Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-imager, r-cran-gplots Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-boundingbox_1.0.1-1.ca2604.1_all.deb Size: 604712 MD5sum: 3f6852de52c8f8eb3d7afe5c570e07b6 SHA1: bb8877c901abd9394686d659cb6e03664a9b1107 SHA256: 71b6ae28db1bed3a7f37ed6d7df074e5ad910410aeaf56a7192126b78880a7ae SHA512: 5981109cf8ea00faaa25e16446428870a565c250cd2daaa3a559d95a1e144040675bf163f8a5c84d91486d140da294d2ab20033914a57638af74799429bded84 Homepage: https://cran.r-project.org/package=boundingbox Description: CRAN Package 'boundingbox' (Create a Bounding Box in an Image) Generate ground truth cases for object localization algorithms. 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Package: r-cran-boussinesq Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-boussinesq_1.0.6-1.ca2604.1_all.deb Size: 29746 MD5sum: aa0d68e41b68ed261ade780f9fe1fcbb SHA1: 3591d3df6b71494ead3d4ad25c7217891f489568 SHA256: ad01aa3b3f1484c7ee72a99f02b82dfb1954787163486c2da167ba2f48c56682 SHA512: 36fa1b61304f4855a2b355954df828615e5e5007c1660686db2ecb1351e4d08a17a02519a9c9dcc77f1a56f987e1d8da58ea3a17d0c7e04315e709e117dbf43f Homepage: https://cran.r-project.org/package=boussinesq Description: CRAN Package 'boussinesq' (Analytic Solutions for (Ground-Water) Boussinesq Equation) A collection of R functions were implemented from published and available analytic solutions for the One-Dimensional Boussinesq Equation (ground-water). 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Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio. Package: r-cran-bqror Architecture: all Version: 1.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-gigrvg, r-cran-truncnorm, r-cran-npflow, r-cran-invgamma, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bqror_1.7.1-1.ca2604.1_all.deb Size: 291128 MD5sum: 05ce6079a46611d07fab12dbc5c56350 SHA1: 565453f764433e2f94fd6b849b183715fdc8b66a SHA256: 2fa30aadbb371280de3fa4fc3ec989bb02d003cddb50a3473547577e534a8066 SHA512: ab959ca62d682ad06300f0f27f571f4662733ac799d7333166dde6e84b2e4238b6fde73011771360462566b8855b8505a80ffd30e83a80cb3ec70d3aac1d792c Homepage: https://cran.r-project.org/package=bqror Description: CRAN Package 'bqror' (Bayesian Quantile Regression for Ordinal Models) Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 . Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 . Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 . Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. . Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. . 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Package: r-cran-brandr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-colorspace, r-cran-dplyr, r-cran-here, r-cran-lifecycle, r-cran-ggplot2, r-cran-yaml Suggests: r-cran-bslib, r-cran-covr, r-cran-hexbin, r-cran-knitr, r-cran-magrittr, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-brandr_0.1.0-1.ca2604.1_all.deb Size: 483924 MD5sum: 9ecda3dc935220da811a6c97ba9024ee SHA1: ddf2f1d7f7abf4133cf5949cc48c568c4f7a59d1 SHA256: a17ae258d0743a466848e2b060cda1e4ea8a288ffa44ff5c7ec6a45fa73e9f18 SHA512: 870a094369bb3746375a05848f56b7deabb36a12c58488fc5eed037307c6ef055b332d89c9d167e552a718bc83a46342b96d685ca4e39340004604eccb4263c4 Homepage: https://cran.r-project.org/package=brandr Description: CRAN Package 'brandr' (Brand Identity Management Using brand.yml Standard) A system to facilitate brand identity management using the brand.yml standard, providing functions to consistently access and apply brand colors, typography, and other visual elements across your R projects. Package: r-cran-brandwatchr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brandwatchr_0.3.0-1.ca2604.1_all.deb Size: 107866 MD5sum: 9aa5c72a7b3272fc8661f8ecdf41dad5 SHA1: e2679d07e714c432ab373f5ae9ffb4889d1e4dca SHA256: 3e290c48eccc11a99539796fa19c0680eede99420a4ff99265c1d83086c30819 SHA512: 0ff0133bf351c89cf24d14e964d46e98d8e9fb4cd3d33c63da9d873a29ed606f0f5041684d3f394d2c962d08cec9449596543e3c87ad0fe84fdff9b88a84f1b6 Homepage: https://cran.r-project.org/package=brandwatchR Description: CRAN Package 'brandwatchR' ('Brandwatch' API to R) Interact with the 'Brandwatch' API . Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories. Also allows you to directly obtain mentions and aggregate data for a specified query or query group. Package: r-cran-brant Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-brant_0.3-0-1.ca2604.1_all.deb Size: 26138 MD5sum: da077acd2294c66c8d4ccec076c53662 SHA1: 5fd14171e62f251fae88901b5edaa024d7e53131 SHA256: 5223851a95428caa0ece111cca523ede4b87ce5f2e18271560b82b4031d95dfa SHA512: 1a386675192b39339af4ea054fd05dfb4eea8e46baa7d534fe64971835fac72684d1b775c5f3d522da2ba4236ded66661162b8531c12581cfbc9a54397710069 Homepage: https://cran.r-project.org/package=brant Description: CRAN Package 'brant' (Test for Parallel Regression Assumption) Tests the parallel regression assumption wit the brant test by Brant (1990) for ordinal logit models generated with the function polr() from the package 'MASS'. 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Package: r-cran-brassica Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-brassica_1.0.3-1.ca2604.1_all.deb Size: 368718 MD5sum: 64913b1cd689bb1f8abd0725d343b4fc SHA1: 2ed7b74d473adf9d3191634b734db5de3c734a6f SHA256: c6602ccaa7dceef9abacc520fd87a74296835c813011db8413e789df90eca052 SHA512: f55f75fc1811138e80154673bb7094fe584cba1b3c6fd504e0ebe5bd8d74de786d2ea90baf0103c88c9775ae06e718fbd578ca70f890faa2f3fb98ed2c81847b Homepage: https://cran.r-project.org/package=brassica Description: CRAN Package 'brassica' (1970s BASIC Interpreter) Executes BASIC programs from the 1970s, for historical and educational purposes. This enables famous examples of early machine learning, artificial intelligence, natural language processing, cellular automata, and so on, to be run in their original form. Package: r-cran-bratteli Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-diagram, r-cran-gmp, r-cran-kantorovich Filename: pool/dists/resolute/main/r-cran-bratteli_1.0.0-1.ca2604.1_all.deb Size: 36646 MD5sum: 20ae51c54b42eefea83bdc78a8bb9a8e SHA1: 86ac910e2b7511a619071acca2fb0690f689f37b SHA256: 9ba375654af6bd8ddb75b906833a57133cdcdebe476b6cc1fafa290e617a2e44 SHA512: 23d13bf7c9dc7db39c2b52c6c90c5c606c08df26748d121f84666a848b73eca9c1396ed9fcad460599cae229b5a8d9a03a82a0760a2a97b897b5ca21ee92df0e Homepage: https://cran.r-project.org/package=bratteli Description: CRAN Package 'bratteli' (Deal with Bratteli Graphs) Utilities for Bratteli graphs. A tree is an example of a Bratteli graph. The package provides a function which generates a 'LaTeX' file that renders the given Bratteli graph. 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Package: r-cran-brazilcrime Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-kableextra, r-cran-tidyr, r-cran-stringr, r-cran-ggcorrplot, r-cran-formatr, r-cran-lubridate, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brazilcrime_0.3.0-1.ca2604.1_all.deb Size: 3250008 MD5sum: 0110415e07214ada8639a5e497342dc0 SHA1: 6095a20de776587d7ccda28a633915784fef4443 SHA256: 37d87b230b573967843ded8236245d8b3223a0f4b87e9f3daf053c0e9c23a3b9 SHA512: d514a5d3ac7d73e183e0f3eae048611bcaaca62c5bdbbdc2fea04a27d4d6007f3b8a8c1a45c5127904b4fe9f9859f38acf9e5be0d5da9c3eb90d079d2400f604 Homepage: https://cran.r-project.org/package=BrazilCrime Description: CRAN Package 'BrazilCrime' (Accesses Brazilian Public Security Data from SINESP Since 2015) Allows access to data from the Brazilian Public Security Information System (SINESP) by state and municipality. It should be emphasized that the package only extracts the data and facilitates its manipulation in R. Therefore, its sole purpose is to support empirical research. All data credits belong to SINESP, an integrated information platform developed and maintained by the National Secretariat of Public Security (SENASP) of the Ministry of Justice and Public Security. . Package: r-cran-brazildataapi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-brazildataapi_0.2.0-1.ca2604.1_all.deb Size: 242226 MD5sum: ee62d37996c48ab1272bb721d50d4d87 SHA1: 84a03e69543ed659a885663ebf2b88cf8c05791e SHA256: ac3dcb1f7d71deb0e636463acc647d54c5b32d55a652f1247b07ec3e76e7fcf8 SHA512: 0829066d5f8aa85da55ac216f3a5e31c831825cadc66b1f7fcc95ea977361a03c20fc3c20f482059892da6f7c404b5a350b9dff0275ec1d6021b6bbc7f66abf9 Homepage: https://cran.r-project.org/package=BrazilDataAPI Description: CRAN Package 'BrazilDataAPI' (Access Brazilian Data via APIs and Curated Datasets) Provides functions to access data from the 'BrasilAPI', 'REST Countries API', 'Nager.Date API', and 'World Bank API', related to Brazil's postal codes, banks, holidays, company registrations, international country indicators, public holidays information, and economic development data. 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Package: r-cran-brazilmet Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-readxl, r-cran-dplyr, r-cran-tibble, r-cran-lubridate, r-cran-sf, r-cran-stringi, r-cran-terra Filename: pool/dists/resolute/main/r-cran-brazilmet_0.4.0-1.ca2604.1_all.deb Size: 197788 MD5sum: aa76a1b3391ea01449a4544618543488 SHA1: 2ae63302c8d4468a65cb105db24accb58bd3370d SHA256: 3f0b8b531b8d8e456360353926b36f99bdaa7d1bbe499badaadab3fbe310425c SHA512: c38f52e8f6a61e0347cb2498c910b1a4f0d94b0cbc2ce3e0bf158b40e32ac0ad423d1a76c8dc96e1ed040133164766c4656803467a78332613d311e3b4500e62 Homepage: https://cran.r-project.org/package=BrazilMet Description: CRAN Package 'BrazilMet' (Download and Processing of Automatic Weather Stations (AWS) Dataof INMET-Brazil) A collection of functions for downloading and processing automatic weather station (AWS) data from INMET (Brazil’s National Institute of Meteorology), designed to support the estimation of reference evapotranspiration (ETo). The package facilitates streamlined access to meteorological data and aims to simplify analyses in agricultural and environmental contexts. Package: r-cran-brcal Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr, r-cran-fields, r-cran-ggplot2, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-xfun, r-cran-gridextra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brcal_1.0.1-1.ca2604.1_all.deb Size: 1009400 MD5sum: d5790cdddeeb2dd6225b7a873e8e6403 SHA1: c1f37e0813a82f50899e234754bfd2a2954d5cbc SHA256: 402e9d7f3a59218e5b00dcd373fc49dbc9279cebfb9df250206f35009aedd378 SHA512: f5093e1ac3c005469ff5179fc28b66ce53204da9ae0ebe7b7da999fb7bc21f4909962c176700673f494fb3676e7c78d9deae73119aa153de417cda7449342d8a Homepage: https://cran.r-project.org/package=BRcal Description: CRAN Package 'BRcal' (Boldness-Recalibration of Binary Events) Boldness-recalibration maximally spreads out probability predictions while maintaining a user specified level of calibration, facilitated the brcal() function. Supporting functions to assess calibration via Bayesian and Frequentist approaches, Maximum Likelihood Estimator (MLE) recalibration, Linear in Log Odds (LLO)-adjust via any specified parameters, and visualize results are also provided. Methodological details can be found in Guthrie & Franck (2024) . Package: r-cran-brclimr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-rlang, r-cran-lobstr, r-cran-magrittr, r-cran-dbi, r-cran-duckdb, r-cran-glue Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-brclimr_0.2.0-1.ca2604.1_all.deb Size: 543194 MD5sum: 00df734e4dfd7eaa1bf8b93c257f86d7 SHA1: eac516e82f2c05085b781fbfdb812d0fb909268c SHA256: e5be18d6847426cc600b79128e6008b37d602d853ce245c5906228b4c7c86d38 SHA512: def0ce1548f5f23420184310e1b55e37b71b61aaf5ef7b62dc5ac1ca3aa19815c9ad6d52f06d6e0e03ddb50f18670609d53960001849d48a260cecc687e6897f Homepage: https://cran.r-project.org/package=brclimr Description: CRAN Package 'brclimr' (Fetch Zonal Statistics of Weather Indicators for BrazilianMunicipalities) Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects. Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames. Package: r-cran-brcore Architecture: all Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2259 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggrepel, r-cran-hmisc, r-cran-magrittr, r-cran-minpack.lm, r-bioc-phyloseq, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-brcore_2.0.7-1.ca2604.1_all.deb Size: 1979590 MD5sum: 2bf64dd4f6eb137ec991633160c5488a SHA1: f972a28a1ffdbb65bb646f3853b278b32551a61d SHA256: 630784ec653ca3664a3a78a689b582d73da4e4d03e62d6f43a44c7dcf1baae17 SHA512: ff2782e596f99ab27384f50ba7a0878858697a5402943a60adc2d5f8a4b3c018572d74291685ad246566c74e0182dd2571d43124ce99bdcc1da79f11c5fc02f5 Homepage: https://cran.r-project.org/package=BRCore Description: CRAN Package 'BRCore' (A Unified Framework for Identification and EcologicalInterpretation of Microbial Data from Bioenergy ResearchCenters) A unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis. 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Package: r-cran-brea Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-coda Filename: pool/dists/resolute/main/r-cran-brea_0.4.2-1.ca2604.1_all.deb Size: 232794 MD5sum: fd4d9c82cdfbc870ae6603a029db0d0f SHA1: 38352b8d8ea7719ae8d8398aa5a2f3f47f533397 SHA256: 60c78178ef57c088c73034edfdab5452d99fc925a546e689b6b11739f098f2ff SHA512: 3c3939a09e50b833e4af650c1b44b90b7a0a78cb3e91ffd086693dd49702f5390615ce9e41a5d5538c9925f2ab2536e29f2f24048acfe38ed71e7ccb8548aa7c Homepage: https://cran.r-project.org/package=brea Description: CRAN Package 'brea' (Bayesian Recurrent Events Analysis) Functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models. Package: r-cran-bread Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-bread_0.4.1-1.ca2604.1_all.deb Size: 71516 MD5sum: 811debc1cef921d6a17493a4f0d56ebf SHA1: 7ca7ee228b027d25ce6766fe34dfc124e1aec511 SHA256: e9bfbbadda038015c6be65ef704922bf5012e63cd3977c9bcfbb8a2c7dc999b5 SHA512: a57a1eda94c9e8af376f16d80a8279b1f93012537b1da838352f5db64cb7d1491e483e9ac8321d7934327caf2f92b2c3559302103a6c5bf1cb5002f1d0a48ed2 Homepage: https://cran.r-project.org/package=bread Description: CRAN Package 'bread' (Analyze Big Files Without Loading Them in Memory) A simple set of wrapper functions for data.table::fread() that allows subsetting or filtering rows and selecting columns of table-formatted files too large for the available RAM. 'b stands for 'big files'. bread makes heavy use of Unix commands like 'grep', 'sed', 'wc', 'awk' and 'cut'. They are available by default in all Unix environments. For Windows, you need to install those commands externally in order to simulate a Unix environment and make sure that the executables are in the Windows PATH variable. To my knowledge, the simplest ways are to install 'RTools', 'Git' or 'Cygwin'. If they have been correctly installed (with the expected registry entries), they should be detected on loading the package and the correct directories will be added automatically to the PATH. Package: r-cran-breadr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2001 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggstatsplot, r-cran-magrittr, r-cran-mass, r-cran-matrixstats, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-matrix, r-cran-fs Filename: pool/dists/resolute/main/r-cran-breadr_1.1.0-1.ca2604.1_all.deb Size: 1397214 MD5sum: 920f34bb2c2917a48085d9f02a72bc55 SHA1: b6f3e75c5cd110aaab5ca1e86dc80dbfb4286607 SHA256: 9857ef80cf75418550a880e613de237bdcad9f185f34bd6679d17f3deb501cdc SHA512: 00fc3f5943c13c008983efbf9206064debed101a55bb890bb534d22fc78a4ae3fc9710760063514ffb48b2b0bdb4b4159405b415ae8f01e92bb93081d7ddfece Homepage: https://cran.r-project.org/package=BREADR Description: CRAN Package 'BREADR' (Estimates Degrees of Relatedness (Up to the Second Degree) forExtreme Low-Coverage Data) The goal of the package is to provide an easy-to-use method for estimating degrees of relatedness (up to the second degree) for extreme low-coverage data. The package also allows users to quantify and visualise the level of confidence in the estimated degrees of relatedness. Package: r-cran-breakaway Architecture: all Version: 4.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1844 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-mass, r-bioc-phyloseq, r-cran-tibble Suggests: r-cran-devtools, r-cran-knitr, r-cran-plyr, r-cran-rcurl, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-breakaway_4.8.4-1.ca2604.1_all.deb Size: 1672286 MD5sum: 36952d33b2a3cc1a17cd37e1cb19864f SHA1: 33f26fe0a037ebd664616211a453aa1f51951747 SHA256: 0a669113d8d9ddfce7a18972b3b0e3c60714a95c00e06abbc086e1d1a284cb85 SHA512: 112be89dfd70652ee12ced31338e66985355577dc467c8cd683430aecb157c563af491743ef1a28be8feb8e0a52266b400e006d67251ebe98b7c7a4280728ea9 Homepage: https://cran.r-project.org/package=breakaway Description: CRAN Package 'breakaway' (Species Richness Estimation and Modeling) Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. 'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) , Willis et al. (2017) , and Willis (2016) , as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) . Package: r-cran-breakdown Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-e1071, r-cran-kernlab, r-cran-xgboost, r-cran-caret, r-cran-randomforest, r-cran-dalex, r-cran-ranger, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-breakdown_0.2.2-1.ca2604.1_all.deb Size: 850922 MD5sum: 40bcccefc11f77168ec541dbf3306b4d SHA1: 5c1e9db5054a962c83a08e0131c0b3c281440a44 SHA256: 81d9e6976bf02bbc13db91f3ea101eb4102a4024e113a3e4ef99eaad7ea831c2 SHA512: bfe83f6038961126312e9f7b0ddf3c74816cde007839e5d76ba6d215871412af35e420e84b8342abc9a52c4332c3807c52f2f49639b06d82ce918f678b077e92 Homepage: https://cran.r-project.org/package=breakDown Description: CRAN Package 'breakDown' (Model Agnostic Explainers for Individual Predictions) Model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. Break Down Plot presents variable contributions in a concise graphical way. This package work for binary classifiers and general regression models. Package: r-cran-breakpoints Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-breakpoints_1.2-1.ca2604.1_all.deb Size: 47926 MD5sum: 126ed8fa59a5566ade4c85ed924b7cff SHA1: 352890ad0af3fb9a66579fd2342205c518940782 SHA256: bf3b8e8b859bdb776d51e7e28f0f47e44d0c10f378b9cf59bcf40ff21ddd4342 SHA512: a9a0af462f32432b746ebc9ffa0a918ae6940c213a9fdaf3f9b518b2e0c0ccce7dd6d1bae07145bfd588308cc57fca19742e4c57f0b4d256717918762aa698ad Homepage: https://cran.r-project.org/package=BreakPoints Description: CRAN Package 'BreakPoints' (Identify Breakpoints in Series of Data) Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney Rank Test, to identify breakpoints in series. For all functions NA is allowed. Since all of the mention methods identify only one breakpoint in a series, a general function to look for N breakpoint is given. Also, the Yamamoto test for climate jump is available. Alexandersson, H. (1986) , Buishand, T. (1982) , Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020) , Mann, H. B., Whitney, D. R. (1947) , Pettitt, A. N. (1979) , Ruxton, G. D., jul (2006) , Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985) . Package: r-cran-breathtestcore Architecture: all Version: 0.8.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ggfittext, r-cran-ggplot2, r-cran-broom, r-cran-mass, r-cran-multcomp, r-cran-nlme, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-signal, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-gridextra, r-cran-qpdf, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-breathteststan, r-cran-covr Filename: pool/dists/resolute/main/r-cran-breathtestcore_0.8.10-1.ca2604.1_all.deb Size: 408318 MD5sum: f15eb10c2ef6e18bd404d8a7cc60c617 SHA1: f0e10478a4799d23cbbd6f0b976bfe211227de11 SHA256: 7694796ca7d90bfdfe65139e6e4770730871a0e7e510d7a59767a88dc880d083 SHA512: 129026067219a5980127df1a263ba1a148c07e90f1a60961394d763f2763a691d82cee849bc859dafca90909ac089758b78f258a2c6bfcfa85500f1ae2e2b889 Homepage: https://cran.r-project.org/package=breathtestcore Description: CRAN Package 'breathtestcore' (Core Functions to Read and Fit 13c Time Series from Breath Tests) Reads several formats of 13C data (IRIS/Wagner, BreathID) and CSV. Creates artificial sample data for testing. Fits Maes/Ghoos, Bluck-Coward self-correcting formula using 'nls', 'nlme'. Methods to fit breath test curves with Bayesian Stan methods are refactored to package 'breathteststan'. For a Shiny GUI, see package 'dmenne/breathtestshiny' on github. Package: r-cran-breeze Architecture: all Version: 0.4-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1858 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate Suggests: r-cran-rcolorbrewer, r-cran-rgooglemaps, r-cran-xml Filename: pool/dists/resolute/main/r-cran-breeze_0.4-4-1.ca2604.1_all.deb Size: 1297502 MD5sum: 48c7ede53d48486e7723ded1b24d1d21 SHA1: e43d25e4f4ea743a54f03352dcdeed7b6c7d1245 SHA256: a96f3fe72ca5c6f17303fdda72d1a969f0efaa5235cac617d27dc12afa4fcfd7 SHA512: 02eeacbc13232d89febf8ae6d92b963bb9fb74a327857dc366d0462a3821938b312f5ff49d8f4dde7997ac8524808f1780f68849c8ea84d5a003419c1def4e76 Homepage: https://cran.r-project.org/package=bReeze Description: CRAN Package 'bReeze' (Functions for Wind Resource Assessment) A collection of functions to analyse, visualize and interpret wind data and to calculate the potential energy production of wind turbines. Package: r-cran-bregr Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-broom.helpers, r-cran-cli, r-cran-dplyr, r-cran-forestploter, r-cran-ggplot2, r-cran-glue, r-cran-insight, r-cran-lifecycle, r-cran-mirai, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-survival, r-cran-tibble, r-cran-vctrs Suggests: r-cran-broom.mixed, r-cran-fs, r-cran-ggalign, r-cran-ggnewscale, r-cran-ggstats, r-cran-ggstatsplot, r-cran-gtsummary, r-cran-ids, r-cran-knitr, r-cran-lme4, r-cran-merderiv, r-cran-qs2, r-cran-rmarkdown, r-cran-testthat, r-cran-ucscxenashiny, r-cran-visreg Filename: pool/dists/resolute/main/r-cran-bregr_1.4.0-1.ca2604.1_all.deb Size: 1896630 MD5sum: 96959ce6c8988fe6e13990fd0b708d72 SHA1: febba8ffa798625f4c46cc0c2e98010869c025e6 SHA256: fcad8e91f345e6a82010defb837586cee220fa11dd639f29b0e040a0c5af2bad SHA512: 3e4d858cbc5b77d71b181f86e8d8797a8a80e11898273066d7c8a81dae759f824adb7c5a603cc71c6eb3fcb0601ea9735f5426a871d9bc259b234681e383d211 Homepage: https://cran.r-project.org/package=bregr Description: CRAN Package 'bregr' (Easy and Efficient Batch Processing of Regression Models) Easily processes batches of univariate or multivariate regression models. Returns results in a tidy format and generates visualization plots for straightforward interpretation (Wang, Shixiang, et al. (2025) ). Package: r-cran-bretigea Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1860 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bretigea_1.0.3-1.ca2604.1_all.deb Size: 1846304 MD5sum: 27cd91945ac59d19482d368434e8297d SHA1: 899261e06e8c1c17caf5230b3e13e8bb0515f37f SHA256: a6e0d86edce2828c70fb1fa888ce8ce41e589d303bda809019dab4397b7dcbfb SHA512: 8b1a82ebacd5aaf64e1c9cf93fe94fac0181d81856c6949910d6e08aa87d0e082cde89fbd389320810034ee9e5d29922e785f1e9f1bfd3e93ed27e568b591439 Homepage: https://cran.r-project.org/package=BRETIGEA Description: CRAN Package 'BRETIGEA' (Brain Cell Type Specific Gene Expression Analysis) Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) . For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) . The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses. Package: r-cran-brew Architecture: all Version: 1.0-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brew_1.0-10-1.ca2604.1_all.deb Size: 55444 MD5sum: 1c44e4047f060e9ff1a64a4fe18baf85 SHA1: 9dee3751c523a65b1f8e5fb6ad70171289fe11b1 SHA256: 30dda59f37c850b280a8ca0a0c1213139f9deedbf01f4acf03713c4bea9d3edc SHA512: a9fa7d1c5a805306546cbfb638377bc25dd7ce363f132ee2ea0022529a26a337f732ac71eaeff4308a0df5f37c35cb84af3d220d73fdffb109a8b2c0c31c9bce Homepage: https://cran.r-project.org/package=brew Description: CRAN Package 'brew' (Templating Framework for Report Generation) Implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module. Package: r-cran-brfinance Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 917 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-httr2, r-cran-lubridate, r-cran-labelled Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-brfinance_0.8.0-1.ca2604.1_all.deb Size: 397566 MD5sum: 74ebd4f3107ef6e669f2fcea17863677 SHA1: a2a31626f27c97ef0aaa95470fba13fade84af96 SHA256: 76fa32e4524fd3e7dd841461e55dc15a1032314ffb82f024e7e8149426501fe7 SHA512: 398305e4583dfd47679eb72becfb8bfe949003c9bc6cd657389c309d7759fc58e746af1342d15efeef1ee7d88b3e210b394798dbd106ba6448ae3bd39d968262 Homepage: https://cran.r-project.org/package=brfinance Description: CRAN Package 'brfinance' (Access to Brazilian Macroeconomic and Financial Time Series) Provides simplified access to selected Brazilian macroeconomic and financial time series from official sources, primarily the Central Bank of Brazil through the SGS (Sistema Gerenciador de Séries Temporais) API. The package enables users to quickly retrieve and visualize indicators such as the unemployment rate and the Selic interest rate using a standardized data structure. It is designed for data access and visualization purposes, without performing forecasts or statistical modeling. For more information, see the official API: . Package: r-cran-brickset Architecture: all Version: 2026.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-piggyback Suggests: r-cran-bookdown, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-brickset_2026.0.0-1.ca2604.1_all.deb Size: 1104332 MD5sum: 4e233734cc28081a11a6e384e4d5f58a SHA1: 25e86415271e44e39a938350d8854c37a15aa515 SHA256: be5328e8cb5f0bc552cb4f7e3767e830fdc6a1416baae272da2b3434bf3fbcfb SHA512: 56ce335e4e4066b507d4dd985a2b380595acd27f8dd996184345d0e942777f26f16f492b261b670f618ec626afddd257af7ad625aa7aa1866ec70a5bc305d2bc Homepage: https://cran.r-project.org/package=brickset Description: CRAN Package 'brickset' (Interface with the Brickset API for Getting Data About LEGO Sets) Interface with the 'Brickset' API for getting data about LEGO sets. Data sets that can be used for teaching and learning without the need of a 'Brickset' account and API key are also included. Includes all LEGO since through the end of 2025. Package: r-cran-brickster Architecture: all Version: 0.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2236 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-ini, r-cran-jsonlite, r-cran-lifecycle, r-cran-nanoarrow, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-tibble Suggests: r-cran-arrow, r-cran-htmltools, r-cran-huxtable, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-brickster_0.2.13-1.ca2604.1_all.deb Size: 1836036 MD5sum: 46fd939ca26f9b2b3e011c9d04359c16 SHA1: 6602c4db928dc0631163da1db242b24fad4cd08d SHA256: 605eaeac6253a929e1f3192153112855388b1e4c3eac7a49cb85f0ac31554460 SHA512: c64e06fd57a52c1eb906f53d418cfe8df75c180ab7dd8f40e47a15b98b43299edb8d544f9247df1c0282a9c5ad24102d2408df970bf7b67aefafcb05e5035c7d Homepage: https://cran.r-project.org/package=brickster Description: CRAN Package 'brickster' (R Toolkit for 'Databricks') Collection of utilities that improve using 'Databricks' from R. Primarily functions that wrap specific 'Databricks' APIs (), 'RStudio' connection pane support, quality of life functions to make 'Databricks' simpler to use. Package: r-cran-bridgedist Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bridgedist_0.1.3-1.ca2604.1_all.deb Size: 79580 MD5sum: afde5ebb42c76cbd26c103edf9bd9288 SHA1: 077cce22c638fb174abac55273c1a19038acecdf SHA256: f7592b8da1ae352e0fc51bd70ce35caa9f716c5a14cc959789ce4b61663e7ed8 SHA512: 8f749b9e00bad4407e815c81fd85f3ffd53128006ebe3e635d0103a05fd81c418d5bb22b634cc42d92dbad611b6a2f1dfa49b617b8288b7a93165d471ec67371 Homepage: https://cran.r-project.org/package=bridgedist Description: CRAN Package 'bridgedist' (An Implementation of the Bridge Distribution with Logit-Link asin Wang and Louis (2003)) An implementation of the bridge distribution with logit-link in R. In Wang and Louis (2003) , such a univariate bridge distribution was derived as the distribution of the random intercept that 'bridged' a marginal logistic regression and a conditional logistic regression. The conditional and marginal regression coefficients are a scalar multiple of each other. Such is not the case if the random intercept distribution was Gaussian. Package: r-cran-bridger2 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-shiny, r-cran-shinydashboard, r-cran-plotly, r-cran-bsda, r-cran-outliers Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bridger2_0.1.0-1.ca2604.1_all.deb Size: 304676 MD5sum: aedb694c23c83197deb20cf976cc24be SHA1: f9204b06d9470811d4f824c509826acd6d65ab36 SHA256: ce1844743d6dd6e5dcdc8eff757dffdc657c855bb77a3616f241ed181f12525d SHA512: 20f01a043cf10aed107b1741bb13ee067a86fd9f16eb795dfe6494d90f75ee2709e9bf23c58efbddcf6801e9691b68dd7a896108fee8e6bed15dc8d1907e3cdf Homepage: https://cran.r-project.org/package=bridger2 Description: CRAN Package 'bridger2' (Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data) BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells. This package provides a series of functions for performing quality check of your BRIC-seq data, calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions. Package: r-cran-bridgesampling Architecture: all Version: 1.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrix, r-cran-brobdingnag, r-cran-stringr, r-cran-coda, r-cran-scales Suggests: r-cran-testthat, r-cran-rcpp, r-cran-rcppeigen, r-cran-r2jags, r-cran-rjags, r-cran-runjags, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-bayesfactor, r-cran-rstan, r-cran-rstanarm, r-cran-nimble, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-bridgesampling_1.2-1-1.ca2604.1_all.deb Size: 1176680 MD5sum: 73e32084b2fe3326afa7d859b6c156f9 SHA1: 405bfeb989cc5b0a8ec27309c8cfb89c34818416 SHA256: e1178f53a270c953c27f8dcf0397569bdf279ecd8a43129afe9eaa06bcbf995f SHA512: 4097173df8c203f141ae3888e3dcf5d1d54ac4ad55a744f09d37392288fadc00431059a30c3ae15f44b6a41f6953da26f5fe69b5da408a1ce2f2db0d5e444908 Homepage: https://cran.r-project.org/package=bridgesampling Description: CRAN Package 'bridgesampling' (Bridge Sampling for Marginal Likelihoods and Bayes Factors) Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, ). Gronau, Singmann, & Wagenmakers (2020) . Package: r-cran-bridgr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 945 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-generics, r-cran-tsbox, r-cran-lubridate, r-cran-forecast, r-cran-xts Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bridgr_0.1.2-1.ca2604.1_all.deb Size: 878510 MD5sum: 383adae627041790c03fd8c5f94df836 SHA1: c55ec1b0175b1231214af75386a912313f73aa18 SHA256: ae9daa17d5bb6c0360614531474741608242278c1b7d18524d2d40284ef76744 SHA512: c982b94de093d2619280e0ec0f66d8504b7faf0b5ffc4b3794dd6c2844a2e3ca61ee09c411bca974c52b3e65bda8e0d3f41e04e64156efb693bc592339d23bb1 Homepage: https://cran.r-project.org/package=bridgr Description: CRAN Package 'bridgr' (Bridging Data Frequencies for Timely Economic Forecasts) Implements bridge models for nowcasting and forecasting macroeconomic variables by linking high-frequency indicator variables (e.g., monthly data) to low-frequency target variables (e.g., quarterly GDP). Simplifies forecasting and aggregating indicator variables to match the target frequency, enabling timely predictions ahead of official data releases. For more on bridge models, see Baffigi, A., Golinelli, R., & Parigi, G. (2004) , Burri (2023) or Schumacher (2016) . Package: r-cran-brightspacer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-config, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-lubridate, r-cran-openssl, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-httptest2, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat, r-cran-withr, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-brightspacer_0.1.0-1.ca2604.1_all.deb Size: 289442 MD5sum: be1a10345a700378b1fde2230e1767f4 SHA1: ca1b9366659744b336ba86568c7b0f20630eb11d SHA256: ccf81c9d37f66998595512421aa0505fea8112e20f1db8cf5dc98b27cb845467 SHA512: 570fe3e2e0595bc4375093fda508de6f8a8644b5c9bf8e5c48cbfc93080bd054fc500c14c9ea5ae8dd27315a6681e97cd6b2c38bef399b87b858f2cbe19e77b8 Homepage: https://cran.r-project.org/package=brightspaceR Description: CRAN Package 'brightspaceR' (Access D2L 'Brightspace' Data Sets via the 'BDS' API) Connect to the D2L 'Brightspace' Data Sets ('BDS') API via 'OAuth2', download all available datasets as tidy data frames with proper types, join them using convenience functions that know the foreign key relationships, and analyse student engagement, performance, and retention with ready-made analytics functions. Package: r-cran-brikmeans Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-cluster, r-cran-depthtools, r-cran-splines2 Filename: pool/dists/resolute/main/r-cran-brikmeans_1.0-1.ca2604.1_all.deb Size: 142232 MD5sum: e3d7c5b2eee22a4ccf3e66a864fd2edf SHA1: 877076f32f0a7c2d9a8eb7e0d1ebf3f3c56401a3 SHA256: cb47fc16b3b5229e2bc20a7e8a08bbcc2220d41e694328658a6b5b46db45bec8 SHA512: 28aad7d0ccbb552087396486c8e5cab8ce039d0396acca110d666288b008ae22f7fef96e68f55ecbe68227e97993fa53fdf4a9513538a79906edb27b983388d0 Homepage: https://cran.r-project.org/package=briKmeans Description: CRAN Package 'briKmeans' (Package for Brik, Fabrik and Fdebrik Algorithms to InitialiseKmeans) Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means. These methods are intended for the clustering of multivariate and functional data, respectively. They make use of the Modified Band Depth and bootstrap to identify appropriate initial seeds for k-means, which are proven to be better options than many techniques in the literature. Torrente and Romo (2021) It makes use of the functions kma and kma.similarity, from the archived package fdakma, by Alice Parodi et al. Package: r-cran-brinda Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-berryfunctions, r-cran-data.table, r-cran-dplyr, r-cran-hmisc, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brinda_0.1.5-1.ca2604.1_all.deb Size: 52808 MD5sum: 435ece11715442c310f31032bef5fef8 SHA1: 12f6ac1f63653b8a7551c371ec8d276f8aed15a0 SHA256: 18ce0bc1e18eb7d2933b9e43dad08db4a0a4450c9feb2311dab872e5d255d712 SHA512: 1a193a1aaf07bcd9f91c64961ba4da6b48cb5cc8953302c8bb0db9b781961119e986080565115f57e35f022df9a2d88a802dc4b758110c3e37f81a416192969b Homepage: https://cran.r-project.org/package=BRINDA Description: CRAN Package 'BRINDA' (Computation of BRINDA Adjusted Micronutrient Biomarkers forInflammation) Inflammation can affect many micronutrient biomarkers and can thus lead to incorrect diagnosis of individuals and to over- or under-estimate the prevalence of deficiency in a population. Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) is a multi-agency and multi-country partnership designed to improve the interpretation of nutrient biomarkers in settings of inflammation and to generate context-specific estimates of risk factors for anemia (Suchdev (2016) ). In the past few years, BRINDA published a series of papers to provide guidance on how to adjust micronutrient biomarkers, retinol binding protein, serum retinol, serum ferritin by Namaste (2020), soluble transferrin receptor (sTfR), serum zinc, serum and Red Blood Cell (RBC) folate, and serum B-12, using inflammation markers, alpha-1-acid glycoprotein (AGP) and/or C-Reactive Protein (CRP) by Namaste (2020) , Rohner (2017) , McDonald (2020) , and Young (2020) . The BRINDA inflammation adjustment method mainly focuses on Women of Reproductive Age (WRA) and Preschool-age Children (PSC); however, the general principle of the BRINDA method might apply to other population groups. The BRINDA R package is a user-friendly all-in-one R package that uses a series of functions to implement BRINDA adjustment method, as described above. The BRINDA R package will first carry out rigorous checks and provides users guidance to correct data or input errors (if they occur) prior to inflammation adjustments. After no errors are detected, the package implements the BRINDA inflammation adjustment for up to five micronutrient biomarkers, namely retinol-binding-protein, serum retinol, serum ferritin, sTfR, and serum zinc (when appropriate), using inflammation indicators of AGP and/or CRP for various population groups. Of note, adjustment for serum and RBC folate and serum B-12 is not included in the R package, since evidence shows that no adjustment is needed for these micronutrient biomarkers in either WRA or PSC groups (Young (2020) ). Package: r-cran-brinton Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3821 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rmarkdown, r-cran-glue, r-cran-pander, r-cran-lubridate, r-cran-tibble, r-cran-sm, r-cran-rcolorbrewer, r-cran-forcats, r-cran-ggally, r-cran-patchwork, r-cran-scales Suggests: r-cran-knitr, r-cran-mass, r-cran-hexbin, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brinton_0.2.7-1.ca2604.1_all.deb Size: 2790180 MD5sum: 1f19525eb211f12502f2568d89b6917e SHA1: 9310691d445b2edd289ba7e183a578494294d422 SHA256: 17e9dccc4a0e71d42a0a15896777ad7af9a3824ca114a2713d2985c746999dfa SHA512: 9f19c636f608d3dcf54c023a42eab9b2d3e316319cfa65e80eb22f804f63e47a64a757203db01095e496685fa4a895f3a5f1fd9f02486e60295f477593d79a3f Homepage: https://cran.r-project.org/package=brinton Description: CRAN Package 'brinton' (A Graphical EDA Tool) An automated graphical exploratory data analysis (EDA) tool that introduces: a.) wideplot graphics for exploring the structure of a dataset through a grid of variables and graphic types. b.) longplot graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. c.) plotup function, which presents a particular graphic for a specific variable of a dataset. The plotup() function also makes it possible to obtain the code used to generate the graphic, meaning that the user can adjust its properties as needed. d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships between all pairs of variables of a certain(s) type(s) in a multivariate data set. Package: r-cran-briqr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-httptest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-briqr_0.1.0-1.ca2604.1_all.deb Size: 18068 MD5sum: 77df0bf75530a0b90196146281492dba SHA1: a3b9a647c70a0b2c1b6c4fbbb8bcc8d844efa593 SHA256: 97fa8d43ea33b1ab61b4413edcca53a4052b12d24e61f7d7b5376695cd34cf9a SHA512: 8684c70d546720487df0d186c90d938c50468763dae29df25b1cc8c4d5b072cd6b91c0ae9b9fd9de98e31e2f8e02321b23f6b0f52bdfd69a09b0d5df5179cf2c Homepage: https://cran.r-project.org/package=briqr Description: CRAN Package 'briqr' (Interface to the 'Briq' API) An interface to the 'Briq' API . 'Briq' is a tool that aims to promote employee engagement by helping employees recognize and reward each other. Employees can praise and thank one another (for achieving a company goal, for example) by giving virtual credits (known as 'briqs' or 'bqs') that can be redeemed for various rewards. The 'Briq' API lets you create, read, update and delete users, user groups, transactions and messages. This package provides functions that simplify getting the users, user groups and transactions of your organization into R. Package: r-cran-brisk Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-hitandrun, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-fs, r-cran-testthat, r-cran-tibble, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-brisk_0.1.1-1.ca2604.1_all.deb Size: 208682 MD5sum: 5d69891d856ebf57869fd7d79c92faf5 SHA1: 19a9d03938eaa8aae80e0a65c6dd2519bc89db3a SHA256: 04851d52269ef5ba2879e07acba8d4575c7185c777610df34c604b1a713c6ba1 SHA512: 68c6c23f774650f6e674764dca2bfe62fe1f3b16d59416224c08d8606ec9de66a28f84f45dc5ee3191436ac7b06ddab7c84752ae0b1a996f8046fc60690768a0 Homepage: https://cran.r-project.org/package=brisk Description: CRAN Package 'brisk' (Bayesian Benefit Risk Analysis) Quantitative methods for benefit-risk analysis help to condense complex decisions into a univariate metric describing the overall benefit relative to risk. One approach is to use the multi-criteria decision analysis framework (MCDA), as in Mussen, Salek, and Walker (2007) . Bayesian benefit-risk analysis incorporates uncertainty through posterior distributions which are inputs to the benefit-risk framework. The brisk package provides functions to assist with Bayesian benefit-risk analyses, such as MCDA. Users input posterior samples, utility functions, weights, and the package outputs quantitative benefit-risk scores. The posterior of the benefit-risk scores for each group can be compared. Some plotting capabilities are also included. Package: r-cran-brlrmr Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-brglm, r-cran-mass, r-cran-profilemodel, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-brlrmr_0.1.7-1.ca2604.1_all.deb Size: 69064 MD5sum: a7b7b72af6dbddea3445f2de950af414 SHA1: ccf6dfc39676be62e37b339dcc7b32ecab3ced1e SHA256: 2c390528e1dae22d6ec7689f09e534d15cd357da0494b6f99876af1d57d86394 SHA512: b271187973a34a01b7275c30965f608bbaec8b3941d0e61b8833dbcba8a5cbbb895413883bbcc70df4ec11cac9934e198b4df97ec0ff7ab30863904a67d29ca4 Homepage: https://cran.r-project.org/package=brlrmr Description: CRAN Package 'brlrmr' (Bias Reduction with Missing Binary Response) Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) . Package: r-cran-brm Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-brm_1.1.1-1.ca2604.1_all.deb Size: 82038 MD5sum: c02e3e9df307183abd7dd7038b8a2211 SHA1: 5b6cb6d2cee88abc2a6a91b3965678cda6dcea69 SHA256: 4f9072e07c0f5eb336840180bab4816f4dd4febf3c87bd9fbf088f88526b7b63 SHA512: 275c431c2b76f9b90e8822367d6b0cd25d89e5359b477bb3deffc6747edaf7a2ee1b47679f560ac9b407e7440f414b30b00dfcba8a42da9ed6cb623e64856213 Homepage: https://cran.r-project.org/package=brm Description: CRAN Package 'brm' (Binary Regression Model) Fits novel models for the conditional relative risk, risk difference and odds ratio . Package: r-cran-brms.mmrm Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-mass, r-cran-posterior, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-trialr, r-cran-zoo Suggests: r-cran-bh, r-cran-emmeans, r-cran-fst, r-cran-gt, r-cran-gtsummary, r-cran-knitr, r-cran-markdown, r-cran-mmrm, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rstan, r-cran-stanheaders, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brms.mmrm_1.1.1-1.ca2604.1_all.deb Size: 1546406 MD5sum: 830e105dbdc77628ea78c7ea86554a1b SHA1: 8d17bc20754a920fdfdaaa1b074689aa089d747c SHA256: ee4c1a66c1e7907f3582fc8d613d3affb10cd04ff4f0df00cc846c3c859057a7 SHA512: 6c7e2b9b35aa6a0280030faded6157d64fed4e34f3abd3addb42bca73b332842f5ea711cbd921d46d94a7cef01bef48681c120133572c2c6deb206fd2485f8b2 Homepage: https://cran.r-project.org/package=brms.mmrm Description: CRAN Package 'brms.mmrm' (Bayesian MMRMs using 'brms') The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a powerful and versatile package for fitting Bayesian regression models. The 'brms.mmrm' R package leverages 'brms' to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) , Mallinckrodt (2008) . Package: r-cran-brms Architecture: all Version: 2.23.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8816 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-ggplot2, r-cran-loo, r-cran-posterior, r-cran-matrix, r-cran-mgcv, r-cran-rstantools, r-cran-bayesplot, r-cran-bridgesampling, r-cran-glue, r-cran-rlang, r-cran-future, r-cran-future.apply, r-cran-matrixstats, r-cran-nleqslv, r-cran-nlme, r-cran-coda, r-cran-abind, r-cran-backports Suggests: r-cran-testthat, r-cran-emmeans, r-cran-projpred, r-cran-priorsense, r-cran-shinystan, r-cran-splines2, r-cran-rwiener, r-cran-rtdists, r-cran-extradistr, r-cran-processx, r-cran-mice, r-cran-spdep, r-cran-mnormt, r-cran-lme4, r-cran-mcmcglmm, r-cran-ape, r-cran-arm, r-cran-statmod, r-cran-digest, r-cran-diffobj, r-cran-betareg, r-cran-r.rsp, r-cran-gtable, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-ragg, r-cran-colorspace, r-cran-mirai, r-cran-future.mirai Filename: pool/dists/resolute/main/r-cran-brms_2.23.0-1.ca2604.1_all.deb Size: 7128338 MD5sum: 8c46c4cbde959a205c0c6cd02d3cce09 SHA1: 07ed8699ce9eb16316300d55de496596b8f2acf6 SHA256: 3a24e859369163c1ad7014ab5f8d34019df24b0f54b8d35d929c75ac4cf42f26 SHA512: b8aa98f65467cc2f217ddfc22883112abb53e9e1b35a60b8ea7890a10aff0a3d63e85389c07cf17972bd0a84c62a6491c6ddb040b797ea72fe285fa121650ce0 Homepage: https://cran.r-project.org/package=brms Description: CRAN Package 'brms' (Bayesian Regression Models using 'Stan') Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) ; Bürkner (2018) ; Bürkner (2021) ; Carpenter et al. (2017) . Package: r-cran-brobdingnag Architecture: all Version: 1.2-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cubature, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brobdingnag_1.2-9-1.ca2604.1_all.deb Size: 951006 MD5sum: 2e74e0357741779929987c29b93ff038 SHA1: 3ca1bc3c11709ca8fbdcc70034e4d109f69affd0 SHA256: af440cca9b3c7e5ea15c295eadb5374f3d7ed2d9d7da2c7e77849e0b8a8f5f6e SHA512: a516cb65895d3b4e6da53a8b35591416c1d41b6000be0c37d57edadb772f2770a7c2b7fdd596eeaae6a1d3f7d7a664102481342ffc99e4609693e0a3b5e81d19 Homepage: https://cran.r-project.org/package=Brobdingnag Description: CRAN Package 'Brobdingnag' (Very Large Numbers in R) Very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. Functionality for complex numbers is also provided. The package includes a vignette that gives a step-by-step introduction to using S4 methods. Package: r-cran-brokenadaptiveridge Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cyclops, r-cran-parallellogger, r-cran-bit64 Suggests: r-cran-testthat, r-cran-survival, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-brokenadaptiveridge_1.0.2-1.ca2604.1_all.deb Size: 28152 MD5sum: d1345c2bad0960d4bc6bfbdfb262a29d SHA1: 88cb9e53bc3d8a464cdb3eedd506896de9031603 SHA256: 3c2a793e00c1d6ea6ea769f393d0536456f9705631cdabc2877c5ec23b5fa613 SHA512: 60f09662a09ddddfe73415815eaa69cf3a1fc1a52e9bfd406b3854931304c12164b298cbdf9f2c889a2313d88604150eff2f760388074e3ad8b453edbfc8051c Homepage: https://cran.r-project.org/package=BrokenAdaptiveRidge Description: CRAN Package 'BrokenAdaptiveRidge' (Broken Adaptive Ridge Regression with Cyclops) Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) and Li et al (2021) . Package: r-cran-brokenstick Architecture: all Version: 2.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-lme4, r-cran-matrixsampling, r-cran-rlang, r-cran-tidyr Suggests: r-cran-agd, r-cran-bookdown, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-mice, r-cran-mvtnorm, r-cran-plyr, r-cran-svglite, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-brokenstick_2.7.0-1.ca2604.1_all.deb Size: 1211876 MD5sum: 8d6f73caf8180777bbe168a4e2c07501 SHA1: 1c9ceacee84fa4db5a261034ef49b41eeee86a95 SHA256: b0a3a993658639621384696763f22350b830edb1518569111e179ff42b820f11 SHA512: 61a661e5e3286720366db85624fe59d9bf72d262d4ec947a45318c4b9653b9a1570b7b88d7c8a8951fde9009abd52bbfc3eef6a0f8cffe2197a784c893478182 Homepage: https://cran.r-project.org/package=brokenstick Description: CRAN Package 'brokenstick' (Broken Stick Model for Irregular Longitudinal Data) Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See for additional documentation on background, methodology and applications. Package: r-cran-brolgar Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4988 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fabletools, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-vctrs Suggests: r-cran-gapminder, r-cran-gghighlight, r-cran-knitr, r-cran-matrix, r-cran-lme4, r-cran-modelr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tsibbledata, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-brolgar_1.0.2-1.ca2604.1_all.deb Size: 3702318 MD5sum: f34c4b585cb087c5d4669f753f815b79 SHA1: 47a056f063fe3e984dc80323908811e571b58a92 SHA256: de3818cf8405b33fd86079483142d1204502b0c843595a5857d8395af95cf336 SHA512: 9cb169d4bc3ccd0ec8bfa95bd563c7d8227b11b3f728a2278965a443228d7e824d69872c5f1e5665623de86099fd7130bf063832bb7b04062642cd8298a1be32 Homepage: https://cran.r-project.org/package=brolgar Description: CRAN Package 'brolgar' (Browse Over Longitudinal Data Graphically and Analytically in R) Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) . Package: r-cran-broom.helpers Architecture: all Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cards, r-cran-cli, r-cran-dplyr, r-cran-labelled, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-betareg, r-cran-biglm, r-cran-brms, r-cran-broom.mixed, r-cran-cmprsk, r-cran-covr, r-cran-effects, r-cran-emmeans, r-cran-fixest, r-cran-forcats, r-cran-gam, r-cran-gee, r-cran-geepack, r-cran-ggplot2, r-cran-ggeffects, r-cran-ggstats, r-cran-glmmtmb, r-cran-glmtoolbox, r-cran-glue, r-cran-gt, r-cran-gtsummary, r-cran-knitr, r-cran-lavaan, r-cran-lfe, r-cran-lme4, r-cran-logitr, r-cran-marginaleffects, r-cran-margins, r-cran-mass, r-cran-mgcv, r-cran-mice, r-cran-mmrm, r-cran-multgee, r-cran-nnet, r-cran-ordinal, r-cran-parameters, r-cran-parsnip, r-cran-patchwork, r-cran-plm, r-cran-pscl, r-cran-quantreg, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-tidycmprsk, r-cran-vgam, r-cran-svyvgam Filename: pool/dists/resolute/main/r-cran-broom.helpers_1.22.0-1.ca2604.1_all.deb Size: 558434 MD5sum: 3d6a68e7ad9c69df2d1528d156337ea1 SHA1: 2993fed65aa139efb59689f7eb781540ad622de1 SHA256: df0444d42d06b9b5c07cc0605e4ec5d8fe9745f18d21d56ebb3b7c0356d10a34 SHA512: a9700bae3afb7b31be30a035ebbbafc40b13f4afece133efe7391f4a749d09b1ef7a8311c2b826cc55be21c9c785254279c9cc384528e03c910b769dff819ff4 Homepage: https://cran.r-project.org/package=broom.helpers Description: CRAN Package 'broom.helpers' (Helpers for Model Coefficients Tibbles) Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more. Package: r-cran-broom.mixed Architecture: all Version: 0.2.9.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-coda, r-cran-dplyr, r-cran-forcats, r-cran-nlme, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-furrr Suggests: r-cran-brms, r-cran-dotwhisker, r-cran-knitr, r-cran-testthat, r-cran-gamlss, r-cran-gamlss.data, r-cran-ggplot2, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-lmertest, r-cran-lme4, r-cran-matrix, r-cran-mcmcglmm, r-cran-mediation, r-cran-mgcv, r-cran-mice, r-cran-ordinal, r-cran-pander, r-cran-pbkrtest, r-cran-posterior, r-cran-rstan, r-cran-rstanarm, r-cran-rstantools, r-cran-r2jags, r-cran-tmb, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-broom.mixed_0.2.9.7-1.ca2604.1_all.deb Size: 5322358 MD5sum: 0622ec975c53079b74868e25f3d10030 SHA1: 037be056c053b92ade0d0dec0586442ff672ed9d SHA256: 5a664bd74a45152bd2864cf12ec36bd1896eb7839e98208e9a56ab1b92670b98 SHA512: 896882daaf72598430f7f438ccf006e4fa206af3c9bd9d84beee508347d0193c9c2b447245d22544395cd78c4058f4fc92d5000b44f736d263530f039b05c3d6 Homepage: https://cran.r-project.org/package=broom.mixed Description: CRAN Package 'broom.mixed' (Tidying Methods for Mixed Models) Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics. Package: r-cran-broom Architecture: all Version: 1.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1856 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-backports, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-aer, r-cran-auc, r-cran-bbmle, r-cran-betareg, r-cran-biglm, r-cran-bingroup, r-cran-boot, r-cran-btergm, r-cran-car, r-cran-cardata, r-cran-caret, r-cran-cluster, r-cran-cmprsk, r-cran-coda, r-cran-covr, r-cran-drc, r-cran-e1071, r-cran-emmeans, r-cran-epir, r-cran-ergm, r-cran-fixest, r-cran-gam, r-cran-gee, r-cran-geepack, r-cran-ggplot2, r-cran-glmnet, r-cran-glmnetutils, r-cran-gmm, r-cran-hmisc, r-cran-interp, r-cran-irlba, r-cran-joinerml, r-cran-kendall, r-cran-knitr, r-cran-ks, r-cran-lahman, r-cran-lavaan, r-cran-leaps, r-cran-lfe, r-cran-lm.beta, r-cran-lme4, r-cran-lmodel2, r-cran-lmtest, r-cran-lsmeans, r-cran-maps, r-cran-margins, r-cran-mass, r-cran-mclust, r-cran-mediation, r-cran-metafor, r-cran-mfx, r-cran-mgcv, r-cran-mlogit, r-cran-modeldata, r-cran-modeltests, r-cran-muhaz, r-cran-multcomp, r-cran-network, r-cran-nnet, r-cran-ordinal, r-cran-plm, r-cran-polca, r-cran-psych, r-cran-quantreg, r-cran-rmarkdown, r-cran-robust, r-cran-robustbase, r-cran-rsample, r-cran-sandwich, r-cran-spatialreg, r-cran-spdep, r-cran-speedglm, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-systemfit, r-cran-testthat, r-cran-tseries, r-cran-vars, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-broom_1.0.13-1.ca2604.1_all.deb Size: 1537166 MD5sum: 6b3d084997293e3a71a4b3cb9de90ab2 SHA1: 621f2d1e378851875a5d4686fd44667ebfdc125a SHA256: bed5a7d543fb17c5b43a7e39d5d169a73465b7893377a9aaf8cbb13e71f65be3 SHA512: fc056a1cebfc1a5c27c9cfcacb276f23a574a3df5d95be6187d9508c37d2a8cf3587a7f954e5be25e4e574acee35f655753c4320afcded722f0e33a4f03c62af Homepage: https://cran.r-project.org/package=broom Description: CRAN Package 'broom' (Convert Statistical Objects into Tidy Tibbles) Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures. Package: r-cran-browndog Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcurl, r-cran-jsonlite, r-cran-httpuv Filename: pool/dists/resolute/main/r-cran-browndog_0.2.1-1.ca2604.1_all.deb Size: 30648 MD5sum: de7f864bd525272de310724c1d9c6816 SHA1: ed9a64a1cc64c3cccd33718a6f92af895d959351 SHA256: 05bbfd43a7fd54700bf8d506d67517523595c4a77ce5fa93cd781ae7cb5479af SHA512: b881ce8d9ab1ac4298d66f1caa4cdb4b43ae12d7fb314bdfa27776b8ed73cadb28e30814ced3ec1abd301a7842266c8dfdcf8f76ac93a34345aa56e570353cd4 Homepage: https://cran.r-project.org/package=BrownDog Description: CRAN Package 'BrownDog' (Brown Dog R Interface) An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services that provides modules to identify the conversion options for a file, to convert file to appropriate format, or to extract data from a file. See for more information. Package: r-cran-brpl Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-brpl_1.0.2-1.ca2604.1_all.deb Size: 1129796 MD5sum: 8aeddd312d2372647d5e0c145181df34 SHA1: cf09891fda90841ef05920dbc3af0c3bd0a35dce SHA256: 9546dbeabbc6a5b828bf222cb3770d25ff21e08c80e8f5b0094b5dc0826e8818 SHA512: 600a91d37c9987e62f8e493ef65c418a3dbb29c0ba86a279ed882451a877b2e6f010bfd2b461c36ad5a402162ae726bdfcb600de8df29bc7fc26e19864bdfb6b Homepage: https://cran.r-project.org/package=BRPL Description: CRAN Package 'BRPL' (Methods for Bivariate Poverty Line Calculations) Provides tools for identifying subgroups within populations based on individual response patterns to specific interventions or treatments. Designed to support researchers and clinicians in exploring heterogeneous treatment effects and developing personalized therapeutic strategies. Offers functionality for analyzing and visualizing the interplay between two variables, thereby enhancing the interpretation of social sustainability metrics. The package focuses on bivariate discriminant analysis and aims to clarify relationships between indicator variables. Package: r-cran-brq Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-brq_3.0-1.ca2604.1_all.deb Size: 160860 MD5sum: 57c8789fa84def345de4ec83d2406760 SHA1: 4ecff9fa203adf2a69abce27ec4fa928d4f54e75 SHA256: 7441aa69e49b2da98b6e3171392731279abb61b9832595b7a837af93a63b575e SHA512: 1af4caff16830f9ca6bd0c413d2d9b96c7ddb20c310b79a866a9fcb7e580acbf55005fb251ef280f8aa5840a42a739be7121585b9332cc38d16d95cc631491d7 Homepage: https://cran.r-project.org/package=Brq Description: CRAN Package 'Brq' (Bayesian Analysis of Quantile Regression Models) Bayesian estimation and variable selection for quantile regression models. Package: r-cran-brsim Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-corrplot, r-cran-rcmdrmisc Filename: pool/dists/resolute/main/r-cran-brsim_0.3-1.ca2604.1_all.deb Size: 28164 MD5sum: 2853c9eb1f572a2d25cf6302b1de9bcc SHA1: 315ab831a2f01d2727dfa258bca41a57ab577957 SHA256: 67dc3210676a7dc8f29cc55b68517025646b771abd8d8fb03467f37ef01d0f00 SHA512: dc49b5db22a86cd391ebe1d91d1e08cd42bc59d1668a2c58b3806f975d5a8db71c271fb7de6bfd2d88ceb20a9fa99ef24a215fed10cd5b722bacc01fadabc435 Homepage: https://cran.r-project.org/package=brsim Description: CRAN Package 'brsim' (Brainerd-Robinson Similarity Coefficient Matrix) Provides the facility to calculate the Brainerd-Robinson similarity coefficient for the rows of an input table, and to calculate the significance of each coefficient based on a permutation approach; a heatmap is produced to visually represent the similarity matrix. Optionally, hierarchical agglomerative clustering can be performed and the silhouette method is used to identify an optimal number of clusters; the results of the clustering can be optionally used to sort the heatmap. Package: r-cran-brucer Architecture: all Version: 2026.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstudioapi, r-cran-data.table, r-cran-rio, r-cran-crayon, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-ggplot2, r-cran-psych, r-cran-afex, r-cran-emmeans, r-cran-effectsize, r-cran-mediation, r-cran-interactions, r-cran-lavaan, r-cran-jtools, r-cran-texreg Suggests: r-cran-pacman, r-cran-glue, r-cran-tibble, r-cran-forcats, r-cran-haven, r-cran-foreign, r-cran-readxl, r-cran-openxlsx, r-cran-clipr, r-cran-cowplot, r-cran-ggtext, r-cran-see, r-cran-car, r-cran-lmtest, r-cran-lme4, r-cran-lmertest, r-cran-nnet, r-cran-vars, r-cran-phia, r-cran-performance, r-cran-mass, r-cran-mumin, r-cran-bayesfactor, r-cran-ggally, r-cran-gparotation Filename: pool/dists/resolute/main/r-cran-brucer_2026.1-1.ca2604.1_all.deb Size: 543874 MD5sum: 03f67ed3589807c2a9eabb5b25f8c5d8 SHA1: 5a718c8b822f360a39180110062db116c3ed781f SHA256: 01f5d557549d1c4c8476ee43c30f3def1ad8b91fe36f2ffb21bda0327636a654 SHA512: 8ef9e99f55f1d1303b0d4e8671787f363814867c24fd9d8749daa09a4be602990548952905c1a221063d00a3fd90c8d12543929a84de1baacdbd21bd882a63f1 Homepage: https://cran.r-project.org/package=bruceR Description: CRAN Package 'bruceR' (Broadly Useful Convenient and Efficient R Functions) Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics. Package: r-cran-brulee Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-coro, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-glue, r-cran-hardhat, r-cran-rlang, r-cran-tibble, r-cran-torch Suggests: r-cran-covr, r-cran-modeldata, r-cran-purrr, r-cran-recipes, r-cran-spelling, r-cran-testthat, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-brulee_0.6.0-1.ca2604.1_all.deb Size: 298710 MD5sum: 4eac0f64218a9320c000f015dbf1540f SHA1: 25a5019f7b948d18ed4a9d0fdf8d1cd0aa20bdf3 SHA256: e108f64f57780e832ff3cb0488e1067a0662dd434771c5f94fc2f728ecd1398d SHA512: 49d5215a9ae8b737a3fa7e5a9739edeb7ed2276ed69103f258365a72eea362e83c2119ad0a53ca2b50aa09af0f4653d45206fa6337fea3393559f23a6a501ade Homepage: https://cran.r-project.org/package=brulee Description: CRAN Package 'brulee' (High-Level Modeling Functions with 'torch') Provides high-level modeling functions to define and train models using the 'torch' R package. 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Package: r-cran-brxx Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-gparotation, r-cran-teachingdemos, r-cran-blavaan, r-cran-blme, r-cran-mass, r-cran-rstan Filename: pool/dists/resolute/main/r-cran-brxx_0.1.2-1.ca2604.1_all.deb Size: 81746 MD5sum: 255737f55a8ecb217460ff41a224fd44 SHA1: 160233080d0978dadd78193cc3f8b284e581fd65 SHA256: 5bd54040f7ef7df6682c8aab758c33062cbabfd471bedd1cd156eb28cd30a3e9 SHA512: 978a0f904d77fab8d1e9fcf3fe6f262392bcf0654cabf87cebef96e53a39fe435366e67ec3a71e747622a78704d1af594b99988cc0ad358792eb920a3d72a54b Homepage: https://cran.r-project.org/package=brxx Description: CRAN Package 'brxx' (Bayesian Test Reliability Estimation) When samples contain missing data, are small, or are suspected of bias, estimation of scale reliability may not be trustworthy. A recommended solution for this common problem has been Bayesian model estimation. Bayesian methods rely on user specified information from historical data or researcher intuition to more accurately estimate the parameters. This package provides a user friendly interface for estimating test reliability. Here, reliability is modeled as a beta distributed random variable with shape parameters alpha=true score variance and beta=error variance (Tanzer & Harlow, 2020) . 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Package: r-cran-bsagri Architecture: all Version: 0.1-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-multcomp, r-cran-mcpan, r-cran-mvtnorm, r-cran-boot, r-cran-mratios Filename: pool/dists/resolute/main/r-cran-bsagri_0.1-10-1.ca2604.1_all.deb Size: 190514 MD5sum: 6fd981d9938544f4d615bdc701c7be9f SHA1: 7465ee955cbf4035c506c728c7f62d1ad28b5521 SHA256: d59cfda5daaae8a47cdbc6443b1b24b4cac59444fa5dd824e22f0c513370d2e9 SHA512: c6fe73ee0525ca68df459570efe74aa30c933733882eadea489bcbe4dd278ba58daf620323d8b02cdd56d81915d9b1030a155b45191e88dc974a1b01fdcd5360 Homepage: https://cran.r-project.org/package=BSagri Description: CRAN Package 'BSagri' (Safety Assessment in Agricultural Field Trials) Collection of functions, data sets and code examples for evaluations of field trials with the objective of equivalence assessment. Package: r-cran-bsam Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-msm, r-cran-mvtnorm, r-cran-rworldxtra, r-cran-sp, r-cran-tibble, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-bsam_1.1.3-1.ca2604.1_all.deb Size: 118298 MD5sum: 3faf3abd6f6cc2d5ffaff2831b42dbb4 SHA1: 0942994ab7f564ed51edca6f2f38da3154f7eb51 SHA256: cc40a7fe5a484ff635a8e7bb2742a53ac26cbd1aa3252243f7159f39188dd1d7 SHA512: c36196972495aecb0c5085179bab61adbd198d0f3337a6a421634cdc140d4ee6839ba72bf0012f82e40b695255007fae6df0a839688656a27416d9e7c4bf9141 Homepage: https://cran.r-project.org/package=bsam Description: CRAN Package 'bsam' (Bayesian State-Space Models for Animal Movement) Tools to fit Bayesian state-space models to animal tracking data. 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These figures can be seamlessly integrated into 'rmarkdown' and 'Quarto' documents, as well as 'shiny' applications, allowing manipulation of elements and reporting actions performed on them. Additional features include pan, zoom in/out functionality, and the ability to export the figures in SVG or PNG formats. Package: r-cran-bsda Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-e1071 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-bsda_1.2.2-1.ca2604.1_all.deb Size: 869170 MD5sum: e0972a216f9a4901dbb3ca0ddeaaf69e SHA1: 113dc533658597f65300e75bde0154c563b275f7 SHA256: 7b64c3093d11d6f71abf87aceff3d0226a86a6189b1074a755a8124a46366721 SHA512: 5988b2364d8ded7b4992e9b4bd9229ed9d008cc407964c79f4092bc645cb6aa782962a7465f70110a786c7d455a904e8ff20e193d149df7d9e69deebba05d643 Homepage: https://cran.r-project.org/package=BSDA Description: CRAN Package 'BSDA' (Basic Statistics and Data Analysis) Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens. 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Provides hypothesis testing tools to evaluate whether a surrogate can reliably estimate the causal effect of a treatment on a primary outcome. Implements the imputation-based Bayesian methodology of Carlotti and Parast (2026) , extending the frequentist rank-based approach of Parast et al. (2024) . Addresses key limitations of the frequentist method, including the lack of causal interpretability and the inability to adjust for covariates in the estimation process. Package: r-cran-bsgof Architecture: all Version: 0.23.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2398 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bsgof_0.23.8-1.ca2604.1_all.deb Size: 2281112 MD5sum: 610140574cb9dfb19d6c48da7ae97330 SHA1: 19cb09653b29cac54c16e00a81aacea138c38088 SHA256: 33578fdca3122095bca184b85c89443497402c33924d8a360237bec2a78161c0 SHA512: 6a5d2a05e2ccf8c601fbe8f39321e3599d2088c1faee31c8a5ce1f1704fd4236f36e7cb86f36eea385b669fdf9f886058e5215fd843f4044902d00748f291ffe Homepage: https://cran.r-project.org/package=bsgof Description: CRAN Package 'bsgof' (Birnbaum-Saunders Goodness-of-Fit Test) Performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) . This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528). Package: r-cran-bsgw Architecture: all Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-survival, r-cran-mfusampler Filename: pool/dists/resolute/main/r-cran-bsgw_0.9.4-1.ca2604.1_all.deb Size: 95464 MD5sum: b4d05f9473266ab83c37e41ec5ea85c6 SHA1: ed7ee7fc65b0b8c2a31b3c2465f969dd99c7a637 SHA256: 3a2a9588f9f54201ca0b7e70db6163d6e0e56c5783e89046a0b6d0fcddcc4839 SHA512: e90c87b82d411630c470b6c0d1bc69fc6e635be76c3ef00dc104479a485fb722106a4494af93a6214d37c00782877d11940d4e88dc4b57fcf92ab86192b7b521 Homepage: https://cran.r-project.org/package=BSGW Description: CRAN Package 'BSGW' (Bayesian Survival Model with Lasso Shrinkage Using GeneralizedWeibull Regression) Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation. Package: r-cran-bshazard Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-epi Filename: pool/dists/resolute/main/r-cran-bshazard_1.2-1.ca2604.1_all.deb Size: 51434 MD5sum: b7a9062c03952474f685443950b428f8 SHA1: 4d6a917d7ed276d87789a60e4b32d96686593957 SHA256: fe3248abdcd25a667a254058b6ce8e9dd9b17b0b0871132b4c95b7e679eb2326 SHA512: 0ee3acb3558d02af0f6b073c1f467a1d00ce6db3595b77bd38a5f8eeff7c535f5db70bf13cea20485dca0ab1b21c6df386338119e24ed6477827eebc47af60e6 Homepage: https://cran.r-project.org/package=bshazard Description: CRAN Package 'bshazard' (Nonparametric Smoothing of the Hazard Function) The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed. The package is based on the work in Rebora P (2014) . Package: r-cran-bsi Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bsi_1.0.0-1.ca2604.1_all.deb Size: 45652 MD5sum: fe85040b6c500a0e51c8ad782da5963e SHA1: 94c1637b5a4133e9204a1f45ea4ef0690fcb1bf6 SHA256: 93a5c8f335287eb02b3382fcf7c857a949766b8e6e4ef36b1f5a5e6f25211cf3 SHA512: 3666c85719af5ebdfc14ec2316e82d8a6cf4f708cbc00ebd60a758fd9f8af58e541638a0fd03cbd532e1a93bc4f7e440c8dc42b9f213a35080a43f2db0e33045 Homepage: https://cran.r-project.org/package=bSi Description: CRAN Package 'bSi' (Modeling and Computing Biogenic Silica ('bSi') from Inland andPelagic Sediments) A collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica 'bSi' in inland and marine sediments. These tools share common data representations and follow a consistent 'API' design. The primary goal of the 'bSi' package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire 'bSi' analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems. Package: r-cran-bsicons Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-htmltools, r-cran-rlang Suggests: r-cran-bslib, r-cran-processx, r-cran-testthat, r-cran-webshot2, r-cran-withr Filename: pool/dists/resolute/main/r-cran-bsicons_0.1.2-1.ca2604.1_all.deb Size: 255284 MD5sum: 2d6ccc1cb372270c0bc3e56f0f5acb20 SHA1: 4bb1682ed7f974fea673de528d66286975420f8b SHA256: bfe8f4da47cfee5b2934617d5be2ce31789b75cddd7db3824f0bc0dd3ce01e06 SHA512: 6f54cd6e04f066ef9e23d4ec105cf82e630c7b381110c2aba89a96a79e4642183808a61c51727dd0cb511820f54a0ce4f3ed472e4e91dec740988a91741dae0a Homepage: https://cran.r-project.org/package=bsicons Description: CRAN Package 'bsicons' (Easily Work with 'Bootstrap' Icons) Easily use 'Bootstrap' icons inside 'Shiny' apps and 'R Markdown' documents. More generally, icons can be inserted in any 'htmltools' document through inline 'SVG'. Package: r-cran-bsims Architecture: all Version: 0.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2734 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-intrval, r-cran-mefa4, r-cran-mass, r-cran-deldir, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-detect, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-bsims_0.3-3-1.ca2604.1_all.deb Size: 1875004 MD5sum: 7a4be9cbd275fe0c668c42b211bbb824 SHA1: d14b485bceba50fa217217dc2b2b7faf76d71094 SHA256: 4cf16bb34ef33497778a82f89b81b78091f6b6793ceb8dabc4f1750eaafe94b2 SHA512: 13ab133f2513e0823cdebb568acc2ed118b88da2a3de3205abfb2ae1800853c0ddfae208f8c8c4d88cba876d4802f3a7a7cbe24be013912231a2d8f07d5a4e42 Homepage: https://cran.r-project.org/package=bSims Description: CRAN Package 'bSims' (Agent-Based Bird Point Count Simulator) A highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 ). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) , Solymos et al. (2013) , and Solymos et al. (2018) , and sound attenuation experiments by Yip et al. (2017) . Package: r-cran-bsitar Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-rstan, r-cran-loo, r-cran-dplyr, r-cran-rlang, r-cran-rdpack, r-cran-insight, r-cran-data.table, r-cran-collapse, r-cran-marginaleffects, r-cran-sitar, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-bayesplot, r-cran-posterior, r-cran-testthat, r-cran-ggridges, r-cran-jtools, r-cran-splines2, r-cran-scales, r-cran-kableextra, r-cran-knitr, r-cran-future, r-cran-future.callr, r-cran-future.apply, r-cran-future.mirai, r-cran-mirai, r-cran-doparallel, r-cran-foreach, r-cran-dofuture, r-cran-fastplyr, r-cran-cheapr, r-cran-dtplyr, r-cran-checkmate, r-cran-installr, r-cran-bayestestr, r-cran-r.utils, r-cran-mass, r-cran-matrix, r-cran-tidyr, r-cran-nlme, r-cran-purrr, r-cran-forcats, r-cran-patchwork, r-cran-tibble, r-cran-pracma, r-cran-extradistr, r-cran-bookdown, r-cran-rmarkdown, r-cran-spelling, r-cran-hmisc, r-cran-growthcleanr, r-cran-boot, r-cran-r.rsp, r-cran-abind, r-cran-glue Filename: pool/dists/resolute/main/r-cran-bsitar_0.3.3-1.ca2604.1_all.deb Size: 7896272 MD5sum: 6d286e9b284ee4eb18eb74b6f0a24236 SHA1: 25269d01ce0d591ab045f16cb871b62e8a4ea948 SHA256: f598e9d520e04312a1a9a251a665e6f1b5e80e4bd70888a5c180f76817f20c2a SHA512: e5fb42ddf484dbb5a83acb1a115a6b145b01faff734963f711cbe0e5311bc12a93e9ecffd6bc93a856d97d314b593b2d37ba5ff54f93f240819be67022dfefb5 Homepage: https://cran.r-project.org/package=bsitar Description: CRAN Package 'bsitar' (Bayesian Super Imposition by Translation and Rotation GrowthCurve Analysis) The Super Imposition by Translation and Rotation (SITAR) model is a shape-invariant nonlinear mixed effect model that fits a natural cubic spline mean curve to the growth data and aligns individual-specific growth curves to the underlying mean curve via a set of random effects (see Cole, 2010 for details). The non-Bayesian version of the SITAR model can be fit by using the already available R package 'sitar'. Unlike the 'sitar' package which allows modelling of a single outcome only, the 'bsitar' package offers great flexibility in fitting models of varying complexities, including joint modelling of multiple outcomes such as height and weight (multivariate model). Additionally, the 'bsitar' package allows for the simultaneous analysis of an outcome separately for subgroups defined by a factor variable such as gender. This is achieved by fitting separate models for each subgroup (for example males and females for gender variable). An advantage of this approach is that posterior draws for each subgroup are part of a single model object, making it possible to compare coefficients across subgroups and test hypotheses. Since the 'bsitar' package is a front-end to the R package 'brms', it offers excellent support for post-processing of posterior draws via various functions that are directly available from the 'brms' package. In addition, the 'bsitar' package includes various customized functions that allow for the visualization of distance (increase in size with age) and velocity (change in growth rate as a function of age), as well as the estimation of growth spurt parameters such as age at peak growth velocity and peak growth velocity. Package: r-cran-bskyr Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-lubridate, r-cran-magick, r-cran-mime, r-cran-opengraph, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-emoji, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vcr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-bskyr_0.4.0-1.ca2604.1_all.deb Size: 442292 MD5sum: b391b08cf1bcd973660b5fb1d3dd5797 SHA1: 14d7a915f8c887b4e77c7ba3f86967c22b7248d5 SHA256: bfc82cfd0485b0e37ac9d4c5b51199de66fbdfb7985c5b8381844274bd9ccd7b SHA512: d468ea50b8bccd6e32d67fc9717368cc6896a4b357ccddaba8c53129a2312e857820139fc5dc6a642d4fd39c590992f64b3df567ba9fd6d9f6bbffc667d82a7e Homepage: https://cran.r-project.org/package=bskyr Description: CRAN Package 'bskyr' (Interact with 'Bluesky' Social) Collect data from and make posts on 'Bluesky' Social via the Hypertext Transfer Protocol (HTTP) Application Programming Interface (API), as documented at . 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Package: r-cran-bspadata Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-spdep, r-cran-pscl, r-cran-pbapply, r-cran-coda Filename: pool/dists/resolute/main/r-cran-bspadata_1.1.0-1.ca2604.1_all.deb Size: 173098 MD5sum: ab395fa30957ec3451eccf7b22e0141b SHA1: 7ebbb226656bf9a127ddfa4c7ac2ecab6477775f SHA256: e0ab4085eaf48a4f79ba115b95c31ba7172086e4f49c4e44e9b24b47f2c2f8f7 SHA512: c9a80c09fd4d74204569b903942aae3284180a3ab229293be6539a2160dda8908ee86be0fc88b0a2a9ba8073291c2b5dced78febe9e4654504b3d44dcec553c9 Homepage: https://cran.r-project.org/package=BSPADATA Description: CRAN Package 'BSPADATA' (Bayesian Proposal to Fit Spatial Econometric Models) The purpose of this package is to fit the three Spatial Econometric Models proposed in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. 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Package: r-cran-bspcov Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gigrvg, r-cran-coda, r-cran-progress, r-cran-bayesfactor, r-cran-mass, r-cran-mvnfast, r-cran-matrixcalc, r-cran-matrixstats, r-cran-purrr, r-cran-dplyr, r-cran-rspectra, r-cran-matrix, r-cran-plyr, r-cran-cholwishart, r-cran-magrittr, r-cran-future, r-cran-furrr, r-cran-ks, r-cran-ggplot2, r-cran-ggmcmc, r-cran-caret, r-cran-fincovregularization, r-cran-mvtnorm, r-cran-patchwork, r-cran-reshape2, r-cran-future.apply Suggests: r-cran-hdbinseg, r-cran-poet, r-cran-tidyquant, r-cran-tidyr, r-cran-timetk, r-cran-quantmod Filename: pool/dists/resolute/main/r-cran-bspcov_1.0.3-1.ca2604.1_all.deb Size: 4212562 MD5sum: fb9e269b6a6532903ae53c27b3a83928 SHA1: 9c29f95428d8a62bb5468dab5acef23aa734d543 SHA256: f180e7690eef9e5d9be7b7fa2b6c0bdc992213ee6cd9fc94a92ea9e937659df7 SHA512: de7e5deafef4e26bf4d0133a0154c876811408819bc1cb7f90b921e666aaec9c929240e504a3daf17de210b355cfdfc96a67fbad1370a008ca37245b81abe9aa Homepage: https://cran.r-project.org/package=bspcov Description: CRAN Package 'bspcov' (Bayesian Sparse Estimation of a Covariance Matrix) Bayesian estimations of a covariance matrix for multivariate normal data. 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The following generalizations of this principle can be used in this package: (1) a vector of multiple budgets can be assigned alongside corresponding thresholds that model degrees of IV invalidity; (2) budgets and thresholds can be chosen using specialist knowledge or varied in a principled sensitivity analysis; (3) treatment effects can be nonlinear and/or depend on multiple exposures (at a computational cost). The methods in this package require only summary statistics. Confidence sets are constructed under the "no measurement error" (NOME) assumption from the Mendelian randomization literature. For further methodological details, please refer to Penn et al. (2024) . 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Package: r-cran-bullishtrader Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bullishtrader_1.0.1-1.ca2604.1_all.deb Size: 124672 MD5sum: be56acdc7a59498f95eb70c49138d874 SHA1: 51de4d6368600df6e027aa7cb50357a7f9227966 SHA256: f94cf24eb05fc829ac9264d8718df6c24c9e0b27a777d8f730bdc8c4d33ec4eb SHA512: a43a24b216daef6a7d555ed04caabba2ba5a66bb4b3c768eb0da69c71cf5da8785cd36e538e0fdab50a92c00e8552bfcb24332ab02606df0cbd3fcc936765259 Homepage: https://cran.r-project.org/package=bullishTrader Description: CRAN Package 'bullishTrader' (Bullish Trading Strategies Through Graphs) Stock, Options and Futures Trading Strategies for Traders and Investors with Bullish Outlook are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). Package: r-cran-bullseye Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3730 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-rlang, r-cran-ggplot2, r-cran-labeling, r-cran-ggiraph, r-cran-dendser, r-cran-tidyr, r-cran-polycor, r-cran-colorspace Suggests: r-cran-desctools, r-cran-acepack, r-cran-energy, r-cran-linkspotter, r-cran-minerva, r-cran-scagnostics, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-correlation, r-cran-palmerpenguins, r-cran-kableextra, r-cran-openintro, r-cran-corrplot Filename: pool/dists/resolute/main/r-cran-bullseye_1.0.1-1.ca2604.1_all.deb Size: 1061092 MD5sum: 9c20ee7b706224f3ca77b17671521ab5 SHA1: f049e30ad893ca8a668c54f6e74071586ef33ce2 SHA256: a37645baf342d0d618c0f95ff0742066cb9c86a38abb884c9a477831edbdd835 SHA512: 5409bab0aad63bc0e37ce7edb67da765c8b7365e6258475684fc2ce25b25380212a3a733622d0bdc5dbf88671ca69b0c6bc93a5ad6347156e5cd6e4dc7840271 Homepage: https://cran.r-project.org/package=bullseye Description: CRAN Package 'bullseye' (Visualising Multiple Pairwise Variable Correlations and OtherScores) We provide a tidy data structure and visualisations for multiple or grouped variable correlations, general association measures scagnostics and other pairwise scores suitable for numerical, ordinal and nominal variables. 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Package: r-cran-bullwhipgame Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-bullwhipgame_0.1.0-1.ca2604.1_all.deb Size: 74308 MD5sum: 7a2cde1e5197e7cfaa6b40a1f98fc19c SHA1: 9670ac20195726d9ffed2e7842ca5eb939c8bb77 SHA256: 70c93dadaa6a9380276cde0a9b95cea609efbf81c292b5a5e571ec943a368352 SHA512: 78ad5c53b83b11c7f3620832dd9e730b1b7b03a5b2c627199080229b8b861dca9d72d7cbcbfaba78c728711ecc61b1050be2f3c33fdac168435e588c29c8af00 Homepage: https://cran.r-project.org/package=bullwhipgame Description: CRAN Package 'bullwhipgame' (Bullwhip Effect Demo in Shiny) The bullwhipgame is an educational game that has as purpose the illustration and exploration of the bullwhip effect,i.e, the increase in demand variability along the supply chain. Marchena Marlene (2010) . 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Package: r-cran-bunching Architecture: all Version: 0.8.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 630 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bunching_0.8.6-1.ca2604.1_all.deb Size: 519230 MD5sum: e9bf64c8eed61b8b092ab7459799ff89 SHA1: d747ce1986fae31578430b87f85ff57afd2bd4f6 SHA256: cba641820cc24e375f5919e55982ec79f8f581cbbf1ce82ea04be7b1b3b1afbf SHA512: 46dda9ca7fc551e02d7e86c3f6577389d9f5a65af09fa3c69b21b0bf28142b09c6093d44c463a0b6a801c8a3f64bbd93e45d1134fa3aea532b7608ba978e3127 Homepage: https://cran.r-project.org/package=bunching Description: CRAN Package 'bunching' (Estimate Bunching) Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) , with lots of functionality to set plot options. Package: r-cran-bunchr Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-bunchr_1.2.1-1.ca2604.1_all.deb Size: 392374 MD5sum: 8800d39b69b66cf5080b38472a55faa1 SHA1: b5969186def628abb7aedad707420652b462de6a SHA256: 5c4a9862361bc90618d531e0e6be34ce1de7173e93f92d0e8115a4840bd6d1e9 SHA512: 65c5e3dfa82610694c5cf1d3b871a6ddc931dfc5a14d9bbc2a124154f0b10e4d09b33fec6b58e7ba947f17b08cb821ccaca8c5cb33cd6aae3f318c8caca95003 Homepage: https://cran.r-project.org/package=bunchr Description: CRAN Package 'bunchr' (Analyze Bunching in a Kink or Notch Setting) View and analyze data where bunching is expected. Estimate counter- factual distributions. For earnings data, estimate the compensated elasticity of earnings w.r.t. the net-of-tax rate. 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Package: r-cran-bundesligr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bundesligr_0.1.0-1.ca2604.1_all.deb Size: 21404 MD5sum: f584b4110b295dfa15d57c6a2bf7130c SHA1: d3c62472c16ee9d9ce608d1cb6c8afdd36614748 SHA256: cf9ca3963729db206b3a042e3f44198541c2a934c3d02e89585a4863ea1af5b5 SHA512: cc567a2eba74e6d52820e61ce7e9374ca696e3693e76fb72b10a5084bfadaa0d43f35470bd100b3186823611e9937b167e5473654be7f23104d638eba36194d0 Homepage: https://cran.r-project.org/package=bundesligR Description: CRAN Package 'bundesligR' (All Final Tables of the Bundesliga) All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016. 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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We also found parametric bootstrap and asymptotic confidence intervals based on the observed Fisher information of scale and shape parameters, and exact confidence intervals for location parameters. Details are in Biplab Paul and Arabin Kumar Dey (2023) "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto distribution with location and scale"; E L Lehmann and George Casella (1998) "Theory of Point Estimation"; Bradley Efron and R J Tibshirani (1994) "An Introduction to the Bootstrap"; A P Dempster, N M Laird and D B Rubin (1977) "Maximum Likelihood from Incomplete Data via the EM Algorithm". 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Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests. 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'calendRio' provides a 'calendR()' function that serves as a drop-in replacement for the upstream version but allows for additional parameters unlocking extra functionality. Package: r-cran-calf Architecture: all Version: 1.0.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-calf_1.0.17-1.ca2604.1_all.deb Size: 113294 MD5sum: 75518dbb062a4311b86027a23d7c994e SHA1: aff8c7d060e00f2134c86c9a3753990984c76dd2 SHA256: 687092baf8b6ad9e42b441ae032a55e9e5732f44c73221cb583f25b2d362cdaa SHA512: 46e0b12b379eb705dd7fc05d6050abd7123178d3a6f9a8d3ad8ae3e6f41fbf333a0d94b813cb41abbce846d78b401917f9846aa524ef8929247b7d741f9a5930 Homepage: https://cran.r-project.org/package=CALF Description: CRAN Package 'CALF' (Coarse Approximation Linear Function) Contains greedy algorithms for coarse approximation linear functions. Package: r-cran-caliberrfimpute Architecture: all Version: 1.0-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-mvtnorm, r-cran-randomforest Suggests: r-cran-missforest, r-cran-rpart, r-cran-survival, r-cran-xtable, r-cran-ranger Filename: pool/dists/resolute/main/r-cran-caliberrfimpute_1.0-8-1.ca2604.1_all.deb Size: 510038 MD5sum: 925422607652e34963f339ce40e510bc SHA1: a0ba38120dcf4f0bef9d97cc91de7554ce6ed47b SHA256: 1bc8f89401695c60bd5b95f3d76072d32d4d9dab868334d4d5b3acef8b605978 SHA512: 609998140b389e9ab370d8e4f08a28e5dca881a89c949aaf1a91181248f26c90528ed23ffc317b09fd8a38f5e15a03e4454b5cb3e0bd45869c319fce815dc4d8 Homepage: https://cran.r-project.org/package=CALIBERrfimpute Description: CRAN Package 'CALIBERrfimpute' (Multiple Imputation Using MICE and Random Forest) Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) . Package: r-cran-calibmsm Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggextra, r-cran-gridextra, r-cran-hmisc, r-cran-mstate, r-cran-rms, r-cran-survival, r-cran-tidyr, r-cran-vgam Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-survminer, r-cran-flexsurv Filename: pool/dists/resolute/main/r-cran-calibmsm_1.1.3-1.ca2604.1_all.deb Size: 1941454 MD5sum: fa750075f1fbff0b8014afaab82a95f5 SHA1: ddfc246e3992baf84e02d8702d778a63a09d9528 SHA256: c176046fd2d70864a7db8d22d18cedb233d402d5afce15b326594ba7b2c7e6f1 SHA512: 634a2fae4d20dfaba7109d32db92aea0fddefca98b0375ef3be7d6f07b7dca65de78b293ac0d46ee31529fa45e39d06933f6f47ffbe13a51068109e514500ea9 Homepage: https://cran.r-project.org/package=calibmsm Description: CRAN Package 'calibmsm' (Calibration Plots for the Transition Probabilities fromMultistate Models) Assess the calibration of an existing (i.e. previously developed) multistate model through calibration plots. Calibration is assessed using one of three methods. 1) Calibration methods for binary logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 2) Calibration methods for multinomial logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 3) Pseudo-values estimated using the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction with landmarking when required. These calibration plots evaluate the calibration (in a validation cohort of interest) of the transition probabilities estimated from an existing multistate model. While package development has focused on multistate models, calibration plots can be produced for any model which utilises information post baseline to update predictions (e.g. dynamic models); competing risks models; or standard single outcome survival models, where predictions can be made at any landmark time. Please see Pate et al. (2024) and Pate et al. (2024) . Package: r-cran-calibrar Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-cmaes, r-cran-deoptim, r-cran-dfoptim, r-cran-gensa, r-cran-minqa, r-cran-optimx, r-cran-foreach, r-cran-lbfgsb3c, r-cran-pso, r-cran-rgenoud, r-cran-soma, r-cran-stringr Suggests: r-cran-desolve, r-cran-ibm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-calibrar_0.9.0-1.ca2604.1_all.deb Size: 560694 MD5sum: 446990030199df387ae6d27b4c8250df SHA1: 55d6a3bf308a886fc6e5879f5367056333406d26 SHA256: f565fd341a461bf76a5de719dfc1a93033844f082ad8422a73639047b7747ed3 SHA512: aa553bac37edbe3199acb54b7d5b9876ef1d2848cdf7e5e8d8baf79c8d1765a05ae2e67a1bece61e53e7d0813bbd5741adabc566b295feff3b0af1afc76e2fdc Homepage: https://cran.r-project.org/package=calibrar Description: CRAN Package 'calibrar' (Automated Parameter Estimation for Complex Models) General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See for more details. Package: r-cran-calibrate Architecture: all Version: 1.7.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-calibrate_1.7.7-1.ca2604.1_all.deb Size: 351190 MD5sum: cc7fcaa07283dba2a7af2bb304ab0b03 SHA1: 0d05bfb5f0a6abc967e495f3f463beb5c9dd081e SHA256: a4420f6847d5c51c83c89fba85578c71bd3b774bf4b3740ee1e80ac25aab061f SHA512: 81fcc463847a70756a5e173be71440a3eaefe93e41669d0fc06ce16fd3c71ddf2661d535df20382bccec48ea89b74381bfe0517980405d4fd8d6b12548bb7bc1 Homepage: https://cran.r-project.org/package=calibrate Description: CRAN Package 'calibrate' (Calibration of Scatterplot and Biplot Axes) Package for drawing calibrated scales with tick marks on (non-orthogonal) variable vectors in scatterplots and biplots. Also provides some functions for biplot creation and for multivariate analysis such as principal coordinate analysis. Package: r-cran-calibratebinary Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gpfit, r-cran-gelnet, r-cran-kernlab, r-cran-randtoolbox Filename: pool/dists/resolute/main/r-cran-calibratebinary_0.1-1.ca2604.1_all.deb Size: 37420 MD5sum: 4e16947d37fb0b2f29e5d03fa5bea45f SHA1: 0cf7b27a47557f8e439c98d4d8cc75adf596fbad SHA256: acc60e84c00eb5d07cc8d904e05012373b4b6323442a24462b395047601be3b7 SHA512: 41b0697e5cfa9437ab35a513dbfea413db5be86588e68e7fc6c41ca6daabc72e24d7817af3e4d1f8e18f3c871c8a35aa4e27c4030b4f64d92a95d9d6e4c8b3b1 Homepage: https://cran.r-project.org/package=calibrateBinary Description: CRAN Package 'calibrateBinary' (Calibration for Computer Experiments with Binary Responses) Performs the calibration procedure proposed by Sung et al. (2018+) . This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest. Package: r-cran-calibratessb Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survey Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-calibratessb_1.4.0-1.ca2604.1_all.deb Size: 190786 MD5sum: 2e3c62b1ef5a00e0fb4e6aced107015f SHA1: 78fd46d0695a450498da9fcab2c3ff1565f73228 SHA256: 12bf420c334d80b34d68914cdebe2cbd9b01d93094336257f16ad40b052d3516 SHA512: 16aed14cc4f7884e2d48a999725ca7f1f970cdfab6f8f1dd7cacaaa01ee24cc414a41f72560950fb6ca37fe4cb9ba1bfbe75bad150b9186fd81271ed7afc8dcc Homepage: https://cran.r-project.org/package=CalibrateSSB Description: CRAN Package 'CalibrateSSB' (Weighting and Estimation for Panel Data with Non-Response) Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the 'survey' package is used for this purpose. It is also possible to use 'ReGenesees', which can be installed from . Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) . 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See Lasai et al. (2025) , De Cock Campo (2023) and Van Calster et al. (2016) . Package: r-cran-calibrator Architecture: all Version: 1.2-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 736 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emulator, r-cran-mvtnorm, r-cran-cubature Filename: pool/dists/resolute/main/r-cran-calibrator_1.2-8-1.ca2604.1_all.deb Size: 635508 MD5sum: 6ec0da6c8840d8a0435e45a3e8bb177f SHA1: 7a533e59915d32c8c5f1ccab1992de76f8ae3c50 SHA256: e53a2faf8b36655b2ba9a49e67156d83a7345627db3dc84feb11ad23002e0676 SHA512: 8f4eb0a8dd79a22e775be7fed6ec0bd7c663ccbf02587dc19cc8286ffed142f1d83fe62003933b6ed8b64520a43f7ddc86632877a69d5ea7c5d97b3cc73d30b6 Homepage: https://cran.r-project.org/package=calibrator Description: CRAN Package 'calibrator' (Bayesian Calibration of Complex Computer Codes) Performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/. Package: r-cran-calibratr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-proc, r-cran-reshape2, r-cran-foreach, r-cran-fitdistrplus, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-calibratr_0.1.2-1.ca2604.1_all.deb Size: 247084 MD5sum: f8f66f1bfc44efbcd87784a7a4878af5 SHA1: a5d66d7c7dd794f5d05bd34a15617d3b11d6e880 SHA256: fda94ca57e395d0a016b8ca27efe3ca9b28efa72191f8c73b436ffe40778d1df SHA512: 80cb9cca99f45311d7d7a276041b680800d7cbe5331e7021fb3449e2673896a66f4513a5c4f132b320c94174690fcc7e0c743e5e193b0abc73a31746872688a4 Homepage: https://cran.r-project.org/package=CalibratR Description: CRAN Package 'CalibratR' (Mapping ML Scores to Calibrated Predictions) Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465. Package: r-cran-calidad Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4603 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-purrr, r-cran-survey, r-cran-kableextra, r-cran-stringr, r-cran-haven Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-srvyr Filename: pool/dists/resolute/main/r-cran-calidad_0.8.2-1.ca2604.1_all.deb Size: 4539062 MD5sum: 88901101233ab1b968ce231d7e040cd5 SHA1: 0cb79199ae58dd5aceec1d1de1016d17374422c0 SHA256: 8f30ba34f8a1307e71639271ed78012d3605aa39aaa85da3124fbb4c539f590f SHA512: 24e15bb929cf8b3bbf6e99478c26bd0397597522908cc1f3db2372a5e92206c32d78693fa4c0a94558289c3ca4ebd20100727bede8037208a4c5830d65512c12 Homepage: https://cran.r-project.org/package=calidad Description: CRAN Package 'calidad' (Assesses the Quality of Estimates Made by Complex Sample Designs) Assesses the quality of estimates made by complex sample designs, following the methodology developed by the National Institute of Statistics Chile (Household Survey Standard 2020, ), (Economics Survey Standard 2024, ) and by Economic Commission for Latin America and Caribbean (2020, ), (2024, ). Package: r-cran-callback Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-callback_0.1.3-1.ca2604.1_all.deb Size: 321204 MD5sum: dd642f7de3eee170d9b0ddac33e5f74e SHA1: 1ecf6b82c5dcdcc8d4e3ba05ac983ada250f7fe8 SHA256: a1e672cc87478dd00c69cd4528138fe944699b4cdfce74125699933aea40f9bc SHA512: 448ac79915ef6cdf9894b7abfdf7fc6ba1bef132de5e664f8a33c46b587d0b3f70fc1a98f9c2a02a1641f6737cc9aa34834c9a8c39ca936c3e01c60c81ef4255 Homepage: https://cran.r-project.org/package=callback Description: CRAN Package 'callback' (Computes Statistics from Discrimination Experimental Data) In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. 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Package: r-cran-callme Architecture: all Version: 0.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-callme_0.1.11-1.ca2604.1_all.deb Size: 48154 MD5sum: 3216d6f6533d5ca4b1599f0e1e017412 SHA1: 94f463d6e10a473c51d1c8e71882ebb28b090f57 SHA256: 3495764f4f527abc955bad593e2cd6f80a47699221a7803efbf6b75ab2c01e51 SHA512: 271bee7ec8a7442c594e6041c0a92da9cd6b35e6ae9e737c63cd7f5fcb9d8fee4f85d104e830ce92e671365aed6bfaa12eba660adb0888e801b19cea89012c6b Homepage: https://cran.r-project.org/package=callme Description: CRAN Package 'callme' (Easily Compile and Call Inline 'C' Functions) Compile inline 'C' code and easily call with automatically generated wrapper functions. By allowing user-defined headers and compilation flags (preprocessor, compiler and linking flags) the user can configure optimization options and linking to third party libraries. Multiple functions may be defined in a single block of code - which may be defined in a string or a path to a source file. Package: r-cran-callr Architecture: all Version: 3.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-processx, r-cran-r6 Suggests: r-cran-asciicast, r-cran-cli, r-cran-mockery, r-cran-ps, r-cran-rprojroot, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-callr_3.7.6-1.ca2604.1_all.deb Size: 443092 MD5sum: fe0824a54028e7e3ee0c1ad0ee2e0a6a SHA1: bc882d969177b61b2e9246e176947202a745d6f0 SHA256: cbcacc9ecaa731e7866eb48d8914f0bc95d18c02d09f13a22deac3363f559323 SHA512: a526bee93339803abf6c30ba59ad46096eea181ae199c26c1cb3db95dc1a30e85e9f1a80c8d1710899dd142b4deb88c09c8e9438ec72d02137d74be43d0b6cae Homepage: https://cran.r-project.org/package=callr Description: CRAN Package 'callr' (Call R from R) It is sometimes useful to perform a computation in a separate R process, without affecting the current R process at all. This packages does exactly that. Package: r-cran-callsync Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-oce, r-cran-seewave, r-cran-signal, r-cran-stringr, r-cran-tuner, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-callsync_0.2.3-1.ca2604.1_all.deb Size: 164776 MD5sum: 8f28cf300e36c8d7e8d7ad00c2f67bba SHA1: 7e37aaca2d85c6423834cdcd80909f055a60461e SHA256: 03a56a4d865162052f05f63a2305cb77268d76cc48172d1bc6bb9f01e3dbbbf1 SHA512: 5d4651c3e7dd117a6f145922e495e3cc512ac4d1d2789dea2fd64559d53cc5db19492c15efb8fab50de4f25edc5a7a8e32aa55d45425a93599918d500476d466 Homepage: https://cran.r-project.org/package=callsync Description: CRAN Package 'callsync' (Recording Synchronisation, Call Detection and Assignment, AudioAnalysis) Intended to analyse recordings from multiple microphones (e.g., backpack microphones in captive setting). It allows users to align recordings even if there is non-linear drift of several minutes between them. A call detection and assignment pipeline can be used to find vocalisations and assign them to the vocalising individuals (even if the vocalisation is picked up on multiple microphones). The tracing and measurement functions allow for detailed analysis of the vocalisations and filtering of noise. Finally, the package includes a function to run spectrographic cross correlation, which can be used to compare vocalisations. It also includes multiple other functions related to analysis of vocal behaviour. Package: r-cran-calmate Architecture: all Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.utils, r-cran-aroma.core, r-cran-mass, r-cran-r.methodss3, r-cran-r.oo, r-cran-matrixstats, r-cran-r.filesets Suggests: r-bioc-dnacopy Filename: pool/dists/resolute/main/r-cran-calmate_0.13.0-1.ca2604.1_all.deb Size: 205358 MD5sum: 1ef956f70bcd5ac3659b6ca7bafc55d4 SHA1: e6708627d58d9b3ecf958eff2d2f7c8b0a6611ee SHA256: ddfa1512dd69f80a5854d3536b98be66e5571c7b02fc9270d2ab9737e4092d9c SHA512: 3fede699d41609139aa7cf3ca3a766e3d49a056245f0849980c877b04e77e21df6bcbeab3eb2cd71db32dd1a5be4581b7c5121c25baaf8010ad103aa7a647611 Homepage: https://cran.r-project.org/package=calmate Description: CRAN Package 'calmate' (Improved Allele-Specific Copy Number of SNP Microarrays forDownstream Segmentation) The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina. Package: r-cran-calmr Architecture: all Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-future, r-cran-future.apply, r-cran-ga, r-cran-ggnetwork, r-cran-ggplot2, r-cran-lifecycle, r-cran-network, r-cran-patchwork, r-cran-progressr, r-cran-rlang Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-calmr_0.8.1-1.ca2604.1_all.deb Size: 891338 MD5sum: 63919f04b54cb90ecdee40d2dbf019a0 SHA1: 0b0d180831d4b0d9235ed0fd28291375694b97de SHA256: 789ae1e817dd45dcb9690cd8f32f09360c641aa24210fba8db49c5ac2cb196f7 SHA512: 186d697a43f962defa0c7812b9c032f32504fc7c022a8419e89f897ba3ef1fa88e94fce4bea715ff1510376ca77cc24f9ee7c9966b0fbf9d8e51658752599825 Homepage: https://cran.r-project.org/package=calmr Description: CRAN Package 'calmr' (Canonical Associative Learning Models and their Representations) Implementations of canonical associative learning models, with tools to run experiment simulations, estimate model parameters, and compare model representations. Experiments and results are represented using S4 classes and methods. Package: r-cran-calms Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-foreign, r-cran-lavaan, r-cran-lsr, r-cran-matchit, r-cran-shiny, r-cran-shinyjs, r-cran-stringr, r-cran-bslib Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rgenoud, r-cran-matching Filename: pool/dists/resolute/main/r-cran-calms_1.0-3-1.ca2604.1_all.deb Size: 1757054 MD5sum: e755b68bacfb8aa6f9f860579e14aed5 SHA1: f2fb1d50b0bcef89989d9046c6ce991d029994fa SHA256: d40fd973158c59b4a1f99f88c86d7fa919e287bed7181cc7f76d54120272c040 SHA512: d735ecb3d5c895040ce7074323bff30ce3155f59aa86f5977e3407efe7604f30f72e06e51410f9e5a771d11dbbf4e7fc575cf1eb269048391ce7b6ca5a64ed73 Homepage: https://cran.r-project.org/package=calms Description: CRAN Package 'calms' (Comprehensive Analysis of Latent Means) Provides a Shiny application to conduct comprehensive analysis of latent means including the examination of group equivalency, propensity score analysis, measurement invariance analysis, and assessment of latent mean differences of equivalent groups with invariant data. Group equivalency and propensity score analyses are implemented using the 'MatchIt' package [Ho et al. (2011) ], ensuring robust control for covariates. Structural equation modeling and invariance testing rely heavily on the 'lavaan' package [Rosseel (2012) ], providing a flexible and powerful modeling framework. The application also integrates modified functions from Hammack-Brown et al. (2021) to support factor ratio testing and the list-and-delete procedure. Package: r-cran-calpassapi Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-digest, r-cran-jsonlite, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-calpassapi_0.0.3-1.ca2604.1_all.deb Size: 37316 MD5sum: 960e90732d842b6426849731ddb48872 SHA1: b0dffe2a8a6d904f7ae0d4550e37904c315489dc SHA256: fe6d249a1857774ec2d51b3e4a5cfbe24df874f2ce2f2e2075a5213022bb8091 SHA512: ec6405752daf18b3c8dade0098a5c46c3fd482c472ce92f22d73f0e5f5d1b9cc79bdf65894e3aa172e49551114de1d58b96d5fe1641bb641219baeb3a859a5d8 Homepage: https://cran.r-project.org/package=calpassapi Description: CRAN Package 'calpassapi' (R Interface to Access CalPASS API) Implements methods for querying data from CalPASS using its API. CalPASS Plus. MMAP API V1. . Package: r-cran-camcorder Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1991 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gifski, r-cran-magick, r-cran-rsvg, r-cran-jsonlite, r-cran-rlang, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-ragg, r-cran-covr, r-cran-testthat, r-cran-patchwork, r-cran-withr, r-cran-dplyr, r-cran-systemfonts, r-cran-scales, r-cran-extrafont Filename: pool/dists/resolute/main/r-cran-camcorder_0.1.0-1.ca2604.1_all.deb Size: 1419490 MD5sum: f03f305f24ff2812b45ede95b509c105 SHA1: 07f15db0929af704002fb67910e7168b5028c878 SHA256: 538345f165efbc0625234587000ef550cbf8e434fca396230f584dfb8573f784 SHA512: 50466d82007d7c7a69c05697e76768f6dd950fe8fdf5391ea8d676ec56ede848885f79556038abee2ec3d33fed0926b0723d2d4f8600e4f16b6ee146e24ce576 Homepage: https://cran.r-project.org/package=camcorder Description: CRAN Package 'camcorder' (Record Your Plot History) Record and generate a 'gif' of your 'R' sessions plots. When creating a visualization, there is inevitably iteration and refinement that occurs. Automatically save the plots made to a specified directory, previewing them as they would be saved. Then combine all plots generated into a 'gif' to show the plot refinement over time. Package: r-cran-camea Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-metafor, r-cran-tibble, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-camea_0.1.2-1.ca2604.1_all.deb Size: 88590 MD5sum: 9e91fabe78482beaf3c1fe35845d94fe SHA1: 4e4b9ab30f2b1be54bdca31084505ff1d470eac3 SHA256: 673874cd59326dd8e29ca5b603c77d2dc3f6a0f71056672d82ab3c1fb0c38884 SHA512: a2e6caca0d21eb23b2db9dd5c2aefc34f4c243baaff1d7eb31fe3946c12a6c19bdcda659bbc777f9a42ff8233ed8be35598af1a799009252bd20c21b96477750 Homepage: https://cran.r-project.org/package=CaMeA Description: CRAN Package 'CaMeA' (Causal Meta-Analysis for Aggregated Data) A tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) . Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the 'metafor' package . Package: r-cran-camerondata Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-camerondata_1.0.0-1.ca2604.1_all.deb Size: 1311640 MD5sum: 26d6b0d00bd39121c477a81805ce416f SHA1: 68f7a58b14425f955edc21e1380388300a8042b3 SHA256: 5f174791d19b1856780cf78bd0e6af31b397de08db88da98b56a2ea6cb7dc7e7 SHA512: e2ae882e5b64e03467f2b5e9d0f058a574bcd1ae1160fd081c8d4fbe4bc6aca3198693a7ae0ee7488c5d8600b6709d16a0bcd0ad1435434c3ea8897cac694035 Homepage: https://cran.r-project.org/package=camerondata Description: CRAN Package 'camerondata' (Datasets from "Microeconometrics: Methods and Applications" byCameron and Trivedi) Quick and easy access to datasets that let you replicate the empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and Applications" (ISBN: 9780521848053).The data are available as soon as you install and load the package (lazy-loading) as data frames. The documentation includes reference to chapter sections and page numbers where the datasets are used. Package: r-cran-campaignmanager Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-campaignmanager_0.1.0-1.ca2604.1_all.deb Size: 22952 MD5sum: e693efa67853c856c453ed36935788ea SHA1: ebe82f363fdeee8ac6ee76956d8aad2742b2caa0 SHA256: 9e0a614ee3371cf358f84c69e115cd28cdfdcb3a945869e3b1ad5a78e28a4a13 SHA512: 162173139cd02e4c43e76068135b63ae52992c6655c698a572ac97838a687c3df1f954f69a09da552775e29fb25c688d8f58d8c704b9abc0a43ac3ca0da88269 Homepage: https://cran.r-project.org/package=campaignmanageR Description: CRAN Package 'campaignmanageR' (Connect to Campaign Manager via the 'Windsor.ai' API) Collect marketing data from Campaign Manager using the 'Windsor.ai' API . Package: r-cran-campfin Architecture: all Version: 1.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringdist, r-cran-stringr, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-campfin_1.0.11-1.ca2604.1_all.deb Size: 644674 MD5sum: cf95d7b7b0cbfc09713c1bb5dbe708f6 SHA1: 6b6dca37b3cbf0d19240251cb9c2f86f96bf416f SHA256: a7390e9ffb6d1bbae26650f2a85591c39ba0e8a93bf19e4663a59b8f614dfe1f SHA512: 69377314685697d0e457af3b71aa78bb903930fce9da1993b94d7b915de51df9b877619d2a2677f334c93e43b449ad2c7bf1b231e4defe0730bb2e5d25939ad3 Homepage: https://cran.r-project.org/package=campfin Description: CRAN Package 'campfin' (Wrangle Campaign Finance Data) Explore and normalize American campaign finance data. Created by the Investigative Reporting Workshop to facilitate work on The Accountability Project, an effort to collect public data into a central, standard database that is more easily searched: . Package: r-cran-campsis Architecture: all Version: 1.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-campsismod, r-cran-assertthat, r-cran-digest, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-mass, r-cran-progressr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-bookdown, r-cran-devtools, r-cran-gridextra, r-cran-knitr, r-cran-mrgsolve, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rxode2, r-cran-stringr, r-cran-testthat, r-cran-tictoc, r-cran-vdiffr, r-cran-xfun Filename: pool/dists/resolute/main/r-cran-campsis_1.8.2-1.ca2604.1_all.deb Size: 996820 MD5sum: 5138eea328ee479b85673be6a7942317 SHA1: 7e6f12174530471872ed5a165d62796964c39817 SHA256: 84854f7a044387748ed2b62085537486f185ae5cfd18251c699837a4346b9792 SHA512: 1041fe8476deeeec9eea642d303ac6d517ff61ab1c533897a89a1ff0d8862c6bf01d0e143b2b02f0394094abdb14e0d5ffdefce3ebec01a5345f480dc501ca4f Homepage: https://cran.r-project.org/package=campsis Description: CRAN Package 'campsis' (Generic PK/PD Simulation Platform Campsis) A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on the R packages 'rxode2' and 'mrgsolve'. Campsis provides an abstraction layer over the underlying processes of defining a PK/PD model, assembling a custom dataset and running a simulation. The package has a strong dependency on the R package 'campsismod', which allows models to be read from and written to files, including through a JSON-based interface, and to be adapted further on the fly in the R environment. In addition, 'campsis' allows users to assemble datasets in an intuitive manner, including via a JSON-based interface to import Campsis datasets defined using formal JSON schemas distributed with the package. Once the dataset is ready, the package prepares the simulation, calls 'rxode2' or 'mrgsolve' (at the user's choice), and returns the results for the given model, dataset and desired simulation settings. The package itself is licensed under the GPL (>= 3); the JSON schema files shipped in inst/extdata are licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). Package: r-cran-campsismod Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-laplacesdemon, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-xfun Filename: pool/dists/resolute/main/r-cran-campsismod_1.3.2-1.ca2604.1_all.deb Size: 1062806 MD5sum: 027510d9cc7e907c77554cffade80415 SHA1: 4da4d17d00461aaef851ff97320762b05f6aff65 SHA256: 024988a84890a5a61a324622b5bed35d181d9dd10886775f7e6ac098120099a3 SHA512: d9b647af2ee38aa9cfb2e207f9ded854e44cac073715ca70123ec0f7be85fe54aa51f521f036460857022b12420ea635968479c87d6c7948b9e8f140a9ac3a44 Homepage: https://cran.r-project.org/package=campsismod Description: CRAN Package 'campsismod' (Generic Implementation of a PK/PD Model) A generic, easy-to-use and expandable implementation of a pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4 class system. This package allows the user to read and write pharmacometric models from and to files, including a JSON-based interface to import Campsis models defined using a formal JSON schema distributed with the package. Models can be adapted further on the fly in the R environment using an intuitive API to add, modify or delete equations, ordinary differential equations (ODEs), model parameters or compartment properties (such as infusion duration or rate, bioavailability and initial values). The package also provides export facilities for use with the simulation packages 'rxode2' and 'mrgsolve'. The package itself is licensed under the GPL (>= 3); the JSON schema file shipped in inst/extdata is licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). This package is designed and intended to be used with the package 'campsis', a PK/PD simulation platform built on top of 'rxode2' and 'mrgsolve'. Package: r-cran-camsrad Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-xml2 Suggests: r-cran-ncdf4, r-cran-roxygen2, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-camsrad_0.3.0-1.ca2604.1_all.deb Size: 32842 MD5sum: e1fc3c1546a72cc95d31f0db0a688d88 SHA1: e817dea19af383afdb23f40fecc8c6f7649e0a7d SHA256: e52fcdd9e04599554fa715f5d4b289c0ae5c1fc6acbe3a0c45e1ed67ea501cc8 SHA512: b78b1a98d126695471c5126afa36f9be248fbabaf63f6eb0bb7873247a94698e93586cd2262a71440ece6004945354bc5ae098aff013db0e3be07eefcfbcdaff Homepage: https://cran.r-project.org/package=camsRad Description: CRAN Package 'camsRad' (Client for CAMS Radiation Service) Copernicus Atmosphere Monitoring Service (CAMS) radiations service provides time series of global, direct, and diffuse irradiations on horizontal surface, and direct irradiation on normal plane for the actual weather conditions as well as for clear-sky conditions. The geographical coverage is the field-of-view of the Meteosat satellite, roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage of data is from 2004-02-01 up to 2 days ago. Data are available with a time step ranging from 15 min to 1 month. For license terms and to create an account, please see . Package: r-cran-camtrapdp Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-eml, r-cran-frictionless, r-cran-memoise, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-uuid Suggests: r-cran-jsonlite, r-cran-lubridate, r-cran-testthat, r-cran-tibble, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-camtrapdp_0.5.0-1.ca2604.1_all.deb Size: 200488 MD5sum: f5828ae4874c51327473090996afb484 SHA1: 7fad18218fa3a93cedfde96642fde9fcd4bf8f2c SHA256: 9e0d7662ff58dcafd6a8c53fee77a4b52a4e8b0a929693f0aa3b6398649354cd SHA512: 83f86d12eb43531362e3e2a64ebdca64438757f3654da6ef601fb81ae4c74def3072d952f1b07ecfe97cc7e82caad1bc17814a314bc0eeed04a0ccad9039f217 Homepage: https://cran.r-project.org/package=camtrapdp Description: CRAN Package 'camtrapdp' (Read and Manipulate Camera Trap Data Packages) Read and manipulate Camera Trap Data Packages ('Camtrap DP'). 'Camtrap DP' () is a data exchange format for camera trap data. With 'camtrapdp' you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. 'camtraptor' or 'camtrapR'. Package: r-cran-camtrapr Architecture: all Version: 3.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9876 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-generics, r-cran-ggplot2, r-cran-leaflet, r-cran-lubridate, r-cran-secr, r-cran-sf, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyjs, r-cran-terra Suggests: r-cran-abind, r-cran-bayesplot, r-cran-callr, r-cran-coda, r-cran-corrplot, r-cran-elevatr, r-cran-inext, r-cran-jsonify, r-cran-jsonlite, r-cran-knitr, r-cran-lattice, r-cran-magick, r-cran-mapview, r-cran-mockery, r-cran-nimble, r-cran-nimbleecology, r-cran-overlap, r-cran-patchwork, r-cran-pbapply, r-cran-plotly, r-cran-psych, r-cran-r.rsp, r-cran-raster, r-cran-reshape2, r-cran-ritis, r-cran-rjags, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-rstudioapi, r-cran-scales, r-cran-shinywidgets, r-cran-stringr, r-cran-taxize, r-cran-tesseract, r-cran-testthat, r-cran-tibble, r-cran-ubms, r-cran-units, r-cran-unmarked, r-cran-viridislite, r-cran-withr, r-cran-zip Filename: pool/dists/resolute/main/r-cran-camtrapr_3.0.4-1.ca2604.1_all.deb Size: 5579494 MD5sum: 36fb94d2707c5c695398ec78e9149290 SHA1: 38c719c6c0744dbf40c731ae8f3693e15f493708 SHA256: efdef57fbeef2658b59de96cab037a7392241a8e83fbbe35dde50565480eb5fb SHA512: 19efdf3f2705caf2c6f4947554ec161e3096080320a6334cdd47816d3e186577011cf3b4deeb75dafbc728b5d691ecdfdbe7d29c06dae9a2fd7fa86f4c75b50a Homepage: https://cran.r-project.org/package=camtrapR Description: CRAN Package 'camtrapR' (Camera Trap Data Management and Analysis Framework) Management and analysis of camera trap wildlife data through an integrated workflow. Provides functions for image/video organization and metadata extraction, species/individual identification. Creates detection histories for occupancy and spatial capture-recapture analyses, with support for multi-season studies. Includes tools for fitting community occupancy models in JAGS and NIMBLE, and an interactive dashboard for survey data visualization and analysis. Features visualization of species distributions and activity patterns, plus export capabilities for GIS and reports. Emphasizes automation and reproducibility while maintaining flexibility for different study designs. Package: r-cran-canadamaps Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4025 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rmapshaper, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-canadamaps_0.1-1.ca2604.1_all.deb Size: 4084660 MD5sum: f74808a44e8adff0c5b2b44029442476 SHA1: b1063bd1456002a0b3c982918e8ea02fe14209a8 SHA256: acfedda7387fa9e5c37d3a46e21a7de373fcefcccd5340d60c84b185fc2e7c7c SHA512: e7187b7d1fb7d4b2aac4d1555b22ec0084679e5aaa4804584d36c5d01f97be226f3c408780fd9aa3a9e9c83884c69ec6f6c416ac7fb7135b10ea2ee3c7541b59 Homepage: https://cran.r-project.org/package=canadamaps Description: CRAN Package 'canadamaps' (Maps of the Political and Administrative Divisions of Canada) Terrestrial maps with simplified topologies for Census Divisions, Agricultural Regions, Economic Regions, Federal Electoral Divisions and Provinces. Package: r-cran-canadianmaps Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sp, r-cran-sf, r-cran-ggrepel, r-cran-rcolorbrewer, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-canadianmaps_2.0.0-1.ca2604.1_all.deb Size: 4229468 MD5sum: 203c39922f8cb68482b0e7e8831ea38f SHA1: fe5ce209880caa486148f585d67601476dd49488 SHA256: 21916ff1b9908b5f12813874f56ceaba1e5133c59f1c5a417db72608ebd1f91b SHA512: d7d333e405393dcb43e77ac6a403e703cc822e58760af4f5c18ddabf019835df323d4b05af0f4a04e70929681952b06686e69a77aca17c246309caadcc573e04 Homepage: https://cran.r-project.org/package=canadianmaps Description: CRAN Package 'canadianmaps' (Effortlessly Create Stunning Canadian Maps) Simple and seamless access to a variety of 'StatCan' shapefiles for mapping Canadian provinces, regions, forward sortation areas, census divisions, and subdivisions using the popular 'ggplot2' package. Package: r-cran-canaper Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-assertr, r-cran-assertthat, r-cran-dplyr, r-cran-future.apply, r-cran-phyloregion, r-cran-progressr, r-cran-purrr, r-cran-tibble, r-cran-vegan Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-future, r-cran-tictoc, r-cran-patchwork, r-cran-testthat, r-cran-stringr, r-cran-magrittr, r-cran-covr, r-cran-picante, r-cran-withr, r-cran-fs, r-cran-readr, r-cran-usethis, r-cran-scales, r-cran-colorblindness Filename: pool/dists/resolute/main/r-cran-canaper_1.0.1-1.ca2604.1_all.deb Size: 534124 MD5sum: 4bed020ed8226ed37d4feca8e2d9c093 SHA1: f0b2a05c7f88e07d90fc840107135ffa2d63ac31 SHA256: 1341bfcbdcd95ed6f36db13180122bc46adec1909935fdd60f21478d7e25c386 SHA512: af399845a4c2d57402d52cae3e2dc2f51370dc1a2bddfb108dd9ffa3c331c1b17740e59c10aaa14f477a493f959b6e5f415a6074d2d4e2e5c3c16701389caf7b Homepage: https://cran.r-project.org/package=canaper Description: CRAN Package 'canaper' (Categorical Analysis of Neo- And Paleo-Endemism) Provides functions to analyze the spatial distribution of biodiversity, in particular categorical analysis of neo- and paleo-endemism (CANAPE) as described in Mishler et al (2014) . 'canaper' conducts statistical tests to determine the types of endemism that occur in a study area while accounting for the evolutionary relationships of species. Package: r-cran-cancensus Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2071 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-sf, r-cran-geojsonsf, r-cran-rlang, r-cran-readr Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-leaflet, r-cran-mapdeck, r-cran-rmarkdown, r-cran-scales, r-cran-sp, r-cran-tidyr, r-cran-lwgeom, r-cran-xml2, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-cancensus_0.6.0-1.ca2604.1_all.deb Size: 1022922 MD5sum: ad17523c72fd2bd39fb9a4ea7d7aa934 SHA1: f0dd1a289a3ef48e6c195ad4796c21329066b090 SHA256: d91116630b98c01fb38eb552358b2b08ad9eac29a6bbafbbf3190c5ae169922c SHA512: 74c4f58ac9f7963570f67c32bc753c21cde4589b5e5a1e95149e45238f4cf73653d7ea59aab52e96594fe3eecfb1bef1a5079a9de335c8102c75672e70870748 Homepage: https://cran.r-project.org/package=cancensus Description: CRAN Package 'cancensus' (Access, Retrieve, and Work with Canadian Census Data andGeography) Integrated, convenient, and uniform access to Canadian Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready tidy data frames and spatial data in multiple formats, as well as convenience functions for working with Census variables, variable hierarchies, and region selection. API keys are freely available with free registration at . Census data and boundary geometries are reproduced and distributed on an "as is" basis with the permission of Statistics Canada (Statistics Canada 1996; 2001; 2006; 2011; 2016; 2021). Package: r-cran-cancerevolutionvisualization Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-gtable, r-cran-plyr, r-cran-stringr, r-cran-boutroslab.plotting.general Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-cancerevolutionvisualization_2.0.1-1.ca2604.1_all.deb Size: 269528 MD5sum: b171df5bf9962767bb5069b5c7d646b4 SHA1: 2a495d3d970dd46108f09fff48f62ef7bd7dac00 SHA256: d0fc11331b1c6193e28118dad6606c043eb955e71916b388633e3219d8cd7fc2 SHA512: 19b7f8c5f1f4674a36c1a952effc6f6fd836f1738a63f169102ae1739a1a7f02a8eb32261f09c470297dfb1b1d14ee3f00a968b2c77de9ef27cde134607adda2 Homepage: https://cran.r-project.org/package=CancerEvolutionVisualization Description: CRAN Package 'CancerEvolutionVisualization' (Publication Quality Phylogenetic Tree Plots) Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user. Package: r-cran-cancergi Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-systemfit, r-bioc-qvalue, r-cran-survival, r-cran-reshape2, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-cancergi_1.0.1-1.ca2604.1_all.deb Size: 729724 MD5sum: cf9bf24feab3905059d02c93aa841037 SHA1: f22a93fa5d505ff46abbd7b935234b0a1e09805e SHA256: 632ed7601db3a60af8211548a44d2821f01108b86710fc79d94b27e20c0be13f SHA512: 5acd0034e7c4882cf790da65d19a04244f3691967986c98457f67c04f0b0476975dd9d806e3c14205c11c79a8672e60da2d60facdb81017ff9c411e88db6c45d Homepage: https://cran.r-project.org/package=cancerGI Description: CRAN Package 'cancerGI' (Analyses of Cancer Gene Interaction) Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network). Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. . Package: r-cran-cancergram Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biogram, r-cran-devtools, r-cran-pbapply, r-cran-ranger, r-cran-shiny, r-cran-stringi, r-cran-dplyr Suggests: r-cran-dt, r-cran-ggplot2, r-cran-pander, r-cran-rmarkdown, r-cran-shinythemes, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-cancergram_1.0.0-1.ca2604.1_all.deb Size: 213072 MD5sum: 387a95926e78d2682c2f3780ea31b124 SHA1: 11afabaa97c19cdc7ac138b5fda87713c3b553ce SHA256: 3f6c89177f2ee27a1d278d213270f5487587b3218e331b8564713ae0967871cb SHA512: eef3abd8b8742aeaebf6b34b45880fd757b52b24ab0165a2a295b6d5f416c267a1d1aee7bce9df7825bcc698ba76a0fa9aae5bc7b2809d5ee5eba6922a6f9ddc Homepage: https://cran.r-project.org/package=CancerGram Description: CRAN Package 'CancerGram' (Prediction of Anticancer Peptides) Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The CancerGram model is too large for CRAN and it has to be downloaded separately from the repository: . For more information see: Burdukiewicz et al. (2020) . Package: r-cran-cancerr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cancerr_0.1.1-1.ca2604.1_all.deb Size: 268564 MD5sum: f1235363140918a74f0911495d54f110 SHA1: 0535a61579dda41865037324a4c68e03ffe3a427 SHA256: 889c3ed210c1dde9c1c976b53fae79c5bb44b1cf724db795663e1d1a85b07fe9 SHA512: 895e93c81fd7d2327619e35ab3f73be3105d8c30011ff165b652c1b04f849771fa5ad84b1845febc907e05c24b0e2e02ec90b16649857ecea7af762e60105267 Homepage: https://cran.r-project.org/package=cancerR Description: CRAN Package 'cancerR' (Classification of Cancer Using Administrative Data) Classifies the type of cancer using routinely collected data commonly found in cancer registries from pathology reports. The package implements the International Classification of Diseases for Oncology, 3rd Edition site (topography), histology (morphology), and behaviour codes of neoplasms to classify cancer type . Classification in children utilize the International Classification of Childhood Cancer by Steliarova-Foucher et al. (2005) . Adolescent and young adult cancer classification is based on Barr et al. (2020) . Package: r-cran-cancerradarr Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7833 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-epitools, r-cran-gtools, r-cran-magrittr, r-cran-openxlsx, r-cran-plyr, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-stringr, r-cran-tidyr Suggests: r-cran-plotly, r-cran-shiny, r-cran-tidyverse, r-cran-dt, r-cran-testthat, r-cran-knitr, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-cancerradarr_3.0.0-1.ca2604.1_all.deb Size: 7171312 MD5sum: 6158a57baa86813f537c918399b4aaa4 SHA1: c917e25633a20557e40ebb160bf103313b1a312e SHA256: cda3b7330903ea41146ca7bfbe0983cafbe41c88c3776389488e388e9b73d384 SHA512: 2e5c98d887555b279a5cb81544428521464155dc8287493620b17cf6b8e30f9d7927c99347b4b78f34badb64218b84faf363993dedb390003a78cded0f25799d Homepage: https://cran.r-project.org/package=cancerradarr Description: CRAN Package 'cancerradarr' (Cancer RADAR Project Tool) Cancer RADAR is a project which aim is to develop an infrastructure that allows quantifying the risk of cancer by migration background across Europe. This package contains a set of functions cancer registries partners should use to reshape 5 year-age group cancer incidence data into a set of summary statistics (see Boyle & Parkin (1991, ISBN:978-92-832-1195-2)) in lines with Cancer RADAR data protections rules. Package: r-cran-candisc Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-heplots, r-cran-car, r-cran-insight, r-cran-dplyr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-rgl, r-cran-cardata, r-cran-corrplot, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart, r-cran-rpart.plot, r-cran-klar, r-cran-lifecycle, r-cran-tidyr, r-cran-testthat, r-cran-rcolorbrewer, r-cran-tinytable Filename: pool/dists/resolute/main/r-cran-candisc_1.1.0-1.ca2604.1_all.deb Size: 581308 MD5sum: 55bcba3d83b27bfc73bab448f18ff283 SHA1: 08af95cdefbeb77985f3b07ffd995797b6707103 SHA256: 63f04de7e89890e2ce250708f7ae2a69008b53b3cac57dde8e6148c38014dc9b SHA512: 3b1413972f80c20a47066c07bb0fec906d86d717a2a72c1d8625829283d87f213d70b75654e6e3765b1d69812485d847cdf2ae0351226202bc9b46a5b4fff7ed Homepage: https://cran.r-project.org/package=candisc Description: CRAN Package 'candisc' (Visualizing Generalized Canonical Discriminant and CanonicalCorrelation Analysis) Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included. Package: r-cran-cane Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-agricolae, r-cran-dplyr, r-cran-emmeans Filename: pool/dists/resolute/main/r-cran-cane_0.1.1-1.ca2604.1_all.deb Size: 49866 MD5sum: 525eb4d59aeb9fba382adfa78bd8f12e SHA1: ab829fce3a9820cb84d5de08584b8cbe3267e7f4 SHA256: d5af8d6768219ffa00d7c81c404b802159f13ea6a4d13cee69653c38e1fd2f9d SHA512: d088bb65286d16662f7d349751c2f23031292910a72c68b05a68561a018964be490aa9d7bd6fdbcc4a25af8ecb25453f5c9d9de0c25c43d902c4042f3ddd6cb8 Homepage: https://cran.r-project.org/package=CANE Description: CRAN Package 'CANE' (Comprehensive Groups of Experiments Analysis for NumerousEnvironments) In many cases, experiments must be repeated across multiple seasons or locations to ensure applicability of findings. A single experiment conducted in one location and season may yield limited conclusions, as results can vary under different environmental conditions. In agricultural research, treatment × location and treatment × season interactions play a crucial role. Analyzing a series of experiments across diverse conditions allows for more generalized and reliable recommendations. The 'CANE' package facilitates the pooled analysis of experiments conducted over multiple years, seasons, or locations. It is designed to assess treatment interactions with environmental factors (such as location and season) using various experimental designs. The package supports pooled analysis of variance (ANOVA) for the following designs: (1) 'PooledCRD()': completely randomized design; (2) 'PooledRBD()': randomized block design; (3) 'PooledLSD()': Latin square design; (4) 'PooledSPD()': split plot design; and (5) 'PooledStPD()': strip plot design. Each function provides the following outputs: (i) Individual ANOVA tables based on independent analysis for each location or year; (ii) Testing of homogeneity of error variances among distinct locations using Bartlett’s Chi-Square test; (iii) If Bartlett’s test is significant, 'Aitken’s' transformation, defined as the ratio of the response to the square root of the error mean square, is applied to the response variable; otherwise, the data is used as is; (iv) Combined analysis to obtain a pooled ANOVA table; (v) Multiple comparison tests, including Tukey's honestly significant difference (Tukey's HSD) test, Duncan’s multiple range test (DMRT), and the least significant difference (LSD) test, for treatment comparisons. The statistical theory and steps of analysis of these designs are available in Dean et al. (2017) and Ruíz et al. (2024). By broadening the scope of experimental conclusions, 'CANE' enables researchers to derive robust, widely applicable recommendations. This package is particularly valuable in agricultural research, where accounting for treatment × location and treatment × season interactions is essential for ensuring the validity of findings across multiple settings. 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Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) . 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Amino acid compositions can be read from FASTA files and used to calculate chemical metrics including carbon oxidation state and stoichiometric hydration state, as described in Dick et al. (2020) . Other properties that can be calculated include protein length, grand average of hydropathy (GRAVY), isoelectric point (pI), molecular weight (MW), standard molal volume (V0), and metabolic costs (Akashi and Gojobori, 2002 ; Wagner, 2005 ; Zhang et al., 2018 ). A database of amino acid compositions of human proteins derived from UniProt is provided. 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Automatically captures the entire analysis environment including R session info, package versions, external tool versions ('Samtools', 'STAR', 'BWA', etc.), 'conda' environments, reference genomes, data provenance with smart checksumming for large files, parameter choices, random seeds, and hardware specifications. Generates executable scripts with 'Docker', 'Singularity', and 'renv' configurations. Integrates with workflow managers ('Nextflow', 'Snakemake', 'WDL', 'CWL') to ensure complete reproducibility of computational research workflows. 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Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019. 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This makes it possible to solve equations symbolically, find symbolic integrals, symbolic sums and other important quantities. Package: r-cran-caradpt Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-caradpt_0.1.0-1.ca2604.1_all.deb Size: 196922 MD5sum: 08e67d26505171ab39381f2b5e511485 SHA1: e3c0d9d7001fed97bc5b28f68b2b155876afe390 SHA256: 7ae1c5707c082963f7b42b7370ba7ca499e34f0744f4f4bd191b19ea3c281509 SHA512: ff6f30fc304b1b5f13b23518a85b44e3baa89d8c6d2b71b47935bcb75dd854581a6a2bdf39e7a7d48d5834e24950750d02021b73190fe2603a89ccde1d648479 Homepage: https://cran.r-project.org/package=caradpt Description: CRAN Package 'caradpt' (Covariate-Adjusted Response-Adaptive Designs for Clinical Trials) Tools for implementing covariate-adjusted response-adaptive procedures for binary, continuous and survival responses. Users can flexibly choose between two functions based on their specific needs for each procedure: use real patient data from clinical trials to compute allocation probabilities directly, or use built-in simulation functions to generate synthetic patient data. Detailed methodologies and algorithms used in this package are described in the following references: Zhang, L. X., Hu, F., Cheung, S. H., & Chan, W. S. (2007) Zhang, L. X. & Hu, F. (2009) Hu, J., Zhu, H., & Hu, F. (2015) Zhao, W., Ma, W., Wang, F., & Hu, F. (2022) Mukherjee, A., Jana, S., & Coad, S. (2024) . Package: r-cran-carbayesdata Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Filename: pool/dists/resolute/main/r-cran-carbayesdata_3.0-1.ca2604.1_all.deb Size: 355722 MD5sum: 75ab7cddfba068d47d0651c052e4310e SHA1: 101c3de2c0cb68462b0fc8ecbb9b632eaf68715e SHA256: d27bb643baa1cddb5ae7cd4f1c18ffd5c0f4e6d65573a740143c361d715e436c SHA512: 1bf4d6b1b641b83152f91788deb4f9a6aeabe402b76c71a39e3130c87a6e2e97bf562a6d482a57b3d22a55c9ada6c273fa787cc3ec572c9837b5a886d223c538 Homepage: https://cran.r-project.org/package=CARBayesdata Description: CRAN Package 'CARBayesdata' (Data Used in the Vignettes Accompanying the CARBayes andCARBayesST Packages) Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ) that make up the 2001 definition of the Greater Glasgow and Clyde health board. Package: r-cran-carbondata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-readxl Suggests: r-cran-arrow, r-cran-nanoparquet, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-carbondata_0.1.0-1.ca2604.1_all.deb Size: 108692 MD5sum: 386aa99cfa19072c46e982a3e633e16c SHA1: 672aa3b99ea1134b24495847469704d6d206fb47 SHA256: 2ad91dd72a6f99d946b8d6633b5a31ee2636eb229947eccb87cc4b7bffb054b5 SHA512: 27db07d970e1e7773ef77b851a30232ddb869c3c5007fd0836684d5930f3b9be3b69ecd3930460bec123ba68062b872f6ff05f97c5488c8a7ea8e4e8aba3d1e8 Homepage: https://cran.r-project.org/package=carbondata Description: CRAN Package 'carbondata' (Access Carbon Market Data from Emissions Trading Systems andVoluntary Registries) Unified access to carbon market data from compliance emissions trading systems ('EU ETS', 'UK ETS', 'RGGI', California Cap-and-Trade) and voluntary carbon markets (Verra, Gold Standard, American Carbon Registry, Climate Action Reserve, via the Berkeley Voluntary Registry Offsets Database and the 'CarbonPlan' 'OffsetsDB' API). Includes cross-market price data from the 'International Carbon Action Partnership' ('ICAP') Allowance Price Explorer , global carbon pricing from the World Bank Carbon Pricing Dashboard , and the historical 'RFF' World Carbon Pricing Database following Dolphin, Pollitt and Newbery (2020) . Data is downloaded from public sources on first use and cached locally. Package: r-cran-carbonpredict Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5049 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-progress, r-cran-lmertest, r-cran-networkd3, r-cran-htmltools, r-cran-htmlwidgets Suggests: r-cran-testthat, r-cran-mockery, r-cran-png Filename: pool/dists/resolute/main/r-cran-carbonpredict_2.0.1-1.ca2604.1_all.deb Size: 4777508 MD5sum: e7caf901ee51ea58553cdec6034c0b06 SHA1: 0b27d500f502b63cccfb482a87f106e2f590742c SHA256: d978c913b02a2ca607e7cdae23f62fc936105515af49561575f5bb8f0c6dbf33 SHA512: 8f58d3cdc1efee675697ab4e71584935cf555d2256b8431475a314db883c106a1f00a07a9da56018d6f17ab1478cc98c949c6dba092ad33575c005d229ceac59 Homepage: https://cran.r-project.org/package=carbonpredict Description: CRAN Package 'carbonpredict' (Predict Carbon Emissions for UK SMEs) Predict Scope 1, 2 and 3 carbon emissions for UK Small and Medium-sized Enterprises (SMEs), using Standard Industrial Classification (SIC) codes and annual turnover data, as well as Scope 1 carbon emissions for UK farms. The 'carbonpredict' package provides single and batch prediction, plotting, and workflow tools for carbon accounting and reporting. The package utilises pre-trained models, leveraging rich classified transaction data to accurately predict Scope 1, 2 and 3 carbon emissions for UK SMEs as well as identifying emissions hotspots. It also provides Scope 1 carbon emissions predictions for UK farms of types: Cereals ex. rice, Dairy, Mixed farming, Sheep and goats, Cattle & buffaloes, Poultry, Animal production and Support for crop production. The methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publications: Phillpotts, A., Owen. A., Norman, J., Trendl, A., Gathergood, J., Jobst, Norbert., Leake, D. (2025) "Bridging the SME Reporting Gap: A New Model for Predicting Scope 1 and 2 Emissions" and Wells, J., Trendl, A., Owen, A., Barrett, J., Gridley, J., Jobst, N., Leake, D. (2025) "A Scalable Tool for Farm-Level Carbon Accounting: Evidence from UK Agriculture". 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Package: r-cran-carcass Architecture: all Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-survival, r-cran-expm, r-cran-arm, r-cran-mass Filename: pool/dists/resolute/main/r-cran-carcass_1.9-1.ca2604.1_all.deb Size: 107090 MD5sum: ea1c7944622a105b6ff8f2b7c37da1c0 SHA1: 8b75a68e877f0c5beb8604edd734ee07d3436efd SHA256: b20a8bd1f55a2a0abbaffb6ca166b0e1a9bed36be594bc0304e951c86786e147 SHA512: 3da04d4155cb203906795dae4ca05ccace2205c9e6ff88cebc20e2209398b4fa56915ef64ca7adca5676f1f89fa01fef6e7a781fd1ca997986083aac252e34f8 Homepage: https://cran.r-project.org/package=carcass Description: CRAN Package 'carcass' (Estimation of the Number of Fatalities from Carcass Searches) The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability. 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Package: r-cran-cardiacdp Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-purrr, r-cran-rcolorbrewer, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cardiacdp_0.4.2-1.ca2604.1_all.deb Size: 241462 MD5sum: e8e7eda26accc6e17466c0f7baea2d60 SHA1: 5b59abb54c3cfdcd59aa5a5d17b85eef5f4393c8 SHA256: c081a9ab63df2078ebfd79c7f58d48aea2de24815a145211538ed6b05e004529 SHA512: 6e56928f1b1815b9e17404ebe7f426632ead0d2394dee7f104fb9d139cee6f99e838518f04da6371dfe779d1165357f4a8b7e675a1422d7ff2da6a108dddb065 Homepage: https://cran.r-project.org/package=CardiacDP Description: CRAN Package 'CardiacDP' (Automated Cardiac Data Processing via ACF, GA & Tracking Index) An algorithm developed to efficiently and accurately process complex and variable cardiac data with three key features: 1. employing autocorrelation to identify recurrent heartbeats and use their periods to compute heart rates; 2. incorporating a genetic algorithm framework to minimize data loss due to noise interference and accommodate within-sequence variations; and 3. introducing a tracking index as a moving reference to reduce errors. Lau, Wong, & Gu (2026) . Package: r-cran-cardidates Architecture: all Version: 0.4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-pastecs, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-cardidates_0.4.9-1.ca2604.1_all.deb Size: 251634 MD5sum: 732d59a53e6e6fc432fedda9b81aee16 SHA1: d55bee06cec9def0f0715cf26f466ff9f1c34038 SHA256: 94a7d14c1929e4f547182605560dd5762834420db3919a730442e3e02623803a SHA512: 03c915b1becc9282a5de0a1a9413f81b38a97229bfedd9f10af30afc76e2da95cf8852334fbe059580c02d537f6fe8efe29c928394c3f749bbd1c1a4690ee7a6 Homepage: https://cran.r-project.org/package=cardidates Description: CRAN Package 'cardidates' (Identification of Cardinal Dates in Ecological Time Series) Identification of cardinal dates (begin, time of maximum, end of mass developments) in ecological time series using fitted Weibull functions. Package: r-cran-cardinalr Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3056 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-geozoo, r-cran-gtools, r-cran-mass, r-cran-mvtnorm, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-langevitour, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cardinalr_1.0.6-1.ca2604.1_all.deb Size: 1643178 MD5sum: 81844414dcde56c5ad7299add6042fe5 SHA1: b95ae8a861aa8485f70149fc9f165253cdd9aefb SHA256: 48cfbfdc5afbe9def5f827a0476de5c559d1aff9717836824e8b8cccebf3974a SHA512: 6ace716e125055b64ae60bf880e1a2e8bb55ec997abb46ee9f593b2ef65807965fd41f820b59311bfd579f98e89c748beaa252efe547468a922807cccefe5b3e Homepage: https://cran.r-project.org/package=cardinalR Description: CRAN Package 'cardinalR' (Collection of Data Structures) A collection of functions to generate a large variety of structures in high dimensions. These data structures are useful for testing, validating, and improving algorithms used in dimensionality reduction, clustering, machine learning, and visualization. Package: r-cran-cardiocurver Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal, r-cran-ggplot2, r-cran-gridextra, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cardiocurver_1.0.0-1.ca2604.1_all.deb Size: 261692 MD5sum: 96255c51fd7e13a7f52043b5ee846cc8 SHA1: beb3239580302aa9f33e0b369f9b205994e690af SHA256: 4ea5582c7adfe474f9e96a2fc49724ed0488f4b8ea2946b5d9cbf9149d96cfe5 SHA512: 39a33a19c96edb5a1fb70ddc4cb5e11cbb1cab7e9cb04ce77fac7be877f4ed1c156c61c83482a16eaa9a9e26ecd13e2cf824cab42a51a843bc900c0908a37422 Homepage: https://cran.r-project.org/package=CardioCurveR Description: CRAN Package 'CardioCurveR' (Nonlinear Modeling of R-R Interval Dynamics) Automated and robust framework for analyzing R-R interval (RRi) signals using advanced nonlinear modeling and preprocessing techniques. The package implements a dual-logistic model to capture the rapid drop and subsequent recovery of RRi during exercise, as described by Castillo-Aguilar et al. (2025) . In addition, 'CardioCurveR' includes tools for filtering RRi signals using zero-phase Butterworth low-pass filtering and for cleaning ectopic beats via adaptive outlier replacement using local regression and robust statistics. These integrated methods preserve the dynamic features of RRi signals and facilitate accurate cardiovascular monitoring and clinical research. Package: r-cran-cardiodatasets Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cardiodatasets_0.2.0-1.ca2604.1_all.deb Size: 537294 MD5sum: 22a55e84369fd298b2245d46535a0a6e SHA1: 283dafaf32695466ee43f99db2c8d56e34562e75 SHA256: f051fdc53b8f1f9c0afc16b2d2bc33e9a09efe9e942f66cd1ec87d9edfb67467 SHA512: 0b414cd0121e817963067b792a880471a01c10d97f6896775c219cdc0328d8666cb61ff24d15e2b78debd5ec68a530e9da2105871b64beb9fc122c4c60c87b4a Homepage: https://cran.r-project.org/package=CardioDataSets Description: CRAN Package 'CardioDataSets' (A Comprehensive Collection of Cardiovascular and Heart DiseaseDatasets) Offers a diverse collection of datasets focused on cardiovascular and heart disease research, including heart failure, myocardial infarction, aortic dissection, transplant outcomes, cardiovascular risk factors, drug efficacy, and mortality trends. Designed for researchers, clinicians, epidemiologists, and data scientists, the package features clinical, epidemiological, and simulated datasets covering a wide range of conditions and treatments such as statins, anticoagulants, and beta blockers. It supports analyses related to disease progression, treatment effects, rehospitalization, and public health outcomes across various cardiovascular patient populations. Package: r-cran-cards Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-cards_0.7.1-1.ca2604.1_all.deb Size: 677442 MD5sum: 3c6ebdef03fcb55bc1ec81ccee4c56ec SHA1: 392c91c470059ce40249a3d1260b4603113429d0 SHA256: a620fcf3f190636d00c912cb0c1d1bbc3493e2b5d04da269639fb99b3eff8dc6 SHA512: 5d218536cd2c6f3321239b059fe710bedc737ac78499990a668032794572605144f8ed32b895c5a98224c423fda672d72cb905bd93672ed73af78c69e146987d Homepage: https://cran.r-project.org/package=cards Description: CRAN Package 'cards' (Analysis Results Data) Construct CDISC (Clinical Data Interchange Standards Consortium) compliant Analysis Results Data objects. 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Package: r-cran-cardx Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cards, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-tidyr Suggests: r-cran-aod, r-cran-broom, r-cran-broom.helpers, r-cran-broom.mixed, r-cran-car, r-cran-effectsize, r-cran-emmeans, r-cran-geepack, r-cran-ggsurvfit, r-cran-lme4, r-cran-parameters, r-cran-smd, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-cardx_0.3.2-1.ca2604.1_all.deb Size: 579790 MD5sum: 582128b33a4e5bed7d0a34c091dff5fe SHA1: 6f5103275e5d7bb55e96aa6cc03f5f29a6b83ff5 SHA256: bc7a13767f66c6d1eae5a9e85b6ed02579c564611255d24fa8a3575cc1655e60 SHA512: cb2c50a4ffdf1acf8997b7489212db59cae5b558f015c51eee90b6002b03e55b50a0d9992eb33df3bd85adcfc25b89db9b73dba6d0796e0f79b213e09a8ec277 Homepage: https://cran.r-project.org/package=cardx Description: CRAN Package 'cardx' (Extra Analysis Results Data Utilities) Create extra Analysis Results Data (ARD) summary objects. The package supplements the simple ARD functions from the 'cards' package, exporting functions to put statistical results in the ARD format. These objects are used and re-used to construct summary tables, visualizations, and written reports. Package: r-cran-care4cmodel Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-ggplot2, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-care4cmodel_1.0.3-1.ca2604.1_all.deb Size: 247288 MD5sum: ba3e7eaf29c5de39a2b74065f9a9211f SHA1: 938de83f2ad16daa96b7df8c035f2fcb22a1c1c6 SHA256: 850aab5d044c8bba808a57472f2601ee8805af300cabccc93706e1f3039171e4 SHA512: 6f840693cc3d1047c8dfb93c5b8bd8f9c6c1e743448726a169f2f9600caaa7f9a289eeeb1972eb1866c8193ee466f66db5a2597a3127ef1c8fe926abefcd1e01 Homepage: https://cran.r-project.org/package=care4cmodel Description: CRAN Package 'care4cmodel' (Carbon-Related Assessment of Silvicultural Concepts) A simulation model and accompanying functions that support assessing silvicultural concepts on the forest estate level with a focus on the CO2 uptake by wood growth and CO2 emissions by forest operations. 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Package: r-cran-care Architecture: all Version: 1.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor Suggests: r-cran-crossval Filename: pool/dists/resolute/main/r-cran-care_1.1.11-1.ca2604.1_all.deb Size: 153882 MD5sum: ddde042e5ba2e1d6d5f12ed33e143578 SHA1: 973a100b3e40ea8a0559e82dc83e842e281313bd SHA256: 6b7752d4b871f86d240cf1b96a03513cb91c97e5eacc2eb988bf762dbb25bf77 SHA512: 1e0c8c7c47090b8a71fc7aab6a93636ea6cb04b93eb7e3d3fecbe893581606a3c48634d4fda075cf30e5d702b5a28a5c924d98f14049da90d223ae41d09d3798 Homepage: https://cran.r-project.org/package=care Description: CRAN Package 'care' (High-Dimensional Regression and CAR Score Variable Selection) Implements the regression approach of Zuber and Strimmer (2011) "High-dimensional regression and variable selection using CAR scores" SAGMB 10: 34, . CAR scores measure the correlation between the response and the Mahalanobis-decorrelated predictors. The squared CAR score is a natural measure of variable importance and provides a canonical ordering of variables. This package provides functions for estimating CAR scores, for variable selection using CAR scores, and for estimating corresponding regression coefficients. Both shrinkage as well as empirical estimators are available. Package: r-cran-carecall Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-spelling, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-carecall_0.1.0-1.ca2604.1_all.deb Size: 91590 MD5sum: 14e291f1728fb399dd89b830350d7247 SHA1: 38caa69d69d251ec6285607153021ad4892c4a28 SHA256: e3032160da459d312d881e5b4318d3c79b49469e3abffb95aef378de265df619 SHA512: 75ebf47a92bbf80025a25c2254b8b9481f423dabb62d37c8d292352b807e0fab442a66af0d9b8d2e7cb1f4ca11ad8b97aeea26d5f7f3a0fec1f1dfc0671b0350 Homepage: https://cran.r-project.org/package=caRecall Description: CRAN Package 'caRecall' (Government of Canada Vehicle Recalls Database API Wrapper) Provides API access to the Government of Canada Vehicle Recalls Database used by the Defect Investigations and Recalls Division for vehicles, tires, and child car seats. The API wrapper provides access to recall summary information searched using make, model, and year range, as well as detailed recall information searched using recall number. Package: r-cran-caredensity Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixextra, r-cran-data.table, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-caredensity_0.1.0-1.ca2604.1_all.deb Size: 58208 MD5sum: fb0d892009caadc143b2c71976956427 SHA1: e29c8ba6d2f6faea4a54ed98b65099810f298601 SHA256: fc48ccd5148169354b39f366dff6dc41d4d48868aca677e21a83e703593bcc2d SHA512: 42bd63c0c6b3e5e02d2f3f1502ea01db7df5ec7d6cff9ce3fd73bdea2dd6268a0d8692bbbcb2b60206b67a9501cc91e0b219fb9504a3357c95c239b6aa599ef5 Homepage: https://cran.r-project.org/package=CareDensity Description: CRAN Package 'CareDensity' (Calculate the Care Density or Fragmented Care Density Given aPatient-Sharing Network) Given a patient-sharing network, calculate either the classic care density as proposed by Pollack et al. (2013) or the fragmented care density as proposed by Engels et al. (2024) . By utilizing the 'igraph' and 'data.table' packages, the provided functions scale well for very large graphs. Package: r-cran-careless Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-careless_1.2.2-1.ca2604.1_all.deb Size: 113890 MD5sum: 68b72ba64ca9c0ef9ce70053ae3aca79 SHA1: fd8623ac9fc6fb7bea56ce0da707f43447445e54 SHA256: 4321853442671f2d26b558abcbcbdb778346d66f6aacb7d8879b8156765e2613 SHA512: 14b48184a8fe111b557ec566d0c0e66f8fdba98071b2d742a5e41a4a6bf2496837ae5afda4ad46dc4d43297bf176dac483b2bfc24b23b50aa2dad964be291a90 Homepage: https://cran.r-project.org/package=careless Description: CRAN Package 'careless' (Procedures for Computing Indices of Careless Responding) When taking online surveys, participants sometimes respond to items without regard to their content. These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing, such as spurious correlations. The 'R' package 'careless' provides solutions designed to detect such careless / insufficient effort responses by allowing easy calculation of indices proposed in the literature. It currently supports the calculation of longstring, even-odd consistency, psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual response variability (also termed inter-item standard deviation). For a review of these methods, see Curran (2016) . Package: r-cran-caresid Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ca, r-cran-ggplot2, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-caresid_0.1-1.ca2604.1_all.deb Size: 24692 MD5sum: dbfd077e0a1545a71ccd4df13758e437 SHA1: ea2395904930fcdbe2d96c9de97fa18068cf6759 SHA256: 43a3f8f1c4aa204ff4d8e17b985c7722fb45652c5af73f7900297264bf3f75ba SHA512: df71bd26e6942e857fbd4bdc8bdeada18229b7c6f50bc279cf3b8d713e84047968f8d30c66c0a8eac831a4937f0d9f938ea9270a66f45adae2ac158dd359096f Homepage: https://cran.r-project.org/package=caresid Description: CRAN Package 'caresid' (Correspondence Analysis Plot and Associations Visualisation) Performs a Correspondence Analysis (CA) on a contingency table and creates a scatterplot of the row and column points on the selected dimensions. 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Package: r-cran-caretensemble Architecture: all Version: 4.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-ggplot2, r-cran-lattice, r-cran-patchwork, r-cran-pbapply, r-cran-rlang Suggests: r-cran-mass, r-cran-catools, r-cran-covr, r-cran-earth, r-cran-gbm, r-cran-glmnet, r-cran-klar, r-cran-knitr, r-cran-lintr, r-cran-mgcv, r-cran-mlbench, r-cran-nnet, r-cran-randomforest, r-cran-rmarkdown, r-cran-rhub, r-cran-rpart, r-cran-spelling, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-caretensemble_4.0.1-1.ca2604.1_all.deb Size: 3124294 MD5sum: 747ce798350339a69cb8656e73d2c493 SHA1: 2568ee615e12d078a3b5d58bf605f419ae63ca36 SHA256: 42d158702d424bd5aaa1985bd3a7a3aca0f6668e64da90a19221f77c4c4896cc SHA512: 522c073d91c50c82f03553171c1df907e8f7f9692557b17658cc4f5b5522377fbb29dcf3747a492648c2f86e07d695d24a9ea20f22036e8ed7f5fecb66542101 Homepage: https://cran.r-project.org/package=caretEnsemble Description: CRAN Package 'caretEnsemble' (Ensembles of Caret Models) Functions for creating ensembles of caret models: caretList() and caretStack(). caretList() is a convenience function for fitting multiple caret::train() models to the same dataset. caretStack() will make linear or non-linear combinations of these models, using a caret::train() model as a meta-model. Package: r-cran-caretforecast Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-caret, r-cran-magrittr, r-cran-dplyr, r-cran-generics Suggests: r-cran-cubist, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-caretforecast_0.1.3-1.ca2604.1_all.deb Size: 2173364 MD5sum: 0e48d5d8fcc0abdcb3a81dcb6f9f8c09 SHA1: 6c5581c1209da71e28805b28ab25c246e756b512 SHA256: 0aed70e3b1a65ca5dad8b879d0991a4c86e7def89ac4041d40da88ce777bc10d SHA512: 7997e0c004da8d13b2892e1c800f631faa25cb49ab0d8937e0cf1c5738180b7db053a585a2d3c6f9927ab6902b3bb528b2aaefaa8d0c9b22a23afcb4f3c9cd31 Homepage: https://cran.r-project.org/package=caretForecast Description: CRAN Package 'caretForecast' (Conformal Time Series Forecasting Using State of Art MachineLearning Algorithms) Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively. Package: r-cran-caretsdm Architecture: all Version: 1.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-blockcv, r-cran-caret, r-cran-checkcli, r-cran-checkmate, r-cran-cli, r-cran-coordinatecleaner, r-cran-data.table, r-cran-dismo, r-cran-dplyr, r-cran-ecdfniche, r-cran-ecospat, r-cran-fs, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggspatial, r-cran-glue, r-cran-gtools, r-cran-httr2, r-cran-lwgeom, r-cran-mapview, r-cran-maxnet, r-cran-parallelly, r-cran-pdp, r-cran-proc, r-cran-progressr, r-cran-purrr, r-cran-raster, r-cran-rgbif, r-cran-rtsne, r-cran-sf, r-cran-stars, r-cran-stringdist, r-cran-stringr, r-cran-terra, r-cran-tidyr, r-cran-usdm Suggests: r-cran-bench, r-cran-biomod2, r-cran-gbm, r-cran-cito, r-cran-covr, r-cran-e1071, r-cran-earth, r-cran-gam, r-cran-here, r-cran-kknn, r-cran-knitr, r-cran-mda, r-cran-naivebayes, r-cran-nnet, r-cran-r.utils, r-cran-randomforest, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-rpart, r-cran-rsnns, r-cran-sdm, r-cran-tibble, r-cran-withr, r-cran-xgboost, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-caretsdm_1.8.3-1.ca2604.1_all.deb Size: 742530 MD5sum: 4bfa8cd5e85f71beb551e9cac7affd7b SHA1: 38964b2d5f9c1cac820e5f32305fb07d6bdf2075 SHA256: cc7aa7ff44ea74ddfd3c833470c437e60f5604b49e3078d743e8e8551b486916 SHA512: 676ef71df027881e081ce81f073f352e070fdf8931318faec17e4ee839c41d2a9fd81c9cc3322521fd01a918fc6c2004b7c15e435ee92fbd13effd5a8c0cc30c Homepage: https://cran.r-project.org/package=caretSDM Description: CRAN Package 'caretSDM' (Build Species Distribution Modeling using 'caret') Use machine learning algorithms and advanced geographic information system tools to build Species Distribution Modeling in a extensible and modern fashion. Package: r-cran-carfima Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-deoptim, r-cran-pracma, r-cran-truncnorm, r-cran-invgamma Filename: pool/dists/resolute/main/r-cran-carfima_2.0.2-1.ca2604.1_all.deb Size: 71464 MD5sum: 1381849e80a94aa9320cc985e66831b0 SHA1: e4fe25a5df4f3d9c852fdc7b835c007f755f13f2 SHA256: 3171aa04a56ce2facc63d48ce16814cff904d766ba9ba653e368028e9e5eae25 SHA512: 36e22bf7c0db8211951ea18494c21f1cb7e828b2cac8595f28e340811d24b8d30d07cbd4544621d94018a38f2bca4b2eb10274d05f34a8c5a170d2aed869a29f Homepage: https://cran.r-project.org/package=carfima Description: CRAN Package 'carfima' (Continuous-Time Fractionally Integrated ARMA Process forIrregularly Spaced Long-Memory Time Series Data) We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via both frequentist and Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. Also, the model can account for heteroscedastic measurement errors, if the information about measurement error standard deviations is known. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces posterior samples of the model parameters via Metropolis-Hastings within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times. Package: r-cran-cargo Architecture: all Version: 0.4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-cargo_0.4.9-1.ca2604.1_all.deb Size: 254568 MD5sum: 74f12f747226fc1680905625000aafbd SHA1: a4516513bea3f8826b1b292dc82a2d280ff46dc2 SHA256: 9d850e6df07cf4be4f1f15dfea390265a5778fb0999beec337bca7e45c4a79f3 SHA512: cd6b621cb2847fcfb058f7c53081f9b83cf72c22f5d45ab27284920f4a8cda383e2e626f80391fe9ffe8d52f41a2ec45e94d01420d1ef1e7d780f6025d555257 Homepage: https://cran.r-project.org/package=cargo Description: CRAN Package 'cargo' (Develop R Packages using Rust) A framework is provided to develop R packages using 'Rust' with minimal overhead, and more wrappers are easily added. Help is provided to use 'Cargo' in a manner consistent with CRAN policies. 'Rust' code can also be embedded directly in an R script. The package is not official, affiliated with, nor endorsed by the Rust project. Package: r-cran-caribou Architecture: all Version: 1.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-caribou_1.1-1-1.ca2604.1_all.deb Size: 55548 MD5sum: 526b7cc50ecab04a5c7e8f6e8c22e074 SHA1: 88f73f23d13a25fe0d74e116491d94d77558d059 SHA256: f19416ef3f93b76befb02c54c1f26a1fc22a78c661e54f44022fb4b4a5cb5f36 SHA512: dfe8fa3acdeecb7e6bcecf17fab9ba213c0090738a303c6258894e30605ef909d3db4b01ffb0af519ec28f54da31cf6b7cc5b97b3c89347f195927dcac903d1f Homepage: https://cran.r-project.org/package=caribou Description: CRAN Package 'caribou' (Estimation of Caribou Abundance Based on Radio Telemetry Data) Estimation of population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990). Package: r-cran-carletonstats Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-patchwork Suggests: r-cran-mass, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-carletonstats_2.2-1.ca2604.1_all.deb Size: 174230 MD5sum: e267cc149d643d5823c2c38726201329 SHA1: a46f70797be0126df739d100a231964900d0c13a SHA256: 8ab3d24b51e6506fcf906cafd1f9e8c8022c809890c6a7eddf429e9e41343254 SHA512: e02b01d7f1142244dd745ae15ca33faf0b6f8564e8a18497cb5cdd6254af159a130e29da149f7c15e937bcb2ad30a3ea2d5f871eba85c213f97c2a0463dc6b8a Homepage: https://cran.r-project.org/package=CarletonStats Description: CRAN Package 'CarletonStats' (Functions for Statistics Classes at Carleton College) Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions. Package: r-cran-carm Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-dplyr, r-cran-mass Filename: pool/dists/resolute/main/r-cran-carm_2.0.0-1.ca2604.1_all.deb Size: 43392 MD5sum: 9638bf019cacc85c032d561e46ccd418 SHA1: d225ad885f7cd996e2888ce18b8a4ab835c445e8 SHA256: 094adc2ddfd743ab47df81dc31ca1f00f5fbea1f7712e9a1cca84536fe7e8fff SHA512: 2230952e0040f17d8717c425ab83ad1a8cb43df4f997f5cb328ec47d631a1687e73c99d0c590bf57ba63d686df44e578bc4bc78d0caf87f2e819e5bbeed7b640 Homepage: https://cran.r-project.org/package=CARM Description: CRAN Package 'CARM' (Covariate-Adjusted Adaptive Randomization viaMahalanobis-Distance) In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2024). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. . In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.. Package: r-cran-caroc Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-caroc_0.1.5-1.ca2604.1_all.deb Size: 229274 MD5sum: 4dbfffc964d8adf675d6ec7a6d46c79f SHA1: 800476bac1940b0ca6095a7234397b6babe8bca7 SHA256: db0f6b5ca12d289aa08c74129315f6cfb2182bed1bb962636248dde189cb9ea6 SHA512: 540b6a1bb91c3da88f0055b5b2192383039cddf25677b3ab3b7d98d2cb8e1255942d8037ec03de5b654a0a3fa6e2894fca6674ca65307b3bf123a2c190cea3e4 Homepage: https://cran.r-project.org/package=caROC Description: CRAN Package 'caROC' (Continuous Biomarker Evaluation with Adjustment of Covariates) Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Methods are described in Ziyi Li, Yijian Huang, Datta Patil, Martin G. Sanda (2021+) "Covariate adjustment in continuous biomarker assessment". Package: r-cran-caroline Architecture: all Version: 0.9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-caroline_0.9.9-1.ca2604.1_all.deb Size: 201744 MD5sum: 958d7130dcf4043b076568a40349e273 SHA1: 709d28aa0249435b143e2bf39c9c7640c5e74c25 SHA256: 9ea90db54f65d0ec9eb8fb7995d5dc27a6e7c71ac54f939ebdfc8cdac320db58 SHA512: 531562e5c5bb69f24de5ec7ba5386276d458e9b2f43a4442e6b41ab0500984942d19bacbcd4549f1a8c65498c8dbb0020aae9c50aaa80734f80c653ed7f3f9b1 Homepage: https://cran.r-project.org/package=caroline Description: CRAN Package 'caroline' (A Collection of Database, Data Structure, Visualization, andUtility Functions for R) The caroline R library contains dozens of functions useful for: database migration (dbWriteTable2), database style joins & aggregation (nerge, groupBy, & bestBy), data structure conversion (nv, tab2df), legend table making (sstable & leghead), automatic legend positioning for scatter and box plots (), plot annotation (labsegs & mvlabs), data visualization (pies, sparge, confound.grid & raPlot), character string manipulation (m & pad), file I/O (write.delim), batch scripting, data exploration, and more. The package's greatest contributions lie in the database style merge, aggregation and interface functions as well as in it's extensive use and propagation of row, column and vector names in most functions. Package: r-cran-carpenter Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-pander, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-carpenter_0.2.3-1.ca2604.1_all.deb Size: 59280 MD5sum: 7c983bd3884dd0a7c0f2f20477d6b27e SHA1: 9e1db32fc563d873909c09ceb1c26010519ec2ce SHA256: fe976a9f23588f7090eda12eb40f7bf59ca1da6dd121b68f1a0693919665b925 SHA512: ec1c32c66e77548b6dfa61e06a5abf604f194d7726e2124b43d4871d007df3762ee47e64b153a019da929cef5d1cf1c50f6747065c3c3af882accfe0ae17274c Homepage: https://cran.r-project.org/package=carpenter Description: CRAN Package 'carpenter' (Build Common Tables of Summary Statistics for Reports) Mainly used to build tables that are commonly presented for bio-medical/health research, such as basic characteristic tables or descriptive statistics. Package: r-cran-carrier Architecture: all Version: 0.3.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lobstr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-carrier_0.3.0.4-1.ca2604.1_all.deb Size: 26640 MD5sum: 1c8243e417968a9fdf3b572f3a6391b8 SHA1: a7da37f3f3b5835e002a7743b24e837a083df3a2 SHA256: 6081f47b232ea5142ea4392bc83a13a300241bb8af684d893bdfac2c1416b43c SHA512: dbdea156dc60102aec7883c0422f204024d0250c9d64d559980fce69d97a11eeda5efe22823c173842d0f1315a2989f8c9a3f3c8edfa036c05ed9c7afb1c8b16 Homepage: https://cran.r-project.org/package=carrier Description: CRAN Package 'carrier' (Isolate Functions for Remote Execution) Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With the carrier package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print at the console with their total size and can be easily tested locally before being sent to a remote. Package: r-cran-carsalgo Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pls, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-carsalgo_0.5.0-1.ca2604.1_all.deb Size: 42454 MD5sum: bc8cebffe676e11d48dc887ac26ebb65 SHA1: 03fce24ef4ae24e698183ecb63a0d306f19c2b3f SHA256: fe9fd1f29bec41bc852216e3a62681452682dbb63fa9c86c8964e8a7e4bb4dc6 SHA512: d0286ac8296d91226fb0f4941b47db35dabde88da7a44533babbd85d6e8c94dd3aeb422083b6019c541170c0fdec67a83afa164b18d412669b3367671b1d51e8 Homepage: https://cran.r-project.org/package=carsAlgo Description: CRAN Package 'carsAlgo' (Competitive Adaptive Reweighted Sampling (CARS) Algorithm) Implements Competitive Adaptive Reweighted Sampling (CARS) algorithm for variable selection from high-dimensional dataset using Partial Least Squares (PLS) regression models. CARS algorithm iteratively applies the Monte Carlo sub-sampling and exponential variable elimination techniques to identify/select the most informative variables/features subjected to minimal cross-validated RMSE score. The implementation of CARS algorithm is inspired from the work of Li et al. (2009) . This algorithm is widely applied in near-infrared (NIR), mid-infrared (MIR), hyperspectral chemometrics areas, etc. Package: r-cran-cartograflow Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-reshape2, r-cran-rlang, r-cran-sf, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-cartography Filename: pool/dists/resolute/main/r-cran-cartograflow_1.0.5-1.ca2604.1_all.deb Size: 371130 MD5sum: e49db736e918dde0fc566dc441e58b91 SHA1: d40867a49b1cf2e5ca7e67a7fbdb56f709057fa0 SHA256: 083f762ea21696d7b342d8d295a951bb457dd5eab830b5f114b09bca8381c23e SHA512: e37fcba2871d39d0227664e66fd491694a1b61ef878b3f55b410ea936f587da926a0902d2427c432c861f3d8a8d08f4fa84678d23f13a0490267699583943bb8 Homepage: https://cran.r-project.org/package=cartograflow Description: CRAN Package 'cartograflow' (Filtering Matrix for Flow Mapping) Functions to prepare and filter an origin-destination matrix for thematic flow mapping purposes. This comes after Bahoken, Francoise (2016), Mapping flow matrix a contribution, PhD in Geography - Territorial sciences. See Bahoken (2017) . Package: r-cran-cartogram Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-packcircles Filename: pool/dists/resolute/main/r-cran-cartogram_0.3.0-1.ca2604.1_all.deb Size: 263112 MD5sum: 116c4f27b0804c6994ee7b588faa0113 SHA1: e57d5d384ef2c036138a1a787fec8ad90378df7a SHA256: 729a3fcab36a47c464af3407d1c5f6f66b1f837c2eacda8a0e5df2afee19eea8 SHA512: 5eac29c3670d1e508bf151ca70d32c68c191f221c67fd646f8900fa04e56b89c63a63113b85841d9d288691c63f85b62a5907da54b7dfa8b72480a33613b6b61 Homepage: https://cran.r-project.org/package=cartogram Description: CRAN Package 'cartogram' (Create Cartograms with R) Construct continuous and non-contiguous area cartograms. Package: r-cran-cartographer Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-sf Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-rnaturalearth, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cartographer_0.2.2-1.ca2604.1_all.deb Size: 162598 MD5sum: 3004d1c3847e137dcbf6507fba0d58e9 SHA1: 9ef50b6c3d79559d199824f765374475108e9800 SHA256: 2ec44b805ac76cff0f13d140ee437386a265ed8692bf848110b4ec0250c11406 SHA512: 79d7d6c81a0e9e28ea26155fe76f317f5090f4a5a33107381bf7363e432474d28c2d0bb404bdd62839e8397c77e1cb3460675ebbf7a5fa87f6eff763b6bd7d95 Homepage: https://cran.r-project.org/package=cartographer Description: CRAN Package 'cartographer' (Turn Place Names into Map Data) A tool for easily matching spatial data when you have a list of place/region names. You might have a data frame that came from a spreadsheet tracking some data by suburb or state. This package can convert it into a spatial data frame ready for plotting. The actual map data is provided by other packages (or your own code). Package: r-cran-cartographr Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3018 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-crayon, r-cran-httr2, r-cran-osmdata, r-cran-ggplot2, r-cran-showtext, r-cran-sysfonts, r-cran-sf Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cartographr_0.2.4-1.ca2604.1_all.deb Size: 2268144 MD5sum: 9bb4c6d0227919902c09b25d03108d3a SHA1: f41a4b88dd7bdf563ab88a747a1158e5d66da061 SHA256: e94098c36e8d19ff4717a94e57f6bd34f0d67a4b2411e903854615b1bf5caa74 SHA512: d10ab40e145658fd6cb19052f14cb3db38dc89856b3ad253e17b461c1a660291ee495992b15b02becfbafcba79850d2e3554e1dd5feb8b3669e46c585c2c91f7 Homepage: https://cran.r-project.org/package=cartographr Description: CRAN Package 'cartographr' (Crafting Print-Ready Maps and Layered Visualizations) Simplifying the creation of print-ready maps, this package offers a user-friendly interface derived from 'ggplot2' for handling OpenStreetMap data. It streamlines the map-making process, allowing users to focus on the story their maps tell. Transforming raw geospatial data into informative visualizations is made easy with simple features 'sf' geometries. Whether for urban planning, environmental studies, or impactful public presentations, this tool facilitates straightforward and effective map creation. Enhance the dissemination of spatial information with high-quality, narrative-driven visualizations! Package: r-cran-cascade Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1899 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-animation, r-cran-cluster, r-cran-igraph, r-cran-lars, r-cran-lattice, r-bioc-limma, r-cran-nnls, r-cran-tnet, r-cran-vgam Suggests: r-cran-r.rsp, r-cran-cascadedata, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cascade_2.4-1.ca2604.1_all.deb Size: 1559176 MD5sum: 9f4451145b99d1e3a99540e1e217d418 SHA1: 43de9a7ebb821666f6b503ad063937ca37f45c69 SHA256: 203253c5ee9b3e2567e9f4aeb5c2865b867d3d9acf6324f7e46fd462d951a2c5 SHA512: a5231474bbdc02ca644983a0bd3d159e06d7e3270c5258fd3715c1a09c9ecd40cd89e818ff67a3f364859446014e74b9c8cc7746f76d700fe40a4ac0344b8538 Homepage: https://cran.r-project.org/package=Cascade Description: CRAN Package 'Cascade' (Selection, Reverse-Engineering and Prediction in CascadeNetworks) A modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) . Package: r-cran-cascadedata Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4224 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cascadedata_1.6-1.ca2604.1_all.deb Size: 4288068 MD5sum: 2b8280e45a0152aa8f7d5e500e89e7d3 SHA1: 37b1909d89ef6886e5817df701ea6b1bec0d383e SHA256: 5a9739078821df5c881d09058eda632cb6254f28618018d5bb308307ac1fcc83 SHA512: d05e17dd00ee55294ff5b42a5db7202cacd6a985716160e180e9c8cfcbb3d2d66107cf51b5b8259522936ebd33e3f8d108a2830521962856eb73a8175211cfd9 Homepage: https://cran.r-project.org/package=CascadeData Description: CRAN Package 'CascadeData' (Experimental Data of Cascade Experiments in Genomics) These experimental expression data (5 leukemic 'CLL' B-lymphocyte of aggressive form from 'GSE39411', ), after B-cell receptor stimulation, are used as examples by packages such as the 'Cascade' one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) . Package: r-cran-cascadeselect Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fontawesome, r-cran-htmltools, r-cran-reactr, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-cascadeselect_1.1.0-1.ca2604.1_all.deb Size: 830952 MD5sum: 9f42f026c7536040be63dcc7dc97723c SHA1: 315f3e10559ad2a9f54a192e73f369e8e311db7a SHA256: cb9feaaa26a53c5a9f8348644b3089cf41368f8d8da6acb5e848c877050d8d1e SHA512: 8e41729df975f9e6721447a24bdc0f5ac4be260b5a44023547b3e56481bbb133a1d0fc8a1ab2718d650986242eeaf4b10635cad575988c1677718dbcb879759b Homepage: https://cran.r-project.org/package=cascadeSelect Description: CRAN Package 'cascadeSelect' (A Cascade Select Input for 'Shiny') Provides a cascade select widget for usage in 'Shiny' applications. This is useful for selection of hierarchical choices (e.g. continent, country, city). It is taken from the 'JavaScript' library 'PrimeReact'. Package: r-cran-cascadess Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-magrittr, r-cran-rlang Suggests: r-cran-cli, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cascadess_0.2.0-1.ca2604.1_all.deb Size: 183436 MD5sum: 1546f20da0ad3130c743ae6ec59d0edd SHA1: a477d7840b07f6b3c466aae032602879ea54b858 SHA256: cbeae41cb5cb85e991365be89c301ed6af5d0129326041209e19b1ff32667559 SHA512: 875d79b8d46216b7e99945ba01851ae02fc9d59064e03efa92389f06b71c32e7ec5e77098c61ff4be26ca58559e8bc2ff8407ce3543b10bf97a99e68e9370787 Homepage: https://cran.r-project.org/package=cascadess Description: CRAN Package 'cascadess' (A Style Pronoun for 'htmltools' Tags) Apply styles to tag elements directly and with the .style pronoun. Using the pronoun, styles are created within the context of a tag element. Change borders, backgrounds, text, margins, layouts, and more. Package: r-cran-cascore Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-testthat, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-cascore_0.1.2-1.ca2604.1_all.deb Size: 61154 MD5sum: dd7bcbfec995728de814c4456d1889d1 SHA1: 2c923cf43f320a50e01c7588a49536a1ce275f3f SHA256: b8d10082c7c04e959ca635b3e6fe530be4e5d229d9a7a8c2ecce48f986d5cac2 SHA512: 7a84622db025112fb58e208d556c43bcd6d5eaf248d4a923df8979f7be7b983bc59639e74d8c2de36e1774389faa437f60963adbfeb87d3c12fa9f530c8332dc Homepage: https://cran.r-project.org/package=CASCORE Description: CRAN Package 'CASCORE' (Covariate Assisted Spectral Clustering on Ratios of Eigenvectors) Functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) . In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks. Package: r-cran-cascsim Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2html, r-cran-fitdistrplus, r-cran-moments, r-cran-copula, r-cran-scatterplot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cascsim_0.4-1.ca2604.1_all.deb Size: 665126 MD5sum: 463e509b6d162dc705bb2569e06c4367 SHA1: d34dba50065d9dc5c5a366093e5f2771e736a9b7 SHA256: f2f1c90d7ffc0d89c31b5d217257cd44c17fb2b3c2e0b3d904dbd02b0c0cbcd6 SHA512: 728005a768b6f1c06164c53b73abef06835e1afe12bb617db5c27b419cf30a2c7c1ceb5d0c79df6dd729b3e5185b756a50396f1009ebdacc7028d1e37b67ad4d Homepage: https://cran.r-project.org/package=cascsim Description: CRAN Package 'cascsim' (Casualty Actuarial Society Individual Claim Simulator) It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. 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Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) , Saarela and Arjas (2015) , and Saarela (2015) . Package: r-cran-casematch Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-casematch_1.1.0-1.ca2604.1_all.deb Size: 48004 MD5sum: 17506acbef9903f54877fb61a2229912 SHA1: 1af8cbc15cb8e85e7d630cfb89eebc630e79f403 SHA256: 50b19ddd2361a8a8eb758b79b288374cf4e3280551b83e837fac5c97d12cedb1 SHA512: df55b94389df44707e40bf984bca50d6f356ecdd2f8f0594d79bb0a2b90054dae61cf0527cd3b14e30b457629150248aba7bf4558ec7a1677fe57030810f247d Homepage: https://cran.r-project.org/package=caseMatch Description: CRAN Package 'caseMatch' (Identify Similar Cases for Qualitative Case Studies) Allows users to identify similar cases for qualitative case studies using statistical matching methods. 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For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). ). Package: r-cran-casidata Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5132 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vcdextra, r-cran-car Filename: pool/dists/resolute/main/r-cran-casidata_0.2.1-1.ca2604.1_all.deb Size: 4708696 MD5sum: a0ef4b969b9187f1c1bcc928a64e5ce1 SHA1: 452ae990bab44e4f3bf2bb81becdefa942b09d7c SHA256: c1847d746644f39ba8283c88f6b3d242438d4d48484e692813c2674af924405a SHA512: 26215be82269e79f32d805258b1fe26acfcc4f1515b1915a0f0ccc65e3229ed61b0b31321284e7f117a27e359f0d3bad921878464e4ee5285d77bdd039196b5b Homepage: https://cran.r-project.org/package=CASIdata Description: CRAN Package 'CASIdata' (Datasets from Computer Age Statistical Inference) Provides the datasets from Efron & Hastie (2016, ISBN: 9781108107952), "Computer Age Statistical Inference: Algorithms, Evidence, and Data Science", in an accessible R format for those who want to use them for study or to try to reproduce analyses from the book. 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The main focus of the package is to enable efficient computation of set retrieval and ranked retrieval metrics across multiple dimensions of a dataset, e.g. document strata or subsets of the label set. The package also provides the possibility of computing bootstrap confidence intervals for all major metrics, with seamless integration of parallel computation and propensity scored variants of standard metrics. 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Package: r-cran-cassandra Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bipartite, r-cran-reshape2, r-cran-magrittr, r-cran-vegan, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-boot, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-proc, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-cassandra_0.2.0-1.ca2604.1_all.deb Size: 168264 MD5sum: dcf279e9f91275b4dbf764c2323d5ef0 SHA1: 9928a707a6d31db0f65d4422ec1724b72544fcbc SHA256: 7e68dd48616e26d1905a08c01b440b68063a2145727f4348d9cd4e7d6e458dcc SHA512: 2a82db192c1d7005fd5a74ff2057b882f02d3d9277b9f26041b67068a13739c8ca20adc6d8186279e940dd4b5787e44c0dc5bac6afdd33361b9b2fd0c810086c Homepage: https://cran.r-project.org/package=cassandRa Description: CRAN Package 'cassandRa' (Finds Missing Links and Metric Confidence Intervals inEcological Bipartite Networks) Provides methods to deal with under sampling in ecological bipartite networks from Terry and Lewis (2020) Ecology Includes tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely missing links. 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Package: r-cran-cassowaryr Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-alphahull, r-cran-splancs, r-cran-interp, r-cran-energy, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-progress, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mgcv, r-cran-ggally, r-cran-tidyr, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-cassowaryr_2.0.2-1.ca2604.1_all.deb Size: 505966 MD5sum: 067184ef70836766bbdcf0e682e9ea42 SHA1: 143eb757fe5130706b9423f2f42dac3eac260f6f SHA256: 9d60314e2943efa814667ac949e1219ab66ac00ee6dcc6bcf8c653c119d284bc SHA512: 9544401e056a4de24f85e06b6056bf73ea5011fb13621bea0cc3a9829c2baecc853cb25994fe0df2c1b3bcc2c7f5c7384c2b359c24543a73cc1004ad149c8ad9 Homepage: https://cran.r-project.org/package=cassowaryr Description: CRAN Package 'cassowaryr' (Compute Scagnostics on Pairs of Numeric Variables in a Data Set) Computes a range of scatterplot diagnostics (scagnostics) on pairs of numerical variables in a data set. A range of scagnostics, including graph and association-based scagnostics described by Leland Wilkinson and Graham Wills (2008) and association-based scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be computed. Summary and plotting functions are provided. 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'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) ; Meyer et al. (2019) ; Meyer and Pebesma (2021) ; Milà et al. (2022) ; Meyer and Pebesma (2022) ; Linnenbrink et al. (2024) ; Schumacher et al. (2025) . The package is described in detail in Meyer et al. (2026) . Package: r-cran-castgen Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-rdpack, r-cran-vcfr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-castgen_1.0.2-1.ca2604.1_all.deb Size: 29750 MD5sum: 8bdc8b5a83a189102f0939d7599e557d SHA1: 4ce74e6fd6427f0f33b8591f89b2bd02dd40f007 SHA256: 79e390ca8186b6f4e8c92f13a7b4f7d056456ea84d675d10e3a5019986809533 SHA512: 33e68997492546515efeed199ef48cc47884c2a2181ea05a9d0fa2b9d20097ccedf8f5799078301a6df2da580df30bcc1843b9bd5dac6d33dd76cf677a6c7ed5 Homepage: https://cran.r-project.org/package=castgen Description: CRAN Package 'castgen' (Estimate Sample Size for Population Genomic Studies) Estimate sample sizes needed to capture target levels of genetic diversity from a population (multivariate allele frequencies) for applications like germplasm conservation and breeding efforts. 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Package: r-cran-cat2cat Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-caret, r-cran-dplyr, r-cran-e1071, r-cran-fixest, r-cran-forcats, r-cran-knitr, r-cran-magrittr, r-cran-randomforest, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-cat2cat_0.6.1-1.ca2604.1_all.deb Size: 2627132 MD5sum: fdb3ad48dcd6067c48ead1b6cb601ca0 SHA1: 715572804d14434d2bdda98a8141023a4390f2e6 SHA256: 6f75cb527b780312b18b28e25891352405bb0f48b9c05ac5a78a2af188a3066a SHA512: 3e390c80642c638a89ffead0b601aaaf7a9d0b14a758b718022d722bf8d7d151bebd878c8c4945e3815d94dfd9bd6881e022a324a29e9b3df43714741517015d Homepage: https://cran.r-project.org/package=cat2cat Description: CRAN Package 'cat2cat' (Handling an Inconsistently Coded Categorical Variable in aLongitudinal Dataset) Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. The main rule is to replicate the observation if it could be assigned to a few categories. Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. This procedure was invented and implemented in the paper by 'Nasinski', 'Majchrowska', and 'Broniatowska' (2020) . Package: r-cran-cata Architecture: all Version: 0.1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cata_0.1.1.2-1.ca2604.1_all.deb Size: 210540 MD5sum: 2fda853b0364d2597441caeee524d8e9 SHA1: 5905b021519e90f605ef8a95db232b9de9cece51 SHA256: d86b182fbacee5f4b6890f67e5cc66056753db6cd0da82675a556175f76e96eb SHA512: 6d748e6ddda8cdff4dd7063ce6eb3377c10fbd1d502fb76ea3aeeb7739858e342f4c4d5f0119e3bb96fe677b0ad1bc4dd3cd4b6c19fcb01d880274ab72fc998d Homepage: https://cran.r-project.org/package=cata Description: CRAN Package 'cata' (Analysis of Check-All-that-Apply (CATA) Data) Package contains functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis are provided; for details, see Meyners, Castura & Carr (2013) . 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This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, ), Li and Huang (2023, ). 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Tools for downloading references and addresses of properties, as well as map images. Package: r-cran-catcont Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-catcont_0.5.0-1.ca2604.1_all.deb Size: 28614 MD5sum: 883210ea3cd5a1591971ca3ddcff08c1 SHA1: 00decb33b3dcdc1082e003a6f07d89d132445db3 SHA256: b4a4e38037c8352b1bae6cbd3b4000693527503cab3823d2087dff73c517e839 SHA512: 7bb5d272eca05957e9dbd2bff67cbce0ddd851410f8ce6cb6adcbe611b6a2e355a5a39b5ffe375c87f636c1642aab0698ac5ea7a29f4bb8a05920bdda9f879e8 Homepage: https://cran.r-project.org/package=catcont Description: CRAN Package 'catcont' (Test, Identify, Select and Mutate Categorical or ContinuousValues) Methods and utilities for testing, identifying, selecting and mutating objects as categorical or continous types. 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The names of the examples refer to the chapter and the data set that is used. Package: r-cran-catdataanalysis Architecture: all Version: 0.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-catdataanalysis_0.1-5-1.ca2604.1_all.deb Size: 71634 MD5sum: d696a36c8432db7c0f9cfedf0da44e72 SHA1: f2e7e4c436509d3ca6499076d45264eed75181b0 SHA256: a7a448c554884f16b2442bcb61a3c1a307b8120ae9ac6693453bfa4687f5b431 SHA512: d8adfb77bbea03b63084b3544bb3228db346607a5e987950bb62cec197b5b2697a3f83d0a3827f952f9c303e73dcd00c039e59ea844da1fc962fbbed6cdd99c2 Homepage: https://cran.r-project.org/package=CatDataAnalysis Description: CRAN Package 'CatDataAnalysis' (Datasets for Categorical Data Analysis by Agresti) Datasets used in the book "Categorical Data Analysis" by Agresti (2012, ISBN:978-0-470-46363-5) but not printed in the book. Datasets and help pages were automatically produced from the source by the R script foo.R, which can be found in the GitHub repository. Package: r-cran-catdyn Architecture: all Version: 1.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1532 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimx, r-cran-bb Filename: pool/dists/resolute/main/r-cran-catdyn_1.1-1-1.ca2604.1_all.deb Size: 1468660 MD5sum: 28a79b0216f212f845cfa87085e90db8 SHA1: 2c64f938d92d3aafbbab78c30863072d829bd712 SHA256: 64db1edf2af1e9a5b547ede740751d69f1e397b607ab47cf30954a1980702252 SHA512: 3c22ada5355457f06f28ad3110f86f7b1f3b19284aa0a86e59cfd0c9b643739c75f28e717bb753590f58f44f78ea45edb05dcf9564673a3f7321d68d9dee6bf8 Homepage: https://cran.r-project.org/package=CatDyn Description: CRAN Package 'CatDyn' (Fishery Stock Assessment by Catch Dynamics Models) Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data. Package: r-cran-categoryencodings Architecture: all Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-sparsepca, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-categoryencodings_1.4.3-1.ca2604.1_all.deb Size: 60228 MD5sum: 03f7830aafb46e645beb968a551e1525 SHA1: 289e029ace6c2bba74803336f8676bdd042037c8 SHA256: 3eac3f97ebb7bc34aa6cdee2562ed09f74ab5c6f21946b4430e06b33e4830aa1 SHA512: a4552b3917f68ac378b1d86e3170873702681f8232231c424ec19d403859065b2df236565c6c2b3f027549dffa6abcd87aa245b7d39f5ce2d02b4c00b80963d5 Homepage: https://cran.r-project.org/package=categoryEncodings Description: CRAN Package 'categoryEncodings' (Category Variable Encodings) Simple, fast, and automatic encodings for category data using a data.table backend. Most of the methods are an implementation of "Sufficient Representation for Categorical Variables" by Johannemann, Hadad, Athey, Wager (2019) , particularly their mean, sparse principal component analysis, low rank representation, and multinomial logit encodings. 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Package: r-cran-catfun Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epitools, r-cran-desctools, r-cran-cli, r-cran-magrittr, r-cran-hmisc, r-cran-broom, r-cran-rlang Suggests: r-cran-testthat, r-cran-dplyr, r-cran-forcats Filename: pool/dists/resolute/main/r-cran-catfun_0.1.4-1.ca2604.1_all.deb Size: 101076 MD5sum: fcf063268946a7cfd676c9f1d9d85b37 SHA1: ec32e3b2ced6b77cfa52ad565426dd575459c46d SHA256: 85ea464a7d746abc7b13465478f562ddcbdb89233ce8989d69de3c61cea5a135 SHA512: cd84083dd205cb1fbf1ce7c58c17fe6c42a02c0d56d931c0c2ff790c4955a82235eb1040080c3bba39ce2c570aadad115db0bd537ba6c0ea605d589c700aeacb Homepage: https://cran.r-project.org/package=catfun Description: CRAN Package 'catfun' (Categorical Data Analysis) Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) and Alan Agresti (2002) . Package: r-cran-cati Architecture: all Version: 0.99.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-ade4, r-cran-ape, r-cran-e1071, r-cran-rastervis, r-cran-hypervolume, r-cran-cluster, r-cran-geometry, r-cran-vegan Suggests: r-cran-lattice, r-cran-entropart, r-cran-fbasics, r-cran-picante, r-cran-mice, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cati_0.99.6-1.ca2604.1_all.deb Size: 351260 MD5sum: 67ef456a2fba179452cfb8064dc267a5 SHA1: 60eecd7fbfef831f7d36f64ab3c1f14e9a7b997d SHA256: 46e49957a6ff489700b856c7ad0ec6ae2991bbcf1f399aca14d6b36f867ff82a SHA512: 6ece53068e3a3fb7f99ec1285c0d9d4142245b608303795a9c61b20d3fd4218099186ec9d3c782b5580a612822fccfedf5726cbbe169a651e84a84c66ad1d18a Homepage: https://cran.r-project.org/package=cati Description: CRAN Package 'cati' (Community Assembly by Traits: Individuals and Beyond) Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models. Provides tools to analyse 'intraspecific' trait variability and its consequences for community assembly. Implements a framework using individual-level trait data to decompose variance at the population, species, and community levels. Methods are described in 'Taudiere' and 'Violle' (2016) . Package: r-cran-catmap Architecture: all Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forestplot, r-cran-metafor Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-catmap_1.6.4-1.ca2604.1_all.deb Size: 57498 MD5sum: 768588bb83154a9bb382369b70944f0e SHA1: df81d8dfeeda74dd8d75f8ce7961b6fdcd780e41 SHA256: 8b0647eae7208055b9b8103f9a29a6df7e1999e56623fea49e4e088eccf76657 SHA512: c458c8a443127337102e2867975db1c653cf13354c96687e776c0ae4ea73f87bd6c41b2d7781d53a30221211b1b66c1398e4dcd3510e698d9e5569595345cf14 Homepage: https://cran.r-project.org/package=catmap Description: CRAN Package 'catmap' (Case-Control and TDT Meta-Analysis Package) Although many software tools can perform meta-analyses on genetic case-control data, none of these apply to combined case-control and family-based (TDT) studies. This package conducts fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986) ] meta-analyses on combined genetic data. Specifically, this package implements a fixed-effects model [Kazeem and Farrall (2005) ] and a random-effects model [Nicodemus (2008) ] for combined studies. Package: r-cran-catmaply Architecture: all Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 21351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-viridis, r-cran-lubridate, r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-catmaply_0.9.5-1.ca2604.1_all.deb Size: 1448788 MD5sum: 5214ef5d10b8e73d427054c53b15e693 SHA1: 2005b0d02966a4b1745838d6903585e14cc7bd28 SHA256: bb70235b3038f75d491da34d6e9b901e66090e1362319368b118ba3f1efc9805 SHA512: 2a6bdfb8ce84495e361ba704b019e877bd55040e4ea073b76138694ecf536550255979fd0a356586760d4f2d2a5c3a9acdeb4101d7acad4e14a65692a2878e46 Homepage: https://cran.r-project.org/package=catmaply Description: CRAN Package 'catmaply' (Heatmap for Categorical Data using 'plotly') Methods and plotting functions for displaying categorical data on an interactive heatmap using 'plotly'. Provides functionality for strictly categorical heatmaps, heatmaps illustrating categorized continuous data and annotated heatmaps. Also, there are various options to interact with the x-axis to prevent overlapping axis labels, e.g. via simple sliders or range sliders. Besides the viewer pane, resulting plots can be saved as a standalone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app. Package: r-cran-catool Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-catool_1.0.1-1.ca2604.1_all.deb Size: 82618 MD5sum: 9724b9c60029b51c28211c74cdb2920a SHA1: 303ad3f4ea38bafa511e76daea43361c64035ed9 SHA256: b74679b41256ecbc29e566d662efe57ab07b915147739cb00458f5e9fe14fac0 SHA512: 5de8a454f0829d792e3f0938911684e70792270e6813268c724cd87393914ad5103148ced189739e28ae6a9c8c27529a9240f1527c04fef832ef1c9aed1200a1 Homepage: https://cran.r-project.org/package=catool Description: CRAN Package 'catool' (Compensation Analysis Tool for Instructor Overload Pay) Calculates equitable overload compensation for college instructors based on institutional policies, enrollment thresholds, and regular teaching load limits. Compensation is awarded only for credit hours that exceed the regular load and meet minimum enrollment criteria. When enrollment is below a specified threshold, pay is prorated accordingly. The package prioritizes compensation from high-enrollment courses, or optionally from low-enrollment courses for fairness, depending on user-defined strategy. Includes tools for flexible policy settings, instructor filtering, and produces clean, audit-ready summary tables suitable for payroll and administrative reporting. Package: r-cran-catpredi Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpe, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-mgcv, r-cran-rgenoud, r-cran-rms, r-cran-survival Filename: pool/dists/resolute/main/r-cran-catpredi_2.0-1.ca2604.1_all.deb Size: 135904 MD5sum: 0fd727c0a883dfa2e367d4780849aa00 SHA1: ad0ee5f2113ade090d6061df2a603909e41a05ce SHA256: 2d0c091a89aa1e7a66593a80a3b88f63c2ed5901b79db1174f8c8bab06113473 SHA512: 5f32b70bb6c9c66e00bef9c1e9a2faf64e723e18eed084bb0d624b247cf546c1243b787cef0db08a597f47c620bd266f0cb3ba931ef28dad542546283ca61595 Homepage: https://cran.r-project.org/package=CatPredi Description: CRAN Package 'CatPredi' (Optimal Categorisation of Continuous Variables in PredictionModels) Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) . I Barrio, MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) . Package: r-cran-catr Architecture: all Version: 3.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-catr_3.17-1.ca2604.1_all.deb Size: 551714 MD5sum: f5dde7680f2388aba2cfd3c740e2fe73 SHA1: 97aeb61e2ab99096a53df0d2d7086c13f2c4c9c6 SHA256: dd002f633e8a40596fc29033000c1e30a4c1d478a4e0936e3400e803cb24b39f SHA512: db3705dc547d3366175de2d32b21dfed7b97956cae3454629a0b838dff02c68610ce9d7ceed157ac432118d129930eb40e7ebdc04fa337617078dac095d9054d Homepage: https://cran.r-project.org/package=catR Description: CRAN Package 'catR' (Generation of IRT Response Patterns under Computerized AdaptiveTesting) Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) ). Package: r-cran-catregs Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2072 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tidyverse, r-cran-ggplot2, r-cran-mass, r-cran-emmeans, r-cran-pscl, r-cran-nnet, r-cran-marginaleffects, r-cran-ggpubr, r-cran-knitr, r-cran-rmarkdown, r-cran-epi, r-cran-dplyr, r-cran-ordinal, r-cran-nlme, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-catregs_1.3-1.ca2604.1_all.deb Size: 1881884 MD5sum: df43b7c22d292b313d4cacee4e88d44b SHA1: 91f55980480ea13ab65370cea1473ba540317620 SHA256: c1a381395520f305390eb4f5668d9b37fb285883cb2666086d227b6bc0b28282 SHA512: 6e070c2ca3705788691900595b8bb59093c401304bc2ef375913e1b8519c142d84cedb6f1abe3bf70729bc1911d08805d37bbe5c4d5352b9cfa1eabfd9c6e3c5 Homepage: https://cran.r-project.org/package=catregs Description: CRAN Package 'catregs' (Post-Estimation Functions for Generalized Linear Mixed Models) Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518). Package: r-cran-cats Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-plotly, r-cran-tidyr, r-cran-doparallel, r-cran-foreach, r-cran-openxlsx, r-cran-forcats, r-cran-epitools, r-cran-zoo, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-cats_1.0.2-1.ca2604.1_all.deb Size: 72394 MD5sum: aba96468988398d4ac0fb13246af931d SHA1: 9ec2434e4a95359a4e408451f91b14d7b0ed9efc SHA256: c6c6b8303d6cb50ac8896645bb72ff1ceb434809b19921dceda72b0d05d47c60 SHA512: 746aee06726e1493af1abbc226f6f7e3f5e127a7a558047c0770c65c1ea39a46fc4cc81bc5c42925bd74464edd1dd51cbf39b92d8fe73aea12d9e3140913edb2 Homepage: https://cran.r-project.org/package=cats Description: CRAN Package 'cats' (Cohort Platform Trial Simulation) Cohort plAtform Trial Simulation whereby every cohort consists of two arms, control and experimental treatment. Endpoints are co-primary binary endpoints and decisions are made using either Bayesian or frequentist decision rules. Realistic trial trajectories are simulated and the operating characteristics of the designs are calculated. Package: r-cran-catseyes Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-emmeans Filename: pool/dists/resolute/main/r-cran-catseyes_0.2.5-1.ca2604.1_all.deb Size: 27612 MD5sum: d79d90c77fe094285962b376ef65d7a4 SHA1: 14d673a09c9ac92aaf4001595c1399e1cefd18e0 SHA256: d159eca5d9eb7a6f4bed70d6f8175fb1cf430c94b8674b54bc65e978999be544 SHA512: bdc59fd1543d1b5df8fcf10cd96e3c0d499d0567cddab8622b2945a633edaadcee5eb62901ec2ebf7596894a7035c2a5372d6801bb56d8b29235a23f38a2a956 Homepage: https://cran.r-project.org/package=catseyes Description: CRAN Package 'catseyes' (Create Catseye Plots Illustrating the Normal Distribution of theMeans) Provides the tools to produce catseye plots, principally by catseyesplot() function which calls R's standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. pmid:24220629. Package: r-cran-catt Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-catt_2.0-1.ca2604.1_all.deb Size: 12200 MD5sum: 5f4a6ea8063cf55837da91de87f2b606 SHA1: 1878499af4854776d43053b76946a8d079a9f796 SHA256: 65f75c57fa31c1414a83e7a2faba0b0920d60ec2710cf3e249aaa2de2ff5e679 SHA512: 82ccbe2067700dbab31cd599ea0dec9aeb08369c8e90a60755d6ae204d23752f5727ea63295cdc307b09472e63d106fb7096a46b9b1b12760424d5f76013763c Homepage: https://cran.r-project.org/package=CATT Description: CRAN Package 'CATT' (The Cochran-Armitage Trend Test) This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default. Package: r-cran-cattexact Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cattexact_0.1.1-1.ca2604.1_all.deb Size: 27160 MD5sum: d952855c49c8099f852da0b5b1995ae8 SHA1: 1e3bae600824ad112c6f28c4b4168149d8ff1cab SHA256: 6bef3f8cb95bcf7a24d715327e550cd10e4df7507d58dddf344b53827e01ef93 SHA512: 65fa1c3f5b0e1cb5b709c7d74a3b51678724d48d16311bb6f6a65ce5b28bad55128abcc69999422f5ff77c3130bf50904ce0c08f7ec2f56d956ba96dfa92e536 Homepage: https://cran.r-project.org/package=CATTexact Description: CRAN Package 'CATTexact' (Computation of the p-Value for the Exact ConditionalCochran-Armitage Trend Test) Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test is performed using an algorithm following an idea by Mehta, et al. (1992) . Package: r-cran-catviz Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-catviz_0.1.1-1.ca2604.1_all.deb Size: 103816 MD5sum: 1ba60f1d20361bd6dddc07d0bef5a914 SHA1: d690ae0c019d5ebb3067c3dda65d84e9106b7235 SHA256: f5bd879761698b748a514f7fe61ed1868eebcde87bc9b6c9d1d3e8199682f008 SHA512: 70836a8d2e99519dd5e135f89dd032751b3b87a04b0e0e45a6734ada246f1e9343daca627458fbb5d1ba7fc8d1bf9c8173f4584d587170d5ee97d0ebeae2f68b Homepage: https://cran.r-project.org/package=catviz Description: CRAN Package 'catviz' (Visualizing Causal Assignment Trees for CSDiD and DR-DDD Designs) Tools for constructing, labeling, and visualizing Causal Assignment Trees (CATs) in settings with staggered adoption. Supports Callaway and Sant'Anna difference-in-differences (CSDiD) and doubly robust difference-in-difference-differences (DR-DDD) designs. The package helps clarify treatment timing, never-treated vs. not-yet-treated composition, and subgroup structure, and produces publication-quality diagrams and summary tables. Current functionality focuses on data-to-node mapping, node counts, cohort-year summaries, and high-quality tree plots suitable for empirical applications prior to estimation. Methods are based on Callaway and Sant'Anna (2021) , Sant'Anna and Zhao (2020) , and Kilanko (2026) . Package: r-cran-cauchypca Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-cauchypca_1.3-1.ca2604.1_all.deb Size: 25062 MD5sum: 9fe87446c55638ba2b52980bb8c72288 SHA1: a9e3ebad101598673980d485f077857e3195f87a SHA256: 8796f34699220cee06d2ac728ef0ddb2174d2f5633394f3d954a10d02630b220 SHA512: 3345f31f0b11797ac6e2a9a9f841b4626e97dc16cac1fe2af15c5abfb1c5f93341dfce19a27d8f1609ea779364dd687f0f7e7e9be231b7f4f483156a678a8fb0 Homepage: https://cran.r-project.org/package=cauchypca Description: CRAN Package 'cauchypca' (Robust Principal Component Analysis Using the CauchyDistribution) A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. . Package: r-cran-causact Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-rlang, r-cran-purrr, r-cran-tidyr, r-cran-igraph, r-cran-stringr, r-cran-cowplot, r-cran-forcats, r-cran-rstudioapi, r-cran-lifecycle, r-cran-reticulate Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-rmarkdown, r-cran-extradistr, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-causact_0.6.0-1.ca2604.1_all.deb Size: 2595656 MD5sum: 43a9609b234220b77198d377340e45e3 SHA1: 20e0cc1573175e6239da0a337c72b2ac7d9642a4 SHA256: e5953813265687887c08e3e8dcd26b048c6f7796ea05ab1406c271d3734a8ee6 SHA512: 1396828e773e29cbbdadb527cb8ad161538909e9cffb1fb0ce15105fe308f7e11d2712bd11964ad6e7051dc730fe99e10cfd89f2444d36f7bc230f76b4c7874a Homepage: https://cran.r-project.org/package=causact Description: CRAN Package 'causact' (Fast, Easy, and Visual Bayesian Inference) Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package. Package: r-cran-causal.decomp Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nnet, r-cran-suppdists, r-cran-psweight, r-cran-rlang, r-cran-dyntxregime, r-cran-distr, r-cran-rpart, r-cran-dplyr, r-cran-modelobj, r-cran-magrittr, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-rpart.plot, r-cran-spelling, r-cran-cbps, r-cran-pbmcapply Filename: pool/dists/resolute/main/r-cran-causal.decomp_0.2.0-1.ca2604.1_all.deb Size: 272898 MD5sum: 30ba87a5f801520d66dbbae31e3351f0 SHA1: 90ef30ab353877e94187be4e73a9464170006f31 SHA256: 29e7789120707a0b1a31abfbecb5445d096c8300271b0ad484557f5608f4d6de SHA512: 0cbf69f45893ec19d7ba81c1555b4f2abf3596685a108262f922bfd7c66c5fa739ac666e760d3bcd5fc8ba23aa3d78cb0b1ba6a25a3c68733bb6b925f72aa275 Homepage: https://cran.r-project.org/package=causal.decomp Description: CRAN Package 'causal.decomp' (Causal Decomposition Analysis) We implement causal decomposition analysis using methods proposed by Park, Lee, and Qin (2022) and Park, Kang, and Lee (2023), which provide researchers with multiple-mediator imputation, single-mediator imputation, and product-of-coefficients regression approaches to estimate the initial disparity, disparity reduction, and disparity remaining (; ). We also implement sensitivity analysis for causal decomposition using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023 ). Finally, we include individualized causal decomposition and sensitivity analyses proposed by Park, Kang, and Lee (2025+) . Package: r-cran-causalbatch Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cdcsis, r-bioc-sva, r-cran-matchit, r-cran-nnet, r-cran-dplyr, r-cran-magrittr, r-bioc-genefilter, r-bioc-biocparallel Suggests: r-cran-tidyr, r-cran-ggpubr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-roxygen2, r-cran-ks, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-causalbatch_1.3.0-1.ca2604.1_all.deb Size: 1093336 MD5sum: f82551bb696b7e2d43301da6bfd38bf2 SHA1: 84fe0807dffbfcd3c30138e4be78f09b8ae92077 SHA256: 50e010591dfda25581bc05fe7b0c6944421feef413eecd1e73fb408c5a4605f8 SHA512: a6148faa66f6e320fe6acd51310ae84833400d41120686f2ab00a9e634546258dc032394021ce057d11dab8a38e70a0e0f976b6e630c89b6f493f5fe25c5b880 Homepage: https://cran.r-project.org/package=causalBatch Description: CRAN Package 'causalBatch' (Causal Batch Effects) Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) and Bridgeford et al. (2023) . Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups. Package: r-cran-causalcmprsk Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-inline, r-cran-doparallel, r-cran-foreach, r-cran-data.table, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-tidyverse, r-cran-cobalt, r-cran-ggsci, r-cran-modeva, r-cran-naniar, r-cran-dt, r-cran-hmisc, r-cran-summarytools Filename: pool/dists/resolute/main/r-cran-causalcmprsk_2.0.0-1.ca2604.1_all.deb Size: 374740 MD5sum: 7d531a671a4e0b10b25489c8a32daef9 SHA1: 6f95d4814d4612247477e48e39a988f803788578 SHA256: 2318e673b4c600bb4c0544bba80b9755b159d9176269daf013f9ac2b8b005805 SHA512: 5edef60fed80413a627758f40cefc1fe21924746195b045670ab144a243c130a0716db76c966e0902bb317ed2b49323a75214a5ea436d1aa26000c3f6e2e25e9 Homepage: https://cran.r-project.org/package=causalCmprsk Description: CRAN Package 'causalCmprsk' (Nonparametric and Cox-Based Estimation of Average TreatmentEffects in Competing Risks) Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) . Package: r-cran-causaldata Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-causaldata_0.1.4-1.ca2604.1_all.deb Size: 3135404 MD5sum: c3558e9d4d8dbe99dba4beeb7486ac1c SHA1: 9240124e57b4840ea8907317747de68551d4b107 SHA256: 28b3b0bd43a75323e2d6d8c3cba8f54c44d8daba11ed95582c3bd3e08203b481 SHA512: e8499d120cb3aa7b6196750aff2488ab7564dec309cc156cd87ad2fa25c44a54a1fe2e7938296806195b8da444c4c7e85f8ad916e9ceed498fc84655e77819f6 Homepage: https://cran.r-project.org/package=causaldata Description: CRAN Package 'causaldata' (Example Data Sets for Causal Inference Textbooks) Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" , first and second edition, Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" . Package: r-cran-causaldef Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1957 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-ggplot2 Suggests: r-cran-survival, r-cran-matchit, r-cran-tmle, r-cran-superlearner, r-cran-grf, r-cran-future.apply, r-cran-glue, r-cran-shiny, r-cran-plumber, r-cran-jsonlite, r-cran-cmprsk, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-causaldef_0.2.0-1.ca2604.1_all.deb Size: 1210070 MD5sum: 82b1b6c5329463068887d3fac124d989 SHA1: b3ad3298a5bd02de9e8759ecab2cc701633ba2f9 SHA256: a25613c9dc1b0f4be9319f0d48dddbbf6c9bcc42199dc93cf3263c3157cc0dae SHA512: 63827b776a4b72ff831fd6dc0caf87a5dda589a18f8f76648026c37340408583d28a49a01bba40cba1ffa4d584679144d2f7de53b131f154c92671b134075b16 Homepage: https://cran.r-project.org/package=causaldef Description: CRAN Package 'causaldef' (Decision-Theoretic Causal Diagnostics via Le Cam Deficiency) Implements Le Cam deficiency theory for causal inference, as described in Akdemir (2026) . Provides theorem-backed bounds together with computable proxy diagnostics for information loss from confounding, selection bias, and distributional shift. Supports continuous, binary, count, survival, and competing risks outcomes. Key features include propensity-score total-variation deficiency proxies, negative control diagnostics, policy regret bounds, and sensitivity analysis via confounding frontiers. Package: r-cran-causaldisco Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-caugi, r-cran-checkmate, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-gtools, r-cran-lifecycle, r-cran-micd, r-cran-pcalg, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-s7, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-mockery, r-cran-quarto, r-cran-rhpcblasctl, r-cran-rjava, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr, r-cran-mice Filename: pool/dists/resolute/main/r-cran-causaldisco_1.1.0-1.ca2604.1_all.deb Size: 4397948 MD5sum: 596c63f7d550aee1616d0e5cb2075398 SHA1: 26ab2c1831aa4b41dc5f04909cddc30c1ffcace7 SHA256: 5aacac09f5fa1e35374bc229037ca27191252c37075c992d6de07af55d10b317 SHA512: 951dfddb21571ce8e0b58555e49b7ffe3f90a3080459ac582fc8ded4d7ab9abb06bec1818a20c98bcef8e5f3518b812a1546b3d90b65fe6d79898d5256eabf33 Homepage: https://cran.r-project.org/package=causalDisco Description: CRAN Package 'causalDisco' (Tools for Causal Discovery on Observational Data) Tools for causal structure learning from observational data, with emphasis on temporally ordered variables. The package implements the Temporal Peter–Clark (TPC) algorithm (Petersen, Osler & Ekstrøm, 2021; ), the Temporal Greedy Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen, 2025; ) and Temporal Fast Causal Inference (TFCI). It provides a unified framework for specifying background knowledge, which can be incorporated into the implemented algorithms from the R packages 'bnlearn' (Scutari, 2010; ) and 'pcalg' (Kalish et al., 2012; ), as well as the Java library 'Tetrad' (Scheines et al., 1998; ). The package further includes utilities for visualization, comparison, and evaluation of graph structures, facilitating performance evaluation and methodological studies. Package: r-cran-causaldrf Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-survey Suggests: r-cran-bayestree, r-cran-dplyr, r-cran-foreign, r-cran-hmisc, r-cran-knitr, r-cran-mass, r-cran-nnet, r-cran-reshape2, r-cran-rmarkdown, r-cran-sas7bdat, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-causaldrf_0.4.2-1.ca2604.1_all.deb Size: 828662 MD5sum: 73f8eb8451d4872f1968540eaf3485ef SHA1: a42ff97ad46cc6b74741a0a66f10a96545357225 SHA256: d77f8942dd42adc1bd2c962cab36ffbbc5128403f700f4aab1dab3343d3681ca SHA512: 2c26d8c1c60716eb9d1386d254fc1c8cf64f011f59592ee374947f49042cb7638e3bc086a7c3161ebc653976c3fe183f7ea93d28af05af92974d2fbb1a668bda Homepage: https://cran.r-project.org/package=causaldrf Description: CRAN Package 'causaldrf' (Estimating Causal Dose Response Functions) Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments. A description of the methods is given in Galagate (2016) . Package: r-cran-causaleffect Architecture: all Version: 1.3.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1042 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-r.rsp, r-cran-xml Filename: pool/dists/resolute/main/r-cran-causaleffect_1.3.15-1.ca2604.1_all.deb Size: 872872 MD5sum: f8a8a3b6512b0b1c3a67bbd3086856d4 SHA1: 135da055eb04f08eeca39caf58ac08a28e494c9e SHA256: 0a64a00240ef80e77d21e2ae0a08e699552021669d3a085c800794df3fba54e9 SHA512: 21d6a0c1f5cf7117daa12a1ee6fa688c66550a2828a8d3b2f9774effc5b3fed86da9162611be3cdb6ba97f813d72c649a098e75f17d10acbf3223147ed1cd424 Homepage: https://cran.r-project.org/package=causaleffect Description: CRAN Package 'causaleffect' (Deriving Expressions of Joint Interventional Distributions andTransport Formulas in Causal Models) Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) , an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) , and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) . All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) . Package: r-cran-causalgam Architecture: all Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gam Filename: pool/dists/resolute/main/r-cran-causalgam_0.1-4-1.ca2604.1_all.deb Size: 50854 MD5sum: 23cc6c4692a64527b217790d9f75314d SHA1: 6784ba66b56fb545f18be63d596395672a149c00 SHA256: b4e26b9bf757808c0f453b8809a66abdc4c89eac96fb71f108a3a27eb3b41509 SHA512: 3bb73f0598b8e8dfb1aea34e9c0d6e677c94f6ebc0318ede4dee2e674fc52f980c46e900df6bb0581adc62a3a78e545f02786946c6cbad0af11afcb7048f765d Homepage: https://cran.r-project.org/package=CausalGAM Description: CRAN Package 'CausalGAM' (Estimation of Causal Effects with Generalized Additive Models) Implements various estimators for average treatment effects - an inverse probability weighted (IPW) estimator, an augmented inverse probability weighted (AIPW) estimator, and a standard regression estimator - that make use of generalized additive models for the treatment assignment model and/or outcome model. See: Glynn, Adam N. and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator." Political Analysis. 18: 36-56. Package: r-cran-causalhypergraph Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-stringr, r-cran-useful, r-cran-cna Filename: pool/dists/resolute/main/r-cran-causalhypergraph_0.1.0-1.ca2604.1_all.deb Size: 47418 MD5sum: d7c82566ab5b040074c69e84e85cccf8 SHA1: 0ebbe639f921868061ce0f32f397eea615e667c5 SHA256: 36d7f7d4b4e4f2f48b8e2d23a0c86b2af875420ddb5db38ae7140028baf74d1f SHA512: 9934443d7a4e5c31a8c526284c251161a9811c07943323ddb6f493ece31d5a26aba7c92a19072a8c9534897879b4cb0a4319dc735fdbfc70c8f530d90b8dfe66 Homepage: https://cran.r-project.org/package=causalHyperGraph Description: CRAN Package 'causalHyperGraph' (Drawing Causal Hypergraphs) Draws causal hypergraph plots from models output by configurational comparative methods such as Coincidence Analysis (CNA) or Qualitative Comparative Analysis (QCA). Package: r-cran-causalimpact Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsts, r-cran-assertthat, r-cran-boom, r-cran-ggplot2, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-causalimpact_1.4.1-1.ca2604.1_all.deb Size: 423928 MD5sum: 52aa41c2619e4b8955fc5fd891cee31f SHA1: 432849bb95628001bdb7bbfba6d8befa0b7cb472 SHA256: 7b1bbb323a48126e21519031d8461708fb720f722802cf4efd100b28817567ac SHA512: 4f47bbe20f6b28a8914952371c760cc0cb09187cd3fc4709d6fbe48db1962fbfb7be0a4be589ebdc691f93ed3bca4d5907e737f0575742688ec93438762354cf Homepage: https://cran.r-project.org/package=CausalImpact Description: CRAN Package 'CausalImpact' (Inferring Causal Effects using Bayesian Structural Time-SeriesModels) Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) . See the package documentation on GitHub to get started. Package: r-cran-causalmbsts Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kfas, r-cran-cholwishart, r-cran-forecast, r-cran-mass, r-cran-matrix, r-cran-mixmatrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-causalmbsts_0.1.1-1.ca2604.1_all.deb Size: 144606 MD5sum: f73a4514b1c6a260946576afca27158e SHA1: 4305db1457ec3808624f6a13913d6a2ccbc91bea SHA256: 209394a57892bb807054a041b078d59991f3a945d1b586fbe225da8eb66a0c02 SHA512: f6d9bf8812ac869f0396330cabfae0f8b7f908de1d7a4f85d1962e467833fa70c28ec072e74b2cd784cfb63e9cf07ffead63fd30d752abd723ef106485bfc9c3 Homepage: https://cran.r-project.org/package=CausalMBSTS Description: CRAN Package 'CausalMBSTS' (MBSTS Models for Causal Inference and Forecasting) Infers the causal effect of an intervention on a multivariate response through the use of Multivariate Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) . The package also includes functions for model building and forecasting. Package: r-cran-causalmetar Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-metafor, r-cran-nnet, r-cran-progress, r-cran-superlearner Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-causalmetar_0.1.3-1.ca2604.1_all.deb Size: 1169174 MD5sum: 48a34506a980ba43d918cbd5253e54cc SHA1: 39446b369a0abae85b8bbb4f2d1de5ae4c6b2e2a SHA256: f6a9e5d7f9c621ba7cf0b08e62a02c21c0d20046fcc2f1579a75cba79e4a1b1c SHA512: 7633f1bc82c9c465fde694c6ff72bb50375096fdafffcfe769d5904bad7e4497ba444dd4b8e7abe5b1b38441f17ed32ecbc699a7b626e3b9d703c406303a8ddc Homepage: https://cran.r-project.org/package=CausalMetaR Description: CRAN Package 'CausalMetaR' (Causally Interpretable Meta-Analysis) Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) , Robertson et al. (2021) , and Wang et al. (2024) . The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2025) for a detailed guide on using the package. Package: r-cran-causalmixgpd Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3556 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-cli, r-cran-coda, r-cran-ggmcmc, r-cran-future, r-cran-future.apply, r-cran-crayon, r-cran-dt, r-cran-kableextra, r-cran-plotly, r-cran-matchit, r-cran-codetools Filename: pool/dists/resolute/main/r-cran-causalmixgpd_0.8.0-1.ca2604.1_all.deb Size: 3038416 MD5sum: 446c37626fc18b5b7e74fb9f56c8a528 SHA1: 8e8fa8b38b974037164789cc952b16a2eaccd573 SHA256: 445c9543b9988d0def83137bcebff6df3780d523774c9b0f8db773cf635a61a3 SHA512: a3a26fe6beed142077672f92ed304e53e0a16608c5daf80acb8f10423e38a36c257a248063a40a17e46200b41c126a11ab21d0fe5dd7fabcf749775117ab3bb2 Homepage: https://cran.r-project.org/package=CausalMixGPD Description: CRAN Package 'CausalMixGPD' (Bayesian Nonparametric Conditional Density Modeling in CausalInference and Clustering with a Heavy-Tail Extension) The presence of a heavy tail is a feature of many scenarios when risk management involves extremely rare events. While parametric distributions may give adequate representation of the mode of data, they are likely to misrepresent heavy tails, and completely nonparametric approaches lack a rigorous mechanism for tail extrapolation; see Pickands (1975) . The package 'CausalMixGPD' implements tools for Bayesian analysis of heavy-tailed outcomes by combining Dirichlet process mixture models for the body of the distribution with optional generalized Pareto tails. The method allows for unconditional and covariate-modulated mixtures, implements MCMC estimation using 'nimble', and extends to mixtures of different arms' outcomes with application to causal inference in the Rubin (1974) framework. Posterior summaries include density functions, quantiles, expected values, survival functions, and causal effects, with an emphasis on tail quantiles and functional measures sensitive to the tail. Package: r-cran-causalmodels Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-causaldata, r-cran-boot, r-cran-multcomp, r-cran-geepack Filename: pool/dists/resolute/main/r-cran-causalmodels_0.2.1-1.ca2604.1_all.deb Size: 104866 MD5sum: 8fdc0a32986295ec0a387de4a01f0488 SHA1: abb203cab3e803217b49f34fd57e9296af0baae4 SHA256: 1aea81dcb5fe41201d1beba7f3422bedac34da4e368cc6446a64e3b66441f5a3 SHA512: 7f356269e0a9a054b547d98c7f82eb50097b3eb37548698395c5efc9ea9a342f93dba3862ccb7f3ce7d4336a7203c62f74d652070c381b2cb128a1f2c8ad02f8 Homepage: https://cran.r-project.org/package=CausalModels Description: CRAN Package 'CausalModels' (Causal Inference Modeling for Estimation of Causal Effects) Provides an array of statistical models common in causal inference such as standardization, IP weighting, propensity matching, outcome regression, and doubly-robust estimators. Estimates of the average treatment effects from each model are given with the standard error and a 95% Wald confidence interval (Hernan, Robins (2020) ). Package: r-cran-causalnet Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-scales, r-cran-cowplot Suggests: r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-causalnet_0.1.0-1.ca2604.1_all.deb Size: 66460 MD5sum: 0caabb8c8aede34b635da710541336de SHA1: fd6494c59d510772b39c91fc8f3169ab0df09286 SHA256: 03b303e8324c53fc799450b994b40eaf86f7909d2635a9f30aef38b9f00bbeb5 SHA512: 15e84f988fd585bacdd4b3c3ac6e631c5515d2d9b9bd71f711fdc1bf8c171e6e9219534d85afa592798f67ce634410fa40eb5652cd7dd3516f0fb9b3a7ea5648 Homepage: https://cran.r-project.org/package=causalnet Description: CRAN Package 'causalnet' (Directed Causal Network Enumeration and Simulation) Enumerate orientation-consistent directed networks from an undirected or partially directed skeleton, detect feedback loops, summarize topology, and simulate node dynamics via stochastic differential equations. Package: r-cran-causaloptim Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2067 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-shiny, r-cran-rcdd Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-causaloptim_1.0.0-1.ca2604.1_all.deb Size: 846160 MD5sum: 39ed2f3330e722795681088534ba150e SHA1: 16620dc8d3bae55867432bfd3f32b743112188f7 SHA256: 2733d95b616cc85da8a87fe7202c1f419d58db4768906187df39fae45e3dfa93 SHA512: a303a9fee9c0023d0059077f05a9cd5ccfbd9319549d5b12b842de04829f66885ddcf1c4943475489059a4b2a9230fba2e0938d2d6ddc4842ce3b26d753592ef Homepage: https://cran.r-project.org/package=causaloptim Description: CRAN Package 'causaloptim' (An Interface to Specify Causal Graphs and Compute Bounds onCausal Effects) When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) . Package: r-cran-causalpaf Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dagitty, r-cran-dplyr, r-cran-forestplot, r-cran-ggdag, r-cran-ggplot2, r-cran-gridextra, r-cran-magrittr, r-cran-mass, r-cran-reshape2, r-cran-rlist Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-causalpaf_1.2.5-1.ca2604.1_all.deb Size: 1151016 MD5sum: b0d4b3b34eab24eee1b8173cba486249 SHA1: 40d90d854c839c259505ba238f4f27160a5a0e51 SHA256: f55cdff92ae710efbea3895251b4d2f04da77bd555687b4aa200bcd80cb3ba75 SHA512: f55cbbc8b86ed9f685760ae45d1c5952a4299578d38c24f4580b6ec0bc30985941f7c2ab4c2a9b62ed60046f04c6bde320e2acbedc901e31308f7a68827fc430 Homepage: https://cran.r-project.org/package=causalPAF Description: CRAN Package 'causalPAF' (Causal Effect for Population Attributable Fractions (PAF)) Calculates population attributable fraction causal effects. The 'causalPAF' package contains a suite of functions for causal analysis calculations of population attributable fractions (PAF) given a causal diagram which apply both: Pathway-specific population attributable fractions (PS-PAFs) O’Connell and Ferguson (2022) and Sequential population attributable fractions Ferguson, O’Connell, and O’Donnell (2020) . Results are presentable in both table and plot format. Package: r-cran-causalplot Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6541 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext Suggests: r-cran-shiny, r-cran-shinythemes Filename: pool/dists/resolute/main/r-cran-causalplot_0.2.1-1.ca2604.1_all.deb Size: 2412612 MD5sum: 49383ad26494f6a5a01f72e7fbc8a37a SHA1: 82cd28ac8ec3f3b9dfba20e636a03f658cbb60d0 SHA256: 4df1dca00c8488fe8b9bc141234fff14461188d8f1217cfe1b53bcdc7ca17cb2 SHA512: cd7d586d31cd728f4862b1eaae2563f4c7790266f6ad55f291b58b42e6d984b815b29561e49ecffdb30d1cad80138ced0aed90b09b2a3539ae5a5191d69f63dc Homepage: https://cran.r-project.org/package=causalplot Description: CRAN Package 'causalplot' (Create Publication-Ready Causal Diagrams) Creates publication-ready causal diagrams using 'ggplot2'. Provides simple templates for common causal diagrams (e.g., mediating mechanisms and parallel pathways) with customizable labels, colors, fonts, and export-friendly defaults. Package: r-cran-causalqual Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aer, r-cran-caret, r-cran-cli, r-cran-ggplot2, r-cran-ggsci, r-cran-grf, r-cran-lmtest, r-cran-magrittr, r-cran-ocf, r-cran-rdrobust, r-cran-sandwich, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-causalqual_1.0.0-1.ca2604.1_all.deb Size: 639086 MD5sum: 22fc78542167b08e5da0f0999a2bf432 SHA1: 7db7e6173c9ab36f3b422ff5059751afa87d7c9b SHA256: ad44d8f3cd093bb7b6aed730aea78a009a03875fb2e79e62832c07bfb989ff68 SHA512: ed06f0442bb746b41b0cf34961d0154302f331aff39d9163a7a87fc7a95c515f03acaf4c5f3d67fba76131277a84d46fe934135d7533ba3d114aa288fea60aff Homepage: https://cran.r-project.org/package=causalQual Description: CRAN Package 'causalQual' (Causal Inference for Qualitative Outcomes) Implements the framework introduced in Di Francesco and Mellace (2025) , shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs. Package: r-cran-causalreg Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-causalreg_0.1.2-1.ca2604.1_all.deb Size: 43924 MD5sum: 7cfa935d9ca6a54fd021d67d191c7ae0 SHA1: 1f3e499b2da95f1afd366c1ddd63d1ee541f5c6b SHA256: 8796a7b1df07fc110118f98c64ea1d005ab387545055ebe716ab90eb278e0587 SHA512: 8bd692dcf818a2527d7f93c436c6cb10216c581d3e6e467908525b892fe088e79cbd5895b40b701f278b25b188feed045bbc2893579fb19aec672bcc0a9ba61e Homepage: https://cran.r-project.org/package=causalreg Description: CRAN Package 'causalreg' (Causal Generalized Linear Models) An implementation of methods for causal discovery in a structural causal model where the conditional distribution of the target node is described by a generalized linear model conditional on its causal parents. Package: r-cran-causalsens Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-causalsens_0.1.3-1.ca2604.1_all.deb Size: 418994 MD5sum: 644662e58493a235f786cd804e844fab SHA1: 7e27d80c5fcf5fc51ba36d65e9deb74da5ada2a4 SHA256: 026fe8d9b14203a6a6ca556d6e749b73aded9e866e19d90034dce026d06aa4f6 SHA512: d49b0f1d344eeb2094b278ba6fdcdde981e1181ea826e5fdb79032f78929316a108dd04ea420aa5e1eacd0d6b71695bac9b342909533d18ee0cb697c3bf6c432 Homepage: https://cran.r-project.org/package=causalsens Description: CRAN Package 'causalsens' (Selection Bias Approach to Sensitivity Analysis for CausalEffects) The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects. Package: r-cran-causalspline Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sandwich, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-cobalt, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-causalspline_0.1.0-1.ca2604.1_all.deb Size: 187144 MD5sum: 55682fa397390b951d962c7361900f64 SHA1: 6f1eec18d8a22076da11e350c6ea19ad683f0c96 SHA256: f9a55a85869de5dbec5b9326c17267dce5a557f51c8e59e139bfb18141a71ed7 SHA512: 50cbea734ae028b43a9a3cbc2849a1bd42b86a18ebebfedf1a3799e8fdb7e6df3f822e88054f31b339e1b79ebfac38561de83825a283eb45b129c557e88b55bd Homepage: https://cran.r-project.org/package=CausalSpline Description: CRAN Package 'CausalSpline' (Nonlinear Causal Dose-Response Estimation via Splines) Estimates nonlinear causal dose-response functions for continuous treatments using spline-based methods under standard causal assumptions (unconfoundedness / ignorability). Implements three identification strategies: Inverse Probability Weighting (IPW) via the generalised propensity score (GPS), G-computation (outcome regression), and a doubly-robust combination. Natural cubic splines and B-splines are supported for both the exposure-response curve f(T) and the propensity nuisance model. Pointwise confidence bands are obtained via the sandwich estimator or nonparametric bootstrap. Also provides fragility diagnostics including pointwise curvature-based fragility, uncertainty-normalised fragility, and regional integration over user-defined treatment intervals. Builds on the framework of Hirano and Imbens (2004) for continuous treatments and extends it to fully nonparametric spline estimation. Package: r-cran-causalweight Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger, r-cran-mvtnorm, r-cran-np, r-cran-larf, r-cran-hdm, r-cran-superlearner, r-cran-glmnet, r-cran-xgboost, r-cran-e1071, r-cran-fastdummies, r-cran-grf, r-cran-checkmate, r-cran-sandwich Filename: pool/dists/resolute/main/r-cran-causalweight_1.1.4-1.ca2604.1_all.deb Size: 2425450 MD5sum: 3742b1adb82b04425ad5d2de66ffe266 SHA1: 581a05e612e8b1e88e5fd08ac936e65f1e79623b SHA256: b1a37f56a5cca63847853ee9666847657fb7e6da1d449eef47bc895170d259d4 SHA512: 24c92b8b279888586ab223eb1f1e60b3891eb5d67a427e45dfcc611a820debb2c1e93f984728ec12cb38e20552379f563597e28d6bc84a5f3dee886e66ad21f6 Homepage: https://cran.r-project.org/package=causalweight Description: CRAN Package 'causalweight' (Estimation Methods for Causal Inference Based on InverseProbability Weighting and Doubly Robust Estimation) Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) , Huber (2012) , Huber (2014) , Huber (2014) , Froelich and Huber (2017) , Hsu, Huber, Lee, and Lettry (2020) , and others. Package: r-cran-causalwins Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drf, r-cran-factominer, r-cran-grf, r-cran-matchit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-wins, r-cran-mass Filename: pool/dists/resolute/main/r-cran-causalwins_0.1.0-1.ca2604.1_all.deb Size: 211998 MD5sum: 23b44e20f3a465e56e3b950669848c11 SHA1: 3342bf44f455e4e8b5162347cec2ba3639850832 SHA256: 0f006d2d4d4eef886e2500990a6b540343415e43892822321e324f0c423700fb SHA512: 961b45609b1edada1898f9dfdfeec3098e2211283140241c0d7f543ed716d648f3b35bee8e9b1952abc3222923803ec6381f8231aa355d0022dbd52ad73a19a8 Homepage: https://cran.r-project.org/package=causalWins Description: CRAN Package 'causalWins' (Compute the Causal Win Ratio Using Nearest Neighbor Matching) Based on “Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis” (M. Even and J. Josse, 2025), this package provides implementations of three approaches - nearest neighbor matching, distributional regression forests, and efficient influence functions - to estimate the causal win ratio, win proportion, and net benefit. Package: r-cran-causcor Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-writexls Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-causcor_0.1.3-1.ca2604.1_all.deb Size: 36806 MD5sum: ff8a1a54a176f85c44e7d431f670b7fb SHA1: be168460e26b8e052d9272c7905e097f325c2da7 SHA256: 81f0efe7fc8e15cab12aba1be2ba9beab4801c73ee4e1e560814e63907944e21 SHA512: 3dfbd7dacc1d1453a6c6963fdc2eea20a00c6b2ece943e8cc69062496fc733f038d14a7d3f51e93ef262abc5cdf294e37d3abcc5440ca4d8c61dab5aa3d60670 Homepage: https://cran.r-project.org/package=CausCor Description: CRAN Package 'CausCor' (Calculate Correlations and Estimate Causality) This tool performs pairwise correlation analysis and estimate causality. Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria. Package: r-cran-causens Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-knitr, r-cran-pkgdown, r-cran-psweight, r-cran-rjags, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-usethis, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-causens_0.0.3-1.ca2604.1_all.deb Size: 569992 MD5sum: 0b944a8e05af6d17e8ba30d97c09aa4b SHA1: 51e413262f88594392f26c142a210d947032ece0 SHA256: 84bb38aa479d76bbde4ef136347f5f7553c9ba366e639ec5369ae3d1a8806ced SHA512: d000dc9c5530e1ed43b859f5c4c75d095e45ed98b5464f64a91a4cb791dc682e83f09419a2a09ed417e55a1c6aa9cad50840aba29ad8abfa509e745a3b145f3c Homepage: https://cran.r-project.org/package=causens Description: CRAN Package 'causens' (Perform Causal Sensitivity Analyses Using Various StatisticalMethods) While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) and Li, Shen, Wu, and Li (2011) ), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) ). Package: r-cran-caustests Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-caustests_1.1.1-1.ca2604.1_all.deb Size: 155366 MD5sum: c7e529ebdd9c34b921f784176ed5f99c SHA1: 24e6e3801aaffbcedf27e936ab6111ace9df0132 SHA256: 07c890b5b4c00d96b474b6606919e363dd5fdee371cc6b3ace0c8a11eb8e4a67 SHA512: 2809d86cbaf846f37912621a8555a456b5d1d653fe86f16a18e50c8ac4378204abd84ca39a32567cb0ad32b34f6e0dcccd0234e49fbb1bde26cc499231ef6974 Homepage: https://cran.r-project.org/package=caustests Description: CRAN Package 'caustests' (Multiple Granger Causality Tests for Time Series and Panel Data) Comprehensive suite of Granger causality tests for time series and panel data. For time series: Toda-Yamamoto (1995) , Fourier-based tests with single frequency (Enders and Jones, 2016) and cumulative frequencies (Nazlioglu et al., 2019) , quantile causality tests (Cai et al., 2023) , and Bootstrap Fourier Granger Causality in Quantiles (Cheng et al., 2021) . For panel data: Panel Fourier Toda-Yamamoto (Yilanci and Gorus, 2020) and Panel Quantile Causality tests (Wang and Nguyen, 2022) , as well as Group-Mean and Pooled Fully Modified OLS estimators for panel cointegrating polynomial regressions (Wagner and Reichold, 2023) . All tests include bootstrap inference for robust p-values. Package: r-cran-cavariants Architecture: all Version: 6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggforce, r-cran-ggrepel, r-cran-gridextra, r-cran-ggplot2, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-cavariants_6.0-1.ca2604.1_all.deb Size: 159002 MD5sum: cd48a565262adcbb364e307a11facaa7 SHA1: 2eb6e2e4a5ec6e43aee961f407891d5d330c2dc5 SHA256: c5656359809e840847e8ec169ef2c2595a0f9f82d75e5fee02922f1b87ff4613 SHA512: d924aceb3e4fdd2840516cc3d30e099e7291620932338b31abfb69df3192fdae02497ef313791fd395c1bf6192fb3f7e1840a9e298361288dd403ed2eebeeb46 Homepage: https://cran.r-project.org/package=CAvariants Description: CRAN Package 'CAvariants' (Correspondence Analysis Variants) Provides six variants of two-way correspondence analysis (ca): simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca, singly ordered non symmetrical ca, and doubly ordered non symmetrical ca. Package: r-cran-cbamodel Architecture: all Version: 0.0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-cbamodel_0.0.1.2-1.ca2604.1_all.deb Size: 22740 MD5sum: 78818f1a340b7470342f13767b7e8022 SHA1: 6c9edbab578bbcfc26e1f9b5ac8d1d73408822e2 SHA256: 1e81d130ea9a090b57fd082c09fbf9551f60019eb11791c37d452d5d3dfa9250 SHA512: 5ca533513456ceecc34b8330e82c90d38f1f3a275a7dd3bab5ca19c90bc504c4562dc0cb5490d8f497c5744e6cd7b8ad22eda3cba0679e1960593de20b1c24a1 Homepage: https://cran.r-project.org/package=CBAModel Description: CRAN Package 'CBAModel' (Stochastic 3D Structure Model for Binder-Conductive AdditivePhase) Simulation of the stochastic 3D structure model for the nanoporous binder-conductive additive phase in battery cathodes introduced in P. Gräfensteiner, M. Osenberg, A. Hilger, N. Bohn, J. R. Binder, I. Manke, V. Schmidt, M. Neumann (2024) . The model is developed for a binder-conductive additive phase of consisting of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains. Package: r-cran-cbanalysis Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cbanalysis_0.2.0-1.ca2604.1_all.deb Size: 18200 MD5sum: fa03bb98368a71e5e6240aa98c2a2746 SHA1: c8899ebd1953b246e837a6a8278aae878c1863f0 SHA256: 85e5fa839635eb4ff55b6db05dff4bf0cddaffd36b33a9acaca0669e45feb9f7 SHA512: 073ad6036c73d875dcead61848e3c9f805d67ba1fb46d38eefecd5c2fdfddba99376eab14f8831ad11dd779bb90d2f7d091deebd0f54baf1a8b7670399073545 Homepage: https://cran.r-project.org/package=cbanalysis Description: CRAN Package 'cbanalysis' (Coffee Break Descriptive Analysis) A set of functions that helps you to generate descriptive statistics based on the variable types. Package: r-cran-cbass Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cbass_0.1-1.ca2604.1_all.deb Size: 75662 MD5sum: 0d01e1ffb39bc5da51122c32087832fb SHA1: c37322786b9a182710c75ef3886919bc25f87b86 SHA256: c8ca33f53453c43196ea7e45f696e365304abff20f1d4cadb74c7015db703cdc SHA512: ff7fdbca90757ff7cf230c8ca81f6d06a80095e70ef6f93bcf0861f0084043e87d10970936067cee6caf5c2f0005973e90ba978fdee85d732acc8be0a8bff688 Homepage: https://cran.r-project.org/package=cbass Description: CRAN Package 'cbass' (Classification -- Bayesian Adaptive Smoothing Splines) Fit multiclass Classification version of Bayesian Adaptive Smoothing Splines (CBASS) to data using reversible jump MCMC. The multiclass classification problem consists of a response variable that takes on unordered categorical values with at least three levels, and a set of inputs for each response variable. The CBASS model consists of a latent multivariate probit formulation, and the means of the latent Gaussian random variables are specified using adaptive regression splines. The MCMC alternates updates of the latent Gaussian variables and the spline parameters. All the spline parameters (variables, signs, knots, number of interactions), including the number of basis functions used to model each latent mean, are inferred. Functions are provided to process inputs, initialize the chain, run the chain, and make predictions. Predictions are made on a probabilistic basis, where, for a given input, the probabilities of each categorical value are produced. See Marrs and Francom (2023) "Multiclass classification using Bayesian multivariate adaptive regression splines" Under review. Package: r-cran-cbassed50 Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-rlog, r-cran-dplyr, r-cran-ggplot2, r-cran-readxl, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cbassed50_0.2.0-1.ca2604.1_all.deb Size: 105144 MD5sum: f8556798b06ea5dac10b51f913b5bb8f SHA1: 2eb5c750e4093a68096f2876823ac6052f921608 SHA256: 4f96396b4e7003e33d6c80c6760626a1f7801f168cf76524a973a6ae5da24cbb SHA512: 4c7d5e0ecab1a91e7f3950a5871946a207eb0bf428f3da00ad8eb65c9a4dddd4bd1bf5a2f4f5b1ef23de7c5eaabb1b44a0256cded419f3d3be691d2fd33493a6 Homepage: https://cran.r-project.org/package=CBASSED50 Description: CRAN Package 'CBASSED50' (Process CBASS-Derived PAM Data) Tools to process CBASS-derived PAM data efficiently. Minimal requirements are PAM-based photosynthetic efficiency data (or data from any other continuous variable that changes with temperature, e.g. relative bleaching scores) from 4 coral samples (nubbins) subjected to 4 temperature profiles of at least 2 colonies from 1 coral species from 1 site. Please refer to the following CBASS (Coral Bleaching Automated Stress System) papers for in-depth information regarding CBASS acute thermal stress assays, experimental design considerations, and ED5/ED50/ED95 thermal parameters: Nicolas R. Evensen et al. (2023) Christian R. Voolstra et al. (2020) Christian R. Voolstra et al. (2025) . 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The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis. Package: r-cran-cbctools Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastdummies, r-cran-ggplot2, r-cran-idefix, r-cran-logitr, r-cran-randtoolbox, r-cran-rlang Suggests: r-cran-here, r-cran-knitr, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-cbctools_0.7.1-1.ca2604.1_all.deb Size: 1316662 MD5sum: c229600e8913f3a0bcd3592ece63ba77 SHA1: 005531a0ae1a23151b7dc273fe97abfa0dd7e5b6 SHA256: 9802a7d29525449df2b53d9f628398f9cf465b1fae9d91d768a6aa719369a776 SHA512: 88e8ab729b2e61facd9943f11e311852c601e5fd7a369fa9150b1c336a92dd6ee4b487378a0baed7f782fd0f46bdd37579988e9ce5b8bfa32b06133996b4e831 Homepage: https://cran.r-project.org/package=cbcTools Description: CRAN Package 'cbcTools' (Design and Analyze Choice-Based Conjoint Experiments) Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, frequency-based designs, and D-optimal designs, as well as "labeled" designs (also known as "alternative-specific designs"), designs with "no choice" options, and designs with dominant alternatives removed. Conveniently inspect and compare designs using a variety of metrics, including design balance, overlap, and D-error, and simulate choice data for a survey design either randomly or according to a utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Bayesian D-efficient designs using the 'cea' and 'modfed' methods are obtained using the 'idefix' package by Traets et al (2020) . Choice simulation and model estimation in power analyses are handled using the 'logitr' package by Helveston (2023) . 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Users can browse and query genomic data on mutations, copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'), microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). See and Gao et al., (2013) for more information on the cBioPortal web resource. 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The confounder blanket learner (CBL) uses sparse regression techniques to simultaneously perform many conditional independence tests, with complementary pairs stability selection to guarantee finite sample error control. CBL is sound and complete with respect to a so-called "lazy oracle", and works with both linear and nonlinear systems. For details, see Watson & Silva (2022) . Package: r-cran-cbpe Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cbpe_0.1.0-1.ca2604.1_all.deb Size: 99848 MD5sum: e1dad864b3137419817cde2b8134eee7 SHA1: 49300499e19a5c241bed0879182e511e0f1477cc SHA256: 2d20828baa1114385ea5aecd8de8ae785e64f9ad0ccb0f01b4efe2e0c6c38884 SHA512: a5adf4c5ee4a82bc88618aecf67e8fdae51e4303aef6dc8bb287594ae42fd6c005b891179e447f5f5342194ea21fb8072e2d0a1a29dd51a16526912d4d09ccfd Homepage: https://cran.r-project.org/package=CBPE Description: CRAN Package 'CBPE' (Correlation-Based Penalized Estimators) Provides correlation-based penalty estimators for both linear and logistic regression models by implementing a new regularization method that incorporates correlation structures within the data. This method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. See Tutz and Ulbricht (2009) and Algamal and Lee (2015) . Package: r-cran-cbps Architecture: all Version: 0.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matchit, r-cran-nnet, r-cran-numderiv, r-cran-glmnet Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cbps_0.24-1.ca2604.1_all.deb Size: 360634 MD5sum: 8c2f35cc830583a6ce5052351621e4c1 SHA1: 94ea079aa2d83df636bfa35c8265a282d0994d9a SHA256: f07af521bcac55e0fba7568cc5cc3951575861386c95cafdb31b48c5bfac2975 SHA512: ab42ff5f0bdf17cb6f70df1622af976eac372eee4e6fa39748b6f18ad73625b28e292afbe20785ae8673fbc3b5526b1a5e5ce3f810568ef1795044499aeac077 Homepage: https://cran.r-project.org/package=CBPS Description: CRAN Package 'CBPS' (Covariate Balancing Propensity Score) Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) . The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements optimal CBPS from Fan et al. (in-press) , several extensions of the CBPS beyond the cross-sectional, binary treatment setting. They include the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) , treatments with three- and four-valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) , propensity score estimation with a large number of covariates from Ning, Peng, and Imai (2020) , and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates. Package: r-cran-cbrt Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cbrt_0.2.0-1.ca2604.1_all.deb Size: 556828 MD5sum: a6cf65652eeef39bae3c83be176f3a6e SHA1: 96ca4706b1293298892cea3fc218467cc90ac0a5 SHA256: f9bc83bc352c97f0f9b802b91c816c0fbd4b135d7870b5693004526800b4f236 SHA512: 72d4ce43f6fc5002c963ac25fb4092220b8e0381ea08f7d0fa79ee1d5130e1d4429e277c48031546d3d7ae8aba00dcb989a55b181536c14f27d31ea9ccaf4f93 Homepage: https://cran.r-project.org/package=CBRT Description: CRAN Package 'CBRT' (CBRT Data on Turkish Economy) The Central Bank of the Republic of Turkey (CBRT) provides one of the most comprehensive time series databases on the Turkish economy. The 'CBRT' package provides functions for accessing the CBRT's electronic data delivery system . It contains the lists of all data categories and data groups for searching the available variables (data series). As of February 17, 2026, there were 47,986 variables in the dataset. The lists of data categories and data groups can be updated by the user at any time. A specific variable, a group of variables, or all variables in a data group can be downloaded at different frequencies using a variety of aggregation methods. 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For more information, see Lee, Glaze, Bradlow, and Kable . 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The central function calculate_price_index() offers a unified interface for running these methods on structured datasets. This package is designed to support index construction workflows for real estate and other domains where quality-adjusted price comparisons over time are essential. The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands. The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024) . For broader methodological context, see Eurostat (2013, ISBN:978-92-79-25984-5, ). 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Control charts are used for monitoring measurement processes, such as those occurring in manufacturing. The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals. Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology. Package: r-cran-cca Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-fields Filename: pool/dists/resolute/main/r-cran-cca_1.2.2-1.ca2604.1_all.deb Size: 61548 MD5sum: 6f2db25f4374088c2ed6a98644e504e7 SHA1: 5d01ffdad1c44c61c6c1be0da9247e1a692eb91f SHA256: 9cac9df557e2cc7b5bdf618647e1e37ca84c5a696f39e6ec069d0f4de4f2818d SHA512: 34bd0a68f55bba93552341c7f6e183e2fde883ecea5bd5f7247038569bfe8dba6a4d9782c04e62a4da1697d32a541cc5abe24dd7378c3ddd30e375074d41c574 Homepage: https://cran.r-project.org/package=CCA Description: CRAN Package 'CCA' (Canonical Correlation Analysis) Provides a set of functions that extend the 'cancor' function with new numerical and graphical outputs. It also include a regularized extension of the canonical correlation analysis to deal with datasets with more variables than observations. Package: r-cran-ccamlrgis Architecture: all Version: 4.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-terra, r-cran-magrittr, r-cran-isoband, r-cran-bezier, r-cran-lwgeom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ccamlrgis_4.3.1-1.ca2604.1_all.deb Size: 4226468 MD5sum: 44170f8282b17e6a66a3cc29a9432989 SHA1: 3a3f71f45aae0bb72455c1d26f53f94e5250f013 SHA256: 34205eed054303c9bcd49faae072c0df4f4724783e65ebd8a7f6240ede784741 SHA512: f3c098de1bb58caac1c085b6e0cb19468072829a66eec05db0934c8c1e7f7de1ba77a10ebc228b92bd7dcd36425ccf16f2fc2e9a7ba5d628f8871e3db2d3b29e Homepage: https://cran.r-project.org/package=CCAMLRGIS Description: CRAN Package 'CCAMLRGIS' (Antarctic Spatial Data Manipulation) Loads and creates spatial data, including layers and tools that are relevant to the activities of the Commission for the Conservation of Antarctic Marine Living Resources. 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Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed to be smooth on the graph. For more details see Donnat and Tuzhilina (2024) and Wu, Tuzhilina and Donnat (2025) . Package: r-cran-cccd Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-proxy, r-cran-deldir, r-cran-fnn Suggests: r-cran-matrix Filename: pool/dists/resolute/main/r-cran-cccd_1.6-1.ca2604.1_all.deb Size: 100504 MD5sum: a28ed501868e89297a4bd7e5563cf417 SHA1: acc4cfc16623ebcdc20b357c5853af4001757381 SHA256: dc630c29f64b40a8f13ad7ea6bb0a29abc093b91724986f38d89f147290f9f29 SHA512: 4da4a397cf5cae777f615f5b50243b26b5b69553a36b8bfe93c2d94af46f3a703151fc3cde04e83fc579e356493ab1e986301a79cf5c0095f43e4f9b07ab4021 Homepage: https://cran.r-project.org/package=cccd Description: CRAN Package 'cccd' (Class Cover Catch Digraphs) Class Cover Catch Digraphs, neighborhood graphs, and relatives. Package: r-cran-cccm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-cccm_0.1.0-1.ca2604.1_all.deb Size: 58908 MD5sum: 7c83757c6394edad10240ffc7904c9b6 SHA1: 0506f4e759f8ce693b2f51c3162ae799b124cfb2 SHA256: 2fd8a2a75c94f11831b4dfc24662bb7115031c7f7a6ea9dc457b86c520b6efd2 SHA512: 8a40827337457ef57688679dfd4835d02a00ac71f8687f5be53bcc3155e4643700b6fa9d9502420a020ba83aabdab219340489f6cf08e8c57b7a145d99e7925a Homepage: https://cran.r-project.org/package=cccm Description: CRAN Package 'cccm' (Crossed Classification Credibility Model) Calculates the credit debt for the next period based on the available data using the cross-classification credibility model. 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The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) and Carrasco et al. (2013) . Package: r-cran-ccd Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2, r-cran-skellam Filename: pool/dists/resolute/main/r-cran-ccd_1.1-1.ca2604.1_all.deb Size: 29774 MD5sum: 3867c0b64ce5fb6d85026e58bd5b0a4a SHA1: 346415f922877d947b61a8712ba86aecbb4f7b3a SHA256: 6683fb4bf7aefb5680313e1f9396812440e08575075bfea675a07e18086a887e SHA512: fa3f64cdbf1f7145774f6e7e1b5b40660e3d55b8bcf24bb3f4da08e968766d81cd15e7aa1361a46ffce9d4ec76dba8eec31527c4a5e6a2dfdf4e3dadacc6016d Homepage: https://cran.r-project.org/package=CCd Description: CRAN Package 'CCd' (The Cauchy-Cacoullos (Discrete Cauchy) Distribution) Maximum likelihood estimation of the Cauchy-Cacoullos (discrete Cauchy) distribution. Probability mass, distribution and quantile function are also included. The reference paper is: Papadatos N. (2022). "The Characteristic Function of the Discrete Cauchy Distribution in Memory of T. Cacoullos". Journal of Statistical Theory Practice, 16(3): 47. . Package: r-cran-ccda Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ccda_1.1.1-1.ca2604.1_all.deb Size: 29354 MD5sum: 4840cb0c8ba96a2fcc49f7627e78d621 SHA1: 2cfe2cbd1572201b9321e483dbfb8ffa77930334 SHA256: 298857352561ffb62225419d4b51aac217fbdf8b18d5d115a82d8dd83ad65a95 SHA512: ffd5a01682483d43cf727fa5fe4f5ab99cf55e47c666e706d9b6147b315e970b8788d3d2705a21c774ab91407815b97a1a8d84560d3276d1280d11f7ae56a0eb Homepage: https://cran.r-project.org/package=ccda Description: CRAN Package 'ccda' (Combined Cluster and Discriminant Analysis) Implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. . 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'ccdR' was developed to streamline the process of accessing the information available through the 'CTX' APIs without requiring prior knowledge of how to use APIs. Most data is also available on the CompTox Chemical Dashboard ('CCD') and other resources found at the EPA Computational Toxicology and Exposure Online Resources . Package: r-cran-cchs Architecture: all Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-cchs_0.4.5-1.ca2604.1_all.deb Size: 92246 MD5sum: 3d4036b495ae5f5fffe50eacebd0bb86 SHA1: 01c102b82e21fb07fbc550f6502f2df1c5e958fe SHA256: 4dce20cd8ac6c03b58b3f956068b27a37286c57b5c2cee838b412eb255a98f22 SHA512: 9fd9bb40b4a39aeffbba0b5628e3b10614cdbbe089d499c5f3fc5f865620cc030d154e4ab8872867c6670c82a581d5f5cfb50b1e2eb435364d495b661eae7018 Homepage: https://cran.r-project.org/package=cchs Description: CRAN Package 'cchs' (Cox Model for Case-Cohort Data with StratifiedSubcohort-Selection) Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), . This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling. Package: r-cran-cchsflow Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-dplyr, r-cran-sjlabelled, r-cran-stringr, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cchsflow_2.1.0-1.ca2604.1_all.deb Size: 1254770 MD5sum: c7f0b07fb28dbb1184849813e4a133df SHA1: 052df08c3b77befb44ef3630cd042e72b315bf63 SHA256: 19546b503bcc9cc2b6c0ee1bec57247bf9583789df34cc04c63aa9b10f9301e4 SHA512: 62d6f6f389e89e1e28321827b7c570909b34ac6d95904ccc42efcecaef46a0964a3f00bcf6530e594423820421e4eb855dd8c270ed6a99a9fc5d70a956062d73 Homepage: https://cran.r-project.org/package=cchsflow Description: CRAN Package 'cchsflow' (Transforming and Harmonizing CCHS Variables) Supporting the use of the Canadian Community Health Survey (CCHS) by transforming variables from each cycle into harmonized, consistent versions that span survey cycles (currently, 2001 to 2018). CCHS data used in this library is accessed and adapted in accordance to the Statistics Canada Open Licence Agreement. This package uses rec_with_table(), which was developed from 'sjmisc' rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation Functions". Journal of Open Source Software, 3(26), 754. . Package: r-cran-cci Architecture: all Version: 0.3.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-caret, r-cran-xgboost, r-cran-ranger, r-cran-data.table, r-cran-e1071, r-cran-rlang, r-cran-progress, r-cran-kknn Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cci_0.3.6.1-1.ca2604.1_all.deb Size: 1185098 MD5sum: bf8a2a9645cfe4f2d3f4a4d12f9556e1 SHA1: ad7329539d00c59d01b07becefe347ab65182bdf SHA256: 9b102235d8c9cb900a0691b4f880bdedb4c4efd101841f7180a7066169a91bd8 SHA512: e087a4882be4e81358917b7b52e2b56d9c2ed3d6f6506ef56e8c756bb7e765bcb6e4e58abf4cb3280aa6720bb01763e6303e9cea65a59dcf6b351df133bfbc32 Homepage: https://cran.r-project.org/package=CCI Description: CRAN Package 'CCI' (Computational Test for Conditional Independence) Tool for performing computational testing for conditional independence between variables in a dataset. 'CCI' implements permutation in combination with Monte Carlo Cross-Validation in generating null distributions and test statistics. For more details see Computational Test for Conditional Independence (2024) . Package: r-cran-ccid Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-idetect, r-cran-hdbinseg, r-cran-genenet, r-cran-gdata Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ccid_1.2.0-1.ca2604.1_all.deb Size: 85022 MD5sum: 03bf2866b8ad55996b2a853807ff50ce SHA1: 364dc5b236b2305281adfa1ec67ff078560ccdc0 SHA256: 761c4fd36de9f4872d853b2b17f5534ce415faa3d259704c94cc2e447aefd638 SHA512: e13d5206e0d6442349645ea480261da1c3dac48eb406203020d9b1f1227f964a582e8b821c6ba5000e6a935c5269210e2695fc170769415f899d502939abfae5 Homepage: https://cran.r-project.org/package=ccid Description: CRAN Package 'ccid' (Cross-Covariance Isolate Detect: a New Change-Point Method forEstimating Dynamic Functional Connectivity) Provides efficient implementation of the Cross-Covariance Isolate Detect (CCID) methodology for the estimation of the number and location of multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. The main routines in the package have been extensively tested on fMRI data. For details on the CCID methodology, please see Anastasiou et al (2022), Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity. Medical Image Analysis, Volume 75. Package: r-cran-cclustr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-e1071, r-cran-fpc, r-cran-viridislite, r-cran-klar, r-cran-clustmixtype, r-cran-proxy, r-cran-mclust Suggests: r-cran-knitr, r-cran-mice, r-cran-mlbench, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cclustr_0.1.1-1.ca2604.1_all.deb Size: 192322 MD5sum: 6cb9de60ec10e5f974d02cb5428b9cf0 SHA1: 24ad8adaf3ba4cf4530d75ddf44536c9ae093d60 SHA256: 929520475b0f6ca4c9fb9ffdc368e21120ccfd812924b319d61fb7ee6907c65d SHA512: 1ca1f4d93fe8c29bb93d73a6133bf1367f918a824b21a3e081680df57b55305a8e81690bc5344da64b8a207c9aff906c1d1df2b9b841d10a6c771254259a66ba Homepage: https://cran.r-project.org/package=cclustr Description: CRAN Package 'cclustr' (Consensus Clustering Methods for Multiple Imputed Data) Provides tools for performing consensus clustering on multiple imputed datasets. The package supports a range of clustering algorithms across imputations, including hierarchical methods (e.g., Ward, single, complete, average) and partition-based approaches such as k-means, k-medoids (PAM), fuzzy clustering, model-based clustering ('mclust'), and methods for mixed or categorical data (k-modes and k-prototypes). A co-assignment matrix is constructed to quantify agreement between partitions, and consensus solutions are derived via hierarchical clustering applied to the resulting dissimilarity matrix. Additional functions are provided for validation and visualization of clustering results, facilitating robust analysis in the presence of missing data. Consensus clustering framework is based on Monti et al. (2003) , rank aggregation methods follow Pihur et al. (2007) , and the PAC (Proportion of Ambiguous Clustering) metric is based on Senbabaoglu et al. (2014) . Package: r-cran-ccm Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ccm_1.2-1.ca2604.1_all.deb Size: 62114 MD5sum: 00effc0c75a9ff152aee931b122933ff SHA1: 83055080efa75ed7cc7b592fe21eff4f3505db37 SHA256: c29bdf41171cd89eb51138e05dd58ada1efe2dbf45ab224cd35001e1c7dc67dd SHA512: 9485dc426ffd9452b849cf2195d8f4c762014beea3b39b9605b9e30889ad01a953ac96d4a396754503e9659269d7328add7e76ac07696b0d4896ca4769cac1cd Homepage: https://cran.r-project.org/package=CCM Description: CRAN Package 'CCM' (Correlation Classification Method) Classification method described in Dancik et al (2011) that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes. 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Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020). 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Including the estimators BT from Bang and Tsiatis (2000) and ZT from Zhao and Tian (2001) . Package: r-cran-ccp Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ccp_1.2-1.ca2604.1_all.deb Size: 45004 MD5sum: bb504fa54f816d4fe1b95789e2acff32 SHA1: 721fe5a82685186a6759aa4cc1fdb694106ee562 SHA256: 675accd4c2ce42879033b70b135b5e00ed36908121b11ec1cc7c33c370b32efa SHA512: ad526cb49bcd5d5e9cca5a353822a0bccb0139284b0b49dd0606b66028d9e5e3f0e189f51642a41bee4416c8a485d1a0420df5d521cabd4154f9f15d11983daa Homepage: https://cran.r-project.org/package=CCP Description: CRAN Package 'CCP' (Significance Tests for Canonical Correlation Analysis (CCA)) Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method. 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Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data. 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See the reference section of GitHub README.md , for details of the methods. 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The method selects discriminative features via a multi-class class separability score (CSS), splits by nearest class centroid, and aggregates tree votes to produce predictions and class probabilities. Returns CSS-based feature importance as well. Amjad Ali, Saeed Aldahmani, Zardad Khan (2025) . 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Geophysical Research Letters, 36, L11708, . ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, . 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Package: r-cran-cdsampling Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-rglpk Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cdsampling_0.1.6-1.ca2604.1_all.deb Size: 135338 MD5sum: 66c0159cd8989d8047edfc1cf7e7d0f2 SHA1: 3ef009dd4869e149b55400fb17e8b56b133261f3 SHA256: 27f9e5631ade2cc3d126fc09fff39d2851b62ece0089f6eea6c7baad44ef1032 SHA512: 032b7d74f46a0e39f1c36cc80f1ff1c26b9a096e50e49e76dcaf8e30aadf06c7a7ec1d336030679ec084eb74a037d71ae247fdfec452eb124c56ad572504c790 Homepage: https://cran.r-project.org/package=CDsampling Description: CRAN Package 'CDsampling' (Constrained Sampling in Paid Research Studies) In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the 'CDsampling' package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025). Package: r-cran-cdse Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-lutz, r-cran-sf, r-cran-terra Suggests: r-cran-maps, r-cran-rsi, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-cdse_0.3.2-1.ca2604.1_all.deb Size: 695756 MD5sum: 69efcb226b4da188416196b52898c35e SHA1: 1888e36a588d90a194e0ba0a3013c6fdea32f104 SHA256: 56e5e30dae026480fadc9850a76823f183dcf95ffbbe0f743c14fa52c9f70737 SHA512: ee0f4b55edbedfb54ccd4e37f200f8b3c509b65641d7bb3f628b9698c33e8858696f5fb5cc5c6390c6560a418e3371cd2dcf83dd94591c6c6313b34848b8d632 Homepage: https://cran.r-project.org/package=CDSE Description: CRAN Package 'CDSE' ('Copernicus Data Space Ecosystem' API Wrapper) Provides interface to the 'Copernicus Data Space Ecosystem' API , mainly for searching the catalog of available data from Copernicus Sentinel missions and obtaining the images for just the area of interest based on selected spectral bands. The package uses the 'Sentinel Hub' REST API interface that provides access to various satellite imagery archives. It allows you to access raw satellite data, rendered images, statistical analysis, and other features. This package is in no way officially related to or endorsed by Copernicus. Package: r-cran-cdsim Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trend, r-cran-truncnorm, r-cran-ncdf4, r-cran-lubridate, r-cran-readr, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr, r-cran-vroom, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cdsim_0.1.2-1.ca2604.1_all.deb Size: 1158980 MD5sum: a03298b40bee7cf9ee0ba60c5abd818c SHA1: ea7076d6ce1e20cce63243a956533126d7d4d22c SHA256: a1d28ef0e9e58387e3e82ccfee2e38559e91539b92e11330996789ed5a65bf45 SHA512: 26547147dcef3e9e0c05f82a4c69402660f1746190ee8c8a983d7693a9e3af02df85e1e1859c99045fb034a05dd9aa37d2718d5be7639f83048096734c83dbb1 Homepage: https://cran.r-project.org/package=CDSim Description: CRAN Package 'CDSim' (Simulating Climate Data for Research and Modelling) Generate synthetic station-based monthly climate time-series including temperature and rainfall, export to Network Common Data Form (NetCDF), and provide visualization helpers for climate workflows. The approach is inspired by statistical weather generator concepts described in Wilks (1999) and Richardson (1981) . Package: r-cran-cdss Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 672 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readods, r-cran-openxlsx Suggests: r-cran-litedown, r-cran-diagrammersvg, r-cran-rsvg, r-cran-png, r-cran-kstmatrix Filename: pool/dists/resolute/main/r-cran-cdss_0.3-1-1.ca2604.1_all.deb Size: 444232 MD5sum: f175e1d275d9b72a6c693fbb3ec0ac7f SHA1: b360f62731316b1db7c71f4ca760135d933823d8 SHA256: e430798add7a20d330075f541abbbddab1512b4048fd40022792c9e6f1620cbc SHA512: 20ea567f805d2e0aa5e910962929fc095d4b9fd0461340d684d4be412c5b473a33e3ef0ac2ab595781295e5e11a9cc472da1f5213d052c0a424ebe462d0cc393 Homepage: https://cran.r-project.org/package=CDSS Description: CRAN Package 'CDSS' (Course-Dependent Skill Structures) Deriving skill structures from skill assignment data for courses (sets of learning objects). Package: r-cran-cdvi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cdvi_0.1.0-1.ca2604.1_all.deb Size: 11994 MD5sum: 1262cdb74d5566b24542947005d153bf SHA1: 89f5141f2482e9f9396f2c954031fe9ca654e073 SHA256: ff69381d1c8bc41bb31cbc3fe78d6c0679481764819f503534f0429aaa8baf56 SHA512: c3ab428c32c33cbab894def21334f61f68f55bc4b31307285a8c6399dd75141f092b8d947f9d0fc672d628280d29dbe3929c5a1139758a001c8718fe7d1f6c48 Homepage: https://cran.r-project.org/package=CDVI Description: CRAN Package 'CDVI' (Cuddy-Della Valle Index for Capturing the Instability in TimeSeries Data) Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022). Package: r-cran-cdvinecopulaconditional Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat, r-cran-vinecopula Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cdvinecopulaconditional_0.1.1-1.ca2604.1_all.deb Size: 134846 MD5sum: 6299de35ff8140947bfb50633bcc471e SHA1: b4619201a7ce3550687b596c22de8660e946fe99 SHA256: f88e2ccfd09d98170a0659c143c2b2e6d722ab70074e4c108506c105563a3805 SHA512: 1a7a5ceaedd7a51d88e3aaa9cb94fccb5f6456d236384ceb546067904085d103f46dcafb68b871ebf51e06884f499744dbfca0c10c07bca932ed3158911f837f Homepage: https://cran.r-project.org/package=CDVineCopulaConditional Description: CRAN Package 'CDVineCopulaConditional' (Sampling from Conditional C- and D-Vine Copulas) Provides tools for sampling from a conditional copula density decomposed via Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a sampling are those which sample as first the conditioning variables (when following the sampling algorithms shown in Aas et al. (2009) ). The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) , and it is a modified version of that from Aas et al. (2009) . A function is available to select the best vine (based on information criteria) among those which allow for such a conditional sampling. The package includes a function to compare scatterplot matrices and pair-dependencies of two multivariate datasets. Package: r-cran-ceas Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-lme4, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ceas_1.3.0-1.ca2604.1_all.deb Size: 2590140 MD5sum: e32b851d2f1c0fada27177612c4e5905 SHA1: 8a00040e86305fdabc5e692496a16122a80f846a SHA256: 58753eb05e1f40e782af2e52d073e4f964c30610face7b56c884f53e1195bed8 SHA512: a9eac5251c24277c5be6c5c7d1fcb2b1f6a182bf2c2ad3168797fca4b57cc328655ed1bc0e205e50f45c80c0b085f707334fa67a194e6afb0e97c0c284d50b3e Homepage: https://cran.r-project.org/package=ceas Description: CRAN Package 'ceas' (Cellular Energetics Analysis Software) Measuring cellular energetics is essential to understanding a matrix’s (e.g. cell, tissue or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization using the methods described by Mookerjee et al. (2017) . Package: r-cran-ceblr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2989 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ceblr_1.0.0-1.ca2604.1_all.deb Size: 2878498 MD5sum: 0aa289b34f4ba73378503c49e2479b17 SHA1: a1fc42361c8b56f84b1da2ddc00957b10f105a66 SHA256: c6f76016b4c9e7fddcdfdc85da2e3882f5af042ea77e202136f0141b4e66b249 SHA512: e44cf3896a89c7e83dba4135b3c59f5aa06d7cb97279c0899d8c51c149f5bf71b09effa60be4a57728ca63f1ba154190410594eb3ffd446007029a350a526357 Homepage: https://cran.r-project.org/package=ceblR Description: CRAN Package 'ceblR' (Extract Data from the Canadian Elite Basketball League) Gather boxscore and play-by-play data from the Canadian Elite Basketball League (CEBL) to create a repository of basic and advanced statistics for teams and players. Package: r-cran-ceda Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3309 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-limma, r-cran-mixtools, r-cran-ggplot2, r-cran-dplyr, r-cran-ggsci, r-cran-ggridges, r-cran-ggprism Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ceda_1.1.1-1.ca2604.1_all.deb Size: 3259348 MD5sum: a9c1397299aab4b85d64439dc479b10b SHA1: 0dff42a62154c5f679412920eb754d62dee47a16 SHA256: 8e758277c7f449881c700441e6dd14ebdab292f31bf9ac2fb2695f9afe7e7c1b SHA512: dc395473821bdde45cad5cef76cc22fbebae3c910bf88002b6437bc5433a3dabc1fb950fbd088582575bac73a5206d8534b7cd7afad15a21888d1e1336df5e3e Homepage: https://cran.r-project.org/package=CEDA Description: CRAN Package 'CEDA' (CRISPR Screen and Gene Expression Differential Analysis) Provides analytical methods for analyzing CRISPR screen data at different levels of gene expression. Multi-component normal mixture models and EM algorithms are used for modeling. Package: r-cran-cedmr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-mediation, r-cran-rlang, r-cran-rms, r-cran-tidyr Suggests: r-cran-cluster, r-cran-konfound, r-cran-mice, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cedmr_0.1.0-1.ca2604.1_all.deb Size: 111844 MD5sum: a1ede2cfc73aab7759991064fa86c6f4 SHA1: 35b006ac1c3a92070d5233d68a40d1371b05f1e0 SHA256: f6ec4c49daa95cd3f1b5dec2b885de7d2a6bb2276ec60e3c423e2583ff36cf45 SHA512: 5e91f881bce70abd607b895dbad59ebb67348a62b8ab0cf236ca0334d18c06f71697478749aebe1cbb5b7f5982a3f1bee84bd3dabd38b06de2abbac103108fc4 Homepage: https://cran.r-project.org/package=CEDMr Description: CRAN Package 'CEDMr' (Capability-Ecological Developmental Model (CEDM) Analysis) Implements the Capability-Ecological Developmental Model (CEDM) for longitudinal and multilevel data. The package supports estimation and interpretation of models examining how socioeconomic status (SES), health indicators, and contextual factors jointly relate to academic outcomes. Functionality includes: (1) classification of ecological capability regimes (amplifying, neutral, compensatory); (2) estimation of moderated multilevel models with higher-order interaction terms; (3) causal mediation analysis using doubly robust estimation; (4) random-effects within-between (REWB) decomposition; (5) nonlinear moderation using restricted cubic splines; (6) clustering of longitudinal health trajectories; and (7) sensitivity analysis using the impact threshold for a confounding variable (ITCV) and robustness-to-replacement (RIR) measures. The package is designed for use with general longitudinal multilevel datasets. Package: r-cran-ceemdanml Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlibeemd, r-cran-tseries, r-cran-forecast, r-cran-fgarch, r-cran-atsa, r-cran-fints, r-cran-lsts, r-cran-earth, r-cran-caret, r-cran-neuralnet, r-cran-e1071, r-cran-pso Filename: pool/dists/resolute/main/r-cran-ceemdanml_0.1.0-1.ca2604.1_all.deb Size: 49490 MD5sum: befd40c1228cc523d90a3035792f9779 SHA1: 9deacbb51489dea45bed7181975b60c943953a18 SHA256: 6d0e04a39b1368aa4c195f06de043d394c28589b5553cb7f9f2517e5de45e9ba SHA512: 02c42c8eb443871dab330dd500a4f282ef63e347c9b52353cf49969fdd05d1f4f5811e8db644f6cca83c8d94fe3c21eed8997c97968f56d1cad4aff7bcca8eaa Homepage: https://cran.r-project.org/package=CEEMDANML Description: CRAN Package 'CEEMDANML' (CEEMDAN Decomposition Based Hybrid Machine Learning Models) Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) . Package: r-cran-celldeep Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-celldeep_1.0.1-1.ca2604.1_all.deb Size: 1490108 MD5sum: 1abc147bfc3eb056388edb0ed6056ed8 SHA1: 0a2182caac2454fe40ef291efb809a65af4b7e5b SHA256: 12988bb253d71d1bd15a49ba721d490186c7f2282d402648b87e05a4541e2371 SHA512: 0a6a4bbce2a209107cf57c2e7d92487cab701ecb266fe2331b9100cfce1644707a98977a915241d7e5e3666b0788dc7db639f89be3fe750b03b9ef4e11319994 Homepage: https://cran.r-project.org/package=CellDEEP Description: CRAN Package 'CellDEEP' (Cell DiffErential Expression by Pooling ('CellDEEP')) Pool cells together before running differentially expression (DE) analysis. Tell 'CellDEEP' how many cells you want to pool together (which shall be determined by the overall cell number of data), then run DE analysis. Cheng et al. (2026) . Package: r-cran-cellgeometry Architecture: all Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-bioc-complexheatmap, r-bioc-delayedarray, r-cran-dplyr, r-bioc-ensembldb, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtools, r-cran-matrixstats, r-cran-mcprogress, r-cran-rlang, r-cran-scales Suggests: r-cran-future.apply, r-cran-ggforce, r-cran-ggsci, r-cran-knitr, r-cran-plotly, r-cran-rfast2, r-cran-rmarkdown, r-cran-seriation, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-cellgeometry_0.6.3-1.ca2604.1_all.deb Size: 2185168 MD5sum: 6a814dbd95fc18674eae05ade77f07f0 SHA1: da1ee0762fc143caba63c79f428e83216b264d53 SHA256: 65d941408514825ebe5a94651326fddc318296cf4613e8226354a27b9879022d SHA512: cfd5be3af78321bd4d9bff1bd441021aa5d943d4323a9eb8fa02ca8e2d305323ed2435a575064c8a56671de3aac696a0f39a576f07bd4c3734e18eb31593f3fb Homepage: https://cran.r-project.org/package=cellGeometry Description: CRAN Package 'cellGeometry' (Geometric Single Cell Deconvolution) Deconvolution of bulk RNA-Sequencing data into proportions of cells based on a reference single-cell RNA-Sequencing dataset using high-dimensional geometric methodology . Package: r-cran-cellkey Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5010 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sdchierarchies, r-cran-rlang, r-cran-digest, r-cran-sdctable, r-cran-ptable, r-cran-cli, r-cran-yaml, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cellkey_1.0.3-1.ca2604.1_all.deb Size: 4962948 MD5sum: 693e6a2123ff8d740614768b4c7d842a SHA1: 3249b1d686c32bc127f4ac27837878831490913e SHA256: 4b3ca22183a9c697c0a561a89083abc916e83c40c741b1ca26cc5187dffabd4d SHA512: 6a3662b37a64812361a5fac554a7f302058ca247d4699809efcd5c043ace42952be68a03a1dab2f36f0b721a3cd901b8e05bac8917e9306083720d3edc4d4f97 Homepage: https://cran.r-project.org/package=cellKey Description: CRAN Package 'cellKey' (Consistent Perturbation of Statistical Frequency- And MagnitudeTables) Data from statistical agencies and other institutions often need to be protected before they can be published. This package can be used to perturb statistical tables in a consistent way. The main idea is to add - at the micro data level - a record key for each unit. Based on these keys, for any cell in a statistical table a cell key is computed as a function on the record keys contributing to a specific cell. Values that are added to the cell in order to perturb it are derived from a lookup-table that maps values of cell keys to specific perturbation values. The theoretical basis for the methods implemented can be found in Thompson, Broadfoot and Elazar (2013) which was extended and enhanced by Giessing and Tent (2019) . Package: r-cran-cellorigins Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iterpc Filename: pool/dists/resolute/main/r-cran-cellorigins_0.1.3-1.ca2604.1_all.deb Size: 346626 MD5sum: 8354ccb4cc1245cd2386eb625cfe7c8d SHA1: df59c63d3af9d1daa38b53474bdda0b9162c7d6c SHA256: 7e60b2974ee9075a58f59b47c41f7f6e6f26332b173b0e6e3ec2235056c83f67 SHA512: 10f31a36c4ef4aee8bb5cc7df450f830e26d52014af2a8f4933b681351d769eb22ca14a052f3d6edb890c970b65bf3d845920fae8fc8b60a9543312c2c5fbab5 Homepage: https://cran.r-project.org/package=cellOrigins Description: CRAN Package 'cellOrigins' (Finds RNASeq Source Tissues Using In Situ Hybridisation Data) Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products. Package: r-cran-cellranger Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rematch, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cellranger_1.1.0-1.ca2604.1_all.deb Size: 102796 MD5sum: d0a2aea65dd20d843acdb1de97f30a58 SHA1: 248f0790e6ba9b94edc54956dbb5db0419c53a74 SHA256: 15263934916b518b1ac2d66f72af7c69b5174c52ecd7de2f98cd4d7021d2540e SHA512: aa467eef598fe0840391ea90bcae2875832bf5b0feb8feccff88bb16df66311cb32eb950bd0f4391ef4f05c66ca7379744af51295a76e6099d3976a5bd2f7cc4 Homepage: https://cran.r-project.org/package=cellranger Description: CRAN Package 'cellranger' (Translate Spreadsheet Cell Ranges to Rows and Columns) Helper functions to work with spreadsheets and the "A1:D10" style of cell range specification. 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Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) , Beltman, Maree, and de Boer (2009) , Gneiting and Schlather (2004) , Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) , Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) , Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) , Textor, Sinn, and de Boer (2013) , Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) . Package: r-cran-cellvolumedist Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-gplots Filename: pool/dists/resolute/main/r-cran-cellvolumedist_1.5-1.ca2604.1_all.deb Size: 52886 MD5sum: 2f77d5bc9fde984ebed8c56ae36bdf9b SHA1: 6f618a8621d8f01067ebe373e49873d4acdb07c0 SHA256: 188b27945258a00beacbab70005b6ac158959eafcfb1048b87fd007af04c5f0d SHA512: 0871a673e9bb3b82a42964b521f2cb538617a6b4127c915f87f8c2e4115fee94a0c008d54f411c1af212cf5772cc3429d35ea0122401ee078e54031bc0744bbb Homepage: https://cran.r-project.org/package=cellVolumeDist Description: CRAN Package 'cellVolumeDist' (Functions to Fit Cell Volume Distributions and Thereby EstimateCell Growth Rates and Division Times) Implements a methodology for using cell volume distributions to estimate cell growth rates and division times that is described in the paper, "Cell Volume Distributions Reveal Cell Growth Rates and Division Times", by Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro Tona and Anne L. Plant, which appeared in the Journal of Theoretical Biology. In order to reproduce the analysis used to obtain Table 1 in the paper, execute the command "example(fitVolDist)". 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Package: r-cran-cemco Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-nnet, r-cran-rootsolve, r-cran-foreach, r-cran-mass, r-cran-mclust, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-cemco_0.2-1.ca2604.1_all.deb Size: 50842 MD5sum: c02dbaba0943a648b89e2142afff5e50 SHA1: 59361b1c430da275b1fccd1fbe35cb29921a8921 SHA256: bd18ff2beb0a87ae95a3f1f19ef8e1a3c40e61a66bea393a4142ba67ddd923b3 SHA512: 6933a7f67bfe161ceea9bf4cb01892c1ec86771e55e902e62a86c724082d2dfb206687c8bb0eaba4efaa4191ccca71a0d9df9ad2d0bcb7ba78d641e71a1cf944 Homepage: https://cran.r-project.org/package=cemco Description: CRAN Package 'cemco' (Fit 'CemCO' Algorithm) 'CemCO' algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) ). 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Package: r-cran-cencrne Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2071 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cencrne_1.0.0-1.ca2604.1_all.deb Size: 2038932 MD5sum: 27889df22899168ac71954316efad4a7 SHA1: 3a9a48373bc0b2211a5f726244fdfd0efc1e96fb SHA256: 5b19982d007afc561ad555c45f66167b9768955d06a0cc8493ab4a840988be3c SHA512: e70719c7707d8f184b71c2879e5619ee7963697d63299036998da3fb3e3660f603aed0725a1c2f1bc721bc1941e63285448b28d5cade00af4d6a54916601ded5 Homepage: https://cran.r-project.org/package=cencrne Description: CRAN Package 'cencrne' (Consistent Estimation of the Number of Communities viaRegularized Network Embedding) The network analysis plays an important role in numerous application domains including biomedicine. Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. This method proposes a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector towards its center and collapses similar community centers with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. Reference: Ren, M., Zhang S. and Wang J. (2022). "Consistent Estimation of the Number of Communities via Regularized Network Embedding". Biometrics, . Package: r-cran-cengam Architecture: all Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-cengam_0.5.4-1.ca2604.1_all.deb Size: 260954 MD5sum: 813f83cf117416dee48c2ddf3307472c SHA1: f2b915b47aba5d1c02d46e48a342689e20b95898 SHA256: 1487e1fd04c178f7e10ef18f5dac02dd485278d05bdc20af0e6003fc9c7ad17f SHA512: f5de5feda29e52a0febb9a1f2532afd65471904c5a275fbe696ac36a68a7d67b2269899611d4ec0d112d0420426a48f16cf04681b3b1de9fdf7b2c27a0762a76 Homepage: https://cran.r-project.org/package=cenGAM Description: CRAN Package 'cenGAM' (Censored Regression with Smooth Terms) Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) . Package: r-cran-censable Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-stringr, r-cran-memoise, r-cran-purrr, r-cran-censusapi, r-cran-tinytiger Suggests: r-cran-roxygen2, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-censable_0.0.8-1.ca2604.1_all.deb Size: 444684 MD5sum: be80ab6c71dd363cad439155e0039b37 SHA1: c5719f5fbee2042ab7bef19e33c6c202600604b9 SHA256: 82c3e01a94416ec712219762ecc5734d34c49c912049068cffa8dfcbb3e424a1 SHA512: 51d6f520358a177be1e6f14b7c32cf92dc6c637ca13b48c78606e16d1593ad948dd2044a5805bb8b893d3f9e5274362ca2c4b8241c5d62afea4350d3e3cf590e Homepage: https://cran.r-project.org/package=censable Description: CRAN Package 'censable' (Making Census Data More Usable) Creates a common framework for organizing, naming, and gathering population, age, race, and ethnicity data from the Census Bureau. Accesses the API . Provides tools for adding information to existing data to line up with Census data. Package: r-cran-censcov Architecture: all Version: 1.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-censcov_1.0-0-1.ca2604.1_all.deb Size: 49698 MD5sum: 100a61ba32524aa280ee4dc15610140f SHA1: d02fcb03afd59b01cdb78dcd256df9d5d8f51a3c SHA256: 6e5067fc20c6df3a1419dad1234fa60f52dd60142694b33669edfc9b8a4132b1 SHA512: 5203dffbce4c584248dd1e6dbff9534dbd143bc2a8d103edd3eb5822fd2412083f9dc1fec6680d2c571a5b53d3c8a6e749380b5071c5d4411e93a97ff7441134 Homepage: https://cran.r-project.org/package=censCov Description: CRAN Package 'censCov' (Linear Regression with a Randomly Censored Covariate) Implementations of threshold regression approaches for linear regression models with a covariate subject to random censoring, including deletion threshold regression and completion threshold regression. Reverse survival regression, which flip the role of response variable and the covariate, is also considered. Package: r-cran-censmfm Architecture: all Version: 3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-momtrunc, r-cran-mvtnorm, r-cran-gridextra, r-cran-ggplot2, r-cran-tlrmvnmvt Suggests: r-cran-mixsmsn Filename: pool/dists/resolute/main/r-cran-censmfm_3.1-1.ca2604.1_all.deb Size: 153944 MD5sum: 45899e7fcb93a993767f0ba20db58e0b SHA1: bd8d669185a4c71dad2fa054a7f90858808c8245 SHA256: b370cbb51cdd4c1df9e64ffe4baf8a17c03ad3dec748f59ea1ccb7337a90aed7 SHA512: b463787c2b9b9b43f2df346023f07a583d79d2561a5f184758c8d4a6c0b38f5e59481dd9b9b7b84202cfe82ca63e686d041ad63d846d2ebe52305591ee905b40 Homepage: https://cran.r-project.org/package=CensMFM Description: CRAN Package 'CensMFM' (Finite Mixture of Multivariate Censored/Missing Data) It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) for the multivariate skew-normal case. Package: r-cran-censo2017 Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-cli, r-cran-crayon, r-cran-rstudioapi Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-dplyr, r-cran-dbplyr, r-cran-ggplot2, r-cran-chilemapas Filename: pool/dists/resolute/main/r-cran-censo2017_0.6.2-1.ca2604.1_all.deb Size: 60260 MD5sum: b40e0061575212db7f44b9607a72cf3a SHA1: 73bbcc789b5a6d0f4e8c175c58ab79f4852191bf SHA256: 2c67bf6b405cc9cb04dfa91cdf181a7cadde723ca1ae1b3d1e87696d137eadcd SHA512: ca88e6b0ecfa7a9ca530632f0eebe8aca6209b646100edf24279552b61dc3b248be6fc9eac1882d0e6d9e774f2512dfaebf3da770811742c84fb706df77540eb Homepage: https://cran.r-project.org/package=censo2017 Description: CRAN Package 'censo2017' (Base de Datos de Facil Acceso del Censo 2017 de Chile (2017Chilean Census Easy Access Database)) Provee un acceso conveniente a mas de 17 millones de registros de la base de datos del Censo 2017. Los datos fueron importados desde el DVD oficial del INE usando el Convertidor REDATAM creado por Pablo De Grande. Esta paquete esta documentado intencionalmente en castellano asciificado para que funcione sin problema en diferentes plataformas. (Provides convenient access to more than 17 million records from the Chilean Census 2017 database. The datasets were imported from the official DVD provided by the Chilean National Bureau of Statistics by using the REDATAM converter created by Pablo De Grande and in addition it includes the maps accompanying these datasets.) Package: r-cran-censobr Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-glue, r-cran-rlang Suggests: r-cran-covr, r-cran-dbi, r-cran-dbplyr, r-cran-geobr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-kableextra, r-cran-knitr, r-cran-scales, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-censobr_0.5.0-1.ca2604.1_all.deb Size: 319148 MD5sum: 827f2629f22335bba2b06e41706065c6 SHA1: bd667484cca3021e869dc8b10fbff1bdd65bf935 SHA256: fd71ef5bd225badfa0836b2cc25c2e53e8fe562368cee9f3ade25df4ebae9fb2 SHA512: f1206c4cfc12bcaa51fe4c824e232e49c26b430b29edee55f0e4cb4e46316d0d4a6750cc8b05fd6a16d63d1cf58106d7dd52a85a41ca4d9b70561d3f93d9f103 Homepage: https://cran.r-project.org/package=censobr Description: CRAN Package 'censobr' (Download Data from Brazil's Population Census) Easy access to data from Brazil's population censuses. The package provides a simple and efficient way to download and read the data sets and the documentation of all the population censuses taken in and after 1960 in the country. The package is built on top of the 'Arrow' platform , which allows users to work with larger-than-memory census data using 'dplyr' familiar functions. . Package: r-cran-censorcopula Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula Filename: pool/dists/resolute/main/r-cran-censorcopula_2.0-1.ca2604.1_all.deb Size: 22744 MD5sum: 40e9058982ce714e5509d012d7eeeed8 SHA1: 8d0d721e5418a22970b596db64a92b6ddb19901a SHA256: b2e158c9f0755bae86a4d11b48fd1c70a0ad5e19d38c403afefdcfbdf93f4b3f SHA512: dadcc57bfb62d25b0ed989c80213e771d08f7c4310a2bb4a5456aae492efd5cbb44ae91c400ac540f988fc9a835c137b3390bf3f033b19795366c5e45fc5f71a Homepage: https://cran.r-project.org/package=censorcopula Description: CRAN Package 'censorcopula' (Estimate Parameter of Bivariate Copula) Implement an interval censor method to break ties when using data with ties to fitting a bivariate copula. Package: r-cran-censored Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parsnip, r-cran-survival, r-cran-cli, r-cran-dials, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-hardhat, r-cran-lifecycle, r-cran-mboost, r-cran-prodlim, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-aorsf, r-cran-coin, r-cran-covr, r-cran-flexsurv, r-cran-glmnet, r-cran-ipred, r-cran-partykit, r-cran-pec, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-censored_0.3.4-1.ca2604.1_all.deb Size: 214362 MD5sum: ec8a30c6d12fe28060c0ec520bf04fea SHA1: d3c42a75744b2c6284c7fdbed230f4f0e0ac72ed SHA256: d62d2504f25edf46cf9762198d53f6ec6a80e67220f72ac4cfed5ffab637424e SHA512: a1e9102f91b04ae751ec082e6434b6f7d66a7f9ed82e1238c08c7bcebdd6c5afa1dd412b2f20182722bef1f7cf6d287d3a2d50ba91e99fbdd5d9dace882d719c Homepage: https://cran.r-project.org/package=censored Description: CRAN Package 'censored' ('parsnip' Engines for Survival Models) Engines for survival models from the 'parsnip' package. These include parametric models (e.g., Jackson (2016) ), semi-parametric (e.g., Simon et al (2011) ), and tree-based models (e.g., Buehlmann and Hothorn (2007) ). Package: r-cran-censoredaids Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mnormt, r-cran-mvtnorm, r-cran-matrixcalc Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-censoredaids_1.0.0-1.ca2604.1_all.deb Size: 434010 MD5sum: 59397383827be9806b266b85b454f7bb SHA1: 23a934071c306f56e7a95f8d885b9aba3922d783 SHA256: c30b654ea45c76c14e4f5c570b8cd1d80ace5228ee81e25d01968d858b0a7403 SHA512: c0bf7cc1df9ca08a5edc0c5462838f37aa9f56a4e6f86f8e2fabaf8019d3573d0536f46d9536e0ec257bc93bc75eb73852a4ebee284efab17628429ec95484cb Homepage: https://cran.r-project.org/package=censoredAIDS Description: CRAN Package 'censoredAIDS' (Estimation of Censored AI/QUAI Demand System via MaximumLikelihood Estimation (MLE)) Tools for estimating censored Almost Ideal (AI) and Quadratic Almost Ideal (QUAI) demand systems using Maximum Likelihood Estimation (MLE). It includes functions for calculating demand share equations and the truncated log-likelihood function for a system of equations, incorporating demographic variables. The package is designed to handle censored data, where some observations may be zero due to non-purchase of certain goods. Package also contains a procedure to approximate demand elasticities numerically and estimate standard errors via Delta Method. It is particularly useful for applied researchers analyzing household consumption data. Package: r-cran-censreg Architecture: all Version: 0.5-38-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-glmmml, r-cran-sandwich, r-cran-misctools, r-cran-plm Suggests: r-cran-aer, r-cran-lmtest Filename: pool/dists/resolute/main/r-cran-censreg_0.5-38-1.ca2604.1_all.deb Size: 208276 MD5sum: 69c8166614d5c54dbc37a97be25fef87 SHA1: 5a5913cc5ee714b5db1397720f6f6d8263195a4f SHA256: 7b9783e24272240f04eb6d74432422e4a721c831384bfffd1acc9c1837abad09 SHA512: 93584f6f0a78575b784c2239de7cfdc610af139e1cc0bf655998b23ceaf1d7ffcd1d8ca43efe91798b1a2ca8ff935eda46a4bda05f2aeced6813a171b06e60f2 Homepage: https://cran.r-project.org/package=censReg Description: CRAN Package 'censReg' (Censored Regression (Tobit) Models) Maximum Likelihood estimation of censored regression (Tobit) models with cross-sectional and panel data. Package: r-cran-censregsmsn Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mnormt, r-cran-cubature, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-censregsmsn_0.0.1-1.ca2604.1_all.deb Size: 124608 MD5sum: 6afdefb5d018c60f020affd18d2a8af3 SHA1: 155277ff2338cfad4fbb6d216ea98a4647731afd SHA256: 9078acf851f013fb47036a14f7df92c56847ac828074852376c715b6345db80d SHA512: 4836bf9ebdc01b9abf94a50128e79daa81cca2bc76fbd5bd9c41e36b48377a482caebf3a8632ea167cf1190cb6502ebfd4136d0bb85af11ac53e3fc921484810 Homepage: https://cran.r-project.org/package=CensRegSMSN Description: CRAN Package 'CensRegSMSN' (Censored Linear Regression Models under Heavy‑tailedDistributions) Functions for fitting univariate linear regression models under Scale Mixtures of Skew-Normal (SMSN) distributions, considering left, right or interval censoring and missing responses. Estimation is performed via an EM-type algorithm. Includes selection criteria, sample generation and envelope. For details, see Gil, Y.A., Garay, A.M., and Lachos, V.H. (2025) . Package: r-cran-censspatial Architecture: all Version: 3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geor, r-cran-rcpp, r-cran-mvtnorm, r-cran-optimx, r-cran-tmvtnorm, r-cran-msm, r-cran-psych, r-cran-numderiv, r-cran-raster, r-cran-moments, r-cran-lattice, r-cran-tlrmvnmvt Filename: pool/dists/resolute/main/r-cran-censspatial_3.6-1.ca2604.1_all.deb Size: 227826 MD5sum: e8fe785de60fcc6ee4b2ec898235cc89 SHA1: 0380f23e5a0500ece05ce5a654f1fb9d89f40648 SHA256: 1687e169346c3278be11ecb4770570e1a2d0ddbb2a451392b185a8b6b6cc8f88 SHA512: 714fb5a65a67da69b81fba2ff8694de5526a8dc42545769abf3c410ee686a18a0ce9de447d268f348089d18058d08cd5ea64592509efec7c6c292fa33be0f8cf Homepage: https://cran.r-project.org/package=CensSpatial Description: CRAN Package 'CensSpatial' (Censored Spatial Models) It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) . Package: r-cran-census2016 Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3575 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-scales, r-cran-testthat, r-cran-png Filename: pool/dists/resolute/main/r-cran-census2016_0.2.0-1.ca2604.1_all.deb Size: 3438592 MD5sum: 81d678d00f845bc308bfd268ffae0107 SHA1: b95b9e4b8131e1744caaadd8b15c3215cd9fbbcb SHA256: a9fbba73208e126d20deee0a9f431b01669a3d6c3002750e9736d99c002474dc SHA512: 1f60ff3de4cbc3af3535e78e6e43f7faab8fad67919bccc9aa51f4a0bc6197e91d8bc774c3a69307e69cbfad5e5a9bc841c356ed6c79c2ff74898010ac9721b9 Homepage: https://cran.r-project.org/package=Census2016 Description: CRAN Package 'Census2016' (Data from the Australian Census 2016) Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis. Package: r-cran-censusapi Architecture: all Version: 0.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-censusapi_0.10.0-1.ca2604.1_all.deb Size: 68536 MD5sum: 9badb664dfc8f786e86f874fb484fee1 SHA1: 9b20092f66e6711d9b412cced104ec0bb74a9e2b SHA256: 2e4f0fa01db19bde99370b437c2046953779ce21516ee1d8400d670032901d92 SHA512: eaa54a33f7173b38b02196c8f33d25c6efa72fc48fe22551bfa6ac4b4cecbdceacf1b42c423afbb05abac6feabe7729d85a80823739a868d08c1f8796a19b302 Homepage: https://cran.r-project.org/package=censusapi Description: CRAN Package 'censusapi' (Retrieve Data from the Census APIs) A wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata. 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Package: r-cran-censusr Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-censusr_0.0.4-1.ca2604.1_all.deb Size: 42002 MD5sum: c472f8cbf82407013d4ed5b2f820c492 SHA1: 654a4a23db325a057ced631bd94b2f1d8985d804 SHA256: ef1476f56a4e0e18a5a23cbd86139fcc82d770171943c3a74c6a16377f82cb11 SHA512: 6e8313d1c8b7d99a4f233c78678d45102c561e37a7e72d92dccbe7574be8896e09115ae8f15e3ae28f690ef3388adc7eb8407122ab012122be7f67658cc06b44 Homepage: https://cran.r-project.org/package=censusr Description: CRAN Package 'censusr' (Collect Data from the Census API) Use the US Census API to collect summary data tables for SF1 and ACS datasets at arbitrary geographies. 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Package: r-cran-cereal Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-cereal_0.1.0-1.ca2604.1_all.deb Size: 47560 MD5sum: 95555c014f62b340d8b479904e9fcea3 SHA1: 846954118a9033cb658f878e91fc4b93b134b55d SHA256: c0249cba2ac8c84d84209fbba0ace02e8689fd10c9f8e94dd2e34a3cc3d94b02 SHA512: b9a792b58a17ff7d60ee85f7e3acf8737052919deff5619cf0687eaa94d6e54fbd0b857bca30082310d049d20c01e937f18f363851a880afe077f7963dbddb67 Homepage: https://cran.r-project.org/package=cereal Description: CRAN Package 'cereal' (Serialize 'vctrs' Objects to 'JSON') The 'vctrs' package provides a concept of vector prototype that can be especially useful when deploying models and code. 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Package: r-cran-ceriolioutlierdetection Architecture: all Version: 1.1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase Suggests: r-cran-rrcov, r-cran-mvtnorm, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-ceriolioutlierdetection_1.1.15-1.ca2604.1_all.deb Size: 64280 MD5sum: b145f9590d524c0b5dfefeffd5a44204 SHA1: 3dd71172f45694a70824f4b572728c44579b124b SHA256: 4e13e78bd5bd1aab703224bfe78b8640c351807ec20754dfac6d5817985d4b68 SHA512: 950997f67b02635b4222ffdd03b2ae8a7bf7d73b575953f1ac412b722a376dbd68d59a857d5df85eaf213e92647e11eb349461736419f78c34b943c21496d336 Homepage: https://cran.r-project.org/package=CerioliOutlierDetection Description: CRAN Package 'CerioliOutlierDetection' (Outlier Detection Using the Iterated RMCD Method of Cerioli(2010)) Implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. 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Package: r-cran-cernaseek Architecture: all Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-survival Filename: pool/dists/resolute/main/r-cran-cernaseek_2.1.3-1.ca2604.1_all.deb Size: 543022 MD5sum: 677a225693e592aa71c51e287e6296a5 SHA1: d3dae11d71bf1d35504d0acc4323668e69eadb6f SHA256: 34ea94dcbcd3750db2be08039e0bd61c6d29e2987b0398d8413fd845e6336d52 SHA512: 84bd42eca2a0f5c185171cb89d18bebe8333190e9dbe3f694e960f8a976c1f6e59f053375a8fef37a460994d59456b609363e3c9068c564d271211d5183f3de8 Homepage: https://cran.r-project.org/package=CeRNASeek Description: CRAN Package 'CeRNASeek' (Identification and Analysis of ceRNA Regulation) Provides several functions to identify and analyse miRNA sponge, including popular methods for identifying miRNA sponge interactions, two types of global ceRNA regulation prediction methods and four types of context-specific prediction methods( Li Y et al.(2017) ), which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. In addition, For predictive ceRNA relationship pairs, this package provides several downstream analysis algorithms, including regulatory network analysis and functional annotation analysis, as well as survival prognosis analysis based on expression of ceRNA ternary pair. Package: r-cran-certainty Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-certainty_1.0.0-1.ca2604.1_all.deb Size: 348576 MD5sum: 8b3be2cd32f0860ea922c581b73aee5f SHA1: 7212bdbd185d482239c328c2ae0e9c275766e225 SHA256: 0ff9c97a43412ef97917475d70b1ad50c88b64b3a8d6321ffe545e39b20b81ec SHA512: a3f6dc30e09e1db9ca00a3c79e0daa62e80668264fc6541b838eacb990fd6a47bbf1e18f775a1115be58f658e026c2cdea54845f52cdad72eb666a66013768da Homepage: https://cran.r-project.org/package=ceRtainty Description: CRAN Package 'ceRtainty' (Certainty Equivalent) Compute the certainty equivalents and premium risks as tools for risk-efficiency analysis. For more technical information, please refer to: Hardaker, Richardson, Lien, & Schumann (2004) , and Richardson, & Outlaw (2008) . Package: r-cran-certara.darwinreporter Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-colourpicker, r-cran-shinyace, r-cran-shinymeta, r-cran-ggplot2, r-cran-xpose, r-cran-certara.xpose.nlme, r-cran-dplyr, r-cran-jsonlite, r-cran-tidyr, r-cran-flextable, r-cran-shinyjqui, r-cran-plotly, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-bslib, r-cran-shinytree, r-cran-sortable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-readr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-certara.darwinreporter_2.0.1-1.ca2604.1_all.deb Size: 342446 MD5sum: 53483239c840885f0f62113fb23b44c7 SHA1: 74f0320f27362c626d885932d34a50d35deda2cf SHA256: 36eb06c0dd998d2a9f7c06c34749a1c9d303fda15113f044ec5db1e9f82b78e3 SHA512: 89826f3afc1fb11f42f770b08be8a379e357f3f492cba81388a8b04677d14f2b8a905d8fba1b7ef05f604867ba6a978a05bd1b01445d6bae76fd7b0cc3cb7537 Homepage: https://cran.r-project.org/package=Certara.DarwinReporter Description: CRAN Package 'Certara.DarwinReporter' (Data Visualization Utilities for 'pyDarwin' Machine LearningPharmacometric Model Development) Utilize the 'shiny' interface for visualizing results from a 'pyDarwin' () machine learning pharmacometric model search. 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From the interface, users can customize model diagnostics and generate the underlying R code to reproduce the diagnostic plots and tables outside of the 'shiny' session. Model diagnostics can be included in a 'rmarkdown' document and rendered to desired output format. Package: r-cran-certara.nlme8 Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-batchtools, r-cran-reshape, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-certara.nlme8_3.0.2-1.ca2604.1_all.deb Size: 488566 MD5sum: 55cd32c239a3f66cbb5c2f756b3af092 SHA1: f35586a6d1f446be0de34fbde68750f792c0db37 SHA256: aa156cfa0027c4a6cc8e5040c7715dec055e13008e86d0794cfc3b7162d922b9 SHA512: edce167d3bf4d6098a74f88ae7ac7bac52d0ecf0f217d223efe49015cd8d94d6d0f47478b9ebc2edca3711335c5b28fc2726b12ba611a8a03e66ad2424deca8b Homepage: https://cran.r-project.org/package=Certara.NLME8 Description: CRAN Package 'Certara.NLME8' (Utilities for Certara's Nonlinear Mixed-Effects Modeling Engine) Perform Nonlinear Mixed-Effects (NLME) Modeling using Certara's NLME-Engine. Access the same Maximum Likelihood engines used in the Phoenix platform, including algorithms for parametric methods, individual, and pooled data analysis. The Quasi-Random Parametric Expectation-Maximization Method (QRPEM) is also supported . Execution is supported both locally or on remote machines. Remote execution includes support for Linux Sun Grid Engine (SGE), Simple Linux Utility for Resource Management (SLURM) grids, Linux and Windows multicore, and individual runs. Package: r-cran-certara.r Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-batchtools, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-plotly, r-cran-reshape, r-cran-remotes, r-cran-rlang, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-shinymaterial, r-cran-shinyjqui, r-cran-sortable, r-cran-ssh, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-certara.r_1.1.0-1.ca2604.1_all.deb Size: 26806 MD5sum: 57b7595d6c6915ef94b6d952fee20fd6 SHA1: 60009bc80a42230d2122600d5706114cf23a1283 SHA256: 0cef00065835bcfd28a65a042eb917e7acae5b778a4cb8a6a37b499655cdff65 SHA512: 9ef69f15d894c846486c27ad8e59f2d0b29f46e181337d0339eb25379a763aa6920630b5a88ac1cd0dfaa14f6e9344cc13f5e716eae2d3513e2a38c3246c4b40 Homepage: https://cran.r-project.org/package=Certara.R Description: CRAN Package 'Certara.R' (Easily Install Pharmacometric Packages and Shiny ApplicationsDeveloped by Certara) A convenient set of wrapper functions to install pharmacometric packages and Shiny applications developed by Certara PMX and Integrated Drug Development (iDD). The functions ensure the successful installation of packages from non-standard repositories. Package: r-cran-certara.rdarwin Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-jsonlite, r-cran-ssh Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-certara.darwinreporter, r-cran-certara.rsnlme, r-cran-geometry Filename: pool/dists/resolute/main/r-cran-certara.rdarwin_1.2.0-1.ca2604.1_all.deb Size: 757758 MD5sum: e105079edaa979508c9253fb84501b0a SHA1: be5d6ae766ccb474f6bdc5f00a361aa86236fec0 SHA256: 496dda6cccda87e736e1685cabe26b8a7476f4bd9120139473bbd999c7238c7b SHA512: 18deb59c9eebe31e7a0cb9d6d4a286107dda750c50022442875add7eb042443582120c2e99ec08df9c5d2c2b1837f7f8eb5ede81a443c3d87642046fca6a9cd4 Homepage: https://cran.r-project.org/package=Certara.RDarwin Description: CRAN Package 'Certara.RDarwin' (Interface for 'pyDarwin' Machine Learning Pharmacometric ModelDevelopment) Utilities that support the usage of 'pyDarwin' () for ease of setup and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) modeling engine. Package: r-cran-certara.rsnlme.modelbuilder Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-certara.rsnlme, r-cran-shinymeta, r-cran-shinyace, r-cran-bslib, r-cran-data.table, r-cran-dt, r-cran-ggplot2, r-cran-ggforce, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-fs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-certara.rsnlme.modelbuilder_3.0.1-1.ca2604.1_all.deb Size: 585104 MD5sum: d7d6ab77f0f5440c13d65aad06315f7c SHA1: a9fe4a4d5f02ff924b45f1245641b38f57af3fba SHA256: db52ee45f45a337da0badcf05496a0141ca43925a405274373c21280fedc3cd8 SHA512: 9e76718f9900af9077cfc363ad7574a554e5ae3bd38211b582c277df08e037fcebc1699c087694b6d017c40ba699696729042db9aa6d1bc65867b6630f6f71cc Homepage: https://cran.r-project.org/package=Certara.RsNLME.ModelBuilder Description: CRAN Package 'Certara.RsNLME.ModelBuilder' (Pharmacometric Model Building Using 'shiny') Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny' interface. The Pharmacometric Modeling Language (PML) code updates in real time given changes to user inputs. Models can be executed using the 'Certara.RsNLME' package. Additional support to generate the underlying 'Certara.RsNLME' code to recreate the corresponding model in R is provided in the user interface. Package: r-cran-certara.rsnlme.modelexecutor Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-certara.rsnlme, r-cran-certara.nlme8, r-cran-shinyace, r-cran-shinymeta, r-cran-bslib, r-cran-htmltools, r-cran-magrittr, r-cran-dplyr, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinywidgets, r-cran-stringr, r-cran-fs, r-cran-ggplot2, r-cran-future, r-cran-promises, r-cran-reshape, r-cran-jsonlite, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-certara.rsnlme.modelexecutor_3.0.2-1.ca2604.1_all.deb Size: 168168 MD5sum: c3a4b0f5a600f5d05aa78dae9ea895e7 SHA1: 9522de07aaddf63da05011d96542ce1fd008b170 SHA256: a41a6c10114e2868ca8e9e65a48a15e854007770304f9ce707d0598d619f4cc5 SHA512: 300e566cf81ce5ef3c8f5ab0502670365759b6dbbaaa747c5497654d918d08a6d458461ba8bbcf8a128dec9994cddabcc980d5eb5d7ba2ef4f06c25c63fcf028 Homepage: https://cran.r-project.org/package=Certara.RsNLME.ModelExecutor Description: CRAN Package 'Certara.RsNLME.ModelExecutor' (Execute Pharmacometric Models Using 'shiny') Execute Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny' interface. Specify engine parameters and select from different run options, including simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive check, and more. Models are executed using the 'Certara.RsNLME' package. Package: r-cran-certara.rsnlme Architecture: all Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2071 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-assertthat, r-cran-certara.nlme8, r-cran-data.table, r-cran-jsonlite, r-cran-ssh Suggests: r-cran-rlang, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-certara.rsnlme_3.1.1-1.ca2604.1_all.deb Size: 1390928 MD5sum: 432c3189cd98a4b8a97254496a05bf58 SHA1: f7841f79165f8095555172b3b7815a0d422eb6b3 SHA256: 8898be24363c2616712084c769be3f659e3f7fba2bc93661857e14a0d4d62324 SHA512: aabc640babb334b46113aa5789de5a28ee510bcaf95cf31909dfe1fb764f7ba66db0899ecf4bdc5f1077956a93b7680f31b003bd6ec9dc0c951388b528e04791 Homepage: https://cran.r-project.org/package=Certara.RsNLME Description: CRAN Package 'Certara.RsNLME' (Pharmacometric Modeling) Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling and simulation with powerful tools for Nonlinear Mixed-Effects (NLME) modeling. The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data, and support integrated use within the Pirana pharmacometric workbench . Execution is supported both locally or on remote machines. Package: r-cran-certara.vpcresults Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinyace, r-cran-shinymeta, r-cran-bslib, r-cran-colourpicker, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-plotly, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinywidgets, r-cran-shinyjqui, r-cran-sortable, r-cran-tidyvpc, r-cran-htmltools, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xpose, r-cran-ggnewscale, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-certara.vpcresults_3.0.2-1.ca2604.1_all.deb Size: 223844 MD5sum: ed8cbf5889d1f7ec6f2684749c1d4e3e SHA1: 0172ef9bb62ef02d38d4faeb0ad13924bbb50d9f SHA256: eb18a458922d41b947388f1773b15d20b077949313fa34de126d7c558a9939e3 SHA512: 02f41d6ad55fe04c23c4c3aed86669c25ad48284acd450781ee5f8b830281776c6e83aea434fc1b9a042f295bbdbd5f3edc040d4e390800ffcaa5fdc88460085 Homepage: https://cran.r-project.org/package=Certara.VPCResults Description: CRAN Package 'Certara.VPCResults' (Generate Visual Predictive Checks (VPC) Using 'shiny') Utilize the 'shiny' interface to parameterize a Visual Predictive Check (VPC), including selecting from different binning or binless methods and performing stratification, censoring, and prediction correction. Generate the underlying 'tidyvpc' and 'ggplot2' code directly from the user interface and download R or Rmd scripts to reproduce the VPCs in R. Package: r-cran-certara.xpose.nlme Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-egg, r-cran-ggally, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-xpose Suggests: r-cran-certara.rsnlme, r-cran-data.table, r-cran-gridextra, r-cran-jsonlite, r-cran-readr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-certara.xpose.nlme_2.0.2-1.ca2604.1_all.deb Size: 1150354 MD5sum: a629bc6c77919c46b170244098d940fc SHA1: eee0033583475d01c755b607e0275295388e6a32 SHA256: 3e1c6e4a968e23afe39e676d85497511041e1fca15cc278503352db10323c687 SHA512: 8e1792bb3423054c14650da81d706f8580fb47fd722e3315ba26356a9206e6eead3c13e48fcaa27e76578060ee4e205246cb561e53304edd6392e47bbfcd2e89 Homepage: https://cran.r-project.org/package=Certara.Xpose.NLME Description: CRAN Package 'Certara.Xpose.NLME' (Enhances 'xpose' Diagnostics for Pharmacometric Models from'Certara.RsNLME' and Phoenix NLME) Facilitates the creation of 'xpose' data objects from Nonlinear Mixed Effects (NLME) model outputs produced by 'Certara.RsNLME' or Phoenix NLME. This integration enables users to utilize all 'ggplot2'-based plotting functions available in 'xpose' for thorough model diagnostics and data visualization. Additionally, the package introduces specialized plotting functions tailored for covariate model evaluation, extending the analytical capabilities beyond those offered by 'xpose' alone. 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Suitable for academic awards, professional recognition, and similar uses. Package: r-cran-ces Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-haven, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-ces_1.0.2-1.ca2604.1_all.deb Size: 108058 MD5sum: b055c888cb5b5d841f65a47459a2e1c1 SHA1: 99a8e37dac85953647424c8c85608cf2fde8288f SHA256: 12220573a4f24a7b1e8881caa960cc91dc99978308a05a1fdc111d65159d28c6 SHA512: e62171375a264ae96b9d9ede7176e0eaaab11539c1ba8ab70cd827bfeb2bcd60e3291e8f7bf186663b55c6ef3724b85535224862f8cb7889884a72a45ca9f550 Homepage: https://cran.r-project.org/package=ces Description: CRAN Package 'ces' (Access to Canadian Election Study Data) Provides tools to easily access and analyze Canadian Election Study data. The package simplifies the process of downloading, cleaning, and using 'CES' datasets for political science research and analysis. The Canadian Election Study ('CES') has been conducted during federal elections since 1965, surveying Canadians on their political preferences, engagement, and demographics. Data is accessed from multiple sources including the 'Borealis' Data repository and the official 'Canadian Election Study' website . This package is not officially affiliated with the Canadian Election Study, 'Borealis' Data, or the University of British Columbia, and users should cite the original data sources in their work. Package: r-cran-cetcolor Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cetcolor_0.2.0-1.ca2604.1_all.deb Size: 373180 MD5sum: 7e9a5a7b82f82e3c8bc31985b2b5824d SHA1: 28a01b79256ed052fb41153211de0bec36d4f83d SHA256: da32089c0dee00939bc35bb528acb4bd1b8ad735d1d8e44a43e68ddcb7a9021d SHA512: 4a4f89adb0fb97feb111da2ead2f9b264318463f75426b7cd9e789ec281a5e8a598389318b4d4fc3a74311bbaddb9abe715f35f7fdd7e60b2b8dd227aa7e7563 Homepage: https://cran.r-project.org/package=cetcolor Description: CRAN Package 'cetcolor' (CET Perceptually Uniform Colour Maps) Collection of perceptually uniform colour maps made by Peter Kovesi (2015) "Good Colour Maps: How to Design Them" at the Centre for Exploration Targeting (CET). Package: r-cran-ceterisparibus Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gower, r-cran-dalex Suggests: r-cran-randomforest, r-cran-ggiraph, r-cran-e1071, r-cran-testthat, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-ceterisparibus_0.6-1.ca2604.1_all.deb Size: 113450 MD5sum: e8cf4cf975fb64d5562ce3db95433abb SHA1: c77b2416a6835f5cb4b1d2d41ba363669e208c98 SHA256: dd71146588f7ca69269d1d6d26e35ef4b90dfd055557a494eba5f7190c29513b SHA512: 79ef8846d76b54e6fa4344eb62bd8138345c2b6b527fd934e2e2f4f895c1f55744bc302da381778894ee15203d7057f1fc669a0a6262642b45fec1040cd626b7 Homepage: https://cran.r-project.org/package=ceterisParibus Description: CRAN Package 'ceterisParibus' (Ceteris Paribus Profiles) Ceteris Paribus Profiles (What-If Plots) are designed to present model responses around selected points in a feature space. For example around a single prediction for an interesting observation. Plots are designed to work in a model-agnostic fashion, they are working for any predictive Machine Learning model and allow for model comparisons. Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package. Package: r-cran-cfa Architecture: all Version: 0.10-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cfa_0.10-2-1.ca2604.1_all.deb Size: 111864 MD5sum: 124881fef5926c77a28c732bf4c4585d SHA1: 01058b128855275aa542e359c45e84fdb524364d SHA256: 5c80742dff533031e6a275b23fde7f3393f99fd20fb3909549a44314823f9630 SHA512: 18be613e3fede71414fad22ac88ef809468be895bf04199176ada26a3a644eafb56d5a1c9e6cd31ee51ea37896e5ac2be701fd490325a04ddc1aafd7f5b99e18 Homepage: https://cran.r-project.org/package=cfa Description: CRAN Package 'cfa' (Configural Frequency Analysis (CFA)) Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm. 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This tool provides functions that enable a robust analysis of colony formation assay (CFA) data in presence or absence of cellular cooperation. The implemented method has been described in Brix et al. (2020). (Brix, N., Samaga, D., Hennel, R. et al. "The clonogenic assay: robustness of plating efficiency-based analysis is strongly compromised by cellular cooperation." Radiat Oncol 15, 248 (2020). ) Power regression for parameter estimation, calculation of survival fractions, uncertainty analysis and plotting functions are provided. 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The main purpose is to compute an encoding (real functional variable) for each state . It also provides functions to perform basic statistical analysis on categorical functional data. Package: r-cran-cfdecomp Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cfdecomp_0.4.0-1.ca2604.1_all.deb Size: 285756 MD5sum: d725a72cda6375458b8144be1c88d9fd SHA1: d35edcd72b1e485d2ed20bb871e696fc42d6104e SHA256: e1003c9f78defb84e344c4ee0022917b8fbc8bd392401cf1d09a7988aff0b43f SHA512: 6b50f10ed211165f98c2e788ed8ff936192850d95bae41bf1469a180c82649b968da2be067ef8ea7bc8a48a22d8ba08dcd630cf4b4801eafae60491bcb7d3dff Homepage: https://cran.r-project.org/package=cfdecomp Description: CRAN Package 'cfdecomp' (Counterfactual Decomposition: MC Integration of the G-Formula) Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) for more information. Package: r-cran-cff Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cff_1.0-1.ca2604.1_all.deb Size: 29600 MD5sum: 052edf3fa00b9ce6cd96f1232d6084c9 SHA1: 518f01e509a4c29f7c728e4f096429b8a72ed2ac SHA256: 879f509407393a45a66fe6b5fc00f197b8cbcbabc1ae4b874c3a9c230b45e8cb SHA512: 6fdc43ca4479a2dd83696d81ccc90f6649319f419729b46c62732c873376cae2832c750ecf429c1d2602008f60d479ddbde7d3361bbee5399bb0e5d5358b66ed Homepage: https://cran.r-project.org/package=CFF Description: CRAN Package 'CFF' (Simple Similarity for User-Based Collaborative Filtering Systems) A simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems, to break free from the complex computation of existing similarity formulas and the ability to solve big data. Package: r-cran-cffdrs Architecture: all Version: 1.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2778 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-geosphere, r-cran-sf, r-cran-terra Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cffdrs_1.9.2-1.ca2604.1_all.deb Size: 1527148 MD5sum: aa8e7912ac896459943cdaed858db0aa SHA1: aa4e1694cdb3e605f9db6254b87b2d1d16166e6f SHA256: dfa86cfe87f2d81d7d39e6614957c180251a6957451c58ce8b4da6aace30ad57 SHA512: a9ce2d8d79daa42d5e6973cd3982ed9136b34bb98cfdfab9c6e9772941ba164ab9dd70045804e8dd77a044f8d8d08ad736e188776653716f3ba4e3578391da30 Homepage: https://cran.r-project.org/package=cffdrs Description: CRAN Package 'cffdrs' (Canadian Forest Fire Danger Rating System) This project provides a group of new functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS) Van Wagner and Pickett (1985) ) at various time scales: the Fire Weather Index (FWI) System Wan Wagner (1985) and the Fire Behaviour Prediction (FBP) System Forestry Canada Fire Danger Group (1992) . Some functions have two versions, table and raster based. 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Provides a simple interface for defining causal diagrams and counterfactual conjunctions. Construction of parallel worlds graphs and counterfactual graphs is carried out automatically based on the counterfactual query and the causal diagram. See Tikka, S. (2023) for a tutorial of the package. 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The package uses efficient sparse matrix representations and provides incremental updates for users, items, and similarity structures through an R6 class-based architecture. See Aggarwal (2016) for an overview. 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Includes Sparse Orthogonal Principal Components (SOPC), and evaluation metrics. Based on Guo G. (2023) . Package: r-cran-cfmortality Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cfmortality_0.3.0-1.ca2604.1_all.deb Size: 14110 MD5sum: 4bea10a130f1f4827c722876f7072e0e SHA1: 350b8e2c22959ebbf217964f193e5e84b4cb3b45 SHA256: 79f4e5df192ed76f0f0aa1c4f0d58b646e27e0683f0c92977c1b857c781892c4 SHA512: abca3eb644621c696a405c00494fb86297ac88c5dba685639d3ee36c628d0afbc594d67fc756ab33d7b19cdbe447f8c9d005435e58bfb8d720c5fc329011f014 Homepage: https://cran.r-project.org/package=cfmortality Description: CRAN Package 'cfmortality' (Cystic Fibrosis Survival Prediction Model Based on StanojevicModel) Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) . Package: r-cran-cfo Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-ggplot2, r-cran-iso, r-cran-pbapply, r-cran-rcolorbrewer, r-cran-scales Filename: pool/dists/resolute/main/r-cran-cfo_2.2.0-1.ca2604.1_all.deb Size: 323226 MD5sum: e5730665e4d83b035f22c78acecfd8b9 SHA1: d231c913845e1f21806aa07187f07ba092d388bd SHA256: c764a663192a3a958a97c8cdad422b7db2709d2297b2e1975a7a708a05caa264 SHA512: 2e45676dc429d71a320d19eb92c620e1e094557bc97d19a74d9845334b7033b5944dd3dbd0ed9d3762f5f8c53799a3134f3f87cbf33bb51832c37488f07b2a67 Homepage: https://cran.r-project.org/package=CFO Description: CRAN Package 'CFO' (CFO-Type Designs in Phase I/II Clinical Trials) In phase I clinical trials, the primary objective is to ascertain the maximum tolerated dose (MTD) corresponding to a specified target toxicity rate. The subsequent phase II trials are designed to examine the potential efficacy of the drug based on the MTD obtained from the phase I trials, with the aim of identifying the optimal biological dose (OBD). The 'CFO' package facilitates the implementation of dose-finding trials by utilizing calibration-free odds type (CFO-type) designs. Specifically, it encompasses the calibration-free odds (CFO) (Jin and Yin (2022) ), randomized CFO (rCFO), precision CFO (pCFO), two-dimensional CFO (2dCFO) (Wang et al. (2023) ), time-to-event CFO (TITE-CFO) (Jin and Yin (2023) ), fractional CFO (fCFO), accumulative CFO (aCFO), TITE-aCFO, and f-aCFO (Fang and Yin (2024) ). It supports phase I/II trials for the CFO design and only phase I trials for the other CFO-type designs. The ‘CFO' package accommodates diverse CFO-type designs, allowing users to tailor the approach based on factors such as dose information inclusion, handling of late-onset toxicity, and the nature of the target drug (single-drug or drug-combination). The functionalities embedded in 'CFO' package include the determination of the dose level for the next cohort, the selection of the MTD for a real trial, and the execution of single or multiple simulations to obtain operating characteristics. Moreover, these functions are equipped with early stopping and dose elimination rules to address safety considerations. Users have the flexibility to choose different distributions, thresholds, and cohort sizes among others for their specific needs. The output of the 'CFO' package can be summary statistics as well as various plots for better visualization. An interactive web application for CFO is available at the provided URL. 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Implements a Kalman filtering framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions, and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications. The methodology is described in more detail in Caspi and Ginker (2026) . Package: r-cran-cforward Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cforward_0.2.0-1.ca2604.1_all.deb Size: 98120 MD5sum: 116871852fb6bbf37bb7abdad98dddab SHA1: 039258933eccace93a09e9471b63aaf6af09bf7b SHA256: 73d9d13c0b10da49f91e9ff3db2779e1dcf50fc90905c6b7de3508ca72cf1f9c SHA512: bf70b43b457d9985e20b20791271b12789315770b59e4f95c58aaed57c2ac85df7ccb267c860f16bcf2fff7a2898f86de967595be3d733ecf7f3e07be314d058 Homepage: https://cran.r-project.org/package=cforward Description: CRAN Package 'cforward' (Forward Selection using Concordance/C-Index) Performs forward model selection, using the C-index/concordance in survival analysis models. 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The CF Metadata Conventions is widely used for distributing files with climate observations or projections, including the Coupled Model Intercomparison Project (CMIP) data used by climate change scientists and the Intergovernmental Panel on Climate Change (IPCC). This package specifically allows the user to work with any of the CF-compliant calendars (many of which are not compliant with POSIXt). The CF time coordinate is formally defined in the CF Metadata Conventions document available at . Package: r-cran-cg Architecture: all Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1918 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-vgam, r-cran-mass, r-cran-lattice, r-cran-survival, r-cran-multcomp, r-cran-nlme, r-cran-rms Filename: pool/dists/resolute/main/r-cran-cg_1.0-4-1.ca2604.1_all.deb Size: 1385102 MD5sum: b25caeb4218515ed1bd03592bdbd0d10 SHA1: 2e0acc3cddc5db6a07b68d4388a033e0217d448c SHA256: cd8a5e1a63120887745994be1afab92974597631620f53491a23a1e59a6e0d45 SHA512: df9fd44ec91a9928b25b8eca06636c3f736634a2a1aef7dff6b90fc3968effcd3a144298c66fccd9f54dd6e29a29eba965d53f7a1e766c243b771fce1de2bdf0 Homepage: https://cran.r-project.org/package=cg Description: CRAN Package 'cg' (Compare Groups, Analytically and Graphically) Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized. Package: r-cran-cgaim Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scam, r-cran-scar, r-cran-quadprog, r-cran-osqp, r-cran-limsolve, r-cran-matrix, r-cran-mass, r-cran-cgam, r-cran-mgcv, r-cran-gratia, r-cran-doparallel, r-cran-coneproj, r-cran-truncatednormal, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cgaim_1.0.3-1.ca2604.1_all.deb Size: 139966 MD5sum: aafc5e180b356bfda119dedde7a08f99 SHA1: 345a74edf1503a22bd573bb31bc6f993e1ad5c6a SHA256: c5450cfd4d7e08df0daa02a1ae25a5115cdbd5e8ca8635457d38bbde36515686 SHA512: 8618051ba4e8801535fd9bf62083886470822d6986005bf87da85eb99dabb91c4ca18342c0e3a201528d0b0cf814bdda69bb0483346cb5d05613bead3f8ef8e2 Homepage: https://cran.r-project.org/package=cgaim Description: CRAN Package 'cgaim' (Constrained Groupwise Additive Index Models) Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" . Package: r-cran-cgal4h Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34635 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cgal4h_0.1.0-1.ca2604.1_all.deb Size: 4093284 MD5sum: dff56d92ca9cc3801f55ffcacbede940 SHA1: 8073d8ab566abfa0964999a896257825ca2b34e1 SHA256: 00fb1ff8f15a34accb5ad039548ea698a68cf5bf04ee43e12cc1e4f15867feff SHA512: 17a285e5aa905641efcc489e97251bea8af6230f5fba2c0cbdd8ac8bec9fe3d9746213a7ea787ab6d378ac11e2acc5f2b678b4a0e96f2b3bfea1501ea52f5bfe Homepage: https://cran.r-project.org/package=cgal4h Description: CRAN Package 'cgal4h' ('CGAL' Version 4 C++ Header Files) 'CGAL' is a C++ library that aims to provide easy access to efficient and reliable algorithms in computational geometry. Since its version 4, 'CGAL' can be used as standalone header-only library and is available under a double GPL-3|LGPL license. . Package: r-cran-cgam Architecture: all Version: 1.32-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coneproj, r-cran-splines2, r-cran-svdialogs, r-cran-statmod, r-cran-lme4, r-cran-matrix, r-cran-ggplot2, r-cran-dplyr, r-cran-zeallot, r-cran-rlang, r-cran-quadprog, r-cran-mass Suggests: r-cran-roxygen2, r-cran-patchwork, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-cgam_1.32-1.ca2604.1_all.deb Size: 1296124 MD5sum: 5408437c2ce406a3ec86ccfb6534b075 SHA1: b91f1c7e9c0b5ccface6e8e57cc5e48513e66bf3 SHA256: df6a9a9b8dca685c797b61500d1e1e91a82ec37bffc98aa3fa32507eeb85679a SHA512: b3e4397af44c383dab06fe932667fdd8c70a5c2d90a5be9f8664b0b3e5c719ade771bf11e659820376e6faa464deba85a8d5547953c81c3d8500fae34dd89147 Homepage: https://cran.r-project.org/package=cgam Description: CRAN Package 'cgam' (Constrained Generalized Additive Model) A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019) for more details. Package: r-cran-cge Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cge_0.3.3-1.ca2604.1_all.deb Size: 286108 MD5sum: b35a45ca6ac0d59b2051107fb550fa6d SHA1: 5d70c8ee77cda0c5abb7a9dd0f71f02e060ac0db SHA256: 281f24ebc21f07551ece9f8452282bbe6de3831b00b3715e63277fac8e720861 SHA512: 4234a2f647ffb7f4a7c79fd9b3cca9244b97111f1f2a9f897dd886c8f7b40358711697ce212b6071f56c9c628344c5c9ef4fe116906c5b04f72d507f9424b09a Homepage: https://cran.r-project.org/package=CGE Description: CRAN Package 'CGE' (Computing General Equilibrium) Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package. Package: r-cran-cgmanalysis Architecture: all Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-parsedate, r-cran-lubridate, r-cran-pracma, r-cran-zoo, r-cran-pastecs, r-cran-readxl, r-cran-readr, r-cran-xml, r-cran-mess, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-cgmanalysis_3.2.0-1.ca2604.1_all.deb Size: 207122 MD5sum: ec4c18c5f0a22cf1d916d741552c6be4 SHA1: dd97bf49b5731eb2cd8a6fc8dc2c2f6c10d19ab1 SHA256: 7aeaeaddf58460344660ba92edbf2e525b3e3b38895d0da99487760941a9521f SHA512: 2e7d13a8410c84b94a51d258cae320426cff0373be14dc41f571ff88014e6c8bb26898169c26f56db1a259dcd49755af405485e7fab4188958efed0aaaf4cbbf Homepage: https://cran.r-project.org/package=cgmanalysis Description: CRAN Package 'cgmanalysis' (Clean and Analyze Continuous Glucose Monitor Data) This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom', 'iPro 2', 'Diasend', 'Libre', or 'Carelink' data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit to download the new-user guide. Package: r-cran-cgmissingdatar Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-fnn, r-cran-metrics, r-cran-ranger Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cgmissingdatar_0.0.1-1.ca2604.1_all.deb Size: 88440 MD5sum: dae88960d028dbb785b313f43504c8f4 SHA1: bdf0b0fc66d287f580b9da8d2ee8e0ad4319adc1 SHA256: 05fc3cf87ca9000d32d2baf0091d14541647575473765b2c27172ffb618526b4 SHA512: eb88f560589f772f9defad61dc8390d51015c1f1eea29f671123e0ce1c56ecbfdac2fb78d63fd0133e9ec183cfcfdf2b25bb8a2fc32a7fd9e3c46245e58e9f1a Homepage: https://cran.r-project.org/package=CGMissingDataR Description: CRAN Package 'CGMissingDataR' (Missingness Benchmark for Continuous Glucose Monitoring Data) Evaluates predictive performance under feature-level missingness in repeated-measures continuous glucose monitoring-like data. The benchmark injects missing values at user-specified rates, imputes incomplete feature matrices using an iterative chained-equations approach inspired by multivariate imputation by chained equations (MICE; Azur et al. (2011) ), fits Random Forest regression models (Breiman (2001) ) and k-nearest-neighbor regression models (Zhang (2016) ), and reports mean absolute percentage error and R-squared across missingness rates. Package: r-cran-cgmquantify Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyverse, r-cran-ggplot2, r-cran-hms, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cgmquantify_0.1.0-1.ca2604.1_all.deb Size: 64098 MD5sum: 4f9de6c7d121de8071bf871a95b4c477 SHA1: 166d15e09e7d48f6cc982946c7415f2c7e46e212 SHA256: 51af411429d79e8c105dc656d7e6e056fba799ea9a8ebb4666c1d7b15c00fc38 SHA512: 836bb184be63498118213b3769e6d9f71d5b55a2840622f690b225d527cb03e79e39168705191d3ea7d9072325831ea4f0f6c636ef41e5e91cbcce9922ebd770 Homepage: https://cran.r-project.org/package=cgmquantify Description: CRAN Package 'cgmquantify' (Analyzing Glucose and Glucose Variability) Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) American Diabetes Association (2020) Kovatchev B (2019) Kovdeatchev BP (2017) Tamborlane W V., Beck RW, Bode BW, et al. (2008) Umpierrez GE, P. Kovatchev B (2018) . Package: r-cran-cgnm Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cgnm_0.9.3-1.ca2604.1_all.deb Size: 583058 MD5sum: 7083bb9dbc832838d864ea498e93c1c3 SHA1: 27519598474ad50fcc4c3d5f004239491142838d SHA256: 040823a25f684baec4d6371db5f7311b97c9fb5857b340dcb4f019598f78ea6e SHA512: d01e8200d10b4125eab212176c7fecbb8ab9a3d9ef188d1da04850b9f9403e69db3a63f27f9de618b0b8d90dd536b89f621e4ad2dbc5eb046e1a41711cc9a206 Homepage: https://cran.r-project.org/package=CGNM Description: CRAN Package 'CGNM' (Cluster Gauss-Newton Method) Find multiple solutions of a nonlinear least squares problem. Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) . Cluster Gauss–Newton method. Optimization and Engineering, 1-31. Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) . Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67. GPT based helper bot available at Package: r-cran-cgp Architecture: all Version: 2.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cgp_2.1-1-1.ca2604.1_all.deb Size: 48350 MD5sum: 68eaf1bdb35e24a074e6af6ae8891b87 SHA1: a4620eb175a1f9cc3610cf7d64f7195225b11d24 SHA256: 2c973e443613d2730b2067cbb9d5916b9815e8ee918e381e073539f4c5d52c51 SHA512: d98a9de1018ebf58bf59e10800a25b104b25854b4826ce365a7fb50ff94a8df9e6143cac5c26f4d1be3b5c88fda87687dbcf552e19b90306fd1b94d6b4c7cac3 Homepage: https://cran.r-project.org/package=CGP Description: CRAN Package 'CGP' (Composite Gaussian Process Models) Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary. Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP. Package: r-cran-cgr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cgr_0.1.0-1.ca2604.1_all.deb Size: 11452 MD5sum: ab6dfd67ac830cffae49670b26c8a21f SHA1: d89c11881e04c71a6b43f252b3f04bedb713ba9f SHA256: 842c4430d1ff16e875624f177249fff46475e9a86d9f9cdc06c1df73bf65f5fd SHA512: aacae023b58e13c9eaad13ee5afe8a6e4308dec6d180d99cce67cf1ce08d66a8432a02c4da17f229e8a664ab0d109af2301f5d65b20d56f2cd69e5bede21e48a Homepage: https://cran.r-project.org/package=CGR Description: CRAN Package 'CGR' (Compound Growth Rate for Capturing the Growth Rate Over thePeriod) The compound growth rate indicates the percentage change of a specific variable over a defined period. It is calculated using non-linear models, particularly the exponential model. To estimate the compound growth rates, the growth model is first converted to semilog form and then analyzed using Ordinary Least Squares (OLS) regression. This package has been developed using concept of Shankar et al. (2022). Package: r-cran-cgwtools Architecture: all Version: 4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp Filename: pool/dists/resolute/main/r-cran-cgwtools_4.1-1.ca2604.1_all.deb Size: 131640 MD5sum: 6f36f056be3a3c98cce467a237f214ff SHA1: ee0a702135ff0e2d53dba6e0a034d3b6de991664 SHA256: 3e0c7829a3b53a619ab5505242339e580544aa71f9810134b2b31617671e4b09 SHA512: 7ba9427e15a8273db540231a090e08adb7f0e89ed5da9a2420898c24378a7e8539eb16a8f22a3320a9c855c9949167497685a0ea59a0f455999a234037de94e4 Homepage: https://cran.r-project.org/package=cgwtools Description: CRAN Package 'cgwtools' (Miscellaneous Tools) Functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' . Functions provided to convert from any base to any other base, and to find the n-th greatest max or n-th least min. In addition, functions which mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. Various other goodies included as well. Package: r-cran-chainbinomial Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics Suggests: r-cran-testthat, r-cran-numderiv, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-chainbinomial_0.1.5-1.ca2604.1_all.deb Size: 90318 MD5sum: d4c0db22ac48d54287d02654f3ae2b90 SHA1: 31fb36ba99f14753b2c417da7c64186c301fde32 SHA256: ef5e5c977b5569fb1f044a7282d10d3733025394c13f54ce1c27fdf42e9c288f SHA512: d31f8fefc17cdb0f07ed0d369579377fb80d54f21f1d46cf6ba73191e29e02797323c2c37907627ec7cc66afc6b2b96cefb59b8c167ec9876cc26ad33a765315 Homepage: https://cran.r-project.org/package=chainbinomial Description: CRAN Package 'chainbinomial' (Chain Binomial Models for Analysis of Infectious Disease Data) Implements the chain binomial model for analysis of infectious disease data. Contains functions for calculating probabilities of the final size of infectious disease outbreaks using the method from D. Ludwig (1975) and for outbreaks that are not concluded, from Lindstrøm et al. (2024) . The package also contains methods for estimation and regression analysis of secondary attack rates. Package: r-cran-chainladder Architecture: all Version: 0.2.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-actuar, r-cran-lattice, r-cran-tweedie, r-cran-systemfit, r-cran-statmod, r-cran-cplm, r-cran-ggplot2, r-cran-mass Suggests: r-cran-runit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-chainladder_0.2.21-1.ca2604.1_all.deb Size: 2192004 MD5sum: fc71eab704562492e347c590b30ca9b8 SHA1: c7714abcd4500315208821b61f394081baf6f102 SHA256: c4c6526e985e3ec0a98870dc5bb3d40abd181cb99f441d2522b659d53f990cba SHA512: ea62bc4464fdce644646ce8b85c30cb63c7a7002d455a6b61bfa3d9b47d86c7c8405dd0b61c77f787339e25d8cc2281c74afe13b38def63e2d6274f50d496031 Homepage: https://cran.r-project.org/package=ChainLadder Description: CRAN Package 'ChainLadder' (Statistical Methods and Models for Claims Reserving in GeneralInsurance) Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II. Package: r-cran-chameleon Architecture: all Version: 0.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-ggplot2, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-chameleon_0.2-3-1.ca2604.1_all.deb Size: 155160 MD5sum: d32ac707488eb1fd5b215f4626b2b95e SHA1: 423bb0b7c11e6e4d79b927dfcba63291f2b53c37 SHA256: de91b03e27bd0b6591e399107d5d06266bfae21c4bd3859e481fe211a7e5aa3d SHA512: a6b6fca17d9161d23aedafdbc93bad238774e918e8d62ee3938a75b82dedaf6ac4236b96463c88f9231888f13ab33f9b9bb8f904acdaf3b2ccc0c5d96bfa9801 Homepage: https://cran.r-project.org/package=chameleon Description: CRAN Package 'chameleon' (Automatic Colors for Multi-Dimensional Data) Assign distinct colors to arbitrary multi-dimensional data, considering its structure. Package: r-cran-chandwich Architecture: all Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sandwich, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chandwich_1.1.6-1.ca2604.1_all.deb Size: 244546 MD5sum: a3043e740253d8082887fa2c5410a19c SHA1: db6644949af1112266aa609431bc2d8b3cba7fec SHA256: ad3705b40c38fe57edaf956200e07f465d89ef526cdfca2e9cfd6b5025238209 SHA512: 85601f35d6de7925b983c795d271c73261226a98b8356866af836e945e3b48039f4d1e483482dd6c2546cb1e0fcbbea8272d5d9caaf65cc46ad0699ee4fd7770 Homepage: https://cran.r-project.org/package=chandwich Description: CRAN Package 'chandwich' (Chandler-Bate Sandwich Loglikelihood Adjustment) Performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) . This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test. Package: r-cran-changepoint.geo Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-changepoint.np, r-cran-ggplot2, r-cran-rdpack Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/resolute/main/r-cran-changepoint.geo_1.0.3-1.ca2604.1_all.deb Size: 260640 MD5sum: d4e0976bec8acb1173b589372c357831 SHA1: 9e8890d19cc445c8c41a55f1fc07f0a4a3ab2838 SHA256: 6dab496c84c50e115be8c90ccdce46787765bd4a0b7e2c4d309afd365f958e91 SHA512: e2c7e164ad4e15d9e790c98b863459d21f997c13c0a6480a93596067c5b75c5eff0b063ffcbd390b7358159fcc7754d064d1203f991a961e49c0187c6097e8bd Homepage: https://cran.r-project.org/package=changepoint.geo Description: CRAN Package 'changepoint.geo' (Geometrically Inspired Multivariate Changepoint Detection) Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) . Package: r-cran-changepoint.influence Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-changepoint.influence_1.0.2-1.ca2604.1_all.deb Size: 98698 MD5sum: 0dbdea3149241b226cda8a2498fd671f SHA1: cb37ee01283ff92ab8b783abcc89f6d397380c1a SHA256: a6f0f3caeb4d38a45de2817d3b2b8dedfd8588dff7003cef9e8c037aa2d6489e SHA512: 315dc8827842ef455949d5d77e4aa58fc3a89ab55a912511acdcfff887f3a30e219ac343560bc2310b54317bc238be109f95edf12f38ef86780208dc6e71b989 Homepage: https://cran.r-project.org/package=changepoint.influence Description: CRAN Package 'changepoint.influence' (Package to Calculate the Influence of the Data on a ChangepointSegmentation) Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2022) . Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this. There are options for different types of graphics to assess the influence. Package: r-cran-changepointsvar Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lars Filename: pool/dists/resolute/main/r-cran-changepointsvar_0.1.2-1.ca2604.1_all.deb Size: 44404 MD5sum: d69c23da17df9e355f319d8f4e0bb15e SHA1: 0c24d266880c99fc0b16d33f2802fdcdbce169b0 SHA256: 794ffa7c4b04d9b88d197675c9b5a415483c9a465d9c56a2fdfa81bc662ab10f SHA512: e409bfb370516572758168e0df64f0ec6ede9402f3eb899345dc124fc7f7a18c6c25075e4d45137aa3e9292963f1f758899f96a12a50e0cc18030736edbecdff Homepage: https://cran.r-project.org/package=changepointsVar Description: CRAN Package 'changepointsVar' (Change-Points Detections for Changes in Variance) Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, . Package: r-cran-changepointtesting Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-changepointtesting_1.2-1.ca2604.1_all.deb Size: 162498 MD5sum: 960792ffe3a62794b76dea79f86841fc SHA1: 2eeeb950ba8fdbb1ecc32de010aebc775b4a8bf8 SHA256: c689cdbf68d30dd8ffed377463f2c96a15ae3059164fcab18ffd3775c7cb182a SHA512: fd3b07ee5462d6041595169af9da10683390f0d0cef1a48e779c1d95ba1f1cedc3d4572ddfb255f242dc8b3d9f9d0fb5ca7066dad42f1b8169506c3f86ab6e1b Homepage: https://cran.r-project.org/package=ChangepointTesting Description: CRAN Package 'ChangepointTesting' (Change Point Estimation for Clustered Signals) A multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values. Package: r-cran-changer Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-available, r-cran-devtools, r-cran-git2r Filename: pool/dists/resolute/main/r-cran-changer_0.0.5-1.ca2604.1_all.deb Size: 20208 MD5sum: af7c3f57d3513d4884b4bdbde3faf655 SHA1: 7833407d037dc4f1bf6e48b05a7656eb82be83ae SHA256: a5d5fe3042660c03f73d10803a1e1447f0c6c1f6deb4f34af7693ca7ef3a6088 SHA512: 5158406b60366a5b72c1de38093eda66ef8512c92487148a919202db32f9f127d60330e9698e61d150ac7136e92737d22e4204a0a70a390ca315b624e86e4c96 Homepage: https://cran.r-project.org/package=changer Description: CRAN Package 'changer' (Change R Package Name) Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task. 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Package: r-cran-channelattributionapp Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-channelattribution, r-cran-shiny, r-cran-data.table, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-channelattributionapp_1.3-1.ca2604.1_all.deb Size: 128088 MD5sum: 7e6c01ad65afcc5e6bbbb8f2346c2413 SHA1: 0d368e82b3fd7a8349c7931bf3c8b01ac90a58fd SHA256: bd80b6b39f0b01aadf1029b137a1ba4ac0a3502baedf7059758ca2c9742b73ba SHA512: daa148e543acf5df6817711f891b5ad69ec045b146dc744084193da05918bf34cd79468b617f4aebe172968fd81f7a1ab92933631efd7e00218340f264809cde Homepage: https://cran.r-project.org/package=ChannelAttributionApp Description: CRAN Package 'ChannelAttributionApp' (Shiny Web Application for the Multichannel Attribution Problem) Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package 'ChannelAttribution'. Package: r-cran-chantrics Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aer, r-cran-chandwich, r-cran-sandwich, r-cran-lmtest, r-cran-rlang, r-cran-progress, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lax, r-cran-covr, r-cran-mass, r-cran-pscl Filename: pool/dists/resolute/main/r-cran-chantrics_1.0.0-1.ca2604.1_all.deb Size: 443018 MD5sum: b2733b0b7c0a30d864725123b356d2e4 SHA1: 138d5854e64fcd6b6b252ea437786c619e28d1ff SHA256: 4c7fbfe69a1d5713141c2af07407bbbbf413370c477ef691aeeba7f927c6cc22 SHA512: 97cbbad68e1df4764d090fc3e2023c8e257ba55dffa22879aabf4d94dc3893297d7e6d339ef92db98177cc77d5c63347d6b927c103f245f153a858ac5c761a74 Homepage: https://cran.r-project.org/package=chantrics Description: CRAN Package 'chantrics' (Loglikelihood Adjustments for Econometric Models) Adjusts the loglikelihood of common econometric models for clustered data based on the estimation process suggested in Chandler and Bate (2007) , using the 'chandwich' package , and provides convenience functions for inference on the adjusted models. Package: r-cran-chaosgame Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-colorramps, r-cran-ggplot2, r-cran-gridextra, r-cran-plot3d, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-chaosgame_1.5-1.ca2604.1_all.deb Size: 93978 MD5sum: 00eeda64eed82a940e65f795978484f1 SHA1: 3f5d0181899b2fd1b2793baf11ff1342c0b407fe SHA256: af5ceab53a1335d66b8185e018b63ced9d9ab5cc8466c534332b1c040f7e583d SHA512: df878eb2e7ed0ae02477ae51478076bb2f10e8ee985515600432e38b6866669144f680e97b34d37b74f3a15722b44d1f61de072f4e4b8eca43b8392ea0c8f825 Homepage: https://cran.r-project.org/package=ChaosGame Description: CRAN Package 'ChaosGame' (Chaos Game) The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. Additionally, the package allows to project the two-dimensional fractal on several three-dimensional surfaces and to transform the fractal into another fractal with uniform marginals. Package: r-cran-chapensk Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel Filename: pool/dists/resolute/main/r-cran-chapensk_0.4-1.ca2604.1_all.deb Size: 251240 MD5sum: 5049884e47ef01a0492a6ce36b11485d SHA1: 664b79780317708ac3104d7c7793440e74c1f1d3 SHA256: db561fd6037cbba7d2d541e0d579c0807a50dbc9a072ea7ed491f488f8af0d49 SHA512: 235f28d7880d2f1e6921539cc57711d3ccfbac35f8eca45b5b4e7600fdde3558a3a44cf41ef4e2b60488001d1cc82e8797a91dab7b554dc6e9bb0821c7b78287 Homepage: https://cran.r-project.org/package=chapensk Description: CRAN Package 'chapensk' (Estimation of Gas Properties from the Lennard-Jones Potential) Estimation of gas transport properties (viscosity, diffusion, thermal conductivity) using Chapman-Enskok theory (Chapman and Larmor 1918, ) and of the second virial coefficient (Vargas et al. 2001, ) using the Lennard-Jones (12-6) potential. Up to the third order correction is taken into account for viscosity and thermal conductivity. It is also possible to calculate the binary diffusion coefficients of polar and non-polar gases in non-polar bath gases (Brown et al. 2011, ). 16 collision integrals are calculated with four digit accuracy over the reduced temperature range [0.3, 400] using an interpolation function of Kim and Monroe (2014, ). Package: r-cran-chapgwas Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-chapgwas_0.1.3-1.ca2604.1_all.deb Size: 37242 MD5sum: f625be1b50eec0260f6decf74d516f0a SHA1: 4e751fba5a8a8d30f691bfbb2ef87a4fccaca989 SHA256: 7151aadb2ea62f0c57509d0f9ff14a18538a320d1867899766fc5cc2ae272d5c SHA512: 21aa4eaa9b2703e64bb05fb3fd6666d2da7777a4b6c9c0be562da94cc931240c855171c9fae2fc4fec71c7c5f7335f696c1cda0411770cf9867ecaa7c1a0cea8 Homepage: https://cran.r-project.org/package=CHAPGWAS Description: CRAN Package 'CHAPGWAS' (CHAP-GWAS: Leveraging Chromosomal Haplotypes to ImproveGenome-Wide Association Studies) CHAP-GWAS (Chromosomal Haplotype-Integrated Genome-Wide Association Study) provides a dynamically adaptive framework for genome-wide association studies (GWAS) that integrates chromosome-scale haplotypes with single nucleotide polymorphism (SNP) analysis. The method identifies and extends haplotype variants based on their phenotypic associations rather than predefined linkage blocks, enabling high-resolution detection of quantitative trait loci (QTL). By leveraging long-range phased haplotype information, CHAP-GWAS improves statistical power and offers a more comprehensive view of the genetic architecture underlying complex traits. Package: r-cran-characterization Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4273 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-andromeda, r-cran-databaseconnector, r-cran-digest, r-cran-featureextraction, r-cran-sqlrender, r-cran-parallellogger, r-cran-resultmodelmanager, r-cran-checkmate, r-cran-dplyr, r-cran-readr, r-cran-rlang Suggests: r-cran-devtools, r-cran-formatr, r-cran-testthat, r-cran-kableextra, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ohdsishinyappbuilder, r-cran-shiny, r-cran-withr Filename: pool/dists/resolute/main/r-cran-characterization_3.0.1-1.ca2604.1_all.deb Size: 3645038 MD5sum: 6c496fdc8419617017c5915a2914755a SHA1: 57dde9114e5c85bab28c59c0bcca71c0bb7e4777 SHA256: 19f231cc5399d47648314ea7e3a8784e828de66dba1f8c437d70b5938dee6c54 SHA512: fad2795cb642e38f05fe75745ecb57450b9460d4af3f1f86dd39e95ec4bee02d59dd4db33f4a9b420b782a8ad0a9ec7cff454a93f4857192a1655853becacc7b Homepage: https://cran.r-project.org/package=Characterization Description: CRAN Package 'Characterization' (Implement Descriptive Studies Using the Common Data Model) An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model. Package: r-cran-charanalysis Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-zoo Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-ggtext, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-charanalysis_2.0.3-1.ca2604.1_all.deb Size: 570146 MD5sum: 2d3acd0f532b652507ba89c6f5cbced8 SHA1: 1ebd6d56baf091f85ab80cd436b859d0d449e4a2 SHA256: 6839ed91da70999044b85c9803fd1af5aa4f13e148262d6ec63d6fd971758b25 SHA512: 5ac031679db1632c2ea166b7aa18402b396328609bb76045b5a888ba903a48e2670e81520f62030d2c6f00558c1beb55d74d66279409124b232eef3049ec2337 Homepage: https://cran.r-project.org/package=CharAnalysis Description: CRAN Package 'CharAnalysis' (Peak Detection and Fire History from Sediment-Charcoal Records) A program for reconstructing local fire histories from high-resolution, continuously sampled lake-sediment charcoal records. 'CharAnalysis' decomposes a charcoal record into low- and high-frequency components and uses locally defined thresholds to separate fire signal from noise, following the approach of Higuera et al. (2009) , with underlying assumptions and rationale described in Higuera et al. (2010) . The package is designed for macroscopic charcoal records with contiguous sampling fine enough to resolve individual fire events, and is not appropriate for low-resolution or discontinuously sampled records. See the package URL for the User's Guide and application examples. Package: r-cran-charcuterie Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vctrs Filename: pool/dists/resolute/main/r-cran-charcuterie_0.0.6-1.ca2604.1_all.deb Size: 319290 MD5sum: dfaad1a573a430623d9ac1a8cc734f21 SHA1: 7e5a48f611ac1e24f4b63a12e9aeb7d2af2aff37 SHA256: fac80cccb021db68ea614851ae17c1bc0273567c339a3fa890cbd727bc7a7b09 SHA512: b54ffa1fc325a40b692d2efec0fe61832e3cb2994d4e3d09d4d5c50be2f72f90282a0be1a5313ab8fc553910a30ecd600b13730572143da4f7f6c6032319333c Homepage: https://cran.r-project.org/package=charcuterie Description: CRAN Package 'charcuterie' (Handle Strings as Vectors of Characters) Creates a new chars class which looks like a string but is actually a vector of individual characters, making 'strings' iterable. 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Package: r-cran-charlatan Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-tibble, r-cran-whisker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ipaddress, r-cran-stringi, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-charlatan_0.6.2-1.ca2604.1_all.deb Size: 4145942 MD5sum: 0b5ecb50583299ad33b4ff3968ba875f SHA1: 7c4c0fc04b8d0cf583711613e1719d6997589bc7 SHA256: dc02a4e1a15e290934bcf51681e1e9579500f67c7079b492ad525669e17320b9 SHA512: da9cc5faa52687d0828708c104cbf01213696a770943361bcb86c5251296446c50185bf479e31a565a6fa6c3ed9dffd51328721af1d3311cbae24448586e16e5 Homepage: https://cran.r-project.org/package=charlatan Description: CRAN Package 'charlatan' (Make Fake Data) Make fake data that looks realistic, supporting addresses, person names, dates, times, colors, coordinates, currencies, digital object identifiers ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers from distributions and within a range. 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Package: r-cran-chatai4r Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3587 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-assertthat, r-cran-clipr, r-cran-crayon, r-cran-rstudioapi, r-cran-future, r-cran-igraph, r-cran-deeprstudio, r-cran-curl, r-cran-base64enc, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-chatai4r_1.3.1-1.ca2604.1_all.deb Size: 2384134 MD5sum: 0fe02d68fe62c6213dd8b3f913a6ff6d SHA1: d05f39f69850a15210cfe3fed1a53778e597d138 SHA256: 606ff8bae302f2a90f79cba1366e77bed209c4445c2d5a833be5965e51c78e60 SHA512: 4a5aaaae8ea6146c79fd96f6b836f519bd9a5ed9f52c5c00f7a8092c9259dea985694ced4404677cf94b4d276f7b2ff994087dc596ab3708e232e1f8350fbab8 Homepage: https://cran.r-project.org/package=chatAI4R Description: CRAN Package 'chatAI4R' (Chat-Based Interactive Artificial Intelligence for R) The Large Language Model (LLM) represents a groundbreaking advancement in data science and programming, and also allows us to extend the world of R. 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Package: r-cran-chatgpt Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clipr, r-cran-httr, r-cran-jsonlite, r-cran-miniui, r-cran-rstudioapi, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-chatgpt_0.2.3-1.ca2604.1_all.deb Size: 146530 MD5sum: 3dfa180a24529a0915d34fa9ad5ee379 SHA1: c5a32eef397869f5d745ecd37fe03a8ac050b621 SHA256: f98fdad3ffb3af20a4fe6df5dae627ff34c7bfc953592dfa98d23fa2647f9336 SHA512: 8ffbcfa188db11a33be7a5ee617def434cd391ebf2e1583ad906b073aac20cb76a9e8d191edd7d7c02edc57f5debe6219a7aee21b91fa57b5d5535bbc2d333a6 Homepage: https://cran.r-project.org/package=chatgpt Description: CRAN Package 'chatgpt' (Interface to 'ChatGPT' from R) 'OpenAI's 'ChatGPT' coding assistant for 'RStudio'. A set of functions and 'RStudio' addins that aim to help the R developer in tedious coding tasks. 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Package: r-cran-cheese Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cheese_0.1.3-1.ca2604.1_all.deb Size: 157924 MD5sum: a448b4e569cd309a56f1ef7840e021d0 SHA1: 21ad5af16f75f3ef982d2e851a29a296fe0ed78a SHA256: 0799b469409647d405960c689a75477f8df4af8f75f4d6234d29b6dfdb5e55d0 SHA512: fb58f29eb02939de93d90b0b5afc2ca1a689dc756e5227edb7726ffe88c12245b3fa078247e87a640b7ba6b823c4ca0cf7fa6d56b237a23bac46cf03587b32fd Homepage: https://cran.r-project.org/package=cheese Description: CRAN Package 'cheese' (Tools for Working with Data During Statistical Analysis) Contains tools for working with data during statistical analysis, promoting flexible, intuitive, and reproducible workflows. 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Package: r-cran-cheetahr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-testthat, r-cran-shiny, r-cran-quarto, r-cran-knitr, r-cran-dplyr, r-cran-palmerpenguins Filename: pool/dists/resolute/main/r-cran-cheetahr_0.3.0-1.ca2604.1_all.deb Size: 1070582 MD5sum: b5fa41778da844a040e53e4d10fef458 SHA1: 25ccde0622c11929135c47127d24af99b92e9f8a SHA256: a455ed75099e69f3eac66563726216fd354316feb119e9cd8bd7bef205c98649 SHA512: cde6f18a59903ca83c6cacdadebb62ff465aae72b38646732ee06af40134f2d1de61b9097ab7729ec783ffd594d1dc760a1c4fe8b06bce4f78e2262b2ee61bae Homepage: https://cran.r-project.org/package=cheetahR Description: CRAN Package 'cheetahR' (High Performance Tables Using 'Cheetah Grid') An R interface to 'Cheetah Grid', a high-performance JavaScript table widget. 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Package: r-cran-chem.databases Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2457 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-install.load, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-chem.databases_1.0.0-1.ca2604.1_all.deb Size: 2478352 MD5sum: 0e6b51995fd959e05efcd6ea61ab8a0f SHA1: 2c48f46a8cfad587568c8785e904509dc4995472 SHA256: 53029f9c156f09383f96cfe2e15663bd77f2607daf3516b8fb81ba17465befc0 SHA512: 5353671b21f6a0054e4e2a2b71eb43aaa74feb4490ed9763a979ef418126e5e8db6c3a5da5ac5017d19050c3223e2983308cba51d051e5a2c980aab5fa77a2a8 Homepage: https://cran.r-project.org/package=chem.databases Description: CRAN Package 'chem.databases' (Collection of 3 Chemical Databases from Public Sources) Contains the Multi-Species Acute Toxicity Database (CAS & SMILES columns only) [United States (US) Department of Health and Human Services (DHHS) National Institutes of Health (NIH) National Cancer Institute (NCI), "Multi-Species Acute Toxicity Database", ] combined with the Toxic Substances Control Act (TSCA) Inventory [United States Environmental Protection Agency (US EPA), "Toxic Substances Control Act (TSCA) Chemical Substance Inventory", ] and the Agency for Toxic Substances and Disease Registry (ATSDR) Database [United States (US) Department of Health and Human Services (DHHS) Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), "Agency for Toxic Substances and Disease Registry (ATSDR) Database", ] in 2 data sets. 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Package: r-cran-chem16s Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4808 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-ggplot2, r-cran-rlang, r-cran-reshape2, r-bioc-phyloseq, r-cran-canprot Suggests: r-cran-tinytest, r-cran-knitr, r-cran-patchwork, r-cran-ggpmisc, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-chem16s_1.2.0-1.ca2604.1_all.deb Size: 3453146 MD5sum: bb405945f80dffb256b5c033aaeff2d4 SHA1: 47afd012539515fc90601fc193d74fd12f63152b SHA256: 5030ac5bc41cf54236e141ce4d350ebce19a8e71b76ff1b3bd03f5cf422e2d24 SHA512: f372c434df9874835bf6109267514636ca93f68022034c63286fc5503878f81b4129718d5f53d9dbd7fbb9f59c227cade47208120d7e2fc9477e9d4bbf745399 Homepage: https://cran.r-project.org/package=chem16S Description: CRAN Package 'chem16S' (Chemical Metrics for Microbial Communities) Combines taxonomic classifications of high-throughput 16S rRNA gene sequences with reference proteomes of archaeal and bacterial taxa to generate amino acid compositions of community reference proteomes. 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(1997) There are also functions estimating the limit of detection (LOD) and limit of quantification (LOQ). The functions work on model objects from - optionally weighted - linear regression (lm) or robust linear regression ('rlm' from the 'MASS' package). 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The selection of the appropriate degradation model and parameter estimation is carried out automatically as far as possible and is driven by a rigorous statistical interpretation of the results. The package integrates already available goodness-of-fit statistics for nonlinear models. In addition it allows data fitting with the nonlinear first-order multi-target (FOMT) model. Package: r-cran-chemist Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-xicor, r-cran-laplacesdemon Filename: pool/dists/resolute/main/r-cran-chemist_0.1.5-1.ca2604.1_all.deb Size: 61016 MD5sum: 8489ad23d182bc70403b4d57d69115cb SHA1: a739492948f69baf7420cea3f700ffe406dff041 SHA256: eb51ccaf14ea68a56ce8447b45dbb0399e3327e38c84484ce8ab2b5cc13e29d1 SHA512: 0771996e15acb6f8e24e404b0eb2b110508b7683bb619f6c70fb4077ee98ad014df4d7cafb1461781d2b44fcad7b4164c178fb2480201aa46479f07e189e834c Homepage: https://cran.r-project.org/package=CHEMIST Description: CRAN Package 'CHEMIST' (Causal Inference with High-Dimensional Error-Prone Covariatesand Misclassified Treatments) We aim to deal with the average treatment effect (ATE), where the data are subject to high-dimensionality and measurement error. This package primarily contains two functions, which are used to generate artificial data and estimate ATE with high-dimensional and error-prone data accommodated. Package: r-cran-chemodiv Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-vegan, r-cran-webchem, r-bioc-fmcsr, r-bioc-chemminer, r-cran-hillr, r-cran-ape, r-cran-gunifrac, r-cran-tidygraph, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-gridextra, r-cran-ggdendro, r-cran-tidyr, r-cran-rlang, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chemodiv_0.3.1-1.ca2604.1_all.deb Size: 578714 MD5sum: 9ec600117350447bac2b72f214c5d0a1 SHA1: cac8fd30d3084137735f810344f525e4bddc1a1f SHA256: 6bb530e57bb0f5f3f8eec5e6e387d0bc1c787bbcc1933d650301d842e4f680e3 SHA512: 63d5e11266426ced3a6e3e01bf458af7f7cb6902e0bffcc50b2611b1559a20d5194d7870db1840fb60ae864aed73ebbe154c55897149b99499e933660bea8584 Homepage: https://cran.r-project.org/package=chemodiv Description: CRAN Package 'chemodiv' (Analysing Chemodiversity of Phytochemical Data) Quantify and visualise various measures of chemical diversity and dissimilarity, for phytochemical compounds and other sets of chemical composition data. Importantly, these measures can incorporate biosynthetic and/or structural properties of the chemical compounds, resulting in a more comprehensive quantification of diversity and dissimilarity. For details, see Petrén, Köllner and Junker (2023) . Package: r-cran-chemometrics Architecture: all Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart, r-cran-class, r-cran-e1071, r-cran-mass, r-cran-nnet, r-cran-pcapp, r-cran-robustbase, r-cran-som, r-cran-lars, r-cran-pls, r-cran-mclust Suggests: r-cran-gclus Filename: pool/dists/resolute/main/r-cran-chemometrics_1.4.4-1.ca2604.1_all.deb Size: 4084178 MD5sum: 4a933aab9525e08db9ba835a2ac0f78f SHA1: 075a6ed61ead68145c3430fe79dc8324574edf18 SHA256: dd463da884dfa92a5d4dd3855bb8a128588ab4af38faa07b534689fc8ca06b5a SHA512: 9c02d3ff8385c2ad0240e1f3e5726155c075d34999629c7465175675de3fb50ddb62f3887169c6fc40d0f06fa6bfe6df03bf2621c456221b10195f88a4278fdd Homepage: https://cran.r-project.org/package=chemometrics Description: CRAN Package 'chemometrics' (Multivariate Statistical Analysis in Chemometrics) R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009). Package: r-cran-chemospec2d Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chemospecutils, r-cran-colorspace, r-cran-readjdx, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-tinytest, r-cran-irlba, r-cran-threeway, r-cran-multiway, r-cran-matrixstats, r-cran-r.utils, r-cran-mlrmbo, r-cran-paramhelpers, r-cran-smoof, r-cran-mlr, r-cran-lhs, r-cran-rcpproll, r-cran-rmarkdown, r-cran-robustbase, r-cran-bookdown, r-cran-cmls Filename: pool/dists/resolute/main/r-cran-chemospec2d_0.5.1-1.ca2604.1_all.deb Size: 385458 MD5sum: 046b9db5665b53d20fd4699a70f26627 SHA1: ad5906c236e9d648767f6da5f711a4d575d8bac6 SHA256: b24382632fb10d3842027c566807b99101df68776234006eb624b9d80349a78c SHA512: 3afaaad085252c72c625d6804b800d5024b3922b608ca224482a2fb4376d8ccf8ef18ab4beffc839365b05bc4059328aea8226be60c9a4607be0b3ff4176ed63 Homepage: https://cran.r-project.org/package=ChemoSpec2D Description: CRAN Package 'ChemoSpec2D' (Exploratory Chemometrics for 2D Spectroscopy) A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. 'ChemoSpec2D' deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the sample from a statistical perspective. In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. 'ChemoSpec2D' takes many of its cues from 'ChemoSpec' and tries to create consistent graphical output and to be very user friendly. Package: r-cran-chemospec Architecture: all Version: 6.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chemospecutils, r-cran-reshape2, r-cran-readjdx, r-cran-patchwork, r-cran-ggplot2, r-cran-plotly, r-cran-lattice, r-cran-magrittr Suggests: r-cran-idpmisc, r-cran-js, r-cran-nbclust, r-cran-clustercrit, r-cran-baseline, r-cran-mclust, r-cran-pls, r-cran-r.utils, r-cran-rcolorbrewer, r-cran-seriation, r-cran-mass, r-cran-pcapp, r-cran-jsonlite, r-cran-signal, r-cran-speaq, r-cran-elasticnet, r-cran-irlba, r-cran-chemometrics, r-cran-amap, r-cran-tinytest, r-cran-roxut Filename: pool/dists/resolute/main/r-cran-chemospec_6.3.1-1.ca2604.1_all.deb Size: 3131330 MD5sum: f8c2f9492f1dae20c83d11a9f7f1ee26 SHA1: a1d938a60c6f2ff798060133d7e46542392265fe SHA256: 6ff19897858f253e69c15051479a9258f15696d586877f437b0443180ebf111c SHA512: 69efba97fafc34424776bd8b85c51dde76f9122475cbfae73ef6d3a63a4292143a2fae2e22fe34358bdcc09bee3a9716fad51fb2e6a7f8eb8a61bc5525ecc787 Homepage: https://cran.r-project.org/package=ChemoSpec Description: CRAN Package 'ChemoSpec' (Exploratory Chemometrics for Spectroscopy) A collection of functions for top-down exploratory data analysis of spectral data including nuclear magnetic resonance (NMR), infrared (IR), Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy. Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate for this type of high-dimensional data are available. ChemoSpec is designed for structured experiments, such as metabolomics investigations, where the samples fall into treatment and control groups. Graphical output is formatted consistently for publication quality plots. ChemoSpec is intended to be very user friendly and to help you get usable results quickly. A vignette covering typical operations is available. Package: r-cran-chemospecutils Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-plotly, r-cran-magrittr Suggests: r-cran-chemospec, r-cran-chemospec2d, r-cran-tinytest, r-cran-robustbase, r-cran-rcolorbrewer, r-cran-amap, r-cran-irlba, r-cran-lattice, r-cran-roxut, r-cran-patchwork, r-cran-threeway, r-cran-multiway, r-cran-mvoutlier Filename: pool/dists/resolute/main/r-cran-chemospecutils_1.0.5-1.ca2604.1_all.deb Size: 235230 MD5sum: c847feda4e9280eca8fe7335e68b03e0 SHA1: efbca62737303799554a01cc5702f671ae5a43dd SHA256: 07bb92a8859f29eb1d1ddefbc25cc47d0ddbddefd56d2a660b81c0ae1cdb5da2 SHA512: 1377185548055708a79db5d4f9711b04e9543423d03e1e44b7b742193b07e7e9e7c83bf8048f3ea64b9b8bc94288bdcffe0b4e2bfc22f79ae9de07d1950940ae Homepage: https://cran.r-project.org/package=ChemoSpecUtils Description: CRAN Package 'ChemoSpecUtils' (Functions Supporting Packages ChemoSpec and ChemoSpec2D) Functions supporting the common needs of packages 'ChemoSpec' and 'ChemoSpec2D'. Package: r-cran-chernoffdist Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsl Filename: pool/dists/resolute/main/r-cran-chernoffdist_0.1.0-1.ca2604.1_all.deb Size: 19190 MD5sum: 0560386677941f4f51a52238d7bacbdc SHA1: 891b8dace09cda266fc955a1fded6225c35a61bf SHA256: 531911d8dd7f231f19155d1cf03b65434fba1016d006716a5ba32febb4feb770 SHA512: 8b787687a4c65ab28d3ccc6a48e8146c98f5b52bf9dac4d7512509a0ece61706a40fc80edc2a48daf356f8b34fc0f9388e71e765590dfa100cab22b9f9a6c074 Homepage: https://cran.r-project.org/package=ChernoffDist Description: CRAN Package 'ChernoffDist' (Chernoff's Distribution) Computes Chernoff's distribution based on the method in Piet Groeneboom & Jon A Wellner (2001) Computing Chernoff's Distribution, Journal of Computational and Graphical Statistics, 10:2, 388-400, . Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2). Package: r-cran-cherry Architecture: all Version: 0.6-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bitops, r-cran-lpsolve, r-cran-hommel Suggests: r-cran-mass, r-bioc-multtest Filename: pool/dists/resolute/main/r-cran-cherry_0.6-15-1.ca2604.1_all.deb Size: 539360 MD5sum: d6aa3ca70fc5fe8015227cb9d9966e69 SHA1: 4afc4933084b1304492015fbe6dad15efe5a5496 SHA256: ad0aad914239e1503bf707722f7d07400ae8445d15bdb1819998c30a5ee1853b SHA512: 3b67a2a8ec7ed1fbc58a9b7a57654774f9c0ca0bcf327660d217c97a4f102301a1d6c8e989c6e29ca878d09278a98ee61b8e0ec3c34e767f0e690be527d1f391 Homepage: https://cran.r-project.org/package=cherry Description: CRAN Package 'cherry' (Multiple Testing Methods for Exploratory Research) Provides an alternative approach to multiple testing by calculating a simultaneous upper confidence bounds for the number of true null hypotheses among any subset of the hypotheses of interest, using the methods of Goeman and Solari (2011) . Package: r-cran-cherryblossom Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cherryblossom_0.1.0-1.ca2604.1_all.deb Size: 1333466 MD5sum: 691687b9f6ad3701605e45a63487a4cb SHA1: 3ab3c65067fe7d34b092adfdc1832e8d75f37fef SHA256: 68cbc45fdee7f51258b90e0ee4375289084e2804d84032b8d6109dc306c74dd7 SHA512: 0efa00909c7616fb7c837aa9acc1bbf76b91f870214d723bb26e8b63a468bd72e635627475b991dfb666fd14d086fab56f5d5725b2f69f03958e642dc4bc80da Homepage: https://cran.r-project.org/package=cherryblossom Description: CRAN Package 'cherryblossom' (Cherry Blossom Run Race Results) Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC. Package: r-cran-chess2plyrs Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chess2plyrs_0.3.0-1.ca2604.1_all.deb Size: 144684 MD5sum: 97c1e6eef725fef74aaf3df96b77ff80 SHA1: 4c85b0c8733e946f419b91e0dd8e97548f8d4271 SHA256: 03653520ace87621fa0ee78670970d8030d9573807565c90d7ec8966bb3e2255 SHA512: 3ec0369ca6583f4eaafbbe41c4adba688aaff2ef9f0d021cf176f101bada0124cf06969ef1436f4062f37598b6299183e82f1fc087c0141188fbb5a121333114 Homepage: https://cran.r-project.org/package=chess2plyrs Description: CRAN Package 'chess2plyrs' (Chess Game Creation and Tools) A chess program which allows the user to create a game, add moves, check for legal moves and game result, plot the board, take back, read and write FEN (Forsyth–Edwards Notation). 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It makes the creation of connectivity matrices easier, i.e. a binary matrix of dimension n x n, where n is the number of nodes (sampling units) indicating the presence (1) or the absence (0) of an edge (link) between pairs of nodes. Different network objects can be produced by 'chessboard': node list, neighbor list, edge list, connectivity matrix. It can also produce objects that will be used later in Moran's Eigenvector Maps (Dray et al. (2006) ) and Asymetric Eigenvector Maps (Blanchet et al. (2008) ), methods available in the package 'adespatial' (Dray et al. (2023) ). This work is part of the FRB-CESAB working group Bridge . Package: r-cran-chessgmoog Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3939 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-chessgmoog_0.1.0-1.ca2604.1_all.deb Size: 3984464 MD5sum: 39c48ca8b18156f3432443363c998bd0 SHA1: dd234e75f83110654271d0e00bb4e734db180070 SHA256: c85cf1dd280220cf2c091248b719bf343d33a488ace6f323eedeb14395ba96fb SHA512: 523e43775800c8a60d527db65a5fc64011e982d1985b5a8f28a91ad3cdd3344b4732b1bbce48e53ae28cf90b88ceff1cdd0c27dad5b488715d4cf0443aa5ab56 Homepage: https://cran.r-project.org/package=ChessGmooG Description: CRAN Package 'ChessGmooG' (FIDE Chess Players Ratings for 2015 and 2020) Datasets of the International Chess Federation's player ratings and country information analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics". Package: r-cran-chest Architecture: all Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-ggplot2, r-cran-survival, r-cran-forestplot, r-cran-mass, r-cran-tibble, r-cran-dplyr Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-chest_0.3.7-1.ca2604.1_all.deb Size: 184210 MD5sum: fe925c2f76fb091727e4c49df2f0031c SHA1: e1d0a2820f0d419ef6ac163b7f4f4957815df79a SHA256: fe05a66d9f95caabae1731d74ddaef589215e5dbb1982cc0f9cc4ca2edd86a86 SHA512: ade822c11f5e1c59f3bcaf66701010334d0f60dc7f8488972a421fe8a3f5e61f63c04f77c0e8fd6b4f5c7c53c78e442ae28e2ae8663789391a91bfbdaf17ef8a Homepage: https://cran.r-project.org/package=chest Description: CRAN Package 'chest' (Change-in-Estimate Approach to Assess Confounding Effects) Applies the change-in-effect estimate method to assess confounding effects in medical and epidemiological research (Greenland & Pearce (2016) ). It starts with a crude model including only the outcome and exposure variables. At each of the subsequent steps, one variable which creates the largest change among the remaining variables is selected. This process is repeated until all variables have been entered into the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest' package has functions for linear regression, logistic regression, negative binomial regression, Cox proportional hazards model and conditional logistic regression. Package: r-cran-chestvolume Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-tidyr, r-cran-geometry, r-cran-ggplot2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chestvolume_1.0.1-1.ca2604.1_all.deb Size: 2546968 MD5sum: fabdbac5871d3ecadd95f8523bd2e8e7 SHA1: 9122360023d45c774554ae9636a409772b77ac65 SHA256: ab7ae0c603f7bdbab4082f5e380deec1292dbd0925c010b312ca8b1d6652cba8 SHA512: 3086363a95cdf72094953ad2c1f99b892710eedb48a164ed35a939033f2ecf849ca975925b124798d4d52564b8b2f6a7f729c3283318b7b022de6a38a71f5d3a Homepage: https://cran.r-project.org/package=ChestVolume Description: CRAN Package 'ChestVolume' (Estimate the Chest Volume with Markers Data) Provides tools to process and analyze chest expansion using 3D marker data from motion capture systems. Includes functions for data processing, marker position adjustment, volume calculation using convex hulls, and visualization in 2D and 3D. Barber et al. (1996) . TAMIYA Hiroyuki et al. (2021) . 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This package implements a structure to reformat the data with 'dunlin', create reporting tables using 'rtables' and 'tern' with standardized input arguments to enable quick generation of standard outputs. In addition, it also provides comprehensive data checks and script generation functionality. Package: r-cran-chi Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-chi_0.1-1.ca2604.1_all.deb Size: 15704 MD5sum: c56e75caa2f5696b60ca30c4c7e8bba9 SHA1: 050b61ebfaf9130bd7af042727635c33a57a5830 SHA256: effa077741a5a093d765e4c759ec926633bb226efabe5019cbe769c0d70ecf9c SHA512: 67610e09e9c0d72415dfa24c724e08fa24e521e547eb8901b0df09ba413862503ca185572dfb0d54870c58b345fcf43dc7df7506f7388b4bf7c540416cba7fad Homepage: https://cran.r-project.org/package=chi Description: CRAN Package 'chi' (The Chi Distribution) Light weight implementation of the standard distribution functions for the chi distribution, wrapping those for the chi-squared distribution in the stats package. 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Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model. Package: r-cran-childdevdata Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-childdevdata_1.1.0-1.ca2604.1_all.deb Size: 1167304 MD5sum: 3c294698adae9bc784a86242b9c69f23 SHA1: 7cc68a1cb0a76d5002c42ae3def5eecd4e843a5c SHA256: fcbdde9b587df03a4746d2a8a2c4eccb5cdf55e1daae0d8bda23155ba6490cba SHA512: a831d363ce4979c71b46ca564a965ca31733ac4980fd31876d18dde9b5df1b411ff358b489d79887512950e31a97548f362c68e423d8637a521aefc9dead5f85 Homepage: https://cran.r-project.org/package=childdevdata Description: CRAN Package 'childdevdata' (Child Development Data) Measuring child development starts by collecting responses to developmental milestones, such as "able to sit" or "says two words". There are many ways to combine such responses into summaries. The package bundles publicly available datasets with individual milestone data for children aged 0-5 years, with the aim of supporting the construction, evaluation, validation and interpretation of methodologies that aggregate milestone data into informative measures of child development. 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For more information on the underlying data, see . Package: r-cran-childeswordfreq Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cachem, r-cran-childesr, r-cran-dplyr, r-cran-memoise, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-writexl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-childeswordfreq_0.2.0-1.ca2604.1_all.deb Size: 64938 MD5sum: 3cdb97f76f2a6c9c8f706b1a9185eca1 SHA1: 335217a9677ec8edc5602ce06ebd63634cd75b0b SHA256: 5fe3b01f819c0813b3fcb6ca0f934e76e3f591921b223e3a4cd5c9ebb30ae683 SHA512: 287864d9fd013a06217fe371f6e3c3222edf795ca719ec63f98db991398b1ce25fbbbf0a47f1e1d2d065cdf93929490f0849b48daa4551d8f20e71c959f3dee4 Homepage: https://cran.r-project.org/package=childeswordfreq Description: CRAN Package 'childeswordfreq' (Word and Phrase Frequency Tools for CHILDES) Tools for extracting word and phrase frequencies from the Child Language Data Exchange System (CHILDES) database via the 'childesr' API. Supports type-level word counts, token-mode searches with simple wildcard patterns and part-of-speech filters, optional stemming, and Zipf-scaled frequencies. Provides normalization per number of tokens or utterances, speaker-role breakdowns, dataset summaries, and export to Excel workbooks for reproducible child language research. The CHILDES database is maintained at . Package: r-cran-childfree Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rio, r-cran-rcurl Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-childfree_0.0.5-1.ca2604.1_all.deb Size: 239510 MD5sum: 62a190ec298f4726e817ad500fec7c4a SHA1: 041f01f8f632ca786da2bcb93ee35af732396cd9 SHA256: 10de4246ce5061c9e3e306ec3e925e922c2c33457c26fafd42b5731a1daea63a SHA512: 10a652d78766e4fc76392eb60e7c369539af7524a97796912d3a256673d989dcd2c5c4440cf4e0ca976d4c4f5393977bc8e7cd0fb4f9ba1a3ff432f47e99533a Homepage: https://cran.r-project.org/package=childfree Description: CRAN Package 'childfree' (Access and Harmonize Childfree Demographic Data) Reads demographic data from a variety of public data sources, extracting and harmonizing variables useful for the study of childfree individuals. The identification of childfree individuals and those with other family statuses uses Neal & Neal's (2024) "A Framework for Studying Adults who Neither have Nor Want Children" ; A pre-print is available at . Package: r-cran-childsds Architecture: all Version: 0.9.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-tidyselect, r-cran-boot, r-cran-class, r-cran-tibble, r-cran-reshape2, r-cran-purrr, r-cran-purrrlyr, r-cran-vgam, r-cran-interp, r-cran-lubridate, r-cran-ggplot2, r-cran-scales, r-cran-desctools, r-cran-colorspace, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-childsds_0.9.11-1.ca2604.1_all.deb Size: 3681162 MD5sum: 3bbf5f8f76d7e738d3d245a57d34cd0a SHA1: 9a40b8321e6793d94d74c97bc9da98a0a5556a7a SHA256: c7ae0be31ed170e36098cebaa18b97ca01d8509ee5e357e38c583a11f8ea0faa SHA512: bed00b017c426a001e0ba08f736b8e29d598bf508a03949241cf53f768fe17d2325c724dd2519319590a04b0940c2e2f539bc1bc8448dbbfcf626a1b0e762b6a Homepage: https://cran.r-project.org/package=childsds Description: CRAN Package 'childsds' (Data and Methods Around Reference Values in Pediatrics) Calculation of standard deviation scores and percentiles adduced from different standards (WHO, UK, Germany, Italy, China, etc). Also, references for laboratory values in children and adults are available, e.g., serum lipids, iron-related blood parameters, IGF, liver enzymes. See package documentation for full list. Package: r-cran-chiledataapi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-scales Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-chiledataapi_0.2.0-1.ca2604.1_all.deb Size: 1396088 MD5sum: 61ef1be10d28e4b9e3be400d0c1ea3a2 SHA1: 0580e98d43c05f4b6920a65d6774f6038fa5e68d SHA256: bd159eabecb020bd507ad453487febc29b0974e974017bb194485763579fe490 SHA512: daa1a917faa74007fd84948a03038bfb5e4166adb276f64da84d809c45146f3db55ab191fbd65ebeffcad14166ce328bdd434fef9ab963a7a62cff60221dee7a Homepage: https://cran.r-project.org/package=ChileDataAPI Description: CRAN Package 'ChileDataAPI' (Access Chilean Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'FINDIC API', 'REST Countries API', 'World Bank API', and 'Nager.Date', retrieving real-time or historical data related to Chile such as financial indicators, holidays, international demographic and geopolitical indicators, and more. Additionally, the package includes curated datasets related to Chile, covering topics such as human rights violations during the Pinochet regime, electoral data, census samples, health surveys, seismic events, territorial codes, and environmental measurements. The package supports research and analysis focused on Chile by integrating open APIs with high-quality datasets from multiple domains. For more information on the APIs, see: 'FINDIC' , 'REST Countries' , 'World Bank API' , and 'Nager.Date' . Package: r-cran-chilemapas Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chilemapas_0.4.0-1.ca2604.1_all.deb Size: 2736800 MD5sum: 845c87f0291ca19e2e7676692b1525d3 SHA1: 2ed504b4225a8ecef57405ab6b7ff2a24d72dfc3 SHA256: 6d23dd610ccf1999997d77cae587b66c00ba3f4af2c1e0ff86dc672307165038 SHA512: b57f5e3e83aa2971353eb0ed45c9c94dc1074c783209ad586a06a2c1577066055abcaa116731ccd8645e2bad6205c54e176028fecd4e40216a34dfec21ee6a26 Homepage: https://cran.r-project.org/package=chilemapas Description: CRAN Package 'chilemapas' (Mapas de las Divisiones Politicas y Administrativas de Chile(Maps of the Political and Administrative Divisions of Chile)) Mapas terrestres con topologias simplificadas. Estos mapas no tienen precision geodesica, por lo que aplica el DFL-83 de 1979 de la Republica de Chile y se consideran referenciales sin validez legal. No se incluyen los territorios antarticos y bajo ningun evento estos mapas significan que exista una cesion u ocupacion de territorios soberanos en contra del Derecho Internacional por parte de Chile. Esta paquete esta documentado intencionalmente en castellano asciificado para que funcione sin problema en diferentes plataformas. (Terrestrial maps with simplified toplogies. These maps lack geodesic precision, therefore DFL-83 1979 of the Republic of Chile applies and are considered to have no legal validity. Antartic territories are excluded and under no event these maps mean there is a cession or occupation of sovereign territories against International Laws from Chile. This package was intentionally documented in asciified spanish to make it work without problem on different platforms.) Package: r-cran-chillmodels Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-chillr, r-cran-dplyr, r-cran-lubridate, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-chillmodels_1.0.2-1.ca2604.1_all.deb Size: 77718 MD5sum: b3798db9c7c4d8f77e3cbc436d00d1a2 SHA1: a2618b759ceba13ac5e466b6ca88baf833fb2e95 SHA256: f084e6b7c4cc7dbf7e0c5e95fb927b91c0fabfad044b457d4f745038f25fd787 SHA512: 15f03a4c2e11bc23f3b66074d2e6593f60459d919f27907ffbd8b1b425af41e9c0ccdb13d895d9b27af77a9e977da647954c4d4ab3c21cfd8b995d7f4ababf9f Homepage: https://cran.r-project.org/package=ChillModels Description: CRAN Package 'ChillModels' (Processing Chill and Heat Models for Temperate Fruit Trees) Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 ), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 ), Model for apple "Gala" (Guak and Nielsen, 2013 ), Taiwan Model (Lu et al., 2012 ), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the 'chillR' package (Luedeling, 2018), Unified Model (Chuine et al., 2016 ) and Heat Restriction model. Package: r-cran-chinapis Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3823 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-chinapis_0.1.1-1.ca2604.1_all.deb Size: 1993830 MD5sum: 3ba8f3e041074392ce38c9fffad34ebd SHA1: b0090ab3d55e783de0ce34ad201e33a52097380a SHA256: a6d1ba6927d7a34d76ca8af8a25db804d34fe909387db696dd19ccfeaf35cb45 SHA512: 2cc16a6ae1c77e6f255aed005ab4e8ffbd4d2cd85c2d1d1b78e9aeb1506b1d4a3233b4e8597400dd9965a14ebbac4066e2e0151d17179b1622c13dc17897697a Homepage: https://cran.r-project.org/package=ChinAPIs Description: CRAN Package 'ChinAPIs' (Access Chinese Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to China, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on China and Hong Kong, covering topics such as air quality, demographics, input-output tables, epidemiology, political structure, names, and social indicators. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-chinesenames Architecture: all Version: 2025.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brucer, r-cran-data.table Suggests: r-cran-babynames, r-cran-car, r-cran-dplyr, r-cran-glue Filename: pool/dists/resolute/main/r-cran-chinesenames_2025.8-1.ca2604.1_all.deb Size: 408474 MD5sum: a605eb75ecc8e1487a53fdde5de667c5 SHA1: 749fe79b6a9176a5c54c90208bace8e42afa15d6 SHA256: e1f08b242022c4f1e5e56a5717d4582a217173175abfdd44e9678030b8ea7c36 SHA512: c567c1db0c95df0f34a611d80a2fd986528f54127950a937071eb8d29fbd2467e31a3d6450ea82937bb66f32fea4201dbfc6f9cc2ecb61fb6b8f58989ba947b1 Homepage: https://cran.r-project.org/package=ChineseNames Description: CRAN Package 'ChineseNames' (Chinese Name Database 1930-2008) A database of Chinese surnames and given names (1930-2008). This database contains nationwide frequency statistics of 1,806 Chinese surnames and 2,614 Chinese characters used in given names, covering about 1.2 billion Han Chinese population (96.8 percent of the Han Chinese household-registered population born from 1930 to 2008 and still alive in 2008). This package also contains a function for computing multiple indices of Chinese surnames and given names for social science research (e.g., name uniqueness, name gender, name valence, and name warmth/competence). Details are provided at . Package: r-cran-chiopendata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-jsonlite, r-cran-httr, r-cran-janitor, r-cran-rlang Suggests: r-cran-curl, r-cran-knitr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-chiopendata_0.1.0-1.ca2604.1_all.deb Size: 88754 MD5sum: c6f2a403105773386192803cc750bf16 SHA1: 113ca238260276bea1e0bd5fd9bcf3a42bc85458 SHA256: 368d266eac6240b6a5c114d77f3a0ba2037ae4bfcf29f4b3cd29171085701d18 SHA512: a430e5d176e02f15b9ddffdb8905d2192f885b2caf6e1aa465c1adc634f85032e79ec4fbfa6a709496d61632b33b087ac2bc3d9d966e302ff982c799e78f44d6 Homepage: https://cran.r-project.org/package=chiOpenData Description: CRAN Package 'chiOpenData' (Convenient Access to Chicago Open Data API Endpoints) Provides simple, reproducible access to datasets from the Chicago Open Data portal . Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. 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Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) . Package: r-cran-chirps Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1920 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-sf, r-cran-terra Suggests: r-cran-climatrends, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chirps_1.1-1.ca2604.1_all.deb Size: 634294 MD5sum: 0f272626a7ffa807d30300846b678412 SHA1: 71980e95ca9ac7329461aae7ba3a613c00125766 SHA256: 3790a93d37119709c8225cb58b883bcb0df900bd79e7686eec786d7d730482fc SHA512: 64d8c9fda42c6687a6b1bf983829c71cc8cca69dbe4ec5bbb1f6b0652d04e9ab516b65b7a0ccd715286761d5c6ba18fd44d369c383221f163a1e682e5bea70dd Homepage: https://cran.r-project.org/package=chirps Description: CRAN Package 'chirps' (API Client for CHIRPS and CHIRTS) API Client for the Climate Hazards Center 'CHIRPS' and 'CHIRTS'. The 'CHIRPS' data is a quasi-global (50°S – 50°N) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S – 70°N), high-resolution data set of daily maximum and minimum temperatures. For more details on 'CHIRPS' and 'CHIRTS' data please visit its official home page . 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References include: Jolliffe I.T. (2002). Principal Component Analysis. and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press. Package: r-cran-chopin Architecture: all Version: 0.9.9-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-anticlust, r-cran-cli, r-cran-dplyr, r-cran-exactextractr, r-cran-future, r-cran-future.apply, r-cran-igraph, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-stars, r-cran-terra, r-cran-mirai, r-cran-collapse Suggests: r-cran-covr, r-cran-devtools, r-cran-targets, r-cran-diagrammer, r-cran-future.mirai, r-cran-knitr, r-cran-lifecycle, r-cran-rmarkdown, r-cran-spatstat.random, r-cran-testthat, r-cran-units, r-cran-withr, r-cran-dggridr, r-cran-h3r Filename: pool/dists/resolute/main/r-cran-chopin_0.9.9-5-1.ca2604.1_all.deb Size: 2737910 MD5sum: 9d690614310df48eca6edb64f0d84f6e SHA1: 2b63f79f9bf3d40dd19f48b3c207224b799060d8 SHA256: e35ec86521ca95790b7429a0398f7e95786746db77d0808060769a456f2408d7 SHA512: 664674163816fe8549f909e9f33e775eaa842047a4b9e503ac161a057c743f145ba1f89f35b8b122b8d097b22a526e356560727288dbba5d25b3a019c87395cc Homepage: https://cran.r-project.org/package=chopin Description: CRAN Package 'chopin' (Spatial Parallel Computing by Hierarchical Data Partitioning) Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on 'future' (Bengtsson, 2024 ) and 'mirai' (Gao et al., 2025 ) parallel back-ends, 'terra' (Hijmans et al., 2025 ) and 'sf' (Pebesma et al., 2024 ) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in 'terra' or 'sf' packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses 'exactextractr' (Baston, 2023 ), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided. Package: r-cran-choplump Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-coin, r-cran-survival Filename: pool/dists/resolute/main/r-cran-choplump_1.1.2-1.ca2604.1_all.deb Size: 595564 MD5sum: 6eb57f142e65dc2e63e765b9077bd2a2 SHA1: 275de41cd2270831328fc9b958e5023f3e9a6aaa SHA256: 544febf87420b084b5eb3e32f6c84b69eb175727861a3e64ab8a252f195b8084 SHA512: f912ec1eb0d3a4da119271f92cba209d4f8d3a316c87fb8c45df3d74f31f11bbf4688d00597613fc65e64d8398e9b032453e70ee4ec0079ef84b98a0c7af1450 Homepage: https://cran.r-project.org/package=choplump Description: CRAN Package 'choplump' (Permutation Test for Some Positive and Many Zero Responses) Calculates permutation tests that can be powerful for comparing two groups with some positive but many zero responses (see Follmann, Fay, and Proschan ). Package: r-cran-chopper Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-normalp, r-cran-purrr, r-cran-ald, r-cran-evd, r-cran-generalizedhyperbolic, r-cran-fgarch, r-cran-forecast, r-cran-imputets, r-cran-changepoint, r-cran-lubridate, r-cran-ggplot2, r-cran-scales Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chopper_1.0-1.ca2604.1_all.deb Size: 73588 MD5sum: e233d3d192a0e4d1118a46b4dabf26f7 SHA1: 2453203cd6bd5f21101029ba23eef1faa763e3a0 SHA256: 1e53ace1108d68993c22be7da123e8b264815b762b5928acf88c683cd26c67c5 SHA512: c02dd47b9ac107868727209e203feb400fe714563a707dcb8f4eee060eb3458a766cb42f86fd665e44483b8692dd0679b1340694a92b2696dee5302ac57d19fa Homepage: https://cran.r-project.org/package=chopper Description: CRAN Package 'chopper' (Changepoint-Aware Ensemble for Probabilistic Modeling) Implements a changepoint-aware ensemble forecasting algorithm that combines Theta, TBATS (Trigonometric, Box-Cox transformation, ARMA errors, Trend, Seasonal components), and ARFIMA (AutoRegressive, Fractionally Integrated, Moving Average) using a product-of-experts approach for robust probabilistic prediction. Package: r-cran-chor Architecture: all Version: 0.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-commonsmath Suggests: r-bioc-graph, r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-cran-chor_0.0-4-1.ca2604.1_all.deb Size: 394912 MD5sum: 3eeb66b3167a4c3c3a6dfd6442841541 SHA1: 22922dfaa36e0625fc89dc5cabeda71e9f65ebe0 SHA256: e62657fefeb3c0e06f2d77db79fbd08090b9a79e03ea80a29d55fef9ab51d992 SHA512: 50b6b223bdeec6d7c0241548c523ab898303f72b9074e33dfa90d925ef2fba2354721f58f31bb4123a4e231cdded8e495d70f440724374a61238156e09ce27b4 Homepage: https://cran.r-project.org/package=ChoR Description: CRAN Package 'ChoR' (Chordalysis R Package) Learning the structure of graphical models from datasets with thousands of variables. More information about the research papers detailing the theory behind Chordalysis is available at (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013). The R package development site is . Package: r-cran-chords Architecture: all Version: 0.95.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chords_0.95.4-1.ca2604.1_all.deb Size: 69284 MD5sum: 8cfb489308c50bf8de64f3c2899bf8cf SHA1: dd571e80a846df77e5b80f407c053f08bec82995 SHA256: 36174740d70691ebbd7002b61fefdd13abd74963a37cd95128ba1d6c3c8ae02d SHA512: fa813dd0fab0a0952eb94b97c86ef2b0daa635ed12992217093f6c346ed1a5caec5f4316db90bff38e33edebfbda7fa31486c9f74ecade281f1c800c812559fb Homepage: https://cran.r-project.org/package=chords Description: CRAN Package 'chords' (Estimation in Respondent Driven Samples) Maximum likelihood estimation in respondent driven samples. 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Package: r-cran-choroplethr Architecture: all Version: 5.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4788 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-stringr, r-cran-ggplot2, r-cran-dplyr, r-cran-r6, r-cran-ggrepel, r-cran-tigris, r-cran-sf, r-cran-tidycensus Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-choroplethr_5.0.1-1.ca2604.1_all.deb Size: 4851482 MD5sum: 0c693663fd0fa855c94bc432449dac1d SHA1: f4016fe7afbeff390e01ce9b45715512dc420b95 SHA256: 95525f130fe5b5dfaa16dd602fdb3539ec5556324caa82e2616be9f44f52de18 SHA512: 4a1165df9a818c96d59339f184bede99d1acae4aa10806b585641c1637d850ac4fbe0eb78579dce41fe2c614c2d353c815a6e6b6b22aab5075e70aef1c763988 Homepage: https://cran.r-project.org/package=choroplethr Description: CRAN Package 'choroplethr' (Create Color-Coded Choropleth Maps in R) Easily create color-coded (choropleth) maps in R. No knowledge of cartography or shapefiles needed; go directly from your geographically identified data to a highly customizable map with a single line of code! Supported geographies: U.S. states, counties, census tracts, and zip codes, world countries and sub-country regions (e.g., provinces, prefectures, etc.). 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Package: r-cran-choroplethrmaps Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2519 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-choroplethrmaps_1.0.1-1.ca2604.1_all.deb Size: 2171456 MD5sum: 03f283649e7a641d1c6c7de53dfd45ff SHA1: af0733fd12ece05ac34a59ee016310dd870731e0 SHA256: 1dbd9c60fc18536607b104553900c0501d2bf6024017444a013b51398d6bc41c SHA512: 20239654937ba6b2d7a506ed973e0003748f35016e9a2049b9a90823fe305dd0d17f50e570e33093fc6971bf47c00dac7ba7048cc780346bd02b9ec91b606aa3 Homepage: https://cran.r-project.org/package=choroplethrMaps Description: CRAN Package 'choroplethrMaps' (Contains Maps Used by the 'choroplethr' Package) Contains 3 maps. 1) US States 2) US Counties 3) Countries of the world. Package: r-cran-christmas Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-animation Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-christmas_1.4.1-1.ca2604.1_all.deb Size: 178732 MD5sum: ccec8c82d3bbee81111c9d6dc10e5b66 SHA1: 1709a6856e7e1288d5caa4bc3a53792ae7aff628 SHA256: ddc86d0f521f698167e7e4dcb1e801334de3398859d12a06bdee7b6b597134b3 SHA512: 8bddfe0ef44427e54a1172ecebd0dd0a5782b684d1779bbdc5b0b454e941e50fd96ceb86b47943d247da3fa981f21d9855db4fa15184e6693756c1782434f8fe Homepage: https://cran.r-project.org/package=christmas Description: CRAN Package 'christmas' (Generation of Different Animated Christmas Cards) Generation of different Christmas cards, most of them being animated. Most of the cards can be generated in three languages (English, Catalan and Spanish). The collection started in 2009. Package: r-cran-chromconverter Architecture: all Version: 0.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bitops, r-cran-fs, r-cran-purrr, r-cran-readxl, r-cran-reticulate, r-cran-stringr, r-cran-tidyr, r-cran-rams, r-cran-tibble, r-cran-xml2, r-cran-bit64, r-cran-data.table, r-cran-base64enc, r-cran-jsonlite, r-cran-digest Suggests: r-cran-ncdf4, r-cran-pbapply, r-cran-testthat, r-bioc-mzr Filename: pool/dists/resolute/main/r-cran-chromconverter_0.7.5-1.ca2604.1_all.deb Size: 768626 MD5sum: c8784ec934cd830cf49ac8834c5fb05f SHA1: 7ea881f6aa4f9f668bf74ca3593ed6491de067ef SHA256: f607ee437da61ee31f4dc08260ac302a52593951fed0b6e74e10659d0a8848ea SHA512: c26bd7e0a44be5aba8dc2ed3ed3f3dfbb4070a18991f596f07c66024547a290acfa9c52e4dc720dc6d556152dbb77b241767d52753e5a70adf47c3e6ad49afe9 Homepage: https://cran.r-project.org/package=chromConverter Description: CRAN Package 'chromConverter' (Chromatographic File Converter) Reads chromatograms from binary formats into R objects. Currently supports conversion of 'Agilent ChemStation', 'Agilent MassHunter', 'Shimadzu LabSolutions', 'ThermoRaw', and 'Varian Workstation' files as well as various text-based formats. In addition to its internal parsers, chromConverter contains bindings to parsers in external libraries, such as 'Aston' , 'Entab' , 'rainbow' , and 'ThermoRawFileParser' . Package: r-cran-chromer Architecture: all Version: 0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-httr Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-chromer_0.10-1.ca2604.1_all.deb Size: 43878 MD5sum: a69d091b35c7fb3526545980e2e74948 SHA1: 1ef3d48a0b97f465059a4c434cd12079011478e7 SHA256: 171e24a1f8038e86abeddccd5d911c2cb6e217401f56937a8a907d1ead67f8c2 SHA512: b88d1d8cc5de9a84fb5b936e27cd09881c48f238948abddcb02db5d57427baca6cd3cf6b75a3cfc55e8932b79e7dfc8768e00210a2790dc58568ffe4b3af2381 Homepage: https://cran.r-project.org/package=chromer Description: CRAN Package 'chromer' (Interface to Chromosome Counts Database API) A programmatic interface to the Chromosome Counts Database (), Rice et al. (2014) . This package is part of the 'ROpenSci' suite (). 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It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the large size of the chromosomes, it is impractical to visualize each element on the same plot. However, the plot provides a magnified view for each of chromosome locus to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications. 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Package: r-cran-chromseq Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-chromseq_0.1.3-1.ca2604.1_all.deb Size: 88506 MD5sum: f1253363e260eb64d46f1146830fd9ba SHA1: 91cfe87d82c499a4b239c864b6a3511db6357772 SHA256: 18d5bccd1c5dfe3ed9ec9481c470f38bf8c3099907bc8e10fa307c2742b3b72e SHA512: 17e3e70b968254672efcbfe8d45c24cab32807b20ed0fd8430c4c1dbc35a89bf6eab7179fc5531ebd9ac52e3872b75975f19e0b0648be4aa09f2410934e14e2c Homepage: https://cran.r-project.org/package=chromseq Description: CRAN Package 'chromseq' (Split Chromosome 'Fasta' File) Chromosome files in the 'Fasta' format usually contain large sequences like human genome. Sometimes users have to split these chromosomes into different files according to their chromosome number. The 'chromseq' can help to handle this. So the selected chromosome sequence can be used for downstream analysis like motif finding. Howard Y. Chang(2019) . 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Details are given in Schomaker, McIlleron, Denti, Diaz (2024) . Package: r-cran-ciecl Architecture: all Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 982 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-stringdist, r-cran-stringr, r-cran-dplyr, r-cran-tibble Suggests: r-cran-comorbidity, r-cran-gt, r-cran-httr2, r-cran-readxl, r-cran-usethis, r-cran-withr, r-cran-writexl, r-cran-testthat, r-cran-knitr, r-cran-litedown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ciecl_0.9.6-1.ca2604.1_all.deb Size: 702208 MD5sum: 3bb4c9c8f29a1bcd3b9c350b5a88ea16 SHA1: d378e9a2e1fb6f8f4001d66c0f03d01b19c12c5c SHA256: 12ed220ea6f8ad626d9688d43a6d490a65bdb46c3f93bd786ec65fe9cb93029b SHA512: 03323f6031a6033cc50dd789dbc74f021224157180fa060a91d05106b74c019c2603a48e74f10d598db8b4c2dff5aa96c95d2e7446a11be6cee754c2f7181b6f Homepage: https://cran.r-project.org/package=ciecl Description: CRAN Package 'ciecl' (International Classification of Diseases 'ICD-10'/'ICD-11' forChile) Tools for working with the International Classification of Diseases ('ICD-10' Chile official 'MINSAL'/'DEIS' v2018). Includes optimized 'SQL' search with 'SQLite', fuzzy matching of medical terms ('Jaro-Winkler'), Charlson and Elixhauser comorbidity calculation, 'WHO' 'ICD-11' 'API' integration, and hierarchical code validation. Data from Centro FIC Chile 'DEIS' . Package: r-cran-ciee Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-lavaan, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ciee_0.1.1-1.ca2604.1_all.deb Size: 369350 MD5sum: 7777152fc639dad62b9e6e458b3d7026 SHA1: ee44ee3810fc57a331ba2499225fcc7a827213c1 SHA256: 6495004f19d06e11e1c1710a1d9e5a529e3290a6cdcc00fd81274092e80cf946 SHA512: 819028a1fce1bb4918d7594f4b77da6bc79dfb2e04507a08efe4d08bc128a8d46e7c706a5bab2b47c22539b8b20b9626f77bb07427e8b06c5478d2e6f5792437 Homepage: https://cran.r-project.org/package=CIEE Description: CRAN Package 'CIEE' (Estimating and Testing Direct Effects in Directed Acyclic Graphsusing Estimating Equations) In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, ) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison. Package: r-cran-cif Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cif_0.1.1-1.ca2604.1_all.deb Size: 133122 MD5sum: e0505c4d423284d7d5028df6b2a0b507 SHA1: c89463538d5ecb5f72d2f8ba98d13a1bc9afce97 SHA256: fc98a5f4504e42af73e20ccc4c217537daaaef3a8ce90a02378256e9a0ebf795 SHA512: 1d86a09eaa423a977d24935a544431b04ae1c95cc1735fb01f283b3bddf1f9c6ceca3c4235278be88d90da19130132f82a5fb0e7acd4d79561459d0ee238943b Homepage: https://cran.r-project.org/package=cif Description: CRAN Package 'cif' (Cointegrated ICU Forecasting) Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, . Package: r-cran-cifinder Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ratesci, r-cran-boot, r-cran-rdpack, r-cran-kableextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cifinder_2.0.0-1.ca2604.1_all.deb Size: 81872 MD5sum: 5e17af104bd5fd481b92823166e26147 SHA1: f37af4679a7c48d9f1311bb6913d216bb6dd8b0f SHA256: 7f21cabb090275cb7fe0f7fced6d8994dcbfb59f42ccf78efd2b44bd952b50e7 SHA512: 634107014385fb72bb2d7ffed301c83144fcd2837b649cc24e025a37922ba8d7f3d53c347114e8ea62052ce62696c631f78fca3b6938fbfe5f663ac52c8f64b7 Homepage: https://cran.r-project.org/package=CIfinder Description: CRAN Package 'CIfinder' (Estimate the Confidence Intervals for Predictive Values) Computes confidence intervals for the positive predictive value (PPV) and negative predictive value (NPV) based on varied scenarios. In situations where the proportion of diseased subjects does not correspond to the disease prevalence (e.g. case-control studies), this package provides two types of solutions: 1) five methods for estimating confidence intervals for PPV and NPV via ratio of two binomial proportions including Gart & Nam (1988), Walter (1975), MOVER-J (Laud, 2017), Fieller (1954), and Bootstrap (Efron, 1979); 2) three direct methods that compute the confidence intervals including Pepe (2003), Zhou (2007), and Delta. In prospective studies where the proportion of diseased subjects is an unbiased estimate of the disease prevalence, this package provides several methods for calculating the confidence intervals for PPV and NPV including Clopper-Pearson, Wald, Wilson, Agresti-Coull, and Beta. See the Details and References sections in the corresponding functions. Package: r-cran-cifti Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-oro.nifti, r-cran-gifti, r-cran-r.utils Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rgl, r-cran-matrixstats Filename: pool/dists/resolute/main/r-cran-cifti_0.5.0-1.ca2604.1_all.deb Size: 1296110 MD5sum: f3b3a679d98bc6f2a5e5603fefe8b93a SHA1: 05a810e26cf4a6a741cb924c0dbda39007234925 SHA256: 462a9ff10576d669a377fec3f88bc76041a13f844c9e0224dc9f24bc8569c584 SHA512: 4bef0bf0089a86010ccc668879aedc5d3fc53baedacd0c677be4a8644a6a73d489b67d61412f99ca294efffdd079c0c70975730449db805ec87d1ec1063f03f4 Homepage: https://cran.r-project.org/package=cifti Description: CRAN Package 'cifti' (Toolbox for Connectivity Informatics Technology Initiative('CIFTI') Files) Functions for the input/output and visualization of medical imaging data in the form of 'CIFTI' files . Package: r-cran-ciftitools Architecture: all Version: 0.19.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7337 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-gifti, r-cran-oro.nifti, r-cran-rnifti, r-cran-rcolorbrewer, r-cran-rgl, r-cran-viridislite, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-htmlwidgets, r-cran-manipulatewidget, r-cran-webshot2, r-cran-knitr, r-cran-rmarkdown, r-cran-png, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ciftitools_0.19.0-1.ca2604.1_all.deb Size: 5450536 MD5sum: babab12cc537865c953ab6ad35312689 SHA1: c189ff1e7681bb3649977e47971932e41647d024 SHA256: 0a5d47ddda2010f7af464069b9f059077338e355ddff555bcc2976867d1b31db SHA512: f4e4275b1c2a40a157daef221dfa4f051ea98beeeab5162c7c7dd71cd8915e16bb9ad7891fd0126258421c7d9452f634a9169d64a208fa8360eb7df580186459 Homepage: https://cran.r-project.org/package=ciftiTools Description: CRAN Package 'ciftiTools' (Tools for Reading, Writing, Viewing and Manipulating CIFTI Files) CIFTI files contain brain imaging data in "grayordinates," which represent the gray matter as cortical surface vertices (left and right) and subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). 'ciftiTools' provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the "dscalar," "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" object, which is structured for convenient access to the data and metadata, and includes support for surface geometry files to enable spatially-dependent functionality such as static or interactive visualizations and smoothing. Package: r-cran-cim Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cim_1.0.0-1.ca2604.1_all.deb Size: 51144 MD5sum: e6b882981a384d3d88431da6338ea09e SHA1: 7e08bf0e80858a0792c16224114d79c1e606a1fc SHA256: 44f4a03ae981de0d5308131c73a0e6cc12375fd9444220b77afd795b3b1d03d1 SHA512: 66e037cad9b032fa198ae98ec87faecdc2779d584efe16beeef6255dfe126437b16bfaf656c3de624066c8def0888002b9c587cccfff61f2339ad12ee4ca33bd Homepage: https://cran.r-project.org/package=CIM Description: CRAN Package 'CIM' (Compositional Impact of Migration) Produces statistical indicators of the impact of migration on the socio-demographic composition of an area. Three measures can be used: ratios, percentages and the Duncan index of dissimilarity. The input data files are assumed to be in an origin-destination matrix format, with each cell representing a flow count between an origin and a destination area. Columns are expected to represent origins, and rows are expected to represent destinations. The first row and column are assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018) for technical details. Package: r-cran-cimir Architecture: all Version: 0.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-glue, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-jsonlite, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cimir_0.4-1-1.ca2604.1_all.deb Size: 109680 MD5sum: a28d61bc46b6d9752cf9e7bc58ab8758 SHA1: 267ed42e6e3eb2f5b7c2b1978fa4c3c261a857fd SHA256: f71df2fdc6a8f2f950a1fec470331422b02d3dde69f15aab299e52f93319c603 SHA512: 747eceb68a2f44a8ca2a80f78402695e613c48bf3d7b57dacb3c9f94996e93961b8622645ab248ffed1efc3a91bafab0f7f03780db41b9082b02436337cf861c Homepage: https://cran.r-project.org/package=cimir Description: CRAN Package 'cimir' (Interface to the CIMIS Web API) Connect to the California Irrigation Management Information System (CIMIS) Web API. See the CIMIS main page and web API documentation for more information. Package: r-cran-cimpleg Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4589 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-assertthat, r-cran-broom, r-cran-butcher, r-cran-caret, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-gtools, r-cran-magrittr, r-cran-matrixstats, r-cran-nnls, r-cran-oner, r-cran-parsnip, r-cran-patchwork, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-scales, r-cran-tibble, r-cran-tictoc, r-cran-tidyr, r-cran-tidyselect, r-cran-tsutils, r-cran-tune, r-cran-vroom, r-cran-workflows, r-cran-yardstick Suggests: r-bioc-biobase, r-bioc-biomart, r-cran-c50, r-cran-circlize, r-bioc-epidish, r-cran-furrr, r-cran-future, r-cran-future.apply, r-bioc-geoquery, r-cran-ggbeeswarm, r-cran-ggsignif, r-cran-glmnet, r-cran-knitr, r-bioc-minfi, r-cran-mltools, r-cran-nmf, r-cran-nnet, r-cran-plyr, r-cran-ranger, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rfast, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-cimpleg_1.0.1-1.ca2604.1_all.deb Size: 4616136 MD5sum: 91fd0bca820648fad2d99a9ca1d28d8a SHA1: 2c4e3865b96b1e2b62a03348f89acf68eb2851ec SHA256: 7e5687e15891974abcbce9a2074ef26cfe0bdd9adbd804bbe8832c4f7a31a93d SHA512: 2f322db1811c379da366e8a72ee2669e532e3f9535f1a29054b6957f4b30b68a886592ec450a0b93c5b25d83926115fc942b11a6d75f4e5fd6ef4cad347e8a94 Homepage: https://cran.r-project.org/package=CimpleG Description: CRAN Package 'CimpleG' (A Method to Identify Single CpG Sites for Classification andDeconvolution) DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. 'CimpleG' is a method for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. 'CimpleG' is time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type (but users can also select more sites if they so wish). Users can train cell type classifiers ('CimpleG' based, and others) and directly apply these in a deconvolution of cell mixes context. Altogether, 'CimpleG' provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution. For more details see Maié et al. (2023) . Package: r-cran-cimtx Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-bart, r-cran-twang, r-cran-arm, r-cran-dplyr, r-cran-matching, r-cran-magrittr, r-cran-weightit, r-cran-tmle, r-cran-tidyr, r-cran-ggplot2, r-cran-cowplot, r-cran-mgcv, r-cran-metr, r-cran-stringr, r-cran-superlearner, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-cimtx_1.2.0-1.ca2604.1_all.deb Size: 265590 MD5sum: 86041082146db301778016f170a1c939 SHA1: ac382aa96c90baa271d156c143dccc80d183cc87 SHA256: e2011761c079aeb1c42e1905091bee5f1c5d4bc67adb0168f7dd6f3a521045a3 SHA512: b34e11a814d630ac3752c7d0c7735437afc5ebfc8cde82d65a977d32a4d27fc04334c53b9cb37c40df9a78b4a4c1d914f6463f5ba3e3d1e561d9b82de9be7c40 Homepage: https://cran.r-project.org/package=CIMTx Description: CRAN Package 'CIMTx' (Causal Inference for Multiple Treatments with a Binary Outcome) Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. . Package: r-cran-cinar Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biocmanager, r-bioc-deseq2, r-cran-dplyr, r-bioc-edger, r-bioc-fgsea, r-bioc-genomicranges, r-cran-ggplot2, r-cran-ggrepel, r-bioc-limma, r-cran-pheatmap, r-bioc-preprocesscore, r-cran-rcolorbrewer, r-bioc-sva, r-cran-writexl Suggests: r-bioc-chipseeker, r-cran-knitr, r-cran-matrix, r-cran-seurat, r-cran-rmarkdown, r-cran-markdown, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene Filename: pool/dists/resolute/main/r-cran-cinar_0.2.6-1.ca2604.1_all.deb Size: 3201512 MD5sum: d80a0dbf3f1b06297a86916a90887ece SHA1: 39ba0fb4510172fc1e8b725b2928525a076e3e9e SHA256: 6cb128152aca39af3b5727693bed11eb13ac90f7eb88ed9211ad359311b27d31 SHA512: d7ac4434086a265baf13b84ee3f284e5dde535573262dfd633cc6b73c819b7894017e84e7559cf50fad95f0dcc36cd96727afc13b017de94139a1c5c152145a4 Homepage: https://cran.r-project.org/package=cinaR Description: CRAN Package 'cinaR' (A Computational Pipeline for Bulk 'ATAC-Seq' Profiles) Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data analyses. This package combines different packages to have an ultimate package for both data analyses and visualization of 'ATAC-seq' data. Methods are described in 'Karakaslar et al.' (2021) . Package: r-cran-cinargenesets Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cinargenesets_0.1.1-1.ca2604.1_all.deb Size: 262990 MD5sum: 7d69fdcffaaea2ade148ef9340abfc20 SHA1: 2a05027b7172fd3938b307bd0589255fdc594deb SHA256: e013121e54eef2431f83697f12280708c2b84abb95f7862af5a40f48d935f633 SHA512: ddfb3540287c6f35d844fc09cab2d205bbc67dd9df8317fa2bf1c3fc866f0ad72e0e6e88e98931f1b4ec1e89a2127ce64c9aa6ee15561ef112f2f53d9f1e5187 Homepage: https://cran.r-project.org/package=cinaRgenesets Description: CRAN Package 'cinaRgenesets' (Ready-to-Use Curated Gene Sets for 'cinaR') Immune related gene sets provided along with the 'cinaR' package. 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The proportions are drawn as circles with different diameters, which makes them ideal for visualizing proportions between planets. Package: r-cran-circletyper Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-circletyper_1.0.2-1.ca2604.1_all.deb Size: 21470 MD5sum: 646ed725e8e6fd1c5d7c07db82762b17 SHA1: cc49d8f77efa47afadcca63e153e4de5aeff91c1 SHA256: c8ed1a6e151c4c09ad16f327dcc65bbf2d2d8057313fbc8215ff134648dba313 SHA512: 4bf4b50a05a8415246b40f3b2d3c90f2b2351db5fabe337269fea66c4b4c87fc7c343e4578befdb34520eb615ba0831d6ef0b33d2cbf08a57429c4fbe75d94e3 Homepage: https://cran.r-project.org/package=circletyper Description: CRAN Package 'circletyper' (Curve Text Elements in 'Shiny' Using 'CircleType.js') Enables curving text elements in 'Shiny' apps. Package: r-cran-circlize Architecture: all Version: 0.4.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-globaloptions, r-cran-shape, r-cran-colorspace Suggests: r-cran-knitr, r-cran-dendextend, r-bioc-complexheatmap, r-cran-gridbase, r-cran-png, r-cran-markdown, r-cran-bezier, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-circlize_0.4.18-1.ca2604.1_all.deb Size: 3292448 MD5sum: 7a6bb39d1aed4c644087070f2e721945 SHA1: 9e632925daafc8819bee7cb5ab2b42052ee02710 SHA256: 47c9830f57667f09891c6d1bdb177bd20265ee88cb2ad1c54ea43816034ad843 SHA512: fb748b1bf40df1806e28c9d81c4c11b35ec222f415b3e5aa90a5d01a5c3ede467c8dc3306d00a90ce12946bd34708c2d51f1ec0749b5700cdf4cd5a929b77510 Homepage: https://cran.r-project.org/package=circlize Description: CRAN Package 'circlize' (Circular Visualization) Circular layout is an efficient way for the visualization of huge amounts of information. Here this package provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of the package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multiple dimensional data. The package is described in Gu et al. 2014 . Package: r-cran-circlizeplus Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5764 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize Suggests: r-cran-png, r-cran-ape, r-cran-dendextend, r-bioc-complexheatmap, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-circlizeplus_0.9.1-1.ca2604.1_all.deb Size: 3857900 MD5sum: 7edb34e4fd613632ee87b46cf19d40b2 SHA1: 90cc77835e09e08cb9ce8d0f735d19249badfac9 SHA256: c7cad82c07a498295f7eb898ae0ebcff58d68aecb59cf47bbbaf5e9d7789804e SHA512: 50df0e9c4ccdaa2610ddcc100bb0869ffd935c8a66f9194f07bb40236a2972015b258be8968eb1e1b56fab370e401ee326ef622703a41f9a9550ea4becbe9157 Homepage: https://cran.r-project.org/package=circlizePlus Description: CRAN Package 'circlizePlus' (Using 'ggplot2' Feature to Write Readable R Code for CircularVisualization) A wrapper for 'circlize'. All components are based on classes and objects. Users can use the addition symbol (+) to combine components for a circular visualization with 'ggplot2' style.The package is described in Zhang Z, Cao T, Huang Y and Xia Y (2025) . Package: r-cran-circmle Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-energy Filename: pool/dists/resolute/main/r-cran-circmle_0.3.0-1.ca2604.1_all.deb Size: 125758 MD5sum: 952e061077c21863f5fc9e0b32e4e938 SHA1: 5b289888f58d549625cac6c442abc53581f62caa SHA256: 609fed9c5ee25a09b57d4009cbc8b62464fb565daf4f65be4a8b95981fd7bf8f SHA512: c6bfb540c283820d422617b3f1c8ebe72e568ed89c68bc9202e1a25250098e624d72f8d744b92be8bd6b10da4611b17da3805a29c04d3826adb8976773767b7a Homepage: https://cran.r-project.org/package=CircMLE Description: CRAN Package 'CircMLE' (Maximum Likelihood Analysis of Circular Data) A series of wrapper functions to implement the 10 maximum likelihood models of animal orientation described by Schnute and Groot (1992) . The functions also include the ability to use different optimizer methods and calculate various model selection metrics (i.e., AIC, AICc, BIC). The ability to perform variants of the Hermans-Rasson test and Pycke test is also included as described in Landler et al. (2019) . The latest version also includes a new method to calculate circular-circular and circular-linear distance correlations. Package: r-cran-circnntsr Architecture: all Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-circnntsr_2.3-1.ca2604.1_all.deb Size: 252014 MD5sum: 5a9c99926cb5dfcbdf3710cf205ad4e6 SHA1: 03e7123f6f39134403c9ea962ce50fbb1afe3006 SHA256: 803c0ccdc14f43cc628d9b9b39488458a06f71db9dc292f0eee1143b29788500 SHA512: 757b555fcb33acc2ee2d38ee1cca5dea6c4ee0d2799d21d3f9852ce9747d87bd142a6b8b7a7d6d17eccc48cec822845f6a7ce311da012dfa72f48e8c675c580f Homepage: https://cran.r-project.org/package=CircNNTSR Description: CRAN Package 'CircNNTSR' (Statistical Analysis of Circular Data using NonnegativeTrigonometric Sums (NNTS) Models) Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more. Package: r-cran-circnntsraxial Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psychtools, r-cran-circnntsr Filename: pool/dists/resolute/main/r-cran-circnntsraxial_0.1.0-1.ca2604.1_all.deb Size: 77362 MD5sum: ddc03a65c7ffa6e08896e5d4afa33896 SHA1: c6791f045157ee829496ee7688b1b3b3bec0c323 SHA256: 3ed7d498cd71dff2eeee053c1f4497ca4e0c3855743039b39ec488cb43a97742 SHA512: ae8f84e15776722f7856d636d10276f54e93ad1a8be3270fad6e1eafd0498e30860c328c7c5d2e3fd7234cd3a5306e6ff115c3f707ead259c384cde03b145472 Homepage: https://cran.r-project.org/package=CircNNTSRaxial Description: CRAN Package 'CircNNTSRaxial' (Axial Data using NNTS Models) Statistical analysis of axial using distributions Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more. Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), ''Multimodal distributions for circular axial data", . 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Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), "Multimodal Symmetric Circular Distributions Based on Nonnegative Trigonometric Sums and a Likelihood Ratio Test for Reflective Symmetry", . 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Users work entirely in R with familiar objects (SpatRaster, file paths) while 'Julia' handles computation invisibly. Supports all four 'Circuitscape' modes (pairwise, one-to-all, all-to-one, advanced) and 'Omniscape' moving-window analysis. Methods are described in McRae (2006) and Landau et al. (2021) . Package: r-cran-circularboxplots Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circular, r-cran-plotrix, r-cran-rgl, r-cran-rcolorbrewer, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-circularboxplots_0.1.2-1.ca2604.1_all.deb Size: 207048 MD5sum: bea27650d376b16d4318abf33b1dc4aa SHA1: a20051f1aa9058e3fe2f8687a08b8d27a3bdeeb0 SHA256: 98a402bc126ba40f02606d6b5166d827c506199d551ddf3336d130029a121276 SHA512: 429983d972ec0da11a35ba07d432b463d0a0fa0cf246998113aa0ec1bd49dd64bcc22c01a59f5a086bb8ba4df32462c84c2928a4e6425439a369d8511f1332ba Homepage: https://cran.r-project.org/package=CircularBoxplots Description: CRAN Package 'CircularBoxplots' (Grouped Boxplots for Circular Data) Plotting functions to create circular boxplots for grouped data. 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Package: r-cran-circularkde Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-circularkde_0.1.1-1.ca2604.1_all.deb Size: 82254 MD5sum: 23c128d0e68ea1ac125d2cd9f487456f SHA1: f82462bdb9c29d7d79780104eac895d05964adf6 SHA256: f01a604823cc13221968be2ebc4fd02eadbdc4eed20bc7cd704b2fdcb69b3144 SHA512: fdf8efe0f8815a08f3bc81a0c5d77e61b6563b00c32c68764cc6b38e998a75a0f37b9709857d3f4f13e21ebbeaef087cfb267d2c32858e4a645e7977f49546ac Homepage: https://cran.r-project.org/package=circularKDE Description: CRAN Package 'circularKDE' (Recent Methods for Kernel Density Estimation of Circular Data) Provides recent kernel density estimation methods for circular data, including adaptive and higher-order techniques. 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We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set. 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Package: r-cran-citan Architecture: all Version: 2025.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-agop, r-cran-rsqlite, r-cran-stringi, r-cran-dbi Filename: pool/dists/resolute/main/r-cran-citan_2025.7.1-1.ca2604.1_all.deb Size: 944372 MD5sum: 356e40382b27cad8e07332d3c857f45c SHA1: ea53bcc6d65cb5975a957671d89859483bc2d245 SHA256: d2f19d2e2498c9de98794d2cdd851eb2f1d91e427d03e713efe379d86fd1884a SHA512: d563f75197245c6c608d5123b374137683a88826ae23163f1dd4b07ade48669ec6550f2621074b3c1123f20c93c7ea323059bf56e126b63759d7861a76279172 Homepage: https://cran.r-project.org/package=CITAN Description: CRAN Package 'CITAN' (CITation ANalysis Toolpack) Supports quantitative research in scientometrics and bibliometrics. Provides various tools for preprocessing bibliographic data retrieved, e.g., from Elsevier's Scopus, computing bibliometric impact of individuals, or modelling phenomena encountered in the social sciences. This package is deprecated; see 'agop' instead. Package: r-cran-citation Architecture: all Version: 0.12.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desc, r-cran-jsonlite, r-cran-withr, r-cran-yaml Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-citation_0.12.2-1.ca2604.1_all.deb Size: 58620 MD5sum: 77cb7005a9d8cd24f06ff24ad4ac8fbb SHA1: ad85fe8a34cf096681b62b42c14352761c2a5384 SHA256: dd26f5ef0a886c31adf5aed09b6d201457b1f1738b040de28cc106ac835ef59c SHA512: 8f63efa566b28b9d2f841d859ddfb420dfa4afaf58febfeea675f685a03270e3de993c333d2542dc00ddacf30af2dc6f65f465c016965c9636c783bfcf2c2d71 Homepage: https://cran.r-project.org/package=citation Description: CRAN Package 'citation' (Software Citation Tools) A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (), extending the functionality already provided by the citation() function in the 'utils' package. Package: r-cran-citationchaser Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-maditr, r-cran-mess, r-cran-networkd3, r-cran-scales, r-cran-tibble, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-citationchaser_0.0.4-1.ca2604.1_all.deb Size: 242748 MD5sum: 58e0916e2defaf5ee5970521535f5cff SHA1: 6c9310d2f54befddc757ae9ed0a1aae569b09412 SHA256: ac0225c5c8234bb99cf53be62e30e8523aea2fd539bdb71d383d9a3780c638c0 SHA512: ab9dac47eb22cd37ae3c15351b8dcdf9bbf5b6ddcb946c323bbf889c27527e18751c0f0b93dc0abe5af372f8ec285e2ec0527941805066d57dfc0811448fcc71 Homepage: https://cran.r-project.org/package=citationchaser Description: CRAN Package 'citationchaser' (Perform Forward and Backwards Chasing in Evidence Syntheses) In searching for research articles, we often want to obtain lists of references from across studies, and also obtain lists of articles that cite a particular study. In systematic reviews, this supplementary search technique is known as 'citation chasing': forward citation chasing looks for all records citing one or more articles of known relevance; backward citation chasing looks for all records referenced in one or more articles. Traditionally, this process would be done manually, and the resulting records would need to be checked one-by-one against included studies in a review to identify potentially relevant records that should be included in a review. This package contains functions to automate this process by making use of the Lens.org API. An input article list can be used to return a list of all referenced records, and/or all citing records in the Lens.org database (consisting of PubMed, PubMed Central, CrossRef, Microsoft Academic Graph and CORE; ). 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Includes a set of functions for getting one identifier type from another, as well as getting references and citations for a given identifier. 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Uses multiply-imputed datasets and cross-validated classifiers to produce a test statistic and p-value, with a sensitivity parameter (kappa) for calibrating interpretation. Wraps the 'citest' 'Python' engine via a local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is needed at runtime. 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Package: r-cran-citmic Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1868 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatch, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-citmic_0.1.3-1.ca2604.1_all.deb Size: 1851908 MD5sum: baf60ec8caf08cb9bd6fad677445041b SHA1: c1c2b9c139404890203df1a1083b606648c5eba1 SHA256: caeb921b3cbf3536476fde7511f98f7941e1868998b2b7d8fe0b1c8b0b68fa14 SHA512: a242ff8bf072f5ca15c1920a76173b0087f18eba996a97a9c06e0df6b32d32499b6d866e5177f051d182949229c74b527320458219b81b0b817cbdef0763e5f2 Homepage: https://cran.r-project.org/package=CITMIC Description: CRAN Package 'CITMIC' (Estimation of Cell Infiltration Based on Cell Crosstalk) A systematic biology tool was developed to identify cell infiltration via Individualized Cell-Cell interaction network. 'CITMIC' first constructed a weighted cell interaction network through integrating Cell-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells. Package: r-cran-cito Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3653 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coro, r-cran-checkmate, r-cran-torch, r-cran-gridextra, r-cran-parabar, r-cran-abind, r-cran-progress, r-cran-cli, r-cran-torchvision, r-cran-tibble, r-cran-lme4 Suggests: r-cran-spelling, r-cran-rmarkdown, r-cran-testthat, r-cran-plotly, r-cran-ggraph, r-cran-igraph, r-cran-ggplot2, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-cito_1.1-1.ca2604.1_all.deb Size: 2578306 MD5sum: 93f1004095a1335aa91525254c31e5ec SHA1: afc00358a1b846b07ef09c1b8aa36c0c518c9507 SHA256: 7bbc72dd978230b078427d8788054c7b9849d41fd202a64e2de3aa06cf7a20b3 SHA512: 0640fac48196492b91f5d00dd70074a7e14fdb5b55d042807be1339f0d45696f984f396c087ec7027452a6953b67eb4247df79048c87296365d6a8a12de81c73 Homepage: https://cran.r-project.org/package=cito Description: CRAN Package 'cito' (Building and Training Neural Networks) The 'cito' package provides a user-friendly interface for training and interpreting deep neural networks (DNN). 'cito' simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems. DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, 'cito' has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. 'cito' optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, 'cito' is computationally efficient because it is based on the deep learning framework 'torch'. The 'torch' package is native to R, so no Python installation or other API is required for this package. Package: r-cran-citools Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1330 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-boot, r-cran-dplyr, r-cran-lme4, r-cran-mass, r-cran-survival Suggests: r-cran-here, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spreda Filename: pool/dists/resolute/main/r-cran-citools_0.6.1-1.ca2604.1_all.deb Size: 732090 MD5sum: 92e669f7f9c9ad97ab319ab64edc6406 SHA1: b1721b1fdcebd9291525456382d05660b261b020 SHA256: b356ab54c96ea46f97267760b016eb21ac2d2639b36cc715346c413804df58fc SHA512: a803861173ac8411b66a6d27e7d8fbcf3144b4bcbf105eaa5266fe40143298e660569cfb21eed0157905a3d33ce02510e53257975fb314a79b73e2d20615db43 Homepage: https://cran.r-project.org/package=ciTools Description: CRAN Package 'ciTools' (Confidence or Prediction Intervals, Quantiles, and Probabilitiesfor Statistical Models) Functions to append confidence intervals, prediction intervals, and other quantities of interest to data frames. All appended quantities are for the response variable, after conditioning on the model and covariates. This package has a data frame first syntax that allows for easy piping. Currently supported models include (log-) linear, (log-) linear mixed, generalized linear models, generalized linear mixed models, and accelerated failure time models. Package: r-cran-citrus Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggally, r-cran-clustmixtype, r-cran-treeclust, r-cran-rpart, r-cran-tibble, r-cran-rpart.plot, r-cran-stringr, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-citrus_1.0.2-1.ca2604.1_all.deb Size: 183850 MD5sum: 32c02117908691e4dad84056404d88e5 SHA1: d7ce58113ac817d73ea93b03a9a72faa55d0149c SHA256: f24285e7050a2050e846dc04421391993070f3c2a18d9cba65d32f18928b1d97 SHA512: a7d582b43d95924f9e7da8e13172b375b56a3090dc66af33064e502d0ecb2b0a3fff0792fb68003806486561e6473842bc198b6a70e6f4219aafee8df65cd2aa Homepage: https://cran.r-project.org/package=citrus Description: CRAN Package 'citrus' (Customer Intelligence Tool for Rapid Understandable Segmentation) A tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting. Users can either run the pipeline as a whole, or choose to run any one of the three individual steps. It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models. Package: r-cran-citsr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-nlme, r-cran-clubsandwich, r-cran-ggplot2, r-cran-aiccmodavg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-citsr_0.1.3-1.ca2604.1_all.deb Size: 41972 MD5sum: c7833d3ac0a7f76f74cd6c7c85a75d42 SHA1: 3862da406b3d3ac15c334bd9f755df592de99e5c SHA256: 4d6ec4ae28445168103f8265fd49f092082beed89591df08e1b37c4ffecb2274 SHA512: 5d6984cb212d94d18a29bfe88b7e053ff7ae31c7a14e49953fa35703e9774308680e4c714bb25ea355e27ce4a3ed0986b23b85de2ad4758dbc34725120dd2f8c Homepage: https://cran.r-project.org/package=citsr Description: CRAN Package 'citsr' (Controlled Interrupted Time Series Analysis and Visualization) Implements controlled interrupted time series (CITS) analysis for evaluating interventions in comparative time-series data. The package provides tools for preparing panel time-series datasets, fitting models using generalized least squares (GLS) with optional autoregressive–moving-average (ARMA) error structures, and computing fitted values and robust standard errors using cluster-robust variance estimators (CR2). Visualization functions enable clear presentation of estimated effects and counterfactual trajectories following interventions. Background on methods for causal inference in interrupted time series can be found in Linden and Adams (2011) and Lopez Bernal, Cummins, and Gasparrini (2018) . Package: r-cran-ciu Architecture: all Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-crayon, r-cran-ggplot2, r-cran-ggbeeswarm Suggests: r-cran-mass, r-cran-caret, r-cran-gbm Filename: pool/dists/resolute/main/r-cran-ciu_0.8-1.ca2604.1_all.deb Size: 176508 MD5sum: 02dfbf21c61d32e16488a2dda3d1392c SHA1: c6068aa7330331bae2191aae19f42df1716842bc SHA256: 8e517db2944b240fc48d78a85cb063d3f086075330175501f0a90ed29aeb06b2 SHA512: 9434de5e6dd4d78106d228682d39d480fa03d2388b6b4e154e6df25dec90de8b0f649eb7d9d6864e58899c7b2464479bd92e6f19d9dbd480f3db0cac624de936 Homepage: https://cran.r-project.org/package=ciu Description: CRAN Package 'ciu' (Contextual Importance and Utility) Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A description of CIU can be found in e.g. Främling (2020) . Package: r-cran-ciuupi2 Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-functional, r-cran-nloptr, r-cran-pracma, r-cran-precisesums, r-cran-statmod Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ciuupi2_1.0.1-1.ca2604.1_all.deb Size: 97468 MD5sum: 8b702e0e7672fe373a480c8e409e3578 SHA1: e5b8dca3cf0503556397c7061285a1ac31b33005 SHA256: 55724f48de3e437e4e891bc7f57e70a94940beb1407ef930065350112bdd1623 SHA512: 0708cdf4f0d128651eb9389e17e59789df84e428d7f605064da91ddf8563a08842e9550a625a0c5b815d2bd28e3409909ab145a3db3ed8881860541d25dd68ef Homepage: https://cran.r-project.org/package=ciuupi2 Description: CRAN Package 'ciuupi2' (Kabaila and Giri (2009) Confidence Interval) Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with unknown variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a specified number. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval is proposed by Kabaila, P. and Giri, K. (2009) . Package: r-cran-ciuupi Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr, r-cran-statmod, r-cran-functional, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ciuupi_1.2.3-1.ca2604.1_all.deb Size: 132606 MD5sum: 2dba70d9a10910678b0a6ccd475bbf36 SHA1: c761ae229cf5d392319b7c7484f9b9b1a9445a89 SHA256: deadd197f0f9742e1bc23b5d1b761ab422b41198dbcecbdf232f341483edf1ec SHA512: 42d0510fe0716ef9520dd9c50fe2cff761d0f59fefe61b4acdb040ffd40b808c603e1d401774ddb325276aa3b7196ce68a32c2853e1fa161e4fe8b7e3b289c60 Homepage: https://cran.r-project.org/package=ciuupi Description: CRAN Package 'ciuupi' (Confidence Intervals Utilizing Uncertain Prior Information) Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with known variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a given value. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval has the following three practical applications. Firstly, if the error variance has been accurately estimated from previous data then it may be treated as being effectively known. Secondly, for sufficiently large (dimension of the response vector) minus (dimension of regression parameter vector), greater than or equal to 30 (say), if we replace the assumed known value of the error variance by its usual estimator in the formula for the confidence interval then the resulting interval has, to a very good approximation, the same coverage probability and expected length properties as when the error variance is known. Thirdly, some more complicated models can be approximated by the linear regression model with error variance known when certain unknown parameters are replaced by estimates. This confidence interval is described in Mainzer, R. and Kabaila, P. (2019) , and is a member of the family of confidence intervals proposed by Kabaila, P. and Giri, K. (2009) . Package: r-cran-civ Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aer, r-cran-kcmeans Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-civ_0.1.0-1.ca2604.1_all.deb Size: 40378 MD5sum: da174229ddb6864efd5380bfe80d5e42 SHA1: 83aed7f6922357eeb65145136ce93de7579c3194 SHA256: 39e6a1239ef5de66b5e39c5d468ed2b08954228c243856b87ce010257e249d0d SHA512: 23c547587adc706b12cb1942398c6d078c45e966308a4b29432bf228c38f11bce22673a3002db8411ce53cb6a47d72ee062b8ec7cc1e34fc379e48534aca1c18 Homepage: https://cran.r-project.org/package=civ Description: CRAN Package 'civ' (Categorical Instrumental Variables) Implementation of the categorical instrumental variable (CIV) estimator proposed by Wiemann (2023) . CIV allows for optimal instrumental variable estimation in settings with relatively few observations per category. To obtain valid inference in these challenging settings, CIV leverages a regularization assumption that implies existence of a latent categorical variable with fixed finite support achieving the same first stage fit as the observed instrument. Package: r-cran-civis Architecture: all Version: 3.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-httr, r-cran-jsonlite, r-cran-memoise Suggests: r-cran-feather, r-cran-ggplot2, r-cran-knitr, r-cran-lifecycle, r-cran-rmarkdown, r-cran-roxygen2, r-cran-mockery, r-cran-r.utils, r-cran-rstudioapi, r-cran-testthat, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-civis_3.1.4-1.ca2604.1_all.deb Size: 3295544 MD5sum: 695db647b7a5c75c4217d1daf08e105d SHA1: 894dddea2073eb5cac13a2301216036e8663df8d SHA256: ff044e45e8dec37b9b712722eda57c9ee56a9bcf7477be890f5368b7bc15f7b6 SHA512: b2b98b2a6f0096380b9b5d3ccdf77910c276912f6f4eaf811a6840331484bf3f21b30e1cf455e0f29742734860a8d5bf05b9b95b6812ba7bc0306436c8d4de2e Homepage: https://cran.r-project.org/package=civis Description: CRAN Package 'civis' (R Client for the 'Civis Platform API') A convenient interface for making requests directly to the 'Civis Platform API' . Full documentation available 'here' . Package: r-cran-ciw Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-xml, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-ciw_0.0.2-1.ca2604.1_all.deb Size: 18750 MD5sum: 4fe7fb7c53d5d7b0b86856dab21a49f2 SHA1: bbbe581f983412780f7d6aa1799e2b17b777cee9 SHA256: dbddf0f35799482fa82c4a4ff835747398c382490b3f2024edcc6d366aa45f47 SHA512: 22265426b7d969ae8e604a771744ec0d855ec39834da681c29d6a1aefba8c5d92d4859698234e210ef153be400e167f9b59df38033cb74a2d0af394d4ab400ed Homepage: https://cran.r-project.org/package=ciw Description: CRAN Package 'ciw' (Watch the CRAN Incoming Directories) Directory reads and summaries are provided for one or more of the subdirectories of the directory, and a compact summary object is returned. The package name is a contraption of 'CRAN Incoming Watcher'. Package: r-cran-cjamp Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimx Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cjamp_0.1.1-1.ca2604.1_all.deb Size: 678580 MD5sum: acd84e1c9fedb8a83958f5042b2273f2 SHA1: 6103ecc74ae2cf1eb0caca9afe28a9d7d245d87e SHA256: 655647228e8a3e263463e294925a6c087f937be5f1b999bc7bbbb0eb06f01a1d SHA512: 57cb8801fa9d6bd261c14f55ddc95350b504d952641474c15271025754bfb90d57193411fbca17d8c63b4277d97fe8133ca685d5aad71ff9ffaa7597ebea2847 Homepage: https://cran.r-project.org/package=CJAMP Description: CRAN Package 'CJAMP' (Copula-Based Joint Analysis of Multiple Phenotypes) We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, ; Konigorski, Yilmaz, Bull, 2014, ). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers. Package: r-cran-cjar Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-httr, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-dplyr, r-cran-jsonlite, r-cran-glue, r-cran-jose, r-cran-tibble, r-cran-lubridate, r-cran-progress, r-cran-vctrs, r-cran-stringr, r-cran-rlang, r-cran-memoise, r-cran-openssl, r-cran-httr2 Filename: pool/dists/resolute/main/r-cran-cjar_0.2.1-1.ca2604.1_all.deb Size: 839874 MD5sum: 3c5fff48b8e8a7cd2bc3e44b8498f54f SHA1: acbc2d358da58ea0b1c0d25651df3cb055e5e6c7 SHA256: 407f1fb770893f0c59d168631de057aa7ebd1ca9e36c46ec2685db25d3fed8b7 SHA512: f1975ea16a016b2bbcdb3f30b56e551f36e91e4a48f3638d4178fd7288fe9d66bf38b1932f840c7d041480fddcfe28581f098a01cc274d3b14435ac3d3182acc Homepage: https://cran.r-project.org/package=cjar Description: CRAN Package 'cjar' (R Client for 'Customer Journey Analytics' ('CJA') API) Connect and pull data from the 'CJA' API, which powers 'CJA Workspace' . The package was developed with the analyst in mind and will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. New features are actively being developed and we value your feedback and contribution to the process. Package: r-cran-cjbart Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bart, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-randomforestsrc, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cjbart_0.3.2-1.ca2604.1_all.deb Size: 300726 MD5sum: a784c1e854b2d119f8c16da8ecbf7e46 SHA1: 20239050ff9524743d69c97f03f1fcb6ea9942e1 SHA256: 9d13ea3a4efa7ac337ad99cbca57dfc152afa3b76120e1158dff888606e44f49 SHA512: defebc60490ee1b3c195b59063fc6ce5c469be29867f81fb8b97fae305bbb7f6a17ce251a87d86b4608e7c01ab865a8e70d5af7b55b88da98d52326dcbd646fa Homepage: https://cran.r-project.org/package=cjbart Description: CRAN Package 'cjbart' (Heterogeneous Effects Analysis of Conjoint Experiments) A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) . This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) , to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects. Package: r-cran-cjive Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve, r-cran-ggplot2, r-cran-reshape2, r-cran-fields, r-cran-gplots, r-cran-psych Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cjive_0.1.0-1.ca2604.1_all.deb Size: 107582 MD5sum: 443ff5c5b2038f0823d19e38a06263b7 SHA1: db3ef8b54863c7970c2b815220884426dce48bf1 SHA256: 503ee0d67b087385f15b41126476dd19bfda3d27085402857326abdef27cf9ba SHA512: bd02bb1717e64c261dda12d2f2f178d9ac5de7571fc9bf4c031a0d01351365dcb49d0f6d9916a0c7fd3495c85bcbfb53895926dcfbcb3f366aa94413006164cc Homepage: https://cran.r-project.org/package=CJIVE Description: CRAN Package 'CJIVE' (Canonical Joint and Individual Variation Explained (CJIVE)) Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can be challenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal component scores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation between corresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, which provides a p-value for each component; and 3) can be used to predict subject scores for out-of-sample observations. Please cite the following article when utilizing this package: Murden, R., Zhang, Z., Guo, Y., & Risk, B. (2022) . Package: r-cran-cjoint Architecture: all Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-lmtest, r-cran-ggplot2, r-cran-survey, r-cran-matrix, r-cran-dt, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cjoint_2.1.3-1.ca2604.1_all.deb Size: 418814 MD5sum: 34e4bea8b1bc0bb9f32d15752d75eaa0 SHA1: 50d6da5100c1af9fafd3510950a5a3683a0c9cb0 SHA256: 74e06f287e614db730baa98bae50e1e84936362b2c79a928a678263d51a143fa SHA512: 274e44ede9f260b6dab1251df5a2da7e79b712bcf89b06c6cc022993adabd2b7b7d10fd000317fabf099d274afc23588fd635e24093a7b4be4cbf2ab7f8f77bf Homepage: https://cran.r-project.org/package=cjoint Description: CRAN Package 'cjoint' (AMCE Estimator for Conjoint Experiments) An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30. Package: r-cran-ckanr Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-crul, r-cran-jsonlite, r-cran-dplyr, r-cran-dbplyr, r-cran-magrittr Suggests: r-cran-sf, r-cran-readxl, r-cran-testthat, r-cran-xml2, r-cran-lazyeval Filename: pool/dists/resolute/main/r-cran-ckanr_0.7.0-1.ca2604.1_all.deb Size: 434370 MD5sum: dcae5eb8008a68ff336d4f7bc4ed1397 SHA1: d603af9551470d2042b3623399964e6ca88d1b88 SHA256: b858fcf4d164bc286f99fa4b46dc244d10686ab7cb0e64a204cc07e5c5c3119f SHA512: e14503ea38bffa554ee4adb8d8453d679d34b678cb3fbac884067ee929b16a3f00bdb152e9c2c6672ae06a22fd81c70270e1039958fa1a3c4d3c0205193ccf1e Homepage: https://cran.r-project.org/package=ckanr Description: CRAN Package 'ckanr' (Client for the Comprehensive Knowledge Archive Network ('CKAN')API) Client for 'CKAN' API (). Includes interface to 'CKAN' 'APIs' for search, list, show for packages, organizations, and resources. In addition, provides an interface to the 'datastore' API. Package: r-cran-ckat Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform Filename: pool/dists/resolute/main/r-cran-ckat_0.1.0-1.ca2604.1_all.deb Size: 35946 MD5sum: 0eb3d5667ba555713d881bc630cf9e1a SHA1: 8a25935c46eb07d9a0dda23def17d2450ec37453 SHA256: 3573648dbfc19c2754724729ab53b6fe9267070eb962742dc4749c3b5ad09d2e SHA512: e3de29c692f24f9b4addead19bbf8988d4f7d4fe3319d945766f970285331b3539b55e20b499ae12e2df5ef243daee958c09b4cbfec10dcb0164ccf6262b3058 Homepage: https://cran.r-project.org/package=CKAT Description: CRAN Package 'CKAT' (Composite Kernel Association Test for Pharmacogenetics Studies) Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials. Package: r-cran-cla Architecture: all Version: 0.96-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fgarch, r-cran-frapo, r-cran-matrix, r-cran-sfsmisc Filename: pool/dists/resolute/main/r-cran-cla_0.96-3-1.ca2604.1_all.deb Size: 1197186 MD5sum: 2787b416c8497760ed9c142938bc02ad SHA1: a731a5dc2efaf7f42c08fff85b6984c8c27b80d1 SHA256: 8d3ac2ad8485fd77fc62506a69aed15c934b15aa72be5773957956cd90d072aa SHA512: 0af5d57104bc325d181ec4d70a77ebff43d4bfc33a2899033597410d6ff5728eed36f50a5e134086b6e6f9dd91da98f18625e465b3eca8a70729f154cd028d7f Homepage: https://cran.r-project.org/package=CLA Description: CRAN Package 'CLA' (Critical Line Algorithm in Pure R) Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical mean-variance portfolio optimization, see Markowitz (1952) . Care has been taken for correctness in light of previous buggy implementations. Package: r-cran-claddis Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2002 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phytools, r-cran-strap, r-cran-clipr, r-cran-geoscale, r-cran-multicool, r-cran-partitions Suggests: r-cran-rgl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-claddis_0.7.0-1.ca2604.1_all.deb Size: 1374400 MD5sum: d0ddfc1124f0dc1a2ff95b85ff6deafa SHA1: b5cb565833aa1a42924e7b9672806a4688acdcf9 SHA256: 42840969eb777d628bb0ba1da7946519121c76dc55e7e6e9bc34ba3bb1ee4857 SHA512: 2b0b485cb8949a36ec848067bf28595f87ea408667723e3e038c49cacba698512d5672d330fd7708d2aba72e12f2bd7c4fef084e75fe3013987b65a1a3f4e099 Homepage: https://cran.r-project.org/package=Claddis Description: CRAN Package 'Claddis' (Measuring Morphological Diversity and Evolutionary Tempo) Measures morphological diversity from discrete character data and estimates evolutionary tempo on phylogenetic trees. Imports morphological data from #NEXUS (Maddison et al. (1997) ) format with read_nexus_matrix(), and writes to both #NEXUS and TNT format (Goloboff et al. (2008) ). Main functions are test_rates(), which implements AIC and likelihood ratio tests for discrete character rates introduced across Lloyd et al. (2012) , Brusatte et al. (2014) , Close et al. (2015) , and Lloyd (2016) , and calculate_morphological_distances(), which implements multiple discrete character distance metrics from Gower (1971) , Wills (1998) , Lloyd (2016) , and Hopkins and St John (2018) . This also includes the GED correction from Lehmann et al. (2019) . Multiple functions implement morphospace plots: plot_chronophylomorphospace() implements Sakamoto and Ruta (2012) , plot_morphospace() implements Wills et al. (1994) , plot_changes_on_tree() implements Wang and Lloyd (2016) , and plot_morphospace_stack() implements Foote (1993) . Other functions include safe_taxonomic_reduction(), which implements Wilkinson (1995) , map_dollo_changes() implements the Dollo stochastic character mapping of Tarver et al. (2018) , and estimate_ancestral_states() implements the ancestral state options of Lloyd (2018) . calculate_tree_length() and reconstruct_ancestral_states() implements the generalised algorithms from Swofford and Maddison (1992; no doi). Package: r-cran-claimsproblems Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometry, r-cran-pracma, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-claimsproblems_1.0.0-1.ca2604.1_all.deb Size: 266968 MD5sum: e497be805900bc03b5a82ab2a1e3a9dd SHA1: 4dc11336c261a852c4288bf4da8e801d7e964d14 SHA256: b966b379a50b94ffdf50df132fc7cdce51274637611301a3902b60e6cc8c3558 SHA512: f80ace1d7629f669fcfa0a050f33ff7c1f69676097814bfeae42deb98b4545206363337a1aaf6873743722b01d4506087fe0245970c4b38f18d1bac36466cd38 Homepage: https://cran.r-project.org/package=ClaimsProblems Description: CRAN Package 'ClaimsProblems' (Analysis of Conflicting Claims) The analysis of conflicting claims arises when an amount has to be divided among a set of agents with claims that exceed what is available. A rule is a way of selecting a division among the claimants. This package computes the main rules introduced in the literature from ancient times to the present. The inventory of rules covers the proportional and the adjusted proportional rules, the constrained equal awards and the constrained equal losses rules, the constrained egalitarian, the Piniles’ and the minimal overlap rules, the random arrival and the Talmud rules. Besides, the Dominguez and Thomson and the average-of-awards rules are also included. All of them can be found in the book by W. Thomson (2019), How to divide when there isn't enough. From Aristotle, the Talmud, and Maimonides to the axiomatics of resource allocation', except for the average-of-awards rule, introduced by Mirás Calvo et al. (2022), . In addition, graphical diagrams allow the user to represent, among others, the set of awards, the paths of awards, the schedules of awards of a rule, and some indexes. A good understanding of the similarities and differences between the rules is useful for better decision-making. Therefore, this package could be helpful to students, researchers, and managers alike. For a more detailed explanation of the package, see Mirás Calvo et al. (2023), . Package: r-cran-clam Architecture: all Version: 2.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rintcal, r-cran-rice Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8 Filename: pool/dists/resolute/main/r-cran-clam_2.6.3-1.ca2604.1_all.deb Size: 177322 MD5sum: 361799029da318d95ac45ce969acc062 SHA1: 3b44c75d6a02a8482312137c660079448a7ebe86 SHA256: 604bcad468b73eba7a2a6d63443ca54ae9634af59bfaff1cb1d11d44d2e75312 SHA512: 8552689cce65b6e3e0fd72b19f77c5a57e549080d423a1195f16677b9f6d3109439862087cc9ccd366ae3c5e329752f56ee978877c4847ca572018c2e068b977 Homepage: https://cran.r-project.org/package=clam Description: CRAN Package 'clam' (Classical Age-Depth Modelling of Cores from Deposits) Performs 'classical' age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010) . Package: r-cran-clampseg Architecture: all Version: 1.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1868 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stepr, r-cran-lowpassfilter Suggests: r-cran-testthat, r-cran-r.cache, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-clampseg_1.2-0-1.ca2604.1_all.deb Size: 1638890 MD5sum: eb0c42930205fd1fd5fd7e974588d03c SHA1: a6ec6016b8d29a28fe313187e06afe9339308d86 SHA256: 9f45455826368cb233b74faa7fae2df5cb9e959942fa731928c7e04f165543c2 SHA512: eb768176af5cdb11f112297b8381deda8c9f89d9e2613bf730af4f2283ccde73950842f305cd4fcbe1f3eaaebcd04cec6928323646de2675ab63e09392c8d9f9 Homepage: https://cran.r-project.org/package=clampSeg Description: CRAN Package 'clampSeg' (Idealisation of Patch Clamp Recordings) Implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF), Hotz et al. (2013) , JUmp Local dEconvolution Segmentation filter (JULES), Pein et al. (2018) , and Heterogeneous Idealization by Local testing and DEconvolution (HILDE), Pein et al. (2021) . Further details on how to use them are given in the accompanying vignette. Package: r-cran-clamr Architecture: all Version: 2.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clamr_2.1-3-1.ca2604.1_all.deb Size: 82012 MD5sum: 8f44f86910f409b8bd3a729f3ebf81a3 SHA1: 8503b003b3781428829bb351de724384b8e386bb SHA256: 63214078eb80834b344202a92108788c6cdb720cedc2e9a41b81f9f2e90db5db SHA512: 8ae5b947522a02a02c0d89f40488ab65cdcf5de60640bafa3ea2478c1a6724f3423e69c75324ebe300ca2ce6b7f79368a86e0deef39e59f0aee59c8f2cf88134 Homepage: https://cran.r-project.org/package=ClamR Description: CRAN Package 'ClamR' (Time Series Modeling for Climate Change Proxies) Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams. 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This package provides methods for detecting and visualizing these overlapping regions using partitional clustering techniques based on nearest neighbor distances. Package: r-cran-clarifai Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clarifai_0.4.2-1.ca2604.1_all.deb Size: 1265820 MD5sum: 04d570b1b86fc1f114d9fc21af184804 SHA1: 48851216c56dffe92a1cedbcb827dc8c28b8a299 SHA256: ad8933739357881d72a78459e42249a19633b6a46f7e8659cac2ad42df428f8f SHA512: d1f0eae0769e01d9ca8cccae796b3f986082d83af10abb81af226675c0eaf1ebdf4d246675b6e10310310ff0ea7e071dbd0d9cea7a3be34e5f573782b4d5721f Homepage: https://cran.r-project.org/package=clarifai Description: CRAN Package 'clarifai' (Access to Clarifai API) Get description of images from Clarifai API. 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Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance. Package: r-cran-classgraph Architecture: all Version: 0.7-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph, r-bioc-rgraphviz Suggests: r-cran-matrix Filename: pool/dists/resolute/main/r-cran-classgraph_0.7-7-1.ca2604.1_all.deb Size: 46024 MD5sum: e13122e51dbe173c732588b3a869a183 SHA1: 4395123be8329895cf3471f847d63649e4eaf5b1 SHA256: 36368a38838f1c42b6528d92a52d9da19a782072a26571cdae2afead020b963a SHA512: 675de0eb9c0e2dbc2c0cf943b1a35b8b27877182741ea6e7412b20d16459966f437e9677acbc3bdada6f4701eb9a0747878da469a3fe692c95cf4d8b89257737 Homepage: https://cran.r-project.org/package=classGraph Description: CRAN Package 'classGraph' (Construct Graphs of S4 Class Hierarchies) Construct directed graphs of S4 class hierarchies and visualize them. 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The package also returns 25 plots, 5 tables and a summary report. Package: r-cran-classifierplots Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-rcpp, r-cran-rocr, r-cran-caret, r-cran-gridextra, r-cran-png Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-classifierplots_1.4.0-1.ca2604.1_all.deb Size: 350866 MD5sum: 19f3948f0046756582a74095c111bd17 SHA1: 0e08e9d7ce16b8f0ab294d46ca90130a8329ac5d SHA256: 8428ef1ca27d396eaae2a8a3da8a6a75218e47eabeaf7a2290422496d8d7bb1f SHA512: 4f948c26cf1de10e6e5a1dd3296211d758e7f8a64857ac673e7f19a2aa95adb8c3f9fac2213d4d7b9899e6f364363a6914226fcadbf29e3142ba90624c787aad Homepage: https://cran.r-project.org/package=classifierplots Description: CRAN Package 'classifierplots' (Generates a Visualization of Classifier Performance as a Grid ofDiagnostic Plots) Generates a visualization of binary classifier performance as a grid of diagnostic plots with just one function call. Includes ROC curves, prediction density, accuracy, precision, recall and calibration plots, all using ggplot2 for easy modification. Debug your binary classifiers faster and easier! 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Package: r-cran-classifyits Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2, r-cran-data.table, r-cran-seqinr Suggests: r-cran-formatr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-classifyits_1.0.2-1.ca2604.1_all.deb Size: 198924 MD5sum: 3c866d8b02f580106f7aa0a07795229f SHA1: 1a867cc47348ed343d2041fb34d9e2899f0ac024 SHA256: 64894e916bc555c4eb588066659ebef0b9f2f190504b9f830703f754be0f04e5 SHA512: 82b8f578be23520c339fa25587a2c3d2a2f0eeb327e88c6695e43ed924fb19ea18b5223e4ac4fdf0ba168c456ace75fc534fee04d5e89c47faabde4ee33fddef Homepage: https://cran.r-project.org/package=ClassifyITS Description: CRAN Package 'ClassifyITS' (Fungal Assignment Pipeline) Fungi are ubiquitous in Earth's wonderfully diverse ecosystems. The 'ClassifyITS' package aids in the taxonomic classification of environmental internal transcribed spacer (ITS) short-read barcoding data. Unlike previous methods, it employs taxon-specific e-value and percent identity cutoffs at each taxonomic rank from kingdom to species. The package takes a conservative approach and outputs both graphics and user-friendly files to help users manually inspect fungal operational taxonomic units (OTUs) that fail classification at relevant levels (e.g., Phylum). 'ClassifyITS' is based on taxonomic cutoff criteria from "The Global Soil Mycobiome consortium dataset for boosting fungal diversity research" (Fungal Diversity, Tedersoo, 2021, ) and "Best practices in metabarcoding of fungi: From experimental design to results" (Molecular Ecology, Tedersoo, 2022, ). Package: r-cran-classmap Architecture: all Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-robustbase, r-cran-e1071, r-cran-cellwise, r-cran-cluster, r-cran-kernlab, r-cran-gridextra, r-cran-rpart, r-cran-randomforest, r-cran-scales, r-cran-lpsolve, r-cran-diptest Suggests: r-cran-knitr, r-cran-reshape2, r-cran-svd, r-cran-rpart.plot, r-cran-nnet, r-cran-robcompositions, r-cran-rmarkdown, r-cran-fairml, r-cran-robusthd, r-cran-vioplot Filename: pool/dists/resolute/main/r-cran-classmap_1.2.7-1.ca2604.1_all.deb Size: 2047840 MD5sum: b4d95443b1fe9bf3325c329fc7bac683 SHA1: ee4b4c9b9f019d08966613854634e352d6829a65 SHA256: 05ab2e60b7999fa526e09c622cbab6114604b6d2a77ac5479ccedf997cbd377b SHA512: 755d925ed737a9055d611691e7ae2ad51d489663a41488b46db5f593fcdb762cc8ddc572137be63e6ae35c40189d7a0b295cf9bfa5f5ee172d65a3de89afac9d Homepage: https://cran.r-project.org/package=classmap Description: CRAN Package 'classmap' (Visualizing Classification Results) Tools to visualize the results of a classification or a regression. The graphical displays include stacked plots, silhouette plots, quasi residual plots, class maps, predictions plots, and predictions correlation plots. Implements the techniques described and illustrated in Raymaekers J., Rousseeuw P.J., Hubert M. (2022). Class maps for visualizing classification results. \emph{Technometrics}, 64(2), 151–165. (open access), Raymaekers J., Rousseeuw P.J.(2022). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. \emph{Journal of Computational and Graphical Statistics}, 31(4), 1332–1343. , and Rousseeuw, P.J. (2026). Explainable Linear and Generalized Linear Models by the Predictions Plot. The American Statistician, 80, 157-163, (open access), and Montalcini, C., Rousseeuw, P.J. (2025). The bixplot: A variation on the boxplot suited for bimodal data, (open access). Examples can be found in the vignettes: "Discriminant_analysis_examples","K_nearest_neighbors_examples", "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples", "Neural_net_examples", "predsplot_examples", and "bixplot_examples". Package: r-cran-clast Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clast_1.0.1-1.ca2604.1_all.deb Size: 97422 MD5sum: 834c39bb2a9b85a51fa516db7aede938 SHA1: a903b36bcce57fc93652f8331e535dcc74b7943c SHA256: 9cbc26728c9af6f3a10d2542f952b4af2ca13fce1e862339c855696ba9ff6305 SHA512: 1e9ee2ab4975b7b9a53a1917594eea4ee5c893cf726a3b02781c5c1ddb07e4af87f4c70f912fe84a4055dca967ec72a9dea368e2506e3cd693c81d0f60f66b7e Homepage: https://cran.r-project.org/package=CLAST Description: CRAN Package 'CLAST' (Exact Confidence Limits after a Sequential Trial) The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, . Package: r-cran-clayringsmiletus Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cluster, r-cran-dbscan, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clayringsmiletus_1.0.2-1.ca2604.1_all.deb Size: 65294 MD5sum: 85708971ef22cb2df2845ba1e02a0d08 SHA1: 0d6a7c143471cc0bb343e482b34be577df7fb37a SHA256: 82e07832a1aefcaccf1ea7d0859a0ef83caeba549368c1892158d40b1634d4b6 SHA512: ae92ea46758438e8bcc1a91e92cc4ce1d6ff5c3e1207c4536c6f52ab059b40023df03aa9fa64f25e8943349c3d0de11bd7a8d63aafabb5cf73b08f671086d1f5 Homepage: https://cran.r-project.org/package=clayringsmiletus Description: CRAN Package 'clayringsmiletus' (Clay Stacking Rings Found in Miletus (Data)) Stacking rings are tools used to stack pottery in a Kiln. A relatively large group of stacking rings was found in the area of the sanctuary of Dionysos in Miletus in the 1970s. Measurements and additional info is gathered in this package and made available for use by other researchers. The data along with its archaeological context and analysis has been published in "Archäologischer Anzeiger" (2020/1, ). 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Many tried to teach us how to write code less dreadful, be it implicitly as B. W. Kernighan and D. M. Ritchie (1988) in 'The C Programming Language' did, be it explicitly as R.C. Martin (2008) in 'Clean Code: A Handbook of Agile Software Craftsmanship' did. So we should check our code for files too long or wide, functions with too many lines, too wide lines, too many arguments or too many levels of nesting. Note: This is not a static code analyzer like pylint or the like. Checkout instead. 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Package: r-cran-clespr Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aer, r-cran-pbivnorm, r-cran-mass, r-cran-magic, r-cran-survival, r-cran-clordr, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-clespr_1.1.2-1.ca2604.1_all.deb Size: 78946 MD5sum: d14fb7e785f8ab15bfc2c9c6a7b656ac SHA1: d6f065e915149948ccfbb25c1b6427dd2cf289b1 SHA256: 6d5698eb72b2341a4bbf3f1482177ead286df31e1d0300126a57403b0163e5ed SHA512: 39ee916231e18f9d24202acd5e11a027978c75b1805b6681fc1a871159558c44932a8281f080a74e399a9761272a870f16f4af6f69713d4fefc676fdd2f76a44 Homepage: https://cran.r-project.org/package=clespr Description: CRAN Package 'clespr' (Composite Likelihood Estimation for Spatial Data) Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) . 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This package is now superseded by the 'cli' package. Please use 'cli' instead in new projects. Package: r-cran-clic Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-laplacesdemon, r-cran-fbasics Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clic_0.1-1.ca2604.1_all.deb Size: 41434 MD5sum: a9297dc15efe80611be5344f8986728c SHA1: bd250dddb49d773349600b7b95195f4601be1f0a SHA256: ada875fa3ec7949fac3e8f5d10b7dcb9cee4ee6534b09daf7a6037093784f120 SHA512: d03603a3da25985d0f82f34be8d9c67973288a94f3bd4f48b2bceb228d0de4cef08c447a210d2fc0079d964f577187df825923b4a1eaa49f03b9f22c9de823bd Homepage: https://cran.r-project.org/package=CLIC Description: CRAN Package 'CLIC' (The LIC for Distributed Cosine Regression Analysis) This comprehensive framework for periodic time series modeling is designated as "CLIC" (The LIC for Distributed Cosine Regression Analysis) analysis. 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Package: r-cran-clickableimagemap Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-ggplotify, r-cran-ggplot2, r-cran-gtable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clickableimagemap_1.0-1.ca2604.1_all.deb Size: 428786 MD5sum: b2c24b3d06e4aab39548d7bc6005d09d SHA1: c288a6355043d675aff9aab5e7e092483da01cdf SHA256: 6a1f3d0d76054d3f9ba49b5ceb7f1987ea0994d3831d858c99058637d2a61512 SHA512: 99924c293930ae8ed13a9768aab1ff6800a755d42865d4987715132a82a2e8de53339be4f467689936e60710b2eb00c784504ec566473ba5dd08ea61bc7ffec4 Homepage: https://cran.r-project.org/package=clickableImageMap Description: CRAN Package 'clickableImageMap' (Implement 'tableGrob' Object as a Clickable Image Map) Implement 'tableGrob' object as a clickable image map. The 'clickableImageMap' package is designed to be more convenient and more configurable than the edit() function. Limitations that I have encountered with edit() are cannot control (1) positioning (2) size (3) appearance and formatting of fonts In contrast, when the table is implemented as a 'tableGrob', all of these features are controllable. In particular, the 'ggplot2' grid system allows exact positioning of the table relative to other graphics etc. Package: r-cran-clickb Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-discreteweibull, r-cran-mclust, r-cran-mcmcpack Suggests: r-cran-seqhmm Filename: pool/dists/resolute/main/r-cran-clickb_0.1-1.ca2604.1_all.deb Size: 37558 MD5sum: 8a8a5eca81ffa6a6e8aa04960ae7aaca SHA1: b4cfea80e3a675a7e238ec4bd5f8b55418bd707f SHA256: 728dbec37287380ed68e31016f7e61ec3c93a2d4f4cd0cca45eba8086c98dabc SHA512: 75bf0a80661536249d612c2f5e75282944620d8cbf7a0ddea633388c15bf843d941517d3f37370bbfcf86a5dcf19e785ba302299596259583720694e1c719039 Homepage: https://cran.r-project.org/package=clickb Description: CRAN Package 'clickb' (Web Data Analysis by Bayesian Mixture of Markov Models) Designed for web usage data analysis, it implements tools to process web sequences and identify web browsing profiles through sequential classification. 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Package: r-cran-clickclustcont Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-clickclustcont_0.1.7-1.ca2604.1_all.deb Size: 176302 MD5sum: 1a943e11bc6ea648c468c29cd53225bd SHA1: a415096126e9353277fc224738518a6a79f1edef SHA256: c6691df47eb0ddea32519c825c2c7ac2102dc59ea7029ff29f2ca462610fbf74 SHA512: 5c7173c8b28015a8fa3942253f93280c487cd05964461adc913578b62e5f6070987ed8343a15aaa04a2a07bd524ebdd71391f19c82f3e1f9e740d74270e4f1ec Homepage: https://cran.r-project.org/package=ClickClustCont Description: CRAN Package 'ClickClustCont' (Mixtures of Continuous Time Markov Models) Provides an expectation maximization (EM) algorithm to fit a mixture of continuous time Markov models for use with clickstream or other sequence type data. Gallaugher, M.P.B and McNicholas, P.D. (2018) . Package: r-cran-clickhousehttp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-httr2, r-cran-jsonlite, r-cran-arrow, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-clickhousehttp_1.0.0-1.ca2604.1_all.deb Size: 229194 MD5sum: 046754d125985eb14a88b7d049d540b4 SHA1: 32ab62aa312872f98da0779cf8aa83b138661507 SHA256: 276849aab991886eb447a742660200803ddac42c7bb3ec486645620da069c6a6 SHA512: a848768ec787da958c6d70f3101897499b854c19bca19efa51d3f6fbe9e6dec49796220c9680117e3c6275e7fd3c9f923c12cc78ec5389ba547920e56663a128 Homepage: https://cran.r-project.org/package=ClickHouseHTTP Description: CRAN Package 'ClickHouseHTTP' (A Simple HTTP Database Interface to 'ClickHouse') 'ClickHouse' () is an open-source, high performance columnar OLAP (online analytical processing of queries) database management system for real-time analytics using SQL. 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Package: r-cran-clickr Architecture: all Version: 0.9.45-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beeswarm, r-cran-future, r-cran-future.apply, r-cran-stringdist Filename: pool/dists/resolute/main/r-cran-clickr_0.9.45-1.ca2604.1_all.deb Size: 184662 MD5sum: 30b55b3d7811ad1abae975d8ae311afe SHA1: 109a3417bec868749a493c112d0eb15f16852b10 SHA256: 54b6883ebdc50b3a64e142100b20aa453e44687e3d5944c4bd48bc7c44a996db SHA512: 63bc9d4dca7555b528a0959ccc7b38a477f38b1b38960b9cccb9325968c053576a9c2d30907ae6c8a840b964a69ada73e2ac7d25408b8d6e64e04836cc235c99 Homepage: https://cran.r-project.org/package=clickR Description: CRAN Package 'clickR' (Semi-Automatic Preprocessing of Messy Data with Change Trackingfor Dataset Cleaning) Tools for assessing data quality, performing exploratory analysis, and semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning. Package: r-cran-clickstream Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-reshape2, r-cran-mass, r-cran-plyr, r-cran-rsolnp, r-cran-arules, r-cran-linprog, r-cran-ggplot2, r-cran-clickclust, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-clickstream_1.3.4-1.ca2604.1_all.deb Size: 320060 MD5sum: b4d4f5e72edf4e3add8b8385896277f4 SHA1: 0aa3a8b1cec1e09c2e5b69cc6a1e452bb6bd79ef SHA256: 42a6c672b206785b2ebedc601dd29734a992bf6469204aa6e8fd679063338ff2 SHA512: b76cc26581b98ba70e793ac1a17f7a3b7825aa093e12b9454b5187638a0532c5bb17e59e17a8750b4cadd166036aea065afbf3e00599ce0f318194d35e02c2cc Homepage: https://cran.r-project.org/package=clickstream Description: CRAN Package 'clickstream' (Analyzes Clickstreams Based on Markov Chains) A set of tools to read, analyze and write lists of click sequences on websites (i.e., clickstream). 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Package: r-cran-clidamonger Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2980 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clidamonger_1.5.0-1.ca2604.1_all.deb Size: 3015434 MD5sum: 3f8deadf59ecfcca2dba1095c4dcda60 SHA1: 9e4921fa4854e3b8d0ac837d4e6842b1ac83d2cd SHA256: 7ae989e5084a52c8cd5b97ce394fc42a5309274eef98ff6cbec32366a6333501 SHA512: 1b258188bb3fa90ea555a608814938606c7333923510a792696a445cbdb209fde71147a8142351483a0951978aa0b35baa1d52293331e960ca10757a1d8c98f5 Homepage: https://cran.r-project.org/package=clidamonger Description: CRAN Package 'clidamonger' (Monthly Climate Data for Germany, Usable for Heating and CoolingCalculations) This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation). Package: r-cran-clidatajp Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-rlang, r-cran-rvest, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clidatajp_0.5.2-1.ca2604.1_all.deb Size: 985834 MD5sum: 49cfff7fd368798b50e5e20b6d383c7e SHA1: 6bc7e0be22ffa10f3460c6d858ec70b00a6afb42 SHA256: a3d38734f02962ffc3f989fd1b367a9600964f60db9e9a0ce6cc1fc65413f1ed SHA512: f561d71c76c9e148357fde76041fcf975be08ef80a58a4d92b0c5cdb2a1e036cb4e6c2925f61d993204b69ea73f79fb96c7cce2c5e1df88ec54a30f109a1b4de Homepage: https://cran.r-project.org/package=clidatajp Description: CRAN Package 'clidatajp' (Data from Japan Meteorological Agency) Includes climate data from Japan Meteorological Agency ('JMA') . 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It is based on the package 'processx' so it does not use shell to start up the process like system() and system2(). It also provides a simpler and cleaner interface than processx::run(). Package: r-cran-cliftlrd Architecture: all Version: 0.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cnltreg, r-cran-liftlrd Suggests: r-cran-fracdiff Filename: pool/dists/resolute/main/r-cran-cliftlrd_0.1-2-1.ca2604.1_all.deb Size: 41818 MD5sum: 7d084b1a3e4b7a9ca61530d49c7e706b SHA1: a8dc2ae3abdaabdc08df4f765d6eb23c106266e0 SHA256: 1785c6b23242f65ca1a38e6960068f54d9e9b91941f2847a183a7dff1d15dd5d SHA512: 49b045197d531e2f5e67abe22ef90856201dec21ff683573545e8d51630c90a0bd8850b943e6f4e096ebb37959f9fec0bdde18c7169d097fb705a632bd1a5db2 Homepage: https://cran.r-project.org/package=CliftLRD Description: CRAN Package 'CliftLRD' (Complex-Valued Wavelet Lifting Estimators of the Hurst Exponentfor Irregularly Sampled Time Series) Implementation of Hurst exponent estimators based on complex-valued lifting wavelet energy from Knight, M. I and Nunes, M. A. (2018) . Package: r-cran-climaemet Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-httr2, r-cran-jsonlite, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-climatol, r-cran-gganimate, r-cran-jpeg, r-cran-knitr, r-cran-lifecycle, r-cran-lubridate, r-cran-mapspain, r-cran-quarto, r-cran-scales, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-climaemet_1.5.1-1.ca2604.1_all.deb Size: 865180 MD5sum: 5d3aa8a7658c7d0b016bc735da6c86d8 SHA1: 8f71b1cbe928482e212a213e4713457a52a93b03 SHA256: 2ae873a976488f380acae638377bf633821bd8c5cd37fd40a9d1fdd4b031d2ad SHA512: 7bee1105da19b807b26fe4cd306b1bd3c825136fde4baae9258ddaca046b55576522c84463bd83f4f29070c7ffc32166f741c356de9c3e4f1822b9633525e039 Homepage: https://cran.r-project.org/package=climaemet Description: CRAN Package 'climaemet' (Climate AEMET Tools) Tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.). Package: r-cran-climarep Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-terra, r-cran-sf, r-cran-tidyterra Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-climarep_1.0-1.ca2604.1_all.deb Size: 2328790 MD5sum: 0d6e1f0ab13479d00b3a4a0dd43da3ca SHA1: 4a9a4a1ff7ca1cc3e7939649326e5bdc07327e15 SHA256: 0722f1ec04a7d8ce33c385741ad29c577a62f44e902c1ead81fe0408abecd0de SHA512: c490c00a47d1e0ee9ef37d6bc65cca66a0e522c715375320e8f2f7860fc64962ee7032bf6c0350c8bd9868c8bd030f38cd31335303a830d66befee6d89fdbdaf Homepage: https://cran.r-project.org/package=ClimaRep Description: CRAN Package 'ClimaRep' (Estimating Climate Representativeness) Offers tools to estimate the climate representativeness of reference polygons and quantifies its transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) and Mingarro and Lobo (2022) . Package: r-cran-climate Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-stringi, r-cran-xml Suggests: r-cran-dplyr, r-cran-knitr, r-cran-maps, r-cran-testthat, r-cran-tidyr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-climate_1.3.0-1.ca2604.1_all.deb Size: 959750 MD5sum: 7c26ea099feafd758c40a9a8aaf9a5fe SHA1: 66025158f7355ed6af7d2efc9d50c29df95ea8a2 SHA256: 71ade74cd769fdce56a2a41163127cd832c95941b82eacc84a44b319c0302b52 SHA512: 74f472949204f5d6d3eb51752de8be8e86372f4a54d6c25211e08860b24d1f4043db833eda12b09f3b27c90860b61fc64ce0593f4024f30b9bff439d40ed0273 Homepage: https://cran.r-project.org/package=climate Description: CRAN Package 'climate' (Interface to Download Meteorological (and Hydrological) Datasets) Automatize downloading of meteorological and hydrological data from publicly available repositories: OGIMET (), University of Wyoming - atmospheric vertical profiling data (), Polish Institute of Meteorology and Water Management - National Research Institute (), and National Oceanic & Atmospheric Administration (NOAA). This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations. Package: r-cran-climatehealth Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-data.table, r-cran-dlnm, r-cran-dplyr, r-cran-epi, r-cran-forcats, r-cran-exactextractr, r-cran-ggplot2, r-cran-ggtext, r-cran-gnm, r-cran-gplots, r-cran-lifecycle, r-cran-lme4, r-cran-lubridate, r-cran-metafor, r-cran-mgcv, r-cran-mixmeta, r-cran-ncdf4, r-cran-patchwork, r-cran-pkgbuild, r-cran-purrr, r-cran-raster, r-cran-rcolorbrewer, r-cran-readr, r-cran-readxl, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-spdep, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tseries, r-cran-tsmodel, r-cran-xfun, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-dt, r-cran-htmltools, r-cran-mockery, r-cran-mvmeta, r-cran-openxlsx, r-cran-patrick, r-cran-pkgload, r-cran-stringdist, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-climatehealth_1.0.1-1.ca2604.1_all.deb Size: 984988 MD5sum: 1fb55e21d3a29a0abb02f0c44c327a7a SHA1: 3c2763e5d8900f1cc2268ea099c29c84011b176e SHA256: 06bf62ea37d72d971f8e387a09db4d7e2e6dcdcb53ffca5b5c04583572ec338e SHA512: 86378c23fa811a6e6a3a8238996820840b48876dba1b7f5175d47ba740e9a5ef7152a3b1795b70e23e3f267adb5c87ef38cabe6f43d5a0d44d7e758e67f041ea Homepage: https://cran.r-project.org/package=climatehealth Description: CRAN Package 'climatehealth' (Statistical Tools for Modelling Climate-Health Impacts) Tools for producing climate-health indicators and supporting official statistics from health and climate data. Implements analytical workflows for temperature-related mortality, wildfire smoke exposure, air pollution, suicides related to extreme heat, malaria, and diarrhoeal disease outcomes, with utilities for descriptive statistics, model validation, attributable fraction and attributable number estimation, relative risk estimation, minimum mortality temperature estimation, and plotting for reporting. These six indicators are endorsed by the United Nations Statistical Commission for inclusion in the Global Set of Environment and Climate Change Statistics. Implemented methods include distributed lag non-linear models (DLNM), quasi-Poisson time-series regression, case-crossover analysis, Bayesian spatio-temporal models using the Integrated Nested Laplace Approximation ('INLA'), and multivariate meta-analysis for sub-national estimates. The package is based on methods developed in the Standards for Official Statistics on Climate-Health Interactions (SOSCHI) project . For methodologies, see Watkins et al. (2025) , Brown et al. (2024) , Pearce et al. (2024) , Byukusenge et al. (2025) , Dzakpa et al. (2025) , and Dzakpa et al. (2025) . Package: r-cran-climatekit Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-terra, r-cran-ncdf4, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-climatekit_0.2.0-1.ca2604.1_all.deb Size: 299238 MD5sum: 73ce85c6f6b8edc08b773f2d9901bf4f SHA1: f908613c41cf588df4466ce08ce9503db1866b74 SHA256: 3646b2141ea35148c41f238c61b1a51182945bbbda945a087252e6a1f0bcc5d9 SHA512: c1e55b515f54e9d7f2ae5fabd0532906eb49aab2789ab0eee8c5c6e7343f3d51e71caf8b5e31b69efa4401fb052fa0bdc277cd0e060843adf5652663d51c49d0 Homepage: https://cran.r-project.org/package=climatekit Description: CRAN Package 'climatekit' (Unified Climate Indices for Temperature, Precipitation, andDrought) Compute the standard suite of climate indices from daily weather observations. Provides the canonical 'ETCCDI' 27 (Expert Team on Climate Change Detection and Indices), the 'ET-SCI' heatwave and cold-wave families plus the Excess Heat Factor of Nairn and Fawcett (2013), and agroclimatic, drought, and human-comfort families. Drought indices ('SPI', 'SPEI') accept a choice of distribution (gamma or Pearson III for SPI; log-logistic or generalised extreme value for SPEI). Reference evapotranspiration is available via Hargreaves and the FAO-56 Penman-Monteith method (Allen et al. 1998). Percentile-based indices support the Zhang (2005) in-base bootstrap. Daily inputs are numeric vectors plus a 'Date' vector; outputs are tidy data frames. Optional gridded support via 'terra' applies any index over a 'SpatRaster' and reads 'netCDF' input. No external API calls; pairs with data packages such as 'readnoaa'. References: Alexander et al. (2006) ; Zhang et al. (2011) ; Zhang et al. (2005) . Package: r-cran-climatestability Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra Suggests: r-cran-rangebuilder, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-climatestability_0.1.4-1.ca2604.1_all.deb Size: 229910 MD5sum: 0243759b8bfe4bf113c198a9f5015ea7 SHA1: 60008c585d124f611f196963332b4e6fc1c497d6 SHA256: 82ec0303662899127a1d751439219feddc777432fa2c3cbe6d10fc71ecd67e34 SHA512: 4b33615099536ae16a290fd36fb2fb1690234fe2ae0cf20f5c310fad6d62094aabebe14da79c5eb223cdf51121996c7a60de43ea059ccf58e2ea78a194ed4aa1 Homepage: https://cran.r-project.org/package=climateStability Description: CRAN Package 'climateStability' (Estimating Climate Stability from Climate Model Data) Climate stability measures are not formalized in the literature and tools for generating stability metrics from existing data are nascent. This package provides tools for calculating climate stability from raster data encapsulating climate change as a series of time slices. The methods follow Owens and Guralnick Biodiversity Informatics. Package: r-cran-climatol Architecture: all Version: 4.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2726 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-evd, r-cran-fields, r-cran-gstat, r-cran-maps, r-cran-mapdata, r-cran-ncdf4, r-cran-raster, r-cran-readxl, r-cran-rodbc, r-cran-sp Filename: pool/dists/resolute/main/r-cran-climatol_4.5-0-1.ca2604.1_all.deb Size: 2364820 MD5sum: 40ad87201a3bfb3a9befd7bc9fce7a63 SHA1: 616c0e271ab34dfe16bb9e34c7a4728c8739abff SHA256: 559a499a6a8277620b13e57bb01effc2d942287af24ed0de20fcd9f7008eaa16 SHA512: 1a033efd23feb4fab682ce366eec023eca38bae1876343642928e46a09164dbf7809b570c84d876198fbf7e4e3b347d6ab7160993a234cd5693448d648e25fbe Homepage: https://cran.r-project.org/package=climatol Description: CRAN Package 'climatol' (Climate Tools (Series Homogenization and Derived Products)) Functions for the quality control, homogenization and missing data filling of climatological series and to obtain climatological summaries and grids from the results. Also functions to display wind-roses, meteograms, Walter&Lieth diagrams, and more. Package: r-cran-climatrends Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nasapower Suggests: r-cran-chirps, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-climatrends_1.2-1.ca2604.1_all.deb Size: 3693920 MD5sum: 36010bd486c4e45ee67441a44b37ac3d SHA1: 37d9ecc13d3ce217a8cd06b6ee00583ad52cf2c0 SHA256: 6b3be113c81bb09b7a20fea8f424758c0d7eb8b893bfa3f331ccfacee42b3fab SHA512: 399fa281197c72747dbff60dc03b4b85a0a709846b45da3883391e216de2f5bd957b53dee5add155e670fbf2f8cfa4ea1e490a093d56e29c5231c94b197e7a44 Homepage: https://cran.r-project.org/package=climatrends Description: CRAN Package 'climatrends' (Climate Variability Indices for Ecological Modelling) Supports analysis of trends in climate change, ecological and crop modelling. Package: r-cran-climclass Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-stringr Filename: pool/dists/resolute/main/r-cran-climclass_2.1.1-1.ca2604.1_all.deb Size: 2172800 MD5sum: a81dc9c1a62b92f932cb755f400ebec6 SHA1: 01525afdba738d290cc896000605ab48960c7cdf SHA256: e25392747f369e6f2a1ac55858c21a642078ace24189020c4b3b418dbb955a4d SHA512: bf06ca92c49c4629c5706565e6cc3401c168abee10a6ab7a756efe83dabc2f084e101ac246a2f19630bd737f37dac1517224be1577d55525c0b094a72948ac5a Homepage: https://cran.r-project.org/package=ClimClass Description: CRAN Package 'ClimClass' (Climate Classification According to Several Indices) Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indices, of water balance after Thornthwaite, of viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy, Bagnouls-Gaussen. Package: r-cran-climd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49761 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-ncdf4, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-climd_0.1.0-1.ca2604.1_all.deb Size: 2555736 MD5sum: 1b7273c90b3277f722a30c8c441e7531 SHA1: 342778057b2db2d2027291b816fd142db47f8e80 SHA256: f87593e6fe1d954a79cafa95f5a25cd84b30e3f47b8f6f496f55ce8c0be9affa SHA512: e0bd3e1097bc1251ad93c553508ce508289601837bd68695dc075267159dea66d30697f9fa4956bc7aab1e4c7008137cef8c1e523cddccf325cbaa51c45dad93 Homepage: https://cran.r-project.org/package=CLimd Description: CRAN Package 'CLimd' (Generating Rainfall Rasters from IMD NetCDF Data) The developed function is a comprehensive tool for the analysis of India Meteorological Department (IMD) NetCDF rainfall data. Specifically designed to process high-resolution daily gridded rainfall datasets. It provides four key functions to process IMD NetCDF rainfall data and create rasters for various temporal scales, including annual, seasonal, monthly, and weekly rainfall. For method details see, Malik, A. (2019).. It supports different aggregation methods, such as sum, min, max, mean, and standard deviation. These functions are designed for spatio-temporal analysis of rainfall patterns, trend analysis,geostatistical modeling of rainfall variability, identifying rainfall anomalies and extreme events and can be an input for hydrological and agricultural models. Package: r-cran-clime Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/resolute/main/r-cran-clime_0.5.0-1.ca2604.1_all.deb Size: 43390 MD5sum: 0c0c83968fdef65d48c497b621ec8d70 SHA1: af7aa82eb7118585ee1191232a86e4c1bf84e744 SHA256: 3417ed85e34dd6b481c7a75fdd05c8c446a44f3289160a1bb0030b86b772ad2d SHA512: 99a8ef6bfb6812c2699023d3f01dabe74a274b2c20d3a330245b4b9f745d210e954dedfa0409dbb8fb4fb18767237d09566a8b1eb2394e806fe334473551569c Homepage: https://cran.r-project.org/package=clime Description: CRAN Package 'clime' (Constrained L1-Minimization for Inverse (Covariance) MatrixEstimation) A robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models. The computation uses linear programming. The method was published in TT Cai, W Liu, X Luo (2011) . Package: r-cran-climenu Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-keypress Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-climenu_0.1.7-1.ca2604.1_all.deb Size: 50442 MD5sum: 1b388050f29634194050892eaae74322 SHA1: fe2d309785f1d458acc59d961c4b28f6b5c8e17f SHA256: 5a16af5564efd3ae0a25b9910343e93f193824a5657a50be67d00bd715c1110b SHA512: bc16eeb1724333b05ac72ffe2c483398b13f6d465538c792a593fe072503839de994c58ea60a2ad84c8081bcaa3ecff1ce0c658f0fbbd0685c5f5fad39ad0776 Homepage: https://cran.r-project.org/package=climenu Description: CRAN Package 'climenu' (Interactive Command-Line Menus) Provides interactive command-line menu functionality with single and multiple selection menus, keyboard navigation (arrow keys or vi-style j/k), preselection, and graceful fallback for non-interactive environments. Inspired by tools such as 'inquirer.js' , 'pick' , and 'survey' . Designed to be lightweight and easy to integrate into 'R' packages and scripts. Package: r-cran-climetrics Architecture: all Version: 1.0-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3829 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rts, r-cran-raster, r-cran-terra, r-cran-zoo, r-cran-yaimpute, r-cran-xts Suggests: r-cran-r.rsp, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-climetrics_1.0-15-1.ca2604.1_all.deb Size: 3168866 MD5sum: ac652fef838110f1e960848f1e1b6533 SHA1: fa418d2ed036fc55b8bccca5e6def3edf7bb94a0 SHA256: ed4242fa837551800da3c72e04a1483e4861f85294117d9f379fc09ce64bc698 SHA512: 43fc2499a12238c0b7bf6e0d1e7d9a6682016d0fe79c53dc84a12abf6b5e16ffb395cd8bd99d57975db4e701cfe01beca711fc4d1088f0dbcc613a7b9272ae35 Homepage: https://cran.r-project.org/package=climetrics Description: CRAN Package 'climetrics' (Climate Change Metrics) A framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time. Package: r-cran-climextremes Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-extremes, r-cran-boot Filename: pool/dists/resolute/main/r-cran-climextremes_0.3.1-1.ca2604.1_all.deb Size: 733826 MD5sum: 4512ca19448e70e3649cbbddc2dfe5c2 SHA1: 0aa5d5fe17c6e6fb5719b683b7884cd17036fbc3 SHA256: 3b4b9cf36100c76c5e5b2d36612f4abe97c20b74ccc5d218c97172b7b510c3a6 SHA512: c19ad5f51b88a3438e28a75276a5aabdf2d3ecddd2fcfa084c5c564ff525bdca7af8e5082978895a13784f0afb20b5a9188f8c2d02331eb2564a558725ed2c87 Homepage: https://cran.r-project.org/package=climextRemes Description: CRAN Package 'climextRemes' (Tools for Analyzing Climate Extremes) Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) . Package: r-cran-climind Architecture: all Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1694 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spei, r-cran-chron, r-cran-weathermetrics Suggests: r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-climind_0.1-3-1.ca2604.1_all.deb Size: 1633540 MD5sum: 39495c63bae84fd11a274d21440455ff SHA1: 8dc8df6dd5f4b8be4104ed1d63c3c8d8b2941390 SHA256: 48e9779588bfa471347238d09d42ef436f33baf64fae550b57a45d86cfa52cec SHA512: c5a8d4091b7bd6c3135b0a1fa76fcb507bbb210d64c2bbd2db00777a473779b87d665e33959bb11fef8e0993dc31092aba152d14700fff2a1916243fdc890c27 Homepage: https://cran.r-project.org/package=ClimInd Description: CRAN Package 'ClimInd' (Climate Indices) Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5). Package: r-cran-climmobtools Architecture: all Version: 1.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lpsolve, r-cran-matrix, r-cran-rspectra Suggests: r-cran-climatrends, r-cran-gosset, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-plackettluce, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-climmobtools_1.8.2-1.ca2604.1_all.deb Size: 635236 MD5sum: a3730e076bfc253667f32ece10c7041b SHA1: fee5d42c00a282ef6f7ff0545502eafe4ef99221 SHA256: b60d4c16eaba3d6619b443f51a4d78242020517fa71fd77e0ff3eb37fa2f5f26 SHA512: 56cce6918e408777c657a0e1f0af9f96f0bde077d170ac255b69215066a040aeae27fa54bfd750378bbb1dd02346ac1aac40c0636fc549d68256d60cda9dba9f Homepage: https://cran.r-project.org/package=ClimMobTools Description: CRAN Package 'ClimMobTools' (API Client for the 'ClimMob' Platform) API client for 'ClimMob', an open source software for decentralized large-N trials with the 'tricot' approach . Developed by van Etten et al. (2019) , it turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions for the target environment, it enables many participants to carry out reasonably simple experiments that taken together can offer even more information. 'ClimMobTools' enables project managers to deep explore and analyse their 'ClimMob' data in R. Package: r-cran-climodr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cast, r-cran-corrplot, r-cran-doparallel, r-cran-dplyr, r-cran-lares, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-terra, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pls, r-cran-randomforest, r-cran-sp Filename: pool/dists/resolute/main/r-cran-climodr_1.0.0-1.ca2604.1_all.deb Size: 1178832 MD5sum: 22d3f1650e893fc79f8db52afa8051e4 SHA1: 5d294ec789d85f2c8a3c57df5921edaa1538e0c0 SHA256: 9dc551da6951d0ca9d790d070f2116dcd81107df105c157ffcd5048412744985 SHA512: 94d286ee84a55ea4a452ae8e71e4f17e7935f2ee671537fc7a916a6a5bd444e86f05fc7b953cb1829100438b996299be052b0dcddba96f302ba6120f00723634 Homepage: https://cran.r-project.org/package=climodr Description: CRAN Package 'climodr' (Climate Modeling with Point Data from Climate Stations) An automated and streamlined workflow for predictive climate mapping using climate station data. Works within an environment the user provides a destined path to - otherwise it's tempdir(). Quick and relatively easy creation of resilient and reproducible climate models, predictions and climate maps, shortening the usually long and complicated work of predictive modelling. For more information, please find the provided URL. Many methods in this package are new, but the main method is based on a workflow from Meyer (2019) and Meyer (2022) , however, it was generalized and adjusted in the context of this package. Package: r-cran-climwin Architecture: all Version: 1.2.33-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-matrix, r-cran-evd, r-cran-lubridate, r-cran-lme4, r-cran-mumin, r-cran-reshape, r-cran-numderiv, r-cran-rcpproll, r-cran-nlme Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-climwin_1.2.33-1.ca2604.1_all.deb Size: 1207052 MD5sum: 3071961a934e5c9459f0b60f2da081bf SHA1: 654ad25b83d5b4ac2c29c992d5805b08a95eddb9 SHA256: 722f92926021a09c8afe51e19755c6154fbfb0f036ceec24366c771c799dc197 SHA512: e9a47e32f197ce1c3766c994ec1910703aaf2a2c4649a8311cb284124be5eef109bb7bb1c7ee0dba1f8c353a83e8620878b45f9991a9819bf9295d6ad43a4fb8 Homepage: https://cran.r-project.org/package=climwin Description: CRAN Package 'climwin' (Climate Window Analysis) Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response. Please see van de Pol et al. (2016) and Bailey and van de Pol (2016) for details. Package: r-cran-clincompare Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-haven, r-cran-rlang, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-openxlsx, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clincompare_1.0.0-1.ca2604.1_all.deb Size: 941650 MD5sum: 5a3c2453dd10a05b9a8ad0554d246c28 SHA1: 0e17545b71b707bffe01dfbb83450b327abb810a SHA256: 668523fa654162dddc5a32e2da20140f5ba9af9a636edeb93a2934c962c964ab SHA512: e0b13768a06489fdad7b67829dd2661e8543036dbdfb93368a46c171ce34191a79432fc7614ee09241f774faad2d6524a8b96a09b7213da09921931d30429245 Homepage: https://cran.r-project.org/package=clinCompare Description: CRAN Package 'clinCompare' (Dataset Comparison with 'CDISC' Validation for Clinical TrialData) A general-purpose toolkit for comparing any two data frames with optional 'CDISC' (Clinical Data Interchange Standards Consortium) validation for clinical trial data. Core comparison functions work on arbitrary datasets: variable-level and observation-level comparison, data type checking, metadata attribute analysis (types, labels, lengths, formats), missing value handling, key-based row matching, tolerance-based numeric comparisons, and group-wise comparisons. Optional z-score outlier detection is available when enabled. When working with clinical data, the package additionally validates 'SDTM' (Study Data Tabulation Model) and 'ADaM' (Analysis Data Model) datasets against CDISC standards (SDTM IG 3.3/3.4, ADaM IG 1.1/1.2/1.3), automatically detecting domains and flagging non-conformant variables. Generates unified comparison reports in text or HTML format with interactive dashboards. For CDISC standards, see . Package: r-cran-clindatareview Architecture: all Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5606 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bookdown, r-cran-clinutils, r-cran-crosstalk, r-cran-data.table, r-cran-ggplot2, r-cran-haven, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-plotly, r-cran-plyr, r-cran-rmarkdown, r-cran-stringr, r-cran-yaml, r-cran-xml2, r-cran-xfun, r-cran-base64enc Suggests: r-cran-countrycode, r-cran-intextsummarytable, r-cran-patientprofilesvis, r-cran-testthat, r-cran-dt, r-cran-scales Filename: pool/dists/resolute/main/r-cran-clindatareview_1.6.2-1.ca2604.1_all.deb Size: 1883498 MD5sum: 676b47e92e3c6fcba4b4005878816c82 SHA1: fa4d3e02e9f011c7bb951f80d599159bc6913e7b SHA256: 3fdca754d995b2cec2e4a523591833cb199517caaa74671f71491770dff07db7 SHA512: cc0be2a99c03ad3e55079e39b97556b5e1be60c0ef46ad78dafa5efec019cbbe7ae5e5888fb8f9ee45b34ff12a1fec359b07ddc3a148776de184f91c89866e63 Homepage: https://cran.r-project.org/package=clinDataReview Description: CRAN Package 'clinDataReview' (Clinical Data Review Tool) Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template 'R Markdown' reports contained in the package. Package: r-cran-clindr Architecture: all Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-shiny, r-cran-foreach, r-cran-dosefinding, r-cran-mvtnorm, r-cran-doparallel, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-glue, r-cran-waiter Filename: pool/dists/resolute/main/r-cran-clindr_2.5.2-1.ca2604.1_all.deb Size: 1633732 MD5sum: 8932eb788188270222d9aa139d2c2314 SHA1: 1a2745f1e7f5e472b7c44fcc7db64e50a55ae05a SHA256: 477345e50314cadb56b848e3951122b2d852ec173e6ca3593109e531adece804 SHA512: 0337053a215bb9cdc610783be398a1ebbe5f1713d4a45625b8b30d160832938651e46d41ebfe3c1213ce5bd621035b38f5a18521c2cc08725e0bd9bcfd15f99b Homepage: https://cran.r-project.org/package=clinDR Description: CRAN Package 'clinDR' (Simulation and Analysis Tools for Clinical Dose ResponseModeling) Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) and Thomas and Roy (2016) Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references. Package: r-cran-clinicalfair Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-clinicalfair_0.1.0-1.ca2604.1_all.deb Size: 245850 MD5sum: e2b2598b7b272361e97d1396f4c62f9c SHA1: 6ff3d9211d81080fcb6ed7db5c3ac865fcb44cec SHA256: 49ca6bafc54f8f689f5136a83b62c3f837bb10663bb01e3188dcae452e952934 SHA512: 4c577b6137d4779814bb31c4f59b25931b6c429179453389fb58ce67432b7a45275eb7644fd6d1f47d8ed2d51c3a43fd7e6cae08484146749e171d417a97f5e5 Homepage: https://cran.r-project.org/package=clinicalfair Description: CRAN Package 'clinicalfair' (Algorithmic Fairness Assessment for Clinical Prediction Models) Post-hoc fairness auditing toolkit for clinical prediction models. Unlike in-processing approaches that modify model training, this package evaluates existing models by computing group-wise fairness metrics (demographic parity, equalized odds, predictive parity, calibration disparity), visualizing disparities across protected attributes, and performing threshold-based mitigation. Supports intersectional analysis across multiple attributes and generates audit reports useful for fairness-oriented auditing in clinical AI settings. Methods described in Obermeyer et al. (2019) and Hardt, Price, and Srebro (2016) . Package: r-cran-clinicalomicsdbr Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-r6, r-cran-dplyr, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-clinicalomicsdbr_1.0.5-1.ca2604.1_all.deb Size: 53810 MD5sum: 48e04a1d51d9b35630b947429fbaa9f8 SHA1: a27bd417b6fea7069284f1ac1057eff071319351 SHA256: fdba4213f65ef61594dc8f2fd3082be946655fbebb1e6247479dafc742091fa0 SHA512: 5c18d67087effb1cc454ff881b42c135d0b8e5c15604f57931d25251f3147ce706168661c879c8949be60cf6d90eaae20bf437eb58480bddc2afb95bc54a9372 Homepage: https://cran.r-project.org/package=clinicalomicsdbR Description: CRAN Package 'clinicalomicsdbR' (Interface with the 'ClinicalOmicsDB' API, Allowing for Easy DataDownloading and Importing) Provides an interface to the 'ClinicalOmicsDB' API, allowing for easy data downloading and importing. 'ClinicalOmicsDB' is a database of clinical and 'omics' data from cancer patients. The database is accessible at . Package: r-cran-clinicalsignificance Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesfactor, r-cran-bayestestr, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-insight, r-cran-lme4, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-clinicalsignificance_3.0.0-1.ca2604.1_all.deb Size: 737274 MD5sum: 35e8efc3972f7d2ccaa0bd78dbcf0e98 SHA1: 02e2108bed3b2af3a51c6cee5b1b7ee05e211f04 SHA256: 19b70510d6f7d13d042e4efb573e30b42e7ba0e8ad45ea1a3adfd8ac4419de77 SHA512: 1c1dad00fa52afc5249a659d0a158dc364e28bea3d32364bb22dde7d08733f4a298e0685c07f8b80e1401c07d81c609a01b2b7fc807137726312f6ed890db742 Homepage: https://cran.r-project.org/package=clinicalsignificance Description: CRAN Package 'clinicalsignificance' (A Toolbox for Clinical Significance Analyses in InterventionStudies) A clinical significance analysis can be used to determine if an intervention has a meaningful or practical effect for patients. You provide a tidy data set plus a few more metrics and this package will take care of it to make your results publication ready. Accompanying package to Claus et al. . Package: r-cran-clinicalutilityrecal Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-caret, r-cran-ggplot2, r-cran-cowplot, r-cran-nloptr Filename: pool/dists/resolute/main/r-cran-clinicalutilityrecal_0.1.0-1.ca2604.1_all.deb Size: 105418 MD5sum: 6fae175144d4dd0c5d5c82aebdd13a21 SHA1: 10f007c70ef5a2d31d34e306a2258ca2eef84179 SHA256: 66decafb7e76c356577d3a72839247f253ef0c41778a8997ce2e8b440b1afaee SHA512: 6aacc52c3f567b416d0ad619736e16729ef3735692757475ef514233fc34b62fdd4d0a62e91c1bce0f7a9d6314316e73c78bc6c8da3c4b6951343e669e79af6f Homepage: https://cran.r-project.org/package=ClinicalUtilityRecal Description: CRAN Package 'ClinicalUtilityRecal' (Recalibration Methods for Improved Clinical Utility of RiskScores) Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. 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Package: r-cran-clinify Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flextable, r-cran-officer, r-cran-magrittr, r-cran-dplyr, r-cran-knitr, r-cran-htmltools, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-tplyr, r-cran-rvest, r-cran-withr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-clinify_0.3.0-1.ca2604.1_all.deb Size: 568818 MD5sum: 1d3d12a02f2e13051e57d3bf3050bee7 SHA1: 5de7ab0e3bc5fb94cd45bae833b3f724f0062f28 SHA256: 99467332af968daea2bafdea3678704dd2635e9976a612472e746a57c97f943a SHA512: 0e6600475cafaa64a3ef07025a9088e911f394e9ad4a835af71eac11c0b2edf50b9a226b072ec594fc565193a2d30c3628267bb3c71f39b8bed93f2137a7b306 Homepage: https://cran.r-project.org/package=clinify Description: CRAN Package 'clinify' (Clinical Table Styling Tools and Utilities) The primary motivation of this package is to take the things that are great about the R packages 'flextable' and 'officer' , take the standard and complex pieces of formatting clinical tables for regulatory use, and simplify the tedious pieces. Package: r-cran-clinmon Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1633 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clinmon_0.6.0-1.ca2604.1_all.deb Size: 514044 MD5sum: 623d1c0cdc1030cd7867eb5d068d0378 SHA1: dd26094a3e7ab4f4c0ea60c9aac91af8c2afa802 SHA256: 7d636cd17aab816da4e827f6886dcc056ce903be0e12e78351aafa71ef480953 SHA512: 08434f283ab06feda59dba2d2ad39431562758397067746ac940f3ab09600eda93d61086353d8a1c8be3a264ab4283459ab8a64f501f8bd239af5d5f41541044 Homepage: https://cran.r-project.org/package=clinmon Description: CRAN Package 'clinmon' (Hemodynamic Calculations from Clinical Monitoring) Every research team have their own script for calculation of hemodynamic indexes. This package makes it possible to insert a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. Package: r-cran-clinpk Architecture: all Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2485 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clinpk_0.13.0-1.ca2604.1_all.deb Size: 745198 MD5sum: 06a31d4208043f26bb38d06a7c462b0c SHA1: 6e5a2a762ff3395e4147b4f0082f38a1afb01a35 SHA256: 6cf50e9b03cbd20a022d58fbd655126f699b521234de88432540bdcaebdd1659 SHA512: 53c693b0dc320d5723ceb87020cc45b774eb04aebbc560b2524ca530a86e5b16e73e870735ecfd622b2ee47e33a819f67645cabdcdf49b8e07d6d1076dba87a5 Homepage: https://cran.r-project.org/package=clinPK Description: CRAN Package 'clinPK' (Clinical Pharmacokinetics Toolkit) Provides equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function. Package: r-cran-clinpubr Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-desctools, r-cran-dplyr, r-cran-fbasics, r-cran-forestploter, r-cran-ggplot2, r-cran-hmisc, r-cran-rlang, r-cran-rms, r-cran-stringi, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tidyr Suggests: r-cran-caret, r-cran-dcurves, r-cran-dtplyr, r-cran-geepack, r-cran-proc, r-cran-resourceselection, r-cran-rstatix, r-cran-tableone, r-cran-testthat, r-cran-timeroc, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-clinpubr_1.3.0-1.ca2604.1_all.deb Size: 963216 MD5sum: 3489035104ff3d649ce85f714d0fbe66 SHA1: b0e8808d4404620e1c064330cc39483de63c7025 SHA256: 5fac60e95269e3ddf21408615548d864e56f54feafa259338cd0f530887925e6 SHA512: 14964e8b4e4304dcec5e38a1393abd5b48be0ceca42aa6d2be2e546b9df8a640a88fab925586e6a2470f54a6932fc684b32ea9544ea5c01b33129db5a8234905 Homepage: https://cran.r-project.org/package=clinpubr Description: CRAN Package 'clinpubr' (Clinical Publication) Accelerate the process from clinical data to medical publication, including clinical data cleaning, significant result screening, and the generation of publish-ready tables and figures. Package: r-cran-clinsig Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clinsig_1.2-1.ca2604.1_all.deb Size: 42650 MD5sum: ca23e0fa3291e7368b9937245998d209 SHA1: 1c90df50d5dc1f01a6d6b787e469249e60e7cef0 SHA256: 2d11dd80d7177e22f40ef1dd4719978976535f14b00ae6bd7c0ba9aa908020b9 SHA512: c302877ed8fffb6b2c748af18807e23c0824b871f18c2f43d228d12351b4cd41166779d4775c39f5851636c167ac72451d0f079185330c7f1b00edfb083657fd Homepage: https://cran.r-project.org/package=clinsig Description: CRAN Package 'clinsig' (Clinical Significance Functions) Functions for calculating clinical significance. Package: r-cran-clinsigmeasures Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clinsigmeasures_1.2-1.ca2604.1_all.deb Size: 49094 MD5sum: 847311f2551ffbfb0936d9840426526e SHA1: e925c61f1fda489d94649951581166b3bff590ef SHA256: 3b0e224a92c4e934deb3b3f9147c9fd79fcff1fff2a3dd20cb7028adf2372b47 SHA512: 1f654ea0bbdd67d92ae39a775d41a959b92796284bf02ca3db7f56f2a2b4e99674611045012e103451c9be8f6a151a7beb5f13d044206c87cec4f05d67c7e2b6 Homepage: https://cran.r-project.org/package=ClinSigMeasures Description: CRAN Package 'ClinSigMeasures' (Clinical Significance Measures) Provides measures of effect sizes for summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, robust effect size, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, Youden index, number needed to treat, number needed to diagnose, and predictive summary index. Package: r-cran-clinspacy Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-data.table, r-cran-assertthat, r-cran-rappdirs, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clinspacy_1.0.2-1.ca2604.1_all.deb Size: 62750 MD5sum: e02b8552ba80cf30792098a33984f583 SHA1: 1024c7cdc0c759db90631745523f6b024773eb38 SHA256: 9001f09d7d8750a5d14eb29df5b17627905504f846b4bd3ca50388d1ef618b83 SHA512: ffa57bef4c7f56dcafeea2a44c9444b902f1002819668d98e66daa9faa353799a22647f1349148e35e60c87e0a26452f8ff82f296bafc8e60ed88e08c179c381 Homepage: https://cran.r-project.org/package=clinspacy Description: CRAN Package 'clinspacy' (Clinical Natural Language Processing using 'spaCy', 'scispaCy',and 'medspaCy') Performs biomedical named entity recognition, Unified Medical Language System (UMLS) concept mapping, and negation detection using the Python 'spaCy', 'scispaCy', and 'medspaCy' packages, and transforms extracted data into a wide format for inclusion in machine learning models. The development of the 'scispaCy' package is described by Neumann (2019) . The 'medspacy' package uses 'ConText', an algorithm for determining the context of clinical statements described by Harkema (2009) . Clinspacy also supports entity embeddings from 'scispaCy' and UMLS 'cui2vec' concept embeddings developed by Beam (2018) . Package: r-cran-clintools Architecture: all Version: 0.9.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal, r-cran-xml2, r-cran-lme4, r-cran-ggplot2, r-cran-proc, r-cran-irr, r-cran-nlme, r-cran-parameters, r-cran-stringi, r-cran-scales, r-cran-dplyr, r-cran-survival, r-cran-pander Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clintools_0.9.10.1-1.ca2604.1_all.deb Size: 791532 MD5sum: b0b5e105d362ab13ee435e797800c2f3 SHA1: 07da41ba30e6a84f499329220dadb85ec9ce057d SHA256: 53325a239ba8eb7b88f22f2d9da0d592e749e9f95ce5248dbcd67049b17f3786 SHA512: 90de9139efc152e4a23050620c06e5c94a980b112d106ed0ad3754154460f295b8d40f7ec66857f6f839bd1d94c4ff7328b69692c1ae119db674a6685f5d92f3 Homepage: https://cran.r-project.org/package=clintools Description: CRAN Package 'clintools' (Tools for Clinical Research) Every research team have their own script for data management, statistics and most importantly hemodynamic indices. The purpose is to standardize scripts utilized in clinical research. The hemodynamic indices can be used in a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. Transfer function analysis (Claassen et al. (2016) ) and Mx (Czosnyka et al. (1996) ) can be calculated using this package. Package: r-cran-clintrialdata Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-connector, r-cran-httr, r-cran-jsonlite, r-cran-piggyback Suggests: r-cran-arrow, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-clintrialdata_0.1.3-1.ca2604.1_all.deb Size: 2950250 MD5sum: 08c5746312e480233dafaa12eb2e09d5 SHA1: 6cb9e7001a08b6aa5a8c78adf43f628c4b50b3ae SHA256: 2b60cbb7ad03ddefffb8a9dda68af39f7c7d2006672674adfdb20563c02ec76b SHA512: ed11cafce89ddd3a7b28a106dbaa4ea1b4ed62d40a4cdafd3e95f79a8c5f80aaeaaa8f6a5e110a9e7b9b71309ed8105773544f4109d92d6447763d44024f2d12 Homepage: https://cran.r-project.org/package=clinTrialData Description: CRAN Package 'clinTrialData' (Clinical Trial Example Datasets) A collection of clinical trial example datasets from multiple sources including the CDISC Pilot 01 study (CDISC ). 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Struggling with complex queries and bulk data extraction? What if you could simplify this process with just a few lines of code? Introducing 'clintrialx' - Fetch clinical trial data from sources like 'ClinicalTrials.gov' and the 'Clinical Trials Transformation Initiative - Access to Aggregate Content of ClinicalTrials.gov' database , supporting pagination and bulk downloads. Also, you can generate HTML reports based on the data obtained from the sources! 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Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate the package functionalities. 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(2019) . Package: r-cran-clonetv2 Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sets, r-cran-ggplot2, r-cran-ggrepel, r-cran-arules, r-cran-dbscan Filename: pool/dists/resolute/main/r-cran-clonetv2_2.2.1-1.ca2604.1_all.deb Size: 2634002 MD5sum: ffa7ec70a268c0542d341b808f02a03f SHA1: 840bf4b2523941ed059b020225386fb89d52cf8a SHA256: 84becea1f766a17fe197492960a2e16875635dd5063a8ce136e6442c3970947e SHA512: c67c3489cd275e3bec49a0cbee76cd7888577b5dca49d6cb159e1db901de263a980502b668d069464e5e4eefef15fc2b1213718708601a553995d3f02a11eb74 Homepage: https://cran.r-project.org/package=CLONETv2 Description: CRAN Package 'CLONETv2' (Clonality Estimates in Tumor) Analyze data from next-generation sequencing experiments on genomic samples. 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See README on for more details. P Yang et al. (2015) . 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Each cluster is supported by a line segment, the position, orientation and length of which guide where the respective points are placed. This package is described in Fachada & de Andrade (2023) . 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See Sobisek, Stachova, Fojtik (2018) . Package: r-cran-clusboot Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fpc, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clusboot_1.2.2-1.ca2604.1_all.deb Size: 52382 MD5sum: ad37409fcea6aab69a8436470a51b699 SHA1: eb4b5ce42a0d026dc6ef4c94652ad9d2c420a225 SHA256: 56c14c263e45d34d3606fd565808b266acb5ccce0d249adbf554605579f8ecde SHA512: 1febb6d409baa6b8ee969bcf966a55880e23d81a093c4b63a25f8b100a46d064b2ef149230ac41f373105b3dae2b089984d340b9e04857fbc8d08b32d9b411ee Homepage: https://cran.r-project.org/package=ClusBoot Description: CRAN Package 'ClusBoot' (Bootstrap a Clustering Solution to Establish the Stability ofthe Clusters) Providing a cluster allocation for n samples, either with an $n \times p$ data matrix or an $n \times n$ distance matrix, a bootstrap procedure is performed. The proportion of bootstrap replicates where a pair of samples cluster in the same cluster indicates who tightly the samples in a particular cluster clusters together. Package: r-cran-clusevol Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1863 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-cluster, r-cran-fpc, r-cran-viridis, r-cran-clustersim, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-clusevol_1.0.1-1.ca2604.1_all.deb Size: 1863446 MD5sum: cb71d0cc5ca0e6794062591a25c807cd SHA1: 5dccff7558666a746f788bb22d976c014765c6e3 SHA256: a2734080c511869450a4525a4e42904c71372c5e00542bb50e8c572dca6f7da2 SHA512: c66318d4e2734972b6041f4d7694095cfa0f2f6edea8c7169f5ab0b727ef8d6ca15b7b8635b2844f816480b7d2af51c6665d4e268e83e239cbfc714be71d4a41 Homepage: https://cran.r-project.org/package=clusEvol Description: CRAN Package 'clusEvol' (A Procedure for Cluster Evolution Analytics) Cluster Evolution Analytics allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) . Package: r-cran-clusscluster Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-venndiagram, r-cran-scales, r-cran-reshape2, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clusscluster_0.1.0-1.ca2604.1_all.deb Size: 210198 MD5sum: 5d6052b50bc6dd931eca380890f2cdcf SHA1: c61d34f23e9dd7aaa9f1b8a236129c280653609c SHA256: 93881b751daf4a1cb9a4d5cb33fee96ec12d1eeae725302df29f172fa0f4617e SHA512: 030137881b90d23350cda227202ca1b06093018f94051ca5fdb6c740906bc2be5e60193a49e297b112f4719a90b5973f825efc554b83f414b7243331f93a3ebc Homepage: https://cran.r-project.org/package=ClussCluster Description: CRAN Package 'ClussCluster' (Simultaneous Detection of Clusters and Cluster-Specific Genes inHigh-Throughput Transcriptome Data) Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, 'ClussCluster' incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells. Package: r-cran-clust.bin.pair Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clust.bin.pair_0.1.2-1.ca2604.1_all.deb Size: 35074 MD5sum: d51f70fcd3ad2bd24d492ff8dd919ea8 SHA1: 5abdb81ec768d13839618025b1756fc4b646af53 SHA256: ad91ff58b6ab6dd81ceb573e8f7c2d28a53c2737ae0c918fcf1d2b31f98895d3 SHA512: 18b37601c4653390d1e3ad9215248149c88039662ccdb112da77723cdee38bc4f2152c6ad053e6a7b73ebaa4b6a047b1353c2c9bde473a57e5615ada6512d146 Homepage: https://cran.r-project.org/package=clust.bin.pair Description: CRAN Package 'clust.bin.pair' (Statistical Methods for Analyzing Clustered Matched Pair Data) Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square statistic. Implemented are the tests Eliasziw (1991) , Obuchowski (1998) , Durkalski (2003) , and Yang (2010) with McNemar (1947) included for comparison. The utility functions nested.to.contingency and paired.to.contingency convert data between various useful formats. Thyroids and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989) respectively. Package: r-cran-clustblock Architecture: all Version: 5.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factominer Suggests: r-cran-clustvarlv Filename: pool/dists/resolute/main/r-cran-clustblock_5.0.0-1.ca2604.1_all.deb Size: 396450 MD5sum: c210023bf1c77f06af7a27ed78809033 SHA1: 2b1e73aff719f11f29281d124e0f5f5ee0f87e52 SHA256: b780acec87e39cc297f6f3f7293716b45f8d7f5276661b2ed2ecb708ae848048 SHA512: 5fa2aff2a8cf0e94549c77f744be372a879a84b36543095b12687850dc378c0038d5826a9d62df00a9a2a2284c41d6adac37a934db3656b850a6564632c881d0 Homepage: https://cran.r-project.org/package=ClustBlock Description: CRAN Package 'ClustBlock' (Clustering of Datasets) Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. Different thresholds per cluster can be sets. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) , Llobell, Vigneau & Qannari (2019) ) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) , Llobell, Giacalone, Labenne & Qannari (2019) ) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data (Llobell, Bonnet & Giacalone (2024) ) . Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of observations (products in sensory analysis) in multi-block context (notably with ClusMB strategy) is also included (Llobell & Giacalone (2025) ).Performing clustering based on CATA and liking at the same time is possible thanks to cluscata_liking function (Llobell & Giacalone (2025) ). Package: r-cran-clustcr2 Architecture: all Version: 1.7.3.01-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-ggplot2, r-cran-ggseqlogo, r-cran-network, r-cran-plyr, r-cran-rcolorbrewer, r-cran-stringr, r-cran-scales, r-cran-sna, r-cran-vlf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustcr2_1.7.3.01-1.ca2604.1_all.deb Size: 252314 MD5sum: 03b561a2c74234e45ff85530a8a4ffdc SHA1: f7f794dd6bd791fb520512537696f221d58aecd3 SHA256: 43379990f60615b553102a4fe2be1a1210f61cfb18eb9b001177e11c92354eec SHA512: 4954dbfa6621485e06a6ffbfa3c58adea63bce79b78ff5f1a8099f857d9a2c520130a6dabbd0533b7e984ac82758ad67f6bb767f48e53befb2e75fee30b13848 Homepage: https://cran.r-project.org/package=ClusTCR2 Description: CRAN Package 'ClusTCR2' (Identifying Similar T Cell Receptor Hyper-Variable Sequenceswith 'ClusTCR2') Enhancing T cell receptor (TCR) sequence analysis, 'ClusTCR2', based on 'ClusTCR' python program, leverages Hamming distance to compare the complement-determining region three (CDR3) sequences for sequence similarity, variable gene (V gene) and length. The second step employs the Markov Cluster Algorithm to identify clusters within an undirected graph, providing a summary of amino acid motifs and matrix for generating network plots. Tailored for single-cell RNA-seq data with integrated TCR-seq information, 'ClusTCR2' is integrated into the Single Cell TCR and Expression Grouped Ontologies (STEGO) R application or 'STEGO.R'. See the two publications for more details. Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman (2021) , Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) . Package: r-cran-clustcurv Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-ggfortify, r-cran-ggplot2, r-cran-gmedian, r-cran-npregfast, r-cran-rcolorbrewer, r-cran-survival, r-cran-survminer Suggests: r-cran-covr, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustcurv_3.0.1-1.ca2604.1_all.deb Size: 1067548 MD5sum: 91e8e1d03df89cb175b1b8ebfb87d0c6 SHA1: 24dd9aa5961be40209022869f97317b11fdd83fb SHA256: 9738579a8ff883b3ba381d62def6193380776852bcec028bffcfa34acc93c14e SHA512: 32cb6d590c30c53f956fa26a13429d96f06357ff8b9553ec7c17053357e0f30c4740b0172399d404de6daa4392ef6b4e7f36e3d2314deea7f1eb82a41abd7da1 Homepage: https://cran.r-project.org/package=clustcurv Description: CRAN Package 'clustcurv' (Determining Groups in Multiples Curves) A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods or other approaches with lower computational cost. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) . Package: r-cran-clusteff Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qrcm, r-cran-cluster, r-cran-fda, r-cran-ggpubr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-clusteff_0.3.1-1.ca2604.1_all.deb Size: 117590 MD5sum: 1bcd195d96623db9f853ae07781d7bdd SHA1: 7d39f208635d6ae4be59d5853bd6ca1818a3c7c8 SHA256: 1654b0f7a8ec1314d37f9cc48faeebeb0867df49ddcc9176cf29b6ebbbcf379b SHA512: 68b67841e37e0ef2124b149e50c1b8d00b1d50a98bf58a6d87a39d27ef28b589374d36014bf9adde939bef8d6d2a96496f2082721fde3b88cad32843223637a7 Homepage: https://cran.r-project.org/package=clustEff Description: CRAN Package 'clustEff' (Clusters of Effects Curves in Quantile Regression Models) Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) . Package: r-cran-cluster.datasets Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cluster.datasets_1.0-1-1.ca2604.1_all.deb Size: 211364 MD5sum: 21941ddb91037d206c7eeefee8f9909c SHA1: badd8a77d50b99a1b22de2ba135b6280826a029b SHA256: 1fe33a02cccf93bcdfbd374d2c250e22a9497213f481d09ad05df592d9983709 SHA512: 2598a543cd7d1782d5b1b122201ca7daf3f62edca1d6c9a28822846e544ec0a007422f7133baf0253513157d7552f95f35e60acfbdef039e707df9328458032b Homepage: https://cran.r-project.org/package=cluster.datasets Description: CRAN Package 'cluster.datasets' (Cluster Analysis Data Sets) A collection of data sets for teaching cluster analysis. Package: r-cran-cluster.obeu Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-cluster, r-cran-clvalid, r-cran-data.tree, r-cran-dendextend, r-cran-jsonlite, r-cran-mclust, r-cran-rcurl, r-cran-reshape, r-cran-reshape2, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cluster.obeu_1.2.3-1.ca2604.1_all.deb Size: 64606 MD5sum: fc1a540d4986d8445bc9de7f504ff83c SHA1: 9f3bb67c0535a9b52387245162be58373e65e68c SHA256: 17e32c57a6c5b84108a7dfae0b7cbfa7b8df9a7b1d8961c85cd53900f3be6b4c SHA512: a47f9134ebdf526a85ed04b7d7e5b4f48653a0ea60383d5f1b63e64708c53cc71c1b4d0b7cd141d7eee0d568a19857cf58fab94fdc09457f4c37bbf3bf35c020 Homepage: https://cran.r-project.org/package=Cluster.OBeu Description: CRAN Package 'Cluster.OBeu' (Cluster Analysis 'OpenBudgets.eu') Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface. Package: r-cran-clusterability Architecture: all Version: 0.2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diptest, r-cran-sparsepca, r-cran-elasticnet, r-cran-cluster Suggests: r-cran-testthat, r-cran-plotly, r-cran-bench Filename: pool/dists/resolute/main/r-cran-clusterability_0.2.3.0-1.ca2604.1_all.deb Size: 92440 MD5sum: bd1f94e9263989319e8b7237cd526225 SHA1: 5005dabae16d22c7fffb62e5d192e71adebbc673 SHA256: a0c1a8062efa4f8ab42842e7d5b609454082e4a811d4f5b1b5adee8c52da9d43 SHA512: d515e4d133ac79358f07655e599d2b177f988e6f522d58e194c3872b56887ef99ec95dcd13d54dbcaa983de700d702a3ffa169a1c3b6b2c9e7952b43c8bfb679 Homepage: https://cran.r-project.org/package=clusterability Description: CRAN Package 'clusterability' (Performs Tests for Cluster Tendency of a Data Set) Test for cluster tendency (clusterability) of a data set. The methods implemented - reducing the data set to a single dimension using principal component analysis or computing pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - are described in Adolfsson, Ackerman, and Brownstein (2019) and Laborde et al. (2023) . Such methods can inform whether clustering algorithms are appropriate for a data set. Package: r-cran-clusterbootstrap Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-clusterbootstrap_2.0.0-1.ca2604.1_all.deb Size: 107610 MD5sum: e063b43d2193bd01b0d479e2bfb5aa5e SHA1: ee88d9820af9b8db68e926357fbbb532a52c6f0e SHA256: 903d7fc42c734765ac60bed54bbc75aaba5f2de1938d503b9125974595df8fe4 SHA512: 685b919ac636b2c119c6f00f0bae3f14f529fc862e47b18fa300907551905b66861dd2d3a430895b749b34cbd1c9cc29ae9ee7d9eea92b40a5e7fbd7da75ae28 Homepage: https://cran.r-project.org/package=ClusterBootstrap Description: CRAN Package 'ClusterBootstrap' (Analyze Clustered Data using the Cluster Bootstrap) Provides functionality for the analysis of clustered data using the cluster bootstrap. Package: r-cran-clusteredmutations Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seriation, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-clusteredmutations_1.0.2-1.ca2604.1_all.deb Size: 954630 MD5sum: 16840200a37d371882307d72b19df865 SHA1: 9d8e6a4179e30928ae2a066997db6b9085f7ed38 SHA256: b4185c09ec7ce3c75051b452a7a0d9a13dc3641e6b796e212406ef73f2783f6b SHA512: 63a4a21c67772257a582b7e84baf0ed90686ea6ff6a9a524f2580ef4256e891734e1a99561caf3c6a971d43ebfe317f1d7709665dfb3a0afc4784037d377e340 Homepage: https://cran.r-project.org/package=ClusteredMutations Description: CRAN Package 'ClusteredMutations' (Location and Visualization of Clustered Somatic Mutations) Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information. Package: r-cran-clustergeneration Architecture: all Version: 1.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-clustergeneration_1.3.8-1.ca2604.1_all.deb Size: 269516 MD5sum: 9b9a6625266bffdec88314a11ab32451 SHA1: d463e5f25f2c791f40666481af5b02d66c5b64a1 SHA256: c7f2785f4221a4401e9565bf7889318011b12a342e3546612c99b1d619a2c338 SHA512: c7d024b12933c653a458f645595e85ca147c4ef801a7dbff9eed3cdc244f0719373df0bea243994218dd207cdb141c9b5c2e8ef0574505ee8f4b52c2620c4b26 Homepage: https://cran.r-project.org/package=clusterGeneration Description: CRAN Package 'clusterGeneration' (Random Cluster Generation (with Specified Degree of Separation)) We developed the clusterGeneration package to provide functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables. Package: r-cran-clustergvis Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorramps, r-cran-dplyr, r-cran-e1071, r-cran-factoextra, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-bioc-singlecellexperiment Suggests: r-bioc-biobase, r-bioc-complexheatmap, r-bioc-clusterprofiler, r-bioc-summarizedexperiment, r-bioc-tcseq, r-cran-circlize, r-cran-igraph, r-cran-knitr, r-bioc-monocle, r-cran-pheatmap, r-cran-rmarkdown, r-cran-seurat, r-cran-wgcna, r-cran-biocmanager Filename: pool/dists/resolute/main/r-cran-clustergvis_0.1.4-1.ca2604.1_all.deb Size: 866926 MD5sum: 01ffb8968fcf6d80eecd99a971abdafa SHA1: 413268f8a842de91a7c80133e88f48c0541277df SHA256: 8e81d3e0feee86937cca48b6dff5ae40d43f5359c80283a33bf1ac2ee78f6e46 SHA512: 506491a8684f0024738b6bc73bdb24b64e436ef91c2c6cf83f608384bc7206014016bb66ae85a38c435fa03052614c66042a4da0beec267eed7cbb3ccf93e03e Homepage: https://cran.r-project.org/package=ClusterGVis Description: CRAN Package 'ClusterGVis' (One-Step to Cluster and Visualize Gene Expression Data) Streamlining the clustering and visualization of time-series gene expression data from RNA-Seq experiments, this tool supports fuzzy c-means and k-means clustering algorithms. It is compatible with outputs from widely-used packages such as 'Seurat', 'Monocle', and 'WGCNA', enabling seamless downstream visualization and analysis. See Lokesh Kumar and Matthias E Futschik (2007) for more details. Package: r-cran-clusterhap Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clusterhap_0.1-1.ca2604.1_all.deb Size: 46896 MD5sum: 28f632dadf9fb7058e7a412a250b84c8 SHA1: 0886495bb645f7b99c605abce25b0b2570907a2d SHA256: 269d2f51c8b5645a062d76ef519dbfd933cb8b1c5d1619bff79d8975ceaa4a24 SHA512: 0a2b6bdc6f4323fd7a2cdc82a2199fdc40213b7769b81519ea3b98fcd88cd2d26e9b862d7d14ff2a4f56574e29b1ba06eb8747f243f530d51064e14ffdb82350 Homepage: https://cran.r-project.org/package=clusterhap Description: CRAN Package 'clusterhap' (Clustering Genotypes in Haplotypes) One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci). clusterhap groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, clusterhap groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, clusterhap calculates such probability from relative frequencies. Package: r-cran-clustering Architecture: all Version: 1.7.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amap, r-cran-apcluster, r-cran-cluster, r-cran-clusterr, r-cran-data.table, r-cran-dplyr, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-gmp, r-cran-pracma, r-cran-pvclust, r-cran-shiny, r-cran-sqldf, r-cran-xtable, r-cran-toordinal Suggests: r-cran-dt, r-cran-shinyalert, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-tidyverse, r-cran-shinycssloaders Filename: pool/dists/resolute/main/r-cran-clustering_1.7.10-1.ca2604.1_all.deb Size: 394730 MD5sum: 9c91ce1a38352bcf3be5cf61f9ba8d41 SHA1: dc12bbaa9930b6dd2c9f61ef0856ca33c7bf3bc1 SHA256: 65c8e22294cccd39fe7f430b5a3539be92ef9f48ca378a481ed4665f8b984a5f SHA512: 016f530a3c376f6f1194340acc3a8d3b1c814be7362f8cd51cdcf51a026067fcca3cefa369c50641f7bafdf66c7e7ce89d218751a5529f868cf968ffdafc6659 Homepage: https://cran.r-project.org/package=Clustering Description: CRAN Package 'Clustering' (Techniques for Evaluating Clustering) The design of this package allows us to run different clustering packages and compare the results between them, to determine which algorithm behaves best from the data provided. See Martos, L.A.P., García-Vico, Á.M., González, P. et al.(2023) "Clustering: an R library to facilitate the analysis and comparison of cluster algorithms.", Martos, L.A.P., García-Vico, Á.M., González, P. et al. "A Multiclustering Evolutionary Hyperrectangle-Based Algorithm" and L.A.P., García-Vico, Á.M., González, P. et al. "An Evolutionary Fuzzy System for Multiclustering in Data Streaming" . Package: r-cran-clustermole Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-bioc-gseabase, r-bioc-gsva, r-cran-magrittr, r-cran-rlang, r-bioc-singscore, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustermole_1.1.1-1.ca2604.1_all.deb Size: 1381740 MD5sum: 6f7630e2bc8c814cecf14519473202dc SHA1: c7ca63ec8f527b00117e777552041120129569de SHA256: 38b0968dc869eeaf73618689a7a12139d416e21bc0a4ed9d257a092db6a530bf SHA512: e22b222cc6e9dd6d161c44b06f552827ed14af57aaf9795e6b428551b6a447706a3020331c604d7b3aac151c6003e71e941545521184520e0b32b0c55ca6f32b Homepage: https://cran.r-project.org/package=clustermole Description: CRAN Package 'clustermole' (Unbiased Single-Cell Transcriptomic Data Cell TypeIdentification) Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases. Package: r-cran-clusternomics Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-plyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mclust, r-cran-gplots, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-clusternomics_0.1.1-1.ca2604.1_all.deb Size: 308480 MD5sum: 5f35e15ac9a2101d73294086085b97eb SHA1: 42560cc4ad78a3f99c1f160ef46adcff22e7862c SHA256: 6ccd500bd1395578a355cd63830c402e5be4b5d817643a20918bd8fa4ad62851 SHA512: 485f436f125f7b24b62f07922acf4716739f9fc3ea7e7187fd587bd5b86b6ca76b916c1608785f29317c6a56b9510a4f0814c8ead4ed0b81b61c87b5c9f30fd3 Homepage: https://cran.r-project.org/package=clusternomics Description: CRAN Package 'clusternomics' (Integrative Clustering for Heterogeneous Biomedical Datasets) Integrative context-dependent clustering for heterogeneous biomedical datasets. Identifies local clustering structures in related datasets, and a global clusters that exist across the datasets. Package: r-cran-clusterranktest Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clusterranktest_1.0-1.ca2604.1_all.deb Size: 28764 MD5sum: 889ee2d77285eda3fda89ccc19952aa3 SHA1: a1bb72761c99a35344792cb574190adce733cc17 SHA256: f09cbf741e5a6d81ad33b851b111f5e52be2a2a3206d3263fb09e2d50d4420db SHA512: 83dcfc4d6495be8e316a66cc5f1094fcf4aebfecf8ed1924412cf49a5d6a874f998726e21f4d5d5e859b9e4dbc70f6b0ca5814cba26a18eb48044683e76f1a0e Homepage: https://cran.r-project.org/package=ClusterRankTest Description: CRAN Package 'ClusterRankTest' (Rank Tests for Clustered Data) Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size. Package: r-cran-clusterrepro Architecture: all Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clusterrepro_0.9-1.ca2604.1_all.deb Size: 20202 MD5sum: 8ac9d3e5fd60fe64b5abb934297a4277 SHA1: 6d82d29f4e3f38b0385c36b259a708e0c6c8a982 SHA256: 93816fcf3629fe31374e98aff4622991fdc16a31892e4022ad05427c350038ec SHA512: f0df30fa9c6b5a327247b24f8a3b078d85004e73dd3dbc2bd54c0bb357ebc110e9b9a2dbc729d08aa420d1ff7db564c177a97dd21cfd97eb78ac75bff5bba4a5 Homepage: https://cran.r-project.org/package=clusterRepro Description: CRAN Package 'clusterRepro' (Reproducibility of Gene Expression Clusters) This is a function for validating microarray clusters via reproducibility, based on the paper referenced below. Package: r-cran-clusterses Architecture: all Version: 2.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aer, r-cran-formula, r-cran-plm, r-cran-sandwich, r-cran-lmtest, r-cran-mlogit, r-cran-dfidx Filename: pool/dists/resolute/main/r-cran-clusterses_2.6.5-1.ca2604.1_all.deb Size: 150830 MD5sum: 45b727d4062b0201c61595f9820e6b21 SHA1: 9d8101fd651021b6736670b32681c4b5e373868e SHA256: b478530cca47a44a51b7c4b03bb52d725cd94278d9da2c045b02be4376d6d2f8 SHA512: 01346cfb15f0557fbafb74280f62989f51965f758edb57c8c4dda5c88bc1c34695264f7816202f7d2a9368858e6f1c7883b93d21abd88c6e4169a207b8f1506d Homepage: https://cran.r-project.org/package=clusterSEs Description: CRAN Package 'clusterSEs' (Calculate Cluster-Robust p-Values and Confidence Intervals) Calculate p-values and confidence intervals using cluster-adjusted t-statistics (based on Ibragimov and Muller (2010) , pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) . Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models. Package: r-cran-clustertend Architecture: all Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clustertend_1.7-1.ca2604.1_all.deb Size: 13124 MD5sum: 1de84c564c3bfd7539dba2c12dc60510 SHA1: 54f786d5cc383f3a92bd010a6b6333665fe4492c SHA256: 8493108bd3cd6758445757afb4c2412442c1c4c8c8391f1ec8ce4de3cf0cd932 SHA512: 42e1bef01103554189c14febd00b63f5e4e4f2b49701a052848f56d5603d2c612bbfdb9094de72445244949419507eb6811851ac9874e3f58aa1e5d080fe7161 Homepage: https://cran.r-project.org/package=clustertend Description: CRAN Package 'clustertend' (Check the Clustering Tendency) Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) . Package: r-cran-clusterv Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-cluster Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-clusterv_1.1.1-1.ca2604.1_all.deb Size: 752824 MD5sum: a94543a38f30560767498aec1b272945 SHA1: d43f5a900a561300f87720662f600296fa2fef2b SHA256: 4bbd541be20799f5f80ae7f39d77e930b4181228780050fa00510dd7a1d0d800 SHA512: e1b738e127095a8995bc4650277020a6fc0aacfc19ce8cd34b0f4aabdb9332bb1f3c37f771dfe2a5de74a3f54c9e9cefcd02ea86ff0d28823c22b7192a6251ad Homepage: https://cran.r-project.org/package=clusterv Description: CRAN Package 'clusterv' (Assessment of Cluster Stability by Randomized Maps) The reliability of clusters is estimated using random projections. A set of stability measures is provided to assess the reliability of the clusters discovered by a generic clustering algorithm. The stability measures are taylored to high dimensional data (e.g. DNA microarray data) (Valentini, G (2005), . Package: r-cran-clustervar Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastdummies, r-cran-mass, r-cran-mvtnorm, r-cran-scales, r-cran-foreach, r-cran-doparallel, r-cran-parabar, r-cran-iterators Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustervar_0.0.8-1.ca2604.1_all.deb Size: 1376288 MD5sum: 89b29aec4d644c9cb260b6d4fdf97eb6 SHA1: 8d37c2f0b2b03e6bc0b6c0cab9998223285e0d7b SHA256: 072ac570e7c4173da55ce04997126110a344d03ef985a8a543034ab94c90e6b1 SHA512: 211dace41ab9443340f18bd91cf354db597d3226f48d044d25dfaf9c6078c23b676e1931331fbb4f7cc9e07e09b869742eb81e070c5b5ba254c74719712239e0 Homepage: https://cran.r-project.org/package=ClusterVAR Description: CRAN Package 'ClusterVAR' (Fitting Latent Class Vector-Autoregressive (VAR) Models) Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters. Package: r-cran-clusterwebapp Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-cluster, r-cran-factoextra, r-cran-ggplot2, r-cran-dbscan, r-cran-mclust, r-cran-kernlab, r-cran-rtsne, r-cran-dt, r-cran-dplyr, r-cran-tidyr, r-cran-mlbench, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-clusterwebapp_0.1.3-1.ca2604.1_all.deb Size: 1290926 MD5sum: 2f568a8644c7b42d8798ba0ff91a6c94 SHA1: 39113b234c4ca1131f443583d0ebed23c311cbce SHA256: 7f3810927b8cf08d9a97cd36dfb4679cdd8c0a46f9dca5cb13657b20906a370f SHA512: 2832ee21df97fbfc6c84f32eb1880c5e77ca95870965c453104feb13ab1ac5dfb60ca727d1756da9b8433bba7212711218a5051fbcaea1cc8bfddd3d92765def Homepage: https://cran.r-project.org/package=clusterWebApp Description: CRAN Package 'clusterWebApp' (Universal Clustering Analysis Platform) An interactive platform for clustering analysis and teaching based on the 'shiny' web application framework. Supports multiple popular clustering algorithms including k-means, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), PAM (Partitioning Around Medoids), GMM (Gaussian Mixture Model), and spectral clustering. Users can upload datasets or use built-in ones, visualize clustering results using dimensionality reduction methods such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), evaluate clustering quality via silhouette plots, and explore method-specific visualizations and guides. For details on implemented methods, see: Reynolds (2009, ISBN:9781598296975) for GMM; Luxburg (2007) for spectral clustering. Package: r-cran-clustgeo Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clustgeo_2.1-1.ca2604.1_all.deb Size: 1171812 MD5sum: 7158d613b963f93c886105694f24adfa SHA1: 0f46ff828b7aff1d4691e3e68d315754b452802b SHA256: c9169eafe232b67e326479edd02c6d1ad56ca1fa00f2be0ce66583f7206d49eb SHA512: ca9ef80882e20c8bc1015fffeb183c824f4e2b332fbc9117dfaf5d9acfb26c9d6994620d73059975ea15b59b489cc95015ea9c3f05f15ce9006f72a961671039 Homepage: https://cran.r-project.org/package=ClustGeo Description: CRAN Package 'ClustGeo' (Hierarchical Clustering with Spatial Constraints) Implements a Ward-like hierarchical clustering algorithm including soft spatial/geographical constraints. Package: r-cran-clustimpute Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clusterr, r-cran-copula, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang, r-cran-knitr Suggests: r-cran-ggextra, r-cran-rmarkdown, r-cran-testthat, r-cran-hmisc, r-cran-tictoc, r-cran-spelling, r-cran-corrplot, r-cran-covr Filename: pool/dists/resolute/main/r-cran-clustimpute_0.2.4-1.ca2604.1_all.deb Size: 611126 MD5sum: a5d922f46139a78c30516305c2923ab6 SHA1: dc7195d21f2fca134e87cf42547878aa39788ab8 SHA256: ead0d33512f02df69f759c1e25e3dd2585c285cbfd71e8c11724148995688d93 SHA512: 7000e58f794711ee0e6745db08b87e2b71160c12db46353a4e6feeeafc6f89a53af9629c1cfa962253cb638168cfd9912dec0baafd5a6a3e13ccb4751003b340 Homepage: https://cran.r-project.org/package=ClustImpute Description: CRAN Package 'ClustImpute' (K-Means Clustering with Build-in Missing Data Imputation) This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details. Package: r-cran-clustlearn Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proxy, r-cran-cli Suggests: r-cran-deldir Filename: pool/dists/resolute/main/r-cran-clustlearn_1.0.0-1.ca2604.1_all.deb Size: 131006 MD5sum: c626e5251d38947e0d7659fc8a1b7060 SHA1: 5de4a4ba06eccc2a40ff3c4cf10a5b71692df7ae SHA256: b3f2d9848f1776db919759342067253d7ea69abf9251a7d99e0c4c03f552a374 SHA512: 9190bc76c362ee7f2932d902b3a2ada4ec6e0908cc734997421d6caab0069cb4ce50bd05f885b203352c3eb672ff272ef1bb943173ad551d9946925de116ecf3 Homepage: https://cran.r-project.org/package=clustlearn Description: CRAN Package 'clustlearn' (Learn Clustering Techniques Through Examples and Code) Clustering methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in Ezugwu et. al., (2022) ; and datasets to test them on, which highlight the strengths and weaknesses of each technique, as presented in the clustering section of 'scikit-learn' (Pedregosa et al., 2011) . Package: r-cran-clustmc Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-procs, r-cran-psych, r-cran-usedist Suggests: r-cran-ggdendro, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustmc_0.1.2-1.ca2604.1_all.deb Size: 108984 MD5sum: 8c2977667a2f8df3a117c71d92e235d7 SHA1: 2e6bc2033b6dc5cc0a00eecef8aa3cd6d98ecc1e SHA256: fd445ead9c5888457c31d6bd70df1ab7d22c1de2e10a8276ab40864a4d307a09 SHA512: 697ee583eb326e8533b6385f167806e34d7aadf31fd6b8bea9821e53e7e8a55e291120d8529e742990517b1f30723707b997067d65c6e32975c807d89c406e24 Homepage: https://cran.r-project.org/package=ClustMC Description: CRAN Package 'ClustMC' (Cluster-Based Multiple Comparisons) Multiple comparison techniques are typically applied following an F test from an ANOVA to decide which means are significantly different from one another. As an alternative to traditional methods, cluster analysis can be performed to group the means of different treatments into non-overlapping clusters. Treatments in different groups are considered statistically different. Several approaches have been proposed, with varying clustering methods and cut-off criteria. This package implements cluster-based multiple comparisons tests and also provides a visual representation in the form of a dendrogram. Di Rienzo, J. A., Guzman, A. W., & Casanoves, F. (2002) . Bautista, M. G., Smith, D. W., & Steiner, R. L. (1997) . Package: r-cran-clustmd Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mclust, r-cran-reshape2, r-cran-mass, r-cran-msm, r-cran-mvtnorm, r-cran-truncnorm, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-clustmd_1.2.2-1.ca2604.1_all.deb Size: 170450 MD5sum: a1b673a7c9d1a3c39e6c5743af09d1d8 SHA1: ad6e19ac159eaab3356109d3d2d4171d6ae62b66 SHA256: 0a87150733ad13c5fb7496f146f7c9f4b3b85cf39842e82b4f6a247d8acdc7ce SHA512: 9ddce4cf1a2172d7907571e97ce4d4a2eb9fa8a480bfa81eab143c561f6b6c77288521868cf8fc9845428e66df00cbb1a33c3ed93e97c03d9862de18db9a291f Homepage: https://cran.r-project.org/package=clustMD Description: CRAN Package 'clustMD' (Model Based Clustering for Mixed Data) Model-based clustering of mixed data (i.e. data which consist of continuous, binary, ordinal or nominal variables) using a parsimonious mixture of latent Gaussian variable models. Package: r-cran-clustmixtype Architecture: all Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-tibble, r-cran-combinat, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustmixtype_0.4-2-1.ca2604.1_all.deb Size: 222358 MD5sum: 85c489c80095a970855e9167eec126c1 SHA1: 4fbc92c35d6eb2f036d8b40b38308955c75802eb SHA256: bb90424687bbdd6b9ad1959fa3a49f702dff1060898352486d0a027da35efc92 SHA512: 40026ddbd8bd037a4bc27b5ee49ea4adc9aa9dcd9ecb5e2a6bbad0561acb29f54777ffa15e7bb7d143f5664e8a8f4e87f15ebab260d3726df502fbe970f8cb2e Homepage: https://cran.r-project.org/package=clustMixType Description: CRAN Package 'clustMixType' (k-Prototypes Clustering for Mixed Variable-Type Data) Functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304. Package: r-cran-clustnet Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bidag, r-cran-pcalg, r-bioc-rbgl, r-cran-clue, r-bioc-graph, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggraph, r-cran-ggpubr, r-cran-ggplot2, r-cran-reshape2, r-cran-car, r-cran-ks, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustnet_1.2.0-1.ca2604.1_all.deb Size: 178640 MD5sum: d712fc99d05194d78cfcf35c5f6f177f SHA1: 65ee246fb2bbdade428cfb5d5162508622675e3e SHA256: 5b98b57e3b54a802c5a64d084ce31c1936e19cbfdc76af8f1d9d2f2cd729f016 SHA512: d2fd8441cb440fadd8fb5d6553d0cc91c91a5070be3374bcb8d06fa2d371dd70be63490be043e972448658bc30bfcc6fa21487a2ce9a805e3354ac6e967eb5da Homepage: https://cran.r-project.org/package=clustNet Description: CRAN Package 'clustNet' (Network-Based Clustering) Network-based clustering using a Bayesian network mixture model with optional covariate adjustment. Package: r-cran-clustofvar Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcamixdata Filename: pool/dists/resolute/main/r-cran-clustofvar_1.2-1.ca2604.1_all.deb Size: 190348 MD5sum: dceed37998a5a93b4d314a7b5b83ab69 SHA1: 9ec53e4056e59969d1007e037b9fc1ff1ddd1168 SHA256: 034eec9800b1d3c0b57b12b9b94b15cf22cd548c7be3725aa47e12b00621bc1e SHA512: 2c705001a76e5fad80a39250eb2ae987727c694121e315d1722fadb4e0ded00c4b87866177c5aabcabce738b42895af7945ea507564037633af3ae737b379c0f Homepage: https://cran.r-project.org/package=ClustOfVar Description: CRAN Package 'ClustOfVar' (Clustering of Variables) Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both. Package: r-cran-clustorus Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2684 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bambi, r-cran-igraph, r-cran-purrr, r-cran-ggplot2, r-cran-rlang, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-clustorus_0.2.2-1.ca2604.1_all.deb Size: 1895094 MD5sum: 9ad9d64678fa32f2fcb3b57dc5cceedf SHA1: 2d89ebcab86806953b98df2d00d1255ad2c61b53 SHA256: 11b86960e689ca3c557ae37a2221dae7eab9696636f6955d3aa7bb33d51a4edf SHA512: 071e10e65173837892d4f972d6960a8ae61099bb2e587e0562e93fad17ad689e28e9acd91f7865a5edf1942e88d7e781ba6d8985b3bce2bb0d4d25f524e925a9 Homepage: https://cran.r-project.org/package=ClusTorus Description: CRAN Package 'ClusTorus' (Prediction and Clustering on the Torus by Conformal Prediction) Provides various tools of for clustering multivariate angular data on the torus. The package provides angular adaptations of usual clustering methods such as the k-means clustering, pairwise angular distances, which can be used as an input for distance-based clustering algorithms, and implements clustering based on the conformal prediction framework. Options for the conformal scores include scores based on a kernel density estimate, multivariate von Mises mixtures, and naive k-means clusters. Moreover, the package provides some basic data handling tools for angular data. Package: r-cran-clustra Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3563 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mgcv, r-cran-mixsim Suggests: r-cran-haven, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clustra_0.2.1-1.ca2604.1_all.deb Size: 2833072 MD5sum: e2e50dad42e530abb0ad930b8e55f750 SHA1: 69378b8da7c107e6ca04733eade070ac4855e3ef SHA256: 739d859312f0144dd2a015e91ab5e3a16a01db286948e17ca9cd89f91fa9c9c0 SHA512: bf8c90d18de2ac01fb77c9eff77feba85f37248a5667c565d5bc4fb99bf5af1645eb82478642aaa730df27f2e135dac1b5d804a81f015bbe2a7aeeac4c67758f Homepage: https://cran.r-project.org/package=clustra Description: CRAN Package 'clustra' (Clustering Longitudinal Trajectories) Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation. Package: r-cran-clustransition Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexclust Filename: pool/dists/resolute/main/r-cran-clustransition_1.0-1.ca2604.1_all.deb Size: 974258 MD5sum: 3148a52104b917f5d0000432580eaffa SHA1: 47446ca616abcddffe393956fea8f1a9ba5a38bf SHA256: 09ac4cd4d94a143daa231db3bed5c491c3ed647b56a371cfe1bcae414467d7aa SHA512: a05a2ff74395adfdc024eebf2667adc4aee66684f0a4bdb1561a1c198e44a3e197dc46785a32e8de425350ed1ec40c391f4586d906bd9b808ece48ab541af295 Homepage: https://cran.r-project.org/package=clusTransition Description: CRAN Package 'clusTransition' (Monitor Changes in Cluster Solutions of Dynamic Datasets) Monitor and trace changes in clustering solutions of accumulating datasets at successive time points. The clusters can adopt External and Internal transition at succeeding time points. The External transitions comprise of Survived, Merged, Split, Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes in location and cohesion of the survived clusters. The package uses MONIC framework developed by Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006) . Package: r-cran-clustrd Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rarpack, r-cran-tibble, r-cran-corpcor, r-cran-ggally, r-cran-fpc, r-cran-cluster, r-cran-dplyr, r-cran-plyr, r-cran-ggrepel, r-cran-ca Filename: pool/dists/resolute/main/r-cran-clustrd_1.4.0-1.ca2604.1_all.deb Size: 338088 MD5sum: 9f50bff8dd4259709de1393a4d10ed1e SHA1: d25a2c7fb5cc69a486829ef2fcfcc9235ce93426 SHA256: 9eb6f5ed92d2392d8837e5ab7366592b62279d2e6bd22f70c23a3976ab070b83 SHA512: b1f314a3e066f4ccec279b5ee24679b6889f20f707b304def2179e1f977ea84432466acb548747f39291a47d651382dbcba4b4106516bb80bc65e06c0b4dbeb8 Homepage: https://cran.r-project.org/package=clustrd Description: CRAN Package 'clustrd' (Methods for Joint Dimension Reduction and Clustering) A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; ). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; ) and reduced K-means (De Soete and Carroll 1994; ); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; ), i-FCB (Iodice D'Enza and Palumbo 2013, ) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; ), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; ), which combine PCA for mixed-type data with K-means. Package: r-cran-clustree Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggraph, r-cran-checkmate, r-cran-igraph, r-cran-dplyr, r-cran-ggplot2, r-cran-viridis, r-cran-rlang, r-cran-tidygraph, r-cran-ggrepel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-singlecellexperiment, r-cran-seurat, r-cran-covr, r-bioc-summarizedexperiment, r-cran-pkgdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-clustree_0.5.1-1.ca2604.1_all.deb Size: 1885354 MD5sum: fe880345e2aaa68b2a46c8d55f63fe17 SHA1: 173b6c43b7b465d15104e76f20b560a2b03d8fe4 SHA256: 1bc3757fb889cbb335665b1e81baeed53ef79d9c33ef40a6feb9811b5cf87268 SHA512: 0dbb00bb4e3ded4c625f0e03057f803a9eecda0b3c553a04e99ea05f6154e55e2d7da1035c31357e79df313c06d030dd2e93edebf86cb74955ccfc9ec86a61d4 Homepage: https://cran.r-project.org/package=clustree Description: CRAN Package 'clustree' (Visualise Clusterings at Different Resolutions) Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases. Package: r-cran-clustringr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringi, r-cran-stringr, r-cran-stringdist, r-cran-igraph, r-cran-assertthat, r-cran-forcats, r-cran-rlang, r-cran-tidygraph, r-cran-ggraph, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-clustringr_1.0-1.ca2604.1_all.deb Size: 417782 MD5sum: 47ae5194d8ba73bc98a0824b81d6da25 SHA1: 1bf1e7ac67445ba0bedcc9ae47f38889f437e926 SHA256: f526ded567cc0d20dce4d9381def27d741cfc411650c2dd3c390f339c19d87cf SHA512: 6660a14b8ae3fdc41ed98faa3a99a082f978f48b02b1d0616805d9c0096a3eed56820da8210c0e0d7b8ace05929f9d36b0c2bec929f11eb8d2550b378b5a96cd Homepage: https://cran.r-project.org/package=clustringr Description: CRAN Package 'clustringr' (Cluster Strings by Edit-Distance) Returns an edit-distance based clusterization of an input vector of strings. Each cluster will contain a set of strings w/ small mutual edit-distance (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by stringdist::stringdist(). The set of all mutual edit-distances is then used by graph algorithms (from package 'igraph') to single out subsets of high connectivity. Package: r-cran-clustshiny Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-klar, r-cran-mass, r-cran-dplyr, r-cran-psycho, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-clustshiny_0.1.0-1.ca2604.1_all.deb Size: 70924 MD5sum: b5a701381debcc705a3bb0a0d3c22d91 SHA1: dee53fa43f59828b47000d69be29579f83423680 SHA256: 06c18bb83391cb7f0b8cac70d558d506a84015b8934b5700ec34f9af246dfef5 SHA512: 18ae4e698a0841e80023665bb0c2d88c3ea286315b42bb6eb6eda3c35a5bd794923884372bca92be7e1e8d9b51032ed172f70d78365836b0bf839a407221158f Homepage: https://cran.r-project.org/package=CLUSTShiny Description: CRAN Package 'CLUSTShiny' (Interactive Document for Working with Cluster Analysis) An interactive document on the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . Package: r-cran-clustvarsel Architecture: all Version: 2.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-matrix, r-cran-bma, r-cran-foreach, r-cran-iterators Suggests: r-cran-mass, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-clustvarsel_2.3.5-1.ca2604.1_all.deb Size: 502632 MD5sum: fa76883c8c3cb127ef8e7cc7433aef72 SHA1: 0a0a0998d310f745223daa9be46cbb18763ce06c SHA256: 6c597f26e7659623abaa2d64a268530ab2a3a05c57b5b7a99be3070d0db44d52 SHA512: bebcadafa9ff0452f2e7bd1ed4ac887a373faf374982dd412cba0c0e1ad7aed3a322791d5dff3c8974b6a9ef83990b83c91af7163aa91a21acf257d3faaac310 Homepage: https://cran.r-project.org/package=clustvarsel Description: CRAN Package 'clustvarsel' (Variable Selection for Gaussian Model-Based Clustering) Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available. Package: r-cran-clvalid Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-class Suggests: r-bioc-biobase, r-bioc-annotate, r-bioc-go.db, r-bioc-moe430a.db, r-cran-rankaggreg, r-cran-kohonen, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-clvalid_0.7-1.ca2604.1_all.deb Size: 607220 MD5sum: b51ca87f6902a2756030a0ba901a82cf SHA1: f9caf2e756084fd00124cc02bdca108a456b0b12 SHA256: c4e871ba159cad641720780858814abb95f4a148084eb8eb7d8e5aca997a6171 SHA512: 1db32b60d33cb8983d9be67c31555b894342f9322b644c4401d2dc58d7e2a689457ebdddb3c782e8c57cef65a568313b63b02db1afa85bcb556d2f34724ca798 Homepage: https://cran.r-project.org/package=clValid Description: CRAN Package 'clValid' (Validation of Clustering Results) Statistical and biological validation of clustering results. This package implements Dunn Index, Silhouette, Connectivity, Stability, BHI and BSI. Further information can be found in Brock, G et al. (2008) . Package: r-cran-cmaes Architecture: all Version: 1.0-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/resolute/main/r-cran-cmaes_1.0-12-1.ca2604.1_all.deb Size: 42962 MD5sum: b91f5f84a9b8453ed5ff4fd526998f56 SHA1: 560b296835efc0056ef57336358b805bf637651b SHA256: 4b4bf115f7133c684d373e92da39f2618b81d772de226faa235a49c633c23537 SHA512: 4569e93aeaec39d21e77a1ed82b8e6cc7e24328f4bbc03b4d3f120dca3d823f871315ac65356a652b4d164550b642a5e681dcb5d4a8e6c45ef73118ad2da933c Homepage: https://cran.r-project.org/package=cmaes Description: CRAN Package 'cmaes' (Covariance Matrix Adapting Evolutionary Strategy) Single objective optimization using a CMA-ES. Package: r-cran-cmaesr Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-paramhelpers, r-cran-bbmisc, r-cran-checkmate, r-cran-smoof, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cmaesr_1.0.3-1.ca2604.1_all.deb Size: 83622 MD5sum: 7c3df37ff3cbccc2e9b4913368c166c2 SHA1: 0aa1d4ba0cdc051114ee582025798a4e12fd9e4e SHA256: 5e2182149e515e854045d3240e0ac31644cb86d89bd1621a8f6dff6ec335a5cf SHA512: 9f4ee8bfc629e0a49f7fc4af4576b7de79f4f4977cf3d339ec22df6afca632059c3be2025ed01ce5f06c1f42e108220c99a02775348bbb3cbb50fc9ea3afc0d5 Homepage: https://cran.r-project.org/package=cmaesr Description: CRAN Package 'cmaesr' (Covariance Matrix Adaptation Evolution Strategy) Pure R implementation of the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES). Package: r-cran-cmahalanobis Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-gridextra, r-cran-matrixstats Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cmahalanobis_1.0.0-1.ca2604.1_all.deb Size: 303404 MD5sum: 8a2c66e250f33585f4b48d14e73b46dc SHA1: 5a396397abde84e696aab8e03e5a51fde7695d8d SHA256: 6c267de91c213d100462e139db9ff06f59149ebff3c9f65a582ed9e8db20b59f SHA512: 54c3a6ab3ebd1f0a9d086f3a1449dbbe3e83d03ef1c011f494b00b0e3b359e3f094517c6cbe59cbe147b12f7c1bc80a14d43ca3df4a7332579a26cbf4f0732be Homepage: https://cran.r-project.org/package=cmahalanobis Description: CRAN Package 'cmahalanobis' (Calculate Distance Measures for DataFrames) It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, Canberra distance, Minkowski dissimilarity (distance defined for p >= 1), Cosine dissimilarity, Bhattacharyya dissimilarity, Jaccard distance, Hellinger distance, Bray-Curtis dissimilarity, Sorensen-Dice dissimilarity between each pair of species in a list of data frames. These statistics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts. Package: r-cran-cmanalysis Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-ggplot2, r-cran-factoextra, r-cran-clue, r-cran-igraph, r-cran-stringr, r-cran-pheatmap Filename: pool/dists/resolute/main/r-cran-cmanalysis_1.0.3-1.ca2604.1_all.deb Size: 107238 MD5sum: d4d63e114f0cafa73dcd8fc1cb69eac3 SHA1: 7571d84df950a06b3735ba6bd52a94c161c80652 SHA256: 6923429d924b645a5606ae04d175d209b04fb1c183158a7bab3e7b39898cba8c SHA512: 4815a234d4c50675962867f123f63a3d627208e2e5cf9e5ea1326abf96f3d8c10eea54d5d58f0226fc4795da7e16f5e8f74a6e147736a8b2c7704aee8e45cd0d Homepage: https://cran.r-project.org/package=cmAnalysis Description: CRAN Package 'cmAnalysis' (Process and Visualise Concept Mapping Data) Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. This package provides functions for processing and visualizing concept mapping data, involving the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. The methodology and its validity are discussed in Kampen, J.K., Hageman, J.A., Breuer, M., & Tobi, H. (2025). "The validity of concept mapping: let's call a spade a spade." Qual Quant. . 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(2011) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization, . It constructs models by using the terms obtained from the forward step of MARS and then estimates parameters by using 'Tikhonov' regularization and conic quadratic optimization. It is possible to construct models for prediction and binary classification. It provides performance measures for the model developed. The package needs the optimisation software 'MOSEK' to construct the models. Please follow the instructions in 'Rmosek' for the installation. 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Selection of the optimal number of components can be done using 'ACMTF_modelSelection()' and 'ACMTFR_modelSelection()'. The CMTF and ACMTF methods were originally described by Acar et al., 2011 and Acar et al., 2014 , respectively. 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Package: r-cran-cnid Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cnid_2.1.1-1.ca2604.1_all.deb Size: 73472 MD5sum: b8a380859b92c4010b887517083eca3a SHA1: 4a0f254b1e195421cc9809c198bed43e87aeb21d SHA256: ce94dbdeb205df19ad0473c2ec2e7e8cc62ddac2662b80d8141bfd983b0d9025 SHA512: ddb3988e62c2c45f9e628f3556f1c582b7f5e72243cb66b87887d1f79637a01da81d6b6dc3caec93c0b77912f587afde7a792fa26b12b508cb3a185a7397fb6f Homepage: https://cran.r-project.org/package=CNID Description: CRAN Package 'CNID' (Get Basic Information from Chinese ID Number) The Chinese ID number contains a lot of information, this package helps you get the region, date of birth, age, age based on year, gender, zodiac, constellation information from the Chinese ID number. 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This study was supported by the National Natural Science Foundation of China (NSFC, Grant No. 42205177). 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The package offers both distribution-free modeling using Taylor polynomials and parametric modeling using the beta-binomial and the 'Sinh-Arcsinh' distribution. Originally developed for achievement tests, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package provides several advantages: It minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and even supports conventional norming. It generates norm tables including confidence intervals. It also includes methods for addressing representativeness issues through Iterative Proportional Fitting. Based on Lenhard et al. (2016) , Lenhard et al. (2019) , Lenhard and Lenhard (2021) and Gary et al. (2023) . Package: r-cran-cnprep Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4077 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-rlecuyer Filename: pool/dists/resolute/main/r-cran-cnprep_2.2-1.ca2604.1_all.deb Size: 4140176 MD5sum: c9ab64c26925e4afc6186812f80915e8 SHA1: 92d63ba0d6b5aad1b197d1672680e66ef6e4693a SHA256: f75f64e3f24b5f1f63ad156b08f6469448e89d64a000672d50a37636554fe639 SHA512: b8f60626bae9c8dff11279906e1a1e54d443c099bee02d297b28283b99428d640f494fcea3d3cd66470854cb47de477ae3487221d5a7c34beb8b2a7a282ea4ba Homepage: https://cran.r-project.org/package=CNprep Description: CRAN Package 'CNprep' (Pre-Process DNA Copy Number (CN) Data for Detection of CN Events) DNA copy number data evaluation using both their initial form (copy number as a noisy function of genomic position) and their approximation by a piecewise-constant function (segmentation), for the purpose of identifying genomic regions where the copy number differs from the norm. Package: r-cran-cnps Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-cnps_1.0.0-1.ca2604.1_all.deb Size: 127798 MD5sum: 3001479df245b23084625bf39aba9f46 SHA1: 256725458fc22ea2641baf58cfe84c3d5ebd4317 SHA256: fd98121752ee59ddcc1aecf07f5f26e6de3b39421e7ca151d2cbad779bcb0bef SHA512: e88f2007ff35f5e96c8247c033666c83e97f9de29f55f112ce364f366cb250f61d108fa5ba9005b34236d5749d8ca63e65803161d7707e271fb801d339c722c9 Homepage: https://cran.r-project.org/package=CNPS Description: CRAN Package 'CNPS' (Nonparametric Statistics) We unify various nonparametric hypothesis testing problems in a framework of permutation testing, enabling hypothesis testing on multi-sample, multidimensional data and contingency tables. Most of the functions available in the R environment to implement permutation tests are single functions constructed for specific test problems; to facilitate the use of the package, the package encapsulates similar tests in a categorized manner, greatly improving ease of use. We will all provide functions for self-selected permutation scoring methods and self-selected p-value calculation methods (asymptotic, exact, and sampling). For two-sample tests, we will provide mean tests and estimate drift sizes; we will provide tests on variance; we will provide paired-sample tests; we will provide correlation coefficient tests under three measures. For multi-sample problems, we will provide both ordinary and ordered alternative test problems. For multidimensional data, we will implement multivariate means (including ordered alternatives) and multivariate pairwise tests based on four statistics; the components with significant differences are also calculated. For contingency tables, we will perform permutation chi-square test or ordered alternative. 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It can be used to extract features from Copy number segment data and use that to find a subset of copy number signatures which can be further used to correlate with other relevant data. For more on 'NMF' see Gaujoux (2013) . 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Creates a "CNV profile curve" to represent an individual’s CNV events across a genomic region so to capture variations in CNV length and dosage. When evaluating association, the CNV profile curve is directly used as a predictor in the regression model, avoiding the need to predefine CNV loci. CNV profile regression estimates CNV effects at each genome position, making the results comparable across different studies. The penalization encourages sparsity in variable selection with a Lasso penalty and encourages effect smoothness between consecutive CNV events with a weighted fusion penalty, where the weight controls the level of smoothing between adjacent CNVs. For more details, see Si (2024) . Package: r-cran-cnvscope Architecture: all Version: 3.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-reshape2, r-cran-magrittr, r-cran-jointseg, r-cran-shiny, r-cran-rcurl, r-cran-foreach, r-bioc-genomicinteractions, r-cran-matrix, r-cran-openimager, r-bioc-biomart, r-cran-matrixstats, r-cran-plyr, r-cran-data.table, r-cran-dplyr, r-cran-doparallel, r-cran-stringr, r-bioc-rtracklayer, r-cran-hmisc Suggests: r-cran-knitr, r-cran-remotes, r-cran-pwr, r-bioc-complexheatmap, r-cran-rmarkdown, r-cran-igraph, r-cran-visnetwork, r-cran-circlize, r-cran-plotly, r-bioc-interactionset, r-bioc-genomicranges, r-bioc-genomicfeatures, r-bioc-iranges, r-cran-rslurm, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-dt, r-cran-logging, r-cran-heatmaply, r-bioc-s4vectors, r-cran-biocmanager, r-cran-shinyjs, r-cran-htmltools, r-cran-htmlwidgets, r-bioc-genomeinfodb, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-tibble, r-cran-smoothie Filename: pool/dists/resolute/main/r-cran-cnvscope_3.7.2-1.ca2604.1_all.deb Size: 2895096 MD5sum: 58bf445a0ecc948650e3c00c817b4c27 SHA1: ac20bab66519dcb0272bd04af676a697bb4f020a SHA256: a22478d7b048adebbe2fc78a175c7f7242afbab4154d6cd5eadb9482e8f3990d SHA512: fd0d0215625dea68971fb87dae4631c82e89910cff72df971aad365a05277575b3fd834917f5d264c881f13d841c7c7a27913f8c0a1b1f00de0a02ee15391b28 Homepage: https://cran.r-project.org/package=CNVScope Description: CRAN Package 'CNVScope' (A Versatile Toolkit for Copy Number Variation Relationship DataAnalysis and Visualization) Provides the ability to create interaction maps, discover CNV map domains (edges), gene annotate interactions, and create interactive visualizations of these CNV interaction maps. 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Package: r-cran-coastlinefd Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-tidyr, r-cran-fields, r-cran-readxl, r-cran-writexl, r-cran-ggplot2, r-cran-progress, r-cran-sfheaders Filename: pool/dists/resolute/main/r-cran-coastlinefd_1.1.2-1.ca2604.1_all.deb Size: 947950 MD5sum: 0cd9cea0bc696204d1b61a6e814c2172 SHA1: 51aeab772d472738b284f051bf0dc4805e0b56e2 SHA256: f80539c86ad00e148994c37fa37b23098f3009ba12b75f232ef3b51c67168de0 SHA512: 422c31ae2f7d4aa7c6f93ba993e3f5b16271f2f126b7bcc1d25ff5e7b0cc07505026a753e857e9d89f1eb6a29091a50770b6762f590206d3570c1f11e58f23b7 Homepage: https://cran.r-project.org/package=CoastlineFD Description: CRAN Package 'CoastlineFD' (Calculation of the Fractal Dimension of a Coastline) Calculating the fractal dimension of a coastline using the boxes and dividers methods. 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Recent developments in enrichment design have introduced biomarker randomness and accounted for the correlation structure between treatment effect and biomarker, resulting in a two-stage threshold enrichment design. We propose novel two-stage enrichment designs capable of handling two or more continuous biomarkers. See Zhang, F. and Gou, J. (2025). Using multiple biomarkers for patient enrichment in two-stage clinical designs. Technical Report. Package: r-cran-cobiclust Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cluster, r-cran-testthat Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-cobiclust_0.1.2-1.ca2604.1_all.deb Size: 59438 MD5sum: 107ff25365d3eb6532a5cceb0e6a13a4 SHA1: 04fbd81bba9d04081e556efb5b5391efd36b92d2 SHA256: 6ad99441e5280164fe9b8ca3a170ef64fe752198dd6ce542b11213848528c656 SHA512: 8b16c674fd34902ab5f4cfac0d9ceade2aee08b97fa73cd849f138f7e7725ae7a02de8bfbb3e7f4811f01d449f6e0cdbdce98c3fd012c8411a558fb5179313e7 Homepage: https://cran.r-project.org/package=cobiclust Description: CRAN Package 'cobiclust' (Biclustering via Latent Block Model Adapted to OverdispersedCount Data) Implementation of a probabilistic method for biclustering adapted to overdispersed count data. It is a Gamma-Poisson Latent Block Model. 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"Clustering dependent observations with copula functions". Statistical Papers, 60, p.35-51. . 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This method is based on an integrated simple additive weighting and compromise exponentially weighted product model. Package: r-cran-cocotest Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Filename: pool/dists/resolute/main/r-cran-cocotest_1.0.3-1.ca2604.1_all.deb Size: 17066 MD5sum: 08b6a279acdbfa52c5240b4c39c1d56c SHA1: fc5502880e962f1261cb999b7fd81471c9604a1c SHA256: 7e434e51c5fe6db4f4fc99f8889a5f3206084ff28b3deb496276ef8aa737fc2d SHA512: dfc43f8b2e9e43f8409ae6ba7ad3071d9658e897eaf12f71235dbbb3b0b63a4b4cd74a0accb191dec7a32af58581ef396a87066d1bef72fe873dd6e1d272a4be Homepage: https://cran.r-project.org/package=cocotest Description: CRAN Package 'cocotest' (Dependence Condition Test Using Ranked Correlation Coefficients) A common misconception is that the Hochberg procedure comes up with adequate overall type I error control when test statistics are positively correlated. However, unless the test statistics follow some standard distributions, the Hochberg procedure requires a more stringent positive dependence assumption, beyond mere positive correlation, to ensure valid overall type I error control. To fill this gap, we formulate statistical tests grounded in rank correlation coefficients to validate fulfillment of the positive dependence through stochastic ordering (PDS) condition. See Gou, J., Wu, K. and Chen, O. Y. (2024). Rank correlation coefficient based tests on positive dependence through stochastic ordering with application in cancer studies, Technical Report. Package: r-cran-cocron Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cocron_1.0-1-1.ca2604.1_all.deb Size: 178982 MD5sum: 3d75dc67e37a68ca9bbc341aef7f93f4 SHA1: ea00c1afd8f9552892f3df214427353943884d4d SHA256: 535e894ed69c93c3f1c336263675dbdf297a4f4918cda0ea7ed7ae9a5c3055ab SHA512: 605b685a09be23131f062253ab768b47b67450f5c2c23d1bcc790e566f9515658035b5281c3eff8ef51a5c949364ba123ffa17ba49563d33db54acb556784a3b Homepage: https://cran.r-project.org/package=cocron Description: CRAN Package 'cocron' (Statistical Comparisons of Two or more Alpha Coefficients) Statistical tests for the comparison between two or more alpha coefficients based on either dependent or independent groups of individuals. A web interface is available at http://comparingcronbachalphas.org. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from https:// rkward.kde.org to use this feature. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. Package: r-cran-coda.pack Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda.base, r-cran-coda.plot Suggests: r-cran-remotes Filename: pool/dists/resolute/main/r-cran-coda.pack_0.1.3-1.ca2604.1_all.deb Size: 23836 MD5sum: 041002c17aaa77a942231f1e18b69f89 SHA1: 6341c8625e54c0476b086819587f0064187d0e38 SHA256: 3cb79c45980c41beb957d63c9dcf65c3211f2539021e54716af4506ae9e9d938 SHA512: 22204cdd91707bcca474f4b5bbc9e80d3ab9bffdafafd728756de2807d5fbc4d0f15cefca1a5d9cc91f025f0af1cf57ee13de4bf3496ffc995bc1a5fdebe6767 Homepage: https://cran.r-project.org/package=coda.pack Description: CRAN Package 'coda.pack' (Meta-Package for Compositional Data Analysis) Meta-package for compositional data analysis. It attaches the main stable packages of the 'coda' ecosystem, currently 'coda.base' and 'coda.plot', and provides helper tools to install development extensions from 'GitHub'. Package: r-cran-coda.plot Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda.base, r-cran-ggplot2, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-coda.plot_0.2.2-1.ca2604.1_all.deb Size: 101216 MD5sum: f2566fe78e37f830262fe857809baeb3 SHA1: 0771521b16f61466a24231847b42bacf182d169b SHA256: 0d4167bf79d6b96e29a17282234c45df9dde5659a4ea398f11cfa27590276c49 SHA512: 3e6310598e5209c813b9d3d31d3cfe696157445eda989cdd83011cf04f8ec1898a138e541562c7350dcf48359fa6a38101ff591e9fc0e35ed8c121fd4149c3fb Homepage: https://cran.r-project.org/package=coda.plot Description: CRAN Package 'coda.plot' (Plots for Compositional Data) Provides a collection of easy-to-use functions for creating visualizations of compositional data using 'ggplot2'. Includes support for common plotting techniques in compositional data analysis. Package: r-cran-coda4microbiome Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-glmnet, r-cran-plyr, r-cran-proc, r-cran-ggpubr, r-cran-ggplot2, r-bioc-complexheatmap, r-cran-circlize, r-cran-survival, r-cran-survminer Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-coda4microbiome_0.2.4-1.ca2604.1_all.deb Size: 370502 MD5sum: e4084386af014140186114ed201742c3 SHA1: e7b7cceb57c63ed8d821c5207cd974aead1616ac SHA256: 80fefa59ac0590be625ea9f5772d64f4dbb16380a5d8108ea1dcd58f9a7b40d3 SHA512: 108c596bbd0156d150970a41d65d8e667afd8e8717fdaf09ad80ae0ad35092555dc6bf4b0f91741b48ad88f3e487d4d58af5266035a06fce1a5f8162fc98733d Homepage: https://cran.r-project.org/package=coda4microbiome Description: CRAN Package 'coda4microbiome' (Compositional Data Analysis for Microbiome Studies) Functions for microbiome data analysis that take into account its compositional nature. Performs variable selection through penalized regression for both, cross-sectional and longitudinal studies, and for binary and continuous outcomes. Package: r-cran-coda Architecture: all Version: 0.19-4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-coda_0.19-4.1-1.ca2604.1_all.deb Size: 324980 MD5sum: d0784cf2313edb6ce70b41ad6603489c SHA1: 9f70ed74995c0a87c3ca7cbd4bcfa99b08164ef6 SHA256: 6e3f21036575fc1f65a5af2f3a2c2ac9787f91f631f60c959f9278a4b9b317eb SHA512: a2d3d073c79f41df2bb23dfecbfd695113b9232a7e7937865f940ac8ddd41c80c2037fa0c5c4754c0ca65cd6d8a19cb678300f9855fb78bb037e51c3b61c8f4a Homepage: https://cran.r-project.org/package=coda Description: CRAN Package 'coda' (Output Analysis and Diagnostics for MCMC) Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain. Package: r-cran-codacore Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1909 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-keras, r-cran-proc, r-cran-r6, r-cran-gtools Suggests: r-cran-zcompositions, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-codacore_0.0.4-1.ca2604.1_all.deb Size: 1085156 MD5sum: 0934b74f69e36d5a96813b9d855719c8 SHA1: 5930405f812a063696dbcec3c2ceaeb9a8aaf95b SHA256: 200749b45a4095b270f1394fa9ec49f0a3a5f22fdad5950b77f5f80cc023c01d SHA512: a0f01f5182ab8178af398147a2e278796e7d01ab803a128d01bf0d3c56eea25631cb9d0749f14e59fa9b8dbefe13bb83435a53b3b7e70bb114123c81b1165f51 Homepage: https://cran.r-project.org/package=codacore Description: CRAN Package 'codacore' (Learning Sparse Log-Ratios for Compositional Data) In the context of high-throughput genetic data, CoDaCoRe identifies a set of sparse biomarkers that are predictive of a response variable of interest (Gordon-Rodriguez et al., 2021) . 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Package: r-cran-codaimpact Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositions Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-sf, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-codaimpact_0.1.0-1.ca2604.1_all.deb Size: 1070274 MD5sum: 374f5a777eacacf77934a8e443b5f6cc SHA1: edd79bb9d9db5e02df042767b2843a0b43400ac9 SHA256: 43d22b423ecb76d17886ae9fd651d8df6804619f835b45d51378c5ed2d21f783 SHA512: eb1e0d890fa8ca4195e15cb37572c7c320703cc3c853ae019e4444851874a54d5c511800231e6d540a5eb27e7b6a6a10ae0ffac4e5fbb469f844dc54cd1ac0e0 Homepage: https://cran.r-project.org/package=CoDaImpact Description: CRAN Package 'CoDaImpact' (Interpreting CoDa Regression Models) Provides methods for interpreting CoDa (Compositional Data) regression models along the lines of "Pairwise share ratio interpretations of compositional regression models" (Dargel and Thomas-Agnan 2024) . The new methods include variation scenarios, elasticities, elasticity differences and share ratio elasticities. These tools are independent of log-ratio transformations and allow an interpretation in the original space of shares. 'CoDaImpact' is designed to be used with the 'compositions' package and its ecosystem. Package: r-cran-codalm Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-squarem, r-cran-future, r-cran-future.apply Suggests: r-cran-knitr, r-cran-gtools, r-cran-remotes, r-cran-testthat, r-cran-rmarkdown, r-cran-ggtern, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-codalm_0.1.3-1.ca2604.1_all.deb Size: 35306 MD5sum: e2b070ac3ee1400672eca161bf26f899 SHA1: 28eb568a3804f827d638f3372883994b6c69e27e SHA256: 5e485bb386fd6e40aaf3cf4957f22c68b8d938b443a437e192353bfc9cb1bb3f SHA512: 09498c129e05b7005dbb08d6c15d5fdae571c98843689dedf560156eacfe368f1b23abaebd604ded2b5e1623bfd796034df1d200b566acccd6edbef186e42719 Homepage: https://cran.r-project.org/package=codalm Description: CRAN Package 'codalm' (Transformation-Free Linear Regression for Compositional Outcomesand Predictors) Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2022) for transformation-free linear regression for compositional outcomes and predictors. Package: r-cran-codalomic Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable, r-cran-ggplot2, r-cran-reshape2, r-cran-compositions, r-cran-mass, r-cran-broom, r-cran-r2jags, r-cran-ggbiplot, r-cran-zcompositions Suggests: r-cran-testthat, r-cran-gtools, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-codalomic_0.1.1-1.ca2604.1_all.deb Size: 948738 MD5sum: 78a095d3381aeb881add45acc56cb694 SHA1: dd4be0564ea66d68f3259d166c96c56f8fa05251 SHA256: 93146ef020c3c8fe10453911655b5559a1e4fb45e65adb6a9f58438f614b3880 SHA512: 445bb091a35a81e8ec221ea3d2a70aed7e69c8d50b06d2e67622989b4b41386e3231dc802e4293271d06eb35f863e62ef09f660296a6d1848a2eebb61649a498 Homepage: https://cran.r-project.org/package=CoDaLoMic Description: CRAN Package 'CoDaLoMic' (Compositional Models to Longitudinal Microbiome Data) Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martí et al (2018, ISBN:978-84-09-07541-6), Creus-Martí et al (2021) and Creus-Martí et al (2022) . Package: r-cran-codaredistlm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositions, r-cran-ggplot2, r-cran-broom, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-codaredistlm_0.1.0-1.ca2604.1_all.deb Size: 186756 MD5sum: 9e78c5e92305a09a1c2dbff750778300 SHA1: e9856e82b68a4494c29832aa739fbb8cd21808d3 SHA256: fb1287107052b9f0efed704cb378498eb5d2c2fc6e76e89f85d36f7ccb2cefa5 SHA512: 1b022b2371fa78736360cc1dbb61509a4bf1203197327118311c9b674dd748eba65cbbaadfeb7ebf11ed3531de73d3e665a49b23cb0420b5d0c62620ea95364c Homepage: https://cran.r-project.org/package=codaredistlm Description: CRAN Package 'codaredistlm' (Compositional Data Linear Models with Composition Redistribution) Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) and Dumuid et al. (2017b) . 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The method is described in Mouresan, Selle and Ronnegard (2019) . Package: r-cran-code Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-desolve, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-code_1.1.1-1.ca2604.1_all.deb Size: 129758 MD5sum: 7c2d6092bf9154d180b3ae2053fe2591 SHA1: 210a51a85b0f376a426e0049bdf091923e96bda3 SHA256: cdcc546ddcf5e983894d2d5bb61669924a48dcd0f6c8ab5b974cba4a415ac372 SHA512: 2c05ccc50494204459b45ae42debff574488f48c8fee8c78977377d19578c636a5b3eb017862963f598952f0d50bc77055a754073bf36bce67e1cd6c864e9685 Homepage: https://cran.r-project.org/package=cOde Description: CRAN Package 'cOde' (Automated C Code Generation for 'deSolve', 'bvpSolve') Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". 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Package: r-cran-codebookr Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-flextable, r-cran-dplyr, r-cran-officer, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-hms, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-codebookr_0.1.9-1.ca2604.1_all.deb Size: 87762 MD5sum: c4ca6ce9c18346207cf4f15cd1f92864 SHA1: 60b9909c153e949dfbfc216f2fc4f4869fa0a4ef SHA256: afc55fa15586d5d24ce2513cf79f3bebc5f08ebd0d4847df833e7a25f4d787d5 SHA512: 58eec481da69c3c0d3ef40afc68a68912fa2daa5681039980f3c396abe3d25ce3ed396fe41bd77c1275fda4feab9c8f77277dad09f40ded4ed4c4526919e4b2f Homepage: https://cran.r-project.org/package=codebookr Description: CRAN Package 'codebookr' (Create Codebooks from Data Frames) Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame. Package: r-cran-codebreaker Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beepr, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-codebreaker_1.0.1-1.ca2604.1_all.deb Size: 79152 MD5sum: 9d18935f668bb90e2fdb504332c3fc7f SHA1: 38d1ed358f784c0c91c50f2f2addbf983ad20a16 SHA256: bea4129376fefa341d823010af76a093a6b1075d179b464147f644028b8096bc SHA512: dc70a5ad58d0ae0daa3dccd9374ba9313610bdb371c6521e1779d91dcdc75b9077a23efe72f83233fd9d10c5da5d5ed71a54509d8fc22498d63bb6c7fa5ad34f Homepage: https://cran.r-project.org/package=codebreaker Description: CRAN Package 'codebreaker' (Retro Logic Game) Logic game in the style of the early 1980s home computers that can be played in the R console. This game is inspired by Mastermind, a game that became popular in the 1970s. Can you break the code? Package: r-cran-codecollection Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epi Filename: pool/dists/resolute/main/r-cran-codecollection_0.1.3-1.ca2604.1_all.deb Size: 1093338 MD5sum: c66f099d16916ab6de1f1e46bb2b1eb0 SHA1: 84d82ccf971f11128036a83b3f315ddcdd9c5cf7 SHA256: 4507d12d83b1a80e28164e2fda386162be6e41f1783aed1914bdf0972c1f2a7c SHA512: a75e2997e81d097f7ee2884e2d919837135dc8a68557495b933f52dce24e6e79c2869f592f505f62e57b60cf69c4b9183ba3ad1a746bb8971e0b153d5e035ca3 Homepage: https://cran.r-project.org/package=codeCollection Description: CRAN Package 'codeCollection' (Collection of Codes with Labels) Includes several classifications such as International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD10), Anatomical Therapeutic Chemical (ATC) Classification, The International Classification of Diseases for Oncology (ICD-O-3), and International Classification of Primary Care (ICPC). Includes function that adds descriptive label to code value. Depending on classification following languages are available: English, Finnish, Swedish, and Latin. Package: r-cran-codecountr Architecture: all Version: 0.0.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-codecountr_0.0.4.8-1.ca2604.1_all.deb Size: 42890 MD5sum: 75b314d64402566df43720219052dbb0 SHA1: 0b3042c41e132d3a2df2e18c5115aa14b47fa6df SHA256: 08a65a4478dc4bfb73ca4ecdd38912b36cda78d2f52fe0e2bcd6ce06edd93774 SHA512: c6a3f0281c181a2fc6ef1889750c943affaa86553eeaebf1df4840dccdc164e9c598aac21fdca2f3c70b91870a6e00c61414c862cfa2dc1ef33ed9bbe9d42a26 Homepage: https://cran.r-project.org/package=codecountR Description: CRAN Package 'codecountR' (Counting Codes in a Text and Preparing Data for Analysis) Data analysis often requires coding, especially when data are collected through interviews, observations, or questionnaires. As a result, code counting and data preparation are essential steps in the analysis process. Analysts may need to count the codes in a text (Tokenization, counting of pre-established codes, computing the co-occurrence matrix by line) and prepare the data (e.g., min-max normalization, Z-score, robust scaling, Box-Cox transformation, and non-parametric bootstrap). For the Box-Cox transformation (Box & Cox, 1964, ), the optimal Lambda is determined using the log-likelihood method. Non-parametric bootstrap involves randomly sampling data with replacement. Two random number generators are also integrated: a Lehmer congruential generator for uniform distribution and a Box-Muller generator for normal distribution. Package for educational purposes. Package: r-cran-codedepends Architecture: all Version: 0.6.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-codetools, r-bioc-graph, r-cran-xml Suggests: r-bioc-rgraphviz, r-cran-runit, r-cran-knitr, r-cran-highlight, r-cran-rjsonio, r-cran-rcurl, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-codedepends_0.6.7-1.ca2604.1_all.deb Size: 887922 MD5sum: 27abdfc1603e2833fe08f34e28037de1 SHA1: ed56a6ca44344298c10d5355221c77c7b6a58f82 SHA256: 2d95ca37455a555bce7d76dc3dbbd85e4477b3bbf1862aa1e294982ad0189742 SHA512: 2478e3eb9a034159ca0ff82a5e67474c17007aae3e985bd288499517505744fa78ca37c9acc01ec5ff0b6273b574d511c2de69fcf4bc8c551f53e6e25ba596b9 Homepage: https://cran.r-project.org/package=CodeDepends Description: CRAN Package 'CodeDepends' (Analysis of R Code for Reproducible Research and CodeComprehension) Tools for analyzing R expressions or blocks of code and determining the dependencies between them. It focuses on R scripts, but can be used on the bodies of functions. There are many facilities including the ability to summarize or get a high-level view of code, determining dependencies between variables, code improvement suggestions. Package: r-cran-codelist Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-simplermarkdown Filename: pool/dists/resolute/main/r-cran-codelist_0.1.0-1.ca2604.1_all.deb Size: 106194 MD5sum: 41cec7b587ed551164d682a12e1e2c52 SHA1: 0eda0d7829937dd375bdc7b1bdf48fb3d9703aa9 SHA256: e54021fc37ef7a4273ff90b2d7116a1d5b9252471982448dad4bd7d1959df1d4 SHA512: bed3b697f2009045d70c6e038785c3633ae85fae64c7a8d0b8004f5e63059d740effbbc70cf95cbed3b89209f695e10b6b071f83c3f1b19959541e48f720565e Homepage: https://cran.r-project.org/package=codelist Description: CRAN Package 'codelist' (Working with Code Lists) Functions for working with code lists and vectors with codes. These are an alternative for factor that keep track of both the codes and labels. Methods allow for transforming between codes and labels. Also supports hierarchical code lists. 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For a given search strategy, a candidate code list will be returned. Package: r-cran-codemeta Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desc, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-codemeta_0.1.1-1.ca2604.1_all.deb Size: 164812 MD5sum: 328df8e94724ac068d4b9e0abed90dd7 SHA1: 0e27a9efd66f7774fc96cdf77aadedbc503b44c3 SHA256: 212c133277c3c6c6a1358a9dcd9aeb9b951940bdd0531868787f9a8a90d6a67e SHA512: aa47f46545da83d2fed31cde23f6a48ee1026683efac80e85bde3a25b8c6cff94e282abdb0dfe8b0209a03cd82f25e571ded7422c34efcce8146568016efd39b Homepage: https://cran.r-project.org/package=codemeta Description: CRAN Package 'codemeta' (A Smaller 'codemetar' Package) The 'Codemeta' Project defines a 'JSON-LD' format for describing software metadata, as detailed at . This package provides core utilities to generate this metadata with a minimum of dependencies. 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This package provides utilities to generate, parse, and modify 'codemeta.json' files automatically for R packages, as well as tools and examples for working with 'codemeta.json' 'JSON-LD' more generally. Package: r-cran-codename Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tibble Filename: pool/dists/resolute/main/r-cran-codename_0.5.0-1.ca2604.1_all.deb Size: 86000 MD5sum: 1b95f19cd3b500cd4de5ff413ac969a3 SHA1: 8c8f1bd39909bb7f7ed290444471dd38c0ef7b02 SHA256: 93d985dd977ca667c598c816bfb46cb5ef94a3b4001c120f7af667b34ea7c7af SHA512: c634850f753cdca1ed866ed959c5c4ca1090a232c2d2a49ec0c03426ccad9f18cf590ed2799e05a7d632330c3ce0ec2b24409a94e766165d5b67c055e72242ac Homepage: https://cran.r-project.org/package=codename Description: CRAN Package 'codename' (Generation of Code Names for Organizations, People, Projects,and Whatever Else) This creates code names that a user can consider for their organizations, their projects, themselves, people in their organizations or projects, or whatever else. The user can also supply a numeric seed (and even a character seed) for maximum reproducibility. Use is simple and the code names produced come in various types too, contingent on what the user may be desiring as a code name or nickname. 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A typical use case considers patient with medically coded data, such as codes from the International Classification of Diseases ('ICD') or the Anatomic Therapeutic Chemical ('ATC') classification system. Functions of the package relies on a triad of objects: (1) case data with unit id:s and possible dates of interest; (2) external code data for corresponding units in (1) and with optional dates of interest and; (3) a classification scheme ('classcodes' object) with regular expressions to identify and categorize relevant codes from (2). It is easy to introduce new classification schemes ('classcodes' objects) or to use default schemes included in the package. Use cases includes patient categorization based on 'comorbidity indices' such as 'Charlson', 'Elixhauser', 'RxRisk V', or the 'comorbidity-polypharmacy' score (CPS), as well as adverse events after hip and knee replacement surgery. Package: r-cran-codestral Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-rstudioapi, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-codestral_0.0.2-1.ca2604.1_all.deb Size: 58988 MD5sum: 1b6561e7c8a4885c17df5c23e4e422cf SHA1: a5d2bf9b8475b3e4d9b356085f288e38e73a6601 SHA256: bd4a5bb05f6219e7129cdc9749e3eda47e5681856b45da72b7685161318c4085 SHA512: bdf471457c90fa3c209d3ad02f09ced83531779927ce81633281f105c5d003bea2aa13faf796bff00760241a7752e98c7c96d4ba9b5ca48df385054b3964ce7d Homepage: https://cran.r-project.org/package=codestral Description: CRAN Package 'codestral' (Chat and FIM with 'Codestral') Create an addin in 'Rstudio' to do fill-in-the-middle (FIM) and chat with latest Mistral AI models for coding, 'Codestral' and 'Codestral Mamba'. For more details about 'Mistral AI API': and . For more details about 'Codestral' model: ; about 'Codestral Mamba': . Package: r-cran-codetools Architecture: all Version: 0.2-20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-codetools_0.2-20-1.ca2604.1_all.deb Size: 89996 MD5sum: d74a71402678859835c8c053af644cbe SHA1: cfc40a51bd14c0f25fed921f7516eab4494f66ef SHA256: 90b2ef2c459e010bcd6cc4f7ede3cf5728b6025040bc1f2e92dc7435292e7ad1 SHA512: 5ef0aca5f9ada31dbae1b81e4cc6aac84f795cd660c5e1b09ec88e237c567d31521b8438aa04c337863cc4c72e194a3ed447a650dac725c186739826922f3f47 Homepage: https://cran.r-project.org/package=codetools Description: CRAN Package 'codetools' (Code Analysis Tools for R) Code analysis tools for R. Package: r-cran-codewhere Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr Suggests: r-cran-httptest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-codewhere_0.1.1-1.ca2604.1_all.deb Size: 14738 MD5sum: 2ec043fae76de0cb1374701cdfa52f45 SHA1: d5c470035c2f17c6bc736c8a158fd5a4883436fa SHA256: 8ecacbee2603e143fc440916d0c34d6ffb486edb60472b8c1f67ab1db02d5401 SHA512: 3efdee18cc33545aabb0214f6833b4b2e59c57f23d60c9fc425934fbd4b59ff58f2e6970fcf379e0217d55ace57bc419c0e5b4c54fb93375231e754b3d4792db Homepage: https://cran.r-project.org/package=codewhere Description: CRAN Package 'codewhere' (Find the Location of an R Package's Code) Find the location of the code for an R package based on the package's name or string representation. Checks on 'CRAN' based on information in the 'URL' field or 'BioConductor' and 'GitHub' based on constructing a URL, and verifies all paths via testing for a successful response. This can be useful when automating static code analysis based on a list of package names, and similar tasks. Package: r-cran-codexcopd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-codexcopd_0.1.0-1.ca2604.1_all.deb Size: 11498 MD5sum: 98e0f1f7bc88aa141d03df2d57e22cbd SHA1: 2ebcae48204c752394a0c23ba54c2628861606a2 SHA256: f63d7563e72b4abf64b427c55f6ceec054efe797038fa4b8b6e89717770ba2ee SHA512: 79fa4d05dd30f476afd81de5bd2e9eadcccbebbd920eb92ff89f8fd56bcb6597dc289c01a78fcc92b3a0229e06c780a2cfc78a2d52f6a6567b4f54fbc8711b58 Homepage: https://cran.r-project.org/package=codexcopd Description: CRAN Package 'codexcopd' (The CODEX (Comorbidity, Obstruction, Dyspnea, and PreviousSevere Exacerbations) Index: Short and Medium-Term Prognosis inPatients Hospitalized for Chronic Obstructive Pulmonary Disease(COPD) Exacerbations) Predicts 3 to 12 months prognosis in Chronic Obstructive Pulmonary Disease (COPD) patients hospitalized for severe exacerbations, as described in Almagro et al. (2014) . 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(2020) . Package: r-cran-coglyphr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sp Suggests: r-cran-imager, r-cran-png, r-cran-jpeg, r-cran-tiff, r-cran-bmp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-coglyphr_1.1.0-1.ca2604.1_all.deb Size: 128644 MD5sum: 5df2f127d04993ff2c9ac8f85b9b20bc SHA1: f57d271e71e439283a87f3e2e8afa1df41dec4a9 SHA256: 58f6dc2b8bf409e01d8ad866d0bf398b76b521b6d758aeb382e1f23afaf1c7dd SHA512: a4fe1407d4358b3f5a8fdfdff2dfd4ea171b9e91fd4b7fca558ddab3ee1f7a59727da6c9a2b7a6f26cd0fe235ebcf608fd5485fc4e3ea97533cb085f9791f576 Homepage: https://cran.r-project.org/package=coglyphr Description: CRAN Package 'coglyphr' (Compute Glyph Centers of Gravity from Image Data) Computes the center of gravity (COG) of character-like binary images using three different methods. This package provides functions for estimating stroke-based, contour-based, and potential energy-based COG. It is useful for analyzing glyph structure in areas such as visual cognition research and font development. The contour-based method was originally proposed by Kotani et al. (2004) and Kotani (2011) , while the potential energy-based method was introduced by Kotani et al. (2006) . Package: r-cran-cogmapr Architecture: all Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph, r-bioc-rgraphviz, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-car Filename: pool/dists/resolute/main/r-cran-cogmapr_0.9.5-1.ca2604.1_all.deb Size: 319602 MD5sum: 68ce55158c361048fa07d23d5bd4805f SHA1: 0a9beca525dd635a6329996889519f09d2667d4b SHA256: 8ed781f1353a40ca3d374fd8ad58aa8b8973b4c55625ac3e93aab0f97a2e220a SHA512: 4e126e587765f90f0c9a4ba5fd4d85883cf4ad81355f2c6e0f9fe4f578d13b8bf6d85c5ed5d1c6fda2d1f024f0d30848787d4cd20893d27e5f93b8dbafdbf9fc Homepage: https://cran.r-project.org/package=cogmapr Description: CRAN Package 'cogmapr' (Cognitive Mapping Tools Based on Coding of Textual Sources) Functions for building cognitive maps based on qualitative data. Inputs are textual sources (articles, transcription of qualitative interviews of agents,...). These sources have been coded using relations and are linked to (i) a table describing the variables (or concepts) used for the coding and (ii) a table describing the sources (typology of agents, ...). Main outputs are Individual Cognitive Maps (ICM), Social Cognitive Maps (all sources or group of sources) and a list of quotes linked to relations. This package is linked to the work done during the PhD of Frederic M. Vanwindekens (CRA-W / UCL) hold the 13 of May 2014 at University of Louvain in collaboration with the Walloon Agricultural Research Centre (project MIMOSA, MOERMAN fund). Package: r-cran-cognitor Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1547 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-config, r-cran-shinyjs, r-cran-httr, r-cran-dplyr, r-cran-base64enc, r-cran-jsonlite, r-cran-paws Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cognitor_1.0.5-1.ca2604.1_all.deb Size: 1311250 MD5sum: ec3d07b1b5c14ae113488dd84c228df9 SHA1: 860dd4e7f030441ecf6330e3f39aafcbdb415a8f SHA256: 8b0343e451f1f5ab2bcfab2cb97bd6c0109c2d185796dc4621dc74358795ac87 SHA512: 0b5f75319ecde90c38391d22222021d36075f4151e1fc91a647d15873248d754a6378dcbbefc474859ece7d6490ddefa05192e1788d2f85478817336b838ebd8 Homepage: https://cran.r-project.org/package=cognitoR Description: CRAN Package 'cognitoR' (Authentication for 'Shiny' Apps with 'Amazon Cognito') Provides authentication for Shiny applications using 'Amazon Cognito' ( ). Package: r-cran-cograph Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4897 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-r6 Suggests: r-cran-backbone, r-cran-braingraph, r-cran-centiserve, r-cran-colorspace, r-cran-digest, r-cran-dplyr, r-cran-gifski, r-cran-gridextra, r-cran-grimport2, r-cran-igraph, r-cran-influencer, r-cran-jsonlite, r-cran-knitr, r-cran-nestimate, r-cran-network, r-cran-qgraph, r-cran-rcolorbrewer, r-cran-reticulate, r-cran-rmarkdown, r-cran-rsvg, r-cran-scales, r-cran-sna, r-cran-testthat, r-cran-tidygraph, r-cran-tna, r-cran-tnet Filename: pool/dists/resolute/main/r-cran-cograph_2.1.1-1.ca2604.1_all.deb Size: 4221658 MD5sum: 683c1bea2ddac8e4115312a25dacb88e SHA1: 50d406ad6cc690b2b0630474fe20f3a67caefeed SHA256: 09b826a6b55d03f2ad1ccc6443f0c177a365c33416454eb84b14f5b97f565363 SHA512: d58d823d2adacb580b3ad3af3f10a407ce61d9d6c8b3b1151db7f99c815e147a29537491489382e091dc4037a277a7baf40b820ad595ba2c5b5c299c363f47a5 Homepage: https://cran.r-project.org/package=cograph Description: CRAN Package 'cograph' (Analysis and Visualization of Complex Networks) Provides tools for the analysis, visualization, and manipulation of dynamical, social (Saqr et al. (2024) ) and complex networks (Saqr et al. (2025) ). The package supports multiple network formats and offers flexible tools for heterogeneous, multi-layer, and hierarchical network analysis with simple syntax and extensive toolset. Package: r-cran-cohetsurr Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-mvtnorm, r-cran-mgcv, r-cran-grf Filename: pool/dists/resolute/main/r-cran-cohetsurr_2.0-1.ca2604.1_all.deb Size: 238774 MD5sum: 7237fc60388250fcc9c8306557d27120 SHA1: 25e5d758fe484073aef2c97d01a7886b0971a69b SHA256: 8d85e44c750ce43902d348bab73f7396710e4e9b82e4772b4307b5183e548a1f SHA512: f4a6fd5fa0d9128f9395c60bfbeecefcf089e422976b188f061184fcf7abb310dc20ccaadc7a7db3e2ff0f83b956725cda2d7e71d5f921f6b7d4fc625d314215 Homepage: https://cran.r-project.org/package=cohetsurr Description: CRAN Package 'cohetsurr' (Assessing Complex Heterogeneity in Surrogacy) Provides functions to assess complex heterogeneity in the strength of a surrogate marker with respect to multiple baseline covariates, in either a randomized treatment setting or observational setting. For a randomized treatment setting, the functions assess and test for heterogeneity using both a parametric model and a semiparametric two-step model. More details for the randomized setting are available in: Knowlton, R., Tian, L., & Parast, L. (2025). "A General Framework to Assess Complex Heterogeneity in the Strength of a Surrogate Marker," Statistics in Medicine, 44(5), e70001 . For an observational setting, functions in this package assess complex heterogeneity in the strength of a surrogate marker using meta-learners, with options for different base learners. More details for the observational setting will be available in the future in: Knowlton, R., Parast, L. (2025) "Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners." A tutorial for this package can be found at . Package: r-cran-cohortbuilder Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-jsonlite, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-glue, r-cran-ggplot2, r-cran-rlang, r-cran-formatr, r-cran-collapse Suggests: r-cran-querybuilder, r-cran-vdiffr, r-cran-testthat, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cohortbuilder_0.4.0-1.ca2604.1_all.deb Size: 578696 MD5sum: d2f7f597621a57ab3bdc41a304aec464 SHA1: fedcf24776335f0dad17382996bdc62545672b4c SHA256: a2f4fb6b34585c39c50f6fe73b05689fa6c88637a3efad05c841943bd36b0bf7 SHA512: d63d00c53fc777d6e2829c92e70c49109b07e4d476ddc5fa10f8fe03425fb9b76750c58caec4458aeb2fa47aa0f034543b454eaf7d87913124c5b0d2d75b8d23 Homepage: https://cran.r-project.org/package=cohortBuilder Description: CRAN Package 'cohortBuilder' (Data Source Agnostic Filtering Tools) Common API for filtering data stored in different data models. Provides multiple filter types and reproducible R code. Works standalone or with 'shinyCohortBuilder' as the GUI for interactive Shiny apps. Package: r-cran-cohortcharacteristics Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-cli, r-cran-stringr, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-snakecase, r-cran-lifecycle, r-cran-purrr, r-cran-clock Suggests: r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-visomopresults, r-cran-cohortconstructor, r-cran-covr, r-cran-dbi, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-drugutilisation, r-cran-dt, r-cran-duckdb, r-cran-flextable, r-cran-ggplot2, r-cran-ggpubr, r-cran-glue, r-cran-gt, r-cran-here, r-cran-hmisc, r-cran-htmltools, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-plotly, r-cran-png, r-cran-reactable, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsvg, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-withr, r-cran-extrafont Filename: pool/dists/resolute/main/r-cran-cohortcharacteristics_1.1.3-1.ca2604.1_all.deb Size: 2679852 MD5sum: 5f576d3734e46143c85ac4d0f9b50fbe SHA1: 8904d6c04e913954dc26327844750afc2137741c SHA256: 92b2cb9a24d53b2b79c04decd5305367ff4bed6a8728f94689cf14ecf8eb0b9e SHA512: 47b626bd7d6d8e96ff7e1fbcbd509ab67f0275883cc22c0d5cc09ab48e771b8294d4b02fb3baeac2c3e5ef5fbaf53c57a6a42cbb9d83ed8f8476a7ba8ea315da Homepage: https://cran.r-project.org/package=CohortCharacteristics Description: CRAN Package 'CohortCharacteristics' (Summarise and Visualise Characteristics of Patients in the OMOPCDM) Summarise and visualise the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Package: r-cran-cohortconstructor Architecture: all Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2018 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-codelistgenerator, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cdmconnector, r-cran-circer, r-cran-cohortcharacteristics, r-cran-covr, r-cran-dbi, r-cran-diagrammer, r-cran-drugutilisation, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-here, r-cran-incidenceprevalence, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-sqlrender, r-cran-stringr, r-cran-testthat, r-cran-tictoc, r-cran-visomopresults, r-cran-systemfonts Filename: pool/dists/resolute/main/r-cran-cohortconstructor_0.6.3-1.ca2604.1_all.deb Size: 1209202 MD5sum: 6602240b4859a77ab6c877318e745dea SHA1: e33ca12060f57ba45edbab86189ffaec6fda40bf SHA256: 55f34663b667ea3b33b322a171fab182936f3c43dab021ba3e2133fe8e916068 SHA512: 67fe1756d10b40ace4340fb9c8cf196959bf7bb738c3cb5cb0a3bf64ecda4a1ab2713bbd3d623e9e34aa58947717de2ecc49b5cb90ef3cedf6a57e680b6f2385 Homepage: https://cran.r-project.org/package=CohortConstructor Description: CRAN Package 'CohortConstructor' (Build and Manipulate Study Cohorts Using a Common Data Model) Create and manipulate study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Package: r-cran-cohortcontrast Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-cdmconnector, r-cran-cohortconstructor, r-cran-omopgenerics, r-cran-lubridate, r-cran-doparallel, r-cran-foreach, r-cran-data.table, r-cran-cli, r-cran-jsonlite, r-cran-nanoparquet Suggests: r-cran-testthat, r-cran-patientprofiles, r-cran-duckdb, r-cran-dbi, r-cran-rpostgres, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-processx, r-cran-bit64, r-cran-reshape2, r-cran-igraph, r-cran-matrix, r-cran-cluster, r-cran-vegan, r-cran-reticulate Filename: pool/dists/resolute/main/r-cran-cohortcontrast_1.0.0-1.ca2604.1_all.deb Size: 4307550 MD5sum: bd1346aaffd72b953665827d137559e1 SHA1: 4e741a13b3a43c42c79320e9be7c93baf4de324b SHA256: b1449d30c0d51600cadf3023ea238fb9c2b948a0c0f218ff1a3b090cfeea21c8 SHA512: a2c67b3b2a9ff040cc96740e75b596ec7170b09c27362f56a68c3a115c4059944b66c6e10224c6782bb07317c5b2f739071662e8e936933a0eadd096c3442cb4 Homepage: https://cran.r-project.org/package=CohortContrast Description: CRAN Package 'CohortContrast' (Enrichment Analysis of Clinically Relevant Concepts in CommonData Model Cohort Data) Identifies clinically relevant concepts in Observational Medical Outcomes Partnership Common Data Model cohorts using an enrichment-based workflow. Defines target and control cohorts and extracts medical interventions that are over-represented in the target cohort during the observation period. Users can tune filtering and selection thresholds. The workflow includes chi-squared tests for two proportions with Yates continuity correction, logistic tests, and hierarchy and correlation mappings for relevant concepts. The results can be optionally explored using the bundled graphical user interface. For workflow details and examples, see . Package: r-cran-cohortgenerator Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2846 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-databaseconnector, r-cran-r6, r-cran-checkmate, r-cran-digest, r-cran-dplyr, r-cran-lubridate, r-cran-parallellogger, r-cran-readr, r-cran-rlang, r-cran-jsonlite, r-cran-resultmodelmanager, r-cran-sqlrender, r-cran-stringi, r-cran-tibble Suggests: r-cran-circer, r-cran-duckdb, r-cran-eunomia, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-zip Filename: pool/dists/resolute/main/r-cran-cohortgenerator_1.1.0-1.ca2604.1_all.deb Size: 1411228 MD5sum: fd69586bbfdcdcbbab042d1a28e96f37 SHA1: 0cbcebb640a86bc50553005e181634ddbd34fc2f SHA256: 9ba67e3f441dae0ddbd295f22794062810e1ec3ce3719309c43908cf7e2df5dc SHA512: e4552b4ac110cc438a097e733ddbc9e9041f706e830af5367a614f3ab9086099fd7e7dea54c37441fec7e5d90db14feb5b4c14f802fb4035c3dc1ba634556739 Homepage: https://cran.r-project.org/package=CohortGenerator Description: CRAN Package 'CohortGenerator' (Cohort Generation for the OMOP Common Data Model) Generate cohorts and subsets using an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) Database. Cohorts are defined using 'CIRCE' () or SQL compatible with 'SqlRender' (). Package: r-cran-cohortplat Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-plotly, r-cran-tidyr, r-cran-doparallel, r-cran-foreach, r-cran-openxlsx, r-cran-forcats, r-cran-epitools, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-cohortplat_1.0.5-1.ca2604.1_all.deb Size: 442986 MD5sum: 9420efbf48c00398ea67993e6e51439f SHA1: fcddb64b517ac880d04ebe46ad9bb5537a39d4dc SHA256: c65e5077e8df921a0c06504b14b3d1dc7011456d861633f9739f1d850c5ea1ad SHA512: 95b0043af27ef308e05d5229071833c186c27e01a3f568d1cae2761f261ce79806e572bdf95266fbf282f880a6fef4305e3fc7c886497253ff746dd0329560b5 Homepage: https://cran.r-project.org/package=CohortPlat Description: CRAN Package 'CohortPlat' (Simulation of Cohort Platform Trials for Combination Treatments) A collection of functions dedicated to simulating staggered entry platform trials whereby the treatment under investigation is a combination of two active compounds. In order to obtain approval for this combination therapy, superiority of the combination over the two active compounds and superiority of the two active compounds over placebo need to be demonstrated. A more detailed description of the design can be found in Meyer et al. and a manual in Meyer et al. . Package: r-cran-cohorts Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-dtplyr, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cohorts_1.0.1-1.ca2604.1_all.deb Size: 1131136 MD5sum: 1725f7487fa7d4e5a1253d141bc48f1b SHA1: 73d1e877fbb411e81e91949d42776d19b64d869c SHA256: e2b51bb2fbbcb6758478050605a8823091c98b2da7511350ec9c9bbd95191a48 SHA512: 63eaeedd62249bc9ede1ee3d04a306d2367d4469aa768d129cc32744d6acebf9958e69e0221555ea75c4120b5d2414e7714a651490de373f2b733ef815000a79 Homepage: https://cran.r-project.org/package=cohorts Description: CRAN Package 'cohorts' (Cohort Analysis Made Easy) Functions to simplify the process of preparing event and transaction for cohort analysis. Package: r-cran-cohortsurvival Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2997 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-clock, r-cran-dbi, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-survival, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-codelistgenerator, r-cran-roxygen2, r-cran-knitr, r-cran-tictoc, r-cran-rmarkdown, r-cran-ggplot2, r-cran-patchwork, r-cran-cmprsk, r-cran-duckdb, r-cran-gt, r-cran-flextable, r-cran-scales, r-cran-visomopresults, r-cran-extrafont, r-cran-cdmconnector Filename: pool/dists/resolute/main/r-cran-cohortsurvival_1.1.1-1.ca2604.1_all.deb Size: 1741276 MD5sum: 1a56a68b71f883826cafaaac6f0a5a5a SHA1: 593637a5a7c9331a95eede5aafc677d367f15d1e SHA256: a7997facc700d8b6268bf40d24694d869ca1f2442c6a722855e4162d9b313199 SHA512: ef362b52ce77362a09a6b4a1eb2e12146b03711c949c3ad0aee0bc2ce39484cb03315c52471489e837ea14eee8581d612cb540e65555d12ec114e38c5343d865 Homepage: https://cran.r-project.org/package=CohortSurvival Description: CRAN Package 'CohortSurvival' (Estimate Survival from Common Data Model Cohorts) Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts. 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Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes 'Epi' package 'Lexis' data. 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Package: r-cran-coinmarketcapr Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-ggplot2, r-cran-data.table, r-cran-curl, r-cran-cli, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-coinmarketcapr_0.4-1.ca2604.1_all.deb Size: 75706 MD5sum: ce3a1f9b51f34de54139a2dbc22741cf SHA1: 06d4f248f9c0a52742292bad7de2c8c14e5bf20f SHA256: d6ab07c9d3d55ba2b6122d394be21e3f752e5b2bc41ebd96ee1c8d77e9d6f68c SHA512: cbe91d7cbed3cf45afa6063d0cde65a9af5cfe8a68e1b327b854001c40a84a3e1fc091e2f2f4ff3ad71ff4f7a007d6696c36a987454a8a2284006e16e268e93f Homepage: https://cran.r-project.org/package=coinmarketcapr Description: CRAN Package 'coinmarketcapr' (Get 'Cryptocurrencies' Market Cap Prices from Coin Market Cap) Extract and monitor price and market cap of 'Cryptocurrencies' from 'Coin Market Cap' . 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The package implements seven classical approaches—Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glaz—along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation. Package: r-cran-coinprofile Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-plyr, r-cran-coin, r-cran-rdpack, r-cran-exactranktests, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-coinprofile_0.1.9-1.ca2604.1_all.deb Size: 188068 MD5sum: db1d1adb5c7e7ae619d3ea0780d6fb95 SHA1: 68dff54a8e0f22fc612633da23daa2fa12f99408 SHA256: 2c6dc5e338faad47bf35a49f425f4163de6feefa47a54cb6dcee6ce54f1ba4cc SHA512: cb05c3872445c1b10d954315c03c5f13e976124efa70e5016ab21f3f0bd6a9809aab35500fcda1ff3943d483dcc18791631b65247028178cda740b9b57c13b8c Homepage: https://cran.r-project.org/package=Coinprofile Description: CRAN Package 'Coinprofile' (Coincident Profile) Builds the coincident profile proposed by Martinez, W and Nieto, Fabio H and Poncela, P (2016) . This methodology studies the relationship between a couple of time series based on the the set of turning points of each time series. The coincident profile establishes if two time series are coincident, or one of them leads the second. 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It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results. Package: r-cran-coint Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-timeseries Suggests: r-cran-cointreg, r-cran-forecast, r-cran-timedate, r-cran-urca, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-coint_0.0.3-1.ca2604.1_all.deb Size: 439660 MD5sum: dc079d16392fd5dd8b8750bce5bf146d SHA1: 5b4db9a38c14571b5149f1b26a35b8228a724c81 SHA256: 9c4169fb8d1ec528fecf64e8bb62df2a1c34997b13a11638a439513cfe376024 SHA512: eb2c8f1b2ac43e60a68e9757dd212148a824b09aa4df0adab8e9ccff5e69947afbecd1cd53cead2aeaa4082f03de417c6221aba74996fb29685bc1a3f12f41c5 Homepage: https://cran.r-project.org/package=COINT Description: CRAN Package 'COINT' (Unit Root Tests with Structural Breaks and Fully-ModifiedEstimators) Procedures include Phillips (1995) FMVAR , Kitamura and Phillips (1997) FMGMM , Park (1992) CCR , and so on. Tests with 1 or 2 structural breaks include Gregory and Hansen (1996) , Zivot and Andrews (1992) , and Kurozumi (2002) . 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Package: r-cran-cointmonitor Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cointreg, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cointmonitor_0.1.0-1.ca2604.1_all.deb Size: 157738 MD5sum: 723d90d627fbce11d8aa34af382020bd SHA1: e39a68c7d2216dcda51ce7fd0750430ba33bbd8b SHA256: 85cead099c95907ca234b633cf2b64e4329cc99fa32df479ae1d25d436b8f843 SHA512: d3a0791828471be822e53bd76034a3d18f26d48ebaad5198a1e8aa841e6669b58ebe3aab691fec4bcfe40ef13146612906523b63b50bf9777a1efdac97bf7048 Homepage: https://cran.r-project.org/package=cointmonitoR Description: CRAN Package 'cointmonitoR' (Consistent Monitoring of Stationarity and CointegratingRelationships) We propose a consistent monitoring procedure to detect a structural change from a cointegrating relationship to a spurious relationship. The procedure is based on residuals from modified least squares estimation, using either Fully Modified, Dynamic or Integrated Modified OLS. It is inspired by Chu et al. (1996) in that it is based on parameter estimation on a pre-break "calibration" period only, rather than being based on sequential estimation over the full sample. See the discussion paper for further information. This package provides the monitoring procedures for both the cointegration and the stationarity case (while the latter is just a special case of the former one) as well as printing and plotting methods for a clear presentation of the results. 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It is well known that in a cointegrating regression the ordinary least squares (OLS) estimator of the parameters is super-consistent, i.e. converges at rate equal to the sample size T. When the regressors are endogenous, the limiting distribution of the OLS estimator is contaminated by so-called second order bias terms, see e.g. Phillips and Hansen (1990) . The presence of these bias terms renders inference difficult. Consequently, several modifications to OLS that lead to zero mean Gaussian mixture limiting distributions have been proposed, which in turn make standard asymptotic inference feasible. These methods include the fully modified OLS (FM-OLS) approach of Phillips and Hansen (1990) , the dynamic OLS (D-OLS) approach of Phillips and Loretan (1991) , Saikkonen (1991) and Stock and Watson (1993) and the new estimation approach called integrated modified OLS (IM-OLS) of Vogelsang and Wagner (2014) . The latter is based on an augmented partial sum (integration) transformation of the regression model. IM-OLS is similar in spirit to the FM- and D-OLS approaches, with the key difference that it does not require estimation of long run variance matrices and avoids the need to choose tuning parameters (kernels, bandwidths, lags). However, inference does require that a long run variance be scaled out. This package provides functions for the parameter estimation and inference with all three modified OLS approaches. That includes the automatic bandwidth selection approaches of Andrews (1991) and of Newey and West (1994) as well as the calculation of the long run variance. 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Package: r-cran-colocboost Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ashr, r-cran-mass, r-cran-susier Filename: pool/dists/resolute/main/r-cran-colocboost_1.0.7-1.ca2604.1_all.deb Size: 3216738 MD5sum: 5daaeeb5f909f229687790c6f1a91876 SHA1: a2984210029ccfdb5940d6052c2074f3d6ecb903 SHA256: c2025c4b171e207c4cf255adb4548d4a304d901b5b5b3b627fb9cf90a8bf49d6 SHA512: 63fe32bdcee4397fb3993190807261b747f2f291a95c6e674964b72195dabae4d8d8d54335e779cde1cb7a4de4ae4ab57a5183949fae358a5996f02b44bc41f6 Homepage: https://cran.r-project.org/package=colocboost Description: CRAN Package 'colocboost' (Multi-Context Colocalization Analysis for QTL and GWAS Studies) A multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies. The ColocBoost model is described in Cao et. al. (2025) . Package: r-cran-colocproptest Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-data.table, r-cran-car, r-cran-coloc Suggests: r-cran-knitr, r-cran-plotrix, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-colocproptest_0.9.3-1.ca2604.1_all.deb Size: 240804 MD5sum: fff89816e46820f75ca32d424054ec48 SHA1: d53678da19d34d812c22372fc998ba2fc7e3f8c8 SHA256: ca7dc3f46cfc829b806e8ecb23fe7d4200d2049799e6229919a71934aa9b02f0 SHA512: 14414a8d5635469cd067de1b8f1d25cbaec5ba5b92c46c1bb4d48b3015a26d127d9ba0d9048567cb333b73e6653f0da059bee4b9ae03a6c3cd8f232c316ec985 Homepage: https://cran.r-project.org/package=colocPropTest Description: CRAN Package 'colocPropTest' (Proportional Testing for Colocalisation Analysis) Colocalisation analysis tests whether two traits share a causal genetic variant in a specified genomic region. Proportional testing for colocalisation has been previously proposed [Wallace (2013) ], but is reimplemented here to overcome barriers to its adoption. Its use is complementary to the fine- mapping based colocalisation method in the 'coloc' package, and may be used in particular to identify false "H3" conclusions in 'coloc'. Package: r-cran-colombiapi Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5240 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-colombiapi_0.3.2-1.ca2604.1_all.deb Size: 4800652 MD5sum: 84d37fcb76043ebe94c323b15722e56a SHA1: 1e72336f061f32adbd62cd10147875ebfdeae4cc SHA256: 67b8eeaab43c86e2d85b1dacf0d5e36e4c3e85ce2f1ca6df02daf8ccd36dcf7d SHA512: 7c33d943c6a1bc8fe2dc7f9a15dceb3340c79e23cbd40ebe63dbadc95dd3aee34f3a775d7a0ee9216c20ae819980b483eb91a2548d0448cfa9ab55bfe5e1f463 Homepage: https://cran.r-project.org/package=ColombiAPI Description: CRAN Package 'ColombiAPI' (Access Colombian Data via APIs and Curated Datasets) Provides a comprehensive interface to access diverse public data about Colombia through multiple APIs and curated datasets. The package integrates four different APIs: 'API-Colombia' for Colombian-specific data including geography, culture, tourism, and government information; 'World Bank API' for economic and demographic indicators; 'Nager.Date' for public holidays; and 'REST Countries API' for general country information. The package enables users to explore various aspects of Colombia such as geographic locations, cultural attractions, economic indicators, demographic data, and public holidays. Additionally, 'ColombiAPI' includes curated datasets covering Bogota air stations, business and holiday dates, public schools, Colombian coffee exports, cannabis licenses, Medellin rainfall, malls in Bogota, as well as datasets on indigenous languages, student admissions and school statistics, forest liana mortality, municipal and regional data, connectivity and digital infrastructure, program graduates, vehicle counts, international visitors, and GDP projections. These datasets provide users with a rich and multifaceted view of Colombian social, economic, environmental, and technological information, making 'ColombiAPI' a comprehensive tool for exploring Colombia's diverse data landscape. For more information on the APIs, see: 'API-Colombia' , 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-colopendata Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1745 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-config, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-sf, r-cran-stringdist, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-leaflet, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-colopendata_1.0.0-1.ca2604.1_all.deb Size: 1144378 MD5sum: 3db4fb3a69a278b391c84303a43f1532 SHA1: e3c45d8e1a1eb1b1a7be5e3287d41a9f641950ee SHA256: 56dcf8433b2de5eb8a8d5e5a82ae7b6d7ba3a5454b54627dc8742063acb72028 SHA512: 687d66abc8e66123470b386ea5c75dc6705ff1c973dcb1d0567cadc6103a2d7b12a67a4e953e1326690cb37516bbd57def83e92b9aae349a6c0e04fa371a4220 Homepage: https://cran.r-project.org/package=ColOpenData Description: CRAN Package 'ColOpenData' (Download Colombian Demographic, Climate and Geospatial Data) Downloads wrangled Colombian socioeconomic, geospatial,population and climate data from DANE (National Administrative Department of Statistics) and IDEAM (Institute of Hydrology, Meteorology and Environmental Studies). It solves the problem of Colombian data being issued in different web pages and sources by using functions that allow the user to select the desired database and download it without having to do the exhausting acquisition process. Package: r-cran-colorblindcheck Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-spacesxyz Suggests: r-cran-rcartocolor, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-colorblindcheck_1.0.4-1.ca2604.1_all.deb Size: 68448 MD5sum: 7201dc72f4d7e37e0a270fcfc183d969 SHA1: f786e606fca424916ef2164a160772fac1d984ee SHA256: 690f581ef205f6af824f634a7108ca8a187135397751e84fd9adba8421825595 SHA512: 98aa493cabf46a4bfca10d783abf3d7fe98cc8ea124fbe5cc193e765c6ca49197bb4dac02cbe71219eb91d15b7241cef209d063cafcadb357e9f8ee6ee086468 Homepage: https://cran.r-project.org/package=colorblindcheck Description: CRAN Package 'colorblindcheck' (Check Color Palettes for Problems with Color Vision Deficiency) Compare color palettes with simulations of color vision deficiencies - deuteranopia, protanopia, and tritanopia. It includes calculation of distances between colors, and creating summaries of differences between a color palette and simulations of color vision deficiencies. This work was inspired by the blog post at . 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Uses CIELAB, RGB, or HSV color spaces. Originally written for use with organism coloration (reef fish color diversity, butterfly mimicry, etc), but easily applicable for any image set. 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Visualize results of hierarchical clustering analyses as dendrograms whose leaves and labels are colored according to sample grouping. Assess whether data point grouping aligns to naturally occurring clusters. 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Includes palettes and functionality from popular packages such as 'viridis', 'RColorBrewer', and base R 'grDevices', as well as 'ggplot2' plot bindings. Users can generate perceptually uniform and colorblind-friendly palettes, adjust palettes in HSL and RGB color spaces, map color gradients to value ranges, and create color-generating functions. 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In addition to enabling display of sequential change in distributions through the use of small multiples, 'colorist' provides functions for extracting several features of interest from a sequence of distributions and for visualizing those features using HCL (hue-chroma-luminance) color palettes. Resulting maps allow for "fair" visual comparison of intensity values (e.g., occurrence, abundance, or density) across space and time and can be used to address questions about where, when, and how consistently a species, group, or individual is likely to be found. Package: r-cran-colorize Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-knitr Suggests: r-cran-quarto, r-cran-glue, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-colorize_0.2.1-1.ca2604.1_all.deb Size: 632612 MD5sum: e71e11f74dbfd7ab9565d0604bd80677 SHA1: 47f749b87df70b38f1147d9b6ca012d6ae55608c SHA256: 53d4bcec116d201ce6f440ae9b5f3fa3653fa9259870b0e59b6580bff8f08583 SHA512: 0ee6fc844bcfa6252b60217f689cf59203fb480646a9ad143239665bc2024cbf9fa40560cab7f10a3677ed8e577223dc075e1c5437ea32a3ac8ee29a6adeebc0 Homepage: https://cran.r-project.org/package=colorize Description: CRAN Package 'colorize' (Render Text in Color for Markdown/Quarto Documents) Provides some simple functions for printing text in color in 'markdown' or 'Quarto' documents, to be rendered as HTML or LaTeX. 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In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with 'ggplot2'. 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When many groups are plotted at the same time on multiple axes, for instance stacked bars or scatter plots, effectively ordering colors becomes difficult. This tool iterates through color combinations to find the best solution to maximize visual distinctness of nearby groups, so plots are more friendly toward colorblind users. This is achieved by two distance measurements, distance between groups within the plot, and CIELAB color space distances between colors as described in Carter et al., (2018) . 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This package is deprecated and will someday be removed; for reasons and details please see the README file. Package: r-cran-colorsgen Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace Filename: pool/dists/resolute/main/r-cran-colorsgen_1.0.0-1.ca2604.1_all.deb Size: 23054 MD5sum: 82bdc776bdbb81cae0f4f31645dabaf2 SHA1: 8012640f7364d5c0064567d5df4fb9ceb0aaf5e0 SHA256: f011b27a844653833ed53bef35b315d3c25a9c32d923602c5fcb0afefb7d9d37 SHA512: 57baabfb327351ce3c8ec5e31c320adf1bcf4ae179d040137063889f0b7c11f671768ae7c6390b53c3cfce8cabfa1ef694f4f12e8eb377c93472b4fbea9ea865 Homepage: https://cran.r-project.org/package=colorsGen Description: CRAN Package 'colorsGen' (Generation of Random Colors) Generation of random colors, possibly with a given hue or a given luminosity. This is a port of the JavaScript library 'randomColor' . 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Build complex systems from simpler parts using a spectral product algebra. For light sources, compute CCT, CRI, SSI, and IES TM-30 reports. For object colors, compute optimal colors and Logvinenko coordinates. Work with the standard CIE illuminants and color matching functions, and read spectra from text files, including CGATS files. Estimate a spectrum from its response. A user guide and 9 vignettes are included. Package: r-cran-colour Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3425 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-jpeg, r-cran-png, r-cran-httr, r-cran-pixmap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales Filename: pool/dists/resolute/main/r-cran-colour_0.1.1-1.ca2604.1_all.deb Size: 2545496 MD5sum: f184f01cee4829f264e9dbbf95c9c0af SHA1: a780b9963a8047cbd7b26bb9c624fc730c2bf9c6 SHA256: 12ca49f2535ac47cee7bf5bd557876171d017920b9bf6933b4687a7e00bc1a39 SHA512: 3dd39245755483b9f8a68a6733d953077d7c29b6fd3c310a841f3df1c121c8a140e2cfdf0f409048b020c8401c0a9c05bacfc011df810c96354a50dbff363ebb Homepage: https://cran.r-project.org/package=colouR Description: CRAN Package 'colouR' (Create Colour Palettes from Images) Can take in images in either .jpg, .jpeg, or .png format and creates a colour palette of the most frequent colours used in the image. 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Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes cyclic and bivariate color palettes. Furthermore, a color for missing values is assigned to each palette. 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Another constraint is that the sum of the beta coefficients equals a constant. References: Hansen, B. E. (2022). Econometrics, Princeton University Press. . Package: r-cran-colt Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-colt_0.1.1-1.ca2604.1_all.deb Size: 165826 MD5sum: c34387881a991f6b1f9c74396dcf01bb SHA1: 6375aee88eb58832d78e2c89c56ba24e2b832f35 SHA256: c46ed3107fc68983c6923e35d802dd8b9acd3210039219703578c6be31eab429 SHA512: 0f41c00b370f13b915d7caf849f9ef2aa661e890dfd37375920dbb95ec9f4a4a17b4d4640bbc1d52fedf4dff4aea7ca2ddc1e471cf14832024fa7dc5e2768d7b Homepage: https://cran.r-project.org/package=colt Description: CRAN Package 'colt' (Command-Line Color Themes) A collection of command-line color styles based on the 'crayon' package. 'Colt' styles are defined in themes that can easily be switched, to ensure command line output looks nice on dark as well as light consoles. Package: r-cran-comato Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-lattice, r-cran-gdata, r-cran-xml, r-cran-cluster, r-cran-clustersim Filename: pool/dists/resolute/main/r-cran-comato_1.1-1.ca2604.1_all.deb Size: 156230 MD5sum: 7a29ffa36659122f2b4dd7d6f669842c SHA1: a7067645c9e6ee733a4646f0657f3408f6fc0a32 SHA256: 8ee9dcd221f0160090015f4fc57ec1284f93f1f8fc623ec641e373f7ae15b1d1 SHA512: 0ee6c48f36dbace27b4cb72edf5e6edae7d46ce92bd82766eb4f458e0053bbd207072348c39fdd74b90782d430e24e7e609c2350a41c5daac10da4e768ab1465 Homepage: https://cran.r-project.org/package=comato Description: CRAN Package 'comato' (Analysis of Concept Maps and Concept Landscapes) Provides methods for the import/export and automated analysis of concept maps and concept landscapes (sets of concept maps). Package: r-cran-combat.enigma Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-caret, r-cran-matrix, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-combat.enigma_1.1.1-1.ca2604.1_all.deb Size: 102406 MD5sum: 322c5f80836aaebb198cb18a37226c0a SHA1: 1555af7418018e99b4a6bcf4aae65b50f280a159 SHA256: 6dc359f3e9d7ac5233b5cfe2777d3ac18caf6e8a94db7e4f8bc20e02c37d2d24 SHA512: c5fdaad88baf6a9786a02c144808099ee88a3c6633842dc669c35b201a1b33ed36ada0848d3795a6dad44d68097480d2825a38d84aff4f1f241c514c6d911bf3 Homepage: https://cran.r-project.org/package=combat.enigma Description: CRAN Package 'combat.enigma' (Fit and Apply ComBat, LMM, or Prescaling Harmonization forENIGMA and Other Multisite MRI Data) Fit and apply ComBat, linear mixed-effects models (LMM), or prescaling to harmonize magnetic resonance imaging (MRI) data from different sites. Briefly, these methods remove differences between sites due to using different scanning devices, and LMM additionally tests linear hypotheses. As detailed in the manual, the original ComBat function was first modified for the harmonization of MRI data (Fortin et al. (2017) ) and then modified again to create separate functions for fitting and applying the harmonization and allow missing values and constant rows for its use within the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium (Radua et al. (2020) ); this package includes the latter version. LMM calls "lme" massively considering specific brain imaging details. Finally, prescaling is a good option for fMRI, where different devices can have varying units of measurement. Package: r-cran-combat Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor Filename: pool/dists/resolute/main/r-cran-combat_0.0.4-1.ca2604.1_all.deb Size: 31194 MD5sum: bb6c518f3ae759661d3114e701011c3f SHA1: 68e3f0030eccef0ca17fcd9abbe1afe5ecbbe5e5 SHA256: 07de7ac1ac5bbfeeb8936242ae69ed5bdcfa5ac58dae880ad52deaf4a08d6123 SHA512: c9f191443db579d9e51f84867cd8016bc5cf4192f4142931d9ad1bb1f1e5d5b06ed0c95d600368ddd030b3366d763418dfe642c43f591d0a6eb31bbea00d21c4 Homepage: https://cran.r-project.org/package=COMBAT Description: CRAN Package 'COMBAT' (A Combined Association Test for Genes using Summary Statistics) Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) . Package: r-cran-combatfamqc Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2414 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-dt, r-cran-shiny, r-cran-car, r-cran-broom, r-cran-pbkrtest, r-cran-rtsne, r-cran-mdmr, r-cran-gamlss, r-cran-lme4, r-cran-mgcv, r-cran-bslib, r-cran-shinydashboard, r-cran-gamlss.dist, r-cran-invgamma, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-testthat, r-cran-remotes, r-cran-plotly, r-cran-quarto, r-cran-spelling, r-cran-systemfonts Filename: pool/dists/resolute/main/r-cran-combatfamqc_1.0.6-1.ca2604.1_all.deb Size: 2236238 MD5sum: 6f3f3f4b260ce19a2d3fee103181057e SHA1: 316fcb0fe9c3ae0762ee4bb6cdad76129ebd58c2 SHA256: f0bfe3d93623b7bc4fde98ed6c996f8084004bd5d517890f600d0403c798f5e0 SHA512: 5e7750c4089844daee259fa85d9731f7b38f6fe0d009c3698f27e2f5e7a308c719ed907c3ab3e0c5911cb223a0bd314ac3385cfd1cc9b70b68e3a05ca3e83bb3 Homepage: https://cran.r-project.org/package=ComBatFamQC Description: CRAN Package 'ComBatFamQC' (Comprehensive Batch Effect Diagnostics and Harmonization) Provides a comprehensive framework for batch effect diagnostics, harmonization, and post-harmonization downstream analysis. Features include interactive visualization tools, robust statistical tests, and a range of harmonization techniques. Additionally, 'ComBatFamQC' enables the creation of life-span age trend plots with estimated age-adjusted centiles and facilitates the generation of covariate-corrected residuals for analytical purposes. Methods for harmonization are based on approaches described in Johnson et al., (2007) , Beer et al., (2020) , Pomponio et al., (2020) , and Chen et al., (2021) . 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Also see Bayer and Hanck (2013) . 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Package: r-cran-combinedevents Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/resolute/main/r-cran-combinedevents_0.1.1-1.ca2604.1_all.deb Size: 90330 MD5sum: 3a1ca36c88378d9b6939adda7c3e099c SHA1: 94d8220921daf574a71ee3150a612f4a31032ecb SHA256: 7a5f11a4556a092336052810bb5dedf98a8de3a220224ae7d4709aaee7e92db7 SHA512: 8c9f5ec3fff49d4761fc2010eb00b36953ba1d3145158be4faa3721372a17dd802eb123da1dab91647513216e42e38ddbed1f435fcd89d7f4429e3916639fc96 Homepage: https://cran.r-project.org/package=combinedevents Description: CRAN Package 'combinedevents' (Calculate Scores and Marks for Track and Field Combined Events) Includes functions to calculate scores and marks for track and field combined events competitions. The functions are based on the scoring tables for combined events set forth by the International Association of Athletics Federation (2001). 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Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested. Package: r-cran-combins Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-combins_1.2-1.ca2604.1_all.deb Size: 55724 MD5sum: cb52020bd1e517ba26e03d889e860102 SHA1: fdb19698a1e2a21a6a82196949666dd41b79cba1 SHA256: 75f199e103697dce91c0de91b52ecf8aa851bb340a274763b56b5afccde18b88 SHA512: 63a53bc7944721ecc25086a58cd32bf1ddde559fd926bc90b20cc0b981788a28fcc9a46b14c12f7bce6f2962644bf8be4ab8ba4841977d921c2f53b8183c4d79 Homepage: https://cran.r-project.org/package=CombinS Description: CRAN Package 'CombinS' (Construction Methods for Series of PBIB Designs via CombinatoryMethod S) Provides constructions of series of partially balanced incomplete block designs (PBIB) based on the combinatory method S, introduced by Rezgui et al. (2014) . This package also offers the associated U-type designs. Version 1.1-1 generalizes the approach to designs with v = wnl treatments. It includes various rectangular and generalized rectangular right angular association schemes with 4, 5, and 7 associated classes. Package: r-cran-combiroc Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1962 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-gtools, r-cran-proc, r-cran-stringr, r-cran-moments Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-httr, r-cran-seurat Filename: pool/dists/resolute/main/r-cran-combiroc_0.3.4-1.ca2604.1_all.deb Size: 1245436 MD5sum: 7a8b9330fe75d144f8f0e9f76f18f6e2 SHA1: daa3d21a1f6f6ad7dc5c273b2b9bb92b432f8806 SHA256: 65e6bc7e25ec6ba2f5b8481073b4711100174a2a464cc807fa73ec31eeb9999c SHA512: 887bf692a64ef0302416de4f61973661705120e87aea85fd2459f5cf3d7bf62eb6b1978671f1fbf38af6d76319bbb22c525f2507e44b9638c4634f6523ccc9bd Homepage: https://cran.r-project.org/package=combiroc Description: CRAN Package 'combiroc' (Selection and Ranking of Omics Biomarkers Combinations Made Easy) Provides functions and a workflow to easily and powerfully calculating specificity, sensitivity and ROC curves of biomarkers combinations. Allows to rank and select multi-markers signatures as well as to find the best performing sub-signatures, now also from single-cell RNA-seq datasets. The method used was first published as a Shiny app and described in Mazzara et al. (2017) and further described in Bombaci & Rossi (2019) , and widely expanded as a package as presented in the bioRxiv pre print Ferrari et al. . Package: r-cran-combo Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-matrix, r-cran-rjags, r-cran-turboem, r-cran-samba Suggests: r-cran-knitr, r-cran-testthat, r-cran-devtools, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-combo_1.2.0-1.ca2604.1_all.deb Size: 682494 MD5sum: 97991fd5043b91af72536ee06c919b0b SHA1: 527eec75c698f19e8d24938ce55da50dff6a656c SHA256: f3bcb45e6035266dae3c0b2b035ed9903dda33708002122d0c0219ace4b7d56e SHA512: 7228f44ab9b88687c5d338c9f91014d62d7204edc99d48e7e27aa271b1947b76fd07d8699f385a12b4494500de6767681aa3064dc1e3236ecf0a3b197d051d2c Homepage: https://cran.r-project.org/package=COMBO Description: CRAN Package 'COMBO' (Correcting Misclassified Binary Outcomes in Association Studies) Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) . Package: r-cran-combss Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-combss_0.1.0-1.ca2604.1_all.deb Size: 74438 MD5sum: a6569ca84737fdab4706f6707257a13a SHA1: 8a00fe22fca2ce048b448b95a0a47bb6d625d147 SHA256: 3cf4ec548856193bdf93a4936d629d03a1665916716d73417f5525a8c92bc413 SHA512: 487a5fc974e2d9dbc3a0c3e4f601d8d4465d952c5e8e4aefd5a772a7f54613df2bbed58fb8836edbc746088c330d4301a56d6a27277d4759893ce83dd0aee8eb Homepage: https://cran.r-project.org/package=combss Description: CRAN Package 'combss' (Continuous Optimisation Towards Best Subset Selection) Best subset selection in generalised linear models via continuous optimisation. Reformulates the NP-hard discrete subset selection problem as a continuous optimisation over the hypercube [0,1]^p, solved via a Frank-Wolfe homotopy algorithm with closed-form ridge inner solves. Supports linear (Gaussian), binary logistic, and multinomial regression. For methodological details see Moka, Liquet, Zhu and Muller (2024) and Mathur, Liquet, Muller and Moka (2026) . Package: r-cran-comclim Architecture: all Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-comclim_0.9.6-1.ca2604.1_all.deb Size: 1072736 MD5sum: 6f542de93482583cb977b6d5931112d3 SHA1: 8b44abb7da0157993e2b93dd67a5d0af08a9370e SHA256: f62eb864d0a3eb710c011e80ec3dbd54674fd8a9127641982186cf65a10e0726 SHA512: 68e27685330a42bdcd9483e06259b0ad4404aaa93baabe17a3666f0162621574dde592452f56b70da9159d983d09b95799c2411b9a18e9b9fbf625bc7dd4f1c5 Homepage: https://cran.r-project.org/package=comclim Description: CRAN Package 'comclim' (Community Climate Statistics) Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage. 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Experiments can be viewed on the 'Comet' online dashboard at . 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See Shih et al. (2019) and Shih et al. (2021) for details under the FGM and general copulas, respectively. 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Australian and Swedish modifications of the Charlson Comorbidity Index are available as well (Sundararajan, 2004 and Ludvigsson, 2021 ), together with different weighting algorithms for both the Charlson and Elixhauser comorbidity scores. 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Package: r-cran-compare Architecture: all Version: 0.2-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-compare_0.2-6-1.ca2604.1_all.deb Size: 503818 MD5sum: 0112ba069caf231b53ebb7186b532f06 SHA1: 26e56e5bb87ae2a66dc1172ad56b7e3f96b2ed73 SHA256: 017fc13fe76139a3bcc544c0526361dcac6fbca81562b3744898dbe1714f90ac SHA512: 2ac3427123d6dd950bb0ed8ba1fc799b630c5c42ed861b46e5ca99c9ea69579fcfc949b7733b48afa1c2d5b70cacd0fb6322dd4a52a5e836a56e49dc51491eda Homepage: https://cran.r-project.org/package=compare Description: CRAN Package 'compare' (Comparing Objects for Differences) Functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical. 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Returns confidence intervals and a p-value for the difference in C-statistics. Useful for evaluating and comparing predictive performance of survival models. Methods implemented for Uno's C are described in Uno et al. (2011) . Package: r-cran-comparedesign Architecture: all Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-ggplot2, r-cran-ggpubr, r-cran-rootsolve, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-comparedesign_2.4.0-1.ca2604.1_all.deb Size: 315388 MD5sum: 8e119437ae2740adb1c5281125283432 SHA1: e621ed7bcadd81617dd8eeacbe0a7d17b148b4f6 SHA256: 6f7615dc288d54067e9b365ed06ba2cd0e4f6c0c3d8b85fae949c012e363366e SHA512: f8649d6c81ef14c1ad324ec9a38295a11c62b036d8c40d9a74fdc8c2501330a48fc7de52671f4c5bad88568aa2acb999f820a273c000a8df49b5a4b0353dd960 Homepage: https://cran.r-project.org/package=CompAREdesign Description: CRAN Package 'CompAREdesign' (Statistical Functions for the Design of Studies with CompositeEndpoints) It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. It also calculates the expected effect and the probability of observing the composite endpoint, among others. The methodology can be found in Bofill & Gómez (2019) and Gómez & Lagakos (2013) . Package: r-cran-comparedf Architecture: all Version: 2.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-htmltable, r-cran-openxlsx, r-cran-tidyr, r-cran-stringr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat, r-cran-futile.logger, r-cran-covr Filename: pool/dists/resolute/main/r-cran-comparedf_2.3.5-1.ca2604.1_all.deb Size: 1069658 MD5sum: 8598935000d8876329d3596bf4761be5 SHA1: 7a6a7bd6de3267cc9617a497a547903f5a1eb4ae SHA256: 9822e308709e9c6da3f7a4d57cd89a4a9c65675d8a4835be73d2fabaabe65d45 SHA512: 6ae5a8f11bb24e521926889d0c8edcceb0b9d9ff1a391038d66d95906e21aa8cfaf35d4170426929fb422ba8e50058cdaacf401faa0b065f9d9583420887c080 Homepage: https://cran.r-project.org/package=compareDF Description: CRAN Package 'compareDF' (Do a Git Style Diff of the Rows Between Two Dataframes withSimilar Structure) Compares two dataframes which have the same column structure to show the rows that have changed. Also gives a git style diff format to quickly see what has changed in addition to summary statistics. Package: r-cran-comparegroups Architecture: all Version: 4.10.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-hardyweinberg, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-chron, r-cran-writexl, r-cran-flextable, r-cran-officer, r-cran-pmcmrplus, r-cran-rmdformats, r-cran-rstatix Suggests: r-cran-tcltk2, r-cran-shiny, r-cran-shinybs, r-cran-shinyjs, r-cran-shinyjqui, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinydashboardplus, r-cran-dt, r-cran-readxl, r-cran-haven Filename: pool/dists/resolute/main/r-cran-comparegroups_4.10.2-1.ca2604.1_all.deb Size: 3365420 MD5sum: b6119313703ea393449e1dfba382f735 SHA1: 19001743fa810c56f8100c817a4916d93c20557c SHA256: 369e693b68fbe5db0062b76399868506c2ae4bcbe18ecaa3a26cb51556508378 SHA512: f44f365e5f3222ad17fb1fdcc0b32c9d5f9dd4b948e31cceb9ce52ea86184adc77a8f027de08d4c6f2b6b7987415c2bd9abe510bd27ceee31603c8f9efad414f Homepage: https://cran.r-project.org/package=compareGroups Description: CRAN Package 'compareGroups' (Descriptive Analysis by Groups) Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data. Package: r-cran-comparemcmcs Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 907 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-r6, r-cran-ggplot2, r-cran-reshape2, r-cran-xtable, r-cran-coda, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rjags, r-cran-rstan Filename: pool/dists/resolute/main/r-cran-comparemcmcs_0.6.0-1.ca2604.1_all.deb Size: 374222 MD5sum: fa126b9a7813db4634897e89f8dcbd95 SHA1: c907c70d9730e0bf3b7b5360e4f8c741c7e46513 SHA256: 6f99924ce0f26aaaa147978ca5bea0182e842b7fab1f8625fdbf6b541ca6f3ff SHA512: 76135d2033370df3d45135db86d553a2c8f9ae3532531251ab9df4c4b85f9cd0b42121174245460264c67dca53ec4e5188094eebcc1b5262a08b61dd4dcc3b47 Homepage: https://cran.r-project.org/package=compareMCMCs Description: CRAN Package 'compareMCMCs' (Compare MCMC Efficiency from 'nimble' and/or Other MCMC Engines) Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the 'nimble' package or from other packages. Plug-ins for JAGS via 'rjags' and Stan via 'rstan' are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components. Package: r-cran-comparemultiplemodels Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ceemdanml Filename: pool/dists/resolute/main/r-cran-comparemultiplemodels_0.1.0-1.ca2604.1_all.deb Size: 15338 MD5sum: c8bd71e55d2ac47ab8aecdf62457401b SHA1: 3cb9677f9bcf3e073d6bf21b9c7a69660503b09a SHA256: 77e3e52c18ea922d2bf6c4227fa2b8342a95769dd518da4dc8a547160ecdd6b2 SHA512: bb5b2b5e93fc945cebff64f1287ab192d3d3e1f528acca406bad5f73a3724bdca30fb37a1ec7e270647b2206e673828bd7d6f0f6f4a44ba476129358bc3fc1c0 Homepage: https://cran.r-project.org/package=CompareMultipleModels Description: CRAN Package 'CompareMultipleModels' (Finding the Best Model Using Eight Metrics Values) In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from 'AllMetrics' package. ‘input_df’ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular model’s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as ‘MIN’ and other values are denoted as ‘NA’. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as ‘MAX’ and other values are denoted as ‘NA’. ‘output_df’ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of ‘input_df’. Values in ‘output_df’ are corresponding ‘NA’, ‘MIN’ or ‘MAX’. Finally, the column containing minimum number of ‘NA’ values is denoted as the best column. ‘min_NA_col’ gives the name of the best column (model). ‘min_NA_values’ are the corresponding metrics values. ‘BestColumn_metrics’ is the data frame (dimension: 1*8) containing different metrics of the best column (model). ‘best_column_results’ is the final result (a list) containing all of these output elements. In special case, if two columns having equal 'NA', it will be checked among these two column which one is having least 'NA' in first five rows and will be inferred as the best. More details about 'AllMetrics' can be found in Garai (2023) . Package: r-cran-comparetests Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-comparetests_1.3-1.ca2604.1_all.deb Size: 38258 MD5sum: 1719b29244feaa4eb8e3e91c69b5eb20 SHA1: 36acf961c2ba78b9609a86c5ebe5583cb0567826 SHA256: 1352e3663e327b9b34ef959c4cb9c032735c6be36fbe92e58dd888726d21b0a5 SHA512: f19139cc6d18c8bb3e6b714fcfd87f0a08f64c48d0c4a69c8d4095734abb220319caf4f61edebea572f84cabf5f40f03c7ede22812ad623b7600e8bee0d0a026 Homepage: https://cran.r-project.org/package=CompareTests Description: CRAN Package 'CompareTests' (Correct for Verification Bias in Diagnostic Accuracy & Agreement) A standard test is observed on all specimens. We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens. Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control. We treat the total sample as stratified two-phase sampling and use inverse probability weighting. We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)). See: Katki HA, Li Y, Edelstein DW, Castle PE. Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) . 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Package: r-cran-comparisonsurv Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-survrm2, r-cran-tshrc, r-cran-muhaz Filename: pool/dists/resolute/main/r-cran-comparisonsurv_1.1.1-1.ca2604.1_all.deb Size: 124250 MD5sum: 4b39e51e0deb7c6baceefbb4b644c54c SHA1: eee3e25151f4a08f56dc37492774ce37e4dfd4b3 SHA256: 5f8e21641739582832411cf533ea8b73da47f2c3bdd162f3e88323b55ba112c7 SHA512: 13fa4ad85ea8334046a11dc57da807a08cde8f98763b10d8702a8b5474c200e615900d5883dbf70d32d7746f3d025f61ce6eaefa909a5d9aba7da444492c8333 Homepage: https://cran.r-project.org/package=ComparisonSurv Description: CRAN Package 'ComparisonSurv' (Comparison of Survival Curves Between Two Groups) Various statistical methods for survival analysis in comparing survival curves between two groups, including overall hypothesis tests described in Li et al. (2015) and Huang et al. (2020) , fixed-point tests in Klein et al. (2007) , short-term tests, and long-term tests in Logan et al. (2008) . Some commonly used descriptive statistics and plots are also included. Package: r-cran-compclassmetrics Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d, r-cran-pracma, r-cran-cubature Filename: pool/dists/resolute/main/r-cran-compclassmetrics_1.0.1-1.ca2604.1_all.deb Size: 99700 MD5sum: 320832177eea1b40f9e1209a742aa288 SHA1: e6fb6a01c6d1b91d189f8b508257cd68e726a553 SHA256: 3b0aff8189138df58db1350d1bb9476d9a4a7c79a21d70073a66ef3b57ffdf84 SHA512: 762befee1bab65f9caf66b07485179a037ba52ee0d6aba240d7a13971a924662546084b2f11da3a8f89645b90d2420737b739569f26681e05d389fb4da31e6be Homepage: https://cran.r-project.org/package=CompClassMetrics Description: CRAN Package 'CompClassMetrics' (Classification Measures when Subclasses are Involved) Accuracy metrics are commonly used to assess the discriminating ability of diagnostic tests or biomarkers. Among them, metrics based on the ROC framework are particularly popular. When classification involves subclasses, the package 'CompClassMetrics' includes functions that can provide the point estimate, confidence interval as well as true values if a parametric setting is known. For more details see Nan and Tian (2025) , Nan and Tian (2023) , Feng and Tian (2020) and Wang et al (2016) . 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Also fits the user specified distribution to a given data set. More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017) CompDist: Multisection composite distributions. Package: r-cran-comperes Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-comperes_0.2.7-1.ca2604.1_all.deb Size: 137552 MD5sum: 86e10405b7faac962b742e3215aee4fc SHA1: fee501dc5e283a7bd278d7f4ba7c29e0ceaa9224 SHA256: 9ba6fe762f612f406eee8a00cf8bb7d518c47badb589c41efcd2292535a5570f SHA512: 3eee0157db9589056ddb4a483b7336d9b3e9f10a668e871e3fedeb31599bfe00fcc3f5469cb73da4049742524cc83c6c86456e6614cbf0d920cf16542e6b5fca Homepage: https://cran.r-project.org/package=comperes Description: CRAN Package 'comperes' (Manage Competition Results) Tools for storing and managing competition results. Competition is understood as a set of games in which players gain some abstract scores. 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The interface is powered by the 'Shiny' web application framework from 'RStudio'. Package: r-cran-compexpdes Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-hadamardr Filename: pool/dists/resolute/main/r-cran-compexpdes_1.0.9-1.ca2604.1_all.deb Size: 122980 MD5sum: 43302ea6dea862b752c24888f4782752 SHA1: 26d2476c9921760058c44ec937e3e2c8a4556167 SHA256: 167e7339aeb755a86a000cd99e4daf0a51a654cd5ef30e4b43a882b0989dffa2 SHA512: 728cd0460fe12c95d493ad32c02ad17bc90b105438707f860da7f042acf4102db3c4e7d8e205bdc1279c9bd0175a47e3f7446bfe4dcd3288e19862c326123948 Homepage: https://cran.r-project.org/package=CompExpDes Description: CRAN Package 'CompExpDes' (Designs for Computer Experimentations) In computer experiments space-filling designs are having great impact. Most popularly used space-filling designs are Uniform designs (UDs), Latin hypercube designs (LHDs) etc. For further references one can see Mckay (1979) and Fang (1980) . In this package, we have provided algorithms for generate efficient LHDs and UDs. Here, generated LHDs are efficient as they possess lower value of Maxpro measure, Phi_p value and Maximum Absolute Correlation (MAC) value based on the weightage given to each criterion. On the other hand, the produced UDs are having good space-filling property as they always attain the lower bound of Discrete Discrepancy measure. Further, some useful functions added in this package for adding more value to this package. Package: r-cran-compgr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-compgr_0.1.3-1.ca2604.1_all.deb Size: 11668 MD5sum: 4d5aa2c2face21184998f542be1898fe SHA1: 866005886a357dd364593d796626806a36211a94 SHA256: 0df58869c34e516697847baab1178197fe1a391e757951ca84c5e2991ea3d4d5 SHA512: 2d0b02904cdd8ec8e455f461ce8c00e54ba95173d421c16a14a00603a2820e01d9f8b2c1b4044e35ebbd3fbcdbe420799c8eacf6c31c6f46a489e26b3391637b Homepage: https://cran.r-project.org/package=CompGR Description: CRAN Package 'CompGR' (Complete Annual Growth Rate Generator) It is designed to streamline the process of calculating complete annual growth rates with user-friendly functions and robust algorithms. It enables researchers and analysts to effortlessly generate precise growth rate estimates for their data. For method details see, Sharma, M.K.(2013) . It offers a comprehensive suite of functions and customisable parameters. Equipped to handle varying complexities in data structures. It empowers users to uncover insightful growth dynamics and make informed decisions. 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Moreover, the variance inflation factor is used to reduce the set of correlated variables. In the case of a discrepancy between the importance and the assigned weight, the script determines weights that allow adjustment of the weights to the intended impact of variables. If the optimised weights are unable to reflect the desired importance, the highly correlated variables are reduced, taking into account variance inflation factor. The final outcome of the script is the calculated value of the composite indicator based on optimal weights and a reduced set of variables, and the linear ordering of the analysed objects. 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For method details see Sendhil, R., Jha, A., Kumar, A. and Singh, S. (2018). , and Wu, T. (2021). . 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Package: r-cran-complmrob Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-ggplot2, r-cran-boot, r-cran-scales Filename: pool/dists/resolute/main/r-cran-complmrob_0.7.1-1.ca2604.1_all.deb Size: 95180 MD5sum: 3c1a737c55d8be2d88bc109cca2a04b5 SHA1: a5d9d9b205f3f02311cf4afb1c86b238c85cf95d SHA256: 40a525197f2afd5c9a48585eb117a94a0430702df8889fcad79a8ffbd36e4d9a SHA512: 6865937bcccd62ae91715b71233d88207a6867e7e85508db373d77acaff7e47bc3fd33e3c80c3926c20a9e944e0cc4a55c7162dde731d2d2f8ba8560d0ca9148 Homepage: https://cran.r-project.org/package=complmrob Description: CRAN Package 'complmrob' (Robust Linear Regression with Compositional Data as Covariates) Robust regression methods for compositional data. The distribution of the estimates can be approximated with various bootstrap methods. These bootstrap methods are available for the compositional as well as for standard robust regression estimates. This allows for direct comparison between them. Package: r-cran-compositereliability Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-plyr, r-cran-psych, r-cran-reshape2, r-cran-tidyr, r-cran-rsolnp Filename: pool/dists/resolute/main/r-cran-compositereliability_1.0.3-1.ca2604.1_all.deb Size: 43838 MD5sum: 2e8c1488e6afc911aa5142f4854d7068 SHA1: 3e1123ecdcc58b9b4904d4f694638e5e51780aed SHA256: 0ae5fceda5ffc1ba93467efa89a4bcb9a32368e6c81da48e6ec55b60b3dfafce SHA512: 97b44e13588be2311e78fbe6b2d340632f6c441d804ba6f191895f78008e238bb380dfaefb85a28476344efd3a6c724b95145e185dbc6847635f1086792d6ea2 Homepage: https://cran.r-project.org/package=CompositeReliability Description: CRAN Package 'CompositeReliability' (Determine the Composite Reliability of a Naturalistic,Unbalanced Dataset) The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts. Package: r-cran-compositereliabilityinnesteddesigns Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-plyr, r-cran-psych, r-cran-reshape2, r-cran-tidyr, r-cran-rsolnp Filename: pool/dists/resolute/main/r-cran-compositereliabilityinnesteddesigns_1.0.4-1.ca2604.1_all.deb Size: 44842 MD5sum: 52c2098d6665886af98740d3a25bae48 SHA1: 747ab6eaaddff880b8b056ad60fa962ba5709229 SHA256: f9a33606a4147cddba9d455107770c3dec1c0b15324ae8976d9a7d92b761af77 SHA512: e70354ccf8bfc1bb2fc6189334552be3662a070c3370142657ff575d03058737e0b3fda479e7eef547d35b7e02b48be1b04be9546f334a2f65f1b93d86d1c660 Homepage: https://cran.r-project.org/package=compositeReliabilityInNestedDesigns Description: CRAN Package 'compositeReliabilityInNestedDesigns' (Optimizing the Composite Reliability in Multivariate NestedDesigns) The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets in nested design as is often the case for Workplace-Based Assessments. This package is designed to estimate composite reliability in nested designs using multivariate generalizability theory and enhance the analysis of assessment data. The package allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations, and options to find the set of weights that maximizes the composite reliability or minimizes the standard error of measurement (SEM). Package: r-cran-compositional.mle Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algebraic.mle, r-cran-mass, r-cran-numderiv Suggests: r-cran-rmarkdown, r-cran-dplyr, r-cran-knitr, r-cran-ggplot2, r-cran-tibble, r-cran-testthat, r-cran-cli, r-cran-future, r-cran-hypothesize, r-cran-likelihood.model Filename: pool/dists/resolute/main/r-cran-compositional.mle_2.0.0-1.ca2604.1_all.deb Size: 552256 MD5sum: 8dea80158e7c963521daf5bebff7252c SHA1: a4d60713a7e0b65a1e7cf5169fa173e3b6937385 SHA256: a2c5b17bf8bacd6d6cc01efac1a2a27ec307e1d4a097261593796a5c403fc965 SHA512: 152b2b29d833702df62d947873e5c91488f6036a0e58fc908e67e6873ba10b6e4d6a6c1a7b0836b13bde198a026584246f9e162cc6df9a065079a551eda6c6b0 Homepage: https://cran.r-project.org/package=compositional.mle Description: CRAN Package 'compositional.mle' (Compositional Maximum Likelihood Estimation) Provides composable optimization strategies for maximum likelihood estimation (MLE). Solvers are first-class functions that combine via sequential chaining, parallel racing, and random restarts. Implements gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free methods with support for constrained optimization and tracing. Returns 'mle' objects compatible with 'algebraic.mle' for downstream analysis. Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization" . Package: r-cran-compositional Architecture: all Version: 8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1262 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bigstatsr, r-cran-cluster, r-cran-emplik, r-cran-glmnet, r-cran-quantreg, r-cran-mass, r-cran-matrix, r-cran-mda, r-cran-minpack.lm, r-cran-mixture, r-cran-nnet, r-cran-osqp, r-cran-quadprog, r-cran-rangen, r-cran-rfast, r-cran-rfast2, r-cran-rgl, r-cran-rnanoflann, r-cran-sn Suggests: r-cran-bigparallelr, r-cran-codalm, r-cran-flexdir Filename: pool/dists/resolute/main/r-cran-compositional_8.2-1.ca2604.1_all.deb Size: 1176328 MD5sum: b368ac18ce474805c32ff8432c49f9ec SHA1: 6cd93b56d0f91b5295454221bf8e2efa3403224d SHA256: 50c1453fc9deb14c5999fdc0374a703e3397e9fe30c0f63e650bfd4709943a6e SHA512: 82d98c9b2824b097e70c265249c8090bc34a1c509cd20b701f401bb8ac331bd628da0dcf10be0d355e45b309544221b283972ce13ba63f75aade5ef36adff4ef Homepage: https://cran.r-project.org/package=Compositional Description: CRAN Package 'Compositional' (Compositional Data Analysis) Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data--based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. . b) Tsagris M. (2014). "The k--NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. . c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. . d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journal of Statistics, 6(2): 47--57. . e) Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved supervised classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. . f) Tsagris M., Preston S. and Wood A.T.A. (2017). "Nonparametric hypothesis testing for equality of means on the simplex". Journal of Statistical Computation and Simulation, 87(2): 406--422. . g) Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. . h) Alenazi A. (2019). "Regression for compositional data with compositional data as predictor variables with or without zero values". Journal of Data Science, 17(1): 219--238. . i) Tsagris M. and Stewart C. (2020). "A folded model for compositional data analysis". Australian and New Zealand Journal of Statistics, 62(2): 249--277. . j) Alenazi A.A. (2022). "f--divergence regression models for compositional data". Pakistan Journal of Statistics and Operation Research, 18(4): 867--882. . k) Tsagris M. and Stewart C. (2022). "A Review of Flexible Transformations for Modeling Compositional Data". In Advances and Innovations in Statistics and Data Science, pp. 225--234. . l) Alenazi A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. . m) Tsagris M., Alenazi A. and Stewart C. (2023). "Flexible non--parametric regression models for compositional response data with zeros". Statistics and Computing, 33(106). . n) Tsagris. M. (2025). "Constrained least squares simplicial--simplicial regression". Statistics and Computing, 35(27). . o) Sevinc V. and Tsagris. M. (2026). "Energy Based Equality of Distributions Testing for Compositional Data". Communications in Statistics--Simulation and Computation. . p) Tsagris M. and Alzeley O. (2025). "Scalable approximation of the transformation--free linear simplicial--simplicial regression via constrained iterative reweighted least squares". . Package: r-cran-compositionalclust Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-doparallel, r-cran-factoextra, r-cran-foreach, r-cran-lowmemtkmeans, r-cran-mixture, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-compositionalclust_1.2-1.ca2604.1_all.deb Size: 68686 MD5sum: 781c706181f8cb08e40ddaf212195657 SHA1: ba4e7af985de519349273906901905c7de03c381 SHA256: 5eaede5146e2caad8c31ad4ac12f0fc05cbdf8f884b729ad597057a96a81c200 SHA512: d04ce8995e227f7332f6eeba51ed2cd9af7563781d4dfe5b664fc888d775bae6919d305fd1f9e7cb7d88c3961ae7fe54ece38ad0c69102bd65228ac9b64a5401 Homepage: https://cran.r-project.org/package=CompositionalClust Description: CRAN Package 'CompositionalClust' (Clustering with Compositional Data) Cluster analysis with compositional data using the alpha--transformation. Relevant papers include: Tsagris M. and Kontemeniotis N. (2025), . Tsagris M.T., Preston S. and Wood A.T.A. (2011), . Garcia-Escudero Luis A., Gordaliza Alfonso, Matran Carlos, Mayo-Iscar Agustin. (2008), . Package: r-cran-compositionalhdda Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-hdclassif, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-compositionalhdda_1.0-1.ca2604.1_all.deb Size: 24188 MD5sum: cef7174a1b6fe2ffc535864deb34a3cf SHA1: 3f54a3ffdd3abcf3c026bf5af89d881c2c62ba05 SHA256: e7502cd9bc72a4562e4bad2384727b12d3fc188df40da47f8bedaae6b6eb855c SHA512: a4ba3ea7db4952b9c5c2d6ab3248dfd61e8a80b860759aff89d9bcc3ad8d0e5de842d0e41eb775910f7b98581d1eed9710b34a838c9bc5306dbf765caceb6201 Homepage: https://cran.r-project.org/package=CompositionalHDDA Description: CRAN Package 'CompositionalHDDA' (High Dimensional Discriminant Analysis with Compositional Data) High dimensional discriminant analysis with compositional data is performed. The compositional data are first transformed using the alpha-transformation of Tsagris M., Preston S. and Wood A.T.A. (2011) , and then the High Dimensional Discriminant Analysis (HDDA) algorithm of Bouveyron C. Girard S. and Schmid C. (2007) is applied. Package: r-cran-compositionalml Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boruta, r-cran-compositional, r-cran-doparallel, r-cran-e1071, r-cran-foreach, r-cran-ranger, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-compositionalml_1.0-1.ca2604.1_all.deb Size: 68846 MD5sum: 014e22c97ce66cef562356cb81b557f1 SHA1: 224f4fdac3342da355240905f2f3ffaf88255116 SHA256: 304a5cdaee922652e3c1b3ac070601703a109c99513a5ac67aebe8ca4cabddb7 SHA512: 1b20f6b43c943de1f2e79f14e7b8e6e2fef5c19dfeb3cfda6809a632b388651b311d031847a09dd7c4cd6aef3f458b48aea07326f0d30235317293a9c27d4071 Homepage: https://cran.r-project.org/package=CompositionalML Description: CRAN Package 'CompositionalML' (Machine Learning with Compositional Data) Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. . Package: r-cran-compositionalnaimp Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-rfast, r-cran-rnanoflann Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-compositionalnaimp_1.0-1.ca2604.1_all.deb Size: 37518 MD5sum: 475aca91502ceb494546e0c9a41e5a6a SHA1: 3032d99d73b099f8b002c1bc3b4a27794811e6f3 SHA256: 7f608b523088bbad9fd77e52bc1e908cc8c6b7f6fa408a7446f44a59d7800de5 SHA512: 3b4d7e3ef33a7a14ab1939bcd99ec86afaa11509aa992f8cc895efe9d88bf3aafe5ff8841152472864e72212f22e6595823343ecc53814fcc1e8d24d71843901 Homepage: https://cran.r-project.org/package=CompositionalNAimp Description: CRAN Package 'CompositionalNAimp' (Missing Value Imputation with Compositional Data) Functions to perform missing value imputation with compositional data using the Jensen-Shannon divergence based k--NN and a--k--NN algorithms. The functions are based on the following paper: Tsagris M., Alenazi A. and Stewart C. (2026). "A Jensen--Shannon divergence based k--NN algorithm for missing value imputation in compositional data". Journal of Applied Statistics (Accepted for publication). Package: r-cran-compositionalsr Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockcv, r-cran-compositional, r-cran-doparallel, r-cran-foreach, r-cran-gslnls, r-cran-minpack.lm, r-cran-rangen, r-cran-rfast, r-cran-sf, r-cran-spmoran Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-compositionalsr_1.4-1.ca2604.1_all.deb Size: 273388 MD5sum: 2e128a46fbc44b687bb6d9a3987efad0 SHA1: 809c48c8994aa8ff2772689e71f001c7eef23d8e SHA256: 152b508e0bf4054744b45002af0a59ef51b4fa843595d41d71d589ed8c5bab32 SHA512: 406d25f5c995232e3bad5a4ec485681262ed7a7cb9108808ec28167e3114ebe724106e8bfbbb6ca2fe5ea983e865937546696f15eba60362a5bfbabd13712709 Homepage: https://cran.r-project.org/package=CompositionalSR Description: CRAN Package 'CompositionalSR' (Spatial Regression Models with Compositional Data) Spatial and non-spatial regression models with compositional responses (and compositional predictors) using the alpha--transformation. Relevant papers include: Tsagris M. and Pantazis Y. (2026), , Tsagris M. (2015), , Tsagris M.T., Preston S. and Wood A.T.A. (2011), . Package: r-cran-compound.cox Architecture: all Version: 3.33-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-survival, r-cran-mass Filename: pool/dists/resolute/main/r-cran-compound.cox_3.33-1.ca2604.1_all.deb Size: 145692 MD5sum: 43cb8f0a6f9e67f63d48e057117a9876 SHA1: 14b76c8fc72699cb17363e86e66d9778737536e8 SHA256: 704c1a3615a1f01dccc02e6faa8ffe64ce34a8ce67fa0c4a0604de4d7366365a SHA512: 3ef0fc847fd6d948fb981308c2cf0870a911e64042f620259990a7870f109e2e40fb8391d25b2d34e15897271a22760e00c53f08108d5d0445d6dfa9eb3c88b5 Homepage: https://cran.r-project.org/package=compound.Cox Description: CRAN Package 'compound.Cox' (Univariate Feature Selection and Compound Covariate forPredicting Survival, Including Copula-Based Analyses forDependent Censoring) Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) , statistical methods in Emura et al (2012 PLoS ONE) , Emura & Chen (2016 Stat Methods Med Res) , and Emura et al (2019). Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023 Biomedicines) and factorial survival analyses (Emura et al 2024 Stat Methods Med Res) . Package: r-cran-compoundevents Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-compoundevents_1.0-1.ca2604.1_all.deb Size: 67466 MD5sum: b1bbaed564a358028fc03f8e46510757 SHA1: 16678b45d9b90aad68f3c94f2bd3b4b7a05b81d5 SHA256: 9aec7502351ff3d7378fb714b7b2a1de8eaf361ef325b60e22e72790feb7cb65 SHA512: 7931467a4121a838cd873a509f8fcaa6a98f8f607fc45dac4fb4ec3aaffbd78a756a03f737c58198132f9f1327300e818d21cc4f23fcef4c4ca8c27e905e1354 Homepage: https://cran.r-project.org/package=CompoundEvents Description: CRAN Package 'CompoundEvents' (Statistical Modeling of Compound Events) Tools for extracting occurrences, assessing potential driving factors, predicting occurrences, and quantifying impacts of compound events in hydrology and climatology. Please see Hao Zengchao et al. (2022) . Package: r-cran-comppareto Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-actuar Filename: pool/dists/resolute/main/r-cran-comppareto_0.1.0-1.ca2604.1_all.deb Size: 33118 MD5sum: 2620bd17e8e4e4b528c55d02fa71325b SHA1: 158ff83ca8d1e8f893a5f8ffcc286325a9f12d47 SHA256: e8ba5364aebb7e27c75b0266662c588cb20a61287cf97604f3538099e3b7293d SHA512: 977ffb0794eec0adc6943d765ffa91ea4e7f6812d7487724555bab4e3a011962c5738e5b8ce62228ff9763796737fcdc9089e4ba4fa988113ae3716a262f6598 Homepage: https://cran.r-project.org/package=CompPareto Description: CRAN Package 'CompPareto' (Discrete Composite Distributions with Pareto Tails) Contains the probability density function, cumulative distribution function, quantile function, and random number generator for composite and discrete composite distributions with Pareto tails. The detailed description of the methods and the applications of the methods can be found in Bowen Liu, Malwane M.A. Ananda (2023) . Package: r-cran-compr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-compr_1.0-1.ca2604.1_all.deb Size: 252362 MD5sum: d9353ab1e4352a2a6171cbebde8ee5b8 SHA1: 261a85097c2473cae392d23a325bda818df33cf4 SHA256: 058ce9af9b50fcf0c916c6ddee3e6d5d141b84e6fe05569370a86774e0242985 SHA512: 1c0d772da65b91eb84788ddfcf5a102220ba00a87e3634278d44d98f4e8d4eb376be347635899174b97af0f9935a86b99e1a10b07a27375aa0ef7dc3732a8522 Homepage: https://cran.r-project.org/package=CompR Description: CRAN Package 'CompR' (Paired Comparison Data Analysis) Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data. 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The functions also allow computing the goodness-of-fit measures namely the Akaike-information-criterion (AIC), the Bayesian-information-criterion (BIC), the minimum value of the negative log-likelihood (-2L) function, Anderson-Darling (A) test, Cramer-Von-Mises (W) test, Kolmogorov-Smirnov test, P-value and convergence status. Moreover, some commonly used data sets from the fields of actuarial, reliability, and medical science are also provided. Related works include: a) Tahir, M. H., & Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3, 1-35. . 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Measures can be calculated in groups or individually. The calculated measure or the resulting vector in table format should help practitioners make more informed decisions. Methods used in this package are from: 1. Chang, E. J., Guerra, S. M., de Souza Penaloza, R. A. & Tabak, B. M. (2005) "Banking concentration: the Brazilian case". 2. Cobham, A. and A. Summer (2013). "Is It All About the Tails? The Palma Measure of Income Inequality". 3. Garcia Alba Idunate, P. (1994). "Un Indice de dominancia para el analisis de la estructura de los mercados". 4. Ginevicius, R. and S. Cirba (2009). "Additive measurement of market concentration" . 5. Herfindahl, O. C. (1950), "Concentration in the steel industry" (PhD thesis). 6. Hirschmann, A. O. (1945), "National power and structure of foreign trade". 7. Melnik, A., O. Shy, and R. Stenbacka (2008), "Assessing market dominance" . 8. Palma, J. G. (2006). "Globalizing Inequality: 'Centrifugal' and 'Centripetal' Forces at Work". 9. Shannon, C. E. (1948). "A Mathematical Theory of Communication". 10. Simpson, E. H. (1949). "Measurement of Diversity" . 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Package: r-cran-condoroptions Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-condoroptions_1.0.1-1.ca2604.1_all.deb Size: 48440 MD5sum: 1c630a3d119568959eb3905b89f11b95 SHA1: 0a9b16148471a4bb9b2bb23ea7aeaeb120cd7dec SHA256: cec59691531c90134948107270ec1f8c10caa2dc7f34fea5c1259a383f134e91 SHA512: b23badb4f82259d97382f50d4d3dc07c3cb9fb56db14af6453716e61c31c705f30ad6d14aaafa22201dc0b2715100b07e5aac39eb9dade8a65c6a023968608e9 Homepage: https://cran.r-project.org/package=condorOptions Description: CRAN Package 'condorOptions' (Trading Condor Options Strategies) Trading of Condor Options Strategies is represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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Also draws random samples from this distribution. Package: r-cran-conductor Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-r6, r-cran-shiny Suggests: r-cran-altdoc, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-conductor_0.1.2-1.ca2604.1_all.deb Size: 98120 MD5sum: 2cedf91b6cce7df4ca93d5fceaaca02a SHA1: bd79dc57b816ca37ec2630af874e99efd6d3667f SHA256: 18c92558179df43fa7553049c6c02c1b529091de29c1293659fbf4a154c9570d SHA512: 529f5f60ed40b0473c946ffbe4faa49878b3c235406f25ecd54c3edd247b07df32bd8b4de340558250a21a6473571be0ff45d83276736a83b474a00eedd9438d Homepage: https://cran.r-project.org/package=conductor Description: CRAN Package 'conductor' (Create Tours in 'Shiny' Apps Using 'Shepherd.js') Enable the use of 'Shepherd.js' to create tours in 'Shiny' applications. Package: r-cran-condvis2 Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2969 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-scales, r-cran-cluster, r-cran-dendser, r-cran-plyr, r-cran-colorspace, r-cran-gower Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-hdrcde, r-cran-scagnostics, r-cran-keras, r-cran-kernlab, r-cran-mclust, r-cran-mass, r-cran-ks, r-cran-mgcv, r-cran-randomforest, r-cran-parsnip, r-cran-mlr, r-cran-c50, r-cran-bartmachine, r-cran-bart, r-cran-caret, r-cran-e1071, r-cran-gbm, r-cran-glmnet, r-cran-glmnetutils, r-cran-mlr3, r-cran-nnet, r-cran-rpart, r-cran-tree, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-condvis2_0.1.2-1.ca2604.1_all.deb Size: 2152268 MD5sum: 8ef7449892787e27f39a3b1527a58a79 SHA1: 52c94d0817a36e59a9e99ee7285ea6d6086bf229 SHA256: 8dcc35a75e98d72aeff8c5f18d8f4876e437a25a049021f3d257a538633775e3 SHA512: faba48029a47533bcdafc63bb50103fd173bd3494fe46fb810ea662a32b6e41830e47ae4cd7afc52d3607a7386085c07df3c16731e1b573725276cc9f8384b14 Homepage: https://cran.r-project.org/package=condvis2 Description: CRAN Package 'condvis2' (Interactive Conditional Visualization for Supervised andUnsupervised Models in Shiny) Constructs a shiny app function with interactive displays for conditional visualization of models, data and density functions. An extended version of package 'condvis'. Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) . Package: r-cran-condvis Architecture: all Version: 0.5-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-rcolorbrewer, r-cran-shiny, r-cran-scagnostics, r-cran-cluster, r-cran-hdrcde, r-cran-gplots, r-cran-tsp, r-cran-dendser, r-cran-testthat, r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-condvis_0.5-2-1.ca2604.1_all.deb Size: 374448 MD5sum: 561de5dbde7770c2994baa7e60b67fc3 SHA1: ee34397ab4529ebdfb1c23bf91c3b84d0f905a7a SHA256: f64262998c66f12c263fb4e776105b515d2446aa2291a5b6f2b0ac35e29078f5 SHA512: 7b86ce3212ae7f776843026af2daa4c09c77064604f717aa1c38358e9caa922d316e04c8c8626684df756f93c8e6b212f50c973172249f167397f2a99bd5f210 Homepage: https://cran.r-project.org/package=condvis Description: CRAN Package 'condvis' (Conditional Visualization for Statistical Models) Exploring fitted models by interactively taking 2-D and 3-D sections in data space. Package: r-cran-conf.design Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-conf.design_2.0.0-1.ca2604.1_all.deb Size: 44744 MD5sum: f437dc0031087bfcb8d8a65b24347aee SHA1: 3a1aa1112b12cc380ce456c53c688962e7062482 SHA256: b36e5883803802bb7683d80f96ba50dee4015642b24fd15f560dca85f1c4b177 SHA512: 1a2a9f90adb63b98ce97b8a4b8c6c61fa61f54d0a0955b0af6a941d08dfe47fa00a82fa331742c59434fe5b2010f4d35d2bd23ed0ebe6b28c71cc54301c3f4d7 Homepage: https://cran.r-project.org/package=conf.design Description: CRAN Package 'conf.design' (Construction of factorial designs) This small library contains a series of simple tools for constructing and manipulating confounded and fractional factorial designs. Package: r-cran-conf Architecture: all Version: 1.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-fitdistrplus, r-cran-pracma, r-cran-rootsolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-conf_1.9.3-1.ca2604.1_all.deb Size: 2110976 MD5sum: 9f3ea55cd2918734ff37dbd40d933fc4 SHA1: cdf7c374ddd8cb0ef40f30e010372cda1827c452 SHA256: 1892c042bb7571dc3224b4530dc6f94fd73cdd99754e35c6b1106b27b515dbd3 SHA512: 12f285323dc59bb508f09a74c67a2d1d8482c58184df5e9e9f4cdb31edc3bcc915d0660b6b80fb551f42edb339e3017de0723eca378c6e5c0b44603aa32a40de Homepage: https://cran.r-project.org/package=conf Description: CRAN Package 'conf' (Visualization and Analysis of Statistical Measures of Confidence) Enables: (1) plotting two-dimensional confidence regions, (2) coverage analysis of confidence region simulations, (3) calculating confidence intervals and the associated actual coverage for binomial proportions, (4) calculating the support values and the probability mass function of the Kaplan-Meier product-limit estimator, and (5) plotting the actual coverage function associated with a confidence interval for the survivor function from a randomly right-censored data set. Each is given in greater detail next. (1) Plots the two-dimensional confidence region for probability distribution parameters (supported distribution suffixes: cauchy, gamma, invgauss, logis, llogis, lnorm, norm, unif, weibull) corresponding to a user-given complete or right-censored dataset and level of significance. The crplot() algorithm plots more points in areas of greater curvature to ensure a smooth appearance throughout the confidence region boundary. An alternative heuristic plots a specified number of points at roughly uniform intervals along its boundary. Both heuristics build upon the radial profile log-likelihood ratio technique for plotting confidence regions given by Jaeger (2016) , and are detailed in a publication by Weld et al. (2019) . (2) Performs confidence region coverage simulations for a random sample drawn from a user- specified parametric population distribution, or for a user-specified dataset and point of interest with coversim(). (3) Calculates confidence interval bounds for a binomial proportion with binomTest(), calculates the actual coverage with binomTestCoverage(), and plots the actual coverage with binomTestCoveragePlot(). Calculates confidence interval bounds for the binomial proportion using an ensemble of constituent confidence intervals with binomTestEnsemble(). Calculates confidence interval bounds for the binomial proportion using a complete enumeration of all possible transitions from one actual coverage acceptance curve to another which minimizes the root mean square error for n <= 15 and follows the transitions for well-known confidence intervals for n > 15 using binomTestMSE(). (4) The km.support() function calculates the support values of the Kaplan-Meier product-limit estimator for a given sample size n using an induction algorithm described in Qin et al. (2023) . The km.outcomes() function generates a matrix containing all possible outcomes (all possible sequences of failure times and right-censoring times) of the value of the Kaplan-Meier product-limit estimator for a particular sample size n. The km.pmf() function generates the probability mass function for the support values of the Kaplan-Meier product-limit estimator for a particular sample size n, probability of observing a failure h at the time of interest expressed as the cumulative probability percentile associated with X = min(T, C), where T is the failure time and C is the censoring time under a random-censoring scheme. The km.surv() function generates multiple probability mass functions of the Kaplan-Meier product-limit estimator for the same arguments as those given for km.pmf(). (5) The km.coverage() function plots the actual coverage function associated with a confidence interval for the survivor function from a randomly right-censored data set for one or more of the following confidence intervals: Greenwood, log-minus-log, Peto, arcsine, and exponential Greenwood. The actual coverage function is plotted for a small number of items on test, stated coverage, failure rate, and censoring rate. The km.coverage() function can print an optional table containing all possible failure/censoring orderings, along with their contribution to the actual coverage function. 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(2024) . Package: r-cran-confidence Architecture: all Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-markdown, r-cran-plyr, r-cran-xtable, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-confidence_1.1-3-1.ca2604.1_all.deb Size: 281314 MD5sum: 7c391862df4e38d3ac8697f4e4f1ba0f SHA1: 9d2e6236ba3b75d2d7e745deeb3cd5eec4414298 SHA256: a6a5e7da4309a4dc72bce49c1436de9386717289d5fbf1d80e3320b7079ebfc4 SHA512: 6592da431ceac36c3ee349c053b38fb1fc4a2e15df00f418355ed1484ad2655d19411cc94e301928018b6efe4dd1792a0ea6573118577d1115bf106a1a46ba3d Homepage: https://cran.r-project.org/package=confidence Description: CRAN Package 'confidence' (Confidence Estimation of Environmental State Classifications) Functions for estimating and reporting multi-year averages and corresponding confidence intervals and distributions. A potential use case is reporting the chemical and ecological status of surface waters according to the European Water Framework Directive. Package: r-cran-confidencecurves Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-desctools, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-confidencecurves_0.2.0-1.ca2604.1_all.deb Size: 37650 MD5sum: 5ade058bd28e434dc65089fc3c7013be SHA1: 6ca88c1a94c7a0862a60c89f46978943324ee576 SHA256: 2e9bcda7c6c71b4b840675131ed1d107e12967676895a1578aa95689d0d2b803 SHA512: eb340ad6dee921d46c77a7837bd9baeb1e17a330bc064430ee01ae12fd3b7dcffcfe956eb3fe85b2ae4b67cd910a42319cb0b57ec01d51fab8920d9b29ed2f8f Homepage: https://cran.r-project.org/package=confidenceCurves Description: CRAN Package 'confidenceCurves' (Frequentist Confidence Analysis for Clinical Trials) Frequentist confidence analysis answers the question: How confident are we in a particular treatment effect? This package calculates the frequentist confidence in a treatment effect of interest given observed data, and returns the family of confidence curves associated with that data. 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It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) . 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From a two-arm, single-stage trial to a perpetually run Adaptive Platform Trial, this package offers vast flexibility to customize your trial and observe operational characterisitics over thousands of instances. 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Read values using a function that determines the current environment and returns the appropriate value. Package: r-cran-configparser Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ini, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-configparser_1.0.0-1.ca2604.1_all.deb Size: 56730 MD5sum: 789b82ee0b65962a212c7635a7219532 SHA1: 8193b797b9aaa2016f40455ddc56486c32f4a87c SHA256: a6c92b9fc4c0366d2e6957a25aff3efd5019a57fe25f790accac095e66612f9f SHA512: acfb2220901aea61e1f836fd22d37045168f693d4719c02833dc50b836726e81414b68a3f66a198f95e1bee1e8bcf886f49f2b9d53e0b7a2d17d0881f8437b54 Homepage: https://cran.r-project.org/package=ConfigParser Description: CRAN Package 'ConfigParser' (Package to Parse an INI File, Including Variable Interpolation) Enhances the 'ini' package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables. Package: r-cran-configr Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-ini, r-cran-yaml, r-cran-rcpptoml, r-cran-stringr, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/resolute/main/r-cran-configr_0.3.5-1.ca2604.1_all.deb Size: 112068 MD5sum: 2364b3a42d2479456df18ec28389685d SHA1: a13beda8b659538f3ef7f4df514a1764c257c675 SHA256: 0860bc48f3579a274cf128ee2b008fe677c85f5f7742454943d8ad4dbfed1266 SHA512: 0909830c678781f9f8cdad429ff66a3f5f83e5bfb6a50ad09196f6d2196b1963ce484d52bad1157acd5141ba06d145ce28d66589ed73cb5ccef96d07fc1ebe99 Homepage: https://cran.r-project.org/package=configr Description: CRAN Package 'configr' (An Implementation of Parsing and Writing Configuration File(JSON/INI/YAML/TOML)) Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'. Package: r-cran-configular Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-here, r-cran-magrittr, r-cran-stringr Suggests: r-cran-covr, r-cran-docopt, r-cran-git2r, r-cran-lintr, r-cran-precommit, r-cran-roxygen2, r-cran-styler, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-configular_0.1.1-1.ca2604.1_all.deb Size: 36652 MD5sum: 1fe6502d68264278161725811d95c464 SHA1: d385d531b4b0563017cba003259ed35e195226a3 SHA256: 34ac74f579a56cdcf43d4dbc2e81942baf960093c99909b89728e84e6214b9ed SHA512: 1242670887001f2f0ac8cde268801a0eed1178e31953268b145221c72b11ea35184e6b38864747845d0847bc96d19b255932060ed98fbc1dfbeea7d5f74263b2 Homepage: https://cran.r-project.org/package=configulaR Description: CRAN Package 'configulaR' (Manage Application Settings via '.env' or '.ini' Files) Provides a simple way to manage application settings by loading configuration values from '.env' or '.ini' files. It supports default values, type casting, and environment variable overrides, enabling a clean separation of configuration from code. Ideal for managing credentials, API keys, and deployment-specific settings. Package: r-cran-configural Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-crayon Filename: pool/dists/resolute/main/r-cran-configural_0.1.5-1.ca2604.1_all.deb Size: 170166 MD5sum: dcfcfb54bfa2b196dce01f277f4833c4 SHA1: 48acbcc3bc54c94f02923188d24f86178810035b SHA256: e68ed396a36c1c742f0cd12974b9d15946da863966a8a137c64750aa21645eb7 SHA512: 553dae7e41429dae37a51f7ca0bc12d0ddbcdd1733cf382e6da3af4cb0f686497821f6f782178fdc1153041ec11a5357ba0e2244a8edca412849c454f93e8805 Homepage: https://cran.r-project.org/package=configural Description: CRAN Package 'configural' (Multivariate Profile Analysis) R functions for criterion profile analysis, Davison and Davenport (2002) and meta-analytic criterion profile analysis, Wiernik, Wilmot, Davison, and Ones (2020) . Sensitivity analyses to aid in interpreting criterion profile analysis results are also included. Package: r-cran-confinterpret Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-confinterpret_1.0.0-1.ca2604.1_all.deb Size: 118860 MD5sum: 07d8ec4538d469457c9f154e836d6f2e SHA1: 3d6614ee5b801bdf7bf1ff1c655cdb164693a82f SHA256: 2fb24ac80940dcda3401cff88c405af79e4f7e6a1601e232cb13fcf3d3b7ca39 SHA512: c10216e48f73a48c40e28b8d623d93e3b86090012ab4649567242e6cc1952d0ff2a0e1f321fe0dd46e979ea0fda871da63ee929e2b19b3fa1016bb056a2783ad Homepage: https://cran.r-project.org/package=confinterpret Description: CRAN Package 'confinterpret' (Descriptive Interpretations of Confidence Intervals) Produces descriptive interpretations of confidence intervals. Includes (extensible) support for various test types, specified as sets of interpretations dependent on where the lower and upper confidence limits sit. Provides plotting functions for graphical display of interpretations. Package: r-cran-confintr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-confintr_1.0.2-1.ca2604.1_all.deb Size: 168098 MD5sum: 5c318e812fd3b6a649c21b524ea7f3f3 SHA1: ab5557ad3809ce0f85aeca9c32bd2a9eeb0a4d0b SHA256: 6fb42368e8595f20645d241fd1a55110e150ddd253eddad784509ab59cda4af1 SHA512: 07f40887c48c0f348135aa68ad840c6caa28ccb661693d4755e4981831c594477c222050c625d66352e5bab07fb46f4bfae086a37e6c0958084bb00c7c6ecb3e Homepage: https://cran.r-project.org/package=confintr Description: CRAN Package 'confintr' (Confidence Intervals) Calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (incl. median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package 'boot'. Both one- and two-sided intervals are supported. Package: r-cran-confintrob Architecture: all Version: 1.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-lme4, r-cran-mass, r-cran-mvtnorm, r-cran-tidyr Suggests: r-cran-robustlmm, r-cran-robustvarcomp, r-cran-lmertest, r-cran-testthat, r-cran-xtable, r-cran-ggplot2, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-confintrob_1.1-1-1.ca2604.1_all.deb Size: 66930 MD5sum: 7d76e630b5d61c78cbac2275e50a2244 SHA1: 90109df99018ad8222fe50d46c5b61be067cf87e SHA256: a7397c492e858afa1b096a606a6093e712e67c9cfdef334ee3f61aca83cdac9c SHA512: 1b99004d60bfce4275dedb9b47a400a634865e97ea617048c171b5608f037186ef6451fe5a5de3d28286f657dca734d801f352abf926c0fe64f8ef3d3b045705 Homepage: https://cran.r-project.org/package=confintROB Description: CRAN Package 'confintROB' (Confidence Intervals for Robust and Classical Linear Mixed ModelEstimators) The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the 'lme4' package and robust estimators from the rlmer() function in the 'robustlmm' package, as well as the varComprob() function in the 'robustvarComp' package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) and Mason, F., Cantoni, E., & Ghisletta, P. (2024) . Package: r-cran-confintvariance Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-confintvariance_1.0.2-1.ca2604.1_all.deb Size: 24730 MD5sum: 99242225c839fddba80a560d298fe281 SHA1: 5c8d679ff0dd804c78916e05c06d7900622325e0 SHA256: dee8f3e82baf4a9e081a3561793edf3f746d47ec35b4c0ba79e18277804631ed SHA512: bad7d52882f1368eba732592624c98aa35eeda0b028ed21a7dc34ec932fe546bb6c1543e8978905ae85c81f7ba32a9186902e0b6131b2def8cfa125b36b06a54 Homepage: https://cran.r-project.org/package=ConfIntVariance Description: CRAN Package 'ConfIntVariance' (Confidence Interval for the Univariate Population Variancewithout Normality Assumption) Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) and the references therein for an overview of unbiased estimation of variances of U-statistics. Package: r-cran-conflicted Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-memoise, r-cran-rlang Suggests: r-cran-callr, r-cran-covr, r-cran-dplyr, r-cran-matrix, r-cran-pkgload, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-conflicted_1.2.0-1.ca2604.1_all.deb Size: 55858 MD5sum: 3298efa2dcd7bcbb37ca948bdea9bd2d SHA1: a25adb5b87d6b2de68e1fd483395ef1ca3916b94 SHA256: ca2238c0196b7310c813cf1732f7b3657f81170216c62b59a0d39e4fdede690c SHA512: 106a0e55739a0fbc6b30e40598ebaf33c53105a48f3422162ffe474ea9e76db81c87ae497210adc24b3ba6129c06db8a60da4f5469e7bd27a2bffc9f7331820e Homepage: https://cran.r-project.org/package=conflicted Description: CRAN Package 'conflicted' (An Alternative Conflict Resolution Strategy) R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. 'conflicted' takes a different approach, making every conflict an error and forcing you to choose which function to use. Package: r-cran-confluxpro Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-rlang, r-cran-furrr, r-cran-progressr, r-cran-ggplot2, r-cran-scales, r-cran-lifecycle Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-diagrammer, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-confluxpro_1.3.1-1.ca2604.1_all.deb Size: 1090278 MD5sum: e963ef62093330083e3122a7fb0a5d8b SHA1: 4ded21b106c7fa0d273cb11440ca61534414f822 SHA256: cfd2b3c9a52cae8597546805fd348f3ca303a5126d7c30c2bdfa471ccf11d397 SHA512: f3d8ac65f1c49f9736ad33cd9de211f87fcfc6ebe5ec20d244bef4ff973a2b7b947b50f3ebf802226bb6bcb9a5aded7b360dc3421d55cdb727ee565d11c09fd5 Homepage: https://cran.r-project.org/package=ConFluxPro Description: CRAN Package 'ConFluxPro' (Soil Gas Analysis and Flux Modeling) Model soil gas fluxes with the Flux-Gradient Method. It includes functions for data handling, a forward and an inverse model for flux modeling and methods for calibration and uncertainty estimation. For more details see Gartiser et al. (2025a) and Gartiser et al. (2025b) . Package: r-cran-confmatrix Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-rdpack, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-confmatrix_0.2.0-1.ca2604.1_all.deb Size: 266988 MD5sum: 7d516fadc2bfb49a178b4ed58f55e629 SHA1: bcfb83a298d6666071a7366039a56737f3898051 SHA256: ea232142014d4b8a6b959986326fb38a2e2ca7e6e819399135a4348cbfb73a8c SHA512: 3039d50bd11fec00598f9d444496426b3aa73c668c150ef4a886058224cea95fd165bfa154bf15e8319b08e919c67dcd099b0fb665564b9e10dcf82eadd9b99d Homepage: https://cran.r-project.org/package=ConfMatrix Description: CRAN Package 'ConfMatrix' (Confusion Matrix) Thematic quality indices are provided to facilitate the evaluation and quality control of geospatial data products (e.g. thematic maps, remote sensing classifications, etc.). The indices offered are based on the so-called confusion matrix. This matrix is constructed by comparing the assigned classes or attributes of a set of pairs of positions or objects in the product and the ground truth. In this package it is considered that the classes of the ground truth correspond to the columns and that the classes of the product to be valued correspond to the rows. The package offers two object classes with their methods: 'ConfMatrix' (Confusion matrix) and 'QCCS' (Quality Control Columns Set). The 'ConfMatrix' class of objects offers more than 20 methods based on the confusion matrix. The 'QCCS' class of objects offers a different perspective in which the ground truth is considered to allow the values of the column marginals to be fixed, see Ariza López et al. (2019) and Canran Liu et al. (2007) for more details. The package was created with 'R6'. Package: r-cran-confmeta Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-meta, r-cran-metafor, r-cran-patchwork, r-cran-replicationsuccess, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bayesmeta, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-confmeta_0.1.0-1.ca2604.1_all.deb Size: 289656 MD5sum: a620a5c3a5f8de3961a44d9b79da70e2 SHA1: 227b613de75ed476376a861cc74d03bec95ef750 SHA256: 32a4764e5025fac0670618906e448b7ca0afdde1d1a88bfb21fded39d862e5e9 SHA512: 33dc00db0dfcba4d7167978308a106a05d455da1c8ef3d60508b8c9e21ab031f17efafe56ffc1ba39e340eea291d3c923dfa570cfca6889883aeee927645643b Homepage: https://cran.r-project.org/package=confMeta Description: CRAN Package 'confMeta' (Confidence Curves and P-Value Functions for Meta-Analysis) Provides tools for the combination of individual study results in meta-analyses using 'p-value' functions. Implements various combination methods including those by Fisher, Stouffer, Tippett, Edgington along with weighted generalizations. Contains functionality for the visualization and calculation of confidence curves and drapery plots to summarize evidence across studies. Package: r-cran-conforest Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-randomforest, r-cran-rann Filename: pool/dists/resolute/main/r-cran-conforest_2.0.1-1.ca2604.1_all.deb Size: 49244 MD5sum: a5d7fb5dd058a0f1d6a77d41f5f4131c SHA1: d36083b24d1a8b2fddc4d62c85409d451a7b324e SHA256: 76a7cb7e1f586732686a877bf8eab8b9069c8afaa05ab7b63c381f43dfac6ad9 SHA512: 5ca8657fd30748c8a5f94344f773fd5e47ff1903305502cd20705200f5051b352e64155f2c2191f202fb0ee02f0415f075835dde3536e4c7a25eb5dcd24d20ad Homepage: https://cran.r-project.org/package=conforest Description: CRAN Package 'conforest' (Conformal Random Forests for Response Surface Emulation) Fits emulators, also known as surrogates or response surfaces, using conformal inference with random forests. The conformal calibration is performed using out-of-bag samples from the forest, eliminating the need for a separate hold-out set. The method is based on Johansson et al. (2014 ). Package: r-cran-conformalbayes Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rstantools, r-cran-loo, r-cran-matrixstats Suggests: r-cran-rstanarm, r-cran-brms, r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-conformalbayes_0.1.4-1.ca2604.1_all.deb Size: 130648 MD5sum: de12c02a544414563ac632d38d0ffdcd SHA1: 1c9c3ae04d44671553d9cbf5f9605bafad7ce2f9 SHA256: 9c98ab374ed5abc39485c0a16cb5e6a3b542633ad0ee1b81d4f94f7215b751e1 SHA512: 69b47c95b613f2601dffaccd1d1cfe4d68edbe2dbe82d2cba09df4e02e63c61405468e703a87678aa6c49f5d6bb9e6370b2116d2782d22fbaeef68ecccd25855 Homepage: https://cran.r-project.org/package=conformalbayes Description: CRAN Package 'conformalbayes' (Jackknife(+) Predictive Intervals for Bayesian Models) Provides functions to construct finite-sample calibrated predictive intervals for Bayesian models, following the approach in Barber et al. (2021) . These intervals are calculated efficiently using importance sampling for the leave-one-out residuals. By default, the intervals will also reflect the relative uncertainty in the Bayesian model, using the locally-weighted conformal methods of Lei et al. (2018) . Package: r-cran-conformalclassification Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-doparallel, r-cran-mlbench Filename: pool/dists/resolute/main/r-cran-conformalclassification_1.0.0-1.ca2604.1_all.deb Size: 51242 MD5sum: b5a8e8147bfb0c82cf89a047b51a495f SHA1: 98b9fa5d055731e84a039465a8ea91e08ca7604a SHA256: 14f7ca6eeb036d551a22ccc532458fa689ca00a9b4fdab05854816616914e483 SHA512: 4e67bc152f7741ce6675873bcc6838e30304b8a9c68624ddd2dbba1a95f9413074c17cdd955a102ab000b75b6bef735f17d1e71535a13c3da94018aeca9bdaa7 Homepage: https://cran.r-project.org/package=conformalClassification Description: CRAN Package 'conformalClassification' (Transductive and Inductive Conformal Predictions forClassification Problems) Implementation of transductive conformal prediction (see Vovk, 2013, ) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems. Package: r-cran-conformalforecast Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-ggdist, r-cran-rlang, r-cran-zoo Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tsibble Filename: pool/dists/resolute/main/r-cran-conformalforecast_0.1.1-1.ca2604.1_all.deb Size: 640034 MD5sum: b5b5826777c6bfc110537844d0eadc5f SHA1: 3bdb0ea0b10bcd93fbd23f9ce63c0e7b8ac62201 SHA256: 38e02843db08682aa7603c1ffb8217b2facabb42d8f738cda8a8335f560a2357 SHA512: ca40b9bb35ccfb5b8cc6db12db451157be48af5798a52420cdedfd6a05e598b42bd08dcfd949dbb04d20ed105b57bcccf4a99eb0615e351485078a132227ea32 Homepage: https://cran.r-project.org/package=conformalForecast Description: CRAN Package 'conformalForecast' (Conformal Prediction Methods for Multistep-Ahead Time SeriesForecasting) Methods and tools for performing multistep-ahead time series forecasting using conformal prediction methods including classical conformal prediction, adaptive conformal prediction, conformal PID (Proportional-Integral-Derivative) control, and autocorrelated multistep-ahead conformal prediction. The methods were described by Wang and Hyndman (2024) . Package: r-cran-conformalinference.fd Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggpubr, r-cran-scales Suggests: r-cran-roahd, r-cran-pbapply Filename: pool/dists/resolute/main/r-cran-conformalinference.fd_1.1.1-1.ca2604.1_all.deb Size: 187658 MD5sum: fe1a4ba52f4050f2f31f9c68b5869ed8 SHA1: f8b7c434ba3a90fd5ded295cc0b0b93df3448513 SHA256: 9ab7826726d11faf0fb103f7343032eec01fc9cb057ba2e23b15367659731470 SHA512: 7c3ef1a4fd00b371a02a9ca2c2245b73459ebeab5c495fe908fa6ebf5267a5ad4c35ce122a29de75555af34d12bb86aefcddb06d03d166915934b91634fa8a59 Homepage: https://cran.r-project.org/package=conformalInference.fd Description: CRAN Package 'conformalInference.fd' (Tools for Conformal Inference for Regression in MultivariateFunctional Setting) It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate 'conformalInference' package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) , Diquigiovanni, Fontana, and Vantini (2021) , Solari, and Djordjilovic (2021) . Package: r-cran-conformalinference.multi Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-glmnet, r-cran-gridextra Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-conformalinference.multi_1.1.2-1.ca2604.1_all.deb Size: 90154 MD5sum: ecd57a0fd660371ce6f899782018f473 SHA1: 1086d9611a140d0a14fe792eeebe733da7ca396b SHA256: e255a5159d340f071015cd6700fdd7e49b1c1a61270ca3dfcc1cf110560b42e3 SHA512: d4ff14310c598d03febd29852ee05e198c2544ac181f84fb1910154ad127d0c6ce925be522ca88768f47c6ef10036edcb707d05883ca52fa293483e3da650189 Homepage: https://cran.r-project.org/package=conformalInference.multi Description: CRAN Package 'conformalInference.multi' (Conformal Inference Tools for Regression with MultivariateResponse) It computes full conformal, split conformal and multi-split conformal prediction regions when the response variable is multivariate (i.e. dimension is greater than one). Moreover, the package also contains plot functions to visualize the output of the full and split conformal functions. To guarantee consistency, the package structure mimics the univariate package 'conformalInference' by Ryan Tibshirani. See Lei, G’sell, Rinaldo, Tibshirani, & Wasserman (2018) for full and split conformal prediction in regression, and Barber, Candès, Ramdas, & Tibshirani (2023) for extensions beyond exchangeability. 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Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) . 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To learn how to use it, check the vignettes for a quick tutorial. The package is based on the work by Yang Y., Kuchibhotla A.,(2021) . 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Includes contour-based sensitivity plots, robustness curves, and benchmark-oriented graphics that help researchers assess how strong omitted confounding would need to be to attenuate, invalidate, or reverse estimated effects. Supports regression-based sensitivity analysis frameworks, including impact threshold approaches (Frank, 2000, ), partial R-squared methods (Cinelli and Hazlett, 2020, ), and E-value style metrics (VanderWeele and Ding, 2017, ). Emphasizes clear, interpretable, and publication-ready graphical summaries for transparent reporting of causal sensitivity analyses across the social, behavioral, health, and educational sciences. 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Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115. 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Package: r-cran-conjurer Architecture: all Version: 1.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1503 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-conjurer_1.7.1-1.ca2604.1_all.deb Size: 353862 MD5sum: 2f7522c998ae110d5386803c5af3e1d7 SHA1: 8fc465b507371752569e9fbd77a4673b4f2ed607 SHA256: 37a5aeecd38f32f1358a1469ece6146ecfb8b0f7204f0b7a6a790e9098de78da SHA512: 2cc117b3b8a43da2417bf918c6ad2cd639abcd1c1f941800949414cd513ffcd8a833816a8384fc831d0e27787dad13a581d6fd0704e7df6f8049730ccff2b789 Homepage: https://cran.r-project.org/package=conjurer Description: CRAN Package 'conjurer' (A Parametric Method for Generating Synthetic Data) Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. This methodology is generic and therefore benefits both the academic and industrial research. Package: r-cran-conmet Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-foreign, r-cran-waiter, r-cran-shinywidgets, r-cran-purrr, r-cran-lavaan, r-cran-summarytools, r-cran-stringr, r-cran-dplyr, r-cran-hmisc, r-cran-semtools, r-cran-openxlsx, r-cran-dt Filename: pool/dists/resolute/main/r-cran-conmet_0.1.0-1.ca2604.1_all.deb Size: 30274 MD5sum: 58ed1414014074748185a4de595a52ef SHA1: 93436c54ebe8bf0faca765b1dbadd2b06272e981 SHA256: c30add73d1bb00252131dde60bd9aa9a08ad100025a8c6b6d2656037a5532184 SHA512: cef3937fcdae4891829223c60c085a7b30270a4050449988e73d51bb833085ced765f350016b16bc980abac4938e287d6c9ddddb85fb5de92abe328e02c51148 Homepage: https://cran.r-project.org/package=conmet Description: CRAN Package 'conmet' (Construct Measurement Evaluation Tool) With this package you can run 'ConMET' locally in R. 'ConMET' is an R-shiny application that facilitates performing and evaluating confirmatory factor analyses (CFAs) and is useful for running and reporting typical measurement models in applied psychology and management journals. 'ConMET' automatically creates, compares and summarizes CFA models. Most common fit indices (E.g., CFI and SRMR) are put in an overview table. ConMET also allows to test for common method variance. The application is particularly useful for teaching and instruction of measurement issues in survey research. The application uses the 'lavaan' package (Rosseel, 2012) to run CFAs. Package: r-cran-connect Architecture: all Version: 0.7.27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qgraph Suggests: r-cran-covr, r-cran-lintr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-connect_0.7.27-1.ca2604.1_all.deb Size: 93566 MD5sum: 8c0c14007f355334069b348e2a852e78 SHA1: 6eb59461d4b7cfd041713fb40766106a2ed4e2b1 SHA256: ea3ac531798294a235ae544348aa96c05beec20911ab7d9dffa191a2d2c560ce SHA512: 9d9200c165eb2cea9675fcea7c015ff9534581e1dd590d9a65ae86262445874b2b2637d0f4f34166d1fd854bfd05382326bd5f6dfdbdc7460d41e2921c009324 Homepage: https://cran.r-project.org/package=ConNEcT Description: CRAN Package 'ConNEcT' (Contingency Measure-Based Networks for Binary Time Series) The ConNEcT approach investigates the pairwise association strength of binary time series by calculating contingency measures and depicts the results in a network. The package includes features to explore and visualize the data. To calculate the pairwise concurrent or temporal sequenced relationship between the variables, the package provides seven contingency measures (proportion of agreement, classical & corrected Jaccard, Cohen's kappa, phi correlation coefficient, odds ratio, and log odds ratio), however, others can easily be implemented. The package also includes non-parametric significance tests, that can be applied to test whether the contingency value quantifying the relationship between the variables is significantly higher than chance level. Most importantly this test accounts for auto-dependence and relative frequency.See Bodner et al.(2021) .Finally, a network can be drawn. Variables depicted the nodes of the network, with the node size adapted to the prevalence. The association strength between the variables defines the undirected (concurrent) or directed (temporal sequenced) links between the nodes. The results of the non-parametric significance test can be included by depicting either all links or only the significant ones. Tutorial see Bodner et al.(2021) . 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API documentation varies by 'Posit Connect' installation and version, but the latest documentation is also hosted publicly at . Package: r-cran-connectcreds Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-rlang Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-connectcreds_0.2.0-1.ca2604.1_all.deb Size: 63272 MD5sum: 9ad538625fcf31c197e32c6479d81bc2 SHA1: f030c3ee59f9ebf9b83a0b97f10f77943a1a3bc6 SHA256: 8dac260ffd8ec9b1a967820f9c8ddf0dbd38f4af0431fd59d9a847401cd49a6e SHA512: dfe08a61aee78a734e380b02c603e2eaa6ac7111e97adc5f00bceb8d5a2e5b69049c5b8e268382c3947d30478602d754ea9f0cae5af56714041b3909f552e7d8 Homepage: https://cran.r-project.org/package=connectcreds Description: CRAN Package 'connectcreds' (Manage 'OAuth' Credentials from 'Posit Connect') A toolkit for making use of credentials mediated by 'Posit Connect'. It handles the details of communicating with the Connect API correctly, 'OAuth' token caching, and refresh behaviour. Package: r-cran-connected Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-lfe, r-cran-reshape2 Suggests: r-cran-agridat, r-cran-dplyr, r-cran-janitor, r-cran-knitr, r-cran-lme4, r-cran-lucid, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-connected_1.1-1.ca2604.1_all.deb Size: 205280 MD5sum: 17dc47f55afc85a193eae61f49b2300e SHA1: b62321100b26682e32a9332350e3d80645cf6137 SHA256: 71f8b2cd800b4ee7f5b166a3e97bfbdd8450314ea5dab42e9ae50c55b3ca58d3 SHA512: 6260342d65a952a22a9b4c2247dbade7bde7dc3188cf73f5a877bf1aeb475ade90c08afd7ed9ad7bed4aa785ec770f64f5fd39cac662065704b0c753fe09fd6e Homepage: https://cran.r-project.org/package=connected Description: CRAN Package 'connected' (Visualize and Improve Connectedness of Factors in Tables) Visualize the connectedness of factors in two-way tables. 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Package: r-cran-connectednessapproach Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3031 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-frequencyconnectedness, r-cran-rmgarch, r-cran-rugarch, r-cran-igraph, r-cran-quantreg, r-cran-mass, r-cran-progress, r-cran-glmnet, r-cran-xts, r-cran-zoo, r-cran-urca, r-cran-moments, r-cran-riskparityportfolio, r-cran-performanceanalytics, r-cran-car, r-cran-l1pack Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-connectednessapproach_1.0.4-1.ca2604.1_all.deb Size: 2976294 MD5sum: 1acfcfac05d66c388d9dcbdcf644c5a6 SHA1: 996c67939a91cc38d9dbfb55d125dfe57f2dca40 SHA256: 777d8a204fa96f368e9435f7614e2ca082863e36d8c33f4cbc188948d3929726 SHA512: e349be85682f6b253edd9b42ec1688832e57a09e79b80ae73dacc835a28ad37829086725ec3fe6b66eb8b312babfee3e81e753edebbac75ea6a7123a29d5ac21 Homepage: https://cran.r-project.org/package=ConnectednessApproach Description: CRAN Package 'ConnectednessApproach' (Connectedness Approach) The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers. Package: r-cran-connection Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-connection_0.1.0-1.ca2604.1_all.deb Size: 17538 MD5sum: d997187594d8b54b08c1052b92131d0a SHA1: aff5c84164d19d3b9db4b68e5276380c00ccd838 SHA256: 42bbeb3dfdcbd57e3a5cc91227d6aa5bbb543221306f27420e19680a1d5b19fe SHA512: 061c7f9f108c57164cb106226eea69a60379246c8f0a4538794d6e0e23121b8108fc98aa10ec532625e546e6e4c9940c8b4c359abffe2b2f06f402b736d9f30b Homepage: https://cran.r-project.org/package=Connection Description: CRAN Package 'Connection' (Measures of Independence and Connection Without Linear Models) Provides tools to measure connection and independence between variables without relying on linear models. Includes functions to compute Eta squared, Chi-squared, and Cramer V. 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It automates the display of schemata, tables, views, as well as the preview of the table's top 1000 records. 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The components, Card, Grid, Table, Search, and Filter can be used to produce a showcase page or gallery contained within a static or interactive R Markdown page. Package: r-cran-connmattools Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-mcmc Filename: pool/dists/resolute/main/r-cran-connmattools_0.3.5-1.ca2604.1_all.deb Size: 266340 MD5sum: 0dd6603ece529d474c798c5779fa4bc3 SHA1: 00e48e8beb98853db624fdd58235a8a0208743d0 SHA256: 19a7106ec8cd2f0118e5513903685e7c039bea867ba4a16591a6862af28950e8 SHA512: 880deb6a6f773ae26a855f2b9415bf39420cf2b28044b0d63bdeb7334043c9e8575ed7c21ad5dd98ebe7ca982ad3abf20538399781ef8a280708f3f7da3ac547 Homepage: https://cran.r-project.org/package=ConnMatTools Description: CRAN Package 'ConnMatTools' (Tools for Working with Connectivity Data) Collects several different methods for analyzing and working with connectivity data in R. Though primarily oriented towards marine larval dispersal, many of the methods are general and useful for terrestrial systems as well. Package: r-cran-conogive Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-mvtnorm, r-cran-checkmate, r-cran-assertthat Suggests: r-cran-testthat, r-cran-psychtools, r-cran-covr Filename: pool/dists/resolute/main/r-cran-conogive_1.0.0-1.ca2604.1_all.deb Size: 115516 MD5sum: b346dd3b55480cb2c328bbb61926ca35 SHA1: efce397d526a703de170a4a870ee2e275e0cce96 SHA256: 11f8874e0fa46170a0b45cd68bce07758b143e3db71146e1d01b720a5afd5d2d SHA512: 228c46831817fe8edf810346e8b4a539aa7b75f517e583aa1635edbaea39bcceda78b48d64fc7aaba4aab9ccab581a0b67eb2b6eb0c788c4385943e56a12f43e Homepage: https://cran.r-project.org/package=conogive Description: CRAN Package 'conogive' (Congeneric Normal-Ogive Model) The congeneric normal-ogive model is a popular model for psychometric data (McDonald, R. P. (1997) ). This model estimates the model, calculates theoretical and concrete reliability coefficients, and predicts the latent variable of the model. This is the companion package to Moss (2020) . Package: r-cran-conover.test Architecture: all Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-conover.test_1.1.7-1.ca2604.1_all.deb Size: 60356 MD5sum: 254fc27e7722dd2a9a455741f656fae0 SHA1: 8b5db0b2e8557fec93cd4702580e949513aefcca SHA256: 47622747e7dd59f1f54262dfb156bca588d2d213323e723f9f6650d9e1ecf578 SHA512: aac605d65b8846fc99f0d29a08c5345affa8921bea26cbe23b805442716d865ea02010274ead82c07f1faa663eb6a0347ef717a5ce561556c116f1e20cb6ca0c Homepage: https://cran.r-project.org/package=conover.test Description: CRAN Package 'conover.test' (Conover-Iman Test of Multiple Comparisons Using Rank Sums) Computes the Conover-Iman test (1979) for 0th-order stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis omnibus test for i0th-order stochastic dominance among k groups (Kruskal and Wallis, 1952). conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference and for mean difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected. Package: r-cran-conrad Architecture: all Version: 1.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-conrad_1.0.0.1-1.ca2604.1_all.deb Size: 45678 MD5sum: 73ab3c782a69766e047cfeea4f3c1f86 SHA1: 8756dc429e632e55033daaad9a37633c41b4d0e7 SHA256: 4eff5681e294936b1556ae543d053a09d283788d5de37fb18d9a1705210dce67 SHA512: 6ec9b0eb3c69aa61234934415e48c8b3dcaa280d15cd4abc567c18d3792069b25d9acfd9bc07b15853cdff1e0e3b02ed0601cf8a76e84e9824193acdfa7de108 Homepage: https://cran.r-project.org/package=conrad Description: CRAN Package 'conrad' (Client for the Microsoft's 'Cognitive Services Text to SpeechREST' API) Convert text into synthesized speech and get a list of supported voices for a region. Microsoft's 'Cognitive Services Text to Speech REST' API supports neural text to speech voices, which support specific languages and dialects that are identified by locale. Package: r-cran-conscir Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4797 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-readr, r-cran-readxl, r-cran-stringr, r-cran-rlang, r-cran-shiny, r-cran-ggplot2, r-cran-dplyr, r-cran-lubridate, r-cran-padr, r-cran-openair Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bslib, r-cran-iapws95, r-cran-ternary Filename: pool/dists/resolute/main/r-cran-conscir_0.3.0-1.ca2604.1_all.deb Size: 3936610 MD5sum: ca2ada1ba457f3450e5bba6477cd7685 SHA1: e37892e928a610e63a72147ce2f1ce9b0d6c2653 SHA256: 684f4df2e882bdaa7fda4652f11eacdbbe3f7c90f31d8ec5873f075268c935c6 SHA512: d59951ee45c70b59f9f55e13cef4b1424d3a28904210952ed941793b800b59fa2360efea65709f37f241fa52e0f75da5a21db5ae13168622f8311ad078d75ab1 Homepage: https://cran.r-project.org/package=ConSciR Description: CRAN Package 'ConSciR' (Tools for Conservation Science) Provides data science tools for conservation science, including methods for environmental data analysis, humidity calculations, sustainability metrics, engineering calculations, and data visualisation. Supports conservators, scientists, and engineers working with cultural heritage preventive conservation data. The package is motivated by the framework outlined in Cosaert and Beltran et al. (2022) "Tools for the Analysis of Collection Environments" . Package: r-cran-consensusclustering Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-igraph, r-cran-cluster, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-consensusclustering_1.5.0-1.ca2604.1_all.deb Size: 120744 MD5sum: 7dab49e2ec2701813414f600ced3529d SHA1: 6f791ab79edef0eef19227c2312b02d7ccf7f1ff SHA256: 31975a60fcd82b09851bf6d4104ad99c1067ddd83ea39672b966b4724e039a69 SHA512: 3b645dd5921a7d1702c39b072c9fe2c31878885ccbdcd857e2e5699879b7c8e4942be438aed8559736530a62ccbf47b6954b769780e32cdd3e54bbbf9a284ee5 Homepage: https://cran.r-project.org/package=ConsensusClustering Description: CRAN Package 'ConsensusClustering' (Consensus Clustering) Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) . 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However, the inherent problems encountered when analyzing single tables are amplified with the generation of multi-block datasets, and finding the relationships between data layers of increasing complexity constitutes a challenging task. For that purpose, a generic methodology is proposed by combining the strength of established data analysis strategies, i.e. multi-block approaches and the Orthogonal Partial Least Squares (OPLS) framework to provide an efficient tool for the fusion of data obtained from multiple sources. The package enables quick and efficient implementation of the consensus OPLS model for any horizontal multi-block data structures (observation-based matching). Moreover, it offers an interesting range of metrics and graphics to help to determine the optimal number of components and check the validity of the model through permutation tests. Interpretation tools include score and loading plots, Variable Importance in Projection (VIP), functionality predict for SHAP computing, and performance coefficients such as R2, Q2, and DQ2 coefficients. J. Boccard and D.N. Rutledge (2013) . Package: r-cran-conserver Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-ggally, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-network, r-cran-rlang, r-cran-scales, r-cran-sna Filename: pool/dists/resolute/main/r-cran-conserver_1.0.4-1.ca2604.1_all.deb Size: 175812 MD5sum: 3699ebf7e497ace33d0d72f78da1af98 SHA1: 3da02b82ea48195eea843a5e3dc0e2a67c5cd517 SHA256: 8c75e409a28efd1e8d51f87eb8b46915b4f77a7104ce8c1895323434e3afee64 SHA512: 915b7b5ec902bc1d85c96d53c4024477d43ad32f0b91d242aedfcbb8d23b485c645e93b016a1de6e33de9a9847212279fbd17a328731ff3f3d35dbc5c0585f19 Homepage: https://cran.r-project.org/package=conserveR Description: CRAN Package 'conserveR' (Identifying Conservation Prioritization Methods Based on DataAvailability) Helping biologists to choose the most suitable approach to link their research to conservation. After answering few questions on the data available, geographic and taxonomic scope, 'conserveR' ranks existing methods for conservation prioritization and systematic conservation planning by suitability. The methods data base of 'conserveR' contains 133 methods for conservation prioritization based on a systematic review of > 12,000 scientific publications from the fields of spatial conservation prioritization, systematic conservation planning, biogeography and ecology. 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It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). The package depends on the 'ConsRank' R package. Options for validate the tree-based method are both test-set procedure and V-fold cross validation. The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index). The package also contains routines to perform the Kemeny-equivalent augmented unfolding. The mds endine is the function 'sacofSym' from the package 'smacof'. Essential references: D'Ambrosio, A., Vera, J.F., and Heiser, W.J. (2021) ; D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) ; D'Ambrosio, A., and Heiser, W.J. (2019) ; D'Ambrosio, A., and Heiser W.J. (2016) ; Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) ; Marden, J.J. . Package: r-cran-consrq Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-rfast Suggests: r-cran-rfast2, r-cran-cols Filename: pool/dists/resolute/main/r-cran-consrq_1.0-1.ca2604.1_all.deb Size: 30710 MD5sum: 0c2c5219f2e4c285b5d9bc6dbc79e561 SHA1: 7b231299853d3552db1cd7b44c94d3f802509ea0 SHA256: 8dfa35fad2dbfae92b06b6a319f7122396bd02aaf332fd0abdc917407999f24c SHA512: 5cfa0ddb8c7724990528b8923779578431b8a93692f924f2671f139efeeedf9d3789893c5f7dedf3a6cd7186285f3967164b0456cf76678c3d7973aeb5c44c6c Homepage: https://cran.r-project.org/package=consrq Description: CRAN Package 'consrq' (Constrained Quantile Regression) Constrained quantile regression is performed. One constraint is that all beta coefficients (including the constant) cannot be negative, they can be either 0 or strictly positive. Another constraint is that the beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. . Package: r-cran-constants Architecture: all Version: 2022.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-errors, r-cran-units, r-cran-quantities, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-constants_2022.0-1.ca2604.1_all.deb Size: 114490 MD5sum: b2709f3e4f1bd6b7dad4f5ed0868c595 SHA1: 1eaa7fd3bcf2527c3d9feee039ea0802f96b207d SHA256: 9a2f0e08f016bc64fffa8b9165c59a342bd75c4b248d95dad6b0853e70350314 SHA512: cd7f66d676aa6dc1483540a1f0e2c7bf9d6fcfccd744e28d57406c784f982955738a0279f40314bee75764b077ce84fc52e76dcfe0dc5041da2387e455d726d8 Homepage: https://cran.r-project.org/package=constants Description: CRAN Package 'constants' (Reference on Constants, Units and Uncertainty) CODATA internationally recommended values of the fundamental physical constants, provided as symbols for direct use within the R language. Optionally, the values with uncertainties and/or units are also provided if the 'errors', 'units' and/or 'quantities' packages are installed. The Committee on Data for Science and Technology (CODATA) is an interdisciplinary committee of the International Council for Science which periodically provides the internationally accepted set of values of the fundamental physical constants. This package contains the "2022 CODATA" version, published on May 2024: Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2024) . Package: r-cran-constellation Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-devtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-constellation_0.2.0-1.ca2604.1_all.deb Size: 558440 MD5sum: 742c9526947ba333f519c4519ccd2742 SHA1: 9d77e365f8c2c0f3f809c1ce387ead1811057d97 SHA256: 802b88931bb871602e80cb716554d896864f02e5b5b19901ac656295b0c15ff9 SHA512: 53a7ae8a44e2fa12c08d2a8d311739742b9203df3c578ee5bbfae0830e2980abac824e22e23f66b27eed2bace651540126ee23dc26e1d40877dcda0eba8629f2 Homepage: https://cran.r-project.org/package=constellation Description: CRAN Package 'constellation' (Identify Event Sequences Using Time Series Joins) Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows. 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Provides easy access to contact data for 177 countries, for use in epidemiological, demographic or social sciences research. Package: r-cran-contactsurveys Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-oai, r-cran-purrr, r-cran-rlang, r-cran-yesno, r-cran-zen4r Suggests: r-cran-socialmixr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-withr, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-contactsurveys_0.1.0-1.ca2604.1_all.deb Size: 61132 MD5sum: 7ff5f65c5ae8f79b54c00d9f7cc97d4f SHA1: f4f664c6ba0b9ef17c90e0b0b1a05f94a3e13283 SHA256: ed570144548699c2d3e535b074864e292308ef3b3a2752bd97c56254d618721e SHA512: 94a17deb90842adb0538214db7a0610ae3f77bee7cc51cc8619080bcb52932855a53b88940543b98fd71c7827d5c7724ce80ab6b50936a6248e678ab2c6c271a Homepage: https://cran.r-project.org/package=contactsurveys Description: CRAN Package 'contactsurveys' (Download Contact Surveys for Use in Infectious Disease Modelling) Download, cache, and manage social contact survey data from the social contact data community on Zenodo () for use in infectious disease modelling. Provides functions to list available surveys, download survey files with automatic caching, and retrieve citations. Contact survey data describe who contacts whom in a population and are used to parameterise age-structured transmission models, for example via the 'socialmixr' package. The surveys available include those from the POLYMOD study (Mossong et al. (2008) ) and other social contact data shared on Zenodo. 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Stage one detects directional information flows between equity markets via Wavelet-Quantile Transfer Entropy, combining maximal-overlap discrete wavelet decomposition (Percival and Walden, 2000, ISBN:9780521685085) with the transfer-entropy estimator of Schreiber (2000) and quantile conditioning following Han, Linton, Oka and Whang (2016) . Stage two attributes each significant directional link to one of five mutually exclusive transmission channels (Trade, Financial, Geopolitical, Behavioural, Monetary Policy) through a multi-method structural identification architecture combining instrumental-variables two-stage least squares with channel-specific external instruments (Stock and Watson, 2018) , LASSO-based instrument selection (Belloni, Chernozhukov and Hansen, 2014) , local projections (Jorda, 2005) , heteroskedasticity-based identification (Rigobon, 2003) , and the Cinelli-Hazlett (2020) robustness-value sensitivity bound. Bundled datasets and replication scripts reproduce the headline findings of Bhandari, Parida and Sahu (2026) ; the package is general-purpose and accommodates user-supplied returns and channel proxies. 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It provides a way to investigate interactively various modes of convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d. random variables. Visualisation of simulated sample paths is possible through interactive plots. The approach is illustrated by examples and exercises through the function 'investigate', as described in Lafaye de Micheaux and Liquet (2009) . The user can study his/her own sequences of random variables. Package: r-cran-convergencedfm Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-vars, r-cran-urca, r-cran-readxl, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-magrittr, r-cran-zoo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-posterior, r-cran-rstan Filename: pool/dists/resolute/main/r-cran-convergencedfm_0.1.4-1.ca2604.1_all.deb Size: 269366 MD5sum: 88e270b0d62018789e5ed1253ceb003a SHA1: 111805afe8088ee5c924cd3c01baacfd03888efe SHA256: 8f8112cd2cffe7e6acdf60e462f8d4f1db1563dbaa24f0d1cd11fb2f953f65f1 SHA512: ef4d62cb0af2462c956cbe7b0c76c0cdc593678623ae9b9f00ecacdf9e03784c66e78a0415deaea9e52e587089856d0fd29ffb2c0d0fdf6c0d412669b05f8258 Homepage: https://cran.r-project.org/package=convergenceDFM Description: CRAN Package 'convergenceDFM' (Convergence and Dynamic Factor Models) Tests convergence in macro-financial panels combining Dynamic Factor Models (DFM) and mean-reverting Ornstein-Uhlenbeck (OU) processes. Provides: (i) static/approximate DFMs for large panels with VAR/VECM stability checks, Portmanteau tests and rolling out-of-sample R^2, following Stock and Watson (2002) and the Generalized Dynamic Factor Model of Forni, Hallin, Lippi and Reichlin (2000) ; (ii) cointegration analysis à la Johansen (1988) ; (iii) OU-based convergence and half-life summaries grounded in Uhlenbeck and Ornstein (1930) and Vasicek (1977) ; (iv) robust inference via 'sandwich' HC/HAC estimators (Zeileis (2004) ) and regression diagnostics ('lmtest'); and (v) optional PLS-based factor preselection (Mevik and Wehrens (2007) ). Functions emphasize reproducibility and clear, publication-ready summaries. 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This package provides functions to calculate several measures of convergence after imputing missing values. The automated downloading of Eurostat data, followed by the production of country fiches and indicator fiches, makes possible to produce automated reports. The Eurofound report () "Upward convergence in the EU: Concepts, measurements and indicators", 2018, is a detailed presentation of convergence. 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Package: r-cran-cooccurrenceaffinity Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biasedurn, r-cran-cowplot, r-cran-ggplot2, r-cran-plyr, r-cran-reshape Filename: pool/dists/resolute/main/r-cran-cooccurrenceaffinity_2.0.0-1.ca2604.1_all.deb Size: 974204 MD5sum: f21acf01be1af5347911bc3c365fce14 SHA1: 359b8de1cfa14f86a22f49eaf5be23f47d079a8a SHA256: 26fbb49c53dad3c8433f603506e56ab92a4e0e674b4f4697ab20885a3a2f226a SHA512: a11f83ad583cd6efd355b1381ce270230867e6e77a9b584bfe0ad7ce6977b96151e0e131e399618271d910c44ff1dd68ca0a2dcfa0f8372b2c3f9964a15f61aa Homepage: https://cran.r-project.org/package=CooccurrenceAffinity Description: CRAN Package 'CooccurrenceAffinity' (Affinity in Co-Occurrence Data) Computes a novel metric of affinity between two entities based on their co-occurrence (using binary presence/absence data). The metric and its maximum likelihood estimator (alpha hat) were advanced in Mainali, Slud, et al, 2021 . Four types of confidence intervals and median interval were developed in Mainali and Slud, 2022 . The `finches` dataset is bundled with the package. 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Package: r-cran-cookiemonster Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-openssl, r-cran-rappdirs, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-urltools, r-cran-vctrs Suggests: r-cran-curl, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cookiemonster_0.1.0-1.ca2604.1_all.deb Size: 54912 MD5sum: 42fb57211ae6de4e97c0dc71a7622bb3 SHA1: a0d38ef30beca8312fe1c1763a7c149c9c418031 SHA256: b677b6f7f7ce79015078dd809c466d2e518f8fda027decc913a0ceefb9198d33 SHA512: 591a0517dd0039115abed4748b8891b5bc29c4599e9bb1e3b6d54edfa32950237681d8b9c68d7603119fefa6d959914e8f72e6b8f3ee59003e635ccedd83e901 Homepage: https://cran.r-project.org/package=cookiemonster Description: CRAN Package 'cookiemonster' (Your Friendly Solution to Managing Browser Cookies) A convenient tool to store and format browser cookies and use them in 'HTTP' requests (for example, through 'httr2', 'httr' or 'curl'). 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Cookies allow websites to persist information about the user and their use of the website. Here we provide tools for working with cookies in 'shiny' apps, in part by wrapping the 'js-cookie' JavaScript library . Package: r-cran-coopgame Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometry, r-cran-rcdd, r-cran-gtools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-coopgame_0.2.2-1.ca2604.1_all.deb Size: 888242 MD5sum: d7ba7d6a60fb68dff03cd1ab0ec853b8 SHA1: 81db933a1b3776fe6728d833a5e11fdb62e302b9 SHA256: 88045229af75256d45e46f5b048498e129ae0f6edfaf849edbd44fdcc5499e73 SHA512: 15648acfcc8748d96c718a6a08bc2a37b60cf99041c98bf03bcc54487e6f00aba50b03619a3989a53b4092860520953bb9da99dd99b28ed10ba357366db29b1a Homepage: https://cran.r-project.org/package=CoopGame Description: CRAN Package 'CoopGame' (Important Concepts of Cooperative Game Theory) The theory of cooperative games with transferable utility offers useful insights into the way parties can share gains from cooperation and secure sustainable agreements, see e.g. one of the books by Chakravarty, Mitra and Sarkar (2015, ISBN:978-1107058798) or by Driessen (1988, ISBN:978-9027727299) for more details. A comprehensive set of tools for cooperative game theory with transferable utility is provided. Users can create special families of cooperative games, like e.g. bankruptcy games, cost sharing games and weighted voting games. There are functions to check various game properties and to compute five different set-valued solution concepts for cooperative games. A large number of point-valued solution concepts is available reflecting the diverse application areas of cooperative game theory. Some of these point-valued solution concepts can be used to analyze weighted voting games and measure the influence of individual voters within a voting body. There are routines for visualizing both set-valued and point-valued solutions in the case of three or four players. Package: r-cran-coordinatecleaner Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2665 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-rgbif, r-cran-rnaturalearth, r-cran-terra, r-cran-tidyselect Suggests: r-cran-countrycode, r-cran-covr, r-cran-knitr, r-cran-magrittr, r-cran-maps, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-sf, r-cran-testthat, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-coordinatecleaner_3.0.1-1.ca2604.1_all.deb Size: 2408076 MD5sum: 54bc87262b7c93332ff4cfe309c8e406 SHA1: 72124f0ca076424dbe96030ddd6d812fb12c9e01 SHA256: 9ff9c08237e88af31ccbba36666e4c3a111e769990242c228877aacd96be31cc SHA512: 7f62230e9ef16a13da86d9fae4ecc6835defbaa7505fd25f75050820033fd5ecd5a6be95d5da50bf6fd390f21a5cf5ae4adddb009c66206ccb4895fa25640ace Homepage: https://cran.r-project.org/package=CoordinateCleaner Description: CRAN Package 'CoordinateCleaner' (Automated Cleaning of Occurrence Records from BiologicalCollections) Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) . Package: r-cran-coortweet Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tidytable, r-cran-rcppsimdjson, r-cran-lubridate, r-cran-igraph, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-coortweet_2.1.2-1.ca2604.1_all.deb Size: 3518014 MD5sum: 7f425af042654d39f90e66f6c8d6449d SHA1: fd3efee5b61186997415ab41f958b2a9493b6dc2 SHA256: d3f37ce27caf6a38bcda8ad4c097634c517f884886eee9e338afb7a89fcb41a5 SHA512: f4bb331e19f66123aae6ec425609276aa55db7e625c62a6ab4dad5b585ebf5bf140e0d555793e725a29101b9d545bdace91d6aa1d5d6f93d824bca27312b96b1 Homepage: https://cran.r-project.org/package=CooRTweet Description: CRAN Package 'CooRTweet' (Coordinated Networks Detection on Social Media) Detects a variety of coordinated actions on social media and outputs the network of coordinated users along with related information. Package: r-cran-copbasic Architecture: all Version: 2.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2981 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lmomco, r-cran-mvtnorm, r-cran-randtoolbox Suggests: r-cran-copula Filename: pool/dists/resolute/main/r-cran-copbasic_2.2.14-1.ca2604.1_all.deb Size: 2668166 MD5sum: c8f408a8a2634a396e2d81e11b02ebbc SHA1: 641a813957d50dcbeacf607ae3f3fb078afb18ff SHA256: c028affaa09dad95bcc15f349a54ea79ad8b37cc2bac82c93c298b74cfb08f55 SHA512: 2fd54da37de5fa36b0109fafa975061dc8425c7db63f4e8d625eccf48f83b5d066e8565a751385a2d28769264e27f8b571efe9fb327593379c995abadc59a045 Homepage: https://cran.r-project.org/package=copBasic Description: CRAN Package 'copBasic' (General Bivariate Copula Theory and Many Utility Functions) Extensive functions for bivariate copula (bicopula) computations and related operations for bicopula theory. The lower, upper, product, and select other bicopula are implemented along with operations including the diagonal, survival copula, dual of a copula, co-copula, and numerical bicopula density. Level sets, horizontal and vertical sections are supported. Numerical derivatives and inverses of a bicopula are provided through which simulation is implemented. Bicopula composition, convex combination, asymmetry extension, and products also are provided. Support extends to the Kendall Function as well as the Lmoments thereof. Kendall Tau, Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer- Wolff Sigma, tail dependency, tail order, skewness, and bivariate Lmoments are implemented, and positive/negative quadrant dependency, left (right) increasing (decreasing) are available. Other features include Kullback-Leibler Divergence, Vuong Procedure, spectral measure, and Lcomoments for fit and inference, Lcomoment ratio diagrams, maximum likelihood, and AIC, BIC, and RMSE for goodness-of-fit. Package: r-cran-copcor Architecture: all Version: 2024.7-31-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-r.rsp, r-cran-survival Filename: pool/dists/resolute/main/r-cran-copcor_2024.7-31-1.ca2604.1_all.deb Size: 58754 MD5sum: 9668a19cb971c34945feb594c429bb24 SHA1: 17dcd02fb92cac69ccb59aed7764ed0b3f936267 SHA256: 7cc4eff473e17993f90a1bc77a98f7b3ddd56c28b1229f4094b479d6e6cbfb96 SHA512: 7d5689ee14d4573721e4336cbae571c3c7303d68fcd6e72e8567c0bcee608b8eecd92749c06a415a22ee7d1bcd902360e9c73c99d6a53b4d5fc561770bb9614b Homepage: https://cran.r-project.org/package=copcor Description: CRAN Package 'copcor' (Correlates of Protection and Correlates of Risk Functions) Correlates of protection (CoP) and correlates of risk (CoR) study the immune biomarkers associated with an infectious disease outcome, e.g. COVID or HIV-1 infection. This package contains shared functions for analyzing CoP and CoR, including bootstrapping procedures, competing risk estimation, and bootstrapping marginalized risks. Package: r-cran-copcts Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-msm, r-cran-copbasic Filename: pool/dists/resolute/main/r-cran-copcts_1.0.0-1.ca2604.1_all.deb Size: 108252 MD5sum: 82d5409a5ce9e6748f4afc443c374b59 SHA1: 9bcfef6647f8c7f4062c1772d13b54d6a620241d SHA256: b6671d8b6113edd38b5ac4f6eb459dc7b874327d70cb713b36fd153dcc8867a5 SHA512: c89768f6c238c6764ff4a67068703489cd593974413c4aa77cb094f97ba537512f7b7f65d86ccce9e548bed9969dea02fb2369e05861e01644b71d8aa961bd6f Homepage: https://cran.r-project.org/package=CopCTS Description: CRAN Package 'CopCTS' (Copula-Based Semiparametric Analysis for Time Series Data withDetection Limits) Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. Estimation of copula parameter(COPC) and Conditional quantile estimation are included for five available copula functions. Copula selection methods based on L2 distance from empirical copula function are also included. Package: r-cran-cope Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maps, r-cran-abind, r-cran-fields, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-cope_0.2.3-1.ca2604.1_all.deb Size: 75846 MD5sum: 5e83bb1737038cb0a3960774f42e5768 SHA1: a8cba659da676f532a9e307474a9520e6e3d74f3 SHA256: 025f3293623993ae8e335f726360aa8063505cb6b1d1be155fe5c7200e401699 SHA512: 3709d1637adaa7ec201b85740d16f79a36cb5ede5c509f6baaa5a2129ade7143900fe59fbb3ef7718a55cfcf1c407335a8d9e1a6cc242f1482e684aff8f8e76f Homepage: https://cran.r-project.org/package=cope Description: CRAN Package 'cope' (Coverage Probability Excursion (CoPE) Sets) Provides functions to compute and plot Coverage Probability Excursion (CoPE) sets for real valued functions on a 2-dimensional domain. CoPE sets are obtained from repeated noisy observations of the function on the entire domain. They are designed to bound the excursion set of the target function at a given level from above and below with a predefined probability. The target function can be a parameter in spatially-indexed linear regression. Support by NIH grant R01 CA157528 is gratefully acknowledged. Package: r-cran-copent Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mnormt Filename: pool/dists/resolute/main/r-cran-copent_0.5-1.ca2604.1_all.deb Size: 42564 MD5sum: e7b208ed4c304879816be2e0fa3489d0 SHA1: 59bfa77efcffc0a82ef09ede6f99cd5e263779d1 SHA256: 8dde88c7518f68702b15b04421621bf58357cfbaf5d95c5071cab2fc264ba691 SHA512: 4f6ea3fa4f97b8e6f1e90b8ce1d8ca7eca602e425fac6160416fda82721cbcffc60d5ec632c858986217de954d041e223713aac0020cb9c2d747461a9ae4c3f8 Homepage: https://cran.r-project.org/package=copent Description: CRAN Package 'copent' (Estimating Copula Entropy and Transfer Entropy) The nonparametric methods for estimating copula entropy, transfer entropy, and the statistics for multivariate normality test and two-sample test are implemented. The methods for estimating transfer entropy and the statistics for multivariate normality test and two-sample test are based on the method for estimating copula entropy. The method for change point detection with copula entropy based two-sample test is also implemented. Please refer to Ma and Sun (2011) , Ma (2019) , Ma (2022) , Ma (2023) , and Ma (2024) for more information. Package: r-cran-copernicusclimate Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2784 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-clipr, r-cran-ggplot2, r-cran-knitr, r-cran-refmanager, r-cran-rmarkdown, r-cran-sf, r-cran-stars, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-copernicusclimate_0.0.5-1.ca2604.1_all.deb Size: 2025546 MD5sum: 2b2a70445c10e75ea6f0782582ac3917 SHA1: 4c05e56792991d9d1a2a112fe26978c00fdf470f SHA256: a3a24f23d2c9b60fd10747d4025b87e2fb82728a343e7c96a43d6f4617b81668 SHA512: 1c27efb6bde9baff25a035b08f4f032a0e57f10e14eb07fd460a8da770b20b02ec1ab1a0938de5c03360b6af27f47206fd28f89794130978ac9e44d8d2e761ac Homepage: https://cran.r-project.org/package=CopernicusClimate Description: CRAN Package 'CopernicusClimate' (Search Download and Handle Data from Copernicus Climate DataService) Subset and download data from EU Copernicus Climate Data Service: . Import information about the Earth's past, present and future climate from Copernicus into R without the need of external software. 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This package provides entry points to several APIs allowing users to access the data directly in R. Package: r-cran-copernicusdem Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-sf, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-data.table, r-cran-fitbitviz, r-cran-mapview, r-cran-terra Filename: pool/dists/resolute/main/r-cran-copernicusdem_1.0.5-1.ca2604.1_all.deb Size: 2658500 MD5sum: 14270f70ec44a48021fbc58a9c4f118c SHA1: fab2801ed864590cc5d744f9ae331bb73f3cce31 SHA256: 82b28d9aeb83d6797aaf08343109a896afa0dff115d29c1b5e1b86f27aed629a SHA512: 75dae3197bc6bb8f3fd6611c37e246622cfddea3e60b1316754ccd822ace677ddf41253cb2de9be96444600a29ef1c8d45139325cfdb7ccad1d8f51a3c1f6eb4 Homepage: https://cran.r-project.org/package=CopernicusDEM Description: CRAN Package 'CopernicusDEM' (Copernicus Digital Elevation Models) Copernicus Digital Elevation Model datasets (DEM) of 90 and 30 meters resolution using the 'awscli' command line tool. The Copernicus (DEM) is included in the Registry of Open Data on 'AWS (Amazon Web Services)' and represents the surface of the Earth including buildings, infrastructure and vegetation. Package: r-cran-copernicusmarine Architecture: all Version: 0.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aws.s3, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-leaflet, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stars, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-cftime, r-cran-clipr, r-cran-cubelyr, r-cran-curl, r-cran-dt, r-cran-knitr, r-cran-lifecycle, r-cran-ncmeta, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-copernicusmarine_0.4.6-1.ca2604.1_all.deb Size: 701254 MD5sum: 57d0409ad964120fbee00468e16f0b27 SHA1: c16d4a12b6c57b21345ade32552bb38faaab0489 SHA256: 55bdc10bb29ecadf64e6a38bef3854e9c61d6d0104102fc1bd3617e78e2966d7 SHA512: 6775eb61116336f4e4214a0906e692f8ff09a89e1d070958b1cd6908271423f65bea70116f03d2a0e2bc3b33a3cd06aa555bf450773641dc17328435a4d1c1e1 Homepage: https://cran.r-project.org/package=CopernicusMarine Description: CRAN Package 'CopernicusMarine' (Search Download and Handle Data from Copernicus Marine ServiceInformation) Subset and download data from EU Copernicus Marine Service Information: . Import data on the oceans physical and biogeochemical state from Copernicus into R without the need of external software. Package: r-cran-copernicusr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-getpass, r-cran-testthat, r-cran-withr, r-cran-terra, r-cran-stars, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-copernicusr_0.1.0-1.ca2604.1_all.deb Size: 71808 MD5sum: cd5de2bdaf3fcf2ed90b4f1507b827f9 SHA1: a76f89ae12839ce1c8e81994c97757bc314ed3e9 SHA256: 1e54d60eb31a0bc719b04facdbcd10120e1171d52deb4631f88e8ed5cc6a21ea SHA512: 5a6097e58530b5a8293dcf77214c4fa7fcc66537709e49c640dcf78c41585c05b7933937a95508821441991b78a02662b6637aee68b743c5f1cb267371cef3fd Homepage: https://cran.r-project.org/package=copernicusR Description: CRAN Package 'copernicusR' (R Interface to Copernicus Marine Service) Provides an R interface to the Copernicus Marine Service for downloading and accessing marine data. Integrates with the official 'copernicusmarine' Python library through 'reticulate'. Requires Python 3.7+ and a free Copernicus Marine account. See and for more information. Package: r-cran-cops Architecture: all Version: 1.12-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cordillera, r-cran-smacofx, r-cran-smacof, r-cran-analogue, r-cran-cmaes, r-cran-crs, r-cran-dfoptim, r-cran-gensa, r-cran-minqa, r-cran-nlcoptim, r-cran-nloptr, r-cran-pso, r-cran-rgenoud, r-cran-rsolnp, r-cran-subplex Suggests: r-cran-r.rsp, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cops_1.12-1-1.ca2604.1_all.deb Size: 582250 MD5sum: 197e13bcf5e5f21fdc1259cfd2521cde SHA1: adef2b00c7270717e908b4ceb4dcc263cf53ff46 SHA256: e42ab07b70d3bb7eec4b5797465731b12d5a88927b94981b8ac6f0fb0556d228 SHA512: d598a1971fd911fd13018be4fc50e708f1a6c41ef99722fa23f3b45879d2430df63474c177b8158d42bee9b75f8ea5b932deaec76f9c22ac9a5fa8c1e74a7181 Homepage: https://cran.r-project.org/package=cops Description: CRAN Package 'cops' (Cluster Optimized Proximity Scaling) Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, ). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, ). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,) and a functions to calculate the phi-distances for count data or histograms. Package: r-cran-copula.markov.survival Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-copula.markov.survival_1.0.0-1.ca2604.1_all.deb Size: 157300 MD5sum: c2677d769b800232c5125d054e0fa31f SHA1: a648c4f226a2d208e06a99f0b7e05b6bc1add761 SHA256: b9024ae568d89931cc2d0f172a5baca49c338987ca3431686517d1d566b48b6a SHA512: 557da2691a419d9df51cc3320c74919cad1c96549ce899c17564b63e15fb818cb9326a5a6081fdb3ae4edb18cae1a181d6d1b7b1f0ceadf742dd6defa5ebf41d Homepage: https://cran.r-project.org/package=Copula.Markov.survival Description: CRAN Package 'Copula.Markov.survival' (Copula Markov Model with Dependent Censoring) Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted). Package: r-cran-copula.markov Architecture: all Version: 2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-copula.markov_2.9-1.ca2604.1_all.deb Size: 238484 MD5sum: b064ba28785d647913898f0893b2a8ab SHA1: cd110a0eba359ddff98e0dd5be157c8c7fb2b077 SHA256: 169ea9c7a7d72f3e119ae86cc157f21ec8cdd61908d4f0b44f6d15548da774ae SHA512: db9fdf4aaea7d74c86e20d217f39c3882c9a90580530061c2e219b64dbe812cf4b558b6dfe82b1a220c22952b079897e71e9f04a9a9cb7a52030433b2baedc97 Homepage: https://cran.r-project.org/package=Copula.Markov Description: CRAN Package 'Copula.Markov' (Copula-Based Estimation and Statistical Process Control forSerially Correlated Time Series) Estimation and statistical process control are performed under copula-based time-series models. Available are statistical methods in Long and Emura (2014 JCSA), Emura et al. (2017 Commun Stat-Simul) , Huang and Emura (2021 Commun Stat-Simul) , Lin et al. (2021 Comm Stat-Simul) , Sun et al. (2020 JSS Series in Statistics), and Huang and Emura (2021, in revision). Package: r-cran-copula.surv Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-copula.surv_3.0-1.ca2604.1_all.deb Size: 178594 MD5sum: d3bc76f9a5e18a5d5e49e338b8e033da SHA1: b5d433777d53e6be265cff9dc656ea101f00bd43 SHA256: 75fc67f3cedae5104c1ef9019d60d2b48c89acf1f9b7b246e17e42254563d1b8 SHA512: bec313ef437c5dd3d3672f209f8cdfc21f21917f3693a6c5b952308e43856efcc8a75c4277a20610d4590ef11590e1347ae34e20011138ba019d663fd379f1ee Homepage: https://cran.r-project.org/package=Copula.surv Description: CRAN Package 'Copula.surv' (Analysis of Bivariate Survival Data Based on Copulas) Simulating bivariate survival data from copula models. Estimation of the association parameter in copula models. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for a given copula model is implemented. See Emura, Lin and Wang (2010) for details. Package: r-cran-copulaboost Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rvinecopulib Filename: pool/dists/resolute/main/r-cran-copulaboost_0.1.0-1.ca2604.1_all.deb Size: 85594 MD5sum: b66381d0c29d0f4bac02e64e302071d3 SHA1: d70187febeac06709a67648111ef7de4c4c89808 SHA256: 3729141683df37b7e021443c881d3dafcc2df85c2ec21092cb0f56c820564d5d SHA512: 6ec66c852cd0d74509141421389ced9e7ee8a8a8871ec7ee78b5bc8341c1d57cdcab2cc790c460b2343a27c06e47c3fc57331ea5da8691ed7a1d8079f2c3b2b0 Homepage: https://cran.r-project.org/package=copulaboost Description: CRAN Package 'copulaboost' (Fitting Additive Copula Regression Models for Binary OutcomeRegression) Additive copula regression for regression problems with binary outcome via gradient boosting [Brant, Hobæk Haff (2022); ]. The fitting process includes a specialised model selection algorithm for each component, where each component is found (by greedy optimisation) among all the D-vines with only Gaussian pair-copulas of a fixed dimension, as specified by the user. When the variables and structure have been selected, the algorithm then re-fits the component where the pair-copula distributions can be different from Gaussian, if specified. Package: r-cran-copulacenr Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-caret, r-cran-copbasic, r-cran-copula, r-cran-corpcor, r-cran-flexsurv, r-cran-foreach, r-cran-icenreg, r-cran-magrittr, r-cran-plotly, r-cran-pracma, r-cran-survival, r-cran-vinecopula Filename: pool/dists/resolute/main/r-cran-copulacenr_1.2.4-1.ca2604.1_all.deb Size: 722172 MD5sum: 9377faeb4b853f37ea1e1485e3edf232 SHA1: 552448624e022581a16f589b464c38ac3702aa23 SHA256: c5b164689c54c73df1e15a8005ed43db0faf154ad00dc7e40bc3f99d5849446e SHA512: e47a2863340835e52d52200d6c582e3315a5b05a0f909ce76929241a51e6417d145c517d89d6e9f8a27a281e0a7b5e2a96eafaab9c84558e625c7325173ffba4 Homepage: https://cran.r-project.org/package=CopulaCenR Description: CRAN Package 'CopulaCenR' (Copula-Based Regression Models for Multivariate Censored Data) Copula-based regression models for multivariate censored data, including bivariate right-censored data, bivariate interval-censored data, and right/interval-censored semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and semiparametric (Cox and transformation) models. Includes methods for convenient prediction and plotting. Also provides a bivariate time-to-event simulation function and an information ratio-based goodness-of-fit test for copula. Method details can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and Sun et al. (2023+) JRSSC. Package: r-cran-copuladata Architecture: all Version: 0.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-copuladata_0.0-2-1.ca2604.1_all.deb Size: 53564 MD5sum: 545900e893d3d19940301d468662890d SHA1: ea5399579458444363c41f5dc7a3e933ff0392de SHA256: c59676ea624b8236cfee84f5782c201a219a7de7c2fd7926932d38ffec821ffa SHA512: 0c21085ca06a0e54a75763f60293c23c1677391f718778b59017eb7a6fdae40c4dca69efe38620a3ee91c6c86009d63e524f9c7fc18c381c4e88bc13558ca0a1 Homepage: https://cran.r-project.org/package=copulaData Description: CRAN Package 'copulaData' (Data Sets for Copula Modeling) Data sets used for copula modeling in addition to those in the R package 'copula'. These include a random subsample from the US National Education Longitudinal Study (NELS) of 1988 and nursing home data from Wisconsin. Package: r-cran-copulareg Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rvinecopulib Filename: pool/dists/resolute/main/r-cran-copulareg_0.1.0-1.ca2604.1_all.deb Size: 40858 MD5sum: c7a3ac61f30fc6f7218e86d081145e1f SHA1: 00002fad226359cdb1f0ac9bf94b787e5d92f6f9 SHA256: b6877eec0e660b0a5d3768a42fe9424deae59b6f8f807989c5827617c1d562f5 SHA512: fb976b78f08e81b97d20e923d79b828c1b202afe7254119f05fa92ebfb35e4757886d75068ab8d82e40fe550366fa6e17f9c2891c7a3b85aea17332f64d8f3a5 Homepage: https://cran.r-project.org/package=copulareg Description: CRAN Package 'copulareg' (Copula Regression) Fits multivariate models in an R-vine pair copula construction framework, in such a way that the conditional copula can be easily evaluated. In addition, the package implements functionality to compute or approximate the conditional expectation via the conditional copula. Package: r-cran-copularemada Architecture: all Version: 1.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-matlab, r-cran-tensor, r-cran-mc2d Filename: pool/dists/resolute/main/r-cran-copularemada_1.7.5-1.ca2604.1_all.deb Size: 567624 MD5sum: 3c58ad3096bd9a7c48de497288b1a102 SHA1: 613e4fb9299a5644ba8f361247c6fcdbfee75ff6 SHA256: bee512f062add1ed7dd1221a4700311f5188e7c2a296e5d8ad7524443d5d0fa3 SHA512: 778d8a554b2e1508418a537d406bd292881fb70d489c79154297e78c9e3b6f8b73f19894972c51c302ced702f78f3fc1d79a4e049e8f7bcc570fd7355273b16d Homepage: https://cran.r-project.org/package=CopulaREMADA Description: CRAN Package 'CopulaREMADA' (Copula Mixed Models for Multivariate Meta-Analysis of DiagnosticTest Accuracy Studies) The bivariate copula mixed model for meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos (2015) and Nikoloulopoulos (2018) . The vine copula mixed model for meta-analysis of diagnostic test accuracy studies accounting for disease prevalence in Nikoloulopoulos (2017) and also accounting for non-evaluable subjects in Nikoloulopoulos (2020) . The hybrid vine copula mixed model for meta-analysis of diagnostic test accuracy case-control and cohort studies in Nikoloulopoulos (2018) . The D-vine copula mixed model for meta-analysis and comparison of two diagnostic tests in Nikoloulopoulos (2019) . The multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic tests with non-evaluable subjects in Nikoloulopoulos (2020) . The one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests in Nikoloulopoulos (2022) . The multinomial six-variate 1-truncated D-vine copula mixed model for meta-analysis of two diagnostic tests accounting for within and between studies dependence in Nikoloulopoulos (2024) . The 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard (Nikoloulopoulos, 2025) . Package: r-cran-copulasfm Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncnorm, r-cran-vinecopula, r-cran-mass Filename: pool/dists/resolute/main/r-cran-copulasfm_0.2.0-1.ca2604.1_all.deb Size: 41320 MD5sum: 8121ed00a72bddfb88b7a22517ff8a48 SHA1: 710da895ff317c09146da6c44ba9553da822f5f1 SHA256: 85862f733b0c7edc45d36e1c25a79d4770fdd7cf60a84eee49d75d063554ba6d SHA512: 506d3c04ed9a1cee08a0425d2f4ff4c1d01c53a2688846d57308e32fdf21fc20a5dbb793102a9c53fb35e459bae3ec08b13c9867eb076f300d3ac8d49a6776c9 Homepage: https://cran.r-project.org/package=copulaSFM Description: CRAN Package 'copulaSFM' (Copula-Based Stochastic Frontier Models) Provides estimation procedures for copula-based stochastic frontier models for cross-sectional data. The package implements maximum likelihood estimation of stochastic frontier models allowing flexible dependence structures between inefficiency and noise terms through various copula families (e.g., Gaussian and Student-t). It enables estimation of technical efficiency scores, log-likelihood values, and information criteria (AIC and BIC). The implemented framework builds upon stochastic frontier analysis introduced by Aigner, Lovell and Schmidt (1977) and the copula theory described in Joe (2014, ISBN:9781466583221). Empirical applications of copula-based stochastic frontier models can be found in Wiboonpongse et al. (2015) and Maneejuk et al. (2017, ISBN:9783319562176). Package: r-cran-copulasim Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mvtnorm, r-cran-rlang, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-testthat, r-cran-ball, r-cran-energy Filename: pool/dists/resolute/main/r-cran-copulasim_0.0.1-1.ca2604.1_all.deb Size: 70508 MD5sum: df65c6d4e0e9806fe0a447ce9ddfe61a SHA1: dc5b26ae4a4277dfe467591ad9ccc51cf09f4627 SHA256: bd89570b5316de13134c089824e96fce97b7bee3df448dead2e8e679f1b8f097 SHA512: 94691a322ed5d94776236d8ab52e3054d6c389d6ebab4830ada20424169e82b93929dc644b62dbe723845d8e546ec6e6c88a4da9e08d78a442644ae263137b31 Homepage: https://cran.r-project.org/package=copulaSim Description: CRAN Package 'copulaSim' (Virtual Patient Simulation by Copula Invariance Property) To optimize clinical trial designs and data analysis methods consistently through trial simulation, we need to simulate multivariate mixed-type virtual patient data independent of designs and analysis methods under evaluation. To make the outcome of optimization more realistic, relevant empirical patient level data should be utilized when it’s available. However, a few problems arise in simulating trials based on small empirical data, where the underlying marginal distributions and their dependence structure cannot be understood or verified thoroughly due to the limited sample size. To resolve this issue, we use the copula invariance property, which can generate the joint distribution without making a strong parametric assumption. The function copula.sim can generate virtual patient data with optional data validation methods that are based on energy distance and ball divergence measurement. The function compare.copula.sim can conduct comparison of marginal mean and covariance of simulated data. To simulate patient-level data from a hypothetical treatment arm that would perform differently from the observed data, the function new.arm.copula.sim can be used to generate new multivariate data with the same dependence structure of the original data but with a shifted mean vector. Package: r-cran-copulasqm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ald, r-cran-vinecopula, r-cran-mass Filename: pool/dists/resolute/main/r-cran-copulasqm_0.1.0-1.ca2604.1_all.deb Size: 46774 MD5sum: 822d4623a490750b6516bec13e541cbf SHA1: ee4de161924a23f7cabe158b2f71b062f910ac7b SHA256: 6fc2a60ed305d046c390bf53878f9d4fbb03739ed41ee8c9057ad0cd3e7ffe5a SHA512: 1df176e3016e04cf1757443cbe42c2508ce0df8874f0dbbd3e363a1579130404157b62e5d5ae1b2df2796c4fe702e137f90266ce57d2847d2dc1e4617452b1ec Homepage: https://cran.r-project.org/package=copulaSQM Description: CRAN Package 'copulaSQM' (Copula Based Stochastic Frontier Quantile Model) Provides estimation procedures for copula-based stochastic frontier quantile models for cross-sectional data. The package implements maximum likelihood estimation of quantile regression models allowing flexible dependence structures between error components through various copula families (e.g., Gaussian and Student-t). It enables estimation of conditional quantile effects, dependence parameters, log-likelihood values, and information criteria (AIC and BIC). The framework combines quantile regression methodology introduced by Koenker and Bassett (1978) with copula theory described in Joe (2014, ISBN:9781466583221). This approach allows modeling heterogeneous effects across quantiles while capturing nonlinear dependence structures between variables. Package: r-cran-copyseparator Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-seqinr, r-cran-stringr, r-cran-kmer, r-bioc-decipher, r-cran-beepr, r-bioc-biostrings, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-copyseparator_1.2.0-1.ca2604.1_all.deb Size: 147080 MD5sum: cf94eddaa69013e12b0e60a963210c2a SHA1: 959bab71286ad3bd09d2f6b1448da8ea6b47ba33 SHA256: 9fb772c091eb64bd5d20d9f138938f613067c6420c0c739dde94d3518c830c33 SHA512: 14785bded6c641e7dc3f3fcad7659dda1a875fce450dcf42ef857ac4d945f636f0336060ab0a0e757c957c34a023a536f611b4ae9224a09c8992f5935a85c676 Homepage: https://cran.r-project.org/package=copyseparator Description: CRAN Package 'copyseparator' (Assembling Long Gene Copies from Short Read Data) Assembles two or more gene copies from short-read Next-Generation Sequencing data. 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The following variables are included in the data set: id, title, book title, authors, address, date, year, editor, journal, volume, pages, publisher, institution, type, tech, note. The data set has a respective gold data set that provides information on which records match based on id. 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Package: r-cran-corbin Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-corbin_1.0.0-1.ca2604.1_all.deb Size: 41248 MD5sum: d49ae4df37df97171250bddb6c102ce3 SHA1: 4839c3cfb18d4dfe236f64e318c4bd8160c9acce SHA256: cea9528b53a58441cd4580f8fcec10d845beec14bbf2988d3b001e0ceb5c7d4c SHA512: 033bf5eb0d825df65dc115ef49eba35725182ecb6115f69aaf4990863252fe92dde42dfe0174dddd00448eecf4ac048fac956f65f3a8eebb23e6ad11d32b809e Homepage: https://cran.r-project.org/package=CorBin Description: CRAN Package 'CorBin' (Generate High-Dimensional Binary Data with CorrelationStructures) We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S., Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See for a detailed presentation of the method. Package: r-cran-corbouli Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rfast, r-cran-rfast2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-corbouli_0.1.5-1.ca2604.1_all.deb Size: 310840 MD5sum: 3de338889437933e07aeb1fb425079e9 SHA1: bb5c22d7a5c7f5b1445114ec7e1d7f48ca7d38f7 SHA256: dc31c8b5b4e3d3d417a2a0842b0127a012c53691fa8308a4ef4d09e547de293a SHA512: 52a2d2dd58daf23278246c1abc66bdb8698acdd50f0555384f85d7cbb8862354f1618681607c45991158f58f4c7f5c25632d87f17f63205a8f7e4126ebb869f3 Homepage: https://cran.r-project.org/package=corbouli Description: CRAN Package 'corbouli' (Corbae-Ouliaris Frequency Domain Filtering) Corbae-Ouliaris frequency domain filtering. According to Corbae and Ouliaris (2006) , this is a solution for extracting cycles from time series, like business cycles etc. when filtering. This method is valid for both stationary and non-stationary time series. Package: r-cran-corclass Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-cairo Filename: pool/dists/resolute/main/r-cran-corclass_0.2.1-1.ca2604.1_all.deb Size: 37588 MD5sum: b34cbf2ce68846a99756f94f999a2686 SHA1: 8661a9d3e13de0a2fd60b7860c2ecfbbae1e62b8 SHA256: f8497f8b3d6f576690012d5b83ab29babd0c4004a55b56989da1b20c020642d2 SHA512: b34808c3f31f73360cbb58b31d5ee63b2ce1d94671212a04626123e392aa24fb3124372ffc9c3ad4390de00aa9758c1c743ce2eea81ef4dc346678497d45c04f Homepage: https://cran.r-project.org/package=corclass Description: CRAN Package 'corclass' (Correlational Class Analysis) Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules. Package: r-cran-cordillera Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbscan Suggests: r-cran-cluster, r-cran-scatterplot3d, r-cran-mass, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-cordillera_1.0-3-1.ca2604.1_all.deb Size: 493056 MD5sum: 0cb67357bc278561de608db9a6a58070 SHA1: f46637a7b80dfbeb22a99e4e833b927303151bde SHA256: 4daadb4aded8987af90083afa564b017a3d545edb9069327be49edea3c9ed1b8 SHA512: 8bffc47c046e98bf76d6fa9baa6532690b399fad57e656b3c30922b62376085faaff0dc4d23054598e1c62e6ddb7f511126c28511707815402365868e45b8901 Homepage: https://cran.r-project.org/package=cordillera Description: CRAN Package 'cordillera' (Calculation of the OPTICS Cordillera) Functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2018) . We provide an R native version with methods for printing, summarizing, and plotting the result. Package: r-cran-core Architecture: all Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-core_3.2-1.ca2604.1_all.deb Size: 99760 MD5sum: d961b18f5d16bdae88727a6b54cd4af9 SHA1: b770a661637d2490c628fa40bfb574ac4504c0a3 SHA256: ee21f05ffbcecb54a623510a8d227b867301b9d7d559cf1ffec8d3aecea56ade SHA512: 3c387ff20da34e221ab3469dedad6515fa538743011f2e17657bdb4b4f04900ae7148b552c233b008fe9e2efbea6b4ad7979ccaf29f569262c0716c1aadfa122 Homepage: https://cran.r-project.org/package=CORE Description: CRAN Package 'CORE' (Cores of Recurrent Events) Given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs. Package: r-cran-corect Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-igraph, r-cran-oro.dicom, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-corect_1.3.3-1.ca2604.1_all.deb Size: 691402 MD5sum: 42b9d0b1256a91bb77b34413060944b8 SHA1: 3f09ebc28d79efb9d5d31b02b61949b10994f284 SHA256: f9cd8d620bc19a1afa104673d39cac09c28199b5afe0c68b4be509f390ab0430 SHA512: 318c0b3981b03b2970b3bf8657302df559217fb7a70413548400272cc91b2fdebeffe7a29a868b85aba6b579e8356f0c8f050dfaa4eabda323d37c9f44e338f3 Homepage: https://cran.r-project.org/package=coreCT Description: CRAN Package 'coreCT' (Programmatic Analysis of Sediment Cores Using ComputedTomography Imaging) Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) . Package: r-cran-coreheat Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase, r-cran-wgcna, r-cran-heatmapflex, r-cran-convertid, r-cran-rappdirs Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-biocmanager, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db Filename: pool/dists/resolute/main/r-cran-coreheat_0.3.2-1.ca2604.1_all.deb Size: 3246538 MD5sum: 733971824bbef01ee4fdd2a64c16c366 SHA1: 40cd309c96cf92da6ac4b78ff417567f485324ea SHA256: cb0732dfcf5f31d21536dc751114b83aee943566a16f375e33cbefd420a523eb SHA512: 1a5c15634d64e33e6719b55f366b61f5542aad5cf45004e1bbe4dc2fdeac1ab1f3a1dd2e8352bd8c0aaa2cb70f9269e10a135c1d74097dc388fc8cf0837a5ee5 Homepage: https://cran.r-project.org/package=coreheat Description: CRAN Package 'coreheat' (Correlation Heatmaps) Create correlation heatmaps from a numeric matrix. Ensembl Gene ID row names can be converted to Gene Symbols using, e.g., BioMart. Optionally, data can be clustered and filtered by correlation, tree cutting and/or number of missing values. Genes of interest can be highlighted in the plot and correlation significance be indicated by asterisks encoding corresponding P-Values. Plot dimensions and label measures are adjusted automatically by default. The plot features rely on the heatmap.n2() function in the 'heatmapFlex' package. Package: r-cran-corehunter Architecture: all Version: 3.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-naturalsort Suggests: r-cran-testthat, r-cran-mockr, r-cran-statmatch Filename: pool/dists/resolute/main/r-cran-corehunter_3.2.3-1.ca2604.1_all.deb Size: 676672 MD5sum: 6890cb18c99df3ea2472556dc2d8281d SHA1: 999f2cfcf8bf4df92ac23b5a761692022b8274f8 SHA256: 38fb33361803840d4f85d2ad2f0a77738f1abe6201b3079c444db3300950296c SHA512: d83c7c045316157c36b4824f4687db045a241cecc24a553998e0579a59888b2a91e2dfea573eef18256da1ced4483572c6e50893ead593bd5a6c34b4ef3656d1 Homepage: https://cran.r-project.org/package=corehunter Description: CRAN Package 'corehunter' (Multi-Purpose Core Subset Selection) Core Hunter is a tool to sample diverse, representative subsets from large germplasm collections, with minimum redundancy. Such so-called core collections have applications in plant breeding and genetic resource management in general. Core Hunter can construct cores based on genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many provided evaluation measures depending on the precise purpose of the core (e.g. high diversity, representativeness, or allelic richness). In addition, multiple measures can be simultaneously optimized as part of a weighted index to bring the different perspectives closer together. The Core Hunter library is implemented in Java 8 as an open source project (see ). Package: r-cran-corella Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3845 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-hms, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-uuid Suggests: r-cran-gt, r-cran-knitr, r-cran-nanoparquet, r-cran-ozmaps, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-corella_0.1.4-1.ca2604.1_all.deb Size: 1383104 MD5sum: 524877379753526d0c734414ed62f6e1 SHA1: 6ddea8ddb0df60cb3850f1ba4476e0b492d5b6ee SHA256: 2ee9ef54a208d3f561fec9002a1bfe076f72f622c35da4e83f96467853a9cde4 SHA512: c0d9670a4e9c59a487b0a12a444ed2f8ad075314dd88921d931d7106137d38d84888907a3aa2195e3a8eb66aea907661db2db4112a53c6b256fa1bfd368c226f Homepage: https://cran.r-project.org/package=corella Description: CRAN Package 'corella' (Prepare, Manipulate and Check Data to Comply with Darwin CoreStandard) Helps users standardise data to the Darwin Core Standard, a global data standard to store, document, and share biodiversity data like species occurrence records. The package provides tools to manipulate data to conform with, and check validity against, the Darwin Core Standard. Using 'corella' allows users to verify that their data can be used to build 'Darwin Core Archives' using the 'galaxias' package. Package: r-cran-coremicrobiomer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatch, r-cran-vegan, r-cran-srs, r-bioc-edger, r-cran-ggplot2, r-cran-ggrepel, r-cran-plotly, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-coremicrobiomer_0.1.0-1.ca2604.1_all.deb Size: 388896 MD5sum: d944d8be8b2c95fe952558e395bce437 SHA1: acc9ed1d3f90b302ba05a4904d1b5d7cf6fd21a0 SHA256: 5e5360c9edf9a2de3b629a90c5530909cf36a43414dbc1a0fe034bfd29e1e711 SHA512: cade31cf23296c1ceae5e01fc635602dd628445dbbaa7d98b5b2332a62755565c7470ccd87462b45841eccb27ab67cc97740b32b9fd38efc064820a229a49c85 Homepage: https://cran.r-project.org/package=CoreMicrobiomeR Description: CRAN Package 'CoreMicrobiomeR' (Identification of Core Microbiome) The Core Microbiome refers to the group of microorganisms that are consistently present in a particular environment, habitat, or host species. These microorganisms play a crucial role in the functioning and stability of that ecosystem. Identifying these microorganisms can contribute to the emerging field of personalized medicine. The 'CoreMicrobiomeR' is designed to facilitate the identification, statistical testing, and visualization of this group of microorganisms.This package offers three key functions to analyze and visualize microbial community data. This package has been developed based on the research papers published by Pereira et al.(2018) and Beule L, Karlovsky P. (2020) . Package: r-cran-corenlp Architecture: all Version: 0.4-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-xml Filename: pool/dists/resolute/main/r-cran-corenlp_0.4-3-1.ca2604.1_all.deb Size: 68126 MD5sum: f552118f20273799718b904d4eb3ce60 SHA1: e860a3f41b0b0d05a57463dca650d54b939acaa2 SHA256: 1c1b4fbb7dfdfd87cf6b5d12455946fbbd928d051507f7fbb9556c75c9ac67c4 SHA512: c2600faec22b6d0eca070384feadedb15c8d5eab28bc8c16b351239c0a2e2cea15ef415ef3c1e26bcd9ff73ffe6ba529e3ca4a328d21cd04cf2ce61a0353a14d Homepage: https://cran.r-project.org/package=coreNLP Description: CRAN Package 'coreNLP' (Wrappers Around Stanford CoreNLP Tools) Provides a minimal interface for applying annotators from the 'Stanford CoreNLP' java library. Methods are provided for tasks such as tokenisation, part of speech tagging, lemmatisation, named entity recognition, coreference detection and sentiment analysis. 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The details of the method are explained in Schafer and Strimmer (2005) and Opgen-Rhein and Strimmer (2007) . The approach is both computationally as well as statistically very efficient, it is applicable to "small n, large p" data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix. 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Package: r-cran-corpmetrics Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-corpmetrics_1.0-1.ca2604.1_all.deb Size: 37652 MD5sum: 63de219181786a6fa58f5917b687caa5 SHA1: 726219b2a5c4589778aa640208045c56610a3a98 SHA256: 84d8f605f4bcd262e368db259911bc9f280a109ac17ac1e4dffb95775b2f3252 SHA512: 4e2a8583dd7534364e9a32f9acbf2a972401842116e922447c4e5d3d0b7d170611975127b1fd65947dcaf1531b55320fa45673c912b6d0bc0e5425beeea99084 Homepage: https://cran.r-project.org/package=corpmetrics Description: CRAN Package 'corpmetrics' (Tools for Valuation, Financial Metrics and Modeling in CorporateFinance) Balance sheet and income statement metrics, investment analysis methods, valuation methods, loan amortization schedules, and Capital Asset Pricing Model. Package: r-cran-corpora Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3435 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-corpora_0.7-1.ca2604.1_all.deb Size: 3415734 MD5sum: b2ca7eefdb859bbeb40f7a30cd627c43 SHA1: a8afaa7854fc3a529d6e27750621eb221e83e4be SHA256: f5cbb4612cbc15318244415edc020eceb68ded2931a4d455d11c2232888dbb5c SHA512: a7761fd3bd842fa0d0abe648c2d6dbf8d1a27c83d30c7f2ec8f5234944eb2cb3dfc6e107586a39f745103e26b5e80b9c2b9b5e94f3b4ce49d6bf3d44ebac056a Homepage: https://cran.r-project.org/package=corpora Description: CRAN Package 'corpora' (Statistics and Data Sets for Corpus Frequency Data) Utility functions for the statistical analysis of corpus frequency data. This package is a companion to the open-source course "Statistical Inference: A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL'). 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The package contains 1) a helper function to convert a data frame to a 'corporaexplorerobject' and 2) a 'Shiny' app for fast and flexible exploration of a 'corporaexplorerobject'. The package also includes demo apps with which one can explore Jane Austen's novels and the State of the Union Addresses (data from the 'janeaustenr' and 'sotu' packages respectively). Package: r-cran-corpower Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-osdesign Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-corpower_1.0.4-1.ca2604.1_all.deb Size: 174538 MD5sum: 0f5b06e2257b877f95abc069c7396f21 SHA1: 071a08b5d6d93882b5b53ea82e271e15c9ff5cde SHA256: cf31fac1040509add6cafd5a91097c3ef7746e241c3a7a83cf21eff115cb3b26 SHA512: 92d8ea7d831de907afc9faa47d07cb2497b152a1c4be4f75840f07d663752e7265a82e52394b882e09e7a43ac36f33feb73c8b57628441262f7822a8a5e61b25 Homepage: https://cran.r-project.org/package=CoRpower Description: CRAN Package 'CoRpower' (Power Calculations for Assessing Correlates of Risk in ClinicalEfficacy Trials) Calculates power for assessment of intermediate biomarker responses as correlates of risk in the active treatment group in clinical efficacy trials, as described in Gilbert, Janes, and Huang, Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials (2016, Statistics in Medicine). The methods differ from past approaches by accounting for the level of clinical treatment efficacy overall and in biomarker response subgroups, which enables the correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection. The methods also account for inter-individual variability of the observed biomarker response that is not biologically relevant (e.g., due to technical measurement error of the laboratory assay used to measure the biomarker response), which is important because power to detect a specified correlate of risk effect size is heavily affected by the biomarker's measurement error. The methods can be used for a general binary clinical endpoint model with a univariate dichotomous, trichotomous, or continuous biomarker response measured in active treatment recipients at a fixed timepoint after randomization, with either case-cohort Bernoulli sampling or case-control without-replacement sampling of the biomarker (a baseline biomarker is handled as a trivial special case). In a specified two-group trial design, the computeN() function can initially be used for calculating additional requisite design parameters pertaining to the target population of active treatment recipients observed to be at risk at the biomarker sampling timepoint. Subsequently, the power calculation employs an inverse probability weighted logistic regression model fitted by the tps() function in the 'osDesign' package. Power results as well as the relationship between the correlate of risk effect size and treatment efficacy can be visualized using various plotting functions. To link power calculations for detecting a correlate of risk and a correlate of treatment efficacy, a baseline immunogenicity predictor (BIP) can be simulated according to a specified classification rule (for dichotomous or trichotomous BIPs) or correlation with the biomarker response (for continuous BIPs), then outputted along with biomarker response data under assignment to treatment, and clinical endpoint data for both treatment and placebo groups. 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Noda (1993) ). Additionally there are two plot functions for the resulting correlation matrix: The first one creates colored 2D plots, while the second one generates 3D plots. 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That is, it visualizes its main constituent, namely the distances of the single values to their respective mean. The visualization thereby shows what the etymology of the word correlation contains: In pairwise combination, bringing back (see package Vignette for more details). I hope that the 'correlatio' package may benefit some people in understanding and critically evaluating what Pearson's correlation coefficient summarizes in a single number, i.e., to what degree and why Pearson's correlation coefficient may (or may not) be warranted as a measure of association. 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Part of the 'easystats' ecosystem. References: Makowski et al. (2020) . 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References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press and Owen A. B. (2001). "Empirical likelihood". Chapman and Hall/CRC Press. ISBN: 9781584880714. 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For more information about the method, refer to the paper Province MA. (2013) . 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This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552. 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The input can be a full data frame; any string and Boolean terms will be dropped as part of functionality. Correlations and p-values are calculated using the 'Hmisc' framework. Output of the correlation_matrix() function is a table of strings; this gets saved out to a '.csv2' with the save_correlation_matrix() function for easy insertion into a paper. For more details about the process, consult . Package: r-cran-corrtoolbox Architecture: all Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-binnonnor, r-cran-binordnonnor, r-cran-genord, r-cran-moments, r-cran-mvtnorm, r-cran-psych Filename: pool/dists/resolute/main/r-cran-corrtoolbox_1.6.4-1.ca2604.1_all.deb Size: 93304 MD5sum: 3a8708e186dd1ec60c10bea389cc33cd SHA1: 3e45534aa532e76557abe92001efa4772e647119 SHA256: 3bdf339477e2bbc05cf5c008b2be99335614a1ff8bbdf56814bc20acf671a291 SHA512: 770b7520192673abd1bcbb5b60a59781904879f64c97f723aec8cc482026462c990ddddb9036a46cfbb2762981a47c6c7c62d81a262ae373b5254a749826e345 Homepage: https://cran.r-project.org/package=CorrToolBox Description: CRAN Package 'CorrToolBox' (Modeling Correlational Magnitude Transformations inDiscretization Contexts) Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) . Package: r-cran-corrviz Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-visnetwork, r-cran-plotly, r-cran-dendser, r-cran-gganimate, r-cran-igraph, r-cran-ggraph, r-cran-circlize, r-cran-ggally, r-cran-purrr, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-gifski Filename: pool/dists/resolute/main/r-cran-corrviz_0.1.0-1.ca2604.1_all.deb Size: 2579514 MD5sum: 8030bd7bfa2c0df7ea29bde20ff65807 SHA1: d4e6fc305a11151e3ecb4c2c62957f3487e219bc SHA256: 745bfd94d4aff12df5a17003ccf2c13fc9ee74a163aef82ea3ce59a9edd2b9ca SHA512: cda59b61e05ff56a1d4cdced01ba6f536ad34a379d86e628a3e1fdc4b6aab0eea2c4a221d7d447c68d31875ce11a9ad74e9ef34a4136b7b463d991b6942d1e36 Homepage: https://cran.r-project.org/package=corrViz Description: CRAN Package 'corrViz' (Visualise Correlations) An investigative tool designed to help users visualize correlations between variables in their datasets. This package aims to provide an easy and effective way to explore and visualize these correlations, making it easier to interpret and communicate results. Package: r-cran-corset Architecture: all Version: 0.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-forecast, r-cran-hts, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-corset_0.1-5-1.ca2604.1_all.deb Size: 27868 MD5sum: 18e5c853acaa716e1257f94b43596f69 SHA1: 4c321b9ee89a24b171a5b2375e45fcf79466b8f9 SHA256: 668dbbf33e5310f27109d857dc9ed29e2800845111fc553a1a7de425eb181c27 SHA512: 71ca4bc20cefd866eb704a1b9646e6888572c127a3f7b33955b1613a32e0f49df39fe27f9d6f0978c5c1a74922ebb54e676410afe30c8738ff3cd20edf05777f Homepage: https://cran.r-project.org/package=corset Description: CRAN Package 'corset' (Arbitrary Bounding of Series and Time Series Objects) Set of methods to constrain numerical series and time series within arbitrary boundaries. Package: r-cran-cortest Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-igraph, r-cran-clustergeneration, r-cran-matrix, r-bioc-biobase, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-cortest_1.0.7-1.ca2604.1_all.deb Size: 133838 MD5sum: d0f4e20b5d5d9e63e07bc0b6c2378dce SHA1: 30c7388b0ed38c01e6e9b25d9cdc8c6d915e8f5f SHA256: 287c096511f4ef3dc42bff876cc74946ce34105f8282109974d413c76f50f37c SHA512: afc709776b37d6246bc8e8094f2340f4698d7986dc6399987d5290e8965fe669a4b97f52f74ccdf5111881aa85c0e617716ede699d29eaf06786e5f5bb7e97df Homepage: https://cran.r-project.org/package=corTest Description: CRAN Package 'corTest' (Robust Tests for Equal Correlation) There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the article titled ''New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs'' published in Scientific Reports. Please see the reference: Yu et al. (2019) . 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Tools execute as R function calls with provenance tracking, and a deterministic retrieval system keeps relevant objects in context across turns. Three entry points: a shell command-line interface (CLI), a console read-eval-print-loop via chat(), and a Model Context Protocol (MCP) server via serve() for external clients. 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An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) . Package: r-cran-cortsinescore Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-magrittr, r-cran-dplyr Suggests: r-cran-tibble Filename: pool/dists/resolute/main/r-cran-cortsinescore_0.1.0-1.ca2604.1_all.deb Size: 13726 MD5sum: ffbbd07ff9a8919b5014969d088a50cd SHA1: ed260a9f1ed17bec613e6b36a55ab27a6d1c3e26 SHA256: 63911d20db7132e5647ed97ad839639e5533eb5d988245b0a9c7c9a5eb2507ef SHA512: c1f5bdc81ec2859792f364e4a6dc5c13d876a1229fd5ed77ae3d789d2220b1ba6bb44dd3e5256fa86d14424f4062255ec24b7234cc84356ca5a5213cc73480d2 Homepage: https://cran.r-project.org/package=CortSineScore Description: CRAN Package 'CortSineScore' (Compute Cortisol Sine Score (CSS) for Diurnal Cortisol Analysis) Computes a single scalar metric for diurnal cortisol cycle analysis, the Cortisol Sine Score (CSS). The score is calculated as the sum over time points of concentration multiplied by sin(2 * pi * time / 24), giving positive weights to morning time points and negative weights to evening ones. The method is model-free, robust, and suitable for regression, classification, clustering, and biomarker research. 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Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with 'corx'. 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Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) . Package: r-cran-coscorr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-coscorr_1.0.0-1.ca2604.1_all.deb Size: 17268 MD5sum: 1ff9f7072fe78ee0383673fbff1bd919 SHA1: fdb1467777ce2b20e8fd7e99f67a2c2d530fa443 SHA256: 7811a233018e6e2ffc043331e9f5a9b455296c29720a3518e087361d2cf3a5dc SHA512: 9a696e05ad2976dbec8fd7683e6e48c264009b9f328b90c1f3ffe4587a03ce9a4b3ea9b0eb35a0dc885ae7db5c95c75135bd8b8f0aab380659697ecec1bab54d Homepage: https://cran.r-project.org/package=cosCorr Description: CRAN Package 'cosCorr' (Cosine-Correlation Coefficient for Vector Variables) Computes the cosine-correlation coefficient for measuring the degree of linear dependence among variables in a multidimensional context. The package implements the generalized cosine-correlation theorem for p-1 variables, providing a quantitative assessment of interrelationships within experimental frameworks. This methodology extends classical correlation measures to higher-dimensional spaces using a dimensional exploration approach based on time scale calculus. Package: r-cran-cosinor2 Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cosinor, r-cran-cowplot, r-cran-scales, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-matrixstats, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cosinor2_0.2.1-1.ca2604.1_all.deb Size: 162004 MD5sum: 42d1178ebaa0716e7f5c4f581954de3c SHA1: 453dec7e87c97864ea2cea736ef08104bef25e91 SHA256: 634e68b9794fe35ad549417678ff8aacbafe3af56798783d1b26eb98ae1d9ece SHA512: 9c6d95f2a22f05dd7cd4c88debdb2138d436921746bcfac0b261b4ed9c22e2f822dc486835d13eecd4ca5eef0fccbbcc4f5e8a85ea0e834aa4c56879443006ee Homepage: https://cran.r-project.org/package=cosinor2 Description: CRAN Package 'cosinor2' (Extended Tools for Cosinor Analysis of Rhythms) Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). . Package: r-cran-cosinor Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-cosinor_1.2.3-1.ca2604.1_all.deb Size: 96350 MD5sum: 273b8dafedb1a8dd9f65b07b428f3e43 SHA1: 78ad7e97e141cbd8c020f5b98256e6601673929e SHA256: 20fae97c01cbefdf37a960b5944b709ed321a0e902a3615a53162cb5d79b3a07 SHA512: bb7658a4dc724378428e0443e3633b80e8329bc2e46d43c07eae273951b37aff74d26171bf548e9359f23b9b3c53eb46bef3082d47e378bdd3d872f2deedf78a Homepage: https://cran.r-project.org/package=cosinor Description: CRAN Package 'cosinor' (Tools for Estimating and Predicting the Cosinor Model) A set of simple functions that transforms longitudinal data to estimate the cosinor linear model as described in Tong (1976). Methods are given to summarize the mean, amplitude and acrophase, to predict the mean annual outcome value, and to test the coefficients. Package: r-cran-cosmic Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-posterior Suggests: r-cran-arrangements, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-mass, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-cosmic_0.5-1.ca2604.1_all.deb Size: 578714 MD5sum: 1743dd791e1cacb4ade706c283aa97dc SHA1: 31ef591e123016393a15508f491c379a958ca6a5 SHA256: bd6d9725ee39a448835a80833c158f7b6180f5bd1dbecec07739b5ee0872df13 SHA512: d74c804d4c550e8442cc71a12b41d0820f67d41d2c68c6eb1a023a781ebf843c03903ec5bfbc2e3cd5ef65c2583bf12ce7fded1d51f5d1c8d27bd2e0e72d1af5 Homepage: https://cran.r-project.org/package=cosmic Description: CRAN Package 'cosmic' (Conditional Ordinal Stereotype Model for Incident-LevelComparison) Implements the Conditional Ordinal Stereotype Model for Incident-Level Comparison (COSMIC), a method for analyzing ordinal outcomes observed across multiple actors within shared events. The model uses a conditional likelihood to remove event-level confounding and estimate actor-specific propensities relative to their peers. Efficient computation is achieved via a dynamic programming algorithm for the Poisson-multinomial normalization term, enabling scalable estimation with Markov chain Monte Carlo. The package provides tools for data preparation, model fitting using Stan, and extraction of posterior summaries for comparative inference. Estimation of police officer propensity to escalate force is the primary motivation for the model. For more details see Ridgeway (2026) "A Conditional Ordinal Stereotype Model to Estimate Police Officers’ Propensity to Escalate Force" . Package: r-cran-cosmicsig Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2959 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-cosmicsig_1.3.1-1.ca2604.1_all.deb Size: 2940194 MD5sum: bc61f8da777eb738515962340399537c SHA1: 0a283425011b153d52dcc12c57d7a476cdcf3b5f SHA256: 31a7c3c3b9f286c1cd75cec6b3bce35c2950aaf0eb1d9d604b00c25d7ea3b858 SHA512: 756cdc77be351fb2d03bb5eac7bf4724a2197414a6cfe59b0b1e0a7acf6e90f2057813ed3dce05e65c576ed52d2366124ed93773df0e8939f7f1beb48e0bc35b Homepage: https://cran.r-project.org/package=cosmicsig Description: CRAN Package 'cosmicsig' (Mutational Signatures from COSMIC (Catalogue of SomaticMutations in Cancer)) A data package with 2 main package variables: 'signature' and 'etiology'. The 'signature' variable contains the latest mutational signature profiles released on COSMIC for 3 mutation types: * Single base substitutions in the context of preceding and following bases, * Doublet base substitutions, and * Small insertions and deletions. 'cosmicsig' stands for COSMIC signatures. Please run ?'cosmicsig' for more information. Package: r-cran-cosmofns Architecture: all Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cosmofns_1.1-2-1.ca2604.1_all.deb Size: 44780 MD5sum: ef628ecd893df8b5450651b96a4ed80c SHA1: 7bdb75df64f77e989532c7a8f7b82efc99b76ca2 SHA256: 29059e9ca8ad7d74e715701174ff9216c825dd5ca14452c25b6e56826aafb7b3 SHA512: 524e3df87d708ddec9366c9cc6691e1923311cb7be1643eb6ad34a45159fa885b625de88cfab6b579c88b1353b37aaece5449c184adb269d5e13eed97c4f82b8 Homepage: https://cran.r-project.org/package=cosmoFns Description: CRAN Package 'cosmoFns' (Cosmological Distances, Times, Luminosities, Etc) Package encapsulates standard expressions for distances, times, luminosities, and other quantities useful in observational cosmology, including molecular line observations. Currently coded for a flat universe only. 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Package: r-cran-cosso Architecture: all Version: 2.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-rglpk, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-cosso_2.1-2-1.ca2604.1_all.deb Size: 187996 MD5sum: 96831dcec17fe8d3e3d6c30c1e361c18 SHA1: 997e2af2eba8bf98c52197e3ba8d39c28389d204 SHA256: 7e04c5acb6894f2bc03eb5ec4cd8fe913f7fe744a3f58e9ecb7d0cc31c220e72 SHA512: 92cf35762c8d7dde03032033eed3baaaae1417f32b8a4ffb27864a7a39bae6b24563f82e59d200b084a6f20e12a18d28e77e4de519c54d4aa87485b64cd610ca Homepage: https://cran.r-project.org/package=cosso Description: CRAN Package 'cosso' (Fit Regularized Nonparametric Regression Models Using COSSOPenalty) The COSSO regularization method automatically estimates and selects important function components by a soft-thresholding penalty in the context of smoothing spline ANOVA models. Implemented models include mean regression, quantile regression, logistic regression and the Cox regression models. Package: r-cran-cost Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-cost_0.1.0-1.ca2604.1_all.deb Size: 277868 MD5sum: 95f6ce4c80a7dbeda274ee05c10cb3c8 SHA1: c1dbff26b3e8c78588f99e48d06563948d14a7f5 SHA256: 5b855b2956dd1c25936d722228876a535ddfd2644031e466e87576b4c7cd8989 SHA512: c3782260da5e498f8d0aaabb4c2e3c8b1054d4dc6ae1a020f11b0c846cd378dbdc986d78c8a8c9156ae446ce38b43de0eeb53be8812f5f32be6598e6404891ef Homepage: https://cran.r-project.org/package=COST Description: CRAN Package 'COST' (Copula-Based Semiparametric Models for Spatio-Temporal Data) Parameter estimation, one-step ahead forecast and new location prediction methods for spatio-temporal data. Package: r-cran-costat Architecture: all Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh Filename: pool/dists/resolute/main/r-cran-costat_2.4.1-1.ca2604.1_all.deb Size: 225698 MD5sum: fbd12f80e36fab893c4b1ec58a390cc0 SHA1: d0b6725e78610c5b499990872a956e1cb9494205 SHA256: 20f80496251fee39cbfd84675dc2a0facdaa94d2c99792f124f59c691b7ed40b SHA512: ba8e092ca5ed2acaf7310d0604751f8bf4156eb709aff8a9fb59738e2193b48c0f676a4929f736d9de64e5df43265cd90d4304678b536f740fdd04ff5ec43533 Homepage: https://cran.r-project.org/package=costat Description: CRAN Package 'costat' (Time Series Costationarity Determination) Contains functions that can determine whether a time series is second-order stationary or not (and hence evidence for locally stationarity). Given two non-stationary series (i.e. locally stationary series) this package can then discover time-varying linear combinations that are second-order stationary. Cardinali, A. and Nason, G.P. (2013) . Package: r-cran-cotima Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-ctsem, r-cran-lavaan, r-cran-foreach, r-cran-matrix, r-cran-mbess, r-cran-crayon, r-cran-psych, r-cran-doparallel, r-cran-rootsolve, r-cran-abind, r-cran-rpushbullet, r-cran-openxlsx, r-cran-zcurve, r-cran-scholar, r-cran-stringi, r-cran-mass Filename: pool/dists/resolute/main/r-cran-cotima_1.0.2-1.ca2604.1_all.deb Size: 4514330 MD5sum: 55979ab8aae1c9ad4025cc6649c2b53a SHA1: d3587c65baf0f21cbe05dfe650e8437e1ff06114 SHA256: eaead299a7ba0fe81240994c83641f7efd74ef446e7828f761ced68234a0a25e SHA512: ab67a8a29c1eb819f3c7b29a6cf88609907c88282430eed5cbd1fb4103515036eeb291ef98ce53ef9eafffb3492c94899e54b95a23ebf499182a62af5f70b46d Homepage: https://cran.r-project.org/package=CoTiMA Description: CRAN Package 'CoTiMA' (Continuous Time Meta-Analysis ('CoTiMA')) The 'CoTiMA' package performs meta-analyses of correlation matrices of repeatedly measured variables taken from studies that used different time intervals. Different time intervals between measurement occasions impose problems for meta-analyses because the effects (e.g. cross-lagged effects) cannot be simply aggregated, for example, by means of common fixed or random effects analysis. However, continuous time math, which is applied in 'CoTiMA', can be used to extrapolate or intrapolate the results from all studies to any desired time lag. By this, effects obtained in studies that used different time intervals can be meta-analyzed. 'CoTiMA' fits models to empirical data using the structural equation model (SEM) package 'ctsem', the effects specified in a SEM are related to parameters that are not directly included in the model (i.e., continuous time parameters; together, they represent the continuous time structural equation model, CTSEM). Statistical model comparisons and significance tests are then performed on the continuous time parameter estimates. 'CoTiMA' also allows analysis of publication bias (Egger's test, PET-PEESE estimates, zcurve analysis etc.) and analysis of statistical power (post hoc power, required sample sizes). See Dormann, C., Guthier, C., & Cortina, J. M. (2019) . and Guthier, C., Dormann, C., & Voelkle, M. C. (2020) . Package: r-cran-cotram Architecture: all Version: 0.6-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tram, r-cran-mlt, r-cran-variables, r-cran-basefun, r-cran-survival, r-cran-qrng Suggests: r-cran-th.data, r-cran-knitr, r-cran-lattice, r-cran-colorspace, r-cran-multcomp, r-cran-mvtnorm, r-cran-numderiv, r-cran-viridis, r-cran-hmsc Filename: pool/dists/resolute/main/r-cran-cotram_0.6-0-1.ca2604.1_all.deb Size: 182998 MD5sum: 582405d768e2ff529a6e35068e9c07a7 SHA1: 45a6244a5d5e66194b28e9ab304735e4872002f4 SHA256: 993fbe2124a78e456a41e4bf1381b938c7943b6f7427cf16867d08ca47253e5f SHA512: e3c203c67dd75dbc6470c57a84afa7cfe4f0b919772259a26d82f950c9f2e79606e3df86e246c0e8a7565f5ad20389e684a935cd8de964a365a36c0684c6ea3c Homepage: https://cran.r-project.org/package=cotram Description: CRAN Package 'cotram' (Count Transformation Models) Count transformation models featuring parameters interpretable as discrete hazard ratios, odds ratios, reverse-time discrete hazard ratios, or transformed expectations. An appropriate data transformation for a count outcome and regression coefficients are simultaneously estimated by maximising the exact discrete log-likelihood using the computational framework provided in package 'mlt', technical details are given in Siegfried & Hothorn (2020) . The package also contains an experimental implementation of multivariate count transformation models with an application to multi-species distribution models . 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Numbered examples correspond to Feb 2011 preprint . Package: r-cran-count Architecture: all Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-msme, r-cran-sandwich, r-cran-mass Filename: pool/dists/resolute/main/r-cran-count_1.3.5-1.ca2604.1_all.deb Size: 381236 MD5sum: b8e186cebb285224c3bd0d07e5e9599b SHA1: f705c353e3cb54e6e1a23dbb23991896f464191e SHA256: 81d42302cdd5285e41da0d49f45390c4a5597c2505821ab6ffab97e70030bb78 SHA512: 8ccb33efad14dfb8c1462b534288b96d251013813445a4f670a9b437e1c6ffe94b6fddb6848d6ac2a1e1b1e3144f9ece84ddf07ae0bbd5e66984c7f2864b6662 Homepage: https://cran.r-project.org/package=COUNT Description: CRAN Package 'COUNT' (Functions, Data and Code for Count Data) Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press). 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The functions allow to compute the MLE for the following distributions such as the Bell distribution, the Borel distribution, the Poisson distribution, zero inflated Bell distribution, zero inflated Bell Touchard distribution, zero inflated Poisson distribution, zero one inflated Bell distribution and zero one inflated Poisson distribution. Moreover, the probability mass function (PMF), distribution function (CDF), quantile function (QF) and random numbers generation of the Bell Touchard and zero inflated Bell Touchard distribution are also provided. Package: r-cran-countdown Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-prismatic Suggests: r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-countdown_0.6.0-1.ca2604.1_all.deb Size: 117794 MD5sum: bebe92a59dd7905cb159fed52d142c3e SHA1: 0838f07e9aca643c7d5f7dc0b29d7eacf7d3fb07 SHA256: 46470b370110608e65e2392cf82b4f824e1c690b41d5d418da4fa9fce8348624 SHA512: 7e8defa10046ef1da68e8611609737e4891e566cee7ae66d6846c8bcb8e3506137e69437af31dcc1f679b93218288426d9bc8803273c7b713e021b2687ee4bf8 Homepage: https://cran.r-project.org/package=countdown Description: CRAN Package 'countdown' (A Countdown Timer for HTML Presentations, Documents, and WebApps) A simple countdown timer for slides and HTML documents written in 'R Markdown' or 'Quarto'. Integrates fully into 'Shiny' apps. Countdown to something amazing. Package: r-cran-counterfactual Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-survival, r-cran-hmisc, r-cran-foreach, r-cran-dorng, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-counterfactual_1.2-1.ca2604.1_all.deb Size: 465784 MD5sum: da0a4aa34df37168a877ddb75fe00a83 SHA1: d62b2d06cb4e62e62fb37b008aca65c061cb2515 SHA256: a39cc94bd15be433d521cc4f204c9f3cddaeacdb1f74078fcc4c690279816e12 SHA512: ee946d3f862940333a5938a1891634440856ddd362d2fc88f3b2f140347e2ef7817152984f6fe97e2ca5cde025b93db2c4e07a09adcb2238581d871d712868ca Homepage: https://cran.r-project.org/package=Counterfactual Description: CRAN Package 'Counterfactual' (Estimation and Inference Methods for Counterfactual Analysis) Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions. Package: r-cran-counterfactuals Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-checkmate, r-cran-statmatch, r-cran-iml, r-cran-data.table, r-cran-paradox, r-cran-miesmuschel, r-cran-bbotk Suggests: r-cran-gower, r-cran-randomforest, r-cran-ggally, r-cran-trtf, r-cran-testthat, r-cran-mass, r-cran-r.rsp, r-cran-cowplot, r-cran-covr, r-cran-ggplot2, r-cran-keras, r-cran-rchallenge, r-cran-gamlss.data, r-cran-partykit, r-cran-mlt, r-cran-variables, r-cran-basefun, r-cran-rmarkdown, r-cran-rpart, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-tidymodels, r-cran-caret, r-cran-mlr Filename: pool/dists/resolute/main/r-cran-counterfactuals_1.0.0-1.ca2604.1_all.deb Size: 1100642 MD5sum: 3c16f68e440406d130a9960ccaa7680f SHA1: 9bea72036b14760556e1b7ef2b7f74161184705e SHA256: ae1dca9250967fedb371ffc62fa0b92d8eb81746c70ec08d8264307750a5065d SHA512: a13c5d51d14f6bf12cd424b49a70ce11dbb33ab73658b502a212b7c792fa67f5ee5fd6e90348961cf4061cbe487d7fd67068ef26f8b9693a5f8a5dd0b8262344 Homepage: https://cran.r-project.org/package=counterfactuals Description: CRAN Package 'counterfactuals' (Counterfactual Explanations) Modular and unified R6-based interface for counterfactual explanation methods. The following methods are currently implemented: Burghmans et al. (2022) , Dandl et al. (2020) and Wexler et al. (2019) . Optional extensions allow these methods to be applied to a variety of models and use cases. Once generated, the counterfactuals can be analyzed and visualized by provided functionalities. The package is described in detail in Dandl et al. (2025) . Package: r-cran-counternull Architecture: all Version: 0.2.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-effsize, r-cran-ggplot2, r-cran-randomizr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-counternull_0.2.12-1.ca2604.1_all.deb Size: 165628 MD5sum: 73b9d5ff1a5e1b0f7480893ec79e141e SHA1: 2d0fd319c916c2a431842bcb3bc0142d00bbb105 SHA256: afb7a16915b379ca0f49541463c9853c8f3aac0f153f2df299052d975d69cf8c SHA512: 8c396e23b61483a2d45b344b7a6ba1123da344464a5a9b39e87a47a3524a4d9d315569a5206f76423d88abc6959c18193f51560a3b71090108fe85d7aff26c06 Homepage: https://cran.r-project.org/package=Counternull Description: CRAN Package 'Counternull' (Randomization-Based Inference) Randomization-Based Inference for customized experiments. Computes Fisher-Exact P-Values alongside null randomization distributions. Retrieves counternull sets and generates counternull distributions. Computes Fisher Intervals and Fisher-Adjusted P-Values. Package includes visualization of randomization distributions and Fisher Intervals. Users can input custom test statistics and their own methods for randomization. Rosenthal and Rubin (1994) . Package: r-cran-countfitter Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-shiny, r-cran-pscl Suggests: r-cran-dplyr, r-cran-dt, r-cran-gridextra, r-cran-knitr, r-cran-pander, r-cran-reshape2, r-cran-rmarkdown, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-countfitter_1.5-1.ca2604.1_all.deb Size: 998570 MD5sum: 814eaf9daa32a70ef1a97fd241da2d81 SHA1: be2f217265ac11e60e0ed312a6d264ae05672f1f SHA256: 69e55f7696efa829c146b75c4eca48196b66db3e0e4b93b4b7d4170818a58537 SHA512: a300e1bb37299f829ab52a7021500710d18a74e652ae368abfaf04d097daf403b42e2d8535503e1f821fa92d6e52680c30e16c9654374afafddbacf9c41f35c6 Homepage: https://cran.r-project.org/package=countfitteR Description: CRAN Package 'countfitteR' (Comprehensive Automatized Evaluation of Distribution Models forCount Data) A large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. ). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set. Package: r-cran-countgmifs Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-countgmifs_0.0.2-1.ca2604.1_all.deb Size: 279846 MD5sum: 4744cbe314e7d112288c13d29580300b SHA1: 471915f515dc5d39496b1a795a2e527bc0ad3f9a SHA256: 377eb4bb9437d2241d893da6516af5faecc0a630379c6cd67103fd62b14e26e8 SHA512: a96a8febfb2836f0e407badcddb2f72f7011e993fe0a9d97949be39509c34d5dd5ca44082611078b12c1468e7b5bd38c60f39d5b801a7efcd5c296cdb94a1f7e Homepage: https://cran.r-project.org/package=countgmifs Description: CRAN Package 'countgmifs' (Discrete Response Regression for High-Dimensional Data) Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. 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For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. . Package: r-cran-countland Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-matrix, r-cran-ggplot2 Suggests: r-cran-tidyverse, r-cran-viridis, r-cran-gridextra, r-cran-igraph, r-cran-rspectra, r-cran-matrixtests, r-cran-rdist, r-cran-seurat, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-countland_0.1.2-1.ca2604.1_all.deb Size: 214652 MD5sum: 5d32751b6c03c8b67354edddca1be8c3 SHA1: 939f1c24baf4749fa075bfa51db8432a92ca39a0 SHA256: 90dfcc64eed62ebf7fe9994bca98b292a95c0b704f1e6542c47bc04b59164639 SHA512: 5032f3574be09ff67879ce438a41d1f6fe5ad652bd392a10503f2f0b1a5d2f96b94da94bbafe650f8fa109ad7d4664b3ac1b88fafb8c1334d88d3638003dc99b Homepage: https://cran.r-project.org/package=countland Description: CRAN Package 'countland' (Analysis of Biological Count Data, Especially from Single-CellRNA-Seq) A set of functions for applying a restricted linear algebra to the analysis of count-based data. See the accompanying preprint manuscript: "Normalizing need not be the norm: count-based math for analyzing single-cell data" Church et al (2022) This tool is specifically designed to analyze count matrices from single cell RNA sequencing assays. The tools implement several count-based approaches for standard steps in single-cell RNA-seq analysis, including scoring genes and cells, comparing cells and clustering, calculating differential gene expression, and several methods for rank reduction. There are many opportunities for further optimization that may prove useful in the analysis of other data. We provide the source code freely available at and encourage users and developers to fork the code for their own purposes. Package: r-cran-countmaskr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2851 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-countmaskr_0.1.1-1.ca2604.1_all.deb Size: 2124084 MD5sum: 1800c93970309070c0785537a78b8daf SHA1: f9d05e6c196d4c7d561c2634e911a0dd2d581bb1 SHA256: 8f81954b491689a03ac7712b38aa6f5121e2e2f22d28674b2a804a83cf59661e SHA512: 85df7560b25f771e64e26b7e3ec955e6459635b96e1a872e4649678085e988702285097d391350d9a3261809ff3475ef9507ef46214ceba096372b79c441aae7 Homepage: https://cran.r-project.org/package=countmaskr Description: CRAN Package 'countmaskr' (Small Cell Masking Tool for One- & Two-Way Tabular Reports) Provides automated small-cell suppression for one- and two-way frequency tables. 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Package: r-cran-countseppm Architecture: all Version: 3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-expm, r-cran-numderiv, r-cran-lmtest Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-countseppm_3.1-1.ca2604.1_all.deb Size: 412414 MD5sum: 9115ed50ec4947c56159645443ed75c5 SHA1: e486f616a1314b6a60ae3136458c25245b1f6878 SHA256: c8a4e78b7c30539ebfad7bc145eefdd5778ad74ac81e53ea410feb17639a0818 SHA512: b8c4c68ffa1677d41f0597e58fccf43719dc25d1d73e1b6a8fdf85dd801f711f2b4f3a6632be70442f0936dd6d49af84909935c0124b58cddfd621d191e294ab Homepage: https://cran.r-project.org/package=CountsEPPM Description: CRAN Package 'CountsEPPM' (Mean and Variance Modeling of Count Data) Modeling under- and over-dispersed count data using extended Poisson process models as in the article Faddy and Smith (2011) Package: r-cran-counttofpkm Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1988 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-complexheatmap, r-cran-circlize Filename: pool/dists/resolute/main/r-cran-counttofpkm_1.0-1.ca2604.1_all.deb Size: 556418 MD5sum: 8b569fb0340b8d315d72d3351e74a0a3 SHA1: a85ca6f6ce783132f0dc15bd0f9bc3cfef3feb84 SHA256: 861a943e9cc75f19ac31bf56bef6e2dac522ab856d71f0effffef6c5e2441124 SHA512: 49fb5c02f39d3df9fa1d3ad15e2275cc5d5bb0dbe81c1674c8c5af6b4d084de3695b9790456370dc7a61659a81d8dc1a4cc83e06adb2e09afb969e8b91dc85c4 Homepage: https://cran.r-project.org/package=countToFPKM Description: CRAN Package 'countToFPKM' (Convert Counts to Fragments per Kilobase of Transcript perMillion (FPKM)) Implements the algorithm described in Trapnell,C. et al. (2010) . This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using 'biomaRt' package, and the mean fragment lengths which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset. 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It offers a simulated testing environment that includes various models like Poisson, Overdispersed Poisson, Zero-inflated Poisson, and Zero-inflated Overdispersed Poisson. The package is capable of executing five specific algorithms: Linear Thompson sampling with log transformation on the outcome, Thompson sampling Poisson, Thompson sampling Negative Binomial, Thompson sampling Zero-inflated Poisson, and Thompson sampling Zero-inflated Negative Binomial. Additionally, it can generate regret plots to evaluate the performance of contextual bandit algorithms. This package is based on the algorithms by Liu et al. (2023) . Package: r-cran-countyhealthr Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-httr, r-cran-stringr, r-cran-jsonlite, r-cran-curl, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-countyhealthr_0.1.5-1.ca2604.1_all.deb Size: 75300 MD5sum: 50a4e6fa21927391ee5d3e2d1504a2c6 SHA1: d2dc21cf01a02db9fe32cd933e87d9d9e0a240d2 SHA256: 71e35be70e59cb22a44e3f53836e70877352d7ee5d6c883d39c097cdfbcf46e2 SHA512: b6b0a15dd8845f0d302ad54e5284fb973fbdfb71f90603aa6130fc8327029f79e9f55563c39554acb33c03942db5ac0ebce3d2f7584fa78dae8a060e26b8a4a0 Homepage: https://cran.r-project.org/package=countyhealthR Description: CRAN Package 'countyhealthR' (Programmatic Access to County Health Rankings & Roadmaps Data) Provides a simple interface to pull County Health Rankings & Roadmaps (CHR&R) county-level health data and metadata directly from 'Zenodo' . Users can retrieve data for CHR&R release years 2010 through 2025. CHR&R data support research and decision-making to promote health equity and policies that help all communities thrive. Package: r-cran-coursekata Architecture: all Version: 0.19.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dslabs, r-cran-ggformula, r-cran-ggplot2, r-cran-glue, r-cran-lifecycle, r-cran-lsr, r-cran-metrics, r-cran-mosaic, r-cran-palmerpenguins, r-cran-purrr, r-cran-remotes, r-cran-rlang, r-cran-supernova, r-cran-vctrs, r-cran-viridislite Suggests: r-cran-fivethirtyeight, r-cran-knitr, r-cran-lubridate, r-cran-mass, r-cran-mockery, r-cran-mockr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-usethis, r-cran-simstudy, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-coursekata_0.19.2-1.ca2604.1_all.deb Size: 462772 MD5sum: 445ab358c454d4dc5f1e3c3fcf09e56f SHA1: c07c0fe679bf79e88793eee149e5bf3678a9edb2 SHA256: ab189f974dc8f4662a12f58de611730f2a6856c543d33e4c181822287964a316 SHA512: 9767de71dea7373e4668eccfe416e6f399f11b31160078ea6465433ce38f989144618b3ff9ee1269603a9efce80b5d8d55e352409a8ca4687b665198ccea70a6 Homepage: https://cran.r-project.org/package=coursekata Description: CRAN Package 'coursekata' (Packages and Functions for 'CourseKata' Courses) Easily install and load all packages and functions used in 'CourseKata' courses. Aid teaching with helper functions and augment generic functions to provide cohesion between the network of packages. Learn more about 'CourseKata' at . Package: r-cran-covadap Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-covadap_1.0.1-1.ca2604.1_all.deb Size: 285268 MD5sum: 44d0a16c1f9197c2e925244e4e6f6c02 SHA1: 16076b5c71072b7b7cb48c14e411622e339ce25f SHA256: 1443f467458172b0bd05466869cbacb73a2cc888523936a1fd7950601bd5a5d5 SHA512: 5cf07cc9ad351904faf2f2a54ad74274bd9b4801a9bea70ce06c1cfac2aaaffd24825c3d0bc3d0616d7f34db620690ced07555f90db8abb8c8d36bbdad7435a7 Homepage: https://cran.r-project.org/package=covadap Description: CRAN Package 'covadap' (Implement Covariate-Adaptive Randomization) Implementing seven Covariate-Adaptive Randomization to assign patients to two treatments. Three of these procedures can also accommodate quantitative and mixed covariates. Given a set of covariates, the user can generate a single sequence of allocations or replicate the design multiple times by simulating the patients' covariate profiles. At the end, an extensive assessment of the performance of the randomization procedures is provided, calculating several imbalance measures. See Baldi Antognini A, Frieri R, Zagoraiou M and Novelli M (2022) for details. Package: r-cran-covalchemy Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-interp, r-cran-clue, r-cran-ggextra, r-cran-gridextra, r-cran-desctools, r-cran-mass Filename: pool/dists/resolute/main/r-cran-covalchemy_1.0.0-1.ca2604.1_all.deb Size: 125534 MD5sum: 7abe367a3f8ea68614b559fa87c41254 SHA1: a9caabdc9cc7da2ea977493443b4389c67172e8e SHA256: 502ccaf6a02aca3bf6ce13ae5b231710936ca66dca667cf127de01d66c71e7a7 SHA512: 9b8eee25dffd061d4e35e6bf55f0b6d640c4d25b549e8955bc6e618bed2a144c54b5331f435377407275746925e8206858f77ab689cb29222536213b34d6aadc Homepage: https://cran.r-project.org/package=covalchemy Description: CRAN Package 'covalchemy' (Constructing Joint Distributions with Control Over StatisticalProperties) Synthesizing joint distributions from marginal densities, focusing on controlling key statistical properties such as correlation for continuous data, mutual information for categorical data, and inducing Simpson's Paradox. Generate datasets with specified correlation structures for continuous variables, adjust mutual information between categorical variables, and manipulate subgroup correlations to intentionally create Simpson's Paradox. Joe (1997) Sklar (1959) . Package: r-cran-covatest Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mathjaxr, r-cran-lubridate, r-cran-v8, r-cran-zoo, r-cran-gstat, r-cran-sp, r-cran-spacetime Suggests: r-cran-sf Filename: pool/dists/resolute/main/r-cran-covatest_1.2.5-1.ca2604.1_all.deb Size: 476792 MD5sum: efd4546b90c7b6369dae51eea4d0bb23 SHA1: 800907e68791551b05b55cd89ee8390226bf55be SHA256: 458a188ee899046d38dcfb458e06504a8111dd9ecc5ec25761c5fc5ab2a606ae SHA512: 16fb9ee115d3895a7e78f4adb4161dec2fb3f6dd838d1186ed6ffa4439e8ec8cf9249350aa73890914b4e70fd9b902e81162ddf73b6fb80cd3a4384bec7ce40e Homepage: https://cran.r-project.org/package=covatest Description: CRAN Package 'covatest' (Tests on Properties of Space-Time Covariance Functions) Tests on properties of space-time covariance functions. Tests on symmetry, separability and for assessing different forms of non-separability are available. Moreover tests on some classes of covariance functions, such that the classes of product-sum models, Gneiting models and integrated product models have been provided. It is the companion R package to the papers of Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability and some classes of space-time covariance function models and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for selecting a class of space-time covariance functions . 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Detailed description of the methods in Chianucci et al. (2022) . Package: r-cran-covests Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-covests_1.1.0-1.ca2604.1_all.deb Size: 274734 MD5sum: ff2c9dff5d4769121492ea685ea1c988 SHA1: 3a4789a90d4cbe6f5b034b707a5424855e74ebc6 SHA256: 368f307d97e47042f4b9dab682c9815b267f5f302f832cf3a42bb9901c89aa50 SHA512: dcfb3feb8df7e0adb5865578205dbd8b59bfe8aed1c81addf12296061aef8053a79ca3a82acc1f5ad425436a6a00f7a5f1d436eeed5d5cf95d6c50999d7c42e3 Homepage: https://cran.r-project.org/package=CovEsts Description: CRAN Package 'CovEsts' (Nonparametric Estimators for Covariance Functions) Several nonparametric estimators of autocovariance functions. Procedures for constructing their confidence regions by using bootstrap techniques. Methods to correct autocovariance estimators and several tools for analysing and comparing them. Supplementary functions, including kernel computations and discrete cosine Fourier transforms. For more details see Bilchouris and Olenko (2025) . 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Originally inspired by the infamous "covfefe" tweet of 2017. 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The datasets reporting the COVID-19 cases are available in two main modalities, as a time series sequences and aggregated data for the last day with greater spatial resolution. Several analysis, visualization and modelling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread. Package: r-cran-covid19 Architecture: all Version: 3.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.utils, r-cran-data.table Suggests: r-cran-rsqlite, r-cran-wbstats Filename: pool/dists/resolute/main/r-cran-covid19_3.0.3-1.ca2604.1_all.deb Size: 34626 MD5sum: 4fc4fb0ab9a282c7fac42579201ae73a SHA1: 4e07508702081faa58d74704fc42546b2016324c SHA256: 979e40b814e6c1b94b42e5c7c9a17f80935b81dda3b81a99b92c86a2eb624d35 SHA512: 4aed70eaad3cb1300464536aabd5f832052f0bea801afee350ec1a3322983aa0d00bfeebd1749caad62e9798938e68c8edb60aaf62a6d95d8e48fe6ef8eeeac3 Homepage: https://cran.r-project.org/package=COVID19 Description: CRAN Package 'COVID19' (R Interface to COVID-19 Data Hub) Provides a daily summary of COVID-19 cases, deaths, recovered, tests, vaccinations, and hospitalizations for 230+ countries, 760+ regions, and 12000+ administrative divisions of lower level. 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The Brazilian COVID-19 data, obtained from the official Brazilian repository at , is available at the country, region, state, and city levels. The package also downloads world-level COVID-19 data from Johns Hopkins University's repository. COVID-19 data is available from the start of follow-up until to May 5, 2023, when the World Health Organization (WHO) declared an end to the Public Health Emergency of International Concern (PHEIC) for COVID-19. Package: r-cran-covid19dbcand Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1939 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.tree, r-cran-dt, r-cran-networkd3 Filename: pool/dists/resolute/main/r-cran-covid19dbcand_0.1.1-1.ca2604.1_all.deb Size: 1495790 MD5sum: 03b638ae2eb8cb1b6dc1c3579525b154 SHA1: 3a11d462d966deac544368be0c692684ffb2f8ba SHA256: cfde1c0022bc127907901fc13055b4309dc645b4dca965f43505fb4b839ed258 SHA512: 53cf726341a990ba35a0f1f7cc63aaaa75a5271a0805dcc550a8c257f2e9d9ec1e47a4ae83fc64af247675ca715b8f1f064617b98035b8df2de01a04dd2eb9cc Homepage: https://cran.r-project.org/package=covid19dbcand Description: CRAN Package 'covid19dbcand' (Selected 'Drugbank' Drugs for COVID-19 Treatment Related Data inR Format) Provides different datasets parsed from 'Drugbank' database using 'dbparser' package. It is a smaller version from 'dbdataset' package. It contains only information about COVID-19 possible treatment. Package: r-cran-covid19france Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-covid19france_0.1.0-1.ca2604.1_all.deb Size: 19198 MD5sum: d13454ff9aab0686dbcb7486185722d6 SHA1: 746615968bd9de26593e7a3288ff355e5aafa535 SHA256: 1623041baa148b20c14f1eb6b03d3d91212333123d873f1650540b4e36f6285e SHA512: a6f3fcc359307cc3e3b0bc619b071156274f80626a0e2403a8a6d5c82e7ad0a1eda3342583e190c0490444813e95e66692a05ff48404d0cca3b097be89c3572a Homepage: https://cran.r-project.org/package=covid19france Description: CRAN Package 'covid19france' (Cases of COVID-19 in France) Imports and cleans 'opencovid19-fr' data on COVID-19 in France. 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Package: r-cran-covidmutations Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4357 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-seqinr, r-cran-stringr, r-cran-ggpubr, r-cran-dplyr, r-cran-venndiagram Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-covidmutations_0.1.3-1.ca2604.1_all.deb Size: 3122262 MD5sum: bace9ea3787b72cc700772634ecc65a5 SHA1: 8638b7b19a4cc6d6f9e56cdbcb75712bb4a195cd SHA256: e2c15b5da2a41206ccf5e21fad54fcc8bd9fdb7ff4d804defa130ae0f4b45c2e SHA512: c85febc436906a331d6a895acad6870c93bc3ca7d0830d1e23970b0492bb76735a7bdb37803e56befc4bb3eb105e2f9ddc0638270cb24b9dd0d8487c58bc73c5 Homepage: https://cran.r-project.org/package=CovidMutations Description: CRAN Package 'CovidMutations' (Mutation Analysis Toolkit for COVID-19 (Coronavirus Disease2019)) A feasible framework for mutation analysis and reverse transcription polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including mutation profile visualization, statistics and mutation ratio of each assay. The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020) . Package: r-cran-covidnor Architecture: all Version: 2023.05.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-glue, r-cran-magrittr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-covidnor_2023.05.18-1.ca2604.1_all.deb Size: 1042524 MD5sum: bcb3c492f7ef2ea1a0882c70b4e8edbf SHA1: ec2732700caafec326c105b1c15f326a050e9810 SHA256: e27db5f6ca9cf0453a629b84ef1d24bda0cb6e661d787d2c4aa2248b187c7f79 SHA512: 197eab2a3b2e2a73f096ed6d8836999d659ea3ae744e58f39315910b6c739856153c51b4168cd6002dfe585a7e209238b43a5877f3848b35b476f0ded74bbce2 Homepage: https://cran.r-project.org/package=covidnor Description: CRAN Package 'covidnor' (Public COVID-19 Data for Norway) Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination. Package: r-cran-covidprobability Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-covidprobability_0.1.0-1.ca2604.1_all.deb Size: 331094 MD5sum: 7fa09fe77729d1a1feda5519cfab96b7 SHA1: d0b51ef0e098351513e98540f621896575ba1e17 SHA256: c81f2194c050702ca6aaeba1148a3eec6d3ecee2b3c768d6dffe6136a599c372 SHA512: 884d5d3e9d533e2064941ed53ac0ce0b21f1d5824416193ddc44ed35766b8402a8b629ca86e933d98897964c1a5057c6db4d40aab2f2006e9f5236a607985b49 Homepage: https://cran.r-project.org/package=covidprobability Description: CRAN Package 'covidprobability' (Estimate the Unit-Wide Probability of COVID-19) We propose a method to estimate the probability of an undetected case of COVID-19 in a defined setting, when a given number of people have been exposed, with a given pretest probability of having COVID-19 as a result of that exposure. Since we are interested in undetected COVID-19, we assume no person has developed symptoms (which would warrant further investigation) and that everyone was tested on a given day, and all tested negative. 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This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise. 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A complete descriptions of the implemented tests can be found in the paper Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017), no. 4, 1431--1461. . 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Marginal information may be fully specified, for which the package implements the VITA (VIne-To-Anything) algorithm Grønneberg and Foldnes (2017) . See also Grønneberg, Foldnes and Marcoulides (2022) . Alternatively, marginal skewness and kurtosis may be specified, for which the package implements the IG (independent generator) and PLSIM (piecewise linear) algorithms, see Foldnes and Olsson (2016) and Foldnes and Grønneberg (2021) , respectively. 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The visualization of that function looks like a quarter segment of a cowbell giving the package its name. The package has been specifically constructed for the case where minimum and maximum value of the dependent and two independent variables are known a prior, which is usually the case when those values are derived from Likert scales. 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The package was originally written for internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package). It has also been used extensively in the book Fundamentals of Data Visualization. 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Package: r-cran-coxaipw Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-randomforestsrc, r-cran-polspline, r-cran-tidyr, r-cran-ranger, r-cran-pracma, r-cran-gbm Filename: pool/dists/resolute/main/r-cran-coxaipw_0.0.3-1.ca2604.1_all.deb Size: 55944 MD5sum: 3f59be20af3266d4f477103246e7de6c SHA1: 0b6ca4fead3e055e4ebda18ad75ee3c36240acbb SHA256: e8f14795f5cebaefe9edc9c2d7d615e1b86f32b3024f026a09193e6c0c250401 SHA512: ef1183d2f25d26ff95de6e8c8faf8b2b182f44533f01ccb2b71de72ddadcc8c6e4de913ddcac9c547cb29be970bbd561188fc61c7b1a635fd0f15a06cf456926 Homepage: https://cran.r-project.org/package=CoxAIPW Description: CRAN Package 'CoxAIPW' (Doubly Robust Inference for Cox Marginal Structural Model withInformative Censoring) Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) ; Rava (2021) . Package: r-cran-coxbcv Architecture: all Version: 0.0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-survival Filename: pool/dists/resolute/main/r-cran-coxbcv_0.0.1.0-1.ca2604.1_all.deb Size: 99038 MD5sum: ca068dd9262ae8137fb56295e99de1b4 SHA1: e5ba92945b22a300cc9691d40582f15a37f366b5 SHA256: 5caa1f668a9c916d7a515a2892f296f50de4771a5f87c3f6697ef192dac047cd SHA512: 3451cb185127005bda1f0b5ebfd18aa8e81fdd60faa5457bfe2aeecfa74ec7753d9c41d2ef7a70e2d4da1351b3ae10fb9ae95d40a83d180cf047388438f12b08 Homepage: https://cran.r-project.org/package=CoxBcv Description: CRAN Package 'CoxBcv' (Bias-Corrected Sandwich Variance Estimators for Marginal CoxAnalysis of Cluster Randomized Trials) The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review). Package: r-cran-coxicpen Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-coxicpen_1.1.0-1.ca2604.1_all.deb Size: 55482 MD5sum: 14a629d9fbececfb039708e1574bf87c SHA1: b3224fc3ee6d36cbe5c6ff705c5787bbdc868b9f SHA256: 3825a6c8b08ba373020e6a53db35f768e2a404ae409bdcbf9dfdd2ebd349b6b5 SHA512: 01633a0813b0287d7629fbbcc1ded4b83cee4f12cc3a4d172d1493559c45871c3a141507fccf5020faa2ba505a45cc9ee1bc531ce4a4e28c04e74c594b804b9f Homepage: https://cran.r-project.org/package=CoxICPen Description: CRAN Package 'CoxICPen' (Variable Selection for Cox's Model with Interval-Censored Data) Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below. 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The method is based on model-X knockoffs as described in Barber and Candes (2015) and fast survival analysis methods from Bi et al. (2020) . A shrinkage algorithmic leveraging accelerates multiple knockoffs generation in large genetic cohorts. This CRAN version uses standard Cox regression for association testing. For enhanced performance on very large datasets, users may optionally install the 'SPACox' package from GitHub which provides saddlepoint approximation methods for survival analysis. 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Package: r-cran-cp Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-cp_1.8-1.ca2604.1_all.deb Size: 185922 MD5sum: a8cbfe2e6dee7dc2b55c5f96c941a640 SHA1: 30e5170d8695ca03cacd8ee4d553a09811f58fcc SHA256: 649b74dc09aead0a6c1e96b09ce0ebb0bad222d53677d3ccc60125ade6ebd667 SHA512: 3ca60395f3b2d4849e5cffac3dcfc1bf8d2354423d97e141f9f3091f797a6eaca12f3e545f1a0618e12ed9a4ca9b3179c113fd015cb6eead6d872cadc0796600 Homepage: https://cran.r-project.org/package=CP Description: CRAN Package 'CP' (Conditional Power Calculations) Functions for calculating the conditional power for different models in survival time analysis within randomized clinical trials with two different treatments to be compared and survival as an endpoint. Package: r-cran-cpa Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cpa_1.0.1-1.ca2604.1_all.deb Size: 40770 MD5sum: 23ed56391dc3f0eced551ad55895fc6c SHA1: dd6e4c795e2e8298e250afdc0ab98b5b01945675 SHA256: 63ff9b8c62be22ef16a461baf13ea5c3bc64f77b84b1049231ab33039976d764 SHA512: 0db7c89fe69ba4b5a7c609488eb0804482fa9f90fb7b3f4853afcf77377dee94894de2c80afb2d1d12e0f7d8c8e4a61efb26c7578d8582d92417cf1b6365e53d Homepage: https://cran.r-project.org/package=cpa Description: CRAN Package 'cpa' (Confirmatory Path Analysis Through 'd-sep' Tests) Functions to test and compare causal models using Confirmatory Path Analysis. Package: r-cran-cpam Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-cli, r-cran-dplyr, r-bioc-edger, r-cran-ggplot2, r-cran-magrittr, r-cran-matrixstats, r-cran-mgcv, r-cran-mvnfast, r-cran-pbmcapply, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scam, r-cran-shiny, r-cran-shinyjs, r-cran-stringr, r-cran-tidyr, r-bioc-tximport Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cpam_0.2.1-1.ca2604.1_all.deb Size: 2577978 MD5sum: 5fac9962e6071e799331964279a5552a SHA1: 022c20d2d8631cfc1c120cc6877f345ad139f794 SHA256: 5f7be6352fdb08a1ddaf1f83223a614f81b812299d249f9e4e4aaea1bca9c082 SHA512: 734435ecb5d3a8ed2fe01ad0e098cf23a2acaa66e737bd3f0c3bc8b2350d4e70b5475b0dab79edb043c5a9c3f5620d68f0ca668c097e756cf5b39f63022ee2a1 Homepage: https://cran.r-project.org/package=cpam Description: CRAN Package 'cpam' (Changepoint Additive Models for Time Series Omics Data) Provides a comprehensive framework for time series omics analysis, integrating changepoint detection, smooth and shape-constrained trends, and uncertainty quantification. It supports gene- and transcript-level inferences, p-value aggregation for improved power, and both case-only and case-control designs. It includes an interactive 'shiny' interface. The methods are described in Yates et al. (2024) . Package: r-cran-cpbayes Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-forestplot, r-cran-purrr, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cpbayes_1.1.0-1.ca2604.1_all.deb Size: 129822 MD5sum: da5d6fc9a0d1d35d125a8e80697eeac4 SHA1: 07309feaa5836cfddc7547d7995c11a99cd62887 SHA256: 89cffedcdf055ba3cbed579b64ae822134f0978651a72547c6e0bcc8fe84f385 SHA512: bf340b113d6f34dbe35310a535800ebe3ff74460bc8eeefa19da246bd220f8cc03ae6bc28dea80037d20e4f75faf8a710b1dd391a49479f85eb0853699b6d851 Homepage: https://cran.r-project.org/package=CPBayes Description: CRAN Package 'CPBayes' (Bayesian Meta Analysis for Studying Cross-Phenotype GeneticAssociations) A Bayesian meta-analysis method for studying cross-phenotype genetic associations. It uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. CPBayes is based on a spike and slab prior. The methodology is available from: A Majumdar, T Haldar, S Bhattacharya, JS Witte (2018) . Package: r-cran-cpc Architecture: all Version: 2.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-dbscan, r-cran-rfast Filename: pool/dists/resolute/main/r-cran-cpc_2.6.2-1.ca2604.1_all.deb Size: 81616 MD5sum: 48fd7fb31660fd24b2a6be9542cc9b15 SHA1: 42fe3cca53c54a4988974e89b3c7a654ee67d5e0 SHA256: 21dbe7041f781cf48e9e68ee69e6c9234daae739f6440b6058c9e8fd50ea6c36 SHA512: 5df39826c5bad9685318cb4d4416f9ae28f32286be9998e78793d191d26d1102514dcb09cb469270574039cc07343aad62f2485631550aa775f087956ff374a3 Homepage: https://cran.r-project.org/package=CPC Description: CRAN Package 'CPC' (Implementation of Cluster-Polarization Coefficient) Implements cluster-polarization coefficient for measuring distributional polarization in single or multiple dimensions, as well as associated functions. Contains support for hierarchical clustering, k-means, partitioning around medoids, density-based spatial clustering with noise, and manually imposed cluster membership. Mehlhaff (2024) . Package: r-cran-cpcat Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cpcat_1.0.0-1.ca2604.1_all.deb Size: 35574 MD5sum: 468ade837f7dbdacaeb264b4afdfba3e SHA1: 1375094775883ef497d94689917250967a375392 SHA256: f0e93ab953c4c275f46040e9dd103c6873d7cba45ddaa205b84aa1939010ff4b SHA512: cb190658295a3544c33850651050be3cc94b28068a7bc7fb8e18e75bb1e22d416a3527aefaee5b0fa2035ed86efb89997db334254c7aaf5828cf12103b602619 Homepage: https://cran.r-project.org/package=CPCAT Description: CRAN Package 'CPCAT' (The Closure Principle Computational Approach Test) P-values and no/lowest observed (adverse) effect concentration values derived from the closure principle computational approach test (Lehmann, R. et al. (2015) ) are provided. The package contains functions to generate intersection hypotheses according to the closure principle (Bretz, F., Hothorn, T., Westfall, P. (2010) ), an implementation of the computational approach test (Ching-Hui, C., Nabendu, P., Jyh-Jiuan, L. (2010) ) and the combination of both, that is, the closure principle computational approach test. Package: r-cran-cpd Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hypergeo, r-cran-rdpack, r-cran-dgof Filename: pool/dists/resolute/main/r-cran-cpd_0.3.3-1.ca2604.1_all.deb Size: 159996 MD5sum: 39fded3f77b256d9fd1b71e7c7ff5da4 SHA1: f711563e9048c8dc4317498cb633a1fce2c9b302 SHA256: 676a08a86437e99ff6311324b2e28b48be5d485741f3b6e4e0f89545f5d64cb8 SHA512: 20edff22f287a086298c97288687f298e476f4b5a89729d602a09a97fa52c7df8daaea6b3c09052a9ac3176ff0d64a355d19d46e78e2a8bf8201599046df25d3 Homepage: https://cran.r-project.org/package=cpd Description: CRAN Package 'cpd' (Complex Pearson Distributions) Probability mass function, distribution function, quantile function and random generation for the Complex Triparametric Pearson (CTP) and Complex Biparametric Pearson (CBP) distributions developed by Rodriguez-Avi et al (2003) , Rodriguez-Avi et al (2004) and Olmo-Jimenez et al (2018) . The package also contains maximum-likelihood fitting functions for these models. Package: r-cran-cpfa Architecture: all Version: 1.2-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-multiway, r-cran-glmnet, r-cran-e1071, r-cran-randomforest, r-cran-nnet, r-cran-rda, r-cran-xgboost, r-cran-foreach, r-cran-doparallel, r-cran-dorng Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cpfa_1.2-9-1.ca2604.1_all.deb Size: 554190 MD5sum: d8b3ef67509b160544259415b0d85977 SHA1: ada724a23bd8c337663054c892cefe0a1f803957 SHA256: 3bea0ce0bdf08442a5c7e665dd2826557ddfb19c8595e5bfdbcf3361c2ce85ef SHA512: 8fc50820bed56f3489d4339e07937e45d5868eb86e576bd247a16ae8f2d2f3d14a26f1894c392a054ad44690b646f5de4c4e5f0e675d4a3668a0a374bdc431f9 Homepage: https://cran.r-project.org/package=cpfa Description: CRAN Package 'cpfa' (Classification with Parallel Factor Analysis) Classification using Richard A. Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model fit to a three-way or four-way data array. See Harshman and Lundy (1994): . Classification using principal component analysis (PCA) fit to a two-way data matrix is also supported. Uses component weights from one mode of a Parafac, Parafac2, or PCA model as features to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Allows for constraints on different tensor modes. Allows for inclusion of additional features alongside features generated by the component model. Supports penalized logistic regression, support vector machine, random forest, feed-forward neural network, regularized discriminant analysis, and gradient boosting machine. Supports binary and multiclass classification. Predicts class labels or class probabilities, and calculates multiple classification performance measures. Implements parallel computing via the 'foreach', 'doParallel', and 'doRNG' packages. Package: r-cran-cpgassoc Architecture: all Version: 2.70-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-cpgassoc_2.70-1.ca2604.1_all.deb Size: 1970744 MD5sum: 05a2b1053056e5d741b9c0f6021ea090 SHA1: 0092aa02fea3cac01653cd8ddffa50885e807700 SHA256: cf8b6fa84233146d1a0233bc18f8b497d026d9194dada2672a8b76b5bb886e3f SHA512: fdab4c7a7586c5346edf4660f7e56603b23b2f5ab8ca14116bc1fc9c06d8e71d2588af591fdee007bcb70a41d58942813113307c387ee212f3474b150876d5d0 Homepage: https://cran.r-project.org/package=CpGassoc Description: CRAN Package 'CpGassoc' (Association Between Methylation and a Phenotype of Interest) Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites. Package: r-cran-cpgfilter Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/resolute/main/r-cran-cpgfilter_1.1-1.ca2604.1_all.deb Size: 19170 MD5sum: 7709450600bd6b3fd4ab9192bce283dd SHA1: 7245ae478c58d765777820b33924c043f9ff227a SHA256: e99e9ea498faf137f16614a9fcbfa1c0aaa142e592ba5d20ed446bbe7c64b60a SHA512: b9462f08fd2f1e12fcbcd7d8b05725746c29a0a00b7a3b557b831ea32fd816872e0e41bdb7d3cd43d8c02a7facacf32b055c8f4f7426f2f1181873392c0d4a05 Homepage: https://cran.r-project.org/package=CpGFilter Description: CRAN Package 'CpGFilter' (CpG Filtering Method Based on Intra-Class CorrelationCoefficients) Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design. Package: r-cran-cpgfr Architecture: all Version: 0.0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-osfr, r-cran-lubridate, r-cran-deflatebr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-cpgfr_0.0.1.0-1.ca2604.1_all.deb Size: 17690 MD5sum: 5db3e8b074b2c95df3ef106efefb9b73 SHA1: 49f70a54ff715a9a9497d1e750e1f562de110164 SHA256: d987e0bd36d451482c485ccf0a7e767bd16b202fb276c57a3a9e7a1ebc0a6766 SHA512: a60f3b5b629a8950e4b22e924203634d4cca3a50e72fa8648e6202a073600c1d84422a15c660529ab362b1b309a0a6f2355082d590b3a0029a057ff1ac8515a2 Homepage: https://cran.r-project.org/package=cpgfR Description: CRAN Package 'cpgfR' (Consolidates Information from the Federal Government PaymentCard) Provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal and a curated dataset hosted on the Open Science Framework . Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism. Package: r-cran-cplots Architecture: all Version: 0.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular Filename: pool/dists/resolute/main/r-cran-cplots_0.5-0-1.ca2604.1_all.deb Size: 77666 MD5sum: c3c7dd5fb6115872389cb655a5819d22 SHA1: ed4e293c59c55c88933e9efdeedecac65ae480db SHA256: 0431c8cb2d495fe66fbf0f50f33f238fe8e79a4f7c1b23ce5717818422a9fe8d SHA512: f1538f84e80fc9f54d6083ab46f58a3579788b726d86bb020a773fcdc56c8773560657fa0b7e31a60c662a9d78a6965c3fcb893677d52b20e27991a8b88bd755 Homepage: https://cran.r-project.org/package=cplots Description: CRAN Package 'cplots' (Plots for Circular Data) Provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include bar plots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms. Package: r-cran-cpmbigdata Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-hmisc, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-benchmarkme Filename: pool/dists/resolute/main/r-cran-cpmbigdata_0.0.2-1.ca2604.1_all.deb Size: 37386 MD5sum: de514e3791026970794f82e7a21d3f54 SHA1: ffc0d2dd12c3b840a070d81d10c60f29e713fedd SHA256: 0566267e4fbf5791e3ad19af324718d2046556b0197b16ce77f218c55f999163 SHA512: 89bae1065ad2af19c5fe2ec6c936e714ab29e32a90ab29d948cbc16f7ba56f7436d3a911a9fbfc9fcd18c2aad8babb9438ce2af8d1c4682cb890e6dfc5be845b Homepage: https://cran.r-project.org/package=cpmBigData Description: CRAN Package 'cpmBigData' (Fitting Semiparametric Cumulative Probability Models for BigData) A big data version for fitting cumulative probability models using the orm() function from the 'rms' package. See Liu et al. (2017) for details. Package: r-cran-cpmcglm Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-plyr, r-cran-abind Filename: pool/dists/resolute/main/r-cran-cpmcglm_1.2-1.ca2604.1_all.deb Size: 85120 MD5sum: 93f6f3f52f3e34c057eec1e25ccda926 SHA1: 33b198158bbc5067b88f5b5255cb31f0591ab940 SHA256: d254219e4403e93164d89156f3925aa5227d82974df787f814308cd92960ac7f SHA512: 57c8722287f6ce769592572daa08db0535f4f16ca6c5f8d87ab5d546a4d2613425ec7f5dc410349757c3a16b0b4ec8acfa5181d9486aec825a921086f71a13e0 Homepage: https://cran.r-project.org/package=CPMCGLM Description: CRAN Package 'CPMCGLM' (Correction of the P-Value after Multiple Coding in GeneralizedLinear Models) We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developed by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available. Package: r-cran-cpmerccutoff Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cpmerccutoff_1.0.0-1.ca2604.1_all.deb Size: 80690 MD5sum: ece2eb4cf3d9b7dbf5027decab6f056f SHA1: b189e379b389738e90f584636da729a7b3a4be0c SHA256: e127621aa2b2c67ec3e952e30536d0deba3cff4810fd539f19923f229d5849c6 SHA512: 71094dd27350caaf4f71e96f11ce648053b3874148dda1061cfc8131530cd2454bfcb01bf0e7038fda8fafa51e6e6cbaf3069279f1e7c6a72ed548533695c422 Homepage: https://cran.r-project.org/package=CpmERCCutoff Description: CRAN Package 'CpmERCCutoff' (Calculation of Log2 Counts per Million Cutoff from ERCC Controls) Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022). This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances. The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes. Package: r-cran-cpmr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-rfast, r-cran-tibble Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-cpmr_0.1.1-1.ca2604.1_all.deb Size: 43046 MD5sum: 3fecde63b0dd5d0f6e3527e9a4bed2e4 SHA1: e6d9c1490c60bbc4cdb268034fe9e0319275fdff SHA256: 9769d28fffd1120b266ba8330f6c2851427d5a7fe65ead92cb842d758da5bdb4 SHA512: a4892ccfe2c051666c1400846040355283514df39a0ad953c3bb33f36eb20c0184c06ee8e29cdc0056866714a527fd8f4b9955b00e31e8c843688cbee79c70a9 Homepage: https://cran.r-project.org/package=cpmr Description: CRAN Package 'cpmr' (Connectome Predictive Modelling in R) Connectome Predictive Modelling (CPM) (Shen et al. (2017) ) is a method to predict individual differences in behaviour from brain functional connectivity. 'cpmr' provides a simple yet efficient implementation of this method. 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Trains a classifier to classify (multivariate) observations as coming from one of several distributions. If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected. 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Package: r-cran-cquad Architecture: all Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-plm, r-cran-formula Filename: pool/dists/resolute/main/r-cran-cquad_2.3-1.ca2604.1_all.deb Size: 197874 MD5sum: 5f5e175cf61bdb068effd8ec6a5766ce SHA1: 939b6825c886e244bf24d6cceeb5c7c079029bf6 SHA256: e27757a7ca35636d5def162248b4ca932ae09f9964c07e5a11d740d01f7aad13 SHA512: 821f8eb1b479f71cd2b6556df6cc1bcf133b47dc171a76342a31ea8511c673d770b569fc767b767729aefeca47c9d68d7bc3408b4d9f30c3cefbdc9a5412d496 Homepage: https://cran.r-project.org/package=cquad Description: CRAN Package 'cquad' (Conditional Maximum Likelihood for Quadratic Exponential Modelsfor Binary Panel Data) Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) . The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) . For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics . Package: r-cran-cr2 Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-matrix, r-cran-generics, r-cran-magrittr, r-cran-broom, r-cran-dplyr, r-cran-performance, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-cr2_0.2.1-1.ca2604.1_all.deb Size: 131950 MD5sum: 91781e8564d595ea85183b4c7de190e4 SHA1: f3358cdaa16031dd10306b062993246a1386d532 SHA256: c731c3a6932701f254ab81462b032fa7f5a39e3d9024ae0781a0bcc0fed3d814 SHA512: bf806761c04f22a7c3e8df91f0a375f956744990dba765a336fff199f989ef1b2637be49678174b635493ba08c2fcc16420baf2f3d9fb939620d55e2ce0832c3 Homepage: https://cran.r-project.org/package=CR2 Description: CRAN Package 'CR2' (Compute Cluster Robust Standard Errors with Degrees of FreedomAdjustments) Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on 'Liang and Zeger' (1986) and Bell and 'McCaffrey' . Functions used in Huang and Li , Huang, 'Wiedermann', and 'Zhang' , and Huang, 'Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness). 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It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) . Package: r-cran-cragg Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expm Suggests: r-cran-testthat, r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cragg_0.0.1-1.ca2604.1_all.deb Size: 34618 MD5sum: 2b9c988e01ce2912a263e2f490375fa6 SHA1: cdddf98378a5fda151820efc4efaf805686fe687 SHA256: 399fea6eb25996d0b880b2f73e1e8d82b0e13bb8faa1c03086f1621f3e9b805c SHA512: f4614b4d2fb8009d3e997a60bccedf7dbf4dca8e8fa7ac3863712d208e80d8eaa9f1acee6132e9341d3889efabe12d5a3a5010ceef2560fd3193042a39de6bf0 Homepage: https://cran.r-project.org/package=cragg Description: CRAN Package 'cragg' (Tests for Weak Instruments in R) Implements Cragg-Donald (1993) and Stock and Yogo (2005) tests for weak instruments in R. 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In a single pass of batched data, the proposed method repeatedly trains a machine learning algorithm and tests its empirical performance. Because it utilizes the entire sample for both learning and evaluation, cramming is significantly more data-efficient than sample-splitting. Unlike cross-validation, Cram evaluates the final learned model directly, providing sharper inference aligned with real-world deployment. The method naturally applies to both policy learning and contextual bandits, where decisions are based on individual features to maximize outcomes. The package includes cram_policy() for learning and evaluating individualized binary treatment rules, cram_ml() to train and assess the population-level performance of machine learning models, and cram_bandit() for on-policy evaluation of contextual bandit algorithms. For all three functions, the package provides estimates of the average outcome that would result if the model were deployed, along with standard errors and confidence intervals for these estimates. Details of the method are described in Jia, Imai, and Li (2024) and Jia et al. (2025) . 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The 'crane' package supplements the functionality of the 'gtsummary' package for creating these often highly bespoke tables in the pharmaceutical industry. Package: r-cran-crank Architecture: all Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-crank_1.1-2-1.ca2604.1_all.deb Size: 79108 MD5sum: 276d70d042a6d40a103b424d1748a29a SHA1: 5836f55ee15193baa7bb431b7df2593d781f16b2 SHA256: 510acfb82c73f10c75a6c7cfd5b7e6b66e4f0f28b62e2d87f633e190c1fe14ae SHA512: 0c9bfe93532c8cae78721415a24c804ac17c55cdf38622c3696ec5be28823a5458ca66f8ca1a9d550516056e57c323f6e487fd90303c99f654aa7cbde45a55ac Homepage: https://cran.r-project.org/package=crank Description: CRAN Package 'crank' (Completing Ranks) Functions for completing and recalculating rankings and sorting. Package: r-cran-cranlike Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-debugme, r-cran-desc, r-cran-rsqlite Suggests: r-cran-covr, r-cran-mockery, r-cran-testthat, r-cran-withr, r-cran-zip Filename: pool/dists/resolute/main/r-cran-cranlike_1.0.3-1.ca2604.1_all.deb Size: 46402 MD5sum: cca19cf3d5cccbdf1cea1604af895c0b SHA1: 476a5643dbf787e06680516de227b406e4933d27 SHA256: 4ac42b772e1a675c52b75129aea21ab3ceeeed6d064adcbd8bc94255f9fae0c8 SHA512: b5925cd3b1d047a569434b1b41e172819cb3e02f164647fbf0f8e9d434432b0e141080bcff859b80b9f56ce9a4bd45f9a362c7eae75185e52c2190dd5e7ca341 Homepage: https://cran.r-project.org/package=cranlike Description: CRAN Package 'cranlike' (Tools for 'CRAN'-Like Repositories) A set of functions to manage 'CRAN'-like repositories efficiently. 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Package: r-cran-cranly Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5443 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-visnetwork, r-cran-colorspace, r-cran-igraph, r-cran-stringr, r-cran-ggplot2, r-cran-countrycode, r-cran-wordcloud, r-cran-tm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cranly_0.6.0-1.ca2604.1_all.deb Size: 1741710 MD5sum: 33b3c699bd00453e30beced6579fd676 SHA1: bdf4a2ebb4d02a65891b292649d62dfcc6dffc67 SHA256: 61e4ed43731d3602081e486a4176d4c890af9d76f45b75df9aae813cc008403a SHA512: 9edf6ba1afb8c552d98a701785dff24e5b6d7a013d83565fdf0b28a4ccef733c851bcf9f724db12b5d5490ebf82eb585835e1c57b53d8980259b13b2faf158c9 Homepage: https://cran.r-project.org/package=cranly Description: CRAN Package 'cranly' (Package Directives and Collaboration Networks in CRAN) Core visualizations and summaries for the CRAN package database. The package provides comprehensive methods for cleaning up and organizing the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances, linking to) and collaboration networks, producing package dependence trees, and for computing useful summaries and producing interactive visualizations from the resulting networks and summaries. The resulting networks can be coerced to 'igraph' objects for further analyses and modelling. Package: r-cran-cransearcher Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1646 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-shiny, r-cran-shinyjs, r-cran-miniui, r-cran-stringr, r-cran-dt, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-cransearcher_1.0.0-1.ca2604.1_all.deb Size: 1631500 MD5sum: 7db6992290c366f7a4f32751cdc88921 SHA1: 1ed784aa5ca3df921742c3c8ec95cb083f76c121 SHA256: 5a91b4093374973bfa2da1db33a5057f4f60fe41f47d4deeb997d24859b0168f SHA512: 8d01c39e717dda51f36ff16835d5c1b6dd1dfb60bc5b1d7d98c8aac4d24126306776b97506a698e961a1ca3f79598f80778f110dd3442b71f8288377e3e0f3e9 Homepage: https://cran.r-project.org/package=CRANsearcher Description: CRAN Package 'CRANsearcher' (RStudio Addin for Searching Packages in CRAN Database Based onKeywords) One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio. Package: r-cran-crassmat Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-svmisc Suggests: r-cran-nmf, r-cran-recommenderlab Filename: pool/dists/resolute/main/r-cran-crassmat_0.0.6-1.ca2604.1_all.deb Size: 98774 MD5sum: 1ab82b781b47e800eea5f9e9a369c0cc SHA1: f4ff0a997af6b9b54bada833968736f1b37a3c16 SHA256: e1502b00aed26db18a8eabe3ebb90950d6256bb28199ac7bfd5c5a5d3778d3c0 SHA512: 103607a1fea79dd4d16de512710a3730dd7241d20f889e742cedaf086b78e923680034bf80f0dcb600a9434015fb4ce5aee0db43bb7a68c779943a6c48f10050 Homepage: https://cran.r-project.org/package=crassmat Description: CRAN Package 'crassmat' (Conditional Random Sampling Sparse Matrices) Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values. 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Provides scales for 'ggplot2' for discrete coloring. Package: r-cran-cre.dcf Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-tibble, r-cran-yaml Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-cre.dcf_0.0.5-1.ca2604.1_all.deb Size: 582628 MD5sum: 88af6fcd5d9694cf8bf3039f6418737c SHA1: 0e03c30d1c9d87f1bdd6eb8b02d6cfb9b7899355 SHA256: 5f0c498d153ba12e073921064acf3c313055e4d6faf1ab05cd74ebef55887d4f SHA512: 5395b1e02c5255c5fadbec7a1fb9558bb13ea7226e74be5d4a00bb82ddc699d2c7d6aee76f95cb38844bc6d763ff75bec2ee0a696e46ad2e2773ec29b846a886 Homepage: https://cran.r-project.org/package=cre.dcf Description: CRAN Package 'cre.dcf' (Discounted Cash Flow Tools for Commercial Real Estate) Provides 'R' utilities to build unlevered and levered discounted cash flow (DCF) tables for commercial real estate (CRE) assets. Functions generate bullet and amortising debt schedules, compute credit metrics such as debt service coverage ratios (DSCR), debt yield ratios, and forward loan-to-value ratios (LTV), and expose an explicit property-level operating chain from gross effective income (GEI) to net operating income (NOI) and property before-tax cash flow (PBTCF). The toolkit supports end-to-end scenario execution from a YAML (YAML Ain't Markup Language) configuration file parsed with 'yaml', includes helpers for effective rent, constrained loan underwriting, and simplified SPV-level tax simulations, and ships reproducible vignettes for methodological and applied use cases. 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It relies on a two-stage pseudo-outcome regression, and it is supported by theoretical convergence guarantees. Bargagli-Stoffi, F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects. arXiv preprint . Package: r-cran-cream Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cream_1.1.1-1.ca2604.1_all.deb Size: 43012 MD5sum: 6abb90259e8e1a2ddef637d6b442b650 SHA1: 741e9c3a40fcd3731c67754bcedc2648f2092ce7 SHA256: e88514c9bf9c097366069e8488e6c71537ecfa66ed1baf2cf354766ce1303d6d SHA512: 206962a2e312232cd71c3046cb71ad8751b58073ebf03433e4158d3d9627ae8ffeb91a8b2797e2ca3e2b533517344b9f7a3c1bb15592dc2bddc7e67d31b55293 Homepage: https://cran.r-project.org/package=CREAM Description: CRAN Package 'CREAM' (Clustering of Genomic Regions Analysis Method) Provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). 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Package: r-cran-createlogicalpcm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-createlogicalpcm_0.1.0-1.ca2604.1_all.deb Size: 18538 MD5sum: 0814a7ef37c6573d120c821a3a45d796 SHA1: 521152632b869ffde9284d3e2b654b4a71f98081 SHA256: 594b3db60409722dbefc31df532687f985653f97f9616fcb38df91ed07370f0a SHA512: dfa7155a0525866954df2ccf0bdfe1c4aea9c00bfb4cf5c7764633475c9048fd629cd4523659287fab61a97fada9197af92c685534c914b8f65e1ba9a0f65b13 Homepage: https://cran.r-project.org/package=createLogicalPCM Description: CRAN Package 'createLogicalPCM' (Create Logical Pairwise Comparison Matrix for the AnalyticHierarchy Process) Create Pairwise Comparison Matrices for use in the Analytic Hierarchy Process. 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Package: r-cran-credentials Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl, r-cran-sys, r-cran-curl, r-cran-jsonlite, r-cran-askpass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-credentials_2.0.3-1.ca2604.1_all.deb Size: 217396 MD5sum: eaf9af062bef13cdb0146923ec65bb92 SHA1: 15231d91a236ac880abd3d415678ea034dcbe76f SHA256: 120fdf3a10819cce87a9cad524cf0b0cadabdf15e5567f5986f24ab482bde317 SHA512: c7b744b7dc6109204ae8a5a2bdbbefb8f6e0fb5a54df91608215a36d20e28d144bf74dd85d8616a265c0183d353077aef0dee9ce67fd207bdabd7078adda303f Homepage: https://cran.r-project.org/package=credentials Description: CRAN Package 'credentials' (Tools for Managing SSH and Git Credentials) Setup and retrieve HTTPS and SSH credentials for use with 'git' and other services. 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Package: r-cran-creditas Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rvest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-creditas_0.3.0-1.ca2604.1_all.deb Size: 29318 MD5sum: ae35288195cd02c732aff7ed07b9d09e SHA1: cadf69c91068c52c4ff89635c616f6f90afcb74a SHA256: 557e9a6ac153af93124c2df1fad236325291dbba8ba060853bfa04544a7078dc SHA512: 401b81a5723ac4deab63d134da5468e6a334b4d9163d1ff296e965205e597fe7e672872cb3a39b91c640db4ac4a48383642ff7e26187f467988f844f622a1809 Homepage: https://cran.r-project.org/package=CRediTas Description: CRAN Package 'CRediTas' (Generate CRediT Author Statements) A tiny package to generate CRediT author statements (). It provides three functions: create a template, read it back and generate the CRediT author statement in a text file. Package: r-cran-creditrisk Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-creditrisk_0.1.7-1.ca2604.1_all.deb Size: 83374 MD5sum: 0bda8b3fc353b352b46bb9b9f249ea6c SHA1: 65c2ae703363a4168c888537dac5d7830f064790 SHA256: d804dfba5a9e47e8c817a15a8e5156c4dfe2fb88654ab72cac879050d74c0b28 SHA512: 27c4fd6ee4528c31d0e5c746f156cf9db599346b07c53401b1f201ef29d38fde6162a254bb1440194dea8c62241da6e4aa3a9b327e50b52426d392219ed6870a Homepage: https://cran.r-project.org/package=CreditRisk Description: CRAN Package 'CreditRisk' (Evaluation of Credit Risk with Structural and Reduced FormModels) Evaluation of default probability of sovereign and corporate entities based on structural or intensity based models and calibration on market Credit Default Swap quotes. References: Damiano Brigo, Massimo Morini, Andrea Pallavicini (2013) . Print ISBN: 9780470748466, Online ISBN: 9781118818589. © 2013 John Wiley & Sons Ltd. Package: r-cran-creds Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-creds_0.1.0-1.ca2604.1_all.deb Size: 21056 MD5sum: 47d7a7fd54c9c78ec848feddadfca615 SHA1: b9a4d6091fe864feb4d1855fa1cb53ca15735e69 SHA256: 21a4024cf992a0a1b0f2aff22090d8ee64513c67edf2bd95f6f2c7c939e0e682 SHA512: c4ba28faf0dfe04d8844e7420e1d46a7c7e2bf13d41c754cd79e02767edef5c49153dafba90f3db790625f046535a03c3be5e0b264d467bf45db00184c5d857d Homepage: https://cran.r-project.org/package=CREDS Description: CRAN Package 'CREDS' (Calibrated Ratio Estimator under Double Sampling Design) Population ratio estimator (calibrated) under two-phase random sampling design has gained enormous popularity in the recent time. This package provides functions for estimation population ratio (calibrated) under two phase sampling design, including the approximate variance of the ratio estimator. The improved ratio estimator can be applicable for both the case, when auxiliary data is available at unit level or aggregate level (eg., mean or total) for first phase sampled. Calibration weight of each unit of the second phase sample was calculated. Single and combined inclusion probabilities were also estimated for both phases under two phase random [simple random sampling without replacement (SRSWOR)] sampling. The improved ratio estimator's percentage coefficient of variation was also determined as a measure of accuracy. This package has been developed based on the theoretical development of Islam et al. (2021) and Ozgul (2020) . Package: r-cran-credsubs Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ff, r-cran-r.rsp, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-credsubs_1.1.1-1.ca2604.1_all.deb Size: 876314 MD5sum: 85875188e74678c48b3471eaefe39f57 SHA1: c3cb57f17f5b1c549815ae4519c2228674f5e3f7 SHA256: 0728c93d8f65556a5616c81bb2e0424c56f0c59c044b93617f9dadb960658eaa SHA512: 2bf363002628c2663d47f66836694fac9d665275df56701ac7f3488ce1e4aa60462f09e6e35188ca5d2542bffe4e7f31882d983fde20aefc879ceea62905bcd4 Homepage: https://cran.r-project.org/package=credsubs Description: CRAN Package 'credsubs' (Credible Subsets) Functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method. Package documentation includes the vignette included in this package, and the paper by Schnell, Fiecas, and Carlin (2020, ). Package: r-cran-creepyalien Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beepr, r-cran-cli, r-cran-glue Filename: pool/dists/resolute/main/r-cran-creepyalien_1.0.0-1.ca2604.1_all.deb Size: 84214 MD5sum: 7ff484d15ed82085386d6190631768a9 SHA1: 0e8d1c56f7db9293067dd25c716ec3cdfee2477b SHA256: 76eb7d2e10edf920199cd402f9604ed4a078e4ce98fe139c049da23be342f1a7 SHA512: 0baf3635356bbb4a45185c2fce04680b623efce1b295bbe77692c5a82cf520c05d550cdbe31c428c6d52e9600342aa31154c43c38a221c8e8c7378cd3edb3f1b Homepage: https://cran.r-project.org/package=creepyalien Description: CRAN Package 'creepyalien' (Puzzle Game for the R Console) Puzzle game that can be played in the R console. Help the alien to find the ship. Package: r-cran-crew.aws.batch Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crew, r-cran-paws.common, r-cran-paws.compute, r-cran-paws.management, r-cran-r6, r-cran-rlang, r-cran-tibble Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-crew.aws.batch_0.1.0-1.ca2604.1_all.deb Size: 549800 MD5sum: 8af8386304d1abd13f1c42a37b69c06b SHA1: 3cabd42f90c5311198a1327e7da23841fd3338f5 SHA256: d1c9d9e8d3d604e8b745c281568e85d23b687fb51b02464398dfa4355f7bdedb SHA512: 5154958e88da01ea9c84f8f5fbee81d21cfead883fee27c32242db1ed7957577d2bcb713cadaad0279b7816c3f8fdbb813e9328f55afa4644310b6437fd990a2 Homepage: https://cran.r-project.org/package=crew.aws.batch Description: CRAN Package 'crew.aws.batch' (A Crew Launcher Plugin for AWS Batch) In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. 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The 'crew.cluster' package extends the 'mirai'-powered 'crew' package with worker launcher plugins for traditional high-performance computing systems. Inspiration also comes from packages 'mirai' by Gao (2023) , 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) , 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischl, and Surmann (2017). . 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The 'NNG'-powered 'mirai' R package by Gao (2023) is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The 'crew' package extends 'mirai' with a unifying interface for third-party worker launchers. Inspiration also comes from packages. 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) , 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischel, and Surmann (2017) . Package: r-cran-cricketdata Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-jsonlite, r-cran-lubridate, r-cran-readr, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-codetools, r-cran-gghighlight, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-here, r-cran-knitr, r-cran-paletteer, r-cran-patchwork, r-cran-rmarkdown, r-cran-r.rsp, r-cran-showtext Filename: pool/dists/resolute/main/r-cran-cricketdata_0.3.0-1.ca2604.1_all.deb Size: 4539868 MD5sum: b1e578dbad7efc7474fa76938d60affd SHA1: b9673542634a3d7cfa72801cfee2382fffc0a020 SHA256: 221475df46fd4770ac22c83561e707425d90582c2c790a20016bd9480bda174a SHA512: f55eb6b80bea6b521406ada5f7b69f8f7d9555d3c94bb38dd186a54ef6d03dde3913b92bb0eff0781c85a3d12244d6aa75ecbd3880272d73f02c80ed06094b3e Homepage: https://cran.r-project.org/package=cricketdata Description: CRAN Package 'cricketdata' (International Cricket Data) Data on international and other major cricket matches from ESPNCricinfo and Cricsheet . 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The toolset can be used for analysis of Tests,ODIs and Twenty20 matches of both batsmen and bowlers. The package can also be used to analyze team performances. Package: r-cran-crimedata Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-osfr, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-crimedata_0.3.5-1.ca2604.1_all.deb Size: 1082870 MD5sum: fdaacbf194ec414b8bb034259ebff512 SHA1: f6484bed5a5839d51f5ec9a00f69dbfdda363222 SHA256: 6ddbc257f0d39c651c2c7a13ce05c8417d0a73ac1f55816601cc8c54ff5ee134 SHA512: 98df188a880eb505f0e249c0db329067bb5787c6cd204d5b1dc5eea2d314f6ba552ba07d8c1400d1fd353578697419fe7cd619f2a3876dbcf74c39136defc162 Homepage: https://cran.r-project.org/package=crimedata Description: CRAN Package 'crimedata' (Access Crime Data from the Open Crime Database) Gives convenient access to publicly available police-recorded open crime data from large cities in the United States that are included in the Crime Open Database . Package: r-cran-crimedatasets Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-crimedatasets_0.1.0-1.ca2604.1_all.deb Size: 2065798 MD5sum: 8afe1c492078a3e894addafadf6d80ac SHA1: c4a78c0580d4e0906e4b7e03bdfaf098de93b25f SHA256: 46b6d6b91995293f25ba85aea3f8a86860fb0ad5f9fe2470566a2c711086f576 SHA512: a13bf511f43e62612cfa88617c18cdd28c53722c52a8c719e7af7058b8cd1591f1d1b6e4e4e93db92b290cdfde6b96bc335869b1db2b1f231e66cc05d2e14cd2 Homepage: https://cran.r-project.org/package=crimedatasets Description: CRAN Package 'crimedatasets' (A Comprehensive Collection of Crime-Related Datasets) A comprehensive collection of datasets exclusively focused on crimes, criminal activities, and related topics. This package serves as a valuable resource for researchers, analysts, and students interested in crime analysis, criminology, social and economic studies related to criminal behavior. Datasets span global and local contexts, with a mix of tabular and spatial data. Package: r-cran-crimeutils Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-ggplot2, r-cran-readr, r-cran-gridextra, r-cran-scales, r-cran-magrittr, r-cran-gt, r-cran-tidyr, r-cran-rlang Suggests: r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-crimeutils_0.5.1-1.ca2604.1_all.deb Size: 99910 MD5sum: 52fb1cc8ab92f679bae73b334ce873ae SHA1: edceeb43e60573b9c69b6127b846f24025744fee SHA256: 7f8ea75c532be8e719f80c2d47b27edfa5efcd4da7b5d942e6857569393e471a SHA512: 5667d4beedf147dc124dcef38bf973f051062127c4e54cd7904d25e21c3bde067a893021eeff0735a0e805fa1dbd1f085d3d0887abe1b8b071a856ba82d16751 Homepage: https://cran.r-project.org/package=crimeutils Description: CRAN Package 'crimeutils' (A Comprehensive Set of Functions to Clean, Analyze, and PresentCrime Data) A collection of functions that make it easier to understand crime (or other) data, and assist others in understanding it. The package helps you read data from various sources, clean it, fix column names, and graph the data. Package: r-cran-crisp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass Filename: pool/dists/resolute/main/r-cran-crisp_1.0.0-1.ca2604.1_all.deb Size: 97148 MD5sum: 2742b69b86436af38ecfec6795f5de85 SHA1: a5265882f16e3b111b6f80b2fac67d70f310eae2 SHA256: 8adae8e67317791a644fd8143c83a22cadc52bd8a30d204437aa3aa52b35a62d SHA512: c641afc406f4e9c50d65c42b4b79b8b7158ff616ffd556820519a3ec8ec7097f701bbb8aa191b545ba8de26b6b703fb280e58118c549ba2d353857611f31c518 Homepage: https://cran.r-project.org/package=crisp Description: CRAN Package 'crisp' (Fits a Model that Partitions the Covariate Space into Blocks ina Data- Adaptive Way) Implements convex regression with interpretable sharp partitions (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 . Package: r-cran-crisprdesignr Architecture: all Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2801 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-cran-gbm, r-bioc-genomicranges, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-s4vectors, r-bioc-rtracklayer, r-cran-stringr, r-cran-vtreat, r-cran-shiny, r-cran-dt Suggests: r-bioc-bsgenome.scerevisiae.ucsc.saccer3 Filename: pool/dists/resolute/main/r-cran-crisprdesignr_1.1.7-1.ca2604.1_all.deb Size: 2516446 MD5sum: 7591d8c1165f9ab165b9850eac06ea82 SHA1: ddbebb7204341956d9af0b3a536d58c40632ef0a SHA256: ee1664854ee5eb830396ba06df55f837140172cfc0fc4cc12955031beea0f46c SHA512: 15aea7edf2bcb31a6cb67901b4519e4709a083deabec9d3d03ecd86e5cee8037eedbd639aeb9e2b6309f2b728fe86dca2a1343b72e5ffeb3a54134c8389187b1 Homepage: https://cran.r-project.org/package=crispRdesignR Description: CRAN Package 'crispRdesignR' (Guide Sequence Design for CRISPR/Cas9) Designs guide sequences for CRISPR/Cas9 genome editing and provides information on sequence features pertinent to guide efficiency. Sequence features include annotated off-target predictions in a user-selected genome and a predicted efficiency score based on the model described in Doench et al. (2016) . Users are able to import additional genomes and genome annotation files to use when searching and annotating off-target hits. All guide sequences and off-target data can be generated through the 'R' console with sgRNA_Design() or through 'crispRdesignR's' user interface with crispRdesignRUI(). CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) and the associated protein Cas9 refer to a technique used in genome editing. Package: r-cran-criticality Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-bnlearn, r-cran-dplyr, r-cran-evd, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-keras, r-cran-magrittr, r-cran-reticulate, r-cran-scales Filename: pool/dists/resolute/main/r-cran-criticality_0.9.3-1.ca2604.1_all.deb Size: 386954 MD5sum: c3d240f87b7d16f2af6b5c0d74ca9d35 SHA1: 841a880fb4d8ae6670c8ba3d83a2e645066c8375 SHA256: b7f7cd8886b323a029559c6e9f6526019bf9d07420ad910b38d3ec4cb3321a92 SHA512: 364e872ece1f4350a303f6d110d7d544556ae9dfecce0741725fa0f4812e0eb0134892076cc512bd0904ce21fc288f05f141c53d505efa717083d5001888c330 Homepage: https://cran.r-project.org/package=criticality Description: CRAN Package 'criticality' (Modeling Fissile Material Operations in Nuclear Facilities) A collection of functions for modeling fissile material operations in nuclear facilities, based on Zywiec et al (2021) . Package: r-cran-criticalpath Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-magrittr, r-cran-r6, r-cran-stringr, r-cran-tibble Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-criticalpath_0.2.1-1.ca2604.1_all.deb Size: 282816 MD5sum: bee4dccd239ef3be3fea32c2e48d6fbc SHA1: 573fe622aacaa63fb3c81d357338e0e47a24decc SHA256: 2e59d2b76e7e103688dc0c887b253c679347a5b140104ec9b60b5aa8c5e7a30a SHA512: c831b89db89ea83480a43b53cf25eaca936ff29ada7989e8a253756630ce149741dc418f574e9e801c7c8b232b773dd1294ae40924c504cdadb82c50d5dc1260 Homepage: https://cran.r-project.org/package=criticalpath Description: CRAN Package 'criticalpath' (An Implementation of the Critical Path Method) An R implementation of the Critical Path Method (CPM). CPM is a method used to estimate the minimum project duration and determine the amount of scheduling flexibility on the logical network paths within the schedule model. The flexibility is in terms of early start, early finish, late start, late finish, total float and free float. Beside, it permits to quantify the complexity of network diagram through the analysis of topological indicators. Finally, it permits to change the activities duration to perform what-if scenario analysis. The package was built based on following references: To make topological sorting and other graph operation, we use Csardi, G. & Nepusz, T. (2005) ; For schedule concept, the reference was Project Management Institute (2017) ; For standards terms, we use Project Management Institute (2017) ; For algorithms on Critical Path Method development, we use Vanhoucke, M. (2013) and Vanhoucke, M. (2014) ; And, finally, for topological definitions, we use Vanhoucke, M. (2009) . Package: r-cran-critpath Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6640 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-ggplot2, r-cran-reshape2, r-cran-dplyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-critpath_0.2.3-1.ca2604.1_all.deb Size: 632776 MD5sum: b195ce634ec3cf873be853bb91980a06 SHA1: 2906803906e725f4f773c20cd34c1cd1084f17f4 SHA256: ee353ffbfa455c89e58db9b2343ef209bb1a9d544e015020b32baa7ebfb411e1 SHA512: c858b64be031b795af9a2f0c6521bd9cade89712dd41ea565538b5c9ca470c4d0bfe48d20f22bf3a5fe8d5f4281407d6d92b40d82c874e43dfefe9af2f6d0964 Homepage: https://cran.r-project.org/package=critpath Description: CRAN Package 'critpath' (Setting the Critical Path in Project Management) Solving the problem of project management using CPM (Critical Path Method), PERT (Program Evaluation and Review Technique) and LESS (Least Cost Estimating and Scheduling) methods. The package sets the critical path, schedule and Gantt chart. In addition, it allows to draw a graph even with marked critical activities. For more information about project management see: Taha H. A. "Operations Research. An Introduction" (2017, ISBN:978-1-292-16554-7), Rama Murthy P. "Operations Research" (2007, ISBN:978-81-224-2944-2), Yuval Cohen & Arik Sadeh (2006) "A New Approach for Constructing and Generating AOA Networks", Journal of Engineering, Computing and Architecture 1. 1-13, Konarzewska I., Jewczak M., Kucharski A. (2020, ISBN:978-83-8220-112-3), Miszczyńska D., Miszczyński M. "Wybrane metody badań operacyjnych" (2000, ISBN:83-907712-0-9). Package: r-cran-crm12comb Architecture: all Version: 0.1.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggforce Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-crm12comb_0.1.12-1.ca2604.1_all.deb Size: 464520 MD5sum: 1c6888de7725e93b48aeaa2668e5de0f SHA1: 5fd8a9f0025da9d15ba00874934190822e9420f5 SHA256: 0312886902b699f394039204b81a5c5c42cff58c3a4d41528d9cb2950d3af774 SHA512: bf016d8db5b73b5011fb80cca210d31e6bda7032f2fb82c6a8546aa4e0a8c5364d2c9922803d286c7ff5b142a04677c66baa52a1b6f60884dcf07c2e3cc2ff6f Homepage: https://cran.r-project.org/package=crm12Comb Description: CRAN Package 'crm12Comb' (Phase I/II CRM Based Drug Combination Design) Implements the adaptive designs for integrated phase I/II trials of drug combinations via continual reassessment method (CRM) to evaluate toxicity and efficacy simultaneously for each enrolled patient cohort based on Bayesian inference. It supports patients assignment guidance in a single trial using current enrolled data, as well as conducting extensive simulation studies to evaluate operating characteristics before the trial starts. It includes various link functions such as empiric, one-parameter logistic, two-parameter logistic, and hyperbolic tangent, as well as considering multiple prior distributions of the parameters like normal distribution, gamma distribution and exponential distribution to accommodate diverse clinical scenarios. Method using Bayesian framework with empiric link function is described in: Wages and Conaway (2014) . Package: r-cran-crmetrics Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggpubr, r-cran-ggrepel, r-cran-magrittr, r-cran-matrix, r-cran-r6, r-cran-scales, r-cran-sccore, r-bioc-sparsematrixstats, r-cran-tibble, r-cran-tidyr Suggests: r-cran-conos, r-cran-data.table, r-cran-markdown, r-cran-pagoda2, r-cran-reticulate, r-bioc-rhdf5, r-cran-seurat, r-cran-soupx, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-crmetrics_0.3.2-1.ca2604.1_all.deb Size: 584802 MD5sum: 44453a2197e6f703de3cd5af49a17a75 SHA1: 3faa4856ede2eec57ab62940f8ea4c573573a7b5 SHA256: 10ffee08830eecce52d12140394c5c7bca58fb426367ba1610a6e2f51450ea15 SHA512: 9d48c45a52315bd7a4ce9daf24c8f4a21ef54f79d75061d9f05c25aeed57802633426e58d6be9fcaefa9713ec3f13154cc481b7f96632e766b9c4366f0f6f792 Homepage: https://cran.r-project.org/package=CRMetrics Description: CRAN Package 'CRMetrics' (Cell Ranger Output Filtering and Metrics Visualization) Sample and cell filtering as well as visualisation of output metrics from 'Cell Ranger' by Grace X.Y. Zheng et al. (2017) . 'CRMetrics' allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. 'CRMetrics' allows for easy removal of ambient RNA using 'SoupX' by Matthew D Young and Sam Behjati (2020) or 'CellBender' by Stephen J Fleming et al. (2022) . Furthermore, it is possible to preprocess data using 'Pagoda2' by Nikolas Barkas et al. (2021) or 'Seurat' by Yuhan Hao et al. (2021) followed by embedding of cells using 'Conos' by Nikolas Barkas et al. (2019) . Finally, doublets can be detected using 'scrublet' by Samuel L. Wolock et al. (2019) or 'DoubletDetection' by Gayoso et al. (2020) . In the end, cells are filtered based on user input for use in downstream applications. Package: r-cran-crmn Architecture: all Version: 0.0.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-pcamethods, r-bioc-biobase Filename: pool/dists/resolute/main/r-cran-crmn_0.0.21-1.ca2604.1_all.deb Size: 440208 MD5sum: f3033cc8ff650e5f9fb3348e047f87f5 SHA1: d144a875272d97f63210c9691c98650b94bb8867 SHA256: f58a5b4b6c284462368cf0e1e66242f7028465861e0af1703d974048c97cc3e4 SHA512: 884ce6f87a1c7786ba8b59c722391036dab678488bf3811334c5c9fd9338b90f065ac6f4b9751c35bb4d5acc092d49917dd4bf698d27446778820435a8330a04 Homepage: https://cran.r-project.org/package=crmn Description: CRAN Package 'crmn' (CCMN and Other Normalization Methods for Metabolomics Data) Implements the Cross-contribution Compensating Multiple standard Normalization (CCMN) method described in Redestig et al. (2009) Analytical Chemistry and other normalization algorithms. Package: r-cran-crmpack Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-checkmate, r-cran-dplyr, r-cran-futile.logger, r-cran-gensa, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-lifecycle, r-cran-magrittr, r-cran-mvtnorm, r-cran-parallelly, r-cran-rdpack, r-cran-rjags, r-cran-rlang, r-cran-survival, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-bookdown, r-cran-broom, r-cran-covr, r-cran-data.tree, r-cran-diagrammer, r-cran-ggmcmc, r-cran-quarto, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-crmpack_2.1.0-1.ca2604.1_all.deb Size: 5118146 MD5sum: 1688aaf84259aa8a356067807fd55c03 SHA1: f2ac562f459da270c5e58d6c1e5e1586564a502c SHA256: 29dc2a29f92594442721e9f5f0ca1fc58110a07a4e143baee2103f01df1dd640 SHA512: c3f98aff533f8b4233fdccfb0994fd65b2464433b86d32cb68113c44c9ee039542196e4f7ffa363ba6fab2f9ba24799197ede75832b09cc553a53e66d07e8761 Homepage: https://cran.r-project.org/package=crmPack Description: CRAN Package 'crmPack' (Object-Oriented Implementation of Dose Escalation Designs) Implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanés Bové et al. (2019) . Package: r-cran-crmreg Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-gplots, r-cran-pcapp, r-cran-plyr, r-cran-robustbase, r-cran-rrcov Filename: pool/dists/resolute/main/r-cran-crmreg_1.0.4-1.ca2604.1_all.deb Size: 92722 MD5sum: 8d996977cf447b523cd067c237bf10f0 SHA1: 81e00845d5e03b30d1b11f5416eae5a0ad85fe67 SHA256: 7803fa91546132c041a5cc2f5489e042556da2f85e497c63a4ca7cd5f38c5093 SHA512: d4658d294c841054ce70dfd738ce17029338e8d0e7669ba4ac743d4d653e6f185319d0f588468ffe678ba750ec1f8ab4384d42bc29afbb54d14a644507ad4538 Homepage: https://cran.r-project.org/package=crmReg Description: CRAN Package 'crmReg' (Cellwise Robust M-Regression and SPADIMO) Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) ) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) ). Package: r-cran-crochet Architecture: all Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-crochet_2.3.0-1.ca2604.1_all.deb Size: 54170 MD5sum: 44105703c3f23b6380ffe9f9cc3070ff SHA1: 7a4b7b784bd30edbbc3fdbd1abb733c5fe3694b4 SHA256: c669a2b49a4c8c3cf4e29a426a2a9603171018c55c6125b0d6f22a434efe5198 SHA512: 225240511e9b25c909e3d0569fdff65deafbee7275cfe3111f0c5f9301570b9298e25bf8c85ca03c6f90df0019b79ca77c290507e8268e929309602622556511 Homepage: https://cran.r-project.org/package=crochet Description: CRAN Package 'crochet' (Implementation Helper for '[' and '[<-' of Custom Matrix-LikeTypes) Functions to help implement the extraction / subsetting / indexing function '[' and replacement function '[<-' of custom matrix-like types (based on S3, S4, etc.), modeled as closely to the base matrix class as possible (with tests to prove it). Package: r-cran-cromwelldashboard Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-stringr, r-cran-httr, r-cran-dplyr, r-cran-dt Filename: pool/dists/resolute/main/r-cran-cromwelldashboard_0.5.1-1.ca2604.1_all.deb Size: 19496 MD5sum: 8d760ed8879872faa440f58c4bc728f2 SHA1: 020453b46952064a497049f22636d8969bda8e66 SHA256: 370adee5714e598697eed2d17737fb6ce03833b24e8ef870a3744ddab669ee6e SHA512: a97d7e70d48c0dcc610b55093ff549ebaecf8f6a40287cc765992e133930af6a376003feecae5a1ced6f03d08da4bdde41ea0f091c07b9a3e16e48c511dfc14c Homepage: https://cran.r-project.org/package=cromwellDashboard Description: CRAN Package 'cromwellDashboard' (A Dashboard to Visualize Scientific Workflows in 'Cromwell') A dashboard supports the usage of 'cromwell'. 'Cromwell' is a scientific workflow engine for command line users. This package utilizes 'cromwell' REST APIs and provides these convenient functions: timing diagrams for running workflows, 'cromwell' engine status, a tabular workflow list. For more information about 'cromwell', visit . Package: r-cran-cronbach Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-rangen, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-cronbach_0.4-1.ca2604.1_all.deb Size: 25628 MD5sum: feda9fc490ca70c5bd9fe976fb768170 SHA1: 18c2ab68a3c45a611635919adb962ec6fc254a67 SHA256: c8ee61dde588f50cac76670aaa20f377f8cea40f3d95bb4e5b8ce0a09adbbb83 SHA512: f0ade938aba862474b6970f8ee1783eab14c247fd4abdc842924656cde7ef8bae8e3580aeab69eaa6ad89cb0151d17cbf33efc70157dd1595d5b1362875e3f74 Homepage: https://cran.r-project.org/package=Cronbach Description: CRAN Package 'Cronbach' (Cronbach's Alpha) Cronbach's alpha and various formulas for confidence intervals. The relevant paper is Tsagris M., Frangos C.C. and Frangos C.C. (2013). "Confidence intervals for Cronbach's reliability coefficient". Recent Techniques in Educational Science, 14-16 May, Athens, Greece. Package: r-cran-crone Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1659 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-crone_0.1.1-1.ca2604.1_all.deb Size: 1288144 MD5sum: 5bf06faf85f807f35682fbc51811c04c SHA1: ba6ffef5688155fdb5858e1296c6d7db551d2cac SHA256: b2b6df266955f8f2817f5fa67963109fac9a29f0f375334d93144978defe19ad SHA512: 80da6540819d6f49a0eb22a6a7b3ac47497c8d95a5307454305b037eb6b0a92148fdecc44ff9f361e7257d058168acd44f8e29b5f644da755a7f7202bcc969c9 Homepage: https://cran.r-project.org/package=crone Description: CRAN Package 'crone' (Structural Crystallography in 1d) Functions to carry out the most important crystallographic calculations for crystal structures made of 1d Gaussian-shaped atoms, especially useful for methods development. Main reference: E. Smith, G. Evans, J. Foadi (2017) . Package: r-cran-cronologia Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-glue Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-cronologia_0.2.0-1.ca2604.1_all.deb Size: 25552 MD5sum: fc2548b3677c8f96a9a306c496188161 SHA1: ea57a07ab5604434e4703ff8522cd7af43637080 SHA256: 2ebff35eea20c1f05262570dfc421e7c6126f22ef5df7292f8c05063ea6a3770 SHA512: dc36d5cfeed94d8b9869c69da951edb6cf48d4a8b202d790d763f1e6765fc831b2cf66b7ab8bb800125f699b692161f6fc8f88926b455d3f1c9e43c9159a052c Homepage: https://cran.r-project.org/package=cronologia Description: CRAN Package 'cronologia' (Create an HTML Vertical Timeline from a Data Frame in'rmarkdown' and 'shiny') Creates an HTML vertical timeline from a data frame as an input for 'rmarkdown' documents and 'shiny' applications. Package: r-cran-cronr Architecture: all Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest Suggests: r-cran-miniui, r-cran-shiny, r-cran-shinyfiles, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-cronr_0.6.5-1.ca2604.1_all.deb Size: 79298 MD5sum: eb0d8616e375efe97e5eaa3bcfbfda8a SHA1: 97836aa334e83bd7b73e556ed8a752c03838c5bc SHA256: b0e3f0f8de95dd06c04e13030dcd6a1e3535063071b4a99bf4e182ae27c27501 SHA512: 3c7114db588d29ca964ae38c6cdd7e00ccb050a1c691421f60dd9e99ca29b7654f8123ac21c57596dbcb8eba9d29d99eaf7e09fcac72303ebbba4e4af3f5c624 Homepage: https://cran.r-project.org/package=cronR Description: CRAN Package 'cronR' (Schedule R Scripts and Processes with the 'cron' Job Scheduler) Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts. Package: r-cran-crookr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-lidr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-crookr_0.1.0-1.ca2604.1_all.deb Size: 369874 MD5sum: 94162800d30e1139da854ca41ac1f59f SHA1: f9c7a91dc716826aa8045a8ccc259f3b7009b946 SHA256: 6126e9fe8bc0591ba43ac08378367a2e4c0813fd3f07e8f752115991ecce1913 SHA512: 897033ad3bb99be4f8c57d1655936ef9e8d31e0fc5347e62b102219906d3d252d5db52a56a6937c790c9ab9ff43dacf75b36e8572061ad894f3f6ea224b10458 Homepage: https://cran.r-project.org/package=crookR Description: CRAN Package 'crookR' (Synthetic Crook Deformations in Stem Point Clouds) Simulates parameterized single- and double-directional stem deformations in tree point clouds derived from terrestrial or mobile laser scanning, enabling the generation of realistic synthetic datasets for training and validating machine learning models in wood defect detection, quality assessment, and precision forestry. For more details see Pires (2025) . Package: r-cran-crop Architecture: all Version: 0.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-crop_0.0-3-1.ca2604.1_all.deb Size: 14484 MD5sum: 1d3d9e0c83a1e14247eb4dae338c5712 SHA1: 8c172277ce482010e2c2b080e1f6ed28123589ae SHA256: d55daebb73242c89a416a19843b828c5a9d306bdce6dbbe368c7c5acddc62ea1 SHA512: b14ae1d3252450fe722e8982b66889c4fbefc93575787ecdbb87d2d5b3dfe115ab0beb2bbc7004a37501bc58c1ca6dbb457a2f7a6bc0966107c58f6eb19b4c73 Homepage: https://cran.r-project.org/package=crop Description: CRAN Package 'crop' (Graphics Cropping Tool) A device closing function which is able to crop graphics (e.g., PDF, PNG files) on Unix-like operating systems with the required underlying command-line tools installed. Package: r-cran-cropbreeding Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-metan, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-cropbreeding_0.1.0-1.ca2604.1_all.deb Size: 30182 MD5sum: e836933a35ea2259f79074042308ff17 SHA1: 1843bff5115dc6a530878e4024c4e255d61ecaa7 SHA256: 262c6f4dbedeb65525e271b5635ac1c87bbac324f7623e72a369a7b84e7f4b75 SHA512: 4993857e0a7ee7969ef834337001ec2f417a15137cd86afe9e45a086df42a0f15b7513c144f6a38d9bd0a3efb70c451b1029f5032d02453c38683c7e577dbd2a Homepage: https://cran.r-project.org/package=CropBreeding Description: CRAN Package 'CropBreeding' (Stability Analysis in Crop Breeding) Provides tools for crop breeding analysis including Genetic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation (PCV), heritability, genetic advance calculations, stability analysis using the Eberhart-Russell model, two-way ANOVA for genotype-environment interactions, and Additive Main Effects and Multiplicative Interaction (AMMI) analysis. These tools are developed for crop breeding research and stability evaluation under various environmental conditions. The methods are based on established statistical and biometrical principles. Refer to Eberhart and Russell (1966) for stability parameters, Fisher (1935) "The Design of Experiments" , Falconer (1996) "Introduction to Quantitative Genetics" , and Singh and Chaudhary (1985) "Biometrical Methods in Quantitative Genetic Analysis" for foundational methodologies. Package: r-cran-cropcircles Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-magick, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-cropcircles_0.2.4-1.ca2604.1_all.deb Size: 62542 MD5sum: 69c7f14fcb069389f39d930706e05589 SHA1: 7bc2ebe0596d547f6ad132ac2b0fb7a5eff7b806 SHA256: 9f48a37bb08879518c47edf2b4bfad33fcf7015776d14dea2b8d7b3c8e5a0180 SHA512: 2967135dc70381b8f916a4fe4d3007709b5ff7c2d6063ff066c1b02b0e63039f97e2dd840ee9a51c4af56b2fbb793115cce533c2559a13b69ce2502eb3dd38ed Homepage: https://cran.r-project.org/package=cropcircles Description: CRAN Package 'cropcircles' (Crops an Image to a Circle) Images are cropped to a circle with a transparent background. The function takes a vector of images, either local or from a link, and circle crops the image. Paths to the cropped image are returned for plotting with 'ggplot2'. Also includes cropping to a hexagon, heart, parallelogram, and square. Package: r-cran-cropdatape Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cropdatape_1.0.0-1.ca2604.1_all.deb Size: 192274 MD5sum: 0a50cbcb6ad035c008b9cc449eaa6827 SHA1: 13e511c2f7a14341189298b365d85c179ace0f88 SHA256: 7c54cc74f14198904ba8dc516e348eece6b8d04a1e5a11aa73a10c66d2218fc2 SHA512: 1d2c928bdce0b56ef46b7f0952029b4fdf9c5206fba6ae522c2d943d52a2263b314314b943ad84813d45d5174a0160f56854dec6faf53d46e7200e31c7a4bb73 Homepage: https://cran.r-project.org/package=cropdatape Description: CRAN Package 'cropdatape' (Open Data of Agricultural Production of Crops of Peru) Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially, in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014. Package: r-cran-cropdemand Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-terra, r-cran-sf, r-cran-tidyr, r-cran-ncdf4 Filename: pool/dists/resolute/main/r-cran-cropdemand_1.0.3-1.ca2604.1_all.deb Size: 3261926 MD5sum: b816f7a0cb96348b6d19fa0e35b8d1b3 SHA1: e8d75b0f60c0cebe39d6735e3a65e7103957d342 SHA256: fae7e7c40e7032744b074bdcf97f01c29361a99b91c30bd93f63b98624f8b88a SHA512: 2932b3dbe3065c08840774f108f905456eb17403b75d31aadd779a7d83890eee94ffdc3fef66c0456e4ce781228f62d8e858637bd4c3a65aa1e48282da2190cf Homepage: https://cran.r-project.org/package=cropDemand Description: CRAN Package 'cropDemand' (Spatial Crop Water Demand for Brazil) Estimation of crop water demand can be processed via this package. As example, the data from 'TerraClimate' dataset () calibrated with automatic weather stations of National Meteorological Institute of Brazil is available in a coarse spatial resolution to do the crop water demand. However, the user have also the option to download the variables directly from 'TerraClimate' repository with the download.terraclimate function and access the original 'TerraClimate' products. If the user believes that is necessary calibrate the variables, there is another function to do it. Lastly, the estimation of the crop water demand present in this package can be run for all the Brazilian territory with 'TerraClimate' dataset. Package: r-cran-cropgrowdays Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-purrrlyr, r-cran-httr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-furrr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cropgrowdays_0.2.2-1.ca2604.1_all.deb Size: 148258 MD5sum: 0c566f5e99bd9a2578cdc9bff5ef4eb9 SHA1: 9c3acf404afc12501c547259aaf8b8695ef6c4a5 SHA256: be32801b6a761ff1a864de701e181de11de721bdc6888f58bac5d1a75118e21e SHA512: 8bd2a9f8b287ac77d841aa2ef68a2f9bc4704414ec8cd268265dd6d88f62549c3aab621179404759969b85933e2290b36aaeb3029ddcad5aab648ae585bc2dff Homepage: https://cran.r-project.org/package=cropgrowdays Description: CRAN Package 'cropgrowdays' (Crop Growing Degree Days and Agrometeorological Calculations) Calculate agrometeorological variables for crops including growing degree days (McMaster, GS & Wilhelm, WW (1997) ), cumulative rainfall, number of stress days and cumulative or mean radiation and evaporation. Convert dates to day of year and vice versa. Also, download curated and interpolated Australian weather data from the Queensland Government DES longpaddock website . This data is freely available under the Creative Commons 4.0 licence. Package: r-cran-crops Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sets, r-cran-reshape, r-cran-tidyverse, r-cran-memoise, r-cran-ggplot2, r-cran-magrittr, r-cran-cowplot, r-cran-tibble, r-cran-rdpack Suggests: r-cran-fpop, r-cran-pacman Filename: pool/dists/resolute/main/r-cran-crops_1.0.3-1.ca2604.1_all.deb Size: 52396 MD5sum: 52d6c3f7429d75c265f5cfd1b2addad0 SHA1: ceeeb1f8431d8b333dcc8e93108b0e4a8ce681c2 SHA256: ccc029fb7a5ea775752369feab76010c5e431032fb7c0d912533936a1dc19c2a SHA512: f68855ef394106949099cd4aca553642571d7233a1ac4ce1947682efbfabae446f89aa06258807761280a0c63909bac91df71d6b1c10df88c20188976192992d Homepage: https://cran.r-project.org/package=crops Description: CRAN Package 'crops' (Changepoints for a Range of Penalties (CROPS)) Implements the Changepoints for a Range of Penalties (CROPS) algorithm of Haynes et al. (2017) for finding all of the optimal segmentations for multiple penalty values over a continuous range. Package: r-cran-cropscaper Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-raster, r-cran-sf, r-cran-data.table, r-cran-httr, r-cran-rjsonio Filename: pool/dists/resolute/main/r-cran-cropscaper_1.1.5-1.ca2604.1_all.deb Size: 93716 MD5sum: f6d529fba2b1848d989f7fd9848403d5 SHA1: b5ff9c8191b7cb7f4e8135abc4c63f2e89759066 SHA256: 134d69a1916436d5aa6a4e9ca8438a448d81b6333b31ddf08de528ed86277318 SHA512: 752af168e456cd9acee977ce3d0214fbab17476eab969c47b418d2285897e3fc3e03ef887b762671623f6095f30406b88f410520e460e70ceac4ef3cbddeef70 Homepage: https://cran.r-project.org/package=CropScapeR Description: CRAN Package 'CropScapeR' (Access Cropland Data Layer Data via the 'CropScape' Web Service) Interface to easily access Cropland Data Layer (CDL) data for any area of interest via the 'CropScape' web service. Package: r-cran-cropwaterbalance Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-powersdi, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cropwaterbalance_0.2.0-1.ca2604.1_all.deb Size: 89606 MD5sum: 85fe94611f0dca8afb9c576f3d6bc174 SHA1: 87ff6004a3200019adf60523b1f6a93ee1024c00 SHA256: 308fdb2a5d80a9e5d5e85256ea2017ea6de3226f383206509d3f3be4f0391336 SHA512: b939b6d8a30ac7a5155289870498e3687b8c59b67f19dbf55cf12baae8b7ad549ad3ffc01f6f25068b88c4bb7ad7eea9c6c1c62e48374c4795d6adfb0ff8ac48 Homepage: https://cran.r-project.org/package=CropWaterBalance Description: CRAN Package 'CropWaterBalance' (Climate Water Balance for Irrigation Purposes) Calculates daily climate water balance for irrigation purposes and also calculates the reference evapotranspiration (ET) using three methods, Penman and Monteith (Allen et al. 1998, ISBN:92-5-104219-5); Priestley and Taylor (1972) ; or Hargreaves and Samani (1985) . Users may specify a management allowed depletion (MAD), which is used to suggest when to irrigate. The functionality allows for the use of crop and water stress coefficients as well. Package: r-cran-cropzoning Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4414 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-tidyr, r-cran-sf, r-cran-ncdf4, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-cropzoning_1.0.3-1.ca2604.1_all.deb Size: 3202644 MD5sum: 72b8428ea2a68a2bfd18185e406ac832 SHA1: 0fb6d98e53911295345f0195f6dc62a2bb932ed1 SHA256: 7e2fee85a3734d70c80dfca033bf554112f416e92a6c5bd923cf84b7f5d09dcd SHA512: b33989dd1e434b7abdd39fdd6f3b13f7f96e2d553e003b220fd3e73699f5e5e81ee8f50080564c7f495fea96b39697b1c0be2450eae3821dedbcc443be4188ed Homepage: https://cran.r-project.org/package=cropZoning Description: CRAN Package 'cropZoning' (Climate Crop Zoning Based in Air Temperature for Brazil) Climate crop zoning based in minimum and maximum air temperature. The data used in the package are from 'TerraClimate' dataset (), but, it have been calibrated with automatic weather stations of National Meteorological Institute of Brazil. The climate crop zoning of this package can be run for all the Brazilian territory. Package: r-cran-crosscarry Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gee, r-cran-ggplot2, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-crosscarry_1.2.0-1.ca2604.1_all.deb Size: 63122 MD5sum: 3a870147cb83445b5ea224fd8c1dd682 SHA1: b929139ae8e27c2976f969827c2dc9c4ad3e075e SHA256: 3c78186ad6cfb74ec347c7617d930226e1d6b35fb7217cffdf1713a7fa2538dc SHA512: 575082392fcdad1dd743aa628259196b89947d46ab1c97a21ed462482a45eb43b70e15d29d621c4377e2a37f90ab7d23a4bcf166f4f0d2bb310fd32c6e7ab2ca Homepage: https://cran.r-project.org/package=CrossCarry Description: CRAN Package 'CrossCarry' (Analysis of Data from a Crossover Design with GEE) Analyze data from a crossover design using generalized estimation equations (GEE), including carryover effects and various correlation structures based on the Kronecker product. 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The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) . Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss. 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Package: r-cran-crso Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-crso_0.1.1-1.ca2604.1_all.deb Size: 133484 MD5sum: 8c3197b69ae8aaece6cc499f531b285f SHA1: 4bdc8ef13746e39225a3ba11e54cb2c70246e216 SHA256: a75cbec25962b4b5a3e5cc5845a9428c79d900a9ee1eb600110d86cd9c38d55f SHA512: 72853873f9a7f7044fb84ec53716d59624ed81c63f198ecf30cf9d92a43d68b4e239f0a1cb4e5651929c05e141d471878d5aa649ef3040dd84487b69aecaf1ad Homepage: https://cran.r-project.org/package=crso Description: CRAN Package 'crso' (Cancer Rule Set Optimization ('crso')) An algorithm for identifying candidate driver combinations in cancer. CRSO is based on a theoretical model of cancer in which a cancer rule is defined to be a collection of two or more events (i.e., alterations) that are minimally sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively are assumed to account for all of ways to cause cancer in the population. In CRSO every event is designated explicitly as a passenger or driver within each patient. Each event is associated with a patient-specific, event-specific passenger penalty, reflecting how unlikely the event would have happened by chance, i.e., as a passenger. CRSO evaluates each rule set by assigning all samples to a rule in the rule set, or to the null rule, and then calculating the total statistical penalty from all unassigned event. CRSO uses a three phase procedure find the best rule set of fixed size K for a range of Ks. A core rule set is then identified from among the best rule sets of size K as the rule set that best balances rule set size and statistical penalty. Users should consult the 'crso' vignette for an example walk through of a full CRSO run. The full description, of the CRSO algorithm is presented in: Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." BioRxiv 674234 [Preprint]. June 19, 2019. . Please cite this article if you use 'crso'. 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The estimator IPW-AUG-GEE is Doubly robust (DR). Package: r-cran-crtsize Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-crtsize_1.2-1.ca2604.1_all.deb Size: 106956 MD5sum: 61849d10cfc7d5db4e2261ae7e14039a SHA1: 234c12d06ab1f058dff9e153ce93793f9997ad16 SHA256: 184ca764364657e28036055dca4f04cec95b9fa2c2cba4c6dd0556fd103c96d4 SHA512: f9799158f9836ed1f9feb769838d35fbc3d186c9c6c52291f640979568f5991b4f4dd5de572cfb83d270d8294f36e6a82adf1fcead0d49ad0cd15c3b6271db07 Homepage: https://cran.r-project.org/package=CRTSize Description: CRAN Package 'CRTSize' (Sample Size Estimation Functions for Cluster Randomized Trials) Sample size estimation in cluster (group) randomized trials. Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012). 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Estimation makes use of recent advancements in Riesz-learning to estimate a set of required nuisance parameters with deep learning. The result is the capability to estimate mediation effects with binary, categorical, continuous, or multivariate exposures with high-dimensional mediators and mediator-outcome confounders using machine learning. 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Package: r-cran-crwbmetareg Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmtest, r-cran-rfast2, r-cran-sandwich Suggests: r-cran-clusterses Filename: pool/dists/resolute/main/r-cran-crwbmetareg_1.0-1.ca2604.1_all.deb Size: 43942 MD5sum: 4cd735ddfb450a622736f70cb853fa16 SHA1: 494c580dfdf41cc3c044aebabdb9fa3a31eb77d6 SHA256: b2d498c57f7cded9cabdfbe933d19169fa0f04ec461ae1f52d40073a96a99533 SHA512: ecf0c501b7a847d79f6c01337fd69df036431ca69d6cd917c3b4a1eb63fce7e1329329d4d9c9770b6900a8a1e5c1fd2212249c9d96df318eb3279bc4f2917db2 Homepage: https://cran.r-project.org/package=crwbmetareg Description: CRAN Package 'crwbmetareg' (Cluster Robust Wild Bootstrap Meta Regression) In meta regression sometimes the studies have multiple effects that are correlated. For this reason cluster robust standard errors must be computed. However, since the clusters are unbalanced the wild bootstrap is suggested. See Oczkowski E. and Doucouliagos H. (2015). "Wine prices and quality ratings: a meta-regression analysis". American Journal of Agricultural Economics, 97(1): 103--121. and Cameron A. C., Gelbach J. B. and Miller D. L. (2008). "Bootstrap-based improvements for inference with clustered errors". The Review of Economics and Statistics, 90(3): 414--427. . 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Package: r-cran-cry Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-cry_0.5.2-1.ca2604.1_all.deb Size: 1621458 MD5sum: dfb138948e6e45d560c8b0604c9ae5ce SHA1: 1f3a2d70773ead8db029dcdad6be9f20dc00713c SHA256: 811e017ac0bdc3c0b6a25520c47144dcff8f6bee683a916c6af95a61527179b3 SHA512: 3a1d761082c0a4536b6c51a9f63a67ccafd64d6f297bb87551446b2f533e827265ba60f40a60a2063fd9353c27a67a02a399e22654ea307165bfab182fd71d36 Homepage: https://cran.r-project.org/package=cry Description: CRAN Package 'cry' (Statistics for Structural Crystallography) Reading and writing of files in the most commonly used formats of structural crystallography. 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Package: r-cran-cryptography Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cryptography_1.0.0-1.ca2604.1_all.deb Size: 45758 MD5sum: ec7eefe2ec36b52250da5df9121387d9 SHA1: 7c360ca8c2f127c89c4e139620d68976f41e2f07 SHA256: 6337827be2b2e9f0264530b82caf615cc5f4aa0fb1db340b933dd735f7d6e7d1 SHA512: 27f2067d3c82fa2b09d8392eb0f22240784042eca2f2607107a607c31c55545ed7006fcefd380403a56af7717ee4a002905a8ca1551bd7c3dc352811f6ea0a29 Homepage: https://cran.r-project.org/package=cryptography Description: CRAN Package 'cryptography' (Encrypts and Decrypts Text Ciphers) Playfair, Four-Square, Scytale, Columnar Transposition and Autokey methods. Further explanation on methods of classical cryptography can be found at Wikipedia; (). 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The data is sourced from major cryptocurrency exchanges via 'curl' and returned in 'xts'-format. The data comes in open, high, low, and close (OHLC) format with flexible granularity, ranging from seconds to months. This flexibility makes it ideal for developing and backtesting trading strategies or conducting detailed market analysis. Package: r-cran-cryptotrackr Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringi, r-cran-openssl, r-cran-digest Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cryptotrackr_1.3.3-1.ca2604.1_all.deb Size: 436220 MD5sum: a96ac9ba039aeeabee943cb3b8991c6e SHA1: 47f871b06084f59e8fe8e6a94418b2de5b6f73a5 SHA256: 07e018606b6bf1ba77b1127c51d073c3a5a06ba557b8a6e53a161acfe945ea40 SHA512: 6c267b64b9a7bba3f874f6c7a0b295d8ce36122010503ae3b27199a78096dbc41ada5ac26e6564ce313f7abf0d47ebdb62b030b0b75986f412a7aea6b41fd58f Homepage: https://cran.r-project.org/package=cryptotrackr Description: CRAN Package 'cryptotrackr' (An Interface to Crypto Data Sources) Allows you to connect to data sources across the crypto ecosystem. This data can enable a range of activity such as portfolio tracking, programmatic trading, or industry analysis. The package is described in French (2024) . Package: r-cran-cryptoverse Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cryptoverse_0.1.0-1.ca2604.1_all.deb Size: 1255398 MD5sum: 461615fa21b21c0d62663f86ca1fc65b SHA1: cc8ec5eefd808a947513e9211073079475dc2fac SHA256: 39c14b3f633bed3e59a70e858b1214e3ee994e2eac6a4a50a252571822d8d3ff SHA512: 724d09a2d567d3f0353300bd50e63a217a7fde05001e1e233768d9ad379557246e41703bb3b3a69319ecea6cb61e624b85f6c272559c361e388df0d8d90ff091 Homepage: https://cran.r-project.org/package=cryptoverse Description: CRAN Package 'cryptoverse' (Visualization and Analytics for the Cryptoverse) Providing data to quickly visualize and analyze data from several cryptocurrencies. Package: r-cran-cryptowatchr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-cryptowatchr_0.2.0-1.ca2604.1_all.deb Size: 61406 MD5sum: 33f44bdd5026f609df4733b7cbaa5b43 SHA1: 34119f5066eec916dc2efdcb91ada3e05edf4b4d SHA256: 3b0badb69f836b3099b9ac78030d77ce2fdb0bd5027ea76adb985cbd123fea50 SHA512: 149e2585898169c2262b1cb737e92cb5ab5aeb49eec555c527b80b543cc2181fcab3c7e6b0326c89a5b6b817dbbd7c6bb59f3d9d54bf6d8bd4c61827d6288e1c Homepage: https://cran.r-project.org/package=cryptowatchR Description: CRAN Package 'cryptowatchR' (An API Wrapper for 'Cryptowatch') An API wrapper for 'Cryptowatch' to get prices and other information (e.g., volume, trades, order books, bid and ask prices, live quotes, and more) about cryptocurrencies and crypto exchanges. See for a detailed documentation. Package: r-cran-cryptrndtest Architecture: all Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ksamples, r-cran-sfsmisc, r-cran-rmpfr, r-cran-lambertw, r-cran-gmp, r-cran-tseries Suggests: r-cran-r.rsp, r-cran-bbotk Filename: pool/dists/resolute/main/r-cran-cryptrndtest_1.2.7-1.ca2604.1_all.deb Size: 341346 MD5sum: ba0b3cea8290c0980e8a50206103d66e SHA1: 0c606436d4c68b86e96509a3cff36765174fb701 SHA256: 9df2547f68806ee0ceda482ce8c74f9cdb51cf7097bb95760369b90eb8b3c19c SHA512: 6ee61c25ad4a5f702d9d6363d57a4d40781fabf9adeb776674871fa148bca5a6ead33cc129fc0dd1bf45406b1c9c514b7aef1ae46c4d22bf1b25f73e90cb15fc Homepage: https://cran.r-project.org/package=CryptRndTest Description: CRAN Package 'CryptRndTest' (Statistical Tests for Cryptographic Randomness) Performs cryptographic randomness tests on a sequence of random integers or bits. Included tests are greatest common divisor, birthday spacings, book stack, adaptive chi-square, topological binary, and three random walk tests (Ryabko and Monarev, 2005) . Tests except greatest common divisor and birthday spacings are not covered by standard test suites. In addition to the chi-square goodness-of-fit test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are also generated by some of the cryptographic randomness tests. Package: r-cran-cryst Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flux, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-cryst_0.1.0-1.ca2604.1_all.deb Size: 117256 MD5sum: f899a06adab83abbd67d40315d0fd7c6 SHA1: 851ec04aa43ab4eebda5dad3be23693b740dc61f SHA256: bbb979d12ab36b40fd20d3e3f068d542252cb6cf7c170d037e277b40e1b92c6d SHA512: c2061a767a1b4b0d74962ce31e8fc0f6e609c89f787f59107c95667a928a085a1585e548fb07343f5a3204122b0ae8455ac6cd953ff1d67d17415da245c9a0dd Homepage: https://cran.r-project.org/package=cryst Description: CRAN Package 'cryst' (Calculate the Relative Crystallinity of Starch by XRD and FTIR) Functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged. Package: r-cran-crystract Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-stringr, r-cran-future, r-cran-future.apply, r-cran-geometry Suggests: r-cran-dt, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-crystract_1.0.0-1.ca2604.1_all.deb Size: 854820 MD5sum: d72ef18369a7acf304a85cee9f9d3391 SHA1: fe736b7f7e80747af4c0574e8b840d136fda7a27 SHA256: 746a7fcf5dbacad4b574df04894be5efdbdd88c86d216821a457da61ed37cbd9 SHA512: cba889bfd8825ef7e0949fd9e30750191e92c6dce686633ebe3009ce8ac248c6bb53a02eb1c96e229309f92f042f051266807ca1c916e7c4c14f454f77a7d48e Homepage: https://cran.r-project.org/package=crystract Description: CRAN Package 'crystract' (Crystallographic Information File (CIF) Data Processing Tools) Provides a suite of functions to parse Crystallographic Information Files (.cif), extracting essential data such as chemical formulas, unit cell parameters, atomic coordinates, and symmetry operations. It also includes tools to calculate interatomic distances, identify bonded pairs using various algorithms (minimum_distance, brunner_nn_reciprocal, econ_nn, crystal_nn), determine nearest neighbor counts, and calculate bond angles. The package is designed to facilitate the preparation of crystallographic data for further analysis, including machine learning applications in materials science. Methods are described in: Brunner (1977) ; Hoppe (1979) ; O'Keeffe (1979) ; Shannon (1976) ; Pan et al. (2021) ; Pauling (1960, ISBN:978-0801403330). Package: r-cran-csa Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-scales, r-cran-reshape2, r-cran-moments, r-cran-lmoments, r-cran-foreach, r-cran-ggpubr, r-cran-raster, r-cran-doparallel Suggests: r-cran-testthat, r-cran-colorspace Filename: pool/dists/resolute/main/r-cran-csa_0.7.1-1.ca2604.1_all.deb Size: 350158 MD5sum: 66a8b6a206f2e2f297e8eed45faf23fd SHA1: fe1da7de4dc6537aae2aaa2cf41feea709487302 SHA256: 6c574eca8a18f649d06418383ac6917e45edae69ec170c21b1a24f844028d366 SHA512: de3f8250aa878d329e2ced1af2d6281c827f80dd17ef4a2d27bd28a8e08565f83fcbc22bc7912ff0e4f9ea605e654532945eb6c7be0befb922552d45d60235ab Homepage: https://cran.r-project.org/package=csa Description: CRAN Package 'csa' (A Cross-Scale Analysis Tool for Model-Observation Visualizationand Integration) Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. 'csa' package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum. Package: r-cran-csalert Architecture: all Version: 2024.6.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-ggplot2, r-cran-glm2, r-cran-cstidy, r-cran-cstime, r-cran-lubridate, r-cran-stringr, r-cran-surveillance Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-glue, r-cran-covidnor, r-cran-csdata, r-cran-csmaps, r-cran-ggrepel, r-cran-plnr Filename: pool/dists/resolute/main/r-cran-csalert_2024.6.24-1.ca2604.1_all.deb Size: 184378 MD5sum: 3d877d8b125c0135e000b599c066acfe SHA1: 16fdef96c1efdb9e17d7f3d53fe247cd558318c5 SHA256: c4c98498b8a85d5ad50054c2b5aa6f2f994c67d5b13ef5ae9e9b0015ed5e33fd SHA512: 0e3e51d62479bf10f7f69d8024b5c6f5f668694f707091f2c12098caa8b8d3678672103eb9b521870ed4a2097364c694c3102c4adf21b7865931190a759b41ab Homepage: https://cran.r-project.org/package=csalert Description: CRAN Package 'csalert' (Alerts from Public Health Surveillance Data) Helps create alerts and determine trends by using various methods to analyze public health surveillance data. The primary analysis method is based upon a published analytics strategy by Benedetti (2019) . Package: r-cran-csampling Architecture: all Version: 1.2-4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-marg, r-cran-statmod, r-cran-survival Filename: pool/dists/resolute/main/r-cran-csampling_1.2-4.1-1.ca2604.1_all.deb Size: 88780 MD5sum: 8232e22dcba6c6a55ccc0ad9e0f029a9 SHA1: f6570f4add44182e5bfc166c0f13e710a33c1aad SHA256: b6c1f5edfb5a2cb9e1723878fdc703b42d8519dbda9b5efa93d2409ade74c7c9 SHA512: ed66f90f31afe91b3a22048e60f9856c6bc262f9993c9547f3ab01df72e78805c8bd8ddf8c3e7d12cfd27b509442c1f4473d66f37ec82aef6426474fcd970161 Homepage: https://cran.r-project.org/package=csampling Description: CRAN Package 'csampling' (Functions for Conditional Simulation in Regression-Scale Models) Implements Monte Carlo conditional inference for the parameters of a linear nonnormal regression model. Package: r-cran-cschange Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-survival, r-cran-boot, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-cschange_0.1.7-1.ca2604.1_all.deb Size: 24796 MD5sum: 26725a7d570dedfdcb740078b3c74076 SHA1: 4a49af63e6342f89c0f5b1a83df2344ba8219836 SHA256: b318d84298ddc9cb8f52332b288501dfab74a8558d1ae289db2465ef92309be9 SHA512: 6aebecef255b366ebe255840a66ba314d602d88537810f2ba09487d6c5abf045e1326ecb5186994cf99a009be44c3a6efe497a594bd5ee2062728fe3b706e93e Homepage: https://cran.r-project.org/package=CsChange Description: CRAN Package 'CsChange' (Testing for Change in C-Statistic) Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers' Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) . Package: r-cran-csci Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exactci Filename: pool/dists/resolute/main/r-cran-csci_0.9.3-1.ca2604.1_all.deb Size: 55168 MD5sum: 6902d9b8a49a4b12862cb12c945e7030 SHA1: 69919eff8488cde7c2bc8ca0a221947322b277ac SHA256: 7f15b89e4f0b583a569e3eafdeb1aaccc5981eeb955828f83d3313413b7a47ab SHA512: 3ed5522a16b732497307419b4bcb752323e2e58bcabcd2eb7ac32ede2cc3001bdd26d47b10af1fe0b54f36f0ece7482dd3114fcbf952862043ff39c29accc54f Homepage: https://cran.r-project.org/package=csci Description: CRAN Package 'csci' (Current Status Confidence Intervals) Calculates pointwise confidence intervals for the cumulative distribution function of the event time for current status data, data where each individual is assessed at one time to see if they had the event or not by the assessment time. Package: r-cran-csclone Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-mcclust, r-cran-moments, r-bioc-dnacopy Filename: pool/dists/resolute/main/r-cran-csclone_1.0-1.ca2604.1_all.deb Size: 55118 MD5sum: b547fdec7b07703ea90a42a73b9d223f SHA1: 4ed0115e79890c9181e206f2e21bbff40864d376 SHA256: b22c67bd5e327d3c59232c3bccb6df98162f0bde87859bffb5150f758b9c84cc SHA512: 3aa8e07acb04b9dc53b25bc48029abf5acfc0ca41ae845a3564340ecc8ba805ab0a09128a530d5d3ded3319c5ceb0a978ebea06616edf5c2daf333e1af3b1e95 Homepage: https://cran.r-project.org/package=CSclone Description: CRAN Package 'CSclone' (Bayesian Nonparametric Modeling in R) Germline and somatic locus data which contain the total read depth and B allele read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster model can deal with the SNVs mutation and the CNAs mutation. Package: r-cran-cscnet Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-survival, r-cran-prodlim, r-cran-riskregression, r-cran-glmnet, r-cran-caret, r-cran-recipes, r-cran-parallelly, r-cran-future, r-cran-furrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cscnet_0.1.4-1.ca2604.1_all.deb Size: 118668 MD5sum: 66e20aecba9394d5d10d307122ad3c20 SHA1: 07e8227708baa5d6242dac2ee345bbe9e4f06dce SHA256: f72cab202010e7d54bb7d73146f1beeed2bb016cebacb51ae64c148e3faddf98 SHA512: a60917b630a0ec2d500b1a0e3120157b55db4530fb9ac531684e906856f5b1c02647d90ed5ca9a5d87e783fc3d5f8cb10c275ac192943667594e7745479f4b14 Homepage: https://cran.r-project.org/package=CSCNet Description: CRAN Package 'CSCNet' (Fitting and Tuning Regularized Cause-Specific Cox Models withElastic-Net Penalty) Flexible tools to fit, tune and obtain absolute risk predictions from regularized cause-specific cox models with elastic-net penalty. Package: r-cran-csdata Architecture: all Version: 2026.3.30-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-broom, r-cran-pxwebapidata, r-cran-crayon, r-cran-dplyr, r-cran-forcats, r-cran-fs, r-cran-geojsonio, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-mapproj, r-cran-ncdf4, r-cran-purrr, r-cran-readxl, r-cran-reshape2, r-cran-rmarkdown, r-cran-rmapshaper, r-cran-rstudioapi, r-cran-stringr, r-cran-sp, r-cran-sf, r-cran-tidyr, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-csdata_2026.3.30-1.ca2604.1_all.deb Size: 1839882 MD5sum: 9e1846bf44e9791bd20df0c38e3cfee0 SHA1: a8a66609f98891bc6748fde5c589eec8a2737577 SHA256: f72910a89a5c1b6a9cc78e4a7987edc30180535ded5c4e8cc84fef7dff8458c2 SHA512: fdd44b054f2c1035052a809704eefe4bd9a041f7b96302911b0e881e53c409e3b2653ff5d1c3aaf08e4cc35dbb136c2826cc6297c5ef7ed19e180f2f46595614 Homepage: https://cran.r-project.org/package=csdata Description: CRAN Package 'csdata' (Structural Data for Norway) Datasets relating to population in municipalities, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2024. Package: r-cran-csdb Architecture: all Version: 2026.5.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-csutil, r-cran-data.table, r-cran-dbi, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-glue, r-cran-odbc, r-cran-r6, r-cran-s7, r-cran-stringr, r-cran-uuid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-digest, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-csdb_2026.5.13-1.ca2604.1_all.deb Size: 379738 MD5sum: 4e7e8d72203d36460b968ee95f96f744 SHA1: 81d097f0e516e09da15926dfbd5b9c2618f93963 SHA256: 2cac91fa93369ee67e4307b85154ae82fbc0eb49273312f2b85a6497c6d4d06e SHA512: 319921f0e077081f8a30166db235d2ffc88c822d6ed1e6b5af854bbe047cdfafe0acd6f8f192d8d5150b66e3d011ce57d56a02d30e85b099c147aa3c92f3acab Homepage: https://cran.r-project.org/package=csdb Description: CRAN Package 'csdb' (An Abstracted System for Easily Working with Databases withLarge Datasets) Provides object-oriented database management tools for working with large datasets across multiple database systems. Features include robust connection management for 'PostgreSQL' databases, advanced table operations with bulk data loading and upsert functionality, comprehensive data validation through customizable field type and content validators, efficient index management, and cross-database compatibility. Designed for high-performance data operations in surveillance systems and large-scale data processing workflows. Package: r-cran-csdm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-xtsum, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-csdm_1.0.1-1.ca2604.1_all.deb Size: 287002 MD5sum: a4ef297cce97846e2c44efbca61cc009 SHA1: ae34049c4efcb821ced93848b6855beee2386a03 SHA256: 865afc22c1fa6b3dcab31e36055e584478e8a5cd189f200fa625fa90c0ff6036 SHA512: 78df92ce45223c68beb4175b436164a3a5403c16670dea337f8ba00336ab6c29242bfa131a9c453deb0710c140d0bdd49e8484fc767540dfeacf0d04169fc08c Homepage: https://cran.r-project.org/package=csdm Description: CRAN Package 'csdm' (Cross-Sectional Dependence Models) Provides estimators and utilities for large panel-data models with cross-sectional dependence, including mean group (MG), common correlated effects (CCE) and dynamic CCE (DCCE) estimators, and cross-sectionally augmented ARDL (CS-ARDL) specifications, plus related inference and diagnostics. Package: r-cran-csdownscale Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cstools, r-cran-abind, r-cran-multiapply, r-cran-nnet, r-cran-plyr, r-cran-s2dv, r-cran-climprojdiags, r-cran-proxy, r-cran-easyverification Filename: pool/dists/resolute/main/r-cran-csdownscale_0.0.2-1.ca2604.1_all.deb Size: 183714 MD5sum: 92c80f2abaa65c8f0dc250564522d97e SHA1: c6de0597ad0a8b6758c0fe0208beec5a4f222318 SHA256: 3660878ba80ad2471511beacafeb0e83389f66f8aa0a961a707ad33d4915b27e SHA512: 2e712efa7176c4b90ccaf5e699d1b6cb9126bc69ff6c8900402e6fa08827940dfa5a5ad7b4968ce1b392484a5a67827c8c5a3201a1b9044ac63110d52745ad0b Homepage: https://cran.r-project.org/package=CSDownscale Description: CRAN Package 'CSDownscale' (Statistical Downscaling of Climate Predictions) Statistical downscaling and bias correction of climate predictions. It includes implementations of commonly used methods such as Analogs, Linear Regression, Logistic Regression, and Bias Correction techniques, as well as interpolation functions for regridding and point-based applications. It facilitates the production of high-resolution and local-scale climate information from coarse-scale predictions, which is essential for impact analyses. The package can be applied in a wide range of sectors and studies, including agriculture, water management, energy, heatwaves, and other climate-sensitive applications. The package was developed within the framework of the European Union Horizon Europe projects Impetus4Change (101081555) and ASPECT (101081460), the Wellcome Trust supported HARMONIZE project (224694/Z/21/Z), and the Spanish national project BOREAS (PID2022-140673OA-I00). Implements the methods described in Duzenli et al. (2024) . Package: r-cran-cseqpat Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlp, r-cran-tm Filename: pool/dists/resolute/main/r-cran-cseqpat_0.1.2-1.ca2604.1_all.deb Size: 16246 MD5sum: df924657d9ab74f403c7d5eaab80f627 SHA1: 4c591e93da5019eae46f315fbbfbf4b36ad79fb4 SHA256: 70851a15f96f98ee64264070f7b213ffdf902ee4c6907c3473ebdc22aa6c3a9f SHA512: 092c343971e0fe0c5e37856abf9a4aa3f1dc55e540ac801568d17e71808eacd62d219f64a0d5286389a133546977240092b8b5db1e1ac64a89211149d5bda7b2 Homepage: https://cran.r-project.org/package=CSeqpat Description: CRAN Package 'CSeqpat' (Frequent Contiguous Sequential Pattern Mining of Text) Mines contiguous sequential patterns in text. Package: r-cran-csesa Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3070 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-csesa_1.2.0-1.ca2604.1_all.deb Size: 888140 MD5sum: 8df80acecd85c3922b9a37a9e23e4c56 SHA1: 10928d7705e64b218471ddc1d879dc04b7c3c8a2 SHA256: 64c3a978202de2c3b2b33c631fee7e89db883229144353342388780c16699168 SHA512: 3d1d3aff25de2a132772ab5e5c8f217b16d989bdfd318e3a6711053e1e6f7d9c42e95e7109e0bdee17f0a52715d86bcb4372ee8b6a8e7585e628e09ba0ba2c68 Homepage: https://cran.r-project.org/package=CSESA Description: CRAN Package 'CSESA' (CRISPR-Based Salmonella Enterica Serotype Analyzer) Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package 'CSESA' to predict the serotype based on this finding. Considering it’s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using 'CSESA' before doing the traditional serotyping. Package: r-cran-cshapes Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rmapshaper, r-cran-sp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cshapes_2.0-1.ca2604.1_all.deb Size: 3698832 MD5sum: 57eda0bd7bef511f8656e3188b82e053 SHA1: 6913544bf094c420a53ea23a6d13ab77906899c5 SHA256: 2ea7a0e4dae52b1ce039ce76356b26269dcf58a5f8141bc304a2952898a9d8e6 SHA512: 0d988cc1aa52237b23195cb9a7e87a0457046ab5e13dde61779c32b30048a3eca3bc3547762c1fb0e60adc1b164f4972f5f2c20e101847ec073697810d8bdcff Homepage: https://cran.r-project.org/package=cshapes Description: CRAN Package 'cshapes' (The CShapes 2.0 Dataset and Utilities) Package for CShapes 2.0, a GIS dataset of country borders (1886-today). Includes functions for data extraction and the computation of distance matrices and -lists. Package: r-cran-cshshydrology Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-lubridate, r-cran-plotrix, r-cran-timedate, r-cran-stringr, r-cran-ggplot2, r-cran-ggspatial, r-cran-dplyr, r-cran-httr2, r-cran-terra, r-cran-tidyterra, r-cran-tidyhydat, r-cran-whitebox, r-cran-circular, r-cran-mgbt, r-cran-outliers, r-cran-teachingdemos, r-cran-kendall Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-readr Filename: pool/dists/resolute/main/r-cran-cshshydrology_1.5.0-1.ca2604.1_all.deb Size: 1234192 MD5sum: 60401d59bc5864b80ad6749c3dfc1289 SHA1: 9415485c8c64adab129d5c27a2c655826d3d4804 SHA256: 684676ca5f790f7702c794bcce2a48b9b4f452cb81479b535d852617e47c7b99 SHA512: b1e47fb8b760b21ac104847d6656e2f70617cd8863c074264e939f8111c2dfd49e6be49d517b62ac08fd7af58e27cbd10868d30c715f548a5eb8db6ef9e87b41 Homepage: https://cran.r-project.org/package=CSHShydRology Description: CRAN Package 'CSHShydRology' (Canadian Hydrological Analyses) A collection of user-submitted functions to aid in the analysis of hydrological data, particularly for users in Canada. The functions focus on the use of Canadian data sets, and are suited to Canadian hydrology, such as the important cold region hydrological processes and will work with Canadian hydrological models. The functions are grouped into several themes, currently including Statistical hydrology, Basic data manipulations, Visualization, and Spatial hydrology. Functions developed by the Floodnet project are also included. CSHShydRology has been developed with the assistance of the Canadian Society for Hydrological Sciences (CSHS) which is an affiliated society of the Canadian Water Resources Association (CWRA). As of version 1.2.6, functions now fail gracefully when attempting to download data from a url which is unavailable. Package: r-cran-csindicators Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multiapply, r-cran-climprojdiags, r-cran-cstools, r-cran-spei, r-cran-lmom, r-cran-lmomco, r-cran-zoo, r-cran-s2dv, r-cran-lubridate, r-cran-geosphere Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-csindicators_1.2.0-1.ca2604.1_all.deb Size: 2185592 MD5sum: 0e0a6260569d19fc187c98431b4c6667 SHA1: 62ac1c41c55ca26388f81694830108c15657201d SHA256: 93de28f36f344e4741e8f536738ee53090b03ca1704f9f3291e4661282461873 SHA512: b147af8d013aa3c87166dcf00befa6b4ba62398bfa40e7250174e5e65c3aef686c60fce55dcbe902e57ca3d195c969242e59308f6ab6a815a647bf52272633d3 Homepage: https://cran.r-project.org/package=CSIndicators Description: CRAN Package 'CSIndicators' (Climate Services' Indicators Based on Sub-Seasonal to DecadalPredictions) Set of generalised tools for the flexible computation of climate related indicators defined by the user. Each method represents a specific mathematical approach which is combined with the possibility to select an arbitrary time period to define the indicator. This enables a wide range of possibilities to tailor the most suitable indicator for each particular climate service application (agriculture, food security, energy, water management, health...). This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time-scales, provided the dimensional structure of the input is maintained. Additionally, the outputs of the functions in this package are compatible with 'CSTools'. This package is described in Pérez-Zanón et al. (2023) and was developed in the context of the H2020 projects MED-GOLD (776467) and S2S4E (776787) projects, as well as the Horizon Europe project MEDEWSA (101121192) and the national project BOREAS (PID2022-140673OA-I00). See Lledó et al. (2019) and Chou et al., 2023 for details. Package: r-cran-csmaps Architecture: all Version: 2025.8.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5834 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-ggrepel, r-cran-leaflet, r-cran-sf, r-cran-csdata Filename: pool/dists/resolute/main/r-cran-csmaps_2025.8.21-1.ca2604.1_all.deb Size: 3837864 MD5sum: 2416c00290f6645a099d1a275a94f8d8 SHA1: d71e788a65ec2472a0e34e2cd1cdd6003a197118 SHA256: e7e9ebfa76e44a374b3b75f00fcf8a594c3ff09ac28a96623af0b225865180ce SHA512: a25f8c51b8850b871cce6df5b5bb9086324f366d84ed84018ec3b18a2a1057a0090c8a0de10343dfd6e426807cf105c3b654fc6e400158be44a3ac90849f85d1 Homepage: https://cran.r-project.org/package=csmaps Description: CRAN Package 'csmaps' (Preformatted Maps of Norway that Don't Need Geolibraries) Provides datasets containing preformatted maps of Norway at the county, municipality, and ward (Oslo only) level for redistricting in 2024, 2020, 2018, and 2017. Multiple layouts are provided (normal, split, and with an insert for Oslo), allowing the user to rapidly create choropleth maps of Norway without any geolibraries. Package: r-cran-csmbuilder Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-desolve, r-cran-rcpp, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-csmbuilder_0.1.0-1.ca2604.1_all.deb Size: 88024 MD5sum: ddc7e131b3486c1cc783a51a76229191 SHA1: fb5254b12d8b74f5171ef63625ac302669142286 SHA256: 379ef843f1d1608c14050f3cd6f7b44670f6a672691918b4fb517b8246699bb7 SHA512: 902a924a19e7527bf50c4b7ec51bbd863894fd1b12e5db6c12d0406da23f26692b1c1490bea51257f6731a1fd9b221d62bb10b4b72e89c9ec2342f750d2662ee Homepage: https://cran.r-project.org/package=csmbuilder Description: CRAN Package 'csmbuilder' (A Collection of Tools for Building Cropping System Models) A collection of tools for designing, implementing, testing, documenting and visualizing dynamic simulation cropping system models. Models are specified as a combination of state variables, parameters, intermediate factors and input data that define a system of ordinary differential equations. Specified models can be used to simulate dynamic processes using numerical integration algorithms. Package: r-cran-csmes Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mco, r-cran-rocr, r-cran-rpart, r-cran-zoo, r-cran-catools, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-csmes_1.0.1-1.ca2604.1_all.deb Size: 108624 MD5sum: 99ba7b6c3dd51466a4a33e6313604421 SHA1: 09582725513e5a9c60526d64f32effc28d4ac401 SHA256: d50e1f0c9e772eeae48499787074de7e3cb2336136c9d001f1b82982fcecadc6 SHA512: 5821ecd4077e57c4f8f9ea3e6f339be7323a9a966533f3fa10b397e2f61cc53c49da14c05761f0f5cf79834f8db3d9eaa971b7adfb9a95c924eec05a6d465685 Homepage: https://cran.r-project.org/package=CSMES Description: CRAN Package 'CSMES' (Cost-Sensitive Multi-Criteria Ensemble Selection for UncertainCost Conditions) Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) ) for cost-sensitive learning under unknown cost conditions. Package: r-cran-csmgmm Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mvtnorm, r-cran-rlang, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-csmgmm_0.4.0-1.ca2604.1_all.deb Size: 114238 MD5sum: 21ee3187420755d17e72f9baa098fcce SHA1: bbb60caa8e56d6d871410d4a6931e1ee8904f698 SHA256: 54c6871c0f4a6ce0397ef957ac0ab8b7ab40f9d020891c0cf014fb7659cbbd78 SHA512: 8b8c3d30872e925506e9bb7ad8fd4c2b218fe6e6afbbcf84eae9fe8d6ec747f14750756a420c83b1d0fd95d20183e30b1f8aad3b5ca10702ae54ffe69ae1d3ee Homepage: https://cran.r-project.org/package=csmGmm Description: CRAN Package 'csmGmm' (Conditionally Symmetric Multidimensional Gaussian Mixture Model) Implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (Journal of the American Statistical Association 2025, ). Package: r-cran-csmpv Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-glmnet, r-cran-hmisc, r-cran-rms, r-cran-forestmodel, r-cran-ggplot2, r-cran-ggpubr, r-cran-survminer, r-cran-xgboost, r-cran-scales, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-csmpv_1.0.5-1.ca2604.1_all.deb Size: 1019392 MD5sum: a1cd2b878f406c53e5066675dd8b5c58 SHA1: 66b6820f2f5e6b7f9ed0ff80296e127dc3134c83 SHA256: 86d7d21abe5b14f6bcd045917362a0a0bfc33480534bf2963033baf2190b34c2 SHA512: 6f3025de0a325a12bf315a36920f3a0b1f5044349340557bb1a7c9094b0ef73ab43ead337f8b32a3eb9c0429dd12dedcc2eb2d7ecda2028c728258bbe6b9ed28 Homepage: https://cran.r-project.org/package=csmpv Description: CRAN Package 'csmpv' (Biomarker Confirmation, Selection, Modelling, Prediction, andValidation) There are diverse purposes such as biomarker confirmation, novel biomarker discovery, constructing predictive models, model-based prediction, and validation. It handles binary, continuous, and time-to-event outcomes at the sample or patient level. - Biomarker confirmation utilizes established functions like glm() from 'stats', coxph() from 'survival', surv_fit(), and ggsurvplot() from 'survminer'. - Biomarker discovery and variable selection are facilitated by three LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging the 'glmnet' R package with additional steps. - Eight versatile modeling functions are offered, each designed for predictive models across various outcomes and data types. 1) LASSO2(), LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection using LASSO methods and construct predictive models based on selected variables. 2) XGBtraining() employs 'XGBoost' for model building and is the only function not involving variable selection. 3) Functions like LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine LASSO-related variable selection with 'XGBoost' for model construction. - All models support prediction and validation, requiring a testing dataset comparable to the training dataset. Additionally, the package introduces XGpred() for risk prediction based on survival data, with the XGpred_predict() function available for predicting risk groups in new datasets. The methodology is based on our new algorithms and various references: - Hastie et al. (1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al. (2021) , - Friedman et al. (2010) , - Simon et al. (2011) , - Harrell (2023) , - Harrell (2023) , - Chen and Guestrin (2016) , - Aoki et al. (2023) . Package: r-cran-csn Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-csn_1.1.3-1.ca2604.1_all.deb Size: 28068 MD5sum: a7f26121944de68fe9ed4f03b9069bd9 SHA1: f2398813e30d24bb735796c739ba8693e6c227f4 SHA256: 6dc605d3862ed7af3a29e3748fa7b9743a6fdba69c80cd9fe349928e8ffb95c3 SHA512: 058cdccfa60d201d355b0a00aa9ce4a7e9d32789f57f4e5d5b241efcfc4c8fa47832a02ac4466737a0ca27d662c880ed8a30007ac1ae33e1ed4fd021a3648b31 Homepage: https://cran.r-project.org/package=csn Description: CRAN Package 'csn' (Closed Skew-Normal Distribution) Provides functions for computing the density and the log-likelihood function of closed-skew normal variates, and for generating random vectors sampled from this distribution. See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004). The closed skew normal distribution, Skew-elliptical distributions and their applications: a journey beyond normality, Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42. 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The Central Statistics Office (CSO) is the national statistical institute of Ireland and 'PxStat' is the CSOs online database of Official Statistics. This database contains current and historical data series compiled from CSO statistical releases and is accessed at . The CSO 'PxStat' Application Programming Interface (API), which is accessed in this package, provides access to 'PxStat' data in JSON-stat format at . This dissemination tool allows developers machine to machine access to CSO 'PxStat' data. Package: r-cran-cspec Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cspec_0.1.2-1.ca2604.1_all.deb Size: 26790 MD5sum: d299a5436fc271fff2561385c34b6c99 SHA1: 71317e95ac1ff725c081f7b91068935dbb53a975 SHA256: 51a584f744fe11078e415a31b1fd435d5f419a4616e2925aff20a132c2d9eb4e SHA512: 5c4c9cd408a426c95ac579484f902b26760e32541b34582ec8c1b880302525bd764fc3e0289f91fcd3e4d05779d8f95c516faaed852f4b94f00e34a8ec2079eb Homepage: https://cran.r-project.org/package=cspec Description: CRAN Package 'cspec' (Complete Discrete Fourier Transform (DFT) and Periodogram) Calculate the predictive discrete Fourier transform, complete discrete Fourier transform, complete periodogram, and tapered complete periodogram. This algorithm is based on the preprint "Spectral methods for small sample time series: A complete periodogram approach" (2020) by Sourav Das, Suhasini Subba Rao, and Junho Yang. 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Jordan and Matt Grossmann (2020) . The Correlates data contains over 2000 variables across more than 100 years that pertain to state politics and policy in the United States. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, create map visualizations, export the citations associated with their searches, and more. Package: r-cran-csppdata Architecture: all Version: 0.2.61-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7776 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-csppdata_0.2.61-1.ca2604.1_all.deb Size: 7928074 MD5sum: 769b7318026b247644aaf15bccf0df02 SHA1: 3fe8320fed161290fbce5517a3234cde7f839ac5 SHA256: a98afa6fd501e19a810e8c454292beff69af9bb1a6e6c86e343879def9d6928e SHA512: 7fbe0a92e0fe796360066bfd9d3a23e3941b40a6e2cb8169e243c4be850fe00194a55eea7796613a9190fe1bf400af8f4f2d1815b4a97d32c6a90ef8107237e2 Homepage: https://cran.r-project.org/package=csppData Description: CRAN Package 'csppData' (Data Only: The Correlates of State Policy Project Dataset) Contains the Correlates of State Policy Project dataset (+ codebook) assembled by Marty P. Jordan and Matt Grossmann (2020) used by the 'cspp' package. The Correlates data contains over 3000 variables across more than 100 years that pertain to state politics and policy in the United States. 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Package functionality and implementation for v 1.0.0 is documented in Carlisle (2022) . Package: r-cran-cthresher Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-cthresher_1.1.0-1.ca2604.1_all.deb Size: 38652 MD5sum: 1e024935622b0fa7b678617fb2d6862d SHA1: 9fe0091daa2551e5121f544ac709d8119b77a08a SHA256: 585a3e5274e9c0b1a21124cb89c7e267ca3d3b5b48d622d662295d179acb19bd SHA512: 76bab61a480a4e5f9a7e16cf6ea0446b383d7d78929a5f7d6fa2ecd39c60d848363c03d62b4d0e9c142f101eb223005e76b53600e7d8845f9b422e67d4651f9b Homepage: https://cran.r-project.org/package=cthreshER Description: CRAN Package 'cthreshER' (Continuous Threshold Expectile Regression) Estimation and inference methods for the continuous threshold expectile regression. It can fit the continuous threshold expectile regression and test the existence of change point, for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted." Package: r-cran-ctlr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-binom, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-lmtest, r-cran-mass, r-cran-mfp2, r-cran-sandwich Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-ctlr_0.1.0-1.ca2604.1_all.deb Size: 333816 MD5sum: 7ac26092bca4a1dda7f843139260afe7 SHA1: 331fdeebd68806c3a82110138fbc00fe987b2e03 SHA256: fbc48aad7acc862ea9372f687a6574d4e3a603a1053cf6305bc42fed59aeb8b6 SHA512: 4b976569d524faf531b5b74ead8e0fb3e19f0f739d0063cfba4163f5a5b6cd99b75b6c831d5aca66b7d2d07d160b146f3bfd4d4bdc7156131c41a67eedb2f6f2 Homepage: https://cran.r-project.org/package=ctlr Description: CRAN Package 'ctlr' (Clinical Tolerance Limits for Assessing Agreement) Implements clinical tolerance limits (CTL) methodology for assessing agreement between two measurement methods. Estimates the true latent trait using Best Linear Unbiased Predictors (BLUP), models bias and variance components, and calculates overall and conditional agreement probabilities. Provides visualization tools including tolerance limit plots and conditional probability of agreement plots with confidence bands. This package is based on methods described in Taffé (2016) , Taffé (2019) , and 'Stata' package Taffé (2025) . Package: r-cran-ctmcmove Architecture: all Version: 1.2.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-matrix, r-cran-fda, r-cran-gdistance, r-cran-sp Suggests: r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-ctmcmove_1.2.10-1.ca2604.1_all.deb Size: 258262 MD5sum: 8cc984d0a965442572ee4029368faaf2 SHA1: c39505aa7e7207006a002ae7df3fc82d2702b655 SHA256: 805bc83a4b321611f9b285845be3095b9f25379b219d497c7c0f6859de2bf51a SHA512: 2a190fad826411284dbde7dae6190d1e09a8ab42ce9258b4334369a89303b8b301482378c0fbc8d9e9151aa91485cc76639fe21a31113fbc18d306509b34d687 Homepage: https://cran.r-project.org/package=ctmcmove Description: CRAN Package 'ctmcmove' (Modeling Animal Movement with Continuous-Time Discrete-SpaceMarkov Chains) Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework. Package: r-cran-ctmle Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-superlearner, r-cran-tmle, r-cran-glmnet Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-ctmle_0.1.2-1.ca2604.1_all.deb Size: 166210 MD5sum: 944c99df8a69de297faa6e153253306e SHA1: 83b6331d9706cbe11a10b5463ba50b6ce8dbcb62 SHA256: 06d22ae4fabea9a90efe421bc8e398bcac7a18d8fef55d0c666a079286cdc54b SHA512: a8f08c7a8e35ee2446fa711bf86cf0f5a5ef37b2d12b079bb280b2d971e9c2f07ddc938e6f5f78c388521343436adbde84f53f50e3affce116bf10acaf5ee408 Homepage: https://cran.r-project.org/package=ctmle Description: CRAN Package 'ctmle' (Collaborative Targeted Maximum Likelihood Estimation) Implements the general template for collaborative targeted maximum likelihood estimation. It also provides several commonly used C-TMLE instantiation, like the vanilla/scalable variable-selection C-TMLE (Ju et al. (2017) ) and the glmnet-C-TMLE algorithm (Ju et al. (2017) ). 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The package is described in Calabrese et al (2016) , with models and methods based on those introduced and detailed in Fleming & Calabrese et al (2014) , Fleming et al (2014) , Fleming et al (2015) , Fleming et al (2015) , Fleming et al (2016) , Péron & Fleming et al (2016) , Fleming & Calabrese (2017) , Péron et al (2017) , Fleming et al (2017) , Fleming et al (2018) , Winner & Noonan et al (2018) , Fleming et al (2019) , Noonan & Fleming et al (2019) , Fleming et al (2020) , Noonan et al (2021) , Fleming et al (2022) , Silva et al (2022) , Alston & Fleming et al (2023) . Package: r-cran-ctmva Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-polynom, r-cran-mass, r-cran-mgcv, r-cran-matrix, r-cran-ggplot2, r-cran-rlang, r-cran-vegan, r-cran-viridislite Suggests: r-cran-dplyr, r-cran-wbwdi Filename: pool/dists/resolute/main/r-cran-ctmva_1.6.0-1.ca2604.1_all.deb Size: 146102 MD5sum: 45771f1a3b45e15f535e63445e29713b SHA1: bb5272e04c0a15edd82438cdb68e54ab3e45082b SHA256: 45c58769f7b4d76617fc3b0c2e42a3ad6735b47d7bf0e6d7978580e1a5bf76cf SHA512: bdf313bcd2b3a4ab91b08e6cd00ebb44efa5823ecd556472b81cc4048b9cc20412b3b6418206a398cc7eda7266ab9dfc8bf7a009ff1319d7930cd77bf70b3ad0 Homepage: https://cran.r-project.org/package=ctmva Description: CRAN Package 'ctmva' (Continuous-Time Multivariate Analysis) Implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" . Package: r-cran-ctnote Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stringr Suggests: r-cran-ape, r-cran-kableextra, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-survival, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-ctnote_0.1.0-1.ca2604.1_all.deb Size: 560498 MD5sum: 5a4d19de210cee73d93b39a1299b3877 SHA1: bcbff5e4b808341dd91ab9ccfdd95e9ea54f7bc1 SHA256: 4ff168a2b9cc83f5dd46f130bd3a2692aba4c7deb276d9350cfdea4dc2ab7117 SHA512: 893624179acb0279f48b3941eaedee7c675616da81b781a951a016da839de47fd805d5648edd55f67ad70e23755dc889ea19eeb0df321f775fd1c68827f1a0d9 Homepage: https://cran.r-project.org/package=CTNote Description: CRAN Package 'CTNote' (CTN Outcomes, Treatments, and Endpoints) The Clinical Trials Network (CTN) of the U.S. National Institute of Drug Abuse sponsored the CTN-0094 research team to harmonize data sets from three nationally-representative clinical trials for opioid use disorder (OUD). The CTN-0094 team herein provides a coded collection of trial outcomes and endpoints used in various OUD clinical trials over the past 50 years. These coded outcome functions are used to contrast and cluster different clinical outcome functions based on daily or weekly patient urine screenings. Note that we abbreviate urine drug screen as "UDS" and urine opioid screen as "UOS". For the example data sets (based on clinical trials data harmonized by the CTN-0094 research team), UDS and UOS are largely interchangeable. Package: r-cran-ctoclient Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-curl, r-cran-httptest2, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ctoclient_0.1.0-1.ca2604.1_all.deb Size: 123666 MD5sum: 3099e3fdd6e5e39e820b6938b5a19fd5 SHA1: 7fd17dff3d5cc83c412f29fb88482cfedfe4823f SHA256: 8e8b8d846ee0878ebdb5c2e441ebac0e9a4fb6bd01570e9e8892878862932b90 SHA512: 7ff348aa8f4a8d38647f858b7c3443138410a3fdf22adb3924e925c70a46a80b2cfaeae0508fceebd020b3420cd471f9a40273c27fb35b05d2bc2c09520fe5eb Homepage: https://cran.r-project.org/package=ctoclient Description: CRAN Package 'ctoclient' (A Modern and Flexible Data Pipeline for 'SurveyCTO') A modern and flexible R client for the 'SurveyCTO', a mobile and offline data collection platform, providing a modern and consistent interface for programmatic access to server resources. Built on top of the 'httr2' package, it enables secure and efficient data retrieval and returns analysis-ready data through optional tidying. It includes functions to create, upload, and download server datasets, in addition to fetching form data, files, and submission attachments. Robust authentication and request handling make the package suitable for automated survey monitoring and downstream analysis. 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Package: r-cran-ctrdata Architecture: all Version: 1.26.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-nodbi, r-cran-rvest, r-cran-stringdist, r-cran-tidyr, r-cran-v8, r-cran-httr2, r-cran-stringi, r-cran-lubridate, r-cran-jqr, r-cran-dplyr, r-cran-zip, r-cran-readr, r-cran-rlang, r-cran-htmlwidgets Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-mongolite, r-cran-tinytest, r-cran-rpostgres, r-cran-duckdb, r-cran-httr, r-cran-tibble, r-cran-clipr, r-cran-chromote Filename: pool/dists/resolute/main/r-cran-ctrdata_1.26.1-1.ca2604.1_all.deb Size: 1827828 MD5sum: 8f2a76ef09479d33e0a01c30717ca7ed SHA1: da1d5f0d88c34ba589ca3ce5219ecb0bce78bb13 SHA256: 0fac1c7a777e653838a2a80021c4961af61638baaac7d60c6935bb33a86a8a02 SHA512: a364b72bc1cfbc444c933da0913fa067b60988ecaf6daf9202d6cfd0d4548690b4ac944912d871bee58d4fcb484522f53c54a1064ad9bc9c156452449b0d91ec Homepage: https://cran.r-project.org/package=ctrdata Description: CRAN Package 'ctrdata' (Retrieve and Analyze Clinical Trials Data from Public Registers) A system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from four public registers, the 'European Union Clinical Trials Register' ('EUCTR', ), 'ClinicalTrials.gov' ( and also translating queries the retired classic interface), the 'ISRCTN' () and the 'European Union Clinical Trials Information System' ('CTIS', ). Trial information is downloaded, converted and stored in a database ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). Protocols, statistical analysis plans, informed consent sheets and other documents in registers associated with trials can also be downloaded. Other functions implement trial concepts canonically across registers, identify deduplicated records, easily find and extract variables (fields) of interest even from complex nested data as used by the registers, merge variables and update queries. The package can be used for monitoring, meta- and trend-analysis of the design and conduct as well as of the results of clinical trials across registers. See overview in Herold, R. (2025) . Package: r-cran-ctrialsgov Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-dbi, r-cran-matrix Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-ctrialsgov_0.2.7-1.ca2604.1_all.deb Size: 3393278 MD5sum: 789ee8801f75eca462a1dc9ba433135e SHA1: aa3148ba0b479f991413a2423fe58007eca0115e SHA256: 520b12087438ef2ed796675c3a3d036591c98fa266f790ffd4584f84d1986a16 SHA512: 75355f8c954aaa1053927eb3289b99a84d0902b4483c4291cea04146934455de3029ed0ed987fd51f8be8abaca0a38d8d8c5f23f515c3994f24496d615a0af69 Homepage: https://cran.r-project.org/package=ctrialsgov Description: CRAN Package 'ctrialsgov' (Query Data from U.S. National Library of Medicine's ClinicalTrials Database) Tools to create and query database from the U.S. National Library of Medicine's Clinical Trials database . 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Wood density profiles (i.e. variations of wood density from pith to bark) can yield important information used for studies in forest resource assessment, wood quality and dendrochronology studies. The first step consists in transforming grey values from the scan images to density values. The packages then proposes a unique method to automatically locate the pith by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree ring profiles (average ring density, earlywood and latewood density, ring width and percent latewood for each ring) are then obtained. 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It provides in-depth analysis at the codon level, including relative synonymous codon usage (RSCU), tRNA weight calculations, machine learning predictions for optimal or preferred codons, and visualization of codon-anticodon pairing. Additionally, it can calculate various gene- specific codon indices such as codon adaptation index (CAI), effective number of codons (ENC), fraction of optimal codons (Fop), tRNA adaptation index (tAI), mean codon stabilization coefficients (CSCg), and GC contents (GC/GC3s/GC4d). It also supports both standard and non-standard genetic code tables found in NCBI, as well as custom genetic code tables. 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See the Journal of Statistical Software reference: . Package: r-cran-cubelyr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cubelyr_1.0.2-1.ca2604.1_all.deb Size: 343026 MD5sum: eade00c7aab7a48f9cb22d80e1cd8f56 SHA1: ba95748d909b8b86eec3f623ba25ec54e46f83dc SHA256: 9f72395c89f02de305be0d763a414d1780d0957f141fd11470f3e79880a376ea SHA512: 13a3c28a38f2872e8158a8120d35412ea66a7f93e4bb4f1db48777a059f6e34a4dc2626d6cc44beba5ac3be655ea87d05e65b2243c98bdd2b1e6cc35c7d7cf0c Homepage: https://cran.r-project.org/package=cubelyr Description: CRAN Package 'cubelyr' (A Data Cube 'dplyr' Backend) An implementation of a data cube extracted out of 'dplyr' for backward compatibility. 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Package: r-cran-cucumber Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-withr Suggests: r-cran-mockery, r-cran-box, r-cran-shinytest2, r-cran-chromote, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-r6, r-cran-pkgdown, r-cran-pkgload, r-cran-muttest Filename: pool/dists/resolute/main/r-cran-cucumber_2.1.1-1.ca2604.1_all.deb Size: 104114 MD5sum: 99b729cc1f68a5050a8f3bc2f75eae43 SHA1: 65b56366609e774c3857f4e8ecfe0fa3542a873d SHA256: a26a4fcfa6ac806c6f0711befc37e7aacd0ad58ec9d19bac313eba8d3e625a13 SHA512: 697bc061a1caa4e3f058c32c9099d600e83287392a9f8341c0b43fbe604b5de8460e9625db6d47922f940b550c211e414ff8234e2279ba99dd30eea214d9433f Homepage: https://cran.r-project.org/package=cucumber Description: CRAN Package 'cucumber' (Behavior-Driven Development for R) Write executable specifications in a natural language that describes how your code should behave. 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Package: r-cran-cuff Architecture: all Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-xtable, r-cran-dt, r-cran-lmertest, r-cran-nlme, r-cran-haven, r-cran-dplyr, r-cran-clipr Filename: pool/dists/resolute/main/r-cran-cuff_1.9-1.ca2604.1_all.deb Size: 113404 MD5sum: 12ccaaae55b99c7e24ef85b4ee6d00dd SHA1: 056243452c26ab124c948bc1901de3a4048f3f63 SHA256: 693bcfe5e598d7023d204c0933d818bf8d9b62c3a05c0e904287274da8cf81a2 SHA512: f2508b36f4c5fdf619ce876d6a38b86944b99c31a1e64b4cc23027ca64a8ea5346c31dd46347ad14972f9284b33a7258344357f461467ed8f5eeb97bdea14148 Homepage: https://cran.r-project.org/package=CUFF Description: CRAN Package 'CUFF' (Charles's Utility Function using Formula) Utility functions that provides wrapper to descriptive base functions like cor, mean and table. It makes use of the formula interface to pass variables to functions. 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Package: r-cran-cultevo Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat, r-cran-hmisc, r-cran-pspearman, r-cran-stringi Suggests: r-cran-memoise, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cultevo_1.0.2-1.ca2604.1_all.deb Size: 256384 MD5sum: b8b6b034e64c6d565c412477a94a6905 SHA1: f5e8f4f88d5767fc3cc717193cfb524d9f324714 SHA256: 5cb38415e3c70479afd5f5a1882c39ec58f71562ccf07923944abf7f0f13ec37 SHA512: f72f91a685c598aa7b4b2fe728a03d13d509ef80461d91b2d7411d12d3785d31c9ceb4e404e7ad9528b96629f59c2aab98e49f60a589c426f278c6f745b96123 Homepage: https://cran.r-project.org/package=cultevo Description: CRAN Package 'cultevo' (Tools, Measures and Statistical Tests for Cultural Evolution) Provides tools for measuring the compositionality of signalling systems (in particular the information-theoretic measure due to Spike (2016) and the Mantel test for distance matrix correlation (after Dietz 1983) ), functions for computing string and meaning distance matrices as well as an implementation of the Page test for monotonicity of ranks (Page 1963) with exact p-values up to k = 22. Package: r-cran-cump Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cump_2.0-1.ca2604.1_all.deb Size: 166540 MD5sum: 2a25efdac21526cb8b1a4516d9c5b7b4 SHA1: 3b72d0ce2a22970a830d2fc8072c0018c16aec16 SHA256: f2ab8b52fc4ec715ee773778345f8c22ccaacfe32c61dc1eb9f951c0b51b96a9 SHA512: a721217155942980413c17c201cfbbe52132506d4bae84e2822a5253a2e8a92c6eb4023a5754c8f8edd9135fb4594ef03cfc27968e8218f9d4d491784afa5033 Homepage: https://cran.r-project.org/package=CUMP Description: CRAN Package 'CUMP' (Analyze Multivariate Phenotypes by Combining Univariate Results) Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy. Package: r-cran-cumprinc Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cumprinc_0.1-1.ca2604.1_all.deb Size: 14534 MD5sum: 200f026f950279959914bb192099c1f1 SHA1: 602e26c4f6bf278a405d4438243fd5db0d144d57 SHA256: 7b5055aacdc20a5a5bc335a39450c986b6a27e25a5e91a136a63969729892f84 SHA512: 2d84234484763a90e35502ab2cee705b0c95815240e4fd6b3d8b295b7040dc98b73a10b42c4282308b3ae54252ba3f460dbf9f952db00dfe4a654229fb3590f3 Homepage: https://cran.r-project.org/package=cumprinc Description: CRAN Package 'cumprinc' (Functions Centered Around Microsoft Excel Cumprinc Function) Provides similar functionality to 'Microsoft Excel' 'CUMPRINC' function . Returns principal remaining at a given month, principal paid in a month, and accumulated principal paid at a given month based on original loan amount, monthly interest rate, and term of loan. Package: r-cran-cumulcalib Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-predtools, r-cran-rmarkdown, r-cran-markdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cumulcalib_0.0.1-1.ca2604.1_all.deb Size: 91556 MD5sum: d86c0a2188c5b5d546c19ea5144088eb SHA1: 5f8d1134d944ff4a424107fa9827e40aac689450 SHA256: cdd0ae05c8ab3071a899ce34a58fdb7db48ea0847e7b64fd6f8dfb70306312c1 SHA512: 33423b18cb5bcdf919d656b30f21878e9a1c514b02d44decef0fd3b35a14da362e2692d8c85910092261dabf103342dc1f6405b3e781f5c9775e9a6f7e565a7e Homepage: https://cran.r-project.org/package=cumulcalib Description: CRAN Package 'cumulcalib' (Cumulative Calibration Assessment for Prediction Models) Tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Patkau (2024) . The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The plot() and summary() methods are implemented for the results returned by cumulcalib(). Package: r-cran-cumulocityr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-cumulocityr_0.1.0-1.ca2604.1_all.deb Size: 262634 MD5sum: 51abf789cf56222e5f657782e5342f86 SHA1: b9af15dd05d19c0a9c50b445c6f57c747e3c92ac SHA256: 195917d9f9ff8153e31a0e6b7f4d2889f4bfe0f9b8e7718305a6b7c5d8e1aa15 SHA512: dc655cc4526c6556fb346d157d1834dc6bdf914b07ad75550c2adafe042d2858212abaca4a822ce67050f72df620ffadda1987dc06a881f317173f075338cd9d Homepage: https://cran.r-project.org/package=cumulocityr Description: CRAN Package 'cumulocityr' (Client for the 'Cumulocity' API) Access the 'Cumulocity' API and retrieve data on devices, measurements, and events. Documentation for the API can be found at . Package: r-cran-cuperdec Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-cuperdec_1.1.0-1.ca2604.1_all.deb Size: 1349772 MD5sum: e956b6023565e93853916ab1226440b7 SHA1: 967df844944ea44e463c630dd7dd174d5d753d8e SHA256: 48d8884f82c85f7c78cf696bf71397eaf3b6ea506549d1c1f55b540269cf6e63 SHA512: 44a4ec0c46c9f22736c1b1d68f533f866bd7d090fb42d4889397bd12f25444c985b1bd003a409b1d0929508119a05e101f7bc008fd9f5bc35dcd332d5de5e3d4 Homepage: https://cran.r-project.org/package=cuperdec Description: CRAN Package 'cuperdec' (Cumulative Percent Decay Curve Generator) Calculates and visualises cumulative percent 'decay' curves, which are typically calculated from metagenomic taxonomic profiles. These can be used to estimate the level of expected 'endogenous' taxa at different abundance levels retrieved from metagenomic samples, when comparing to samples of known sampling site or source. Method described in Fellows Yates, J. A. et. al. (2021) Proceedings of the National Academy of Sciences USA . Package: r-cran-cure Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rstpm2, r-cran-date, r-cran-numderiv, r-cran-statmod, r-cran-relsurv, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-cure_1.1.1-1.ca2604.1_all.deb Size: 1054604 MD5sum: ba97ad0b4da8f8f4299a94d8980d2bcf SHA1: 694ab40a633337e8c3e10a6e8b2dce88c0a4ff3c SHA256: fe25c95aa53ebf2b9f78a85aba5d58e070dd7568a5fcd128017403c050ea1687 SHA512: 0a7879f2cd8ef6b013777956505a790a697ff83ce1607a48a82a1626528b0b07f23f54f8360f15c8ea9efd60a59b0d2eb3bc4d4db802a7059c0b81f9ef3f4d2b Homepage: https://cran.r-project.org/package=cuRe Description: CRAN Package 'cuRe' (Parametric Cure Model Estimation) Contains functions for estimating generalized parametric mixture and non-mixture cure models , loss of lifetime, mean residual lifetime, and crude event probabilities. Package: r-cran-curedepcens Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dlm, r-cran-formula, r-cran-rootsolve, r-cran-survival, r-cran-matrixstats Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-curedepcens_0.1.0-1.ca2604.1_all.deb Size: 138910 MD5sum: eef75c68a385bc42872422a9c7937661 SHA1: dcd09bc7426008dc108dad92ed0eef1be09b1d27 SHA256: 1c2bf96290001236351a904520d36393e36f19a72d643880cc96324efde228c4 SHA512: c79469d2531bee9a3027bbcd01a3bd4c92ccb8f24f41f77685f5c4ce43e4f73ab8070acb9736e7543ebdd8caa76d8997e551f2ef1399d9ad8e61bc07d8b0f675 Homepage: https://cran.r-project.org/package=CureDepCens Description: CRAN Package 'CureDepCens' (Dependent Censoring Regression Models with Cure Fraction) Cure dependent censoring regression models for long-term survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the cure fraction and the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2022) . Package: r-cran-curephem Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xfun, r-cran-devtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-curephem_0.3.2-1.ca2604.1_all.deb Size: 145894 MD5sum: 1eb4f05f53bda7ca96f66a9051c9944b SHA1: cc87cedca7098e1e112196040a800d2f00fe43f4 SHA256: 36c9b135a26b7ff04917b26f51e63259e415453c15b72727774220cee876c632 SHA512: 3cd8238fc5b874cb98df05d4ccde71c2a53c5ffda5044c9b2e6a4d8ac3cb661c03174ba6d68d0f75752e9b86d7acc877c18386cb4e9208ab83cabb02f3d95ab7 Homepage: https://cran.r-project.org/package=curephEM Description: CRAN Package 'curephEM' (NPMLE for Logistic-Cox Cure-Rate Model) Expectation-Maximization (EM) algorithm for point estimation and variance estimation to the nonparametric maximum likelihood estimator (NPMLE) for logistic-Cox cure-rate model with left truncation and right- censoring. See Hou, Chambers and Xu (2017) . Package: r-cran-cureplots Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cureplots_1.1.1-1.ca2604.1_all.deb Size: 70304 MD5sum: 335387fa2e9c4e7bad90ba23c562c9ef SHA1: e3a89d7e262b68c8f00d7ce48be9bd42f0b92c34 SHA256: 91ada8a6bc0c30705578f4b6bbe6131a5c8ebd7e9b0cb4173550e67b7cc7f0a0 SHA512: 67e6f2c88f43f2c85431bf9a5c675e4369cf261e3a7c8f8125bc8cc9b4180b8bad0909ea825b53b4c8d0534a3c046966127f39df3f4fcf8065385561b3986dde Homepage: https://cran.r-project.org/package=cureplots Description: CRAN Package 'cureplots' (CURE (Cumulative Residual) Plots) Creates 'ggplot2' Cumulative Residual (CURE) plots to check the goodness-of-fit of a count model; or the tables to create a customized version. A dataset of crashes in Washington state is available for illustrative purposes. Package: r-cran-curesurv Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4086 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-survival, r-cran-numderiv, r-cran-randtoolbox, r-cran-bbmle, r-cran-optimx, r-cran-formula, r-cran-deriv, r-cran-statmod Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-xhaz, r-cran-survexp.fr Filename: pool/dists/resolute/main/r-cran-curesurv_0.1.2-1.ca2604.1_all.deb Size: 1945914 MD5sum: 9050f8e8762d6442d9111db08162696c SHA1: 1a2fb3fe5b58e0e4ee14d70b7eca15d3b61cd17b SHA256: 9f3567b6b0b90f61bead0b18187664e850b61508a79610db39cf88b611ab4c14 SHA512: ee0026b95d0dcfab4103029d0aa866c43c700bc5d0339b68b5b6ac31ec89ca293e08ecf2ce0e22d3d022bd9c7fbdb3a6d6b384e8889082f72428bdd179ed76fe Homepage: https://cran.r-project.org/package=curesurv Description: CRAN Package 'curesurv' (Mixture and Non Mixture Parametric Cure Models to Estimate CureIndicators) Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) . 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You can find the full API documentation at . Package: r-cran-currentsurvival Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-cmprsk Filename: pool/dists/resolute/main/r-cran-currentsurvival_1.1-1.ca2604.1_all.deb Size: 159942 MD5sum: 66850a4b2b9999d97a87c1f806ea5038 SHA1: 1c24dfeca41efbf11ab5adc944188aa8bd49caa4 SHA256: 20dac1920c20451124588431be1121000fddc58b6b7309f1258ff95390a43212 SHA512: 6a49e5181238d28e0724a874be8792c7e054baf8171619b6ed057114f13673f721e4c945fe63fa8f7352845d7eb5c2f9dc2274779528f444b7348467b6cdebba Homepage: https://cran.r-project.org/package=currentSurvival Description: CRAN Package 'currentSurvival' (Estimation of CCI and CLFS Functions) The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). 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Package: r-cran-customerscoringmetrics Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-customerscoringmetrics_1.0.0-1.ca2604.1_all.deb Size: 75472 MD5sum: e4461a0979b5f34929c62dad4d2af7db SHA1: 0e53f8f18804b51ebd9ca0c9af58dcea4bf0b180 SHA256: b2d837f8b382da2e945472efdfae3614068830e0eff7479ac7e67ddc1dc96d28 SHA512: 28f3a569c620e1b9271326fc5e0568461501811380b3f8f3420cd4091a90267c396cfa6e59295dffef511da794a7252d0579ad5f4c4696a0b2cebad7e6642eec Homepage: https://cran.r-project.org/package=CustomerScoringMetrics Description: CRAN Package 'CustomerScoringMetrics' (Evaluation Metrics for Customer Scoring Models Depending onBinary Classifiers) Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions. Package: r-cran-customiser Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs, r-cran-rmarkdown, r-cran-knitr, r-cran-rlang, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-customiser_0.1.1-1.ca2604.1_all.deb Size: 17858 MD5sum: 5089691cf97afa0d58442e57fc5818d0 SHA1: cbfe2e1d28b7b6048c25780ecc6bbe1b3691a7fd SHA256: 65ad2b7a55943218f870a06c7bfbdb72a8f7471e382ab5d6a05f5d9942cb3617 SHA512: e223611c61eb6b59e91f00174782ea1078dbd7551ad9e0df2cde603aa4c689a1b170ffd5295a43cf860ce75a6e21b2654436e8fb2a9792125fa07b1e442f44ec Homepage: https://cran.r-project.org/package=customiser Description: CRAN Package 'customiser' (Use R Markdown to Write your "Rprofile") A simple way to write ".Rprofile" code in an R Markdown file and have it knit to the correct location for your operating system. Package: r-cran-customizedtraining Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-customizedtraining_1.3-1.ca2604.1_all.deb Size: 95460 MD5sum: 32ec4606438582e660d4767b7acf2925 SHA1: c85d277862f9169ab51b8997d38fc7c2f68de094 SHA256: 2b9c4747dbab6714626e4c9b2ec26f0e9267b93d631232c038ef87695f8da02b SHA512: aa3405bf259d1bd9254bd241467b1945e32afdb64a9cfb2dcae826fd34056044526742fc7d1e533dfcb86cdf6938757cc0e345b3f6caea74ba0a7a0bd6c7ee73 Homepage: https://cran.r-project.org/package=customizedTraining Description: CRAN Package 'customizedTraining' (Customized Training for Lasso and Elastic-Net RegularizedGeneralized Linear Models) Customized training is a simple technique for transductive learning, when the test covariates are known at the time of training. The method identifies a subset of the training set to serve as the training set for each of a few identified subsets in the training set. This package implements customized training for the glmnet() and cv.glmnet() functions. Package: r-cran-customknitrender Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-customknitrender_1.0.2-1.ca2604.1_all.deb Size: 10814 MD5sum: 04b8425a380a43bbc7d2ac6bab474621 SHA1: 41077696fb67369715cc172a9dc25d8b664da43a SHA256: 6ad458c9c82fe5e4a9c6938eb7de8659770e187029127666977a2fe8552796a0 SHA512: db802d26dc9fac76e9e5de448878ca43592d9c8a2de6b9ad7afa24ebf74a0e7c92c858bf76e79d52382a6b5e1c93cf0f591a72565f8c43d889b4fc86f2a2b1fc Homepage: https://cran.r-project.org/package=customknitrender Description: CRAN Package 'customknitrender' (Easily Switch Output Format of 'Rmarkdown' Files with SharedFrontmatter) Define the output format of 'rmarkdown' files with shared output 'yaml' frontmatter content. Rather than modifying a shared 'yaml' file, use integers to easily switch output formats for 'rmarkdown' files. Package: r-cran-custosascensor Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-custosascensor_0.1.0-1.ca2604.1_all.deb Size: 55630 MD5sum: 649eb51a2287cedc320ad571c1560cfc SHA1: 5f4851d2c6788f332c8437f123a25f49bb8c3c4d SHA256: 78fc143f265a75a26b83854e1e3ab2bfc8e26f7675f5ebd873960f3196d64e63 SHA512: 88878abf1ca532077db2ca6514d7a891b99eee555a2ad90ee75276ade973f1cf2bb16e920521f6360b437cd1d4e825167a8f036d4be57dc89d83b60be95e5672 Homepage: https://cran.r-project.org/package=CustosAscensor Description: CRAN Package 'CustosAscensor' (Costs Allocation for the Installation of an Elevator) Calculate the distribution of costs for the installation of an elevator based on the different distribution rules. Package: r-cran-cusumcharter Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-cusumcharter_0.1.0-1.ca2604.1_all.deb Size: 230150 MD5sum: 91024a0c2ec278b041b1d78d51793282 SHA1: 003e44a606845c754bf7d28f53bc193675cd26ba SHA256: 887bd85947ee10d5a080c6b15510520ecde00d3054b8e526f3bb80251a5753f6 SHA512: 60aa6e311f17dede5c3cd8f470e2dca3f34c10e04f84e468be95d067a1fc5f038597d3ab1c02fdd68121d1d81b68cbd1bf81527f6e428fe4b4bdc6e25aab27d8 Homepage: https://cran.r-project.org/package=cusumcharter Description: CRAN Package 'cusumcharter' (Easier CUSUM Control Charts) Create CUSUM (cumulative sum) statistics from a vector or dataframe. Also create single or faceted CUSUM control charts, with or without control limits. Accepts vector, dataframe, tibble or data.table inputs. Package: r-cran-cutoff Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-set, r-cran-do, r-cran-rocit Filename: pool/dists/resolute/main/r-cran-cutoff_1.3-1.ca2604.1_all.deb Size: 55304 MD5sum: 27da72ce662e9758b253141b75c95f59 SHA1: bf8fc21c877dc5df547b6c8228ca840ce6c7627c SHA256: cda9ddc2210c1708ce77ab45ca5eccce07097ff9c9242cf798a1940ff7633a2f SHA512: 71beb3464e3a54d3557920e1404323910d576d09fd01c347b19d226af70933a4fe35b193129b30387d391682e1cb4af27e2ed608c2074905bff11027d6e02fd1 Homepage: https://cran.r-project.org/package=cutoff Description: CRAN Package 'cutoff' (Seek the Significant Cutoff Value) Seek the significant cutoff value for a continuous variable, which will be transformed into a classification, for linear regression, logistic regression, logrank analysis and cox regression. First of all, all combinations will be gotten by combn() function. Then n.per argument, abbreviated of total number percentage, will be used to remove the combination of smaller data group. In logistic, Cox regression and logrank analysis, we will also use p.per argument, patient percentage, to filter the lower proportion of patients in each group. Finally, p value in regression results will be used to get the significant combinations and output relevant parameters. In this package, there is no limit to the number of cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods, typical Bonferroni and Duglas G (1994) , to adjust the p value, Missing values will be deleted by na.omit() function before analysis. Package: r-cran-cutools Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cutools_0.1.0-1.ca2604.1_all.deb Size: 28384 MD5sum: 8685c49f758b2f6027a93fe58aca5e00 SHA1: 48f2bbedc3d7470091e23c16dbcb96ae9d9644b0 SHA256: 3ba8a91eb535e086162e5b1330a7ac49ddabf28280f277921350b9c7562a70a6 SHA512: 22a30558b3d683de08e064b9573c2587ffb01557f33aaa607520102956a5a797bb15bc8be623764920274878da018f161503a942f097f252d9ea598fcd35ce46 Homepage: https://cran.r-project.org/package=CUtools Description: CRAN Package 'CUtools' (Clinical Utility Tools to Analyze a Predictive Model) Package to analyze the clinical utility of a biomarker. It provides the clinical utility curve, clinical utility table, efficacy of a biomarker, clinical efficacy curve and tests to compare efficacy between markers. Package: r-cran-cutpoint Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-plotly, r-cran-rcppalgos, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cutpoint_1.0.0-1.ca2604.1_all.deb Size: 1103058 MD5sum: c9cb82381f00926589b1e04a5d123290 SHA1: ff94b9cba64d13cff3e0ff55fb7917a82ea82e0a SHA256: d469cc6b292baaa0c4c232ff18b94ce93de1b8ab9ff1c350ed7a54204ab51a0c SHA512: a3155ce13408e6d7ef7d177657fbf9f7e91556bc8af1310de1b5619108809aa927e360c415fe3c8d6ad94edeb17f5c803deb90383c1dceb28bdfc2810ee51546 Homepage: https://cran.r-project.org/package=cutpoint Description: CRAN Package 'cutpoint' (Estimate Cutpoints of Metric Variables in the Context of CoxRegression) Estimate one or two cutpoints of a metric or ordinal-scaled variable in the multivariable context of survival data or time-to-event data. Visualise the cutpoint estimation process using contour plots, index plots, and spline plots. It is also possible to estimate cutpoints based on the assumption of a U-shaped or inverted U-shaped relationship between the predictor and the hazard ratio. Govindarajulu, U., and Tarpey, T. (2022) . Package: r-cran-cutpointsoehr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-cutpointsoehr_0.1.2-1.ca2604.1_all.deb Size: 26016 MD5sum: c82cfb82bb154cc4465d1bdc29b6e4ee SHA1: 6b1708fdb74ce19b166ec6d3330e6334da35018a SHA256: 64034a66a073e86f24a6151f4af7d04049b8d53a0c2739f09e08f0a31bd88d82 SHA512: 66aa28c219e373dd3a587d97ddc6a86bb05b86122144467818ed4a742f0eaa6acaad73153faf584dfbf6eff1ef2c96cb5b1f1e5ab71a0b8ff6e5bb5342bf86d7 Homepage: https://cran.r-project.org/package=CutpointsOEHR Description: CRAN Package 'CutpointsOEHR' (Optimal Equal-HR Method to Find Two Cutpoints for U-ShapedRelationships in Cox Model) Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values. 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Some of the methods implemented in the package are novel, as described in the package vignettes; for general introductions to cross-validation, see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical Learning, Second Edition". Package: r-cran-cvap Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-censable, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cvap_0.1.6-1.ca2604.1_all.deb Size: 334884 MD5sum: 50a0540c258b4c657b7f55b676f2900f SHA1: c8929b7eac822b959c27dea78b9dffc67ba6c8c4 SHA256: c257afcafd46ab18ea97345a1e3780eaaf1c7678197329d248b7b44cc096fecf SHA512: a815b67d962ba075a2c5fc253c9941c1afd0a10408f57718b36acf6e08260a506097bdeda488db3b137926000f4267d931cb0668368825632aa4ba30affc1fa4 Homepage: https://cran.r-project.org/package=cvap Description: CRAN Package 'cvap' (Citizen Voting Age Population) Works with the Citizen Voting Age Population special tabulation from the US Census Bureau . Provides tools to download and process raw data. Also provides a downloading interface to processed data. Implements a very basic approach to estimate block level citizen voting age population from block group data. Package: r-cran-cvar Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbutils, r-cran-rdpack Suggests: r-cran-testthat, r-cran-fgarch, r-cran-performanceanalytics Filename: pool/dists/resolute/main/r-cran-cvar_0.6-1.ca2604.1_all.deb Size: 137900 MD5sum: 94f076da4a4d5205ec4da4efbdf55f99 SHA1: 58704f7bc37220d7e4bd18601f22504bec3837f8 SHA256: 5187827ca11750c2a1e7ea45761305b56bbd117f889a569af43e236db1ccf952 SHA512: c36196c14a26216420fb2f8ee48aee2c70fcb11b6232f236be9fe1f9cba38850811ac251dbc747afd72b40e2575e78dcd965a1d3a3bf21b914d1536617615228 Homepage: https://cran.r-project.org/package=cvar Description: CRAN Package 'cvar' (Compute Expected Shortfall and Value at Risk for ContinuousDistributions) Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator, probability density function, or data. ES is also known as Conditional Value at Risk (CVaR). Virtually any continuous distribution can be specified. The functions are vectorized over the arguments. The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002) . Some support for GARCH models is provided, as well. 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The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively. One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods. The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package. Package: r-cran-cvcovest Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-matrix, r-cran-origami, r-cran-coop, r-cran-rdpack, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-assertthat, r-cran-rspectra, r-cran-ggplot2, r-cran-ggpubr, r-cran-rcolorbrewer, r-cran-rmtstat Suggests: r-cran-future, r-cran-future.apply, r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-cvcovest_1.2.2-1.ca2604.1_all.deb Size: 621930 MD5sum: 82d447a8ade6e7492d936fb684eb8117 SHA1: 7f29628fe131fceca1ff2c4f873b7e88ddb01026 SHA256: 42539fac32867e54891555c7a498d93ff1280783ca4e134601a3b4a02d3b8107 SHA512: 9fc582f1bc26cffb3a97835aacee9783ccccbf6be7b72bbddd4f77486499f09226339e9b8a643c6ccc907979d357398d3c72b5cb5f7cde1c990f57f72ad71811 Homepage: https://cran.r-project.org/package=cvCovEst Description: CRAN Package 'cvCovEst' (Cross-Validated Covariance Matrix Estimation) An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) to identify the optimal estimator from among a prespecified set of candidates. 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References are 'Panichkitkosolkul' (2013) , 'Altunkaynak' & 'Gamgam' (2018) , 'Albatineh', 'Kibria', Wilcox & 'Zogheib' (2014) . Package: r-cran-cvcrand Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tableone Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cvcrand_0.1.1-1.ca2604.1_all.deb Size: 166796 MD5sum: 772f35bada318a65afb4044da0e01eeb SHA1: ce9956d95db6e47c2de20940c895bd42a0df2453 SHA256: d15b288f35367b635b6a846dda950beeb9c66abd3dbb3148536f78444295d3e9 SHA512: 0c6ec4d8fb275847e4ddcd7db6fdb53289710ae96ff879fc65c9c26c1e101035f928d07b31af6b96322e7338a5762ac2914e919fd8a2a99b81427b14639c31ea Homepage: https://cran.r-project.org/package=cvcrand Description: CRAN Package 'cvcrand' (Efficient Design and Analysis of Cluster Randomized Trials) Constrained randomization by Raab and Butcher (2001) is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) . Motivated from Li, et al. (2016) , the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials. Package: r-cran-cvd Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1770 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cvd_1.0.2-1.ca2604.1_all.deb Size: 1594636 MD5sum: b790ab534c0ef6f735171c9cd64e5b9a SHA1: f967e0c6b7528f628ec71d461770ea5e5d649a42 SHA256: caaee5d8a913e785afa088516ff5fd0ae99b19e448a28fc5f69ec886f3e4b36f SHA512: fe26afbe3335e1d3c730ded4f664b55b64751a0145a4c78d8ef6bc7ad071ce655c905a08761aac95fc8e1cbd1afa7079ee1e31db213d8af5a26888329d98446a Homepage: https://cran.r-project.org/package=CVD Description: CRAN Package 'CVD' (Color Vision Deficiencies) Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation. Package: r-cran-cvdprevent Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cachem, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-memoise, r-cran-purrr, r-cran-rappdirs, r-cran-tibble, r-cran-tidyr Suggests: r-cran-forcats, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-mockery, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-cvdprevent_0.2.5-1.ca2604.1_all.deb Size: 456492 MD5sum: 9c0d6efe8974e954331dc7a323445f74 SHA1: fd32edbf84b7eb39c8d67c028f86a4424eea6613 SHA256: d148d557a3f94bddee00b7dc87119563dba990db7b2bc996fcf296e6f5871098 SHA512: 24b7563f93c54f4094cd3e89ad0824b38c7db73de69945bb6ab6e173cc52ffcef5cc4f6323b421cf587e31508919839b966f7c624acc896a0804673fd4fc9b11 Homepage: https://cran.r-project.org/package=cvdprevent Description: CRAN Package 'cvdprevent' (Access and Analyse Data from the 'CVD Prevent' API) Provides an R interface to the 'CVD Prevent' application programming interface (API), allowing users to retrieve and analyse cardiovascular disease prevention data from primary care records across England. The Cardiovascular Disease Prevention Audit (CVDPREVENT) automatically extracts routinely held GP health data to support national reporting and improvement initiatives. See the API documentation for details: . Package: r-cran-cvequality Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-ggbeeswarm, r-cran-covr Filename: pool/dists/resolute/main/r-cran-cvequality_0.2.0-1.ca2604.1_all.deb Size: 71182 MD5sum: 42fe5ca2a6a3bdcca6dc9f7bb1da7311 SHA1: e530eb989b3f97ace59d4a44d672e57628a91893 SHA256: 7896da38d3ea64a97fee72e25e128ca9e1541db4809d0805d44c526a0891a838 SHA512: c6a6a858cb78b98ab58f42759e07d2dc350d95de3fdcf7630c1c94117e64b54d91f8b91d6d8dc0f3ed76c84241f790fdd9eb0dd4b29f4320b3872fd1f5adad31 Homepage: https://cran.r-project.org/package=cvequality Description: CRAN Package 'cvequality' (Tests for the Equality of Coefficients of Variation fromMultiple Groups) Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) asymptotic test and Krishnamoorthy and Lee's (2014) modified signed-likelihood ratio test. See the vignette for more, including full details of citations. Package: r-cran-cvgee Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-geepack, r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-cvgee_0.3-0-1.ca2604.1_all.deb Size: 199730 MD5sum: cf067dcb8b83569ce8f0316645eecbb8 SHA1: 912dfa188cafeb37062d0cc610bf125e3eb837d4 SHA256: 2ae9ffc9781c2832154bbbb822e16e3fa4e7847297d54f87b85f7cad9fa94d9c SHA512: daf1a0957d0a971a95854b44b4ed40b3866f22a16650267079525bd6b2b1484373d1ff27da2fdd6a5ca0072121fe228d28db1f0859fba1ab7ed68ee3dad5c203 Homepage: https://cran.r-project.org/package=cvGEE Description: CRAN Package 'cvGEE' (Cross-Validated Predictions from GEE) Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) . Package: r-cran-cvglasso Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-glasso Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-pkgdown, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-cvglasso_1.0.1-1.ca2604.1_all.deb Size: 155972 MD5sum: a54720eaa72d654b602c1d3e169fbf33 SHA1: 1cefceca08fdfa49687dfdb2880c4dfb2696136b SHA256: 3a2e60ca459710d09e1fa9dd4cc174182c9735f6401d80833ef17aa6720b4cdd SHA512: b18bed6fb8b70d3d73ccfb66a910efd0ac56aabaee278cd1b718803bddd7d4c6c62a70656f867754605bd45ffe31279100be3b24796e8f1a29f30ba24f8e129e Homepage: https://cran.r-project.org/package=CVglasso Description: CRAN Package 'CVglasso' (Lasso Penalized Precision Matrix Estimation) Estimates a lasso penalized precision matrix via blockwise coordinate descent (BCD). This package is a simple wrapper around the popular 'glasso' package and extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations. See Friedman et al. (2008) for details regarding the estimation method. Package: r-cran-cvmaplfam Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-quadprog, r-cran-mgcv, r-cran-mass Filename: pool/dists/resolute/main/r-cran-cvmaplfam_0.1.1-1.ca2604.1_all.deb Size: 58278 MD5sum: 2124c855833fbea5bb7cba0aa8634a0e SHA1: 175fcf2c6a2402fc3b8ddf61a72a1d313460b732 SHA256: 2f462a96978db11cabc1eac85d8969bdf06294925f47f12e98de1f16239e0d7c SHA512: b178b96072a6f063858806aff31fbf246aeb5117e0e9dc5d969c8cd15aeabb710e89bcdb69a5a8ea7479707391480c597b76d69e718ed10d689e847542772e0e Homepage: https://cran.r-project.org/package=cvmaPLFAM Description: CRAN Package 'cvmaPLFAM' (Cross-Validation Model Averaging for Partial Linear FunctionalAdditive Models) Produce an averaging estimate/prediction by combining all candidate models for partial linear functional additive models, using multi-fold cross-validation criterion. More details can be referred to arXiv e-Prints via . Package: r-cran-cvmdisc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cvmdisc_0.1.0-1.ca2604.1_all.deb Size: 51302 MD5sum: 866ad677a7190fddeb864aa8aa997cc8 SHA1: 43d49a07508df691b9242ade9b08ccd47dd9f4ea SHA256: d4a98253f330bbeb452b90bf54bc7964d0a94fdade388acda2c25dd840a48088 SHA512: 6e7e3b61cf1f640f3ed501238337fbe208a7599d197f4776f21e04052286d5b8d45b5cf607c93bc963af2124996781fc12bd25e6f0f2677016a16d7e7673f72d Homepage: https://cran.r-project.org/package=cvmdisc Description: CRAN Package 'cvmdisc' (Cramer von Mises Tests for Discrete or Grouped Distributions) Implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) and Lockhart, Spinelli and Stephens (2007) (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) . Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values. Package: r-cran-cvmgof Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-cvmgof_1.0.3-1.ca2604.1_all.deb Size: 111774 MD5sum: ef5fae74eea5056012c6e27496ad81be SHA1: bab2d49f81cbadf33f5552873d4a81899dd50300 SHA256: 579a383667558078867d1345aa5f83aad6c196bd823346ace1224e694ee69de3 SHA512: 43e4bc4837d3fdeab0f0b0ae50856d3cc8b47c693c163f0c2753ce56754654fb7061e1e851b72b91ab5935b6928cde28b2d179c1a9e0cc7dc920230e1bd35524 Homepage: https://cran.r-project.org/package=cvmgof Description: CRAN Package 'cvmgof' (Cramer-von Mises Goodness-of-Fit Tests) It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model. Package: r-cran-cvmortalitymult Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stmomo, r-cran-forecast, r-cran-gnm, r-cran-tmap, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cvmortalitymult_1.1.1-1.ca2604.1_all.deb Size: 1982472 MD5sum: e971bfebeedc26f9ceba7b146fe16b2d SHA1: a9183a25b72d85b22a3832ca330c6df318b11cae SHA256: 0141eebd7e2114f8a2dcc575216124d7291b86d2fbb3681d1ebef424dbdd837d SHA512: d6b06b7ccb992533db9590683c245763e5d70c3e6c94f65a5ec80ebc2cacd878d9ae2104b9baf8e3ac77749aebdee2e7fa1e0a6758f1c90cf44bbb7c3480969e Homepage: https://cran.r-project.org/package=CvmortalityMult Description: CRAN Package 'CvmortalityMult' (Cross-Validation for Multi-Population Mortality Models) Implementation of cross-validation method for testing the forecasting accuracy of several multi-population mortality models. The family of multi-population includes several multi-population mortality models proposed through the actuarial and demography literature. The package includes functions for fitting and forecast the mortality rates of several populations. Additionally, we include functions for testing the forecasting accuracy of different multi-population models. References, . Atance, D., Debon, A., and Navarro, E. (2020) . Bergmeir, C. & Benitez, J.M. (2012) . Debon, A., Montes, F., & Martinez-Ruiz, F. (2011) . Lee, R.D. & Carter, L.R. (1992) . Russolillo, M., Giordano, G., & Haberman, S. (2011) . Santolino, M. (2023) . Package: r-cran-cvms Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4798 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-groupdata2, r-cran-lifecycle, r-cran-lme4, r-cran-mumin, r-cran-parameters, r-cran-plyr, r-cran-proc, r-cran-purrr, r-cran-rearrr, r-cran-recipes, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-auc, r-cran-covr, r-cran-e1071, r-cran-furrr, r-cran-ggimage, r-cran-ggnewscale, r-cran-knitr, r-cran-merderiv, r-cran-nnet, r-cran-randomforest, r-cran-rmarkdown, r-cran-rsvg, r-cran-testthat, r-cran-xpectr Filename: pool/dists/resolute/main/r-cran-cvms_2.0.0-1.ca2604.1_all.deb Size: 3531334 MD5sum: 44d1d83b43991fa9031a8aaf56310d05 SHA1: d7e560535cbb46a7a2ea8be4a1ea07a1a97a6805 SHA256: 2f865ec6df9e139297d80ef4844dd47f319472fd4b54edc31cf2eea77b0bec5d SHA512: 4e528aa07bb25ee530d1b53b35b5b9427d2035c92be865ca4fb19afb39531720c9221109ab23a10a6bbeef07229c15c0d963f7607a1456a798258628eaa5e0c4 Homepage: https://cran.r-project.org/package=cvms Description: CRAN Package 'cvms' (Cross-Validation for Model Selection) Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134). Package: r-cran-cvrisk Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-preventr Suggests: r-cran-testthat, r-cran-covr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cvrisk_1.2.1-1.ca2604.1_all.deb Size: 475784 MD5sum: 8e04182cb7065da919eebea656a79d2a SHA1: c7bd5a1941925c804be064b7628c6afe2aa8b6bc SHA256: b997522f056b64353eb279e44764341ebb9d742ad90f2d07009b59f14779d5bc SHA512: bb86ae2006e14838c455da6a993f0e0a79c7d9c77ca4b100ba9803760e162b268e4584460e3c53e465d4494a96f4d489e6a3c36b5fc111ac87137d514b8df156 Homepage: https://cran.r-project.org/package=CVrisk Description: CRAN Package 'CVrisk' (Compute Risk Scores for Cardiovascular Diseases) Calculate various cardiovascular disease risk scores from the Framingham Heart Study (FHS), the American College of Cardiology (ACC), and the American Heart Association (AHA) as described in D’agostino, et al (2008) , Goff, et al (2013) , and Mclelland, et al (2015) , and Khan, et al (2024) . Package: r-cran-cvsem Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cvsem_1.0.0-1.ca2604.1_all.deb Size: 39666 MD5sum: 4961f95f9d82db929e0f5e75f83f2bfe SHA1: 9c893c09f84e5f2d413e75db16ffc2ad35776e3e SHA256: a0d027b15f14355a14731a42c9c2576be555bb5d9787e2693cb5153a99ce10a0 SHA512: 7c036ebc68fe9fe304ca85169582c630d0351d0dcaec72e051b8c0cc963519507ec5dc0d02de205df629e855e004addee86360bc6834aaeed1888ff02e30a3bc Homepage: https://cran.r-project.org/package=cvsem Description: CRAN Package 'cvsem' (SEM Model Comparison with K-Fold Cross-Validation) The goal of 'cvsem' is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using 'lavaan' syntax. 'cvsem' computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) . The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors. Package: r-cran-cvst Architecture: all Version: 0.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernlab, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-cvst_0.2-3-1.ca2604.1_all.deb Size: 86640 MD5sum: aa4bb0e8871b793f9dfdfa1af71f2ca3 SHA1: d3288ab7783efd4222b5db9a1340d8c8a585ba6a SHA256: cf3eaabbc666accc36768b0f9e853ac5adc1a5844520d0be012e271cb0873e83 SHA512: b130e2c8e087d7b9ea7c43262ea15f1eda64a4de9de4e5e34b4246f68acdd035e677f99e92cafa347c883210688f0c24bf13b38ee6dc84e846e5167671780e8a Homepage: https://cran.r-project.org/package=CVST Description: CRAN Package 'CVST' (Fast Cross-Validation via Sequential Testing) The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts. Package: r-cran-cvthresh Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh, r-cran-ebayesthresh Filename: pool/dists/resolute/main/r-cran-cvthresh_1.1.2-1.ca2604.1_all.deb Size: 79850 MD5sum: 6a477395d6758ebf59808c0bd3930438 SHA1: 4e61e4bd10f3be55099c1eff99a42d5f16c48965 SHA256: 36e5f8fe86efed0414016165f2d1555246de0ac9bb9183af7d4657e44e949e75 SHA512: 33a10675967d4333881743197216511bdb7ce764ec46abe10276199f590e96a729e2cabe8c8dbdba91079ed90c6a78ced3a1432a939e5b45bd79ca82c515152a Homepage: https://cran.r-project.org/package=CVThresh Description: CRAN Package 'CVThresh' (Level-Dependent Cross-Validation Thresholding) The level-dependent cross-validation method is implemented for the selection of thresholding value in wavelet shrinkage. This procedure is implemented by coupling a conventional cross validation with an imputation method due to a limitation of data length, a power of 2. It can be easily applied to classical leave-one-out and k-fold cross validation. Since the procedure is computationally fast, a level-dependent cross validation can be performed for wavelet shrinkage of various data such as a data with correlated errors. Package: r-cran-cvtools Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-cvtools_0.3.3-1.ca2604.1_all.deb Size: 199102 MD5sum: 6ff1b10be9bd8b721cd468f6b462c2b3 SHA1: 7d12be69cae6e56b2254d49559e5765a446e54ee SHA256: 6de8b3a8195681269d0da80ac32b80dc6713560eb96794a730be4933b9f300c2 SHA512: 728dd3e1581b9d7367b792c414e704d31f264695947d94859266235465e3222bd32e4055ee86d44b644ec188a56890bb45e8e36c51bd08d4f7cbba48375947bf Homepage: https://cran.r-project.org/package=cvTools Description: CRAN Package 'cvTools' (Cross-Validation Tools for Regression Models) Tools that allow developers to write functions for cross-validation with minimal programming effort and assist users with model selection. Package: r-cran-cwot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatest, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-cwot_0.1.0-1.ca2604.1_all.deb Size: 26542 MD5sum: f8f814429aec460d090c8244f0e1c198 SHA1: 3258ad15bb836ea633baabafd7d00b55866e9fc6 SHA256: 3631387bde76b8e8ad5b1f2494ff84980dfe13bd87ef72f64d7a13bdc85fea6f SHA512: 7270bb8a409d0227cb7eaeecde9a6985515e81463c1d2336376d80ef62737aa20713bc16dc4ab4cc81b87438704e1a4e9d006675ce0a92e95b107192ca65df8d Homepage: https://cran.r-project.org/package=cwot Description: CRAN Package 'cwot' (Cauchy Weighted Joint Test for Pharmacogenetics Analysis) A flexible and robust joint test of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures, Cauchy weighted joint test (CWOT) and adaptively weighted joint test (AWOT), are proposed to accurately calculate the joint test p-value. The proposed methods are evaluated through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in pharmacogenetics (PGx) association studies. For reference, see Hong Zhang, Devan Mehrotra and Judong Shen (2022) . Package: r-cran-cxr Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2059 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-optimx Suggests: r-cran-bb, r-cran-deoptimr, r-cran-dfoptim, r-cran-dplyr, r-cran-gensa, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-minqa, r-cran-nloptr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-ucminf Filename: pool/dists/resolute/main/r-cran-cxr_1.1.1-1.ca2604.1_all.deb Size: 1317982 MD5sum: 61780425d7e0a8556b3789fb865f9d38 SHA1: acdc9cbf5070b83a55378025fd8b979b8768cda7 SHA256: 619e9bb6613876a50eb8a1c3a50063403af4d402209393ff5bf2842036744568 SHA512: 078389297dae350af5d6b38de9aa64734f51844bf6830a0689e28006ead99f558dfeb887def2c3967a813fa953640bf1903600ba300bc67cf3567311dd68d882 Homepage: https://cran.r-project.org/package=cxr Description: CRAN Package 'cxr' (A Toolbox for Modelling Species Coexistence in R) Recent developments in modern coexistence theory have advanced our understanding on how species are able to persist and co-occur with other species at varying abundances. However, applying this mathematical framework to empirical data is still challenging, precluding a larger adoption of the theoretical tools developed by empiricists. This package provides a complete toolbox for modelling interaction effects between species, and calculate fitness and niche differences. The functions are flexible, may accept covariates, and different fitting algorithms can be used. A full description of the underlying methods is available in García-Callejas, D., Godoy, O., and Bartomeus, I. (2020) . Furthermore, the package provides a series of functions to calculate dynamics for stage-structured populations across sites. 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Package: r-cran-cyclestreets Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1547 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-curl, r-cran-dplyr, r-cran-data.table, r-cran-geojsonsf, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-progressr, r-cran-rcppsimdjson, r-cran-readr, r-cran-sf, r-cran-stringr, r-cran-stringi Suggests: r-cran-covr, r-cran-od, r-cran-stplanr Filename: pool/dists/resolute/main/r-cran-cyclestreets_1.0.3-1.ca2604.1_all.deb Size: 1481834 MD5sum: 337233b9f6939e41ac47df0918fa966f SHA1: 00594bca9a6612ee69cade05c4870a959dd21b60 SHA256: 0601f92aa5e85d198827e6d88312e12748831b1107bad2119fb35395787ab161 SHA512: 1dbbebe1db4a8ac4257100ca26d1641c1b560d2104dac220be496631923444ffb7eb28655024c42f516e9055d585822c8c25ea66fa594719fab3776314c32333 Homepage: https://cran.r-project.org/package=cyclestreets Description: CRAN Package 'cyclestreets' (Cycle Routing and Data for Cycling Advocacy) An interface to the cycle routing/data services provided by 'CycleStreets', a not-for-profit social enterprise and advocacy organisation. The application programming interfaces (APIs) provided by 'CycleStreets' are documented at (). The focus of this package is the journey planning API, which aims to emulate the routes taken by a knowledgeable cyclist. An innovative feature of the routing service of its provision of fastest, quietest and balanced profiles. These represent routes taken to minimise time, avoid traffic and compromise between the two, respectively. Package: r-cran-cyclocomp Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-crayon, r-cran-desc, r-cran-remotes, r-cran-withr Suggests: r-cran-processx, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cyclocomp_1.1.2-1.ca2604.1_all.deb Size: 33108 MD5sum: f4da81b9e2099087f7476bf5b88d94c1 SHA1: 49b97f881583a03ff2973f6b357cdc7a76485675 SHA256: 0232af26135394eacbde13705d8ea285ee9e6115411e1d34d89075b2e88c00d7 SHA512: 12ffccd7245cdebbbf8266171db298e4f211e2aee4917efe357026f6be2c378161b5dfdbd0c43fad9fa7baf1d6d2e019bf1e9393b77ea87a009cf7f891f82828 Homepage: https://cran.r-project.org/package=cyclocomp Description: CRAN Package 'cyclocomp' (Cyclomatic Complexity of R Code) Cyclomatic complexity is a software metric (measurement), used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program's source code. It was developed by Thomas J. McCabe, Sr. in 1976. Package: r-cran-cycloids Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cycloids_1.0.2-1.ca2604.1_all.deb Size: 402644 MD5sum: cc1645aa9dffe691a5bc8d79291f1384 SHA1: 68ef0a598c6f8e75868158333eeee34a702bba11 SHA256: 82099326525ee691b6ad0b65adb787851c9266e396a4a64a197a311ba67a8256 SHA512: c9b384585b49ddee039bb3f5abe7847f97d000ead552e9f6f311cd8174ffc219c7162fb7142940582cb08df531bc36ec4534d1343e1b886a3ee4861c3ae922e2 Homepage: https://cran.r-project.org/package=cycloids Description: CRAN Package 'cycloids' (Tools for Calculating Hypocycloids, Epicycloids, Hypotrochoids,and Epitrochoids) Tools for calculating coordinate representations of hypocycloids, epicyloids, hypotrochoids, and epitrochoids (altogether called 'cycloids' here) with different scaling and positioning options. The cycloids can be visualised with any appropriate graphics function in R. Package: r-cran-cyclomort Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-lubridate, r-cran-magrittr, r-cran-mvtnorm, r-cran-plyr, r-cran-scales, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-cyclomort_1.0.3-1.ca2604.1_all.deb Size: 246678 MD5sum: d213a8561cb4c243ec26301b6b7cc872 SHA1: 7690a15c37a2ae7b6987a8e72b18aef91f309d86 SHA256: ce5d50d21ff4d15a2b750b45888313d8e47e8006427aa05bfda01d3fb294de74 SHA512: e0c5214922d383be57ac5fddcfb829d563bd4f238d0df30824a4475d4f212662f810247f288879175746e00a8f6e932ccb44d30afc7fffa5f9b407e2748b7669 Homepage: https://cran.r-project.org/package=cyclomort Description: CRAN Package 'cyclomort' (Survival Modeling with a Periodic Hazard Function) Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period. The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) ). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations. Thus, the respective periods of higher mortality can be identified as "mortality seasons". Package: r-cran-cyclotomic Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-intmap, r-cran-gmp, r-cran-maybe, r-cran-memoise, r-cran-numbers, r-cran-verylargeintegers Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cyclotomic_1.3.0-1.ca2604.1_all.deb Size: 214484 MD5sum: e5e58a41b5906450b503c0ada51a02b3 SHA1: e94f2b60b9ab4c49a1f7dfb0b9657e2fa4a7f9cd SHA256: f4be9fed11dec3914500557bdaaa036209e305e3477f2dd8651e2f89927c4d0c SHA512: 06c848058fa7c209d794b8f66d81f3c50078a7a6d2f682f8f7029cd6daee835f3a5c6f006915b2d6374a7248284cb813b783a6fba2389af883dd3013a05a00b7 Homepage: https://cran.r-project.org/package=cyclotomic Description: CRAN Package 'cyclotomic' (The Field of Cyclotomic Numbers) The cyclotomic numbers are complex numbers that can be thought of as the rational numbers extended with the roots of unity. They are represented exactly, enabling exact computations. They contain the Gaussian rationals (complex numbers with rational real and imaginary parts) as well as the square roots of all rational numbers. They also contain the sine and cosine of all rational multiples of pi. The algorithms implemented in this package are taken from the 'Haskell' package 'cyclotomic', whose algorithms are adapted from code by Martin Schoenert and Thomas Breuer in the 'GAP' project (). Cyclotomic numbers have applications in number theory, algebraic geometry, algebraic number theory, coding theory, and in the theory of graphs and combinatorics. They have connections to the theory of modular functions and modular curves. 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These copulas are especially useful for modeling correlation in discrete-time movement data. Methods for density, (conditional) distribution, random number generation, bivariate dependence measures and fitting parameters using maximum likelihood and other approaches. The package also contains methods for visualizing movement data and copulas. 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English: It provides a Portuguese translated version of the datasets listed above. 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Package: r-cran-dagassist Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-crayon, r-cran-dagitty, r-cran-magrittr, r-cran-writexl, r-cran-dplyr, r-cran-ggplot2, r-cran-dotwhisker Suggests: r-cran-devtools, r-cran-fixest, r-cran-ggdag, r-cran-knitr, r-cran-modelsummary, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-diagrammer, r-cran-marginaleffects, r-cran-weightit, r-cran-directeffects, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-dagassist_0.2.8-1.ca2604.1_all.deb Size: 845232 MD5sum: d0bf0c6f703fad30ca5f8d96a30e29a8 SHA1: 9095c24c9269707c2ec2a1d9941edb5090ef5715 SHA256: 6f0bd85844c6b4e116ee0c2dcfe5ad0b338f9213318a5ea4523a5077b27821b2 SHA512: 3aec99ee0f10ac8fcf572f86368a4d0c607a6f04299cc4c78e9b764e4b26e21d997521591aad99443bde8bb938bd274dd71da13b2e0ed39abbc46417e8fe9fc4 Homepage: https://cran.r-project.org/package=DAGassist Description: CRAN Package 'DAGassist' (Test Robustness with Directed Acyclic Graphs) Provides robustness checks to align estimands with the identification that they require. Given a 'dagitty' object and a model specification, 'DAGassist' classifies variables by causal roles, recovers a target estimand, and generates a report comparing the original model with DAG-derived adjustment sets. Exports publication-grade reports in 'LaTeX', 'Word', 'Excel', 'dotwhisker', or plain text/'markdown'. 'DAGassist' is built on 'dagitty', an 'R' package that uses the 'DAGitty' web tool () for creating and analyzing DAGs. Methods draw on Pearl (2009) and Textor et al. (2016) . Package: r-cran-daghmm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-future, r-cran-matrixstats, r-cran-prroc, r-cran-bnlearn, r-cran-bnclassify Filename: pool/dists/resolute/main/r-cran-daghmm_0.1.1-1.ca2604.1_all.deb Size: 76182 MD5sum: ff9b697b67d3d363daaef78823e41b51 SHA1: 6d7838e094f23bf630fa3f0f8b6124f12e5e0e10 SHA256: 8dcc1839ed4f0cf7b7d2c76d03e65dd19eb80bb3710ff0974ee27a4a612a609e SHA512: 4e6ff734324016f13fa83f6773e46f5965869b478e10b9de3437e26af7ff390d0999db913296c1a3f55bdeae7366bcc95285cc27a648f747b23ab91c1d57a0e9 Homepage: https://cran.r-project.org/package=dagHMM Description: CRAN Package 'dagHMM' (Directed Acyclic Graph HMM with TAN Structured Emissions) Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path. Package: r-cran-dagirlite Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4378 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-knitr Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dagirlite_0.1.0-1.ca2604.1_all.deb Size: 4336542 MD5sum: ef35c5d82f71b1bc1eb36653591209d5 SHA1: b89eee7e6dc9a89c77f6d908602fc3acc3fc104a SHA256: 311d330b1f73a7dd4206952ddc7c0f78761ebf17dbb3cac38ae5ab25846f4269 SHA512: 4716912650efb10cbe8ee0b437a01da7a7db928065a27f91625fbe3839c270e352bde764f1ed5e0bc37a17aa2bd65fb0cb3f65a4dacfbf8b2e9743cb25d07567 Homepage: https://cran.r-project.org/package=dagirlite Description: CRAN Package 'dagirlite' (Spatial Vector Data for Danmarks Administrative GeografiskeInddeling DAGI) Compressed spatial vector data originally from saved as Simple Features, SF, objects with data on population, age and gender from Statistics Denmark . Package: r-cran-dagitty Architecture: all Version: 0.3-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-v8, r-cran-jsonlite, r-cran-boot, r-cran-mass Suggests: r-cran-igraph, r-cran-knitr, r-cran-base64enc, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown, r-cran-lavaan, r-cran-ccp, r-cran-fastdummies Filename: pool/dists/resolute/main/r-cran-dagitty_0.3-4-1.ca2604.1_all.deb Size: 359372 MD5sum: 5384e00a40ae646a9b614530e6bef143 SHA1: f883ae45b8d8d940a744a3183a9747d59dd6888e SHA256: ad880fe1fd69ef63dd9c33fa4d1a732563f41b9e92a7e5af532f8f4b7d5de23c SHA512: 41de1a25bfb6c4c59160c88e2b1f69deaa25d74f652fd5201e19838dd448c96cac4166c0bb23b7107778c7b19a53358e794d52ee09b8b125700537549032978c Homepage: https://cran.r-project.org/package=dagitty Description: CRAN Package 'dagitty' (Graphical Analysis of Structural Causal Models) A port of the web-based software 'DAGitty', available at , for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation. Package: r-cran-dagr Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dagitty Filename: pool/dists/resolute/main/r-cran-dagr_1.2.1-1.ca2604.1_all.deb Size: 190784 MD5sum: 2bd1c4f69d35ef83d080da14c17701f9 SHA1: 3a389b364313537dc444ea2d3f88e3965ae60729 SHA256: e61ecaa32fa016dff63b363847a09df5510538cc00b5dffe46f770e3d528cea5 SHA512: e88e90c6a78d76c97d89f46128ee3f374b0acd61d5ce587c464149dc0e5c24203bc148e9fd8c6cf2f86a3b3a0dc23bec52cc2922ada365d926dc4fd2366d97d3 Homepage: https://cran.r-project.org/package=dagR Description: CRAN Package 'dagR' (Directed Acyclic Graphs: Analysis and Data Simulation) Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777. Package: r-cran-dagwood Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dagitty Suggests: r-cran-ggdag Filename: pool/dists/resolute/main/r-cran-dagwood_0.1.4-1.ca2604.1_all.deb Size: 28686 MD5sum: c22f73a94411d26db062b93c7e383751 SHA1: 6994a0e3a7785bf43d4246b353bb94b305c849bf SHA256: 4e02159fa297886a95733c5d1c46a5fafb1f6d0af6618cf2c1b72235f0c008c6 SHA512: 86ba8fe467576bb9a0ec9e4dcd8d1461dfa2d90f99cc96ccd87ab136145e5c5cc311fe87f4401392fac8eb9482da0f00742a3efcd2e0b414b7b8ee2bb9afc31f Homepage: https://cran.r-project.org/package=dagwood Description: CRAN Package 'dagwood' (DAGs with Omitted Objects Displayed (DAGWOOD)) DAGs With Omitted Objects Displayed (DAGWOOD) is a framework to help reveal key hidden assumptions in a causal DAG. This package provides an implementation of the DAGWOOD algorithm. Further description can be found in Haber et al (2022) . Package: r-cran-dail Architecture: all Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deflatebr, r-cran-dplyr, r-cran-janitor, r-cran-lubridate, r-cran-magrittr, r-cran-rcurl, r-cran-readr, r-cran-stopwords, r-cran-stringr, r-cran-tidytext Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dail_1.5.2-1.ca2604.1_all.deb Size: 1376214 MD5sum: 6c1dcacf5835b75746c53b8d69e4229b SHA1: ba910c632adbb501907b776117039f1e8a030ec5 SHA256: ba451191a36cfa908c2d33b02bb8a76ff01473523f7806fa52b5a162fbf24d57 SHA512: e982248a1c77e0403a9270610d022d46f98273e3e33e5fcd46b1ef43cb6b0c4bd7717f3a2b2b79ed0e4cb9dcc448ddf0d2af708f253c8395cfd5083e03594666 Homepage: https://cran.r-project.org/package=dail Description: CRAN Package 'dail' (Data from Access to Information Law) Downloads the public data available from the Brazilian Access to Information Law and and performs a search on information requests and appeals made since 2015. 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Package: r-cran-daiquiri Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-readr, r-cran-ggplot2, r-cran-scales, r-cran-cowplot, r-cran-rmarkdown, r-cran-reactable, r-cran-xfun Suggests: r-cran-covr, r-cran-knitr, r-cran-testthat, r-cran-codemetar, r-cran-vdiffr, r-cran-shiny, r-cran-diffviewer Filename: pool/dists/resolute/main/r-cran-daiquiri_1.2.1-1.ca2604.1_all.deb Size: 947354 MD5sum: 3f022f65b8513497f76648106a12ac42 SHA1: 7761ed7a7857163f32f3f651dae0335e1199c08c SHA256: fcc24bfa76d296bfde9a7b4ec466f4b2bc02e2fc0cb3bd7b13f4268f2e3682e1 SHA512: 2e05b490a034fc20b1dbbec270b9e5d1dd8b0fd9b52a93986c8d18272b6a892d985bcaf22b7c8cfdbd767491fbd3e4b7f2c773c6e30276486d6e8afda37bc4b0 Homepage: https://cran.r-project.org/package=daiquiri Description: CRAN Package 'daiquiri' (Data Quality Reporting for Temporal Datasets) Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event). 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'Document AI' is a powerful server-based OCR service that extracts text and tables from images and PDF files with high accuracy. 'daiR' gives R users programmatic access to this service and additional tools to handle and visualize the output. See the package website for more information and examples. Package: r-cran-daisieprep Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phylobase, r-cran-ggplot2, r-cran-scales, r-bioc-ggtree, r-cran-daisie, r-cran-castor, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-diversitree, r-cran-corhmm, r-cran-tidyr, r-cran-dplyr, r-cran-ggimage Filename: pool/dists/resolute/main/r-cran-daisieprep_1.0.1-1.ca2604.1_all.deb Size: 2166638 MD5sum: 059726ff130eb1e73465b3e9874b54c9 SHA1: 0215112dd1d39826fb9e596834bf6cae6ded053f SHA256: 0984ac5646e83c952932ffe4193c01945ba29eb45e50c5a0aec2dad66167a10c SHA512: 104b05bea76581ab3e6cc18f5cefa4cc9b75a269d3e83ce368171dabb30a9e8ce6cde7109f5304f88a4ce62ea1408b3016328ce0a99a7db72b40fd6ffde14c96 Homepage: https://cran.r-project.org/package=DAISIEprep Description: CRAN Package 'DAISIEprep' (Extracts Phylogenetic Island Community Data from PhylogeneticTrees) Extracts colonisation and branching times of island species to be used for analysis in the R package 'DAISIE'. It uses phylogenetic and endemicity data to extract the separate island colonists and store them. Package: r-cran-daks Architecture: all Version: 2.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-relations, r-cran-sets Filename: pool/dists/resolute/main/r-cran-daks_2.1-3-1.ca2604.1_all.deb Size: 712072 MD5sum: 6b5299e59f92ae8ab5f7ced72b3d18d6 SHA1: 7f41738c654041bb3b466f2a0428231f68d8a529 SHA256: e76d8a1b4f0ce690d528a974d15983e7aa7c6b540586b5ecc25613b4941b3550 SHA512: c63f820fde6218b193a0749c5ad86d8d8ae8776b577cde328f61cb43a83da930df611ea890ed4baf140fbffbc5b56adfc719a0b2105bc5ea02223223317d15cb Homepage: https://cran.r-project.org/package=DAKS Description: CRAN Package 'DAKS' (Data Analysis and Knowledge Spaces) Functions and an example dataset for the psychometric theory of knowledge spaces. This package implements data analysis methods and procedures for simulating data and quasi orders and transforming different formulations in knowledge space theory. See package?DAKS for an overview. Package: r-cran-dalex Architecture: all Version: 2.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1374 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ibreakdown, r-cran-ingredients, r-cran-kernelshap Suggests: r-cran-gower, r-cran-ranger, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dalex_2.5.3-1.ca2604.1_all.deb Size: 1081582 MD5sum: 30e80dff071e58522a539698d4cf96dd SHA1: f9f870cdd8260a2d5d74e80fe0f9af5d48bf9881 SHA256: 225dd5fada47807b629ea6576ef51460d309da062736e6a21352cff58714858b SHA512: 9173069fa9d600720c426d3d82daca1a2e6d1e810aa447b3fb7b6280af7523d95527bc7948c5bbcb506dc48fcdfba851c98c271358b40e9976801ecb9f1d4d99 Homepage: https://cran.r-project.org/package=DALEX Description: CRAN Package 'DALEX' (moDel Agnostic Language for Exploration and eXplanation) Any unverified black box model is the path to failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore the model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) . Package: r-cran-dalextra Architecture: all Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-ggplot2 Suggests: r-cran-auditor, r-cran-gbm, r-cran-ggrepel, r-cran-h2o, r-cran-iml, r-cran-ingredients, r-cran-lime, r-cran-localmodel, r-cran-mlr, r-cran-mlr3, r-cran-ranger, r-cran-recipes, r-cran-reticulate, r-cran-rmarkdown, r-cran-rpart, r-cran-stacks, r-cran-xgboost, r-cran-testthat, r-cran-tidymodels Filename: pool/dists/resolute/main/r-cran-dalextra_2.3.1-1.ca2604.1_all.deb Size: 366526 MD5sum: 1fd2fda209c77f4397e0bddf896258ed SHA1: 2c73380ff88669509b8302695c3eb5de82816621 SHA256: 9e66c6f1f5c97828507b36ede7885d6b1ce38952eb116aa5b07fc31476f993f8 SHA512: 017c327fc7aa4d20976a78adb1fc5a9748b435c495446ac8bb836e8d14f88608f0c08dc68bd20344ca029739d79f0f40923e41b2c9a21e9cf7ec43b2926fae60 Homepage: https://cran.r-project.org/package=DALEXtra Description: CRAN Package 'DALEXtra' (Extension for 'DALEX' Package) Provides wrapper of various machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the interpretable machine learning, there are more and more new ideas for explaining black-box models, that are implemented in 'R'. 'DALEXtra' creates 'DALEX' Biecek (2018) explainer for many type of models including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. Important part of the package is Champion-Challenger analysis and innovative approach to model performance across subsets of test data presented in Funnel Plot. Package: r-cran-dalsm Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubicbsplines, r-cran-mass, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-dalsm_0.9.1-1.ca2604.1_all.deb Size: 368210 MD5sum: 910a847ab7c6701cedc19fe0b323eefe SHA1: d1e92e3a116de1ec179fd53cf80d6a363ef1e533 SHA256: ecf41a178c63f258fac4efd9ba4ae899e0d6dc5671218b02d285007d4246db7a SHA512: 32461b28778509dcd22c21122665ca01289de70b8ec1e95069d99219bb635a71f46695e07bc5ae05435ea0af2b612a484f1b56d8e5131a3649012f4510ee77a3 Homepage: https://cran.r-project.org/package=DALSM Description: CRAN Package 'DALSM' (Nonparametric Double Additive Location-Scale Model (DALSM)) Fit of a double additive location-scale model with a nonparametric error distribution from possibly right- or interval censored data. The additive terms in the location and dispersion submodels, as well as the unknown error distribution in the location-scale model, are estimated using Laplace P-splines. For more details, see Lambert (2021) . 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The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) . Package: r-cran-daltoolboxdp Architecture: all Version: 1.3.747-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tspredit, r-cran-daltoolbox, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-daltoolboxdp_1.3.747-1.ca2604.1_all.deb Size: 277646 MD5sum: 89fbc96ac6d61a7c9cc54c0a652d7787 SHA1: 7444f544d0365c773bda3bf6fc74c95721ffab19 SHA256: 037adaec6c413e0563f3ca5b085ecfdcca0ad57dc0ab42820f908ff239b07c06 SHA512: 21771b6893fe4bdb84f8d96feef78be3acac702de7238020b9587b855e285e0db82dd18e3edf9757b0b2b07487d2b48b8667935e073b3b344f8072058d26b380 Homepage: https://cran.r-project.org/package=daltoolboxdp Description: CRAN Package 'daltoolboxdp' (Deep Python Extensions for 'daltoolbox') Extends 'daltoolbox' with Python-backed components for deep learning, scikit-learn classification, and time-series forecasting through 'reticulate'. 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Package: r-cran-dam Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dam_0.0.1-1.ca2604.1_all.deb Size: 9804 MD5sum: 4919a84039ccee94346638fd2bab32e9 SHA1: 057506bddb67ccbe4abc6be3d9358157157e539d SHA256: 55fa55c326278c5cd9d0ea253e1ac4fe0f0081ffe61895f0559ef8c4f60a034a SHA512: 8e03b06924c389c93dbf391946fccc5dd65ddc0de80550f2f3a7956f70cd1b584a010a082a914efe811c25be2101e739c3e03cb2303315598fce1f38c538c6dd Homepage: https://cran.r-project.org/package=dam Description: CRAN Package 'dam' (Data Analysis Metabolomics) A collection of functions which aim to assist common computational workflow for analysis of matabolomic data.. 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Package: r-cran-damocles Architecture: all Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caper, r-cran-ape, r-cran-desolve, r-cran-matrixstats, r-cran-expm, r-cran-picante, r-cran-matrix, r-cran-ddd, r-cran-hmisc Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-damocles_2.3-1.ca2604.1_all.deb Size: 150104 MD5sum: 5a11c511a5b08360f3577546a7293b3f SHA1: 200498eba9e4bdce6adcc311ff0f1ceac308d8ce SHA256: ae418bc3b5abede21166ed6876d777bffc9a954fee180396da37a237ba3987f4 SHA512: 7ac4a8cb67d39b14b29e13387b5d08182169a2d49e9b82be070f9e25ecf3f0db40a8d4184369edef8e3a978ee2589f66069671cf55e4fe01cd977554066c93ab Homepage: https://cran.r-project.org/package=DAMOCLES Description: CRAN Package 'DAMOCLES' (Dynamic Assembly Model of Colonization, Local Extinction andSpeciation) Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history. 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This package was developed with funding from the National Institutes of Allergy and Infectious Diseases of the National Institutes of Health under award no. R01AI138783. The content of this package is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The theoretical underpinnings of 'dampack''s functionality are detailed in Hunink et al. (2014) . 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Package: r-cran-dandelion Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph Suggests: r-bioc-qvalue Filename: pool/dists/resolute/main/r-cran-dandelion_0.1.0-1.ca2604.1_all.deb Size: 61780 MD5sum: 4e4021e019dbaf08a99b779b286a28fc SHA1: 85dc0b0c5d097142f45994bb7e2e3d4f1554e740 SHA256: ad2027efdbf29caabd0b2e4f9ed1109879255e42df4cd8a0397344db92f88b5e SHA512: ed884e529164bfc99627d92d79098a1ed4b6dc5e06ec23d6d3c06854d11072499b1daaca660c8fe8aa3a515ad60553ce8e8b1bef0dfd8daec0709101faa394cc Homepage: https://cran.r-project.org/package=DANDELION Description: CRAN Package 'DANDELION' (Discovery of Candidate Disease Genes Using Trans-RegulatoryEffects) Implements the DANDELION method for prioritizing candidate disease-related proximal genes by integrating trans-regulatory association p-values and gene-level trait association p-values. The statistical testing framework builds on the divide-aggregate composite-null test described by Liu et al. (2022) . The biological application and SNP/gene-based prioritization workflow are motivated by Salamone et al. (under review), "Leveraging trans-gene regulation prioritizes central genes and pathways in asthma". The package provides functions for identifying distal-proximal gene pairs, organizing significant pairs into genomic loci, and visualizing resulting gene networks. Package: r-cran-dani Architecture: all Version: 0.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epi Filename: pool/dists/resolute/main/r-cran-dani_0.1-1-1.ca2604.1_all.deb Size: 38590 MD5sum: b222a9aa71be9ca9a0bd54225baad308 SHA1: 00b07d7ef2ef727f2e607d7a8be12c0a997b2a13 SHA256: f8b3db98096c747d48043469a97d0f9bba4cd093d45d86640197b125e10191f2 SHA512: bfbda64444d71d101e9d59d91ca8305c09aa82ecd12609e8ac6ced38485abb672da221243ef5c73ee81e01774620b0e83a91b744979ce715ede68cee3beeae60 Homepage: https://cran.r-project.org/package=dani Description: CRAN Package 'dani' (Design and Analysis of Non-Inferiority Trials) Provides tools to help the design and analysis of resilient non-inferiority trials. 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Daniel's Biostatistics, Tenth Edition) Functions to accompany Wayne W. Daniel's Biostatistics: A Foundation for Analysis in the Health Sciences, Tenth Edition. Package: r-cran-dapper Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-checkmate, r-cran-furrr, r-cran-memoise, r-cran-posterior, r-cran-progressr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dapper_1.1.0-1.ca2604.1_all.deb Size: 83532 MD5sum: 93f1aac58b5ef41ae6928ad2dd267014 SHA1: 3bed5ea4e10a0aff8a92505ecafad29a8b37e343 SHA256: ea78a8018049126de1f933e2daeacab128ca22d927d359b52fd3008bc521d0cd SHA512: 031e9d494daf163b2b37ab196340fcc48b3ca95d17ed399974ac5d87ecb5cb477050c74f90174f3995b23af0b49364223154f4768d203ab29798ed70c099ec8d Homepage: https://cran.r-project.org/package=dapper Description: CRAN Package 'dapper' (Data Augmentation for Private Posterior Estimation) A data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample() function takes an existing sampler as input and automatically constructs a privacy-aware sampler. The process of constructing a sampler is simplified through the specification of four independent modules, allowing for easy comparison between different privacy mechanisms by only swapping out the relevant modules. Probability mass functions for the discrete Gaussian and discrete Laplacian are provided to facilitate analyses dealing with privatized count data. The output of dapper_sample() can be analyzed using many of the same tools from the 'rstan' ecosystem. For methodological details on the sampler see Ju et al. (2022) , and for details on the discrete Gaussian and discrete Laplacian distributions see Canonne et al. (2020) . Package: r-cran-daqapo Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-stringdist, r-cran-stringr, r-cran-tidyr, r-cran-xesreadr, r-cran-rlang, r-cran-bupar, r-cran-readr, r-cran-edear, r-cran-magrittr, r-cran-purrr, r-cran-glue, r-cran-miniui, r-cran-shiny, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-daqapo_0.3.2-1.ca2604.1_all.deb Size: 186060 MD5sum: f741ad50b12929510d8caf40a0afc4d7 SHA1: 861f5024d83fd1b3cbf50877658c49ef0d05dd10 SHA256: c80398a1280da2f9deb713026e2ab929c049f1464007b8593267e8256c7a2f06 SHA512: ab8526d0e66bafa70ad6593f9cb9aa350582b505209b64fb60c557dfba5b906846557ea86ea45e90ef2a02e57e8bbf98074f799081457e2c96202313df9dc0c1 Homepage: https://cran.r-project.org/package=daqapo Description: CRAN Package 'daqapo' (Data Quality Assessment for Process-Oriented Data) Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...). Package: r-cran-darand Architecture: all Version: 0.0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-darand_0.0.1.2-1.ca2604.1_all.deb Size: 30400 MD5sum: 83fbcf526e93f2c07f11d2912252120d SHA1: 630575c54a4fe89eb1f11c3c03d744c6ab7bf0c1 SHA256: ba85cba339016a73afbad60d829bcb03da471851ece081c3e6863f11e81e9936 SHA512: 588e4287e7cddfcd59ad42483a20b3bfe0cbebe09a12c3d8a405f3e4ca001a72338bfb97f39008243d2ec422a2b68e59cf95ff453fe3b4a24a7fe0c9ce729b27 Homepage: https://cran.r-project.org/package=DArand Description: CRAN Package 'DArand' (Differential Analysis with Random Reference Genes) Differential Analysis of short RNA transcripts that can be modeled by either Poisson or Negative binomial distribution. The statistical methodology implemented in this package is based on the random selection of references genes (Desaulle et al. (2021) ). Package: r-cran-dark Architecture: all Version: 0.9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dark_0.9.9-1.ca2604.1_all.deb Size: 399974 MD5sum: a9285db34b1a3dbd12a08234836417d4 SHA1: c2714ce70369091757b41ef4ff551199d7603d99 SHA256: 4ca3cd1993c730e1885cfbbab4d1df6ec023de765845db407807db83556585be SHA512: 11c175e34a00f9cac968b3692aaedaa9b4392ffbbea54615c928106e1d99e2404ffc771bfeeb734eef2b02c2ad7ba79bec5195ba7f934d107c52b8a77383c331 Homepage: https://cran.r-project.org/package=Dark Description: CRAN Package 'Dark' (The Analysis of Dark Adaptation Data) The recovery of visual sensitivity in a dark environment is known as dark adaptation. In a clinical or research setting the recovery is typically measured after a dazzling flash of light and can be described by the Mahroo, Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take dark adaptation data and use nonlinear regression to find the parameters of the model that 'best' describe the data. They do this by firstly, generating rapid initial objective estimates of data adaptation parameters, then a multi-start algorithm is used to reduce the possibility of a local minimum. There is also a bootstrap method to calculate parameter confidence intervals. The functions rely upon a 'dark' list or object. This object is created as the first step in the workflow and parts of the object are updated as it is processed. Package: r-cran-darkdiv Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-darkdiv_0.3.0-1.ca2604.1_all.deb Size: 35642 MD5sum: 897c410d02df6b3aa29b5e2c84d46179 SHA1: ff9269b78af101c23488ac0bb850039caddc445c SHA256: c9dd1b1a82ecf664fb7dc84124fd1de4b3fcf679f3dcf6288dfe92e2fc80fa39 SHA512: 863cc64341dd606ba415c23fec8d71bf63f5a98a88ad9873a3631e3c46b313dc369226a82e7a4853ab7f4cf68b74fe92d2356a2a2178ad3f585240468e0c7d8c Homepage: https://cran.r-project.org/package=DarkDiv Description: CRAN Package 'DarkDiv' (Estimating Dark Diversity and Site-Specific Species Pools) Estimation of dark diversity and site-specific species pools using species co-occurrences. It includes implementations of probabilistic dark diversity based on the Hypergeometric distribution, as well as estimations based on the Beals index, which can be transformed to binary predictions using different thresholds, or transformed into a favorability index. All methods include the possibility of using a calibration dataset that is used to estimate the indication matrix between pairs of species, or to estimate dark diversity directly on a single dataset. See De Caceres and Legendre (2008) , Lewis et al. (2016) , Partel et al. (2011) , Real et al. (2017) for further information. Package: r-cran-darksky Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-gridextra, r-cran-gtable, r-cran-ggplot2, r-cran-plyr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-darksky_1.3.0-1.ca2604.1_all.deb Size: 41666 MD5sum: 762cade5374de37b7e2d6a1f2c4566b4 SHA1: 604a81e2592a0c5fb758d5687058eb81be83f407 SHA256: 44cdb182cc92e632164357a4a6b4673add738f7fe7ef9510fbbd8ce80ddb30a5 SHA512: 6ebf0dee7482a358414ba1760b27b1358efff2d8bdcb889acb41f95b5bb655bba1a545e6aacde01725c4778436aea71817769eba97fc7c1fba8a64f5d9688529 Homepage: https://cran.r-project.org/package=darksky Description: CRAN Package 'darksky' (Tools to Work with the 'Dark Sky' 'API') Provides programmatic access to the 'Dark Sky' 'API' , which provides current or historical global weather conditions. Package: r-cran-dartr.base Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-dartr.data, r-cran-adegenet, r-cran-ape, r-cran-crayon, r-cran-data.table, r-cran-foreach, r-cran-gridextra, r-cran-patchwork, r-cran-plyr, r-cran-reshape2, r-bioc-snprelate, r-cran-stampp, r-cran-stringr, r-cran-tidyr, r-cran-mass, r-cran-bigstatsr, r-cran-bigsnpr, r-bioc-snpstats, r-cran-gtools Suggests: r-cran-boot, r-cran-devtools, r-cran-directlabels, r-cran-dismo, r-cran-doparallel, r-cran-expm, r-cran-gdistance, r-cran-gganimate, r-cran-ggrepel, r-cran-gtable, r-cran-ggthemes, r-cran-gplots, r-cran-hardyweinberg, r-cran-hierfstat, r-cran-igraph, r-cran-iterpc, r-cran-knitr, r-cran-label.switching, r-cran-lattice, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-markdown, r-cran-mmod, r-cran-networkd3, r-cran-pegas, r-cran-pheatmap, r-cran-plotly, r-cran-poppr, r-cran-proxy, r-cran-purrr, r-bioc-qvalue, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rgl, r-cran-rmarkdown, r-cran-rrblup, r-cran-scales, r-cran-seqinr, r-cran-sf, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-siber, r-cran-stringi, r-cran-tibble, r-cran-vcfr, r-cran-zoo, r-cran-viridis, r-cran-fields, r-cran-testthat, r-cran-ggtern, r-cran-dendextend, r-cran-ggdendro, r-cran-terra, r-cran-r.utils Filename: pool/dists/resolute/main/r-cran-dartr.base_1.2.3-1.ca2604.1_all.deb Size: 3750630 MD5sum: 2d82f19392c4ac1decbeda425a01add1 SHA1: fdec96feb8188e288d6645d3a7636918495a5449 SHA256: 189b3d44e73ad7433cb6681ef196f18ed824adab5a2c24987bbeab00d1803a6b SHA512: 10ee55ecf75fd515c930c6f836ba5d6043cba5336df5893bdb13d63558ed6cc5127152b04cd3677b015cdfe18dae5234fb30b188064191519d507deaf5053a32 Homepage: https://cran.r-project.org/package=dartR.base Description: CRAN Package 'dartR.base' (Analysing 'SNP' and 'Silicodart' Data - Basic Functions) Facilitates the import and analysis of 'SNP' (single nucleotide 'polymorphism') and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' (Diversity Arrays Technology), however, data from other sequencing platforms can be used once 'SNP' or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of 'SNP' and 'silicodart' data, for reporting on and filtering on various criteria (e.g. 'callrate', 'heterozygosity', 'reproducibility', maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. 'dartR.base' is the 'base' package of the 'dartRverse' suits of packages. To install the other packages, we recommend to install the 'dartRverse' package, that supports the installation of all packages in the 'dartRverse'. If you want to cite 'dartR', you find the information by typing citation('dartR.base') in the console. Package: r-cran-dartr.captive Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-dartr.sim, r-cran-adegenet, r-cran-crayon, r-cran-ggplot2, r-cran-patchwork, r-cran-stringr, r-cran-data.table, r-cran-gridextra, r-cran-magrittr, r-cran-reshape2, r-cran-tidyr, r-cran-digest Suggests: r-cran-siber, r-cran-gplots, r-cran-fields, r-cran-igraph, r-cran-rrblup, r-cran-scales, r-cran-spelling, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-dartr.captive_1.2.2-1.ca2604.1_all.deb Size: 1255454 MD5sum: 985e8b680851a4008fb87cb3859a5ba6 SHA1: ee48f0e83528eb948d4cb7fbad6af6656a9f05e6 SHA256: 43181fffa9708ff381ef0501d52cfa362d8744b4c3d788f86f7f0f53f0156347 SHA512: a558a7751f767de2c8f43d6d030a2bcf9fdee84d46c9ecba57f693f4ba39718ad9a752a3adeb6034ff9765a966cee6dc562934116fdfe17f299e60145f893077 Homepage: https://cran.r-project.org/package=dartR.captive Description: CRAN Package 'dartR.captive' (Analysing 'SNP' Data to Support Captive Breeding) Functions are provided that facilitate the analysis of SNP (single nucleotide polymorphism) data to answer questions regarding captive breeding and relatedness between individuals. 'dartR.captive' is part of the 'dartRverse' suit of packages. Gruber et al. (2018) . Mijangos et al. (2022) . Package: r-cran-dartr.data Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-adegenet Filename: pool/dists/resolute/main/r-cran-dartr.data_1.2.2-1.ca2604.1_all.deb Size: 5369162 MD5sum: 5ebc3f4bcc71edddabfffc230ea43a4a SHA1: d7a4ab5a59aab80479965563aa2007c018e7fc0d SHA256: f5f753848d48864a328e02f5a9f72e8fefa0c314f2ecfbeeacc93e75cef76510 SHA512: 8271dd5491922497b167e2ee869e5b559384fbf68cf4d75016b574f639fb5aaa10bb5fc5d02cfe2b8cc9d5d808b5d974c6635a45cbdda298d3c4fa59cfef3c71 Homepage: https://cran.r-project.org/package=dartR.data Description: CRAN Package 'dartR.data' (Auxiliary Data Package for Our Main Package 'dartR') Data package for 'dartR'. Provides data sets to run examples in 'dartR'. This was necessary due to the size limit imposed by 'CRAN'. The data in 'dartR.data' is needed to run the examples provided in the 'dartR' functions. All available data sets are either based on actual data (but reduced in size) and/or simulated data sets to allow the fast execution of examples and demonstration of the functions. Package: r-cran-dartr.popgen Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-mass, r-cran-dplyr, r-cran-patchwork, r-cran-crayon, r-cran-ggplot2, r-cran-data.table, r-cran-stringr, r-cran-furrr, r-cran-future, r-cran-ggdendro, r-bioc-lea, r-cran-pillar, r-cran-plyr, r-cran-terra, r-cran-purrr, r-cran-ggpmisc, r-cran-r.utils, r-cran-ape Suggests: r-cran-siber, r-cran-expm, r-cran-fields, r-cran-gplots, r-cran-gridextra, r-cran-igraph, r-cran-iterpc, r-cran-label.switching, r-cran-leaflet, r-cran-proxy, r-bioc-qvalue, r-cran-raster, r-cran-reshape2, r-cran-scales, r-bioc-snpstats, r-cran-tidyr, r-cran-viridis, r-cran-zoo, r-cran-gsubfn, r-cran-sp Filename: pool/dists/resolute/main/r-cran-dartr.popgen_1.2.2-1.ca2604.1_all.deb Size: 1199282 MD5sum: 5906a28860ede8af55acd723808b9e61 SHA1: 085d5733de055cfc3079949d8e8226bdc98ca6d4 SHA256: d9f95cbf23d6cb2122116a895f1041754042e25c6acc55ae06d406c45e025fc5 SHA512: 6b65b85f50805d0d114111c575c9cdd5a5bedee02a2b38f87efbe46a00bbe83257cbaa7867f6794619f1fe3fcdfe3a0138013db2fe087e6af6282da2d9d18c31 Homepage: https://cran.r-project.org/package=dartR.popgen Description: CRAN Package 'dartR.popgen' (Analysing 'SNP' and 'Silicodart' Data Generated by Genome-WideRestriction Fragment Analysis) Facilitates the analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. 'dartR.popgen' provides a suit of functions to analyse such data in a population genetics context. It provides several functions to calculate population genetic metrics and to study population structure. Quite a few functions need additional software to be able to run (gl.run.structure(), gl.blast(), gl.LDNe()). You find detailed description in the help pages how to download and link the packages so the function can run the software. 'dartR.popgen' is part of the the 'dartRverse' suit of packages. Gruber et al. (2018) . Mijangos et al. (2022) . Package: r-cran-dartr.sexlinked Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-doparallel, r-cran-ggplot2, r-cran-foreach, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-dartr.sexlinked_1.2.2-1.ca2604.1_all.deb Size: 509688 MD5sum: 49ea585c701e8c53f8a9e35e350e777a SHA1: 7089cf8442c833e80e53a4e1444d168a2f34b008 SHA256: 3827279cd62ed3bb5e4a26bb398c1d1873c9c86fef0df68f9c7ed8ef515ec54e SHA512: 98e02b8f3cb2d9b35dd9e1bb0a96f39c043757279ef56daf22cc12e06e60de48c92481eaf5d318558d9219c4d6ad4d962d3c749016dd7d5082345d3e1c8f262d Homepage: https://cran.r-project.org/package=dartR.sexlinked Description: CRAN Package 'dartR.sexlinked' (Analysing SNP Data to Identify Sex-Linked Markers) Identifies, filters and exports sex linked markers using 'SNP' (single nucleotide polymorphism) data. To install the other packages, we recommend to install the 'dartRverse' package, that supports the installation of all packages in the 'dartRverse'. If you want understand the applied rational to identify sexlinked markers and/or want to cite 'dartR.sexlinked', you find the information by typing citation('dartR.sexlinked') in the console. Package: r-cran-dartr.sim Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-ggplot2, r-cran-dartr.popgen, r-cran-adegenet, r-cran-shiny, r-cran-fields, r-cran-stringi, r-cran-stringr, r-cran-data.table, r-cran-rcpp, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-hierfstat, r-cran-reshape2, r-cran-foreach, r-cran-ggrepel, r-cran-dplyr, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-dartr.sim_1.2.2-1.ca2604.1_all.deb Size: 1391118 MD5sum: fd47b90704117355304a0b58e8495069 SHA1: 2c7f517d87ddaca160ef299c8764ce7c9727b353 SHA256: 51e681d345e7f98037bbc483d512c6326f20274a3a21db55484ff140d0e21df4 SHA512: cf0d3681395b0e724805c51683ae7ee0f2a363fa43b31ac2311e37d8c13e5ce6c35dbb888df2b2f471f26f8d3ecd83908eceb77e66548fc5d887baa7e939f829 Homepage: https://cran.r-project.org/package=dartR.sim Description: CRAN Package 'dartR.sim' (Computer Simulations of 'SNP' Data) Allows to simulate SNP data using genlight objects. For example, it is straight forward to simulate a simple drift scenario with exchange of individuals between two populations or create a new genlight object based on allele frequencies of an existing genlight object. Package: r-cran-dartr.spatial Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-crayon, r-cran-stampp, r-cran-raster, r-cran-sp, r-cran-tidyr, r-cran-vegan, r-cran-mass, r-cran-ggplot2, r-cran-data.table, r-cran-dismo Suggests: r-cran-mmod, r-cran-gdistance, r-cran-gplots, r-cran-rrblup, r-cran-terra, r-cran-sf, r-cran-popgenreport Filename: pool/dists/resolute/main/r-cran-dartr.spatial_1.2.2-1.ca2604.1_all.deb Size: 1314822 MD5sum: 5afe329c7210b725cf8c36ecf314d983 SHA1: c436c34bdfb0ba1207edae68ccb3ec008e9ab35c SHA256: 8e72d10a0ec8fdfe676e64cec3f0963ce8e310c7b598e876de86c7cc24109069 SHA512: 5978ec2cc2b89d4afd6638dd8a28254e5821f829eed60d44d8689d9c7cc9f9531488b5f16d46da2412b92a6d7e8af0651895f622b9149fb791e3a69d4e1610da Homepage: https://cran.r-project.org/package=dartR.spatial Description: CRAN Package 'dartR.spatial' (Applying Landscape Genomic Methods on 'SNP' and 'Silicodart'Data) Provides landscape genomic functions to analyse 'SNP' (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. 'dartR.spatial' is a package that belongs to the 'dartRverse' suit of packages and depends on 'dartR.base' and 'dartR.data'. Package: r-cran-dartr Architecture: all Version: 2.9.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6071 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adegenet, r-cran-ggplot2, r-cran-dplyr, r-cran-dartr.data, r-cran-ape, r-cran-crayon, r-cran-data.table, r-cran-fields, r-cran-foreach, r-cran-gridextra, r-cran-mass, r-cran-patchwork, r-cran-plyr, r-cran-popgenreport, r-cran-raster, r-cran-reshape2, r-cran-shiny, r-bioc-snprelate, r-cran-sp, r-cran-stampp, r-cran-stringr, r-cran-tidyr, r-cran-gsubfn, r-cran-purrr Suggests: r-cran-boot, r-cran-devtools, r-cran-directlabels, r-cran-dismo, r-cran-doparallel, r-cran-expm, r-cran-gdistance, r-cran-ggtern, r-cran-gganimate, r-cran-ggrepel, r-cran-gtable, r-cran-ggthemes, r-cran-gplots, r-cran-hardyweinberg, r-cran-hierfstat, r-cran-igraph, r-cran-iterpc, r-cran-knitr, r-cran-label.switching, r-cran-lattice, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-markdown, r-cran-mmod, r-cran-networkd3, r-cran-pegas, r-cran-pheatmap, r-cran-plotly, r-cran-poppr, r-cran-proxy, r-bioc-qvalue, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rgl, r-cran-rmarkdown, r-cran-rrblup, r-cran-scales, r-cran-seqinr, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-siber, r-bioc-snpstats, r-cran-stringi, r-cran-terra, r-cran-tibble, r-cran-vcfr, r-cran-zoo, r-cran-viridis, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-dartr_2.9.9.5-1.ca2604.1_all.deb Size: 5267120 MD5sum: 623f363fe50ef40bc8dfb9803f5b6f20 SHA1: dcca3236606ebf170d4b71192ca6ff98948b202e SHA256: 8228161d2f10dd4564aa7cb81b120a0d946b035fd4c8884ed7656ad6920137bc SHA512: 30be86734bf200ef2819add03f6870cc69f65ee3dc4ea15e62df0fddb520869e85f98a1ce40ec5260c10f49cb2f5689cdca6812c1e412717611487bbc00e4eb7 Homepage: https://cran.r-project.org/package=dartR Description: CRAN Package 'dartR' (Importing and Analysing 'SNP' and 'Silicodart' Data Generated byGenome-Wide Restriction Fragment Analysis) Functions are provided that facilitate the import and analysis of 'SNP' (single nucleotide polymorphism) and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' (Diversity Arrays Technology), however, data from other sequencing platforms can be used once 'SNP' or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of 'SNP' and 'silicodart' data, for reporting on and filtering on various criteria (e.g. 'CallRate', heterozygosity, reproducibility, maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. 'dartR' supports also the analysis of 3rd party software package such as 'newhybrid', 'structure', 'NeEstimator' and 'blast'. Since version 2.0.3 we also implemented simulation functions, that allow to forward simulate 'SNP' dynamics under different population and evolutionary dynamics. Comprehensive tutorials and support can be found at our 'github' repository: github.com/green-striped-gecko/dartR/. If you want to cite 'dartR', you find the information by typing citation('dartR') in the console. Package: r-cran-dartrverse Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1701 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-devtools, r-cran-rcurl, r-cran-httr Suggests: r-cran-dartr.base, r-cran-dartr.data, r-cran-dartr.sim, r-cran-dartr.captive, r-cran-dartr.popgen, r-cran-dartr.spatial Filename: pool/dists/resolute/main/r-cran-dartrverse_1.0.6-1.ca2604.1_all.deb Size: 1680270 MD5sum: d71e2308903dc284f8e0a961cd205b32 SHA1: 7722fa8add2842396f0697a422dc59fb5f14a96e SHA256: fa50010e04565f768bc68c3c9cfcc9a0f4dadee63973278776c702f1c1b34c7c SHA512: f68ff0324294c80116639e7fe5808bf7b12cff950aaf5709c211efdebe2919a02b02191396d91e4f8bd2fb4b74d163b59e8a0d712c7140d1e3c429f51e7f996e Homepage: https://cran.r-project.org/package=dartRverse Description: CRAN Package 'dartRverse' (Install and Load the 'dartRverse' Suits of Packages) Provides a single function that supports the installation of all packages belonging to the 'dartRverse'. The 'dartRverse' is a set of packages that work together to analyse SNP (single nuclear polymorphism) data. All packages aim to have a similar 'look and feel' and are based on the same type of data structure ('genlight'), with additional metadata for loci and individuals (samples). For more information visit the 'GitHub' pages . Package: r-cran-dasguptr Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2610 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-dasguptr_2.1.0-1.ca2604.1_all.deb Size: 471372 MD5sum: 9b1fa269ee0c6efae3fb9128419917fb SHA1: 552011315b78a2701c800a1c3485ab9c69e42365 SHA256: bafb7f8fdc445adfad02785e4690699668a1e42f6edb4eeff83ea11c5e1c0037 SHA512: f789a23afd44e843c463918e07f5a90c7abd309408d3bcd08f47a947432cb0d5850ed7becf870620571695e1feb2abfb069c34ab5adf800b0f364e8a0b0bb52f Homepage: https://cran.r-project.org/package=DasGuptR Description: CRAN Package 'DasGuptR' (Das Gupta Standardisation and Decomposition) Implementation of Das Gupta's standardisation and decomposition of population rates, as set out "Standardization and decomposition of rates: A user’s manual", Das Gupta (1993) . The goal of these methods is to calculate adjusted rates based on compositional 'factors' and quantify the contribution of each factor to the difference in crude rates between populations. The package offers functionality to handle various scenarios for any number of factors and populations, where said factors can be comprised of vectors across sub-populations (including cross-classified population breakdowns), and with the option to specify user-defined rate functions. Package: r-cran-dashboardthemes Architecture: all Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-testthat, r-cran-lintr, r-cran-knitr, r-cran-rmarkdown, r-cran-glue, r-cran-covr Filename: pool/dists/resolute/main/r-cran-dashboardthemes_1.1.6-1.ca2604.1_all.deb Size: 91176 MD5sum: 5a42def6e4ab7c332ccdf6b7baa3f821 SHA1: bfc10bc102f18360c299b7ce2a81370a6d896f0c SHA256: ce4c07efb3f36fee1dc358770096f676b10242e74ac8e6ccbdeb34d49b76ddde SHA512: 70c08704eda7b436db0611718a58f41bc7d64332e1f41b8927a89f68507c7b851de487b86b5da29abe31678f7de483b49096d9bef16744cd08ebd58d29572838 Homepage: https://cran.r-project.org/package=dashboardthemes Description: CRAN Package 'dashboardthemes' (Customise the Appearance of 'shinydashboard' Applications usingThemes) Allows manual creation of themes and logos to be used in applications created using the 'shinydashboard' package. Removes the need to change the underlying css code by wrapping it into a set of convenient R functions. Package: r-cran-dasst Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2451 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dasst_0.3.4-1.ca2604.1_all.deb Size: 1019532 MD5sum: 74ab3466e62ee08c5847174124d726de SHA1: bf675f91d3c9923f42ace686009526417a1a1ec8 SHA256: 8a474817213ab54d4f901a3f182b9674a261d5d3ca80b7a5605f1930d4a54829 SHA512: d0a877aaeed7ddd2524131cf4ddf6f964ae1ce2e9b0095b8f5dd04eaeb6d5d1270aba847eb8880db983d171a00b1cd2245948a2474f2bfd2c5cf3e26d8f62b4c Homepage: https://cran.r-project.org/package=Dasst Description: CRAN Package 'Dasst' (Tools for Reading, Processing and Writing 'DSSAT' Files) Provides methods for reading, displaying, processing and writing files originally arranged for the 'DSSAT-CSM' fixed width format. 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Aggregate, cumulate, print, plot, convert to and from data.frame and more. Useful for decision trees, machine learning, finance, conversion from and to JSON, and many other applications. Package: r-cran-data.validator Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertr, r-cran-shiny.semantic, r-cran-knitr, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-r6, r-cran-rlang, r-cran-rmarkdown, r-cran-htmltools, r-cran-htmlwidgets, r-cran-tibble Suggests: r-cran-covr, r-cran-fs, r-cran-lintr, r-cran-magrittr, r-cran-rcmdcheck, r-cran-readr, r-cran-shiny, r-cran-spelling, r-cran-targets, r-cran-testthat, r-cran-visnetwork, r-cran-withr Filename: pool/dists/resolute/main/r-cran-data.validator_0.2.1-1.ca2604.1_all.deb Size: 624606 MD5sum: 64bb05c78501ad7baf2c9cad271ba67e SHA1: b7bfdd347cb56f155604ef3a391045588a7d07ce SHA256: 5030da366eb3299acfec981223a7748d03820760e951a739c21fddec34395c80 SHA512: 7cc1e97d11a36421b58fa46e0ab92c8646eece73405c3d8696fbecb85eb16b16eee48674b758ce84c05f57a9c0d7c9960c4fca7613c888d5af58f51a0f890b93 Homepage: https://cran.r-project.org/package=data.validator Description: CRAN Package 'data.validator' (Automatic Data Validation and Reporting) Validate dataset by columns and rows using convenient predicates inspired by 'assertr' package. Generate good looking HTML report or print console output to display in logs of your data processing pipeline. Package: r-cran-data360r Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-reshape2, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-data360r_1.0.9-1.ca2604.1_all.deb Size: 30288 MD5sum: 70439f61713c1785638027cc395242b9 SHA1: c0d296eb0a884d3a54fe349823c42ae326b73622 SHA256: 48f33fe2d91cbd42d501016d0065d5fdfcd2845bc02aa41425271ccf93111830 SHA512: 72239463a0b5e9f5ad454388bcd51651cd1ab70fe57687fa8cc33210195612f814885b93d870ec02f213c5e50e01ea1859b56b11f0cc046f97fe50fabf2cda30 Homepage: https://cran.r-project.org/package=data360r Description: CRAN Package 'data360r' (Wrapper for 'TCdata360' and 'Govdata360' API) Makes it easy to engage with the Application Program Interface (API) of the 'TCdata360' and 'Govdata360' platforms at and , respectively. These application program interfaces provide access to over 5000 trade, competitiveness, and governance indicator data, metadata, and related information from sources both inside and outside the World Bank Group. Package functions include easier download of data sets, metadata, and related information, as well as searching based on user-inputted query. Package: r-cran-databaseconnector Architecture: all Version: 7.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-sqlrender, r-cran-stringr, r-cran-readr, r-cran-rlang, r-cran-dbi, r-cran-urltools, r-cran-bit64, r-cran-checkmate, r-cran-digest, r-cran-dbplyr Suggests: r-cran-aws.s3, r-cran-r.utils, r-cran-withr, r-cran-testthat, r-cran-dbitest, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-ssh, r-cran-andromeda, r-cran-dplyr, r-cran-rpostgres, r-cran-odbc, r-cran-duckdb, r-cran-bigrquery, r-cran-pool, r-cran-parallellogger, r-cran-azurestor Filename: pool/dists/resolute/main/r-cran-databaseconnector_7.1.0-1.ca2604.1_all.deb Size: 1235726 MD5sum: 2ac4768de46d305cb99be38b40cd0a56 SHA1: e3e324911b8621fd763e9fa6c2b465a0854aca45 SHA256: ac62443734da70f8cd2695d560fd1150441e67115d202e0e91678261626c0576 SHA512: a00258e23d06fddd81cdf58673040b8fd9be4cb9e6cd80deadc060e7b9d263e75d84e8a02abeb7a88b1cab951100ca43428742a3922907d6b5c9e684999acf41 Homepage: https://cran.r-project.org/package=DatabaseConnector Description: CRAN Package 'DatabaseConnector' (Connecting to Various Database Platforms) An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server', 'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', 'Snowflake', 'Spark', 'SQLite', and 'InterSystems IRIS'). Also includes support for fetching data as 'Andromeda' objects. Uses either 'Java Database Connectivity' ('JDBC') or other 'DBI' drivers to connect to databases. 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It is originated from a author's project which focuses on creative performance in online education environment. The resulting paper of that study will be published soon. 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Methods align with Zhu et al. (2025) and the associated software resource Zhu (2025) . 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The main types of data addressed are spatial (coordinates, longitude and latitude) and taxonomic data (ranking and nomenclature validity) with some additional options for user-determined dataset refinement. Combined or individual calls to the online repositories of the Global Biodiversity Information Facility (GBIF) via 'rgbif' and the Integrated Taxonomic Information System (ITIS) via 'taxize' enable built-in taxonomic checks. Package: r-cran-datana Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hmisc, r-cran-scales Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-datana_1.1.5-1.ca2604.1_all.deb Size: 5236020 MD5sum: 381ea8653dfdcf29114e50f1bfa63b0c SHA1: a2397bccc4ae1f4b8f7047afbef49efea73e180a SHA256: e542768d366c8735c30b9ba8585b05fbc9bd00ee97f047ef174d127f3b2e01b1 SHA512: 24c9c7b985648663f7435bab4087f2bca45d7e2376b443e5fca9fa43d5f236d46773cdb1f9e822c49f6fdf80ffebc9f01c76fb53aec6ec544a56ef589c7473b8 Homepage: https://cran.r-project.org/package=datana Description: CRAN Package 'datana' (Datasets and Functions to Accompany Analisis De Datos Con R) Datasets and functions to accompany the book 'Analisis de datos con el programa estadistico R: una introduccion aplicada' by Salas-Eljatib (2021, ISBN: 9789566086109). The package helps carry out data management, exploratory analyses, and model fitting. Package: r-cran-datanugget Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-rfast Filename: pool/dists/resolute/main/r-cran-datanugget_1.4.0-1.ca2604.1_all.deb Size: 82074 MD5sum: a4e71c5710867ddabadfeb177dd32a9a SHA1: a0e4570b1ac8097888e695f7007d42a5a86def43 SHA256: 5c3dec882a05139a7f9b4811861173096b5ea140c744e3307ca95506df610ae4 SHA512: 06958caaf360ab1ab1190ea5cd85d6106a7c85fec76ceceba7d0bc7e53ab8834daf855614d1b9ac9a6f4d07db50e8b47a4e872d04b45695c702f0c0d48e86cd5 Homepage: https://cran.r-project.org/package=datanugget Description: CRAN Package 'datanugget' (Create, and Refine Data Nuggets) Creating, and refining data nuggets. Data nuggets reduce a large dataset into a small collection of nuggets of data, each containing a center (location), weight (importance), and scale (variability) parameter. Data nugget centers are created by choosing observations in the dataset which are as equally spaced apart as possible. Data nugget weights are created by counting the number observations closest to a given data nugget center. We then say the data nugget 'contains' these observations and the data nugget center is recalculated as the mean of these observations. Data nugget scales are created by calculating the trace of the covariance matrix of the observations contained within a data nugget divided by the dimension of the dataset. Data nuggets are refined by 'splitting' data nuggets which have scales or shapes (defined as the ratio of the two largest eigenvalues of the covariance matrix of the observations contained within the data nugget) Reference paper: [1] Beavers, T. E., Cheng, G., Duan, Y., Cabrera, J., Lubomirski, M., Amaratunga, D., & Teigler, J. E. (2024). Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure. Journal of Computational and Graphical Statistics, 1-21. [2] Cherasia, K. E., Cabrera, J., Fernholz, L. T., & Fernholz, R. (2022). Data Nuggets in Supervised Learning. \emph{In Robust and Multivariate Statistical Methods: Festschrift in Honor of David E. Tyler} (pp. 429-449). Cham: Springer International Publishing. 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Swap Execution Facilities (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are traded on or reported to those facilities (respectively). This package provides you the ability to get this data from supported sources. 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Each 'DataONE' repository implements a consistent repository application programming interface. Users call methods in R to access these remote repository functions, such as methods to query the metadata catalog, get access to metadata for particular data packages, and read the data objects from the data repository. Users can also insert and update data objects on repositories that support these methods. 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These data may consist of multiple data files and associated meta data and ancillary files. Individual data objects have associated system level meta data, and data files are linked together using the OAI-ORE standard resource map which describes the relationships between the files. The OAI- ORE standard is described at . Data packages can be serialized and transported as structured files that have been created following the BagIt specification. The BagIt specification is described at . 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Potentially time consuming processing of raw data sets into analysis ready data sets is done in a reproducible manner and decoupled from the usual 'R CMD build' process so that data sets can be processed into R objects in the data package and the data package can then be shared, built, and installed by others without the need to repeat computationally costly data processing. The package maintains data provenance by turning the data processing scripts into package vignettes, as well as enforcing documentation and version checking of included data objects. Data packages can be version controlled on 'GitHub', and used to share data for manuscripts, collaboration and reproducible research. 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The datasets represent a larger and quirkier object lesson that is typically taught via Anscombe's Quartet (available in the 'datasets' package). Anscombe's Quartet contains four very different distributions with the same summary statistics and as such highlights the value of visualisation in understanding data, over and above summary statistics. As well as being an engaging variant on the Quartet, the data is generated in a novel way. The simulated annealing process used to derive datasets from the original Datasaurus is detailed in "Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing" . 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The package is lightweight (no dependencies, pure R implementations) and relies only on R's standard classes to represent dates and times ('Date' and 'POSIXt'); it aims to provide efficient implementations, through vectorisation and the use of R's native numeric representations of timestamps where possible. 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The package automatically detects date patterns within data frame columns and converts them to consistent ISO-formatted dates, with optional imputation of missing day or month components based on user-defined rules. It also includes functionality to identify inserted, deleted, and updated records, as well as column- and value-level changes, when comparing old and new versions of a data frame. Only one date format may be applied within a single column. Package: r-cran-datoramar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-datoramar_0.1.0-1.ca2604.1_all.deb Size: 15284 MD5sum: 11c4474a7f4b610773132cc8b9d1b1c4 SHA1: d58a38df44a26d210e8c1de6e4e8493e188d7be1 SHA256: f287323322d145b8a6d3d7708ecde9e551d113cdb57513743dc5c5e5c094f1bf SHA512: 892cd480ebd1096019642103e66b51532090dba715f5d1ce4f1f1a6a80034b8119c9040a4ad78c4443aa26187e5ee87046b42d72ff0aad0dc6bc56413ee7b2f9 Homepage: https://cran.r-project.org/package=datoramar Description: CRAN Package 'datoramar' (Interface to the 'Datorama' API) A thin wrapper around the 'Datorama' API. Ideal for analyzing marketing data from . Package: r-cran-datos Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-babynames, r-cran-dplyr, r-cran-forcats, r-cran-fueleconomy, r-cran-gapminder, r-cran-ggplot2, r-cran-lahman, r-cran-nasaweather, r-cran-nycflights13, r-cran-palmerpenguins, r-cran-modeldata, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-yaml Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-datos_0.5.1-1.ca2604.1_all.deb Size: 160042 MD5sum: eff7adad18dc7ac8289c98a5c5aadf1e SHA1: 2b37e8e4ae35443290db659e359065f88e51382b SHA256: a3a92b3bf7cfb451c92f7cfcf6671bb56d688c3c2f6fe15b0538206450bb8ae1 SHA512: ce690e4e357dda90413dd6ec721b32ab8b626e84a49f2a1647033a6ba7fd438b7e5f894e47f36e753a00276402e7e92004a7763e79224cf5e407ec78928097db Homepage: https://cran.r-project.org/package=datos Description: CRAN Package 'datos' (Traduce al Español Varios Conjuntos de Datos de Práctica) Provee una versión traducida de los siguientes conjuntos de datos: 'airlines', 'airports', 'AwardsManagers', 'babynames', 'Batting', 'credit_data', 'diamonds', 'faithful', 'fueleconomy', 'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers', 'mpg', 'mtcars', 'atmos', 'palmerpenguins', 'People, 'Pitching', 'planes', 'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b', 'table5', 'vehicles', 'weather', 'who'. English: It provides a Spanish translated version of the datasets listed above. Package: r-cran-datplot Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-devtools, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggridges, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-datplot_1.1.1-1.ca2604.1_all.deb Size: 330870 MD5sum: a6c84956768d7cdfca26c8fb4fefdf01 SHA1: 732935a4f0a533393a9c69a22c0b1c36d29aee94 SHA256: f4b42b27751e774377a73e7496bc7e73f3867dcf26a635aa657a6060e2f906df SHA512: 795addad9d60e0aaa76395c4af84acf5ab08bc5248e8355197c39b9d8f2fe7fad38e95f6e8c70954894b29d30d1a14a4b2e6fa0f2777872a8aac84018acbc0d1 Homepage: https://cran.r-project.org/package=datplot Description: CRAN Package 'datplot' (Preparation of Object Dating Ranges for Density Plots (AoristicAnalysis)) Converting date ranges into dating 'steps' eases the visualization of changes in e.g. pottery consumption, style and other variables over time. This package provides tools to process and prepare data for visualization and employs the concept of aoristic analysis. Package: r-cran-datr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-datr_0.1.0-1.ca2604.1_all.deb Size: 26798 MD5sum: ac364b8d462e731ba1f6dfc913437bbd SHA1: ba7a2bc63421cc22f2f7ff63c350a155d9b74faf SHA256: 8f8f464b611137aac5d8254d4990b26d445b8f87d1d596a05575ca7b6c977109 SHA512: dbe6eb2060e303842dd8d8c79e31c3e4b53168419801260a7201671477b39fbb4894a81bbb53e7e4ce9de39f8c770ea2ef26fc22decdc786b844985d68a4b275 Homepage: https://cran.r-project.org/package=datr Description: CRAN Package 'datr' ('Dat' Protocol Interface) Interface with the 'Dat' p2p network protocol . Clone archives from the network, share your own files, and install packages from the network. Package: r-cran-datrprofile Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-odbc, r-cran-dplyr, r-cran-rsqlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-datrprofile_0.1.0-1.ca2604.1_all.deb Size: 71062 MD5sum: 10671d6a61f53b4e8bdab04634b943da SHA1: a8d4514e099aed7f3db1067a7c0f710690c491e2 SHA256: bd0b9627096deb28c1fb0d310809d9c05db1a94f14b194837c2422984a6f9028 SHA512: 4931a973c9d8714a160b59052058ebe4553dad77490613fbe8507b1e430290557ab23bf13436fe7032e34f7088a0dc0476fe62b0b6d4458c5f1a74f04796b658 Homepage: https://cran.r-project.org/package=datrProfile Description: CRAN Package 'datrProfile' (Column Profile for Tables and Datasets) Profiles datasets (collecting statistics and informative summaries about that data) on data frames and 'ODBC' tables: maximum, minimum, mean, standard deviation, nulls, distinct values, data patterns, data/format frequencies. Package: r-cran-daur Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-daur_1.0-1.ca2604.1_all.deb Size: 33190 MD5sum: 5e21e62842a468449b5d98eeb2a58a88 SHA1: 6402407646bbdde566da64f73c12c93a5773e809 SHA256: 2b31ed1c1edd910b7aa6c6861b5d7a0156cbf06372958b8511c11751c3b868ef SHA512: b0673fd21e09c045e58a69713301f4a5e7ee7fe3371def78fbbf0fcbc99525930b414530ee439f012333633aae68b3fac91fca32fb390cf0d51d1c320d797860 Homepage: https://cran.r-project.org/package=dauR Description: CRAN Package 'dauR' (Datasets for "Sampling and Data Analysis Using R: Theory andPractice") Provides several datasets used throughout the book "Sampling and Data Analysis Using R: Theory and Practice" by Islam (2025, ISBN:978-984-35-8644-5). The datasets support teaching and learning of statistical concepts such as sampling methods, descriptive analysis, estimation and basic data handling. These curated data objects allow instructors, students and researchers to reproduce examples, practice data manipulation and perform hands-on analysis using R. Package: r-cran-davies Architecture: all Version: 1.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1931 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-davies_1.2-1-1.ca2604.1_all.deb Size: 1935128 MD5sum: b8a45211fd307d32f9ed48f228108c13 SHA1: 5a27bd9425925e7e3a23367670512e4c7a95eb5a SHA256: 63d1f146d1ede09d73efc7695b49c124dc3e07df8d34d9f4809bb5234db584f7 SHA512: 5ce533335f872a9cad9b065ef0a9b56ea2bca2336f7c4a2abce0fc5f01db2a459fbb4a94d60bf9194c0ec41ba622601fa571d2d72cd8709f8edcaf9f4dfac923 Homepage: https://cran.r-project.org/package=Davies Description: CRAN Package 'Davies' (The Davies Quantile Function) Various utilities for the Davies distribution. 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The development of this package is completely independent from the government agency, Klimadatastyrelsen, who maintains the API. Package: r-cran-daymetr Architecture: all Version: 1.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-ncdf4, r-cran-httr, r-cran-tidyr, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-markdown, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-daymetr_1.7.1-1.ca2604.1_all.deb Size: 290824 MD5sum: a29655646c5dc00af105f1fda6b4f171 SHA1: 491405e1f7ab8d9aae035e882b1889626a1c1859 SHA256: 141df4f1c334163b14c595cf917fdf3ac000bec35768b02b8e533715434e8d2b SHA512: 8e7ab2011b21f1347baf205f156757e79f76e3ca7611660229781b3f826b46398c6dd589181692085debd6b9272455d4aef7499dfb02af272ce951a4a05575b5 Homepage: https://cran.r-project.org/package=daymetr Description: CRAN Package 'daymetr' (Interface to the 'Daymet' Web Services) Programmatic interface to the 'Daymet' web services (). Allows for easy downloads of 'Daymet' climate data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded (netCDF) data are provided. Package: r-cran-days2lessons Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-days2lessons_0.1.3-1.ca2604.1_all.deb Size: 44728 MD5sum: 7172b9639e5d53ee8ecfaba17b9c7e76 SHA1: 484b2ca489f976bb9526297c30ccb8bc84a4171f SHA256: 8203d18c1b2edf9c184fa5e2147f28eab921db2c53d9b9ec26d6c29912f72062 SHA512: 9e31d600bd66de8b8d5d2030ab99c3828d0e86c22301bf2f64e3d552f7fcb463f44068dd21e36c44f68a88015da11ca719e67fbd177470293a55b98e2ab05d12 Homepage: https://cran.r-project.org/package=days2lessons Description: CRAN Package 'days2lessons' (Distributes Teachers Lessons On Days in a Balanced Manner) The set of teacher/class lessons is completed with a column that allocates a day to each lesson, so that the distribution of lessons by day, by class, and by teacher is as uniform as possible. . Package: r-cran-daysupply Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lme4, r-cran-rlang, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-daysupply_0.1.0-1.ca2604.1_all.deb Size: 37074 MD5sum: 313ca411f9d738528179eca77a9761e5 SHA1: dd5aac1e9d2f64c73d4235278c7dcc29dcbe0291 SHA256: e2f62d5a65b73d155c62db9aa6b9138138072af4be505eb6381537be23c1e9c9 SHA512: 70ebbdcddbb07856c765dc6ab3d8fefc9dc8675248b3fe6f29ee0eb246d38d498e037260a873d482ff7e6e8caac99e838927c57b8e5c9dd1c7c41d63e4e58134 Homepage: https://cran.r-project.org/package=daySupply Description: CRAN Package 'daySupply' (Calculating Days' Supply and Daily Dose of Prescriptions) Allows clinicians and researchers to compute daily dose (and subsequently days' supply) for prescription refills using the following methods: Fixed window, fixed tablet, defined daily dose (DDD), and Random Effects Warfarin Days' Supply (REWarDS). Daily dose is the computed dose that the patient takes every day. For medications with fixed dosing (e.g. direct oral anticoagulants) this is known and does not need to be estimated. For medications with varying dose such as warfarin, however, the daily dose should be assumed or estimated to allow measurement of drug exposure. Days’ supply is the number of days that patients’ supply of medication will last after each prescription fill. Estimating days’ supply is necessary to calculate drug exposure. The package computes days’ supply and daily dose at both the prescription and patient levels. Results at the prescription level are denoted with “-Rx-” and those at patient level are denoted with “-Pt-”. Package: r-cran-dbacf Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-dbacf_0.2.8-1.ca2604.1_all.deb Size: 50116 MD5sum: 91c8883da7ccbd225e04edac059900b7 SHA1: 504dcfa50bef584bf420089231f7a96eefa91f56 SHA256: 41029e91d922d3b0958bbf5d9b26b82d9f59a9f83a35c4a9e835e03e26e47e61 SHA512: d30108330e52cf86918769f8e9bcbeb45878166a46221485923bcc155318ea3a3f6e31479dd07c6b3fb068da3d569baabb60da0b99181d770ab33fb767efdeb8 Homepage: https://cran.r-project.org/package=dbacf Description: CRAN Package 'dbacf' (Autocovariance Estimation via Difference-Based Methods) Provides methods for (auto)covariance/correlation function estimation in change point regression with stationary errors circumventing the pre-estimation of the underlying signal of the observations. Generic, first-order, (m+1)-gapped, difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) . Bias-reducing, second-order, (m+1)-gapped, difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) . Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) . It also includes a general projection-based method for covariance matrix estimation. Package: r-cran-dbcvindex Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dbcvindex_1.6-1.ca2604.1_all.deb Size: 29648 MD5sum: 3fca787c249092b26289d2d1ea478e44 SHA1: 9aa77e74d0e5d4d8431061a11929f35f48de2aff SHA256: b4e7cfac8e8c766a0c85bd83f2263d5b177f683ade71fcd5372b0ecd3134997c SHA512: 2638c64f4a846df98e6d289f9cdaa633d5d90670794e0d6c67c94512e57638b216b25aa59b8053e778a4000f7de524573afb89b7ae0abe273daecce70c9979a9 Homepage: https://cran.r-project.org/package=DBCVindex Description: CRAN Package 'DBCVindex' (Calculates the Density-Based Clustering Validation (DBCV) Index) A metric called 'Density-Based Clustering Validation index' (DBCV) index to evaluate clustering results, following the 'R' implementation by Pablo Andretta Jaskowiak. Original 'DBCV' index article: Moulavi, D., Jaskowiak, P. A., Campello, R. J., Zimek, A., and Sander, J. (April 2014), "Density-based clustering validation", Proceedings of SDM 2014 -- the 2014 SIAM International Conference on Data Mining (pp. 839-847), . A more recent article on the 'DBCV' index: Chicco, D., Sabino, G.; Oneto, L.; Jurman, G. (August 2025), "The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters", PeerJ Computer Science 11:e3095 (pp. 1-), . Package: r-cran-dbd Architecture: all Version: 0.0-22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-hmm.discnp, r-cran-mass, r-cran-rmutil, r-cran-spcadjust Filename: pool/dists/resolute/main/r-cran-dbd_0.0-22-1.ca2604.1_all.deb Size: 224230 MD5sum: 635fd27c590124ecc384874d3ab11f53 SHA1: 935194339bbc1845cfe7c4efdd410911f9d34786 SHA256: 3a3420264419af0c420bfae7ca479dac7134bf0adc35e74bc166d924135e0b5c SHA512: ca1afab892f0f42a2860f65353f914dfbd4b5714fbfa595f456079348d19d2c2362bda72b3eee52cb5a8744c5bce9d3e7a20d36c096c3f102563cebafb64bc3c Homepage: https://cran.r-project.org/package=dbd Description: CRAN Package 'dbd' (Discretised Beta Distribution) Tools for working with a new versatile discrete distribution, the db ("discretised Beta") distribution. This package provides density (probability), distribution, inverse distribution (quantile) and random data generation functions for the db family. It provides functions to effect conveniently maximum likelihood estimation of parameters, and a variety of useful plotting functions. It provides goodness of fit tests and functions to calculate the Fisher information, different estimates of the hessian of the log likelihood and Monte Carlo estimation of the covariance matrix of the maximum likelihood parameter estimates. In addition it provides analogous tools for working with the beta-binomial distribution which has been proposed as a competitor to the db distribution. Package: r-cran-dbfit Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfit Filename: pool/dists/resolute/main/r-cran-dbfit_2.0-1.ca2604.1_all.deb Size: 93530 MD5sum: 8949e2dfed8990a78b6db3b35e1e455e SHA1: db7858023490bbebdbc6aa79cae95f8c8a4cfb72 SHA256: 0d27cb7fddc93a5f426b85b908345eb5303db05c174f22f2f75b31228d975edb SHA512: 24f50c57514c83a7ddd53172adb52b7f0a958a401d9f4b6f72e9cc09333c83c631b3c40f86f636e331d4a897d5afb0e2255ebb9efc6cd457a402a3a8b4cd3b96 Homepage: https://cran.r-project.org/package=DBfit Description: CRAN Package 'DBfit' (A Double Bootstrap Method for Analyzing Linear Models withAutoregressive Errors) Computes the double bootstrap as discussed in McKnight, McKean, and Huitema (2000) . The double bootstrap method provides a better fit for a linear model with autoregressive errors than ARIMA when the sample size is small. 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Package: r-cran-dcorvs Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcov, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-dcorvs_1.1-1.ca2604.1_all.deb Size: 31456 MD5sum: 47f5fe8b1302f854243c91ea1c7a85d3 SHA1: c8a0e857a75103d8121d778871203270f0b3fe2e SHA256: 39ef17670a35f43f1d2844cd1efc13ab46d707dfa33195c44ca398b0da7065e7 SHA512: c2c51e9800870de957a0f6d44b01a5a7d21bc86f4e3d6fc3badfdcb8745f6a517ceb45bc6e095f3e61d50dd012441b67f03c599a2ce9b0dd7c0f4339b10dcfa2 Homepage: https://cran.r-project.org/package=dcorVS Description: CRAN Package 'dcorVS' (Variable Selection Algorithms Using the Distance Correlation) The 'FBED' and 'mmpc' variable selection algorithms have been implemented using the distance correlation. The references include: Tsamardinos I., Aliferis C. F. and Statnikov A. (2003). "Time and sample efficient discovery of Markovblankets and direct causal relations". In Proceedings of the ninth ACM SIGKDD international Conference. . Borboudakis G. and Tsamardinos I. (2019). "Forward-backward selection with early dropping". Journal of Machine Learning Research, 20(8): 1--39. . Huo X. and Szekely G.J. (2016). "Fast computing for distance covariance". Technometrics, 58(4): 435--447. . Package: r-cran-dcovts Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcov, r-cran-doparallel, r-cran-foreach, r-cran-rangen, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-dcovts_1.5-1.ca2604.1_all.deb Size: 180580 MD5sum: a6386341026a93deb7698072e6612f05 SHA1: 7685a179f4c1faa778d51fac43163618221fb8b9 SHA256: ab110a708772ac68bd8cfe03398bcda252182255be3071a1dc7988b7269c69cb SHA512: 83f217d6b1d1639b85c829e0e2c6975ff35e3eb5164bfa2c464f353e735ba4dd46b78885be7ca3b579fbb4ca9b4a07af5799e690143e0f4ce922dace9dbb062d Homepage: https://cran.r-project.org/package=dCovTS Description: CRAN Package 'dCovTS' (Distance Covariance and Correlation for Time Series Analysis) Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. References include: a) Edelmann Dominic, Fokianos Konstantinos and Pitsillou Maria (2019). 'An Updated Literature Review of Distance Correlation and Its Applications to Time Series'. International Statistical Review, 87(2): 237--262. . b) Fokianos Konstantinos and Pitsillou Maria (2018). 'Testing independence for multivariate time series via the auto-distance correlation matrix'. Biometrika, 105(2): 337--352. . c) Fokianos Konstantinos and Pitsillou Maria (2017). 'Consistent testing for pairwise dependence in time series'. Technometrics, 59(2): 262--270. . d) Pitsillou Maria and Fokianos Konstantinos (2016). 'dCovTS: Distance Covariance/Correlation for Time Series'. R Journal, 8(2):324-340. . 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Decision-analytic techniques allow assessment of clinical outcomes, but often require collection of additional information may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. See the following references for details on the methods: Vickers (2006) , Vickers (2008) , and Pfeiffer (2020) . Package: r-cran-dcvar Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-posterior, r-cran-loo, r-cran-ggplot2, r-cran-patchwork, r-cran-bayesplot, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-sn Filename: pool/dists/resolute/main/r-cran-dcvar_0.2.0-1.ca2604.1_all.deb Size: 590430 MD5sum: 69aac9dcc9946d67ddd2250582ade4d9 SHA1: fc37c85551c93c32ae13470c5fff0c3553b02745 SHA256: d50b7f837a5e96f3eeae5d52e9dd2cf465c3bd9e129db52a537df72aa59f443c SHA512: d906468ad0ac0ecb378df48cf9bacfadbd4eae373adfc9e5ed20a03dede2d3346aa75c9d6ea92083a165888812f7bf3f9c27fe39dda032da8001beb25189f5cd Homepage: https://cran.r-project.org/package=dcvar Description: CRAN Package 'dcvar' (Dynamic Copula VAR Models for Time-Varying Dependence) Fits Bayesian copula vector autoregressive models for bivariate time series with dynamic, regime-switching, and constant dependence structures. The package includes simulation, data preparation, estimation with 'Stan' through 'rstan' or 'cmdstanr', posterior summaries, diagnostics, trajectory extraction, fitted and predictive summaries, and approximate leave-one-out cross-validation model comparison for supported fits. For Bayesian computation and model comparison, see Carpenter et al. (2017) and Vehtari, Gelman and Gabry (2017) . Package: r-cran-dda Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dhsic, r-cran-energy, r-cran-foreach Suggests: r-cran-boot, r-cran-doparallel, r-cran-devtools, r-cran-interactions, r-cran-iterators, r-cran-lmtest, r-cran-moments, r-cran-spelling, r-cran-testthat, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-dda_0.1.1-1.ca2604.1_all.deb Size: 158976 MD5sum: 935d2d826deab0f4c9b990592fcaf72c SHA1: d479549336d45ebfcf10ce099a2636b04c00d66f SHA256: 405e6217537ae2e1677512445159167685133bc72094100f26bf8a26a73b9ea0 SHA512: d6d040eee5b8f39c123aaec8d681155c8efbf91c6c4c49e333551381f22ecd3f7523ab46099268ea188cf7788e3ac0458de0609d4d6d71487277024181dc7a62 Homepage: https://cran.r-project.org/package=dda Description: CRAN Package 'dda' (Direction Dependence Analysis) A collection of tests to analyze the causal direction of dependence in linear models (Wiedermann, W., & von Eye, A., 2025, ISBN: 9781009381390). The package includes functions to perform Direction Dependence Analysis for variable distributions, residual distributions, and independence properties of predictors and residuals in competing causal models. In addition, the package contains functions to test the causal direction of dependence in conditional models (i.e., models with interaction terms) For more information see . Package: r-cran-ddecompose Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rifreg, r-cran-formula, r-cran-hmisc, r-cran-pbapply, r-cran-sandwich, r-cran-ranger, r-cran-fastglm Suggests: r-cran-testthat, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-ddecompose_1.0.0-1.ca2604.1_all.deb Size: 1342254 MD5sum: dff979b7889883d95a301af6619d47c9 SHA1: 3150d30e32e94fed46ef1eb7dcac134de54fb92d SHA256: ad7cbe24ddeedef4b16e659e6688c913314b6e183938d38d83c0262e82c79eb6 SHA512: 911e383dececded83f29bb7938408e3800aea668a248c7834ab57a5f132be5abfd9434af69d1250d538d0dac8f9a5ac8814863cb011611855b39e73102811093 Homepage: https://cran.r-project.org/package=ddecompose Description: CRAN Package 'ddecompose' (Detailed Distributional Decomposition) Implements the Oaxaca-Blinder decomposition method and generalizations of it that decompose differences in distributional statistics beyond the mean. The function ob_decompose() decomposes differences in the mean outcome between two groups into one part explained by different covariates (composition effect) and into another part due to differences in the way covariates are linked to the outcome variable (structure effect). The function further divides the two effects into the contribution of each covariate and allows for weighted doubly robust decompositions. For distributional statistics beyond the mean, the function performs the recentered influence function (RIF) decomposition proposed by Firpo, Fortin, and Lemieux (2018). The function dfl_decompose() divides differences in distributional statistics into an composition effect and a structure effect using inverse probability weighting as introduced by DiNardo, Fortin, and Lemieux (1996). The function also allows to sequentially decompose the composition effect into the contribution of single covariates. References: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2018) . "Decomposing Wage Distributions Using Recentered Influence Function Regressions." Fortin, Nicole M., Thomas Lemieux, and Sergio Firpo. (2011) . "Decomposition Methods in Economics." DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. (1996) . "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach." Oaxaca, Ronald. (1973) . "Male-Female Wage Differentials in Urban Labor Markets." Blinder, Alan S. (1973) . "Wage Discrimination: Reduced Form and Structural Estimates." Package: r-cran-ddesonn Architecture: all Version: 7.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-dplyr, r-cran-openxlsx, r-cran-tidyr, r-cran-proc, r-cran-prroc, r-cran-reshape2, r-cran-digest, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-quantmod, r-cran-randomforest, r-cran-reticulate, r-cran-zoo, r-cran-readxl, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-ddesonn_7.1.11-1.ca2604.1_all.deb Size: 9711484 MD5sum: 8ebedf472adc068a5f22d9c4f3653592 SHA1: e8b90589dc826f5ca51ede7e50612ec750f843de SHA256: 0be2a8d18b3a13fb1d995e0108b306faaa0380c43be3419d230080462d36a64d SHA512: 9b11c20325377f7577ae818ba122821499ebc9a7f039f2ef051af98ff4c2fae73b260d153c89eb86ecdd089e46712225d99d14ed45d40b244e54e42a5ab77c9b Homepage: https://cran.r-project.org/package=DDESONN Description: CRAN Package 'DDESONN' (A Deep Dynamic Experimental Self-Organizing Neural NetworkFramework) Provides a fully native R deep learning framework for constructing, training, evaluating, and inspecting Deep Dynamic Ensemble Self Organizing Neural Networks at research scale. The core engine is an object oriented R6 class-based implementation with explicit control over layer layout, dimensional flow, forward propagation, back propagation, and transparent optimizer state updates. The framework does not rely on external deep learning back ends, enabling direct inspection of model state, reproducible numerical behavior, and fine grained architectural control without requiring compiled dependencies or graphics processing unit specific run times. Users can define dimension agnostic single layer or deep multi-layer networks without hard coded architecture limits, with per layer configuration vectors for activation functions, derivatives, dropout behavior, and initialization strategies automatically aligned to network depth through controlled replication or truncation. Reproducible workflows can be executed through high level helpers for fit, run, and predict across binary classification, multi-class classification, and regression modes. Training pipelines support optional self organization, adaptive learning rate behavior, and structured ensemble orchestration in which candidate models are evaluated under user specified performance metrics and selectively promoted or pruned to refine a primary ensemble, enabling controlled ensemble evolution over successive runs. Ensemble evaluation includes fused prediction strategies in which member outputs may be combined through weighted averaging, arithmetic averaging, or voting mechanisms to generate consolidated metrics for research level comparison and reproducible per-seed assessment. The framework supports multiple optimization approaches, including stochastic gradient descent, adaptive moment estimation, and look ahead methods, alongside configurable regularization controls such as L1, L2, and mixed penalties with separate weight and bias update logic. Evaluation features provide threshold tuning, relevance scoring, receiver operating characteristic and precision recall curve generation, area under curve computation, regression error diagnostics, and report ready metric outputs. The package also includes artifact path management, debug state utilities, structured run level metadata persistence capturing seeds, configuration states, thresholds, metrics, ensemble transitions, fused evaluation artifacts, and model identifiers, as well as reproducible scripts and vignettes documenting end to end experiments. Kingma and Ba (2015) "Adam: A Method for Stochastic Optimization". Hinton et al. (2012) "Neural Networks for Machine Learning (RMSprop lecture notes)". Duchi et al. (2011) "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization". Zeiler (2012) "ADADELTA: An Adaptive Learning Rate Method". Zhang et al. (2019) "Lookahead Optimizer: k steps forward, 1 step back". You et al. (2019) "Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (LAMB)". McMahan et al. (2013) "Ad Click Prediction: a View from the Trenches (FTRL-Proximal)". Klambauer et al. (2017) "Self-Normalizing Neural Networks (SELU)". Maas et al. (2013) "Rectifier Nonlinearities Improve Neural Network Acoustic Models (Leaky ReLU / rectifiers)". Package: r-cran-ddi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-ddi_0.1.0-1.ca2604.1_all.deb Size: 19138 MD5sum: bfec699cc8bfe8ceee796480515804b9 SHA1: de0169597dc7d91b8dee4587c22da347679f9670 SHA256: a285ed2c11b1a22efc35280337d617b783cb801b2a2bc61bcdcd6dab7c8a3c37 SHA512: c2d7bfad3ecf6ad4b0b4632c5381d1bad01ecb26bf4ed7228564c18dde34ea81bd16303226db91c994abe9c3ef70206be99c8be4f7b1c3ab7a9f409a9c8f965c Homepage: https://cran.r-project.org/package=ddi Description: CRAN Package 'ddi' (The Data Defect Index for Samples that May not be IID) Implements Meng's data defect index (ddi), which represents the degree of sample bias relative to an iid sample. The data defect correlation (ddc) represents the correlation between the outcome of interest and the selection into the sample; when the sample selection is independent across the population, the ddc is zero. Details are in Meng (2018) , "Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election." Survey estimates from the Cooperative Congressional Election Study (CCES) is included to replicate the article's results. Package: r-cran-ddiv Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-segmented, r-cran-qpdf Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ddiv_0.1.1-1.ca2604.1_all.deb Size: 402952 MD5sum: c70bd0cef076e02f6529761c2efdb92a SHA1: e70007dc72425fea8f0ffe51e26685ec2fa866e5 SHA256: 49c6136a18a554e2b3aa28372c678d6735edf4e967a92e154e17b409da0900f9 SHA512: 2a1038e59eb227c2ee1c914684e8face3dec1d26be799b5dd53a52a45b5e5cafac4cdaf27e50191d9ff4414c6fa3d5cb2aad75ca3a177ea7e0d41198f882f97c Homepage: https://cran.r-project.org/package=ddiv Description: CRAN Package 'ddiv' (Data Driven I-v Feature Extraction) The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be found in the following paper. [1] Ma, X. et al, 2019. . Package: r-cran-ddiwr Architecture: all Version: 0.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admisc, r-cran-base64enc, r-cran-declared, r-cran-digest, r-cran-xml2, r-cran-haven, r-cran-readxl, r-cran-writexl Filename: pool/dists/resolute/main/r-cran-ddiwr_0.19-1.ca2604.1_all.deb Size: 405610 MD5sum: 2e28acef02584d3fb7deaba07c2a0f68 SHA1: dc2e3da8dcdebd5056b9d2b1b87938e4a53497bb SHA256: 568a41bc92b364b4adb1324efac2f1b4e0107884762ef10051c09dbdc90d2a8d SHA512: 4950b3bd7cd5a1c49ac079c7a6aa2fd92510fc41f13c83a07e9de7942f6f2d7fd3d2689537d523f5808a6fc5ac1b9918f2751b75b7ec62cc79bc780886ba18df Homepage: https://cran.r-project.org/package=DDIwR Description: CRAN Package 'DDIwR' (DDI with R) Useful functions for various DDI (Data Documentation Initiative) related inputs and outputs. Converts data files to and from DDI, SPSS, Stata, SAS, R and Excel, including user declared missing values. 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Package: r-cran-ddm Architecture: all Version: 1.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ddm_1.0-0-1.ca2604.1_all.deb Size: 180534 MD5sum: 2893cc9e84e812a76951e0c09f520319 SHA1: 6ef4bc50bdc31e162fc16bbf3c60cf7799784293 SHA256: 74394abceec5bfcf715027a5b2ffa0a1a75a1750f898e0a39b602d46643ecad3 SHA512: ee3c5fef7b1d5554b80b1c9644e7f011c24890bacdd7537dc624f2f7664038d6771624867b23b66af4293f7d67dfaea4ba303654d51c454a008bb93e51a7eb8a Homepage: https://cran.r-project.org/package=DDM Description: CRAN Package 'DDM' (Death Registration Coverage Estimation) A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. 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The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package. 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Package: r-cran-ddpm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ddpm_0.1.0-1.ca2604.1_all.deb Size: 196946 MD5sum: 9d68ea4bd06a5e484137e2c1815b3b97 SHA1: 8d3f5f83930ea9976029838ecbd223c0f180b488 SHA256: bed9b2e04ab1acbaa478d6c0ddce3d99b57754f42ea52af32e61f22f73faad3c SHA512: 16f86a3c91255bfdf860cac86a5b149e83f5d89e91a55e27e0a85ae739bed057f2c1330b2fd6426dc2372543993c7c2480027e3d240e2ed5d2bbab87acb83809 Homepage: https://cran.r-project.org/package=DDPM Description: CRAN Package 'DDPM' (Data Sets for Discrete Probability Models) A wide collection of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. 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Package: r-cran-ddpna Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggfun, r-cran-ggrepel, r-cran-megena, r-cran-igraph, r-cran-hmisc, r-cran-plyr, r-cran-scales, r-cran-venndiagram Suggests: r-cran-wgcna, r-bioc-biostrings, r-bioc-impute, r-cran-ggfortify Filename: pool/dists/resolute/main/r-cran-ddpna_0.4.1-1.ca2604.1_all.deb Size: 2435022 MD5sum: 43835a49ab3efbfed2fe0193efa4957c SHA1: b0cdbfe87bb575f0067640ae3d896bf4d084fc43 SHA256: f6151975184526b8aca7e0656741d2594d60f7a7ccd12527119b511dee6b60b0 SHA512: 215105a024d6938d2fdcfd9073f3559af4140107dd68de1f6dcfaaff7186a585855bb3063e6dbec35aab12e52346d2bb0ff7156c48e01554a3ba2bfda5bfcf6c Homepage: https://cran.r-project.org/package=DDPNA Description: CRAN Package 'DDPNA' (Disease-Drived Differential Proteins Co-Expression NetworkAnalysis) Functions designed to connect disease-related differential proteins and co-expression network. 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Reference: Li et al. (2023) . Package: r-cran-deadband Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ttr Filename: pool/dists/resolute/main/r-cran-deadband_0.1.0-1.ca2604.1_all.deb Size: 652530 MD5sum: 08104c446d5e74897009e6cd94d918aa SHA1: 96d1461f7e6439d63507b9633d629078b54615bf SHA256: 1d0c3fe98cf2bbc6e84c4f89c3bccc324f7950d5ec70f921063907b16fbc4538 SHA512: 5cef9056116563ad69c0e6fe93ae8725f4b758a0b60ca8cda8e04aad794b25880592a4610734f6fff18929164fa123869f166b1cda2842811d4320bbe2038d17 Homepage: https://cran.r-project.org/package=deadband Description: CRAN Package 'deadband' (Statistical Deadband Algorithms Comparison) Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms. Package: r-cran-dear Architecture: all Version: 1.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 831 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-ggplot2, r-cran-tidyr, r-cran-plotly, r-cran-igraph, r-cran-writexl, r-cran-dplyr, r-cran-gridextra, r-cran-optisolve Filename: pool/dists/resolute/main/r-cran-dear_1.5.4-1.ca2604.1_all.deb Size: 788454 MD5sum: 96b76c86f3d0a73fc1ea2b32f7ca229f SHA1: e29c600364cfb5cece3fd2abee216b48b2925bf2 SHA256: 73b1acfbc107b4e8cf90c4abbc8767e58ee0396a1d184adb7bcdb30c6a56ff72 SHA512: dfeb17811942189e67ada461f1dab1e3cf3e55cc44a1cc341b4a01530dfb37d92c39f5b47307118c018db5bc10af4d3b6ab5fd52f7ccedfb69ff6a58dafb5670 Homepage: https://cran.r-project.org/package=deaR Description: CRAN Package 'deaR' (Conventional and Fuzzy Data Envelopment Analysis) Set of functions for Data Envelopment Analysis, including classical, fuzzy, cross-efficiency, bootstrapping, and Malmquist models. See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). , Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). . 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It also implements calibration using negative control outcomes to enhance robustness. 'debiasedTrialEmulation' facilitates effect estimation for both binary and time-to-event outcomes, supporting risk ratio (RR), odds ratio (OR), and hazard ratio (HR) as effect measures. It integrates statistical modeling and visualization tools to assess covariate balance, equipoise, and bias calibration. Additional methods—including approaches to address immortal time bias, information bias, selection bias, and informative censoring—are under development. Users interested in these extended features are encouraged to contact the package authors. 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Package: r-cran-deboinr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-ggplot2, r-cran-gridextra, r-cran-pracma, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-deboinr_1.0-1.ca2604.1_all.deb Size: 601780 MD5sum: 5bc878ec925f990bb405edbaf5ef0eef SHA1: cc82b77c18c4ad13d79addb1ecc77a52834f65e9 SHA256: db59ed158ed1ee000325db44cf71327d9b93e38987f3382b8bd9346421133997 SHA512: e5bb2f41f2b8d08e720d257f3b2791ef33b6aeb514607130e772d8596ac9b2dc1d2f387bf8c594b5a8d8c9b0e5edf24f29049d9b4955da67cf5ab698a154ad23 Homepage: https://cran.r-project.org/package=DeBoinR Description: CRAN Package 'DeBoinR' (Box-Plots and Outlier Detection for Probability DensityFunctions) Orders a data-set consisting of an ensemble of probability density functions on the same x-grid. Visualizes a box-plot of these functions based on the notion of distance determined by the user. Reports outliers based on the distance chosen and the scaling factor for an interquartile range rule. For further details, see: Alexander C. Murph et al. (2023). "Visualization and Outlier Detection for Probability Density Function Ensembles." . 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Designed for terminal workflows and artificial intelligence (AI) agent consumption, offering views including hotspot analysis, call trees, source context, caller/callee relationships, and memory allocation breakdowns. Package: r-cran-debtkit Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-debtkit_0.1.2-1.ca2604.1_all.deb Size: 140016 MD5sum: 1db03dc6ee06c914fb7fa3d0a94d755a SHA1: cc19a1d3d54b5da943d7cf7c9577e0dc203956d3 SHA256: 9db74885c135d60c5bdb4ff2270939bb33660fbbb7f38dabece0a66e0762079c SHA512: 091623ad9b0101ec3e97547a49fe2db0d238aad599572415e4f9569a3f23016a3989fac747d1ba739d47bd60e229f727b25572ed95ac9cfa416a77c43c5888d1 Homepage: https://cran.r-project.org/package=debtkit Description: CRAN Package 'debtkit' (Debt Sustainability Analysis and Fiscal Risk Assessment) Analyses government debt sustainability using the standard debt dynamics framework from Blanchard (1990) and the IMF Debt Sustainability Analysis methodology (IMF, 2013) and the Sovereign Risk and Debt Sustainability Framework (IMF, 2022). Projects debt-to-GDP paths, decomposes historical debt changes into interest, growth, and primary balance contributions, and estimates fiscal reaction functions following Bohn (1998) . Produces stochastic fan charts via Monte Carlo simulation, standardised stress tests, and IMF- style heat map risk assessments. Computes S1/S2 sustainability gap indicators used by the European Commission. All methods are pure computation with no external dependencies beyond base R; works with fiscal data from any source. 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Package: r-cran-debugr Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rprojroot, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-debugr_0.0.1-1.ca2604.1_all.deb Size: 35054 MD5sum: 7e302233a771c6cf4d5aecd2ad599f55 SHA1: 4dac4d50b30d27a0106dd5a16561b999d4e639c6 SHA256: 9ceb8661c092027d785bfc7299a1e0eab0cbd298c13809aedbbd88b1783a1dba SHA512: 7187157a53359b461dcdce59572ef59bc8587788e32fba329e1b9d808fbd7436d51db04560c97a18e2ca8c453f18e7a72b4817c41e4066d2b2b33d5ca3716b83 Homepage: https://cran.r-project.org/package=debugr Description: CRAN Package 'debugr' (Debug Tool to Watch Objects/Expressions While Running an RScript) Tool to print out the value of R objects/expressions while running an R script. Outputs can be made dependent on user-defined conditions/criteria. Debug messages only appear when a global option for debugging is set. This way, 'debugr' code can even remain in the debugged code for later use without any negative effects during normal runtime. Package: r-cran-decide Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-decide_1.3-1.ca2604.1_all.deb Size: 64860 MD5sum: 8305137812afc5aefab669d4522fb7ae SHA1: 81635bcb977964eb43c25f8407a55d75ca2e953e SHA256: cafb8d6f4d138706f3e7d829b8c411186adce464ca8d5276c948be9f40b98f1b SHA512: 6fd7716db1ae236f3aeae5cf71fb6770e81ef1ead87751fc68baad6cb05afaf6c0f46e2144a7dda58a86e06fc6da7d0534bbff1be0b0943628eb053e632882da Homepage: https://cran.r-project.org/package=DECIDE Description: CRAN Package 'DECIDE' (DEComposition of Indirect and Direct Effects) Calculates various estimates for measures of educational differentials, the relative importance of primary and secondary effects in the creation of such differentials and compares the estimates obtained from two datasets. Package: r-cran-decision Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-decision_0.1.0-1.ca2604.1_all.deb Size: 13792 MD5sum: e558c51cf360d4fec877a4f2f80c6a70 SHA1: 8b853587c531025dff93e7d9e9a71766a9b27185 SHA256: eef49f3f50cf5fbf0f3a36916c990ec5c38ec7da11bf3e18368c0877629c9d76 SHA512: 9ad814ffeba68dab9b9556c800ec13fdbcfbd9fa83f9f2f0ce5cd05f08ddc3886cd3fe6f11ac4fd5139539cd5912d18316a1a0f792abe03795660a7dba14cf21 Homepage: https://cran.r-project.org/package=decision Description: CRAN Package 'decision' (Statistical Decision Analysis) Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making. Package: r-cran-decisiondrift Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-tibble Suggests: r-cran-decisionpaths, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-decisiondrift_0.1.0-1.ca2604.1_all.deb Size: 193846 MD5sum: d2c6f06c34e558b54509706d2a48a49d SHA1: af034d3f56eea5269d123d343382b1fde76ad967 SHA256: e02d4f768c46f0e3498a8df9145261fa6df2735f95ef8b2509a9228cee7e1595 SHA512: 27615d49a785a8dae6aafb43026ac7cce9935eee9878b12b4bb4c2238bc410f3a6c331f67c98953e7a19bbefd49c7660377197f4ea5c721737d0c7fa4d42fe4e Homepage: https://cran.r-project.org/package=DecisionDrift Description: CRAN Package 'DecisionDrift' (Detecting, Decomposing, and Stress-Testing Temporal Change inRepeated Decision Systems) Tools for detecting, decomposing, and stress-testing temporal drift in repeated binary decision systems. Complements the 'decisionpaths' package by shifting focus from path construction to system-level change over time. Implements five core analytic modules: (1) prevalence drift — did the overall decision rate change over time?; (2) transition drift — did the probability of switching or persisting change?; (3) entropy and stability trends — did path complexity evolve?; (4) group-differential drift — did the system drift differently across subgroups?; (5) change-point and regime-shift detection — did the system change abruptly after a policy or model update? Additionally provides a robustness module for testing stability of drift conclusions across analytic choices, and a sensitivity module for probing vulnerability to data problems including missingness, miscoding, and threshold shifts. Defines four original drift indices: the Decision Drift Index (DDI), Transition Drift Index (TDI), Group Differential Drift (GDD), and Cumulative Drift Burden (CDB). Applications include algorithmic audit, AI governance, education, health, and organisational research. Package: r-cran-decisionpaths Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-decisionpaths_0.1.0-1.ca2604.1_all.deb Size: 84920 MD5sum: e1e256504661632515b8880a84a46797 SHA1: 2f25d30b3d26062030ef977dec82a7fe5c7d7047 SHA256: 984ea570016ce875250075db62878ee470edabb79d74f095f40202cd70b83bac SHA512: aec6ac19cfb87b3aeb42830ecabc9494eb56e8c1ac2a574295d44c971c5a235604fab25d2a71890f396f370c388eceff53c84be9c2b224163c19219dc0de942a Homepage: https://cran.r-project.org/package=decisionpaths Description: CRAN Package 'decisionpaths' (Construct and Audit Longitudinal Decision Paths) Tools for constructing and auditing longitudinal decision paths from panel data. Implements a decision infrastructure framework for representing institutional AI systems as generators of time-ordered binary decision sequences. Provides functions to build path objects from panel data, summarise per-unit descriptors (dosage, switching rate, onset, duration, longest run), compute the Decision Reliability Index (DRI) following Cronbach (1951) , estimate Shannon decision-path entropy following Shannon (1948) , classify systems by infrastructure type (static, periodic, continuous, human-in-the-loop), and evaluate subgroup disparities in decision exposure and stability. Applications include education, policy, health, and organisational research. 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Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own. 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Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued. 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Function "code" can be used to code plain text to code if there is a 1:1 relation between the two. The concept relies on 'keyvalue' objects used for translation. There are several 'keyvalue' objects included in the areas of geographical regional codes, administrative health care unit codes, diagnosis codes and more. It is also easy to extend the use by arbitrary code sets. Package: r-cran-decompml Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-nnfor, r-cran-rlibeemd, r-cran-vmdecomp Filename: pool/dists/resolute/main/r-cran-decompml_0.1.1-1.ca2604.1_all.deb Size: 80468 MD5sum: 4d787cb70c02efa6c93f7a0049c9ebf3 SHA1: e6fba36a67b0403224d6d680357c983363a7516e SHA256: 62318a342b1e1cd5bbdb7d53c62bea03b3a16c2a125c3f6442981ead8c4208ed SHA512: 8a34ecb54f2919d092d7732763d2dd64792d2a0b6698b026b872df97bf0be23d277327e1a075c43e72f030aa2a58a2e578d275e75cfbeadb576bf072d44f2846 Homepage: https://cran.r-project.org/package=decompML Description: CRAN Package 'decompML' (Decomposition Based Machine Learning Model) The hybrid model is a highly effective forecasting approach that integrates decomposition techniques with machine learning to enhance time series prediction accuracy. Each decomposition technique breaks down a time series into multiple intrinsic mode functions (IMFs), which are then individually modeled and forecasted using machine learning algorithms. The final forecast is obtained by aggregating the predictions of all IMFs, producing an ensemble output for the time series. The performance of the developed models is evaluated using international monthly maize price data, assessed through metrics such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). For method details see Choudhary, K. et al. (2023). . Package: r-cran-decomposedpsf Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psf, r-cran-rlibeemd, r-cran-forecast, r-cran-tseries Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-decomposedpsf_0.2-1.ca2604.1_all.deb Size: 29282 MD5sum: 76e45e1422e89c361176233dd2a0eba2 SHA1: e20338e43ec7852a1a06f5d997cd31dcffe6a220 SHA256: 0bcbaeaf1f4a9e6127e9993fb3cb770d7b5148d0fd4ba451499cf8831fd17c3f SHA512: 04cc019c3de8a4991012c0833c1e7a2c86227430d7676405e39b72d6623cbf54170186dff69e642f71fcbbc19f8e4daf0e251d1b41739209e71c39fa0b68d29a Homepage: https://cran.r-project.org/package=decomposedPSF Description: CRAN Package 'decomposedPSF' (Time Series Prediction with PSF and Decomposition Methods (EMDand EEMD)) Predict future values with hybrid combinations of Pattern Sequence based Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based hybrid methods. Package: r-cran-decomposer Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 932 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-usethis, r-cran-tictoc, r-cran-stratigrapher, r-cran-hexbin, r-cran-colorramps, r-cran-dplyr Suggests: r-cran-emd, r-cran-rssa, r-cran-astrochron Filename: pool/dists/resolute/main/r-cran-decomposer_1.0.7-1.ca2604.1_all.deb Size: 820230 MD5sum: 321a12fadd9087183de5b33107c0f472 SHA1: ae3f14ebac43cce6fc73dc6fb07c805b2761d3ab SHA256: d12d58e9b0da8906733ca32253c1855ba38872fe7d0b376cd5980d4ba1455204 SHA512: 6e4898c9cc27bcbd84ee1956cf84de104c84f82f27f18e5800a7e63ad04d056f985b6b1e9a3d491f4133c467406d3f1df0ecb082590c503d4d53913aaf8f14be Homepage: https://cran.r-project.org/package=DecomposeR Description: CRAN Package 'DecomposeR' (Empirical Mode Decomposition for Cyclostratigraphy) Tools to apply Ensemble Empirical Mode Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new algorithm, extricate, that performs EEMD in seconds, a linear interpolation algorithm using the greatest rational common divisor of depth or time, different algorithms to compute instantaneous amplitude, frequency and ratios of frequencies, and functions to verify and visualise the outputs. The functions were developed during the CRASH project (Checking the Reproducibility of Astrochronology in the Hauterivian). When using for publication please cite Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2022, "A decomposition approach to cyclostratigraphic signal processing". Earth-Science Reviews 225 (103894). . 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(2005). A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research, 12, pp.141–172. In addition, there is a decomposition function for disease cause breakdown and a couple helpful plot functions. Package: r-cran-deconvolver Architecture: all Version: 1.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2890 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-deconvolver_1.2-1-1.ca2604.1_all.deb Size: 1679018 MD5sum: 91825a499d79de8c91e6da9d967eded5 SHA1: d523ea312f00a7c6ac24bd9cfa0ae91965867c6b SHA256: 569defa6efef455f896ec1aed1d42251ab61ac0d5543780f0e78ed86dbf04752 SHA512: 69540356bd8386dc3bff35b2b338a6eafe321d0655e3ac4c17690738dfe9593ffb7eae18c100cd8358c680046028efb4d379ff7494c6d93bbf8255ce173b7972 Homepage: https://cran.r-project.org/package=deconvolveR Description: CRAN Package 'deconvolveR' (Empirical Bayes Estimation Strategies) Empirical Bayes methods for learning prior distributions from data. 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The method is as described in: Campos, D.F., (1984, ISBN:9686194444). 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The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way. It also adds some popularity measures for the packages e.g. in the form of download count through the 'cranlogs' package. Uses the CRAN metadata database and Bioconductor metadata . Other data acquire functions are: get_dependencies(), get_downloads() and get_description(). The deepdep_shiny() function runs shiny application that helps to produce a nice 'deepdep' plot. Package: r-cran-deepgmm Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-deepgmm_0.2.1-1.ca2604.1_all.deb Size: 86956 MD5sum: 1541470db7ff0512161eb0091324bfcb SHA1: 4b555fd2ca2d9efc1b7967723286ef73d6868316 SHA256: e65950423bcf94838e46e27676493e33f18fb9130b4faf9ed7aea8cbf193807e SHA512: e873321cf3978bdbca2de63c8a3f1c64a53e76b49150e38c3810ca243544b52360ebceb04d0c13327da26179b48c72a864f07dbed26b1818bfe24366bc58ed08 Homepage: https://cran.r-project.org/package=deepgmm Description: CRAN Package 'deepgmm' (Deep Gaussian Mixture Models) Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) provide a generalization of classical Gaussian mixtures to multiple layers. Each layer contains a set of latent variables that follow a mixture of Gaussian distributions. To avoid overparameterized solutions, dimension reduction is applied at each layer by way of factor models. Package: r-cran-deeplearningcausal Architecture: all Version: 0.0.107-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocr, r-cran-caret, r-cran-neuralnet, r-cran-superlearner, r-cran-ggplot2, r-cran-tidyr, r-cran-magrittr, r-cran-reticulate, r-cran-keras3, r-cran-hmisc Suggests: r-cran-testthat, r-cran-dplyr, r-cran-class, r-cran-xgboost, r-cran-randomforest, r-cran-glmnet, r-cran-ranger, r-cran-gam, r-cran-e1071, r-cran-gbm, r-cran-tensorflow Filename: pool/dists/resolute/main/r-cran-deeplearningcausal_0.0.107-1.ca2604.1_all.deb Size: 342380 MD5sum: 5ba2f0bc5ceddfb93413547ad5527ee3 SHA1: ac834f32ebd55a93bc102f77c1ac1c99590f2d05 SHA256: b1768060597b010da71d8ff5e899ffb70217f9140eea90f05e70aa78411c4037 SHA512: f5b7ea460a66ea33eb9dcf5560645bd0700bed9273f87aa6daa8126498af12f798c075c77478a206b1e06237b225d21590256e4e8a8c65c031fc83a8c3733370 Homepage: https://cran.r-project.org/package=DeepLearningCausal Description: CRAN Package 'DeepLearningCausal' (Causal Inference with Super Learner and Deep Neural Networks) Functions for deep learning estimation of Conditional Average Treatment Effects (CATEs) from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements the conformal prediction framework that enables computation and illustration of conformal prediction (CP) intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with treatment noncompliance using weighted ensemble learning via the super learner approach and R neural networks. Package: r-cran-deeplr Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-utf8, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-tokenizers, r-cran-jsonlite, r-cran-readr Suggests: r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-deeplr_2.1.0-1.ca2604.1_all.deb Size: 169790 MD5sum: 9fbaf0d04505b17da5d34c490416b425 SHA1: 9361d7369f4a5d7a4804b011aaee921bce78673a SHA256: 7e76e1d52758420a940126d7f05fe846e8c09e1eca1e9703d3b8ab44dd736454 SHA512: 4d22db7e6757ad46e68076f329d4644ecd4899eb5dddaa40cc114bd6b3f751216d59a454635ce3daf938a59395c6efe9b589bbadf7ec39bdcca1255256bdf1c6 Homepage: https://cran.r-project.org/package=deeplr Description: CRAN Package 'deeplr' (Interface to the 'DeepL' Translation API) A wrapper for the 'DeepL' API , a web service for translating texts between different languages. A DeepL API developer account is required to use the service (see ). Package: r-cran-deepmou Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-skmeans, r-cran-extradistr, r-cran-dplyr, r-cran-rfast, r-cran-entropy, r-cran-ggplot2, r-cran-mass Filename: pool/dists/resolute/main/r-cran-deepmou_0.1.1-1.ca2604.1_all.deb Size: 127256 MD5sum: a574785c2f559a45477031d7520a528d SHA1: ed8a6684ab58efd9a6b5f1a07deac11300528a23 SHA256: 0a92540bbf87eb5740a87a8290f2d0df5aea3f478c681fea23909c8121edb153 SHA512: 5fdaf7e9650fca4627f70b9ba600a4581454c8d7e4e2e8a82eec3569b45541e5d0b3b3198c79efa793b8ab246bd4930c8882b3d89a0aa850f596e04efa04443c Homepage: https://cran.r-project.org/package=deepMOU Description: CRAN Package 'deepMOU' (Clustering of Short Texts by Mixture of Unigrams and Its DeepExtensions) Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). ), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) ) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) ). There are also functions for graphical representation of clusters obtained. Package: r-cran-deepnet Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-deepnet_0.2.1-1.ca2604.1_all.deb Size: 60220 MD5sum: 75d7729b6576b73b8382f3b0b2c96390 SHA1: ddbde9270517a979c1e91e6fb5e62cf74b758eb0 SHA256: 6fea398a580b782c725aaa457a94547e354a3315b00681261c241a007f5efd01 SHA512: e4ba3cf1012e17c583835863bfaf8e7d2b282d1ab61d0bb3de382b8c46c36ca3698e2cdbb64a2e4192f78f3d6023caf4e35af30eee230e482e41e45498502190 Homepage: https://cran.r-project.org/package=deepnet Description: CRAN Package 'deepnet' (Deep Learning Toolkit in R) Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on. Package: r-cran-deepnn Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-deepnn_1.2-1.ca2604.1_all.deb Size: 161458 MD5sum: 210c69f3913342120af8f1a476a10baa SHA1: 468e74dab0bbddd7411fa0397e5cc94d4ac0cca5 SHA256: e888c7ac6ee87ae1bd3f16b0aa4e24d5cfd15468bd1db6aac11e157a7c0b7ec5 SHA512: 8c89d413ebff1b8465825272bfb32666b880a5ab5ddb049f095683d4e555bdd0556010ea945e232d9b49931045fd5e7ed8272be1d3e8473fb43d833753205f2b Homepage: https://cran.r-project.org/package=deepNN Description: CRAN Package 'deepNN' (Deep Learning) Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) Neural Networks YouTube playlist. Michael A. Nielsen Neural Networks and Deep Learning. Package: r-cran-deeprstudio Architecture: all Version: 0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-assertthat, r-cran-clipr, r-cran-rstudioapi, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-deeprstudio_0.0.9-1.ca2604.1_all.deb Size: 818768 MD5sum: 6dec3f213d0d1ce07425380dccd6e8b9 SHA1: ae7cf219e98cb066905559cfff56b8bde57fcb01 SHA256: a6d8f846a7b5fa7d49be229551a5585a1a6e9553b24d7b509a39225bf6fe55ff SHA512: 2f0d99f7077ff46983c2eaa34bb69a09599578ae87e0745601fc9d3f8c3b171e9bcbff7b87435873da11e9d08dba8ce206ec5212e659d228158952b3319bd6dd Homepage: https://cran.r-project.org/package=deepRstudio Description: CRAN Package 'deepRstudio' (Seamless Language Translation in 'RStudio' using 'DeepL' API and'Rstudioapi') Enhancing cross-language compatibility within the 'RStudio' environment and supporting seamless language understanding, the 'deepRstudio' package leverages the power of the 'DeepL' API (see ) to enable seamless, fast, accurate, and affordable translation of code comments, documents, and text. This package offers the ability to translate selected text into English (EN), as well as from English into various languages, namely Japanese (JA), Chinese (ZH), Spanish (ES), French (FR), Russian (RU), Portuguese (PT), and Indonesian (ID). With much of the text being written in English, the emphasis is on compatibility from English. It is also designed for developers working on multilingual projects and data analysts collaborating with international teams, simplifying the translation process and making code more accessible and comprehensible to people with diverse language backgrounds. This package uses the 'rstudioapi' package and 'DeepL' API, and is simply implemented, executed from addins or via shortcuts on 'RStudio'. With just a few steps, content can be translated between supported languages, promoting better collaboration and expanding the global reach of work. The functionality of this package works only on 'RStudio' using 'rstudioapi'. Package: r-cran-deepspat Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-matrix, r-cran-reticulate, r-cran-keras, r-cran-tensorflow, r-cran-tfprobability, r-cran-evd, r-cran-spatialextremes, r-cran-fields Filename: pool/dists/resolute/main/r-cran-deepspat_0.3.1-1.ca2604.1_all.deb Size: 497392 MD5sum: 86e1c0419be1aa380dbc293090fbed04 SHA1: 1104662c257ee3cbb33aa4ee81ad4ac11c26423c SHA256: 2129e0c44965784f8dc090c4fc973183b7566bbe229fee28ec6527fef7719cc4 SHA512: 20e7989f0554ed080c9f28fe59d1ba820723c23d40fc839f4d114918febc1ccf772efbbed958f2853d035af0046c56269920213b69d0d4dfb0b6d1ab9223c14d Homepage: https://cran.r-project.org/package=deepspat Description: CRAN Package 'deepspat' (Deep Compositional Spatial Models) Deep compositional spatial models are standard spatial covariance models coupled with an injective warping function of the spatial domain. The warping function is constructed through a composition of multiple elemental injective functions in a deep-learning framework. The package implements two cases for the univariate setting; first, when these warping functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. In the multivariate setting only the former case is available. Estimation and inference is done using `tensorflow`, which makes use of graphics processing units. For more details see Zammit-Mangion et al. (2022) , Vu et al. (2022) , Vu et al. (2023) , and Shao et al. (2025) . Package: r-cran-deeptime Architecture: all Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4740 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deeptimedata, r-cran-ggplot2, r-cran-ggforce, r-cran-gridextra, r-cran-gtable, r-cran-lattice, r-cran-rlang, r-cran-scales, r-cran-ggfittext, r-cran-curl, r-cran-cli, r-cran-lifecycle, r-cran-grimport2, r-cran-ggh4x Suggests: r-cran-geomtextpath, r-cran-phytools, r-cran-dplyr, r-cran-divdyn, r-cran-gsloid, r-cran-ape, r-cran-palaeoverse, r-cran-paleotree, r-cran-disprity, r-bioc-ggtree, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-ggpattern, r-cran-ggrepel, r-cran-rmacrostrat, r-cran-svglite Filename: pool/dists/resolute/main/r-cran-deeptime_2.3.1-1.ca2604.1_all.deb Size: 3661080 MD5sum: 0c86c0cfe8b47bfd20322c815b80ef05 SHA1: 3557e1afd12d5725d72f918786142cada21d37b9 SHA256: c6b208a1d551b863f5a711acc9db094d53000852dbe190061450d7d0ea5e91df SHA512: bf751cc67a08fc8b43c3d21aa76e134a19bdfe59a1830022502d4171f37ad572773b436da33de290fed69aa603c94f988862d1c857b42b6506958d5b3d1bcebe Homepage: https://cran.r-project.org/package=deeptime Description: CRAN Package 'deeptime' (Plotting Tools for Anyone Working in Deep Time) Extends the functionality of other plotting packages (notably 'ggplot2') to help facilitate the plotting of data over long time intervals, including, but not limited to, geological, evolutionary, and ecological data. The primary goal of 'deeptime' is to enable users to add highly customizable timescales to their visualizations. Other functions are also included to assist with other areas of deep time visualization. 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Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user’s gene lists, aiding in hypothesis generation and data-driven literature review. Sokolowski, Dustin J., et al. "Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression." Nucleic Acids Research Genomics and Bioinformatics (2023). 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This model is proposed as an alternative to the widespread measure Cohen kappa coefficient, which performs poorly when the marginal distributions are very asymmetric (Martin-Andres and Femia-Marzo (2004), ; Martin-Andres and Femia-Marzo (2008) ). The package also contains a function to perform a massive analysis of multiple raters against a gold standard. A shiny app is also provided to obtain the measures of nominal agreement between two raters. 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Package: r-cran-demokde Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-demokde_1.0.1-1.ca2604.1_all.deb Size: 27326 MD5sum: 30cc960568715390b332d5db35589531 SHA1: e1380925d0e025e2a0a4a1e82b75de1d9d20273f SHA256: 94c81ccd9139841e7b1c69fda40c4e742e46bd5f48be84071b171af963b22fa2 SHA512: d8de2bae73eb5950595fcc5712494cda64f042223481088540c7dfc06fff908b3f72bd0514aa9e471ce87a7a1f285a651e6bf9f4ee59d290b9494a24ab8c3ddb Homepage: https://cran.r-project.org/package=demoKde Description: CRAN Package 'demoKde' (Kernel Density Estimation for Demonstration Purposes) Demonstration code showing how (univariate) kernel density estimates are computed, at least conceptually, and allowing users to experiment with different kernels, should they so wish. The method used follows directly the definition, but gains efficiency by replacing the observations by frequencies in a very fine grid covering the sample range. A canonical reference is B. W. Silverman, (1998) . NOTE: the density function in the stats package uses a more sophisticated method based on the fast Fourier transform and that function should be used if computational efficiency is a prime consideration. Package: r-cran-demokin Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5801 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-progress, r-cran-matrixcalc, r-cran-matrix, r-cran-mass, r-cran-igraph, r-cran-magrittr, r-cran-data.table, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-demokin_1.0.3-1.ca2604.1_all.deb Size: 3040224 MD5sum: d65aae2be3f9ff04bb4c72df47232c2b SHA1: 1e8b6e8e035ceefc3d74fff03f61d9a5cfc343ae SHA256: 9f4863a17093f66c3dd9da2745a8fe97622c7837504fdb119fff1b1346bac279 SHA512: 0ee981e33a0ee9902357916ac437cec84b3a3a06d14f15f00c25846e3e12ed48d2ccd296a4ccf19da26d598d8962dd1f28bfcf7ff224c0a91e7919f993f58527 Homepage: https://cran.r-project.org/package=DemoKin Description: CRAN Package 'DemoKin' (Estimate Population Kin Distribution) Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model. Package: r-cran-demor Architecture: all Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-demor_1.0.10-1.ca2604.1_all.deb Size: 202442 MD5sum: 728ad660be1f8c672f5a6169575944cc SHA1: a8e71b733bcba33e04fb0c282b4a66002d3b3214 SHA256: 85c24abe899a6e7b199ff02114e2fae7e6a90c4c2c67e2b4fb0ce65980cc7687 SHA512: 126fe8549dbbd51e535f1ae6941e83ab5517ac1941e018b8becd09a6ff75552a315c341466cf4db5ddcd7569aa89bd0800828f539a8861ae16bb7d6d59aac157 Homepage: https://cran.r-project.org/package=demor Description: CRAN Package 'demor' (Methods for Demographic Analysis) Implements life tables, fertility and mortality indicators, decomposition methods, Lee-Carter mortality forecasting, Leslie matrices, and population pyramids for demographic analysis. Methods are described in Preston et al. (2001, ISBN:1557864519) and Ustyuzhanin (2025) . Package: r-cran-demoshiny Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-demoshiny_0.1-1.ca2604.1_all.deb Size: 241470 MD5sum: 9af2a71898c8490ad753a8734387709e SHA1: cf750034b86b283b8e56332d82cba372c1cccabb SHA256: cf0a0914176ebf8f8d6a22be70771f6db44fb5676a71f710f4e8c5c1dbe5dbb5 SHA512: da61aed63afa1d1a904a39ba3ef2cc6ff9c4e287135277b53d88fbf2f47a990572a1e229287cdbfe2bdcdf5e02db84153aae0709188d39bf3e99537d655100ee Homepage: https://cran.r-project.org/package=demoShiny Description: CRAN Package 'demoShiny' (Runs a 'Shiny' App as Demo or Lists All Demo 'Shiny' Apps) Mimics the demo functionality for 'Shiny' apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic). Package: r-cran-demulticoder Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3894 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-furrr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble Suggests: r-cran-biocmanager, r-bioc-biostrings, r-bioc-dada2, r-bioc-shortread, r-bioc-phyloseq, r-cran-rmarkdown, r-cran-rcppparallel, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-demulticoder_0.1.2-1.ca2604.1_all.deb Size: 1492808 MD5sum: 1d018b8c17631eeba7a21047d0262d7e SHA1: fdc3ed2d62e09f63b706187a95e9cc5b77b272a1 SHA256: b1bb3edfccb0ecbcc00bf59c79d5d1103ae149bf81bf6bfb4b9cb4c7c9d1f19f SHA512: c6ada29272223e7145e05baba63e47e8baae76c4bfba938a18fce94c59ffe5c9cf2e83b6548c2f7e39dfb72f45bb1dffaeeb4e84f701650adafa3942ed0d7866 Homepage: https://cran.r-project.org/package=demulticoder Description: CRAN Package 'demulticoder' (Simultaneous Analysis of Multiplexed Metabarcodes) A comprehensive set of wrapper functions for the analysis of multiplex metabarcode data. It includes robust wrappers for 'Cutadapt' and 'DADA2' to trim primers, filter reads, perform amplicon sequence variant (ASV) inference, and assign taxonomy. The package can handle single metabarcode datasets, datasets with two pooled metabarcodes, or multiple datasets simultaneously. The final output is a matrix per metabarcode, containing both ASV abundance data and associated taxonomic assignments. An optional function converts these matrices into 'phyloseq' and 'taxmap' objects. For more information on 'DADA2', including information on how DADA2 infers samples sequences, see Callahan et al. (2016) . For more details on the demulticoder R package see Sudermann et al. (2025) . Package: r-cran-dendextend Architecture: all Version: 1.19.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6999 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-seriation, r-cran-colorspace, r-cran-ape, r-cran-microbenchmark, r-cran-gplots, r-cran-heatmaply, r-cran-dynamictreecut, r-cran-pvclust, r-cran-corrplot, r-cran-dendser, r-cran-mass, r-cran-cluster, r-cran-fpc, r-cran-circlize, r-cran-covr Filename: pool/dists/resolute/main/r-cran-dendextend_1.19.1-1.ca2604.1_all.deb Size: 4824912 MD5sum: 4eb7be651eaafa368f4a35f5d99d7324 SHA1: 6c8aebb06dfee73f49d197d9654d893f793750ea SHA256: 544fa0323571fffa477cc4bce4724049b4edab6423818842912b3b03b1081be6 SHA512: c703f24ce1c6b19086b46e9372e9fdac13e4d9a4544fe52d35f37eadf45af5e6fd9917c98538480ae1d09ff0dab89491d36bd1bda46fac7b1ab9d51baf5ed3b4 Homepage: https://cran.r-project.org/package=dendextend Description: CRAN Package 'dendextend' (Extending 'dendrogram' Functionality in R) Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another. Package: r-cran-dendroanalyst Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2658 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyverse, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-readxl, r-cran-tibble, r-cran-tidyr, r-cran-zoo, r-cran-forecast, r-cran-mgcv, r-cran-minpack.lm, r-cran-pspline, r-cran-moments, r-cran-signal, r-cran-readr, r-cran-boot, r-cran-rlang, r-cran-changepoint, r-cran-waveletcomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dendroanalyst_0.1.6-1.ca2604.1_all.deb Size: 2321784 MD5sum: 4064df9969f4e35bf4a151f231208034 SHA1: e8adbbb60672bb9692fafd1d8223ca631184ea10 SHA256: a4e8ad876e9ea402fbeea07db820f89b253810d8833b49f1349c1d885ce65f43 SHA512: 5170ed3432fc4287f25fc85a80b691be8002d90ebe65e4a0294c6da04eadbf31b4590e56941715bb144f43e8e3eb8cb807838624ea113943c812fa9f362e924e Homepage: https://cran.r-project.org/package=dendRoAnalyst Description: CRAN Package 'dendRoAnalyst' (A Tool for Processing and Analyzing Dendrometer Data) There are various functions for managing and cleaning data before the application of different approaches. This includes identifying and erasing sudden jumps in dendrometer data not related to environmental change, identifying the time gaps of recordings, and changing the temporal resolution of data to different frequencies. Furthermore, the package calculates daily statistics of dendrometer data, including the daily amplitude of tree growth. Various approaches can be applied to separate radial growth from daily cyclic shrinkage and expansion due to uptake and loss of stem water. In addition, it identifies periods of consecutive days with user-defined climatic conditions in daily meteorological data, then check what trees are doing during that period. Package: r-cran-dendroextras Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dendroextras_0.2.3-1.ca2604.1_all.deb Size: 32664 MD5sum: 48b924f69a52526f8c2fecf6c8e4fe8a SHA1: bede522a054a8218384aeb2f735bcfec6467783d SHA256: fa53b200c9ad385475c56cdeb0425ba9b267a18e2ce7f73f76a514b315967dc2 SHA512: cf9973b5cbf33d2d95a3961a5bf97f1b5338e4bc097dd9621c54335bc98a063cfbb028594eecc2371e2857d44ae1e8c8bd95072de91fcbfda48ad5f19d0e0af9 Homepage: https://cran.r-project.org/package=dendroextras Description: CRAN Package 'dendroextras' (Extra Functions to Cut, Label and Colour Dendrogram Clusters) Provides extra functions to manipulate dendrograms that build on the base functions provided by the 'stats' package. The main functionality it is designed to add is the ability to colour all the edges in an object of class 'dendrogram' according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut 'dendrogram' and 'hclust' objects and to set/get labels. Package: r-cran-dendrometer Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 934 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-pspline, r-cran-zoo Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-dendrometer_1.1.1-1.ca2604.1_all.deb Size: 569228 MD5sum: 3a3f433fc2fe22bb41b539bcea9c37c6 SHA1: 4aea4262bfb16183928c113b175001e725a15961 SHA256: 280cebfb86081affa3c00364b48dfb01535565cbde6b28cf1729ac89aaf9e3f2 SHA512: 451ae20a1d78359ea94f1382b89a6fe3c1ee61c67d9ec91ff54f49a20e652b7193830665497b4cf44da4611f04a18e6dc36e68089d101932057a09466d39c0ef Homepage: https://cran.r-project.org/package=dendrometeR Description: CRAN Package 'dendrometeR' (Analyzing Dendrometer Data) Various functions to import, verify, process and plot high-resolution dendrometer data using daily and stem-cycle approaches as described in Deslauriers et al, 2007 . For more details about the package please see: Van der Maaten et al. 2016 . Package: r-cran-dendrometry Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1288 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dendrometry_0.0.4-1.ca2604.1_all.deb Size: 523646 MD5sum: a5010c46c9fb5b5c3f4d19a0df883c29 SHA1: 32e93e58cde3c73db3d407bbaaf3db956b97ffe7 SHA256: 2ea09e605f081fa53894fceaf29fcd4ba2c0f29ca9d6be75d0865841a3567780 SHA512: 903fad245c8086b2e9bf9b1d737091ad17b00451999e017c41224bcee27ad31249a6e9ad3928458a73d565e8fe42678f8a11b9285c7fcfee9e25af7988c1f249 Homepage: https://cran.r-project.org/package=dendrometry Description: CRAN Package 'dendrometry' (Forest Estimations and Dendrometric Computations) Computation of dendrometric and structural parameters from forest inventory data. The objective is to provide a user-friendly R package for researchers, ecologists, foresters, statisticians, loggers and other persons who deal with forest inventory data. The package includes advanced distribution fitting capabilities with multiple estimation methods (Maximum Likelihood, Maximum Product Spacing with ties correction methods following Cheng & Amin (1983), and Method of Moments) for probability distributions commonly used in forestry. Visualization tools with confidence bands using delta method and parametric bootstrap are provided for three-parameter Weibull distribution fitting to diameter data. Useful conversion of angle value from degree to radian, conversion from angle to slope (in percentage) and their reciprocals as well as principal angle determination are also included. Position and dispersion parameters usually found in forest studies are implemented. The package contains Fibonacci series, its extensions and the Golden Number computation. Useful references are Arcadius Y. J. Akossou, Soufianou Arzouma, Eloi Y. Attakpa, Noël H. Fonton and Kouami Kokou (2013) , W. Bonou, R. Glele Kakaï, A.E. Assogbadjo, H.N. Fonton, B. Sinsin (2009) , R. C. H. Cheng and N. A. K. Amin (1983) , and R. C. H. Cheng and M. A. Stephens (1989) . Package: r-cran-dendronetwork Architecture: all Version: 0.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3425 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplr, r-cran-igraph, r-cran-stringr, r-cran-reshape2, r-bioc-rcy3, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-tidyr, r-cran-foreach, r-cran-lifecycle, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-diagrammer Filename: pool/dists/resolute/main/r-cran-dendronetwork_0.5.5-1.ca2604.1_all.deb Size: 1425708 MD5sum: 135fde04868f02190d0d1ca9eebc1fb6 SHA1: a80c68d1efb89310259c3a1b3389e9fa92e80972 SHA256: 4c8bcf17ea81e557b9d475fc580a3aa7fc7922f3621033d234131d7914b69f95 SHA512: 6fa3f5eb1c64d89ff66c4e2d6a95eecc37fd6007f27736209ecbe4a7d6262a2fba1f89e1468bd8c65ae2228fac8f69a29485e0c8202ea0bfbb48fd91860bad2b Homepage: https://cran.r-project.org/package=dendroNetwork Description: CRAN Package 'dendroNetwork' (Create Networks of Dendrochronological Series using PairwiseSimilarity) Creating dendrochronological networks based on the similarity between tree-ring series or chronologies. The package includes various functions to compare tree-ring curves building upon the 'dplR' package. The networks can be used to visualise and understand the relations between tree-ring curves. These networks are also very useful to estimate the provenance of wood as described in Visser (2021) or wood-use within a structure/context/site as described in Visser and Vorst (2022) . Package: r-cran-dendrosync Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-dendrosync_0.1.5-1.ca2604.1_all.deb Size: 128462 MD5sum: da75f2ffc0605b2028a71d3273e358d1 SHA1: 9a9bcd9dd6e0fc009b85ac8681bf1af96ac59f40 SHA256: 23755cd34cfc0b6d1c8273eaac931850332474fd1c6b2efce9bc69ccb95a3c80 SHA512: bf7735440a445e88560872fcdda74a472ce3ad0365f2805c2883e08122ef44600cce287617c92fa316c27e25718f7b549503dc51ce19df529befe1de46296c44 Homepage: https://cran.r-project.org/package=DendroSync Description: CRAN Package 'DendroSync' (A Set of Tools for Calculating Spatial Synchrony BetweenTree-Ring Chronologies) Provides functions for the calculation and plotting of synchrony in tree growth from tree-ring width chronologies (TRW index). It combines variance-covariance (VCOV) mixed modelling with functions that quantify the degree to which the TRW chronologies contain a common temporal signal. It also implements temporal trends in spatial synchrony using a moving window. These methods can also be used with other kind of ecological variables that have temporal autocorrelation corrected. Package: r-cran-dendrotools Architecture: all Version: 1.2.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3318 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-brnn, r-cran-reshape2, r-cran-scales, r-cran-oce, r-cran-mlmetrics, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-plotly, r-cran-randomforest, r-cran-cubist, r-cran-lubridate, r-cran-psych, r-cran-boot, r-cran-viridis, r-cran-dplr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dendrotools_1.2.16-1.ca2604.1_all.deb Size: 1552472 MD5sum: 14cb4c2467e4e3f893d371cbb43bb0dd SHA1: 60ed2cc1f0a6af947309fd3c17fc47af5708aaa2 SHA256: b5f7dbc31ee16787b14d7424e5fce5bc4c58a1374775ebcf808b7487e20f505b SHA512: 98a0f7bab8b65613fdc2a2c024080618c9055968454605dda5c0f9ae3164ef12f80d665a4e6318e4ec9b30186c842165c8526f77b1dd992dc9e2967cacca7abd Homepage: https://cran.r-project.org/package=dendroTools Description: CRAN Package 'dendroTools' (Linear and Nonlinear Methods for Analyzing Daily and MonthlyDendroclimatological Data) Provides novel dendroclimatological methods, primarily used by the Tree-ring research community. There are four core functions. The first one is daily_response(), which finds the optimal sequence of days that are related to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), which implements partial correlations in the analysis of daily response functions. For the enthusiast of monthly data, there is monthly_response() function. The last core function is compare_methods(), which effectively compares several linear and nonlinear regression algorithms on the task of climate reconstruction. Package: r-cran-dendsort Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1608 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-gplots, r-cran-seriation, r-cran-gapmap, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dendsort_0.3.4-1.ca2604.1_all.deb Size: 1144768 MD5sum: 77908b315b3e5c560861164239dafa68 SHA1: 9b922f9b53d09d46db62c4a32d11c46153cff270 SHA256: 0924fdcb50e8dc8e26080c9aa1f857dda59618ee6f522f04911ee9876e4d5c53 SHA512: 0ff8f3549b91f26a4b7c6e6d076184c725b6d9e4b0d5d1b71ef61808c82b7d741338cd75d7b55a88ddc0143dc291b60fac30df6c41c3f78be1bf1d1fdace6e79 Homepage: https://cran.r-project.org/package=dendsort Description: CRAN Package 'dendsort' (Modular Leaf Ordering Methods for Dendrogram Nodes) An implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization. This method is described in "dendsort: modular leaf ordering methods for dendrogram representations in R", F1000Research 2014, 3: 177 . Package: r-cran-denguedatahub Architecture: all Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rvest, r-cran-stringr, r-cran-lifecycle, r-cran-magrittr, r-cran-here, r-cran-purrr, r-cran-xml2, r-cran-tabulapdf, r-cran-rlang Suggests: r-cran-roxygen2, r-cran-tsibble Filename: pool/dists/resolute/main/r-cran-denguedatahub_4.1.0-1.ca2604.1_all.deb Size: 1315120 MD5sum: 150a12beb81839a15e1ea3d5d6900d17 SHA1: e9d59f8d38b91665ffa62280dd450d10253245ee SHA256: 14bb9641e159e77dea65eb023d5938f7f7c17ca8dd0d55bb74964603b668f5c9 SHA512: a71d984665104543cc860c37307226b09084c089162a944f17aa2002b042a903f696724911a1a8238ef33e023131c8ad31af3a9c7b52792f248ce83e00ba5b51 Homepage: https://cran.r-project.org/package=denguedatahub Description: CRAN Package 'denguedatahub' (A Tidy Format Datasets of Dengue by Country) Provides a weekly, monthly, yearly summary of dengue cases by state/ province/ country. 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This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), , and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution. 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(2011) ), RuLSIF (Yamada et al. (2011) ), and KLIEP (Sugiyama et al. (2007) ). Package: r-cran-denstest Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-denstest_1.0.0-1.ca2604.1_all.deb Size: 85042 MD5sum: 7012beae041de6641b533e44164d639d SHA1: 43a3bfca312d22f9ef7b62f9312237fdf1dc178d SHA256: ba3372d8e5f30d9514d6b707cff5bc28621217a93027b8ade594709df1a6d4d9 SHA512: e9f84d444e03453cc88a36ec34e474f7a7f9395ff3d1b1087f2fdd415d6a55c233844c66cc980636cd3561f6d2d5c1685161627fff9f07e7935ac94388fea184 Homepage: https://cran.r-project.org/package=denstest Description: CRAN Package 'denstest' (Density Equality Testing) Methods for testing the equality between groups of estimated density functions. 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Using the functions in the package, that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data using other functions in this package (as in the manuscript) or from some other source. The package provides a vignette which walks through the analysis in the publication, as well as a function to generate a project skeleton for such an analysis. 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Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface. Package: r-cran-descriptivewh Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-descriptivewh_1.0.3-1.ca2604.1_all.deb Size: 28826 MD5sum: afd1b7ba44da357c8740a2487eab375b SHA1: dd2abd09a6f7b01d118f4ae912658bd84d07a0d3 SHA256: 55e7c947988bd2945a01131653a299b5812bf6fd78e635098af8b4516ef52b63 SHA512: be1d3adc044681c4fadae1bd291a26ec9fe96ebcec92e3677738cd42fa68b584e4d5805b7edb612330043ba86241e38fa2a20800dde5708e4d687ae1e60b5e20 Homepage: https://cran.r-project.org/package=DescriptiveWH Description: CRAN Package 'DescriptiveWH' (Descriptive Statistics) Exploratory analysis of a data base. Using the functions of this package is possible to filter the data set detecting atypical values (outliers) and to perform exploratory analysis through visual inspection or dispersion measures. 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Though there are other packages which does similar job but each of these are deficient in one form or other, in the measures generated, in treating numeric, character and date variables alike, no functionality to view these measures on a group level or the way the output is represented. Given the foremost role of the descriptive statistics in any of the exploratory data analysis or solution development, there is a need for a more constructive, structured and refined version over these packages. This is the idea behind the package and it brings together all the required descriptive measures to give an initial understanding of the data quality, distribution in a faster,easier and elaborative way.The function brings an additional capability to be able to generate these statistical measures on the entire dataset or at a group level. It calculates measures of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures are generated based on the data types as required by them,rather than applying numerical measures on character and data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a summary report for the data. 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Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs. Also, they can be used for the design of randomized experiments, for example, for matching before randomization. By default, 'designmatch' uses the 'highs' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface. For their installation, please follow the instructions at and . We have also included directions in the gurobi_installation file in the inst folder. 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Jones and C.J. Nesbitt (1997, ISBN: 978-0938959465), "Actuarial Mathematics for Life Contingent Risks" by Dickson, David C. M., Hardy, Mary R. and Waters, Howard R (2009) and "Life Contingencies" by Jordan, C. W (1952) . It also contains functions for equivalent interest and discount rate calculation, present and future values of annuities, and loan amortization schedule. 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Package: r-cran-devrate Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1926 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-minpack.lm Filename: pool/dists/resolute/main/r-cran-devrate_0.2.6-1.ca2604.1_all.deb Size: 1282648 MD5sum: 973c6577f3e50a170d74dfe46f141957 SHA1: 2a04cf464138bb304f6304f87423f1ae5c5374cd SHA256: db2d701df2b62baef3ea3582f9f9a2d740fc705be8dda9f1308ad25c075cb9db SHA512: c61bde91105cf03e8067d7c4b5bfc8c9bee48522f5d2a3c26d897db8a58843c5158fea7a7f7bb29265865971e3d840edbbb83e74d3996b805eb592ae9495fd81 Homepage: https://cran.r-project.org/package=devRate Description: CRAN Package 'devRate' (Quantify the Relationship Between Development Rate andTemperature in Ectotherms) A set of functions to quantify the relationship between development rate and temperature and to build phenological models. The package comprises a set of models and estimated parameters borrowed from a literature review in ectotherms. The methods and literature review are described in Rebaudo et al. (2018) , Rebaudo and Rabhi (2018) , and Regnier et al. (2021) . An example can be found in Rebaudo et al. (2017) . Package: r-cran-devtools Architecture: all Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-usethis, r-cran-cli, r-cran-desc, r-cran-ellipsis, r-cran-fs, r-cran-lifecycle, r-cran-memoise, r-cran-miniui, r-cran-pak, r-cran-pkgbuild, r-cran-pkgdown, r-cran-pkgload, r-cran-profvis, r-cran-rcmdcheck, r-cran-rlang, r-cran-roxygen2, r-cran-rversions, r-cran-sessioninfo, r-cran-testthat, r-cran-urlchecker, r-cran-withr Suggests: r-cran-biocmanager, r-cran-callr, r-cran-covr, r-cran-curl, r-cran-digest, r-cran-dt, r-cran-foghorn, r-cran-gh, r-cran-httr2, r-cran-knitr, r-cran-lintr, r-cran-quarto, r-cran-remotes, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-spelling, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-devtools_2.5.2-1.ca2604.1_all.deb Size: 464476 MD5sum: 12e415c1deca6ee681717a3dddd58d14 SHA1: 2ebb1002c1e7b5402d9ae1970b0b4fd5d5baf739 SHA256: a553495b06404a78f7a7ace55921873e60e53f8d64dd83330e721a57ac05478d SHA512: 1b848751974fb46230fe68c77050e1ff454373a79e76c9febd4f3df52f40d1f632268ab15cbc93dd06998b5ccc24966afc12a63096e37dd9c33dfee588f9d8a0 Homepage: https://cran.r-project.org/package=devtools Description: CRAN Package 'devtools' (Tools to Make Developing R Packages Easier) Collection of package development tools. Package: r-cran-devtreatrules Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-dyntxregime, r-cran-modelobj Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-devtreatrules_1.1.0-1.ca2604.1_all.deb Size: 397348 MD5sum: 5ede32eecf139dcacc57b97c6cdc0702 SHA1: 03522e7cc937cfb4796c12e45ace5ba291ca0e78 SHA256: d856538d5898d4e88a7adb3117070be663fefccbed0dbd472d7c904ac35d6364 SHA512: db3299e431480d1564c1b72ad0152e6e09b9e39bcfb7a6fcdabe5cd316818545315f60975611dacfedcefa7307590448daec33e140cfd7bd343ae3261e98580a Homepage: https://cran.r-project.org/package=DevTreatRules Description: CRAN Package 'DevTreatRules' (Develop Treatment Rules with Observational Data) Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) ; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) . Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample. Package: r-cran-dexir Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-stringr Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-ggally, r-cran-fmsb Filename: pool/dists/resolute/main/r-cran-dexir_1.0.2-1.ca2604.1_all.deb Size: 553774 MD5sum: dde4ac5c0c368dfdf8b48b6d2768cb4e SHA1: 1d04ead3fd152b577dc9a20f914870763d9d48e9 SHA256: 5f93c6d56a443b8f63c1cb9dd960468dc54aebbab364f3544a4f21dc5f4ccbf6 SHA512: 665e6e7920cbf9d2120e093b65860749d28ebcb313e42fcc8399f6a82efe48c4aaba4c2db4cf7a9f4bb75e5af61d57d83e69a6fe9888ea8c177f11204bf9a9f5 Homepage: https://cran.r-project.org/package=DEXiR Description: CRAN Package 'DEXiR' ('DEXi' Library) A software package for using 'DEXi' models. 'DEXi' models are hierarchical qualitative multi-criteria decision models developed according to the method DEX (Decision EXpert, ), using the program 'DEXi' () or 'DEXiWin' (). A typical workflow with 'DEXiR' consists of: (1) reading a '.dxi' file, previously made using the 'DEXi' software (function read_dexi()), (2) making a data frame containing input values of one or more decision alternatives, (3) evaluating those alternatives (function evaluate()), (4) analyzing alternatives (selective_explanation(), plus_minus(), compare_alternatives()), (5) drawing charts. 'DEXiR' is restricted to using models produced externally by the 'DEXi' software and does not provide functionality for creating and/or editing 'DEXi' models directly in 'R'. Package: r-cran-dexisensitivity Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-algdesign, r-cran-plotrix, r-cran-genalg, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-xml2 Suggests: r-cran-sloop, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-dexisensitivity_1.0.2-1.ca2604.1_all.deb Size: 433110 MD5sum: 3456818c5f75377f3b266c4be61c4088 SHA1: b7441f27eadcae86ef4ff519b77bf06f7badf8d7 SHA256: 9217f170d9dbf9abe2d2fc81cc95c3267a0a592f5a20745e6d9d1007f36f6646 SHA512: 384bc855af3f580e7df3a38531b4fb0c3dea268a0ae89b9c75cdc9249cd488884b107e54a9fea8afbbc8f89541f11217dab98b702292a64a83b12972f5955274 Homepage: https://cran.r-project.org/package=dexisensitivity Description: CRAN Package 'dexisensitivity' ('DEXi' Decision Tree Analysis and Visualization) Provides a versatile toolkit for analyzing and visualizing 'DEXi' (Decision EXpert for education) decision trees, facilitating multi-criteria decision analysis directly within R. Users can read .dxi files, manipulate decision trees, and evaluate various scenarios. It supports sensitivity analysis through Monte Carlo simulations, one-at-a-time approaches, and variance-based methods, helping to discern the impact of input variations. Additionally, it includes functionalities for generating sampling plans and an array of visualization options for decision trees and analysis results. A distinctive feature is the synoptic table plot, aiding in the efficient comparison of scenarios. Whether for in-depth decision modeling or sensitivity analysis, this package stands as a comprehensive solution. Definition of sensitivity analyses available in Carpani, Bergez and Monod (2012) and detailed description of the package soon available in Alaphilippe et al. (2025) . 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The methods are described in Manly & Alberto (2017, ISBN:9781498728966), Rencher (2002, ISBN:0-471-41889-7), and Tabachnik & Fidell (2019, ISBN:9780134790541). 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Included are Bayesian analogues to the frequentist Mann-Whitney U test, the Wilcoxon Signed-Ranks test, Kendall's Tau Rank Correlation Coefficient, Goodman and Kruskal's Gamma, McNemar's Test, the binomial test, the sign test, the median test, as well as distribution-free methods for testing contrasts among condition and for computing Bayes factors for hypotheses. The package also includes procedures to estimate the power of distribution-free Bayesian tests based on data simulations using various probability models for the data. The set of functions provide data analysts with a set of Bayesian procedures that avoids requiring parametric assumptions about measurement error and is robust to problem of extreme outlier scores. 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The DGEobj has data slots for row (gene), col (samples), assays (matrix n-rows by m-samples dimensions) and metadata (not keyed to row, col, or assays). A set of accessory functions to deposit, query and retrieve subsets of a data workflow has been provided. Attributes are used to capture metadata such as species and gene model, including reproducibility information such that a 3rd party can access a DGEobj history to see how each data object was created or modified. Since the DGEobj is customizable and extensible it is not limited to RNA-seq analysis types of workflows -- it can accommodate nearly any data analysis workflow that starts from a matrix of assays (rows) by samples (columns). 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The implementations follow Ming & Guillas (2021) and Ming, Williamson, & Guillas (2023) and Ming & Williamson (2023) . To get started with the package, see . Package: r-cran-dharma Architecture: all Version: 0.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4603 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-gap, r-cran-lmtest, r-cran-ape, r-cran-qgam, r-cran-lme4 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-kernsmooth, r-cran-sfsmisc, r-cran-mass, r-cran-mgcv, r-cran-mgcviz, r-cran-spamm, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-phylolm Filename: pool/dists/resolute/main/r-cran-dharma_0.4.7-1.ca2604.1_all.deb Size: 3220284 MD5sum: 99010178c6089e898e9da84f866ff42b SHA1: e9e947eb99daf68df7768feb228ca640263147dc SHA256: 897a966bee0b99bd076e274ca6e6d23843ddd5da55a4c77f71fc1db6e48aa1c3 SHA512: 9ddaa5a64006b4cf317394b42d2d3dd713d7434be0fd4f093f9fddfe613c505604c9ed00673243b81d4079702e2caac6d1610b77453ad8d6d3e8d86f07fb2750 Homepage: https://cran.r-project.org/package=DHARMa Description: CRAN Package 'DHARMa' (Residual Diagnostics for Hierarchical (Multi-Level / Mixed)Regression Models) The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive', and 'spaMM'; phylogenetic linear models from 'phylolm' (classes 'phylolm' and 'phyloglm'); generalized additive models ('gam' from 'mgcv'); 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation. Package: r-cran-dhbins Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-dhbins_1.1-1.ca2604.1_all.deb Size: 117496 MD5sum: 2e3fffcfd6efa27875fe5f8dac335c87 SHA1: 89d40d011a440714d5e63893dc28612ff76fdff1 SHA256: 211315a08388b1ff3d6e382a27fbc4b9bcd33ce0a9f7f6ca91884052095bc856 SHA512: b3cfdeb7ce50ff22e8a25ebe9097b38a5d9fad3c83d30f040211b6ae64fab6bbdd7596ccf5dcb2e570a283099a4822266a71b00e16a99d26c8c9a749e5addbdb Homepage: https://cran.r-project.org/package=DHBins Description: CRAN Package 'DHBins' (Hexmaps for NZ District Health Boards) Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. 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The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes. Package: r-cran-dhglm Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-boot, r-cran-mass, r-cran-car, r-cran-sandwich Filename: pool/dists/resolute/main/r-cran-dhglm_2.0-1.ca2604.1_all.deb Size: 482056 MD5sum: 34d2494497418bd7bfa2201e8b8e7d03 SHA1: 382504ff7b20d226ebfeb93dfa814c08488e39af SHA256: 0ce93f05805658829d72814921742a44db033b4d184218b0a5891dae759ee473 SHA512: 6aa1c8d08095c7e796bc05ccee84ecf7c4e479c749ffbcb8fcd0825570a0bbbb8063349c3f09bc3d20b42c362cee07c1023f81494e5237da53ef92c7c66c75cf Homepage: https://cran.r-project.org/package=dhglm Description: CRAN Package 'dhglm' (Double Hierarchical Generalized Linear Models) Implements double hierarchical generalized linear models in which the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models. Package: r-cran-dhs.rates Architecture: all Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape, r-cran-survey, r-cran-haven, r-cran-matrixstats, r-cran-dplyr, r-cran-rlang, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dhs.rates_0.9.2-1.ca2604.1_all.deb Size: 262990 MD5sum: 2bf1293c0246cd3d6de20c6bc06b958f SHA1: 8532effde742b120acee248f83b10c546ce6aaa7 SHA256: 2883b04dcd77a4e334d0e47d7607f815f34b7b9fcb5c2c9fa0862f0e6f942b5d SHA512: a11cc4be6a262b10b37ef3ac2f10ca17dfbf46c4507f9cb6107003ff6fc09e9473473b4221e5b8b7273249fd7d9389edf351bfa965008fcebf9aa235fc4558b5 Homepage: https://cran.r-project.org/package=DHS.rates Description: CRAN Package 'DHS.rates' (Calculates Demographic Indicators) Calculates key indicators such as fertility rates (Total Fertility Rate (TFR), General Fertility Rate (GFR), and Age Specific Fertility Rate (ASFR)) using Demographic and Health Survey (DHS) women/individual data, childhood mortality probabilities and rates such as Neonatal Mortality Rate (NNMR), Post-neonatal Mortality Rate (PNNMR), Infant Mortality Rate (IMR), Child Mortality Rate (CMR), and Under-five Mortality Rate (U5MR), and adult mortality indicators such as the Age Specific Mortality Rate (ASMR), Age Adjusted Mortality Rate (AAMR), Age Specific Maternal Mortality Rate (ASMMR), Age Adjusted Maternal Mortality Rate (AAMMR), Age Specific Pregnancy Related Mortality Rate (ASPRMR), Age Adjusted Pregnancy Related Mortality Rate (AAPRMR), Maternal Mortality Ratio (MMR) and Pregnancy Related Mortality Ratio (PRMR). In addition to the indicators, the 'DHS.rates' package estimates sampling errors indicators such as Standard Error (SE), Design Effect (DEFT), Relative Standard Error (RSE) and Confidence Interval (CI). The package is developed according to the DHS methodology of calculating the fertility indicators and the childhood mortality rates outlined in the "Guide to DHS Statistics" (Croft, Trevor N., Aileen M. J. Marshall, Courtney K. Allen, et al. 2018, ) and the DHS methodology of estimating the sampling errors indicators outlined in the "DHS Sampling and Household Listing Manual" (ICF International 2012, ). Package: r-cran-dhsage Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dhsage_0.1.0-1.ca2604.1_all.deb Size: 77090 MD5sum: e78647577685a9cd76ad6e7669b6fd2f SHA1: c241b839ba418b341351bb94f4d6f3b2916d3fbf SHA256: 1215ddc03ef06a5d510d01682be0755d80964500aab902544d41a633d078001f SHA512: 4e2650b494478ff5815e3d1d07fb5089091af1177679e1e7d51a5fba4caf0608e7789a337900c43fd8d6edee6b3a92b055f35fa20631ff8a2cb75f54e417a6e6 Homepage: https://cran.r-project.org/package=dhsage Description: CRAN Package 'dhsage' (Reproductive Age Female Data of Various Demographic HealthSurveys) We provide 70 data sets of females of reproductive age from 19 Asian countries, ranging in age from 15 to 49. The data sets are extracted from demographic and health surveys that were conducted over an extended period of time. Moreover, the functions also provide Whipple’s index as well as age reporting quality such as very rough, rough, approximate, accurate, and highly accurate. 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It can also be extended to other datasets. The package supports spatial correlation index construction and visualization, along with empirical Bayes approximation of regression coefficients in a multistage setup. The main functionality is repeated regression — for example, if we have to run regression for n groups, the group ID should be vertically composed into the variable for the parameter `location_var`. It can perform various kinds of regression, such as Generalized Regression Models, logit, probit, and more. Additionally, it can incorporate interaction effects. The key benefit of the package is its ability to store the regression results performed repeatedly on a dataset by the group ID, along with respective p-values and map those estimates. 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The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood and represents a proxy measure of aging and also can be served as a sensitive predictor of survival. For more information, see (i)"Accumulation of Deficits as a Proxy Measure of Aging" by Arnold B. Mitnitski et al. (2001), The Scientific World Journal 1, ; (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" by Arnold B Mitnitski et al. (2001), BMC Geriatrics2002 2(1), . 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Implements calibration, stability and tail diagnostics, including tail support, threshold elasticity, posterior error probability (PEP) reliability, and equal-chance checks. If you used this package in your research, please cite the associated preprint . Detailed examples of using this package can also be found on the GitHub repository (). 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The algorithms are based in Dielman (2005) , Elian et al. (2000) and Dodge (1997) . This package builds on the 'quantreg' package, which is a well-established package for tuning quantile regression models. There are also tests to verify if the errors have a Laplace distribution based on the work of Puig and Stephens (2000) . Package: r-cran-diagmeta Architecture: all Version: 0.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-diagmeta_0.5-1-1.ca2604.1_all.deb Size: 160626 MD5sum: 73c88dc4cd058a78911ac5cfe096c173 SHA1: 00bd026bd2e3c84894dea22fea5817c8f946e645 SHA256: 5ae4448826145738e9170f0b09777b09f320e663cccad0efb008207292d17d1a SHA512: 1c96011e5a274c86a038cf6ca8f86d88142671d15ab1f5aabf2a398a4d665d09d2c010447fce4b48f1fa80cdfaa87c4a242c858e28df67dc68a9b060829e7208 Homepage: https://cran.r-project.org/package=diagmeta Description: CRAN Package 'diagmeta' (Meta-Analysis of Diagnostic Accuracy Studies with SeveralCutpoints) Provides methods by Steinhauser et al. (2016) for meta-analysis of diagnostic accuracy studies with several cutpoints. 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Package: r-cran-diagram Architecture: all Version: 1.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shape Filename: pool/dists/resolute/main/r-cran-diagram_1.6.5-1.ca2604.1_all.deb Size: 662082 MD5sum: 18b072907d9c26f7592578ace1028f1d SHA1: 9f3152cdcc356c7ca3ca7ca9f323e755d804103c SHA256: 42d16685405e4304ddf3159cd208f18efe1617a6b11e3bfe4233800992526d64 SHA512: bf07e1a9414d4325ed90c7bbc6229093352cce3c745e2c41bd06d46659f4069573478741f872219048befddbf9ad3acc5941fe7f831f4e10ad44fac55155702f Homepage: https://cran.r-project.org/package=diagram Description: CRAN Package 'diagram' (Functions for Visualising Simple Graphs (Networks), PlottingFlow Diagrams) Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, visualising webs, electrical networks, etc. Support for the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter M.J. Herman (2009), Springer. and the book "Solving Differential Equations in R" by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer. Includes demo(flowchart), demo(plotmat), demo(plotweb). 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Package: r-cran-diallelanalysisr Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-diallelanalysisr_0.6.0-1.ca2604.1_all.deb Size: 78364 MD5sum: 9997690a7f9ba4285bd4967646ab7c91 SHA1: 5c6c3a42f8e91e8ed4c514ea59d0a2100aeeec82 SHA256: ab63aeb858f17c826515d160ee56eb502a3c2788a4f49be7aa458ab1561d1e74 SHA512: b1d76213e5f75454afd4a9d55d3440f8ad689ecd64d5540658ecf4ba98332dcdbe71bbe1dabff5324826f47f6cf8c6a2eefb090c8b1d0cfb303401ecd9360582 Homepage: https://cran.r-project.org/package=DiallelAnalysisR Description: CRAN Package 'DiallelAnalysisR' (Diallel Analysis with R) Performs Diallel Analysis with R using Griffing's and Hayman's approaches. Four different Methods (1: Method-I (Parents + F1's + reciprocals); 2: Method-II (Parents and one set of F1's); 3: Method-III (One set of F1's and reciprocals); 4: Method-IV (One set of F1's only)) and two Models (1: Fixed Effects Model; 2: Random Effects Model) can be applied using Griffing's approach. 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These tools can be used to define objects for creating, simulating, or validating values for such parameters. Package: r-cran-dialvalidator Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-stringr Suggests: r-cran-xml2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dialvalidator_0.1.0-1.ca2604.1_all.deb Size: 159804 MD5sum: c887742787bdab22dcf434b70bfa8d25 SHA1: cfad99ae322644d9de0296f90b42cffbae493292 SHA256: 52d12c17a1112c6bbb2f418f138a47aa566c829c6def3280b13eac6d181171f2 SHA512: 4fcfd6303c99908c059a62382d6923ff76e6ba7b0be1dc35234b885914981227a08adbc6c6a56aac78fe8fbc73d1483e80b0a2beb3f8f82c082fb3228bdef660 Homepage: https://cran.r-project.org/package=dialvalidator Description: CRAN Package 'dialvalidator' (Phone Number Validation Using Google's 'libphonenumber' Metadata) Parses, validates, formats, and classifies phone numbers using Google's 'libphonenumber' metadata. Covers 240+ territories with support for mobile, landline, toll-free, and other number types. Unlike 'dialr', this package requires no Java runtime — metadata is parsed ahead of time from the upstream 'PhoneNumberMetadata.xml' and shipped as a bundled R object. Functions accept character vectors and return simple R types (logical, character, data.frame). Package: r-cran-diaplt Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-diaplt_1.4.0-1.ca2604.1_all.deb Size: 53436 MD5sum: 2243a0b9ba2c1fc072aee822f0af335a SHA1: 645701fe19b5af67fc45a796910972cc8e263564 SHA256: 15bd9f51a644d2239a2e2daf09876654e29677f03d1cfdfc3abbf6e9602f3855 SHA512: fd459e96a27bbd939e45ca111b729b7e73c724cd7237db87b582dd9cf48bbe7fdb627d85bf403402eab26411747a7bbfb0a5acfff5dd90d079814e3ffacce33a Homepage: https://cran.r-project.org/package=diaplt Description: CRAN Package 'diaplt' (Beads Summary Plot of Ranges) Visualize one-factor data frame. Beads plot consists of diamonds of each factor of each data series. A diamond indicates average and range. Look over a data frame with many numeric columns and a factor column. Package: r-cran-diario Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-tibble, r-cran-httr2, r-cran-keyring Filename: pool/dists/resolute/main/r-cran-diario_0.1.1-1.ca2604.1_all.deb Size: 151362 MD5sum: ff6cd9e6f54c2530eef9a203d1916341 SHA1: dd44b6694e7ad487131e63f96eeec67196c6d3c9 SHA256: 30f320f910aa9569f4b85e9434d55b5606d148409e82827a12250d3381728f0c SHA512: ea50312c6340d3b4b8204a021103fbd9ba4ffdd926b160620c62f29543c25eba6de5f012272e704bb639b6ecf273964f18dc01aa815885c3c8729d604c1c8d14 Homepage: https://cran.r-project.org/package=diario Description: CRAN Package 'diario' ('R' Interface to the 'Diariodeobras' Application) Provides a set of functions for securely storing 'API' tokens and interacting with the system. Includes convenient wrappers around the 'httr2' package to perform authenticated requests, retrieve project details, tasks, reports, and more. Package: r-cran-diathor Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringdist, r-cran-vegan, r-cran-ggplot2, r-cran-tidyr, r-cran-data.table, r-cran-purrr, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-diathor_0.1.5-1.ca2604.1_all.deb Size: 2635816 MD5sum: 8d9d562b40bb63faa26764b77cc4ca1d SHA1: 12eba44d78e2c369f9763f350cc0943c2ac22459 SHA256: 31de763416fa30104c1ea229603d095576794eaaa891f95de3bb0fb2ad22ef05 SHA512: 47312a1509b31ee392d1e404bc2b7777a5774a5e73fc992d53ea0cee8ed9aa7069d7c01572b3bcf5ff3111b1eae93b153da5e09e087d3fd99b3f0ebf54e0ff62 Homepage: https://cran.r-project.org/package=diathor Description: CRAN Package 'diathor' (Calculate Ecological Information and Diatom Based Indices) Calculate multiple biotic indices using diatoms from environmental samples. Diatom species are recognized by their species' name using a heuristic search, and their ecological data is retrieved from multiple sources. It includes number/shape of chloroplasts diversity indices, size classes, ecological guilds, and multiple biotic indices. It outputs both a dataframe with all the results and plots of all the obtained data in a defined output folder. - Sample data was taken from Nicolosi Gelis, Cochero & Gómez (2020, ). - The package uses the 'Diat.Barcode' database to calculate morphological and ecological information by Rimet & Couchez (2012, ),and the combined classification of guilds and size classes established by B-Béres et al. (2017, ). - Current diatom-based biotic indices include the DES index by Descy (1979) - EPID index by Dell'Uomo (1996, ISBN: 3950009002) - IDAP index by Prygiel & Coste (1993, ) - ID-CH index by Hürlimann & Niederhauser (2007) - IDP index by Gómez & Licursi (2001, ) - ILM index by Leclercq & Maquet (1987) - IPS index by Coste (1982) - LOBO index by Lobo, Callegaro, & Bender (2002, ISBN:9788585869908) - SLA by Sládeček (1986, ) - TDI index by Kelly, & Whitton (1995, ) - SPEAR(herbicide) index by Wood, Mitrovic, Lim, Warne, Dunlop, & Kefford (2019, ) - PBIDW index by Castro-Roa & Pinilla-Agudelo (2014) - DISP index by Stenger-Kovács et al. (2018, ) - EDI index by Chamorro et al. (2024, ) - DDI index by Álvarez-Blanco et al. (2013, ) - PDISE index by Kahlert et al. (2023, ). Package: r-cran-dibble Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-memoise, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dibble_0.3.2-1.ca2604.1_all.deb Size: 126880 MD5sum: 8dcc775e306e7d231f15d6086befdcfe SHA1: d954079b1cb528eacb6c30155e0348aa4e995674 SHA256: ec157041e76f85679473df1fd5f2db97385dffdb5c50328130a5651914f15801 SHA512: 1f2cbea95dd933f5717a3dd69387f4365f068b7ad55d18a20026109043238d79088e99cceecd0afacf8599adb39c2ec2db287a470c9b480da34deac7fd59dbd6 Homepage: https://cran.r-project.org/package=dibble Description: CRAN Package 'dibble' (Dimensional Data Frames) Provides a 'dibble' that implements data cubes (derived from 'dimensional tibble'), and allows broadcasting by dimensional names. Package: r-cran-dice Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-dice_1.2-1.ca2604.1_all.deb Size: 30668 MD5sum: 713bcb89d9da6cb1395ff9f97db6a46c SHA1: 33041ca4e14514a3d3dc5a4ab75ed4bbc0783e2f SHA256: 48896e2ea02ee683e521f2072e98f88e779420deb714df8f9f1eba9dec9aa214 SHA512: ddd17c58fad3de8432b9b3b9c1b45dfdb3932400e7f4914b1762d30fa10b88cc3288a94f3f9f5400ac6a7c97c9f8fdff3df9cae1433bd03168c93f482989a5b8 Homepage: https://cran.r-project.org/package=dice Description: CRAN Package 'dice' (Calculate probabilities of various dice-rolling events) This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice. 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Package: r-cran-diegr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2069 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-gganimate, r-cran-plotly, r-cran-rgl, r-cran-sp, r-cran-scales, r-cran-purrr, r-cran-tidyr Suggests: r-cran-av, r-cran-gifski, r-cran-magick, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-diegr_0.2.0-1.ca2604.1_all.deb Size: 1895696 MD5sum: 0079dd906f48ef0e1af07ab98a7fd1cf SHA1: 70a038897167bfe372407be76b1eed3e5888b2fd SHA256: ed6632a2dabd7f20ba3da41b7ed37ba74e880209f1930dc14d8deee8d1542973 SHA512: ca3834907e56b378fc69da7c28904bc6167bb732ac7613c1a700c20200a3eb555f9f5156ba1548187b1c97dbbf833b8758cfac56f7cc1d48e4a899da99be5925 Homepage: https://cran.r-project.org/package=diegr Description: CRAN Package 'diegr' (Dynamic and Interactive EEG Graphics) Allows to visualize high-density electroencephalography (HD-EEG) data through interactive plots and animations, enabling exploratory and communicative analysis of temporal-spatial brain signals. Funder: Masaryk University (Grant No. MUNI/A/1457/2023). Package: r-cran-diemr Architecture: all Version: 1.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-vcfr, r-cran-data.table, r-cran-circlize Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-diemr_1.5.5-1.ca2604.1_all.deb Size: 1306372 MD5sum: cb0ffeab3a146e358f028d76c4125148 SHA1: 5a866f9c039b925edb616b201cc994b72c4d2ef9 SHA256: 78203ce0c414d3093373c65fb921204e7a79f3f67b0a9113b5e1222daed7c25e SHA512: e1be0209ba1b527f155fb0e5e005f62243273f015c0d86029342e74fee1b2e0533c551981d169081d2721a2518285a5884c95256bad61fe5417e59acf3adc00b Homepage: https://cran.r-project.org/package=diemr Description: CRAN Package 'diemr' (Genome Polarization via Diagnostic Index ExpectationMaximization) Implements a likelihood-based method for genome polarization, identifying which alleles of SNV markers belong to either side of a barrier to gene flow. The approach co-estimates individual assignment, barrier strength, and divergence between sides, with direct application to studies of hybridization. Includes VCF-to-diem conversion and input checks, support for mixed ploidy and parallelization, and tools for visualization and diagnostic outputs. Based on diagnostic index expectation maximization as described in Baird et al. (2023) . Package: r-cran-dietcost Architecture: all Version: 1.0.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-rlang, r-cran-dplyr, r-cran-tidyselect, r-cran-xlsx, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-dietcost_1.0.0.0-1.ca2604.1_all.deb Size: 266486 MD5sum: cee7103c0b148dbc2197bcee87a19c5f SHA1: 3a5671cf1b72c5882399ba23a54f9040ef52d809 SHA256: c88a26d3d4087c53325bfba09dff2ba70952b51710b0f6129e4a3d71d0c15e91 SHA512: b6c9e5083c34ceea713fb01c6d7fa8080869bc60fd0deda54450f4cc3726577137ddc8c51bdc5147ef51184ec2d80e3be2db4f33b91398fa1619f3adae5bd192 Homepage: https://cran.r-project.org/package=DIETCOST Description: CRAN Package 'DIETCOST' (Calculate the Cost and Environmental Impact of a Ideal Diet) Easily perform a Monte Carlo simulation to evaluate the cost and carbon, ecological, and water footprints of a set of ideal diets. Pre-processing tools are also available to quickly treat the data, along with basic statistical features to analyze the simulation results — including the ability to establish confidence intervals for selected parameters, such as nutrients and price/emissions. A 'standard version' of the datasets employed is included as well, allowing users easy access to customization. This package brings to R the 'Python' software initially developed by Vandevijvere, Young, Mackay, Swinburn and Gahegan (2018) . Package: r-cran-dietr Architecture: all Version: 1.1.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 934 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfishbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dietr_1.1.6-1-1.ca2604.1_all.deb Size: 389178 MD5sum: 2aa50b565b4016ab8171c566fc52889d SHA1: 4cea0cfc20aed6b8255565dc5a60e2dd9a169a25 SHA256: 3f2bd0efb1e951381c29e0228ac8a5e5351fc56429271f54c1b67cdc4e53d74a SHA512: 4d19f9faf0164d6a295c39aeb38dad9054ade0723b642287fc3b8cee5cb2c2fdbac1e22b974694b6bc44ff390d331904e485887b2a21d929183aed3a6056dc79 Homepage: https://cran.r-project.org/package=dietr Description: CRAN Package 'dietr' (Diet Estimated Trophic Levels) Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Borstein (2020) . Package: r-cran-diezeit Architecture: all Version: 0.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brew, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-diezeit_0.1-0-1.ca2604.1_all.deb Size: 130570 MD5sum: ce3e4875aa6566f6c5fbfc16c7287db4 SHA1: f52f1d1ff6138ad9707592fb5adbbf6a85b96237 SHA256: b8f28513288e5ac093056e2740db53dcef21d287fd85359a095ba1cfa8214a1e SHA512: 391cff2f660bb2bca6c5f110be8d0ad9175a69b6eb254387f9193841823e7e542ec2c7992ee0bff78d6d6746fa5723804d93dd17414b67524ad7471088537530 Homepage: https://cran.r-project.org/package=diezeit Description: CRAN Package 'diezeit' (R Interface to the ZEIT ONLINE Content API) A wrapper for the ZEIT ONLINE Content API, available at . 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage. Package: r-cran-difboost Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-penalized, r-cran-stabs Filename: pool/dists/resolute/main/r-cran-difboost_0.4-1.ca2604.1_all.deb Size: 30772 MD5sum: 3cd2db4ce600547cb3ed83a424bfb7cb SHA1: 255acbb5e73538a7110a36fd08ec3380d279bc72 SHA256: 82f92ad27b1fed9b9912613336654e402220ed07c294c57f635e34fa62336622 SHA512: 2350ab7b86545537b679e166e6ef8d91645f03ab5e7a0133b1d1813d1600c9ee56c19fdadcd5ae84c9afa96d28e40c98764eff6126dfe955017c774dcb749e45 Homepage: https://cran.r-project.org/package=DIFboost Description: CRAN Package 'DIFboost' (Detection of Differential Item Functioning (DIF) in Rasch Modelsby Boosting Techniques) Performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) . Package: r-cran-difconet Architecture: all Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gplots, r-cran-stringr, r-cran-data.table, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-difconet_1.0-4-1.ca2604.1_all.deb Size: 83246 MD5sum: 2e463dbae5e18ab174d6291c3a6b9547 SHA1: ecb2433050a6cff8ffaf453e7ec2de0ef88f0e91 SHA256: 4062fce30726896eda95fb5a2730ba64b7414a2930471812a1c74405c2f8ffd4 SHA512: cc91d3187b58288635b52bae061985dee905b31b48f6107c6d6b61ef73ba76a4a273d61c10a675a5eaa3cd20cef37fdfe1db2948dad17cfc6f2258166c4ebbc4 Homepage: https://cran.r-project.org/package=difconet Description: CRAN Package 'difconet' (Differential Coexpressed Networks) Estimation of DIFferential COexpressed NETworks using diverse and user metrics. This package is basically used for three functions related to the estimation of differential coexpression. First, to estimate differential coexpression where the coexpression is estimated, by default, by Spearman correlation. For this, a metric to compare two correlation distributions is needed. The package includes 6 metrics. Some of them needs a threshold. A new metric can also be specified as a user function with specific parameters (see difconet.run). The significance is be estimated by permutations. Second, to generate datasets with controlled differential correlation data. This is done by either adding noise, or adding specific correlation structure. Third, to show the results of differential correlation analyses. Please see for further information. Package: r-cran-diffcor Architecture: all Version: 0.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-diffcor_0.8.4-1.ca2604.1_all.deb Size: 68902 MD5sum: e955a4bdd86e918a2f8557624f6254e4 SHA1: e28a2ce48039e3400788b38837b95c243f293aee SHA256: a9882aa18e9129853183d41e7a5030a64f9378eb2f7e041a197f6d7bc09ade1a SHA512: 86d5c94c4b15dbbc688b4117e60cb4d74cee794f96744df0411d8b5e5826f06182b979fdedf0563a8876789b88c63709eb0c152c21f69111fc0cdb19f99060e7 Homepage: https://cran.r-project.org/package=diffcor Description: CRAN Package 'diffcor' (Fisher's z-Tests Concerning Differences Between Correlations) Computations of Fisher's z-tests concerning different kinds of correlation differences. The 'diffpwr' family entails approaches to estimating statistical power via Monte Carlo simulations. Important to note, the Pearson correlation coefficient is sensitive to linear association, but also to a host of statistical issues such as univariate and bivariate outliers, range restrictions, and heteroscedasticity (e.g., Duncan & Layard, 1973 ; Wilcox, 2013 ). Thus, every power analysis requires that specific statistical prerequisites are fulfilled and can be invalid if the prerequisites do not hold. To this end, the 'bootcor' family provides bootstrapping confidence intervals for the incorporated correlation difference tests. Package: r-cran-diffcorr Architecture: all Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-igraph, r-bioc-multtest, r-bioc-pcamethods Suggests: r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-diffcorr_0.4.5-1.ca2604.1_all.deb Size: 725842 MD5sum: 489c0eca5450bdae9f7792d45a003932 SHA1: 700af7696df475a93664ed56d91f29cead113f33 SHA256: eddf8444c4a489b07c6ebc46ad306e762384b4ea7e2ed74c5526ef6e50555c2e SHA512: ea224c2b90ec19a32a4a46a655d4e2b76f7e86d74ea4f3f1bafd9cab36e9a252b70eedd85eca1b9c2ed3ebe2c8b9c8522417c2a1e6f50273f03506c196b5f56d Homepage: https://cran.r-project.org/package=DiffCorr Description: CRAN Package 'DiffCorr' (Analyzing and Visualizing Differential Correlation Networks inBiological Data) A method for identifying pattern changes between 2 experimental conditions in correlation networks (e.g., gene co-expression networks), which builds on a commonly used association measure, such as Pearson's correlation coefficient. This package includes functions to calculate correlation matrices for high-dimensional dataset and to test differential correlation, which means the changes in the correlation relationship among variables (e.g., genes and metabolites) between 2 experimental conditions. Package: r-cran-diffdf Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-assertthat Suggests: r-cran-testthat, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-dplyr, r-cran-stringi, r-cran-stringr, r-cran-devtools, r-cran-covr, r-cran-bit64, r-cran-withr Filename: pool/dists/resolute/main/r-cran-diffdf_1.1.2-1.ca2604.1_all.deb Size: 134776 MD5sum: 42cbc53f8350d3358b979e2806b0bead SHA1: 67c30fea82aa6954086d161ba121a7d08c2c126d SHA256: 2dd4107b8c73399379073656f031bc7456b3fa612b7f2657d5e835220cdaad19 SHA512: 8fe3e2b9882efdfcdcf6f7bd67f221dfbd76cfdf26f9f602b2bbee1564c145783bb64d9747d1581b27a07d7a9b3eeeba3d7be474310192f284485548aa5a5460 Homepage: https://cran.r-project.org/package=diffdf Description: CRAN Package 'diffdf' (Dataframe Difference Tool) Functions for comparing two data.frames against each other. The core functionality is to provide a detailed breakdown of any differences between two data.frames as well as providing utility functions to help narrow down the source of problems and differences. Package: r-cran-diffdfs Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-janitor, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-diffdfs_0.9.0-1.ca2604.1_all.deb Size: 15452 MD5sum: f5c87e74a9ffe696cf3adacc90d8559a SHA1: b7c9ad817420894d6f9d9ae4378f2684882a19d5 SHA256: d76436293640774859b97fe3a4e3378c09b30e8e412b5fa61a8ec07b1f926fcb SHA512: dc259a4dbb7a86f6711ca69ecfa95f167d6114900e76bb2e907d5c0d82709a1452bdac20ef08f246144c81e68425168e281960ddd4df081053a2e50874dd7729 Homepage: https://cran.r-project.org/package=diffdfs Description: CRAN Package 'diffdfs' (Compute the Difference Between Data Frames) Shows you which rows have changed between two data frames with the same column structure. Useful for diffing slowly mutating data. Package: r-cran-diffee Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4074 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-pcapp Filename: pool/dists/resolute/main/r-cran-diffee_1.1.0-1.ca2604.1_all.deb Size: 4127996 MD5sum: 0997587bbf9df5aa7d1e5c3565f9f3d4 SHA1: 5893d044576b2f001deebafaf6542a80a096a308 SHA256: 05a4598cbdbb2e09a2ea628e69e4b6beb3810a6942f7621ee8f0eae4cc163337 SHA512: b54a30c0edf85e98a0ae5030ebf75af0e3d6c30f3c72119732f0ddb9fbdfa1a04dc7905dc622323ed70c4227bf12e45e19f788ee67eedb73f43ac34c6de7ecb6 Homepage: https://cran.r-project.org/package=diffee Description: CRAN Package 'diffee' (Fast and Scalable Learning of Sparse Changes in High-DimensionalGaussian Graphical Model Structure) This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) . Package: r-cran-diffenrich Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-here, r-cran-rlang, r-cran-stringr, r-cran-reshape2, r-cran-ggnewscale Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-diagram Filename: pool/dists/resolute/main/r-cran-diffenrich_0.1.2-1.ca2604.1_all.deb Size: 964516 MD5sum: e50571b6b0cc548962339b33233b84bf SHA1: 8391c07c9f0cd617b04aca9ea3ff61da66126320 SHA256: 071d4fff025cb200f342de9a92d77e9adefb74d39b664a550a237ba9248569ce SHA512: 17a5f99848577a559e8f1a7d68baa79990cb0036050e2da07a4fac3ee56cb2f82521917d1ca316015c98d2e3d34856ef562e3af596ab2b5d1c996a3aeed3c03c Homepage: https://cran.r-project.org/package=diffEnrich Description: CRAN Package 'diffEnrich' (Given a List of Gene Symbols, Performs Differential EnrichmentAnalysis) Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) . Given a list of gene symbols, 'diffEnrich' will perform differential enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. This package provides a number of functions that are intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will download and clean species specific ENTREZ gene IDs and map them to their respective KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly enriched pathways from two gene sets, and finally, the user will identify pathways that are differentially enriched between the two gene sets. In addition to the analysis pipeline, this package also provides a plotting function. Package: r-cran-diffeqr Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11067 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-diffeqr_2.1.0-1.ca2604.1_all.deb Size: 521144 MD5sum: 72f040f13ab89d12d393ca060b66af24 SHA1: 5acef32016ae2d552394050a3a6405b2c7fcfadf SHA256: c1f820a14515519596c4fb5682c1ae2249b0155e707881028917117348b8e2e4 SHA512: d73402e53acb3bac6f5a80017a10df7f5a2302ca5af354ffe7a80c30996720fae1fe1c3b0936d794764db00c8ee46d84ced7508389f04cb72e441807367e23dc Homepage: https://cran.r-project.org/package=diffeqr Description: CRAN Package 'diffeqr' (Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)) An interface to 'DifferentialEquations.jl' from the R programming language. It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs). 'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information, see Rackauckas and Nie (2017) . 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This package includes clinical trials, observational studies, experimental datasets, cohort data, and case series involving gastrointestinal disorders such as gastritis, ulcers, pancreatitis, liver cirrhosis, colon cancer, colorectal conditions, Helicobacter pylori infection, irritable bowel syndrome, intestinal infections, and post-surgical outcomes. The datasets support educational, clinical, and research applications in gastroenterology, public health, epidemiology, and biomedical sciences. Designed for researchers, clinicians, data scientists, students, and educators interested in digestive diseases, the package facilitates reproducible analysis, modeling, and hypothesis testing using real-world and historical data. Package: r-cran-diginorm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-diginorm_0.1.0-1.ca2604.1_all.deb Size: 69616 MD5sum: de1b70feda7374472500cece6023d7d6 SHA1: d273ac9d28b644534699243ac3aac68741b4874f SHA256: 479328341b305a14df7a3202d4404f4739c51a925a618ad064d68d677c719b34 SHA512: 1c1ab319508920fa9e924c2d2515142a84cfc4bd0e2e4e408b551866dbed0e4715209d6ec583c3ee59c4b2fd9469766153582d706152c8bae59e77d9f6722687 Homepage: https://cran.r-project.org/package=digiNORM Description: CRAN Package 'digiNORM' (Data-Driven Digital PCR Normalization) Adopts the general least squares-based data-driven normalization strategy developed by Heckmann et al. (2011) to correct for technical variance in gene expression data generated via digital polymerase chain reaction (dPCR). Performs normalization of raw copy numbers and also calculates relative variability metrics that can be used to assess the impact of normalization on variance. Package: r-cran-digirhythm Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-readr, r-cran-magrittr, r-cran-dplyr, r-cran-xts, r-cran-pracma, r-cran-ggplot2, r-cran-lubridate, r-cran-stringr, r-cran-zoo, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-digirhythm_2.4-1.ca2604.1_all.deb Size: 1695776 MD5sum: 5ac82b1a985077c3c2f51271fe2267bc SHA1: a8f6d3a4e21449b2373758347dca8c77c31fd995 SHA256: cb075588572a8add5efd71f7b6903f49cfe32a07c228ee13763b6658abe82462 SHA512: 870871762b0054d50dfaac42b70f73d6692f8feadeae6d4219962057f22aa3e4d9c1fc194879d36d29e6334033b4c14d063ab7c6c00feeb0beb065787f0ec1d4 Homepage: https://cran.r-project.org/package=digiRhythm Description: CRAN Package 'digiRhythm' (Analyzing Animal's Rhythmicity) Analyze and visualize the rhythmic behavior of animals using the degree of functional coupling (See Scheibe (1999) ), compute and visualize harmonic power, actograms, average activity and diurnality index. Package: r-cran-digitalpcr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-digitalpcr_1.1.0-1.ca2604.1_all.deb Size: 16778 MD5sum: f714e57310f80631e369fbe80df8dfdd SHA1: b2719340d599f85f2e6a9c1e8a2e23a2f7f71b7c SHA256: b977d5b751a3746953ce0a32fc18d7e5dbcc875b9bd328726cc48a5f2f9e96a7 SHA512: 51433ed6d7f7de142178d4aa67d5b4bd7aa8b2727f55934dc89e7ce9c7fdd835242158c7f54041bf49b08916344c517af04b8bf7232d388695b5b7775644c2e2 Homepage: https://cran.r-project.org/package=digitalPCR Description: CRAN Package 'digitalPCR' (Estimate Copy Number for Digital PCR) The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute. Package: r-cran-digittests Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-benford.analysis, r-cran-benfordtests, r-cran-beyondbenford, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-digittests_0.1.2-1.ca2604.1_all.deb Size: 86806 MD5sum: 4badc82136086e90b3f51e79edfd0534 SHA1: f68c6dc50deb6191183624261d93603fbfc3b712 SHA256: 0edb9f82dd0f1a0de050ff48fd8bd4ac48386d0d399e5a93531f1c138dafdf2a SHA512: 8acf23ba58b52eda3b985aad9a9cb9907d6e9e1abd93985a56edfe90adbb14e4e9c18cb7bcbf45ef64c5bc29eee63b7d3e3cb62297d8ae231e1db81d99c8f52a Homepage: https://cran.r-project.org/package=digitTests Description: CRAN Package 'digitTests' (Tests for Detecting Irregular Digit Patterns) Provides statistical tests and support functions for detecting irregular digit patterns in numerical data. The package includes tools for extracting digits at various locations in a number, tests for repeated values, and (Bayesian) tests of digit distributions. Package: r-cran-digss Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-viridis, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-digss_1.0.2-1.ca2604.1_all.deb Size: 144036 MD5sum: 98b5f0d133abf5742d6fdcbd83b3bdde SHA1: be1da0d89d0b9c291811d5ef8c8bfb4711b9553f SHA256: 42dbd1b9055e724d504d452ea724dd9b706d9ac84d501db8ed1376a5092f63e3 SHA512: 188a9a084a118ef4626d31d2417aae776ef31cbcae0fcd7d248c0cbafbc9f3ee568d9f61852e6d2ef6f521a9f314ef8f35572e66d303d88afce034e3cefac765 Homepage: https://cran.r-project.org/package=DIGSS Description: CRAN Package 'DIGSS' (Determination of Intervals Using Georeferenced Survey Simulation) Simulation tool to estimate the rate of success that surveys possessing user-specific characteristics have in identifying archaeological sites (or any groups of clouds of objects), given specific parameters of survey area, survey methods, and site properties. The survey approach used is largely based on the work of Kintigh (1988) . Package: r-cran-dilp Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dilp_1.1.0-1.ca2604.1_all.deb Size: 139514 MD5sum: 4b385f3ee1f9c179cda697d683683cda SHA1: f94b1ef9256a1dcedb4846746484ad5e6dafa50a SHA256: d6f60a89cf8fbd23b40f8c95f1237f8f5c27def9fb5f0aa1857661d6b4ad82d2 SHA512: fb2465debb70b2b66699acef0fb4e1821ebf87446ba3bec698b0b6810df18cbba37d9f7aa3deaa8eca592230ed0314cf81a349903d07331d3f4f7a85858630f6 Homepage: https://cran.r-project.org/package=dilp Description: CRAN Package 'dilp' (Reconstruct Paleoclimate and Paleoecology with Leaf Physiognomy) Use leaf physiognomic methods to reconstruct mean annual temperature (MAT), mean annual precipitation (MAP), and leaf dry mass per area (Ma), along with other useful quantitative leaf traits. Methods in this package described in Lowe et al. (in review). Package: r-cran-dimensio Architecture: all Version: 0.14.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arkhe, r-cran-khroma Suggests: r-cran-fontquiver, r-cran-knitr, r-cran-markdown, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-dimensio_0.14.1-1.ca2604.1_all.deb Size: 1187398 MD5sum: 6f7f96a683496cb150f55937a24a1e9d SHA1: 591a9af2620b9fe8c4fba9d35c561e48f94acb50 SHA256: 3bbd9c7ce91ac77faf737cbb64cc0d72c68476e58f7658c23d59800d152df414 SHA512: 837e8615a9762483a286c0a99e283dac161beb26cfb6f6c99ba590fa133217e44365647e17a0703e940d78d41127b0ff605588b40ecef3f686c48af8e1538cd5 Homepage: https://cran.r-project.org/package=dimensio Description: CRAN Package 'dimensio' (Multivariate Data Analysis) Simple Principal Components Analysis (PCA) and (Multiple) Correspondence Analysis (CA) based on the Singular Value Decomposition (SVD). This package provides S4 classes and methods to compute, extract, summarize and visualize results of multivariate data analysis. It also includes methods for partial bootstrap validation described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et al. (2006, ISBN: 978-2-10-049616-7). Package: r-cran-dimensionsr Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-bibliometrix, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dimensionsr_0.0.3-1.ca2604.1_all.deb Size: 73350 MD5sum: d988218c53d367255b4ba90ebb260055 SHA1: 459b1aa10d21277229e7fb7d82ee5e59d0333029 SHA256: 814c2647337159f4ff99870b3388c12720cbb810db656002c14f045497c89a44 SHA512: 6e42339792e9ae182328fade7f641bd7fcdbfb08446a7e516d6d73b01aec4ade69a9eaa6f142900e50d21023a59a6282e2bd8b77f287a68faf8ac675b485904e Homepage: https://cran.r-project.org/package=dimensionsR Description: CRAN Package 'dimensionsR' (Gathering Bibliographic Records from 'Digital ScienceDimensions' Using 'DSL' API) A set of tools to extract bibliographic content from 'Digital Science Dimensions' using 'DSL' API . Package: r-cran-dimodels Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hnp, r-cran-rootsolve, r-cran-multcomp, r-cran-multcompview Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-dimodels_1.3.3-1.ca2604.1_all.deb Size: 457452 MD5sum: e39a99874f840adc7789993a47e97cef SHA1: b8d8d167de4f42158d097cb66d7d95bd3043dda2 SHA256: d9248aa13269a2f45803b2182ab78f6877099c46710ea6b5c3b76b8295f0d079 SHA512: ec7d9bcded2675c3cfd515ba304efaea439a0ed7ad4af218662be32217db0fcb3955c6c84adf39569b3d596943299dfa4ca7d551200fa0bf18d32c1db0a75643 Homepage: https://cran.r-project.org/package=DImodels Description: CRAN Package 'DImodels' (Diversity-Interactions (DI) Models) The 'DImodels' package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) . Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function). Package: r-cran-dimodelsmulti Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dimodels, r-cran-plyr, r-cran-dplyr, r-cran-nlme, r-cran-reshape2, r-cran-knitr, r-cran-matrix, r-cran-shiny Suggests: r-cran-ggplot2, r-cran-dimodelsvis, r-cran-cli, r-cran-fansi, r-cran-crayon, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-mass, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dimodelsmulti_1.2.1-1.ca2604.1_all.deb Size: 819120 MD5sum: 61ae2c0c45d898f44551feeea63652d9 SHA1: 02d940045b1433a8f726e434f2f775d218d0c0eb SHA256: 3320a73ae4a8fc4f1eb3b798166e264c37b9537636b23a5eb7a3606ef69b3bee SHA512: 384e9c25387e7f00776b0f38ec41886a227d9b73e24ae38a0242028b7695226205f4b7a380a14d45abff9d003e1a26e0b4ce0f0ea7ab59b1d5dde6b915272ae4 Homepage: https://cran.r-project.org/package=DImodelsMulti Description: CRAN Package 'DImodelsMulti' (Fit Multivariate Diversity-Interactions Models with RepeatedMeasures) An add-on package to 'DImodels' for the fitting of biodiversity and ecosystem function relationship study data with multiple ecosystem function responses and/or time points. This package uses the multivariate and repeated measures Diversity-Interactions (DI) methods developed by Kirwan et al. (2009) , Finn et al. (2013) , and Dooley et al. (2015) . Package: r-cran-dimodelsvis Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-colorspace, r-cran-dimodels, r-cran-dplyr, r-cran-forcats, r-cran-ggfortify, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-insight, r-cran-metr, r-cran-pieglyph, r-cran-plotwidgets, r-cran-rlang, r-cran-scales, r-cran-tidyr Suggests: r-cran-dimodelsmulti, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-plotly, r-cran-randomforest, r-cran-cowplot, r-cran-nnet, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-dimodelsvis_1.0.4-1.ca2604.1_all.deb Size: 1222778 MD5sum: fc7244ce38f26a869af57a713a1b3c20 SHA1: 319f3afb82ce1c688ee1dc408dbffb31536f426d SHA256: 41f5f731e0f5754cf1250e73c8d54c6637f9a1d2fa7936ab9e1c97584abe5fd1 SHA512: fcd0f0627664d9460eed6e53e0879e79181b119e54d91172a4f9b22e0c51755d8eb89633a5dfe636e82b2ac9330b099ea22cbf806f918caf8fd63e0efae53205 Homepage: https://cran.r-project.org/package=DImodelsVis Description: CRAN Package 'DImodelsVis' (Visualising and Interpreting Statistical Models Fit toCompositional Data) Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object. Package: r-cran-dimora Architecture: all Version: 0.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-numderiv, r-cran-forecast, r-cran-reshape2, r-cran-desolve Filename: pool/dists/resolute/main/r-cran-dimora_0.3.6-1.ca2604.1_all.deb Size: 119892 MD5sum: f38a88dc8c9b4372406096ef8458845f SHA1: 5754ec52eef115eee095f8354d3e2eff83db2375 SHA256: 0adac52bdf6ceda02b13390fcdbdc9fd87fc97e38ec2a53395ba3c62a493ecee SHA512: faeaa465e78580f6bf10505538ab95fb0352ebef6c8e68d986426a5a3f0ffcd1c8d85944bdfde96ff85d579028510aa04dd192c040892961e1096493f05f1a7c Homepage: https://cran.r-project.org/package=DIMORA Description: CRAN Package 'DIMORA' (Diffusion Models R Analysis) The implemented methods are: Standard Bass model, Generalized Bass model (with rectangular shock, exponential shock, and mixed shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model, and UCRCD model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function. The UCRCD model (Unbalanced Competition and Regime Change Diachronic) is a diffusion model used to capture the dynamics of the competitive or collaborative transition. Package: r-cran-dinamic.duo Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biomart, r-cran-dinamic, r-cran-plyr, r-cran-httr Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-dinamic.duo_1.0.4-1.ca2604.1_all.deb Size: 1342556 MD5sum: 7ba9bc15314c2a499ce6ace4d46143eb SHA1: 7349215cee273b02ba5612250c2a66842afd80df SHA256: c1350cbb992c993f1687248db9079e9f617c04eeec9737d50f0430a9bd0acdc3 SHA512: 53690679d0e020bf9b437a344d9711710bb55a2b4c09321b237d6fb7d0292cd764c1445623169a65f68f8deecaf6965c6b1f70c45464cf5286625ac95edd5e33 Homepage: https://cran.r-project.org/package=DiNAMIC.Duo Description: CRAN Package 'DiNAMIC.Duo' (Finding Recurrent DNA Copy Number Alterations and Differences) In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. Here we use cyclic shift permutations to identify recurrent copy number alterations in a single cohort or recurrent copy number differences in two cohorts based on a common set of genomic markers. Additional functionality is provided to perform downstream analyses, including the creation of summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al. (2011) and leverages the theory developed in Walter et al. (2015) . An article describing DiNAMIC.Duo by Walter et al. (2022) can be found at . Package: r-cran-dinamic Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-dinamic_1.0.1-1.ca2604.1_all.deb Size: 136560 MD5sum: 34cdef1c035874e73d1c13fcb72a647b SHA1: 41dbe6197727a88a152199777ee0327261d92781 SHA256: d1dc4b67698dbd6e5ff9c2e0cb36b347d4e98fc35817992edb5ae780a549c484 SHA512: dad250334690f67b91658ff7afd2d01c0c267921288db19f1cb8d1d734c0dee2c1fddfab58dce7403f1ff0d4e795138945f46759ac2b04daeaa2112a1a431f7c Homepage: https://cran.r-project.org/package=dinamic Description: CRAN Package 'dinamic' (A Method to Analyze Recurrent DNA Copy Number Aberrations inTumors) In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. This package implements the DiNAMIC procedure for assessing the statistical significance of recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685). Package: r-cran-dineq Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-dineq_0.1.0-1.ca2604.1_all.deb Size: 216420 MD5sum: 999a40cd1066fbda5a3ed4efda024be1 SHA1: 022f379994f88bdce57e1af06f5f8d78b31f2f97 SHA256: fb3639755fff0abedf173d1c103f148593fd5d1a94402c62b1d8cb68786b3ee5 SHA512: 91747980a294c46ed37dffd89aeefdc4243b7dc3d1db0fee78b325fa0b09e48f386e4fb5b78003913a667289f1a44fc349bba12d47fd721deadd5b55d4ef0390 Homepage: https://cran.r-project.org/package=dineq Description: CRAN Package 'dineq' (Decomposition of (Income) Inequality) Decomposition of (income) inequality by population sub groups. For a decomposition on a single variable the mean log deviation can be used (see Mookherjee Shorrocks (1982) ). For a decomposition on multiple variables a regression based technique can be used (see Fields (2003) ). Recentered influence function regression for marginal effects of the (income or wealth) distribution (see Firpo et al. (2009) ). Some extensions to inequality functions to handle weights and/or missings. 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Basic arithmetic operations (such as adding and subtracting) are supported, as well as formatting and converting to and from standard R date types. 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Using the framework of linear model-based differential expression comparisons ('limma' package), time-course expression patterns for genes in different conditions are compared and analyzed for significant pattern changes. For reference, see: Greenham K, Sartor RC, Zorich S, Lou P, Mockler TC and McClung CR. eLife. 2020 Sep 30;9(4). . Package: r-cran-diphiseq Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-diphiseq_0.2.0-1.ca2604.1_all.deb Size: 55700 MD5sum: eaa3ecdf4a3825d14d5eeedebe2efec3 SHA1: 7e88fe2b7f66d75d4e5d217b0448825be2c818d3 SHA256: 312235511fe0fca6b7358f5ce0d684e55fa746d77e1a6eeca29a2371861281ae SHA512: 54294934ac2543f585dff751f32f8d5c793c7b68c0c2739158ca5a273859502e00bd9f7d87af91e5d98bca46be56fdcb4f41a9d3e4582ab21538f3acac97b887 Homepage: https://cran.r-project.org/package=DiPhiSeq Description: CRAN Package 'DiPhiSeq' (Robust Tests for Differential Dispersion and DifferentialExpression in RNA-Sequencing Data) Implements the algorithm described in Jun Li and Alicia T. Lamere, "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes" (Unpublished). Detects not only genes that show different average expressions ("differential expression", DE), but also genes that show different diversities of expressions in different groups ("differentially dispersed", DD). DD genes can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi. (2) Added a function for estimating the proportion of outliers in the data. (3) Modified the input parameter names for clarity, and modified the output format for the main function. Package: r-cran-dips Architecture: all Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-mvnfast, r-cran-rlemon Suggests: r-cran-optmatch Filename: pool/dists/resolute/main/r-cran-dips_0.6.4-1.ca2604.1_all.deb Size: 124916 MD5sum: b528b5cbe432ef34defceb8ec1c1098d SHA1: e3210c98502e77c8c6edb28a42fe3b96ee374b9f SHA256: 3a8568d090afbfa4f19de59d850fd13cb033df5a897a352999eddc140dd375f9 SHA512: 3bdf782be57fad080dcf9382ea78182ed64db99c31a6fca185a76c7fa47de1a0f8ff470a1d9bf093441f7368ae7f2c025aa2554253e2981db9226158c5693e00 Homepage: https://cran.r-project.org/package=DiPs Description: CRAN Package 'DiPs' (Directional Penalties for Optimal Matching in ObservationalStudies) Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. 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Package: r-cran-directedclustering Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-igraph Filename: pool/dists/resolute/main/r-cran-directedclustering_1.0.0-1.ca2604.1_all.deb Size: 39408 MD5sum: c8a2da86c38cd7dffeca8d5daf58c5d2 SHA1: f066401dcadd98269f47a59273a6d711315384ef SHA256: 4f64fcb09e33c2833566ecae1fe638f4426d8b5cfb2f9290141de74fe2b0a487 SHA512: 2a39b4a1fb993804661bbd9b64a9c9db5254715e83989b7f59ea47de867fba708b85b608fb129d412e4fbd298748b0d30c95048c3bd00ad1b8012982cd5f5732 Homepage: https://cran.r-project.org/package=DirectedClustering Description: CRAN Package 'DirectedClustering' (Directed Weighted Clustering Coefficient) Allows the computation of clustering coefficients for directed and weighted networks by using different approaches. It allows to compute clustering coefficients that are not present in 'igraph' package. A description of clustering coefficients can be found in "Directed clustering in weighted networks: a new perspective", Clemente, G.P., Grassi, R. (2017), . 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Package: r-cran-directional Architecture: all Version: 7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1026 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigstatsr, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-rfast, r-cran-rfast2, r-cran-rnanoflann, r-cran-rgl, r-cran-rnaturalearth, r-cran-sf Suggests: r-cran-bigreadr Filename: pool/dists/resolute/main/r-cran-directional_7.5-1.ca2604.1_all.deb Size: 952530 MD5sum: 09ffbda1b7b8a7d7b14dfacfc14245a8 SHA1: 16895884f4a15b1369dbedae12d64735697959fa SHA256: ba7f9019ce0f7d5238050a832d6f92fbf3cf9b5e805c4156ee0af924b85d4e43 SHA512: 37dfa4f708e2b15905e403536d4e96199f956f96f353b9fc844c359ba9405ad4126cd6a4f2acc4218debd051bb9016f3dc123c1df0607ffb25ea90a39577ba18 Homepage: https://cran.r-project.org/package=Directional Description: CRAN Package 'Directional' (A Collection of Functions for Directional Data Analysis) A collection of functions for directional data (including massive data, with millions of observations) analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include: a) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2018). "An elliptically symmetric angular Gaussian distribution". Statistics and Computing 28(3): 689-697. . b) Tsagris M. and Alenazi A. (2019). "Comparison of discriminant analysis methods on the sphere". Communications in Statistics: Case Studies, Data Analysis and Applications 5(4):467--491. . c) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2020). "Spherical regression models with general covariates and anisotropic errors". Statistics and Computing 30(1): 153--165. . d) Tsagris M. and Alenazi A. (2024). "An investigation of hypothesis testing procedures for circular and spherical mean vectors". Communications in Statistics-Simulation and Computation, 53(3): 1387--1408. . e) Yu Z. and Huang X. (2024). A new parameterization for elliptically symmetric angular Gaussian distributions of arbitrary dimension. Electronic Journal of Statistics, 18(1): 301--334. . f) Tsagris M. and Alzeley O. (2025). "Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling". Australian & New Zealand Journal of Statistics, 67(1): 77--103. . g) Tsagris M., Papastamoulis P. and Kato S. (2025). "Directional data analysis: spherical Cauchy or Poisson kernel-based distribution". Statistics and Computing, 35:51. . h) Alzeley O. and Tsagris (2026). On the generalized circular projected Cauchy distribution. . 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See Yang P et al (2014) ; and Yang P et al. (2016) . Package: r-cran-directstandardisation Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-directstandardisation_1.3-1.ca2604.1_all.deb Size: 39554 MD5sum: 1ac462f3f441dbdb8c670871b9212668 SHA1: abbd90a50e9e6f319e1d03713146da549032a9e9 SHA256: 95016e26a2741480b5079d8df758144ef0eb1efb00ec4238eb654f450df1ec18 SHA512: 50d86f7547f706d562bd6403baf7d46a7d3934f0ae0fa559f191553137eb37b1b7148f47b3617c55dbfcb1ed1761ad7c0290e41651d8e4b88f70c57d4823b6a7 Homepage: https://cran.r-project.org/package=DirectStandardisation Description: CRAN Package 'DirectStandardisation' (Adjusted Means and Proportions by Direct Standardisation) Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset. Package: r-cran-dirichletprocess Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-ggplot2, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-dirichletprocess_0.4.2-1.ca2604.1_all.deb Size: 768564 MD5sum: 75b50b983ad780b9c906aaae489eee04 SHA1: d2d82703733b543b3eb2ad536bb092ffb1ea4b8d SHA256: cc38039c481370c46902f1cf3568becf0c78214d07f81828a9357ed2a7f9f3ea SHA512: 15951dfd15a1a698cebed1fa1d299937596579094e1d00708545dab50179e026a3bee9997f74aa2767d2a28c39d05b441f4849e667972826b1f0022f8d5b90ca Homepage: https://cran.r-project.org/package=dirichletprocess Description: CRAN Package 'dirichletprocess' (Build Dirichlet Process Objects for Bayesian Modelling) Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) , among many other sources. Package: r-cran-dirmr Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lava, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-dirmr_0.5.0-1.ca2604.1_all.deb Size: 123556 MD5sum: e40a2d630336088ebb7c6f3164bc6c3c SHA1: f9bd724751a9a63e70b02421b1cbacb24f135fb7 SHA256: 7fbc9d1d7b206803f57a6f3c75ebf7dc9ec73be1f28e27930215766f5a06d93f SHA512: 38572301fa6b957af7e9282f7527a21f63b5944e65f21e402ac56723cc3c60aa7c7706fdf8b242040013a6d13c34bd5c6dc8fd6108ba210cfb26ac11c27c9e02 Homepage: https://cran.r-project.org/package=DIRMR Description: CRAN Package 'DIRMR' (Distributed Imputation for Random Effects Models with MissingResponses) By adding over-relaxation factor to PXEM (Parameter Expanded Expectation Maximization) method, the MOPXEM (Monotonically Overrelaxed Parameter Expanded Expectation Maximization) method is obtained. Compare it with the existing EM (Expectation-Maximization)-like methods. Then, distribute and process five methods and compare them, achieving good performance in convergence speed and result quality.The philosophy of the package is described in Guo G. (2022) . Package: r-cran-dirmult Architecture: all Version: 0.1.3-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dirmult_0.1.3-5-1.ca2604.1_all.deb Size: 74552 MD5sum: 3a989f0513b62f9f135d16f69b0dacb2 SHA1: 65d19c3c0cb1c5d3c0493078b355a22468c75d90 SHA256: 628035bb46e065335519a2b4586f2fa34ef6c31a3a9b764d24245074014187e2 SHA512: af4a08efcb301d3ba1577d47cdebe263a6b7c00432a15bfadb05870ac039952fb2c74280f20a3f4cf6d7f81871dd49a3935a9ee71924c95c149092655574fe66 Homepage: https://cran.r-project.org/package=dirmult Description: CRAN Package 'dirmult' (Estimation in Dirichlet-Multinomial Distribution) Estimate parameters in Dirichlet-Multinomial and compute log-likelihoods. Package: r-cran-disagg2 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynomf Filename: pool/dists/resolute/main/r-cran-disagg2_0.1.0-1.ca2604.1_all.deb Size: 19552 MD5sum: b5a999a934bb53ab1b583b5c2bde9b82 SHA1: 0502da958f4c555a66e185775e2d12627c3f18e0 SHA256: ca5df3cbe25bceabee9dd7e7fb531bd313655c796dd10ec02ede37808b2326f0 SHA512: 2ac46ffe87e69f32bd1006ccc9abb4ce00d5db4dbdd90bffe655fe6502dc4ba6830d0ca5ec3454eef3948dcc8cdaf3e34feb6480fdd79ac6c77c7349502e90d8 Homepage: https://cran.r-project.org/package=disagg2 Description: CRAN Package 'disagg2' (Support Functions for Time Series Analysis Book) Contains the support functions for the Time Series Analysis book. We present a function to calculate MSE and MAE for inputs of actual and forecast values. We also have the code for disaggregation as found in Wei and Stram (1990, ), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series"). 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The aggregated sum of the resulting time series is strictly equal to the low-frequency time series within the benchmarking window. Typically, the low-frequency time series is an annual one, unknown for the last year, and the high frequency one is either quarterly or monthly. See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8, ). Package: r-cran-disaggregatets Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-matrix, r-cran-lars, r-cran-zoo, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-corrplot, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-disaggregatets_3.0.1-1.ca2604.1_all.deb Size: 149134 MD5sum: 29c7294067c9e40a2c30205afabf05b2 SHA1: 1f61b645c1296045b7cdab860824d97ea658a624 SHA256: 92f6803a85d789e7c216bce97f0caca57cf7eba3917c158b3587f8c39f4c2cbd SHA512: e6e5aeb3850ad08b1bd080d016faa09092773f06cd271c73330e64556d06acf89815eafd4184f71bac9a68e2a8570f6718770800123d74e25d5c6bc9dff62c6f Homepage: https://cran.r-project.org/package=DisaggregateTS Description: CRAN Package 'DisaggregateTS' (High-Dimensional Temporal Disaggregation) Provides tools for temporal disaggregation, including: (1) High-dimensional and low-dimensional series generation for simulation studies; (2) A toolkit for temporal disaggregation and benchmarking using low-dimensional indicator series as proposed by Dagum and Cholette (2006, ISBN:978-0-387-35439-2); (3) Novel techniques by Mosley, Gibberd, and Eckley (2022, ) for disaggregating low-frequency series in the presence of high-dimensional indicator matrices. Package: r-cran-disagmethod Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom, r-cran-ltsa, r-cran-zoo, r-cran-xts, r-cran-tsbox, r-cran-tswge Filename: pool/dists/resolute/main/r-cran-disagmethod_0.1.1-1.ca2604.1_all.deb Size: 62066 MD5sum: d0f3b3127d0d8ef56b7490cec97f4af0 SHA1: eb427807fa8cef5178b097aeaf04d813f8a1e599 SHA256: ed133c9e9dea47fa5e420d52e28fe6925f9f6d1775319f1dc09d8b1216c14586 SHA512: d5fd152545c03c4f151fad1ed19625f0de5adc22af74081b3dd508621c82cfd48dea970f908ba8ce99e0964afc7a348b4369703afac3d78dce8d8abf5fb48e55 Homepage: https://cran.r-project.org/package=disagmethod Description: CRAN Package 'disagmethod' (Autoregressive Integrated Moving Average (ARIMA) BasedDisaggregation Methods) We have the code for disaggregation as found in Wei and Stram (1990, ), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series" in Statistical Science I, Nova Publishing). The disaggregation models have different orders of the moving average component. These are based on ARIMA models rather than differencing or using similar time series. 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Intelligent cleaning and normalization techniques transform raw commentary into structured data, ensuring precise extraction of disaster-specific insights. Collective sentiments of affected communities are quantitatively scored and qualitatively categorized, providing a multifaceted view of societal responses under duress. Interactive geographic maps and temporal charts illustrate the evolution and spatial dispersion of emotional reactions and impact indicators. Package: r-cran-disastr.api Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-disastr.api_1.0.6-1.ca2604.1_all.deb Size: 20904 MD5sum: e2d2742bf1d425ba1ed0a721178caabe SHA1: 8646fed7f6f93f12aae222e4333bcc7ef72c81eb SHA256: caed8c59fd23fc339fc054594a3e9a902b1a5d313a482f7c21163dd98efd2b60 SHA512: 987584b297e157fc882032510b32cc8c286ae207a766f489050775293f97c17fe38728911349edb81c770fb3fb76e401116a7e6b99cd79e521d34dec5f0c1c94 Homepage: https://cran.r-project.org/package=disastr.api Description: CRAN Package 'disastr.api' (Wrapper for the UN OCHA ReliefWeb Disaster Events API) Access and manage the application programming interface (API) of the United Nations Office for the Coordination of Humanitarian Affairs' (OCHA) ReliefWeb disaster events at . The package requires a minimal number of dependencies. It offers functionality to retrieve a user-defined sample of disaster events from ReliefWeb, providing an easy alternative to scraping the ReliefWeb website. It enables a seamless integration of regular data updates into the research work flow. 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Although the calculation of AUC is standardized, there are differences in AUC based on some assumptions. For example, Myerson et al. (2001) assumed that (with delay discounting data) a researcher would impute an indifference point at zero delay equal to the value of the larger, later outcome. However, this practice is not clearly followed. This imputed zero-delay indifference point plays an important role in log and ordinal versions of AUC. Ordinal and log versions of AUC are described by Borges et al. (2016). The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)], impute indifference points when x = 0, calculate ordinal AUC in the case of Halton sampling of x-values, and account for probability discounting AUC. 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Package: r-cran-discfrail Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-discfrail_0.2-1.ca2604.1_all.deb Size: 391630 MD5sum: 9a01fca0fcd12901c4464e2babf2a459 SHA1: 53a0b91f9547cde33c579378e67c951c480218d3 SHA256: dc07cd83c45fd0510089842a77e40ef75ff2ca77c79bf0f525d45aaf794e0141 SHA512: e8ef3300655e682175b38aa58148d0b2cfcb760289ba635cc13d238d702d43a520fd33b2ac0793eb5f762c195d18e1fdbe66d56e10598899c3ee41ddc2d8774a Homepage: https://cran.r-project.org/package=discfrail Description: CRAN Package 'discfrail' (Cox Models for Time-to-Event Data with Nonparametric DiscreteGroup-Specific Frailties) Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) . There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function. Package: r-cran-discharge Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmom, r-cran-ggplot2, r-cran-circstats, r-cran-checkmate, r-cran-boot Filename: pool/dists/resolute/main/r-cran-discharge_1.0.0-1.ca2604.1_all.deb Size: 275894 MD5sum: eb492e6402eedb6ae2186d5249316eaf SHA1: e88e6b684b7eea0e2b28116ae48ace2ae50a0e7f SHA256: b51d904b3a4eb59255e3eb52dc5989060b0f60842129d89967755267ab6516bc SHA512: 94ce390097178c2ec7caa0766670bced371478beaeab344388cbe80b4d7170e51d849346f30c219787bbe81de41ecccab6e40ef39958c33a2a9c23df019757ee Homepage: https://cran.r-project.org/package=discharge Description: CRAN Package 'discharge' (Fourier Analysis of Discharge Data) Computes discrete fast Fourier transform of river discharge data and the derived metrics. The methods are described in J. L. Sabo, D. M. Post (2008) and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) . Package: r-cran-disclap Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-disclap_1.5.1-1.ca2604.1_all.deb Size: 23360 MD5sum: ec1d845bdb6f85e330f4a244bdbefb55 SHA1: efc56c89d9b2d072f6f14be7a599ba5da418c911 SHA256: 7862d0fc1cb69270bf2fbd4025ffbcfe670c24755134a8ca471cd1b44ca68c76 SHA512: 9d72df665a61e1009c196af3300b4dcef0215625abc03f740bd08eb2f18c419a392ec130dd0ecd8da8a863fdf383b12e5289793d2444fead725c481653c57d68 Homepage: https://cran.r-project.org/package=disclap Description: CRAN Package 'disclap' (Discrete Laplace Exponential Family) The discrete Laplace exponential family for use in fitting generalized linear models. Package: r-cran-disclosur Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-pdftools, r-cran-qdap, r-cran-sentimentanalysis, r-cran-stringi, r-cran-stringr, r-cran-syuzhet, r-cran-zoo, r-cran-snowballc, r-cran-tm, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-disclosur_0.6.0-1.ca2604.1_all.deb Size: 194184 MD5sum: 0a033255e5a4fd8090c26bb31440e21b SHA1: c384cf437fd94eb3ce089350613ea940e86c3e7c SHA256: a7c21bfe9696ede736082523406d2d8baba4162c12d7a49801bb94feaceb28c0 SHA512: f2010558bb6ddf14d282412d31355e57f2590bbf1e3379fc5f7045480daa2027b25ec42803c317245099601f358a3032d0d922dcdb5a74e405447d8544de0a61 Homepage: https://cran.r-project.org/package=disclosuR Description: CRAN Package 'disclosuR' (Text Conversion from Nexis Uni PDFs to R Data Frames) Transform 'newswire' and earnings call transcripts as PDF obtained from 'Nexis Uni' to R data frames. Various 'newswires' and 'FairDisclosure' earnings call formats are supported. Further, users can apply several pre-defined dictionaries on the data based on Graffin et al. (2016) and Gamache et al. (2015). Package: r-cran-discnorm Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-arules, r-cran-sirt, r-cran-mass, r-cran-pbivnorm, r-cran-cubature, r-cran-copula, r-cran-mnormt, r-cran-gofkernel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-discnorm_0.2.1-1.ca2604.1_all.deb Size: 49414 MD5sum: e4904f5cbfff9b87bfbcf3d091584b1d SHA1: 8f94890d80acd25d439bec26fa8f3f398800a03f SHA256: 61a64fed47bb834343ebd8421e7a095109b23eba99c1d98d2b6b6943b6754133 SHA512: 31e6f0fe4d05f364d3ed2baf7a3b58795ec2e5ee7b8fa18429304f265e4939b9c14df825a0ce1e6012fa0ba14165d67e6447575a684943b3301385ae70f14e4d Homepage: https://cran.r-project.org/package=discnorm Description: CRAN Package 'discnorm' (Test for Discretized Normality in Ordinal Data) Tests whether multivariate ordinal data may stem from discretizing a multivariate normal distribution. The test is described by Foldnes and Grønneberg (2019) . In addition, an adjusted polychoric correlation estimator is provided that takes marginal knowledge into account, as described by Grønneberg and Foldnes (2022) . Package: r-cran-discocvi Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn Suggests: r-cran-dbscan, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-discocvi_0.1.1-1.ca2604.1_all.deb Size: 105178 MD5sum: 9df73fd3b1a63621aa08adf54011eef4 SHA1: 0a5a14123481f694b91c180902cb923f58d95be3 SHA256: db86b1c929e829eed1ca2665c48a72f64a0cf7a61db8674018b2ab3161bb6701 SHA512: 2f732ba159147ba245bb41d475a2adabd4ccd90e7671f200301bffc424b782f0e1293761e032d808eb600c9afcecafa7feb0796868845da518dbb4d8ae05a7a3 Homepage: https://cran.r-project.org/package=discoCVI Description: CRAN Package 'discoCVI' (Implementation of the DISCO Metric for Internal ClusteringEvaluation) Implementation of the DISCO (Density-based Internal Score for Clusterings with nOise) metric, a cluster validity index for evaluating density-based clustering results without ground truth labels. DISCO is the first index to explicitly assess the quality of noise point assignments in addition to cluster quality. It uses density-connectivity distance derived from a minimum spanning tree of the mutual-reachability graph, providing interpretable, bounded scores in [-1, 1]. Higher scores indicate better clustering. Based on Beer et al. (2025) . Package: r-cran-discord Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2418 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nlsylinks, r-cran-ggpedigree, r-cran-bgmisc, r-cran-dt, r-cran-broom, r-cran-dplyr, r-cran-gridextra, r-cran-ggextra, r-cran-ggplot2, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-scales, r-cran-sessioninfo, r-cran-stargazer, r-cran-snakecase, r-cran-testthat, r-cran-tidyverse, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-discord_1.3-1.ca2604.1_all.deb Size: 1607672 MD5sum: 83672b737969dd02b36d02742d5f11e5 SHA1: 7f9a337780dc1780854b6271701b5f088dea95e8 SHA256: 70e72645f77cd2a70950e56dc01091d441fe102d91fb1b32f62b41554fb81815 SHA512: 824a5793efbf323db428995ce67ccd80870446073ef4f874be3a6df1c3bdb05913861ea062d370f5ab0747baecec344e8de0e09ca5c67c97572ce62822aae27b Homepage: https://cran.r-project.org/package=discord Description: CRAN Package 'discord' (Functions for Discordant Kinship Modeling) Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 ], [Sims, Trattner, and Garrison, 2024 ] for empirical examples, and [Garrison and colleagues for theoretical work ]. Package: r-cran-discos Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2075 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-cvxr, r-cran-pracma, r-cran-rdpack, r-cran-evmix, r-cran-extremestat, r-cran-mass Suggests: r-cran-haven, r-cran-latex2exp, r-cran-knitr, r-cran-rmarkdown, r-cran-maps, r-cran-testthat, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-discos_0.1.3-1.ca2604.1_all.deb Size: 2024564 MD5sum: 1a7030a1e78947822ba20a01b2c1b1e2 SHA1: d624e031efa6747169a2001420d8184ee9327fc0 SHA256: faeb87adaec64bf040b1398b9aaf8c4ab312f5af9d70c44190175658d50481b5 SHA512: 27d2925a9c161ae7c9ba305abe300045d295e008033be90c53db48509d4905710986f0785f288b327f4841a6ca9fbc0eb2d7e6fe238aab4aad29a5038ecf6dad Homepage: https://cran.r-project.org/package=DiSCos Description: CRAN Package 'DiSCos' (Distributional Synthetic Controls Estimation) The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) , that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them. Package: r-cran-discoursegt Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggally, r-cran-ggplot2, r-cran-ggrepel, r-cran-igraph, r-cran-network Suggests: r-cran-biocmanager, r-cran-formatr, r-cran-ggpubr, r-cran-knitr, r-cran-markdown, r-cran-r.rsp, r-cran-rmarkdown, r-cran-rticles, r-cran-sna, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-discoursegt_1.2.0-1.ca2604.1_all.deb Size: 433434 MD5sum: 50b35fd1491e91e9faf483975d8095d3 SHA1: 8b3b3e64f791814877017f0bec94f9271a14c68e SHA256: c74d3148d129e18ba3b5e96cfe13826767e183cd5468b9ee2d91db2dd5c10efd SHA512: 2c94dc14505b697f1a3710b0797001d57e44a364f81bb19bee976550365ce40ab76cfc450655f602f1fe520608617346dbd794335b00a6628d0b7e4102acba08 Homepage: https://cran.r-project.org/package=discourseGT Description: CRAN Package 'discourseGT' (Analyze Group Patterns using Graph Theory in EducationalSettings) Analyzes group patterns using discourse analysis data with graph theory mathematics. Takes the order of which individuals talk and converts it to a network edge and weight list. Returns the density, centrality, centralization, and subgroup information for each group. Based on the analytical framework laid out in Chai et al. (2019) . Package: r-cran-discover Architecture: all Version: 3.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-rlang, r-cran-golem, r-cran-shiny, r-cran-config, r-cran-plotly, r-cran-loader, r-cran-cluster, r-cran-ggplot2, r-cran-shinyjs, r-cran-shinyace, r-cran-ggdendro, r-cran-echarts4r, r-cran-htmltools, r-cran-factominer, r-cran-htmlwidgets, r-cran-colourpicker, r-cran-shinydashboard, r-cran-shinycustomloader, r-cran-shinydashboardplus Filename: pool/dists/resolute/main/r-cran-discover_3.1.7-1.ca2604.1_all.deb Size: 442136 MD5sum: 8e1c043f503810693197b20a7827d45d SHA1: 208561be16f5ef67636486615c5abe0cc5b60838 SHA256: 0367074e541077eb1bb6ca97e62d728e6bf1143a11815aee5bf031e439b188b9 SHA512: b53ebf7ee1aa5ef6303ef52873b5c81ba85de7c65a762f9a26a69bee291d0d97f82bec0e9595bad48b614553ec8cf8811c08b1b304aeb428c9ec6eebe4815e7b Homepage: https://cran.r-project.org/package=discoveR Description: CRAN Package 'discoveR' (Exploratory Data Analysis System) Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method. Package: r-cran-discoverableresearch Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-ngram, r-cran-readr, r-cran-stringdist, r-cran-stringi, r-cran-stopwords, r-cran-synthesisr, r-cran-tm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-discoverableresearch_0.0.1-1.ca2604.1_all.deb Size: 358972 MD5sum: f481ebf8cfbbac1bb146ff6eb09d4b94 SHA1: 81718ceb46ea6576e54c7572829b52466609a027 SHA256: 3d0e328e2e4ef0bf7892e0ffdfb7b1f968ec77ee311251c2fa416447a4c0a81d SHA512: 234e68758e2440a0bd69bd9324bbe1d04beecb56a85f8b01b93e755689e0eea72e9b8c23ec4349ec93ac9843d712aa669507fdc9d32c8b137681c9b51cad962b Homepage: https://cran.r-project.org/package=discoverableresearch Description: CRAN Package 'discoverableresearch' (Checks Title, Abstract and Keywords to Optimise Discoverability) A suite of tools are provided here to support authors in making their research more discoverable. check_keywords() - this function checks the keywords to assess whether they are already represented in the title and abstract. check_fields() - this function compares terminology used across the title, abstract and keywords to assess where terminological diversity (i.e. the use of synonyms) could increase the likelihood of the record being identified in a search. The function looks for terms in the title and abstract that also exist in other fields and highlights these as needing attention. suggest_keywords() - this function takes a full text document and produces a list of unigrams, bigrams and trigrams (1-, 2- or 2-word phrases) present in the full text after removing stop words (words with a low utility in natural language processing) that do not occur in the title or abstract that may be suitable candidates for keywords. suggest_title() - this function takes a full text document and produces a list of the most frequently used unigrams, bigrams and trigrams after removing stop words that do not occur in the abstract or keywords that may be suitable candidates for title words. check_title() - this function carries out a number of sub tasks: 1) it compares the length (number of words) of the title with the mean length of titles in major bibliographic databases to assess whether the title is likely to be too short; 2) it assesses the proportion of stop words in the title to highlight titles with low utility in search engines that strip out stop words; 3) it compares the title with a given sample of record titles from an .ris import and calculates a similarity score based on phrase overlap. This highlights the level of uniqueness of the title. This version of the package also contains functions currently in a non-CRAN package called 'litsearchr' . Package: r-cran-discovr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8061 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr, r-cran-ggplot2, r-cran-glue, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-discovr_1.0.0-1.ca2604.1_all.deb Size: 2485108 MD5sum: 57d8b9cdcc04a96c2a8d58ef34703cf2 SHA1: 3f304162e43865f53c7d7cf913a4a6b313b2045d SHA256: 9974e1bc5aff2b2e178fd30c07e47f78fb7daab70fcc9692db461bcec36162b9 SHA512: e0bcb525b1720ddd757e1a73f1153ea7dd3b7e23233a0fbba95abfaca12e7db12886f1ad32917235feacdb1a510f60345c8ee44a922fe2afcce9a02c329fcce6 Homepage: https://cran.r-project.org/package=discovr Description: CRAN Package 'discovr' (Interactive Tutorials and Data for "Discovering Statistics UsingR and RStudio") Interactive 'R' tutorials and datasets for the textbook Field (2026), "Discovering Statistics Using R and RStudio", . Interactive tutorials cover general workflow in 'R' and 'RStudio', summarizing data, visualizing data, fitting models and bias, correlation, the general linear model (GLM), moderation, mediation, missing values, comparing means using the GLM (analysis of variance), comparing adjusted means (analysis of covariance), factorial designs, multilevel models, repeated measures designs, growth models, exploratory factor analysis (EFA), loglinear analysis, and logistic regression. There are no functions, only datasets and interactive tutorials. Package: r-cran-discretedatasets Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 957 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate Filename: pool/dists/resolute/main/r-cran-discretedatasets_0.2.0-1.ca2604.1_all.deb Size: 861124 MD5sum: fcba34242a0e80200506b488736465ba SHA1: 93a51c44f68787d846ece831156b59698f9b917d SHA256: 35ae1050ccc82cf0e31be29674f827092bb103b3fcd23dda5a048280a5aaf62d SHA512: c0ee5b3a7b1960b8708adf6c1d63ae461fc0273f914d0b05f211c30809b6c71cf938a18cae6a9327ec34a83c91ac30f490ea297e15c04361a4564b97df8ef988 Homepage: https://cran.r-project.org/package=DiscreteDatasets Description: CRAN Package 'DiscreteDatasets' (Example Data Sets for Use with Discrete Statistical Tests) Provides several data sets for use with discrete statistical tests and discrete multiple testing procedures. Package: r-cran-discretegapstatistic Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cultevo, r-cran-magrittr, r-cran-ggplot2, r-cran-pheatmap, r-cran-dplyr, r-cran-polychrome, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-tidyr, r-bioc-complexheatmap, r-cran-cluster Suggests: r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-discretegapstatistic_1.1.2-1.ca2604.1_all.deb Size: 1826016 MD5sum: 44f7913c910b1ca47a6daf1b85a6bcae SHA1: f60115ef8f8d7f8a9b23719552bdad5e239a2f58 SHA256: f279d3bd9a4f50071402b7f1dfbace450e7a1422fe769e0b6f6a3cf9bf65b64e SHA512: 93257975e1779bb83bdb299bf5d4282e98666bb1f68dec9ecb70e9384d7ff59d8464f31b1dcc4cba282ef8ea89c17b8b55f1601951d1d291564da8074cc55707 Homepage: https://cran.r-project.org/package=DiscreteGapStatistic Description: CRAN Package 'DiscreteGapStatistic' (An Extension of the Gap Statistic for Ordinal/Categorical Data) The gap statistic approach is extended to estimate the number of clusters for categorical response format data. This approach and accompanying software is designed to be used with the output of any clustering algorithm and with distances specifically designed for categorical (i.e. multiple choice) or ordinal survey response data. Package: r-cran-discreteinverseweibull Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp Filename: pool/dists/resolute/main/r-cran-discreteinverseweibull_1.0.2-1.ca2604.1_all.deb Size: 43362 MD5sum: 1740025d1aca60d841c94f0b6e02a9da SHA1: 10107003c3173dfb2ca39dad35c578347cc22f38 SHA256: b60c5a4c72fecbe5a7d4e8b8e03909b0903382b88fc98d5159ce8977a7bf297e SHA512: 6c116364a3fab66a355df887b92432501483e0dddc768dececc4c62349fde8f32b7e28ec08956408c793a7087543e349a4a30f1e0725a60099e6dda99ea57c35 Homepage: https://cran.r-project.org/package=DiscreteInverseWeibull Description: CRAN Package 'DiscreteInverseWeibull' (Discrete Inverse Weibull Distribution) Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution. Package: r-cran-discretelaplace Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-discretelaplace_1.1.1-1.ca2604.1_all.deb Size: 58116 MD5sum: 8d7a818dd6efe1f6ec8bd7ba39e8b512 SHA1: 4e4e75f2c1c882f570338641989aa2d898430936 SHA256: 4763321f51b0352c826a30fa953ed3de240930334852a9cef5fd58ac8c4e2d85 SHA512: 7a13a032f00d1d5d55aa303f35c048fc84fc9993057de824c4588bf477ea7008dcd5b26cfcd38cea0df34ae27fa9e117352f7cd99ccb891f91c14a9c8bbeaddd Homepage: https://cran.r-project.org/package=DiscreteLaplace Description: CRAN Package 'DiscreteLaplace' (Discrete Laplace Distributions) Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions. Package: r-cran-discreteqvalue Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-coin, r-cran-exactranktests Filename: pool/dists/resolute/main/r-cran-discreteqvalue_1.1-1.ca2604.1_all.deb Size: 36382 MD5sum: ee000738bbed0bebcbe697efb9812b3b SHA1: a24fe40dde55f1325f7ad0958f3becca2c95a155 SHA256: 519a3cbb671ec154fb94515b84a42385418fad18c17aa90eb617c7d592d078f7 SHA512: f77379c3d09a0b48cb7d5fd7a1bc26c7f041cb4826fd792a9494bcceabb4910fd0e2a27d226121bc5ec6882c3505ad7889dc990669d0066cc97982e3caf4f19d Homepage: https://cran.r-project.org/package=DiscreteQvalue Description: CRAN Package 'DiscreteQvalue' (Improved q-Values for Discrete Uniform and Homogeneous Tests) We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), . The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, p0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi0 estimators which take into account the discrete distribution of the P-values. 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Series can be traversed, combined using arithmetic operations, tested for membership, and queried for limit points ("sinks"), without explicit enumeration of all elements. 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Package: r-cran-discretization Architecture: all Version: 1.0-1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-discretization_1.0-1.1-1.ca2604.1_all.deb Size: 99484 MD5sum: 99bf32850736303ac9c545901c103863 SHA1: 3a0810dd64b3270819c9dfca822783346644dacc SHA256: 0481ca190f2bdd2c84a303c66ba2e627f753c2ea6ceee682eea70d4979909f3f SHA512: 51c508b58452c59b72dab0b35e43c99675ccfbc4be4b939556907a4d3f888efe1518636b4cef9e8189fbc94c87240d6608c16a7237377433a706abe6a8d4a442 Homepage: https://cran.r-project.org/package=discretization Description: CRAN Package 'discretization' (Data Preprocessing, Discretization for Classification) A collection of supervised discretization algorithms. It can also be grouped in terms of top-down or bottom-up, implementing the discretization algorithms. 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Models include flavors of discriminant analysis, such as linear (Fisher (1936) ), regularized (Friedman (1989) ), and flexible (Hastie, Tibshirani, and Buja (1994) ), as well as naive Bayes classifiers (Hand and Yu (2007) ). Package: r-cran-discrtr Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rmdformats Suggests: r-cran-dfidx, r-cran-readr Filename: pool/dists/resolute/main/r-cran-discrtr_0.0.1-1.ca2604.1_all.deb Size: 1930832 MD5sum: 54b79aa2a899aa55832dfc96417acc76 SHA1: 0b8ce25e13a44efd4e3119618ba437c49e5feade SHA256: 54e64a7e1eca5caba1cb1c4016070b3db251c261b05bd3e9555f4ca4bba371fb SHA512: 506c393733963dd71e39b5c1a3f75de4e5bb3b1a424be7eeff4633f3a7dc2f0b1e2df2f7dd65719e217d611d0fa01d53893111ec0d208d0053c96ab713497d27 Homepage: https://cran.r-project.org/package=discrtr Description: CRAN Package 'discrtr' (A Companion Package for the Book "Discrete Choice Analysis with'R'") Templates and data files to support "Discrete Choice Analysis with R", Páez, A. and Boisjoly, G. (2023) . 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Package: r-cran-discursive Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowballc, r-cran-stm, r-cran-stringr, r-cran-tm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-discursive_0.1.1-1.ca2604.1_all.deb Size: 248650 MD5sum: c8cbfd1634fa5d8d4d558a53644e1c7e SHA1: e733836d1587b6aed7da6e61f553bbf4165b8927 SHA256: 55f31cae9c564ca763a6da750c939931116105b142b455575047f0d09019a03e SHA512: 8cacfd9c409144783f63bbe3abc52d0ff2036c6cbaa8f2b67c18ad466a0a931993c2ffa6c8230485819af7f985f2225a2d3cac37a189a4e8e3a809591c5b3b06 Homepage: https://cran.r-project.org/package=discursive Description: CRAN Package 'discursive' (Measuring Discursive Sophistication in Open-Ended SurveyResponses) A simple approach to measure political sophistication based on open-ended survey responses. Discursive sophistication captures the complexity of individual attitude expression by quantifying its relative size, range, and constraint. For more information on the measurement approach see: Kraft, Patrick W. 2023. "Women Also Know Stuff: Challenging the Gender Gap in Political Sophistication." American Political Science Review (forthcoming). 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These data are described and made available by Elith et al (2020) to compare species distribution modelling methods. 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Package: r-cran-diseasystore Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-isoweek, r-cran-jsonlite, r-cran-lubridate, r-cran-pkgcond, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-r6, r-cran-scdb, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-devtools, r-cran-duckdb, r-cran-ggplot2, r-cran-here, r-cran-knitr, r-cran-lintr, r-cran-microbenchmark, r-cran-odbc, r-cran-pkgdown, r-cran-rmarkdown, r-cran-rsqlite, r-cran-rpostgres, r-cran-testthat, r-cran-tibble, r-cran-spelling, r-cran-usethis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-diseasystore_0.3.3-1.ca2604.1_all.deb Size: 711950 MD5sum: 4f50c60ba30b49d416bc4fdf00caecae SHA1: 4720b7d8e785b7be05a4e4c4a016b7343d307c62 SHA256: 41e461f22b261d896d0162d9fd5e52590b0f53d7e0d75ec40a54a1c138e86b2d SHA512: a8bb1bc39ba529362a677a5465f0f4606a92543d8501b8df4ea55474494f67b27ab98d6d4284c57b825f6e3babe9f4fc61492877e32171015b93f9d6b6a2f9ea Homepage: https://cran.r-project.org/package=diseasystore Description: CRAN Package 'diseasystore' (Feature Stores for the 'diseasy' Framework) Simple feature stores and tools for creating personalised feature stores. 'diseasystore' powers feature stores which can automatically link and aggregate features to a given stratification level. These feature stores are automatically time-versioned (powered by the 'SCDB' package) and allows you to easily and dynamically compute features as part of your continuous integration. 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The observed gene expression in bulk tumor sample is modeled by a log-normal distribution with the location parameter structured as a linear combination of the component-specific gene expressions. 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This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects. 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California Community Colleges Chancellor's Office (2017). Percentage Point Gap Method. . California Community Colleges Chancellor's Office (2014). Guidelines for Measuring Disproportionate Impact in Equity Plans. . 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The package is a dependency for mvp-type packages that use the STL map class: it traps plausible idiom that is ill-defined (implementation-specific) and returns an informative error, rather than returning a possibly incorrect result. To cite the package in publications please use Hankin (2022) . 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Angeles Serrano, Marian Boguna and Alessandro Vespignani in "Extracting the multiscale backbone of complex weighted networks", Proceedings of the National Academy of Sciences 106 (16), 2009. This implementation of the algorithm supports both directed and undirected networks. 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Based on convex hull or integrated covariance Mahalanobis, several indicators are implemented for inter and intra batch dispersion analysis. It is designed to facilitate robust statistical assessment of data variability, supporting applications in exploratory data analysis and quality control, for such datasets as the one found in metabololomics studies. For more details see Salanon (2024) and Salanon (2025) . Package: r-cran-displease Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-farver Filename: pool/dists/resolute/main/r-cran-displease_1.0.0-1.ca2604.1_all.deb Size: 18980 MD5sum: a373234e47ceb2b3fdaf60f695eb013c SHA1: 969a7c2b0570a54fa3f0e511122b87ff01753cc5 SHA256: a541be2f44e4cc81e1a92a7433991db2bc0dbc61a1e78e5a1a2a1b54854c1448 SHA512: 51c59ab4946143a719995d4ef1ce5576678a272797dee87df40c23932f4647c9e6fa01d1fc5e50c9697705eb1e75ee2ae0f4e3832f9407a95359caa617707fdd Homepage: https://cran.r-project.org/package=displease Description: CRAN Package 'displease' (Numeric and Color Sequences with Non-Linear Interpolation) When visualising changes between two values over time, a strict linear interpolation can look jarring and unnatural. By applying a non-linear easing to the transition, the motion between values can appear smoother and more natural. This package includes functions for applying such non-linear easings to colors and numeric values, and is useful where smooth animated movement and transitions are desired. Package: r-cran-dispmod Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-car Filename: pool/dists/resolute/main/r-cran-dispmod_1.2-1.ca2604.1_all.deb Size: 53834 MD5sum: cf731152dd30d9e78c392dd15c2a66b1 SHA1: a9c30026fcae89be1bd97358eab7c5588e0e36f4 SHA256: 52f85153d3b75e5988a0cff679d0fcd38d4100521d0cf154da8ab7de39ef5da4 SHA512: 1a23830c97fde7c5bb36166dba1239ce319197dbd845774883c8509870fc5211a956d215569b4886ccbe2cd638b30599b0b2240c8e7cd3df6959cfa77632c9cb Homepage: https://cran.r-project.org/package=dispmod Description: CRAN Package 'dispmod' (Modelling Dispersion in GLM) Functions for estimating Gaussian dispersion regression models (Aitkin, 1987 ), overdispersed binomial logit models (Williams, 1987 ), and overdispersed Poisson log-linear models (Breslow, 1984 ), using a quasi-likelihood approach. Package: r-cran-disposables Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-disposables_1.0.3-1.ca2604.1_all.deb Size: 22930 MD5sum: 2a9cedad9e43d1a3f3923a598d8acba1 SHA1: e0976f1569050f91f802457ddbbc801dd6849ced SHA256: d22713d1a759d6069bc20f1dbaf668d33c05c2ecc5040ce9f5aa48db85bfc5c4 SHA512: 34783ce85cd8af0a7cde09d6f36b309d52923ba9d8699eb8ad23ac190e274c36b56f0d0d6152eb33cd29d1a08c64feafad92d6e13a86eab7915d81a58a4a49cd Homepage: https://cran.r-project.org/package=disposables Description: CRAN Package 'disposables' (Create Disposable R Packages for Testing) Create disposable R packages for testing. You can create, install and load multiple R packages with a single function call, and then unload, uninstall and destroy them with another function call. This is handy when testing how some R code or an R package behaves with respect to other packages. Package: r-cran-dispositioneffect Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-lubridate, r-cran-magrittr, r-cran-progress Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-covr, r-cran-tidyr, r-cran-skimr, r-cran-ggplot2, r-cran-ggridges, r-cran-furrr, r-cran-future, r-cran-foreach, r-cran-doparallel, r-cran-bench Filename: pool/dists/resolute/main/r-cran-dispositioneffect_1.0.1-1.ca2604.1_all.deb Size: 1233150 MD5sum: a517456a612b21e45c22adf31570132c SHA1: 6adb801a3d1f528ece2cc148c59c225f26470acb SHA256: 6964277a0e34e6baecf2e9871d05caec8f094364cc0ab0892811b7e8b71c8023 SHA512: 79b467c4481b780a8f70d0e7b08f7099752a1b255b0ea6d9d665c55e1c590e09e3ddc74946a9b2112f96fb4af68b896d88e76086caae72ffdd1569c5b3e51408 Homepage: https://cran.r-project.org/package=dispositionEffect Description: CRAN Package 'dispositionEffect' (Analysis of Disposition Effect on Financial Portfolios) Evaluate the presence of disposition effect and others irrational investor's behaviors based solely on investor's transactions and financial market data. 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Package: r-cran-disscqn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-spader, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-disscqn_0.1.0-1.ca2604.1_all.deb Size: 63708 MD5sum: aad817d80ab5cd1257d9ab496a2f3a08 SHA1: 78942a242be03f337a1eeb7db6d5fabe95fa66ec SHA256: 89929a1de1509092dfe4451a18bb2ec9185257bad6dc19f90f7ff95e3aef4b97 SHA512: d13589a306ddbd6d6e59d030d1442a109efff2919146964205ce0f83a36976e0bed9513222ced9fe01e1a6b6d0c66a9ea93d3dfe3ac2272aad3a2c30e0d460d4 Homepage: https://cran.r-project.org/package=dissCqN Description: CRAN Package 'dissCqN' (Multiple Assemblage Dissimilarity for Orders q = 0-N) Calculate multiple or pairwise dissimilarity for orders q = 0-N (CqN; Chao et al. 2008 ) for a set of species assemblages or interaction networks. 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The original dissever algorithm was published by Malone et al. (2012) , and extended by Roudier et al. (2017) . Package: r-cran-dissmod Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc, r-cran-matrixcalc, r-cran-psych, r-cran-mass Filename: pool/dists/resolute/main/r-cran-dissmod_1.0.0-1.ca2604.1_all.deb Size: 400528 MD5sum: 56ea2a342672e6324e13ae7de7954048 SHA1: e2a7dca41407ad6fee73eee580a5a94983ad4dc3 SHA256: fd9b7572bc43a6823c98ad5093997f6cdd9d65ca89a484588f2215e8f4dfdde5 SHA512: 894e0b15dc35a4cf1e85c60ce9abe9852e0af6ab125314c7f0dc54d608a8952adc8f12440874212cfe7c94039833ffcde8e3a1bd4f28cf8b5b3b4d5c9f771000 Homepage: https://cran.r-project.org/package=DiSSMod Description: CRAN Package 'DiSSMod' (Fitting Sample Selection Models for Discrete Response Variables) Tools to fit sample selection models in case of discrete response variables, through a parametric formulation which represents a natural extension of the well-known Heckman selection model are provided in the package. The response variable can be of Bernoulli, Poisson or Negative Binomial type. The sample selection mechanism allows to choose among a Normal, Logistic or Gumbel distribution. Package: r-cran-dist.structure Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-algebraic.dist Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-withr Filename: pool/dists/resolute/main/r-cran-dist.structure_0.5.0-1.ca2604.1_all.deb Size: 228054 MD5sum: 65fd9d03bbd4b67d240a83d214f7b641 SHA1: 445b26fa492476011852fe0bbba34974f2780ce2 SHA256: 94c4e95b8aebbd7355cd41a90f1997ec5afa2eb45e494662852ef7def306f75a SHA512: d7d362bfd6408112e872c166aba0baa8e1e36469ef655b304d9881613761afa70669e30a6e8741ee97554abba77d99b84a55125b06cf6104a8a09095b9cac5b0 Homepage: https://cran.r-project.org/package=dist.structure Description: CRAN Package 'dist.structure' (Structured Random Variables for Reliability System Distributions) Extends the 'algebraic.dist' distribution algebra to random variables with internal structure: coherent reliability systems decomposed into components arranged by a structure function (series, parallel, k-out-of-n, bridge, and arbitrary topologies via minimal path sets). Every 'dist_structure' object is a 'dist', so the full distribution algebra (mean, vcov, sampler, surv, cdf) works automatically via default methods that compose component-level distributions through the topology. Adds structural queries: structure function evaluation, minimal path and cut sets, system signature, critical states, dual, Birnbaum structural importance, and system reliability. Topology shortcut constructors (series_dist, parallel_dist, kofn_dist, bridge_dist) produce ready-to-use dists from component dists and a chosen structure. Package: r-cran-distance Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mrds, r-cran-dplyr, r-cran-rlang, r-cran-rdpack Suggests: r-cran-rmarkdown, r-cran-kableextra, r-cran-bookdown, r-cran-knitr, r-cran-covr, r-cran-progress, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-activity, r-cran-testthat, r-cran-optimx, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-distance_2.0.1-1.ca2604.1_all.deb Size: 395756 MD5sum: 2940cba146804ca3025ea0a2839c931e SHA1: 7a75f184d9abfb607456ff16db33c0c48256f2ac SHA256: 4623b2c844d4326e5928f338331309493065c7820fdf828e08a4aa3b9ec22697 SHA512: 6d445e3948a54464476b91429b0f0d25bda24da41fa6be1817d6588d4c74b96077c92a825b3f3de5af9a69a8c0b8de0516e45b1f9d5abc03283ac0ad7f5d6bcb Homepage: https://cran.r-project.org/package=Distance Description: CRAN Package 'Distance' (Distance Sampling Detection Function and Abundance Estimation) A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) for more information on methods and for example analyses. Package: r-cran-distancehd Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-distancehd_1.2-1.ca2604.1_all.deb Size: 345160 MD5sum: 85c8af6a66bf4ad7e3ad1c77077f936c SHA1: 40024ee1fde4d8082a8b83de01e8bff41996b21b SHA256: 1410447cabab622e93ff435ea971eb7433a36c5d6f149f19793244e415f5ad97 SHA512: d453110b55280ea2a5d14ff08883696681681876018f5b6858487828b99e02087d24147112c8f44aa1c7f00a8d8e3dfe7064d0fe7e65886cdadbd4386867691e Homepage: https://cran.r-project.org/package=distanceHD Description: CRAN Package 'distanceHD' (Distance Metrics for High-Dimensional Clustering) We provide three distance metrics for measuring the separation between two clusters in high-dimensional spaces. The first metric is the centroid distance, which calculates the Euclidean distance between the centers of the two groups. 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Package: r-cran-distanceto Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-nabor, r-cran-geodist Suggests: r-cran-fasterize, r-cran-knitr, r-cran-rmarkdown, r-cran-raster, r-cran-tinytest, r-cran-lwgeom Filename: pool/dists/resolute/main/r-cran-distanceto_0.0.3-1.ca2604.1_all.deb Size: 71226 MD5sum: 42bf0e69e6ad2cafb448b0103228db67 SHA1: c79fc947f3b1daa517c00f0b5ce6ad7ee6ee0416 SHA256: cf656626a3028ef5fa3d520d2de030a9d714cb578edcd0aa19bc2e95d5eebc8d SHA512: 78e9311a5f209284658ecc68b68e71e2688f9c3be720c4d8ff9ca198b8da3966104f11495c03a7558dca780040f8d6abe37b04d9488268002b331adc883a6ae2 Homepage: https://cran.r-project.org/package=distanceto Description: CRAN Package 'distanceto' (Calculate Distance to Features) Calculates distances from point locations to features. The usual approach for eg. resource selection function analyses is to generate a complete distance to features surface then sample it with your observed and random points. Since these raster based approaches can be pretty costly with large areas, and often lead to memory issues in R, the distanceto package opts to compute these distances using efficient, vector based approaches. As a helper, there's a decidedly low-res raster based approach for visually inspecting your region's distance surface. But the workhorse is distance_to. Package: r-cran-distatisr Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-prettygraphs, r-cran-car, r-cran-readxl, r-cran-dplyr, r-cran-tidytext Filename: pool/dists/resolute/main/r-cran-distatisr_1.1.2-1.ca2604.1_all.deb Size: 326860 MD5sum: cb6fadd545f7b9463d7e63955a9f8613 SHA1: d66dbaf50bb98259894e90f15ddd75cdddb96124 SHA256: 41645988d58f7d9aa6eae89d09b29d75e49161d81486846b97078ad5bf5c9402 SHA512: cc616e899568ac143617a4f91ff0fef42ab987c364e8deb2b6917789912ff337864ff9617f28a0f06ecd44c364a8294f8ca630d1a14ad1ef34ec35b2d45ae3aa Homepage: https://cran.r-project.org/package=DistatisR Description: CRAN Package 'DistatisR' (DiSTATIS Three Way Metric Multidimensional Scaling) Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). DiSTATIS and CovSTATIS are used to analyze multiple distance/covariance matrices collected on the same set of observations. These methods are based on Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012) . 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For a review of discretisation methods, see Chakraborty (2015) . 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The method provides estimates with standard error of a comparison of proportions (difference, odds ratio and risk ratio) derived, with similar precision, from a comparison of means. See the URL below or for more information. 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As fitness function a mixture of the chi square test for distributions and a novel measure for approximating the common area under curves between multiple Gaussians is used. The package presents an alternative to the commonly used Likelihood Maximization as is used in Expectation Maximization. The algorithm and applications of this package are published under: Lerch, F., Ultsch, A., Lotsch, J. (2020) . The evolution is based on the 'GA' package: Scrucca, L. (2013) while the Gaussian Mixture Logic stems from 'AdaptGauss': Ultsch, A, et al. (2015) . 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'disttools' can be used to extract the distance between any pair or combination of points encoded by a 'dist' object using only the indices of those points. This is an improvement over existing functionality, which requires either coercing a 'dist' object into a matrix or calculating the one dimensional index corresponding to a pair of observations. Coercion to a matrix is undesirable because doing so doubles the amount of memory required for storage. In contrast, there is no inherent downside to the latter solution. However, in part due to several edge cases, correctly and efficiently implementing such a solution can be challenging. 'disttools' abstracts away these challenges and provides a simple interface to access the data in a 'dist' object using the latter approach. 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It allows creation of finely-tuned, publication-quality figures from single function calls. Visualizations include scatter plots, compositional bar plots, violin, box, and ridge plots, and more. Customization ranges from size and title adjustments to discrete-group circling and labeling, hidden data overlay upon cursor hovering via ggplotly() conversion, and many more, all with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). Package: r-cran-ditwah Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-ditwah_1.0.1-1.ca2604.1_all.deb Size: 49442 MD5sum: f638dec17c61bca868848b3d4a815259 SHA1: c3556ed82462d9761f6213155277db0c7074ac89 SHA256: 9eda87b5fb8b9aa61a96a5eee162ae25e711e283cee2754ce98526a144b52910 SHA512: 9a736909f253523cead704d7f364551da46ccc0cc601c62fffc1dc95c2d021762124dae26561589dd153d03dbfcbdb0748f64bf6dd8a8a105495eec0782c6023 Homepage: https://cran.r-project.org/package=Ditwah Description: CRAN Package 'Ditwah' (Ditwah Storm Data and Tools for Storm Monitoring and EarlyWarning November 2025, Sri Lanka) The Ditwah storm began impacting Sri Lanka on 25 November 2025. Ditwah provides a collection of tidy, well-structured datasets to support storm data management, monitoring, and early warning applications in Sri Lanka. The publicly available data were converted to tidy data format for easy analysis. The package processes weather data, flood data and situation report data (families affected, etc.). The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics. Package: r-cran-ditwahlandslide Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-plotly, r-cran-stringr, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-ditwahlandslide_1.2.0-1.ca2604.1_all.deb Size: 46534 MD5sum: cff35dc9cf4dfe5474b76fe458560cdf SHA1: 316d6d8cc3e2635a35c3c786235c0071df063a7d SHA256: 058e3250c27f4bcd53993df199343dda51522d082b1743ae18495aab048babb0 SHA512: 1e35af7cc16505b4b67e80939e508d3d187bca22930b32b31de3c6d825f0779bd769075cde097a66b9fe93db7403bff6f8ec5f39db305f69dc5d5e10344df500 Homepage: https://cran.r-project.org/package=ditwahLandslide Description: CRAN Package 'ditwahLandslide' (Early Warning Information on Landslides in Sri Lanka During theDitwah Storm) Provides curated early warning data on landslides in Sri Lanka during the Ditwah storm. It includes structured, machine-readable tidy dataset. This is developed for education and research purposes. Package: r-cran-dive Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-fme, r-cran-sp Filename: pool/dists/resolute/main/r-cran-dive_1.3-1.ca2604.1_all.deb Size: 3226284 MD5sum: 115d0e21f833ddd20647175669157c20 SHA1: 065d3de3499c00c49192f2d065428d5b2d0ae801 SHA256: dd8da05e00172946412599424473a8185c747224aa1dfa81fbfe8f8cf5d3b034 SHA512: 3e21224443f3de15cd39a43922881bbb9ba392dbc0b0f8934c9dd80639fcefba1463e32176499ba71539e81854e2f889b25ab83e5edb022fcb3a30cf3f319a12 Homepage: https://cran.r-project.org/package=DivE Description: CRAN Package 'DivE' (Diversity Estimator) Contains functions for the 'DivE' estimator . 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These tools work with and complement those of the 'tidyverse' suite, extending the grammar of 'ggplot2' to become a grammar of interactive graphics. The suite provides many visual tools designed for moderately (100s of variables) high dimensional data analysis, through 'zenplots' and novel tools in 'loon', and extends the 'ggplot2' grammar to provide parallel coordinates, Andrews plots, and arbitrary glyphs through 'ggmulti'. The 'diveR' package gathers together and installs all these related packages in a single step. Package: r-cran-diverge Architecture: all Version: 2.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncnorm Filename: pool/dists/resolute/main/r-cran-diverge_2.0.6-1.ca2604.1_all.deb Size: 377426 MD5sum: 3a1e41ac8367f90abfe358fac838facc SHA1: 78603b60b31c0ef2daeb8e9d43f9b0fcacaecea8 SHA256: d1c819c48724076214e58fa5f21ae95f2c14429f9016d91f1477ee9278515521 SHA512: 526e9349b6d33c1d3673aea7f1e88afb6e13c4ce0fe10191955f20074348329a87e02d0423a87984230d7cbe77fb04e24f4be6ea42b0017b34ecf1f555157e1a Homepage: https://cran.r-project.org/package=diverge Description: CRAN Package 'diverge' (Evolutionary Trait Divergence Between Sister Species and OtherPaired Lineages) Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework. Package: r-cran-diversificationr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-diversificationr_0.1.0-1.ca2604.1_all.deb Size: 40436 MD5sum: 741de2a98c2770f939649741a1f66627 SHA1: 4c31cf25aa40efb8f380dd3729987ff447b057b2 SHA256: 0ca3d9f4d996c43a751a988be761288d43483169cef4db7ad9224e9d620eafa6 SHA512: c9888d215d1e42a5df0903df61b001474a9f91e0f48704802d88e3f32b669b309ffdb63b2cf8962ad2edb95ef87f221723608c30c5511a220a2373984914eed3 Homepage: https://cran.r-project.org/package=DiversificationR Description: CRAN Package 'DiversificationR' (Econometric Tools to Measure Portfolio Diversification) Diversification is one of the most important concepts in portfolio management. This framework offers scholars, practitioners and policymakers a useful toolbox to measure diversification. Specifically, this framework provides recent diversification measures from the recent literature. These diversification measures are based on the works of Rudin and Morgan (2006) , Choueifaty and Coignard (2008) , Vermorken et al. (2012) , Flores et al. (2017) , Calvet et al. (2007) , and Candelon, Fuerst and Hasse (2020). Package: r-cran-diversityarch Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-diversityarch_0.3.0-1.ca2604.1_all.deb Size: 66146 MD5sum: ff96bacf9d9377dd48e10022d3d7822e SHA1: f3cb564dc50e6a457029b1a4a26e0cbb21a51853 SHA256: ad23fb094a7f680af9b17852bc7daf592a1f361c7802bfe0bbfa7168cbb3ef99 SHA512: 351d895f2daeab22c734de3ffd30a1dc826e62bf1504ee026245f625547feddd0488954a78cf768316e9c0f4d2f2e192ddde164c84c4ee0f04cced93598a4d7b Homepage: https://cran.r-project.org/package=diversityArch Description: CRAN Package 'diversityArch' (Computes Diversity Indices with Archaeological Data) Companion package of Arnaud Barat, Andreu Sansó, Maite Arilla-Osuna, Ruth Blasco, Iñaki Pérez-Fernández, Gabriel Cifuentes-Alcobenda, Rubén Llorente, Daniel Vivar-Ríos, Ella Assaf, Ran Barkai, Avi Gopher, & Jordi Rosell-Ardèvol (2025), "Quantifying Diversity through Entropy Decomposition. Insights into Hominin Occupation and Carcass Processing at Qesem cave". 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Implements a wide range of classical and entropy-based diversity indices, including Berger-Parker, Simpson (and related variants), Shannon, Brillouin, McIntosh, Margalef, Menhinick and Smith-Wilson. Supports permutation-based hypothesis tests for comparing groups with respect to diversity (global and pairwise comparisons), as well as confidence interval estimation using multiple bootstrap methods. Includes functionality for generating diversity profiles based on parametric families such as Hill numbers, Rényi entropy, and Tsallis entropy. The methods are applicable to ecological community data (species abundance counts) and genetic or phenotypic class frequency data. 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It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, 'DIVINE' makes the process more efficient, transparent, and reproducible. 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Spatial and temporal beta diversity can be partitioned into replacement and richness difference components. It also calculates standardized effect size for FD and PD alpha diversity and the average individual traits across multilayer rasters. The layers of the raster represent species, while the cells represent communities. Methods details can be found at Cardoso et al. 2022 and Heming et al. 2023 . Package: r-cran-divseg Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rlang, r-cran-dplyr, r-cran-tidyselect, r-cran-tibble, r-cran-units Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-divseg_0.1.0-1.ca2604.1_all.deb Size: 294042 MD5sum: 672e22fa82cbe3278c70b4102fd806d8 SHA1: bf86feeecbb6375b9ebe6ae60d7588ad20f3e0ee SHA256: c69859bbafbb02611768edf94a45bddb3b38ad5f27ea7a31e0ddc745c18757a7 SHA512: cf1c082a47ec1ecc9b5488ea7ebdef94b18c71b838a4f9e9fd46bbccfb6483ac4fa0f54cb010865ac0286cd584881611ac167dc11d8f2d40dfded4b60cab15e5 Homepage: https://cran.r-project.org/package=divseg Description: CRAN Package 'divseg' (Calculate Diversity and Segregation Indices) Implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) . Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a 'tidyselect' based workflow. 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Calculate common biodiversity and range-size metrics on subsampled data. Background theory and practical considerations for the methods are described in Antell and others (2024) . Package: r-cran-diyar Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1775 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-diyar_0.5.1-1.ca2604.1_all.deb Size: 1370382 MD5sum: dd6d407d956fcf61b8dae69bd0741224 SHA1: 960ff7e681b8211870a6e992b93e709b4c612a3d SHA256: 4cd1877a26bb3de9889b9ee6a1fc40531810882f34fdaf4af423974cec908797 SHA512: 68ca0323e04837b2bcabb3e7be8923fda1cb290accb9ba2af127ad9fb61a3ec7c18017581e0e9bdbef2a5ed2ec99d88ea05628d00300806638b332bdd179fd89 Homepage: https://cran.r-project.org/package=diyar Description: CRAN Package 'diyar' (Record Linkage and Epidemiological Case Definitions in 'R') An R package for iterative and batched record linkage, and applying epidemiological case definitions. 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The selected model consists of a subset of numerical regressors and partitions of levels of factors. Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel and Agnieszka Sołtys, 2023. Improving Group Lasso for High-Dimensional Categorical Data. In: Computational Science – ICCS 2023. Lecture Notes in Computer Science, vol 14074, p. 455-470. Springer, Cham. . Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. . Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. . Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. . 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This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and often impose further restrictions on the parameters. 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This package is meant to be useful both to players and Dungeon Masters (DMs). Some functions apply to many tabletop role-playing games (e.g., dice rolling), but others are focused on Fifth Edition (a.k.a. "5e") and where possible both the 2014 and 2024 versions are supported. Package: r-cran-dnetfinder Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flare Filename: pool/dists/resolute/main/r-cran-dnetfinder_1.1-1.ca2604.1_all.deb Size: 55890 MD5sum: d43a9a27322a5b6c937c9872494c74c5 SHA1: 5fa0632a3cc9561fec3eb6758a346b12ce204564 SHA256: 1a92872dfc2430ad8adcb739dab66e2aaf6fd87acd7d59f34aa9b8b46e100009 SHA512: 1e6c33adb1a3317a299b2752882b4b298ceac3bbd8f54d0bf579d57783d9b16e9ff021b0f47aca4d052a9363fecfafe6a3e93967ab68dc046deb8c96bfdb0b4d Homepage: https://cran.r-project.org/package=DNetFinder Description: CRAN Package 'DNetFinder' (Estimating Differential Networks under Semiparametric GaussianGraphical Models) Provides a modified hierarchical test (Liu (2017) ) for detecting the structural difference between two Semiparametric Gaussian graphical models. 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Package: r-cran-dnmf Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-matrix, r-cran-gplots, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-dnmf_1.4.2-1.ca2604.1_all.deb Size: 38256 MD5sum: 7f7af46143074cd0a3a0d2c26afa2edf SHA1: 50ca5219d0b867073835019dc386173850fd6267 SHA256: 413e928554e4e787f36b5ecb85b4493a238cd249cfad54d48fec846744d7f3a3 SHA512: 82bcad5b5b88018557624c89b5a91024a9de5c4c324270af3bd256485494017e6181980f51032874000f5bd1e45f5f368cbb77f1e27d1264a69522247df2b883 Homepage: https://cran.r-project.org/package=DNMF Description: CRAN Package 'DNMF' (Discriminant Non-Negative Matrix Factorization) Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535. 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Package: r-cran-do3pca Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdimtools, r-cran-ape, r-cran-phytools, r-cran-matrixcalc, r-cran-mclust, r-cran-nloptr, r-cran-ratematrix Filename: pool/dists/resolute/main/r-cran-do3pca_1.0.0-1.ca2604.1_all.deb Size: 54758 MD5sum: 1dd22c71b2e3b8de649fb9c0595eda44 SHA1: 79f1af8c04be7d8beedcda152997862ff7be99fc SHA256: 48284659f766f782191e94854ec3dd2130f0e5c6f4921c5d19d0b415c45df5ce SHA512: 8289693c54b8c967601c50db2c6da4179d7d5825f33998a3b70117e8e08a557ba108ed4b58337c46e5be9f3bfa223d0199c6a39826316cfbf20eacc9548a3db7 Homepage: https://cran.r-project.org/package=do3PCA Description: CRAN Package 'do3PCA' (Probabilistic Phylogenetic Principal Component Analysis) Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. 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Package: r-cran-dobin Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbscan, r-cran-ggplot2, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-outlierso3, r-cran-fnn Filename: pool/dists/resolute/main/r-cran-dobin_1.0.4-1.ca2604.1_all.deb Size: 558712 MD5sum: 89e3dbc9b20eba03c1cc392346899c2c SHA1: fe2a28e0ffdc7a4be8f793da06ce09ab4bd98511 SHA256: ee38b39d5910733b4b725d9cd33a35fbf3f8b709b8aee3b3819a623ee02231b8 SHA512: 9b6726e447e0e3dfbde7aef96046e56c4b67dd8128032d604e30f79be4cba2e1a19a747c39070c07c6fbe3418794edd30d74c7031736cfe0c97f62b85ae5ede5 Homepage: https://cran.r-project.org/package=dobin Description: CRAN Package 'dobin' (Dimension Reduction for Outlier Detection) A dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. 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Package: r-cran-doe.base Architecture: all Version: 1.2-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-conf.design, r-cran-vcd, r-cran-combinat, r-cran-mass, r-cran-lattice, r-cran-numbers, r-cran-partitions Suggests: r-cran-frf2, r-cran-doe.wrapper, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-doe.base_1.2-5-1.ca2604.1_all.deb Size: 1743280 MD5sum: 88b930d6c2fca2b0017778e4b0a9ac82 SHA1: c88a42c41ede38cf06a850d250c38b1064d676af SHA256: 04b84f953e038b5053d3b7a0a1b79a7c225fcaf103e4962123df130c65a1f4e2 SHA512: eeddf5be83da7fae1a032d144aab1efa65a0eedc3090f4bb0539804c7753030521527c6725d90f06f9336e5c7ccc4e01c590755c8c7692f4eeed9fcf5895d89b Homepage: https://cran.r-project.org/package=DoE.base Description: CRAN Package 'DoE.base' (Full Factorials, Orthogonal Arrays and Base Utilities for DoEPackages) Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. 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Package: r-cran-doe.wrapper Architecture: all Version: 0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-frf2, r-cran-doe.base, r-cran-rsm, r-cran-lhs, r-cran-dicedesign, r-cran-algdesign Suggests: r-cran-skpr Filename: pool/dists/resolute/main/r-cran-doe.wrapper_0.13-1.ca2604.1_all.deb Size: 212886 MD5sum: 376f8803e0d9493954b1f2dd5d763aff SHA1: 407c6dd1c0f38df80c5f4dd72f959edaf983ef85 SHA256: 001928529b03949417aab0219fd6f06051ad55f0529f85ced76c8c0b116c4658 SHA512: 7beb06726574d6341d4988946c30373b8c418244e7d346d1a43b5b4257dbb5cef68690a3b7ed5521ea92151cbf92c5e09ffd11986d072a27bfd574d59efa11b4 Homepage: https://cran.r-project.org/package=DoE.wrapper Description: CRAN Package 'DoE.wrapper' (Wrapper Package for Design of Experiments Functionality) Various kinds of designs for (industrial) experiments can be created. 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Package: r-cran-doem Architecture: all Version: 0.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1349 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-doem_0.0.0.1-1.ca2604.1_all.deb Size: 1342490 MD5sum: 737b85fb3cb5b0f596d8c0c944dac861 SHA1: 4229fc5a1add5c065a4f7038bc3fe52d7b6338b8 SHA256: 18f8707f8d689a6a9e736f852c150cd15c6caa69ae66ef5aba2ef0cdf0c1f036 SHA512: f908d7f6fc660a0dad4e0e9cfb79ad5d34a85a9caf0a7288511b63bb0c4681b0166352233c1d3f9c2e46d5b86a51413dd328cd115742d31163fe533c9653b901 Homepage: https://cran.r-project.org/package=DOEM Description: CRAN Package 'DOEM' (The Distributed Online Expectation Maximization Algorithms toSolve Parameters of Poisson Mixture Models) The distributed online expectation maximization algorithms are used to solve parameters of Poisson mixture models. The philosophy of the package is described in Guo, G. (2022) . 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Package: r-cran-domean Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-domean_0.1-1.ca2604.1_all.deb Size: 41422 MD5sum: 37488b83c76166a36545cb8e039d3937 SHA1: d824b8d79017e15755f24930024e2515f894ddae SHA256: 230df95a463006fb3d2b4bd2a60f03d8907d732582e9530ab4c0017b2a46cf58 SHA512: 717170eb17e577d4fa3de0be7e0ed96f39cb8c46b8906fc590475912b0026b214a581579efe380bfe4abcf08118691b9d4567024bc7c151b5f949d117696336f Homepage: https://cran.r-project.org/package=Domean Description: CRAN Package 'Domean' (Distributed Online Mean Tests) Distributed Online Mean Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. 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The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) and Azen, R., & Budescu, D. V. (2003) for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) , Azen, R., & Traxel, N. (2009) and Luo, W., & Azen, R. (2013) , respectively. Package: r-cran-domino Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-domino_0.3.1-1.ca2604.1_all.deb Size: 45880 MD5sum: 76d15b8e5bf84bd15687eb5d53aad78c SHA1: 3d5caf72d340a4eaf55fcfc70fb19cdd01779f8e SHA256: 1f43c2cab8b7db1238d22fdc7251cf8dbaae79b6e4ce98a24681d0432a33cca0 SHA512: 43f1b35ea7be385470d83dc8db295c9d5692c9b5a19ecd0a4ba294b95674493ab3c62c89e27b4261e77453e7e936e88a94b8c5cf1412ba19c0d83c26c1f9eb7a Homepage: https://cran.r-project.org/package=domino Description: CRAN Package 'domino' (R Console Bindings for the 'Domino Command-Line Client') A wrapper on top of the 'Domino Command-Line Client'. It lets you run 'Domino' commands (e.g., "run", "upload", "download") directly from your R environment. 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Package: r-cran-donutsk Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-glue, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-donutsk_0.1.1-1.ca2604.1_all.deb Size: 1241046 MD5sum: 0ff13a54ed6c5327587f8d9f46fe044d SHA1: ba1faf597755bf2ef9c45bf98f9d0917ad0b75e5 SHA256: b07cba3d45e2f55a1db912310b4c252ce2253a5eccc6930ca37c0a14e33da91f SHA512: b240ff264065911b14d9c5ae7a844d85e436770039618433a00578d1b149bad8e8c3958dc0a63097b196edaeae9055820e7baf4c91452363f861147ca70d24b9 Homepage: https://cran.r-project.org/package=donutsk Description: CRAN Package 'donutsk' (Construct Advanced Donut Charts) Build donut/pie charts with 'ggplot2' layer by layer, exploiting the advantages of polar symmetry. Leverage layouts to distribute labels effectively. Connect labels to donut segments using pins. Streamline annotation and highlighting. Package: r-cran-doofa Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-combinat Filename: pool/dists/resolute/main/r-cran-doofa_1.0-1.ca2604.1_all.deb Size: 41522 MD5sum: e2f0427b8fd03787e43679f2f8a228c9 SHA1: 323c64f04f0d1b639c23dab001610d7d76c82f85 SHA256: 7fc0f53d5fc3847ae0c233af3ba82d9eab3febd98d08608d72c0d95b29f97bec SHA512: 4e5c60a8380d28983f1e8d5dea59cc872d63b2abf9f92d99e57509d2c42bbc376c9fa99315bb69ec986fb5cddc15d05f06a8a5fb591e45ee5b58267a9ab5411c Homepage: https://cran.r-project.org/package=doofa Description: CRAN Package 'doofa' (Designs for Order-of-Addition Experiments) A facility to generate efficient designs for order-of-additions experiments under pair-wise-order model, see Dennis K. J. Lin and Jiayu Peng (2019)."Order-of-addition experiments: A review and some new thoughts". Quality Engineering, 31:1, 49-59, . It also provides a facility to generate component orthogonal arrays under component position model, see Jian-Feng Yang, Fasheng Sun & Hongquan Xu (2020): "A Component Position Model, Analysis and Design for Order-of-Addition Experiments". Technometrics, . 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Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate, bivariate analysis and parse it into HTML. It also allows you to visualize all your data with graphic representation. 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As he claims, 'MATLAB' indeed might have been the most suitable language when he originally wrote the functions, but, with growing popularity of R it is not entirely valid. As 'Dowd's' code was not intended to be error free and were mainly for reference, some functions in this package have inherited those errors. An attempt will be made in future releases to identify and correct them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. It should be noted that 'Dowd' offers both 'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more recent version of 'MMR1' toolbox and they both have mostly similar function. The toolbox mainly contains different parametric and non parametric methods for measurement of market risk as well as backtesting risk measurement methods. 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Package: r-cran-dqcheckr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readr, r-cran-dbi, r-cran-rsqlite, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr, r-cran-tidyr, r-cran-yaml, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dqcheckr_0.1.2-1.ca2604.1_all.deb Size: 168650 MD5sum: 201bfa8dd86ca77994145a8071edc9ac SHA1: 53c6248871cf5dac90e6c53d44aac38ac50fb750 SHA256: 73ef86da81cdf9a0b208401f915634890b7e5ab131b49f4334ee35d6c5204648 SHA512: 9750150f256ce16f218992f0377c09b3d2758bb3f367fbf53617c4594406ccaac1a4626f981fd7a436cd67970e877ca42c6f947c9e9bd141e045c08193ac1c4f Homepage: https://cran.r-project.org/package=dqcheckr Description: CRAN Package 'dqcheckr' (Automated Data Quality Checks for Recurring Dataset Deliveries) Automates quality verification of recurring external dataset deliveries. For each new file arrival, it runs single-snapshot quality checks, compares the file to the previous delivery, writes a self-contained 'HTML' report, and records summary statistics in a local 'SQLite' database for long-term trend tracking. Supports 'CSV' and fixed-width formats. Custom organisation-specific checks can be supplied as plain R files. Package: r-cran-dqtg.seq Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-data.table, r-cran-doparallel, r-cran-openxlsx, r-cran-qtl, r-cran-stringr, r-cran-writexl, r-cran-vroom, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-dqtg.seq_1.0.2-1.ca2604.1_all.deb Size: 949450 MD5sum: 34212c0fe82e95621c058334ad761a24 SHA1: ade10ce054743f03a625dd5f4d937bfde25941b6 SHA256: 8ed8e214075bc3fede8f13f84b3e552ef5b54c6382e59240226434ff38fc7006 SHA512: 1dc55d47d71c0f3e115d3d6cf6c2d1bad56edec7f52267936586948b3e53b7db8c0a14df95d57b28604c0feb76638c3eaa031a89a1435348a53f57143588a1e3 Homepage: https://cran.r-project.org/package=dQTG.seq Description: CRAN Package 'dQTG.seq' (A BSA Software for Detecting All Types of QTLs in BC, DH, RILand F2) The new (dQTG.seq1 and dQTG.seq2) and existing (SmoothLOD, G', deltaSNP and ED) bulked segregant analysis methods are used to identify various types of quantitative trait loci for complex traits via extreme phenotype individuals in bi-parental segregation populations (F2, backcross, doubled haploid and recombinant inbred line). The numbers of marker alleles in extreme low and high pools are used in existing methods to identify trait-related genes, while the numbers of marker alleles and genotypes in extreme low and high pools are used in the new methods to construct a new statistic Gw for identifying trait-related genes. dQTG-seq2 is feasible to identify extremely over-dominant and small-effect genes in F2. Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM (2022, ). Package: r-cran-dr4pl Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-tensor, r-cran-rdpack, r-cran-generics, r-cran-rlang, r-cran-glue Suggests: r-cran-drc, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-dr4pl_2.0.0-1.ca2604.1_all.deb Size: 591286 MD5sum: f6d231db23c365079991936ef9e1551f SHA1: 502367d55f2eeb0fe11016ba613b258a52dd88d1 SHA256: 3a1dd436143f5ec2afa8ce3bac7b32fa555ac5dac4f05680ea9f1d5327f38707 SHA512: 93e02006c4298a759345424daca460103dffd0d3917c87bbcfb9db49f531ea5607d66d4ea427202bc2f766fbae2de70e44d609d228154d3e3b6e46e781614f6f Homepage: https://cran.r-project.org/package=dr4pl Description: CRAN Package 'dr4pl' (Dose Response Data Analysis using the 4 Parameter Logistic (4pl)Model) Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems. Gadagkar, S. R. and Call, G. B. (2015) Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) . Package: r-cran-dr Architecture: all Version: 3.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-dr_3.0.11-1.ca2604.1_all.deb Size: 440416 MD5sum: d40d32fd30c1dd7f234519b09e268679 SHA1: 61fbec6b279a1dacb392b6cbc55c01a8af9614ba SHA256: 2d1e38018009b865cad90a48c4726d31469376ea399901fad601328c98c37e56 SHA512: dd5cc175277590c0e6c0b65becbec92f87bbac36a5c7bd39c96481d8693a0d4d0eb94f00889d55147a7580f9b9befb294748af38d7e816a831d7cdfa5b524f02 Homepage: https://cran.r-project.org/package=dr Description: CRAN Package 'dr' (Methods for Dimension Reduction for Regression) Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified. Package: r-cran-dragonking Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dragonking_0.1.0-1.ca2604.1_all.deb Size: 29828 MD5sum: 0d0ad49b2bfa8391cfe90d9844ab7143 SHA1: 35467d069ddab6286db747291f67270ff683454a SHA256: a6ffac89beb2c7eeb3c352ad51711fe16e69605a180e13a027c3d6b639c9d619 SHA512: 9d5aaaebc70623241d8551ddaeb1e097570782c07d007d1dffc267e5bba6111dcb6ebc23e2ce95cab4d9f1c9307325341f5ae052fe7f6436056eadb0baaf025e Homepage: https://cran.r-project.org/package=dragonking Description: CRAN Package 'dragonking' (Statistical Tools to Identify Dragon Kings) Statistical tests and test statistics to identify events in a dataset that are dragon kings (DKs). The statistical methods in this package were reviewed in Wheatley & Sornette (2015) . Package: r-cran-dragracer Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dragracer_0.1.7-1.ca2604.1_all.deb Size: 232578 MD5sum: 047a066b1ce99ad11e7dee2e58cab493 SHA1: 2aedc76cbb7705500ca19fed55c09aba7729df7b SHA256: 974e21b9b8f3cd14cf726c90751e389d6f3864992ed797dd22b8f7a6d0d012f6 SHA512: 3b86d3cebdb320ca3bbcb07fb978cf93347be6f389ae2d33ce7555bed8c7849a1d2bc33168ca03c704ec39312e0b459c49a52a5eeab5dd0ad1d33d9ca26bdede Homepage: https://cran.r-project.org/package=dragracer Description: CRAN Package 'dragracer' (Data Sets for RuPaul's Drag Race) These are data sets for the hit TV show, RuPaul's Drag Race. Data right now include episode-level data, contestant-level data, and episode-contestant-level data. This is a work in progress, and a love letter of a kind to RuPaul's Drag Race and the performers that have appeared on the show. This may not be the most productive use of my time, but I have tenure and what are you going to do about it? I think there is at least some value in this package if it allows the show's fandom to learn more about the R programming language around its contents. Package: r-cran-dragular Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dragular_0.3.1-1.ca2604.1_all.deb Size: 27054 MD5sum: 979ae98cf579137ec89ba0307d1db612 SHA1: 584f342933a87c1c305725d687d0a8d6d7ff0cf0 SHA256: 892d4f0b622df5c4f98520fbe48ca4b560370d0ee8e8f2df7de7b8d5e55b59de SHA512: 4dd938d90b96f32929b8e51ca9d527669a9852945bae8fe9db25ca88a3459d00a45d6e0e7e7ca13895fef9e66f49658b6abcd91783d4d4943aba00d0aef417a7 Homepage: https://cran.r-project.org/package=dragulaR Description: CRAN Package 'dragulaR' (Drag and Drop Elements in 'Shiny' using 'Dragula JavascriptLibrary') Move elements between containers in 'Shiny' without explicitly using 'JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables. Package: r-cran-drake Architecture: all Version: 7.13.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2677 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64url, r-cran-digest, r-cran-igraph, r-cran-rlang, r-cran-storr, r-cran-tidyselect, r-cran-txtq, r-cran-vctrs Suggests: r-cran-abind, r-cran-bindr, r-cran-callr, r-cran-cli, r-cran-clustermq, r-cran-crayon, r-cran-curl, r-cran-data.table, r-cran-downloader, r-cran-fst, r-cran-future, r-cran-ggplot2, r-cran-ggraph, r-cran-keras, r-cran-knitr, r-cran-lubridate, r-cran-networkd3, r-cran-prettycode, r-cran-progress, r-cran-rcpp, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-styler, r-cran-testthat, r-cran-tibble, r-cran-txtplot, r-cran-usethis, r-cran-visnetwork, r-cran-webshot Filename: pool/dists/resolute/main/r-cran-drake_7.13.11-1.ca2604.1_all.deb Size: 2278748 MD5sum: 1b20b9373a747acb8f5c96a8dc47d0c8 SHA1: 056ad829f2da7c6a85877606bcf6e053fdd16cec SHA256: fc7cd3ee544bfd60c0f9a3b5b3ec3057d7964ff0b025698467a6d9a92a716d8a SHA512: f987c9460cf7908863b66264b57434361380a23aba967b600ace0cfa3275789578f49dc31c9ad51577702d0264fc975e36fc88b5980ff559f1297ab61010b7f5 Homepage: https://cran.r-project.org/package=drake Description: CRAN Package 'drake' (A Pipeline Toolkit for Reproducible Computation at Scale) A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website and the online manual . Package: r-cran-dramaanalysis Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape2, r-cran-readr, r-cran-data.table, r-cran-httr, r-cran-git2r, r-cran-xml2, r-cran-tokenizers, r-cran-stringr Suggests: r-cran-testthat, r-cran-fmsb, r-cran-knitr, r-cran-magrittr, r-cran-highcharter, r-cran-rmarkdown, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-dramaanalysis_3.0.2-1.ca2604.1_all.deb Size: 752964 MD5sum: 6ed7f2950ff8bf10767132455ff6358a SHA1: 5d9c5dfa703b1eb11be2b9a26be80547e6f69626 SHA256: 1cf607d9bc6fce2fcbe7675f635cb0ca94c4b2f9e7f0269ef6884d772e59fcc9 SHA512: c9d0b09ec87be4fd2a940469b0633401dcfcb7b6d2b2ddf44b6305e7880262fc39f4300c01401eccb0f0ac6787973174befac57435505356f814f532a108c324 Homepage: https://cran.r-project.org/package=DramaAnalysis Description: CRAN Package 'DramaAnalysis' (Analysis of Dramatic Texts) Analysis of preprocessed dramatic texts, with respect to literary research. The package provides functions to analyze and visualize information about characters, stage directions, the dramatic structure and the text itself. The dramatic texts are expected to be in CSV format, which can be installed from within the package, sample texts are provided. The package and the reasoning behind it are described in Reiter et al. (2017) . Package: r-cran-drape Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-doubleml, r-cran-glmnet, r-cran-hdi, r-cran-knitr, r-cran-matrix, r-cran-mlr3, r-cran-paradox, r-cran-partykit, r-cran-rjson, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-drape_0.0.2-1.ca2604.1_all.deb Size: 112560 MD5sum: 6c4407ae8d5e644af9c1e7409887e552 SHA1: 6fa4592823abc09bcdd0a13ac3d5e13a2d1ea080 SHA256: d763a19525197614203023f6bc3d33d806bbb975ad452656be4af8400805a193 SHA512: 669d443c21ca0a731dbe8485aaa8ef6ec1d4e43e26791e63c7d3e86055dea8a177939becb770f551163b3728588cb3486f8352b24332547edf1a4116928d91e1 Homepage: https://cran.r-project.org/package=drape Description: CRAN Package 'drape' (Doubly Robust Average Partial Effects) Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) . Package: r-cran-drat Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-git2r, r-cran-simplermarkdown Filename: pool/dists/resolute/main/r-cran-drat_0.2.5-1.ca2604.1_all.deb Size: 196410 MD5sum: c138da558f998ef9f4ba829f61a44f77 SHA1: 3ced614e3f21883f1b8c7c268df687b20d791ce1 SHA256: 0cf46745350bf8f778fc40283a1a6374f3d33cc1125bfd25ca36b84a86486159 SHA512: b91c67f247d22f1f3b6f4cf1e93e4d30b13beb897643e4375c9efdf9d3225c8e1e45f0bf6239e923038f078ccdbe26ed850abebc3438db0ed0f995b5c696755a Homepage: https://cran.r-project.org/package=drat Description: CRAN Package 'drat' ('Drat' R Archive Template) Creation and use of R Repositories via helper functions to insert packages into a repository, and to add repository information to the current R session. Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template. Package: r-cran-draw Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-draw_1.0.0-1.ca2604.1_all.deb Size: 78182 MD5sum: 59345027aa8034b57513f2bd826f4299 SHA1: 69fb4ce80a200c772b4eb074a93eeed76e57d685 SHA256: 1fcd26518c390e76640db7d1359482ae4360d743ada9538f6251af0d839acde2 SHA512: ac6a616f928b3ef01cc7f9c2abc15dafea75a44d441e73a63b57d378213062910e8e303b351e0cbc2a0b2ce2e44718acccf0460ded2cd95ce43574700f4e6648 Homepage: https://cran.r-project.org/package=draw Description: CRAN Package 'draw' (Wrapper Functions for Producing Graphics) A set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings. Compatible with and based on the R 'grid' package. Package: r-cran-drawer Architecture: all Version: 0.2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-magrittr, r-cran-glue, r-cran-bsplus, r-cran-shiny, r-cran-stringr Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-drawer_0.2.0.1-1.ca2604.1_all.deb Size: 191330 MD5sum: d30b2f706473f2a4b8ceda8c060bd4b4 SHA1: 7c69811775e2106400c9d96f4c1ea7dfede12ba3 SHA256: 589acda779eb5256f2a6f8ec577c68f3a01fe6c1787e8529005144d2e830e112 SHA512: c42b7206dd1570ee98098e00dff07f79ef80d1fb04fd8012d38ee1cfe7e33748d0bb6ac85c2ae4d95e83e0cb88b7bc07b5ba7ac1784b31fceb36acb6af11ebc5 Homepage: https://cran.r-project.org/package=drawer Description: CRAN Package 'drawer' (An Interactive HTML Image Editing Tool) An interactive image editing tool that can be added as part of the HTML in Shiny, R markdown or any type of HTML document. Often times, plots, photos are embedded in the web application/file. 'drawer' can take screenshots of these image-like elements, or any part of the HTML document and send to an image editing space called 'canvas' to allow users immediately edit the screenshot(s) within the same document. Users can quickly combine, compare different screenshots, upload their own images and maybe make a scientific figure. Package: r-cran-drawr Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rocr Filename: pool/dists/resolute/main/r-cran-drawr_1.0.3-1.ca2604.1_all.deb Size: 46102 MD5sum: f5a904bfd4506ce48e0448a3c8885ac5 SHA1: 6fec8af0e58a6a0de947b7c2ca9d446e2dc5bd19 SHA256: 21cc6e8cc925a0ba0d49d5afd19f59c8aa40318112ab29415a0cd5d083098a99 SHA512: 04702c9680030724a4853e3239c5a8e52d00d3fcfc3cb7547bcf56051802a791f6ef0565994808d33cb6eb8cc305b2b6afdd86efe4093b85a3abfd66aef005d8 Homepage: https://cran.r-project.org/package=DRaWR Description: CRAN Package 'DRaWR' (Discriminative Random Walk with Restart) We present DRaWR, a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types, preserving more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only the relevant properties. We then rerank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. Package: r-cran-drawsample Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lattice, r-cran-tibble, r-cran-psych, r-cran-moments, r-cran-readxl, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-xlsx Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drawsample_1.0.2-1.ca2604.1_all.deb Size: 248372 MD5sum: 45417f289c7ced654ab180802a609f88 SHA1: e7f2897f6dc2005734bcf5729b565224cdda4a5f SHA256: b1ec371968198c9b23b7ef8fb4331fef7b229963887285bd99303069b2b9b570 SHA512: b348ec8737fe815e0b39940c8dcbc469275e52606623f91e41670087f965c8c0fcd6b894d355089bf8917cb3b60f996f9efc67a98a11cfb67e65626f0de82571 Homepage: https://cran.r-project.org/package=drawsample Description: CRAN Package 'drawsample' (Draw Samples with the Desired Properties from a Data Set) A tool to sample data with the desired properties.Samples can be drawn by purposive sampling with determining distributional conditions, such as deviation from normality (skewness and kurtosis), and sample size in quantitative research studies. For purposive sampling, a researcher has something in mind and participants that fit the purpose of the study are included (Etikan,Musa, & Alkassim, 2015) .Purposive sampling can be useful for answering many research questions (Klar & Leeper, 2019) . Package: r-cran-drayl Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-rconics, r-cran-rmutil, r-cran-cubature Filename: pool/dists/resolute/main/r-cran-drayl_1.0-1.ca2604.1_all.deb Size: 41288 MD5sum: 3a48d82c95623eb7706166f3c304f743 SHA1: 808dd0522ecf7e48024671304366140aac58e207 SHA256: 7f7cd8025f14cb8396202cd9befa88c414568648068b0df8c6c84dbe17f74dc7 SHA512: c60dd3abeb74e71b7c88673c133b3d64d01b4c76f35668586c83f408efdac58b83ebeabc2792f9e238f52ec34359d618a03200a428fcfdb919fc86f3f8aac3a0 Homepage: https://cran.r-project.org/package=DRAYL Description: CRAN Package 'DRAYL' (Computation of Rayleigh Densities of Arbitrary Dimension) We offer an implementation of the series representation put forth in "A series representation for multidimensional Rayleigh distributions" by Wiegand and Nadarajah . Furthermore we have implemented an integration approach proposed by Beaulieu et al. for 3 and 4-dimensional Rayleigh densities (Beaulieu, Zhang, "New simplest exact forms for the 3D and 4D multivariate Rayleigh PDFs with applications to antenna array geometrics", ). Package: r-cran-drbats Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-ade4, r-cran-coda, r-cran-mass, r-cran-matrix, r-cran-sde Suggests: r-cran-fda, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drbats_0.1.6-1.ca2604.1_all.deb Size: 1280116 MD5sum: f8025e888257cdd3aa1d75bdbc5ccb4d SHA1: 247bc954a8b00fe7442edbb19e307834c0b3ec62 SHA256: 6a747031903785c66c2a6a2b3eb08fc50479efbe8378b451abf935cd61cb54d8 SHA512: 217fda7acbb66fa52786c115921d2e3647885e8975f63b6dbddfe4adf000ae7c0540aaf4e144458c2d77446db6721d97364f01f76de015d83274230189af52c3 Homepage: https://cran.r-project.org/package=DrBats Description: CRAN Package 'DrBats' (Data Representation: Bayesian Approach That's Sparse) Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016. Package: r-cran-drc Architecture: all Version: 3.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 991 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-car, r-cran-gtools, r-cran-multcomp, r-cran-plotrix, r-cran-scales Filename: pool/dists/resolute/main/r-cran-drc_3.0-1-1.ca2604.1_all.deb Size: 901894 MD5sum: 2364edabc0bf0b883f5f6a3d22876934 SHA1: 3815fbdb849d2b9aaa9eb5b845575153772a3c5e SHA256: 9fff55ae14a19f8949db4aa7a33533f20cb928181da1b9972edeea2f30b81797 SHA512: ece4f9b9b9be67f459d19ac974688c03b77a65433668e12c7db33d40867fcbbe21a85e04b8697b978dbbcd635c092b949682cad0e97baf51e1199790ea2c86e6 Homepage: https://cran.r-project.org/package=drc Description: CRAN Package 'drc' (Analysis of Dose-Response Curves) Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions. Package: r-cran-drcarlate Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-mass, r-cran-stringr, r-cran-splus2r, r-cran-glmnet, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-drcarlate_1.2.0-1.ca2604.1_all.deb Size: 195530 MD5sum: 4e556491babbbb83a67191e9c9ec789e SHA1: 34836a17326bb88399730e4df7defd28f99d298a SHA256: 18054925444647824c267040b4a39c75bc26019846298d954c03d2b21553bda8 SHA512: 98cba467cf0a83ef3054e022f5fb9ebd34a1223ab86240397f0a453a9d05e0d7f93e114f297872d3fdd3bf2605e07ab64ac396733a1a7ca2f458ac645f6c6b0f Homepage: https://cran.r-project.org/package=drcarlate Description: CRAN Package 'drcarlate' (Improving Estimation Efficiency in CAR with Imperfect Compliance) We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022) . Package: r-cran-drclass Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-drclass_0.1.0-1.ca2604.1_all.deb Size: 49846 MD5sum: 03ef281c59c5b946d9de57cc30832adc SHA1: 827b8a15b87956480829e8349cbe135315b0c8d6 SHA256: 01c405045f15c55295f25b83fe3d23d63dfc844c8bc7a7cc3fce2476c92bccab SHA512: 2b3bceb7d905c83872b0ea672381b0a5efd444f5daa829b55c0a1cff2c4dd092f4853156a33437162c0b4787ebf149b3942fe484ad7ee284865fa59f20a3d765 Homepage: https://cran.r-project.org/package=DRclass Description: CRAN Package 'DRclass' (Consider Ambiguity in Probabilistic Descriptions Using DensityRatio Classes) Consider ambiguity in probabilistic descriptions by replacing a parametric probabilistic description of uncertainty by a non-parametric set of probability distributions in the form of a Density Ratio Class. This is of particular interest in Bayesian inference. The Density Ratio Class is particularly suited for this purpose as it is invariant under Bayesian inference, marginalization, and propagation through a deterministic model. Here, invariant means that the result of the operation applied to a Density Ratio Class is again a Density Ratio Class. In particular the invariance under Bayesian inference thus enables iterative learning within the same framework of Density Ratio Classes. The use of imprecise probabilities in general, and Density Ratio Classes in particular, lead to intervals of characteristics of probability distributions, such as cumulative distribution functions, quantiles, and means. The package is based on a sample of the distribution proportional to the upper bound of the class. Typically this will be a sample from the posterior in Bayesian inference. Based on such a sample, the package provides functions to calculate lower and upper class boundaries and lower and upper bounds of cumulative distribution functions, and quantiles. Rinderknecht, S.L., Albert, C., Borsuk, M.E., Schuwirth, N., Kuensch, H.R. and Reichert, P. (2014) "The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction." Environmental Modelling & Software. 62, 300-315, 2014. . Sriwastava, A. and Reichert, P. "Robust Bayesian Estimation of Value Function Parameters using Imprecise Priors." Submitted. . Package: r-cran-drcseedgerm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-drcte, r-cran-plyr, r-cran-dplyr, r-cran-mvtnorm, r-cran-survival Filename: pool/dists/resolute/main/r-cran-drcseedgerm_1.0.1-1.ca2604.1_all.deb Size: 368500 MD5sum: 721834fb59c8dbe72ff592659b8fc67e SHA1: 5d19b0c82cc4b65fe58ade1ef932d5265f77fda9 SHA256: 43932bd37b9a9aec6ec3c746bab0c6d500618a96f55ae1b04821006ea0f5ea20 SHA512: 6a9188137ed5589201a3c759da0f2356902916a86eee1c913f2b997e53a226b7ea74211e9c59be470803f237303d8baa3a8886688c7bdcf17a6bd1bfe02ca216 Homepage: https://cran.r-project.org/package=drcSeedGerm Description: CRAN Package 'drcSeedGerm' (Utilities for Data Analyses in Seed Germination/Emergence Assays) Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 . Contains several datasets for practicing. Package: r-cran-drcte Architecture: all Version: 1.0.65-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-plyr, r-cran-nor1mix, r-cran-mclust, r-cran-survival, r-cran-sandwich, r-cran-lmtest, r-cran-dplyr, r-cran-multcomp, r-cran-tidyr, r-cran-mass, r-cran-tibble, r-cran-car Filename: pool/dists/resolute/main/r-cran-drcte_1.0.65-1.ca2604.1_all.deb Size: 492882 MD5sum: c52b353424d65dcb43ba7dd46ed7cd5f SHA1: aed2c669e01040e8b1009679466d90b504991d67 SHA256: eda345bb32c0fbe2816e1aa0d98591f6c7ddf20e32ea790678e62f02a26e4fca SHA512: da5504b3b71fd950983a3de713bd2c2263fb6534438fe4c1e94b87cd093f7de8eafc6bbb772c7e158eb35a08b3a5e90e7096a0ae48384405f1dabd3965a6d712 Homepage: https://cran.r-project.org/package=drcte Description: CRAN Package 'drcte' (Statistical Approaches for Time-to-Event Data in Agriculture) A specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 . Package: r-cran-drda Architecture: all Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drda_2.0.5-1.ca2604.1_all.deb Size: 1064404 MD5sum: e82c55b81b20bc10ef23241c0ea2fe34 SHA1: 966105ea6133436cd77cf4d5eb94f215847e21cd SHA256: d196896c9ca3c47d36710806a38a28553c931c9d3c76a0a45a0b0655eaf22c85 SHA512: f66a6bcffbf8106d1f82d4fe61360b50750202cd4fbf84c2088e63d29c4d0016843a61bdf9a264733ab47cf5a1c1e4c28efdad09a8e4cf7cfab7fa4b8ab1f71f Homepage: https://cran.r-project.org/package=drda Description: CRAN Package 'drda' (Dose-Response Data Analysis) Fit logistic functions to observed dose-response continuous data and evaluate goodness-of-fit measures. See Malyutina A., Tang J., and Pessia A. (2023) . Package: r-cran-drdata Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny Suggests: r-cran-shinydashboard, r-cran-plotly, r-cran-dt, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-readxl, r-cran-caret, r-cran-randomforest, r-cran-rpart, r-cran-rpart.plot, r-cran-e1071, r-cran-class, r-cran-nnet, r-cran-colourpicker, r-cran-glmnet, r-cran-cluster, r-cran-dbscan, r-cran-ggally, r-cran-gbm, r-cran-proc, r-cran-reshape2, r-cran-scales, r-cran-nortest, r-cran-tseries, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-drdata_0.2.0-1.ca2604.1_all.deb Size: 66986 MD5sum: 709fe1875a3c990ac32d309380705773 SHA1: c24dd8b701920db1b0022bf324d5e70c5bcd1373 SHA256: 6f31247b531dc0789d3debe4f81c257004128f8c9f412fb74b6e44a42035a1b5 SHA512: 989cd9f332241e6b92bff9e5e4147e67787ca30606aacf47997ae033b6971838ec1872798d31e1b32b96e0b429324fdd64e5c290d7ab81c3c745c86f45fc4491 Homepage: https://cran.r-project.org/package=DrData Description: CRAN Package 'DrData' (Interactive Statistical Analysis and Machine Learning Platform) A 'Shiny'-based interactive platform for end-to-end data science workflows. Provides modules for data import (CSV, 'Excel', RDS, TXT), data preprocessing (missing value imputation, encoding, scaling, outlier removal), exploratory data analysis with interactive plots and normality tests, supervised learning (regression and classification each with eight algorithms), and unsupervised learning (k-means, hierarchical clustering, density-based spatial clustering of applications with noise). Designed for students and practitioners in data science and artificial intelligence. Package: r-cran-drdimont Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-dplyr, r-cran-stringr, r-cran-wgcna, r-cran-rfast, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-magrittr, r-cran-rlang, r-cran-reticulate Suggests: r-bioc-go.db, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-drdimont_0.1.6-1.ca2604.1_all.deb Size: 1144246 MD5sum: b6bbf9de783bbccebf0873f4c7f6f6ab SHA1: 8a2518feeea4895fd14196fc0744cde1f1e8c638 SHA256: 4f57c33745898b87329b5048c195225c6f945af65e00476495b2849446802a45 SHA512: 151367030b2386b321276be5ce1cb59f2ef3870694f5d080f92a068185a9f3f3bc89d2fcdcfd47638989393c866739efaf6d7e958058fa75bc22e5a38f79f4cc Homepage: https://cran.r-project.org/package=DrDimont Description: CRAN Package 'DrDimont' (Drug Response Prediction from Differential Multi-Omics Networks) While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. We present a novel network analysis pipeline, DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) . The package license applies only to the software and explicitly not to the included data. Package: r-cran-drdrtest Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-superlearner Filename: pool/dists/resolute/main/r-cran-drdrtest_0.1-1.ca2604.1_all.deb Size: 53944 MD5sum: da6248c0a234ecbf7f8a92d13ec40b5a SHA1: c1d772434e0071d7f98176b2dcf25ad830396d64 SHA256: 4ca7822aa65375bfb00573aa8718e545922f80c724bc0231ff903375bd3157e6 SHA512: 4847a689b41797292eb912abe4afccef4feca22857f51080902bbc45b1e32e59d587612a343a5a5507ee504c6b7f14282b7e7cfed59383d9057d99db2c862ff4 Homepage: https://cran.r-project.org/package=DRDRtest Description: CRAN Package 'DRDRtest' (A Nonparametric Doubly Robust Test for Continuous TreatmentEffect) Implement the statistical test proposed in Weng et al. (2021) to test whether the average treatment effect curve is constant and whether a discrete covariate is a significant effect modifier. Package: r-cran-dreamer Architecture: all Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1361 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-ellipsis, r-cran-ggplot2, r-cran-purrr, r-cran-rootsolve, r-cran-rjags, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-fs, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-dreamer_3.2.0-1.ca2604.1_all.deb Size: 984026 MD5sum: 5df248a8244dac669bb4cb7c14484a57 SHA1: d2ee7f67b3f8b651bc5dd11e3f42d4fa53a6ae91 SHA256: 37e15c0d2078ca4d4bb86640414102f51614ac5b5224bd64d254bc6adfa13d2b SHA512: 01da3e12e768da3f7b339d4991db5b28e8af3808eec7f7daa2f72c0d4b8d801e3f50372284848bc3fd30ad4ec3b4fdad738dfca0b1e3687f57114f86d8d24d44 Homepage: https://cran.r-project.org/package=dreamer Description: CRAN Package 'dreamer' (Dose Response Models for Bayesian Model Averaging) Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) . Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019). Package: r-cran-dreamerr Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1768 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-stringmagic Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dreamerr_1.5.0-1.ca2604.1_all.deb Size: 948266 MD5sum: 6d963b6439e7b8a5166c5a56306145ee SHA1: 68b73c1d17869a7addfda2d6ebf5be49152d7e18 SHA256: 8d33da84b9e6063cb210a5731cdfc7bc13af7a71bd96a9ae028dc6343f145b98 SHA512: 9945497299f8fc36f5c243a08c85840c79485e54c6b10eb9a34c3c93f4fd3dc55e4f658b4ec5518cbf9c778f529319f838b9d5702c33314fe0beaa13fe62ba51 Homepage: https://cran.r-project.org/package=dreamerr Description: CRAN Package 'dreamerr' (Error Handling Made Easy) Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). Provides a simple, powerful and flexible way to check the arguments passed to functions. The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging. Package: r-cran-dregar Architecture: all Version: 0.1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-msgps Filename: pool/dists/resolute/main/r-cran-dregar_0.1.4.0-1.ca2604.1_all.deb Size: 50346 MD5sum: 9ca2162b878504d1a2628437162da285 SHA1: 4649803b9a99ff7f3b95b8173442a02141cb1b25 SHA256: b2ea9d13a46d44e2480e6e7743862afb3285fad69e428f4e2c6f1caffdd80d27 SHA512: 4d693655cff3b19def439921d964bff104ce94f8223f6db47980b8868d660f0ad4d8bbd43f4447d9febc0116dbda8cbf2c2cb1b31bfad872cbfca4ac7e17cd04 Homepage: https://cran.r-project.org/package=DREGAR Description: CRAN Package 'DREGAR' (Regularized Estimation of Dynamic Linear Regression in thePresence of Autocorrelated Residuals (DREGAR)) A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters. Package: r-cran-drglm Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-speedglm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drglm_1.1-1.ca2604.1_all.deb Size: 57612 MD5sum: 9961309ec2b48d412ee60a8d5b281d74 SHA1: 1c6f5b1f4c721960f788f19b7f89aa501307eb6e SHA256: 79432075473328439dbb26daa07081504956c05dbc8be58201a4642e500af72b SHA512: 1200a3e30add9e83740c2c4b88557ffea934c16e7c121304f2783490272d47bac7a69acbbc31792f7a6047e8cebbd64121187a208c2fc2bdbc07d19845bacfba Homepage: https://cran.r-project.org/package=drglm Description: CRAN Package 'drglm' (Fitting Linear and Generalized Linear Models in "Divide andRecombine" Approach to Large Data Sets) To overcome the memory limitations for fitting linear (LM) and Generalized Linear Models (GLMs) to large data sets, this package implements the Divide and Recombine (D&R) strategy. It basically divides the entire large data set into suitable subsets manageable in size and then fits model to each subset. Finally, results from each subset are aggregated to obtain the final estimate. This package also supports fitting GLMs to data sets that cannot fit into memory and provides methods for fitting GLMs under linear regression, binomial regression, Poisson regression, and multinomial logistic regression settings. Respective models are fitted using different D&R strategies as described by: Xi, Lin, and Chen (2009) , Xi, Lin and Chen (2006) , Zuo and Li (2018) , Karim, M.R., Islam, M.A. (2019) . Package: r-cran-drhotnet Architecture: all Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbsmapping, r-cran-raster, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-drhotnet_2.3-1.ca2604.1_all.deb Size: 397788 MD5sum: d4603955cba25ca3f763b5b872106013 SHA1: df071a1f729aad6cb42ee346eedb69e2861ee071 SHA256: 3ce15a7f1c4cfc1c91d48be63cc9a5428e29a34d537c51650b238acfdb2fbb26 SHA512: cdce62c2f19060bdc69dfaadaf3445752a37be38e6d5298d8053752f796b74911d90c51e7d6b08aa7a5d8836b9e5456e187a2004267f8c508ce54a93731a71d1 Homepage: https://cran.r-project.org/package=DRHotNet Description: CRAN Package 'DRHotNet' (Differential Risk Hotspots in a Linear Network) Performs the identification of differential risk hotspots (Briz-Redon et al. 2019) along a linear network. Given a marked point pattern lying on the linear network, the method implemented uses a network-constrained version of kernel density estimation (McSwiggan et al. 2017) to approximate the probability of occurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) . The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented. Package: r-cran-drhur Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3768 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr Suggests: r-cran-rmarkdown, r-cran-scales, r-cran-modelsummary, r-cran-knitr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-drhur_1.1.0-1.ca2604.1_all.deb Size: 3554482 MD5sum: 640423e7a158c29c4805d1da09f868bf SHA1: bb7f4a95979db9dd8f9421aed11e9091876e9c95 SHA256: 5c75a2d1927634c476234fd9af951cd9774940d83c38bdbd07c49e48b320e311 SHA512: 34b4e022c80b61fae736b3ab17fb1930177f5722d02231c12401efd6368427cafe0f6f4249b36ad22e86296e8e0bac38e99f78720597c21fd41ff6db27e5c5c0 Homepage: https://cran.r-project.org/package=drhur Description: CRAN Package 'drhur' (Learning R with Dr. Hu) Tutarials of R learning easily and happily. Package: r-cran-drhutools Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1810 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-htmltools, r-cran-sf, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-leaflet, r-cran-sp, r-cran-gganimate, r-cran-magick, r-cran-webshot, r-cran-animation, r-cran-png Suggests: r-cran-knitr, r-cran-remotes, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-drhutools_1.1.0-1.ca2604.1_all.deb Size: 1623252 MD5sum: ba7c85c1d058c3ec4ba87a986d060505 SHA1: 1aa06b933b88d6c56157c757d0743a5cde10760f SHA256: 9aa5e17a277fe1f025828057e8db70f17b833544bc037a28656d86a2f206ffb5 SHA512: 54f2a7614aec95b0aedbf76bf56b7f5a64a75d14e584737eb19545cab9340dd3ca932c81184d768080662e810a157134220ceeb2bfa4dfb0cf128b43293cb4bb Homepage: https://cran.r-project.org/package=drhutools Description: CRAN Package 'drhutools' (Political Science Academic Research Gears) Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, set up color-blind palette, and test for Type I error (false positives) in Qualitative Comparative Analysis (QCA) for crisp-set, multi-value, and fuzzy-set variants. Package: r-cran-drifter Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-dplyr, r-cran-tidyr, r-cran-ingredients Suggests: r-cran-testthat, r-cran-ranger Filename: pool/dists/resolute/main/r-cran-drifter_0.2.1-1.ca2604.1_all.deb Size: 38650 MD5sum: ae710b1f2dbb51c6e906dc66102fe222 SHA1: c26054ae9d056956590c7383c8ae6a5dcd9df967 SHA256: d1363f5b85366110a5a5fd5eb194430d7dcb50cfb497c4b1d511e2866d87537f SHA512: ffd08bb6f85d52a4c1d33c691fa59dd3c21de8cf9b2f6c419fd30535877d06b272eae116daf38c1f842f9f7516adba17a26a2ebc20408c7153abd36ccebf95a1 Homepage: https://cran.r-project.org/package=drifter Description: CRAN Package 'drifter' (Concept Drift and Concept Shift Detection for Predictive Models) Concept drift refers to the change in the data distribution or in the relationships between variables over time. 'drifter' calculates distances between variable distributions or variable relations and identifies both types of drift. Key functions are: calculate_covariate_drift() checks distance between corresponding variables in two datasets, calculate_residuals_drift() checks distance between residual distributions for two models, calculate_model_drift() checks distance between partial dependency profiles for two models, check_drift() executes all checks against drift. 'drifter' is a part of the 'DrWhy.AI' universe (Biecek 2018) . Package: r-cran-drillr Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr Filename: pool/dists/resolute/main/r-cran-drillr_0.1-1.ca2604.1_all.deb Size: 42608 MD5sum: 129daacbd5471d26659ffe1347d81333 SHA1: 47c716db6e9998af7f94005722dad0e36b3f72e0 SHA256: 856c33afbaccac11f23f351dc14ceaa72afa55489f33391a8e58ecb3eda2760b SHA512: 55a1b0301d775eae2ddcdb22bd14c612d30c1121958cf15d7e89ec5df61dc4e1584dc3b70c9c0a184fe759345f1189576f3adeafe1e9213ac659e19bf8b858bf Homepage: https://cran.r-project.org/package=DrillR Description: CRAN Package 'DrillR' (R Driver for Apache Drill) Provides a R driver for Apache Drill, which could connect to the Apache Drill cluster or drillbit and get result(in data frame) from the SQL query and check the current configuration status. This link contains more information about Apache Drill. Package: r-cran-drimmr Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seqinr, r-cran-ggplot2, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-dplyr, r-cran-rdpack, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-drimmr_1.0.3-1.ca2604.1_all.deb Size: 229198 MD5sum: 2c3ca812a03688c9fa8f868fff6f8846 SHA1: 699ce302e9c9ba71f7e021484ab2652078b0dd75 SHA256: f805a83847f01eedd4ef4d67e240b16921299f5fb1b8ff1b749fffa40f7348b1 SHA512: 216ed4bf65481c930606006696cd064348b1b5288bc5da904ad7cb2b18edf3491cfac7e865e6c2effe4a7a325f2775e342ed796aca5dec930468be180e838c5d Homepage: https://cran.r-project.org/package=drimmR Description: CRAN Package 'drimmR' (Estimation, Simulation and Reliability of Drifting Markov Models) Performs the drifting Markov models (DMM) which are non-homogeneous Markov models designed for modeling the heterogeneities of sequences in a more flexible way than homogeneous Markov chains or even hidden Markov models. In this context, we developed an R package dedicated to the estimation, simulation and the exact computation of associated reliability of drifting Markov models. The implemented methods are described in Vergne, N. (2008), and Barbu, V.S., Vergne, N. (2019) . Package: r-cran-driver Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-randomforest, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-s4vectors, r-cran-rlang Suggests: r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-driver_0.5.0-1.ca2604.1_all.deb Size: 2880478 MD5sum: a1dcfe28c18cac3ac77ce6c5fc8cd0b5 SHA1: f71cf785992c3d9622265862a5cb22076ffa4e81 SHA256: 7a9867c50c5d6ef47f485cca8d0a79a6b3a061266b3f7752ce901faa20ac9625 SHA512: ecc380b597fe74afebad4da5d432b318d39274e872325080c05a6162a81ab1eadbbaca869ff4dd1255a3cefae3ea38b17f267f545c5b3905705a96fd478e0126 Homepage: https://cran.r-project.org/package=driveR Description: CRAN Package 'driveR' (Prioritizing Cancer Driver Genes Using Genomics Data) Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations. 'driveR' is a tool for personalized or batch analysis of genomic data for driver gene prioritization by combining genomic information and prior biological knowledge. As features, 'driveR' uses coding impact metaprediction scores, non-coding impact scores, somatic copy number alteration scores, hotspot gene/double-hit gene condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG pathways. It uses these features to estimate cancer-type-specific probability for each gene of being a cancer driver using the related task of a multi-task learning classification model. The method is described in detail in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics . Package: r-cran-drmaic Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-boot, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drmaic_0.1.0-1.ca2604.1_all.deb Size: 361140 MD5sum: e789aae8448dba78ff820026803d12e0 SHA1: 5dd3ba431b4f60690e4f0a7bd6f850ab18fb54b8 SHA256: b1d8a3b79e2836ec63678011b82b8017f79d2a20280033d660f27a898bc18ee0 SHA512: ea26c018209bb2168aa58a7493c8f951f2c44bdfd1b3a7dc2d65bb8aabf2c093011ce5e24532b4cd84d9006e58d3587cc7dd355b87307ae415b149f142e61d8d Homepage: https://cran.r-project.org/package=drMAIC Description: CRAN Package 'drMAIC' (Doubly Robust Matching-Adjusted Indirect Comparison for HTA) Implements Doubly Robust Matching-Adjusted Indirect Comparison (DR-MAIC) for population-adjusted indirect treatment comparisons in health technology appraisal (HTA). The package provides: (1) standard MAIC via entropy balancing / exponential tilting; (2) augmented/doubly robust MAIC combining inverse probability weighting with outcome regression; (3) comprehensive covariate balance diagnostics including standardised mean differences, Love plots, and effective sample size; (4) sensitivity analyses including E-values, weight trimming, and variable exclusion analyses; (5) bootstrap confidence intervals; and (6) submission-ready outputs aligned with NICE Decision Support Unit Technical Support Document 18, Cochrane Handbook guidance on indirect comparisons, and ISPOR best practice guidelines. Supports binary (risk difference, risk ratio, odds ratio) and time-to-event (hazard ratio) outcomes. Package: r-cran-drmeta Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-metafor, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drmeta_0.1.0-1.ca2604.1_all.deb Size: 150580 MD5sum: 92ae10e2a3f4e1c768854a95b90a34e1 SHA1: 2c235d98413a504aa982a40bb3701c22750d913c SHA256: 578450b51666389637f6ae5d2aeae43fe03f67cb4b224ef8932b560feb71e1e7 SHA512: 6e455cf03f6f892fd754b5d0481e0b20208d980d462b72ccd320385caabf7bc7b9cddc87cffe61b1662242759097b7d456a8cf58258eb94bd03af7076b6b7434 Homepage: https://cran.r-project.org/package=drmeta Description: CRAN Package 'drmeta' (Design-Robust Meta-Analysis via Variance-Function Models) Implements Design-Robust Meta-Analysis (DR-Meta), a variance-function random-effects framework in which between-study heterogeneity is modelled as a function of a study-level design robustness index, allowing heterogeneity to depend systematically on study quality or design strength rather than being treated as a single nuisance parameter. The package provides profiled restricted maximum likelihood (REML) estimation of the overall effect and variance-function parameters, study-specific weights, heterogeneity diagnostics (tau-squared, I-squared), influence and leave-one-out analysis, and graphical tools including forest plots and influence plots. The DR-Meta framework nests classical fixed-effects and standard random-effects meta-analysis as special cases, making it a strict generalisation of existing approaches. Package: r-cran-droll Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ryacas Suggests: r-cran-covr, r-cran-ggplot2, r-cran-mockery, r-cran-testthat, r-cran-tibble, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-droll_0.1.0-1.ca2604.1_all.deb Size: 243786 MD5sum: a38465de41c16d2b8b27bf04ec8a35ca SHA1: 00d0a57f5d3dfcc265b466e982de483779e0c4db SHA256: 090e7c623c3a5ab1d5500003fedb10d26ca1280f4c22e12f5083f1d816991dd8 SHA512: 1c52fb88ecd6f5854192e7ad92ce46a6e36d9d2266fdd221b3a781d3489b69d2efcd09db7b727f6efd99f11a7c2225470b7688ac07ca8f3e013a04c74db3881a Homepage: https://cran.r-project.org/package=droll Description: CRAN Package 'droll' (Analyze Roll Distributions) A toolkit for parsing dice notation, analyzing rolls, calculating success probabilities, and plotting outcome distributions. Package: r-cran-dromics Architecture: all Version: 2.6-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5674 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-limma, r-bioc-deseq2, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-ggfortify, r-cran-rlang Suggests: r-cran-shiny, r-cran-shinybs, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinywidgets, r-cran-sortable, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-sva, r-cran-venndiagram, r-cran-plotly, r-cran-svglite Filename: pool/dists/resolute/main/r-cran-dromics_2.6-2-1.ca2604.1_all.deb Size: 3642674 MD5sum: d9103b95e89997af9bc1481fd14f185e SHA1: 5fa694f63d9a29362cd376ac16f1ca65e48734f2 SHA256: 60fe9ee25bba6d8e8893f4efc3877515be2afbab2f50388f433d27111e930fdb SHA512: 3db3ff23f46c30bd0cc0071de639d598b7d35e900c36ad03fa52df7ef701f74f9be45d8cd04e19c9cdc3af572934bca2deeb20259ae95a5afee395f1927fffdf Homepage: https://cran.r-project.org/package=DRomics Description: CRAN Package 'DRomics' (Dose Response for Omics) Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) and Larras et al (2018) . Package: r-cran-drone Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-drone_1.0.0-1.ca2604.1_all.deb Size: 59262 MD5sum: 9fd9f6dd586fa434638e474a7097135f SHA1: 37928ee2db3d9768d964ed8a7fe4659a6e66d1ff SHA256: 4b063f637c921ca953d58bf430e75624c8eeeec3687f36dafd781e8ea612f060 SHA512: e3cf36681da6587597c8027e5bd591bea75fdb2ad7a4ed680c010d89e4a57f6e456a5f3059cdf379ce7e35c4320aef6987ee8a30e8b954e037f4f0376d51f8ac Homepage: https://cran.r-project.org/package=drone Description: CRAN Package 'drone' (Data for Data Visualisation Geometries Encyclopedia) This is the companion package to the Data Visualization Geometries Encyclopedia, providing seamless access to the associated data. Package: r-cran-dropr Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-data.table, r-cran-survival, r-cran-lifecycle Suggests: r-cran-dt, r-cran-shinydashboard, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dropr_1.0.6-1.ca2604.1_all.deb Size: 332498 MD5sum: e528bba3be89579a0b9de48900587441 SHA1: cd74f916c04ec2f4d88effaba1e4b6c3244e6667 SHA256: c86bc24aec41124343b53238265c9419da4894de4e0089d8db266cc9ae6923fe SHA512: cf38db0946dbdf44ee34b143fbaa82aff809cb4fcbdde5ee558118b4328cc66b4903149c422b8df551fa5cbe45a2371624647f12e6a3c2a3fed6fe0f5a385930 Homepage: https://cran.r-project.org/package=dropR Description: CRAN Package 'dropR' (Dropout Analysis by Condition) Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi squared), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Article published in _Behavior Research Methods_ on 'dropR' by the authors: Dropout analysis: A method for data from Internet-based research and 'dropR', an R-based web app and package to analyze and visualize dropout. (2025) . Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001) ; Ulf-Dietrich Reips (2002) . Package: r-cran-droptest Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-droptest_0.1.3-1.ca2604.1_all.deb Size: 47278 MD5sum: 57615cecabec520116b8547aa2ca1380 SHA1: 5d580e0f9d3cb1970f7bd6f63268802ba5ca3019 SHA256: d3059002da152f7b5645a0a1fba6455971fb03c72860847851d976abc1726071 SHA512: 25676d67f6e79db98b76f39eb639e5393bb611f9e36952da48767a7262a845f9ac57e2e740174f484b83c42b87d56ce99d38df86c3bafb7b70644f176a91bfd8 Homepage: https://cran.r-project.org/package=droptest Description: CRAN Package 'droptest' (Simulates LOX Drop Testing) Generates simulated data representing the LOX drop testing process (also known as impact testing). A simulated process allows for accelerated study of test behavior. Functions are provided to simulate trials, test series, and groups of test series. Functions for creating plots specific to this process are also included. Test attributes and criteria can be set arbitrarily. This work is not endorsed by or affiliated with NASA. See "ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized Liquid and Gaseous Oxygen Environments" . Package: r-cran-drord Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-vgam, r-cran-ordinal, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggsci Filename: pool/dists/resolute/main/r-cran-drord_1.0.1-1.ca2604.1_all.deb Size: 279230 MD5sum: 6dffe743749af9dd1bb1bfefb851fad1 SHA1: 9c3fef3ad9911ecfdecbea86069d0c1040026a1e SHA256: 8acee82d555f63a60648c3a9e509a95d02903ff705edcf29786e39cf6e25c010 SHA512: 17c036ec53a147d80deb06a3ce9051fb5a9295ee68e096e050a87b3edad853bf242312c198c48d107d31ee5decfa548a84e1c74baa2b96605f5f74fce8e9b2bf Homepage: https://cran.r-project.org/package=drord Description: CRAN Package 'drord' (Doubly-Robust Estimators for Ordinal Outcomes) Efficient covariate-adjusted estimators of quantities that are useful for establishing the effects of treatments on ordinal outcomes. Package: r-cran-drought Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-corrplot Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-drought_1.2-1.ca2604.1_all.deb Size: 50756 MD5sum: 312a26048398aeb26b022fbf31cb6db4 SHA1: 1cf75f13db8f3ee2ee6df0dc914470da1068a846 SHA256: 510e1193a94978615b9891555d057672684919fb1cebe148b788106cb96c8aa6 SHA512: 1a9bdcb6f3d9622c57aadd34ebb82ef132ae6703af95530327b5385267ad656f22e6b7502017cc590e561a13cfa1b492f3e36274a5dd5d5fca3fbde8134bcf02 Homepage: https://cran.r-project.org/package=drought Description: CRAN Package 'drought' (Statistical Modeling and Assessment of Drought) Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP), drought risk assessments, and drought propagation are also provided. Please see Hao Zengchao et al. (2017) . Package: r-cran-drpop Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gam, r-cran-janitor, r-cran-reshape2, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-superlearner, r-cran-ggplot2, r-cran-nnet, r-cran-nnls, r-cran-ranger Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drpop_0.0.3-1.ca2604.1_all.deb Size: 153600 MD5sum: b91e48c516cae22cae1b1d8cc684fda5 SHA1: 8cc05a8ff180098133733c9ad01fcabb168995e8 SHA256: 50b03f1708b3b8413b77865b1107345bf445bef5dbe2ab026c8b937f2cc6b1da SHA512: e87b3afd870b8a2d03696a192f6f54342ba8a81fd67c47ecbfe5dddea981016d9c2786e475dbf60b06f42dc98a0b045a476c9ba1a8209c083a6cfc4cf1467eea Homepage: https://cran.r-project.org/package=drpop Description: CRAN Package 'drpop' (Efficient and Doubly Robust Population Size Estimation) Estimation of the total population size from capture-recapture data efficiently and with low bias implementing the methods from Das M, Kennedy EH, and Jewell NP (2021) . 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Package: r-cran-drquality Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-databionicswarm Suggests: r-cran-plotly, r-cran-geometry, r-cran-deldir, r-cran-fcps, r-cran-projectionbasedclustering, r-cran-datavisualizations, r-cran-fastknn, r-cran-ggplot2, r-cran-pcapp, r-cran-pracma, r-cran-spdep, r-cran-igraph, r-cran-cccd, r-cran-sf Filename: pool/dists/resolute/main/r-cran-drquality_0.2.1-1.ca2604.1_all.deb Size: 90600 MD5sum: 71a87083601f287f451ae278e3a12f5b SHA1: 3c82f8a1b51524189ae8a8a699e58f17dd20a9a1 SHA256: 242835cb183d17e96ee14f95944a7d7ff42d7242a3c1356fa13b7f412c07409d SHA512: 2cf4f5693a381846014227631d3a67c1d45cf965d42233663228c926ed97bd803e90d41bd5859988b796c2542a164c71d629f9ecbf358d362e75280290409ba0 Homepage: https://cran.r-project.org/package=DRquality Description: CRAN Package 'DRquality' (Quality Measurements for Dimensionality Reduction) Several quality measurements for investigating the performance of dimensionality reduction methods are provided here. 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Package: r-cran-drugdevelopr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-mvtnorm, r-cran-cubature, r-cran-msm, r-cran-mass, r-cran-progressr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-kableextra, r-cran-magrittr, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-drugdevelopr_1.0.2-1.ca2604.1_all.deb Size: 919306 MD5sum: 252189bf568646aea1ecbced6eb9f221 SHA1: d88401c607ce58f1dd71aa251e51b06a387be3e2 SHA256: 1c12317e2f30a2e903d218e08fe32a1b150224c1f97d7b6223196e24b9ab8e40 SHA512: 3277547abd09b8e7cc5342ce5e5d5ffbccafbdf9c6c8c6b8ca1ab3afc488da5fdaddb9b141bafc04a49d2ee32acf3d647693a87d745027c8ffb6a43990c96ad0 Homepage: https://cran.r-project.org/package=drugdevelopR Description: CRAN Package 'drugdevelopR' (Utility-Based Optimal Phase II/III Drug Development Planning) Plan optimal sample size allocation and go/no-go decision rules for phase II/III drug development programs with time-to-event, binary or normally distributed endpoints when assuming fixed treatment effects or a prior distribution for the treatment effect, using methods from Kirchner et al. (2016) and Preussler (2020). Optimal is in the sense of maximal expected utility, where the utility is a function taking into account the expected cost and benefit of the program. It is possible to extend to more complex settings with bias correction (Preussler S et al. (2020) ), multiple phase III trials (Preussler et al. (2019) ), multi-arm trials (Preussler et al. (2019) ), and multiple endpoints (Kieser et al. (2018) ). Package: r-cran-drugexposurediagnostics Architecture: all Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4637 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cdmconnector, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-checkmate, r-cran-glue, r-cran-drugutilisation, r-cran-omopgenerics, r-cran-r6 Suggests: r-cran-testthat, r-cran-duckdb, r-cran-odbc, r-cran-dbi, r-cran-knitr, r-cran-rmarkdown, r-cran-zip, r-cran-lubridate, r-cran-tibble, r-cran-dt, r-cran-sqlrender, r-cran-ggplot2, r-cran-plotly, r-cran-tictoc, r-cran-here, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-shinyjs, r-cran-shinytest2 Filename: pool/dists/resolute/main/r-cran-drugexposurediagnostics_1.1.7-1.ca2604.1_all.deb Size: 968080 MD5sum: 53508d7fc401eff3d0504280b6a3413d SHA1: ef2502ecd8b3f6b0f9dfd438815e92e9acfb84ce SHA256: 94c8b3a4cb2242ab4df65dcb7e5234d2c1a2c19ca09cc4c9e3983377bf729bf7 SHA512: b83ae23b675acf983249788adb49783e6d89d70ea7a2d12421dae02cd46798a420fedea553d3bd64018c434e161a90710ad3fbeb83ad6a1b2a980afcdf8b2d52 Homepage: https://cran.r-project.org/package=DrugExposureDiagnostics Description: CRAN Package 'DrugExposureDiagnostics' (Diagnostics for OMOP Common Data Model Drug Records) Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model. Package: r-cran-drugsens Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1337 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-knitr, r-cran-roxygen2, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drugsens_0.1.0-1.ca2604.1_all.deb Size: 133552 MD5sum: c50490277edb99418e6f54eb696b70ad SHA1: 0fe1c9f22e0c568fb30a824a1ffec61d6d23dce4 SHA256: c0f25934aac0359fed0580c05cedef74cb8c589c69d3b2329894aa05e071bccf SHA512: 4e2ba8940dbc4df27e142ec21b23b23e89133d7234ae8194f7c3615507ca942d439c6d9a51790f4824d1d46dc2690b1e282aee6baa4095a1aad8ea62cbe78116 Homepage: https://cran.r-project.org/package=drugsens Description: CRAN Package 'drugsens' (Automated Analysis of 'QuPath' Output Data and MetadataExtraction) A comprehensive toolkit for analyzing microscopy data output from 'QuPath' software. Provides functionality for automated data processing, metadata extraction, and statistical analysis of imaging results. The methodology implemented in this package is based on Labrosse et al. (2024) "Protocol for quantifying drug sensitivity in 3D patient-derived ovarian cancer models", which describes the complete workflow for drug sensitivity analysis in patient-derived cancer models. Package: r-cran-drugsim2dr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-pheatmap, r-cran-tidyr, r-cran-reshape2, r-cran-fastmatch Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-drugsim2dr_0.1.2-1.ca2604.1_all.deb Size: 2468406 MD5sum: 32b92c6b2d77e6f3b32abde0735a00f9 SHA1: 04d4f4af1f46dbdda3be73d1a6626927470a4c15 SHA256: 39e68c9f41025e5d6b456113f8466292066c320b9a9c88a237d5e00df778b8a7 SHA512: e28d78e108cb8164751b6623c56ae2325d1f85ef89ad5d9a0feca287002dbbd2018b62ebe0a6845598104cfeb16ec47fcd8a5c5d9b42893939ad042fa2c8df6e Homepage: https://cran.r-project.org/package=DrugSim2DR Description: CRAN Package 'DrugSim2DR' (Predict Drug Functional Similarity to Drug Repurposing) A systematic biology tool was developed to repurpose drugs via a drug-drug functional similarity network. 'DrugSim2DR' first predict drug-drug functional similarity in the context of specific disease, and then using the similarity constructed a weighted drug similarity network. Finally, it used a network propagation algorithm on the network to identify drugs with significant target abnormalities as candidate drugs. Package: r-cran-drugutilisation Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-codelistgenerator, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-cohortconstructor, r-cran-cohortsurvival, r-cran-covr, r-cran-dbi, r-cran-duckdb, r-cran-flextable, r-cran-ggplot2, r-cran-ggtext, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-visomopresults Filename: pool/dists/resolute/main/r-cran-drugutilisation_1.1.0-1.ca2604.1_all.deb Size: 678548 MD5sum: 32b3404f30839079b881f373c2a794f2 SHA1: 80422a3ed59c1f193090afbd2995637b64d4d00e SHA256: 8611d5d96af24ed8e01ad3843db6f2d7948904b0638e3ebf8e4b9382e6230699 SHA512: 9cb774d65902de13ed982c2e2750a98da7173b7d7b0005718f9b37ef5c74605e64483fa7531823d2105a4d1a6b218371e228f0713546c729615fef12e9a4a66f Homepage: https://cran.r-project.org/package=DrugUtilisation Description: CRAN Package 'DrugUtilisation' (Summarise Patient-Level Drug Utilisation in Data Mapped to theOMOP Common Data Model) Summarise patient-level drug utilisation cohorts using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. 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Package: r-cran-drumr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-audio, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-drumr_0.1.0-1.ca2604.1_all.deb Size: 1944488 MD5sum: 29f2cf09d60a39d9b1d7463e756e425c SHA1: c655200427a398f89766e8123bf76edec49efcc4 SHA256: 5e61fb58a778fb568c1e53339d0ce17cfe1a364a8c1f5f216877d4abcbf11871 SHA512: f66625353d8cdf1e7cc1f2278e756df434799f345b3918bc4eebdc958bedc9971c3568461d5c1df1c90ea8acbd59f7cfa830ae79d0d7ba5efcb52c9224088e9e Homepage: https://cran.r-project.org/package=drumr Description: CRAN Package 'drumr' (Turn R into a Drum Machine) Includes various functions for playing drum sounds. beat() plays a drum sound from one of the six included drum kits. tempo() sets spacing between calls to beat() in bpm. Together the two functions can be used to create many different drum patterns. 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The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results. Package: r-cran-ds4psy Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-unikn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ds4psy_1.3.0-1.ca2604.1_all.deb Size: 931222 MD5sum: 1ae8d3cbb1e15bd11a2f99f153f008c2 SHA1: 2764480424a4f86f5d41f20b1546ba26edb17424 SHA256: 0cf8a82527ee0d348ef3da506bd5ba268d00a3e0b9ab65968663952aebfc9762 SHA512: d2d3e38c8784f1581551bba0f6028bb728ca46897a51291d8a01f7fd0fb7e082844053f051617d901e6e60a7c333ddf947d04d3bebf89964ec3490b29ab27b4b Homepage: https://cran.r-project.org/package=ds4psy Description: CRAN Package 'ds4psy' (Data Science for Psychologists) All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2026, ), freely available at . The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The 'ds4psy' package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in 'ds4psy' are designed to be explicit and instructive, rather than efficient or elegant. 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Bundesbank Discussion Paper 41/2018. 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The purpose of the package is to help individuals learn about within-host infection and immune response modeling from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models. 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The dataSplit() function will help you divide the data according to specific requirements, and you can refer to the par.default() function to set the parameters for data splitting. The getAUC() function will help you measure the similarity of distribution features between the data subsets. For more information about the data splitting algorithms, please refer to: Chen et al. (2022) , Zheng et al. (2022) . 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Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibody derived tags (ADT) in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', 'ASAP-seq', 'TEA-seq', 'proteogenomic' data from the Mission Bio platform, etc. See the vignette for tutorials on how to integrate dsb with 'Seurat' and 'Bioconductor' and how to use dsb in 'Python'. Please see our paper Mulè M.P., Martins A.J., and Tsang J.S. Nature Communications 2022 for more details on the method. 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Package: r-cran-dsbayes Architecture: all Version: 2023.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb Filename: pool/dists/resolute/main/r-cran-dsbayes_2023.1.0-1.ca2604.1_all.deb Size: 75194 MD5sum: c8ebc6f997cff7c9c2b9f9508ff8f110 SHA1: f8358228562e320b9c1033a5773d15cddee18179 SHA256: ef9336729ce059ea29853497ba972a9b8df1ae9d6258e0458a4e79a7198591b0 SHA512: b1148ecc5e17ac72e1b7b893c8096b84ac2c71447fe1143db1bf8476cd86bcc953cb2bdb8ab12d8ec1109692046a5900e89bd3c36c54e6d9fe97d196730432c0 Homepage: https://cran.r-project.org/package=DSBayes Description: CRAN Package 'DSBayes' (Bayesian Subgroup Analysis in Clinical Trials) Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881. 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The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) . Package: r-cran-dsge Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-coda, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dsge_1.0.0-1.ca2604.1_all.deb Size: 525846 MD5sum: 9f0a93abcee3045bc52dea62526240f4 SHA1: 30197550e43d9d9fdbf81845c30ffc614ed8bd0e SHA256: 3b77cbf0c98a158bb4ea0639766fc7aceaca498b3b6782911db0a26432002f20 SHA512: d7feae8d2a294bfb28a1358a10f930f117e7cfb1147816004c1247937897970be799132d6e6c9fd127ffaef2ce05ad5cc88fa94c852b2d8e800f99a18172058d Homepage: https://cran.r-project.org/package=dsge Description: CRAN Package 'dsge' (Dynamic Stochastic General Equilibrium Models) Specify, solve, and estimate dynamic stochastic general equilibrium (DSGE) models by maximum likelihood and Bayesian methods. Supports both linear models via an equation-based formula interface and nonlinear models via string-based equations with first-order perturbation (linearization around deterministic steady state). Solution uses the method of undetermined coefficients (Klein, 2000 ). Likelihood evaluated via the Kalman filter. Bayesian estimation uses adaptive Random-Walk Metropolis-Hastings with prior specification. Additional tools include Kalman smoothing, historical shock decomposition, local identification diagnostics, parameter sensitivity analysis, second-order perturbation, occasionally binding constraints, impulse-response functions, forecasting, and robust standard errors. 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Package: r-cran-dsims Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dssd, r-cran-mrds, r-cran-distance, r-cran-sf, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-mgcv, r-cran-rstudioapi, r-cran-gridextra, r-cran-rlang Suggests: r-cran-testthat, r-cran-bookdown, r-cran-pbapply, r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dsims_1.0.6-1.ca2604.1_all.deb Size: 431566 MD5sum: f2b554fe6e968a94f80d2502f5fcb420 SHA1: 5c706c7327779a50386752d31393611b409d329d SHA256: 0263ea05695874a94c4d7feb3b9176b6fa1d08c4453ebdc467ba42087d7a8e6b SHA512: d8399c60bb03fa0c67aa64b57a23bce53b995dbb2f18b4e5bb7fcda7e912f0445f9ef8d376fed9f68cbe2b76fe2a43959c36fa45e660ca8a0e1f776895a6558b Homepage: https://cran.r-project.org/package=dsims Description: CRAN Package 'dsims' (Distance Sampling Simulations) Performs distance sampling simulations. 'dsims' repeatedly generates instances of a user defined population within a given survey region. It then generates realisations of a survey design and simulates the detection process. The data are then analysed so that the results can be compared for accuracy and precision across all replications. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under a number of simulations so that users can be confident that they have achieved a robust survey design before deploying vessels into the field. The distance sampling designs used in this package from 'dssd' are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). General distance sampling methods are detailed in Introduction to Distance Sampling: Estimating Abundance of Biological Populations, Buckland et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating animal/plant abundance with distance sampling at . 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Dataset containing information about job listings for data science job roles. 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This 'DataSHIELD Interface' implementation is for analyzing datasets living in the current R session. The purpose of this is primarily for lightweight 'DataSHIELD' analysis package development. 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A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation. 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Furthermore, three different types of drifting semi-Markov models are considered. These models differ in the number of transition matrices and sojourn time distributions used for the computation of a number of semi-Markov kernels, which in turn characterize the drifting semi-Markov kernel. For the parametric model estimation and specification, several discrete distributions are considered for the sojourn times: Uniform, Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric model specification makes no assumptions about the shape of the sojourn time distributions. Semi-Markov models are described in: Barbu, V.S., Limnios, N. (2008) . Drifting Markov models are described in: Vergne, N. (2008) . Reliability indicators of Drifting Markov models are described in: Barbu, V. S., Vergne, N. (2019) . We acknowledge the DATALAB Project (financed by the European Union with the European Regional Development fund (ERDF) and by the Normandy Region) and the HSMM-INCA Project (financed by the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005). 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These designs can be assessed for various effort and coverage statistics. Once the user is satisfied with the design characteristics they can generate a set of transects to use in their distance sampling survey. Many of the designs implemented in this R package were first made available in our 'Distance' for Windows software and are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). Find out more about estimating animal/plant abundance with distance sampling at . 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Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions. 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A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) ). There are two examples, one concerning unemployment from Lalive et al. (2006) , the other concerning smoking and periodontal disease from Rosenbaum (2017) . 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The package provides tools for processing data, several ways of estimating parametric and nonparametric multistate models, and an extensive set of Markov chain methods which use transition probabilities derived from the multistate model. Some of the implemented methods are described in Schneider et al. (2024) , Dudel (2021) , Dudel & Myrskylä (2020) , van den Hout (2017) . Package: r-cran-dtp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-gtools, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-dtp_0.1.0-1.ca2604.1_all.deb Size: 54724 MD5sum: f5826f51fca9884ff527f330e98d857b SHA1: 6c1daa05b120ecd048e674bb768cc9890fa745e3 SHA256: 26fcbc7503adfc767ee74f7434a0730ec0b8dab857e995e5199d183bc3d6ccf5 SHA512: b65db7c9927cfb3866d515ad4f46fd0df2021f0398043b530792a6c5e74d5a212288746c56a12ed3303a38161856ebb32740cf8590da29d44dc494d9f80c3287 Homepage: https://cran.r-project.org/package=dtp Description: CRAN Package 'dtp' (Dynamic Panel Threshold Model) Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) ) in which they extended the (Hansen (1999) ) original static panel threshold estimation and the Caner and (Hansen (2004) ) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used. Package: r-cran-dtpcrm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagram, r-cran-dfcrm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dtpcrm_0.1.1-1.ca2604.1_all.deb Size: 180660 MD5sum: b8800006f1baee64f3eb051b42147027 SHA1: b97439aadfc875962bc080e6e02abb6e032c51b3 SHA256: 259b6fb6657c5bc2db0b8395ad5abdc81f5b5d90a0f42f38d222d28724235132 SHA512: 4f6c809c43524d11d9df763af1d0d5d4065ac4eeb484d7b8fed9d0b0ab8d52a9e52097ef3bd050a39b22f51c02fd863174fc48e4dc68788f2b0fa3358608ad5d Homepage: https://cran.r-project.org/package=dtpcrm Description: CRAN Package 'dtpcrm' (Dose Transition Pathways for Continual Reassessment Method) Provides the dose transition pathways (DTP) to project in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, deescalate or stop early) using all the accumulated toxicity information; See Yap et al (2017) . DTP can be used as a design and an operational tool and can be displayed as a table or flow diagram. The 'dtpcrm' package also provides the modified continual reassessment method (CRM) and time-to-event CRM (TITE-CRM) with added practical considerations to allow stopping early when there is sufficient evidence that the lowest dose is too toxic and/or there is a sufficient number of patients dosed at the maximum tolerated dose. 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The goal of 'dtplyr' is to allow you to write 'dplyr' code that is automatically translated to the equivalent, but usually much faster, data.table code. 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As you filter, mutate, and join your way through a data set, 'dtrackr' seamlessly keeps track of your data flow and makes publication ready documentation of a data pipeline simple. Package: r-cran-dtreg Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-r6, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sets, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dtreg_1.1.2-1.ca2604.1_all.deb Size: 145160 MD5sum: ead48e50600531dfeee1164b2506ed12 SHA1: 63f32475c931ee8a8af2698234ed5f2eee946540 SHA256: 29ee644fc6dbf901f0f820115faa6904df5f267d818a68da8fa846f0bf0345bd SHA512: 54ce403bce1161190b9b31f1e160eefd85f6fad73150eca0323863f1b7de3ec806aad7b8213f20918032b4baacca85b53e0ec0b4ef3dd93e9f4f44d4b705c4d8 Homepage: https://cran.r-project.org/package=dtreg Description: CRAN Package 'dtreg' (Interact with Data Type Registries and Create Machine-ReadableData) You can load a schema from a DTR (data type registry) as an R object. 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Package: r-cran-dtrlearn2 Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernlab, r-cran-mass, r-cran-matrix, r-cran-foreach, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-dtrlearn2_1.1-1.ca2604.1_all.deb Size: 126640 MD5sum: 9ef6a99dc3ba5d6c6f7990a486d42f36 SHA1: 08747190c940532347925177983d75718eeac164 SHA256: 5eba4018612f00111c1e82cf69a992d9c2508c04e625c16572b381229a1a2df4 SHA512: 1f955d4ec9540971f4f9afe9d2ddfa7b76fbb88099060c789a1e6ef4916ab4b52721fad5a58d7656c82be7c59872b92aaba0656afd5db14f1c2c7915b07788da Homepage: https://cran.r-project.org/package=DTRlearn2 Description: CRAN Package 'DTRlearn2' (Statistical Learning Methods for Optimizing Dynamic TreatmentRegimes) We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation. Package: r-cran-dtrreg Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggplotify, r-cran-nnet, r-cran-r6 Filename: pool/dists/resolute/main/r-cran-dtrreg_2.4-1.ca2604.1_all.deb Size: 416054 MD5sum: 1a6dff7c9274c3b82ca2bb4fb5b22cbf SHA1: f8a549d9e7be5252997b5490a6c938d459a72088 SHA256: b60f071b0bbc260aa0ce78601f1bd0c875057661fe35f085423ddefbd22ff355 SHA512: 272f3a86ef3e9b1d77032412914f01b80f64f4bba6615e65b122b22249c5c8674732d4c4452f7cefae5f9851619108be573032e26f155e4bc850f97b594b6df8 Homepage: https://cran.r-project.org/package=DTRreg Description: CRAN Package 'DTRreg' (DTR Estimation and Inference via G-Estimation, Dynamic WOLS,Q-Learning, and Dynamic Weighted Survival Modeling (DWSurv)) Dynamic treatment regime estimation and inference via G-estimation, dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference via bootstrap and recursive sandwich estimation. Estimation and inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). Extension to continuous treatment variables. Wallace et al. (2017) ; Simoneau et al. (2020) . Package: r-cran-dts Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-actuar, r-cran-expint Filename: pool/dists/resolute/main/r-cran-dts_0.1.1-1.ca2604.1_all.deb Size: 96322 MD5sum: 2637bb9f2f9e04379a7e5dc0a07ad009 SHA1: 1ffa96a2dae454d400c5e20e126bcf7c10e29feb SHA256: 8ff9d1753baf585bb096fbd3871386c022ade0f3d3418eca2f40faa9d3308546 SHA512: 55c12fee071a486b10aabd3a89c49fee6f8d03268495223f63ce70cadee83858562cbe07c07787ae97897ce1123fc2fa4dd57e971aecff9ad24519ffadcf63c3 Homepage: https://cran.r-project.org/package=DTS Description: CRAN Package 'DTS' (Discrete Tempered Stable Distributions) Methods for evaluating the probability mass function, cumulative distribution function, and generating random samples from discrete tempered stable distributions. For more details see Grabchak (2021) . Package: r-cran-dtsea Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-bioc-fgsea, r-cran-igraph, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-bioc-biocparallel, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dtsea_0.0.3-1.ca2604.1_all.deb Size: 346444 MD5sum: 0183075960430c8711682e3174ff24c8 SHA1: c686f20b95453c4c072e7d4e4e87d831074df683 SHA256: c760a5c21e29f86b8c616259fb2b2aa40d6d325453c05c8edef661d75b58fec7 SHA512: 777865d058f96b8ee87015aa434722ad8e5d30b9075c868c514083c49842f2a362d296e97fa8033411e8edd54a8422cd9a6bec37fb4f4bdd9fc55045be1b26f2 Homepage: https://cran.r-project.org/package=DTSEA Description: CRAN Package 'DTSEA' (Drug Target Set Enrichment Analysis) It is a novel tool used to identify the candidate drugs against a particular disease based on the drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. (See Gómez-Carballa et al. (2022) and Feng et al. (2022) for disease expression profiles; see Wishart et al. (2018) and Gaulton et al. (2017) for drug target information; see Kanehisa et al. (2021) for the details of KEGG database.) Package: r-cran-dtsg Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1769 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-r6, r-cran-timechange Suggests: r-cran-dygraphs, r-cran-fasttime, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rcppcctz, r-cran-rmarkdown, r-cran-runner, r-cran-tinytest, r-cran-units Filename: pool/dists/resolute/main/r-cran-dtsg_2.1.0-1.ca2604.1_all.deb Size: 375922 MD5sum: da91b9d6b0f14609c2707c777e9217d9 SHA1: 0dfa575a4e4c89818b1cfab5defd3a81bb1c18a5 SHA256: 2a1b835a52cf9bb9a6318f76e018da6a36cf9f6d26e5ee35401e48e119c4741a SHA512: 89b7ace5af76429fc52c00ccf335f8e4ee5e5c1e23fcf0ec4dad880c2eb3b734c3a7eff88773ebe33f754a746d17569380ea4a9562359acdf3e7d3c6892e6a71 Homepage: https://cran.r-project.org/package=DTSg Description: CRAN Package 'DTSg' (A Class for Working with Time Series Data Based on 'data.table'and 'R6' with Largely Optional Reference Semantics) Basic time series functionalities such as listing of missing values, application of arbitrary aggregation as well as rolling (asymmetric) window functions and automatic detection of periodicity. As it is mainly based on 'data.table', it is fast and (in combination with the 'R6' package) offers reference semantics. In addition to its native R6 interface, it provides an S3 interface for those who prefer the latter. Finally yet importantly, its functional approach allows for incorporating functionalities from many other packages. Package: r-cran-dtt Architecture: all Version: 0.1-2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dtt_0.1-2.1-1.ca2604.1_all.deb Size: 21124 MD5sum: 47af3bc80a0e98ae75036d80df395ae3 SHA1: c460bb5dca4462ba4d50906bc47fca609e2b9f19 SHA256: 49d93c864233efc4a9ef8d9b4bc447d3031f4e2e49e3f90295f4a0a3f526c434 SHA512: 053b388efc2147fc53b13c95929079ae0aa6102767d7badf2ebdb3a022f106ea7bc5c1c54ff827539c05de58ca2d1cd626cb01112cd15b06a484591b0625cbc5 Homepage: https://cran.r-project.org/package=dtt Description: CRAN Package 'dtt' (Discrete Trigonometric Transforms) Provides functions for 1D and 2D Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Hartley Transform (DHT). Package: r-cran-dttr2 Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chk, r-cran-hms, r-cran-lifecycle Suggests: r-cran-rlang, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dttr2_0.5.2-1.ca2604.1_all.deb Size: 254508 MD5sum: 78b1fae475e84105fe93370ea3892a70 SHA1: 6f939b1ca4365dc85e774d2470de78d81443f3ed SHA256: 1b05bd219c4540a196de2ae02496284b117f8118e87da19aa5b26f226a10fae0 SHA512: e3bee3ec01397fc4ca1d8d772f6d3bee7e2b8c462d6b5a2e8d465f6441c9f7128fdf4352f7d31e707ed6a4375aa0658ace016f958b878e309ad2b4ff6385e883 Homepage: https://cran.r-project.org/package=dttr2 Description: CRAN Package 'dttr2' (Manipulate Date, POSIXct and hms Vectors) Manipulates date ('Date'), date time ('POSIXct') and time ('hms') vectors. Date/times are considered discrete and are floored whenever encountered. Times are wrapped and time zones are maintained unless explicitly altered by the user. Package: r-cran-dtwbi Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dtw, r-cran-rlist, r-cran-e1071, r-cran-entropy, r-cran-lsa Filename: pool/dists/resolute/main/r-cran-dtwbi_1.1-1.ca2604.1_all.deb Size: 75998 MD5sum: 3c1e6bc76e40a1aeedf1ad5c1ba745a9 SHA1: 1622492a7f2c6a8e9702ee7a584f5095af9d70d1 SHA256: 00390d163f23b4745afaab54ba6239ec136596210424ab99076598cef90e127b SHA512: 96124734a44a534a3400f661706fbd7efb038233c072b3853f698cc9dfe5e6dc82a1a97e2be89e818c137c774ff03febb6819c22b92a4dc3e59d7599bc643e47 Homepage: https://cran.r-project.org/package=DTWBI Description: CRAN Package 'DTWBI' (Imputation of Time Series Based on Dynamic Time Warping) Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), . Performance criteria are added to compare similarity between two signals (query and reference). 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These include cleaning accidental text, contingent calculations, counting missing data, and building summarizations of the data. Package: r-cran-dtwrappers Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dtwrappers_0.0.2-1.ca2604.1_all.deb Size: 89056 MD5sum: c1a6c8a4984823b9633cd716ad19ee08 SHA1: 743bbbc2845642e5f50d522d45c660b1e918ddce SHA256: bbbc5cee86636e5b477618a97271c9d2220cdf225ada5dc55effff7ccc04169a SHA512: 43ca11d218354b9d5775a5984d9983075dc50978b349c6bf145472e231d822c754c6ccd7332d51987a79fbb9e12dccf9dbd04c317c33500b569953dd6f2e1cdc Homepage: https://cran.r-project.org/package=DTwrappers Description: CRAN Package 'DTwrappers' (Simplified Data Analysis with Wrapper Functions for the'Data.Table' Package) Provides functionality for users who are learning R or the techniques of data analysis. Written as a collection of wrapper functions, the 'DTwrapper' package facilitates many core operations of data processing. This is achieved with relatively few requirements about the order of the processing steps or knowledge of specialized syntax. 'DTwrappers' creates coding results along with translations to data.table's code. This enables users to benefit from the speed and efficiency of data.table's calculations. Furthermore, the package also provides the translated code for educational purposes so that users can review working examples of coding syntax and calculations. Package: r-cran-dtwumi Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dtw, r-cran-rlist, r-cran-e1071, r-cran-entropy, r-cran-lsa, r-cran-dtwbi Filename: pool/dists/resolute/main/r-cran-dtwumi_1.0-1.ca2604.1_all.deb Size: 72548 MD5sum: 1f1e15156be12938187f61236c1f4f0b SHA1: 3569dde4221f2898dccf0b0183089a62b40b29d8 SHA256: 6fe58ce1e385f97cccbdd20e02902a0ff2a6920583bb08aca1e8ebf23f30bd0e SHA512: 352fbd5a77c638275ef9ecd486b7e3312152b5209d1c4b13ec3769cfb3b6098806385f0fa3020d31df3c3fc1d0f6d4a27d394ba950ba4db6f9555fd30c39e54d Homepage: https://cran.r-project.org/package=DTWUMI Description: CRAN Package 'DTWUMI' (Imputation of Multivariate Time Series Based on Dynamic TimeWarping) Functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) : a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package 'DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided. Package: r-cran-dual Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dual_0.0.6-1.ca2604.1_all.deb Size: 242544 MD5sum: c8fead8f07ea6b85da62b5ffb87302c5 SHA1: b60352102bbe2fdf502c821af1cb6548fa00571e SHA256: b70e686e691a5e1281c1012b649138dfb7f34b4edc33a13d74b372ca786f4d3e SHA512: 0be3808f306f4c6b6c5d1f09b080c517d55b0b5f7117d7e515202ddff92e26a33c487d42bd3f52f841596264569f1061d315a1c97226ddf3323754b92b1369b2 Homepage: https://cran.r-project.org/package=dual Description: CRAN Package 'dual' (Automatic Differentiation with Dual Numbers) Automatic differentiation is achieved by using dual numbers without providing hand-coded gradient functions. The output value of a mathematical function is returned with the values of its exact first derivative (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind (2018) . Package: r-cran-dualscale Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eba, r-cran-ff, r-cran-ggplot2, r-cran-ggrepel, r-cran-matrix, r-cran-matrixcalc, r-cran-rcolorbrewer, r-cran-glue Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-dualscale_1.0.0-1.ca2604.1_all.deb Size: 137502 MD5sum: 257132f1ed51a47adbbd02f8107ea560 SHA1: 843fbc1377b5df6ea0a5e7c2b2e80cdf230ca794 SHA256: 20e7cb3953da828f3a0666bdfe7492ade34a79d192cfdd730df216312f2fc4ca SHA512: e0d37d60a9c032393b50fafc65e6171587b21c7984313bd5f200a1a154bcf828f0d0895291fafedbde7db368f92964e9527a9b8fb3959557784a583329688eef Homepage: https://cran.r-project.org/package=dualScale Description: CRAN Package 'dualScale' (Dual Scaling Analysis of Data) Dual Scaling, developed by Professor Shizuhiko Nishisato (1994, ISBN: 0-9691785-3-6), is a fundamental technique in multivariate analysis used for data scaling and correspondence analysis. Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets. Package: r-cran-duawranglr Architecture: all Version: 0.6.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-readxl, r-cran-readr, r-cran-digest, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-duawranglr_0.6.7-1.ca2604.1_all.deb Size: 88020 MD5sum: 6dea835dac3645b84cb2413b84e32248 SHA1: 637ee6355fb0fa3fac6ce06f2af24853dfd66fba SHA256: a0d691f5c450991c761487a100698725efb5867bcc0392903e7f8826dc644e0f SHA512: 6d044c848ebfbed221424fa5037da42bf75113b9e2d50f34b9baa569087bacca451aab87533474257dcb7fb4433c1233fe177276c682429a52d1c1d2aebde9fc Homepage: https://cran.r-project.org/package=duawranglr Description: CRAN Package 'duawranglr' (Securely Wrangle Dataset According to Data Usage Agreement) Create shareable data sets from raw data files that contain protected elements. Relying on master crosswalk files that list restricted variables, package functions warn users about possible violations of data usage agreement and prevent writing protected elements. Package: r-cran-dub Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dub_0.2.0-1.ca2604.1_all.deb Size: 21746 MD5sum: 44d2adc41fbe52351e15f594dbbd1e7a SHA1: 8549e074b96813983f6601f36bfaa5dcf711cd33 SHA256: 8e49ef24a6e8981cbce0107cdee429099f5757976402a19558489cfafa43a9c7 SHA512: 5563bdd67fcb45a55185b9d3a08ecfb1038871d9e38eb7017550271db1a22e08569a48a41e85a37d3bd5f74069b84f49ec77fd0625ebbf33b33a49931abb20b9 Homepage: https://cran.r-project.org/package=dub Description: CRAN Package 'dub' (Unpacking Assignment for Lists via Pattern Matching) Provides an operator for assigning nested components of a list to names via a concise pattern matching syntax. 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Package: r-cran-duckdbfs Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-glue Suggests: r-cran-curl, r-cran-sf, r-cran-jsonlite, r-cran-spelling, r-cran-minioclient, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-duckdbfs_0.1.2-1.ca2604.1_all.deb Size: 407430 MD5sum: 37b3383aa3b84e6ff417176b427efc1c SHA1: 581e344d64e5534fbf96489f55e58baf3a80afed SHA256: 06d58fed2f24d8ccd1cb1421e54462511768639bef465d70a367c14a5ac3edfc SHA512: 3c66f0f95f0687d15c432bf4a30a92d76c2c77e4d71be328a8be5884d2cc5906c99772e435a0986e99ed155325d3bde3ce4b2c5280c1181dcdc7b7ddf78771ac Homepage: https://cran.r-project.org/package=duckdbfs Description: CRAN Package 'duckdbfs' (High Performance Remote File System, Database and 'Geospatial'Access Using 'duckdb') Provides friendly wrappers for creating 'duckdb'-backed connections to tabular datasets ('csv', parquet, etc) on local or remote file systems. This mimics the behaviour of "open_dataset" in the 'arrow' package, but in addition to 'S3' file system also generalizes to any list of 'http' URLs. Package: r-cran-duckduckr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-duckduckr_1.0.0-1.ca2604.1_all.deb Size: 14910 MD5sum: 24c5eababd7cca66418a0108d728cb3c SHA1: e3fc2a183ade29b3e96b1e6dd0c8fbfe646e7ea1 SHA256: 8370ed3eb9687fdab03dce5134465fd8207b0633e14f3a95f2cd7656591872ce SHA512: 8040362a8a74bc1aed5ef5adffcd41bf8e12691e906b3fbb43ceffd5aca1f44cdb1bac19753a8f791474c496ae581617bbbf9499eeb4251605da2c048e49ca70 Homepage: https://cran.r-project.org/package=duckduckr Description: CRAN Package 'duckduckr' (Simple Client for the DuckDuckGo Instant Answer API) Programmatic access to the DuckDuckGo Instant Answer API . Package: r-cran-duckh3 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-duckspatial, r-cran-glue Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-duckh3_0.1.0-1.ca2604.1_all.deb Size: 1727318 MD5sum: 2c5a7c56328360e6999ba1899a589de8 SHA1: 125e10171a59cd5d692a014adc600c93036bbdca SHA256: 5bd532ada0fd4edaab58ef83369528380b7236fb0dfbc7290d5a8d7406cc4f6c SHA512: 3719959f863977ff80ab9de98e6b256d043997565359dc8002ef7f35d098c1fff7bde764245bf9a84b07b6576c497713e2c9ca24c47872c39f0b1e9ab9ebc5c7 Homepage: https://cran.r-project.org/package=duckh3 Description: CRAN Package 'duckh3' (H3 Extension of 'DuckDB') Fast & memory-efficient functions to analyze and manipulate large data sets. It leverages the fast analytical capabilities of 'DuckDB' and its spatial extension (see ) while maintaining compatibility with R’s spatial data ecosystem to work with spatial vector data. Package: r-cran-duckplyr Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1579 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-collections, r-cran-dbi, r-cran-duckdb, r-cran-glue, r-cran-jsonlite, r-cran-lifecycle, r-cran-magrittr, r-cran-memoise, r-cran-pillar, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-arrow, r-cran-brio, r-cran-callr, r-cran-conflicted, r-cran-constructive, r-cran-curl, r-cran-dbplyr, r-cran-hms, r-cran-knitr, r-cran-lobstr, r-cran-lubridate, r-cran-nycflights13, r-cran-palmerpenguins, r-cran-prettycode, r-cran-purrr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-duckplyr_1.2.1-1.ca2604.1_all.deb Size: 1022612 MD5sum: 60d31d5aa9955165f3d61bc6cbd0de4d SHA1: 2a868064b6d51a511e70851268e37afcac94063e SHA256: f1586079c9325fa13be21faac8383a6a08edff9bbfb3c30fe9cf6f8bab539231 SHA512: feea3296bbe6212211cc19cfeb85895e589ca214f7d5d91c9f9be7629a7a681710229a85742f4f2fc282b140f94b8e6f2e162b08630ca0c318908f1dff70e529 Homepage: https://cran.r-project.org/package=duckplyr Description: CRAN Package 'duckplyr' (A 'DuckDB'-Backed Version of 'dplyr') A drop-in replacement for 'dplyr', powered by 'DuckDB' for performance. Offers convenient utilities for working with in-memory and larger-than-memory data while retaining full 'dplyr' compatibility. Package: r-cran-duckspatial Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4111 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-geoarrow, r-cran-glue, r-cran-jsonlite, r-cran-lifecycle, r-cran-nanoarrow, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-units, r-cran-uuid, r-cran-withr, r-cran-wk Suggests: r-cran-areal, r-cran-bench, r-cran-duckdbfs, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-patchwork, r-cran-quadkeyr, r-cran-quarto, r-cran-rmarkdown, r-cran-scales, r-cran-terra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-duckspatial_1.1.0-1.ca2604.1_all.deb Size: 2325672 MD5sum: 01531fc98b251b07ed0631830f6debc7 SHA1: e8a574d5b88f42a0bbdf5f9b2e7eb3921724bc61 SHA256: 79ca3361a2246c5f2ea2472f70dcc69c3b6dd875ab2f48d0127e2d9aa804f74b SHA512: a449a1a6927f85068f26261565922e20366e7be301fe6ec7d9950e45f5972b826057b9c538c15568b104c424318f658ee5881d995c0d16300f241096d9f8a2ff Homepage: https://cran.r-project.org/package=duckspatial Description: CRAN Package 'duckspatial' (R Interface to 'DuckDB' Database with Spatial Extension) Fast & memory-efficient functions to analyze and manipulate large spatial data data sets. It leverages the fast analytical capabilities of 'DuckDB' and its spatial extension (see ) while maintaining compatibility with R’s spatial data ecosystem to work with spatial vector data. Package: r-cran-duet Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-kza, r-cran-patchwork, r-cran-reshape2, r-cran-rjson, r-cran-rlang, r-cran-signal, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-duet_0.1.1-1.ca2604.1_all.deb Size: 149814 MD5sum: cb08aadb0ddc51fdc144ddbfa4bb691e SHA1: 1f257d63b6b7af4b1850ffe219979d8a7a16ca32 SHA256: c63fe11481bb5e39f32f120869e63f48b108336cbb2ec3f02e951bef3a62ae58 SHA512: 72954f5528681d630535c1dbf05527a788ac432a7d36d5dadcd1e41046b6adbbe3d6589581bb9f1df61b0ff35287a7c5e3a5ab02c2a5040a2af2185c27d6fba4 Homepage: https://cran.r-project.org/package=duet Description: CRAN Package 'duet' (Analysing Non-Verbal Communication in Dyadic Interactions fromVideo Data) Analyzes non-verbal communication by processing data extracted from video recordings of dyadic interactions. It supports integration with open source tools, currently limited to 'OpenPose' (Cao et al. (2019) ), converting its outputs into CSV format for further analysis. The package includes functions for data pre-processing, visualization, and computation of motion indices such as velocity, acceleration, and jerkiness (Cook et al. (2013) ), facilitating the analysis of non-verbal cues in paired interactions and contributing to research on human communication dynamics. Package: r-cran-dumbbell Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1481 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tidyverse, r-cran-ggplot2, r-cran-rlang, r-cran-data.table, r-cran-rstatix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dumbbell_0.1-1.ca2604.1_all.deb Size: 711448 MD5sum: 6b6c4018b3d204cbc97f6d1c9ac68168 SHA1: a0ddb7ea268f133bddb6a9761775246c1868ecb5 SHA256: 922b0116444203e06952ab0770c6010d63f401d08009e0c513b2c1e4a6676611 SHA512: ee88c2cd40ca8e7f684f8345fcb758bde8d0464ceb91b833344872b3bbf0ac94a7bfae9e91cae4bea086382c9f5ccbdd5c70e301722c09a904146fe1cbf8770b Homepage: https://cran.r-project.org/package=dumbbell Description: CRAN Package 'dumbbell' (Displaying Changes Between Two Points Using Dumbbell Plots) Creates a Dumbbell Plot. Package: r-cran-dummy Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dummy_0.1.3-1.ca2604.1_all.deb Size: 19038 MD5sum: c3f30af78b37ea7e5f16b2b3c131de4f SHA1: 4f15f7d0755f7277f63360ca0ff7fb3a02b6c4e4 SHA256: ed35a5f240868acc6d5538df960a3b07370f118719329ec9cfe53ea1d2c38413 SHA512: 89df4ad65c7ddd82ecb396755f55c54532bd40255e3692a9f73b2febe7fdcce7a2e358e64f375f5c227a3e9823670ebdd2d6c3dc01a2548d8bc04cf3b15b40a2 Homepage: https://cran.r-project.org/package=dummy Description: CRAN Package 'dummy' (Automatic Creation of Dummies with Support for PredictiveModeling) Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set). Package: r-cran-dundermifflin Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-dundermifflin_0.1.1-1.ca2604.1_all.deb Size: 1439370 MD5sum: c077326e894dc4780e39d39a08109feb SHA1: 34a84041095618c02f54cb205785f297c6b63595 SHA256: d1a468622f339b07b2bb97791e3fccfc242995d0488041b724eed9dc9b8b9b10 SHA512: 419d0afc9292cf6478bd43182b2c6fcdc525e4a34b33df5827110e9148c23cc5a6ca83a7276d860ba7531dadb3a5ea9a2c6488f0819cd3ef8798164fb08ab38b Homepage: https://cran.r-project.org/package=dundermifflin Description: CRAN Package 'dundermifflin' (The Office Quotes on-Demand) Provides functions to randomly select, return, and print quotes or entire scenes from the American version of the show the Office. Receive laughs from one of of the greatest sitcoms of all time on demand. Add these functions to your '.Rprofile' to get a good laugh everytime you start a new R session. Package: r-cran-dunlin Architecture: all Version: 0.1.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-forcats, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-dunlin_0.1.12-1.ca2604.1_all.deb Size: 155976 MD5sum: 01dbf1e56ecd387053725643d72ef3ef SHA1: cb2b2596935af0a277259c8009dbe25a782a9103 SHA256: 9d11c947e55c67b6924876feacb0afb86a8094119df65aecd23af3201ca4f631 SHA512: 9c2b19f51e0baae7e53e392d14e2a7dfe4a93dbc1cd0a9bc9b4ce0c09b9462c384ae9857ef150079f497a0923ff6d471efcb8940379ba4a2388b1a03b2c391b6 Homepage: https://cran.r-project.org/package=dunlin Description: CRAN Package 'dunlin' (Preprocessing Tools for Clinical Trial Data) A collection of functions to preprocess data and organize them in a format amenable to use by chevron. 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'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference and for mean difference. 'dunn.test' accounts for tied ranks. 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(2018) for more details. Package: r-cran-dwlm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dwlm_0.1.0-1.ca2604.1_all.deb Size: 34200 MD5sum: 2d6632a3b7a24d69a1fe9d0f2531adb3 SHA1: ffe4c28240fb4ddf9efab1da50e73ac4bd4924bb SHA256: b4a8b8ad6e465fb5bdaeef5f17734fcd2f9a40b3d06e587ae1fb3bb8463f1700 SHA512: 5f9ab495d1c37236bfd9de07ee7e50fd8c642fae294a27fa81c331cec521e05b2bdb36120543bafb6c5ed1b203e1b4dbe642123f47045faa9532a2179671dd1f Homepage: https://cran.r-project.org/package=dwlm Description: CRAN Package 'dwlm' (Doubly Weighted Linear Model) This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) and Ripley & Thompson(1987) . 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However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly,our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Dampened weighted least squares ('DWLS') is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. See: for more information about the development of 'DWLS' and the methods behind our functions. 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Package: r-cran-dyncorr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpridge Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-dyncorr_1.1.0-1.ca2604.1_all.deb Size: 105614 MD5sum: 2f4a724877b934d36d6e2e9b9fb8a508 SHA1: 6225e1693bc4a2b26c38b1179a610d5ea07df4ea SHA256: 5ececb3ed5ed255a037a4ae620d3e6bed7a902236421390afbdf769be9bd400a SHA512: 66e30116b39e4054da054b4524a006e985578c4916a898c1ebcc4103680bc2348094b8dee86cd22d51f63ead3db9c99cc6e25b67a0d97bbd56849a32bcc76b98 Homepage: https://cran.r-project.org/package=dynCorr Description: CRAN Package 'dynCorr' (Dynamic Correlation Package) Computes dynamical correlation estimates and percentile bootstrap confidence intervals for pairs of longitudinal responses, including consideration of lags and derivatives. 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Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8. 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Much of NASA's public data catalogs hosted and maintained by its 12 Distributed Active Archive Centers ('DAACs') are now made available on the Amazon Web Services 'S3' storage. However, accessing this data through the standard 'S3' API is restricted to only to compute resources running inside 'us-west-2' Data Center in Portland, Oregon, which allows NASA to avoid being charged data egress rates. This package provides public access to the data from any networked device by using the 'EarthData' login application programming interface (API), , providing convenient authentication and access to cloud-hosted NASA 'EarthData' products. This makes access to a wide range of earth observation data from any location straight forward and compatible with R packages that are widely used with cloud native earth observation data (such as 'terra', 'sf', etc.) Package: r-cran-earthtones Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maptiles, r-cran-terra, r-cran-sf Suggests: r-cran-testthat, r-cran-cluster, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-earthtones_0.2.0-1.ca2604.1_all.deb Size: 25440 MD5sum: 89f1edcbdd03197192d912e676584dff SHA1: ac3387d427c47c319dcbb5b2ee3483397e70a825 SHA256: 497588eb97d526a2b704f6e846d5c3fd5e8cfe00ca8de71b92aa6bd3004d1881 SHA512: 7e0a4ca396fb5c4991cb662b4a09db913d62d73c8ca4b90ca6f90634be88ad75675a91e74d29bf39e386261577f93600487f2548535b92f3a857ca0c558b2586 Homepage: https://cran.r-project.org/package=earthtones Description: CRAN Package 'earthtones' (Derive a Color Palette from a Particular Location on Earth) Downloads a satellite image via ESRI and maptiles (these are originally from a variety of aerial photography sources), translates the image into a perceptually uniform color space, runs one of a few different clustering algorithms on the colors in the image searching for a user-supplied number of colors, and returns the resulting color palette. 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(2022) , Palau et al. (2023) , Salazar de Pablo et al. (2025) . Package: r-cran-easy.utils Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fastmatch, r-cran-rlang, r-cran-scales Filename: pool/dists/resolute/main/r-cran-easy.utils_0.1.0-1.ca2604.1_all.deb Size: 46524 MD5sum: 42ce278e17cb7cd01c30aeac7fc95567 SHA1: 4f17a563429bccdc10c9caa09cd51428a18de56d SHA256: acdc091501c78278e6544771e3520651d56bcc7b7d793d06f1d92ecf97e76820 SHA512: aa759c7daf000d5fc60e61f285a5fc46b6605c9a6bf47d52a9f1001e43fed14efd554b82354865ac6ba30ae7bf0e0f01b06a4f91ec7663b6ebf2df0be608ca68 Homepage: https://cran.r-project.org/package=easy.utils Description: CRAN Package 'easy.utils' (Frequently Used Functions for Easy R Programming) Some utility functions for validation and data manipulation. 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(Rosvall M, Bergstrom CT (2010) Mapping Change in Large Networks. PLoS ONE 5(1): e8694. Their graphical grammar however is a bit more complex then that of a regular x/y plots. The 'ggalluvial' package made a great job of translating that grammar into 'ggplot2' syntax and gives you many options to tweak the appearance of an alluvial plot, however there still remains a multi-layered complexity that makes it difficult to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple interface to this package that allows you to produce a decent alluvial plot from any dataframe in either long or wide format from a single line of code while also handling continuous data. It is meant to allow a quick visualisation of entire dataframes with a focus on different colouring options that can make alluvial plots a great tool for data exploration. 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This package can perform descriptive statistics according to different data types. If the data is a continuous variable, the mean and standard deviation or median and quartiles are automatically output; if the data is a categorical variable, the number and percentage are automatically output. In addition, if you enter two variables in this package, the two variables will be described and their relationships will be tested automatically according to their data types. For example, if one of the two input variables is a categorical variable, another variable will be described hierarchically based on the categorical variable and the statistical differences between different groups will be compared using appropriate statistical methods. And for groups of more than two, the post hoc test will be applied. For more information on the methods we used, please see the following references: Libiseller, C. and Grimvall, A. (2002) , Patefield, W. M. (1981) , Hope, A. C. A. (1968) , Mehta, C. R. and Patel, N. R. (1983) , Mehta, C. R. and Patel, N. R. (1986) , Clarkson, D. B., Fan, Y. and Joe, H. (1993) , Cochran, W. G. (1954) , Armitage, P. (1955) , Szabo, A. (2016) , David, F. B. (1972) , Joanes, D. N. and Gill, C. A. (1998) , Dunn, O. J. (1964) , Copenhaver, M. D. and Holland, B. S. (1988) , Chambers, J. M., Freeny, A. and Heiberger, R. M. (1992) , Shaffer, J. P. (1995) , Myles, H. and Douglas, A. W. (1973) , Rahman, M. and Tiwari, R. (2012) , Thode, H. J. (2002) , Jonckheere, A. R. (1954) , Terpstra, T. J. (1952) . 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Package: r-cran-eatme Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qcr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eatme_0.1.0-1.ca2604.1_all.deb Size: 85530 MD5sum: 38577bd9d7131c87319263afcc8bf4df SHA1: b537ffdf2b7108efff35d66eb9da771ee5a09922 SHA256: 8b79054b7f8b2e333c5e1a625e53b0296d387c545d084c14cde336aba0aa7d85 SHA512: ad795674734808f1621abc0a9e6da77b1988427352055b89a589b85f6f19349958eb2f4de04c049509c5812d35dfd40318e9c3fe0f4134d5e36980da84bf4ec7 Homepage: https://cran.r-project.org/package=EATME Description: CRAN Package 'EATME' (Exponentially Weighted Moving Average with Adjustments toMeasurement Error) The univariate statistical quality control tool aims to address measurement error effects when constructing exponentially weighted moving average p control charts. The method primarily focuses on binary random variables, but it can be applied to any continuous random variables by using sign statistic to transform them to discrete ones. With the correction of measurement error effects, we can obtain the corrected control limits of exponentially weighted moving average p control chart and reasonably adjusted exponentially weighted moving average p control charts. The methods in this package can be found in some relevant references, such as Chen and Yang (2022) ; Yang et al. (2011) ; Yang and Arnold (2014) ; Yang (2016) and Yang and Arnold (2016) . Package: r-cran-eatrep Architecture: all Version: 0.15.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-bifiesurvey, r-cran-progress, r-cran-lavaan, r-cran-hmisc, r-cran-fmsb, r-cran-mice, r-cran-boot, r-cran-car, r-cran-reshape2, r-cran-plyr, r-cran-combinat, r-cran-miceadds, r-cran-tidyr, r-cran-effectliter, r-cran-estimatr, r-cran-eattools, r-cran-eatgads, r-cran-janitor, r-cran-msm, r-cran-checkmate, r-cran-lifecycle, r-cran-dplyr, r-cran-future, r-cran-reformulas, r-cran-stringr Suggests: r-cran-weights, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-eatrep_0.15.3-1.ca2604.1_all.deb Size: 1223254 MD5sum: 274639a44191ca481503a2f6c7770b69 SHA1: cecda3d91afd0ea319941239a79d03ec9280810b SHA256: b4ceba9b5528c26703258f83bf2965fd8b1ddd67dd8e9cc274fd677617ca4fb7 SHA512: 85fecc8675827d8e28d97628b03faba104fa3cb1791898fdc6d7dec294c237fc472aaee4e07568ccb132e1324cf8fe73c7edd2bd727f4b505ce4f72e568accaf Homepage: https://cran.r-project.org/package=eatRep Description: CRAN Package 'eatRep' (Educational Assessment Tools for Replication Methods) Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles, mean comparisons using weighted effect coding, generalized linear models, and linear multilevel models) in complex survey designs comprising multiple imputed or nested imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. See the package documentation for a more detailed description along with references. Package: r-cran-eattools Architecture: all Version: 0.7.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringi, r-cran-checkmate Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-eattools_0.7.9-1.ca2604.1_all.deb Size: 404076 MD5sum: 7156d1506025481df94f3be3f7ee732f SHA1: ffcb136d1d082b9fe745f345a8fcc54e6cb53d77 SHA256: 8915d9e6bae09d1e6ccd0925c72c36677863d17da1c594a6e2164488f8ba5679 SHA512: d57d23de4dfe5edc8839ae8655ff2c268a2630fa3543d126b52d8512a5a77c6d082f9efddcba0ae9691c40fdddd800e9de2f0faf41702a2da2329c2f51881060 Homepage: https://cran.r-project.org/package=eatTools Description: CRAN Package 'eatTools' (Miscellaneous Functions for the Analysis of EducationalAssessments) Miscellaneous functions for data cleaning and data analysis of educational assessments. Includes functions for descriptive analyses, character vector manipulations and weighted statistics. Mainly a lightweight dependency for the packages 'eatRep', 'eatGADS', 'eatPrep' and 'eatModel' (which will be subsequently submitted to 'CRAN'). The function for defining (weighted) contrasts in weighted effect coding refers to te Grotenhuis et al. (2017) . Functions for weighted statistics refer to Wolter (2007) . Package: r-cran-eava Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 764 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eava_1.0.0-1.ca2604.1_all.deb Size: 305338 MD5sum: a862903ec4fdebe3cf83d83a31341045 SHA1: 9a08875251edbd30c89e1e8788948e94221dd019 SHA256: 949b6059619024337cd388ccae858de21fff344f38307a6c2287cd84b5fc7e7d SHA512: 5ad88acfa2910970df0efe7472cf1584416d5c60c6817a871362465ca4259a22a6a4dd60812aea4b9b568b3270596ce83851186c7ff7433f1fddb959d1e2a414 Homepage: https://cran.r-project.org/package=EAVA Description: CRAN Package 'EAVA' (Deterministic Verbal Autopsy Coding with Expert Algorithm VerbalAutopsy) Expert Algorithm Verbal Autopsy assigns causes of death to 2016 WHO Verbal Autopsy Questionnaire data. odk2EAVA() converts data to a standard input format for cause of death determination building on the work of Thomas (2021) . codEAVA() uses the presence and absence of signs and symptoms reported in the Verbal Autopsy interview to diagnose common causes of death. A deterministic algorithm assigns a single cause of death to each Verbal Autopsy interview record using a hierarchy of all common causes for neonates or children 1 to 59 months of age. Package: r-cran-eba Architecture: all Version: 1.10-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-psychotools Filename: pool/dists/resolute/main/r-cran-eba_1.10-1-1.ca2604.1_all.deb Size: 237776 MD5sum: 93bea0e1ad3634c176315d1511c59bd0 SHA1: b72ec7745d34b0e5f319a6313b3a1bef58118cc4 SHA256: cb901cefeeec6f67b01e8c1513b9740958617ad4bbf1cd1b990da6e1404f541c SHA512: e9045634b2bca6d5914eae58c0ac076996c1af2dd7a0c566128fca89b34a44caa6d3a1a121925fcb1a00a524b0156554a09b42259c7b2cb468f2c4c4e8ae74ef Homepage: https://cran.r-project.org/package=eba Description: CRAN Package 'eba' (Elimination-by-Aspects Models) Fitting and testing multi-attribute probabilistic choice models, especially the Bradley-Terry-Luce (BTL) model (Bradley & Terry, 1952 ; Luce, 1959), elimination-by-aspects (EBA) models (Tversky, 1972 ), and preference tree (Pretree) models (Tversky & Sattath, 1979 ). Package: r-cran-ebal Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ebal_0.2.1-1.ca2604.1_all.deb Size: 72968 MD5sum: 37a734acba062148df3ebb0fd5e0115b SHA1: 78d7c0946b2ff9fe19e60ceeeb570f72858e3a57 SHA256: 95c8654f43f76eae4e0f661e9bac13313fa52e162b9df1695660ab71f206009d SHA512: 3fd463fc727a04bb0c502e8ab524f509886c1c3186c30549ec454269e0026a59e455264db1d3c5df8cc225ecf1ce104fcba80b2302577f2aff5169be059d8121 Homepage: https://cran.r-project.org/package=ebal Description: CRAN Package 'ebal' (Entropy Reweighting to Create Balanced Samples) Implements entropy balancing, a data preprocessing procedure described in Hainmueller (2012, ) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user-specified moment conditions. Useful for creating balanced samples in observational studies with a binary treatment where the control group is reweighted to match the covariate moments of the treatment group, and for reweighting a survey sample to known characteristics from a target population. Package: r-cran-ebase Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-lubridate, r-cran-r2jags, r-cran-rjags, r-cran-tidyr, r-cran-truncnorm, r-cran-zoo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-ebase_1.1.0-1.ca2604.1_all.deb Size: 822242 MD5sum: fd3a962e5596a63c4abbd90d1bffbea6 SHA1: d4e85b3c199bd24f1d55b1d2c705f21f7b005001 SHA256: 22907c9363c8711999f0a67d64db9ea579b55d51b48fd8bee5f4f9780f177532 SHA512: 910220213a389fe8e1f0050f2cf70ab548ffcea864060cddf5c218aa76a15ad2ab258537df7c8acca90a266aa7ffac599eb23afb9800d3ab3785bb81170b5cfa Homepage: https://cran.r-project.org/package=EBASE Description: CRAN Package 'EBASE' (Estuarine Bayesian Single-Station Estimation Method forEcosystem Metabolism) Estimate ecosystem metabolism in a Bayesian framework for individual water quality monitoring stations with continuous dissolved oxygen time series. A mass balance equation is used that provides estimates of parameters for gross primary production, respiration, and gas exchange. Methods adapted from Grace et al. (2015) and Wanninkhof (2014) . Details in Beck et al. (2024) . Package: r-cran-ebayesthresh Architecture: all Version: 1.4-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-ebayesthresh_1.4-12-1.ca2604.1_all.deb Size: 772830 MD5sum: 478872e973a1d8f73dfdafe96ca98bd9 SHA1: d1f206c5075227ef9f5a650300465c0353f9432d SHA256: 33336335b9a6336d23565f09a0a0cbc60c324195934dbe7ae652c944d4a181f7 SHA512: 73fd8bd48b1ad0555846af27b9dc4904966bf8a20787f9bd03cd234b35963b16d468219666890d1fd577136202dbe28de7180b66766c67727f5ceff74250e4cf Homepage: https://cran.r-project.org/package=EbayesThresh Description: CRAN Package 'EbayesThresh' (Empirical Bayes Thresholding and Related Methods) Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package. Package: r-cran-ebchs Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-fda Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ebchs_0.1.1-1.ca2604.1_all.deb Size: 92464 MD5sum: b572fbdb2485b79fe726c5439864190e SHA1: 123ee3424e828333a5b63415c37efdf23f8fe412 SHA256: c6c5ed99e7a850738fadc997d53ccf0d1d0a08267453e113f363b6701e12136c SHA512: bfb8c0eb0546cfec9583e54df90d93b2c88189ae77741b081491472bbe41250a57c57c18aad96375f79e36bf3d036787ab32be27dc9e6c6cb1400ad5d677f12a Homepage: https://cran.r-project.org/package=EBCHS Description: CRAN Package 'EBCHS' (An Empirical Bayes Method for Chi-Squared Data) We provide the main R functions to compute the posterior interval for the noncentrality parameter of the chi-squared distribution. The skewness estimate of the posterior distribution is also available to improve the coverage rate of posterior intervals. Details can be found in Du and Hu (2022) . Package: r-cran-ebci Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat, r-cran-lpsolve, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ebci_1.0.0-1.ca2604.1_all.deb Size: 195566 MD5sum: d3c8668724144c7bc5b5d642e47f5d33 SHA1: acd1ac26820b6435ea9086ea54a87e89f65fbf94 SHA256: 1e962ead8b6a7d12be29b2616e7256e9da823f55a548c19f30ab50a275a8837f SHA512: 336311c7685fa3a5c17277344fae9c98ace23848e1dd08a47bc5bb6c642622dac0f011efed9293e8d89bdb8985e46a209fa0d9a8f07944dde4bb7e3da555e89e Homepage: https://cran.r-project.org/package=ebci Description: CRAN Package 'ebci' (Robust Empirical Bayes Confidence Intervals) Computes empirical Bayes confidence estimators and confidence intervals in a normal means model. The intervals are robust in the sense that they achieve correct coverage regardless of the distribution of the means. If the means are treated as fixed, the intervals have an average coverage guarantee. The implementation is based on Armstrong, Kolesár and Plagborg-Møller (2020) . Package: r-cran-ebcobart Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbarts, r-cran-loo, r-cran-posterior, r-cran-univariateml, r-cran-extradistr Filename: pool/dists/resolute/main/r-cran-ebcobart_1.1.2-1.ca2604.1_all.deb Size: 252756 MD5sum: 411a5983c14761ddba46ceabab7536d2 SHA1: 294d941de79ee726ea9372fb2b9ade5888cf26d9 SHA256: 36c30f30853ab202ae71663cc9c516cbe028c566bb530ef7615016bd7fb8561e SHA512: be9687cad4050a9ad6733c29f9bd19ccc29be6a7b797b3cd63921bedc01a840dc3dcc9e43609a36f85d757a9c5b1303d2592b9121701ae76543b4516e3732c6a Homepage: https://cran.r-project.org/package=EBcoBART Description: CRAN Package 'EBcoBART' (Co-Data Learning for Bayesian Additive Regression Trees) Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the 'dbarts' 'R' package. See Goedhart and others (2023) for details. Package: r-cran-ebdm Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ebdm_3.0.1-1.ca2604.1_all.deb Size: 57296 MD5sum: aae65e576c91b816d67617f5c93aed2a SHA1: d081c8ee3931d20661550cd026ef590e5efb3fd6 SHA256: 40e8f679ec4c111f7c7f22f4c95684de39f50d4d441e503bc742448cdf63280e SHA512: 8a82e78eb3495e3aa45edec9fe5e8a1bd3e68a03a9ddbf94d3d0022de7add04ce53e8e90de02509f321e58b453934823a4790d8177510fe4b9a3a391e21e652c Homepage: https://cran.r-project.org/package=ebdm Description: CRAN Package 'ebdm' (Estimating Bivariate Dependency from Marginal Data) Provides statistical methods for estimating bivariate dependency (correlation) from marginal summary statistics across multiple studies. The package supports three modules of bivariate joint distribution estimated from marginal summary data: (1) two binary, (2) two continuous, (3) one binary and one continuous These methods enable privacy-preserving joint estimation when individual-level data are unavailable. The approaches are detailed in Shang, Tsao, and Zhang (2025a) and Shang, Tsao, and Zhang (2025b) . Package: r-cran-ebgenotyping Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ebgenotyping_2.0.1-1.ca2604.1_all.deb Size: 31632 MD5sum: c0e423ed29e783aa536d8ba838f1874c SHA1: 49c12a8fceb55321cb1ac5070b6aebee6a7fb8e0 SHA256: d8ce2408663a50d05880b1e2480247e534cac9ee4f7950266120e66148e679f6 SHA512: 0bd64232c98003186c96e768fa94df618ea3d0c669deb674d90148910bc905799675fe543a7a08bbc5b2ff5ec06d3b64e96e5c4a07aa8f0b8be17455c884dc11 Homepage: https://cran.r-project.org/package=ebGenotyping Description: CRAN Package 'ebGenotyping' (Genotyping and SNP Detection using Next Generation SequencingData) Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method. 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Package: r-cran-ebm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-ggplot2, r-cran-lattice Suggests: r-cran-htmltools, r-cran-islr2, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-ebm_0.1.0-1.ca2604.1_all.deb Size: 509180 MD5sum: 7fe20275734ce4a204ed655793e22e15 SHA1: 276dedb2e0eb2bc4ca18170f14130757ab35e539 SHA256: 98ee7f473348f90f1c47298d8da0b34cccf59c056ce86151195fd1a75d79d44b SHA512: 52f6e78814fe05c21f63a88e97de0a5ea8fc08e88b78e73bf3c57aff3e24e6cc07f36c686736e2f6879cb4ae2bedfa266692d03b9cda373b21aaf7ca472c8e6b Homepage: https://cran.r-project.org/package=ebm Description: CRAN Package 'ebm' (Explainable Boosting Machines) An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) for details. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable. Package: r-cran-ebmc Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-rpart, r-cran-c50, r-cran-randomforest, r-cran-proc, r-cran-smotefamily Filename: pool/dists/resolute/main/r-cran-ebmc_1.0.1-1.ca2604.1_all.deb Size: 70404 MD5sum: 74975e5423f8ccfcdb4418f2ea9a05da SHA1: 15f175a2fe498b8cb57f7279d270d2997b9dfb95 SHA256: 8c2ede9f40306d72ceebf741a8e720a5c42db8e61de431db26b245d036d9c99f SHA512: f918ec4db35cd9f266fc398fa50c38519032ea5f7ee966489b0e5e65bd5f608b167f6662e0b67c08415e85c0823f371761e8457e8c3f8d1371f25dd5d842b295 Homepage: https://cran.r-project.org/package=ebmc Description: CRAN Package 'ebmc' (Ensemble-Based Methods for Class Imbalance Problem) Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) , Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) , R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) , Shuo Wang and Xin Yao (2009) , Yoav Freund and Robert E. Schapire (1997) . Package: r-cran-ebnm Architecture: all Version: 1.1-42-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ashr, r-cran-mixsqp, r-cran-truncnorm, r-cran-trust, r-cran-deconvolver, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-rebayes, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-mcmc, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-ebnm_1.1-42-1.ca2604.1_all.deb Size: 868268 MD5sum: 95351d525f4d49ab8b13505be77870ac SHA1: 83285413fd16768f256671178c362d5a32df9299 SHA256: 4027e87999e2598516255c6fc94b44853a73a628aeefcce42dd0600f4ce2bce2 SHA512: 7add2a53760b970d68defdf7e63693eb81cadae4e083b676dd1158b1b33267ca467dfa2672c2600909f515a354c0792707d569139c7f59c1138636db8eeb8f8b Homepage: https://cran.r-project.org/package=ebnm Description: CRAN Package 'ebnm' (Solve the Empirical Bayes Normal Means Problem) Provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). Our JSS article, Willwerscheid, Carbonetto, and Stephens (2025) , provides a detailed introduction to the package. Package: r-cran-ebprs Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocr, r-cran-bedmatrix, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-ebprs_2.1.0-1.ca2604.1_all.deb Size: 55932 MD5sum: b3f5e30d0a3bd400e5fa4d6d1130aff5 SHA1: 7b765eaabf249fd274772f3ca67ca0e8598fba9a SHA256: 11ce9881ab530e4d1af6ae9a6d5a86bef4ff8e1b749d72db206d8fe99f59c46b SHA512: c7c89589f4de2d39d188e746c83588deb839ad5ccc09e01dc199f7fa5900e286de9f8c0a69b7a4651438e39bffa116a0352cda064b01d4fe1717836bae49d891 Homepage: https://cran.r-project.org/package=EBPRS Description: CRAN Package 'EBPRS' (Derive Polygenic Risk Score Based on Emprical Bayes Theory) EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) for a detailed presentation of the method. Package: r-cran-ebrahim.gof Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-resourceselection, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-ebrahim.gof_1.0.0-1.ca2604.1_all.deb Size: 33042 MD5sum: 11a26dea5dd9c798b13172752b1efdd7 SHA1: 7e6cb463654ff149184a7dc9b82b3dc4b7d91f70 SHA256: 70a2fe2c1b8229d5225d87298ca05c0e58ce56d34ce5fbd94fce1f5e13b19905 SHA512: ca297c0888ac7b97e1414033be432e9982c47f7be56b7307b1203c0f0061b3ac11a917efaf06690a4cd26729fce67c079caf66b138433419bf5946c4940b957b Homepage: https://cran.r-project.org/package=ebrahim.gof Description: CRAN Package 'ebrahim.gof' (Ebrahim-Farrington Goodness-of-Fit Test for Logistic Regression) Implements the Ebrahim-Farrington goodness-of-fit test for logistic regression models, particularly effective for sparse data and binary outcomes. This test provides an improved alternative to the traditional Hosmer-Lemeshow test by using a modified Pearson chi-square statistic with data-dependent grouping. The test is based on Farrington (1996) theoretical framework but simplified for practical implementation with binary data. Includes functions for both the original Farrington test (for grouped data) and the new Ebrahim-Farrington test (for binary data with automatic grouping). For more details see Hosmer (1980) and Farrington (1996) . Package: r-cran-ebrank Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ebrank_1.0.0-1.ca2604.1_all.deb Size: 34452 MD5sum: 81b5a31a3bf73780c4012d5685a05cc0 SHA1: 384f756b8f9fbce19e345b464b451d1b40c07d06 SHA256: f2712249049f79b91412a16c55f3e8a7e4afa1a1b5dd7c09f14a162b183141c3 SHA512: e6bb52fdbe5e1164ac2b3ff71a9dd524ffb27b669fe444e0e389b6bbfe570eb2441d40c7d1ddc2812949764aab00fa5c352376231d1424240c3eb63774419abb Homepage: https://cran.r-project.org/package=EBrank Description: CRAN Package 'EBrank' (Empirical Bayes Ranking) Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given. Package: r-cran-ebvcube Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3069 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-curl, r-bioc-delayedarray, r-cran-ggplot2, r-bioc-hdf5array, r-cran-httr, r-cran-jsonlite, r-cran-memuse, r-cran-ncdf4, r-cran-ncmeta, r-cran-reshape2, r-bioc-rhdf5, r-cran-stringr, r-cran-terra, r-cran-tidyterra, r-cran-withr Suggests: r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ebvcube_0.5.2-1.ca2604.1_all.deb Size: 2150582 MD5sum: 2291ccd2989d5a6db2c1d6cf1ad7371c SHA1: b7190bebbffe5f0754ba2aedc526f7c11d103681 SHA256: bcd7a3dda12ca04aa30ac66d0998c0f42badf6e2c60e1c49c7ca0d70d45f693b SHA512: 1e9c34bed1833516e2a866170924ccc242b38602935c3505fe1a91bd554ad3bc2718b560966d0dcdbb607618cdbda4c108ee11ffabcb4adf7b4953675790af09 Homepage: https://cran.r-project.org/package=ebvcube Description: CRAN Package 'ebvcube' (Working with netCDF for Essential Biodiversity Variables) The concept of Essential Biodiversity Variables (EBV, ) comes with a data structure based on the Network Common Data Form (netCDF). The 'ebvcube' 'R' package provides functionality to easily create, access and visualise this data. The EBV netCDFs can be downloaded from the EBV Data Portal: Christian Langer/ iDiv (2020) . Package: r-cran-ebx Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-r6, r-cran-base64enc Suggests: r-cran-testthat, r-cran-withr, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-ebx_1.0.0-1.ca2604.1_all.deb Size: 452538 MD5sum: 1eb7db012e546ce5135c04524dac66ae SHA1: a2b892c216b653e274cac6e563eff916b3c5a01b SHA256: 3d185354035512ac894dce18ac1797c7ca6e465da9a4472f15d5cebe63f7654b SHA512: f330a5608968963bd1b967276eaba2e357ea8f7d447c5e5f2d1a8538d7634df348091b486b96941515a231bc4b8dadfab0c6e66dc901803008be08c0d18c8b6b Homepage: https://cran.r-project.org/package=ebx Description: CRAN Package 'ebx' ('Earth Blox' API Client) Client library for the 'Earth Blox' API (). Provides authentication and endpoints for interacting with 'Earth Blox' geospatial analytics services. Compatible with 'Shiny' applications. Package: r-cran-ec50estimator Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-drc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggridges, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-ec50estimator_0.1.0-1.ca2604.1_all.deb Size: 124230 MD5sum: dc0209a21c0a66cf81c92e2b37a4813d SHA1: ecf037bec4d6b9623713f8409de9d5ca47fe3a95 SHA256: 069f34228537ce43ffc89274a8379cc2ae3adf61fd3545eb4018b7faced87cea SHA512: 395100192946b8bd083e36ef3811cf3a017efd8400627f251e7c83dabd485cb2f90f452f3e0f66bcdb82a015709ea0d7b136536ac6c6acad7d34adfcb8260497 Homepage: https://cran.r-project.org/package=ec50estimator Description: CRAN Package 'ec50estimator' (An Automated Way to Estimate EC50 for Stratified Datasets) An implementation for estimating Effective control to 50% of growth inhibition (EC50) for multi isolates and stratified datasets. It implements functions from the drc package in a way that is displayed a tidy data.frame as output. Info about the drc package is available in Ritz C, Baty F, Streibig JC, Gerhard D (2015) . 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Contains consistent and easy wrapper functions of 'stat', 'vegan', and 'labdsv' packages, and visualisation functions of ordination and clustering. Package: r-cran-ecb Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-rsdmx, r-cran-xml2, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-lubridate, r-cran-ggplot2, r-cran-testthat, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-ecb_0.4.3-1.ca2604.1_all.deb Size: 443658 MD5sum: a30ec5eec474dbe080a43c15358bfdec SHA1: ec8b1f4575e1c01ed952b0d0afbf517ee625b9f1 SHA256: e88e561a0159b40f157f35275e4240f65d81a0ee00a4e4169a8fa352386b02bb SHA512: 856559a40c634630c70d4471a0caca0809c46fa4463f16e0546b2485e30c6eddba378035f5ca74e817e001f1548fbd273ff77806af29c5dc7d43c062710cf50b Homepage: https://cran.r-project.org/package=ecb Description: CRAN Package 'ecb' (Programmatic Access to the European Central Bank's Data Portal) Provides an interface to the European Central Bank's Data Portal API, allowing for programmatic retrieval of a vast quantity of statistical data. 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You can pass in an English or Chinese sentence, ecce package support both English and Chinese translation. It also support browse translation results in website. In addition, also support obtain the pinyin of the Chinese character, you can more easily understand the pronunciation of the Chinese character. 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Package: r-cran-ecdfht Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-ecdfht_0.1.1-1.ca2604.1_all.deb Size: 2104740 MD5sum: fa56e86aa4a9d184a4cfa126786335c2 SHA1: c44d5ae1bbfe08e182121013702e353c38204f4b SHA256: 50846f31e6c9958c2c91063488df25c8fd731b6e7bed17837eaaa07b122cebf0 SHA512: 105683f7007875a316e1cf1c04944398f071bd5696c8108dbad47982413e21e78e07fbda18a6b4941eaded52e8879c3d9d6217a4d15c9bc982c191d25ea9c50e Homepage: https://cran.r-project.org/package=ecdfHT Description: CRAN Package 'ecdfHT' (Empirical CDF for Heavy Tailed Data) Computes and plots a transformed empirical CDF (ecdf) as a diagnostic for heavy tailed data, specifically data with power law decay on the tails. Routines for annotating the plot, comparing data to a model, fitting a nonparametric model, and some multivariate extensions are given. 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Often these tests are performed according to the procedures described in 'ISO GUIDE 35:2017'. The 'eCerto' package contains a 'Shiny' app which provides functionality to load, process, report and backup data recorded during CRM production and facilitates following the recommended procedures. It is described in Lisec et al (2023) and can also be accessed online without package installation. Package: r-cran-ecfun Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2636 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-tis, r-cran-jpeg, r-cran-mass, r-cran-stringi, r-cran-xml2, r-cran-bma, r-cran-mvtnorm, r-cran-rvest, r-cran-readr, r-cran-rworldmap Suggests: r-cran-car, r-cran-desctools, r-cran-ecdat, r-cran-maps, r-cran-gridbase, r-cran-knitr, r-cran-rmarkdown, r-cran-invgamma, r-cran-ipumsr, r-cran-lubridate, r-cran-bayesplot, r-cran-bssm, r-cran-ggplot2, r-cran-tibble, r-cran-kableextra, r-cran-openxlsx, r-cran-fitdistrplus, r-cran-purrr, r-cran-markdown, r-cran-envstats, r-cran-drc, r-cran-zoo, r-cran-prodlim, r-cran-plyr, r-cran-trampr, r-cran-raster, r-cran-readxl, r-cran-pandoc Filename: pool/dists/resolute/main/r-cran-ecfun_0.4.0-1.ca2604.1_all.deb Size: 807730 MD5sum: dc21d78a390a2f581135a3a578150159 SHA1: d6cf35cb606cc7451d2cc2b469ace7ef22184c0d SHA256: 167220896adf2912bb2408884e66495950d322f5e23abb4b905aa09755de177e SHA512: 839486dd1c11a596d8bd07b86677adc4d196d1386448d54d6abf3f8a67c92baedfbe8d0cc0e10c42586e3696f13ac5abcd796dc76fed6154151fdd4f92137ab1 Homepage: https://cran.r-project.org/package=Ecfun Description: CRAN Package 'Ecfun' (Functions for 'Ecdat') Functions and vignettes to update data sets in 'Ecdat' and to create, manipulate, plot, and analyze those and similar data sets. Package: r-cran-ecg Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ecg_0.5.2-1.ca2604.1_all.deb Size: 113756 MD5sum: 19e58e480ed8e7e6011f5bbca067dae9 SHA1: 856ace3b9f14810a8b9ba13689cbde48348ba617 SHA256: 96605b37ddd0a9d142a47762c888f46070ea1fd9205f98c65e28f7cd058709ff SHA512: 079921aa51e0e52599d1ec13c87b44e15131986803d9d79f7cae607f58af5d0dc0454ce6003b829881ae905e21b6c0a17e86712f980f11dbf8023e77de1c75b6 Homepage: https://cran.r-project.org/package=ECG Description: CRAN Package 'ECG' (Center of Gravity Methods) Implementation of the Centre of Gravity method and the Extrapolated Centre of Gravity method. It supports replicated observations. Cameron, D.G., et al (1982) JCGM (2008) . Package: r-cran-echarts2shiny Architecture: all Version: 0.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-echarts2shiny_0.2.13-1.ca2604.1_all.deb Size: 307538 MD5sum: 8d8d6419ec9b4dac6c9ddea8f2e0ed68 SHA1: dc5d1124c316afc06f6e229a7faee8bbf5f99e91 SHA256: 055500ff9d0bf976e52a87478419ebef57d6504c99defae26cda02ea4cfd4582 SHA512: 3467ca7d01c8c03598f1b5bba1355b5254bd837351f654a2da7b5118de4044a27145c099ab63437d733000da324948093fbcf76e6755e2ba6a64ccd8825a46aa Homepage: https://cran.r-project.org/package=ECharts2Shiny Description: CRAN Package 'ECharts2Shiny' (Embedding Interactive Charts Generated with ECharts Library intoShiny Applications) Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (). 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Package: r-cran-echarty Architecture: all Version: 1.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-dplyr, r-cran-data.tree Suggests: r-cran-htmltools, r-cran-shiny, r-cran-jsonlite, r-cran-crosstalk, r-cran-testthat, r-cran-sf, r-cran-leaflet, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-echarty_1.7.2-1.ca2604.1_all.deb Size: 686950 MD5sum: 524676f4df8135774900ae59bcedae58 SHA1: 2c0352f596a4c5a105a57ae1969cbe93167be51b SHA256: 34d85ff7b0b3e3c1a154ee4b644dbdae8e0a7607d81853aa4ff6bfba652936a9 SHA512: dc669f60d87cfa4cd23d099cfa47cea3d870788b7646345825e0e92c695d9ad94704eb2b13de055feb4cb1b8b21739d50a2c8cf0e3a42b87220ac99a9a2b8d00 Homepage: https://cran.r-project.org/package=echarty Description: CRAN Package 'echarty' (Minimal R/Shiny Interface to JavaScript Library 'ECharts') Deliver the full functionality of 'ECharts' with minimal overhead. 'echarty' users build R lists for 'ECharts' API. Lean set of powerful commands. Package: r-cran-echem Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1374 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d, r-cran-animation Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-echem_1.0.0-1.ca2604.1_all.deb Size: 1170566 MD5sum: 31d4ebe990664fb58913956ddd92fe64 SHA1: 397852dba6b559dbaad9bc7d6ad2ae9e01496998 SHA256: d109f0a070894f9bdf493b3d365fe457591c3afa79c3400ef8b9bf1db2d806b9 SHA512: b9d70083f2dcc1418ca51521433460cb1791157a807929926d5ee7336a598cf416efae7938a25a15a84c1a8bf6de1c765d77a992edcbec8c4ffa20f643d44583 Homepage: https://cran.r-project.org/package=eChem Description: CRAN Package 'eChem' (Simulations for Electrochemistry Experiments) Simulates cyclic voltammetry, linear-sweep voltammetry (both with and without stirring of the solution), and single-pulse and double-pulse chronoamperometry and chronocoulometry experiments using the implicit finite difference method outlined in Gosser (1993, ISBN: 9781560810261) and in Brown (2015) . Additional functions provide ways to display and to examine the results of these simulations. The primary purpose of this package is to provide tools for use in courses in analytical chemistry. Package: r-cran-echo.find Architecture: all Version: 4.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-echo.find_4.0.1-1.ca2604.1_all.deb Size: 126442 MD5sum: 83829188913db7daa9dac8071d3afc33 SHA1: 0f59b309be5866bd7d0d01566bba84f8fd860e6b SHA256: b3b45fbf20b0ab5a29ceaa7eb76fd38bb9a95515c02d25b9881f783468e0e019 SHA512: 961f91c585a9fec23fa69d5f65880ddd44648d365d45169c5777fc71b6a00cb41c8afb7588300702d4276368c77f7092c4baa5bcf14fdf67485d5b8a84207c7c Homepage: https://cran.r-project.org/package=echo.find Description: CRAN Package 'echo.find' (Finding Rhythms Using Extended Circadian Harmonic Oscillators(ECHO)) Provides a function (echo_find()) designed to find rhythms from data using extended harmonic oscillators. For more information, see H. De los Santos et al. (2020) . Package: r-cran-echo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-echo_0.1.0-1.ca2604.1_all.deb Size: 23634 MD5sum: 513e500bee063f0b971598673abe02b2 SHA1: e8001d3ebb6da5affbc3542b755c447892a98161 SHA256: 1313a80d4cc65a1536ce1ebd063cb64854a42d298de01f6d219d1922b5d64f4d SHA512: 0c116ff840bcbaab97b52234b207b75be1e6ac9d2a293e3fcb536abf1868379da1e13d8151e6c9119c39dff08f30c0c8f952b6f0f4afdde7e608c7e0811c807b Homepage: https://cran.r-project.org/package=echo Description: CRAN Package 'echo' (Echo Code Evaluations) Capture code evaluations and script executions by expressions, outputs, and condition calls for logging. 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Package: r-cran-echor Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-plyr, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-httptest, r-cran-testthat, r-cran-utf8 Filename: pool/dists/resolute/main/r-cran-echor_0.1.9-1.ca2604.1_all.deb Size: 796606 MD5sum: 58f26ed99e55033f2b6c4d4e80a55b61 SHA1: 826368a8e2de1a9977c316c4f0436ffd6a0ba92f SHA256: 5bb96ebca38969c9876f921c612111c41dc9e7f97396c179818b51028f012bbf SHA512: 3b2b53309a69694c1a28cdd31a0f59a09ac5200ffeef4a62bf9f3db6c6e06591bd031933af69040e780c18770d180f06fc6d7dcd226135d1702bbde1bccb7956 Homepage: https://cran.r-project.org/package=echor Description: CRAN Package 'echor' (Access EPA 'ECHO' Data) An R interface to United States Environmental Protection Agency (EPA) Environmental Compliance History Online ('ECHO') Application Program Interface (API). 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Package: r-cran-ecic Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-patchwork, r-cran-progress, r-cran-progressr Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-ecic_0.0.4-1.ca2604.1_all.deb Size: 599174 MD5sum: f7442bf80b484df061534151c1efe5e2 SHA1: e7c4c6ac5861b2758a73e09c4dc36e5be7e97acc SHA256: d56cd383aab26a5101984ef79db3f5c3ee58185e9cd9c0e194c8e24c8951391b SHA512: 4b5b0b5a21e586a708aa25be96de56c57252969a9d8d295897cde7b806e22349324508291a56dd3da8c3bfe3eb52ff3b9bb67f9254d722e09a78a25778fa67d0 Homepage: https://cran.r-project.org/package=ecic Description: CRAN Package 'ecic' (Extended Changes-in-Changes) Extends the Changes-in-Changes model a la Athey and Imbens (2006) to multiple cohorts and time periods, which generalizes difference-in-differences estimation techniques to the entire distribution. Computes quantile treatment effects for every possible two-by-two combination in ecic(). Then, aggregating all bootstrap runs adds the standard errors in summary_ecic(). Results can be plotted with plot_ecic() aggregated over all cohort-group combinations or in an event-study style for either individual periods or individual quantiles. Package: r-cran-ecipex Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chnosz Filename: pool/dists/resolute/main/r-cran-ecipex_1.1.1-1.ca2604.1_all.deb Size: 41342 MD5sum: f141cf12fa38d40b6fe681f152bafadf SHA1: 2720da76d246c8b0b892a0f722a7b187752b443a SHA256: 103c4ef2d342b36a514dd92ea865a4562d18d58702ea839504605c97f22f9236 SHA512: 2a2fe4303d9c6e0e2a27b94bf0c962b847c1e818b6292f2f425329b320307f9013eb5de5ab702b02dc0e0afd6bfde7403e6a4362c45ebb7f483c5541f1a06f67 Homepage: https://cran.r-project.org/package=ecipex Description: CRAN Package 'ecipex' (Efficient Calculation of Fine Structure Isotope Patterns viaFourier Transforms of Simplex-Based Elemental Models) Provides a function that quickly computes the fine structure isotope patterns of a set of chemical formulas to a given degree of accuracy (up to the limit set by errors in floating point arithmetic). A data-set comprising the masses and isotopic abundances of individual elements is also provided and calculation of isotopic gross structures is also supported. Package: r-cran-eclipseplot Architecture: all Version: 0.9.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-ggforce, r-cran-cowplot, r-cran-magrittr, r-cran-tidyr, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-eclipseplot_0.9.7-1.ca2604.1_all.deb Size: 48800 MD5sum: 10f984ac563ec862ee076db92175eb34 SHA1: c4c5457afbdb7abe7adfd0880ef5dc317a6a6153 SHA256: bd869d2f106026d7125cc2a646a13ef3228a22d5e1a99ae48deaf9d41b93232f SHA512: 38a177f4e2d8eb03c19b13b6e330166b18f9ac7196724202fb2d7641979970ce1af40f1c76340b62a0071829e04d56a8e431b54777c27a083024bac8c66176b0 Homepage: https://cran.r-project.org/package=eclipseplot Description: CRAN Package 'eclipseplot' (Graphical Visualizations for ROBUST-RCT Risk of Bias Assessments) Provides visual representations of risk-of-bias assessments using the ROBUST-RCT framework, as described in Wang et al. (2025) . The graphical visualization displays both factual evaluation (Step 1) and judgment (Step 2). Package: r-cran-eclrmc Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-softimpute Filename: pool/dists/resolute/main/r-cran-eclrmc_1.0-1.ca2604.1_all.deb Size: 29268 MD5sum: c0309cb3d4be26bcb411cad97ddf7d7b SHA1: 511f7c87786ec50e5755655e9c81470f8ab5ddb2 SHA256: cf5619e394901523e1881765aba87350ebb7813009a432fbc17fd99b4dc902ee SHA512: c4d29d0dfcb0b90257539f04645ef9d8d89728ccd02c0ace02f75041b5fc97cb10e4bc5deb00b734cb0b74a200635c4bba6d1d51254d7e2785828131036a4901 Homepage: https://cran.r-project.org/package=ECLRMC Description: CRAN Package 'ECLRMC' (Ensemble Correlation-Based Low-Rank Matrix Completion) Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) . 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Package: r-cran-ecmle Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-idpmisc, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ecmle_0.1.0-1.ca2604.1_all.deb Size: 72456 MD5sum: 987b94f93218717404efd7d0ec89a065 SHA1: ad0551a85b759ac0600285bf029fd2f74da814e5 SHA256: 0a0f2b9a5f62e3060c28f9b8f0cb423bd5bff8005dde68b8ee2fcc631a6312b9 SHA512: 16a13fe0e34d9881bfbe50c6e135d5ad6323f9360d57d4759ab3ba9fe0734c8725bff367d04c7bf682a12898288d7df72246a093a6b99d5749d0d356599139b1 Homepage: https://cran.r-project.org/package=ECMLE Description: CRAN Package 'ECMLE' (Approximating Evidence via Bounded Harmonic Means) Implements the Elliptical Covering Marginal Likelihood Estimator (ECMLE), a geometric method for approximating marginal likelihood from posterior draws and log-posterior evaluations. The method constructs a collection of non-overlapping ellipsoids in a high-posterior-density region, computes the covered volume, and combines this with posterior sample coverage to estimate model evidence. It is designed to stabilize harmonic-mean-based evidence approximation and can be applied in multimodal settings. The methodology is described in Naderi et al. (2025) . 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Allows for easy downloads of weather forecasts and climate reanalysis data in R. Data stores covered include the Climate Data Store (CDS; ), Atmosphere Data Store (ADS; ) and Early Warning Data Store (CEMS; ). Package: r-cran-ecocbo Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-parabar, r-cran-parallelly, r-cran-vegan, r-cran-ssp, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ecocbo_1.0.0-1.ca2604.1_all.deb Size: 2063322 MD5sum: eac9f54eda5703b9c44225db84f5aea1 SHA1: 0e658c6a0fc065be3083d66a7df1a3d8cad499ab SHA256: 3ec9432ae63a9cb1e966bf9ed84ce489f45787f4c2ba10dbf704cd9d0b828018 SHA512: 1a136ce4c7dc4346abcacdf3c0b3d5e2fffa7ded8d2cf2a0785a58ae4f735be91a76d22ae73ecfda337b5c07de6f50766cbcfb9bd04fc4c2c4d1fb43195dd34d Homepage: https://cran.r-project.org/package=ecocbo Description: CRAN Package 'ecocbo' (Calculating Optimum Sampling Effort in Community Ecology) A system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with prep_data() which formats and arranges the initial data, and then the optimization follows the following procedure of four functions: (1) prep_data() takes the original dataset and creates simulated sets that can be used as a basis for estimating statistical power and type II error. (2) sim_beta() is used to estimate the statistical power for the different sampling efforts specified by the user. (3) sim_cbo() calculates then the optimal sampling effort, based on the statistical power and the sampling costs. Additionally, (4) scompvar() calculates the variation components necessary for (5) Underwood_cbo() to calculate the optimal combination of number of sites and samples depending on either an economic budget or on a desired statistical accuracy. Lastly, (6) plot_power() helps the user visualize the results of sim_beta(). Package: r-cran-ecochange Architecture: all Version: 2.9.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sf, r-cran-rastervis, r-cran-sp, r-cran-ggplot2, r-cran-landscapemetrics, r-cran-tibble, r-cran-httr, r-cran-getpass, r-cran-rlang, r-cran-lattice, r-cran-rasterdt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-curl, r-cran-xml2, r-cran-rvest, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-ecochange_2.9.3.3-1.ca2604.1_all.deb Size: 952918 MD5sum: 44b1536b91a4751fd8bbd70a13222136 SHA1: 2b1526f99e1f4cfd68661e53f60f4aaefbace8fc SHA256: d15c8197e7f140f03ac4a852f50117c202c4775a57f6e10e97b8865615a2f044 SHA512: ab28e1490ef70b67015153d3af350db8cb627bdeb949538295a6214370200ca9ae04391084d276af62b627b1a62d97f6e0dd73ba4469da45266c261ac3298206 Homepage: https://cran.r-project.org/package=ecochange Description: CRAN Package 'ecochange' (Integrating Ecosystem Remote Sensing Products to Derive EBVIndicators) Essential Biodiversity Variables (EBV) are state variables with dimensions on time, space, and biological organization that document biodiversity change. Freely available ecosystem remote sensing products (ERSP) are downloaded and integrated with data for national or regional domains to derive indicators for EBV in the class ecosystem structure (Pereira et al., 2013) , including horizontal ecosystem extents, fragmentation, and information-theory indices. To process ERSP, users must provide a polygon or geographic administrative data map. Downloadable ERSP include Global Surface Water (Peckel et al., 2016) , Forest Change (Hansen et al., 2013) , and Continuous Tree Cover data (Sexton et al., 2013) . 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Methods described in Popovic, GC., Hui, FKC., Warton, DI., (2018) . Package: r-cran-ecode Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-rlang, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-ecode_0.1.0-1.ca2604.1_all.deb Size: 185122 MD5sum: 8bd862935d2517fa3c23dda05fa4ce6d SHA1: 38432de69f7983a64cdaec0d450357c968f33fe8 SHA256: 44ef16140ebb5ed3e0dcae8a4882646163723fffb5c95ca603fd0cf30a380005 SHA512: 689fedd040b3592d1541b19eabd6f871e3d005b61657d4c3562b1ac3d479e1a14b497026b8a30474768d5d2850da39da97ed73d351ea386cb29cd7605bb05a6c Homepage: https://cran.r-project.org/package=ecode Description: CRAN Package 'ecode' (Ordinary Differential Equation Systems in Ecology) A framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). 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Package: r-cran-ecohydmod Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ecohydmod_1.0.0-1.ca2604.1_all.deb Size: 26840 MD5sum: f2792ca51e4a34dd5a4715835bac2d62 SHA1: 91eb1105f08587d61cc588cc972839772637180a SHA256: c3643c39783cb4a1a2a747838d73381f7c60cc5b67949b604b206a13594173ba SHA512: bf11cc82b2504faadf00292ae467ed774c28aee75f3dd0bbf3081af10e9b1d32b26ba0a8280a5063123023020d7c690a64bb1130863248c2d696173d7b202276 Homepage: https://cran.r-project.org/package=Ecohydmod Description: CRAN Package 'Ecohydmod' (Ecohydrological Modelling) Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) . 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This package provides bug fixes, generalizations and implementations of many state of the art measures. The measures are described in the papers: Lorena et al. (2019) and Lorena et al. (2018) . 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''Ecological Inference and Aggregate Analysis of Election''. PhD Dissertation. University of Michigan. Thomsen, S.R. (1987, ISBN:87-7335-037-2). ''Danish Elections 1920 79: a Logit Approach to Ecological Analysis and Inference''. Politica, Aarhus, Denmark. Package: r-cran-ecoltest Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ecoltest_0.0.1-1.ca2604.1_all.deb Size: 31194 MD5sum: c163762ed54560c6c6099aa8faa739de SHA1: 87405dd82c8f8340f7f3e85baa1ebf69c45b036b SHA256: 5b14e3c296fb8d90e03ce66c1161d5c01488ec7657f0eedca9cdfc1127257dc2 SHA512: 998aa765fce30bfbd8fc607cc71908bd5829b8e096dbe6af353acb4ce20f15cda39bd35dcb8e4d42150bb502ea204d99ecfc8385b1efa83cbb83514aea88d9ee Homepage: https://cran.r-project.org/package=ecolTest Description: CRAN Package 'ecolTest' (Community Ecology Tests) Functions and data sets to perform and demonstrate community ecology statistical tests, including Hutcheson's t-test (Hutcheson (1970) , Zar (2010) ISBN:9780321656865). Package: r-cran-ecometrics Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-forecast, r-cran-ggplot2, r-cran-insight, r-cran-lmtest, r-cran-moments, r-cran-tibble, r-cran-tseries Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-ecometrics_0.1.1-1.ca2604.1_all.deb Size: 107846 MD5sum: 8ff64cbc55fdb763754d99161bf6369f SHA1: e56e74c73279af6f178ff9f5f0d1b200bff9cd66 SHA256: c82ba9d535a780b117c90f05f559e6a8c401e0a69eaec8415bcb9e25503ca165 SHA512: f32c72a059ebe81360b65365a267cfdc7b95906d8656c5e91c837b4c82498d90dccb4262ef870ca0e8825327a9de19b599e260ff7301f02ef293e0e388567a66 Homepage: https://cran.r-project.org/package=EcoMetrics Description: CRAN Package 'EcoMetrics' (Econometrics Model Building) An intuitive and user-friendly package designed to aid undergraduate students in understanding and applying econometric methods in their studies, Tailored specifically for Econometrics and Regression Modeling courses, it provides a practical toolkit for modeling and analyzing econometric data with detailed inference capabilities. 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Heavy backends (Stan) are optional and never used in examples or tests. Package: r-cran-econdatasets Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-econdatasets_0.1.0-1.ca2604.1_all.deb Size: 28318 MD5sum: 34d3584eca5533818d3654b597128a75 SHA1: 257284b31c3a7865875d4ce10d1bf02784498e32 SHA256: 3bd281e0c4182ce390f5a815d5f2c6aa22ab2f9694d9424de7704b4a30c1f783 SHA512: adf4a1e3f97cf11c4e27b7a86ce6cb022ba6fe0e8704b893a43a12cbd386007220858fb207f804981488daa49abd7a7f73fc9b0ffc7b8684dbba4a8673b9af78 Homepage: https://cran.r-project.org/package=econdatasets Description: CRAN Package 'econdatasets' (Easily Download 'EconDataverse' Datasets) The 'EconDataverse' is a universe of open-source packages to work seamlessly with economic data. 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Package: r-cran-econid Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-econid_0.0.3-1.ca2604.1_all.deb Size: 411430 MD5sum: a6f844c07099ebb80285d6a0f3c0d473 SHA1: 96f9bd27a46968bca05f0a0edbb811f5eb9ebab5 SHA256: 56a120dc37b37d9dbef42a3687e6a1675bb16ca4fccb8a8ddaf7cecdc9ea9fea SHA512: 2a85822d44f30337b6349e9321cd96fb160078ea2a9b51f83ea5068bd41d23ccc7f745070e00c8cfb04d80ba3010e65de0c1503bec266119d1c3dd071a80f901 Homepage: https://cran.r-project.org/package=econid Description: CRAN Package 'econid' (Economic Entity Identifier Standardization) Provides utility functions for standardizing economic entity (economy, aggregate, institution, etc.) name and id in economic datasets such as those published by the International Monetary Fund and World Bank. 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First, model parameters are compiled as a data set and associated metadata for over 300 habitat suitability models developed by the U.S. Fish and Wildlife Service (USFWS 1980, ). Second, functions for conducting habitat suitability analyses both for the models described above as well as generic user-specified model parameterizations. Third, a suite of decision support tools for conducting cost-effectiveness and incremental cost analyses (Robinson et al. 1995, IWR Report 95-R-1, U.S. Army Corps of Engineers). 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Package: r-cran-ecos Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-xml, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-ecos_0.1.7-1.ca2604.1_all.deb Size: 307026 MD5sum: 4fe3859aa4fd290c4e678c8da4eb1d9c SHA1: 313ff2e1c6b3b9b01034838fd7c4b97c0064b966 SHA256: ec3bf47d7697d4a70fcf195a27c720ce18203a2d66c8b959bbd3d3f8b22a2ada SHA512: 9a5366cd5b7bb1440d22b0bf0c9760f370ce79e633af88e264751b8deb0f5260b2e19392c181dec5a6984955cb8d8f650da57ad6d560ce905f432e9ac7472e95 Homepage: https://cran.r-project.org/package=ecos Description: CRAN Package 'ecos' (Economic Statistics System of the Bank of Korea) API wrapper to download statistical information from the Economic Statistics System (ECOS) of the Bank of Korea . Package: r-cran-ecosim Architecture: all Version: 1.3-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-stoichcalc Filename: pool/dists/resolute/main/r-cran-ecosim_1.3-5-1.ca2604.1_all.deb Size: 116878 MD5sum: c91ad8a92107d9676181e9283e483adc SHA1: 7fb64ed2f93bc4622a9ab3e236a681cac093286a SHA256: 1e966825d262c4d07de62073acc5ea9cf7d0a032c7eb01bc01538cd9e53d9d6d SHA512: 817dd55e9216c0743d0a9d61dd5c1e31149ea3c71464f626b60b65497efed2628eebc9f8d7c5407ac2c236fa63f200d9aa660cab5976c3b00ff614d890ed5fcc Homepage: https://cran.r-project.org/package=ecosim Description: CRAN Package 'ecosim' (Toolbox for Aquatic Ecosystem Modeling) Classes and methods for implementing aquatic ecosystem models, for running these models, and for visualizing their results. Package: r-cran-ecospace Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fd Suggests: r-cran-vegan, r-cran-knitr, r-cran-rmarkdown, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-ecospace_1.4.2-1.ca2604.1_all.deb Size: 213730 MD5sum: 824b4d16c6c7255d9888d065e9b11571 SHA1: 41fa1105071e13f9dbc28ec942b54f4c325e20fa SHA256: c1baa593b627652db29eb2639bbc30d4402ebad1cfa183f25c9ca09d4304a165 SHA512: 7cbe386e548c5f0249018819eabb8400cc5e2dbcac967e366ba44bc6ed80a7e1076b9c57c3f57ec0008c59bb145b8eb1eb3bbb344d70ec21c379e0d9e586b180 Homepage: https://cran.r-project.org/package=ecospace Description: CRAN Package 'ecospace' (Simulating Community Assembly and Ecological DiversificationUsing Ecospace Frameworks) Implements stochastic simulations of community assembly (ecological diversification) using customizable ecospace frameworks (functional trait spaces). Provides a wrapper to calculate common ecological disparity and functional ecology statistical dynamics as a function of species richness. Functions are written so they will work in a parallel-computing environment. 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Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) for details. Package: r-cran-ecostate Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtmb, r-cran-tmb, r-cran-mass, r-cran-checkmate, r-cran-ggplot2, r-cran-ggnetwork, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ecostate_0.3.0-1.ca2604.1_all.deb Size: 199986 MD5sum: 73feba3fe151fd68762be9c31849ac79 SHA1: 6bd59269cb17f2d38d4ec08b831ab44bc38f83fc SHA256: 73e874052256d26823eb1dc0e1007a1db706dff1e68429ab1e44e4b188c0eb0a SHA512: 401b08fe31ef4be0207f028feaea5c832f2f049ed4cf82a64a354d3c768413e8bd3dce7d8a2551c86975224de5161ddfc23c207e7437b4939b7e7ea78b50cea9 Homepage: https://cran.r-project.org/package=ecostate Description: CRAN Package 'ecostate' (State-Space Mass-Balance Model for Marine Ecosystems) Fits a state-space mass-balance model for marine ecosystems, which implements dynamics derived from 'Ecopath with Ecosim' ('EwE') while fitting to time-series of fishery catch, biomass indices, age-composition samples, and weight-at-age data. 'Ecostate' fits biological parameters (e.g., equilibrium mass) and measurement parameters (e.g., catchability coefficients) jointly with residual variation in process errors, and can include Bayesian priors for parameters. Package: r-cran-ecostats Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvabund, r-cran-ecocopula, r-cran-get, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ade4, r-cran-caper, r-cran-car, r-cran-corrplot, r-cran-daag, r-cran-dharma, r-cran-dplyr, r-cran-gclus, r-cran-ggally, r-cran-ggplot2, r-cran-ggthemes, r-cran-glmnet, r-cran-gllvm, r-cran-glmmtmb, r-cran-gparotation, r-cran-grplasso, r-cran-lattice, r-cran-leaps, r-cran-lme4, r-cran-mcmcglmm, r-cran-multcomp, r-cran-nlme, r-cran-ordinal, r-cran-permute, r-cran-pgirmess, r-cran-phylobase, r-cran-phylosignal, r-cran-psych, r-cran-reshape2, r-cran-smatr, r-cran-testthat, r-cran-vegan, r-cran-vgam, r-cran-covr Filename: pool/dists/resolute/main/r-cran-ecostats_1.2.2-1.ca2604.1_all.deb Size: 2639930 MD5sum: ab9565a7b5a2d29ac92a7012d29d7381 SHA1: a150582d07b0aa45cac21300d6fda1d7feea2da4 SHA256: c6a61c396648980f6874e75531878fb4bbee6407b0c108965c40ebad8f6a8dfb SHA512: 5be0c4d33354df437b28812d571978aaf4b2ab880042be9de639025e816b39e6a43a230901e76ef62118daa258b0ffc2091c9cb9906ab6b66d8ae651c1ae031e Homepage: https://cran.r-project.org/package=ecostats Description: CRAN Package 'ecostats' (Code and Data Accompanying the Eco-Stats Text (Warton 2022)) Functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises. Package: r-cran-ecostatscale Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-desolve Filename: pool/dists/resolute/main/r-cran-ecostatscale_1.1-1.ca2604.1_all.deb Size: 49690 MD5sum: 10137afa9b1d95a0d5bc49efe7553170 SHA1: 1179ce4e02f367f4e7bca96aeab66093e6a93c6d SHA256: ee1dcb1b9eee55cbaac7650e6c57420bc2c3f2446f3f96d18a94d94908d6ec94 SHA512: 877d9955aa89d2ede721677d4867a0104a1a19dbc11923ec5a9d1e209bdc141b379aac104d6ae70bb6df93326b9d78dd38ede8c1c1d236945774f5cccb37a598 Homepage: https://cran.r-project.org/package=ecostatscale Description: CRAN Package 'ecostatscale' (Statistical Scaling Functions for Ecological Systems) Implementation of the scaling functions presented in "General statistical scaling laws for stability in ecological systems" by Clark et al in Ecology Letters . Includes functions for extrapolating variability, resistance, and resilience across spatial and ecological scales, as well as a basic simulation function for producing time series, and a regression routine for generating unbiased parameter estimates. See the main text of the paper for more details. Package: r-cran-ecoteach Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ecoteach_0.1.0-1.ca2604.1_all.deb Size: 183800 MD5sum: 2d62cd2737d5b490dc990d032dbffd32 SHA1: fa401e8330dcb74327b1991fb7d57b8978b5e770 SHA256: 3f1d1a55d776a0c6fe4659220c712882946465afeeaade9458d87faa447e4ab0 SHA512: 3261282a97944142b065b3f06211785bf9ab43b05079a2087f46b93fd1d127203ebc64a13ba5e3766823b8e9deb5c9e570ddaccee37768b2d67c86fee0234c9a Homepage: https://cran.r-project.org/package=ecoteach Description: CRAN Package 'ecoteach' (Educational Datasets for Ecology and Agriculture) A collection of curated educational datasets for teaching ecology and agriculture concepts. Includes data on wildlife monitoring, plant treatments, and ecological observations with documentation and examples for educational use. All datasets are derived from published scientific studies and are available under CC0 or compatible licenses. Package: r-cran-ecotolerance Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-raster, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-terra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ecotolerance_0.1.0-1.ca2604.1_all.deb Size: 70612 MD5sum: ccc5020330ffb6b8af5c4c253a74f861 SHA1: 7e58d56ac0f3293da953a762ad43dbb51704ab40 SHA256: 3b858ceab9e5fe85abd12992ff1605c8491ea35e816011314886096007b279c8 SHA512: 675e709907d55cf5129114525fa76941ffc2f58e4de8dc47f5b947c1ba64b85df922aa324c11638290875ed1f6d6c377ca6d2f8a8a0a32b7e5eb643a8295e9d5 Homepage: https://cran.r-project.org/package=ecoTolerance Description: CRAN Package 'ecoTolerance' (Ecological Tolerance Indices) Computes the Road Tolerance Index (RTI) and the Human Footprint Tolerance Index (HFTI) for species occurrence data. It automates data cleaning and integrates spatial data (roads and human footprint) to produce reproducible tolerance metrics for biodiversity and conservation research. The HFTI calculation is based on the global human footprint dataset by Mu et al. (2022) . This package is part of a PhD thesis focused on amphibian ecology in Brazil. Package: r-cran-ecotonefinder Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-colorspace, r-cran-corrplot, r-cran-e1071, r-cran-ggplot2, r-cran-qgraph, r-cran-igraph, r-cran-philentropy, r-cran-plyr, r-cran-purrr, r-cran-reshape, r-cran-rlang, r-cran-rmisc, r-cran-vegan, r-cran-vegclust, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-ecotonefinder_0.2.3-1.ca2604.1_all.deb Size: 214248 MD5sum: 4b6205c532aab581cbd7a8942a4e6b08 SHA1: 1c5e4fc99a57cd9a0d9ecd1ca49aa1a6b4b0d74b SHA256: 4161a2495e05fcb821410cdfdfa18d14bc0318e4bdafb6babd1d47ab50198c88 SHA512: ec61e8a4be3e2606d7fbddec91af8b47838b3527e89c91a931bf65c9e8cf0af5f8ae9b2654f1667f0726c9f505822b8424bef4e9dff6d61fb1062cf2f5f8d910 Homepage: https://cran.r-project.org/package=EcotoneFinder Description: CRAN Package 'EcotoneFinder' (Characterising and Locating Ecotones and Communities) Analytical methods to locate and characterise ecotones, ecosystems and environmental patchiness along ecological gradients. Methods are implemented for isolated sampling or for space/time series. It includes Detrended Correspondence Analysis (Hill & Gauch (1980) ), fuzzy clustering (De Cáceres et al. (2010) ), biodiversity indices (Jost (2006) ), and network analyses (Epskamp et al. (2012) ) - as well as tools to explore the number of clusters in the data. Functions to produce synthetic ecological datasets are also provided. Package: r-cran-ecotourism Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6080 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-ggbeeswarm, r-cran-sf, r-cran-knitr, r-cran-lubridate, r-cran-quarto, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ecotourism_0.1.0-1.ca2604.1_all.deb Size: 4536850 MD5sum: c8ca29d19c92baefc7cc483f8b8de151 SHA1: 32b2bff01699af7ab61ecf12ec6413666679af0b SHA256: a18e4e63299fbd5d694d7594b6f8ad5c194b6375e32f31f979ea9713cb9e3537 SHA512: a88e71f55f0004b667c3e5bb3116720319e3630e2e27baaac455745452dbde22f42fde1bf40c175ef0e699612486e5ce124226a17177ecb3be395cf1f909808c Homepage: https://cran.r-project.org/package=ecotourism Description: CRAN Package 'ecotourism' (Collection of Data on Wildlife Sightings, Tourism Counts, andWeather from Australia) This is a collection of data files for exploring sightings of wild things, relative to weather and tourism patterns in Australia. Package: r-cran-ecotox Architecture: all Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-covr, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-hmisc, r-cran-openxlsx, r-cran-readr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ecotox_1.4.4-1.ca2604.1_all.deb Size: 93414 MD5sum: 8c75e0a78e67c1880929d7a90d22503e SHA1: 4f70a6df5b1e6746755b5808029054f89d29b03a SHA256: 60f948426187be9ff8c9071a93827b59f28dbbdf3b12e1f9796db11168d9e242 SHA512: feede66b91f0867fb09b282928702e8b006333f251bdc367d24c224d872ce31280df9f200574ac7a21dbd2ae21045d6dae2e2d523f62b97b13eb373876d4b101 Homepage: https://cran.r-project.org/package=ecotox Description: CRAN Package 'ecotox' (Analysis of Ecotoxicology) A simple approach to using a probit or logit analysis to calculate lethal concentration (LC) or time (LT) and the appropriate fiducial confidence limits desired for selected LC or LT for ecotoxicology studies (Finney 1971; Wheeler et al. 2006; Robertson et al. 2007). The simplicity of 'ecotox' comes from the syntax it implies within its functions which are similar to functions like glm() and lm(). In addition to the simplicity of the syntax, a comprehensive data frame is produced which gives the user a predicted LC or LT value for the desired level and a suite of important parameters such as fiducial confidence limits and slope. Finney, D.J. (1971, ISBN: 052108041X); Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) ; Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K. (2007, ISBN: 0849323312). Package: r-cran-ecotoxicology Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ecotoxicology_1.0.1-1.ca2604.1_all.deb Size: 212844 MD5sum: bcf1b89a72069761a0d8e4d1e4bb7aca SHA1: 64ce068dd445b2ada37cd8111ccadc2394da0235 SHA256: 4a8403457f8de046d6af29d28adfb30df918d4f0c2e0798b927deb937694839b SHA512: bba4d7a860418f585cc1cbc41bc091f0e1415115a6058e804a544ca1668366cc57d128196bab830597611e2e44d4452afed792dbc10f5b8dfd84bf60688a6c15 Homepage: https://cran.r-project.org/package=ecotoxicology Description: CRAN Package 'ecotoxicology' (Methods for Ecotoxicology) Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis. Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book. Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included. Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard. 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As the online search interface doesn't come with an API, this package provides the means to easily access and search the database in R. To this end, all raw tables are downloaded from the EPA website and stored in a local SQLite database . 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Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package 'bupaR'. Package: r-cran-edecob Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-survival Filename: pool/dists/resolute/main/r-cran-edecob_1.2.2-1.ca2604.1_all.deb Size: 97304 MD5sum: 5da5467de8072d4d5eb580233892b87d SHA1: d06174c84258d80070d4d0b180047e6a4c78d5dc SHA256: cb002049b28c8db0ad537417f92f584b7aba7990daf20cfc3c929bf28e55a473 SHA512: b0e805289fe18d7d3e073bb387c2a8250e27375d60d077f9fe003207d8c68e2b04ead1428bbc8087878e89fb5df6894f4683e87460c6ebc938b598a4a3206015 Homepage: https://cran.r-project.org/package=edecob Description: CRAN Package 'edecob' (Event Detection Using Confidence Bounds) Detects sustained change in digital bio-marker data using simultaneous confidence bands. Accounts for noise using an auto-regressive model. Based on Buehlmann (1998) "Sieve bootstrap for smoothing in nonstationary time series" . Package: r-cran-edf Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-edf_1.0.0-1.ca2604.1_all.deb Size: 33838 MD5sum: ef06bbab498f3ef13d155ba38309556b SHA1: e8e6a051f59a025d41d05466cbafeae6bfe4ffce SHA256: 5bec72fb08cf3bf38b71238646efe15796939233cac3916583011f10ae71d65a SHA512: b001200672de653182ebc8be06a1a29a12954a3a00f7f4f7d86983c1e0ccfaf765e2eea2d589fca31e4fe1937e0ab14561f7cbe9f087d0dd133bbe62126e2ca1 Homepage: https://cran.r-project.org/package=edf Description: CRAN Package 'edf' (Read Data from European Data Format (EDF and EDF+) Files) Import physiologic data stored in the European Data Format (EDF and EDF+) into R. Both EDF and EDF+ files are supported. Discontinuous EDF+ files are not yet supported. Package: r-cran-edfinr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-edfinr_0.1.1-1.ca2604.1_all.deb Size: 177242 MD5sum: bf61f6c4af8467ee1e24c6cc4c338f42 SHA1: 4d799c28213af028302625b14999b31495340592 SHA256: be511d28f05eadb9daf42df46356bf7472290c4fed5327c78a8b2e6424cc5ae5 SHA512: 380ddcefbbde0b23497302565119d191296211ab957e0fc49e89bda7c03c35550565e7dfad78fad5435329f0e09ff2b4fabf21fbb1fb39540704018d3da426dc Homepage: https://cran.r-project.org/package=edfinr Description: CRAN Package 'edfinr' (Access Tidy Education Finance Data) Provides easy access to tidy education finance data using Bellwether's methodology to combine NCES F-33 Survey, Census Bureau Small Area Income Poverty Estimates (SAIPE), and community data from the ACS 5-Year Estimates. The package simplifies downloading, caching, and filtering education finance data by year and state, enabling researchers and analysts to explore K-12 education funding patterns, revenue sources, expenditure categories, and demographic factors across U.S. school districts. Package: r-cran-edfreader Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1712 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-edfreader_1.2.1-1.ca2604.1_all.deb Size: 382708 MD5sum: 78a9b668378fc8a41e70b17090359d1c SHA1: fa55d8ac055b078e636fd273bd2d66eab9214ead SHA256: 6ad37bc29062996912eda8e736ead1adf54b7dca3a30512fffded4057f3ba4e1 SHA512: 74ad99500da45b76a841fdad0ec6f87d45171f978cd251ddae6e4999352e498c770d0152c2cf30c3ecad421ecaa39cab676456edbf3c2432fef24b98f437916c Homepage: https://cran.r-project.org/package=edfReader Description: CRAN Package 'edfReader' (Reading EDF(+) and BDF(+) Files) Reads European Data Format files EDF and EDF+, see , BioSemi Data Format files BDF, see , and BDF+ files, see . The files are read in two steps: first the header is read and then the signals (using the header object as a parameter). Package: r-cran-edfun Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-edfun_0.2.0-1.ca2604.1_all.deb Size: 300666 MD5sum: 615aab765b4fd1f93f871b79fe4c05f9 SHA1: ae9fb17ed91a55ed976e8a8b357f086b8223fc3e SHA256: 7ebed555738ffed17fff7bd063d916e03763815190bfa0687e1ad0e1d0e9ce33 SHA512: 58894a771996d98d3a16a787a39e3dfbb1112421a38242321b0b11d14f7bc2cab05267e8238747412d3502ddca6e36c240f7b1a7fef30a93a5e1ef5a922a6f9a Homepage: https://cran.r-project.org/package=edfun Description: CRAN Package 'edfun' (Creating Empirical Distribution Functions) Easily creating empirical distribution functions from data: 'dfun', 'pfun', 'qfun' and 'rfun'. Package: r-cran-edgar Architecture: all Version: 2.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.utils, r-cran-tm, r-cran-xml, r-cran-stringr, r-cran-stringi, r-cran-qdapregex, r-cran-httr Filename: pool/dists/resolute/main/r-cran-edgar_2.0.8-1.ca2604.1_all.deb Size: 472516 MD5sum: 305cd31465a82303526999bdbf8b5f75 SHA1: 698e5e71e89eb619909a5a6a32cacd83bbc22623 SHA256: 86163a99bcc3bc63a3d3f5ed52c4e3d8e139e2d3e69858e70a3b481f454808ce SHA512: 84da9118a1afc8c7c7a8b0e38967a24c0dc12971440019b3f2b915bce9455475207378955aa1c6c092b83a54f9b552e91aff5de1919ff5d8bdf9dbae06f800a2 Homepage: https://cran.r-project.org/package=edgar Description: CRAN Package 'edgar' (Tool for the U.S. SEC EDGAR Retrieval and Parsing of CorporateFilings) In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). 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Package: r-cran-edgarfundamentals Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-rlang, r-cran-tidyquant Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-edgarfundamentals_0.1.2-1.ca2604.1_all.deb Size: 45488 MD5sum: 8a69e07561fc849a488870d31a07be09 SHA1: f59ff154b843edcf616b58ca4c10f47ed2d5b043 SHA256: b200fcd1771951039050a024b229c0a7a5e2e9028cab8c103764d3704bcf90da SHA512: ec48a2ac6278389d75eb440b11b217a2a48b503b6609db6d56c77e78699cf425d8045788a6c617929875abd25609a342409f8b1a10cd5676ddfbdeb904d590fa Homepage: https://cran.r-project.org/package=edgarfundamentals Description: CRAN Package 'edgarfundamentals' (Retrieve Fundamental Financial Data from SEC 'EDGAR') Provides a simple, ticker-based interface for retrieving fundamental financial data from the United States Securities and Exchange Commission's 'EDGAR' 'XBRL' API . Functions return key financial ratios including earnings per share, return on equity, return on assets, debt-to-equity, current ratio, gross margin, operating margin, net margin, price-to-earnings, price-to-book, and dividend yield for any publicly traded U.S. company. Data is sourced directly from company 10-K annual filings, requiring no API key or paid subscription. Designed for use in quantitative finance courses and research workflows. 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Package: r-cran-edgebundler Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-rjson, r-cran-igraph, r-cran-shiny Suggests: r-cran-knitr, r-cran-huge Filename: pool/dists/resolute/main/r-cran-edgebundler_0.1.4-1.ca2604.1_all.deb Size: 96672 MD5sum: 12ac364a16b6ae9cb55f47474649060f SHA1: d3b57f8de5bf632775a02df032a4122f552be821 SHA256: d15bbf2ce2d3d2d2a855228f3fbe778c816ab210ce35450fc351b2c11336779a SHA512: e513d9d1792abf566611031ae8fc51d6ba33f95dc35c19df9d6efda4128779949e334435a992e0f24e34ae0f4e4736947b8daaf010a0e2c61d73e644c6297a98 Homepage: https://cran.r-project.org/package=edgebundleR Description: CRAN Package 'edgebundleR' (Circle Plot with Bundled Edges) Generates interactive circle plots with the nodes around the circumference and linkages between the connected nodes using hierarchical edge bundling via the D3 JavaScript library. See for more information on D3. Package: r-cran-edgecorr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-edgecorr_1.0-1.ca2604.1_all.deb Size: 28880 MD5sum: 094ebf123c3b5cb5cc803f5b7898119f SHA1: ec1e2dc142b2777fc80c5232d865559af978bd50 SHA256: ef149982b9b9988df22adce974c4b28eff46236dd77b62963954962ad8f83d82 SHA512: 9b4a23310a9de0e19ab8198e3df2c68b53adee926912ba5b918b07338f69984f538aca8d5655d73897e42443e5994c8859069dc6c7684acd1547476d81c8cec4 Homepage: https://cran.r-project.org/package=edgeCorr Description: CRAN Package 'edgeCorr' (Spatial Edge Correction) Facilitates basic spatial edge correction to point pattern data. Package: r-cran-edgedata Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-edgedata_0.2.0-1.ca2604.1_all.deb Size: 192724 MD5sum: 5953478f0d446484f1f43c71f9574428 SHA1: f5096fbc3ba8891d08a6d0dd5bd601735f900aca SHA256: 6e114adb449960778ca06586359d9cdaca91045666602e38bfca7066270783f1 SHA512: feea54c0b6f87e4d6dd4882ef75985c3b427b66dbea78bbe143130d0adabb1e4d2b6e04d2666e017eb397478d0936929c0f3b4e1812d9421cbfddd39471c1582 Homepage: https://cran.r-project.org/package=edgedata Description: CRAN Package 'edgedata' (Datasets that Support the EDGE Server DIY Logic) Datasets from most recent CCIIO DIY entry in a tidy format. These support the Centers for Medicare and Medicaid Services' (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows health insurance issuers to calculate member risk profiles under the Health and Human Services-Hierarchical Condition Categories (HHS-HCC) regression model. This regression model is used to calculate risk adjustment transfers. Risk adjustment is a selection mitigation program implemented under the Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA. Under the ACA, health insurance issuers submit claims data to CMS in order for CMS to calculate a risk score under the HHS-HCC regression model. However, CMS does not inform issuers of their average risk score until after the data submission deadline. These data sets can be used by issuers to calculate their average risk score mid-year. More information about risk adjustment and the HHS-HCC model can be found here: . Package: r-cran-edibble Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3741 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-tibble, r-cran-cli, r-cran-pillar, r-cran-tidyselect, r-cran-nestr, r-cran-algdesign, r-cran-dae, r-cran-r6, r-cran-lifecycle, r-cran-dplyr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-openxlsx2, r-cran-visnetwork, r-cran-blocksdesign, r-cran-knitr, r-cran-scales, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-edibble_1.1.1-1.ca2604.1_all.deb Size: 3102402 MD5sum: 880ff4a1a88e61daa021406ca986a546 SHA1: 5c6170cd5783b7a789318d98015244fdf1c55563 SHA256: 96270a258569ddcc1f3f879530a0554532013cde5f3f959f29f43055628fb143 SHA512: 167be78dc4204b2e58b0057f985eaeb7fdf69acd33d6895216d5628813538333229d65f457dd032158d77817e1558c52dade49d127e54fcb73681f9c1952c408 Homepage: https://cran.r-project.org/package=edibble Description: CRAN Package 'edibble' (Encapsulating Elements of Experimental Design) A system to facilitate designing comparative (and non-comparative) experiments using the grammar of experimental designs . An experimental design is treated as an intermediate, mutable object that is built progressively by fundamental experimental components like units, treatments, and their relation. The system aids in experimental planning, management and workflow. Package: r-cran-ediblecity Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-magrittr, r-cran-stars, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-ediblecity_0.2.2-1.ca2604.1_all.deb Size: 2191496 MD5sum: d537f5ad82661c6a3f02b47f76b19988 SHA1: 99c12042924875c5e4d466015135bade85ae636f SHA256: 44d64e0ddd6a91bcc9f5ae09322eb084b15a69c941e03ed5840bbda0fe41730f SHA512: eaa00fba52cedb4922899e8eead0bae11ad41083b345f0e99f9520a15f1be398195a8c500f5899fc2e36c8180f0bd724dc7f69e47caf940a6f3aa1f8d7e06607 Homepage: https://cran.r-project.org/package=ediblecity Description: CRAN Package 'ediblecity' (Modeling Urban Agriculture at City Scale) The purpose of this package is to estimate the potential of urban agriculture to contribute to addressing several urban challenges at the city-scale. Within this aim, we selected 8 indicators directly related to one or several urban challenges. Also, a function is provided to compute new scenarios of urban agriculture. Methods are described by Pueyo-Ros, Comas & Corominas (2023) . Package: r-cran-edison Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-edison_1.1.2-1.ca2604.1_all.deb Size: 257764 MD5sum: 8292debb52bfcda084fdf4e0a25844ce SHA1: 508cc265065b3bfc130caf51ecd907b02a75d565 SHA256: 6fada26e774797e2ed3bca0b02a7e2beaa44d1b7f2e5a39dbd6d60f818a034b2 SHA512: 60f4bf8f035ccb270f38dd51a152dd04abe1b3fc97bfe776553de0010f9a57e5e3c646067fbf79c1fd4e36bc81fb29c638d8bf797a020e57d1eaa535d060a929 Homepage: https://cran.r-project.org/package=EDISON Description: CRAN Package 'EDISON' (Network Reconstruction and Changepoint Detection) Package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression) runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty. Package: r-cran-editbl Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinyjs, r-cran-dt, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-uuid, r-cran-fontawesome Suggests: r-cran-testthat, r-cran-dtplyr, r-cran-data.table, r-cran-vctrs, r-cran-rsqlite, r-cran-dbplyr, r-cran-glue, r-cran-dbi, r-cran-bit64, r-cran-knitr, r-cran-dm Filename: pool/dists/resolute/main/r-cran-editbl_1.3.0-1.ca2604.1_all.deb Size: 639552 MD5sum: b5aec2ab46906c4bf5576cc6dcfe6218 SHA1: d21f373506cca140b756e642d59488c6d6180352 SHA256: 0e14d1b754d6a05e67d7e92db7227f6680cb596e9388541538f83c422ddbbc02 SHA512: 722e07d66fc6fe9e81db10f7ed2f244eda70cc28a3acc2456c7b2d4d894c81857e0ea2707850d12b9e99c301c7d3ba90a20ea93f52df5bed54e481edc3138da1 Homepage: https://cran.r-project.org/package=editbl Description: CRAN Package 'editbl' ('DT' Extension for CRUD (Create, Read, Update, Delete)Applications in 'shiny') The core of this package is a function eDT() which enhances DT::datatable() such that it can be used to interactively modify data in 'shiny'. By the use of generic 'dplyr' methods it supports many types of data storage, with relational databases ('dbplyr') being the main use case. Package: r-cran-editdata Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2892 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-dt, r-cran-tibble, r-cran-dplyr, r-cran-rio, r-cran-magrittr, r-cran-shinywidgets, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-editdata_0.1.8-1.ca2604.1_all.deb Size: 2112692 MD5sum: c712e82fcb89112d7f4a33b2546cf344 SHA1: 4441326b52366c68a4dde97117f80f5d2b8e7a4d SHA256: 51bef1ce4e40c1979be5aa429354299db6ea1e04e8ca8a28fe5b0653f3de60ba SHA512: b8e161648fa41da0afe48562f2570b863527ef471b7a494bcc02d7ea0b9b937757be8d53fb483002ed228efe7e9b55b15ab215c75d1a2745bdb7dda5f7529945 Homepage: https://cran.r-project.org/package=editData Description: CRAN Package 'editData' ('RStudio' Addin for Editing a 'data.frame') An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame' without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications. Package: r-cran-editrules Architecture: all Version: 2.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-lpsolveapi Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-editrules_2.9.6-1.ca2604.1_all.deb Size: 520148 MD5sum: 3a5673c33147ada894e7038836816ed1 SHA1: 4bea87f312c0b269f9f2889db01a61f0da12f5fa SHA256: 5aa98363447d96aaf5d5891a693f9932628c861c2b73c2c8a5695c7d622077df SHA512: 3693891457777dc759ce3424f44c01095f4fdbfd7a72dd5a60ee2f77a07b65846422e88bb138f5e3e74b535d35271b6484c07517e3774b0257f21fcb17b051ed Homepage: https://cran.r-project.org/package=editrules Description: CRAN Package 'editrules' (Parsing, Applying, and Manipulating Data Cleaning Rules) Please note: active development has moved to packages 'validate' and 'errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package. Package: r-cran-ediutils Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-xml2 Suggests: r-cran-knitr, r-cran-readr, r-cran-vcr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ediutils_2.1.0-1.ca2604.1_all.deb Size: 354164 MD5sum: 3bcd521ed91749ad0c4f120327d87849 SHA1: e59af5a6b9c3a69ed68073e2178bb91340fb13a9 SHA256: daa06140e9762dd58d0b137cbcde861947ae9ebc4b8c620ec3a78eb36211e155 SHA512: b3f1f9e977b18b000f2009f7b56533c7fe9a00c74fbed06502728e6d7c0f4caca8a4bf4d17380b5713903883da9777e72877e318ea8cf3954f77786a26ef40c5 Homepage: https://cran.r-project.org/package=EDIutils Description: CRAN Package 'EDIutils' (An API Client for the Environmental Data Initiative Repository) A client for the Environmental Data Initiative repository REST API. The 'EDI' data repository is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the 'PASTA+' software stack and was developed in collaboration with the US 'LTER' Network . 'EDIutils' includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users. Package: r-cran-edl Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotfunctions, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-edl_1.1-1.ca2604.1_all.deb Size: 399540 MD5sum: 6576bef574a4659a90f46c2580d913c4 SHA1: 43c279301b2340c5fdbfa90ef076cc221d3be469 SHA256: d2fe0917c7d27694a5395d60bc8b09cf6cd33406a17f035d5811855be1078d1e SHA512: 32a490195473fa56509d7ddaf44e4bae360636601bff8392f3512c5a5017cf0b514e02b734a69128925e615233112d404c33084ce3ab025400e2c9bfb17977e7 Homepage: https://cran.r-project.org/package=edl Description: CRAN Package 'edl' (Toolbox for Error-Driven Learning Simulations with Two-LayerNetworks) Error-driven learning (based on the Widrow & Hoff (1960) learning rule, and essentially the same as Rescorla-Wagner's learning equations (Rescorla & Wagner, 1972, ISBN: 0390718017), which are also at the core of Naive Discrimination Learning, (Baayen et al, 2011, ) can be used to explain bottom-up human learning (Hoppe et al, ), but is also at the core of artificial neural networks applications in the form of the Delta rule. This package provides a set of functions for building small-scale simulations to investigate the dynamics of error-driven learning and it's interaction with the structure of the input. For modeling error-driven learning using the Rescorla-Wagner equations the package 'ndl' (Baayen et al, 2011, ) is available on CRAN at . However, the package currently only allows tracing of a cue-outcome combination, rather than returning the learned networks. To fill this gap, we implemented a new package with a few functions that facilitate inspection of the networks for small error driven learning simulations. Note that our functions are not optimized for training large data sets (no parallel processing), as they are intended for small scale simulations and course examples. (Consider the python implementation 'pyndl' for that purpose.) Package: r-cran-edmdata Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-edmdata_1.3.0-1.ca2604.1_all.deb Size: 667132 MD5sum: 153500d0a95cfff6d7737eba9c530bf0 SHA1: 16504713de6906010a4b65bf6eec3d99b05aff52 SHA256: 24b31d1176338fa3b720118d14a9d2e83fc943e7ce9480880d8b6f9847d9c94d SHA512: c77a9cdea11897ba2275ef6525b81f5ad2a74b5afde1c52cdb9c933991eaf95ae4b7f61d50645c350f6917c045e33395911137b935a03a19399ad52579057045 Homepage: https://cran.r-project.org/package=edmdata Description: CRAN Package 'edmdata' (Data Sets for Psychometric Modeling) Collection of data sets from various assessments that can be used to evaluate psychometric models. These data sets have been analyzed in the following papers that introduced new methodology as part of the application section: Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) , Culpepper, S. A., & Balamuta, J. J. (2021) , Yinghan Chen et al. (2021) , Yinyin Chen et al. (2020) , Culpepper, S. A. (2019a) , Culpepper, S. A. (2019b) , Culpepper, S. A., & Chen, Y. (2019) , Culpepper, S. A., & Balamuta, J. J. (2017) , and Culpepper, S. A. (2015) . Package: r-cran-ednafuns Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-googlesheets4, r-bioc-phyloseq, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vegan, r-cran-ggplot2, r-cran-vroom, r-bioc-biostrings, r-cran-stringr, r-cran-tidyselect, r-cran-readr Suggests: r-cran-insect, r-cran-testthat, r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ednafuns_0.1.0-1.ca2604.1_all.deb Size: 140478 MD5sum: ea2b4699b262efa38f798052ba7f66fb SHA1: 163c36f818719bcb54b222274e3004442fa48218 SHA256: 57d08d664affe575f589f0ce6d3822218f97e5029cd564326ce4e91e0016c9ce SHA512: 5fe135ac139974ec1ca934e38b87d3a37cfbcd3721493a27de3117592805c3a7d2e37f9cca0a66305302420b91d9da4b5eff3c6942871c0967ed3fa3b9e38de0 Homepage: https://cran.r-project.org/package=eDNAfuns Description: CRAN Package 'eDNAfuns' (Working with Metabarcoding Data in a Tidy Format) A series of R functions that come in handy while working with metabarcoding data. The reasoning of doing this is to have the same functions we use all the time stored in a curated, reproducible way. In a way it is all about putting together the grammar of the 'tidyverse' from Wickham et al.(2019) with the functions we have used in community ecology compiled in packages like 'vegan' from Dixon (2003) and 'phyloseq' McMurdie & Holmes (2013) . The package includes functions to read sequences from FAST(A/Q) into a tibble ('fasta_reader' and 'fastq_reader'), to process 'cutadapt' Martin (2011) 'info-file' output. When it comes to sequence counts across samples, the package works with the long format in mind (a three column 'tibble' with Sample, Sequence and counts ), with functions to move from there to the wider format. Package: r-cran-edne.eq Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-edne.eq_1.0-1.ca2604.1_all.deb Size: 36476 MD5sum: 4b6de30ce11d9dc35771267bc40e9622 SHA1: 4f7370735f88162b5554f690d6fe06c02cdb3589 SHA256: 9be596c900044cf8eaeaa4c76c0eb83c81f31039d7045ec0950078b931b24349 SHA512: 695249bfd079ff3fa718e24d25dccbd5c9d4fd8a71e40e7dbf7f75a888c3f07cbbfd0f36d277373f88a65d0ca3998e7c9980a7b6214401ee341d768eaa4971af Homepage: https://cran.r-project.org/package=EDNE.EQ Description: CRAN Package 'EDNE.EQ' (Implements the EDNE-Test for Equivalence) Package implements the EDNE-test for equivalence according to Hoffelder et al. (2015) . "EDNE" abbreviates "Euclidean Distance between the Non-standardized Expected values". The EDNE-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Euclidean distance. The test is an asymptotically valid test for the family of distributions fulfilling the assumptions of the multivariate central limit theorem (see Hoffelder et al.,2015). The function EDNE.EQ() implements the EDNE-test for equivalence according to Hoffelder et al. (2015). The function EDNE.EQ.dissolution.profiles() implements a variant of the EDNE-test for equivalence analyses of dissolution profiles (see Suarez-Sharp et al.,2020 ). EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the differences of the expected values of both dissolution profile populations is statistically significantly smaller than 10 [\% of label claim]. The current regulatory standard approach for equivalence analyses of dissolution profiles is the similarity factor f2. The statistical hypotheses underlying EDNE.EQ.dissolution.profiles() coincide with the hypotheses for f2 (see Hoffelder et al.,2015, Suarez-Sharp et al., 2020). Package: r-cran-edoif Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-distr, r-cran-igraph, r-cran-ellipsis, r-cran-simpleboot, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-edoif_0.1.4-1.ca2604.1_all.deb Size: 240938 MD5sum: 8c0df87adc852789663955d3cbd3f44e SHA1: 1e94116cd3426b1ee2954be39c3eec3584bd871a SHA256: 32882bda08a98ef440a8c5022ed737a79d6af455258de6d2135899be06030723 SHA512: 48321826e29ff7e41c7cf9c1944394a36c5b0802f597f3d165c5e6cbd4b9afe665dfbf954d5933fa4db1e8778b0577fdd8fe00eab8adb5d4ed362b616614a206 Homepage: https://cran.r-project.org/package=EDOIF Description: CRAN Package 'EDOIF' (Empirical Distribution Ordering Inference Framework (EDOIF)) A non-parametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of mean-difference confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) . Package: r-cran-edotrans Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abcanalysis, r-cran-opgmmassessment Filename: pool/dists/resolute/main/r-cran-edotrans_0.2.5-1.ca2604.1_all.deb Size: 71480 MD5sum: 7bc65d451b36a68e8dc1f67311b97e7b SHA1: d615615ffabcf1e500a8820047623de752ae8834 SHA256: 827eafcac2ebbad8b1bbdd462492dccd4f050b09e053650e3c8c74b68260d070 SHA512: 2da20197a737b97a6e9e452369c45f0ce3c7175384d7068ce9fb54eaaa3258c98584ddbacb1a87bdb0811c83fb2d104dfe0095e5409484bd7feadb65bb1899f1 Homepage: https://cran.r-project.org/package=EDOtrans Description: CRAN Package 'EDOtrans' (Euclidean Distance-Optimized Data Transformation) A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance. Package: r-cran-edstan Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-edstan_1.1.0-1.ca2604.1_all.deb Size: 82786 MD5sum: 3792a522f15dd1a40d35ba3df890df08 SHA1: d8fd8440258fea49e0e2e3d21c7f8c4354812114 SHA256: f00e77dda00157bbe820bc4624d049acb4aa3f22a6a4689790b74c279483ea2a SHA512: ca4ffae548fa1c3af3b7030960b7b5ce770f2102cbec33883ca7bbddc00af632758e7f02e03b7b8eea042792d2765d40cf20c45a1f0a3f5d765b3c8f28aa0b6d Homepage: https://cran.r-project.org/package=edstan Description: CRAN Package 'edstan' (Stan Models for Item Response Theory) Streamlines the fitting of common Bayesian item response models using Stan. Package: r-cran-edsurvey Architecture: all Version: 4.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2947 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-lfactors, r-cran-dire, r-cran-data.table, r-cran-formula, r-cran-glm2, r-cran-haven, r-cran-laf, r-cran-lifecycle, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-naepprimer, r-cran-quantreg, r-cran-readxl, r-cran-tibble, r-cran-wcorr, r-cran-naepirtparams, r-cran-wemix, r-cran-xtable, r-cran-xml2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-withr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-edsurvey_4.0.7-1.ca2604.1_all.deb Size: 2397268 MD5sum: 7780a362cc234e15902e18cc3706ff17 SHA1: 5dd247bc8612044b6163744968630b9b817856ee SHA256: 07f73809a5031e0dbc421559905920ff1429fe67df10a217d6089154318e0333 SHA512: 8038ae70c3b5f639502c35bd090a87d14ec00937e38413e7c828113fb19d7c2c7d595df55d6cf668224712e70ce91b35126d3ed875665be955878b523bd51cae Homepage: https://cran.r-project.org/package=EdSurvey Description: CRAN Package 'EdSurvey' (Analysis of NCES Education Survey and Assessment Data) Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) , including National Assessment of Educational Progress (NAEP) data and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) , including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) , including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd). Package: r-cran-educabr Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6861 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-lifecycle, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-systemfonts, r-cran-testthat, r-cran-textshaping, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-educabr_0.9.1-1.ca2604.1_all.deb Size: 5217108 MD5sum: 601431910d6c2231dd6315b645cb1188 SHA1: 3ce3e18047d308aed746eb920c5ff2a0e1e0905d SHA256: 739405c7f8eeb1031c0698c692d4b8ccd899a56d382e5adb91fd4e977ffb07f5 SHA512: da9d64d67be9ab335ea2b6b680387e60550599df7a40138ddc8558c958902c0a2d4c92935b12756b7cae7e7f83c0ea28d38375ff27ad4e02989a0c9287cb7dd6 Homepage: https://cran.r-project.org/package=educabR Description: CRAN Package 'educabR' (Download and Process Brazilian Education Data from INEP) Download and process public education data from INEP (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira). Provides functions to access microdata from the School Census (Censo Escolar), ENEM (Exame Nacional do Ensino Médio), SAEB (Sistema de Avaliação da Educação Básica), Higher Education Census (Censo da Educação Superior), ENADE (Exame Nacional de Desempenho dos Estudantes), ENCCEJA (Exame Nacional para Certificação de Competências de Jovens e Adultos), IDD (Indicador de Diferença entre os Desempenhos Observado e Esperado), CPC (Conceito Preliminar de Curso), IGC (Índice Geral de Cursos), CAPES graduate education data, FUNDEB (Fundo de Manutencao e Desenvolvimento da Educacao Basica), IDEB (Índice de Desenvolvimento da Educação Básica), and other educational datasets. Returns data in tidy format ready for analysis. Data source: INEP Open Data Portal . Package: r-cran-educationdata Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-educationdata_0.1.5-1.ca2604.1_all.deb Size: 346726 MD5sum: bc5c317b7535c16c4864bff9a6b18f52 SHA1: 6838605b35d0f2dda2aaab8c9393e3b87b453360 SHA256: ff61a86b98e6721694395e2c6ffc68f915b2565812ac7d30537efcbe0aeae154 SHA512: fb841ec7bf53ed1a757d1ed384706d72566aa2c58bf6d6e81819acf57575778ac33ed1c1a9633b2464e69e41345c98f86b54a5000deb2d8960c63eea8ad5ce35 Homepage: https://cran.r-project.org/package=educationdata Description: CRAN Package 'educationdata' (Retrieve Records from the Urban Institute's Education DataPortal API) Allows R users to retrieve and parse data from the Urban Institute's Education Data API into a 'data.frame' for analysis. Package: r-cran-educationr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-educationr_0.1.0-1.ca2604.1_all.deb Size: 225216 MD5sum: ecf366fba19c24b1d67adbcd3ae2bfe2 SHA1: 47dea09118ec07a4d786bc8f506ecaa511c12932 SHA256: b98c45690ee15abdd0eb0ecbeec67e3935b4a5850e9461a8bfb724f505a59a6c SHA512: ae3b430db598b3be3c4dc028a2583626cf89ede524c781afc3f13f273ce8654ad4ca6bd1ed4ac3e32218782964096dcde0fa19e9312cd061672b2c91986c9d6c Homepage: https://cran.r-project.org/package=educationR Description: CRAN Package 'educationR' (A Comprehensive Collection of Educational Datasets) Provides a comprehensive collection of datasets related to education, covering topics such as student performance, learning methods, test scores, absenteeism, and other educational metrics. This package serves as a resource for educational researchers, data analysts, and statisticians to explore and analyze data in the field of education. Package: r-cran-educineq Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ineq, r-cran-flexsurv Filename: pool/dists/resolute/main/r-cran-educineq_0.1.0-1.ca2604.1_all.deb Size: 400176 MD5sum: 49a8afc80bca00062c8e95f2e095cb27 SHA1: 2a08e4f5bdee19ec4890de0b08c0849e7f315b66 SHA256: bc2da4177464c1186954905f83607f55a6f120f65022cbdc73932e6ff8e17c00 SHA512: a335c88bb1810a0a0be93cfe2558c38bf92ef1e82b5a2bfb8645e2f8fe0a61fcf92a6a3336eea1ed47d5a542a53f3f6e857a8bc84ddd9732b167668020dfc63e Homepage: https://cran.r-project.org/package=educineq Description: CRAN Package 'educineq' (Compute and Decompose Inequality in Education) Easily compute education inequality measures and the distribution of educational attainments for any group of countries, using the data set developed in Jorda, V. and Alonso, JM. (2017) . The package offers the possibility to compute not only the Gini index, but also generalized entropy measures for different values of the sensitivity parameter. In particular, the package includes functions to compute the mean log deviation, which is more sensitive to the bottom part of the distribution; the Theil’s entropy measure, equally sensitive to all parts of the distribution; and finally, the GE measure when the sensitivity parameter is set equal to 2, which gives more weight to differences in higher education. The decomposition of these measures in the components between-country and within-country inequality is also provided. Two graphical tools are also provided, to analyse the evolution of the distribution of educational attainments: The cumulative distribution function and the Lorenz curve. Package: r-cran-edwards97 Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-tibble, r-cran-broom, r-cran-cli, r-cran-withr, r-cran-glue Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-edwards97_0.1.1-1.ca2604.1_all.deb Size: 376570 MD5sum: 34d8881357de7d78b636c789ec7f9770 SHA1: 3c2563488f1ee516c70acfaee3fb2061614ef69e SHA256: c9206849322707943f233956bd242bed8eaa06047fe68494067d4172c84d766e SHA512: f22053a829d440b19d9730f67fac15dbcf9bdefdf80eb6a6717623745224465529099951a9525d86df1fa517bdd1bdf06fe13658abdc515010686b8bab3fd96b Homepage: https://cran.r-project.org/package=edwards97 Description: CRAN Package 'edwards97' (Langmuir Semi-Empirical Coagulation Model) Implements the Edwards (1997) Langmuir-based semi-empirical coagulation model, which predicts the concentration of organic carbon remaining in water after treatment with an Al- or Fe-based coagulant. Data and methods are provided to optimise empirical coefficients. Package: r-cran-ee.data Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7306 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nnet, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-ee.data_0.1.1-1.ca2604.1_all.deb Size: 7444800 MD5sum: 8ba2d7afa6156f1c1cf8bc2d75175a04 SHA1: 5a26323e5ebe2696e9d42e509b57b0efdf9302f1 SHA256: 81258bba5cb72b82e120bc1351c09e028275cf18c62b821d38086bd72b1748c0 SHA512: b38188b1b81620b42dae33a2baa7a93da47de967e04a9eb56081cd46ce3f4807926e5fa8abdd7da7008693936a483d48723dacbc01f7769c261f195a0ca7e9d3 Homepage: https://cran.r-project.org/package=EE.Data Description: CRAN Package 'EE.Data' (Objects for Predicting Energy Expenditure) This is a data-only package containing model objects that predict human energy expenditure from wearable sensor data. Supported methods include the neural networks of Montoye et al. (2017) and the models of Staudenmayer et al. (2015) , one a linear model and the other a random forest. The package is intended as a spoke for the hub-package 'accelEE', which brings together the above methods and others from packages such as 'Sojourn' and 'TwoRegression.' 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See the web page for details on the EEA's Air Quality Download Service. The package allows dynamically mapping the stations, summarising and time aggregating the measurements and building spatial interpolation maps. See the web page for further information on EEA activities and history. Further details, as well as, an extended vignette of the main functions included in the package, are available at the GitHub web page dedicated to the project. 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Package: r-cran-eefanalytics Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2jags, r-cran-ggplot2, r-cran-lme4, r-cran-mvtnorm, r-cran-coda, r-cran-mcmcvis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eefanalytics_1.1.5-1.ca2604.1_all.deb Size: 260994 MD5sum: a983263f57ac43fb70d10dd36d396090 SHA1: 1fac4d5143bb777d48933527c664c81dae07ef51 SHA256: c222eb7c6bdf17b15472e516e03c389bce8300f4b13b70f405984bae4107add0 SHA512: ef290080cb1d28c7a75c1b7f9890ae2dbee58fa9b36a034324b8d0b147204a8a2fda87fa159333a9cefe23186911bed7df61cf2f1869d7bcffcd3784e11e1676 Homepage: https://cran.r-project.org/package=eefAnalytics Description: CRAN Package 'eefAnalytics' (Robust Analytical Methods for Evaluating EducationalInterventions using Randomised Controlled Trials Designs) Analysing data from evaluations of educational interventions using a randomised controlled trial design. 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Package: r-cran-eeml Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcs, r-cran-weightedensemble, r-cran-topsis Filename: pool/dists/resolute/main/r-cran-eeml_0.1.1-1.ca2604.1_all.deb Size: 20772 MD5sum: 80a6f9666e15db6cd6467793fbb6563e SHA1: efd123ee1f14f42fcc9c5d4cf411744081c10a56 SHA256: 00074d53d3f4a4eef802e2c30dc96d20e83d67e6eb9021478c511a9135374100 SHA512: 5afd26144b4d1c969dad80388dbfba80831d1de058e1afd789f0bc679d4b8e00931bdcc536ebe64e5682587808f536e04ea4facbc15b599d40ff0c15314c3834 Homepage: https://cran.r-project.org/package=EEML Description: CRAN Package 'EEML' (Ensemble Explainable Machine Learning Models) We introduced a novel ensemble-based explainable machine learning model using Model Confidence Set (MCS) and two stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. 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Package: r-cran-efa.dimensions Architecture: all Version: 0.1.8.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-polycor, r-cran-efatools, r-cran-mirt, r-cran-gparotation Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-efa.dimensions_0.1.8.6-1.ca2604.1_all.deb Size: 1016354 MD5sum: 64bd8fc777d69a9603db073cf3d8fadc SHA1: f114690de2ea4cb83e8a1d8470fffea4ef251e10 SHA256: 4e1a46819d560f17b70724e6eaa1fb1398415c9e216e22a38db4b3a7d774ed9b SHA512: e3a1b31009bc77732f86658bf49b31bc6caa190ac063c0b0109c50213513bb25bf79deec634456d75cabf75870b71099f13f5983e7d3a1df725dd3dbdee36f48 Homepage: https://cran.r-project.org/package=EFA.dimensions Description: CRAN Package 'EFA.dimensions' (Exploratory Factor Analysis Functions for AssessingDimensionality) Functions for eleven procedures for determining the number of factors, including functions for parallel analysis and the minimum average partial test. There are also functions for conducting principal components analysis, principal axis factor analysis, maximum likelihood factor analysis, image factor analysis, and extension factor analysis, all of which can take raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, gamma correlations, or polychoric correlations. Varimax rotation, promax rotation, and Procrustes rotations can be performed. Additional functions focus on the factorability of a correlation matrix, the congruences between factors from different datasets, the assessment of local independence, the assessment of factor solution complexity, internal consistency, and for correcting Pearson correlation coefficients for attenuation due to unreliability. Auerswald & Moshagen (2019, ISSN:1939-1463); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2); Mulaik (2010, ISBN:978-1-4200-9981-2); O'Connor (2000, ); O'Connor (2001, ISSN:0146-6216). 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Package: r-cran-eff2 Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcalg, r-bioc-rbgl, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-qgraph Filename: pool/dists/resolute/main/r-cran-eff2_1.0.2-1.ca2604.1_all.deb Size: 195504 MD5sum: 6a1781bb22f750d2ebf9bed29fd1b686 SHA1: df2e115d89dfbfb77145b95c243761a584f14109 SHA256: 748945bb250069aa9445bb05e032bef72690566e5b7ae0d3e8576a1e70efa3a5 SHA512: 5d0fb46bd267d7414198657aa977b32e8d148dc3b582d121931681fbe923b05e1f208299f20bbb363691353961de05183ae7f993413eac584779438641edcef9 Homepage: https://cran.r-project.org/package=eff2 Description: CRAN Package 'eff2' (Efficient Least Squares for Total Causal Effects) Estimate a total causal effect from observational data under linearity and causal sufficiency. 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Package: r-cran-effclust Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fixest Suggests: r-cran-plm, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-effclust_0.8.0-1.ca2604.1_all.deb Size: 135294 MD5sum: 86f7a70773893995e854ee564fb6fee7 SHA1: 9f005467f92c728c77b6594f5e97879c63549ad0 SHA256: c5846ecab8865b6094ef4317e5d7c36c2b69bdb87069d8d79147c57cd909f04a SHA512: 65e7aec3b77fd58a138aae840094a0cbb920b585533b943f3efaedc4c9ae231ebc466e16514b01bc1af36cabce7c478e48dfa1ac151b3ac22281927820e521f6 Homepage: https://cran.r-project.org/package=effClust Description: CRAN Package 'effClust' (Calculate Effective Number of Clusters for a Linear Model) Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) . 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It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design. Package: r-cran-effectstars2 Architecture: all Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam, r-cran-misctools Suggests: r-cran-diflasso, r-cran-difboost, r-cran-vgamdata Filename: pool/dists/resolute/main/r-cran-effectstars2_0.1-3-1.ca2604.1_all.deb Size: 107352 MD5sum: b52209f00fe40b249b7318bf9eeb035d SHA1: f16ace5e5d8ca0a27653bfa49af3db904cfe083a SHA256: d752cd5ff8d1918057a576883dcc40d110702ea0c331ed51ca23752b16ae3534 SHA512: ecf4914fa22c9ce18eff24954d44b1f6bb0478c19f3ba5808c928687ee3894561de8637a1edbadd00082226e12e72959e485e75a0bbc0dda71327285826b6fbe Homepage: https://cran.r-project.org/package=EffectStars2 Description: CRAN Package 'EffectStars2' (Effect Stars) Provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) . 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Supports several data formats, including the export formats of 'EgoNet', 'EgoWeb 2.0' and 'openeddi'. An interactive (shiny) app for the intuitive visualization of ego-centered networks is provided. Also included are procedures for creating and visualizing Clustered Graphs (Lerner 2008 ). Package: r-cran-egret Architecture: all Version: 3.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dataretrieval, r-cran-survival, r-cran-fields, r-cran-truncnorm, r-cran-foreach, r-cran-mass Suggests: r-cran-egretci, r-cran-knitr, r-cran-rmarkdown, r-cran-extrafont, r-cran-testthat, r-cran-rkt, r-cran-doparallel, r-cran-pkgdown, r-cran-png, r-cran-dplyr, r-cran-zyp, r-cran-lubridate, r-cran-covr Filename: pool/dists/resolute/main/r-cran-egret_3.0.11-1.ca2604.1_all.deb Size: 3229828 MD5sum: 1ee53dffb05b32a18adc8a390ef90949 SHA1: 8f843dbc2a040bd333f5b1e960836e230ac52a5e SHA256: 91cc95ab6d4e201768712e87bd7736bdd9d99569917e45cced17d38deb640621 SHA512: 331d7791c794fc73e3e5fe947df2aee44b108d13f06c35cd95e2082ce359959d09abd083793e963eeb63aa952514f35d4897a843cadc7c28f81cf5a67bffab58 Homepage: https://cran.r-project.org/package=EGRET Description: CRAN Package 'EGRET' (Exploration and Graphics for RivEr Trends) Statistics and graphics for streamflow history, water quality trends, and the statistical modeling algorithm: Weighted Regressions on Time, Discharge, and Season (WRTDS). Package: r-cran-egretci Architecture: all Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-egret, r-cran-binom, r-cran-foreach Suggests: r-cran-knitr, r-cran-testthat, r-cran-doparallel, r-cran-iterators, r-cran-rmarkdown, r-cran-pkgdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-egretci_2.0.5-1.ca2604.1_all.deb Size: 2566862 MD5sum: b68014240c76e7aea9b7f53c08cd66a2 SHA1: bff7d678e1fb1932e8d65542ec320bd379bf5ac7 SHA256: ca73552a4119797cc2cf0be88dd4584377ef508f6d321fb0383b821b35871759 SHA512: 5fde8c3f0fa708180ee1abc0a3b68f04c8ce2e4569fa2f9a80103d38de1e59ec51ee90ca53ee2a2fcdc908692f1a170c9bef96dcdf7bbf9288ecd6cad83fa675 Homepage: https://cran.r-project.org/package=EGRETci Description: CRAN Package 'EGRETci' (Exploration and Graphics for RivEr Trends Confidence Intervals) Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) , and expanded in Hirsch and De Cicco (2015) . The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) . Package: r-cran-egrni Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-gdata, r-cran-mass, r-cran-readr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-egrni_0.1.6-1.ca2604.1_all.deb Size: 770062 MD5sum: 0d4e1c7d62a14fd3287b449ee4923e0b SHA1: ab434c60b9dab659e1344d62519da8e783951a98 SHA256: 8e31c6f77711eb1e3a1ef2d4654f2842f22574b9155ed5a8ed45f2da9f4362a7 SHA512: 5984adb6d7fcf15c7900ff6deadf7e178ac7ea950827b28746eb8305fe4465c85de09fdef6522ccb73070f65a9a449aecf119606c15c18772b755c3e29170591 Homepage: https://cran.r-project.org/package=EGRNi Description: CRAN Package 'EGRNi' (Ensemble Gene Regulatory Network Inference) Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. . Package: r-cran-egst Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-mvtnorm, r-cran-mass, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-egst_1.0.0-1.ca2604.1_all.deb Size: 197316 MD5sum: 09a7f6904c38b9dbb4ec11159a973a09 SHA1: 4de77c574b3045276a7caa350ad15e8c476902ff SHA256: e908630f351e7534c466cf1e31fa15440195a3507339d404d0e41d6d8d484423 SHA512: fe87a9925cc30352db68ace7671689e02f2e7ea7fd8531b7168d7eec145b5a3aacad58da97abaa3182b5193c931d5e733d469074191c735e8f7ae9f4f25e2af1 Homepage: https://cran.r-project.org/package=eGST Description: CRAN Package 'eGST' (Leveraging eQTLs to Identify Individual-Level Tissue of Interestfor a Complex Trait) Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) . Package: r-cran-ehagof Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ehagof_0.1.1-1.ca2604.1_all.deb Size: 66860 MD5sum: 32ee5b78a82481e605c8bb8c6b923f61 SHA1: 3e36885511f0da8a246a18092f4b66fffa498a7d SHA256: 4bc6d3b6cd7dd31b3a714e679bbb875b234f566be25beb2a9c503596b36d6407 SHA512: 3ce75a4570b75bb4a4344d718fcdc9513bb4de6d1a30634a63495e5adc6cdc94973f2659f6eb756316336edd6f38260b90c6d01fd3f3e385b567f9d86763e894 Homepage: https://cran.r-project.org/package=ehaGoF Description: CRAN Package 'ehaGoF' (Calculates Goodness of Fit Statistics) Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), corrected Akaike's information criterion (CAIC), Mean Square Error (MSE), Bayesian Information Criterion (BIC) and Normalized Mean Square Error (NMSE). Package: r-cran-ehdprep Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1611 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-forcats, r-cran-stringr, r-cran-purrr, r-cran-tidyr, r-cran-kableextra, r-cran-magrittr, r-cran-tibble, r-cran-scales, r-cran-rlang, r-cran-quanteda, r-cran-tm, r-cran-pheatmap, r-cran-igraph, r-cran-tidygraph, r-cran-readr, r-cran-readxl, r-cran-knitr Suggests: r-cran-testthat, r-cran-ggraph, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ehdprep_1.4.0-1.ca2604.1_all.deb Size: 1317234 MD5sum: 5e5a619759a1be8fd0b4339d4822b79a SHA1: 49bb6dff5a2c6e336809df1f3adb8ed903ea981c SHA256: 84e8f259307bd9a4b02dd60d7450d8447413e07dd08548053d452bb840c88130 SHA512: 81cc146cbabf24f344f89a01e12e441b8318d7fc3d053b4669a41a1f61fb4aeabe7503f96ee2cc50d4a1b14090a26e95e691142287f2b1caf67ca56c7e28d663 Homepage: https://cran.r-project.org/package=eHDPrep Description: CRAN Package 'eHDPrep' (Quality Control and Semantic Enrichment of Datasets) A tool for the preparation and enrichment of health datasets for analysis (Toner et al. (2023) ). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. 'eHDPrep' also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies. Package: r-cran-ehelp Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-ehelp_1.2.1-1.ca2604.1_all.deb Size: 90192 MD5sum: ca7350ce84c4d4919b523b1e4891cfe4 SHA1: a4ed3b8ff67628c5717f125ba7b6d1f270a1057e SHA256: b885efc9f3da4616556f85bd2c1f14aa083bb64180bd2e91192cdd6165e00d1e SHA512: e41ec1d54486eab1311e19b6235413361d02f596ac31d7020c47546b7abec272613e7c4374bc8ee312427c33428f1e31d0910590f9088227d6236f7654140649 Homepage: https://cran.r-project.org/package=ehelp Description: CRAN Package 'ehelp' (Enhanced Help to Enable "Docstring"-Comments in Users Functions) By overloading the R help() function, this package allows users to use "docstring" style comments within their own defined functions. 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The 'EHR' package provides modules to perform diverse medication-related studies using data from EHR databases. Especially, the package includes modules to perform pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, Stein, Bejan, Denny, and Van Driest (2020) . Additional modules will be added in future. In addition, this package provides various functions useful to perform Phenome Wide Association Study (PheWAS) to explore associations between drug exposure and phenotypes obtained from EHR data, as outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) . Package: r-cran-ehrtemporalvariability Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plotly, r-cran-zoo, r-cran-xts, r-cran-lubridate, r-cran-rcolorbrewer, r-cran-viridis, r-cran-scales, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-dbscan, r-cran-webshot, r-cran-httr Filename: pool/dists/resolute/main/r-cran-ehrtemporalvariability_1.2.2-1.ca2604.1_all.deb Size: 2753966 MD5sum: 9ed509fa95c0e4ceaacffaac3f5dcd2b SHA1: 72b92e43ca77884e955d0733dffc499ecc520712 SHA256: 11ead27d78006c016da7e80dd2f242150e151ae9b1b7d93e464f3ae4e07894f1 SHA512: f7bdd3e8741899e0f8391546e0d95bf720322b3add3ca36d38bcfa6c3d1620fa37a672521cda3ee934feae704186f524a67e895eb3a88987243f3157903c500b Homepage: https://cran.r-project.org/package=EHRtemporalVariability Description: CRAN Package 'EHRtemporalVariability' (Delineating Temporal Dataset Shifts in Electronic Health Records) Functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. 'EHRtemporalVariability' is particularly suitable for multi-modal data and categorical variables with a high number of values, common features of biomedical data where traditional statistical process control or time-series methods may not be appropriate. 'EHRtemporalVariability' allows you to explore and identify dataset shifts through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. An additional 'EHRtemporalVariability' Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding. (Sáez et al. 2020) . Package: r-cran-ehymet Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clustercrit, r-cran-kernlab, r-cran-tf Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ehymet_0.1.1-1.ca2604.1_all.deb Size: 198926 MD5sum: ed7fb744bde13ab9624b84e02f5bed5c SHA1: 4a7feafff172b616c6496f1f597bc05cc438a880 SHA256: 4513d51abef90ce6080f6ba89207b1490bd3e335175ce1ce17697b183ab94ecf SHA512: 2314ce0aefada1f25782c498f9d2c67e75bf95232bc1b40f1460f0066696e2576a4297f4f624b292c59a4d7c7800656fb94a700167dda9a23294c841a8aac82b Homepage: https://cran.r-project.org/package=ehymet Description: CRAN Package 'ehymet' (Methodologies for Functional Data Based on the Epigraph andHypograph Indices) Implements methods for functional data analysis based on the epigraph and hypograph indices. These methods transform functional datasets, whether in one or multiple dimensions, into multivariate datasets. The transformation involves applying the epigraph, hypograph, and their modified versions to both the original curves and their first and second derivatives. The calculation of these indices is tailored to the dimensionality of the functional dataset, with special considerations for dependencies between dimensions in multidimensional cases. This approach extends traditional multivariate data analysis techniques to the functional data setting. A key application of this package is the EHyClus method, which enhances clustering analysis for functional data across one or multiple dimensions using the epigraph and hypograph indices. See Pulido et al. (2023) and Pulido et al. (2024) . Package: r-cran-ei.datasets Architecture: all Version: 0.0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4041 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-ei.datasets_0.0.1-3-1.ca2604.1_all.deb Size: 4086940 MD5sum: 32dc684f63e1bd528a31d616bd6ae565 SHA1: 152e09ba66fdd51b5d7a6a4961e2ab0a4aa5ae7b SHA256: aa89ba4e8dbb88ceb1e125aba19038187b65c3de13d89043938370e3f48ea5b4 SHA512: a4074c0dce50b4e9c01b4637636ce12e0963b1167344bc890eb3bc35e0f1002489e6367a25d50f511419d89bdf2bc0f976da1bb1a655f1159bf9f1b1180d3894 Homepage: https://cran.r-project.org/package=ei.Datasets Description: CRAN Package 'ei.Datasets' (Real Datasets for Assessing Ecological Inference Algorithms) Provides more than 550 data sets of actual election results. Each of the data sets includes aggregate party and candidate outcomes at the voting unit (polling stations) level and two-way cross-tabulated results at the district level. These data sets can be used to assess ecological inference algorithms devised for estimating RxC (global) ecological contingency tables using exclusively aggregate results from voting units. Reference: Pavía (2022) . Package: r-cran-ei Architecture: all Version: 1.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eipack, r-cran-mvtnorm, r-cran-msm, r-cran-tmvtnorm, r-cran-ellipse, r-cran-plotrix, r-cran-mass, r-cran-ucminf, r-cran-cubature, r-cran-mnormt, r-cran-foreach, r-cran-sp Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-ei_1.3-3-1.ca2604.1_all.deb Size: 585064 MD5sum: 4ea38e2eff837f8f443de4faa8b0710c SHA1: 5b6a573e90c48108fb5e12d3d2f73c0d2ca40acf SHA256: e53b195625781b6555474323b3e1dbcbfa13732a44b113ef5c27d1b51bf64e35 SHA512: 75041c513f076a9431befcc6010d29ed714d72cd2eccb27b3b9dab97777333562bcd3ce5aeb8045b613d530a60abafe1c2f1d4a2d5513365e9b8fb2c60491e75 Homepage: https://cran.r-project.org/package=ei Description: CRAN Package 'ei' (Ecological Inference) Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407. Package: r-cran-eia Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-httr, r-cran-jsonlite, r-cran-memoise, r-cran-lubridate Suggests: r-cran-testthat, r-cran-knitr, r-cran-covr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-eia_0.4.2-1.ca2604.1_all.deb Size: 291830 MD5sum: 1ddebe5dbe22c3f5439914853cbb3479 SHA1: 3cb4e0ae4cfccfb3fee1f95f172fed9efef44ba1 SHA256: 11938c2dd690391814910233a806745d30b277fb1b7b9ad0797d642df09118e0 SHA512: 949d9ec59406f34924cec4e3af9092c108c91563ff9cbc3c19a2c21009e1fd89b6ff9e4748838e8754cc37061bfbae7f8bb818b93a60d2a799cf6209bac3df74 Homepage: https://cran.r-project.org/package=eia Description: CRAN Package 'eia' (API Wrapper for U.S. Energy Information Administration ('EIA')Open Data) Provides API access to data from the U.S. Energy Information Administration ('EIA') . Use of the EIA's API and this package requires a free API key obtainable at . This package includes functions for searching the EIA data directory and returning time series and geoset time series datasets. Datasets returned by these functions are provided by default in a tidy format, or alternatively, in more raw formats. It also offers helper functions for working with EIA date strings and time formats and for inspecting different summaries of series metadata. The package also provides control over API key storage and caching of API request results. Package: r-cran-eiaapi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-knitr, r-cran-plotly, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eiaapi_0.2.0-1.ca2604.1_all.deb Size: 477442 MD5sum: a50ed6888dd7dbbfa13c377a90f39f99 SHA1: 43b95e59c8aa22fd003899db4491136bfe52a8f2 SHA256: cc2636678445402804fc5a3ba9e5c7573ba56673268f9d73dcb6225ec9ced579 SHA512: fbb2973c03d2efbe3e17d558c08ac57993fba2f2a7495a9791355a627f2b6b7481b8958027971d249cb8c6c11ae1fa81aadae1be0d050c2867095ce036ac47ad Homepage: https://cran.r-project.org/package=EIAapi Description: CRAN Package 'EIAapi' (Query Data from the 'EIA' API) Provides a function to query and extract data from the 'US Energy Information Administration' ('EIA') API V2 . The 'EIA' API provides a variety of information, in a time series format, about the energy sector in the US. The API is open, free, and requires an access key and registration at . 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(2012), as extension of the model proposed in Brown and Payne (1986). Allows incorporation of covariates. References: Brown, P. and Payne, C. (1986). ''Aggregate data, ecological regression and voting transitions''. Journal of the American Statistical Association, 81, 453–460. . Forcina, A., Gnaldi, M. and Bracalente, B. (2012). ''A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy''. Statistical Methods & Applications, 21, 109–119. . Pavia, J.M, and Forcina, A. (2026). ''Simulating electoral behavior''. Modeling Decisions for Artificial Intelligence, MDAI 2025. Lecture Notes in Computer Science, vol 15957, Torra, V., Narukawa, Y., Domingo-Ferrer, J. (eds), Springer, Cham, pp. 54-65. . Acknowledgements: The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIAICO/2023/031) and MICIU/AEI/10.13039/501100011033/FEDER, EU (grant PID2021-128228NB-I00) for supporting this research. Package: r-cran-eientropy Architecture: all Version: 0.0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-here Filename: pool/dists/resolute/main/r-cran-eientropy_0.0.1.4-1.ca2604.1_all.deb Size: 104194 MD5sum: 052a70530c045a94707665d7797d37fb SHA1: ac1b683a68e57fb79db724c37d21584b4c08321e SHA256: cd967dccd55152004ce3492ee19524a1c99f4fcbc8c0c13ffbf652e76275c532 SHA512: 0aa40840ddb91de426cd8ca053eef742811a23825b36646659f18ea8308d73a6fd23e9ebec9132fbea381decebaa39bad122e088719578f146984ec16ce7cae7 Homepage: https://cran.r-project.org/package=EIEntropy Description: CRAN Package 'EIEntropy' (Ecological Inference Applying Entropy) Implements two estimations related to the foundations of info metrics applied to ecological inference. These methodologies assess the lack of disaggregated data and provide an approach to obtaining disaggregated territorial-level data. For more details, see the following references: Fernández-Vázquez, E., Díaz-Dapena, A., Rubiera-Morollón, F. et al. (2020) "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach." . Díaz-Dapena, A., Fernández-Vázquez, E., Rubiera-Morollón, F., & Vinuela, A. (2021) "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom." . 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Acknowledgements: The authors wish to thank Generalitat Valenciana, Consellería de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031) for supporting this research. 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For more theoretical details on our methods and algorithms see Steyer et al. (2023, ) and Steyer et al. (2023, ). 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Package: r-cran-elastes Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-elasdics, r-cran-mgcv, r-cran-sparseflmm, r-cran-orthogonalsplinebasis Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-rmarkdown, r-cran-shapes Filename: pool/dists/resolute/main/r-cran-elastes_0.1.7-1.ca2604.1_all.deb Size: 114952 MD5sum: 2020fffa500b9017f3742ba6c97fe533 SHA1: 8ed924efc8c05d362b13a9541d1866a9e233353c SHA256: 696a6f31a21a5d6b963137b4828e9ba285f604e6cab2c2e4e209d879eda061f8 SHA512: 234880021a4f6644e218613400d2bd755458e78fad1d51a7a429bcb3407891e9d494889c330489112fab9fdb95118b4f07f29d3e1c60f768224e720b875e029b Homepage: https://cran.r-project.org/package=elastes Description: CRAN Package 'elastes' (Elastic Full Procrustes Means for Sparse and Irregular PlanarCurves) Provides functions for the computation of functional elastic shape means over sets of open planar curves. 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Package: r-cran-elastic Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-crul, r-cran-jsonlite, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-elastic_1.2.2-1.ca2604.1_all.deb Size: 760960 MD5sum: f6bd23f103a6da2095ee6498681e9f05 SHA1: c15752b3bcbaaf6df41bc59d122d32759ad27ff3 SHA256: eee19410aba29bdacaa58ef2fc4fb561fbc12fd4f275af3aad9dcbd016c68268 SHA512: 5ca460fb28bfbd9ddca2bd1b24a8d243e145af2f24aef1f1675923eda7c4cf1ba5c7ccb469916e1db76627ebbb444753605bb4ea423c16645a5584a78193674d Homepage: https://cran.r-project.org/package=elastic Description: CRAN Package 'elastic' (Database Interface to 'Elasticsearch' and 'OpenSearch') Connect to 'Elasticsearch' and 'OpenSearch', 'NoSQL' databases built on the 'Java' Virtual Machine and using the 'Apache' 'Lucene' library. 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Package: r-cran-elasticnet Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lars Filename: pool/dists/resolute/main/r-cran-elasticnet_1.3-1.ca2604.1_all.deb Size: 233388 MD5sum: bdb5ba4cbc30f2cd27ad99db136df397 SHA1: 3f841723c21c57cf9eb7e5ba6c464d18f3c037f1 SHA256: 1fd2dde16613298bdc6a8e2cc1ce104c695fe5c6a619f860a373e23055056aba SHA512: 9b353f0f1467ef6d94aa210920b6de747b76e324ad05c3eb81833062a5c47e01937f1f7bd0bc3301c88b7f0652cd2377fec9ae746408df7cd2724d2e81a6776e Homepage: https://cran.r-project.org/package=elasticnet Description: CRAN Package 'elasticnet' (Elastic-Net for Sparse Estimation and Sparse PCA) Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for doing sparse PCA. Package: r-cran-elechemr Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-elechemr_1.2.0-1.ca2604.1_all.deb Size: 187018 MD5sum: a1e72f2df5b5c584f66c66accc32511b SHA1: 2de6bd28cb861cf5d48e2aa1ec86b4b83aa409b3 SHA256: 73ea30978e31f0b39e2144b8fef3b59c0f0c0239067abdf96a5b011712c49c1a SHA512: adb657e737ce4d74fbc118ceacb3312140a852fd91b55aa7feecee1351cad0670b75ebb77ae629c09a9c36dcf892127d6552c5cb1f1342672fa669901881ad5d Homepage: https://cran.r-project.org/package=EleChemr Description: CRAN Package 'EleChemr' (Electrochemical Reactions Simulation) Digital simulation of electrochemical processes. Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.Update 1.2.0 improved the accuracy of the measurements and allow personalized data resolution for simulation. Bibliography regarding this methods can be found in the following texts. Dieter Britz, Jorg Strutwolf (2016) . Allen J. Bard, Larry R. Faulkner (2000) . 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References: Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). . Penades, A and Pavia, J.M. (2025) ''The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''. Acknowledgements: The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research. Package: r-cran-elections Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-elections_1.0.1-1.ca2604.1_all.deb Size: 12744 MD5sum: f4d3b2a85ee00ea3a1bd19c0a4541272 SHA1: 3db685f3128f18c916597ab1a8ec53535be2d4f0 SHA256: 6b8a02f6083ba5fbc7e985d814ae5c91ca927da21b0d9f37f96f10f5e6f17618 SHA512: c1c507704c7ff1ae873eba539a08c08e8a050452938c25407e2faa014ff3ac3dcaf6e4a3c343f350f9c091f0b229f24414340dd8147b64c043e4bf32aca52e92 Homepage: https://cran.r-project.org/package=elections Description: CRAN Package 'elections' (USA Presidential Elections Data) This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in . Package: r-cran-electionsbr Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-data.table, r-cran-haven, r-cran-readr, r-cran-httr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-electionsbr_0.5.0-1.ca2604.1_all.deb Size: 158456 MD5sum: aa0102c54c38c97aa799954236158573 SHA1: 09928b4be36e0b3f1e51321341a2e057ab4848d2 SHA256: b452868315f476091bd59a2e0817a5b6c0ced13e69ed717c220e01ad775f3e46 SHA512: 1b88350cb8b29da56e41b15d0ceb6d4f8a5703ca9e52b6f307290d9a7af5a9616bce9eef05a282cab6048b787b6e9b13d3cd6df0407f27b6277bed96d22e78d1 Homepage: https://cran.r-project.org/package=electionsBR Description: CRAN Package 'electionsBR' (R Functions to Download and Clean Brazilian Electoral Data) Offers a set of functions to easily download and clean Brazilian electoral data from the Superior Electoral Court and 'CepespData' websites. Among other features, the package retrieves data on local and federal elections for all positions (city councilor, mayor, state deputy, federal deputy, governor, and president) aggregated by state, city, and electoral zones. Package: r-cran-electivity Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-usethis Filename: pool/dists/resolute/main/r-cran-electivity_1.0.2-1.ca2604.1_all.deb Size: 33324 MD5sum: f2a6f6728fa744f2b33a9d27a833a80d SHA1: 67f075269b39150960eb499b26594b7d81443ec5 SHA256: b56a67271d9838e5a6f28d078ec34913a67e2e58963716580f05eafba092683a SHA512: 24f55bac6226d404ea7b0157e9f92f1d1cc4a261ee86be20f323cb0789f1a440081a6e803a151b8cd67d389d567928f50110fb6f44543f5c84793bb5f8e44842 Homepage: https://cran.r-project.org/package=electivity Description: CRAN Package 'electivity' (Algorithms for Electivity Indices) Provides all electivity algorithms (including Vanderploeg and Scavia electivity) that were examined in Lechowicz (1982) , plus the example data that were provided for moth resource utilisation. Package: r-cran-electoral Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ineq, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-electoral_0.1.4-1.ca2604.1_all.deb Size: 46076 MD5sum: 4836edcb8fe557c1b52b0ea3d5adc383 SHA1: 0d9ab6c1c30ec4ffb999fa6c1b42a3e6e3bc8f28 SHA256: 63a361d9e5b4f13402e9683133d555be85b1315ae6d25b64d1774bad9fd19a54 SHA512: 1746e8bbfd1f2bd127b807885eaeeb854d23a7222284c57440dbc4998555480c037ddb7f6c6196261b9629fec99b17909cd90e732ccd0924bcce45e7c6dd3a3b Homepage: https://cran.r-project.org/package=electoral Description: CRAN Package 'electoral' (Allocating Seats Methods and Party System Scores) Highest averages & largest remainders allocating seats methods and several party system scores. Implemented highest averages allocating seats methods are D'Hondt, Webster, Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague, equal proportions and Adams. Implemented largest remainders allocating seats methods are Hare, Droop, Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders. The main advantage of this package is that ties are always reported and not incorrectly allocated. Party system scores provided are competitiveness, concentration, effective number of parties, party nationalization score, party system nationalization score and volatility. References: Gallagher (1991) . Norris (2004, ISBN:0-521-82977-1). Laakso & Taagepera (1979) . Jones & Mainwaring (2003) . Pedersen (1979) . Golosov (2010) . Golosov (2014) . Package: r-cran-elevatr Architecture: all Version: 0.99.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-progressr, r-cran-sf, r-cran-terra, r-cran-future, r-cran-furrr, r-cran-purrr, r-cran-units, r-cran-slippymath, r-cran-curl, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-progress Filename: pool/dists/resolute/main/r-cran-elevatr_0.99.1-1.ca2604.1_all.deb Size: 340460 MD5sum: e01059013855e33aff724d9dd42ce331 SHA1: 40774b7de81e9d0c181c9039dddab5fa8df398fa SHA256: 732791c58578c25a9144cc9848ebe512dea501bbff2abc5ea1000c9042b7a4c8 SHA512: 98f40da7580578f31f5a703485b68943e6588d06a5d332a33801f98c0835c7e6855b41e3c25cabc6ab807f4eb24ac4d0d27f59438d347742d0b882465914528d Homepage: https://cran.r-project.org/package=elevatr Description: CRAN Package 'elevatr' (Access Elevation Data from Various APIs) Several web services are available that provide access to elevation data. This package provides access to many of those services and returns elevation data either as an 'sf' simple features object from point elevation services or as a 'raster' object from raster elevation services. In future versions, 'elevatr' will drop support for 'raster' and will instead return 'terra' objects. Currently, the package supports access to the Amazon Web Services Terrain Tiles , the Open Topography Global Datasets API , and the USGS Elevation Point Query Service . Package: r-cran-elexr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-elexr_1.0-1.ca2604.1_all.deb Size: 11846 MD5sum: bf6053e4383e42a7cf7b1780dbc416be SHA1: a8a954c18a45c81b98d018648f8fefade5484c34 SHA256: f2d3691cdd22ec40a42d648cded3bfed56ba4fc7f0f619c7988a28cf2d6e3790 SHA512: 500a3d82d18f517167ba23350c3e8da0e899e7ae7bd10912bf4dfdf88dc9a3f675994012bf460ac2579f3d8307f3e65b1b136cc9a203a1da617e2f3eb0149432 Homepage: https://cran.r-project.org/package=elexr Description: CRAN Package 'elexr' (Load Associated Press Election Results with Elex) Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results. Package: r-cran-elfgen Architecture: all Version: 2.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-quantreg, r-cran-ggplot2, r-cran-testit, r-cran-scales, r-cran-sqldf, r-cran-curl, r-cran-sbtools, r-cran-nhdplustools Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-elfgen_2.3.5-1.ca2604.1_all.deb Size: 218052 MD5sum: 05f514d75ad7dab6a2550fa803154644 SHA1: 7c868b12b2b07b52d15c5847874ceee33b8f45c0 SHA256: 62d09a1f47ebc13f04abd01228eb4bbfabda23904cfecc1e37ff0631c477f7f6 SHA512: 12b2cbbb1443aa4e21379344b4d2716fa657e80effd2c64db828e218fed9fcd2927484524a418556524183b23b3db109e6caa16ac0f8bef1482b03f1ced647d4 Homepage: https://cran.r-project.org/package=elfgen Description: CRAN Package 'elfgen' (Ecological Limit Function Model Generation and Analysis Toolkit) A toolset for generating Ecological Limit Function (ELF) models and evaluating potential species loss resulting from flow change, based on the 'elfgen' framework. ELFs describe the relation between aquatic species richness (fish or benthic macroinvertebrates) and stream size characteristics (streamflow or drainage area). Journal publications are available outlining framework methodology (Kleiner et al. (2020) ) and application (Rapp et al. (2020) ). Package: r-cran-elhmc Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emplik, r-cran-plyr, r-cran-mass Filename: pool/dists/resolute/main/r-cran-elhmc_1.2.1-1.ca2604.1_all.deb Size: 31508 MD5sum: 75775bf4c6ca1fffc2d6a4c33905c2b3 SHA1: fed1a024613de3da8e2bb1887220d5906c2b97b8 SHA256: 1ade3e5c075e96ce235c6033277483b3f5b8c1fe27ce9a15dc549a223e9264c8 SHA512: e9e59fde49824fbc65e48c5fcc4d0d5b50d50a244ad3409f4334162d2eaaab830348b94b348943113c7a1a986b417114f92648377c78d6e11077198f6b9d6ac3 Homepage: https://cran.r-project.org/package=elhmc Description: CRAN Package 'elhmc' (Sampling from a Empirical Likelihood Bayesian Posterior ofParameters Using Hamiltonian Monte Carlo) A tool to draw samples from a Empirical Likelihood Bayesian posterior of parameters using Hamiltonian Monte Carlo. Package: r-cran-elic Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-distr, r-cran-distrellipse, r-cran-mass Suggests: r-cran-testthat, r-cran-sn Filename: pool/dists/resolute/main/r-cran-elic_0.1.0-1.ca2604.1_all.deb Size: 41462 MD5sum: 557b0dabe95683b06578bf7cfaee13cd SHA1: 175cee09f62a5a4c9d4348e9dc4e6db10958d68a SHA256: 915ac114096e3cc7409898e04eb3e2457d14ce4047f89c9c9c50f85ed98879a1 SHA512: f4e66bbd0b53e3c05d45d6f8956f4b4622b4001deb693270e9212ab2d0e338dfe9a448811e93e943b32f8d6630c56cd854c4654a3314daca42ff088801c04d57 Homepage: https://cran.r-project.org/package=ELIC Description: CRAN Package 'ELIC' (LIC for Distributed Elliptical Model) This comprehensive toolkit for Distributed Elliptical model is designated as "ELIC" (The LIC for Distributed Elliptical Model Analysis) analysis. It is predicated on the assumption that the error term adheres to a Elliptical distribution. The philosophy of the package is described in Guo G. (2020) . 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Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs. Feng (2018) . 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Drawing on characteristics of classical test theory, Exploratory Likert Scaling (ELiS) supports the user exploring multiple one-dimensional data structures. In common research practice, however, EFA remains the go-to method to uncover the (underlying) structure of a data set. Orthogonal dimensions and the potential of overextraction are often accepted as side effects. As described in Müller-Schneider (2001) ), ELiS confronts these problems. As a result, 'elisr' provides the platform to fully exploit the exploratory potential of the multiple scaling approach itself. 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Package: r-cran-elitism Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-elitism_1.1.1-1.ca2604.1_all.deb Size: 90980 MD5sum: e48562ad792b2d3744a340e96cbdf132 SHA1: 0d188a6737c9ce5d73c687d88d1d06962f81149a SHA256: d4197fc4850977f6a0ea9acf276422a88ee8c78e8235dba945a9b26dcca50eb0 SHA512: 2c9b4500db71711c0cd875085fd51a35552dbb54a1e1f138a55b16f4cae1fb334f03ee3d7e376760fb1c9a3cdb832fe10a485a5f649d76615634b528fb6eb031 Homepage: https://cran.r-project.org/package=elitism Description: CRAN Package 'elitism' (Equipment for Logarithmic and Linear Time Stepwise MultipleHypothesis Testing) Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). 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One function for linear regression, a second for logistic regression and a last one for generalized linear models. Package: r-cran-elliplot Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-elliplot_1.3.0-1.ca2604.1_all.deb Size: 42494 MD5sum: 39f23bd3ac42521b90bee92fdd97ca92 SHA1: 1b77ba48ffc46c37d294dd1d39bb13705fbbbb2b SHA256: 1ab1b49e09973713504667e976fca21b314dea88c3f463b8ded79a9feba9f88a SHA512: 84136097a743b51f12d29c2afc642a7c7fc756ae5c60275c2ebef83503590174e781644b190a9fc04579fca8c54b95d2d4f7f5a5e568df0e74c5807da72fff88 Homepage: https://cran.r-project.org/package=elliplot Description: CRAN Package 'elliplot' (Ellipse Summary Plot of Quantiles) Correlation chart of two set (x and y) of data. Using Quantiles. Visualize the effect of factor. 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This package provides several statistical tests for elliptical symmetry that are described in Babic et al. (2021) . Package: r-cran-ellmer Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2450 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-coro, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-later, r-cran-lifecycle, r-cran-promises, r-cran-r6, r-cran-rlang, r-cran-s7, r-cran-tibble, r-cran-vctrs Suggests: r-cran-connectcreds, r-cran-curl, r-cran-gargle, r-cran-gitcreds, r-cran-jose, r-cran-knitr, r-cran-magick, r-cran-openssl, r-cran-otel, r-cran-otelsdk, r-cran-paws.common, r-cran-png, r-cran-rmarkdown, r-cran-shiny, r-cran-shinychat, r-cran-testthat, r-cran-vcr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-ellmer_0.4.1-1.ca2604.1_all.deb Size: 1734574 MD5sum: 9ab429899447423c1f2ebba56c71e5d5 SHA1: 898ab188d4e5ad3b2826c7624520c98caf470287 SHA256: a2e4783926f3d90fc72d5898bd218d03eadcc3e044dcd3a4f2fff8ea66651d88 SHA512: be21511f4ff4ef3501b1c2c8dcdb13718e792f3286f4ec63d9d1d4dc85ea03915ccf90260d9b1ce4b0046a92b61a09f3937fca69b0c66146311e26be1121c83a Homepage: https://cran.r-project.org/package=ellmer Description: CRAN Package 'ellmer' (Chat with Large Language Models) Chat with large language models from a range of providers including 'Claude' , 'OpenAI' , and more. Supports streaming, asynchronous calls, tool calling, and structured data extraction. Package: r-cran-elmethodvar Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1599 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-elmethodvar_0.1-1.ca2604.1_all.deb Size: 1600324 MD5sum: 5e97bb13f646c6af7fa942258af8a99d SHA1: e6d9b13115f64a0f4bacb74082123aa78d42c5e1 SHA256: f6a87e990350822447977b597d29552c5ab320ad194dc4b4ebf80568cb5483f5 SHA512: f4db2156525ed1d3bc1103e0797bc6cb7e238a3a98151e3c4c29abe1b9f2ff8988f1bc397a7c6981e524b79bd6589a4bf43571c887f3c89b32e665f3100b6314 Homepage: https://cran.r-project.org/package=ELmethodVar Description: CRAN Package 'ELmethodVar' (Empirical Likelihood Inference of Variance Components in LinearMixed-Effects Models) Provides empirical likelihood-based methods for the inference of variance components in linear mixed-effects models. Package: r-cran-elmr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-elmr_1.0-1.ca2604.1_all.deb Size: 33238 MD5sum: a4eaab1cc303420ef45cedc93422f12f SHA1: 7b8ccf126f04ee01dab95b3898703978b7dd9b6d SHA256: 038a56c757e8dbd7ab0a7197d40c45bed86c65aa79bfba0739fdad9f47c07bf8 SHA512: c4d857dd30e406866d410917b536928145c23e65bce65a3f38f126b9c3556c20c8ce41fccc574512d67e7864e87aa37aef10f318a22adf93382c4747090bd5a7 Homepage: https://cran.r-project.org/package=ELMR Description: CRAN Package 'ELMR' (Extreme Machine Learning (ELM)) Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. 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Package: r-cran-elmso Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-elmso_1.0.1-1.ca2604.1_all.deb Size: 27938 MD5sum: 05bb6e20ab4a3c4efedcbcc17e4f5ae6 SHA1: ce4f853041acb3b95e9d4aa8c6b37bf645ddbeee SHA256: 7d974b39902dc20b0143b8418086371520f9b466329067cab9eb21121fdb7d01 SHA512: e4300c9f8967f7cd69f722a337903e7a5a1bae0a947b031727d263701991af006f640f3562c49f8dbc6b0245ef7865e2b3a8fe334b16894c0ac2df4e04f7c060 Homepage: https://cran.r-project.org/package=ELMSO Description: CRAN Package 'ELMSO' (Implementation of the Efficient Large-Scale Online DisplayAdvertising Algorithm) An implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and for a full-text version of the paper). 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Package: r-cran-elnnpairedcov Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase, r-cran-mass, r-bioc-limma Filename: pool/dists/resolute/main/r-cran-elnnpairedcov_0.3.2-1.ca2604.1_all.deb Size: 255684 MD5sum: eee882a29ecf84ccd9310605799c15c8 SHA1: 07f36be3ecc3254170b680c9eec094933f919cda SHA256: c650455fb1bc471cf1d04bfdd00f3bbbd37e01e0e74e7d8d14df70649a686d2e SHA512: a6bcc5a96f9e76ce02b1d9962d6b953630b2cb3d17ccde160e94d3de08f68f9e97c765df6f47e9c041bfb32ee09e75d54846d40913c6cd79336a772ed9d2fc35 Homepage: https://cran.r-project.org/package=eLNNpairedCov Description: CRAN Package 'eLNNpairedCov' (Model-Based Gene Selection for Paired Data) Model-based clustering for paired data based on the regression of a mixture of Bayesian hierarchical models on covariates. Zhang et al. (2023) . 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Package: r-cran-eltr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eltr_0.1.0-1.ca2604.1_all.deb Size: 58416 MD5sum: f68dfc323c221bec9b64fc484e5851a5 SHA1: 1a54ac12353a5e9a5a0b5b42b7d0547e651c9a7b SHA256: 982979266e59391e1195d7c585f6d12e0c5efa967015e212aa49eb54994822dc SHA512: 1ae072fa6141cdb19b8d5ed0593123d49e9c9fae19346b1a5411edaf7d71a3503c852b0d3a164a0eb20042160dfb1cf8dce044fe20367b34974ab4b473337757 Homepage: https://cran.r-project.org/package=eltr Description: CRAN Package 'eltr' (Utilise Catastrophe Model Event Loss Table Outputs) Provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution. Package: r-cran-em.fuzzy Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-distrib Filename: pool/dists/resolute/main/r-cran-em.fuzzy_1.0-1.ca2604.1_all.deb Size: 67516 MD5sum: 661163c0c934224ad99bb0c0ed0dd471 SHA1: 5b6293d7e463f069a7bc2819ab0ac37da2eb9bcb SHA256: 6ce9c93d63f52c15f8aa987c1ac2103ca2f9383f642b69dc56ac987c749bd089 SHA512: cda6933d95d72c1a0b1fd7fdebb4162e96d1fe14ec5746e7b2aaf8431989b3d35cfc58993c71984907818153df0b96d699cc339a249743f69b895d0878074753 Homepage: https://cran.r-project.org/package=EM.Fuzzy Description: CRAN Package 'EM.Fuzzy' (EM Algorithm for Maximum Likelihood Estimation by Non-PreciseInformation) The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) . Package: r-cran-emailjsr Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shiny.i18n, r-cran-httr, r-cran-shinybrowser Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-emailjsr_0.0.2-1.ca2604.1_all.deb Size: 147164 MD5sum: 43249d66730f4070cab5627f7cd945d2 SHA1: a26a6cb60cc1a43ccfcf59e6499e7abb5493b01b SHA256: 136adccd6e873e140fe74a8a51c21bbe62459052f324b6003781e6b73615aa35 SHA512: abc65e2bc6d8cafd9777d1c48e22f1634638c677ae232a1029d8c5e41830c6b605ac3ca038d167d25af9eda78aca2842b253977cb6be736636ff8a5ed74048c3 Homepage: https://cran.r-project.org/package=emailjsr Description: CRAN Package 'emailjsr' ('emailjs' Support) Use 'emailjs' API easily in 'R'. This package is not official. . You can send e-mail with 'emailjs' with function, based on 'httr'. You can also make a 'shiny' ui and server function. It can be used for making feedback form, inquiry, and so on. Package: r-cran-emailvalidation Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-emailvalidation_0.1.0-1.ca2604.1_all.deb Size: 20090 MD5sum: 2ae941f116a5d20ace718cf7c2bb11fa SHA1: 5cb31b0e5999c1669c9a90d7d055300fc62f1e21 SHA256: bdbf465a920055a4b17654bc54eab56d1f7899a266dddccc43a91d5d8af71603 SHA512: 40fcc49568f6fef697d6e9cae4bf981f0a8756a024130e3f1d6a214f11d79434bec33fe72ebedc1a06ec0804dc9308cab97247c6f006181e371638ea8df05cc3 Homepage: https://cran.r-project.org/package=emailvalidation Description: CRAN Package 'emailvalidation' (Client for the 'emailalvalidation.io' E-Mail Validation API) An R client for the 'emailvalidation.io' e-mail verification API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at . Package: r-cran-emar Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-emar_1.0.0-1.ca2604.1_all.deb Size: 19506 MD5sum: 11b5fb1f0e7ec2307d182879b76e818a SHA1: e1dfd99fb7a1ed3c77ee242a4c050ef7e017f703 SHA256: 9613c55ef2b467fb84af458a7f4ff5ec6b067934c9d93f08a241209e0e432577 SHA512: 619ffbd84d255adc6216471176d73267bd606450670abb52468b4218a87e7b186816cf36a97d6fbb5c94550b4c5b965f3d75333ef6c5e31e81ef77a8ea3e7036 Homepage: https://cran.r-project.org/package=EMAR Description: CRAN Package 'EMAR' (Empirical Model Assessment) A tool that allows users to generate various indices for evaluating statistical models. The fitstat() function computes indices based on the fitting data. The valstat() function computes indices based on the validation data set. Both fitstat() and valstat() will return 16 indices SSR: residual sum of squares, TRE: total relative error, Bias: mean bias, MRB: mean relative bias, MAB: mean absolute bias, MAPE: mean absolute percentage error, MSE: mean squared error, RMSE: root mean square error, Percent.RMSE: percentage root mean squared error, R2: coefficient of determination, R2adj: adjusted coefficient of determination, APC: Amemiya's prediction criterion, logL: Log-likelihood, AIC: Akaike information criterion, AICc: corrected Akaike information criterion, BIC: Bayesian information criterion, HQC: Hannan-Quin information criterion. The lower the better for the SSR, TRE, Bias, MRB, MAB, MAPE, MSE, RMSE, Percent.RMSE, APC, AIC, AICc, BIC and HQC indices. The higher the better for R2 and R2adj indices. Petre Stoica, P., Selén, Y. (2004) \n Zhou et al. (2023) \n Ogana, F.N., Ercanli, I. (2021) \n Musabbikhah et al. (2019) . Package: r-cran-emas Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mediation, r-cran-multilevel, r-cran-ggplot2, r-cran-lavaan Filename: pool/dists/resolute/main/r-cran-emas_0.2.4-1.ca2604.1_all.deb Size: 231328 MD5sum: 57edce8fbcdaec482d3b47ad2ad8b9de SHA1: e93604335e4699c52f4e27ab98fe470f924db866 SHA256: ee36a628e9d18798f40276904c08276f840c8b652408e1c860badfc994b7f9c5 SHA512: 75008bad4e3dd808aaccd37306cdd5966baaafc8cbe5dbde1dc7f9dcf84a65f04e5a2ccbf31e6df1fb3188b871b29631c8b9ad93b6dfaa32a19ab900000b33bb Homepage: https://cran.r-project.org/package=EMAS Description: CRAN Package 'EMAS' (Epigenome-Wide Mediation Analysis Study) DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) , Turner, S. D. (2018) , Rosseel, D. (2012) . 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Effect encodings using simple generalized linear models or nonlinear models can be used. There are also functions for dimension reduction and other approaches. Package: r-cran-embryogrowth Architecture: all Version: 2025.12.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-optimx, r-cran-numderiv, r-cran-ggplot2, r-cran-helpersmg, r-cran-rdpack Suggests: r-cran-entropy, r-cran-shiny, r-cran-coda, r-cran-polynom, r-cran-car, r-cran-gam, r-cran-pbapply, r-cran-cranlogs, r-cran-mapdata Filename: pool/dists/resolute/main/r-cran-embryogrowth_2025.12.22-1.ca2604.1_all.deb Size: 3277088 MD5sum: 69386dfa2ca04315b87f91af86f83c65 SHA1: 062cc4c561b4b5b9fea098c4e0805d1a92b59c45 SHA256: f625628dbc7a2bf190a97e932f6afabc15b7210dc8cf121f8aca56de0eb1eb41 SHA512: 7da655d5e0afc04192da1d845de074c71cf402a49cbe2345e7b2a35065a6be8bf891a1a386a69834a068cc4b8d341a836c1c04bf19e74dd895508db293244b98 Homepage: https://cran.r-project.org/package=embryogrowth Description: CRAN Package 'embryogrowth' (Tools to Analyze the Thermal Reaction Norm of Embryo Growth) Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See for tsd functions; see for thermal reaction norm of embryo growth. 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So far, estimation methods comprise direct estimation, the model-based unit-level approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010) ), the area-level model (see "Estimates of income for small places: An application of James-Stein procedures to Census Data" by Fay and Herriot (1979) ) and various extensions of it (adjusted variance estimation methods, log and arcsin transformation, spatial, robust and measurement error models), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel. For a detailed description of the package and the methods used see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in R". 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These indices help assess the relationship between biodiversity and multiple ecosystem functions. For more details, see Byrnes et al. (2014) and Chao et al. (2024) . Package: r-cran-emg Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-moments Filename: pool/dists/resolute/main/r-cran-emg_1.0.9-1.ca2604.1_all.deb Size: 37048 MD5sum: e8918eeeeaef1e9519d30df6c47ceded SHA1: 93a102a1823a26fb5851475481787dbff4728a8f SHA256: 52817babb79688c08fb7ddcfd138021db0aa5ccf7d2bdad0cad8900b3251d2a3 SHA512: 2a05eb1ad57fe4dee01b229328670ac402d2dc6bf08669115feed2f25976e67ee443e859005c3cbcb5fded2bee6a9d473d3875ff0ebc1991dfdcfa2b0fa5f3af Homepage: https://cran.r-project.org/package=emg Description: CRAN Package 'emg' (Exponentially Modified Gaussian (EMG) Distribution) Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution. 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The package supports user-specified link functions, includes methods for parameter estimation and model diagnostics, and provides residual analysis tailored for cure models. The classical theory methods used are described in Berkson, J. and Gage, R. P. (1952) , Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) , Bazán, J., Torres-Avilés, F., Suzuki, A. and Louzada, F. (2017). Package: r-cran-emhawkes Architecture: all Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-misctools Filename: pool/dists/resolute/main/r-cran-emhawkes_0.9.8-1.ca2604.1_all.deb Size: 266028 MD5sum: c1d7d181b5b2a2a094f5470d005dd646 SHA1: 89a569d8a22a8bbe3cb05b5184822aa3a1205106 SHA256: 3d0f093004ba2b6cbae3523201113ec505a7497b19d9965d14cbfc4d5edeb277 SHA512: 3d49ce4fa8934bca9f759b58717a504e29ee3e7eb23d3eb7bb59fa5bcdaa4256a4523cb98a14edd804ba39cc8e19026e9ab130c30710136db4ebd0c67647f728 Homepage: https://cran.r-project.org/package=emhawkes Description: CRAN Package 'emhawkes' (Exponential Multivariate Hawkes Model) Simulate and fitting exponential multivariate Hawkes model. This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) , with an exponential kernel and fits the parameters from the data. 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Emissions can be calculated both using emission factors and activity data (Schuch et al 2018) or using pollutant inventories (Schuch et al., 2018) . Functions to process individual point emissions, line emissions and area emissions of pollutants are available as well as methods to incorporate alternative data for Spatial distribution of emissions such as satellite images (Gavidia-Calderon et. al, 2018) or openstreetmap data (Andrade et al, 2015) . Package: r-cran-emjmcmc Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-glmnet, r-cran-biglm, r-cran-hash, r-cran-bas, r-cran-stringi, r-cran-speedglm, r-cran-withr Suggests: r-cran-testthat, r-cran-bindata, r-cran-clustergeneration, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-emjmcmc_1.5.0-1.ca2604.1_all.deb Size: 357960 MD5sum: b26fe14e22944602e56f86aef07b1890 SHA1: 751dd077fc7b379af86dee999bdd76237f3f8029 SHA256: 0f803016bde0f74d60924a8c1c76608044895b7874151c71b228220f0c137997 SHA512: 98b084c2073034acb4fd8975b41bca171a397d62d6efeffcd2e4ca3f7274c74ddbc18740e114102a2e023b2d88ead077f495c0a4a0617cacac90d9193388501c Homepage: https://cran.r-project.org/package=EMJMCMC Description: CRAN Package 'EMJMCMC' (Evolutionary Mode Jumping Markov Chain Monte Carlo ExpertToolbox) Implementation of the Mode Jumping Markov Chain Monte Carlo algorithm from Hubin, A., Storvik, G. (2018) , Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Storvik, G., & Frommlet, F. (2020) , Hubin, A., Storvik, G., & Frommlet, F. (2021) , and Hubin, A., Heinze, G., & De Bin, R. (2023) , and Reversible Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Frommlet, F., & Storvik, G. (2021) , which allow for estimating posterior model probabilities and Bayesian model averaging across a wide set of Bayesian models including linear, generalized linear, generalized linear mixed, generalized nonlinear, generalized nonlinear mixed, and logic regression models. Package: r-cran-eml Architecture: all Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-digest, r-cran-emld, r-cran-jqr, r-cran-jsonlite, r-cran-uuid, r-cran-rmarkdown, r-cran-dplyr Suggests: r-cran-knitr, r-cran-taxadb, r-cran-tibble, r-cran-testthat, r-cran-covr, r-cran-units, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinyjs, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-eml_2.0.7-1.ca2604.1_all.deb Size: 779670 MD5sum: b33e4ec93e462a9873809fed48f4ef49 SHA1: 1e835b8e30a5b6e900fb2bacd498d467d7650f89 SHA256: ac66146e3e471c505bb672f156ca14a2c44d18b7e383ec4eef9c8cbdfa026ffc SHA512: 1cca3597107d4fba5439917a6d91f6c8365b3339f3fcaa6bd52a1e5ccc81510ead6ac7774e67359bd775b5a86c6feda19cdcbd33b8cbfcd1a97879aae95049ba Homepage: https://cran.r-project.org/package=EML Description: CRAN Package 'EML' (Read and Write Ecological Metadata Language Files) Work with Ecological Metadata Language ('EML') files. 'EML' is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), . Package: r-cran-emld Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-jsonlite, r-cran-jsonld, r-cran-yaml Suggests: r-cran-spelling, r-cran-testthat, r-cran-magrittr, r-cran-rmarkdown, r-cran-covr, r-cran-knitr, r-cran-rdflib, r-cran-jqr Filename: pool/dists/resolute/main/r-cran-emld_0.5.3-1.ca2604.1_all.deb Size: 329742 MD5sum: 84314bbc83ae389df837185e7b4065fd SHA1: a4a95e79fe6dcaed3e45e5aae68dfd45187053e2 SHA256: 54213e8fcad05a56246ea93663b9978ecfe589dfbae50e2b5157f3c7caedd5cb SHA512: 41749eb13f13883efc06a8436a54415efe094ccca49278ebbdce0e0cfa33d2ebe252d325748821f4aa29adce8f9df772ddf0d5768bfdef5549bb632e4e393ec5 Homepage: https://cran.r-project.org/package=emld Description: CRAN Package 'emld' (Ecological Metadata as Linked Data) This is a utility for transforming Ecological Metadata Language ('EML') files into 'JSON-LD' and back into 'EML.' Doing so creates a list-based representation of 'EML' in R, so that 'EML' data can easily be manipulated using standard 'R' tools. This makes this package an effective backend for other 'R'-based tools working with 'EML.' By abstracting away the complexity of 'XML' Schema, developers can build around native 'R' list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the 'JSON-LD' representation enables the use of developer-friendly 'JSON' parsing and serialization that may facilitate the use of 'EML' in contexts outside of 'R,' as well as the informatics-friendly serializations such as 'RDF' and 'SPARQL' queries. Package: r-cran-emli Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-emli_0.3.0-1.ca2604.1_all.deb Size: 56572 MD5sum: 6dcb229db56e68eb14974128bd3a836f SHA1: 403b7d0dde1ec984ceacf75b692d094211e0fd01 SHA256: fbd473db02f891ff9b895ffbe6b5ea3fdd89dbac75bee68f80cca1eb0d058370 SHA512: a2d9b9e705d7e88777172b0298a5f66bb6a267b2174f7380d7afe2035e6300d236ed618e2921dc3820f9d3cb1aa5e0aca30b8799701f8d84efd102616329603d Homepage: https://cran.r-project.org/package=EMLI Description: CRAN Package 'EMLI' (Computationally Efficient Maximum Likelihood Identification ofLinear Dynamical Systems) Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models representing linear dynamical systems. Currently, two such algorithms (one offline and one online) are implemented for the single-output cumulative structural equation model with an additive-noise output measurement equation and assumptions of normality and independence. The corresponding scientific papers are referenced in the descriptions of the functions implementing these algorithms. Package: r-cran-emln Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2226 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-igraph, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-matrix, r-cran-dt, r-cran-hablar, r-cran-rlang Suggests: r-cran-bipartite, r-cran-httpuv, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-emln_1.2.0-1.ca2604.1_all.deb Size: 1502964 MD5sum: 5511046e0d2b52b428b5323e43eb709b SHA1: fd81bc718360e96d0b035d2c6b4781d85cd54d39 SHA256: 3fcac7594f608316cdb97513b6a2b36eca08b9411b3fbe7e84b79560ec1a4427 SHA512: 1a51a10c422331f0a1fba8230e8281a1bb2ed525036463e2422ad37e170cf4955b04147042bcc6276040c01436837202358e231db9772491b36ea10abb78be95 Homepage: https://cran.r-project.org/package=emln Description: CRAN Package 'emln' (Organize, Handle, and Explore Ecological Multilayer Networks) Data and analysis of ecological multilayer networks, including standardization of data structures and functions to convert between them. Includes an interactive multilayer network visualizer (beta, paper forthcoming), and a collection of 78 empirical ecological multilayer network datasets. This work was supported by research grant ISF (Israel Science Foundation) 1281/20 to Shai Pilosof. Noa Frydman (2023) . Package: r-cran-emmageo Architecture: all Version: 0.9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gparotation, r-cran-nnls, r-cran-catools, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-emmageo_0.9.9-1.ca2604.1_all.deb Size: 591504 MD5sum: d54ae74b4350b4d510915bc613f87371 SHA1: 1b4f3c01710919be8919c2f6e84ac9540433a2f0 SHA256: 48dee4d98a296900f7dfdf8db59285531c8911c2473a4cd5aa79477c6f99f507 SHA512: 90913a298c91d26d2c9de3747d2d2c2eb03d3d952939006dfa94bb9a3d789a932acf6315ed0fcbbb848b9db7acdc0401f5554e7c488fa0d4aa0afafa29dedfcf Homepage: https://cran.r-project.org/package=EMMAgeo Description: CRAN Package 'EMMAgeo' (End-Member Modelling of Grain-Size Data) End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose. Package: r-cran-emmeans Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-estimability, r-cran-mvtnorm, r-cran-numderiv, r-cran-rlang Suggests: r-cran-bayesplot, r-cran-bayestestr, r-cran-biglm, r-cran-brms, r-cran-car, r-cran-coda, r-cran-compositions, r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-lme4, r-cran-lmertest, r-cran-logspline, r-cran-mass, r-cran-mediation, r-cran-mgcv, r-cran-multcomp, r-cran-multcompview, r-cran-mumin, r-cran-nlme, r-cran-ordinal, r-cran-pbkrtest, r-cran-rmarkdown, r-cran-robmixglm, r-cran-rsm, r-cran-sandwich, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-emmeans_2.0.3-1.ca2604.1_all.deb Size: 2126682 MD5sum: 3f164708f3be4431d3c5a61e7d63c51a SHA1: f834421f241945b3a5491b13013c232e3bf2e34d SHA256: a48268c9dc8a7aa14c0f28fd4ca24998ec74658f91eb558b62526ad716df5202 SHA512: 87aa78e2fee4e4fe1261f2bdd82dd5b28e4b9be939efb65d3e65024eba6cffaa49aad3c6909e8d68000b6148567f2ef0d37152e80f12ed65061a23ba4a2b0f06 Homepage: https://cran.r-project.org/package=emmeans Description: CRAN Package 'emmeans' (Estimated Marginal Means, aka Least-Squares Means) Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 . Package: r-cran-emmixssl Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-emmixssl_1.1.1-1.ca2604.1_all.deb Size: 296348 MD5sum: d141935841f56dec9153a99269a6032f SHA1: 0d01bc1704e8599c70d4c08d6cd8c9360cda2233 SHA256: 59edd4151e31cb52d66cf48e2cd3a1e5034a3f8e9f9c64469de1e2a30f0cb13d SHA512: 8e9e9983643c35b05710fa3600a5ab40c05d2adecee1e6d7cace826b10d287d67b507aae2e7ff9559b170f8e6a82caefb5741e6e7be42eb60664c2c7b1c5499a Homepage: https://cran.r-project.org/package=EMMIXSSL Description: CRAN Package 'EMMIXSSL' (Semi-Supervised Gaussian Mixture Model with a Missing-DataMechanism) The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayes’ rule. Package: r-cran-emmli Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-emmli_0.0.3-1.ca2604.1_all.deb Size: 75716 MD5sum: ce25b2673c4d7e191b50dc88f15ad340 SHA1: f5e954039c4f42e7e3fba0298106c5d104bef64c SHA256: 100a49a6ddcff5c926bb039291882aaaec79d0bbe6b79b786d459d7a430e8153 SHA512: 9e12cee1bdf7df18346b79bf1b4c1beefb0908f8c7beee99256de86e73aa09dd2bdd1c96d5840732ed344c7772a2d54898801bac6a1fb79189971074449bec34 Homepage: https://cran.r-project.org/package=EMMLi Description: CRAN Package 'EMMLi' (A Maximum Likelihood Approach to the Analysis of Modularity) Fit models of modularity to morphological landmarks. Perform model selection on results. Fit models with a single within-module correlation or with separate within-module correlations fitted to each module. Package: r-cran-emmreml Architecture: all Version: 3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-emmreml_3.1-1.ca2604.1_all.deb Size: 47376 MD5sum: 140214bb5736dabf2eaad49ca77be9f7 SHA1: 44648ffcb2e20ebe82130991d635dc2177d5d3e8 SHA256: bc84e7b3ec6a0c274540c1a4f0509f01e315b3cc428711924f84494b098b332c SHA512: 2e687cdc2ee90ac6b924c3e98b4df1401a65cb9e87971e0b65ef1fc15d922e92503df211ace0d0814f703f3ed8729122bb12ec07ede72acb072509e014251af0 Homepage: https://cran.r-project.org/package=EMMREML Description: CRAN Package 'EMMREML' (Fitting Mixed Models with Known Covariance Structures) The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm. 'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm. 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Package: r-cran-emotions Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-orthopolynom, r-cran-quantreg, r-cran-minpack.lm, r-cran-tidyr, r-cran-ggplot2, r-cran-ggridges, r-cran-parameters, r-cran-rlang, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-emotions_1.3-1.ca2604.1_all.deb Size: 894896 MD5sum: f9ed4481d137835d76f0d3b69c6048d6 SHA1: 723203c8dd548ab092314e89645820c62add4f7e SHA256: 5eea47196d790064891d24e2b3899bc446bb0b526d450a76a65f072f404b4c8a SHA512: 4d807ffe1065db30323d4f48ea18ca50cb52283b36674f18fbd59fca4764609b0f785fcd725389c75bde8d832736271a9c571b19212c8d32fa84c312acd18bb3 Homepage: https://cran.r-project.org/package=EMOTIONS Description: CRAN Package 'EMOTIONS' (Ensemble Models for Lactation Curves) Lactation curves describe temporal changes in milk yield and are key to breeding and managing dairy animals more efficiently. The use of ensemble modeling, which consists of combining predictions from multiple models, has the potential to yields more accurate and robust estimates of lactation patterns than relying solely on single model estimates. The package EMOTIONS fits 47 models for lactation curves and creates ensemble models using model averaging based on Akaike information criterion (AIC), Bayesian information criterion (BIC), root mean square percentage error (RMSPE) and mean squared error (MAE), variance of the predictions, cosine similarity for each model's predictions, and Bayesian Model Average (BMA). The daily production values predicted through the ensemble models can be used to estimate resilience indicators in the package. The package allows the graphical visualization of the model ranks and the predicted lactation curves. Additionally, the packages allows the user to detect milk loss events and estimate residual-based resilience indicators. Package: r-cran-emov Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-emov_0.1.1-1.ca2604.1_all.deb Size: 68890 MD5sum: 1e3aac03ef3d6a6169f74e1e8b626b04 SHA1: 938729bb1456fb9a0a59d6c893ea4fa17bf8a942 SHA256: c3fde682d835c3ab138c9062d383d3c90adabf93f536b192a2013cbc69595785 SHA512: 07c564dc5ad9cd184c078f1950c31a7052adae1959a20e399291e9229af3235b8bd6cde4ffae7665c66678e37430710edef3da047bf1116ad2ae76ee09d7deb7 Homepage: https://cran.r-project.org/package=emov Description: CRAN Package 'emov' (Eye Movement Analysis Package for Fixation and Saccade Detection) Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position. Package: r-cran-emp Architecture: all Version: 2.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocr Filename: pool/dists/resolute/main/r-cran-emp_2.0.6-1.ca2604.1_all.deb Size: 25198 MD5sum: 67f353bc859b91420d320b9b6cf639ef SHA1: 3c9b0f4fc27637deedac56d8d17db28924de53d6 SHA256: 0aa33a6c28dd7407559dfe67f58c91edde057f91d0d6e691887c206681ba59c7 SHA512: 6413d094b5204c309a29df43a2865488b945625266ca40d3739289e885131a25e5cc0a6c8af867434d71b6b4183d2df373bf4a17e87f82757f3f8010f11743f5 Homepage: https://cran.r-project.org/package=EMP Description: CRAN Package 'EMP' (Expected Maximum Profit Classification Performance Measure) Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) , . Package: r-cran-empeaksr Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-empeaksr_0.3.1-1.ca2604.1_all.deb Size: 99464 MD5sum: dabd14a03a99b901814e8feefbe87586 SHA1: c84bf9efbd0ff393785d3f8eb097e43ae4cae99a SHA256: 0c1cec89253b1160475b2068bd54178e5a8b9e3fd5eeb84b8928e5369f66b7e0 SHA512: f9e720da2c2c9982c104e7c144a35bface24155729905040f3bc6b876e384f544207827044d60423c103d6d64bff901d629d0b38866981fc36b3a3364f0d6180 Homepage: https://cran.r-project.org/package=EMpeaksR Description: CRAN Package 'EMpeaksR' (Conducting the Peak Fitting Based on the EM Algorithm) The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) , . The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed. 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Highlighting is displayed in any console supporting 'ANSI' colours, and can be converted to 'HTML', 'typst', 'latex' and 'SVG'. 'quarto' and 'rmarkdown' rendering are directly supported. It is also possible to add colour to regular expression matches and highlight differences between two arbitrary R objects. Package: r-cran-empiricaldynamics Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall, r-cran-cvxr, r-cran-minpack.lm, r-cran-signal, r-cran-lmtest, r-cran-tseries, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-clarabel, r-cran-scs, r-cran-osqp, r-cran-ecosolver, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-empiricaldynamics_0.1.3-1.ca2604.1_all.deb Size: 445060 MD5sum: b408cd95424cddc094184e094b0eabfc SHA1: 2b1500329fe9658e785f8b492bbf08a490de8952 SHA256: d0184827d22785ba4237540d9506f26405cbd9694da8348bd35489ced9c2dffa SHA512: cea6b730c5a01e5cccef67a6184a51af2c00fee72da55c3116179cb2887ab4799420023bd515ccfe74ab51172be5e1cc37680738e3e6e8c72cc2c5ddb1383a92 Homepage: https://cran.r-project.org/package=EmpiricalDynamics Description: CRAN Package 'EmpiricalDynamics' (Empirical Discovery of Differential Equations from Time SeriesData) A comprehensive toolkit for discovering differential and difference equations from empirical time series data using symbolic regression. The package implements a complete workflow from data preprocessing (including Total Variation Regularized differentiation for noisy economic data), visual exploration of dynamical structure, and symbolic equation discovery via genetic algorithms. It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide industrial-grade robustness, physics-informed constraints, and rigorous out-of-sample validation. Designed for economists, physicists, and researchers studying dynamical systems from observational data. Package: r-cran-emplik2 Architecture: all Version: 1.33-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-emplik2_1.33-1.ca2604.1_all.deb Size: 81612 MD5sum: c099e196563ed648805ab6e4c07e7e0f SHA1: 1e78573c792cd51a9da9e6b4f5e40af4288a5e20 SHA256: dbb84b57da1ce170ac0a89a4a9108c2e7aaacc7ce2cad4339092e955dace91e4 SHA512: b4942f4d65d3e11240fb5ccd0741d5f639d52080c47a97c4a0af245096c8d4f551f3d7ed8f4c8f545bdb89c286d5179529613306f9b2b6f5dcc3f5700cfd570d Homepage: https://cran.r-project.org/package=emplik2 Description: CRAN Package 'emplik2' (Empirical Likelihood Ratio Test for Two-Sample U-Statistics withCensored Data) Calculates the empirical likelihood ratio and p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with possible censored data. Package: r-cran-emplikauc Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emplik2, r-cran-rootsolve Filename: pool/dists/resolute/main/r-cran-emplikauc_0.5-1.ca2604.1_all.deb Size: 447420 MD5sum: 07b8ffbd6bb79f6f88532326db32d96c SHA1: 0045469355fdfa2d0956f180931b4b88774f7fcb SHA256: cdbcc5f5e8436285870de7ec109c8a2ad2d4898d30eb9ea1a64d8e6be16dfffd SHA512: b693fced9622daf10ea7dc392fa1b980c7ea923807e943647cb6efe0d1df157116c6e62bf6f30b7e7f923e3a0593f3d8869f446a8ed79d0cd304f808e119d12f Homepage: https://cran.r-project.org/package=emplikAUC Description: CRAN Package 'emplikAUC' (Empirical Likelihood Ratio Test/Confidence Interval for AUC orpAUC) Test hypotheses and construct confidence intervals for AUC (area under Receiver Operating Characteristic curve) and pAUC (partial area under ROC curve), from the given two samples of test data with disease/healthy subjects. The method used is based on TWO SAMPLE empirical likelihood and PROFILE empirical likelihood, as described in . Package: r-cran-emplikcs Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-monotone Filename: pool/dists/resolute/main/r-cran-emplikcs_0.3-1.ca2604.1_all.deb Size: 80310 MD5sum: 30c2b3eb2235888ad93f84b03f0a690f SHA1: 1a36905c8fb559d92dac8199dca2d2407b05564f SHA256: dc885fe1bb06e1440524a3c590829479806fd902ab09cda4e75b003d94a7fc57 SHA512: d6d3dd6ca59e4a94737068d8d443f586d33afc9bf0cc613e35572b93146806b6eb6055ac1315b6bda51018bad34f68ff51ab4aed07f58c5b9770d22143fbf2c6 Homepage: https://cran.r-project.org/package=emplikCS Description: CRAN Package 'emplikCS' (Empirical Likelihood with Current Status Data for Mean,Probability, Hazard) Compute the empirical likelihood ratio, -2LogLikRatio (Wilks) statistics, based on current status data for the hypotheses about the parameters of mean or probability or weighted cumulative hazard. Package: r-cran-emreliability Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-emreliability_1.0.0-1.ca2604.1_all.deb Size: 165528 MD5sum: ab155edd5c7971640325a6d7c85d2089 SHA1: ac2173e56768d6ca19079a6f7f8700f5408a42f0 SHA256: 2d8a8d3cab62b867602e031b0088b2f44d5335f22b307215ff404f1274c16ba4 SHA512: 01e564565ee18a1bf9e97c771ab5bad1bf2d6276ea23437d47651c29d46aab09c512664e158405f3952c5cc167bcb00abea33ddf43062d4270c70366315aedf1 Homepage: https://cran.r-project.org/package=emreliability Description: CRAN Package 'emreliability' (Test Reliability and CSEM in Educational Measurement) Provides functions for computing test reliability and conditional standard error of measurement (CSEM) based on the methods described in the Reliability in Educational Measurement chapter of the 5th edition of "Educational Measurement" by Lee and Harris (2025, ISBN:9780197654965). Package: r-cran-ems Architecture: all Version: 1.3.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-survival, r-cran-rms Filename: pool/dists/resolute/main/r-cran-ems_1.3.11-1.ca2604.1_all.deb Size: 696296 MD5sum: dd42d63a0e6fea3472b516cdc53ebad1 SHA1: fcf101564fec2375f623492ca926f7f234cb0b18 SHA256: 8f3b9bd4f272f9a05d4c095fe961b9f7f0599c25da5e93e9028dbbc0974b7dd4 SHA512: 47a6ba819d68f22f0eba3f82a35799c6bc82adf740666e5f4ad7c0898540f7d1c6660b7a54892905215d596fcb90bb821bd00e60482c0b652adeb09a34f35d42 Homepage: https://cran.r-project.org/package=ems Description: CRAN Package 'ems' (Epimed Solutions Collection for Data Editing, Analysis, andBenchmark of Health Units) Collection of functions related to benchmark with prediction models for data analysis and editing of clinical and epidemiological data. Package: r-cran-emsaov Architecture: all Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-emsaov_2.3-1.ca2604.1_all.deb Size: 71864 MD5sum: c89b3b7233bbc055fce0bbc282804faf SHA1: c05c2a39b5b08a3a73983c06923e391b3b35ec61 SHA256: bcb504a84193b1007def3c0257233b594c5a9b0bdaebad6beccb33303003b21f SHA512: b1d4a2e8d5e4f7ef83cae1dd909d628230fa588ead78711a9dd43ad70582de52d02b719edc42d167e1c45b698732bbdad3b4a8cdeef26a80fc7baf1ac88c4bdd Homepage: https://cran.r-project.org/package=EMSaov Description: CRAN Package 'EMSaov' (The Analysis of Variance with EMS) Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS. Package: r-cran-emsc Architecture: all Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3585 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-emsc_0.9.4-1.ca2604.1_all.deb Size: 1832810 MD5sum: 947e2ff02eef2d2bf2e5920d14f23d37 SHA1: e0fe5f188462ea8150c85cb91724eb5c228a2762 SHA256: 4779161b5ad1ba9b2a3e47905e4afa5f18244d0d81670adce90818cf621e0053 SHA512: 83d19472f6d2acf1715ba93ab93c7895ae422adf90fd28afe29a64e862d6f680400b8c5e0a0633a0323e9c019773f7fbf0eaa3e66e8700f0f25de078e0b3c74b Homepage: https://cran.r-project.org/package=EMSC Description: CRAN Package 'EMSC' (Extended Multiplicative Signal Correction) Background correction of spectral like data. Handles variations in scaling, polynomial baselines, interferents, constituents and replicate variation. Parameters for corrections are stored for further analysis, and spectra are corrected accordingly. Package: r-cran-emsnm Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-emsnm_1.0-1.ca2604.1_all.deb Size: 66550 MD5sum: 52d6b0cb9b7e3339317f56b06a79ca4f SHA1: fd4cb5ab3efcb5bff25b12b1172621171f4f8438 SHA256: dbec438386f31e3460cada9b335c0b32d800e7d30bb56500702f26029eb6bcda SHA512: c2702ac3ed7a7d2d133b182a7ea203faf219d923147ea837da3e197f3a85eb03367284cff9c9973568db38e3e7f0b41678a9d0c5067192a6e7373114d8e48485 Homepage: https://cran.r-project.org/package=EMSNM Description: CRAN Package 'EMSNM' (EM Algorithm for Sigmoid Normal Model) It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure. Package: r-cran-emss Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sampleselection, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-emss_1.1.1-1.ca2604.1_all.deb Size: 79694 MD5sum: 60b3de4d3e09a84eb7635ca4c5735021 SHA1: f629fe9d855258d427fe537844b96e4d7bb2b8a8 SHA256: e041a55c71d8cebaa4b2baf83927cf558c4f91b1a86f39a259f97f964a757615 SHA512: e9fc694c16bcd02657e1eebcea93bd69d4046e20d74e3ec6157da3db144c982113141d7a217a4315dfb00e8cd3c87b79132139047a66a6ea1c6026fe9566d636 Homepage: https://cran.r-project.org/package=EMSS Description: CRAN Package 'EMSS' (Some EM-Type Estimation Methods for the Heckman Selection Model) Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM’s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) . Package: r-cran-emstreer Architecture: all Version: 3.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlpack, r-cran-scatterplot3d, r-cran-ggplot2, r-cran-sf Filename: pool/dists/resolute/main/r-cran-emstreer_3.1.3-1.ca2604.1_all.deb Size: 50440 MD5sum: b5615de3532a9e1b771673e979536dae SHA1: 3c2dc532813aa053869ac7d607a4fcbab3ea6349 SHA256: f1381c08572376dfc201b90974942acb5be5ea67de867097ddfc4c782f29aae2 SHA512: 9ce15d0c9988100b2900ade138537597bb96148d03fe2d4d090e59b263f1a8abc9db9f2f9249878a5a3a277329e4640ef9b577da2da1ceffb561a5f10791be7b Homepage: https://cran.r-project.org/package=emstreeR Description: CRAN Package 'emstreeR' (Tools for Fast Computing and Visualizing Euclidean MinimumSpanning Trees) Fast and easy computation of Euclidean Minimum Spanning Trees (EMST) from data, relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013). 'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, ), which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the style of the 'base', 'scatterplot3d', or 'ggplot2' libraries; and functions to export the MST output to shapefiles. Package: r-cran-emt Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-emt_1.3.2-1.ca2604.1_all.deb Size: 38654 MD5sum: 56ae098f0c8de6197a38cb13be7a2065 SHA1: 5c8a49d0ff6cb50a5c3cd94302d7bbd79a981841 SHA256: ac5ed9cc9eb651a8370b111c0e1e25f5dccc811502280782aec8d1e6f4f4f15c SHA512: 9e222b3dfaf51424a2f2d4b81946e4a32ecdebd41d485ab594e5700b370858bce7622649b3fb474f91abf456101566a9b5b3f5a90dded036a8aef5e62ba9bc2a Homepage: https://cran.r-project.org/package=EMT Description: CRAN Package 'EMT' (Exact Multinomial Test: Goodness-of-Fit Test for DiscreteMultivariate Data) Goodness-of-fit tests for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. Monte Carlo methods are provided to make the package capable of solving high-dimensional problems. Package: r-cran-emulator Architecture: all Version: 1.2-24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-emulator_1.2-24-1.ca2604.1_all.deb Size: 326298 MD5sum: 77b84c5f62e53d2566bae8f06c44639f SHA1: efd8831a23f3f91d08cb306ee8e9f841e7de3d98 SHA256: 8613987aa0fd398b44c9a1e24121402f9f853405c0a92acec572bb794ba9b7d9 SHA512: b35fac6a8fad19f41cbf93d4cba728691b0f20241d0b266fca4e46a71c265375115719e01e47257a7922c0a3bd6891863ef6c34b0e72ca540ce06970459a5fd3 Homepage: https://cran.r-project.org/package=emulator Description: CRAN Package 'emulator' (Bayesian Emulation of Computer Programs) Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently. Package: r-cran-emur Architecture: all Version: 2.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3627 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-stringr, r-cran-uuid, r-cran-base64enc, r-cran-shiny, r-cran-wrassp, r-cran-jsonlite, r-cran-rsqlite, r-cran-dbi, r-cran-httpuv, r-cran-dplyr, r-cran-readr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-mime, r-cran-rstudioapi, r-cran-httr, r-cran-v8, r-cran-cli, r-cran-fs, r-cran-tidyselect Suggests: r-cran-mass, r-cran-ggplot2, r-cran-testthat, r-cran-compare, r-cran-knitr, r-cran-rmarkdown, r-cran-matlabr, r-cran-r.matlab Filename: pool/dists/resolute/main/r-cran-emur_2.6.0-1.ca2604.1_all.deb Size: 2793754 MD5sum: 4eaf0b78ef17c8f22127ade0c3acf5d4 SHA1: d7e70fd08ccc7d9eacf17355b2f262618900d463 SHA256: 8c0ae26daca6fb28b6af8ce4ffdb2d8361182be38dbf7d340b9cb6809b0d06f5 SHA512: a0cf8b77e2bcec21cd9e84ea955aa13d2dee7af0365b8d2481e018e3f872416c2fad6d28b218c9651d33af9c1f811c5081d9c82006e663453ac453f60aba534c Homepage: https://cran.r-project.org/package=emuR Description: CRAN Package 'emuR' (Main Package of the EMU Speech Database Management System) Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See for more details. Package: r-cran-enaho Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-enaho_0.2.5-1.ca2604.1_all.deb Size: 237612 MD5sum: 6ed90762dc3e03a2c0d13d313ec97819 SHA1: fae7e8f1aec6c99686b90b17eb14f78c274d5767 SHA256: 5f940c31acf1e7dd8d6cb368aebfdf47d8c7b8f8a574911e9222486d50de0ba1 SHA512: fc8f786c43856d3ba25ef3379269e6fee426388c28787d07419ae446551034fc6c464586042cf82493ed0dba846979771cf395dd830e787136c425c5429d290a Homepage: https://cran.r-project.org/package=enaho Description: CRAN Package 'enaho' (Encuesta Nacional de Hogares (Peruvian Home National Survey)) Descarga, lee y analiza bases de la Encuesta Nacional de Hogares (ENAHO) y otras encuestas del Instituto Nacional de Estadística e Informática (INEI) del Perú. (Downloads, reads, and combines data from the Peruvian Home National Survey and other surveys from the National Institute for Statistics (INEI).) Package: r-cran-encode Architecture: all Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-encode_0.3.7-1.ca2604.1_all.deb Size: 46940 MD5sum: 8dd23d341c7f8b30b88b40c20065f8f1 SHA1: 7566818ea25bb757201bc54d823ff1e7f006a519 SHA256: 2bb7858113a344d008e5e121db2579416611683618de589e3c17c65ecc93c0eb SHA512: 2b8255492c5f8a0ea4dd938c93e5d1fc700b6b948132c1ecee40f6b23c88eacbb74ddda3c338199307bdd3f960634aef2bdec8527c71b6b47e7f50a8c50ed8a7 Homepage: https://cran.r-project.org/package=encode Description: CRAN Package 'encode' (Represent Ordered Lists and Pairs as Strings) Interconverts between ordered lists and compact string notation. 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The statistics are standardised to a normal distribution, and the null hypothesis is that the larger model contains no additional useful information. P-values will be provided in the output. Package: r-cran-encryptedrmd Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sodium, r-cran-readr, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-encryptedrmd_0.2.1-1.ca2604.1_all.deb Size: 38388 MD5sum: 46721cc9096f97c1132e3043c9b471a6 SHA1: b161bd5d3f8d8b7a24a301ea04cfe7fe759cfa34 SHA256: 77b8f195512899851744baef12730f676974a81bc7dca514d0c712f6fc365cc1 SHA512: 9ffaa0ddf7938eb8575e01c8995db2e1e8e125a986b89095fd1d29080a3abc56b26cfd7be71292e87def50c6a9a1fa03dfd643e809d56701fd6028506744db26 Homepage: https://cran.r-project.org/package=encryptedRmd Description: CRAN Package 'encryptedRmd' (Encrypt Html Reports Using 'Libsodium') Create encrypted html files that are fully self contained and do not require any additional software. 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Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns. A public key can be shared to allow others to encrypt data to be sent to you. This is particularly aimed a healthcare settings so patient data can be pseudonymised. Package: r-cran-endogenous Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-endogenous_1.0-1.ca2604.1_all.deb Size: 55876 MD5sum: a950067352cd0317b0b2a82919e38efe SHA1: edd8cd1f2a44285b414475eaea354f707e1672ac SHA256: f6dfbfb9ab1aba34c850acd634615716d79603b22adf6ab0e59a5fbd1aa6b4da SHA512: a91b3753d2692e353ae59e67c84b768d4411f6115fb5bcc41062f2b429bbf5b1b21e2eff5397e4925017ff1d92817942ff02c74dc372d7cba298a5cc54c38647 Homepage: https://cran.r-project.org/package=endogenous Description: CRAN Package 'endogenous' (Classical Simultaneous Equation Models) Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification. Package: r-cran-endtoend Architecture: all Version: 2.29-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pastecs, r-cran-ggplot2 Suggests: r-cran-hopbyhop, r-cran-opportunistic Filename: pool/dists/resolute/main/r-cran-endtoend_2.29-1.ca2604.1_all.deb Size: 37500 MD5sum: 6689212b9dd767c092737b6f45e0e40c SHA1: f9c542b651de1d0a191fb2ef3ba10f61c2dd4bd2 SHA256: 5ddf40bbd0cdea24106140f4f5a49c5e91d81150e121a759c79feff2a76aad08 SHA512: 200350a58f773f007bf76674777779154f3f8b9ca4c5e817c4741f156808012d636212d39ea4843bb48708d310039dc84c711a3e47856475385742dd537a210c Homepage: https://cran.r-project.org/package=endtoend Description: CRAN Package 'endtoend' (Transmissions and Receptions in an End to End Network) Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. 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Package: r-cran-energygof Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-energy, r-cran-gsl, r-cran-boot, r-cran-fitdistrplus, r-cran-statmod Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-energygof_0.1-1.ca2604.1_all.deb Size: 185574 MD5sum: 6f1cb1f2b5a75c3e4ab8150daf56298a SHA1: 56693b813918d30211e7b37a930d9aa64b348f70 SHA256: 988fcc7a40ffca80536546224f3093aa7b25d77b28f6ed2b747097f68468f4a3 SHA512: afc7bd9bdfcb1bfbf2f70d27cc8a5861a9af78247f38196992d41c734a04b66a00ef84df08b5012328540c25d55421c96cd2305c6d22b3287c0ef842c2888fd7 Homepage: https://cran.r-project.org/package=energyGOF Description: CRAN Package 'energyGOF' (Goodness-of-Fit Tests for Univariate Data via Energy) Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap. Package: r-cran-energyonlinecpm Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-energy, r-cran-mass Filename: pool/dists/resolute/main/r-cran-energyonlinecpm_1.0-1.ca2604.1_all.deb Size: 26130 MD5sum: 88bfcaf8559f2169c556a40c9925d1d5 SHA1: 4caf6be7939480e985c22d389229f237f69f0907 SHA256: 5bf0185a67a3a79735766155d4234556ce2adee7b5a1286fb4a0cf6bfe07840a SHA512: 758eeb41fb3eb649fed99e8910dabba970c022d8ef98b49389abf268db6cff1b5efe1f292a7e8b0aa3dd5d6f49d451ace36d881526a8d540d4e3dcf28e6ee69b Homepage: https://cran.r-project.org/package=EnergyOnlineCPM Description: CRAN Package 'EnergyOnlineCPM' (Distribution Free Multivariate Control Chart Based on EnergyTest) Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) . 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This package contains Standard cost engineering and engineering economics methods that are applied to convert between present, future, and annualized costs. Newnan D. (2020) “Engineering Economic Analysis”. Package: r-cran-engression Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch Filename: pool/dists/resolute/main/r-cran-engression_0.1.6-1.ca2604.1_all.deb Size: 49020 MD5sum: 164568b1cd2d91fa10b32d5eb174853c SHA1: 9271b72f489f386b83f2b8ee3e706bf996c7ea48 SHA256: f4269a449ef42a9be26e557a0baad6756b36faa2e1d01642ff9a7b3f9c1d4375 SHA512: 7e4fcceaa76b844b6989834808619584d6461456d85855f4fdaa6022397f9d1c6a17ef04c0b8a564db3d5764786d951cf40ae805320b7a93eae84b5a34b12acc Homepage: https://cran.r-project.org/package=engression Description: CRAN Package 'engression' (Engression Modelling) Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). . 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Package: r-cran-enhancer Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4337 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-enhancer_1.1.1-1.ca2604.1_all.deb Size: 4114170 MD5sum: a4d0b710afad1fe13344af7a51ae0883 SHA1: 5fd5160a8721bfeb00001aa729a58e92a7d86a42 SHA256: d054164f1ed0c3aaac3c93a20b7537de9f5e456f50d2273986d9717a0e5c7afc SHA512: 190ad8f285b102c9abb8b12d0e292695898724e7cfb47085b92be536779fc414fac2d0beea4104682b99c0bffb1da0f289b44c18071147abc953700f118f2840 Homepage: https://cran.r-project.org/package=enhancer Description: CRAN Package 'enhancer' (Mixed-Effects Models Enhancing Functions) Special functions that enhance other mixed effect model packages by creating overlayed, reduced rank, and reduced model matrices together with multiple data sets to practice the use of these models. For more details see Covarrubias-Pazaran (2016) . Package: r-cran-enmeval Architecture: all Version: 2.0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-maxnet, r-cran-predicts, r-cran-foreach, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-glmnet, r-cran-rangemodelmetadata, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-rjava, r-cran-spocc, r-cran-rcolorbrewer, r-cran-sf, r-cran-blockcv, r-cran-devtools, r-cran-tibble, r-cran-ecospat, r-cran-geodata, r-cran-usdm Filename: pool/dists/resolute/main/r-cran-enmeval_2.0.5.2-1.ca2604.1_all.deb Size: 2494140 MD5sum: 6e5cb16f1d665c4f733a95e81c816c22 SHA1: c7d21d96f988941d05f28c85024e2f201a8814bc SHA256: bc14901ca6b7ac81deb81fab4a8bb7afb86809932d794e7780b283a63cecee35 SHA512: e5686c2384ee3c571e3a664842a551781f9750656326a343a50de8cffd93439d125fedc4b01d12b86ce20bf992e96047efe0b3c467ba1f339d5ec15a6c40bd74 Homepage: https://cran.r-project.org/package=ENMeval Description: CRAN Package 'ENMeval' (Automated Tuning and Evaluations of Ecological Niche Models) Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: . Package: r-cran-enmsdmx Architecture: all Version: 1.2.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aiccmodavg, r-cran-boot, r-cran-cowplot, r-cran-data.table, r-cran-doparallel, r-cran-dt, r-cran-foreach, r-cran-gbm, r-cran-ggplot2, r-cran-ks, r-cran-maxnet, r-cran-mgcv, r-cran-omnibus, r-cran-predicts, r-cran-ranger, r-cran-rjava, r-cran-scales, r-cran-sf, r-cran-shiny, r-cran-sp, r-cran-statisfactory, r-cran-terra Filename: pool/dists/resolute/main/r-cran-enmsdmx_1.2.12-1.ca2604.1_all.deb Size: 1988474 MD5sum: cfa578add74da2897e57620fdd04a34a SHA1: 8dacad943f1196a018769c369a86adf5cc4a46e3 SHA256: 4c2bc656d79a415d2ecaf10cf9c1062f4b16e7062891e9e340035abd0a17ab0c SHA512: a7178a694ae3458bb32953ec6ad174acd76e96b48a2c07147c13a0455da6824d46d404ae198f2baa838bc7369972f05f77acf42254bddffff16919880d86fe1b Homepage: https://cran.r-project.org/package=enmSdmX Description: CRAN Package 'enmSdmX' (Species Distribution Modeling and Ecological Niche Modeling) Implements species distribution modeling and ecological niche modeling, including: bias correction, spatial cross-validation, model evaluation, raster interpolation, biotic "velocity" (speed and direction of movement of a "mass" represented by a raster), interpolating across a time series of rasters, and use of spatially imprecise records. The heart of the package is a set of "training" functions which automatically optimize model complexity based number of available occurrences. These algorithms include MaxEnt, MaxNet, boosted regression trees/gradient boosting machines, generalized additive models, generalized linear models, natural splines, and random forests. To enhance interoperability with other modeling packages, no new classes are created. The package works with 'PROJ6' geodetic objects and coordinate reference systems. Package: r-cran-enmtools Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dismo, r-cran-knitr, r-cran-ggplot2, r-cran-gridextra, r-cran-lhs, r-cran-magrittr, r-cran-enmeval, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-ggpubr, r-cran-forcats, r-cran-terra, r-cran-raster Suggests: r-cran-testthat, r-cran-hypervolume, r-cran-leaflet, r-cran-mgcv, r-cran-ecospat, r-cran-randomforest, r-cran-ranger, r-cran-caret, r-cran-calibratr, r-cran-ape, r-cran-resourceselection, r-cran-reshape2, r-cran-vip, r-cran-pdp, r-cran-fastshap, r-cran-viridis, r-cran-progress, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/resolute/main/r-cran-enmtools_1.1.5-1.ca2604.1_all.deb Size: 1659858 MD5sum: 6364828c702e2901b9eb158c0d20a53e SHA1: 1390f91e1ac919a82ee1827070698a532633bdb5 SHA256: ed62d2041120d2584bed0a9354a19a870e94cac928de59f5a40dbaaad38f7b37 SHA512: becbd28002180a2a5eee1a40f0020422f2f2f92a323bd2a9d97e680c431a2d36f7381619d6a9f671afa7d5f8aad9cf9ddca4719dc0d869c707d762a2655047e2 Homepage: https://cran.r-project.org/package=ENMTools Description: CRAN Package 'ENMTools' (Analysis of Niche Evolution using Niche and Distribution Models) Constructing niche models and analyzing patterns of niche evolution. Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography. Warren, D.L., R.E. Glor, and M. Turelli (2008) Glor, R.E., and D.L. Warren (2011) Warren, D.L., R.E. Glor, and M. Turelli (2010) Cardillo, M., and D.L. Warren (2016) D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) . Package: r-cran-enpls Architecture: all Version: 6.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-spls, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-reshape2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-enpls_6.1.1-1.ca2604.1_all.deb Size: 1807692 MD5sum: 4466d64b582e649ab22b4cd59a84e5dc SHA1: 23c22de2fcfae6d36a00a9d8db33b9a80cdf6bb0 SHA256: 809b64b388714a521797fbe2760db4882f6cf2cfa002daf9a191b3cb8b4f4110 SHA512: 8432adbb5b952f6c3858be42f4ca761674d7c8053a1f36ab6b8058fbe9ad9dbf583565e961b652be6001f280727c38e1548743b332ff5996e5c45bfb7b3553e6 Homepage: https://cran.r-project.org/package=enpls Description: CRAN Package 'enpls' (Ensemble Partial Least Squares Regression) An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions. Package: r-cran-enrichintersect Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1387 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-networkd3, r-cran-jsonlite, r-cran-htmlwidgets, r-cran-webshot2 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-enrichintersect_0.7-1.ca2604.1_all.deb Size: 1107954 MD5sum: 7b3607265685e8599ea87207b1f3f97e SHA1: 26d47d871c6ee04dae78286c980fa515bb8a269c SHA256: a5a6951705f219b72f79ac18ccc4565b68018d5b8acd72fb657ba859efa7ef24 SHA512: 7032d77ecf7aaa978ea31060ace6fd353281c229dbaff497da3a049e8b1e3f12f9eeef46edfce2e307727d6806333625fcc2393db268b707d735d27dcbad77d3 Homepage: https://cran.r-project.org/package=EnrichIntersect Description: CRAN Package 'EnrichIntersect' (Enrichment Analysis and Intersecting Sankey Diagram) A flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al., 2022 ) or similar methods. Package: r-cran-enrichr Architecture: all Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-curl, r-cran-rjson, r-cran-ggplot2, r-cran-writexls Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-enrichr_3.4-1.ca2604.1_all.deb Size: 255184 MD5sum: 9d96dcfd9cd97070e852abb914be5aa7 SHA1: 19d3323aeea585d6f251006c1b98f5f6789e56d7 SHA256: 6a7851395158efdeadf0bf8085cd338f5307c7c201308756a49a3a61da2a2c31 SHA512: d1e2d2616058e02f3a2b1f2bf5b7c220265d0cffef328dcf90eb440350b85b3fca2856e9776f31eff1bd0f098827324cb42f9b9bdfd839be886bfd7b911b8b3b Homepage: https://cran.r-project.org/package=enrichR Description: CRAN Package 'enrichR' (Provides an R Interface to 'Enrichr') Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See for further details. Package: r-cran-enrichwith Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-whisker, r-cran-suppdists, r-cran-brglm, r-cran-brglm2, r-cran-ggplot2, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-enrichwith_0.5.0-1.ca2604.1_all.deb Size: 273790 MD5sum: ef8fc3b30955ebc36af74ec9ffcaebdf SHA1: 2d939765c95b3666598441b3d500756ef0af90a2 SHA256: a8f698fe819c9f3d321696d9544719142e9c854e033dd433fff6fa9b0a9e4876 SHA512: 66ddf7bd52bbf1bdbda38f4716d4bbc2a1021d40afdc94a451de361ba340856b0cf474867d1214a757d779b68f7c13774597538cf14e102f16f3841baea449c7 Homepage: https://cran.r-project.org/package=enrichwith Description: CRAN Package 'enrichwith' (Methods to Enrich R Objects with Extra Components) Provides the "enrich()" method for augmenting list-like R objects with additional, model-specific components. Methods are currently available for objects of class "family", "link-glm", "lm", "glm", and "betareg". Enriched objects retain their original class and remain compatible with existing methods. For example, enriching a "glm" object produces an "enriched_glm" object that also inherits from "glm". In addition to the standard components, the "enriched_glm" object includes methods for simulation and functions to compute scores, observed and expected information matrices, first-order bias, and other model quantities such as densities, probabilities, and quantiles, which can be evaluated at use-supplied parameter values. The package also provides tools for generating customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects. Package: r-cran-enscat Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2716 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dendextend, r-cran-ggplot2, r-cran-ggdendro, r-cran-seqinr Filename: pool/dists/resolute/main/r-cran-enscat_1.1-1.ca2604.1_all.deb Size: 2591084 MD5sum: 00f27504a93566e34f52b84b53d827a3 SHA1: 7003cf3cba43276deb2fbdb0c070525436c6bde5 SHA256: f396c63a89d66e91f93be717bba8c93bf3cf7534e62a9d28b1393d286fb3f60d SHA512: e51dae092f9589e30b55b82ee990b9a555663c279b51632d74b8b035acee155ba4617a0e35ceec9d3f54982517217d40a4a98dabc592f9f51e497e6959e315df Homepage: https://cran.r-project.org/package=EnsCat Description: CRAN Package 'EnsCat' (Clustering of Categorical Data) An implementation of the clustering methods of categorical data discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical data via ensembling dissimilarity matrices. Preprint . Package: r-cran-ensemblebma Architecture: all Version: 5.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2763 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chron Suggests: r-cran-fields, r-cran-maps Filename: pool/dists/resolute/main/r-cran-ensemblebma_5.1.8-1.ca2604.1_all.deb Size: 2704106 MD5sum: 293d38238170b9e942e940d7b803ef76 SHA1: 7fee60cb985dc8842978623a33e14ccb423245d8 SHA256: 6e0d8449b6f75bda938ea7c1352cf6ee322404a86c1ffc036970b9f0371c1913 SHA512: 8401fc682933abde273705bd0618942b6b1ae607d8c0f43dc464ecc7794389bfde7f0d8f2fff90ec11bdf9a2d44dc4dc73a987e1556e9162f21a52ac41d46c53 Homepage: https://cran.r-project.org/package=ensembleBMA Description: CRAN Package 'ensembleBMA' (Probabilistic Forecasting using Ensembles and Bayesian ModelAveraging) Bayesian Model Averaging to create probabilistic forecasts from ensemble forecasts and weather observations . Package: r-cran-ensemblemos Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ensemblebma, r-cran-chron, r-cran-evd Suggests: r-cran-fields, r-cran-maps Filename: pool/dists/resolute/main/r-cran-ensemblemos_0.8.2-1.ca2604.1_all.deb Size: 385598 MD5sum: d2a85aabfd09f66fdacf567aa9bd5ddc SHA1: 921934f9d57c3b73f97f5e1d51d325927e6521f7 SHA256: 5a4ad05eaf8604812d3979e981b87f362e4d27fa5626810bf60713ebced48223 SHA512: 053bebb77fa9e11da2ce527b81c0440fccf4e87a9fcf12dad5a97138ecf5c511f9f20ffa499aa28cb975a46018fb244059cf8eef1b73ad631c9d0c8b88eb0223 Homepage: https://cran.r-project.org/package=ensembleMOS Description: CRAN Package 'ensembleMOS' (Ensemble Model Output Statistics) Ensemble Model Output Statistics to create probabilistic forecasts from ensemble forecasts and weather observations. Package: r-cran-ensemblepp Architecture: all Version: 1.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ensemblebma, r-cran-crch, r-cran-gamlss, r-cran-ensemblemos, r-cran-specsverification, r-cran-scoringrules, r-cran-glmx, r-cran-ordinal, r-cran-proc, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-ensemblepp_1.0-0-1.ca2604.1_all.deb Size: 252736 MD5sum: dda9f4ba4366fafba038d57ff709cf09 SHA1: 2420fb78045e40db14eda7a2dbcd4df0366d9ea4 SHA256: b3fe2bbe7a8e93a30f999c64806b20533e2b39aabeead7d8bd698930cdf82ddf SHA512: 0f86e4ae2dc704cccacc4b4480575ef0da04acdc7408be2eecebd993d9c6996e17ad3f383a76c306a4388e25ecbf57e111a9ad4fb2e16761930e080c2a35c7ec Homepage: https://cran.r-project.org/package=ensemblepp Description: CRAN Package 'ensemblepp' (Ensemble Postprocessing Data Sets) Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided. Package: r-cran-ensembletax Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-bioc-biostrings, r-bioc-decipher, r-cran-stringr, r-cran-ggplot2, r-cran-reshape2, r-cran-usethis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-ensembletax_1.1.1-1.ca2604.1_all.deb Size: 1986986 MD5sum: e9f3969475526b754b80c58e11f2e66c SHA1: edab5a52ab6b24aa12a1dd4d9ad527991fe6de3a SHA256: 761b430eefa9bbf88c7efb6b026f3948df427601a3ff20ead1f996e6446fd2d9 SHA512: f52d6147372f9689570e1e22afcd9e2bd008d8b26daa7f7aa14fcb6dd0a915d356e4608e9ca1ed9ee89596cc2a1197bf35400d128954f3cdfc1a86a5acaeee8f Homepage: https://cran.r-project.org/package=ensembleTax Description: CRAN Package 'ensembleTax' (Ensemble Taxonomic Assignments of Amplicon Sequencing Data) Creates ensemble taxonomic assignments of amplicon sequencing data in R using outputs of multiple taxonomic assignment algorithms and/or reference databases. Includes flexible algorithms for mapping taxonomic nomenclatures onto one another and for computing ensemble taxonomic assignments. Package: r-cran-enshuman Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7468 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-enshuman_1.0.0-1.ca2604.1_all.deb Size: 7610098 MD5sum: f3c6ae82b43e938b9901d3eb7b88d20c SHA1: 6f013395031c7eec953b96d5d00579aab4a65b6e SHA256: 5d2893418694fe062c7b0aee4fd7b8bddefd422a5c53b32e5b84c7ee1f6508f7 SHA512: ecd2c40e25365805594317b79731c84d74296bcd77b833392fe318b755392e18a60c3aa9a55910fe0b343a65a178e25f19b639d6decce305e2edf2b33c3f9e2d Homepage: https://cran.r-project.org/package=enshuman Description: CRAN Package 'enshuman' (Human Gene Annotation Data from 'Ensembl') Gene information from 'Ensembl' genome builds 'GRCh38.p14' and 'GRCh37.p13' to use with the 'topr' package. The datasets were originally downloaded from and and converted into the format required by the 'topr' package. See to see the required format. 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For classification models, the plots are heatmaps, for regression, scatterplots. 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This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis. Package: r-cran-envir Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-envir_0.3.0-1.ca2604.1_all.deb Size: 52904 MD5sum: ba27cee603f8565add9c65ee4f38825c SHA1: fe7016bcae8f5b198f23939af9d3b3521f792d33 SHA256: 56c275b01d76f5575d477da076d5b56dbbcd097f77d72ad27c9e1178ed496659 SHA512: e8e23d94e7b26ce9df2d31f058c791e54323aa0c4d5ca678ddff6b36c550382b6fe1d434c208eefcf55a900359987e1d431fa1105952b4dca42faaebbd8dd8c4 Homepage: https://cran.r-project.org/package=envir Description: CRAN Package 'envir' (Manage R Environments Better) Provides a small set of functions for managing R environments, with defaults designed to encourage usage patterns that scale well to larger code bases. 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Package: r-cran-envnj Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-bio3d, r-cran-phangorn, r-cran-philentropy, r-cran-seqinr, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-envnj_0.1.3-1.ca2604.1_all.deb Size: 145242 MD5sum: b57962f7b5117d4f0c529bd7f37e1260 SHA1: f7e275960b88a207b9d7919b9d12a3856e233473 SHA256: f27d3434efe82fcea31218f66d486ddb25b38d1df394b41fecd2c2b6b0f65234 SHA512: 030d5dd09410eb3bb1208d4975d34764bb6a157a8bc9114439703a403361692c33b8d13bbd33ad3070f76d04fa6feab66f8685660b67467f4c02fa83b91fda35 Homepage: https://cran.r-project.org/package=EnvNJ Description: CRAN Package 'EnvNJ' (Whole Genome Phylogenies Using Sequence Environments) Contains utilities for the analysis of protein sequences in a phylogenetic context. Allows the generation of phylogenetic trees base on protein sequences in an alignment-independent way. Two different methods have been implemented. One approach is based on the frequency analysis of n-grams, previously described in Stuart et al. (2002) . The other approach is based on the species-specific neighborhood preference around amino acids. Features include the conversion of a protein set into a vector reflecting these neighborhood preferences, pairwise distances (dissimilarity) between these vectors, and the generation of trees based on these distance matrices. Package: r-cran-envoutliers Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-car, r-cran-changepoint, r-cran-ecp, r-cran-ismev, r-cran-lokern, r-cran-robustbase Suggests: r-cran-openair Filename: pool/dists/resolute/main/r-cran-envoutliers_1.1.0-1.ca2604.1_all.deb Size: 212800 MD5sum: ecb5a26296cb7fbc11953c01c92e05c5 SHA1: de46bc76f2d42f9aa1f8f8934ea26ad239f44793 SHA256: 21fd67b4067218e1c23858e939037342f420e9ab55b0d4a82f7d880861dc4e4c SHA512: de152bcd45808db84635a326d7cc16705325fdb0cc0bc630375915ffb1393ed0fc88d7d12e26a1acb8b7bb738fc27f2de34b2366174e3eb1236a7631ed91655d Homepage: https://cran.r-project.org/package=envoutliers Description: CRAN Package 'envoutliers' (Methods for Identification of Outliers in Environmental Data) Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) ) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) ) and the third method (Holesovsky, Campulova and Michalek (2018) ) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) ). Package: r-cran-envsetup Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-fs, r-cran-purrr, r-cran-rlang, r-cran-usethis, r-cran-envnames Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-kableextra, r-cran-magrittr, r-cran-devtools, r-cran-readr, r-cran-tidyr, r-cran-withr, r-cran-lintr, r-cran-styler, r-cran-renv, r-cran-covr Filename: pool/dists/resolute/main/r-cran-envsetup_0.3.0-1.ca2604.1_all.deb Size: 219332 MD5sum: fd24fb78c08d83b921d332a79ff1037d SHA1: 5371b9f459af81687cc132fd499824578041441a SHA256: f04977beebfc3035504aeee46bc0b088956c44f0943d240a1f6b4231d3813f70 SHA512: ddcb03ed94eee16144a099fc5adbf3a52acf60ae04f2e2d800e984e5023efd9f1fc3e3d70390550aa6b88bf871ae0ca90c96090949f740143bb7325fee62763d Homepage: https://cran.r-project.org/package=envsetup Description: CRAN Package 'envsetup' (Support the Setup of the R Environment for Clinical TrialProgramming Workflows) The purpose of this package is to support the setup the R environment. 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Package: r-cran-envstat Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-rstudioapi, r-cran-yaml Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-envstat_0.0.3-1.ca2604.1_all.deb Size: 27828 MD5sum: 4c90cdc3acac26172595c7994544ed50 SHA1: 7b2a1dfcefc3bd8928efd7f54bab47ab17759294 SHA256: 1f8e088224bf6c880cfc14cb1f7257b2b201a8baefb5a64a057be817154d80d6 SHA512: 6ac4c15c0f8c18c0d7cbba1cb9f92e70505435fe37957c9512cda953122d1860513918f5a53ae543fac38937174a88824fc31b3c4c7cd7a02f666460da652808 Homepage: https://cran.r-project.org/package=envstat Description: CRAN Package 'envstat' (Configurable Reporting on your External Compute Environment) Runs a series of configurable tests against a user's compute environment. 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Package: r-cran-envstats Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-nortest Suggests: r-cran-lattice, r-cran-qcc, r-cran-sp, r-cran-boot, r-cran-tinytest, r-cran-covr, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-envstats_3.1.0-1.ca2604.1_all.deb Size: 6235644 MD5sum: ad8d1836a4fe5dfb92b1946064d8e4cc SHA1: df1f5633236be58d9076bb3533922e4c064117fc SHA256: 1b9bfd3e58b0caa188ec78661505997ff3ed76b621d56720159a55733e67eba5 SHA512: 25ae24683827ff2475d922d18f20ab92b0f8243e527df6ad6e0c8df15aa162e118609b5dcd2395a923e16b83260222061b74f885ba1906f9af1c5679d1e5c433 Homepage: https://cran.r-project.org/package=EnvStats Description: CRAN Package 'EnvStats' (Package for Environmental Statistics, Including US EPA Guidance) Graphical and statistical analyses of environmental data, with focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. Major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. Numerous built-in data sets from regulatory guidance documents and environmental statistics literature. Includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, ). 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Package: r-cran-eoa3 Architecture: all Version: 1.0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-actuar, r-cran-genest, r-cran-mass, r-cran-rjags, r-cran-survival, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-eoa3_1.0.0.2-1.ca2604.1_all.deb Size: 98624 MD5sum: 89f98cd195479570e3f7d25d6f9e9c52 SHA1: 91fc1b13a5c49798d49ffe2f5b93256cccea075f SHA256: e272fb3372e6deb104b0727d23fb88794d360446b18b36630a5516b8db7f586d SHA512: 49f42d2f9407385a7980ef1ea000b0176bb647dfff4cd631ff3516328f06e6f15faaddde2d86686387820a242a6c70f97898b5528d45d79d685d5b5db5dc4756 Homepage: https://cran.r-project.org/package=eoa3 Description: CRAN Package 'eoa3' (Wildlife Mortality Estimator for Low Fatality Rates andImperfect Detection) Evidence of Absence software (EoA) is a user-friendly application for estimating bird and bat fatalities at wind farms and designing search protocols. 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Package: r-cran-eodhdr2 Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-tidyr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eodhdr2_0.5.2-1.ca2604.1_all.deb Size: 243612 MD5sum: c39a1862894b55164b9d54cf87f01d90 SHA1: 94c1e28e6f81bd09b88d8819e9db602a636cda0b SHA256: 41907d9199708c94e65ca275a16a38a4973ceb3e297fd02905aa082ed25206a0 SHA512: d4960fc76e6dfb5d8ad7692d256c23ce99a59321457b9f3b73b7201ef968f054193a9526aa8b98b182059dcbdbd9e87afa7e91beb2c11068ea79acb429ee3548 Homepage: https://cran.r-project.org/package=eodhdR2 Description: CRAN Package 'eodhdR2' (Official R API for Fetching Data from 'EODHD') Second and backward-incompatible version of R package 'eodhd' , extended with a cache and quota system, also offering functions for cleaning and aggregating the financial data. 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And This package also provide write out figures with lots of different formats. Since people may work on the platform without GUI support, the package also provide function to easily write out figures to lots of different type of formats. Now this package provide function to extract colors from all types of figures and pdf files. Package: r-cran-eor Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-eor_0.4.0-1.ca2604.1_all.deb Size: 199076 MD5sum: 15cc1b832b17ac6b88e4418b7bd67ba1 SHA1: bbdc79722dc283f82f85e2ff4bccce1eac24c1cf SHA256: 3f89d3d4d02316eb54213fb2b91d2569c9f0bf0fbaa6a19e95a5cdc0aede935a SHA512: 146d85c238fe1438bdee46593265fe434aa7df4af3fab47b9362bed4c689698f7f9a288669bec628419384d99891ca3b0870623e97e5a650446bf80c982fbf91 Homepage: https://cran.r-project.org/package=eoR Description: CRAN Package 'eoR' (Data Management Package (Exposure and Occurrence Data in R)) This data management package provides some helper classes for publicly available data sources (HMD, DESTATIS) in Demography. Similar to ideas developed in the Bioconductor project we strive to encapsulate data in easy to use S4 objects. 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Package: r-cran-epandist Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-epandist_1.1.1-1.ca2604.1_all.deb Size: 37516 MD5sum: c5256071877f854c24f69b18c3768870 SHA1: 95b8d5bc0f9b74fc976525383dc7e56f7d0e0c0c SHA256: 3f5bfc92e360f99744d8424eeb49ffa95dcc6c89f935db755d953411071074c3 SHA512: bf7e3878727d4b0070f2bd0df200aaf10ff4092cc87a9486f1617acc2aeaea4ceb8f6f7e4dea702aaca45e27088f85d7c131a227e4ac03178cf33ef72ab63748 Homepage: https://cran.r-project.org/package=epandist Description: CRAN Package 'epandist' (Statistical Functions for the Censored and UncensoredEpanechnikov Distribution) Analyzing censored variables usually requires the use of optimization algorithms. 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Package: r-cran-epanetreader Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-epanet2toolkit, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-epanetreader_1.0.0-1.ca2604.1_all.deb Size: 171454 MD5sum: 67ab037b1760d504724875696d448917 SHA1: d13f023984c17b2aa88c591738f406f7ee251595 SHA256: 6082bb8f6b4e6924bb3f09b40432934f4bbd9cbf51354441a890fb390c2c0918 SHA512: ddaf4cc381ee924f859c160daa31ab7721dea7312f737420ef85a66ac7269e4fcefd0fb33c437d9c33ca6de8827ea44ed4292d2300e4cce1af248c7bd93a8577 Homepage: https://cran.r-project.org/package=epanetReader Description: CRAN Package 'epanetReader' (Read Epanet Files into R) Reads water network simulation data in 'Epanet' text-based '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'. Provides basic summary information and plots. The README file has a quick introduction. See for more information on the Epanet software for modeling hydraulic and water quality behavior of water piping systems. Package: r-cran-epca Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-irlba, r-cran-matrix, r-cran-gparotation Suggests: r-cran-elasticnet, r-cran-ggcorrplot, r-cran-tidyverse, r-cran-rmarkdown, r-cran-reshape2, r-cran-markdown, r-cran-rspectra, r-cran-matlabr, r-cran-knitr, r-cran-pma, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-epca_1.1.0-1.ca2604.1_all.deb Size: 137476 MD5sum: 34d5e0c4d57259c8b82968bb77bef054 SHA1: 6ad08f0260b8f27cb9c51473b65c280f479f4f9d SHA256: 5cbed731451ef4eaa463292ffe381b3022aff7d18ca0a1bf307591f44387e506 SHA512: 369243dbd0c487b317d8df181bfa68cc3c36056baac143c21a88f14a3d492fea6fa090bcccdc5c5c21eca55c409b48bf28b913606eb2a25042381c7a72a38139 Homepage: https://cran.r-project.org/package=epca Description: CRAN Package 'epca' (Exploratory Principal Component Analysis) Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation. Package: r-cran-epcr Architecture: all Version: 0.11.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4775 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-hamlet, r-cran-survival, r-cran-timeroc, r-cran-pracma, r-cran-bolstad2, r-bioc-impute Suggests: r-cran-mass, r-cran-rocr, r-cran-c060, r-cran-matrix, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-epcr_0.11.0-1.ca2604.1_all.deb Size: 4699282 MD5sum: 2efe6e8287e5b736f28d69848d3ed254 SHA1: e65c9dfac4102087b1fed314958365563191068c SHA256: e633c085cc0f391b73edc63a2fba7a6736d178ec098eed176a63eb28bd09f571 SHA512: a144413401c81142dffae2f3307868176e48973e116ec382ed291b7693bccffe19fb150b39988a3701783b67330a47e95bdede5e6ca5ad029c9b3f939ff435df Homepage: https://cran.r-project.org/package=ePCR Description: CRAN Package 'ePCR' (Ensemble Penalized Cox Regression for Survival Prediction) The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge presented in Guinney J, Wang T, Laajala TD, et al. (2017) is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects. Package: r-cran-epe4md Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4674 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-scales, r-cran-purrr, r-cran-tidyr, r-cran-readxl, r-cran-jrvfinance, r-cran-janitor, r-cran-furrr, r-cran-zoo, r-cran-lubridate, r-cran-tsibble, r-cran-stringr, r-cran-fabletools, r-cran-feasts, r-cran-tibble, r-cran-assertthat, r-cran-forcats, r-cran-future, r-cran-magrittr, r-cran-readr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-epe4md_0.1.4-1.ca2604.1_all.deb Size: 3865876 MD5sum: 5be31474249110af8ac7c505788ac512 SHA1: 1162c08c4a17a661a0b18a3437f432fab7029b4a SHA256: e97483207f1a7a5ed31715054431b7761ec8b660cac3ac2038dff91c54bcaf7a SHA512: 44abb7078ea55248cb1cfd40cc61b4a5341353f33dc326d6c3395aa82e7435b7ef5d0b98f84ab29a557f718b282654270b63eca9654ec9f97eb645c6c8debbf1 Homepage: https://cran.r-project.org/package=epe4md Description: CRAN Package 'epe4md' (EPE's 4MD Model to Forecast the Adoption of DistributedGeneration) EPE's (Empresa de Pesquisa Energética) 4MD (Modelo de Mercado da Micro e Minigeração Distribuída - Micro and Mini Distributed Generation Market Model) model to forecast the adoption of Distributed Generation. Given the user's assumptions, it is possible to estimate how many consumer units will have distributed generation in Brazil over the next 10 years, for example. In addition, it is possible to estimate the installed capacity, the amount of investments that will be made in the country and the monthly energy contribution of this type of generation. . Package: r-cran-eph Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1789 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-expss, r-cran-purrr, r-cran-tibble, r-cran-stringr, r-cran-readxl, r-cran-tidyr, r-cran-zoo, r-cran-leaflet, r-cran-htmltools, r-cran-rlang, r-cran-cli, r-cran-httr, r-cran-curl, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-readr, r-cran-forcats, r-cran-httptest Filename: pool/dists/resolute/main/r-cran-eph_1.0.2-1.ca2604.1_all.deb Size: 1357570 MD5sum: 8f606c9f5e941f1ed77a668b3f6ce5af SHA1: 25a3549855c254d51529b67f7dc191d4ea4f5c5c SHA256: 9f69a9726267634b91e233118d184b11478952de0359b7cd8b11c4bc84b33c6b SHA512: b858bc1063fd4b0ad9b8270b4ec02817e15f358059708650c6d61ce8092935d87e6580efd2837b25a25834c8905b0ba72ab9f3e32c2cba79834dca2de947af2d Homepage: https://cran.r-project.org/package=eph Description: CRAN Package 'eph' (Argentina's Permanent Household Survey Data and ManipulationUtilities) Tools to download and manipulate the Permanent Household Survey from Argentina (EPH is the Spanish acronym for Permanent Household Survey). e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets, calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology. organize_panels() is used to concatenate observations from different periods, and organize_labels() adds the official labels to the data. The implemented methods are based on INDEC (2016) . As this package works with the argentinian Permanent Household Survey and its main audience is from this country, the documentation was written in Spanish. Package: r-cran-epibasix Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-epibasix_1.5-1.ca2604.1_all.deb Size: 92270 MD5sum: 9d5ddc290f93e9d862eadda32a5277e4 SHA1: 0a21bfd48ba6aeb08409ce9317f8091e1578a7f7 SHA256: eb6de9656741e746613710f7c55f16d529914d205326361893794e80cc039f27 SHA512: cfc605e5e9a783c10ea62132f7321162339bbec0b1942559cdb374426348fc9ae70a752a86d62d9f218cb89a842267bc92f56c8e03b4743a0df692f078a1bfcb Homepage: https://cran.r-project.org/package=epibasix Description: CRAN Package 'epibasix' (Elementary Epidemiological Functions for Epidemiology andBiostatistics) Contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. Source code is commented throughout to facilitate modification. The target audience includes advanced undergraduate and graduate students in epidemiology or biostatistics courses, and clinical researchers. Package: r-cran-epicasting Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-metrics, r-cran-wavelets Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-epicasting_0.1.0-1.ca2604.1_all.deb Size: 21296 MD5sum: 474f3e0d8039df607e49813ec4d2dc57 SHA1: 3f0ce405cf15f66141c0d0450f663e5269fed0d1 SHA256: 191a7c4e3066cb641f62122fed9f6e01eeb4ccbf9bfed852e23e16b220bfd0e4 SHA512: be51c0988ffeda689a1e745d696eb040461fee750e699a25d9c5f3bc8d4162173b9dd6bae5fa337bc4102be6cf20a2f3eaa348f43edba113b0fca72883babf5c Homepage: https://cran.r-project.org/package=epicasting Description: CRAN Package 'epicasting' (Ewnet: An Ensemble Wavelet Neural Network for Forecasting andEpicasting) Method and tool for generating time series forecasts using an ensemble wavelet-based auto-regressive neural network architecture. This method provides additional support of exogenous variables and also generates confidence interval. This package provides EWNet model for time series forecasting based on the algorithm by Panja, et al. (2022) and Panja, et al. (2023) . Package: r-cran-epichains Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3882 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate Suggests: r-cran-bookdown, r-cran-dplyr, r-cran-epicontacts, r-cran-ggplot2, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-truncdist Filename: pool/dists/resolute/main/r-cran-epichains_0.1.1-1.ca2604.1_all.deb Size: 1362532 MD5sum: c7ab70e6f7f5ae832da6652107ca9770 SHA1: 842241ee5f3300f746b8305eb9a888879a503499 SHA256: fa6f4ce36cd95033212969f5dde417ec0f5be487f8ad8b2284d241f6e85ceaff SHA512: 8c1213c9e2a7b7b9f1658e2bc10aae0f2dcc42783f1ca1e873713ce1157b24bdea29493081cdc8c4d5abaf1240a1e14a737be9625315dcdbf209df7a1d68f3a2 Homepage: https://cran.r-project.org/package=epichains Description: CRAN Package 'epichains' (Simulating and Analysing Transmission Chain Statistics UsingBranching Process Models) Provides methods to simulate and analyse the size and length of branching processes with an arbitrary offspring distribution. These can be used, for example, to analyse the distribution of chain sizes or length of infectious disease outbreaks, as discussed in Farrington et al. (2003) . Package: r-cran-epicmodel Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-checkmate, r-cran-cli, r-cran-dagitty, r-cran-diagrammer, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-gtools, r-cran-prompter, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-shinyalert, r-cran-shinyjs, r-cran-shinythemes, r-cran-spsutil, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-ggdag, r-cran-ggforce, r-cran-ggraph, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-epicmodel_0.2.1-1.ca2604.1_all.deb Size: 762192 MD5sum: 58334087ee024bfe29b2ade499b8c83d SHA1: 1c55ff1f836937551b27e351f29d68be11b668a0 SHA256: fe43749b19e3f549837414d32bc13822008ed876e394fd011fa4bf253a383e7e SHA512: 31b023eedbc0b0b496e379bd39f23e417c24d05976d582f963faaafd5128a300f81aa43c4c26c1e97a43bf86661ebb1a20c718d10dbbe00791d06bf59861ceb2 Homepage: https://cran.r-project.org/package=epicmodel Description: CRAN Package 'epicmodel' (Causal Modeling in Epidemiology) Create causal models for use in epidemiological studies, including sufficient-component cause models as introduced by Rothman (1976) . 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It implements travel times estimated in Bravo-Vega C., Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method (Bortman, M. (1999) ). Package: r-cran-epicontacts Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3598 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-visnetwork, r-cran-threejs Suggests: r-cran-outbreaks, r-cran-testthat, r-cran-covr, r-cran-shiny, r-cran-readr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-epicontacts_1.1.4-1.ca2604.1_all.deb Size: 1054146 MD5sum: 4b5aa5b2005ef04e700d9ad130a28799 SHA1: 2fd6440fb289b9d031e205bc52ade1c2f4cf39d5 SHA256: 0cbab71ebf25b7ea99310d44a691917c562c4a237bb0ed47eeb30eba6a08079b SHA512: 4786b183c82dbce8e6bebbe8d8e97068a1a2bef2313e1ef57e9a514bb31d82de796163facab2d96ce95c4af53cf80f9970ac6370d6898581aea273fed1ce1295 Homepage: https://cran.r-project.org/package=epicontacts Description: CRAN Package 'epicontacts' (Handling, Visualisation and Analysis of Epidemiological Contacts) A collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics. Package: r-cran-epicurve Architecture: all Version: 2.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-isoweek, r-cran-scales, r-cran-timedate, r-cran-rcolorbrewer, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-epicurve_2.4-2-1.ca2604.1_all.deb Size: 263334 MD5sum: 9ce1fb5b2d676c49a252030a71457c2a SHA1: 203402c452b961d0befbbd5b92d01e1ff6793ba6 SHA256: 5121e68e7f20656bfcc7ef36fe0fbd850f067883e791bac7f2a19eff2b3cf83e SHA512: 8140cfe9fdf9caad84f29d9849e032940d0f601ffae3667dddb469f097ffc9f536816562a603a287e042ed27b0dd25f8318238970a7ac654b35673b94c261dab Homepage: https://cran.r-project.org/package=EpiCurve Description: CRAN Package 'EpiCurve' (Plot an Epidemic Curve) Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data. Package: r-cran-epidata Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-rvest, r-cran-xml2, r-cran-tidyr, r-cran-readr, r-cran-stringi, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-epidata_0.4.0-1.ca2604.1_all.deb Size: 506094 MD5sum: 6ccafa4328e9079aad099a4a65f26ecf SHA1: 7575a2749d8e7a04c06698cac0edfe75eecb644b SHA256: e0abc504cc455bf1219a89bd71c42d70e49776ce047113081d198f566b108fc9 SHA512: 2dac79e46783e9eab087d457d13ecfd34049d51315f737276c48227333b59b21569815e1b55f9f5780ab0b28b5ab4c0e94c135cd2f6ed3322c17848d17eb669e Homepage: https://cran.r-project.org/package=epidata Description: CRAN Package 'epidata' (Tools to Retrieve Economic Policy Institute Data LibraryExtracts) The Economic Policy Institute () provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. It is compiled from Economic Policy Institute analysis of government data sources. Use it to research wages, inequality, and other economic indicators over time and among demographic groups. Data is usually updated monthly. Package: r-cran-epidatr Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cachem, r-cran-checkmate, r-cran-cli, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-mmwrweek, r-cran-openssl, r-cran-purrr, r-cran-rappdirs, r-cran-rlang, r-cran-tibble, r-cran-usethis, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-mapproj, r-cran-maps, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-epidatr_1.2.2-1.ca2604.1_all.deb Size: 415370 MD5sum: b0d9a1f33a8fd7a276fba5aaf0af50f9 SHA1: 1cdb435e1c1cbc38cf17ce4bfacfdd0255777e57 SHA256: 6873ffaf0b9bb78e464f899aca9a60b307db045c5bde02a95f2f82885e16d93c SHA512: 43ed2b58e658de57e188619b85749004eb1bc48525d6477e723590f32d996db30e3bc99707fe0b453981fe4fec3deef1ecf7bcf020712d00809a9bc3e109c02b Homepage: https://cran.r-project.org/package=epidatr Description: CRAN Package 'epidatr' (Client for Delphi's 'Epidata' API) The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. . Package: r-cran-epideaths Architecture: all Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-epideaths_1.1.8-1.ca2604.1_all.deb Size: 46020 MD5sum: d6d11585f2c8a1f9efa4775c88093cdb SHA1: dc231f55a0752fe6c086ea54982049d06c7cb8fd SHA256: 00558b191e5e6e6e46909bdfa863f77a3ef6bf5da4db1653942762534d7f9912 SHA512: aa6fc7992359a2f04f62c7da23a3e02556dac0a0cfde3dd662a0008b6c98ee47d27424fa28d175ec3b2bfd7b299e7f95114cc4af473aa782931b5732c0799abf Homepage: https://cran.r-project.org/package=epiDeaths Description: CRAN Package 'epiDeaths' (Functions for Calculating Mortality Indicators) Provides functions for calculating mortality indicators. These include geometric interpolation between two periods and projections for future years, as described in the textbook by Laurenti, Mello Jorge, Lebrão and Gotlieb (2005, ISBN:9788512408309), the standardised mortality ratio (Bruce, Pope and Stanistreet, 2018, ISBN:9781118665411), the age-adjusted mortality rate (direct standardisation), years of potential life lost (Gardner and Sanborn, 1990, ; Ma, Ward, Siegel and Jemal, 2015, ), and age-standardised years of potential life lost (Silva Filho et al., 2024 ). Confidence intervals for the standardised mortality ratio are obtained according to Vandenbroucke (1982) and Ulm (1990) . The function also includes a function that produces a graph similar to an age pyramid. Package: r-cran-epidict Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clipr, r-cran-dplyr, r-cran-readxl, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-dt, r-cran-knitr, r-cran-matchmaker, r-cran-rmarkdown, r-cran-sitrep, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-epidict_0.3.0-1.ca2604.1_all.deb Size: 422034 MD5sum: 59a1c6179386a8e466d87b6a1e4c7e13 SHA1: 5b0fd54227ef61964b627b55ac267fc1818c7323 SHA256: 03c5a719df24508bdca84d7f1890d6514d686d3a7ca72e29926fc7d14ffe6fb0 SHA512: ed56ad017ca5f5b86b0ec232a866e72624b7ec097642b0baba743a3e612fb4b61eb35f426414be55ad7dce13fec87e082fad95f2ab3d732596ba6fe008b039be Homepage: https://cran.r-project.org/package=epidict Description: CRAN Package 'epidict' (Epidemiology Data Dictionaries and Random Data Generators) The 'R4EPIs' project seeks to provide a set of standardized tools for analysis of outbreak and survey data in humanitarian aid settings. This package currently provides standardized data dictionaries from Medecins Sans Frontieres Operational Centre Amsterdam for outbreak scenarios (Acute Jaundice Syndrome, Cholera, Diphtheria, Measles, Meningitis) and surveys (Retrospective mortality and access to care, Malnutrition, Vaccination coverage and Event Based Surveillance) - as described in the following . In addition, a data generator from these dictionaries is provided. It is also possible to read in any Open Data Kit format data dictionary. Package: r-cran-epidigir Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-sp, r-cran-tm, r-cran-glmnet, r-cran-caret, r-cran-survival Suggests: r-cran-kernlab, r-cran-randomforest, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-usethis, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-epidigir_0.1.2-1.ca2604.1_all.deb Size: 82968 MD5sum: c22f63e692be74dc873615719d4f048d SHA1: 93250bb68c2d58c48f21f0920a24ee3b963c9166 SHA256: bfd66835f76415e89bf92dbbf43d3d91c19a237e78f9f5b14a2c7620556acac3 SHA512: e7ff68d94a7066e17f10cfc9a770f737a0230d618039e369cde6e62ed896e9c0015c120ae6744b9ab3b30a5ee8c4093e3bbe34b99e7143182708a8d655e24dc1 Homepage: https://cran.r-project.org/package=EpidigiR Description: CRAN Package 'EpidigiR' (Digital Epidemiological Analysis and Visualization Tools) Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) and Wickham et al. (2019, ISBN:9781492052040>. Package: r-cran-epidisplay Architecture: all Version: 3.7.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-survival, r-cran-mass, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-epidisplay_3.7.0.0-1.ca2604.1_all.deb Size: 669802 MD5sum: f83793d8475216c0a2e6928ec5ea576d SHA1: fda5d8aaed675de333563e2c45f5acf5cce9468c SHA256: 7e1998cc18d2b4efdbee684fbf8102f90499751d572a978ba0f2ee56cd2ea838 SHA512: 18aff8277d95073b7a32285865a449293ab312896160cbcea982e176a99eed8b3aaffda87982cf44985327cc6e68c1fdf896806f4187207ad0e989a65ab428ef Homepage: https://cran.r-project.org/package=epiDisplay Description: CRAN Package 'epiDisplay' (Epidemiological Data Display Package) Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at ''. 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Includes functions to calculate area-under-the-curve summaries, fit and compare exponential, monomolecular, logistic, and Gompertz models using linear or nonlinear regression, work with single or multiple epidemics, and produce 'ggplot2'-based visualizations. Also includes an experimental powdery mildew dataset for reproducible teaching and research workflows. See Madden, Hughes, and van den Bosch (2007) for background on the epidemiological methods. 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Currently we have functionalities for simplifying overlapping time intervals, Charlson comorbidity score constructors for Danish data, getting frequency for multiple variables, getting standardized output from logistic and log-linear regressions, sibling design linear regression functionalities a method for calculating the confidence intervals for functions of parameters from a GLM, Bayes equivalent for hypothesis testing with asymptotic Bayes factor, and several help functions for generalized random forest analysis using 'grf'. 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This is part of the 'R4Epis' project . Package: r-cran-epilogi Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-epilogi_1.2-1.ca2604.1_all.deb Size: 22826 MD5sum: 840cc077ba38a9433a5491ef4224037b SHA1: cf23308794ef9dab7d3643a3a20a4927f52a4644 SHA256: dc98c1176cedfca268ebe6236b895e5af6c3d01c94aa26b9f62e6ac371c5c2ad SHA512: 03971b66ff6ba32071d0e305ba6d0568bcfafd1373faa074f8c6b9bc41bee77c384f23c689fb3e136a6edb440c0727f9465618eb46c39a18cdc5808b58dab62b Homepage: https://cran.r-project.org/package=epilogi Description: CRAN Package 'epilogi' (The 'epilogi' Variable Selection Algorithm for Continuous Data) The 'epilogi' variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics, 39(9): btad545. . 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Bjornstad (ISBN 978-3-319-97487-3) . The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course . 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Supports SEIR (Susceptible-Exposed-Infectious-Recovered), SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased), SVEIRD (Susceptible-Vaccinated-Exposed-Infectious-Recovered-Deceased), and age-stratified compartmental models with flexible intervention functions (spline-based, Gaussian process, or user-defined). Inference is available via maximum likelihood or sequential Monte Carlo (SMC, also known as particle filtering) with no external binary dependencies. Includes a dependency-free real-time effective reproduction number (Rt) estimator, spatial multi-patch models with gravity-model mobility, ensemble forecasting via Bayesian model averaging (BMA), and proper scoring rules including CRPS (Continuous Ranked Probability Score), coverage, and MAE (Mean Absolute Error) for forecast evaluation. Methods follow Anderson and May (1991, ISBN:9780198545996), Doucet, de Freitas, and Gordon (2001) , Cori et al. (2013) , and Gneiting and Raftery (2007) . Package: r-cran-epiomics Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-qgcomp, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-epiomics_1.2.0-1.ca2604.1_all.deb Size: 393686 MD5sum: c769abcdac450b8f88581442f656cd38 SHA1: 6d96478af65e1d7d6d8218cb66718f00d633d837 SHA256: 653b50f80f317fa446ef5b82ecc4bb47e0b4bba63b1be603b1687bb31359d07b SHA512: 2951e79a43a6b4a49a30f2d684360b696c0f7eaefa0e3ba4b4d973cf2d3ddd0813b5da89e37fd8b2f17699b2688cd41c1acba080021f8f0e695395798e87fb5e Homepage: https://cran.r-project.org/package=epiomics Description: CRAN Package 'epiomics' (Analysis of Omics Data in Observational Studies) A collection of fast and flexible functions for analyzing omics data in observational studies. Multiple different approaches for integrating multiple environmental/genetic factors, omics data, and/or phenotype data are implemented. This includes functions for performing omics wide association studies with one or more variables of interest as the exposure or outcome; a function for performing a meet in the middle analysis for linking exposures, omics, and outcomes (as described by Chadeau-Hyam et al., (2010) ); and a function for performing a mixtures analysis across all omics features using quantile-based g-Computation (as described by Keil et al., (2019) ). Package: r-cran-epiparameter Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cachem, r-cran-checkmate, r-cran-cli, r-cran-distcrete, r-cran-distributional, r-cran-epiparameterdb, r-cran-lifecycle, r-cran-pillar, r-cran-rlang Suggests: r-cran-bookdown, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-epiparameter_0.4.1-1.ca2604.1_all.deb Size: 833046 MD5sum: 836bd2c8a0448e428da69cfb0d1df3cb SHA1: fd2b937a499ff4e5fb1b8ad858ceb97a379e886d SHA256: 097676780460cd0061bde7598875f4ef9e5f4437895e11ff3ffc58d79da4c36f SHA512: 9893fe12d2c481f8ba6674e1192f977189af470a864daa27b7786a84033f6c54b5848d79793e09fede8ac44405d45ae41258cc168dc83009a4e027283e1a141e Homepage: https://cran.r-project.org/package=epiparameter Description: CRAN Package 'epiparameter' (Classes and Helper Functions for Working with EpidemiologicalParameters) Classes and helper functions for loading, extracting, converting, manipulating, plotting and aggregating epidemiological parameters for infectious diseases. Epidemiological parameters extracted from the literature are loaded from the 'epiparameterDB' R package. Package: r-cran-epiparameterdb Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dt, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-epiparameterdb_0.1.0-1.ca2604.1_all.deb Size: 184978 MD5sum: e032f275e72b4d18b54d735ac62f8989 SHA1: adf0234a8985074a8e59a93904983f26f1130cb0 SHA256: 2cccb3cacf02834c81685dfd6324f845281f12343c6e6310518d81e4c0ab7406 SHA512: f5a1503d50c15ad7435d610c0681ad5d160516a7a5cdcd3e03c95566db2a54765f742617be42fb13b6e1bc83aba7e8bcffd4d52bcfa678d461b5c7edaa188f35 Homepage: https://cran.r-project.org/package=epiparameterDB Description: CRAN Package 'epiparameterDB' (Database of Epidemiological Parameters) A data package containing a database of epidemiological parameters. It stores the data for the 'epiparameter' R package. Epidemiological parameter estimates are extracted from the literature. Package: r-cran-epir Architecture: all Version: 2.0.93-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5469 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-biasedurn, r-cran-pander, r-cran-sf, r-cran-lubridate, r-cran-zoo, r-cran-flextable, r-cran-officer Suggests: r-cran-mass, r-cran-dplyr, r-cran-foreign, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-plyr, r-cran-rcolorbrewer, r-cran-scales, r-cran-sp, r-cran-spdata, r-cran-spatstat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-epir_2.0.93-1.ca2604.1_all.deb Size: 1765496 MD5sum: 2ccb24d85ff8e157fa333c5b512cbcf8 SHA1: eecafa6adf000038aab5587a3907ddf15aef59fc SHA256: d8313c36f1448031b3b1e42ff2bb339e467bd2dbd262bb844d280983af201351 SHA512: ae02c6512d81d1622445733be0732802b7ee4835db294f43b21a0c30d38e34d1729fc6e40ded3aa85f783903fbd038e63701c66a618d7e17805836063a561f77 Homepage: https://cran.r-project.org/package=epiR Description: CRAN Package 'epiR' (Tools for the Analysis of Epidemiological Data) Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses. Package: r-cran-epireport Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3248 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-officer, r-cran-flextable, r-cran-zoo, r-cran-png, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-epireport_1.0.4-1.ca2604.1_all.deb Size: 2607494 MD5sum: ad199bb711bb7dc52bf780a14e674f4e SHA1: 2c9a3de7aad2874351798c30be77705e20e1d38c SHA256: 5ad9cdf8591c39431ce20596c6f3d90fba402acfacfbeabc2a5e5701368f8f92 SHA512: 86503173f7b2faf92fd7282c528287d11ec8cc2dc885bc54b7a01ad0140c447e44ec79c5c61ef06ddd0aa75b108a611210fff1638907e0f4a98f9a7d391d8927 Homepage: https://cran.r-project.org/package=EpiReport Description: CRAN Package 'EpiReport' (Epidemiological Report) Drafting an epidemiological report in 'Microsoft Word' format for a given disease, similar to the Annual Epidemiological Reports published by the European Centre for Disease Prevention and Control. Through standalone functions, it is specifically designed to generate each disease specific output presented in these reports and includes: - Table with the distribution of cases by Member State over the last five years; - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, by month, over the past five years; - Trend plot with the trend and number of cases at the European Union / European Economic Area level, by month, over the past five years; - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level. Two types of datasets can be used: - The default dataset of dengue 2015-2019 data; - Any dataset specified as described in the vignette. Package: r-cran-episemble Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3962 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-tidyverse, r-cran-seqinr, r-bioc-biostrings, r-cran-splitstackshape, r-cran-entropy, r-cran-party, r-cran-stringr, r-cran-tibble, r-cran-doparallel, r-cran-e1071, r-cran-caret, r-cran-randomforest, r-cran-gbm, r-cran-foreach, r-cran-ftrcool, r-cran-iterators Filename: pool/dists/resolute/main/r-cran-episemble_0.1.1-1.ca2604.1_all.deb Size: 981636 MD5sum: a53a512af7b6c4a6f9fdf661cb1e2ef1 SHA1: 02028e7033b37de8a1d3fca1b699884cf923a0f0 SHA256: c46c0b99a17d27d2b65969ba5480842e9de95c60f7eb187498c16cbcd4a84537 SHA512: e8952cdef2ae67b1064b3d92ab7663f520f79ba2b6a4e5091ebc7d0ddbdb0e2aad6e925da6b6f01349fe6be48206e0bdf72c947e718b88372349f13f9b9209be Homepage: https://cran.r-project.org/package=EpiSemble Description: CRAN Package 'EpiSemble' (Ensemble Based Machine Learning Approach for PredictingMethylation States) DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This 'EpiSemble' package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) . Package: r-cran-episensr Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-actuar, r-cran-boot, r-cran-cli, r-cran-dagitty, r-cran-forcats, r-cran-ggdag, r-cran-magrittr, r-cran-trapezoid, r-cran-triangle, r-cran-truncnorm, r-cran-mass, r-cran-lifecycle Suggests: r-cran-aplore3, r-cran-covr, r-cran-directlabels, r-cran-knitr, r-cran-lattice, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-episensr_2.1.0-1.ca2604.1_all.deb Size: 849170 MD5sum: aad580ea6a022185d8c2f39f878a1f65 SHA1: 16caa22d3ca24e3345357383afca607df18e2250 SHA256: d94572fe39738f31cc9bd9989f02772f98884c8392cccb9366ec110fac4cd1fd SHA512: 289edf14306abc56977ea32cae6c64f77a8e73e9682e438c1c87202281fd15b73e7586139ea1fbf4adff44cf5a3303555582f5ebb57284a51abceff69c0cbf38 Homepage: https://cran.r-project.org/package=episensr Description: CRAN Package 'episensr' (Basic Sensitivity Analysis of Epidemiological Results) Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Fox M.P., MacLehose R.F., and Lash T.L. "Applying Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021). Package: r-cran-episignaldetection Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-isoweek, r-cran-rmarkdown, r-cran-shiny, r-cran-surveillance Suggests: r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-pander Filename: pool/dists/resolute/main/r-cran-episignaldetection_0.1.3-1.ca2604.1_all.deb Size: 498506 MD5sum: 11cef2e127fc6279cab320686b43c04d SHA1: 0f3f49481eb2d4d97960e1536a11f1b737c0ce45 SHA256: 7b7e7d3e8cad0eaaf8749ae3f9858c6ce9b7b40f2c840837f1a41a3b6e9cf090 SHA512: 8fed9fc68a12b510dddbba7e09e294a009c8d025471d184a451549e2bdff7b31a1ab74451169f6768b03706808d6df35ebe346b05cda44aa1a510e6349ddedea Homepage: https://cran.r-project.org/package=EpiSignalDetection Description: CRAN Package 'EpiSignalDetection' (Signal Detection Analysis) Exploring time series for signal detection. It is specifically designed to detect possible outbreaks using infectious disease surveillance data at the European Union / European Economic Area or country level. Automatic detection tools used are presented in the paper "Monitoring count time series in R: aberration detection in public health surveillance", by Salmon (2016) . The package includes: - Signal Detection tool, an interactive 'shiny' application in which the user can import external data and perform basic signal detection analyses; - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs. This report can also be stratified by population characteristics (see 'Population' variable). This project was funded by the European Centre for Disease Prevention and Control. Package: r-cran-episimr Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-desolve, r-cran-openxlsx, r-cran-dplyr, r-cran-dt, r-cran-shinythemes Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-episimr_1.1-1.ca2604.1_all.deb Size: 24814 MD5sum: 84a153db2a7108c2cd33fe4ea5b9056e SHA1: f4226a9e9f94dc92aa2fbc8c414b8eafcc931e7e SHA256: 2a3f7b3e014a34dff0d2abd265898d4d1b5680a3d3a81a547358340833ca1e57 SHA512: b5cd928c49fe5f29a8fb38fd8dc1500f17972c746bdcf464a721c1cea90f36fc94ea78f4ce9a59b36894de2749d1b2e8603064e25af59de04b635cf501abf30f Homepage: https://cran.r-project.org/package=EpiSimR Description: CRAN Package 'EpiSimR' (A 'Shiny' App to Simulate the Dynamics of Epidemic and EndemicDiseases Spread) The 'EpiSimR' package provides an interactive 'shiny' app based on deterministic compartmental mathematical modeling for simulating and visualizing the dynamics of epidemic and endemic disease spread. It allows users to explore various intervention strategies, including vaccination and isolation, by adjusting key epidemiological parameters. The methodology follows the approach described by Brauer (2008) . Thanks to 'shiny' package. Package: r-cran-episomer Architecture: all Version: 3.0.35-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4555 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-dt, r-cran-emayili, r-cran-future, r-cran-httr, r-cran-htmltools, r-cran-jsonlite, r-cran-keyring, r-cran-ggplot2, r-cran-janitor, r-cran-magrittr, r-cran-plotly, r-cran-processx, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-openxlsx, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-xtable, r-cran-httr2, r-cran-lubridate, r-cran-sf, r-cran-cli Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-episomer_3.0.35-1.ca2604.1_all.deb Size: 3212994 MD5sum: e2dea544c38dcc59773bae24c63f295d SHA1: e0269924b69432e64819bb546573b241767b3ac4 SHA256: 3feb9312e3c0f65a6424e431d3b80cd49f5b08d0689e48d616b46b18f438fc15 SHA512: 82453afb01bf0a27bf7793ea5d7439570f9b2a269ba1885991a80082d42f7fab65ac309ca6027b9fb74fb0a3522b0e34c696931dbbbe61f5b86c8adb256d933a Homepage: https://cran.r-project.org/package=episomer Description: CRAN Package 'episomer' (Early Detection of Public Health Threats from Social Media Data) It allows you to automatically monitor trends of social media messages by time, place and topic aiming at detecting public health threats early through the detection of signals (i.e., an unusual increase in the number of messages per time, topic and location). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information on the original package 'epitweetr' is available in the peer-review publication Espinosa et al. (2022) . Package: r-cran-epistandard Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dbi, r-cran-duckdb, r-cran-incidenceprevalence, r-cran-omopgenerics Filename: pool/dists/resolute/main/r-cran-epistandard_0.2.0-1.ca2604.1_all.deb Size: 851002 MD5sum: 7c1759cc808d1160b813ac23f4493532 SHA1: 47cad9cced86738086d8af1b5e6149772baa3b5e SHA256: c942c28124f9026fded4f0b46d6caf5aca798de35af24b1e9a6bf59cb00c7a33 SHA512: c855c2d6e513941be563c36ea83d417e90efcf18b2c85dee6cb076ae2cd378f12912b287f56227c272a5462cfeae181d3626f9f20da0ae02c37b41608d209844 Homepage: https://cran.r-project.org/package=EpiStandard Description: CRAN Package 'EpiStandard' (Directly Standardise Rates by Age) Provides functions for age standardisation of epidemiological measures such as incidence and prevalence rates. It allows users to apply standard population structures to observed age-specific estimates in order to obtain comparable summary measures across populations or time periods. Functions support calculation of standardised rates, outcome counts, and corresponding confidence intervals. The tools are designed to facilitate reproducible and transparent adjustment for differences in age distributions in epidemiological and public health research. Package: r-cran-epistats Architecture: all Version: 1.6-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epir, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-epistats_1.6-2-1.ca2604.1_all.deb Size: 401568 MD5sum: 4f617f046ff0afecde81930bd79cd520 SHA1: a761c4ffcee73b9f4fb441f512d06322da0828ad SHA256: bb8778f913c23bb1fce87b5517360029cc32a895eb1910e3591d2de9aaf4c00a SHA512: 45ae4aee01725f6a13ef43d5d51f8d75232a5e6fc51f4298d366b044f5b0dd9e8fa7ced97ea022ee704558da0cdb1d5f7c29810654de241cb2eddf619185de3f Homepage: https://cran.r-project.org/package=EpiStats Description: CRAN Package 'EpiStats' (Tools for Epidemiologists) Provides set of functions aimed at epidemiologists. The package includes commands for measures of association and impact for case control studies and cohort studies. It may be particularly useful for outbreak investigations including univariable analysis and stratified analysis. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands. References - Cornfield, J. 1956. A statistical problem arising from retrospective studies. In Vol. 4 of Proceedings of the Third Berkeley Symposium, ed. J. Neyman, 135-148. Berkeley, CA - University of California Press. Woolf, B. 1955. On estimating the relation between blood group disease. Annals of Human Genetics 19 251-253. Reprinted in Evolution of Epidemiologic Ideas Annotated Readings on Concepts and Methods, ed. S. Greenland, pp. 108-110. Newton Lower Falls, MA Epidemiology Resources. Gilles Desve & Peter Makary, 2007. 'CSTABLE Stata module to calculate summary table for cohort study' Statistical Software Components S456879, Boston College Department of Economics. Gilles Desve & Peter Makary, 2007. 'CCTABLE Stata module to calculate summary table for case-control study' Statistical Software Components S456878, Boston College Department of Economics. Package: r-cran-epistemicgametheory Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-epistemicgametheory_0.1.2-1.ca2604.1_all.deb Size: 33372 MD5sum: 7584ca0a3e4c3c79238e4d0a0adf890a SHA1: d503e42623f1ff22ebed453092096365a994fad4 SHA256: c163377fc5fec4ebfd3cfbc4730adb22462dd4780b4d4c66a8be3c6bc34ca993 SHA512: 3168176d31fd287a6caa24647580aff577476e2995dd6e1a7933aeec36d49c4b6017e3f933cb19cddd1ba1bd8005b06266f08d044f4f7dc51b3b775e943cb01e Homepage: https://cran.r-project.org/package=EpistemicGameTheory Description: CRAN Package 'EpistemicGameTheory' (Constructing an Epistemic Model for the Games with Two Players) Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality. Package: r-cran-epitab Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kableextra, r-cran-knitr, r-cran-mass, r-cran-survival, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-epitab_0.2.2-1.ca2604.1_all.deb Size: 154324 MD5sum: 1162b1f7e91e302a5fd797bde21ac548 SHA1: 12c9b99d74a9da9bf2af68f33e5b2432bd706621 SHA256: 7fa36f49c3e1ef0551e5cf1015107831c04e15bb1de8883247e3955a2c89dc00 SHA512: 4598836c52a0a0460cdf138fdaf207e8b0f9aa5c9be51d01c317284ac8b214f9615e5a632304d612bd5767b365c7cdf8f40a681772edb920401c8dccb97cbb4b Homepage: https://cran.r-project.org/package=epitab Description: CRAN Package 'epitab' (Flexible Contingency Tables for Epidemiology) Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: 'HTML', 'LaTeX', and 'Excel'. 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The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) , Mueller, Bernd et al. (2017) , Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) , and Mueller, Bernd et al. (2022) . 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Oh, Ensemble patch transformation: a flexible framework for decomposition and filtering of signal, EURASIP Journal on Advances in Signal Processing 30 (2020) 1-27 . H.-S. Oh, D. Kim, Image decomposition by bidimensional ensemble patch transform, Pattern Recognition Letters 135 (2020) 173-179 . 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Includes functions for conducting multilevel analyses using both Bayesian and Frequentist methods. Supports futility and superiority analyses through Bayesian approaches, along with visualization tools to aid interpretation and presentation of results. Package: r-cran-epubr Architecture: all Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-xslt, r-cran-magrittr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readr Filename: pool/dists/resolute/main/r-cran-epubr_0.6.5-1.ca2604.1_all.deb Size: 525116 MD5sum: 43d1290a95e72d5c3198ae54d2d9d320 SHA1: e29866dedbe65681f443f09ed07317df1154acd1 SHA256: 2a9f1d662315b8c592bb66d30ae63baec9ec6ded1985186949088e3fa7a7bcdd SHA512: 042c98ea31bf686f77c71664234ad6e80def9384439db485f8b291f45f98770956b8fd3b385f8cf98c036d464d643823f5ace56ae9afac78d53205d02a2900ec Homepage: https://cran.r-project.org/package=epubr Description: CRAN Package 'epubr' (Read EPUB File Metadata and Text) Provides functions supporting the reading and parsing of internal e-book content from EPUB files. The 'epubr' package provides functions supporting the reading and parsing of internal e-book content from EPUB files. E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. E-book formatting is not completely standardized across all literature. It can be challenging to curate parsed e-book content across an arbitrary collection of e-books perfectly and in completely general form, to yield a singular, consistently formatted output. Many EPUB files do not even contain all the same pieces of information in their respective metadata. EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with 'epubr'. Text is read 'as is' for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. Substantive changes are expected to be performed subsequently by the user as part of their text analysis. Additional text cleaning can be performed at the user's discretion, such as with functions from packages like 'tm' or 'qdap'. Package: r-cran-epxtor Architecture: all Version: 0.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-httr Filename: pool/dists/resolute/main/r-cran-epxtor_0.4-1-1.ca2604.1_all.deb Size: 35202 MD5sum: fa96d60c6ef7b45a89beb923a97dde03 SHA1: 0587441a920f748a2bd090b643c2d8e03517fc93 SHA256: 906a867fbac0e781cd8a6b2c65be6c20a1fc2adc2690309086aa6c0941750cd6 SHA512: c159a7b232dd5674b9f96a303f741d2ee0fc084f386cc12896ae438aca032d7e49481ea5c88eb47744bec7a2679b1441c31039bf045c970318608023ca63f0ff Homepage: https://cran.r-project.org/package=epxToR Description: CRAN Package 'epxToR' (Import 'Epidata' XML Files '.epx') Import data from 'Epidata' XML files '.epx' and convert it to R data structures. Package: r-cran-eq5d Architecture: all Version: 0.16.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-testthat, r-cran-shiny, r-cran-dt, r-cran-mime, r-cran-readxl, r-cran-ggplot2, r-cran-ggiraph, r-cran-ggiraphextra, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-fsa, r-cran-pmcmrplus, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-eq5d_0.16.3-1.ca2604.1_all.deb Size: 3062472 MD5sum: ca5e34998a844ed7903cbac812a568a8 SHA1: eab3c19691ee8da3179a0b34fb942763b6e0de59 SHA256: a14721febef18926922264bd22ad205bfe9904ceda0e0396c585509e988b7512 SHA512: 00349090b65eea158059c576b6c922154581369a698c159ea5a3f997285342c67478231a5e2353fd6a0264a6f15828d149427cb9be380e8eea39fef800076c8f Homepage: https://cran.r-project.org/package=eq5d Description: CRAN Package 'eq5d' (Methods for Analysing 'EQ-5D' Data and Calculating 'EQ-5D' IndexScores) EQ-5D is a popular health related quality of life instrument used in the clinical and economic evaluation of health care. Developed by the EuroQol group , the instrument consists of two components: health state description and evaluation. For the description component a subject self-rates their health in terms of five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression using either a three-level (EQ-5D-3L, ) or a five-level (EQ-5D-5L, ) scale. Frequently the scores on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. The eq5d package provides methods to calculate index scores from a subject's dimension scores. 33 TTO and 11 VAS EQ-5D-3L value sets including those for countries in Szende et al (2007) and Szende et al (2014) , 49 EQ-5D-5L EQ-VT value sets, the EQ-5D-5L crosswalk value sets developed by van Hout et al. (2012) , the crosswalk value sets for Bermuda, Jordan and Russia and the van Hout (2021) reverse crosswalk value sets. 13 EQ-5D-Y-3L value sets are also included as are the NICE 'DSU' age-sex based EQ-5D-3L to EQ-5D-5L and EQ-5D-5L to EQ-5D-3L mappings. Methods are also included for the analysis of EQ-5D profiles, including those from the book "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) . Additionally a shiny web tool is included to enable the calculation, visualisation and automated statistical analysis of EQ-5D data via a web browser using EQ-5D dimension scores stored in CSV or Excel files. 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It assesses five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale. Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, which are critical in clinical and economic evaluations of healthcare and in population health surveys. The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk by van Hout et al. (2021) (mapping EQ-5D-3L responses to EQ-5D-5L index values). Users are allowed to add and/or remove user-defined value sets. Additionally, the package provides tools to analyze EQ-5D data according to the recommended guidelines outlined in "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) . 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Package: r-cran-eqrn Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coro, r-cran-dofuture, r-cran-evd, r-cran-foreach, r-cran-future, r-cran-ismev, r-cran-magrittr, r-cran-torch Filename: pool/dists/resolute/main/r-cran-eqrn_0.1.2-1.ca2604.1_all.deb Size: 430674 MD5sum: fdb982c490fe177b2b2d8fbe33e8a50f SHA1: 899df8d2844fdc18ab95de363be4c1190084537a SHA256: 7882aca427dacb373c3298f417fb35896b28b338998a5f385f62187e449293b7 SHA512: 8b900aa74c7024a2b7368956fb9b94c2fdc6868cce59b918f3f336726cda65a1b61cc12865c5a8e094f4d98839e7c2a1924aa6bc733686262060088d5858f24b Homepage: https://cran.r-project.org/package=EQRN Description: CRAN Package 'EQRN' (Extreme Quantile Regression Neural Networks for Risk Forecasting) This framework enables forecasting and extrapolating measures of conditional risk (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities, using extreme value statistics and flexible neural network architectures. It allows for capturing complex multivariate dependencies, including dependencies between observations, such as sequential dependence (time-series). The methodology was introduced in Pasche and Engelke (2024) (also available in preprint: Pasche and Engelke (2022) ). Package: r-cran-eqtesting Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-eqtesting_0.1.1-1.ca2604.1_all.deb Size: 60096 MD5sum: c55771cbac9fabc1a98d07d415ee1827 SHA1: 5152827a8084ccc48303a9f524c75e8f2daa1c12 SHA256: 05a20d362ae7f339cd24c26ec4cb1be2df34fc790b4c10dbb2cc6749825d724e SHA512: 7ab33fad44dab2470a70a7a6be8364bc34b7f252f3e321d113d643215388839d24fdfb48aa3ce12deea96f995f095e8e4fb73ad7af3cea31363866bb341f3c72 Homepage: https://cran.r-project.org/package=eqtesting Description: CRAN Package 'eqtesting' (Equivalence Testing Functions) Contains several functions for equivalence testing and practical significance testing. First, the tsti() command provides an automatic computation of three-sided testing results for a given estimate, standard error, and region of practical equivalence. For details, see Goeman, Solari, & Stijnen (2010) and Isager & Fitzgerald (2024) . Second, the lddtest() command performs logarithmic density discontinuity equivalence testing for regression discontinuity designs. For reference, see Fitzgerald (2025) . 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Gurusamy,K (2025). 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Gurusamy,K (2025) and Gurusamy,K (2025). Package: r-cran-equalprognosis Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-base64enc, r-cran-calibrationcurves, r-cran-mime, r-cran-predtools, r-cran-proc, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-equalprognosis_0.1.3-1.ca2604.1_all.deb Size: 258094 MD5sum: a11c4571a1f89d18cbe226b0f79806e9 SHA1: 6d222122bd0520ab5a5825801f0160a466589f28 SHA256: 56c68c3fef0ee9f296c5bbd9dc0910f11f5a268dcff766f606c975a963eae6fc SHA512: 8c95467c4c741f7d342ce069b00c27bc648d170825eabf7ef0641a6a11e095e57bd9c4a805573fff52eae77b6a61231845ce1b615e828d450c399192f417eb50 Homepage: https://cran.r-project.org/package=EQUALPrognosis Description: CRAN Package 'EQUALPrognosis' (Analysing Prognostic Studies) Functions that help with analysis of prognostic study data. This allows users with little experience of developing models to develop models and assess the performance of the prognostic models. This also summarises the information, so the performance of multiple models can be displayed simultaneously. This minor update fixes issues related to memory requirements with large number of simulations and deals with situations when there is overfitting of data. Gurusamy, K (2026). Package: r-cran-equalrepeat Architecture: all Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringr, r-cran-ggplot2, r-cran-desctools, r-cran-zip, r-cran-cowplot, r-cran-rstatix, r-cran-forecast, r-cran-tseries, r-cran-urca, r-cran-vars, r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-equalrepeat_0.4.5-1.ca2604.1_all.deb Size: 180112 MD5sum: 8402ed45e56787c40d96cd474ca7346a SHA1: e2228f5358b2dc1442ff26a1ce1509c109f8af7f SHA256: 2357c4966aec840282a30fa31aa23bf564881c776c2075b4cac35286081f58ef SHA512: e97dc8136ee2fb35ab69f1007f938b37a2c9e06ddba49820d1b372fb4bb1b6cfeec33aaab8bc2ae6dcbba6f976b7b7f050cce8ddacb5f7500938ad56fcc0455e Homepage: https://cran.r-project.org/package=EQUALrepeat Description: CRAN Package 'EQUALrepeat' (Algorithm Driven Time Series Analysis for Researchers withoutCoding Skills) Support functions for R-based 'EQUAL-STATS' software which automatically classifies the data and performs appropriate statistical tests. 'EQUAL-STATS' software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024). Package: r-cran-equalstats Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-shiny, r-cran-zip, r-cran-ggplot2, r-cran-desctools, r-cran-cowplot, r-cran-boot, r-cran-pwr, r-cran-ggcorrplot, r-cran-survival, r-cran-nnet, r-cran-mass, r-cran-lmtest, r-cran-proc, r-cran-thresholdroc, r-cran-ggsurvfit, r-cran-lme4, r-cran-mclogit, r-cran-ordinal, r-cran-coxme, r-cran-mumin Filename: pool/dists/resolute/main/r-cran-equalstats_0.5.1-1.ca2604.1_all.deb Size: 597984 MD5sum: 73acd19a4ee41831d411a03adcf6be4c SHA1: 59f1cad040224c32763029adfe5830eecfe4d80d SHA256: 7f1a943dce9f6f0d5ea0a6bc18008f637d4384da1a73445dfa028da53efce714 SHA512: dadadbc87a059c651be3d5c4110287b44af663a5c17d99f6455dba9a44785754d7c627dcd017ae52059cd68a7549a0310fc2d5c0ef68202b92ec607fb388da6d Homepage: https://cran.r-project.org/package=EQUALSTATS Description: CRAN Package 'EQUALSTATS' (Algorithm Driven Statistical Analysis for Researchers withoutCoding Skills) Support functions for R-based 'EQUAL-STATS' software which automatically classifies the data and performs appropriate statistical tests. 'EQUAL-STATS' software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024). Package: r-cran-equatags Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-xslt, r-cran-katex Filename: pool/dists/resolute/main/r-cran-equatags_0.2.2-1.ca2604.1_all.deb Size: 31990 MD5sum: ca77b185f52dc4bb1eb1219bad91100a SHA1: 3ca55e255b7ec2d2cec4e7f6f85d9ac38e3ed5e5 SHA256: 325634306db6278b460d1d6d708a6280538f3a4cce700595f198cf8c004503c7 SHA512: e43e816e2bc6d32ef259899196243b1864040e48464e45cba48365620b42cf8518c48a5e4c10db86ab67a8d76851abd2097d8b88ada0cc9e279ac84b4338a5cc Homepage: https://cran.r-project.org/package=equatags Description: CRAN Package 'equatags' (Equations to 'XML') Provides function to transform latex math expressions into format 'HTML' or 'Office Open XML Math'. The 'XML' result can then be included in 'HTML', 'Microsoft Word' documents or 'Microsoft PowerPoint' presentations by using a 'Markdown' document or the R package 'officer'. Package: r-cran-equate Architecture: all Version: 2.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2794 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-equate_2.0.9-1.ca2604.1_all.deb Size: 2142246 MD5sum: 23fb95307d169f14b7e180dee01a265e SHA1: 1764dc9c5a8f3cc596dab9dc3cef70d8921ba7dc SHA256: d29d13779e1c436fd66c6ca991341e38bdc6545334dcb951c6dde85936d78a78 SHA512: 6e17b1d867706fc609e81bbd0f03330bb452f9bf6b3ce8e63a40314a4f827b50670f829b39a092d8230de87c0bfe4545f77c8e0a804ddad8b90d1d4119241d66 Homepage: https://cran.r-project.org/package=equate Description: CRAN Package 'equate' (Observed-Score Linking and Equating) Contains methods for observed-score linking and equating under the single-group, equivalent-groups, and nonequivalent-groups with anchor test(s) designs. Equating types include identity, mean, linear, general linear, equipercentile, circle-arc, and composites of these. Equating methods include synthetic, nominal weights, Tucker, Levine observed score, Levine true score, Braun/Holland, frequency estimation, and chained equating. Plotting and summary methods, and methods for multivariate presmoothing and bootstrap error estimation are also provided. Package: r-cran-equateirt Architecture: all Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1941 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-mirt Suggests: r-cran-knitr, r-cran-ltm, r-cran-rmarkdown, r-cran-sna Filename: pool/dists/resolute/main/r-cran-equateirt_2.5.2-1.ca2604.1_all.deb Size: 1112786 MD5sum: 14742d3ec2272ba19499bcc760a117a9 SHA1: 8c4f3d837154b20576e504126cab16bfb1950fe2 SHA256: a8ca7133d962a28450f278cdf2d5603d54517e4a1b0d04568e359fab29be8523 SHA512: f82223b5611e14af642eca67b8e14206020d16137ff13b011e393c65f854f5c112683e4d917a6e279a6ddeef501b7a00559980a94c6eed325083464395bf6707 Homepage: https://cran.r-project.org/package=equateIRT Description: CRAN Package 'equateIRT' (IRT Equating Methods) Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items (Battauz (2013) , Battauz (2015) ). Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) ). The package includes tests to assess the stability of the equating transformations (Battauz(2022) ). Package: r-cran-equatiomatic Architecture: all Version: 0.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-broom.mixed, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-covr, r-cran-shinywidgets, r-cran-forecast, r-cran-ggplot2, r-cran-latex2exp, r-cran-lme4, r-cran-mass, r-cran-ordinal, r-cran-parsnip, r-cran-recipes, r-cran-workflows, r-cran-testthat, r-cran-gtsummary, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-equatiomatic_0.4.8-1.ca2604.1_all.deb Size: 1057532 MD5sum: 78a913f72c92586090e8923b75d45b59 SHA1: b1917e38e22ec9d6e56289b1438760974e6aa69b SHA256: 4377e4f5ec8b0efed7069155987ea4e2b6e9f99c87b5fcad1b1ca8f33ddd0b4e SHA512: e5bb1737ed98201a150c87d58eefc39bb12ce88801db3c35258518202dfd1b48599bc982ea5219ca841470333b574f5f49115af2b0b4549d3374abc4f21ee436 Homepage: https://cran.r-project.org/package=equatiomatic Description: CRAN Package 'equatiomatic' (Transform Models into 'LaTeX' Equations) The goal of 'equatiomatic' is to reduce the pain associated with writing 'LaTeX' formulas from fitted models. The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding 'LaTeX' code for the model. Package: r-cran-equibspd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-equibspd_0.1.0-1.ca2604.1_all.deb Size: 22022 MD5sum: 1cb34733a65898d61386bd63ad926a65 SHA1: ab793fa06485265344839ff34d36428623e10e91 SHA256: ba52969ba5e824efffa75befaad47bf93fbac28ee8ae0c57acd7fde4c9ba033d SHA512: 722169bc3adc335eeed58929abeb9929764b92532653a3c5d34e891b1b73094db53d0ec914d8a9ab0b66f17ede268f2a594aefdc634ce6e26f56cef5a4c48c48 Homepage: https://cran.r-project.org/package=equiBSPD Description: CRAN Package 'equiBSPD' (Equivalent Estimation Balanced Split Plot Designs) In agricultural, post-harvest and processing, engineering and industrial experiments factors are often differentiated with ease with which they can change from experimental run to experimental run. This is due to the fact that one or more factors may be expensive or time consuming to change i.e. hard-to-change factors. These factors restrict the use of complete randomization as it may make the experiment expensive and time consuming. Split plot designs can be used for such situations. In general model estimation of split plot designs require the use of generalized least squares (GLS). However for some split-plot designs ordinary least squares (OLS) estimates are equivalent to generalized least squares (GLS) estimates. These types of designs are known in literature as equivalent-estimation split-plot design. For method details see, Macharia, H. and Goos, P.(2010) .Balanced split plot designs are designs which have an equal number of subplots within every whole plot. This package used to construct equivalent estimation balanced split plot designs for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function equivalent_BSPD(). Package: r-cran-equil2 Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-units Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-equil2_1.0.0-1.ca2604.1_all.deb Size: 57038 MD5sum: c281536938861c90306992d15197d18f SHA1: d23731e59261a734fcb0ff848666102d1ab51653 SHA256: 80930121493d289471221bcddbd91835a8c027e72b47bcbc9bc754e35a85827e SHA512: 5c131fe100bd8b247ce69385f005c955e8d6bfb136dcbc90e21aa070389a9afbed8830c4fe30acc0dcf2b91382e225a05381e5067ab2416df229b0a2d8ae7038 Homepage: https://cran.r-project.org/package=equil2 Description: CRAN Package 'equil2' (Calculate Urinary Saturation with the EQUIL2 Algorithm) Saturation of ionic substances in urine is calculated based on sodium, potassium, calcium, magnesium, ammonia, chloride, phosphate, sulfate, oxalate, citrate, ph, and urate. This program is intended for research use, only. The code within is translated from EQUIL2 Visual Basic code based on Werness, et al (1985) "EQUIL2: a BASIC computer program for the calculation of urinary saturation" to R. The Visual Basic code was kindly provided by Dr. John Lieske of the Mayo Clinic. Package: r-cran-equisurv Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-eha Filename: pool/dists/resolute/main/r-cran-equisurv_0.1.0-1.ca2604.1_all.deb Size: 54582 MD5sum: 81f3449af4a704ab2db0008ac67ee17c SHA1: 27db389313a8d2c93ad961511adaa6a8e5cec78a SHA256: 71edf5ef94fa110124b33e2681ac85fc82c750be8a0cff0690b14838274dc862 SHA512: 6a6e5efa1306d93afed005629b853bf1b6a83474feecbfe660442215bb2df16b5b5c9798a5430d9c1b0aade9a312244c90339a3446451c23ee5aac44c8ac3ee5 Homepage: https://cran.r-project.org/package=EquiSurv Description: CRAN Package 'EquiSurv' (Modeling, Confidence Intervals and Equivalence of SurvivalCurves) We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points. For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula. The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration. All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data . Package: r-cran-equivalencetest Architecture: all Version: 0.0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom, r-cran-rootsolve, r-cran-cubature, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-equivalencetest_0.0.1.1-1.ca2604.1_all.deb Size: 59432 MD5sum: 25b0f6936060168550c50c7f60bb0957 SHA1: 9302fe8eb9e33ab1d9f64be764254192cddc3c0d SHA256: 9dd8814f5df375cc807f8cb942ef2b475c324ae0d94a336441fb4ab5dd1676d2 SHA512: 5d6f3b932d93e4522958feab56c09d3d78fee86b59a0a6bc907addf299650de3b8b8e6da763a5d44c3293925979d4aad1a73b74d4de272d218cbe4478504a696 Homepage: https://cran.r-project.org/package=equivalenceTest Description: CRAN Package 'equivalenceTest' (Equivalence Test for the Means of Two Normal Distributions) Two methods for performing equivalence test for the means of two (test and reference) normal distributions are implemented. The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results. 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The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories. Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. . Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. . 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Indicators of electoral volatility, electoral disproportionality, party nationalization and the effective number of parties are included. Package: r-cran-esback Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-esreg Filename: pool/dists/resolute/main/r-cran-esback_0.3.1-1.ca2604.1_all.deb Size: 110122 MD5sum: ab7b4bf038baddf478384645c3be45b2 SHA1: ed6f694eab99510683606cac968126a059abbf51 SHA256: 462fb71eebe19aed2423ca85bc1d9926b29913170b35f741ee35d94a97c3a045 SHA512: 59f8a48895d2aa281ca72d7e63b6b0ba20d34c0b0a88f46980700bbbba8ca628cfa2a90d4bc448d807e5e607972534af2f7dad8b3891a775e312e36a6091efb0 Homepage: https://cran.r-project.org/package=esback Description: CRAN Package 'esback' (Expected Shortfall Backtesting) Implementations of the expected shortfall backtests of Bayer and Dimitriadis (2020) as well as other well known backtests from the literature. Can be used to assess the correctness of forecasts of the expected shortfall risk measure which is e.g. used in the banking and finance industry for quantifying the market risk of investments. A special feature of the backtests of Bayer and Dimitriadis (2020) is that they only require forecasts of the expected shortfall, which is in striking contrast to all other existing backtests, making them particularly attractive for practitioners. Package: r-cran-esc Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-esc_0.5.1-1.ca2604.1_all.deb Size: 171702 MD5sum: dd09e581f91e17a985db3d5abd0cbcc3 SHA1: 3483c3583db275c5f02e629f90fb4a805cb8d94e SHA256: e7f34181bcba95f097384f95ec009f7da4af3f741d18f5b7f34513906dd7e398 SHA512: 2bde59ddc26d0e901cf935faf0288f20452761684cca6f3c78959cfab075191ebc06e1147e156d4ef23192df06012303a212d9a80507f5889da3523f622cd213 Homepage: https://cran.r-project.org/package=esc Description: CRAN Package 'esc' (Effect Size Computation for Meta Analysis) Implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson () in R. Based on the input, the effect size can be returned as standardized mean difference, Cohen's f, Hedges' g, Pearson's r or Fisher's transformation z, odds ratio or log odds, or eta squared effect size. Package: r-cran-escalation Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17067 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-ggplot2, r-cran-gtools, r-cran-dfcrm, r-cran-boin, r-cran-trialr, r-cran-diagrammer, r-cran-rcolorbrewer, r-cran-viridis, r-cran-binom, r-cran-r6, r-cran-mvtnorm, r-cran-iso, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-escalation_0.2.3-1.ca2604.1_all.deb Size: 1703612 MD5sum: fa5c58c3d4e1d954cdd3f5771f7ed1b0 SHA1: 217248e795d10fbfff656ff0cb62ce00b9744e4d SHA256: 69fb56fea892f25e2a79f2680f522ba75a0c76617b64e6a401c65266fcc4378f SHA512: 4777fd8f87ca503b03d3df430c25271cb6bd2e46585df65f6d739ad11c03bfb5bbe8a65d54b268a269493f45d3f3388adb409e0e38f589cd8b81ab5b351a3a18 Homepage: https://cran.r-project.org/package=escalation Description: CRAN Package 'escalation' (A Modular Approach to Dose-Finding Clinical Trials) Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) , the toxicity probability interval (TPI) design by Ji et al. (2007) , the modified TPI (mTPI) design by Ji et al. (2010) , the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) , EffTox by Thall & Cook (2004) ; the design of Wages & Tait (2015) , and the 3+3 described by Korn et al. (1994) . All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients. Package: r-cran-esci Architecture: all Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggbeeswarm, r-cran-ggdist, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-jmvcore, r-cran-legendry, r-cran-metafor, r-cran-mathjaxr, r-cran-multcomp, r-cran-r6, r-cran-rdpack, r-cran-rlang, r-cran-sadists, r-cran-statpsych, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-esci_1.0.10-1.ca2604.1_all.deb Size: 3050688 MD5sum: b0bba9a3bb432e568fe6fec08f3bbd8f SHA1: f64427e9fdb3fae3fdac924e11593e58c3fe016e SHA256: 849c24e38a7fcc39998ee090056a737484be3e9888c92938739bbf8b6ee6f368 SHA512: bb5817c231af901e60e275cc2a48d7a4e1bf3deca267c8d20716137b84c598754079587644d0c7ca6156f61fabc6b34a12f403f8189b1d4ac1a7059cd7b1bfd5 Homepage: https://cran.r-project.org/package=esci Description: CRAN Package 'esci' (Estimation Statistics with Confidence Intervals) A collection of functions and 'jamovi' module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on 'statpsych' and 'metafor'. This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions. Data sets are included for all examples from Cumming & Calin-Jageman (2024) . Package: r-cran-escvtmle Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-superlearner, r-cran-origami, r-cran-dplyr, r-cran-tidyselect, r-cran-mass, r-cran-stringr, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-escvtmle_0.0.2-1.ca2604.1_all.deb Size: 123118 MD5sum: 328add8f84b27b496fd072ece4c5d633 SHA1: 3ebbd48705eb5277604554168e4227d4e175c7bc SHA256: d1ca1c24e611f0335a5424053049014dfb3b645dfd60acfa4f0b6d7210db5f20 SHA512: 148d6237a760a5b40b7f614872edf3da6a24aed35ff40404f3431cacd5c4f8f8e2cfef944f22efb572b05def55d077884778f41dff3030df1c5e132f66559b1f Homepage: https://cran.r-project.org/package=EScvtmle Description: CRAN Package 'EScvtmle' (Experiment-Selector CV-TMLE for Integration of Observational andRCT Data) The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) . Package: r-cran-esdesign Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-esdesign_1.0.3-1.ca2604.1_all.deb Size: 124342 MD5sum: 3f2c305a9c78c6f134a429507a607bc1 SHA1: bf1312ab9749332251a400c3173fee088b28c834 SHA256: 5a35650a5fbc0c71d91c68e2e43abf69cb17a91534e60b10be73382dd598d2d5 SHA512: b3f67bbe3327edbc2c61f99b4ad379d75d7a41f547988373c3411bed98071edd878915144a22403fcb330e5c6ac8cc80d1ed1c62b4003966de3acb75e9f66f5c Homepage: https://cran.r-project.org/package=esDesign Description: CRAN Package 'esDesign' (Adaptive Enrichment Designs with Sample Size Re-Estimation) Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation presented in Lin et al. (2021) . In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) , the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR. Package: r-cran-esdm Architecture: all Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4630 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rocr, r-cran-sf, r-cran-shiny, r-cran-units Suggests: r-cran-colorramps, r-cran-colourpicker, r-cran-dichromat, r-cran-dt, r-cran-knitr, r-cran-leafem, r-cran-leaflet, r-cran-maps, r-cran-raster, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-shinybusy, r-cran-shinydashboard, r-cran-shinyjs, r-cran-testthat, r-cran-tmap, r-cran-viridis, r-cran-zip Filename: pool/dists/resolute/main/r-cran-esdm_0.4.4-1.ca2604.1_all.deb Size: 3686408 MD5sum: 53c1ac3b877b68aab6f7b30ee1c246a1 SHA1: 30946fd80497ed42cd19db9ce0aeabc202ceae90 SHA256: dfb174393b80c63a3197190936cac49afd52af5e0aa92b6b1e3d6cb08f230c2a SHA512: 0698811b8237d8fc0d7ce007bc76b052e6ec3ee1ab03509e934444e5f6fcfe43b2fba932f86ef8d2a68bb8f415f91d4a4207ef643de6bccde402e91f0af8801c Homepage: https://cran.r-project.org/package=eSDM Description: CRAN Package 'eSDM' (Ensemble Tool for Predictions from Species Distribution Models) A tool which allows users to create and evaluate ensembles of species distribution model (SDM) predictions. Functionality is offered through R functions or a GUI (R Shiny app). This tool can assist users in identifying spatial uncertainties and making informed conservation and management decisions. The package is further described in Woodman et al (2019) . Package: r-cran-eselect Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-comparedesign Filename: pool/dists/resolute/main/r-cran-eselect_1.1-1.ca2604.1_all.deb Size: 66910 MD5sum: 2a1c2d96ede9017885f6343d681fc4ce SHA1: 28b2813a376fa5a923e790ba941e23ef53e0e30c SHA256: 34a2e441119fe5fe216e2564b2a8a0557131841f24f8b42959bc8de118b9a217 SHA512: 5b956007ef0a359c6bc0233515466c34e00099dbe261d38c34f1260159e8436ff8fa81dbfb75f369cf2dd6bab91f419d892bddf13c64dbc73f6f15467e238d77 Homepage: https://cran.r-project.org/package=eselect Description: CRAN Package 'eselect' (Adaptive Clinical Trial Designs with Endpoint Selection andSample Size Reassessment) Endpoint selection and sample size reassessment for multiple binary endpoints based on blinded and/or unblinded data. Trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation between endpoints. The implemented design is proposed in Bofill Roig, M., Gómez Melis, G., Posch, M., and Koenig, F. (2022). . Package: r-cran-esem Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gparotation, r-cran-lavaan, r-cran-magrittr, r-cran-psych, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-esem_2.0.0-1.ca2604.1_all.deb Size: 111336 MD5sum: fd883177109b47e9ce26401c048e4dfd SHA1: 526bc91508bb81f720a6b809bb4c79a593a2858a SHA256: 2abb2e9f7c97d7e4b50f74c8a80139bc1fc48e6cffc72ee55006744429a391a4 SHA512: 2239c7f0915a2c84ee02823fd3a08a8b13031010977588d7c5067b58f3dc4cf169b48feea5831976b4fb3afc35b4660e33755fd320f7b05c309f2f8d75b0e8cc Homepage: https://cran.r-project.org/package=esem Description: CRAN Package 'esem' (Exploratory Structural Equation Modeling ESEM) A collection of functions developed to support the tutorial on using Exploratory Structural Equiation Modeling (ESEM) (Asparouhov & Muthén, 2009) ) with Longitudinal Study of Australian Children (LSAC) dataset (Mohal et al., 2023) . The package uses 'tidyverse','psych', 'lavaan','semPlot' and provides additional functions to conduct ESEM. The package provides general functions to complete ESEM, including esem_c(), creation of target matrix (if it is used) make_target(), generation of the Confirmatory Factor Analysis (CFA) model syntax esem_cfa_syntax(). A sample data is provided - the package includes a sample data of the Strengths and Difficulties Questionnaire of the Longitudinal Study of Australian Children (SDQ LSAC) in sdq_lsac(). 'ESEM' package vignette presents the tutorial demonstrating the use of ESEM on SDQ LSAC data. Package: r-cran-esg Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-esg_1.3-1.ca2604.1_all.deb Size: 178448 MD5sum: 856329720a40579e463bc2a47f75fa0b SHA1: 9388b57bc96a1b26b5cd91a2016bede779e9acb2 SHA256: af0f68f97187a43c12fa4a6e1607afaf88063edb6224705c58ff77c2aba19c62 SHA512: 22528b28409c99fe54481d4c07459d1e9cfd64f950f01d0b336821a630959e660af528c24ba643d19c81c4841d15cfaabfc22fffd9119073e328344c6b46676b Homepage: https://cran.r-project.org/package=ESG Description: CRAN Package 'ESG' (A Package for Asset Projection) Presents a "Scenarios" class containing general parameters, risk parameters and projection results. Risk parameters are gathered together into a ParamsScenarios sub-object. 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Package: r-cran-eshrink Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-eshrink_0.2.0-1.ca2604.1_all.deb Size: 66570 MD5sum: 21e6f65f85aa0eeff850890bcb601c75 SHA1: 579d4f299cfc3e4b783709ddd9ef2ad68a720d1c SHA256: 26631648c9904a6d7c21167af78fd36246e908725782e234cbef0f4b19cab286 SHA512: 2f9109f6449b2d21422af9425f73ebf8b4a73990a0dfce73b6276fc70a1a5c1ef752d8e6483de60d417e6ad371feec51ddcc0f242cf49a086ef6a3a73ecb9f8c Homepage: https://cran.r-project.org/package=eshrink Description: CRAN Package 'eshrink' (Shrinkage for Effect Estimation) Computes shrinkage estimators for regression problems. Selects penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) for more details. 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There are several functions available by 1) including a time-varying transmission modifier, 2) adding a time-dependent quarantine compartment, 3) adding a time-dependent antibody-immunization compartment. Wang L. (2020) . 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It empowers users with capabilities to extract critical insights during preprocessing, conducts thorough data quality assessments (e.g., design and sampling scheme checks, compliance rate, careless responses), and generates visualizations and concise summary tables tailored specifically for ESM data. Additionally, it streamlines the creation of informative and interactive preprocessing reports, enabling researchers to transparently share their dataset preprocessing methodologies. Finally, it is part of a larger ecosystem which includes a framework and a web gallery (). Package: r-cran-esquisse Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-datamods, r-cran-downlit, r-cran-ggplot2, r-cran-htmltools, r-cran-jsonlite, r-cran-phosphoricons, r-cran-rlang, r-cran-rstudioapi, r-cran-scales, r-cran-shiny, r-cran-shinybusy, r-cran-shinywidgets, r-cran-zip Suggests: r-cran-officer, r-cran-rvg, r-cran-rio, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggthemes, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-esquisse_2.1.0-1.ca2604.1_all.deb Size: 1553868 MD5sum: c5d1f2de535f5756f92b8cb779d75fac SHA1: fc7cefeb8ef321dc8c5bd67030d3346afc9e42e4 SHA256: 190ca8b59cd6e8b884928d1c2bc62c4ccd10dfdb267e8ae579f5e181abe69c7e SHA512: e3a4d0cc7783e9bc14371d01f0b2011547ee83cfae5cf9970518fdaf5ea7d366bc8f0a1bc7cfc264f687dcc4d43dbff524f77a4a4d71dc13c1273f1287b5e613 Homepage: https://cran.r-project.org/package=esquisse Description: CRAN Package 'esquisse' (Explore and Visualize Your Data Interactively) A 'shiny' gadget to create 'ggplot2' figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot. 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The package is intended to support textbook examples by distributing data in a form that is easy for students and instructors to access within R. Current functionality includes packaged datasets and convenience wrappers for functions from 'ez', 'pwr', and 'WebPower' for analysis of variance and statistical power calculations. Package: r-cran-essurvey Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-haven, r-cran-rvest, r-cran-tibble Suggests: r-cran-foreign, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-essurvey_1.0.8-1.ca2604.1_all.deb Size: 126782 MD5sum: 534d9d0090699f605589c73cb4c877b1 SHA1: 47daf4b9a119879d1ee80a813d8184e81bc82e2c SHA256: 3d15374a30b3c3bb8456a4bfc463ee129d5636a906282c3644e53404dfe346c4 SHA512: 52310ca801a5210f65d46f53606a0d9cad95ef94b668c0074e04ceafbe8868ae3013f1e5b8f59f04e5aec9e8ca1f625314a86d2bfd66e50e8b07a67912f2a2a9 Homepage: https://cran.r-project.org/package=essurvey Description: CRAN Package 'essurvey' (Download Data from the European Social Survey on the Fly) Download data from the European Social Survey directly from their website . There are two families of functions that allow you to download and interactively check all countries and rounds available. Package: r-cran-estadistica Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2039 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-plotly, r-cran-ggplot2, r-cran-rio, r-cran-shiny, r-cran-shinydashboard, r-cran-knitr, r-cran-forecast, r-cran-openxlsx, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-estadistica_1.2.2-1.ca2604.1_all.deb Size: 1243762 MD5sum: d134d1fae82b08f0a820d5fb10959acc SHA1: ec6e2c2d202081cf09dd16042134fef38ba02679 SHA256: 7d7da2af195601cfbe59779c0cfbb2c43874d9168b046dfb682270b5cdbabb1c SHA512: 7f8e09a21f167fc9082b267087cf1fe93901d48bae9083f7614e516f8cee4b02d2638ff4514d919a9928e39cdb414057ecb08c8f273c1e7f96b85a53f96f2dd1 Homepage: https://cran.r-project.org/package=estadistica Description: CRAN Package 'estadistica' (Fundamentos de estadística descriptiva e inferencial) Este paquete pretende apoyar el proceso enseñanza-aprendizaje de estadística descriptiva e inferencial. Las funciones contenidas en el paquete 'estadistica' cubren los conceptos básicos estudiados en un curso introductorio. Muchos conceptos son ilustrados con gráficos dinámicos o web apps para facilitar su comprensión. This package aims to help the teaching-learning process of descriptive and inferential statistics. The functions contained in the package 'estadistica' cover the basic concepts studied in a statistics introductory course. Many concepts are illustrated with dynamic graphs or web apps to make the understanding easier. See: Esteban et al. (2005, ISBN: 9788497323741), Newbold et al.(2019, ISBN:9781292315034 ), Murgui et al. (2002, ISBN:9788484424673) . Package: r-cran-estar Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-vegan, r-cran-zoo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-cran-hesim, r-cran-cowplot, r-cran-viridis, r-cran-forcats, r-cran-tidyr, r-cran-marss, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-estar_1.0-1-1.ca2604.1_all.deb Size: 1850368 MD5sum: c8e0ff5618e90cc5eb51391b0097908b SHA1: 15979b172dec932cd7ca38580093e37fbefd0e47 SHA256: febc808be3372e44b084531f064a88036b787e40063f425f05dab571a439ced3 SHA512: e7760070aa719f564480f801c463ec9d5dc21ddb78015d5e31b1d72ef5da1d69614e781397519fec603bf0dbeeedf483fad95f8b5e579c02f5ecb310b5e5e699 Homepage: https://cran.r-project.org/package=estar Description: CRAN Package 'estar' (Ecological Stability Metrics) Standardises and facilitates the use of eleven established stability properties that have been used to assess systems’ responses to press or pulse disturbances at different ecological levels (e.g. population, community). There are two sets of functions. The first set corresponds to functions that measure stability at any level of organisation, from individual to community and can be applied to a time series of a system’s state variables (e.g., body mass, population abundance, or species diversity). The properties included in this set are: invariability, resistance, extent and rate of recovery, persistence, and overall ecological vulnerability. The second set of functions can be applied to Jacobian matrices. The functions in this set measure the stability of a community at short and long time scales. In the short term, the community’s response is measured by maximal amplification, reactivity and initial resilience (i.e. initial rate of return to equilibrium). In the long term, stability can be measured as asymptotic resilience and intrinsic stochastic invariability. Figueiredo et al. (2025) . 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Tsagris M. and Papadakis M. (2025). . 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Package: r-cran-estempmm Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4026 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-mass, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-estempmm_0.3.2-1.ca2604.1_all.deb Size: 2485430 MD5sum: a7581b876db6b8d0611c3826dd5f59d1 SHA1: 8f407c8c4401705c7480866f2e84962c106b86b2 SHA256: 6f25b8e6383555fca91b5426b62a4c2b6f2397464c533fbaae0726cea175e462 SHA512: 6c8213244c2a234bd52e9021fab6f898e721846af1feb0a553c3906a79e755372db69836665b00908cb1cff0ba7e311c1525fb28159ee705d3dc2183d375cd0b Homepage: https://cran.r-project.org/package=EstemPMM Description: CRAN Package 'EstemPMM' (Polynomial Maximization Method for Non-Gaussian Regression) Implements the Polynomial Maximization Method ('PMM') for parameter estimation in linear and time series models when error distributions deviate from normality. The 'PMM2' variant achieves lower variance parameter estimates compared to ordinary least squares ('OLS') when errors exhibit significant skewness. The 'PMM3' variant (S=3) targets symmetric platykurtic error distributions, reducing variance when excess kurtosis is negative. Includes automatic method selection ('pmm_dispatch'), linear regression, 'AR'/'MA'/'ARMA'/'ARIMA' models, and bootstrap inference. Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) , Zabolotnii, Tkachenko, and Warsza (2022) , and Zabolotnii, Tkachenko, and Warsza (2023) , and Zabolotnii (2025) . 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The germination time of the seed is inversely proportion to the difference between soil water potential and the base seed water potential which is described by hydro time model (Bradford, 2002 ). The parameters of the model like speed of germination, stress tolerance, uniformity of germination are unknown fixed values (Ghosh et al., 2026 ) which are to be estimated using statistical regression model where the validity of the adopted statistical model has been established theoretically. The package will help to estimate the tuning parameter for proportion of viable seeds along with standard error and p- values for inference. 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It has two main functionalities, the first being about prediction models of covariates and environmental processes, while the second deals with the estimation of genetic parameters and selection strategies. Designed for researchers and professionals in genetics and environmental sciences, the package combines statistical methods for modeling and data analysis. This includes the plastochron estimate proposed by Porta et al. (2024) , Stress indices for genotype selection referenced by Ghazvini et al. (2024) , the Environmental Stress Index described by Tazzo et al. (2024) , industrial quality indices of wheat genotypes (Szareski et al., 2019), , Ear Indexes estimation (Rigotti et al., 2024), , Selection index for protein and grain yield (de Pelegrin et al., 2017), , Estimation of the ISGR - Genetic Selection Index for Resilience for environmental resilience (Bandeira et al., 2024) , estimation of Leaf Area Index (Meira et al., 2015) , Restriction of control variability (Carvalho et al., 2023) , Risk of Disease Occurrence in Soybeans described by Engers et al. (2024) and estimation of genetic parameters for selection based on balanced experiments (Yadav et al., 2024) . 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Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation. Package: r-cran-estimatew Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-plot.matrix, r-cran-r6 Filename: pool/dists/resolute/main/r-cran-estimatew_0.2.0-1.ca2604.1_all.deb Size: 332442 MD5sum: baf5546feb1eb278214f0752e3ef4802 SHA1: 9891f387566036653a98473824f440a6e09555a6 SHA256: 4baf8c5d189021f6ea516f4cbab3dd159a14e5ed07b749c22955031a86e0a2d4 SHA512: d66f0150c0e922b25fbba7410e2bb45fe5aefd283d2412db30f826e831ecf793223b1ac8775215f97a83b1f7fdd3a5ca6218cd90f3b189fb7f7261bf7e4e6de3 Homepage: https://cran.r-project.org/package=estimateW Description: CRAN Package 'estimateW' (Estimation of Spatial Weight Matrices) Bayesian estimation of spatial weight matrices in spatial econometric panel models. 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The first was originally proposed by Olley, G.S. and Pakes, A. (1996) . The second by Levinsohn, J. and Petrin, A. (2003) . And the third by Wooldridge (2009) . Package: r-cran-esvis Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-hmisc, r-cran-tibble Suggests: r-cran-testthat, r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-esvis_0.3.1-1.ca2604.1_all.deb Size: 1196002 MD5sum: c522401c3db70cb61779b0e40f49fecb SHA1: 3fa851e95a335488372203195db52c80f7a49ad7 SHA256: 8fdde2f35b6c85bc8c4a01e33be26ebb4a0828348b2a3e603c9f3fccb5804bf2 SHA512: 992384a71f3d03aaeac62b4a2c141838a6a0f25f4b688c1110a11a74e1a2875cf2ba1688e5e9490fa68cd9740c7177468b8cd9bcfad2deead6c3452e83999829 Homepage: https://cran.r-project.org/package=esvis Description: CRAN Package 'esvis' (Visualization and Estimation of Effect Sizes) A variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; ), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; ), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified. Package: r-cran-esviz Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maps, r-cran-climprojdiags, r-cran-sf, r-cran-ggplot2, r-cran-rnaturalearth, r-cran-cowplot, r-cran-s2dv, r-cran-rcolorbrewer, r-cran-cstools, r-cran-easyncdf, r-cran-dplyr, r-cran-plyr, r-cran-data.table, r-cran-reshape2, r-cran-scales, r-cran-foreach, r-cran-rlang, r-cran-kableextra, r-cran-gridextra, r-cran-gtable, r-cran-doparallel, r-cran-mapproj, r-cran-webshot2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-rnaturalearthdata Filename: pool/dists/resolute/main/r-cran-esviz_0.0.2-1.ca2604.1_all.deb Size: 673798 MD5sum: 81f242c433f2df9f7d1673399c8c293f SHA1: ab5345adf4ed15afc85baee56bce156a44386670 SHA256: ce889ab74ffdb473ffe1f1388d920b0fca2aef70a637ea6777ab92540b984ed7 SHA512: edf0ba7e2802b5da85ecb80797241e288aca1bb74e7a95e1a19fc93130e20f5500195c12b478a0c45e2c8aea61cd678ce49986d024fa792892840a66f3a4ac4e Homepage: https://cran.r-project.org/package=esviz Description: CRAN Package 'esviz' (Plotting Functions for Climate Science and Services) A plotting package for climate science and services. Provides a set of functions for visualizing climate data, including maps, time series, scorecards and other diagnostics. Some functions are adapted and extended from the 's2dv' and 'CSTools' packages (Manubens et al. (2018) ; Pérez-Zanón et al. (2022) ), with more consistent and integrated functionalities. Package: r-cran-et.nwfva Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-et.nwfva_0.2.0-1.ca2604.1_all.deb Size: 1234838 MD5sum: b967235205cd7f1102c9295516355f61 SHA1: 427c61062b216aca00a15a37949dff5cb2c7c16b SHA256: 090ee4fa061987ae6340669868c97429dbebf7a162656b78e2abc559ad3bed2e SHA512: f7943a001416951a7217484dcc52af8c581e3e31043fee7885194d842f4f3b1cd7ae988f17629a9a93d8bfbd44b50e6fa718ce5182065e15be5756520f25f888 Homepage: https://cran.r-project.org/package=et.nwfva Description: CRAN Package 'et.nwfva' (Forest Yield Tables for Northwest Germany and their Application) The new yield tables developed by the Northwest German Forest Research Institute (NW-FVA) provide a forest management tool for the five main commercial tree species oak, beech, spruce, Douglas-fir and pine for northwestern Germany. The new method applied for deriving yield tables combines measurements of growth and yield trials with growth simulations using a state-of-the-art single-tree growth simulator. By doing so, the new yield tables reflect the current increment level and the recommended graduated thinning from above is the underlying management concept. The yield tables are provided along with methods for deriving the site index and for interpolating between age and site indices and extrapolating beyond age and site index ranges. The inter-/extrapolations are performed traditionally by the rule of proportion or with a functional approach. Package: r-cran-etable Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-etable_1.3.1-1.ca2604.1_all.deb Size: 135730 MD5sum: 34dedde2aa5451abf157d5a284096677 SHA1: b7d2ec18266295047f17a34dafc2bdd6ae4c33a1 SHA256: e708d99d23dd184ed3066772964801cc62a28e53ac47ee58363edb74b2eb7322 SHA512: ad74d17220377a250ed0e23e5c40ae09873db14484bc90a7ea5431b05137bc7c9df0960c9d461c3c85ec8ce956adcc5b72e71fc7e0c4303f57e16f2efe0ec518 Homepage: https://cran.r-project.org/package=etable Description: CRAN Package 'etable' (Easy Table) Creates simple to highly customized tables for a wide selection of descriptive statistics, with or without weighting the data. Package: r-cran-etasbootstrap Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-etas, r-cran-mass, r-cran-spatstat.geom Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-etasbootstrap_0.2.1-1.ca2604.1_all.deb Size: 400816 MD5sum: 2b5e33caf40479ed9decc4f390bb73f1 SHA1: 715c2a826c645d77e8c9f4e462b01296839665b8 SHA256: 3c10f5ca8e4f159feb25925c5026d07ac1b605a08bb09d38d1e1034d5c0c2246 SHA512: 0d9b1e3b57e7a97c753ad0ae22caa5ff62aa2a787d5d3ff36e2b07899033a953edc24ccd6ff23401995b26f440aad2f7eae8b501c460cc265c2264db35591bbb Homepage: https://cran.r-project.org/package=ETASbootstrap Description: CRAN Package 'ETASbootstrap' (Bootstrap Confidence Interval Estimation for 'ETAS' ModelParameters) The 2-D spatial and temporal Epidemic Type Aftershock Sequence ('ETAS') Model is widely used to 'decluster' earthquake data catalogs. Usually, the calculation of standard errors of the 'ETAS' model parameter estimates is based on the Hessian matrix derived from the log-likelihood function of the fitted model. However, when an 'ETAS' model is fitted to a local data set over a time period that is limited or short, the standard errors based on the Hessian matrix may be inaccurate. It follows that the asymptotic confidence intervals for parameters may not always be reliable. As an alternative, this package allows for the construction of bootstrap confidence intervals based on empirical quantiles for the parameters of the 2-D spatial and temporal 'ETAS' model. This version improves on Version 0.1.0 of the package by enabling the study space window (renamed 'study region') to be polygonal rather than merely rectangular. A Japan earthquake data catalog is used in a second example to illustrate this new feature. Package: r-cran-etc Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-simcomp, r-cran-multcomp, r-cran-mratios Filename: pool/dists/resolute/main/r-cran-etc_1.5-1.ca2604.1_all.deb Size: 55840 MD5sum: 548339891754031ff92729c919ce22ae SHA1: 10b537674c7881df0f8d8d32dde5160ddfd69584 SHA256: 297eb012ae06f6810595014faa58b20acb0e6de109ac1adda287a4d9ad112c88 SHA512: 3c7619cc095dd6119e13a0a2e4b494ac6cfe5625a3aec6c20702d74af0ea267abcfa685a93c752cb494642a086c7a9a551f3b96cafef1f63137c8d32d5859465 Homepage: https://cran.r-project.org/package=ETC Description: CRAN Package 'ETC' (Equivalence to Control) Treatments of a one-way layout, being equivalent to a control, can be selected with this package. Bonferroni adjusted "two one-sided t-tests" (TOST) and related simultaneous confidence intervals are given for both differences or ratios of means of normally distributed data. For the case of equal variances and balanced sample sizes for the treatment groups, the single-step procedure of Bofinger and Bofinger (1995) can be chosen. For non-normal data, the Wilcoxon test is applied. Package: r-cran-etdqualitizer Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-etdqualitizer_1.0.0-1.ca2604.1_all.deb Size: 141864 MD5sum: 130701cf06745e29d215bef584da8039 SHA1: f0ea5526a77b81f4574ac47d6be14147344dbe3d SHA256: df1f583d9cda1d1ce884bc77f58dafc0eca7f9c7ffc2fe6db66b382d59c20653 SHA512: eb196bc6d4c84845681ea414b482270d227d04e319acfbb95f643ab769c52a71a92b534dbdbcd6c45fc40297c5767af36d3dfd0016a8112d84d99b49779d55ea Homepage: https://cran.r-project.org/package=ETDQualitizer Description: CRAN Package 'ETDQualitizer' (Automated Eye Tracking Data Quality Determination forScreen-Based Eye Trackers) Compute common data quality metrics for accuracy, precision and data loss for screen-based eye trackers. The package supports gaze input in screen pixels or degrees and reports angular measures in degrees where appropriate. If you use this package, please cite Niehorster, D.C., Nyström, M., Hessels, R.S., Benjamins, J.S., Andersson, R., and Hooge, I.T.C. (2026). The fundamentals of eye tracking part 7: Determining data quality. Behavior Research Methods. . Package: r-cran-ethnobotanyr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4851 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-cran-cowplot, r-cran-dplyr, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2, r-cran-magrittr Suggests: r-cran-broom, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-vegan, r-cran-bnlearn, r-cran-causaleffect, r-cran-grain, r-cran-pbapply, r-cran-tidyselect, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-ethnobotanyr_0.2.0-1.ca2604.1_all.deb Size: 2499804 MD5sum: 0be28e3793c0e70935e56d60937ce1e6 SHA1: a268593b8e4c03b606c7157b605dd14c0d8226f6 SHA256: 40c6f6559c22523e9d6060e297127b1f2a73810200c17c9ec798d72336bd28b6 SHA512: 290536d2a4762c7a02eb49b2fd80ddc9fabcedb6c913d236c6c4615ae27b1ac0e71b84d8a910dcdd40e1765064f5a5ca3e7bf85113e1bfabfc0c09ba3f75d738 Homepage: https://cran.r-project.org/package=ethnobotanyR Description: CRAN Package 'ethnobotanyR' (Ethnobotanical Analysis, Decision-Framing, and TEK Modeling) Tools for quantifying Traditional Ecological Knowledge (TEK), modeling TEK in decision frameworks, and designing structured decision-framing exercises in conservation and development contexts. The package implements quantitative ethnobotany indices (Use Value, Relative Frequency of Citation, etc.) but positions them within a larger framework of Bayesian modeling and participatory decision analysis. Includes critical assessment of indices' limitations and case studies of participatory workshops. Package: r-cran-etl Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dbi, r-cran-dbplyr, r-cran-downloader, r-cran-fs, r-cran-janitor, r-cran-lubridate, r-cran-readr, r-cran-rlang, r-cran-rvest, r-cran-tibble, r-cran-usethis, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rsqlite, r-cran-rpostgres, r-cran-rmariadb, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-etl_0.4.3-1.ca2604.1_all.deb Size: 127392 MD5sum: b3a86a4bf6698d5b911fa037bebd873b SHA1: dca50d43f7877fffbd9e73e2a0189accbfd5904b SHA256: 249246eee9116220004637a31103bc78a3ba1e9721f0b939ce9c2a8371d742e0 SHA512: 8b27fd023a4c74471b9f83745c1c9b08d2364b26dcada52d592dfe4c1fce44090bc831832b2eda414fed65af7ed6a950910058c8cd44ea60d3876e6431f209ba Homepage: https://cran.r-project.org/package=etl Description: CRAN Package 'etl' (Extract-Transform-Load Framework for Medium Data) A predictable and pipeable framework for performing ETL (extract-transform-load) operations on publicly-accessible medium-sized data set. This package sets up the method structure and implements generic functions. Packages that depend on this package download specific data sets from the Internet, clean them up, and import them into a local or remote relational database management system. Package: r-cran-etlutils Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ff, r-cran-bit Suggests: r-cran-rsqlite, r-cran-zoo, r-cran-dbi, r-cran-rodbc, r-cran-rjdbc Filename: pool/dists/resolute/main/r-cran-etlutils_1.6-1.ca2604.1_all.deb Size: 102324 MD5sum: dbc812cbfa7c925f301271a8cdd9fee0 SHA1: a6defaddd57d86ebc1e9b67d2ecd19c5c351255f SHA256: b461455de5cadb58d75c16a65e0d685b950b90bd7d60678d680fed93081bd1e7 SHA512: 0a8c30fa68fe2fffb48dda1d01e2a36261ba94eafed7b7bdba42950c2833819ec4c2f97d972bd0cc15222f4f67497bf65d030bbdecec1bf6b2198211bdbc3740 Homepage: https://cran.r-project.org/package=ETLUtils Description: CRAN Package 'ETLUtils' (Utility Functions to Execute Standard Extract/Transform/LoadOperations (using Package 'ff') on Large Data) Provides functions to facilitate the use of the 'ff' package in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL', 'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to do fast left outer join merging based on 'match', factorisation of data and a basic function for re-coding vectors. Package: r-cran-etrader Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-urltools, r-cran-dplyr, r-cran-rvest, r-cran-purrr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-etrader_0.1.5-1.ca2604.1_all.deb Size: 113158 MD5sum: 99596afa9424a2cddd57951892846703 SHA1: dc7b37698b6abe1312773076e8de36c9b727233c SHA256: 8b13cee2ebf437727e410c00e703d54218374fa4827d2d6c09d03e98ffe1dfd8 SHA512: 7c284957e5e75d11011ef15aa8ce9eda0fd42acc718bfec50de7f63e445928cd7ff69d337f2159297daa79b2450666fc61bbd7a62790ec7d5eb03f6530f64359 Homepage: https://cran.r-project.org/package=etrader Description: CRAN Package 'etrader' ('ETRADE' API Interface for R) Use R to interface with the 'ETRADE' API . Functions include authentication, trading, quote requests, account information, and option chains. A user will need an ETRADE brokerage account and 'ETRADE' API approval. See README for authentication process and examples. Package: r-cran-etree Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3894 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-braingraph, r-cran-cluster, r-cran-energy, r-cran-fda.usc, r-cran-igraph, r-cran-networkdistance, r-cran-partykit, r-cran-survival, r-cran-tda, r-cran-usedist Suggests: r-cran-knitr, r-cran-mlmetrics, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-etree_0.1.0-1.ca2604.1_all.deb Size: 3127958 MD5sum: 327ff0d98b36d9685dd575dc26cfe328 SHA1: 8b84cf32e983bd0f94a9c431fff8368d917ea042 SHA256: f44d568dec0752e772634098df2d326a1234c996578a9edcba1851d563e117ad SHA512: a2497033aa4b59d6b2fca6f0cb2bfe433ebf69290c90635b2941f73c5cb1d9b21c40a59bd8de485db04c1c86cd473d9788bb4fb42956ef4242b81364ad355167 Homepage: https://cran.r-project.org/package=etree Description: CRAN Package 'etree' (Classification and Regression with Structured and Mixed-TypeData) Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon 'partykit' to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) . Package: r-cran-etrep Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1918 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-shapes, r-cran-morpho, r-cran-matlib, r-cran-rspincalc, r-cran-rotations, r-cran-rvcg, r-cran-fields, r-cran-truncnorm, r-cran-htmlwidgets Filename: pool/dists/resolute/main/r-cran-etrep_1.2.1-1.ca2604.1_all.deb Size: 1902070 MD5sum: 30ed2bba006d1712d161537f78cb9291 SHA1: 50ef6029618e2b24c3b5bc35f017f3b1c7fa81d2 SHA256: 5b5ba1254a39d2a4238520067b8b0211890bb1b8cda52bd6229aabfc3b4b2b0d SHA512: 41766e0effdd4b063dba0e9a20e978a59318d0a4cad252b58204ed3d0563b77aa897861278bd44ac1c09c1b8a92163140c89f5d3fcf19a4755d11f756d978d5b Homepage: https://cran.r-project.org/package=ETRep Description: CRAN Package 'ETRep' (Analysis of Elliptical Tubes Under the Relative CurvatureCondition) Analysis of elliptical tubes with applications in biological modeling. The package is based on the references: Taheri, M., Pizer, S. M., & Schulz, J. (2024) "The Mean Shape under the Relative Curvature Condition." Journal of Computational and Graphical Statistics and arXiv . Mohsen Taheri Shalmani (2024) "Shape Statistics via Skeletal Structures", PhD Thesis, University of Stavanger, Norway . Key features include constructing discrete elliptical tubes, calculating transformations, validating structures under the Relative Curvature Condition (RCC), computing means, and generating simulations. Supports intrinsic and non-intrinsic mean calculations and transformations, size estimation, plotting, and random sample generation based on a reference tube. The intrinsic approach relies on the interior path of the original non-convex space, incorporating the RCC, while the non-intrinsic approach uses a basic robotic arm transformation that disregards the RCC. Package: r-cran-etrm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 921 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-etrm_1.0.1-1.ca2604.1_all.deb Size: 721970 MD5sum: 2a7b1c220b908b0b0f9b48ac9186b6cc SHA1: e6e386c4da2fc3f223d605ce0ba47b920d0f2376 SHA256: 4dd99b41375ded25ac4aaae9e9954f90729f869bd92a6f20ce53c40904214147 SHA512: bd3174c016fe7bcadd5fbba01ff0d29e7ebdf9b363a8c34143e22d79b0b557c3c36208ad8adc402122543fe7e7de2f3b1add502990cb53aeda911dda79aac549 Homepage: https://cran.r-project.org/package=etrm Description: CRAN Package 'etrm' (Energy Trading and Risk Management) Provides a collection of functions to perform core tasks within Energy Trading and Risk Management (ETRM). Calculation of maximum smoothness forward price curves for electricity and natural gas contracts with flow delivery, as presented in F. E. Benth, S. Koekebakker, and F. Ollmar (2007) and F. E. Benth, J. S. Benth, and S. Koekebakker (2008) . Portfolio insurance trading strategies for price risk management in the forward market, see F. Black (1976) , T. Bjork (2009) , F. Black and R. W. Jones (1987) and H. E. Leland (1980) . 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Package: r-cran-etsi Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hetsurr Filename: pool/dists/resolute/main/r-cran-etsi_1.0-1.ca2604.1_all.deb Size: 106460 MD5sum: 3fbd36e8dbc91c6770668f9a9c15bc3a SHA1: 61108743b23cf62ae1da3a5ce90d4e8bcd35e427 SHA256: 78867786ddb8c7d56e029c2e964e3651993bef89b0e37992863da00733c8dc5c SHA512: 6d4aa1997762a956241697f9978ab38fac3b1424de978dab28ee8217a5dbf8bf0f7e4e7116bb62d7d36de17de76de97888618b15a5aa4e58fb52171847f27da8 Homepage: https://cran.r-project.org/package=etsi Description: CRAN Package 'etsi' (Efficient Testing Using Surrogate Information) Provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086, . A tutorial for this package can be found at . 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This package helps a user guess four things (mean, MD, scaled MSD, and RMSD) before they get the SD. 1) The package displays the Empirical Cumulative Distribution Function (ECDF) of the given data. The user must choose the value of the mean by equating the areas of two colored (blue and green) regions. The package gives feedback to improve the choice until it is correct. Alternatively, the reader may continue with a different guess for the center (not necessarily the mean). 2) The user chooses the values of the Mean Deviation (MD) based on the ECDF of the deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 3) The user chooses the Scaled Mean Squared Deviation (MSD) based on the ECDF of the scaled square deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 4) The user chooses the Root Mean Squared Deviation (RMSD) by ensuring that its intersection with the ECDF of the deviations is at the same height as the intersection between the scaled MSD and the ECDF of the scaled squared deviations. 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Package: r-cran-eva Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2761 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-envstats Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spatialextremes Filename: pool/dists/resolute/main/r-cran-eva_0.2.6-1.ca2604.1_all.deb Size: 2280304 MD5sum: 9a41a4bf3a3f47dcd453497613acf56a SHA1: 0f0f29d26feb2ad491a4befec79fa13d8e04e50e SHA256: 4e8d2104c4b0e3b2bee105e34d3c4bd94c04901b6cd59f22729ec56c5c3578a9 SHA512: 76d6102bd8f43c6a3330e62211341b879c5d8c1d5b541bc197c1fcc0f29cdfe171b74f75c436576e6b31e30eeffe0f5af9535a58c187d9d12664ca6105b5339c Homepage: https://cran.r-project.org/package=eva Description: CRAN Package 'eva' (Extreme Value Analysis with Goodness-of-Fit Testing) Goodness-of-fit tests for selection of r in the r-largest order statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the Generalized Pareto distribution (GPD). Random number generation and density functions for the GEVr distribution. Profile likelihood for return level estimation using the GEVr and Generalized Pareto distributions. P-value adjustments for sequential, multiple testing error control. Non-stationary fitting of GEVr and GPD. Bader, B., Yan, J. & Zhang, X. (2016) . Bader, B., Yan, J. & Zhang, X. (2018) . 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'evapoRe' enables users to download, validate, visualize, and analyze multi-source ET data across various spatio-temporal scales. Also, the package offers calculation methods for estimating potential ET (PET), including temperature-based, combined type, and radiation-based approaches described in : Oudin et al., (2005) . 'evapoRe' supports hydrological modeling, climate studies, agricultural research, and other data-driven fields by facilitating access to ET data and offering powerful analysis capabilities. Users can seamlessly integrate the package into their research applications and explore diverse ET data at different resolutions. 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Package: r-cran-evcgsampler Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3443 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-osqp, r-cran-patchwork, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-evcgsampler_1.0.0-1.ca2604.1_all.deb Size: 2278746 MD5sum: 1bb0a624dd5e62ef28d16b490008cf63 SHA1: d040a759cc6e7252db263be2b13ba49cc32d4ca5 SHA256: 861ae863d20e4107e95c6bf1be59da1dd991341d990af29d455451e83019439e SHA512: 1e172b987ab9467e955472d54ab31a310db398c942c4159f1704075f7979d623a1bf6267dc07f4b4872ff437a59f5f0a9ac2d8ec505e3f6b0af24e324e06827a Homepage: https://cran.r-project.org/package=eVCGsampler Description: CRAN Package 'eVCGsampler' (VCG Sampling using Energy-Based Covariate Balancing) Provides a principled framework for sampling Virtual Control Group (VCG) using energy distance-based covariate balancing. The package offers visualization tools to assess covariate balance and includes a permutation test to evaluate the statistical significance of observed deviations. Package: r-cran-evchargcost Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-evchargcost_0.1.0-1.ca2604.1_all.deb Size: 24152 MD5sum: 9102400618182837a3397efb0013658f SHA1: fa308c8511dafb7b8b1d761463fd98c4686cd1b6 SHA256: b81979290b0e9f2a8ff7fddc498b5ebd46c608f0a9d1354db634cd258d26c4b2 SHA512: c17da38060f9fcfe80b2d803535be1a9e7cecb6663562d42bbc2db56d77f6845199e79a1c4e704917155df1e0f857402195022f8bf7f881ebdf07c5cc243d9f7 Homepage: https://cran.r-project.org/package=EVchargcost Description: CRAN Package 'EVchargcost' (Computes and Plot the Optimal Charging Strategy for ElectricVehicles) The purpose of this library is to compute the optimal charging cost function for a electric vehicle (EV). It is well known that the charging function of a EV is a concave function that can be approximated by a piece-wise linear function, so bigger the state of charge, slower the charging process is. Moreover, the other important function is the one that gives the electricity price. This function is usually step-wise, since depending on the time of the day, the price of the electricity is different. Then, the problem of charging an EV to a certain state of charge is not trivial. This library implements an algorithm to compute the optimal charging cost function, that is, it plots for a given state of charge r (between 0 and 1) the minimum cost we need to pay in order to charge the EV to that state of charge r. The details of the algorithm are described in González-Rodríguez et at (2023) . Package: r-cran-evclass Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-ibelief, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-evclass_2.0.2-1.ca2604.1_all.deb Size: 321656 MD5sum: 794bddd05ddbd0f1d67869a366ebb8be SHA1: 76c4872a05c6675db371d4fe4a3dc03bc0c952f2 SHA256: fe21823f361dc3f040191a188f7e70dc5b17964e9972f701283146349a8974ac SHA512: c86a1db8c52ced0c77bdd24862f471ebdeaa8c8ae2b00a7b5f10a018ce32562db2e7a730275437353c17fec243663ab22c9bb13521fe102e47a22dc1c270202c Homepage: https://cran.r-project.org/package=evclass Description: CRAN Package 'evclass' (Evidential Distance-Based Classification) Different evidential classifiers, which provide outputs in the form of Dempster-Shafer mass functions. The methods are: the evidential K-nearest neighbor rule, the evidential neural network, radial basis function neural networks, logistic regression, feed-forward neural networks. Package: r-cran-evcombr Architecture: all Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-evcombr_0.1-4-1.ca2604.1_all.deb Size: 132872 MD5sum: d6a5ef8ee6735ec0a46f9eddb9b90713 SHA1: 3f262e96496d6fab165d64e9cac412dccd859d78 SHA256: ac87a84aa89d66c6f0bd7560e8c8182a372b3f16b1086dbca9c274ced5d739be SHA512: d6023ff41bfe7fb0234a1b509ee838e5842bde5a1d7b7a65f17ebfd6e201a3d672125234df45e8e07489ac3ce99200fccb16876f716f29ac28732b45ba3f8d18 Homepage: https://cran.r-project.org/package=EvCombR Description: CRAN Package 'EvCombR' (Evidence Combination in R) Combine pieces of evidence in the form of uncertainty representations. Package: r-cran-eve Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-eve_1.0-1.ca2604.1_all.deb Size: 55164 MD5sum: 87df7d3b1aca54ab9ba10f46efdc1bae SHA1: 7abaac6cb50f7612fe96a15362076b0daf6f8813 SHA256: d5cc35429f03d6f7edd922c82e4045375fc3796eb807a81d738f4f8aa1d22c4e SHA512: 01f1a72d9a5c10bde05b19a1cc5c6dc49eba45a18ef606a7d22cf1278def3da745fd4354abe2ebc87e17d6caf135cdc921ff655abf958d99142ef94475f11176 Homepage: https://cran.r-project.org/package=eve Description: CRAN Package 'eve' (The Eigenvalues Entropy as a Classifier Evaluation Measure) The confusion matrix (CM) is used to get a classifier's evaluation measure in order to select a method among many. A stochastic matrix and its transformation are computed from the CM. The eigenvalues of the transformed symmetric matrix are used to get an entropy which appears to be a good evaluation measure. Many other measures, commonly used, are provided for comparison purpose. Package: r-cran-evenbreak Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-evenbreak_1.0-1.ca2604.1_all.deb Size: 268582 MD5sum: 67fd716f6e438ca6ef310fa3d68d76e0 SHA1: 97da30a327d1660dfb9b5d26aa8496f0e458f3aa SHA256: 84e77cb00540b49d251cf87c99199de78d430532691a301157a298a0da7953a0 SHA512: 5856653fc0dec00522f916f959d3266776309a9e06b49ea34b7e84a234c6d54fec90f2edbb0e1478c8a1708a96b00430aace27060cb1150bb8fad2f20b42e5e2 Homepage: https://cran.r-project.org/package=evenBreak Description: CRAN Package 'evenBreak' (A Posteriori Probs of Suits Breaking Evenly Across Four Hands) We quantitatively evaluated the assertion that says if one suit is found to be evenly distributed among the 4 players, the rest of the suits are more likely to be evenly distributed. Our mathematical analyses show that, if one suit is found to be evenly distributed, then a second suit has a slightly elevated probability (ranging between 10% to 15%) of being evenly distributed. If two suits are found to be evenly distributed, then a third suit has a substantially elevated probability (ranging between 30% to 50%) of being evenly distributed.This package refers to methods and authentic data from Ely Culbertson , Gregory Stoll , and details of performing the probability calculations from Jeremy L. Martin , Emile Borel and Andre Cheron (1954) "The Mathematical Theory of Bridge",Antonio Vivaldi and Gianni Barracho (2001, ISBN:0 7134 8663 5) "Probabilities and Alternatives in Bridge", Ken Monzingo (2005) "Hand and Suit Patterns" Ken Monzingo (2005) "Hand and Suit Patterns" . Package: r-cran-eventdatar Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2278 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eventdatar_0.3.1-1.ca2604.1_all.deb Size: 1380794 MD5sum: d281f069ee7a11089dfd656f977cc4a9 SHA1: 1c33a27600ef8b440212fe637c068152c51cdd48 SHA256: 51fe34232a587ae33623ca010c728510f4e7685e411e270c219bc9c3e6dce57c SHA512: e336f95a9e8793c38e3f8b24e93354d0168cc83b6f422644eb5c00bf75980e34092f6f97960577100d5c6f2985d95279676d817cbfca86039ddbaa1f7b3f9db8 Homepage: https://cran.r-project.org/package=eventdataR Description: CRAN Package 'eventdataR' (Event Data Repository) Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages. Janssenswillen et al. (2020) . Package: r-cran-eventinterval Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-eventinterval_1.3-1.ca2604.1_all.deb Size: 37106 MD5sum: fd4314bc00552eae4e1c43e1621fa71f SHA1: 65775245da5fa23273c0498c33dbc9d028cbb6ee SHA256: 654cc7c34d57928e8a6b12d6437e73d80c6584f0e7cb9c7b8016e92802144253 SHA512: a2e9bc0875a86d108bc8851b9bf8eaabc701339c753e5bdeb7dbbc4dcf9cc55b43556b0b1be91f960f11491ea68f7f02e4712598ca09a3ff5c631db374ad28f7 Homepage: https://cran.r-project.org/package=eventInterval Description: CRAN Package 'eventInterval' (Sequential Event Interval Analysis) Functions for analysis of rate changes in sequential events. Package: r-cran-eventpred Architecture: all Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-ggplot2, r-cran-plotly, r-cran-survival, r-cran-matrix, r-cran-mvtnorm, r-cran-rstpm2, r-cran-numderiv, r-cran-purrr, r-cran-flexsurv, r-cran-erify, r-cran-shiny, r-cran-rlang, r-cran-lrstat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eventpred_0.2.9-1.ca2604.1_all.deb Size: 646458 MD5sum: 2a02c89c0a3105167da462534a92621d SHA1: 5da93cece7265a1df777a4375f49e573488de247 SHA256: c7612484f1e6e8e6d06387b8221ed3ab2c7107068148b9c803d89817810a6bb7 SHA512: 02884510b455e1f25ca8210ddd05fe40a9fa4dd3330e9af896ec41d2f2919a107c539c360ab3140bf77a2eca7eebd48adea0ea4c1501256d1461118b1747fd58 Homepage: https://cran.r-project.org/package=eventPred Description: CRAN Package 'eventPred' (Event Prediction) Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations. Package: r-cran-eventpredincure Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-perm, r-cran-msm, r-cran-mlecens, r-cran-kmsurv, r-cran-dplyr, r-cran-rlang, r-cran-plotly, r-cran-survival, r-cran-matrix, r-cran-mvtnorm, r-cran-rstpm2, r-cran-numderiv, r-cran-tmvtnsim, r-cran-erify, r-cran-lubridate, r-cran-flexsurv, r-cran-mass Filename: pool/dists/resolute/main/r-cran-eventpredincure_1.0-1.ca2604.1_all.deb Size: 365444 MD5sum: 5376a24ab87a9fb95e3de95b84b9ce54 SHA1: 8d0f08146c3510339c38c961fd7bdf34d70e8c59 SHA256: ecb5e554c08a314f6b08680070708bd41adcc7888f9e87d79500334a292f7040 SHA512: 1382d8dac71dfc35f1119767f91570441d0592b29efef93cf9247c2b98b8d9cc12872babe07acdbdee26d93326f5dcf8a309b323e337ede20434c539a070989e Homepage: https://cran.r-project.org/package=EventPredInCure Description: CRAN Package 'EventPredInCure' (Event Prediction Including Cured Population) Predicts enrollment and events assumed enrollment and treatment-specific time-to-event models, and calculates test statistics for time-to-event data with cured population based on the simulation.Methods for prediction event in the existence of cured population are as described in : Chen, Tai-Tsang(2016) . Package: r-cran-eventreport Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-scales, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-tidyverse, r-cran-tinytable Filename: pool/dists/resolute/main/r-cran-eventreport_0.1.2-1.ca2604.1_all.deb Size: 618862 MD5sum: 35ee4d988eb1845fb00337fa22ecec3a SHA1: 89f0d983722e53e82881765c7c7431641ee2d0de SHA256: 4f5719c3eeacd328a86a2a8658455220b55e314ee1e9b4e5a908fc7a249a0db6 SHA512: 8a4ef717738d3d0cbb8169a48830a3ba7679643911200f010bbc9e2f46cf81bf162cbd7829dba1f4fc8d7545d8a88055e083643af1f555031a9b25f2fe4b601a Homepage: https://cran.r-project.org/package=eventreport Description: CRAN Package 'eventreport' (Diagnose, Visualize, and Aggregate Event Report Level Data) Diagnose, visualize, and aggregate event report level data to the event level. Users provide an event report level dataset, specify their aggregation rules, and the package produces a dataset aggregated at the event level. Also includes the Modes and Agents of Election-Related Violence in Côte d'Ivoire and Kenya (MAVERICK) dataset, an event report level dataset that records all documented instances of electoral violence from the first multiparty election to 2022 in Côte d'Ivoire (1995-2022) and Kenya (1992-2022). For more details see van Baalen and Höglund (2026) . Users of the enclosed MAVERICK dataset should also cite van Baalen and Höglund (2026) . Package: r-cran-eventstream Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-tensora, r-cran-glmnet, r-cran-dbscan, r-cran-mass, r-cran-changepoint, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eventstream_0.1.1-1.ca2604.1_all.deb Size: 4594036 MD5sum: d403ad500fd2022ae1743dc6086b21d2 SHA1: 918890bf90cd44342ec0d242f54788e9c65b2b1d SHA256: 0ee95cd099d46f5bec15f4f4ac1bc3f139f3e239f4d5052c8dbb830617470496 SHA512: b0f19a894774a74af75edb603f80bb23904a86f3200e50312549c482eb18961b8c256ba3c35d5c41fbabd54d4913a3e7c5aa4e713f6f903eeb8af159a452a70d Homepage: https://cran.r-project.org/package=eventstream Description: CRAN Package 'eventstream' (Streaming Events and their Early Classification) Implements event extraction and early classification of events in data streams in R. It has the functionality to generate 2-dimensional data streams with events belonging to 2 classes. These events can be extracted and features computed. The event features extracted from incomplete-events can be classified using a partial-observations-classifier (Kandanaarachchi et al. 2018) . Package: r-cran-eventstudyr Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-data.table, r-cran-dplyr, r-cran-estimatr, r-cran-fixest, r-cran-ggplot2, r-cran-mass, r-cran-rlang, r-cran-pracma, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eventstudyr_1.2.0-1.ca2604.1_all.deb Size: 242780 MD5sum: b03dc478e0748af72ba6f5d95b9b9a75 SHA1: 993094aee9e1163d5263fefe9d8eb91bc3f868fd SHA256: 9d192c1cb222ab833cb738884d2537dd8fb37fbe138761b5e2fb6cee82f668f1 SHA512: ae8695d8abb9e87e79518864eddd8aa4e751ab0c8753aa024bef33693038aa250a68f9c894ef6cbfe6ac379dc2988fd51d6225e62fedb2e773ad168cc84aed0e Homepage: https://cran.r-project.org/package=eventstudyr Description: CRAN Package 'eventstudyr' (Estimation and Visualization of Linear Panel Event Studies) Estimates linear panel event study models. Plots coefficients following the recommendations in Freyaldenhoven et al. (2021) . Includes sup-t bands, testing for key hypotheses, least wiggly path through the Wald region. Allows instrumental variables estimation following Freyaldenhoven et al. (2019) . Package: r-cran-eventtrack Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-muhaz Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rpact, r-cran-fitdistrplus, r-cran-gestate Filename: pool/dists/resolute/main/r-cran-eventtrack_1.0.4-1.ca2604.1_all.deb Size: 122208 MD5sum: 6209f2df15d4d083801757e28fc61ddd SHA1: be999d9093b969e214739c1b2c931a0f4b571b1f SHA256: a7416c1ebef3fcdd14fbf6aecb1bc88232dd6508b3c130438dd7758bfe30eb78 SHA512: 5c2da3b8a1566046524be723c6cfc155585a4de1c104c9dd11ae69229b9c9e2b9a2a7fd3a9d8036693259bac6db510fb8ea7210d51381510ffed88ca06ceec98 Homepage: https://cran.r-project.org/package=eventTrack Description: CRAN Package 'eventTrack' (Event Prediction for Time-to-Event Endpoints) Implements the hybrid framework for event prediction described in Fang & Zheng (2011, ). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential. Package: r-cran-eventwinratios Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-eventwinratios_1.0.0-1.ca2604.1_all.deb Size: 101724 MD5sum: 1dc69b9fb3f54b35af02bd24595eaa4b SHA1: 8f69cba18dcddc1d5159c06b0706e0a4515cfe77 SHA256: b93adddc06545ed8f28e8278ae32f0177f0e6a190fd64a37df9b8a2ea9f277ae SHA512: bff4660b7dd82cd535fe2951642c4f8aaf602be628465754dee5cdc3e7938aef6966e522735f6ac4e04b60a345b6121ead443c25d82235c5ebcfb946514ed648 Homepage: https://cran.r-project.org/package=EventWinRatios Description: CRAN Package 'EventWinRatios' (Event-Specific Win Ratios for Terminal and Non-Terminal Events) Provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets. Package: r-cran-evi Architecture: all Version: 0.2.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-evi_0.2.0-0-1.ca2604.1_all.deb Size: 90696 MD5sum: a6006fe00d4925369762f597b7958e2f SHA1: 105332febcb3c5c82916b18053c92a6898a0fab7 SHA256: ef1aacab4001f05a40198b2c20326e70b7d711488ea10e81d429dfd6d511e025 SHA512: b1927429e600bf4fb878feab1586e6661f6806c7f41bb29f5c7813fc5fa2b10d572dccb2ed1a43e06ec037340bee269f7f00c6ab6ea7f6d5faea931e1d3a55fe Homepage: https://cran.r-project.org/package=EVI Description: CRAN Package 'EVI' (Epidemic Volatility Index as an Early-Warning Tool) This is an R package implementing the epidemic volatility index (EVI), as discussed by Kostoulas et. al. (2021) and variations by Pateras et. al. (2023). EVI is a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Package: r-cran-evian Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-profilelikelihood, r-cran-sandwich, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-evian_2.1.0-1.ca2604.1_all.deb Size: 120828 MD5sum: d5db22c695c571b07565a3be8751b9f2 SHA1: 6b5c2f3e8a89f57fce53b70b21bddcb925020b4d SHA256: cc5b6a0bdf265c3755a11031cfc6f9866f52ce6dbe5c1fbb8cd732e7dcaae965 SHA512: 9bed437712511d6046ad9b9f9ce7132c253fc78b04589c496c9abd40edfb4824cbfedde1456b4af29ce8a5de17d9944640e20f9316f0bf753dd3c83219f0ffb3 Homepage: https://cran.r-project.org/package=evian Description: CRAN Package 'evian' (Evidential Analysis of Genetic Association Data) Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package: Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) . Strug, L. J., & Hodge, S. E. (2006) . Royall, R. (1997) . Package: r-cran-evidencefactors Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sensitivitymv Filename: pool/dists/resolute/main/r-cran-evidencefactors_1.8-1.ca2604.1_all.deb Size: 47044 MD5sum: e21e1039692a8f02de32f214b6c0d875 SHA1: c2e493035a6fa913608f63105ee07f85c01bd48d SHA256: 8fc75cb7ca8dc0e502f5a27438e6aaab99ad962b646075c0ee623ccc13a6ba2f SHA512: 99093bd3433286f07a387a77da872f129f5618dd13bc5a1a09018ae276a42a2bdccf0d72fa9911de62a97c04f246ec1ddd296a7bbacde009f6c0a68efe2946eb Homepage: https://cran.r-project.org/package=evidenceFactors Description: CRAN Package 'evidenceFactors' (Reporting Tools for Sensitivity Analysis of Evidence Factors inObservational Studies) Provides tools for integrated sensitivity analysis of evidence factors in observational studies. When an observational study allows for multiple independent or nearly independent inferences which, if vulnerable, are vulnerable to different biases, we have multiple evidence factors. This package provides methods that respect type I error rate control. Examples are provided of integrated evidence factors analysis in a longitudinal study with continuous outcome and in a case-control study. Karmakar, B., French, B., and Small, D. S. (2019). 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The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) . 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Rosenbaum (2021) . Package: r-cran-eviewsr Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-magrittr, r-cran-xts, r-cran-zoo, r-cran-rdpack Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-eviewsr_0.1.7-1.ca2604.1_all.deb Size: 2265836 MD5sum: c6625ade3445b4510b571430d3a112db SHA1: c56e62dc50aa769995920548475d117e71a20de5 SHA256: 8cb10e71729a11ab17e2950fc2540c22727bfe56c20189b063d2730e96a7b4e0 SHA512: e09bc6f01c1e815a4bfff0381e0ec62beeb722b6f0d71f05c681b22605f48c4340a6560fc2d0f92cba389ba067f471fe1e3b6f09901edde97f05ed28d93913c4 Homepage: https://cran.r-project.org/package=EviewsR Description: CRAN Package 'EviewsR' (A Seamless Integration of 'EViews' and R) It allows running 'EViews' () program from R, R Markdown and Quarto documents. 'EViews' (Econometric Views) is a statistical software for Econometric analysis. This package integrates 'EViews' and R and also serves as an 'EViews' Knit-Engine for 'knitr' package. Write all your 'EViews' commands in R, R Markdown or Quarto documents. For details, please consult our peer-review article Mati S., Civcir I. and Abba S.I (2023) . 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Package: r-cran-evir Architecture: all Version: 1.7-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-evir_1.7-4-1.ca2604.1_all.deb Size: 465522 MD5sum: 91bbff7d5d634ebacbe12c8ac5274bfd SHA1: 69bec4ac5fe3ccdf6019b86f09f0fd594cf321be SHA256: 83c129da5378d903f6de19d1d40840c33822434fd47ebb501945030c9d9b8300 SHA512: a2c5702a44778af7b36d490b47babad0e013685fabb69ba7b0cf1960f403b392e0d52e7a75a538a859abedb2ecfcec281ebd113d86490a6e0364254da224f025 Homepage: https://cran.r-project.org/package=evir Description: CRAN Package 'evir' (Extreme Values in R) Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions. Package: r-cran-evmissing Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlssx, r-cran-itp, r-cran-nieve, r-cran-revdbayes, r-cran-rust Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-evmissing_1.0.2-1.ca2604.1_all.deb Size: 336502 MD5sum: 4d507f7183a90292b4446b8efe2b1d34 SHA1: 850ba1a7bd29ac6a0745ffba4d6174a8a2e0e49a SHA256: 94960c248090f620871191d706eb9869595520cb8b26020f8fda090365cf6b76 SHA512: 5f06507f78a9dd3ccc78bbcb6e2f70af5b85a0dd3ad1842387345110ec6a00e2f116ca46d86914e3026a87b8e945ea6847ad023bdc98adf9dbb8f21cd7feb557 Homepage: https://cran.r-project.org/package=evmissing Description: CRAN Package 'evmissing' (Extreme Value Analyses with Missing Data) Performs likelihood-based extreme value inferences with adjustment for the presence of missing values based on Simpson and Northrop (2026) . A Generalised Extreme Value distribution is fitted to block maxima using maximum likelihood estimation, with the location and scale parameters reflecting the numbers of non-missing raw values in each block. A Bayesian version is also provided. For the purposes of comparison, there are options to make no adjustment for missing values or to discard any block maximum for which greater than a percentage of the underlying raw values are missing. Example datasets containing missing values are provided. Package: r-cran-evmix Architecture: all Version: 2.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5041 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gsl, r-cran-sparsem Filename: pool/dists/resolute/main/r-cran-evmix_2.12-1.ca2604.1_all.deb Size: 4892160 MD5sum: a9509549a975884dd11dc1f1be368fa5 SHA1: ce9cdf2a05bedafa44bacbc349790669d3214c48 SHA256: 980f7a806b9d7f87a690d52a2d4def03570b7c92c8957c204dd016e63b5cd38c SHA512: 1eab31573d961442be1eee3fa60c3282c2da4dc48a178c34f71270bf88a709e7f48b7c4c7e02ad5b7bdfdfef465c852793e22463deb2500522b4ac09166920da Homepage: https://cran.r-project.org/package=evmix Description: CRAN Package 'evmix' (Extreme Value Mixture Modelling, Threshold Estimation andBoundary Corrected Kernel Density Estimation) The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code. Package: r-cran-evmr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eva, r-cran-lmomco, r-cran-numderiv, r-cran-rsolnp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-evmr_0.1.0-1.ca2604.1_all.deb Size: 373422 MD5sum: 80e77ff16f18c4b69ea7cf609e99dd7a SHA1: 761d2f43be74901e891eae8d5402b5be42751f8c SHA256: 6d7b4bf96406456d18c8e40b0b2b931ba31cda5aa5a90dbbf1d4edbaf6c8bcac SHA512: cfaf5abdce6d3301f1e161166bcbbc7ede7119fa4e1a96d11be64f28123b0287d8ab829355e1e83414456fe6b971c2cc557bf9a894c488363cc2f611f2d1bce9 Homepage: https://cran.r-project.org/package=evmr Description: CRAN Package 'evmr' (Extreme Value Modeling for r-Largest Order Statistics) Tools for extreme value modeling based on the r-largest order statistics framework. The package provides functions for parameter estimation via maximum likelihood, return level estimation with standard errors, profile likelihood-based confidence intervals, random sample generation, and entropy difference tests for selecting the number of order statistics r. Several r-largest order statistics models are implemented, including the four-parameter kappa (rK4D), generalized logistic (rGLO), generalized Gumbel (rGGD), logistic (rLD), and Gumbel (rGD) distributions. The rK4D methodology is described in Shin et al. (2022) , the rGLO model in Shin and Park (2024) , and the rGGD model in Shin and Park (2025) . The underlying distributions are related to the kappa distribution of Hosking (1994) , the generalized logistic distribution discussed by Ahmad et al. (1988) , and the generalized Gumbel distribution of Jeong et al. (2014) . Penalized likelihood approaches for extreme value estimation follow Martins and Stedinger (2000) and Coles and Dixon (1999) . Selection of r is supported using methods discussed in Bader et al. (2017) . The package is intended for hydrological, climatological, and environmental extreme value analysis. Package: r-cran-evola Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-enhancer Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-evola_1.0.7-1.ca2604.1_all.deb Size: 316752 MD5sum: 54eb4a67bb8e8fad94dc5f4c4fd90a14 SHA1: 9110b88d75ce1abaa2d0bb4934321f0c7bdd83af SHA256: 4459f90c41caecdd50ea5e6f0ce9907dcd188d168cab04f7fe894eaffa166b2a SHA512: 92810fd5a85098429a30417008067aeb833de959acee25d1b8c30ebadf3bb9b47746355048cbd733f95d89102538a818b6968ace9d83d35e5122244fac656ca3 Homepage: https://cran.r-project.org/package=evola Description: CRAN Package 'evola' (Evolutionary Algorithm) Runs an evolutionary algorithm using the 'AlphaSimR' machinery . 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Functions include sending text, media (image/video/document), audio, stickers, geographic locations, contacts, polls, interactive lists and button messages. Also includes number verification and structured CLI logging for debugging. Package: r-cran-evolutionarygames Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-geometry, r-cran-ggplot2, r-cran-interp, r-cran-mass, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-evolutionarygames_0.1.2-1.ca2604.1_all.deb Size: 2966748 MD5sum: dbf1d7ae73101ff4ff1a624a8ec43d71 SHA1: 4ddfaa0a1b061efb2ab546a55421cb26c498dec3 SHA256: 014c4e4948353193ccd31c7b499ea5e26a8a55face86d4538d5045b5b4176a04 SHA512: 4190e04c7620e530b09c6a058ff55c3e063e0f541b79d64072f696d9f0aadd4e199dbb9c3885b4d595e1665fcca55e834d391aeca3bb0e738385147b63d26d98 Homepage: https://cran.r-project.org/package=EvolutionaryGames Description: CRAN Package 'EvolutionaryGames' (Important Concepts of Evolutionary Game Theory) Evolutionary game theory applies game theory to evolving populations in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive set of tools to illustrate the core concepts of evolutionary game theory, such as evolutionary stability or various evolutionary dynamics, for teaching and academic research is provided. Package: r-cran-evolvability Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-matrix, r-cran-ape, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-evolvability_2.0.1-1.ca2604.1_all.deb Size: 308062 MD5sum: a2334c8a0abe266b2dfd240783a62ad2 SHA1: 6dfef1010b9872015af37439744dd81ce6be47c1 SHA256: 748707a0505bf0a61cb0273f74386ecc0b70e85ad560502e1767dcb021a12dfd SHA512: 45cf9b384cd595cfdcf7859335393a9831b42d7a66c4102718ececbee6b9be87f68eaa6070f3ad3fdf08e9f132ca65037481e8e1c80313e829298a7724775aa2 Homepage: https://cran.r-project.org/package=evolvability Description: CRAN Package 'evolvability' (Calculation of Evolvability Parameters) Provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology ). The package was released with Bolstad et al. (2014) , which contains some examples of use. Package: r-cran-evolved Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-diversitree, r-cran-phytools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lsd, r-cran-paleobiodb, r-cran-bammtools, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-evolved_1.0.0-1.ca2604.1_all.deb Size: 3063446 MD5sum: 3a0c103bb340808875953ac62754fa89 SHA1: 74e86fd485d60770c7ac992b8fe84f510c655063 SHA256: 70904c2a04d0acf5aa15cbe749cc4a4e59bd4e3dabeb52822a2c50b9941449dc SHA512: b4f528f91161b385a00b149bc6932be72fddd31b84f4d726084496ce9355667316d301fc283c222fc4ce9cb8f76a7fc453fef3bdfd1cce45b863bbd21fc0d3c5 Homepage: https://cran.r-project.org/package=evolved Description: CRAN Package 'evolved' (Open Software for Teaching Evolutionary Biology at MultipleScales Through Virtual Inquiries) "Evolutionary Virtual Education" - 'evolved' - provides multiple tools to help educators (especially at the graduate level or in advanced undergraduate level courses) apply inquiry-based learning in general evolution classes. In particular, the tools provided include functions that simulate evolutionary processes (e.g., genetic drift, natural selection within a single locus) or concepts (e.g. Hardy-Weinberg equilibrium, phylogenetic distribution of traits). More than only simulating, the package also provides tools for students to analyze (e.g., measuring, testing, visualizing) datasets with characteristics that are common to many fields related to evolutionary biology. Importantly, the package is heavily oriented towards providing tools for inquiry-based learning - where students follow scientific practices to actively construct knowledge. For additional details, see package's vignettes. 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Package: r-cran-evoper Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rrepast, r-cran-logger, r-cran-boot, r-cran-reshape, r-cran-ggplot2, r-cran-desolve, r-cran-plot3d, r-cran-plyr, r-cran-data.table, r-cran-rnetlogo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-evoper_0.7.0-1.ca2604.1_all.deb Size: 518948 MD5sum: 040036e01f7ab7453936476a01272e01 SHA1: 1336cbd299f0043fe08b8002200452a4832cd654 SHA256: e85b0d9c812ca26e15d369460d1e77bf3f96e57945d122f15f3005dbb80564aa SHA512: 924cd94660f031142288c3048cc016a98d3a0785c5c598e7c0f6558b1c55610a4eb86f5637e26bea9ef0ed25e1b51ba2f3f17d15cef24274397100223f6cb56a Homepage: https://cran.r-project.org/package=evoper Description: CRAN Package 'evoper' (Evolutionary Parameter Estimation for 'Repast Simphony' Models) The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible package providing optimization driven parameter estimation methods using metaheuristics and evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization for continuous domains, Tabu Search, Evolutionary Strategies, ...) which could be more efficient and require, in some cases, fewer model evaluations than alternatives relying on experimental design. Currently there are built in support for models developed with 'Repast Simphony' Agent-Based framework () and with NetLogo () which are the most used frameworks for Agent-based modeling. 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It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics. 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It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics. Package: r-cran-ewr Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ewr_1.4-1.ca2604.1_all.deb Size: 40206 MD5sum: 080298ce4c817bd3eb05fc9942e4bfbd SHA1: bce3c296e62cf121c5703924e1609fbc77bea449 SHA256: a3b996bc3228f97dbfcba398bae16f3c413eac1d194331411f136339d1a12d58 SHA512: 2ff99b4f42d2e81a71615817dd8eb80ceb1a4534f4dbcba2cdcd99c8cb1829308279d6b3fd0dc92cc17f4e04ab88cbfefefe0ff9055c72e81e2a8f75371ac5bc Homepage: https://cran.r-project.org/package=EwR Description: CRAN Package 'EwR' (Econometrics with R) Function and data sets in the book entitled "R ile Temel Ekonometri", S.Guris, E.C.Akay, B. Guris(2020). The book published in Turkish. 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EWS are a toolbox for policymakers to prevent or attenuate the impact of economic downturns. Modern EWS are based on the econometric framework of Kauppi and Saikkonen (2008) . Specifically, this framework includes four dichotomous models, relying on a logit approach to model the relationship between yield spreads and future recessions, controlling for recession risk factors. These models can be estimated in a univariate or a balanced panel framework as in Candelon, Dumitrescu and Hurlin (2014) . This package provides both methods for estimating these models and a dataset covering 13 OECD countries over a period of 45 years. In addition, this package also provides methods for the analysis of the propagation mechanisms of an exogenous shock, as well as robust confidence intervals for these response functions using a block-bootstrap method as in Lajaunie (2021). This package constitutes a useful toolbox (data and functions) for scholars as well as policymakers. 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(2022) Preprint . 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Gives melded confidence intervals for the binomial case (Fay, et al, 2015, ). Gives boundary-optimized rejection region test (Gabriel, et al, 2018, ), an unconditional exact test for the situation where the controls are all expected to fail. Gives confidence intervals compatible with exact McNemar's or sign tests (Fay and Lumbard, 2021, ). For review of these kinds of exact tests see Fay and Hunsberger (2021, ). Package: r-cran-exact Architecture: all Version: 3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve Filename: pool/dists/resolute/main/r-cran-exact_3.3-1.ca2604.1_all.deb Size: 204108 MD5sum: 729ac788100a7a12a48418ec1a701886 SHA1: 578eb90047d27d4680df0abff51bb52da5462eaa SHA256: e51a84cea5127f135d6e8a02fe1081886c59bd65dd984358d1b82de8192bb302 SHA512: 53c0d4c01a2733c841a8d1873741eab84bbabe67ebc044bf9ad416d5ceb428deecccf696ab1c64f5a0a35cf5480a4cb4d0ab9016deb420f1de7d41c5180befa6 Homepage: https://cran.r-project.org/package=Exact Description: CRAN Package 'Exact' (Unconditional Exact Test) Performs unconditional exact tests and power calculations for 2x2 contingency tables. For comparing two independent proportions, performs Barnard's test (1945) using the original CSM test (Barnard, 1947 ), using Fisher's p-value referred to as Boschloo's test (1970) , or using a Z-statistic (Suissa and Shuster, 1985, ). For comparing two binary proportions, performs unconditional exact test using McNemar's Z-statistic (Berger and Sidik, 2003, ), using McNemar's conditional p-value, using McNemar's Z-statistic with continuity correction, or using CSM test. Calculates confidence intervals for the difference in proportion. This package interacts with pre-computed data available through the ExactData R package, which is available in a 'drat' repository. Install the ExactData R package from GitHub at . The ExactData R package is approximately 85 MB. Package: r-cran-exactamente Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-exactamente_0.1.1-1.ca2604.1_all.deb Size: 74514 MD5sum: ac14c62990684193a3c1858f129564b1 SHA1: 7bdefae2cc38fe4390c7997a4e900a1f3314f950 SHA256: 37508de034f685deb6ca18218c48eb951837d5f50ae62bed72338410b66c8bee SHA512: 407dce6fa705c2ddf271bec6f2a2b023b1a095c13d5222e7fd387d0078668336f75b14d353dd635f940106fde6b2371d28d32fff3c519fc54928d8893187db1a Homepage: https://cran.r-project.org/package=exactamente Description: CRAN Package 'exactamente' (Explore the Exact Bootstrap Method) Researchers often use the bootstrap to understand a sample drawn from a population with unknown distribution. The exact bootstrap method is a practical tool for exploring the distribution of small sample size data. For a sample of size n, the exact bootstrap method generates the entire space of n to the power of n resamples and calculates all realizations of the selected statistic. The 'exactamente' package includes functions for implementing two bootstrap methods, the exact bootstrap and the regular bootstrap. The exact_bootstrap() function applies the exact bootstrap method following methodologies outlined in Kisielinska (2013) . The regular_bootstrap() function offers a more traditional bootstrap approach, where users can determine the number of resamples. The e_vs_r() function allows users to directly compare results from these bootstrap methods. To augment user experience, 'exactamente' includes the function exactamente_app() which launches an interactive 'shiny' web application. This application facilitates exploration and comparison of the bootstrap methods, providing options for modifying various parameters and visualizing results. Package: r-cran-exactci Architecture: all Version: 1.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ssanv, r-cran-testthat Suggests: r-cran-rmarkdown, r-cran-blakerci, r-cran-knitr, r-cran-exact2x2 Filename: pool/dists/resolute/main/r-cran-exactci_1.4-5-1.ca2604.1_all.deb Size: 213514 MD5sum: 50668de2b10bc406fef473addc24399c SHA1: 3013f5a562a17974027a25cb77a69f058e31e68a SHA256: a851aeac877f992f05b7adb083cf904522466e289db2b00f29656c6042752596 SHA512: d0d6ac46c341e8ffe8983019c0964359663d590072a4c1acb6b52137b9d3a609a8c3125ed139af6ef8a953bf75efb56f66ec67fda90c933caf2932885c45da0e Homepage: https://cran.r-project.org/package=exactci Description: CRAN Package 'exactci' (Exact P-Values and Matching Confidence Intervals for SimpleDiscrete Parametric Cases) Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases (see Fay (2010) ). Package: r-cran-exactcidiff Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-exactcidiff_2.1-1.ca2604.1_all.deb Size: 55354 MD5sum: a83e8487d419bd7630183d9353f192c1 SHA1: bf95f77ed40e930969ce5daf2ac42671bfd79274 SHA256: d01e45f59554f56c968e3b340f15974b4eda2ad29e6896b06b44304033932267 SHA512: 0589d7650540dd5138ebbfbca7d1dff7ad0ac427aa072c46224732e38447ff21ee3e8795357d540bae885e98682d4c8415fcf5dadec8858737af14b9c9d8bbc3 Homepage: https://cran.r-project.org/package=ExactCIdiff Description: CRAN Package 'ExactCIdiff' (Inductive Confidence Intervals for the Difference Between TwoProportions) This is a package for exact Confidence Intervals for the difference between two independent or dependent proportions. Package: r-cran-exactcione Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-exactcione_1.0.5-1.ca2604.1_all.deb Size: 100658 MD5sum: f68ce08dc21e3cedd2497d083674ea55 SHA1: 7ea9f3f86057c69303c1fb52bdf25fdf0ec09a30 SHA256: 73f1435c7862365ff6cc231837e5742bc2a3d5821c792fd1cfb0b46b35a919bd SHA512: 63827ba95beb076491783f90a21da0637d092423ff8d4fd28290e3d68f7f3169d9020078673a35ba2e8807b7dcd60fd01e85a8c714f8865170de6bae6f58120a Homepage: https://cran.r-project.org/package=ExactCIone Description: CRAN Package 'ExactCIone' (Admissible Exact Intervals for One-Dimensional DiscreteDistributions) Construct the admissible exact intervals for the binomial proportion, the Poisson mean and the total number of subjects with a certain attribute or the total number of the subjects for the hypergeometric distribution. Both one-sided and two-sided intervals are of interest. This package can be used to calculate the intervals constructed methods developed by Wang (2014) and Wang (2015) . Package: r-cran-exactcox Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biasedurn Filename: pool/dists/resolute/main/r-cran-exactcox_0.1.0-1.ca2604.1_all.deb Size: 20924 MD5sum: fa493b1eafffbf7f9ed751c5607b9936 SHA1: fefe7582f21362ea7282a056aee166f5338d462f SHA256: 7745f24b45c0044ef222309112270490645202f3c8f9312b5b5729b3151f0881 SHA512: 2df4aa3d4af67d344b1f82d68cea4459165efa1a20d7503ef3b5dc4de1546f8d19a5b768cf61e96a2debfed29469b7e54022225cdae7093fa22cfdc400e1343e Homepage: https://cran.r-project.org/package=ExactCox Description: CRAN Package 'ExactCox' (Exact Test and Exact Confidence Interval for the Cox Model) Performs the exact test on whether there is a difference between two survival curves. Exact confidence interval for the hazard ratio can also be generated for the Cox model. Package: r-cran-exactltre Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc, r-cran-popdemo Filename: pool/dists/resolute/main/r-cran-exactltre_0.1.2-1.ca2604.1_all.deb Size: 3257348 MD5sum: 3ae1cbb3ce2f659b131d5391d6e7a9a7 SHA1: 1c95dbe6ec48ef942563a8f10c07b0f0ceb26ee2 SHA256: 853b0984c5fb22c13ffefc3b7bab9082c0c1f361bc400b70e5a09d0baff4c740 SHA512: cfb723516808c8c5ea3759e2aa3609f378e1837a27c59cf84a3b3ae5e969f0097e74d1c0afad3714b4363ad095337cc4f1a1e3ff26e5f4b52265335fe9850b96 Homepage: https://cran.r-project.org/package=exactLTRE Description: CRAN Package 'exactLTRE' (An Exact Method for Life Table Response Experiment (LTRE)Analysis) Life Table Response Experiments (LTREs) are a method of comparative demographic analysis. The purpose is to quantify how the difference or variance in vital rates (stage-specific survival, growth, and fertility) among populations contributes to difference or variance in the population growth rate, "lambda." We provide functions for one-way fixed design and random design LTRE, using either the classical methods that have been in use for several decades, or an fANOVA-based exact method that directly calculates the impact on lambda of changes in matrix elements, for matrix elements and their interactions. The equations and descriptions for the classical methods of LTRE analysis can be found in Caswell (2001, ISBN: 0878930965), and the fANOVA-based exact methods are described in Hernandez et al. (2023) . We also provide some demographic functions, including generation time from Bienvenu and Legendre (2015) . For implementation of exactLTRE where all possible interactions are calculated, we use an operator matrix presented in Poelwijk, Krishna, and Ranganathan (2016) . Package: r-cran-exactmed Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 607 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brglm2, r-cran-sandwich, r-cran-lmtest, r-cran-pkgcond, r-cran-mlogit, r-cran-dfidx, r-cran-nnet Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-exactmed_0.3.0-1.ca2604.1_all.deb Size: 278384 MD5sum: 619fd79cd937f4cb9ec1db46957b22c7 SHA1: 45c1410ec05341eaa467aaee21a5f4eab4710887 SHA256: 689a11bb5273a88e3488fea1139f9e1348111f7b62e770820288776fc2b9f174 SHA512: 4a133e9cb5a2122c437b330e3d49f732d12d7650ce34ce3357b7e460d0e46c7e00763e60a7ffc80895692a5751aadb8985f8c28c5abaac6d4a2538bb824178e3 Homepage: https://cran.r-project.org/package=ExactMed Description: CRAN Package 'ExactMed' (Exact Mediation Analysis for Binary Outcomes) A tool for conducting exact parametric regression-based causal mediation analysis of binary outcomes as described in Samoilenko, Blais and Lefebvre (2018) ; Samoilenko, Lefebvre (2021) ; and Samoilenko, Lefebvre (2023) . 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It is designed for multiple-choice, true-false, and open-ended questions. The toolkit is usable with datasets in 1-0 or other formats. Key analyses include difficulty, discrimination, response-option analysis, and reports. The classical test theory methods used are described in Ebel and Frisbie (1991, ISBN:978-0132892314). Package: r-cran-exampadata Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2083 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-exampadata_0.5.0-1.ca2604.1_all.deb Size: 2070156 MD5sum: 7e58dbee88320e26e17cdb45384a5c4e SHA1: ef8175df047a69d2bc7af48a3eef4288892e59b2 SHA256: 3a0ec61b3b65d20ca9357b39bf848c6d7e8dffcbed3d3793a825ea7bfd5c2365 SHA512: 2367d53a1f64a580d66cfa4dcba72cc7cbd36fc6f1447be92484129fd061c782ddddf3343cc8eb8151d7a7185ec91ca6ff2a5896f15f67bd86fd84d479ae557f Homepage: https://cran.r-project.org/package=ExamPAData Description: CRAN Package 'ExamPAData' (Data Sets for Predictive Analytics Exam) Contains all data sets for Exam PA: Predictive Analytics at . 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Sort of like python 'doctests' for R. Package: r-cran-exams.forge.data Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exams, r-cran-exams.forge Suggests: r-cran-aer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-exams.forge.data_0.1.3-1.ca2604.1_all.deb Size: 435858 MD5sum: d2720c038e8cdabbf7abe084c8480444 SHA1: cc4487190e576f7802111cc9ad167adabadb1eb5 SHA256: 24dc94ff4cc83176e98df036b5e349c6760eff211630900559aa9492876acd2f SHA512: 746d4b702746959852a4dc3293a4667d41dde8f409b0057093caa15d7026f9d3ef69cb3da259c34677c8140fd0ba560ec1d9fb39496c357f80c4bff902f1b7bf Homepage: https://cran.r-project.org/package=exams.forge.data Description: CRAN Package 'exams.forge.data' (Sample and Precomputed Data for Use with 'exams.forge') Provides a small collection of datasets supporting Pearson correlation and linear regression analysis. It includes the precomputed dataset 'sos100', with integer values summing to zero and squared sum equal to 100. For other values of 'n' and user-defined parameters, the 'sos()' function from the 'exams.forge' package can be used to generate datasets on the fly. In addition, the package contains around 500 german R Markdown exercises that illustrate the usage of 'exams.forge' commands. 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(2009) . Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'. Package: r-cran-exams.mylearn Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exams, r-cran-glue, r-cran-stringr, r-cran-stringi, r-cran-pkgbuild, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-exams.mylearn_1.4-1.ca2604.1_all.deb Size: 42338 MD5sum: 6e787fc3f70c18c2cb28f895ae50dffe SHA1: 48e5cc8a570ac75fc10d687b659bc4a9e8177785 SHA256: 68ff93d22d418e76dad3d2011df77d0b69aabb127493d674fb3f3a02422006a1 SHA512: fdb1994bccfc5242e30a0b0bb0ee169e5b77fb97dc988a246337f5347a3a3ba9c831d5719df6c9c16f88feb908eef99e2782127ebb210abc0f5d91c65b99ca2c Homepage: https://cran.r-project.org/package=exams.mylearn Description: CRAN Package 'exams.mylearn' (Question Generation in the 'MyLearn' XML Format) Randomized multiple-select and single-select question generation for the 'MyLearn' teaching and learning platform. Question templates in the form of the R/exams package (see ) are transformed into XML format required by 'MyLearn'. 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Package: r-cran-exams2ilias Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-exams Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-exams2ilias_0.0.1-1.ca2604.1_all.deb Size: 96174 MD5sum: 6408ee72cb4dfc93a8ad05232fbc84b9 SHA1: ee835ba48c9abd8a6fdb71ac29dcb0ff83cf090b SHA256: 4da5f0ffa01f39bf4900cc82170b2f60039828a76dfd91df221ae937f736829b SHA512: d04a2dda84899654d1de08ac2a98fc7c323bf627b0c223385ff98b28c345ef2f7ece727c28d151c38ac4b8ac0c8326746b0e26455cb85a192ce76c3728a73139 Homepage: https://cran.r-project.org/package=exams2ilias Description: CRAN Package 'exams2ilias' (ILIAS Export Interface for R/Exams) Utilities for exporting exercises from 'R/exams' to question pools for the learning management system 'ILIAS'. The package implements a Question and Test Interoperability 1.2 rendering path tailored to 'ILIAS' and writes the question-pool XML layout validated for imports in 'ILIAS' 9.17. Supported exercise types include single-choice, multiple-choice, numeric, string, and combined gap questions. The underlying 'R/exams' framework is described in Zeileis, Umlauf, and Leisch (2014) . 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Package: r-cran-exgaussestim Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-nloptr, r-cran-invgamma, r-cran-dlm, r-cran-fitdistrplus, r-cran-gamlss.dist Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-exgaussestim_0.1.2-1.ca2604.1_all.deb Size: 34238 MD5sum: 8464c7c030404904a1690ca308c38c3c SHA1: e9d50e7dda518d791d29f46b823a57c33bd4ced5 SHA256: f2b7acbeeb10cd28de9d88212edd4f6d7609ed04364aae81418b940aef53d60d SHA512: 859d0f0068421d5103c10011e88af9e89e6a1fb87f43f4c3683d41cfb008b5a16f98980526c6f2afb884a5837922806be40c0e5f2649f926cde795f4c5fe62e2 Homepage: https://cran.r-project.org/package=ExGaussEstim Description: CRAN Package 'ExGaussEstim' (Quantile Maximization Likelihood Estimation and BayesianEx-Gaussian Estimation) Presents two methods to estimate the parameters 'mu', 'sigma', and 'tau' of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The 'QMLE' method allows a choice between three different estimation algorithms for these parameters : 'neldermead' ('NEMD'), 'fminsearch' ('FMIN'), and 'nlminb' ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) ; McCormack, P. D., & Wright, N. M. (1964) ; Van Zandt, T. (2000) ; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) ; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) . Package: r-cran-exhaustiverasch Architecture: all Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-erm, r-cran-psychotree, r-cran-psych, r-cran-tictoc, r-cran-psychotools, r-cran-pairwise, r-cran-arrangements, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-exhaustiverasch_0.3.7-1.ca2604.1_all.deb Size: 255632 MD5sum: d8a92cb9daca57693be2651670962500 SHA1: f7641123a0d07c7c061be5f2d64014b209bf7f7b SHA256: 0f053d9c714911ea0700a77a8dbaf54d5a56debfbb7a79202f37810e7c0e09b5 SHA512: 4827f1c354191f0ff95c3721164930a2b8d2db263deed928ce0409712f179eb12695c0408f34883df1c5c82332219e974e3d0384eacef0ed3a44b67e8753bc2e Homepage: https://cran.r-project.org/package=exhaustiveRasch Description: CRAN Package 'exhaustiveRasch' (Item Selection and Exhaustive Search for Rasch Models) Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages 'eRm', 'psychotools' or 'pairwise' can be used. Package: r-cran-exifr Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-plyr, r-cran-tibble, r-cran-jsonlite, r-cran-rappdirs Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-exifr_0.3.2-1.ca2604.1_all.deb Size: 2142322 MD5sum: bc8fa770bda8e3a6e6bf31d885b5eb5d SHA1: 4f0c60fe4899124c381bcee7a2fb41ed99507831 SHA256: f7206d83b2944634293eb67f898e1662f1ff0a8b4cb5c34b405bfae1023d69c2 SHA512: 046252b162c420ca4ffc0f702601a2086ce51fb46569bc13815ba5db9a6ad13ef82fbbf7faaa9d882f4fa5d4952d155930bcc536592224545a919204534e164a Homepage: https://cran.r-project.org/package=exifr Description: CRAN Package 'exifr' (EXIF Image Data in R) Reads EXIF data using ExifTool and returns results as a data frame. 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Package: r-cran-exnruleensemble Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn Filename: pool/dists/resolute/main/r-cran-exnruleensemble_0.1.1-1.ca2604.1_all.deb Size: 26400 MD5sum: 08ddb26b92623c443612b98938a0fc06 SHA1: cfc011b7120f0676b82fb0c076abe71945745b76 SHA256: 0a1c4640f7f8bafe77c63af37cd5a14a8bed877cdbce7fd7d797fd816bb2370b SHA512: 0bebe60233ec2dc7807a43d55c3cf63190543a27c4adc90639f1fdfbf7b5437df773c090a8e97329180fde5a992166354d6eeac1c8e7b7cfbdf70e3740750d63 Homepage: https://cran.r-project.org/package=ExNRuleEnsemble Description: CRAN Package 'ExNRuleEnsemble' (A k Nearest Neibour Ensemble Based on Extended NeighbourhoodRule) The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) . Package: r-cran-expanalysis3d Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-fields, r-cran-magrittr, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-expanalysis3d_0.1.3-1.ca2604.1_all.deb Size: 142700 MD5sum: 4ef758875022f9ea67659492bd1602d9 SHA1: 4f4e749c021600e322f2ff875c2bbeb4c053771e SHA256: 16cb478e0f7f247406a6ff5985dff47b6103c144b6d8ceb27824fc696a9eff96 SHA512: 3a5dc10f70c8645f210f579ee7057b8df817d64e601af0fe8f55dd8be780f2e1592a86bdcdd218dbeb14709c47919f39d876c4d453695381835a3aa48e88e4f9 Homepage: https://cran.r-project.org/package=ExpAnalysis3d Description: CRAN Package 'ExpAnalysis3d' (Pacote Para Analise De Experimentos Com Graficos De SuperficieResposta) Pacote para a analise de experimentos havendo duas variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico. Sao ajustados 12 modelos de regressao multipla e plotados graficos de superficie resposta (Hair JF, 2016) .(Package for the analysis of experiments having two explanatory quantitative variables and one quantitative dependent variable. The experiments can be without repetitions or with a statistical design. Twelve multiple regression models are fitted and response surface graphs are plotted (Hair JF, 2016) ). Package: r-cran-expandar Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-corrplot, r-cran-multiwayvcov, r-cran-lmtest, r-cran-plm, r-cran-stargazer, r-cran-scales, r-cran-shiny, r-cran-dt, r-cran-openssl, r-cran-tictoc, r-cran-shinycssloaders, r-cran-kableextra, r-cran-rio, r-cran-zip, r-cran-rlang Suggests: r-cran-gapminder, r-cran-rmarkdown, r-cran-htmltools, r-cran-knitr, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-expandar_0.5.3-1.ca2604.1_all.deb Size: 3321016 MD5sum: e497517dd190905fd55e017f64df03e5 SHA1: b887fcad20d395cda62f5ed867a89909476824c7 SHA256: f4feea384e89e597692cd3564e8ea63f7182504abc77c9b7c6518eedb50e0985 SHA512: dbf03682841ff80e25dd4fca136b7de4f48144f0dbfceefdd827784c2318d2a0b01553d40df3b6aa050ef65c564ad762a5eea54ba9584b443f62ccff113abd87 Homepage: https://cran.r-project.org/package=ExPanDaR Description: CRAN Package 'ExPanDaR' (Explore Your Data Interactively) Provides a shiny-based front end (the 'ExPanD' app) and a set of functions for exploratory data analysis. Run as a web-based app, 'ExPanD' enables users to assess the robustness of empirical evidence without providing them access to the underlying data. You can export a notebook containing the analysis of 'ExPanD' and/or use the functions of the package to support your exploratory data analysis workflow. Refer to the vignettes of the package for more information on how to use 'ExPanD' and/or the functions of this package. Package: r-cran-expandfunctions Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-orthopolynom, r-cran-polynom, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-expandfunctions_0.1.0-1.ca2604.1_all.deb Size: 76506 MD5sum: ecd7108387b3b9e9ff09f9a14d0991d9 SHA1: c00bcf9eb619c67c622aa3d6d206d727cc4dcbbb SHA256: 0dbc6056651241a05e0ff8b95efbc63890d5ba04e32c525fbf9b7eb43aaa13cc SHA512: b38e02afc45f9e4e8c6998d23a9de6e98f7792e79a5789b817109e6db1008ec4114f2c89ce86fd7f8e9b126256b2c6ceab662ab94fa5a404fcb2e818f0a86ba6 Homepage: https://cran.r-project.org/package=expandFunctions Description: CRAN Package 'expandFunctions' (Feature Matrix Builder) Generates feature matrix outputs from R object inputs using a variety of expansion functions. The generated feature matrices have applications as inputs for a variety of machine learning algorithms. 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Additionally, there are utilities for replacing features, removing rows with NAs, creating matrix samples of a given distribution, a simple wrapper for LASSO with CV, a Freeman-Tukey transform, generalizations of the outer function, matrix size-preserving discrete difference by row, plotting, etc. Package: r-cran-expar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-expar_0.1.0-1.ca2604.1_all.deb Size: 34552 MD5sum: 3864750041906364b96417b616cf5d6e SHA1: 2992d3bc21bd68c0fb4e277302a09b8947715990 SHA256: ffe436e35df918f0c8d53521b1fd8e86c5048aec919db3f2398bdb8d2f2e91ca SHA512: 7844d36f572c11631fdd6543b83419181d49ea101b20b3e241907803d07cd48576bfa6987fa1eadb83725349574e40db1e4f6c0c766e329998c0b35b0662c36b Homepage: https://cran.r-project.org/package=EXPAR Description: CRAN Package 'EXPAR' (Fitting of Exponential Autoregressive (EXPAR) Model) The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided. Package: r-cran-exparma Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-exparma_0.1.0-1.ca2604.1_all.deb Size: 24160 MD5sum: eb5657fcb7cd96a0c7e1229080d358da SHA1: 6c4ff4e835e2c460699478e955feb5f73e858203 SHA256: e32b1c1b4afe9456f9c233e72aea71b18efc6b8fcd5a4008cdf4f3454b14f76f SHA512: 1374e6a260ec3401629028a63d08b372842938703ca7fee664b228db59167fc3d54b09520ec7fa0066c5f6cf613ce3e51e4efd8bc1bf00d331261f3fcd00ca17 Homepage: https://cran.r-project.org/package=EXPARMA Description: CRAN Package 'EXPARMA' (Fitting of Exponential Autoregressive Moving Average (EXPARMA)Model) The amplitude-dependent autoregressive time series model (EXPAR) proposed by Haggan and Ozaki (1981) was improved by incorporating the moving average (MA) framework for capturing the variability efficiently. Parameters of the EXPARMA model can be estimated using this package. The user is provided with the best fitted EXPARMA model for the data set under consideration. 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Package: r-cran-expdes.pt Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stargazer Filename: pool/dists/resolute/main/r-cran-expdes.pt_1.2.2-1.ca2604.1_all.deb Size: 574186 MD5sum: ea0db87769c6e826ee8f6bb4e57c2ef9 SHA1: 86bb6d8ba696180fc595237396defe6643055ca1 SHA256: ee0a9a4336cf6b5bfdc0e5aef6d5a1381e1d2e93e9090dd05a423366fcf42262 SHA512: a7c0d337d8f264a90ece8487d0357c78072d784b07f815c15661121a7ef84c32a917d0e2c88ec5101494be40d4f14cd7abc198c8911ce1f86e54ddd198e9815b Homepage: https://cran.r-project.org/package=ExpDes.pt Description: CRAN Package 'ExpDes.pt' (Pacote Experimental Designs (Portugues)) Pacote para análise de delineamentos experimentais (DIC, DBC e DQL), experimentos em esquema fatorial duplo (em DIC e DBC), experimentos em parcelas subdivididas (em DIC e DBC), experimentos em esquema fatorial duplo com um tratamento adicional (em DIC e DBC), experimentos em fatorial triplo (em DIC e DBC) e experimentos em esquema fatorial triplo com um tratamento adicional (em DIC e DBC), fazendo analise de variancia e comparacao de multiplas medias (para tratamentos qualitativos), ou ajustando modelos de regressao ate a terceira potencia (para tratamentos quantitativos); analise de residuos (Ferreira, Cavalcanti and Nogueira, 2014) . Package: r-cran-expdes Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stargazer Filename: pool/dists/resolute/main/r-cran-expdes_1.2.2-1.ca2604.1_all.deb Size: 569368 MD5sum: 4cbf15309267a1f84458952b869eafbe SHA1: 5034d494319f1b4fbacdb177ca926bfb4024f081 SHA256: f9204c383473d895e4d751858e82b46204e13b2886c9429f5ac62a8bc39899b7 SHA512: bca7830d19d6ae35e845bad490126d0b78ef9691d4575fd6035395a8007e9fafd6980e76f767e1fc166bae7366434841a33137000529c9733db964974cee13bc Homepage: https://cran.r-project.org/package=ExpDes Description: CRAN Package 'ExpDes' (Experimental Designs Package) Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) . 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For quality control it provides functions to subset a representative sample. 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It provides a suite of tools to simplify data exploration such as benchmarking, comparing groups, and checking for differences. The outputs translate statistical approaches in applied experience research to human readable output. Package: r-cran-experimentr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-experimentr_0.1.0-1.ca2604.1_all.deb Size: 64114 MD5sum: 31dcb43eb82b71e8a156a6602c4a7df6 SHA1: dc1642cda7d881a397a5aefb69d34089a2b03a0c SHA256: 73efc2ca6173610739a64471fafa185643e2c132c5280f9c0f1c861352045fa4 SHA512: 7e4724c11f485c113b346734a3965f186ae5dbfe267ba845f2a2b005cb6706ada14ea74c671c2c2e581f40550bd0892c69470fe6abe102c42b76094c7a7402c0 Homepage: https://cran.r-project.org/package=experimentr Description: CRAN Package 'experimentr' (Datasets Used in Social Science Experiments: A Hands-onIntroduction) Contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at . Package: r-cran-expertchoice Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-far, r-cran-dplyr, r-cran-doe.base, r-cran-rlist, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-expertchoice_0.2.0-1.ca2604.1_all.deb Size: 314554 MD5sum: 2f5793d3e69b37ce2e445f50fe6c50bc SHA1: 7c08460353511a4b739d55b99a9227698e8ab316 SHA256: df882ad0253387cc13242ea10fb040a3fb0ded8683f38984e6f44f753f258f9f SHA512: b616d486b2aa666e086e892dd2fd2068bf0e89192ef1e887e8c16439b743841bb5985d6c808f15843b54952697d734a7fbba84f8c38a929ea26fb34f21c4b2c0 Homepage: https://cran.r-project.org/package=ExpertChoice Description: CRAN Package 'ExpertChoice' (Design of Discrete Choice and Conjoint Analysis) Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) . Package: r-cran-expgenetic Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-deseq2, r-cran-futile.logger, r-cran-ggplot2, r-cran-ggsci, r-cran-plyr, r-cran-venndiagram Filename: pool/dists/resolute/main/r-cran-expgenetic_0.1.0-1.ca2604.1_all.deb Size: 139180 MD5sum: 04fcffc7286d635aa466a6475c9a721a SHA1: 2eab0c7f2e3d08c17cd2bf0a9af28321b5eee064 SHA256: 7416e04ff9ceb70645881877e5b14823c0064add4a845852fa44df7a6188f3af SHA512: 6ca1d24a6f2a3c1e459c4f56cbf510114927affdab2a38514ac66d552d438800aee57981ec0c023c40dc84d4faca6391401b71274418c23620b48c152b06285d Homepage: https://cran.r-project.org/package=ExpGenetic Description: CRAN Package 'ExpGenetic' (Non-Additive Expression Analysis of Hybrid Offspring) Three functional modules, including genetic features, differential expression analysis and non-additive expression analysis were integrated into the package. And the package is suitable for RNA-seq and small RNA sequencing data. Besides, two methods of non-additive expression analysis were provided. One is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the gene in hybrid offspring with the expression level in parents. For non-additive expression analysis of RNA-seq data, it is only applicable to hybrid offspring (including two sub-genomes) species for the time being. Package: r-cran-expirest Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-lifecycle Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-expirest_0.1.7-1.ca2604.1_all.deb Size: 333778 MD5sum: ecd5c18af8b1f34c666b98127f9ae209 SHA1: 55ae033f460da96a3806689b709c84fbc25fc5de SHA256: 53143d99323b63c7e9cf296b1646bda6dbad4e36120b22c7d500122e716a8b87 SHA512: b7dae3d5736243f71e8fefea14bbb9b222b5621ceab78215745ba29fa8b249d1f9de4543758ee41b0dbf5cc56ad62211c650d366b40fc157ae50aab81bb3ad34 Homepage: https://cran.r-project.org/package=expirest Description: CRAN Package 'expirest' (Expiry Estimation Procedures) The Australian Regulatory Guidelines for Prescription Medicines (ARGPM), guidance on "Stability testing for prescription medicines", recommends to predict the shelf life of chemically derived medicines from stability data by taking the worst case situation at batch release into account. Consequently, if a change over time is observed, a release limit needs to be specified. Finding a release limit and the associated shelf life is supported, as well as the standard approach that is recommended by guidance Q1E "Evaluation of stability data" from the International Council for Harmonisation (ICH). Package: r-cran-explainer Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvms, r-cran-data.table, r-cran-dplyr, r-cran-egg, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggpubr, r-cran-magrittr, r-cran-plotly, r-cran-tibble, r-cran-tidyr, r-cran-writexl, r-cran-gridextra, r-cran-scales Suggests: r-cran-cowplot, r-cran-mlr3, r-cran-mlr3learners, r-cran-knitr, r-cran-broom, r-cran-iml, r-cran-forcats, r-cran-mlr3viz, r-cran-plotroc, r-cran-psych, r-cran-reshape2, r-cran-remotes, r-cran-mlbench, r-cran-ranger, r-cran-precrec, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-explainer_1.0.2-1.ca2604.1_all.deb Size: 164064 MD5sum: 80df4e88864b60a45f7372e39c4a3b3b SHA1: fa9795280dd7bb551737f63921397a4b6f4e951e SHA256: 22b5da5a1dae341effeec6c23c801589236bd7a8e237a929da1c40099817b576 SHA512: 4be55f347254a2a4cab902f3152bdfda3ace5ac5175411cffc21143c930643987b66f76ee6bb3e320a57a1afa9a470edab9313a1f3585dd09ca29003c38fe71d Homepage: https://cran.r-project.org/package=explainer Description: CRAN Package 'explainer' (Machine Learning Model Explainer) It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements. Package: r-cran-explodelayout Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-explodelayout_0.1.3-1.ca2604.1_all.deb Size: 72508 MD5sum: e6929fbc83c8faffc091d5e2df0f6d62 SHA1: b03857a74a35bd3308b383ce478e51b8e8e7bf07 SHA256: 714b2e6639b2caa7e07bfe7455cbd4ae4a9612740b1ae6a7287890bd59f6aece SHA512: 0ddcf2fafb34aa15a93b5daf5019155a7865e2f8ae133c87c5d421666368d24b474be5b8d1296a493c2d046566538f9aec65e92d68feed1c1d59b860f6fd6660 Homepage: https://cran.r-project.org/package=ExplodeLayout Description: CRAN Package 'ExplodeLayout' (Calculate Exploded Coordinates Based on Original NodeCoordinates and Node Clustering Membership) Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual “hairball” that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be “exploded” to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which “explodes” the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl] that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) ). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks. 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Package: r-cran-exploratory Architecture: all Version: 0.3.31-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-ggplot2, r-cran-ggridges, r-cran-lemon, r-cran-lm.beta, r-cran-mediation, r-cran-remotes, r-cran-shiny, r-cran-shinydashboard, r-cran-weights Suggests: r-cran-moments Filename: pool/dists/resolute/main/r-cran-exploratory_0.3.31-1.ca2604.1_all.deb Size: 195876 MD5sum: 7466c57fa9c97ff6a98cebf1438a4d3f SHA1: 91c73803c8b0c3e918657f36916690fc2d0a5e47 SHA256: 8e585292abdfcfe964e9ace6fc0725192121a7938ee28b2de0a8bd4c3890828b SHA512: 1fe85dde3c1920943e1e080d56ffe6718539e429a2cd4470a7678aae78d276f6c58aa3b5515c085817dba0b4eead1b1e58e6ed5e3d00387d0ad0e3aa1f32036b Homepage: https://cran.r-project.org/package=exploratory Description: CRAN Package 'exploratory' (A Tool for Large-Scale Exploratory Analyses) Conduct numerous exploratory analyses in an instant with a point-and-click interface. With one simple command, this tool launches a Shiny App on the local machine. Drag and drop variables in a data set to categorize them as possible independent, dependent, moderating, or mediating variables. Then run dozens (or hundreds) of analyses instantly to uncover any statistically significant relationships among variables. Any relationship thus uncovered should be tested in follow-up studies. This tool is designed only to facilitate exploratory analyses and should NEVER be used for p-hacking. Many of the functions used in this package are previous versions of functions in the R Packages 'kim' and 'ezr'. Selected References: Chang et al. (2021) . Dowle et al. (2021) . Kim (2023) . Kim (2021) . Kim (2020) . Simmons et al. (2011) Tingley et al. (2019) . Wickham et al. (2020) . 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Package: r-cran-extremeci Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dofuture, r-cran-dplyr, r-cran-evd, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-extremeci_0.2.1-1.ca2604.1_all.deb Size: 226248 MD5sum: 9e46b493fbe5e12d8e0b1824f869ce50 SHA1: 4d525f0a334c83e1fb136121e1cb5748b4c5cde9 SHA256: 678e1159a685a3780a44c01b42c4ba3bb12810c9896034734a6318bcd8e58c51 SHA512: e5ec852661ae14e9380c84c6601d555fc8aa2bf120afb653c5c3c3ee5035402eddd2095ce1e032d054c8bc24cd48a12170f125766e341945836b422c314a5f90 Homepage: https://cran.r-project.org/package=ExtremeCI Description: CRAN Package 'ExtremeCI' (Realistic Confidence Intervals for Non-Stationary Extreme ValueStatistics) This framework provides versatile algorithms to efficiently infer confidence intervals for extreme value statistics, such as extreme quantiles and return levels, that are representative of the asymmetric uncertainty spread, using extreme value theory extrapolation and the profile likelihood (see e.g., Coles (2001) ). Unlike existing algorithms, the CI endpoints are found without the need for a strict prespecified range, can be covariate-dependent, and can be based on weighted samples. This package is motivated by Zeder et al. (2023) and by Pasche et al. (2026) . Package: r-cran-extremefit Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3130 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-extremefit_1.1.0-1.ca2604.1_all.deb Size: 2812524 MD5sum: 41c63f585b6eef7692d6ed413ae8f2d9 SHA1: 575729557fb888973726eb906ed6d1b9dfc91cae SHA256: 698c00f1e7eea9d9c9205c065c1af59ae4a6c7b357c28214936af7efb4ed3931 SHA512: f2fa7774e324aaa6fa25e9d24a6ee77bdf39ebe8befec7c2ce51b48162fc033ca4e597c2ba5c4e4a07a4bade944dd9526963e21d02ef329f5c2f5d1cb8d4c91c Homepage: https://cran.r-project.org/package=extremefit Description: CRAN Package 'extremefit' (Estimation of Extreme Conditional Quantiles and Probabilities) Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities. Package: r-cran-extremeindex Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 829 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-evd, r-cran-gmm, r-cran-evir Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-extremeindex_0.0.3-1.ca2604.1_all.deb Size: 803528 MD5sum: 9b5b296d26673ab3387fed580e6bc532 SHA1: 955ffb4ed9879fdf8dde018f04df9f167b06f86e SHA256: d29635f6aa875c0f73c1f0039b9d882903f67703156f820ad16157da4b46c07b SHA512: 47cefd215e2d5f2f5f77df822448031962717cb2046c9df1a44aa5a3bbe0ce9c5dbb0b159c23ea677d4252976802d55234b6f239ee5bf9b30e03e22c41fb02dc Homepage: https://cran.r-project.org/package=extremeIndex Description: CRAN Package 'extremeIndex' (Forecast Verification for Extreme Events) An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) . Package: r-cran-extremes Architecture: all Version: 2.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmoments, r-cran-distillery Suggests: r-cran-fields Filename: pool/dists/resolute/main/r-cran-extremes_2.2-1-1.ca2604.1_all.deb Size: 1121024 MD5sum: 2a1bc5f59731b15492c9b59cd5d1aa8e SHA1: 28c9fd3d77213f019dfb9427ff29308e18403300 SHA256: 2bbd7f0a4a009d1baf4f344700ebd53f568e1482ddae18689ff97b39bbd0af1f SHA512: fa6d4dd26ae9b4fd1e80738375fb51315c2dbce6959a9e7381c9c7e42204851d426cff3d9d286246368d07ce7807c3a4c718f6ef098ebdfd41dd6300a60e1f42 Homepage: https://cran.r-project.org/package=extRemes Description: CRAN Package 'extRemes' (Extreme Value Analysis) General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) and for bootstrapping, please see Gilleland (2020) . Package: r-cran-extremestat Architecture: all Version: 1.5.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmomco, r-cran-berryfunctions, r-cran-pbapply, r-cran-rcolorbrewer, r-cran-evir, r-cran-ismev, r-cran-fextremes, r-cran-extremes, r-cran-evd, r-cran-renext Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-extremestat_1.5.12-1.ca2604.1_all.deb Size: 702990 MD5sum: d00f1c84f4ffa01700c1ebf8d8b721dc SHA1: 98c16a2fcba6661bd7de08f509346bc1247d4349 SHA256: e673fa92525e2a4f4566a28c30bb401a6f5c3f7c0088ae418a835efd85488c81 SHA512: dc3fd2f66993c764158d41d5b995d86606d3e4dd4a519211cc21a8da7ea1535dbc316b9ec3b13be77f5476b8d7960a34af832c6452a56db0d44d7ecb8f1700cc Homepage: https://cran.r-project.org/package=extremeStat Description: CRAN Package 'extremeStat' (Extreme Value Statistics and Quantile Estimation) Fit, plot and compare several (extreme value) distribution functions. Compute (truncated) distribution quantile estimates and plot return periods on a linear scale. On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...] ISBN 1463508417. Package: r-cran-extremevalues Architecture: all Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-extremevalues_2.4.1-1.ca2604.1_all.deb Size: 404938 MD5sum: 3e2e55f86c0545116a7fffc981527d97 SHA1: 572ea1c21e9af866df6b1b1935b0d6e2c6789fbf SHA256: edd6454cfc294f9a9b3110870f62662dc70fbfa1263914ad23acb1e53e828001 SHA512: f9d0c0ee69a0ff049c155906a7c2edba576429d1a0fa867724eef6442302fef1dff0ff3ebc2f14578aad36d1028ce0c01ecb7afaa9d440b7183e5fe51de582cc Homepage: https://cran.r-project.org/package=extremevalues Description: CRAN Package 'extremevalues' (Univariate Outlier Detection) Detect outliers in one-dimensional data. Package: r-cran-extremogram Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-mass Filename: pool/dists/resolute/main/r-cran-extremogram_1.0.2-1.ca2604.1_all.deb Size: 69152 MD5sum: f6ec4ed75f0a9c6f629ca670ba84220f SHA1: 8c892dfef19b2137da4ff62140416f8e2c815e63 SHA256: 6d52dce6a1bfc546ff721e2654af4a9fcc3e7f6a44d5a122ed48470b1aa7aa70 SHA512: 5782e70830b77872d333bdb4d03c9b6b1108de087304c829da18e9da838445ad1065259b5ceb6d95d6af269ec7b4421ac11aa39247a26b99c779f911377d8320 Homepage: https://cran.r-project.org/package=extremogram Description: CRAN Package 'extremogram' (Estimation of Extreme Value Dependence for Time Series Data) Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms. Package: r-cran-extrpatt Architecture: all Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1793 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-mass Filename: pool/dists/resolute/main/r-cran-extrpatt_0.1-4-1.ca2604.1_all.deb Size: 1751612 MD5sum: 75f52107b14818e50b42af5b015278c0 SHA1: 40e80ec2f7d1727fd59ab161deedd34d741432b8 SHA256: 79e0d5f8f435b29f213e58b23e653fbf5ee15400783b964713173ec4fec63f6d SHA512: e29f23c9c23ab5ac6f8ef8d0b73a6ec9fd0efb2c461a6bccc46112efc01d8e2c086f810274dd54ad4ea7eff145662ad715c9d369cd92d6b8a3aea2e11d5906a9 Homepage: https://cran.r-project.org/package=ExtrPatt Description: CRAN Package 'ExtrPatt' (Spatial Dependencies and Indices for Extremes) An implementation of 1) the tail pairwise dependence matrix (TPDM) as described in Jiang & Cooley (2020) 2) the extremal pattern index (EPI) as described in Szemkus & Friederichs ('Spatial patterns and indices for heatwave and droughts over Europe using a decomposition of extremal dependency'; submitted to ASCMO 2023). Package: r-cran-exvatools Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-openxlsx, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-exvatools_1.0.0-1.ca2604.1_all.deb Size: 419338 MD5sum: 676fe48afff39c4fa29eee1c438f449c SHA1: 2497296d138abb688fdd9f1a780fb8b05b0b762f SHA256: d0f1e3e947df3e472afa6772be857dc79c819508bf3e76579c058c0d5f1b3914 SHA512: 294a93fe7f94affc7fa169c8ee31c825fb86f45efa2d4199908e1f44ae3f4ae81da1f196fad7a06946f2debaea1cbbc989002d5216515dc3cdda9ca3f71f1727 Homepage: https://cran.r-project.org/package=exvatools Description: CRAN Package 'exvatools' (Value Added in Exports and Other Input-Output Table AnalysisTools) Analysis of trade in value added with international input-output tables. Includes commands for easy data extraction, matrix manipulation, decomposition of value added in gross exports and calculation of value added indicators, with full geographical and sector customization. Decomposition methods include Borin and Mancini (2023) , Miroudot and Ye (2021) , Wang et al. (2013) and Koopman et al. (2014) . Package: r-cran-eye Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-english, r-cran-lubridate, r-cran-magrittr, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-eyedata, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eye_1.3.0-1.ca2604.1_all.deb Size: 204094 MD5sum: f1d6da6c15d332358bf94dada77b7e73 SHA1: dd1127f47f1ea62124b2dff00ab5c667921db197 SHA256: de64478d99d7c47adb9e00caf7706c4273b6fb1844882b0ca6c5dbb2754af3dc SHA512: 72eb018a344d86fc8db1a9906134b71f01932912c9bf08ca272da562469e52d174119f03b4529091dfd221701cdc127c441aa8a16e9efed8a863237fdb657321 Homepage: https://cran.r-project.org/package=eye Description: CRAN Package 'eye' (Analysis of Eye Data) There is no ophthalmic researcher who has not had headaches from the handling of visual acuity entries. Different notations, untidy entries. This shall now be a matter of the past. Eye makes it as easy as pie to work with VA data - easy cleaning, easy conversion between Snellen, logMAR, ETDRS letters, and qualitative visual acuity shall never pester you again. The eye package automates the pesky task to count number of patients and eyes, and can help to clean data with easy re-coding for right and left eyes. It also contains functions to help reshaping eye side specific variables between wide and long format. Visual acuity conversion is based on Schulze-Bonsel et al. (2006) , Gregori et al. (2010) , Beck et al. (2003) and Bach (2007) . Package: r-cran-eyedata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-eyedata_0.1.0-1.ca2604.1_all.deb Size: 916106 MD5sum: 3c6bfee1fe94c07af433943314f10fea SHA1: dea3fa52bbcf5862c2336b0923517adff22bb5c8 SHA256: 2a773f1300df64047987d73f64c1392c94a8ee98b44b9b5b18c9b73ed30bce20 SHA512: 1361afdede08032b512a6ca8845a9b7e05e0b1fbd5d339ca7dd78ba29681e308702005386e327feca195e301c6fb8cf31d9d8b4f91ed0ea0bc11e76c376041d4 Homepage: https://cran.r-project.org/package=eyedata Description: CRAN Package 'eyedata' (Open Source Ophthalmic Data Sets Curated for R) Open source data allows for reproducible research and helps advance our knowledge. The purpose of this package is to collate open source ophthalmic data sets curated for direct use. This is real life data of people with intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF), due to age-related macular degeneration or diabetic macular edema. Associated publications of the data sets: Fu et al. (2020) , Moraes et al (2020) , Fasler et al. (2019) , Arpa et al. (2020) , Kern et al. 2020, . Package: r-cran-eyelinker Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-readr, r-cran-intervals Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-eyelinker_0.2.2-1.ca2604.1_all.deb Size: 1615468 MD5sum: 5b3f41945cb81743ad0535c6eb3baf22 SHA1: 2e6d181b94c98ed3ed720dfcccac75ba551418fe SHA256: 5de53cdfc3bc39577781070e94d03ee85078feabd1c7626efeff9c45b6acf838 SHA512: 9c42796ea7dda57a4acc670116c0f8f9350418b639fb42d2c03a2441078b8898e6e0e550a85a23bb1bca25c0e28bf4e4540c5bfc21822530a6165a9f6c142baa Homepage: https://cran.r-project.org/package=eyelinker Description: CRAN Package 'eyelinker' (Import ASC Files from EyeLink Eye Trackers) Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for analysis and visualization. Package: r-cran-eyeris Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5788 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eyelinker, r-cran-dplyr, r-cran-gsignal, r-cran-purrr, r-cran-zoo, r-cran-cli, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-progress, r-cran-data.table, r-cran-withr, r-cran-lifecycle, r-cran-mass, r-cran-viridis, r-cran-fields, r-cran-jsonlite, r-cran-rmarkdown, r-cran-dbi, r-cran-glue, r-cran-base64enc Suggests: r-cran-arrow, r-cran-duckdb, r-cran-knitr, r-cran-testthat, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-eyeris_3.0.1-1.ca2604.1_all.deb Size: 3487344 MD5sum: 4c9369d6f5d9bad159e68ff41e4c4119 SHA1: 6a9858ec9a3c411e1e7f715f5cd9ccc7d262550b SHA256: 559f5d24c01c510c33b66407c3c4f5843af128908bb53663a4e51dff6585c335 SHA512: 3b183b8297db7b32ce0ea1c5e5350113e15d32e1905d5be284214a8847f223315432d889fb6384f16423ce61855547d9660445aed9b132d1dfecfc151ce44fd7 Homepage: https://cran.r-project.org/package=eyeris Description: CRAN Package 'eyeris' (Flexible, Extensible, & Reproducible Pupillometry Preprocessing) Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) ; Kret & Sjak-Shie (2018) ; Strauch (2024) ]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) ]. 'eyeris' provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) ; Gorgolewski et al. (2016) ]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology. Package: r-cran-eyetools Architecture: all Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4052 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggforce, r-cran-ggplot2, r-cran-viridis, r-cran-glue, r-cran-hdf5r, r-cran-lifecycle, r-cran-magick, r-cran-pbapply, r-cran-rlang, r-cran-zoo, r-cran-png Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eyetools_0.9.2-1.ca2604.1_all.deb Size: 3661354 MD5sum: 8dce8fbdefc4bff5d2a49a5726d267cd SHA1: 8a01ff97d84388fdf48cfcda4d31ca5696575ee2 SHA256: 01e7e50b18fa2ea4971b86018fe6029431e692d1e5ad144c42c340b424224efb SHA512: 03f415edee22a6082be0b0cfc307b8d96d7ae0277e8998c7eca163c2b10eefbb5e9b8830a41a6b123d27e9fec70baaff219971dd729f7a0becc63e6b64fd7658 Homepage: https://cran.r-project.org/package=eyetools Description: CRAN Package 'eyetools' (Analyse Eye Data) Enables the automation of actions across the pipeline, including initial steps of transforming binocular data and gap repair to event-based processing such as fixations, saccades, and entry/duration in Areas of Interest (AOIs). It also offers visualisation of eye movement and AOI entries. These tools take relatively raw (trial, time, x, and y form) data and can be used to return fixations, saccades, and AOI entries and time spent in AOIs. As the tools rely on this basic data format, the functions can work with data from any eye tracking device. Implements fixation and saccade detection using methods proposed by Salvucci and Goldberg (2000) . Package: r-cran-eyetrackingr Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-broom, r-cran-broom.mixed, r-cran-ggplot2, r-cran-lazyeval, r-cran-rlang, r-cran-zoo, r-cran-tidyr, r-cran-purrr Suggests: r-cran-pbapply, r-cran-knitr, r-cran-lme4, r-cran-glmmtmb, r-cran-mass, r-cran-matrix, r-cran-testthat, r-cran-rmarkdown, r-cran-domc, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-eyetrackingr_0.2.2-1.ca2604.1_all.deb Size: 897452 MD5sum: 3ce02485ac81a61fe1b8908dea34dbf3 SHA1: 74f0599290caa6b77b9586691ef775658198a9c8 SHA256: 6d1b35ffc6a489d71f312b3896592503a669115187e78f339b44885fd99383a0 SHA512: e68c82967d73f452381e0e4461e4d3b66493118a35fe6323d56763635d9f85929b99f56355a0078594ed4a94e8e175adb7e6204c1235ff11d194ad500ff9d505 Homepage: https://cran.r-project.org/package=eyetrackingR Description: CRAN Package 'eyetrackingR' (Eye-Tracking Data Analysis) Addresses tasks along the pipeline from raw data to analysis and visualization for eye-tracking data. Offers several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches. For references to the approach see Mirman, Dixon & Magnuson (2008) , and Barr (2008) . Package: r-cran-eyetrackr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eyetrackr_1.0.1-1.ca2604.1_all.deb Size: 2937442 MD5sum: fb179f239bd19b439d179aac79050f45 SHA1: 155b625bc018edd052330c1b2c43d0488abeb8fc SHA256: 3429a17f6f36569d4808ecbf89c8b69f535b389efc087516b2f0461c9ec7dd3e SHA512: 85c196405b402e920765b927d67dc6d0c608284e51f7cc406833205c692dca0fa145729e1cf9dcf9c1ff4e7156f488900a36a2b4647b76066fab5911f8c94ed7 Homepage: https://cran.r-project.org/package=eyeTrackR Description: CRAN Package 'eyeTrackR' (Organising and Analysing Eye-Tracking Data) A set of functions for organising and analysing datasets from experiments run using 'Eyelink' eye-trackers. Organising functions help to clean and prepare eye-tracking datasets for analysis, and mark up key events such as display changes and responses made by participants. Analysing functions help to create means for a wide range of standard measures (such as 'mean fixation durations'), which can then be fed into the appropriate statistical analyses and graphing packages as necessary. Package: r-cran-ez.combat Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ez.combat_1.0.0-1.ca2604.1_all.deb Size: 29086 MD5sum: 5af208ca816ab30e5307dcb8c83c927a SHA1: 208cd48b23f7378a1a431c89d81c3908218b79ae SHA256: 50b975b9263d93639b0ef10f0ceaff0ca02cf6bb6d3ccc2e9f8363a4a682b4e5 SHA512: 98bd884988325aab0fcc50a3d9fc31e075a04a8c70822b2258ae9962c8b5a2aa35b4ba33c94191010b5a6fc75c036cdb44bb90a20ba2fd461bed051902375e2f Homepage: https://cran.r-project.org/package=ez.combat Description: CRAN Package 'ez.combat' (Easy ComBat Harmonization) A dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects. Johnson WE & Li C (2007) "Adjusting batch effects in microarray expression data using empirical Bayes methods." Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnes M, Phillips ML, Trivedi MH, Weissman MM, & Shinohara RT (2017) "Harmonization of cortical thickness measurements across scanners and sites." Fortin J-P, Parker D, Tun B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, & Shinohara RT (2017) "Harmonization of multi-site diffusion tensor imaging data." Package: r-cran-ez Architecture: all Version: 4.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-ggplot2, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-plyr, r-cran-reshape2, r-cran-scales, r-cran-stringr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-ez_4.5-0-1.ca2604.1_all.deb Size: 335088 MD5sum: f4868c2b04296b4e3449574865868ba4 SHA1: e82f60a97e801132ff305aa225157cd4f950af41 SHA256: 652608bca9cec0218a83954e0e80d80928973909a2479d9fc3057ca11206cf2e SHA512: 4c0346430175fa3e01ac5987825012b08d8148bfe5693b73e0ee56954aca4e9d689a145a781a01283fb9f064226c4555a1f6b3ac5b6f8c25d56117fd93a67b0d Homepage: https://cran.r-project.org/package=ez Description: CRAN Package 'ez' (Easy Analysis and Visualization of Factorial Experiments) Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. 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Package: r-cran-ezbakr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-bioc-tximport Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ezbakr_0.1.0-1.ca2604.1_all.deb Size: 867052 MD5sum: c6dd539da9fefd0d2b9fe1e7dd96db54 SHA1: 892144b9dfdc0768cd3084b8d5dc759534f385b7 SHA256: 2f9baa5a29a8660203a08efa88503f3ae47788d26be702131b68388baf901d7c SHA512: f782c977bb7c1cada0a81ecc507a7d94eadb40e484723f81f00d81e26b1735bd2581cc33c845c1af6f23e6b5c5515682fe17b34ecd34acba809c7c24717e6727 Homepage: https://cran.r-project.org/package=EZbakR Description: CRAN Package 'EZbakR' (Analyze and Integrate Any Type of Nucleotide Recoding RNA-SeqData) A complete rewrite and reimagining of 'bakR' (see 'Vock et al.' (2025) ). Designed to support a wide array of analyses of nucleotide recoding RNA-seq (NR-seq) datasets of any type, including TimeLapse-seq/SLAM-seq/TUC-seq, Start-TimeLapse-seq (STL-seq), TT-TimeLapse-seq (TT-TL-seq), and subcellular NR-seq. 'EZbakR' extends standard NR-seq standard NR-seq mutational modeling to support multi-label analyses (e.g., 4sU and 6sG dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. 'EZbakR' also generalized dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, 'EZbakR' implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. Package: r-cran-ezcox Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forestmodel, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-survival, r-cran-tibble, r-cran-utf8 Suggests: r-cran-covr, r-cran-furrr, r-cran-future, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ezcox_1.0.4-1.ca2604.1_all.deb Size: 622826 MD5sum: 5819d06dbc83405ce2a88df2ab60d299 SHA1: e2ae0a0a85a506d087bfaaa65afd2f8f38ff31f6 SHA256: de76e130b5b98ec7a71ffbd5a63ba8d380574fab04202d3bcca55e5f82a63836 SHA512: eb4ef4b9a238de374189a1ac6063504445310860f7cdc48529dec9d624affe28f8ca796b80a4c441adbc8bbf914aca38411f7674ffea9f8c3761347f134668db Homepage: https://cran.r-project.org/package=ezcox Description: CRAN Package 'ezcox' (Easily Process a Batch of Cox Models) A tool to operate a batch of univariate or multivariate Cox models and return tidy result. Package: r-cran-ezcutoffs Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-ggplot2, r-cran-lavaan, r-cran-moments, r-cran-progress Suggests: r-cran-boot Filename: pool/dists/resolute/main/r-cran-ezcutoffs_1.0.2-1.ca2604.1_all.deb Size: 53364 MD5sum: 1a8af2de0b0a734e8939f13074d289e8 SHA1: 72f2ee51808b78472fdd81cb88a2c5a6a32e5ec8 SHA256: e3738de4dca944a414b3e9d3ccb7268ee06d4c8f33f0e34fd5f11ae03394f3c2 SHA512: 049d6262d1011cbcc27f8e2e2db40f500488204898c816e6e3119d1dff901a0a09d2fd1f9963fbfef30b5f8f5998963d3053bbfc08ec510a9c9d888692b21414 Homepage: https://cran.r-project.org/package=ezCutoffs Description: CRAN Package 'ezCutoffs' (Fit Measure Cutoffs in SEM) Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. 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Package: r-cran-ezec Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-dplyr Suggests: r-cran-testthat, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ezec_1.0.2-1.ca2604.1_all.deb Size: 93766 MD5sum: 9db799c1fdfa08fe493378f3f315f7ae SHA1: 6deaf133d88788d8454be478e5b3c625aedf8b7c SHA256: 55ea463cfd070f3e3d486ab5145dad22b75466fd6e8c86801e04b0d65f2dee8c SHA512: 3ac35e377266f903fe707ea1fd03b6fb3e93f320dc921ecf312c3e4266d6c34e5d8bf03db2013e57ac1b458e5ed8e6d1402cc3646c577c686f4f26ba5628eae3 Homepage: https://cran.r-project.org/package=ezec Description: CRAN Package 'ezec' (Easy Interface to Effective Concentration Calculations) Because fungicide resistance is an important phenotypic trait for fungi and oomycetes, it is necessary to have a standardized method of statistically analyzing the Effective Concentration (EC) values. This package is designed for those who are not terribly familiar with R to be able to analyze and plot an entire set of isolates using the 'drc' package. Package: r-cran-ezecm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ellipse, r-cran-klar, r-cran-lhs, r-cran-mcmcpack, r-cran-rdpack, r-cran-mvnfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-laplacesdemon Filename: pool/dists/resolute/main/r-cran-ezecm_1.0.0-1.ca2604.1_all.deb Size: 333016 MD5sum: 3de0bef46a89d6ec07b9aa5fc35c0ec2 SHA1: 8a55c3fc520d2ce47ebd36036b164f3fccaca61a SHA256: 5c8cfd28af3970be590c78d2fd7ecd8edc533037a6c48d90d8945d7b8e5a4ab3 SHA512: 186c97e2195845fd3e64558f59fb8bb7eda8b5d07f36e56b003ac14e468bec6ac8cc7436c428786d80cdf5826a2baaee96b89f0835cdc40cd15c4a31c038a23e Homepage: https://cran.r-project.org/package=ezECM Description: CRAN Package 'ezECM' (Event Categorization Matrix Classification for NuclearDetonations) Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at . Package: r-cran-ezfragility Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epoch, r-cran-ggplot2, r-cran-viridis, r-cran-ggtext, r-cran-glue, r-cran-rlang, r-cran-foreach, r-cran-progress, r-cran-ramify, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dosnow, r-cran-gsignal Filename: pool/dists/resolute/main/r-cran-ezfragility_2.1.1-1.ca2604.1_all.deb Size: 2676862 MD5sum: 5c6456e590a452315bc218f7e870005a SHA1: 6c99d30735d50f4fb48ce0474621c9a606814aa7 SHA256: ffc6ba0e8c7b1d0c59716c35b9c9d00e6c1fe36802b3a3b7c4a2048888d4bbac SHA512: 5f41d7ddd9822cf99a705646ca9381e69db8431b24fa4551425fdfc890cd60cbc025d38f157507dfd26eaf58a2bf7b9bfb4864cd56981f07ad584d208ff80105 Homepage: https://cran.r-project.org/package=EZFragility Description: CRAN Package 'EZFragility' (Compute Neural Fragility for Ictal iEEG Time Series) Provides tools to compute the neural fragility matrix from intracranial electrocorticographic (iEEG) recordings, enabling the analysis of brain dynamics during seizures. The package implements the method described by Li et al. (2017) and includes functions for data preprocessing ('Epoch'), fragility computation ('calcAdjFrag'), and visualization. Package: r-cran-ezgp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ezgp_0.1.0-1.ca2604.1_all.deb Size: 798264 MD5sum: f554b13a086e0561e9742fb3e9fa5034 SHA1: 192b602dce1fcb0beeeadc6f5b00956dface1c2b SHA256: 0109a057da3f5c44b767d6324a9c0047ed303403f9cda10890d81a1446c248d7 SHA512: 5d66e3a4ca6501f090908a34a9f5cd684311b74f3aca7afceed5b427d7b38f3aaff36c9fee19af481259b13bfeb2bf7017d09adfc40f932b5665736e6fc9bc2e Homepage: https://cran.r-project.org/package=EzGP Description: CRAN Package 'EzGP' (Easy-to-Interpret Gaussian Process Models for ComputerExperiments) Fit model for datasets with easy-to-interpret Gaussian process modeling, predict responses for new inputs. The input variables of the datasets can be quantitative, qualitative/categorical or mixed. The output variable of the datasets is a scalar (quantitative). The optimization of the likelihood function can be chosen by the users (see the documentation of EzGP_fit()). The modeling method is published in "EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors" by Qian Xiao, Abhyuday Mandal, C. Devon Lin, and Xinwei Deng (2022) . Package: r-cran-ezknitr Architecture: all Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-markdown, r-cran-r.utils Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ezknitr_0.6.3-1.ca2604.1_all.deb Size: 44062 MD5sum: bec86a7dc3b02060eda605bea03da72b SHA1: fca47b9f77826618ebef78d28fb0d43288868138 SHA256: 54f781459a92346f761e4ce72308b333e3d4c29ebb3c2e0c45176d311d2666e2 SHA512: 21a5f24dcae542802f868b90c04ad01dfc4abd71989df2beb635b1551387252e94469fedeca3d72de3ac6d7aad5143b2892fc07837de64e43f9b1a365cc13521 Homepage: https://cran.r-project.org/package=ezknitr Description: CRAN Package 'ezknitr' (Avoid the Typical Working Directory Pain When Using 'knitr') An extension of 'knitr' that adds flexibility in several ways. 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Package: r-cran-ezmmek Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-assertable, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-nls2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ezmmek_0.2.4-1.ca2604.1_all.deb Size: 464508 MD5sum: eebcdcee8cb9e5835fb4746999f0d998 SHA1: b89a44c2952c33418c67d8b73dc573e1e61a56ee SHA256: 5226a34c961d8a8e7db007f8889bd26eda498db542df8dfc84672a3256c77c4c SHA512: 75fc01e4048af59fb53b18d5d1233b125f4296c7b7951138119df7ff9955827fe967fbcc101bab25100b3d6cf1fef82f23d3aaa4ef3d937f601011fb4aa3c1bd Homepage: https://cran.r-project.org/package=ezmmek Description: CRAN Package 'ezmmek' (Easy Michaelis-Menten Enzyme Kinetics) Serves as a platform for published fluorometric enzyme assay protocols. 'ezmmek' calibrates, calculates, and plots enzyme activities as they relate to the transformation of synthetic substrates. At present, 'ezmmek' implements two common protocols found in the literature, and is modular to accommodate additional protocols. Here, these protocols are referred to as the In-Sample Calibration (Hoppe, 1983; ) and In-Buffer Calibration (German et al., 2011; ). protocols. By containing multiple protocols, 'ezmmek' aims to stimulate discussion about how to best optimize fluorometric enzyme assays. A standardized approach would make studies more comparable and reproducible. Package: r-cran-ezplot Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lubridate, r-cran-rlang Suggests: r-cran-covr, r-cran-dt, r-cran-e1071, r-cran-ggrepel, r-cran-knitr, r-cran-miniui, r-cran-rmarkdown, r-cran-rocr, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-tsibbledata Filename: pool/dists/resolute/main/r-cran-ezplot_0.8.2-1.ca2604.1_all.deb Size: 694866 MD5sum: b53acf07ecd732e89b1487e6f5189917 SHA1: 1224a495397a4963c771288b9ec563ecaa58e2fa SHA256: 447749111efd4f9fe001db17bfc1db4402effe7bd8986ec22f8d9c3f31fb62c3 SHA512: 51b4676f929700d12bf880f1c19c56d7449effe6062d176d88c05ca2b274f72c9392b3e86b0dd89e4c1fbc9d07abfc8bcc8276c23d8f1f08c1ff35fae40a9ec8 Homepage: https://cran.r-project.org/package=ezplot Description: CRAN Package 'ezplot' (Functions for Common Chart Types) Wrapper for the 'ggplot2' package that creates a variety of common charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce typing. Package: r-cran-ezr Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-ggplot2, r-cran-ggridges, r-cran-moments, r-cran-shiny, r-cran-shinydashboard, r-cran-weights Filename: pool/dists/resolute/main/r-cran-ezr_0.1.5-1.ca2604.1_all.deb Size: 78044 MD5sum: 0c4b56c7e86a9e1fbf7548b82d9d3932 SHA1: f038f48261b6cc823fa6407861aef4e47a27781a SHA256: ccfa147994081e9efd562a3f6f69c78d84e81ede643ff64b0e0b5563d2d30da4 SHA512: 82cd182c83d53f8b3e8655ecd026339e700f71dbad1b4b51b1a6c4a7d2ef529526ae7cc9dd3848899490671553f2deb08d74574c46920d985be384575bbebaf2 Homepage: https://cran.r-project.org/package=ezr Description: CRAN Package 'ezr' (Easy Use of R via Shiny App for Basic Analyses of ExperimentalData) Runs a Shiny App in the local machine for basic statistical and graphical analyses. The point-and-click interface of Shiny App enables obtaining the same analysis outputs (e.g., plots and tables) more quickly, as compared with typing the required code in R, especially for users without much experience or expertise with coding. Examples of possible analyses include tabulating descriptive statistics for a variable, creating histograms by experimental groups, and creating a scatter plot and calculating the correlation between two variables. Package: r-cran-eztrack Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-geosphere, r-cran-leaflet, r-cran-adehabitathr, r-cran-magrittr, r-cran-htmltools, r-cran-ggplot2, r-cran-readxl, r-cran-kableextra, r-cran-dplyr Suggests: r-cran-knitr, r-cran-viridislite, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eztrack_0.1.0-1.ca2604.1_all.deb Size: 381870 MD5sum: 2e094f264a7d4aaf27e0f85c62603586 SHA1: b6f3b7aa5b006ffe7f3ecf1671a1de0beb3ee7b0 SHA256: b09220f93c41c7116272729ddfebc192afaac6ae195dcf8d32663d644a2d1de2 SHA512: 21c0265473a99707fa85449c97323f24ab9c211d1d0d03ff97ea2915d7d7b620cd3cae7b06b0c7f8fe7fad1f42c5efcc97d338d22b18c34245c10768aa0a81c4 Homepage: https://cran.r-project.org/package=ezTrack Description: CRAN Package 'ezTrack' (Exploring Animal Movement Data) Streamlines common steps for working with animal tracking data, from raw telemetry points to summaries, interactive maps, and home range estimates. Designed to be beginner-friendly, it enables rapid exploration of spatial and movement data with minimal wrangling, providing a unified workflow for importing, summarizing, and visualizing, and analyzing animal movement datasets. Package: r-cran-eztune Architecture: all Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2058 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ada, r-cran-e1071, r-cran-ga, r-cran-gbm, r-cran-optimx, r-cran-rpart, r-cran-glmnet, r-cran-rocr, r-bioc-biocstyle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mlbench, r-cran-doparallel, r-cran-dplyr, r-cran-yardstick, r-cran-rsample Filename: pool/dists/resolute/main/r-cran-eztune_3.1.1-1.ca2604.1_all.deb Size: 1493128 MD5sum: 7763bf0375eea3a590e6187b0c088c21 SHA1: 503ef3d7e84f185a1f8d2d6e0196a7c1b248d778 SHA256: 86763b5f8c91ef356f3683c21f6b28ee6a05d8cc886492c760dd4d4d7a98f773 SHA512: db5a19e7208d9fa37cad0b1e5ba0944fce8aba713f70a7f99d6304276449a51f4f2f1b5fd8660a5a7d221a601c961f2c2b390e21a717ecf1804250fe2fdeb1a1 Homepage: https://cran.r-project.org/package=EZtune Description: CRAN Package 'EZtune' (Tunes AdaBoost, Elastic Net, Support Vector Machines, andGradient Boosting Machines) Contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function eztune.cv will compute a cross validated error rate. The purpose of eztune_cv is to provide a cross validated accuracy or MSE when resubstitution or validation data are used for optimization because error measures from both approaches can be misleading. Package: r-cran-f1datar Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-glue, r-cran-magrittr, r-cran-tibble, r-cran-jsonlite, r-cran-httr2, r-cran-memoise, r-cran-janitor, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-lifecycle, r-cran-cli, r-cran-rappdirs, r-cran-cachem, r-cran-withr Suggests: r-cran-ggplot2, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-f1datar_2.0.1-1.ca2604.1_all.deb Size: 2612226 MD5sum: 61f7d6b75d1e95280d645e9610ac1677 SHA1: 96162a704fa6a999e7294a9b77fd213ae413f815 SHA256: 2dfc0a53d1db19a370814a4b64f3369400cd3edb701eb4bdace27e5358ccfa06 SHA512: 2b4cc86c889d7d5dcfb5fad192570da46f117273da154e9078760a81b9a2409a9274d9b5fc5e1d6ff78ccd6dce37583e07f492bc184fbcd43859182a4f9a87de Homepage: https://cran.r-project.org/package=f1dataR Description: CRAN Package 'f1dataR' (Access Formula 1 Data) Obtain Formula 1 data via the 'Jolpica API' and the unofficial API via the 'fastf1' 'Python' library . Package: r-cran-f1pits Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-readr, r-cran-tibble, r-cran-httr, r-cran-jsonlite, r-cran-f1datar Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-f1pits_1.3.1-1.ca2604.1_all.deb Size: 80206 MD5sum: fb06d3f6c1c3b3f55b7ff9255d13b5f6 SHA1: 3c8fb20638758f1fee9f3a756103286a7f2caccb SHA256: 241c5a1654d9380e3ccfd949da68392b716f4cfe3d3ccc975fed2379c22d5723 SHA512: 520d32844edd18450a41a5793d15207374f5f9be6113fe29366f12cf90050d975f23c053c034164765989cc7db278de4878d604e13003ef7cf27361ec5ff49cb Homepage: https://cran.r-project.org/package=f1pits Description: CRAN Package 'f1pits' (F1 Pit Stop Datasets) Formula 1 pit stop data. The package provides information on teams and drivers across seasons (2019 or higher). It also includes a function to visualize pit stop performance. Package: r-cran-faasr Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-cli, r-cran-jsonvalidate, r-cran-uuid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-faasr_2.0.0-1.ca2604.1_all.deb Size: 140138 MD5sum: 94f83cc0bdb4b51135f6405e06cc79b0 SHA1: f4778621e0e2338415999b68ea275a4830d27c0d SHA256: d921e7bd0f0e214466a2ac3c36b5d6a71c93857faa6617294b0f601b4fbf0151 SHA512: 4859ad22329fcb70d524299daf3314c427e955750fd00706cbbce14fe20695a87a73811aacd39ecda2ba701bbb31be2e13a8ebffa5d071fb8facf27ce767f78f Homepage: https://cran.r-project.org/package=FaaSr Description: CRAN Package 'FaaSr' ('FaaSr' Local Test Development Package) Provides a local execution environment for testing and developing the 'FaaSr' workflows without requiring cloud infrastructure. The 'FaaSr' package enables R developers to validate and test workflows locally before deploying to Function-as-a-Service (FaaS) platforms. Key features include: 1) Parsing and validating JSON workflow configurations compliant with the 'FaaSr' schema 2) Simulated S3 storage operations using local file system with local logging 3) Support for conditional branching 4) Support for parallel rank functions execution 5) Workflow cycle detection and validation 6) No cloud credentials or infrastructure required for testing This package is designed for development and testing purposes. For production deployment to cloud FaaS platforms, use the main 'FaaSr' package available at . Package: r-cran-fabci Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-fabci_0.3-1.ca2604.1_all.deb Size: 70622 MD5sum: a8cbe67c6e7e7f10dbee82e7e960ce43 SHA1: a1fe9574c81fa2c5fb968ee14e1fa51b9a6aa482 SHA256: a9f5705c0c6b40398888882766290b427f3d05c84b6a26873dcf3b88096db2d8 SHA512: 580df5724ff2f103ac3ca4d59f5df2978f51dde8f660bdeea2d3646838038a435e3cb470d15119a4d0eb4840efd9fea654e3ca5719372194dc89b6b3a7f86dfe Homepage: https://cran.r-project.org/package=fabCI Description: CRAN Package 'fabCI' (FAB Confidence Intervals) Frequentist assisted by Bayes (FAB) confidence interval construction. See 'Adaptive multigroup confidence intervals with constant coverage' by Yu and Hoff and 'Exact adaptive confidence intervals for linear regression coefficients' by Hoff and Yu . Package: r-cran-fabinference Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-fabinference_0.1-1.ca2604.1_all.deb Size: 67470 MD5sum: d5934e0e9be45e92156c8a70a4e466e8 SHA1: 923d31cc35e18cfa5115c1723ab12cf5889effb3 SHA256: caa42709b66917676bff87e26be2436122db66c5faaf6a3722a9062021b94413 SHA512: 814879a59db3ada63eedad3e037b5b9186d58ada1cd4c922682c564ce793b799edc7d20f2d0a6fd160f782f811563bd1a8147762eb597e397b7b9090f0f982ea Homepage: https://cran.r-project.org/package=FABInference Description: CRAN Package 'FABInference' (FAB p-Values and Confidence Intervals) Frequentist assisted by Bayes (FAB) p-values and confidence interval construction. See Hoff (2019) "Smaller p-values via indirect information", Hoff and Yu (2019) "Exact adaptive confidence intervals for linear regression coefficients", and Yu and Hoff (2018) "Adaptive multigroup confidence intervals with constant coverage". Package: r-cran-fabisearch Architecture: all Version: 0.0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nmf, r-cran-rgl, r-cran-reshape2, r-cran-foreach, r-cran-doparallel, r-cran-dorng Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fabisearch_0.0.4.5-1.ca2604.1_all.deb Size: 4449544 MD5sum: 1d8c682e162b17ae2c815ba1a2832416 SHA1: 682797e0282deba637a2c50d56c910e3af969d4b SHA256: 358a080546dc226edfe3bf1d297407bd3c690e4ac5add1ff2d08b79b8270006e SHA512: 61bb7de2cb185bfb95551aaad21a22c88fc560b0ee288360910d792fb23a1f9eb8c288d7fb7dfba093fa8a8c9b2f79384f9de47c96ceb6fd71b40e84debb8c8d Homepage: https://cran.r-project.org/package=fabisearch Description: CRAN Package 'fabisearch' (Change Point Detection in High-Dimensional Time Series Networks) Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and the location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. It requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021) . For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint. Package: r-cran-fable.ata Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fabletools, r-cran-ataforecasting, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tsibble, r-cran-distributional, r-cran-tsbox, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-fable.ata_0.0.6-1.ca2604.1_all.deb Size: 64374 MD5sum: be6383f84620ae66d2b3e5dcc52ef9cd SHA1: d54ed18c39bf61a6900f697cc71413466ef549f5 SHA256: bfc98e210012076ad789ae4dcadf06e4fdc4e760132cf38f3e6d4e07209b1061 SHA512: 6882154c5a1950ce27f64e323b18d9f2fb2dd98f78701629e629261b44ec63da4be3fe6de6ea758d9b3af98595535cad666b3e8169ea770ce070fbfdf3bf5b27 Homepage: https://cran.r-project.org/package=fable.ata Description: CRAN Package 'fable.ata' ('ATAforecasting' Modelling Interface for 'fable' Framework) Allows ATA (Automatic Time series analysis using the Ata method) models from the 'ATAforecasting' package to be used in a tidy workflow with the modeling interface of 'fabletools'. This extends 'ATAforecasting' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) ), an alternative to exponential smoothing (described in Yapar (2016) , Yapar et al. (2017) ), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). Package: r-cran-fable.prophet Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fabletools, r-cran-rlang, r-cran-tsibble, r-cran-lubridate, r-cran-prophet, r-cran-dplyr, r-cran-distributional Suggests: r-cran-tsibbledata, r-cran-testthat, r-cran-ggplot2, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fable.prophet_0.1.0-1.ca2604.1_all.deb Size: 510528 MD5sum: 743d15ab70f1fad58ae01b4cfae7dbd9 SHA1: 053a6720c1eae720b1ac1eb1ae27c60705c51d31 SHA256: f1a5fdd84f743f686d313e594f999f8ef7a7d807202ddb981ec88c50270a7596 SHA512: 5b0578b4daad99a336222af3cecb8ecf87c60bfbe8ea7ccf9364f12df3723c4b0f6b6d5a6f33dcc1f1180818a6deefc6b7d1cb862eb687119eb4e1ba20850741 Homepage: https://cran.r-project.org/package=fable.prophet Description: CRAN Package 'fable.prophet' (Prophet Modelling Interface for 'fable') Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. Package: r-cran-fablecount Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fabletools, r-cran-tscount, r-cran-glarma, r-cran-fable, r-cran-dplyr, r-cran-tsibble, r-cran-tibble, r-cran-tidyr, r-cran-distributional, r-cran-lubridate, r-cran-stringr, r-cran-tsibbledata Suggests: r-cran-rcpp, r-cran-rlang, r-cran-covr, r-cran-feasts, r-cran-forecast, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fablecount_0.1.0-1.ca2604.1_all.deb Size: 96774 MD5sum: 27ff008f78bd7ef0e368c00e639959ec SHA1: 8b36be13686e2e13a76820cd0ae60f93f9aa4674 SHA256: bffeb9bff7af3bf4195104784283e40a75c5a7ff0bb98ec21df0054039abe8ae SHA512: 43330f3ce91ebfb1e46d587e1fd70720f66879f02ac487ed7e082d0e1e23ee3c9aeae30c8f782125d78cd74b46fe01247ed5e2fea0a068922272a574c59b0742 Homepage: https://cran.r-project.org/package=fableCount Description: CRAN Package 'fableCount' (INGARCH and GLARMA Models for Count Time Series in FableFramework) Provides a tidy R interface for count time series analysis. It includes implementation of the INGARCH (Integer Generalized Autoregressive Conditional Heteroskedasticity) model from the 'tscount' package and the GLARMA (Generalized Linear Autoregressive Moving Averages) model from the 'glarma' package. Additionally, it offers automated parameter selection algorithms based on the minimization of a penalized likelihood. Package: r-cran-fabletools Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tsibble, r-cran-tibble, r-cran-ggplot2, r-cran-tidyselect, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-generics, r-cran-r6, r-cran-vctrs, r-cran-distributional, r-cran-progressr, r-cran-lifecycle, r-cran-ggdist, r-cran-scales, r-cran-cli Suggests: r-cran-covr, r-cran-crayon, r-cran-fable, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-pillar, r-cran-feasts, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tsibbledata, r-cran-lubridate, r-cran-urca, r-cran-mvtnorm, r-cran-matrix, r-cran-ggtime Filename: pool/dists/resolute/main/r-cran-fabletools_0.7.0-1.ca2604.1_all.deb Size: 687984 MD5sum: 230c87cbcbc82c572c3456ec533b5c7d SHA1: cd9e57bfdb9e095c7c98732510e09abe708f0316 SHA256: b242bb9da8c544e7a5debcc2619782a82f423409166d52ec7dbd286376a4637f SHA512: 97a3c79a57d8a44f888f49cc717a32f012016b01c11095ecc1759b5e179822d8cde6920b7bc60b13664bd8f82397f0eacae6cdabe2586c379ad7eb73a399347d Homepage: https://cran.r-project.org/package=fabletools Description: CRAN Package 'fabletools' (Core Tools for Packages in the 'fable' Framework) Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis. Package: r-cran-fabr Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-usethis, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-janitor, r-cran-fs, r-cran-readr, r-cran-readxl, r-cran-writexl, r-cran-haven, r-cran-lubridate, r-cran-bookdown, r-cran-xfun, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-fabr_2.1.1-1.ca2604.1_all.deb Size: 138644 MD5sum: 9ade8162d9b5295af56159c967e9b26e SHA1: 1fff21a862b3f2a7030553b8b093a25d80953be8 SHA256: cb8396f4c96ea58741fa1336abb811fa931ca8d0220951dd97080e7cb97f4481 SHA512: 33f9dc233ddd94cf992407597f9a69b85478000f065d71bdda28f352b2eecca055a46ee2fd7f0fac8f7692ea205af374dac745e35d71501f9dd3f0142f81a1b1 Homepage: https://cran.r-project.org/package=fabR Description: CRAN Package 'fabR' (Wrapper Functions Collection Used in Data Pipelines) The goal of this package is to provide wrapper functions in the data cleaning and cleansing processes. These function helps in messages and interaction with the user, keep track of information in pipelines, help in the wrangling, munging, assessment and visualization of data frame-like material. Package: r-cran-fabricatr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-data.table, r-cran-mvnfast, r-cran-mass, r-cran-extradistr Filename: pool/dists/resolute/main/r-cran-fabricatr_1.0.2-1.ca2604.1_all.deb Size: 146940 MD5sum: efd396ccddef6af09a10d3b99b963ead SHA1: fcf686024a5dffdcb9774509ffe8c5238657a594 SHA256: f5bbc74cc7e935787653a46cd5e68a31fdac501779fd2f7ba7c5bae67dca5f23 SHA512: 00988ac6d7321870c1851a8b4d1810e752650c645ebed731acfaa0af83bb118eda04ca026d177496c903f77feee9f2b677ab329f324f5cad7d5816a8e494306f Homepage: https://cran.r-project.org/package=fabricatr Description: CRAN Package 'fabricatr' (Imagine Your Data Before You Collect It) Helps you imagine your data before you collect it. Hierarchical data structures and correlated data can be easily simulated, either from random number generators or by resampling from existing data sources. This package is faster with 'data.table' and 'mvnfast' installed. 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Package: r-cran-facmodcs Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-xts, r-cran-zoo, r-cran-performanceanalytics, r-cran-lattice, r-cran-sn, r-cran-tseries, r-cran-robustbase, r-cran-robstattm Suggests: r-cran-pcra, r-cran-corrplot, r-cran-lmtest, r-cran-rugarch, r-cran-hh Filename: pool/dists/resolute/main/r-cran-facmodcs_1.0-1.ca2604.1_all.deb Size: 427244 MD5sum: d725acc7d0a3ac7a48511315c9a8e286 SHA1: a9873f83b83390d53d2a370ee80ec4f8b6b4e1b4 SHA256: 65437033eb9faf6ad1204663517acf161bf5fab799b21d65d53cd30ffa97b23f SHA512: 0a4a9b54a4ae773babf3f5f9e5fd98cbab247e16e813277cc7b587fb33621f730fbb43aa1efe116e476715d5be0eed93391eaec4437eeda51945ac5624603286 Homepage: https://cran.r-project.org/package=facmodCS Description: CRAN Package 'facmodCS' (Cross-Section Factor Models) Linear cross-section factor model fitting with least-squares and robust fitting the 'lmrobdetMM()' function from 'RobStatTM'; related volatility, Value at Risk and Expected Shortfall risk and performance attribution (factor-contributed vs idiosyncratic returns); tabular displays of risk and performance reports; factor model Monte Carlo. The package authors would like to thank Chicago Research on Security Prices,LLC for the cross-section of about 300 CRSP stocks data (in the data.table object 'stocksCRSP', and S&P GLOBAL MARKET INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and "factor exposures") fundamental data on the 300 companies in the data.table object 'factorSPGMI'. The 'stocksCRSP' and 'factorsSPGMI' data are not covered by the GPL-2 license, are not provided as open source of any kind, and they are not to be redistributed in any form. 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Unique in providing not only classical least squares, but also modern robust model fitting methods which are not much influenced by outliers. Includes returns and risk decompositions, with user choice of standard deviation, value-at-risk, and expected shortfall risk measures. "Robust Statistics Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, M. Salibian-Barrera (2019) . Package: r-cran-factchar Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-factchar_1.0-1.ca2604.1_all.deb Size: 69794 MD5sum: e5fbe59cf6b23fb0c00f3a427612b2e9 SHA1: 62d29cc79ce79c0be06bb89075e86c9c6febe85b SHA256: 04c59bcb062e45766449745266dbbf0f9ae9685826af8688d4f5e08ed19d4cd1 SHA512: 6d2419dc397f66932bd0090b5d9d402dfd18be700a8a6d41a0ff131a1349356b64b4af5aae3d0ee0c9f9604fe05459511592197cde90a09369b4420616b6b218 Homepage: https://cran.r-project.org/package=FactChar Description: CRAN Package 'FactChar' (Characterization and Diagnostic Tools for Factorial BlockDesigns) Description: Provides comprehensive tools for analysing and characterizing mixed-level factorial designs arranged in blocks. Includes construction and validation of incidence structures, computation of C-matrices, evaluation of A-, D-, E-, and MV-efficiencies, checking of orthogonal factorial structure (OFS), diagnostics based on Hamming distance, discrepancy measures, B-criterion, Es^2 statistics, J2-distance and J2-efficiency, Phi-p optimality, and symmetry conditions for universal optimality. The methodological framework follows foundational work on factorial and mixed-level design assessment by Xu and Wu (2001) , and Gupta (1983) . These methods assist in selecting, comparing, and studying factorial block designs across a range of experimental situations. 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This package includes tools to check whether a design has orthogonal factorial structure (OFS) with balance or not and is able to find the orthogonality deviation value if not having OFS. This package includes function to evaluate efficiency factor of all factorial effects in two situations, in the first situation if the design is verified with OFS and balance then calculate the efficiencies of all factorial effects using a specific analytical procedure and in the second situation if the design is verified with non-OFS and balance then a new general method has been developed and used to calculate efficiencies under the condition that the design should be proper and equi-replicated, See Gupta, S.C. and Mukerjee, R. (1987): "A Calculus for factorial arrangements". Lecture Notes in Statistics. No. 59, Springer-Verlag, Berlin, New York, . For the easy use of package, 'shiny' app is used for giving inputs and inputs validation. 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Package: r-cran-fake Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-mass, r-cran-rdpack, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fake_1.5.0-1.ca2604.1_all.deb Size: 365832 MD5sum: 93b690e0f0d9568cc1fe9ba507b2ba40 SHA1: 9db4809358b0eb41eadcb068a802f2ed16463507 SHA256: 8b4c187691e08455d291c29c840d5fdffd88b98a232e96853179c78f367a1f75 SHA512: 9d2c7585cd94e3f32ffc40a974eff0ac79e32b611aea0851a71813e32a29d408256d0b59863a28b68342aab3f7887a60c7b5961a2cbf5f1fbec43bb5584908b3 Homepage: https://cran.r-project.org/package=fake Description: CRAN Package 'fake' (Flexible Data Simulation Using the Multivariate NormalDistribution) This R package can be used to generate artificial data conditionally on pre-specified (simulated or user-defined) relationships between the variables and/or observations. Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures (B Bodinier et al (2021) ). 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Methods are related to approaches in Nowok, Raab and Dibben (2016) and the foundation-model overview by Bommasani et al. (2021) . Package: r-cran-fakemake Architecture: all Version: 1.11.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fritools, r-cran-igraph, r-cran-makefiler Suggests: r-cran-cleanr, r-cran-covr, r-cran-cyclocomp, r-cran-devtools, r-cran-hunspell, r-cran-knitr, r-cran-lintr, r-cran-pkgbuild, r-cran-pkgload, r-cran-rasciidoc, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rprojroot, r-cran-runit, r-cran-spelling, r-cran-testthat, r-cran-withr, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-fakemake_1.11.1-1.ca2604.1_all.deb Size: 74230 MD5sum: 8fa29b124a0d8707cf52bc2993af8846 SHA1: 0163d0980361cc4c40428eb941ad383e36e845d7 SHA256: 19fee3dda52abcb0b712cae2a5915f28fb501953bf42278f19370c1d4b3dcffd SHA512: 34695de9551b15dc2c2e4d5e1119522992ed8f0f24f3ca5d2e0f50ab896d493e50b695b6a768d910781af30eb351a69f1135ec0af756293516b09b0dfe259a5d Homepage: https://cran.r-project.org/package=fakemake Description: CRAN Package 'fakemake' (Mock the Unix Make Utility) Use R as a minimal build system. This might come in handy if you are developing R packages and can not use a proper build system. Stay away if you can (use a proper build system). Package: r-cran-fakir Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1740 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-attempt, r-cran-charlatan, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-withr Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fakir_1.0.0-1.ca2604.1_all.deb Size: 1602702 MD5sum: b5910bcebc19059771354c5b4aa4603b SHA1: e11d6cd833ff71d7d9bbf1bff1ad252021234ddd SHA256: 1554dd8cd108cff65521377929a303433fa7f84dd09e237c47d4847268e62668 SHA512: 623e098cb8d9fc649201cd1ea7ca82e3e02cb25ea2cebb4e28db0fb11739ff9b004317138729fb395fdd12f71022934a7d6eb2d9ac1aedc938f0bf1de39feac3 Homepage: https://cran.r-project.org/package=fakir Description: CRAN Package 'fakir' (Generate Fake Datasets for Prototyping and Teaching) Create fake datasets that can be used for prototyping and teaching. This package provides a set of functions to generate fake data for a variety of data types, such as dates, addresses, and names. It can be used for prototyping (notably in 'shiny') or as a tool to teach data manipulation and data visualization. Package: r-cran-fakmct Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fakmct_0.1.0-1.ca2604.1_all.deb Size: 43904 MD5sum: 9cd3367c3b6306765aa104d2b8c52766 SHA1: d80c8eccf6f6f7bf0bb86c0f8ebe2adcf4d35986 SHA256: 8d2def8576081267e6a1c81e1bc0b2f7b30c2d22801214990716294f82c12cdf SHA512: 774c489245ef5d08852fbf6b49a2a51eab6df8be683dd2a7f95a07bae1f7b369c842d97b8339b5dc5bfbcb8f1c18c0f67827f10736315ee5890de3f7f248590b Homepage: https://cran.r-project.org/package=fakmct Description: CRAN Package 'fakmct' (Fuzzy Adaptive Resonance Theory K-Means Clustering Technique) A set of function for clustering data observation with hybrid method Fuzzy ART and K-Means by Sengupta, Ghosh & Dan (2011) . Package: r-cran-fam.recrisk Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fam.recrisk_0.1-1.ca2604.1_all.deb Size: 34400 MD5sum: 4acd2ed1735ab17aadd4be5d30530d6f SHA1: 168294b50ea35870e9b979a113afb9787b6c4f02 SHA256: 29c16b068fbf141b8a5900121fc528781d23cb1f096abae0e9aee9e188c086c9 SHA512: cb741a171926aee3e30c289422ef859b4c79902dce83bf237941df97c5f0944a6f37fe7f5673f8742f49e25991e31b4aef177343d62c8886944a053861e18ad9 Homepage: https://cran.r-project.org/package=fam.recrisk Description: CRAN Package 'fam.recrisk' (Familial Recurrence Risk) Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families. Package: r-cran-fameta Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lipidms, r-cran-rmarkdown, r-cran-knitr, r-cran-accucor, r-cran-scales, r-cran-gtools, r-cran-minpack.lm, r-cran-tidyr, r-cran-plyr, r-cran-gplots Filename: pool/dists/resolute/main/r-cran-fameta_0.1.7-1.ca2604.1_all.deb Size: 410706 MD5sum: df547c5d8052b39611f03dd4a98e910e SHA1: 09aac9f1e6fc8ac3a52fe13522291f95db1cb234 SHA256: 9c6e5a4eb244263f3275c5f9ad933e72deaf853fda6f64c366a4e27f4ae2bf9f SHA512: 17bedf491d23e9a80328d7e855d2d12a7be9adb8cca053d75c0eb4042890c2deec5df0a467288834b7c3f0ca678039930e56bf708510796d40d66322ba9c9b82 Homepage: https://cran.r-project.org/package=FAMetA Description: CRAN Package 'FAMetA' (Fatty Acid Metabolic Analysis) Fatty acid metabolic analysis aimed to the estimation of FA import (I), de novo synthesis (S), fractional contribution of the 13C-tracers (D0, D1, D2), elongation (E) and desaturation (Des) based on mass isotopologue data. Package: r-cran-famevent Architecture: all Version: 3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-kinship2, r-cran-truncnorm, r-cran-eha, r-cran-pracma, r-cran-cmprsk, r-cran-matrixcalc Filename: pool/dists/resolute/main/r-cran-famevent_3.3-1.ca2604.1_all.deb Size: 547986 MD5sum: 96596b08819ce9dcdbc4a54e5eb9479b SHA1: 43002205d821f1245828badaa2323ac4a1d4a6b4 SHA256: cce09351be265d8be47aaec7bacba5baa6110d9188e92dfc6141deaeaa005719 SHA512: 4ee17003ff13b89a91fdce7c0c8bd8a0aa1a0f55a57638ff94176b6bc3739cf33fc8b22924d328fa3e8595d158a93d9673daf43bd7174e967cea0700fbd9299d Homepage: https://cran.r-project.org/package=FamEvent Description: CRAN Package 'FamEvent' (Family Age-at-Onset Data Simulation and Penetrance Estimation) Simulates age-at-onset traits associated with a segregating major gene in family data obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment correction is utilized to estimate age-dependent penetrance functions either parametrically from the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm can infer missing genotypes and carrier probabilities estimated from family's genotype and phenotype information or from fitted models. Plot functions include pedigrees of simulated families and predicted penetrance curves based on specified parameter values. For more information see Choi, Y.-H., Briollais, L., He, W. and Kopciuk, K. (2021) FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs, Journal of Statistical Software 97 (7), 1-30. Package: r-cran-familial Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-depthproc, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/resolute/main/r-cran-familial_1.0.7-1.ca2604.1_all.deb Size: 76968 MD5sum: 5dad92011e19f9361da2484fed849244 SHA1: 3e1d45882bf5046d72e337bc5c1b15b2a92ec3b5 SHA256: 7ceb3a08bc1ab878d35c43642443bd464b34b50f5c2ed3746e54d2b5ede201c5 SHA512: 069f7b015548fc0cbb85d458f78b9ef69d177df9d1ce744e1669c75d82bcd89b2a54c53a6cc45dff7931f89bef1c926de7602aae3e4c23775cb1e02c0a287893 Homepage: https://cran.r-project.org/package=familial Description: CRAN Package 'familial' (Statistical Tests of Familial Hypotheses) Provides functionality for testing familial hypotheses. Supports testing centers belonging to the Huber family. Testing is carried out using the Bayesian bootstrap. One- and two-sample tests are supported, as are directional tests. Methods for visualizing output are provided. Package: r-cran-familiar Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6609 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rlang, r-cran-rstream, r-cran-survival Suggests: r-cran-bart, r-cran-callr, r-cran-cluster, r-cran-corelearn, r-cran-coro, r-cran-dynamictreecut, r-cran-e1071, r-cran-fastcluster, r-cran-fastglm, r-cran-ggplot2, r-cran-glmnet, r-cran-gtable, r-cran-harmonicmeanp, r-cran-isotree, r-cran-knitr, r-cran-labeling, r-cran-lagp, r-cran-maxstat, r-cran-microbenchmark, r-cran-nnet, r-cran-paletteer, r-cran-power.transform, r-cran-praznik, r-cran-proxy, r-cran-randomforestsrc, r-cran-ranger, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-xml2, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-familiar_2.0.1-1.ca2604.1_all.deb Size: 4564368 MD5sum: c7eff474b548a28c72a7a281d8b0e40c SHA1: ade8d441c70b6b59fe0022c366b41ff2d5605a84 SHA256: c574d4af5c63a0f1043c337273390ca3302d7ee4113d9524c32c437da8e6cce7 SHA512: 78f79d94e74fd4a51e58f4ff84599e7bfc6471f58c24110f44a921b555f9e9cabb572e4d483b6205ca83a162f786f6bdba77bd5ce97a619db936ed993b74f8e8 Homepage: https://cran.r-project.org/package=familiar Description: CRAN Package 'familiar' (End-to-End Automated Machine Learning and Model Evaluation) Single unified interface for end-to-end modelling of regression, categorical and time-to-event (survival) outcomes. Models created using familiar are self-containing, and their use does not require additional information such as baseline survival, feature clustering, or feature transformation and normalisation parameters. Model performance, calibration, risk group stratification, (permutation) variable importance, individual conditional expectation, partial dependence, and more, are assessed automatically as part of the evaluation process and exported in tabular format and plotted, and may also be computed manually using export and plot functions. Where possible, metrics and values obtained during the evaluation process come with confidence intervals. Package: r-cran-families Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-msm, r-cran-reshape, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lubridate, r-cran-xml2, r-cran-plyr, r-cran-virtualpop Filename: pool/dists/resolute/main/r-cran-families_2.0.2-1.ca2604.1_all.deb Size: 478942 MD5sum: 7925495f5bf43d5907bc6b7c81f79403 SHA1: bf83da186a195e27b626dc55879e1b4513aee4ae SHA256: a72b5a966a75f11280d8fa3242f859a9c331105042394852844b9bfac9be8939 SHA512: 8714f5b29c86aa0a3c3cc34719660c170cb4435c586cf7f13f9e274458f8cff3b0595a5ed55e643e7b7a485701440b29769bc8a7ca414ab57f2afee685c1edfe Homepage: https://cran.r-project.org/package=Families Description: CRAN Package 'Families' (Kinship Ties in (Virtual) Multi-Generation Populations) Tools to study lineages, grandparenthood, loss of close relatives, kinship networks and other topics in multi-generation populations. Package: r-cran-famish Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1905 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-distionary, r-cran-fitdistrplus, r-cran-ismev, r-cran-lmom, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-famish_0.2.0-1.ca2604.1_all.deb Size: 1842624 MD5sum: f136f2811d65812ff31065f2a7b17352 SHA1: c9b51d41030d4d50ed0e613fa1e2842a667d8ebb SHA256: 72de0750920e49b18917b8a0be12b2e6f91dfedec3faa9c32d0681da0ce34506 SHA512: 3090df6ca10ad865073f4fa4f453566dd8324e294eb284c77593165f2fe152347cb0be562bf94331890e385f2a6c58e5086ed35d55347e58d93a38308bd84e33 Homepage: https://cran.r-project.org/package=famish Description: CRAN Package 'famish' (Flexibly Tune Families of Probability Distributions) Fits probability distributions to data and plugs into the 'probaverse' suite of R packages so distribution objects are ready for further manipulation and evaluation. Supports methods such as maximum likelihood and L-moments, and provides diagnostics including empirical ranking and quantile score. Package: r-cran-famos Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-r.utils Suggests: r-cran-future.batchtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-famos_0.3.1-1.ca2604.1_all.deb Size: 152406 MD5sum: acabf1a51e548710d24d26b273f6681d SHA1: 38281e787d36a460d4c729e15914cbda3b7c5f7b SHA256: 8e85ff2c356e96703949997a69a3817709efbe0483a134b0a916c11ea77a21b0 SHA512: 6ad8040f511e4b77fb8a99062a412e47b9ffcd0d80168c32dea374f164749561ef3053cb40713d9dae90f213f040a9c0ac0c780ec64e4b51f5150139f1a7789d Homepage: https://cran.r-project.org/package=FAMoS Description: CRAN Package 'FAMoS' (A Flexible Algorithm for Model Selection) Given a set of parameters describing model dynamics and a corresponding cost function, FAMoS performs a dynamic forward-backward model selection on a specified selection criterion. It also applies a non-local swap search method. Works on any cost function. For detailed information see Gabel et al. (2019) . Package: r-cran-famskatrc Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-kinship2, r-cran-coxme, r-cran-bdsmatrix Filename: pool/dists/resolute/main/r-cran-famskatrc_1.1.0-1.ca2604.1_all.deb Size: 41308 MD5sum: f85ca61641d73908f30e4485d2245719 SHA1: 6bec8d423d136d2f25ef687ba9980d42a6d0e117 SHA256: 9814c85b2cf847d6aec9d11a615101779098ef5864b7f531b89bc463255059b9 SHA512: 7631da2d8a46bef87abd789b5afd8f34f01873f2e9786c916a58ca5a497c7f3d3ee70fa9565e4cb1c1d6c1d6ec0354bfe927738eafe6ad1f4bece8b15b481419 Homepage: https://cran.r-project.org/package=famSKATRC Description: CRAN Package 'famSKATRC' (Family Sequence Kernel Association Test for Rare and CommonVariants) FamSKAT-RC is a family-based association kernel test for both rare and common variants. This test is general and several special cases are known as other methods: famSKAT, which only focuses on rare variants in family-based data, SKAT, which focuses on rare variants in population-based data (unrelated individuals), and SKAT-RC, which focuses on both rare and common variants in population-based data. When one applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one applies famSKAT-RC and set the kinship matrix (fullkins) to the identity matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also include a small sample synthetic pedigree to demonstrate the method with. For more details see Saad M and Wijsman EM (2014) . Package: r-cran-fancova Architecture: all Version: 0.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fancova_0.6-1-1.ca2604.1_all.deb Size: 99564 MD5sum: 944685fffc71e163459ea640a25a29b9 SHA1: 601f1c0003049852ae525f4827da0ed68b8eef4c SHA256: b6ddaf080cc4a457995436065eda11c184ed095fc9421dd0f2d991bc78a01022 SHA512: 6d8a6b7f7e228f5508dd1795bab0b7d11ef20ec3c9c92113ed1f9d11ca3fc13514feb747b4ac2210c86ae4ed9586e77f62aea914c60fc5f7b3b53ce62acfc2c1 Homepage: https://cran.r-project.org/package=fANCOVA Description: CRAN Package 'fANCOVA' (Nonparametric Analysis of Covariance) A collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. 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Package: r-cran-fastfocal Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-withr, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-fastfocal_0.1.3-1.ca2604.1_all.deb Size: 303964 MD5sum: 8d4d51437169bb315f7dff5f8d679e15 SHA1: 7c46757f1a459bcd9f0d6474cbdd70a31e082f83 SHA256: 16341332717b6146f62924fe212c3f85f937cb146e3da1c476a7dcd94e30b8d4 SHA512: 7e156cee896a67238b0b82f431799a886d3c58b12227583f6412eafe6a507a17f1849648f03b732efa409074739645f183300783c77b532168e43d6283713abe Homepage: https://cran.r-project.org/package=fastfocal Description: CRAN Package 'fastfocal' (Fast Multiscale Raster Extraction and Moving Window Analysiswith FFT) Provides fast moving-window ("focal") and buffer-based extraction for raster data using the 'terra' package. 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Package: r-cran-fastgraph Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-jmuoutlier Filename: pool/dists/resolute/main/r-cran-fastgraph_2.1-1.ca2604.1_all.deb Size: 87074 MD5sum: 687a1dafc10d2d67f8290043910e6059 SHA1: 1066f2129574e0327d6b622ecb3aa505e7ec268f SHA256: bbf455bd2c720373dd6ac63b9207b590d068a1824e19fda302fc3e65a057d086 SHA512: 579d8a3442c7544891051e07868f11f932d8aa2f3172f440a3f3f5c765e1796a762a9d10f373ee3b2fc6507ecb2135afb038eae3493d19cdcd2bc02bb461a65f Homepage: https://cran.r-project.org/package=fastGraph Description: CRAN Package 'fastGraph' (Fast Drawing and Shading of Graphs of Statistical Distributions) Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title. Package: r-cran-fastimputation Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat, r-cran-caret, r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-fastimputation_2.2.1-1.ca2604.1_all.deb Size: 623834 MD5sum: 1105d47c24f21d940ae3e2ea1563f312 SHA1: e10a9b545819bfe116d142e797115bd5cf25a611 SHA256: b45af2ca7fa5238eb2d71bfb99abf773e02ebd79072c034d6b9a4644cf241407 SHA512: 7380785accb40e3afffb4c215280db9078a2063e41289acf45413b87a38d7bc3ca2eb0b100e85d55e6c17c05322294f45fd760d9ffa1d9cb6c5f31996ce30351 Homepage: https://cran.r-project.org/package=FastImputation Description: CRAN Package 'FastImputation' (Learn from Training Data then Quickly Fill in Missing Data) TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class 'FastImputationPatterns'. FastImputation() function uses this 'FastImputationPatterns' object to impute (make a good guess at) missing data in a single line or a whole data frame of data. This approximates the process used by 'Amelia' but is much faster when filling in values for a single line of data. Package: r-cran-fastkm Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rarpack Suggests: r-cran-skat, r-cran-survival Filename: pool/dists/resolute/main/r-cran-fastkm_1.2-1.ca2604.1_all.deb Size: 87904 MD5sum: d1ca7dfa2f929a5487d0698e57bb5fa7 SHA1: ecd7a90a0aaef0631e85f9b7f82bd1c98d7a9b8a SHA256: 85e40e2edf203539f8df756c1a05383e55e70e3d5d198077070e3d399d988c9c SHA512: ae9b23c0c2f265b9b96a1d8224273cf69caeaa90cf9310e701fe882dd61d55bae70557a055031d6bdc7488429e10f98d2489ad36f9aba566e23f46605b855ff7 Homepage: https://cran.r-project.org/package=FastKM Description: CRAN Package 'FastKM' (A Fast Multiple-Kernel Method Based on a Low-Rank Approximation) A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from . Package: r-cran-fastknn Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pdist, r-cran-assertthat Filename: pool/dists/resolute/main/r-cran-fastknn_0.0.1-1.ca2604.1_all.deb Size: 19636 MD5sum: 336c39b3f0c98fad23195cce55d49ab1 SHA1: 1be6dd19ae4d52f55067ec00590299a9362f4356 SHA256: 8786d68d80849d5f52ea37bb1679be24e7dbde4ae306752df462b109557900b0 SHA512: 165278da8dfab9e5c847d3428d57bc9c0b3571b79b8e8f4eacec484a0e9459ee86f6f8301a2b5eae912251a01dd21335d0abcd1e9ab0f40cd1205d64802f55a8 Homepage: https://cran.r-project.org/package=FastKNN Description: CRAN Package 'FastKNN' (Fast k-Nearest Neighbors) Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time. Package: r-cran-fastlogitme Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fastlogitme_0.1.0-1.ca2604.1_all.deb Size: 24972 MD5sum: e9853410ac8459fc1f66be96cd6bd99e SHA1: 8f796200006f545250074bc39edc1587cf4eee83 SHA256: 69e8f7feff20ae441698a41d44d7c78fb35e2c0081fe09e327050a42c603a89e SHA512: 3931f34405e1549d443653d6ebb4068fcd4faabf3d227897252cd81495dfb756f93f94fa7f5d92739f9586379ae08e30fafb4787dbc79052fc516d31beb8d9a5 Homepage: https://cran.r-project.org/package=fastlogitME Description: CRAN Package 'fastlogitME' (Basic Marginal Effects for Logit Models) Calculates marginal effects based on logistic model objects such as 'glm' or 'speedglm' at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power. Package: r-cran-fastml Architecture: all Version: 0.7.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-recipes, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-rsample, r-cran-parsnip, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-tibble, r-cran-rlang, r-cran-dials, r-cran-rcolorbrewer, r-cran-dofuture, r-cran-foreach, r-cran-finetune, r-cran-future, r-cran-viridislite, r-cran-magrittr, r-cran-proc, r-cran-janitor, r-cran-stringr, r-cran-broom, r-cran-tidyr, r-cran-purrr, r-cran-survival, r-cran-xgboost Suggests: r-cran-testthat, r-cran-withr, r-cran-baguette, r-cran-c50, r-cran-dalex, r-cran-discrim, r-cran-fairmodels, r-cran-flexsurv, r-cran-ibreakdown, r-cran-iml, r-cran-lime, r-cran-modelstudio, r-cran-pdp, r-cran-plsmod, r-cran-probably, r-cran-ranger, r-cran-rstpm2, r-cran-survrm2, r-cran-aorsf, r-cran-censored, r-cran-crayon, r-cran-kernlab, r-cran-klar, r-cran-kknn, r-cran-keras, r-cran-lightgbm, r-cran-rstanarm, r-cran-patchwork, r-cran-ggally, r-cran-glmnet, r-cran-themis, r-cran-dt, r-cran-upsetr, r-cran-vim, r-cran-dbscan, r-cran-gridextra, r-cran-htmlwidgets, r-cran-kableextra, r-cran-moments, r-cran-naniar, r-cran-plotly, r-cran-scales, r-cran-skimr, r-cran-sparsediscrim, r-cran-knitr, r-cran-rmarkdown, r-cran-pec Filename: pool/dists/resolute/main/r-cran-fastml_0.7.8-1.ca2604.1_all.deb Size: 1118256 MD5sum: 108906bc0883315855da294b06a5180f SHA1: fbd40186a73816fa12ae8ae85e600eccfa033df2 SHA256: 80da5369352bf75971b0857cc150a44db84a618db824ee6419160c771de647f1 SHA512: ea195aa69bd8ffef381a7f925c1069d279584c562fe84bb672a48b7a622272e3521d2b8012bfed83e1df2d93dd7cec68fa7f4205b3f13aec58e10c2b5b3738b2 Homepage: https://cran.r-project.org/package=fastml Description: CRAN Package 'fastml' (Guarded Resampling Workflows for Safe and Automated MachineLearning in R) Provides a guarded resampling workflow for training and evaluating machine-learning models. When the guarded resampling path is used, preprocessing and model fitting are re-estimated within each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates with established engines in the 'tidymodels' ecosystem, and aims to improve evaluation reliability by coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight AutoML-style workflow by automating model training, resampling, and tuning across multiple algorithms, while keeping evaluation design explicit and user-controlled. Package: r-cran-fastnaivebayes Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fastnaivebayes_2.2.1-1.ca2604.1_all.deb Size: 1215156 MD5sum: f8bf0cc58c5dd5b5dd51fd9c3c501ebc SHA1: c7e9264ef5f5d5b66fc78905a552cb48fbd5df7a SHA256: 90385d86ef71f96dd0800f6bc3ad393effbb474305f00fea193c3cd3357855c3 SHA512: 0709d28b05f9a1ff215a1c30e17b38ab4086ace3c83f2e94c6ab00418ac2b67e2b6cff7bfba95edb0af89c38262556f97de2e953079ad2c6241009fbe59ca6e6 Homepage: https://cran.r-project.org/package=fastNaiveBayes Description: CRAN Package 'fastNaiveBayes' (Extremely Fast Implementation of a Naive Bayes Classifier) This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) . Any issues can be submitted to: . Package: r-cran-fastnet Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-tidygraph Filename: pool/dists/resolute/main/r-cran-fastnet_1.0.0-1.ca2604.1_all.deb Size: 179526 MD5sum: a0e3553635885216a9c4ee8468138255 SHA1: 82aa337d31cbdfc36e168c7656903cb762b12843 SHA256: 6a29574e7f348e10e7c0064e9e5a94aea1a00e4a0f7ccd0d9498320e1fd85501 SHA512: 86582d4ca584a8c3bd0919ecd4623bfc1371a6d3807cefaf37460774a04d865f1c1ef3b165cd90e379ddfc7c0615942c77c7516ff0dda407a3552328e5e83f2a Homepage: https://cran.r-project.org/package=fastnet Description: CRAN Package 'fastnet' (Large-Scale Social Network Analysis) We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics. Details can be found in the accompanying scientific paper on the Journal of Statistical Software, . Package: r-cran-fastonlinecpt Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-fastonlinecpt_1.0-1.ca2604.1_all.deb Size: 133418 MD5sum: 052f669b56202a4e3aa0a8d48966e220 SHA1: f1290a3472e2e9052137b6af67a3a6a7b241ffc8 SHA256: ce700d240b793144b21026cb2621f7a1c2794cae2e80b625aa4aa84e8254f317 SHA512: 2ecde872413cb71fb7240861dc75f45a678748cce1f93858102594a3ed41b4b23ad600c6434325f9997b1c9702b683e85c27cbe0f5d4fe81cfc564861e0d8636 Homepage: https://cran.r-project.org/package=fastOnlineCpt Description: CRAN Package 'fastOnlineCpt' (Online Multivariate Changepoint Detection) Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay. Package: r-cran-fastqcr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gridextra, r-cran-ggplot2, r-cran-magrittr, r-cran-readr, r-cran-rmarkdown, r-cran-rvest, r-cran-tibble, r-cran-tidyr, r-cran-scales, r-cran-xml2, r-cran-rlang Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-fastqcr_0.1.3-1.ca2604.1_all.deb Size: 1282044 MD5sum: 00f01596c9107c66ea405430038403e4 SHA1: 3f022be6c3b5aa56ac61336789ed41941c5d8698 SHA256: bb5f8e96569350d7df68550b3326997a2a558185c399fb057395e62e0bfa82ef SHA512: 89b3ce9d5e21ca66a83dcf3b4633ab2571a4f0cec6be32920441bb5d79754f0992fe36b985b487fc000a4e7fafa50951829c87aea02faba0943bbe4a5d9c7c0e Homepage: https://cran.r-project.org/package=fastqcr Description: CRAN Package 'fastqcr' (Quality Control of Sequencing Data) 'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data. It produces, for each sample, an html report and a compressed file containing the raw data. If you have hundreds of samples, you are not going to open up each 'HTML' page. You need some way of looking at these data in aggregate. 'fastqcr' Provides helper functions to easily parse, aggregate and analyze 'FastQC' reports for large numbers of samples. It provides a convenient solution for building a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations. Package: r-cran-fastqrs Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-copula Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sampleselection, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-fastqrs_1.0.0-1.ca2604.1_all.deb Size: 264308 MD5sum: 0c1edeba65cd1e6a8c44a833fe869f8c SHA1: d0b4242cbe145ea6c1915ec63189a36512bac353 SHA256: 0e02edce6ef625f72ccf8abf2ca287660d5a6003d6f5627de449f05e7e6652c3 SHA512: 0de739d8e165592a7bbd7603f6174154d7bb8739f1e5c5ccdd9de333fb990b80d83b46944ce3164bff971da345a847c2dc6ae3e95f86531d7ec89d5fbcef2a72 Homepage: https://cran.r-project.org/package=fastqrs Description: CRAN Package 'fastqrs' (Fast Algorithms for Quantile Regression with Selection) Fast estimation algorithms to implement the Quantile Regression with Selection estimator and the multiplicative Bootstrap for inference. This estimator can be used to estimate models that feature sample selection and heterogeneous effects in cross-sectional data. For more details, see Arellano and Bonhomme (2017) and Pereda-Fernández (2024) . Package: r-cran-fastr2 Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2083 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mosaic, r-cran-maxlik, r-cran-numderiv, r-cran-dplyr, r-cran-ggplot2, r-cran-lattice, r-cran-misctools Suggests: r-cran-ggformula, r-cran-mosaiccalc, r-cran-tidyr, r-cran-readr, r-cran-mass, r-cran-faraway, r-cran-hmisc, r-cran-daag, r-cran-multcomp, r-cran-vcd, r-cran-car, r-cran-alr4, r-cran-corrgram, r-cran-bradleyterry2, r-cran-cubature, r-cran-knitr, r-cran-mosaicdata, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fastr2_1.2.5-1.ca2604.1_all.deb Size: 1151056 MD5sum: 1cd5e8279bcdd542003aac08e95c0fa8 SHA1: 8d94520f6537f645c8de228c05b2d1557dca644a SHA256: ee897b30834740be0dba3a37e13edd1eda2cc5eab28ff4d30a99ba9ba856d20f SHA512: 61d53f1d401656c451334b1b027cb4bf087205c058c7750c7b78a396f9680bc53596e370bf57ab60f4a24b466856e9190e14dff3f1999241fe46ced64634e285 Homepage: https://cran.r-project.org/package=fastR2 Description: CRAN Package 'fastR2' (Foundations and Applications of Statistics Using R (2nd Edition)) Data sets and utilities to accompany the second edition of "Foundations and Applications of Statistics: an Introduction using R" (R Pruim, published by AMS, 2017), a text covering topics from probability and mathematical statistics at an advanced undergraduate level. R is integrated throughout, and access to all the R code in the book is provided via the snippet() function. Package: r-cran-fastreg Architecture: all Version: 0.8.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-haven, r-cran-osdc, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-uuid Suggests: r-cran-crew, r-cran-dbplyr, r-cran-devtools, r-cran-duckdb, r-cran-qs2, r-cran-quarto, r-cran-targets, r-cran-testthat, r-cran-tidyselect, r-cran-withr Filename: pool/dists/resolute/main/r-cran-fastreg_0.8.17-1.ca2604.1_all.deb Size: 730986 MD5sum: 205f82bb6450123d8a8024e89fa894c6 SHA1: 00f3df3f72fbfd1d2cc9837dcdee9c8d4472579a SHA256: 33bfbc20dab284c4c1de8bc23978cac3556f104218bf9ae9d07eb5f140d8ba11 SHA512: 7c724104e9127a2709ceaa2ee3e8f56f51994a627a1565ad4435ff11d69529d467877a36339937f069ae02538e6ea76abb87fe41b50a80baafe710dd2c833274 Homepage: https://cran.r-project.org/package=fastreg Description: CRAN Package 'fastreg' (Fast Conversion and Querying of Danish Registers with 'Parquet') Converts large Danish register files ('sas7bdat') into 'Parquet' format with year-based 'Hive' partitioning and chunked reading for larger-than-memory files. Supports parallel conversion with a 'targets' pipeline and reading those registers into 'DuckDB' tables for faster querying and analyses. Package: r-cran-fastrep Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-janitor, r-cran-kableextra, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fastrep_0.7-1.ca2604.1_all.deb Size: 117852 MD5sum: 12f00762dab63c0a4e012b755cc6f82c SHA1: a10b6fa9ca2b0a7a4eb1db40079efc00b2894b90 SHA256: 0f18d794004a45d805c5450c88568ea12e1bb10e2081cd275730c6a63b80cb54 SHA512: 96f9a49e78775f9e6f63044e499c939274b2444da3a9996a1f779341ebb83af7f540c9822b9a9d00407f3bb5dac4a3e871da35abfa2c6f243eeaa6304dbc9a8d Homepage: https://cran.r-project.org/package=fastrep Description: CRAN Package 'fastrep' (Time-Saving Package for Creating Reports) Provides templates for reports in 'rmarkdown' and functions to create tables and summaries of data. Package: r-cran-fastrerandomize Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-assertthat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fastrerandomize_0.3-1.ca2604.1_all.deb Size: 2084144 MD5sum: 4127f276c616534fe12fc1494d16f217 SHA1: 2d578fadcd3c2d3363da26f10ccc7880d3ac3218 SHA256: 1ad0efadee8f0fafc9f22a5d57fdc452c0e398fb990c10091e090b47b7115ce3 SHA512: c1e1962e078c1ad41e0a4b14af78954d3bf146b0a4d2ff8480085b6bdb7858bfb4fae4d93f8fc445be9d84bc9fd42eba5ac546eb87f9eb2646e78b0b8029be43 Homepage: https://cran.r-project.org/package=fastrerandomize Description: CRAN Package 'fastrerandomize' (Hardware-Accelerated Rerandomization for Improved Balance) Provides hardware-accelerated tools for performing rerandomization and randomization testing in experimental research. Using a 'JAX' backend, the package enables exact rerandomization inference even for large experiments with hundreds of billions of possible randomizations. Key functionalities include generating pools of acceptable rerandomizations based on covariate balance, conducting exact randomization tests, and performing pre-analysis evaluations to determine optimal rerandomization acceptance thresholds. The package supports various hardware acceleration frameworks including 'CPU', 'CUDA', and 'METAL', making it versatile across accelerated computing environments. This allows researchers to efficiently implement stringent rerandomization designs and conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) . Package: r-cran-fastret Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-cluster, r-cran-data.table, r-cran-dt, r-cran-future, r-cran-ggplot2, r-cran-glmnet, r-cran-htmltools, r-cran-openxlsx, r-cran-promises, r-cran-rcdk, r-cran-rlang, r-cran-shiny, r-cran-shinyhelper, r-cran-shinyjs, r-cran-withr, r-cran-xgboost Suggests: r-cran-callr, r-cran-caret, r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-languageserver, r-cran-lintr, r-cran-pkgdown, r-cran-pkgbuild, r-cran-pkgload, r-cran-rmarkdown, r-cran-servr, r-cran-tibble, r-cran-testthat, r-cran-toscutil, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-fastret_1.3.0-1.ca2604.1_all.deb Size: 1496918 MD5sum: aa757b8e8f2ef9e796c8d59c63b7bb72 SHA1: ac0830f97a44f5817b0ef223c4901b4e1ba79fa2 SHA256: 19a1e0117793965cbe4d63ad4e9f4a2f51a3a2ca5a7379e823544934667f42f0 SHA512: 525f1a27242f38826deddb2574c03073cc798c6d714c9b67b8fc1118f10dff22b43a87385dbde27631422f49dfb2bad7dd480a40d217563726042462b766bbe1 Homepage: https://cran.r-project.org/package=FastRet Description: CRAN Package 'FastRet' (Retention Time Prediction in Liquid Chromatography) A framework for predicting retention times in liquid chromatography. Users can train custom models for specific chromatography columns, predict retention times using existing models, or adjust existing models to account for altered experimental conditions. The provided functionalities can be accessed either via the R console or via a graphical user interface. Related work: Bonini et al. (2020) . Package: r-cran-fastrg Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-igraph, r-cran-rlang, r-cran-rspectra, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr Suggests: r-cran-covr, r-cran-irlba, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fastrg_0.4.0-1.ca2604.1_all.deb Size: 498154 MD5sum: 107301a8b18c846acdbf1bd708152c83 SHA1: 9d1f9f711c7afec70b0aed3a91682e4d33e694bf SHA256: fe1e3776de6e22207311107b460abdc37bd58e674838261b2f45a2e40d2e6b75 SHA512: ffbaa3fa69dac85000197376ffaecd2076791eb33effb79121610a7f08ef98d8f5e4f08969a16b993f2d33ecb46c8c54d22ec88b280ee8c5afd19c71a7f82b61 Homepage: https://cran.r-project.org/package=fastRG Description: CRAN Package 'fastRG' (Sample Generalized Random Dot Product Graphs in Linear Time) Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) . The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes. Package: r-cran-fastrhockey Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2094 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-progressr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-testthat, r-cran-usethis, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-fastrhockey_0.4.0-1.ca2604.1_all.deb Size: 2078888 MD5sum: e580f3b49599825ba858364cde9d8c2d SHA1: 9521331cc7449006c23759ae757010225aafae0d SHA256: fe20e94315b19db40efeededef174d7803d0d860ab205b8b076ca2931bd05dd4 SHA512: 3d20220204c6620b0bcaba341a068a56d350b813aa67297545a39c79cb78cfd8300a5220dfa4c3d3c456e3e55d4885d34cfcdf486a293f745d64ac759d4c56a0 Homepage: https://cran.r-project.org/package=fastRhockey Description: CRAN Package 'fastRhockey' (Functions to Access Premier Hockey Federation and NationalHockey League Play by Play Data) A utility to scrape and load play-by-play data and statistics from the Premier Hockey Federation (PHF) , formerly known as the National Women's Hockey League (NWHL). 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Package: r-cran-faststepgraph Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-faststepgraph_0.1.1-1.ca2604.1_all.deb Size: 327380 MD5sum: 584750d14cd529967db4b6a1ec97ff23 SHA1: fbb240d37ccd842d577026e858cf06c91b864965 SHA256: 5a0c39da67adb7a62fd669b99f58cb576a8c4001a4e0ce0733fa82cfaef3ad49 SHA512: 6189bae27559d9830b49e0f4eacce2f8915d66d34c0eb6e8f157045bfa92ce433cc9aa475591a969449fe6783ddd0e6a27e2adc80d8b5a3a80847cf1928a803f Homepage: https://cran.r-project.org/package=FastStepGraph Description: CRAN Package 'FastStepGraph' (A Fast Algorithm for Sparse Precision Matrix Estimation) It implements an improved and computationally faster version of the original Stepwise Gaussian Graphical Algorithm for estimating the Omega precision matrix from high-dimensional data. Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) . Package: r-cran-fastts Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ncvreg, r-cran-rcpproll, r-cran-rlang, r-cran-yardstick Suggests: r-cran-covr, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-fastts_1.0.3-1.ca2604.1_all.deb Size: 394842 MD5sum: 072cba3742ecf4fad7d423c1ecdd6f3f SHA1: 01167bd4f4ff9cb19e9582e5ebce28196c82a194 SHA256: 0b14afe59fff20cbcf1f5b5a36e5a65221e3b62a054a447a77c2624c4017e42c SHA512: ef33f54b3f0e0f4d4ab4f39c859e58ca3adc50530b9d2b3403f18f428408e61690abdba10846b178aa2e9abe7b1504ba2c6e7f01c1f537d85cb7bb4750ad7330 Homepage: https://cran.r-project.org/package=fastTS Description: CRAN Package 'fastTS' (Fast Time Series Modeling for Seasonal Series with ExogenousVariables) An implementation of sparsity-ranked lasso and related methods for time series data. This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2024) , which also describes this package in greater detail. The sparsity-ranked penalization methods for time series implemented in 'fastTS' can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The method is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature. Package: r-cran-fastverse Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-collapse, r-cran-kit, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fastverse_0.3.4-1.ca2604.1_all.deb Size: 107356 MD5sum: 985d716beb064c30c8b2e1b20bf5b4f6 SHA1: 269816e7d25121e6332a472139f2d91e6f4b44cb SHA256: 868b619dc81e2c27008708148858c43c7904a85390e0b5ac1167f9fc2a039060 SHA512: 894aab4ee9eb279df56fcdc2ac45c7eb69937b3230fafb990905f4bd74be31676dfb0017821c016bb013366c50f25e9c8653afb4b4c518c85e42f9c9014a335a Homepage: https://cran.r-project.org/package=fastverse Description: CRAN Package 'fastverse' (A Suite of High-Performance Packages for Statistics and DataManipulation) Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. 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Package: r-cran-fat2lpoly Architecture: all Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1402 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kinship2, r-cran-multgee Filename: pool/dists/resolute/main/r-cran-fat2lpoly_1.2.6-1.ca2604.1_all.deb Size: 417186 MD5sum: ee4482c2b9d78860073a21f6264c8948 SHA1: aa3831c08095871c7aef74239535a332b78e5469 SHA256: f491d1f5160c03656cbf96883dbc363193b14c2664997beb6d9dad9463b53747 SHA512: 856613750243a63d6fc9cac0a51ebdbe234aee7b503e1444b0a64e9be396561afd27ebd197445dfe8d574d7dec5fd9a30e6c2e8f2d60212c60dec5f555242794 Homepage: https://cran.r-project.org/package=fat2Lpoly Description: CRAN Package 'fat2Lpoly' (Two-Locus Family-Based Association Test with Polytomous Outcome) Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models defined by some design matrix. 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Herman, J.S., Sagar, Grün D. (2018) . 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Package: r-cran-faunabr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4566 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-data.table, r-cran-httr, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-faunabr_1.0.1-1.ca2604.1_all.deb Size: 3078864 MD5sum: 36f7785ba848949278778f07d81987ea SHA1: e77ea339b7ee528de834a552310e1172d5750793 SHA256: b656e722a9c487bf06fc72cebf3dc2223ffa0e39020a64b40d757ae5105492ff SHA512: 8ce7c36892ca09fed871e994c28392dfea045bb8f3e031c430b3c85ba23d4453187bee473fa323d7ff7cf8b163f6d35da6ff75e4ac5bc99781784983c9ab8a1c Homepage: https://cran.r-project.org/package=faunabr Description: CRAN Package 'faunabr' (Explore Catálogo Taxônomico da Fauna do Brasil Database) A collection of functions designed to retrieve, filter and spatialize data from the Catálogo Taxônomico da Fauna do Brasil. 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Package: r-cran-fda Architecture: all Version: 6.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4769 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fds, r-cran-desolve Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-fda_6.3.0-1.ca2604.1_all.deb Size: 2593224 MD5sum: 8d7a21ceb32b41d4a730ba4871fead54 SHA1: 1b964e4e1b4d8cdc70ba9cbd7b7cbd3c597e43e2 SHA256: 795ac3d731df6fd84b9cfe11535c2bd33fc77b0c197e92c933f5ca943b8c21dc SHA512: 14ae834bf5fb6a546d6629181e7ceac7bb09902cddaac0c476b70bfa9e70229d0ac2e6a195fdbf96162bd61278b6f335c480d5e95fa06f70d35cce83aba0af3a Homepage: https://cran.r-project.org/package=fda Description: CRAN Package 'fda' (Functional Data Analysis) These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from . Package: r-cran-fdaacf Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-pracma, r-cran-fda, r-cran-vars Suggests: r-cran-testthat, r-cran-fields Filename: pool/dists/resolute/main/r-cran-fdaacf_1.0.0-1.ca2604.1_all.deb Size: 116674 MD5sum: af2418d07d606b86505c72addce64c73 SHA1: 1c2dd6be642e5447244db84ecf742d1bfeef80b8 SHA256: c1a1d814b9c92c28966ba732e1e6af6ce0795ad4188945ff1010371f2d6b41dd SHA512: 503c3f5149c0ca21fd498188dfa0612c34911267ed0b66c84e895110b5428a6c2e4d4508c420186f53841b17ed7d09669d4b1abe2b525d40e8141e3685359597 Homepage: https://cran.r-project.org/package=fdaACF Description: CRAN Package 'fdaACF' (Autocorrelation Function for Functional Time Series) Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) . The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise. Package: r-cran-fdamocca Architecture: all Version: 0.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-foreach, r-cran-doparallel, r-cran-mvtnorm, r-cran-fda Filename: pool/dists/resolute/main/r-cran-fdamocca_0.1-2-1.ca2604.1_all.deb Size: 1435886 MD5sum: 268c2616a1cf20136282b1d2d41a3210 SHA1: c452b48a1af416dbac8cedbc308ab986cf8f0f5d SHA256: 4491c063cfbadb987f6175eaa34413686b5b249dcbcab46375200eddd6d31ac1 SHA512: d1f4dea195014cb32bf3a87088b418436e4d74fed12c35a08e602545bbb73236f854f0dd3418215e96a48f2bdab080fe5e0d69c2d12cd576e1b647811d9def25 Homepage: https://cran.r-project.org/package=fdaMocca Description: CRAN Package 'fdaMocca' (Model-Based Clustering for Functional Data with Covariates) Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) . The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate. Package: r-cran-fdanova Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-doparallel, r-cran-ggplot2, r-cran-doby, r-cran-mass, r-cran-magic, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-fdanova_0.1.2-1.ca2604.1_all.deb Size: 521414 MD5sum: aa38c08df7be287fd90eec758c27db4d SHA1: 6f697ec2a12426da5ce6baadb68d498ffcab50d2 SHA256: 4342a75510d3c4f944ff740926eb8b07783fa9d1a967e4b80484f41f9a1137a5 SHA512: 76505301d8f92721922aff87611abd4d1f38eec840e4204b526f0bb1e87fbad6333efb17b5851a235ef257ac0318aacb2579d67d31d22c392588e23bec3c09bb Homepage: https://cran.r-project.org/package=fdANOVA Description: CRAN Package 'fdANOVA' (Analysis of Variance for Univariate and Multivariate FunctionalData) Performs analysis of variance testing procedures for univariate and multivariate functional data (Cuesta-Albertos and Febrero-Bande (2010) , Gorecki and Smaga (2015) , Gorecki and Smaga (2017) , Zhang et al. 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Package: r-cran-fdapoifd Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-magrittr, r-cran-reshape2, r-cran-patchwork, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fdapoifd_2.0.1-1.ca2604.1_all.deb Size: 438204 MD5sum: 0b4f33a4be5467d0fb431ef169b07e96 SHA1: da0870bdc889cbf09cd7242bd5a5e10d6ffe1762 SHA256: b4bcdacc16417b0dac51d7ae51977774567667f090dfa29b30e20688746e89dc SHA512: 04cba07ff82826b39bf373163091f914b62479e4c473356219ff21c2eed7e795662e91ba9bce37a53bf5b7e7823f74eab662969d559b13a39791c1a806307c36 Homepage: https://cran.r-project.org/package=fdaPOIFD Description: CRAN Package 'fdaPOIFD' (Partially Observed Integrated Functional Depth) Integrated Functional Depth for Partially Observed Functional Data and applications to visualization, outlier detection and classification. It implements the methods proposed in: Elías, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, . Elías, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, . Elías, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, . Package: r-cran-fdatest Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda Filename: pool/dists/resolute/main/r-cran-fdatest_2.1.1-1.ca2604.1_all.deb Size: 251082 MD5sum: 6067a5aee1fb0635124f655919da0eef SHA1: 2aa37769b0007d67d7bd3f30c3cc724c55e88c67 SHA256: c7c902c1ad35baf6474c76dda40508f1b3083e3daa7f4e28953853f0c7f13c0a SHA512: 8cf334413649725419372aa3d42263a223c76c4a708b6b4423d7bab58a254b79cf813777e1df39d3a0c1768faa9f3953ce8a899f592d921dfd9b2c4e17fc7f63 Homepage: https://cran.r-project.org/package=fdatest Description: CRAN Package 'fdatest' (Interval Testing Procedure for Functional Data) Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data. 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Package: r-cran-fdclassify Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-modeest Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fdclassify_0.1.0-1.ca2604.1_all.deb Size: 91054 MD5sum: 7d0010b1ef2cc55579579b499837c2d1 SHA1: 87effdd8e51d27c8750946a36de725e96e8fc548 SHA256: 7f87349e7f7c66d9bdf0f00f65c1710a3778593601e8837ef7e6d7442215fd9f SHA512: 80d0ced45164074023fe06bb42d317487bf2acc9c5ab3aef9c79ea6e7ac3a8fa52ee4881e9e6ea2a948011a89d141d705412c0cae6b9f29c7db16f93c3a4731f Homepage: https://cran.r-project.org/package=fdclassify Description: CRAN Package 'fdclassify' (Supervised Classification for Functional Data via Signed Depth) Provides a suite of supervised classifiers for functional data based on the concept of signed depth. The core pipeline computes Fraiman-Muniz (FM) functional depth in either its Tukey or Simplicial variant, derives a signed depth by comparing each curve to a reference median curve via the signed distance integral, and feeds the resulting scalar summary into several classifiers: the k-Ranked Nearest Neighbour (k-RNN) rule, a moving-average smoother, a kernel-density Bayes rule, logistic regression on signed depth and distance to the mode, and a generalised additive model (GAM) classifier. Cross-validation routines for tuning the neighbourhood size k and parametric bootstrap confidence intervals are also included. 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Provides support for various estimators; supports robust, bootstrap, and jackknife variance; returns dynamic, pre/event/post aggregates and raw means; and includes helpers for data preparation and plotting. Methodology follows Xu, Zhao and Ding (2026) . Package: r-cran-fdm2id Architecture: all Version: 0.9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arules, r-cran-arulesviz, r-cran-factominer, r-cran-mclust, r-cran-nnet, r-cran-pls Suggests: r-cran-car, r-cran-caret, r-cran-class, r-cran-cluster, r-cran-e1071, r-cran-fds, r-cran-flexclust, r-cran-fpc, r-cran-glmnet, r-cran-ibr, r-cran-irr, r-cran-kohonen, r-cran-leaps, r-cran-mass, r-cran-mda, r-cran-meanshiftr, r-cran-questionr, r-cran-randomforest, r-cran-rocr, r-cran-rpart, r-cran-rpart.plot, r-cran-rtsne, r-cran-snowballc, r-cran-text2vec, r-cran-stopwords, r-cran-wordcloud, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-fdm2id_0.9.9-1.ca2604.1_all.deb Size: 1408886 MD5sum: 7a35c6518cb902c88f19edabbf743648 SHA1: 296052fcd877954f37636564703f739b4f88e98e SHA256: ce0705a0dbfba0c6acd64a83cd1c149a9b3c8aba063c44d80cb3c0a5e099c8b1 SHA512: 023d5a0acd5c5d5bd07d69520e3f891c609fc541aa6fab481777377e2c0489ae6566998b908d466aa7cd051e8c9a7d11c98a788b82456ce6cbf082a2cf8402f5 Homepage: https://cran.r-project.org/package=fdm2id Description: CRAN Package 'fdm2id' (Data Mining and R Programming for Beginners) Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle". Package: r-cran-fdp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-fdp_1.0.0-1.ca2604.1_all.deb Size: 284880 MD5sum: e73b87b21792cc394a42437269b50d3a SHA1: 3ef824a782cadb4d3cead623cfad044b1366b490 SHA256: 97ccf67b38d1a3aba8442d4cec131b013b71cc5f8e9d2543ba834182e738cb15 SHA512: 11f205d674a513050d52df1cea71729b44df47adf0561780bad64080234be94871cfbd94bf6594d413305c31e783831a23f4e47bbe2439e17b7fa49c48740717 Homepage: https://cran.r-project.org/package=fdp Description: CRAN Package 'fdp' (f-Differential Privacy and Gaussian Differential Privacy) Constructs and visualises trade-off functions for f-differential privacy (f-DP) as introduced by Dong et al. (2022) . 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This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success. 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Package: r-cran-featureimpcluster Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-flexclust, r-cran-clustmixtype, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-attempt, r-cran-clustimpute, r-cran-covr Filename: pool/dists/resolute/main/r-cran-featureimpcluster_0.1.5-1.ca2604.1_all.deb Size: 60086 MD5sum: c00ad613aac933a7bfcd761f672b62be SHA1: 96d3968aea76e776e9f76ff3cb16470601d8c0a5 SHA256: 708580f767704742fd2cd008d22e7b793122ad3a104518097af08d2aeb55fdad SHA512: 0ccc1a67e0e21a8562d7668b5841c316cae1bd8ceb722cba6c3bc023954dc7875724352858352abfd09234d4a46c9d180d38914c0a3114a78905af723c8d8064 Homepage: https://cran.r-project.org/package=FeatureImpCluster Description: CRAN Package 'FeatureImpCluster' (Feature Importance for Partitional Clustering) Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: . Package: r-cran-features Architecture: all Version: 2025.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lokern Filename: pool/dists/resolute/main/r-cran-features_2025.1-1.ca2604.1_all.deb Size: 26468 MD5sum: f9ae8e919025cea606020a6dce779944 SHA1: a7aba0c4202d22d3acefc6b43e0b3b71c740a639 SHA256: 8cd438e1b9feb6cdd039ef08418d99fd8295aab0cccd265e7eb9591e2c1b3380 SHA512: d32a4005ab54ef48c9f0c85e87216cff06a22c49da2a8bd718e1feaac643e5df43737250329f6e80662f4b616c951f5629163ecfda3a5bd5d49666e4abbf6cce Homepage: https://cran.r-project.org/package=features Description: CRAN Package 'features' (Feature Extraction for Discretely-Sampled Functional Data) Discretely-sampled function is first smoothed. Features of the smoothed function are then extracted. Some of the key features include mean value, first and second derivatives, critical points (i.e. local maxima and minima), curvature of cunction at critical points, wiggliness of the function, noise in data, and outliers in data. Package: r-cran-featureterminator Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-caret, r-cran-tibble, r-cran-dplyr, r-cran-lattice, r-cran-e1071, r-cran-randomforest Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-featureterminator_1.0.0-1.ca2604.1_all.deb Size: 126654 MD5sum: d7da1e9493b800397dea2d2a2b6fde92 SHA1: e69a1ba595c8a4970b740415fcf14eb67645023a SHA256: 1edbb0c12db159eb888a011406c9eeface9b92466dd5bf806d393cb4fe3568f9 SHA512: 756bc724fe7406752f5fd9aecbc28a653b01ee66acbe199e774f1b02e3f2f392dc01dd8d57848514cf1ca3b41e86f2a38c27d89e0a2b603bdd8f0b4eb8d13bfb Homepage: https://cran.r-project.org/package=FeatureTerminatoR Description: CRAN Package 'FeatureTerminatoR' (Feature Selection Engine to Remove Features with MinimalPredictive Power) The aim is to take in data.frame inputs and utilises methods, such as recursive feature engineering, to enable the features to be removed. What this does differently from the other packages, is that it gives you the choice to remove the variables manually, or it automated this process. Feature selection is a concept in machine learning, and statistical pipelines, whereby unimportant, or less predictive variables are eliminated from the analysis, see Boughaci (2018) . Package: r-cran-featurizer Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-featurizer_0.2-1.ca2604.1_all.deb Size: 36168 MD5sum: bbe8e9badbf60bfac655241a3e4cf297 SHA1: 06db5d677d656b83222b0292f5a3b9b39e14755f SHA256: 07bb37c4685871a54275a2660d67dee816090e7e8573b4c894fdd61b30caeb40 SHA512: 80b3a54457b094845fdf1bd6cedd57fca0861d4a4e749abe7a3b7f505d0844d21fdaad3b9d039c91c87a32636916bf8f3e7c947995169fcc09e3d1fe5896670a Homepage: https://cran.r-project.org/package=featurizer Description: CRAN Package 'featurizer' (Some Helper Functions that Help Create Features from Data) A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down. Package: r-cran-fec16 Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-vroom, r-cran-usethis Suggests: r-cran-testthat, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-fs, r-cran-lubridate, r-cran-ggplot2, r-cran-stringr, r-cran-utf8 Filename: pool/dists/resolute/main/r-cran-fec16_0.1.6-1.ca2604.1_all.deb Size: 2168132 MD5sum: a66553309e338bbbb96a9dcf7793bf27 SHA1: 2e441dbd4efdb62e0d2862d6eab272eccb3f0ac1 SHA256: f6856bc75ebb1188952a2ce9b2705323221cb361c77f0e31111f90a8e9bce7b2 SHA512: 862904004caf9e1cff154be66dea0571ea00452f202bde069a64430b2c036faea5933f783bb168160c3256b0e59b949d89706bfc9d282b48d0a51888a979fea7 Homepage: https://cran.r-project.org/package=fec16 Description: CRAN Package 'fec16' (Data Package for the 2016 United States Federal Elections) Easily analyze relational data from the United States 2016 federal election cycle as reported by the Federal Election Commission. 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Currently, the package enables extraction from nine datasets: The National Elevation Dataset digital elevation models ( 1 and 1/3 arc-second; USGS); The National Hydrography Dataset (; USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (; NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (; GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 4, available from the Oak Ridge National Laboratory's Distributed Active Archive Center (; DAAC); the International Tree Ring Data Bank; the National Land Cover Database (; NLCD); the Cropland Data Layer from the National Agricultural Statistics Service (; NASS); and the PAD-US dataset of protected area boundaries (; USGS). Package: r-cran-federalregister Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-curl, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-federalregister_0.2.0-1.ca2604.1_all.deb Size: 44630 MD5sum: 82954bbb506ad8a86b0e633fb77b1c54 SHA1: 5456cd4f59ad9943cb80ae4fbe58ce7684d2f6b8 SHA256: aef1ab79919cc205102752edc738f717d38a166d881f2c09c1553476cfabb615 SHA512: 1db19d04f30a4b38a560577141fbf86520a997b3ffe3d11009a2868bcdf411b9d7e5144be39b31ff8e5895f08fcf76c8daf22290dfe28e9f11d1756a46c412cf Homepage: https://cran.r-project.org/package=federalregister Description: CRAN Package 'federalregister' (Client Package for the U.S. Federal Register API) Access data from the Federal Register API . Package: r-cran-fedirt Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-pracma, r-cran-shiny, r-cran-httr, r-cran-callr, r-cran-dt, r-cran-ggplot2, r-cran-shinyjs Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fedirt_1.1.0-1.ca2604.1_all.deb Size: 146418 MD5sum: c468a48d64356025875eb61a060beae8 SHA1: 31d3e017da94d690d7a0f540c5daae8d80355c4c SHA256: cffcf99aee8e04ae030735cf747e6a40da22a75736ab623b690ab767d88269a8 SHA512: db7113e8a62dad7466b510fc79d9bd9f52d9d49b8bc462b4a2ec38b5291176fd52c226fb2961026af220a8b2d9ba8bab9f350667511fc887564b4ad9bc43bdaf Homepage: https://cran.r-project.org/package=FedIRT Description: CRAN Package 'FedIRT' (Federated Item Response Theory Models) Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly 'shiny' application is included. 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Package: r-cran-fedz1 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fedz1_0.1.0-1.ca2604.1_all.deb Size: 255918 MD5sum: 6838b1eff9649791b552ed312d6ec294 SHA1: 110900db6c8e44fddbcd1652b88d016c0422dfd2 SHA256: c65d3692ea941a4e502e8c58c428a3368cea7cb83efa6bef879005e22a2c15fa SHA512: 91ac741598055140b4c3e5ac08d92def67959b337f554664d59dd5f213b8613d51acc7c06d09953f1e0e874889dca804c8f9bc343487726ddfe654fcae15aae2 Homepage: https://cran.r-project.org/package=fedz1 Description: CRAN Package 'fedz1' (An Easier Access to Financial Accounts of the United States(Z.1)) Flow of funds are financial accounts that are provided by Federal Reserve quarterly. The package contains all datasets , tables and descriptions with functions to understand series and explore them. Package: r-cran-fee Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oneinfl Filename: pool/dists/resolute/main/r-cran-fee_1.0.0-1.ca2604.1_all.deb Size: 53024 MD5sum: ec1384ed86ffd34017fda9727576fb16 SHA1: 25ea398a960a20427b65a61a9e7405d6d12cfe6f SHA256: 5a3ff5b9f49a3617d09a0b1a7451aa664c384d6a154a67d7dc17693b385f2f84 SHA512: 9b37a414dd8e94c685c0a19f8fd82772b3413af1fbae0b25a6ba5c344d55b7079158310ac95132229d38fd41fda75e3d5bb87753cd26e924284604bfa17c9639 Homepage: https://cran.r-project.org/package=fee Description: CRAN Package 'fee' (Estimate the First-Exposure Effect (FEE) using Count Data Models) Estimates the first-exposure effect (FEE) using a one-inflated positive Poisson model, or a one-inflated zero-truncated negative binomial model. In addition, estimates the marginal FEE, and standard errors for the FEE and marginal FEE. Package: r-cran-feisr Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-plm, r-cran-rdpack, r-cran-dplyr Suggests: r-cran-texreg, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-feisr_1.3.1-1.ca2604.1_all.deb Size: 288074 MD5sum: 8872b2eb7194f5dc26daedfd5c380fa0 SHA1: e729a3e784874094d90e0f4d720f815722d3a380 SHA256: 5f3b4a7f9980da4b4974ee11373740dc7673d442ef41d64b18593de8b1716737 SHA512: 0601d48c9142f987a965def0e0ca1243963e0d8fd1db570eb2fe1b93e6b84894e234624fb4ce52ab3ca0a38ad99d6c4173e48230836ac5ec3b1c22a147483857 Homepage: https://cran.r-project.org/package=feisr Description: CRAN Package 'feisr' (Estimating Fixed Effects Individual Slope Models) Provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package 'plm' by Croissant and Millo (2008) . In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) ; Polachek and Kim (1994) ; Ruettenauer and Ludwig (2020) ; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test. Package: r-cran-fejiv Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-haven Filename: pool/dists/resolute/main/r-cran-fejiv_0.1.1-1.ca2604.1_all.deb Size: 22926 MD5sum: 96e8caff38c40643d538e5b3c67139c8 SHA1: a2f22d6cc790bc73b88fbe8f935149107d70c1fd SHA256: 72b349201b19ed36567835b28d64f6891c867b9be70c597d17937038eeb5332c SHA512: 9cb31cd5a2cf0aba79d35a415b5a6844a40471c751a164f20ee7618121fe66b209965810ce2f5ae147be7da979d5a9eb2c699cc57b1ca1e69fb3dceac926492f Homepage: https://cran.r-project.org/package=fejiv Description: CRAN Package 'fejiv' (Fixed Effect Jackknife Instrumental Variables Estimation) Implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) , allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by Słoczyński (2024) as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) . Package: r-cran-felp Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-prettycode, r-cran-callr, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-htmltools, r-cran-magrittr, r-cran-matrixstats, r-cran-memoise, r-cran-miniui, r-cran-reactable, r-cran-rstudioapi, r-cran-rlang, r-cran-shiny, r-cran-stringi Suggests: r-cran-knitr, r-cran-printr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-felp_0.6.0-1.ca2604.1_all.deb Size: 85714 MD5sum: 2256e29b3d47e26d80261d7a0db08deb SHA1: 9c33b8942244aa66474c09d569c170dca37ded93 SHA256: 76e7436fa8c220f31ba05451d016c6596e8d213c97ae3a10b65670fd8037d91c SHA512: 84611312a2b5a1f79d601eb80094bcef47ad5d4f38f7dbc2d1279cf8689a85cb564809875a15c275d967604dcbd3e4ab81ae89694f55120538597a26417a6f26 Homepage: https://cran.r-project.org/package=felp Description: CRAN Package 'felp' (Functional Help for Functions, Objects, and Packages) Enhance R help system by fuzzy search and preview interface, pseudo-postfix operators, and more. The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects. The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo). Package: r-cran-feltr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-httptest2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-feltr_0.1.0-1.ca2604.1_all.deb Size: 192420 MD5sum: 7f48baaf933fe89e7bc725a6a72e6644 SHA1: f790b8c6a4aa143ef12e7ce2a96b6722ffdae503 SHA256: 27239b86ef6b758db50020e61430cf892fa5cde633712337039de06ca41d9e13 SHA512: 5c61992d0334c7219b86d210ef40e2fd0a37527664b5fce97d5a7a140a9aba4ea0a16b2506cae402894c494b0e40af21a306746b5e6c9b095bd994cdf8157af2 Homepage: https://cran.r-project.org/package=feltr Description: CRAN Package 'feltr' (Access the Felt API) Upload, download, and edit internet maps with the Felt API (). Allows users to create new maps, edit existing maps, and extract data. Provides tools for working with layers, which represent geographic data, and elements, which are interactive annotations. Spatial data accessed from the API is transformed to work with 'sf'. Package: r-cran-fence Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lme4, r-cran-ggplot2, r-cran-sae, r-cran-fields, r-cran-snowfall, r-cran-snow Suggests: r-cran-pscl Filename: pool/dists/resolute/main/r-cran-fence_1.0-1.ca2604.1_all.deb Size: 152264 MD5sum: 34a6eb3987211bd16a39a9bbcf0baa5f SHA1: dba878efea26ad84a6a82e1783009f8559836f4e SHA256: 907c15d544428da613b0a1240a01af20f6291a73936804fa18f13bd6ee5d7931 SHA512: e3ce337bad9d14cc55cab1aea9f2a4ce9236de33cff743fc9a44fe3fe22faa09a169babc7a09d42b7618b7336253b1879b93b050a5fa00505be1ffc983841379 Homepage: https://cran.r-project.org/package=fence Description: CRAN Package 'fence' (Using Fence Methods for Model Selection) This method is a new class of model selection strategies, for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from among those within the fence according to a criterion which can be made flexible. References: 1. Jiang J., Rao J.S., Gu Z., Nguyen T. (2008), Fence Methods for Mixed Model Selection. The Annals of Statistics, 36(4): 1669-1692. . 2. Jiang J., Nguyen T., Rao J.S. (2009), A Simplified Adaptive Fence Procedure. Statistics and Probability Letters, 79, 625-629. 3. Jiang J., Nguyen T., Rao J.S. (2010), Fence Method for Nonparametric Small Area Estimation. Survey Methodology, 36(1), 3-11. . 4. Jiming Jiang, Thuan Nguyen and J. Sunil Rao (2011), Invisible fence methods and the identification of differentially expressed gene sets. Statistics and Its Interface, Volume 4, 403-415. . 5. Thuan Nguyen & Jiming Jiang (2012), Restricted fence method for covariate selection in longitudinal data analysis. Biostatistics, 13(2), 303-314. . 6. Thuan Nguyen, Jie Peng, Jiming Jiang (2014), Fence Methods for Backcross Experiments. Statistical Computation and Simulation, 84(3), 644-662. . 7. Jiang, J. (2014), The fence methods, in Advances in Statistics, Hindawi Publishing Corp., Cairo. . 8. Jiming Jiang and Thuan Nguyen (2015), The Fence Methods, World Scientific, Singapore. . 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Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subject’s gait deviates from an average normal profile and to represent this deviation as a single number. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern. This R package provides tools for computing the Functional Gait Deviation Index, a novel index for quantifying gait pathology using multivariate functional principal component analysis. The package supports analysis at the level of both legs combined, individual legs, and individual joints/planes. It includes functions for functional data preprocessing, multivariate functional principal component decomposition, FGDI computation, and visualisation of gait abnormality scores. Further details can be found in Minhas, S. K., Sangeux, M., Polak, J., & Carey, M. (2025). The Functional Gait Deviation Index. Journal of Applied Statistics . 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Package: r-cran-findingit Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-crayon, r-cran-htmlwidgets Suggests: r-cran-shiny, r-cran-r.utils Filename: pool/dists/resolute/main/r-cran-findingit_0.1.1-1.ca2604.1_all.deb Size: 54286 MD5sum: bd0938f056441323d55f7caf24986e35 SHA1: 6510adb5c34ed30641843e4224252eb05f867f50 SHA256: 2de5a3623663b509a2ef9c07088fa4bbe179178b25845020067d9417c82159ad SHA512: 1f9655f01c109d1e5e91545a8bf9800dd4fe8674fd6964bb20f0245462838c93ccb6e80657bb1ee86655a1174725f32d8112e8bc1607d4f45af2ad4791d5e3fb Homepage: https://cran.r-project.org/package=findInGit Description: CRAN Package 'findInGit' (Find Pattern in Files of All Branches of a 'git' Repository) Creates a HTML widget which displays the results of searching for a pattern in files in a given 'git' repository, including all its branches. 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Package: r-cran-findit Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-glmnet, r-cran-lars, r-cran-matrix, r-cran-quadprog, r-cran-glinternet, r-cran-igraph, r-cran-sandwich, r-cran-lmtest, r-cran-limsolve Filename: pool/dists/resolute/main/r-cran-findit_1.3.0-1.ca2604.1_all.deb Size: 427586 MD5sum: a8caa6820fbfc76711e37a6f16a887a3 SHA1: 623a1013eebd0d322bf5757964e91fa86bd999ab SHA256: ffbe345b3db7c7dcfa9a92ca232a7bc99d4de1e02d512810fcde9e80c5abb055 SHA512: c791a93314d5fe65ca8d73c80af5ee1db3b3eed832d6a76a445d857e1a9d71690bc266f32099f981c6807ca528663567a488776fc8860d335aef7e09995520b0 Homepage: https://cran.r-project.org/package=FindIt Description: CRAN Package 'FindIt' (Finding Heterogeneous Treatment Effects) The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013). 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Package: r-cran-findn Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-scales Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-findn_0.2.0-1.ca2604.1_all.deb Size: 46462 MD5sum: 02ef60d1600d8f397d5a65e5aec6a42b SHA1: c498cbb08495dae602e404c6d73d09a526b089e9 SHA256: 46eb913e3d93af763b206a7af175cc054c182d9636d0f216ac9b37f515023948 SHA512: d06524653150c2d6c750419913866db32ab8c7798f99b8513894f607e7ebc111299fd4b9ea9f8c38628251a33badfeb5f98bae4556d6d036417a0b9ccdeca7cd Homepage: https://cran.r-project.org/package=findn Description: CRAN Package 'findn' (Simulation Based Sample Size Estimation) Estimates the sample size for a test or a trial based on repeated simulation using a model based approach. Implements a method by Maruo et al. (2018) and an extension. 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Package: r-cran-findr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pdftools, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-findr_0.2.1-1.ca2604.1_all.deb Size: 477166 MD5sum: 3227dc65656222d4042e65ea80b8c7d6 SHA1: fb4a6f68b9c9120e41e81ce9d189ff062420cfee SHA256: fc17f9792ec408520b33bf637ad43014bc1a222a94d8fac9bde21eb1fd63014c SHA512: 287505933508a63b45bdc6a23ec554691fac6f3afb437e393129ee8c8c0b260a8e284a244da07bd48b479363b1ff0cd8676b1edd4789c1e5232148198bad6918 Homepage: https://cran.r-project.org/package=findR Description: CRAN Package 'findR' (Find Code Snippets, R Scripts, R Markdown, PDF and Text Fileswith Pattern Matching) Scans all directories and subdirectories of a path for code snippets, R scripts, R Markdown, PDF or text files containing a specific pattern. Files found can be copied to a new folder. Package: r-cran-findsvi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2752 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tidycensus, r-cran-tidyr, r-cran-tidyselect, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet, r-cran-htmltools, r-cran-glue, r-cran-sf, r-cran-ggplot2, r-cran-reactable, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-findsvi_0.2.0-1.ca2604.1_all.deb Size: 2216962 MD5sum: 9d63b88d7f49e3a32f0e4a508b31ed48 SHA1: a8e4c45168515a5fe359a9b7425477e6305f3b0f SHA256: 1d3a6691838da7e680852f58fe289d92669389b3a48464cf074028844088137b SHA512: e08996d3b4a352187e850b65dc2fd829e41bd328a5e0507457a5d46f6c7893dc8350be1b01ed9b1e5f39107fe36d47cde6ce068df0fe5764f88aeecc085826e7 Homepage: https://cran.r-project.org/package=findSVI Description: CRAN Package 'findSVI' (Calculate Social Vulnerability Index for Communities) Developed by CDC/ATSDR (Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry), Social Vulnerability Index (SVI) serves as a tool to assess the resilience of communities by taking into account socioeconomic and demographic factors. Provided with year(s), region(s) and a geographic level of interest, 'findSVI' retrieves required variables from US census data and calculates SVI for communities in the specified area based on CDC/ATSDR SVI documentation. Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) . Package: r-cran-findviews Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-scales, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-findviews_0.1.3-1.ca2604.1_all.deb Size: 145234 MD5sum: 936c37743348de3451cd55f67424450d SHA1: fd3842016f4b04f06607d3d3cf6a9666178a2c5b SHA256: b7dd570fc25c32ce5a8818f54cd218d69627d6709ab4801ff4d5f55a53916c07 SHA512: 640fef7c47aede5498cc3546d436fa9ed3925553d5fea781ef0fdfa4d6fd64e5b97d69b4429834de13921ef361e5dc19e30f07f2ee7426fe9629dc0d0c3771a4 Homepage: https://cran.r-project.org/package=findviews Description: CRAN Package 'findviews' (A View Generator for Multidimensional Data) A tool to explore wide data sets, by detecting, ranking and plotting groups of statistically dependent columns. Package: r-cran-finepop2 Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-finepop2_0.4-1.ca2604.1_all.deb Size: 117266 MD5sum: 533fc82a104f988844b05da298123b24 SHA1: 79975a9c8d6d8e6c57016c769159551ebac7dd2b SHA256: 5e4b82ed10f9effc9ac69556c315e9f6ee5a1a09799d63e54f59cb71a03faeac SHA512: f76eae0309b96a5bdd40cdb1b392c86f8ad822ba4a55ba2591ba2d340b829ee739b2a44f6002971e12660eef4e6051b5135e08daf2708d4a65b8587313a411d0 Homepage: https://cran.r-project.org/package=FinePop2 Description: CRAN Package 'FinePop2' (Fine-Scale Population Analysis (Rewrite forGene-Trait-Environment Interaction Analysis)) Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate 'GENEPOP' format data files). For more information, see Kitada, Nakamichi and Kishino (2020) . Package: r-cran-finepop Architecture: all Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ape Filename: pool/dists/resolute/main/r-cran-finepop_1.5.2-1.ca2604.1_all.deb Size: 429300 MD5sum: 353f42c5b4a78beb2c41609aabc21182 SHA1: 0e8a096f67be002b8832407744462d3fd4c8bfc9 SHA256: b289042c618dafcc2ce6f275a8286980412cb761dad8440c014b31d2dcec2144 SHA512: abd80ac5d602a1dd8e951b0b0b121c46d0a1ddefa3367f9e5cd999298873d51c777ae730562757da9f87ab156577fe38a1965913c05043c9347ffd766c6f9d48 Homepage: https://cran.r-project.org/package=FinePop Description: CRAN Package 'FinePop' (Fine-Scale Population Analysis) Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files. Package: r-cran-finetune Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tune, r-cran-cli, r-cran-dials, r-cran-dplyr, r-cran-ggplot2, r-cran-parsnip, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-workflows Suggests: r-cran-bradleyterry2, r-cran-covr, r-cran-discrim, r-cran-kknn, r-cran-klar, r-cran-lme4, r-cran-modeldata, r-cran-ranger, r-cran-recipes, r-cran-rpart, r-cran-rsample, r-cran-spelling, r-cran-testthat, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-finetune_1.3.0-1.ca2604.1_all.deb Size: 221298 MD5sum: 3edf0cc95deef1fb2eae542ef3564ee6 SHA1: 5cfc88ac887d5f3d7af92440deb7efbfb64f6f35 SHA256: 38dea9b5c57d3481d67c043bd201fa3910a5034d418c85495745becfc6b51b15 SHA512: 1a6bc1f5f73c7739c4d880942c3fb8740f3af4dcd62c78603496befec8765ac33a31e27504177ec608753523ab7fccfd66c67f35fb67d14a1e9617977e73157b Homepage: https://cran.r-project.org/package=finetune Description: CRAN Package 'finetune' (Additional Functions for Model Tuning) The ability to tune models is important. 'finetune' enhances the 'tune' package by providing more specialized methods for finding reasonable values of model tuning parameters. Two racing methods described by Kuhn (2014) are included. An iterative search method using generalized simulated annealing (Bohachevsky, Johnson and Stein, 1986) is also included. Package: r-cran-finiteruinprob Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sdprisk, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-finiteruinprob_0.6-1.ca2604.1_all.deb Size: 134564 MD5sum: f9f65e5509b6afbe4a43c29979cdc396 SHA1: 101f378bd23312c17da584df301e635533539492 SHA256: 2c33c83a9f24a421914561eaab729a541e466a259bb42ca8f98160b9a5e07ca9 SHA512: f546ce710b9c7635588cc9862dea9fde3feae433c3e364bba1b4c1512644ec39c6209ac1006e458d3b3ff0536b1a4858aea5ae4f35cf010bd7639eb9f3305efa Homepage: https://cran.r-project.org/package=finiteruinprob Description: CRAN Package 'finiteruinprob' (Computation of the Probability of Ruin Within a Finite TimeHorizon) In the Cramér–Lundberg risk process perturbed by a Wiener process, this packages provides approximations to the probability of ruin within a finite time horizon. Currently, there are three methods implemented: The first one uses saddlepoint approximation (two variants are provided), the second one uses importance sampling and the third one is based on the simulation of a dual process. This last method is not very accurate and only given here for completeness. Package: r-cran-finlabr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-ggplot2, r-cran-performanceanalytics, r-cran-zoo, r-cran-class, r-cran-quantmod, r-cran-reshape2, r-cran-mclust, r-cran-shiny Suggests: r-cran-bslib, r-cran-ttr, r-cran-dt, r-cran-xts, r-cran-yfr, r-cran-cryptoquotes, r-cran-tidyquant, r-cran-rtsne, r-cran-umap, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-finlabr_1.0.0-1.ca2604.1_all.deb Size: 491448 MD5sum: 27fa522717c08b503c6ec6f82cd04522 SHA1: 1f1134ad797fc7f309adcbf1b373c1a02ce39bb2 SHA256: fed3aa83ce78d51b8029040ef5597c485692c96dcdec3b5c8efa90390ae538d4 SHA512: 151e26b6c5243f4d54c8a113c53e66bc0660de8ccee8a592e19ccf90d3c408ad4d73560e3b26c2c5b57f8947d47af86a50d5bb9c9e0366ba6e0d2b8602a1cfcb Homepage: https://cran.r-project.org/package=finlabR Description: CRAN Package 'finlabR' (Portfolio Analytics and Simulation Toolkit) Tools for portfolio construction and risk analytics, including mean-variance optimization, conditional value at risk (expected shortfall) minimization, risk parity, regime clustering, correlation analysis, Monte Carlo simulation, and option pricing. 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Package: r-cran-finna Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-xml2, r-cran-jsonlite, r-cran-ggplot2, r-cran-readr, r-cran-tibble, r-cran-curl, r-cran-progress, r-cran-purrr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-finna_0.1.1-1.ca2604.1_all.deb Size: 110462 MD5sum: fda4984129036932b1a41a92a8809427 SHA1: f59e6d820e12d7b67a60d8a62e536b90df499049 SHA256: 29004fd14e862d73ede3f406d96a44895334c8c17b7fc55a7b22527d9af554c7 SHA512: f3e57bc7a718bd9c19acffb74fdf29bf4c1450627f45f4c87a141065ed5c1c3b5fce712fca2f2b434b9f6fb7a8f8587063aac67530b8c2750aa4867ccff62fa3 Homepage: https://cran.r-project.org/package=finna Description: CRAN Package 'finna' (Access the 'Finna' API) Provides functions to access and retrieve metadata from the 'Finna' API , which aggregates content from Finnish archives, libraries, and museums. Package: r-cran-finnishgrid Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 673 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-finnishgrid_0.2.0-1.ca2604.1_all.deb Size: 483318 MD5sum: fb703e43d77c870f42b2949053f18cb3 SHA1: c39d1e390110dcf85fd7b606d3ed00d193dee82d SHA256: 18028d6c15bf2e5460efb67e7507377895eb45c0b449095850734a3bf9775315 SHA512: 19f714f842e2885043a0bc7e3451b55fccd3ef963c58a8501a992ffb2a490ca8c076b6b65c3f416518eb62ec1dbc645a943d5e11b8fdbf419e1f5bd03d9b8dcc Homepage: https://cran.r-project.org/package=finnishgrid Description: CRAN Package 'finnishgrid' ('Fingrid Open Data API' R Client) R API client package for 'Fingrid Open Data' on the electricity market and the power system. get_data() function holds the main application logic to retrieve time-series data. API calls require free user account registration. Data is made available by Fingrid Oyj and distributed under Creative Commons 4.0 . Package: r-cran-finnsurveytext Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-stopwords, r-cran-stringr, r-cran-textrank, r-cran-tibble, r-cran-tidyr, r-cran-udpipe, r-cran-wordcloud Suggests: r-cran-dt, r-cran-htmlwidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyjs, r-cran-survey Filename: pool/dists/resolute/main/r-cran-finnsurveytext_2.1.1-1.ca2604.1_all.deb Size: 657884 MD5sum: c661db3c6d0f6d177d6231bdef3e6ec5 SHA1: 13aead7ff44527e233149266aeddb54e6d8b5974 SHA256: e322c57ea9521db23d5e76afd1220fe32a0a9a3d35a3db49afeb166b6bc8b7d2 SHA512: 897107f3814d23cebdfc246f9bfff5854aaa95bd5b7e85aa68b4cf1f512707cdc6c84d8c1d8195e4cf2e356b8eafc0b61c9bfe0573ba9761ecc8d1ea941cb2ce Homepage: https://cran.r-project.org/package=finnsurveytext Description: CRAN Package 'finnsurveytext' (Analyse Open-Ended Survey Responses in Finnish) Annotates Finnish textual survey responses into CoNLL-U format using Finnish treebanks from using UDPipe as described in Straka and Straková (2017) . Formatted data is then analysed using single or comparison n-gram plots, wordclouds, summary tables and Concept Network plots. The Concept Network plots use the TextRank algorithm as outlined in Mihalcea, Rada & Tarau, Paul (2004) . Package: r-cran-finnts Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltime, r-cran-cli, r-cran-cubist, r-cran-dials, r-cran-digest, r-cran-doparallel, r-cran-dplyr, r-cran-earth, r-cran-feasts, r-cran-foreach, r-cran-fs, r-cran-generics, r-cran-glue, r-cran-glmnet, r-cran-gtools, r-cran-hts, r-cran-kernlab, r-cran-lubridate, r-cran-magrittr, r-cran-parsnip, r-cran-plyr, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-rules, r-cran-snakecase, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-timetk, r-cran-tune, r-cran-vroom, r-cran-workflows Suggests: r-cran-arrow, r-cran-azurestor, r-cran-boruta, r-cran-corrr, r-cran-knitr, r-cran-microsoft365r, r-cran-notebookutils, r-cran-reactable, r-cran-rmarkdown, r-cran-sparklyr, r-cran-testthat, r-cran-vip Filename: pool/dists/resolute/main/r-cran-finnts_0.6.0-1.ca2604.1_all.deb Size: 840366 MD5sum: 4af94a750b2dee81864888ec18ac151a SHA1: e1b25a69a46108d624c40ad3413d4eb209f2dce0 SHA256: aa9d158823cf9d772ceb7c1c95c79f502046f6262c8c69de72d4c98697a57389 SHA512: dc303a9cd6cc0804e34434c3982fc229dfa2119af8af5f042363366654c29fc00be1cb63e5c9e6a0966bcd3265e0695dc67fc12e429f1bf48dae06c9695dc70b Homepage: https://cran.r-project.org/package=finnts Description: CRAN Package 'finnts' (Microsoft Finance Time Series Forecasting Framework) Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting! Package: r-cran-finto Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-httr, r-cran-dplyr, r-cran-jsonlite, r-cran-tidyr, r-cran-stringr, r-cran-purrr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-finto_0.1.1-1.ca2604.1_all.deb Size: 126486 MD5sum: a75570f9e3a61747bcfbfd23a36eb8bb SHA1: bb660ef7ad6d9381d940fb17ebae2302852d8c05 SHA256: f19cd26372cdae14739e1e8bf219aef2baa71ef4fbae86d798a938fc27b72c2a SHA512: d555daba07eb8fe66d86ba68ad0b8eee441766d71fb6563668c280d23c413dea30ad5381bb6f32776136efc11e7f4c850d421efaa46d52df1c8e878686a86fc7 Homepage: https://cran.r-project.org/package=finto Description: CRAN Package 'finto' (Access the 'Finto' API) Access and retrieve vocabulary data 'Finto' API , which is a centralized service for interoperable thesauri, ontology and classification schemes for different subject areas. 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Implements a cascading strategy: Exact -> Fuzzy ('zoomerjoin') -> 'FTS5' ('SQLite') -> Rarity Weighted. References: Beniamino Green (2025) ; . Package: r-cran-first Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-twinning Filename: pool/dists/resolute/main/r-cran-first_2.1-1.ca2604.1_all.deb Size: 60240 MD5sum: f47cd1d25559b307dc70c3ef08fdb6b5 SHA1: f033ee623b776c74aa1eb8aaa323bd5c8ed07146 SHA256: de3bdcb79f4f4c4fa23cb7dbddb1efdd2d00431eab3a7dd7c670e7be0eb806d1 SHA512: 6ced4d63c19c453f8073c5c107469385baf19546b4fc69008c1958b19c0cd360d6402261978351b9d19e536b763755d12229db38dcec43e16081c3192562b0b7 Homepage: https://cran.r-project.org/package=first Description: CRAN Package 'first' (Factor Importance Ranking and Selection using Total Indices) A model-independent factor importance ranking and selection procedure based on total Sobol' indices. Please see Huang and Joseph (2025) . This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873. Package: r-cran-fiscal Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fiscal_1.0.2-1.ca2604.1_all.deb Size: 140800 MD5sum: a2095790050e0f4350d12f5ca0343f1e SHA1: 0009a4d0acb0fe9fda07ce01661dca86ed9f52ad SHA256: 167ce735023ae45cbe982fe6ad980e903eb5e92ba50a46ba1f973e9f7fbefc61 SHA512: b9b02650b25b7ddcdff1658ea0cf5bf5ed0c746a0e270b84bda8e20d36b71d8733ef108bb0c428b336ad14e4434ab3c8fa2490f2a883e60d6165498a2283d322 Homepage: https://cran.r-project.org/package=Fiscal Description: CRAN Package 'Fiscal' (Income Tax Calculations (UK)) Income tax calculations for England, Northern Ireland and Wales. Estimate annual income tax within the different taxation bands at specified levels of both taxable income and the Personal Allowance, emulating the results obtained at . Calculate the standard Personal Allowance at various levels of taxable income. Estimate the personal allowance required to recoup a specified amount of income tax. Package: r-cran-fish Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda.usc, r-cran-kernsmooth Filename: pool/dists/resolute/main/r-cran-fish_1.1-1.ca2604.1_all.deb Size: 23414 MD5sum: 4d3ab2c9ceb5ea94208c61d41336a220 SHA1: a81cca990f8bbb9ad2bf0de9271483f4d4b369e9 SHA256: fd3c9b1f97d42d06dd5a8aca61b216c5b6e8d401260b945641932e1eb22de90c SHA512: 6bc4fe88a5dc47b351e996ea4bade0373fcdae30f53333a267de6086242a3f4e0f25d5c92879bcb62894ead43b708f7f1011fdf21a902bac488a29f6151edd4f Homepage: https://cran.r-project.org/package=FiSh Description: CRAN Package 'FiSh' (Fisher-Shannon Method) Proposes non-parametric estimates of the Fisher information measure and the Shannon entropy power. More theoretical and implementation details can be found in Guignard et al. . A 'python' version of this work is available on 'github' and 'PyPi' ('FiShPy'). Package: r-cran-fishbc Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-chk, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-fishbc_0.2.1-1.ca2604.1_all.deb Size: 58714 MD5sum: a5ab36fa6b97c2e06931434e16053ed3 SHA1: 77c7989792b8ad516582372a6826c86e39e84f7f SHA256: 5c759d17474cd456eaa04216cc51a1682e960bdbbe301be8a84aa4e17ea8c32d SHA512: 263cb67a2515ba70293c7c95bd45e38a7b9a57d1b1e3dbffc58df6557197ce82dfa3ea672863129f9a18fd893c9c75311cb647d7732d862b2dbad7d9ab5953aa Homepage: https://cran.r-project.org/package=fishbc Description: CRAN Package 'fishbc' (Fishes of British Columbia) Provides raw and curated data on the codes, classification and conservation status of freshwater fishes in British Columbia. Marine fishes will be added in a future release. Package: r-cran-fishdata Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3660 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-magrittr, r-cran-dm, r-cran-ggplot2, r-cran-tidyr, r-cran-diagrammersvg, r-cran-diagrammer Filename: pool/dists/resolute/main/r-cran-fishdata_1.0.1-1.ca2604.1_all.deb Size: 1404698 MD5sum: b846ecbfee5fe6bbebea2673b0707cda SHA1: 6106ebbbc507f209beb6022868519f65982e7ec7 SHA256: e39a7c35f4b60c1d5dcfc665d1936c36d0ba964e9e11c386f6f5a5c9b1c71a03 SHA512: 1a24108d70c8a3db633402b01d00c485c82326a9e67d5a8f210c684076bfebe9cebecaf33a220ad11fe47b657f2ba1cece412c60c6933296d5b352a55e3f3676 Homepage: https://cran.r-project.org/package=fishdata Description: CRAN Package 'fishdata' (A Small Collection of Fish Population Datasets) A collection of four datasets based around the population dynamics of migratory fish. Datasets contain both basic size information on a per fish basis, as well as otolith data that contains a per day record of fish growth history. All data in this package was collected by the author, from 2015-2016, in the Wellington region of New Zealand. Package: r-cran-fishdiver Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-factominer, r-cran-geometry, r-cran-ggplot2, r-cran-gridextra, r-cran-lubridate, r-cran-moments, r-cran-patchwork, r-cran-colorspace, r-cran-rgl, r-cran-rfast, r-cran-rlang, r-cran-scales, r-cran-suncalc, r-cran-tidyr, r-cran-waveletcomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fishdiver_1.1.0-1.ca2604.1_all.deb Size: 5399904 MD5sum: 67b6c0634d28171ba0e1d0fa7f06dd1a SHA1: c99f4757ecfded71eb96dcf9d65cfaae26df9e06 SHA256: e680daa55388d3fe37696bcc7015766f53930809883706e517de31d20516cb9d SHA512: 8fa5f704806bde49d74c9e77b72a0b046a62fa3c19866fdaf2d7a13a43f868538a01871017367a1c6c6aee6d90a73fa3ca7bef7ea2dbf7592b05311f24d49a9b Homepage: https://cran.r-project.org/package=FishDiveR Description: CRAN Package 'FishDiveR' (Classify Aquatic Animal Behaviours from Vertical Movement Data) Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering. Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics. Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster. Based on principals of combining CWT with k-means first developed by Sakamoto (2009) and redeveloped by Beale (2026) . Package: r-cran-fisherem Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-elasticnet, r-cran-ggplot2, r-cran-ellipse, r-cran-plyr Suggests: r-cran-testthat, r-cran-aricode Filename: pool/dists/resolute/main/r-cran-fisherem_1.6-1.ca2604.1_all.deb Size: 225450 MD5sum: 8fc6852838f4f271bb2bc9c19ffa9909 SHA1: e21f8164bb088c34e3b41d8a01859ec795627137 SHA256: 7291cd587ec2155cda667669bd0a5bc11189c5a9dacdbdbd83d08cac5ffd999c SHA512: 34389643d03a981e7169e7dd4e10dce1e35ecf7ce0de2bf7e84a3441b6f1ee2e9b4a5659fc94b1a6db2d9f70c3f86d25f9bd3d22e766760f33bca33aaa83c11e Homepage: https://cran.r-project.org/package=FisherEM Description: CRAN Package 'FisherEM' (The FisherEM Algorithm to Simultaneously Cluster and VisualizeHigh-Dimensional Data) The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) , is an efficient method for the clustering of high-dimensional data. FisherEM models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data. A sparse version of Fisher-EM algorithm is also provided. Package: r-cran-fisheye Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-fisheye_0.2.0-1.ca2604.1_all.deb Size: 16546 MD5sum: 1486df6dae0b65190cac88eebaa8b5e1 SHA1: 9ff3fc047b552c22bd889b10941ccd3196c1ad23 SHA256: 657b05ebe185fac10f80ff9143b3fdace36ccc776782f5a5e386cdf8b221ad0d SHA512: 32f51dff0ba6b88a8afa4da13e5348ea073b5c94bb48ff631896b300cfc25c960cb5e483ae63a1c8b19d4ab93e2c02128ebe11b98b606be8068fca746e33bdbb Homepage: https://cran.r-project.org/package=fisheye Description: CRAN Package 'fisheye' (Transform Base Maps Using Log-Azimuthal Projection) Base maps are transformed to focus on a specific location using an azimuthal logarithmic distance transformation. Package: r-cran-fishgrowth Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtmb Suggests: r-cran-areaplot Filename: pool/dists/resolute/main/r-cran-fishgrowth_1.0.4-1.ca2604.1_all.deb Size: 225638 MD5sum: 0c20829c9993f115caaf6e75c1b542ad SHA1: 3a949eda42489e1a7dad85b947d141e1a0d948dc SHA256: a51fd0cf0356b6883e72a747999918845eb1c1bd1822c96ec3c254bce3a151a3 SHA512: 17f31dbe9138a95db78b83181fac9cbd1b5598e2fd17835bf321df9c67a74ca4d8e5c5ac46648340decf7de47c2939e926842e72b2a8a484e3023b86f82a5e2e Homepage: https://cran.r-project.org/package=fishgrowth Description: CRAN Package 'fishgrowth' (Fit Growth Curves to Fish Data) Fit growth models to otoliths and/or tagging data, using the 'RTMB' package and maximum likelihood. The otoliths (or similar measurements of age) provide direct observed coordinates of age and length. The tagging data provide information about the observed length at release and length at recapture at a later time, where the age at release is unknown and estimated as a vector of parameters. The growth models provided by this package can be fitted to otoliths only, tagging data only, or a combination of the two. Growth variability can be modelled as constant or increasing with length. Package: r-cran-fishkirkko2015 Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fishkirkko2015_1.0.0-1.ca2604.1_all.deb Size: 14622 MD5sum: 9843f07595f1fa63226bd98c956c1bf1 SHA1: db86d24fbc51707d41cc8908f98d4c91fccb1fe8 SHA256: 8054aa4acb1a338904d9721715354527115b4c5f36909d6309bf74399ed8be16 SHA512: 61c01d0d8b31d6e88aba14392618e2c2c8a3b625a0337df40afdffde4a322f781189d11a50e33f7504a55e282a6875fa7ebe88a0a2e08685c9524f0365042fef Homepage: https://cran.r-project.org/package=fishkirkko2015 Description: CRAN Package 'fishkirkko2015' (Dataset of Measurements of Fish Species at Kirkkojarvi Lake,Finland) Dataset of 302 measurements of 11 fish species to accompany the manuscript "Length-weight relationships of six freshwater fish species from lake Kirkkojarvi, Finland". Package: r-cran-fishmechr Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1727 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-gsignal, r-cran-pracma, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fishmechr_1.0.3-1.ca2604.1_all.deb Size: 1168220 MD5sum: e03e7a98fe072a7707fca83e66dfe8b3 SHA1: 895f4964514e5b815ae324bcf30b1b53be9d0f60 SHA256: 4f8e513c466f77baabeca9227356a9c705f35987ea92b8e1f18cb9c1c06f7ab1 SHA512: 583be5efa65db63c955e62a0341269727418c389d9369c691ae237dec68158e1c8eb09a1d8bc231ce6ade61cfc0d5f31e4229a6b85519eca8c159b44a5d7b107 Homepage: https://cran.r-project.org/package=fishmechr Description: CRAN Package 'fishmechr' (Computes Kinematic Parameters for Swimming) Processes tracked points on a fish's body and uses them to estimate standard kinematic parameters such as tail beat amplitude and frequency, body wavelength and wavespeed. As part of this, it also estimates the location of the center of mass and the principal swimming axis. The techniques are described in detail in the main vignette and are published in the book chapter Hawkins, O.H., Di Santo, V., Tytell, Eric.D., 2025. "Biomechanics and energetics of swimming", in: Higham, T.E., Lauder, G.V. (Eds.), Integrative Fish Biomechanics, Fish Physiology. Academic Press. . Package: r-cran-fishphylomaker Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 940 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-fishtree, r-cran-geiger, r-cran-knitr, r-cran-phytools, r-cran-progress, r-cran-rfishbase, r-cran-rmarkdown Suggests: r-cran-markdown, r-cran-gh Filename: pool/dists/resolute/main/r-cran-fishphylomaker_0.2.0-1.ca2604.1_all.deb Size: 702910 MD5sum: e3b6083722b8d0f5ea46c3c426d5cc34 SHA1: 68a7c7869e0f83a2ca03843f62180017dd067e38 SHA256: 48951e3e482d58f9861273b59bffa1e17f06aa44a14c7da63d40b39c3287d3cf SHA512: 77760904cd2b2637d9c50153857158a84b37929129e1d46a15eb52943bb40790ee290632344dcbf6c704d76cdad74ef2e849512e04e872b2731870b8bc6b209b Homepage: https://cran.r-project.org/package=FishPhyloMaker Description: CRAN Package 'FishPhyloMaker' (Phylogenies for a List of Finned-Ray Fishes) Provides an alternative to facilitate the construction of a phylogeny for fish species from a list of species or a community matrix using as a backbone the phylogenetic tree proposed by Rabosky et al. (2018) . Package: r-cran-fishproxcompanalyzer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fishproxcompanalyzer_0.1.0-1.ca2604.1_all.deb Size: 22088 MD5sum: fa43a70f2629eb25e226133f2857fdeb SHA1: cb9d392b8c503c1aed6671c5f74b7b52969da546 SHA256: ecac9e5b5aaad06e56b28b28c614db483f37e42d284f3ee289eb0906e0b70e69 SHA512: fd216123731e81ac5db31a8228c46968155512678a708032a488f91fe325d70dfc7905a8516b5557841f363898cc0c4e31b3dc90790bc2cf19382752440d2d13 Homepage: https://cran.r-project.org/package=FishProxCompAnalyzer Description: CRAN Package 'FishProxCompAnalyzer' (Proximate Composition Analysis of Fish and Feed Ingredients) The proximate composition analysis is the quantification of main components that constitutes nutritional profile of any food and food products including fish, shellfish, fish feed and their ingredients. Understanding this composition is essential for evaluating their nutritional value and for making informed dietary choices. The primary components typically analyzed include; moisture/ water in foods, crude protein, crude fat/ lipid, total ash, fiber and carbohydrates AOAC(2005,ISBN:0-935584-77-3). In case of fish, shellfish and its products, the proximate composition consists of four primary constituents - water, protein, fat, and ash (mostly minerals). Fish exhibit significant variation in their chemical makeup based on age, sex, environment, and season, both within the same species and between individual fish. There is minimal fluctuation in the content of ash and protein. The lipid concentration varies remarkably and is inversely correlated with the water content. In case of fish, carbohydrates are present in minor quantity so that are quantified by subtracting total of other components from 100 to get percentage of carbohydrates. Package: r-cran-fishrman Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 921 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-countrycode, r-cran-dplyr, r-cran-ggplot2, r-cran-golem, r-cran-httr, r-cran-jsonlite, r-cran-maps, r-cran-sf, r-cran-shiny, r-cran-shinybs, r-cran-shinyjs, r-cran-shinywidgets, r-cran-viridis Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fishrman_1.2.3-1.ca2604.1_all.deb Size: 596862 MD5sum: 6ff3d99df9d00786f4386bf3e105bd16 SHA1: dd055e4580c1a92dbe2871482ecd56fb21ea54b5 SHA256: 0fa6f53b65e8fcea8638b0409af0a234f9dc8b8c0afbe2bda897eb4a3d4dc67c SHA512: c2bc39ce39694586271affb061fb550296909a7f6b5dea8f6518943d0979b9d1f520698101b03a4d8ad8441166a7a37226446d48206531987cae9ab261e9591c Homepage: https://cran.r-project.org/package=fishRman Description: CRAN Package 'fishRman' (The Fisheries Scientist's Toolbox) A bundle of analytics tools for fisheries scientists. A 'shiny' R App is included for a 'no-code' solution for retrieval, analysis, and visualization. Package: r-cran-fishstat Architecture: all Version: 2026.1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4848 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-areaplot Filename: pool/dists/resolute/main/r-cran-fishstat_2026.1.0.0-1.ca2604.1_all.deb Size: 4758230 MD5sum: 7544d82c78dff5c3a9796cd7595f5af0 SHA1: 42109f319cc77aeab577f0e2b91d4ecea5fbf13a SHA256: db1ed4f8b60c2f45b4ef0ae528004a56a0ff2a26faf5249a817af5f3a72f00e0 SHA512: 1046c69bd9a2fc5be63fe37952066f0cecbef97a98b8dd2ed93732ea391e6bbfe13ba5ceb366e5486e7bf9b07436a3d22702a54e0420c645a18bbefa83374536 Homepage: https://cran.r-project.org/package=fishstat Description: CRAN Package 'fishstat' (Global Fishery and Aquaculture Statistics) The Food and Agriculture Organization of the United Nations (FAO) FishStat database is the leading source of global fishery and aquaculture statistics and provides unique information for sector analysis and monitoring. This package provides the global production data from all fisheries and aquaculture in R format, ready for analysis. Package: r-cran-fishtree Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-jsonlite, r-cran-memoise, r-cran-rlang Suggests: r-cran-diversitree, r-cran-geiger, r-cran-hisse, r-cran-knitr, r-cran-markdown, r-cran-phytools, r-cran-picante, r-cran-rfishbase, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fishtree_0.3.4-1.ca2604.1_all.deb Size: 392580 MD5sum: c124a5fa7f89a55f65d9794103c5feb3 SHA1: d3ef1c424962dc703e67766097382405e2fa576d SHA256: 02689884061d6571af302fd413120b23460ee275406d58c82d2d6c8750b8dcc8 SHA512: 158da46950e62b31c98ff7ddc13308c9fe29388cc7fb591896f0ce0805d6bf6c43e0e2d6d46cac11204ad2bba772dc4e0aaebe4edf764c5fe9fb2b22d3a95925 Homepage: https://cran.r-project.org/package=fishtree Description: CRAN Package 'fishtree' (Interface to the Fish Tree of Life API) An interface to the Fish Tree of Life API to download taxonomies, phylogenies, fossil calibrations, and diversification rate information for ray-finned fishes. Package: r-cran-fishualize Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-png, r-cran-downloader, r-cran-httr, r-cran-magrittr, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-scales, r-cran-rlang, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rfishbase, r-cran-rnaturalearth, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-fishualize_0.2.3-1.ca2604.1_all.deb Size: 1019322 MD5sum: 21fc1e3f9bbf845de83a1ac469090f51 SHA1: 019b7fd72f0052a5fc101add98665bc3fec719c3 SHA256: be9a062a21b9df637c23014238f2de17bde8154210fb32506bb29f514be191f6 SHA512: d7a9e8c69027eecc40191c7aa866f699bcd196be194b30a66569c6b900c2230552a68ebd9e48d35afe478a2550440f048a8cfaa22c8cdd595210932d55c97088 Homepage: https://cran.r-project.org/package=fishualize Description: CRAN Package 'fishualize' (Color Palettes Based on Fish Species) Implementation of color palettes based on fish species. Package: r-cran-fit.models Architecture: all Version: 0.64-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-fit.models_0.64-1.ca2604.1_all.deb Size: 134248 MD5sum: d379426e4bfee42b865b321dd5dde35a SHA1: 8df62fc732aaf8d00f5016b9185d1ca8f120dc7d SHA256: 4c4abee798d9ccd15130b434657b37ca21d31cf883c0858c4826a67ca725851d SHA512: 8b8972c7752255e45d11e17a878a702f39082250f008e7e83d1d3be73e29c173685b779bab55f6c40b24e4d1ff38f62a3cd720a16b593bb473298f0ce1c2efc0 Homepage: https://cran.r-project.org/package=fit.models Description: CRAN Package 'fit.models' (Compare Fitted Models) The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002) 'Robust Library User's Guide' ). The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally. Package: r-cran-fitbitr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-checkmate, r-cran-devtools, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fitbitr_0.3.0-1.ca2604.1_all.deb Size: 446812 MD5sum: 0eaddcb4d7ecd94fb997c97b29b47ac0 SHA1: d3e87def1ce2f5bc3925a41c095485b57bf48257 SHA256: bddf05cfc872f6d3424f9f0120840b3299406b28bdc329bee0b3a80ea16f9a7d SHA512: bb71ce352e77c9e31f2fcba5a16ee02924924323eec19f8be421729c29dc67c1c8c6bf7bc8c4b3211ebfadf17e3db73d514f7810c918e0fee2828cddf2593200 Homepage: https://cran.r-project.org/package=fitbitr Description: CRAN Package 'fitbitr' (Interface with the 'Fitbit' API) Many 'Fitbit' users, and R-friendly 'Fitbit' users especially, have found themselves curious about their 'Fitbit' data. 'Fitbit' aggregates a large amount of personal data, much of which is interesting for personal research and to satisfy curiosity, and is even potentially useful in medical settings. The goal of 'fitbitr' is to make interfacing with the 'Fitbit' API as streamlined as possible, to make it simple for R users of all backgrounds and comfort levels to analyze their 'Fitbit' data and do whatever they want with it! Currently, 'fitbitr' includes methods for pulling data on activity, sleep, and heart rate, but this list is likely to grow in the future as the package gains more traction and more requests for new methods to be implemented come in. You can find details on the 'Fitbit' API at . Package: r-cran-fitbitscraper Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggthemes Filename: pool/dists/resolute/main/r-cran-fitbitscraper_0.1.8-1.ca2604.1_all.deb Size: 58774 MD5sum: 8753c20f9820737c7b612f80d05a198e SHA1: 1941c031768a2e3d66fb1969e40bd51b98f03e3a SHA256: 9e023468897ef02a425723e018609a1572d1f955cff3b1de10f3e5f2e6d201ae SHA512: 47e66a41b1c65946c000523d68fe7b342d1937003ad2681838e0f65c1e025f16ec25fa22eda7a713fbf55b4cb7d88532724b3f148d8d04bd13b1127a82351f12 Homepage: https://cran.r-project.org/package=fitbitScraper Description: CRAN Package 'fitbitScraper' (Scrapes Data from Fitbit) Scrapes data from Fitbit . This does not use the official API, but instead uses the API that the web dashboard uses to generate the graphs displayed on the dashboard after login at . Package: r-cran-fitbitviz Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-ggplot2, r-cran-lubridate, r-cran-patchwork, r-cran-data.table, r-cran-viridis, r-cran-scales, r-cran-ggthemes, r-cran-paletteer, r-cran-xml, r-cran-hms, r-cran-leaflet, r-cran-sf, r-cran-rstudioapi, r-cran-leafgl, r-cran-raster, r-cran-terra, r-cran-magrittr, r-cran-lifecycle, r-cran-reshape2 Suggests: r-cran-copernicusdem, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-rgl, r-cran-rayshader, r-cran-magick Filename: pool/dists/resolute/main/r-cran-fitbitviz_1.0.8-1.ca2604.1_all.deb Size: 2144076 MD5sum: c19242d92de7a05c761b9e330c727d5d SHA1: 12454eac9703996fda8c9c62cc75b1a0a95721b0 SHA256: 30146d0e395696416448621b1bd8f8d7cc657dcf64bc4029b5094f4904ef6f19 SHA512: f818f431c86ba9474f521c0f18cb108055ac422dd03b33cf4ad4b6602e585b84ce43ec5af0e84e4f15d8fa556341dc5a8b0ea50950ceabfef4d9152e08cca4cd Homepage: https://cran.r-project.org/package=fitbitViz Description: CRAN Package 'fitbitViz' ('Fitbit' Visualizations) Visualization of pre-downloaded 'Fitbit' personal health data using 'ggplot2' Visualizations, 'Leaflet' and 3-dimensional 'Rayshader' Maps. The 3-dimensional 'Rayshader' Map requires the installation of the 'CopernicusDEM' R package which includes the 30- and 90-meter elevation data. Package: r-cran-fitconic Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-fitconic_1.2.1-1.ca2604.1_all.deb Size: 92376 MD5sum: a3a3a7dd449aa3c50f511e7f65653efb SHA1: 9f63dd9ba32a2e3ce0d57d2ecd14163d7d40137c SHA256: da4c862a7abbbec587e121a89e7580488db631731cf2cc3588b37ba0f64150b8 SHA512: 9ee3c46103348f87941ca122c11c8881dcd928057a75f0ea8f860f65c0f75cb982be32264838ce6d936d654a48379d58881d543d397ec86e12e15e5b1a5ae5c5 Homepage: https://cran.r-project.org/package=fitConic Description: CRAN Package 'fitConic' (Fit Data to Any Conic Section) Fit data to an ellipse, hyperbola, or parabola. Bootstrapping is available when needed. The conic curve can be rotated through an arbitrary angle and the fit will still succeed. Helper functions are provided to convert generator coefficients from one style to another, generate test data sets, rotate conic section parameters, and so on. References include Nikolai Chernov (2014) "Fitting ellipses, circles, and lines by least squares" ; A. W. Fitzgibbon, M. Pilu, R. B. Fisher (1999) "Direct Least Squares Fitting of Ellipses" IEEE Trans. PAMI, Vol. 21, pages 476-48; N. Chernov, Q. Huang, and H. Ma (2014) "Fitting quadratic curves to data points", British Journal of Mathematics & Computer Science, 4, 33-60; N. Chernov and H. Ma (2011) "Least squares fitting of quadratic curves and surfaces", Computer Vision, Editor S. R. Yoshida, Nova Science Publishers, pp. 285-302. Package: r-cran-fitdistcp Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4084 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mev, r-cran-extradistr, r-cran-gnorm, r-cran-fdrtool, r-cran-pracma, r-cran-rust, r-cran-actuar, r-cran-fextremes Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fitdistcp_0.2.3-1.ca2604.1_all.deb Size: 3547634 MD5sum: fbb778ad09e5d15fb18600789b111dc0 SHA1: d446e3609dc71ca39946de05e3e8e558d51eeb06 SHA256: e71bacfc362ead8b2fbd9fa5cdb80b7d271e2546c78a1ae4c58e983ba51eb211 SHA512: 2073f9b62e98a69fe9a0f4f232b7db8f020531a48da126c5cae925e53a02abec76386b91d00128ed19fafb75076ccb9e18ec9e01f02b4c1bf5d5db0bf142bebb Homepage: https://cran.r-project.org/package=fitdistcp Description: CRAN Package 'fitdistcp' (Distribution Fitting with Calibrating Priors for Commonly UsedDistributions) Generates predictive distributions based on calibrating priors for various commonly used statistical models, including models with predictors. Routines for densities, probabilities, quantiles, random deviates and the parameter posterior are provided. The predictions are generated from the Bayesian prediction integral, with priors chosen to give good reliability (also known as calibration). For homogeneous models, the prior is set to the right Haar prior, giving predictions which are exactly reliable. As a result, in repeated testing, the frequencies of out-of-sample outcomes and the probabilities from the predictions agree. For other models, the prior is chosen to give good reliability. Where possible, the Bayesian prediction integral is solved exactly. Where exact solutions are not possible, the Bayesian prediction integral is solved using the Datta-Mukerjee-Ghosh-Sweeting (DMGS) asymptotic expansion. Optionally, the prediction integral can also be solved using posterior samples generated using Paul Northrop's ratio of uniforms sampling package ('rust'). Results are also generated based on maximum likelihood, for comparison purposes. Various model selection diagnostics and testing routines are included. Based on "Reducing reliability bias in assessments of extreme weather risk using calibrating priors", Jewson, S., Sweeting, T. and Jewson, L. (2024); . Package: r-cran-fitdistrplus Architecture: all Version: 1.2-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4002 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-rlang Suggests: r-cran-actuar, r-cran-rgenoud, r-cran-mc2d, r-cran-gamlss.dist, r-cran-knitr, r-cran-ggplot2, r-cran-generalizedhyperbolic, r-cran-rmarkdown, r-cran-hmisc, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-fitdistrplus_1.2-6-1.ca2604.1_all.deb Size: 2863916 MD5sum: 0c1f5b4d568dd201fd943eb5259dec69 SHA1: 8af2c8672274eae4e7d8a61c93a760a1625298db SHA256: 861028d8b495bd06bcabe46260e44421d5bb49515773d80b4a24fba8fd8a7c48 SHA512: ca6e686c643b8d1477d8e07152f768de90b9841056c5d05331cf161b087e322c25d98b681addc82aac8f6e6a4ac97be5780136cc639debca05b43c3a3df8f779 Homepage: https://cran.r-project.org/package=fitdistrplus Description: CRAN Package 'fitdistrplus' (Help to Fit of a Parametric Distribution to Non-Censored orCensored Data) Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation. Package: r-cran-fitdynmix Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evir, r-cran-mass, r-cran-pracma, r-cran-rdpack, r-cran-ks Filename: pool/dists/resolute/main/r-cran-fitdynmix_1.0.2-1.ca2604.1_all.deb Size: 107606 MD5sum: 546997b3df3c3656ade2e6202930ab64 SHA1: 3a088397b0b3b461edc43d565d5a73e76472ae1a SHA256: 8f2c97fa7c079cc4bf78ad6230ad9cc513f5a55778245aab4d052f49e5d07795 SHA512: 81e1604b0929057fa4b84b132625ca1d4a66ea311124fbd5ada10ff079f353406f96dbf41abff7c9e2ac1e1b7577dcbf99ab19fcee86416e8cb35eb7f1ba6d39 Homepage: https://cran.r-project.org/package=FitDynMix Description: CRAN Package 'FitDynMix' (Estimation of Dynamic Mixtures) Estimation of a dynamic lognormal - Generalized Pareto mixture via the Approximate Maximum Likelihood and the Cross-Entropy methods. See Bee, M. (2023) . Package: r-cran-fitheavytail Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1334 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-icsnp, r-cran-mvtnorm, r-cran-ghyp, r-cran-numderiv Suggests: r-cran-ggplot2, r-cran-reshape2, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fitheavytail_0.2.0-1.ca2604.1_all.deb Size: 743104 MD5sum: 807956069dc1878556b879e162ac68e5 SHA1: 7851b6a9964e68533473f6db9462b708867041dd SHA256: d9f32f41dd66f7a11b479eaa8cec5f4482b78400127c026d8a944d0eb4644e3c SHA512: 1d1b4bbee5291b514d35b0feb44c98fd6b77e2c50cce1fe77d91374a2f8fa5f82487c4b1d0c6f78a4bce4b372c11f2961118f10d19fbf37f1c0598ee361fbbe9 Homepage: https://cran.r-project.org/package=fitHeavyTail Description: CRAN Package 'fitHeavyTail' (Mean and Covariance Matrix Estimation under Heavy Tails) Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021). Package: r-cran-fitlandr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-furrr, r-cran-future.apply, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-mass, r-cran-numderiv, r-cran-plotly, r-cran-purrr, r-cran-r.utils, r-cran-rfast, r-cran-rlang, r-cran-rootsolve, r-cran-simlandr, r-cran-sparsevfc, r-cran-tidyr Suggests: r-cran-akima, r-cran-colorramps, r-cran-future, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-fitlandr_0.1.1-1.ca2604.1_all.deb Size: 289282 MD5sum: 2f774e50024a0a72a83547185c5753b2 SHA1: e3341880a2c7ef3d9ae060e90f03a48a537ae33a SHA256: 0e8c40c17e1f7cc270bf6df5e8817b6053d1f2d65fa5d70b2a803f9023d009cd SHA512: c84e72de4128a786d387e185209590c36653f27dfe2d5db2b2e6dd2eb204109844734b18f84cd312ab60bb96f8a46251d3a27340385c171e6eca28f4a4ab2d51 Homepage: https://cran.r-project.org/package=fitlandr Description: CRAN Package 'fitlandr' (Fit Vector Fields and Potential Landscapes from IntensiveLongitudinal Data) A toolbox for estimating vector fields from intensive longitudinal data, and construct potential landscapes thereafter. The vector fields can be estimated with two nonparametric methods: the Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche (2018) and the Sparse Vector Field Consensus (SparseVFC) algorithm by Ma et al. (2013) . The potential landscapes can be constructed with a simulation-based approach with the 'simlandr' package (Cui et al., 2021) , or the Bhattacharya et al. (2011) method for path integration . Package: r-cran-fitmix Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-fitmix_0.1.1-1.ca2604.1_all.deb Size: 43910 MD5sum: 7f954336eff4269187dcecae424fadd1 SHA1: ac8a1c6ffadf0ccc57c527087b6e05180dcfff9c SHA256: 629c30ef0640b4642cad6fc3a8dc5166200c4cac102a8aef2426c4aa148c3568 SHA512: 9c103077e3529fe5f181c97dea2f212f054b983fcdb0354914a6b02983e21ba1f65dfe8cc6e5167764bb0bfb014664a9d8d6da3bd5b74551cbcb6c3f5360fd12 Homepage: https://cran.r-project.org/package=fitmix Description: CRAN Package 'fitmix' (Finite Mixture Model Fitting of Lifespan Datasets) Fits the lifespan datasets of biological systems such as yeast, fruit flies, and other similar biological units with well-known finite mixture models introduced by Farewell et al. (1982) and Al-Hussaini et al. (2000) . Estimates parameter space fitting of a lifespan dataset with finite mixtures of parametric distributions. Computes the following tasks; 1) Estimates parameter space of the finite mixture model by implementing the expectation maximization (EM) algorithm. 2) Finds a sequence of four goodness-of-fit measures consist of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log-likelihood) statistics. 3)The initial values is determined by k-means clustering. Package: r-cran-fitnmr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9685 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-abind, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-gslnls Filename: pool/dists/resolute/main/r-cran-fitnmr_1.0-1.ca2604.1_all.deb Size: 7028668 MD5sum: 02aba09f6d65a6e9581dc293a3970571 SHA1: 3791c73d3253eff4f8ce281e2af1ccff06e1db6f SHA256: 059cfe0f090280b809407bef34cbd07fc0b70c4ff7064af5258b69e9f69bfdbb SHA512: 03627f9b9479f82e94e91abef0e763fdd31090dfe465b42f8c6ab9a756e608d267d39f6c9b124e4082c212c6994941d714fa01fe474b32efd85b7c28fe0c8c39 Homepage: https://cran.r-project.org/package=fitnmr Description: CRAN Package 'fitnmr' (Multidimensional Nuclear Magnetic Resonance Peak Fitting andAnalysis) Tools for fitting and analyzing 1D-4D nuclear magnetic resonance spectra with analytical models of peak shapes and peak groups. The package reads spectra in 'NMRPipe' format, builds constrained parameter structures for chemical shifts, line widths, scalar couplings, volumes, and phases, and performs nonlinear least-squares optimization for iterative peak discovery or simultaneous fits across multiple spectra. It also provides methods for visualization, preprocessing, and kinetic analysis of 1D time-series data, including automated phase optimization, solvent suppression, time-domain correction for frequency shifts and line broadening, modeling spectra as linear combinations of two component spectra, and exponential rate fitting. Package: r-cran-fitodbod Architecture: all Version: 1.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-hypergeo, r-cran-mass, r-cran-mvtnorm, r-cran-rdpack Suggests: r-cran-flextable, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridextra, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-fitodbod_1.5.5-1.ca2604.1_all.deb Size: 729300 MD5sum: 48b06c27eed9c90893cde990fa64e63d SHA1: 6653599a2f0a506500366594cecf6ed826f890c6 SHA256: 7200616789ecb0391ebcbc1b76a10e4935edfa78d1f8d823ab2720dabd9cacb6 SHA512: 80087da1be55cf672dc1975c67811169df209643ba3cb5916116111a8c7c7746211975d5e1963da8976186f9d1d66e78748b9c6eb053caaf7d663dcdd16ca219 Homepage: https://cran.r-project.org/package=fitODBOD Description: CRAN Package 'fitODBOD' (Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD) Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) ) and Alternate Binomial Distributions (ABD) (Paul (1985) ), also Journal article to use the package(). Package: r-cran-fitodbodrshiny Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-config, r-cran-flextable, r-cran-ggplot2, r-cran-golem, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyscreenshot Filename: pool/dists/resolute/main/r-cran-fitodbodrshiny_1.0.2-1.ca2604.1_all.deb Size: 2796904 MD5sum: 486ca74e54e8ec8e8cf415b325a23753 SHA1: 6cc5b7b03f1e6fc3ccf7647ca11daf646738d123 SHA256: 4b5d94b9bae053c24fe28db62e8224f06991da4c4002edbac9c95abe570ed825 SHA512: bb47a5c1bd85d14ddbee791da0da78dc570dd2a6bc80ca1c4071209a3518639aef8c3c7d4723d862b4d14b81012d7d1e57e9932cab113c88d3e2ba5d9e4e892c Homepage: https://cran.r-project.org/package=fitODBODRshiny Description: CRAN Package 'fitODBODRshiny' ('Shiny' Application for R Package 'fitODBOD') For binomial outcome data Alternate Binomial Distributions and Binomial Mixture Distributions are fitted when overdispersion is available. Package: r-cran-fitplc Architecture: all Version: 1.2-3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-car Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fitplc_1.2-3.1-1.ca2604.1_all.deb Size: 89142 MD5sum: d8566e063a26cdcc66c22ea7e5729040 SHA1: f2e8d8faec5f06d49d57df62cdfff45ed80de423 SHA256: 8b140a995710349b034fcf6761ff83ef088f664fc52d8c105ff82818c5b33e98 SHA512: e809323f3183c479c8b8b8d41800e22d972af68b4518afefa714e5789634814c57f941b55db866935666ec7071cba2981f8168f1d7e04d17495dbc3f946b2ca1 Homepage: https://cran.r-project.org/package=fitplc Description: CRAN Package 'fitplc' (Fit Hydraulic Vulnerability Curves) Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods. Package: r-cran-fitplotr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fitplotr_0.1.0-1.ca2604.1_all.deb Size: 57194 MD5sum: 61a6e29c6b5c14560982ea01036553a9 SHA1: 36353e318184d836bb5b57554dd55fab308b8741 SHA256: 71e49ce3238abc0d34b3e8554d17cbe1e230629f63edfe6365c1eb252c19f16e SHA512: e147328721371417006444fa629af365ac9b1f93ce5fc283daec50ccf270c8eb03a8dc0f49d66fd3713e089080e83e044d73580eb0cab13897b835478b633851 Homepage: https://cran.r-project.org/package=fitPlotR Description: CRAN Package 'fitPlotR' (Plotting Probability Distributions) Provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) . Package: r-cran-fitpoly Architecture: all Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-devemf, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fitpoly_4.0.0-1.ca2604.1_all.deb Size: 1860954 MD5sum: d35b38371ed32cc3ba137aa5fa5894a1 SHA1: 362d110f7df45f2c1a7e44df31bb276039d636f9 SHA256: ce9bfc80ad15696b99401c4b4136a754b82abea2a3b04c99be2d0c1e787e3828 SHA512: 59cd76915d53fcdf8562079a31c5ff9ea99037ca077da86ebea7ee623a90698b81f7e1b07abc8daac168b425be17a9a07116e0ad9af2592c6f5dedb0542bc719 Homepage: https://cran.r-project.org/package=fitPoly Description: CRAN Package 'fitPoly' (Genotype Calling for Bi-Allelic Marker Assays) Genotyping assays for bi-allelic markers (e.g. SNPs) produce signal intensities for the two alleles. 'fitPoly' assigns genotypes (allele dosages) to a collection of polyploid samples based on these signal intensities. 'fitPoly' replaces the older package 'fitTetra' that was limited (a.o.) to only tetraploid populations whereas 'fitPoly' accepts any ploidy level. Reference: Voorrips RE, Gort G, Vosman B (2011) . New functions added on conversion of data from SNP array software formats, drawing of XY-scatterplots with or without genotype colors, checking against expected F1 segregation patterns, comparing results from two different assays (probes) for the same SNP, recovery from a saveMarkerModels() crash. Package: r-cran-fitps Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-dplyr, r-cran-hmisc, r-cran-iterators, r-cran-knitr, r-cran-ks, r-cran-pbapply, r-cran-rdpack, r-cran-readxl, r-cran-vgam Suggests: r-cran-sp, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-fitps_1.0.1-1.ca2604.1_all.deb Size: 486120 MD5sum: da2ae43ea4ed57e3a1a33d9f7f8c2381 SHA1: 5f9aac156026a61fcdf44dcdce6b670591b83652 SHA256: a6f51d31a4bd2f5dc84a7244ff426867cc90a32a9b335452225defb68cd78295 SHA512: 39a0d0919775ee8d0038c0cae80e5644e6d944835755835288ef91196c34ce8e803a5ab9d88024632f04d667a9a238e4318556c801b6c7443e3742af310438a1 Homepage: https://cran.r-project.org/package=fitPS Description: CRAN Package 'fitPS' (Fit Zeta Distributions to Forensic Data) Fits Zeta distributions (discrete power laws) to data that arises from forensic surveys of clothing on the presence of glass and paint in various populations. The general method is described to some extent in Coulson, S.A., Buckleton, J.S., Gummer, A.B., and Triggs, C.M. (2001) , although the implementation differs. Package: r-cran-fitscape Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fitscape_0.1.0-1.ca2604.1_all.deb Size: 25004 MD5sum: 6cf74917c8f545259b60055e3ba1c8b1 SHA1: 99af1388fff7c1e82015decbe755f8e50e941fea SHA256: cb130a6375ed866ae5d652b661741b723e9c10b2f2c04578df4923e838790a22 SHA512: 10c36a31dec24617da18ddb5d32f7f595a5dad769bbd102f69a5c58cc569969aedc5bec0d3331cd762797b18d677ac7d5638d2d42c2a5b3b796d902586ccfc0d Homepage: https://cran.r-project.org/package=fitscape Description: CRAN Package 'fitscape' (Classes for Fitness Landscapes and Seascapes) Convenient classes to model fitness landscapes and fitness seascapes. A low-level package with which most users will not interact but upon which other packages modeling fitness landscapes and fitness seascapes will depend. Package: r-cran-fitsio Architecture: all Version: 2.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fitsio_2.1-6-1.ca2604.1_all.deb Size: 139678 MD5sum: 0d5734035ff396bd748d368e69bc9936 SHA1: 691c6bc4cd5b9eba7d11d368c6ce0d8140f6b47e SHA256: d60d3ca15b1413a40f1a894875f053776994b43d20450ee6f591eab020dcc424 SHA512: ebe93666b6647ae797e546067dbf52c7eaf20b62e923da9d2686bd1c594cf6bd6f615eadd79f0e9dddbfc8713caf0293f848fc46acf7bbe5bc61b7782943e7f6 Homepage: https://cran.r-project.org/package=FITSio Description: CRAN Package 'FITSio' (FITS (Flexible Image Transport System) Utilities) Utilities to read and write files in the FITS (Flexible Image Transport System) format, a standard format in astronomy (see e.g. for more information). Present low-level routines allow: reading, parsing, and modifying FITS headers; reading FITS images (multi-dimensional arrays); reading FITS binary and ASCII tables; and writing FITS images (multi-dimensional arrays). Higher-level functions allow: reading files composed of one or more headers and a single (perhaps multidimensional) image or single table; reading tables into data frames; generating vectors for image array axes; scaling and writing images as 16-bit integers. Known incompletenesses are reading random group extensions, as well as complex and array descriptor data types in binary tables. Package: r-cran-fitter Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-shiny, r-cran-dplyr, r-cran-maxlik, r-cran-r.utils Suggests: r-cran-actuar, r-cran-ald, r-cran-benchden, r-cran-biasedurn, r-cran-bridgedist, r-cran-davies, r-cran-discreteinverseweibull, r-cran-discretelaplace, r-cran-discreteweibull, r-cran-emdbook, r-cran-emg, r-cran-envstats, r-cran-evd, r-cran-evir, r-cran-extdist, r-cran-extremefit, r-cran-fadist, r-cran-fattailsr, r-cran-fbasics, r-cran-fextremes, r-cran-flexsurv, r-cran-gambin, r-cran-genbinomapps, r-cran-generalizedhyperbolic, r-cran-gld, r-cran-gldex, r-cran-glogis, r-cran-gsm, r-cran-hermite, r-cran-hyperbolicdist, r-cran-kscorrect, r-cran-loglognorm, r-cran-marg, r-cran-mc2d, r-cran-minimax, r-cran-msm, r-cran-normallaplace, r-cran-normalp, r-cran-paretoposstable, r-cran-pearsonds, r-cran-poistweedie, r-cran-polyaaeppli, r-cran-qmap, r-cran-qrm, r-cran-reins, r-cran-reliar, r-cran-renext, r-cran-revdbayes, r-cran-rmkdiscrete, r-cran-rmtstat, r-cran-sadists, r-cran-skellam, r-cran-skewhyperbolic, r-cran-skewt, r-cran-smr, r-cran-sn, r-cran-stabledist, r-cran-statmod, r-cran-trapezoid, r-cran-triangle, r-cran-truncnorm, r-cran-variancegamma Filename: pool/dists/resolute/main/r-cran-fitter_0.2.0-1.ca2604.1_all.deb Size: 73068 MD5sum: eb895416114bb7dc98f99f5ba524f99b SHA1: 7563915e9663ed40e14067e3fbeb6aea68cbd1c7 SHA256: 1718266bee0c09050305251996b78150810d8f4e020e514d1d5e8fc6dd4d16b1 SHA512: 39d154151bac92dd360c3d7214151c699eced0bb47ce8ae57706ed57c47bc6ec42f6c2695ba1a1699b30775f7b4025f097d539eb53496f414fc7021702015b31 Homepage: https://cran.r-project.org/package=fitteR Description: CRAN Package 'fitteR' (Fit Hundreds of Theoretical Distributions to Empirical Data) Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a 'shiny' app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test. Package: r-cran-fitultd Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-adgoftest, r-cran-fitdistrplus, r-cran-assertthat, r-cran-mass, r-cran-purrr, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-fitultd_3.1.0-1.ca2604.1_all.deb Size: 60916 MD5sum: 05823c0c402f7eff9c2b148a48450ca9 SHA1: e0a43921fee443d1018403c915713a55ce878bf5 SHA256: b7cb4ff397b4f9067294a4530ebf968ac71a43b207bf6a85fdd25a9ea4237808 SHA512: 4ab2cf96b3ab853d9c8ecf202de0ffae31d8c14c34a49d8129518c8c7232af8d29e32d057224f36fcac85bf3b0ea26e06c562fc5c20da5c8903ea415ef75aa73 Homepage: https://cran.r-project.org/package=FitUltD Description: CRAN Package 'FitUltD' (Fit Univariate Mixed and Usual Distributions) Extends the fitdist() (from 'fitdistrplus') adding the Anderson-Darling ad.test() (from 'ADGofTest') and Kolmogorov Smirnov Test ks.test() inside, trying the distributions from 'stats' package by default and offering a second function which uses mixed distributions to fit, this distributions are split with unsupervised learning, with Mclust() function (from 'mclust'). Package: r-cran-fitur Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus, r-cran-actuar, r-cran-e1071, r-cran-ggplot2, r-cran-goftest, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fitur_0.6.2-1.ca2604.1_all.deb Size: 125432 MD5sum: f9103df1706987fe62baf05b691c71e3 SHA1: 601bdeede66f24e023dec9f6ff92942234ab0ec0 SHA256: 9ca62dd231aa5613cf151d0a608f20712c9128f949170488425b201bbde367a1 SHA512: 4aa6666eaf7a83ec9c07e0585751238e5e1ceecb5d3cc6f5f0a7a065aefb0eb36d0f0e2f44d147f7a8602c928c3a72ac9526a9d345ec02ae509144a53ed406e6 Homepage: https://cran.r-project.org/package=fitur Description: CRAN Package 'fitur' (Fit Univariate Distributions) Wrapper for computing parameters for univariate distributions using MLE. 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Package: r-cran-fitvarmxid Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-simstatespace Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fitvarmxid_1.0.3-1.ca2604.1_all.deb Size: 182382 MD5sum: c7ae393bd6e4503e3f9ed47dbaa324e8 SHA1: a51948164ea600094cc1409ec58e4f9986cb514f SHA256: bea347e8595acbe88f1346da803d8559c17eff74bb40839f1e39b8dc27826527 SHA512: 2032f3ce0a7004dce7115aa75d084375cbe94c3a0be9c363b6ed37707c07b71dcce1c35e169d5748feb2eaaa96f051aa9a2b2ddef95410476a6feaef8b3f2068 Homepage: https://cran.r-project.org/package=fitVARMxID Description: CRAN Package 'fitVARMxID' (Fit the Vector Autoregressive Model for Multiple Individuals) Fit the vector autoregressive model for multiple individuals using the 'OpenMx' package (Hunter, 2017 ). 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'fitzRoy' provides a range of functions for accessing publicly available data from 'AFL Tables' , 'Footy Wire' and 'The Squiggle' . Further functions allow for easy processing, cleaning and transformation of this data into formats that can be used for analysis. 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The package's name derives from a play on the fact that lipid scrambling is also sometimes referred to as 'flipping'. The package is originally published as Cotton, R.J., Ploier, B., Goren, M.A., Menon, A.K., and Graumann, J. (2017). "flippant–An R package for the automated analysis of fluorescence-based scramblase assays." BMC Bioinformatics 18, 146. . 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The full variety of 'brms' formula-based effects structures are available to use in multiple classes of occupancy model, including single-season models, models with data augmentation for never-observed species, dynamic (multiseason) models with explicit colonization and extinction processes, and dynamic models with autologistic occupancy dynamics. Formulas can be specified for all relevant distributional terms, including detection and one or more of occupancy, colonization, extinction, and autologistic depending on the model type. Several important forms of model post-processing are provided. References: Bürkner (2017) ; Carpenter et al. (2017) ; Socolar & Mills (2023) . 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The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688). 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Lee, Adam, Kang, & Whittaker (2023). . This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036. 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Package: r-cran-fluorojip Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fluorojip_0.1.1-1.ca2604.1_all.deb Size: 498804 MD5sum: 6c3e6c317b0eee1618d7003f06a1ab26 SHA1: c36f0ca208f3a1f81e9aa4fb925b5adbebab6dc2 SHA256: 1af4eba0f024483bcfd23aba40e56ded383c2dda7aa533516dd6ebe8c0452e19 SHA512: 1cf7d3a017cc0a5c32aba753a89c14c2c50831a25b944551ee9382fe5482beb06c6199a7135c88b6b843a98947019ab5a14e48baa82a5c393ea60812bb206f9f Homepage: https://cran.r-project.org/package=fluorojip Description: CRAN Package 'fluorojip' (Analysis of Chlorophyll a Fluorescence Transient Parameters) Computes chlorophyll a fluorescence transient parameters from fluorescence summary data, including minimum and maximum fluorescence, selected transient steps, and area. Provides standard photosynthetic performance indices and fluxes per reaction center and per cross section. Includes helpers to read exported trace tables in supported 'csv' formats and validation workflows based on bundled example files, as well as visualization tools and an interactive 'shiny' interface. The implemented calculations are based on Strasser et al. (2004) and Stirbet and Govindjee (2011) . Package: r-cran-fluosurv Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/resolute/main/r-cran-fluosurv_1.0.0-1.ca2604.1_all.deb Size: 982550 MD5sum: 5dc42370242aebb507e460e8f48e3dea SHA1: 00290201f03802ff305b59cc24b9a53d88402877 SHA256: 4b4f5bfefe2f2055b5342ace042aaf77f357b227fe25a9370d1771005ffd43c1 SHA512: bd7f48f952a7846b03eb47c0d14c209fbaaa2905ec8943f3afc3d261384b29db6e6cfda6e2e8d97e44d4efab5f6e315c2db08c9dfce84b8264e9f9dd064a9632 Homepage: https://cran.r-project.org/package=fluoSurv Description: CRAN Package 'fluoSurv' (Estimate Insect Survival from Fluorescence Data) Use spectrophotometry measurements performed on insects as a way to infer pathogens virulence. Insect movements cause fluctuations in fluorescence signal, and functions are provided to estimate when the insect has died as the moment when variance in autofluorescence signal drops to zero. The package provides functions to obtain this estimate together with functions to import spectrophotometry data from a Biotek microplate reader. Details of the method are given in Parthuisot et al. (2018) . Package: r-cran-fluspect Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-fluspect_1.0.0-1.ca2604.1_all.deb Size: 54524 MD5sum: 58f337ea31c3400e83ba76f17a29afc2 SHA1: 1cd98683cf22086c43f8f4b559da839ab53cd427 SHA256: 1102911687a5cac569d72f1d8e78046fdede3b02e9030886ba85e7af8da7a318 SHA512: 1b9cf992bd5714d94a8ee553ecf8836da434e3bd22e4ef260afc731550dc57ee6d7551ac396083d70d6b2e14db5181199ab26759c795fc955c5da09ccd95c3b0 Homepage: https://cran.r-project.org/package=fluspect Description: CRAN Package 'fluspect' (Fluspect-B) A model for leaf fluorescence, reflectance and transmittance spectra. It implements the model introduced by Vilfan et al. (2016) . Fluspect-B calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II. Package: r-cran-flux Architecture: all Version: 0.3-0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-catools Filename: pool/dists/resolute/main/r-cran-flux_0.3-0.1-1.ca2604.1_all.deb Size: 603394 MD5sum: 980e9e9ff003707e2e11a8fe2611b7c6 SHA1: 3ad4b4ba6019464d16414e3872390e4f750ed688 SHA256: 36bb131c92b91441df73803f35c44490ec59728d116149e0067968e5d506eda4 SHA512: 560bc4d9e17d552e56537e3828c15bbd3b3250494f460799edf4e63d612b3578bfb41cc6bf4877f619757da888da75eb355d11bb52694b17a89aae0b2118be10 Homepage: https://cran.r-project.org/package=flux Description: CRAN Package 'flux' (Flux Rate Calculation from Dynamic Closed Chamber Measurements) Functions for the calculation of greenhouse gas flux rates from closed chamber concentration measurements. The package follows a modular concept: Fluxes can be calculated in just two simple steps or in several steps if more control in details is wanted. Additionally plot and preparation functions as well as functions for modelling gpp and reco are provided. Package: r-cran-fluxfinder Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-jsonlite, r-cran-lubridate, r-cran-mass Suggests: r-cran-gasfluxes, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-fluxfinder_1.2.2-1.ca2604.1_all.deb Size: 190328 MD5sum: 321b58e16ba3178757762f5bce8d9f2d SHA1: f7186a20a64ee2fab76c234f771dbdb329c192e8 SHA256: 03886929d259c546f81b8ab9b27d438f1c4b02817ae401d75c2c056f7da28a51 SHA512: 6cdef07adb881d2b42f7bb47a09ac88c0a57b7ecf545bf6e9f1fa255aad3ef258ae636da76450f1635a22716c701d0f7576867541827af2d36cd5843aac8f714 Homepage: https://cran.r-project.org/package=fluxfinder Description: CRAN Package 'fluxfinder' (Parsing, Computation, and Diagnostics for Greenhouse GasMeasurements) Parse static-chamber greenhouse gas measurement files generated by a variety of instruments; compute flux rates using multi-observation metadata; and generate diagnostic metrics and plots. Designed to be easy to integrate into reproducible scientific workflows. Package: r-cran-fluxfixer Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gsignal, r-cran-lubridate, r-cran-magrittr, r-cran-ranger, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-xts, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fluxfixer_1.1.0-1.ca2604.1_all.deb Size: 1497748 MD5sum: e7935d5aeffdee6d026988bffc025f9c SHA1: b34a99e4ec3d373947ee1fad12755a21c5300865 SHA256: aee5bc54459c9ef6fcfe5e13dd6ad8f3c1c690fe0727161cae6a8df719e81d86 SHA512: cb10997d7dff55f3760e153cf39ef07de5db6bbc1fc84f289221d743061d0396185f9d9fc859063368dd8ae4ece19beddaa6c4f0ef302acfed32f11a90212232 Homepage: https://cran.r-project.org/package=fluxfixer Description: CRAN Package 'fluxfixer' (Advanced Framework for Sap Flow Data Post-Process) Provides a flexible framework for post-processing thermal dissipation sap flow data using statistical methods and machine learning. This framework includes anomaly correction, outlier removal, gap-filling, trend removal, signal damping correction, and sap flux density calculation. The functions in this package can also apply to other time series with various artifacts. Package: r-cran-fluxible Architecture: all Version: 1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-haven, r-cran-lubridate, r-cran-rlang, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-zoo, r-cran-progress, r-cran-purrrlyr, r-cran-tidyselect, r-cran-lifecycle, r-cran-forcats, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-tidyverse, r-cran-fs, r-cran-licoread, r-cran-readr Filename: pool/dists/resolute/main/r-cran-fluxible_1.3.6-1.ca2604.1_all.deb Size: 2513886 MD5sum: d61537f2b42fcdc09bdace3f5a861b5b SHA1: 44d547851a4b18d08acd4f5239194dfc1957eb90 SHA256: 1f732a29623a6ab9b54e426a029df74500f3dd9ef30d0e95d0dbfa1e1d34d478 SHA512: 622189515475a77f8041d74efdb2caee9fc375cff7238e188cafcf09a86acdb0d405fa1edbb6ddc39d58f539864bd0c3c2511b74b5fc518b71d693938380ad64 Homepage: https://cran.r-project.org/package=fluxible Description: CRAN Package 'fluxible' (Ecosystem Gas Fluxes Calculations for Closed Loop Chamber Setup) Toolbox to process raw data from closed loop flux chamber (or tent) setups into ecosystem gas fluxes usable for analysis. It goes from a data frame of gas concentration over time (which can contain several measurements) and a meta data file indicating which measurement was done when, to a data frame of ecosystem gas fluxes including quality diagnostics. Organized with one function per step, maximizing user flexibility and backwards compatibility. Different models to estimate the fluxes from the raw data are available: exponential as described in Zhao et al (2018) , exponential as described in Hutchinson and Mosier (1981) , quadratic, and linear. Other functions include quality assessment, plotting for visual check, calculation of fluxes based on the setup specific parameters (chamber size, plot area, ...), gross primary production and transpiration rate calculation, and light response curves. Package: r-cran-fluxpoint Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockmatrix, r-cran-corpcor, r-cran-doparallel, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-matrix, r-cran-nnls, r-cran-pracma, r-cran-simdesign Filename: pool/dists/resolute/main/r-cran-fluxpoint_0.1.2-1.ca2604.1_all.deb Size: 134134 MD5sum: e7fdb6d996897c1e6a953a3fa82f65e5 SHA1: de15e7cfb4fd4a8e9cde94586ab186141db0b238 SHA256: 437244d3d0b43145b9d11ae59c306ecdac85a723e61317c905559d826c46cfe8 SHA512: 63dd752e085c67d8ba26aaa90f8ae5615e846a5d56cba089b092d38c7731ebcc483d930a2ddea5ef713e1645735b498eda4a43ca242f767cf18143e4ec6e6de0 Homepage: https://cran.r-project.org/package=FluxPoint Description: CRAN Package 'FluxPoint' (Change Point Detection for Non-Stationary and Cross-CorrelatedTime Series) Implements methods for multiple change point detection in multivariate time series with non-stationary dynamics and cross-correlations. The methodology is based on a model in which each component has a fluctuating mean represented by a random walk with occasional abrupt shifts, combined with a stationary vector autoregressive structure to capture temporal and cross-sectional dependence. The framework is broadly applicable to correlated multivariate sequences in which large, sudden shifts occur in all or subsets of components and are the primary targets of interest, whereas small, smooth fluctuations are not. Although random walks are used as a modeling device, they provide a flexible approximation for a wide class of slowly varying or locally smooth dynamics, enabling robust performance beyond the strict random walk setting. Package: r-cran-fluxseparator Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-hmr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-ttr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fluxseparator_1.0.1-1.ca2604.1_all.deb Size: 89444 MD5sum: 409069cb9cd1818e32ef199feac10928 SHA1: e22043b422e07b295c1c0cc06bd381cba442e998 SHA256: 521664b10901d571c75a1a4fa297e5fdcfff90095ffb6f086b8973aa7a80c7dd SHA512: 3fb87488ccfe772121f03d6d39902d386105fe5ba825c9bf43af24570edb89caf08851ab4997d87ace0d1e68b8327c201f7e1071b3c380f696121b56d599d9ea Homepage: https://cran.r-project.org/package=FluxSeparator Description: CRAN Package 'FluxSeparator' (Separation of Diffusive and Ebullitive Fluxes) Separates diffusive and ebullitive (bubble) fluxes from continuous concentration measurements using a running variance approach. Ebullitive events are identified when the running variance exceeds a user-set threshold. Diffusive fluxes are calculated via linear regression on the non-ebullitive portion of the data. See Sø et al. (2024) for details. Package: r-cran-fluxtools Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plotly, r-cran-readr, r-cran-shiny, r-cran-tibble Suggests: r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-shinywidgets, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fluxtools_0.7.1-1.ca2604.1_all.deb Size: 270874 MD5sum: b7d7172a8b54a469397a75b97902c258 SHA1: 69e0d7160fa476ebf9cb3ee60353d160d039502f SHA256: b6324bd2bed45dc6955fe22829547e1826c09bb63a658d2f1f6f3b55c7e3da7a SHA512: 25828306224edc547f03c19976bf3330b135be411a0648258c93ecf252d1b83c2b1fc288c634975defdadbe7d1f6b5310ebab564f9988df7eec66b9e780f45c3 Homepage: https://cran.r-project.org/package=fluxtools Description: CRAN Package 'fluxtools' (A 'shiny' App for Reproducible QA/QC of Eddy Covariance Data) An interactive 'shiny'-based tool for exploration and quality assurance and quality control (QA/QC) of eddy covariance flux tower data processing. It generates data-point removal code via user-directed selection from a scatterplot, and can export a cleaned .csv with removed points set to NA plus an R script for reproducibility. Reference: Key (2025) . Package: r-cran-fluxweb Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-fluxweb_2.0.1-1.ca2604.1_all.deb Size: 265516 MD5sum: 8bd1265236117f084cbd044232aadb4a SHA1: 40d9da06290d1fbbb9c27a1bb3f98192319acd73 SHA256: 720c77cccb66d802a8e1e5ac503e8b261471d12bde91b105576cdcfd0cecdbf2 SHA512: 74ca0e8fad44d05c735b70c4a0598648a259ba2bd39df4aa5f25f654287dbaac56dda1b5621d07da47254830060a59dddbcb6d916e986f8334e9d35eb5f54f0a Homepage: https://cran.r-project.org/package=fluxweb Description: CRAN Package 'fluxweb' (Estimate Energy Fluxes in Food Webs) Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) to calculate energy fluxes, which are also used to calculate equilibrium stability. Package: r-cran-fma Architecture: all Version: 2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-fma_2.5-1.ca2604.1_all.deb Size: 473602 MD5sum: b07654f3c1d7be6621e3ec989a1a2225 SHA1: a0da8341e9bef75eb4398b1af9f8a53f3f4237a6 SHA256: 059b73e905658cd0c93d63c96c88a8970516d8759b2f602de01195165e28ffda SHA512: 565bcc28334b196416134dc7340ee91217f370121eab4df75b609f104f71ffed24a7a46c228dbe09fcc804d7417bbcf535aebed7c710aa47c7da5b6603387324 Homepage: https://cran.r-project.org/package=fma Description: CRAN Package 'fma' (Data Sets from "Forecasting: Methods and Applications" byMakridakis, Wheelwright & Hyndman (1998)) All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) . Package: r-cran-fmadist Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus, r-cran-actuar, r-cran-envstats, r-cran-extradistr, r-cran-mass, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-fmadist_0.1.2-1.ca2604.1_all.deb Size: 40944 MD5sum: fb4bfc1a78b726de59ba7fd5a7f35b09 SHA1: 6bf5caa651069866d786482a04474437f184e25e SHA256: 3d82b0a8e19284cad341a43a5c9595d66c138e2c93bfa48fd234c4209a460439 SHA512: fa2b084b4dfe93608c352c9ebf499c145a7619ae10e8efc1c474d3fee9e9869cac658f85f99c7d84ed7fa80fbd5c8a0c067e1582031b07621c5fd07d0e3d8835 Homepage: https://cran.r-project.org/package=FMAdist Description: CRAN Package 'FMAdist' (Frequentist Model Averaging Distribution) Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586. Package: r-cran-fmat Architecture: all Version: 2026.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-data.table, r-cran-stringr, r-cran-forcats, r-cran-rvest, r-cran-psych, r-cran-irr, r-cran-glue, r-cran-crayon, r-cran-cli, r-cran-purrr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-brucer, r-cran-psychwordvec, r-cran-text, r-cran-sweater, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-fmat_2026.1-1.ca2604.1_all.deb Size: 116354 MD5sum: 239cdec996bb08164cc5cee08e59fde4 SHA1: 060425f935a3b706e1d92b6f8ecc810c7e36d3ee SHA256: 6646e5964e7007adcf947ee681e310fc4387a78b6f05028b3041880bc319cf25 SHA512: 552203e37e870af71d36ab4a8518b51cc83988978eed3532661854df8e2949aeb90fb0ebac4458521c0d30bb97b58a7dd31e1f260e3d5db1efa6afde895374a9 Homepage: https://cran.r-project.org/package=FMAT Description: CRAN Package 'FMAT' (The Fill-Mask Association Test) The Fill-Mask Association Test ('FMAT') is an integrative, probability-based social computing method using Masked Language Models to measure conceptual associations (e.g., attitudes, biases, stereotypes, social norms, cultural values) as propositional semantic representations in natural language. Supported language models include 'BERT' and its variants available at 'Hugging Face' . Methodological references and installation guidance are provided at . Package: r-cran-fmc Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalrsd Filename: pool/dists/resolute/main/r-cran-fmc_1.0.1-1.ca2604.1_all.deb Size: 18752 MD5sum: 9bbfb852dd3025a908dd0a1d86957dc5 SHA1: 380c6cfc9bea3cc68f283f3e06d66c6d760412a1 SHA256: bd1592b28c8b466ad9fb5db530d7bda507abddef1800dbb32cc9ec22fdba0759 SHA512: 1063864e58075a27ae81a71135ab05598298bd64663adf5a082a8d9c35f9b8641fe8dde1ca18a6104f7a9ba7c253cf9eb50d3f11de7651fac6fc6dc9ae472c16 Homepage: https://cran.r-project.org/package=FMC Description: CRAN Package 'FMC' (Factorial Experiments with Minimum Level Changes) Generate cost effective minimally changed run sequences for symmetrical as well as asymmetrical factorial designs. Package: r-cran-fmcensskewreg Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-momtrunc, r-cran-mnormt, r-cran-sn, r-cran-truncdist, r-cran-mixsmsn Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fmcensskewreg_0.1.1-1.ca2604.1_all.deb Size: 119940 MD5sum: ed21bb98ce0670040175946b772980f6 SHA1: 2ae4b1986e689ed004ed32edf5ecf56a1f443314 SHA256: 606369b210671bceedcf88b6af9a349e9a81bf74fef1f4f43b7a4f9e1f0f3d85 SHA512: 439e67a0e76b1d9d2dbe7dca0c2fd0435e0791493bcf87dbeebe6f8b79fec5ccbf09e880f55f83bf52490ca2fc3cafdedfc8c8e1dfbdaad5838bb406c13600d8 Homepage: https://cran.r-project.org/package=FMCensSkewReg Description: CRAN Package 'FMCensSkewReg' (Finite Mixture of Censored Regression Models with SkewedDistributions) Provides an implementation of finite mixture regression models for censored data under four distributional families: Normal (FM-NCR), Student t (FM-TCR), skew-Normal (FM-SNCR), and skew-t (FM-STCR). The package enables flexible modeling of skewness and heavy tails often observed in real-world data, while explicitly accounting for censoring. Functions are included for parameter estimation via the Expectation-Maximization (EM) algorithm, computation of standard errors, and model comparison criteria such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Efficient Determination Criterion (EDC). The underlying methodology is described in Park et al. (2024) . Package: r-cran-fmcmc Architecture: all Version: 0.5-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1839 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-mass, r-cran-matrix Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-mcmc, r-cran-tinytest, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-fmcmc_0.5-2-1.ca2604.1_all.deb Size: 1350590 MD5sum: 0cfff4d9f9702cdde89dda2f10883603 SHA1: c9f6a08e86d46ce2efa8bfd008cfff3d8dea9d7b SHA256: 143b20967728a3420535f6c8bb7ef150b09db844fea90a9f17b9745bebea3f3a SHA512: ebb9b1c79f8447e788b89c215804000a4a260f91693e91949e53a714d94caf5df0634f0e0c1a8330de8d12f15e698d3bdaef2c447e8c1e4c31077e890b90950e Homepage: https://cran.r-project.org/package=fmcmc Description: CRAN Package 'fmcmc' (A friendly MCMC framework) Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) Adaptive Metropolis, Vihola (2012) Robust Adaptive Metropolis, and Thawornwattana et al. (2018) Mirror transition kernels. Package: r-cran-fmeffects Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-partykit, r-cran-ggparty, r-cran-ggplot2, r-cran-cowplot, r-cran-r6, r-cran-testthat Suggests: r-cran-caret, r-cran-furrr, r-cran-future, r-cran-hexbin, r-cran-knitr, r-cran-mlr3verse, r-cran-parallelly, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-tidymodels Filename: pool/dists/resolute/main/r-cran-fmeffects_0.1.4-1.ca2604.1_all.deb Size: 2617110 MD5sum: c276e08778397f82cd6bd9938ba4c1de SHA1: a0d952c8755a041cb4201c26ffa5aefc6ce1e3ba SHA256: 96862ece3b53659c8a4798930f438d066a5a08386c6676bde2ebaff9cd6801c1 SHA512: cb768bdd411d3bb2f6e62d4248d124160811bdbeb1b02a0f1db38adef02b82a776e4b78c1a8af81f9a64ca999f61c0b43052c3b9003ec87db2baca95297b428d Homepage: https://cran.r-project.org/package=fmeffects Description: CRAN Package 'fmeffects' (Model-Agnostic Interpretations with Forward Marginal Effects) Create local, regional, and global explanations for any machine learning model with forward marginal effects. 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(2014) ). Package: r-cran-fmsb Architecture: all Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-epi, r-cran-vcd Filename: pool/dists/resolute/main/r-cran-fmsb_0.7.6-1.ca2604.1_all.deb Size: 398052 MD5sum: 0cd775fb5986c163c49e84d51c8db08d SHA1: 3e58a3d22f8d1aedfd3d7cf6c49db84268ef7445 SHA256: 6d2ae31b5308341d0c09b9f514fe802eeb74c938c429401290ef2145297fe31d SHA512: 66eac7aea861708e4c7b7750c37d5d5beae30d64230a0547b851656c69128a5af83e2d73803d8ba88f37a09667da884dfca3b758f72c0cc586ece4513795c83e Homepage: https://cran.r-project.org/package=fmsb Description: CRAN Package 'fmsb' (Functions for Medical Statistics Book with some Demographic Data) Several utility functions for the book entitled "Practices of Medical and Health Data Analysis using R" (Pearson Education Japan, 2007) with Japanese demographic data and some demographic analysis related functions. 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Provides multiple Diebold-Mariano test implementations based on fixed-smoothing approaches, including fixed-b methods such as Kiefer and Vogelsang (2005) , and applications to tests for equal predictive accuracy as in Coroneo and Iacone (2020) , alongside conventional large-sample approximations. HAR inference involves nonparametric estimation of the long-run variance, and a key tuning parameter (the truncation parameter) trades off size and power. Lazarus, Lewis, and Stock (2021) theoretically characterize the size-power frontier for the Gaussian multivariate location model. 'ForeComp' computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute finite-sample size and power, it fits a best approximating ARMA process to the input data and reports how the truncation parameter performs and how robust testing outcomes are to its choice. 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In particular, a carbon sequestration potential productivity calculation method based on the potential mean annual increment is proposed. This package is applicable to both natural forests and plantations. It can quantitatively assess stand’s potential productivity, realized productivity, and possible improvement under certain site, and can be used in many aspects such as site quality assessment, tree species suitability evaluation, and forest degradation evaluation. Reference: Lei X, Fu L, Li H, et al (2018) . Fu L, Sharma R P, Zhu G, et al (2017) . 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This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) , and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) . Package: r-cran-forestecology Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1673 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-glue, r-cran-ggplot2, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggridges, r-cran-mvnfast, r-cran-sf, r-cran-sfheaders, r-cran-snakecase, r-cran-tibble, r-cran-yardstick, r-cran-forcats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-patchwork, r-cran-blockcv Filename: pool/dists/resolute/main/r-cran-forestecology_0.2.1-1.ca2604.1_all.deb Size: 1344598 MD5sum: d561a305bc227a473389ec502218133d SHA1: ec1457d9629bd577fdc21bfa5daf570b0cf6db85 SHA256: 4e13607d51dcc8c9e96fd9d50b6bdaf25e783410659caf68352730e5579ea965 SHA512: 4bf20e3c6135b23ff9899369246f1e261646e05f4306231c78ad12ca1d4ffc3f032a4725acdce81cb2c60ecf492730f33f62fef2e187988aae033a2fa03c4984 Homepage: https://cran.r-project.org/package=forestecology Description: CRAN Package 'forestecology' (Fitting and Assessing Neighborhood Models of the Effect ofInterspecific Competition on the Growth of Trees) Code for fitting and assessing models for the growth of trees. In particular for the Bayesian neighborhood competition linear regression model of Allen (2020): methods for model fitting and generating fitted/predicted values, evaluating the effect of competitor species identity using permutation tests, and evaluating model performance using spatial cross-validation. Package: r-cran-forested Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-forested_0.2.0-1.ca2604.1_all.deb Size: 852594 MD5sum: e92eefa7e0ad99bd72a97e168f184736 SHA1: e03a0b59a2ade7f16d5d03e37f797584c9f28a93 SHA256: f94ee3c60fc69ac896500396bc8bbd1d1ca9b2d868f74a25127d8cbd5a0cc246 SHA512: e103913ded265a9e4f692e992a606641180cb62a3c08fea46c6ce61dba385dadfd2a403d80b976b15ca8429c9c864b3d10fa4f928c588f6638c6ec8b1141f2af Homepage: https://cran.r-project.org/package=forested Description: CRAN Package 'forested' (Forest Attributes in U.S. States) A small subset of plots throughout the U.S. are sampled and assessed "on-the-ground" as forested or non-forested by the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program, but the FIA also has access to remotely sensed data for all land in the country. The 'forested' package contains data frames intended for use in predictive modeling applications where the more easily-accessible remotely sensed data can be used to predict whether a plot is forested or non-forested. Currently, the package provides data for Washington and Georgia. Package: r-cran-forestelementsr Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-magrittr, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-rdpack, r-cran-tidyr, r-cran-vctrs, r-cran-stringr, r-cran-tidyselect, r-cran-doby Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-forestelementsr_2.2.0-1.ca2604.1_all.deb Size: 1288970 MD5sum: 88c4406bdcd6c4818655756f4dfd5f62 SHA1: 5fe0b5e219cdc8f5ad7608f52b407487c4952167 SHA256: 58f82ddc1bfe97065b61e48a5caa4cb62a36b3f34c616e13d8fe00e34ec7de34 SHA512: ff15e93ad4d13146a6d6c184788859139c2b28d3218f0810ad2395cfbd10c1a1f9ea4a434b048d37167398e7f962d466f7071e9fda56cf347d4d488bb07186ce Homepage: https://cran.r-project.org/package=ForestElementsR Description: CRAN Package 'ForestElementsR' (Data Structures and Functions for Working with Forest Data) Provides generic data structures and algorithms for use with forest mensuration data in a consistent framework. The functions and objects included are a collection of broadly applicable tools. More specialized applications should be implemented in separate packages that build on this foundation. Documentation about 'ForestElementsR' is provided by three vignettes included in this package. For an introduction to the field of forest mensuration, refer to the textbooks by Kershaw et al. (2017) , and van Laar and Akca (2007) . Package: r-cran-foresterror Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-purrr Suggests: r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-foresterror_1.1.0-1.ca2604.1_all.deb Size: 54598 MD5sum: 4c6fe44cd7fecf6e4435bd4ddd6f2baa SHA1: 9dd0e78da299f541e8de55787b7aaa50b0eab6f4 SHA256: f7dbbbe90d1cf86f24ee6a08fdc562fd5ac2c67b94d5db500dfdbb843f774e29 SHA512: 236d046d57a81644da27921d2b8bb3ba09515d1ca0eda9f6aaef5d0bd0c7534bcc6d538d5febc9e088c4bcc076a076de0a0ecc7ce1c6f19a9098e0bf9d9e6585 Homepage: https://cran.r-project.org/package=forestError Description: CRAN Package 'forestError' (A Unified Framework for Random Forest Prediction ErrorEstimation) Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R. Package: r-cran-forestfit Architecture: all Version: 2.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ars, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-forestfit_2.4.3-1.ca2604.1_all.deb Size: 452770 MD5sum: 1b47ecb02941d4f9dfa81e9175b0d1b3 SHA1: 8aa9783f03305518227a6c771ec48cf13756acf0 SHA256: 12f6843e732a4b2283e4ee4c304de819e679a2b7d2ef663ffa2c00351add672e SHA512: ff74db73032020360b02accf1d35c2aba10b51614bdba21b2af5ef4355250db526abe644ed0f8ef6fab0589ee9aaa311b8b8c797f6179097fc45264c2896275d Homepage: https://cran.r-project.org/package=ForestFit Description: CRAN Package 'ForestFit' (Statistical Modelling for Plant Size Distributions) Developed for the following tasks. 1 ) Computing the probability density function, cumulative distribution function, random generation, and estimating the parameters of the eleven mixture models. 2 ) Point estimation of the parameters of two - parameter Weibull distribution using twelve methods and three - parameter Weibull distribution using nine methods. 3 ) The Bayesian inference for the three - parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to grouped data using three methods including approximated maximum likelihood, expectation maximization, and maximum likelihood. 5 ) Estimating the parameters of the gamma, log-normal, and Weibull mixture models fitted to the grouped data through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve fitted to the height - diameter observation, 7 ) Estimating parameters, computing probability density function, cumulative distribution function, and generating realizations from gamma shape mixture model introduced by Venturini et al. (2008) , 8 ) The Bayesian inference, computing probability density function, cumulative distribution function, and generating realizations from univariate and bivariate Johnson SB distribution, 9 ) Robust multiple linear regression analysis when error term follows skewed t distribution, 10 ) Estimating parameters of a given distribution fitted to grouped data using method of maximum likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through the Bayesian, method of moment, conditional maximum likelihood, and two - percentile method. Package: r-cran-forestgapr Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-powerlaw, r-cran-raster, r-cran-sp, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-vgam, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-forestgapr_0.1.7-1.ca2604.1_all.deb Size: 526654 MD5sum: 35672602b500bc5469987de54deb11a5 SHA1: a2dbf1e8b9f89564fdcef088f714bc3a06f00a78 SHA256: 0a4ad40f8968eac698ba78f3ce3a21df17afeedcd3f090d6faed67c956e87f42 SHA512: d63eb88c6dfa1deb8e8b99b50b50a82ea2aefdf24b8732135fa5ad7942a40a3695fd83aebf584d5326521c8b136f7d58036f64b6ae9c2b70862eb16c965831ab Homepage: https://cran.r-project.org/package=ForestGapR Description: CRAN Package 'ForestGapR' (Tropical Forest Canopy Gaps Analysis) Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps. Details were published in Silva and others (2019) . Package: r-cran-forestgym Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-forestgym_1.0.0-1.ca2604.1_all.deb Size: 44854 MD5sum: 4460f67980474c780191ffbd6506c480 SHA1: 708474dfb1d19089cc1f3a274ff762c3ca312cec SHA256: 9a811c37177d60e002e4e85b198d1804c64b3c7c9ee2e659b42a15a47ef11509 SHA512: e8b7ef17456fecf12c9509926d482f400b7948e4ba6c3008be6baff12ba6618bfe7483cea9eac5f919baa01e05a66feb7180e1dfd426ac716e1fc5d1c7aee6ba Homepage: https://cran.r-project.org/package=forestGYM Description: CRAN Package 'forestGYM' (Forest Growth and Yield Model Based on Clutter Model) The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021). Guera O, Silva J, Ferreira R, et al(2019). Package: r-cran-foresthes Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-foresthes_2.0.1-1.ca2604.1_all.deb Size: 68506 MD5sum: 77b100f72c2c44306b9e2d24574003c3 SHA1: 6564d4f053557708a135935d24ac713741ee73ce SHA256: 93dfc83ac4e78ab9a9adc5d6bd1be2abf3b6de708f7bfb718f6fbb981d1ecdd8 SHA512: 38a61ad60ac19da146dbaf6a28dd3718027f517695e38a1b8b92f0813cf98727159395a65e56e3ce87a697e69531aca68b535c3c76edaff7f61aa867d6c1d11b Homepage: https://cran.r-project.org/package=forestHES Description: CRAN Package 'forestHES' (Forest Health Evaluation System at the Forest Stand Level) Assessing forest ecosystem health is an effective way for forest resource management.The national forest health evaluation system at the forest stand level using analytic hierarchy process, has a high application value and practical significance. The package can effectively and easily realize the total assessment process, and help foresters to further assess and management forest resources. Package: r-cran-forestinventory Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-forestinventory_1.0.0-1.ca2604.1_all.deb Size: 1351386 MD5sum: 4b0350b538c68dcbfc069ba91ca1cce7 SHA1: c13471f2f6cd6c1a35da00ac009bef82158febbe SHA256: b643f09186c74b77e7b1c3f491c2a3923453c11d5aedd40902eb8bcc9c83e155 SHA512: 5456becec7b25e058bc04f2f2c6b2c78b86374e64aaa6cc76f7ac1eca87ccda8ed63f0c1a73307b1a59dbcaa10ea6744f09045b7253fab82843e71a1e6804af5 Homepage: https://cran.r-project.org/package=forestinventory Description: CRAN Package 'forestinventory' (Design-Based Global and Small-Area Estimations for MultiphaseForest Inventories) Extensive global and small-area estimation procedures for multiphase forest inventories under the design-based Monte-Carlo approach are provided. The implementation has been published in the Journal of Statistical Software () and includes estimators for simple and cluster sampling published by Daniel Mandallaz in 2007 (), 2013 (, , , ) and 2016 (). It provides point estimates, their external- and design-based variances and confidence intervals, as well as a set of functions to analyze and visualize the produced estimates. The procedures have also been optimized for the use of remote sensing data as auxiliary information, as demonstrated in 2018 by Hill et al. (). 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Includes functionality for adverse event filtering, incidence-based group filtering, hover-over reveals, and search and sort operations. The workflow allows for metadata construction, data preparation, output formatting, and interactive plot generation. 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There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). . 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It also supports mixing text with mathematical expressions. The package extends the application of forest plots beyond traditional meta-analyses, offering a more general version of the original 'rmeta' package’s forestplot() function. It relies heavily on the 'grid' package for rendering the plots. 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References: Atkins et al. 2018 ; Hardiman et al. 2013 ; Parker et al. 2004 . Package: r-cran-forestrk Architecture: all Version: 0.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-ggplot2, r-cran-rapportools, r-cran-partykit, r-cran-pkgkitten, r-cran-knitr, r-cran-mlbench Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-forestrk_0.0-5-1.ca2604.1_all.deb Size: 380184 MD5sum: caffc6186e6f486132099e0588a6e058 SHA1: 14b2b91b78e186e83068ccdca99945dfa7ba8162 SHA256: 94fabfffd9be81418d1ba6c225c68638ca364e0be178ba2a200535fe7cfe32f8 SHA512: e5fae238c9a80b9ef8eed3f5251a0642b56ff304d2abf9f9136208135f6e8b964ee57bc38bda37cce0b4212516d330bfc98d520dbc2ad84d88c80fc5cf2f602c Homepage: https://cran.r-project.org/package=forestRK Description: CRAN Package 'forestRK' (Implements the Forest-R.K. 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Package: r-cran-fossilbrush Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1915 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-curl, r-cran-data.table, r-cran-pbapply, r-cran-stringdist, r-cran-stringr, r-cran-matrix Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-fossilbrush_1.0.6-1.ca2604.1_all.deb Size: 1820684 MD5sum: d7fafb0498de66f3a5984b239e07710e SHA1: 55d6ce07c2c673a8e322d9386bd607f155814640 SHA256: 315485125427c4b59777e89127dff806f7bdb26039fbe40038678fdc9ea3202c SHA512: 119d9644402162bda54daed76cf42f9e9d6bf92bd1ee626c74041621f1cbc9ff6ccccf6daa14cffeaaec405582521078866ddc1e37665530d672d59965b5dbaf Homepage: https://cran.r-project.org/package=fossilbrush Description: CRAN Package 'fossilbrush' (Automated Cleaning of Fossil Occurrence Data) Functions to automate the detection and resolution of taxonomic and stratigraphic errors in fossil occurrence datasets. Functions were developed using data from the Paleobiology Database. 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Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility. Package: r-cran-fourinarow Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-fourinarow_0.1.1-1.ca2604.1_all.deb Size: 46114 MD5sum: 452390c87189b2e732a58e01a9d99ce0 SHA1: 774a22ef27cf7737e6129f957f1a9304d13c1936 SHA256: 8e4a1b2026dbdb06938da1a4a0ce505c26baefee3fc294d16248b74d723ed9b7 SHA512: a820cc4d924fb64609d231abde4f3dc869d50d47cf5dca554030220c9cf258b7f30c9b489ca64f1f42d5b76e3ae9089dd2e5e03e63863b53145d77571d582f15 Homepage: https://cran.r-project.org/package=fourinarow Description: CRAN Package 'fourinarow' (Play "Four in a Row") Play or simulate games of "Four in a Row" in the R console. This package is designed for educational purposes, encouraging users to write their own functions to play the game automatically. It contains a collection of built-in functions that play the game at various skill levels, for users to test their own functions against. Package: r-cran-fourscores Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fourscores_1.5.1-1.ca2604.1_all.deb Size: 55448 MD5sum: 4d5ad8d97b606f860582fa2c33b66abc SHA1: e5bc6ea2cfcd6c34ead525fb0799ff9f4ba79e2a SHA256: 2f1d5019e5e60b1652ee9bfde850b1d11ecfabc57937b3565e20d5578b0131ac SHA512: de0ca7d13d0dc1011be8d86f2f4b3d195324291dea8ecb6e662f65f6f2cc12de4342b369163b1d59028b9ada6d9f18212e3c6f22dbb4077c2fdf4448a7d0608e Homepage: https://cran.r-project.org/package=FourScores Description: CRAN Package 'FourScores' (A Game for Human vs. Human or Human vs. AI) A game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin. Package: r-cran-fourwayhmm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-withr, r-cran-snow, r-cran-dosnow, r-cran-foreach, r-cran-mclust, r-cran-tensor, r-cran-tidyr, r-cran-data.table, r-cran-laplacesdemon Filename: pool/dists/resolute/main/r-cran-fourwayhmm_1.0.0-1.ca2604.1_all.deb Size: 85230 MD5sum: f5de2ef7d7cedcc535fe4226120f64e0 SHA1: 0507669167885114090d14c08d87b8c81dbebf96 SHA256: 9048faf0e96520e1bf6b13fe0c647d6fa2ef501d0caf7187eee91fe6d2a3305b SHA512: 09a9631c03f62d6d043dbf79a01118260326268ecc270deb17bcfc7e1bbb59373efb6248c52976ee1be8ff7a83a6cd5fcd178d88ae94ed8b826f009c437c4440 Homepage: https://cran.r-project.org/package=FourWayHMM Description: CRAN Package 'FourWayHMM' (Parsimonious Hidden Markov Models for Four-Way Data) Implements parsimonious hidden Markov models for four-way data via expectation- conditional maximization algorithm, as described in Tomarchio et al. (2020) . The matrix-variate normal distribution is used as emission distribution. For each hidden state, parsimony is reached via the eigen-decomposition of the covariance matrices of the emission distribution. This produces a family of 98 parsimonious hidden Markov models. Package: r-cran-fpa Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape, r-cran-fields Filename: pool/dists/resolute/main/r-cran-fpa_1.0-1.ca2604.1_all.deb Size: 59978 MD5sum: 209a1571e1220245de97ea8a54fa5b2a SHA1: 5b25f7a571818b546270139d6ff0a22676215f40 SHA256: 550e505b7a6479f03e7ae3e98ef81c230ca6def6ca36dbfaece9654cbcd33eaf SHA512: 30f8f084b91092311ddb1c2586c3eedf529ace4f034a681cb1efbcc3d023a6eb49a2a1cad0eb62a0b1272d2c34de05d0b1f01ced8eb77a1e1a8e000d427644d6 Homepage: https://cran.r-project.org/package=fpa Description: CRAN Package 'fpa' (Spatio-Temporal Fixation Pattern Analysis) Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing within moments. The other three functions are supportive functions which visualize the eye fixation patterns. 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See the manuscript by Koner and Luo (2023) for details of the PASS formula and computational details. The details of the testing procedure for univariate and multivariate response are presented in Wang (2021) and Koner and Luo (2023) respectively. Package: r-cran-fpc Architecture: all Version: 2.2-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 897 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-cluster, r-cran-mclust, r-cran-flexmix, r-cran-prabclus, r-cran-class, r-cran-diptest, r-cran-robustbase, r-cran-kernlab Suggests: r-cran-tclust, r-cran-pdfcluster, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-fpc_2.2-14-1.ca2604.1_all.deb Size: 852578 MD5sum: 1aa0eb461bc5a6f658c88f223c76deff SHA1: 31d349b3f8e4397becda567a953ac753b1145637 SHA256: 34ac1190c4f10a62f38440108e0eb8c5450f555a550f72f63c4a9868ed59ee82 SHA512: 1741230eae734659dc5bf6e4b563069d260f145f4f340c68ff3cc405dccc3c3609ac67fce5134745c7e90ccbe8d568700d3fad3b4c171c831d380776a4192b5c Homepage: https://cran.r-project.org/package=fpc Description: CRAN Package 'fpc' (Flexible Procedures for Clustering) Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc. Package: r-cran-fpca3d Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fpca3d_1.0-1.ca2604.1_all.deb Size: 23028 MD5sum: 17b90f6f06be16c29766750df896dff1 SHA1: 2b977313eea85213343c3d573a5d9e2f7f377f35 SHA256: 71b7dc8f8fae472e6eae13ea015f830ef6d0a9de649aa91cac93d9cf40d9bf62 SHA512: 6b91d3b1156e2be3f736bd1a2b498ca255fee8ce7cc84201d95e8966645b8c8af6aaa873d54f15b43edad2cb5f5e35e3d3436645a550f00d59bd96960e98d644 Homepage: https://cran.r-project.org/package=FPCA3D Description: CRAN Package 'FPCA3D' (Three Dimensional Functional Component Analysis) Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) . Package: r-cran-fpcdpca Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc, r-cran-rsvd Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fpcdpca_0.4.0-1.ca2604.1_all.deb Size: 2456706 MD5sum: c0c8eaf3a4fa70f0dc51e7b3ae856d84 SHA1: 0e24c270487e37ed7ae79123b589294e23b2652f SHA256: 46c81535036b4754aac7c3c16818ab5804d9138e65d560f54012b4494f79b74b SHA512: cbc5c5d9b81ede289805fa534a4dc5ec7bec6381767ca9dc0829bbc8d2d25e8b9f1b9e86a5f721e221d97ab472a596c4988ac6b4f421235dd9647d031119ff05 Homepage: https://cran.r-project.org/package=FPCdpca Description: CRAN Package 'FPCdpca' (The FPCdpca Criterion on Distributed Principal ComponentAnalysis) We consider optimal subset selection in the setting that one needs to use only one data subset to represent the whole data set with minimum information loss, and devise a novel intersection-based criterion on selecting optimal subset, called as the FPC criterion, to handle with the optimal sub-estimator in distributed principal component analysis; That is, the FPCdpca. The philosophy of the package is described in Guo G. (2025) . Package: r-cran-fpcompare Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fpcompare_0.2.4-1.ca2604.1_all.deb Size: 21354 MD5sum: 6299dcfbcad0d263386f20afd8167d18 SHA1: 9174de8b8ad034274a64bf27edf00839c21fd86e SHA256: e5b9f3588935faf37da812bfac58bfb0eecf89d9b25e2edd2c2777f8d5edd502 SHA512: b7ebea2eca0d77719b057919ee4b4b103463c9a70e04b373c34821688da95d85e21df29ff3e6b1c751296f6abc17c873724fa4654de355680c36f9452505dc38 Homepage: https://cran.r-project.org/package=fpCompare Description: CRAN Package 'fpCompare' (Reliable Comparison of Floating Point Numbers) Comparisons of floating point numbers are problematic due to errors associated with the binary representation of decimal numbers. Despite being aware of these problems, people still use numerical methods that fail to account for these and other rounding errors (this pitfall is the first to be highlighted in Circle 1 of Burns (2012) 'The R Inferno' ). This package provides new relational operators useful for performing floating point number comparisons with a set tolerance. Package: r-cran-fpdclustering Architecture: all Version: 2.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-threeway, r-cran-mvtnorm, r-cran-exposition, r-cran-cluster, r-cran-rootsolve, r-cran-mass, r-cran-klar, r-cran-ggally, r-cran-ggplot2, r-cran-ggeasy Filename: pool/dists/resolute/main/r-cran-fpdclustering_2.3.5-1.ca2604.1_all.deb Size: 2113382 MD5sum: db29bc2e0886c2b2a070a7b15796d342 SHA1: c3eeb7b74b7f9970b8d9761f0990875743b37233 SHA256: 48f9ce50887d3e50dc1ce8a8237da5c2454483e6d7bf5dec12ff0d1c08ef4ec6 SHA512: f1eb514ab21c62ea5dc2d9009380b6203ff2c87160c680ff95a3557595ddd9bece3fabb0f83e5d2204d740a6553349d34b34d7274f7317f96e93838114764e18 Homepage: https://cran.r-project.org/package=FPDclustering Description: CRAN Package 'FPDclustering' (PD-Clustering and Related Methods) Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets. 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Package: r-cran-fracarma Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-fracdiff Filename: pool/dists/resolute/main/r-cran-fracarma_0.1.0-1.ca2604.1_all.deb Size: 12854 MD5sum: fb9d42d9bfe998131525dcdbe62d3a35 SHA1: a8ebb95bc6c8a03ffd4bca9f5cf37951e2501cce SHA256: 13ac38a0777dca6e22572b62459fa23eed78b61b32e676f3c2895e23bf2f4657 SHA512: 3790b36447142a7f07046d6c38ab2088a73018748618237b9a3c877d28f7d07623f835428f22a74c6918f4a5a80c1b8962c855924ea206af3cb94f568cd2687c Homepage: https://cran.r-project.org/package=fracARMA Description: CRAN Package 'fracARMA' (Fractionally Integrated ARMA Model) Implements fractional differencing with Autoregressive Moving Average models to analyse long-memory time series data. Traditional ARIMA models typically use integer values for differencing, which are suitable for time series with short memory or anti-persistent behaviour. In contrast, the Fractional ARIMA model allows fractional differencing, enabling it to effectively capture long memory characteristics in time series data. The ‘fracARMA’ package is user-friendly and allows users to manually input the fractional differencing parameter, which can be obtained using various estimators such as the GPH estimator, Sperio method, or Wavelet method and many. Additionally, the package enables users to directly feed the time series data, AR order, MA order, fractional differencing parameter, and the proportion of training data as a split ratio, all in a single command. The package is based on the reference from the paper of Irshad and others (2024, ). Package: r-cran-fracdist Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fracdist_0.1.1-1.ca2604.1_all.deb Size: 1095522 MD5sum: acf9660fcfdb7c77a9418c20e503569a SHA1: 8aaa458af739e4efe637e1e8d59583963f1728b9 SHA256: 7e7d5e548a0754863cd4b8d2b0b6eb135a918d2bb5c23ffdb09b68c25a3ee250 SHA512: 08bad2418c73db311ae7aaf76fdf1109d5b5c76a3d07ebcd0a47f9c60fa7ba4e6b4c4ad182644535647da407d189038f4d126c7e1985b01602fb2b7c752a116e Homepage: https://cran.r-project.org/package=fracdist Description: CRAN Package 'fracdist' (Numerical CDFs for Fractional Unit Root and Cointegration Tests) Calculate numerical asymptotic distribution functions of likelihood ratio statistics for fractional unit root tests and tests of cointegration rank. 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'FracFixR' addresses the fundamental challenge in fractionated RNA-seq experiments where library preparation and sequencing depth obscure the original proportions of RNA fractions. It reconstructs original fraction proportions using non-negative linear regression, estimates the "lost" unrecoverable fraction, corrects individual transcript frequencies, and performs differential proportion testing between conditions. Supports any RNA fractionation protocol including polysome profiling, sub-cellular localization, and RNA-protein complex isolation. 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Package: r-cran-fractaldim Architecture: all Version: 0.8-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Suggests: r-cran-wavelets, r-cran-pcapp, r-cran-snowft Filename: pool/dists/resolute/main/r-cran-fractaldim_0.8-5-1.ca2604.1_all.deb Size: 134462 MD5sum: 95fac4e09f91cdbff8784195de0b4283 SHA1: 34754d0bb4d4914b7d5d9293019b5bfdee48d7cd SHA256: 1e799d5e5f466cc627828b066c856d78fa9a8024812ee108440cdf6baa11f747 SHA512: 118a6d60126d36251c7391a1d96a8ee737c5191040b04449aa40dede273e80bf27515496084cbcb86111ee373487aa4cce1a76b958212ab8219c6993b382c695 Homepage: https://cran.r-project.org/package=fractaldim Description: CRAN Package 'fractaldim' (Estimation of Fractal Dimensions) Implements various methods for estimating fractal dimension of time series and 2-dimensional data . 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Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) . Package: r-cran-fractd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-imager, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fractd_0.1.0-1.ca2604.1_all.deb Size: 204328 MD5sum: 00662f214b3f47ebd0a2d3f3c3e2596b SHA1: 7dbe055429a453f0c2182c817bfa7be5e3c6b7a7 SHA256: 5c9129c20654abfab82e9cc935c7e3532ef61ffa0f844ae6a4669084e7780a88 SHA512: 5950f0db611e21f652483a6247b8578721d3fe67dc84f6eba58dc7e8d6b60683a2d297cd9e9eb27b376a7bbbb0ee64fb2ce7abd213e2ec89066c3e6a8e0265e2 Homepage: https://cran.r-project.org/package=fractD Description: CRAN Package 'fractD' (Estimation of Fractal Dimension of a Black Area in 2D and 3D(Slices) Images) Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) . Package: r-cran-fraction Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fraction_1.1.1-1.ca2604.1_all.deb Size: 21662 MD5sum: c30ee8bf331b241bf0340a16f6108399 SHA1: 93c665c83333f8edc6020d9e44be9c93c1632575 SHA256: 5ee85e66ec1591053aad05ccfbe51b0657e6249932a05b970336d236cd9df9e8 SHA512: 5eb53e33c8c1c55db82285cdab56436871d50a6f6a465e893590aed6f177375df17128169c04f7047d73c00350ebd5a6c9bb3103b47db7a6f21cff9c94caf971 Homepage: https://cran.r-project.org/package=FRACTION Description: CRAN Package 'FRACTION' (Numeric Number into Fraction) Turn numeric,data.frame,matrix into fraction form. Package: r-cran-fragility Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta, r-cran-metafor, r-cran-netmeta, r-cran-plotrix Filename: pool/dists/resolute/main/r-cran-fragility_1.6.1-1.ca2604.1_all.deb Size: 401158 MD5sum: 934a089d291663c3b3302609dd533d10 SHA1: 422ac55f73f347f6a62743bc64acc7d1400ec0fa SHA256: e5fab13d6e8196bd69334ddc06a45c2bf5756380dcb0b66759256b2743f16076 SHA512: 58f9ba8839b230c6e2dbded52efee5d502c2064450cba75afbb611660a1feee717d431784341af25d0c850635503e2871b87ef2bbe7f6d2d3c121ae78d3b63f1 Homepage: https://cran.r-project.org/package=fragility Description: CRAN Package 'fragility' (Assessing and Visualizing Fragility of Clinical Results withBinary Outcomes) A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 ; Lin, 2021 ), conventional pairwise meta-analyses (Atal et al., 2019 ), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 ). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 ) and detailed in Lin and Chu (2022 ). Package: r-cran-fragman Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1810 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fragman_1.0.9-1.ca2604.1_all.deb Size: 1814838 MD5sum: 16758ec388b48723bc6d5d376dcf0fb3 SHA1: 402f9a28811583ef55d3dc0a02ffe9b164c823fb SHA256: a190448ef9aabcac10719b5ec1b163d4f5445085eeb66b27a5c9b02550c76750 SHA512: f8960028ef2af54411cf8b429f99fafe4d3b70a53ae851ff8841c01c1e6bd35326fa1a856f0274e05b9d05fbf92404ffabf82523968054ffce1e3026d4dc783a Homepage: https://cran.r-project.org/package=Fragman Description: CRAN Package 'Fragman' (Fragment Analysis in R) Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. 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Package: r-cran-frailtycomprisk Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-dplyr, r-cran-tibble, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-frailtycomprisk_0.1.1-1.ca2604.1_all.deb Size: 91270 MD5sum: d10d639b859ee95e89d9b2f7e71df839 SHA1: 3a1beb719f683eea3ba78759964b59061c7298da SHA256: 76a6364497f3258051a344d958ea03ec98370e3114338d728d54b1539b02754e SHA512: b1f904b96e1f2c04cc2763d3c2c4d313b3d55405803809bde284d2d9a864b0bf9798f4f43c7c99668cbcde2a18872af8172bedb35dc90bbe5c4757a2e8484984 Homepage: https://cran.r-project.org/package=FrailtyCompRisk Description: CRAN Package 'FrailtyCompRisk' (Competing Risks Models for Multi-Center Survival Data withFrailty) Implements methods for analyzing competing risks data in multi-center survival studies using frailty models. 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See Beyene and Chen (2024) . Package: r-cran-frair Architecture: all Version: 0.5.203-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-lamw, r-cran-boot, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-frair_0.5.203-1.ca2604.1_all.deb Size: 202450 MD5sum: 4c98709c6efa58146278e5baaecae410 SHA1: 4aaa82457cfe1dc952437a724a6e81fc3aaa924f SHA256: 9d71106f23a66027bc185af1040a892a4691ece38aee5ccc9067091e43277952 SHA512: 03ae5d9ddf12038058b609a0874360d4f465dbc25984c80e7b5e97e31053b3231322506f8f5a0700b36e847b384ee224d5e390e69690adf7d76b842364fea3cf Homepage: https://cran.r-project.org/package=frair Description: CRAN Package 'frair' (Tools for Functional Response Analysis) Tools to support sensible statistics for functional response analysis. 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The majority of commands wrap 'dplyr' mutate statements in a convenient way to concisely solve common issues that arise when tidying small to medium data sets. Includes smart defaults and allows flexible selection of columns via 'tidyselect'. 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In contrast to classic sampling theory, where only one sampling frame is considered, dual frame methodology assumes that there are two frames available for sampling and that, overall, they cover the entire target population. Then, two probability samples (one from each frame) are drawn and information collected is suitably combined to get estimators of the parameter of interest. Package: r-cran-framework Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dbi, r-cran-rsqlite, r-cran-yaml, r-cran-fs, r-cran-readr, r-cran-dotenv, r-cran-openssl, r-cran-lubridate, r-cran-jsonlite, r-cran-plumber Suggests: r-cran-testthat, r-cran-arrow, r-cran-aws.s3, r-cran-aws.signature, r-cran-biocmanager, r-cran-cli, r-cran-cyclocomp, r-cran-devtools, r-cran-dplyr, r-cran-dt, r-cran-duckdb, r-cran-ggplot2, r-cran-haven, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-languageserver, r-cran-odbc, r-cran-pool, r-cran-prismjs, r-cran-r.utils, r-cran-readxl, r-cran-remotes, r-cran-renv, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rpostgres, r-cran-tm, r-cran-usethis, r-cran-withr, r-cran-writexl Filename: pool/dists/resolute/main/r-cran-framework_1.0.2-1.ca2604.1_all.deb Size: 4085294 MD5sum: edf03b0f55c08bb162b906a6e4a44d61 SHA1: 58e000ee6d60624b16bb8199065a6bd058a58071 SHA256: e06aaf0d58d77aad455079ad4b793791f0647f80c1d90b3878c35f23e8b1eb03 SHA512: 2ad1f62eb8fb524812f6f8e446b92dbd76216865da617110483335c5a97b3011bec1b8d0c0c38a795e5307c55514b5f7fa9b1ebe2cebf9e4948c91cbbeef4aff Homepage: https://cran.r-project.org/package=framework Description: CRAN Package 'framework' (Structured Data Science Project Scaffolding) Project scaffolding and workflow tools for reproducible data science. 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Package: r-cran-franc Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-franc_1.1.4-1.ca2604.1_all.deb Size: 493314 MD5sum: 20abe7e0cec0b5cd4f6c81112ad0ca58 SHA1: d90f99a2db92f964faebb850576db751db2f2a98 SHA256: ad6d2f9c995f86167d6662f0db4514028c113a8ad55259ec672c98ae3ae1852b SHA512: 4898ca33e24accf618c823d6b60d6b871bc57331ee05e5abca952c8716927522661c41916e93d01316f3575ae4d406fe0471c8d0ed90ec8e2e0bf9f56582703a Homepage: https://cran.r-project.org/package=franc Description: CRAN Package 'franc' (Detect the Language of Text) With no external dependencies and support for 335 languages; all languages spoken by more than one million speakers. 'Franc' is a port of the 'JavaScript' project of the same name, see . Package: r-cran-frapo Architecture: all Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cccp, r-cran-rglpk, r-cran-timeseries Filename: pool/dists/resolute/main/r-cran-frapo_0.4-2-1.ca2604.1_all.deb Size: 3111704 MD5sum: bc614a6e4abf7fc5c051860da451efe6 SHA1: 5cbb699eba28d5b5b1e5f79445ab77d9537e4308 SHA256: e3487f37e8d49dd06a432360dc4ec6f04e80e7aa7477b3157003bf25f25d9257 SHA512: bc0d4cce54923fa5975deec0eaa93daf6f102d41ee5008ea48af9c1fb50a653c6db40b83fe72d2577b290f47e6772a15f9d3585dcef277ac7a5551e44c71c973 Homepage: https://cran.r-project.org/package=FRAPO Description: CRAN Package 'FRAPO' (Financial Risk Modelling and Portfolio Optimisation with R) Accompanying package of the book 'Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package. Package: r-cran-frapp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2091 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-shiny, r-cran-gplots Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-frapp_1.0.0-1.ca2604.1_all.deb Size: 1793278 MD5sum: e1ff82097f1b32f4e834d9e1786833af SHA1: c137779f17a43ac751fc2786b24ff18fa069e6ff SHA256: 6966618aa1bedef881aa0610877e18d1fe7756cba486ec978002bf442c7c18b9 SHA512: 653552dfcecebc31d57bc7388a671361b39abae45df08b87ccf6b76325c9bdaa1bd1d7e672291385506bbd9c99c7b76c91d38f8ec7dcc938cc7f983d835de3de Homepage: https://cran.r-project.org/package=FRApp Description: CRAN Package 'FRApp' (FRAP Data Analysis Using Nonlinear Mixed Effect Models with'shiny') Analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments using nonlinear mixed-effects regression models and analysis of the results. 'FRApp' is not limited to the analysis of FRAP experiments only. Any nonlinear mixed-effects models with an asymptotic exponential functional relationship to hierarchical data in various domains can be fitted. The analysis of data available in the package is presented in Di Credico, G., Pelucchi, S., Pauli, F. et al. (2025) . Package: r-cran-frapplot Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-frapplot_0.1.3-1.ca2604.1_all.deb Size: 26566 MD5sum: 2d451c6db2ca7aaf1056e52193899f1e SHA1: a85b81253a7334398b97f1787146d0985bd93858 SHA256: f05e8c52020689408b2d035886c041a7aa42b936d3d5a0ca1444f9878d96d3d8 SHA512: 05c7e9573d77ffd2c481e7063d32f4af27e39462b480c5565fc9503d8032ff55b0e78a9ff9ecc73e680ea232f714af7908559b2669f335aedcdf3e9f4b40f370 Homepage: https://cran.r-project.org/package=frapplot Description: CRAN Package 'frapplot' (Automatic Data Processing and Visualization for FRAP) Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace 'ImageJ' (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) ; Day, Charles A. (2012) . Package: r-cran-frb Architecture: all Version: 2.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrcov, r-cran-corpcor Suggests: r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-frb_2.0-1-1.ca2604.1_all.deb Size: 622040 MD5sum: a08a63934c53259ec729cfdf6a084e32 SHA1: 572e5aefb66c49947452d42d22cb5108cae70aa1 SHA256: b614b9181fddc33c0615ad09fdbc1f6d6211027b905c7b46af96d3c8a8ffbec5 SHA512: b74c669c7df932e48c857fcbd8070dd28bc1f1646d6c045e6a4b100d60c61bc7d3b2888f6d10c477ca57dde7a7632d07d3b6ab9d113ab2d5e3030b9520bb6eb7 Homepage: https://cran.r-project.org/package=FRB Description: CRAN Package 'FRB' (Fast and Robust Bootstrap) Perform robust inference based on applying Fast and Robust Bootstrap on robust estimators (Van Aelst and Willems (2013) ). This method constitutes an alternative to ordinary bootstrap or asymptotic inference. procedures when using robust estimators such as S-, MM- or GS-estimators. The available methods are multivariate regression, principal component analysis and one-sample and two-sample Hotelling tests. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap. Package: r-cran-frbinom Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-frbinom_1.0.0-1.ca2604.1_all.deb Size: 43080 MD5sum: 5e57b0d5b52a462f051c2e0444f9a526 SHA1: 81af805e4efb5d18091c0cb7057fb1b478ae7c3c SHA256: cde2508e00698cc9370e5be165f32b41c06324d2757cdc89e28fbbda7cf26e78 SHA512: 28128818b7443a0edc7ece00906fe81b93b1550696d91a79009568ece840f745edcd82d9b41cd351dcbbd9d2dcff535fc295a533bede23b5d0c68af072a37d39 Homepage: https://cran.r-project.org/package=frbinom Description: CRAN Package 'frbinom' (Fractional Binomial Distributions) Generating fractional binomial random variables and computing density, cumulative distribution, and quantiles of fractional binomial distributions. (Lee, J. (2023) .) Package: r-cran-frbs Architecture: all Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1610 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-class, r-cran-e1071, r-cran-xml, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-frbs_3.2-0-1.ca2604.1_all.deb Size: 1307660 MD5sum: 48365bafc7380f64212683a777c5c519 SHA1: 6b11fd86a857e230494f8203c05529b9ee8be7ce SHA256: 10eb8159d8dca4fa035d561672adecb7929f567161adce15b5b2bf5a9af4c3c7 SHA512: e3cca059ad31c3727490783f78ef549303c517fed569d95d339a46f6c1e1d2593e71c9cda61be44eb8fb1bd04ff2942bb5c0cf96d40859786c0f121c161bd774 Homepage: https://cran.r-project.org/package=frbs Description: CRAN Package 'frbs' (Fuzzy Rule-Based Systems for Classification and Regression Tasks) An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named 'frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from 'frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community. 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Please see the following for details: Raul Cruz-Cano, Mei-Ling Ting Lee, Fast regularized canonical correlation analysis, Computational Statistics & Data Analysis, Volume 70, 2014, Pages 88-100, ISSN 0167-9473 . 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Compute and plot the fuzzy membership functions of the methods, and the expected length compared with the infimum. 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The core of this package is Fréchet regression for random objects with Euclidean predictors, which allows one to perform regression analysis for non-Euclidean responses under some mild conditions. Examples include distributions in 2-Wasserstein space, covariance matrices endowed with power metric (with Frobenius metric as a special case), Cholesky and log-Cholesky metrics, spherical data. References: Petersen, A., & Müller, H.-G. (2019) . 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'FRED' is maintained by the 'Federal Reserve Bank of St. Louis' and contains over 800,000 time series from 118 sources covering GDP, employment, inflation, interest rates, trade, and more. Dedicated functions fetch series observations, search for series, browse categories, releases, and tags, and retrieve series metadata. Multiple series can be fetched in a single call, in long or wide format. Server-side unit transformations (percent change, log, etc.) and frequency aggregation are supported, with readable transform aliases such as 'yoy_pct' and 'log_diff'. Real-time and vintage helpers (built on 'ALFRED') return a series as it appeared on a given date, the first-release version, every revision, or a panel of selected vintages. Data is cached locally for subsequent calls. This product uses the 'FRED' API but is not endorsed or certified by the 'Federal Reserve Bank of St. Louis'. 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These frequency domain methods for dimensionality reduction of multivariate time series were introduced by David Brillinger in his book Time Series (1974). We follow implementation guidelines as described in Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component . 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Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield. 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Procedures in the package allow to i) downscale daily meteorological variables to hourly values (Forster et al (2016) ), ii) estimate chilling and forcing heat accumulation (Miranda et al (2019) ), iii) estimate plant phenology (Schwartz (2012) ), iv) calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr et al (2017) ), v) estimate the incidence of weather-related disorders in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi) estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)). 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Package: r-cran-fsdam Architecture: all Version: 2024.7-30-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kyotil, r-cran-reticulate Suggests: r-cran-r.rsp, r-cran-runit Filename: pool/dists/resolute/main/r-cran-fsdam_2024.7-30-1.ca2604.1_all.deb Size: 69238 MD5sum: d880cdab6e89376f3789887544a41c6b SHA1: 41d368d8535a455c79e268462612fbd7f0c88b5c SHA256: 63037a9fb785fbedc68e753b0280a4cffba3377eba98b2c40040ced11349d753 SHA512: d8e5d181e7949b3f01ab8737c016741c6ce1eb524bb8c533fc6c8334f57b1efcf7abb5bcb0aee9edc7eede58ee5029dde512af0ca7cf03fba7d271351aeeef2d Homepage: https://cran.r-project.org/package=FSDAM Description: CRAN Package 'FSDAM' (Forward Stepwise Deep Autoencoder-Based Monotone NLDR) FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints. Package: r-cran-fsdar Architecture: all Version: 0.9-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3083 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rjava, r-cran-ggplot2 Suggests: r-cran-robustbase, r-cran-rrcov, r-cran-mass Filename: pool/dists/resolute/main/r-cran-fsdar_0.9-1-1.ca2604.1_all.deb Size: 2269806 MD5sum: c497b3b8b046cd3df084cddb5f9870d3 SHA1: 874bdbc3a048be7eb5dadd16dc1e94aa21e5d60c SHA256: 401437ca88e0d9e2ff6d2725015e79faaac360d199a3439e184a95ed1bd731f3 SHA512: 024d45849de52ad46bd73a6d95322800093c0edbf021868785ac22d4a23700e12d680a7014d540c5c60d15c7bceb795d33a0953102427197e3ae70f1129c5e24 Homepage: https://cran.r-project.org/package=fsdaR Description: CRAN Package 'fsdaR' (Robust Data Analysis Through Monitoring and DynamicVisualization) Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis (FSDA)' which is comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as a function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis. Package: r-cran-fselector Architecture: all Version: 0.34-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-entropy, r-cran-randomforest, r-cran-rweka Suggests: r-cran-mlbench, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-fselector_0.34-1.ca2604.1_all.deb Size: 93720 MD5sum: 903d0f5487591530393640e07deefe9b SHA1: 0c47eed49304ba747f328a309a967fd145863106 SHA256: 698049101ffd39fdd3d3a8f5031f9593ea1572858f2aca26b34e21eb0be62d06 SHA512: 48799a3663e5d65de55cd24a199d4fe01632d7ece7c8e596d01645e9d2bebd397a1ad00550ca86a422d70d93550821a4af1ef282f129f9fdc6e96744267c492c Homepage: https://cran.r-project.org/package=FSelector Description: CRAN Package 'FSelector' (Selecting Attributes) Functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible. Package: r-cran-fsemipar Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1722 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-grpreg, r-cran-dicekriging, r-cran-gtools, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-gridextra, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-fsemipar_1.1.1-1.ca2604.1_all.deb Size: 1694316 MD5sum: efd1c2ca93bf785cf938cd68ff9e39c3 SHA1: f63513588a5e51aeb7154fd29f3273e03cd7201c SHA256: 499e7bd9a72d805c65b00381397d53de4377ff44c9620eb51618ab472ab69cd5 SHA512: 2fd5c26b9d5f5e20671c30cc73983c190dc5d06f1e3933ec09c93426559d59d0b58a06ce7696ef1b4adfcd13e9f160b7b1d29f449f8c170ef3c2c0ce30c6f8b7 Homepage: https://cran.r-project.org/package=fsemipar Description: CRAN Package 'fsemipar' (Estimation, Variable Selection and Prediction for FunctionalSemiparametric Models) Routines for the estimation or simultaneous estimation and variable selection in several functional semiparametric models with scalar responses are provided. 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Package: r-cran-fsia Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fsia_1.1.1-1.ca2604.1_all.deb Size: 555794 MD5sum: 66439b29807a2435ecf47e0c1d1825c1 SHA1: 3556f4b55cc57510bcf9c40f56b72621a75b9b8d SHA256: a810ffa8fbfef71d64faebbf895c6ce6c2c012811865208f496b85499d102587 SHA512: bf5df2ad532755982b378c33b8be3c0dfca4c637b34ccaea644fb913c3d23fa614ef84e5e49db3f123a4689bdc65d6e2d926fd4ae338943242190f36093cc5fe Homepage: https://cran.r-project.org/package=fsia Description: CRAN Package 'fsia' (Import and Analysis of OMR Data from FormScanner) Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from . 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The goal is to be able to interface with 'FSL' completely in R, where you pass R objects of class 'nifti', implemented by package 'oro.nifti', and the function executes an 'FSL' command and returns an R object of class 'nifti' if desired. Package: r-cran-fsm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fsm_1.0.0-1.ca2604.1_all.deb Size: 99666 MD5sum: c54421e7a7e1cada60658d4a9dbeb256 SHA1: 97f49c673bf086799920511a7ee56c9de1574fb5 SHA256: 3ab3762d44c4192e3924fd03f2ecab2aa5f4f3b16c97accd97e4449e7a9e7b9d SHA512: 7db689bcd4a9a06b231ecd017b4d9196d818cf30f840cc28c00c4f2825bbd727418f4648b9072a1dfef97c02d9227df998951054379845e6e0bda4a365d2c497 Homepage: https://cran.r-project.org/package=FSM Description: CRAN Package 'FSM' (Finite Selection Model) Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) for details on the original version of the FSM. Package: r-cran-fsn Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-fsn_0.4-1.ca2604.1_all.deb Size: 41084 MD5sum: 85525528a61d4aeb473bd70bdf8b21ec SHA1: 253d8825e9ffc876aa1feb66f5a6515078ffff89 SHA256: 81b9ffb75f5d2d1773ba75375950b4607d9eb9ec0918efac8d917e51b9299b3b SHA512: eb012f761011515063e2fc7e830a7c7133eb2fd2c64d7643cad6f519b4be64e5fcf67d27ad36e67a90469b9b8d6422faa1e899d2d93e1edec15e95db9e65bb1c Homepage: https://cran.r-project.org/package=fsn Description: CRAN Package 'fsn' (Rosenthal's Fail Safe Number and Related Functions) Estimation of Rosenthal's fail safe number including confidence intervals. The relevant papers are the following. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2014). "Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number". International Scholarly Research Notices, Volume 2014. . Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2017). "Exploring the distribution for the estimator of Rosenthal's fail-safe number of unpublished studies in meta-analysis". Communications in Statistics-Theory and Methods, 46(11):5672--5684. . Package: r-cran-fspe Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-corpcor Filename: pool/dists/resolute/main/r-cran-fspe_0.1.2-1.ca2604.1_all.deb Size: 31476 MD5sum: 45abd6e0dd628b494f04716b612e7475 SHA1: 0f552b027c3d91493b6ee8a5cdf38f89104021b1 SHA256: 26947c1be67c7ff49cbba5a3d5622bad2b4a3de53e16d869433d5c8985ff53c3 SHA512: 7859b5d43ff70acc8b181c416fde7f9305227e3c96998328a263831afdc25132cf25d8ced2ee6c99a9189a27e9c7c6b34b5124f1284edd27976167c3997acbf2 Homepage: https://cran.r-project.org/package=fspe Description: CRAN Package 'fspe' (Estimating the Number of Factors in EFA with Out-of-SamplePrediction Errors) Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) . 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It employs fuzzy set theory and fuzzy logic as foundation to deal with spatial fuzziness. It mainly implements underlying concepts defined in the following research papers: (i) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types" ; (ii) "A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets" ; (iii) "Spatial Data Types for Heterogeneously Structured Fuzzy Spatial Collections and Compositions" ; (iv) "Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems" ; (v) "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models" . Package: r-cran-fsrm Architecture: all Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-plyr, r-cran-reshape2, r-cran-ggplot2, r-cran-scales, r-cran-foreign, r-cran-tcltk2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-fsrm_0.6.5-1.ca2604.1_all.deb Size: 232122 MD5sum: d3157bec5e9ba7d721b0f68c82f9857c SHA1: 4b1cf0f6744892802ca0c03136836de1e6b3dda8 SHA256: fa9085034def58b9369e12a0126556a96e5fffaf2ea265b1c087f971f66c68dd SHA512: 10492e854eee317376b85f641d881895a49395bd563066757e88330ecc164b175d189b4618daabac4c700fc9647360c4f749ce97d6309f4d5662083cca0dda44 Homepage: https://cran.r-project.org/package=fSRM Description: CRAN Package 'fSRM' (Social Relations Analyses with Roles ("Family SRM")) Social Relations Analysis with roles ("Family SRM") are computed, using a structural equation modeling approach. 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Package: r-cran-fst4pg Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fpopw, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-gplots, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-fst4pg_1.0.0-1.ca2604.1_all.deb Size: 1236172 MD5sum: b58e5a1e817b0d60eb1c91b4fc538f58 SHA1: 035f76b2e00e0ee5973020de5418fe9f437ed41e SHA256: 8705583a09bed0903e3d6be3b5b4f73c0fe222c016cb916a5d0c73359948cb4f SHA512: ad2da8c6e3c5b39772531181dd26e0a8a1ed8cad72485ef29b89431f88cdf4e27480fdd64e15e135174ac6c77277007a331e9160a51f8252aa319dea1ea7ce51 Homepage: https://cran.r-project.org/package=fst4pg Description: CRAN Package 'fst4pg' (Genetic Distance Segmentation for Population Genetics) Provides efficient methods to compute local and genome wide genetic distances (corresponding to the so called Hudson Fst parameters) through moment method, perform chromosome segmentation into homogeneous Fst genomic regions, and selection sweep detection for multi-population comparison. When multiple profile segmentation is required, the procedure can be parallelized using the future package. Package: r-cran-fstability Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fstability_0.1.2-1.ca2604.1_all.deb Size: 12770 MD5sum: 058ce10cba277fa6de70d24555fd52a7 SHA1: 693d68a70e31f082c497905776c72d4ee1beeab3 SHA256: 02e1df5fec6229f9abd666d8e9a2173a7604e53a829e747bf2819a0911b6b7ab SHA512: 45fad82ed7cf41e7a7c040b47b9be3a25ba30e9c064a733148059cff52093d85ce53af562c700e44b4aae9a6ef97f8c687cb0f2c87ae2cf787613a337a575b66 Homepage: https://cran.r-project.org/package=Fstability Description: CRAN Package 'Fstability' (Calculate Feature Stability) Has two functions to help with calculating feature selection stability. 'Lump' is a function that groups subset vectors into a dataframe, and adds NA to shorter vectors so they all have the same length. 'ASM' is a function that takes a dataframe of subset vectors and the original vector of features as inputs, and calculates the Stability of the feature selection. The calculation for 'asm' uses the Adjusted Stability Measure proposed in: 'Lustgarten', 'Gopalakrishnan', & 'Visweswaran' (2009). 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The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) . 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Features include spanning header, grouping rows, parsing markdown and so on. Package: r-cran-ftrading Architecture: all Version: 3042.79-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-fbasics Suggests: r-cran-runit Filename: pool/dists/resolute/main/r-cran-ftrading_3042.79-1.ca2604.1_all.deb Size: 97408 MD5sum: 47c20208e696984ba80e44d3288bf5fc SHA1: 5081b01882ee3c9b2bdc6e211ab8b0bdffd23c3e SHA256: c3e960420463af4e04198106e6f39022121666e334f09c25068b4c94ae681623 SHA512: 8b0be9f04dd77ca6dfcb42270bd75bda79845958ea3fbb65ce0f2c978cb00f2e8782f871ddb83f237210d0693258dc6f103bd1d416c52c5e97fb0757fbd35aba Homepage: https://cran.r-project.org/package=fTrading Description: CRAN Package 'fTrading' (Rmetrics - Trading and Rebalancing Financial Instruments) A collection of functions for trading and rebalancing financial instruments. It implements various technical indicators to analyse time series such as moving averages or stochastic oscillators. Package: r-cran-ftrcool Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2782 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ftrcool_2.0.0-1.ca2604.1_all.deb Size: 1455598 MD5sum: 2a974cb48d72a8459415dc3b93341db2 SHA1: 21366c3c1ea357505332887cdd814aa85d8f7b09 SHA256: 71eb643f02cb4f33a03c4780da5b5fc4cbfa1fdeb1a5f02ff1a201c94bd69115 SHA512: 0f441bb733b05b716c0eff3831213cbbb518537278540787840ddcc5c9af7c39ca23d3f7ccf6d6b0780440c4cb8cd3c6717d78b5c3e164a05ae961bd7d85ef9e Homepage: https://cran.r-project.org/package=ftrCOOL Description: CRAN Package 'ftrCOOL' (Feature Extraction from Biological Sequences) Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from nucleotide and peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) . 'iFeature' 'Z. Chen et al.' (2018) . . 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) . 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) . 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) . 'W. Chen et al.' 'Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome' (2015) . 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Functions support creating height-above-ground (HAG) metrics, tiling, and sampling point clouds, generating training datasets, applying trained models to new point clouds, and producing per-point fuel classes such as stems, branches, foliage, and surface fuels. These tools support workflows for forest structure analysis, wildfire behavior modeling, and fuel complexity assessment. Deep learning segmentation relies on the PointNeXt architecture described by Qian et al. (2022) , while ground classification utilizes the Cloth Simulation Filter algorithm by Zhang et al. (2016) . 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Package: r-cran-fundiversity Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-geometry, r-cran-matrix, r-cran-vegan Suggests: r-cran-future, r-cran-knitr, r-cran-memoise, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fundiversity_1.1.1-1.ca2604.1_all.deb Size: 342930 MD5sum: 6813279e08ed2696c0cd9e4da51b5256 SHA1: 5ff701e3ed8d93a101297ffd321e698a76d8f256 SHA256: b4dd9bd84bebc9787a94d623f752d879a055ba2216af51530d60b055d48b23f8 SHA512: d4c024c65be26042909b7e51b9599635ca533beafbdf5a23c979db01c2d3be9a05da0d4cb0dd76758be06042a8763f4e72add7e1fb7def4589a701db9ae2c1e7 Homepage: https://cran.r-project.org/package=fundiversity Description: CRAN Package 'fundiversity' (Easy Computation of Functional Diversity Indices) Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 ). Provides efficient, modular, and parallel functions to compute functional diversity indices (preprint: ). Package: r-cran-funfem Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-fda, r-cran-elasticnet Filename: pool/dists/resolute/main/r-cran-funfem_1.2-1.ca2604.1_all.deb Size: 390446 MD5sum: 88ec295a05b7e86222124f0b6876e501 SHA1: ee084b38d9125e9b546b218da2ac628414a0a2d2 SHA256: fb49491f1fd2f59210644f69699782c0aa02e19c482d9cfba145fff1dbfd706f SHA512: ebd7899ea131a0068f30328c235bc6e32a5f98ebf3de397a4e11753aceb8bb629596b2a34f76c4588f797e4efcc5841ba788a4eb32b327b84cb8438b27975502 Homepage: https://cran.r-project.org/package=funFEM Description: CRAN Package 'funFEM' (Clustering in the Discriminative Functional Subspace) The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace. Package: r-cran-fungp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-knitr, r-cran-scales, r-cran-microbenchmark, r-cran-dofuture, r-cran-dorng, r-cran-future, r-cran-progressr Filename: pool/dists/resolute/main/r-cran-fungp_1.0.0-1.ca2604.1_all.deb Size: 704288 MD5sum: b7bf271c7083b0434936c9e142ab4724 SHA1: 83db244b5c625f3423354ebff769730510920446 SHA256: 74180836e069cb24deeeaafdd909da4e59885e4d63fbd87f9762974aaae5384f SHA512: 56949629d0e85785123dcecc88ee879011ef0ae49b0f7f79f066ff1f640dc1a33fff48ed5da77d404f95d91df4670f841b30d04dc930f5be8ceb2583e90c338d Homepage: https://cran.r-project.org/package=funGp Description: CRAN Package 'funGp' (Gaussian Process Models for Scalar and Functional Inputs) Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). An in-depth tutorial in the use of funGp is provided in Betancourt et al. (2024) and Metamodeling background is provided in Betancourt et al. (2020) . The algorithm for structural parameter optimization is described in . Package: r-cran-funhddc Architecture: all Version: 2.3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-fda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-funhddc_2.3.1.1-1.ca2604.1_all.deb Size: 497072 MD5sum: e5649f67957c4fe9690fa6f53bb538c9 SHA1: fb9c6e62978ae6a5ceb505c5f501a02f474fcdd3 SHA256: 862395d05b139bb47532463a2cf86e361db169d2ef0756d0491d48acd5fb12de SHA512: 7f59834ea60ab423b04d319fe2b8f78967ac010821ae784d1a8415e58f3c47e80f7104682899b8b6e2e841eafc2a10f41463e366e812fa6da2226383e91da7a2 Homepage: https://cran.r-project.org/package=funHDDC Description: CRAN Package 'funHDDC' (Univariate and Multivariate Model-Based Clustering inGroup-Specific Functional Subspaces) The funHDDC algorithm allows to cluster functional univariate (Bouveyron and Jacques, 2011, ) or multivariate data (Schmutz et al., 2018) by modeling each group within a specific functional subspace. Package: r-cran-funihc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-cluster, r-cran-mclust Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-funihc_0.1.0-1.ca2604.1_all.deb Size: 171538 MD5sum: 1ca93a41779df4aa8a163e6703a7f243 SHA1: b7ad111891c21fc0e68c2c5f15b08aa93ed3ad50 SHA256: c4b9b1075ae7b1dcd90a74e0bcf67601342a0f94a2ad4ca7d83a823f1c40c12c SHA512: 41f07f3bfacaa2829763cb5770c94c41d936276e2a69ce125ebc6cb1a5b2135636dfab3b728f524a09eef75525f1128fc6b0a77f9893b6a0246f51115bfaf9cd Homepage: https://cran.r-project.org/package=funIHC Description: CRAN Package 'funIHC' (Functional Iterative Hierarchical Clustering) Functional clustering aims to group curves exhibiting similar temporal behaviour and to obtain representative curves summarising the typical dynamics within each cluster. A key challenge in this setting is class imbalance, where some clusters contain substantially more curves than others, which can adversely affect clustering performance. While class imbalance has been extensively studied in supervised classification, it has received comparatively little attention in unsupervised clustering. This package implements functional iterative hierarchical clustering ('funIHC'), an adaptation of the iterative hierarchical clustering method originally developed for multivariate data, to the functional data setting. For further details, please see Higgins and Carey (2024) . Package: r-cran-funique Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-funique_0.0.1-1.ca2604.1_all.deb Size: 927464 MD5sum: 7b33366314d5311a7c30995f0f8813e4 SHA1: e020bfc8298ef7bf2e64b5f1f248cf817222f539 SHA256: f6adaa274ebf6c4d52e3e4904a57c4c5d6d64262d5dbcb064a121b8c578cd8e0 SHA512: 1cf4bea4e94e1f1744ce50c6d5446386287da11872b22dffc467847ebaa4c46fc453e3828ace32a266a898a3607f37ffa72df18832f522506ef24866ba4b374c Homepage: https://cran.r-project.org/package=funique Description: CRAN Package 'funique' (A Faster Unique Function) Similar to base's unique function, only optimized for working with data frames, especially those that contain date-time columns. Package: r-cran-funkycells Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3482 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-ggplot2, r-cran-rpart, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-proc, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-funkycells_1.1.1-1.ca2604.1_all.deb Size: 3095376 MD5sum: 335ddc12ed233573da534fa5709f78fa SHA1: 497c159b817f69ccced894eb9fcf1dd52fd290df SHA256: 5782de99808ed0b084ee7f24c11dd83fa36b42c95161c99e6cdf160ba2148a26 SHA512: df3e1f12a1f37a55301c318df471e2524c0f245b18f6d32443e99074757f5c1eff49b45add06abe59131eb6f245074a51b0568a756047dd189df413b128b897b Homepage: https://cran.r-project.org/package=funkycells Description: CRAN Package 'funkycells' (Functional Data Analysis for Multiplexed Cell Images) Compare variables of interest between (potentially large numbers of) spatial interactions and meta-variables. Spatial variables are summarized using K, or other, functions, and projected for use in a modified random forest model. The model allows comparison of functional and non-functional variables to each other and to noise, giving statistical significance to the results. Included are preparation, modeling, and interpreting tools along with example datasets, as described in VanderDoes et al., (2023) . 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Funky heatmaps can be fine-tuned by providing annotations of the columns and rows, which allows assigning multiple palettes or geometries or grouping rows and columns together in categories. Saelens et al. (2019) . Package: r-cran-funmediation Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-refund, r-cran-tvem, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-funmediation_1.0.2-1.ca2604.1_all.deb Size: 141524 MD5sum: f844849fc8baa6d31165f712fc02f816 SHA1: 6a2a9a214295a89bd59676b0ccc58834342f1c53 SHA256: f7cf4dfe7dd081e83c83a83b057aedbc614a80447535060a64b156d862227806 SHA512: 44002a673f611e27ea399eea158bcd23d49264dcc3b9e6df2edba3dd96cfcf97c2ad1aa532c062a2796efffcc5057f0f3847dcfa0a5be04770a2da55ff1b5a56 Homepage: https://cran.r-project.org/package=funmediation Description: CRAN Package 'funmediation' (Functional Mediation for a Distal Outcome) Fits a functional mediation model with a scalar distal outcome. The method is described in detail by Coffman, Dziak, Litson, Chakraborti, Piper & Li (2021) . The model is similar to that of Lindquist (2012) although allowing a binary outcome as an alternative to a numerical outcome. The current version is a minor bug fix in the vignette. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Package: r-cran-funmodeling Architecture: all Version: 1.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-rocr, r-cran-ggplot2, r-cran-gridextra, r-cran-pander, r-cran-reshape2, r-cran-scales, r-cran-dplyr, r-cran-rlang, r-cran-rcolorbrewer, r-cran-moments, r-cran-entropy, r-cran-cli, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-funmodeling_1.9.6-1.ca2604.1_all.deb Size: 1000088 MD5sum: 0c267988ecc9bbe6107cea57443f412b SHA1: 9845781227d6ba95c75c99b42c95c94559afcc2b SHA256: 3019ec481080c8579dbefbdc4a5c11600a7e4ca4e4707dccc6e070e762c43e54 SHA512: 9783465ccf1abe10c80603c44b07e02629c525036fb66d9b5ee37cf629f82ea67ec49c0a645e3f4e3e879dbb23a662886607fd7a9d17d423be7296f600f29420 Homepage: https://cran.r-project.org/package=funModeling Description: CRAN Package 'funModeling' (Exploratory Data Analysis and Data Preparation Tool-Box) Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book () are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions. Package: r-cran-funneljoin Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-broom, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-forcats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-funneljoin_0.2.0-1.ca2604.1_all.deb Size: 174824 MD5sum: 58707c5691611c70d79a37a1def043be SHA1: c42f564e6d53c32bc474dbffc86349864130a588 SHA256: b74f0bdad463dc96b04e4818dec5bb5d9115cd5f81df55a3d6447e35cd069fce SHA512: d7d1cda0d4b23cf89452432de474704073a29b90f8c6cbb21fa2db598938ffc24b29239bea553c5f7fdf7ff17c112279c8e6f51df53a03fdd2851d1f657b3c48 Homepage: https://cran.r-project.org/package=funneljoin Description: CRAN Package 'funneljoin' (Time-Based Joins to Analyze Sequences of Events) Time-based joins to analyze sequence of events, both in memory and out of memory. after_join() joins two tables of events, while funnel_start() and funnel_step() join events in the same table. With the type argument, you can switch between different funnel types, like first-first and last-firstafter. 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Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored. 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Package: r-cran-funprog Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-purrr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-funprog_0.3.0-1.ca2604.1_all.deb Size: 33998 MD5sum: 613943cd1613aaba4db2a08fb18e5607 SHA1: 7bcf56c0e374fc41185a0dc6accfe03b9e1fdbfb SHA256: 6d73cffb9b0c2f79a68315ce01ffe0121fcb75c21235f64b62eb5e27f51092f4 SHA512: d6cb86eafdb76b98de7613c6b1d67169c3c3ca1247bce00198b8d6bd7dfb329c84891c182e33554b0a0dbf04371fb20c0edaad8c7f91205308e575ac5bf8872f Homepage: https://cran.r-project.org/package=funprog Description: CRAN Package 'funprog' (Functional Programming) High-order functions for data manipulation : sort or group data, given one or more auxiliary functions. Functions are inspired by other pure functional programming languages ('Haskell' mainly). 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Package: r-cran-funrar Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-matrix Suggests: r-cran-ade4, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidytext Filename: pool/dists/resolute/main/r-cran-funrar_1.5.0-1.ca2604.1_all.deb Size: 540492 MD5sum: ab17bfcf007edbecd5a2e3d3e7ebebb3 SHA1: e6017c269b3a909f6c525eafaf7775a52229f7f1 SHA256: 6b25c861e62714c5d596edfff0f99d619ea9fc231e43a1d18d2c190d92c28274 SHA512: 8614ea3195f11d2877f199d761a307892a1101ffa103b72a268553e1a3bfd55c52410274d8d6097642aee6bcf59dda924d32b9024f2ce71f5be7e4ee60486cbf Homepage: https://cran.r-project.org/package=funrar Description: CRAN Package 'funrar' (Functional Rarity Indices Computation) Computes functional rarity indices as proposed by Violle et al. (2017) . Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. 'funrar' is presented in Grenié et al. (2017) . Package: r-cran-funreg Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-funreg_1.2.2-1.ca2604.1_all.deb Size: 411074 MD5sum: b80caf3c5a893ab3c923a53b4e555292 SHA1: 5862393b046b1d184aad8240fde1d2290974d77c SHA256: 1f0a38f8bda5014cab9befbd8715eae556229eddf33a9fccb7723b8b68bdce5a SHA512: 8ab3bcc044206de51acf522d1ce39dd99315299fb19b4775b7107513fe44c8a33e1f5f18dd365a12ecb6eeb2cd63f10046fe0797077743473757fc163a1e90d4 Homepage: https://cran.r-project.org/package=funreg Description: CRAN Package 'funreg' (Functional Regression for Irregularly Timed Data) Performs functional regression, and some related approaches, for intensive longitudinal data (see the book by Walls & Schafer, 2006, Models for Intensive Longitudinal Data, Oxford) when such data is not necessarily observed on an equally spaced grid of times. The approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu, Caffo, and Reich (2011) and the approach taken in their sample code, but with some modifications to make it more feasible to use with long rather than wide, non-rectangular longitudinal datasets with unequal and potentially random measurement times. It also allows easy plotting of the correlation between the smoothed covariate and the outcome as a function of time, which can add additional insights on how to interpret a functional regression. Additionally, it also provides several permutation tests for the significance of the functional predictor. The heuristic interpretation of ``time'' is used to describe the index of the functional predictor, but the same methods can equally be used for another unidimensional continuous index, such as space along a north-south axis. Note that most of the functionality of this package has been superseded by added features after 2016 in the 'pfr' function by Jonathan Gellar, Mathew W. McLean, Jeff Goldsmith, and Fabian Scheipl, in the 'refund' package built by Jeff Goldsmith and co-authors and maintained by Julia Wrobel. The development of the funreg package in 2015 and 2016 was part of a research project supported by Award R03 CA171809-01 from the National Cancer Institute and Award P50 DA010075 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse, the National Cancer Institute, or the National Institutes of Health. Package: r-cran-funspace Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-ks, r-cran-mgcv, r-cran-missforest, r-cran-mass, r-cran-paran, r-cran-vegan, r-cran-phytools, r-cran-viridis Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-funspace_0.2.2-1.ca2604.1_all.deb Size: 1354064 MD5sum: 32d59ea0b16be0a1369487ea39a72b5b SHA1: c9de8323146259acc9b356ffae110b0f0c8e3fe1 SHA256: b77971ec19b76ad27fd5b591f40ed03a23f7f7394c41a8421b18d3cd14dd52d3 SHA512: 562ab719169ffc86f0bc6f50e6809eeeb26b15b5d999ff1fbc8c38aa6c9ce5b6c7d450881bd6ac86928b56fb8bc51f648cf12adf4f3eee4fc5ca4bf046a1f4f8 Homepage: https://cran.r-project.org/package=funspace Description: CRAN Package 'funspace' (Creating and Representing Functional Trait Spaces) Estimation of functional spaces based on traits of organisms. The package includes functions to impute missing trait values (with or without considering phylogenetic information), and to create, represent and analyse two dimensional functional spaces based on principal components analysis, other ordination methods, or raw traits. It also allows for mapping a third variable onto the functional space. See 'Carmona et al. (2021)' , 'Puglielli et al. (2021)' , 'Carmona et al. (2021)' , 'Carmona et al. (2019)' for more information. Package: r-cran-funspotr Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-glue, r-cran-knitr, r-cran-httr, r-cran-callr, r-cran-readr, r-cran-here, r-cran-formatr, r-cran-fs, r-cran-tibble, r-cran-import, r-cran-lifecycle Suggests: r-cran-remotes, r-cran-visnetwork, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-funspotr_0.0.5-1.ca2604.1_all.deb Size: 241312 MD5sum: 15252ba8f2a97bbf3d2e41347e231e34 SHA1: 9e0b576c166b52d45cc186ff131108d75fbf2fc3 SHA256: bc5bfdf33b690f2bad419193decf6cf564cf68d2b55ad40de7870a0d064b7014 SHA512: 4312c535e0805a8895a3fe71ad1752c7189175ef4f0a870f5519a88227faa46ec16fd195963d5a1d5c13d2279aa4c8a758dfb62eb4cbe98e17557f620aa666aa Homepage: https://cran.r-project.org/package=funspotr Description: CRAN Package 'funspotr' (Spot R Functions & Packages) Helpers for parsing out the R functions and packages used in R scripts and notebooks. 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Other competitive state-of-the-art approaches proposed by Chakraborty and Chaudhuri (2015) , Horvath et al (2013) or Cuevas et al (2004) are also included in the package, as well as procedures to run test result comparisons and power analysis using simulations. Package: r-cran-funsurv Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mfpca, r-cran-mass, r-cran-fundata, r-cran-matrix, r-cran-ggplot2, r-cran-reda Filename: pool/dists/resolute/main/r-cran-funsurv_1.0.0-1.ca2604.1_all.deb Size: 65958 MD5sum: aa8493d056298f266735a537601eac4e SHA1: 7929df3ed51bdcc74c22dd45c3e3b7eaf0fe028f SHA256: e671aa49efa701b92432c2d37374ea517fefd2f7779e8dde47216342c70d737e SHA512: 28087f0fed9491708823c2867c6123ee292e236e491c26fe3700aea1cebd4a27e9446a543802058ef7c1ba7e7f32675d4ef8f2c99f62c9759cd00bebfc343d6b Homepage: https://cran.r-project.org/package=FunSurv Description: CRAN Package 'FunSurv' (Modeling Time-to-Event Data with Functional Predictors) A collection of methods for modeling time-to-event data using both functional and scalar predictors. 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Package: r-cran-funta Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-funta_0.1.1-1.ca2604.1_all.deb Size: 24572 MD5sum: 96f14393deb8cbc2fe594621a06eb1c1 SHA1: aabbac0732dc13f6eb060efdee09742ec929a942 SHA256: 0bc1fd366750bd0ef4b31bd0e52543b1dd1408c557bbae3b93eb099a2ddedc4d SHA512: aaceef3bbee0cce9e85c66b03d081674cbcff46b2bdcc857543200eb167cf180c797597c8f373f2aefa8c521a6dcf9b08e15d6668629137451850ad02215d082 Homepage: https://cran.r-project.org/package=FUNTA Description: CRAN Package 'FUNTA' (Functional Tangential Angle Pseudo-Depth) Contains functions to compute the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, for details. 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Package: r-cran-funviewr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-codetools, r-cran-visnetwork, r-cran-igraph, r-cran-htmltools, r-cran-magrittr Suggests: r-cran-htmlwidgets Filename: pool/dists/resolute/main/r-cran-funviewr_0.1.1-1.ca2604.1_all.deb Size: 42962 MD5sum: 2d95c0146bbaf13b993efdec72aaa021 SHA1: 1c6fc2101ca7bb33e52ed835b97d4e3be087a9d0 SHA256: 40528d5387b6e77261267ec876562efbf40358402bb2f462b16b5cf5f01c883a SHA512: ce8ee48e95f04bf8bf810b6e693ea6268e04ce23cea8302ff087a89b4479806daaf1c66a134233beb3e02a446257c6ee2777dbbd9e17a11f0f1a4f088e969ed4 Homepage: https://cran.r-project.org/package=funviewR Description: CRAN Package 'funviewR' (Visualize Function Call Dependencies in R Source Code) Provides tools to analyze R source code and detect function definitions and their internal dependencies across multiple files. 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Package: r-cran-funwithnumbers Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpfr, r-cran-gmp, r-cran-bigbits Filename: pool/dists/resolute/main/r-cran-funwithnumbers_1.2-1.ca2604.1_all.deb Size: 78402 MD5sum: 27b6a34704980b931264e0369e752da4 SHA1: 3803783ad8eb7aed72758d43c2996f61876bfd41 SHA256: 424aa243bfa2da4b4c5d22d1cf7ad5890398c12b4d84b4f9b21d260b8b9f71ae SHA512: 5d6a086f71f0537bce0cd63179188b4ae162c09231d61f6976349c2e40473983e96f5a9186fb48271f763dc974a9237cfb45edd3476d79ae0af1a5396b932e61 Homepage: https://cran.r-project.org/package=FunWithNumbers Description: CRAN Package 'FunWithNumbers' (Fun with Fractions and Number Sequences) A collection of toys to do things like generate Collatz and other interesting sequences, calculate a fraction which is a close approximation to some value (e.g., 22/7 or 355/113 for pi), and so on. 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These furniture-themed functions are designed to simplify common tasks in quantitative analysis. Other data summary and cleaning tools are also available. Package: r-cran-furrr Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1094 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-globals, r-cran-purrr, r-cran-rlang, r-cran-vctrs Suggests: r-cran-carrier, r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-parallelly, r-cran-testthat, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-furrr_0.4.0-1.ca2604.1_all.deb Size: 1002796 MD5sum: b4ddc326f80b8b3e80c5e6f138807b51 SHA1: d6e3d24e3d1f6f87086142bcd27b75060a28a9ff SHA256: f978863ab748ffa93cddefcea3bf22680241a7ac08893b74640c4329b0fb5065 SHA512: 029ba9ecb367c5603fddc373bd32e1387c17429ea90741ccfb3ef5e8ba7ad8faa9cda753963b8fd560fbd1c128ae11b4e0ba0b6b2be150eef85aa5337a8e5e76 Homepage: https://cran.r-project.org/package=furrr Description: CRAN Package 'furrr' (Apply Mapping Functions in Parallel using Futures) Implementations of the family of map() functions from 'purrr' that can be resolved using any 'future'-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. Package: r-cran-fusedmgm Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastdummies, r-cran-bigmemory, r-cran-gplots, r-cran-bigalgebra, r-cran-biganalytics Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fusedmgm_0.1.2-1.ca2604.1_all.deb Size: 437334 MD5sum: afe45fa235edf33cd33dccbe2f1f96f8 SHA1: 7718541af292763592081da048c22edc462a458a SHA256: 1000f14462767dd24d6aa10b195e1b648b235581c4fad68f0c49856bf13bcd12 SHA512: a181a46ac51005f4c3bc9ba6edc4f3b2cbfd40d4944dde374fc557a334aa9333fd8de40290c5531d5e1b2e77d27147af5eca638fd64ccce026a3ca7c9cd37d6e Homepage: https://cran.r-project.org/package=fusedMGM Description: CRAN Package 'fusedMGM' (Implementation of Fused MGM to Infer 2-Class Networks) Implementation of fused Markov graphical model (FMGM; Park and Won, 2022). The functions include building mixed graphical model (MGM) objects from data, inference of networks using FMGM, stable edge-specific penalty selection (StEPS) for the determination of penalization parameters, and the visualization. For details, please refer to Park and Won (2022) . Package: r-cran-fusedtree Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-splittools, r-cran-survival, r-cran-treeclust, r-cran-partykit Suggests: r-cran-rpart Filename: pool/dists/resolute/main/r-cran-fusedtree_1.1.0-1.ca2604.1_all.deb Size: 153380 MD5sum: 18dc315eaf5d257ece1a8e29e7b2903a SHA1: 738d01dc58982c394ef998ffba5d479c5b01db40 SHA256: 1c9a0616d9b73b0197539002186ddad62516d69c8b982b43518a77c623c9ca8d SHA512: 0a7dc4b865ecc4b1406edba0f09d62e5207cef0bd0984207f69daddfb0648d1bfeb83715a6ef1c250c2c03bc7674dce0d3e684a0aab1c12594d096e0ab2b6a08 Homepage: https://cran.r-project.org/package=fusedTree Description: CRAN Package 'fusedTree' (Fused Partitioned Regression for Clinical and Omics Data) Fit (generalized) linear regression models in each leaf node of a tree. The tree is constructed using clinical variables only. The linear regression models are constructed using (high-dimensional) omics variables only. The leaf-node-specific regression models are estimated using the penalized likelihood including a standard ridge (L2) penalty and a fusion penalty that links the leaf-node-specific regression models to one another. The intercepts of the leaf nodes reflect the effects of the clinical variables and are left unpenalized. The tree, fitted with the clinical variables only, should be constructed outside of the package with the 'rpart' 'R' package. See Goedhart and others (2024) for details on the method. Package: r-cran-fusemlr Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1349 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-digest Suggests: r-cran-testthat, r-cran-upsetr, r-cran-caret, r-cran-ranger, r-cran-glmnet, r-cran-boruta, r-cran-knitr, r-cran-rmarkdown, r-cran-proc, r-cran-checkmate Filename: pool/dists/resolute/main/r-cran-fusemlr_0.0.4-1.ca2604.1_all.deb Size: 1069646 MD5sum: 83d90b23dfcac901f5aa46b8facd95bf SHA1: c260a7650d99cf06720a30f33c668cce90456022 SHA256: 323004bc05109e0ae4c9e00afbef7cca97e92fc6551cb44f14c326554f99933f SHA512: 1c94147057b3507366bca61acdbbbdecb0fa202c0390b548d2ee37bac5df5ce2e63554a28f136b84476e66ae95374616fd0622dbd03ae41d144bd2371b3a0b39 Homepage: https://cran.r-project.org/package=fuseMLR Description: CRAN Package 'fuseMLR' (Fusing Machine Learning in R) Recent technological advances have enable the simultaneous collection of multi-omics data i.e., different types or modalities of molecular data, presenting challenges for integrative prediction modeling due to the heterogeneous, high-dimensional nature and possible missing modalities of some individuals. We introduce this package for late integrative prediction modeling, enabling modality-specific variable selection and prediction modeling, followed by the aggregation of the modality-specific predictions to train a final meta-model. This package facilitates conducting late integration predictive modeling in a systematic, structured, and reproducible way. 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Package: r-cran-fusionclust Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fusionclust_1.0.0-1.ca2604.1_all.deb Size: 40454 MD5sum: 0027f143c4dc69a86d32de7f4bc196ea SHA1: 2823ad81b31cce4f70b52f2aa14ee3706ec3f605 SHA256: 76f8f9f17347e31af8fbb70e90b78b12257521c621e24e9e15ffac847eb1ba7e SHA512: 47b6d1e7c785001491ad31e5ff5961c7a9591f214fa51170c4891359a0d239856431306598526dea1ca5ba6d09dc5048241148c6cd2169c9919eea9b788955d7 Homepage: https://cran.r-project.org/package=fusionclust Description: CRAN Package 'fusionclust' (Clustering and Feature Screening using L1 Fusion Penalty) Provides the Big Merge Tracker and COSCI algorithms for convex clustering and feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) and T.Banerjee et al. (2017) for more details. Package: r-cran-fusionlearn Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2986 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-ggplot2, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-fusionlearn_0.2.1-1.ca2604.1_all.deb Size: 2741022 MD5sum: cd9f99135f6b1887a8a8c2858581a156 SHA1: 402b0c539d96cbdb404014ab869fe6466ba15f20 SHA256: d5ba44086a9421efc83dfd4cad9d13ba63a53212a4e019db32834e37999f47c0 SHA512: 3930bbfbbf7a9ee2ae10f7cbdfa9ccd5643c71551aa76654782c91d0c1e55cc8aa61692657fe68d2242cd873799654dd156ba46547f137e27b5f6212d9ddd82e Homepage: https://cran.r-project.org/package=FusionLearn Description: CRAN Package 'FusionLearn' (Fusion Learning) The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) . Package: r-cran-futile.logger Architecture: all Version: 1.4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lambda.r, r-cran-futile.options Suggests: r-cran-testit, r-cran-jsonlite, r-cran-httr, r-cran-crayon, r-cran-rsyslog, r-cran-glue Filename: pool/dists/resolute/main/r-cran-futile.logger_1.4.9-1.ca2604.1_all.deb Size: 113986 MD5sum: a5e38d9758675f2f3ab1075ddfbd794a SHA1: 36656215d820e70acdb3a37b94789662b786ce6c SHA256: cbcaa967c0257cdfc6f3676db29d40cc845f5df53a4d3e6165675fd59a9cff0b SHA512: efdb1d73c6fc197a2e20975b1f5dedcb2c5ad7adc30af3f7147fd5584e073253ad0f9516abe46864e1332f055f432005b97c4b08d1d63dffe31c744c454fdc52 Homepage: https://cran.r-project.org/package=futile.logger Description: CRAN Package 'futile.logger' (A Logging Utility for R) Provides a simple yet powerful logging utility. Based loosely on log4j, futile.logger takes advantage of R idioms to make logging a convenient and easy to use replacement for cat and print statements. Package: r-cran-futile.options Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-futile.options_1.0.1-1.ca2604.1_all.deb Size: 19770 MD5sum: 659a07091a1dc0d2c36750d9a1865c04 SHA1: 50228b88f7d8f3a96343e4590f8aa9831b9018cb SHA256: d5c43b24d7bc47fe36b23bcc37af740be1e8c708a77d2f6e1e27460470bea04e SHA512: 5e235c4a85015e2c222f0847fcbf01df45398aaf049af96da3f578a8117b38f504772adbef0f3e3bc3d241bc251e4cb98c1e4b154fa64773211ff608cca3a323 Homepage: https://cran.r-project.org/package=futile.options Description: CRAN Package 'futile.options' (Futile Options Management) A scoped options management framework. Used in other packages. Package: r-cran-futility Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-futility_0.5-1.ca2604.1_all.deb Size: 274522 MD5sum: 0d84e57d5654afcd89b03e7debcda457 SHA1: f22722e1b4fa737a7cec8323395e6c97e68677e0 SHA256: d3dc5a49978acac6ea9df5cec5b7b777a17fef143a409eba020463e35a7608b0 SHA512: d70c6bf100f3f053393d33b64c31e4aff3c0c1113fe576b726873ae3100d269768b381270c5f2f4900fbec1b12719143a61f0b8e72542f2034064d9d0fb4103f Homepage: https://cran.r-project.org/package=futility Description: CRAN Package 'futility' (Interim Analysis of Operational Futility in Randomized Trialswith Time-to-Event Endpoints and Fixed Follow-Up) Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results. Package: r-cran-future.apply Architecture: all Version: 1.20.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-globals Suggests: r-cran-listenv, r-cran-r.rsp, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-future.apply_1.20.2-1.ca2604.1_all.deb Size: 190656 MD5sum: 0eb32ccea3f85efd99dcd018a39d40d0 SHA1: 0ad1fc2520052a760bc969c004c1b8f22ce2800d SHA256: fc7aa2ef01fd1a3025fae6082bf5da2bcdcf3e46e8f9627c9db9270691b3ca3d SHA512: c466f604a3ee6d37049a43190973b6fa2b986e69b8299d1fbcaab1919888c9ebb74eb346896c1c3a5b7820206f670bd040dd00ff2bf3fd98b0619d2a5c0f3686 Homepage: https://cran.r-project.org/package=future.apply Description: CRAN Package 'future.apply' (Apply Function to Elements in Parallel using Futures) Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework . Package: r-cran-future.batchtools Architecture: all Version: 0.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-parallelly, r-cran-batchtools, r-cran-checkmate, r-cran-stringi Suggests: r-cran-globals, r-cran-future.apply, r-cran-listenv, r-cran-markdown, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-future.batchtools_0.22.0-1.ca2604.1_all.deb Size: 289920 MD5sum: e538e432b7c8cea0b8fcf9c084e03bec SHA1: f60779903f4457f95e44c4f02ee64c2aaa3bb65c SHA256: 4ec03ef245c8782816e1dec1794f6a562f6dc00f6738caf167c1f6702a181014 SHA512: 1c13d22132fca36b675d37c7c8b8f5c9bea2dc99026fba5420dc76d8def5725210a8636773ff85813d6a078d9fcb105dd659a153d4a35b88cd55a715cbf75ba8 Homepage: https://cran.r-project.org/package=future.batchtools Description: CRAN Package 'future.batchtools' (A Future API for Parallel and Distributed Processing using'batchtools') Implementation of the Future API on top of the 'batchtools' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'. Package: r-cran-future.callr Architecture: all Version: 0.10.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-parallelly, r-cran-callr Suggests: r-cran-globals, r-cran-future.apply, r-cran-listenv, r-cran-markdown, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-future.callr_0.10.2-1.ca2604.1_all.deb Size: 86648 MD5sum: e6cc3c565e4e0c1d796e474cec5fc1ff SHA1: 4463cb0eb5d2814949e67400c0c0c73e3ed4e215 SHA256: e69139fa909ebc64cc97d40a077e5c946faab069dc1a2e2ca561e5b402af504d SHA512: a4ce97c857ecd85dbab440783f3da17609cd3e1f73d5ae798b3fbc2f01df55ef959b9e589435e024e571a9afed4ff753636108786236d51e8245b4abda281873 Homepage: https://cran.r-project.org/package=future.callr Description: CRAN Package 'future.callr' (A Future API for Parallel Processing using 'callr') Implementation of the Future API on top of the 'callr' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine. Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession') and socket connections, the 'callr' backend provided here can run more than 125 parallel R processes. Package: r-cran-future.mirai Architecture: all Version: 0.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-mirai, r-cran-parallelly Suggests: r-cran-future.tests, r-cran-future.apply, r-cran-listenv Filename: pool/dists/resolute/main/r-cran-future.mirai_0.10.1-1.ca2604.1_all.deb Size: 82148 MD5sum: 21a2acedde8e5c84cf50704d01de351a SHA1: 68a3fd66955a6ce12cab9d39a6db18c884afe5ce SHA256: 92704c1392ca1e8dc24882fc662a2aafd3d692e5a1b1619b1d3db5ea4154a15c SHA512: b06846c53c75addeba5c0111d8facfab600390fd558c58b20a4e4eb7dde2435df00604f600996645b0b3be7edc336b9bdb70994043eaf81c4e2930197ea2a520 Homepage: https://cran.r-project.org/package=future.mirai Description: CRAN Package 'future.mirai' (A 'Future' API for Parallel Processing using 'mirai') Implementation of the 'Future' API on top of the 'mirai' package . By using this package, you get to take advantage of the benefits of 'mirai' plus everything else that 'future' and the 'Futureverse' adds on top of it. It allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local machine or across remote machines. Contrary to back-ends relying on the 'parallel' package (e.g. 'multisession') and socket connections, 'mirai_cluster' and 'mirai_multisession', provided here, can run more than 125 parallel R processes. As a reminder, regardless which future backend is used by the user, the code does not have to change, it gives identical results, and behaves exactly the same. Package: r-cran-future.tests Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-cli, r-cran-crayon, r-cran-prettyunits, r-cran-sessioninfo Suggests: r-cran-commonmark, r-cran-base64enc, r-cran-data.table, r-cran-ff Filename: pool/dists/resolute/main/r-cran-future.tests_0.9.0-1.ca2604.1_all.deb Size: 194508 MD5sum: 2079e8352855257812f829a29339e99d SHA1: 3a8e0217ef67b39b13c04cf8f48f5887cbfca12c SHA256: 1bfd3e5320a81251e38124701e205259d47c1e62e575b49bc8333b116cbd6bb1 SHA512: 1b4b035ee2104f9aae6f632b0ee1f752352fad4dbb1dec27b41b79cbe6a1462d0fcb918c1abade3f3094313634c39a0dce3a52ef52ff72a80929148a6fc4dd88 Homepage: https://cran.r-project.org/package=future.tests Description: CRAN Package 'future.tests' (Test Suite for 'Future API' Backends) Backends implementing the 'Future' API , as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API. The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines. Package: r-cran-future Architecture: all Version: 1.70.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-globals, r-cran-listenv, r-cran-parallelly Suggests: r-cran-rhpcblasctl, r-cran-r.rsp, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-future_1.70.0-1.ca2604.1_all.deb Size: 926508 MD5sum: d8a43ff5bbc5ba8096306fe2df19ee88 SHA1: 2a27eb4478ba1aa395ded06db91f34a05777f491 SHA256: df5aa0378e22c7b9a6afdb751183b3d5b70b5276a3716e98cd870627fa0ec4a0 SHA512: 1514f314ff918411b2c063e3532cfcb0bb0b00cf8c4e04c486660fdc4696492a6a479ed5068134e799ac79064fa3b2e537a005a32bc7deb80bff3c6a48e69c69 Homepage: https://cran.r-project.org/package=future Description: CRAN Package 'future' (Unified Parallel and Distributed Processing in R for Everyone) The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multisession)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers, etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures. Package: r-cran-futureverse Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-futurize, r-cran-future.apply, r-cran-furrr, r-cran-dofuture, r-cran-progressr Suggests: r-cran-future.batchtools, r-cran-future.callr, r-cran-future.mirai, r-cran-rhpcblasctl Filename: pool/dists/resolute/main/r-cran-futureverse_0.2.0-1.ca2604.1_all.deb Size: 34394 MD5sum: 7885843e6ed9391104865556c2618337 SHA1: 1065a261b55138c790ec4b2fb6224c69c66ea7f6 SHA256: 45f90a394287ddd00aac9bcd850f81b25a22ee4ef3ddd9ab96e3b74a8991ed4b SHA512: 7fc5c00a600eb1625d6b03c20b9f2404dc76ad4a15b1eb288d8b5557c92b1172ca3df8bf5da12ebfa73a4be0f15efd81faafcead3c206acadb1fabb32d271b94 Homepage: https://cran.r-project.org/package=futureverse Description: CRAN Package 'futureverse' (Install 'Futureverse' in One Go) The 'Futureverse' is a set of packages for parallel and distributed processing with the 'future' package at its core, cf. Bengtsson (2021) . This package is designed to make it easy to install common 'Futureverse' packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each 'Futureverse' package as a dependency as needed. Package: r-cran-futurize Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future Suggests: r-cran-future.apply, r-cran-foreach, r-cran-dofuture, r-cran-purrr, r-cran-furrr, r-cran-crossmap, r-cran-plyr, r-cran-pbapply, r-bioc-biocparallel, r-cran-boot, r-cran-caret, r-cran-randomforest, r-bioc-deseq2, r-bioc-fgsea, r-bioc-genomicalignments, r-bioc-rsamtools, r-cran-fwb, r-cran-gamlss, r-cran-glmmtmb, r-cran-glmnet, r-bioc-gsva, r-cran-kernelshap, r-cran-lme4, r-cran-metafor, r-cran-mgcv, r-cran-partykit, r-cran-riskregression, r-bioc-scater, r-bioc-scuttle, r-bioc-singlecellexperiment, r-bioc-sva, r-cran-seriation, r-cran-simdesign, r-cran-shapr, r-cran-strucchange, r-cran-tm, r-cran-vegan, r-cran-commonmark, r-cran-base64enc Filename: pool/dists/resolute/main/r-cran-futurize_0.3.0-1.ca2604.1_all.deb Size: 423802 MD5sum: 554ed1f1f6e00f6d3189f03833b56336 SHA1: 31007978134343b92efc537cd93570cbf45004f0 SHA256: a2fcd02670d4d6ec12fc2b1a9acd35c181138708ad892e2acd5607e3fc794124 SHA512: 05c65b63a4784fcf25f75e00b688a93f66ca4de3b17da5cd0a037f1a6d18bf56556fb36836bd46f16d812fa2de776a60888c8f11aaae054694f0d2bb173ebd34 Homepage: https://cran.r-project.org/package=futurize Description: CRAN Package 'futurize' (Parallelize Common Functions via One Magic Function) The futurize() function transpiles calls to sequential map-reduce functions such as base::lapply(), purrr::map(), 'foreach::foreach() %do% { ... }' into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the 'future' ecosystem . By combining this function with R's native pipe operator, you have a convenient way for speeding up iterative computations with minimal refactoring, e.g. 'lapply(xs, fcn) |> futurize()', 'purrr::map(xs, fcn) |> futurize()', and 'foreach::foreach(x = xs) %do% { fcn(x) } |> futurize()'. Other map-reduce packages that can be "futurized" are 'BiocParallel', 'plyr', 'crossmap', 'pbapply' packages. There is also support for a growing set of domain-specific packages on CRAN (e.g. 'boot', 'caret', 'fgsea', 'fwb', 'gamlss', 'glmmTMB', 'glmnet', 'kernelshap', 'lme4', 'metafor', 'mgcv', 'partykit', 'riskRegression', 'seriation', 'shapr', 'SimDesign', 'strucchange', 'tm', 'TSP', and 'vegan') and on Bioconductor (e.g. 'DESeq2', 'GenomicAlignments', 'GSVA', 'Rsamtools', 'scater', 'scuttle', 'SingleCellExperiment', and 'sva'). Package: r-cran-fuzzr Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-progress, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fuzzr_0.2.2-1.ca2604.1_all.deb Size: 50000 MD5sum: fac38b1295263470d9c56717e25b0d41 SHA1: dd0f0e90fc137d650fbb73e7a863eeeb26974d9e SHA256: e2e757595a0a21c81b4b1aea287e52156daea44f59a3c7bbacaa03a6be629721 SHA512: 2c7658a5284715b559d2dc22e9d41f47eedaffd48a8a766a6127b5f633d740d71f2184c42db098eb6350978aaead9679f4cd2037fb0892e88d4e611645ffa9cb Homepage: https://cran.r-project.org/package=fuzzr Description: CRAN Package 'fuzzr' (Fuzz-Test R Functions) Test function arguments with a wide array of inputs, and produce reports summarizing messages, warnings, errors, and returned values. Package: r-cran-fuzzy.p.value Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers Filename: pool/dists/resolute/main/r-cran-fuzzy.p.value_1.1-1.ca2604.1_all.deb Size: 51262 MD5sum: e8509ac4918ed846eac66f2fda3334cd SHA1: d750414e57510cbd42ec2d1f3b378b9ce67952da SHA256: 3b22597f972087ce6a1002787e3310049e027c23afb5a95ade75dedc14c4d873 SHA512: 88f72915e3209a1c2531fa13b5a6521638a2b033d7620ee866a285a175f9eeeb9cfff6dbb30bf1e961a455c50c367c1140cc97d02a45fef55406586d1fb7fc24 Homepage: https://cran.r-project.org/package=Fuzzy.p.value Description: CRAN Package 'Fuzzy.p.value' (Computing Fuzzy p-Value) The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package. Package: r-cran-fuzzyahp Architecture: all Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fuzzyahp_0.9.5-1.ca2604.1_all.deb Size: 186350 MD5sum: e78f712e21c91b006f3e3fe9ff710f6a SHA1: 16886458922ffa4d9c719b72cdb183f2c7c429ff SHA256: c4a7159d6a9694e93af39e0bdfff2a306ee3940ac6cf27137eb4a7e57561ee72 SHA512: ebd5ea1ac19e2adfcc37b4add30788d2f72d2b25cbb674ed637dfb2dd8f6a201883cef5c29013790001cbf50d6906d7d3f1d7488c6a7c58c799127e855e2bf0a Homepage: https://cran.r-project.org/package=FuzzyAHP Description: CRAN Package 'FuzzyAHP' ((Fuzzy) AHP Calculation) Calculation of AHP (Analytic Hierarchy Process - ) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights. Package: r-cran-fuzzyclass Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-catools, r-cran-doparallel, r-cran-dplyr, r-cran-e1071, r-cran-envstats, r-cran-foreach, r-cran-mass, r-cran-mvtnorm, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-rootsolve, r-cran-tibble, r-cran-tidyr, r-cran-trapezoid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-maxlik Filename: pool/dists/resolute/main/r-cran-fuzzyclass_0.1.7-1.ca2604.1_all.deb Size: 527004 MD5sum: 3b57d9305b10eb159b499efe9f39b4db SHA1: 8ee8472163cfaf6ab785a0e4e837b33b4b2bcc18 SHA256: bf719b63bbb8a296b452c950f411537e671cd010db9329223cba1ca2f76c9979 SHA512: 927399337638b27429603202fc7d852aacd94baa2504054527accb6302883328530c46948d8f3148a9478a76ba9ead485747416ae8b26d0812fdd5042fe0cedf Homepage: https://cran.r-project.org/package=FuzzyClass Description: CRAN Package 'FuzzyClass' (Fuzzy and Non-Fuzzy Classifiers) It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) . Package: r-cran-fuzzyforest Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-mvtnorm Suggests: r-cran-wgcna, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fuzzyforest_1.0.8-1.ca2604.1_all.deb Size: 826116 MD5sum: 5ecb84f1fa2839ae289f77edaa6f8b1f SHA1: 68d8151c8951f24e9dc206508635eeb9d16c8358 SHA256: ef550755ffe476676656be8a8e7ca656ee282f9540b15f19a92f501f35548bec SHA512: 1cb4bb987bbdc2fa8e6fb0a79afa78fcb30067419ced4f07cec02461bb1a83ce870a9e0e33b3374feba16db5a7c5bc81c7501c0e12106e35e363c0e0c242ead0 Homepage: https://cran.r-project.org/package=fuzzyforest Description: CRAN Package 'fuzzyforest' (Fuzzy Forests) Fuzzy forests, a new algorithm based on random forests, is designed to reduce the bias seen in random forest feature selection caused by the presence of correlated features. Fuzzy forests uses recursive feature elimination random forests to select features from separate blocks of correlated features where the correlation within each block of features is high and the correlation between blocks of features is low. One final random forest is fit using the surviving features. This package fits random forests using the 'randomForest' package and allows for easy use of 'WGCNA' to split features into distinct blocks. See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) for further details. Package: r-cran-fuzzyimputationtest Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzysimres, r-cran-fuzzynumbers, r-cran-missforest, r-cran-miceranger, r-cran-vim, r-cran-fuzzyresampling, r-cran-mice Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fuzzyimputationtest_0.5.2-1.ca2604.1_all.deb Size: 141254 MD5sum: bdc5eec858c21dd48ddfa0bbf93b9b16 SHA1: 416d8683c103d554c5d94128b8a712b18b3e2cea SHA256: eadf1c07e2fb1d2b3f9a375da7e589c7d9e50330630dbc78d137f55fc6fcb8d5 SHA512: 7bfaec8ea6f93401c590b47e18d0ac05a5dd544a9ee3365b9ccafbb0e0e45264982dcc94a03722da0c45200a3f93b3b90968a26fcdcf94beaa3261c4bd7fe298 Homepage: https://cran.r-project.org/package=FuzzyImputationTest Description: CRAN Package 'FuzzyImputationTest' (Imputation Procedures and Quality Tests for Fuzzy Data) Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets. 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Implementations include string distance and regular expression matching. Package: r-cran-fuzzylink Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rfast, r-cran-reshape2, r-cran-stringdist, r-cran-stringr, r-cran-httr, r-cran-jsonlite, r-cran-httr2, r-cran-ranger, r-cran-ellmer Filename: pool/dists/resolute/main/r-cran-fuzzylink_0.4.1-1.ca2604.1_all.deb Size: 63268 MD5sum: 1248b35109afd42c7e055e992cf508a3 SHA1: 7d8b67b4124929f116b75739cfc3ce3bfd580a1b SHA256: 1789742425a3e1e05f3020ba6c194557da68b29528f2f24949d6839eda68809a SHA512: 5528e2822079bf0b9f34f582eec88d70fa1ff5f8e5ef933dd23b5738619f850d7d1c974ad643678069d08082de2b551442dc1ad1130f0127bf516541affb4a27 Homepage: https://cran.r-project.org/package=fuzzylink Description: CRAN Package 'fuzzylink' (Probabilistic Record Linkage Using Pretrained Text Embeddings) Links datasets through fuzzy string matching using pretrained text embeddings. Produces more accurate record linkage when lexical string distance metrics are a poor guide to match quality (e.g., "Patricia" is more lexically similar to "Patrick" than it is to "Trish"). Capable of performing multilingual record linkage. Methods are described in Ornstein (2025) . Package: r-cran-fuzzylp Architecture: all Version: 0.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-roi, r-cran-fuzzynumbers, r-cran-roi.plugin.glpk Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-fuzzylp_0.1-7-1.ca2604.1_all.deb Size: 421450 MD5sum: d39d878feb8d0fe2f690a9a18a3f504f SHA1: 5f041b4523d2f3ee7a8b7eb944cdf809e4c0a0e6 SHA256: b84007bf2294e438b9cc5635885fa810dc15f6289074029d76962d54ad72c9fc SHA512: b25aa4e53db4ce174332fece93dd283eef67842e1ecc7f83ed74e0922d7d022078760100607a4814c26394fce13470f16d57ff181403fda5fdff4c9edcbedf36 Homepage: https://cran.r-project.org/package=FuzzyLP Description: CRAN Package 'FuzzyLP' (Fuzzy Linear Programming) Provides methods to solve Fuzzy Linear Programming Problems with fuzzy constraints (following different approaches proposed by Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy technological matrix. Package: r-cran-fuzzym Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fuzzym_0.1.0-1.ca2604.1_all.deb Size: 652032 MD5sum: 7cd2a9bfbf177a73eda5a03038f3263c SHA1: 8f59c61c3b16777be5fb75f7030dacdf2c456a4c SHA256: 8d1011e19b3b72cd19fb47e25f40c4af7be8fb01245d3357c86a1ddb8ba9ebb5 SHA512: 7ec9a322fa31787b98e765c48bc50206130bec38e278861ffc7b2ca6f5f22c04e00b06173d975650f186202e30cb01c4513e171680dee0991c52870acccff2c3 Homepage: https://cran.r-project.org/package=FuzzyM Description: CRAN Package 'FuzzyM' (Fuzzy Cognitive Maps Operations) Contains functions for operations with fuzzy cognitive maps using t-norm and s-norm operators. T-norms and S-norms are described by Dov M. Gabbay and George Metcalfe (2007) . System indicators are described by Cox, Earl D. (1995) . Executable examples are provided in the "inst/examples" folder. Package: r-cran-fuzzynumbers.ext.2 Architecture: all Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers Filename: pool/dists/resolute/main/r-cran-fuzzynumbers.ext.2_3.2-1.ca2604.1_all.deb Size: 48932 MD5sum: 665efccbe8df414f0aa50413340a5da6 SHA1: 57f3d288c62726826028474206d592eff1322971 SHA256: b80ceeb5e1daeb81bba7521daae063de70b9588b7e4c29f89940efda19251681 SHA512: d0a561eb2212430123df660f17d36449c85de38deb4af643d9e66d466a83a7c8fee31b5a7ae84c1955c78144bb486f204e3d02f0882ecd5f4914ac155d78cfac Homepage: https://cran.r-project.org/package=FuzzyNumbers.Ext.2 Description: CRAN Package 'FuzzyNumbers.Ext.2' (Apply Two Fuzzy Numbers on a Monotone Function) One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result. Package: r-cran-fuzzynumbers Architecture: all Version: 0.4-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-digest Filename: pool/dists/resolute/main/r-cran-fuzzynumbers_0.4-7-1.ca2604.1_all.deb Size: 788090 MD5sum: 2658e6e259e41fd6551a059f7ecd3565 SHA1: 50bba659d7785dbdf6f3f041cfc54bd7692ca9d6 SHA256: dfca196ac699ad65ca3b1aa5ae7b8c8fe6cb6e373c20c609b8797cc0afafa008 SHA512: 82c00616f756c3c6c25eded8a88111b0992a026137521404706a9b72d622a548edfdcb49b4203c2fbcb267255f9ceac0876cab41de71e7a61dbec387b3e07395 Homepage: https://cran.r-project.org/package=FuzzyNumbers Description: CRAN Package 'FuzzyNumbers' (Tools to Deal with Fuzzy Numbers) S4 classes and methods to deal with fuzzy numbers. They allow for computing any arithmetic operations (e.g., by using the Zadeh extension principle), performing approximation of arbitrary fuzzy numbers by trapezoidal and piecewise linear ones, preparing plots for publications, computing possibility and necessity values for comparisons, etc. Package: r-cran-fuzzypovertyr Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-sampling, r-cran-ecp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-fuzzypovertyr_3.0.2-1.ca2604.1_all.deb Size: 642838 MD5sum: 2d07a68d88168574b76587d400a181e1 SHA1: 9ada073a4ab0956ca24071d64d9b360549f363e9 SHA256: f23abade4794fbdeab5e007244751615b13625c3256551b969c37e050f037bed SHA512: 9222954f52228363dffb3543ae06ab47a2094b5f14e0e3a9412dc44256e188cd2bbe7215633d98cd40fc6934f8f930e7f0039dacbbf0c9e81c20e5c55ec08e22 Homepage: https://cran.r-project.org/package=FuzzyPovertyR Description: CRAN Package 'FuzzyPovertyR' (Estimation of Fuzzy Poverty Measures) Estimates fuzzy measures of poverty and deprivation. It also estimates the sampling variance of these measures using bootstrap or jackknife repeated replications. Package: r-cran-fuzzyq Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-fuzzyq_0.1.0-1.ca2604.1_all.deb Size: 58876 MD5sum: 7fd92f15f1c7bf81d911a0c278e37800 SHA1: 0688962cd43ab08c541a5190d66ace809efd9b17 SHA256: e21ec0ba05c11bd418d57669538bc140aa7cc3852366892d3e727c340cf782c4 SHA512: aa81149f36326353686b5d556968274f9f549d94a380f6c6ba917f0dfe6ad30088f8cf3857886c538439cf93db94872a50fe00997105bda235b12a851dc63bf3 Homepage: https://cran.r-project.org/package=FuzzyQ Description: CRAN Package 'FuzzyQ' (Fuzzy Quantification of Common and Rare Species) Fuzzy clustering of species in an ecological community as common or rare based on their abundance and occupancy. It also includes functions to compute confidence intervals of classification metrics and plot results. See Balbuena et al. (2020, ). Package: r-cran-fuzzyr Architecture: all Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-fuzzyr_2.3.2-1.ca2604.1_all.deb Size: 320584 MD5sum: f027b9f246a76634069e5fb04bc63c53 SHA1: edc1141b0813df3a57f023ee192bc57f71f8bdd8 SHA256: 6a1604cc46e14e4b3bca6a1a773b382f8bb521bb4e87f8ca12277e6aad31303e SHA512: fa37ed68ff65c69e2daa45e4f8aa780b02c7dbe5f6b118f3858f5a72f143e0f77ff3cedaab0a01d64e68f8af06231628df02a225d3da0feddba0dd97a57d2ab7 Homepage: https://cran.r-project.org/package=FuzzyR Description: CRAN Package 'FuzzyR' (Fuzzy Logic Toolkit for R) Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic. This toolkit includes with graphical user interface (GUI) and an adaptive neuro- fuzzy inference system (ANFIS). This toolkit is a continuation from the previous package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA) and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham. A big thank you to the many people who have contributed to the development/evaluation of the toolbox. Please cite the toolbox and the corresponding paper when using it. More related papers can be found in the NEWS. Package: r-cran-fuzzyresampling Architecture: all Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-fuzzyresampling_0.6.4-1.ca2604.1_all.deb Size: 416516 MD5sum: b58094a193b5c496507bed8f05834938 SHA1: 0496bbd99776196bcd73aa488fa4563b2ba4430f SHA256: dfaecabf08be091c80c14923ea9c5a6be8bdcd6a40cb289444876e124df5e28d SHA512: c8176964773e80e7f13e51681b2528d37c2b52478a371fe80a98c0e253d88ab9a533157afe04f45b7c796e090a90c638bcb419387f6df97d9d437fb19aea3b62 Homepage: https://cran.r-project.org/package=FuzzyResampling Description: CRAN Package 'FuzzyResampling' (Resampling Methods for Triangular and Trapezoidal Fuzzy Numbers) The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317) "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data. The main aim of the algorithms implemented in this package is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample. To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. , Grzegorzewski et al. (2020) , Grzegorzewski et al. (2020) , Grzegorzewski and Romaniuk (2022) , Romaniuk and Hryniewicz (2019) ). Some additional procedures related to these resampling methods are also provided, like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers") and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, )). Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions. Package: r-cran-fuzzysim Architecture: all Version: 4.54-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-modeva, r-cran-stringi Suggests: r-cran-aod, r-cran-geodist, r-cran-phylolm, r-cran-raster, r-cran-terra Filename: pool/dists/resolute/main/r-cran-fuzzysim_4.54-1.ca2604.1_all.deb Size: 446382 MD5sum: 2ed36890930ffa382c014d944ead0cfc SHA1: ff3b6ce5dde0412a2921f739544b442f0d25f196 SHA256: 00cff0e380b14ecec54de4c0cc7d4c1b0cc92fba5197d69130d9cc7bc3e17f7d SHA512: 4eec826cb44cd5d560f0e94a8380f365d014721dfc85ea8ce5342dffbd7d2689988fc9bf56077a7a2ed2eb1488ad9c827357c28cbbfa08e5b237252f1d5d74dd Homepage: https://cran.r-project.org/package=fuzzySim Description: CRAN Package 'fuzzySim' (Fuzzy Similarity in Species Distributions) Functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, fuzzy loss, fuzzy gain), and for data preparation, such as obtaining unique abbreviations of species names, defining the background region, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting among (pseudo)absences to address survey bias, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) . Package: r-cran-fuzzyspec Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-thresher, r-cran-fclust, r-cran-mclust, r-cran-mvtnorm, r-cran-np, r-cran-ggplot2, r-cran-viridislite Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-patchwork, r-cran-devtools, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-fuzzyspec_1.0.0-1.ca2604.1_all.deb Size: 226174 MD5sum: 142f712279e53c6a93cac23a80833167 SHA1: 699da97ec40afb7d78299a1e2fa48557340c5329 SHA256: 9ee9a5a07018e813432a0094b72899ba81dd870ae8751685ccc159367838b494 SHA512: 93f498a58ab61b8116f7796121ada84effe9ed66a44724a314910d6fdb76d69bea6239d2c3dab4f7476aa4fe9f3974a48d5e0491c7f700e0ee23daf44daf223e Homepage: https://cran.r-project.org/package=FuzzySpec Description: CRAN Package 'FuzzySpec' (Fuzzy Spectral Clustering with Variable-Weighted AdjacencyMatrices) Implementation of the FVIBES, the Fuzzy Variable-Importance Based Eigenspace Separation algorithm as described in the paper by Ghashti, J.S., Hare, W., and J.R.J. Thompson (2025). Variable-Weighted Adjacency Constructions for Fuzzy Spectral Clustering. Submitted. Package: r-cran-fuzzystattra Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fuzzystattra_1.0-1.ca2604.1_all.deb Size: 165748 MD5sum: ae98c90487fd46070ed058d2d34f5dcb SHA1: 7616e890455c0254db365f2f997bf79e0df8c0fc SHA256: 2b7cf57d0f3a323f3308c32a803bef8c69639ccdf89914f6f7bf89244f81ce72 SHA512: 4e2838f441be8d7394bfe472e1b27cce318d2f20ced90baca55459636de26eef3fa593036058c2a21f0d76b4d408770d717e59a0cc52fa95498e265e784ff44a Homepage: https://cran.r-project.org/package=FuzzyStatTra Description: CRAN Package 'FuzzyStatTra' (Statistical Methods for Trapezoidal Fuzzy Numbers) The aim of the package is to provide some basic functions for doing statistics with trapezoidal fuzzy numbers. In particular, the package contains several functions for simulating trapezoidal fuzzy numbers, as well as for calculating some central tendency measures (mean and two types of median), some scale measures (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and one diversity index and one inequality index. Moreover, functions for calculating the 1-norm distance, the mid/spr distance and the (phi,theta)-wabl/ldev/rdev distance between fuzzy numbers are included, and a function to calculate the value phi-wabl given a sample of trapezoidal fuzzy numbers. Package: r-cran-fuzzysts Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-polynom Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fuzzysts_0.4-1.ca2604.1_all.deb Size: 856606 MD5sum: 8574fd23b0c08c49a2d1728484756a84 SHA1: e6f6182b84788686b91b5c75062970817b008c76 SHA256: b907cf95e390dfdc4f7d1063aa89ee8add67e4d6ba790a23fb3bb816ce9a0481 SHA512: 987390ce560d7da00edad65c90bd87b55e6242e897b409d2024ede234a09b7a11b2630c44619c7e7571952a50a72f75b6dc46cc36d50018338bbfda034729aa8 Homepage: https://cran.r-project.org/package=FuzzySTs Description: CRAN Package 'FuzzySTs' (Fuzzy Statistical Tools) The main goal of this package is to present various fuzzy statistical tools. It intends to provide an implementation of the theoretical and empirical approaches presented in the book entitled "The signed distance measure in fuzzy statistical analysis. Some theoretical, empirical and programming advances" . For the theoretical approaches, see Berkachy R. and Donze L. (2019) . For the empirical approaches, see Berkachy R. and Donze L. (2016) ). Important (non-exhaustive) implementation highlights of this package are as follows: (1) a numerical procedure to estimate the fuzzy difference and the fuzzy square. (2) two numerical methods of fuzzification. (3) a function performing different possibilities of distances, including the signed distance and the generalized signed distance for instance with all its properties. (4) numerical estimations of fuzzy statistical measures such as the variance, the moment, etc. (5) two methods of estimation of the bootstrap distribution of the likelihood ratio in the fuzzy context. (6) an estimation of a fuzzy confidence interval by the likelihood ratio method. (7) testing fuzzy hypotheses and/or fuzzy data by fuzzy confidence intervals in the Kwakernaak - Kruse and Meyer sense. (8) a general method to estimate the fuzzy p-value with fuzzy hypotheses and/or fuzzy data. (9) a method of estimation of global and individual evaluations of linguistic questionnaires. (10) numerical estimations of multi-ways analysis of variance models in the fuzzy context. The unbalance in the considered designs are also foreseen. Package: r-cran-fuzzywuzzyr Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fuzzywuzzyr_1.0.6-1.ca2604.1_all.deb Size: 191576 MD5sum: b64b4a028a896dfd7efa5f40f09f1b6c SHA1: 4c4b7a5a4bb07aae4345794620f4ec138e971bd4 SHA256: d2204d15602f278ab33a189c218ef8bd13b0212d90a8200644217a7b3d6cb9ef SHA512: e162cd5514401f492f0c06b7c1b7f9b92d58516348c1ca1365119b244e0db6afa402f8dba3db518a12c0cb91b8401e13ad9087131ff17a92cfe02030319ad1b5 Homepage: https://cran.r-project.org/package=fuzzywuzzyR Description: CRAN Package 'fuzzywuzzyR' (Fuzzy String Matching) Fuzzy string matching implementation of the 'fuzzywuzzy' 'python' package. It uses the Levenshtein Distance to calculate the differences between sequences. Package: r-cran-fwb Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-chk, r-cran-pbapply, r-cran-generics Suggests: r-cran-survival, r-cran-cobalt, r-cran-boot, r-cran-mvtnorm, r-cran-sandwich, r-cran-ggdist, r-cran-lmtest, r-cran-nnet, r-cran-future, r-cran-future.apply, r-cran-testthat, r-cran-waldo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fwb_0.5.1-1.ca2604.1_all.deb Size: 275220 MD5sum: cc95be81828ce27231714f25f16ac3d3 SHA1: 7445945a80bac2651f480137ef6c057413b355ff SHA256: 39eedf677fa2dcdfa0b87896677d5a969d421eb192aea10752b88bf1f331230a SHA512: ebb0676e3670b802b5d5a4d5eae7d8a7ed9856fcc9282f2794f8d1cb02e3f8ebf7596b1f77ee7cdd3219adcfce98e4f6cd9c93f549b796fff6a8046d5ab3ee4e Homepage: https://cran.r-project.org/package=fwb Description: CRAN Package 'fwb' (Fractional Weighted Bootstrap) An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the 'boot' package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) for details. Package: r-cran-fwdselect Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvtools, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-fwdselect_2.1.1-1.ca2604.1_all.deb Size: 178556 MD5sum: 85b3255dc4fe2588453d5d2dbc4d5d01 SHA1: 85f798efda1ec2c8b35649ac4c149cda546898b6 SHA256: 81c2ee2d6318de779963830b8329d3908b35a669d0b6d6c57dde417120bd3e6c SHA512: 239571a959ff35f6b48528b38803bdd840cc6c30d1ba09d4579471538f24f0d8844f1fbb2e59c29f21feb3a57c4ef46ebcd902602add01ba37c917b9654d6b06 Homepage: https://cran.r-project.org/package=FWDselect Description: CRAN Package 'FWDselect' (Selecting Variables in Regression Models) A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric). Implemented methodology described in: M. Sestelo, N. M. Villanueva, L. Meira-Machado and J. Roca-Pardiñas (2016). FWDselect: an R package for variable selection in regression models. The R Journal, 8 (1), 132-148. . Package: r-cran-fwlplot Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1933 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fixest, r-cran-tinyplot Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-fwlplot_0.3.0-1.ca2604.1_all.deb Size: 1730000 MD5sum: 179fbcb28b8017ed4f9c88d6f86676e0 SHA1: 6ce95248ead1bbb8888a68e9d34ccc78f4090443 SHA256: 6d55e65d1d9b80b439410127773f65bbce987e9c61327363609012c056fd34af SHA512: 43e3b1119db1e48438836f4cc222260a2d07d1dbb28b4912f580207dc38a676cac74044237598fb64d2050a68a7060dc5abeff52b2bfae8b6cdbf4fa13c311f1 Homepage: https://cran.r-project.org/package=fwlplot Description: CRAN Package 'fwlplot' (Scatter Plot After Residualizing Using 'fixest' Package) Creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the 'fixest' package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) . Package: r-cran-fwrgb Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-imager, r-cran-neuralnet Filename: pool/dists/resolute/main/r-cran-fwrgb_0.1.0-1.ca2604.1_all.deb Size: 254696 MD5sum: 4373d750084439df0631f955f5179d15 SHA1: ab66969a1edaa97e2aa31c0b108ca0bef1d4f667 SHA256: a95f20f51da0e218d1f84946251a4982e0b27cf91d39e6b1ae1015e5441ca66a SHA512: fa63cc973df380abba58eb6432d0b6e226f76fda979de6388889f6c9813cbc6677436606a2363d70542098dd62094e9e237199b16ba3fddbc76d35ed90e4b7b4 Homepage: https://cran.r-project.org/package=FWRGB Description: CRAN Package 'FWRGB' (Fresh Weight Determination from Visual Image of the Plant) Fresh biomass determination is the key to evaluating crop genotypes' response to diverse input and stress conditions and forms the basis for calculating net primary production. However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsu’s thresholding method Otsu, N. (1979) to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected area. After that, the projected area is used as input to the machine learning model (Linear Model, Artificial Neural Network, and Support Vector Regression) to determine the plant's fresh weight. Package: r-cran-fwtraits Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-jsonlite, r-cran-rstudioapi, r-cran-r.cache Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-vcr, r-cran-dplyr, r-cran-tidytext, r-cran-testthat, r-cran-fd, r-cran-tidyr, r-cran-tibble, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-fwtraits_1.0.0-1.ca2604.1_all.deb Size: 1472004 MD5sum: 111bcf1616805bfb03a87237c5e23c07 SHA1: 7354eb2f887c0d4cd6c00d3bade280bf9f456e4b SHA256: c6f6fc00fc841b240bc19758b4fbc8ab50f2d82805076a240625cc2fb629816a SHA512: 295a9dbc63c4cc26e672a75db3dd19c8c344ec3c35847c8baf8d03e14c79bab1e4df4e39f9bfeeae5c2d197ae1db42c38e347c2577b023d2910d3799d04e9067 Homepage: https://cran.r-project.org/package=fwtraits Description: CRAN Package 'fwtraits' (Extract Species Ecological Parameters fromWww.freshwaterecology.info) Support the extraction and seamless integration of species ecological traits or preferences from the www.freshwaterecology.info into several ecological model workflows. During data extraction, different taxonomic levels are acceptable, including species, genus, and family, based on the availability of data in the database. The data is cached after the first search and can be accessed during and after online interactions. Only scientific names are acceptable in the search; local or English names are not allowed. A user API key is required to start using the package. Package: r-cran-fxl Architecture: all Version: 1.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5381 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-here, r-cran-tidyverse, r-cran-scales, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-fxl_1.7.3-1.ca2604.1_all.deb Size: 4205084 MD5sum: 7418cfc3ffd51f791b3d2af2432afae8 SHA1: d97dbdcbf8a0fc8f3e73ff6dfb16d6d166682859 SHA256: cd4a0293c4a09f1d63b7d402f68edfbe894634231e30e9de13cbee3678ef64d6 SHA512: 98d2b0b09c5f2394679e76e76735470a607efa48d07c7dd5f6cf965c9788ed82be7753a1b22ed95b8c4bed6d1b1dbfde15d2fe03fa6ccea4b13b4603fc3ecbd3 Homepage: https://cran.r-project.org/package=fxl Description: CRAN Package 'fxl' ('fxl' Single Case Design Charting Package) The 'fxl' Charting package is used to prepare and design single case design figures that are typically prepared in spreadsheet software. With 'fxl', there is no need to leave the R environment to prepare these works and many of the more unique conventions in single case experimental designs can be performed without the need for physically constructing features of plots (e.g., drawing annotations across plots). Support is provided for various different plotting arrangements (e.g., multiple baseline), annotations (e.g., brackets, arrows), and output formats (e.g., svg, rasters). Package: r-cran-fxregime Architecture: all Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-strucchange, r-cran-car, r-cran-sandwich Suggests: r-cran-lmtest, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-fxregime_1.0-4-1.ca2604.1_all.deb Size: 1748122 MD5sum: 2f05b7bbfddf4b93fdbe449c7874ddc1 SHA1: e34ba3c063f4b6efdd651f57014775efe88d7702 SHA256: 416fe4a66bee06e4204ae97aab95eeb6cb717cf2e051ebf25205467342167fc7 SHA512: 93d0848633a3613d3b1094055d35d5bd88d6763b90ab55b928716c0e667af2f64609b7335479d221db86dd01506e85dca808729aeca49ea62d94eb100c656f95 Homepage: https://cran.r-project.org/package=fxregime Description: CRAN Package 'fxregime' (Exchange Rate Regime Analysis) Exchange rate regression and structural change tools for estimating, testing, dating, and monitoring (de facto) exchange rate regimes. Package: r-cran-fxtwapls Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-geosphere, r-cran-ggplot2, r-cran-jops, r-cran-mass, r-cran-progressr Suggests: r-cran-magrittr, r-cran-progress, r-cran-scales, r-cran-tictoc Filename: pool/dists/resolute/main/r-cran-fxtwapls_0.1.3-1.ca2604.1_all.deb Size: 122526 MD5sum: 77835974d7570520a2e4bde6eeee000b SHA1: 59e6d910c278105458a6f5879e0cb5118c258560 SHA256: 17e6b89775fa81fec513a587fa730c2d0cc6686298550ad80d6a39d3f631dac5 SHA512: 20220bae9b67b4ab2d700b7ae79919a30c20b0fac053627f0dfa2a5e1f2c8715e489c9fa27225cce8e72c6ef4fe7ac8f8172298393201571344e0e11343fba52 Homepage: https://cran.r-project.org/package=fxTWAPLS Description: CRAN Package 'fxTWAPLS' (An Improved Version of WA-PLS) The goal of this package is to provide an improved version of WA-PLS (Weighted Averaging Partial Least Squares) by including the tolerances of taxa and the frequency of the sampled climate variable. This package also provides a way of leave-out cross-validation that removes both the test site and sites that are both geographically close and climatically close for each cycle, to avoid the risk of pseudo-replication. 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As such, many databases need to represent and validate financial years efficiently. While the use of integer years with a convention that they represent the year ending is common, it may lead to ambiguity with calendar years. On the other hand, string representations may be too inefficient and do not easily admit arithmetic operations. This package tries to make validation of financial years quicker while retaining clarity. Package: r-cran-g.data Architecture: all Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-g.data_2.4.1-1.ca2604.1_all.deb Size: 130326 MD5sum: ddab58edc95e3b56f838571664ef628a SHA1: fa2dede793ef6c463dc2d05a808b4beeec5447ab SHA256: 782ad9a4869c3f25722ed6b18ed681efda5141c7fde216483743d2bd9d6dfe93 SHA512: 7098257cc87f63d31c952ca7993c70cb276bb471f7f95f1b3fea3b0756be3b4b39a4780228e7465ca1f1d8d37684ec9e53717cb111dfdebc488b6d5432b763e3 Homepage: https://cran.r-project.org/package=g.data Description: CRAN Package 'g.data' (Delayed-Data Packages) Create and maintain delayed-data packages (ddp's). Data stored in a ddp are available on demand, but do not take up memory until requested. 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Package: r-cran-g.ridge Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-g.ridge_1.0-1.ca2604.1_all.deb Size: 21578 MD5sum: a401512eddb86625edb214db752cca90 SHA1: 93dc6bc5d4a4c49dce3dfc09569649895dceb0f0 SHA256: 2b97a232629a8ca8e0f416bce40f9e0edbed2c07a8a4a11f12574200c2b30f1f SHA512: 56e451f2b0e13bfa86a1e4140144dcf75663423d457e8d46a3c5b59f587b82dd78126209666021f02dad52d648631d86a7d5a1d10a4c87107c4cfa87c32e8b91 Homepage: https://cran.r-project.org/package=g.ridge Description: CRAN Package 'g.ridge' (Generalized Ridge Regression for Linear Models) Ridge regression due to Hoerl and Kennard (1970) and generalized ridge regression due to Yang and Emura (2017) with optimized tuning parameters. These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors. 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Package: r-cran-g6r Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4744 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-igraph, r-cran-roxy.shinylive, r-cran-testthat, r-cran-withr, r-cran-stringr, r-cran-htmltools, r-cran-bslib Filename: pool/dists/resolute/main/r-cran-g6r_0.6.0-1.ca2604.1_all.deb Size: 1141198 MD5sum: aeb5d0aff7e678400d2d892b258463e4 SHA1: 6ae38df20f1a792239a541efb6e07a1fd420ebfb SHA256: 55d8f70a5c53aae23aa9215f5560f10f701faf26865995fe6d424c332db3eb98 SHA512: a4a845bef57596261509a42ce968c0cfeef9b1941d3e06fce3cd6d495eb8740b13a67166ba6b883ed430437f4ca8f18c80ab4b6c3f50291b0e73d699ead1f3de Homepage: https://cran.r-project.org/package=g6R Description: CRAN Package 'g6R' (Graph Visualisation Engine Widget for R and 'shiny' Apps) Create stunning network experiences powered by the 'G6' graph visualisation engine 'JavaScript' library . 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Package: r-cran-gaawr2 Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gap, r-cran-gap.datasets, r-cran-ggplot2, r-cran-survival, r-cran-rdpack Suggests: r-cran-blr, r-cran-bglr, r-bioc-biomart, r-cran-bookdown, r-cran-cairo, r-bioc-ensdb.hsapiens.v75, r-bioc-ensembldb, r-cran-gmmat, r-cran-hardyweinberg, r-cran-httr, r-cran-httpuv, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-mcmcglmm, r-cran-plumber, r-cran-powereqtl, r-cran-r2jags, r-cran-regress, r-bioc-rsamtools, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-gaawr2_0.0.7-1.ca2604.1_all.deb Size: 653714 MD5sum: 5d5066666fd928a7cbea78336e82d93f SHA1: 023057339358f61fa0a84e8135b7c3e65a041d26 SHA256: c1b113cebbd4fd18d8942217bfac8e6806e40dc744e8ab02b678ad444f70932e SHA512: 6a7812980f8fb39cab5b9cfb40a5bed361cdceab22e0dc67f102c19438aea821041f65bd1987d043eb6ee575dadbb61334fb414725f0e22d78fac7d2bf6a1e73 Homepage: https://cran.r-project.org/package=gaawr2 Description: CRAN Package 'gaawr2' (Genetic Association Analysis) This is a companion to Henry-Stewart talk by Zhao (2026, ), which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. The earlier version by Zhao (2009, ) provides a brief introduction to these topics. Package: r-cran-gabb Architecture: all Version: 0.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-egg, r-cran-ggforce, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggplotify, r-cran-ggpubr, r-cran-tidyr, r-cran-hotelling, r-cran-pheatmap, r-cran-vegan Suggests: r-cran-factominer, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gabb_0.3.10-1.ca2604.1_all.deb Size: 85840 MD5sum: 787721d301ec28f43de4e7396a24d091 SHA1: 6bae5c714390e8c0b3ae37f379de274b93c369ea SHA256: aab0e205b8b7d9e222772f53809eac4f606cc9608fcc2a92dd99baf3482b5e6f SHA512: 9e43a612a5d17ecbdb8710da36fdd785c6dc94c7c633187d3943c801b573f1faef6a0e225784063937d89e03feeaaf0067f72917ddb1644d2284d746bc414d94 Homepage: https://cran.r-project.org/package=GABB Description: CRAN Package 'GABB' (Facilitation of Data Preparation and Plotting Procedures for RDAand PCA Analyses) Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects. Inputs are : data frame, RDA (package 'vegan') and PCA (package 'FactoMineR') objects. Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters. Package: r-cran-gace Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-gace_1.0.0-1.ca2604.1_all.deb Size: 44966 MD5sum: 57cde887d919f6a5424d63401ea6b54c SHA1: fa4729e02a0a993207a9c503285dbec42923c073 SHA256: df9414a75049421a9d168822691ff3db318664896d77c81324a762b4d23f31d9 SHA512: f4970bfd5677f05b5bfe35a554bca2e4c94cfd334af2025bc37410b3a95ab8896402e63feff7cf5be1889983e394e7be7004ee9c3e4977984a98152acd85af7c Homepage: https://cran.r-project.org/package=GACE Description: CRAN Package 'GACE' (Generalized Adaptive Capped Estimator for Time SeriesForecasting) Provides deterministic forecasting for weekly, monthly, quarterly, and yearly time series using the Generalized Adaptive Capped Estimator. The method includes preprocessing for missing and extreme values, extraction of multiple growth components (including long-term, short-term, rolling, and drift-based signals), volatility-aware asymmetric capping, optional seasonal adjustment via damped and normalized seasonal factors, and a recursive forecast formulation with moderated growth. The package includes a user-facing forecasting interface and a plotting helper for visualization. Related forecasting background is discussed in Hyndman and Athanasopoulos (2021) and Hyndman and Khandakar (2008) . The method extends classical extrapolative forecasting approaches and is suited for operational and business planning contexts where stability and interpretability are important. Package: r-cran-gacff Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gacff_1.0-1.ca2604.1_all.deb Size: 79062 MD5sum: d3eb7b1970d02c9edaa2d667fa6142d1 SHA1: 1ca31653b0117cd1be64cf83cbccb5bef1dcb347 SHA256: ac1d2c7264e52e416eaa0011900eee5c23f80dc1d3ecf6f0d74b939c558cdbf1 SHA512: 0b7525b3aaf1a0e6a9ac2435c25ac74eb9fedef48ecb731fe8e359c1aba3c82f34df58585b02a7eb5316f09edbca5be5739ca844b6138eef7cd71f13ea55d68b Homepage: https://cran.r-project.org/package=GACFF Description: CRAN Package 'GACFF' (Genetic Similarity in User-Based Collaborative Filtering) The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method. By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed. This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others. First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user. By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions. The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem. Package: r-cran-gad Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/resolute/main/r-cran-gad_2.0-1.ca2604.1_all.deb Size: 125438 MD5sum: 669fbfdb59b5d89b1203d336afe39715 SHA1: bfead67d45fec0a3bffd75b86f4822d3b8ac75da SHA256: 4584d68b01fcebb4654b5372ddf7afc442947b98c12ed94d9227547787fd3f81 SHA512: 7b606d11ddbb402382ebfed927e93d5d4189b8a36e0dd5585f4afd77a0f59ebb0f96d22b621e522eb37b67eb99c5be7069c0ddd1a1fa59d074b403551acd0603 Homepage: https://cran.r-project.org/package=GAD Description: CRAN Package 'GAD' (Analysis of Variance from General Principles) Analysis of complex ANOVA models with any combination of orthogonal/nested and fixed/random factors, as described by Underwood (1997). There are two restrictions: (i) data must be balanced; (ii) fixed nested factors are not allowed. 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Package: r-cran-gadget3 Architecture: all Version: 0.15-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-rlang, r-cran-tmb Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-unittest Filename: pool/dists/resolute/main/r-cran-gadget3_0.15-1-1.ca2604.1_all.deb Size: 1296226 MD5sum: e304438c70967620d3b96e94680d7057 SHA1: 1c384c9d2f5c377ac537d5f5e5f2ae9795d46616 SHA256: a3d13b08b076f35279815a1a4fab0051394697edcacbd681c2ae0a70d992c34d SHA512: 89e227b98f7980fac321f4f1f3df2601b56d0a3c0a718637a47ead5f676edcab6a264fd108994e81e7945af0dabe18b048e8a69eb6913dac15b2497e4e28bbe4 Homepage: https://cran.r-project.org/package=gadget3 Description: CRAN Package 'gadget3' (Globally-Applicable Area Disaggregated General Ecosystem ToolboxV3) A framework to assist creation of marine ecosystem models, generating either 'R' or 'C++' code which can then be optimised using the 'TMB' package and standard 'R' tools. Principally designed to reproduce gadget2 models in 'TMB', but can be extended beyond gadget2's capabilities. Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) "TMB: Automatic Differentiation and Laplace Approximation.". Begley, J., & Howell, D. (2004) "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.". Package: r-cran-gagblup Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1611 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-gagblup_1.0-1.ca2604.1_all.deb Size: 1580866 MD5sum: 0c8725dc29f4f2996bc87191d7d99dab SHA1: 48bbec2b723dffca9125d7a26c33afe98727f0c3 SHA256: 9e5d7dcdbbbdf3c5136948fefa5b7dbb0cbb1d7ec8d090ea088301539617d0c3 SHA512: 6da3cb497de635582fd275d94f265c54ef61a82b90be7d24d25325eaee98ce8b599b5599ef80d73dd39b5007bb63b22a4e6e846485eef88cbfe391063358beaa Homepage: https://cran.r-project.org/package=GAGBLUP Description: CRAN Package 'GAGBLUP' (Genetic Algorithm Assisted Genomic Best Liner UnbiasedPrediction) Performs genetic algorithm (Scrucca, L (2013) ) assisted genomic best liner unbiased prediction for genomic selection. 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Methods included are ANOVA and Average / Range methods. Requires balanced study. Package: r-cran-gainml Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-fnn Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gainml_0.1.0-1.ca2604.1_all.deb Size: 116642 MD5sum: f476fd595dfc4c502b638bc171053d06 SHA1: 4412f6355bc238038922a815d2fb423cbde22a54 SHA256: 3a6e2ebb1db77e3e70562d95f98d62a22193d95c96310a01851771d4fc6494e1 SHA512: 375c2c7fff3104c5b9f9af5be1e841260796105fdcaead91cb6b82b607371b53b1b74289e78742a646ceaf73faf9aaf8f8407e0c039ff6ccec415471bbe5e94d Homepage: https://cran.r-project.org/package=gainML Description: CRAN Package 'gainML' (Machine Learning-Based Analysis of Potential Power Gain fromPassive Device Installation on Wind Turbine Generators) Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators. H. Hwangbo, Y. Ding, and D. Cabezon (2019) . Package: r-cran-gains Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1982 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gains_1.2-1.ca2604.1_all.deb Size: 1900918 MD5sum: 8550c4432e94dccb1e227b071c10a878 SHA1: 75bd6fb40796a8b2e6551af202603499292aa672 SHA256: 4caafa84f4de5b20169ee0dbf8532e8b02d87228185d58cf52e3341cc77bc538 SHA512: bab1d48c23a46bc976bc673234107ec1e0744d2b7e0d81f5dc384cf56727a274ee14d1a95b306919f4c59fafb7a091a2e719317ce25c7b4efbcebd5cd774cf70 Homepage: https://cran.r-project.org/package=gains Description: CRAN Package 'gains' (Lift (Gains) Tables and Charts) Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications. The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11. Package: r-cran-gaipe Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gaipe_1.1-1.ca2604.1_all.deb Size: 38290 MD5sum: bfab48e764a3519a0153a5722f726c0c SHA1: 70b268a61df37a00e8dde6ab1ea3d959ec7abdc6 SHA256: 220954a65c187d4d0469f1ad4c24eb9c265b35c2515a3898150d9bf6e21751cf SHA512: c5f8aa9ed45a6f67325a493e9c1ac31a9d72302d222d694b66b41f24eea5f7082f895f844735f21c4e39d7227faa30cec02ecc795c95a4555b17c3a6ced7e047 Homepage: https://cran.r-project.org/package=GAIPE Description: CRAN Package 'GAIPE' (Graphical Extension with Accuracy in Parameter Estimation(GAIPE)) Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) . And, it can also implement AIPE on RMSEA and power analysis on RMSEA. Package: r-cran-galah Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2710 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-potions, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2 Suggests: r-cran-covr, r-cran-gt, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-pkgdown, r-cran-reactable, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-galah_2.2.0-1.ca2604.1_all.deb Size: 1799544 MD5sum: 2c5aad95134a315a2260bb339cbb700a SHA1: 933fd115dc221aea87c06f1a4bccf5696ace7859 SHA256: d9c504787dd07f58521f08c530afdcc961f78e1529d888bf2f3b8fb091a5e4ad SHA512: cfd7db102a7febde03edcf99a241a9ca21701de6fa13d1c84fe4cdbd9d5ea1c30cdbf4fe5e41c60e9085b1261d76d5b84b2fae83a7a9caaf5252e8c9ff76ac71 Homepage: https://cran.r-project.org/package=galah Description: CRAN Package 'galah' (Biodiversity Data from the GBIF Node Network) The Global Biodiversity Information Facility ('GBIF', ) sources data from an international network of data providers, known as 'nodes'. Several of these nodes - the "living atlases" () - maintain their own web services using software originally developed by the Atlas of Living Australia ('ALA', ). 'galah' enables the R community to directly access data and resources hosted by 'GBIF' and its partner nodes. Package: r-cran-galahad Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-galahad_2.0.0-1.ca2604.1_all.deb Size: 63888 MD5sum: 65654cef9ef6522b9c72d4a7dfb61166 SHA1: 7fd76699628faafb8044d25cd816773296cf8291 SHA256: 80457a1aca8de64f09dd47677397728d4d0168ccba9b43573f55d38b2436a991 SHA512: 6e9e34ad92408c822066c920cb8297cb6a73622f2be979d819cea2f30c2b42a54fa50a46f6f953ff2622b10a9e2d6fe915aaa94dcefe8d3bbdfa1e90c76232e3 Homepage: https://cran.r-project.org/package=GALAHAD Description: CRAN Package 'GALAHAD' (Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer withSoftplus Reparameterization and Trust-Region Control) Implements the GALAHAD algorithm (Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer), updated in version 2 to replace the hard-clamp positivity constraint of v1 with a numerically smooth softplus reparameterization, add rho-based trust-region adaptation (actual vs. predicted objective reduction), extend convergence detection to include both absolute and relative function-stall criteria, and enrich the per-iteration history with Armijo backtrack counts and trust-region quality ratios. Parameters constrained to be positive (rates, concentrations, scale parameters) are handled in a transformed z-space via the softplus map so that gradients remain well-defined at the constraint boundary. A two-partition API (positive / euclidean) replaces the three-way T/P/E partition of v1; the legacy form is still accepted for backwards compatibility. Designed for biological modeling problems (germination, dose-response, prion RT-QuIC, survival) where rates, concentrations, and unconstrained coefficients coexist. Developed at the Minnesota Center for Prion Research and Outreach (MNPRO), University of Minnesota. Based on Conn et al. (2000) , Barzilai and Borwein (1988) , Xu and An (2024) , Polyak (1969) , Nocedal and Wright (2006, ISBN:978-0-387-30303-1), and Dugas et al. (2009) . Package: r-cran-galaxias Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corella, r-cran-cli, r-cran-delma, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-usethis, r-cran-withr, r-cran-zip Suggests: r-cran-gt, r-cran-here, r-cran-janitor, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-r.utils, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-galaxias_0.1.2-1.ca2604.1_all.deb Size: 424810 MD5sum: be09346d94c26496bb0891a10caf8803 SHA1: 1726823d2d1009ab7248a8a4a58e1263e3877283 SHA256: c68e7edada8e41401d0a083b9ef43e8cebb3c722c4191a399ceea38365b40763 SHA512: 3c915a7b950ee151c539d5205ea692284547b432338183c0438f6928ce29cfc47d7ff0115bd83bca96aef2213a1445f0dd6f349be596f176333dd7b555a6899a Homepage: https://cran.r-project.org/package=galaxias Description: CRAN Package 'galaxias' (Describe, Package, and Share Biodiversity Data) The Darwin Core data standard is widely used to share biodiversity information, most notably by the Global Biodiversity Information Facility and its partner nodes; but converting data to this standard can be tricky. 'galaxias' is functionally similar to 'devtools', but with a focus on building Darwin Core Archives rather than R packages, enabling data to be shared and re-used with relative ease. For details see Wieczorek and colleagues (2012) . Package: r-cran-galaxyr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-galaxyr_0.1.1-1.ca2604.1_all.deb Size: 273848 MD5sum: b5508f46b2e173d8875bbda23b0a95ac SHA1: 844c40c6959f061378d5e2a4a2331aab769ed104 SHA256: 0f99fe40bbd181b22d5c52247f687a99ee15468d3b0900cadf19f6e036bba629 SHA512: e1436a738e683d89881b6612f29a1b00854b963eab4cc10e5eb7af4d5b22141a29669f1ff18e4d4de43ba4587ddf46882e1696a99a575c411db9b799448b4fa6 Homepage: https://cran.r-project.org/package=GalaxyR Description: CRAN Package 'GalaxyR' ('Galaxy' API Implementation) On 'Galaxy' platforms like 'Galaxy Europe' , many tools and workflows can run directly on a high-performance computer. 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Package: r-cran-galigor Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-rstudioapi, r-cran-tibble, r-cran-gargle, r-cran-ryandexdirect, r-cran-rfacebookstat, r-cran-rvkstat, r-cran-rmytarget, r-cran-rym, r-cran-getproxy, r-cran-rgoogleads, r-cran-rappsflyer Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-galigor_0.2.5-1.ca2604.1_all.deb Size: 73210 MD5sum: 465e9a84c05caca4f4922deebca97912 SHA1: f2002eb54478efb651e61e5f3484e0016225494c SHA256: 7aa57cb5679dcaede868ceb6d55e76d3ad96869b12b39f70614ca96d364f83e9 SHA512: 00892e25c631262c5380ee35b439e3ea4dbea3887663ff58760f254ea60a1d984e48ac1857895c5134bb66889b595ec8331c2546032684087fca6b179205e08a Homepage: https://cran.r-project.org/package=galigor Description: CRAN Package 'galigor' (Collection of Packages for Internet Marketing) Collection of packages for work with API 'Google Ads' , 'Yandex Direct' , 'Yandex Metrica' , 'MyTarget' , 'Vkontakte' , 'Facebook' and 'AppsFlyer' . This packages allows you loading data from ads account and manage your ads materials. Package: r-cran-galisats Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-png Filename: pool/dists/resolute/main/r-cran-galisats_2.2.0-1.ca2604.1_all.deb Size: 191676 MD5sum: 8ef16c789a710a77f9dd0cb8719fe753 SHA1: 8ffb681c279925a87eb6c26b59c08b73fbc2a3c7 SHA256: 66c77f42163e1c53dd6f8960ffc0cad7430c5b6db034db842f4564aa1e668777 SHA512: ed9a8064eec4c1e7e3d5f499e725c011acba1f7c386891cbb0093668bd1186dbac51f9fc4fcb52ef21e5feb7bc9e7924ea1e5e0a46d902dcafd07834263da163 Homepage: https://cran.r-project.org/package=galisats Description: CRAN Package 'galisats' (Configuration of Jupiter's Four Largest Satellites) Calculate, plot and animate the configuration of Jupiter's four largest satellites (known as Galilean satellites) for a given date and time (UTC - Coordinated Universal Time). The galsat() function returns numerical values of the satellites’ positions. x – the apparent rectangular coordinate of the satellite with respect to the center of Jupiter’s disk in the equatorial plane in the units of Jupiter’s equatorial radius; X is positive toward the west, y – the apparent rectangular coordinate of the satellite with respect to the center of Jupiter’s disk from the equatorial plane in the units of Jupiter’s equatorial radius; Y is positive toward the north. For more details see Meeus (1988, ISBN 0-943396-22-0) "Astronomical Formulae for Calculators". The galsat_animate() function creates an animation of the Galilean satellites' positions. You provide the starting time, duration, the time step between frames, and the pause between frames. The function delta_t() returns the value of delta-T in units of seconds. Package: r-cran-gallery Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gallery_1.0.0-1.ca2604.1_all.deb Size: 171892 MD5sum: 4334ccb7f5670155eb1aaea79ebb451d SHA1: b9a3e680162c0e7e098601a50a60ea7eabcebf24 SHA256: 424fae386198f1df1b98ae61cbe60f25c6770393b96175f874ffb6412afc3b90 SHA512: c1b5f6b924f3c176dd1d90c3b2c2750af3448865effa3e6db8e255b8f08b0acbdc82ccde9803d015360603c73b1638cf7f0a538c4c3f6eebbfc0c12ac57986ca Homepage: https://cran.r-project.org/package=gallery Description: CRAN Package 'gallery' (Generate Test Matrices for Numerical Experiments) Generates a variety of structured test matrices commonly used in numerical linear algebra and computational experiments. 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Package: r-cran-gallo Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5090 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-rcolorbrewer, r-bioc-rtracklayer, r-cran-stringr, r-cran-unbalhaar, r-cran-dt, r-cran-webshot, r-cran-igraph, r-cran-visnetwork, r-cran-compquadform, r-cran-matrix, r-cran-reticulate Suggests: r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gallo_2.0-1.ca2604.1_all.deb Size: 2741208 MD5sum: ff4ebb89bb13378428315edc43048b4b SHA1: ea9b6bd3295656a41f2fac6684d32afeeb9185ae SHA256: c5f8c7433daeb551cc33fccf5de5aca7ac9893edb364e2dc2a9b2935d8dc4e6f SHA512: d17069dc76471314685300346f46c775631fcc3cbb885f0fb01131935b0552f1d11404f4ce9ec01574d498382521ecffb171653ee2275bfe7f0bfefe979bcfd3 Homepage: https://cran.r-project.org/package=GALLO Description: CRAN Package 'GALLO' (Genomic Annotation in Livestock for Positional Candidate LOci) The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others. Package: r-cran-galts Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-genalg, r-cran-deoptim Filename: pool/dists/resolute/main/r-cran-galts_1.3.2-1.ca2604.1_all.deb Size: 26636 MD5sum: 6866004e8479db6494bf9beeff4d1014 SHA1: 375e266500d7be023970e14f9378c82b1e5e8231 SHA256: e5c2ce654e1ea08cc121fb2057bd1b7408df1c3a7c30fc519b243284c56e0223 SHA512: e6a9bff27add0c070e450b666ca166268b1eda2631f71cb607b744a5f6a7235cfa5fe28cdcef3d8a6f26a5b3ba9f16ab4d87984f8cdf64d638f113fd328204a4 Homepage: https://cran.r-project.org/package=galts Description: CRAN Package 'galts' (Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares)Estimation) Includes the ga.lts() function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS (Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time. Version >=1.3 includes the function medmad for fast outlier detection in linear regression. Package: r-cran-gam.hp Architecture: all Version: 0.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-gam.hp_0.0-5-1.ca2604.1_all.deb Size: 49240 MD5sum: d538b17d39c1179e2c085a00b28eb194 SHA1: 3fc6e61a98eb6cecd147257ba34d8ed5486c53f1 SHA256: 5c7819871ab3c5fa45fefb627cd6a203cdedc48a4698c97cc4e52ffe138cd6ca SHA512: 6d71bcf5e1868b3db146455df8aa85fee715e9f58a3e4516859e9d1f5bfe630b476c105159c928d0e07c21416a4f46f9f7c5a936d5dd230398c8d6d2c8d75239 Homepage: https://cran.r-project.org/package=gam.hp Description: CRAN Package 'gam.hp' (Hierarchical Partitioning of Adjusted R2 and Explained Deviancefor Generalized Additive Models) Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of 'gam()' and 'bam()' in 'mgcv' package, applying the algorithm in this paper: Lai(2024) . Package: r-cran-gamair Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1845 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mgcv, r-cran-lattice, r-cran-mass, r-cran-nlme, r-cran-lme4, r-cran-geor, r-cran-survival Filename: pool/dists/resolute/main/r-cran-gamair_1.0-2-1.ca2604.1_all.deb Size: 1784590 MD5sum: 099543b59ea5339230fe561a821bbe6e SHA1: c07e211fa039cb9217472345a679a8bb2f6af358 SHA256: b751d2f5cc34a22c9401b87923945a0ff8789c7320ee8fab27e1126fc0384c09 SHA512: 597172306a8f2826ab7ab2c4a00fb1319f25761ef273952207fa28111a1013c4ab685b5236fcdc1657f724696bf81772a99ef35a617d9fbce9e465620d26cf85 Homepage: https://cran.r-project.org/package=gamair Description: CRAN Package 'gamair' (Data for 'GAMs: An Introduction with R') Data sets and scripts used in the book 'Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC. Package: r-cran-gambin Architecture: all Version: 2.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-gtools, r-cran-foreach Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gambin_2.5.0-1.ca2604.1_all.deb Size: 112450 MD5sum: 7e9fcd95acc65b55c73bedd6f213b399 SHA1: 83f68ffe40eeabe491f0bd351c86d81b58039db7 SHA256: c32a29a5ee58c81209534ace04920bc983f8a1ff5661b4b6b65e7bdddd41548a SHA512: 68eeb7dfa8176f00b3dbd7e358529c2ce28deeedc9fa60bcb0668d4e528a40133192b350d01ea94e8e4b37291dac49732ce4ad8cc25a684a04ba691942bd1a14 Homepage: https://cran.r-project.org/package=gambin Description: CRAN Package 'gambin' (Fit the Gambin Model to Species Abundance Distributions) Fits unimodal and multimodal gambin distributions to species-abundance distributions from ecological data, as in in Matthews et al. (2014) . 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For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007. 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Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at , and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. 'gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's 'Struggle for Existence' in the journal Ecology and Evolution. Package: r-cran-gaussdiff Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gaussdiff_1.1.1-1.ca2604.1_all.deb Size: 21210 MD5sum: 6fedb9f038c7cf5695bea1f6684d6dc1 SHA1: b995b33bb63bcb9c98d8b4ead7add40a0bcec1c9 SHA256: dfb7586a175fe8a60f257a99a0b45d1a6f4065d9b9af0f9b795d925bb228bbf7 SHA512: 6cd5dc331008b435c5d65fa5995c5f46a4495247fdf93a18f6b48bfe75bc6852d79ba24bef5bfe6450daa253d8ebd6a0ef83db197c12f1175225db29a37692c0 Homepage: https://cran.r-project.org/package=gaussDiff Description: CRAN Package 'gaussDiff' (Difference Measures for Multivariate Gaussian ProbabilityDensity Functions) A collection difference measures for multivariate Gaussian probability density functions, such as the Euclidea mean, the Mahalanobis distance, the Kullback-Leibler divergence, the J-Coefficient, the Minkowski L2-distance, the Chi-square divergence and the Hellinger Coefficient. Package: r-cran-gaussfacts Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gaussfacts_0.0.2-1.ca2604.1_all.deb Size: 28436 MD5sum: afd10404f71cd786fba83b2e0553d59c SHA1: 012151a4687d8fc452639b515a1acf23e021acbe SHA256: b47a9a361120c0ece23dc0534be14603ae9d132ca296b9e3853037f3d67a429f SHA512: 1c7e771d039cc4c62276a527512993ec0e5b3aa870a6f355a6a9fa6e792f7c4de20481414ea4dbb6e57541a6660eac26a5d310070c8ae50857f88d1af73a360e Homepage: https://cran.r-project.org/package=gaussfacts Description: CRAN Package 'gaussfacts' (The Greatest Mathematician Since Antiquity) Display a random fact about Carl Friedrich Gauss based the on collection curated by Mike Cavers via the site. Package: r-cran-gaussplotr Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-metr, r-cran-rgl, r-cran-viridislite Suggests: r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gaussplotr_0.2.5-1.ca2604.1_all.deb Size: 292182 MD5sum: 831bc2967d8ee4f75471fa1bc76cb792 SHA1: 9ff42be9d531953275ab9e64158356c857b3d714 SHA256: 3e60ff105ae0dd3fb8a70f34b5c9b19a0a761464ce3629ba1b4060f7897d6ca3 SHA512: 0555f391af31c83fc0d264cdd3b2fcb1fa05d340de079a9fdf573c4714032f9d8bc87fa5c74b41ce117aa81030144411208f8a3e2ee15cc7cbdf309907a72ce1 Homepage: https://cran.r-project.org/package=gaussplotR Description: CRAN Package 'gaussplotR' (Fit, Predict and Plot 2D Gaussians) Functions to fit two-dimensional Gaussian functions, predict values from fits, and produce plots of predicted data via either 'ggplot2' or base R plotting. Package: r-cran-gaussquad Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-orthopolynom, r-cran-polynom Filename: pool/dists/resolute/main/r-cran-gaussquad_1.0-3-1.ca2604.1_all.deb Size: 198392 MD5sum: f1d60afa1e32eff810d917c2fff2ec0c SHA1: b05346200839065850d3c2b7e50ee4b0570a8370 SHA256: df2c8babb0cd28b01eddcffbc11ded64cd7e84422d493101aa2e538989dd1ece SHA512: b90028ef9072735603f37cc6b9675479bc2709e0ce9a5f008b0ad1c9b55f51114b578d7f6b4b8f125a5ff40790041d43eb2b7c575214c39be595da8603c5b142 Homepage: https://cran.r-project.org/package=gaussquad Description: CRAN Package 'gaussquad' (Collection of Functions for Gaussian Quadrature) A collection of functions to perform Gaussian quadrature with different weight functions corresponding to the orthogonal polynomials in package orthopolynom. Examples verify the orthogonality and inner products of the polynomials. Package: r-cran-gaussratiovegind Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gaussratiovegind_3.0.0-1.ca2604.1_all.deb Size: 73582 MD5sum: 5f51052d646025edba35936f17e9a9f5 SHA1: b9490f084e6a911e485ea77a9b3dbbbb537f5095 SHA256: ccba63fbdea1695dc7a0a8a751bd5f729059a8d81d77b12df7936fc6c82295e6 SHA512: 7e971992270accf626d87165a870cfe485f6fccaf41bd300d9a570c34a25b0a76ba3f2351bb9253df2d3779daed068db9e7b665789e9eaed568d654c6fc1bb93 Homepage: https://cran.r-project.org/package=gaussratiovegind Description: CRAN Package 'gaussratiovegind' (Distribution of Gaussian Ratios) It is well known that the distribution of a Gaussian ratio does not follow a Gaussian distribution. The lack of awareness among users of vegetation indices about this non-Gaussian nature could lead to incorrect statistical modeling and interpretation. This package provides tools to accurately handle and analyse such ratios: density function, parameter estimation, simulation. An example on the study of chlorophyll fluorescence can be found in A. El Ghaziri et al. (2023) and another method for parameter estimation is given in Bouhlel et al. (2023) . Package: r-cran-gausssuppression Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ssbtools, r-cran-regsdc, r-cran-matrix, r-cran-ellipsis, r-cran-rlang Suggests: r-cran-formattable, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lpsolve, r-cran-rsymphony, r-cran-rglpk, r-cran-slam, r-cran-highs, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-gausssuppression_1.3.0-1.ca2604.1_all.deb Size: 590812 MD5sum: a30703ae52d6979e21a4b1f5fbd48390 SHA1: d9d12cb580949cc628d3407054c6c67c02ee1d3d SHA256: c43ac8477b4fd3d8dd56d1ec877e062338f61fd51d2006905bd2cfec6264c896 SHA512: e25bc6f41b5dd8629d50897f4d1af8dfd9b342948337d87206ca016aa2ac18809acfb7d9abfde3e993858105511306993802e95c64f483f3d1ced354b736cf3d Homepage: https://cran.r-project.org/package=GaussSuppression Description: CRAN Package 'GaussSuppression' (Tabular Data Suppression using Gaussian Elimination) A statistical disclosure control tool to protect tables by suppression using the Gaussian elimination secondary suppression algorithm (Langsrud, 2024) . A suggestion is to start by working with functions SuppressSmallCounts() and SuppressDominantCells(). These functions use primary suppression functions for the minimum frequency rule and the dominance rule, respectively. Novel functionality for suppression of disclosive cells is also included. General primary suppression functions can be supplied as input to the general working horse function, GaussSuppressionFromData(). Suppressed frequencies can be replaced by synthetic decimal numbers as described in Langsrud (2019) . Package: r-cran-gawdis Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fd, r-cran-ga Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gawdis_0.1.5-1.ca2604.1_all.deb Size: 95084 MD5sum: abebb1640d2dc08f786c0da7fb869f88 SHA1: d645627cfbdaaf0b6aeaf18f51d892d6e02c9ffb SHA256: b824441551387392ca61f8640d631aeb3c84453d8412bdbaea325b7956f6c13d SHA512: d1c52f365c07c52daf6289d6b684715e5f2866b459ef6e79127358a97db2f305e565d95ff8825dbf1b704ebc13a5dbfd8a6250396dbdd056b1f5b425023a71be Homepage: https://cran.r-project.org/package=gawdis Description: CRAN Package 'gawdis' (Multi-Trait Dissimilarity with more Uniform Contributions) R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function. Package: r-cran-gb2 Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-hypergeo, r-cran-laeken, r-cran-numderiv, r-cran-survey Suggests: r-cran-simframe Filename: pool/dists/resolute/main/r-cran-gb2_2.1.2-1.ca2604.1_all.deb Size: 245290 MD5sum: a923001e38d2c414f23d1ed3c8e144a3 SHA1: 39d1bd4a2f916dffb2e415958b0bc63f3e169d0b SHA256: 63fa4c80de8cf28e0928cfffb1cd90e31289df1bba37bfbe7391945f8567d2ba SHA512: 5d8fbc7659dabda940752e4d3621c9095b78abe5109b91f314205606c7ecbbef9975e2f3e34138305eba73e40abadf1c67f9e42139557378a78f3efcfefe0582 Homepage: https://cran.r-project.org/package=GB2 Description: CRAN Package 'GB2' (Generalized Beta Distribution of the Second Kind: Properties,Likelihood, Estimation) The GB2 package explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distribution are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots. Package: r-cran-gb2group Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gb2, r-cran-minpack.lm, r-cran-ineq, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-gb2group_0.3.0-1.ca2604.1_all.deb Size: 186258 MD5sum: a43e1e123c0042e07db619fdf0e3c435 SHA1: bccbe6890533af2fb4df60b6237e960277cb6dfa SHA256: 8871c21cf3aa3d47c8eb9731e837c2f504951c35324c4174ca0d774b224a9e7a SHA512: 3aa9b82903129179a34cac60ae98bb9100024cc3ac6d2112c1c9455c23b425f7e4dc165a5953f134063f483c3fce33a3658b8c3e019f6487eeb883bb871ccb43 Homepage: https://cran.r-project.org/package=GB2group Description: CRAN Package 'GB2group' (Estimation of the Generalised Beta Distribution of the SecondKind from Grouped Data) Estimation of the generalized beta distribution of the second kind (GB2) and related models using grouped data in form of income shares. The GB2 family is a general class of distributions that provides an accurate fit to income data. 'GB2group' includes functions to estimate the GB2, the Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. 'GB2group' deploys two different econometric strategies to estimate these parametric distributions, the equally weighted minimum distance (EWMD) estimator and the optimally weighted minimum distance (OMD) estimator. Asymptotic standard errors are reported for the OMD estimates. Standard errors of the EWMD estimates are obtained by Monte Carlo simulation. See Jorda et al. (2018) for a detailed description of the estimation procedure. Package: r-cran-gb5mcpred Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-tidyverse, r-cran-seqinr, r-bioc-biostrings, r-cran-splitstackshape, r-cran-entropy, r-cran-party, r-cran-stringr, r-cran-tibble, r-cran-doparallel, r-cran-e1071, r-cran-caret, r-cran-randomforest, r-cran-gbm, r-cran-foreach, r-cran-ftrcool, r-cran-iterators Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gb5mcpred_0.1.0-1.ca2604.1_all.deb Size: 2524382 MD5sum: 4f07688e78813e6fc2abca6c9cde5008 SHA1: 8d850b43e1f5f952f0de6038049dbf7afadb9775 SHA256: 49e1affef2fd41b3e39b9113213b77d4c4c404e8ac4ad63c944e0f7a7686b176 SHA512: aa882351153510ab9d22a1663919e167808a488963e70aa63712757b3a32772c351fee7cd0f4fad988bbe8941e0d0a97db6ca10837f10bd66cee01db4219d3bc Homepage: https://cran.r-project.org/package=GB5mcPred Description: CRAN Package 'GB5mcPred' (Gradient Boosting Algorithm for Predicting Methylation States) DNA methylation of 5-methylcytosine (5mC) is the result of a multi-step, enzyme-dependent process. Predicting these sites in-vitro is laborious, time consuming as well as costly. This ' Gb5mC-Pred ' package is an in-silico pipeline for predicting DNA sequences containing the 5mC sites. It uses a machine learning approach which uses Stochastic Gradient Boosting approach for prediction of the sequences with 5mC sites. This package has been developed by using the concept of Navarez and Roxas (2022) . Package: r-cran-gbass Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-gigrvg, r-cran-bass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lhs, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gbass_2.0.1-1.ca2604.1_all.deb Size: 200936 MD5sum: de3296583f7c98b47b48d0182e6fd629 SHA1: 76f5f5a34b5b9e5348a0fd242bee9bab93ae5f96 SHA256: 29c786504fad9f4f6319cddeda32dbfe07ff0d91934761e61fc3a707ace86f44 SHA512: abfcf3c6050d6247dd1d13a6f54442ea01685535443e8628707eb2590631eb73f72df2a8b121e35f54a7aedb811d5231825a819ea581e77b1559a074b3a8fbbe Homepage: https://cran.r-project.org/package=GBASS Description: CRAN Package 'GBASS' (Generalized Bayesian Adaptive Smoothing Splines) Bayesian nonlinear regression under a range of likelihood models using generalized Bayesian adaptive smoothing splines. Robust regression with Student's t likelihoods, quantile regression, and related latent-scale models are included as special cases. Package: r-cran-gbfs Architecture: all Version: 1.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-jsonlite, r-cran-lubridate, r-cran-httr, r-cran-purrr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-gbfs_1.3.10-1.ca2604.1_all.deb Size: 71762 MD5sum: 372d3e5ef75925db7d4b70d561a17802 SHA1: 26f58c28c0570cfafe182bf22fe49b20eca5a637 SHA256: b4ec6cfa0c7f0873181c1d9fb4386d6c8f2972b347fc64167c12226dde7d765d SHA512: 88641e27f13004e228cff58e7dbc06d625dbdba7b983acd7151831eabcf3ecaf7f5f376fca844218e4a6cae6ff71463a384a3cef9d5b6df3523cf882129a6269 Homepage: https://cran.r-project.org/package=gbfs Description: CRAN Package 'gbfs' (Interface with Live Bikeshare Data) Supplies a set of functions to interface with bikeshare data following the General Bikeshare Feed Specification, allowing users to query and accumulate tidy datasets for specified cities/bikeshare programs. Package: r-cran-gbifdb Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-duckdbfs Suggests: r-cran-spelling, r-cran-dbplyr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-minioclient Filename: pool/dists/resolute/main/r-cran-gbifdb_1.0.0-1.ca2604.1_all.deb Size: 293676 MD5sum: aad69c2b4329672e5a95079c4ae00bcb SHA1: 3bd6feb3e7c8639b16862b9d7a281968813ff575 SHA256: fd65351e874c1190ee9cba2c402106953044565b14bc120477c11193872e2476 SHA512: bbe316f929593d45c350207d354000b973427534dac5f4f4efddda017bcb124c03c54133f7db5ca0473ace48489480d04bec7259a539e95a76b13bdcc3dbbdfb Homepage: https://cran.r-project.org/package=gbifdb Description: CRAN Package 'gbifdb' (High Performance Interface to 'GBIF') A high performance interface to the Global Biodiversity Information Facility, 'GBIF'. In contrast to 'rgbif', which can access small subsets of 'GBIF' data through web-based queries to a central server, 'gbifdb' provides enhanced performance for R users performing large-scale analyses on servers and cloud computing providers, providing full support for arbitrary 'SQL' or 'dplyr' operations on the complete 'GBIF' data tables (now over 1 billion records, and over a terabyte in size). 'gbifdb' accesses a copy of the 'GBIF' data in 'parquet' format, which is already readily available in commercial computing clouds such as the Amazon Open Data portal and the Microsoft Planetary Computer, or can be accessed directly without downloading, or downloaded to any server with suitable bandwidth and storage space. The high-performance techniques for local and remote access are described in and respectively. Package: r-cran-gbm.auto Architecture: all Version: 2024.10.01-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beepr, r-cran-dismo, r-cran-dplyr, r-cran-gbm, r-cran-ggmap, r-cran-ggplot2, r-cran-ggspatial, r-cran-lifecycle, r-cran-lubridate, r-cran-mapplots, r-cran-metrics, r-cran-readr, r-cran-sf, r-cran-stars, r-cran-starsextra, r-cran-stringi, r-cran-tidyselect, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-gbm.auto_2024.10.01-1.ca2604.1_all.deb Size: 3851660 MD5sum: 96a29fddcda8b1c5a0a5cfedc917cd01 SHA1: 76a19ae2d45357cc2f09f4d4eb7c1d205f0f3aa7 SHA256: 2d7abb77626549d761ebcad4dcb40242fdc209136882a9fe4a9a61d3eb0b2977 SHA512: 0cd0c3d7c894e4eae65e1fa4e0dfbb2d3b592992ab1c3277cbf9d62fa50ad639b1e0fd85e225170ec007abbef35ed30ba66845389609497978c1f37acdb779c7 Homepage: https://cran.r-project.org/package=gbm.auto Description: CRAN Package 'gbm.auto' (Automated Boosted Regression Tree Modelling and Mapping Suite) Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around 'gbm' (gradient boosting machine) functions in 'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around 'gbm' (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 'Working Guide' ; workflow follows Appendix S3. See for published guides and papers using this package. 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Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages. 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The underlying alignment procedure comprises three sequential steps. (1) Full alignment of samples by linear transformation of retention times to maximise similarity among homologous peaks (2) Partial alignment of peaks within a user-defined retention time window to cluster homologous peaks (3) Merging rows that are likely representing homologous substances (i.e. no sample shows peaks in both rows and the rows have similar retention time means). The algorithm is described in detail in Ottensmann et al., 2018 . 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Details can be found in "Generalised Canonical Correlation Estimation of the Multilevel Factor Model." Lin and Shin (2025) . Package: r-cran-gcd Architecture: all Version: 4.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster Filename: pool/dists/resolute/main/r-cran-gcd_4.0.7-1.ca2604.1_all.deb Size: 1418748 MD5sum: 3c44114f4571fb65aaefb179bf52de08 SHA1: 3513a581b8aa9705b2945a047af2370d8e33e856 SHA256: 13a01c11e07370a2b9ffc4a58322d9222f7ca35217ea66c60d583ed1dd951c72 SHA512: 736f467d395eae89665bf8c30a49946444cf451888568a627d78ded076efa5f31c4d05a397225745a6cf7f8a83c1a00afa49f8f4b85d0c2d8a669a5b17b059d7 Homepage: https://cran.r-project.org/package=GCD Description: CRAN Package 'GCD' (Global Charcoal Database) Contains the Global Charcoal database data. 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Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F, and Mell LK (2014) . 2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) . 3. Lunn, Mary, and Don McNeil (1995) . 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The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) ). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) ) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints. 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It parses Basic Local Alignment Search Tool (BLAST) results in tab-delimited format produced by tools like NCBI BLAST+ and Diamond BLASTp, filters Open Reading Frames (ORFs) by length, detects contiguous clusters of reference genes, optionally extracts genomic coordinates, merges functional annotations, and generates publication-ready arrow plots. The package works seamlessly with or without the coding sequences input and skips plotting when no functional groups are found. For more details see Li et al. (2023) . 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It allows for flexible estimation of the outcome model, especially penalized regressions (Lasso, Ridge, or Elasticnet) for binary, continuous, counting, or right-censored time-to-event outcomes. Average treatment effect among the entire population (ATE) or among the treated population (ATT) can be estimated. The method for time-to-events is described by Chatton et al. (2020) . For a binary outcome, details are available in the paper proposed by Chatton et al. (2022) . 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Tools for reshaping common plate reader outputs into 'tidy' formats and merging them with design information, making data easy to work with using 'gcplyr' and other packages. Also streamlines common growth curve processing steps, like smoothing and calculating derivatives, and facilitates model-free characterization and analysis of growth data. See methods at . 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The package implements widely used models including Dagnelie P., Rondeux J. & Palm R. (2013, ISBN:9782870161258) "Cubage des arbres et des peuplements forestiers - Tables et equations" , Vallet P., Dhote J.-F., Le Moguedec G., Ravart M. & Pignard G. (2006) "Development of total aboveground volume equations for seven important forest tree species in France" , Pauwels D. & Rondeux J. (1999, ISSN:07779992) "Tarifs de cubage pour les petits bois de meleze (Larix sp.) en Ardenne" , Massenet J.-Y. (2006) "Chapitre IV: Estimation du volume" , France Valley (2025) "Bilan Carbone Forestier - Methodologie" . Its modular structure allows transparent integration of bibliographic or user-defined allometric relationships. 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'GECal' incorporates design weights into the constraints to maintain design consistency, rather than including them in the objective function itself. Package: r-cran-gecko Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1972 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-sp, r-cran-geosphere, r-cran-red, r-cran-biomod2, r-cran-kernlab, r-cran-ggplot2, r-cran-sf, r-cran-stringr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-gecko_1.0.3-1.ca2604.1_all.deb Size: 1141598 MD5sum: 19e698a577782b44a6181dffeeafadef SHA1: d13883c587417df5f5f689d3313239c118901971 SHA256: d9c3251be7aa6b32fde853d5df396c5b5d3ae1d09745f9707fa1b1196af21644 SHA512: 095731b05be94c0281490fe1166fb1ebb7113e3e7056eda9a70a0406f515d486aa254084412aba5a706412e4cd9464caabf032d244e7eb4e5716d9d7903fc43d Homepage: https://cran.r-project.org/package=gecko Description: CRAN Package 'gecko' (Geographical Ecology and Conservation Knowledge Online) Includes a collection of geographical analysis functions aimed primarily at ecology and conservation science studies, allowing processing of both point and raster data. Now integrates SPECTRE (), a dataset of global geospatial threat data, developed by the authors. Package: r-cran-geeasy Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geepack, r-cran-ggplot2, r-cran-geem, r-cran-lme4, r-cran-matrix, r-cran-mess Suggests: r-cran-testthat, r-cran-mumin Filename: pool/dists/resolute/main/r-cran-geeasy_0.1.3-1.ca2604.1_all.deb Size: 126338 MD5sum: 41671bc9eee6d206731f71c03599026c SHA1: 2e6276fb7c58b6e5d48b6cca67d948baff1cc917 SHA256: 7b421d7b407c1e80f82bec5734819de90db146f777f076811110ccfe493e3655 SHA512: b9b50bb8a1b1c170c1e0bab26ac546174e7f51b067ef999e0edacf7937d7464fede8795714c1391f574f6713065cffc88c61fe9caacffce25fe44f2ae50fa05b Homepage: https://cran.r-project.org/package=geeasy Description: CRAN Package 'geeasy' (Solve Generalized Estimating Equations for Clustered Data) Estimation of generalized linear models with correlated/clustered observations by use of generalized estimating equations (GEE). See e.g. Halekoh and Højsgaard, (2005, ), for details. Several types of clustering are supported, including exchangeable variance structures, AR1 structures, M-dependent, user-specified variance structures and more. The model fitting computations are performed using modified code from the 'geeM' package, while the interface and output objects have been written to resemble the 'geepack' package. The package also contains additional tools for working with and inspecting results from the 'geepack' package, e.g. a 'confint' method for 'geeglm' objects from 'geepack'. Package: r-cran-geecrt Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rootsolve, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-geecrt_1.1.5-1.ca2604.1_all.deb Size: 324962 MD5sum: 3e2e370a006b8ccb8969b94fcbe10bd2 SHA1: 623444299edfc86f730054d8b3b93e3264f800b5 SHA256: 3c2b00237c05dafa5c3c8e7e173490aca714320ef4757a6bfb2a9a02c223bf32 SHA512: 4f55652ecb14d69e06831bb932a6bd05d21d35ccc48e46b9f6dbfbe270460a718e109f13e29860a82d51868f4c847280422cbe2a3c3a9a9fc99c6b1a079a43ab Homepage: https://cran.r-project.org/package=geeCRT Description: CRAN Package 'geeCRT' (Bias-Corrected GEE for Cluster Randomized Trials) Population-averaged models have been increasingly used in the design and analysis of cluster randomized trials (CRTs). To facilitate the applications of population-averaged models in CRTs, the package implements the generalized estimating equations (GEE) and matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by Li et al. (2022) specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives a simple and efficient estimating equations approach based on the cluster-period means to estimate the intervention effects as well as correlation parameters. In addition, the package also provides functions for generating correlated binary data with specific mean vector and correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) or the conditional linear family method in Qaqish (2003) . Package: r-cran-geecure Architecture: all Version: 1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-cran-mass, r-cran-geepack Filename: pool/dists/resolute/main/r-cran-geecure_1.0-6-1.ca2604.1_all.deb Size: 164860 MD5sum: 5b217dd1d70eafb948dcee4f50f3bc6d SHA1: 2aec102297d5189490edaa03f0552c6350299107 SHA256: d16dd404c5e35d5e56226c2b79bddb04dd3933255e367101ae011e7b54dde6e6 SHA512: 1beda0acb95c38dbab4a69c3823b048807047d20054ead1319344da347ce5ff361ff5289902fb1a262b7c211666debcbf266f0dfe665edfe50b4c9cde7503154 Homepage: https://cran.r-project.org/package=geecure Description: CRAN Package 'geecure' (Marginal Proportional Hazards Mixture Cure Models withGeneralized Estimating Equations) Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) . Package: r-cran-geelite Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 902 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rnaturalearthdata, r-cran-rnaturalearth, r-cran-googledrive, r-cran-data.table, r-cran-reticulate, r-cran-rstudioapi, r-cran-geojsonio, r-cran-lubridate, r-cran-jsonlite, r-cran-magrittr, r-cran-progress, r-cran-reshape2, r-cran-rsqlite, r-cran-stringr, r-cran-crayon, r-cran-dplyr, r-cran-h3jsr, r-cran-knitr, r-cran-purrr, r-cran-tidyr, r-cran-rgee, r-cran-cli, r-cran-sf Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-optparse, r-cran-leaflet, r-cran-withr Filename: pool/dists/resolute/main/r-cran-geelite_1.0.6-1.ca2604.1_all.deb Size: 408400 MD5sum: 5958bd6738b2e4376f21933e57edc776 SHA1: 7ff1dbef4f375f0d8d7cb258115b3a89257ff463 SHA256: 15bd50651f7375cacd1a8e9335fb5eeb2373384e2c324bd556fdd549ea21f81c SHA512: d5a390b408fd60b8a5ab0b26ff1045dd463d04463346d2d20e30853c28525b963132d47dc12544dfbb60811d2d174923576e28259b79eeaf2b958dd5b3529c10 Homepage: https://cran.r-project.org/package=geeLite Description: CRAN Package 'geeLite' (Building and Managing Local Databases from 'Google Earth Engine') Simplifies the creation, management, and updating of local databases using data extracted from 'Google Earth Engine' ('GEE'). It integrates with 'GEE' to store, aggregate, and process spatio-temporal data, leveraging 'SQLite' for efficient, serverless storage. The 'geeLite' package provides utilities for data transformation and supports real-time monitoring and analysis of geospatial features, making it suitable for researchers and practitioners in geospatial science. For details, see Kurbucz and Andrée (2025) "Building and Managing Local Databases from Google Earth Engine with the geeLite R Package" . Package: r-cran-geem Architecture: all Version: 0.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-geepack, r-cran-testthat, r-cran-mumin Filename: pool/dists/resolute/main/r-cran-geem_0.10.1-1.ca2604.1_all.deb Size: 81120 MD5sum: 92d5f7196f7a12a81b05cc2ca814b153 SHA1: 44c04b20355f6cf0fbc5ce5a7d1680312f67e95c SHA256: f7a4319c5829cf37ecb2db575f2f34ab474d2ecc8d90f1ffde40ddda7a753778 SHA512: f64a5446c88c16a011699b20df15c5f24a376e6ccfd95191bbe928840c5257de9754c7cf2863000cbdc55498bee397aee8388d492c77375e122dc2c1001022d6 Homepage: https://cran.r-project.org/package=geeM Description: CRAN Package 'geeM' (Solve Generalized Estimating Equations) GEE estimation of the parameters in mean structures with possible correlation between the outcomes. User-specified mean link and variance functions are allowed, along with observation weighting. The 'M' in the name 'geeM' is meant to emphasize the use of the Matrix package, which allows for an implementation based fully in R. Package: r-cran-geemediate Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gee Filename: pool/dists/resolute/main/r-cran-geemediate_1.1.4-1.ca2604.1_all.deb Size: 30270 MD5sum: 160d5a9084eb4ff157bd2d459c8c82d6 SHA1: 6fb5b63eb07f84ae2e146e1be138bee42582844f SHA256: ea8ecab8c41bdc287f7bbba551f070741697e7f38deb9d6d4021df6cec89bb09 SHA512: 986f803ceb4864e591ce99af3c0b99b958c1234bfea8cb0f2826a72d5f0bf459d4ddb8c3874a4c6b90ad7d9a12c58c2de783194b88e96efc7d9fd81b99db5796 Homepage: https://cran.r-project.org/package=GEEmediate Description: CRAN Package 'GEEmediate' (Mediation Analysis for Generalized Linear Models Using theDifference Method) Causal mediation analysis for a single exposure/treatment and a single mediator, both allowed to be either continuous or binary. The package implements the difference method and provides point and interval estimates as well as testing for the natural direct and indirect effects and the mediation proportion. Nevo, Xiao and Spiegelman (2017) . Package: r-cran-geesmv Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-gee, r-cran-matrixcalc, r-cran-mass Filename: pool/dists/resolute/main/r-cran-geesmv_1.3-1.ca2604.1_all.deb Size: 139446 MD5sum: fe3d60f8a1c85e99acb693fdbb6f47e6 SHA1: 390b1c91359f941148a45b6b52c99ddb8d7bf549 SHA256: 5604b0205272e1a76c3a497ea54271a62462e35093b014af3c35e58b61979b72 SHA512: a08017033483f44c4ca46e40a9b77d7d1bd0c12c6d6ca72635b1b5ff628070b799e381948b40f1500206402248155071194308d7b23bb30c726acb84f3e0ee59 Homepage: https://cran.r-project.org/package=geesmv Description: CRAN Package 'geesmv' (Modified Variance Estimators for Generalized EstimatingEquations) Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance. Package: r-cran-geess Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geess_1.0.1-1.ca2604.1_all.deb Size: 60388 MD5sum: 4ca80f7177efa867fab466e60d1e7f5d SHA1: 1228f88cd4258fec208b770564bc051ab4680bc4 SHA256: 2d00803e8843acb1955268ecddb01c8a729728a67d3581293554d70ce2e06cf4 SHA512: 283d85360c5d3921dd66fdd23bd63756bf19c302651284f4327acfd27794cb4c2603ac8515333945c23675628d84cbc2ab70372c6b110254eca9351c74d63be2 Homepage: https://cran.r-project.org/package=geess Description: CRAN Package 'geess' (Modified Generalized Estimating Equations for Small-Sample Data) Analyze small-sample clustered or longitudinal data using modified generalized estimating equations with bias-adjusted covariance estimator. The package provides any combination of three modified generalized estimating equations and 11 bias-adjusted covariance estimators. Package: r-cran-geessbin Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geessbin_1.0.2-1.ca2604.1_all.deb Size: 72386 MD5sum: 6a058c73faeca3eb83ffc3640fa0d4a8 SHA1: 61b3f7163d868a7697950f78b0d9885dd324fd10 SHA256: 29c0e615c39ea40b8b599beed73e3a47e6ef6683a093d719ee8c5902a4339607 SHA512: 6b03e347efc2cca9d28f76f1504cc9d8dd3a796091e1cc4d4c3ffd1baf2f8170688a0bbd0db0cb2018f70cd88c7c6819773456e00b3293ca7b9a8f105ce945a3 Homepage: https://cran.r-project.org/package=geessbin Description: CRAN Package 'geessbin' (Modified Generalized Estimating Equations for Binary Outcome) Analyze small-sample clustered or longitudinal data with binary outcome using modified generalized estimating equations (GEE) with bias-adjusted covariance estimator. The package provides any combination of three GEE methods and 12 covariance estimators. Package: r-cran-geex Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rootsolve, r-cran-numderiv, r-cran-lme4 Suggests: r-cran-testthat, r-cran-knitr, r-cran-dplyr, r-cran-moments, r-cran-sandwich, r-cran-inferference, r-cran-xtable, r-cran-aer, r-cran-icsnp, r-cran-mass, r-cran-gee, r-cran-saws, r-cran-rmarkdown, r-cran-geepack, r-cran-covr, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-geex_1.1.1-1.ca2604.1_all.deb Size: 849094 MD5sum: 0a220021f92bdfbd2fd315038ba6934b SHA1: 6716f6c703a27574420f92e6550e8107bbbdd5da SHA256: 2bbfaf035b5dfececa69a47647d295cc07e9cd3ffb0a4976af713809e8e834fe SHA512: f946c6c4228bfc8e46a8c08220939f878046eb7f74f925a99dcc2cf08c409abdd52d206c1005be99c04821f6791a285381f55e180251835a203f8c6786b71e30 Homepage: https://cran.r-project.org/package=geex Description: CRAN Package 'geex' (An API for M-Estimation) Provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) ). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) . Also provides an API to compute finite-sample variance corrections. Package: r-cran-geinfo Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-glmnet, r-cran-rvest, r-cran-dplyr, r-cran-pheatmap Filename: pool/dists/resolute/main/r-cran-geinfo_1.0-1.ca2604.1_all.deb Size: 108716 MD5sum: 3502863a1db0a706ac9e3dcd72292346 SHA1: 8713365e78d8571e64be936b7665f0650a2d1543 SHA256: fa294a24bdc74afeb63b63960905f6741dd082598b4d6356f2f73717cb7aa7bc SHA512: 7cccd88ab76abfa2e236889f208a9edee7a63dd74ef43ed72fd02d26069c1a5fda633bb1b2d7a9b9ec5f261fd142c74580c2b31d357addfe1fa03c98ca8acde9 Homepage: https://cran.r-project.org/package=GEInfo Description: CRAN Package 'GEInfo' (Gene-Environment Interaction Analysis Incorporating PriorInformation) Realize three approaches for Gene-Environment interaction analysis. All of them adopt Sparse Group Minimax Concave Penalty to identify important G variables and G-E interactions, and simultaneously respect the hierarchy between main G and G-E interaction effects. All the three approaches are available for Linear, Logistic, and Poisson regression. Also realize to mine and construct prior information for G variables and G-E interactions. Package: r-cran-geint Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-bindata, r-cran-nleqslv, r-cran-pracma, r-cran-speedglm, r-cran-rje, r-cran-geepack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geint_1.1-1.ca2604.1_all.deb Size: 141542 MD5sum: da0e81413f37500d97d4372c164b309c SHA1: 0048d698ebe7053948082053b61d549c0d785ac8 SHA256: dd65bfcf7fbe4764e271849fb74200ac90dfec163c6ee0e47cf46800369f8f00 SHA512: 27d33ac915f4b7f3f227aa3313795809cfa4456c7787fd02b3871871c69f144d8104eedfdc72119144fe81e0ea89a57ab4877b7871f3f17e3803f74afda0d20c Homepage: https://cran.r-project.org/package=GEint Description: CRAN Package 'GEint' (Misspecified Models for Gene-Environment Interaction) The first major functionality is to compute the bias in regression coefficients of misspecified linear gene-environment interaction models. The most generalized function for this objective is GE_bias(). However GE_bias() requires specification of many higher order moments of covariates in the model. If users are unsure about how to calculate/estimate these higher order moments, it may be easier to use GE_bias_normal_squaredmis(). This function places many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate normal distribution) and is thus able to automatically calculate many of the higher order moments automatically, necessitating only that the user specify some covariances. There are also functions to solve for the bias through simulation and non-linear equation solvers; these can be used to check your work. Second major functionality is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample performance than other inference procedures for testing GxE interaction. More details on these functions are available in Sun, Carroll, Christiani, and Lin (2018) . Package: r-cran-geinter Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pcapp, r-cran-hmisc, r-cran-survival, r-cran-quantreg, r-cran-reshape2, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-geinter_0.3.2-1.ca2604.1_all.deb Size: 3119546 MD5sum: 0b22bf7f8b66ed595f7e3eff1b6a2543 SHA1: 103b041fae324589cb2208ad19e57210940504f7 SHA256: 14f06da2da3bb4f3518f668f3ab5676c5e9ece17997f65fabc5ba4302774ff24 SHA512: 1a85f5599f5b0989c1ddcbe4a4eea1f119743c585687753567480e71d0b9f5a9cab8670d989a05ebde901a1216c5c464b24c848a87610f672c962f0fdc261f16 Homepage: https://cran.r-project.org/package=GEInter Description: CRAN Package 'GEInter' (Robust Gene-Environment Interaction Analysis) Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), ; Mengyun Wu et al (2017), ; Yaqing Xu et al (2018), ; Yaqing Xu et al (2019), ; Mengyun Wu et al (2021), ). 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By using these datasets instead of lists of male and female names, this package is able to more accurately infer the gender of a name, and it is able to report the probability that a name was male or female. GUIDELINES: This method must be used cautiously and responsibly. Please be sure to see the guidelines and warnings about usage in the 'README' or the package documentation. See Blevins and Mullen (2015) . Package: r-cran-genderapi Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-genderapi_1.0.3-1.ca2604.1_all.deb Size: 27216 MD5sum: 5b996651ae9739e9bae55acf3e7a3d6e SHA1: 3b87d9dc69e847f09bf5382cc2d46a6ec8ba526f SHA256: aa7a573070914188d1cc8ec9bed87c15bce2ee034fad7faf84f1786d5cd02083 SHA512: acd40414f6cb07a4e7d320e1f6ccc63ffb05fa4fcdab2b9fff7e23979953b10799f7f10239e6f3036dbe2fb5344ab7fd6678ce77bcd52bef5db65a24a8542ddd Homepage: https://cran.r-project.org/package=genderapi Description: CRAN Package 'genderapi' (Client for 'GenderAPI.io') Provides an interface to the 'GenderAPI.io' web service () for determining gender from personal names, email addresses, or social media usernames. Functions are available to submit single or batch queries and retrieve additional information such as accuracy scores and country-specific gender predictions. This package simplifies integration of 'GenderAPI.io' into R workflows for data cleaning, user profiling, and analytics tasks. Package: r-cran-genderbr Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4697 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-httr, r-cran-purrr, r-cran-torch Suggests: r-cran-testthat, r-cran-covr, r-cran-httr2, r-cran-luz Filename: pool/dists/resolute/main/r-cran-genderbr_1.3.0-1.ca2604.1_all.deb Size: 4572766 MD5sum: 82442816d3df47cc06194ceb6015f8c2 SHA1: 2b27af18f0c9aa3ed6fce7cd5f01381228fbef5f SHA256: 5108f9f1b247d5f63407d3cb9a657b9906773bc722977cb858f233549dc188b4 SHA512: bd86151fa55e18ff9694e7fde1e12c4847a41bf4c4e715088277d000a5d676fa98fc2c63559dd1535972ec5eae4e9fff23b218f8f3f0064ea765772030c5f086 Homepage: https://cran.r-project.org/package=genderBR Description: CRAN Package 'genderBR' (Predict Gender from Brazilian First Names) A generalized method to predict and report gender from Brazilian first names using the Brazilian Institute of Geography and Statistics' Census data and neural networks. Package: r-cran-gendercoder Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bs4dash, r-cran-haven, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-gendercoder_0.1.1-1.ca2604.1_all.deb Size: 147408 MD5sum: 75da44df4f3b1018a4ee73a3c06c82e2 SHA1: f6bc5f27d16b2ab378ec66da0ff2a5d2ad768165 SHA256: dce0754434a13f7cad5739165625513802352ccc53fd722fb418e903260cff3c SHA512: aae02d5b23fa09132ab6638ee71ffa29f93b259b651b9675e4a46f06740b18816dae138294917133490252ecbb620e1a318ebe3774489482d75d4cce81f395df Homepage: https://cran.r-project.org/package=gendercoder Description: CRAN Package 'gendercoder' (Recodes Sex/Gender Descriptions into a Standard Set) Provides dictionary-based tools for recoding free-text gender responses into consistent categories while preserving gender diversity where possible. The package standardises spelling, capitalization, whitespace, and common variants through curated named character-vector dictionaries, supports either detailed or collapsed output categories, and can retain original unmatched responses for manual review. It also includes helpers for creating custom dictionaries from approximate string matches and a local interactive application for recoding uploaded data files. Package: r-cran-genderinfer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-binom Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-genderinfer_0.1.0-1.ca2604.1_all.deb Size: 482260 MD5sum: 803af699e3ed39d932e4a84193763ac2 SHA1: d3361430d408f15be62b5948e7da3d19d32524c4 SHA256: 3c9703b4bf2b9accdf49e600d920f2c5a166e6f805a1ef366e92f9c13a254fae SHA512: 3049cb7e242a721f4c14bd2a0328e2180f07874e361603c50cb3e7f7901f23e1fd1abbb46dd14f6be7b259fc0b744161bf1fff61c02c95f561ce0e654bbde950 Homepage: https://cran.r-project.org/package=GenderInfer Description: CRAN Package 'GenderInfer' (This is a Collection of Functions to Analyse Gender Differences) Implementation of functions, which combines binomial calculation and data visualisation, to analyse the differences in publishing authorship by gender described in Day et al. (2020) . It should only be used when self-reported gender is unavailable. Package: r-cran-genderstat Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-genderstat_0.1.5-1.ca2604.1_all.deb Size: 66728 MD5sum: 36f5d7d403ad47ab4b1b45a179545c8b SHA1: 92c7b6e3a5ffba1b892e8f0983f518b2595001c7 SHA256: 60502cb144fd7858f5f2d70f4e7aa04eb0781378e8474f1966942c59b3bb3786 SHA512: 7d209413d8a97fb3f2bd933f4e3a1c699191cd451f8b6f02d36ead73abf7255d1cae55145696cd6b3df6a64073b16f83fc16e66af434db48fc71f0b8c5b689c9 Homepage: https://cran.r-project.org/package=genderstat Description: CRAN Package 'genderstat' (Quantitative Analysis Tools for Gender Studies) Provides tools for quantitative analysis in gender studies, including functions to calculate various gender inequality metrics such as the Gender Pay Gap, Gender Inequality Index (GII), Gender Development Index (GDI), and Gender Empowerment Measure (GEM). Also includes extracted secondary example datasets for practice and learning purposes, which were obtained from the UNDP Human Development Reports Data Center and the World Bank Gender Data Portal by the author the dataset is available on . References: Miller, Kevin; Vagins, Deborah J. (2021) . Jacques Charmes & Saskia Wieringa (2003) . Gaëlle Ferrant (2010) . Package: r-cran-gendist Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gendist_2.0-1.ca2604.1_all.deb Size: 86896 MD5sum: ab3530dcc97a75c282d920e989313035 SHA1: d63cb9698d57afec73176c3eb335f00b75c6e9fe SHA256: 6ee37fddd399d4e00710b4f566a4829f150f8a8bfcc9728397baa3976926d593 SHA512: bb9c7f26067efa6d78c509ee4ad21cd5c2a1d00aa481cc18a4dee288e25d6beac69e137f1dadc63d1632dbbdd97cfe9087a60f8ea060b1f3da6aa167608667bd Homepage: https://cran.r-project.org/package=gendist Description: CRAN Package 'gendist' (Generated Probability Distribution Models) Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models. Package: r-cran-geneacore Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-signal, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-genearead, r-cran-geneaclassify Filename: pool/dists/resolute/main/r-cran-geneacore_1.2.0-1.ca2604.1_all.deb Size: 2007868 MD5sum: de3469d9421f3189137afb57d4ed70b5 SHA1: a33bfcb50b1b31fb558b2e81256c8de4ecdf8205 SHA256: e14d2e6b91f4144eead3afb4db035251780cb03fc21e33d39f1c31a10811837e SHA512: 14ff5c311f7e173096bc7d08f01da60770c6f39becc66df842bb29824a2776a4bea800f6e38d10c60c8573502b465607eccd6e388633913d1f76a1b14088621a Homepage: https://cran.r-project.org/package=GENEAcore Description: CRAN Package 'GENEAcore' (Pre-Processing of 'GENEActiv' Data) Analytics to read in and segment raw 'GENEActiv' accelerometer data into epochs and events. For more details on the 'GENEActiv' device, see . Package: r-cran-genecycle Architecture: all Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-longitudinal, r-cran-fdrtool Filename: pool/dists/resolute/main/r-cran-genecycle_1.1.6-1.ca2604.1_all.deb Size: 187752 MD5sum: 37626251a5f0840945b910d4629d869a SHA1: b557cd794879a795afb1d5b9bed3c16ab3c6de82 SHA256: ed461a13bbcec1dd13ad4772abaef13fb3f0032cc3c613f644d247ca83a959b6 SHA512: 2de49e117593d5a4a3f7cde67f959c1e38be86e2c2bd9494a2d020d2ca6b4a7859e1b851b00083e813e33b9282b09050902b13839a702715774e8f582631fa6b Homepage: https://cran.r-project.org/package=GeneCycle Description: CRAN Package 'GeneCycle' (Identification of Periodically Expressed Genes) The GeneCycle package implements the approaches of Wichert et al. (2004) , Ahdesmaki et al. (2005) and Ahdesmaki et al. (2007) for detecting periodically expressed genes from gene expression time series data. Package: r-cran-geneexpressionfromgeo Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-bioc-biobase, r-bioc-annotate, r-bioc-geoquery Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-geneexpressionfromgeo_1.3-1.ca2604.1_all.deb Size: 30278 MD5sum: 8679c35678ab03453c88fa4afca90df0 SHA1: 6293ba98b7147e8460675c2b106f9268a3a2df36 SHA256: 4493b05de3d49441518bc99fb23c0190a896d0b6e0e0e8a7c465b2f974bd8ec9 SHA512: b0940014bbd9aee675155e3d2062426c2e8587352292e69adbf4fcb23a90f70ba860e5d61eac24b9973832fb846732542f128fd859807a3ac597d300cd205f3c Homepage: https://cran.r-project.org/package=geneExpressionFromGEO Description: CRAN Package 'geneExpressionFromGEO' (Easily Downloads a Gene Expression Dataset from a GEO Code andRetrieves the Gene Symbols of Its Probesets) A function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL . More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, . Package: r-cran-genef Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-genef_1.0.1-1.ca2604.1_all.deb Size: 34394 MD5sum: 44efc290ce16b4a4eaa001f324760dde SHA1: 28d4665024cf3dde1dd3a66b8b85e3cb56dd846e SHA256: 515921d2dc34af0454bb10c5bf30aebdbd303b07f7319924346b003e975cda6d SHA512: 2b95eb22707b5f89a7b01111c36ddd0d388939200f4c1af21038524592870d75cbe1ed3e74a4ac9b55491376b344584ac9bd46993ccbfb96fb92642084403695 Homepage: https://cran.r-project.org/package=GeneF Description: CRAN Package 'GeneF' (Package for Generalized F-Statistics) Implementation of several generalized F-statistics. The current version includes a generalized F-statistic based on the flexible isotonic/monotonic regression or order restricted hypothesis testing. Based on: Y. Lai (2011) . Package: r-cran-genehummus Architecture: all Version: 1.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rentrez, r-cran-stringr, r-cran-dplyr, r-cran-httr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-genehummus_1.0.11-1.ca2604.1_all.deb Size: 89624 MD5sum: a8dcf22e814f14451c78de94909f54df SHA1: 564afe4cc5623dd3811c8b1f032691000261212b SHA256: b87c8b67d9497cfea707a1de71533a04dc67e85e0b4ff4073dfa54607370a012 SHA512: 04f85410d247c3d7bae858916667d6e118a37a067279bd10b9f5699334d72d5f082961ea58da288787905dabfe799c6a5cb01e3f5aa8a19970f9676dada86972 Homepage: https://cran.r-project.org/package=geneHummus Description: CRAN Package 'geneHummus' (A Pipeline to Define Gene Families in Legumes and Beyond) A pipeline with high specificity and sensitivity in extracting proteins from the RefSeq database (National Center for Biotechnology Information). Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family. The pipelines implements an automatic approach for the identification of gene families based on the conserved domains that specifically define that family. See Die et al. (2018) for more information and examples. Package: r-cran-genekitr Architecture: all Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2834 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-clusterprofiler, r-cran-dplyr, r-cran-europepmc, r-cran-fst, r-cran-geneset, r-cran-ggplot2, r-cran-ggraph, r-cran-ggvenn, r-cran-igraph, r-cran-magrittr, r-cran-openxlsx, r-cran-stringr, r-cran-stringi, r-cran-tidyr, r-cran-rlang Suggests: r-bioc-annotationdbi, r-cran-cowplot, r-cran-complexupset, r-cran-forcats, r-bioc-fgsea, r-cran-futile.logger, r-cran-ggplotify, r-cran-ggsci, r-cran-ggrepel, r-cran-ggridges, r-cran-ggnewscale, r-cran-goplot, r-bioc-gosemsim, r-cran-labeling, r-cran-pheatmap, r-cran-tm, r-cran-treemap, r-cran-rcolorbrewer, r-cran-rcurl, r-cran-reshape2, r-cran-rio, r-bioc-rrvgo, r-cran-testthat, r-cran-wordcloud, r-cran-knitr, r-cran-rmarkdown, r-cran-xml, r-cran-xml2, r-cran-httr Filename: pool/dists/resolute/main/r-cran-genekitr_1.2.8-1.ca2604.1_all.deb Size: 2805880 MD5sum: 8f95a7632620225573556721f4270ac0 SHA1: 33501fc02873950ab154c3b7c52206f25cc69c4e SHA256: 932f4d6e316003b09a1dec25a35017f6cdd93b994f3b9e800139a36490fa0957 SHA512: 0ce2dede90c17f6e2022e2f058769b0d962c7a18e1b0e94269c3699c43b73949791b9ec156b3f947bdc83d05a36f20d84add1b4c9aeb843c8af35eee4fd7d5a8 Homepage: https://cran.r-project.org/package=genekitr Description: CRAN Package 'genekitr' (Gene Analysis Toolkit) Provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. 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In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions). Package: r-cran-genenmf Architecture: all Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppml, r-cran-matrix, r-cran-seurat, r-cran-cluster, r-cran-lsa, r-cran-irlba, r-cran-pheatmap, r-cran-dendextend, r-cran-viridis, r-cran-colorspace Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-fgsea, r-cran-msigdbr Filename: pool/dists/resolute/main/r-cran-genenmf_0.9.2-1.ca2604.1_all.deb Size: 817048 MD5sum: 7208ffe6f14d976d57a344f2dd301b96 SHA1: eb81ab1e82b34d4ab0d7dfb7eee6ffc22696eddd SHA256: 7aa069dad6126ad09cab9e832852db1124309eb8f9ccdcbc71ab04fadb35e658 SHA512: 1dd0b4ec374582d2d8988ae32c56dfebd058c91fe22056a9934a0494f0b3a0c3ebad225eadffc0496a60a05afcb8350695542c46fdaadfc8c862ffbf11a07396 Homepage: https://cran.r-project.org/package=GeneNMF Description: CRAN Package 'GeneNMF' (Non-Negative Matrix Factorization for Single-Cell Omics) A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures. Package: r-cran-genenr Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-stringr, r-cran-httr, r-cran-rvest, r-cran-xml2, r-cran-writexl, r-cran-vcfr, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-genenr_2.0.1-1.ca2604.1_all.deb Size: 113372 MD5sum: 1887f93d20fd0baf9e045067c4b2f36e SHA1: ac06653d8415f29ea5a442816ab5c465c5db6b8d SHA256: 243e1c684385fffcdf15851b1dec11805deece5be76c81313287ef8b9077fc2e SHA512: 46de14eb3c6d7e3310371c9a180bb1e6f71da708b44423d8be8ae08acc22c2a52b883fabbbbefeecdad0767ff7063111d5ad9df9da215405f9ad3136446ee27d Homepage: https://cran.r-project.org/package=geneNR Description: CRAN Package 'geneNR' (Automated Gene Identification for Post-GWAS and QTL Analysis) Facilitates the post-Genome Wide Association Studies (GWAS) and Quantitative Trait Loci (QTL) analysis of identifying candidate genes within user-defined search window, based on the identified Single Nucleotide Polymorphisms (SNPs) as given by Mazumder AK (2024) . It supports candidate gene analysis for wheat and rice. Just import your GWAS result as explained in the sample_data file and the function does all the manual search and retrieve candidate genes for you, while exporting the results into ready-to-use output. Package: r-cran-genepopstats Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vcfr Filename: pool/dists/resolute/main/r-cran-genepopstats_0.1.0-1.ca2604.1_all.deb Size: 182066 MD5sum: 5ffddd08ed2608535d8f80469898da9b SHA1: a22acfc26f7d7ad0520c16f9d6554afb04fb3002 SHA256: c7424c363891f9c38860b3b1de47271fead98a43bf82a347e8e69f3bd89e1ef0 SHA512: 6661d51dfe9eb724edbb9896d8da67679f3b4a3d648e84d31b69f6c1b3496da554597760403b2b6f3b344741c82794205906d19ce20c2529a89dfa9d1c2d934a Homepage: https://cran.r-project.org/package=GenePopStats Description: CRAN Package 'GenePopStats' (Population Genetics Statistics for Selective Sweep) Selective Sweep can be calculated by five significant Population Genetics Statistics such as "Pi", "Wattersons_theta", "Tajima_D", "Kelly_ZnS" and "Omega" Statistics in specified chromosomal region. It has been developed by using the concept of "Kern" and "Schrider" (2018). Package: r-cran-generalcorr Architecture: all Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3036 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-np, r-cran-xtable, r-cran-meboot, r-cran-psych, r-cran-lattice Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-generalcorr_1.2.6-1.ca2604.1_all.deb Size: 2636400 MD5sum: abcd79c45618dbb2ceb65fbbfd0e15cf SHA1: 778dc23c5ed097112b34eba9365106eeb18ba8d4 SHA256: 3b36e2dcc8c721f0d37d1d012f1d06b70c0ef1447bf50f9c485fee25883a1171 SHA512: c8cb1d5e8d8ac308b7e6ea9a8bfea7651bc955703b744c38955e700db757c39c7f7c5a2853900bad540385969c5e8f2a0922e076b1a12bc3a11c15f7323962a9 Homepage: https://cran.r-project.org/package=generalCorr Description: CRAN Package 'generalCorr' (Generalized Correlations, Causal Paths and Portfolio Selection) Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with 'boot' provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}. Package: r-cran-generalhoslem Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape, r-cran-mass Suggests: r-cran-nnet, r-cran-mlogit, r-cran-ordinal Filename: pool/dists/resolute/main/r-cran-generalhoslem_1.3.4-1.ca2604.1_all.deb Size: 54338 MD5sum: 53089aa2d10120f49df016106b3cdd2d SHA1: 33f29393c9a7fa48ac3890d6ca57f8f79a8d4924 SHA256: 6a04909348e2549210a1884e8c64490cead1237cc5101f63cb13c0248c5cba15 SHA512: 9f8490899702000fdcd09686ba7a5b642af6ef84bf28f3c341e6f14418be249f8792c53443d37d25600b7a7bfd90a8a20d9d92b414cf586ee0df3f0d16f9583f Homepage: https://cran.r-project.org/package=generalhoslem Description: CRAN Package 'generalhoslem' (Goodness of Fit Tests for Logistic Regression Models) Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models. Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests (ordinal). Package: r-cran-generalizedhyperbolic Architecture: all Version: 0.8-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-distributionutils, r-cran-mass Suggests: r-cran-variancegamma, r-cran-actuar, r-cran-skewhyperbolic, r-cran-runit Filename: pool/dists/resolute/main/r-cran-generalizedhyperbolic_0.8-7-1.ca2604.1_all.deb Size: 587248 MD5sum: 5cb3d0d6c6d404cc3e6d6111a39fb9e7 SHA1: 09a70a006e0c76a49fddbde4966e8892c31d6992 SHA256: 6a18f75b86893c2462103ae8e869a8af4767494f48667a333143b5c495dd00f3 SHA512: 4c2286dfc27e7d84aba35c1f7c34327f2c225611b4af7c00b2efadb0824732454eb2e0f9aab20149f3bd3d0e9dfa1d42084b069e42bba9c2fd36a9f52bc0f120 Homepage: https://cran.r-project.org/package=GeneralizedHyperbolic Description: CRAN Package 'GeneralizedHyperbolic' (The Generalized Hyperbolic Distribution) Functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, normal inverse Gaussian distribution and generalized inverse Gaussian distribution, including fitting of these distributions to data. Linear models with hyperbolic errors may be fitted using hyperblmFit. Package: r-cran-generaloaxaca Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-generaloaxaca_1.0-1.ca2604.1_all.deb Size: 35372 MD5sum: 6aa8873e482fd7c19b148acaaac45133 SHA1: a2c2730440e0492d73cf14f5150e683d122b9afc SHA256: b64aaec9e3b0248765bcdcb2278cee1afbe22b19732f9fbd8830fd57494e8f9c SHA512: dfdb11e0161422b682324248435bbedfca73bd6e3bf95d7a35f057cd964ac4b035a64e9591bb26ceb27ca8a2c59e009ee2a0337195331366930a52ea14242608 Homepage: https://cran.r-project.org/package=GeneralOaxaca Description: CRAN Package 'GeneralOaxaca' (Blinder-Oaxaca Decomposition for Generalized Linear Model) Perform the Blinder-Oaxaca decomposition for generalized linear model with bootstrapped standard errors. The twofold and threefold decomposition are given, even the generalized linear model output in each group. Package: r-cran-generalrss Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emplik, r-cran-rootsolve Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-generalrss_0.1.3-1.ca2604.1_all.deb Size: 100628 MD5sum: be3d691b9138144df43a831e7db44c18 SHA1: a4df2a408ee527e4d1e6a60b8dfc030c1d578ac0 SHA256: 36bb9c0faa11ca4c57a1c8d033fbf48c025e78d5803adf05f5c17e6bb85ff510 SHA512: 55a3d3d7aa63d11fbd2953f74ee0982cd6c2ec9d095f4439563c92d8558385a2c0c6a953e1c0742abf2a6579c0d235d33e2aabcf3b2e928c11efca1d96f477d4 Homepage: https://cran.r-project.org/package=generalRSS Description: CRAN Package 'generalRSS' (Statistical Tools for Balanced and Unbalanced Ranked SetSampling) Ranked Set Sampling (RSS) is a stratified sampling method known for its efficiency compared to Simple Random Sampling (SRS). When sample allocation is equal across strata, it is referred to as balanced RSS (BRSS) whereas unequal allocation is called unbalanced RSS (URSS), which is particularly effective for asymmetric or skewed distributions. This package offers practical statistical tools and sampling methods for both BRSS and URSS, emphasizing flexible sampling designs and inference for population means, medians, proportions, and Area Under the Curve (AUC). It incorporates parametric and nonparametric tests, including empirical likelihood ratio (LR) methods. The package provides ranked set sampling methods from a given population, including sampling with imperfect ranking using auxiliary variables. Furthermore, it provides tools for efficient sample allocation in URSS, ensuring greater efficiency than SRS and BRSS. For more details, refer e.g. to Chen et al. (2003) , Ahn et al. (2022) , and Ahn et al. (2024) . Package: r-cran-generator Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-generator_0.1.0-1.ca2604.1_all.deb Size: 53586 MD5sum: 2c6ad0635aee5baa188db6135fb7f11a SHA1: 4ffe7fb16a7a22717e43330a48af493e8be81625 SHA256: 985f246e180bd06a60468a02598e3133b165cc5d404d9888c73f7caf1b3396e4 SHA512: ccf4feef0a63913de116c4e00f33545a569162e1173345d0a491b2f96cb7aaba934b2e71545617eec7c06ac176f63f327cad403101b51419993db6fc6a3cb330 Homepage: https://cran.r-project.org/package=generator Description: CRAN Package 'generator' (Generate Data Containing Fake Personally IdentifiableInformation) Allows users to quickly and easily generate fake data containing Personally Identifiable Information (PII) through convenience functions. Package: r-cran-genericml Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mlr3, r-cran-mlr3learners, r-cran-sandwich, r-cran-lmtest, r-cran-splitstackshape, r-cran-abind Suggests: r-cran-glmnet, r-cran-ranger, r-cran-rpart, r-cran-e1071, r-cran-xgboost, r-cran-kknn, r-cran-dicekriging, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-genericml_0.2.2-1.ca2604.1_all.deb Size: 270770 MD5sum: 3e001d93536a6ad591ef39447b24e584 SHA1: eb88cf9a16322b926d0d008943c965279220c1b0 SHA256: d64028bd46ffe1ed63584683a78bd1d9cd151dbf46993221d5b6810fb0769aef SHA512: d4b34e5c600cefc590e61a6a42cf71b01eba7a4e4cb58f0f26b56315d86c0532da9941fd46b01ca5b9c848232bd3e449f03a141b0e8edaa522dee62b2af89a3d Homepage: https://cran.r-project.org/package=GenericML Description: CRAN Package 'GenericML' (Generic Machine Learning Inference) Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) . This package's workhorse is the 'mlr3' framework of Lang et al. (2019) , which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported. Package: r-cran-generics Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-pkgload, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-generics_0.1.4-1.ca2604.1_all.deb Size: 78970 MD5sum: 20d7a06ed55ce653c028700886970918 SHA1: 96c4b54426727a5403cc611a20096e512bbd59f0 SHA256: 46906c192e67d8695d77987b2995c2baa6efdd228b4ae534a10336780e4e28d1 SHA512: 7993facd9ec7d2fd8b0d0238a6e635d4f50fecb630b7346fe25e6bcaab75a5c2291c83a197025ecb6f2cffdbb8d734eae2793eb462230b9e53de1492925b2a66 Homepage: https://cran.r-project.org/package=generics Description: CRAN Package 'generics' (Common S3 Generics not Provided by Base R Methods Related toModel Fitting) In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics. Package: r-cran-genero Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-genero_0.1.0-1.ca2604.1_all.deb Size: 259894 MD5sum: 0ea598bf253a8416ac340fa161677a9e SHA1: 7c1d9266badef28eb2bc7436ffdb472301f777b4 SHA256: 31e12acbbb1507c6cb569c5932ad031c5c2cf5e6770b3415f3574ce1ec95ecf4 SHA512: b5d7a7f5cd92614252398be82655b83faf00944cf788ccf0109c12c597ddbbda5f693094f0771fd03589cad058c27fbadfc3738128b43d5ad0721d8682283934 Homepage: https://cran.r-project.org/package=genero Description: CRAN Package 'genero' (Estimate Gender from Names in Spanish and Portuguese) Estimate gender from names in Spanish and Portuguese. Works with vectors and dataframes. The estimation works not only for first names but also full names. The package relies on a compilation of common names with it's most frequent associated gender in both languages which are used as look up tables for gender inference. Package: r-cran-genescape Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-corpcor Filename: pool/dists/resolute/main/r-cran-genescape_1.0-1.ca2604.1_all.deb Size: 31866 MD5sum: 9322895a1ebbee66aee62d645c53a604 SHA1: 63c846afb5d5fb3c9eaf40386dff742ad1f57d38 SHA256: 2c01801d371d1c53e404cb97febeaee1b093ff267f00eaf0ae25b126758996ee SHA512: be4b9fc0a7a1328f933cf312eb3581a8fdcf0b2c8fe4c5087d15b64364fa661a57af5951d527ac44354fd9c061ca1a00d22397ba72ef082936a8e9f1acc1ac6d Homepage: https://cran.r-project.org/package=GeneScape Description: CRAN Package 'GeneScape' (Simulation of Single Cell RNA-Seq Data with Complex Structure) Simulating single cell RNA-seq data with complicated structure. This package is developed based on the Splat method (Zappia, Phipson and Oshlack (2017) ). 'GeneScape' incorporates additional features to simulate single cell RNA-seq data with complicated differential expression and correlation structures, such as sub-cell-types, correlated genes (pathway genes) and hub genes. Package: r-cran-genescorer Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-genescorer_0.2.0-1.ca2604.1_all.deb Size: 19904 MD5sum: 1c1d33392a4518f53c18e1a221f803e4 SHA1: d2cc7e4b2d1da2e8e312cfd2430cf04763c0b2de SHA256: 598673c6abcfcab8ce7d47d2165b2a15a5cf6edd4ba97cdcf2ea1ea5a60578ee SHA512: 920fc768868323fc48cb6e29ecf40c1fc25d8ec1ce6054ef06e6098789fe44d4874e3bb9ff769caddaa360d14692d0cf90d649bf6b03d1ea98004a8bc6306007 Homepage: https://cran.r-project.org/package=GeneScoreR Description: CRAN Package 'GeneScoreR' (Gene Scoring from Count Tables) Provides methods for automatic calculation of gene scores from gene count tables, including a Z-score method that requires a table of samples being scored and a count table with control samples; a geometric mean method that does not rely on control samples; and a principal component-based method that summarizes gene expression using user-selected principal components. The Z-score and geometric mean approaches are described in Kim et al. (2018) . Package: r-cran-geneset Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rcurl, r-cran-fst, r-cran-stringi, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geneset_0.2.7-1.ca2604.1_all.deb Size: 364732 MD5sum: 8067468d1263129debe2eb1527e7e5ea SHA1: e63fb67c58dbb2e4c58f129e83bf85200ddbf17b SHA256: 284fbf47f09fc7ecb74c7f433c7ba7d0bd1c8ddf00470dbe47b2a5c5fa45b19e SHA512: 0347b5f6a8ac77fcbbf2983c177ba813e2478ced2f9d8ec5602f8eec5b2fa9e3c563fb6e35499f225a075d6b5136ed850c9cc4bbca389756e74311f234e58dec Homepage: https://cran.r-project.org/package=geneset Description: CRAN Package 'geneset' (Get Gene Sets for Gene Enrichment Analysis) Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) ), 'KEGG' (Minoru et al. (2000) ), 'WikiPathway' (Marvin et al. (2020) ), 'MsigDb' (Arthur et al. (2015) ), 'Reactome' (David et al. (2011) ), 'MeSH' (Ish et al. (2014) ), 'DisGeNET' (Janet et al. (2017) ), 'Disease Ontology' (Lynn et al. (2011) ), 'Network of Cancer Genes' (Dimitra et al. (2019) ) and 'COVID-19' (Maxim et al. (2020) ). Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of terms ID and term description. Package: r-cran-genesysr Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-dplyr, r-cran-readr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-genesysr_2.2.0-1.ca2604.1_all.deb Size: 106006 MD5sum: 6b99962757643b19c24e4b833989857f SHA1: 3f687a74c95dd01b865f68842ce544260f39da93 SHA256: 36445b5bb52fc27ab3c2311d1fd1105fce136bc4fb567fa1c73d11db086ad082 SHA512: 27f065d23f82ca15ece25594a96a7cbae7967af64c3dd92f4e558b3ae5152f23e0b94a109bd161595887967a0409dc911e0ad28081c32d48997b0cf2fea72e3d Homepage: https://cran.r-project.org/package=genesysr Description: CRAN Package 'genesysr' (Genesys PGR Client) Access data on plant genetic resources from genebanks around the world published on Genesys (). Your use of data is subject to terms and conditions available at . 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Implements the Additive Main effects and Multiplicative Interaction (AMMI) model (Gauch, 1992, ISBN:9780444892409) and the Site Regression (SREG) model (Cornelius, 1996, ). To ensure reliable results even with outliers or missing data, it includes robust versions of AMMI (Rodrigues et al., 2016, ) and SREG (Angelini et al., 2022, ). Furthermore, the package offers advanced imputation techniques for multi-environment data, covering classical methodologies (Arciniegas-Alarcón et al., 2014, ) and recently published imputation methods for MET data (Angelini et al., 2024, ). 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It estimates the effects of each variable while fully adjusting for all other variables that are measured in at least one of the studies. Using algebraic relationships between regression parameters in different dimensions, a set of moment equations is specified for estimating the parameters of a maximal model through information available on sets of parameter estimates from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. These equations are solved using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. The proposed framework is implemented using iterated reweighted least squares algorithm for fitting generalized linear regression models. For more details about the method, please see pre-print version of the manuscript on generalized meta-analysis by Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018) .The current version (0.2.0) is updated to address some of the stability issues in the previous version (0.1). Package: r-cran-geno2proteo Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-iranges, r-cran-r.utils, r-cran-runit Filename: pool/dists/resolute/main/r-cran-geno2proteo_0.0.6-1.ca2604.1_all.deb Size: 1992426 MD5sum: d90ac8d6ff56d0e380c2edf2d98a11ca SHA1: 726e9d4ed330482c2f0a97d47c75edb3937782d6 SHA256: d131ca485b5b54f8315f8b311ec124c2dd3b44291481980b9371faa96dcf7758 SHA512: 3c58da86a9ed7ebdd4841864dc0fa4b3ee779649499c125d11c64a62dfbfc4abee965639066bac97564a58ce127f5394a0fef7ab24dc244aaf291b9c06f4c600 Homepage: https://cran.r-project.org/package=geno2proteo Description: CRAN Package 'geno2proteo' (Finding the DNA and Protein Sequences of Any Genomic orProteomic Loci) Using the DNA sequence and gene annotation files provided in 'ENSEMBL' , the functions implemented in the package try to find the DNA sequences and protein sequences of any given genomic loci, and to find the genomic coordinates and protein sequences of any given protein locations, which are the frequent tasks in the analysis of genomic and proteomic data. Package: r-cran-genomic.autocorr Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-reshape Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-genomic.autocorr_1.0-1-1.ca2604.1_all.deb Size: 24636 MD5sum: 9070b870caa935656cdb546835d94f41 SHA1: 1c4362f86be85d0db6670799d5e5834bf8b3c0ab SHA256: 111e73d32e900db96a469972de2d10a8d0cc3106d5e65029925d73a699538ae8 SHA512: 7c7888c06186c97e55d93bdc236e243944b0be98902e5cd67b2f5f8949a8858859c140db66e907b963088f23ea931c14dd3097e5e02664349ba82f41639c8c85 Homepage: https://cran.r-project.org/package=genomic.autocorr Description: CRAN Package 'genomic.autocorr' (Models Dealing with Spatial Dependency in Genomic Data) Local structure in genomic data often induces dependence between observations taken at different genomic locations. Ignoring this dependence leads to underestimation of the standard error of parameter estimates. This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function. Package: r-cran-genomicper Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-genomicper_1.8-1.ca2604.1_all.deb Size: 170128 MD5sum: 4872e7d99d56023240f4eb81b708d64b SHA1: 5a05b2fcde20d1ac3d8bef39ff0a0a41bde9a031 SHA256: 2bb3fe6dd3a270ab7be402941de2623f654da5ab3c53122dd1618d72717dd798 SHA512: 06e624ba8c5d68cb0e5f25b87a9fb6e8873d991d4eed626f37bac588664a5ff26ca73c2f9ab5296bbc8cbd0d9fd9f4e7e08b8fe483f1d740640c33fa399c979c Homepage: https://cran.r-project.org/package=genomicper Description: CRAN Package 'genomicper' (Circular Genomic Permutation using Genome Wide Associationp-Values) Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user. Cabrera et al (2012) . Package: r-cran-genomicsig Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kaos, r-bioc-biostrings, r-cran-entropy, r-cran-seqinr Filename: pool/dists/resolute/main/r-cran-genomicsig_0.1.0-1.ca2604.1_all.deb Size: 49880 MD5sum: 41bbe35d35463f526eb2fd2656096369 SHA1: 5ee58fead74c7ddde5d13209d203d94c12d93ce2 SHA256: 1b23c66848460ea947160c73f41a1f4bd8cb4b547ae4ca84a552389403e3b6cb SHA512: 871e6f270de617b6dbdbd32ee8c32c6eb97328765967fa50417c8f23c1343c4a59dbcfb564f4369bc608e2a49da1cc5690f96fa1e7a03b548db31aed2bdf4be9 Homepage: https://cran.r-project.org/package=GenomicSig Description: CRAN Package 'GenomicSig' (Computation of Genomic Signatures) Genomic signatures represent unique features within a species' DNA, enabling the differentiation of species and offering broad applications across various fields. This package provides essential tools for calculating these specific signatures, streamlining the process for researchers and offering a comprehensive and time-saving solution for genomic analysis.The amino acid contents are identified based on the work published by Sandberg et al. (2003) and Xiao et al. (2015) . The Average Mutual Information Profiles (AMIP) values are calculated based on the work of Bauer et al. (2008) . The Chaos Game Representation (CGR) plot visualization was done based on the work of Deschavanne et al. (1999) and Jeffrey et al. (1990) . The GC content is calculated based on the work published by Nakabachi et al. (2006) and Barbu et al. (1956) . The Oligonucleotide Frequency Derived Error Gradient (OFDEG) values are computed based on the work published by Saeed et al. (2009) . The Relative Synonymous Codon Usage (RSCU) values are calculated based on the work published by Elek (2018) . 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Package: r-cran-genotriplo Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinybs, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-htmltools, r-cran-processx, r-cran-rlang, r-cran-rmixmod, r-cran-shiny, r-cran-shinythemes, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-genotriplo_1.1.3-1.ca2604.1_all.deb Size: 350498 MD5sum: b11b4f887fe32a45e4686c407bff62ad SHA1: 45531bd2c9c3352a98272622956c29001a44dd3a SHA256: 70316b94b4d3908e31aaaf1eab7083351e0f7927e34336a2e7855c247c485f2b SHA512: e29555fa0dde6deb7422171c77fe6275ca8430a0ef699db634f3551ae7f97ffb361da87ddbaa157f6c4d7245a72e4c0addd94a0ea9b34ac5cc038302c5055b3e Homepage: https://cran.r-project.org/package=GenoTriplo Description: CRAN Package 'GenoTriplo' (Genotyping Triploids (or Diploids) from Luminescence Data) Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids. Two main functions: Run_Clustering() that regroups individuals with a same genotype based on proximity and Run_Genotyping() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny(). 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Package: r-cran-genpathmox Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-csem, r-cran-diagram, r-cran-matrixcalc Filename: pool/dists/resolute/main/r-cran-genpathmox_1.1-1.ca2604.1_all.deb Size: 242936 MD5sum: ab537c4d4ce0dbf5c2d0aa2db23f9b73 SHA1: 2db298458be068b5e9a0f3746c4795196b166bb6 SHA256: 59d17b9483024ea4bc5a444579358a8b731befa77340560e37f1c37070ff6f74 SHA512: 98acd1067b3a728fd7d3ce5a71480e357550df5470d623315a31086d7354ba37d69dff7012fe3e17c531a1ff7f35f4955480d01a566f38fa0a093cbae357fdd4 Homepage: https://cran.r-project.org/package=genpathmox Description: CRAN Package 'genpathmox' (Pathmox Approach Segmentation Tree Analysis) It provides an interesting solution for handling a high number of segmentation variables in partial least squares structural equation modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, and Aluja,(2016)) including the F-coefficient test (Lamberti, Sanchez, and Aluja,(2017)) to detect the path coefficients responsible for the identified differences). The package also allows running the hybrid multi-group approach (Lamberti (2021) ). 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Two layers are always active: structured logging with real traceback and per-case timing; and reproducibility capture, which records the R version, loaded package versions, execution environment, the exact iteration mask, and a stat-based fingerprint of every input file referenced in the mask (with a diff_inputs() helper to detect silent drift between runs). Parallel execution (built on the 'future' framework, Bengtsson (2021) ), non-blocking background jobs, and opt-in progress reporting (via 'progressr') are implemented as optional, composable layers. Further layers (error replay, content-hash input fingerprinting, content-based case identifiers) are planned and will remain composable with the default layers. Package: r-cran-genpwr Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-nleqslv, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-genpwr_1.0.4-1.ca2604.1_all.deb Size: 506878 MD5sum: ac26f491ec2fbf8140cb5a771c3cb6dc SHA1: 2913109c7eca7bbb517d97e5bd50e5adc60d9377 SHA256: 892060c7b24a75b9f8e02dc7efd2e7d130dba133ce9eea69f607dee9a3d5113a SHA512: 3bd78432ad107848cc2a58e60f40081ee93d297840e272aefe4fc82c867b447cba3b10405260d11be77e19a525e4de397cb6346e2228363efd743b0aca9301c0 Homepage: https://cran.r-project.org/package=genpwr Description: CRAN Package 'genpwr' (Power Calculations Under Genetic Model Misspecification) Power and sample size calculations for genetic association studies allowing for misspecification of the model of genetic susceptibility. "Hum Hered. 2019;84(6):256-271.. Epub 2020 Jul 28." Power and/or sample size can be calculated for logistic (case/control study design) and linear (continuous phenotype) regression models, using additive, dominant, recessive or degree of freedom coding of the genetic covariate while assuming a true dominant, recessive or additive genetic effect. In addition, power and sample size calculations can be performed for gene by environment interactions. These methods are extensions of Gauderman (2002) and Gauderman (2002) and are described in: Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification. American Society of Human Genetics. October 2018, San Diego. Package: r-cran-genridge Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-rgl, r-cran-colorspace Suggests: r-cran-mass, r-cran-bestglm, r-cran-vcdextra Filename: pool/dists/resolute/main/r-cran-genridge_0.8.0-1.ca2604.1_all.deb Size: 333844 MD5sum: 42fa92b90290d4cbb94b79af0643a67a SHA1: 61b7727295dc492355c7c31f5cabb3a6c133c91f SHA256: 9ca7731518313927ab76cfe6c28f8f8565cdaa27f4092c7d89e494ebb14691d3 SHA512: 5cb19a6b639f65d7fd894e4cf28907115af45134afc3122291b4a4dfe43e1a9caf33066a931333b865c2ade15951d62a30f0ec9b870d651f984634b3e4c9de01 Homepage: https://cran.r-project.org/package=genridge Description: CRAN Package 'genridge' (Generalized Ridge Trace Plots for Ridge Regression) The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors. Package: r-cran-genseir Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-minpack.lm, r-cran-nlsr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-genseir_0.1.1-1.ca2604.1_all.deb Size: 99458 MD5sum: d910b7147056ecb050598336e8b97688 SHA1: 5561b9e188e98d04813edf18333e03f485ff2b5a SHA256: ec9998d29aed9215323235a6c783fa153db821e529b285aec0c78398e19b703e SHA512: 4988f81d0de16dafa91fcf132d5cc98d2fa5c8d25ecd2b040db0b5f3b224574fafdb52615a2a9915e2bccc1d15265bf435ca7933dc2852f6b0421ed39ff9121d Homepage: https://cran.r-project.org/package=genSEIR Description: CRAN Package 'genSEIR' (Predict Epidemic Curves with Generalized SEIR Modeling) Performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) . Package: r-cran-genset Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-genset_0.1.1-1.ca2604.1_all.deb Size: 54182 MD5sum: 03505ba51ab6ab235c377b6a54137c0a SHA1: a5c1c4b272ca76924f6cfd3dd0474a480a30d55e SHA256: 8a5b0bac1ec830094d8a7f89b0a9c5d0c3ec6ddde800287339aeedcd57aa3bbb SHA512: 9e8c777b96ffef16604caf9a76e9109ebad866e610302d457e4aec58b20273641a4e3c71a1d11fad85e808b436eb307f444262f5aa552c32edc274dc21eb3510 Homepage: https://cran.r-project.org/package=genset Description: CRAN Package 'genset' (Generates Multiple Data Sets) Generate multiple data sets for educational purposes to demonstrate the importance of multiple regression. The genset function generates a data set from an initial data set to have the same summary statistics (mean, median, and standard deviation) but opposing regression results. Package: r-cran-gensphere Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvmesh, r-cran-geometry, r-cran-sphericalcubature, r-cran-rgl, r-cran-simplicialcubature Filename: pool/dists/resolute/main/r-cran-gensphere_1.3-1.ca2604.1_all.deb Size: 123830 MD5sum: 3d3186f0aedbdc8be838e059bceaf9da SHA1: c22f6cdec625665c992c50150c308d681a51bc5c SHA256: f533dbe9971f8f06786b43fad9f76769c4b8978654bde4e495fbdaaeac8ed4c9 SHA512: f5f0f80394acfc11d778c522da87e46643376a57f5a3e4eac841a08e7bbda9fc119378cbfdcc3b158efb642c60d3849865fa6cb7436aac3718ee4de506c90b72 Homepage: https://cran.r-project.org/package=gensphere Description: CRAN Package 'gensphere' (Generalized Spherical Distributions) Define and compute with generalized spherical distributions - multivariate probability laws that are specified by a star shaped contour (directional behavior) and a radial component. The methods are described in Nolan (2016) . Package: r-cran-genstab Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-genstab_1.0.0-1.ca2604.1_all.deb Size: 46778 MD5sum: 15a85561ee1f04a05ae576e9614f1927 SHA1: b86e4397d36d4a4e9246bb2e4dc7a04c784b2895 SHA256: cc1633449a680a0f9405aa401954a1601f276723e504ed7ec7a96744a3c80caf SHA512: cc4ef28e897e2f821483d3ec037715463e965ffb6ff8fd8f268b99d2c8121b60eab273fa101451a7d980ecb15dcae3b7b4fa95028f73f832a090e7f53eb7caf7 Homepage: https://cran.r-project.org/package=genstab Description: CRAN Package 'genstab' (Resampling Based Yield Stability Analyses) Several yield stability analyses are mentioned in this package: variation and regression based yield stability analyses. Resampling techniques are integrated with these stability analyses. The function stab.mean() provides the genotypic means and ranks including their corresponding confidence intervals. The function stab.var() provides the genotypic variances over environments including their corresponding confidence intervals. The function stab.fw() is an extended method from the Finlay-Wilkinson method (1963). This method can include several other factors that might impact yield stability. Resampling technique is integrated into this method. A few missing data points or unbalanced data are allowed too. The function stab.fw.check() is an extended method from the Finlay-Wilkinson method (1963). The yield stability is evaluated via common check line(s). Resampling technique is integrated. Package: r-cran-gentag Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gentag_1.0-1.ca2604.1_all.deb Size: 70330 MD5sum: 933edd9718104cd24080a3750861c674 SHA1: 00e4ee58d1d162f2ea5ba9bb90a67616537f17e9 SHA256: a7baa8a78e171aa03b465acb3419389d6f8b1ceb2d3acb2285f632909ef917dd SHA512: b177c1eb3460643fa0fbe756572b3d8bb01fa263d93d7969e133dec1ffd1f5ad598a60e02daaa978476dec7ab5e2792b25ab1e3983fc867cc32de474daa9f2a1 Homepage: https://cran.r-project.org/package=GenTag Description: CRAN Package 'GenTag' (Generate Color Tag Sequences) Implement a coherent and flexible protocol for animal color tagging. 'GenTag' provides a simple computational routine with low CPU usage to create color sequences for animal tag. 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Package: r-cran-gents Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gents_0.1.4-1.ca2604.1_all.deb Size: 1468248 MD5sum: f3014e2e353ee1b4afce188ad5127911 SHA1: 738ff537df39be18d2daaf7a4d15fa4ac545f0b8 SHA256: dbb2bba45d362d0c91535d476e7c40dbf4f90492f9d2dc12a676427adeb3e0cc SHA512: 3b667fd22bea6997e88b1079539541007e3aa78e02241589a57597a755d1c81c6b54ceeecf8e8eeb5c50b6c58fbaa0664815661c4bab13b4ced6c2efc43e09b7 Homepage: https://cran.r-project.org/package=genTS Description: CRAN Package 'genTS' (R Shiny App for Creating Simplified Trial Summary (TS) Domain) Make it easy to create simplified trial summary (TS) domain based on FDA FDA guide . Package: r-cran-gentwoarmstrialsize Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trialsize, r-cran-dplyr, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gentwoarmstrialsize_0.0.5-1.ca2604.1_all.deb Size: 235852 MD5sum: 9d98a22599f57d98af75644cd0a47306 SHA1: 31294dd902473a28a8879715c676e44c7ee8a974 SHA256: f5f090f0521b52398d4cff4d5c7f72ee9e5080e7711d7a7b0fbb318d653b99b4 SHA512: fe3baf832509edcd2042578b50a04bb27510b3920729e8042d79b94014b5ff4b332db989a7c77cdc6974542d2bdde70ac0e023cd2836c07b9aa3e01ecf3cb6fb Homepage: https://cran.r-project.org/package=GenTwoArmsTrialSize Description: CRAN Package 'GenTwoArmsTrialSize' (Generalized Two Arms Clinical Trial Sample Size Calculation) Two arms clinical trials required sample size is calculated in the comprehensive parametric context. The calculation is based on the type of endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover), hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms noncompliance rates and expected loss of follow-up. Methods are described in: Chow SC, Shao J, Wang H, Lokhnygina Y (2017) , Wittes, J (2002) , Sato, T (2000) , Lachin J M, Foulkes, M A (1986) , Whitehead J(1993) , Julious SA (2023) . Package: r-cran-genwin Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pspline Filename: pool/dists/resolute/main/r-cran-genwin_1.0-1.ca2604.1_all.deb Size: 983350 MD5sum: 5f2af8ddd66531e1d0ca1ec6e6697f33 SHA1: 9dd62d1a5961210153bdf107d415409ca41d7524 SHA256: 03d6f1c3d21af68f96b5fe2939fdbc601232579ca13c94cd3fb17f8924c6af37 SHA512: f03eb9c69413df4440b0b772855a02d17c939f592ad46741176a31f895b9ffc0a7ed1995f459b36ae75b9c887af90fb4137d709dca38ea44860cb34b1b16f9fe Homepage: https://cran.r-project.org/package=GenWin Description: CRAN Package 'GenWin' (Spline Based Window Boundaries for Genomic Analyses) Defines window or bin boundaries for the analysis of genomic data. Boundaries are based on the inflection points of a cubic smoothing spline fitted to the raw data. 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Package: r-cran-geoar Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-httr2, r-cran-promises, r-cran-assertthat, r-cran-attempt, r-cran-tidyr, r-cran-stringr, r-cran-magrittr, r-cran-curl, r-cran-glue, r-cran-leaflet, r-cran-jsonlite, r-cran-purrr Suggests: r-cran-testthat, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-geofacet, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-geoar_1.2.2-1.ca2604.1_all.deb Size: 658364 MD5sum: d8ea73bc99629c1387a8483e092a375c SHA1: d987ccd3af7d5183458925dbdece2c8223d3e470 SHA256: 2a44984e053f7b27c0adb08a44c6682a94f97c912b449ff75622d27f2550135b SHA512: 626d52c22fc46db5b05cda50c687d2ded11fdd327c865ea3bd6917ceef1230dce14684da2bff90c2ca2a6bf434fd34607c6722e2b038a3121d9b88858cf12ada Homepage: https://cran.r-project.org/package=geoAr Description: CRAN Package 'geoAr' (Argentina's Spatial Data Toolbox) Collection of tools that facilitates data access and workflow for spatial analysis of Argentina. Includes historical information from censuses, administrative limits at different levels of aggregation, location of human settlements, among others. Since it is expected that the majority of users will be Spanish-speaking, the documentation of the package prioritizes this language, although an effort is made to also offer annotations in English. 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Package: r-cran-geobounds Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-countrycode, r-cran-dplyr, r-cran-httr2, r-cran-sf Suggests: r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-quarto, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-geobounds_0.1.1-1.ca2604.1_all.deb Size: 399330 MD5sum: b98a47282a0753eac81001f2541a4ea3 SHA1: 625cb9814dd4490a09a34d47c022fa4df1706088 SHA256: c6c1d800895161e41c3893636997ac482972c37d1232c6fab5e85c42e55cd69f SHA512: fc13be56e8b3b8f4a0bc8e23189b7a7a37d92620b2fea5e3e1687718fc73f4074c729e7628d1715678342a79a6037d72a27292d3af703a5191e18b9fbe336da1 Homepage: https://cran.r-project.org/package=geobounds Description: CRAN Package 'geobounds' (Download Map Data from 'geoBoundaries') Tools to download data from 'geoBoundaries' . Several administration levels available. See Runfola, D. et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLOS ONE 15(4): 1-9. . 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The package offers a wide range of spatial data sets available at various geographic scales and for various years with harmonized attributes, projection and fixed topology. All functions allow for seamless integration sf, DuckDB and Arrow. Package: r-cran-geocacher Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-tibble, r-cran-threewords Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geocacher_0.1.0-1.ca2604.1_all.deb Size: 42552 MD5sum: ed1a1bf3b0585184a0d4722d1727cf43 SHA1: f56993c670a6ab743d56bd38f7aa0d2f5067fb4e SHA256: 3f4c222d03e016bd0eed18a2766c386fb087b8fbe27afe926b634db90c112340 SHA512: 5fc8ab1bf4b65f67447c96efa9bde91c11f5f084685370036d6d828917aca74584360c37d6d911a785cbf5be09ee537fcaebd78788620dc7e870d043875c402d Homepage: https://cran.r-project.org/package=geocacheR Description: CRAN Package 'geocacheR' (Tools for Geocaching) Tools for solving common geocaching puzzle types, and other Geocaching-related tasks. Package: r-cran-geocausal Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crsuggest, r-cran-ggthemes, r-cran-data.table, r-cran-dplyr, r-cran-furrr, r-cran-ggplot2, r-cran-ggpubr, r-cran-mclust, r-cran-progressr, r-cran-purrr, r-cran-sf, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.model, r-cran-spatstat.univar, r-cran-spatstat.random, r-cran-terra, r-cran-tidyr, r-cran-tidyselect, r-cran-tidyterra Suggests: r-cran-elevatr, r-cran-gridextra, r-cran-knitr, r-cran-readr, r-cran-gridgraphics Filename: pool/dists/resolute/main/r-cran-geocausal_0.4.0-1.ca2604.1_all.deb Size: 1223132 MD5sum: 5bb29b74597c97b1a6698fbdbda8ae05 SHA1: efc81d9fe24719db4f3f099b5ed47a7ee7bb466d SHA256: 04e9926f181823750e8fbfd9cfe0b5fe0db4b18ab770180a9b5fb968f3b08c49 SHA512: b8a9112600c5e6ba5cda2c747c72bb8b4d2ac7544a231d282cf48333a0cef375d51171af1ada59b21596c362278bd4edefad6d98dc17e587e27c0e3e546c78c2 Homepage: https://cran.r-project.org/package=geocausal Description: CRAN Package 'geocausal' (Causal Inference with Spatio-Temporal Data) Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) and Mukaigawara, et al. (2024) . 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O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatística (IBGE), órgão oficial de estatísticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatística (IBGE), Brazil's official statistics and geography agency.) Package: r-cran-geodadata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3347 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sf Filename: pool/dists/resolute/main/r-cran-geodadata_0.1.0-1.ca2604.1_all.deb Size: 3343162 MD5sum: 61b9fdc6106a0b51140b27d5304b80e0 SHA1: 124f3cb276147825d479551affe2836e1899463e SHA256: 4a36ce17419d243c2271dcf33975adb54fbef68d58f55af14df9453ee62dcac1 SHA512: 9908d03e17d02a49a0fad8acd9efa78aa33fd422a28ae30a3343afacfbe99a86b7accbf470ffbe3bba466bc7e127d0da872b2dda2df3fcaeb559e9c4ea788cf6 Homepage: https://cran.r-project.org/package=geodaData Description: CRAN Package 'geodaData' (Spatial Analysis Datasets for Teaching) Stores small spatial datasets used to teach basic spatial analysis concepts. Datasets are based off of the 'GeoDa' software workbook and data site developed by Luc Anselin and team at the University of Chicago. Datasets are stored as 'sf' objects. Package: r-cran-geodata Architecture: all Version: 0.6-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rappdirs Suggests: r-cran-jsonlite, r-cran-r.utils, r-cran-httr, r-cran-archive Filename: pool/dists/resolute/main/r-cran-geodata_0.6-9-1.ca2604.1_all.deb Size: 268774 MD5sum: 28b2b2e09fe33eb0e19c5b7f0ed34f10 SHA1: e1df145fc5aa360aa39aed0d8780cc6a80d72b0e SHA256: bee7792d6565d7b6d17866d17767209daa00eddf8c08bea7eb61a1a708095126 SHA512: 2add20d4157e078cce9a0d5ca0fc1bb4c782c61487af819337091169bdac6e3f0bf568d8f7d543e4cb4fb6dba53a423a8489940534d39b06354dc0d85da425db Homepage: https://cran.r-project.org/package=geodata Description: CRAN Package 'geodata' (Access Geographic Data) Functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data. Package: r-cran-geodeltaaudit Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3286 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geodeltaaudit_0.1.0-1.ca2604.1_all.deb Size: 1213148 MD5sum: ae8231bb6c6311adf2b55a7217bc3916 SHA1: 15d73429d999e20ba66d946a2b5df1f388b2b91d SHA256: 56f4b0ee4bc4d35cbb047f6bb031054e52548d38e1fe2ceee136b9f6828f0b71 SHA512: e21accf8edf038303307754151c61506ee98efcd470b1cfc362facb6c82ff9fb7127f83d8ed3c82e87dd39f79a806456de4fec07eff24ba91e9ca244ebf947bb Homepage: https://cran.r-project.org/package=geoDeltaAudit Description: CRAN Package 'geoDeltaAudit' (Quantifying Variable Change Induced by Administrative BoundaryTransformations) Tools for auditing how analytic variables change when data are transformed across administrative boundary systems. The package is agnostic to data source, variable type, and administrative geography, and is designed to quantify transformation-induced change without attributing blame to any specific boundary definition or allocation scheme. Package: r-cran-geodetector Architecture: all Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2654 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-geodetector_1.0-5-1.ca2604.1_all.deb Size: 1684294 MD5sum: f2cd3832745fdf0dae0045751d1b2383 SHA1: c02ed6bb267153ae7da789861e6f7d2787d1f569 SHA256: 0b4e0b0af281804225f9a4b2bce53c2b36811afcaf40a996de31a83ababdd5cb SHA512: 2d027a68c71224522535354cfb4a183166f9a741e17b61da7ab0ad297883e8a7eb32e1d29176b5d6cb750f70a1dadb4ed9240a59212a34f4b0b34296c7498d04 Homepage: https://cran.r-project.org/package=geodetector Description: CRAN Package 'geodetector' (Stratified Heterogeneity Measure, Dominant Driving ForceDetection, Interaction Relationship Investigation) Spatial stratified heterogeneity (SSH), referring to the within strata are more similar than the between strata, a model with global parameters would be confounded if input data is SSH. Note that the "spatial" here can be either geospatial or the space in mathematical meaning. Geographical detector is a novel tool to investigate SSH: (1) measure and find SSH of a variable Y; (2) test the power of determinant X of a dependent variable Y according to the consistency between their spatial distributions; and (3) investigate the interaction between two explanatory variables X1 and X2 to a dependent variable Y (Wang et al 2014 , Wang, Zhang, and Fu 2016 ). Package: r-cran-geodimension Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3078 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-snakecase, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geodimension_2.0.0-1.ca2604.1_all.deb Size: 3029466 MD5sum: 0ff8d99bd91f748d916c7aa1688d1262 SHA1: a89a7d8077f7d961552c106be12388243e7aec76 SHA256: 1f84b56c80fcb6a69e06bbbc02e4c7aeb68532a47ab23d682232f5585869d9b0 SHA512: 5475013f0687de22fe7439e25321470b385860fe2c8084a4916550986cc54eecd5a4283d6deb70b5bc32b27a18c5845c6ad63dcc032efe5526505ed0ac5bb733 Homepage: https://cran.r-project.org/package=geodimension Description: CRAN Package 'geodimension' (Definition of Geographic Dimensions) The geographic dimension plays a fundamental role in multidimensional systems. To define a geographic dimension in a star schema, we need a table with attributes corresponding to the levels of the dimension. Additionally, we will also need one or more geographic layers to represent the data using this dimension. The goal of this package is to support the definition of geographic dimensions from layers of geographic information related to each other. It makes it easy to define relationships between layers and obtain the necessary data from them. Package: r-cran-geodl Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-luz, r-cran-torchvision, r-cran-dplyr, r-cran-terra, r-cran-multiscaledtm, r-cran-psych, r-cran-coro, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-geodl_0.3.1-1.ca2604.1_all.deb Size: 594578 MD5sum: 3bc3a11678f54331bd1e48abdd7a453b SHA1: 38d8917d35789454e4bb7f8d400da4a9c659039e SHA256: 3c3c847ffefcff8aa6b434c6a4aeaf2a659b89a7205a868cbe2ecb08adfd5979 SHA512: 7fc1403da29916c31deba6ac3c2baeeb52ae98fa30bc6b57f4a5b394a539cd5a05b3c11bb8e50da577ac68881b1343b7be5942ff34420be27c77fda4d518fc2a Homepage: https://cran.r-project.org/package=geodl Description: CRAN Package 'geodl' (Geospatial Semantic Segmentation with Torch and Terra) Provides tools for semantic segmentation of geospatial data using convolutional neural network-based deep learning. Utility functions allow for creating masks, image chips, data frames listing image chips in a directory, and DataSets for use within DataLoaders. Additional functions are provided to serve as checks during the data preparation and training process. A UNet architecture can be defined with 4 blocks in the encoder, a bottleneck block, and 4 blocks in the decoder. The UNet can accept a variable number of input channels, and the user can define the number of feature maps produced in each encoder and decoder block and the bottleneck. Users can also choose to (1) replace all rectified linear unit (ReLU) activation functions with leaky ReLU or swish, (2) implement attention gates along the skip connections, (3) implement squeeze and excitation modules within the encoder blocks, (4) add residual connections within all blocks, (5) replace the bottleneck with a modified atrous spatial pyramid pooling (ASPP) module, and/or (6) implement deep supervision using predictions generated at each stage in the decoder. A unified focal loss framework is implemented after Yeung et al. (2022) . We have also implemented assessment metrics using the 'luz' package including F1-score, recall, and precision. Trained models can be used to predict to spatial data without the need to generate chips from larger spatial extents. Functions are available for performing accuracy assessment. The package relies on 'torch' for implementing deep learning, which does not require the installation of a 'Python' environment. Raster geospatial data are handled with 'terra'. Models can be trained using a Compute Unified Device Architecture (CUDA)-enabled graphics processing unit (GPU); however, multi-GPU training is not supported by 'torch' in 'R'. Package: r-cran-geodrawr Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-leaflet, r-cran-shiny, r-cran-shinydashboard Filename: pool/dists/resolute/main/r-cran-geodrawr_2.0.0-1.ca2604.1_all.deb Size: 29978 MD5sum: e6ac46914fb475df38131aeedda87e55 SHA1: f9faa7dd396c1b8b0bb81fe2452a3839fdb9619d SHA256: ad6a73bf648656e14b2dfb53b06ab6aac712d44c8a2ea921c2ba7acf4af50a41 SHA512: cc00689115a9da8550cca85f1d0baca2697def7982508e5bb0461dd2d3eb62bee5eb860f9057b71caadbed8f4d7a8e14f2e0fb33c0815266a1f5ee9d66d05458 Homepage: https://cran.r-project.org/package=geodrawr Description: CRAN Package 'geodrawr' (Making Geospatial Objects) Draw geospatial objects by clicks on the map. This packages can help data analyst who want to check their own geospatial hypothesis but has no ready-made geospatial objects. Package: r-cran-geodregr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-zipfr Filename: pool/dists/resolute/main/r-cran-geodregr_0.2.0-1.ca2604.1_all.deb Size: 146602 MD5sum: 805bbfd4047a368acbed393cfcfc0dff SHA1: b70b03f80f017acef5ba2c3a0db8337efffce012 SHA256: 1d6d2dac71f7666ac9b76406b7c1adb55413247c70b24f6f6283db443bd73572 SHA512: 8c019854f806a8499e920a6cec3378b33118f63bd8efa0dbe530ee005e04f27f51ff90425a8dc1ef831c59d2d31c83659e0e33a305b45f437e1dfda81c2d3176 Homepage: https://cran.r-project.org/package=GeodRegr Description: CRAN Package 'GeodRegr' (Geodesic Regression) Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) . Also see Fletcher, P. T. (2013) . Package: r-cran-geoelectrics Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rgl, r-cran-fields Suggests: r-cran-testthat, r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-geoelectrics_0.2.2-1.ca2604.1_all.deb Size: 899868 MD5sum: e9e093883527416974fff43141a12406 SHA1: c781ac58434b58ec299bff343a2a73227ebc0af2 SHA256: 46a4a7b42ca376eeee6192a2f1a99c1df5a5377905074f8cd568d6a2efdd3234 SHA512: a2170e9c67bb9b21caa28a3e0073d42f3de8c0611c338f9bed7198e85edcf22dac4065db3301d97bb63817c9d0e2a4c93ee5ebc94203056a5dcd01aa2ad16d6e Homepage: https://cran.r-project.org/package=geoelectrics Description: CRAN Package 'geoelectrics' (3D-Visualization of Geoelectric Resistivity Measurement Profiles) Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions. Package: r-cran-geofacet Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1804 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-rnaturalearth, r-cran-sp, r-cran-ggrepel, r-cran-gridextra, r-cran-geogrid, r-cran-rlang, r-cran-httr2 Suggests: r-cran-sf, r-cran-testthat, r-cran-covr, r-cran-lintr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-geofacet_0.2.4-1.ca2604.1_all.deb Size: 1195164 MD5sum: 73a6f47da238ff37fe9aaf377249369e SHA1: d3451857132d3241d6767b292183adf2b82f80cd SHA256: 82e6a5eb2e87714161f20527e05bc7bf83a266a6d0c575026aea7a74c2c4f58c SHA512: cba373edff73db94dc9ebcdb32464e14e6bd09aa62efeb26f3fe175077cf1a8bdc5f77c8dcb7c429225faabedf7dd9296b47e3c82ed1438d37b39c4c956b6597 Homepage: https://cran.r-project.org/package=geofacet Description: CRAN Package 'geofacet' ('ggplot2' Faceting Utilities for Geographical Data) Provides geographical faceting functionality for 'ggplot2'. Geographical faceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation. Package: r-cran-geofi Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4866 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httpcache, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-xml2, r-cran-yaml Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggrepel, r-cran-geofacet, r-cran-htmltools, r-cran-httptest, r-cran-janitor, r-cran-knitr, r-cran-leaflet, r-cran-patchwork, r-cran-readr, r-cran-rmarkdown, r-cran-sotkanet, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-geofi_1.2.1-1.ca2604.1_all.deb Size: 3317900 MD5sum: aa805434037fdfee11cab1c90f16e0a4 SHA1: 070af0ec816fdf1b9522a9581c0a72af014205e1 SHA256: a5fdc393a7f585f70f906bccdcbc859fb36570b2e2ab2759855a3ad21a4f311a SHA512: eb7af682d26086286e2f0f68c0ddfc4799eaa9d83aa2747502a44c2c9fa21605dc8614b1fd09f0b2159eef6e57f3c9c669d08149f5090d5ca34833547358f1c8 Homepage: https://cran.r-project.org/package=geofi Description: CRAN Package 'geofi' (Access Finnish Geospatial Data) Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API , the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations. Package: r-cran-geoflow Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-dotenv, r-cran-benchmarkme, r-cran-httr, r-cran-mime, r-cran-jsonlite, r-cran-yaml, r-cran-xml, r-cran-xml2, r-cran-rdflib, r-cran-curl, r-cran-whisker, r-cran-digest, r-cran-dplyr, r-cran-readr, r-cran-arrow, r-cran-zip, r-cran-png, r-cran-uuid, r-cran-sf, r-cran-sfarrow, r-cran-lwgeom, r-cran-smoothr, r-cran-terra, r-cran-geometa, r-cran-geosapi, r-cran-geonapi, r-cran-geonode4r, r-cran-ows4r Suggests: r-cran-testthat, r-cran-readxl, r-cran-gsheet, r-cran-googledrive, r-cran-dbi, r-cran-rapiclient, r-cran-rmariadb, r-cran-rpostgres, r-cran-rpostgresql, r-cran-rsqlite, r-cran-ncdf4, r-cran-thredds, r-cran-eml, r-cran-emld, r-cran-datapack, r-cran-dataone, r-cran-rgbif, r-cran-ocs4r, r-cran-zen4r, r-cran-atom4r, r-cran-d4storagehub4r, r-cran-rmarkdown, r-cran-dataverse, r-cran-blastula, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-geoflow_1.2.1-1.ca2604.1_all.deb Size: 1766014 MD5sum: a22722ebc361b157321ace809e63c134 SHA1: 9277251d61037b6011c60a43be264328c0ab0181 SHA256: 1e5eaebd9e3c033cdc0756f733a4026f6a167ad7b4702b94390856197f76a780 SHA512: dab94414473a34c294ca0cfd3ea0708b8f56398f45d170c1d6f0bfab0b0426821395bec2c15dc7267c3bd70d1db5150ea647737c46e1f9ef6bae8ef37b14a60e Homepage: https://cran.r-project.org/package=geoflow Description: CRAN Package 'geoflow' (Orchestrate Geospatial (Meta)Data Management Workflows andManage FAIR Services) An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with 'FAIR' (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the 'DublinCore' standard (), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with 'ISO/TC211' () and 'OGC' () geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. 'INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. 'PostGIS'), metadata catalogues (eg. 'GeoNetwork', 'CSW' servers), data servers (eg. 'GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of 'EML' metadata, and their management with 'DataOne' servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of 'CF' conventions, 'NetCDF' data format and 'OPeNDAP' access protocol, and their management with 'Thredds' servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', 'DataCite'), to facilitate the publication of data within (meta)data repositories such as 'Zenodo' () or DataVerse (). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference 'Zenodo' 'DOI' in 'GeoNetwork'/'GeoServer' metadata, or vice versa reference 'GeoNetwork'/'GeoServer' links in 'Zenodo' or 'EML' metadata). The use of standardized configuration files ('JSON' or 'YAML' formats) allow fully reproducible workflows to facilitate the work of data and information managers. Package: r-cran-geogam Architecture: all Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4750 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-mgcv, r-cran-grpreg, r-cran-mass Suggests: r-cran-raster, r-cran-sp Filename: pool/dists/resolute/main/r-cran-geogam_0.1-4-1.ca2604.1_all.deb Size: 4815036 MD5sum: d4ebbd282cd36f2d24c2223718361f5b SHA1: be2d17c69b1ee7415affa2768aecd00dbfa3fac3 SHA256: 43a8646608c7734dc9bf8a2c0bafdf1a0e068c122e8dcb7091b71d08081c46f9 SHA512: 3cc4a318b4ef6048ad6316b76e91d5f6950724cade47c771ad882d062e4fb9f8d6bef8fd8fe91e978b544fd01498a7ce51eac30111b706a559a06138bdf4fa47 Homepage: https://cran.r-project.org/package=geoGAM Description: CRAN Package 'geoGAM' (Select Sparse Geoadditive Models for Spatial Prediction) A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) . Package: r-cran-geogenr Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6855 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geomultistar, r-cran-httr, r-cran-readr, r-cran-rolap, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-dbi, r-cran-dbplyr, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-dm, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-rsqlite, r-cran-snakecase, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geogenr_2.0.1-1.ca2604.1_all.deb Size: 4856096 MD5sum: d90a021ef82999bb90b036660d8a57a4 SHA1: 032b1c20f4986d7cf2e555fc1043a83040908597 SHA256: 3822c761b9d6202bd19f6e6dbe55810c26e39ac63ca3b2db2eb4028b738a8465 SHA512: a22f235454b1a2ac95661dd1ca125dac126415895546ed903f17e2b907b570d50a3c19d2c9925a55cf4c727209d2a2c810603ab7de813b3e55e4cd04d36bfd2b Homepage: https://cran.r-project.org/package=geogenr Description: CRAN Package 'geogenr' (Generator from American Community Survey Geodatabases) The American Community Survey (ACS) offers geodatabases with geographic information and associated data of interest to researchers in the area. The goal of this package is to generate objects that allow us to access and consult the information available in various formats, such as in 'GeoPackage' format or in multidimensional 'ROLAP' (Relational On-Line Analytical Processing) star format. Package: r-cran-geohabnet Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-geosphere, r-cran-igraph, r-cran-terra, r-cran-yaml, r-cran-stringr, r-cran-memoise, r-cran-viridislite, r-cran-beepr, r-cran-rnaturalearth, r-cran-future.apply, r-cran-future, r-cran-magrittr, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-devtools, r-cran-knitr, r-cran-lintr, r-cran-mockthat, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geohabnet_2.2-1.ca2604.1_all.deb Size: 798604 MD5sum: 7aa3e2c7804323b17e49904e1d62edf8 SHA1: a184364c3e12db1ff73067f4766687540238a9f1 SHA256: c697c64e7b7177d9e0cb36c4e8be8c50622332d494b506bf6e504cf186a54e63 SHA512: 14356320a7134e94638292078967476f7350403687016fe36dc500a8cca3c08166306c94824dd7750ce540216ff9a07bae95696f4f6e690eab0c54a36aaeb647 Homepage: https://cran.r-project.org/package=geohabnet Description: CRAN Package 'geohabnet' (Geographical Risk Analysis Based on Habitat Connectivity) The 'geohabnet' package is designed to perform a geographically or spatially explicit risk analysis of habitat connectivity. Xing et al (2021) proposed the concept of cropland connectivity as a risk factor for plant pathogen or pest invasions. As the functions in 'geohabnet' were initially developed thinking on cropland connectivity, users are recommended to first be familiar with the concept by looking at the Xing et al paper. In a nutshell, a habitat connectivity analysis combines information from maps of host density, estimates the relative likelihood of pathogen movement between habitat locations in the area of interest, and applies network analysis to calculate the connectivity of habitat locations. The functions of 'geohabnet' are built to conduct a habitat connectivity analysis relying on geographic parameters (spatial resolution and spatial extent), dispersal parameters (in two commonly used dispersal kernels: inverse power law and negative exponential models), and network parameters (link weight thresholds and network metrics). The functionality and main extensions provided by the functions in 'geohabnet' to habitat connectivity analysis are a) Capability to easily calculate the connectivity of locations in a landscape using a single function, such as sensitivity_analysis() or msean(). b) As backbone datasets, the 'geohabnet' package supports the use of two publicly available global datasets to calculate cropland density. The backbone datasets in the 'geohabnet' package include crop distribution maps from Monfreda, C., N. Ramankutty, and J. A. Foley (2008) "Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022" and International Food Policy Research Institute (2019) "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4". Users can also provide any other geographic dataset that represents host density. c) Because the 'geohabnet' package allows R users to provide maps of host density (as originally in Xing et al (2021)), host landscape density (representing the geographic distribution of either crops or wild species), or habitat distribution (such as host landscape density adjusted by climate suitability) as inputs, we propose the term habitat connectivity. d) The 'geohabnet' package allows R users to customize parameter values in the habitat connectivity analysis, facilitating context-specific (pathogen- or pest-specific) analyses. e) The 'geohabnet' package allows users to automatically visualize maps of the habitat connectivity of locations resulting from a sensitivity analysis across all customized parameter combinations. The primary functions are msean() and sensitivity analysis(). Most functions in 'geohabnet' provide three main outcomes: i) A map of mean habitat connectivity across parameters selected by the user, ii) a map of variance of habitat connectivity across the selected parameters, and iii) a map of the difference between the ranks of habitat connectivity and habitat density. Each function can be used to generate these maps as 'final' outcomes. Each function can also provide intermediate outcomes, such as the adjacency matrices built to perform the analysis, which can be used in other network analysis. Refer to article at to see examples of each function and how to access each of these outcome types. To change parameter values, the file called 'parameters.yaml' stores the parameters and their values, can be accessed using 'get_parameters()' and set new parameter values with 'set_parameters()'. Users can modify up to ten parameters. Package: r-cran-geoheatmap Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4948 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geofacet, r-cran-statebins, r-cran-ggplot2, r-cran-plotly, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-viridislite, r-cran-testthat, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-geoheatmap_0.1.0-1.ca2604.1_all.deb Size: 1811356 MD5sum: 5f4fd04e839872636b37881c0773f445 SHA1: 0ab84606564d1be21372a6ba750154d1ec7af6d2 SHA256: 17cd65735846307a1f023b6d44bfa6786b072b788ca27dbf44467e1e2a439c76 SHA512: 46884b005ed386b29c753ac499a5dc00770ec119556ced44200f09a35c6d2564e6b186c4f5b114a8f8e8b3178d6c7c7bce92870cf80fb3f9c08c899af4bab2e8 Homepage: https://cran.r-project.org/package=geoheatmap Description: CRAN Package 'geoheatmap' (Create Geospatial Cartogram Heatmaps) The functionality provided by this package is an expansion of the code of the 'statebins' package, created by B. Rudis (2022), . It allows for the creation of square choropleths for the entire world, provided an appropriate specified grid is supplied. Package: r-cran-geojson Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-jsonlite, r-cran-protolite, r-cran-jqr, r-cran-magrittr, r-cran-lazyeval Suggests: r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-stringi, r-cran-covr Filename: pool/dists/resolute/main/r-cran-geojson_0.3.5-1.ca2604.1_all.deb Size: 1026988 MD5sum: c569d24c0e224eb021676a6fb072e5a2 SHA1: f5da50e3a7742d8b8ce00931a94b3b3a6c970ce6 SHA256: 0b6493799b959f5bcacdede306676012640c6b839640a0ebd57994e2c3484c35 SHA512: 2e19a70a03500d68e11e43b175b89822a4cf089ee73708c8738772d62b08cfa3129c367a5c123db3146ac7899b838e51193f758e6c7491353a62c13ee78fcc85 Homepage: https://cran.r-project.org/package=geojson Description: CRAN Package 'geojson' (Classes for 'GeoJSON') Classes for 'GeoJSON' to make working with 'GeoJSON' easier. Includes S3 classes for 'GeoJSON' classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited 'GeoJSON'; and serializing to/from 'Geobuf' binary 'GeoJSON' format. Package: r-cran-geojsonio Architecture: all Version: 0.11.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2501 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-geojson, r-cran-geojsonsf, r-cran-jqr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-sf, r-cran-sp, r-cran-v8 Suggests: r-cran-covr, r-cran-dbi, r-cran-gistr, r-cran-leaflet, r-cran-maps, r-cran-rpostgres, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-geojsonio_0.11.3-1.ca2604.1_all.deb Size: 716172 MD5sum: e526dfdbaf04f10a6ddf1933ef526ce5 SHA1: 5e5d2d8e7ef050bc7b93db3df75964844a4a7e32 SHA256: a9ece26d0a7696d89a3c002f267287ec2c517cef600f3c6b6fbf44dd276c50a3 SHA512: 1253032c4d2c038527daf0fd73209e3fe5e9321fe3140837b83d1a09ee28abd90aad35a96cb32530721bfc44b717a4d27468a1f0d1efc274fe3758186cb9cc58 Homepage: https://cran.r-project.org/package=geojsonio Description: CRAN Package 'geojsonio' (Convert Data from and to 'GeoJSON' or 'TopoJSON') Convert data to 'GeoJSON' or 'TopoJSON' from various R classes, including vectors, lists, data frames, shape files, and spatial classes. 'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'. Package: r-cran-geomapdata Architecture: all Version: 2.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1277 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-geomap Filename: pool/dists/resolute/main/r-cran-geomapdata_2.0-2-1.ca2604.1_all.deb Size: 1269528 MD5sum: 7e5e17379f419fdd03e76a7d4b292046 SHA1: efc6a51f64fbc769085a6b614bb3816e1020994e SHA256: 8a5c15e9722b60e8f41ebef3b0600370bdf419711f42fa323227a3c9d483b55a SHA512: bbac755b820657a1abf4887e339ad7b9c5b1ad0b87abdedfa89130c36a35d7f2a1bc6758bcbd563ac5d2cfe600d096dec6489e27a83c543d8f282dd5042e4a45 Homepage: https://cran.r-project.org/package=geomapdata Description: CRAN Package 'geomapdata' (Data for Topographic and Geologic Mapping) Data sets included here are for use with package GEOmap. These include world map, USA map, Coso map, Japan Map. Package: r-cran-geomarchetypal Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-geometry, r-cran-archetypal, r-cran-doparallel, r-cran-plot3d, r-cran-distances, r-cran-rlang, r-cran-magrittr, r-cran-dplyr, r-cran-mirai, r-cran-abind, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-geomarchetypal_1.0.3-1.ca2604.1_all.deb Size: 572178 MD5sum: bbf056801ae4a3f3d6536a30f3679a19 SHA1: 4ddcc21dfe7c0feff965e254a1da5af87358ee84 SHA256: 9a0c76904b4e0129110c459a13af68e74ca3f52c0f852efe35bd486613f4d90b SHA512: 925fa51adbd3e24db1b68b39ade2990425299b41bef6ac66d6734464855bbbe8a8f929b6ef2d7c3435270ede62f429c2b8db30741f90b894b1e1aa0a86a34a4a Homepage: https://cran.r-project.org/package=GeomArchetypal Description: CRAN Package 'GeomArchetypal' (Finds the Geometrical Archetypal Analysis of a Data Frame) Performs Geometrical Archetypal Analysis after creating Grid Archetypes which are the Cartesian Product of all minimum, maximum variable values. Since the archetypes are fixed now, we have the ability to compute the convex composition coefficients for all our available data points much faster by using the half part of Principal Convex Hull Archetypal method. Additionally we can decide to keep as archetypes the closer to the Grid Archetypes ones. Finally the number of archetypes is always 2 to the power of the dimension of our data points if we consider them as a vector space. Cutler, A., Breiman, L. (1994) . Morup, M., Hansen, LK. (2012) . Christopoulos, DT. (2024) . Package: r-cran-geomaroc Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geomaroc_0.1.1-1.ca2604.1_all.deb Size: 38196 MD5sum: 55028b5a5a903c2c2b0b83b5747644f0 SHA1: 4a165afbba194813e7f9d64d03001e680ee23423 SHA256: c25c7b2265da296f58067e216248aee403b34d273d7b1eabb031ee5a5b64c5a9 SHA512: 37399230e764fae14fdfdcd2de1d12da790ea5511af16c9d93f942fb7f0c0a2f26131a684a4edbd5fac62fcab604af8125340594cc3876ddca6ca948ed255de8 Homepage: https://cran.r-project.org/package=geomaroc Description: CRAN Package 'geomaroc' (Easily Visualize Geographic Data of Morocco) Tools to easily visualize geographic data of Morocco. This package interacts with data available through the 'geomarocdata' package, which is available in a 'drat' repository. The size of the 'geomarocdata' package is approximately 12 MB. Package: r-cran-geomerge Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-spdep, r-cran-raster, r-cran-terra, r-cran-sf, r-cran-geosphere, r-cran-lubridate, r-cran-ggplot2, r-cran-gridextra, r-cran-scales Filename: pool/dists/resolute/main/r-cran-geomerge_0.3.4-1.ca2604.1_all.deb Size: 1635352 MD5sum: 1c86fba8d37fa4123c82675558897f90 SHA1: fcb10c71c5d5a773ab8e904151d2e5a3f957f653 SHA256: 83c2f6592abc591782adbbf8c9d5969647e9bf86c580cee93e7e88a4b5230978 SHA512: fb336232b8d6b610118f97ee537769ed4728a9a38ceef6c2f4ca9f8436d2b4ffe7ca149acf604df3e82dfaff4716f80b93c5a385f31f6c4e87cb06d311223f29 Homepage: https://cran.r-project.org/package=geomerge Description: CRAN Package 'geomerge' (Geospatial Data Integration) Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution. Package: r-cran-geometa Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 22075 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-xml, r-cran-httr, r-cran-jsonlite, r-cran-keyring, r-cran-readr, r-cran-crayon Suggests: r-cran-sf, r-cran-ncdf4, r-cran-eml, r-cran-emld, r-cran-units, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-geometa_0.9.3-1.ca2604.1_all.deb Size: 9522684 MD5sum: 575c5bba7e7d43e7ae69db8f235ee8fa SHA1: 202f9c5b7f0d837060911f49b915967ccec41185 SHA256: 6cb2ab3873add19d4e0759996de3eae6ba9e0adbaa3345d68f7634a688a517ec SHA512: 2fb8c272fd856dcd151832a5c57d5a9e4dba24debdc5aeb8b2fd3f385d770237cdc9c1641cc990ba31408504ae1f62cdc07bbcfa4807a01a05d643ea59652121 Homepage: https://cran.r-project.org/package=geometa Description: CRAN Package 'geometa' (Tools for Reading and Writing ISO/OGC Geographic Metadata) Provides facilities to read, write and validate geographic metadata defined with ISO TC211 / OGC ISO geographic information metadata standards, and encoded using the ISO 19139 and ISO 19115-3 (XML) standard technical specifications. This includes ISO 19110 (Feature cataloguing), 19115 (dataset metadata), 19119 (service metadata) and 19136 (GML). Other interoperable schemas from the OGC are progressively supported as well, such as the Sensor Web Enablement (SWE) Common Data Model, the OGC GML Coverage Implementation Schema (GMLCOV), or the OGC GML Referenceable Grid (GMLRGRID). Package: r-cran-geometricmorphometricsmix Architecture: all Version: 0.6.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-mclust Suggests: r-cran-ape, r-cran-morpho, r-cran-geometry, r-cran-nlshrink, r-cran-lmf, r-cran-mass, r-cran-clustergeneration, r-cran-ggplot2, r-cran-rmpfr, r-cran-knitr, r-cran-rmarkdown, r-cran-phytools, r-cran-mvmorph, r-cran-testthat, r-cran-future, r-cran-future.apply Filename: pool/dists/resolute/main/r-cran-geometricmorphometricsmix_0.6.1.1-1.ca2604.1_all.deb Size: 273628 MD5sum: 6d49f4ce894a14fbcb5f0b1f8f9c0357 SHA1: 3d4d0f74fe8eaa9b338fff8bb584b665c6729a91 SHA256: 562a91e22882bc3d7f6d7902ba9971f2985ac54c1b6c9eccd41d8d9b77d84106 SHA512: 6e39ca44f44985fda1eed5ebe42ed6002adff0bbd7a352d7efa14858924cc454fe009433209a48e5580f51861a4f7772c80def4886fb710d98e207ff9f7d73f6 Homepage: https://cran.r-project.org/package=GeometricMorphometricsMix Description: CRAN Package 'GeometricMorphometricsMix' (Heterogeneous Methods for Shape and Other Multidimensional Data) Tools for geometric morphometric analyses and multidimensional data. Implements methods for morphological disparity analysis using bootstrap and rarefaction, as reviewed in Foote (1997) . Includes integration and modularity testing, following Fruciano et al. (2013) , using Escoufier's RV coefficient as test statistic as well as two-block partial least squares - PLS, Rohlf and Corti (2000) . Also includes vector angle comparisons, orthogonal projection for data correction (Burnaby (1966) ; Fruciano (2016) ), and parallel analysis for dimensionality reduction (Buja and Eyuboglu (1992) ). Package: r-cran-geomod Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-caret, r-cran-rastervis, r-cran-randomforest, r-cran-arm, r-cran-kernlab, r-cran-e1071, r-cran-cubist, r-cran-rpart, r-cran-ranger, r-cran-qrnn, r-cran-quantregforest, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-geomod_0.1.0-1.ca2604.1_all.deb Size: 111902 MD5sum: f839547d53567ce70ec72dda399618a1 SHA1: feced529d47e1481c342ff8cbe470d53090ae4a8 SHA256: 1ff589032bd2246d54c661d21f96dceacb11c73686563d941fa332d918d2bb86 SHA512: ac25aff6aaa83f05c9f6765bb9edd4bd42add777ced06fe969a25bd69a03860f657a1a51812c71ae177176b9f01d8aca7f56a289f064f94edba6d243da882b8b Homepage: https://cran.r-project.org/package=geomod Description: CRAN Package 'geomod' (A Computer Program for Geotechnical Investigations) The 'geomod' does spatial prediction of the Geotechnical soil properties. It predicts the spatial distribution of Geotechnical properties of soil e.g. shear strength, permeability, plasticity index, Standard Penetration Test (SPT) counts, etc. The output of the prediction takes the form of a map or a series of maps. It uses the interpolation technique where a single or statistically “best” estimate of spatial occurrence soil property is determined. The interpolation is based on both the sampled data and a variogram model for the spatial correlation of the sampled data. The single estimate is produced by a Kriging technique. Package: r-cran-geomongo Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-r6, r-cran-geojsonr, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-geomongo_1.0.3-1.ca2604.1_all.deb Size: 302882 MD5sum: 9c8dca1dd3efcfd16875b310c199cae1 SHA1: 37e745ac093439f16827ea83ab2d76b9497d031e SHA256: e3238e4eb85fe426b1fdb8726ac7bdfea40189f7d2f6a4f77c2f1359f4cf51f2 SHA512: 68266f834ce106715af35b9087dd53174b745f3b5001b129d02c40b0e49ebf727ad1f742cd5b9a4de2cdd4032bc6695277d808b8c7b1ff01b64f2bd69860b2c2 Homepage: https://cran.r-project.org/package=GeoMongo Description: CRAN Package 'GeoMongo' (Geospatial Queries Using 'PyMongo') Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions. Package: r-cran-geomorph Architecture: all Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1961 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrpp, r-cran-matrix, r-cran-jpeg, r-cran-ape, r-cran-ggplot2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-geomorph_4.1.0-1.ca2604.1_all.deb Size: 1872752 MD5sum: 3c5874d8a97103e1773b896aec6e2419 SHA1: a96854448471b5c8efc1915ca6b7e934c78d57d7 SHA256: fedfc723303ed24155902f29085443c29d85fea4c05efedfec25c29437792d83 SHA512: 21d0ea8bf2a92fab177a090d9c730db9f3ee9c48bcc3a0e3b134c1967e97d56d1460a98730cea4b72b2e58dfb4c83ddfb7f256f3545ecc1c1d4020bc164e0844 Homepage: https://cran.r-project.org/package=geomorph Description: CRAN Package 'geomorph' (Geometric Morphometric Analyses of 2D and 3D Landmark Data) Read and manipulate landmark data, generate shape variables via Procrustes analysis for points, curves and surfaces, perform shape analyses, and provide graphical depictions of shapes and patterns of shape variation. 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Curved text makes it easier to directly label paths or neatly annotate in polar co-ordinates. Package: r-cran-geomultistar Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-purrr, r-cran-rlang, r-cran-rsqlite, r-cran-sf, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geomultistar_1.2.2-1.ca2604.1_all.deb Size: 744566 MD5sum: 112e65bae148f6930411a41d703e96ea SHA1: 04bcbe4e2348c763d25d86ded05b54cf80e8b295 SHA256: ae14071fdc581463264843837a3cdccac035c20ae7daeef4bda2dd709a97eafb SHA512: 55e78dfe03439ae40246529c82b50a53a595024ae01018e0f470122a327fa5d4e72e2306f40b455f81318c24a6674c97632bc4aa694e34c8bbea67e456a41720 Homepage: https://cran.r-project.org/package=geomultistar Description: CRAN Package 'geomultistar' (Multidimensional Queries Enriched with Geographic Data) Multidimensional systems allow complex queries to be carried out in an easy way. The geographical dimension, together with the temporal dimension, plays a fundamental role in multidimensional systems. Through this package, vector geographic data layers can be associated to the attributes of geographic dimensions, so that the results of multidimensional queries can be obtained directly as vector layers. The multidimensional structures on which we can define the queries can be created from a flat table or imported directly using functions from this package. Package: r-cran-geonames Architecture: all Version: 0.999-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson Filename: pool/dists/resolute/main/r-cran-geonames_0.999-1.ca2604.1_all.deb Size: 64302 MD5sum: 6ebcef0b07679795c938fae8e3c15219 SHA1: 4860de5c512595ce48ad149eb880ae93a528f18f SHA256: c0cde7b914c73a803530189d959fdb14c23a98104939b0198110d02c5b5d8c61 SHA512: 30b604668f177b45ab2fad9791ab6500a8000e5dd9ef9812b02e1bb6e1117f7649114f61a9312d7711ffd01bbaaca2babe2315c2cc19231680aea0e1a553c01a Homepage: https://cran.r-project.org/package=geonames Description: CRAN Package 'geonames' (Interface to the "Geonames" Spatial Query Web Service) The web service at provides a number of spatial data queries, including administrative area hierarchies, city locations and some country postal code queries. A (free) username is required and rate limits exist. Package: r-cran-geonapi Architecture: all Version: 0.8-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometa, r-cran-keyring, r-cran-r6, r-cran-openssl, r-cran-httr, r-cran-xml, r-cran-plyr Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-geonapi_0.8-1-1.ca2604.1_all.deb Size: 389230 MD5sum: 09fe5aad3008efe8696518400cb16575 SHA1: 3c6cf5e4d83c11d67e7d34811000f8e58b5b2b83 SHA256: 4458ec7698167802298c2dca227280940a14e2bb05da598026780f2b1142f88a SHA512: e0e5fe80ae067bdd052fe3be3a757912809d9b8dc34677d5e9f93e081faecc66896b9dbd9720360de8971d56954abb72be0d1d1d5336860e14b2dcf7e1e8605d Homepage: https://cran.r-project.org/package=geonapi Description: CRAN Package 'geonapi' ('GeoNetwork' API R Interface) Provides an R interface to the 'GeoNetwork' API () allowing to upload and publish metadata in a 'GeoNetwork' web-application and expose it to OGC CSW. Package: r-cran-geonetwork Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-igraph, r-cran-sf Suggests: r-cran-devtools, r-cran-knitr, r-cran-maps, r-cran-mapview, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spdata, r-cran-testthat, r-cran-tmaptools Filename: pool/dists/resolute/main/r-cran-geonetwork_0.6.0-1.ca2604.1_all.deb Size: 95972 MD5sum: d0f46d7c7e135fb446fd2eccd024e979 SHA1: 860749809deb56e9c614663c3318de15a6a642e6 SHA256: a4c21c9385fb97cfc95a956bf61d79037ce47680ed21528221fbe5212f0f903a SHA512: 0d76d00b09d5d3c73f77437164b8c30d9e20f7ce5d8bc03eed41689563aab9c4009bd2478ec467af038c215c612e9cf9f5fae146224cb649bd5b664bd8864a95 Homepage: https://cran.r-project.org/package=geonetwork Description: CRAN Package 'geonetwork' (Geographic Networks) Provides classes and methods for handling networks or graphs whose nodes are geographical (i.e. locations in the globe). 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Package: r-cran-geonode4r Architecture: all Version: 0.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-openssl, r-cran-httr, r-cran-keyring, r-cran-readr Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-covr, r-cran-shiny, r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-geonode4r_0.1-2-1.ca2604.1_all.deb Size: 161062 MD5sum: cbaa2b47b4232eaae336ea80a1907d22 SHA1: 5347c61110592223c9e9b4cec0ef3e6b93bb586f SHA256: 3d6bdffc3c66f50bb4a9e361fc44b26caa20198c11d30a10d950a1c206ce7b07 SHA512: f986d407ad2d0d52326a62362a1f0d00012fe95cf42b252733c1b14f1c9f18db81bb5b2c30888d31634ac81ea1530f496ebb0f7991b1436a30b00bd59e6ef9ea Homepage: https://cran.r-project.org/package=geonode4R Description: CRAN Package 'geonode4R' (Interface to 'GeoNode' REST API) Provides an interface to the 'GeoNode' API, allowing to upload and publish metadata and data in 'GeoNode'. For more information about the 'GeoNode' API, see . Package: r-cran-geonuts Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eurostat, r-cran-ggplot2, r-cran-giscor, r-cran-units, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geonuts_1.0.1-1.ca2604.1_all.deb Size: 196782 MD5sum: a1a7902e9aab0a483edc9640ea313650 SHA1: 8c62697422d7c8ba13c5f52fbf13bf295649f0f3 SHA256: 3fca24b449cc42e1139e8143581b00d43f50b40aacef6945ee8a16514a4b4214 SHA512: 97e44eee93b74c7d0a42921ff81f8e493bccdc6ba58a160681d3d545b3e9f3460f4284965588eca20805108d17a9c01659de46e27aff427746355161f37625b9 Homepage: https://cran.r-project.org/package=geonuts Description: CRAN Package 'geonuts' (Identification and Visualisation of European NUTS Regions fromGeolocations) Provides functions to identify European NUTS (Nomenclature of Territorial Units for Statistics) regions for geographic coordinates (latitude/longitude) using Eurostat geospatial boundaries. Includes map-based visualisation of the matched regions for validation and exploration. Designed for regional data analysis, reproducible workflows, and integration with common geospatial R packages. Package: r-cran-geoperu Architecture: all Version: 0.0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geoperu_0.0.0.2-1.ca2604.1_all.deb Size: 404728 MD5sum: a762823a81fcd4a11c0e912bd1111024 SHA1: c76856c525b39f8855a3b799f59f4ce00963125b SHA256: f2218cfd5bc6023614989a68def2e3a3fa1af8fd4857c61ac351d232d657a111 SHA512: a73ffb1b9f81d592c0d0f5f90d48d286f02e73e1f21779b58695793d45515bd3e61aa98eb5bb91a7f7130e5a5eca0fd11a387344b298279230b66bc817082a84 Homepage: https://cran.r-project.org/package=geoperu Description: CRAN Package 'geoperu' (Download Spatial Datasets of Peru) Provides convenient access to the official spatial datasets of Peru as 'sf' objects in R. This package includes a wide range of geospatial data covering various aspects of Peruvian geography, such as: administrative divisions (Source: INEI ), protected natural areas (Source: GEO ANP - SERNANP ). All datasets are harmonized in terms of attributes, projection, and topology, ensuring consistency and ease of use for spatial analysis and visualization. Package: r-cran-geoprofiler Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-tectonicr, r-cran-terra, r-cran-units Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyterra Filename: pool/dists/resolute/main/r-cran-geoprofiler_0.0.3-1.ca2604.1_all.deb Size: 585642 MD5sum: 0d90e9d3ffff707ed08a7ee4b892f997 SHA1: fd3563663baa6544f9bb7c5c8c8fdacbee8de096 SHA256: 49da445ab4736d54881db5c29cfb867a0235d82981462d7432becdd016b816ea SHA512: fdee4228aa20bd5a1207a23a9ce9372396a09e9b2824cc45fc5a3a630bd46312546572dd299a0fc0b783aff9b5691917db4e8702d59251d113b8fd121d400790 Homepage: https://cran.r-project.org/package=geoprofiler Description: CRAN Package 'geoprofiler' (Perpendicular Line Transects for Geosciences) Toolset to create perpendicular profile graphs and swath profiles. Method are based on coordinate rotation algorithm by Schaeben et al. (2024) . Package: r-cran-geopsych Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/resolute/main/r-cran-geopsych_0.1.0-1.ca2604.1_all.deb Size: 47304 MD5sum: a3c58bc34da624d12f9ed4f461e146dc SHA1: 5f9bff7eb5d6b38d733428bc1dc17b39a09a9444 SHA256: 3a7a719f45e106f8c2e2f5e9a1c37921bf158f77cebf034adec170b404c38bc4 SHA512: b793bbffee41ca4a69f25c185907bcc6720fa927aede855a3d42e6ac16d21753039c66b7d8918a57a14071474f7f94a4533894c1677d60d8ad3b7e03f1b60304 Homepage: https://cran.r-project.org/package=geopsych Description: CRAN Package 'geopsych' (Methods of Applied Psychology and Psychometrics in GeographicalAnalysis) Integrating applied psychological and psychometric methods into geographical analysis. With the emergence of geo-referenced questionnaires, spatially explicit psychological and psychometric methods can offer a geographically contextualised approach that reflects latent traits and processes at a more local scale, leading to more tailored research and decision-making processes. The implemented methods include Geographically Weighted Cronbach's alpha and its bandwidth selection. See Zhang & Li (2025) . Package: r-cran-georange Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-proj4, r-cran-raster, r-cran-moments, r-cran-velociraptr Filename: pool/dists/resolute/main/r-cran-georange_0.1.0-1.ca2604.1_all.deb Size: 158558 MD5sum: b1631d883c81b94dee6e87abc71a0a34 SHA1: 174684e2e4ddbecd513136bc84786674dc0c022f SHA256: 8acb6bfcd80708555a5d6d1059e444b0b73fd32cdc7bac16ec4e36b8c2074005 SHA512: f788fbc3373c054c1b87d4b7e551a0a423efbd1221b844905db5b680f27784398e9686810e602e2acc21873166c3d07d71fea18fb5b161f628beaf350349efb6 Homepage: https://cran.r-project.org/package=GeoRange Description: CRAN Package 'GeoRange' (Calculating Geographic Range from Occurrence Data) Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied. Package: r-cran-georefdatar Architecture: all Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-spelling, r-cran-testthat, r-cran-readxl, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-georefdatar_0.6.5-1.ca2604.1_all.deb Size: 230286 MD5sum: 94f5b6eb45acc4bcb200b172f05338d6 SHA1: 606fa37f506dd3acb2eabe4e845816708cd70bdd SHA256: c1d6b122ddf486b9837c3f1858f9944845d0265d1085392c44bca97816c69371 SHA512: 322e0838b51161e6c96285e30bf529be6246ab59d2781eb022409eceaad55b230ec0a5cd10891e8e63ce5db38bbc4046be3a23282d955eb47f5d9a0f24180038 Homepage: https://cran.r-project.org/package=georefdatar Description: CRAN Package 'georefdatar' (Geosciences Reference Datasets) Reference datasets commonly used in the geosciences. 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Package: r-cran-georob Architecture: all Version: 0.3-23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-abind, r-cran-constrainedkriging, r-cran-fields, r-cran-lmtest, r-cran-nlme, r-cran-nleqslv, r-cran-quantreg, r-cran-robustbase, r-cran-snowfall Suggests: r-cran-gstat, r-cran-multcomp, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-georob_0.3-23-1.ca2604.1_all.deb Size: 2203324 MD5sum: debf3d9472215fcd475eb242c14b156a SHA1: 9156ba1ef2dd2f6261cc3c7d28909d357347909f SHA256: 207b34887c730dc58f0ecb86f564b82620f2acb39a945e603fe183fefd90732f SHA512: 0b232f611e9669f173aa9bb3a019406dbe625fea927e1c3a01e48330a10bc4e5ac4f94890701ef10a565f79c4f381cdc6f9df2aab9c1bfa931f318d3027a521a Homepage: https://cran.r-project.org/package=georob Description: CRAN Package 'georob' (Robust Geostatistical Analysis of Spatial Data) Provides functions for efficiently fitting linear models with spatially correlated errors by robust (Kuensch et al. (2011) ) and Gaussian (Harville (1977) ) (Restricted) Maximum Likelihood and for computing robust and customary point and block external-drift Kriging predictions (Cressie (1993) ), along with utility functions for variogram modelling in ad hoc geostatistical analyses, model building, model evaluation by cross-validation, (conditional) simulation of Gaussian processes (Davies and Bryant (2013) ), unbiased back-transformation of Kriging predictions of log-transformed data (Cressie (2006) ). Package: r-cran-geosae Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-geosae_0.1.0-1.ca2604.1_all.deb Size: 37898 MD5sum: 69f3535cc1cae6bd84c49cd20b8ac00b SHA1: dd8fc81ce1dc8d8a2c6d819aa49257eba6b07d78 SHA256: 5da0acc24c950b37edee88fe635cbe4336d7331496c43eb4079a3735354be1f5 SHA512: 408df00d0c2e3914d785cb395f412660ea0dde9a153d917dd97a1f6c098f6af79394672390f8d1ee0c1c3a42fd8bf713bddb1ff278a7bf29dd9dc21d954ba8b1 Homepage: https://cran.r-project.org/package=geoSAE Description: CRAN Package 'geoSAE' (Geoadditive Small Area Model) This function is an extension of the Small Area Estimation (SAE) model. Geoadditive Small Area Model is a combination of the geoadditive model with the Small Area Estimation (SAE) model, by adding geospatial information to the SAE model. This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016), and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018). Package: r-cran-geosapi Architecture: all Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cli, r-cran-openssl, r-cran-httr, r-cran-xml2, r-cran-magrittr, r-cran-keyring, r-cran-readr Suggests: r-cran-testthat, r-cran-waldo, r-cran-roxygen2, r-cran-shiny, r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-geosapi_0.8-1.ca2604.1_all.deb Size: 1929908 MD5sum: c6e9e33192e6ecc0833f9795d150fdf5 SHA1: 0f47e262334f21f45449da0944f02f4374cf838d SHA256: a1c6230149da1e8fc2b077fc8ae177d4660ebd51d923d5e2746b48bf843f5ff4 SHA512: fdbbbdfdbe00a6bba16fc18e4e80cd659b5ce6a402eefc8122b4820c8a3e1a600e8971e08ae41d58762e13c8edffcdcdd3bca017b4fdf99784a17658bd6e195b Homepage: https://cran.r-project.org/package=geosapi Description: CRAN Package 'geosapi' (GeoServer REST API R Interface) Provides an R interface to the GeoServer REST API, allowing to upload and publish data in a GeoServer web-application and expose data to OGC Web-Services. The package currently supports all CRUD (Create,Read,Update,Delete) operations on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes, layers, styles, as well as vector data upload operations. For more information about the GeoServer REST API, see . Package: r-cran-geoscale Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-geoscale_2.0.1-1.ca2604.1_all.deb Size: 94998 MD5sum: b6f07b87e79c4e76643b1ae76ef2a910 SHA1: 80b795a1fe0539a344b6cedf12fbc93fbbc195a5 SHA256: 7a0011a4a903272164dbcf4391e16fa03322c214658eafa57af51ea34c969b17 SHA512: f4a8c08b14c2e7203534093cf94d3e8e71ca6c31d052c2ce0f53cd406217216a7c334a0090aa8a3f368ff44b8684309b5d1a69603651e52cafc8dc968b6b4141 Homepage: https://cran.r-project.org/package=geoscale Description: CRAN Package 'geoscale' (Geological Time Scale Plotting) Functionality for adding the geological timescale to bivariate plots. Package: r-cran-geosimilarity Architecture: all Version: 3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-magrittr, r-cran-ggrepel Suggests: r-cran-cowplot, r-cran-viridis, r-cran-car, r-cran-desctools, r-cran-performanceanalytics, r-cran-testthat, r-cran-sdsfun, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-geosimilarity_3.9-1.ca2604.1_all.deb Size: 1470108 MD5sum: c289858c841531c0aa75ce4586265273 SHA1: 5897edf72deee8b5e0619a0300a877fcc31ffb0f SHA256: a51b69a3fbd51d9eb140f227f0fe32364a2eb0731b7c2b3ea72dc86cebfca309 SHA512: 3491121368bd3bf22f69bd18fca229d089fb5366abed95063599fe6a068d8ed10e077d534cd0d56936f10af056f159bbc882f522bf043e5151f713289ea745bb Homepage: https://cran.r-project.org/package=geosimilarity Description: CRAN Package 'geosimilarity' (Geographically Optimal Similarity) Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) . GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) . GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty. Package: r-cran-geospark Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparklyr, r-cran-dplyr, r-cran-dbplyr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-geospark_0.3.1-1.ca2604.1_all.deb Size: 20264 MD5sum: 621aea86b4f1eb66093b1955aaaadd97 SHA1: 9eeee5326daddec94edc5aa0931f305f64af9d61 SHA256: f71e0282282a81fb03ef78d80984effce75799944ab9d03b79c55472dfc1e959 SHA512: bdac00ae7b42ba07fc5bbb7dbe8ef0d64947d555d4d1d3d5f2641a3f0d651e820b50ce72bcc7d6553164f97d95a603f8885af9e1aa4509a0816665a31b3d8ce3 Homepage: https://cran.r-project.org/package=geospark Description: CRAN Package 'geospark' (Bring Local Sf to Spark) R binds 'GeoSpark' extending 'sparklyr' R package to make distributed 'geocomputing' easier. Sf is a package that provides [simple features] access for R and which is a leading 'geospatial' data processing tool. 'Geospark' R package bring the same simple features access like sf but running on Spark distributed system. Package: r-cran-geospatialsuite Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2322 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-leaflet, r-cran-magrittr, r-cran-mice, r-cran-rcolorbrewer, r-cran-rnaturalearth, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-tigris, r-cran-viridis Suggests: r-cran-knitr, r-cran-pkgnet, r-cran-rmarkdown, r-cran-kableextra, r-cran-nhdplustools, r-cran-zipcoder, r-cran-tidygeocoder, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geospatialsuite_0.2.0-1.ca2604.1_all.deb Size: 1080726 MD5sum: 675a74ae5707959a7b31af13eaffaea3 SHA1: 250dc1e882a0c8d4d2b319d961ef8d2ab055222a SHA256: fbe3290869a704e7d3cc593a89d56f84212d183da826296b18373680f9d45e7c SHA512: d1d55a5483d38c3bd406e40984f3eae7ea762976ea48ab785c68db833bd708c620a150b14eb6bd702aaf9acfc37f45423fc784b4041f57c5035c69cf079b4693 Homepage: https://cran.r-project.org/package=geospatialsuite Description: CRAN Package 'geospatialsuite' (Comprehensive Geospatiotemporal Analysis and MultimodalIntegration Toolkit) A comprehensive toolkit for geospatiotemporal analysis featuring 60+ vegetation indices, advanced raster visualization, universal spatial mapping, water quality analysis, CDL crop analysis, spatial interpolation, temporal analysis, and terrain analysis. Designed for agricultural research, environmental monitoring, remote sensing applications, and publication-quality mapping with support for any geographic region and robust error handling. Methods include vegetation indices calculations (Rouse et al. 1974), NDVI and enhanced vegetation indices (Huete et al. 1997) , (Akanbi et al. 2024) , spatial interpolation techniques (Cressie 1993, ISBN:9780471002556), water quality indices (McFeeters 1996) , and crop data layer analysis (USDA NASS 2024) . Funding: This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. Package: r-cran-geospt Architecture: all Version: 1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-genalg, r-cran-mass, r-cran-sp, r-cran-minqa, r-cran-fields, r-cran-gsl, r-cran-plyr, r-cran-teachingdemos, r-cran-sgeostat Filename: pool/dists/resolute/main/r-cran-geospt_1.0-6-1.ca2604.1_all.deb Size: 539326 MD5sum: a19feb99d418c5ade16531106ecbae9b SHA1: 3d3d76eebdac72e40826e9e569931bad468bc0a8 SHA256: d72482cba3057b1f50303a22ef50e1503c634657c1fd4cd4877354d024f8e5a9 SHA512: d7fb09a236a42334979e0a86226558e47ab3f542c6bd9d37a99d998aa4f1f7b6df518471ab274932175750f0e70a7b1ecc6f81b78beb6418ff17071ba88c8ac8 Homepage: https://cran.r-project.org/package=geospt Description: CRAN Package 'geospt' (Geostatistical Analysis and Design of Optimal Spatial SamplingNetworks) Estimation of the variogram through trimmed mean, radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, pocket plot, and design of optimal sampling networks through sequential and simultaneous points methods. Package: r-cran-geosptdb Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmatch, r-cran-fields, r-cran-sp, r-cran-mass, r-cran-minqa, r-cran-gsl, r-cran-geospt Filename: pool/dists/resolute/main/r-cran-geosptdb_1.0-3-1.ca2604.1_all.deb Size: 467936 MD5sum: ed4852fb74ff7cd0115b3710edae49e0 SHA1: f2918eaea829849eb2b5c8926dfad9f21bf114d8 SHA256: 60a656e71571477f6edd1162f45f0454bf23854d8c943c03488d92dc696651f0 SHA512: 81df78a0f411215fc42e7d8772e66f0db52759b0fa96e263141b0786eab5e02b92fffb2f4c7738d66671431869041722f8ff9da4b3c1ccb1e212872d2257116e Homepage: https://cran.r-project.org/package=geosptdb Description: CRAN Package 'geosptdb' (Spatio-Temporal Radial Basis Functions with Distance-BasedMethods (Optimization, Prediction and Cross Validation)) Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance. Package: r-cran-geostats Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 932 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-geostats_1.6-1.ca2604.1_all.deb Size: 899920 MD5sum: 2aef7cfbb228085384d11af22d0f9068 SHA1: f8b2697dee93b1b170e50c92a2ce09331db5bdf0 SHA256: c1079255c36528a9599202db4fda34a1ad5874c69f9abecd9dd89a349a3d5534 SHA512: 80499346885e178727dd644f9629c2bc470a2eec398c367348c6c12f12d961a6009031127542d7173cee2dd2c41859b366aa63bbe05b02c937c2ad740bbd07ae Homepage: https://cran.r-project.org/package=geostats Description: CRAN Package 'geostats' (An Introduction to Statistics for Geoscientists) A collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as 'compositions', 'robCompositions', 'sp', 'gstat' and 'RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at . Package: r-cran-geotargets Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-targets, r-cran-rlang, r-cran-cli, r-cran-terra, r-cran-withr, r-cran-zip, r-cran-lifecycle, r-cran-gdalraster Suggests: r-cran-crew, r-cran-knitr, r-cran-ncmeta, r-cran-rmarkdown, r-cran-sf, r-cran-stars, r-cran-testthat, r-cran-fs, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-geotargets_0.3.1-1.ca2604.1_all.deb Size: 598624 MD5sum: 835a5492f9969206a2bb7243a888aae3 SHA1: 0baaca398bff1624308746aa564ecef98f0cfb24 SHA256: ad014d72ba67d57701d5d39a9b67a670f79f39ff1b6c0b536f51a3e3e402d89a SHA512: f775d56015591191d0f7d3a7deed2ff8772499c00eec56f5ad6c47f54f554971c6d73493b2a03a9c375ed0daa5519b74578cd528e5d5a495a3c7407e08e94e36 Homepage: https://cran.r-project.org/package=geotargets Description: CRAN Package 'geotargets' ('targets' Extensions for Geographic Spatial Formats) Provides extensions for various geographic spatial file formats, such as shape files and rasters. Currently provides support for the 'terra' geographic spatial formats. See the vignettes for worked examples, demonstrations, and explanations of how to use the various package extensions. Package: r-cran-geothinner Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-fields, r-cran-foreach, r-cran-matrixstats, r-cran-nabor, r-cran-sf, r-cran-terra Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-s2, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-geothinner_2.1.1-1.ca2604.1_all.deb Size: 1840666 MD5sum: 0319739b218efc3512bda2275394bb02 SHA1: c406d2de1c467ec7a900bd5b0539ad31a0cb53a5 SHA256: 47eae7781737b0e022cf7dd6100dca464b8c16ce154879b8e917493bfe6fe8db SHA512: 61dad1149eec10d9facddc85f2a41eae785654d8be2b4ef1a9091b78f2d7ca98f63bf66c6c11cfd41a7724be1957f0093f4c8fff5cec9d50e469670bcf3e2136 Homepage: https://cran.r-project.org/package=GeoThinneR Description: CRAN Package 'GeoThinneR' (Efficient Spatial Thinning of Species Occurrences) Provides efficient geospatial thinning algorithms to reduce the density of coordinate data while maintaining spatial relationships. Implements K-D Tree and brute-force distance-based thinning, as well as grid-based and precision-based thinning methods. For more information on the methods, see Elseberg et al. (2012) . Package: r-cran-geotools Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1583 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-geotools_0.1-1.ca2604.1_all.deb Size: 1549076 MD5sum: 1be5883d1c265f5c91aa38b2ab8aba9d SHA1: 6a04f68fe23fe74959f0890e951c5fb15fdadae7 SHA256: 0ef663d714d8bc13022886d0bbd6028d99c44ad0bf15bd27c19bcb0cd3f5d136 SHA512: 5434b6fd7b59c78896b6d73c27f581286c75535fbe490b442a72526b9b825b01e86912610ec5f2803fd25a9de8e7c01424b1d33986e7b23059f5d1566bfde721 Homepage: https://cran.r-project.org/package=geotools Description: CRAN Package 'geotools' (Geo tools) Tools Package: r-cran-geotoolsr Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geor, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-geotoolsr_1.2.1-1.ca2604.1_all.deb Size: 75298 MD5sum: d3d7350c6a1b73cdf01918a675af9513 SHA1: 843b0d9b22d3881a8151d1da16989e934b0972a2 SHA256: 035bdb6835360201574e10dca6d5400be293f28ba93b7904122eb92cfda5f9b8 SHA512: 0dec60ea0ee05eb562584f0e290688d6f39c61e3755fa26e1f559f3b98425cbc547288faffce0aeebf2ec6da323dc28e661f5ca74838c9c8cbf3cc771f4bd11f Homepage: https://cran.r-project.org/package=geotoolsR Description: CRAN Package 'geotoolsR' (Tools to Improve the Use of Geostatistic) The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure. There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms. Package: r-cran-geotopbricks Architecture: all Version: 1.5.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3881 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-stringr, r-cran-zoo, r-cran-sf, r-cran-terra Suggests: r-cran-soilwater Filename: pool/dists/resolute/main/r-cran-geotopbricks_1.5.9.1-1.ca2604.1_all.deb Size: 3253296 MD5sum: e30442dcf63aa82f6774ec6d4af32572 SHA1: 8715277e6bf9c912b0dd3c93abe976b907b33cd8 SHA256: 1a4ca73a0004f39eae4496c77f675f3b2acbfdebdb51dabcf88681c696609482 SHA512: 36cbd0812adb7cd1e12ce664c1ea5cb2927415c505fd5db88757588851734dec3254db6e567f8226fc11e421a8e20ea262e6a4c38f59f2750852a18323e514a3 Homepage: https://cran.r-project.org/package=geotopbricks Description: CRAN Package 'geotopbricks' (An R Plug-in for the Distributed Hydrological Model GEOtop) It analyzes raster maps and other information as input/output files from the Hydrological Distributed Model GEOtop. 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The package, methods, and case-studies are described in Messier, Reif, and Marvel (2025) and Eccles et al. (2023) . 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References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. . Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. . Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. . 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Package: r-cran-geslar Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 685 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-cli Suggests: r-cran-bslib, r-cran-dt, r-cran-knitr, r-cran-leaflet, r-cran-leafpop, r-cran-lubridate, r-cran-patchwork, r-cran-plotly, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyalert, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-geslar_1.0-1-1.ca2604.1_all.deb Size: 186682 MD5sum: 918933afd4a65665944cebb7f86e2bfe SHA1: 951b6e1d2137992ffcf17f37fe87a55a0efd27ec SHA256: dea3b3cf4fc35dabcaa3e6d6868804712cdb34492d730b6d97061bc3222f4d01 SHA512: cef1807dcfb82980dcd7e6b26709c7972ae739a2f4e0661d183c271b1f8437a16a3f221700a93050ea8edb409d02a3016b37bdd2d232f6d96587e4e85c66838d Homepage: https://cran.r-project.org/package=geslaR Description: CRAN Package 'geslaR' (Get and Manipulate the GESLA Dataset) Promote access to the GESLA (Global Extreme Sea Level Analysis) dataset, a higher-frequency sea-level record data from all over the world. It provides functions to download it entirely, or query subsets directly into R, without the need of downloading the full dataset. Also, it provides a built-in web-application, so that users can apply basic filters to select the data of interest, generating informative plots, and showing the selected sites. Package: r-cran-gestalt Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-magrittr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gestalt_0.2.0-1.ca2604.1_all.deb Size: 195590 MD5sum: a09fcb9d3cc1f3f43d846e4e57ad6c02 SHA1: 2b6e210ee291589f57e79bc19a3eea1141cc8bf1 SHA256: db46c8700c4ff67865e9f7be4fc3e5054599808acaeb2f94b2565de487429aab SHA512: 5baa5b760eab669d4a4b59c90bae0deca8807a50a1138c983295a58871490bed0ce4b48059f3178cdf95da2804ca5d08dd20f81aeca6d0613406dd31da33c5db Homepage: https://cran.r-project.org/package=gestalt Description: CRAN Package 'gestalt' (Tools for Making and Combining Functions) Provides a suite of function-building tools centered around a (forward) composition operator, %>>>%, which extends the semantics of the 'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables you to construct composite functions that can be inspected and transformed as list-like objects. In conjunction with %>>>%, a compact function constructor, fn(), and a partial-application constructor, partial(), are also provided; both support quasiquotation. Package: r-cran-gestate Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-shiny, r-cran-shinythemes, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gestate_1.6.0-1.ca2604.1_all.deb Size: 714100 MD5sum: 8e4b5481323bdbef84b586655d3c143e SHA1: 5e7451918e0d3291db1bd236b2c15cce7d6551a2 SHA256: c632c8f97b7df2cd8ff650b2bf6c9499df3f9b1a94137ad8ac3d8dc4d6cccfeb SHA512: 5736423097d3f08db4fb7f4aa9d80730014ba7a38a253f17df367a22e4beaa4dac08ad919ab601a6f7f06086916e94e7896d16cf5a40502ea30a8f286fba3072 Homepage: https://cran.r-project.org/package=gestate Description: CRAN Package 'gestate' (Generalised Survival Trial Assessment Tool Environment) Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves). Package: r-cran-get Architecture: all Version: 1.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4421 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-ggplot2, r-cran-gridextra, r-cran-viridislite Suggests: r-cran-crayon, r-cran-geor, r-cran-gstat, r-cran-fda, r-cran-fda.usc, r-cran-locfit, r-cran-mvtnorm, r-cran-patchwork, r-cran-quantreg, r-cran-r.rsp, r-cran-sf, r-cran-sp, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.knet, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-spatstat.random, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-get_1.0-7-1.ca2604.1_all.deb Size: 4194082 MD5sum: b4857422603077f5ae640dbd5983b25f SHA1: edf67060026f997f8e2ae2b995ffe78f2f3c9a4c SHA256: e8f909d49a78f031bfde445959a691c481e1b28db04569c7f18894f2a5dfbd00 SHA512: 81125b5235989bd6516dc2e752dccec4525c3c8ed6b769001b41ff6a7ac992824d8cf06f49b823e674f0b023adf898977d9370005fae2e3bbbbaeb9f7bbd5477 Homepage: https://cran.r-project.org/package=GET Description: CRAN Package 'GET' (Global Envelopes) Implementation of global envelopes for a set of general d-dimensional vectors T in various applications. A 100(1-alpha)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and Mrkvička (2024) , Myllymäki et al. (2017) , Mrkvička and Myllymäki (2023) , Mrkvička et al. (2016) , Mrkvička et al. (2017) , Mrkvička et al. (2020) , Mrkvička et al. (2021) , Myllymäki et al. (2021) , Mrkvička et al. (2022) , Dai et al. (2022) , Dvořák and Mrkvička (2022) , Mrkvička et al. (2023) , and Konstantinou et al. (2024) . Package: r-cran-getable Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-jsonlite, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-getable_1.0.3-1.ca2604.1_all.deb Size: 24186 MD5sum: 4ea654ded51c9e810749d5c8c145a3f3 SHA1: ea0c33695026c856d5e5a47884d5ba2164727669 SHA256: 4f1294e647320f87fceae0aef2adf3aab5aa7f42bb600cb13c2cdf89f698ec64 SHA512: e84ee32704af5c70fb8070afaafcefecea7582979bc0db2c44d19dd87f5793f60f55e4d04ecef0a03439c3438d3963cf6a8e12f455ea1616441b6c7591a2f8d0 Homepage: https://cran.r-project.org/package=getable Description: CRAN Package 'getable' (Fetching Tabular Data "Onload" in Compiled R Markdown HTMLDocuments) Dynamically retrieve data from the web to render HTML tables on inspection in R Markdown HTML documents. Package: r-cran-getbcbdata Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-future, r-cran-furrr, r-cran-jsonlite, r-cran-memoise, r-cran-purrr, r-cran-cli, r-cran-parallelly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-getbcbdata_0.9.1-1.ca2604.1_all.deb Size: 34776 MD5sum: fce445a6c45eec8f54fe9e3a92739446 SHA1: acd9c2aab6e32b3d04539f82287cfd9f0899781e SHA256: aa908a438b8e4960e46a9f9996be527744c0b7cea1c6f513cc97c9cc5ea85a4c SHA512: 1df43a65a5c45f9b8b75916c3e89d08073ebd15554eba9994ab4135d1704df061ca64e895806554d2329214c5f759f274db2aa3beb59baa702d2ea47dc3f98b5 Homepage: https://cran.r-project.org/package=GetBCBData Description: CRAN Package 'GetBCBData' (Imports Datasets from BCB (Central Bank of Brazil) using ItsOfficial API) Downloads and organizes datasets using BCB's API . Offers options for caching with the 'memoise' package and , multicore/multisession with 'furrr' and format of output data (long/wide). Package: r-cran-getcrucldata Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-data.table, r-cran-fs, r-cran-httr2, r-cran-rlang, r-cran-terra Suggests: r-cran-knitr, r-cran-r.utils, r-cran-rmarkdown, r-cran-roxygen2, r-cran-roxyglobals, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-getcrucldata_2.0.0-1.ca2604.1_all.deb Size: 589554 MD5sum: 7e8e7d24f2f596cd668b5eeb3d4cb4b6 SHA1: 717236f710ec53a9425e07526b91c93ed10ce03b SHA256: 222192a9d9286356fceb5a0d3b138cd2348ba30f7181e15472f5d6315faef8d3 SHA512: 931ef391a71e2694d81264d90caca12850aefe9a8160db8307dc6387e476b05b565e4479c45578c797b0054b3b34039a8ed8e95120af56c69889df4111ebf301 Homepage: https://cran.r-project.org/package=getCRUCLdata Description: CRAN Package 'getCRUCLdata' ('CRU' 'CL' v. 2.0 Climatology Client) Provides functions that automate downloading and importing University of East Anglia Climate Research Unit ('CRU') 'CL' v. 2.0 climatology data, facilitates the calculation of minimum temperature and maximum temperature and formats the data into a data.table object or a 'terra' 'SpatRaster' object. 'CRU' 'CL' v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arc minutes (0.1666667 degree) resolution. For more information see the description of the data provided by the University of East Anglia Climate Research Unit, . Package: r-cran-getdesigns Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-getdesigns_1.2.0-1.ca2604.1_all.deb Size: 21086 MD5sum: 344a8069819db588edc47c7a000b4974 SHA1: ff87e44f862a8214ce4315d897d4201a73d1d399 SHA256: c75678812c7b2c4a7b4db4b73f378bd6d8862a74678c4664fb37992adba49de7 SHA512: d76c977624ae817915c0d1fe7890267d9c1ee0068eadb92d53787265b8582322bfd8c4a0ee9b5d72ed5ee2fb3c88f076a3c18fe25c1f6a2deeb8fa01cd558719 Homepage: https://cran.r-project.org/package=GETdesigns Description: CRAN Package 'GETdesigns' (Generalized Extended Triangular Designs ('GETdesigns')) Since their introduction by Bose and Nair (1939) , partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,…,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs. Package: r-cran-getdfpdata2 Architecture: all Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-xml2, r-cran-dplyr, r-cran-readr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-memoise, r-cran-rvest, r-cran-tidyr, r-cran-rcurl, r-cran-shiny, r-cran-writexl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-covr, r-cran-fs Filename: pool/dists/resolute/main/r-cran-getdfpdata2_0.6.3-1.ca2604.1_all.deb Size: 52850 MD5sum: cb6fd4159d2b936e4dc37dd13a931d0d SHA1: e99f8197480eeb84dba2ef5e49712b073ce5f1d8 SHA256: a5745391bddd4c1138586752d0bc625e9fa5bdb34ac53ec70ec9f8b08773e4e8 SHA512: 73df7d565113d8329c2b3b846d6dcbfab7f765a75ef3d7e12962ab904c751e8671e23302307657d12e06e530ac45a62962fed2b9fc0391857dc5f2a700492406 Homepage: https://cran.r-project.org/package=GetDFPData2 Description: CRAN Package 'GetDFPData2' (Reading Annual and Quarterly Financial Reports from B3) Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange . All data is downloaded and imported from CVM's public ftp site . 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These are web based interfaces for all financial reports of companies traded at Bovespa. The package is specially designed for large scale data importation, keeping a tabular (long) structure for easier processing. Package: r-cran-getdteval Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-formulaic, r-cran-microbenchmark Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat, r-cran-covr, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-getdteval_0.0.2-1.ca2604.1_all.deb Size: 259968 MD5sum: 64154834f4b3bceba30759bb35478fd1 SHA1: fa0c070a2a13433641c3139dd1b2d252e20311fb SHA256: fa88d340d56c2212274eb3d42c799cbcb1c19bbf106ef314aef30f5867f8c282 SHA512: b9905b9cdeba1e6ece3c46bf352ecb0619df04f418b2a4bedd11162f0452c5007c47c3701847c7807892d776ecb328e1b50e8442791fd454e88d96961c128fe9 Homepage: https://cran.r-project.org/package=getDTeval Description: CRAN Package 'getDTeval' (Translating Coding Statements using get() and eval() forImproved Run-Time Coding Efficiency) The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design. The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs. 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All data is downloaded and imported from the ftp site . Package: r-cran-getlattes Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-dplyr, r-cran-tibble, r-cran-janitor, r-cran-purrr Suggests: r-cran-knitr, r-cran-rlang, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-getlattes_1.0.0-1.ca2604.1_all.deb Size: 4196390 MD5sum: 7445da40d4959488d246df670c91b6d1 SHA1: 866b7bb88fd8d2efa411da7a30d8a3fe87e6f225 SHA256: 4af4aa9bb79cd9a4dc81aab8c0e264ae835e5c3163d6cae8a584487916e71053 SHA512: 9581813c783132def742770802406e8b6aed80a400cbce07f179e82ab9770054158ba3e85561aa95c61a60ea7725d1c83b54151b2b7896c2e743069277a50ff0 Homepage: https://cran.r-project.org/package=getLattes Description: CRAN Package 'getLattes' (Import and Process Data from the 'Lattes' Curriculum Platform) Tool for import and process data from 'Lattes' curriculum platform (). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in 'Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. 'getLattes' package allows work with 'Lattes' data exported to XML format. 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Package: r-cran-ggblanket Architecture: all Version: 20.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1467 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-blends, r-cran-farver, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggrefine, r-cran-jumble, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-snakecase, r-cran-stringr, r-cran-viridis Suggests: r-cran-dplyr, r-cran-flexoki, r-cran-forcats, r-cran-hexbin, r-cran-knitr, r-cran-lubridate, r-cran-palmerpenguins, r-cran-patchwork, r-cran-quantreg, r-cran-rmarkdown, r-cran-sf, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggblanket_20.0.0-1.ca2604.1_all.deb Size: 1095020 MD5sum: 02efed2d7cba14be0eb7e4ac177ef774 SHA1: 65bab4eb37a117171d50103999a65d8921328e62 SHA256: b2697e0f671e06d6e21fb41513a93841fd977575a4acddccd0eaaba214ca5af1 SHA512: b5c0f3dd43f4061086932f4c8e1adc930492eac689aff35dd4e0c967c22c5a05b2cb56bf19830f78f812b4328b4dd86ac6f850ceed73c4bf8be5f5ba4aba083e Homepage: https://cran.r-project.org/package=ggblanket Description: CRAN Package 'ggblanket' (Publication-Quality 'ggplot2' Visualisation) Wrapper 'ggplot2' functions for publication-quality visualisation. 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Package: r-cran-ggcleveland Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang, r-cran-magrittr, r-cran-readr, r-cran-egg, r-cran-vctrs, r-cran-lattice, r-cran-tibble, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggcleveland_0.1.0-1.ca2604.1_all.deb Size: 941444 MD5sum: 2d3a6ad3ed8f1c19f3f0137a48f43c44 SHA1: 7055ecd209b46a89d9c741da0ced6fe38600ff0d SHA256: 63603596a8ed3fd87efe9696d08c133dd8f4f3748a19464a5c5d28a26abcbd79 SHA512: 7a0c186bee8ca4fafa28e7db4dfafd5c8482d49eb2d4afdc4fe25d068f6972fcacc7d9a6fb75a41ee3192a17ba859936a28251380f2c5ecbc159ec09f7d267c9 Homepage: https://cran.r-project.org/package=ggcleveland Description: CRAN Package 'ggcleveland' (Implementation of Plots from Cleveland's Visualizing Data Book) William S. Cleveland's book 'Visualizing Data' is a classic piece of literature on Exploratory Data Analysis. Although it was written several decades ago, its content is still relevant as it proposes several tools which are useful to discover patterns and relationships among the data under study, and also to assess the goodness of fit o a model. This package provides functions to produce the 'ggplot2' versions of the visualization tools described in this book and is thought to be used in the context of courses on Exploratory Data Analysis. Package: r-cran-ggcompare Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-ggcompare_0.0.6-1.ca2604.1_all.deb Size: 141510 MD5sum: 4584db34bf0f9acb7ef1214b605e1105 SHA1: 5ad002cc53ee02a5c4247662d5cab874c607f794 SHA256: d6764379dba149e6b55c8c6699d34b25f10a71a7588d326fdbc5ca78645e5eef SHA512: b832a5b66a230b51b9176806cfc70b59f92e3933b241776798b110404973504fb50f26104e115aaba3be4c671107d87b9964717a310ab02a82217f19b492ffb4 Homepage: https://cran.r-project.org/package=ggcompare Description: CRAN Package 'ggcompare' (Mean Comparison in 'ggplot2') Add mean comparison annotations to a 'ggplot'. This package provides an easy way to indicate if two or more groups are significantly different in a 'ggplot'. Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric). Package: r-cran-ggcorrheatmap Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 823 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-dplyr, r-cran-dendextend, r-cran-ggnewscale, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggcorrheatmap_0.3.0-1.ca2604.1_all.deb Size: 742486 MD5sum: d918929b5949e0f91cc5048476ace6da SHA1: 993512e1e66bf90687941c1491aceb3568cc1d3b SHA256: 0be8816d6c90f6b1b4ba986445051838960abb087edd711cbcb794d809dc79c1 SHA512: 573d14a283fa61f6af6255e7fa70b258c4790550676d7d37a53ad1ea01d74311fd25f57fbe6181816aaeb39e7a0b8c3a9c1ec8b5547a3fe1364c37828034ee66 Homepage: https://cran.r-project.org/package=ggcorrheatmap Description: CRAN Package 'ggcorrheatmap' (Make Flexible 'ggplot2' Correlation Heatmaps) Create correlation heatmaps with 'ggplot2' and customise them with flexible annotation and clustering. Symmetric heatmaps can use triangular or mixed layouts, removing redundant information or displaying complementary information in the two halves. There is also support for general heatmaps not displaying correlations. Package: r-cran-ggcorrplot Architecture: all Version: 0.1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-spelling, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggcorrplot_0.1.4.1-1.ca2604.1_all.deb Size: 30798 MD5sum: 689176acc90596dd797be1ba16ae0e36 SHA1: bd8a7ec889888bef63b667e2f2f45a556d07f6a3 SHA256: d8315f5b02485e62949221369cb35b9a3be3fb224e65e77bca82bcfbe39830e7 SHA512: 0fc55d6a46aad76e1f1f374140d00bfb69b793c25e3e5d4a7180e78eb28561f61294a415724000133556a890e0fb991d244c07e50ab04615c79f99f90d91b249 Homepage: https://cran.r-project.org/package=ggcorrplot Description: CRAN Package 'ggcorrplot' (Visualization of a Correlation Matrix using 'ggplot2') The 'ggcorrplot' package can be used to visualize easily a correlation matrix using 'ggplot2'. It provides a solution for reordering the correlation matrix and displays the significance level on the plot. It also includes a function for computing a matrix of correlation p-values. Package: r-cran-ggdag Architecture: all Version: 0.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2598 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dagitty, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggraph, r-cran-ggrepel, r-cran-igraph, r-cran-magrittr, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidygraph Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-ggdag_0.2.13-1.ca2604.1_all.deb Size: 1888904 MD5sum: 73d9eddbc1904e3f92d057958612f2af SHA1: b87f567ba8cdbfa3bb26ed67e2200b6af93c21d5 SHA256: ca5eb884ca175e1169da4db4e1cc3a023642b1e93b8a281862164c92567db1b2 SHA512: e6b8fde89d2d3373701ca3df9e59776392d8f6d00dfc48a6fb7844ed40fb4e315bf29665cf44635953cfdb1e3d874b74ced940196b008a6023985dd8f321327e Homepage: https://cran.r-project.org/package=ggdag Description: CRAN Package 'ggdag' (Analyze and Create Elegant Directed Acyclic Graphs) Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses the 'DAGitty' web tool () for creating and analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as well as common analytic and graphical functions, such as determining adjustment sets and node relationships. Package: r-cran-ggdaynight Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggdaynight_0.1.3-1.ca2604.1_all.deb Size: 185488 MD5sum: e16f1550690ebb6247ea418c1a8a648d SHA1: 3007a24e5220e70321066c43df8df7c5c7ef7546 SHA256: ec65812ac4055e30ecc70e0cf09a02998138e7dfde5af88b9d790c5e00d4c5a8 SHA512: 197c1ae60ec0a1926658bb9c9aeb2c7b58d1730e7d1b48f547aedc9747f60a27522715298dfc826ea80f4ead933322d04af418d1c0edcf206347c70c1ba05fef Homepage: https://cran.r-project.org/package=ggdaynight Description: CRAN Package 'ggdaynight' (Add Day/Night Patterns to 'ggplot2' Plots) It provides a custom 'ggplot2' geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. It is useful for visualizing data that spans multiple days and for highlighting diurnal patterns. Package: r-cran-ggdemetra Architecture: all Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4095 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rjdemetra, r-cran-ggrepel, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggdemetra_0.2.9-1.ca2604.1_all.deb Size: 2434908 MD5sum: c9ec82b873d80f15ad236f23c083cc03 SHA1: 94635ba738b3b1896085c4f9f030a90646f9993b SHA256: 8cc69f11699430591967abcace9b2d76ca61f1d404e36c22474980a6c5fd39ca SHA512: 226bf707d88be90909285bcdd8d5171030a62c7810783ae0b7a5d5caf6c9858a971f60a559d661d676689b39950f2898371d86308286bd1c92d9f65868e3e788 Homepage: https://cran.r-project.org/package=ggdemetra Description: CRAN Package 'ggdemetra' ('ggplot2' Extension for Seasonal and Trading Day Adjustment with'RJDemetra') Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (), the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks. Package: r-cran-ggdendro Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-rpart, r-cran-tree, r-cran-testthat, r-cran-knitr, r-cran-cluster, r-cran-scales, r-cran-spelling, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-ggdendro_0.2.0-1.ca2604.1_all.deb Size: 200780 MD5sum: 2266be6dec525ba0e693a2997009590d SHA1: fc79d3105e6470e7bc1ba264efa156090d7096c3 SHA256: 3247e0e2241c9380c40c9e3a15eebc28d63e1c2759c613d58a6f8f43c7072484 SHA512: 8da438c46418c5d5065217781367bd078843f826a6449d5e7eeb19a945ff1cdbd5fd802b7657c8c8495dabe650ebd149732ac52eaac357dba77d282bc6bdbbbb Homepage: https://cran.r-project.org/package=ggdendro Description: CRAN Package 'ggdendro' (Create Dendrograms and Tree Diagrams Using 'ggplot2') This is a set of tools for dendrograms and tree plots using 'ggplot2'. The 'ggplot2' philosophy is to clearly separate data from the presentation. Unfortunately the plot method for dendrograms plots directly to a plot device without exposing the data. The 'ggdendro' package resolves this by making available functions that extract the dendrogram plot data. The package provides implementations for 'tree', 'rpart', as well as diana and agnes (from 'cluster') diagrams. Package: r-cran-ggdensity Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-isoband, r-cran-vctrs, r-cran-tibble, r-cran-mass, r-cran-scales Suggests: r-cran-vdiffr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggdensity_1.0.1-1.ca2604.1_all.deb Size: 369770 MD5sum: b76eeca496a158d88f78308af0194215 SHA1: 6474a070e5dbf6ffc9f7439a531b622399dc6e18 SHA256: 5153b4c545d2401983d5cfbf5c9c4daabc101aea90d09c7c4ac8d606d725031f SHA512: d0213896678c728bc4996821c31584b78eb73cc4f39cd3bb33e6d26af681a5b310c7d1eeba5d685045c248dd46fda40fce7614b4e247bdc1e0d3424495c3c6e5 Homepage: https://cran.r-project.org/package=ggdensity Description: CRAN Package 'ggdensity' (Interpretable Bivariate Density Visualization with 'ggplot2') The 'ggplot2' package provides simple functions for visualizing contours of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators as well as more interpretable visualizations based on highest density regions instead of the traditional height of the estimated density surface. Package: r-cran-ggdiagram Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9936 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrowheadr, r-cran-bezier, r-cran-cli, r-cran-dplyr, r-cran-farver, r-cran-geomtextpath, r-cran-ggarrow, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext, r-cran-janitor, r-cran-lavaan, r-cran-magick, r-cran-magrittr, r-cran-pdftools, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-signs, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tinter, r-cran-tinytex, r-cran-vctrs Suggests: r-cran-knitr, r-cran-marquee, r-cran-quarto, r-cran-rmarkdown, r-cran-simstandard, r-cran-spelling, r-cran-testthat, r-cran-tidyverse, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-ggdiagram_0.1.1-1.ca2604.1_all.deb Size: 6990312 MD5sum: ae3caf0d7ce8a04f524e518c8190de0b SHA1: b706b54cf58b0be391cb94d46fdbb18f773fa43a SHA256: 2d64dd0072376fde712d96318cc0c56a2523f28506fc9875c7713e134c887d90 SHA512: a2418259bd291896121bc47a40330476d8355689c605e05fe45898e104dcf0d8d8ca8414ff7644faedb2db37266d614f0e1a3c235298ea2bd611d926bf7d527b Homepage: https://cran.r-project.org/package=ggdiagram Description: CRAN Package 'ggdiagram' (Object-Oriented Diagram Plots with 'ggplot2') Creates diagrams with an object-oriented approach. Geometric objects have computed properties with information about themselves (e.g., their area) or about their relationships with other objects (e.g, the distance between their edges). The objects have methods to convert them to geoms that can be plotted in 'ggplot2'. Package: r-cran-ggdibbler Architecture: all Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6451 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-distributional, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-lifecycle, r-cran-scales, r-cran-tidyr, r-cran-tibble, r-cran-cli, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-mgcv, r-cran-fable, r-cran-urca, r-cran-gganimate, r-cran-tidyverse, r-cran-tidygraph, r-cran-ggthemes, r-cran-gifski, r-cran-ggridges, r-cran-quantreg, r-cran-ggdist, r-cran-ggraph, r-cran-feasts, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-ggdibbler_0.6.5-1.ca2604.1_all.deb Size: 5582914 MD5sum: 5e630d550122cc3dcf3b8d33ff9834b9 SHA1: 96764d680b1d93a21a57958502b0dc3f8b1530fe SHA256: 04cdc3e512bcaf39599015ec5b6580949b7dc2c6f9a33c63bfa33c73a3e7c958 SHA512: 0fffdafd16a91e39a82e7fd8bcda6149f17b61dd0675272a9574f593c15bc321d69969c8ff715fe9284f97c260a0fad5e2c9596f92f65cc26b0447b99b5865b0 Homepage: https://cran.r-project.org/package=ggdibbler Description: CRAN Package 'ggdibbler' (Add Uncertainty to Data Visualisations) A 'ggplot2' extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas 'ggdibbler' differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace any of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart. Package: r-cran-ggdiceplot Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-legendry, r-cran-scales, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-ggdiceplot_1.2.0-1.ca2604.1_all.deb Size: 78694 MD5sum: e683dffbef79072f36f929935b47006a SHA1: 5e0f7c223026115e8313e961f20c0de7fab34f39 SHA256: 5cb7ccbc4e334050cea773a365f732e8b4457c8acdf06ad9a8d40b926b0ed927 SHA512: ce9712914f907c2c4b41b5325bd458cc3a9901c80e2da769126be8958dc2750e5e9e436b832c005cd0db0ed5c7686291a3bc4102c0150990b40a445a237f4d18 Homepage: https://cran.r-project.org/package=ggdiceplot Description: CRAN Package 'ggdiceplot' (DicePlot Visualization for 'ggplot2') Provides 'ggplot2' extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes 'geom_dice()' for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously. Package: r-cran-ggdnavis Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggnewscale, r-cran-ggplot2, r-cran-magick, r-cran-png, r-cran-ragg, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bslib, r-cran-colourpicker, r-cran-jsonlite, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggdnavis_1.0.0-1.ca2604.1_all.deb Size: 752942 MD5sum: 6eefb04421475f079a9f6faa7fa8efa6 SHA1: 0ad56957d6b8489e83b5b5ccfc1fe143ed866314 SHA256: 65e5c9a0871c8d062d76cc4370e3e2093325707a1bd9476b259668074fa6456d SHA512: ba1341cc3f4716bf4e9cf3b131c488ca48c607cb90b3a91b90124ee4809e7562f01fc798e8a505dfcd477330fa56d30c46d8412166d90b4886e85e52c534b06a Homepage: https://cran.r-project.org/package=ggDNAvis Description: CRAN Package 'ggDNAvis' ('ggplot2'-Based Tools for Visualising DNA Sequences andModifications) Uses 'ggplot2' to visualise either (a) a single DNA/RNA sequence split across multiple lines, (b) multiple DNA/RNA sequences, each occupying a whole line, or (c) base modifications such as DNA methylation called by modified bases models in Dorado or Guppy. Functions starting with visualise_<>() are the main plotting functions, and functions starting with extract_and_sort_<>() are key helper functions for reading files and reformatting data. Source code is available at , a full non-expert user guide is available at , and an interactive web-app version of the software is available at . Package: r-cran-ggdoe Architecture: all Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-insight Suggests: r-cran-roxygen2, r-cran-tibble, r-cran-mass, r-cran-viridislite, r-cran-ggrepel, r-cran-patchwork, r-cran-unrepx, r-cran-geomtextpath, r-cran-rsm, r-cran-lhs, r-cran-doe.base Filename: pool/dists/resolute/main/r-cran-ggdoe_0.8-1.ca2604.1_all.deb Size: 813762 MD5sum: 3cc9a19056930dab86a99221fad69d76 SHA1: 8b0e8167df5b7d087b697df9603bf5416353a063 SHA256: 306df5f4c2204f54f03361e8186a6deefcfa960ccd931bc3808a4687937e5849 SHA512: 246fc1d7095b037ebbb29df6a404db6d6319f6559860affcdff5de615a89b04b6e954f16e0c91167ac04ad161ed61c0abf69c93ca52aa4f4d8a2ecaa75e6a1e7 Homepage: https://cran.r-project.org/package=ggDoE Description: CRAN Package 'ggDoE' (Modern Graphs for Design of Experiments with 'ggplot2') Generate commonly used plots in the field of design of experiments using 'ggplot2'. 'ggDoE' currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot, regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs, contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design. Package: r-cran-ggdoubleheat Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-ggnewscale Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-dplyr, r-cran-scales, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ggdoubleheat_0.1.3-1.ca2604.1_all.deb Size: 205884 MD5sum: bbc44bdc5c859b8bcf979f3257f49377 SHA1: 40eb7db7bfff41a2edd761411e826aa6313df12d SHA256: ea7bacb89c54386d5242feede783d6647e02afd996babb75f9117084aba899ae SHA512: f2c80084da743a6784f89fb4fd6bb0957c80c5d107bf3087fc0e94e46d7160430d87ec299932ee70fb7d7b20884862d5dfb43046c250c457566e379ab3f0678d Homepage: https://cran.r-project.org/package=ggDoubleHeat Description: CRAN Package 'ggDoubleHeat' (A Heatmap-Like Visualization Tool) A data visualization design that provides comparison between two (Double) data sources (usually on a par with each other) on one reformed heatmap, while inheriting 'ggplot2' features. Package: r-cran-gge Architecture: all Version: 1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nipals, r-cran-reshape2 Suggests: r-cran-agridat, r-cran-knitr, r-cran-lattice, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gge_1.10-1.ca2604.1_all.deb Size: 158694 MD5sum: 6cb906ee3e95b104aeff580b001a249e SHA1: ee5256107499b21765ab72437f65e73d30af2802 SHA256: 8d82f23e537ff81b1ccaedc31d590884eb55c39e235f3ecea86a3f0a406db2e8 SHA512: f4beedf1b661c968c188f9899b0741bbc197e45ff0f9fcb71c691c4e9a8651a00eabf783b17741d220b1798dafc385be32eb134d4bb19f17749d029dd682b49e Homepage: https://cran.r-project.org/package=gge Description: CRAN Package 'gge' (Genotype Plus Genotype-by-Environment Biplots) Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) . 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Notable among these are the bivariate peelings surveyed by Green (1981, ISBN:978-0-471-28039-2), the bag-and-bolster plots proposed by Rousseeuw &al (1999) , and the minimum spanning trees used by Jolliffe (2002) to represent high-dimensional relationships among data in a low-dimensional plot. Additionally, biplots of singular value--decomposed tabular data, such as from principal components analysis, make use of vectors, calibrated axes, and other representations of variable elements to complement point markers for case elements; see Gabriel (1971) and Gower & Harding (1988) for original proposals. Because they treat the abscissa and ordinate as commensurate or the data elements themselves as point masses or unit vectors, these multivariable tools can be thought of as belonging to geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) for foundations. 'gggda' extends Wickham's (2010) layered grammar of graphics with statistical transformation ("stat") and geometric construction ("geom") layers for many of these tools, as well as convenience coordinate systems to emphasize intrinsic geometry of the data. 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The plotting functions produce 'ggplot' objects which may be easily manipulated or extended. Use 'ggmice' to inspect missing data, develop imputation models, evaluate algorithmic convergence, or compare observed versus imputed data. Package: r-cran-ggmix Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-matrix Suggests: r-cran-rspectra, r-cran-popkin, r-cran-bnpsd, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggmix_0.0.2-1.ca2604.1_all.deb Size: 1253408 MD5sum: 931ccb9a194411130014a3fb60f69990 SHA1: b0f67ffc7c09fa3d26e3d3dc02ac21c81a05f7cd SHA256: d6d215ee3cabd36d26d6ec9c3f8ad59861d00f2c227531fcdf82d0964bcf7f4b SHA512: e57e23206b6a6fd2710992b7179e781ee60198b8834c65d1353f76cb0a41457f6ccadf870f1e514b654e7c5803bd7cf9d60a70af4e9aed4541624262584e9050 Homepage: https://cran.r-project.org/package=ggmix Description: CRAN Package 'ggmix' (Variable Selection in Linear Mixed Models for SNP Data) Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, . Package: r-cran-ggmr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ggmr_0.1.1-1.ca2604.1_all.deb Size: 25722 MD5sum: 3da002fd1582f01d87546bae65285b1c SHA1: 3a0c3859d3a79deff95db8a611295d31e29e3b90 SHA256: dedeb9046c277c25ac4b578de7e07a1d5e89c49c314548df207734005d14226c SHA512: 059df17802c6da81915d68aa4bbf2d82c5bbc2a8240aef7df210e063749c2a5d8989b45676a4d336c994289528cadfbf5ae05804f993f0ec59d7ca4b57dbbe32 Homepage: https://cran.r-project.org/package=ggmr Description: CRAN Package 'ggmr' (Generalized Gauss Markov Regression) Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) . Package: r-cran-ggmridge Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ggmridge_1.5-1.ca2604.1_all.deb Size: 73318 MD5sum: baa0653d3babf61984392c62009c47be SHA1: 6239a2d24026cde1c3e3674eebc745c2cb343ef5 SHA256: d3522c193199a05611674de1597b60979049d625a9b152f3cd3ad6cb4a0eef62 SHA512: 190bf0598bb98bea6137a3b0a26fa8d8855c46d1204d012c67ff7b0848627d449d3ca7b607a0dbb5b91125ec2254668f8f91d0746063d427a2193741363454e7 Homepage: https://cran.r-project.org/package=GGMridge Description: CRAN Package 'GGMridge' (Gaussian Graphical Models Using Ridge Penalty Followed byThresholding and Reestimation) Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. 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Package: r-cran-ggmrscu Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ggmrscu_0.1.0-1.ca2604.1_all.deb Size: 77338 MD5sum: 47f31a0f85d1d8ab134364a4e1388a59 SHA1: 22280fac0f0ecc11e326ae70e8756ea26ab8792a SHA256: 52e330ebfae6984dd9b918c17170c42b67f8c071da309f1fa84f976e030c966c SHA512: d51ba7bee02f0effac2e68906d453bebec51ba66346edca5588671a335f6d95322052d49b4cbbdb38d84bca0a7a3f212e29d9bc747ef2406f522539a1551b94f Homepage: https://cran.r-project.org/package=ggmRSCU Description: CRAN Package 'ggmRSCU' (Visualizing Multi-Species Relative Synonymous Codon Usage andExtensible Data Exploration) Facilitates efficient visualization of Relative Synonymous Codon Usage patterns across species. 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Package: r-cran-ggmuller Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ape, r-cran-rlang Suggests: r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggmuller_0.7.0-1.ca2604.1_all.deb Size: 304140 MD5sum: 8564c5d2a8aac5ae890d73a438515801 SHA1: 2a837ad1bb158bffcdb86c6b5f79778097edf0b4 SHA256: e6b87a0e0f050687d56af905a73d07d7d3ce24fff13c858754e2b4cd218ac5c2 SHA512: 1e41f9250024f69022bd85acbafe9a5e1a985e7828bd84373b5377092a1fd8b6182335cee8ef08abd5bdfe58efa9a86b0aa1079366845c8ccac5025b3019ca6f Homepage: https://cran.r-project.org/package=ggmuller Description: CRAN Package 'ggmuller' (Create Muller Plots of Evolutionary Dynamics) Create plots that combine a phylogeny and frequency dynamics. Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo". 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Plotting oceanographic spatial data is made as simple as feasible, but also flexible for custom modifications. Data that contain geographic information from anywhere around the globe can be plotted on maps generated by the basemap() or qmap() functions using 'ggplot2' layers separated by the '+' operator. The package uses spatial shape- ('sf') and raster ('stars') files, geospatial packages for R to manipulate, and the 'ggplot2' package to plot these files. The package ships with low-resolution spatial data files and higher resolution files for detailed maps are stored in the 'ggOceanMapsLargeData' repository on GitHub and downloaded automatically when needed. Package: r-cran-ggokabeito Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2783 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-ggraph, r-cran-igraph, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggokabeito_0.1.0-1.ca2604.1_all.deb Size: 2302090 MD5sum: 75b57aca949ed4c7ad530fb11e49817d SHA1: 45e38cf1a9c1c2a52616f5e317a6625393a46e50 SHA256: 1c23938c5acf6d9d0b468b2b282de8c3986b3c9ef0b9d05dbe0e98a1b341b0a6 SHA512: abca37ad178d15a8978197c2e269cfaf2ebb005d612887080d834098425b8a7c74a5192cc61346e31d0fc6989b081e8f364817c17f8db7e34fc4f9cd721f3a44 Homepage: https://cran.r-project.org/package=ggokabeito Description: CRAN Package 'ggokabeito' ('Okabe-Ito' Scales for 'ggplot2' and 'ggraph') Discrete scales for the colorblind-friendly 'Okabe-Ito' palette, including 'color', 'fill', and 'edge_colour'. 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Package: r-cran-ggoutlier Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastknn, r-cran-foreach, r-cran-doparallel, r-cran-scales, r-cran-rcolorbrewer, r-cran-ggforce, r-cran-rlang, r-cran-tidyr, r-cran-rnaturalearth, r-cran-sf, r-cran-ggplot2, r-cran-cowplot Suggests: r-cran-rnaturalearthdata Filename: pool/dists/resolute/main/r-cran-ggoutlier_1.0.2-1.ca2604.1_all.deb Size: 481178 MD5sum: 184b9cf68e216dfeafa6fc5fa5978bf3 SHA1: 09b1a04b96d91f6a9f630bfce2e38c07e42f8ef1 SHA256: c6026aa463259fe908fde82e5b744e56c68e69d384d4d2d6aa58f78ba6bcf09c SHA512: 36b4527338175c9e602659a23be9f28daa7e9cbc0b854e972981697b6b44c1e7943cc2e41b1b8856036b67bc67aecfe7728d5edcc120cfa5643b53237e36fdfa Homepage: https://cran.r-project.org/package=GGoutlieR Description: CRAN Package 'GGoutlieR' (Identify Individuals with Unusual Geo-Genetic Patterns) Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) . It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample. Package: r-cran-ggpackets Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-dplyr, r-cran-backports, r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-covr Filename: pool/dists/resolute/main/r-cran-ggpackets_0.2.2-1.ca2604.1_all.deb Size: 803880 MD5sum: 3d0edb2b636b2ea206b30371ca8b5678 SHA1: 5ce542792aa0dd78367c3a83078c261885db633f SHA256: 1dff61004eeb22a1f266d6548d14f96266c0e0f7090c74bf96fa368c3443e7e8 SHA512: cf8eedba05fc1fe7f14c7c02ec3ed229ebbab2e302e3380dfc0091577519efa7e0c9ecb3cbdf4ab82e26f74515747a2a039fbfa932599a5ea740dfa87251bc24 Homepage: https://cran.r-project.org/package=ggpackets Description: CRAN Package 'ggpackets' (Package Plot Layers for Easier Portability and Modularization) Create groups of 'ggplot2' layers that can be easily migrated from one plot to another, reducing redundant code and improving the ability to format many plots that draw from the same source 'ggpacket' layers. 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Package: r-cran-ggpca Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1072 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-golem, r-cran-shiny, r-cran-rlang, r-cran-rtsne, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-umap Suggests: r-cran-knitr, r-cran-tibble, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggpca_0.1.3-1.ca2604.1_all.deb Size: 550572 MD5sum: 4c6ded7140c2d359afc105805baabeaa SHA1: 93ec8e5f22e306be6ecde0d9c72bf2f2495aa735 SHA256: a90f425956ad414f27ff0451ab36877da0e4648dab50113f7a17149a577b0e34 SHA512: 473399b74bc251019b799f1e892e2f5e28a0a8b723f403435e7737c7bfc7ca4d801533d6d248acc0aea7d8fc5c0237caea49c8e024ad7bd273dca28964ab1769 Homepage: https://cran.r-project.org/package=ggpca Description: CRAN Package 'ggpca' (Publication-Ready PCA, t-SNE, and UMAP Plots) Provides tools for creating publication-ready dimensionality reduction plots, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). 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Package: r-cran-ggpedigree Architecture: all Version: 1.1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bgmisc, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-plotly, r-cran-scales, r-cran-tidyr Suggests: r-cran-selectr, r-cran-kinship2, r-cran-quadprog, r-cran-ggrepel, r-cran-paletteer, r-cran-mockery, r-cran-patchwork, r-cran-viridis, r-cran-knitr, r-cran-tidyverse, r-cran-purrr, r-cran-data.table, r-cran-discord, r-cran-openmx, r-cran-nlsylinks, r-cran-rmarkdown, r-cran-tibble, r-cran-corrplot, r-cran-showtext, r-cran-sysfonts, r-cran-withr, r-cran-htmlwidgets, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggpedigree_1.1.1.1-1.ca2604.1_all.deb Size: 1090732 MD5sum: 9281e4b5d128e8f4535b0308334b4c5a SHA1: 1d65e247ae1c27c3d1e07b1e2f683e4cf839f077 SHA256: 8ae03728b50b12220dbafab1fd0c9557e7432b6089fdaf6dad6e4bd14272cf90 SHA512: 82065baeffc8b0fe94337630042da51cdf95b9855e07752c73af0fcb2fcc7793cc6ca0c6b7933fa1275024e00f5ff24562c2cab66c8cd689e59057718a609a89 Homepage: https://cran.r-project.org/package=ggpedigree Description: CRAN Package 'ggpedigree' (Visualizing Pedigrees with 'ggplot2' and 'plotly') Provides plotting functions for visualizing pedigrees and family trees. The package complements a behavior genetics package 'BGmisc' [Garrison et al. (2024) ] by rendering pedigrees using the 'ggplot2' framework. Features include support for duplicated individuals, complex mating structures, integration with simulated pedigrees, and layout customization. Due to the impending deprecation of kinship2, version 1.0 incorporates the layout helper functions from kinship2. The pedigree alignment algorithms are adapted from 'kinship2' [Sinnwell et al. (2014) ]. We gratefully acknowledge the original authors: Jason Sinnwell, Terry Therneau, Daniel Schaid, and Elizabeth Atkinson for their foundational work. Package: r-cran-ggperiodic Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-sticky, r-cran-tidyselect, r-cran-data.table Suggests: r-cran-covr, r-cran-knitr, r-cran-maps, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggperiodic_1.0.3-1.ca2604.1_all.deb Size: 336068 MD5sum: e44ea4b10e461986c658d053e8a6f1d5 SHA1: 04d6e6bf9774c225b2da0f8da54e8f5d997801e0 SHA256: 7b909a1882f8e98031665db49d50a0c2295912c67f2201dd9ffb7e1b7d2a0fa0 SHA512: 9d1138f4179c60c698b076a281cfcc08ce94e0fbe9e2863fa668aa6f59351f1097b658bb3e89b63d311386b0663df1a8db489872e2d7a11ea1ce328843ddabab Homepage: https://cran.r-project.org/package=ggperiodic Description: CRAN Package 'ggperiodic' (Easy Plotting of Periodic Data with 'ggplot2') Implements methods to plot periodic data in any arbitrary range on the fly. Package: r-cran-ggpicrust2 Architecture: all Version: 2.5.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5745 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aplot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggh4x, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-ggprism, r-cran-patchwork, r-cran-ggplotify, r-cran-magrittr, r-cran-progress, r-cran-tidygraph, r-cran-ggraph Suggests: r-bioc-biobase, r-cran-ggdendro, r-bioc-keggrest, r-bioc-complexheatmap, r-bioc-biocgenerics, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-aldex2, r-bioc-deseq2, r-bioc-edger, r-cran-ggally, r-bioc-limma, r-cran-microbiomestat, r-bioc-summarizedexperiment, r-cran-circlize, r-bioc-lefser, r-bioc-maaslin2, r-bioc-metagenomeseq, r-bioc-fgsea, r-bioc-clusterprofiler, r-bioc-enrichplot, r-bioc-dose, r-cran-ggvenndiagram, r-cran-upsetr, r-cran-igraph, r-cran-ggridges, r-cran-ggrepel, r-cran-logging Filename: pool/dists/resolute/main/r-cran-ggpicrust2_2.5.16-1.ca2604.1_all.deb Size: 4468656 MD5sum: 3bf120f62498dbcc1d28e929da957a80 SHA1: cd6b9e232619bd63bbe8fd73c23c7df46e3c358e SHA256: 9ee751dfc87c459fa9770cff21ac03c030a9295914dec4d3973dca341e6c16f4 SHA512: 69b5822d08988c8432518b1bbf124a9054a0006abe0c6e00c724f321f810289788373f4abc1dfaf2aeee69270ac27174d82b4828936ec42ca8b0eb9dbeb22dda Homepage: https://cran.r-project.org/package=ggpicrust2 Description: CRAN Package 'ggpicrust2' (Make 'PICRUSt2' Output Analysis and Visualization Easier) Provides a convenient way to analyze and visualize 'PICRUSt2' output with pre-defined plots and functions. Allows for generating statistical plots about microbiome functional predictions and offers customization options. Features a one-click option for creating publication-level plots, saving time and effort in producing professional-grade figures. Streamlines the 'PICRUSt2' analysis and visualization process. For more details, see Yang et al. (2023) . Package: r-cran-ggplate Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5160 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-forcats, r-cran-farver Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggplate_0.3.1-1.ca2604.1_all.deb Size: 4729702 MD5sum: a4d9e16300e744821c966e94f5565daa SHA1: 4eddd0d1c3cdc116d89105ebc349a769618582d7 SHA256: 3b2495f8ce562642b908259ded1107d8776314990acb090a8011540508cb5620 SHA512: a03e4ebca8abefa14088ae3f9c6a101ddc37d30d8134dd0b1e4d087cc655b1cde064e554292acc7ec2ddee15e8b137cb23662ccc53561a371db9108eebeb1551 Homepage: https://cran.r-project.org/package=ggplate Description: CRAN Package 'ggplate' (Create Layout Plots of Biological Culture Plates and Microplates) Enables users to create simple plots of biological culture plates as well as microplates. 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Package: r-cran-ggplot.multistats Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-scales, r-cran-hexbin, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-ggplot.multistats_1.0.1-1.ca2604.1_all.deb Size: 34474 MD5sum: a4022a05819f4cace452688643d81cbd SHA1: 2c206d51f652bd75fec5830d35645d1615d3b1e3 SHA256: c69e8e1737d6849e0130722baf58204a985e30d6c8bab9cb226e55a4742d6b79 SHA512: a647cc68ed37e68488dd4cc2ec6acb896740f097c1f8227bb7e483fae61c3ac2a8e0ed3858c6db14228d1200e6fe27df15e783fab8edab525641db12ec402cc3 Homepage: https://cran.r-project.org/package=ggplot.multistats Description: CRAN Package 'ggplot.multistats' (Multiple Summary Statistics for Binned Stats/Geometries) Provides the ggplot binning layer stat_summaries_hex(), which functions similar to its singular form, but allows the use of multiple statistics per bin. 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Package: r-cran-ggplot2.utils Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-checkmate, r-cran-envstats, r-cran-ggpp, r-cran-ggstats, r-cran-survival Suggests: r-cran-dplyr, r-cran-lifecycle, r-cran-testthat, r-cran-tibble, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggplot2.utils_0.3.3-1.ca2604.1_all.deb Size: 1259370 MD5sum: c7fec0b7ed6e8562a083ebf4f37d5f69 SHA1: d324450fcc1085a6c54d4776a69c42f50dd91011 SHA256: c4c89e1a69aa6b32bd3b8bb81234e0e463cb6bd47cc1e23f078839fe0deaedfe SHA512: d5ecdd9233eb1b02e0274dce8a8ae6829934aa054ef4548012b625fcef43d8d1c72e83ef16727cd56b2825fbc16a4bbb0f6d977377ff7b8ad7c644117d6e8776 Homepage: https://cran.r-project.org/package=ggplot2.utils Description: CRAN Package 'ggplot2.utils' (Selected Utilities Extending 'ggplot2') Selected utilities, in particular 'geoms' and 'stats' functions, extending the 'ggplot2' package. This package imports functions from 'EnvStats' by Millard (2013), 'ggpp' by Aphalo et al. (2023) and 'ggstats' by Larmarange (2023), and then exports them. This package also contains modified code from 'ggquickeda' by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions to plots. All functions are tested to make sure that they work reliably. Package: r-cran-ggplot2 Architecture: all Version: 4.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-gtable, r-cran-isoband, r-cran-lifecycle, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-vctrs, r-cran-withr Suggests: r-cran-broom, r-cran-covr, r-cran-dplyr, r-cran-ggplot2movies, r-cran-hexbin, r-cran-hmisc, r-cran-hms, r-cran-knitr, r-cran-mapproj, r-cran-maps, r-cran-mass, r-cran-mgcv, r-cran-multcomp, r-cran-munsell, r-cran-nlme, r-cran-profvis, r-cran-quantreg, r-cran-quarto, r-cran-ragg, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-rpart, r-cran-sf, r-cran-svglite, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-ggplot2_4.0.3-1.ca2604.1_all.deb Size: 7781032 MD5sum: 2a305860e5b60a1e1421cffd3414340d SHA1: f05f77cceff2767deacf4696b79758ecdc17465f SHA256: ddce2c65b57618cfadf425258a8223527136347241bb01ff3ae66ebcacf2cffd SHA512: 751d82fde8ea471d8785cdd13144cbac6ce2abde1a040b1fc2a5d63bdf00ce8270a9fbaa96ffd568e30196ab19d4ab6054ad83832a67628cf77c417e065e41a8 Homepage: https://cran.r-project.org/package=ggplot2 Description: CRAN Package 'ggplot2' (Create Elegant Data Visualisations Using the Grammar of Graphics) A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. Package: r-cran-ggplot2movies Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1280 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ggplot2movies_0.0.1-1.ca2604.1_all.deb Size: 1267798 MD5sum: 9a3030a416e9cde53654c3245ff9e26a SHA1: 318991f2255d28ebad00c31d19d6be0a838fc902 SHA256: 9a6d30908407bc1aa3669bf47b0d9a8c877d9fdc0b75b8cbba5ea4be064daea1 SHA512: b3c5ea50e6ebd0f5d04834f405dffc1698cda361714544a80ff357e1f4fd796d5684015430c3786593585e64ece9a15a3ed08fca5ca06a7061add812257166a7 Homepage: https://cran.r-project.org/package=ggplot2movies Description: CRAN Package 'ggplot2movies' (Movies Data) A dataset about movies. This was previously contained in ggplot2, but has been moved its own package to reduce the download size of ggplot2. Package: r-cran-ggplotassist Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1418 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-shinywidgets, r-cran-shinyace, r-cran-stringr, r-cran-tidyverse, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-scales, r-cran-ggthemes, r-cran-gcookbook, r-cran-moonbook, r-cran-editdata Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-ggplotassist_0.1.3-1.ca2604.1_all.deb Size: 756034 MD5sum: c0e23dd8dd3a933bfac6772e3fee5d99 SHA1: 48631625d2f86c7a58df4e91a3f1bdf969f5e6b6 SHA256: d8c721b17acbf23007f5891cc0fd5b263a3ad039c2a657f899debe9ec8fcd97d SHA512: c61aa970df5d044832dea61a2feb340e6648118ef1851b8cdb09b8bb9949af5aa5ee66a74ba04ed2518681fa6f23782bc819b41c5ed581f61809e84d25bd6a81 Homepage: https://cran.r-project.org/package=ggplotAssist Description: CRAN Package 'ggplotAssist' ('RStudio' Addin for Teaching and Learning 'ggplot2') An 'RStudio' addin for teaching and learning making plot using the 'ggplot2' package. You can learn each steps of making plot by clicking your mouse without coding. You can get resultant code for the plot. Package: r-cran-ggplotgui Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-shiny, r-cran-ggplot2, r-cran-stringr, r-cran-readr, r-cran-haven, r-cran-readxl, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-ggplotgui_1.0.0-1.ca2604.1_all.deb Size: 625314 MD5sum: 095d13d2e18f289fd3bfa832ca0a6023 SHA1: ee0e7cd72fa304b06d644301ade2ef0808d3beff SHA256: 7d125743caecb48a0cfe65e97e0f78b5981c28a6ecde105afc4254dce377d8d1 SHA512: 96cfc20a2cbeb483cf95e6b5aa9b9a6d734fe2f99069765486253c17394afc0a31f29c1f0e452ee5197fb7c1c3511bbe3759bdabb55eb8d6c492679e69acc029 Homepage: https://cran.r-project.org/package=ggplotgui Description: CRAN Package 'ggplotgui' (Create Ggplots via a Graphical User Interface) Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided. Package: r-cran-ggplotify Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridgraphics, r-cran-rlang, r-cran-yulab.utils Suggests: r-cran-aplot, r-cran-colorspace, r-cran-cowplot, r-cran-ggimage, r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-prettydoc, r-cran-vcd Filename: pool/dists/resolute/main/r-cran-ggplotify_0.1.3-1.ca2604.1_all.deb Size: 140052 MD5sum: 26dcc9152b0e897647eb8393ef54f48a SHA1: 67ba5fa047572db05e6ca5942ae4457c1dacd167 SHA256: 42b7b446ebfaa28f9b0349d22280534f051aea0d6319412381aa40de1288c5cc SHA512: 35f60e6cac3556248f3a5ef16137bb8b793ade878b93b0ab29be26c153667b3d41b3c0978a09256f23fff8de97ad29b88c0c690b809029503397fd5f74941493 Homepage: https://cran.r-project.org/package=ggplotify Description: CRAN Package 'ggplotify' (Convert Plot to 'grob' or 'ggplot' Object) Convert plot function call (using expression or formula) to 'grob' or 'ggplot' object that compatible to the 'grid' and 'ggplot2' ecosystem. With this package, we are able to e.g. using 'cowplot' to align plots produced by 'base' graphics, 'ComplexHeatmap', 'eulerr', 'grid', 'lattice', 'magick', 'pheatmap', 'vcd' etc. by converting them to 'ggplot' objects. Package: r-cran-ggpmisc Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2461 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggpp, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-generics, r-cran-polynom, r-cran-confintr, r-cran-splus2r, r-cran-tibble, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-catools, r-cran-quantreg Suggests: r-cran-broom, r-cran-broom.mixed, r-cran-robustbase, r-cran-mass, r-cran-lmodel2, r-cran-nlme, r-cran-smatr, r-cran-multcomp, r-cran-multcompview, r-cran-mixtools, r-cran-segmented, r-cran-gginnards, r-cran-ggrepel, r-cran-ggtext, r-cran-xdvir, r-cran-marquee, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggpmisc_0.7.0-1.ca2604.1_all.deb Size: 1747532 MD5sum: f4f1ebe0e056a6162cb90a2a95448622 SHA1: f9ddd91972885c4d4f79a841082b902f21fb9e2b SHA256: 0d9b9bbdcb1810c34694ea024c7ab50aeabddc818cd8506496873510e46d8765 SHA512: 991cd9341ef01cb7c41bf1fa178628bf716ca34061df448546a23af1c8729aa07c0a20c7681864e5c11aff1e2b11c8dc4b17079a9364b981410aab969cb09224 Homepage: https://cran.r-project.org/package=ggpmisc Description: CRAN Package 'ggpmisc' (Miscellaneous Extensions to 'ggplot2') Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Statistics to locate and tag peaks and valleys and to label plots with the equation of a fitted polynomial model by ordinary least squares, major axis, quantile and robust and resistant regression approaches. Line and model equation for Normal mixture models. Labels for P-value, R^2 or adjusted R^2 or information criteria for fitted models; parametric and non-parametric correlation; ANOVA table or summary table for fitted models as plot insets; annotations for multiple pairwise comparisons with adjusted P-values. Model fit classes for which suitable methods are provided by package 'broom' and 'broom.mixed' are supported as well as user-defined wrappers on model fit functions, allowing model selection and conditional labelling. Scales and stats to build volcano and quadrant plots based on outcomes, fold changes, p-values and false discovery rates. Package: r-cran-ggpmx Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6842 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-yaml, r-cran-r6, r-cran-gtable, r-cran-ggplot2, r-cran-ggforce, r-cran-magrittr, r-cran-stringr, r-cran-assertthat, r-cran-ggally, r-cran-zoo, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-checkmate, r-cran-scales Suggests: r-cran-testthat, r-cran-xtable, r-cran-vdiffr, r-cran-rxode2, r-cran-nlmixr2est, r-cran-nlmixr2data, r-cran-nlme, r-cran-xgxr, r-cran-withr, r-cran-lifecycle Filename: pool/dists/resolute/main/r-cran-ggpmx_1.3.2-1.ca2604.1_all.deb Size: 3959022 MD5sum: b492b040b979f45c67873be722eea385 SHA1: 9a0381a3661a84783ebdf349b7799f2fc0328909 SHA256: efe69cd4d842ad955abf48d5a2627fee4685131dedd24c405b1fdf1c5e57c860 SHA512: 2a09c729822067764adaf50262e988dd275886c7bd07bfcbaa84fab23d4a7cbc369441de2fc5406270d08efe0dea94b2389546b268acd2d1e8652416b2e81960 Homepage: https://cran.r-project.org/package=ggPMX Description: CRAN Package 'ggPMX' ('ggplot2' Based Tool to Facilitate Diagnostic Plots for NLMEModels) At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling activities in order to reduce the overall effort required for generating such plots. For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP . While not required, you can get/install the 'R' 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url . When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required Chart Data instead of exporting it from the 'Monolix' gui. Package: r-cran-ggpointless Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2275 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-cli, r-cran-farver, r-cran-scales, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat, r-cran-ragg, r-cran-vdiffr, r-cran-spelling, r-cran-withr Filename: pool/dists/resolute/main/r-cran-ggpointless_0.3.0-1.ca2604.1_all.deb Size: 2122838 MD5sum: 734484952d9786c82aa3f0544828be4e SHA1: 1ffe738cfc7eda12fb308f57bd1d16151c516f9f SHA256: 5349c0610b36aeebfa09b2cea094f710210bda10e3769709155211a8e04068ff SHA512: b2dd243235cabeef33b0e8a179f786ed87c64e65e914462b8f35d44362b0771a2edb63868b5d14a46b446d9d1a607f83e505b43e2001322e4a8c2c40245e1703 Homepage: https://cran.r-project.org/package=ggpointless Description: CRAN Package 'ggpointless' (Extra Geometries and Stats for 'ggplot2') A collection of layers for 'ggplot2'. Provides geoms built on linear and radial gradients from the 'grid' package, giving areas, bars, paths, rectangles, and ridgelines a fading or glowing visual effect. Also includes mathematically driven layers — catenary curves, Chaikin's corner-cutting smoothing (Chaikin, 1974, ), and Fourier-series reconstruction — plus Lexis diagrams, isotype bar charts. Package: r-cran-ggpolar Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-ezcox, r-cran-ggnewscale, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-cli Filename: pool/dists/resolute/main/r-cran-ggpolar_0.2.2-1.ca2604.1_all.deb Size: 70648 MD5sum: 9ba7013793a3e8f5b5bb5f11d71e87c7 SHA1: 7562e63c668a43189dc9f6a1f0dc9964e57cdec3 SHA256: 567869ce765b1cae40e53acbc56cba165e483e0af4e1c7db68fb20f1e1f3702b SHA512: fbfb913db2d4097b93af3ca74d33189e6409dd0e42330d729e0a694bbce1f74a7ca52da45f3c9d608acf11be287270643bfa16a74475e2bf5261a519e6b5668d Homepage: https://cran.r-project.org/package=ggpolar Description: CRAN Package 'ggpolar' (Dots and Their Connections in Polar Coordinate System) Provides basic graphing functions to fully demonstrate point-to-point connections in a polar coordinate space. 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Package: r-cran-ggpop Architecture: all Version: 1.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1702 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-ggimage, r-cran-magick, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-fontawesome, r-cran-rsvg, r-cran-cli, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-ggforce, r-cran-gganimate, r-cran-ggrepel, r-cran-ggtext, r-cran-scales, r-cran-reactable Filename: pool/dists/resolute/main/r-cran-ggpop_1.7.1-1.ca2604.1_all.deb Size: 1096286 MD5sum: 17a99cb8b61041967980aae64318f84f SHA1: a5713979bf1c848f0d02af742ec843f580324c76 SHA256: 47cebf73dcbc71fb25bc067f1e53fda976e19cb0f3d3a2e51d367826f72cd5d7 SHA512: e83bd7d9b98da5822217145602aa0beee9fa10525738f37ef5998e940d0e60461dc041d8ac585eebe143335ebabbbb2d3f11a9c62550ecb677b4104f2488f7e3 Homepage: https://cran.r-project.org/package=ggpop Description: CRAN Package 'ggpop' (Icon-Based Population Charts and Plots for 'ggplot2') Create engaging population and point plots charts in R. 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Geometries: geom_table(), geom_plot() and geom_grob() add insets to plots using native data coordinates, while geom_table_npc(), geom_plot_npc() and geom_grob_npc() do the same using "npc" coordinates through new aesthetics "npcx" and "npcy". Statistics: select observations based on 2D density. Positions: radial nudging away from a center point and nudging away from a line or curve; combined stacking and nudging; combined dodging and nudging. Package: r-cran-ggprism Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4081 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-ggplot2, r-cran-glue, r-cran-gtable, r-cran-rlang, r-cran-scales, r-cran-tibble Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggbeeswarm, r-cran-ggnewscale, r-cran-knitr, r-cran-magrittr, r-cran-patchwork, r-cran-rmarkdown, r-cran-rstatix, r-cran-tidyr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-ggprism_1.0.7-1.ca2604.1_all.deb Size: 2742652 MD5sum: a8c50959a9ac83c249ebbe7302ac311f SHA1: b85421a644ca48242adefd149c219493a16d4919 SHA256: 7110cc27eb7264952b160b6f9dcf18b71b3b4079f0515457e94d5a48c3363e61 SHA512: 9459b2d590af887e6995cfbd0202fccbdc20715c0f5fb4203d689abeb08962901b91f0dc9b8ee223fe38a9a33e3968542d098fc6b5ea30a065e3a02490955b02 Homepage: https://cran.r-project.org/package=ggprism Description: CRAN Package 'ggprism' (A 'ggplot2' Extension Inspired by 'GraphPad Prism') Provides various themes, palettes, and other functions that are used to customise ggplots to look like they were made in 'GraphPad Prism'. 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However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots. Package: r-cran-ggpval Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggpval_0.2.5-1.ca2604.1_all.deb Size: 1106726 MD5sum: c18b3f4577fa5376e63c371b43297e3a SHA1: a884e04105381d9ded2e9a5d560f6f41abf27b91 SHA256: 188a75cb64a6d385ea11c3f3f6518f765a38a93af3f90c1bf3176f03d582b318 SHA512: 62228f00751f944b7ceed74115f30b0c26b0df951eedc194d53bede6004172a85ee3012416bd5dce1f61ff2e5331c43c30abb58dcc3d2eced025268fe5c36266 Homepage: https://cran.r-project.org/package=ggpval Description: CRAN Package 'ggpval' (Annotate Statistical Tests for 'ggplot2') Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and adds the resulting p-values to the plot with an annotation bar. Visualizing group differences are frequently performed by boxplots, bar plots, etc. Statistical test results are often needed to be annotated on these plots. This package provides a convenient function that works on 'ggplot2' objects, performs the desired statistical test between groups of interest and annotates the test results on the plot. Package: r-cran-ggqqunif Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-ggqqunif_0.1.5-1.ca2604.1_all.deb Size: 92378 MD5sum: ec38e380cf45326cc908a4f7415531ce SHA1: 950f964523f801ab373983ec757c547c27db2d31 SHA256: dc4e6cb0f4114eedcb1f9d31e766c1657c56689f85a47325cbf49a6425f7cf24 SHA512: aa0e89b52876c8ebad890bef439e6a0207a41fde47482188497778e09bfe727d3c1ebb548c902fc302d6b1d54b1a88aa5d7274a3b30b335523683ae21e7ee192 Homepage: https://cran.r-project.org/package=ggQQunif Description: CRAN Package 'ggQQunif' (Compare Big Datasets to the Uniform Distribution) A quantile-quantile plot can be used to compare a sample of p-values to the uniform distribution. 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Package: r-cran-ggquickeda Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colourpicker, r-cran-dplyr, r-cran-data.table, r-cran-dt, r-cran-formula, r-cran-forcats, r-cran-ggally, r-cran-ggbeeswarm, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggrepel, r-cran-ggridges, r-cran-ggpubr, r-cran-ggstance, r-cran-glue, r-cran-gridextra, r-cran-hmisc, r-cran-markdown, r-cran-patchwork, r-cran-plotly, r-cran-quantreg, r-cran-rlang, r-cran-rms, r-cran-rpostgres, r-cran-scales, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinyjqui, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble, r-cran-tidyr, r-cran-table1, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggquickeda_0.3.3-1.ca2604.1_all.deb Size: 2947434 MD5sum: baedf131a87417f7b50a470dc42caf38 SHA1: 2d745b89286ad2bbe8f98b38e2c2d3a0332b8162 SHA256: f791dbb8a837220d8921088952f48c35f24a20d2bc2817cb3ec600f68e75ab8a SHA512: 90bc1ddb0c19f375324e7ff22098912d8b84962710fdfbf795f583b6a9a2adaa86fa9a607ede3696791d09d41d6cd6126fa0ce890a3328acbf735023fcb54a21 Homepage: https://cran.r-project.org/package=ggquickeda Description: CRAN Package 'ggquickeda' (Quickly Explore Your Data Using 'ggplot2' and 'table1' SummaryTables) Quickly and easily perform exploratory data analysis by uploading your data as a 'csv' file. 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Package: r-cran-ggquiver Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 787 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-maps, r-cran-sf, r-cran-pkgdown, r-cran-vdiffr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggquiver_0.4.0-1.ca2604.1_all.deb Size: 677288 MD5sum: 399b36e15637ac8e58760dd58b3d700a SHA1: 898b9c78c62ce81a01258c386bc38ba2dc2a7de0 SHA256: b186d2020c3bfe98783f81993d76e22e159e62d8e0c88ecb0318244ec476003c SHA512: b58a57bcd23772be3b8dca30d7dd2708f5e139cea78eebd13ef23a8e72ae7189dccdc6a9ac501912fb28f942db97261bac9df35c28bdfef4f4518ab434dedb3e Homepage: https://cran.r-project.org/package=ggquiver Description: CRAN Package 'ggquiver' (Quiver Plots for 'ggplot2') An extension of 'ggplot2' to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. 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Implements visualisations of the methods described in Breiman (2001) and Ishwaran, Kogalur, Blackstone, and Lauer (2008) . 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We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome. 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Package: r-cran-ggsced Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggh4x, r-cran-gtable, r-cran-assert Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-ggsced_0.1.6-1.ca2604.1_all.deb Size: 148840 MD5sum: a270ff290cad1b481ba13df1ad8db347 SHA1: 90e6f8053987acd0388b0cb3dd7ef4b62bb48fc8 SHA256: 461781fe6b6e1784a1e7678e95261dbc3b00af90654cb8a485942495e54c427e SHA512: 03eaa67926933368c5f7481098105da1caff2faef60e49d296e14ab03e4cdab67bd99a9a02c1de0a2a960612dc30c829269c59d5d5c68d8d172384b2b0dde24c Homepage: https://cran.r-project.org/package=ggsced Description: CRAN Package 'ggsced' (Utilities and Helpers for Single Case Experimental Design (SCED)using 'ggplot2') Provides specialized visualization tools for Single-Case Experimental Design (SCED) research using 'ggplot2'. SCED studies are a crucial methodology in behavioral and educational research where individual participants serve as their own controls through carefully designed experimental phases. This package extends 'ggplot2' to create publication-ready graphics with professional phase change lines, support for multiple baseline designs, and styling functions that follow SCED visualization conventions. Key functions include adding phase change demarcation lines to existing plots and formatting axes with broken axis appearance commonly used in single-case research. Package: r-cran-ggsci Architecture: all Version: 5.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3832 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-scales Suggests: r-cran-gridextra, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggsci_5.0.0-1.ca2604.1_all.deb Size: 2082198 MD5sum: b345ea8fbef7d6bdd14b77fa561f4ebc SHA1: 937c50886fc3df916337e1d16d1ac433f92911ad SHA256: a551f276919508c6a3e8ca93667eac40a2859e5fc1fa7adc904b944dbdde6b2d SHA512: de0377f77571727b2d57b9f65b543145fef0cff45ede2d51d51f4433ccb8d3c8b09890d10e9bb45cc6339218570fa937afa5ae67d23e85f5cd226230dd9e0948 Homepage: https://cran.r-project.org/package=ggsci Description: CRAN Package 'ggsci' (Scientific Journal and Sci-Fi Themed Color Palettes for'ggplot2') A collection of 'ggplot2' color palettes inspired by plots in scientific journals, data visualization libraries, science fiction movies, and TV shows. Package: r-cran-ggscidca Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cmprsk, r-cran-e1071, r-cran-ggplot2, r-cran-kernlab, r-cran-randomforest, r-cran-reshape2, r-cran-survival, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-ggscidca_0.2.8-1.ca2604.1_all.deb Size: 267830 MD5sum: a72de455b85d8e2a1089b08d4e6b8419 SHA1: d555260870574ccb8c81f924767c2f887d7e54a8 SHA256: d6dfab0f4be6df950662f49e630e78ce245d65af076e061ddd8c11ea60ebce77 SHA512: 4616cc07f84245c4c903c9401af8f5815ee10cd5002c1427add601473060541b6ec40f35becc8acb51ef677f4705395ded88f564a1aaeab9b415283e2485084e Homepage: https://cran.r-project.org/package=ggscidca Description: CRAN Package 'ggscidca' (Plotting Decision Curve Analysis with Coloured Bars) Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The 'ggscidca' package adds coloured bars of discriminant relevance to the traditional decision curve. Improved practicality and aesthetics. This method was described by Balachandran VP (2015) . Package: r-cran-ggscribe Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-farver, r-cran-ggplot2, r-cran-glue, r-cran-rlang, r-cran-scales Suggests: r-cran-blends, r-cran-dplyr, r-cran-flexoki, r-cran-ggrefine, r-cran-ggwidth, r-cran-jumble, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-ggscribe_0.1.1-1.ca2604.1_all.deb Size: 558958 MD5sum: a5d317953bc6941a173b89f55f1c6520 SHA1: b01df093b8f909dc2706d10249a5199cb6ceb666 SHA256: 8cf2648768a610082b846a234998e1b2db01436ad260542866a9c733d68b9a55 SHA512: b5ff109657c47011dd31ba03142e6de8c271b68fa91e2e7c00744837966759128717cccb7c8ba00d58fe3fe1a711424b0669f6610e40495e83d110bec6c0feec Homepage: https://cran.r-project.org/package=ggscribe Description: CRAN Package 'ggscribe' (Publication-Quality 'ggplot2' Annotation) Annotation helper functions for publication-quality 'ggplot2' visualisation. These functions make it easier to annotate plots in a way that stays consistent with the set theme. Package: r-cran-ggseg.formats Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-rlang, r-cran-sf, r-cran-tidyr Suggests: r-cran-covr, r-cran-devtools, r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggseg.formats_0.0.2-1.ca2604.1_all.deb Size: 3808216 MD5sum: d595bba2b6aff9d40ebe5bece24691ec SHA1: 581204f190e461122cc430d34b73a78ab3c069df SHA256: ab1aaaa01a53b8e7205654300e9093efd564ca6e199258e51faac9f818cbcd33 SHA512: c0fcb060475cb09584030600af210fbf4abb2548ef388ee7196133406535cdc6183dabbfb68f10ce5f9ee7835464ee1aa989b2f5d5c01f497443110e22b2336d Homepage: https://cran.r-project.org/package=ggseg.formats Description: CRAN Package 'ggseg.formats' (Brain Atlas Data Structures for the 'ggseg' Ecosystem) Provides the 'ggseg_atlas' S3 class used across the 'ggseg' ecosystem for 2D and 3D brain visualisation. Ships three bundled atlases ('Desikan-Killiany', 'FreeSurfer' 'aseg', 'TRACULA') and functions for querying, subsetting, renaming, and enriching atlas objects. Also includes readers for 'FreeSurfer' statistics files. Package: r-cran-ggseg.meshes Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-freesurfer, r-cran-freesurferformats, r-cran-gifti, r-cran-ggseg.formats, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggseg.meshes_0.0.1-1.ca2604.1_all.deb Size: 2926958 MD5sum: 07f8d004773342e39b4c80942aa4dd04 SHA1: f895e6c51fb3128d76e9fc7b5ce85665ca3f7b24 SHA256: c029e13fbd943df6e4a63bea84a096d1eb86bd3f16b841313d4fe9f440d8ff3c SHA512: f3bc250efa711d2e58de06651a4156f10b54761aa538eef7bd08baf916362fa09615183ce99c7cd81be74c2c363939b1684de22cada5bb04855675004a0664c3 Homepage: https://cran.r-project.org/package=ggseg.meshes Description: CRAN Package 'ggseg.meshes' (Additional Brain Surface Meshes for the 'ggsegverse' Ecosystem) Provides additional brain surface meshes for cortical and cerebellar visualisation in the 'ggsegverse' ecosystem. Cortical surfaces include pial, white, midthickness, semi-inflated, sphere, smoothwm, and orig at fsaverage5 resolution. Cerebellar surfaces include the Spatially Unbiased Infratentorial Template (SUIT) flatmap. All meshes follow the same vertices/faces data frame format used by 'ggseg.formats' and 'ggseg3d'. Package: r-cran-ggseg3d Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2700 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggseg.formats, r-cran-htmlwidgets, r-cran-knitr, r-cran-lifecycle, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-webshot2 Suggests: r-cran-ggseg.meshes, r-cran-png, r-cran-rayshader, r-cran-rgl, r-cran-rmarkdown, r-cran-shiny, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggseg3d_2.1.1-1.ca2604.1_all.deb Size: 1615038 MD5sum: 844d672d77e602e9b88e7730b816a4f4 SHA1: 146d8a690579d456902dafa00164bf7fb9bb0fe0 SHA256: 99c7e0062593fb6029c86be629f1a97e679f854e49b09f9cca0698f8912a497b SHA512: 338d5f75a9593c11da0834a8d4f7d20cbee1cc411e69efd43dfeecd9022d72ffb1fc9fba4b7705e6220b26ad22dde0f8f21fa6f3a7df25ab0c5dd551f8e7ad1b Homepage: https://cran.r-project.org/package=ggseg3d Description: CRAN Package 'ggseg3d' (Interactive 3D Brain Atlas Visualization) Plot brain atlases as interactive 3D meshes using 'Three.js' via 'htmlwidgets', or render publication-quality static images through 'rgl' and 'rayshader'. A pipe-friendly API lets you map data onto brain regions, control camera angles, toggle region edges, overlay glass brains, and snapshot or ray-trace the result. Additional atlases are available through the 'ggsegverse' r-universe. Mowinckel & Vidal-Piñeiro (2020) . Package: r-cran-ggseg Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-ggseg.formats, r-cran-lifecycle, r-cran-sf, r-cran-tidyr Suggests: r-cran-covr, r-cran-devtools, r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggseg_2.1.1-1.ca2604.1_all.deb Size: 3251318 MD5sum: 77232788461241be3406d8f5b6d15d72 SHA1: 553bda979a407e3d105386065e8120676b09e56a SHA256: fa733af740116845e59608f268d670e30e452257c10fbcd771650bc97aeaf4a0 SHA512: 5c46ba9ccccc51768651da73950f17c45648a73945348bfca6501882f1625a4e44a8f2fb41aac5bfd4570b8e77eec0a26aae133d95081d2c2e6efa004d4921b5 Homepage: https://cran.r-project.org/package=ggseg Description: CRAN Package 'ggseg' (Plotting Tool for Brain Atlases) Provides a 'ggplot2' geom and position for visualizing brain region data on cortical, subcortical, and white matter tract atlases. Brain atlas geometries are stored as simple features ('sf'), enabling seamless integration with the 'ggplot2' ecosystem including faceting, custom scales, and themes. Mowinckel & Vidal-Piñeiro (2020) . Package: r-cran-ggsegmentedtotalbar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-forcats Suggests: r-cran-testthat, r-cran-devtools, r-cran-remotes, r-cran-roxygen2, r-cran-usethis, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggsegmentedtotalbar_0.1.0-1.ca2604.1_all.deb Size: 97494 MD5sum: c87b1a4a0aa19da5cc6e6747b863792c SHA1: 7d64b39b49bd3168caf47fe05b2360f7a65fcac0 SHA256: a0d15968445ead91b3bad64e507e629c9f794521c6aca5c76260523ae8704c40 SHA512: bd8b13d2f9f776266cd5e88c7b3e299daf44ff3ecb07f86abc2656e69d89cda7d964ffc5f63d7fc79d586693e085f6c324ad90ef82f3b04135cb183c772c68ce Homepage: https://cran.r-project.org/package=ggsegmentedtotalbar Description: CRAN Package 'ggsegmentedtotalbar' (Create a Segmented Total Bar Plot with Custom Annotations andLabels) It provides a better alternative for stacked bar plot by creating a segmented total bar plot with custom annotations and labels. 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Package: r-cran-ggsem Architecture: all Version: 0.9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blavaan, r-cran-diagrammersvg, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-lavaan, r-cran-network, r-cran-purrr, r-cran-qgraph, r-cran-rcolorbrewer, r-cran-rlang, r-cran-rtsne, r-cran-semplot, r-cran-smplot2, r-cran-stringr, r-cran-tidyr, r-cran-tidysem, r-cran-umap, r-cran-xml2 Suggests: r-cran-colourpicker, r-cran-dt, r-cran-diagrammer, r-cran-memoise, r-cran-shiny, r-cran-shinyjs, r-cran-svglite Filename: pool/dists/resolute/main/r-cran-ggsem_0.9.9-1.ca2604.1_all.deb Size: 801424 MD5sum: bf7b56e9b11e6cfb533b4d8d453ffcb9 SHA1: 551f9256037751cd13044f83ee616518ec34c0de SHA256: 8c8952d71f83b17d319c0aadc6c38e38cff2cac0a71e550110f03dd1d12f834a SHA512: ec32f7939cd400cb0bfec9a06ec19e2b8bc075aab3a8a9c3b02d5fb4ecdbce479b9c1b7c66080556ecf3d6c57e462b81a0b5ee8b7930ee9db4d9e118574d727a Homepage: https://cran.r-project.org/package=ggsem Description: CRAN Package 'ggsem' (Interactive Structural Equation Modeling (SEM) and Multi-GroupPath Diagrams) Provides an interactive workflow for visualizing structural equation modeling (SEM), multi-group path diagrams, and network diagrams in R. Users can directly manipulate nodes and edges to create publication-quality figures while maintaining statistical model integrity. Supports integration with 'lavaan', 'OpenMx', 'tidySEM', and 'blavaan' etc. Features include parameter-based aesthetic mapping, generative AI assistance, and complete reproducibility by exporting metadata for script-based workflows. Package: r-cran-ggseqlogo Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggseqlogo_0.2.2-1.ca2604.1_all.deb Size: 738906 MD5sum: d8009e616085f1dfe604cf69939119fa SHA1: 7832ce48a17fc06a73e614d5d74e1133cfb1c908 SHA256: 1734a88d9564aa6d49cd9b87bad77ce6241966d8a4b8a8f5a8773678b02924a0 SHA512: abefd98f18431c44e8c2d4e07ea76ddc00ef18f88377ec66cf8452f2d339e5bfb4c7605bae9ee31fe48ba760b0aae2cd8627973f14735e249fdcd12481634030 Homepage: https://cran.r-project.org/package=ggseqlogo Description: CRAN Package 'ggseqlogo' (A 'ggplot2' Extension for Drawing Publication-Ready SequenceLogos) The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots. Package: r-cran-ggseqplot Architecture: all Version: 0.8.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-colorspace, r-cran-dplyr, r-cran-forcats, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggtext, r-cran-glue, r-cran-haven, r-cran-patchwork, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-tidyr, r-cran-traminer Suggests: r-cran-covr, r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggseqplot_0.8.9-1.ca2604.1_all.deb Size: 1117080 MD5sum: a23bed497172d7cae25e64a6671e1a12 SHA1: 351d64b40a19def5985fed80780ebdb26392cbbf SHA256: 13558f711078f188af1aed3aa1a9d4d1bb4453c17de9090d31ee8b5dc736eb5d SHA512: 16a82eac092672fc2ce28518426747c19168b2d03bed5f6d89fe5414982c70d4c84999a42db3e5e7e9fc534fbca967162e416867e1f6ed1e8e12e4e0495cf2f8 Homepage: https://cran.r-project.org/package=ggseqplot Description: CRAN Package 'ggseqplot' (Render Sequence Plots using 'ggplot2') A set of wrapper functions that mainly re-produces most of the sequence plots rendered with TraMineR::seqplot(). Whereas 'TraMineR' uses base R to produce the plots this library draws on 'ggplot2'. The plots are produced on the basis of a sequence object defined with TraMineR::seqdef(). The package automates the reshaping and plotting of sequence data. Resulting plots are of class 'ggplot', i.e. components can be added and tweaked using '+' and regular 'ggplot2' functions. Package: r-cran-ggshadow Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-glue, r-cran-vctrs, r-cran-cli Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-ggshadow_0.0.5-1.ca2604.1_all.deb Size: 588560 MD5sum: 978786287bb390c12a915eed6f783f42 SHA1: b857b14830ab6ac36d4a38bb105b75b1e1000a3f SHA256: ab5efdcc220ad500fb44510651a95ad77fa220bf59ec6af590535fd921f996e7 SHA512: 046db6721618b688aa2c980d2d577a2a918da1595439938b51332db20dc566979828dc9078bdce2af450cc402a22268d4dd48cceda93737fcf5e984fbe4b90b9 Homepage: https://cran.r-project.org/package=ggshadow Description: CRAN Package 'ggshadow' (Shadow and Glow Geoms for 'ggplot2') A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a neon glow around lines to get a steampunk style. Package: r-cran-ggside Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3772 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-rlang, r-cran-scales, r-cran-cli, r-cran-glue, r-cran-tibble, r-cran-vctrs, r-cran-s7, r-cran-lifecycle Suggests: r-cran-tidyr, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-ggdendro, r-cran-viridis, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-ggside_0.4.1-1.ca2604.1_all.deb Size: 2996416 MD5sum: 2990b1226b34c989e87b7c58aae0122e SHA1: b0f646b034328be6ad6d54b2a886bc6a9286d723 SHA256: 1b68e79ac071d2cb1dfa5b9fd630671b1bdfe04f9ecf97e06b658f34c2153d2d SHA512: d58e1266012c5bcecac74ada26773907f81d4873cf34a18d7b2cff0b97b8d467153dc5fb8689108b780a2975aca1608c18ac36cf853b5033607028ed1c653e6d Homepage: https://cran.r-project.org/package=ggside Description: CRAN Package 'ggside' (Side Grammar Graphics) The grammar of graphics as shown in 'ggplot2' has provided an expressive API for users to build plots. 'ggside' extends 'ggplot2' by allowing users to add graphical information about one of the main panel's axis using a familiar 'ggplot2' style API with tidy data. This package is particularly useful for visualizing metadata on a discrete axis, or summary graphics on a continuous axis such as a boxplot or a density distribution. Package: r-cran-ggsignif Architecture: all Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggsignif_0.6.4-1.ca2604.1_all.deb Size: 567510 MD5sum: a52a2cff51671d201252cb6587f74000 SHA1: 4369aeba8c0cca48f7a2b129729857d8f9041980 SHA256: 03d635e9c8025d864ab08b78cc7bd2f6390e22764feeb1d2b6056da992cc142d SHA512: 14a42c4666408aeab8c9815d3d0a2bbd2fd31c2fa027c8330fabd4535cc69963cb7620c93687d4368bcc7a30a4f86c47d4e986344840a2bf65d46c1b0c6a4522 Homepage: https://cran.r-project.org/package=ggsignif Description: CRAN Package 'ggsignif' (Significance Brackets for 'ggplot2') Enrich your 'ggplots' with group-wise comparisons. This package provides an easy way to indicate if two groups are significantly different. 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Package: r-cran-ggskewboxplots Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-waldo, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggskewboxplots_1.0.0-1.ca2604.1_all.deb Size: 90776 MD5sum: 1ad5c09a0de22403525f81f8d4144d87 SHA1: 2a9ebac511db80b1e10e969d77a41881ad79ef7e SHA256: f47787e97091f7bb4ef7f419b44cdf403343951b6ca87c4de6279ccf2ab7ab7c SHA512: f4159773e5ae1c8f52d6993f072b0415e25c023bc3efd19288a61f2a91b5b347c1882078a7a27862f74743931115b6e9442cf530c6199f1d60cca9c074a013c7 Homepage: https://cran.r-project.org/package=ggskewboxplots Description: CRAN Package 'ggskewboxplots' (Skew Boxplot Geoms for 'ggplot2') Provides 'ggplot2' extensions for creating skewed boxplots using several statistical methods (Kimber, 1990 ; Hubert and Vandervieren, 2008 ; Adil et al., 2015 ; Babura et al., 2017 ; Walker et al., 2018 ). 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Package: r-cran-ggsky Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggsky_0.1.0-1.ca2604.1_all.deb Size: 447504 MD5sum: fa278d19586b2a07129e54f8f5226acb SHA1: 4f837730abf86ce932016ab55e30bf458bc66b66 SHA256: af7cfb1e118b62c5b5dcd7b235f61c53d2248c1a3079475778aea32c43fdc9fe SHA512: 30250855aa83e482a85d3c477001f55ce0fbd8d08120b0e6ccc87f74fd6d0063ed0a781e82bd87b9a1cfaa10ef03acde904d5af74e2dbba1916d988fd78bed2b Homepage: https://cran.r-project.org/package=ggsky Description: CRAN Package 'ggsky' (Galactic and Equatorial Coordinate Implementation for 'ggplot2') Simple tools to draw sky maps in 'ggplot2' using galactic or equatorial coordinates. Includes custom coordinate systems, grid labels, and helpers for sky map breaks. Package: r-cran-ggsmc Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-poorman, r-cran-ggplot2, r-cran-gganimate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggsmc_0.2.0-1.ca2604.1_all.deb Size: 1123338 MD5sum: 6e5f35ff1743655e018ceba09965a566 SHA1: 7b58be9dfdc25c1a79b8bf1302ed5faaf03e4f21 SHA256: b11dc6e521e8b5613c1a9fd0a41b3b05d423e8bde931f6db97f043b6afbf543e SHA512: 7dd61b0bb82a8586d26115aaaab80b8f0f5638269638b2e949e05059bdab523b2e774235c9a68871f42359b2be0df8823c531908472678ca38f6a34710dda959 Homepage: https://cran.r-project.org/package=ggsmc Description: CRAN Package 'ggsmc' (Visualising Output from Sequential Monte Carlo andEnsemble-Based Methods) Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions. Package: r-cran-ggsoccer Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-ggsoccer_0.2.0-1.ca2604.1_all.deb Size: 269902 MD5sum: 6a7d905c06b332902282c666736010bd SHA1: afa2aac7f64a92fe12e746ea0a0ed21173d93c5e SHA256: 971910bf8767b2402253e91e9b042f9166e0185f282da34e6b0387299e1d5d3e SHA512: 8e05d38acf9dd613787ad60321bebcd4149c69f63d5f1aa0d3a210287f94561049677883b1e0c0bc0837acdf9823bf25dab83313a31a606679f7884b8fb598dc Homepage: https://cran.r-project.org/package=ggsoccer Description: CRAN Package 'ggsoccer' (Plot Soccer Event Data) The 'ggplot2' package provides a powerful set of tools for visualising and investigating data. The 'ggsoccer' package provides a set of functions for elegantly displaying and exploring soccer event data with 'ggplot2'. 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Package: r-cran-ggsolvencyii Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-covr, r-cran-ggmap, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggsolvencyii_0.1.2-1.ca2604.1_all.deb Size: 319192 MD5sum: 29c59ebbc3a397b202b705ff101ae665 SHA1: bd441a5bfe578a8766d38828d6ec142b4328e950 SHA256: e1aee1949535c485d1412d25dc18a1348c9253062637b3e722e053b3c1b9568e SHA512: 2704790b22de38c73605435ee7ada6a457d1069610f166b02b86fc48c9f867f9384b86273090bc8925ba1ed3445d845745193985e511dce42c508db6c2f910db Homepage: https://cran.r-project.org/package=ggsolvencyii Description: CRAN Package 'ggsolvencyii' (A 'ggplot2'-Plot of Composition of Solvency II SCR: SF and IM) An implementation of 'ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014. . Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided. The structure files can be adopted for localization or for insurance companies who use Internal Models (IM). Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts. With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment). Package: r-cran-ggsom Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-ggplot2, r-cran-kohonen, r-cran-assertthat, r-cran-data.table, r-cran-entropy, r-cran-tibble Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggsom_0.4.0-1.ca2604.1_all.deb Size: 38540 MD5sum: 00d5493abf13fbedba100ab1a0c96046 SHA1: b1164016a462299bc678e539d8926f8b7d7ded08 SHA256: fa4266a0ac7236e0da3fb275df5f684a26d23f908ae693523c8173e631c002c9 SHA512: 1b24d37536ae7ad8e2394d59c514e40429bcf2fe813312da1644e3d210b61d7f9b3970eceef731b777d1ea70bed20b0d9c21b487993054c5da989f03b1fe874c Homepage: https://cran.r-project.org/package=ggsom Description: CRAN Package 'ggsom' (New Data Visualisations for SOMs Networks) The aim of this package is to offer more variability of graphics based on the self-organizing maps. 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Package: r-cran-ggspectra Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology, r-cran-ggplot2, r-cran-photobiologywavebands, r-cran-scales, r-cran-ggrepel, r-cran-lubridate, r-cran-rlang, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-ggspectra_0.4.0-1.ca2604.1_all.deb Size: 2664240 MD5sum: 232f31b702db67834ecaae4e8d629080 SHA1: 5384a6e8bcb45bb860bab980652421e46d50422b SHA256: ea0014fdffd398c07bcc46a5cbb36303a5ef509ff3c6ab7963334f594331583a SHA512: 91bb4ac3db94fa13dfc83bc0fbf96463fdf2697041492ceb0bdcf6d978c593823dad160e9b26c68fde7614293396c208e1a4694532764e9fa81c4a8cd87d1880 Homepage: https://cran.r-project.org/package=ggspectra Description: CRAN Package 'ggspectra' (Extensions to 'ggplot2' for Radiation Spectra) Additional annotations, stats, geoms and scales for plotting "light" spectra with 'ggplot2', together with specializations of ggplot() and autoplot() methods for spectral data and waveband definitions stored in objects of classes defined in package 'photobiology'. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-ggstackplot Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-lifecycle, r-cran-tidyselect, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-cowplot, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-scales, r-cran-pangaear Filename: pool/dists/resolute/main/r-cran-ggstackplot_0.4.1-1.ca2604.1_all.deb Size: 3436240 MD5sum: 8602c35d71f19c1c0a5c4ce6e7539f0d SHA1: 2f2751fee94cb7273911babeeef64035de1cbf7d SHA256: b84e34f420da56cc9bc65d4a47f4644f629d9c57bf6ae794ec63974fa05278ea SHA512: 90b2b30596161ce721682d737492843e4e0034330a3935b0371597b9d69999920623fcc8fe27df682188324f62e392e71ddb7629272923ca0588c2b2dc7df61f Homepage: https://cran.r-project.org/package=ggstackplot Description: CRAN Package 'ggstackplot' (Create Overlapping Stacked Plots) Easily create overlapping grammar of graphics plots for scientific data visualization. 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Package: r-cran-ggstar Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 882 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-gridextra, r-cran-cli, r-cran-rlang, r-cran-ggiraph Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-prettydoc, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-ggstar_1.0.6-1.ca2604.1_all.deb Size: 293524 MD5sum: 72244c056c50351d3f168dae1261e3a2 SHA1: 68aa3b182b9dc6b28aec93510ca69f387cb36725 SHA256: 24e84dc236e0d2457f71ebea31d3b5d5c5bfbf5106bf9273196b94cf178b2ce8 SHA512: 2dee21bb22e691260af14779e758017723ce7d105f5bee0e3dcdd2e836d8b76d0dcf887a11093553220fcefa76798a61ce94128b85f14142a3a2a053e5a29eb8 Homepage: https://cran.r-project.org/package=ggstar Description: CRAN Package 'ggstar' (Multiple Geometric Shape Point Layer for 'ggplot2') To create the multiple polygonal point layer for easily discernible shapes, we developed the package, it is like the 'geom_point' of 'ggplot2'. It can be used to draw the scatter plot. Package: r-cran-ggstats Architecture: all Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1943 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lifecycle, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr Suggests: r-cran-betareg, r-cran-broom, r-cran-broom.helpers, r-cran-emmeans, r-cran-glue, r-cran-gtsummary, r-cran-knitr, r-cran-labelled, r-cran-reshape, r-cran-rmarkdown, r-cran-nnet, r-cran-parameters, r-cran-pscl, r-cran-testthat, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggstats_0.13.0-1.ca2604.1_all.deb Size: 1259922 MD5sum: 8aac1954d20bb9d461ac68442b0d8406 SHA1: cf93c01fe898c8edf38e6eae024c8a20cd41e36e SHA256: 328ab0b078f81d3a7940e61bd8f184ac134a298b4ff8f3f33c41611e0b84e8ad SHA512: fac377fe259d7610c1ba31bd1a1a1d831fad2d35b87ab8c08e3eba9bf196807ccd6fba4e5a04deb7470162dae7c2e50760a590953e5872573a4bc8d50ee1f1c9 Homepage: https://cran.r-project.org/package=ggstats Description: CRAN Package 'ggstats' (Extension to 'ggplot2' for Plotting Stats) Provides new statistics, new geometries and new positions for 'ggplot2' and a suite of functions to facilitate the creation of statistical plots. Package: r-cran-ggstatsplot Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3815 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-correlation, r-cran-datawizard, r-cran-dplyr, r-cran-forcats, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggside, r-cran-ggsignif, r-cran-glue, r-cran-insight, r-cran-paletteer, r-cran-parameters, r-cran-patchwork, r-cran-performance, r-cran-purrr, r-cran-rlang, r-cran-statsexpressions, r-cran-tidyr Suggests: r-cran-afex, r-cran-bayesfactor, r-cran-bayestestr, r-cran-gapminder, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-metabma, r-cran-metafor, r-cran-metaplus, r-cran-patrick, r-cran-psych, r-cran-rmarkdown, r-cran-rstantools, r-cran-survival, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-withr, r-cran-wrs2 Filename: pool/dists/resolute/main/r-cran-ggstatsplot_1.0.0-1.ca2604.1_all.deb Size: 3241086 MD5sum: 30791003edbbef3b05ae70aaf13fba08 SHA1: 69b6e6e5e0219690d6a2561980cd430650e8bf2d SHA256: 995f5748c262ad0a02ceebf5af9c5e94c1166ce9c1eac507d0543a9be91499eb SHA512: 66b9e426e9bcc8774e91109fd8e3370c462007ad8ddea0c58fa62d53e9c038633ad444415737f7c487995af97b2016705427401968a502337c0cc25ea769b720 Homepage: https://cran.r-project.org/package=ggstatsplot Description: CRAN Package 'ggstatsplot' ('ggplot2' Based Plots with Statistical Details) Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) . Package: r-cran-ggstudent Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-scales Filename: pool/dists/resolute/main/r-cran-ggstudent_0.1.2-1.ca2604.1_all.deb Size: 59054 MD5sum: 31c080d2d4a6c71c8eee7a1506389799 SHA1: 8c09242a0e08791eba1bf1e82458fed4a31f36de SHA256: e8d5877ac5890cc2c410e05e2632681c19994e498a9a46798ba05212c3efb161 SHA512: 31dd242e7316d24e20d3d5c88e82e75a968fd688aae57355c698763b39baaf88f267977a375330c5dfaccaa6ba949c3f89b032e73ccae25a55280393c71849a0 Homepage: https://cran.r-project.org/package=ggstudent Description: CRAN Package 'ggstudent' (Continuous Confidence Interval Plots using t-Distribution) Provides an extension to 'ggplot2' (Wickham, 2016, ) for creating two types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean. These plots contain multiple user-defined confidence areas with varying colours, defined by the underlying t-distribution used to compute standard confidence intervals for the mean of the normal distribution when the variance is unknown. Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the shape (horizontal width) is defined by the probability density function of the t-distribution. These visualizations are studied in (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) . Package: r-cran-ggsurveillance Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-isoweek, r-cran-legendry, r-cran-lubridate, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggrepel, r-cran-hmisc, r-cran-knitr, r-cran-outbreaks, r-cran-plotly, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggsurveillance_0.5.2-1.ca2604.1_all.deb Size: 962576 MD5sum: 9a07c948f7f81f8f18ecca022ee17e30 SHA1: 383ab0776fee19a997bc145a90691be07185f133 SHA256: 88c604bcdf54c7cb4c032f16763a5b99021d8e8f8857a7089ce7f7da61ac3f24 SHA512: 82e71ab8a86f1428c52b563a2493dd4a6ec333cc9933a4fe17edf439d195850263220aa902cf3a37638b27d22a2fe94f6f5c821a4687865ec21e6c7c4f16b6b8 Homepage: https://cran.r-project.org/package=ggsurveillance Description: CRAN Package 'ggsurveillance' (Tools for Outbreak Investigation/Infectious Disease Surveillance) Create epicurves, epigantt charts, and diverging bar charts using 'ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation (time series data). Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the 'ggplot2', (D) labelling of the last value of a time-series. An introduction on how to use epicurves can be found on the US CDC website (2012, ). Package: r-cran-ggsurvey Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-survey, r-cran-hexbin, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-ggsurvey_1.0.0-1.ca2604.1_all.deb Size: 72792 MD5sum: 60512c8c0f4c9dec226775c80b718260 SHA1: 84c4b0388c0a9da4324d900ad5036c7be6c73811 SHA256: 7aaf43c4105ccf31896e735e5261cc74ee8d077ea501a2f4fead3841cf41b83e SHA512: 772a054da7c8f60cbf868e5726e6612a64813b13aa2eff16e6aed658e28593482ee3c05b06de4c1ed14b574dd8fd6b762d395c97de0b92e548c459b0fdec4956 Homepage: https://cran.r-project.org/package=ggsurvey Description: CRAN Package 'ggsurvey' (Simplifying 'ggplot2' for Survey Data) Functions for survey data including svydesign objects from the 'survey' package that call 'ggplot2' to make bar charts, histograms, boxplots, and hexplots of survey data. Package: r-cran-ggsurvfit Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-gtable, r-cran-patchwork, r-cran-rlang, r-cran-survival, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tidycmprsk, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-ggsurvfit_1.2.0-1.ca2604.1_all.deb Size: 574494 MD5sum: 8e9758a0563307b644b831919d08d692 SHA1: 4c1b63c265d5620c9f1987a2422d8614df17398d SHA256: 3dc10ca4bea5c2c2e68cdefafedcff8afb7c0aacc790ded78eeb586649cf7dc1 SHA512: a427311df9bf4d0724c410a51ab96b8bbb2dfdad8e4a0e46ddd68e99074f242794cf5fac322879feb7b539df70589301b2f3474d1cac573d2089612198bd0759 Homepage: https://cran.r-project.org/package=ggsurvfit Description: CRAN Package 'ggsurvfit' (Flexible Time-to-Event Figures) Ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper 'ggplot2' geoms or stat methods, allowing the functions from this package to be combined with any function or customization from 'ggplot2' and other 'ggplot2' extension packages. Package: r-cran-ggswissmaps Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1952 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-sf, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggswissmaps_0.1.2-1.ca2604.1_all.deb Size: 1665790 MD5sum: 9614fa6cafb54deeae19e794321eb048 SHA1: 45bfa5a30f50bd0aca257924ba8826bce40220f0 SHA256: c438f5168af6e1fede666e9c2206e8e4026aa2f19a5d80497d77ddd3883ace56 SHA512: 9427083bab96348e51322bb00d96ed6061f91d5c32490ada5b9fe7fc4d9c0cc9a1f76db23adc255cd9d50d4f5b0111783da0eea02e4bd1d69b86db93f2fb5683 Homepage: https://cran.r-project.org/package=ggswissmaps Description: CRAN Package 'ggswissmaps' (Offers Various Swiss Maps as Data Frames and 'ggplot2' Objects) Offers various swiss maps as data frames and 'ggplot2' objects and gives the possibility to add layers of data on the maps. 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Package: r-cran-ggtangle Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggfun, r-cran-ggplot2, r-cran-ggrepel, r-cran-igraph, r-cran-rlang, r-cran-yulab.utils Suggests: r-cran-aplot, r-cran-cli, r-cran-ggiraph, r-cran-ggnewscale, r-bioc-ggtree, r-cran-quarto, r-cran-scatterpie Filename: pool/dists/resolute/main/r-cran-ggtangle_0.1.2-1.ca2604.1_all.deb Size: 560978 MD5sum: 28d95c6ee4d3bd03c75c5db3dd21d4f6 SHA1: 50d69474c819d2d960c9d52f16c842904dab05da SHA256: 189fbdbb1517342a9ca05a020417711fe12b057e91a9f3663c6e29eb647584dd SHA512: 4ca90ef7a897283e52f8352f120845f6842818bb1bf8f1fc77fe447bbb41de99e8aaa87ff53c18b1a06659d11725b4da40efa98901853ea7e51885224ec3c691 Homepage: https://cran.r-project.org/package=ggtangle Description: CRAN Package 'ggtangle' (Draw Network with Data) Extends the 'ggplot2' plotting system to support network visualization. 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Package: r-cran-ggtaxplot Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tidyverse, r-cran-scales, r-cran-rcolorbrewer, r-cran-cluster, r-cran-vegan, r-cran-ggalluvial, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-ggtaxplot_0.0.1-1.ca2604.1_all.deb Size: 57338 MD5sum: 038c20caf919df5e919dd8f242723e92 SHA1: d4881a6946e226c896bac026848f316a74815e94 SHA256: f5369faa648222fe07da4dcd8db2123dd39f8b010786dc930acf926d05d2c8cc SHA512: b349d8c64237b803d11e148c7a4be4e072148b5239a62bcc2add38a284da5c75110485b822f4c39562adbccf49addf5cdba231750c361146f2312af02ec6fc07 Homepage: https://cran.r-project.org/package=ggtaxplot Description: CRAN Package 'ggtaxplot' (Create Plots to Visualize Taxonomy) Provides a comprehensive suite of functions for processing and visualizing taxonomic data. 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Package: r-cran-ggtrace Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ggtrace_0.2.0-1.ca2604.1_all.deb Size: 1141650 MD5sum: 840c8edc313b95b80ae84e5e9145cea9 SHA1: 1dc0e8a224e4452795d1499b7d8b9c244af6753e SHA256: 354c44853b29a2332ae94667098a48db3b262ffc00a62bbad1e3a99f97403bbd SHA512: bff457beb0a1bcb545225722b121a20446ccc2e7c7a174e9ad01e2e6b45b1f5d85eacd2a7cd27832c5fc5dffffb3cb01c50b822080771f4c850d4085efb7f43a Homepage: https://cran.r-project.org/package=ggtrace Description: CRAN Package 'ggtrace' (Trace and Highlight Groups of Data Points) Provides 'ggplot2' geoms that allow groups of data points to be outlined or highlighted for emphasis. 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For a general overview of the methods used in this package, see Ritz and Streibig (2008) and Greenwell and Schubert Kabban (2014) . Package: r-cran-ggtricks Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2924 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggtricks_0.1.0-1.ca2604.1_all.deb Size: 2756278 MD5sum: 896fcfc676a51a2f23f2d1b665cbabe9 SHA1: dc769cb47d80707c7acf9c9f73c5fcc64ff7c3f0 SHA256: 5b96c1a15d661b5022738d52e79dead83e923c2ce4ab78ee7cdbb77c0d3e0823 SHA512: 66fa1a8ba5f5da48b7cc5fceb2d3df3deb22fef20da5217dc87e9e08b16a2ca70371572d45591463d60e1b65421a6dd2cadaf6621ad725da99ccf5c3ce705a20 Homepage: https://cran.r-project.org/package=ggtricks Description: CRAN Package 'ggtricks' (Create Sector and Other Charts Easily Using Grammar of Graphics) A collection of several geoms to create graphics, using 'ggplot2' and the Cartesian coordinate system. 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Package: r-cran-ggum Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-abind, r-cran-viridis, r-cran-rdpack, r-cran-xlsx Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggum_0.5-1.ca2604.1_all.deb Size: 238076 MD5sum: 655a009f239d237ee4092e109063b08b SHA1: 3c3bac5893eb00b7687b129454cb18813314a3d8 SHA256: 522bff9fe2fd86b44202145e5e703679c328cf7df6d9f0a7cafe8a1f3370707d SHA512: d4b3041f2400cf2d89877f361501319e272e30eacf5d878e47d74e0bb230920595c67fad8f9aad6758430006235a6093916a322094041ca5e645b7b003ad79e3 Homepage: https://cran.r-project.org/package=GGUM Description: CRAN Package 'GGUM' (Generalized Graded Unfolding Model) An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) ). It allows to simulate data sets based on the GGUM. 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Package: r-cran-ggupset Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2830 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-tibble, r-cran-rlang, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggupset_0.4.1-1.ca2604.1_all.deb Size: 1993320 MD5sum: 2dbf64f88084df0031a04f1975b287c8 SHA1: 0d6c9c1e2e6e9bba18187cd5069e4fb54fcd1dd3 SHA256: bb4b43715325ac3329c47fb44d559dee7edf2fa1ad26148e875d005b3d2f5319 SHA512: ba72d909fac2b5141b6db367c63a0a6301fd7a891ec35bc19df17f1ec7822844a41ad6b868c974705ea960216baf0eecfef1553e49235dc5f6132dbe8152ad46 Homepage: https://cran.r-project.org/package=ggupset Description: CRAN Package 'ggupset' (Combination Matrix Axis for 'ggplot2' to Create 'UpSet' Plots) Replace the standard x-axis in 'ggplots' with a combination matrix to visualize complex set overlaps. 'UpSet' has introduced a new way to visualize the overlap of sets as an alternative to Venn diagrams. This package provides a simple way to produce such plots using 'ggplot2'. In addition it can convert any categorical axis into a combination matrix axis. Package: r-cran-ggvariant Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cli, r-cran-scales Suggests: r-cran-plotly, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ggvariant_0.1.0-1.ca2604.1_all.deb Size: 131744 MD5sum: 8b85a251a3952694456fea68f52da145 SHA1: 4d8a61fbd084892885a2975ef8208cb92dd1c9ad SHA256: 083a29bfd430f9172c86e9109ac88496dfd30e9b196f9e901e47dbb4ed5cd601 SHA512: 1bbb82ea46d7fe28d332c9834345d0a260cce461be0c5c6aab7bb7dfd614586bfdd2c335877d7fca87ad022fe7ddbdd327d5e210cbebc7d68bdcf416592a7ab8 Homepage: https://cran.r-project.org/package=ggvariant Description: CRAN Package 'ggvariant' (Tidy, 'ggplot2'-Native Visualization for Genomic Variants) A simple, opinionated toolkit for visualizing genomic variant data using a 'ggplot2'-native grammar. Accepts VCF files or plain data frames and produces publication-ready lollipop plots, consequence summaries, mutational spectrum charts, and cohort-level comparisons with minimal code. Designed for both wet-lab biologists and experienced bioinformaticians. Package: r-cran-ggvegan Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-dplyr, r-cran-tibble, r-cran-vctrs, r-cran-ggplot2, r-cran-tidyr, r-cran-ggrepel, r-cran-generics Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-permute, r-cran-withr Filename: pool/dists/resolute/main/r-cran-ggvegan_0.2.1-1.ca2604.1_all.deb Size: 229878 MD5sum: 197af1dbd875933d9c968a9958747e2b SHA1: d93fe7fb8c1eb96539153da5797db812529427b8 SHA256: ab1a7ca1f27dc5bee817deeb9057b03835552b0119d0585f0d52614af105896b SHA512: 1e0c09c2c2f466301a9d3de1d6c1e4dc0efce35ea69100ab5e2af0534b7dc9ca475a7ca5864d8fff015f911444be3f05a74b014f2b22fa50ec459abd91a5d6ee Homepage: https://cran.r-project.org/package=ggvegan Description: CRAN Package 'ggvegan' ('ggplot2' Plots for the 'vegan' Package) Functions to produce 'ggplot2'-based plots of objects produced by functions in the 'vegan' package. Provides 'fortify()', 'autoplot()', and 'tidy()' methods for many of 'vegan''s functions. The aim of 'ggvegan' is to make it easier to work within the 'tidyverse' with 'vegan'. Package: r-cran-ggvenn Architecture: all Version: 0.1.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ggvenn_0.1.19-1.ca2604.1_all.deb Size: 77802 MD5sum: 28ea63ac1e9bf386c49c6a57ab92dd1f SHA1: afba0f9c04d568f427d87e2c2799a71b2da30c8d SHA256: 6905cccd1e672cebc911e59acff68f59042079bc462512fd3e2023eafc885f2a SHA512: 7d8845a995d73da8e768ecb8fdb3a756f8416e3f2d1850df8a2e314bce6e1b4d1c8f4a4dc5b70381ee2359f1f5c3c5c381ce6535382a3d2c37fa4bb512602af3 Homepage: https://cran.r-project.org/package=ggvenn Description: CRAN Package 'ggvenn' (Draw Venn Diagram by 'ggplot2') An easy-to-use way to draw pretty Venn diagrams using 'ggplot2'. This package provides functions to create Venn diagrams with customizable colors, labels, and styling options. Package: r-cran-ggvenndiagram Architecture: all Version: 1.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-aplot, r-cran-venn, r-cran-yulab.utils, r-cran-forcats Suggests: r-cran-testthat, r-cran-knitr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-shiny, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ggvenndiagram_1.5.7-1.ca2604.1_all.deb Size: 3831736 MD5sum: a110bed798bdb31f909de26c98088776 SHA1: c00b0d23be2f45a4ce2740edc796633314661740 SHA256: bf6c24c4c73d6afbc7593b1715c1df99d26a5bc34b2cdfdc412180e4f0d5e53c SHA512: baee73df1eec1eb403705229f207d965a3af99a6b70b5f6d371843d4d1abe7964de37576db2ee56b3000cbf63a604e9a440ceeb2956fd6c91c645df8a7e3d322 Homepage: https://cran.r-project.org/package=ggVennDiagram Description: CRAN Package 'ggVennDiagram' (A 'ggplot2' Implement of Venn Diagram) Easy-to-use functions to generate 2-7 sets Venn or upset plot in publication quality. 'ggVennDiagram' plot Venn or upset using well-defined geometry dataset and 'ggplot2'. The shapes of 2-4 sets Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which are developed and imported from another package 'venn', authored by Adrian Dusa. We provided internal functions to integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections of them, then separately plot Venn in four components, set edges/labels, and region edges/labels. From version 1.0, it is possible to customize these components as you demand in ordinary 'ggplot2' grammar. From version 1.4.4, it supports unlimited number of sets, as it can draw a plain upset plot automatically when number of sets is more than 7. 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Incluye datos de ecologia, salud publica, educacion, economia y biodiversidad para la ensenanza de visualizacion de datos con 'ggplot2'. 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The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures. Package: r-cran-ghrmodel Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6527 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dlnm, r-cran-dplyr, r-cran-ggplot2, r-cran-ghrexplore, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-sf, r-cran-sn, r-cran-rcolorbrewer, r-cran-colorspace, r-cran-testthat, r-cran-spdep, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ghrmodel_0.1.1-1.ca2604.1_all.deb Size: 5464698 MD5sum: 69d23cc705b1501eae9824e0952a39b7 SHA1: 47325c3f8f678fe33a7c1c24828bc45a8a0ef67f SHA256: 43550aa6be07468cf5706eac1b8fab3660be8f73b5db1012612781bd6e685afd SHA512: f0a68f75feac981d3442f76e7012f72872f6df5577d554e2f97c7d6698c1c4be8a2a565d02f995f69a003c3e324133f89c271740150f9b823f1a4b519e0c2e59 Homepage: https://cran.r-project.org/package=GHRmodel Description: CRAN Package 'GHRmodel' (Bayesian Hierarchical Modelling of Spatio-Temporal Health Data) Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the 'INLA' framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) . Package: r-cran-ghs Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ghs_0.1-1.ca2604.1_all.deb Size: 1055008 MD5sum: 908db94ec2857d26f6ce825d68a3fd3e SHA1: 22090c83ce95ff97c9216b6a08b7b8d1545995d2 SHA256: 208cf9b4f41ec3cecb2d13cffc074f0f48a1b117bfaacef02f002f4793f3a352 SHA512: 37e1e09fad0eea2714084d979f18be6cecf05f24690d0d5f57c8603570dbaf551d9cd95d664b54386a968dc9aff1d53d0ff38d1d1a275ca0545a580b9a3eabcd Homepage: https://cran.r-project.org/package=GHS Description: CRAN Package 'GHS' (Graphical Horseshoe MCMC Sampler Using Data Augmented BlockGibbs Sampler) Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) . The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) , and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) . Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) . Package: r-cran-ghypernet Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbmcapply, r-cran-plyr, r-cran-numbers, r-cran-purrr, r-cran-dplyr, r-cran-rlang, r-cran-reshape2, r-cran-rootsolve, r-cran-texreg Suggests: r-cran-biasedurn, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggraph, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ghypernet_1.1.2-1.ca2604.1_all.deb Size: 905144 MD5sum: aa0debbf7c74b9c2b27bca0cb4fd339f SHA1: 623d77229e774468c51a216d83329b331cac51e1 SHA256: 039d86c3f617a3abf1a8a80c6e3f7c3ff159e93132b9d1ff81712dd2a84d9f12 SHA512: 5b7fd3fb9bb766d94df8a83416a317fed7b4d00b4d167cd40d88d0563355bc340a647eb28d289367de0e4959e242cee9c7b9eaab54abb72c365b063e4638a431 Homepage: https://cran.r-project.org/package=ghypernet Description: CRAN Package 'ghypernet' (Fit and Simulate Generalised Hypergeometric Ensembles of Graphs) Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG). To learn how to use it, check the vignettes for a quick tutorial. Please reference its use as Casiraghi, G., Nanumyan, V. (2019) together with those relevant references from the one listed below. The package is based on the research developed at the Chair of Systems Design, ETH Zurich. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) . Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) . Casiraghi, G., (2017) . Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) . Casiraghi, G. (2019) . Casiraghi, G., Nanumyan, V. (2021) . Casiraghi, G. (2021) . Package: r-cran-giant Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1334 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fdrtool, r-cran-st, r-bioc-limma, r-bioc-globaltest, r-bioc-deseq2, r-bioc-globalancova Filename: pool/dists/resolute/main/r-cran-giant_1.3.4-1.ca2604.1_all.deb Size: 1281866 MD5sum: d8954f60f39414e65382d5cca98106d3 SHA1: eb5a69dd86698fa142b7ec78761192d89a9c2ad4 SHA256: 6d67ef0f39745d5bd3f2d2801d54d0eab222cdec4f5a5c11ec68646910232d49 SHA512: 226b1bc1b5103b08ea435ba06fd193c15acbd8381d31f01f306355426fffd93c50214f2fb489472cad30ed3c040f2531be7d5c636fc5302ab27a66a51a9538da Homepage: https://cran.r-project.org/package=GiANT Description: CRAN Package 'GiANT' (Gene Set Uncertainty in Enrichment Analysis) Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets, Schmid et al. (2016) . 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More information about the GIFT database can be found at and the map of available floras can be visualized at . The API and associated queries can be accessed according the following scheme: . Package: r-cran-gifti Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-base64enc, r-cran-r.utils Suggests: r-cran-rgl, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-gifti_0.9.0-1.ca2604.1_all.deb Size: 70094 MD5sum: 72aa4015fc8088d70d967e230a233934 SHA1: ab94103ec6f65b387e34161dd520577bc7084d5f SHA256: 515230761ff006c9940e2815f98bea52afabbbabec58453770bd81db062cf313 SHA512: 05a46dd761b3d7a1dbd930f5f1cd1812a27a9e64e4f51fe1521c4f1c0e89e9c235fda57e009bd8392bde100c7c68024ae2c0b21455ac6f6f97e18bd04da28858 Homepage: https://cran.r-project.org/package=gifti Description: CRAN Package 'gifti' (Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information) Functions to read in the geometry format under the 'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 'GIFTI' . These files contain surfaces of brain imaging data. Package: r-cran-giftr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-giftr_0.1.0-1.ca2604.1_all.deb Size: 72408 MD5sum: 690842f1cd70c4b1092f642b1a4ee964 SHA1: f4963681de6f20b3ee3a3be9cc2247558941b07a SHA256: f880e0f4b71742003b48d6c40cfe76d9435fb137cb23f14c4378fc86cc660f16 SHA512: eef8090a8b09c0d430de80c242cb3ff0a4d5d5ca4826686ad5cf08bca0ec94e01a13d10afcb4ea6afc0203a93cf78ee1e82ce5fa61ebc547f240bb64b043849e Homepage: https://cran.r-project.org/package=GIFTr Description: CRAN Package 'GIFTr' (GIFT Questions Format Generator from Dataframes) A framework and functions to create 'MOODLE' quizzes. 'GIFTr' takes dataframe of questions of four types: multiple choices, numerical, true or false and short answer questions, and exports a text file formatted in 'MOODLE' GIFT format. You can prepare a spreadsheet in any software and import it into R to generate any number of questions with 'HTML', 'markdown' and 'LaTeX' support. Package: r-cran-gillespiessa Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1930 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gillespiessa_0.6.2-1.ca2604.1_all.deb Size: 1257594 MD5sum: a983d642dac62dd4341499c0e1253708 SHA1: 3121886694392747966aafd7454e2b9694d720d1 SHA256: 2ae055eef955dd3d1068d7d61071eb9899b7f5827570339201070ad8c3e69b63 SHA512: 5f9553022dfb95368afe7d99208c11740e447f032e9f087c1a7664fdb136a6716b4aa33f33d6a7782d82132d53fd06124841f9e87d55f4806ba460e28b5accde Homepage: https://cran.r-project.org/package=GillespieSSA Description: CRAN Package 'GillespieSSA' (Gillespie's Stochastic Simulation Algorithm (SSA)) Provides a simple to use, intuitive, and extensible interface to several stochastic simulation algorithms for generating simulated trajectories of finite population continuous-time model. Currently it implements Gillespie's exact stochastic simulation algorithm (Direct method) and several approximate methods (Explicit tau-leap, Binomial tau-leap, and Optimized tau-leap). The package also contains a library of template models that can be run as demo models and can easily be customized and extended. Currently the following models are included, 'Decaying-Dimerization' reaction set, linear chain system, logistic growth model, 'Lotka' predator-prey model, Rosenzweig-MacArthur predator-prey model, 'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model. Pineda-Krch et al. (2008) . Package: r-cran-gilmour Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gilmour_0.1.1-1.ca2604.1_all.deb Size: 645582 MD5sum: dc106b795e5d7deb1a137aa0e2b84685 SHA1: ea7d73af44b7c8f3cfe877a4be2b99db0212169b SHA256: 363aa05ba68e54570b1eb466fbb9ff10b16f8aa958d415e092c9035161f7eb0a SHA512: c55473bf1e4a52e8da817f7e7a2011cac63a45edd606b128c61f068686034f4aeed587329819e782b5e184c86945066a0d98e34fb0f10c9992da7dade42c69cf Homepage: https://cran.r-project.org/package=gilmour Description: CRAN Package 'gilmour' (The Interpretation of Adjusted Cp Statistic) Several methods may be found for selecting a subset of regressors from a set of k candidate variables in multiple linear regression. One possibility is to evaluate all possible regression models and comparing them using Mallows's Cp statistic (Cp) according to Gilmour original study. Full model is calculated, all possible combinations of regressors are generated, adjusted Cp for each submodel are computed, and the submodel with the minimum adjusted value Cp (ModelMin) is calculated. To identify the final model, the package applies a sequence of hypothesis tests on submodels nested within ModelMin, following the approach outlined in Gilmour's original paper. For more details see the help of the function final_model() and the original study (1996) . Package: r-cran-gim Architecture: all Version: 0.33.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gim_0.33.1-1.ca2604.1_all.deb Size: 372550 MD5sum: 246e2857096d1a950f41c0980bf0b856 SHA1: 7e19bb8b5cef254225d0790beaf332b354303e59 SHA256: 0d9c6f8c14062533068db655c14ba53570a7edebdee4cad7341f501a912c3371 SHA512: ff33026235f56c2ebce2c90d3478f74e759573ef1a4b3b62d69a3ca4c805e7499f24b30eec7c57f83b31e205d3ec8a7104b23c467fddeb1c047ef27ff014f72d Homepage: https://cran.r-project.org/package=gim Description: CRAN Package 'gim' (Generalized Integration Model) Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020). Package: r-cran-gimap Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14009 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-dplyr, r-cran-tidyr, r-cran-rmarkdown, r-cran-vroom, r-cran-ggplot2, r-cran-magrittr, r-cran-pheatmap, r-cran-purrr, r-cran-janitor, r-cran-stringr, r-cran-httr, r-cran-jsonlite, r-cran-openssl Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-kableextra, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-gimap_1.1.2-1.ca2604.1_all.deb Size: 3045034 MD5sum: 62a13694fc8dc75af1f18fd414baa26e SHA1: ca0beb24b5eb0b6d4b907fc2524f9c461a488259 SHA256: f5402782ec273a124a70fd4cf868d87603fb2e2756136cf8c20c3abe01c0a5c9 SHA512: 1195a640f3536db1b9be056f7b01ee147bb2bd1fc33bf94fed459392f3ec9c71f6e4c29f7dc6c293a1ed38b694e4e2df4543be6648406cde4b625c1406467a58 Homepage: https://cran.r-project.org/package=gimap Description: CRAN Package 'gimap' (Calculate Genetic Interactions for Paired CRISPR Targets) Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the 'gimap' package is genetic interaction scores which are the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) . Package: r-cran-gimme Architecture: all Version: 10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2042 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-igraph, r-cran-qgraph, r-cran-data.tree, r-cran-miivsem, r-cran-imputets, r-cran-nloptr, r-cran-mass, r-cran-tseries Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gimme_10.0-1.ca2604.1_all.deb Size: 1988756 MD5sum: e908fae1015eb9fbfe8a44cdf2a5613f SHA1: cf0d02c7cc62847655cb502fab8f9c57f760a7a5 SHA256: 5752e5c5c40dc7e40870b1d1952b49122dcb7da6425167e9f6df2959f8de2fd8 SHA512: b31e7480131595d1436a994014c0878e6f0193e32bedab6b31c7bbbcd9c5b60b1a89ab0af3942842339121cd714ba62ca1fccc25093614205770691d12b6e5c9 Homepage: https://cran.r-project.org/package=gimme Description: CRAN Package 'gimme' (Group Iterative Multiple Model Estimation) Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) . Package: r-cran-gimmegvar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-graphicalvar, r-cran-here, r-cran-qgraph, r-cran-png Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gimmegvar_0.1.0-1.ca2604.1_all.deb Size: 198968 MD5sum: 4b19a8681a3a6cbfa619ec93909372b3 SHA1: 31900b7bc25a6d57f159beda514b06fc07e860a2 SHA256: 66f622a59f05b6570493ed352388dba76ad854fba8ceef66c2ba9fa4b441af37 SHA512: c500d9f215863b86234a46ac880b476557bb4982a12a95343a396b7bb831eef9847e11bf139bae01edb872a7b6d5d7c5c8249803366c15f82509a75f6943ce6a Homepage: https://cran.r-project.org/package=GIMMEgVAR Description: CRAN Package 'GIMMEgVAR' (Group Iterative Multiple Model Estimation with 'graphicalVAR') Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the 'graphicalVAR' approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) . Package: r-cran-gimmemyplot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-rstatix, r-cran-scales, r-cran-stringr, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gimmemyplot_0.1.0-1.ca2604.1_all.deb Size: 87718 MD5sum: a8ea29321f1f9a709dddf08c704734b9 SHA1: 2ff3e1d28e9b4b9f682b32197360bfa192436596 SHA256: aff9d13006d6c69dc05e33c52e702a327195953964e247fda19a485316402ce6 SHA512: 542c3b8c17c2505fcee053f151abb9c03eb5db4bd563e6914b3ec775ba76188327456c0f304d8388fc160fe6b51ae14b6b5a5349c41a7ee804dacd432a31c22f Homepage: https://cran.r-project.org/package=GimmeMyPlot Description: CRAN Package 'GimmeMyPlot' (Graphical Utilities for Visualizing and Exploring Data) Simplifies the process of creating essential visualizations in R, offering a range of plotting functions for common chart types like violin plots, pie charts, and histograms. With an intuitive interface, users can effortlessly customize colors, labels, and styles, making it an ideal tool for both beginners and experienced data analysts. Whether exploring datasets or producing quick visual summaries, this package provides a streamlined solution for fundamental graphics in R. 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Package: r-cran-gimms Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-curl, r-cran-kendall, r-cran-ncdf4, r-cran-zyp Suggests: r-cran-checkmate, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-gimms_1.2.5-1.ca2604.1_all.deb Size: 401742 MD5sum: 7e3adf5a1380337c09a572a6733576dd SHA1: 713d0d6ef19ccdc2e10061be90a1eec4c42f5593 SHA256: 04323315a1687a0b66921659c8a35a433248e6c5da7f2f7f4f259e4e54042a86 SHA512: 43c7d95ae6526f5b94db4b03384a94c6bf331a61485c8e99214351bbaf8553d9a33fe4da34030f750ec3bcec4788555fbfbd212f141812bfa5cbeca8acc82b2b Homepage: https://cran.r-project.org/package=gimms Description: CRAN Package 'gimms' (Download and Process GIMMS NDVI3g Data) This is a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets; import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread 'raster' package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon 'Raster*' images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation. 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It supports the identification of credible sets of genetic variants. Package: r-cran-gini Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gini_0.1.0-1.ca2604.1_all.deb Size: 27970 MD5sum: 0291459dd075e653b89fb3b84326ee54 SHA1: b4aa024c81edd0c52470bf2124bf995cbd1e68ed SHA256: 1b5465c621967aef72b55fc1e66401844322a1566ca13e981501e75614969b92 SHA512: 297ff2cf5f0d8d6c899c0beea0c1cae47053d8f2dc7b1cf8bb9ea6956e5a51067d820c2f505afc2afd49046bdda8b5b2c3fd28f4ea089e7dba2e7703ce6d4f9c Homepage: https://cran.r-project.org/package=Gini Description: CRAN Package 'Gini' (Gini Coefficient) Providing various equations to calculate Gini coefficients. The methods used in this package can be referenced from Brown MC (1994) . Package: r-cran-ginici Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggpubr Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-ginici_0.1.3-1.ca2604.1_all.deb Size: 284000 MD5sum: 4ea0378cdfa4e8920757b8984a5130f5 SHA1: 9221fdb5e9e15bc493898ec0b8043393b98fd163 SHA256: f0be5f70e8cfc4488b9d86d5db6daf14d020cc090f6a08075ada8bc10ba4f6db SHA512: 252adafa3db7d113358c526c900553e4aea17c8af86f104971950f9ba66bdc6c9edee6c62387a96087b1b5239bc58d16159c772465c0ee722fae0d0c102e6ede Homepage: https://cran.r-project.org/package=giniCI Description: CRAN Package 'giniCI' (Gini-Based Composite Indicators) An implementation of Gini-based weighting approaches in constructing composite indicators, providing functionalities for normalization, aggregation, and ranking comparison. Package: r-cran-ginidecomply Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dineq, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ginidecomply_1.0.1-1.ca2604.1_all.deb Size: 41328 MD5sum: 36c3709fc6fa4be457d955a73a356362 SHA1: 94bc8564fce38f7afc29915f119eb0e2002c9c7e SHA256: 2fa361e053c22cbfb647eb0972fdf072f1698d02453f30f8929c97bfcf2cae8b SHA512: 13ec73184ce81437e1765c8d7c8b5750e15cdffaa07bf1462f529223bdccfdfb9e1fdc0e7b714f8ec5ddba6907bcc897b349344e385f4556c5c713ac62826460 Homepage: https://cran.r-project.org/package=GiniDecompLY Description: CRAN Package 'GiniDecompLY' (Gini Decomposition by Income Sources) Estimation of the effect of each income source on income inequalities based on the decomposition of Lerman and Yitzhaki (1985) . Package: r-cran-ginsarcorw Architecture: all Version: 1.15.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-circular, r-cran-sp Filename: pool/dists/resolute/main/r-cran-ginsarcorw_1.15.8-1.ca2604.1_all.deb Size: 3008278 MD5sum: ae9252b57eeb23d372d3b13ac49a13c8 SHA1: 72a89abe0da4594a1a12f394fa42f0709fb90ae0 SHA256: 6bab194410472e76be8e4ac039a3844a4e0ea18d11124c9cc349a0de30f671b5 SHA512: 3b915ac58dfdea7bf0b4aca5f841cf83739fc3d1bdf802de12d49dd5df0c94b18078bab8d1ffe45c2d1a25b3fcac8cdf961b1c51661c86489ea141dd4ab32fb7 Homepage: https://cran.r-project.org/package=GInSARCorW Description: CRAN Package 'GInSARCorW' (GACOS InSAR Correction Workflow) A workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework. References: Yu, C., N. T. Penna, and Z. Li (2017) . Yu, C., Li, Z., & Penna, N. T. (2017) . Yu, C., Penna, N. T., and Li, Z. (2017) . Package: r-cran-gipfrm Architecture: all Version: 3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gipfrm_3.1-1.ca2604.1_all.deb Size: 40588 MD5sum: 494597dc6fb4c0dafa9431958dd6a552 SHA1: 5bdbb9e70b8eb601e6d27a5a48c8831897286142 SHA256: 19a21f2cebf82a98941474643293e527513cb81614c3ba49203589b249a5b9d9 SHA512: 0c629eb03102658f9a4057ae80d8c53f29c578ee5538179cfd9de9dbd0687c5de60d6ee598146209baeaca3395bac6ff50b2316ca4e00513bb53fb19571f67bd Homepage: https://cran.r-project.org/package=gIPFrm Description: CRAN Package 'gIPFrm' (Generalized Iterative Proportional Fitting for Relational Models) Maximum likelihood estimation under relational models, with or without the overall effect. Package: r-cran-giplot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-giplot_0.1.0-1.ca2604.1_all.deb Size: 21728 MD5sum: f2fd5e3ca5ec62ade1611bb5c07026d8 SHA1: 3fc214bc59982507d7aec9061d49dde073e28657 SHA256: 33a3bc000c37cc82f17027d4345cf4b6905c01c7c1bf43b95216206187356668 SHA512: 15197b6daca73463c0e357ccbfa2db35df1f3fe061606360976e2577e63eb69282b5bdf6f7c908210fc88b995bdc6d0b2afa43575fd39449af751cfeac3764c4 Homepage: https://cran.r-project.org/package=GIplot Description: CRAN Package 'GIplot' (Gaussian Interval Plot (GIplot)) The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean and the standard deviation of a quantitative variable. It also flags potential outliers (together with their frequencies) that are c standard deviations away from the mean. Package: r-cran-gips Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numbers, r-cran-permutations, r-cran-rlang Suggests: r-cran-daag, r-cran-dplyr, r-cran-ggplot2, r-cran-hash, r-cran-hsaur2, r-cran-knitr, r-cran-mass, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-spelling, r-cran-stringi, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-gips_1.2.3-1.ca2604.1_all.deb Size: 835078 MD5sum: 38dc95da0760e087d8d8e60539558acd SHA1: 4760f110fd91ecd359fb3f5cd35158d37af6e200 SHA256: efa12dd7dde7cbe312d4a86b9bca7848a3a6ed6011c3c803cce68844ede1435e SHA512: 074b5ea34d71f5d26fb6f9c2f0e84f006b193d860eb981c075ddd3819688ad7bea9205e6250058069bd8c0cf2057b23461768be8c95c0613ae445b9429f19ce1 Homepage: https://cran.r-project.org/package=gips Description: CRAN Package 'gips' (Gaussian Model Invariant by Permutation Symmetry) Find the permutation symmetry group such that the covariance matrix of the given data is approximately invariant under it. Discovering such a permutation decreases the number of observations needed to fit a Gaussian model, which is of great use when it is smaller than the number of variables. Even if that is not the case, the covariance matrix found with 'gips' approximates the actual covariance with less statistical error. The methods implemented in this package are described in Graczyk et al. (2022) . Documentation about 'gips' is provided via its website at and the paper by Chojecki, Morgen, Kołodziejek (2025, ). 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The package leverages methodology from the 'gips' framework to identify and impose permutation structures that act as a form of regularization, improving stability and interpretability in settings with symmetric or exchangeable features. Several discriminant analysis variants are provided, including pooled and class-specific covariance models, as well as multi-class extensions with shared or independent symmetry structures. For more details about 'gips' methodology see and Graczyk et al. (2022) and Chojecki, Morgen, Kołodziejek (2025, ). Package: r-cran-giscor Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2692 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-countrycode, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-rappdirs, r-cran-sf, r-cran-testthat, r-cran-tibble Suggests: r-cran-dplyr, r-cran-eurostat, r-cran-ggplot2, r-cran-knitr, r-cran-quarto, r-cran-withr Filename: pool/dists/resolute/main/r-cran-giscor_1.1.0-1.ca2604.1_all.deb Size: 2508856 MD5sum: b2708bfad863cef3559b074cd27156c3 SHA1: b08db358ea07c0a82d1031f15139f87549b594fa SHA256: 1f817b0965a7fb0ae41ab29261733a4a113f3751a446d08d236438645820be06 SHA512: 6cde5ef8d77469f5b6233017f4e5245ae36c0fd0d7fcdd6201d386397aea2f2972d505cb37cd7ea8bb37b8d6388e9d7e3fc9a5208bc7b5cc236a843247793434 Homepage: https://cran.r-project.org/package=giscoR Description: CRAN Package 'giscoR' (Download Map Data from GISCO API - Eurostat) Tools to download data from the GISCO (Geographic Information System of the Commission) Eurostat database . Global and European map data available. This package is in no way officially related to or endorsed by Eurostat. Package: r-cran-gisintegration Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shapefiles, r-cran-tm, r-cran-syn, r-cran-recordlinkage, r-cran-stringr, r-cran-sf Filename: pool/dists/resolute/main/r-cran-gisintegration_1.0-1.ca2604.1_all.deb Size: 36912 MD5sum: 4c5ed3a11fc0cc49aacc2925f8ebe0b7 SHA1: e92d3ea2644ba196176398284ef75a55d9018703 SHA256: 107d656130cb09e08538859e2f241246fc94f7b3d68a8aea7ef9515c15348db9 SHA512: 90567ab497a3506de25022f464568ce489032c8f145aeeccbd67da2b90f771c7b01f119fd984e1dd7a60360e8e224852580ddcc845c56c12c2f091d4b2d3d0f7 Homepage: https://cran.r-project.org/package=GISINTEGRATION Description: CRAN Package 'GISINTEGRATION' (GIS Integration) Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases , the R package 'GISINTEGRATION' offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. 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Package: r-cran-gistools Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-rcolorbrewer, r-cran-mass Filename: pool/dists/resolute/main/r-cran-gistools_1.0-2-1.ca2604.1_all.deb Size: 3567946 MD5sum: 0e42351713389c5d3f023487857d2a3d SHA1: 651d74556cf1aede6bcdfa1ae153af5d581731ef SHA256: 884aa062884a67808ef3c4e9606f54ce3c8b198d08c3ed948b5a697ab6422cb5 SHA512: ec6afbf8bf150122231e383356b03381874162431e6654d97431b34c32235695694cb3229bd617845dec50287255ddfff870aada4b9facae0d803f7536bdb04f Homepage: https://cran.r-project.org/package=GISTools Description: CRAN Package 'GISTools' (Further Capabilities in Geographic Information Science) Mapping and spatial data manipulation tools - in particular drawing thematic maps with nice looking legends, and aggregation of point data to polygons. 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Package: r-cran-git2rdata Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1463 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-git2r, r-cran-yaml Suggests: r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-microbenchmark, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-git2rdata_0.5.2-1.ca2604.1_all.deb Size: 881316 MD5sum: ac4042f1896dccc0447016c037ad7939 SHA1: a3966931873d40f8aeca4129c90b883f9697ed93 SHA256: b3544d112e642086f3af0981a6ba19c4e3e291ff8ee39b1d79c2dd2fbe9f1e09 SHA512: b374e39ff06415cc2d0d9adbee1808f0474abbb67930e58feee76637fae7911c193adbfde37d1a839479befdcd712fe160fc8306a0d44108405df71379817500 Homepage: https://cran.r-project.org/package=git2rdata Description: CRAN Package 'git2rdata' (Store and Retrieve Data.frames in a Git Repository) The git2rdata package is an R package for writing and reading dataframes as plain text files. 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Package: r-cran-git4r Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-git2r, r-cran-diffr Suggests: r-cran-gitcreds, r-cran-rstudioapi, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-git4r_0.1.2-1.ca2604.1_all.deb Size: 524016 MD5sum: f7cf4f12b01d782b6d17b1664345c1f3 SHA1: 4f52971d7c1dd36563cd5319e67b7fc28a869355 SHA256: 83f75c3cb76c87b37ca453afe6d21fc4f7eef40071244294f46d69bbe29991ec SHA512: c2cc5f7aa96ead2e1917247fa411fd37067e370123e5bda9a96ae9497f15683ec66f15d93bcb2448ec8b2821f2ea993a6787da0c8d2050fc10b478d03194a397 Homepage: https://cran.r-project.org/package=git4r Description: CRAN Package 'git4r' (Interactive Git for R) An interactive git user interface from the R command line. Intuitive tools to make commits, branches, remotes, and diffs an integrated part of R coding. 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Package: r-cran-gitcreds Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-codetools, r-cran-covr, r-cran-knitr, r-cran-mockery, r-cran-oskeyring, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-gitcreds_0.1.2-1.ca2604.1_all.deb Size: 87494 MD5sum: 7705fbd00041545cc0157ba2df274ef2 SHA1: 90c449c1635fe08644ec71386f6de0c5b9c1d2fa SHA256: f9f784397e94039d7d7dff5d9a0fda3a727d06a92d72824f673157699861dc00 SHA512: 4ac3e265873e3fdfc5a612d55a90bc212aaeafb24641c7f4487675801ae07999eaf7a8c78f36b4eca1bc85850b2b282a2e9a1a911b319e619e577e4b38bd7b75 Homepage: https://cran.r-project.org/package=gitcreds Description: CRAN Package 'gitcreds' (Query 'git' Credentials from 'R') Query, set, delete credentials from the 'git' credential store. Manage 'GitHub' tokens and other 'git' credentials. 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The package provides a tab navigation component and a searchable multi-select widget with multiple checkbox indicator styles, select-all controls, and customizable colour themes. The widgets are compatible with standard 'shiny' layouts and 'bs4Dash' dashboards. 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The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed. Package: r-cran-gldreg Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gldex, r-cran-ddst Suggests: r-cran-mass, r-cran-quantreg Filename: pool/dists/resolute/main/r-cran-gldreg_1.1.2-1.ca2604.1_all.deb Size: 154814 MD5sum: 90c946b6a235b552ef9b63f5a6ef14b9 SHA1: 1e3be4c82d9394579434bc2a00251b378b041413 SHA256: 8f45ba01ec9e14fe23eaf743d52c1197d73d109405ce64c7b03f963669f2871a SHA512: ce93dbdb320ce99c912ec23576203a35e9249ef32136ace29547c5c194a033b87fb850a9df310132fc3df2febb6781befd1bb9fa718de467efb294ad5ba9c937 Homepage: https://cran.r-project.org/package=GLDreg Description: CRAN Package 'GLDreg' (Fit GLD Regression/Quantile/AFT Model to Data) Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model". Package: r-cran-gldrm Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gldrm_1.6-1.ca2604.1_all.deb Size: 116466 MD5sum: b5c225d0151dad3c488592afda27f7ed SHA1: bd114b8a17c2f3acaa6e72ddb939ee524711b731 SHA256: 07e830b493286a8d9f9747e85ecbe7cdbd40c2a4b9d1d178518dd22dea0623ca SHA512: 4b59056d86c5ad85268b7ae8f1d077f4e137d84ee8de8786cad23719a575935fa80f9b9acf8bb82c9f5c0c2cbd1048e73d6820a32a4084a4f46c8a7ae61b8cf3 Homepage: https://cran.r-project.org/package=gldrm Description: CRAN Package 'gldrm' (Generalized Linear Density Ratio Models) Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) . Huang and Rathouz (2012) . Rathouz and Gao (2008) . Package: r-cran-gleam Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gleam_0.8.0-1.ca2604.1_all.deb Size: 2828756 MD5sum: e9d7980d5ea7549c5919c979d01df6db SHA1: a493bb0af854d22f26b3d44ce35f713361708b16 SHA256: e67d91f98a830f844322181ccb293ff19cd33bd9b8eded69a2c4a3f45b207868 SHA512: d1fe62f211d5bcdb5a20e44dbfc2e2d525d5d71cad529d60e3f7f1c27fbdd784ed6624e21455fc4ce537bdf71d7978f20db0aca277b2b9c06e98a2f0d069621e Homepage: https://cran.r-project.org/package=gleam Description: CRAN Package 'gleam' (Global Livestock Environmental Assessment Model (GLEAM-X)) The official implementation of the Global Livestock Environmental Assessment Model (GLEAM) of the Food and Agriculture Organization of the United Nations (FAO) in R. GLEAM-X provides a modular, transparent framework for simulating livestock production systems and quantifying their environmental impacts. Methodological background: MacLeod et al. (2017) "Invited review: A position on the Global Livestock Environmental Assessment Model (GLEAM)" . Further information: . Package: r-cran-glioblastomaehrsdata Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dataexplorer, r-cran-flextable, r-cran-ggplot2, r-cran-rmarkdown, r-cran-summarytools, r-cran-table1, r-cran-tinytex Suggests: r-cran-ragg, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-glioblastomaehrsdata_1.1.0-1.ca2604.1_all.deb Size: 78172 MD5sum: 32f7bf4912d6a103d9583537b9e899df SHA1: ce4225a32adf68b85e635876aee90c3659537692 SHA256: 4b6ef1d3a04ba1c6ef4a501b2f4c2307b89b0425aeca5942610bcd99340fdbe5 SHA512: 4eb9a03ecafd3b4e631ad80f0ffac737cb9c22a2e85f6e7683ee43c81a0facc8bb48db2212437a2338c474422cf86ed57f8751c9e59b398a7444c0953854f3cb Homepage: https://cran.r-project.org/package=glioblastomaEHRsData Description: CRAN Package 'glioblastomaEHRsData' (Descriptive Analysis on Three Glioblastoma EHRs Datasets) Provides functions to load and analyze three open Electronic Health Records (EHRs) datasets of patients diagnosed with glioblastoma, previously released under the Creative Common Attribution 4.0 International (CC BY 4.0) license. Users can generate basic descriptive statistics, frequency tables and save descriptive summary tables, as well as create and export univariate or bivariate plots. The package is designed to work with the included datasets and to facilitate quick exploratory data analysis and reporting. More information about these three datasets of EHRs of patients with glioblastoma can be found in this article: Gabriel Cerono, Ombretta Melaiu, and Davide Chicco, 'Clinical feature ranking based on ensemble machine learning reveals top survival factors for glioblastoma multiforme', Journal of Healthcare Informatics Research 8, 1-18 (March 2024). . Package: r-cran-glm.predict Architecture: all Version: 4.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-aer, r-cran-survival, r-cran-mass, r-cran-mlogit, r-cran-dfidx, r-cran-survey, r-cran-lme4, r-cran-vgam Suggests: r-cran-ggplot2, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-glm.predict_4.3-2-1.ca2604.1_all.deb Size: 309646 MD5sum: 8f3e5b7b752676ca2f0feccb84e8bde4 SHA1: 0f9efeb867cdecdbd9f0838d16edcbbbc7a25605 SHA256: 55c4fc9d96e894c47b5d0becc59be9ff9bbb85e168e39bc12701ff31b05e2834 SHA512: 124879ec5b8c2241181b72b21aab169eaba8f20bdba5b22d968d94ea3a6d0caffaa2e2a7774b5bcf0139730b5122bd46a14d415f67f54b52aaa9f21da0eac648 Homepage: https://cran.r-project.org/package=glm.predict Description: CRAN Package 'glm.predict' (Predicted Values and Discrete Changes for Regression Models) Functions to calculate predicted values and the difference between the two cases with confidence interval for lm() [linear model], glm() [generalized linear model], glm.nb() [negative binomial model], polr() [ordinal logistic model], vglm() [generalized ordinal logistic model], multinom() [multinomial model], tobit() [tobit model], svyglm() [survey-weighted generalised linear models] and lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) . Package: r-cran-glm2 Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-glm2_1.2.1-1.ca2604.1_all.deb Size: 50084 MD5sum: 46375c76142d30e7d76249c43a4596a2 SHA1: 185eec5208967f1c090327b7e4bc9aa1029de74f SHA256: 63e61c96ec9418051ed2a7ed55258ca6bec680b7711920c38b29f6b104a92af4 SHA512: 6a996ab3f6529d466a8f091afef161cf59d96320515dc683892e7dc21ac17e02f6527b88b036e2d5cf42a8ba2f7ba9b276d73f3486ec789a84daa1695bc3ff69 Homepage: https://cran.r-project.org/package=glm2 Description: CRAN Package 'glm2' (Fitting Generalized Linear Models) Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm. Package: r-cran-glm4 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixmodels Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-glm4_0.1.0-1.ca2604.1_all.deb Size: 255558 MD5sum: 0ccb15e122dc72596fd23f01fd965f54 SHA1: 0de1b6845a78a232e5737556d54221fdc9a9fc03 SHA256: 506d5fe8532f09e98254e1f43afd5ae7e96244da07d666a18326854edcf57f4f SHA512: 1fb352de5cff4971f568c70f2e00117eb05a72f5bf0ea51c55507d5101005175e9ffd10dba46447b143d7613b5940a2c30c7d4b789dd4faf3aa5ddf168377571 Homepage: https://cran.r-project.org/package=glm4 Description: CRAN Package 'glm4' (Fitting Generalized Linear Models Using Sparse Matrices) Fits Generalised Linear Models (GLMs) with sparse and dense 'Matrix' matrices for memory efficiency. Acts as a wrapper for the glm4() function in the 'MatrixModels' package , but adds convenient model methods and functions designed to mimic those associated with the glm() function from the 'stats' package. 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The methods implemented are based on Brown (2018, ISBN:978-3-319-93547-8) and Debelak et al. (2022, ISBN:978-1-138-71046-7) . 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Use branch and bound algorithm so we do not have to fit all models. Package: r-cran-glmc Architecture: all Version: 0.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emplik Filename: pool/dists/resolute/main/r-cran-glmc_0.4-1-1.ca2604.1_all.deb Size: 87874 MD5sum: 4535abddee9f9b9ac1ea686b8078bf22 SHA1: 152916b87ff974776f6de4df127da887ed3301ee SHA256: 8b043fac3f336675a1f474d0a17e5d20a846e90cc622325f59ebdc4dcc582ec3 SHA512: de6811f0923eac77810630dad78eb7e57c7ea4fb6238295de581583e86171971ec4c73eb4559d3c8d27e3e87b0e6223ad9a0918779cdc8c5ed3efeab1041c770 Homepage: https://cran.r-project.org/package=glmc Description: CRAN Package 'glmc' (Fitting Generalized Linear Models Subject to Constraints) Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters. Package: r-cran-glme Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-reshape, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-glme_0.1.0-1.ca2604.1_all.deb Size: 36826 MD5sum: edb8018d44bf393f3f107feb267f824c SHA1: e0a99a59ee06fa0af8dad9656135302fe0f3e55e SHA256: fc858dfdc427af4788dd6e403db143756264b7913cdab104e8cdf36d3f132532 SHA512: fafe1e42dbab657de6da0545def58aef939ac91ac8cd07004f47dfa5b3eddc3b0d1966bc84114036a4e6084d29ab0b9480e5fc3aca8d0323500b78f9cf6c9f68 Homepage: https://cran.r-project.org/package=glme Description: CRAN Package 'glme' (Generalized Linear Mixed Effects Models) Provides Generalized Inferences based on exact distributions and exact probability statements for mixed effect models, provided by such papers as Weerahandi and Yu (2020) under the widely used Compound Symmetric Covariance structure. The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference. 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The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; ). For detecting and modeling subgroups in growth curves with GLMM trees see Fokkema & Zeileis (2024; ). 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The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) , Maiti and Pradhan (2009) , Maity, Pradhan, and Das (2019) for further methodological details. 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Package: r-cran-glmmadaptive Architecture: all Version: 0.9-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-matrixstats Suggests: r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-multcomp, r-cran-emmeans, r-cran-estimability, r-cran-effects, r-cran-dharma, r-cran-optimparallel Filename: pool/dists/resolute/main/r-cran-glmmadaptive_0.9-7-1.ca2604.1_all.deb Size: 374982 MD5sum: b5947994b75d7682ed236b615b4e0240 SHA1: 1060c7c9560db6ea5edcb276db3aa6e5997916dc SHA256: d883b54a40c7415a1a1cb703b27f12630491854e2dcd2e4d8272768615900911 SHA512: 78cbee6a8aa311a30974a20d12bf0fd5399dca42fc095d7a84f620e25bc7d4eafed7ca0b0d16e54d55842cb0272071151a5688a3a0222fbf1808b8a3c90382c9 Homepage: https://cran.r-project.org/package=GLMMadaptive Description: CRAN Package 'GLMMadaptive' (Generalized Linear Mixed Models using Adaptive GaussianQuadrature) Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) . Package: r-cran-glmmcosinor Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cowplot, r-cran-ggforce, r-cran-ggplot2, r-cran-glmmtmb, r-cran-lme4, r-cran-rlang, r-cran-scales Suggests: r-cran-cosinor, r-cran-covr, r-cran-dharma, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ftextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-glmmcosinor_0.2.1-1.ca2604.1_all.deb Size: 1150056 MD5sum: 0412c129aee2f485d7d6458ea463a888 SHA1: d71245688bc7ef3d68e511feed7515a15b892e8d SHA256: 7b39b5b58662b9070a95837eee172978fd7e6e44a22570964313ebd248f5d8c0 SHA512: 6c7abf594b8a9e200ef326e35fb9e70a5d04eec8a93fd09d6dc1d0d480fa8661c8bc57015bec1fc7794e12b989da167d4863095c891f2f12be1635d458c3d380 Homepage: https://cran.r-project.org/package=GLMMcosinor Description: CRAN Package 'GLMMcosinor' (Fit a Cosinor Model Using a Generalized Mixed Modeling Framework) Allows users to fit a cosinor model using the 'glmmTMB' framework. This extends on existing cosinor modeling packages, including 'cosinor' and 'circacompare', by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) . Package: r-cran-glmmfel Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-numderiv Suggests: r-cran-testthat, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-nlme, r-cran-mvglmmrank, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-glmmfel_1.0.5-1.ca2604.1_all.deb Size: 131484 MD5sum: 67a829b2f21da2f30cb0412df04af493 SHA1: ac3d472d9f3bd6374b07ea14a3c96c0e190d451f SHA256: a5937cf6937ff4a5756c6e8a9e2f98135329525af9f4b65227aba19da0b1909b SHA512: 26fc257b6d4daf4681ff59b98f9c586698504c161ddbede3ffdf0113926ad0589eeed2f68d88867a7e8c19294756db14a28416fa523054e82200b533bc6dfc76 Homepage: https://cran.r-project.org/package=glmmFEL Description: CRAN Package 'glmmFEL' (Generalized Linear Mixed Models via Fully Exponential Laplace inEM) Fit generalized linear mixed models (GLMMs) with normal random effects using first-order Laplace, fully exponential Laplace (FEL) with mean-only corrections, and FEL with mean and covariance corrections in the E-step of an expectation-maximization (EM) algorithm. The current development version provides a matrix-based interface (y, X, Z) and supports binary logit and probit, and Poisson log-link models. An EM framework is used to update fixed effects, random effects, and a single variance component tau^2 for G = tau^2 I, with staged approximations (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A pseudo-likelihood engine glmmFEL_pl() implements the working-response / working-weights linearization approach of Wolfinger and O'Connell (1993) , and is adapted from the implementation used in the 'RealVAMS' package (Broatch, Green, and Karl (2018)) . The FEL implementation follows Karl, Yang, and Lohr (2014) and related work (e.g., Tierney, Kass, and Kadane (1989) ; Rizopoulos, Verbeke, and Lesaffre (2009) ; Steele (1996) ). Package code was drafted with assistance from generative AI tools. Package: r-cran-glmmisrep Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-poisson.glm.mix Filename: pool/dists/resolute/main/r-cran-glmmisrep_0.1.1-1.ca2604.1_all.deb Size: 263374 MD5sum: 17e549d84ea70e890fb2515a0be2af66 SHA1: d1b8dffd9d97b11f226500209cb50eeb9acf613a SHA256: 9c77cb6a0c6feeea1f134e4655ce6abf9b578d34cca851c49119cbab034694c8 SHA512: 579216a0c05d1c072735e68609ffa78f75a834de4a49a449fa9eca60553eef43c1a0eaa5c9560ed216702cf5036fe89e6325b7d059e75b26bfb8313f196ed076 Homepage: https://cran.r-project.org/package=glmMisrep Description: CRAN Package 'glmMisrep' (Generalized Linear Models Adjusting for Misrepresentation) Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) . Package: r-cran-glmmrr Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-lattice, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-glmmrr_0.6.0-1.ca2604.1_all.deb Size: 588598 MD5sum: 42c7f89e9872c5b750ee994b88402191 SHA1: 9a7918411670c7ccbabcf6637f2e7440e4e2b840 SHA256: 075a023435b1e2d8e690559cac817a12038e67fead43b182a7b132c759acba3e SHA512: b6ba6f0b387d7797ee51322eb3ec7c315c359411678cc5b3c6ecce10973ebd27dbe6345a441c66a41330228e3a7fa8fae6d36bd902961beae87f860d1f9ec8f4 Homepage: https://cran.r-project.org/package=GLMMRR Description: CRAN Package 'GLMMRR' (Generalized Linear Mixed Model (GLMM) for Binary RandomizedResponse Data) Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. . Package: r-cran-glmmselect Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-glmmselect_1.2.0-1.ca2604.1_all.deb Size: 109274 MD5sum: b50cd1ea78d45b646843e202df9611a7 SHA1: ffc21491900b8c5977949b4cf2cc32494bab3a1e SHA256: b8ae5e9b81fa691ea48a944ee5e13c8ec4e3a9752f4cc807939e63ebc8b4575e SHA512: 3bc7e4d0263b7d1a9c00af0d250be1b9d64c68d7e8ed57b06c691bf0e0acf1e6a6f4383fdaa4c5c16618c6536f247c7400925f97f3539f53ec09ce7ff516a2f8 Homepage: https://cran.r-project.org/package=GLMMselect Description: CRAN Package 'GLMMselect' (Bayesian Model Selection for Generalized Linear Mixed Models) A Bayesian model selection approach for generalized linear mixed models. Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. 'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. 'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review. Package: r-cran-glmmseq Architecture: all Version: 0.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2974 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-emmeans, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmmtmb, r-cran-mass, r-cran-lme4, r-cran-lmertest, r-cran-kableextra, r-cran-mcprogress, r-cran-plotly, r-bioc-qvalue, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-deseq2, r-bioc-edger Filename: pool/dists/resolute/main/r-cran-glmmseq_0.5.7-1.ca2604.1_all.deb Size: 1652256 MD5sum: 013ecd76b6a9ac86dffc5469460013a5 SHA1: 035adb7b399845d0767ea8462a397e8220a988ea SHA256: 28755eeaccaaec748a7148b6fcce8902ab5af40be4f2de69adf58bde40f473e9 SHA512: 38e284bd656dcc395a53d87ba1e6a9410933c30ff08198817595ff8cd2726290fa31db3c53049a1733db50e8d31d79fbf4286d8ee0bc21cfc45c67303b6ee9d1 Homepage: https://cran.r-project.org/package=glmmSeq Description: CRAN Package 'glmmSeq' (General Linear Mixed Models for Gene-Level DifferentialExpression) Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine . Package: r-cran-glmnetcr Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-glmnetcr_1.0.7-1.ca2604.1_all.deb Size: 807038 MD5sum: 961de9c9990abfdf0f0cb83abebeb92d SHA1: 76b0c6aa2bd736b958410159258dad891f37628f SHA256: b89bcb21dcb3dfd3f10516cf1ffd707738edb19610d9f7312293c2fa7e3614aa SHA512: a801b2facbd7d3265bbda3022abb3c8be15aba6a8193418ca95d16bf3eef5cc875c3495a9b6cc65d6de72a1f149c24acfbaa634192d0145e32fb6160b06ab06b Homepage: https://cran.r-project.org/package=glmnetcr Description: CRAN Package 'glmnetcr' (Fit a Penalized Constrained Continuation Ratio Model forPredicting an Ordinal Response) Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring. Package: r-cran-glmnetr Architecture: all Version: 0.6-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-matrix, r-cran-xgboost, r-cran-smoof, r-cran-mlrmbo, r-cran-paramhelpers, r-cran-randomforestsrc, r-cran-rpart, r-cran-torch, r-cran-aorsf Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-glmnetr_0.6-3-1.ca2604.1_all.deb Size: 2814584 MD5sum: 66d97aaf686b894e6e7ceba0087702e4 SHA1: 703863656022b76a27aac800ef2a954b35e4c876 SHA256: deb35b4206f85c224ea1efe2a306cf867bb7b83ab3e3fc68872ef51de85ecc7e SHA512: 8d29b8fe8c80ec8a02f3beb688d16dd606af08411c990a30d0a04c14bee4ccdabb6ac5737ec9548eaea6149d55b00c5d7b15c7f781abbdba32e30b0a93f436ff Homepage: https://cran.r-project.org/package=glmnetr Description: CRAN Package 'glmnetr' (Nested Cross Validation for the Relaxed Lasso and Other MachineLearning Models) Cross validation informed Relaxed LASSO (or more generally elastic net), gradient boosting machine ('xgboost'), Random Forest ('RandomForestSRC'), Oblique Random Forest ('aorsf'), Artificial Neural Network (ANN), Recursive Partitioning ('RPART') or step wise regression models are fit. Cross validation leave out samples (leading to nested cross validation) or bootstrap out-of-bag samples are used to evaluate and compare performances between these models with results presented in tabular or graphical means. Calibration plots can also be generated, again based upon (outer nested) cross validation or bootstrap leave out (out of bag) samples. Note, at the time of this writing, in order to fit gradient boosting machine models one must install the packages 'DiceKriging' and 'rgenoud' using the install.packages() function. For some datasets, for example when the design matrix is not of full rank, 'glmnet' may have very long run times when fitting the relaxed lasso model, from our experience when fitting Cox models on data with many predictors and many patients, making it difficult to get solutions from either glmnet() or cv.glmnet(). This may be remedied by using the 'path=TRUE' option when calling glmnet() and cv.glmnet(). Within the 'glmnetr' package the approach of path=TRUE is taken by default. other packages doing similar include 'nestedcv' , 'glmnetSE' which may provide different functionality when performing a nested CV. Use of the 'glmnetr' has many similarities to the 'glmnet' package and it could be helpful for the user of 'glmnetr' also become familiar with the 'glmnet' package , with the "An Introduction to 'glmnet'" and "The Relaxed Lasso" being especially useful in this regard. Package: r-cran-glmnetse Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-glmnetse_0.0.1-1.ca2604.1_all.deb Size: 44916 MD5sum: 10f242de7cfec6390388496a84e418c3 SHA1: 12cd9d6195628062e301ee249c89c81b381697a8 SHA256: 7b0fb2bc7674775d781a41986c2466d6351ea0e52afae78b39834c2502e91181 SHA512: f10fe931980bcab7d58d82df645ec994afed434d3e6ead3c86248dbf58296169e66508a28b013496f9833697f52b0ec7bfac07dcd90053a7a3acd821bc9a1d57 Homepage: https://cran.r-project.org/package=glmnetSE Description: CRAN Package 'glmnetSE' (Add Nonparametric Bootstrap SE to 'glmnet' for SelectedCoefficients (No Shrinkage)) Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or 'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process. The methods are described in Friedman et al. (2010) and Simon et al. (2011) . Package: r-cran-glmnetutils Architecture: all Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-doparallel, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-glmnetutils_1.1.9-1.ca2604.1_all.deb Size: 104720 MD5sum: 459e74b01c39c585a37edfe2f666d462 SHA1: ebb3b75f1da08d909178dfe2dab26db887e33e9f SHA256: 81b1b90f89d722cc5b5e567c873ca6e08b5cb009d2dc1f94cb34d1aa5d2e19be SHA512: 9c0fdf4863a8a382906d0b296507ef55dd85b8873133fc2d4f9f1ddc6c94eb8b6a5fb3d3af18d1e6d8ad6b18ca71ba04cb74f4e06d8871e58bc93e36a5343222 Homepage: https://cran.r-project.org/package=glmnetUtils Description: CRAN Package 'glmnetUtils' (Utilities for 'Glmnet') Provides a formula interface for the 'glmnet' package for elasticnet regression, a method for cross-validating the alpha parameter, and other quality-of-life tools. Package: r-cran-glmom Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmomco, r-cran-nleqslv, r-cran-robustbase, r-cran-ismev, r-cran-rsolnp, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-glmom_1.3.1-1.ca2604.1_all.deb Size: 363114 MD5sum: df9f3c4a4eb5056a9d52f1e416d1e1ae SHA1: c1442101e5c8d71cea0ea340981237876883014d SHA256: 34e83a00ea5f822697bd77fdbec129871438cee6132045453d8f1c390844cc5c SHA512: fd40657ab0f6e437472c3c04153606266716f8b82257c8f936b57d37ae7879efd67066886d2b46f01a2df8f4aaf59c8c4e5339f565fe578caccf694885f9daab Homepage: https://cran.r-project.org/package=GLmom Description: CRAN Package 'GLmom' (Generalized L-Moments Estimation for Extreme Value Distributions) Provides generalized L-moments estimation methods for the generalized extreme value ('GEV') distribution. Implements both stationary 'GEV' and non-stationary 'GEV11' models where location and scale parameters vary with time. Includes various penalty functions ('Martins'-'Stedinger', Park, Cannon, 'Coles'-Dixon) for shape parameter regularization. Also provides model averaging estimation ('ma.gev') that combines MLE and L-moment methods with multiple weighting schemes for robust high quantile estimation. The 'GLME' methodology is described in Shin et al. (2025a) . The non-stationary L-moment method is based on Shin et al. (2025b) . The model averaging method is described in Shin et al. (2026) . See also 'Hosking' (1990) for L-moments theory and 'Martins' and 'Stedinger' (2000) for penalized likelihood methods. Package: r-cran-glmpack Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pbrackets, r-cran-nnet, r-cran-effects, r-cran-aer, r-cran-pscl, r-cran-foreign, r-cran-matrix, r-cran-lme4, r-cran-lmtest, r-cran-sandwich, r-cran-censreg, r-cran-plm Filename: pool/dists/resolute/main/r-cran-glmpack_0.1.0-1.ca2604.1_all.deb Size: 122072 MD5sum: 0ddba2870b119256569355fc9797ed46 SHA1: edd061a3d055dba373c5731243059ff94a984e6a SHA256: 6edaaf30c3db9712573458dbf5d8db729911559c41e9da6779bdeeb0c9efeaf6 SHA512: 83108ab9f55a8bda035245763f78da9b6ad167f6bd91a33a3881291dad3fa1f68eab74eb9b187174b5860107e7471546c616411a9bb4e21762af99f16417fad2 Homepage: https://cran.r-project.org/package=GLMpack Description: CRAN Package 'GLMpack' (Data and Code to Accompany Generalized Linear Models, 2ndEdition) Contains all the data and functions used in Generalized Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create all models, tables, and plots are included for each data set. Package: r-cran-glmpathcr Architecture: all Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmpath Filename: pool/dists/resolute/main/r-cran-glmpathcr_1.0.10-1.ca2604.1_all.deb Size: 808266 MD5sum: a46980be9a3f477e59b51fc4665443bd SHA1: d60fc00e8e5b525518cc0a2264cd4ac74d3cb000 SHA256: 44aa8f991a811e9436016d0c9a0fa661fb259d8a02df9982b74d88bd392f6798 SHA512: e0d42587bcfcf013953d0c4f0db42deb9b0d0e31c9802aebf0400127aff376e253e240b6338de2318b6cc4a5c01bd70bbf976d5d79495b03fa7cd9e3ed2d10f0 Homepage: https://cran.r-project.org/package=glmpathcr Description: CRAN Package 'glmpathcr' (Fit a Penalized Continuation Ratio Model for Predicting anOrdinal Response) Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots as described by Archer and Williams (2012) . Package: r-cran-glmpca Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-logisticpca, r-cran-markdown, r-cran-matrix, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-glmpca_0.2.0-1.ca2604.1_all.deb Size: 226104 MD5sum: 066b8876eb7180d994d55c3fbd4fc412 SHA1: 4003c96575425a8b7f068bbca1d725e78b473471 SHA256: 88dbf82ebdc9639658414f245bac2819e0ac3133525e3fb34ed8a4a83bae767d SHA512: 605e49d81540fd4b96e9176a0669b99bc12d46cf08affd8505c74b019357ccd649b6a799023a8e0896f315bb2c4883c6cd0e55133a7df801dee54276c2bd9811 Homepage: https://cran.r-project.org/package=glmpca Description: CRAN Package 'glmpca' (Dimension Reduction of Non-Normally Distributed Data) Implements a generalized version of principal components analysis (GLM-PCA) for dimension reduction of non-normally distributed data such as counts or binary matrices. Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) . Townes FW (2019) . 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The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The details of methods can be found in "Tian, Y., & Feng, Y. (2023). Transfer learning under high-dimensional generalized linear models. Journal of the American Statistical Association, 118(544), 2684-2697.". 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The package includes functions to retrieve metadata, filter by bounding box, and download building height tiles. 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Package: r-cran-glorenz Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lorenzregression, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-glorenz_0.1.1-1.ca2604.1_all.deb Size: 20518 MD5sum: 026bca9630d4fc498b41c48b99367919 SHA1: cfecebd8c74aa7745f89d4c5cd17017badcbf7d0 SHA256: b95c3b9002ac0ac750e4ced10f84fa9c7b942b40965f3b703fca874deab6a202 SHA512: ced8efe9773d396cd52a89827658251d25adb9cf8e200384d2010781e502617eb27583c4cc6e5ea83f7f7ae9a7cf6e46f62df41a48f10b9c695e1caf2f833e44 Homepage: https://cran.r-project.org/package=glorenz Description: CRAN Package 'glorenz' (Transformed and Relative Lorenz Curves for Survey Weighted Data) Functions for constructing Transformed and Relative Lorenz curves with survey sampling weights. Given a variable of interest measured in two groups with scaled survey weights so that their hypothetical populations are of equal size, tlorenz() computes the proportion of members of the group with smaller values (ordered from smallest to largest) needed for their sum to match the sum of the top qth percentile of the group with higher values. rlorenz() shows the fraction of the total value of the group with larger values held by the pth percentile of those in the group with smaller values. Fd() is a survey weighted cumulative distribution function and Eps() is a survey weighted inverse cdf used in rlorenz(). Ramos, Graubard, and Gastwirth (2025) . Package: r-cran-glossa Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2628 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bs4dash, r-cran-shiny, r-cran-automap, r-cran-blockcv, r-cran-dbarts, r-cran-dplyr, r-cran-dt, r-cran-geothinner, r-cran-ggplot2, r-cran-htmltools, r-cran-leaflet, r-cran-markdown, r-cran-mcp, r-cran-proc, r-cran-sf, r-cran-shinywidgets, r-cran-sparkline, r-cran-svglite, r-cran-terra, r-cran-tidyterra, r-cran-waiter, r-cran-zip Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-matrixstats, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-glossa_1.2.4-1.ca2604.1_all.deb Size: 1451036 MD5sum: 0164d2388c8814944622bea343bd3ec9 SHA1: 653b15558842db79e5f82351230c7119fe1da315 SHA256: eb43a4026b33889587f7d51d62d24715ff750ad64c519579ae16478c2b67a225 SHA512: 413467573809cd6de719ec878ba326f65124bbcb77c8416507ad4cd3a9fbba5d7ef1f31b7b5fc43002acf9d620b78209946d0475ce3975df4fda85cdf2a6e94a Homepage: https://cran.r-project.org/package=glossa Description: CRAN Package 'glossa' (User-Friendly 'shiny' App for Bayesian Species DistributionModels) A user-friendly 'shiny' application for Bayesian machine learning analysis of marine species distributions. GLOSSA (Global Ocean Species Spatio-temporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) ) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales. Optionally, pseudo-absences can be generated within the environmental space using the external package 'flexsdm' (not on CRAN), which can be downloaded from ; this functionality is used conditionally when available and all core features work without it. Package: r-cran-glossary Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kableextra, r-cran-knitr, r-cran-markdown, r-cran-rvest, r-cran-xml2, r-cran-yaml Suggests: r-cran-covr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-glossary_1.0.0-1.ca2604.1_all.deb Size: 68766 MD5sum: 50187b16d14d311d84b1af35fd44ca0a SHA1: d6d42bd892eddd319d00ac209cbae8b93452bc63 SHA256: 94e81f6d7b0cd42d8bf7f2960c65a974c89b7ed9f07f9c3abff66fb9001e815f SHA512: be3ac114a4fe4e11fc9204993640965e99016bcc9923ed3eab45658a2fe699aa7327fc8882f63d4a5ccaa96f9e159404d79a01c3bee50a15d3681e6a4c4be10b Homepage: https://cran.r-project.org/package=glossary Description: CRAN Package 'glossary' (Glossaries for Markdown and Quarto Documents) Add glossaries to markdown and quarto documents by tagging individual words. Definitions can be provided inline or in a separate file. 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Package: r-cran-glscalibrator Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-maps, r-cran-dplyr, r-cran-lubridate, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-glscalibrator_0.1.0-1.ca2604.1_all.deb Size: 147204 MD5sum: 15beb5ff292a464c6ea3b610cdceb423 SHA1: 6f92af3a9f41ed162c172cab7cc2af0f2604057e SHA256: f5179f3db93125dd0074670ecbcd71d856ff49ddebb088fc90b95c5587bbe9fb SHA512: a052d39df92c5229583399427b08f6b49cbdc9a114121adb7b38a86a13bb41ad5f6a0327dfdfe4c76475292c1d07eebef1f39cf7767366c71feba8fae852b2d3 Homepage: https://cran.r-project.org/package=glscalibrator Description: CRAN Package 'glscalibrator' (Automated Calibration and Analysis of 'GLS' (Global LocationSensor) Data) Provides a fully automated workflow for calibrating and analyzing light-level geolocation ('GLS') data from seabirds and other wildlife. The 'glscalibrator' package auto-discovers birds from directory structures, automatically detects calibration periods from the first days of deployment, processes multiple individuals in batch mode, and generates standardized outputs including position estimates, diagnostic plots, and quality control metrics. Implements the established threshold workflow internally, following the methods described in 'SGAT' (Wotherspoon et al. (2016) ), 'GeoLight' (Lisovski et al. (2012) ), and 'TwGeos' (Lisovski et al. (2019) ). Package: r-cran-glsm Architecture: all Version: 0.0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-vgam, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-glsm_0.0.0.6-1.ca2604.1_all.deb Size: 53814 MD5sum: 1688fefb64f9c385b7a032d4f4670a95 SHA1: cd7513849e8b9b8e0fe99adec5d033ee3ad72b1d SHA256: 2975ca8c51a25cbfdb6a3c0bf0d7dd50270d035e0388cc3f783d35346fc52f60 SHA512: 87f6f2d029a555137ed537a657e0b2fa577298a3022d845a20710f15f730b4294b92f4e4e41f99bbc6146ebdb3852742db22dbfc78fae31a343e589993455e8d Homepage: https://cran.r-project.org/package=glsm Description: CRAN Package 'glsm' (Saturated Model Log-Likelihood for Multinomial Outcomes) When the response variable Y takes one of R > 1 values, the function 'glsm()' computes the maximum likelihood estimates (MLEs) of the parameters under four models: null, complete, saturated, and logistic. It also calculates the log-likelihood values for each model. This method assumes independent, non-identically distributed variables. For grouped data with a multinomial outcome, where observations are divided into J populations, the function 'glsm()' provides estimation for any number K of explanatory variables. Package: r-cran-glsme Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor Suggests: r-cran-ape, r-cran-mvslouch Filename: pool/dists/resolute/main/r-cran-glsme_1.0.5-1.ca2604.1_all.deb Size: 72190 MD5sum: cf9c9f011cbe41342ddd138a3ba2bf6b SHA1: ebbfad2cdd9eaf654ffb008df406b165b1db2aec SHA256: 4b9ea8b83d214c14dd40367cc73dfd326637aa5871a4b16516f6489c2544d949 SHA512: c6ef34e5d37598be300ec0b37803fa851338ad6636c0d8ca6e610cda109036b0d594b9bac49505ff5571fb62557c40330c1c8e4551d37c9c0f49f8b8ed41019c Homepage: https://cran.r-project.org/package=GLSME Description: CRAN Package 'GLSME' (Generalized Least Squares with Measurement Error) Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these. Package: r-cran-glsup Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-glsup_1.0.1-1.ca2604.1_all.deb Size: 23546 MD5sum: 52b32469683ae30a83fde919cd8a33b9 SHA1: 43ef2263667a014494ce9a5ef08f7d46794a3213 SHA256: 167c532d8fa6ce4c740851d23563ce3417ed1378616b5123876b508f59912b4e SHA512: 855c3f28b0e1a361703af4e01ecf243841523a0822da9b00b23111de7492280543196bfa8e7db89266caffafc7378554e4d8c0842ecc361810d77e4585b93bea Homepage: https://cran.r-project.org/package=GLSUP Description: CRAN Package 'GLSUP' (Generalised Linear Step-Up Procedure for Multiple HypothesisTesting) Performs the Generalised Linear Step-up Procedure (GLSUP) with a flexible user-defined sizing function. Functions are also available for creating common sizing functions. Package: r-cran-gluedo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue Filename: pool/dists/resolute/main/r-cran-gluedo_0.1.0-1.ca2604.1_all.deb Size: 15290 MD5sum: 1e5223af71742ec72786aa62dc4b8693 SHA1: 4e4f4c736ac7d92499811efdb91eff671958a1e8 SHA256: be7d8db07bfdbbad5160d121862a88917561d1cab727ec787868671924738cf0 SHA512: 1bd416d91a059acedb56775ae755550b0f2cf2bc34eeb31b62b80f28fc21a8f5307983f89575b033723a6beaf7c26eda8ab0e1bb14d02112d1e73054dcc8986c Homepage: https://cran.r-project.org/package=glueDo Description: CRAN Package 'glueDo' (Wrapper Functions for the 'glue' Library) Provides convenient wrapper functions around the 'glue' library for common string interpolation tasks. The package simplifies the process of combining 'glue' string templating with common R functions like message(), warning(), stop(), print(), cat(), and file writing operations. Instead of manually calling glue() and then passing the result to these functions, 'glueDo' provides direct wrapper functions that handle both steps in a single call. This is particularly useful for logging, error handling, and formatted output in R scripts and packages. The main reference for the underlying 'glue' package is Hester and Bryan (2022) . Package: r-cran-gluedown Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-magrittr Suggests: r-cran-covr, r-cran-dplyr, r-cran-httr, r-cran-knitr, r-cran-markdown, r-cran-mockr, r-cran-rmarkdown, r-cran-rvest, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-gluedown_1.0.9-1.ca2604.1_all.deb Size: 272360 MD5sum: 87f1a007dcc87d5fa927a25c37621320 SHA1: 40d4c7a62f033b02750865e02e3e71283c606cc2 SHA256: ca9a86c9be832fa20b633f4f14ce848e0729154f61b292a8591a1e7ce9b92ec5 SHA512: dffa181349df654d9aa2b059134c2b24c6b94fdb63990fe4125b2d609650a593b442f96c61be482562c27699b28c18ba7bbe2f4a8189b7bb9c4c69ca1a11a379 Homepage: https://cran.r-project.org/package=gluedown Description: CRAN Package 'gluedown' (Wrap Vectors in Markdown Formatting) Ease the transition between R vectors and markdown text. With 'gluedown' and 'rmarkdown', users can create traditional vectors in R, glue those strings together with the markdown syntax, and print those formatted vectors directly to the document. This package primarily uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous CommonMark specification by John MacFarlane (2019) . Package: r-cran-gluvarpro Architecture: all Version: 7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-pracma, r-cran-scales, r-cran-tidyr, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-gluvarpro_7.0-1.ca2604.1_all.deb Size: 303444 MD5sum: faa3e01c4ae00946f9023922cb6b6b3b SHA1: f3555d3d6abeb51cc6715b6d2614b9121381a988 SHA256: c63e7392e992f82a1d2d4555d021999fba7c2bb2b93fbed6ad6622850c287da9 SHA512: 1494eefb928eb35518c09e7ab8903622ea9ada81521db5784309a53ad2ec3eb2f3cb1c3af5d7a2eef74f3f028d5a63d68b40071b205f42f10457413323f34655 Homepage: https://cran.r-project.org/package=gluvarpro Description: CRAN Package 'gluvarpro' (Glucose Variability Measures from Continuous Glucose MonitoringData) Calculate different glucose variability measures, including average measures of glycemia, measures of glycemic variability and measures of glycemic risk, from continuous glucose monitoring data. Boris P. Kovatchev, Erik Otto, Daniel Cox, Linda Gonder-Frederick, and William Clarke (2006) . Jean-Pierre Le Floch, Philippe Escuyer, Eric Baudin, Dominique Baudon, and Leon Perlemuter (1990) . C.M. McDonnell, S.M. Donath, S.I. Vidmar, G.A. Werther, and F.J. Cameron (2005) . Everitt, Brian (1998) . Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) . Dougherty, R. L., Edelman, A. and Hyman, J. M. (1989) . Tukey, J. W. (1977) . F. John Service (2013) . Edmond A. Ryan, Tami Shandro, Kristy Green, Breay W. Paty, Peter A. Senior, David Bigam, A.M. James Shapiro, and Marie-Christine Vantyghem (2004) . F. John Service, George D. Molnar, John W. Rosevear, Eugene Ackerman, Leal C. Gatewood, William F. Taylor (1970) . Sarah E. Siegelaar, Frits Holleman, Joost B. L. Hoekstra, and J. Hans DeVries (2010) . Gabor Marics, Zsofia Lendvai, Csaba Lodi, Levente Koncz, David Zakarias, Gyorgy Schuster, Borbala Mikos, Csaba Hermann, Attila J. Szabo, and Peter Toth-Heyn (2015) . Thomas Danne, Revital Nimri, Tadej Battelino, Richard M. Bergenstal, Kelly L. Close, J. Hans DeVries, SatishGarg, Lutz Heinemann, Irl Hirsch, Stephanie A. Amiel, Roy Beck, Emanuele Bosi, Bruce Buckingham, ClaudioCobelli, Eyal Dassau, Francis J. Doyle, Simon Heller, Roman Hovorka, Weiping Jia, Tim Jones, Olga Kordonouri,Boris Kovatchev, Aaron Kowalski, Lori Laffel, David Maahs, Helen R. Murphy, Kirsten Nørgaard, Christopher G.Parkin, Eric Renard, Banshi Saboo, Mauro Scharf, William V. Tamborlane, Stuart A. Weinzimer, and Moshe Phillip.International consensus on use of continuous glucose monitoring.Diabetes Care, 2017 . Package: r-cran-glvmfit Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-glvmfit_0.1.0-1.ca2604.1_all.deb Size: 129604 MD5sum: 959c2894180d1cb6319be224952c44f8 SHA1: b1c84852d970c1c47935c15b763fcd97fa6d4406 SHA256: 790abab9ada43177f52889ddacb16c280be26f93d7a18007d0f8897480125dfc SHA512: 99e650fa73867178cfadaa60bf8ee4bdedcc0faef8f6157da781a71bff1ece64d19406a7632da6aebf92ce8e072b80e271f8682811ab5ad160d4ae3ef4f10754 Homepage: https://cran.r-project.org/package=glvmfit Description: CRAN Package 'glvmfit' (Methods to Assess Generalized Latent Variable Model Fit) Provides residual global fit indices for generalized latent variable models. Package: r-cran-glyparse Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glyrepr, r-cran-igraph, r-cran-purrr, r-cran-rlang, r-cran-rstackdeque, r-cran-stringr, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-glue, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-glyparse_0.6.0-1.ca2604.1_all.deb Size: 123998 MD5sum: b9ee3212a7ab3887009569679caeb1f2 SHA1: a38d887314adb72a8575750618f1f0cf90c746a5 SHA256: 46eaa63876c5d488dd4676ba8f6a9fd0ab8aae2aa5013ee12c29428176a93e7b SHA512: a760e3bc69b6717a4c097221095c08a11fb5719027cc1260a7a9763c6fbf4d06c6d97fa4ce8061032cd7b3b4160bbb17d1c379f810fce98eed059c4d49586199 Homepage: https://cran.r-project.org/package=glyparse Description: CRAN Package 'glyparse' (Parsing Glycan Structure Text Representations) Provides functions to parse glycan structure text representations into 'glyrepr' glycan structures. 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Supports varying levels of monosaccharide specificity (e.g., "Hex" or "Gal") and ambiguous linkages. Provides robust parsing and generation of IUPAC-condensed structure strings. Optimized for vectorized operations on glycan structures, with efficient handling of duplications. As the cornerstone of the glycoverse ecosystem, this package delivers the foundational data structures that power glycomics and glycoproteomics analysis workflows. 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Based on the method proposed by Zheng, Shi, and Zhang (2012) . Package: r-cran-gmdatabase Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rmysql, r-cran-foreach, r-cran-digest, r-cran-shiny Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-gmdatabase_0.5.1-1.ca2604.1_all.deb Size: 293940 MD5sum: bf8843ca952e1d90143fb581d9e318a6 SHA1: 75fae8ffa51292ee60721fcbc65981beee683d1b SHA256: ba8b0af4495a493466895452a8fc9a553159cce2a395fe5f7dd14825d67369a3 SHA512: 8f887d75519e02ce3ab0f1791eae87449a7456ba17fe27b002c18a6658a7bff0a2eac05acab73cdc73d611c107a8af750be5416c91424c13802780485d184dd0 Homepage: https://cran.r-project.org/package=gmDatabase Description: CRAN Package 'gmDatabase' (Accessing a Geometallurgical Database with R) A template for a geometallurgical database and a fast and easy interface for accessing it. 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There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response (Dag et al., 2019, ). Package: r-cran-gmdh Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-gmdh_1.6-1.ca2604.1_all.deb Size: 36004 MD5sum: ee60d7e6eaa00b7ef8b6b7a60d3fe28b SHA1: 1267e1ec7fca4ce08c6551694cca52978527172b SHA256: 37457387dd466175ce111414763b00d98732e287985d3828d9eaef70e73e5ba4 SHA512: 86b18ccc1736748cc1f7978ca0a6937cee73866d31831780a1361c6d3a2b2593a3dbcb1165187597895969b79a037825ce1984d8ae5c96f522b6103d8d3a2ca6 Homepage: https://cran.r-project.org/package=GMDH Description: CRAN Package 'GMDH' (Short Term Forecasting via GMDH-Type Neural Network Algorithms) Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. 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Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons. Package: r-cran-gmfamm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bamlss, r-cran-mgcv, r-cran-mass, r-cran-matrix Suggests: r-cran-testthat, r-cran-tidyverse, r-cran-jmbayes2, r-cran-registr, r-cran-fundata, r-cran-mfpca, r-cran-mjmbamlss, r-cran-refund Filename: pool/dists/resolute/main/r-cran-gmfamm_0.1.1-1.ca2604.1_all.deb Size: 168178 MD5sum: a062112573023889e8b910233bbb2413 SHA1: 100911ad2b7fba5c9ba450e592e2fee152576f82 SHA256: 0dd1283c0c3fa9894c3f4663614bf07840dccca4504ce0acc108ce84e2419d85 SHA512: 7315ca859774009be8853fc8d319a54f9eab455263f672da31a4feff002b9120c528c5eda01540e8f3f92fdb1077da247acefd2c168146f74b48067e969f25df Homepage: https://cran.r-project.org/package=gmfamm Description: CRAN Package 'gmfamm' (Generalized Multivariate Functional Additive Models) Supply implementation to model generalized multivariate functional data using Bayesian additive mixed models of R package 'bamlss' via a latent Gaussian process (see Umlauf, Klein, Zeileis (2018) ). 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For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) . Package: r-cran-gmgm Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-visnetwork Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gmgm_1.1.3-1.ca2604.1_all.deb Size: 732714 MD5sum: 5410d70ac85b8ed56128eda274b4c8aa SHA1: e26c1a9e7515ec632132d06fa9126e5040d9541b SHA256: 25098bde5dba2059988e4e5ed7f713aa51bb2c6aa815093cb0b0044e178a85a7 SHA512: b790d045b61ea813b84a7093d3eb143516755e6a1e5b429905093518285d098d8f9ddfb2ef99be28cefa8eb4524af830c9f787098d2ccc63fa759ec2733afac5 Homepage: https://cran.r-project.org/package=gmgm Description: CRAN Package 'gmgm' (Gaussian Mixture Graphical Model Learning and Inference) Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) . 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Initialization methods are compared using log-likelihood, and the best-fitting model can be selected using BIC. Methods build on initialization strategies for finite mixture models described in Michael and Melnykov (2016) and Biernacki et al. (2003) , and on the EM algorithm of Dempster et al. (1977) . Background on model-based clustering includes Fraley and Raftery (2002) and McLachlan and Peel (2000, ISBN:9780471006268). 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Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) , Savi Virolainen (2025) , Savi Virolainen (in press) . 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In addition to our classic distribution functions here, we calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions. Calculates the Value at Risk (VaR) and Average VaR are another important risk factors which are estimated by using well-known distribution functions. Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to study the properties of risk measures. 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The 'GNRS' is a batch application for resolving & standardizing political division names against standard name in the geonames database . The 'GNRS' resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the 'GNRS' attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The 'GNRS' works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the 'GNRS' does not attempt to match state or county. 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Methods are described in Sosa et al. (2023) . 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The godley R package offers tools to dynamically define model structures by adding variables and specifying governing systems of equations. With it, users can analyze how different macroeconomic structures affect key variables, perform parameter sensitivity analyses, introduce policy shocks, and visualize resulting economic scenarios. The accounting structure of SFC models follows the approach outlined in the seminal study by Godley and Lavoie (2007, ISBN:978-1-137-08599-3), ensuring a comprehensive integration of all economic flows and stocks. The algorithms implemented to solve the models are based on methodologies from Kinsella and O'Shea (2010) , Peressini and Sullivan (1988, ISBN:0-387-96614-5), and contributions by Joao Macalos. Package: r-cran-goeveg Architecture: all Version: 0.7.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-fields, r-cran-mgcv, r-cran-hmisc, r-cran-data.table Suggests: r-cran-biodiversityr, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-goeveg_0.7.10-1.ca2604.1_all.deb Size: 216670 MD5sum: 33d4bdf41adfe7ff854f6d18077957d0 SHA1: f71dc818a432df4ff5b929924307201069e5837f SHA256: 9e0c001e17939a7fb72587478fe3f2c3b0e46504d990c18ea0be2d88589a38ab SHA512: 74e3b73a230376a96981b3b97f4fcf3e8a27163d26f86de3063c0dfb3b3f2b53e1875b3c791542adca8a47b5fa068893802c838f09db6a41f081b841b691fce4 Homepage: https://cran.r-project.org/package=goeveg Description: CRAN Package 'goeveg' (Functions for Community Data and Ordinations) A collection of functions useful in (vegetation) community analyses and ordinations. 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Package: r-cran-gofedf Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-mass, r-cran-glm2, r-cran-statmod Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gofedf_1.1.0-1.ca2604.1_all.deb Size: 141624 MD5sum: 5880574e355257730bf30087a30dd3d6 SHA1: 04fc7fc737e062789b2b8280ec129ce3c3f04681 SHA256: fd4e9c5dda926db8c0d31b69ba4050977d2f310b91179c87a551377d4876d664 SHA512: 9e7007205d04b77fde170f0f5cbeb6aee13241db9c63cb766ff07dfc45de61c8d478ffee3cc2ab2063bdda58d3fa794587ca6c683fe81f6b1d91539dc5da678e Homepage: https://cran.r-project.org/package=gofedf Description: CRAN Package 'gofedf' (Goodness of Fit Tests Based on Empirical Distribution Functions) Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test if a sample follows Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) . Package: r-cran-gofgamma Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gofgamma_1.0-1.ca2604.1_all.deb Size: 69498 MD5sum: b8a7d5cb3de055f0dd875c46df175c2f SHA1: 135e6d1d7e020c3df9d417601fb6421a7a48b4f7 SHA256: f1c50f418e2850a49e5f4a70b62fedfeb2604e04ce7d8283e7241d0dc9c12060 SHA512: 79789814cc611ccc6a3ecd1cc85b3b19bb70a7d65b343ceb966a01b0b62974b42ddf6cae3277652ff1264a142c9ad497c948afa109c048eafdb7448eccf4e7b2 Homepage: https://cran.r-project.org/package=gofgamma Description: CRAN Package 'gofgamma' (Goodness-of-Fit Tests for the Gamma Distribution) We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) . The recent procedures presented in Henze, Meintanis & Ebner (2012) and Betsch & Ebner (2019) are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) . Package: r-cran-gofig Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-rmutil Filename: pool/dists/resolute/main/r-cran-gofig_1.0-1.ca2604.1_all.deb Size: 83552 MD5sum: 42370a3a662643b13ce94d84b9ca86ce SHA1: 4015f58fdff92a3a5d1afe2156170e76de65574f SHA256: 80b40305f99ea10ae8a33ebe224d8b5bf3f69835fcf70328d6b5102ecf02cd26 SHA512: ecdb074c8ff1c6163f692f46cb962eb5d41e112f1e1df182242a48cad0117841e786bfbc2bdf32b440dbd76db619858a346d51a063c3314ad1c9f3781b95ef56 Homepage: https://cran.r-project.org/package=gofIG Description: CRAN Package 'gofIG' (Goodness-of-Fit Tests for the Inverse Gaussian Distribution) We implement various tests for the composite hypothesis of testing the fit to the family of inverse Gaussian distributions. Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. (2022) , as well as two tests from Henze and Klar (2002) . Additionally, the package implements a test proposed by Baringhaus and Gaigall (2015) . For each test a parametric bootstrap procedure is implemented. 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Supports both 'knitr' and interactive execution within 'RStudio'. Package: r-cran-gofkernel Architecture: all Version: 2.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/resolute/main/r-cran-gofkernel_2.1-3-1.ca2604.1_all.deb Size: 62634 MD5sum: 6acbf11d0256a6d406c815a5e4197eb9 SHA1: cc00d50a207d36d17feed58aee30ebac2df820b8 SHA256: 3d63eb2d166d7f4e134fb8419d8315b88a36904ced5667705578e4bca0e2bf0d SHA512: b61829877491493262d21cb6f29cb2931c92ee32b71c73f486e53f631b95794c3525af3a5060a595a741b793dbd87fcc58a501b44e13553feaa6c1d349c49a06 Homepage: https://cran.r-project.org/package=GoFKernel Description: CRAN Package 'GoFKernel' (Testing Goodness-of-Fit with the Kernel Density Estimator) Tests of goodness-of-fit based on a kernel smoothing of the data. References: Pavía (2015) . Package: r-cran-gofreg Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-ggplot2, r-cran-r6, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gofreg_1.0.0-1.ca2604.1_all.deb Size: 841750 MD5sum: 9f6530a79abcfdfc8002de3de164b1bb SHA1: c8904a18d15b5a6c6c0e1d4ffdf1523bb95f2b3f SHA256: bc7e826d601435b7a670839be542d5b2ff2b1be963f58ccc9d291b549eeccc16 SHA512: 29eba2af0261d71fcee72962288cfe0e060c9e7aeb0c477ab925b03df7ea692bef509551f66c0fd2eff76d90c4366502ab277ad05f7e12c22fc5c6f6b8f57bba Homepage: https://cran.r-project.org/package=gofreg Description: CRAN Package 'gofreg' (Bootstrap-Based Goodness-of-Fit Tests for Parametric Regression) Provides statistical methods to check if a parametric family of conditional density functions fits to some given dataset of covariates and response variables. Different test statistics can be used to determine the goodness-of-fit of the assumed model, see Andrews (1997) , Bierens & Wang (2012) , Dikta & Scheer (2021) and Kremling & Dikta (2024) . As proposed in these papers, the corresponding p-values are approximated using a parametric bootstrap method. Package: r-cran-gofshiny Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-rhandsontable Filename: pool/dists/resolute/main/r-cran-gofshiny_0.1.0-1.ca2604.1_all.deb Size: 69838 MD5sum: 98613f144597c2635b126dd7130ab28b SHA1: b17b10be0e1b9f697ad74321098c27184f188712 SHA256: 8a77f5b5747e63ae6b6b24ed4a928fc62373dbce87de88af463fd4ba3fffec93 SHA512: 9b7437a048f3132ed71106d9fdaceabe3d53ca16e264250c564b1c7276a094a021ad57a6094429d76b47edc287f9c3746d90cd09e6183f69c9cacd68fb9f225c Homepage: https://cran.r-project.org/package=GOFShiny Description: CRAN Package 'GOFShiny' (Interactive Document for Working with Goodness of Fit Analysis) An interactive document on the topic of goodness of fit analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . 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Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions. Package: r-cran-gogarch Architecture: all Version: 0.7-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fgarch, r-cran-fastica Filename: pool/dists/resolute/main/r-cran-gogarch_0.7-6-1.ca2604.1_all.deb Size: 760812 MD5sum: f3f139c944a4df061c3b8693606b565c SHA1: 071203e1c9114983b1d9673ba3ddc67ea0568b41 SHA256: f2c1d10abf45a0d88f8d1128db975b5d4f60b64465efd03c08b6db35bcc40d8c SHA512: 49a498b39214518bd44a3bedcd2a2880e7f50f90470ac6aca01cf2ed5ffad0e0cf18568d4f9f6e066599befa00eabe98ef448fa96e97f9a0184f076098850ffa Homepage: https://cran.r-project.org/package=gogarch Description: CRAN Package 'gogarch' (Generalized Orthogonal GARCH (GO-GARCH) Models) Provision of classes and methods for estimating generalized orthogonal GARCH models. 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Package: r-cran-gominer Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-hgnchelper, r-cran-randomgodb, r-cran-gplots, r-cran-vprint Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gominer_1.3-1.ca2604.1_all.deb Size: 3807138 MD5sum: 6250618b13fefc05e9db99279acfb029 SHA1: 8ebf29993358d0890b7a8d29a8ea3fbcd2e6a51d SHA256: afeb64073619acbf14f26639a4bf410c15bd18dcc53969b39f8812904891dbec SHA512: a18458268fecd4f4ccc27f4611c2be7963082531b68a71c577023a015a05262f6b9d97cbd5e54e04c27ce88ff05b755467de3862b34b0ccbffb87310d8a66928 Homepage: https://cran.r-project.org/package=GoMiner Description: CRAN Package 'GoMiner' (Automate the Mapping Between a List of Genes and Gene OntologyCategories) In gene-expression microarray studies, for example, one generally obtains a list of dozens or hundreds of genes that differ in expression between samples and then asks 'What does all of this mean biologically?' 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The package offers many alternative regression models, such linear, robust, survival, multivariate etc., including k-fold cross-validation. References: Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. . Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). "The gamma-OMP algorithm for feature selection with application to gene expression data". IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214--1224. . 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Package: r-cran-googlenlp Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-readr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-googlenlp_0.2.0-1.ca2604.1_all.deb Size: 46522 MD5sum: 679f996f8712e4afffafe6213c8e9308 SHA1: b12466f35b14a4b6d75629d9109b19c70bb90b25 SHA256: e31119b0c42953cd604d515730fa7e4de5c1a8767070e69864f11e006161b6a1 SHA512: 023e999c95599657901a0be82594e5a043b511486b364bc7f48d482322d2f371c245160d5648c0809ac0daa33262ef6e32db38b569004285e3ffacfaad5bdf5e Homepage: https://cran.r-project.org/package=googlenlp Description: CRAN Package 'googlenlp' (An Interface to Google's Cloud Natural Language API) Interact with Google's Cloud Natural Language API (v1) via R. 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Package: r-cran-googlepublicdata Architecture: all Version: 0.16.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-readxl Suggests: r-cran-testthat, r-cran-covr, r-cran-googlevis Filename: pool/dists/resolute/main/r-cran-googlepublicdata_0.16.1-1.ca2604.1_all.deb Size: 77690 MD5sum: a8e5d52f92c1caa1a71f10a47a25f58b SHA1: e589e435037ce5732977e9ac75d58f6cb8c7f2b9 SHA256: 796a94f3e07ce2f04615f11ece4bc1fab089e7266716dea5283ce16770836d65 SHA512: c916fef0575c43154aa930d30a9ad84c77d75a76db8d7a9a290280aa58398556f15e00b350d6196952d726b50de0d80cfc1b60b12fbb7480a087c44b4d97b548 Homepage: https://cran.r-project.org/package=googlePublicData Description: CRAN Package 'googlePublicData' (Working with Google's 'Public Data Explorer' DSPL Metadata Files) Provides a collection of functions to set up 'Google Public Data Explorer' data visualization tool with your own data, building automatically the corresponding DataSet Publishing Language file, or DSPL (XML), metadata file jointly with the CSV files. 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Package: r-cran-gor Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gor_2.0-1.ca2604.1_all.deb Size: 279818 MD5sum: 284c7cf45ca1c418a81d460b7d2481ef SHA1: 44b2a40e66a9ffc14bc3a47f13a5abe87a15f69a SHA256: f095ca168ac236fb18c59bc2945305e2121ba4377a94c512d5c1b6662d0df8e7 SHA512: a9faabdcf15a39f479a06e1abc83905c5fd94a9cfe0b01c13a83a025e4b73aa08e6ab03ede7a452f2aa2062c30a2ab7bd3986aa3c5fe5b8d59d1caeecb1e76e1 Homepage: https://cran.r-project.org/package=gor Description: CRAN Package 'gor' (Algorithms for the Subject Graphs and Network Optimization) Informal implementation of some algorithms from Graph Theory and Combinatorial Optimization which arise in the subject "Graphs and Network Optimization" from first course of the EUPLA degree of Data Engineering in Industrial Processes. 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Package: r-cran-goric Architecture: all Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-quadprog, r-cran-mvtnorm, r-cran-mass, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-goric_1.1-3-1.ca2604.1_all.deb Size: 134434 MD5sum: 77f28081c6c53ba31e83edf9e8dd3dbc SHA1: 80d1b4efe44be37143b59f4b337d4b2ee6f92c3d SHA256: b20cb7336db582d7cb65acafcae718eb0007d4ef0fb6083b4fb9329ede206c22 SHA512: 51525dc091697aafbb9771010bd3bf1db74ad9a33ae98835d8da80a22777a8a6edd486a171f0536dceaf11536be5ba0a4d847870073ffe16af3283ad67a42647 Homepage: https://cran.r-project.org/package=goric Description: CRAN Package 'goric' (Generalized Order-Restricted Information Criterion) Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate linear models and generalised linear models. 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This collection requires a rich class of models and can be a very useful building block for a beginner. 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(2002) at for more details. Package: r-cran-gpltr Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gpltr_1.5-1.ca2604.1_all.deb Size: 521620 MD5sum: 83600b8f5f276146baf0e45eef92b205 SHA1: e06767550520837b0f01d4860cf8ff8ea98f82e1 SHA256: 42286fe166fea2a2440dd7a6d5047acfd89158d82aba3dcb0c22a1bb2da3c390 SHA512: 8b7a0513c5a13c62901d4feac8fabc94a72836bd72da3f3a586d4da0448c5459a654825213a8171cbbf1d2297f09a9376aebe78bbacc1425cabff5cff8c48494 Homepage: https://cran.r-project.org/package=GPLTR Description: CRAN Package 'GPLTR' (Generalized Partially Linear Tree-Based Regression Model) Combining a generalized linear model with an additional tree part on the same scale. A four-step procedure is proposed to fit the model and test the joint effect of the selected tree part while adjusting on confounding factors. We also proposed an ensemble procedure based on the bagging to improve prediction accuracy and computed several scores of importance for variable selection. See 'Cyprien Mbogning et al.'(2014) and 'Cyprien Mbogning et al.'(2015) for an overview of all the methods implemented in this package. 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Package: r-cran-gpom Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-rgl, r-cran-float Suggests: r-cran-signal, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gpom_1.4-1.ca2604.1_all.deb Size: 4775214 MD5sum: 53dd53056b4e56dd995212809583aaee SHA1: 6c3db19c18e73b29242bafb2474fcc679112d2eb SHA256: 35fdfcdd386bc99ea3f6d8e71af90e106ed171da4455806a7e14c9f2b3d423cc SHA512: 95807ec724876a846c448fc35b74013e238e371c1d853584cdf6d8c52136d47387ebab3a2494936cbbdea58651c992bead6ed47a093c15a59de6b12f5d20da8c Homepage: https://cran.r-project.org/package=GPoM Description: CRAN Package 'GPoM' (Generalized Polynomial Modelling) Platform dedicated to the Global Modelling technique. Its aim is to obtain ordinary differential equations of polynomial form directly from time series. 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Package: r-cran-gpp Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan Filename: pool/dists/resolute/main/r-cran-gpp_0.1-1.ca2604.1_all.deb Size: 77454 MD5sum: 45b72a633eab477779b607cf63256904 SHA1: 10d8c2b313e126fadbb533b4fcbff148cdb0fcff SHA256: cca5bafacbebd6c3b341722a28e294c29f464eef216a7f1ec8d7fd73e45feafa SHA512: 3c84eb39e0182050c2e9dc56279ff62a585671f5d27d51db294b45b34e5a51dfe87b18afcb38ed7b97dd082b1c3ed13f6d4c4a24dcefbc7bd370789f0b87246c Homepage: https://cran.r-project.org/package=GPP Description: CRAN Package 'GPP' (Gaussian Process Projection) Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. 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The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. This package uses Markov Chain Monte Carlo (MCMC) to sample from posterior probability distribution by 'rstan' package in R. Details are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012) , Wang H, Liddell CA, Coates MM, et al. (2014) and Mohammadi, Parsaeian, Mehdipour et al. (2017) . 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A single scalar balancing score is calculated for any generalized propensity score vector with three or more treatments. This balancing score is used for propensity score matching and stratification in outcome analyses when analyzing either ordinal or multinomial treatments. 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Plots interactive cluster maps and provides a summary dataframe with attributes for each cluster commonly used as covariates in subsequent modeling efforts. Additional functions provide individual keyhole markup language plots for quick assessment, and export of global positioning system exchange format files for navigation purposes. Methods can be found at . Package: r-cran-gptoolsstan Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gptoolsstan_1.0.0-1.ca2604.1_all.deb Size: 36936 MD5sum: 0aecf931184aa9c9567f63f02a5109c8 SHA1: fa856056c432ea3197076339562231a12e0c2c33 SHA256: 3935d45fbac414b4b7721187068cb06c1513761e5ce1ea714256636f5b18d867 SHA512: 5dda0065c22db6ae7a2a5b6a8e634ba91b2a7f3c9318d3724d0fbd8ac7a367904dee4b77d15386a51d43c2a29cf2d95cc46f7cf3d4fe757f63dce03091eb438a Homepage: https://cran.r-project.org/package=gptoolsStan Description: CRAN Package 'gptoolsStan' (Gaussian Processes on Graphs and Lattices in 'Stan') Gaussian processes are flexible distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. This package implements two methods for scaling Gaussian process inference in 'Stan'. First, a sparse approximation of the likelihood that is generally applicable and, second, an exact method for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility functions are provided to compile and fit 'Stan' models using the 'cmdstanr' interface. References: Hoffmann and Onnela (2025) . Package: r-cran-gptr Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-rcurl Filename: pool/dists/resolute/main/r-cran-gptr_0.7.0-1.ca2604.1_all.deb Size: 14456 MD5sum: 64b22eec3258be05cd598f7e2394242f SHA1: f73f893d3e5800e6c3fc7952094eb86c4e14cc60 SHA256: b78eca3784d3e89cff4c52fbf52f6bfd0ce1f430cf4464ea2d445f97cb77cac9 SHA512: 5d63a7ebb90c038cfab761cf8dc819e1e67f864e7bfeb27fff6bb221f6139aafb0eb466b1f7df7c1fafb3c7ca0446daad6ae52d0f062b9b340f91b2be6ee868f Homepage: https://cran.r-project.org/package=gptr Description: CRAN Package 'gptr' (A Convenient R Interface with the OpenAI 'ChatGPT' API) A convenient interface with the OpenAI 'ChatGPT' API . 'gptr' allows you to interact with 'ChatGPT', a powerful language model, for various natural language processing tasks. The 'gptr' R package makes talking to 'ChatGPT' in R super easy. It helps researchers and data folks by simplifying the complicated stuff, like asking questions and getting answers. With 'gptr', you can use 'ChatGPT' in R without any hassle, making it simpler for everyone to do cool things with language! Package: r-cran-gptreeo Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-hash, r-cran-dicekriging, r-cran-mlegp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gptreeo_1.0.1-1.ca2604.1_all.deb Size: 481012 MD5sum: 9186137ec4a90c50d4b7c18696de4315 SHA1: 5ac4a3c18db01018cd58a2e46267110479d7395a SHA256: e720a1c680cc2e660b9e9ca723207d57909e83037c5fb597b318328b7d265cf5 SHA512: a82ab6d4c80e017025f8cc0cdba7851a8566f2d3541743493c0aef1a790d32a64ee67896ca89ca0d64736f3825a7a590910c73db7c5d9353f0b092a722d15d8f Homepage: https://cran.r-project.org/package=GPTreeO Description: CRAN Package 'GPTreeO' (Dividing Local Gaussian Processes for Online Learning Regression) We implement and extend the Dividing Local Gaussian Process algorithm by Lederer et al. (2020) . Its main use case is in online learning where it is used to train a network of local GPs (referred to as tree) by cleverly partitioning the input space. In contrast to a single GP, 'GPTreeO' is able to deal with larger amounts of data. The package includes methods to create the tree and set its parameter, incorporating data points from a data stream as well as making joint predictions based on all relevant local GPs. Package: r-cran-gptstudio Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-bslib, r-cran-cli, r-cran-colorspace, r-cran-curl, r-cran-fontawesome, r-cran-glue, r-cran-htmltools, r-cran-htmlwidgets, r-cran-httr2, r-cran-ids, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-rstudioapi, r-cran-rvest, r-cran-shiny, r-cran-shiny.i18n, r-cran-sseparser, r-cran-stringr, r-cran-waiter, r-cran-yaml Suggests: r-cran-azurermr, r-cran-knitr, r-cran-mockr, r-cran-rmarkdown, r-cran-shinytest2, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-gptstudio_0.4.0-1.ca2604.1_all.deb Size: 633340 MD5sum: 14b3e9d3bd399af058caceb2cf5a6096 SHA1: c005d2c590476fbce8a174888b6168eee4aa4cf4 SHA256: f14690400c8d9debc2c4383da3ef1f5e82f22adec016c702324cb512dea8ac9e SHA512: 63fcafffbb04d72a2c6355609be2b251cd987642a7a28c928bd2678af782a47b9ae4a9e74a8b154e8d2dd084708df65a42ac1867895d3561c2a0c7efe8fdd11c Homepage: https://cran.r-project.org/package=gptstudio Description: CRAN Package 'gptstudio' (Use Large Language Models Directly in your DevelopmentEnvironment) Large language models are readily accessible via API. This package lowers the barrier to use the API inside of your development environment. For more on the API, see . Package: r-cran-gptzeror Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-lifecycle, r-cran-tidyr Suggests: r-cran-httptest2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gptzeror_0.0.2-1.ca2604.1_all.deb Size: 45086 MD5sum: 258fa2bdd27b423325da59996e529868 SHA1: b458b1a88c5054b10e088a12bf8028f073fa82c3 SHA256: d475a2e1dcba06486de250b0babe79ea6fe093203a0bd5db1818c4fba3364001 SHA512: 900d20a2c26b2039810a646e6125d96494c6f27a6ac882605dc52c1735abe765fce3605fbc49b529bc0e3ccddd94ad9f8298b281795d740c62372b63d39ec61d Homepage: https://cran.r-project.org/package=gptzeror Description: CRAN Package 'gptzeror' (Identify Text Written by Large Language Models using 'GPTZero') An R interface to the 'GPTZero' API (). Allows users to classify text into human and computer written with probabilities. Formats the data into data frames where each sentence is an observation. Paragraph-level and document-level predictions are organized to align with the sentences. Package: r-cran-gpumatrix Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3851 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-torch, r-cran-tensorflow, r-cran-matrix, r-cran-matrixstats, r-cran-float, r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gpumatrix_1.0.3-1.ca2604.1_all.deb Size: 2683624 MD5sum: e6d8654e9831583288a44b80e873ab53 SHA1: a1182a9aef4b448acadfba5d2771c9195d9bcf03 SHA256: 336bf464413cac129f95c2a31828814155edbc6a3b83a653f738680536340d8d SHA512: 572321687b5d489c3200c5cc1419f3bafb25b5ca2ad6755de4d5cc144d584e1c8c4e724a3eb1801b701a5b55e004ee7f0df0a766e906c26fa29fee1a12e1d39a Homepage: https://cran.r-project.org/package=GPUmatrix Description: CRAN Package 'GPUmatrix' (Basic Linear Algebra with GPU) GPUs are great resources for data analysis, especially in statistics and linear algebra. Unfortunately, very few packages connect R to the GPU, and none of them are transparent enough to run the computations on the GPU without substantial changes to the code. The maintenance of these packages is cumbersome: several of the earlier attempts have been removed from their respective repositories. It would be desirable to have a properly maintained R package that takes advantage of the GPU with minimal changes to the existing code. We have developed the 'GPUmatrix' package (available on CRAN). 'GPUmatrix' mimics the behavior of the Matrix package and extends R to use the GPU for computations. It includes single(FP32) and double(FP64) precision data types, and provides support for sparse matrices. It is easy to learn, and requires very few code changes to perform the operations on the GPU. 'GPUmatrix' relies on either the 'Torch' or 'Tensorflow' R packages to perform the GPU operations. We have demonstrated its usefulness for several statistical applications and machine learning applications: non-negative matrix factorization, logistic regression and general linear models. We have also included a comparison of GPU and CPU performance on different matrix operations. 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Package: r-cran-gpyramid Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gpyramid_0.0.1-1.ca2604.1_all.deb Size: 62616 MD5sum: 84296c18e49ddd1c72dd7dafe8969ec1 SHA1: c2cc0500b5cfae9e0680fe0fe54ea28514eb3258 SHA256: c7115cfac900191175ac5720827bd1f691bd68a60cafcecc757ab4574c47ec9d SHA512: 029ad24aae56bfb98d5b68982bfb40fae72c68a09280f9c62f460a8445b6c9c34268f7dc02ff146d64426e868e9869bfa333b15e3a10b5b049b2b47ff368ea93 Homepage: https://cran.r-project.org/package=gpyramid Description: CRAN Package 'gpyramid' (Identify Efficient Crossing Schemes for Gene Pyramiding) Calculates the cost of crossing in terms of the number of individuals and generations, which is theoretically formulated by Servin et al. (2004) . 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Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included. 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For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) . 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This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), ). 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Package: r-cran-grader Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-callr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-grader_2.0.1-1.ca2604.1_all.deb Size: 131178 MD5sum: b20776b4b9633af4146df843871365b7 SHA1: a23d9caffa39921bee182e1855a77929a2605470 SHA256: b32bb33a863431e7c9e7ba9806f7dbe091b7617b71c716ce479e452e84eaf012 SHA512: 165e5013b5df0866d0d4bd449593491d23d0b88c3c98544719f130a0b23eb8f539f0218e712761ba40cdfca11543a8494c9a7c6ab26428c6b6d576cf8ffaa082 Homepage: https://cran.r-project.org/package=gradeR Description: CRAN Package 'gradeR' (Helps Grade Assignment Submissions in Common R Formats) After being given the location of your students' submissions and a test file, the function runs each file that is an R script, R Markdown file, or Quarto document, and evaluates the results from all the given tests. Results are neatly returned in a data frame that has a row for each student, and a column for each test. Package: r-cran-gradient Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-gradient_1.0.1-1.ca2604.1_all.deb Size: 40732 MD5sum: d20480dd032414f0197d6336d01a995c SHA1: 2310cb5feedb86e3401009508e278bafc2472903 SHA256: 7bc3b7db88c32ba5aee633d2c3ade904903f98f60e3dcf62d0633387205f5d49 SHA512: 65c5e2d453b2008790244af7aaf78c213a9d63935f066587136d1ba19d32a9d4bf00d6dbd4bc8511d3931094b9d20e6fb6151657b6a6b817866ad0fe72f25d52 Homepage: https://cran.r-project.org/package=graDiEnt Description: CRAN Package 'graDiEnt' (Stochastic Quasi-Gradient Differential Evolution Optimization) An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; ). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially. Package: r-cran-gradlasso Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gradlasso_0.1.1-1.ca2604.1_all.deb Size: 138500 MD5sum: 2e1703efea5c3783c3ea13c22bdca3a3 SHA1: c453d62bc9aef96f15c8b01e2c87b26b70dc51e1 SHA256: a5b9eb621f28f21ed2052a1b393ffc2b74ebb3abb989f3c68cdb7fccdd0850f0 SHA512: e1b6b3cb32e500f01f8667e180fe46877d6ec2a29b9e4a2e125edf94d741f5b3214767cd0a19f50b342078815286c883478341d1fc5b1c98ff8d03462224a7fe Homepage: https://cran.r-project.org/package=gradLasso Description: CRAN Package 'gradLasso' (Gradient Descent LASSO with Stability Selection and BootstrappedConfidence Intervals) Implements LASSO regression using gradient descent with support for Gaussian, Binomial, Negative Binomial, and Zero-Inflated Negative Binomial (ZINB) families. Features cross-validation for determining lambda, stability selection, and bootstrapping for confidence intervals. Methods described in Tibshirani (1996) and Meinshausen and Buhlmann (2010) . Package: r-cran-grafify Architecture: all Version: 5.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-car, r-cran-dplyr, r-cran-emmeans, r-cran-hmisc, r-cran-lme4, r-cran-lmertest, r-cran-magrittr, r-cran-mgcv, r-cran-patchwork, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-matrix, r-cran-rlang, r-cran-rmarkdown, r-cran-pbkrtest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-grafify_5.1.0-1.ca2604.1_all.deb Size: 4238490 MD5sum: 1a4163234dde16caca8a931230cd831e SHA1: ffd02efccdacbbd294c0630fc7bbea2d54eec0af SHA256: 6d84e19952323dd71954df08001f59c4f25d55592f4384dcdda874683837a47e SHA512: b1babb11ddf0f4fd113ae9d2df2d87e18156a57ed2f731d62bb9b736bcddfe37b172cb8771ebed977d061bdb98f8be4032620d9fbc7ea551300410535dc9c9bf Homepage: https://cran.r-project.org/package=grafify Description: CRAN Package 'grafify' (Easy Graphs for Data Visualisation and Linear Models for ANOVA) Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage . Citation: . Package: r-cran-grafzahl Architecture: all Version: 0.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-lime, r-cran-quanteda, r-cran-reticulate Suggests: r-cran-knitr, r-cran-quanteda.textmodels, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-grafzahl_0.0.12-1.ca2604.1_all.deb Size: 1127880 MD5sum: 806a044d9229f0c7106e1847f56ad141 SHA1: 10a19f980111d298b737661271e3d3d65bee519a SHA256: 7e3722d50c0257bacde62074807580c6d238b1627c47e0af5724c28d63694b00 SHA512: f7f936bb69145c0ea9a9f88ec4284a58421534ebfc6849b49592016d08e793d7f315466b80f4af1376a991da87eef7ee29ca714b228718763b8b69b03714025e Homepage: https://cran.r-project.org/package=grafzahl Description: CRAN Package 'grafzahl' (Supervised Machine Learning for Textual Data Using Transformersand 'Quanteda') Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) to modern Transformer-based text classification models (Wolf et al., 2020) , in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' . More information: . Package: r-cran-gramevol Architecture: all Version: 2.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rex, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-gramevol_2.1-4-1.ca2604.1_all.deb Size: 401998 MD5sum: 7e0bf57e0676e2142f8912cbd6ae974e SHA1: d336beecf076b8cb851bc88b59db8a4e31e0830e SHA256: d1c25f3f5a79d96d6bca26043b2f6dbff656abc100f49e8430b067869b6f0180 SHA512: aa00d897c795a14064352cf4b41fb9705bd6053d89c492453aa0bbb65c6587c2199d12dc7a9333abed3001d80105106e57c9a9b87319b6b402d990a674a7f7ed Homepage: https://cran.r-project.org/package=gramEvol Description: CRAN Package 'gramEvol' (Grammatical Evolution for R) A native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function. Package: r-cran-gramquad Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-gramquad_0.1.1-1.ca2604.1_all.deb Size: 14282 MD5sum: 1072a76ec9f5896719fbcc60b92daefb SHA1: 0d8d438f0dd90fb8144f052f60082f3f4c65c93f SHA256: b7692fc425863994ac90aadccc3e7d20675c25d5424498cb80d124a97f756fb8 SHA512: b0c07645a56971a2fd98dd9564cbe558271692c387e2ccb0f2a8e91a4443ce2949aeff997902067da1835a161d155922e0cc99477d41c4ae5fd7b6a57e185b47 Homepage: https://cran.r-project.org/package=GramQuad Description: CRAN Package 'GramQuad' (Gram Quadrature) Numerical integration with Gram polynomials (based on [math.NA] 28 Jun 2021, by Irfan Muhammad [School of Computer Science, University of Birmingham, UK]). Package: r-cran-grand Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-grand_0.9.1-1.ca2604.1_all.deb Size: 105340 MD5sum: cb70612a491364dfe3c83cbc0101f3f5 SHA1: 8e85578d09f5fdc156ab9819f4bd9d66272b763b SHA256: b6dc4a42f1cbd04179f883897f9b214411940aa3c0860f0e8d3406c59ec9cdc5 SHA512: 12526e78fc0075d43c7cd6810bf83449e38cc7d78af6fde45e8869bf3d70a139efc689310f69befd376b037d8ad9831011985762033e16f800ecef89a0330069 Homepage: https://cran.r-project.org/package=grand Description: CRAN Package 'grand' (Guidelines for Reporting About Network Data) Interactively applies the Guidelines for Reporting About Network Data (GRAND) to an 'igraph' object, and generates a uniform narrative or tabular description of the object. Package: r-cran-grandpriv Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-envstats, r-cran-rmutil, r-cran-rspectra, r-cran-diffpriv, r-cran-truncnorm, r-cran-randnet, r-cran-igraph, r-cran-hcd, r-cran-transport Filename: pool/dists/resolute/main/r-cran-grandpriv_0.1.3-1.ca2604.1_all.deb Size: 56096 MD5sum: 8e95f0843f41bd72aab4ff1e6c1d0b2b SHA1: 48851f222d50d408e8393df0dc9afa2a99b09a6f SHA256: 39c46a392a5a75f59e6718ca37c86b589df5247eeeb3816bc7e04a413210745a SHA512: 44afc3dc0f37381f7d95ed0c9647dfefeeeca9b9c8bedf75ee9a661b0bf38c4a4c4de6c98a247c717e215653cab425300eee1016e2f7533418c5f22bf7405fa1 Homepage: https://cran.r-project.org/package=GRANDpriv Description: CRAN Package 'GRANDpriv' (Graph Release with Assured Node Differential Privacy) Implements a novel method for privatizing network data using differential privacy. Provides functions for generating synthetic networks based on LSM (Latent Space Model), applying differential privacy to network latent positions to achieve overall network privatization, and evaluating the utility of privatized networks through various network statistics. The privatize and evaluate functions support both LSM and RDPG (Random Dot Product Graph). For generating RDPG networks, users are encouraged to use the 'randnet' package . For more details, see the "proposed method" section of Liu, Bi, and Li (2025) . Package: r-cran-grangers Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vars, r-cran-tseries Filename: pool/dists/resolute/main/r-cran-grangers_0.1.0-1.ca2604.1_all.deb Size: 110000 MD5sum: 34cbbf9204154cb526dae973f3378ba2 SHA1: f7fb04d595a4c43992fb5f3d2813480125e6c90d SHA256: d382c8d3ca70b0ad54190e933093f600d122963af68d83bddf79256c3e57cd3c SHA512: 340ae5f3c05d2b145a31e9681ccf7b7b4dadbefea4ce366e4ef6ec60708308300e092d2db88ba88c892c29b755e35cd1817a25af765be66055f0b0771b5a0edd Homepage: https://cran.r-project.org/package=grangers Description: CRAN Package 'grangers' (Inference on Granger-Causality in the Frequency Domain) Contains five functions performing the calculation of unconditional and conditional Granger-causality spectra, bootstrap inference on both, and inference on the difference between them via the bootstrap approach of Farne' and Montanari, 2018 . Package: r-cran-grangersearch Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vars, r-cran-rlang, r-cran-tibble, r-cran-generics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-grangersearch_0.1.0-1.ca2604.1_all.deb Size: 112032 MD5sum: 0f9624a2a627e6b27cd27b113bf887ce SHA1: 7a1e61d89d7ef198eaa4cdbae051bf67a6d8db43 SHA256: decf1233f0d7be31ef10efaa0a80e4d89327ab2a7446adebe942c2493472a483 SHA512: 06cd3056c5ce7718c53719a980184f22d8dbb80b052936789102e8666eb41be787fb1ecb5ffe18e806b69fb6ecdf9bb32419d88836c8b116eb7710ef024f22e7 Homepage: https://cran.r-project.org/package=grangersearch Description: CRAN Package 'grangersearch' (Granger Causality Testing for Time Series) Performs Granger causality tests on pairs of time series to determine causal relationships. Uses Vector Autoregressive (VAR) models to test whether one time series helps predict another beyond what the series' own past values provide. Returns structured results including p-values, test statistics, and causality conclusions for both directions. Package: r-cran-granova Architecture: all Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car Suggests: r-cran-mgcv, r-cran-rgl, r-cran-mass, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-granova_2.3-1.ca2604.1_all.deb Size: 83646 MD5sum: 01195e1c11b5cd81e6fcb38604266878 SHA1: 8cfb9a767dc4a529f27e9ec4f927819472a93aae SHA256: 1da5ad30439232d099d028418b86e481e42134f19ffe1458ab9c65aaf22794f2 SHA512: 7865a44516b6c06ed04ef13901f95ff6b8c02022335eba5f1686128dc213b3cbf14cea32d623ce6a834d71faa0e65cb07156239e8953a05e4bea64b98e4e14e1 Homepage: https://cran.r-project.org/package=granova Description: CRAN Package 'granova' (Graphical Analysis of Variance) This small collection of functions provides what we call elemental graphics for display of analysis of variance results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) , Robert M. Pruzek and James E. Helmreich . The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular analysis of variance methods. These functions can be particularly helpful for students and non-statistician analysts. But these methods should be quite generally helpful for work-a-day applications of all kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. Package: r-cran-granovagg Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-granovagg_1.4.1-1.ca2604.1_all.deb Size: 142350 MD5sum: 6626d35ab97389d293d957a7decdaadd SHA1: b69041e36c7fd65d7b833b9e6b39e4580efec406 SHA256: 12219b45e2a29d5fac3fe1a1a1c0eedf32a92b55001f430fd34c7fd3fa1a7cb0 SHA512: ff600793e8f557534e9e28526e4ba575ea8c468a1eb379d15617bf22c1e2ecd454a9ae647c31cf5b40920f80efce4d00283171ac5080992df413b236d3243e5a Homepage: https://cran.r-project.org/package=granovaGG Description: CRAN Package 'granovaGG' (Graphical Analysis of Variance Using ggplot2) Create what we call Elemental Graphics for display of anova results. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular anova methods. This package represents a modification of the original granova package; the key change is to use 'ggplot2', Hadley Wickham's package based on Grammar of Graphics concepts (due to Wilkinson). The main function is granovagg.1w() (a graphic for one way ANOVA); two other functions (granovagg.ds() and granovagg.contr()) are to construct graphics for dependent sample analyses and contrast-based analyses respectively. (The function granova.2w(), which entails dynamic displays of data, is not currently part of 'granovaGG'.) The 'granovaGG' functions are to display data for any number of groups, regardless of their sizes (however, very large data sets or numbers of groups can be problematic). For granovagg.1w() a specialized approach is used to construct data-based contrast vectors for which anova data are displayed. The result is that the graphics use a straight line to facilitate clear interpretations while being faithful to the standard effect test in anova. The graphic results are complementary to standard summary tables; indeed, numerical summary statistics are provided as side effects of the graphic constructions. granovagg.ds() and granovagg.contr() provide graphic displays and numerical outputs for a dependent sample and contrast-based analyses. The graphics based on these functions can be especially helpful for learning how the respective methods work to answer the basic question(s) that drive the analyses. This means they can be particularly helpful for students and non-statistician analysts. But these methods can be of assistance for work-a-day applications of many kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. In the case of granovagg.1w() and granovagg.ds() several arguments are provided to facilitate flexibility in the construction of graphics that accommodate diverse features of data, according to their corresponding display requirements. See the help files for individual functions. Package: r-cran-grantham Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-grantham_0.1.4-1.ca2604.1_all.deb Size: 227784 MD5sum: fd285a03b5e122eea8167971467a25ff SHA1: 08b0ede380dd347246042ee817814538fb827cc3 SHA256: aa9d6f4ea7421bb1ad8cd17668fbb8fe633fb193a253b4131cecd88107cb7d7f SHA512: 5b5297f2c240bd5e770280b38795cd1e9e3503a21ddaead86b7c8d73d5fc4ecec6ea6c7c0b500061f620f99f29679f0be353677933840ed2e4d7f69d15274d48 Homepage: https://cran.r-project.org/package=grantham Description: CRAN Package 'grantham' (Calculate the Grantham Distance) A minimal set of routines to calculate the Grantham distance . The Grantham distance attempts to provide a proxy for the evolutionary distance between two amino acids based on three key chemical properties: composition, polarity and molecular volume. In turn, evolutionary distance is used as a proxy for the impact of missense mutations. The higher the distance, the more deleterious the substitution is expected to be. Package: r-cran-grape Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-grape_0.1.1-1.ca2604.1_all.deb Size: 39088 MD5sum: ee78d6c3a07e2b9fde9bda90fb884cd0 SHA1: bbd49dc4755a3cb2661941359c71f8bcbaca9beb SHA256: 92543a2fc508c4da79a8b3bc3983739d52282e71faaf19913c3938ef69536115 SHA512: c6f95940bb584e36eddc2273deaa20b05600a70e9b7bc8250cac74c9ce40da3f3a8959580fbeff8eea83194b00713807774f6191b63fe82db366871e049cece4 Homepage: https://cran.r-project.org/package=GRAPE Description: CRAN Package 'GRAPE' (Gene-Ranking Analysis of Pathway Expression) Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for summarizing the consensus behavior of biological pathways in the form of a template, and for quantifying the extent to which individual samples deviate from the template. GRAPE templates are based only on the relative rankings of the genes within the pathway and can be used for classification of tissue types or disease subtypes. GRAPE can be used to represent gene-expression samples as vectors of pathway scores, where each pathway score indicates the departure from a given collection of reference samples. The resulting pathway- space representation can be used as the feature set for various applications, including survival analysis and drug-response prediction. Users of GRAPE should use the following citation: Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics, 18:317 (June 2017). Package: r-cran-grapes Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-grapes_1.0.0-1.ca2604.1_all.deb Size: 83500 MD5sum: 9adaac24cbc2a32d98b0bb2d14cf3363 SHA1: 27e008491e644e62d72c0f44c0b2f6cc1bbedff8 SHA256: 4efe3fcba37c24fca2d5b9ac8930cf047592b5269bcbbf0ded9b003bb6fcf670 SHA512: 322c1c94d871a143d850a6921e49b3688f22fef62a6574e4330c520ad9e9572a122ca69d0f1514746d6c2fee1c425293f47676fbdcd14caebbc780f6abf84e1f Homepage: https://cran.r-project.org/package=grapes Description: CRAN Package 'grapes' (Make Binary Operators) Turn arbitrary functions into binary operators. Package: r-cran-grapesagri1 Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinywidgets, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-magrittr, r-cran-summarytools, r-cran-dplyr, r-cran-pastecs, r-cran-ggpubr, r-cran-hmisc, r-cran-corrplot, r-cran-ggplot2, r-cran-reshape2, r-cran-gridgraphics, r-cran-rcolorbrewer, r-cran-desplot, r-cran-agricolae, r-cran-paireddata, r-cran-gtools, r-cran-rdpack Suggests: r-cran-shinytest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-grapesagri1_1.1.0-1.ca2604.1_all.deb Size: 3503754 MD5sum: 7daf02cbcecbc70a8f018ad5a36a5344 SHA1: 37ac7f183b8afa64a03651c863ae8b5615e66a33 SHA256: 6c8bb86619ec66b32d92a227fdeacd6135642327bbfef642c41f3568c709d1df SHA512: d6d98125d7fb121b6a9c3fb54a77774a3de22c93171923cf5aeb6d32b074ec0977b3ab5dc8fbeeec12ec09a051dd16f269ae5154f5ef154c561a53dee960b904 Homepage: https://cran.r-project.org/package=grapesAgri1 Description: CRAN Package 'grapesAgri1' (Collection of Shiny Apps for Agricultural Research Data Analysis) Allows user to have graphical user interface to perform analysis of Agricultural experimental data. On using the functions in this package a Interactive User Interface will pop up. Apps Works by simple upload of files in CSV format. Package: r-cran-graph3d Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-lazyeval Suggests: r-cran-shiny, r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-graph3d_0.2.0-1.ca2604.1_all.deb Size: 105000 MD5sum: 64080495a14b266f2c435f6618c6d42b SHA1: d834c342cdc14b1d4e02df513f04cd9becdac1e0 SHA256: b09895ddb6857e8dcee8e4a2a2fdc795a656b29eb31b65892d05508110c71fd4 SHA512: 96d5df13ca41815e1b437829877989e4f85c5ccf32ea9a783400f3958bf57fd851a007c5784801dfe507484aab6c7c189c30c15e8eeeed7b86737ddb565a5e8b Homepage: https://cran.r-project.org/package=graph3d Description: CRAN Package 'graph3d' (A Wrapper of the JavaScript Library 'vis-graph3d') Create interactive visualization charts to draw data in three dimensional graphs. The graphs can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. Based on the 'vis.js' Graph3d module and the 'htmlwidgets' R package. Package: r-cran-graph4lg Architecture: all Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adegenet, r-cran-ggplot2, r-cran-stringr, r-cran-igraph, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-matrix, r-cran-vegan, r-cran-pegas, r-cran-mass, r-cran-tidyr, r-cran-sp, r-cran-sf, r-cran-hierfstat, r-cran-rappdirs, r-cran-gdistance, r-cran-raster, r-cran-foreign, r-cran-ecodist, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-graph4lg_1.8.0-1.ca2604.1_all.deb Size: 2086960 MD5sum: 2dcb9d5499f4cc8b66a476086fbef95a SHA1: 278faa3882cadb34ccb5262aa09b5c20cd52f935 SHA256: a7c59a3ffdb45885efa99ad35bc9c00db872615a0e96fb4060c0af9f1bb85588 SHA512: 082ad18b638f1c95987cb0468993a07aa5b24b19d36464712d6d0287dbe7c60b9bc2b6803207dad40b7b7cbb657f5760647c14891e66503dbcee6dd4178fc694 Homepage: https://cran.r-project.org/package=graph4lg Description: CRAN Package 'graph4lg' (Build Graphs for Landscape Genetics Analysis) Build graphs for landscape genetics analysis. This set of functions can be used to import and convert spatial and genetic data initially in different formats, import landscape graphs created with 'GRAPHAB' software (Foltete et al., 2012) , make diagnosis plots of isolation by distance relationships in order to choose how to build genetic graphs, create graphs with a large range of pruning methods, weight their links with several genetic distances, plot and analyse graphs, compare them with other graphs. It uses functions from other packages such as 'adegenet' (Jombart, 2008) and 'igraph' (Csardi et Nepusz, 2006) . It also implements methods commonly used in landscape genetics to create graphs, described by Dyer et Nason (2004) and Greenbaum et Fefferman (2017) , and to analyse distance data (van Strien et al., 2015) . Package: r-cran-graphclust Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockmodels, r-cran-igraph, r-cran-sclust Filename: pool/dists/resolute/main/r-cran-graphclust_1.3-1.ca2604.1_all.deb Size: 116124 MD5sum: 9031d7c4b38eda6fb3644ce784094b98 SHA1: 81ad866da1f15069579a42e367e9c7e91b22433b SHA256: f6a8f07f93694414b93923c85c778e05f7b83f91a3976538a31c385e1ea77454 SHA512: 3c861e5bef6e790e58836ab806118117cb0d5846fa3440e08781f5070f264754c892e3c5cf930ad1b98d877a08c614255f12e444f1b6430e5aa00bc0cb2f043f Homepage: https://cran.r-project.org/package=graphclust Description: CRAN Package 'graphclust' (Hierarchical Graph Clustering for a Collection of Networks) Graph clustering using an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models. The method is described in the article "Model-based clustering of multiple networks with a hierarchical algorithm" by T. Rebafka (2022) . Package: r-cran-grapher Architecture: all Version: 1.9-86-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-grapher_1.9-86-5-1.ca2604.1_all.deb Size: 694514 MD5sum: 8618e6c3770a0214bcb52c5793847e2b SHA1: 641a6679a2435271dad9b680882861bf2df9867e SHA256: ea4ea361d41cb668f469ae1d10c48e38ff4e67de4129fba6a16db1d1f4cbe73e SHA512: 65287b53d8c740a7d88b176f0bd41c17e11081d356c79fd085f8556d1eed8f17d147589c41801c7c896b32de91355b27ef6eff73f357ec8e568740e17b852717 Homepage: https://cran.r-project.org/package=GrapheR Description: CRAN Package 'GrapheR' (A Multi-Platform GUI for Drawing Customizable Graphs in R) A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot. Package: r-cran-grapherator Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate, r-cran-reshape2, r-cran-vegan, r-cran-ggplot2, r-cran-lhs, r-cran-deldir Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-grapherator_1.0.0-1.ca2604.1_all.deb Size: 756752 MD5sum: 74ee44eb1942d3587843adcba5b7cc7e SHA1: 7271f9784b4d7253f610bd0bde3c53f6a01c98e3 SHA256: 3bc9a79b992093d1a7cc33d37a184ac84aeed88a701b8b0e64ef83fd2bebca9b SHA512: d0f9b33dae8a9e686bc26e88479046ca5ebee881f246b534c38be51d8df93a9aabef3c0305e1f4adcb10250aa4f23361f9266dab68b8575b2be8bfa46b536863 Homepage: https://cran.r-project.org/package=grapherator Description: CRAN Package 'grapherator' (A Modular Multi-Step Graph Generator) Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights. Package: r-cran-graphframes Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparklyr, r-cran-tibble, r-cran-forge Suggests: r-cran-testthat, r-cran-covr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-graphframes_0.1.2-1.ca2604.1_all.deb Size: 68970 MD5sum: 5993bc2efff31a6ef4f0dd263e24b544 SHA1: dd2244bec85328c430d698f61dc68e11332f5b4d SHA256: 50ffd3f87c5083c36ca81eace3299a591b81502f819d4ad81542f9a058b968aa SHA512: 70af747d4e55ac146e15aeeb4042a4c13e4132c8cc311abffc16ddbaa492aaf7489ea9452f2fe89f80f7b3cf5c63af03062656024fcc3dffbd78646c9ec0091c Homepage: https://cran.r-project.org/package=graphframes Description: CRAN Package 'graphframes' (Interface for 'GraphFrames') A 'sparklyr' extension that provides an R interface for 'GraphFrames' . 'GraphFrames' is a package for 'Apache Spark' that provides a DataFrame-based API for working with graphs. 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Methods are provided for exact simulation and statistical inference for multivariate Pareto distributions on graphical structures as described in the paper 'Graphical Models for Extremes' by Engelke and Hitz (2020) . 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Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) , Lu (2016) , and Xi et al. (2017) . This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs. Package: r-cran-graphon Architecture: all Version: 0.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-roptspace, r-cran-rdpack Suggests: r-cran-igraph Filename: pool/dists/resolute/main/r-cran-graphon_0.3.6-1.ca2604.1_all.deb Size: 80148 MD5sum: b660471a27f7e88e6ac18a727d96999c SHA1: 5b6f7b27be5655caa682748f0e8ed768b13a5398 SHA256: 380b3713aa6551f56bc4d57b29c059e4209ef4a0fc749a59b2c87beda823908e SHA512: f9527b8ce8b46a0aae44b4747295a7444897697ebb00aa0a40700d1b0dc9b2c323b206ae6ff14974383aa6262735654154d38815d5f025b62c1628b95798456d Homepage: https://cran.r-project.org/package=graphon Description: CRAN Package 'graphon' (A Collection of Graphon Estimation Methods) Provides a not-so-comprehensive list of methods for estimating graphon, a symmetric measurable function, from a single or multiple of observed networks. 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Package: r-cran-graphonmix Architecture: all Version: 0.0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 830 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-imager Suggests: r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-graphonmix_0.0.1.0-1.ca2604.1_all.deb Size: 606378 MD5sum: dbf78bfb2dc92fc7ff7da733aeb88a6c SHA1: 0b01fd01f746c24cec16d83d61c16f6bac85681d SHA256: 84e2c3bf0f4185100c104e5ab7e0e30c8fa2028943aa22303982745d3d8764d3 SHA512: 65ac76ec923e478ee79a7589980e6175c146f30746fec2d351db5c840f2b9fd5c43c8f2acb4ec5191febddfc52bc6491a67881ed0629ec9af2ad876f16c28d9b Homepage: https://cran.r-project.org/package=graphonmix Description: CRAN Package 'graphonmix' (Generates Mixture Graphs from Dense and Sparse Graphons) Generates (U,W) mixture graphs where U is a line graph graphon and W is a dense graphon. Graphons are graph limits and graphon U can be written as sequence of positive numbers adding to 1. Graphs are sampled from U and W and joined randomly to obtain the mixture graph. Given a mixture graph, U can be inferred. Kandanaarachchi and Ong (2025) . Package: r-cran-graphpaf Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-gtools, r-cran-madness, r-cran-mass, r-cran-reshape2, r-cran-survival Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-graphpaf_2.0.1-1.ca2604.1_all.deb Size: 1470706 MD5sum: 709fe5fea03c23077ddfc37fa7ef249f SHA1: c43185e9caff41b0213f0e0c1d2cb654e2bcc616 SHA256: c0ce2db778e8ceec7a4867ebfb525eabbc8cf3616a69b7aaadede56ecc6644af SHA512: 0a0924e03ff1511e6fddc242b354b975163d4f33ffc3b6e442b44a8a18eab2a9d2cc724e502a43477814488de485098f7357aab3e8c9ea0ca99066390b5a36e8 Homepage: https://cran.r-project.org/package=graphPAF Description: CRAN Package 'graphPAF' (Estimating and Displaying Population Attributable Fractions) Estimation and display of various types of population attributable fraction and impact fractions. As well as the usual calculations of attributable fractions and impact fractions, functions are provided for attributable fraction nomograms and fan plots, continuous exposures, for pathway specific population attributable fractions, and for joint, average and sequential population attributable fractions. Package: r-cran-graphranktest Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-nbpmatching Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-graphranktest_0.1-1.ca2604.1_all.deb Size: 32890 MD5sum: a70d0a9dab330fd499814065f17f4ac4 SHA1: 65882be720248541b20fec74dd9bd2c2bfd411da SHA256: 90f3dbd579ba472b0681d2825a2269eec1debf5cbc9427f6ad1ee2395c222911 SHA512: 37d20a0881eddb7093a3bb041f6b178eb92b9104ff59abe34871686e34c6c81c79fb4f9e238566b64f686939a41febb6f7468168ff36b064d5937fbb9ccbc89d Homepage: https://cran.r-project.org/package=GraphRankTest Description: CRAN Package 'GraphRankTest' (Rank in Similarity Graph Edge-Count Two-Sample Test (RISE)) Implements the Rank In Similarity Graph Edge-count two-sample test (RISE) for high-dimensional and non-Euclidean data. The method constructs similarity-based graphs, such as k-nearest neighbor graph (k-NNG), k-minimum spanning tree (k-MST), and k-minimum distance non-bipartite pairing (k-MDP), and evaluates rank-based within-sample edge counts with asymptotic and permutation p-values. For methodological details, see Zhou and Chen (2023) . Package: r-cran-graphsim Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2683 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gplots, r-cran-igraph, r-cran-mvtnorm, r-cran-matrixcalc, r-cran-matrix Suggests: r-cran-devtools, r-cran-knitr, r-cran-markdown, r-cran-prettydoc, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat, r-cran-scales, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-graphsim_1.0.4-1.ca2604.1_all.deb Size: 1611978 MD5sum: 0e15ffb822f3d8bb1849cae402740d4b SHA1: dc5179706c378334299c3f318dcb00f1c1f6a47d SHA256: d1da2188d64ac587063e9636464c52fc15d20d3560bd6e5577e4a26969d30fcc SHA512: 384a652cb4c057691b451190a43c174a42ec0ae6e3a0ffa8ce98a9cf102aabf713139d29ca21258420132a13800f4fde09475c164f9bddccfc966c758ac71304 Homepage: https://cran.r-project.org/package=graphsim Description: CRAN Package 'graphsim' (Simulate Expression Data from 'igraph' Networks) Functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph' structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses. Package: r-cran-graposas Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-graposas_1.0.0-1.ca2604.1_all.deb Size: 39072 MD5sum: 3a03a6a903c6181cb54b053f6d95e29d SHA1: 7135c5363a23be5b443c9936f2d9073e7d526b71 SHA256: a461d2453f278fc8e99b6d030bb32e7ed439fdbf5d8548e3bdd7ec54d2d43cfe SHA512: ad0a8679dc162e9adab41964d3b4975c6f31966bbf21cae7d0c208c9a45622f5548e5f2e0401b93f171944e7397dcced108a734f5f530c41b0bdf838e4529b33 Homepage: https://cran.r-project.org/package=graposas Description: CRAN Package 'graposas' (Graphical Approach Optimal Sample Size) Graphical approach provides a useful framework for multiplicity adjustment in clinical trials with multiple endpoints. This package includes statistical methods to optimize sample size over initial weight and transition probability in a graphical approach under a common setting, which is to use marginal power for each endpoint in a trial design. See Zhang, F. and Gou, J. (2023). Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment, Technical Report. 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Package: r-cran-grcdata Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr, r-cran-cubature Filename: pool/dists/resolute/main/r-cran-grcdata_1.0-1.ca2604.1_all.deb Size: 53014 MD5sum: de5ed00cb637f700f13889fba23833ef SHA1: 62e22905e2aca24816a52930d7c8b950367e7aa0 SHA256: e1009c6d8251e07d9469d2a100ed1d71f7e7e846f3d44c4868f0831d7dc24679 SHA512: 570391148b163ccc8138318cff196f05f1dfcc5f908987b5910b13006495fca0f9f5fe13c4efe94a9449518a92201e819b983de055b82dd12ed9a7ab58df8cc2 Homepage: https://cran.r-project.org/package=GRCdata Description: CRAN Package 'GRCdata' (Parameter Inference and Optimal Designs for Grouped and/orRight-Censored Count Data) We implement two main functions. The first function uses a given grouped and/or right-censored grouping scheme and empirical data to infer parameters, and implements chi-square goodness-of-fit tests. 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Package: r-cran-greekletters Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-assertthat Suggests: r-cran-clisymbols, r-cran-swirlify, r-cran-swirl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-greekletters_1.0.4-1.ca2604.1_all.deb Size: 33118 MD5sum: 6f7d91fb00f68f9b2bb87a2aa58339dd SHA1: 6082944cc443e24b176f436fb5f3122dba3e48f0 SHA256: a87e6ea233ded6632c9373efdfa6ddea85c6e5efb962f465b2fa8de86cd50fa7 SHA512: ab59104415872d60478b2df213fa1476c61d87a728ea5f1269012595371745671d622b579fcd3bcfac243bab480c5690e1a4000b0a08c795af332daf316d0aba Homepage: https://cran.r-project.org/package=greekLetters Description: CRAN Package 'greekLetters' (Routines for Writing Greek Letters and Mathematical Symbols onthe 'RStudio' and 'RGui') An implementation of functions to display Greek letters on the 'RStudio' (include subscript and superscript indexes) and 'RGui' (without subscripts and only with superscript 1, 2 or 3; because 'RGui' doesn't support printing the corresponding Unicode characters as a string: all subscripts ranging from 0 to 9 and superscripts equal to 0, 4, 5, 6, 7, 8 or 9). The functions in this package do not work properly on the R console. Characters are used via Unicode and encoded as UTF-8 to ensure that they can be viewed on all operating systems. Other characters related to mathematics are included, such as the infinity symbol. All this accessible from very simple commands. This is a package that can be used for teaching purposes, the statistical notation for hypothesis testing can be written from this package and so it is possible to build a course from the 'swirlify' package. Another utility of this package is to create new summary functions that contain the functional form of the model adjusted with the Greek letters, thus making the transition from statistical theory to practice easier. In addition, it is a natural extension of the 'clisymbols' package. 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Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. In addition to the descriptive methods, there are functions for the robust covariance matrix provided by the 'sandwich' package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for time-splitting a dataset when modeling non-proportional hazards in Cox regressions. 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Pounds, Stan, et al. (2013) . Package: r-cran-gripp Architecture: all Version: 0.2.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gensa, r-cran-ga Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gripp_0.2.21-1.ca2604.1_all.deb Size: 392978 MD5sum: 7f4945b83d29902bfd3fb996b712eda1 SHA1: ce3b06f7f8e0185e80ce17cf53cc8b3763c729b0 SHA256: 23c47bc29c834e5659a3cc611afdee607ecfd1e883c0285da79629bfd335edf0 SHA512: 93a4d8362b1c149e7403cf358b4808baa19d4aa8b37d3c47c15a4cb39c99789d48ae13a83ca000aba8284adbf3190bf3275f057d344e4a26029efa72673439e3 Homepage: https://cran.r-project.org/package=gripp Description: CRAN Package 'gripp' (General Inverse Problem Platform) Set of functions designed to solve inverse problems. The direct problem is used to calculate a cost function to be minimized. 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Baumont "Model Predicting Dynamics of Biomass, Structure and Digestibility of Herbage in Managed Permanent Pastures. 1. Model Description." (2006) ). The implementation in this package contains a few additions to the above cited version of ModVege, such as simulations of management decisions, and influences of snow cover. As such, the model is fit to simulate grass growth in mountainous regions, such as the Swiss Alps. The package also contains routines for calibrating the model and helpful tools for analysing model outputs and performance. 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Package: r-cran-growthcurveme Architecture: all Version: 0.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-flextable, r-cran-ggplot2, r-cran-investr, r-cran-knitr, r-cran-magrittr, r-cran-minpack.lm, r-cran-moments, r-cran-patchwork, r-cran-rlang, r-cran-saemix, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-viridis Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-growthcurveme_0.1.11-1.ca2604.1_all.deb Size: 470386 MD5sum: cb2f3d2d676e49ce6b4d5c4490d7f7ce SHA1: 76c146d9aadfe432791175216a0bc3804f0866ce SHA256: a504556e346c78034df3d504435305aba4712b65017f761a58a625d984873475 SHA512: fb1820785715830331f774309a829c61b28b5ac55eedbc3e7b65eaa035172dd90a177e2835302014a954fea7e26afaf674ad79a9ea693c5d627cc689e5db90f6 Homepage: https://cran.r-project.org/package=GrowthCurveME Description: CRAN Package 'GrowthCurveME' (Mixed-Effects Modeling for Growth Data) Simple and user-friendly wrappers to the 'saemix' package for performing linear and non-linear mixed-effects regression modeling for growth data to account for clustering or longitudinal analysis via repeated measurements. The package allows users to fit a variety of growth models, including linear, exponential, logistic, and 'Gompertz' functions. For non-linear models, starting values are automatically calculated using initial least-squares estimates. The package includes functions for summarizing models, visualizing data and results, calculating doubling time and other key statistics, and generating model diagnostic plots and residual summary statistics. It also provides functions for generating publication-ready summary tables for reports. Additionally, users can fit linear and non-linear least-squares regression models if clustering is not applicable. The mixed-effects modeling methods in this package are based on Comets, Lavenu, and Lavielle (2017) as implemented in the 'saemix' package. Please contact us at models@dfci.harvard.edu with any questions. Package: r-cran-growthcurver Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm Suggests: r-cran-testthat, r-cran-knitr, r-cran-dplyr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-growthcurver_0.3.1-1.ca2604.1_all.deb Size: 317596 MD5sum: 33c6a89d7f99f1b9d91e2fdeecbb2fed SHA1: 397ff693228c0f7b27c330f236ab58b9fbc9fdb8 SHA256: e2f7007bd381e0e91423bcd7eb1ad944dbdaee00f6fb4d4de80c330f7be76038 SHA512: b1aec9397f71568dcd119d4ed7886c716abb332a94902493f0aa83e01ceb71e9a35748b392353dca82fb7c0f42bb83c7d035ef6df2f53723702036e647330f7c Homepage: https://cran.r-project.org/package=growthcurver Description: CRAN Package 'growthcurver' (Simple Metrics to Summarize Growth Curves) Fits the logistic equation to microbial growth curve data (e.g., repeated absorbance measurements taken from a plate reader over time). From this fit, a variety of metrics are provided, including the maximum growth rate, the doubling time, the carrying capacity, the area under the logistic curve, and the time to the inflection point. Method described in Sprouffske and Wagner (2016) . Package: r-cran-growthdecomp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fracdiff Filename: pool/dists/resolute/main/r-cran-growthdecomp_0.1.0-1.ca2604.1_all.deb Size: 11426 MD5sum: 29c3a0c628399a1ac8daa7bddc099911 SHA1: dc9261938ffa76f9c0e205bfdf27d922ec6e1c91 SHA256: 845359c96332b51998648db39d66686c90e6bfeca22b82644226fa4436817017 SHA512: f67023eb15bd7b3fcbd0a44782db2e3687c1c3ac49ea0a5f042a7f6ef446f5b1975aa10a293144478728346985e5fc1b8fcf313112dc0ada3a436cabaed9b2a5 Homepage: https://cran.r-project.org/package=growthDecomp Description: CRAN Package 'growthDecomp' (Decomposition of Growth Trends) Decomposes observed growth in agricultural and livestock systems into interpretable component effects. Depending on the application, the total change in output can be attributed to components such as area effect, yield effect, herd or slaughter effect, productivity effect, and interaction effect. Details can be found in Rakshit and Bardhan (2026) . 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Package: r-cran-growthpheno Architecture: all Version: 3.1.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4679 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dae, r-cran-dplyr, r-cran-ggally, r-cran-ggplot2, r-cran-hmisc, r-cran-jops, r-cran-rcolorbrewer, r-cran-readxl, r-cran-reshape, r-cran-stringi Suggests: r-cran-testthat, r-cran-nlme, r-cran-r.rsp, r-cran-scales Filename: pool/dists/resolute/main/r-cran-growthpheno_3.1.18-1.ca2604.1_all.deb Size: 4576096 MD5sum: a7f47be1c8be0590efd1f97d2867eefb SHA1: 5de1d70c0db4f9e1def0d929d268cc197ae8e96e SHA256: 0fec2a2892e68ab8dd919655de4ff137cfc6c5d3e29c219c5b342982626abbcc SHA512: 6d1a03c26ecb33f0aa269430da0be52d9bc7f6c888fb8980778b20b04d017d76bef86079e0243406b6adbd45a34deea52eac5c07fbc5cfaf659643ece8891b4f Homepage: https://cran.r-project.org/package=growthPheno Description: CRAN Package 'growthPheno' (Functional Analysis of Phenotypic Growth Data to Smooth andExtract Traits) Assists in the plotting and functional smoothing of traits measured over time and the extraction of features from these traits, implementing the SET (Smoothing and Extraction of Traits) method described in Brien et al. (2020) Plant Methods, 16. Smoothing of growth trends for individual plants using natural cubic smoothing splines or P-splines is available for removing transient effects and segmented smoothing is available to deal with discontinuities in growth trends. There are graphical tools for assessing the adequacy of trait smoothing, both when using this and other packages, such as those that fit nonlinear growth models. A range of per-unit (plant, pot, plot) growth traits or features can be extracted from the data, including single time points, interval growth rates and other growth statistics, such as maximum growth or days to maximum growth. The package also has tools adapted to inputting data from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer 3D (see for more information). The package 'growthPheno' can also be installed from . 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Supports flexible detrending and climate–growth modeling via generalized additive mixed models (Wood 2017, ISBN:978-1498728331) and the 'mgcv' package (), enabling robust analysis of non-linear trends and autocorrelated data. Provides standardized visual reporting, including summaries, diagnostics, and model performance. Compatible with '.rwl' files and tailored for the Canadian Forest Service Tree-Ring Data (CFS-TRenD) repository (Girardin et al. (2021) ), offering a comprehensive and adaptable framework for dendrochronologists working with large and complex datasets. 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Package: r-cran-gsmams Architecture: all Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gsmams_0.7.2-1.ca2604.1_all.deb Size: 152064 MD5sum: 298c2e74f4b66e553ec12888f509c7ea SHA1: e6ad99d2afeaf4ea7bdebde5935e9337ae438fd4 SHA256: fb4806f1db45b840fd9879428a1c1a305cf7630a962f29e654606d034a644d57 SHA512: 94a751a4d69073d1c3cc5bd2f642c59483a19fecaca4f3c5e257a9660ec1c7f8b546889b55fbb742fb409b01082a2c6a98b0ad0e70c990161fb72591ae486107 Homepage: https://cran.r-project.org/package=gsMAMS Description: CRAN Package 'gsMAMS' (Group Sequential Designs of Multi-Arm Multi-Stage Trials) It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. 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Package: r-cran-gson Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-rlang, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-gson_0.1.0-1.ca2604.1_all.deb Size: 219290 MD5sum: a2efc029e37d3e03b6dea069e4588b22 SHA1: ad90739feefe569f0bfbf1963ffffb00027cf135 SHA256: 6eabd9bc68c984b4f1f50b0865307007b690d64f3dacc6400def5185c5296af7 SHA512: 60104883a46bed6065c6ac0c285e05abd056b91daca139e1faead99865454f1100851b5982d4f7d0f876047c4679f5ac2560323843f0839c4c457cce2939aac0 Homepage: https://cran.r-project.org/package=gson Description: CRAN Package 'gson' (Base Class and Methods for 'gson' Format) Proposes a new file format ('gson') for storing gene set and related information, and provides read, write and other utilities to process this file format. 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Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017". 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Package: r-cran-gsrs Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-gsrs_0.1.1-1.ca2604.1_all.deb Size: 280546 MD5sum: 5367df436c564ecde9796bdb61f0d262 SHA1: d33cf1c85d8dd4c73dbe4c0bd8392ab979bd0ac6 SHA256: fc541988ffdca7627a219d2e5c65073162d4231051c6b2aaf4cb17b32b9a4606 SHA512: 04ed5a6f232ae8e2da83de87a941df4766b9283e599254a3e3ca68dbf38e1af2040059ace8e3c3fd0df8e9bbeaf146d1800b3f0c6722e8bfb1323410626fdf7c Homepage: https://cran.r-project.org/package=gsrs Description: CRAN Package 'gsrs' (A Group-Specific Recommendation System) A group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System for the details. 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Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48. Package: r-cran-gsse Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-zoo Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-gsse_0.1-1.ca2604.1_all.deb Size: 69806 MD5sum: 8968509716e025651507f409bcb4d7ea SHA1: 1fdb802eba330cb37b3d09a242964e78da7ddea4 SHA256: 5dce7dabdec271f10f3d419946ca638d0e6c966c83726184c1dc36fafcc75a14 SHA512: 8f05d7589997ba683ccfdf2d9b71be68dc58d739be4ca8c8139188c136d827f1a00ffbaec162f25f511b209fa298eec3e52df0c7ca7a89a9c951e37dd1c0da3e Homepage: https://cran.r-project.org/package=GSSE Description: CRAN Package 'GSSE' (Genotype-Specific Survival Estimation) We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees. We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function. The estimators are calculated via an expectation-maximization algorithm. Package: r-cran-gsstda Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4965 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-survival, r-cran-visnetwork, r-bioc-complexheatmap, r-cran-circlize, r-cran-devtools Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gsstda_1.0.0-1.ca2604.1_all.deb Size: 4054218 MD5sum: 5c8f79f5a560d32f3a996057676d3db2 SHA1: 9e05e40c236619d990446be060a442379258a22f SHA256: 7bf1c6ad91187f301c94cfd0dfe7856d866f8625bf93545a8a8b8153c6e86586 SHA512: 585d902d1148a789eca623671c335a6d7b304a649acb0312edb691e6629d9ae3ebdcef29aa764f1cba2407cf9791c5c3b172029c3c7b525d05aa406d46004101 Homepage: https://cran.r-project.org/package=GSSTDA Description: CRAN Package 'GSSTDA' (Progression Analysis of Disease with Survival using TopologicalData Analysis) Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" . Package: r-cran-gstar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-xts, r-cran-zoo, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gstar_0.1.0-1.ca2604.1_all.deb Size: 53030 MD5sum: a4505c53dc76c003ee820799150baff0 SHA1: bfe8f4dc3ce842fcca84634d7a999213d4b76644 SHA256: b8a653d1d473586e2d803afd6fdb284e6376966430f6eeddb135b99d15089783 SHA512: 38cfe76052d16863e039b481508886d101b4a4cab798d67b1f6cac3075117d5f9a66c08f656e0a9963e99dbc9871070d5c4f8c62e551054a6ddd6c9ee1a42d6a Homepage: https://cran.r-project.org/package=gstar Description: CRAN Package 'gstar' (Generalized Space-Time Autoregressive Model) Multivariate time series analysis based on Generalized Space-Time Autoregressive Model by Ruchjana et al.(2012) . 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Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available. See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data . 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Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD - Simpósio Brasileiro de Banco de Dados, 2021 ). 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Package: r-cran-gtexr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-curl, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-gtexr_0.2.1-1.ca2604.1_all.deb Size: 414350 MD5sum: 397738c487118aa4e6bfcf4e4f712dd9 SHA1: 6c52a93b849e5e5d340e0d878e4aa94d1d87d26a SHA256: a265ddcf5c306ddef258bec9b990fb68549c13d84dd091d5f9ec1b186c47966a SHA512: 43ca41c39bac4e007f79c18a275bedc6c49dbb7e6574c6100cddc3f10bfcad08278365489e26dc68ee6e380a6bccf85def260c27b1743e4d072c96db85b8947d Homepage: https://cran.r-project.org/package=gtexr Description: CRAN Package 'gtexr' (Query the GTEx Portal API) A convenient R interface to the Genotype-Tissue Expression (GTEx) Portal API. The GTEx project is a comprehensive public resource for studying tissue-specific gene expression and regulation in human tissues. Through systematic analysis of RNA sequencing data from 54 non-diseased tissue sites across nearly 1000 individuals, GTEx provides crucial insights into the relationship between genetic variation and gene expression. This data is accessible through the GTEx Portal API enabling programmatic access to human gene expression data. For more information on the API, see . Package: r-cran-gtextras Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gt, r-cran-commonmark, r-cran-dplyr, r-cran-fontawesome, r-cran-ggplot2, r-cran-glue, r-cran-htmltools, r-cran-paletteer, r-cran-rlang, r-cran-scales, r-cran-knitr, r-cran-cli Suggests: r-cran-base64enc, r-cran-bitops, r-cran-covr, r-cran-fs, r-cran-hms, r-cran-magrittr, r-cran-rvest, r-cran-sass, r-cran-stringr, r-cran-svglite, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-webshot2, r-cran-xml2, r-cran-lifecycle Filename: pool/dists/resolute/main/r-cran-gtextras_0.6.2-1.ca2604.1_all.deb Size: 4001198 MD5sum: aabd10a4bda20f4954bdea66f0904c3b SHA1: f2c320a202af73bf28d30f769898569c10b632a6 SHA256: 4c35cfb712f74a7b56c33454c42db69c8309fb2567cee92f656fed76be077e90 SHA512: 46f853a6825bba9b1a425e5b6a8cda71f54153db9c96a6ae0dcd14804028dde01871768362892e94cd1bf3f215eaf2c17dfdd3768b521e3aa32bf0497938d12c Homepage: https://cran.r-project.org/package=gtExtras Description: CRAN Package 'gtExtras' (Extending 'gt' for Beautiful HTML Tables) Provides additional functions for creating beautiful tables with 'gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions. Package: r-cran-gtexture Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dlookr, r-cran-dplyr, r-cran-fitscape, r-cran-igraph, r-cran-magrittr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gtexture_1.0.1-1.ca2604.1_all.deb Size: 107178 MD5sum: 547f8576ece78a3bef6aaaa3a6c0d125 SHA1: 7e2aecbd018969eaab5b157819e0996000fa45a8 SHA256: 414068424ec59ea3c952b9e71d2a4707f7b9547c16f335cbba965721923bd570 SHA512: 1c19a60eee5849e4ea7a3048a5e4c99e5ce687ba7966f938272768055b9fcb0cde380f0010b5cd10240d1fdec78049d409835767560396ee6455edfdd22eec92 Homepage: https://cran.r-project.org/package=gtexture Description: CRAN Package 'gtexture' (Generalized Application of Co-Occurrence Matrices and HaralickTexture) Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was developed as part of the author's publication in Physics in Medicine and Biology Barker-Clarke et al. (2023) . A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) . Package: r-cran-gtfs2emis Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-furrr, r-cran-future, r-cran-gtfs2gps, r-cran-sf, r-cran-sfheaders, r-cran-terra, r-cran-units, r-cran-parallelly Suggests: r-cran-gtfstools, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gtfs2emis_0.1.2-1.ca2604.1_all.deb Size: 2489664 MD5sum: cc0057fe9a42441071427e22454f68d9 SHA1: a635609d589c03e6617dc638928ec0bc70cd8e89 SHA256: f919905140dbb7898d73acc2ef78544f9b80474aafdc9ae275a35edafcd37ab8 SHA512: 880564fdb60d77d4d760ff407b6a574e9d87ed8e334b7a35c2da4f960e7116b20086fb698ad099d85c33854eb1be47a78fbf1f6a505a768c696f88452825aa96 Homepage: https://cran.r-project.org/package=gtfs2emis Description: CRAN Package 'gtfs2emis' (Estimating Public Transport Emissions from General Transit FeedSpecification (GTFS) Data) A bottom up model to estimate the emission levels of public transport systems based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) Public transport data in the GTFS standard format; and ii) Some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the package estimates several pollutants at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics. A full description of the methods used in the 'gtfs2emis' model is presented in Vieira, J. P. B.; Pereira, R. H. M.; Andrade, P. R. (2022) . Package: r-cran-gtfsio Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-fs, r-cran-zip, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-gtfsio_1.2.1-1.ca2604.1_all.deb Size: 358986 MD5sum: be758d90851b7a21ddea3ff08dade746 SHA1: 2edbf670c1470bba6f137dab9d8767637a7abdbf SHA256: be11ac99d15229490560fd6dbfde9d0adf6f173b763fc9dbf7dc8b680cd7a272 SHA512: 6df82cbaa3c19b9fc5452eaf64684b580e92804f05c2593415d4ecddd8b512e4892f5e11c7e274e7853840e85611f37dd3164fef9cbdf02fcaa0aca457215cfe Homepage: https://cran.r-project.org/package=gtfsio Description: CRAN Package 'gtfsio' (Read and Write General Transit Feed Specification (GTFS) Files) Tools for the development of packages related to General Transit Feed Specification (GTFS) files. Establishes a standard for representing GTFS feeds using R data types. Provides fast and flexible functions to read and write GTFS feeds while sticking to this standard. Defines a basic 'gtfs' class which is meant to be extended by packages that depend on it. And offers utility functions that support checking the structure of GTFS objects. Package: r-cran-gtheoryr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gtheoryr_0.1.0-1.ca2604.1_all.deb Size: 29064 MD5sum: f868f6a110536cc541304afb0f5825b2 SHA1: 7121d436f3a2300c1e283e57247553c31d83ea47 SHA256: 5fee0315656926b0cd1010be171449eb24d9ed451fe29072b7dc5841e4af521a SHA512: b01f34c4fa49fa77b33c485c77555261f497805f2202ff5ad802713f93fab10272d5ac620f31396174826fb7763002045f21b23d3fbf9edbbc6c9556eea12a5f Homepage: https://cran.r-project.org/package=gtheoryr Description: CRAN Package 'gtheoryr' (Simple Generalizability Theory for Crossed and Nested Designs) Provides a small, beginner-friendly interface for estimating variance components in simple generalizability theory designs. The package currently supports a fully crossed persons-by-items design and a simple items-within-person nested design, along with design-study summaries for relative and absolute decisions. Package: r-cran-gto Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gt, r-cran-magrittr, r-cran-officer, r-cran-rlang, r-cran-xml2 Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tibble, r-cran-covr Filename: pool/dists/resolute/main/r-cran-gto_0.1.2-1.ca2604.1_all.deb Size: 283582 MD5sum: a0697b5cde640f262f76c16c2652f058 SHA1: ae19088ee0a9372f96ffa73fce72dc47fd77fca4 SHA256: e6d7355220f5dd74c33cf776a51161b6bbb2955416ff0a06d201ac4b1a87430b SHA512: 780d20e6226cc6806e91bc8bcd087b8265d4d35541ae847f7e7786f5e763d6eadf8f5734b9bc6933f706eba0a5cab3b5ae66f8688afe205bb95d96ca0cd82e3d Homepage: https://cran.r-project.org/package=gto Description: CRAN Package 'gto' (Insert 'gt' Tables into Word Documents) Insert tables created by the 'gt' R package into 'Microsoft Word' documents. This gives users the ability to add to their existing word documents the tables made in 'gt' using the familiar 'officer' package and syntax from the 'officeverse'. Package: r-cran-gtranslate Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-rvest Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gtranslate_0.0.1-1.ca2604.1_all.deb Size: 14294 MD5sum: 394aa93c3942058d97c77669bf061c54 SHA1: eff2c0ef0fb8215052b713b6214c7e6e5a929fb2 SHA256: 06df0808e6bfac6273253bebe2caa967cf1fe30072c83eda5f0ae93c9853d341 SHA512: 9aea5e80676acf376e8ba59fad8aa3d7218b3c01175f1002f9cd6d7cc614855911d7a96761abad5b31d408ff4bfdb54173d48f6068d2807275520963cdf18cae Homepage: https://cran.r-project.org/package=gtranslate Description: CRAN Package 'gtranslate' (Translate Between Different Languages) The goal of this package is to translate between different languages without any Google API authentication which is pain and you must pay for the key, This package is free and lightweight. Package: r-cran-gtreg Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1467 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-glue, r-cran-gtsummary, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gtreg_0.4.2-1.ca2604.1_all.deb Size: 1294832 MD5sum: d63a5f3b808bba95d900f687b5935a65 SHA1: 7ba1ab6f0ba7df492a3f74295dc6e5f16734faf0 SHA256: 2805bb7eebfd2394b85b330929f84534e6d6f1d339ba5d516184a851803093fb SHA512: 58fde0e6a27e6078b4b196f5d0b03213dc616ff9857c567f5c813b90b356f4e3247bcba5ac02d2af19f6e14d003cacfee8fc162ba0a0ce838deba430e9f312ba Homepage: https://cran.r-project.org/package=gtreg Description: CRAN Package 'gtreg' (Regulatory Tables for Clinical Research) Creates tables suitable for regulatory agency submission by leveraging the 'gtsummary' package as the back end. 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Designed to be accessible for researchers, particularly those in Low- and Middle-Income Countries (LMIC). Built upon widely accepted statistical methods, including logistic regression (Hosmer et al. 2013, ISBN:9781118548429), log-binomial regression (Spiegelman and Hertzmark 2005 ), Poisson and robust Poisson regression (Zou 2004 ), negative binomial regression (Hilbe 2011, ISBN:9780521179515), and linear regression (Kutner et al. 2005, ISBN:9780071122214). Leverages multiple dependencies to ensure high-quality output and generate reproducible, publication-ready tables in alignment with best practices in epidemiology and applied statistics. Package: r-cran-gtrendshealth Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gtrendshealth_1.0.0-1.ca2604.1_all.deb Size: 40640 MD5sum: b20a93c0d7fae751bf742718d5ff69eb SHA1: 53cae59842adf336e9f5be81ece7a62d3a4ba203 SHA256: a7764d4170aefbfff85708a908406c0e8a709a3184dde929db32f705df5a73ca SHA512: 7b8faa77a0d20e549f13cd7f8d33b2e94d66438bc99848c35129d6c19bf5a6cebfd876d6b9aa5b6daaca1d2725afe7ca8fdadb82e3ce1745871d118146d83d74 Homepage: https://cran.r-project.org/package=gtrendshealth Description: CRAN Package 'gtrendshealth' (Query the 'Google Trends for Health' API) Connects to the 'Google Trends for Health' API hosted at , allowing projects authorized to use the health research data to query 'Google Trends'. 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Trends (number of hits) over the time as well as geographic representation of the results can be displayed. Package: r-cran-gtrt Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-timeseriesdatasets Filename: pool/dists/resolute/main/r-cran-gtrt_0.1.0-1.ca2604.1_all.deb Size: 61690 MD5sum: 6e6c46271c218d5422b07581752062b4 SHA1: 3e9ecb5052d420f845823465aa3e9aa5f46ac784 SHA256: 736e9cfb59ce78aba0fe983b2922257bfba0f93be40b4ed84e1a2b8b897fcc72 SHA512: 6f2c479e96401837cd62bfb4bd7daf1e98a0921af4909aa23fd0307ed21b66449c0da25299107571c06a720eeb548afad731b74610d30d982e6344aee1f10b0f Homepage: https://cran.r-project.org/package=GTRT Description: CRAN Package 'GTRT' (Graph Theoretic Randomness Tests) A collection of functions for testing randomness (or mutual independence) in linear and circular data as proposed in Gehlot and Laha (2025a) and Gehlot and Laha (2025b) , respectively. Package: r-cran-gtsummary Architecture: all Version: 2.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cards, r-cran-cardx, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-gt, r-cran-lifecycle, r-cran-rlang, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-aod, r-cran-broom, r-cran-broom.helpers, r-cran-broom.mixed, r-cran-car, r-cran-cmprsk, r-cran-effectsize, r-cran-emmeans, r-cran-flextable, r-cran-geepack, r-cran-ggstats, r-cran-huxtable, r-cran-insight, r-cran-kableextra, r-cran-knitr, r-cran-lme4, r-cran-mice, r-cran-nnet, r-cran-officer, r-cran-openxlsx, r-cran-parameters, r-cran-parsnip, r-cran-rmarkdown, r-cran-smd, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-withr, r-cran-workflows Filename: pool/dists/resolute/main/r-cran-gtsummary_2.5.0-1.ca2604.1_all.deb Size: 1728680 MD5sum: 3a87921b563673b676bfde5dc2656d5c SHA1: 3d0e8f481a6d6cd651289e75143736b0e0e889e9 SHA256: e78ceeb7eb1622de73a84df72c2843d2fa7b99131ee4277d196cea428198dd79 SHA512: b4ffa8250b70929ae584053b39f83f34c86bcb0d4371e7b687b611f240822e8000cbb89bb6318248e8f8cd9e2d65d3863af908a52930ed1ca32d2e0646897b4c Homepage: https://cran.r-project.org/package=gtsummary Description: CRAN Package 'gtsummary' (Presentation-Ready Data Summary and Analytic Result Tables) Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers. Package: r-cran-gtwas Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gtwas_1.1.1-1.ca2604.1_all.deb Size: 123088 MD5sum: 2f3a6a777d98e270e165f69a4850ab82 SHA1: be44baee1475c6513a55b5f4442ea850e09f7497 SHA256: d9bcfd6a642672407aaddb3891ffd218e7c66a915d8f715421a96b4669b4e3e2 SHA512: 769bced8036f8f0dc9d28ac8cb4e36a732f63be511fc5ed1bff739a24540f6561754ea5f8ff15bcbd4dbb6ab6b86a2daaa710363ff870c29894e7ca0e5d2da0d Homepage: https://cran.r-project.org/package=gtWAS Description: CRAN Package 'gtWAS' (Genome and Transcriptome Wide Association Study) Quantitative trait loci mapping and genome wide association analysis are used to find candidate molecular marker or region associated with phenotype based on linkage analysis and linkage disequilibrium. Gene expression quantitative trait loci mapping is used to find candidate molecular marker or region associated with gene expression. In this package, we applied the method in Liu W. (2011) and Gusev A. (2016) to genome and transcriptome wide association study, which is aimed at revealing the association relationship between phenotype and molecular markers, expression levels, molecular markers nested within different related expression effect and expression effect nested within different related molecular marker effect. F test based on full and reduced model are performed to obtain p value or likelihood ratio statistic. The best linear model can be obtained by stepwise regression analysis. Package: r-cran-guaguas Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3677 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-guaguas_0.3.0-1.ca2604.1_all.deb Size: 3632396 MD5sum: 4b8d959576c0fe7ed4492a03d562df02 SHA1: c3d11b9892d2a44bc236ae3c61936af62f35a1aa SHA256: 646ebb64cfa01d6b4b3bce0cd34d7a3896ab778062acf7f02c67166979fcf703 SHA512: 620929a68f212ceea88a8ba40bd318c566cb1da31efd1df1583eb18ff38e4a0dd1251eeaf543deaa07552be2894a2a10c3de473d2704f60042c8f865aa272cbd Homepage: https://cran.r-project.org/package=guaguas Description: CRAN Package 'guaguas' (Nombres Inscritos en Chile (1920 - 2021)) Datos de nombres inscritos en Chile entre 1920 y 2021, de acuerdo al Servicio de Registro Civil. English: Chilean baby names registered from 1920 to 2021 by the Civil Registry Service. Package: r-cran-guardianapi Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lubridate, r-cran-ggplot2, r-cran-covr, r-cran-scales, r-cran-readr Filename: pool/dists/resolute/main/r-cran-guardianapi_0.1.1-1.ca2604.1_all.deb Size: 483078 MD5sum: bfc7d9250cf5781f54ae5259245e262e SHA1: 0b6607649cadfc647f4735389dc87ea88cc56fe3 SHA256: d579bc247c12fe17ab94288135441e4ba9ee435830c035d55c99bfa8add98f57 SHA512: 1bee75edff54583403080577c96bb0fdc512f03f758fdc521e7c02b74eb96924737e0ffc002cf64f48c3cf610b908c9592cf2921455f2cc66659f448577cb57a Homepage: https://cran.r-project.org/package=guardianapi Description: CRAN Package 'guardianapi' (Access 'The Guardian' Newspaper Open Data API) Access to 'The Guardian' newspaper's open API , containing all articles published in 'The Guardian' from 1999 to the present, including article text, metadata, tags and contributor information. 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Guerry and others, and statistical and graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and development of statistical and graphic methods for multivariate data in a geospatial context of historical interest. Package: r-cran-guescini Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2469 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-guescini_0.1.0-1.ca2604.1_all.deb Size: 2352124 MD5sum: ea7ef280c3044d5f6f12e9af566e693e SHA1: caf3ec7b9ef1e09a22dbe7c0ee34be21a15e4658 SHA256: c6d54348a4d5ab80a101d44752501eb6b6c5f73ecee51de4b5b9cc380e9a4fb8 SHA512: d833548e4083f46d2fc11f95872b771b44507274cd50f9b7545b927226fa8c2dcae7b197950aae8ddf8c4700be22d8fefd524deb54744a7f3d5d1d97cef21b16 Homepage: https://cran.r-project.org/package=guescini Description: CRAN Package 'guescini' (Real-Time PCR Data Sets by Guescini et al. (2008)) Real-time quantitative polymerase chain reaction (qPCR) data by Guescini et al. (2008) in tidy format. This package provides two data sets where the amplification efficiency has been modulated: either by changing the amplification mix concentration, or by increasing the concentration of IgG, a PCR inhibitor. Original raw data files: and . Package: r-cran-guess Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lintr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-guess_0.3.0-1.ca2604.1_all.deb Size: 99074 MD5sum: b4e8e621f73011961b50e28eead7f742 SHA1: 8b6e8bc6b9cf79c2c3221ae8ce3c857b4b681de7 SHA256: 85f435238faeb76bb327ece8b5bdbe54647b4098c8cf7f1a815c48b048f4f033 SHA512: 6be8733aa4c1c6fbbc2d6871f22cd34631d8a73b092cb49b5e6bea535e33c6ee4d2fb9f14714dd2b5a9c7c2408cacc6a0e689226cadf03c5745d1b3d97a22343 Homepage: https://cran.r-project.org/package=guess Description: CRAN Package 'guess' (Adjust Estimates of Learning for Guessing) Provides tools to adjust estimates of learning for guessing-related bias in educational and survey research. Implements standard guessing correction methods and a sophisticated latent class model that leverages informative pre-post test transitions to account for guessing behavior. The package helps researchers obtain more accurate estimates of actual learning when respondents may guess on closed-ended knowledge items. For theoretical background and empirical validation, see Cor and Sood (2018) . Package: r-cran-guest Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xicor, r-cran-network, r-cran-ggally Suggests: r-cran-sna Filename: pool/dists/resolute/main/r-cran-guest_0.2.0-1.ca2604.1_all.deb Size: 160224 MD5sum: 6ae742c398f88a4023d5341f974527fd SHA1: 50973cbdadcdf1bca6d6058d01556fd1b39d42aa SHA256: 2a146428de9781676b0dea63df6913b3e1168d9fadddb619f1a2911ddd76c870 SHA512: d5d18e4957185957622eb4efcc5897c5fde2d9fc75a71a4507fe09e77fea2571aade268e401fbded71b8a6efd85951898d2c9512d8fdd7ce8edee0ce7760dd0b Homepage: https://cran.r-project.org/package=GUEST Description: CRAN Package 'GUEST' (Graphical Models in Ultrahigh-Dimensional and Error-Prone Datavia Boosting Algorithm) We consider the ultrahigh-dimensional and error-prone data. Our goal aims to estimate the precision matrix and identify the graphical structure of the random variables with measurement error corrected. We further adopt the estimated precision matrix to the linear discriminant function to do classification for multi-label classes. Package: r-cran-guidedpls Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-irlba Suggests: r-cran-fields, r-cran-geigen, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-guidedpls_1.1.0-1.ca2604.1_all.deb Size: 293766 MD5sum: 5b7c54708d9570d981fbf55a1e52be19 SHA1: 0343ca76eac08242654d54a806f99d2b1dafa1d0 SHA256: de329c83f2c00934317d198d1612e704d758ae4856122cbdec83c2c3c693d6cc SHA512: 104f5d82668e0e26da677b63aaa75b4119610d5cf42dd9b476ffd5010772b9c3c8b8e403b1bbb1c8edc8a144f2844f52465ac24407bd79e5ad43ab371a168015 Homepage: https://cran.r-project.org/package=guidedPLS Description: CRAN Package 'guidedPLS' (Supervised Dimensional Reduction by Guided Partial Least Squares) Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md . Package: r-cran-guider Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-labelled, r-cran-lifecycle, r-cran-pak, r-cran-patchwork, r-cran-purrr, r-cran-renv, r-cran-rlang, r-cran-rstudioapi, r-cran-scales, r-cran-srvyr, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-broom, r-cran-broom.helpers, r-cran-cardx, r-cran-dt, r-cran-factominer, r-cran-ggupset, r-cran-ggstats, r-cran-gt, r-cran-gtsummary, r-cran-htmltools, r-cran-htmlwidgets, r-cran-khroma, r-cran-nnet, r-cran-parameters, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-traminer, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-guider_0.9.0-1.ca2604.1_all.deb Size: 260284 MD5sum: b177cfad0a8806b36a8062d801acbadc SHA1: 35aa86011a295dd0cfe19d100b0c8351676d1a2b SHA256: dff4541b705d05d0cf4acb5a8ad48b8826e3f4e0262aa297e99eacb62f91a1a9 SHA512: 966288fa3767b2260d770aba8361b3d75d105320f077124dfab43fd79c019fea5cbbeb9371d850e59794ae611f80368d05285ac469e4818c686d268543412397 Homepage: https://cran.r-project.org/package=guideR Description: CRAN Package 'guideR' (Miscellaneous Statistical Functions Used in 'guide-R') Companion package for the manual 'guide-R : Guide pour l’analyse de données d’enquêtes avec R' available at . 'guideR' implements miscellaneous functions introduced in 'guide-R' to facilitate statistical analysis and manipulation of survey data. Package: r-cran-guildai Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-rappdirs, r-cran-yaml, r-cran-config, r-cran-rlang, r-cran-rstudioapi, r-cran-readr, r-cran-dplyr, r-cran-processx, r-cran-tibble Suggests: r-cran-fs, r-cran-envir, r-cran-rmarkdown, r-cran-quarto, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-guildai_0.0.1-1.ca2604.1_all.deb Size: 253342 MD5sum: 8c9f9a918bc69565dbd6f85b8f33b8c3 SHA1: 534f126ff523569a2df8894c732f47deea747b42 SHA256: 2bbe4023ab8610b18c94bf9127c37f163fec8972d91663d1ef734ca43567d4b7 SHA512: 0a1302335d1083a31a6f5eb7b691741f01c43ad39f8394d5b28b273a76bf83991f379cbce9cc8b262396635d0f88f6e922584435541de7ea9e0a324659d82f8d Homepage: https://cran.r-project.org/package=guildai Description: CRAN Package 'guildai' (Track Machine Learning Experiments) 'Guild AI' is an open-source tool for managing machine learning experiments. 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Package: r-cran-guiplot Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-svglite, r-cran-dt, r-cran-rlang, r-cran-magrittr, r-cran-r6, r-cran-excelr, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-guiplot_0.5.0-1.ca2604.1_all.deb Size: 212610 MD5sum: fe76ab7e0ad14a116307db04be591347 SHA1: bd0edfbfd6bbd451100e371c6de09b995d463cf9 SHA256: 9fe717f1a879e0364ed40773977ee2cd281f3f5b425881a19eb01fa0189aec28 SHA512: 322efea49c272e1fa1bd0d39d492569868c9641da5017ababe5f068f63495a90f1c9486bc35f134544c543cae3c8d89c1f046ed7f1af207957b641fe53eada41 Homepage: https://cran.r-project.org/package=guiplot Description: CRAN Package 'guiplot' (User-Friendly GUI Plotting Tools) Create a user-friendly plotting GUI for 'R'. In addition, one purpose of creating the 'R' package is to facilitate third-party software to call 'R' for drawing, for example, 'Phoenix WinNonlin' software calls 'R' to draw the drug concentration versus time curve. 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If the requested 'R' package does not exist in 'Guix' at this time, the package and all its missing dependencies will be imported recursively and the generated package definitions will be written to ~/.Rguix/packages.scm. This record of imported packages can be used later to reproduce the environment, and to add the packages in question to a proper 'Guix' channel (or 'Guix' itself). guix.install() not only supports installing packages from CRAN, but also from Bioconductor or even arbitrary 'git' or 'mercurial' repositories, replacing the need for installation via 'devtools'. Package: r-cran-gulfm Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-gulfm_0.5.0-1.ca2604.1_all.deb Size: 32886 MD5sum: 0874c6c871a7069afd304cd6469c2f40 SHA1: 95358ab27753dc3d255b3f4812bf792dde126476 SHA256: 11e9f411cf7c62f4c500e065337f4b6e885e5b47a945ce1dec721a74d7cc6448 SHA512: ad27013c05281eb924f10ea7a5c0e37aa4fbfb84cf1173057495c906feff0a2f8faa15045f335db817811678356ca5b84c7dec34bf8dc9c06a074c65ad9273e4 Homepage: https://cran.r-project.org/package=GulFM Description: CRAN Package 'GulFM' (General Unilateral Load Estimator for Two-Layer Latent FactorModels) Implements general unilateral loading estimator for two-layer latent factor models with smooth, element-wise factor transformations. We provide data simulation, loading estimation,finite-sample error bounds, and diagnostic tools for zero-mean and sub-Gaussian assumptions. A unified interface is given for evaluating estimation accuracy and cosine similarity. The philosophy of the package is described in Guo G. (2026) . 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Package: r-cran-gwrpvr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gwrpvr_1.0-1.ca2604.1_all.deb Size: 48324 MD5sum: a848f8989103578fefaccd6821b261db SHA1: 2bac1ba94212d5b16857ad4807abc8f9f98b3f35 SHA256: cd3fdfd57f2f4846e9744711f0571673e1b9280da495ebba649c022b0a69a649 SHA512: 77d2a703fe64253c11decf28e638f306b87bda27b7aca7de71129a33ca8a3caf90b5a51fa8a086a6f4ef3c218c409f93d0ae8857d9ab9da00582cb820651a0f9 Homepage: https://cran.r-project.org/package=gwrpvr Description: CRAN Package 'gwrpvr' (Genome-Wide Regression P-Value (Gwrpv)) Computes the sample probability value (p-value) for the estimated coefficient from a standard genome-wide univariate regression. It computes the exact finite-sample p-value under the assumption that the measured phenotype (the dependent variable in the regression) has a known Bernoulli-normal mixture distribution. Finite-sample genome-wide regression p-values (Gwrpv) with a non-normally distributed phenotype (Gregory Connor and Michael O'Neill, bioRxiv 204727 ). Package: r-cran-gwrr Architecture: all Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-lars Filename: pool/dists/resolute/main/r-cran-gwrr_0.2-2-1.ca2604.1_all.deb Size: 68776 MD5sum: bbffa00759939b7881f659c870cec223 SHA1: 4ead4607b5109544c4afc2301a93fbc4b3f547ae SHA256: a2c7b9671075886535ac1129f4f7b7542d59be58fdffac0612465fa2e15282b9 SHA512: ac0ffa90bb1afb6e031ac1da0a8f2cab1465c0984306e5b9a045c670dfb161a53e98c4088d7bfdb79eee04158be8b46d512fcc21383b1af4c9d1ec19ea1c1cf8 Homepage: https://cran.r-project.org/package=gwrr Description: CRAN Package 'gwrr' (Fits Geographically Weighted Regression Models with DiagnosticTools) Fits geographically weighted regression (GWR) models and has tools to diagnose and remediate collinearity in the GWR models. Also fits geographically weighted ridge regression (GWRR) and geographically weighted lasso (GWL) models. See Wheeler (2009) and Wheeler (2007) for more details. Package: r-cran-gwsdat Architecture: all Version: 3.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3774 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deldir, r-cran-digest, r-cran-geometry, r-cran-kendall, r-cran-lattice, r-cran-lubridate, r-cran-mass, r-cran-matrix, r-cran-officer, r-cran-raster, r-cran-readxl, r-cran-rhandsontable, r-cran-sf, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinyjs, r-cran-sm, r-cran-sp, r-cran-splancs, r-cran-zoo Suggests: r-cran-dbi, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-gwsdat_3.3.0-1.ca2604.1_all.deb Size: 2466652 MD5sum: 4f10b6394004d077fc75052643d93753 SHA1: 8f92b0772ed7ebb1c5ec8d4a2bd6abec3847adbd SHA256: eefadc63a4497b6e68ec8f03afc066e1d4db51e073d82ca01fff334c012a2b8a SHA512: b5e070dd8f4c7ec98a4bed85c2b59550cd5111bfbbdb7487f2829729ca63291be9593c8dc0f74d9ea57c8d774b1ffc553ac819c4af9c7379250b5529f0273239 Homepage: https://cran.r-project.org/package=GWSDAT Description: CRAN Package 'GWSDAT' (GroundWater Spatiotemporal Data Analysis Tool (GWSDAT)) Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format. Package: r-cran-gwsignif Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gwsignif_1.2.1-1.ca2604.1_all.deb Size: 22286 MD5sum: 7b353a4f57e24ad3cefb5518efdc2257 SHA1: be10e59497f5c1528412ec9ff78cd670b4d7205a SHA256: 03bb36718145cc65cb443abd402acc8ee8f1a652e55a205e6b9d7810365114f2 SHA512: 6b5e73ca5a1789938bde85bd4967e35fe83c75833bed5b6c32f3100d9ad29b5e168e3ba55a28729ece0f9c9af8ba0db34f0b0bf55d58eb6ef0041829c7048c19 Homepage: https://cran.r-project.org/package=GWsignif Description: CRAN Package 'GWsignif' (Estimating Genome-Wide Significance for Whole Genome SequencingStudies, Either Single SNP Tests or Region-Based Tests) The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome. Package: r-cran-gwzinbr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/resolute/main/r-cran-gwzinbr_0.1.0-1.ca2604.1_all.deb Size: 157860 MD5sum: b5fae57b9b92262c2963e0cab61b96d9 SHA1: 8e9fa9ec9b864122ff3fec4a05073d1ec296089a SHA256: 7f2677435d10a699300525acb094b0fcd17ba9efce90eb6b991474cc7a57969d SHA512: 0a3484aefaa9fe4550d91c8a8af10b748de5454a302be6a2f7372223dadd69136dc5a7de083b05bdf4d1b1cd03662b3882c1acdb34b349a61d7c07ed61a83ca8 Homepage: https://cran.r-project.org/package=gwzinbr Description: CRAN Package 'gwzinbr' (Geographically Weighted Zero Inflated Negative BinomialRegression) Fits a geographically weighted regression model using zero inflated probability distributions. Has the zero inflated negative binomial distribution (zinb) as default, but also accepts the zero inflated Poisson (zip), negative binomial (negbin) and Poisson distributions. Can also fit the global versions of each regression model. Da Silva, A. R. & De Sousa, M. D. R. (2023). "Geographically weighted zero-inflated negative binomial regression: A general case for count data", Spatial Statistics . Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). "Geographically weighted regression: a method for exploring spatial nonstationarity", Geographical Analysis, . Yau, K. K. W., Wang, K., & Lee, A. H. (2003). "Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros", Biometrical Journal, . Package: r-cran-gxeprs Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1414 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gxeprs_1.2-1.ca2604.1_all.deb Size: 986084 MD5sum: f72e82519f1012fa442dff525824f55b SHA1: ee092c59e8f570ea22f4a8c1119587ccd9e9b755 SHA256: 0645aa3be8bce83931e5a7954f9242070c7b0fd72b5c67b71b8952cfd4b4f5f6 SHA512: ebec3ccf17eadf3d204dc088a4848a630e16b6ad32d87e8dcf1a0c49f0c8dc234abbda1939944085c8d8bebfe9e4c49261c3e904ca0ba99854a8b458507daa7d Homepage: https://cran.r-project.org/package=GxEprs Description: CRAN Package 'GxEprs' (Genotype-by-Environment Interaction in Polygenic Score Models) A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) , (Pandis et al., 2013) , (Peyrot et al., 2018) and (Song et al., 2022) , as well as newly proposed models for genomic risk prediction (refer to the URL for more details). Package: r-cran-gxescanr Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lsreg, r-cran-binarydosage Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gxescanr_3.0.0-1.ca2604.1_all.deb Size: 142618 MD5sum: 5f74a9eec0e359f9ea9ed28f2081a2f3 SHA1: 93dc4a4894932e661abf76fa0652b329b741dfb0 SHA256: 605b2e28692981d2a7a9ea14b3c5e91b091a976c2477072e2ecd27912487a86e SHA512: a6c42242ab89176054545a9792e29e6f4e721a08b10974350965a8a20046b7934d7c27526fa8d2596c54c9fef9fe3f8cde0b26546e260f470f839762d1b849ec Homepage: https://cran.r-project.org/package=GxEScanR Description: CRAN Package 'GxEScanR' (Run GWAS/GWEIS Scans Using Binary Dosage Files) Tools to run genome-wide association study (GWAS) and genome-wide by environment interaction study (GWEIS) scans using the genetic data stored in a binary dosage file. The user provides a data frame with the subject's covariate data and the information about the binary dosage file returned by the BinaryDosage::getbdinfo() routine. Package: r-cran-gym Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gym_0.1.0-1.ca2604.1_all.deb Size: 49670 MD5sum: 2d76a13165189f2e8c11c862f0322adf SHA1: 44fb08757b82fb3d68eb07a1afc9a76d6a41bbaf SHA256: 49b7896389ec63f087f42b9fb5df123782f2de5ee616e00c73028aa896d4a27a SHA512: b3eedd93788d91db99e1df97147ac40d05977d0664f5ec5661b6f892256ec5e3fe79e29172b020b98ea7b2c45aedbd765b029d7ff4ef3c2acd73ed0b24b9c312 Homepage: https://cran.r-project.org/package=gym Description: CRAN Package 'gym' (Provides Access to the OpenAI Gym API) OpenAI Gym is a open-source Python toolkit for developing and comparing reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API, and enables access to an ever-growing variety of environments. For more details on OpenAI Gym, please see here: . For more details on the OpenAI Gym API specification, please see here: . Package: r-cran-h0 Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-h0_1.0.1-1.ca2604.1_all.deb Size: 31134 MD5sum: 1de95aecc54df0c6b96052363ffab007 SHA1: 2870424d5f6074ae3bf12e607a59c7829d0cd69a SHA256: ddf41d55032152ce0edca35a3f8233359013050c5f60efaa500600ffe7576989 SHA512: f02ff2539d3b8c12cb0c7b2a1fc629c4c22e03c00d32e378d1a1b2a0fbb567d847b92d7b0a14def911ee9221abcb2f51460f5fc24eea772cf2c78ff74a9b5e5a Homepage: https://cran.r-project.org/package=h0 Description: CRAN Package 'h0' (A Robust Bayesian Meta-Analysis for Estimating the HubbleConstant via Time Delay Cosmography) We provide a toolbox to conduct a Bayesian meta-analysis for estimating the current expansion rate of the Universe, called the Hubble constant H0, via time delay cosmography. The input data are Fermat potential difference and time delay estimates. 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Package: r-cran-h2o4gpu Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-reticulate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-h2o4gpu_0.3.3-1.ca2604.1_all.deb Size: 96026 MD5sum: 743780a82a77b07ee79a7f27709fe9e3 SHA1: f2bb5d9120368259c3c08edfbbbf86fc60316f96 SHA256: 2b1e85d8ab47244a8e7af72e35b3f15417a623ec3599714e8647c970c81ccc38 SHA512: 4c51a3c79bbee48efdfa006c67c6fb6f230e4e2f144b56247639a179beae000f43187011fa8e87ec030c2b93f3f63ad97a67ca3dc680fb6ad60913300ff4181a Homepage: https://cran.r-project.org/package=h2o4gpu Description: CRAN Package 'h2o4gpu' (Interface to 'H2O4GPU') Interface to 'H2O4GPU' , a collection of 'GPU' solvers for machine learning algorithms. 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Package: r-cran-h2otools Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-h2o, r-cran-curl, r-cran-boot Filename: pool/dists/resolute/main/r-cran-h2otools_0.4-1.ca2604.1_all.deb Size: 51630 MD5sum: 545e0c183e8553f7368d7696d539cb02 SHA1: 46b84c6d48c9f684679ed5fab88b86ab6d840164 SHA256: 4ba736f63c3dd5fba5b62e64a61ab241bda616eaef51963b49bd6046b73a6c2a SHA512: b25beeeda1f2555eef50ca117d9c49d2cc51938813c736c870c88af3501adaf4834a011386e239994b4ab7c1b89c45b3f766270116d03a9ad7714c5e2bb2b4e7 Homepage: https://cran.r-project.org/package=h2otools Description: CRAN Package 'h2otools' (Machine Learning Model Evaluation for 'h2o' Package) Enhances the H2O platform by providing tools for detailed evaluation of machine learning models. It includes functions for bootstrapped performance evaluation, extended F-score calculations, and various other metrics, aimed at improving model assessment. Package: r-cran-h2x2factorial Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-h2x2factorial_2.0.0-1.ca2604.1_all.deb Size: 116136 MD5sum: 8a0a7267c55729d140c6f3f1019c5e45 SHA1: d87ef5bdf6592b85d528d55e0333f4f2837d4199 SHA256: 4346a2d1caea4aab123e750c0eec0fc93fa33e9b21c7732567fc6c67dbc847a9 SHA512: fe0afea0d2796f54577b4d5460f1b6a1cd3c18d6e445df5205b2ae877fa4b2d40e63542192cc0e3ccea38cb92bb0ec2cb35eaf73e8246a5136b517137018599a Homepage: https://cran.r-project.org/package=H2x2Factorial Description: CRAN Package 'H2x2Factorial' (Sample Size Calculation in Hierarchical 2x2 Factorial Trials) Implements the sample size methods for hierarchical 2x2 factorial trials under two choices of effect estimands and a series of hypothesis tests proposed in "Sample size calculation in hierarchical 2x2 factorial trials with unequal cluster sizes" (under review), and provides the table and plot generators for the sample size estimations. Package: r-cran-h3jsr Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-sf, r-cran-tidyr, r-cran-v8 Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-h3jsr_1.3.1-1.ca2604.1_all.deb Size: 695610 MD5sum: feead72abd11a51fe2ba4a0ac81a36a0 SHA1: 886d5f65c14c23bbc9ab6508112f513906756e01 SHA256: 6df933113ba1d09dcda2ddb0b4d2a767661f387fb2a892c1bec86bcde35f4b1f SHA512: 36a26b3f6098030b91e9aec2418ebbcab8d0fc3545c47e525a3b2a36f276fe2e24952f16fa6c1cb8fdec6ffeb83ea7d132b46032fd059a70c15e1d1058d89e75 Homepage: https://cran.r-project.org/package=h3jsr Description: CRAN Package 'h3jsr' (Access Uber's H3 Library) Provides access to Uber's H3 library for geospatial indexing via its JavaScript transpile 'h3-js' and 'V8' . Package: r-cran-h3sdm Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-terra, r-cran-spatialsample, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-ecospat, r-cran-dalex, r-cran-stacks Suggests: r-cran-ggplot2, r-cran-paisaje, r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-tidyr, r-cran-themis, r-cran-dalextra, r-cran-ingredients, r-cran-exactextractr, r-cran-landscapemetrics, r-cran-h3jsr, r-cran-tidyterra, r-cran-spocc, r-cran-tidymodels, r-cran-workflowsets, r-cran-ranger, r-cran-xgboost, r-cran-ggbrick, r-cran-parsnip, r-cran-tidyverse, r-cran-rbiodatacr Filename: pool/dists/resolute/main/r-cran-h3sdm_0.1.2-1.ca2604.1_all.deb Size: 1583992 MD5sum: f34135fe67e0c1959c471bd62b1137aa SHA1: ec2264962018653b224e33a31690bab5c2889093 SHA256: 9586f53e3cc8922cc8d7d78f18cba24b183b1747a8a5de83854bb9ba232a824b SHA512: 228fb15fc477da6675bc551a915a49452228dcafc5af005a7a2e3dff6cbefb91c8991037b4db8256c95772928bdd25ca2c9fc8a80862ef6a95f480fa994bd450 Homepage: https://cran.r-project.org/package=h3sdm Description: CRAN Package 'h3sdm' (Species Distribution Modeling with H3 Grids) Provides tools for species distribution modeling using H3 hexagonal grids (Uber Technologies Inc., 2022, ). Facilitates retrieval of species occurrence records, generation of H3 grids, computation of landscape metrics, and preparation of spatial data for modern species distribution models workflows. Designed for biodiversity and landscape ecology research. Package: r-cran-haarfisz Architecture: all Version: 4.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh Filename: pool/dists/resolute/main/r-cran-haarfisz_4.5.4-1.ca2604.1_all.deb Size: 54446 MD5sum: d8bb40c0fd02194dfa20aaeb2a5d14ac SHA1: 6d128d61a88abcde8250b705b8f9a8d6baaaaec2 SHA256: dd52ad20a22bd9d5c348f5781c789ede0475f12dfa5b1c0098ffc6dbd83d81b7 SHA512: 49ddaa930c2ce9f1e294422de7ca942f5102b2191dc6ffb4641f6a86cc56c44bf8134984ef040561ca347cbaa9089c8b32af0f0f8dc45f5d53884a325ad640ca Homepage: https://cran.r-project.org/package=haarfisz Description: CRAN Package 'haarfisz' (Software to Perform Haar Fisz Transforms) A Haar-Fisz algorithm for Poisson intensity estimation. Will denoise Poisson distributed sequences where underlying intensity is not constant. Uses the multiscale variance-stabilization method called the Haar-Fisz transform. Contains functions to carry out the forward and inverse Haar-Fisz transform and denoising on near-Gaussian sequences. Can also carry out cycle-spinning. Main reference: Fryzlewicz, P. and Nason, G.P. (2004) "A Haar-Fisz algorithm for Poisson intensity estimation." Journal of Computational and Graphical Statistics, 13, 621-638. . Package: r-cran-hablar Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2402 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-lubridate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-gapminder, r-cran-diagrammer, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-hablar_0.3.2-1.ca2604.1_all.deb Size: 599842 MD5sum: 652cb858b06021bff5696fa10235160b SHA1: d783edeed8e3bce794a07fb05efe04365f5d68b4 SHA256: 7480a8873012afe8985e86c396a95cf12b66fa4793e1eac4d1be49b6249f90a8 SHA512: 3cf6b34ce88df4837b476cfc506895b61c87ab63d6f1f4bd8117081d11dafa8755aeb03637a8b672a8e540700fd5270695bf5152bf46e9a359e5439532323de1 Homepage: https://cran.r-project.org/package=hablar Description: CRAN Package 'hablar' (Non-Astonishing Results in R) Simple tools for converting columns to new data types. Intuitive functions for columns with missing values. Package: r-cran-habtools Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4850 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-terra, r-cran-rvcg, r-cran-sp, r-cran-geometry, r-cran-concaveman, r-cran-magrittr, r-cran-purrr, r-cran-ks, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rgl, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-habtools_1.1.1-1.ca2604.1_all.deb Size: 4365270 MD5sum: 2b2be7e684c46798d82efce22369eabf SHA1: d7c5d2c88b021c9c71dd1e6de08343f54c3b9ac2 SHA256: b67fd1ec5776d01e34c1aaef4dc7b541e711630d939e552519b0c562d59c364a SHA512: 9fbb14cee03d0081de283a3b4d647605d68d46db0976f64e9b82fb2eac165956d2634a78bf041e13c2c190aa2eb8a1c7671d2635b6da4b827a37224daae31f1b Homepage: https://cran.r-project.org/package=habtools Description: CRAN Package 'habtools' (Tools and Metrics for 3D Surfaces and Objects) A collection of functions for sampling and simulating 3D surfaces and objects and estimating metrics like rugosity, fractal dimension, convexity, sphericity, circularity, second moments of area and volume, and more. Package: r-cran-hac Architecture: all Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula Filename: pool/dists/resolute/main/r-cran-hac_1.1-2-1.ca2604.1_all.deb Size: 1041714 MD5sum: 011fa5c87d1ffecd501d1ee77d5f946a SHA1: da45867d3c0eae9decebb335178e4385abb7362f SHA256: 9b61bab73b1a778b7c767d2eb7425802798cfa207c74f3da6e13dd423332bd49 SHA512: 5e2bb41631e1a0bbdd0eafcb6463a6ddf84acfc9e8afd5d9dca4b0df539d01a802eb121d42da291e7b8e46b5a460a1592b9cb6427b1a5ede3fa9e5a1a322e319 Homepage: https://cran.r-project.org/package=HAC Description: CRAN Package 'HAC' (Estimation, Simulation and Visualization of HierarchicalArchimedean Copulae (HAC)) Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC). Package: r-cran-hackernews Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-covr, r-cran-testthat, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-hackernews_0.2.2-1.ca2604.1_all.deb Size: 473198 MD5sum: 6e1fec6d38075a13dfbf247f580c28f1 SHA1: bb1d612556e2f3843a71a435b55571956e233516 SHA256: b105c92b8f9f881c473484428a2e3b2290cf429403e4aa974dc5ce4f7c8f3a05 SHA512: 64abb8e9ced176a081e34d2aa6eeb02e1801ee0ad4c5e50a1eda1cdda0aa36a4fe303af3bb537a5ceb73f6d8bf9f29200c8ccde8c367a2fc1dd8966ed6fd5372 Homepage: https://cran.r-project.org/package=hackeRnews Description: CRAN Package 'hackeRnews' (Wrapper for the 'Official Hacker News' API) Use the Official Hacker News API through R. Retrieve posts, articles and other items in form of convenient R objects. Package: r-cran-hadamardr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numbers, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-hadamardr_1.0.0-1.ca2604.1_all.deb Size: 233440 MD5sum: 7ff6f1dc1e136e3519672b4a503c2e99 SHA1: 93042061e0cb3781d04ce96967683c189252781a SHA256: 72dedda6c691fb7ba8a9bec3de59e9b49b25e6326d5b8bfd82d8efd3bfd6626e SHA512: 06cafd67c9611cbc8fee1b3b0715698990f3489d94cc1628899637c9e9de1f8a3a94ee27ccbeb2bb8166c557bf6ae39aded60c05adb779ad9ed25e4d9dd8c450 Homepage: https://cran.r-project.org/package=HadamardR Description: CRAN Package 'HadamardR' (Hadamard Matrix Generation) Generates Hadamard matrices using different construction methods. For those who want to generate Hadamard matrix, a generic function, Hadamard_matrix() is provided. For those who want to generate Hadamard matrix using a particular method, separate functions are available. See Horadam (2007, ISBN:9780691119212) Hadamard Matrices and their applications, Princeton University Press for more information on Hadamard Matrices. Package: r-cran-hadex2 Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8630 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-gridextra, r-cran-magick, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-r3dmol, r-cran-remotes, r-cran-stringi, r-cran-tidyr, r-cran-ggiraph Suggests: r-cran-bookdown, r-cran-digest, r-cran-knitr, r-cran-magrittr, r-cran-pander, r-cran-renv, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-testthat, r-cran-vdiffr, r-cran-scales, r-cran-shiny, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-hadex2_1.0.0-1.ca2604.1_all.deb Size: 3969262 MD5sum: bae5941548219533140429910c52cebe SHA1: 79b07540484524719aeb2fa3caa268877420047e SHA256: 57542177d8b915ebc01d5f7065715c02e2501bc0e79743277d008040b3c2187b SHA512: 561c412ab2ed304b53886c08d41903b3a8d27fd04c831d48063d66c0501732f84edd848ae0ec2ee819e5bffda9388dcbdef54dd0b58f1b1c2a3f6fbd881ea768 Homepage: https://cran.r-project.org/package=HaDeX2 Description: CRAN Package 'HaDeX2' (Analysis and Visualisation of Hydrogen/Deuterium Exchange MassSpectrometry Data) Processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS). 'HaDeX2' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including uncertainty propagation, data aggregation and visualization on 3D structure. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the accompanying 'shiny' app. Package: r-cran-hadex Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7755 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-latex2exp, r-cran-reshape2, r-cran-readr, r-cran-readxl, r-cran-shiny, r-cran-tidyr Suggests: r-cran-spelling, r-cran-covr, r-cran-digest, r-cran-dt, r-cran-gridextra, r-cran-gsubfn, r-cran-knitr, r-cran-pander, r-cran-renv, r-cran-rmarkdown, r-cran-shinycssloaders, r-cran-shinyhelper, r-cran-shinyjs, r-cran-stringr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-hadex_1.2.3-1.ca2604.1_all.deb Size: 2021190 MD5sum: d9a88c8809be5eaa5904087238c39110 SHA1: a4b74df5c870b6d8e5da614ecd74fa3f096b9f37 SHA256: 5522e677f5239729d93285d961d026af7bd9da5262ad43bdb3f9449702b914be SHA512: 6e618a33f06be65f4c92b1512bbc409430aea141995b028c2aa04b1285bb589207d3e61f5f7cea6afbc1cf8f5a04a786d0cca7b01963fb2fcb4d5bc93f2fc543 Homepage: https://cran.r-project.org/package=HaDeX Description: CRAN Package 'HaDeX' (Analysis and Visualisation of Hydrogen/Deuterium Exchange MassSpectrometry Data) Functions for processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS) (). 'HaDeX' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including novel uncertainty intervals. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the in-built 'Shiny' app. Package: r-cran-hadibds Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hadibds_1.0.1-1.ca2604.1_all.deb Size: 19050 MD5sum: 296a54863a1f8bff4c5be9d7533cb401 SHA1: a0bbebc6a5aad515443fc5e880700843bda99c0d SHA256: e297fe72f62298d64d8d96ddecb881b8560b9e0b42dae7c09bfc420d1949da36 SHA512: aeaa8ddf812b06e352d209e5cea76b56a0d9049e686e5523dff6b35d230b40907ed18b41cb3ced59734c8dd4201516ed9790462368ddfb98686b0b20aa3f0e3d Homepage: https://cran.r-project.org/package=HadIBDs Description: CRAN Package 'HadIBDs' (Incomplete Block Designs using Hadamard Matrix (HadIBDs)) Hadamard matrix based statistical designs are of immense importance as the resultant designs carry various desirable characterizing properties. Constructing Partially Balanced Incomplete Block Designs (PBIBds) using Kronecker product of incidence matrices of Balanced Incomplete Block (BIB) and Partially Balanced Incomplete Block (PBIB) designs is much evident from literature. Here, we have constructed Incomplete Block Designs (IBDs) based on Hadamard matrices and Kronecker product of Hadamard matrices. Package: r-cran-hagis Architecture: all Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1649 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-pander Suggests: r-cran-ape, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-vdiffr, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-hagis_4.0.0-1.ca2604.1_all.deb Size: 725264 MD5sum: 41c5251bfc9f1f77c6d719673f6ee8b6 SHA1: c65b2c63c935da4b639feee24e4469ca3886f7cd SHA256: a3fc89aeb07594ed689ac73ad80e62ba8821ff87dc8892f7dee3403001726c89 SHA512: 46dfbf7822295f6335171a4eb8ec5d7457f14f715bc3ffe0bddc859da5d00d74857e9c8f57da32ec05949e7b82101cf03a350223147623ce4cd1ca299aa4283c Homepage: https://cran.r-project.org/package=hagis Description: CRAN Package 'hagis' (Analysis of Plant Pathogen Pathotype Complexities, Distributionsand Diversity) Analysis of plant pathogen pathotype survey data. Functions provided calculate distribution of susceptibilities, distribution of complexities with statistics, pathotype frequency distribution, as well as diversity indices for pathotypes. This package is meant to be a direct replacement for Herrmann, Löwer and Schachtel's (1999) Habgood-Gilmour Spreadsheet, 'HaGiS', previously used for pathotype analysis. Package: r-cran-hakaiapi Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-r6, r-cran-readr, r-cran-tibble Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-withr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hakaiapi_1.0.5-1.ca2604.1_all.deb Size: 69416 MD5sum: bc8c44e35658cf6cadde97fbb9cc873d SHA1: ef3311258c227ef4e802183ed97ae6c9b84c879d SHA256: 74af722c5ac352fb89c81fbc8dafe8919791c59891b40ee3fdee46f83f366a13 SHA512: 010f056f75514bf8669349a4623ea42829d2e06a2944947ef5779d3dabd644a94d48b541d3245a973d6c8f9800a7091c1d180213b57d2e23940af9792760f4ec Homepage: https://cran.r-project.org/package=hakaiApi Description: CRAN Package 'hakaiApi' (Authenticated HTTP Request Client for the 'Hakai' API) Initializes a class that obtains API credentials and provides a method to use those credentials to make GET requests to the 'Hakai' API server. Usage instructions are documented at . Package: r-cran-haldensify Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-ggplot2, r-cran-data.table, r-cran-matrixstats, r-cran-future.apply, r-cran-assertthat, r-cran-hal9001, r-cran-origami, r-cran-stringr, r-cran-rlang, r-cran-scales, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-future Filename: pool/dists/resolute/main/r-cran-haldensify_0.2.8-1.ca2604.1_all.deb Size: 230294 MD5sum: ed0303b110650fdeac99e1b8fe9e8e97 SHA1: 6b9e4fde51e277286b127fc19e9e0716c9983b8a SHA256: 4652f39f4834b5163fab5e9e5253eda6061ca8d1bd05183d064745136502bd10 SHA512: dc254e41394137f08b656c595bd46256788427dca5b7c38e357c9f84d4a7d3e8166df3329f3f20e54b0d428d3977785ff84124ad327046eb8518696abf379a4e Homepage: https://cran.r-project.org/package=haldensify Description: CRAN Package 'haldensify' (Highly Adaptive Lasso Conditional Density Estimation) An algorithm for flexible conditional density estimation based on application of pooled hazard regression to an artificial repeated measures dataset constructed by discretizing the support of the outcome variable. To facilitate flexible estimation of the conditional density, the highly adaptive lasso, a non-parametric regression function shown to estimate cadlag (RCLL) functions at a suitably fast convergence rate, is used. The use of pooled hazards regression for conditional density estimation as implemented here was first described for by Díaz and van der Laan (2011) . Building on the conditional density estimation utilities, non-parametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using conditional density estimation to estimate the generalized propensity score. Non-parametric IPW estimators based on this can be coupled with undersmoothing of the generalized propensity score estimator to attain the semi-parametric efficiency bound (per Hejazi, Díaz, and van der Laan ). Package: r-cran-halfcircle Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-halfcircle_0.1.0-1.ca2604.1_all.deb Size: 247606 MD5sum: 6e98156c08f8effcc8beaad7288691a3 SHA1: d6b171aa986c938a76032191890901b09f57d1d9 SHA256: 857478f3f1cfd0743b32f667d1c9c47d62ff36369713478eb34cb9d77682c7fc SHA512: 4391cb08ad239790a1a6dfcb8e13b2c895b38916a34efe5a20cef7173fa8c41277f33d6b2a4bc881d545e77bcf342acc2057d4a9ff24e460653c7fa5e12239b8 Homepage: https://cran.r-project.org/package=halfcircle Description: CRAN Package 'halfcircle' (Plot Halfcircle Diagram) There are growing concerns on flow data in diverse fields including trade, migration, knowledge diffusion, disease spread, and transportation. The package is an effective visual support to learn the pattern of flow which is called halfcircle diagram. The flow between two nodes placed on the center line of a circle is represented using a half circle drawn from the origin to the destination in a clockwise direction. Through changing the order of nodes, the halfcircle diagram enables users to examine the complex relationship between bidirectional flow and each potential determinants. Furthermore, the halfmeancenter function, which calculates (un) weighted mean center of half circles, makes the comparison easier. Package: r-cran-halfmoon Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-gtsummary, r-cran-propensity, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-smd, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tidysmd, r-cran-vctrs Suggests: r-cran-cards, r-cran-cardx, r-cran-cobalt, r-cran-covr, r-cran-mgcv, r-cran-survey, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-halfmoon_0.2.0-1.ca2604.1_all.deb Size: 1233564 MD5sum: 511d379a08ddd4234e2120c24f375b87 SHA1: 04bc8e24bb2838f698639b89b5eed553d161095c SHA256: bbf7c26c5d2e82c066d30b25b08fe71156f8a80e3a4cc0add8807e0e84fc7eea SHA512: 2b3c32ab7c944cd8c08f1abd58e6dd841d645678fd91918465f1454c19a2212c9056e96937a8655126ffbfa6ad470235a3992dc54ed6256b640eb20fbe6a02d2 Homepage: https://cran.r-project.org/package=halfmoon Description: CRAN Package 'halfmoon' (Techniques to Build Better Balance) Build better balance in causal inference models. 'halfmoon' helps you assess propensity score models for balance between groups using metrics like standardized mean differences and visualization techniques like mirrored histograms. 'halfmoon' supports both weighting and matching techniques. Package: r-cran-ham Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2943 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ham_1.2.0-1.ca2604.1_all.deb Size: 2351652 MD5sum: 34debe0745fed97677ee8e948b1e9b6c SHA1: c54bfe280f4e77ccadb8e3ebc6d7f50d16c4bf61 SHA256: 68bd8718a1612c8c822a19916fa70bc5f1bdfd80ab31000e76ef219075484a52 SHA512: f120cb607c20ceaf6329f5e9b5ddb96aa26ad02ab07dab67f1777106fab3ab3b9d9a3ee60f372cc62d2d1b59a3dd5991591956d7dcee956146c383930b1ed45b Homepage: https://cran.r-project.org/package=ham Description: CRAN Package 'ham' (Healthcare Analysis Methods) Conducts analyses for healthcare program evaluations or intervention studies. Calculates regression analyses for standard ordinary least squares (OLS or linear) or logistic models. Performs regression models used for causal modeling such as differences-in-differences (DID) and interrupted time series (ITS) models. Provides limited interpretations of model results and a ranking of variable importance in models. Performs propensity score models, top-coding of model outcome variables, and can return new data with the newly formed variables. Conducts Bayesian analysis summaries and graphs, decision curve analysis, and produces some Shewhart control charts. Also performs Cronbach's alpha for various scale items (e.g., survey questions). See Github URL for examples in the README file. For more details on the statistical methods, see Allen & Yen (1979, ISBN:0-8185-0283-5), Angrist & Pischke (2009, ISBN:9780691120355), Cohen (1988, ISBN:0-8058-0283-5), Gebski (2012) , Gelman & Goodrich (2019) , Harrell (2016, ISBN:978-3-319-19424-0), Kline (1999, ISBN:9780415211581), Kruschke (2014, ISBN:9780124058880), Linden (2015) , Merlo (2006) , Muthen & Satorra (1995) , Rabe-Hesketh & Skrondal (2008, ISBN:978-1-59718-040-5), Ryan (2011, ISBN:978-0-470-59074-4), and Vickers & Elkin (2006) . 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Package: r-cran-haplocatcher Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-caret, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-lattice, r-cran-randomforest Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-haplocatcher_1.0.4-1.ca2604.1_all.deb Size: 937646 MD5sum: ad7e30c09f87f8836621e5b431d17c45 SHA1: 8c21fb832764e7ad36fec29b2594dc11f5625117 SHA256: 6837199e74490ff6b5a03e974f331fd677edf4495d5e2eb45f235595c0dc7a20 SHA512: 912783f3b334c5a9067211e2e7ac33b7010b96efd76e886187cbc5f1f6cfdbe551139753c394fe88b63859c26c34e906534d1951194c66703d66e231213318ea Homepage: https://cran.r-project.org/package=HaploCatcher Description: CRAN Package 'HaploCatcher' (A Predictive Haplotyping Package) Used for predicting a genotype’s allelic state at a specific locus/QTL/gene. 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Package: r-cran-haplosim Architecture: all Version: 1.8.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pedigree Filename: pool/dists/resolute/main/r-cran-haplosim_1.8.4.2-1.ca2604.1_all.deb Size: 205182 MD5sum: 2cc6d806442726e043849d6495657dfe SHA1: b15c8bb0d1b63af68783eef7a1ac71ec7197b5ce SHA256: 92bf09b5bc5ea9bcc504f2b2d98a90317db64b2968915d2116b1ffc3ba80b684 SHA512: 7fa334066eb14af3de00b23c56fdfbf85595f3b2f5b5db3f73807fe00f3e67547a94a0c9d06913b3d731c5ab339492e2872bee2c14914e31574e9c5a993fef46 Homepage: https://cran.r-project.org/package=HaploSim Description: CRAN Package 'HaploSim' (Functions to Simulate Haplotypes) Simulate haplotypes through meioses. Allows specification of population parameters. Package: r-cran-haplotyper Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-haplotyper_0.1-1.ca2604.1_all.deb Size: 46018 MD5sum: efaf5c38374bf64ff1021d047cbe0f0c SHA1: e143ceedaaaaff06f8f499bd05d6c5e7cb2eabf4 SHA256: 26225606bf3c8a269d27f18afc5c6491c4c37b3637ddd1848a36ec05ba637320 SHA512: 5581e0642dc8a154c39a407accc9030baf3bcbd24fb5013f30308986503b28bfa831771ad87568a653a8026279b109c0fe0a4bcd850d1b79a07aecee817f753e Homepage: https://cran.r-project.org/package=haplotyper Description: CRAN Package 'haplotyper' (Tool for Clustering Genotypes in Haplotypes) Function to identify haplotypes within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL. 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Package: r-cran-haplotypes Architecture: all Version: 1.1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-sna, r-cran-ape, r-cran-phangorn, r-cran-plotrix Filename: pool/dists/resolute/main/r-cran-haplotypes_1.1.3.2-1.ca2604.1_all.deb Size: 572544 MD5sum: 54dd12a9b610539ec8fb57d80cef1625 SHA1: bd6f9d41dc59de76a69b1bda99e68256b56a1a74 SHA256: 64a0e50bfb600a22b97d64f48e78c4054388924e4ae324d1b9066d42deb18c0b SHA512: 98ccc51bc082dd3df41b280e677367c94f31374018f34f2663cc3301a3757845350c46237060a94cd1ccdd015b5e062bae3511ae3a1ddbdce4d7bdb67de3016b Homepage: https://cran.r-project.org/package=haplotypes Description: CRAN Package 'haplotypes' (Manipulating DNA Sequences and Estimating Unambiguous HaplotypeNetwork with Statistical Parsimony) Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel-coding method (only simple indel-coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapsing identical DNA sequences into haplotypes or inferring haplotypes using user-provided absolute pairwise character difference matrix. This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks. Package: r-cran-haplovar Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-dbscan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-haplovar_0.1.1-1.ca2604.1_all.deb Size: 935078 MD5sum: 4cf67403aef30c11a2bff12b96a24627 SHA1: aeb96aa42e2f2d5f5dd419daa1e295834c8a3e60 SHA256: 9a4a5d447ae5b9fe0536e4f4d75f256e6e3716d33a63aa0d9dfc0e72f0d0467c SHA512: a2cd2ea5e615a6fc72c2002eaee7968e45721ca1aba5cba6b7f8beca3a0c433bd21ace8c17094e8c34bd1fa0a4c30b24d5e0eac5c8145ac4b0bba4c7a4a3835d Homepage: https://cran.r-project.org/package=HaploVar Description: CRAN Package 'HaploVar' (Defining Local Haplotype Variants for Use in Trait Associationand Trait Prediction Analyses) A local haplotyping tool for use in trait association and trait prediction analyses pipelines. 'HaploVar' enables users take single nucleotide polymorphisms (SNPs) (in VCF format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the output to be compatible with a wide range of trait association and trait prediction tools. The local haplotypes are calculated from the LD matrix using a clustering algorithm called density-based spatial clustering of applications with noise ('DBSCAN') (Ester et al., 1996) . Package: r-cran-happign Architecture: all Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr2, r-cran-sf, r-cran-terra, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-httptest2, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/resolute/main/r-cran-happign_0.3.8-1.ca2604.1_all.deb Size: 1537420 MD5sum: a83fd5d7a2f55f6eac5fcaaa2b5304eb SHA1: 09851d6c9f541e26b267078fd0a49b515e9bd271 SHA256: b80fa18b7b943538aff5216dade9726cc813faea52f3312a933489c075a30dc4 SHA512: 1401fb36ed2eafa84f81cf7a4732bb0d89bd38ec7df288e2828722e956ee09827c1c7debd96041005d69c99641c3dc376001960e0ed12dae39f3d2b45b75c859 Homepage: https://cran.r-project.org/package=happign Description: CRAN Package 'happign' (R Interface to 'IGN' Web Services) Automatic open data acquisition from resources of IGN ('Institut National de Information Geographique et forestiere') (). Available datasets include various types of raster and vector data, such as digital elevation models, state borders, spatial databases, cadastral parcels, and more. 'happign' also provide access to API Carto (). Package: r-cran-happytime Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-happytime_0.1.0-1.ca2604.1_all.deb Size: 28444 MD5sum: a1fe3b42d1e6ffafb81325a3d62952c0 SHA1: faca3bf87a8de96aa136940586627da6ef39add9 SHA256: b995ec77128be4e015455c9e95f879e478f266136e1311a1dab537ccd2adf0bb SHA512: 05c2ee0fcc804457f5059751e10abca64b424eed4cf3694eeb830972a47d5df5fc82f006650c7328c4b9a92fb4ca317fe41ffd4f33afe75b632c250094c1d6bd Homepage: https://cran.r-project.org/package=happytime Description: CRAN Package 'happytime' (Two Games to Relieve the Boredom) There are two interesting games in this package, one is 2048 games(for windows), using up and down to control the direction until there is a 2048 figure. And the other is 'what to eat today',preparing for people who choose difficulties, including most of the delicious Cantonese cuisine. Package: r-cran-harbinger Architecture: all Version: 2.0.757-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tspredit, r-cran-changepoint, r-cran-daltoolbox, r-cran-forecast, r-cran-ggplot2, r-cran-hht, r-cran-rcpphungarian, r-cran-dplyr, r-cran-dtwclust, r-cran-rugarch, r-cran-patchwork, r-cran-stringr, r-cran-strucchange, r-cran-tsmp, r-cran-wavelets, r-cran-zoo Suggests: r-cran-ocp Filename: pool/dists/resolute/main/r-cran-harbinger_2.0.757-1.ca2604.1_all.deb Size: 563690 MD5sum: 2862504307c5baf7a286ecc44b7bb0c0 SHA1: bbd3423cc378e9cacfc6c0a7ef25813fba466105 SHA256: 3fda2f644d17d73f8a111c5948d097f97d25f8301982c1ae7469e49e64dd6414 SHA512: eaec3f1df876ab9ba89746a97e1ec7533c6748dd58336b10430f5796eda2c70b893a7dd90bdbe6238712d070e85b71958110c627a4c9446a79c8f67d9ec5ea6f Homepage: https://cran.r-project.org/package=harbinger Description: CRAN Package 'harbinger' (A Unified Time Series Event Detection Framework) By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) . Package: r-cran-hardhat Architecture: all Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-sparsevctrs, r-cran-tibble, r-cran-vctrs Suggests: r-cran-covr, r-cran-crayon, r-cran-devtools, r-cran-knitr, r-cran-matrix, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-hardhat_1.4.3-1.ca2604.1_all.deb Size: 854564 MD5sum: 8bc35cb0c8f0b7f3be1f5622a879421b SHA1: 90386a6ba3762b3e5e32a05e2ebfa56f9d3b8c3d SHA256: 85c0e7b6229d4ef7f2a44f9a4fea2504bc3a29805d6ab1601c1cfaa27e11a6d5 SHA512: 0274eea6ad89c5eee888b17a06aac1fb9725c1fbafe10dda3cc4dc2c0365cfe6cd7dc1e55d163b750abf604f1ababd55ea67ae3201803f2e96071a0aa8d2aefa Homepage: https://cran.r-project.org/package=hardhat Description: CRAN Package 'hardhat' (Construct Modeling Packages) Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input. Package: r-cran-harmonicmeanp Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmstable Suggests: r-cran-knitr, r-cran-ape, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-harmonicmeanp_3.0.1-1.ca2604.1_all.deb Size: 531910 MD5sum: 834fd74b6edda4b3d8accc0f0ea3334c SHA1: 1e861af9e428c12a3fa5ab1377a04676ce8c8a30 SHA256: eb79c424bd3ecbec5580251cbd77848440c9be25a1cf5381f4b109d51cdbd269 SHA512: 00717201a12c7546600c31946061e591b98ed0b70c4068465e6de8a89183dad2e2375665d8d5596c6ad8fdd559077c399bf41795d165c7fe56ce30c7b2a419de Homepage: https://cran.r-project.org/package=harmonicmeanp Description: CRAN Package 'harmonicmeanp' (Harmonic Mean p-Values and Model Averaging by Mean MaximumLikelihood) The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended. Package: r-cran-harmonizer Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3326 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-harmonizer_0.3.2-1.ca2604.1_all.deb Size: 1687542 MD5sum: dc9f987190c66ca2e1643f41743b8037 SHA1: 3726bfccc3550a4ab64cdfdaa766442246955409 SHA256: 281bff414e816290bf6dd3a568857cf82ded867eee2a029dd0d4b71c2866f76e SHA512: b4d5d5d3a63ed54aad65787485faa072a8b92c0e7263a01f861e6f294477e803398fcbff6468a69ea12054fa02e4848992c95befeadd95a89d9c47b4dd6360aa Homepage: https://cran.r-project.org/package=harmonizer Description: CRAN Package 'harmonizer' (Harmonizing CN8 and PC8 Product Codes) Several functions are provided to harmonize CN8 (Combined Nomenclature 8 digits) and PC8 (Production Communautaire 8 digits) product codes over time and the classification systems HS6 and BEC. Harmonization of CN8 codes are possible by default from 1995 to 2022 and of PC8 from 2001 to 2021, respectively. Package: r-cran-harmonydata Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-uuid, r-cran-base64enc, r-cran-jsonlite, r-cran-assertthat, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-harmonydata_0.3.1-1.ca2604.1_all.deb Size: 35144 MD5sum: aeb954f1db12ec6981d6c76f23619952 SHA1: b9f104dd1998dbc387d3739bb2413c319c896430 SHA256: 285e7cae3aad0a284127c93ed138b611c6cb3a01f115b535cfcdbbce7cb4f5b3 SHA512: ed0a350d9cb7102bae1258b9d36977e1f7d7c2710ffa257876eed98413d6aeea9823447e3eb5c2dd91d379282407dce8029c4e369e3c6fc6610c8cc43bd1891e Homepage: https://cran.r-project.org/package=harmonydata Description: CRAN Package 'harmonydata' (R Library for 'Harmony') 'Harmony' is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try 'Harmony' at and you can read our blog at or at . Documentation at . Package: r-cran-harplus Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-openxlsx, r-cran-haven, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-harplus_1.2.0-1.ca2604.1_all.deb Size: 729982 MD5sum: c452f947e654c1c636e9dc0d10286879 SHA1: e036c00fff6bcdd8d7be6e13e8a05a823dd4298f SHA256: 5fbda970deee26f6f430c326474f0a28c8a01f7f7d418a66b87911313ab9824d SHA512: 1d12aa6b9cd720275d7dc6d85c538da6a3c8c2761ec69b039c61a2081f55aec91f573b2d79866b6216d54b58d8b8b3d015c6ea4bf09c591118ff103acdbaf0b7 Homepage: https://cran.r-project.org/package=HARplus Description: CRAN Package 'HARplus' (Enhanced R Package for 'GEMPACK' .har and .sl4 Files) Provides tools for processing and analyzing .har and .sl4 files, making it easier for 'GEMPACK' users and 'GTAP' researchers to handle large economic datasets. It simplifies the management of multiple experiment results, enabling faster and more efficient comparisons without complexity. Users can extract, restructure, and merge data seamlessly, ensuring compatibility across different tools. The processed data can be exported and used in 'R', 'Stata', 'Python', 'Julia', or any software that supports Text, CSV, or 'Excel' formats. Package: r-cran-harr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-harr_1.1.0-1.ca2604.1_all.deb Size: 57816 MD5sum: c81600de4df44540a097b204821303bd SHA1: 62b608a52b366140a046f78d349b152e8df47fa1 SHA256: 30b8f0ccc83537df736a0223f79fab98cef38876f99c97e9a2b2495fb9d3f297 SHA512: 8c64d04742bff76cfab1e5adf8a7393039612b75beaac83bb5e2389ae58a23e8bf547ca71e9f5f381fb0e4b46bf4c7673e543f5a14534d25fa904f4e5f957d45 Homepage: https://cran.r-project.org/package=HARr Description: CRAN Package 'HARr' ('HAR' ('GEMPACK') File Read/Write Utility) HAR files are generated and consumed by 'GEMPACK' applications. This package reads/writes 'HAR' files (and 'SL4' files) directly using basic R functions. Package: r-cran-harrietr Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-bioc-ggtree, r-cran-magrittr, r-cran-lazyeval, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-harrietr_0.2.3-1.ca2604.1_all.deb Size: 368854 MD5sum: f47fa7a7c1245d4c14c5e2d1266bb728 SHA1: f5dbfbe482d4b58cd388aa7c95f5de80531e13ad SHA256: 01965d89718613bd4c6a4b6c6c5a89ddb19f30a1151543f875492ae8440dc02a SHA512: 99fe1d6067a005db7170d5505573ab46995deea9c932913817ab5759e1e6c69353c79bc2ede638f502f051b19f8832a51696b29f2596a970e3f2133fd51f8465 Homepage: https://cran.r-project.org/package=harrietr Description: CRAN Package 'harrietr' (Wrangle Phylogenetic Distance Matrices and Other Utilities) Harriet was Charles Darwin's pet tortoise (possibly). 'harrietr' implements some function to manipulate distance matrices and phylogenetic trees to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse' tools. Package: r-cran-harrypotter Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-hexbin, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-harrypotter_2.1.1-1.ca2604.1_all.deb Size: 229352 MD5sum: f31a1a7d5abdd117abd72915cf2f3c84 SHA1: 11084a1077a8fefa6fb2448d0149d2b1ea60befb SHA256: 39ae90e4a7262ba24ff9b4ffe65ab090a2075447ce58393bceb5f004d186219e SHA512: f1abb9217715f30ddbbd09bffbb0ea1d172125dfe197982c814fd31346e1dbfafcc5b7ef790fe62bfce1bd0259aa0fec8c642f551fa3ea9a2336d2c1fda845d4 Homepage: https://cran.r-project.org/package=harrypotter Description: CRAN Package 'harrypotter' (Palettes Generated from All "Harry Potter" Movies) Implementation of characteristic palettes inspired in the Wizarding World and the Harry Potter movie franchise. Package: r-cran-harvest.tree Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-harvest.tree_1.1-1.ca2604.1_all.deb Size: 63992 MD5sum: 27c6e13362893a4f18a5e29f79e41805 SHA1: 8d5412c835f0d718815b62b5c2ce95d05c2f1272 SHA256: fea91c6c3dbd904ff1d0184f328b40b5cb837518ab3f210211b4c0e7246dec5d SHA512: 789f76692a54cc2942e2836a7a0473ae1c5f58cc753bd45969f7e65af5163e238afb53b04b3f072a02a410e6d331cef478c0da5a8bb01844b6ddcedfaaa317e8 Homepage: https://cran.r-project.org/package=Harvest.Tree Description: CRAN Package 'Harvest.Tree' (Harvest the Classification Tree) Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information. Package: r-cran-hash Architecture: all Version: 2.2.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-rbenchmark Filename: pool/dists/resolute/main/r-cran-hash_2.2.6.4-1.ca2604.1_all.deb Size: 189394 MD5sum: 2c0da8808018556f0b4d0598fffc320e SHA1: 7c5bea390e8739f5462de3794271df49dbb19e6d SHA256: e79240ef9e8df70b2bae4b6f8b96265ff7a10b6f04e3f25f03384be8f869bf6a SHA512: b6300a863adebdbd3f5aa719d696146eb855fc564f41759f1fb527c4e52addb93dec7786fd85bfb6611c5fe29b3c5f3a865a644bd18f22fd93a0cb7ddfa15e23 Homepage: https://cran.r-project.org/package=hash Description: CRAN Package 'hash' (Full Featured Implementation of Hash Tables/AssociativeArrays/Dictionaries) Implements a data structure similar to hashes in Perl and dictionaries in Python but with a purposefully R flavor. For objects of appreciable size, access using hashes outperforms native named lists and vectors. Package: r-cran-hashids Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hashids_0.9.0-1.ca2604.1_all.deb Size: 44720 MD5sum: 8d5d5796ad1e44899942eb27f09beb47 SHA1: 83ea80c3a066925e9d19ee98e92a2a6f78a24109 SHA256: b78d3c5203aca5e95002345763395b0385f7a838fd5ed61cc2f1fde22d66b1d5 SHA512: 5c7b84ae8caa949f0d9084bf445c1325325bf166fbf3112f225f8a4656c97b05434301793306032c4920cfc796b1d56e3d073d79ca480b5735b857aaf3776e1f Homepage: https://cran.r-project.org/package=hashids Description: CRAN Package 'hashids' (Generate Short Unique YouTube-Like IDs (Hashes) from Integers) An R port of the hashids library. hashids generates YouTube-like hashes from integers or vector of integers. Hashes generated from integers are relatively short, unique and non-seqential. hashids can be used to generate unique ids for URLs and hide database row numbers from the user. By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other. hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer. Package: r-cran-hassani.sacf Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hassani.sacf_2.0-1.ca2604.1_all.deb Size: 16016 MD5sum: e88c078278e3e9949818bf7cc4e7ed15 SHA1: 5a0f9e9795955e81507347f9a7f7e20f92c8086d SHA256: 032885b2a7259271187c7d128fff8744963fb2a93b87c5035901aed8fc69f066 SHA512: af16277035fa30ebb4a7eb904e1a3a0879ae3b6e09d3309e149ab89d0a4afd8ce3835ee94f101f040fdd968fb54e275c3c633bbd50271117195500df3f72baca Homepage: https://cran.r-project.org/package=Hassani.SACF Description: CRAN Package 'Hassani.SACF' (Computing Lower Bound of Ljung-Box Test) The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. The Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to the number of lags involved in the test and therefore it should be used with extra caution. The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) . Package: r-cran-hassani.silva Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hassani.silva_1.0-1.ca2604.1_all.deb Size: 17134 MD5sum: f5cc9cbf19a3e584f9e740b59985ce99 SHA1: d7f04711fb40090ba0b64de2dfb4f06d7015274c SHA256: 7f3e4f9cbf7ac96b026f35423bdd5396d827343198f50d8d537a3bbbdf12d0b6 SHA512: c7f84037a8d13badd9326d7ec28e3693265805617fd13c17b666fd03a9dbc7441415f90d44771083f0decde71a30b349d92f24cb1e28a579af98b57d6a826911 Homepage: https://cran.r-project.org/package=Hassani.Silva Description: CRAN Package 'Hassani.Silva' (A Test for Comparing the Predictive Accuracy of Two Sets ofForecasts) A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. KSPA test has been described in : Hassani and Silva (2015) . Package: r-cran-hassediagrams Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-mass Suggests: r-cran-dae, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-hassediagrams_2.0-1.ca2604.1_all.deb Size: 264590 MD5sum: 291d900e0550987508c734f69df7e3e4 SHA1: a587bf199c8e1803e5d412dc738ea8b36aaa5ffd SHA256: cd9c4ac9bb6fe2863aec8cbb73fd3e3fafb9ffcfd2d9b20b44a98ef8b7ede533 SHA512: 70e6a9861a5c06ee8ce7897bac0af90e436423c9f434a25cc4ba5757090eed639e17dbf7132067f31146336901a4699d46426461fe68c9e0da3ac48fffebb17e Homepage: https://cran.r-project.org/package=hassediagrams Description: CRAN Package 'hassediagrams' (Hasse Diagram of the Layout Structure and Restricted LayoutStructure) Returns a Hasse diagram of the layout structure (Bate and Chatfield (2016)) or the restricted layout structure (Bate and Chatfield (2016)) of an experimental design. 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Package: r-cran-hatemicoint Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hatemicoint_1.0.1-1.ca2604.1_all.deb Size: 39886 MD5sum: 1c44c80d9c66d58787b5b7c9825ee25e SHA1: 974a28fbf9899d14d235de9bb4da7c5178b8f7d5 SHA256: caa8e00ff0fa08dd64b4e44628c71f82c2d2f0e7ed14ee76e05ae31d2c54c584 SHA512: dc1f581277b70d2f1437fdd407bc955fee42921a1761e18c04e613928c8dc0e1497a73ac7bfabe77dbf8e534da04bfa442acd059bec0e22f58a07956420ae1f9 Homepage: https://cran.r-project.org/package=hatemicoint Description: CRAN Package 'hatemicoint' (Hatemi-J Cointegration Test with Two Unknown Regime Shifts) Implements the Hatemi-J (2008) cointegration test which allows for two unknown structural breaks (regime shifts) in the cointegrating relationship. The test provides three test statistics: ADF* (Augmented Dickey-Fuller), Zt* (Phillips-Perron Z_t), and Za* (Phillips-Perron Z_alpha), along with endogenously determined break dates. Critical values are based on simulations from Hatemi-J (2008) . 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The modeling framework follows the methodological foundations described in area-level models. This package is designed to facilitate a principled Bayesian workflow, enabling users to conduct prior predictive checks, model fitting, posterior predictive checks, model comparison, and sensitivity analysis in a coherent and reproducible manner. It supports flexible model specifications via 'brms' and promotes transparency in model development, aligned with the recommendations of modern Bayesian data analysis practices, implementing methods described in Rao and Molina (2015) . Package: r-cran-hbstm Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1797 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-fbasics, r-cran-maps Filename: pool/dists/resolute/main/r-cran-hbstm_1.0.2-1.ca2604.1_all.deb Size: 1487094 MD5sum: 47fc7f9c48c4c1c23f6419a9c5295198 SHA1: 80acbafa6a5d2d10c4e471c5ef6f0b9bf41ec1f6 SHA256: 2fcb73b017f42fdf9ccb600d99b43f96bf282effd5ee0cf1c6cefac0e870f471 SHA512: 3107d9774a9351ded88a4d542ff7dc4af933f603b69eb82c6d60715fa550c0dc575128910ed03180085ac8205a2f6573b53c55874052f7cf1788a66322a468ba Homepage: https://cran.r-project.org/package=HBSTM Description: CRAN Package 'HBSTM' (Hierarchical Bayesian Space-Time Models for Gaussian Space-TimeData) Fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models. Package: r-cran-hcandersenr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4368 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hcandersenr_0.2.0-1.ca2604.1_all.deb Size: 4317698 MD5sum: f41adc0d9b56881346405c9ca72c9a3e SHA1: 1488cc7a7159b4ac999943f3307a7968d16bd3da SHA256: f5cd26213f20e63e1b9a92179a7988d4b479c07b224b78efb8792bca4c8a0a59 SHA512: 7e350eb1b9f139a3b4ae85f433bc63d0dc2cdd7d0adc91a8de35d13c042e6757d87f06307da966a9b4ed2a97812d5abcbd8d24d9a475b118c317e211e49bd696 Homepage: https://cran.r-project.org/package=hcandersenr Description: CRAN Package 'hcandersenr' (H.C. Andersens Fairy Tales) Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French. Package: r-cran-hcci Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hcci_1.2.0-1.ca2604.1_all.deb Size: 50012 MD5sum: bc51ce7a118a170f309bf3fa8dc355cd SHA1: bc34d6adad244a2012884a39653d57d7b50f3645 SHA256: 15db00300c4805ce347a95a5b105f9e2441b22ff7973a8cc8733a5ec125799f7 SHA512: 7f4676ede0121b1d2c7042a01ac0e96c67ad669492f1b48548473f42b865c85bfc991f291f2653b4fbd094d667ab653956d36e5f2de1be5829e5d53c04377c46 Homepage: https://cran.r-project.org/package=hcci Description: CRAN Package 'hcci' (Interval Estimation of Linear Models with Heteroskedasticity) Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at . The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) , while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) . The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) . This bootstrap scheme is known as weighted or wild bootstrap. Package: r-cran-hcd Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-randnet, r-cran-rspectra, r-cran-irlba, r-cran-data.tree, r-cran-data.table, r-cran-stringr, r-cran-dendextend Filename: pool/dists/resolute/main/r-cran-hcd_1.0-1.ca2604.1_all.deb Size: 53292 MD5sum: 69fc20e40a218718d77a9a45d7b3a7fa SHA1: b099c29527d7528a042e9a765c996f41284ddea7 SHA256: 58dcfe9d453a531f47352b3bf7317fc4ab8b96ef98b6b1a11059180d8ac3b735 SHA512: f72fdda934a5748cab37446e29c9f9b8e40a133c733c4ce85480c14f2e983eeb00ce27d108f9a1a6b0525bad41d638fe708aab1c0bccb024f8822495243d3302 Homepage: https://cran.r-project.org/package=HCD Description: CRAN Package 'HCD' (Hierarchical Community Detection by Recursive Partitioning) Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) . The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities. Package: r-cran-hce Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6151 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hce_0.9.3-1.ca2604.1_all.deb Size: 1144074 MD5sum: 1dbe369f3f6f72953ca08435d67b47ee SHA1: dd343a6f60ddf8cfe0b3d83e5d7288bc64ce3b0e SHA256: 7750bd11f329ba61acccdc1e029f0a388f0e0311067f45548d73d650db91a3ec SHA512: e8b2ba94b0e09ded4beb50d2b7964dc415c02b0cb9c820e4106243c1645d0b7a992fe0f23262da033c657b64a8a2958987629d6c028248c1a54a13b94ae11afd Homepage: https://cran.r-project.org/package=hce Description: CRAN Package 'hce' (Design and Analysis of Hierarchical Composite Endpoints) Simulate and analyze hierarchical composite endpoints. Includes implementation for the kidney hierarchical composite endpoint as defined in Heerspink HL et al (2023) “Development and validation of a new hierarchical composite end point for clinical trials of kidney disease progression” (Journal of the American Society of Nephrology 34 (2): 2025–2038, ). Win odds, also called Wilcoxon-Mann-Whitney or success odds, is the main analysis method. Other win statistics (win probability, win ratio, net benefit) are also implemented in the univariate case, provided there is no censoring. The win probability analysis is based on the Brunner-Munzel test and uses the DeLong-DeLong-Clarke-Pearson variance estimator, as described by Brunner and Konietschke (2025) in “An unbiased rank-based estimator of the Mann–Whitney variance including the case of ties” (Statistical Papers 66 (1): 20, ). Includes implementation of a new Wilson-type, compatible confidence interval for the win odds, as proposed by Schüürhuis, Konietschke, Brunner (2025) in “A new approach to the nonparametric Behrens–Fisher problem with compatible confidence intervals.” (Biometrical Journal 67 (6), ). Stratification and covariate adjustment are performed based on the methodology presented by Koch GG et al. in “Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them” (Statistics in Medicine 17 (15-16): 1863–92). For a review, see Gasparyan SB et al (2021) “Adjusted win ratio with stratification: Calculation methods and interpretation” (Statistical Methods in Medical Research 30 (2): 580–611, ). Package: r-cran-hchinamap Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-dplyr, r-cran-shiny, r-cran-colourpicker Filename: pool/dists/resolute/main/r-cran-hchinamap_0.1.0-1.ca2604.1_all.deb Size: 564006 MD5sum: 46d3fe62d416101a906446db6f56c8ca SHA1: 905bc0687d0beef5a1421d2ec2d652701b1216fe SHA256: e382af1de89181433a3a611c4e909cad469c0157baa94cb1ac214203aa873c14 SHA512: b1e2886ed0630d9ff2086ab65262cd5119bcc70f30f4778bf289ca0346677f4ab792ac06d3dd6a8c9f67af7477dccd4929d85b253481db67ec9c89bd3dc998a7 Homepage: https://cran.r-project.org/package=hchinamap Description: CRAN Package 'hchinamap' (Mapping China and Its Provinces) By binding R functions and the 'Highmaps' chart library, 'hchinamap' package provides a simple way to map China and its provinces. 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(2017) for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood. 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The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test. 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Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) for a description of some of our methods. 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The package supports pre index and post index analysis, patient cohort comparison, and customizable summaries and visualizations for clinical and health economics research. Methods implemented are based on Scott et al. (2022) and Xia et al. (2024) . 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The options for interim look boundaries are efficacy only, efficacy & futility or futility only. It also provides the function to monitor the trial for any unplanned look. The package is based on Jianrong Wu, Xiaoping Xiong (2016) and Jianrong Wu, Yimei Li (2020) . 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We propose a new estimate of the regularization parameter based on an estimated lower bound of the proportion of false null hypotheses (Meinshausen and Rice (2006) ). The bound is estimated by applying the empirical null distribution of the higher criticism statistic, a second-level significance testing, which is constructed by dependent p-values from a multi-split regression and aggregation method (Jeng, Zhang and Tzeng (2019) ). An estimate of tuning parameter in penalized regression is decided corresponding to the lower bound of the proportion of false null hypotheses. Different penalized regression methods are provided in the multi-split algorithm. 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The package provides streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) mapping files and Summary Trend Tables, enabling researchers and analysts to efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to CCSR categories and access HCUP statistical reports. Key features include: direct download from HCUP website, multiple output formats (long/wide/default), cross-classification support, version management, citation generation, and intelligent caching. The package does not redistribute HCUP data files but facilitates direct download from the official HCUP website, ensuring users always have access to the latest versions and maintain compliance with HCUP data use policies. This package only accesses free public tools and reports; it does NOT access HCUP databases (NIS, KID, SID, NEDS, etc.) that require purchase. For more information, see . 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With 'hdar', researchers and data scientists can integrate the extensive HDA datasets into their R workflows, enhancing their data analysis capabilities. Comprehensive information on the API functionality and usage is available at . Package: r-cran-hdbma Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2jags, r-cran-gplots, r-cran-mass, r-cran-survival, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-hdbma_1.0-1.ca2604.1_all.deb Size: 181624 MD5sum: 6a1aab2f7c4e113d2dc9f680f3d90d92 SHA1: e0cef4fc4b5a5c3179c097b56544e927c8991391 SHA256: 2e871022a4d5564c35614d14b350bc77eb32dddf35a2640d07ad5a6a6b551d9c SHA512: a78c1c7251aec227a15d96acd649cdc428d57a016be5f15acd823f66465fb6598cc321462c1a6a91c12c01492d715531ea70d6134e9c7053a12a0581c392a75b Homepage: https://cran.r-project.org/package=hdbma Description: CRAN Package 'hdbma' (Bayesian Mediation Analysis with High-Dimensional Data) Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators. Package: r-cran-hdbrr Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2917 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-bigstatsr, r-cran-mass Filename: pool/dists/resolute/main/r-cran-hdbrr_1.1.4-1.ca2604.1_all.deb Size: 2598816 MD5sum: 1e09313358582ea59ca2fca521b3a03f SHA1: 8688a0562fb46ce63b44f737a3e3f359f6f4cd36 SHA256: 489bf481ca4e745d71edc847df4b03b0b2cf706ed407d2ed02b392a13becaebf SHA512: a7e5308721f9b7e116c37408e3b85e24507d10aadf19356f2e5d2fe0ce5974904a9a3403f824135ac0aea85932611c78098879661b575dfc1d87da5c318de947 Homepage: https://cran.r-project.org/package=HDBRR Description: CRAN Package 'HDBRR' (High Dimensional Bayesian Ridge Regression without MCMC) Ridge regression provide biased estimators of the regression parameters with lower variance. The HDBRR ("High Dimensional Bayesian Ridge Regression") function fits Bayesian Ridge regression without MCMC, this one uses the SVD or QR decomposition for the posterior computation. Package: r-cran-hdcate Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-r6, r-cran-hdm, r-cran-locpol, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xfun, r-cran-randomforest, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes Filename: pool/dists/resolute/main/r-cran-hdcate_0.1.0-1.ca2604.1_all.deb Size: 187422 MD5sum: 69593346597e9cb7ed0611fe208858fa SHA1: 14bbd5bb1ff4e2a6734965ee661ce6dd9a58e8a1 SHA256: 8434d086fb8e973e619abcf2e4202c1e875aa078152ae324ac269a8f039c1141 SHA512: 488e156214b56b4089d26d64931ac44e62638b6f6b1b54b82b5a5b85f4759fafcbd3b2b1a93aa5698614ec56987f62971e4f9e85c8983752f1bf39b87d24aef5 Homepage: https://cran.r-project.org/package=hdcate Description: CRAN Package 'hdcate' (Estimation of Conditional Average Treatment Effects withHigh-Dimensional Data) A two-step double-robust method to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the “double robustness” property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics . Package: r-cran-hdci Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-slam, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-lattice, r-cran-matrix, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-hdci_1.0-2-1.ca2604.1_all.deb Size: 106468 MD5sum: b56b579895e4811153548d349baafa56 SHA1: d98be29f3efe1725fa44efc1a8d09d8ef8dd61b6 SHA256: 2aef15479fb00235b7a1fce56f195efb5164654793ff2c018f72ff077536ba80 SHA512: 1bbfae6f051c7ff8f470061e3d1362bb946ac9cf16da079c50cbd3d1e30d98d081a3871ab7887edc8c8a3e47c01822f91366afdcad03765151daa6a7b6c1dd4d Homepage: https://cran.r-project.org/package=HDCI Description: CRAN Package 'HDCI' (High Dimensional Confidence Interval Based on Lasso andBootstrap) Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge. 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Jimenez-Varon, Ying Sun and Han Lin Shang (2024, Journal of Computational and Graphical Statistics). Package: r-cran-hdi Architecture: all Version: 0.1-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scalreg, r-cran-mass, r-cran-glmnet, r-cran-linprog Suggests: r-cran-matrix Filename: pool/dists/resolute/main/r-cran-hdi_0.1-10-1.ca2604.1_all.deb Size: 2408498 MD5sum: 8641701f11ca62951aca9d68aa3a86a0 SHA1: c6ec08add9d04282640951301bb9c7de3a21168f SHA256: b838bd457b4300128484d1c50b36ff657a8a77c8f584397e0eae65c077a7bbf3 SHA512: ed2dd4f8cbc5093114ce6f84f1c86a20db7fdf79015439dfba61c97b829065481988c464fd6a06e3d28320506543cb37df992a8a6612f5ff57b7313bc037bb11 Homepage: https://cran.r-project.org/package=hdi Description: CRAN Package 'hdi' (High-Dimensional Inference) Implementation of multiple approaches to perform inference in high-dimensional models. Package: r-cran-hdimpute Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-missranger, r-cran-plyr, r-cran-purrr, r-cran-magrittr, r-cran-tibble, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-missforest, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-hdimpute_0.2.1-1.ca2604.1_all.deb Size: 84190 MD5sum: 50e0a74f0af2a46a54034f97812aad72 SHA1: 2af0a22e2f5357937084346052340933d2674800 SHA256: 3b7a8bf8bd2cfd337918e30b318c685cc75fd57e1ab3280153be00be3456bfb9 SHA512: 9daad929fa2c96a8c361511aeb74a1c8c16e813245917a3d0942113f82294ce2a979479ab5a8b01c24cb2e4b3269cc2185cc16775fcc101a2873b22ec0abe1de Homepage: https://cran.r-project.org/package=hdImpute Description: CRAN Package 'hdImpute' (A Batch Process for High Dimensional Imputation) A correlation-based batch process for fast, accurate imputation for high dimensional missing data problems via chained random forests. See Waggoner (2023) for more on 'hdImpute', Stekhoven and Bühlmann (2012) for more on 'missForest', and Mayer (2022) for more on 'missRanger'. 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Package: r-cran-hdir Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-npcirc, r-cran-circular, r-cran-rgl, r-cran-directional, r-cran-movmf Suggests: r-cran-ggplot2, r-cran-maps, r-cran-mapproj, r-cran-dirstats Filename: pool/dists/resolute/main/r-cran-hdir_1.1.3-1.ca2604.1_all.deb Size: 360166 MD5sum: 0defb32cfd9f6fdf3ee9da2409edf755 SHA1: a626eef27856365ea583847d5f730771f58f520a SHA256: 6f6febf108abc96a996c935bbd59cfeea56f1b4b667432a715b1fff7b3a19931 SHA512: 38c0641c54034d11e9f868f06150ad0fdc38adc29f3fab80fec73ee526bbf074d66a5d0b18de316ad93faab1ad0894c77432190deab95f86a03215d8a8931017 Homepage: https://cran.r-project.org/package=HDiR Description: CRAN Package 'HDiR' (Directional Highest Density Regions) We provide an R tool for computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) . This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences. Package: r-cran-hdivar Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hdivar_1.0.2-1.ca2604.1_all.deb Size: 68422 MD5sum: a1e010af582a1a370bf2fb53cfae838f SHA1: e14ffb5cdd6bbdc88464ed1856ef4425c731ffce SHA256: a01fa44e2aad701624cb057bd0f9a0e8201a27a1749d7ab2874153d494c808aa SHA512: 676b1ea7a8cbe63aee786910274c75925a2287a7fe902dfec838f4a303b52812de6a70c3af4919830659bc6cffa3d4ff6d8837dda87f046d2762f57912b98e02 Homepage: https://cran.r-project.org/package=hdiVAR Description: CRAN Package 'hdiVAR' (Statistical Inference for Noisy Vector Autoregression) The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica. Package: r-cran-hdm Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-glmnet, r-cran-ggplot2, r-cran-checkmate, r-cran-formula Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-xtable, r-cran-mvtnorm, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-hdm_0.3.2-1.ca2604.1_all.deb Size: 1649264 MD5sum: 87d65f54d28d30aba69e9b002f813265 SHA1: ac16538cc556e844c33b277bd5c523b1b2b13190 SHA256: 016010c90fe828694758113fd33fc652cdfc6556abcc760937e34c0dd669acb1 SHA512: 4abf283747cce57dc5a73ae86f66be39c0a1c106532d73dad338a7e42cde94b9d88bd48f1968f9a3b12ce50c38032dedac284fc8f348aa6b889721add809d8f4 Homepage: https://cran.r-project.org/package=hdm Description: CRAN Package 'hdm' (High-Dimensional Metrics) Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) . Package: r-cran-hdmfa Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-hdmfa_0.1.1-1.ca2604.1_all.deb Size: 70976 MD5sum: e8c4a28798b9d76ae83661385389e571 SHA1: 2f32cf0febe397ab34d1ac8b1801e901e136a415 SHA256: c6de8b6f084896f8a9a22f23d6b1584019a323511b49c89ca403a305c412f8e9 SHA512: 3505742f76e966e58484f539b28e153f91890eb487ced13c59562bd9718ee2cfa6097fca83f14a1b7173f1a5cd9845bb6c831cd50590a95d1a29d59822769e58 Homepage: https://cran.r-project.org/package=HDMFA Description: CRAN Package 'HDMFA' (High-Dimensional Matrix Factor Analysis) High-dimensional matrix factor models have drawn much attention in view of the fact that observations are usually well structured to be an array such as in macroeconomics and finance. In addition, data often exhibit heavy-tails and thus it is also important to develop robust procedures. We aim to address this issue by replacing the least square loss with Huber loss function. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's Frobenius norm, which leads to a weighted iterative projection approach to compute and learn the parameters and thereby named as Robust-Matrix-Factor-Analysis (RMFA), see the details in He et al. (2023). The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm (IHR), see the details in He et al. (2023) . In this package, we also provide the algorithm for alpha-PCA by Chen & Fan (2021) , the Projected estimation (PE) method by Yu et al. (2022). In addition, the methods for determining the pair of factor numbers are also given. Package: r-cran-hdmt Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-bioc-qvalue Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-hdmt_1.0.5-1.ca2604.1_all.deb Size: 1858582 MD5sum: 35b32403709adad2f39bab83e8deb125 SHA1: 8107d03e2cc7bfa96fc06022bcdfc00b7106fb7c SHA256: a6e1ece809fee30b0005962083c220b664d6b3782945c243bddf693a8a1e9e9c SHA512: 7c1c32fc76fa7708b4b8ee32d2925521416d4c9a13b37bdace8a6c8959b8ba666b162a578ea89cd71e39472a3c676f64639f7712565fd9a864912cd9e29a7e0e Homepage: https://cran.r-project.org/package=HDMT Description: CRAN Package 'HDMT' (A Multiple Testing Procedure for High-Dimensional MediationHypotheses) A multiple-testing procedure for high-dimensional mediation hypotheses. Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods for mediation analyses, the popular joint significance (JS) test yields an overly conservative type I error rate and therefore low power. In the R package 'HDMT' we implement a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when using JS for testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of three component null hypotheses and deriving the corresponding mixture distribution of null p-values. Results of the data examples include better-behaved quantile-quantile plots and improved detection of novel mediation relationships on the role of DNA methylation in genetic regulation of gene expression. With increasing interest in mediation by molecular intermediaries such as gene expression, the proposed method addresses an unmet methodological challenge. Methods used in the package refer to James Y. Dai, Janet L. Stanford & Michael LeBlanc (2020) . Package: r-cran-hdmtd Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-igraph Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hdmtd_0.1.4-1.ca2604.1_all.deb Size: 519990 MD5sum: 3bdabc708de330c09d7cb2b324b588ad SHA1: 6e9ae3eb5034505756f473327ce15fb3b07d8054 SHA256: f02af48286d813ae03570076e9d4b025c49c57ac18622386137156436efcee48 SHA512: 8282e356f4ad28cbb81329919c81c76adedd4141e1c99adc87388a4d5ffae3addc3942e5bf667b5d29de6f233f5fa40d9bade5f04fc89259d8419412a3f4848e Homepage: https://cran.r-project.org/package=hdMTD Description: CRAN Package 'hdMTD' (Inference for High-Dimensional Mixture Transition DistributionModels) Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, 'hdMTD' includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) . 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Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) . 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We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's L2 norm, which turns out to do Principal Component Analysis (PCA) on the weighted sample covariance matrix and thereby named as Huber PCA. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm. In this package we also provide the code for traditional PCA, the Robust Two Step (RTS) method by He et al. (2022) and the Quantile Factor Analysis (QFA) method by Chen et al. (2021) and He et al. (2023). 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The techniques developed in Bodnar et al. (2018 , 2019 , 2020 , 2021 ) are central to the package. They provide simple and feasible estimators and tests for optimal portfolio weights, which are applicable for 'large p and large n' situations where p is the portfolio dimension (number of stocks) and n is the sample size. The package also includes tools for constructing portfolios based on shrinkage estimators of the mean vector and covariance matrix as well as a new Bayesian estimator for the Markowitz efficient frontier recently developed by Bauder et al. (2021) . 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In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, 'HDStIM' labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots. 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The package accompanies the paper Chen, Keilbar, Su and Wang (2023) "Inference on many jumps in nonparametric panel regression models". arXiv preprint . 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'Metopio' health atlases store open public health data. See what topics (or indicators) are available among specific populations, periods, and geographic layers. Download relevant data along with geographic boundaries or point datasets. Spatial datasets are returned as 'sf' objects. 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Package: r-cran-healthdb Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clock, r-cran-data.table, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-healthdb_0.5.0-1.ca2604.1_all.deb Size: 300732 MD5sum: 9cd0960aa26ae4accd41a2a755b3f053 SHA1: 5713f0391650e424e9d24036f7d45d76f72c770f SHA256: 1d9c59882a6aeaa0e1070a045800f47180a6881fc4976f5851e92d0871fa1f65 SHA512: 74b8c00c5148d70e7b3c8106973e625789f7c69b8494a54ab572bb57e6b515e77766668075ca4e37c540a1841a4187d6ca77cbbf06d15538af03ff99c391221b Homepage: https://cran.r-project.org/package=healthdb Description: CRAN Package 'healthdb' (Working with Healthcare Databases) A system for identifying diseases or events from healthcare databases and preparing data for epidemiological studies. It includes capabilities not supported by 'SQL', such as matching strings by 'stringr' style regular expressions, and can compute comorbidity scores (Quan et al. (2005) ) directly on a database server. The implementation is based on 'dbplyr' with full 'tidyverse' compatibility. Package: r-cran-healthequal Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1570 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-emmeans, r-cran-marginaleffects, r-cran-rlang, r-cran-srvyr, r-cran-survey Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-sandwich, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-healthequal_1.0.1-1.ca2604.1_all.deb Size: 825614 MD5sum: 3a729d9ff4c9b8301af2b9459bc786bd SHA1: 0d943b77dc1c64c598d7b83bdda263eb3d234169 SHA256: fa805b1c4c538e054016ba27f04b57a7a71d0f490c9cbe02b23a896c3b34e303 SHA512: f50671846ebac7854c2c7c13c365bad29022271fc08ac35a5acb3d8804c2a745b139d84615da04fb3b0be569d0e13a4ec2f4d305498a5aaf58ef67fab44ecb50 Homepage: https://cran.r-project.org/package=healthequal Description: CRAN Package 'healthequal' (Compute Summary Measures of Health Inequality) Compute 21 summary measures of health inequality and its corresponding confidence intervals for ordered and non-ordered dimensions using disaggregated data. Measures for ordered dimensions (e.g., Slope Index of Inequality, Absolute Concentration Index) also accept individual and survey data. Package: r-cran-healthfinance Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-readr, r-cran-scales, r-cran-shiny, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-healthfinance_0.1.0-1.ca2604.1_all.deb Size: 31062 MD5sum: b87e21c5af11252120c9f6ecb0872ba7 SHA1: 6a3074233bcd626b3eb8320d95c4b7444e3eacc3 SHA256: f2a9742bd12b4779b9e5e59a39f8cf9f86e80cd96f42861999a330238d73f8e6 SHA512: e6db3e5f29a0ba911dd03f55ecf033535e64b0933eb2f606a5aa1db954b02b57f291a9a6aa32866de39189603579a4cdf965bab5e82071ed3a7f679d437e40cf Homepage: https://cran.r-project.org/package=healthfinance Description: CRAN Package 'healthfinance' (Financial Projections and Planning for Health Care Practices) Provides a shiny interface for a free, open-source managerial accounting-like system for health care practices. This package allows health care administrators to project revenue with monthly adjustments and procedure-specific boosts up to a 3-year period. Granular data (patient-level) to aggregated data (department- or hospital-level) can all be used as valid inputs provided historical volume and revenue data is available. For more details on managerial accounting techniques, see Brewer et al. (2015, ISBN:9780078025792). Package: r-cran-healthiar Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1912 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-rdpack Suggests: r-cran-sf, r-cran-terra, r-cran-exactextractr, r-cran-testthat, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-healthiar_0.2.4-1.ca2604.1_all.deb Size: 1390564 MD5sum: 7307a7da0e7697a1083e6334d5e76eae SHA1: 6e885af4c5c3ea8f850cadeff126930b9f37536c SHA256: 0a8dd2d404c1c5b3b953f739d0c8a3ffe499a1cfd2968a96ab6393e3088c53cd SHA512: a74f3f6f265acb87ce1152006a4f1b6a34db3a41f2fdf855d43b0f89d03c19a6f245f6ecd69a4ef7fcaea13346de20f93981336ec6c33efe7dca7d074b0869b3 Homepage: https://cran.r-project.org/package=healthiar Description: CRAN Package 'healthiar' (Quantifying and Monetizing Health Impacts Attributable toExposure) This R package has been developed with a focus on air pollution and noise but can be applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package. Selection of relevant references (in chronological order): WHO (2003) , Murray et al. (2003) , Miller & Hurley (2003) , Steenland & Armstrong (2006) , WHO (2011) , GBD 2019 Risk Factors Collaborators (2020) . Package: r-cran-healthmarkers Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pkgload, r-cran-cvrisk, r-cran-di, r-cran-rspiro, r-cran-pooledcohort, r-cran-qrisk3, r-cran-riskscorescvd, r-cran-usethis, r-cran-withr, r-cran-mice, r-cran-missforest Filename: pool/dists/resolute/main/r-cran-healthmarkers_0.1.2-1.ca2604.1_all.deb Size: 1182622 MD5sum: 7141790ebeb5d8d542e2a024d61fee2b SHA1: 4e56a50a8b88f835a281d50ec5988ba00ba621a1 SHA256: d925003c5511f45a90392dc0a1ab6184ca4a29b075fc18fa32e3065e5a8f605c SHA512: 46e322aee80d0bd74a4459123adab3ae4417581f7741f51db1fc0a7767a41a425e7e59aefac17554a024e58750084d985677a9b1d5702e27c4d59c689d9443db Homepage: https://cran.r-project.org/package=HealthMarkers Description: CRAN Package 'HealthMarkers' (Toolkit for Clinical, Metabolic, and Cardiovascular BiomarkerCalculations) Computes over 50 specialist health marker functions covering insulin sensitivity and resistance indices (fasting, oral glucose tolerance test, adipose-tissue, tracer, and dual-energy X-ray absorptiometry (DXA)-based), glycaemic and lipid markers, atherogenic and metabolic syndrome scores, liver steatosis and fibrosis scores, and cardiovascular risk algorithms (Framingham Heart Study equations, atherosclerotic cardiovascular disease (ASCVD) Pooled Cohort Equations, the QRISK3 cardiac risk score, and Systematic Coronary Risk Evaluation 2 (SCORE2) including the Older Persons variant (SCORE2-OP)). Also implements renal function (estimated glomerular filtration rate (eGFR), Kidney Failure Risk Equation (KFRE), chronic kidney disease (CKD) staging), pulmonary function (spirometry z-scores, Body-mass index, airflow Obstruction, Dyspnea, and Exercise capacity index (BODE)), inflammatory markers and the inflammatory age clock (iAge), hormonal panels, body composition and anthropometric z-scores, bone turnover markers and fracture risk (Fracture Risk Assessment Tool (FRAX)), frailty and comorbidity indices (Rockwood, Charlson), psychiatric rating scales, and biomarker panels from alternative biofluids (urine, saliva, sweat). Missing value imputation helpers, pre or post computation normalization and a unified all_health_markers() dispatcher that returns all requested marker groups as a single wide tibble are included. Package: r-cran-healthmotionr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15700 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-healthmotionr_0.2.0-1.ca2604.1_all.deb Size: 15599142 MD5sum: 9da17cbe71567ab40f44395d2ea10d04 SHA1: d3c32b53e020e1e822776f355a78d8e2888f06c9 SHA256: 205f7a4f1fb16fd988012a27b05f989cdb2bf867d0aed6ec7a054df402ce61b8 SHA512: 1d4aaa3177d6c893a7af52c47a51c58279c2e5a9d6a2b59fdb4ecaa8f94ccdccd34c22f150313ac662b6c5d152c6ab9bb3ad10f6e2bba8d4f023a42e26c37b0e Homepage: https://cran.r-project.org/package=healthmotionR Description: CRAN Package 'healthmotionR' (A Comprehensive Collection of Health and Human Motion Datasets) Provides a broad collection of datasets focused on health, biomechanics, and human motion. It includes clinical, physiological, and kinematic information from diverse sources, covering aspects such as surgery outcomes, vital signs, rheumatoid arthritis, osteoarthritis, accelerometry, gait analysis, motion sensing, and biomechanics experiments. Designed for researchers, analysts, and students, the package facilitates exploration and analysis of data related to health monitoring, physical activity, and rehabilitation. Package: r-cran-healthyr.ai Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 808 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-yardstick, r-cran-broom, r-cran-ggrepel, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-forcats, r-cran-recipes, r-cran-purrr, r-cran-h2o, r-cran-dials, r-cran-parsnip, r-cran-tune, r-cran-workflows, r-cran-modeltime Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-healthyr.data, r-cran-scales, r-cran-tidyselect, r-cran-janitor, r-cran-timetk, r-cran-plotly, r-cran-rsample, r-cran-kknn, r-cran-hardhat, r-cran-uwot, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-healthyr.ai_0.1.1-1.ca2604.1_all.deb Size: 595032 MD5sum: 6a9d58f8aa9078c102417c6664b2080d SHA1: c9dc56cbe5a130db6b6bda2ca9abaa9f0b0e4df1 SHA256: a91c5f5e80e0d190835c1ce6cd14e6c7b9ec56abe8a4877a5e0c69dc69edb52b SHA512: 0e846631d81894af47cc6a898019babc68a07015289b874cf8c0df84f8e69978322ec72824a3a1e2ec64bdb2569472b7bf6934e0fdba7086f3930778556d207a Homepage: https://cran.r-project.org/package=healthyR.ai Description: CRAN Package 'healthyR.ai' (The Machine Learning and AI Modeling Companion to 'healthyR') Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. Package: r-cran-healthyr.data Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-janitor, r-cran-dplyr, r-cran-httr2, r-cran-stringr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-healthyr.data_1.2.0-1.ca2604.1_all.deb Size: 4297838 MD5sum: ced3ac09f9df5c6171f2c31e30f7e32a SHA1: d8f2844014f50f7b8101022e02d5d4182d70d505 SHA256: 9c7bbd4264b6fc14f1a642c570b6be4f78fe7056de027a91a63b9023b92517c5 SHA512: 850b743fccfcf57822e81edc2a16cf609cbf59eaa65a0e243758e2f82d4451a562461dfb3e1e3760df003be247c13198f0f8dcc5bc1b9e2e294e1d8bffc734e1 Homepage: https://cran.r-project.org/package=healthyR.data Description: CRAN Package 'healthyR.data' (Data Only Package to 'healthyR') Provides data for functions typically used in the 'healthyR' package. Package: r-cran-healthyr.ts Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-timetk, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-lubridate, r-cran-plotly, r-cran-recipes, r-cran-modeltime, r-cran-cowplot, r-cran-forcats, r-cran-stringi, r-cran-parsnip, r-cran-workflowsets, r-cran-hardhat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-rsample, r-cran-healthyr.ai, r-cran-stringr, r-cran-forecast, r-cran-tidymodels, r-cran-glue, r-cran-xts, r-cran-zoo, r-cran-tsa, r-cran-tune, r-cran-dials, r-cran-workflows, r-cran-tidyselect, r-cran-glmnet, r-cran-earth, r-cran-smooth, r-cran-kernlab Filename: pool/dists/resolute/main/r-cran-healthyr.ts_0.3.2-1.ca2604.1_all.deb Size: 2232528 MD5sum: 0f37c765cb18344ae86d272de35e80ae SHA1: 662cf0ca80f7ec8c805e9862b0f1b7e020730006 SHA256: 46800160ece8174572aad4d769a10fd9198060d3f65547fd09b8d5052d313462 SHA512: 3c10c6cdc30e993375d3abfc657284efc1c74ec646f0540bc490b5fd2c9d527825cda663ac504f97acc57d16eea99b8e2bed678a058aba95b3afd6235189fc37 Homepage: https://cran.r-project.org/package=healthyR.ts Description: CRAN Package 'healthyR.ts' (The Time Series Modeling Companion to 'healthyR') Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. Package: r-cran-healthyr Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-timetk, r-cran-ggplot2, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-stringr, r-cran-writexl, r-cran-cowplot, r-cran-scales, r-cran-sqldf, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pacman, r-cran-healthyr.data, r-cran-broom, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-healthyr_0.2.2-1.ca2604.1_all.deb Size: 2444546 MD5sum: f57708ed2be207b96d1754c8f52327a4 SHA1: 2d240b95d7534cf54c3d6de0ff894ff1d5fb041c SHA256: cdfdec78907f1eba54ac76ac42c69b1cb5c3d1da5aa2ba95ac0e650f3a945fa6 SHA512: a90dfb62d32315466147a5711e38c59d57060e066e82512c11c339d44cadb7d6a92b8d3e7c2976b743b6a2bd266800a8b86002b11f03da1b9c93239811453b97 Homepage: https://cran.r-project.org/package=healthyR Description: CRAN Package 'healthyR' (Hospital Data Analysis Workflow Tools) Hospital data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include average length of stay, readmission rates, average net pay amounts by service lines just to name a few. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. Package: r-cran-heaping Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus, r-cran-envstats Suggests: r-cran-vim, r-cran-ranger, r-cran-data.table, r-cran-ggplot2, r-cran-simpop, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-heaping_0.1.0-1.ca2604.1_all.deb Size: 527778 MD5sum: f095ddfed7d5198624ad356df8ddfb25 SHA1: e6cb0a7aa04ee7db960093f1f782f1edb47f6f4b SHA256: 5fd07c1c6378bd9cc2f1c03df43754e8582d0ab111a7858b634328d8b35aed82 SHA512: a3f27c089e7d15c2ad558345cc47f0022b8051a112fc19e59ff9183b009271399fb7d17cbb8b145f8a78ea3bb5f25fa27ac20f4bb6046d77bd79346896a0f048 Homepage: https://cran.r-project.org/package=heaping Description: CRAN Package 'heaping' (Correction of Heaping on Individual Level) Provides methods for correcting heaping (digit preference) in survey data at the individual record level. Age heaping, where respondents disproportionately report ages ending in 0 or 5, is a common phenomenon that can distort demographic analyses. Unlike traditional smoothing methods that only correct aggregated statistics, this package corrects individual values by replacing a calculated proportion of heaped observations with draws from fitted truncated distributions (log-normal, normal, or uniform). Supports 5-year and 10-year heaping patterns, single heap correction, and optional model-based adjustment to preserve covariate relationships. Package: r-cran-heapsofpapers Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aws.s3, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-magrittr, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-heapsofpapers_0.1.0-1.ca2604.1_all.deb Size: 2059506 MD5sum: 99f79539486b245f6df949228ae8c81f SHA1: c3e21d11cb5d895550ce69d816a349ae7aa98934 SHA256: af2ebede24e58abaaa284ae1f1ee1c8919d51621da574972a485b8203edf9eff SHA512: ddcab5da961f6c0051a41d755a3d3c526915364f3ec64793ab575af6d99125ac7345012853c18933ca23fd4b0a37bb225ae76b87ed2580c694fbb46776a10e58 Homepage: https://cran.r-project.org/package=heapsofpapers Description: CRAN Package 'heapsofpapers' (Easily Download Heaps of PDF and CSV Files) Makes it easy to download a large number of files such as PDF files and CSV files, while automatically slowing down requests, letting you know where it is up to, and adjusting for files that have already been downloaded. Package: r-cran-heartbeatr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-av, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-transformr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-heartbeatr_1.0.0-1.ca2604.1_all.deb Size: 2136880 MD5sum: 2ff286e48cf8ee052b14f880fc15c154 SHA1: 0674d681c2f90c3b6ba39b7f6fd3a8d16de8fca9 SHA256: 5a4161c81cdce92e60c998ed558118660ac88e5cedfce455ee9df16bd11df92b SHA512: 2f5f4697a2b79fa655cceb81ccd747030bbe858d57eb74cc0a77681bdbc5a4c6775b18c19494eb71e18465b1567fbc456c9732f03f7f101de4c321622a1c5928 Homepage: https://cran.r-project.org/package=heartbeatr Description: CRAN Package 'heartbeatr' (A Workflow to Process Data Collected with PULSE Systems) Given one or multiple paths to files produced by a PULSE multi-channel or a PULSE one-channel system () from a single experiment: [1] check pulse files for inconsistencies and read/merge all data, [2] split across time windows, [3] interpolate and smooth to optimize the dataset, [4] compute the heart rate frequency for each channel/window, and [5] facilitate quality control, summarising and plotting. Heart rate frequency is calculated using the Automatic Multi-scale Peak Detection algorithm proposed by Felix Scholkmann and team. For more details see Scholkmann et al (2012) . Check original code at . ElectricBlue is a non-profit technology transfer startup creating research-oriented solutions for the scientific community (). Package: r-cran-heatex Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-heatex_1.0-1.ca2604.1_all.deb Size: 33036 MD5sum: e6bafca377056c472c6be339dd2afd94 SHA1: e5432f9071e2622f9dc9f374998b320b69d7e8e6 SHA256: 5ab9148f7cffd3d852d810b0769078ec7a85a59b5472f8832b499f704ae82110 SHA512: b6984315d3c3b294b282a9cdc9f0e92a4cfe79a57de344cd790858cf0bc34f77a8621f6830655dd01ab8894885cbe1235cb36cef9ff7f23de5efcc29bfc78894 Homepage: https://cran.r-project.org/package=heatex Description: CRAN Package 'heatex' (Heat exchange calculations during physical activity) The heatex package calculates heat storage in the body and the components of heat exchange (conductive, convective, radiative, and evaporative) between the body and the environment during physical activity based on the principles of partitional calorimetry. The program enables heat exchange calculations for a range of environmental conditions when wearing various clothing ensembles. Package: r-cran-heatmap3 Architecture: all Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastcluster Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-heatmap3_1.1.9-1.ca2604.1_all.deb Size: 174218 MD5sum: b454882432b7c33e4880241eec52fdb0 SHA1: 347a02c6cdb449dfdc1f39883ba68ee3fb5f333b SHA256: fbab32d999721bcdbd85d60b8643ad02f9e3a0f7fb06cb07ea51ba1a3b7fa934 SHA512: a0bc5fe2fec80b397e2c00c1bdd7332fd7168ca2f064c297218b4c3b485ee33cc79a9a0a7907b01aa20eefc26cb378d615c6969eb711ad496e96e867d6ef51ea Homepage: https://cran.r-project.org/package=heatmap3 Description: CRAN Package 'heatmap3' (An Improved Heatmap Package) An improved heatmap package. Completely compatible with the original R function 'heatmap', and provides more powerful and convenient features. Package: r-cran-heatmapfit Architecture: all Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-heatmapfit_2.0.4-1.ca2604.1_all.deb Size: 38978 MD5sum: 8b01464063ee43a41e1592f7a8718be0 SHA1: 1df1386e806bda8ff160d892fb5c4e523fa56da1 SHA256: 87c6e64dfc950a540bd0d95d183f77e883c936d7db6b9031d54e59588f483606 SHA512: 1a36f15113e32e9da1ca99423a520c33b425184b3605b3a1240ed16308762477129cefdfb3dabf320a70a9999ce0619136cdcc0f7c3d68f7d2ae35fb1f6f048e Homepage: https://cran.r-project.org/package=heatmapFit Description: CRAN Package 'heatmapFit' (Fit Statistic for Binary Dependent Variable Models) Generates a fit plot for diagnosing misspecification in models of binary dependent variables, and calculates the related heatmap fit statistic described in Esarey and Pierce (2012) . Package: r-cran-heatmapflex Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase, r-bioc-heatplus, r-cran-rcolorbrewer Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-heatmapflex_0.1.2-1.ca2604.1_all.deb Size: 1022584 MD5sum: e2438803ba69fcd92212072917eea38c SHA1: 0f14e5f63f4e605145f958723f9ce144a23361c5 SHA256: b0a606e23f90a05efb1b91fbf6583a416d8c5071fcf52a60ed47aba0b6ce38cf SHA512: 660cd18edd080a4910f55532d7398893aea1f4db04c0a5a3c7a4876b2aa66b5279a08257475dbabe85d3d494a1d345e5d4177fc03eb33fb53700658e5824ff72 Homepage: https://cran.r-project.org/package=heatmapFlex Description: CRAN Package 'heatmapFlex' (Tools to Generate Flexible Heatmaps) A set of tools supporting more flexible heatmaps. The graphics is grid-like using the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set, e.g., clustered and ordered by a supervised clustering method. 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Package: r-cran-heatmapr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-heatmapr_1.1.0-1.ca2604.1_all.deb Size: 616458 MD5sum: c4c59f3342cfb316a5b913dd735338e8 SHA1: f4b907ee18f25c7654a5389db5c992bfbc2d5353 SHA256: e28beabb3ac3dd28a7929611033ebe882103fc064e8e0aa141d73f70b5b8dab2 SHA512: 50d84eaf020799fc91124c9753b7421e0caebcb07e0b0dceb0ecc6c784b574780493a03294fb2190ee681ba5ff5778345d8b1409fd0255721c3e08cdef9e7d97 Homepage: https://cran.r-project.org/package=HeatmapR Description: CRAN Package 'HeatmapR' (Create Heatmaps Using Base Graphics) Provides a lightweight framework for creating high quality, complex heatmaps using base graphics. Supports hierarchical clustering with dendrograms, column and row scaling, cluster sub-divisions, customizable cell colours, shapes and sizes, legends, and flexible layouts for arranging multiple heatmaps. Designed to return plot objects that can be easily arranged with other plots without sacrificing resolution. Methods for hierarchical clustering and distance computations are described in Murtagh and Contreras (2012) . Dendrogram visualisation methods are based on the 'ggdendro' package by de Vries and Ripley (2020) . 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Decision Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011; Siebert, U. et al. State-Transition Modeling. Medical Decision Making 32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, heterogeneity analysis, time dependency on state-time and model-time (semi-Markov and non-homogeneous Markov models), etc. Package: r-cran-heimdall Architecture: all Version: 1.2.727-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-daltoolbox, r-cran-ggplot2, r-cran-metrics, r-cran-reticulate, r-cran-proc, r-cran-car Filename: pool/dists/resolute/main/r-cran-heimdall_1.2.727-1.ca2604.1_all.deb Size: 186488 MD5sum: f90551e0c63c870114817bda0406a160 SHA1: 03b46f5850042948e4920bd93a1975ca2c7d7cb6 SHA256: 8688594e5ac8c969a95c691cbdf3244d21d725d28890d60528470006ba1c861c SHA512: 9b96080e04378ce4ef3d9502ae9910aa8f3f4c556bd43de674580d31ce9b3484e3617549b9a8470506f073631f0255e10cd5b3e57cbfcadb45fb5298855a9d29 Homepage: https://cran.r-project.org/package=heimdall Description: CRAN Package 'heimdall' (Drift Adaptable Models) In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. 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Package: r-cran-herer Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-curl, r-cran-data.table, r-cran-flexpolyline, r-cran-jsonlite, r-cran-sf, r-cran-stringr Suggests: r-cran-covr, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-knitr, r-cran-leafpop, r-cran-lwgeom, r-cran-mapview, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-herer_1.1.0-1.ca2604.1_all.deb Size: 840366 MD5sum: f3d931687bfb11625368b926b1797622 SHA1: 9b6b213693c22015e0cf9c0c89d14722b3e7f3ac SHA256: 6026e35a18d560de2115e43ca2d4dd8693235b2892a8b1a70609d31a7425bfad SHA512: fa56e8b0940d2da9c5bca152eeb94fc8bc14bd04ba97be5a7fc795490b0cc9647ff19f28b1fccce93331c13de4edab4d674ae6c97efd3d8cea62855759cce695 Homepage: https://cran.r-project.org/package=hereR Description: CRAN Package 'hereR' ('sf'-Based Interface to the 'HERE' REST APIs) Interface to the 'HERE' REST APIs : (1) geocode and autosuggest addresses or reverse geocode POIs using the 'Geocoder' API; (2) route directions, travel distance or time matrices and isolines using the 'Routing', 'Matrix Routing' and 'Isoline Routing' APIs; (3) request real-time traffic flow and incident information from the 'Traffic' API; (4) find request public transport connections and nearby stations from the 'Public Transit' API; (5) request intermodal routes using the 'Intermodal Routing' API; (6) get weather forecasts, reports on current weather conditions, astronomical information and alerts at a specific location from the 'Destination Weather' API. Locations, routes and isolines are returned as 'sf' objects. Package: r-cran-heritability Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-heritability_1.4-1.ca2604.1_all.deb Size: 1372488 MD5sum: 8fcfdfdc25326afc9e6ee29bf2b1a25e SHA1: 93f3009adb73551ca1c13d0ee4be5c9987e6bbd3 SHA256: dcaf5378d0263da269da55686fa9df6697531cacb1a37a6fec2156c82ebc0ab0 SHA512: f799518022c6280d9dbeadcaf1ae2cff7b70eb7fd00ac1454065727fcc5caac36904df683799666cc8a0af2b7b34a436c462da0820b7295c06b512e767fd9ece Homepage: https://cran.r-project.org/package=heritability Description: CRAN Package 'heritability' (Marker-Based Estimation of Heritability Using Individual Plantor Plot Data) Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana. Package: r-cran-heritable Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-emmeans, r-cran-matrix, r-cran-stringr, r-cran-vctrs Suggests: r-cran-testthat, r-cran-agridat, r-cran-knitr, r-cran-rmarkdown, r-cran-lme4, r-cran-pbkrtest, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-here Filename: pool/dists/resolute/main/r-cran-heritable_0.1.0-1.ca2604.1_all.deb Size: 175492 MD5sum: 166b4ccce456e954adeaf2a487b33f55 SHA1: a2b3ad9cfaec01e0d2ce998ef88b3c4184597c5e SHA256: 497592d809c5866e93236b8a6efbb78de2bd4be932dca644cad9f78af3564395 SHA512: 7f3bee21d894fa6d9c58d416edc5dd37bec664a010fb6af49159da3c895f3ec766c43c9c5feccd019456e5276fe04ce0f797e725f40e660bd6cd61d7ad4061c5 Homepage: https://cran.r-project.org/package=heritable Description: CRAN Package 'heritable' (Heritability Estimation from Mixed Models) Reporting heritability estimates is an important to quantitative genetics studies and breeding experiments. Here we provide functions to calculate various broad-sense heritabilities from 'asreml' and 'lme4' model objects. All methods we have implemented in this package have extensively discussed in the article by Schmidt et al. (2019) . Package: r-cran-hermite Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik Filename: pool/dists/resolute/main/r-cran-hermite_1.1.2-1.ca2604.1_all.deb Size: 72818 MD5sum: dace144d4aa9da98b0c5576f1ba42867 SHA1: c30a784bbcd9fb0c1ac3240f739757f2ac93b8d4 SHA256: 2dd07955dbc03a24da74eac6f2c118f5cf68a2e165d692729a5cdb81f4ff5138 SHA512: 917cf070e53edd206a9d8e5dbae511ce1465297ba006d2bb000f14f081c7999d1af2ebe19035421c973ddce9af3dfb5dd04525c6ba5dcbbd2a11a213039fcfca Homepage: https://cran.r-project.org/package=hermite Description: CRAN Package 'hermite' (Generalized Hermite Distribution) Probability functions and other utilities for the generalized Hermite distribution. Package: r-cran-hero Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-optimx, r-cran-pbapply, r-cran-sf, r-cran-sp, r-cran-fields Suggests: r-cran-autoimage, r-cran-devtools, r-cran-fda, r-cran-igraph, r-cran-testthat, r-cran-future.apply, r-cran-rmpi Filename: pool/dists/resolute/main/r-cran-hero_0.6-1.ca2604.1_all.deb Size: 2542812 MD5sum: 39a748eaea50bf19cbe20099982447fd SHA1: 05527241be12fd3a672bad988e86803ac489ca00 SHA256: 83ecc8320602f68806e1101e4e624379a34843a508d04f85aba116630a81ec92 SHA512: 83792f4b5218836b31a78fbfc10dec4eadf332a670537975d57f97083054d32b1d07825e7dcc2743ae38a5ed1a9ce70195d9b446dd59104bca98752f8231f9fa Homepage: https://cran.r-project.org/package=hero Description: CRAN Package 'hero' (Spatio-Temporal (Hero) Sandwich Smoother) An implementation of the sandwich smoother proposed in Fast Bivariate Penalized Splines by Xiao et al. (2012) . A hero is a specific type of sandwich. Dictionary.com (2018) describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes. Also implements the spatio-temporal sandwich smoother of French and Kokoszka (2021) . Package: r-cran-hessrna Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-bioc-deseq2, r-cran-ssizerna, r-cran-rdpack Suggests: r-cran-car, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hessrna_1.0.1-1.ca2604.1_all.deb Size: 39088 MD5sum: 7affc35fc514fd54d3b32cce015dcc8b SHA1: 6dfeccad0c4e18f568c039699e64c4f192f7525d SHA256: 5ceefa9aded7f2b7eca17a4653ac330ee66f0011ccc874bed449f9f5b351ad81 SHA512: 2125074d0675cad63df300c5621b5eb1d410bdfb1d22f378d93e07e243624269455fcc635f5b425454be6c5d38d848f8bf7245827583e1699649333495b1709f Homepage: https://cran.r-project.org/package=HEssRNA Description: CRAN Package 'HEssRNA' (Heritability-Based Estimation of Sample Size for RNA-Seq Data) Provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. 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Package: r-cran-hetcorfs Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polycor, r-cran-dplyr, r-cran-cluster, r-cran-psych Filename: pool/dists/resolute/main/r-cran-hetcorfs_1.0.1-1.ca2604.1_all.deb Size: 52726 MD5sum: 9ba7c99c0c26660977ce5df8f3438f2b SHA1: 2d85800938d553c5d67ed23d7d7aff9652af0a33 SHA256: 04a41b0db1d968609ed7e8bc5d78e2d78046ebcd7e1884c5b48730125bb8493e SHA512: b3141d672c07d643847c55f46d939c9beefe5f0ca86675e3d7d2ccfdad3eea03dac1be4a6850a50c7c33a0d0c73b3a6fe3d6d2319648b71a4a0efc4ffd15ea6a Homepage: https://cran.r-project.org/package=hetcorFS Description: CRAN Package 'hetcorFS' (Unsupervised Feature Selection using the HeterogeneousCorrelation Matrix) Unsupervised multivariate filter feature selection using the UFS-rHCM or UFS-cHCM algorithms based on the heterogeneous correlation matrix (HCM). The HCM consists of Pearson's correlations between numerical features, polyserial correlations between numerical and ordinal features, and polychoric correlations between ordinal features. Tortora C., Madhvani S., Punzo A. (2025). "Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix." International Statistical Review . This work was supported by the National Science foundation NSF Grant N 2209974 (Tortora) and by the Italian Ministry of University and Research (MUR) under the PRIN 2022 grant number 2022XRHT8R (CUP: E53D23005950006), as part of ‘The SMILE Project: Statistical Modelling and Inference to Live the Environment’, funded by the European Union – Next Generation EU (Punzo). Package: r-cran-heterfunctionaldata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-heterfunctionaldata_0.1.0-1.ca2604.1_all.deb Size: 63118 MD5sum: b83cdb4b5148f6c7ec0d1d56a112c8ba SHA1: fe1d4ffbc130862361f76f2796520d0d7939a74c SHA256: eef9ddf1064b87d6eb883b626294415025e00e8ec30701275509ca028da285b0 SHA512: 3fa4961a1650b959de7140006c47fa412b5dc5c7e233fbe42c019c3f61583647f4a02d646d8394650d0c58e18f6dc51d1c8602f8bdb8d0c69ee76ea73361ab00 Homepage: https://cran.r-project.org/package=HeterFunctionalData Description: CRAN Package 'HeterFunctionalData' (Test of No Main and/or Interaction Effects in Functional Data) Distribution free heteroscedastic tests for functional data. The following tests are included in this package: test of no main treatment or contrast effect and no simple treatment effect given in Wang, Higgins, and Blasi (2010) , no main time effect, and no interaction effect based on original observations given in Wang and Akritas (2010a) and tests based on ranks given in Wang and Akritas (2010b) . Package: r-cran-heterocop Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-igraph, r-cran-matrixcalc, r-cran-foreach, r-cran-stringr, r-cran-dosnow, r-cran-huge Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-heterocop_1.0.1-1.ca2604.1_all.deb Size: 103912 MD5sum: c99ecb16baa30f195542f5eb5612d1f2 SHA1: f8012f11ed593af0d4f2433cc0303eb4023e9e81 SHA256: fd5368f96998cf02ea5da0d30ea9e969690a8a98edac9f4689f2a83d00323c91 SHA512: 2b1e3b414810926e63d6c26d632db71136c09fa5ed8066febc9b529a337544f09b5b801ff36c3cda2da59531771e041e88c2b19a4f120751012fc5e265899beb Homepage: https://cran.r-project.org/package=heterocop Description: CRAN Package 'heterocop' (Semi-Parametric Estimation with Gaussian Copula) A method for estimating the correlation matrix of the Gaussian copula from the observed data. 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Package: r-cran-heteromixgm Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-tmvtnorm, r-cran-glasso, r-cran-bdgraph, r-cran-mass Filename: pool/dists/resolute/main/r-cran-heteromixgm_2.0.2-1.ca2604.1_all.deb Size: 110236 MD5sum: c77d3e46a96dd3f37368788cf5f0c10a SHA1: 1932237f5dbef05aac03ba9d08f2e880e10ae114 SHA256: 12fa0caa4a4ac11039ec6234933fee6fcecb0ec163d744ba72f207ccb14f27ea SHA512: aa99137ea67eff1d33541b01f40258905d1f66f752f04faf7c401dceaf14fa774696393550dce82b75de6749ca21e126712f7b564df5923a79cb1c7e8f8d5ace Homepage: https://cran.r-project.org/package=heteromixgm Description: CRAN Package 'heteromixgm' (Copula Graphical Models for Heterogeneous Mixed Data) A multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the 'heteromixgm' package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, a function that plots partial correlation graphs based on the selected precision matrices, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2024) . Package: r-cran-hetools Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hetools_1.0.0-1.ca2604.1_all.deb Size: 16504 MD5sum: 1f0b3262c206e8a79e3167d4811d084c SHA1: c7dd3d9f9b403ab9096660ed7dc74259cd153371 SHA256: 11fdb67c4904d80ee053e119b9895673b65a72daaa2ed89ec38a09dcaf0eb308 SHA512: 90b62705fc5a43011d9eb7ee49a32c5edd1ad56cbcb7b15ce060e1fe45d49a8c63f518b13b0694c5af24c29b5cb3ac50a909c350f4954a47e507a0f267ccb396 Homepage: https://cran.r-project.org/package=HEtools Description: CRAN Package 'HEtools' (Homomorphic Encryption Polynomials) Homomorphic encryption (Brakerski and Vaikuntanathan (2014) ) using Ring Learning with Errors (Lyubashevsky et al. (2012) ) is a form of Learning with Errors (Regev (2005) ) using polynomial rings over finite fields. Functions to generate the required polynomials (using 'polynom'), with various distributions of coefficients are provided. Additionally, functions to generate and take coefficient modulo are provided. 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Connecting post-perturbation cells via cellular trajectories to untreated cells (e.g. by leveraging metabolic labeling information) enables exploitation of intercellular heterogeneity as a combined knock-down and overexpression screen to identify pathway modulators, termed Heterogeneity-seq (see 'Berg et al' ). This package contains functions to generate cellular trajectories based on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing) time courses, functions to identify pathway modulators and to visualize the results. 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More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press. 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The small depressions are combined into a single "meta" depression which explicitly models the hysteresis between the storage of water and the connected/contributing areas of the depressions. The largest (greater than 5% of the total depressional area) depression (if it exists) is represented separately to model its gatekeeping, i.e. the blocking of upstream flows until it is filled. The methodolgy is described in detail in Shook and Pomeroy (2025, ). 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Package: r-cran-hglm Architecture: all Version: 2.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-hglm.data Filename: pool/dists/resolute/main/r-cran-hglm_2.2-1-1.ca2604.1_all.deb Size: 518348 MD5sum: ca3889da3c43610f531ff3c8d71c1cc5 SHA1: d293bbbf109ee20a913893b47b7d69ae4160d8bd SHA256: bca9d5c9db5444d7b03766136c6c495a812a29d2a8e39ef6e83d8cc35f337ca8 SHA512: f497a1c564a36bb8fe3d98a9d20e84c08bda65bae54498dc35162b9386a064e217a9353390843bed605f37de5eb27ce1fd8df51b1276a93d3c17b63371c71e9d Homepage: https://cran.r-project.org/package=hglm Description: CRAN Package 'hglm' (Hierarchical Generalized Linear Models) Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) . We provide an implementation (Ronnegard, Alam and Shen 2010) following Lee, Nelder and Pawitan (2006) with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) . 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The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster. Package: r-cran-hgnc Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1023 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-memoise, r-cran-prettyunits, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble Suggests: r-cran-lubridate, r-cran-spelling, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-hgnc_0.3.0-1.ca2604.1_all.deb Size: 939278 MD5sum: ddc5e353297c45863c42617706fda993 SHA1: 5edf37cad2493dfeb49d2075a48495a81a9b853f SHA256: 0e768bc1de046b9747801a1e884b05c6afb67e308700496b22102a0bc0fc3ff0 SHA512: 2c774c214bac4f5984e7283d566dfd705a5518291aafa726268cc9b5798d652b8511f927f7ff6daf0080a6050ae6b48f20b9723af55b51db7c8266ae66dae99d Homepage: https://cran.r-project.org/package=hgnc Description: CRAN Package 'hgnc' (Import Human Gene Nomenclature) A set of routines to quickly download and import the HUGO Gene Nomenclature Committee (HGNC) data set on mapping of gene symbols to gene entries in other genomic databases or resources. 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Also contains functions for reversibly converting between HGNC symbols and valid R names. Package: r-cran-hgraph Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hgraph_0.1.0-1.ca2604.1_all.deb Size: 133176 MD5sum: 3b106eefc623ad518862ad2eaa0d9833 SHA1: 72b92cffc4b4a8c961b41cb96ab6f5d795121ce9 SHA256: 3b579241011c291d87c506231fb05db51c67d408d73db95351c830deeae14b50 SHA512: 3dbc7ba60fd6da0595660a7b0968a8c3bbb7f6788a6036c6a02f4514e5bf868dfdb5f376a25b27c177ab2218907b98ee2aa5a629484e564aaee5f9314de47427 Homepage: https://cran.r-project.org/package=HGraph Description: CRAN Package 'HGraph' (Use Graph Structure to Travel) It is used to travel graphs, by using DFS and BFS to get the path from node to each leaf node. Depth first traversal(DFS) is a recursive algorithm for searching all the vertices of a graph or tree data structure. Traversal means visiting all the nodes of a graph. Breadth first traversal(BFS) algorithm is used to search a tree or graph data structure for a node that meets a set of criteria. It starts at the tree’s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level. Also, it provides the matrix which is reachable between each node. Implement reference about Baruch Awerbuch (1985) . 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Package development functionalities includes among others tools such as cross-referencing package imports with the description file, analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub. Some of the other functionalities include automatic package installation and loading, plotting points without overlap, creating nice breaks for plots, overview tables and many more handy utility functions. 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Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package. Package: r-cran-hhh4contacts Architecture: all Version: 0.13.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-surveillance, r-cran-sp Suggests: r-cran-mass, r-cran-lattice, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-hhh4contacts_0.13.4-1.ca2604.1_all.deb Size: 327272 MD5sum: 0feefebd618009e2818d930bb430c0d7 SHA1: 40fe9540a78fbb28e6c3ae9a40db4823182ecb60 SHA256: b487359c5de05638d79463c96726cc284f52c937ebb062c3b6309d9d18244825 SHA512: ceb46f9c28c2416a3c970073646585ca7769387b74bdc32ec3bb7924ccdb47b7acaef63b86e62cc3f8c3869fc8693b630526a34bfaeeb44af97d1ca346d8e361 Homepage: https://cran.r-project.org/package=hhh4contacts Description: CRAN Package 'hhh4contacts' (Age-Structured Spatio-Temporal Models for Infectious DiseaseCounts) Meyer and Held (2017) present an age-structured spatio-temporal model for infectious disease counts. The approach is illustrated in a case study on norovirus gastroenteritis in Berlin, 2011-2015, by age group, city district and week, using additional contact data from the POLYMOD survey. This package contains the data and code to reproduce the results from the paper, see 'demo("hhh4contacts")'. Package: r-cran-hhi Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-hhi_1.2.0-1.ca2604.1_all.deb Size: 15670 MD5sum: 0fa66ee7dae197a41ecb409860157c4e SHA1: 68d4f69873a964c85eb2b12c7b75133fcfad750c SHA256: a4ede74589a19266f3cf96457cf9f77b22c6a41816823ca8297db708e4919894 SHA512: f41fc5ae6ec3428fe53866cedad55a33b856f6af0ba10f64f93a2af7d5198cc7d2328bec84ef5074718dd887c780cc839c140bc4def747822ed299ed93b95134 Homepage: https://cran.r-project.org/package=hhi Description: CRAN Package 'hhi' (Calculate and Visualize the Herfindahl-Hirschman Index) Based on the aggregated shares retained by individual firms or actors within a market or space, the Herfindahl-Hirschman Index (HHI) measures the level of concentration in a space. This package allows for intuitive and straightforward computation of HHI scores, requiring placement of objects of interest directly into the function. The package also includes a plot function for quick visual display of an HHI time series using any measure of time (year, quarter, month, etc.). For usage, please cite the Journal of Open Source Software paper associated with the package: Waggoner, Philip D. (2018) . Package: r-cran-hhmr Architecture: all Version: 0.0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-rlang, r-cran-ggplot2, r-cran-patchwork, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hhmr_0.0.1.1-1.ca2604.1_all.deb Size: 613958 MD5sum: c1567c596c6e061e4366037519fe843d SHA1: 2703b167b5517015d38e7f0dcd60215376f77b32 SHA256: 59d962b4cae204365e1481e15dfb4c9c9be4b72d7ed7372e67546ebade31b051 SHA512: 890a9befc126832816b695bd506a8d9e9aa4a7ad18e3284943d431f70d04b067323f78b53760c5d8765035e765ca8260bc43fcbbdfcbdafe510ad2cdd0c79c00 Homepage: https://cran.r-project.org/package=hhmR Description: CRAN Package 'hhmR' (Hierarchical Heatmaps) Allows users to create high-quality heatmaps from labelled, hierarchical data. Specifically, for data with a two-level hierarchical structure, it will produce a heatmap where each row and column represents a category at the lower level. These rows and columns are then grouped by the higher-level group each category belongs to, with the names for each category and groups shown in the margins. While other packages (e.g. 'dendextend') allow heatmap rows and columns to be arranged by groups only, 'hhmR' also allows the labelling of the data at both the category and group level. Package: r-cran-hhp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-bioc-fmrs Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hhp_1.0.0-1.ca2604.1_all.deb Size: 506956 MD5sum: 4d2a0927cbd0dbdcbbeebdf8c5c123ff SHA1: b023d6662ef427a7376e5ecf5953c9423f11994e SHA256: fadb0c84a2b9d7685858f51592d31b7e74425fef5a0544d2d001110bee2dfa19 SHA512: e512c72afcef233b763d7358274e146adcfbda26293c1a6fd4d6ce88c2b285f99112e36cf233b3a372890631cbd7807b2123302db51a5d6a9eaeb785e71cee47 Homepage: https://cran.r-project.org/package=HhP Description: CRAN Package 'HhP' (Hierarchical Heterogeneity Analysis via Penalization) In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. Reference: Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, . 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Package: r-cran-hicocietyexample Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7801 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hicocietyexample_1.0.0-1.ca2604.1_all.deb Size: 7711174 MD5sum: d669a8c81177effebb129b88b3dc028d SHA1: 19fd1392685216fab466180b65adfc32d0f605d0 SHA256: ef56e37b0039af1a1892ad19aa3e7b5f29c729e9fd3ad52714b1186bea01464a SHA512: 04e8920aec21a5f6fc1a08f802f10290ca31f97c1f161ff93ac49da3882de1459c6609a395bb30effc7966cf24e9a06bb098720163e71de4eb0a835e58877c35 Homepage: https://cran.r-project.org/package=HiCocietyExample Description: CRAN Package 'HiCocietyExample' (Example HiC and Two 'HiCociety' Outputs for Demonstration andTesting) Provides an example HiC dataset and two examples of 'HiCociety' outputs from a function named hic2community(). The data are intended for demonstration purposes only and kept small enough to be distributed via CRAN. Package: r-cran-hicp Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-restatapi, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hicp_1.1.0-1.ca2604.1_all.deb Size: 242200 MD5sum: 23337f0213b33fff5fc388a471b76118 SHA1: 066b558174ae27375c06e34167e04eb779014f5c SHA256: b7a5c67043b24a45c4e87274c413974be3343fa5fc03bd7e0741f193cd4b8134 SHA512: ebdd0c8aa7142c1269f4cb8af85f6b106439b193c990599d1bdc0440bcf4b153d034035ded9a60c49314249ca16d54272c48296d8ecbddab4c2035e5310a2083 Homepage: https://cran.r-project.org/package=hicp Description: CRAN Package 'hicp' (Harmonised Index of Consumer Prices) The Harmonised Index of Consumer Prices (HICP) is the key economic figure to measure inflation in the euro area. The methodology underlying the HICP is documented in the HICP Methodological Manual (). Based on the manual, this package provides functions to access and work with HICP data from Eurostat's public database (). 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Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering. Package: r-cran-hiddenf Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hiddenf_2.0-1.ca2604.1_all.deb Size: 81948 MD5sum: 138469fdb46b79d40889365c2df51974 SHA1: 92d201fe5bc2a32282dcfecd12a409a0dcc7a33a SHA256: 199762ed08307d2de003393fbe5d27b29b3cbf1b0ae8ff35e08bab062c11e601 SHA512: b636766334334401a18c6acf47c26287ae9bae334d2f06a6e3f0d1f2addb39340f8aa508827af183973f3ee0d156003cbf7fa069cb2bbcfcafc765a8860d8567 Homepage: https://cran.r-project.org/package=hiddenf Description: CRAN Package 'hiddenf' (The All-Configurations, Maximum-Interaction F-Test for HiddenAdditivity) Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies. 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(2013) . 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Methods used in the package refer to Zhang H, Zheng Y, Hou L, Liu L, HIMA: An R Package for High-Dimensional Mediation Analysis. Journal of Data Science. (2025). . 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Package: r-cran-hindex Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-ggplot2, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hindex_0.2.0-1.ca2604.1_all.deb Size: 152978 MD5sum: 54361f3e694e89827ec0f87c07bc6d29 SHA1: 4d5af6b10e54ec4b3d93b5e6cd0d7c58d4c24365 SHA256: 6ad0d9675b0e4d0310fb8506a0f22a8a5d9ddca4433ed8126cf5e4b49b06badf SHA512: 79b26f477cc4daa00f2043816953b2ba756fc5686b891404bf6f05dca67aefc8ae32694bba1a8ddd1a07fbcd049fb38f0fc870f1cc9dca27f043395a7be6cf29 Homepage: https://cran.r-project.org/package=hindex Description: CRAN Package 'hindex' (Simulating the Development of h-Index Values) H-index and h-alpha are a bibliometric indicators. This package provides functions to simulate how these indicators may develop over time for a given set of researchers and to visualize the simulation data. The implementation is based on the 'STATA' ado h-index and is described in more detail in Bornmann et al. (2019) . Package: r-cran-hindexcalculator Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hindexcalculator_1.0.0-1.ca2604.1_all.deb Size: 336086 MD5sum: 7057d2f50caede5ab57234c04ba4a233 SHA1: 510e7f9f4901a61c1f9bb9ba4034bd5e93ddedfa SHA256: 8f45ac8b76185c41a8130ae8e1a829db9d87e9316538f48a36b5b41e8eb66019 SHA512: 05863491b10c70fbaafebf334c95112e1ab04ba5fc1c07939df3e4ecbdeb12eaa79744be2377353c40f982c3868053faf1c748427d43b3ccaea7458e02c24792 Homepage: https://cran.r-project.org/package=hindexcalculator Description: CRAN Package 'hindexcalculator' (H-Index Calculator using Data from a Web of Science (WoS)Citation Report) H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format. 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Package: r-cran-hiphop Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hiphop_0.0.1-1.ca2604.1_all.deb Size: 890954 MD5sum: 0231a39e079fbd91ea74fc6e32b4e7d9 SHA1: 6cfa1adb8c21215274acbe23f55126340d98118a SHA256: 4564e8767d1d0582f06e98c502d81905d915ba2154db87af3f7f4814791baddd SHA512: 166eeca65bd9d3c9a7bf3490734aed2203c03af0b7ae4e26845ca20c80a1a701d34a1748f783bcec88d32533b793b90ed6a7efa15d88ced3581c1080a73f043d Homepage: https://cran.r-project.org/package=hiphop Description: CRAN Package 'hiphop' (Parentage Assignment using Bi-Allelic Genetic Markers) Can be used for paternity and maternity assignment and outperforms conventional methods where closely related individuals occur in the pool of possible parents. The method compares the genotypes of offspring with any combination of potentials parents and scores the number of mismatches of these individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). It elaborates on a prior exclusion method based on the Homozygous Opposite Test (HOT; Huisman 2017 ) by introducing the additional exclusion criterion HIPHOP (Homozygous Identical Parents, Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential parents are excluded if they have more mismatches than can be expected due to genotyping error and mutation, and thereby one can identify the true genetic parents and detect situations where one (or both) of the true parents is not sampled. Package 'hiphop' can deal with (a) the case where there is contextual information about parentage of the mother (i.e. a female has been seen to be involved in reproductive tasks such as nest building), but paternity is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, because there is no contextual information on which female laid eggs and which male fertilized them (e.g. polygynandrous mating system where multiple females and males deposit young in a common nest, or organisms with external fertilisation that breed in aggregations). For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L., Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. (in revision). HIPHOP: improved paternity assignment among close relatives using a simple exclusion method for bi-allelic markers. Molecular Ecology Resources, DOI to be added upon acceptance. Package: r-cran-hippie Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstudioapi, r-cran-sourcetools Suggests: r-cran-testthat, r-cran-withr, r-cran-fs Filename: pool/dists/resolute/main/r-cran-hippie_0.1.0-1.ca2604.1_all.deb Size: 100734 MD5sum: 42e119ea9a70008d2749502a83c1e247 SHA1: 0031ed0ce04e4dd20650780437d5f153c7dfa193 SHA256: 806f60b47b30c1e7528e3b56d08f8fde8b00f89165e5030680a437b79a6821f0 SHA512: 0fcc2a52886406521e3636463939f946e07838f8e3c41c64dd859a582f8ad4e3239f192ac574bc03d5254aaa8351bfe430b62abfc59bbd209f4f2532d64b039d Homepage: https://cran.r-project.org/package=hippie Description: CRAN Package 'hippie' (Hippie Code Completion in 'RStudio') An 'RStudio' Addin for Hippie Expand (AKA Hippie Code Completion or Cyclic Expand Word). This type of completion searches for matching tokens within the user's current source editor file, regardless of file type. By searching only within the current source file, 'hippie' offers a fast way to identify and insert completions that appear around the user's cursor. Package: r-cran-hirisplexr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bedmatrix, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hirisplexr_0.1.0-1.ca2604.1_all.deb Size: 24908 MD5sum: ccb0a9a83ed21cf092fd7be0c7eb557f SHA1: b2231fcf455110f9addb484a16b371a530d6b16f SHA256: 0d35d9a4c8f263d5b9dec3e7f09cdad3d9cffffe5a40c16626d226bb73356a03 SHA512: 27d4626a886dbfa317c3bf618c997c6e6beeda2a69358511854287c85585876b847dbfcd01545480381c6ff83b4c5694c4e615ed977eaa032b858accaf10539f Homepage: https://cran.r-project.org/package=hirisplexr Description: CRAN Package 'hirisplexr' (From 'PLINK' to 'HIrisPlex') Read 'PLINK' 1.9 binary datasets (BED/BIM/FAM) and generate the CSV files required by the Erasmus MC 'HIrisPlex' / 'HIrisPlex-S' webtool . It maps 'PLINK' alleles to the webtool's required 'rsID_Allele' columns (0/1/2/NA). No external tools (e.g., 'PLINK CLI') are required. Package: r-cran-histdat Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-histdat_0.2.0-1.ca2604.1_all.deb Size: 90208 MD5sum: eaab8dbc96ad9768a3fb265bcb9bc874 SHA1: 72874fd25d6c01d531c5da61cc902209bccc60cc SHA256: 330f5f3d8f068fc3648885a2e09881d787a1c5b0a3ab3bbe0142271638cb30d4 SHA512: acf4c9760c8a37bf9f1dadc890bf6b39238f5aa0c97207cf5401c38cf166c5235442b3b6362fc83491b7838e72368080118bbfae0a715a9e458cb954c7fa39d5 Homepage: https://cran.r-project.org/package=HistDat Description: CRAN Package 'HistDat' (Summary Statistics for Histogram/Count Data) In some cases you will have data in a histogram format, where you have a vector of all possible observations, and a vector of how many times each observation appeared. You could expand this into a single 1D vector, but this may not be advisable if the counts are extremely large. 'HistDat' allows for the calculation of summary statistics without the need for expanding your data. Package: r-cran-histdata Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1235 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gtools, r-cran-kernsmooth, r-cran-maps, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-proto, r-cran-reshape, r-cran-plyr, r-cran-lattice, r-cran-jpeg, r-cran-car, r-cran-gplots, r-cran-sp, r-cran-heplots, r-cran-knitr, r-cran-rmarkdown, r-cran-effects, r-cran-lubridate, r-cran-gridextra, r-cran-vcd, r-cran-mass, r-cran-forcats Filename: pool/dists/resolute/main/r-cran-histdata_1.0.0-1.ca2604.1_all.deb Size: 706298 MD5sum: cd30f5f96be775390509e19e423fa196 SHA1: 31785af8a8fc0061ccb9335641c1cf06b03f9f78 SHA256: 7bbf7fd974c3dede090230be52112d0ba16a5b940d0f83983bfcf0c4e6ece32d SHA512: 2322a4f70aa040378036d9e0c25e6327ca8e9eb32ce8170d2d3ba82cbdcbd292b46194b9c9bd5ea6425b455ccd53b62ffa432862e4a6615691bfb45ef6b947e0 Homepage: https://cran.r-project.org/package=HistData Description: CRAN Package 'HistData' (Data Sets from the History of Statistics and Data Visualization) The 'HistData' package provides a collection of small data sets that are interesting and important in the history of statistics and data visualization. The goal of the package is to make these available, both for instructional use and for historical research. Some of these present interesting challenges for graphics or analysis in R. Package: r-cran-histogram Architecture: all Version: 0.0-25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-histogram_0.0-25-1.ca2604.1_all.deb Size: 63048 MD5sum: 15745c158ca6770e6c85aef79de34a6d SHA1: 199fd345c773a5e24013db1fabb9fa835b7071a2 SHA256: e446f32da68d16993ca56ae891320604e947fcce097f880382f6aed2dbe9dbfd SHA512: a79104743352b684e6568e81f880f099c608bbf9bb9e34e6c87ab022d2539164c73cde4d97e712f71facec1fa7c67a82c3e8b36dd5954e2494d953c4975a0b4e Homepage: https://cran.r-project.org/package=histogram Description: CRAN Package 'histogram' (Construction of Regular and Irregular Histograms with DifferentOptions for Automatic Choice of Bins) Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010). Package: r-cran-histogramtools Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ash, r-cran-hmisc Suggests: r-cran-emdist, r-cran-gdata, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-histogramtools_0.4.1-1.ca2604.1_all.deb Size: 444172 MD5sum: 9ba28e6980a5afb7897442666aa5ca50 SHA1: 5c55426bb27fbe2755acbc0be9c1419c1c498137 SHA256: 9b600f3a2e4e27e8b9b44ad00dbba86fc9c087094e88a1cacee82b8036372716 SHA512: 554bd3234bb8fc2d5bf7890cc8b9a5b9cd4a15f46f67ead7e6d0a96ad53e6d9656ace4f44247bc14602d219a1046a788c986505980f68f2afd36446af8442296 Homepage: https://cran.r-project.org/package=HistogramTools Description: CRAN Package 'HistogramTools' (Utility Functions for R Histograms) Provides a number of utility functions useful for manipulating large histograms. This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning. It also provides a protocol buffer representation of R's native histogram class to allow histograms over large data sets to be computed and combined in distributed analytical pipelines. Implements bin-by-bin histogram distance measures described in Rubner, Tomasi and Guibas (2000) , Swain and Ballard (1991) , and Puzicha, Hofmann and Buhmann (1997) , and average shifted histograms as in Scott (2015, ISBN:9781118575536). Package: r-cran-historicalborrow Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-posterior, r-cran-rjags, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-historicalborrow_1.1.1-1.ca2604.1_all.deb Size: 215788 MD5sum: ba9bdc6a28805427ccedcd30ad3ca64e SHA1: 26c6391ef7f1ee20e63ee5c686a1975815a442b9 SHA256: 5952ef040808cad9cead466fdd602492ba0c5b1563aa7c4c3b89dfae7f9bd000 SHA512: 6ffd96bdcb0fe0dd64bbce9cf0913030a3e97d0702aad6b3ebe2eaa11c006c80b46df49cc91c0bf45692266a9c9f5414fad008cd9cac1ea4bba2bcc5cc6a1e91 Homepage: https://cran.r-project.org/package=historicalborrow Description: CRAN Package 'historicalborrow' (Non-Longitudinal Bayesian Historical Borrowing Models) Historical borrowing in clinical trials can improve precision and operating characteristics. This package supports a hierarchical model and a mixture model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through benchmark models (independent and pooled). Some of the methods are discussed by Viele et al. (2013) . Package: r-cran-historydata Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-historydata_0.1-1.ca2604.1_all.deb Size: 346092 MD5sum: a26858c712d0383fd52dabd07a044988 SHA1: 384ee543eb057343ac379ee35dfb270b1d4aac63 SHA256: 86fbd97db24d0599903108dd65a8aeb577d2e8be48300cc531ee52269cdfbfb4 SHA512: df620c8446e9e336ff7c23a09c94e92b4284d1258e7acb9c37150d40532935b63a018d8162b9d42a9606c7f46e424524f92dee71adc456b74f7b9917d9dca1f0 Homepage: https://cran.r-project.org/package=historydata Description: CRAN Package 'historydata' (Data Sets for Historians) These sample data sets are intended for historians learning R. They include population, institutional, religious, military, and prosopographical data suitable for mapping, quantitative analysis, and network analysis. Package: r-cran-histoslider Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-htmltools, r-cran-reactr, r-cran-shiny Suggests: r-cran-testthat, r-cran-shinytest2, r-cran-bslib Filename: pool/dists/resolute/main/r-cran-histoslider_0.1.1-1.ca2604.1_all.deb Size: 146494 MD5sum: c9713d08a5fa1a9aaa9f23fbb7fe15c5 SHA1: e37626c4bcaab9f71d9890e5864f22a11def16ed SHA256: fbeeea41b469c2fef3d3018ffe5d2496b4cafc5ed57edf24d3a78ddd1d548225 SHA512: 4e79c352edbfd0d2a469b39525960d998b337f8779b8350684103f694a33688965c373a4cdc9bd4fc2ab5e738cfc6c7021304817df2f691caa1f3fecffe19236 Homepage: https://cran.r-project.org/package=histoslider Description: CRAN Package 'histoslider' (A Histogram Slider Input for 'Shiny') A histogram slider input binding for use in 'Shiny'. Currently supports creating histograms from numeric, date, and 'date-time' vectors. Package: r-cran-hivdata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hivdata_0.1.0-1.ca2604.1_all.deb Size: 24838 MD5sum: 8cc924ba254707cd9dfc8430eb7b8ddc SHA1: f7453beb41cc4fede840efe9339550e10225e11c SHA256: eae72e70615b877794420ab2d8719cd485dcb43a69a131af18d8597e56b41723 SHA512: 82fd57a7546b762ac900b84872aa4f50254fee57693c8092bf71af796407a90a6b8b8ca904dd77cdbb2b875d15869bf04aac4549e9a4dceae052f9712cb7cea7 Homepage: https://cran.r-project.org/package=hivdata Description: CRAN Package 'hivdata' (Six-Year Chronological Data of HIV and ART Cases in Pakistan) We provide the monthly number of HIV and antiretroviral therapy (ART) cases of male, female, children and transgender as well as for the whole of Pakistan reported at various treatment centers in Pakistan from January 2016 to December 2021. Related works include: a) Imran, M., Nasir, J. A., & Riaz, S. (2018). Regional pattern of HIV cases in Pakistan. Journal of Postgraduate Medical Institute, 32(1), 9-13. . Package: r-cran-hive Architecture: all Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-xml Filename: pool/dists/resolute/main/r-cran-hive_0.2-2-1.ca2604.1_all.deb Size: 94760 MD5sum: 07c5cf2910bc96452602e1db18f8c44c SHA1: cb8cf0aaa691cc86bd91d1c0e42545bf4b97a3de SHA256: 4ff1f5de30d103861e5338146e8e51f9fc05f12a9d1e0a90c6fc3530932966c6 SHA512: 2f45be1abb976d8d66d22177b0b03c45128c6e05070002ec813e498197dc4f8a424640fa87e8cc203d7c9ce50324d9d9b94e9f0ad5ef2a4458bd7738a7896344 Homepage: https://cran.r-project.org/package=hive Description: CRAN Package 'hive' (Hadoop InteractiVE) Hadoop InteractiVE facilitates distributed computing via the MapReduce paradigm through R and Hadoop. An easy to use interface to Hadoop, the Hadoop Distributed File System (HDFS), and Hadoop Streaming is provided. Package: r-cran-hiver Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-jpeg, r-cran-png, r-cran-rcolorbrewer, r-cran-rgl, r-cran-xtable Suggests: r-cran-bipartite Filename: pool/dists/resolute/main/r-cran-hiver_0.4.0-1.ca2604.1_all.deb Size: 676784 MD5sum: 4d7c52e434c5ddb5c41e3a29599c7bbe SHA1: a5b8024ded2f2e23d037f3d38524551c96d25dfe SHA256: 118d8722b8f8808f1c7c9785a962a3ec5bec6d6c16e887e5de053aecae8aabe8 SHA512: 885aac42bcf99560d6e33837390a8b4d1d47b06f33806306448593616d789039d2f775ee1b8d6bae544837e4349ce6e6c979e75d9709a5c7fa36a1a01f5156cb Homepage: https://cran.r-project.org/package=HiveR Description: CRAN Package 'HiveR' (2D and 3D Hive Plots for R) Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics. Package: r-cran-hiviz Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-readxl, r-cran-haven, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-plotly, r-cran-tidyr, r-cran-wordcloud, r-cran-ggrepel, r-cran-paletteer, r-cran-shinywidgets Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hiviz_0.1.2-1.ca2604.1_all.deb Size: 130256 MD5sum: 4747c60d18d3e317cee5588b9cd5c53c SHA1: 9146fedc5bd6c85fce5ca8c0219799c735f0a424 SHA256: 98e0299244fa6dd7f4e0305124b5092199ac5f2d245f022e6280ae5a5576f99e SHA512: 6f1ce9edb2a834520b118a77ebb330433e89d1cc4b0c80ed1de58fc9f77810779674d39b5f155151a394fb76abd724a13e8446582aa4e47cb00a5e14f31c244e Homepage: https://cran.r-project.org/package=HIViz Description: CRAN Package 'HIViz' (Interactive Dashboard for 'HIV' Data Visualization) An interactive 'Shiny' dashboard for visualizing and exploring key metrics related to HIV/AIDS, including prevalence, incidence, mortality, and treatment coverage. The dashboard is designed to work with a dataset containing specific columns with standardized names. These columns must be present in the input data for the app to function properly: year: Numeric year of the data (e.g. 2010, 2021); sex: Gender classification (e.g. Male, Female); age_group: Age bracket (e.g. 15–24, 25–34); hiv_prevalence: Estimated HIV prevalence percentage; hiv_incidence: Number of new HIV cases per year; aids_deaths: Total AIDS-related deaths; plhiv: Estimated number of people living with HIV; art_coverage: Percentage receiving antiretroviral therapy (ART); testing_coverage: HIV testing services coverage; causes: Description of likely HIV transmission cause (e.g. unprotected sex, drug use). The dataset structure must strictly follow this column naming convention for the dashboard to render correctly. Package: r-cran-hjam Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-dplyr, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hjam_1.0.0-1.ca2604.1_all.deb Size: 714954 MD5sum: 0adb3c61d2e5c63855af80ce9a27c0c4 SHA1: f9ac2c8c9872be9f36d740514084491fca0c7c91 SHA256: f802c7777027086d5e02d34013a240800acec15f1d940b5e97806bd8e6c9d638 SHA512: 595dc2dfb3cace868937e8e4db416c3ca6bfd591e463c9a3a40ef45fdb041e86c2a812618aafa7125f0db83b4470c6608ded932ac21f8a5b402ebb3beafa0015 Homepage: https://cran.r-project.org/package=hJAM Description: CRAN Package 'hJAM' (Hierarchical Joint Analysis of Marginal Summary Statistics) Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." . Package: r-cran-hk80 Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hk80_0.0.2-1.ca2604.1_all.deb Size: 80394 MD5sum: e74092442739da16efff1eef6bbfe84c SHA1: dc8bedb7f18c89a373489cdc43e435959628937b SHA256: 0e793d6484c6047fd58889ac4bd0533e699e343a7e8afec7786a397552d4ea49 SHA512: 0405c7cf4ffa76400ca61d7d996df0422f869653d9b2dd277cd3ab01034f8dc8fecb7236fee38234bf7bb014a2067b193db85cec1632f4a6639fb9ed2f27178b Homepage: https://cran.r-project.org/package=HK80 Description: CRAN Package 'HK80' (Conversion Tools for HK80 Geographical Coordinate System) This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995). Package: r-cran-hkdatasets Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fst Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-here Filename: pool/dists/resolute/main/r-cran-hkdatasets_1.0.0-1.ca2604.1_all.deb Size: 358984 MD5sum: 7841efadd964de2c55fb59e4f64a3400 SHA1: 29cf5ea04ac524a079c07bbe1ee3db950c56de61 SHA256: 3514879690ccd7be521512ef9931655dab3a6e5c1599e2212159ff6c0b8a57f0 SHA512: a3b7f6750caa93aca46ea2ed169178ff4f8f228ddec965a0ea818261ee4e1f406347dc61ab3e6b7248135567bb7d2433667de5d808021d05a9e163c954933550 Homepage: https://cran.r-project.org/package=hkdatasets Description: CRAN Package 'hkdatasets' (Datasets Related to Hong Kong) Datasets related to Hong Kong, including information on the 2019 elected District Councillors ( and ) and traffic collision data from the Hong Kong Department of Transport (). All of the data in this package is available in the public domain. Package: r-cran-hkrbook Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1672 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-shinydashboardplus, r-cran-dt, r-cran-highlight, r-cran-formatr, r-cran-scatterplot3d Suggests: r-cran-cluster, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hkrbook_0.1.3-1.ca2604.1_all.deb Size: 836188 MD5sum: 25ceb0ae0808aa1554a66f236d7de765 SHA1: 1ea93f42294251d73b301608a430292fba202e75 SHA256: a7e90b5a78bf6720687461c92d73ef31f4b44782cbd88b77c3bf2d334c03d622 SHA512: 16ec055e5930100aa3a58ea0c504543d4b215442592015362e5847d3da0f467c006191eba5d33274e92db703f8842f1cb58e64a4bcdb4e2c5f9b0dad4097901d Homepage: https://cran.r-project.org/package=HKRbook Description: CRAN Package 'HKRbook' (Apps and Data for the Book "Introduction to Statistics") Functions, Shiny apps and data for the book "Introduction to Statistics" by Wolfgang Karl Härdle, Sigbert Klinke, and Bernd Rönz (2015) . Package: r-cran-hlar Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-tidyverse, r-cran-dplyr, r-cran-reshape2, r-cran-schoolmath, r-cran-tibble, r-cran-tidyselect, r-cran-stringr, r-cran-purrr, r-cran-tidyr, r-cran-readr, r-cran-janitor Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hlar_1.0.0-1.ca2604.1_all.deb Size: 190026 MD5sum: 526d58427173c1f74165cb48fc0df3e7 SHA1: ad9e6e3f4910e7f17e774daf0f92ad07cd84e5de SHA256: c4ce21c403782db634c05ca18ec1ae867624ecdda6c0170c8519ace4b3230aeb SHA512: 729ebe298f85611abfa0d43b028323284f9dcfaf230db9ebd0d3841b11904c791ac7fe1d29e007bce236844795689b0527a4fc69bfe36add3a7515b4e4bf416d Homepage: https://cran.r-project.org/package=hlaR Description: CRAN Package 'hlaR' (Tools for HLA Data) A streamlined tool for eplet analysis of donor and recipient HLA (human leukocyte antigen) mismatch. Messy, low-resolution HLA typing data is cleaned, and imputed to high-resolution using the NMDP (National Marrow Donor Program) haplotype reference database . High resolution data is analyzed for overall or single antigen eplet mismatch using a reference table (currently supporting 'HLAMatchMaker' versions 2 and 3). Data can enter or exit the workflow at different points depending on the user's aims and initial data quality. Package: r-cran-hlatools Architecture: all Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-dplyr, r-cran-fmsb, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-xfun Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hlatools_1.6.3-1.ca2604.1_all.deb Size: 590562 MD5sum: f4eb3847fabb7f69052a3a4fb0a83277 SHA1: df68aa8c445561c6972b669472e187972355fc65 SHA256: 93138543623decc866b97a8347bbd4d675987f4ca79c9fa57fb01d41030bc15f SHA512: 69ea071761d8a51cb562056a0cc688b40803f55a8d9b881f4dcc4c5491850420adf47ac6352b177ecfeef2e21fc0aa7b3fe4360fc1e39a14eced1826db318d93 Homepage: https://cran.r-project.org/package=HLAtools Description: CRAN Package 'HLAtools' (Toolkit for HLA Immunogenomics) A toolkit for the analysis and management of data for genes in the so-called "Human Leukocyte Antigen" (HLA) region. Functions extract reference data from the Anthony Nolan HLA Informatics Group/ImmunoGeneTics HLA 'GitHub' repository (ANHIG/IMGTHLA) , validate Genotype List (GL) Strings, convert between UNIFORMAT and GL String Code (GLSC) formats, translate HLA alleles and GLSCs across ImmunoPolymorphism Database (IPD) IMGT/HLA Database release versions, identify differences between pairs of alleles at a locus, generate customized, multi-position sequence alignments, trim and convert allele-names across nomenclature epochs, and extend existing data-analysis methods. 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Package: r-cran-hlmlab Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-scales Suggests: r-cran-shiny, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hlmlab_0.1.0-1.ca2604.1_all.deb Size: 48046 MD5sum: e208926f11989b38b9809cb47256a8ca SHA1: 4dd68434c50d72b3c5660ace32e0f93597a6efe5 SHA256: eac88185520db467f6c43d98232ea1325b8556bb0c7039e5f62c062835c07df2 SHA512: 058466af528bd509f402220fb1742c67209c8d02bbf6ef18176dcd9a53fa3c61d2137766022d09fdb32abc75594f3bce5917f2b0687f3c65abf3ff149eaaa054 Homepage: https://cran.r-project.org/package=hlmLab Description: CRAN Package 'hlmLab' (Hierarchical Linear Modeling with Visualization andDecomposition) Provides functions for visualization and decomposition in hierarchical linear models (HLM) for applications in education, psychology, and the social sciences. Includes variance decomposition for two-level and three-level data structures following Snijders and Bosker (2012, ISBN:9781849202015), intraclass correlation (ICC) estimation and design effect computation as described in Shrout and Fleiss (1979) , and contextual effect decomposition via the Mundlak (1978) specification distinguishing within- and between-cluster components. Supports visualization of random slopes and cross-level interactions following Hofmann and Gavin (1998) and Hamaker and Muthen (2020) . Multilevel models are estimated using 'lme4' (Bates et al., 2015 ). An optional 'Shiny' application enables interactive exploration of model components and parameter variation. The implementation follows the multilevel modeling framework of Raudenbush and Bryk (2002, ISBN:9780761919049). Package: r-cran-hlrhotrix Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hlrhotrix_0.1.0-1.ca2604.1_all.deb Size: 213112 MD5sum: f3d2b46442add177734e31926b79659f SHA1: 160a2ca08ae8019fbf67a6ba5764c5973456f0d2 SHA256: 41593d3bd868ff8ffcfe73b88002d07ba370baaa8268655b67f95a10dc332783 SHA512: f3018eaa87118d249e9d4b781dffd7b346e3aa67f9b640c56c865a349cab6f76cd001e7fecbba907a944d079c62ec3a3756c96aa503358ceeb3c6d0bc02290f5 Homepage: https://cran.r-project.org/package=hlrhotrix Description: CRAN Package 'hlrhotrix' (Algebraic Operations and Visualisation for Hl-Rhotrices) Provides constructors for hl-rhotrices of dimension 2, 4 and 6, together with computation of the determinant, adjoint, inverse and eigenvalues under the Robust Multiplication Method (RMM). A 'ggplot2'-based function visualises the rhomboidal layout and the decomposition into principal minors. Package: r-cran-hmc Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-irlba, r-cran-pma, r-cran-mass, r-cran-grpreg Filename: pool/dists/resolute/main/r-cran-hmc_1.2-1.ca2604.1_all.deb Size: 120714 MD5sum: d95014e6747cf4051316ceaa550baaa5 SHA1: 0ce728fb3547df72b49c744b8947da9d8808da56 SHA256: a515d096671e6400badd2ec05c415dff4ca5a782359beaf62f41b0572ad90017 SHA512: 7323bc3ebd9fa9def69e57e0cc739361c2a55ffa856247c0e81234a7075584a760dd53a2f8e514693822332651b69b8e1844a6c5635fc3ff4e0c85deb07dd79a Homepage: https://cran.r-project.org/package=HMC Description: CRAN Package 'HMC' (High-Dimensional Mean Comparison with Projection andCross-Fitting) Provides interpretable high-dimensional mean comparison methods (HMC). For example, users can apply these methods to assess the difference in gene expression between two treatment groups. It is not a gene-by-gene comparison. Instead, the methods focus on the interplay between features and identify those that are predictive of the group label. The tests are valid frequentist procedures and yield sparse estimates indicating which features contribute to the group differences. Package: r-cran-hmclearn Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1655 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-cran-lme4, r-cran-cardata, r-cran-mlbench, r-cran-ggplot2, r-cran-mlmrev, r-cran-testthat, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-hmclearn_0.0.5-1.ca2604.1_all.deb Size: 1067448 MD5sum: b0774d0c814d3b120a977961763c2b3a SHA1: bf3a24e89bee6774f029ce65ec6a1ae2a05fc9e1 SHA256: af6cd11548563be52911eeea7836e11837b580c88acc44e8bb9f9dc1c466083f SHA512: 00100fb5aae37b3650da1509900a5a783d3a6822ef563702ec137e8d8efbe67c3d88cd8efe0db8e6330e0b04b3c2d716ee05432a10574e039d51a5dbe3e34c24 Homepage: https://cran.r-project.org/package=hmclearn Description: CRAN Package 'hmclearn' (Fit Statistical Models Using Hamiltonian Monte Carlo) Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models. All of the code is written in R. Theoretical references are listed below:. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" , Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" , Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174. Package: r-cran-hmda Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-h2o, r-cran-shapley, r-cran-autoensemble, r-cran-h2otools, r-cran-splittools, r-cran-psych, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-hmda_0.3.0-1.ca2604.1_all.deb Size: 1179538 MD5sum: 244beafc4b0461ee028436b43ebf1e5d SHA1: a9e169213f7214ab5164d893593af225f6093409 SHA256: 3dea6a60d9301ec79c1eeb9ed3f26421d4ab1105c22cb11ac4793c285e863478 SHA512: 870d7124a855c57cfeaf2ad30be09c352e4144ca06eb3df07d761a061bb6163fa79cbd03a83d63bf39746951bf6cbe99c241369e1098ecd14cf8351012c3092f Homepage: https://cran.r-project.org/package=HMDA Description: CRAN Package 'HMDA' (Holistic Multimodel Domain Analysis for Exploratory MachineLearning) Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological conditions. Additionally, HMDA includes automated procedures for feature selection based on WMSHAP ratios and performs dimension reduction analyses to identify underlying structures among features. For more details see Haghish (2025) . 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These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (), The Japanese Mortality Database (), and the Canadian Human Mortality Database (). Arguments and data are standardized. Package: r-cran-hmer Architecture: all Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-ggplot2, r-cran-lhs, r-cran-mass, r-cran-r6, r-cran-viridis, r-cran-mvtnorm, r-cran-ggally, r-cran-rlang, r-cran-isoband, r-cran-cluster, r-cran-pdist, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-desolve, r-cran-testthat, r-cran-covr, r-cran-progressr Filename: pool/dists/resolute/main/r-cran-hmer_1.6.2-1.ca2604.1_all.deb Size: 3190824 MD5sum: 3aa0762a3372f8ff23b63e1e366f7a9e SHA1: d8e0a59278bf2e638b7e577b1a6884fd7d485757 SHA256: 0662e823b9784cf8f3c9a83fb2ba3542724fff1c32014944d8957edb446eaa6f SHA512: 7ec3120dea1c040cbd2a499a9393e69e0b50a6b0a5a8ba471dc5b10b2ef4056ace13fe748b6472b1891adec1d37e1f8c1fb807ad0701be4e8454939d985e30be Homepage: https://cran.r-project.org/package=hmer Description: CRAN Package 'hmer' (History Matching and Emulation Package) A set of objects and functions for Bayes Linear emulation and history matching. Core functionality includes automated training of emulators to data, diagnostic functions to ensure suitability, and a variety of proposal methods for generating 'waves' of points. For details on the mathematical background, there are many papers available on the topic (see references attached to function help files or the below references); for details of the functions in this package, consult the manual or help files. Iskauskas, A, et al. (2024) . Bower, R.G., Goldstein, M., and Vernon, I. (2010) . Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) . Package: r-cran-hmetad Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1169 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brms, r-cran-abind, r-cran-dplyr, r-cran-glue, r-cran-posterior, r-cran-rlang, r-cran-stringr, r-cran-tidybayes, r-cran-tidyr Suggests: r-cran-colorspace, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-hmetad_0.1.2-1.ca2604.1_all.deb Size: 627798 MD5sum: 328aafe18c12dbd1119a47554d04b509 SHA1: 5ad15b42c42ba72e80971b1e411f279741f73739 SHA256: a5314676f3a8edbaad6c8397190112ad5d09a850e8bd5d728a54aab58a6cc9cc SHA512: 272ddf6f2d0faabd6328a06a7956d9c433ada25fec62b0e2f12b35334e44e597a6f23da55d20922d6befa9b27ba6bb1f97203a6ba02ce78cd5626cbcd9711428 Homepage: https://cran.r-project.org/package=hmetad Description: CRAN Package 'hmetad' (Fit the Meta-D' Model of Confidence Ratings Using 'brms') Implementation of Bayesian regressions over the meta-d' model of psychological data from two alternative forced choice tasks with ordinal confidence ratings. For more information, see Maniscalco & Lau (2012) . The package is a front-end to the 'brms' package, which facilitates a wide range of regression designs, as well as tools for efficiently extracting posterior estimates, plotting, and significance testing. 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The goodness-of-fit test is based on a Cramer-von Mises statistic and uses Rosenblatt's transform and parametric bootstrap to estimate the p-value. The proposed methodologies are described in Nasri, Remillard and Thioub (2020) . 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Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014). . Package: r-cran-hmmr Architecture: all Version: 1.0-0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 470 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-depmixs4 Suggests: r-cran-boot Filename: pool/dists/resolute/main/r-cran-hmmr_1.0-0.1-1.ca2604.1_all.deb Size: 437436 MD5sum: d2a3c8ea38cb81a07d9e35cfcadd7622 SHA1: a86532d299111b52268a5d354170628683da4d54 SHA256: 45e6a06adea803435de84a5be603f033bf31a0d7adfd3e6db1ed890c2d403879 SHA512: e3525b039b2bb389b85dc85d999f66b12167d3d554b67f9884a6ff1cc9341c1a7f076f7aaf597ff1925eef743e149ff06bd3fe69c09a42c919afd9fae210b2c5 Homepage: https://cran.r-project.org/package=hmmr Description: CRAN Package 'hmmr' ("Mixture and Hidden Markov Models with R" Datasets and ExampleCode) Datasets and code examples that accompany our book Visser & Speekenbrink (2021), "Mixture and Hidden Markov Models with R", . 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This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) . 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Package: r-cran-hnmf Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nmf, r-cran-oro.nifti, r-cran-nnls, r-cran-rasterimage, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hnmf_1.0-1.ca2604.1_all.deb Size: 87950 MD5sum: 97625d9e494e563406bc50c10e6a9932 SHA1: 6b1e3e6809a4e7b7edc2bb4369ac4e97784f3207 SHA256: da894a52ddd98d248f584a542e4d41815bcf2a54409be7345f6ab37eb6ba1af7 SHA512: ca93037e8bad8f35efc030695136b551560915ae69d1cbe4b238950451699325a297cd8895294afc6762251f02e06463636cca4aeef4fe6301046c93fd797273 Homepage: https://cran.r-project.org/package=hNMF Description: CRAN Package 'hNMF' (Hierarchical Non-Negative Matrix Factorization) Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available. 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A few example datasets are included. Package: r-cran-hnpclassifier Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-e1071, r-cran-nnet, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-hnpclassifier_0.1.0-1.ca2604.1_all.deb Size: 77054 MD5sum: a7ec19915338a4958344289cf428baa4 SHA1: 16ccda9dd72370fcc5b2a7248f3d7f8cc6596bab SHA256: d828b793409d265b7ce69fce28d847424c6ec04b89632f82890d994e74bd87a3 SHA512: 0ddf33d19c899d27903dc5105752a2977a523504eca260debe7ee290f82ee3f905f2054d914d2a424fc775c92ec3c5f5a622659efbeff3898faf2baca7bcd655 Homepage: https://cran.r-project.org/package=HNPclassifier Description: CRAN Package 'HNPclassifier' (Hierarchical Neyman-Pearson Classification for Ordered Classes) The Hierarchical Neyman-Pearson (H-NP) classification framework extends the Neyman-Pearson classification paradigm to multi-class settings where classes have a natural priority ordering. This is particularly useful for classification in unbalanced dataset, for example, disease severity classification, where under-classification errors (misclassifying patients into less severe categories) are more consequential than other misclassifications. The package implements H-NP umbrella algorithms that controls under-classification errors under user specified control levels with high probability. It supports the creation of H-NP classifiers using scoring functions based on built-in classification methods (including logistic regression, support vector machines, and random forests), as well as user-trained scoring functions. For theoretical details, please refer to Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li & Xin Tong (2024) . 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Further, sets of miRNA target genes can be identified by using the targetscan.org API. Package: r-cran-hoardr Architecture: all Version: 0.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1883 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rappdirs, r-cran-digest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hoardr_0.5.5-1.ca2604.1_all.deb Size: 579828 MD5sum: f79e551186b074ae3e82f2d1f9875b15 SHA1: eff99df5793b2d155e2f99754fa711fe7e0ae403 SHA256: f45974c5d5099c6675b31ad535678f059e10e253eb8f142756f50e4177bb4814 SHA512: 719644884fd8aaad612e16bad13e73ab07b74443a26502f9788ba696c256d529b5e6ddcc08a3ffe2528fc627f363559008db0841045b2e9b5b40ef3d3a5749da Homepage: https://cran.r-project.org/package=hoardr Description: CRAN Package 'hoardr' (Manage Cached Files) Suite of tools for managing cached files, targeting use in other R packages. Uses 'rappdirs' for cross-platform paths. 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Package: r-cran-hoasso Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-hoasso_1.0.1-1.ca2604.1_all.deb Size: 26382 MD5sum: 208642a24871c6452edc4ced242f4105 SHA1: e467548d69678bde893d8e712b90fd4cfb29c50b SHA256: 4719419362ef822c7a09b23e26c08622e8f8b64c9b096613a27eadfd1e80c0d2 SHA512: 937e62e0c6959346fbad5aac9d852732108244479643646e0ace270c4c8c70fe8aedf1d5f2e1d1ff887d1d68ea11e43c8d146dbaaaf3fa2a6ac1f03a211a26c7 Homepage: https://cran.r-project.org/package=HOasso Description: CRAN Package 'HOasso' (Higher Order Assortativity for Complex Networks) Allows to evaluate Higher Order Assortativity of complex networks defined through objects of class 'igraph' from the package of the same name. The package returns a result also for directed and weighted graphs. References, Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2017) Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2021) Arcagni, A., Cerqueti, R., & Grassi, R. (2023) . Package: r-cran-hockeystick Architecture: all Version: 0.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1713 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-readr, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-scales, r-cran-rvest, r-cran-tibble, r-cran-treemapify, r-cran-rcolorbrewer, r-cran-jsonlite, r-cran-readxl Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spelling, r-cran-viridislite, r-cran-quarto, r-cran-svglite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hockeystick_0.8.7-1.ca2604.1_all.deb Size: 888634 MD5sum: 039ab0da3c4ae0daa9845bcdafb4ce11 SHA1: 71ca4539ba99cc4aa812e5a4e4aca985bc5f0da1 SHA256: 1b17c4841b4d8776a44db6bc5720b93a8ba3a860289200e9e388365f56bf3b35 SHA512: c1079f80ebc8f8ca3b9e210e74b52a7bf8d1d87fb9ba1c37e2e21379783bd72bc019c039b1ac9141aaa91dde9715ec5faebbb0fde99ebe4829df73aada72eeb0 Homepage: https://cran.r-project.org/package=hockeystick Description: CRAN Package 'hockeystick' (Download and Visualize Essential Climate Change Data) Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory , Global Carbon Project , NASA GISTEMP , National Snow and Sea Ice Data Center , CSIRO , NOAA Laboratory for Satellite Altimetry and HURDAT Atlantic Hurricane Database , Vostok Paleo carbon dioxide and temperature data: . Package: r-cran-hodgestools Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-readr, r-cran-ini, r-cran-qqman, r-cran-data.table, r-cran-recordlinkage Filename: pool/dists/resolute/main/r-cran-hodgestools_1.0.0-1.ca2604.1_all.deb Size: 296270 MD5sum: eb0f822c0b67015e5906b85c8683d1e8 SHA1: 10ada8aefe65a5b9196d6cf039d88c5cf71ded7f SHA256: d9a5686af1366f90e9df70ec74eb1174716f73f85a0a66f28028627a829dbf6f SHA512: f10bc485d65fa5e73a691d2bbeb372b20b4b7c5f5843ba4093ba8995d9210b2e9637701423197637128bd0a406ec5268cf2c78899b988e2f12ac4142b6b097af Homepage: https://cran.r-project.org/package=HodgesTools Description: CRAN Package 'HodgesTools' (Common Use Tools for Genomic Analysis) Built by Hodges lab members for current and future Hodges lab members. Other individuals are welcome to use as well. Provides useful functions that the lab uses everyday to analyze various genomic datasets. Critically, only general use functions are provided; functions specific to a given technique are reserved for a separate package. As the lab grows, we expect to continue adding functions to the package to build on previous lab members code. Package: r-cran-hoifcar Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brglm2, r-cran-bb, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-mass Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-hoifcar_1.1.1-1.ca2604.1_all.deb Size: 178418 MD5sum: bfa4aff440827a157e3678a08d587f7e SHA1: c59e949deba79390ee7813261f234f595fa4c578 SHA256: ab47936e5062c18152ddd8a79c0a9ccf630683d35ba39803aa83ef3792e0accb SHA512: f792cb6d637293dcec53eefa95275775ec3bdeb02f24d0ffcc05225fade4ca2059c5836bea9fbbef220aae05d2e34d207d8d73daa6ee519b398876f3421abb7f Homepage: https://cran.r-project.org/package=HOIFCar Description: CRAN Package 'HOIFCar' (Covariate Adjustment in RCT by Higher-Order Influence Functions) Estimates treatment effects using covariate adjustment methods in Randomized Clinical Trials (RCT) motivated by higher-order influence functions (HOIF). Provides point estimates, oracle bias, variance, and approximate variance for HOIF-adjusted estimators. For methodology details, see Zhao et al. (2024) and Gu et al. (2025) . Package: r-cran-holi Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-ggplot2, r-cran-likelihoodasy, r-cran-mass, r-cran-pool, r-cran-rpostgres, r-cran-shiny, r-cran-shinythemes, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-holi_0.1.1-1.ca2604.1_all.deb Size: 72570 MD5sum: 0875f9335b4097a7570b1ee494bfbeb5 SHA1: 19ca4241e3a4ca24c773af837eae5273636def88 SHA256: 2cc5771b9a2ae716dcf5a6fe7785de546bd3cd8e62616bbe0e4ba36be450b494 SHA512: 3fb83f630256de578140cd58b6b3cfa2444d0f2aab1ee30880ff3b9905c8ba54f217cc0d8529f104e509112dc49c11d51fb3b00518774cc54f483cd3804651b8 Homepage: https://cran.r-project.org/package=holi Description: CRAN Package 'holi' (Higher Order Likelihood Inference Web Applications) Higher order likelihood inference is a promising approach for analyzing small sample size data. The 'holi' package provides web applications for higher order likelihood inference. It currently supports linear, logistic, and Poisson generalized linear models through the rstar_glm() function, based on Pierce and Bellio (2017) and 'likelihoodAsy'. The package offers two main features: LA_rstar(), which launches an interactive 'shiny' application allowing users to fit models with rstar_glm() through their web browser, and sim_rstar_glm_pgsql(), which streamlines the process of launching a web-based 'shiny' simulation application that saves results to a user-created 'PostgreSQL' database. 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The 'holiglm' package simplifies estimating HGLMs using convex optimization. Additional information about the package can be found in the reference manual, the 'README' and the accompanying paper . Package: r-cran-holland Architecture: all Version: 0.1.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mplusautomation, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-holland_0.1.2-4-1.ca2604.1_all.deb Size: 211786 MD5sum: 119de89a9800a531e98e23eb56155aaa SHA1: d01125a4ae917366cf66586e6413e52cddaf8371 SHA256: 368e6b46a822b950a5aefead24f37bace915c1a471d6afbab7c546073b68ce25 SHA512: 57f418fc6295d32405425d9fd52384bafddff59569df1bd58b0084adfe39e167c15635e2a01e9a06c61064ee4a7942401480e7e0c4eaa3665a3e1f35dc93749d Homepage: https://cran.r-project.org/package=holland Description: CRAN Package 'holland' (Statistics for Holland's Theory of Vocational Choice) Offers a convenient way to compute parameters in the framework of the theory of vocational choice introduced by J.L. Holland, (1997). A comprehensive summary to this theory of vocational choice is given in Holland, J.L. (1997). Making vocational choices. A theory of vocational personalities and work environments. Lutz, FL: Psychological Assessment. Package: r-cran-hollr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-pbapply, r-cran-reticulate Filename: pool/dists/resolute/main/r-cran-hollr_1.0.0-1.ca2604.1_all.deb Size: 30020 MD5sum: ebbc93287c5b16ee8832d828c0df3c4f SHA1: 7b3537cdc99d7f68fda19583c676eff6ced6e2e9 SHA256: 90b8531d4a2576e9814521b41f983a35fd42850c094b861179c24674dbc14740 SHA512: a393276d7768fd49afcd57878349f0ee97f20b12c6a342c02b528c0522654f1e7912ce3859336cf0311c7fd2536d757446e6125de03c733f05c8e2b964a320fc Homepage: https://cran.r-project.org/package=hollr Description: CRAN Package 'hollr' (Chat Completion and Text Annotation with Local and OpenAI Models) Enables chat completion and text annotation with local and 'OpenAI' language models, supporting batch processing, multiple annotators, and consistent output formats. Package: r-cran-holobiont Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-bioc-phyloseq, r-cran-phytools, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-castor, r-cran-vegan, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-holobiont_0.1.3-1.ca2604.1_all.deb Size: 165294 MD5sum: a61768d36500c4ad4dbce771b92251e4 SHA1: 208526cf84919322f608cc1acd8910a1bac86ff8 SHA256: a63411a3129e0ac026391ae77d0e893c3046bf8b5be49761d3c116ec6a8f218d SHA512: 0252d65a5c8ad472d6a5e6f70f45c69d23b0885dde1419c95566349228a770fc46e05a04691b8020cee1c4fe96464de7eaa2964056919e2acb52e6ad80efdacd Homepage: https://cran.r-project.org/package=holobiont Description: CRAN Package 'holobiont' (Microbiome Analysis Tools) We provide functions for identifying the core community phylogeny in any microbiome, drawing phylogenetic Venn diagrams, calculating the core Faith’s PD for a set of communities, and calculating the core UniFrac distance between two sets of communities. All functions rely on construction of a core community phylogeny, which is a phylogeny where branches are defined based on their presence in multiple samples from a single type of habitat. Our package provides two options for constructing the core community phylogeny, a tip-based approach, where the core community phylogeny is identified based on incidence of leaf nodes and a branch-based approach, where the core community phylogeny is identified based on incidence of individual branches. We suggest use of the microViz package. Package: r-cran-holodeck Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-assertthat Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-mice, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-holodeck_0.2.2-1.ca2604.1_all.deb Size: 257804 MD5sum: a7684e419265678b20b5f80609c4f7f4 SHA1: 0caed47c0a264d9247b832e944da33bad047d385 SHA256: 99bdaef7918a6a5a889db4848357c1ab4f374a56d89b063999ba168bc87f7c9b SHA512: 462f7bbf40304af051bfe99029bb73f551a7b4f5030db6b9c09604ea4a58ec0cec6ef048f9147b909cc2e074ef2ba567e4c7b340808894db9bbb64a01c86862c Homepage: https://cran.r-project.org/package=holodeck Description: CRAN Package 'holodeck' (A Tidy Interface for Simulating Multivariate Data) Provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets with groups of variables with different degrees of variance, covariance, and effect size. Package: r-cran-holomics Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bs4dash, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-igraph, r-cran-openxlsx, r-cran-readxl, r-cran-shiny, r-cran-shinyalert, r-cran-shinybusy, r-cran-shinyjs, r-cran-shinyvalidate, r-cran-shinywidgets, r-cran-stringr, r-cran-tippy, r-cran-visnetwork, r-bioc-mixomics, r-bioc-biocparallel Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-badger Filename: pool/dists/resolute/main/r-cran-holomics_1.2.1-1.ca2604.1_all.deb Size: 3198396 MD5sum: e154879232431104d37f39f177018723 SHA1: 588bb25a03d6370bddcdef4f80c56bfcbc55e6be SHA256: 8be618b06d03f4e8e90cb30ae62b27bfd4ea94ab722262c7bd8288b6e6574886 SHA512: bde4e8023176fa7ea2caf39ee1d0fff7b77be047679d0cc5ec94cb7244046de34da623d821f30508cbcb11492351d75a2fd13e7a647821b57e1952c2255c0a1d Homepage: https://cran.r-project.org/package=Holomics Description: CRAN Package 'Holomics' (A User-Friendly R 'shiny' Application for Multi-Omics DataIntegration and Analysis) A 'shiny' application, which allows you to perform single- and multi-omics analyses using your own omics datasets. After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended 'Holomics' workflow. Methods used in the package were implemented in the package 'mixomics' by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) and are described there in further detail. Package: r-cran-homals Architecture: all Version: 1.0-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-scatterplot3d Filename: pool/dists/resolute/main/r-cran-homals_1.0-11-1.ca2604.1_all.deb Size: 552750 MD5sum: ee7dff88ba9d26dd219b7a9b19613e96 SHA1: 340f2a61c79dbc2d59f7882fa984ccee34e4f0ba SHA256: fd6a19f1676fefe7bb5c277a0df6cb63714380dd8c0eb7b9035baaa95fd5e88b SHA512: f07099533dd5dd16053ff5b9847af1ca269992c06a687b3277a18a5db84faaf62ffd15883cca8813603afcab9aeb9b17aca1a402c10210c60067c073fb546e9d Homepage: https://cran.r-project.org/package=homals Description: CRAN Package 'homals' (Gifi Methods for Optimal Scaling) Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis. Package: r-cran-homeric Architecture: all Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-homeric_0.1-3-1.ca2604.1_all.deb Size: 17446 MD5sum: 8b8a7463f3059963d45454f741491c3f SHA1: 7519fd0ffe1ab4ba3eab059352fe61cd828aa0f8 SHA256: 5d1d7c5903fa6f72c1a2f9d30003e3c460f471197d5949eaf16288a2d76c9b32 SHA512: cfeec7370d39daf913f20909cc064f7538fe24fae2d43a70c09732dacddf86a736bb2f514edb698ae7446070fba8382ab7ac9269d2bb5aecbf3131b4cfedfc3e Homepage: https://cran.r-project.org/package=Homeric Description: CRAN Package 'Homeric' (Doughnut Plots) A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts. Package: r-cran-homnormal Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-huxtable Filename: pool/dists/resolute/main/r-cran-homnormal_0.1-1.ca2604.1_all.deb Size: 72508 MD5sum: c6e36ea512c080fb948fa608e9c93847 SHA1: 7da7e3e282baed87650c264e9ec989886eb919c3 SHA256: 79b0db48a72b297df930ce788244228dff2d1ba2f7589494020acfa5c5291829 SHA512: 76b861089c62e70f2a09ec9a0f34c63dc4a285cecdfd86ba7c0dfcfb837db49051de2786715e5f2d83805d76195729ea9e65b4ddf4fccc66fe2e015b8d691d67 Homepage: https://cran.r-project.org/package=homnormal Description: CRAN Package 'homnormal' (Tests of Homogeneity of Variances) Most common exact, asymptotic and resample based tests are provided for testing the homogeneity of variances of k normal distributions under normality. These tests are Barlett, Bhandary & Dai, Brown & Forsythe, Chang et al., Gokpinar & Gokpinar, Levene, Liu and Xu, Gokpinar. Also, a data generation function from multiple normal distribution is provided using any multiple normal parameters. Bartlett, M. S. (1937) Bhandary, M., & Dai, H. (2008) Brown, M. B., & Forsythe, A. B. (1974). Chang, C. H., Pal, N., & Lin, J. J. (2017) Gokpinar E. & Gokpinar F. (2017) Liu, X., & Xu, X. (2010) Levene, H. (1960) Gökpınar, E. (2020) . Package: r-cran-homologene Architecture: all Version: 1.4.68.19.3.27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4084 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-r.utils Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-homologene_1.4.68.19.3.27-1.ca2604.1_all.deb Size: 3751386 MD5sum: d44c3965758b6a682f4e5effbac8afe2 SHA1: e1a938d24a8cd3a83ef69aba91333eadb4d881d3 SHA256: 3218618adfa7530a1d14f2c8d9dbcea20d06cf6094537f886455bb98e2441efb SHA512: b7819e5fec32171b74a7f92ddf343700b9d31ff6f6ea74fb6fe56787a077946906dce5e4269ecf6e80f9fcd3dfde73af6d3a4f38ed01d2aa7ea68467585153a7 Homepage: https://cran.r-project.org/package=homologene Description: CRAN Package 'homologene' (Quick Access to Homologene and Gene Annotation Updates) A wrapper for the homologene database by the National Center for Biotechnology Information ('NCBI'). It allows searching for gene homologs across species. Data in this package can be found at . The package also includes an updated version of the homologene database where gene identifiers and symbols are replaced with their latest (at the time of submission) version and functions to fetch latest annotation data to keep updated. Package: r-cran-homomorpher Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-gmp, r-cran-sodium Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-dplyr, r-cran-magrittr, r-cran-digest Filename: pool/dists/resolute/main/r-cran-homomorpher_0.3-1.ca2604.1_all.deb Size: 887770 MD5sum: 6ccdf6e6beb8ef3bc54501a25929e19a SHA1: 2faf4a0e0929014bc87950a4c22f43440b3f08df SHA256: 72f76ac33a849a6fc109c1c92d577a7d1c45add01ad595ab09ee9624c074fef0 SHA512: 86c4db67b409fda1f91badac88138bcdd42c98392d9f63f9d53f30518d550d4a7e8003209d4faad5af002b13534844caa5d769ec1bd1e39241d515851c4d8dd4 Homepage: https://cran.r-project.org/package=homomorpheR Description: CRAN Package 'homomorpheR' (Homomorphic Computations in R) Homomorphic computations in R for privacy-preserving applications. Currently only the Paillier Scheme is implemented. Package: r-cran-homomorphicencryption Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom, r-cran-hetools Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-homomorphicencryption_0.9.0-1.ca2604.1_all.deb Size: 68636 MD5sum: 49a06edd771af089905c8b9f7c02f342 SHA1: 144e529f305ba5d3878ec1c7f5b0d370612c63d8 SHA256: 22e2a624a7ef6019b4028601f433c2dcced1b357dc200d658da4d3a63e0ae250 SHA512: 64b0c0e5abc6b2534dc2691750daf49d8c36c35903ee66fec0f96a495b4524862331984d9e0eab37edb2767c0b9f218a9fdf33a424fdaffbcec7feeb0317ea11 Homepage: https://cran.r-project.org/package=HomomorphicEncryption Description: CRAN Package 'HomomorphicEncryption' (BFV, BGV, CKKS Schema for Fully Homomorphic Encryption) Implements the Brakerski-Fan-Vercauteren (BFV, 2012) , Brakerski-Gentry-Vaikuntanathan (BGV, 2014) , and Cheon-Kim-Kim-Song (CKKS, 2016) schema for Fully Homomorphic Encryption. The included vignettes demonstrate the encryption procedures. Package: r-cran-honestdid Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-foreach, r-cran-matrixstats, r-cran-cvxr, r-cran-ecosolver, r-cran-latex2exp, r-cran-lpsolveapi, r-cran-matrix, r-cran-pracma, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-rglpk, r-cran-mvtnorm, r-cran-truncatednormal Suggests: r-cran-knitr, r-cran-testthat, r-cran-haven, r-cran-lfe, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-honestdid_0.2.8-1.ca2604.1_all.deb Size: 809666 MD5sum: 309cd0dc4aa76599dfe1bd4bfe2bc5c4 SHA1: e9f0596f8d01a9d23aa516f612b2b386da2968f8 SHA256: 5054d3a592588e562ab752cfddc4504698635b3aa69dd98e94f8f8369799ae20 SHA512: b6fec43352e045af2c12e5f3bcbac1998bb6ae8513c452e14c9dd140e9a768e149139c6d282d01ae4ba0ae35fa3dbcccac9a4ca8328886a160ab49b7840bb438 Homepage: https://cran.r-project.org/package=HonestDiD Description: CRAN Package 'HonestDiD' (Robust Inference in Difference-in-Differences and Event StudyDesigns) Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) , "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets. Package: r-cran-hoopr Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2737 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-progressr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-tidyr Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-xml2, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-hoopr_3.0.0-1.ca2604.1_all.deb Size: 2571150 MD5sum: 04078439fa65f14f173f47035fcdad90 SHA1: ad8a524dacabc550761b582bfab5c7e49e685507 SHA256: 4871e67df28726cad51b719a515edf494c284500db1b10edc36a910920ebc21a SHA512: 3aae88a195aadf9d598f2d36def8a781362845b88d8ee134ed2d4c7a2f976a40b5121935b1d3772c1b079f0bafd632fd69374a7481b26d5faf2ef5aa1f03d2e9 Homepage: https://cran.r-project.org/package=hoopR Description: CRAN Package 'hoopR' (Access Men's Basketball Play by Play Data) A utility to quickly obtain clean and tidy men's basketball play by play data. Provides functions to access live play by play and box score data from ESPN with shot locations when available. It is also a full NBA Stats API wrapper. It is also a scraping and aggregating interface for Ken Pomeroy's men's college basketball statistics website. It provides users with an active subscription the capability to scrape the website tables and analyze the data for themselves. Package: r-cran-hopbyhop Architecture: all Version: 3.41-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pastecs, r-cran-ggplot2 Suggests: r-cran-endtoend, r-cran-opportunistic Filename: pool/dists/resolute/main/r-cran-hopbyhop_3.41-1.ca2604.1_all.deb Size: 40806 MD5sum: 23a3d09cbf019a90dd4ad3e4495a3b70 SHA1: 055aa6033a79145929af39d886980de11b463adb SHA256: 276eeb22960af630e68c1aa3579e7b042bda4f1d9c059cc04ff04ea5d2f56b29 SHA512: 02e955c3314646ba3ecb5a8e0481b39e8f312e3be71f493f03444ab966a21727d068cb72b33d52cb75017957c1b628d67a1b9eccc10c5415984e5f11c5657166 Homepage: https://cran.r-project.org/package=hopbyhop Description: CRAN Package 'hopbyhop' (Transmissions and Receptions in a Hop by Hop Network) Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities. Package: r-cran-hopkins Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-donut, r-cran-pdist, r-cran-rann Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spatstat.data, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hopkins_1.1-1.ca2604.1_all.deb Size: 79664 MD5sum: 6fde515ed8d73a5cef24b7f3ec5c6c43 SHA1: 727702aca9b46d9ca56902d0818c03357111d24b SHA256: 68df91798c8cedba452be74b5e73b41f1a1c6d34e6e9ff729c7c5b859e6e837a SHA512: 7b0b81b2e00db380e6889ef40528d63a6e2a249b9c4347758165ff8c5c72b32adcc77caa7d384812eeedfe4731a4f0ca331039a605b78979f76608205e2827a7 Homepage: https://cran.r-project.org/package=hopkins Description: CRAN Package 'hopkins' (Calculate Hopkins Statistic for Clustering) Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) . Package: r-cran-horm Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-orthopolynom, r-cran-quantmod, r-cran-rsm Filename: pool/dists/resolute/main/r-cran-horm_0.1.4-1.ca2604.1_all.deb Size: 164424 MD5sum: 0d3d59543cccc4f9ae674c8ec7027de1 SHA1: 17423bd32a0ad579a1f0bfc323776593453c441b SHA256: 68d4b668da8aaba53b0e68b4be5e60a141391cf02ce340f04fd4340d125ecc8f SHA512: 2a5067abb70668211a3d76c97fc5c54e8bb270c242a99ee6b5f8ed7cb07aee28d834d15f4a0ab92a390e942b47822f64d5870cd62c1437f5eeb45c82819b9b69 Homepage: https://cran.r-project.org/package=HoRM Description: CRAN Package 'HoRM' (Supplemental Functions and Datasets for "Handbook of RegressionMethods") Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares. Package: r-cran-hornpa Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hornpa_1.1.1-1.ca2604.1_all.deb Size: 15032 MD5sum: d8d579cfcfa3d3cfc8074d905e0c733f SHA1: f94311999a99d80245abe7e210cb1f6d2d6c643e SHA256: e34f1273425caef7cd8da6b1367c43933e07a72c4d21917b4183957320477365 SHA512: 36cabc937d4cd6ab04aade2b7788f3a47d58d014b4f3f672329b67c39263cc10b5fe0ae81c27f5f699aa6890e0bc5106b9a23d690a41c495debe955311740fa9 Homepage: https://cran.r-project.org/package=hornpa Description: CRAN Package 'hornpa' (Horn's (1965) Test to Determine the Number of Components/Factors) A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis ). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185. Package: r-cran-horsekicks Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-horsekicks_1.0.2-1.ca2604.1_all.deb Size: 49510 MD5sum: bf39237abc6ea7c36c1bb345801ecb11 SHA1: 2e8a64c33c8ccb9c08c1529781d8ebbed628ef0a SHA256: cc0a53a57414d51918292d2a0096f410019784ce25fbee9e7dc731711a1ccf46 SHA512: 92f12e0c23283b98ba635020bdf9ba6e9f7d73f77c637cbf0b424344bc02203c7dea8c00bd1c19ede8697143acaf1b5e8e8ee868e59a26d202018ef87fd5393d Homepage: https://cran.r-project.org/package=Horsekicks Description: CRAN Package 'Horsekicks' (Provide Extensions to the Prussian Army Death by Horsekick Data) We provide extensions to the classical dataset "Example 4: Death by the kick of a horse in the Prussian Army" first used by Ladislaus von Bortkeiwicz in his treatise on the Poisson distribution "Das Gesetz der kleinen Zahlen", . As well as an extended time series for the horse-kick death data, we also provide, in parallel, deaths by falling from a horse and by drowning. Package: r-cran-horseshoenlm Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-msm Suggests: r-cran-boot, r-cran-pgdraw, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-horseshoenlm_0.0.6-1.ca2604.1_all.deb Size: 71394 MD5sum: 52140d828430204a726af866aae098b0 SHA1: 4fe5c9865d05e5b5fb2fbd9cd600f0221e15c5cb SHA256: 59ed08465297deb48140a59a23df28aa6c64dae6b2ae004be9e4443e55d9ee30 SHA512: a592c8e7ccf651161d5a2c37818b26f95db06a29ea2308a1b9b0d1934a2778b09fb15b2043a481ba162e7d72bb951170d9476a6201168b9870b21ead65eb171c Homepage: https://cran.r-project.org/package=horseshoenlm Description: CRAN Package 'horseshoenlm' (Nonlinear Regression using Horseshoe Prior) Provides the posterior estimates of the regression coefficients when horseshoe prior is specified. The regression models considered here are logistic model for binary response and log normal accelerated failure time model for right censored survival response. The linear model analysis is also available for completeness. All models provide deviance information criterion and widely applicable information criterion. See Maity et. al. (2019) Maity et. al. (2020). 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Package: r-cran-hotspot Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hotspot_1.0-1.ca2604.1_all.deb Size: 87732 MD5sum: d743b22eb3a5bf54c57826657608e625 SHA1: 522ee6dd5e3d74ab01ad99624b52827d4fab9e29 SHA256: d31d4a4a9157a25bca7e21f55524d07bd62d9373d5d05cdd58fc8ad65d164e66 SHA512: 5d596bf7be2d4009dcb389dfbd1740d21820f31cc370b0cf08495fb6854d08bc70b536c6563e728f841d50e6cc24f3f5c6b1a8bb8a6b83514aa34cc6f06cfce7 Homepage: https://cran.r-project.org/package=hotspot Description: CRAN Package 'hotspot' (Software Hotspot Analysis) Contains data for software hotspot analysis, along with a function performing the analysis itself. 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Grouping of markers is based on the correlation matrix and a representative marker is suggested for each group. Employing the correlation matrix, optimal sample size can be derived for association studies based on a SNP-BLUP approach. The implementation relies on paternal half-sib families and biallelic markers. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. Wittenburg, Bonk, Doschoris, Reyer (2020) "Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations" . Carlson, Eberle, Rieder, Yi, Kruglyak, Nickerson (2004) "Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium" . 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Package: r-cran-htgm2d Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-gplots, r-cran-jaccard, r-cran-vprint, r-cran-randomgodb, r-cran-hgnchelper Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-htgm2d_1.1.1-1.ca2604.1_all.deb Size: 943626 MD5sum: bae541b691c522a6d7b60c095ee55563 SHA1: b97fdd49c1a6729e297a2809d2e190401c7e1264 SHA256: e58d0aba34ec622159eadd6cf4e82adcf81a8eddb673be548d05e23b76a2df7d SHA512: ab159c0515e4c2af012ce026052dab1ea85d6742af5ade54f299836ea26d1cef6243d359fabb9174f6a97e7218f38036d02be1ee431dc94589e10fb2fdd1a2aa Homepage: https://cran.r-project.org/package=HTGM2D Description: CRAN Package 'HTGM2D' (Two Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I developed a two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'). I generate a 2D heat map whose axes are any two of BP, MF, or CC, and the value within a picture element of the heat map reflects the Jaccard metric p-value for the number of genes in common for the corresponding pair. Package: r-cran-htgm3d Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-htgm2d, r-cran-r2html, r-cran-rgl, r-cran-vprint, r-cran-randomgodb, r-cran-stringr, r-cran-salesforcer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-htgm3d_1.0.3-1.ca2604.1_all.deb Size: 1316892 MD5sum: 3e6a9a4454592728d1769be317a837b1 SHA1: de700cb19d46a380f167db1c8d60575190749cbe SHA256: cc96763f941ca8f9012e3caa27b698b31cf9da39d29b610c95aef63a42c7f8ea SHA512: 43747f21c4e4aec59d2889596657508237935171fd36a0eabe0be5abc9766d295f5e47179f9176dab54dc4885bcde1418ab2304d12fef9b23c8d37aed71fa607 Homepage: https://cran.r-project.org/package=HTGM3D Description: CRAN Package 'HTGM3D' (Three Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. To capture the benefit of all three ontologies, I developed 'HTGM3D', a three-dimensional version of 'GoMiner'. Package: r-cran-htgm4d Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-htgm2d, r-cran-gplots, r-cran-vprint, r-cran-randomgodb, r-cran-hgnchelper, r-cran-png, r-cran-magick, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-htgm4d_1.0-1.ca2604.1_all.deb Size: 2151288 MD5sum: b6aaafed0072c9fd2cd8cc7635b0cfeb SHA1: 8c53405e40d12c25cf024aff0ade4196b610e2c8 SHA256: f5c94097e48d7280a8cadeadea5210cdaf25cc8f5d6de1325318749bb3322f5d SHA512: bd915564674ca4a2fbc4c0a62fcd2f21155f92a9928583ba76abe13dcfbbe044bb36c728ae330316507738a5c76b204488da79534ec8beb2f769818c68e98b0a Homepage: https://cran.r-project.org/package=HTGM4D Description: CRAN Package 'HTGM4D' (Four Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I now present an enhancement of the existing two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'), which is called 'HTGM4D'. The original 'HTGM2D' is augmented by adding two instances of the original 'GoMiner' genes versus categories heatmaps, aligned with the categories axes of the 'HTGM2D' heatmap. Package: r-cran-htgm Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-gplots, r-cran-vprint Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-htgm_1.2-1.ca2604.1_all.deb Size: 845170 MD5sum: 81f14d438222b9a6131044b5705ca973 SHA1: a5ab065f73821c7a70f68628cc93830ff0d76ab6 SHA256: 066e543368fb36465beeb4411439ab4943b94551161550c94fecad40dc11d056 SHA512: bc4df1e3690986b3f248b7b4e85ccf77fdbde7383a2c89da0cbf94a7189ef0f0ed309376d45dac66f6dcbcf3ef55a63b59b979e205993959ed64e34fa41d7719 Homepage: https://cran.r-project.org/package=HTGM Description: CRAN Package 'HTGM' (High Throughput 'GoMiner') Two papers published in the early 2000's (Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) and (Zeeberg, B.R., Qin, H., Narashimhan, S., et al. (2005) ) implement 'GoMiner' and 'High Throughput GoMiner' ('HTGM') to map lists of genes to the Gene Ontology (GO) . Until recently, these were hosted on a server at The National Cancer Institute (NCI). In order to continue providing these services to the bio-medical community, I have developed stand-alone versions. The current package 'HTGM' builds upon my recent package 'GoMiner'. The output of 'GoMiner' is a heatmap showing the relationship of a single list of genes and the significant categories into which they map. 'High Throughput GoMiner' ('HTGM') integrates the results of the individual 'GoMiner' analyses. The output of 'HTGM' is a heatmap showing the relationship of the significant categories derived from each gene list. The heatmap has only 2 axes, so the identity of the genes are unfortunately "integrated out of the equation." Because the graphic for the heatmap is implemented in Scalable Vector Graphics (SVG) technology, it is relatively easy to hyperlink each picture element to the relevant list of genes. By clicking on the desired picture element, the user can recover the "lost" genes. 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Package: r-cran-html5 Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-html5_1.0.2-1.ca2604.1_all.deb Size: 266176 MD5sum: a8312be2c1f3a1b81b199041a22ff495 SHA1: aad604343ec4e2ace13e2bfa6bbc2a27dc371376 SHA256: 8afe2857102dc37ac31ebc6423c08768f015b044d19edeb3c877c206ef3cc204 SHA512: 579235d645baf29e4e44890909c7829fd263b2039e44e43a9f51768f5be5f4278c48dae98b5d8452aea6db2954813c69ebbee3b1b38b539a563b5d249c27890a Homepage: https://cran.r-project.org/package=html5 Description: CRAN Package 'html5' (Creates Valid HTML5 Strings) Generates valid HTML tag strings for HTML5 elements documented by Mozilla. Attributes are passed as named lists, with names being the attribute name and values being the attribute value. Attribute values are automatically double-quoted. To declare a DOCTYPE, wrap html() with function doctype(). Mozilla's documentation for HTML5 is available here: . Elements marked as obsolete are not included. Package: r-cran-htmlreportr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1899 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mime, r-cran-ggplot2, r-cran-knitr, r-cran-xfun, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-htmlreportr_1.0.0-1.ca2604.1_all.deb Size: 1221308 MD5sum: ef7acc0f2240a5951a8540e4ac41459e SHA1: 6950209159d57937c65d3be45634d9aac0cd31c2 SHA256: 4bfbfc744e40c2c0270c35f0123627de282b9831732071af1a6a2859f86579f0 SHA512: 7a3ccd828d9e96f0893cccdbeafa0b5b9b42053ad27904b7d664199e4bf17c76e924b61cb13eaccb1792e8598fd3bdf5cefe1a3c4987cd21abb47395a46ef4c4 Homepage: https://cran.r-project.org/package=htmlreportR Description: CRAN Package 'htmlreportR' ('HTML' Reporting Made Simple(R)) Create compressed, interactive 'HTML' (Hypertext Markup Language) reports with embedded 'Python' code, custom 'JS' ('JavaScript') and 'CSS' (Cascading Style Sheets), and wrappers for 'CanvasXpress' plots, networks and more. Based on , its sister project. Package: r-cran-htmltable Architecture: all Version: 2.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-knitr, r-cran-magrittr, r-cran-checkmate, r-cran-htmlwidgets, r-cran-htmltools, r-cran-rstudioapi Suggests: r-cran-testthat, r-cran-xml, r-cran-xml2, r-cran-hmisc, r-cran-rmarkdown, r-cran-chron, r-cran-lubridate, r-cran-tibble, r-cran-purrr, r-cran-tidyselect, r-cran-glue, r-cran-rlang, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-htmltable_2.5.0-1.ca2604.1_all.deb Size: 396494 MD5sum: a4d22dbe32e3763126a05c25c282264e SHA1: 119ed341519497936a8f72820177882571298333 SHA256: 67af0d7accf9f1a1167cd3afadc169c5136fe0b45f51ce7bd282ee414814964a SHA512: fdfa4319e6954f24fbc078334b6b8b2e7df8b7726b82c1b10c149359884d8400498fc5a55ac2446ee2c18d75aec0be5548ab5474a2c73db80d9b2c3aa541332f Homepage: https://cran.r-project.org/package=htmlTable Description: CRAN Package 'htmlTable' (Advanced Tables for Markdown/HTML) Tables with state-of-the-art layout elements such as row spanners, column spanners, table spanners, zebra striping, and more. While allowing advanced layout, the underlying css-structure is simple in order to maximize compatibility with common word processors. The package also contains a few text formatting functions that help outputting text compatible with HTML/LaTeX. 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Package: r-cran-htmlwidgets Architecture: all Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2080 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-htmlwidgets_1.6.4-1.ca2604.1_all.deb Size: 416148 MD5sum: 18aed3aa09b87b3fde8ef681a10d8e85 SHA1: ebb91b9971e72317847ff09e5fe43f81cc1a8918 SHA256: fd4454d74babaf54c4074f63d6be607cc0ad909999cb86b542daf62073929802 SHA512: 3d09d2ea9b8463ef730fb429164b4af9717d81ada619b5bc034bd304be098ab3fced154572aa03b40fb3b842ce2c5eb8516f148cc5dfea13b458bf358fb37b70 Homepage: https://cran.r-project.org/package=htmlwidgets Description: CRAN Package 'htmlwidgets' (HTML Widgets for R) A framework for creating HTML widgets that render in various contexts including the R console, 'R Markdown' documents, and 'Shiny' web applications. Package: r-cran-htmxr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-plumber2 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-svglite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-htmxr_0.2.0-1.ca2604.1_all.deb Size: 650834 MD5sum: f75038e253d5a51b248c7692f9ab5b64 SHA1: 10abdba770a2ebeeab6a05385e0ceecd4906e21a SHA256: 02da1a2f08a030ce8eb12d1cb9a10808426ed8dc79e30e1a9799c3ff425aa13c SHA512: 94f500f7c33565a2d443eb31743b45c0869011f6d40d03b2534a9af7eba589e8e079c89431d24ea6c93c7aa139ac55760c166d3b037061366ba4c36af0809dfc Homepage: https://cran.r-project.org/package=htmxr Description: CRAN Package 'htmxr' (Build Modern Web Applications with 'htmx' and 'plumber2') A lightweight framework for building server-driven web applications in 'R'. 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Package: r-cran-htrspranalysis Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-readxl, r-cran-openxlsx, r-cran-minpack.lm, r-cran-zoo, r-cran-gridextra, r-cran-readr, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-ggplot2, r-cran-purrr, r-cran-forcats, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-htrspranalysis_0.1.3-1.ca2604.1_all.deb Size: 1249940 MD5sum: 90eed6507e311032bce6210e2d7877be SHA1: 230415df6b38c637ac44461389ed1b1ae90e2453 SHA256: 853e3a8a1ba75456e19589805deba088703a0d9862f07f8e1652d16967cf3f9f SHA512: a7ce2d16a670fb2c1737ba3098755bdce15fc980bc48bb8c988e10559cdd298edb92e51eb52d6b2a1df76700e78d7e50ce6c64430057e30d2acac43062f77abb Homepage: https://cran.r-project.org/package=htrSPRanalysis Description: CRAN Package 'htrSPRanalysis' (Analysis of Surface Plasmon Resonance Data) Analysis of Surface Plasmon Resonance (SPR) and Biolayer Interferometry data, with automations for high-throughput SPR. This version of the package fits the 1: 1 binding model, with and without bulkshift. It offers optional local or global Rmax fitting. The user must provide a sample sheet and a Carterra output file in Carterra's current format. There is a utility function to convert from Carterra's old output format. The user may run a custom pipeline or use the provided 'Runscript', which will produce a pdf file containing fitted Rmax, ka, kd and standard errors, a plot of the sensorgram and fits, and a plot of residuals. The script will also produce a .csv file with all of the relevant parameters for each spot on the SPR chip. Package: r-cran-htrx Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastglm, r-cran-caret, r-cran-glmnet, r-cran-tune, r-cran-recipes Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-htrx_1.2.4-1.ca2604.1_all.deb Size: 828654 MD5sum: c18b46b36730c3453a7f43d6d5c1f50f SHA1: f7557841e7c00a1eb04e7355c1cb25001bf4f89a SHA256: fe66098b314258cdfa01569f86d3bbd579006abeedf59b33dcfb03b96c8dc5fb SHA512: 4ffb01422aeaf28020d8f53367c3c597d9ef040936f1fa24aa0d26d3dcaf005f717c2fcf9c67298a38aa22bd4f98e0e6b77a4e8a625dad4896d3574b676cbeba Homepage: https://cran.r-project.org/package=HTRX Description: CRAN Package 'HTRX' (Haplotype Trend Regression with eXtra Flexibility (HTRX)) Detection of haplotype patterns that include single nucleotide polymorphisms (SNPs) and non-contiguous haplotypes that are associated with a phenotype. Methods for implementing HTRX are described in Yang Y, Lawson DJ (2023) and Barrie W, Yang Y, Irving-Pease E.K, et al (2024) . Package: r-cran-htscluster Architecture: all Version: 2.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-edger, r-cran-plotrix, r-cran-capushe Suggests: r-bioc-htsfilter, r-bioc-biobase Filename: pool/dists/resolute/main/r-cran-htscluster_2.0.11-1.ca2604.1_all.deb Size: 511720 MD5sum: 050b54a8441feb32ad28533232f558cc SHA1: 5b4d7789013d2c86bc066b186b08e1ea43ad4856 SHA256: dc63ca5ede1e040a58273345c24a1f7f0103437095999710486572c92e12de09 SHA512: ceb9ea705fff679839a89f4a90234afd3d9b2c6b4bc3ded9534670f61915b45e26fc7822402af9097830d470ab671d7bcc05634952cdd99341422b6a487881e1 Homepage: https://cran.r-project.org/package=HTSCluster Description: CRAN Package 'HTSCluster' (Clustering High-Throughput Transcriptome Sequencing (HTS) Data) A Poisson mixture model is implemented to cluster genes from high- throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters). Package: r-cran-htsdegenerater Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparsem Suggests: r-cran-forecast, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-htsdegenerater_0.1.0-1.ca2604.1_all.deb Size: 36332 MD5sum: 735292aa32caf767e8c99785fb5e1083 SHA1: c173a486960b358099d3af1bf83b46a94a0a8a6c SHA256: 4b6ec0bb4d719f43444a4ae333a574957e71b15fb3d324b6d9a86bfacc5b248c SHA512: 8c113495b509f7dbdb739558d12055c67f60070654028b248b80f21394abbc584f50f89e7069196f058721230abede2ed08db8fd0b666e5c0e98c1da808644bb Homepage: https://cran.r-project.org/package=htsDegenerateR Description: CRAN Package 'htsDegenerateR' (Degenerate Hierarchical Time Series Reconciliation) Takes the MinT implementation of the 'hts' package and adapts it to allow degenerate hierarchical structures. Instead of the "nodes" argument, this function takes an S matrix which is more versatile in the structures it allows. For a demo, see Steinmeister and Pauly (2024). The MinT algorithm is based on Wickramasuriya et al. (2019). Package: r-cran-htseed Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-htseed_0.1.0-1.ca2604.1_all.deb Size: 27548 MD5sum: b5739ef6724fd24f3019bd1493bbd95a SHA1: aab317abcd323d5dd1f737e94bd91b5176de18c3 SHA256: fc8eea8638de4ce5b53d1953161576df37efc3402f658ae97d209f211f63892d SHA512: 54df2c6c3bc55087473ef67f41d1d78fb6a6cc54b2ddbaff92bbc8266159221901094520e08be7046991e808d17d687d9dd2dbeea4d25633abfe217cc9f90d8c Homepage: https://cran.r-project.org/package=HTSeed Description: CRAN Package 'HTSeed' (Fitting of Hydrotime Model for Seed Germination Time Course) The seed germination process starts with water uptake by the seed and ends with the protrusion of radicle and plumule under varying temperatures and soil water potential. Hydrotime is a way to describe the relationship between water potential and seed germination rates at germination percentages. One important quantity before applying hydrotime modeling of germination percentages is to consider the proportion of viable seeds that could germinate under saturated conditions. This package can be used to apply correction factors at various water potentials before estimating parameters like stress tolerance, and uniformity of the hydrotime model. Three different distributions namely, Gaussian, Logistic, and Extreme value distributions have been considered to fit the model to the seed germination time course. Details can be found in Bradford (2002) , and Bradford and Still(2004) . Package: r-cran-htseedglm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-htseedglm_0.1.0-1.ca2604.1_all.deb Size: 19714 MD5sum: 2f321e56f8a2fe782c541f9a7830662f SHA1: cdd7b1ef4199b71ad21801aa038bc2478b1bdc59 SHA256: 2db1df4f5669d0b1513c93109e5887430b27587b7245da57836c24040a717e46 SHA512: 3dcdb79668e7205194154a2deb5b7d037b3b538a8803e7fb40e38d209be106ca5909d0b85a8039744f4eb09c90f7e8ae71e54099df7c91b62308ddc82055cbe1 Homepage: https://cran.r-project.org/package=HTSeedGLM Description: CRAN Package 'HTSeedGLM' (Hydro Thermal Time Analysis of Seed Germination UsingGeneralised Linear Model) Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is also performed in the analysis. Details can be found in Kebreab and Murdoch (1999) and Bradford (2002) . Package: r-cran-httkexamples Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4906 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httk, r-cran-rmarkdown, r-cran-knitr, r-cran-rdpack Suggests: r-cran-dplyr, r-cran-tidyverse, r-cran-xlsx, r-cran-metrics, r-cran-ggplot2, r-cran-ggforce, r-cran-ggpubr, r-cran-ggrepel, r-cran-viridis, r-cran-ggh4x, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-pracma, r-cran-cgwtools, r-cran-openxlsx, r-cran-ggstar, r-cran-latex2exp, r-cran-smatr, r-cran-reshape, r-cran-gdata, r-cran-censreg, r-cran-gmodels, r-cran-gplots, r-cran-scales, r-cran-colorspace, r-cran-gridextra, r-cran-rvcheck Filename: pool/dists/resolute/main/r-cran-httkexamples_0.0.1-1.ca2604.1_all.deb Size: 1835908 MD5sum: a94e4b5c79bd2d2d7ac4e1be3c12cc1e SHA1: df4b5b29f6140b22139f62c139caaeb57047835f SHA256: 71824402b4b7e461833a7237c8a3cae1280cc85e0331b67ecad439eb13325f69 SHA512: de7b134d64fa83939b9bcae5337d9dbb3328db8bd8654db563846207f3eb109026b35c5598d861be2f38c3c5dd601e8e3c1dbdfd14949feb48902b6c2d965c71 Homepage: https://cran.r-project.org/package=httkexamples Description: CRAN Package 'httkexamples' (High-Throughput Toxicokinetics Examples) High throughput toxicokinetics ("HTTK") is the combination of 1) chemical-specific in vitro measurements or in silico predictions and 2) generic mathematical models, to predict absorption, distribution, metabolism, and excretion by the body. HTTK methods have been described by Pearce et al. (2017) () and Breen et al. (2021) (). Here we provide examples (vignettes) applying HTTK to solve various problems in bioinformatics, toxicology, and exposure science. In accordance with Davidson-Fritz et al. (2025) (), whenever a new HTTK model is developed, the code to generate the figures evaluating that model is added as a new vignettte. Package: r-cran-httpcache Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-httr Suggests: r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-httpcache_1.2.0-1.ca2604.1_all.deb Size: 61880 MD5sum: 5eb84ebb73f20e1c13e1eef5a29f86af SHA1: dcc18ae2acf838edd0c841ccffc4d6dfe6c34504 SHA256: b7659a2f15260e998759a9416f41825503320664acd66cf37adbcab7d9336deb SHA512: bf34b93d8a3728e884a8a9c16571fe48ff3cb9353987a3ebec9f2d922da5d428509d8c998bbfc018047ff4bb8d35df9c29623f7be1408c696afd3ee7fc877caa Homepage: https://cran.r-project.org/package=httpcache Description: CRAN Package 'httpcache' (Query Cache for HTTP Clients) In order to improve performance for HTTP API clients, 'httpcache' provides simple tools for caching and invalidating cache. It includes the HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in replacements for those in the 'httr' package. These functions are cache-aware and provide default settings for cache invalidation suitable for RESTful APIs; the package also enables custom cache-management strategies. Finally, 'httpcache' includes a basic logging framework to facilitate the measurement of HTTP request time and cache performance. Package: r-cran-httpcode Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-httpcode_0.3.0-1.ca2604.1_all.deb Size: 33846 MD5sum: 97ec319869c4f17ba633a70afe313235 SHA1: eee365c773e6479e8797a9db90eb42680deaa54e SHA256: 9c906b41bba2725c74ccbb5dd5417c81fed92c0c64bf030e370d85f58fbd9e3e SHA512: ebb16f811c9556588329e12e4af7b1beadf1d24467c8d6d7149fbac508ced44ad6453ac66c5d101a5e767bb95b160268bde50c97ccdeb422cc8fc81adeda0a24 Homepage: https://cran.r-project.org/package=httpcode Description: CRAN Package 'httpcode' ('HTTP' Status Code Helper) Find and explain the meaning of 'HTTP' status codes. Functions included for searching for codes by full or partial number, by message, and get appropriate dog and cat images for many status codes. Package: r-cran-httping Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-httpcode, r-cran-lobstr Suggests: r-cran-testthat, r-cran-webfakes Filename: pool/dists/resolute/main/r-cran-httping_0.3.0-1.ca2604.1_all.deb Size: 24562 MD5sum: 212f1d1b2c498c32a129fcfcabff6062 SHA1: 5f6f41231c77673176f3d5bdcce2c782cff04793 SHA256: 241fce4b62a73a631957923e3c483e89a10954b10a253968705bad40dfae0411 SHA512: 39923defec44223a5b37e2bcee68fb36bca6df40c83f6963b1c855ac3a79be3e283efe01f4e4072451d6a3f81a399c659511e64366ecc96bb5c6cd490899486e Homepage: https://cran.r-project.org/package=httping Description: CRAN Package 'httping' ('Ping' 'URLs' to Time 'Requests') A suite of functions to ping 'URLs' and to time 'HTTP' 'requests'. Designed to work with 'httr'. Package: r-cran-httpproblems Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-httpproblems_1.0.1-1.ca2604.1_all.deb Size: 22324 MD5sum: 5e8ef5de0c286a3d662808f630e03be7 SHA1: 4c7a18c4791b29bc4b65c4cabbe36f00becfc141 SHA256: da624142c15ab51c105cda25a2a5ccf83cf0ec143a6b99aba4c1855fab2b50ea SHA512: 41727ea2641f78b05d54db0b87ec6f0ecde4f59ac2465283fa60f593281f0eb195a71ddf1f9ca7a79c6e855a0de38a899cb2191512fddb145c536f148c9d18d8 Homepage: https://cran.r-project.org/package=httpproblems Description: CRAN Package 'httpproblems' (Report Errors in Web Applications with 'Problem Details' (RFC7807)) Tools for emitting the 'Problem Details' structure defined in 'RFC' 7807 for reporting errors from 'HTTP' servers in a standard way. Package: r-cran-httptest2 Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-testthat Suggests: r-cran-curl, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-spelling, r-cran-webfakes, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-httptest2_1.2.2-1.ca2604.1_all.deb Size: 133894 MD5sum: 1261f5da37442bb138e9b9859e198fd7 SHA1: 7ed808867450a9e63e17f5d7bd11f1a7712ee46f SHA256: bad7f659dc85a3d67f6358b3196f6efc0a8b7f3263ff1673a0d4fec979681322 SHA512: 5034994e99f9a8b92d580637759ff0f21dd913aed5c40f8261f388facc7cef188b86c022d0bc3d5471dfe55da12ae97e0e7d18e8bd9a7800ec62cbfdb502465e Homepage: https://cran.r-project.org/package=httptest2 Description: CRAN Package 'httptest2' (Test Helpers for 'httr2') Testing and documenting code that communicates with remote servers can be painful. This package helps with writing tests for packages that use 'httr2'. It enables testing all of the logic on the R sides of the API without requiring access to the remote service, and it also allows recording real API responses to use as test fixtures. The ability to save responses and load them offline also enables writing vignettes and other dynamic documents that can be distributed without access to a live server. Package: r-cran-httptest Architecture: all Version: 4.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-curl, r-cran-digest, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-spelling, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-httptest_4.2.3-1.ca2604.1_all.deb Size: 164992 MD5sum: a2974f64005244e4f9507f30ef3519ae SHA1: 479d95a08e27da1227c60616a3725a245aaab4ae SHA256: c537b79813c1d6421e88d450bf2c2d6388e30e3209557f4d5c52258120353ade SHA512: ba5a836543e9cca8cfb1848f957f7a937e9538fda796092d083a5750f515d4be5681b2ad5d682d4d25172c139b00b535c8a5766e774567309a44d59d42446e1b Homepage: https://cran.r-project.org/package=httptest Description: CRAN Package 'httptest' (A Test Environment for HTTP Requests) Testing and documenting code that communicates with remote servers can be painful. Dealing with authentication, server state, and other complications can make testing seem too costly to bother with. But it doesn't need to be that hard. This package enables one to test all of the logic on the R sides of the API in your package without requiring access to the remote service. Importantly, it provides three contexts that mock the network connection in different ways, as well as testing functions to assert that HTTP requests were---or were not---made. It also allows one to safely record real API responses to use as test fixtures. The ability to save responses and load them offline also enables one to write vignettes and other dynamic documents that can be distributed without access to a live server. Package: r-cran-httr2 Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-openssl, r-cran-r6, r-cran-rappdirs, r-cran-rlang, r-cran-vctrs, r-cran-withr Suggests: r-cran-askpass, r-cran-bench, r-cran-clipr, r-cran-covr, r-cran-docopt, r-cran-httpuv, r-cran-jose, r-cran-jsonlite, r-cran-knitr, r-cran-later, r-cran-nanonext, r-cran-otel, r-cran-otelsdk, r-cran-paws.common, r-cran-promises, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-webfakes, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-httr2_1.2.2-1.ca2604.1_all.deb Size: 791604 MD5sum: 580c7840f48438018e413b6ac0006b95 SHA1: abe0549b0fb3297b324502dd95d1721e08d4be8c SHA256: 6a00e7746dd45de4276183afda894bcb39775685d7fe96ca2a59b411c12fb9bf SHA512: 1ac9b7a4a3a3819a4e7ef5c819507e37bdf52f5055588b07121e378eb7fe263ba52512b54afe312e3078ed6e580f207e408d29f2421389f761926b96437fdc66 Homepage: https://cran.r-project.org/package=httr2 Description: CRAN Package 'httr2' (Perform HTTP Requests and Process the Responses) Tools for creating and modifying HTTP requests, then performing them and processing the results. 'httr2' is a modern re-imagining of 'httr' that uses a pipe-based interface and solves more of the problems that API wrapping packages face. Package: r-cran-httr Architecture: all Version: 1.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-mime, r-cran-openssl, r-cran-r6 Suggests: r-cran-covr, r-cran-httpuv, r-cran-jpeg, r-cran-knitr, r-cran-png, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-httr_1.4.8-1.ca2604.1_all.deb Size: 469022 MD5sum: 9bf14cb32a11ac07ce4649cf7cc2afec SHA1: a833ec9315b639ddbf4295f4a8532e45e1d91a82 SHA256: a9e9078dda8622adb8b079783ed58cee568af58c76b2552a945c86b4b15eb7a4 SHA512: 60c4f9b3cbdd46764a5a64d976f5fdd3e7f5160f3bfbbf288d15a983da367805f869d697202aa5f3da8cde954427b739804105e9b99c397bc25ac5e7f19def11 Homepage: https://cran.r-project.org/package=httr Description: CRAN Package 'httr' (Tools for Working with URLs and HTTP) Useful tools for working with HTTP organised by HTTP verbs (GET(), POST(), etc). Configuration functions make it easy to control additional request components (authenticate(), add_headers() and so on). Package: r-cran-hubeau Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-purrr, r-cran-tibble, r-cran-urltools Suggests: r-cran-ggplot2, r-cran-hmisc, r-cran-knitr, r-cran-leafpop, r-cran-lubridate, r-cran-mapview, r-cran-rmarkdown, r-cran-sf, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hubeau_0.5.2-1.ca2604.1_all.deb Size: 894182 MD5sum: ffaa81fd7ce7631e3cb1d161d223998a SHA1: dc22d9eff8e5774dd8a4bed8eddc49f9818c7701 SHA256: d76393ecaf7bab9428698ecd2b0ededd54f3e4997a4bb4bbcabd7a7a4afaf996 SHA512: 34953d19f110208f539abd2d35558daa431d8c2fe8185fa3ee0f5266e3231b3c904120a78117b1a27a064c4b5d64ab3b2956c859f9f7bffb99ae26631507b18c Homepage: https://cran.r-project.org/package=hubeau Description: CRAN Package 'hubeau' (Get Data from the French National Database on Water 'Hub'Eau') Collection of functions to help retrieving data from 'Hub'Eau' the free and public French National APIs on water . Package: r-cran-hubensembles Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-distfromq, r-cran-dplyr, r-cran-hubutils, r-cran-lifecycle, r-cran-matrixstats, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hubensembles_1.0.0-1.ca2604.1_all.deb Size: 112266 MD5sum: 7eddc2d82bfc259ca05dfeb17959002d SHA1: 23ec89adfad468c43ff837a2429652d4df4c24bf SHA256: 57cb5c5dbad68b54a6cce5756237384e3b53081b2315cb90519c0c280bb35582 SHA512: 41d8120c45f6f352fa76c5f0469079b7a393b9cb0bcb6e25f4dc6213e609344ce2030a96eb854b62b4e32d91a09bf8a15101f5f282151fbc47ff2b91ca42026f Homepage: https://cran.r-project.org/package=hubEnsembles Description: CRAN Package 'hubEnsembles' (Ensemble Methods for Combining Hub Model Outputs) Functions for combining model outputs (e.g. predictions or estimates) from multiple models into an aggregated ensemble model output. 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Used to interact with 'hubverse' schema, Hub configuration files and model outputs and designed to be primarily used internally by other 'hubverse' packages. See Reich et al. (2022) for an overview of Collaborative Hubs. 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Package: r-cran-hwig Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-asnipe, r-cran-spatsoc, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hwig_0.0.2-1.ca2604.1_all.deb Size: 31526 MD5sum: b5d3c1a083ce1e1985a5d2f2502b30a3 SHA1: ebdcc74b6c2f45ba93efed23d60add5e063a2f8f SHA256: 2bcdd445b42e52e017673caedfa97145e451523ddbde69c73084134c6086c4d7 SHA512: 7996fd4bf3d87114159ba6b207a7d5eb1b1452205d06c80a1519382994c81fde9b955663572c58c61003c5dfbf81da4521762941e9afb265cc4beebe665fa586 Homepage: https://cran.r-project.org/package=hwig Description: CRAN Package 'hwig' (Half-Weight Index Gregariousness) The half-weight index gregariousness (HWIG) is an association index used in social network analyses. It extends the half-weight association index (HWI), correcting for level of gregariousness in individuals. 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Package: r-cran-hwsdr Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-httr, r-cran-dplyr Suggests: r-cran-ncdf4, r-cran-knitr, r-cran-markdown, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hwsdr_1.2-1.ca2604.1_all.deb Size: 1175216 MD5sum: f0dfad9f61df89f531fb6a645ef4e7ac SHA1: 8c160a994361fb8bdc4884440b5ab3a1c7fc0d76 SHA256: f7368e33292f883a406d7641a65c7a01b628946ca04d16d64408a6d9e72b6229 SHA512: 31c62a2dea4b6ba4d285a2b1b1032b16528e1fe17f17e6423513b2fa3512c9898045687fe5742eb5b6072489a1c0f2c21a54e317694ba6a8754515ee734bfe00 Homepage: https://cran.r-project.org/package=hwsdr Description: CRAN Package 'hwsdr' (Interface to the 'HWSD' Web Services) Programmatic interface to the Harmonized World Soil Database 'HWSD' web services (). Allows for easy downloads of 'HWSD' soil data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded data are provided. Package: r-cran-hwwntest Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom, r-cran-wavethresh Filename: pool/dists/resolute/main/r-cran-hwwntest_1.3.2-1.ca2604.1_all.deb Size: 129426 MD5sum: e82da3973d9fda48ac7c3e32bf6bd686 SHA1: 915de81f4e6ce954d08a45323f5ae99e6ec36ce8 SHA256: f785ddb46b4e8e6473f3ebdb9eff42ff95126eb49628e30db006964a3b521084 SHA512: bf076e96d711df30583217eef80b46527dbd16e20bb695b7c2268b268333e20740d366684b3271a4b2cce3e38afcb79b69f1d0bcaf530e411379ec1a806a021a Homepage: https://cran.r-project.org/package=hwwntest Description: CRAN Package 'hwwntest' (Tests of White Noise using Wavelets) Provides methods to test whether time series is consistent with white noise. Two new tests based on Haar wavelets and general wavelets described by Nason and Savchev (2014) are provided and, for comparison purposes this package also implements the B test of Bartlett (1967) . Functionality is provided to compute an approximation to the theoretical power of the general wavelet test in the case of general ARMA alternatives. Package: r-cran-hybriddesign Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testit, r-cran-resourceselection Filename: pool/dists/resolute/main/r-cran-hybriddesign_1.0-1.ca2604.1_all.deb Size: 42868 MD5sum: 7590f6ef19604080bbb9eb9375099708 SHA1: 4fbac021ce1a6d66a263ee3457a625ca88c48f8c SHA256: 9d5b8344692abc4dc56adfa82b3ba140d11a453c692b3a48e95fe314ae8f104c SHA512: 89df7607db8e215bf057c2af40c77e60f4567af93402e6b8674a77f5c379e81844a500f0fa89c228271f0631ace38973d8856f0a49c711acdd0c669107917c03 Homepage: https://cran.r-project.org/package=HybridDesign Description: CRAN Package 'HybridDesign' (Hybrid Design for Phase I Dose-Finding Studies) The Hybrid design is a combination of model-assisted design (e.g., the modified Toxicity Probability Interval design) with dose-toxicity model-based design for phase I dose-finding studies. The hybrid design controls the overdosing toxicity well and leads to a recommended dose closer to the true maximum tolerated dose (MTD) due to its ability to calibrate for an intermediate dose. More details can be found in Liao et al. 2022 . Package: r-cran-hybridehr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-lubridate, r-cran-magrittr, r-cran-jsonlite, r-cran-openxlsx, r-cran-dbi, r-cran-rsqlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hybridehr_0.2.0-1.ca2604.1_all.deb Size: 70864 MD5sum: 68186b3238be7c4e454bbc5baf4f4bac SHA1: 72b06ba465661e6a87b7e2ed6b8afd782a5bff33 SHA256: 3d7c1241ba71d899d5cb0972bfaa1951038f82f27f972f5448ac50371e35e826 SHA512: 0362d3534c6a51eccc130f61bac915e66b69a2fdb5813ba2d9cd6cace8e8a4d9ffa13432f21fc3246b2c6b237ee6732f4fb9023974bd00d8d9dd3a73df9a0866 Homepage: https://cran.r-project.org/package=hybridEHR Description: CRAN Package 'hybridEHR' (Synthetic Hybrid Electronic Health Record Generation forSARS-Related Research and CT Views) Generates synthetic electronic health record data, including patients, encounters, vitals, laboratory results, medications, procedures, and allergies. The package supports optional SARS-focused and computed tomography (CT) research views and export to CSV, SQLite, and Excel formats for research and development workflows. Package: r-cran-hybridmicrobiomes Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-compositions, r-cran-rlang, r-cran-permanova, r-cran-vegan, r-cran-ks, r-cran-ape, r-cran-geometry, r-cran-stereomorph, r-bioc-phyloseq Filename: pool/dists/resolute/main/r-cran-hybridmicrobiomes_0.1.1-1.ca2604.1_all.deb Size: 124926 MD5sum: 864887c46e7e3d9436e420f4f3926aef SHA1: 649e549937d66288ed9d56dcf7e298d0744a2487 SHA256: e68873121c6aeb5c35c3640645e7c8833872033151aa827bdaa73e891ccb52c1 SHA512: 29d358e7c9def79af3ea0933d62835544840bb4b33429775de2783ab53c4315db127ee574c96bdad2699bdbe20e0bc1c262518e83244ee499854d70fa80c262a Homepage: https://cran.r-project.org/package=HybridMicrobiomes Description: CRAN Package 'HybridMicrobiomes' (Analysis of Host-Associated Microbiomes from Hybrid Organisms) A set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz"). Package: r-cran-hybridmodels Architecture: all Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-ggplot2, r-cran-gillespiessa, r-cran-reshape2, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-hybridmodels_0.3.8-1.ca2604.1_all.deb Size: 139762 MD5sum: 6e2e4ecee5f26f1cda580f00a0381183 SHA1: 1ef2980cd559298c090e62ffe283204d53f33c5f SHA256: 7d23293e302cc6195ce5b5749d8a01e8fd2b3fbca57753ae119784170fd37d29 SHA512: f2e082261a66cf04d892f5abc701302eba9d30cca9fe67f5328edd25ea8626a1b92cb3fbbcfd905cba5975cbe2197681e1679e14287b9b1a84451b6b6e47db80 Homepage: https://cran.r-project.org/package=hybridModels Description: CRAN Package 'hybridModels' (An R Package for the Stochastic Simulation of Disease Spreadingin Dynamic Networks) Simulates stochastic hybrid models for transmission of infectious diseases in dynamic networks. It is a metapopulation model in which each node in the network is a sub-population and disease spreads within nodes and among them, combining two approaches: stochastic simulation algorithm () and individual-based approach, respectively. Equations that models spread within nodes are customizable and there are two link types among nodes: migration and influence (commuting). More information in Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) . Package: r-cran-hybridogram Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pheatmap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hybridogram_0.3.2-1.ca2604.1_all.deb Size: 17312 MD5sum: f51ff0d2784c7d0b9f7dcc2188ec5773 SHA1: b8dc4c875a92d72f2eda37b34ee0d8a036892778 SHA256: ef0c7353d80ea60c6de8c28284d54e98158ac60e3b26841ae23a6e647d2feb28 SHA512: a912445ce9d96427b8a61968d0b72877564602b3ce19877d7dd4835596cc0d291cdcbac0a2940f0f703fbc6d2da827456df5baeb01c4093b868bace058ecf2aa Homepage: https://cran.r-project.org/package=hybridogram Description: CRAN Package 'hybridogram' (Function that Creates a Heat Map from Hybridization Data) Using hybrid data, this package created a vividly colored hybrid heat map. The input is two files which are auto-selected. The first file has three columns, the first two for pairs of species, with the third column for the hybrid experiment code (an integer). The second file is a list of code and their descriptions in two columns. The output is a figure showing the hybrid heat map with a color legend. Package: r-cran-hybridts Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-nnfor, r-cran-waveletarima, r-cran-metrics Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-hybridts_0.1.0-1.ca2604.1_all.deb Size: 50914 MD5sum: 08b1f3dc7e023d3cb2c5900075a668ca SHA1: 0b23f372d9f1a1aaac243fdeb18368c568a4c2e6 SHA256: 5af7f3c0636526f64bc701243432d3bd9a868196298590250ce6f54132d1137a SHA512: 453d488bda34eaf6d4cb4f9ad5110f609b5900d727b7b69cf84d7693d2f5c171137e8b35dba21780c2ca42948c898438a070716b5764143dff1cc9b74aecfe05 Homepage: https://cran.r-project.org/package=hybridts Description: CRAN Package 'hybridts' (Hybrid Time Series Forecasting Using Error Remodeling Approach) Method and tool for generating hybrid time series forecasts using an error remodeling approach. These forecasting approaches utilize a recursive technique for modeling the linearity of the series using a linear method (e.g., ARIMA, Theta, etc.) and then models (forecasts) the residuals of the linear forecaster using non-linear neural networks (e.g., ANN, ARNN, etc.). The hybrid architectures comprise three steps: firstly, the linear patterns of the series are forecasted which are followed by an error re-modeling step, and finally, the forecasts from both the steps are combined to produce the final output. This method additionally provides the confidence intervals as needed. Ten different models can be implemented using this package. This package generates different types of hybrid error correction models for time series forecasting based on the algorithms by Zhang. (2003), Chakraborty et al. (2019), Chakraborty et al. (2020), Bhattacharyya et al. (2021), Chakraborty et al. (2022), and Bhattacharyya et al. (2022) . Package: r-cran-hyd1d Architecture: all Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-httr2, r-cran-curl Suggests: r-cran-dbi, r-cran-rpostgresql, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-devtools, r-cran-pkgdown, r-cran-roxygen2, r-cran-revealjs, r-cran-shiny, r-cran-shiny.i18n, r-cran-shinytime, r-cran-lubridate, r-cran-usethis, r-cran-yaml, r-cran-desc Filename: pool/dists/resolute/main/r-cran-hyd1d_0.5.4-1.ca2604.1_all.deb Size: 1774976 MD5sum: f1760ab213173dae713b324266d4b323 SHA1: a17006c699c6e41429eefc85f3a6875513dfb414 SHA256: d762f7f4d23faf8fb8b21363e74f186c13496bfcba651783ffd045030d08951c SHA512: 0172dfd1602165f6ea96452f30290e119913e15b728e9d51c5d1afc95cf9386a1344dfc564927ee67dcba92fb0ab9d82ff6ed6969bafcedc5776e930a1d0ba2a Homepage: https://cran.r-project.org/package=hyd1d Description: CRAN Package 'hyd1d' (1d Water Level Interpolation along the Rivers Elbe and Rhine) An S4 class and several functions which utilize internally stored datasets and gauging data enable 1d water level interpolation. The S4 class (WaterLevelDataFrame) structures the computation and visualisation of 1d water level information along the German federal waterways Elbe and Rhine. 'hyd1d' delivers 1d water level data - extracted from the 'FLYS' database - and validated gauging data - extracted from the hydrological database 'WISKI7' - package-internally. For computations near real time gauging data are queried externally from the 'PEGELONLINE REST API' . Package: r-cran-hydflood Architecture: all Version: 0.5.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-raster, r-cran-hyd1d, r-cran-rdpack, r-cran-httr2, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-pkgdown, r-cran-roxygen2, r-cran-testthat, r-cran-plot3d, r-cran-shiny, r-cran-shinyjs, r-cran-shiny.i18n, r-cran-leaflet, r-cran-leaflet.extras, r-cran-pangaear, r-cran-rgrass, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-hydflood_0.5.10-1.ca2604.1_all.deb Size: 1790656 MD5sum: 7b9f0f58268607a31d77933de9256101 SHA1: 66b0c5f5234ea7c670763a7cc13a472792412abd SHA256: 5b66d4d588bb37a33d0ea82ec20cce1a2222108b7330f1e99ab0eb4304f6e726 SHA512: 23a237e29e1d2e3066ef46054ffd4eb92ee55139c82577fd1904cea1acda738d75cd7d1780141c9660569abbc1e4e46b965471b96859bf9faa8486098867f07d Homepage: https://cran.r-project.org/package=hydflood Description: CRAN Package 'hydflood' (Flood Extents and Duration along the Rivers Elbe and Rhine) Raster based flood modelling internally using 'hyd1d', an R package to interpolate 1d water level and gauging data. The package computes flood extent and duration through strategies originally developed for 'INFORM', an 'ArcGIS'-based hydro-ecological modelling framework. It does not provide a full, physical hydraulic modelling algorithm, but a simplified, near real time 'GIS' approach for flood extent and duration modelling. Computationally demanding annual flood durations have been computed already and data products were published by Weber (2022) . Package: r-cran-hydra Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-igraphdata, r-cran-matrix, r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-hydra_0.1.0-1.ca2604.1_all.deb Size: 49676 MD5sum: 596d751cde0d5c5dbdf4afcac8905de4 SHA1: 703f86ce1a7d3e913fd418f04ea4c3052c663faa SHA256: dd9e7471ed4a2c38eb43a771df04b3239b4b88fabc7815e9335a0e7046088a74 SHA512: f1722b3bf4996acd05c389f9d7f3e874deb897e3211bce346ffee5e196ea565eacccad627706f2711a7c127fbe86d0139aaecd0376f1a55b74be41ac1b72e45a Homepage: https://cran.r-project.org/package=hydra Description: CRAN Package 'hydra' (Hyperbolic Embedding) Calculate an optimal embedding of a set of data points into low-dimensional hyperbolic space. This uses the strain-minimizing hyperbolic embedding of Keller-Ressel and Nargang (2019), see . Package: r-cran-hydraulics Architecture: all Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtools, r-cran-pracma, r-cran-purrr, r-cran-reshape2, r-cran-tibble, r-cran-units Suggests: r-cran-formatdown, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hydraulics_0.7.2-1.ca2604.1_all.deb Size: 822344 MD5sum: 58b4c3992fbd4217871c2ebef7133703 SHA1: f64cc0232089c4dbd44be9ec1a654329e1d738bf SHA256: 064ff70522733a4317cca9cacca7d1b7d984a7f8b6874a7f52b0473c1f29fe42 SHA512: 6d7056f0aab963f8d4bed8948b5f7e23630798c7410bd677c96ef11c30630f311b8de510a682649fa033f89e8252d64aeaebbef64ca9da42d5eb444b2b9f2d2a Homepage: https://cran.r-project.org/package=hydraulics Description: CRAN Package 'hydraulics' (Basic Pipe and Open Channel Hydraulics) Functions for basic hydraulic calculations related to water flow in circular pipes both flowing full (under pressure), and partially full (gravity flow), and trapezoidal open channels. For pressure flow this includes friction loss calculations by solving the Darcy-Weisbach equation for head loss, flow or diameter, plotting a Moody diagram, matching a pump characteristic curve to a system curve, and solving for flows in a pipe network using the Hardy-Cross method. The Darcy-Weisbach friction factor is calculated using the Colebrook (or Colebrook-White equation), the basis of the Moody diagram, the original citation being Colebrook (1939) . For gravity flow, the Manning equation is used, again solving for missing parameters. The derivation of and solutions using the Darcy-Weisbach equation and the Manning equation are outlined in many fluid mechanics texts such as Finnemore and Maurer (2024, ISBN:978-1-264-78729-6). Some gradually- and rapidly-varied flow functions are included. For the Manning equation solutions, this package uses modifications of original code from the 'iemisc' package by Irucka Embry. Package: r-cran-hydreng Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hydreng_1.0.0-1.ca2604.1_all.deb Size: 217270 MD5sum: a693819b74886f4253d960db3cbd6f4d SHA1: e216b5b437efbeaaa07ad45a02cf2d0ef038f786 SHA256: e885b407b017df0b87a5c91590940d6145520fb067927fae36db4e3494f25f94 SHA512: 474f36069b3c8506c9f1067f626bf47373cae1761d015a88b60be32fd183fff4c51dab887140ed4893d39c793ebbdded8013ca6c8eadae5dc4c56238714cbaa7 Homepage: https://cran.r-project.org/package=hydReng Description: CRAN Package 'hydReng' (Hydraulic Engineering Tools) The 'hydReng' package provides a set of functions for hydraulic engineering tasks and natural hazard assessments. It includes basic hydraulics (wetted area, wetted perimeter, flow, flow velocity, flow depth, and maximum flow) for open channels with arbitrary geometry under uniform flow conditions. For structures such as circular pipes, weirs, and gates, the package includes calculations for pressure flow, backwater depth, and overflow over a weir crest. Additionally, it provides formulas for calculating bedload transport. The formulas used can be found in standard literature on hydraulics, such as Bollrich (2019, ISBN:978-3-410-29169-5) or Hager (2011, ISBN:978-3-642-77430-0). Package: r-cran-hydrocal Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hydrocal_1.0.0-1.ca2604.1_all.deb Size: 66404 MD5sum: 9b9c25eb69774b8ef23bc43661bbf73e SHA1: a60b62c56ebe07f78eb3d8ac48ee746f0ffafbf8 SHA256: a2d6140eded8dde9ce0ff35957a8e8ef8771e56103b74ce9c68c6a9d056e9753 SHA512: 9ead4170109dc5e8ae8172e4e5d1d292c409df42974f8d6c6b4cefe46cf821271cb4e393a61737f2a045de5198993858c9126b6c83250224dc509cb6625aaf93 Homepage: https://cran.r-project.org/package=HYDROCAL Description: CRAN Package 'HYDROCAL' (Hydraulic Roughness Calculator) Estimates frictional constants for hydraulic analysis of rivers. This HYDRaulic ROughness CALculator (HYDROCAL) was previously developed as a spreadsheet tool and accompanying documentation by McKay and Fischenich (2011, ). Package: r-cran-hydrocode Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/resolute/main/r-cran-hydrocode_1.0.3-1.ca2604.1_all.deb Size: 4813564 MD5sum: aed9b478e83c507768c99714cbc675f9 SHA1: 626f0fae715119b1e8e5d7bbd05d4587d24c1f98 SHA256: b07b9f5c660283e359eb4b6f71c947de67f65cf7b49edf61b64ffac185d213e4 SHA512: 065539a531d8aefca39f4c0c070eb62620c6fa50b06c83bd6d974548f9c533fd4e0772f59869afa165aa33789570a0fe3736daced1754f10344415a1b55d32e6 Homepage: https://cran.r-project.org/package=HydroCode Description: CRAN Package 'HydroCode' (Hydrological Codes) Pfafstetter Hydrological Codes as cited in Verdin and Verdin (1999) are decoded for upstream or downstream queries. Package: r-cran-hydrodcindex Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-hydrodcindex_1.0.0-1.ca2604.1_all.deb Size: 143666 MD5sum: f362d9d686d3e3c7c11f81acdfe8512a SHA1: 07703ab3d8905199bcc59839bcd4c9fad32eef1e SHA256: d3f089a39e893787015e7009993193a36a99091dfe43cef7bd06983edf530514 SHA512: 4f6c292e97d8b89bb76d72482e31605f436e7fe5260e15c5f5adc3a1e5b73cf22a2099b196d5626a3b4a7b3f87958b81a7f6b26b967b2a0af86bf908a64734f8 Homepage: https://cran.r-project.org/package=hydroDCindex Description: CRAN Package 'hydroDCindex' (Duration Curve Hydrological Model Indexes) Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) . Package: r-cran-hydrodownloadr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dataretrieval, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-pdftools, r-cran-progress, r-cran-rappdirs, r-cran-ratelimitr, r-cran-rlang, r-cran-rsqlite, r-cran-sf, r-cran-magrittr, r-cran-tibble, r-cran-cellranger, r-cran-stringi, r-cran-stringr Suggests: r-cran-odbc, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rvest, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2, r-cran-cachem, r-cran-curl, r-cran-memoise, r-cran-testthat, r-cran-writexl Filename: pool/dists/resolute/main/r-cran-hydrodownloadr_0.1.3-1.ca2604.1_all.deb Size: 986952 MD5sum: 00d6491908dfd69038e34b3deff87ed3 SHA1: 979b38e24c51960edb72098a44c234e008749374 SHA256: 91bc4e0dfce8d57e32495318244aa23d5ec96d55f68bcf53a4e904c598136a0d SHA512: 747d73f75796ef4006684562eb417923237c35e1635d866079347152025827f9767ac8cd16f8eff184ff5f59c38c779c4636be1b244638795dfb5c15c2fb1aab Homepage: https://cran.r-project.org/package=hydrodownloadR Description: CRAN Package 'hydrodownloadR' (Hydrologic Station Catalogs and Time Series from Public APIs) Provides a unified, extensible interface to discover hydrologic stations and download daily time series (e.g., water discharge, water level, water temperature, and several other water quality parameter) from national and regional public APIs. Includes a provider registry, S3 generics 'stations' and 'timeseries', licensing metadata, date-range and 'complete history' modes, rate limiting and retries, optional authentication via environment variables, tidy outputs, UTF-8 to ASCII transliteration, and WGS84 coordinates. Designed for reproducible workflows and straightforward addition of new providers. Background and use cases are described in Farber et al. (2025) and Farber et al. (2023) . Package: r-cran-hydroeval Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hydroeval_0.1.0-1.ca2604.1_all.deb Size: 164486 MD5sum: 78630432c0d5f59f84aea5a41f8480e3 SHA1: 0f0ee4dc2ad387c6998dbff60a18a68b9f0d7b33 SHA256: bb2f5673134b987b7cd7bd1131c20e96a2d16bdeae213e0c1969b012b8d4c52f SHA512: fa30ebb9a33c8b485a6d6df48bca9ef4d2adb7b203608de4ccad0c44878a1ebdcfed1497dfdf44c87fa9f1aa26a8a65fca20f7b94f5c000d4be783a4f7643902 Homepage: https://cran.r-project.org/package=hydroeval Description: CRAN Package 'hydroeval' (Hydrological Evaluation Metrics and Goodness-of-Fit Summaries) Computes scalar performance metrics and goodness-of-fit summaries for comparing simulated and observed hydrological or regression values. Provides error metrics, percentage bias, Nash-Sutcliffe efficiency, Kling-Gupta efficiency variants, correlation measures, agreement indices, and comparison tables via gof() and gof_compare(). Metric definitions are registry-governed with shared validation, provenance-aware wrappers, and explicit handling of undefined or degenerate cases. Methods include Nash and Sutcliffe (1970) , Gupta et al. (2009) , Kling et al. (2012) , and Willmott et al. (2012) . Package: r-cran-hydroevents Architecture: all Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3415 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hydroevents_0.13.0-1.ca2604.1_all.deb Size: 3168558 MD5sum: eb3318c9f09c7e1c5b8665c7afd6146f SHA1: f06ccf8d298cf03d899fecc148d5951b25ba2270 SHA256: 77db8f8893c5139c1601a98c41b9e9473e18c22dae5ecfd4100a15cd6dcb24ad SHA512: f00a02a282ee2f5750cd66cc169e2f9920affe759eb9fe815496d032c032c862c4a489d8f5a6059a9c53fdc2d536983b751348fc542c0161f1aeb6d02c7d11fb Homepage: https://cran.r-project.org/package=hydroEvents Description: CRAN Package 'hydroEvents' (Extract Event Statistics in Hydrologic Time Series) Events from individual hydrologic time series are extracted, and events are matched across multiple time series. The package has been applied in studies such as Wasko and Guo (2022) and Mohammadpour Khoie, Guo and Wasko (2025) . Package: r-cran-hydrogeo Architecture: all Version: 0.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hydrogeo_0.6-1-1.ca2604.1_all.deb Size: 150284 MD5sum: ea9ba34fa98ddc26c06d8e0bcf108124 SHA1: 25becfbb69c381e3b9987b2dacb6f385f1f0d025 SHA256: aabdaf1b6acedf14ed2334f7fadaa33969743bc2425d86f1c407d167adf1e049 SHA512: 2395f6e8065d6ecc026a878fb6b5caed04c8cdc2fa7ee22693a1c6b69ce7e553f58112f2be5c4b16096c51aeb4dea3efd02f572940ded135a4dac9a0d8c966ab Homepage: https://cran.r-project.org/package=hydrogeo Description: CRAN Package 'hydrogeo' (Groundwater Data Presentation and Interpretation) Contains one function for drawing Piper diagrams (also called Piper-Hill diagrams) of water analyses for major ions. Package: r-cran-hydrogof Architecture: all Version: 0.7-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-hydrotsm, r-cran-xts Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hydrogof_0.7-0-1.ca2604.1_all.deb Size: 2932766 MD5sum: e50c96bfe53a1f12c00e17c0c00d082e SHA1: e17c09cb3d61585ad062e3731976faf6102d84ce SHA256: 83e51db7f814312614315576a804df3e5a970f6d660cc2162a87fb10479ccbbd SHA512: a694254aed586f34c7a14860fc11c7edd6141730c22535032ac2e65b4dc2b8edaea656d76189340eb79cc957c141c80508b61870d6f5d6db3d539ebd7457dafa Homepage: https://cran.r-project.org/package=hydroGOF Description: CRAN Package 'hydroGOF' (Goodness-of-Fit Functions for Comparison of Simulated andObserved Hydrological Time Series) S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed. Package: r-cran-hydroloom Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4448 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-sf, r-cran-units, r-cran-pbapply, r-cran-tidyr, r-cran-rann, r-cran-rlang, r-cran-fastmap Suggests: r-cran-testthat, r-cran-nhdplustools, r-cran-future, r-cran-lwgeom, r-cran-future.apply, r-cran-knitr, r-cran-gifski, r-cran-mapview, r-cran-webshot, r-cran-geos Filename: pool/dists/resolute/main/r-cran-hydroloom_1.1.3-1.ca2604.1_all.deb Size: 2135038 MD5sum: 88cec87449c7025781a99929950b32df SHA1: 0d8ad73e6e55b07d4483c65f506e53e0b6ef2a51 SHA256: 142e88b1c1f6e04c32e9ebaf147cf74a60dbbfa9073fbb3f07edaa8ca5d3b8a3 SHA512: 3f874abac3b380e2c8b42d33c32f97d8855d0539ca807bfd2babb268f910fb7bfa1f94da1f9adf7a945db3c6775e92d1ed92e7d1c49bec84400d345b5710278d Homepage: https://cran.r-project.org/package=hydroloom Description: CRAN Package 'hydroloom' (Utilities to Weave Hydrologic Fabrics) A collection of utilities that support creation of network attributes for hydrologic networks. Methods and algorithms implemented are documented in Moore et al. (2019) ), Cormen and Leiserson (2022) and Verdin and Verdin (1999) . Package: r-cran-hydrome Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-minpack.lm, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-hydrome_2.1.2-1.ca2604.1_all.deb Size: 96102 MD5sum: a8fd05ee85dc572d6d89855d6a80f77c SHA1: dd1f49a73e7050af33d6686397177bea9dce4d27 SHA256: 852dc96df8e83af550c06ecfbabc5ed61a5940a024848c556d61119933cec2ae SHA512: 15f4041c35863a6da0da918f2124d43336dd45e3dda637c26fbd02171a769ebc0c692a422d59f80616abcb9653d44dc9b7244a9c1556a79247b0c4ebf2ed0952 Homepage: https://cran.r-project.org/package=HydroMe Description: CRAN Package 'HydroMe' (Estimating Water Retention and Infiltration Model Parametersusing Experimental Data) Estimates the parameters of infiltration and water retention models using the curve-fitting methods as shown in Omuto and Gumbe (2009) . The models considered are those that are commonly used in soil science. Version 2 of the package has new models for water retention characteristic curves. Package: r-cran-hydromopso Architecture: all Version: 0.1-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 713 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-randtoolbox, r-cran-lhs, r-cran-hydrotsm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-smoof, r-cran-hydrogof, r-cran-airgr, r-cran-tuwmodel Filename: pool/dists/resolute/main/r-cran-hydromopso_0.1-14-1.ca2604.1_all.deb Size: 692740 MD5sum: 73161cea81865766dfe319d860109571 SHA1: 3580c113a712c7297d91648a2ac7f930a2e7c9f9 SHA256: 5eb48f3a2977416a1c0abffebc982d493c54286c61baaa00195e58aeaa4b1282 SHA512: 999b97e4ac29e158f33d878febb7409544c458b2a0227b62fa1751f39b7d8b06abd9cc51e11221eaf45e42228b534a6d0642694bf01a1f3f2614018f27619f21 Homepage: https://cran.r-project.org/package=hydroMOPSO Description: CRAN Package 'hydroMOPSO' (Multi-Objective Optimisation with Focus on Environmental Models) State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) . This package is inspired by and closely follows the philosophy of the single objective 'hydroPSO' R package ((Zambrano-Bigiarini & Rojas, 2013) ), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., 'TUWmodel', 'GR4J', 'GR6J'). However, the main focus of 'hydroMOPSO' is optimising environmental and other real-world models that need to be run from the system console (e.g., 'SWAT+'). 'hydroMOPSO' communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, 'hydroMOPSO' can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, 'hydroMOPSO' is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time. Package: r-cran-hydropeak Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-hydropeak_0.1.2-1.ca2604.1_all.deb Size: 84198 MD5sum: 666815effb135d612761b1051a730525 SHA1: b808b6a2c8fab0488332837cfe30d11298c68842 SHA256: c9352e69c0fa9e666201019d7e2f1b5e30e1b075defeabf3e4677004dc997909 SHA512: 3998aedb67cc08b9e9ad345711d11547fca5ba178cd5db55f8a7d4f1c913239f12867f26a83308ff9f1eb5ec4c1e247ff7237eb6fb0752d5cb66d39553ad5abb Homepage: https://cran.r-project.org/package=hydropeak Description: CRAN Package 'hydropeak' (Detect and Characterize Sub-Daily Flow Fluctuations) An important environmental impact on running water ecosystems is caused by hydropeaking - the discontinuous release of turbine water because of peaks of energy demand. An event-based algorithm is implemented to detect flow fluctuations referring to increase events (IC) and decrease events (DC). For each event, a set of parameters related to the fluctuation intensity is calculated. The framework is introduced in Greimel et al. (2016) "A method to detect and characterize sub-daily flow fluctuations" and can be used to identify different fluctuation types according to the potential source: e.g., sub-daily flow fluctuations caused by hydropeaking, rainfall, or snow and glacier melt. This is a companion to the package 'hydroroute', which is used to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path as proposed and validated in Greimel et al. (2022). Package: r-cran-hydroportailstats Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evd, r-cran-mvtnorm, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hydroportailstats_1.1.0-1.ca2604.1_all.deb Size: 206398 MD5sum: 0de651a7e98786da8d62b61edda3e06f SHA1: c66427934e577885d0d5b2df305760c2ecac7679 SHA256: 823bd80fbb3f5ebf46109da0e6f00c44482422561b96fe8ad1b7fc0a4802c0bd SHA512: 4b28153062f59f24b1240ffbc8b3deade59e087d85447499cf856f72e378368fca943f454c3490b802051138bf7ab6fd143acef99f3ef2c1617d2f28347880d5 Homepage: https://cran.r-project.org/package=HydroPortailStats Description: CRAN Package 'HydroPortailStats' ('HydroPortail' Statistical Functions) Statistical functions used in the French 'HydroPortail' . This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities. Technical details are available (in French) in Renard (2016) . Package: r-cran-hydroroute Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggpmisc, r-cran-ggplot2, r-cran-gridextra, r-cran-hydropeak, r-cran-lubridate, r-cran-reshape2, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-hydroroute_0.1.2-1.ca2604.1_all.deb Size: 813106 MD5sum: 144ed2466714dc36d412a3736ab3afca SHA1: 61aeb780abc83737f8452422302fa770162427e7 SHA256: 6abe1cfc7203673dde3620476a4eac2f1a140ea26e825f59863e8a6d3acb2fc1 SHA512: 99a1a59cbfd70a67739619dc988acb60c2e639f59e161c460e1bd808ec9880e6d0910d6ca4f80b343dd7500fdb94bf9b2bde7ba6a991ac2affb336993b83cd9b Homepage: https://cran.r-project.org/package=hydroroute Description: CRAN Package 'hydroroute' (Trace Longitudinal Hydropeaking Waves) Implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022) . The identification of associated events is based on the event detection implemented in 'hydropeak'. Package: r-cran-hydrostate Architecture: all Version: 0.2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-sn, r-cran-truncnorm, r-cran-diagram, r-cran-padr, r-cran-zoo, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/resolute/main/r-cran-hydrostate_0.2.0.0-1.ca2604.1_all.deb Size: 3083210 MD5sum: 90db63ea8167600586970c3c766e99e1 SHA1: aa990868d7a3be352965645501181a9b535522a9 SHA256: 4eb787866bef9cb7121f2a068833e12cc6e4438fbf96124a3fd92be7becd261e SHA512: 80ce1a612552034e4fc713a8c507d9610a4dc6c99d2b35565990d7e5bfb92ab4dfb5c051172660f4f9a2f5169321e9066de5fa4561ff5d6265e71ee972ed9d9a Homepage: https://cran.r-project.org/package=hydroState Description: CRAN Package 'hydroState' (Hidden Markov Modelling of Hydrological State Change) Identifies regime changes in streamflow runoff not explained by variations in precipitation. The package builds a flexible set of Hidden Markov Models of annual, seasonal or monthly streamflow runoff with precipitation as a predictor. Suites of models can be built for a single site, ranging from one to three states and each with differing combinations of error models and auto-correlation terms. The most parsimonious model is easily identified by AIC, and useful for understanding catchment drought non-recovery: Peterson TJ, Saft M, Peel MC & John A (2021) . Package: r-cran-hydrostats Architecture: all Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-hydrostats_0.2.9-1.ca2604.1_all.deb Size: 211308 MD5sum: e7ee299e32c11a5ace07cae28c9dd4af SHA1: 48a0c2ecca00c349dd5d4bc4fcf4ea2482665e53 SHA256: de8ccee612222b3cad56bf36d352b48f7a777d0bf375556d2ddfb4969000eac1 SHA512: fba19ebe28411ae73adcd99137ea77ce1245daa8edbd71de0ac37430aa1b694c9efb2e23d09476c41d80eca0b23167ce4b8258d862fdd48f1b2f5776b17d35ea Homepage: https://cran.r-project.org/package=hydrostats Description: CRAN Package 'hydrostats' (Hydrologic Indices for Daily Time Series Data) Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology. Package: r-cran-hydrotoolkit Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-lubridate, r-cran-readxl, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-hydrotoolkit_0.1.0-1.ca2604.1_all.deb Size: 1276368 MD5sum: 756e7499403f97ac519d08c656bdb0f6 SHA1: 5caf8a8c8857a8981ebc4a9f116eb5be29f5809f SHA256: 3a92bbc1c7440fbdd0b8b35f87856b6f830e30b314ee90fa1d71d04cc7f00b76 SHA512: 4ab7e498752be9d80519011209d1384419e34a2ad93d7316c2973a5945fbcf9ebdbdd99020526ec443de0f4c3449842ff0a77655f6030db3519e6b3448936892 Homepage: https://cran.r-project.org/package=hydroToolkit Description: CRAN Package 'hydroToolkit' (Hydrological Tools for Handling Hydro-Meteorological Data fromArgentina and Chile) Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile. Package: r-cran-hydrotsm Architecture: all Version: 0.8-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-e1071, r-cran-lattice, r-cran-classint, r-cran-timechange Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-hydrotsm_0.8-6-1.ca2604.1_all.deb Size: 4078214 MD5sum: f27512954277a91e799a08f89ae5600a SHA1: 750bff2a506bf89615498822fdd810c7502157cc SHA256: 9b7c7e43afd9a2134f941879049c2dc74f1f162e54a8a334afce7a1f42c82ffc SHA512: cf512c6ec4296588b48a07b4b2cfc693b5f177efc988e0a2f05f88f3f2128dea0fec85d5f9073ae14c21f281f6277d159e67c8eb5b0e1f38acd7d6e6f670d250 Homepage: https://cran.r-project.org/package=hydroTSM Description: CRAN Package 'hydroTSM' (Time Series Management and Analysis for Hydrological Modelling) S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. 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Package: r-cran-hyfo Architecture: all Version: 1.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-zoo, r-cran-sf, r-cran-plyr, r-cran-moments, r-cran-lmom, r-cran-maps, r-cran-sp, r-cran-ncdf4, r-cran-mass, r-cran-data.table Suggests: r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hyfo_1.4.6-1.ca2604.1_all.deb Size: 675520 MD5sum: 08ab5dbd87dccc0e6f1367093501735e SHA1: 6264d761bdaf0e368558aabfd6cce4e6b94a0ef0 SHA256: 574b97c5b893700714eb6ad37513918be352a4c26e7ed4e20e8ba3a36026c111 SHA512: 2286766cfc707b292789d5ec1b8aeef6e1aa923e5977e0e3845c6486b2447887ef1a196421a49eb9f5e03a0136849a8c7050f1eec42c8c0d2a6ccfecaa1a2d4e Homepage: https://cran.r-project.org/package=hyfo Description: CRAN Package 'hyfo' (Hydrology and Climate Forecasting) Focuses on data processing and visualization in hydrology and climate forecasting. 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Package: r-cran-hyperbolicdist Architecture: all Version: 0.6-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-actuar Filename: pool/dists/resolute/main/r-cran-hyperbolicdist_0.6-5-1.ca2604.1_all.deb Size: 365124 MD5sum: 4216ca07fe40d236d13bd9c44b631f57 SHA1: 625e218daafbf45be3c6e5ed2812c4fd6a23508f SHA256: f037271a1da892602057171ee657b74e9f4c0c35a7c8cf0e975dd2a509911aaa SHA512: 3df95df4eac84f66f2474f41a90a09dd848baa182856ec59df17e83e24af5273f6e0161b5a4f9acc743708ddcb6cf57b2787f702a999eb6a0e52902106f4e80b Homepage: https://cran.r-project.org/package=HyperbolicDist Description: CRAN Package 'HyperbolicDist' (The Hyperbolic Distribution) Maintenance has been discontinued for this package. It has been superseded by 'GeneralizedHyperbolic'. 'GeneralizedHyperbolic' includes all the functionality of 'HyperbolicDist' and more and is based on a more rational design. 'HyperbolicDist' provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, including fitting of the hyperbolic to data. Package: r-cran-hypercube Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-stringr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-hypercube_0.2.1-1.ca2604.1_all.deb Size: 209726 MD5sum: c20d432ee9caaab23eb132a33abef666 SHA1: ad169323c23a0ea48f266cd9687c65e13c71a785 SHA256: 766f554b06689fe110126f35e38bd3b349f9f0443ccc06b70feb89e5c0dca5d0 SHA512: 6a9586749c344351cbc915b49568dcc582dd0ea8aa83e75519f88a6afd771b6894ad5dedd08c2c7dc199c7b009d9560b45f98a0226b510ae1ee3836214754d85 Homepage: https://cran.r-project.org/package=hypercube Description: CRAN Package 'hypercube' (Organizing Data in Hypercubes) Provides functions and methods for organizing data in hypercubes (i.e., a multi-dimensional cube). Cubes are generated from molten data frames. 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Several functions are provided for extracting key elements from the tabular datasets. Package: r-cran-i3pack Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gbm, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-stv, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-i3pack_0.1.0-1.ca2604.1_all.deb Size: 103738 MD5sum: 5748f8ca4cfe53685af0b8cf1d20b0e1 SHA1: 3869ed55ea59b6a1be0d26698d11caf77302b64b SHA256: f36ec6a4a2e15b3900162bf3d07579a998db827cbf06af51748b917cdfaaa941 SHA512: 0bfa86a1c41226c6015094569fb55de2448156783447ba808df442b49849d44480fbb1b508e952adcfe479ca557a097ff05ec3f5035f60940bbf22784c5f7eac Homepage: https://cran.r-project.org/package=i3pack Description: CRAN Package 'i3pack' (Incentives for Inter- And Intra-Party Electoral Competition) Suite of functions that help simulate elections under different electoral systems, which are then used to compute incentives generated by these systems in terms of the inter- and intra-party dimensions of electoral competition. 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Package: r-cran-iadapt Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-iadapt_2.0.1-1.ca2604.1_all.deb Size: 221868 MD5sum: 919c99639b3d702c5a0b8364229d4903 SHA1: 5783f7f15e0a46f386c076a83fb880174862c1cf SHA256: 4be0769a576b2d9fd3ba69b6595c58b4f31d5fa600bd99a3990b9fd546728724 SHA512: f6f57f7274846f959ba763cda2fd7de50d2cc19632cc9f5fdaae4348b59bc919c177450da2b696d8a16a7f7db4c37170841a8e25ce904eb06f60297490f53749 Homepage: https://cran.r-project.org/package=iAdapt Description: CRAN Package 'iAdapt' (Two-Stage Adaptive Dose-Finding Clinical Trial Design) Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading . Package: r-cran-iadf Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manipulate, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-dplr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-lazyeval, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-iadf_0.1.3-1.ca2604.1_all.deb Size: 163218 MD5sum: 81530018ea71316d6e5e6bdda9e3e241 SHA1: 9d51169755b0865d34e2e4d0cf7336fe059b5a4a SHA256: 5c27e1841e25a94284bd902902a29e72d5362e49415191e0b23246021182f933 SHA512: 1d3ef766b782d9ec1034de048fd618f126ae910e1b6001cc62eea16ebc0fb7e60475872f96995d9f1522c1a0729058b4ca1ae240f966e4794979d9a2e789bb30 Homepage: https://cran.r-project.org/package=iadf Description: CRAN Package 'iadf' (Analysis of Intra Annual Density Fluctuations) Calculate false ring proportions from data frames of intra annual density fluctuations. Package: r-cran-iadt Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpfr, r-cran-mgcv, r-cran-rdpack, r-cran-mvnfast Filename: pool/dists/resolute/main/r-cran-iadt_1.2.1-1.ca2604.1_all.deb Size: 31656 MD5sum: 06ac958d37ec15a282bbf9c8dd7317c4 SHA1: 4cdd92529d445aa071354277cdd32845ba22d5b9 SHA256: de88a0e6ea783da98c1496adf4080e83a5fe3bb1db0486d8fd60ed6cce90e087 SHA512: dbef7d39e86af2850a4aafad3bc95a92ce7b7682237c236137bbfe8fb53aaeaa6cab2c935958d337d7970c5fb366ff6eb96e17b213580b72c2308337dd565f62 Homepage: https://cran.r-project.org/package=IADT Description: CRAN Package 'IADT' (Interaction Difference Test for Prediction Models) Provides functions to conduct a model-agnostic asymptotic hypothesis test for the identification of interaction effects in black-box machine learning models. The null hypothesis assumes that a given set of covariates does not contribute to interaction effects in the prediction model. The test statistic is based on the difference of variances of partial dependence functions (Friedman (2008) and Welchowski (2022) ) with respect to the original black-box predictions and the predictions under the null hypothesis. The hypothesis test can be applied to any black-box prediction model, and the null hypothesis of the test can be flexibly specified according to the research question of interest. Furthermore, the test is computationally fast to apply as the null distribution does not require resampling or refitting black-box prediction models. Package: r-cran-iai Architecture: all Version: 1.10.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall, r-cran-stringr, r-cran-rlang, r-cran-lifecycle, r-cran-rappdirs, r-cran-ggplot2, r-cran-cowplot, r-cran-rjson Suggests: r-cran-testthat, r-cran-covr, r-cran-xml2, r-cran-withr Filename: pool/dists/resolute/main/r-cran-iai_1.10.2-1.ca2604.1_all.deb Size: 514750 MD5sum: 9350673dcab87cdc85977fdf9a9dfa82 SHA1: 23587c469d506e703631552c381caf60d5472215 SHA256: 2cf098fd1949c4f32f9c68aaefd52fa3fb406397a6bd544b50ee4fb27c06dbcc SHA512: f834596be08b02148e067c8a910187abf528e79634daf02ef89bd585ce627c21987b3830633664dffaaba0c909e444c026cb4c7efafdeca155be81b7c841f67c Homepage: https://cran.r-project.org/package=iai Description: CRAN Package 'iai' (Interface to 'Interpretable AI' Modules) An interface to the algorithms of 'Interpretable AI' from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available. Package: r-cran-ialiquor Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4063 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ialiquor_0.1.0-1.ca2604.1_all.deb Size: 3900314 MD5sum: 09fe8ea938c8958e8beeae8ec7e62cc0 SHA1: b02fd43c244f5afa3145520f6ea167a5a90c7263 SHA256: a1424185d4f8be4e8c2c2d8fb24727e1224056017426f9b8e318ba684916fe29 SHA512: 35b6dd8fb746696b259af92ce477fb0800e12bb6402ce36e43c903f753630d4fa74c0532facc93f0108df92e52808878be73a438513e14a8712bf60de30049d1 Homepage: https://cran.r-project.org/package=ialiquor Description: CRAN Package 'ialiquor' (Monthly Iowa Liquor Sales Summary) Provides a monthly summary of Iowa liquor (class E) sales from January 2015 to October 2020. See the package website for more information, documentation and examples. Data source: Iowa Data portal . Package: r-cran-ials Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra, r-cran-pracma, r-cran-hdmfa Filename: pool/dists/resolute/main/r-cran-ials_0.1.3-1.ca2604.1_all.deb Size: 29844 MD5sum: 2616082bd6cff116789b898d35fa11e6 SHA1: ff910b38d9637de8c3d7f44770e14ca6f7ca0ab1 SHA256: bd272f905f2a18b59387a51c64e926f989b36a442b52e1c1217e7611753f1f69 SHA512: 67351f79be3183574385ffe7cb5e29c1b50a106678c6ba40d746b0b9d12da97bb4888c2d5dce24fab5a2e7aae6235fa3a1d5f2d5f71dbec8adf5b3d86333a2f5 Homepage: https://cran.r-project.org/package=IALS Description: CRAN Package 'IALS' (Iterative Alternating Least Square Estimation forLarge-Dimensional Matrix Factor Model) The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) . Package: r-cran-iarm Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-erm, r-cran-ggplot2, r-cran-gridextra, r-cran-hmisc, r-cran-psychotools, r-cran-vcdextra Filename: pool/dists/resolute/main/r-cran-iarm_0.4.3-1.ca2604.1_all.deb Size: 170420 MD5sum: 95f64168792569da67a071bac2182eab SHA1: 5af8c8fe42096c515e78c795d77181cb9681b725 SHA256: 98fadc21cf9ff538c94906a5864ed2f84ca120d31d2fe8b2242a43b63f6ff46d SHA512: 802222acb9c10b54bfeb2da46a43d10a65f57798bb4b7710d76453eaedf1ec0bb80060c00760376a420226a4cc4afb64a241d30c4ea55e48a0e1b8668636320b Homepage: https://cran.r-project.org/package=iarm Description: CRAN Package 'iarm' (Item Analysis in Rasch Models) Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0). Package: r-cran-iasd Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-iasd_1.1.1-1.ca2604.1_all.deb Size: 33686 MD5sum: 58a3456be9db6cb7a6b4a810d6980853 SHA1: 516928f9648fb2e4e5a1246b1b8f9224333d6561 SHA256: 15b2320b09746827453a3a2ecc544c96eb5ff57d9b9e6340043820e412dfb4a9 SHA512: e41bb99e3fe46e7f31cd761431c61f68f5768a3b5db15566f166fcb7af7f393c8a9eaaaa9875989e94f4869c8a1647209d0d032dd82c612d5e1967b1abc01dbf Homepage: https://cran.r-project.org/package=IASD Description: CRAN Package 'IASD' (Model Selection for Index of Asymmetry Distribution) Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal. Package: r-cran-iatanalytics Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-iatanalytics_0.2.0-1.ca2604.1_all.deb Size: 25182 MD5sum: 545ebaca3054249eafe8d47580d8ee8a SHA1: 36cf865f98ca11b0b0ad4ab41f1c6f61479def85 SHA256: a926dee0f16270f101fc64f09d9f274aeca690d60a711d794df93655984eadf4 SHA512: 4cd2d23e3a34a0b84a7e0efe83bdc8bd322723b83d2ebce4a86279ef991e24bbdf95e4fa157a11749c331f29db68c26665bf3310156cc37ea0b8dbaefe512dcf Homepage: https://cran.r-project.org/package=IATanalytics Description: CRAN Package 'IATanalytics' (Compute Effect Sizes and Reliability for Implicit AssociationTest (IAT) Data) Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) . IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) . The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). A sample dataset (titled 'sampledata') is included in this package to practice with. Package: r-cran-iatscore Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-iatscore_0.2.0-1.ca2604.1_all.deb Size: 24848 MD5sum: f4c4d341e87b44b3229dbd2177e6210f SHA1: ae19c57f17c1605ee3026bcdc52441cc324edd86 SHA256: 2b81eefa9987369dba66d321a7fd96cf2c81e119df91f4e5ff9c05c0746af014 SHA512: d829a86f9b8e8df6c1a26311b55e3a423ca4a0f027ac20d3975ee8555bf47c6abccf6dc9f24d6beeb93b6a8d0aff7de770974579cbf7c6dfcde23d55f8375ed1 Homepage: https://cran.r-project.org/package=IATScore Description: CRAN Package 'IATScore' (Scoring Algorithm for the Implicit Association Test (IAT)) This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) . IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) . Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with. Package: r-cran-iatscores Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-reshape2, r-cran-qgraph Suggests: r-cran-nparcomp Filename: pool/dists/resolute/main/r-cran-iatscores_0.2.8-1.ca2604.1_all.deb Size: 107298 MD5sum: 839a971d48433ac5a3c40f1ef2c0877c SHA1: 80a34220830b7cf48d76bc8f69dc7188109f91c9 SHA256: 395de34413a1d3dd7781ff4bfe67fcc930294a40754b5d0787fcb2a74be6ad49 SHA512: 9806927203366947a730396e1bdcc2c1a01ee6c6c9dde8ccb5c794b97960fb9563e255676ab931a31661a71165319b8689ef7d81d764451928d64cd8157b5140 Homepage: https://cran.r-project.org/package=IATscores Description: CRAN Package 'IATscores' (Implicit Association Test Scores Using Robust Statistics) Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, ) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, ). Package: r-cran-ib Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 887 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-lme4, r-cran-formula, r-cran-mass, r-cran-matrix, r-cran-rdpack, r-cran-vgam Suggests: r-cran-testthat, r-cran-knitr, r-cran-nlraa, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ib_0.2.1-1.ca2604.1_all.deb Size: 555580 MD5sum: 1c3c1ec4b52977c9b1a0cbb60800c7f6 SHA1: a8db7659052a03d3c8f00adf1a01b6df8b20dd60 SHA256: 46d9a868857998b6c912300025cc556ccf4e3067ca3764393c8ec20c05e0b508 SHA512: acbde2c03e0fc9869ffe9877c5d45e81a2c75141ce6c60326d4a0abe557f6a4b8abaf72f5720ba1038b10fed4263fd09ecdc24091556ffb68741d40cdfecaee1 Homepage: https://cran.r-project.org/package=ib Description: CRAN Package 'ib' (Bias Correction via Iterative Bootstrap) An implementation of the iterative bootstrap procedure of Kuk (1995) to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique. 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Package: r-cran-ibcf.mtme Architecture: all Version: 1.6-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lsa, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-ibcf.mtme_1.6-0-1.ca2604.1_all.deb Size: 90274 MD5sum: c3b51192363cfe42141b60665cd09836 SHA1: 7b730c9e7c0b1cbc50366f13e788c55503d7e2d7 SHA256: 33f0c2e35c13cc3e0d06fb1f2629641f34ae95357b360f9962e27b059ed396da SHA512: b3c9afa72bdd0f9e42aab6fec16491dfa73b285e4c5ca83d5e1457f80f98cde47650fbc522938f4cdba9ed3d2ee201645949102cf2cce22538f6fc6ff3d2e92d Homepage: https://cran.r-project.org/package=IBCF.MTME Description: CRAN Package 'IBCF.MTME' (Item Based Collaborative Filtering for Multi-Trait andMulti-Environment Data) Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) . Package: r-cran-ibd Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-car, r-cran-emmeans, r-cran-multcomp Suggests: r-cran-multcompview Filename: pool/dists/resolute/main/r-cran-ibd_1.6-1.ca2604.1_all.deb Size: 124278 MD5sum: 2eec173ab12af3e27079716075e3c3de SHA1: b97a481a3708c60548f218a0e43781c6a5aba3ea SHA256: f427ec6f167e5ad0be215e6246c3473395182433a52416a36ad4eb15996c3fc4 SHA512: d8db258bf84615feecc4a84b69a3a9abb37fdce6b7ab65966819d7ce0031278b313ca3abcd62d2940b163af66cfc7014cff8fed9ffd3f42e2f5aea7c788521af Homepage: https://cran.r-project.org/package=ibd Description: CRAN Package 'ibd' (Incomplete Block Designs) A collection of several utility functions related to binary incomplete block designs. Contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. Contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported. Package: r-cran-ibdfindr Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forrel, r-cran-ggplot2, r-cran-ibdsim2, r-cran-pedtools, r-cran-ribd Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ibdfindr_0.3.1-1.ca2604.1_all.deb Size: 353672 MD5sum: d4b099cd4986b7e0855d70cd7683398f SHA1: 6399b32c0f6b87d412eed13e2ed30d3649d021ac SHA256: 07f18ce7895a03c722225bc67f6c0e264d00b891ea62bdc557ac9bf6c9241744 SHA512: 31f0eaa93bd5c17b1681437d62e54fae8ffe31c362e43d379de0358e04bb27adef6e0783e1624efe7f261368454e8f023fbefd9f4fd30134c3f10f23b1d34b4b Homepage: https://cran.r-project.org/package=ibdfindr Description: CRAN Package 'ibdfindr' (HMM Toolkit for Inferring IBD Segments from SNP Genotypes) Implements continuous-time hidden Markov models (HMMs) to infer identity-by-descent (IBD) segments shared by two individuals from their single-nucleotide polymorphism (SNP) genotypes. Provides posterior probabilities at each marker (forward-backward algorithm), prediction of IBD segments (Viterbi algorithm), and functions for visualising results. Supports both autosomal data and X-chromosomal data. Package: r-cran-ibdinfer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crossdes, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ibdinfer_0.1.0-1.ca2604.1_all.deb Size: 35108 MD5sum: d1c651d62b50d5f47f8aecdd6129bf62 SHA1: 8d83bbe21ee82c6f3aa6869c21ee3520e86d7975 SHA256: 6c3af74dc566f733cbd8ae8c767d31d0c30190e6e803fe68889607a03232d1d5 SHA512: 592854df87434f513a20f4a3d05d62cb1f610ceba92d277e66ba92c369a1fd8fd189d8864529bc6d4b5bdb4b9ae2954b79a6fe18dbc95c0ba980570bef713492 Homepage: https://cran.r-project.org/package=IBDInfer Description: CRAN Package 'IBDInfer' (Design-Based Causal Inference Method for Incomplete BlockDesigns) This R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on 'Koo and Pashley' (2026) , it includes block-level estimators and extends to unit-level effects using 'Horvitz-Thompson' and 'Hájek' estimators. The package also provides asymptotic confidence intervals to support valid statistical inference. Package: r-cran-ibelief Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ibelief_1.3.1-1.ca2604.1_all.deb Size: 87120 MD5sum: 03881d65bf7d31b1426a8cd5865ba08d SHA1: 9ed981d4f18d6eabe69bf3f2a2c7197346b1712c SHA256: c00c949ed3adab6365984e9377761e16f8705ec5e25d7de69bed1f30c6672743 SHA512: e4b506f42955533b79eeae9ef6dfbdde36e95e936773c8f6d7aee4adc5791ad4c74a6a44ce612815a84258079a6529af29ee12ca95272b7c50992ff0a366dfd9 Homepage: https://cran.r-project.org/package=ibelief Description: CRAN Package 'ibelief' (Belief Function Implementation) Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets , evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and six decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management. Package: r-cran-ibfs Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ibfs_1.0.0-1.ca2604.1_all.deb Size: 32000 MD5sum: 9fdf16f77dd5774d2c63145efa256b97 SHA1: 6cdd97249408139cf43505632191020bc6030f54 SHA256: 78639175eacc76360def4c8d3a4c7cc489c35f2ba381ceff86644a9fa70582d2 SHA512: 143d6577fff84395ddb2688675c90f592c42856e3908fa5365271354bc63ec7ea23f48e8522a1db26cd40a5e7e7e6ecd82a01f31a518d4363ed2b66480ebbb02 Homepage: https://cran.r-project.org/package=IBFS Description: CRAN Package 'IBFS' (Initial Basic Feasible Solution for Transportation Problem) The initial basic feasible solution (IBFS) is a significant step to achieve the minimal total cost (optimal solution) of the transportation problem. 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Query aggregates, variables, localities, periods, and metadata from surveys and censuses conducted by 'IBGE'. Package: r-cran-ibkrcp Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ibkrcp_0.1.1-1.ca2604.1_all.deb Size: 52042 MD5sum: 538b73653e0cdef7362af0f55ed2917c SHA1: 77d0350d67ef940dbd9b48f13109f6fb46fd6adf SHA256: 40f4b2975876bf20684e897bab5bfe2e10ac7999516281144d006c57f3926b84 SHA512: d30641c9bbced912ea57cf1617f1580155593777722bb56993b840974af38d9007642b6b7132a24b25cdeed2cf8a18cd44a28621e30289136f0e369de5f02e4b Homepage: https://cran.r-project.org/package=ibkrcp Description: CRAN Package 'ibkrcp' (R Client for the Interactive Brokers Client Portal API) Provides a lightweight R interface to the Interactive Brokers (IBKR) Client Portal REST API. 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Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the 'SparkR' package (). The optional 'ggplot2' package is needed for the plot.idaLm() function only. 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Supports additive attributions and attributions with interactions. The Break Down Table shows contributions of every variable to a final prediction. The Break Down Plot presents variable contributions in a concise graphical way. This package works for classification and regression models. It is an extension of the 'breakDown' package (Staniak and Biecek 2018) , with new and faster strategies for orderings. It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) This package is a part of the 'DrWhy.AI' universe (Biecek 2018) . Package: r-cran-ibrokers Architecture: all Version: 0.10-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-ibrokers_0.10-2-1.ca2604.1_all.deb Size: 505510 MD5sum: e58050cba0391e598ae4214a9ace0776 SHA1: 039cc0ff0a2ea9594dfe49aaf36c9485761b0374 SHA256: 33a34900a018dcf023d064c070da70aded6cd64592211bc7330be984f0991204 SHA512: 4388ad39e93992691f1b78e100cfa34e751dbda1acef96fc5e7b8b7eeeb49e4d1a64ff820c3e9920d77ecd7e459af8d17fd3d004922509feabe03bd47f276773 Homepage: https://cran.r-project.org/package=IBrokers Description: CRAN Package 'IBrokers' (R API to Interactive Brokers Trader Workstation) Provides native R access to Interactive Brokers Trader Workstation API. Package: r-cran-ibrtools Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-data.table, r-cran-gtools, r-cran-tidyr, r-cran-fmsb, r-cran-binhf, r-cran-tidyselect Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ibrtools_0.1.3-1.ca2604.1_all.deb Size: 57632 MD5sum: daf214896af5cc9de96a2149e2add6e0 SHA1: 935d41b7ff8b0c79db0a34e49d7abb625896fb00 SHA256: 87596cf598232ba2828e4852c70db174003bb53e2093cb59b8a055f573a63e2e SHA512: b56fb1dd17f0041e70755a0f233bcee7ce18e325fc9a877ddfba0ef719e72e82934b45a218bd7da230b563f95717ff51adce6c75a530390ea46d2f04305bad35 Homepage: https://cran.r-project.org/package=IBRtools Description: CRAN Package 'IBRtools' (Integrating Biomarker-Based Assessments and Radarchart Creation) Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). . Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). . Minato N. (2022). . Package: r-cran-ic.infer Architecture: all Version: 1.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-mvtnorm, r-cran-boot, r-cran-kappalab Suggests: r-cran-relaimpo Filename: pool/dists/resolute/main/r-cran-ic.infer_1.1-7-1.ca2604.1_all.deb Size: 330978 MD5sum: d42b8e0116c92c6ed4cd9b3387711be5 SHA1: 5d9134b37a4ca736fd417735597f7069b9423d07 SHA256: 1bf56617d9cbdaff8fcc9863f55349c7392cbb3404452c0fb596d0a1eda89df8 SHA512: eda3fc85ff94d57690048c7d54ea0ff582d0ca7b534d84ccc229c76715b1ab0ff4a58d8ec2fbef22291c89b71a097bf5b1de8c28f493a2389c89c18335eaf929 Homepage: https://cran.r-project.org/package=ic.infer Description: CRAN Package 'ic.infer' (Inequality Constrained Inference in Linear Normal Situations) Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included. Package: r-cran-ic10 Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pamr, r-bioc-impute, r-cran-ic10trainingdata Filename: pool/dists/resolute/main/r-cran-ic10_2.0.2-1.ca2604.1_all.deb Size: 78930 MD5sum: ad756bdab1b97334ffec870be758cefb SHA1: 9d3225cc424ca3917034726b268136a806ddba95 SHA256: d49044d9d2890369ab40396fec0de24f8ab0a2ccf13e0f14624a4cddea2f85b9 SHA512: 677d61d2dc1a4dab608cab4762d7b7ac10beec552eb5af5a27e87082ee015b07d5928040e186de18c0e2d21102ec375c240b68afa16dc36f43a78badb25c1cfc Homepage: https://cran.r-project.org/package=iC10 Description: CRAN Package 'iC10' (A Copy Number and Expression-Based Classifier for Breast Tumours) Implementation of the classifier described in the paper Ali HR et al (2014) . It uses copy number and/or expression form breast cancer data, trains a Tibshirani's 'pamr' classifier with the features available and predicts the iC10 group. Package: r-cran-ic10trainingdata Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5765 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ic10trainingdata_2.0.1-1.ca2604.1_all.deb Size: 5856058 MD5sum: 1de2fc019eab111e632d07a678329bf9 SHA1: e0ae031e4bcdd28ecdee9cf302eadc5451aefb6f SHA256: 9fe60c4e3b06a65676a642745dc4b0b159718e3f030b7ba8c3610b5eb8fa3a85 SHA512: 57145ea29dbb82c4b0d93a2d01f8560f481c4e2224849432469f84b175c304ec477edf979ce73ae30b6e082c29a44c8c16fe7a842e9510a77648e8a0aff425e3 Homepage: https://cran.r-project.org/package=iC10TrainingData Description: CRAN Package 'iC10TrainingData' (Training Datasets for iC10 Package) Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package. Package: r-cran-ica Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ica_1.0-3-1.ca2604.1_all.deb Size: 86506 MD5sum: b792700d47399f77f21266e2dd8ebbdf SHA1: 9aeb51f7323ce4742cf308009b52c52673644104 SHA256: 2cc4291fc75b3e8c6d4188c17db101925854839e062d42d8e9c6330620c361ea SHA512: f460abc2873ffda13e8a2906b85f495a722897de7cc24a293c952e56ce76b357eb4a819baef2ee765fc466dbc8d986cb58e30c1b15a38b9f9d71211742cf2d35 Homepage: https://cran.r-project.org/package=ica Description: CRAN Package 'ica' (Independent Component Analysis) Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE). 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It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) based on neutral theory model and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) . 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Package: r-cran-icarh Architecture: all Version: 2.0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-mass, r-cran-ggplot2, r-cran-glue, r-cran-rcurl, r-bioc-kegggraph, r-cran-igraph, r-cran-reshape2, r-cran-mc2d, r-cran-abind, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-icarh_2.0.2.1-1.ca2604.1_all.deb Size: 125406 MD5sum: 3f7d11e540caabcaf0af3d2e3575bb9d SHA1: 1713b8a95397db902639eb2c5defd852bf36ae1a SHA256: bd57ceaf994a8b57cfa781d89e3a9ef562fe3be07b62723a5bd1e902d004416b SHA512: 6f397bfee6ed963272072e666d65612e56afcc2c9c2c6498726ed4abfdf2910878bc392b276cd5714425e7dac21b2ac53ad8cf84a6090ac0a03bec1a1b784481 Homepage: https://cran.r-project.org/package=iCARH Description: CRAN Package 'iCARH' (Integrative Conditional Autoregressive Horseshoe Model) Implements the integrative conditional autoregressive horseshoe model discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. BMC Bioinformatics 21, 11 (2020) . The model consists in three levels: Metabolic pathways level modeling interdependencies between variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify potential associations between heterogeneous omic variables via a Horseshoe prior and experimental design level to capture experimental design conditions through a mixed-effects model. The package also provides functions to simulate data from the model, construct pathway matrices, post process and plot model parameters. Package: r-cran-icarus Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-rglpk, r-cran-slam, r-cran-xtable, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-icarus_0.3.3-1.ca2604.1_all.deb Size: 3980776 MD5sum: eb02542f2e71c38ecf337e37b45f3668 SHA1: 14cd8c9b097e7b19560dab8cb13c22ab08b8804a SHA256: 1941cd42c3f0994a40297f6bcd856d700268c9619bb52118cdeada0e962fa65d SHA512: d0de99bfefedceceae4697ba96c46308b8f8aa91660ed5d33f35073238770dd6a33f88e3352a5e980cbc03441ff87a9c1cdc14d26697b30993b5e4d6d43c51c7 Homepage: https://cran.r-project.org/package=icarus Description: CRAN Package 'icarus' (Calibrates and Reweights Units in Samples) Provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to 'icarus'. In addition to calibration (with linear, raking and logit methods), 'icarus' features functions for calibration on tight bounds and penalized calibration. Package: r-cran-icbiomark Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-dplyr, r-cran-purrr, r-cran-latex2exp, r-cran-matrixstats, r-cran-ggplot2, r-cran-gglasso, r-cran-prroc Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-icbiomark_0.1.4-1.ca2604.1_all.deb Size: 3877116 MD5sum: ae3bdde077678ec2c3c24b100db2dc27 SHA1: 55a0b8489cd7ed2b3fdc57ddf0da742d21889e9c SHA256: 1b8ec66661f1c78be6386f1941da691d9e914972d96005f0af0e72f8aa5fcea0 SHA512: f26c2b227d2864349ba65de7f6ccae309546deb50e20dbbbc57d32a43d461ae284cd810a2545107c1c335caef3cfb664cbaf64510ad5db28c77c0e7c5073d865 Homepage: https://cran.r-project.org/package=ICBioMark Description: CRAN Package 'ICBioMark' (Data-Driven Design of Targeted Gene Panels for EstimatingImmunotherapy Biomarkers) Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) . This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models. Package: r-cran-icc.sample.size Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-icc.sample.size_1.1-1.ca2604.1_all.deb Size: 29590 MD5sum: 6a12aff1572e95f1737ab134eba31d6d SHA1: 0cafec11a8408f35d36c645553f4d5d000bf6d81 SHA256: ca5d5a88b6fee7cdb46ac465ec94c77795da317f85d273f53e57224771bca8f9 SHA512: e70be5469df5032a15444c9ab47065340d9497c7ba11f8361bc05d619721b6ee65ccfd0373fba941ea13cee7913b944d3319eeb417604e8cab258ea20e54e507 Homepage: https://cran.r-project.org/package=ICC.Sample.Size Description: CRAN Package 'ICC.Sample.Size' (Calculation of Sample Size and Power for ICC) Provides functions to calculate the requisite sample size for studies where ICC is the primary outcome. Can also be used for calculation of power. In both cases it allows the user to test the impact of changing input variables by calculating the outcome for several different values of input variables. Based off the work of Zou. Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Statistics in medicine, 31(29), 3972-3981. Package: r-cran-icc Architecture: all Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-icc_2.4.0-1.ca2604.1_all.deb Size: 47222 MD5sum: be779a80c8296366d54db4709eb16651 SHA1: 07f3d4c2d511d9fb6619929acf625a43f868cb9b SHA256: 8670a5acf95874b89853529c5bc1dcb8303e8a8de4220080716e5bde70709e4f SHA512: 9f51f6686e128ab43bb021db29db7c863a82a63356acf127e9e657a508bbb7a7774e2f27a4a026c7e3a641ad8783534cc035f0db4ec3fd188252c3e5f02ba755 Homepage: https://cran.r-project.org/package=ICC Description: CRAN Package 'ICC' (Facilitating Estimation of the Intraclass CorrelationCoefficient) Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width. 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Several of the implemented approaches are based on the work of Donner and Zou (2002) . Package: r-cran-icccounts Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmmtmb, r-cran-ggplot2, r-cran-deriv, r-cran-gridextra, r-cran-vgam, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-icccounts_1.1.4-1.ca2604.1_all.deb Size: 282400 MD5sum: 81f5f14ff43a4ac0db0b5bf3c5a045d3 SHA1: 3bf94c2750949e8cbab3cb640c00b943c32c9382 SHA256: 26e5d68ec60e9725cf6c7908ef5cfd96eadb909a67397a93532bcfa88effd544 SHA512: ff984c0edad39b388cbe1d079ab5d194ef42d7ff4990bcd97a921bf5c9e54199f9ca22c963c1d7fe66c0c709b8875188ab14ce9f9198a3c7c7eadfca02e1cab2 Homepage: https://cran.r-project.org/package=iccCounts Description: CRAN Package 'iccCounts' (Intraclass Correlation Coefficient for Count Data) Estimates the intraclass correlation coefficient (ICC) for count data to assess repeatability (intra-methods concordance) and concordance (between-method concordance). In the concordance setting, the ICC is equivalent to the concordance correlation coefficient estimated by variance components. The ICC is estimated using the estimates from generalized linear mixed models. The within-subjects distributions considered are: Poisson; Negative Binomial with additive and proportional extradispersion; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial with additive and proportional extradispersion. The statistical methodology used to estimate the ICC with count data can be found in Carrasco (2010) . Package: r-cran-iccde Architecture: all Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-iccde_0.3.8-1.ca2604.1_all.deb Size: 22116 MD5sum: 458eda6e85bbd73d0883aa55614b35c5 SHA1: 48669b7ea50b7ccb538e754563dbd4c8558fc003 SHA256: fd1db9885d39b0b2fc4b8e5effb2b9f80f164407b7f8681370d08528eac27230 SHA512: 0f26bb0dc27a11e794da3b982f2a0a665f9363ca5ee2c9f58fa676ab04ccfcaa17bcf815bb3742fc2c0c49102b371443560b6565a87c7fcca9427e3a34e53139 Homepage: https://cran.r-project.org/package=iccde Description: CRAN Package 'iccde' (Computation of the Double-Entry Intraclass Correlation) The functions compute the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). Profiles comprising correlations are automatically transformed according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score prior to computation of the double-entry intraclass correlation (McCrae, 2008). See Furr (2010) or McCrae (2008) for details. Package: r-cran-iccforest Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-survival, r-cran-icenreg, r-cran-ipred Suggests: r-cran-ltrctrees, r-cran-inum Filename: pool/dists/resolute/main/r-cran-iccforest_0.5.1-1.ca2604.1_all.deb Size: 59708 MD5sum: 3a6d36764a54e2285046e8ccafdd366c SHA1: ffdc5a969b4e3ab780c8ca9b46bad21bcc8ba7ef SHA256: f5ccfe3dc9610f6a81f2cca4f4b8548470ce9b3f7ca653629c2aca4012e95dd0 SHA512: e2cf1fe0f5236b40064f6c5872e2a8302040e439c51e134c71a0611ff47721e202d3ea71aee82e7e44fd78ff62e0488fe6342a4ef757fc9c335e6b44764ab0ae Homepage: https://cran.r-project.org/package=ICcforest Description: CRAN Package 'ICcforest' (An Ensemble Method for Interval-Censored Survival Data) Implements the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2019) . Package: r-cran-iccmult Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dirmult, r-cran-gtools, r-cran-iccbin, r-cran-lme4 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-iccmult_1.0.1-1.ca2604.1_all.deb Size: 35264 MD5sum: d209c2fda89405cb41ab611990bb45f5 SHA1: 2ce024a5a6a231e41e0cdf24441a4ec8fafc991e SHA256: 05e43aeef23eb0ded936ceb8748721196bf1d34a241849301dce2f770ec21028 SHA512: 4464c6b448bbb22a0e59c873f502b107c42df14b699ea73e41415109adcae08226cbbf5a921cffb9f0439722c96dbd396cfd4ed82117c7cf667e6e0989e7a299 Homepage: https://cran.r-project.org/package=iccmult Description: CRAN Package 'iccmult' (Intracluster Correlation Coefficient (ICC) in ClusteredCategorical Data) Assists in generating categorical clustered outcome data, estimating the Intracluster Correlation Coefficient (ICC) for nominal or ordinal data with 2+ categories under the resampling and method of moments (MoM) methods, with confidence intervals. Package: r-cran-icctraj Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magic, r-cran-trajectories, r-cran-sp, r-cran-spacetime, r-cran-purrr, r-cran-furrr, r-cran-progressr, r-cran-parallelly, r-cran-future Filename: pool/dists/resolute/main/r-cran-icctraj_1.1.0-1.ca2604.1_all.deb Size: 151886 MD5sum: dbfb52eb96a10512c1c7f5634fe0b03c SHA1: 6bdd7050f751af4d61995e4331193c700017ee37 SHA256: 3cf30b314a702e5474b5c0a93cd255c3416e64f398734e0e4a6a2970cf4edba9 SHA512: 87b0c6770dd537bbbc4436127a9cb5b6b5e3305ee00cf72b7706c0671c9c9c68bf0b8fee9494bf4a63753a9c91e77e494befd58f30c17c1dc6665ce494530718 Homepage: https://cran.r-project.org/package=iccTraj Description: CRAN Package 'iccTraj' (Estimates the Intraclass Correlation Coefficient for TrajectoryData) Estimates the intraclass correlation coefficient for trajectory data using a matrix of distances between trajectories. The distances implemented are the extended Hausdorff distances (Min et al. 2007) and the discrete Fréchet distance (Magdy et al. 2015) . Package: r-cran-icd.data Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5004 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-icd.data_1.0-1.ca2604.1_all.deb Size: 4408186 MD5sum: 64ad16a4d2862fd6fa77f3de2d03adda SHA1: bcc8b93deabbd0b50adbb8c79dcf0d07ba64b107 SHA256: e2a2eee2b2074548743a0faad04a84d90491525388e60dc823ddea1e7227cb55 SHA512: c7cda10c3b69d2ffdaec829941905c2ae9633b42aedb32cfa0e2f3f6175ea996ce06b18419ff7638be910c4cf3c72f2c01199d3f109de7451cc1eb30c9e12103 Homepage: https://cran.r-project.org/package=icd.data Description: CRAN Package 'icd.data' (International Classifcation of Diseases (ICD) Data) Data from the United States Center for Medicare and Medicaid Services (CMS) is included in this package. There are ICD-9 and ICD-10 diagnostic and procedure codes, and lists of the chapter and sub-chapter headings and the ranges of ICD codes they encompass. There are also two sample datasets. These data are used by the 'icd' package for finding comorbidities. Package: r-cran-icd10gm Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringi, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rvest Filename: pool/dists/resolute/main/r-cran-icd10gm_1.2.5-1.ca2604.1_all.deb Size: 1282190 MD5sum: 2eaebec16b55e048a3a088dabe465c56 SHA1: 18ae5777e8dbecfe54dcb8d9cf57ebd44fe1544e SHA256: 53f3e993ebe6bb427be1641cfcc9faa9334c29c78e59b817f2970621ba10d9f3 SHA512: 93456a61cfd3bb2b68428a6d5dfae622f6771780b6990bac79152b5026ad87156c8b8c8171bc45e96c3a790cdc306e7272ea366d713cbe0d2996fd7367025c16 Homepage: https://cran.r-project.org/package=ICD10gm Description: CRAN Package 'ICD10gm' (Metadata Processing for the German Modification of the ICD-10Coding System) Provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information , and are redistributed in accordance with their license. Package: r-cran-icdcomorbid Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-icdcomorbid_1.0.0-1.ca2604.1_all.deb Size: 49046 MD5sum: bab1c4d7ba4c4c1c17e8e5ec1a826180 SHA1: cdc1641c225c1f004a1787dc0fa649ae6e01af8a SHA256: 01582614e45973cb2fbe0d2c909e727d704bad95337fe50f696d014c0da73f90 SHA512: d044702b4477ae49eb41e54e785c58af8b7bf5322cf2164355ef2825b0ceab460d45d4d0a2708315a551243d2eb724f93f46f233c0dac03c63ff7044ee707234 Homepage: https://cran.r-project.org/package=icdcomorbid Description: CRAN Package 'icdcomorbid' (Mapping ICD Codes to Comorbidity) Provides tools for mapping International Classification of Diseases codes to comorbidity, enabling the identification and analysis of various medical conditions within healthcare data. Package: r-cran-icdglm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-icdglm_1.0.0-1.ca2604.1_all.deb Size: 51908 MD5sum: 2c4fb92fe3102f4053c3b85dacfe4756 SHA1: dc460543e0914ed850a4f1b6378ba57b5be53ac1 SHA256: 4e76f0228a4bbe2aec956b4e5d8405fe6b85e02ed7e58215bcc1d819cde8428d SHA512: b088ad219913f5e797f4ea27614c3c301c7f6882d9479cc5645f48d015d924da9209fb6f21aa4a54a31a9a226bea6f91c5b608d6dad56a90b625bfc044b2b727 Homepage: https://cran.r-project.org/package=icdGLM Description: CRAN Package 'icdGLM' (EM by the Method of Weights for Incomplete Categorical Data inGenerlized Linear Models) Provides an estimator for generalized linear models with incomplete data for discrete covariates. The estimation is based on the EM algorithm by the method of weights by Ibrahim (1990) . Package: r-cran-icdpicr2 Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-icdpicr2_2.1.0-1.ca2604.1_all.deb Size: 688842 MD5sum: 149265460d81c3e4283c0b27ab5d9db9 SHA1: fb27d2f833d892282284730c045fec98b2a5a254 SHA256: cc05866ba322e676a321ddeb9f829a421d577c678d3be3fb4177b1d89b084890 SHA512: 2c25b2daff13e6dec51be6aad91b554033039d043262e00ed838ceb3e06f6977922ec7e31457853eeebb72239fac26af35a9b6fcc227d4ffa3065d16959e72d4 Homepage: https://cran.r-project.org/package=icdpicr2 Description: CRAN Package 'icdpicr2' (Categorize Injury Diagnosis Codes) Functions read a dataframe containing one or more International Classification of Diseases Tenth Revision codes per subject. They return original data with injury categorizations and severity scores added. Package: r-cran-icdpicr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-rcmdcheck Filename: pool/dists/resolute/main/r-cran-icdpicr_1.0.1-1.ca2604.1_all.deb Size: 1423078 MD5sum: 7c93ea09c15bffaff1898f1f7e338f4e SHA1: 6953634ccfdd009e2a2b466daf2905156322d081 SHA256: e19e0af77de240da50a54c22817a73657372d4312d3b4dd6c47b39565bae20cb SHA512: d23a5f39cf65a0140027edbf6dfac5e03fc307882314b88cd09489c7b0bf9adc7960d0022fdf268ca7ce505b87ccba513d617e2f5196516d664b548d74e798bb Homepage: https://cran.r-project.org/package=icdpicr Description: CRAN Package 'icdpicr' ('ICD' Programs for Injury Categorization in R) Categorization and scoring of injury severity typically involves trained personnel with access to injured persons or their medical records. 'icdpicr' contains a function that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS') from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury severity scoring. 'ICDPIC' was originally developed in 'Stata', and 'icdpicr' is an open-access update that accepts both 'ICD-9' and 'ICD-10' codes. Package: r-cran-icecdr Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-glue, r-cran-httr2, r-cran-rlang Suggests: r-cran-here, r-cran-ncdf4, r-cran-rcdo, r-cran-testthat, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-icecdr_1.2.0-1.ca2604.1_all.deb Size: 153762 MD5sum: 4019b58329c5937ee602841a90e4c1d6 SHA1: f7e2e13eda743e2c9ce2d40e11356be25429c0db SHA256: b58baa4b3bb7b754daa88991a67331dd1212032d7bc6075bc2b43b4908d1e9ea SHA512: 6c991412128289fcd916baa72bb484aa2e69b760889ee6ed836a7a8c18489d5cc5ae211fe253825c49cc72fa34cf63e8256f057997695939ecbbb5dcd80fc991 Homepage: https://cran.r-project.org/package=icecdr Description: CRAN Package 'icecdr' (Download Sea Ice Concentration Data from the NSIDC Climate DataRecord) Programmatic access to NSIDC's sea ice concentration CDR via ERDAPP server and Sea Ice index . Supports caching results and optional fixes for some inconsistencies of the raw files. 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Wrap an expression in ic() to print the expression, its value and (where available) its source location. Debugging output can be toggled globally without modifying code. Package: r-cran-iced Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-knitr, r-cran-lavaan, r-cran-mass, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-iced_0.0.1-1.ca2604.1_all.deb Size: 772840 MD5sum: fe82be437fd51e6b5b849e22feea09c5 SHA1: 0b085759d45e6abd9c4267e07a8ee7a895118e02 SHA256: 7b880b7b60b906803ad37e2eb42a87fa0bf258d8666aa0a5f07120218f07abdd SHA512: fad5ca12dbf367919118fbb7a13789e0caf0b248ea6abe98ab4a8d8c812767f78c549260682674f6e82d18de85b367184f5407db41b1183b50e3565e002a35fc Homepage: https://cran.r-project.org/package=ICED Description: CRAN Package 'ICED' (IntraClass Effect Decomposition) Estimate test-retest reliability for complex sampling strategies and extract variances using IntraClass Effect Decomposition. Developed by Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" Also includes functions to simulate data based on sampling strategy. Unofficial version release name: "Good work squirrels". Package: r-cran-icehmeasures Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-survey, r-cran-rlang, r-cran-car Suggests: r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-icehmeasures_1.1.0-1.ca2604.1_all.deb Size: 69060 MD5sum: 0c1f8265440b2a8a588f7cf0305feb7f SHA1: dfe8cd14103d563494070e7eddfe9ed0285171b0 SHA256: 209b7a1c4c870745b19bab24d19252674d3ca5b5bea0d096ee6b3b369a55af76 SHA512: 2f487b73696f406b74d4d2f7204cb88e3411cb0160fc8c82cfa770356f482ed0d86e29fc31f3dd45e926af56fce16c1ed947ddf58fef44fc7654d7cab4a96016 Homepage: https://cran.r-project.org/package=ICEHmeasures Description: CRAN Package 'ICEHmeasures' (The Equiplot Graph and Complex Inequality Measures) Generates the equiplot, an iconic dot-plot graph for visualizing inequalities, as well as three complex inequality measures: the slope index of inequality, the concentration index and the mean absolute difference to the mean. For more details see World Health Organization (2013) . Package: r-cran-icensbkl Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mass, r-bioc-icens, r-cran-teachingdemos, r-cran-mvtnorm, r-cran-smoothsurv, r-cran-gtools Suggests: r-cran-interval, r-cran-logspline, r-cran-mixak, r-cran-bayessurv, r-cran-fhtest, r-cran-icenreg, r-cran-frailtypack, r-cran-mlecens, r-cran-cubature, r-cran-runjags, r-cran-coda, r-cran-dynsurv Filename: pool/dists/resolute/main/r-cran-icensbkl_1.5-1.ca2604.1_all.deb Size: 355072 MD5sum: a3068ccc31b94d494d243d9852449dba SHA1: 24845f59cb8324ab2257cc12c381054fa312b4ff SHA256: 7bef8d05432b25a57f2d3fbf20890bcc02091b70c659b82f6477c835b27811be SHA512: 1c9f4bccf18f8b1e578419a5dfef7b97e72f1c57b7423672fbc7e63dc1720e49f3aa7c86656bab2851925795eee945deb9d07f18d60e5f351f07591827714784 Homepage: https://cran.r-project.org/package=icensBKL Description: CRAN Package 'icensBKL' (Accompanion to the Book on Interval Censoring by Bogaerts,Komarek, and Lesaffre) Contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017, ISBN: 978-1-4200-7747-6) "Survival Analysis with Interval-Censored Data: A Practical Approach" . Full R code related to the examples presented in the book can be found at . Packages mentioned in the "Suggests" section are used in those examples. Package: r-cran-icertool Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-purrr, r-cran-dt, r-cran-tidyverse, r-cran-readxl, r-cran-ggplot2, r-cran-ggrepel, r-cran-digest, r-cran-shinyhelper Filename: pool/dists/resolute/main/r-cran-icertool_0.0.3-1.ca2604.1_all.deb Size: 29960 MD5sum: f0bb155ee056594938935b450f2114a4 SHA1: bc359ef6025d13cb43ec9bea172c4c99b030040b SHA256: c81f9c5b4e65a0f83d9d4298fc2453092324d1f68c02a2cdbb20b1a85b23a717 SHA512: a8d5b65e4b539ce13a71a934a32eec0af09af6026328d469e218f08f6024182e3819bff121a076e0a4db94b3c3e031a99ccda7e9f7a7e2bb773e831910abdee3 Homepage: https://cran.r-project.org/package=icertool Description: CRAN Package 'icertool' (Calculate and Plot ICER) The app will calculate the ICER (incremental cost-effectiveness ratio) Rawlins (2012) from the mean costs and quality-adjusted life years (QALY) Torrance and Feeny (2009) for a set of treatment options, and draw the efficiency frontier in the costs-effectiveness plane. The app automatically identifies and excludes dominated and extended-dominated options from the ICER calculation. Package: r-cran-icesadvice Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-icesadvice_2.1.1-1.ca2604.1_all.deb Size: 61258 MD5sum: 685cc1d23dca63a2917e99b32468dda8 SHA1: 4ae9a90a3770ce6c45beb01d018766e203821284 SHA256: 04b8ffcc708500885c695a3fc51d301f0babd0f3393f298618bbdd947578cdb4 SHA512: f7c0bf552bbbcd5c23170781cce0627f70e0909a045cc5c134a3ac8d818cca83a040025b30e6bbd5cefa52d91d3e8e44d537f9416f1c9831145a8bcfed5c4e50 Homepage: https://cran.r-project.org/package=icesAdvice Description: CRAN Package 'icesAdvice' (Functions Related to ICES Advice) A collection of functions that facilitate computational steps related to advice for fisheries management, according to ICES guidelines. These include methods for calculating reference points and model diagnostics. 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ICES is an organization facilitating international collaboration in marine science. 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Package: r-cran-icods Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-icods_1.2-1.ca2604.1_all.deb Size: 232246 MD5sum: 24f700ef78c1b4566e96e9fed2bf0188 SHA1: 5a6cf1bba8811e3b039fab83ee97d77fa5084348 SHA256: c1a1d1bd8811077fa83fc9c83e4c40172347900c9edb00d49a400922e2d8dc4d SHA512: f37ef6e26172f7e5070150bf759d7ad123acf68e322653aec2a552def63a8aaae18af6dfce0437c92f1010a2f3da40865b93d22b54242423bc5fe844fa87d65c Homepage: https://cran.r-project.org/package=ICODS Description: CRAN Package 'ICODS' (Data Analysis for ODS and Case-Cohort Designs withInterval-Censoring) Sieve semiparametric likelihood methods for analyzing interval-censored failure time data from an outcome-dependent sampling (ODS) design and from a case-cohort design. Zhou, Q., Cai, J., and Zhou, H. (2018) ; Zhou, Q., Zhou, H., and Cai, J. (2017) . 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However, there are some major disadvantages of training such networks via the widely accepted 'gradient-based backpropagation' algorithm, such as convergence to local minima, dependencies on learning rate and large training time. These concerns were addressed by Huang et al. (2006) , wherein they introduced the Extreme Learning Machine (ELM), an extremely fast learning algorithm for SLFNs which randomly chooses the weights connecting input and hidden nodes and analytically determines the output weights of SLFNs. It shows good generalized performance, but is still subject to a high degree of randomness. To mitigate this issue, this package uses a dimensionality reduction technique given in Hyvarinen (1999) , namely, the Independent Component Analysis (ICA) to determine the input-hidden connections and thus, remove any sort of randomness from the algorithm. This leads to a robust, fast and stable ELM model. Using functions within this package, the proposed model can also be compared with an existing alternative based on the Principal Component Analysis (PCA) algorithm given by Pearson (1901) , i.e., the PCA based ELM model given by Castano et al. (2013) , from which the implemented ICA based algorithm is greatly inspired. Package: r-cran-icpack Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rlang, r-cran-gridextra, r-cran-checkmate, r-cran-matrixstats, r-cran-dplyr, r-cran-reshape2, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-flexsurv, r-cran-lemon, r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-icpack_0.1.0-1.ca2604.1_all.deb Size: 203854 MD5sum: 398dca8d4b660b59ee7f2af88407ef47 SHA1: 9c2ffa0002dfbb365d9421858c65f1c462feb1bc SHA256: 39ab23a38a6c25e13045ae481fc54fa5f7978d71bcbe910d6b0a20c106103ed1 SHA512: 2d99df59eb6829a4870539091ce422bd11524b79a07397206962f7953adf09f5bb38d85648f5bfde0bc9aaf911633fa6c847681563e95b7d958f8ad93fd5a66d Homepage: https://cran.r-project.org/package=icpack Description: CRAN Package 'icpack' (Survival Analysis of Interval-Censored Data) Survival analysis of interval-censored data with proportional hazards, and an explicit smooth estimate of the baseline log-hazard with P-splines. Package: r-cran-icpsrdata Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rvest, r-cran-purrr, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-icpsrdata_0.6.1-1.ca2604.1_all.deb Size: 424218 MD5sum: 0596f3ed760a2851e2f5f43fe9f4489b SHA1: 976d92490c80829e428ac42b64597cf60cdc2de7 SHA256: aef5b3b9cdccc95abf76daef8020431198e1216712f67e56a1cca59af1bcd2ba SHA512: 30332500db28ea57676d83c1953a51078e73a41664ce2f52a8ce0cce59cad133dcf01db8b142518b7ec30ebbd4a3bfa77900848c2d6eecb7e2a07843cb37d785 Homepage: https://cran.r-project.org/package=icpsrdata Description: CRAN Package 'icpsrdata' (Reproducible Data Retrieval from the ICPSR Archive) Reproducible, programmatic retrieval of datasets from the Inter-university Consortium for Political and Social Research archive. Package: r-cran-ics Architecture: all Version: 1.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-survey Suggests: r-cran-pixmap, r-cran-robustbase, r-cran-mass, r-cran-icsnp, r-cran-testthat, r-cran-icsoutlier Filename: pool/dists/resolute/main/r-cran-ics_1.4-2-1.ca2604.1_all.deb Size: 1136544 MD5sum: 3a32fa49d1ca7c8e8b13cc35a90135f9 SHA1: bce4d5f3a5c976f14e6ae57a03e5686e8a42dccc SHA256: 3abde0dcd580702fefce4535ffd8476f9bcc7395a9ff02f8d9358bcde706d34f SHA512: 4e8cc34b6fdb94af38cce8245a275e5f0615c3fc78c83bad91d0ab23031668724a5efd988d2a03c27105641153825c5ac0e14939fbbc34a3455258e73c3023b2 Homepage: https://cran.r-project.org/package=ICS Description: CRAN Package 'ICS' (Tools for Exploring Multivariate Data via ICS/ICA) Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009, ) and Oja, Sirkia and Eriksson's (AJS, 2006, ) method of two different scatter matrices to obtain an invariant coordinate system or independent components, depending on the underlying assumptions. Package: r-cran-icsoutlier Architecture: all Version: 0.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1068 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ics, r-cran-moments, r-cran-mvtnorm Suggests: r-cran-icsclust, r-cran-repplab, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-icsoutlier_0.4-1-1.ca2604.1_all.deb Size: 1022294 MD5sum: c528dcc2525308d449ecb905dea920ca SHA1: fc6ce69d0fdb3ef138a0798f0099b0f2d20861ad SHA256: cbe9966a38be26e41b228fd5862de20455cbb6a7800c22b6015436a8d5cc3ceb SHA512: 7dca072fcd13eb243614a3c39be23863fe976dd36b01556539d4bbf46f2793e4b002eb38e6fcee620ef5608d7460dc5f4ea2310c26ecde09a164d23140e1e71d Homepage: https://cran.r-project.org/package=ICSOutlier Description: CRAN Package 'ICSOutlier' (Outlier Detection Using Invariant Coordinate Selection) Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers. Package: r-cran-icss Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstack Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-icss_1.1-1.ca2604.1_all.deb Size: 25058 MD5sum: 10047e2731c7c778abd9ac4058a97e96 SHA1: 9a5eeeeaf64665d5be870e1982b2cce6fcdfc63b SHA256: d875dc9f4547a293c0dfdd96822e7aff99f0bfa0105f4e6c6b9739079b71db91 SHA512: 23fe00ae7ab1519f80b0f0d4cb7f0407d979aea7c0213c5dcfa1b85ed13c56ad68d0e0142a40fe0fa74787428bc8cb65e054d5848af6b97b3803a35961781e30 Homepage: https://cran.r-project.org/package=ICSS Description: CRAN Package 'ICSS' (ICSS Algorithm by Inclan/Tiao (1994)) The Iterative Cumulative Sum of Squares (ICSS) algorithm by Inclan/Tiao (1994) detects multiple change points, i.e. structural break points, in the variance of a sequence of independent observations. 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See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 . Package: r-cran-idarps Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-idarps_0.0.6-1.ca2604.1_all.deb Size: 133834 MD5sum: 7c6e21d2363dace64c1a618238b58fcd SHA1: f84478387c54b149ce09ea307885ca6e636754db SHA256: cb9d849dee6ec499539c540f8ffaf944dc1899529a6ac62f2184f9daf4e47843 SHA512: 0a5c5ad1b19591f00bc41b5a17c51a2779eef845fb803a603463f961712809fa56128bcb340dceb2374779b464f2a3e44b4ba46c1c1deffcbd7452fbaea52201 Homepage: https://cran.r-project.org/package=idarps Description: CRAN Package 'idarps' (Datasets and Functions for the Class "Modelling and DataAnalysis for Pharmaceutical Sciences") Provides datasets and functions for the class "Modelling and Data Analysis for Pharmaceutical Sciences". The datasets can be used to present various methods of data analysis and statistical modeling. 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Results are returned as R data frames. For more information about the IDB API, visit . Package: r-cran-idcard Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-idcard_0.3.0-1.ca2604.1_all.deb Size: 33600 MD5sum: e6f21dd663c7969bb1f510115d8e7d20 SHA1: 053fe4ceb084e7358f0cd0d37ee689db777589c4 SHA256: 09b851dfc47e070ef26740133466e5f1c548b185dba0de917688130f5706f2c0 SHA512: e70b995646eefdc2d11c44c222cbb32d7edcdbf4802bdb8e4a1ae5c24023778780ae2414099799a165ac73c80b24e80ebfe13293ccd433b457e7dd513349c499 Homepage: https://cran.r-project.org/package=IDCard Description: CRAN Package 'IDCard' (Update Chinese ID Card Number to Eighteen Digits) The digits of the old version (before 2000 year) of 'Chinese ID Card Number' is 15, this package aims to update to the current version of 18 digits. 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Package: r-cran-ide Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-sp, r-cran-spacetime, r-cran-dplyr, r-cran-tidyr, r-cran-frk, r-cran-deoptim, r-cran-sparseinv Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-ide_0.3.1-1.ca2604.1_all.deb Size: 264534 MD5sum: 97de13756984de6df3d6b4cd086d1512 SHA1: 29abaac10a8a3d71ff1408e2b000aa6ee1939d35 SHA256: 3dac704c38e6c3992051fc83e02e0b55866dd6e937b68835caa8b49666c0cf43 SHA512: 54b91d471cae3658c3c30074f0546e9b1b4075ea5a40df3e9a40d6659fac0d5d99daa82fc11b69ca8a57b71871e5f035c686aeb3bde938b2ff4b753ae9883fae Homepage: https://cran.r-project.org/package=IDE Description: CRAN Package 'IDE' (Integro-Difference Equation Spatio-Temporal Models) The Integro-Difference Equation model is a linear, dynamical model used to model phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011, ISBN:978-0-471-69274-4) or Dewar et al. (2009) . At the heart of the model is the kernel, which dictates how the process evolves from one time point to the next. Both process and parameter reduction are used to facilitate computation, and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted by maximum likelihood, and estimation is carried out using an evolution algorithm. Package: r-cran-ideafilter Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-ggplot2, r-cran-pillar, r-cran-purrr, r-cran-rcolorbrewer, r-cran-shiny, r-cran-shinytime Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest, r-cran-shinytest2, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ideafilter_0.2.1-1.ca2604.1_all.deb Size: 206632 MD5sum: d5e4baa57e4607192a995307f843324d SHA1: 63d0707ac5d4a6c7927f893dbd4e24ebffe801cb SHA256: 873bd4cc836aa8686025c8f1888488f25688b63347295e74fa5f9816e037abd4 SHA512: 8db851d273b44e4840d01b051ffd25f66f6b4ec96a6c4aa0a7c8270b6c65f722afc3823ab2d74dc94911107600092412ab9c66ab531f915b6e16d8e30e88e60d Homepage: https://cran.r-project.org/package=IDEAFilter Description: CRAN Package 'IDEAFilter' (Agnostic, Idiomatic Data Filter Module for Shiny) When added to an existing shiny app, users may subset any developer-chosen R data.frame on the fly. That is, users are empowered to slice & dice data by applying multiple (order specific) filters using the AND (&) operator between each, and getting real-time updates on the number of rows effected/available along the way. Thus, any downstream processes that leverage this data source (like tables, plots, or statistical procedures) will re-render after new filters are applied. The shiny module’s user interface has a 'minimalist' aesthetic so that the focus can be on the data & other visuals. In addition to returning a reactive (filtered) data.frame, 'IDEAFilter' as also returns 'dplyr' filter statements used to actually slice the data. Package: r-cran-idealstan Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1330 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-svdialogs, r-cran-tidyr, r-cran-stringr, r-cran-bayesplot, r-cran-posterior, r-cran-gghighlight, r-cran-ggrepel, r-cran-ggplot2, r-cran-tidybayes, r-cran-lazyeval, r-cran-rlang, r-cran-ggthemes, r-cran-forcats, r-cran-ordbetareg, r-cran-scales, r-cran-shiny, r-cran-tibble Suggests: r-cran-quarto, r-cran-knitr, r-cran-rmarkdown, r-cran-loo, r-cran-lubridate, r-cran-rstan, r-cran-shinystan, r-cran-pscl, r-cran-tinytable, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-idealstan_1.0-1.ca2604.1_all.deb Size: 659276 MD5sum: 2cec3a87be9466934409c7492ec98a83 SHA1: 952dd358bbdb21abe9f275cf2543397bfab8762a SHA256: c92254aa8234fc7a741740edd5f0b05ac117999038aa55c9ebdcd67de2504086 SHA512: 64226388baebfcf22a871162a786e2fecf5e834bf97c4305bbf6cf3316131782ec48a74c3746599318bda1f7945e1af554a4c9eeabdba1d46e6edc916bbdf82a Homepage: https://cran.r-project.org/package=idealstan Description: CRAN Package 'idealstan' (Robust Measurement with 'Stan') Offers item-response theory (IRT) ideal-point measurement modeling for diverse distributions, missing data, and over-time variation. Full and approximate Bayesian sampling with 'Stan' (). Package: r-cran-ideamdb Architecture: all Version: 0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 783 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ideamdb_0.0.9-1.ca2604.1_all.deb Size: 400622 MD5sum: 3c1e78ba93889cb7d64dc8a3803056dd SHA1: aeee6df7be00d546fd28d2603989d3acf060bd74 SHA256: 4bcb96b359cbd5de8908f674969a745501cd794fadfe1298dbc60d086d3a8852 SHA512: 4617e2c0dfff0758549dfcb0dd4cc7b5bf776f43b178af72d4eafa6c7c3b4e76f8ec56dfc4c2f702a8f3521054b6fa7db1a69441d66317936587b52f0aa7f936 Homepage: https://cran.r-project.org/package=ideamdb Description: CRAN Package 'ideamdb' (Easy Manipulation of IDEAM's Climatological Data) Time series plain text conversion and data visualization. It allows to transform IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales) daily series from plain text to CSV files or data frames in R. Additionally, it is possible to obtain exploratory graphs from times series. IDEAM’s data is freely delivered under formal request through the official web page . Package: r-cran-ideanet Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraphdata, r-cran-cliquepercolation, r-cran-cluster, r-cran-colorspace, r-cran-concorr, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridgraphics, r-cran-igraph, r-cran-intergraph, r-cran-jsonlite, r-cran-magrittr, r-cran-matrix, r-cran-moments, r-cran-network, r-cran-readxl, r-cran-reshape2, r-cran-rlang, r-cran-rspectra, r-cran-shiny, r-cran-sna, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-dt, r-cran-devtools, r-cran-egor, r-cran-ergm, r-cran-shinythemes, r-cran-shinywidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-shinycssloaders, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-ideanet_1.1.1-1.ca2604.1_all.deb Size: 2702450 MD5sum: 51f5a1cf610603741f759aa3734059fe SHA1: 043fe80d0503f79eccbdd30e952253d3003eb367 SHA256: 46180157309e1b7494abcee9b7babd7b0ac05d1dc03a878ff935501db5bbc202 SHA512: 49751f07f317d3506044c15893a793292d581a4e57a32ae138df79f70c1f769d93d939a601f1060a08f3b4d2d7aee620465cb4bee39e0dfc1d90450f142ebb93 Homepage: https://cran.r-project.org/package=ideanet Description: CRAN Package 'ideanet' (Integrating Data Exchange and Analysis for Networks ('ideanet')) A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding. Package: r-cran-ideatools Architecture: all Version: 3.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-readxl, r-cran-tibble, r-cran-stringi, r-cran-ggimage, r-cran-pdftools, r-cran-ggpubr, r-cran-ggplot2, r-cran-rlang, r-cran-ggtext, r-cran-rmarkdown, r-cran-shiny, r-cran-openxlsx Suggests: r-cran-covr, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-ideatools_3.5.2-1.ca2604.1_all.deb Size: 2334910 MD5sum: 8d4a093235a6e15459655bd9da2634d3 SHA1: 11052776353e74d1b69b2158285c820ad8333386 SHA256: 56cdea3c397d939644e6dc0d87752df847f4458f4c1ac8b76b22b35de9839427 SHA512: a97d2c96b9ebad50b269c10615649e454ffa55615f5b20b47af9dec49603ed30a2977f4f044a6bea7af4149df43b09492bd370eaa6c1caac2a3d67bdd5a96bc4 Homepage: https://cran.r-project.org/package=IDEATools Description: CRAN Package 'IDEATools' (Individual and Group Farm Sustainability Assessments using theIDEA4 Method) Collection of tools to automate the processing of data collected though the IDEA4 method (see Zahm et al. (2018) ). Starting from the original data collecting files this packages provides functions to compute IDEA indicators, draw modern and aesthetic plots, and produce a wide range of reporting materials. Package: r-cran-idendr0 Architecture: all Version: 1.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tkrplot Suggests: r-cran-dendser, r-cran-cluster, r-cran-rcolorbrewer, r-cran-loon, r-cran-mass, r-bioc-flowstats, r-cran-hyperspec Filename: pool/dists/resolute/main/r-cran-idendr0_1.5.4-1.ca2604.1_all.deb Size: 1555898 MD5sum: 668c190fb2087a2347e75c9b0cbe3ed0 SHA1: 8585e28b9e215e5896b9609943c9aa7944e32d5d SHA256: 5b22484b664ad53085f9745ce4ba562f8e95486ee5412dfc5c5258739a713684 SHA512: c260b004ecf4e30c56f2c7197de089a22af44179bfcfda19e2481a73db6647270957ecb3f16d5bd53eb73f8f87ad426a8fd1be119df1dcafd864b44f8a4f33db Homepage: https://cran.r-project.org/package=idendr0 Description: CRAN Package 'idendr0' (Interactive Dendrograms) Interactive dendrogram that enables the user to select and color clusters, to zoom and pan the dendrogram, and to visualize the clustered data not only in a built-in heat map, but also in 'GGobi' interactive plots and user-supplied plots. This is a backport of Qt-based 'idendro' () to base R graphics and Tcl/Tk GUI. Package: r-cran-ider Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-glm2 Filename: pool/dists/resolute/main/r-cran-ider_0.1.1-1.ca2604.1_all.deb Size: 933828 MD5sum: 44da1661a0a11b819463ba525d719d4c SHA1: bd1a71069f1aa3df6642a0d5fa32afd12a5cbd98 SHA256: 0adb734b91aa196b15a616b8abea365fe4ac3d4bd58eb80c48c14d6010c4fc65 SHA512: c2233c8ea16fd25b62f526eb9becd35ccade5f960b65dfc8c60a37352c1e8e6be40ad90d127fcd5551adf77e2681d7b1a0e7e2c4f8296571d12e5b8faa843f4c Homepage: https://cran.r-project.org/package=ider Description: CRAN Package 'ider' (Various Methods for Estimating Intrinsic Dimension) An implementation of various methods for estimating intrinsic dimension of vector-valued dataset or distance matrix. Most methods implemented are based on different notion of fractal dimension such as the capacity dimension, the box-counting dimension, and the information dimension. Package: r-cran-idetect Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-idetect_0.1.1-1.ca2604.1_all.deb Size: 155196 MD5sum: a08e8f5c4369eb2d2337c7e29f906ca5 SHA1: 557cbf7517802625c97d9b18540d1c5e8e3e7655 SHA256: aaab18221eb131caea43b79efd057704a4e72a4662af2dd757db706626a3d0ca SHA512: dd286bde1fb48e0b8f3fea2d52d0503d4171181eb389649015115947f578868958e8260970889235e59fc69a90bfb7f7d4e56c737940becd342206a2818c5a92 Homepage: https://cran.r-project.org/package=IDetect Description: CRAN Package 'IDetect' (Isolate-Detect Method for Multiple Change-Point Detection) The IDetect provides efficient implementation of the ID methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the `deterministic + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, continuous piecewise-linear signal, continuous piecewise-linear signal with a heavy-tailed noise. Package: r-cran-idf Architecture: all Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evd, r-cran-ismev, r-cran-rcpproll, r-cran-pbapply, r-cran-fastmatch Filename: pool/dists/resolute/main/r-cran-idf_2.1.3-1.ca2604.1_all.deb Size: 284948 MD5sum: e75b4fc5ff3b899efbe684fb9507f9a7 SHA1: e3ba4e3b918da636bad7aa168ff59e4d5d5b5e9a SHA256: e33b2087f0e3418cccf1394b8bea246901be026f49187e96103342c5d910dc96 SHA512: 7557c7d380772254384414f71e10c1b7c4464dba217473c4508c1eb2aad6f874834b2c2d61b3b2f4ec1dda8be5d1c32aed52480e59143ee23eb1018e8d3e00e0 Homepage: https://cran.r-project.org/package=IDF Description: CRAN Package 'IDF' (Estimation and Plotting of IDF Curves) Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, ]. The package 'IDF' provides functions to estimate IDF parameters for given precipitation time series on the basis of a duration-dependent generalized extreme value distribution [Koutsoyiannis et al., 1998, ]. Package: r-cran-idingo Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-mvtnorm, r-cran-glasso, r-cran-ggmridge, r-cran-visnetwork, r-cran-scales Filename: pool/dists/resolute/main/r-cran-idingo_1.0.4-1.ca2604.1_all.deb Size: 306144 MD5sum: 864f74efaebaf7fee434a78cb05af83a SHA1: 451dfe008e29108d112f4af0ffced612501fce6d SHA256: 4617cbb5400839b7b118bbb2aaf47591a67a0018857086dcf7600f7dd09c485c SHA512: 133088e8a8837521c179e54e2650a4f5d4027c1e66e4312d6a52cb3a30b558a0fc1bd69ef9845bb6f55e6cbe1e7825bed1faaacb0dbe6e18395142d37cc7c036 Homepage: https://cran.r-project.org/package=iDINGO Description: CRAN Package 'iDINGO' (Integrative Differential Network Analysis in Genomics) Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) and Ha et. al., (2015) . Package: r-cran-idiogramfish Architecture: all Version: 2.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3844 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-crayon, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-scales Suggests: r-cran-rentrez, r-cran-ggplot2, r-cran-ggpubr, r-bioc-ggtree, r-cran-phytools, r-bioc-treeio, r-cran-rmdformats, r-cran-knitr, r-cran-kableextra, r-cran-rvcheck, r-cran-badger, r-cran-rmarkdown, r-cran-rcurl, r-cran-shiny, r-cran-shinydashboard, r-cran-rhandsontable, r-cran-gtools, r-cran-rclipboard, r-cran-clipr, r-cran-shinyjs Filename: pool/dists/resolute/main/r-cran-idiogramfish_2.0.13-1.ca2604.1_all.deb Size: 1944580 MD5sum: c20e34d8d4c63e1f244bbb8e7ee33eef SHA1: 8d52a9a6e9f7469f0013c804e259a002d9bac012 SHA256: dab64dd149cba162436044713a666a96ecdd2288b64f6f400964e1d380cbc722 SHA512: 39a469c9df68d89ac7b7d72792ed6c5a20ffc8e192de3e2e30a27665a0170f07d34d493a0d0bf6bc182a135024967b55923459fe35804e266b52bb373dbdd389 Homepage: https://cran.r-project.org/package=idiogramFISH Description: CRAN Package 'idiogramFISH' (Shiny App. Idiograms with Marks and Karyotype Indices) Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids. Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangle, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) , karyotype indices of Watanabe et al. (1999) and Romero-Zarco (1986) and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964). Package: r-cran-idiolect Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 929 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quanteda, r-cran-caret, r-cran-dplyr, r-cran-fdrtool, r-cran-ggplot2, r-cran-kgrams, r-cran-pbapply, r-cran-proc, r-cran-proxy, r-cran-quanteda.textstats, r-cran-spacyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-readtext, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-idiolect_1.2.0-1.ca2604.1_all.deb Size: 770806 MD5sum: ddbb97ecb1f676e96dfd8bed376eb280 SHA1: 47f9d37b2bcac49e0beb4f30bf19e5a2f862a5d6 SHA256: 16efcb19f5dd2bc2439432177154bec0b7e3fd66d96e308448ea5def9ce34d90 SHA512: d143066f175afa329053020ed23edfa720695835a3d9bffe3d2ae097608649ae5e854a90ce46f54f82eadf61f5fe1a4c54ca59b862075610fa963c5e04ff4197 Homepage: https://cran.r-project.org/package=idiolect Description: CRAN Package 'idiolect' (Forensic Authorship Analysis) Carry out comparative authorship analysis of disputed and undisputed texts within the Likelihood Ratio Framework for expressing evidence in forensic science. This package contains implementations of well-known algorithms for comparative authorship analysis, such as Smith and Aldridge's (2011) Cosine Delta or Koppel and Winter's (2014) Impostors Method , as well as functions to measure their performance and to calibrate their outputs into Log-Likelihood Ratios. Package: r-cran-idionomics Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-forcats, r-cran-forecast, r-cran-ggplot2, r-cran-metafor, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-idionomics_0.1.0-1.ca2604.1_all.deb Size: 182506 MD5sum: 5c12b64ee0aeb14b0c597837b66b5b59 SHA1: 5ef58eeb07c47282fc55a2f0fe3f73bc0ced8b0f SHA256: 016561fe20e4b7ad8b7bf597b278d810ae6a5b18fb6b2a5d552e640f342ea076 SHA512: 58ccb53379e9a1f1525957ea48d54ff1f0ee6670579faaf2be0928227ab352cae311674558a75797c670b31d2d0a6620e09cde0f921aa2de4b25318a500b89ec Homepage: https://cran.r-project.org/package=idionomics Description: CRAN Package 'idionomics' (Conduct Idionomic Analyses for Time Series Modeling) A toolkit for idionomic science, a research philosophy that places the unit of the ensemble (individual/couple/group) at the center of analysis. Rather than assuming a common distribution, a similar enough process for each unit, and fitting a single model to the whole ensemble, idionomic methods model each unit separately, then aggregate upward if sensible. The group-level picture emerges from individual results, not the other way around, while explicitly evaluating whether aggregation is reasonable given the measured level of heterogeneity of effects. The package is built around intensive longitudinal data where each participant contributes a time series. It provides a pipeline from preprocessing through modeling to group-level summaries. Current functions: data quality screening (i_screener()), within-person standardization (pmstandardize()), linear detrending (i_detrender()), per-subject ARIMAX (AutoRegressive Integrated Moving Average with eXogenous inputs) modeling and meta-analysis (iarimax()), individual p-values (i_pval()), Sign Divergence and Equisyncratic Null tests (sden_test()), and directed loop detection (looping_machine()). Methods are described in Hernandez et al. (2024) , Ciarrochi et al. (2024) , and Sahdra et al. (2024) . Package: r-cran-idlfm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-sparsearray Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-idlfm_1.0.0-1.ca2604.1_all.deb Size: 30244 MD5sum: a336ebad213495ef261c3d433aef5470 SHA1: f9bbb74f9ead3488602c778be8dd22c907b8d5de SHA256: e8a94f145dc5bf5c80ca9c01a8ba2a6aac78ba52a25d96654afe4b4da53db7f4 SHA512: 131d414432611e24c0474d36aea6a2f38b3d088d8ad7f77654ab4e61ce7ba6d6468ddf02c27417ecf8d8bf2f07befd634fe71e9a0f11b5521456b9667fae12e9 Homepage: https://cran.r-project.org/package=IDLFM Description: CRAN Package 'IDLFM' (Individual Dynamic Latent Factor Model) A personalized dynamic latent factor model (Zhang et al. (2024) ) for irregular multi-resolution time series data, to interpolate unsampled measurements from low-resolution time series. Package: r-cran-idm Architecture: all Version: 1.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-animation, r-cran-corpcor, r-cran-ca, r-cran-ggrepel Suggests: r-cran-caret Filename: pool/dists/resolute/main/r-cran-idm_1.8.3-1.ca2604.1_all.deb Size: 514836 MD5sum: 950b19e0b776229fa26b68416b80703d SHA1: 4fc6b853957985166bcb78f4a6926f2c087c77c5 SHA256: 740d19b9ac0acc6bf91a4cf85da3df82e1f45c884fbcff61487eb6754af2787f SHA512: cb76a6f60088ca125c9ba988e4675d363a886b83ab8233a3735a47ecb158f97b7c5be78710c307fa13b50f528c3e78c6fff429e4f49d71f31350432ebaa1bb59 Homepage: https://cran.r-project.org/package=idm Description: CRAN Package 'idm' (Incremental Decomposition Methods) Incremental Multiple Correspondence Analysis and Principal Component Analysis. 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This difficulty arises from different mappings between raw scores and scale scores, complex mathematical relationships, adjustments based on judgmental procedures, and diverse equating functions applied to different assessment forms. An alternative method involves running simulations to explore the effect of incrementing raw scores on mean scale scores. The 'idmact' package provides an implementation of this approach based on the algorithm detailed in Schiel (1998) which was developed to help interpret differences between mean scale scores on the American College Testing (ACT) assessment. The function idmact_subj() within the package offers a framework for running simulations on subject-level scores. In contrast, the idmact_comp() function provides a framework for conducting simulations on composite scores. 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(2019) : "Measuring individual identity information in animal signals: Overview and performance of available identity metrics". 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And the Morisita estimator is used for the ID estimation, but other tools are included as well. 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For reference, see below: 1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018) 2. Wu, S., Wu, H. (2013) 3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016) . 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It permits to work simultaneously with various testing materials, from standard univariate, and functional data analysis (FDA) perspectives. The univariate approach based on ASTM E691-08 consist of estimating the Mandel's h and k statistics to identify those laboratories that provide more significant different results, testing also the presence of outliers by Cochran and Grubbs tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey tests) to test differences in means corresponding to different laboratories per each material. Taking into account the functional nature of data retrieved in analytical chemistry, applied physics and engineering (spectra, thermograms, etc.). ILS package provides a FDA approach for finding the Mandel's k and h statistics distribution by smoothing bootstrap resampling. Package: r-cran-ilsamerge Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2025 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ilsamerge_1.4.0-1.ca2604.1_all.deb Size: 247132 MD5sum: ccdc5f2b9278f8b327e08fdaf4ac9b12 SHA1: 672a57f7ccbaa384cf5584b18121f1d73a202c37 SHA256: 1dbe1c5e40de515d21c7407dfa6f28168a9ef8f4fc1e40922b8c71730ca064b1 SHA512: 5878f82ad29619605ec4ad7bc2279394e4de447ed42dc7a2b568409713ca598b4f606ddc84b37104484c9d23d952e989da0e22f314940437039da90437d9f0aa Homepage: https://cran.r-project.org/package=ILSAmerge Description: CRAN Package 'ILSAmerge' (Merge and Download International Large-Scale Assessments (ILSA)Data) Merges and downloads 'SPSS' data from different International Large-Scale Assessments (ILSA), including: Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), and others. 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The method begins with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution of model parameters. Accepted points result in model predictions that are within the initially specified tolerance intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) . 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The functions provide scores for several basic aesthetic principles that facilitate fluent cognitive processing of images: contrast, complexity / simplicity, self-similarity, symmetry, and typicality. See Mayer & Landwehr (2018) and Mayer & Landwehr (2018) for the theoretical background of the methods. 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The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity. However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting. 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Package: r-cran-imageseg Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-keras, r-cran-magick, r-cran-magrittr, r-cran-purrr, r-cran-tibble, r-cran-foreach, r-cran-doparallel, r-cran-dplyr Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-imageseg_0.5.0-1.ca2604.1_all.deb Size: 3688950 MD5sum: b0a62e9d5e8da93494365dde3a286a9b SHA1: d6d830e2e897f9c73cd19d3dbe0ed763b2317a51 SHA256: 06c1c427192c4f8edc97d13376a10ddb75411d45d9f142892ccca7c22ff661f6 SHA512: c5e0434600f0ae8a56f0b97a44d04b3b2a864a3e03010224e1bb0519ad817bf5f30645fd2b662868bc01e2a4e0082729fb8dd2e4833e6248474204456baeccf5 Homepage: https://cran.r-project.org/package=imageseg Description: CRAN Package 'imageseg' (Deep Learning Models for Image Segmentation) A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) and the U-Net++ architecture by Zhou et al. (2018) . We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation. 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Package: r-cran-imfweo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-tidyr Suggests: r-cran-curl, r-cran-testthat, r-cran-withr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-imfweo_0.1.0-1.ca2604.1_all.deb Size: 46992 MD5sum: 8c01d2217ba34f8d8bb01625df66ad4f SHA1: d357a121f0cc81d270f522d869d916a1d19eb27e SHA256: d26d7787755cd6f260cc696ea441603c8fdd4af69b6292821c5dc00d93803fac SHA512: 5db070879f2bc106cc711f8ceb311ab621d4f40f09cc6dfe80c632a6af078dc57a704c486f261a6ff23838536cfb7531bf209ca871e462e869121397cd043a8e Homepage: https://cran.r-project.org/package=imfweo Description: CRAN Package 'imfweo' (Seamless Access to IMF World Economic Outlook (WEO) Data) Provides tools to download, process, and analyze data from the International Monetary Fund's World Economic Outlook (WEO) database . 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Package: r-cran-imifa Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3827 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-mclust, r-cran-mvnfast, r-cran-rfast, r-cran-slam, r-cran-viridislite Suggests: r-cran-gmp, r-cran-knitr, r-cran-mcclust, r-cran-rmarkdown, r-cran-rmpfr Filename: pool/dists/resolute/main/r-cran-imifa_2.2.0-1.ca2604.1_all.deb Size: 3661862 MD5sum: 5528170f33d5386842bcdfd98972781d SHA1: 5fac6d49cf55c880b87f826e353f759e6931a082 SHA256: b7f6dbf1e2fca5b5874771e9136065bec1431ced27cb27ec4f9f5d7d97fafecc SHA512: adf45664afe4d70121697d7bc9c2505c5123a3714609aca9999af382bbd1a0c937c824de74544679ea6cd649883b6ffe63afca747f0d9e8abc2bdaac63d33309 Homepage: https://cran.r-project.org/package=IMIFA Description: CRAN Package 'IMIFA' (Infinite Mixtures of Infinite Factor Analysers and RelatedModels) Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) . The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty. Package: r-cran-imix Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-mvtnorm, r-cran-mixtools, r-cran-mclust, r-cran-ggplot2, r-cran-mass Filename: pool/dists/resolute/main/r-cran-imix_1.1.5-1.ca2604.1_all.deb Size: 153028 MD5sum: e5ed4e4132525bf286576ed54d8d2b80 SHA1: 1ea1dd79f2ba68634b12227217ce6d94a32bebc4 SHA256: 24d55ac46b816e76aa5cfe2a771a39256bb82df854db6fa83194c9b43cb321d1 SHA512: 32e2dc671970acd54c441cb9b042ef3d306ea0047fdc26a4c2a37184b9d65e2e5559a35c8e063eba3b15552d42418586cea7ff8e95936bcc9b29dec98e2e448c Homepage: https://cran.r-project.org/package=IMIX Description: CRAN Package 'IMIX' (Gaussian Mixture Model for Multi-Omics Data Integration) A multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. 'IMIX' can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. 'IMIX' uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. 'IMIX' features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) . Package: r-cran-iml Architecture: all Version: 0.11.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-formula, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-metrics, r-cran-r6 Suggests: r-cran-bench, r-cran-bit64, r-cran-caret, r-cran-covr, r-cran-e1071, r-cran-future.callr, r-cran-glmnet, r-cran-gower, r-cran-h2o, r-cran-keras, r-cran-knitr, r-cran-mass, r-cran-mlr, r-cran-mlr3, r-cran-party, r-cran-partykit, r-cran-patchwork, r-cran-randomforest, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat, r-cran-yaimpute Filename: pool/dists/resolute/main/r-cran-iml_0.11.4-1.ca2604.1_all.deb Size: 662520 MD5sum: 610044534763f8b97ae2a840ab609042 SHA1: 66a43f8908892de333b8474fecb955a81f425ad6 SHA256: eb3f54a3e572addfe69ba274de2454970415b7c6c77606174763aa196dc82d27 SHA512: c582d4f4f735abae762a20a1c43e07491df9edf961d644afa7945bd8234bfba5eb6a86a494f216170eb0055847f79273219cde3837c3840507ce13294b1f8658 Homepage: https://cran.r-project.org/package=iml Description: CRAN Package 'iml' (Interpretable Machine Learning) Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) , accumulated local effects plots described by Apley (2018) , partial dependence plots described by Friedman (2001) , individual conditional expectation ('ice') plots described by Goldstein et al. (2013) , local models (variant of 'lime') described by Ribeiro et. al (2016) , the Shapley Value described by Strumbelj et. al (2014) , feature interactions described by Friedman et. al and tree surrogate models. Package: r-cran-immailgun Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr Filename: pool/dists/resolute/main/r-cran-immailgun_0.1.2-1.ca2604.1_all.deb Size: 45048 MD5sum: 35de27ae76d514e1c0fcf35768e25002 SHA1: a89f27be8401caa8aa6e9d2c3343bc20f0a4c1b9 SHA256: f4b87df296753448df37a45fdfddd0e64da8fb8e7fe937ffa47b1d9ec414c2f3 SHA512: de69e7967fbe8cef39b32ce0337a9d089ede62a53596725a9250479e23c07cb380773b26b61abb6af7888a493d8da1723f2e31162fd02087ca53dccfcd4a9c68 Homepage: https://cran.r-project.org/package=IMmailgun Description: CRAN Package 'IMmailgun' (Send Emails using 'Mailgun') Send emails using the 'mailgun' api. To use this package you will need an account from . Package: r-cran-imml Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rpart, r-cran-caret, r-cran-randomforest, r-cran-e1071, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-imml_0.1.5-1.ca2604.1_all.deb Size: 24124 MD5sum: c99002beb1b335537910bd3b31276339 SHA1: 256273370b7950334c5d3c39c41f357ad07fcd1a SHA256: a8d97c83aba0c58277ef80e6b6bfa8abf00e933ae328aeda443b8e88ceb83b9d SHA512: 84f06c843abd02b856959b35c58a60272aa49bb42ae6673fcda19602cd61e763a62e94546f5656e772d412249475ab0364d39e49172b6d565817d957e4f30129 Homepage: https://cran.r-project.org/package=ImML Description: CRAN Package 'ImML' (Machine Learning Algorithms Fitting and Validation for Forestry) Fitting and validation of machine learning algorithms for volume prediction of trees, currently for conifer trees based on diameter at breast height and height as explanatory variables. Package: r-cran-immunaut Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-plyr, r-cran-dplyr, r-cran-caret, r-cran-proc, r-cran-prroc, r-cran-rlang, r-cran-rtsne, r-cran-dbscan, r-cran-fnn, r-cran-igraph, r-cran-fpc, r-cran-mclust, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-clustersim, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-immunaut_1.0.2-1.ca2604.1_all.deb Size: 179258 MD5sum: d68804a1dc435b642aa1e5dd8ca1d662 SHA1: 26abb729f920d8b6d68fbb9fb2b63e23137f9b17 SHA256: 8e7760c950881aa234148865a1de82bfdf8340a3a5fafcb48f9e448c7c652999 SHA512: be354365cb83b9087c8dcb65887a9caeb5ddd1d80c0185644f262663fda2fbec39f0023c92f7912a52e0a7d0c423c4720621f1a4da97c817aedc563a4d3d5f14 Homepage: https://cran.r-project.org/package=immunaut Description: CRAN Package 'immunaut' (Machine Learning Immunogenicity and Vaccine Response Analysis) Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. 'Immunaut' integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations. Package: r-cran-immundata Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-duckplyr, r-cran-checkmate, r-cran-cli, r-cran-dbplyr, r-cran-ggplot2, r-cran-glue, r-cran-jsonlite, r-cran-lifecycle, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-tibble Suggests: r-cran-duckdb, r-cran-rmarkdown, r-cran-testthat, r-cran-seurat Filename: pool/dists/resolute/main/r-cran-immundata_0.0.7-1.ca2604.1_all.deb Size: 3576660 MD5sum: 6b13476029d9629ae4067e610374a6ae SHA1: 2baa195f44f0e1e3b0a45f4aa8bf2246a028032b SHA256: 0908fefc5f4be1a444d66c2f33151ec94003aac14d7ac0cdc777a559ad61fcd0 SHA512: 211a82d3a45813e3079e272597180badea576ca391f5128aba6933fbc68e83be4765662550d47c28613742065116fb820a11fbe8c836c4f5325265bf17819e14 Homepage: https://cran.r-project.org/package=immundata Description: CRAN Package 'immundata' (A Unified Data Layer for Large-Scale Single-Cell, Spatial andBulk Immunomics) Provides a unified data layer for single-cell, spatial and bulk T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data. Package: r-cran-immunesigr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2248 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-immunesigr_0.1.0-1.ca2604.1_all.deb Size: 830324 MD5sum: 917ea1a3d528b7b85c97d249ba5eaf8c SHA1: bbd8a7636bcfa4e29c914cec36f7c2750a2e306b SHA256: b5f8a017bedf80e7237f242b61d078c721df4ff52e801fe0916da04e520a64cf SHA512: ba31abf14ef21a43966b8f54048b216eb94b2e7498754a6d1e1c99fc788c60b254890597121b974077df46748484ded53e821bc9dbdb185ac59fbf7e0b619e4d Homepage: https://cran.r-project.org/package=ImmuneSigR Description: CRAN Package 'ImmuneSigR' (Immune Cell Signature Retrieval and Single-Cell Scoring) Provides a literature-derived database of immune cell markers formatted as Gene Matrix Transposed (GMT) files. 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Package: r-cran-immunogenetr Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2852 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-immunogenetr_1.2.0-1.ca2604.1_all.deb Size: 297304 MD5sum: a1d22674e1048c6d2a021ae00493cc4b SHA1: 528e994d09180d10841fe5d8a1e4f61db247785f SHA256: 62a0e39129b53fde14a1676720d477f9000e334e0ba9ba266ea5e69341ac63d9 SHA512: 8414fe6b8fb99aaee6ee33620d852e9b2b0a1fe19d8807b8e76811dc141dbc0918374ce451157774f9770d82b145bbb5dc2ec468853c68359e1ea5cd71837b3a Homepage: https://cran.r-project.org/package=immunogenetr Description: CRAN Package 'immunogenetr' (A Comprehensive Toolkit for Clinical HLA Informatics) A comprehensive toolkit for clinical Human Leukocyte Antigen (HLA) informatics, built on 'tidyverse' principles and making use of genotype list string (GL string, Mack et al. 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Package: r-cran-imneuron Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlmetrics, r-cran-ggplot2, r-cran-neuralnet Filename: pool/dists/resolute/main/r-cran-imneuron_0.1.0-1.ca2604.1_all.deb Size: 23412 MD5sum: f66e3b0d1e0a0a8f65ada8b23e0f4f27 SHA1: 78ba192fc2ddd4de65e62a0f525e12a03a4f627f SHA256: 933029649024c3589b3d7a6419ca1430520fd23b18427e7adfd448be4c92ea8d SHA512: 949b9c1197e77e9a8593182135d7a860004bc389ebbdbe2f95baef8b6bf77d7cc1c044ccb895630453392cf8f9340d28370c46ebdd7239df4449fc83a5dcfcb8 Homepage: https://cran.r-project.org/package=Imneuron Description: CRAN Package 'Imneuron' (AI Powered Neural Network Solutions for Regression Tasks) It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics. Package: r-cran-imnn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlmetrics, r-cran-ggplot2, r-cran-neuralnet Filename: pool/dists/resolute/main/r-cran-imnn_0.1.0-1.ca2604.1_all.deb Size: 22170 MD5sum: b4ef329f4b5349b83f543a26041c885f SHA1: 18165151861a9801959021e49ee91cdeaa2a0233 SHA256: d7f60a48a19c4e693457a6e6d3fc74e0c63c72654e4611895abf4c3aabd437f1 SHA512: cd0f61e0cad35c944434a33520c7d592c7ccb6f4288f20ada9a50e1fb92dd6a07d6ea9b7e225b674547408c2bdc3439f11289e0483497519b3ee2f0071334b02 Homepage: https://cran.r-project.org/package=ImNN Description: CRAN Package 'ImNN' (Neural Networks for Predicting Volume of Forest Trees) Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) . Package: r-cran-imp Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-imp_1.1-1.ca2604.1_all.deb Size: 63630 MD5sum: 6eb04dd6735c24d9dedb1d237b000340 SHA1: 469c107a30b92e083c43e193da90f023b73308b6 SHA256: 89c92d85d4c5b7be91dc1b1ab31aaf199b18b79b2c206fd6838794d07a2661f3 SHA512: 6500d044fb53fdc016aca6ed0c3652560268e1443244e6074ca5822477a20e3ca59d227f98a34d2268358f209c0a2c6b7233b1670363f3039a0a2179cb69cbc6 Homepage: https://cran.r-project.org/package=IMP Description: CRAN Package 'IMP' (Interactive Model Performance Evaluation) Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps). Package: r-cran-impact Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-impact_0.1.1-1.ca2604.1_all.deb Size: 15968 MD5sum: bbb8b1b53c641c05b0f2ae7d55b1d18d SHA1: 83daca8c7e3b6e973df165253d5d7a469ba29859 SHA256: baa73cd52c53d7911fe6d9c5b2973e79805a79f425936022eef4fafd9892371b SHA512: 1679b6af45869da9c7336d3c0f1bf7e4e523a17d77875b26676f113c69c31f71e40aec2473fae5c8c536c69dd3c43be074abd3ca7debcd1f13783bfdb8e87273 Homepage: https://cran.r-project.org/package=IMPACT Description: CRAN Package 'IMPACT' (The Impact of Items) Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items. Package: r-cran-impectr Architecture: all Version: 2.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-impectr_2.5.5-1.ca2604.1_all.deb Size: 266064 MD5sum: c0e1de4f4cba4760b68a4a27d50dc5ce SHA1: 88712aac1f0bd07a88d4a21902d506856bee293a SHA256: ac480c188427ed60894baa8263adf03e35cb8fb23b9e72d8a06944c06242d7ec SHA512: 88cb30578a3266917d5252b19a91523b970b3563ed225b3ecb2c8de277617239b7d3a5590f43a61ae2f18fe3ffcdac3ba48d34607db5e6025bcbadecef892a86 Homepage: https://cran.r-project.org/package=impectR Description: CRAN Package 'impectR' (Access Data from the 'Impect' API) Pull data from the 'Impect' Customer API . The package can retrieve data such as events or match sums. Package: r-cran-impermanentlosscalc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-impermanentlosscalc_0.1.0-1.ca2604.1_all.deb Size: 25622 MD5sum: d4b725260ee843b718e43e9d6a06bb09 SHA1: 94cae4676428073fef5b5e298824d02d4ce962db SHA256: f3de97efc4a489061dd3826b9ab76b5841bfd07b72592ccdf8e03e1c9dc7f190 SHA512: b3b2f8aa3729840f66fd556b0376f2ad07c48a9150a72f45049c709f2f32b3911c2e0c4a4301b089975c0ddc956cd6c2ff189dd3308ce89753ef2e7830bd9cfe Homepage: https://cran.r-project.org/package=impermanentlosscalc Description: CRAN Package 'impermanentlosscalc' (Calculate Impermanent Loss in Automated Market Maker (AMM)Liquidity Pools) Computes the key metrics for assessing the performance of a liquidity provider (LP) position in a weighted multi-asset Automated Market Maker (AMM) pool. Calculates the nominal and percentage impermanent loss (IL) by comparing the portfolio value inside the pool (based on the weighted geometric mean of price ratios) against the value of simply holding the assets outside the pool (based on the weighted arithmetic mean). The primary function, `impermanent_loss()`, incorporates the effect of earned trading fees to provide the LP's net profit and loss relative to a holding strategy, using a methodology derived from Tiruviluamala, N., Port, A., and Lewis, E. (2022) . Package: r-cran-impimp Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-impimp_0.3.1-1.ca2604.1_all.deb Size: 65188 MD5sum: 46fb3918237a98652c2adaf65450045f SHA1: f767e9a8118d75397f9848e56da19b6cbfe76043 SHA256: 03036f19b8ab841821ac654ef769e5c1fd0bfe3c3a3e2aa08456ecaccd365d2a SHA512: 9826905b1d17c0a38c12f0081db4a1a4970bfefed61cc7c066a4c4d68c41eeeaefeb6f9b9636de6d10e46aa10b239708396e9762429143709f7c4f2db6ea1f9f Homepage: https://cran.r-project.org/package=impimp Description: CRAN Package 'impimp' (Imprecise Imputation for Statistical Matching) Imputing blockwise missing data by imprecise imputation, featuring a domain-based, variable-wise, and case-wise strategy. Furthermore, the estimation of lower and upper bounds for unconditional and conditional probabilities based on the obtained imprecise data is implemented. Additionally, two utility functions are supplied: one to check whether variables in a data set contain set-valued observations; and another to merge two already imprecisely imputed data. The method is described in a technical report by Endres, Fink and Augustin (2018, ). 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Impala enables low-latency 'SQL' queries on data stored in the 'Hadoop' Distributed File System '(HDFS)', Apache 'HBase', Apache 'Kudu', Amazon Simple Storage Service '(S3)', Microsoft Azure Data Lake Store '(ADLS)', and Dell 'EMC' 'Isilon'. See for more information about Impala. 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Package: r-cran-importanceindice Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-importanceindice_0.0.2-1.ca2604.1_all.deb Size: 75858 MD5sum: 825a59ff884548867cca1835240904ff SHA1: 1fba6ff074ae09d3e747ba96ce69e30e020b712e SHA256: df08ff51f9e293149545145eae89cc9fccfe6a6c283c461ec8d24416f60480b3 SHA512: fc685bb63d144f6f0a5d5df82ee761fb4bb58d9f039cd1773f5ee82d0d81ff6c2cfe35820d53693dc3728259b6e32f9e45026eafa1ea6a780b881e5c28a29d91 Homepage: https://cran.r-project.org/package=ImportanceIndice Description: CRAN Package 'ImportanceIndice' (Analyzing Data Through of Percentage of Importance Indice andIts Derivations) The Percentage of Importance Indice (Percentage_I.I.) bases in magnitudes, frequencies, and distributions of occurrence of an event (DEMOLIN-LEITE, 2021) . This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of loss or income gain, on the productive system. The Percentage_I.I. = [(ks1 x c1 x ds1)/SUM (ks1 x c1 x ds1) + (ks2 x c2 x ds2) + (ksn x cn x dsn)] x 100. key source (ks) is obtained using simple regression analysis and magnitude (abundance). Constancy (c) is SUM of occurrence of L.S. or S.S. on the samples (absence = 0 or presence = 1), and distribution source (ds) is obtained using chi-square test. This index has derivations: i.e., i) Loss estimates and solutions effectiveness and ii) Attention and non-attention levels (DEMOLIN-LEITE,2024) . 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See . Package: r-cran-imprecise101 Architecture: all Version: 0.2.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tolerance, r-cran-pscl Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-imprecise101_0.2.2.4-1.ca2604.1_all.deb Size: 60272 MD5sum: c675d0bbb8b04babfd1d28d775bf2e90 SHA1: c909a0f3e4df23bfb4d2bd936ac116bff5bcfe33 SHA256: 6be675bbf9a0f622874bdd01bde5cf9deb75665c61270a10986e66e88c3d0ec4 SHA512: 3fb42fc31badc6270acd38d86cd79879e4c04408200a30fc2cd835a900843a0e75520237abdeb1caa6b20178d2896e7ac3fd583344aef507d65c6cb30bd535bb Homepage: https://cran.r-project.org/package=imprecise101 Description: CRAN Package 'imprecise101' (Introduction to Imprecise Probabilities) An imprecise inference presented in the study of Walley (1996) is one of the statistical reasoning methods when prior information is unavailable. Functions and utils needed for illustrating this inferential paradigm are implemented for classroom teaching and further comprehensive research. Two imprecise models are demonstrated using multinomial data and 2x2 contingency table data. The concepts of prior ignorance and imprecision are discussed in lower and upper probabilities. Representation invariance principle, hypothesis testing, decision-making, and further generalization are also illustrated. Package: r-cran-impressionist.colors Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3564 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-impressionist.colors_1.0-1.ca2604.1_all.deb Size: 3618082 MD5sum: 6740851d1e55faee763b30e38b389598 SHA1: 63b836879d8e041659f9cf5e017c6a27ecb8ce19 SHA256: 58ca05cf43d72318037f7242ec3da80ea16e73ad8dcbca55a4233a09e76e2788 SHA512: f1df7fa67cdf667d6fc4575407c5745a996ca685737d1194a631b7e918d43cc93d3afdde37c0fe2f20648cd0ac613fa467301aba0dac6488417e49ec3708bf6f Homepage: https://cran.r-project.org/package=impressionist.colors Description: CRAN Package 'impressionist.colors' (Impressionism's Color Palettes) Provides color palettes from Impressionism and post-Impressionism artworks. This package allows to select colors combinations while looking at the original paintings where colors were sampled from. Package: r-cran-imprinting Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-cowplot, r-cran-ggplot2, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-imprinting_0.1.1-1.ca2604.1_all.deb Size: 400522 MD5sum: 9220b99c4f46abe46e9db196b1a154b2 SHA1: d5a44094b06a2567042583d231b62d8a4ab027ab SHA256: ba97cd3c08785e0430ed239060890cf0df3cf7bdac8da75ab601e0f97ef9509a SHA512: 7be9c67990dfff32983fc3c651055dcdde1dde3df4d5c239878929b1263bcaa537f104a02782fde40085850eed16cd1dd7ce7d2b26025ad3d25da6d21c3b51c1 Homepage: https://cran.r-project.org/package=imprinting Description: CRAN Package 'imprinting' (Calculate Birth Year-Specific Probabilities of Immune Imprintingto Influenza) Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) . Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc. Package: r-cran-impshrinkage Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-impshrinkage_1.0.0-1.ca2604.1_all.deb Size: 112612 MD5sum: 99464ccff8d2a74a6390dea544aaaacf SHA1: 007279231b9200e65cd2652773d12bd51f284b55 SHA256: e941c8eb8247b7de9b89bd9dca9fd4a55917624f67ac8e3199c1c2c466622853 SHA512: 849748f6ad7ad4f9f81132fd017d64d3814d990e7fb97ae071847eab722736a3020ee108044811f1ee8175b7093c205ccbb0791f7009a44873e16ae988433c6e Homepage: https://cran.r-project.org/package=ImpShrinkage Description: CRAN Package 'ImpShrinkage' (Improved Shrinkage Estimations for Multiple Linear Regression) A variety of improved shrinkage estimators in the area of statistical analysis: unrestricted; restricted; preliminary test; improved preliminary test; Stein; and positive-rule Stein. More details can be found in chapter 7 of Saleh, A. K. Md. E. (2006) . Package: r-cran-imputefin Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1474 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-zoo, r-cran-mvtnorm, r-cran-magrittr Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-xts Filename: pool/dists/resolute/main/r-cran-imputefin_0.1.2-1.ca2604.1_all.deb Size: 1072608 MD5sum: 469108c939747226880d881fe386a596 SHA1: 20791fafb173c5f3c7e7ca47b3b416c17827fa82 SHA256: d885cf70a2e67b36dc7a7f9745b74abafa67efbd3b51b6aaca0e40596665c050 SHA512: 6bdb5e5bdd1d4849f86fbc93b6918c40ca890b3f9aeff9e1427d5106b42b7f1b803133cbb6ddb942b6f4d5b64c585536539a4b254916f8e813b60d957560c879 Homepage: https://cran.r-project.org/package=imputeFin Description: CRAN Package 'imputeFin' (Imputation of Financial Time Series with Missing Values and/orOutliers) Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed. The package is based on the paper: J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172. . Package: r-cran-imputegeneric Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gower, r-cran-parsnip Suggests: r-cran-missmethods, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-imputegeneric_0.1.0-1.ca2604.1_all.deb Size: 58848 MD5sum: dc763f4babae03ea3fc97a639002cdcf SHA1: 2efe0eff069f300e2abbc94aeee8e4630dfc30b8 SHA256: da7183ae83bd6687920a15862470c9b69e0006a3bcd8c37e18a0bde164ec4f90 SHA512: 3206448e005860749a891548ef67cfd04ac6e39a3cca9a43d394a8663d6e6906834f23e0ddda6cf3e2933c21220ec26576c417e299f957f98184ac85e076af37 Homepage: https://cran.r-project.org/package=imputeGeneric Description: CRAN Package 'imputeGeneric' (Ease the Implementation of Imputation Methods) The general workflow of most imputation methods is quite similar. The aim of this package is to provide parts of this general workflow to make the implementation of imputation methods easier. The heart of an imputation method is normally the used model. These models can be defined using the 'parsnip' package or customized specifications. The rest of an imputation method are more technical specification e.g. which columns and rows should be used for imputation and in which order. These technical specifications can be set inside the imputation functions. Package: r-cran-imputelcmd Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tmvtnorm, r-cran-norm, r-bioc-pcamethods, r-bioc-impute Filename: pool/dists/resolute/main/r-cran-imputelcmd_2.1-1.ca2604.1_all.deb Size: 635862 MD5sum: 9995c630b10ecf732d32dcd46f4468e2 SHA1: 0da536cc7a15d125de4ce492012b3127c624b291 SHA256: ed6281e4d1d28ad7214e5191947cb9ab5ffd18ef060d5b35513b94c32e605707 SHA512: f47d581d72d2da7c87aeb2c4a00041b927d7d80c357c4554798d61fb1e403be3ea3799d7893dc2177aaee8ade3591f56d4f2f17f360da6eb825355c92f6b53f2 Homepage: https://cran.r-project.org/package=imputeLCMD Description: CRAN Package 'imputeLCMD' (A Collection of Methods for Left-Censored Missing DataImputation) A collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR) mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR. Package: r-cran-imputelongicovs Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-imputelongicovs_0.1.0-1.ca2604.1_all.deb Size: 410040 MD5sum: ece9ad5ab92a36f44d03bdcc29ab84b5 SHA1: 94965cdd6515c2622de73a6b5c9dfbfdc71043c3 SHA256: ee8cea3f663927c7d76afd67b846e7f0d590bdcfe3f0bf1582a8be22689854e2 SHA512: 372d439ee9ba2c84e0a3783e5358019b6719afa3c0d34773fc3f380d24aadba7c0269558c572027984af850414e07320f5fcb7c69261b00cf32a1678f5c4df37 Homepage: https://cran.r-project.org/package=ImputeLongiCovs Description: CRAN Package 'ImputeLongiCovs' (Longitudinal Imputation of Categorical Variables via a JointTransition Model) Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) for an overview. Package: r-cran-imputemissings Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-imputemissings_0.0.4-1.ca2604.1_all.deb Size: 22484 MD5sum: 6b5ceb9e61e395f56885aa772db78574 SHA1: 737aefbb5028abdaadbfba182d9245cd3ff6f578 SHA256: 86a73d2f7fe40abe0be11dfe743eab671e1bc2cb14c55790f9fb5934c591b676 SHA512: 16149179ebace182d6d2585ffbfe9bcbecbb0db4096bab72bbdec7659d3cc5ba36acb58cfb713b38eda37e276d717545f76f83296dd255e1dd7d104d3f13c12a Homepage: https://cran.r-project.org/package=imputeMissings Description: CRAN Package 'imputeMissings' (Impute Missing Values in a Predictive Context) Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest. Package: r-cran-imputer Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-caret, r-cran-glmnet, r-cran-pls, r-cran-cubist, r-cran-ridge, r-cran-gbm, r-cran-mboost, r-cran-rpart, r-cran-earth Filename: pool/dists/resolute/main/r-cran-imputer_2.2-1.ca2604.1_all.deb Size: 418920 MD5sum: fbc14c7642276ae2254e1964fd33c39f SHA1: df61a9804c87ced6ab019657ca12ae57bfb8a2bd SHA256: a353e7a2d3d188c880c4fb41f80fc9a571e12c6dd49f88ea7b99d88dcfbadf12 SHA512: 4942a88a2bf0e9315f03990a73d6de31a974ead8e081cf1d75a41e1387d4da04b86e83c207d1ca3e9a1a0896e69e72babed4fa6c3ee2efebe9b812eb8211b96c Homepage: https://cran.r-project.org/package=imputeR Description: CRAN Package 'imputeR' (A General Multivariate Imputation Framework) Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS. Package: r-cran-imputeree Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-stringr, r-cran-purrr, r-cran-rlang, r-cran-broom Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-imputeree_0.0.5-1.ca2604.1_all.deb Size: 92538 MD5sum: 36ceea7a81f458bc8e70d33645f562f9 SHA1: 16ac93136a542982a95c1cd7b47b6d67485a2fca SHA256: 71ecbaa1e93dea7acff56bcb51faea4ecaf0e04b5e97b27a2e8026f65e1ffddc SHA512: b7ca390f571ce20f99cfa522db4236d2a733541597bb5b7122493ef726839b603f1895f913ffb67cf4e856b347fd9ac233bdc669c32d8e9499dd77b589c20ab6 Homepage: https://cran.r-project.org/package=imputeREE Description: CRAN Package 'imputeREE' (Impute Missing Rare Earth Element Data in Zircon) Set of functions to impute missing rare earth data, calculate La and Pr concentrations and Ce anomalies in zircons based on the Chondrite-Onuma and Chondrite-Lattice of Carrasco-Godoy and Campbell (2023) and the Logarithmic regression from Zhong et al. (2019) . Package: r-cran-imputerobust Architecture: all Version: 1.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-mice, r-cran-purrr, r-cran-extremevalues, r-cran-gamlss.dist, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-imputerobust_1.3-1-1.ca2604.1_all.deb Size: 55304 MD5sum: d4533cb540b54a1d462ce9f55506eca3 SHA1: 28b5a1dfdf32ab82b4cd4526b2f2b0f73d4f0101 SHA256: a52a250a4c162c82858e73f39ed4b0ebc93868ec8d499add4f539d22df85d3fa SHA512: 6538cb0a23bd5370c980b5d00d949faf0106a70ba48ad0cd7a14fb72324e97e3247dd1b83dd99590f56528a75a2fda0d86f619280be6348284ea7b8b2e56dbf5 Homepage: https://cran.r-project.org/package=ImputeRobust Description: CRAN Package 'ImputeRobust' (Robust Multiple Imputation with Generalized Additive Models forLocation Scale and Shape) Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess . Package: r-cran-imputetestbench Architecture: all Version: 3.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-imputets, r-cran-reshape2, r-cran-tidyr, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-imputetestbench_3.0.3-1.ca2604.1_all.deb Size: 53574 MD5sum: 64661b39e9a0510ce5a4b3ba4493292e SHA1: 08afe14d7a95a15be6f2a2544a85e03c057f80ae SHA256: 57a1d5d7c8d0cd4572320c562aa0670269a9143a1e2f368004b508b185da5ece SHA512: 7e7cd2886b62d3f10afb5fb5344d72347a212dd5ca11e7eff5457469891239e779549b06ac8339d9dd4f97aeca4e1fc5f3c9d257595f5fb16ce3397233b2d743 Homepage: https://cran.r-project.org/package=imputeTestbench Description: CRAN Package 'imputeTestbench' (Test Bench for the Comparison of Imputation Methods) Provides a test bench for the comparison of missing data imputation methods in uni-variate time series. Imputation methods are compared using different error metrics. Proposed imputation methods and alternative error metrics can be used. Package: r-cran-imrmc Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-imrmc_2.1.0-1.ca2604.1_all.deb Size: 4981826 MD5sum: 3abba054a904aa7b41be8930d29b43b5 SHA1: 725c069af6ba03ba7cb66b2ff04da69d9ec92904 SHA256: 14bd71506435ad6d38019c138637ad0552e61f211bbb163f1f434712d25aa4fd SHA512: a2c598a1d72f42975b45549e80f45a6b296febf3c6f38487fa9c7accaa1bf0a8e0ff8c82ac1e5f554fce996e5e01c6d211dd615b03687fbf73659b0aed4aca44 Homepage: https://cran.r-project.org/package=iMRMC Description: CRAN Package 'iMRMC' (Multi-Reader, Multi-Case Analysis Methods (ROC, Agreement, andOther Metrics)) This software does Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: . Package: r-cran-imsig Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hiclimr, r-cran-rcolorbrewer, r-cran-igraph, r-cran-ggplot2, r-cran-gridextra, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-imsig_1.1.3-1.ca2604.1_all.deb Size: 307656 MD5sum: 85f376155fc38f5e70a580c14b64cd8e SHA1: 8caa86636e70bf518fd205d93bc08a91051c71c5 SHA256: ef96f94fa9c5332dd04d0d0bd7df79f99a712bcf318095cc6f4a1bbf39b980f6 SHA512: 2842b87eaf29f2c9b11dc819a567a515ffb0fc4325e0f255f076c7a99234cf37665dae5438894061a8f4be34a0994a047c66751587d17469bdf79e539ac31f22 Homepage: https://cran.r-project.org/package=imsig Description: CRAN Package 'imsig' (Immune Cell Gene Signatures for Profiling the Microenvironmentof Solid Tumours) Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data. Package: r-cran-imtest Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltm, r-cran-mass, r-cran-lme4, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-imtest_1.0.0-1.ca2604.1_all.deb Size: 124648 MD5sum: bc900fa88e0070c774ed1e2099017499 SHA1: 237b8a42d6cbe8173c5c056719ed6854bbd9d877 SHA256: 74b956df3b1f9ce503ff8053725a5e5f3c435c157d767ac8b7d8013ca5fa9102 SHA512: 94801ce3af3c1c3bcf5901aff054977461abc477a382e21f3654ddeca230e8f61b474c9cb395fe89be99f54e7182bb19c27573fe8124f99ea21fa96f5725a487 Homepage: https://cran.r-project.org/package=IMTest Description: CRAN Package 'IMTest' (Information Matrix Test for Generalized Partial Credit Models) Implementation of the information matrix test for generalized partial credit models. Package: r-cran-imv Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-lme4, r-cran-mirt, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-imv_0.3-1.ca2604.1_all.deb Size: 59716 MD5sum: 09629d6fa6da89cc819b7d5d82aaf1b0 SHA1: 5be110538f9b46de1fa99a349af642c67e06e6b4 SHA256: 616c1907f16692eb0ff18d0408ed9835924e6ad340d43b495462e005735d8077 SHA512: 656008969798673d09f799963afbf33391beb8f69c06728d4768738cc2c6ae7b2cbff518ea4f49f1ad42ae03a144fe3510e6a79106997d66533da2d8c2b3a38b Homepage: https://cran.r-project.org/package=imv Description: CRAN Package 'imv' (Model Comparison via the 'InterModel Vigorish' ('IMV')) Computes the 'InterModel Vigorish' ('IMV'), a metric for comparing the predictive accuracy of two models for binary outcomes. The 'IMV' is derived from the expected value of a bettor using one model's predicted probabilities against those of a competing model, and is estimated via k-fold cross-validation. Methods are provided for generalized linear models, mixed-effects models ('lme4'), and item response theory models ('mirt'). See . Package: r-cran-imvol Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-nls2, r-cran-caret, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-imvol_0.1.0-1.ca2604.1_all.deb Size: 29652 MD5sum: 93d1895a125b90603eb401343044cede SHA1: 86f9b4aa7ebe1464f759793f340c5d70228dfcd3 SHA256: f84ee621c016a76ab998e5e553869ec504b39803bf8a8af56e2b01157699322f SHA512: 1220e159438a631d9ffc2d3523ba4a84bf9af591ac833b910d2f47a71a0fe79a64f978111e125421ce56aa96bd0050fd1578a94268a35197cc1c5fab91b341e9 Homepage: https://cran.r-project.org/package=ImVol Description: CRAN Package 'ImVol' (Volume Prediction of Trees Using Linear and Nonlinear AllometricEquations) Volume prediction is one of challenging task in forestry research. This package is a comprehensive toolset designed for the fitting and validation of various linear and nonlinear allometric equations (Linear, Log-Linear, Inverse, Quadratic, Cubic, Compound, Power and Exponential) used in the prediction of conifer tree volume. This package is particularly useful for forestry professionals, researchers, and resource managers engaged in assessing and estimating the volume of coniferous trees. This package has been developed using the algorithm of Sharma et al. (2017) . Package: r-cran-inaparc Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kpeaks, r-cran-lhs Suggests: r-cran-ggally, r-cran-nbclust, r-cran-cluster, r-cran-factoextra, r-cran-vegclust Filename: pool/dists/resolute/main/r-cran-inaparc_1.2.1-1.ca2604.1_all.deb Size: 270654 MD5sum: 6c674e811bbbb83085549cd32c48ae39 SHA1: 51fad68ed45ff8bb51f41d7f4d151969f87652e0 SHA256: c3c6bc940c1e4444c16e93a4bcc26fdacaab7842b11ea46aca7455da6913f417 SHA512: 46251f7d2c19c053977a8e60f36d29ac74fd462e8fe2ce14511cae22e774dee8e66f88978ae9799525a034f7dc9dc2ddd90e0457e0571fd4d40f3a7e6d3be334 Homepage: https://cran.r-project.org/package=inaparc Description: CRAN Package 'inaparc' (Initialization Algorithms for Partitioning Cluster Analysis) Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting the algorithms. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the centers of clusters, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'. Package: r-cran-inbreedr Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-inbreedr_0.3.3-1.ca2604.1_all.deb Size: 405570 MD5sum: aef5267ec184bfb22bda7d12ea931309 SHA1: 0fa9f847658a7839acf1d3a7403b195cb11f28dd SHA256: 9c3da6e4f175263d931281b53bbe35f3ddcfccf6ebbd86d6663cb5d8c74a6bcd SHA512: 5b3e38c459c20133d09df912a0d86490e5ac9c9fc5b7d9a9317af2e1c2daa9e82c41a96b05beff1d253b27e78d9a37f1479d969ad4690d97b9e6f25ea3accb11 Homepage: https://cran.r-project.org/package=inbreedR Description: CRAN Package 'inbreedR' (Analysing Inbreeding Based on Genetic Markers) A framework for analysing inbreeding and heterozygosity-fitness correlations (HFCs) based on microsatellite and SNP markers. 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These functions accept a vector as an optional first argument, allowing conditional statements to be built using the 'magrittr' dot operator. The functions also coerce all outputs to the same type, meaning you no longer have to worry about using specific typed variants of NA or explicitly declaring integer outputs, and evaluate outputs somewhat lazily, so you don't waste time on long operations that won't be used. Package: r-cran-incidence2 Architecture: all Version: 2.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-grates, r-cran-data.table, r-cran-pillar, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-tidyselect, r-cran-rlang, r-cran-vctrs, r-cran-ympes Suggests: r-cran-outbreaks, r-cran-ggplot2, r-cran-scales, r-cran-litedown, r-cran-testthat, r-cran-citools, r-cran-withr Filename: pool/dists/resolute/main/r-cran-incidence2_2.6.4-1.ca2604.1_all.deb Size: 617548 MD5sum: 7d83fa52bd786f60e84e3f1d05234bec SHA1: 84bfbf6fe64ba50be232033e2a6261edaf9a6df3 SHA256: 8f9b6b0837c1cc0bf2baed26b05fac000dab17c615173760e8fc85068493b042 SHA512: 58262962f925288109d4421b99b364083750b11d25f73edcd9f33631b8a4e8d477c780ad29245234d0fb2fb87dbba5dec4d00968228973af5ffd390c79e62847 Homepage: https://cran.r-project.org/package=incidence2 Description: CRAN Package 'incidence2' (Compute, Handle and Plot Incidence of Dated Events) Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence2' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. Package: r-cran-incidence Architecture: all Version: 1.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2361 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-aweek Suggests: r-cran-magrittr, r-cran-outbreaks, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-incidence_1.7.6-1.ca2604.1_all.deb Size: 1384624 MD5sum: cadde86fc7d3e8291d441c3818918339 SHA1: 64438fcf028da902bf584b11de42de81263b0ab5 SHA256: c5d0af2bfc39f358fbea731782a5d0e26503f84456dfdd2019781e6f201c181d SHA512: cd6bd2f3f71d75de9d3cbb6db8cee456fd9ff09b0a0dbf32475a50c3a3d5994d29347414440a8cb7e3b3886666e62ec7c41a67f077533bca18dc76184bed1e33 Homepage: https://cran.r-project.org/package=incidence Description: CRAN Package 'incidence' (Compute, Handle, Plot and Model Incidence of Dated Events) Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON () toolkit for outbreak analysis. Package: r-cran-incidenceprevalence Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cdmconnector, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-magrittr, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-dbplyr, r-cran-rmarkdown, r-cran-rpostgres, r-cran-duckdb, r-cran-dbi, r-cran-odbc, r-cran-here, r-cran-hmisc, r-cran-epitools, r-cran-tictoc, r-cran-testthat, r-cran-spelling, r-cran-gt, r-cran-flextable, r-cran-ggplot2, r-cran-scales, r-cran-visomopresults, r-cran-patchwork, r-cran-binom Filename: pool/dists/resolute/main/r-cran-incidenceprevalence_1.2.1-1.ca2604.1_all.deb Size: 803578 MD5sum: 26e8d6f33e51df744285c467f1fe6ca8 SHA1: 96b84c13446186c8b5995d46114f6686b4e11f67 SHA256: b5ff9742db999f1757135bbb89af350904995a6c420e2d5ff6e45bec3f20310f SHA512: c5df3eaa2986ae196ff0d49d7d614fb1fa1aced5d5096f5d4e147eabfecfc2db8a6e00a3f67ce08b146191e297b4209f9bb59d20d45942286bdf868588c5d89f Homepage: https://cran.r-project.org/package=IncidencePrevalence Description: CRAN Package 'IncidencePrevalence' (Estimate Incidence and Prevalence using the OMOP Common DataModel) Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort. Package: r-cran-incidental Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-matrixstats, r-cran-numderiv, r-cran-dlnm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-incidental_0.1-1.ca2604.1_all.deb Size: 460180 MD5sum: e7e16895c5203e04a97b1111ba1a049a SHA1: d3afb1867a38eb12fd01a818a2b2e361ec7040bd SHA256: 4d4dda1fd7555633530592893b10d0281884b5ef94c76b778a72959327315967 SHA512: 6dc10526fab222122ffc7d4d4e2e77bc3ca5dd6a7eb98926c5b30328fa0c834a9f978c58eb9de22ce5ecccb1f0db47b96f6ec82029076f2a89e8cbefd755b754 Homepage: https://cran.r-project.org/package=incidental Description: CRAN Package 'incidental' (Implements Empirical Bayes Incidence Curves) Make empirical Bayes incidence curves from reported case data using a specified delay distribution. Package: r-cran-incidentally Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-xml2 Suggests: r-cran-backbone, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-incidentally_1.0.4-1.ca2604.1_all.deb Size: 540412 MD5sum: 476e971bd0b7f33cedf53ba5e6dba271 SHA1: d977cc875d6f6729c6542aa7a2189f356fb8ab43 SHA256: 50df1a3260b5896d495e3b5e4a78f5725b977dc89e5bb3f16f96bdf8460be6a2 SHA512: f74c88c017e63954b64336b18263377aeff4b9d4196580a44eb541245fe757c4e29c719640e1d23f95aa82bc3ea1f9480a73dd84a20334e2f4891d9b2739f362 Homepage: https://cran.r-project.org/package=incidentally Description: CRAN Package 'incidentally' (Generates Incidence Matrices and Bipartite Graphs) Functions to generate incidence matrices and bipartite graphs that have (1) a fixed fill rate, (2) given marginal sums, (3) marginal sums that follow given distributions, or (4) represent bill sponsorships in the US Congress . It can also generate an incidence matrix from an adjacency matrix, or bipartite graph from a unipartite graph, via a social process mirroring team, group, or organization formation , or examine the space of binary matrices with fixed marginals. Package: r-cran-incompair Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-incompair_0.1.0-1.ca2604.1_all.deb Size: 53762 MD5sum: 4acd0ca38038f7f57a2123d5e9ee10fc SHA1: d73277d209227d53b64d344170aec2db1ddac849 SHA256: 50d4688184d65037fa02f0026954af55bcc67f0d255392f554ce92399db6aee6 SHA512: 16d87373288fd0a9fbdd0b86f56766a00a614f003942c5cb954d11e6e677f119bd5f82d0a3951d56e7c4caaace5ee487fedd810946d86fac600294c5334a4fa6 Homepage: https://cran.r-project.org/package=IncomPair Description: CRAN Package 'IncomPair' (Comparison of Means for the Incomplete Paired Data) Implements a variety of nonparametric and parametric methods that are commonly used when the data set is a mixture of paired observations and independent samples. The package also calculates and returns values of different tests with their corresponding p-values. Bhoj, D. S. (1991) "Testing equality of means in the presence of correlation and missing data". Dubnicka, S. R., Blair, R. C., and Hettmansperger, T. P. (2002) "Rank-based procedures for mixed paired and two-sample designs". Einsporn, R. L. and Habtzghi, D. (2013) "Combining paired and two-sample data using a permutation test". Ekbohm, G. (1976) "On comparing means in the paired case with incomplete data on both responses". Lin, P. E. and Stivers, L. E. (1974) On difference of means with incomplete data". Maritz, J. S. (1995) "A permutation paired test allowing for missing values". Package: r-cran-incr Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-suncalc, r-cran-lubridate Suggests: r-cran-codetools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-incr_2.1.1-1.ca2604.1_all.deb Size: 179924 MD5sum: acbe4fedf3e1cecedf479e82e99379f2 SHA1: 7757c53641f57e862dbc826039e8148c475139a0 SHA256: 1058e2c63aa64520d770701da109279165276eedde1540adad74ea25b4d67ee2 SHA512: 5cab189e023b10a102bb04f9afb0062816761355959a0212d000d4709ecd2c85fc0df2e6b1d32138f7626c9b896872c91d9ffd1f1f30c89fffb78bf2080f4bf1 Homepage: https://cran.r-project.org/package=incR Description: CRAN Package 'incR' (Analysis of Incubation Data) Suite of functions to study animal incubation. At the core of incR lies an algorithm that allows for the scoring of incubation behaviour. Additionally, several functions extract biologically relevant metrics of incubation such as off-bout number and off-bout duration - for a review of avian incubation studies, see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) edited by D. Charles Deeming and S. James Reynolds . 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Implements and extends the method of Kassanjee et al. (2012) . Package: r-cran-incvcommunitydetection Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rspectra, r-cran-clusterr, r-cran-irlba, r-cran-cluster, r-cran-rfast, r-cran-data.table, r-cran-imifa Suggests: r-cran-latentnet, r-cran-network, r-cran-rdist, r-cran-igraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-incvcommunitydetection_0.1.0-1.ca2604.1_all.deb Size: 288044 MD5sum: 373d1a3b5fe60427f10ed559d06e7cdd SHA1: fe76228e6f1587e0ba5ed45f5fb343d9c617d7cc SHA256: 48a035b16407b210dffec38c8a4e6c31c131cb3b38c823775a8b90f55f3462f4 SHA512: 5494d0b128e73298cdccf70fa0a965966483a5b6d02067957d7e130e2e57b0549dc2ff993c68516038f4db2690bc7d5ac8dcbbc9c5410bd89ef6c0d968a7b269 Homepage: https://cran.r-project.org/package=INCVCommunityDetection Description: CRAN Package 'INCVCommunityDetection' (Inductive Node-Splitting Cross-Validation for CommunityDetection) Implements Inductive Node-Splitting Cross-Validation (INCV) for selecting the number of communities in stochastic block models. Provides f-fold and random-split node-level cross-validation, along with competing methods including CROISSANT, Edge Cross-Validation (ECV), and Node Cross-Validation (NCV). Supports both SBM and Degree-Corrected Block Models (DCBM), with multiple loss functions (L2, binomial deviance, AUC). Includes network simulation utilities for SBM, RDPG, and latent space models. Package: r-cran-indago Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1840 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigtabulate, r-cran-bsicons, r-cran-bslib, r-cran-callr, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-fs, r-cran-ggplot2, r-cran-ggrepel, r-cran-heatmaply, r-cran-hmisc, r-cran-htmltools, r-cran-magrittr, r-cran-matrixstats, r-cran-memuse, r-cran-paletteer, r-cran-pheatmap, r-cran-plotly, r-cran-r.devices, r-cran-readr, r-cran-reshape2, r-cran-rintrojs, r-cran-seqinr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spscomps, r-cran-svglite, r-cran-tibble, r-cran-tidyr, r-cran-upsetjs, r-cran-upsetr Suggests: r-bioc-biocgenerics, r-cran-biocmanager, r-bioc-biostrings, r-bioc-edger, r-cran-harrypotter, r-bioc-htsfilter, r-bioc-limma, r-cran-oompabase, r-cran-palr, r-bioc-rsamtools, r-bioc-rsubread, r-bioc-rtracklayer, r-bioc-s4vectors, r-bioc-shortread, r-cran-testthat, r-bioc-xvector Filename: pool/dists/resolute/main/r-cran-indago_1.0.3-1.ca2604.1_all.deb Size: 853618 MD5sum: b78b4030fd8e1409d7d48250852662f1 SHA1: fd21cbbbddb18caef4e89219bb097b84c9565ff2 SHA256: a81a1b5cbd935452bd16af2f78ba1842f30d8aa10ca9b8b0a58e0c4e07548ec2 SHA512: e9bb58b72c3c6889ded3f75f0572a6e92264d360be52c54dcf0ebded3fa2564bb5fbac217dbd7f09603b07982643b959e2d78007d3d74c44df9c06113d76e617 Homepage: https://cran.r-project.org/package=inDAGO Description: CRAN Package 'inDAGO' (A GUI for Dual and Bulk RNA-Sequencing Analysis) A 'shiny' app that supports both dual and bulk RNA-seq, with the dual RNA-seq functionality offering the flexibility to perform either a sequential approach (where reads are mapped separately to each genome) or a combined approach (where reads are aligned to a single merged genome). The user-friendly interface automates the analysis process, providing step-by-step guidance, making it easy for users to navigate between different analysis steps, and download intermediate results and publication-ready plots. 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Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors. Package: r-cran-index0 Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-index0_0.0.1-1.ca2604.1_all.deb Size: 16974 MD5sum: 1a69d33e85144b1a4bb2b8d204390f60 SHA1: ad192c30398f9a7ce3eaea52fda49ae2e183d3e7 SHA256: ec13e4adc5853b301284e55db348a0bc57bc228424d61ded60040b44f550b355 SHA512: 9b58ae0602dc00d12b1e4b14a55a94d761fac6085a5ee208e97bce3f76cb47731f751e5cea5ee98f9a1c3dde04731e9d33acc288e78ffb2a9bb4199776f84b26 Homepage: https://cran.r-project.org/package=index0 Description: CRAN Package 'index0' (Zero-Based Indexing in R) Extract and replace elements using indices that start from zero (rather than one), as is common in mathematical notation and other programming languages. 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Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) . 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It has support for many standard bilateral indexes as well as multilateral index number methods such as GEKS, GEKS-Tornqvist (or CCDI), Geary-Khamis and the weighted time product dummy (for details on these methods see Diewert and Fox (2020) ). It also supports updating of multilateral indexes using several splicing methods. 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Package: r-cran-indiapis Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-indiapis_0.1.0-1.ca2604.1_all.deb Size: 820886 MD5sum: e8dd3edcfe333707b51d99a6068abc63 SHA1: 9fcc9248e46d90d4e16a955f7a6131ddb67213ff SHA256: e1d004f5da284de603e35ec7d684f8b2c387bab5d682c765a6558ded01270d07 SHA512: 40676d8887b44fce3dcbcf80a4b1c22654be1d8a2b7e614e8898054211da2b959c59e365d18c7bf7b56ffc6d77f7d76b605e41cfcc5473f20cc19aee76bdcbd8 Homepage: https://cran.r-project.org/package=IndiAPIs Description: CRAN Package 'IndiAPIs' (Access Indian Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to India, such as economic indicators, and international demographic and geopolitical indicators. 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Package: r-cran-indicspecies Architecture: all Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-permute Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-indicspecies_1.8.0-1.ca2604.1_all.deb Size: 340886 MD5sum: e6aa409afe89340269ffee3533ad4a4f SHA1: 143197f53ade94cb37aa0b4b45135fb96e8937bd SHA256: 4d317dd719b113921b5caf30f4f0ca78969a40983543863462dc5920a328b011 SHA512: 6e3561ec64528f77dd6501288becb9998a9b978b919b1990ddad1811d4b7ba19f6056be585030ad70693f4fdc2e3c8d015962798e0999249f52ad78528d9477c Homepage: https://cran.r-project.org/package=indicspecies Description: CRAN Package 'indicspecies' (Relationship Between Species and Groups of Sites) Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) ]. 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Package: r-cran-ineapir Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1008 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ineapir_0.2.5-1.ca2604.1_all.deb Size: 915266 MD5sum: 4bbc778f4aed720ab16e10fcb349fb9d SHA1: 513bb8fdc3dd337bb5a6b50acf1ab2fc60aa3e92 SHA256: 6367374541003a156eea2cb317cb2e19d769cfd77dcca9da94a05e69fc0851cf SHA512: 7b2f3f81634f8fded67bcbb745c3d0b7f1d47ecab8d52d985a4b0b761212e55f28656514d000285c25cefcad4ab65e780d908b4218ab46f136aa2aa022e30443 Homepage: https://cran.r-project.org/package=ineapir Description: CRAN Package 'ineapir' (Obtaining Data Published by the National Statistics Institute) Get open statistical data and metadata disseminated by the National Statistics Institute of Spain (INE). The functions return data frames with the requested information thanks to calls to the 'INE' API . 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It is based on data analysis courses offered at the Instituto de Ecología AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) , Manrique-Ascencio et al (2024) , Ruiz-Guerra et al(2017) , Juarez-Fragoso et al (2024) , Papaqui-Bello et al (2024) . 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We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities. 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Package: r-cran-infixit Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-infixit_0.3.1-1.ca2604.1_all.deb Size: 485226 MD5sum: 350802f2caa5b9ec37445dc33f9eace8 SHA1: ee1658f163eed9fe0bb2a5aedc2e745dcf26ecc5 SHA256: 88eede4ccf09d363a8e58e8aa4301f059b42376a2b3a2a865bfe5b109adf493c SHA512: ac6098866f1812eee18745733cdeeafd47536311025dd7357dabbea332ee8f4ff375e4f95de2ef869ebdf77a9cfe87bd41b71f653b45336ef8a9289237746368 Homepage: https://cran.r-project.org/package=infixit Description: CRAN Package 'infixit' (Helpful Additional Infix Functions) Infix functions in R are those that comes between its arguments such as %in%, +, and *. These are useful in R programming when manipulating data, performing logical operations, and making new functions. 'infixit' extends the infix functions found in R to simplify frequent tasks, such as finding elements that are NOT in a set, in-line text concatenation, augmented assignment operations, additional logical and control flow operators, and identifying if a number or date lies between two others. Package: r-cran-inflater Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-inflater_0.1.3-1.ca2604.1_all.deb Size: 87670 MD5sum: 86ae19abce348340598846d9a2abf0bc SHA1: cd3da365b3df0559e66973ce04b0e61ba10052fc SHA256: 26d3480e595246c2c533305db3851686297e4d41d071437d792211eb642949b9 SHA512: 3f035c10b78da0dbd2d709a94408c14583ef4fa77086266e178d6213d69d627ded2f66414c5f81c02060b7955af4e28adbb430505fe3be20fb17549317ddc129 Homepage: https://cran.r-project.org/package=inflateR Description: CRAN Package 'inflateR' (Inflation Adjustment for Historical Currency Values) Convert historical monetary values into their present-day equivalents using bundled CPI (Consumer Price Index) and GDP deflator data sourced from the World Bank Development Indicators. Supports British pounds (GBP), Australian dollars (AUD), US dollars (USD), Euro (EUR), Canadian dollars (CAD), Japanese yen (JPY), Chinese yuan (CNY), Swiss francs (CHF), New Zealand dollars (NZD), Indian rupees (INR), South Korean won (KRW), Brazilian reais (BRL), and Norwegian krone (NOK). Currency codes and country names are both accepted as input. 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Computes weighted contributions of price index components, core inflation measures (trimmed mean, weighted median, exclusion-based) following Bryan and Cecchetti (1994) , inflation persistence via sum-of-AR-coefficients, diffusion indices, Phillips curve estimation, breakeven inflation, and trend inflation using the Beveridge-Nelson decomposition and Hodrick-Prescott filter. All functions are pure computation and work with price data from any source. Package: r-cran-inflection Architecture: all Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-inflection_1.3.7-1.ca2604.1_all.deb Size: 277908 MD5sum: 581a328be99efff38d4b2639bbb8879b SHA1: 69be22d694cd5471f3fce36e703e8a13556bd9a3 SHA256: 2eac7bc41493a5f83082d914c75357561b04ebd40aaf359673d287843cee5350 SHA512: 961a4fd9cc29e0ca07798b3ded1459336edc2f87e6a1d59c20e63134bba2f07fa5fa4980ccd2c1124af389929fccac641c16217ce66725bc9c4d43baa69f7feb Homepage: https://cran.r-project.org/package=inflection Description: CRAN Package 'inflection' (Finds the Inflection Point of a Curve) Implementation of methods Extremum Surface Estimator (ESE) and Extremum Distance Estimator (EDE) to identify the inflection point of a curve . Christopoulos, DT (2014) . Christopoulos, DT (2016) . Christopoulos, DT (2016) . 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McCracken (2022) . Package: r-cran-inflongitudinal Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-mice Filename: pool/dists/resolute/main/r-cran-inflongitudinal_0.1.0-1.ca2604.1_all.deb Size: 812224 MD5sum: b1f243565e4489cea3aef40e67dfe0e1 SHA1: 59a08d796b518a14c3fe0742767e93fbafe25a94 SHA256: c47f0ef13944ac81b7fcfbb85b564c3a110ff67f269b64b9160501280deb3391 SHA512: 8a2965a25851826bd94db099e32925fe0d4ddeae3210bef314146e70f050831fb38885e1f78cd9a0c15b299437b0718a5996178350ac83fdef67596429200e12 Homepage: https://cran.r-project.org/package=Inflongitudinal Description: CRAN Package 'Inflongitudinal' (Detecting Influential Subjects in Longitudinal Data) Provides methods for detecting influential subjects in longitudinal data, particularly when observations are collected at irregular time points. The package identifies subjects whose response trajectories deviate substantially from population-level patterns, helping to diagnose anomalies and undue influence on model estimates. Package: r-cran-influence.me Architecture: all Version: 0.9-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-influence.me_0.9-10-1.ca2604.1_all.deb Size: 84218 MD5sum: 88a56edd051aaf33542fd97116740740 SHA1: 1b69b7cf3f8bbf864acb44e7f06e5026d12f70cb SHA256: 575ed39a69ad6c07a77a841b9755bdc94432914522b573a8da541d10737516cf SHA512: 1dda272fb0b1cce2ca54d60b9d1ca64a0b1cc35760ed6433db01072e429dfcefee113e461536007f2d98f03957087958f778c9692365d332c0fad58a72682695 Homepage: https://cran.r-project.org/package=influence.ME Description: CRAN Package 'influence.ME' (Tools for Detecting Influential Data in Mixed Effects Models) Provides a collection of tools for detecting influential cases in generalized mixed effects models. It analyses models that were estimated using 'lme4'. The basic rationale behind identifying influential data is that when single units are omitted from the data, models based on these data should not produce substantially different estimates. To standardize the assessment of how influential a (single group of) observation(s) is, several measures of influence are common practice, such as Cook's Distance. In addition, we provide a measure of percentage change of the fixed point estimates and a simple procedure to detect changing levels of significance. Package: r-cran-influence.sem Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan Filename: pool/dists/resolute/main/r-cran-influence.sem_2.4-1.ca2604.1_all.deb Size: 78866 MD5sum: 1864ceca9f74101c694d77a9fa38ea39 SHA1: 4d508857527c9fc63ec7e9e226be921a4350bfb3 SHA256: 9fb34bf5682493acd2a1b4ef4657bad272809faeb905524cab9432adcdb975cd SHA512: 3695a2fa56fcd7b6d6608e8158ee19d88ac1f078743bbfbece8c3a48d7f44d2d78aa31a8c2aa275297320669998ec30aa232e80895c87dff47386b711e665674 Homepage: https://cran.r-project.org/package=influence.SEM Description: CRAN Package 'influence.SEM' (Case Influence in Structural Equation Models) A set of tools for evaluating several measures of case influence for structural equation models. Package: r-cran-influenceauc Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geigen, r-cran-ggplot2, r-cran-ggrepel, r-cran-rocr Filename: pool/dists/resolute/main/r-cran-influenceauc_0.1.2-1.ca2604.1_all.deb Size: 65090 MD5sum: c55916f5367fa633df75fc6454ff1f78 SHA1: 279ab9e3842c3d6b4bd73caf98d9d282ea05e17e SHA256: 1826bfc3f21f14e42a9cf6acd65f24f2955ec5730f6eda29d1da94a233bcba84 SHA512: b3e7e36c02cc598148e697069abfd7b8efe284e88e3cbda566b63042fbab3a5c3ca7326c5a0d2b45f6931d7bcadca1b68ec464a8373830007a2843cb19496f05 Homepage: https://cran.r-project.org/package=influenceAUC Description: CRAN Package 'influenceAUC' (Identify Influential Observations in Binary Classification) Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization. Package: r-cran-influenceborrowing Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-krls Filename: pool/dists/resolute/main/r-cran-influenceborrowing_0.1.0-1.ca2604.1_all.deb Size: 59568 MD5sum: e3171670de2f5eeeb4a94d8133de4578 SHA1: 98c470c85184d5ac31ccbfbc4109a4b6160fe21c SHA256: 628516492641069a54e600e35c53c3a199f22287e49d09275e94473474076f59 SHA512: e3b5d11d5520ca0d601a97c1e7cae5bb956fdf4f9a3e36dea7c309aff9e52b7f25ba24519fbddfc995c4c0dd265a9d4d173d937847e7b7560450736337c19554 Homepage: https://cran.r-project.org/package=InfluenceBorrowing Description: CRAN Package 'InfluenceBorrowing' (Adaptive Influence-Based Borrowing for Hybrid Control Trials) Implements the adaptive influence-based borrowing framework proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper ``Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls" for augmenting Randomized Controlled Trials (RCTs) with External Control (EC) data. This package provides a comprehensive workflow to: (1) quantify the comparability of external control samples using influence scores approximated via the influence function of the M-estimator; (2) construct candidate borrowing subsets and select the optimal subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic differences in external outcomes using R-learner methods implemented via Ordinary Least Squares or Kernel Ridge Regression. Package: r-cran-influential Architecture: all Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-janitor, r-cran-ranger, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-hmisc, r-cran-mgcv, r-cran-nortest, r-cran-nns, r-cran-biocmanager, r-cran-readr, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-colourpicker, r-cran-magrittr, r-cran-dt, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-influential_2.3.0-1.ca2604.1_all.deb Size: 1084286 MD5sum: 601433612148e8c88ac4a2f87ec92233 SHA1: 0b0e16650ac811c8f6efe16d503af62d82460b19 SHA256: 4db9ffc0c163c5acad50fc7e894b24f3b1fc2277b9aed617808cfab483877a5f SHA512: 3b8b73c99e4f4f101746f75626e61eef597e9b6dc9466b8f8d4334f0cb2bd1195aaf7ee60891b91bbb98d5ac7a1b786d080f33d664ceb3cabb824a6a42849b22 Homepage: https://cran.r-project.org/package=influential Description: CRAN Package 'influential' (Identification and Classification of the Most Influential Nodes) Provides functions for classification and ranking of candidate features, reconstruction of networks from adjacency matrices and data frames, topological analysis, and calculation of centrality measures. The package includes the SIRIR model, which combines leave-one-out cross-validation with the conventional SIR model to rank vertex influence in an unsupervised manner. Additional functions support assessment of dependence and correlation between network centrality measures, as well as estimation of conditional probabilities of deviation from their corresponding means in opposite directions. 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This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided. 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In order to make the package as efficient as possible, aggregations are done in data.table and creation of WOE vectors can be distributed across multiple cores. The package also supports exploration for uplift models (NWOE and NIV). 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Gamma Imputation described in and Risk Score Imputation described in . Package: r-cran-informativesci Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmcp, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-informativesci_1.0.4-1.ca2604.1_all.deb Size: 109844 MD5sum: 03952ddfe05c580d132baab8757f843c SHA1: 64bc7ddfc3184a0659cf55dcf75a4b9c1a720c3b SHA256: 9003ebcb5d0f9a70ecaebe8442e172f031bfdc76a85d9976b25b3e313d37a4a0 SHA512: 1e32da774da3c836ce97795e6454fefabb4babf1735565e9c436e2dbdb693310e0be809b1e5baa986a11a33249ec83bbfd927d5cc3bcfe07607cb99196d6a649 Homepage: https://cran.r-project.org/package=informativeSCI Description: CRAN Package 'informativeSCI' (Informative Simultaneous Confidence Intervals) Calculation of informative simultaneous confidence intervals for graphical described multiple test procedures and given information weights. Bretz et al. (2009) and Brannath et al. (2024) . Furthermore, exploration of the behavior of the informative bounds in dependence of the information weights. Comparisons with compatible bounds are possible. Strassburger and Bretz (2008) . 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The package uses the optimization software gurobi obtainable from , together with its associated R package, also called gurobi; see: . The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374 . 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The procedure to compute the informative set adjusts the method proposed by Mariani et al. (2022a) and Mariani et al. (2022b) to gross returns of financial assets. This is accomplished through an adaptive algorithm that identifies sub-groups of gross returns in each iteration by approximating their distribution with a sequence of two-component log-normal mixtures. These sub-groups emerge when a significant change in the distribution occurs below the median of the financial returns, with their boundary termed as the “change point" of the mixture. The process concludes when no further change points are detected. The outcome encompasses parameters of the leftmost mixture distributions and change points of the analyzed financial time series. The functionalities of the INFOSET package include: (i) modelling asset distribution detecting the parameters which describe left tail behaviour (infoset function), (ii) clustering, (iii) labeling of the financial series for predictive and classification purposes through a Left Risk measure based on the first change point (LR_cp function) (iv) portfolio construction (ptf_construction function). The package also provide a specific function to construct rolling windows of different length size and overlapping time. 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Package: r-cran-injector Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-injector_0.2.4-1.ca2604.1_all.deb Size: 28088 MD5sum: 7de89f964576379e9c11a4231398256a SHA1: 0175df739606e86e420edbea0f4ad9626c62eeaa SHA256: afdbab27cf951f24e0c33fcc68c260d39c8fc35c499b880a96bff8634679ae04 SHA512: a0c6ba34e1c238a5eb9d62b61659dd43c7c4311dc2f4f4111ea44267ecfbaa38c12b39a97804777c3454623c684c58b6c39f8730c5240fd04cc2cd4e26942208 Homepage: https://cran.r-project.org/package=injectoR Description: CRAN Package 'injectoR' (R Dependency Injection) R dependency injection framework. Dependency injection allows a program design to follow the dependency inversion principle. The user delegates to external code (the injector) the responsibility of providing its dependencies. This separates the responsibilities of use and construction. Package: r-cran-injuryseverityscore Architecture: all Version: 0.0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-injuryseverityscore_0.0.0.2-1.ca2604.1_all.deb Size: 35842 MD5sum: 21eb92205a5eccd3e81f81e0261d6ad2 SHA1: 4804e042570b33793fbeb3012b6b1f68ac07d648 SHA256: 11fed6898e3214dae40fbd9ce4608db90678ab538bfabd8873c1893351eec358 SHA512: 0fac1ccb08ddef1a169a76cd8577405b508032e9eda9dd8649653d20f7a1014186d9b50e0b205f79cca9588e7f4d59e1122ecda8090d157ef8293089d0ce167c Homepage: https://cran.r-project.org/package=InjurySeverityScore Description: CRAN Package 'InjurySeverityScore' (Translate ICD-9 into Injury Severity Score) Calculate the injury severity score (ISS) based on the dictionary in 'ICDPIC' from . The original code was written in 'STATA 11'. The original 'STATA' code was written by David Clark, Turner Osler and David Hahn. I implement the same logic for easier access. Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. "ICDPIC: Stata module to provide methods for translating International Classification of Diseases (Ninth Revision) diagnosis codes into standard injury categories and/or scores," Statistical Software Components S457028, Boston College Department of Economics, revised 29 Oct 2010. 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The 'injurytools' package provides standardized routines and utilities that simplify such analyses. It offers functions for data preparation, informative visualizations and descriptive and model-based analyses. 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Package: r-cran-insane Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3731 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggpubr, r-cran-glue, r-cran-patchwork, r-cran-purrr, r-cran-readxl, r-cran-shiny, r-cran-tidyr Suggests: r-cran-covr, r-cran-roxygen2, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-insane_1.0.3-1.ca2604.1_all.deb Size: 2577126 MD5sum: 71844ef7030f49afcad6cb07a6279340 SHA1: 086723d2e05832d866a335b1f67e8514d2aa5602 SHA256: 7c6b477b4658daa38223369f152253dc1370286e775028afb1cebe156f903a55 SHA512: 1362c0511fb3795438063d15f3c6f0d557a197d978cb97c03008f13a5a967a6346031b059d64df8228ebb003aef42d660d9baf5c5ddb221c7296d0ed22dc2ffc Homepage: https://cran.r-project.org/package=insane Description: CRAN Package 'insane' (INsulin Secretion ANalysEr) A user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) assays in pancreatic beta cells or islets. The package allows the user to import several sets of experiments from different spreadsheets and to perform subsequent steps: summarise in a tidy format, visualise data quality and compare experimental conditions without omitting to account for technical confounders such as the date of the experiment or the technician. Together, insane is a comprehensive method that optimises pre-processing and analyses of GSIS experiments in a friendly-user interface. The Shiny App was initially designed for EndoC-betaH1 cell line following method described in Ndiaye et al., 2017 (). 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See Wilkinson et al (2018) . Package: r-cran-insectlabelr Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-shiny Suggests: r-cran-testthat, r-cran-shinyjs, r-cran-readxl, r-cran-readr, r-cran-readods, r-cran-dt, r-cran-editbl Filename: pool/dists/resolute/main/r-cran-insectlabelr_1.0.4-1.ca2604.1_all.deb Size: 90260 MD5sum: 52162a796e56ec4f336cbec34aa530bf SHA1: dacfc454a401d10ff34973419598e9dbea73866f SHA256: 255faf5f863ec76f180a640a04f9ce27546e8cf100ff27d826a6b847441b9433 SHA512: 23cd63b1389f533bc7c60e8ebfcc5cd1bfd59cbce82a2f1a4e31bb53f047cf972cd813ec6bd65a6746e7961335d8c82986b68c8e065f46799b1b9121dba96ec9 Homepage: https://cran.r-project.org/package=InsectLabelR Description: CRAN Package 'InsectLabelR' (Create Labels for Insect in Collection) Streamlines the creation of high-quality labels for insect pinning. By taking a dataset as input, the package allow to generate printable labels in 'LaTeX' and PDF format, helping researchers and entomologists maintain accurate and standardized specimen records. Requires a compatible installation of 'pdflatex' (e.g. ). For enhanced accessibility, the package includes a user-friendly 'shiny' application (accessible online ), which provides a graphical interface for generating labels without requiring programming expertise. 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Package: r-cran-insetplot Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-insetplot_1.4.0-1.ca2604.1_all.deb Size: 180860 MD5sum: 409fba0f12dd591d74667887052c633a SHA1: 40ac01c7baefcaf437bc4762e69b242c8e15594b SHA256: 6450e61d9110e8c9b8593800b04e198ca1ae922dd935466766432901d159c00c SHA512: 2fde409549e2182913818deff665bfb8f4ca5bba59488d98977ae4e370ef221b5dd4d61c158549d857ae542becb8ca46e21b1e62e577b7b85d299709f5072149 Homepage: https://cran.r-project.org/package=insetplot Description: CRAN Package 'insetplot' (Inset Plots for Spatial Data Visualization) Tools for easily and flexibly creating 'ggplot2' maps with inset maps. One crucial feature of maps is that they have fixed coordinate ratios, i.e., they cannot be distorted, which makes it difficult to manually place inset maps. This package provides functions to automatically position inset maps based on user-defined parameters, making it extremely easy to create maps with inset maps with minimal code. Package: r-cran-inshiny Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-htmltools, r-cran-rlang, r-cran-shiny, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-inshiny_0.1.4-1.ca2604.1_all.deb Size: 142998 MD5sum: 2533cb3c5482bfff89d6c8fcd4f01207 SHA1: 18abcefa1ad4be2655ef7e47b07f893ea84d4f05 SHA256: e90e9cb77a10fa35cdb82ffc5b733b3f97a2826c36f12a4b51320c91d91ffe87 SHA512: 8f2e93aba19eeca0f5f48be86d8bfda3f0fdd43933b058d48f0477d51d40e1d16f9020f0855d3f30252231f350ce8af3b1032a739fd07da55791d1922df48156 Homepage: https://cran.r-project.org/package=inshiny Description: CRAN Package 'inshiny' (Compact Inline Widgets for 'shiny' Apps) Provides a basic set of compact widgets for 'shiny' apps which occupy less space and can appear inline with surrounding text. Package: r-cran-insight Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3476 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-aer, r-cran-afex, r-cran-aod, r-cran-ape, r-cran-bayesfactor, r-cran-bayestestr, r-cran-bbmle, r-cran-bsda, r-cran-bdsmatrix, r-cran-betareg, r-cran-biglm, r-cran-bh, r-cran-blavaan, r-cran-blme, r-cran-boot, r-cran-brms, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-censreg, r-cran-cgam, r-cran-clubsandwich, r-cran-cobalt, r-cran-coxme, r-cran-cplm, r-cran-crch, r-cran-curl, r-cran-datawizard, r-cran-dbarts, r-cran-effectsize, r-cran-emmeans, r-cran-epir, r-cran-estimatr, r-cran-feisr, r-cran-fixest, r-cran-fungible, r-cran-fwb, r-cran-gam, r-cran-gamlss, r-cran-gamlss.data, r-cran-gamm4, r-cran-gbm, r-cran-gee, r-cran-geepack, r-cran-geor, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-glmtoolbox, r-cran-gmnl, r-cran-gt, r-cran-httptest2, r-cran-httr2, r-cran-interp, r-cran-ivreg, r-cran-jm, r-cran-knitr, r-cran-lavaan, r-cran-lavasearch2, r-cran-lcmm, r-cran-lfe, r-cran-lme4, r-cran-lmertest, r-cran-lmtest, r-cran-logistf, r-cran-logitr, r-cran-marginaleffects, r-cran-mass, r-cran-matrix, r-cran-mclogit, r-cran-mclust, r-cran-mcmcglmm, r-cran-mertools, r-cran-metabma, r-cran-metadat, r-cran-metafor, r-cran-metaplus, r-cran-mgcv, r-cran-mhurdle, r-cran-mice, r-cran-mlogit, r-cran-mmrm, r-cran-modelbased, r-cran-multgee, r-cran-mumin, r-cran-mvtnorm, r-cran-nestedlogit, r-cran-nlme, r-cran-nnet, r-cran-nonnest2, r-cran-ordinal, r-cran-panelr, r-cran-parameters, r-cran-parsnip, r-cran-pbkrtest, r-cran-performance, r-cran-phylolm, r-cran-plm, r-cran-proreg, r-cran-pscl, r-cran-psych, r-cran-quantreg, r-cran-rcpp, r-cran-rcppeigen, r-cran-reformulas, r-cran-recipes, r-cran-rmarkdown, r-cran-rms, r-cran-robustbase, r-cran-robustlmm, r-cran-rpart, r-cran-rstanarm, r-cran-rstantools, r-cran-rstpm2, r-cran-rstudioapi, r-cran-rwiener, r-cran-sandwich, r-cran-sdmtmb, r-cran-sampleselection, r-cran-serp, r-cran-speedglm, r-cran-statmod, r-cran-survey, r-cran-survival, r-cran-svylme, r-cran-testthat, r-cran-tidymodels, r-cran-tinytable, r-cran-tmb, r-cran-truncreg, r-cran-tune, r-cran-tweedie, r-cran-vgam, r-cran-weightit, r-cran-withr, r-cran-workflows Filename: pool/dists/resolute/main/r-cran-insight_1.5.1-1.ca2604.1_all.deb Size: 2483952 MD5sum: 4ee4cd1392ff8dd995fc90ebaf67195f SHA1: f8f169bf2130f3a0173ebf5f7d513fe3bb56d3dd SHA256: 750caa73ce83b65b5047375144dd93c93aba47198523c20df6b7807d8e1cc19c SHA512: ac9c54d81df25433b480fb149fdc07531a51a37125f24654843d6958c3234622f1de7c260255e25ec004b670c33f26457cfdb88f1bcf8680b0567427cb95cad4 Homepage: https://cran.r-project.org/package=insight Description: CRAN Package 'insight' (Easy Access to Model Information for Various Model Objects) A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 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Package: r-cran-insilicova Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4844 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-coda, r-cran-ggplot2, r-cran-interva5 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-insilicova_1.4.2-1.ca2604.1_all.deb Size: 4076828 MD5sum: 0af1a376019909b9609cc3ccba395b10 SHA1: 4f9cb5aecc3646a99c78ccfd07617350359ed7cc SHA256: 55a771706835b4c86d5e8e5d30a68f02c200f24e963b7b05bffe10cdeffc21b1 SHA512: 4e4256372a187cce6f53da8eca3e32c4cee69da9658a23f9fd2acc93a53d1cca6e11aa813ebb2ff1801ab21a50362755e49aa9ad934805ead4b8499049a93056 Homepage: https://cran.r-project.org/package=InSilicoVA Description: CRAN Package 'InSilicoVA' (Probabilistic Verbal Autopsy Coding with 'InSilicoVA' Algorithm) Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) . It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output. Package: r-cran-inspectchangepoint Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-inspectchangepoint_1.2-1.ca2604.1_all.deb Size: 86998 MD5sum: 04831d4553e5c2677fb113a2e4b40175 SHA1: 1596003f1a03a95f4f8f02f4bcfd48efcbd0b5e8 SHA256: ab87af66e71d4a4c832ed713a2bb707d0952a2a48bfec8b8f338965cdefd607b SHA512: acc1a609439eb9015a91b5bd86732a39aebb239eb19f01aa9be5b2dbdf37e2d21448d99f89fa078d4ce701a6b6de4464fda8e0da49a2b953551d3751c262e676 Homepage: https://cran.r-project.org/package=InspectChangepoint Description: CRAN Package 'InspectChangepoint' (High-Dimensional Changepoint Estimation via Sparse Projection) Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme. 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It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling). Package: r-cran-inspector Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pcal Filename: pool/dists/resolute/main/r-cran-inspector_1.0.3-1.ca2604.1_all.deb Size: 111350 MD5sum: e65d9682785d30b1aa9e9c4cbc72354c SHA1: 09f8b7d6517754fb020a21c446dfa0083d93fb33 SHA256: d67fd6b97d8b95889070ba412d7abf02897be75be5924f44cd04ebd9a1437131 SHA512: 3641ad60b097e162edc56d21e9711efecf186d5aef2a36b9496c66def4f0c145746a4ebf37037140b8b24bc07513ad92bf60b8a55f9f8c5ac06f12128bce5c2a Homepage: https://cran.r-project.org/package=inspector Description: CRAN Package 'inspector' (Validation of Arguments and Objects in User-Defined Functions) Utility functions that implement and automate common sets of validation tasks. 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R is great for installing software. Through the 'installr' package you can automate the updating of R (on Windows, using updateR()) and install new software. Software installation is initiated through a GUI (just run installr()), or through functions such as: install.Rtools(), install.pandoc(), install.git(), and many more. The updateR() command performs the following: finding the latest R version, downloading it, running the installer, deleting the installation file, copy and updating old packages to the new R installation. 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Package: r-cran-insurancedata Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-insurancedata_1.0-1.ca2604.1_all.deb Size: 626552 MD5sum: 8f013957b241b7454c7d61004c6d49f6 SHA1: ec10e94de7e28e85b2c7c678aa41fd4cee1949a0 SHA256: 6b09eaae34134500b935688ca7dd36af3f6a925542b860760a811550c2112fbd SHA512: b5f33415a22df5e9c293ba4f93bdd7e4ebefc45dd014013d07d7c0a9eedb8f048a564b9f8a1531c14293f9f4d1e15e7083f4f1978a8d4fa88cdf985e89c6f417 Homepage: https://cran.r-project.org/package=insuranceData Description: CRAN Package 'insuranceData' (A Collection of Insurance Datasets Useful in Risk Classificationin Non-life Insurance) Insurance datasets, which are often used in claims severity and claims frequency modelling. 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Package: r-cran-intccr Architecture: all Version: 3.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-alabama, r-cran-doparallel, r-cran-foreach, r-cran-mass, r-cran-splines2 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-intccr_3.0.4-1.ca2604.1_all.deb Size: 1969752 MD5sum: b12dfe27980a2d5904237b6f5d40c9e5 SHA1: 3a960affc337315731a5de7173bc400b3b7fbea1 SHA256: 606080d2e810f62226a57b1087b3c37a1a13eee719399c0c4674f7561bb714f1 SHA512: 2887729c854f2681136526f6dcd07f0e3833277a8ef52940d294b0e4244026cb5d74194c05fb11a3d960fd012b5c40e3ef3bc063792af87c57f7f30a0988ed99 Homepage: https://cran.r-project.org/package=intccr Description: CRAN Package 'intccr' (Semiparametric Competing Risks Regression under IntervalCensoring) Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi{10.1002/sim.7350} and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi{10.1093/biostatistics/kxaa052}. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models. 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The goal is to identify genes that are altered in cancer either marginally or consistently across different assays. The heterogeneity among different platforms and different samples are automatically adjusted so that the overall alteration magnitude can be accurately inferred. See Tong and Coombes (2012) . 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Items are automated where possible, and are grouped into eight domains, including unusual data patterns, baseline characteristics, correlations, date violations, patterns of allocation, internal and external inconsistencies, and plausibility of data. The package may be applied by evidence synthesists, editors, and others to determine whether a randomised controlled trial may be considered trustworthy to contribute to the evidence base that informs policy and practice. For more details, see Hunter et al. (2024) and in the same issue of Research Synthesis Methods. Package: r-cran-inteli Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-inteli_0.1.2-1.ca2604.1_all.deb Size: 121552 MD5sum: 9b69832085e24fbf85a15d9fc94fd3f4 SHA1: e8f4366190d5728a974ef5626868b31404fbab3c SHA256: 46e125cf976aa868edc4618aac7afc21d3785c807a5c704d690a44e1bf313339 SHA512: cd7b8c6046ee4909a0b9ccf462048e7fdc3769c51a8544f7c6145a90b21fe5d57139ff7e95ec7017019cdce8731ae16bfaa8928c6d4b82d8f2a313134256d4f3 Homepage: https://cran.r-project.org/package=inteli Description: CRAN Package 'inteli' (Interval Estimation by Likelihood Method) Currently used CI method has its limitation when the test statistics are asymmetrical (chi-square test, F-test) or the model functions are non-linear. It can be overcome by using the likelihood functions for the interval estimation. 'inteli' package now supports interval estimation for the mean, variance, variance ratio, binomial distribution, Poisson distribution, odds ratio, risk difference, relative risk and their likelihood function plots. Testing functions are also provided. Package: r-cran-intendo Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dbi, r-cran-dplyr, r-cran-duckdb, r-cran-pointblank, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-intendo_0.1.1-1.ca2604.1_all.deb Size: 83016 MD5sum: 813302f6cbb9183706caf5f3285912d3 SHA1: 4ad3f1490d20a0aee68267f01eaee0868733ef12 SHA256: 89346fb583f4eace0a7b2fd3bd2fb5fb0d8c97706df08f084d53839cee4db9cb SHA512: 925b75cd7134a3aa0764db36d183a506c3fd1524151352f11effa85aaf3d62c339eb237d26f802f4314d8a7c871d5ff7d4aeffb932030f4208a73c7fc3fce302 Homepage: https://cran.r-project.org/package=intendo Description: CRAN Package 'intendo' (A Group of Fun Datasets of Various Sizes and Differing Levels ofQuality) Four datasets are provided here from the 'Intendo' game 'Super Jetroid'. It is data from the 2015 year of operation and it comprises a revenue table ('all_revenue'), a daily users table ('users_daily'), a user summary table ('user_summary'), and a table with data on all user sessions ('all_sessions'). These core datasets come in different sizes, and, each of them has a variant that was intentionally made faulty (totally riddled with errors and inconsistencies). This suite of tables is useful for testing with packages that focus on data validation and data documentation. Package: r-cran-intensegrid Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-intensegrid_0.1.2-1.ca2604.1_all.deb Size: 65798 MD5sum: 530a982e545c61e50cd1bf419ad9f9a3 SHA1: 4a7f09aa868187aab10fc87dc2a2522227787b1f SHA256: 9ffe87866e1bfc0e924ea8ce1f213a895a314fd3086886a88657e56466d444f5 SHA512: e56890d351d96128ac8bc702f5807f676ed1e49824d4f92c27a238cd7af2bd05b11cd7c170f83ff13291e0be156eb97c154f858f78ddba1a46b300d88fd3491e Homepage: https://cran.r-project.org/package=intensegRid Description: CRAN Package 'intensegRid' (R Wrapper for the Carbon Intensity API) Electricity is not made equal and it vary in its carbon footprint (or carbon intensity) depending on its source. This package enables to access and query data provided by the Carbon Intensity API (). National Grid’s Carbon Intensity API provides an indicative trend of regional carbon intensity of the electricity system in Great Britain. Package: r-cran-intensitynet Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2793 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-intergraph, r-cran-matrix, r-cran-sna, r-cran-spatstat.geom, r-cran-spdep, r-cran-viridis Suggests: r-cran-spatstat, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-intensitynet_1.4.0-1.ca2604.1_all.deb Size: 2464178 MD5sum: a02647405940f2c00c5d2f60727f835a SHA1: f05e5a63e9f9bba1320703fff2d9eb93009a5cae SHA256: c1520498ab1371fc939e2e4d87bd3a7434ee9018d4092dd28738832dc00596ad SHA512: 41419a89098c4e581a2316871ae2647d93c7d85bc944938116468780362189d8dace426ecddc97aee4662862865625a46056910df2b23b6936bdc7267c9da76f Homepage: https://cran.r-project.org/package=intensitynet Description: CRAN Package 'intensitynet' (Intensity Analysis of Spatial Point Patterns on Complex Networks) Tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks. This package is based on the following research: Eckardt and Mateu (2018) . Eckardt and Mateu (2021) . Package: r-cran-inteq Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-inteq_1.1-1.ca2604.1_all.deb Size: 435546 MD5sum: 245491c7d2ed196fd0452f12caf4ed66 SHA1: 23b72d4c5beaa44f4a51c6b77f17f5a0ef279c14 SHA256: 5e86604de9b5edc17c40ab03fe578a2c18230a7ea69173df8c2c610dfa640037 SHA512: 9bdfa0c4a7a9903978b3db1dec573544370fefa35c901ec9fc1bab187e1fea9494af247cb7f1a562fe1d57c100e351ae48d923b9003e9680171036ad133f4509 Homepage: https://cran.r-project.org/package=inteq Description: CRAN Package 'inteq' (Numerical Solution of Integral Equations) An R implementation of Matthew Thomas's 'Python' library 'inteq'. First, this solves Fredholm integral equations of the first kind ($f(s) = \int_a^b K(s, y) g(y) dy$) using methods described by Twomey (1963) . Second, this solves Volterra integral equations of the first kind ($f(s) = \int_0^s K(s,y) g(t) dt$) using methods from Betto and Thomas (2021) . Third, this solves Voltera integral equations of the second kind ($g(s) = f(s) + \int_a^s K(s,y) g(y) dy$) using methods from Linz (1969) . Package: r-cran-interactionpower Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-polynom, r-cran-chngpt, r-cran-rlang, r-cran-tidyr, r-cran-ggbeeswarm, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-interactionpower_0.2.4-1.ca2604.1_all.deb Size: 437714 MD5sum: 87f98cff9c7a4507806da6ad946236af SHA1: eb85e74539ecfbf95d4068c8b5d77e213062151c SHA256: 252b006538ef1d16fb4d6e4f43bf04f61bfcbe49472c01e491aed67c3a668ef4 SHA512: 491a683f792bef1b54f22d5621c7be442eb432d049d3d40116dea408605c2dbbb345b726104af253fae331356fbdf256674c35fe33e13ab23e615abccbaba4e7 Homepage: https://cran.r-project.org/package=InteractionPoweR Description: CRAN Package 'InteractionPoweR' (Power Analyses for Interaction Effects in Cross-SectionalRegressions) Power analysis for regression models which test the interaction of two or three independent variables on a single dependent variable. Includes options for correlated interacting variables and specifying variable reliability. Two-way interactions can include continuous, binary, or ordinal variables. Power analyses can be done either analytically or via simulation. Includes tools for simulating single data sets and visualizing power analysis results. The primary functions are power_interaction_r2() and power_interaction() for two-way interactions, and power_interaction_3way_r2() for three-way interactions. The function run_pos_power_search() provides a stability analysis for two-way interactions. Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM (2023). "Tutorial: Power analyses for interaction effects in cross-sectional regressions." . If you use the stability analyses, please cite: Castillo A, Miller JD, Vize C, Baranger DAA, Lynam DR. "When Do Interaction/Moderation Effects Stabilize in Linear Regression?". Package: r-cran-interactionr Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-msm, r-cran-car, r-cran-officer, r-cran-flextable Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-interactionr_0.1.7-1.ca2604.1_all.deb Size: 134526 MD5sum: 7d4d2630e46fb8c1b0136b74e08a0be8 SHA1: b161ab5c312840c1ccffb89b70e591b10e0b309d SHA256: 65b9b31a633050ce4cf06c11d682a540485d45ccd1b4d073aa49065eac2baa73 SHA512: 974935fb07ae436b7eafc85e19cb83da131bb8b8609d8b2105d7331f6172e68e92bac843eee26fe19a825c6a012a213a3938971c5019066c48c03ccf7ed093b5 Homepage: https://cran.r-project.org/package=interactionR Description: CRAN Package 'interactionR' (Full Reporting of Interaction Analyses) Produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) []. It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), []), variance recovery method (see Zou (2008), []), or percentile bootstrapping (see Assmann et al. (1996), []). 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Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); Durrleman S, Simon R. (1989) ; Greenland S. (1995) . 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Functionality includes visualization of two- and three-way interactions among continuous and/or categorical variables as well as calculation of "simple slopes" and Johnson-Neyman intervals (see e.g., Bauer & Curran, 2005 ). These capabilities are implemented for generalized linear models in addition to the standard linear regression context. Package: r-cran-interactiontest Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-interactiontest_1.2-1.ca2604.1_all.deb Size: 39910 MD5sum: 84a3ccc9d46e5d38ddfc48ec6f19ce7f SHA1: aee35c3f627c6cefc1203a39bf7917f38548add9 SHA256: c529b7ba8945ceb8f7b727854b6316179c585717171c3cc311b7a931f1427f86 SHA512: b524899092f4ae2612c83bf3bf7f964970645ca76fb2c26f0207f45ae07e32cf2460779e0e6ef3cd3423473cfdecb9ecf89f4bd926bdb7c0bf9ab651d7e63990 Homepage: https://cran.r-project.org/package=interactionTest Description: CRAN Package 'interactionTest' (Calculates Critical Test Statistics to Control False DiscoveryRates in Marginal Effects Plots) Implements the procedures suggested in Esarey and Sumner (2017) for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms. 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Traditional time series intervention models, viz. Autoregressive Integrated Moving Average model with exogeneous variables (ARIMA-X) and Artificial Neural Networks with exogeneous variables (ANN-X), rely on linear intervention functions such as step or ramp functions, or their combinations. In this package, the Gompertz, Logistic, Monomolecular, Richard and Hoerl function have been used as non-linear intervention function. The equation of the above models are represented as: Gompertz: A * exp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) * exp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K - A) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl: a*(b^t)*(t^c).This package introduced algorithm for time series intervention analysis employing ARIMA and ANN models with a non-linear intervention function. This package has been developed using algorithm of Yeasin et al. and Paul and Yeasin . 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In particular, this package provides S4 classes, methods, and functions in order to compute basic arithmetic and statistical operations with interval-valued data; prepare customized plots; associate each interval-valued response to its equivalent Likert-type and visual analogue scales answers through the minimum theta-distance and the mid-point criteria; analyze the reliability of respondents' answers from the internal consistency point of view by means of Cronbach's alpha coefficient; and simulate interval-valued responses in this type of questionnaires. The package also incorporates some real-life data that can be used to illustrate its working with several non-trivial reproducible examples. The methodology used in this package is based in many theoretical and applied publications from SMIRE+CoDiRE (Statistical Methods with Imprecise Random Elements and Comparison of Distributions of Random Elements) Research Group () from the University of Oviedo (Spain). 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Package: r-cran-invasioncorrection Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-invasioncorrection_0.1-1.ca2604.1_all.deb Size: 20162 MD5sum: 94ec46c65209a973e768d9a4d24abbad SHA1: 29bbdb7973e42818262fb8c9528c26421c796ea3 SHA256: 9c003eb1d4d55c7143cdafa18ebdec8c49420503d0e1f47dce0f2f22b738dc49 SHA512: f527b3cb43f257eb1d87cd4b35c4d4bb2aaca24a3e35bf4299db128d4ed2ed8a3efa4a60be8abe9fd07adb4b222e2e9435f5df684f4f7261371093a9397d5d91 Homepage: https://cran.r-project.org/package=InvasionCorrection Description: CRAN Package 'InvasionCorrection' (Invasion Correction) The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates. Package: r-cran-invctr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-plyr Suggests: r-cran-knitr, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-invctr_0.2.0-1.ca2604.1_all.deb Size: 119304 MD5sum: 4ebee72c69f16b1fb9464338c463c02e SHA1: 58a0ba877a2a9b40af2c65844cbbbfb754335fb7 SHA256: 419a295927dff516c24bdec2396d9a3f81cb3a74566be130ed9059ba8d784eb5 SHA512: f40b9acdb4f5693d9354f7b9782542a05c4392c1d355477a7a2aa0aa523fe509a1f30549e2076ea1e796d0543fe5a6f30987bc1de713e68c17cb85df53016603 Homepage: https://cran.r-project.org/package=invctr Description: CRAN Package 'invctr' (Infix Functions For Vector Operations) Vector operations between grapes: An infix-only package! 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Package: r-cran-inventorize Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-plotly, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-inventorize_1.1.2-1.ca2604.1_all.deb Size: 450552 MD5sum: 740c6553238c3f881f1b29036a0ea77b SHA1: 491b3c1904b398e5b3f70e1494b94f6bdcf25623 SHA256: b37a0e0bd834d0bdd5a8a5a8d98f4ac835aeb35934f5bec9c018311eee0cdc32 SHA512: f1b09018bd2a21ce0450cb546a68916dcdd758c94b82a5636a56170a6356bf07ae6243d3306c50f71ee78f7f2a0bd98dbaba1b34cd9e361e166b259e8cad2b2c Homepage: https://cran.r-project.org/package=inventorize Description: CRAN Package 'inventorize' (Inventory Analytics, Pricing and Markdowns) Simulate inventory policies with and without forecasting, facilitate inventory analysis calculations such as stock levels and re-order points,pricing and promotions calculations. The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations. The package includes revenue management techniques such as Multi-product optimization,logit and polynomial model optimization. The functions are referenced from : 1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. 2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition. 3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. 4-Ballou, R.H. Business Logistics Management. 5-MIT Micromasters Program. 6- Columbia University course for supply and demand analysis. 8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W For further details or correspondence :, . Package: r-cran-inverseregex Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-inverseregex_0.2.0-1.ca2604.1_all.deb Size: 42748 MD5sum: 407413214fa0cf177a1b54e8737c5e8d SHA1: b8fc2e6049b38128ab2a0e5accbf16f29d056ebb SHA256: 1644ab0e94509aa231d361dc40a4b79bd5f103bf70640880adc37abe5519dabe SHA512: 6ba73313bacaaba58143d44eba30772a29e39ba16ac8f387297b449d649acb7081e7fecb47d312802d846a38e59c231c77bbfb59bc5d2ffdf8da80a25f2d904d Homepage: https://cran.r-project.org/package=inverseRegex Description: CRAN Package 'inverseRegex' (Reverse Engineers Regular Expression Patterns for R Objects) Reverse engineer a regular expression pattern for the characters contained in an R object. 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Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions. Package: r-cran-invitrotkdata Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1511 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-invitrotkdata_0.0.2-1.ca2604.1_all.deb Size: 1425432 MD5sum: 9ffcba0ae4417ed240b9d15b35ca2f07 SHA1: 084a3f4981ac93dda77f40c1c6a51182bcf22d02 SHA256: dd6f25b283dbb512b151b026b7c7362af15b7025afdaf7dbab731e8bafcb74f4 SHA512: 055665854ce157db345ad12800275bfb96a37398f8d8e8bb4d67a2e4dd71cc81ac6966f768ce32f55deff6a6906f1809a66ec1b49e927b46b9f96cc5c4a43e18 Homepage: https://cran.r-project.org/package=invitroTKdata Description: CRAN Package 'invitroTKdata' (In Vitro Toxicokinetic Data Processed with the 'invitroTKstats'Pipeline) A collection of datasets containing a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). 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The package was developed to perform frequentist and Bayesian estimation on a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The methods provided by the package were described in Wambaugh et al. (2019) . 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The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019) . The package can also be used as a teaching demo for introductory Bayesian courses. 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Package: r-cran-inzightplots Architecture: all Version: 2.16.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-chron, r-cran-colorspace, r-cran-dichromat, r-cran-dplyr, r-cran-emmeans, r-cran-expss, r-cran-hexbin, r-cran-hms, r-cran-inzightmr, r-cran-inzighttools, r-cran-lubridate, r-cran-magrittr, r-cran-quantreg, r-cran-rlang, r-cran-s20x, r-cran-scales, r-cran-stringr, r-cran-units, r-cran-survey Suggests: r-cran-covr, r-cran-forcats, r-cran-dbi, r-cran-dbplyr, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-ggridges, r-cran-ggtext, r-cran-ggthemes, r-cran-gridsvg, r-cran-hextri, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rsqlite, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-inzightplots_2.16.0-1.ca2604.1_all.deb Size: 1285074 MD5sum: 245665875063b4cdc9f335a7b15e5b5c SHA1: b6678f922f530584f7bd0d0766729403ee71c548 SHA256: 60eae6b93a7a85ded29200b9138d324a74f8c9c33d41db269cc03a7177c2dd4f SHA512: 61e14d9998f6b7e018b72b71497cc14c5961dbda80097af7f86d91d6b3ad889b0ab3611614a3b5a5ae7269784fce5cf66b1e1da5ca009d2eed2eccb73885d7d6 Homepage: https://cran.r-project.org/package=iNZightPlots Description: CRAN Package 'iNZightPlots' (Graphical Tools for Exploring Data with 'iNZight') Simple plotting function(s) for exploratory data analysis with flexible options allowing for easy plot customisation. 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Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) , Winters (1960) , Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess". Package: r-cran-io Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-filenamer, r-cran-stringr Suggests: r-cran-xml, r-bioc-rhdf5, r-cran-yaml, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-io_0.3.2-1.ca2604.1_all.deb Size: 97932 MD5sum: 7fc4dedc0d8fd339d90d08b4f808d43a SHA1: d438899074d120169e6b5769e0845eea5a42c848 SHA256: 193ea9583d2fcff3d50d2aa2b30af00e29da29ae26c306fe713c053de9cc9e8c SHA512: c46279b2f72133c97ab3f5b6929d040c49ff084ff815d09ba590d07bcf626c1c3956aed3fa8e38cb0baebcaca977207caf81d879c1e3e4a311710c0242ecc6f0 Homepage: https://cran.r-project.org/package=io Description: CRAN Package 'io' (A Unified Framework for Input-Output Operations in R) One function to read files. One function to write files. One function to direct plots to screen or file. Automatic file format inference and directory structure creation. 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(1951) ); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) , Blair and Miller (2009) , Antras et al (2012) , Hummels, Ishii, and Yi (2001) ); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) , Sonis, Hewings, and Guo (2006) ); and a variety of table operators. Package: r-cran-iobr Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4215 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-bioc-gsva, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble, r-cran-tidyr Suggests: r-bioc-biocparallel, r-bioc-biomart, r-cran-circlize, r-bioc-clusterprofiler, r-bioc-complexheatmap, r-cran-corrplot, r-bioc-deseq2, r-cran-doparallel, r-bioc-dose, r-cran-e1071, r-bioc-easier, r-bioc-enrichplot, r-cran-factoextra, r-cran-factominer, r-cran-foreach, r-cran-ggdensity, r-cran-ggpp, r-cran-ggpubr, r-cran-ggsci, r-cran-gridextra, r-cran-hmisc, r-cran-knitr, r-bioc-limma, r-cran-limsolve, r-bioc-maftools, r-cran-mass, r-cran-matrix, r-cran-msigdbr, r-cran-nbclust, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-patchwork, r-cran-pmcmrplus, r-cran-pracma, r-bioc-preprocesscore, r-cran-prettydoc, r-cran-proc, r-cran-psych, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-rocr, r-cran-sampling, r-cran-scales, r-cran-seurat, r-cran-seuratobject, r-bioc-sva, r-cran-testthat, r-cran-tidyheatmap, r-cran-timeroc, r-cran-webr, r-cran-wgcna Filename: pool/dists/resolute/main/r-cran-iobr_2.2.1-1.ca2604.1_all.deb Size: 3538668 MD5sum: d22a46f7c7bf9def55d66fb462a3adb0 SHA1: 3d78fcc20e014e3dda67ffdedcad160d6cf76d0f SHA256: 5841b1db989ab5252bd92a7d4108ffcaaa2c50090d2050d2f0f9b195fd55d92d SHA512: ae6da7a4bdc394b7edddab48aba9fdfc0eacb72484b25a55acf7b0af3c16ee203136adf9f7c6fe49037cbcf510b5f89e121a74f2285b1040621bf863154486e1 Homepage: https://cran.r-project.org/package=IOBR Description: CRAN Package 'IOBR' (Immune Oncology Biological Research) Provides six modules for tumor microenvironment (TME) analysis based on multi-omics data. 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Part of rOpenGov for open source open government initiatives. Package: r-cran-ip2location.io Architecture: all Version: 0.0.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-ip2location.io_0.0.0-2-1.ca2604.1_all.deb Size: 21668 MD5sum: 377d13524824afd6e4650b41ce7d039c SHA1: 1697309fa11679d45fdc06a1a34c0d570bd27cf8 SHA256: d13f4d2cd46b1bf9c51f9ee7847ccebc6acadc4e72eb87b4138184329873b117 SHA512: 7313bd9676882f706ea207983bd10c19cf7784705d10be13c55fea778bdf1308334ac78fe58ae8342767e780e8d79fd798d74d06c8a09b70d971cb1d7c85c0dd Homepage: https://cran.r-project.org/package=ip2location.io Description: CRAN Package 'ip2location.io' (Batch IP Data Retrieval and Storage Using 'IP2Location.io') A system for submitting multiple IP information queries to 'IP2Location.io'’s IP Geolocation API and storing the resulting data in a dataframe. 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Package: r-cran-ip2location Architecture: all Version: 8.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite, r-cran-ggplot2, r-cran-maps, r-cran-scales Filename: pool/dists/resolute/main/r-cran-ip2location_8.1.3-1.ca2604.1_all.deb Size: 29974 MD5sum: 71e4f0ea22d0efbc550ea35214df75f8 SHA1: 7496e366226ca1bc0d90504478292e92e80df936 SHA256: 1bc8c4f3344cd926a6345fea49c951e0d76a68316e5123e98ec7de9d84b1ac91 SHA512: 03f4c70ed1d047e0e1d16ea3f1045c6eda2cce3733a73e31d303ee5310da9402bde8862dcdf0c6efb9710e81fc5064c452514f4dcafc45aa839d714eae547cca Homepage: https://cran.r-project.org/package=ip2location Description: CRAN Package 'ip2location' (Lookup for IP Address Information) Enables the user to find the country, region, district, city, coordinates, zip code, time zone, ISP, domain name, connection type, area code, weather, Mobile Country Code, Mobile Network Code, mobile brand name, elevation, usage type, address type, IAB category and Autonomous system information that any IP address or hostname originates from. Supported IPv4 and IPv6. Please visit to learn more. You may also want to visit for free database download. This package requires 'IP2Location Python' module. At the terminal, please run 'pip install IP2Location' to install the module. Package: r-cran-ip2locationio Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-ip2locationio_1.1.0-1.ca2604.1_all.deb Size: 23464 MD5sum: ebd88c170e9e70cf24706cad78be9f29 SHA1: 8500c906fa6493770c7e2f87481ef3c0003f653a SHA256: 453965e0f1cd3c6b7e54b4c3398f878273f09dd78e5f264170cdac4670acb8cd SHA512: 7aa9b071099d6fdba830045a7df66731b850b4d7ef29a136658e0ce4f07145d984a2da619d64b4bb616c3922c445c692bac818096116deaca71694d543cf49e5 Homepage: https://cran.r-project.org/package=ip2locationio Description: CRAN Package 'ip2locationio' (Lookup Geolocation and Proxy Information using 'IP2Location.io'API) Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too. This package uses the 'IP2Location.io' API to query this data. To get started with a free API key, sign up here . Package: r-cran-ip2proxy Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite, r-cran-ggplot2, r-cran-maps, r-cran-scales Filename: pool/dists/resolute/main/r-cran-ip2proxy_1.2.0-1.ca2604.1_all.deb Size: 33668 MD5sum: 5482bfd73cac6fec790d1029b4dac515 SHA1: 873d089c7f44a766d97bfa28e0e4c7524f67f8cd SHA256: 0e107efc7bec34e4d151434b4ae0ba1956e0e36427f49c17b93e442ccb8b5098 SHA512: 22328f2cb5bc7554b0c791550fb0c8331644913c91cd84f43d65d9ac9297a40e94834265e1868b895fad2c2a049dc777f876038635088167cf85d65df485b0f2 Homepage: https://cran.r-project.org/package=ip2proxy Description: CRAN Package 'ip2proxy' (Lookup for IP Address Proxy Information) Enable user to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits. The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit for free database download. Package: r-cran-ip2whois Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-ip2whois_1.0.0-1.ca2604.1_all.deb Size: 30358 MD5sum: 943e4383b8daa60ac65b04ad9bea71be SHA1: cf257e172f5ba53ecc9d6e860920aeb587fce5d6 SHA256: 7ac5bff1cc96ac899e0823fd6a63612a9d80a3b7ca8369fd2e76dcfe61d96352 SHA512: 47b47d6fd6bdcfdc9fcbf08b5496a0a72209900c8409b6695b610b008c66b8c18d480f2ecd0719b9d2d48187e146e2e20b1df738371e99d9c000c60522c3f187 Homepage: https://cran.r-project.org/package=ip2whois Description: CRAN Package 'ip2whois' (Lookup 'WHOIS' Information for a Particular Domain) Easily implement the checking of 'WHOIS' information for a particular domain. 'IP2WHOIS' supports the query for 1113 Top-level Domains(TLDs) and 634 Country Code Top-level Domains(ccTLDs). To get started with a free API key, you may sign up at here . Package: r-cran-ipa Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ipa_0.1.0-1.ca2604.1_all.deb Size: 72478 MD5sum: 7fa3e8ba1441b07663ad1290564bbdf0 SHA1: 04b809b39406b2dace3688f9c833b789a0f45593 SHA256: 929fa35b244b06cb0de0a69cc7545247fc6d1d6b15d6b91fd9df7a9082e1f5d3 SHA512: dc19e9995b6d48d652d6486539609557a9f6a226c4231071cd5a92dbd6e2c4e578a6f88c816eac4f70b541edf66a8c99fd75d5e1cfbf2d27c20acfe6e8bb4163 Homepage: https://cran.r-project.org/package=ipa Description: CRAN Package 'ipa' (Convert Between Phonetic Alphabets) Converts character vectors between phonetic representations. Supports IPA (International Phonetic Alphabet), X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet), and ARPABET (used by the CMU Pronouncing Dictionary). Package: r-cran-ipadmixture Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-treemap, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ipadmixture_0.1.2-1.ca2604.1_all.deb Size: 1007320 MD5sum: 5284ebe8a014f9af39b16dee8fd0fd93 SHA1: 4d381124149f8243dc70b2112d1b74b009af984a SHA256: f2458cca6b7fa17eb231fff5cc26692e5194e36c3fed6f4c37f52faeba30396b SHA512: ca1b75610bb9cf4e2dab78dbffb66b4639bc41cc258c184b3f792544b2e7041432f138a337eb7ffea37b72b06d130ceee912b5b127c2a41c6e41800413ba52ae Homepage: https://cran.r-project.org/package=ipADMIXTURE Description: CRAN Package 'ipADMIXTURE' (Iterative Pruning Population Admixture Inference Framework) A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) . Package: r-cran-ipag Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ipag_0.1.0-1.ca2604.1_all.deb Size: 134034 MD5sum: 3d0c01b13b22e52f690449b391cdfea1 SHA1: 791a8bfec6d2d431587ea3425ad2d64f9808bcb6 SHA256: 5a5e695096a2159b5e69ff11686ee72d34cb40582852329ac6025fd4f6cd4e55 SHA512: c11e4fafe8e101dca42a565af3a3f86fa9dee4db334e46d48c77bb9ce4cb3b43c6de50132d79613ff27ef1a8c40ec041eca4bd12bf38a987744865a37571ef98 Homepage: https://cran.r-project.org/package=IPAG Description: CRAN Package 'IPAG' (Tools for IPAG Courses) Provides a collection of intuitive and user-friendly functions for computing confidence intervals for common statistical tasks, including means, differences in means, proportions, and odds ratios. The package also includes tools for linear regression analysis and several real-world datasets intended for teaching and applied statistical inference. Package: r-cran-ipanema Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-rmysql Filename: pool/dists/resolute/main/r-cran-ipanema_1.2.0-1.ca2604.1_all.deb Size: 43824 MD5sum: 6eb7118ffac42667a51087834c9afa71 SHA1: c58b3efc17ce18a47615b0fadf9a8549bdd97221 SHA256: bd86ab6f2f2e273292716c426216d91b09ec3e3434444cbcb95e7e5070210c0e SHA512: f5d2d73781ab66e1be887d04d88fbf1864a29f7b5d0b04f6df56c3b6c56f1cc87dd2064ffdca4a22c582ba648af9efe0a1468928327e94ee22e982864c85db64 Homepage: https://cran.r-project.org/package=ipanema Description: CRAN Package 'ipanema' (Read Data from 'LimeSurvey') Read data from 'LimeSurvey' () in a comfortable way. Heavily inspired by 'limer' (), which lacked a few comfort features for me. Package: r-cran-ipbase Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ipbase_0.1.1-1.ca2604.1_all.deb Size: 25902 MD5sum: d288e8fae1b12025c2e4fc3ec716b062 SHA1: f3b2d85fc65a2f7f2a7b7abcd5fe2445a4057035 SHA256: 293e5252191baf60f8a46559dd4d3ffd82df9cc0d0156e82b1135a1a00ca9567 SHA512: daa7de13e78c850456aa47bbacd81d15e1f5c701c55512d6ab891adf4f4d09b67dd9199015531246bf973f03b762964e2e7299d55392f61df5673018b87d370f Homepage: https://cran.r-project.org/package=ipbase Description: CRAN Package 'ipbase' (Client for the 'ipbase.com' IP Geolocation API) An R client for the 'ipbase.com' IP Geolocation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at . Package: r-cran-ipc Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-shiny, r-cran-txtq Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-future, r-cran-promises, r-cran-redux Filename: pool/dists/resolute/main/r-cran-ipc_0.1.4-1.ca2604.1_all.deb Size: 356800 MD5sum: 9b3145d62ff7db03c9141236a68471f4 SHA1: 65a25fc9b393556495d3637f0f48efc01421ab35 SHA256: 8f22ef1a6172e97cb46a4ad4bb0a449b7ae5e8f1211aac14d9ca43a4d1d8f30a SHA512: 0f33a2c19c1621ce4270a109c5782eb09fa80e8dfa8b1038a6279b098ad5273c3e47442ebe1531238e80674fe883ed4922e37099b15973fd957989ff1dca99ea Homepage: https://cran.r-project.org/package=ipc Description: CRAN Package 'ipc' (Tools for Message Passing Between Processes) Provides tools for passing messages between R processes. Shiny examples are provided showing how to perform useful tasks such as: updating reactive values from within a future, progress bars for long running async tasks, and interrupting async tasks based on user input. Package: r-cran-ipcwk Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survival Filename: pool/dists/resolute/main/r-cran-ipcwk_1.0-1.ca2604.1_all.deb Size: 25198 MD5sum: 667719e29d55074037ee85f2543a08d2 SHA1: 9f499a219bff974ea40902dcfc866220153bf096 SHA256: acf6d6270ef477a70893b13d48af057f95c4ab5d24977dd7a2130a9f9ab9ac3f SHA512: 3c63fec9f40691de217b119bf2ca1654f1594f57e717382c7414450de34a1ca5a51219a4776cded6b9ebec3c92459be3d51bac851aa797bcb5d2dfeca5fd3763 Homepage: https://cran.r-project.org/package=IPCWK Description: CRAN Package 'IPCWK' (Kendall's Tau Partial Corr. for Survival Trait and Biomarkers) We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) . Package: r-cran-ipd Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3093 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-caret, r-cran-gam, r-cran-ranger, r-cran-mass, r-cran-randomforest, r-cran-tibble Suggests: r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyverse, r-cran-xfun, r-bioc-biocstyle, r-cran-biocmanager Filename: pool/dists/resolute/main/r-cran-ipd_0.4.1-1.ca2604.1_all.deb Size: 1580024 MD5sum: f37773d50e523e1ad06801aecc607584 SHA1: eab061fbd98d82d3d4dac21cfaa843bbce2ef9c1 SHA256: bc84ceef138b363f39e4af60dc0c29eba8545df5c290c53b97956b7570634ddc SHA512: c550ef8255c78b144d7b9025659b3dd2c6e49f0b3ce060bdaa2e47d1a9c73a39079dd76faab57cc5f0a1b82d95f3813090c184acebef6ac7ecd66af6fad0b2f3 Homepage: https://cran.r-project.org/package=ipd Description: CRAN Package 'ipd' (Inference on Predicted Data) Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2025) . Package: r-cran-ipdfromkm Architecture: all Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-survival, r-cran-gridextra, r-cran-readbitmap Filename: pool/dists/resolute/main/r-cran-ipdfromkm_0.1.10-1.ca2604.1_all.deb Size: 119692 MD5sum: 334a699731a18684b1768e2311d69611 SHA1: 6398ba50b5954690bc4405b9ab39119f473c6eb6 SHA256: df3a57aab16f2c29ec163b2ae3c4698551437c69c7d4358850fa7c480dff73dd SHA512: d396ff8fe51ae9bd3c956e8fb77726fa950d670651623f52387c2009c2273de9e730fe9ee26929a922e933137f496867d8a3bab95f0a40a50cff9dc0d351d4f4 Homepage: https://cran.r-project.org/package=IPDfromKM Description: CRAN Package 'IPDfromKM' (Map Digitized Survival Curves Back to Individual Patient Data) An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. ’s digitize package (2011) . For more complex and tangled together graphs, digitizing software, such as 'DigitizeIt' (for MAC or windows) or 'ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) with some modifications. Package: r-cran-ipdw Architecture: all Version: 2.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdistance, r-cran-sf, r-cran-raster Suggests: r-cran-gstat, r-cran-gdata, r-cran-spatstat, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ipdw_2.0-0-1.ca2604.1_all.deb Size: 213816 MD5sum: f37339e841f119587223cd4b2900b10b SHA1: b0ec8844fce3c385d63b43932daca7254883ecd0 SHA256: 0dffdd43fd9bfd28250b86a28188464ec6c609c2ec2a0564e0b2945e6fd27e91 SHA512: a687f06ccf4366ebf7da3eccbf5b5746774050dfae5a1a0324ca762d9ae67261e889fe79aa6e8ebaf63959628e1e957aca11e3971ad56e1d7d246a4162584f04 Homepage: https://cran.r-project.org/package=ipdw Description: CRAN Package 'ipdw' (Spatial Interpolation by Inverse Path Distance Weighting) Functions are provided to interpolate geo-referenced point data via Inverse Path Distance Weighting. Useful for coastal marine applications where barriers in the landscape preclude interpolation with Euclidean distances. Package: r-cran-ipeadatar Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-curl, r-cran-rlang, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-sjlabelled Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ipeadatar_0.1.6-1.ca2604.1_all.deb Size: 67328 MD5sum: f3606d87573073101261b40263223f8e SHA1: 831f0c1da6566a2df09ab77d8766e8cf1996d1cf SHA256: 707025e5f56bbd808c83ee767a29848c4dbe7c0491ae12c8e4ca3bf4f6639316 SHA512: f26269cea5e72a21f943d2733694bace894a2c16b0cfbe99e6623bc45aeb4e75a765905848d7c6435848c3d80bc797e2c021809e410358a308eea723f40f1d13 Homepage: https://cran.r-project.org/package=ipeadatar Description: CRAN Package 'ipeadatar' (API Wrapper for 'Ipeadata') Allows direct access to the macroeconomic, financial and regional database maintained by Brazilian Institute for Applied Economic Research ('Ipea'). This R package uses the 'Ipeadata' API. For more information, see . 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Package: r-cran-ipec Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ipec_1.1.2-1.ca2604.1_all.deb Size: 219706 MD5sum: 6ff2fb5c5f66bc285b497636a4a5d605 SHA1: f43071af0fa8695d1245b4fc6868cc7e08f0b7e5 SHA256: f04d89b10d181fcb0612bca3aec3433bcb6d6a40a671b8bc2e833a00d0ef2992 SHA512: bd45e15f5fdd055ec3b68ad14999c5c4ec14d1ee11646eb32f2c17a17043a42930698eae0901c2a61e6aaddaaaf6e0ba8059b80f8f8bc99ae0536a661d9b37ab Homepage: https://cran.r-project.org/package=IPEC Description: CRAN Package 'IPEC' (Root Mean Square Curvature Calculation) Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) and Ratkowsky and Reddy (2017) for details. 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The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice. 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Inverse probability of treatment weighting (IPTW) is used to construct a pseudopopulation in which all individuals receive a specified intervention, enabling assessment of agreement between predicted risks under the intervention and observed outcomes in the pseudo-population corresponding to that intervention. Package supports binary and time-to-event outcomes under binary interventions made at a single time point. Performance measures supported are AUC (Area Under the receiving operating characteristic Curve), Brier score, observed-expected ratio, and calibration plots. Methods implemented in this package are based on work by Keogh and Van Geloven (2024) . 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Package: r-cran-ipgeolocation Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2 Filename: pool/dists/resolute/main/r-cran-ipgeolocation_0.1.0-1.ca2604.1_all.deb Size: 11936 MD5sum: e7e18205c744b3886641f4f3448a9b5f SHA1: e2c0207a124200a4a04b56037cf14a0f74d9ae55 SHA256: f9bdb3d8a6ab17d5646280b364a9f7cdeddd994198b2f2949b924b282be18546 SHA512: 656171728fda6030edf574568897339f309be1f8cc56ec8f5683b6058dfad9283d675674b9202bb7605807046b3feada344844003a27127f43483b24f215966a Homepage: https://cran.r-project.org/package=ipgeolocation Description: CRAN Package 'ipgeolocation' (Client for the 'IPGeolocation.io IP Location API') Provides functions to query the 'IPGeolocation.io IP Location API' (). Supports retrieval of IP location, ASN, network, currency, timezone, abuse, and security data. 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The goal of the implemented algorithm is to estimate the individual average cost of each disease, starting from the global health costs available for each patient. Package: r-cran-iprism Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hmisc, r-cran-tidyr, r-cran-igraph, r-cran-pbapply, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-iprism_0.1.1-1.ca2604.1_all.deb Size: 2693324 MD5sum: bfb42f50ce00fa19a95e9bcbf1ad0499 SHA1: ef8892a83ce04f894d28d3c7f1b60c71215a993e SHA256: 77fd4424cd9dc564f95be3b0a795b5888e3050d7f53486a59d90f9317d608523 SHA512: 372803f15db8ddeed969c9cde78957b82b616eb0cc000aa8425eed1b1c7ff107f54a822bfaf5c31faa7a293dcde735997e6d262c2adf6bf36236c0f2adb0ce26 Homepage: https://cran.r-project.org/package=iPRISM Description: CRAN Package 'iPRISM' (Intelligent Predicting Response to Cancer Immunotherapy ThroughSystematic Modeling) Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms. Package: r-cran-iprsue Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-bigstatsr, r-cran-logistf Filename: pool/dists/resolute/main/r-cran-iprsue_1.0.0-1.ca2604.1_all.deb Size: 186072 MD5sum: bf667b231520b69fcfd1a76f953bee76 SHA1: f771417b087f39c6b89742b6ecc70125ddfcb060 SHA256: 9f85549c9cd731a117cc6cda8d96b2b9adda897097d4249ca2e60d03f36ab9e8 SHA512: 864b5195bee54c71831b519228cfc9ba7924a1b62a2cd1ee0b1cc20b780f08240c87f6777be2ac7ba19ddc0584adf68e3f63511025af770d38b81b39480ae4b6 Homepage: https://cran.r-project.org/package=iPRSue Description: CRAN Package 'iPRSue' (Individual Polygenic Risk Score Uncertainty Estimation) Provides tools for estimating uncertainty in individual polygenic risk scores (PRSs) using both sampling-based and analytical methods, as well as the Best Linear Unbiased Estimator (BLUE). These methods quantify variability in PRS estimates for both binary and quantitative traits. See Henderson (1975) for more details. Package: r-cran-ips Architecture: all Version: 0.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-phangorn, r-cran-plyr, r-cran-xml Filename: pool/dists/resolute/main/r-cran-ips_0.0.13-1.ca2604.1_all.deb Size: 406698 MD5sum: c64968345e7c80636eca68a9080e51dd SHA1: 1d9455e2e45a8e074ec18f94e7f8970a6847998f SHA256: f23ac8093b07d593ff447fca5a59127434ebb80ce03580573f70e2be1072f78c SHA512: 21c42709c0864026fe9ebf9e641d1d9967df74d668679f9e9e60b0bdbd2c0c87ac9536aee2c33bd44355a7cf80eeaf8b74b1203e700ffd5df67153960d422804 Homepage: https://cran.r-project.org/package=ips Description: CRAN Package 'ips' (Interfaces to Phylogenetic Software in R) Functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states. Package: r-cran-ipsfs Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ipsfs_1.0.0-1.ca2604.1_all.deb Size: 223040 MD5sum: 125189652ed21700be3e60e9b5e089bf SHA1: 067d77bdb644439324666ac1eac5eda4685237d4 SHA256: 445c2064c6829052389e468159c68dbd6c292754e9cf43e4983b16a54a01e440 SHA512: 9532168007b8dc00edf23f7e6546f894013c67ede0ed7c1138e9283aa2bbb81d2da8d594f8656641bf2e1cc043ef3e367ec013f0108bcad4a63c015ed28543f8 Homepage: https://cran.r-project.org/package=ipsfs Description: CRAN Package 'ipsfs' (Intuitionistic, Pythagorean, and Spherical Fuzzy SimilarityMeasure) Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets. Package: r-cran-ipsrdbs Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-ggplot2, r-cran-extradistr Suggests: r-cran-xtable, r-cran-ggally, r-cran-magick, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-huxtable, r-cran-rcolorbrewer, r-cran-markdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-cran-ipsrdbs_1.0.0-1.ca2604.1_all.deb Size: 3738938 MD5sum: c157bff128fe41476a1eb335fcc1fc33 SHA1: 9577a4904a3c653a0884885aa611af36509aacb0 SHA256: dea5cb2bc6c5458042bb0ecc952053dcb129cf41a771d5727a1669ab951fd6a4 SHA512: 4e374b13c03866c86de5e69bceb4320e1b0f787b55e5c25b9666c426c96df7b4e6206bd8ed5eaf97cea88cb60eddba323cfa47de7e5b9666800ad77e0275a38b Homepage: https://cran.r-project.org/package=ipsRdbs Description: CRAN Package 'ipsRdbs' (Introduction to Probability, Statistics and R for Data-BasedSciences) Contains data sets, programmes and illustrations discussed in the book, "Introduction to Probability, Statistics and R: Foundations for Data-Based Sciences." 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Generate and download data through the IPUMS API and load IPUMS files into R with their associated metadata to make analysis easier. IPUMS data describing 1.4 billion individuals drawn from over 750 censuses and surveys is available free of charge from the IPUMS website . 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Package: r-cran-ipw Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nnet, r-cran-survival, r-cran-geepack Suggests: r-cran-nlme, r-cran-survey, r-cran-boot, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ipw_1.3.0-1.ca2604.1_all.deb Size: 221590 MD5sum: e2e6c3f573452c5eee304e3bee8b7f45 SHA1: 9c947c897f769864e7daa968046345b6969f1186 SHA256: 299a972573ec598ed46d7c60cef7736af352b0304058b9a6593d24236600cf06 SHA512: a6deafe1e5a51a3c0b49f9f8bb992ea296b4747ece5b5f69b340c7bcd28045c07668c4d2209129334b930da1ce18b77dfd4c7891c4ea3596261849e390020920 Homepage: https://cran.r-project.org/package=ipw Description: CRAN Package 'ipw' (Estimate Inverse Probability Weights) Functions to estimate the probability to receive the observed treatment, based on individual characteristics. The inverse of these probabilities can be used as weights when estimating causal effects from observational data via marginal structural models. Both point treatment situations and longitudinal studies can be analysed. The same functions can be used to correct for informative censoring. Package: r-cran-ipwboxplot Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-isotone Suggests: r-cran-mice, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ipwboxplot_0.1.2-1.ca2604.1_all.deb Size: 66046 MD5sum: 485154dbd6b0388b5f15f63a454301df SHA1: 5f7b682611129280e6bf681875609ca7a6ea3528 SHA256: dff02f3e2e020eb075ab92a2987f6f1db0a7d355900e7663ceac79d29e6ed69d SHA512: 0194238df2c2a2975af9524741b8ea29087537930524036c2e6c253dde4d69e8a106a7f1d6d3de569c2ee4beda5db456d581b34e21751daa8e4d1c964e142873 Homepage: https://cran.r-project.org/package=IPWboxplot Description: CRAN Package 'IPWboxplot' (Adapted Boxplot to Missing Observations) Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or modelled using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) ", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method. Package: r-cran-ipwcoxcsv Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-ipwcoxcsv_1.0-1.ca2604.1_all.deb Size: 47266 MD5sum: e359f9b26ce22f82a42a52327d5ae6c7 SHA1: 95218369f86b0e91d88366437d2c3ecf62753d5a SHA256: f83da2203a8ddcdbc69c05b969239a9713908a5d56fdcd9b2dbc9e48f5b587eb SHA512: 401723ee5acb410bee707ba2861af458ab81371327676baf92bad90dbe4d5646c7b9e45478de650069cc2b49b058366c893752cf3965aae11284b56b4d425be5 Homepage: https://cran.r-project.org/package=ipwCoxCSV Description: CRAN Package 'ipwCoxCSV' (Inverse Probability Weighted Cox Model with Corrected SandwichVariance) An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. 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This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) . 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There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) ), perform multiple outputation (Pullenayegum EM (2016) ) and fit semi-parametric joint models (Liang Y (2009) ). 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Most of the statistical procedures implemented in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, Advanced Analytics, LLC. 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Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi{10.1037/1082-989X.1.1.30} and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi{10.1214/aoms/1177732186}. 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Suitable for beginners who are learning item response theory. 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The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) ; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) ; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) ; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) . Thus, both Bayesian modal estimates and maximum likelihood estimates are available. Package: r-cran-irtdemo Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-fgarch Filename: pool/dists/resolute/main/r-cran-irtdemo_0.1.5-1.ca2604.1_all.deb Size: 24288 MD5sum: 4968035e8c45af4bb85b6ac1c7026155 SHA1: f303c4d3feb83c0f71f158a7105ee2344edf10f8 SHA256: dea2e818930a26ea1ff425f2305df050288f18f05f4ff12ccbf3bb40896e47e3 SHA512: 985ff4fa437c7e7558d302f38a61c730010e07716ca4adf7ff655f96df3659c7a0c95748621ff7c43ce22c988b9da21cde6ea743715f50f221fec7dc4354070f Homepage: https://cran.r-project.org/package=irtDemo Description: CRAN Package 'irtDemo' (Item Response Theory Demo Collection) Includes a collection of shiny applications to demonstrate or to explore fundamental item response theory (IRT) concepts such as estimation, scoring, and multidimensional IRT models. Package: r-cran-irtest Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betafunctions, r-cran-dcurver, r-cran-ggplot2, r-cran-usethis, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-irtest_2.2.0-1.ca2604.1_all.deb Size: 582290 MD5sum: 6e3a0fe34d9a2e92aa2c040ef0c35579 SHA1: e16e4020c8db802e42e95647002b49a9e0efd804 SHA256: d087a151dd558f1782e3f8ab76caf5c405e4d2ab36f5a8fc0ab299b76b0b052b SHA512: b9e4642c0d5df73c423ae4595064e88fed89bd2f6dbabe392d5063565a1baed24bc41b2e889f8cd7e9ecd539058e134413abd4763c77d262cdf4283ae39776b2 Homepage: https://cran.r-project.org/package=IRTest Description: CRAN Package 'IRTest' (Parameter Estimation of Item Response Theory with Estimation ofLatent Distribution) Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm (Bock \& Aitkin, 1981 ). Within parameter estimation algorithm, several methods for latent distribution estimation are available. Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution. 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Package: r-cran-irtpwr Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mirt, r-cran-deriv, r-cran-digest, r-cran-spatstat.random, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-irtpwr_1.0.3-1.ca2604.1_all.deb Size: 172920 MD5sum: 054e538d963fbc2f78aa8471e96cd539 SHA1: a8bf6e642da7a4032ca748fb5a6fb4597c745616 SHA256: b348d552650b9d61cfda9faa28e4d99413351a2df1d96eb7b4549970c0774244 SHA512: 2d39d12468c1129f755fbbc23a543d914f8616cd9987d47fb49cd17e08c0e34955ae039869bb5995fd9ae054d4c2abf06630735ced5e606ebe7197e2bdd7f08a Homepage: https://cran.r-project.org/package=irtpwr Description: CRAN Package 'irtpwr' (Power Analysis for IRT Models Using the Wald, LR, Score, andGradient Statistics) Implementation of analytical and sampling-based power analyses for the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied to item response theory (IRT) models that are fitted using marginal maximum likelihood estimation. The methods are described in our paper (Zimmer et al. (2022) ). 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The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2022) ). The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() and read.parscale() functions, respectively (Weeks (2010) ). The bisection() function was written by modifying the bisection() function (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve scoring in the est_score() function was written by modifying the irt.eq.tse() function (González (2014) ). In est_score() function, the code of weighted likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions of the catR package (Magis & Barrada (2017) ). 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The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) . 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Implements the 10-decision framework from Schroeders and Gnambs (2025) as a three-step workflow: specify the data-generating model with irt_design(), add study conditions with irt_study(), and run simulations with irt_simulate(). Supports one-parameter logistic (1PL), two-parameter logistic (2PL), and graded response models with missing-completely-at-random (MCAR), missing-at-random (MAR), booklet, and linking missingness mechanisms. Results include mean squared error (MSE), bias, root mean squared error (RMSE), standard error (SE), and coverage criteria with summary and plot methods. 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Implements methods described in: Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) , Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) , Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019) , and Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020) . 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Package: r-cran-islandcodes Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-countrycode Suggests: r-cran-dplyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-islandcodes_0.1.1-1.ca2604.1_all.deb Size: 51240 MD5sum: ff357ac10ba595f210550dc18b1227af SHA1: 100f5ae261c0ecbc33b8e43ab73741dc2c7b1f88 SHA256: e1f05942e80a000efe9f751f2e2cc0b7fbd4ad60d42c5936e097f4a205d0f544 SHA512: ec7d58597d0bdeaaa8439b9f0dca9784b53bb12d837a5b96c2ecafdebed00ffbc5c82807b44080e62c2f20656b8d14b11ff368edec823f10c6258f68d3c047ba Homepage: https://cran.r-project.org/package=islandcodes Description: CRAN Package 'islandcodes' (Reference Data and Helpers for Small Island States andTerritories) A curated reference list of countries and territories with classifications for Small Island Developing States (SIDS), sub-national island jurisdictions (SNIJ), World Bank region and income group, and political association. 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Package: r-cran-ismtchile Architecture: all Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-ismtchile_2.1.5-1.ca2604.1_all.deb Size: 209684 MD5sum: 8f8000de23b4dd20e45c5c1de9da55eb SHA1: 76b1e297c2e164ec9735827870219579819675ed SHA256: 4b688d221ba9b7e133000d20c68661729f3557cc16cbf230aec8841999c7ab82 SHA512: ec4b8cd699a6b3c9296bf3a6daadc12ce2e3852575c22d63ab9f33e2d901559d9fcfc834ce562d0e6b9fc884aa777a0198ec092138c2d4ceda0f8223a41c28d6 Homepage: https://cran.r-project.org/package=ismtchile Description: CRAN Package 'ismtchile' (Calculating Socio Material Territorial Index) Paquete creado con el fin de facilitar el cálculo y distribución del índice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodología completa está disponible en "ISMT" () [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" () [Observatorio de Ciudades UC (2019)]. Package: r-cran-ismtools Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-visnetwork, r-cran-viridis, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ismtools_0.1.0-1.ca2604.1_all.deb Size: 122154 MD5sum: aa50c24cf6c2312c799bf8286ad2be6c SHA1: 438587f85935f43256b10fcb2eafb4f5105b0c5e SHA256: f0c4bd83819624da872daad82d3dddf0135b8117c0d9cdbefcf6167e3d5dc192 SHA512: 9ae7f9aac7cdf5296bf7f10aca6d42170ea8ac54da6f1fc25dde61e9b5e17b2582180973b85ad102877bdf2be421d2477649e8b0005940d7cea0be5a76b00b26 Homepage: https://cran.r-project.org/package=ISMtools Description: CRAN Package 'ISMtools' (Interpretive Structural Modelling Analysis Tools) A comprehensive toolkit for Interpretive Structural Modelling (ISM) analysis. Provides functions for creating adjacency matrices from various input formats including SSIM (Structural Self-Interaction Matrix), computing reachability matrices using Warshall's algorithm, performing hierarchical level partitioning, MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis, and visualizing ISM structures through both static and interactive diagrams. ISM is a methodology for identifying and summarizing relationships among specific elements which define an issue or problem, as described in Warfield (1974) . Package: r-cran-isni Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-mvtnorm, r-cran-nnet, r-cran-matrixcalc, r-cran-formula, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-isni_1.3-1.ca2604.1_all.deb Size: 201802 MD5sum: e9ed79f23af0c4e430107899a111c7f6 SHA1: 4e37ffcf390e35bac37023b9ee16c58d4b99d109 SHA256: 63d5ec131b10ff089262750fc6751eeeb82b468a3d81de0efbf6c413562b3509 SHA512: bf398fb5777c3f117387d1317029c6156911b7c93b6d1a9e72f5599974a75098bcdf6d89294a440558feabb8cca688e0d103eaccb91b2814ec3b864881ed7ee6 Homepage: https://cran.r-project.org/package=isni Description: CRAN Package 'isni' (Index of Local Sensitivity to Nonignorability) The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random is with respect to the assumption of ignorability. For a tutorial, download at . For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) and Ma Troxel and Heitjan (2005) and Xie (2008) and Xie (2012) and Xie and Qian (2012) . Package: r-cran-iso11784tools Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-stringi, r-cran-dplyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-iso11784tools_1.2.0-1.ca2604.1_all.deb Size: 61906 MD5sum: 214cf1ba7395986b3019e3cdf28c0eca SHA1: 28c525c52eff8a5672043b21b70549985590235c SHA256: 44a10ca7a92d3e7ade9151758bf2ec4f591aee5c22d44c5ed143691616ea2bc9 SHA512: 15a0e0b2477bddd1d23a95e9072115a2b18c097804f5168343676322ce1de2576b0049bb5712e9f226ab86971cd93830ad6bdf3c2d1d8ca51cb8d8bfc680aa73 Homepage: https://cran.r-project.org/package=ISO11784Tools Description: CRAN Package 'ISO11784Tools' (ISO11784 PIT Tag ID Format Converters) Some tools to assist with converting International Organization for Standardization (ISO) standard 11784 (ISO11784) animal ID codes between 4 recognised formats commonly displayed on Passive Integrated Transponder (PIT) tag readers. The most common formats are 15 digit decimal, e.g., 999123456789012, and 13 character hexadecimal 'dot' format, e.g., 3E7.1CBE991A14. These are referred to in this package as isodecimal and isodothex. The other two formats are the raw hexadecimal representation of the ISO11784 binary structure (see ). There are two 'flavours' of this format, a left and a right variation. Which flavour a reader happens to output depends on if the developers decided to reverse the binary number or not before converting to hexadecimal, a decision based on the fact that the PIT tags will transmit their binary code Least Significant Bit (LSB) first, or backwards basically. Package: r-cran-isoboost Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-isotone, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-isoboost_1.0.2-1.ca2604.1_all.deb Size: 102434 MD5sum: fe453124dfc8811bf1d7ddc87c0279cb SHA1: 30b6b27406b21393288b3be7dc31da8364264199 SHA256: 776ef4af868db2d67862ed8578d922bd763edb9d0a472e7a17e7f78a7a9b502e SHA512: 9f48e397ff08b24441c5eaefa86b9e29a65a1540c16d170bdb81305258dbf6f47464fa0564a4d00b596136332271968fd7c3ca3bebf68e3109b65fd600ca4348 Homepage: https://cran.r-project.org/package=isoboost Description: CRAN Package 'isoboost' (Isotonic Boosting Classification Rules) In classification problems a monotone relation between some predictors and the classes may be assumed. In this package 'isoboost' we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. Package: r-cran-isobxr Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-readxl, r-cran-dplyr, r-cran-data.table, r-cran-desolve, r-cran-rlang, r-cran-ggplot2, r-cran-ggrepel, r-cran-qgraph, r-cran-writexl, r-cran-r.utils, r-cran-fs, r-cran-magrittr, r-cran-tidyr, r-cran-gridextra, r-cran-purrr, r-cran-reshape2, r-cran-tictoc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-mass, r-cran-roxygen2, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-isobxr_2.0.0-1.ca2604.1_all.deb Size: 509498 MD5sum: 90f82b9ebcef4f12b59ce69e3daf2f54 SHA1: b3cd597ec0c8e9c5a0b847ca23f63ae7611ca99e SHA256: b1b8562295f4b983b62ee3dff77adbd2de1339aa33de5cb5275b097bc8ebc742 SHA512: b8566396104b22df37677c33ad91baa00f6e38769437377f4ee43f479259859c97098e3400d205a46b125704400e9c04dcc9a9d689b424e7d859946bb828e2d1 Homepage: https://cran.r-project.org/package=isobxr Description: CRAN Package 'isobxr' (Stable Isotope Box Modelling in R) A set of functions to run simple and composite box-models to describe the dynamic or static distribution of stable isotopes in open or closed systems. The package also allows the sweeping of many parameters in both static and dynamic conditions. The mathematical models used in this package are derived from Albarede, 1995, Introduction to Geochemical Modelling, Cambridge University Press, Cambridge . Package: r-cran-isocalcr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-isocalcr_0.1.1-1.ca2604.1_all.deb Size: 288430 MD5sum: 8718d77501bf9d765f9028409f72223a SHA1: dd13f792249f566e7108f2b18cc7ab21762d7ec9 SHA256: 7df5fe66c6a1f15bfe590514323c7a8c7fdfa90568560ee57b89665334891d1b SHA512: e2972f34451e9c6faa0ca4f022db319c0ea5b25c95948c451dbfbfeb2fa7cb29f0fa2274e9b7db2662fb1017d9610a4f3e8b14fcb4c8a24e277450797b3c8f1b Homepage: https://cran.r-project.org/package=isocalcR Description: CRAN Package 'isocalcR' (Isotope Calculations in R) Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Mathias and Hudiburg (2022) in Global Change Biology . Package: r-cran-isocat Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-plyr, r-cran-sp, r-cran-magrittr, r-cran-foreach Suggests: r-cran-dendextend, r-cran-doparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-pvclust, r-cran-rmarkdown, r-cran-rastervis, r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-isocat_0.3.0-1.ca2604.1_all.deb Size: 477322 MD5sum: e10dadfed678f6c5efa57e237f5126a0 SHA1: b0e8968c59a034a7df787fd8cdcbcbde46ebaf8b SHA256: 3711328d6036ec51ba2d10fedf2e65d1914682d5dbc169c63e57ad8922e8f6f9 SHA512: 86ba0369157201dfd5ab80fbe7fc0fd1eb4d78d85dc4914c20af9a1e21b53addb0be81a51cc0ae2e22c171ad314b1ecf50bd578fda3f98459a7c149f04072219 Homepage: https://cran.r-project.org/package=isocat Description: CRAN Package 'isocat' (Isotope Origin Clustering and Assignment Tools) This resource provides tools to create, compare, and post-process spatial isotope assignment models of animal origin. It generates probability-of-origin maps for individuals based on user-provided tissue and environment isotope values (e.g., as generated by IsoMAP, Bowen et al. [2013] ) using the framework established in Bowen et al. (2010) ). The package 'isocat' can then quantitatively compare and cluster these maps to group individuals by similar origin. It also includes techniques for applying four approaches (cumulative sum, odds ratio, quantile only, and quantile simulation) with which users can summarize geographic origins and probable distance traveled by individuals. Campbell et al. [2020] establishes several of the functions included in this package . Package: r-cran-isocodes Architecture: all Version: 2026.03.28-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-isocodes_2026.03.28-1.ca2604.1_all.deb Size: 215862 MD5sum: 1944934fe1c32cf405a1cd8c0d916fd3 SHA1: 5fbbb1f2b38e81e8d06d42ce0b093cdf461458bd SHA256: 07487490e21063d4eb101cb08872990e0c6d1ff2de760e093f6c4c0f8e0c63a8 SHA512: 73b23b0de7f005bf6027df0acb99b9c2c7e8d7ce924ca7e89cb7cd02179ef07b3ac18d7e11e5201d5d2a32697dd6f2e7560816c6e2f21e75468111166fe9a9d2 Homepage: https://cran.r-project.org/package=ISOcodes Description: CRAN Package 'ISOcodes' (Selected ISO Codes) ISO language, territory, currency, script and character codes. Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217 currency codes, ISO 15924 script codes, and the ISO 8859 character codes as well as the UN M.49 area codes. Package: r-cran-isocor Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-config, r-cran-dt, r-cran-golem, r-cran-maldiquant, r-cran-markdown, r-cran-plyr, r-cran-shiny, r-cran-shinyalert, r-cran-shinyjs Suggests: r-cran-shinytest2, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-isocor_0.2.8-1.ca2604.1_all.deb Size: 3563122 MD5sum: 43ea61fec7ba489a9cb14c0101d637d6 SHA1: 62fab2e8439224533cb5d248c7c570f247534e34 SHA256: 9297808a23509aa5c3371a2de23f297e3b1c1971fd5e6689533317f5b9a82513 SHA512: 7905c19a96f214e8ba1080be35264e7c0201b4ddc1b0860d46ce686af4c7a1c3ef02ac040cd439662fcc01bd082afc31f1da44f9dcd745374a9c09b60a88b296 Homepage: https://cran.r-project.org/package=IsoCor Description: CRAN Package 'IsoCor' (Analyze Isotope Ratios in a 'Shiny'-App) Analyzing Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) measurement data to evaluate isotope ratios (IRs) is a complex process. The 'IsoCor' package facilitates this process and renders it reproducible by providing a function to run a 'Shiny'-App locally in any web browser. In this App the user can upload data files of various formats, select ion traces, apply peak detection and perform calculation of IRs and delta values. Results are provided as figures and tables and can be exported. The App, therefore, facilitates data processing of ICP-MS experiments to quickly obtain optimal processing parameters compared to traditional 'Excel' worksheet based approaches. A more detailed description can be found in the corresponding article . The most recent version of 'IsoCor' can be tested online at . Package: r-cran-isocountry Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-isocountry_0.6.0-1.ca2604.1_all.deb Size: 26254 MD5sum: 0e9d28e759e45a1dcaf8eb236825281b SHA1: 3a7adf8953f03f74101b1cd3510646e5d7b1875c SHA256: 5ed7134a3c7e4afaa6836e78045570d2e9b4fde4eaa96e975d006745ba512fdf SHA512: b7c6f1858ec9a7d61d58210c9958cdbf63d4afb75cc7ebbf80740ff47ffb6e1b3eed57d6707329e73735f8c39a02dc54071b90b1839a578f587bd725fd2c4265 Homepage: https://cran.r-project.org/package=isocountry Description: CRAN Package 'isocountry' (ISO 3166-1 Country Codes) ISO 3166-1 country codes and ISO 4217 currency codes provided by the International Organization for Standardization. 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Use it to quickly calculate isotope fractionation factors, and apply paleothermometry equations. Package: r-cran-isokernel Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rann, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-isokernel_0.1.0-1.ca2604.1_all.deb Size: 15224 MD5sum: a0c87daad19a2a28749ca7b5ef2361b7 SHA1: 2ffa7aa582c2cc932b98a7aadd603c4a708ea06d SHA256: f8a01b60acab8ee77e455a461d81390e0f70f440d344950b2d8bb974f2f1c7ac SHA512: d885ce0d0930dacc5511754074f2c26d63e6aaa37a88f508e37a6fcee86323e305775b6ee1c084305b5e1023ee31df301dc1b7ea0ef6ebc5f3d25948ed2ba9fb Homepage: https://cran.r-project.org/package=isokernel Description: CRAN Package 'isokernel' (Isolation Kernel) Implementation of Isolation kernel (Qin et al. (2019) ). 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Citation: Kantnerova et al. (Nature Protocols, 2024). Package: r-cran-isopam Architecture: all Version: 3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-cluster, r-cran-fastkmedoids, r-cran-future, r-cran-future.apply, r-cran-ps, r-cran-proxy, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-isopam_3.6-1.ca2604.1_all.deb Size: 126268 MD5sum: 9df89ac21f35e71052c9102bf63c240b SHA1: 3795b00cdfc98bdab12601860c864cb75abb2d5c SHA256: 5a514aa46e13c3b1abf46cd84608c8816afdf43a5c19d9770647aa6bb6e45d34 SHA512: ce2556e90a140663ec9bcc402b7548e4a0411a6475d0b8d63c335bc43c8365f723b52e867097bdc487bf9bb556d062f971c61c6de6ea2102e42ead156c5a5e98 Homepage: https://cran.r-project.org/package=isopam Description: CRAN Package 'isopam' (Clustering of Sites with Species Data) Clustering algorithm developed for use with plot inventories of species. 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Package: r-cran-isopleuros Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1420 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-interp, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-isopleuros_1.4.0-1.ca2604.1_all.deb Size: 528886 MD5sum: d69fc3069d97e869e25506a5963b9fe6 SHA1: e25aee3d7b9bd3e6efbc47d14ae504b8fad2f3e6 SHA256: 27edebf4edeffde150d5061b4392b98b6a2304f5970769c1c1483d45233934a0 SHA512: 57e0984e72f5220ee035c182288c91ffa15471696e7397687d8a6abe0804de80fa11cf3d451cbead2e53f4f92517ffadb3c34b811fbc9ca9b86520d4f7b8f09c Homepage: https://cran.r-project.org/package=isopleuros Description: CRAN Package 'isopleuros' (Ternary Plots) Ternary plots made simple. This package allows to create ternary plots using 'graphics'. It provides functions to display the data in the ternary space, to add or tune graphical elements and to display statistical summaries. It also includes common ternary diagrams which are useful for the archaeologist (e.g. soil texture charts, ceramic phase diagram). Package: r-cran-isoplotr Architecture: all Version: 6.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1556 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-isoplotr_6.8-1.ca2604.1_all.deb Size: 1420308 MD5sum: 34678366337ab32b9c56e1cdfa6ed29d SHA1: b95fb563bf436e4cd92070edbe1c3668dd710d70 SHA256: 947ed48cddc30884a1ecbbbcb4d5dd607de10a32831ec9eb51ba684060d06676 SHA512: ad9b7998737f8478f1bfad7516794e196cee49c9ef725992fcd28e7d91819f259bdd65008a7e84ce72f848ae6b3380eded709906f9d76d3a3b8e2c9b406a2f24 Homepage: https://cran.r-project.org/package=IsoplotR Description: CRAN Package 'IsoplotR' (Statistical Toolbox for Radiometric Geochronology) Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington', 'Ludwig' and Omnivariant Generalised Least-Squares ('OGLS') approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots. 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This package is essentially a simplified interface to several other packages which implements a new statistical framework based on mixed models. It uses 'spaMM' for fitting and predicting isoscapes, and assigning an organism's origin depending on its isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for plotting the maps produced with 'terra' using 'lattice'. 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The package contains functions to obtain and analyze incomplete split-plot designs for three kinds of situations namely (i) when blocks are complete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments, (ii) when blocks are incomplete with respect to main plot treatments and main plots are complete with respect to subplot treatments and (iii) when blocks are incomplete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments. 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Package: r-cran-iterors Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-reticulate, r-cran-combinat, r-cran-testthat, r-cran-foreach, r-cran-iterators, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-iterors_1.0.1-1.ca2604.1_all.deb Size: 334642 MD5sum: cd88eb651e18935c03af837f3ec070d8 SHA1: afe7759ff9afa73378c3480adf3c3ec22fb63163 SHA256: 8bc426daba7b442e8eaef8efce2766ba8ae4efd1956a4e9a3aee7a5fbdc5b1f9 SHA512: 0a49cfc12735eb5adf5118d1f9fe36a8f0795d16aeefa52442cd8376ddfaec2a2a2d027443c58dc3c1e136f936f15015307352804b79bd291e35c6ffc5d56ca3 Homepage: https://cran.r-project.org/package=iterors Description: CRAN Package 'iterors' (Fast, Compact Iterators and Tools) A fresh take on iterators in R. 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We based this methodology on the RV coefficient (Robert & Escoufier, 1976, ), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, ). 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Data sets are 'aHDL' from Rosenbaum (2023a) and 'bingeM' from Rosenbaum (2023b) . The function makematch() uses two-criteria matching from Zhang et al. (2023) to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) ) and matching with fine or near-fine balance (Rosenbaum et al. (2007) and Yang et al (2012) ). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'. 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Functions are provided for parsing XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. Multivariate methods are implemented with appropriate data transformation. Package: r-cran-itrimhoch Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-itrimhoch_1.0.0-1.ca2604.1_all.deb Size: 107452 MD5sum: 76a169742bfc9f43adf481a0ebc46f4c SHA1: 2d3bbd466349f2a23e015d24a4154bcd31cec78c SHA256: 195bc03fb708a11468c10cf64c679cca85c8f038d59e472000d9d921cb5f3076 SHA512: 9f612df527c7b1227b6c24363d6a3e5a8ab976f14a66348e2f0baa13db3a424d5c513ae50a36b992f2d1160beaf4c0cc4a274d7e7d73e7639f4c666c507eb4e3 Homepage: https://cran.r-project.org/package=itrimhoch Description: CRAN Package 'itrimhoch' (Improved Trimmed Weighted Hochberg Procedures and Sample SizeOptimization) The improved trimmed weighted Hochberg procedure provides increased statistical power and relaxes the dependence assumptions for familywise error rate control compared to the original weighted Hochberg procedure. This package computes the boundaries required for implementing the proposed methodology and includes sample size optimization methods. See Gou, J., Chang, Y., Li, T., and Zhang, F.(2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report. Package: r-cran-its.analysis Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-car, r-cran-forecast, r-cran-boot, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-its.analysis_1.6.0-1.ca2604.1_all.deb Size: 37514 MD5sum: 7176990ad67568235f9a3728580cd165 SHA1: 439b08d08bc55d31b77526d487d844999959a592 SHA256: 44ae626802bb2036a9b53874e172051b0a1bc76e0a432d8fd2f34c2b95f5e847 SHA512: 055ab47d8a3314a9ef04f1017065cfa03d08d0066c7c5e6b9567bd2860c701d971b020259ecb440e4525940d4adcfbc19b323790f8968cca73440222e6de0f6a Homepage: https://cran.r-project.org/package=its.analysis Description: CRAN Package 'its.analysis' (Running Interrupted Time Series Analysis) Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' for an overview of the method. Package: r-cran-itsadug Architecture: all Version: 2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4872 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-plotfunctions Suggests: r-cran-knitr, r-cran-xtable, r-cran-sp, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-itsadug_2.5-1.ca2604.1_all.deb Size: 3298942 MD5sum: fe7af02e325cc1498bedfdca2b5996cc SHA1: b6f516ad161bd07421285bdac7a7ce57f4d53470 SHA256: ec5f0a50809835020215815828ec37a4688980467b413552b11d36d3edbe9692 SHA512: bb91e370eb77ea9a5f3ea1a86705a9e2547834a462d37aab4080c51aa5d323c6985e6e6b6d8b1ce6d4aa07c9f0695f4f7ac7d49fa0b7e3069bc3123d7ad48045 Homepage: https://cran.r-project.org/package=itsadug Description: CRAN Package 'itsadug' (Interpreting Time Series and Autocorrelated Data Using GAMMs) GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts). Package: r-cran-itscalledsoccer Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httpcache, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-r6, r-cran-tidyr, r-cran-stringi, r-cran-rlang, r-cran-data.table, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-itscalledsoccer_0.3.2-1.ca2604.1_all.deb Size: 552900 MD5sum: 63efbbc3871322f8fa2fc2aac1fbf0e6 SHA1: d4761f4da93a5fe0a768fdb1ddda2ee30f2575f8 SHA256: b015612b687bbce91b5b99e188fc2ddbda6b5723de7fdb8c174f80dec780833f SHA512: 276dc8afe0cb7edde1bdffb86afb10e85fd35ca498464710d7109a6112acb37522e0f5c7d330987fe4a4c7263ff165e8387566e78a7faafa58731a27ff12b27e Homepage: https://cran.r-project.org/package=itscalledsoccer Description: CRAN Package 'itscalledsoccer' (American Soccer Analysis API Client) Provides a wrapper around the same API that powers the American Soccer Analysis app. Package: r-cran-itsdm Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-fastshap, r-cran-ggplot2, r-cran-isotree, r-cran-mgcv, r-cran-ncdf4, r-cran-outliertree, r-cran-patchwork, r-cran-raster, r-cran-rlang, r-cran-rocit, r-cran-sf, r-cran-stars, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-itsdm_0.2.2-1.ca2604.1_all.deb Size: 1457426 MD5sum: 2c53989ca632bdda04d0903e7ac73606 SHA1: c45829fc26580a23dd53c73a433dcb28e1f05f72 SHA256: 896a6e3a6b8517b8707645dc0074cb2bbb3e859dcaf9f7369f9e2ac9d875678a SHA512: 6ad9a4c2de1cb018a1257128cc7b910b97620f4ecd8c0c859df7c272081f6a854d4d4180dd3f3cf5801776b9599bcaf72c5cc005e1c5a6a2cd42c6f4fa9dd3b8 Homepage: https://cran.r-project.org/package=itsdm Description: CRAN Package 'itsdm' (Isolation Forest-Based Presence-Only Species DistributionModeling) Collection of R functions to do purely presence-only species distribution modeling with isolation forest (iForest) and its variations such as Extended isolation forest and SCiForest. See the details of these methods in references: Liu, F.T., Ting, K.M. and Zhou, Z.H. (2008) , Hariri, S., Kind, M.C. and Brunner, R.J. (2019) , Liu, F.T., Ting, K.M. and Zhou, Z.H. (2010) , Guha, S., Mishra, N., Roy, G. and Schrijvers, O. (2016) , Cortes, D. (2021) . Additionally, Shapley values are used to explain model inputs and outputs. See details in references: Shapley, L.S. (1953) , Lundberg, S.M. and Lee, S.I. (2017) , Molnar, C. (2020) , Štrumbelj, E. and Kononenko, I. (2014) . itsdm also provides functions to diagnose variable response, analyze variable importance, draw spatial dependence of variables and examine variable contribution. As utilities, the package includes a few functions to download bioclimatic variables including 'WorldClim' version 2.0 (see Fick, S.E. and Hijmans, R.J. (2017) ) and 'CMCC-BioClimInd' (see Noce, S., Caporaso, L. and Santini, M. (2020) . Package: r-cran-itsmr Architecture: all Version: 1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-itsmr_1.11-1.ca2604.1_all.deb Size: 172248 MD5sum: 1df990700ccfdd663e656fd7b1951b0b SHA1: b9fec6d2fb8cf134ee30c31af32756eee05ce9c2 SHA256: 12ded6d1653ea3fd602d31e734a821a2cae469e9f2c1a40ca0d1cd87709330f4 SHA512: af90e770c169307eb1a60328e563dbde4375663e57692de9a7362dee0bf2a0868f891bec0df067c46d1632bf79a5fc740ed29b1bc1262228ade21f391e8598a3 Homepage: https://cran.r-project.org/package=itsmr Description: CRAN Package 'itsmr' (Time Series Analysis Using the Innovations Algorithm) Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. Package: r-cran-iucnr Architecture: all Version: 0.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-stringr Suggests: r-cran-rlang, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-iucnr_0.0.0.1-1.ca2604.1_all.deb Size: 21684 MD5sum: b1fce18a78128a8ffef606bbd925ee74 SHA1: 54ac34c34f9decd8f1ebcb1f1d5989b9b35072bf SHA256: 162b8e150258c6ebb162b711b1d8618256eb1f5181d321d77ffb8584f52a62a3 SHA512: 1da0f8a41a50525797c000bbd7371ee0133b10e5d6e0ab5be1a212305a63b86d03370b9c7f3b395f82161afca16a8b8ebdf8a57f3679f9847eedf790ff24b685 Homepage: https://cran.r-project.org/package=iucnr Description: CRAN Package 'iucnr' (IUCN Red List Data) Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the 'iucnrdata' package, available on GitHub . To install the data package, use pak::pak('PaulESantos/iucnrdata'). Package: r-cran-iv.sensemakr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sensemakr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-iv.sensemakr_0.1.0-1.ca2604.1_all.deb Size: 5223824 MD5sum: 902aac179cf9915db76f89816b285c54 SHA1: f31874ef082d44d9fbf8529fa6323ef6d72656f4 SHA256: 725b5e7e67a04a38a73326633711284d9fd485382662119dd2c64eeb9d4ffc6b SHA512: b4cd07180ea808f56cc988be83ad2eda736533b3d1335a5c6a7f1dbbcfe7f147b90f9bfe3503eedd4203ec79c334b9b87411bd4b610d0e458862c6b8daadac22 Homepage: https://cran.r-project.org/package=iv.sensemakr Description: CRAN Package 'iv.sensemakr' (Sensitivity Analysis Tools for Instrumental Variable Estimates) Implements a suite of sensitivity analysis tools for instrumental variable estimates as described in Cinelli and Hazlett (2025) . 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(2011) and Lee et al. (2007) . Package: r-cran-ivcheck Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-fixest, r-cran-ivreg, r-cran-modelsummary, r-cran-broom, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ivcheck_0.1.1-1.ca2604.1_all.deb Size: 253022 MD5sum: e8487b528f7b874afb412b94d3904f35 SHA1: c219eb75f515f0713b3724c5678e2d60d98a4e05 SHA256: b6e573eea27d282541954baa0d5440c275f4fb84b9e93112c53808513674903e SHA512: 743725b3655d98cbb3f185e8559767c0e0200d909097de431e5f5f0b2e57027ba22ba0c90da5df83696d4ec49706a42234b0cd9c2fcd2ac9082be02f35cf9902 Homepage: https://cran.r-project.org/package=ivcheck Description: CRAN Package 'ivcheck' (Tests for Instrumental Variable Validity) Implements tests for the identifying assumptions of instrumental variable models, the local exclusion restriction and monotonicity conditions required for local average treatment effect identification. Covers Kitagawa (2015) , Mourifie and Wan (2017) , and Frandsen, Lefgren, and Leslie (2023) . Includes a one-shot wrapper that runs all applicable tests on a fitted instrumental variable model. Dispatches on 'fixest' and 'ivreg' model objects. Package: r-cran-ivcor Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-bwquant, r-cran-quantdr Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ivcor_0.1.0-1.ca2604.1_all.deb Size: 59074 MD5sum: f6cf78aaa81e14928859c03231592ebb SHA1: ea94d31b358b27a9802df0fb515337467a4f8c10 SHA256: 0d6718fcc3c2065d7babf5898013c3b3c675886a9ee82fe455f8652146ade92a SHA512: 66858a8739f43ea5352f1086522aaeb4cd3e960f1fe533a65946f45c714de482ab2885dc3b2fd4c65de0500dce2627e4b6ad3beb19a1ae1eedd17beb8af657e9 Homepage: https://cran.r-project.org/package=IVCor Description: CRAN Package 'IVCor' (A Robust Integrated Variance Correlation) A integrated variance correlation is proposed to measure the dependence between a categorical or continuous random variable and a continuous random variable or vector. This package is designed to estimate the new correlation coefficient with parametric and nonparametric approaches. Test of independence for different problems can also be implemented via the new correlation coefficient with this package. Package: r-cran-ivd Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-coda, r-cran-ggplot2, r-cran-patchwork, r-cran-future, r-cran-future.apply, r-cran-rstan, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ivd_1.0.0-1.ca2604.1_all.deb Size: 322188 MD5sum: 5c6f696f440bc328d35187b9fae1d360 SHA1: f893fe14f8284405168ee1ff096a559674b8e654 SHA256: c18b75889128a993b340d28b20a99ba96fcd0d23dc4c709019ecac62d95a08e5 SHA512: 8e6cf0df6ce2fb4e6b7cb85e308640f7a7995f301ed8c1e89f05d834fc79e0cc0dfe42dad6442556162947b3f7aa9036cfb6b5301d036b99fff487d4af7e2e4d Homepage: https://cran.r-project.org/package=ivd Description: CRAN Package 'ivd' (Individual Variance Detection) Fit mixed-effects location scale models with spike-and-slab priors on the location random effects to identify units with unusual residual variances. 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This package implements the method described in Marbach and Hangartner (2020) and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) . 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Package: r-cran-ivdml Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ranger, r-cran-xgboost Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ivdml_1.0.1-1.ca2604.1_all.deb Size: 99992 MD5sum: 867d753e2b901717d120956a36badbab SHA1: cb74d912395f552c2efae1b2e3a92883021295fb SHA256: d6056658f027da06732e8277c55e4002dab7fd09165ddb2f205c852bfe65cb38 SHA512: b25bd9badf0e94f1739dc9d759b7bbdc13587841054d6eb1f74f915a8634d3f15b343e18227f3b3bd41fb9ba390d1bf0c06376aac6b20ccd6864f9c0df193522 Homepage: https://cran.r-project.org/package=IVDML Description: CRAN Package 'IVDML' (Double Machine Learning with Instrumental Variables andHeterogeneity) Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" . 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Furthermore, indicator saturation methods can be used to detect outliers and structural breaks in the sample. Package: r-cran-ivitr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-randomforest, r-cran-dplyr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-ivitr_0.1.0-1.ca2604.1_all.deb Size: 61102 MD5sum: 8d35f5a620e29f359542c36b5e500a4a SHA1: 769897f59a39aef9fec17877f3aec17f7523da11 SHA256: 6bfb5f2d6c0ce0a8c7bb4daeb292b338ba9fb69393553f76480c5ab33d45e85e SHA512: fb69f31281c8fb8090d1c1d0828fe0663f77f9399997e5c63a4af56e618c6d6ee71eb6b0a064c9531a673abc362bf43af2343bc0508f1af9b048c3316aa696c0 Homepage: https://cran.r-project.org/package=ivitr Description: CRAN Package 'ivitr' (Estimate IV-Optimal Individualized Treatment Rules) A method that estimates an IV-optimal individualized treatment rule. An individualized treatment rule is said to be IV-optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. Please refer to for more details on the methodology and some theory underpinning the method. Function IV-PILE() uses functions in the package 'locClass'. Package 'locClass' can be accessed and installed from the 'R-Forge' repository via the following link: . Alternatively, one can install the package by entering the following in R: 'install.packages("locClass", repos="")'. Package: r-cran-ivmodel Architecture: all Version: 1.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-formula, r-cran-reshape2, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ivmodel_1.9.1-1.ca2604.1_all.deb Size: 416688 MD5sum: bb68fcc4dee69c2d21ce0a858931b668 SHA1: d08e3691276aee898ddbac38d6452920bd1a7198 SHA256: fd621eb9c9e528c8f196408dbe126b0ee4eea22039ec660a32729772ca42be8f SHA512: f79974d1deb8fb20063b5c3c910e459640e327c083a52b7357cb67748a03448b4173ecaf4bfb1814f52848b4acbd48734104e32935bb1f7691380f5286b02795 Homepage: https://cran.r-project.org/package=ivmodel Description: CRAN Package 'ivmodel' (Statistical Inference and Sensitivity Analysis for InstrumentalVariables Model) Carries out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics. See Kang, Jiang, Zhao, and Small (2020) for details. Package: r-cran-ivmte Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula Suggests: r-cran-slam, r-cran-lpsolveapi, r-cran-rmosek, r-cran-testthat, r-cran-data.table, r-cran-splines2, r-cran-future.apply, r-cran-future, r-cran-matrix, r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-aer, r-cran-lsei, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-ivmte_1.4.0-1.ca2604.1_all.deb Size: 1499634 MD5sum: 51893124572587cce3954ae32ee04d5f SHA1: 595fc60007a7ece5f02cf60064fa725c40a5a8da SHA256: e6b87c88beb878fea4c0825d523941d8fd01e0eec44962025da25b31648410c6 SHA512: 99cdfc9b62afcab4bfdd0079cfa8cbed851c279fad4189df31e72709f71d2ea64c69302649c41498e41af10d40d0583fb6b9238da7dc3f4737915ddd9c7fdad9 Homepage: https://cran.r-project.org/package=ivmte Description: CRAN Package 'ivmte' (Instrumental Variables: Extrapolation by Marginal TreatmentEffects) The marginal treatment effect was introduced by Heckman and Vytlacil (2005) to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) . This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) , and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using either linear programming or quadratically constrained quadratic programming. Support for four solvers is provided. Gurobi and the Gurobi R API can be obtained from . CPLEX can be obtained from . CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be obtained from . The lp_solve library is freely available from , and is included when installing its API 'lpSolveAPI', which is available from CRAN. Package: r-cran-ivo.table Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-flextable, r-cran-checkmate, r-cran-gt, r-cran-officer, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-palmerpenguins Filename: pool/dists/resolute/main/r-cran-ivo.table_0.7.1-1.ca2604.1_all.deb Size: 630704 MD5sum: 3e01bc608e176529ddd5f859c362d488 SHA1: ed81a05219fb131b34a83ac3aa90f9df1b2f7884 SHA256: d4dda612ae8c71a187dff4b6d4c3e4e49c78900ad1971dc92c1508f14f6d2c4c SHA512: 824d954d6d88d1d4e8b35ac43c226885b74a82c811027f3d8df8be846ccd6bd4979115546ce3cef9d8a7947f47715be381df3f3b6aace31faa388b291c149052 Homepage: https://cran.r-project.org/package=ivo.table Description: CRAN Package 'ivo.table' (Nicely Formatted Contingency Tables and Frequency Tables) Nicely formatted frequency tables and contingency tables (1-way, 2-way, 3-way and 4-way tables), that can easily be exported to HTML or 'Office' documents. Designed to work with pipes. Package: r-cran-ivolcano Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42654 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggiraph, r-cran-ggrepel, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-patchwork, r-cran-rlang Suggests: r-bioc-clusterprofiler, r-bioc-enrichplot, r-cran-fanyi, r-bioc-org.hs.eg.db, r-cran-quarto, r-cran-yulab.utils Filename: pool/dists/resolute/main/r-cran-ivolcano_0.0.5-1.ca2604.1_all.deb Size: 2646254 MD5sum: fe75d2569f88c3a4993c32135c976001 SHA1: 1d16ae63de5918ff4987ca43721337eb164f285f SHA256: 4b1312a7efe8d54628c061cca8713e42271ce94698ff36063e2297a31b3f5723 SHA512: 619205f3bf06c9c02a62866869a97eee0a6695bb68aacda53f57f7c40beac47ac110ba48eaeb1b2764941e4b295edc5d1ef543fd690f78800868503871c433c1 Homepage: https://cran.r-project.org/package=ivolcano Description: CRAN Package 'ivolcano' (Interactive Volcano Plot) Generate interactive volcano plots for exploring gene expression data. Built with 'ggplot2', the plots are rendered interactive using 'ggiraph', enabling users to hover over points to display detailed information or click to trigger custom actions. Package: r-cran-ivpp Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bootnet, r-cran-clustergeneration, r-cran-dplyr, r-cran-mvtnorm, r-cran-psychonetrics, r-cran-graphicalvar, r-cran-lifecycle, r-cran-future.apply, r-cran-future, r-cran-networktools, r-cran-qgraph, r-cran-fmsb Filename: pool/dists/resolute/main/r-cran-ivpp_1.1.2-1.ca2604.1_all.deb Size: 102624 MD5sum: 3360e649b8cbd86992ae28d9699cb552 SHA1: 6b28753b279f6e7286394b7e3a315a88735e4ead SHA256: b10572a42a1c4b972107ea4f36ccb9663f6f96b0d9788fc365873af742303338 SHA512: e6b0150cf38b43b1c7ef879f11b4d7ef06223e666345f2edf2222a649db85bc36ba7858d16ec3a035bdabfe52f4a29f12d7ec7b97797eec9a502752f49de4d50 Homepage: https://cran.r-project.org/package=IVPP Description: CRAN Package 'IVPP' (Invariance Partial Pruning Test) An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning. The package also allows you to compute centrality measures and use radar chart to plot. Analysis of bridge centralities by community pairs is also possible (e.g., the bridge strength from depression to anxiety, and from depression to panic disorder). Package: r-cran-ivreg Architecture: all Version: 0.6-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-formula, r-cran-lmtest, r-cran-mass Suggests: r-cran-aer, r-cran-effects, r-cran-knitr, r-cran-insight, r-cran-rmarkdown, r-cran-sandwich, r-cran-testthat, r-cran-modelsummary, r-cran-gt, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-ivreg_0.6-7-1.ca2604.1_all.deb Size: 907438 MD5sum: d4d00702260a67e26b867756e49914b1 SHA1: e728f7247c3e2be942df24f76602fb0a59af81df SHA256: a6fb429447192e043fc254afb2487bd39f2ba519c2fe973d9ed4ce409ac29695 SHA512: e5ef480eda3610c27bea3096e86c6d141468c7ef81e144cc9a841720a3258f2fe129755dd4f93bb89ebc1287fb797e6e5e85fe2a3459a828c25f507992ca8af6 Homepage: https://cran.r-project.org/package=ivreg Description: CRAN Package 'ivreg' (Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM',with Diagnostics) Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools. Package: r-cran-ivs Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-vctrs Suggests: r-cran-bit64, r-cran-clock, r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ivs_0.2.0-1.ca2604.1_all.deb Size: 407284 MD5sum: 2213a9f9269fa3930635bf53ddc62b60 SHA1: eb2821859aca1225c122fbff00ef05e9f4203215 SHA256: 5d5baaedf1cd207592737e55f778f1c783998c45739daa8b7422488f9c62510c SHA512: 128c992b944b03d961436760b08f1f41e121f4a0254488486b36cf530f64825d519e21eefb201e2733e49bf65cf39cb6a27036cc6c7879d8683121fd7bbf1b1c Homepage: https://cran.r-project.org/package=ivs Description: CRAN Package 'ivs' (Interval Vectors) Provides a library for generic interval manipulations using a new interval vector class. 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Package: r-cran-ivyplot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-ivyplot_0.1.0-1.ca2604.1_all.deb Size: 15656 MD5sum: a1a949cf9369e6e2389dd0cacceb4b4d SHA1: 2040945fe9719c5599cd0fcbc021fd1ba615b6af SHA256: 70e8b899b75168be33a56152fcf1c7f37713765166ea22c22a5e34fe9b3c96aa SHA512: 0cb8d6bde5a050941b48fde20601bb9d25cd91ab5bf3dca3e0f3673721d26aafac7f0e44339a98a1378411e0ac302ece6e720c3db775ecaa4581ce8fb774c1e9 Homepage: https://cran.r-project.org/package=IVYplot Description: CRAN Package 'IVYplot' (Produces an IVY Plot (Similar to Dot Plot) with/withoutFrequencies) For a single variable, the IVY Plot stacks tied values in the form of leaflets. Five leaflets join to form a leaf. Leaves are stacked vertically. At most twenty leaves are shown; For high frequency, each leaflet may represent more than one observation with multiplicity declared in the subtitle. Package: r-cran-iwaqr Architecture: all Version: 1.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggthemes, r-cran-ggrepel, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-iwaqr_1.8.4-1.ca2604.1_all.deb Size: 96212 MD5sum: 6dc16bb891694eb7f97bb78f39a422de SHA1: cced48882c7b23b2436274fd2ae7ec24bf623c8f SHA256: c5ab81be0a51d33e1e6235ef654a6b9e30c11c02efb978c6ae3cbc0f1d3785cd SHA512: 1c6e9d588bb5c5793f9a53ccaab704adc0474661c442ed48b4d5f7f5728878256d576c619bf9f5deb74fdccff3eaffd6ba92f5f8e92823fb17f30456df7a44e1 Homepage: https://cran.r-project.org/package=iwaqr Description: CRAN Package 'iwaqr' (Irrigation Water Quality Assessment and Visualizations) Calculates irrigation water quality ratios and has functions that could be used to plot several popular diagrams for irrigation water quality classification. Package: r-cran-iweigreg Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-trust Filename: pool/dists/resolute/main/r-cran-iweigreg_1.1-1.ca2604.1_all.deb Size: 343082 MD5sum: 05489e838c2c881c4e647c501b9267a3 SHA1: e26f111ff363a69187ac16851962c293d1ad566e SHA256: d34ec2dcc12ecd40defedccfa3873d5269e5785d415e65e76b74bacef8fb0c26 SHA512: 676de86b79948882c7d1d03af7cc74b6d07dcd0cc741b7dfff2133d54c31927a732bbbc98063215a9f85ebdde3da8a1a32b9253465e503d5a35df758aa136ee6 Homepage: https://cran.r-project.org/package=iWeigReg Description: CRAN Package 'iWeigReg' (Improved Methods for Causal Inference and Missing Data Problems) Improved methods based on inverse probability weighting and outcome regression for causal inference and missing data problems. 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Such models exist in the literature, but the source code to run them is not always available. 'IxPopDyMod' provides an easy way for these models to be written and shared. Package: r-cran-ixsurface Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ixsurface_0.1.0-1.ca2604.1_all.deb Size: 85912 MD5sum: 237548d45b77147c3209547b033b6047 SHA1: 59868856a8fe11b25759d07f1d9ada44d0ec8677 SHA256: 7de7f2a3eb3a5021100afc4958fb36942fc1f8183183df7ceb2f5d9d632fd6c2 SHA512: 74765c1755f6926e6f800257a33a6afe4be959ac344a2c84b6884dc004e79213e47cab5194e9eb57778a9c29028cc4b52964e98b3c7962b2ba75884f9d449e70 Homepage: https://cran.r-project.org/package=ixsurface Description: CRAN Package 'ixsurface' (Interactive 3D Surface Plots for Multi-Factor InteractionVisualization) Visualize interactions between multiple experimental factors using interactive 3D surface plots powered by 'plotly'. Instead of examining combinatorial pairwise interaction plots, map factor combinations to response surfaces and use surface crossings as geometric indicators of interaction effects. Supports continuous, categorical, and mixed factor designs with automatic binning for continuous conditioning variables. Package: r-cran-izid Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr, r-cran-rootsolve, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-izid_0.0.1-1.ca2604.1_all.deb Size: 162784 MD5sum: cd17f2465e46f0ddd0c5d26bd9026270 SHA1: 27bc1c77fea9f4991487a06f4f8a9aa6b8606973 SHA256: 6ae0e9b90b9a2be0bbdd58a987e8282bdc3295d8a2dbaee8805a17229bdf1dfa SHA512: 4ba2ad5628e672ed587b98d604e39adbe7cb2a40f2db9a1bb6a8f298dda2f763bb3744ee2a9f7fb2559cc546fc867132fe9a4474e8811ead712bca786d6e4279 Homepage: https://cran.r-project.org/package=iZID Description: CRAN Package 'iZID' (Identify Zero-Inflated Distributions) Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) . 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Package: r-cran-izmir Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rvest, r-cran-stringr, r-cran-jsonlite, r-cran-readr, r-cran-openxlsx, r-cran-magrittr Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-covr, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-izmir_0.1.0-1.ca2604.1_all.deb Size: 17962 MD5sum: bb86327a3babb2e580b73c598b7d4010 SHA1: 989db9166cb47a1913ee4146e0c71d00411cc7bb SHA256: 995cf24bd728703b840d3bad5589dc9024aca43616ac1723aa0686f32431721f SHA512: b5355a84140367234cacf2f10a1fc18b4d7ccd810505f52c6250c6456ee82670a01b9c2ecf62b731c1bccaf36b4e1530f3832c81aa91e029526500de96b1ec1b Homepage: https://cran.r-project.org/package=izmir Description: CRAN Package 'izmir' (R Wrapper for Izmir Municipality Open Data Portal) Call the data wrappers for Izmir Metropolitan Municipality's Open Data Portal. This will return all datasets formatted as Excel files (.csv or .xlsx), as well as datasets that require an API key. Package: r-cran-jaatha Architecture: all Version: 3.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-r6 Suggests: r-cran-boot, r-cran-coala, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jaatha_3.2.5-1.ca2604.1_all.deb Size: 296280 MD5sum: f9bd35bcfe4deb419ef48c081702c50b SHA1: c247197961a0d822ba49f06bada26eada2dc8610 SHA256: fd130de8e7990d14ce708676e38ab11e4ec9ef8cfad1f66365201e9d65d32d0f SHA512: 1531b3424a43b3bd67a2c2e93c7f61c6f2c9c3178ab5acf7132586327ebdda860f4bb4104cb21a6793df98737ee98671beef56370357e4304ff0618040fbd798 Homepage: https://cran.r-project.org/package=jaatha Description: CRAN Package 'jaatha' (Simulation-Based Maximum Likelihood Parameter Estimation) An estimation method that can use computer simulations to approximate maximum-likelihood estimates even when the likelihood function can not be evaluated directly. It can be applied whenever it is feasible to conduct many simulations, but works best when the data is approximately Poisson distributed. It was originally designed for demographic inference in evolutionary biology (Naduvilezhath et al., 2011 , Mathew et al., 2013 ). It has optional support for conducting coalescent simulation using the 'coala' package. Package: r-cran-jab.adverse.reactions Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tm, r-cran-stringi, r-cran-bracer, r-cran-mgsub, r-cran-qdapregex, r-cran-stringr, r-cran-data.table, r-cran-xmlconvert, r-cran-jsonlite, r-cran-anytime, r-cran-cffr, r-cran-rbibutils, r-cran-install.load Filename: pool/dists/resolute/main/r-cran-jab.adverse.reactions_1.0.3-1.ca2604.1_all.deb Size: 36010 MD5sum: 1e65c796045a624bf5ba95973f6acaf5 SHA1: d874b809e7042259fc7722336da6387e0d06b739 SHA256: a02315347974e0a2b1588e6a8ec42f56b9d8a4bb0261daecb28aa9a2f00c7f12 SHA512: 4cc2800f7733f4e2c3380b737c5f529f5ce559bd59ec6f12f3bb1de1754284129157183b0350b0af535587812f76dc17066d9e4fb7697528051e416ed7de934f Homepage: https://cran.r-project.org/package=jab.adverse.reactions Description: CRAN Package 'jab.adverse.reactions' (Possible Adverse Events/Reactions from theVaccinations/Experimental Gene Therapies) Provides data about the possible adverse events/reactions resulting from being injected with a vaccine/experimental gene therapy. Currently, this data set only includes information from six reference sources. Refer to the CITATION.cff file for the complete citations of the reference sources. For information about vaccination$/immunization$ hazards, visit , , , and . Package: r-cran-jackknifer Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-future.apply Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-jackknifer_2.0.0-1.ca2604.1_all.deb Size: 34542 MD5sum: 9ca6d85c4b00cfba861a5d526c9583ea SHA1: 4e558c73c5e31a7ded27a2e02a4130d5a80d0325 SHA256: f3668cf2ebb0fc43f0ce50b5c6ee3745a8e765848225a6450ff24d1b346d5144 SHA512: 88f8595585b92bd4f1f7263d58f949b77ce754892e47cbe2e3ef7c177cca49829e762e7e5444af06663e328ff65e49cd8f6bdb408ec478e0f5c1c7262b4c9487 Homepage: https://cran.r-project.org/package=jackknifeR Description: CRAN Package 'jackknifeR' (Delete-d Jackknife for Point and Interval Estimation) Implements delete-d jackknife resampling for robust statistical estimation. The package provides both weighted (HC3-adjusted) and unweighted versions of jackknife estimation, with parallel computation support. Suitable for biomedical research and other fields requiring robust variance estimation. Package: r-cran-jackstrap Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fbasics, r-cran-benchmarking, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-doparallel, r-cran-reshape, r-cran-tidyr, r-cran-scales, r-cran-plyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-jackstrap_0.1.0-1.ca2604.1_all.deb Size: 118576 MD5sum: a26f616ba276e129589e625101f77824 SHA1: e1c193a1af343aa9c761ae7fe8ce5eee05c8de3b SHA256: c86231715adc1ae311fb4e17a3931919bb63ed25943fec7ae0b70ad308f3db71 SHA512: ddcf1ee257db14d161f495767692ef1c9295bf4a9349cc00450a6f9294f25043e6bd1cbc6051059d0e2857c4fc774a506dceb1e20eebacbbad4b9036d701534c Homepage: https://cran.r-project.org/package=jackstrap Description: CRAN Package 'jackstrap' (Correcting Nonparametric Frontier Measurements for Outliers) Provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." . Package: r-cran-jackstraw Architecture: all Version: 1.3.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-irlba, r-cran-rsvd, r-cran-clusterr, r-cran-cluster, r-cran-bedmatrix, r-cran-genio Suggests: r-bioc-qvalue, r-bioc-lfa, r-bioc-gcatest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jackstraw_1.3.21-1.ca2604.1_all.deb Size: 433244 MD5sum: 19bb334946ce9e2b55a0c85059487b0b SHA1: 1e6c63a64be0e4580bef07f6f58b26b4c26e3de5 SHA256: a49ad715c28449c72c6d1ed3b748542f18941473762765442a27ccac4d735b6f SHA512: 470ce719c890acf9210fec799ad1243c89ee02c132db985152be40b1bbc0f6b6a2bcad4bd8ab0857c663f2b460748ee47e899f28ae8c9722c48aff3760973e96 Homepage: https://cran.r-project.org/package=jackstraw Description: CRAN Package 'jackstraw' (Statistical Inference for Unsupervised Learning) Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq). Package: r-cran-jacpop Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-jacpop_0.6-1.ca2604.1_all.deb Size: 14986 MD5sum: 040ac8e3faaa4d2353ffe386fcb84a66 SHA1: 5f986e74bf19e167a1ad68cca96ba093f7c43759 SHA256: 82f4114249ad4a87a4941d2b232da7df19af5f9808c78496653f4393372f519e SHA512: 73fb5dbdb4b82a661fa3c0c1af2a0b7c76f73b912ca2503f73cf6cdfe5a5d7f681f4983e4b5ac8f6934916c885ac11dfffe589e8f5a1661cc848ca4b7ca76adc Homepage: https://cran.r-project.org/package=jacpop Description: CRAN Package 'jacpop' (Jaccard Index for Population Structure Identification) Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis. Package: r-cran-jacquard Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-jacquard_1.0.2-1.ca2604.1_all.deb Size: 923890 MD5sum: 36efd6b99268f4275bcba2e11cd004f1 SHA1: abb09d250b23248182024037dcd6db37ff486336 SHA256: 6945466007cb3cb2ffbf92dec188f976ca18bcde30610c2703d2139d3a623b71 SHA512: d58505e8eb66909c7ba6cd460b131114ea11478687a9d2e92f71534e37028f1bbf005db1dbed44d411bab9cc71a6fce49b3a640ac729efc904fe79925a5b2437 Homepage: https://cran.r-project.org/package=Jacquard Description: CRAN Package 'Jacquard' (Estimation of Jacquard's Genetic Identity Coefficients) Contains procedures to estimate the nine condensed Jacquard genetic identity coefficients (Jacquard, 1974) by constrained least squares (Graffelman et al., 2024) and by the method of moments (Csuros, 2014) . These procedures require previous estimation of the allele frequencies. Functions are supplied that estimate relationship parameters that derive from the Jacquard coefficients, such as individual inbreeding coefficients and kinship coefficients. Package: r-cran-jadelizardoptions Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-jadelizardoptions_1.0.1-1.ca2604.1_all.deb Size: 25076 MD5sum: 975f4514ec789714c45d45dba93f6dbd SHA1: 2865ebf19c1993595f7f94e48e5666becb668392 SHA256: bf067d0dc864e5f01c610d873b45fb450d89c9652187d7371935ac5df00f2753 SHA512: f7cd383097fd1dea03d8d800071a54247e2e1e008feac44571ab5b695423f48eedea80cac4f74020d8cbec9efd98200acd6080797af5136ab9da371d1250a832 Homepage: https://cran.r-project.org/package=jadeLizardOptions Description: CRAN Package 'jadeLizardOptions' (Trading Jade Lizard Option Strategies) Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Russell A. Stultz (“The option strategy desk reference: an essential reference for option traders (First edition.)”, 2019, ISBN: 9781949443912). Package: r-cran-jage Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-data.table, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jage_0.1.0-1.ca2604.1_all.deb Size: 64136 MD5sum: dcb55689ee2d5927d965f1cd1911c3f9 SHA1: 7607daa94447f7b97245802ed4452b1da14fd8ab SHA256: 5ad8d1c7e5879e8dc36ab55aa63f25ddadfd6b6d1b80afa8f9e2e705687ac485 SHA512: 67b5904e83b3fc2aa9014080a74cb2ed055e97cbf8929e1a0a153e8491e1971f1fd25477716c54ba9a136b4718141e54d753abf1721782ec0d172a15354942cf Homepage: https://cran.r-project.org/package=jage Description: CRAN Package 'jage' (Estimation of Developmental Age) Bayesian methods for estimating developmental age from ordinal dental data. For an explanation of the model used, see Konigsberg (2015) . For details on the conditional correlation correction, see Sgheiza (2022) . Dental scoring is based on Moorrees, Fanning, and Hunt (1963) . Package: r-cran-jaggr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formatr, r-cran-glue Filename: pool/dists/resolute/main/r-cran-jaggr_0.1.1-1.ca2604.1_all.deb Size: 71180 MD5sum: dd76b186ded595ec50264a5f271355a8 SHA1: 800d8999fb3091b5de417619de259f4c9d5d0f15 SHA256: c5782c3c6c762eb9b383c6c88665902e79ccd00190b5600e4a4411cac5ea4456 SHA512: de1dae8d0c5ca4ab554fb3c7934bcb4f26c0369f7c110d59d82ddfb47778d0f5d2b7cdb92b7e478ffbbd955f7a9cb2f5e2671d85ec24ea562b37cf7fea50101f Homepage: https://cran.r-project.org/package=jaggR Description: CRAN Package 'jaggR' (Supporting Files and Functions for the Book Bayesian Modellingwith 'JAGS') All the data and functions used to produce the book. We do not expect most people to use the package for any other reason than to get simple access to the 'JAGS' model files, the data, and perhaps run some of the simple examples. The authors of the book are David Lucy (now sadly deceased) and James Curran. It is anticipated that a manuscript will be provided to Taylor and Francis around February 2020, with bibliographic details to follow at that point. Until such time, further information can be obtained by emailing James Curran. Package: r-cran-jagshelper Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4010 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jagsui, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jagshelper_0.4.1-1.ca2604.1_all.deb Size: 3863906 MD5sum: 3a2a0bd0489fa0d72f5da993f62778c7 SHA1: 02dd473478e1443dac3646116bdd69d2f75ecb20 SHA256: 078166ca3d37687642f4aaba670fdf057c397e5cf7714d0ad618d1e707f14760 SHA512: 23bb203c1251e53e766cfe4199d16f089bad86d9fdb94809cb1cbaca48d7a9132522fa0a9db1d161676a3d2c54c66ed0243ce7b269e9ee52a24df2c0ffc81935 Homepage: https://cran.r-project.org/package=jagshelper Description: CRAN Package 'jagshelper' (Extracting and Visualizing Output from 'jagsUI') Tools are provided to streamline Bayesian analyses in 'JAGS' using the 'jagsUI' package. Included are functions for extracting output in simpler format, functions for streamlining assessment of convergence, and functions for producing summary plots of output. Also included is a function that provides a simple template for running 'JAGS' from 'R'. Referenced materials can be found at . Package: r-cran-jagstargets Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-fst, r-cran-posterior, r-cran-purrr, r-cran-qs2, r-cran-r2jags, r-cran-rjags, r-cran-rlang, r-cran-secretbase, r-cran-targets, r-cran-tarchetypes, r-cran-tibble, r-cran-tidyselect, r-cran-withr Suggests: r-cran-dplyr, r-cran-fs, r-cran-knitr, r-cran-r.utils, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-jagstargets_1.2.2-1.ca2604.1_all.deb Size: 385916 MD5sum: 85828a963524688b26c4b17996622c1d SHA1: 8f5d0fb222014dcd0de1f8cf1e82c67a91a8857c SHA256: 9206a459664461030c21807981c2fe2eafdb9cfe6bcb920fddffd6616d2d2acb SHA512: 19174757ddda132647ce4aa675943d4a31d4146a43398e873b6206159cb3935a4c3fa4ad09e60d02daa4829a5e6b317cf4aa585cda13740725b1eea5238eddee Homepage: https://cran.r-project.org/package=jagstargets Description: CRAN Package 'jagstargets' (Targets for JAGS Pipelines) Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'jagstargets' R package is leverages 'targets' and 'R2jags' to ease this burden. 'jagstargets' makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. For the underlying methodology, please refer to the documentation of 'targets' and 'JAGS' (Plummer 2003) . Package: r-cran-jagsui Architecture: all Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rjags Suggests: r-cran-knitr, r-cran-markdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-jagsui_1.6.3-1.ca2604.1_all.deb Size: 1526986 MD5sum: d4cf18f556ebd424cf624041e24b85d8 SHA1: 0e96c5431be97269de2cbcada81263c5ab35b882 SHA256: 1bdf7073d616c6c860869895d61040a6cbf104bb885fab20dcee872dea020980 SHA512: 42c359f87242cdd42a376d3a82468ad8217ac7f191099b459c697d6eaca5391dd598c5cf448d5e10378e0465b0cb208f906c0f324f1c7ceccfc7b9c205310c96 Homepage: https://cran.r-project.org/package=jagsUI Description: CRAN Package 'jagsUI' (A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses) A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers. Package: r-cran-jalcal Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-jalcal_0.3.0-1.ca2604.1_all.deb Size: 23972 MD5sum: 5963fee1323e5bdc3b5e2ec0e1edd997 SHA1: 927e642393a91be848306efee94f588f83b88acc SHA256: 81c3f5f2bdb7aedbe68b62ca18252f19cff0083be6f03479f302366f20d114fd SHA512: 13218ee1eb9bb8800bc3247870fa2e2719caeec1fe09905240f1c1ce384a72522b8c3aee4c8a298d950f5835d4e7aa1451c4e4450575f31eadfaadc82d1f2605 Homepage: https://cran.r-project.org/package=jalcal Description: CRAN Package 'jalcal' (Convert Between Jalaali (Persian or Solar Hijri) and GregorianCalendar Dates) The Jalaali calendar, also known as the Persian or Solar Hijri calendar, is the official calendar of Iran and Afghanistan. It starts on Nowruz, the spring equinox, and follows an astronomical system for determining leap years. Each year consists of 365 or 366 days, divided into 12 months. This package provides functions for converting dates between the Jalaali and Gregorian calendars. The conversion calculations are based on the work of Kazimierz M. Borkowski (1996) (), who used an analytical model of Earth's motion to compute equinoxes from AD 550 to 3800 and determine leap years based on Tehran time. Package: r-cran-jamba Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-rcolorbrewer, r-cran-kernsmooth, r-cran-withr Suggests: r-cran-crayon, r-cran-farver, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jamba_1.0.4-1.ca2604.1_all.deb Size: 2096454 MD5sum: 9521387d6e55068f039ff59a13912907 SHA1: 1887f74a9653dbb420ba504c9f9dfe7da97e605b SHA256: 0ed07fae991f6f60bac9a180b09b79d4a8447da787ab9b99994e148597168ad2 SHA512: 5d03a5226f97daed5f064fa065b0cc3b5b6a75a2c296bf5e6b2328753680ab2595dc5d6531cd0d92e7b392215da32d59ed888d5d275754b01fda1f8d98ea1f88 Homepage: https://cran.r-project.org/package=jamba Description: CRAN Package 'jamba' (Just Analysis Methods Base) Just analysis methods ('jam') base functions focused on bioinformatics. Version- and gene-centric alphanumeric sort, unique name and version assignment, colorized console and 'HTML' output, color ramp and palette manipulation, 'Rmarkdown' cache import, styled 'Excel' worksheet import and export, interpolated raster output from smooth scatter and image plots, list to delimited vector, efficient list tools. Package: r-cran-jamendor Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-jamendor_0.1.1-1.ca2604.1_all.deb Size: 131810 MD5sum: 7cbc1942c2621e67b88b52c0061f09d9 SHA1: e81d784c2914eb333352579bee314906d76574eb SHA256: cb73183ba62393e64f7bce503673021ae140e238165c2e571fbceb4bd563107a SHA512: a0fb55f10b13c9417115ad68bebbf9deb0589ff95c5078e166500db97ac60ef4851deb0de96383f925c594ad3f795688b76d4ae328087d172b963d01b094e908 Homepage: https://cran.r-project.org/package=JamendoR Description: CRAN Package 'JamendoR' (Access to 'Jamendo' API) Provides an interface to 'Jamendo' API . Pull audio, features and other information for a given 'Jamendo' user (including yourself!) or enter an artist's -, album's -, or track's name and retrieve the available information in seconds. Package: r-cran-janeaustenr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1616 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-janeaustenr_1.0.0-1.ca2604.1_all.deb Size: 1619990 MD5sum: 58ba71f52dbd8b604114e10e9430eacf SHA1: a5f8069818f338839a1deb345b38a47aa7b85953 SHA256: 23f317c7465131e4e1e40ae1983dd426ecceec42da4eb96e679dbbbf7c3fe8a9 SHA512: f229996f91f965df688cd885d98334c8c16e569a1f8b11d0864ed44cc872a287a012b4b548c6dbb8114c506492e827f0708056dcc17a1ef7388aef99a1196d86 Homepage: https://cran.r-project.org/package=janeaustenr Description: CRAN Package 'janeaustenr' (Jane Austen's Complete Novels) Full texts for Jane Austen's 6 completed novels, ready for text analysis. These novels are "Sense and Sensibility", "Pride and Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion". Package: r-cran-janitor Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-snakecase, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-dbplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-sf, r-cran-testthat, r-cran-tibble, r-cran-tidygraph Filename: pool/dists/resolute/main/r-cran-janitor_2.2.1-1.ca2604.1_all.deb Size: 268574 MD5sum: 765f2df1e1b3eb16f1c30bcb52a2e14a SHA1: c1a2457a70c49f81bc409fcc5d43b08f385862df SHA256: b9a7b574b0ae2253485cde9932ac444f1e8a18d13440c9f99bea60d6d8fee21d SHA512: a8b54b235c2c67c7bc0dce8ea56719d3318e7770cac501418d4d80e1051939db920239cf3290997685d7c55e7ebd745af2df08e9b1c34d370c74f2dd1541f43b Homepage: https://cran.r-project.org/package=janitor Description: CRAN Package 'janitor' (Simple Tools for Examining and Cleaning Dirty Data) The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and explore duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel. This package follows the principles of the "tidyverse" and works well with the pipe function %>%. janitor was built with beginning-to-intermediate R users in mind and is optimized for user-friendliness. Package: r-cran-janus Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-dplyr, r-cran-purrr, r-cran-forcats, r-cran-tictoc, r-cran-readr, r-cran-ggplot2, r-cran-narray, r-cran-lubridate, r-cran-rcppalgos, r-cran-rmpfr, r-cran-metrics, r-cran-statrank, r-cran-hash, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-janus_1.0.0-1.ca2604.1_all.deb Size: 55544 MD5sum: 81407ec6aee3972e3a4f27ff53e304e7 SHA1: f9f0fd31e099307805e556bf5342fee2c0dbbef7 SHA256: 6dddf06f48464405f75bd19f351bb9b58e467c1f436d9dbc2caf9db43f6411d9 SHA512: b025c025c81e9afd63ab80d957ba970bd96e2a0bc05b714e29b5e1b0ce248b1aaab12dad4881809ca988c53d63c6f401b14255dd6e0f48743047954748e2ce4c Homepage: https://cran.r-project.org/package=janus Description: CRAN Package 'janus' (Optimized Recommending System Based on 'tensorflow') Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using 'tensorflow'. Package: r-cran-janusplot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ggplot2, r-cran-patchwork, r-cran-cli, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-agridat, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-mass, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-janusplot_0.1.0-1.ca2604.1_all.deb Size: 2455536 MD5sum: 95ef2a3e993a347a6b181b7b380cf1eb SHA1: 96d22dd2b4290a789f6a8932cdafc81f81477d4e SHA256: cd6dcb6008f7f27f38427829c7d5acbdee174c53de7c3acbb5d94c49b78472b4 SHA512: 8aa2a20d95bd17da7ca34457a688626ede128eedd2bca6b77e85bfc13f7c5b5a19b2c31efb02564fec34658d12d04468758283faeaa1742ea1bb45696b7b90d7 Homepage: https://cran.r-project.org/package=janusplot Description: CRAN Package 'janusplot' (Asymmetric Smoothed-Association Matrices via GAM Fits) Render a pairwise, asymmetric smoothed-association matrix of continuous variables. Each cell shows the fitted spline from an 'mgcv' generalised additive model, with the upper triangle displaying 'gam(x_j ~ s(x_i))' and the lower triangle 'gam(x_i ~ s(x_j))'. Unlike Pearson's correlation matrix, the visualisation is intentionally asymmetric, revealing heteroscedasticity, leverage, and directional non-linearity that a single scalar correlation hides. An asymmetry index and a 24-category shape taxonomy quantify the directional difference and qualitative form of each fitted smooth. Package: r-cran-japanapis Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-japanapis_0.1.1-1.ca2604.1_all.deb Size: 664726 MD5sum: d8a81a20bec6679962db95a5a3bd5fa6 SHA1: ba82123cf790480af5413d7941951f9477844b55 SHA256: 529179880591b736df58b0a748e3bf969df4f7514eefcab329e4056b22616bd4 SHA512: adece4d86dccc22ffdcb944c3ab14a8ec88a9de863dc6a8597d6d3a6977db6a41cc9653bacf0cdd68fe0143117a11afa684e30799d7598e87facab1a1b222f8d Homepage: https://cran.r-project.org/package=JapanAPIs Description: CRAN Package 'JapanAPIs' (Access Japanese Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to Japan, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on Japan, covering topics such as natural disasters, economic production, vehicle industry, air quality, demographics, and administrative divisions. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-japanstat Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr, r-cran-pillar, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-keyring, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-japanstat_0.1.0-1.ca2604.1_all.deb Size: 64530 MD5sum: 9ccdc43fa936d9ef47af1ce88da55ff8 SHA1: 4dd3c51f08235322974d00467be619dd4b0f1fc8 SHA256: eab2da0406c1d0ef79e10e901622d6682096e015ffb0c794166af0264e6b4057 SHA512: 1b5a3d34d866a0132412151a6a356a308099b0f41cc1da10aed1a55ea7744aeac6e63a24d2118d6251dbd187027462e947785494aad8c58d1c3356b56229105a Homepage: https://cran.r-project.org/package=japanstat Description: CRAN Package 'japanstat' (Tools for Easy Use of 'e-Stat' API) Provides tools for using the API of 'e-Stat' (), a portal site for Japanese government statistics. Includes functions for automatic query generation, data collection and formatting. Package: r-cran-jarbes Architecture: all Version: 2.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-r2jags, r-cran-ggplot2, r-cran-ggextra, r-cran-mass, r-cran-gridextra, r-cran-bookdown, r-cran-tidyr, r-cran-kableextra, r-cran-ggally, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-jarbes_2.5.0-1.ca2604.1_all.deb Size: 607394 MD5sum: 58ead0c73aa70642cbdfe70726b1958d SHA1: d8c950b54d6271571783d1f5fc027bfc5f6352f7 SHA256: bd0ec4dd65ef54e0d0c5100e475744d7bc2f4021954f7678059e31c6c4a249c3 SHA512: 7dd61c43d66852d27222fb08ee68b9879e0b31f969b91af5a36d8dba7f25eef9a9f1c3e6b89b134022b33522e119ebc4f22281a745a99a628f7c7a7ace08a3cd Homepage: https://cran.r-project.org/package=jarbes Description: CRAN Package 'jarbes' (Just a Rather Bayesian Evidence Synthesis) Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS). Package: r-cran-jatsdecoder Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlp, r-cran-opennlp Filename: pool/dists/resolute/main/r-cran-jatsdecoder_1.3.1-1.ca2604.1_all.deb Size: 576178 MD5sum: 5dd8ccaf506928f7714af99b2fec39da SHA1: 5afc9c704c0c4c6e465c34a8703dc4909a734d7e SHA256: f25f45e69a7b896ba300ba61121e48f9168ace3fe52c18a8ec7bbd56f1238c4f SHA512: 0dec4f310b8870f9d91b1e361f8b58a4af72d6d92dc2a5b9ef018c14e4b02f53c34f9656a8b556cb80121c9d17f92ead88cd2fa70aa3647d2f7340a96dc8b39e Homepage: https://cran.r-project.org/package=JATSdecoder Description: CRAN Package 'JATSdecoder' (A Metadata and Text Extraction and Manipulation Tool Set) Provides a function collection to extract metadata, sectioned text and study characteristics from scientific articles in 'NISO-JATS' format. Articles in PDF format can be converted to 'NISO-JATS' with the 'Content ExtRactor and MINEr' ('CERMINE', ). For convenience, two functions bundle the extraction heuristics: JATSdecoder() converts 'NISO-JATS'-tagged XML files to a structured list with elements title, author, journal, history, 'DOI', abstract, sectioned text and reference list. study.character() extracts multiple study characteristics like number of included studies, statistical methods used, alpha error, power, statistical results, correction method for multiple testing, software used. The function get.stats() extracts all statistical results from text and recomputes p-values for many standard test statistics. It performs a consistency check of the reported with the recalculated p-values. An estimation of the involved sample size is performed based on textual reports within the abstract and the reported degrees of freedom within statistical results. In addition, the package contains some useful functions to process text (text2sentences(), text2num(), ngram(), strsplit2(), grep2()). See Böschen, I. (2021) Böschen, I. (2021) , Böschen, I. (2023) , and Böschen, I. (2024) . Package: r-cran-javateak Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-javateak_1.0-1.ca2604.1_all.deb Size: 17624 MD5sum: 24a2f46b696d764aad4e89d36736ab24 SHA1: 7e2da2e13c47005d8e7d338c252ba457ea63b051 SHA256: 77f89b65381cd27894e2e9381c9f2823ac86db5b07267910c0222a5cb47ae9a1 SHA512: d0ee2415d1277c3eca91c2f96e3582fdfbb2585fea26ee89ec02a78176ff6fe37908080611a09937b033f39c111023b16c1012f3462a2110e077bbda105da17d Homepage: https://cran.r-project.org/package=javateak Description: CRAN Package 'javateak' (Javanese Teak Above Ground Biomass Estimation) Simplifies the process of estimating above ground biomass components for teak trees using a few basic inputs, based on the equations taken from the journal "Allometric equations for estimating above ground biomass and leaf area of planted teak (Tectona grandis) forests under agroforestry management in East Java, Indonesia" (Purwanto & Shiba, 2006) . This function is most reliable when applied to trees from the same region where the equations were developed, specifically East Java, Indonesia. This function help to estimate the stem diameter at the lowest major living branch (DB) using the stem diameter at breast height with R^2 = 0.969. Estimate the branch dry weight (WB) using the stem diameter at breast height and tree height (R^2 = 0.979). Estimate the stem weight (WS) using the stem diameter at breast height and tree height (R^2 = 0.997. Also estimate the leaf dry weight (WL) using the stem diameter at the lowest major living branch (R^2 = 0.996). Package: r-cran-jaya Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-evaluate, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jaya_1.0.3-1.ca2604.1_all.deb Size: 62956 MD5sum: 89a9bc88eecb4391031237464d14094a SHA1: cbd09cd185360500fec7d7e18496e9c0fe4460b6 SHA256: b2281b0ad494e08fb810a695e54f87173cb7a32bf56fcf81673e86562bcab4d0 SHA512: f75b2fd3351647aac4516aa54d6798e2edc2169e9a609d95588c12f1e8b04d4ab0903c819e5b07508bc60de227a4a008215ada0f17217cf50ab0477be18de6b9 Homepage: https://cran.r-project.org/package=Jaya Description: CRAN Package 'Jaya' (Gradient-Free Optimization Algorithm for Single andMulti-Objective Problems) An implementation of the Jaya optimization algorithm for both single-objective and multi-objective problems. Jaya is a population-based, gradient-free optimization algorithm capable of solving constrained and unconstrained optimization problems without hyperparameters. This package includes features such as multi-objective Pareto optimization, adaptive population adjustment, and early stopping. For further details, see R.V. Rao (2016) . Package: r-cran-jbrowser Architecture: all Version: 0.10.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-reactr, r-cran-stringr, r-cran-magrittr, r-cran-readr, r-cran-jsonlite, r-cran-httpuv, r-cran-mime, r-cran-cli, r-cran-ids, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-jbrowser_0.10.2-1.ca2604.1_all.deb Size: 1023674 MD5sum: 2b6f1d3deb31678f5bbd7fcaf72aea1d SHA1: 3958ccb7dbe098fc3ecc9add782659360e9956e0 SHA256: 471e660ae3aac0a8d09780ff52d526623fc7f67e7a649b810bafe1bc7dee4ebb SHA512: a9ac19cf45d98e6a93a53e207d8b608514a6e0741425f73210e1c68c7d069436992b7c0b2217fb291bd050a883c2510d712451c4cb2e3acb5fbb61ec761472a8 Homepage: https://cran.r-project.org/package=JBrowseR Description: CRAN Package 'JBrowseR' (An R Interface to the JBrowse 2 Genome Browser) Provides an R interface to the JBrowse 2 genome browser. Enables embedding a JB2 genome browser in a Shiny app or R Markdown document. The browser can also be launched from an interactive R console. The browser can be loaded with a variety of common genomics data types, and can be used with a custom theme. Package: r-cran-jcalendar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-jcalendar_0.1.0-1.ca2604.1_all.deb Size: 153194 MD5sum: 56c5a614aa237aa75b362d97a3457cb3 SHA1: 3fb2a7a76fbe3c45139e5ec50901572138fc8845 SHA256: aaea278af5dc09b73be56c2ac51ffee52017266c984edb7bf94ed970ad7f739c SHA512: bb64079e8b71a5e616f038b8c98a097cc6c409bf4f3b662f0c1e1900ea1718b254a29a4126b690009ed85498f1ffb365102ca666d62258e07220e6dc1cd42b94 Homepage: https://cran.r-project.org/package=jcalendaR Description: CRAN Package 'jcalendaR' (Interconversion Between the Japanese Calendar System and theWestern Calendar) This is a set of simple utility functions to perform mutual conversion between the current Japanese calendar system that Wareki, the old Japanese calendar system that the Kyureki calendar and the Julian and Gregorian calendar. To calculate each calendar method, it converts to the Julian Day Number. Package: r-cran-jcext Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-rcolorbrewer, r-cran-maps, r-cran-rworldmap, r-cran-ggplot2, r-cran-sp Suggests: r-cran-ncdf4 Filename: pool/dists/resolute/main/r-cran-jcext_0.1.1-1.ca2604.1_all.deb Size: 2232988 MD5sum: 81658a2813fb78017ae9d16672624143 SHA1: 1362260e8873391b0133d743ff42770e3a4c39bd SHA256: 9f39f08990460102c3c9396b3d1688b5e9987afbf65b17c178ef5bee9750103f SHA512: 7c5b7955e1629f7883072257212afb18bbef6f3a655d3d5795a82c1fefe0306c678a02f863b4b09eec0aec146e78e4e20a386efa5fd3538cd48c7b9db0e4b08e Homepage: https://cran.r-project.org/package=jcext Description: CRAN Package 'jcext' (Extended Classification of Weather Types) Provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather types during the period considered for the analysis. See Otero et al., (2017) for more information. Package: r-cran-jcolors Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-jcolors_0.0.5-1.ca2604.1_all.deb Size: 665728 MD5sum: e64b14242c4e3f67293179af8a59bb93 SHA1: 561554bc41c21bfb5cbe59d78e83e28bb8c52923 SHA256: 7c7a26fa6a56fbcb09cb97919ed07b7f3b13183cf3244445bb470acbccd94141 SHA512: bb662bb87f96251512fbd32a9b7676c1373cf8d6b8ab928b93d96e58668a6702c261db23eb780fa142394b58d55ff376a6829fa3ad8e46d70a5f5bc60abd3a8c Homepage: https://cran.r-project.org/package=jcolors Description: CRAN Package 'jcolors' (Colors Palettes for R and 'ggplot2', Additional Themes for'ggplot2') Contains a selection of color palettes and 'ggplot2' themes designed by the package author. 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Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547. 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The process of stock prices are represented as Geometric Brownian motion [Black (1973) ] or jump diffusion processes [Kou (2002) ]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other. Package: r-cran-jds.rmd Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bookdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-jds.rmd_0.3.4-1.ca2604.1_all.deb Size: 47442 MD5sum: 1e75d610e2c2c0eb7a0ee5b24242552b SHA1: 8e20a1158321a6a72fc651eee0ffd047df4383bf SHA256: 87308a22711285a148c14b94d354851fb2e677fd6907dea4b21208704d6ff44a SHA512: 2ed4b1f1dcb75883da9fa2959db8582382541fcaf83a9fe7b922ca0bce22843cfa367cdb058cbd3418ae95d317d4cb7666ba5be14cd10ff93bc03bdd2e2d9c19 Homepage: https://cran.r-project.org/package=jds.rmd Description: CRAN Package 'jds.rmd' (R Markdown Templates for Journal of Data Science) Customized R Markdown templates for authoring articles for Journal of Data Science. Package: r-cran-jeek Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-pcapp, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-jeek_1.1.1-1.ca2604.1_all.deb Size: 448316 MD5sum: dacf33a1f43b4bba68ddac510fa13836 SHA1: 14ce1cbd1df50652b4a0ae667fe19a9a11b20cd0 SHA256: 85c577c11ad64868d4c628d4956c49c3547b16d4a0ba46b47090820ad48a9aa9 SHA512: 261632914d6551cf9be3b5c7010a9d157a118c7084f29cbcbb0c4ae049f4d861cf045f981b2becbb160006250f537163234b64ea3c520a7ae94d412a50877add Homepage: https://cran.r-project.org/package=jeek Description: CRAN Package 'jeek' (A Fast and Scalable Joint Estimator for Integrating AdditionalKnowledge in Learning Multiple Related Sparse GaussianGraphical Models) Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeek) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) . 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Useful for handling complex survival and longitudinal data in clinical research. Package: r-cran-jmbig Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jmbayes2, r-cran-joinerml, r-cran-rstanarm, r-cran-fastjm, r-cran-dplyr, r-cran-nlme, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-jmbig_0.1.3-1.ca2604.1_all.deb Size: 197710 MD5sum: 2885f11a2adf4b20d1a8052351cd8e13 SHA1: a1c427ddd5aad4236adb50b53f5a3af861a8cbf4 SHA256: 251fb7d1ab7269470f72e2e19eb659dd434555023e2b945a5190923ed038d68b SHA512: c8b696ce1359a403bebbf69b22082a4a613c0eab9dbcc8593fe002c2a1c1f2ca63cfabdc551fc2d7d31a6914ed42f5f6e0b76fadf36eca8894ed134b62f949f0 Homepage: https://cran.r-project.org/package=jmBIG Description: CRAN Package 'jmBIG' (Joint Longitudinal and Survival Model for Big Data) Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences. Package: r-cran-jmdem Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-jmdem_1.0.1-1.ca2604.1_all.deb Size: 247074 MD5sum: 819a2eb5369d83cac228863b0346375d SHA1: ac2ed29c5443135337e51452cedbfd23d63c88b2 SHA256: f19c544fa081126d8c235f0437b86d6f1e1d3357a6336b1101f3774255719044 SHA512: f8342a000e026252ec820939b97b09d43be9529a1348eaecc4fdb5a45063e140851bcabd8d049eb3eee3617c2b01a7cb701c120224e821e7fa06892445e44c9d Homepage: https://cran.r-project.org/package=jmdem Description: CRAN Package 'jmdem' (Fitting Joint Mean and Dispersion Effects Models) Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. 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Package: r-cran-jpmesh Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1709 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-memoise, r-cran-miniui, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-shiny, r-cran-tibble, r-cran-units, r-cran-magrittr, r-cran-vctrs Suggests: r-cran-knitr, r-cran-lintr, r-cran-lwgeom, r-cran-testthat, r-cran-rmarkdown, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-jpmesh_2.1.0-1.ca2604.1_all.deb Size: 1151166 MD5sum: 20297884ded9bb3d79963cb86f33bc98 SHA1: 226b1a5f2398d0ed8b9b9c121f04893e7da0a571 SHA256: 13b891e60be30221115eccf79164c9c9d21e5c0f6d07513edc248bbecf0a6210 SHA512: 692b597fe7b424490db2153c181c3acdf7c736a8747f22a8378f5b4cd6307614f1180412a66e0bb95dbcf6ab47d32f5109ccdd1b6d909930729c1a8872cf8b3d Homepage: https://cran.r-project.org/package=jpmesh Description: CRAN Package 'jpmesh' (Utilities for Japanese Mesh Code) Helpful functions for using mesh code (80km to 100m) data in Japan. 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Package: r-cran-jpsurv Architecture: all Version: 3.0.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-scales Filename: pool/dists/resolute/main/r-cran-jpsurv_3.0.20-1.ca2604.1_all.deb Size: 1303512 MD5sum: c862b2a4243a491e42dd02a0cc2f0388 SHA1: 38180ea9da72a0fb14f6a52466bd884bf53032f1 SHA256: dfbc5374745a1ee2ad40bc7a2d547dac828f29f03b5d0eca56a1970681fadea2 SHA512: beff7652e0d7ee79024ac1918938b48494ca159ffc25e2f69014de8a421da1e937d71691286308b120d94a5c1c15bbf1547388918008017a078a923e71dc48fe Homepage: https://cran.r-project.org/package=JPSurv Description: CRAN Package 'JPSurv' (Joinpoint Model for Relative and Cause-Specific Survival) Contains functions for fitting a joinpoint proportional hazards model to relative survival or cause-specific survival data, including estimates of joinpoint years at which survival trends have changed and trend measures in the hazard and cumulative survival scale. See Yu et al.(2009) . Package: r-cran-jqbr Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-bslib, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jqbr_1.0.4-1.ca2604.1_all.deb Size: 70010 MD5sum: f196b2736bf05115fc12ed802092e5d4 SHA1: ed8ba0505ce94a15e6266c1cdbae117b19e1f0a9 SHA256: 68ede2883b387845072c9c3ab02cdb18b1797757609539ae123a4e94a256cd72 SHA512: dda763b69e454baec621f19f4b59adce1245f924f27e88004e4a36f297e2f43cd01f23d4e176ac93188769a0837af7d89b02f04425241a7beba0483c92496c5d Homepage: https://cran.r-project.org/package=jqbr Description: CRAN Package 'jqbr' ('jQuery QueryBuilder' Input for 'Shiny') A highly configurable 'jQuery' plugin offering a simple interface to create complex queries/filters in 'Shiny'. The outputted rules can easily be parsed into a set of 'R' and/or 'SQL' queries and used to filter data. Custom parsing of the rules is also supported. For more information about 'jQuery QueryBuilder' see . Package: r-cran-jql Architecture: all Version: 3.6.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-pdist, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-jql_3.6.9-1.ca2604.1_all.deb Size: 50626 MD5sum: dcdbbcb44c234003955da7afa1c2f0f8 SHA1: 744a77697df5ec700aa053e3e328f170fe5f1535 SHA256: 4789a6173e9e0b72966575f3b08a358ba912cec462ec22ab3f4cfb42176cd457 SHA512: 382a38bea94914fac327720bb69883c36c5f6f28a42b47ab069336507c845434389e0560a920cda6f299345f7700b08a4ab56bc3f8407c4c2315a7a4de0a43af Homepage: https://cran.r-project.org/package=JQL Description: CRAN Package 'JQL' (Jump Q-Learning for Individualized Interval-Valued Dose Rule) We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap. Package: r-cran-jrc Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httpuv, r-cran-jsonlite, r-cran-stringr, r-cran-stringi, r-cran-mime, r-cran-r6, r-cran-r.utils Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jrc_0.6.0-1.ca2604.1_all.deb Size: 179338 MD5sum: d4bada8dc63fb8fceb26fe7c2ef67be5 SHA1: 565239afe9e2ca93f255ab9d7b8c3746b6b47da1 SHA256: 20dbd4c7f660e4fdb6b2f8ceeae2ab116cb2e5b670d8b70ea36a4f03b2059803 SHA512: b14d9abd51b6f05b6840441c641296bf5555662dfbb13980ffe86351e5985f5e67195663631e0ff97cd76b352f4cf163562d346ce9e00f859f56f6d2452bb9ab Homepage: https://cran.r-project.org/package=jrc Description: CRAN Package 'jrc' (Exchange Commands Between R and 'JavaScript') An 'httpuv' based bridge between R and 'JavaScript'. Provides an easy way to exchange commands and data between a web page and a currently running R session. 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The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area in a conservation analysis. The algorithm is described in: Miranda-Esquivel, D (2016) . 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A function is used to automatically compare and select models, as well as to present a variety of model-based statistics. Plotting functions are used to present category curves, as well as information, reliability and standard error functions. Package: r-cran-jrvfinance Architecture: all Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-jrvfinance_1.4.3-1.ca2604.1_all.deb Size: 104214 MD5sum: 5397a5a43334534e98bd2597ccab2f03 SHA1: e5a5d6eb737160e914c32f71887534e5ea1f8c8a SHA256: 80867a55ff81f40815f5382ccf80a9e60b8d483ca0fd324c49fe9c17bb3e54d1 SHA512: 6572d25b71126e284ef5fc0ca47cc12187c418bd55c25a1f9e1ea39b4e2209860b9dfccae94f5e7505e34016eb7e6effc02acc4931a62fa30a64fa17924b66b2 Homepage: https://cran.r-project.org/package=jrvFinance Description: CRAN Package 'jrvFinance' (Basic Finance; NPV/IRR/Annuities/Bond-Pricing; Black Scholes) Implements the basic financial analysis functions similar to (but not identical to) what is available in most spreadsheet software. This includes finding the IRR and NPV of regularly spaced cash flows and annuities. Bond pricing and YTM calculations are included. In addition, Black Scholes option pricing and Greeks are also provided. 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Package: r-cran-kanova Architecture: all Version: 0.3-20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.random Suggests: r-cran-r.rsp, r-cran-devore7 Filename: pool/dists/resolute/main/r-cran-kanova_0.3-20-1.ca2604.1_all.deb Size: 740176 MD5sum: 059747562c70554246e9e0ee8349b716 SHA1: 0d3299810d093637505fbe27eba7cc5f8d492044 SHA256: 8945ddaec8edaf9e22363e61d50dabd50d775d9b71ec89dbaf3aa8a37cb71a7b SHA512: 791f467b56e7163a6720fbaf120d4de9706a2b051e0cc08bcce58910ec28c6f7a6653bf8fb122f0428e2b5088ad67f2ffc97945988514f58c94b2e975e37292b Homepage: https://cran.r-project.org/package=kanova Description: CRAN Package 'kanova' (Quasi Analysis of Variance for K-Functions) One-way and two-way analysis of variance for replicated point patterns, grouped by one or two classification factors, on the basis of the corresponding K-functions. Package: r-cran-kantorovich Architecture: all Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-gmp, r-cran-lpsolve, r-cran-rcdd, r-cran-rglpk, r-cran-slam, r-cran-ompr, r-cran-ompr.roi, r-cran-roi.plugin.glpk Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kantorovich_3.2.0-1.ca2604.1_all.deb Size: 64046 MD5sum: 900fdf2fa9866570b9be099e979213f9 SHA1: 9cc0975010623815480c99460e23b4fb11e851d6 SHA256: bf4dccda0184127b7674c94327c2f136277aa01d61728b01887866076278213f SHA512: d759f9236271c83e01e1223f29cafa81231d39a82ba15c76c7d5ab35532e271ba3f25bd24ed85fb5934da9f93cc6cacac2524635487a1786b66fb5d6a6445e0b Homepage: https://cran.r-project.org/package=kantorovich Description: CRAN Package 'kantorovich' (Kantorovich Distance Between Probability Measures) Computes the Kantorovich distance between two probability measures on a finite set. The Kantorovich distance is also known as the Monge-Kantorovich distance or the first Wasserstein distance. Package: r-cran-kaos Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-kaos_0.1.2-1.ca2604.1_all.deb Size: 34382 MD5sum: 578e432bcb6a6c5548d077784263054e SHA1: 29941407f0c8a5712b19f0a3bbf0b0b8cfb176b7 SHA256: 5283729a566e0c1c34ed9ab553eda21e32926228414876dd4a393e908acf5cff SHA512: 1e2ed419a2c4a4fcf5b37c969ae6c847711ace4b03ee1e9860952ba3ff08345c55c26beb11d7848863b36270fd3b0bb4f07121aeac8ac8b623e02eafac50e62f Homepage: https://cran.r-project.org/package=kaos Description: CRAN Package 'kaos' (Encoding of Sequences Based on Frequency Matrix Chaos GameRepresentation) Sequences encoding by using the chaos game representation. Löchel et al. (2019) . Package: r-cran-kaphom Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kaphom_0.3-1.ca2604.1_all.deb Size: 35964 MD5sum: b1a9db2c36c993c15dfaeb15b2cce5bb SHA1: 6bec03c0f62cf8c661f3a32ec6759e8b8d8e01b7 SHA256: 08949d8558e8bd1f4dc2bce6e8e64d88777e6aaef9f7ab59415d11e73bd5b3d9 SHA512: 08a7a7f18737eadfbeb0bdbb9db2f6bbe0024e13b314179f3b15a2c111137ae8729fd1c842484856ec95d636b3296a7e7b8f9a1349939671e1ff9e5bad5d0ddb Homepage: https://cran.r-project.org/package=kaphom Description: CRAN Package 'kaphom' (Test the Homogeneity of Kappa Statistics) Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Muammer Albayrak, Kemal Turhan, Yasemin Yavuz, Zeliha Aydin Kasap (2019) Jun-mo Nam (2003) Jun-mo Nam (2005) Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) Allan Donner, Michael Eliasziw, Neil Klar (1996) . Package: r-cran-kappagold Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-irr, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kappagold_0.4.0-1.ca2604.1_all.deb Size: 84562 MD5sum: 1c141358b26f445eac2b16f492662ae1 SHA1: 8eff679ddee0e3018dfe4583ba368bc278d84708 SHA256: 56836ed332bc395f6704363f552eb7a9d8789d353d6ecedefbff949063f44d93 SHA512: 73f00aafa3866e67163a9b8e3a0dc5273e02b6cb0a9ff520af989a6f9488547ee9f116652567890da16e446747052b621439f9df29dfeb8def92d33c5dc0767b Homepage: https://cran.r-project.org/package=kappaGold Description: CRAN Package 'kappaGold' (Agreement of Nominal Scale Raters (with a Gold Standard)) Estimate agreement of a group of raters with a gold standard rating on a nominal scale. For a single gold standard rater the average pairwise agreement of raters with this gold standard is provided. For a group of (gold standard) raters the approach of S. Vanbelle, A. Albert (2009) is implemented. Bias and standard error are estimated via delete-1 jackknife. Package: r-cran-kappagui Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-irr Filename: pool/dists/resolute/main/r-cran-kappagui_2.0.2-1.ca2604.1_all.deb Size: 30858 MD5sum: b4f4f87a98ce8a11f6d8705fe569b674 SHA1: 0fb6496d81b741668ea0bc88683ab69a5fcb7ee0 SHA256: f036925ab1215fabb82ccedb9c29b2663439ff83f4d8af9b40892363430d8348 SHA512: 84c77e53482a5b252099e6f438e09e213ee72c5170e5d76c7a56ed85440e945312c56c34d822bca28827e58f27290aa67456f3de1caf87746a891350a8d4e780 Homepage: https://cran.r-project.org/package=KappaGUI Description: CRAN Package 'KappaGUI' (An R-Shiny Application for Calculating Cohen's and Fleiss' Kappa) Offers a graphical user interface for the evaluation of inter-rater agreement with Cohen's and Fleiss' Kappa. The calculation of kappa statistics is done using the R package 'irr', so that 'KappaGUI' is essentially a Shiny front-end for 'irr'. Package: r-cran-kappasize Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kappasize_1.2-1.ca2604.1_all.deb Size: 320530 MD5sum: 1b0f23ccdb318dd04c439fb312c7d8b5 SHA1: 107415ae9b37970cac54f0a9d40dbf060b3b48c7 SHA256: 330ffb91e2e4f0531dec1bf6e20cda84dc344d97a1908f19004c50a5c20e9ec2 SHA512: b737562c1a171028ed9507df58afe57525c62012cd2130c4969cf8194fc68c16ed6403e1d6dec781c5e68cf89f2d0e1c37a45b5a489e48d2a4c7a0e5e93471fa Homepage: https://cran.r-project.org/package=kappaSize Description: CRAN Package 'kappaSize' (Sample Size Estimation Functions for Studies of InterobserverAgreement) Contains basic tools for sample size estimation in studies of interobserver/interrater agreement (reliability). Includes functions for both the power-based and confidence interval-based methods, with binary or multinomial outcomes and two through six raters. Package: r-cran-karaoke Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tuner, r-cran-seewave Filename: pool/dists/resolute/main/r-cran-karaoke_3.0-1.ca2604.1_all.deb Size: 12882 MD5sum: 95b0ea32a3510e6f150efbd70cf191e2 SHA1: 48a86f9536238a7bcba962fd3515460f3c495959 SHA256: b7d4d1143127223a331626e9183ab87c2e24e36182714f3f9c603aebe14e7d42 SHA512: d3849af158f399d3d1d483f055119e3c0442a95e1cff8a2c003f2755e208717d4cec74fc97995ce0c55db6151b9ddfd0dc08dbff17d19c3efd60b6177c01ed88 Homepage: https://cran.r-project.org/package=karaoke Description: CRAN Package 'karaoke' (Remove Vocals from a Song) Attempts to remove vocals from a stereo '.wav' recording of a song. Package: r-cran-kardl Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-msm, r-cran-lmtest, r-cran-nlwaldtest, r-cran-car, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-officer, r-cran-flextable, r-cran-equatags, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-dplyr, r-cran-mass, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kardl_1.3.1-1.ca2604.1_all.deb Size: 409346 MD5sum: eb8b2fd5f349d38758acab5f64edaa98 SHA1: 038741d7e75d68627956ea2251fea671af216fcd SHA256: 006bfc04eed22ac41313167cf9be02be3c1df0efc1fc9ef5dbc1bbc4e99946a5 SHA512: 19eb704c7ff64d8c2ce4c62cef683e358e15d2687d9a9842e7722896541f136595ba001ab84585794c2e6ec40a4f4e471b77cdedca41084f20ef501f74f40140 Homepage: https://cran.r-project.org/package=kardl Description: CRAN Package 'kardl' (Make Symmetric and Asymmetric ARDL Estimations) Implements estimation procedures for Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) models, which allow researchers to investigate both short- and long-run relationships in time series data under mixed orders of integration. The package supports simultaneous modeling of symmetric and asymmetric regressors, flexible treatment of short-run and long-run asymmetries, and automated equation handling. It includes several cointegration testing approaches such as the Pesaran-Shin-Smith F and t bounds tests, and narayan test. Methodological foundations are provided in Pesaran, Shin, and Smith (2001) and Shin, Yu, and Greenwood-Nimmo (2014, ISBN:9780123855079). Package: r-cran-karen Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-gaussquad, r-cran-scales, r-cran-mvtnorm, r-cran-tmvtnorm, r-cran-mass, r-cran-igraph, r-cran-xtable, r-cran-stringr, r-cran-abind, r-cran-expm Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-karen_1.0-1.ca2604.1_all.deb Size: 667874 MD5sum: 757174f116cce1085d35f82702bd590e SHA1: 9e6ffabcd5db6dbd145ee68265c8f97b03d5a37e SHA256: 7e292e943555014a0d5c184e901cbc3257f922923496b1e5dc8f3bb00d4623a6 SHA512: 7060da1bf991a4231f16c049a5dea69d87548e5491e41b9d186e8e8ec7130602eb32f30089165dfa7ff2979b36671b75fe7dd0f5dfe3689253f4d8a9f67c20ac Homepage: https://cran.r-project.org/package=Karen Description: CRAN Package 'Karen' (Kalman Reaction Networks) This is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing. This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) . Package: r-cran-karlen Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-karlen_0.0.2-1.ca2604.1_all.deb Size: 2846908 MD5sum: 39ff6e3a35c87d5ccb62201a5322bdd0 SHA1: 6fb245c73c1efe8cbcea876311d5af1cb6b94f75 SHA256: 862da6b2a7a7ac5fb356db6f72b5109ea21c8c4aea900806d15f80f2e7833e1f SHA512: 4934035b48677f06b56550bf6c9b470c06391d1e02b2c9da71e0ee517713d8bc67e1468aed56f7b26c29722713199f96caa140df8f2f3d4fc70040d067c6ab15 Homepage: https://cran.r-project.org/package=karlen Description: CRAN Package 'karlen' (Real-Time PCR Data Sets by Karlen et al. (2007)) Real-time quantitative polymerase chain reaction (qPCR) data sets by Karlen et al. (2007) . Provides one single tabular tidy data set in long format, encompassing 32 dilution series, for seven PCR targets and four biological samples. The targeted amplicons are within the murine genes: Cav1, Ccn2, Eln, Fn1, Rpl27, Hspg2, and Serpine1, respectively. Dilution series: scheme 1 (Cav1, Eln, Hspg2, Serpine1): 1-fold, 10-fold, 50-fold, and 100-fold; scheme 2 (Ccn2, Rpl27, Fn1): 1-fold, 10-fold, 50-fold, 100-fold and 1000-fold. For each concentration there are five replicates, except for the 1000-fold concentration, where only two replicates were performed. Each amplification curve is 40 cycles long. Original raw data file is Additional file 2 from "Statistical significance of quantitative PCR" by Y. Karlen, A. McNair, S. Perseguers, C. Mazza, and N. Mermod (2007) . Package: r-cran-karsts Architecture: all Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3501 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-mvn, r-cran-tcltk2, r-cran-tserieschaos, r-cran-stlplus, r-cran-tseries, r-cran-forecast, r-cran-stinepack, r-cran-missforest, r-cran-nonlineartseries, r-cran-zoo, r-cran-rgl, r-cran-mgcv, r-cran-infotheo, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-karsts_2.4.1-1.ca2604.1_all.deb Size: 3053062 MD5sum: b894ce197984664b2cb2a0a7423e95d5 SHA1: 424624ab13a247b52cca486750faae47baec26cc SHA256: 6861bdf98f58fbd1725a2d2197c58f1848aa152a6a17b4f80a991513b3782e45 SHA512: 2ddefbe6dbb7fc40420ccbbe1a8532f80a9939e325d27d6efac74e3bcecdeda3e6d88efb094377596b6675a0a4f1dc8a87991ec960443740cd8f923f1dde30e7 Homepage: https://cran.r-project.org/package=KarsTS Description: CRAN Package 'KarsTS' (An Interface for Microclimate Time Series Analysis) An R code with a GUI for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) ) and filling methods (Moffat et al. (2007) ), as well as tools to manipulate easily time series and gap sets. Package: r-cran-karyotapr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1903 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-circlize, r-cran-cli, r-bioc-complexheatmap, r-cran-dbscan, r-cran-dplyr, r-cran-fitdistrplus, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-ggplot2, r-cran-gtools, r-bioc-iranges, r-cran-magrittr, r-cran-purrr, r-bioc-rhdf5, r-cran-rlang, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr, r-cran-umap, r-cran-viridislite Suggests: r-bioc-biostrings, r-cran-knitr, r-cran-rmarkdown, r-bioc-rsamtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-karyotapr_1.0.2-1.ca2604.1_all.deb Size: 1431332 MD5sum: 0ca747256a3f6789e2581e618f1c5e5a SHA1: 6a01691ee97fceaa019290a5ee8f6c2a00713def SHA256: c5080622f37567e20667accd42c788334a2efa77449bc6c5e19e4fe86342c799 SHA512: 1ed9a63f1cec8838dbf3d411f72fc3ef08fea2728f714514101164e7c425002df2a4f33c0e795cfd3f3de7137b3b90a656142bf2ca9cb40d1b3fa3a3f0a437ad Homepage: https://cran.r-project.org/package=karyotapR Description: CRAN Package 'karyotapR' (DNA Copy Number Analysis for Genome-Wide Tapestri Panels) Analysis of DNA copy number in single cells using custom genome-wide targeted DNA sequencing panels for the Mission Bio Tapestri platform. Users can easily parse, manipulate, and visualize datasets produced from the automated 'Tapestri Pipeline', with support for normalization, clustering, and copy number calling. Functions are also available to deconvolute multiplexed samples by genotype and parsing barcoded reads from exogenous lentiviral constructs. Package: r-cran-katex Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-v8 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-katex_1.5.0-1.ca2604.1_all.deb Size: 187338 MD5sum: fcb57292c8d418bfb9cbb72e4a65da97 SHA1: 509deb7f54b5f9aaf4f31e40b1072d46e4884886 SHA256: f225c6565789c61dee48728f95b38bd87710b4a95e234d54a0873e5f488604fe SHA512: 7c1b3de4bbd37606196465e70dbfa18d47ad02e8f65b38d45f0e9420e96653bc0c8f60e7ceeecb55f942926eda1796a68f7d75a2d2b958305b6b5164b5dacb6b Homepage: https://cran.r-project.org/package=katex Description: CRAN Package 'katex' (Rendering Math to HTML, 'MathML', or R-Documentation Format) Convert latex math expressions to HTML and 'MathML' for use in markdown documents or package manual pages. The rendering is done in R using the V8 engine (i.e. server-side), which eliminates the need for embedding the 'MathJax' library into your web pages. In addition a 'math-to-rd' wrapper is provided to automatically render beautiful math in R documentation files. Package: r-cran-kayadata Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-forcats, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-scales, r-cran-purrr Suggests: r-cran-broom, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-kayadata_1.4.0-1.ca2604.1_all.deb Size: 700086 MD5sum: 44ebccce581621ac5a380d93c36280bd SHA1: 3d86ad38c4e126572371a0510f13199195ab4858 SHA256: 7e7ae6f7d3a6e37d3a8027d4ce88f910eb137dc4abc4016c0b9e4fd45aa51937 SHA512: 1f2e6ea6cd89fdfa972d52c1981fc7518f612ecb77d270a074025201a9949b9f051103c9f7c77e899d2ea2b0a2a690950ee6fc158cc11150f532c4d20a7e85cb Homepage: https://cran.r-project.org/package=kayadata Description: CRAN Package 'kayadata' (Kaya Identity Data for Nations and Regions) Provides data for Kaya identity variables (population, gross domestic product, primary energy consumption, and energy-related CO2 emissions) for the world and for individual nations, and utility functions for looking up data, plotting trends of Kaya variables, and plotting the fuel mix for a given country or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, "Environment, Energy, and Economy: Strategies for Sustainability" (United Nations University Press, 1998) and ) expresses a nation's or region's greenhouse gas emissions in terms of its population, per-capita Gross Domestic Product, the energy intensity of its economy, and the carbon-intensity of its energy supply. Package: r-cran-kbmvtskew Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kbmvtskew_1.1.0-1.ca2604.1_all.deb Size: 41156 MD5sum: a18d4c9c0adff3cd49cf8fbe8df1ed40 SHA1: 8807155f99bd28c67bb4d61ab07fe046c0e2faff SHA256: 7528d2f2f16a01577a10b990b37a3c7ee710dbea1e4f3e2ffd863f93dc69285d SHA512: f2f45f190e24a579222cf255670753c59e728c8882e4cb04a32166df769bdb6efef697a10c1b6cf6250d21ec39272664d3b6706c0eb44f0825db91309dcbc27c Homepage: https://cran.r-project.org/package=KbMvtSkew Description: CRAN Package 'KbMvtSkew' (Khattree-Bahuguna's Univariate and Multivariate Skewness) Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearson’s coefficient of skewness, Bowley’s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) . Package: r-cran-kcmeans Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-mass, r-cran-matrix Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kcmeans_0.1.0-1.ca2604.1_all.deb Size: 26832 MD5sum: eb714cf29e00636c3f616be39403551b SHA1: fb47272417be662e9ece085bf450ad16f3be6f4f SHA256: a007073f11321c46786e25e9ded47cadffa1226e11cfc7a0ce504347d920cee3 SHA512: 1844b0a53635437a069fe4dc0a0452422d3eac411907c9bafb5b0dd349a3235b6fa2cb0580a9c109517e2e97196eab270a9b3aaee1b08234b13fe43e5421bdad Homepage: https://cran.r-project.org/package=kcmeans Description: CRAN Package 'kcmeans' (Conditional Expectation Function Estimation withK-Conditional-Means) Implementation of the KCMeans regression estimator studied by Wiemann (2023) for expectation function estimation conditional on categorical variables. 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Ngounou Bakam and Denys Pommeret (2022) and Yves I. Ngounou Bakam and Denys Pommeret (2022) . Package: r-cran-kcsknnshiny Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rhandsontable, r-cran-dplyr, r-cran-caret, r-cran-fnn Filename: pool/dists/resolute/main/r-cran-kcsknnshiny_0.1.0-1.ca2604.1_all.deb Size: 21982 MD5sum: 0402cde44e00b09404d6864f41e1e997 SHA1: 45c549274ee7e14015f9a1c889defb0633316ef5 SHA256: 088e978afbda878fed47ddfffba89e1018214a4d393b526e99a857e47f40fa22 SHA512: 2cc46872be717c142e49781f7e3a507430b41f855a7cc46b93fcd3e5e66288966fb932d0a5b9cec8691ba6723bb32fa74d55f633e2e713193502d5ed9be3ee53 Homepage: https://cran.r-project.org/package=KCSKNNShiny Description: CRAN Package 'KCSKNNShiny' (K-Nearest Neighbour Classifier) It predicts any attribute (categorical) given a set of input numeric predictor values. 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Package: r-cran-kcsnbshiny Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-rhandsontable, r-cran-dplyr, r-cran-caret, r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-kcsnbshiny_0.1.0-1.ca2604.1_all.deb Size: 20958 MD5sum: 8df6490e3d9082ec261220be227e6eab SHA1: eb59f2e26c679893ceb6de08b9dd9ee62c6dfbfd SHA256: 5ba254604186f9b556a07f64d82ac0942682e4ff41fa10f5744c47bb1a5b8527 SHA512: 55022e91db2d6cba99dd2df489aba0f69ec3206e8be26098ec0d4f8d2c3e8e6d15498011b878023695ddfcb925d639e6ed3ef7103ce7b52eec68c9cb0d5ed225 Homepage: https://cran.r-project.org/package=KCSNBShiny Description: CRAN Package 'KCSNBShiny' (Naive Bayes Classifier) Predicts any variable in any categorical dataset for given values of predictor variables. If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. The user can upload their own datasets and select what variable they want to predict. A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown. Package: r-cran-kdensity Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-univariateml Suggests: r-cran-extradistr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kdensity_1.2.0-1.ca2604.1_all.deb Size: 317270 MD5sum: 5cd64d53cb726f5c65f60c4138a1fdd8 SHA1: 55f718983aca825054268be83bd56920cad989ca SHA256: 95f4ce6d7c2876e9dfd7e85033ef8882acc8912e8d64845281f52d727486719e SHA512: b87935c5863c039add7b12cb76f3250bfcf31afa67f947bf061358e2cd60e0e44ca334df91f0940891a696734711b0967ff8d39ec9e6923ce14b501f96c9cd93 Homepage: https://cran.r-project.org/package=kdensity Description: CRAN Package 'kdensity' (Kernel Density Estimation with Parametric Starts and AsymmetricKernels) Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) . Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) , the beta kernel of Chen (1999) , and the copula kernel of Jones & Henderson (2007) . User-supplied kernels, parametric starts, and bandwidths are supported. Package: r-cran-kdglm Architecture: all Version: 1.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10739 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr, r-cran-rfast, r-cran-generics, r-cran-rlang, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plotly, r-cran-tidyverse, r-cran-spdep, r-cran-sf, r-cran-terra, r-cran-geodata Filename: pool/dists/resolute/main/r-cran-kdglm_1.2.14-1.ca2604.1_all.deb Size: 2262864 MD5sum: 224cf071d1f663c44519e12a6d9acedc SHA1: e0a4120af6e37f589ca22a8df9e5711cc2cebe0d SHA256: 8e09474d589d6acc94bb0848a62b8591adb15c6fce3ea4cc89b5ca67da511031 SHA512: 2cb1286154d165c4bc6530ec4277874e9ec5df926324fac5ffacbfa2d74e33fef5815a17bdf870d87fce75d83985c7e7db6a993f98645d40008ca089e24a6d45 Homepage: https://cran.r-project.org/package=kDGLM Description: CRAN Package 'kDGLM' (Bayesian Analysis of Dynamic Generalized Linear Models) Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024) and dos Santos Jr. et al. (2024). Package: r-cran-kdist Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kdist_0.2-1.ca2604.1_all.deb Size: 25944 MD5sum: 9ecb4958102a1519165c1e8a9a815691 SHA1: bb293bb354d0903b1b0fd3443d6bea3c72f5538e SHA256: 4ed1ba8c73f7d3c241e84e8c740ff3c6d038828e4fd2d34aeea6211775aa1cd1 SHA512: 4db2efb6f376c5b99aa7409982dde56de226c8630df7dc3db7949f19b42e763e23d4c840097948208864eb0ac90c9273ca54a24a3c809404dc1895ff87126ded Homepage: https://cran.r-project.org/package=kdist Description: CRAN Package 'kdist' (K-Distribution and Weibull Paper) Density, distribution function, quantile function and random generation for the K-distribution. A plotting function that plots data on Weibull paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc. Package: r-cran-kdml Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-np, r-cran-mass, r-cran-markdown Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kdml_1.1.1-1.ca2604.1_all.deb Size: 156022 MD5sum: bb23835e7e02ed8a873d702656237d4e SHA1: 8cd956f403bc9f41016f779f0c2750c9e9a5e4a5 SHA256: 70112b1b7ed3b681764c12f41c77e5e77c23b58b2950fc1715d8c0b2809359b7 SHA512: 9135147e0f64881ef1d25c837eded9fcb74ea4913c60645ead8156b9fb00e8b5db375033d65e731204b588c3eeaf2e79408a4ce49e5514269075ee9811abaf1a Homepage: https://cran.r-project.org/package=kdml Description: CRAN Package 'kdml' (Kernel Distance Metric Learning for Mixed-Type Data) Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) for dkps() methodology, and Ghashti (2024) for dkss() methodology. 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It removes related individuals based on kinship or identity-by-descent (IBD) scores while prioritizing subjects with phenotypes of interest. This approach helps maximize the retention of informative subjects, particularly for rare or valuable traits, and improves statistical power in genetic and epidemiological studies. KDPS supports both categorical and quantitative phenotypes, composite scoring, and customizable pruning strategies using a fuzziness parameter. Benchmark results show improved phenotype retention and high computational efficiency on large-scale datasets like the UK Biobank. Methods used include Manichaikul et al. (2010) for kinship estimation, Purcell et al. (2007) for IBD estimation, and Bycroft et al. (2018) for UK Biobank data reference. 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Package: r-cran-kendallrandomwalks Architecture: all Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-actuar Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-kendallrandomwalks_0.9.4-1.ca2604.1_all.deb Size: 217538 MD5sum: 21d32bdb8b0c9e4377ae13a691b58073 SHA1: 35836ff3e05404336a7809084be421c702d6a1ce SHA256: 7ca08e47b836901c782f2f1cebb685ec00444d39219770a0ddfb997c61cb278a SHA512: 4a22a7d3024ae599bb0d877386ceebc7ab211423925d08c3f2f6d8c68d2beb2f02a00af40dbb2195465a8759f9c2e79f1efe8877ef4927ec242b53a68c8fc521 Homepage: https://cran.r-project.org/package=kendallRandomWalks Description: CRAN Package 'kendallRandomWalks' (Simulate and Visualize Kendall Random Walks and RelatedDistributions) Kendall random walks are a continuous-space Markov chains generated by the Kendall generalized convolution. This package provides tools for simulating these random walks and studying distributions related to them. For more information about Kendall random walks see Jasiulis-Gołdyn (2014) . Package: r-cran-keng Architecture: all Version: 2026.3.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-effectsize, r-cran-tidyr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-keng_2026.3.19-1.ca2604.1_all.deb Size: 161652 MD5sum: 99d9ad817fe7e1ec2bd853a808875e4e SHA1: 74aa06c381d4f0fd4b5c3cb7822992c5e4399d41 SHA256: 32abdd03388c93e521906a79c0e9fa96bb0000500f33abed7bcc63d7e611ec8c SHA512: b7dc529bbc96a16999193830e11457deef8bd2c84f0564bce74d6cb088711c100321ba1212925ce548a9ec23d6cb95d7537dd8f83ba2e74e06107c9bdf9b1e03 Homepage: https://cran.r-project.org/package=Keng Description: CRAN Package 'Keng' (Knock Errors Off Nice Guesses) Miscellaneous functions and data used in psychological research and teaching. Keng currently has a built-in dataset depress, and could (1) scale a vector; (2) divide a vector into three groups, (3) compute the cut-off values of Pearson's r with known sample size; (4) test the significance and compute the post-hoc power for Pearson's r with known sample size; (5) conduct a priori power analysis and plan the sample size for Pearson's r; (6) compare lm()'s fitted outputs using R-squared, f_squared, post-hoc power, and PRE (Proportional Reduction in Error, also called partial R-squared or partial Eta-squared); (7) calculate PRE from partial correlation, Cohen's f, or f_squared; (8) conduct a priori power analysis and plan the sample size for one or a set of predictors in regression analysis; (9) conduct post-hoc power analysis for one or a set of predictors in regression analysis with known sample size; (10) randomly pick numbers for Chinese Super Lotto and Double Color Balls; (11) assess course objective achievement in Outcome-Based Education. Package: r-cran-kensyn Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-lme4, r-cran-metafor Filename: pool/dists/resolute/main/r-cran-kensyn_0.3-1.ca2604.1_all.deb Size: 71660 MD5sum: e56129baf2f3f17811ace48ce3f9d375 SHA1: c0144e9e57d928018c3e9ab304688ea05d92da24 SHA256: 353040dc266be0eea68dcfe3db18f51a7321c37f75f2c2cfe6cea2e6cfc0f42d SHA512: 6622a7fcef0322a5bb83f7a319bc749919827703300e8c74a74d19400d93af36264194a183026265d489e64f906cfed8127ddf0500eb07ca54bc2a4cb962bfaf Homepage: https://cran.r-project.org/package=KenSyn Description: CRAN Package 'KenSyn' (Knowledge Synthesis in Agriculture - From Experimental Networkto Meta-Analysis) Demo and dataset accompaying the books : De l'analyse des réseaux expérimentaux à la méta-analyse: Méthodes et applications avec le logiciel R pour les sciences agronomiques et environnementales (Published 2018-06-28, Quae, for french version) by David Makowski, Francois Piraux and Francois Brun - Knowledge Synthesis in Agriculture : from Experimental Network to Meta-Analysis (in preparation for 2018-06, Springer , for English version) by David Makowski, Francois Piraux and Francois Brun A full description of all the material is in both books. ACKNOWLEDGMENTS : The French network "RMT modeling and data analysis for agriculture" () have contributed to the development of this R package. This project and network are lead by ACTA (French Technical Institute for Agriculture) and was funded by a grant from the Ministry of Agriculture and Fishing of France. Package: r-cran-kepted Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expm, r-cran-compquadform, r-cran-cubature Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kepted_0.2.0-1.ca2604.1_all.deb Size: 58976 MD5sum: 17d7cf5c71775d2ee00ebd4812bf18e2 SHA1: 412f1d5d39d96ec0adb9750baca70de52b9b69cf SHA256: 21a792f13da62a83ffceb78c4d64e216527b0e6c503d6da4fd1a80c6862a78a9 SHA512: 09bc0aa8b7888a822357b7c30b6b17330db8458e29597cc2a80a7fd07c3c0c3e6623df55ceec20c0d76a65e947260842db3f00e1568a30a3d5e01cfcb68ac2e7 Homepage: https://cran.r-project.org/package=KEPTED Description: CRAN Package 'KEPTED' (Kernel-Embedding-of-Probability Test for Elliptical Distribution) Provides an implementation of a kernel-embedding of probability test for elliptical distribution. This is an asymptotic test for elliptical distribution under general alternatives, and the location and shape parameters are assumed to be unknown. Some side-products are posted, including the transformation between rectangular and polar coordinates and two product-type kernel functions. See Tang and Li (2024) for details. Package: r-cran-kequate Architecture: all Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltm, r-cran-equateirt, r-cran-mirt Filename: pool/dists/resolute/main/r-cran-kequate_1.6.4-1.ca2604.1_all.deb Size: 916368 MD5sum: 2db5e62dd3156d3083ea9da1db0077ed SHA1: d80ae73862a108b17bf3fa8b72493e8c77bb089d SHA256: bb4f7cd2d69bdbc0a2dfe83b0741d646d7793e7205dcc5a0259e759783c1b6ad SHA512: f76d69b53080e5766b3db371365cc3e39871200b905ceb4dc00ad312cf66ca07fb5b68c3ac39e75ec1ad1c5ef56fdaa12e87556e3742eeb7f8589ad163630703 Homepage: https://cran.r-project.org/package=kequate Description: CRAN Package 'kequate' (The Kernel Method of Test Equating) Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) and Andersson, B. and Wiberg, M. (2017) using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data. Package: r-cran-keras3 Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16635 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-reticulate, r-cran-tensorflow, r-cran-tfruns, r-cran-magrittr, r-cran-zeallot, r-cran-fastmap, r-cran-glue, r-cran-cli, r-cran-rlang, r-cran-dotty Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-callr, r-cran-tfdatasets, r-cran-withr, r-cran-png, r-cran-jsonlite, r-cran-purrr, r-cran-rstudioapi, r-cran-r6, r-cran-jpeg Filename: pool/dists/resolute/main/r-cran-keras3_1.5.1-1.ca2604.1_all.deb Size: 12309564 MD5sum: 9701cfa463b6a8e35b8ad2891985b9fd SHA1: b2afeb020cdad06b3f1d826429e5a4c82367b5e1 SHA256: 14d8c5022df63cd423508981f0a2134f89fba2d79e5fee376f3acf506692df01 SHA512: a110f55e3841aa7b49565298ce738808e202b3f3b4e6482cce2baef0aadf3c65ff1770c58fc69391d08d87f884f12d05d57599f45ff9bfdffa2e958139453310 Homepage: https://cran.r-project.org/package=keras3 Description: CRAN Package 'keras3' (R Interface to 'Keras') Interface to 'Keras' , a high-level neural networks API. 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Package: r-cran-kerasnip Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1320 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-generics, r-cran-parsnip, r-cran-rlang, r-cran-keras3, r-cran-tibble, r-cran-purrr, r-cran-dplyr, r-cran-cli, r-cran-recipes, r-cran-reticulate, r-cran-lobstr Suggests: r-cran-testthat, r-cran-bundle, r-cran-butcher, r-cran-modeldata, r-cran-tidymodels, r-cran-finetune, r-cran-tune, r-cran-dials, r-cran-workflows, r-cran-rsample, r-cran-knitr, r-cran-lme4, r-cran-rmarkdown, r-cran-future, r-cran-ggplot2, r-cran-mgcv, r-cran-probably Filename: pool/dists/resolute/main/r-cran-kerasnip_0.1.2-1.ca2604.1_all.deb Size: 491512 MD5sum: ecb5eeca1629bf11a9820373541d14fc SHA1: b6d7e762c77aab24b49f750375b0f8c3ead597a6 SHA256: 4235761470febad4559d2f283f10b1e0c89b0820f939f11a8d25698d0be6a778 SHA512: b66b344fbf8e3acff4c21b8901a55fc8aa415ecd089718aa56894bc488ceed082539f3848d0f59852c685b9cf6983092b8a3c2e696988b5a9ee315432299fbb4 Homepage: https://cran.r-project.org/package=kerasnip Description: CRAN Package 'kerasnip' (A Bridge Between 'keras' and 'tidymodels') Provides a seamless bridge between 'keras' and the 'tidymodels' frameworks. It allows for the dynamic creation of 'parsnip' model specifications for 'keras' models. Package: r-cran-kerdaa Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-kerdaa_0.1.1-1.ca2604.1_all.deb Size: 20798 MD5sum: a4cfae7d6c56c6541130105e136d239f SHA1: d5daf7dafe7b48c6d6b6f396eaa239e3de9e1a36 SHA256: e1b3b40bf6f2666252cd91b3c40647305a87090e8fea12245874bbd94ce5e4a1 SHA512: 57bbe74750627046d2a7a565afa1612c957c67821e6aa55862c61dbc55e439b0ffe30db24868281a2862450c00d75aad3c672b542a56336469b7a6687cd1eeb0 Homepage: https://cran.r-project.org/package=kerDAA Description: CRAN Package 'kerDAA' (New Kernel-Based Test for Differential Association Analysis) A new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions. Song, H. and Wu, M.C. (2023) . Package: r-cran-kerdiest Architecture: all Version: 1.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-chron, r-cran-date, r-cran-evir Filename: pool/dists/resolute/main/r-cran-kerdiest_1.3-1-1.ca2604.1_all.deb Size: 98856 MD5sum: 5914a56911e8a17d5cabc8d81d3a7c55 SHA1: 26ba3be4b2f65caed69387421f245e958826c9cd SHA256: 60eb1c0ca99c9ef41d4461875dd002f4ab45958300ce1a5d48d46e4269ede874 SHA512: 9c084515cc78446238a3735f6e9f5af77315bcf15da37cdbb41bc04c4a99f52cb7d858b3ecbcd552d9a4a3a6bd129c0c3790577f4bc6ba9b7b6b7f8eeea99a37 Homepage: https://cran.r-project.org/package=kerdiest Description: CRAN Package 'kerdiest' (Nonparametric Kernel Estimation of Distribution Function) Nonparametric kernel distribution function estimation is performed. Three bandwidth selectors are implemented: the plug-in selectors of Altman and Leger and of Polansky and Baker, and the cross-validation selector of Bowman, Hall and Prvan. The exceedance function, the mean return period and the return level are also computed. For details, see Quintela-del-Río and Estévez-Pérez (2012) . Package: r-cran-kernelfactory Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-auc, r-cran-genalg, r-cran-kernlab Filename: pool/dists/resolute/main/r-cran-kernelfactory_0.3.0-1.ca2604.1_all.deb Size: 54812 MD5sum: c97759047cfccedfafad4cd67f605b22 SHA1: 06c62b3e27348b1d54eb63f1c9602ef5368dbdcc SHA256: 53447bbef7ea76b899ab69d897cec9f676a8ceeaa13be8757431b7414a6dbf98 SHA512: 40e0c015743f99e28fd606a98013d2b52423d100541791d3bdeca2afce7b36370a3805ed4d22fe0e7cedf1d4ac5e35429226857394d6f75c2dd51aa020f7e393 Homepage: https://cran.r-project.org/package=kernelFactory Description: CRAN Package 'kernelFactory' (Kernel Factory: An Ensemble of Kernel Machines) Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. 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Package: r-cran-kernelheaping Architecture: all Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ks, r-cran-sparr, r-cran-sp, r-cran-plyr, r-cran-dplyr, r-cran-fastmatch, r-cran-fitdistrplus, r-cran-gb2, r-cran-magrittr, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-kernelheaping_2.3.0-1.ca2604.1_all.deb Size: 170224 MD5sum: 25dd18ca913015f2aae27725043a474b SHA1: 1885cbbfd9440e8a997a4b2b75b6eaaaeb6ef3d3 SHA256: fec2ef377c3fa62942eb8396225cc569eb2d1472a361f15e629da238d4301b90 SHA512: 3bf625058d824db11b95717683afaea244ff3a1392641adf5f5a03928ff8e2a72e1aa4ef5c7eed1784282d3738cec94ea800bf3d2fff4da9ff2735f0690b41d7 Homepage: https://cran.r-project.org/package=Kernelheaping Description: CRAN Package 'Kernelheaping' (Kernel Density Estimation for Heaped and Rounded Data) In self-reported or anonymised data the user often encounters heaped data, i.e. data which are rounded (to a possibly different degree of coarseness). 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Package: r-cran-kernelphil Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-benchmarkme, r-cran-directlabels, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-hmisc, r-cran-pbapply, r-cran-reshape2, r-cran-rlang, r-cran-terra, r-cran-wordspace Filename: pool/dists/resolute/main/r-cran-kernelphil_0.2-1.ca2604.1_all.deb Size: 124926 MD5sum: 544ba49c96a9b791cb79e96184e70b05 SHA1: 1688c0e4060e7e252707a318dbf517a15203a863 SHA256: ecb7a1c1f693a9d31dd6dd5404e2e0c3f08700512fd22f2060a0b45277f53cff SHA512: c02a4fd6003c1b087130be661be12f56fb7a60b5ddd27bf5ef07a0ad2d2d8160af95aa303ade4f112243317a7389af259858e62fbb4072cb977ab81aeff4c803 Homepage: https://cran.r-project.org/package=kernelPhil Description: CRAN Package 'kernelPhil' (Kernel Smoothing Tools for Philology and Historical Dialectology) Contains kernel smoothing tools designed for use by historical dialectologists and philologists for exploring spatial and temporal patterns in noisy historical language data, such as that obtained from historical texts. 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Package: r-cran-kernelshap Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kernelshap_0.9.1-1.ca2604.1_all.deb Size: 255290 MD5sum: 5538a2361897c0a00a88ad9a41d00382 SHA1: 96c1b22a14665886624626495a4f0093408a82a1 SHA256: 03078a19bfc7627e8aa9cf46b3f66d1d0bb96039b5760c386657d3411d0fa4dc SHA512: 9131c34ea62db7e9e016d0e5a9e2d5a105c6d0c4f0609d2f9fa987d8a214cc12cb3a12333b41d2be33669e91d380ef823481415277f12452822b986b74be0cc5 Homepage: https://cran.r-project.org/package=kernelshap Description: CRAN Package 'kernelshap' (Kernel SHAP) Efficient implementation of Kernel SHAP (Lundberg and Lee, 2017, ) permutation SHAP, and additive SHAP for model interpretability. For Kernel SHAP and permutation SHAP, if the number of features is too large for exact calculations, the algorithms iterate until the SHAP values are sufficiently precise in terms of their standard errors. The package integrates smoothly with meta-learning packages such as 'tidymodels', 'caret' or 'mlr3'. It supports multi-output models, case weights, and parallel computations. Visualizations can be done using the R package 'shapviz'. Package: r-cran-kernhaz Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-foreach, r-cran-doparallel, r-cran-ga Suggests: r-cran-survival Filename: pool/dists/resolute/main/r-cran-kernhaz_0.1.0-1.ca2604.1_all.deb Size: 101620 MD5sum: 89a44edd694e6e5b3a3102e31e563150 SHA1: a7556946fb39cc96f0894ed435f47db601293057 SHA256: 1ad751d92522e6b7cdd158a40d1cbceb4a43cbf2ba1c292f605e39b55f28e67e SHA512: 7389674a23cd1c6b7311b9fc6415775568083e141d6516c2496fc6cf52e4c22be30c9ae92ca67476ccbafcc3bb93d1eac3258cddd467b99706ac97a5b9c9e868 Homepage: https://cran.r-project.org/package=kernhaz Description: CRAN Package 'kernhaz' (Kernel Estimation of Hazard Function in Survival Analysis) Producing kernel estimates of the unconditional and conditional hazard function for right-censored data including methods of bandwidth selection. Package: r-cran-kernopt Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kernopt_1.0.0-1.ca2604.1_all.deb Size: 165168 MD5sum: 54204ed1d0fecdfb6cf42c5a92f60870 SHA1: 430355b2afacc0bd68714242e0fc23c63a6a39e9 SHA256: 24013d22aa74a9bf4e2fb60c69736d9636948061478bd0e7c98fbd483c43937c SHA512: 359625fb378c795ac672f09eacf1c7af3da13757ee9405d9db20552db9c68dd647012913596d8f98ad738f9448a84bf98ad1180f3db48822dbc3cf8db259c248 Homepage: https://cran.r-project.org/package=kernopt Description: CRAN Package 'kernopt' (Estimating Count Data Distributions with Discrete OptimalSymmetric Kernel) Implementation of Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by T. Senga Kiessé and G. Durrieu (2024) .The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels. Package: r-cran-kernplus Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-kernsmooth, r-cran-mixtools Filename: pool/dists/resolute/main/r-cran-kernplus_0.1.2-1.ca2604.1_all.deb Size: 67694 MD5sum: 62fd9a03d873eb24bebad80c82fee0c4 SHA1: 135a00880e099c51b32672ccdb51999dfd345bc7 SHA256: 8a20df5e7925516b2591352a0ebebdee4b6cfd9e9513938611180aabe6ec60c1 SHA512: 98d886e26a2403ebef1f670e61902534e5a0fc4a10ba1870bad85e42cacb14b953f4181590a492539d489eeb868410fe92abe8ce5d0b5a5f9c49adfef1dccf1e Homepage: https://cran.r-project.org/package=kernplus Description: CRAN Package 'kernplus' (A Kernel Regression-Based Multidimensional Wind Turbine PowerCurve) Provides wind energy practitioners with an effective machine learning-based tool that estimates a multivariate power curve and predicts the wind power output for a specific environmental condition. Package: r-cran-kernscr Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-readxl, r-bioc-org.hs.eg.db Filename: pool/dists/resolute/main/r-cran-kernscr_1.0.7-1.ca2604.1_all.deb Size: 102302 MD5sum: ab6889e8d4ffb7ae6b827a219f74c27c SHA1: 5dcdb1c88de2715db7a15595c9477625845d2b2d SHA256: 505203b9847ae0f9b954d8d432c850c22e2313614e03363982a0fbdbd3f8a101 SHA512: 47429265eaa2c78419253e0958e7ac780dc8a26a81c52c66f4dc4c71216cecacd9226306a694536711b15efd68b5d42a158152d6d174d88919ab013f8487e150 Homepage: https://cran.r-project.org/package=kernscr Description: CRAN Package 'kernscr' (Kernel Machine Score Test for Semi-Competing Risks) Kernel Machine Score Test for Pathway Analysis in the Presence of Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) . Package: r-cran-kernstadapt Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-misc3d, r-cran-sparr, r-cran-spatstat.explore, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.utils, r-cran-spatstat.linnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-kernstadapt_0.4.0-1.ca2604.1_all.deb Size: 1569574 MD5sum: aefa1e973d7f8baecf124959199adc5d SHA1: c99e5b09756d0e7f461b118d1a758aa1ffec2ee8 SHA256: 40800e4a2ed044805ecd600e3a59d59b565424709697b07069d0507b277c1d19 SHA512: 83e035d68a11a396c30d5550f93cb4b008e5e30fe524bd8a1c0487bf86ed6d059392b4621b3987682dbb14a5cad88ae658905e458c59c08a915cfcd5ee6d966d Homepage: https://cran.r-project.org/package=kernstadapt Description: CRAN Package 'kernstadapt' (Adaptive Kernel Estimators for Point Process Intensities onLinear Networks) Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). . Package: r-cran-kerntools Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-kernlab, r-cran-reshape2, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kerntools_1.2.1-1.ca2604.1_all.deb Size: 1213050 MD5sum: 0da215ff338ee2a5675909292bb651f4 SHA1: 9a82abeaceb0ede448569f3f7705441da1ed4151 SHA256: 0162c6d4943e171151b2089d710cb77a94a6975bf88af714431592598528a701 SHA512: 49ce4cd225acb2f7fb0679c0247e1dbd40bcc418762bfc67955efa44236db7654ba90713d581c46369853c6b46574a657e9a0044034465c7e7cbfe370938711e Homepage: https://cran.r-project.org/package=kerntools Description: CRAN Package 'kerntools' (Kernel Functions and Tools for Machine Learning Applications) Kernel functions for diverse types of data (including, but not restricted to: nonnegative and real vectors, real matrices, categorical and ordinal variables, sets, strings), plus other utilities like kernel similarity, kernel Principal Components Analysis (PCA) and features' importance for Support Vector Machines (SVMs), which expand other 'R' packages like 'kernlab'. Package: r-cran-kertests Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kertests_0.1.4-1.ca2604.1_all.deb Size: 24774 MD5sum: 95a5a476d845aa80500a82d54a4b65cc SHA1: 6f25861a6730e34f476992aabdf2bde90886a7f9 SHA256: a6a92776dada032c87aa57b9417683e43d680567da86864e5f6a212f4323cd67 SHA512: ff85190d1fd6cc00110290c020925ec14f9b2a1c0bdac59077a06c3b067dccb96d22ac52f204643bbe4c6ee74e16005a79f08848cd59c3bae4b5e3152d1bc37e Homepage: https://cran.r-project.org/package=kerTests Description: CRAN Package 'kerTests' (Generalized Kernel Two-Sample Tests) New kernel-based test and fast tests for testing whether two samples are from the same distribution. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2023) . Package: r-cran-kesernetwork Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinybs, r-cran-shiny, r-cran-htmltools, r-cran-config, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-plotly, r-cran-reactable, r-cran-rintrojs, r-cran-rlang, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyhelper, r-cran-shinywidgets, r-cran-stringr, r-cran-visnetwork, r-cran-yaml Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kesernetwork_0.1.0-1.ca2604.1_all.deb Size: 699260 MD5sum: 7968b36ed3426ce4efd2f133a30a538f SHA1: fcaaf664f5446dfabf604501914cc6276d2ffcea SHA256: 93db2f6d08b0b7ddebc0f8b2dee29343e4d5a5ab117761cff4d1313e8b679d11 SHA512: d5288667898a45400a6f6634aec850daa9b646372f948f9cc98d59ebd6fd6871ead36c7f508a50bc2deaa869cf63252b079f1ef855b9374238512ebdd6797216 Homepage: https://cran.r-project.org/package=kesernetwork Description: CRAN Package 'kesernetwork' (Visualization of the KESER Network) A shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) . Package: r-cran-keyboard Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-iso, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-keyboard_0.1.3-1.ca2604.1_all.deb Size: 270020 MD5sum: 36a8de988b331f81430e458709ca9b11 SHA1: 3736d5a88c6a63f69eea35444ac1a96a0ce45595 SHA256: a061aa49a0379fca40239ed95c3128a6adef1339abe8d38670371396b5b45a11 SHA512: e6b85b0f85ae40e2b5c9c8dff02491891c56ceb53fb91da4d95d7b3495bb9cac41d7cee1503fd04ac88de299e9c46b7b97ccfec24d73720eceec85ead5d635b5 Homepage: https://cran.r-project.org/package=Keyboard Description: CRAN Package 'Keyboard' (Bayesian Designs for Early Phase Clinical Trials) We developed a package 'Keyboard' for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The 'Keyboard' designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) , Li DH, Whitmore JB, Guo W, Ji Y. (2017) , Liu S, Johnson VE (2016) , Zhou Y, Lee JJ, Yuan Y (2019) , Pan H, Lin R, Yuan Y (2020) ). The 'Keyboard' package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials. For more details about how to use this packge, please refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022) "A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs". Manuscript submitted for publication. Package: r-cran-keyed Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-digest, r-cran-lifecycle, r-cran-pillar, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-joinspy, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-uuid Filename: pool/dists/resolute/main/r-cran-keyed_0.2.0-1.ca2604.1_all.deb Size: 164996 MD5sum: 62f771693e5e17878f5c8c8d8a38435f SHA1: a91d020d4ab06f30f9302e3c48897de8857b2ef6 SHA256: 9121c0f617c52e8aed896baad680c4561a2893d6b9eec5f549df2b9bc7120073 SHA512: b009c17840dc2475db6bbfe9b991f1c11294f2c91b6d2cd401f058bcc60329458d39282618f280bed54c42719afefd41674238c21486c2b9fb81c74c6cb97de0 Homepage: https://cran.r-project.org/package=keyed Description: CRAN Package 'keyed' (Explicit Key Assumptions for Flat-File Data) Helps make implicit data assumptions explicit by attaching keys to flat-file data that error when those assumptions are violated. Designed for CSV-first workflows without database infrastructure or version control. Provides key definition, assumption checks, join diagnostics, and automatic drift detection via watched data frames that snapshot before each transformation and report cell-level changes. Package: r-cran-keyholder Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-keyholder_0.1.8-1.ca2604.1_all.deb Size: 91082 MD5sum: 25bb15db49f371f91220cafbbe56b71b SHA1: 10cce060407b79d020a563c0c5373b5811757d92 SHA256: 162b282f7010f75d64f78f36056a6f1397e7131719545dbce928b901415f206f SHA512: 4fb9432d188ac5db245088dab5fc5b5a38ee7b5a4769867939628de692c9f190e4b8a1d5879d797f0c41d36971e9f285d51d87570f078093d09df01ca9bb3a6b Homepage: https://cran.r-project.org/package=keyholder Description: CRAN Package 'keyholder' (Store Data About Rows) Tools for keeping track of information, named "keys", about rows of data frame like objects. This is done by creating special attribute "keys" which is updated after every change in rows (subsetting, ordering, etc.). This package is designed to work tightly with 'dplyr' package. 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Package: r-cran-keyringr Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-keyringr_0.4.0-1.ca2604.1_all.deb Size: 64078 MD5sum: 70f86905b2fdad417ac3ca01a07646b8 SHA1: a1cbc0f7ec9dc8f43a47e0e4540560bec99c4206 SHA256: 79408738e792d292401933d6a0a4ac01b678c4dd353f34a76a709ac23792c449 SHA512: a096d8464d2d257ef734399f57f5713bb95a7f4f5cfa6afd1aca410701fbc9979c7e58686b878cc28faa384ef1ccf70ddb932f6ef8641bf01da4fb38500692c6 Homepage: https://cran.r-project.org/package=keyringr Description: CRAN Package 'keyringr' (Decrypt Passwords from Gnome Keyring, Windows Data ProtectionAPI and macOS Keychain) Decrypts passwords stored in the Gnome Keyring, macOS Keychain and strings encrypted with the Windows Data Protection API. Package: r-cran-keys Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-keys_0.1.1-1.ca2604.1_all.deb Size: 132930 MD5sum: ca41dba61dbb1d702157f4885a0c12f7 SHA1: bf02d883649feab5f2b567addbd8db3051d35e2b SHA256: 06fa85a317b3637b6c3a974fcaa9a5e29e1b05e6b45f292102e0e019ac62da2e SHA512: a6874c11c7313e3bf813082f0609460d4a9ec2e51642589f5d9e3f23a190c78cb3274dfef1bd2d88de796f751a90c197b269251351dbd63c91c8fc3a12a16ec1 Homepage: https://cran.r-project.org/package=keys Description: CRAN Package 'keys' (Keyboard Shortcuts for 'shiny') Assign and listen to keyboard shortcuts in 'shiny' using the 'Mousetrap' Javascript library. Package: r-cran-kfa Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3520 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-doparallel, r-cran-flextable, r-cran-foreach, r-cran-gparotation, r-cran-knitr, r-cran-lavaan, r-cran-officer, r-cran-rmarkdown, r-cran-semtools, r-cran-simstandard Suggests: r-cran-semplot Filename: pool/dists/resolute/main/r-cran-kfa_0.2.2-1.ca2604.1_all.deb Size: 3381622 MD5sum: ef77b1a62d279977d25b294acfb76a74 SHA1: c64dd5bc3fd1111db73497d1eda66f79f27205a6 SHA256: 92244c22777a633eff78089b35c47a4bb5c8cefddd2f08b817941708b6d05b20 SHA512: 934c52873e2fc1f3fbca9353576cc9ea6045a623c420ace0a4017dba6d8651ae35dbf4941be7eaf25785cbba6033c40a2bc3e662e904fb472fa4fa31ce8aa4e8 Homepage: https://cran.r-project.org/package=kfa Description: CRAN Package 'kfa' (K-Fold Cross Validation for Factor Analysis) Provides functions to identify plausible and replicable factor structures for a set of variables via k-fold cross validation. The process combines the exploratory and confirmatory factor analytic approach to scale development (Flora & Flake, 2017) with a cross validation technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009) . Also available are functions to determine k by drawing on power analytic techniques for covariance structures (MacCallum, Browne, & Sugawara, 1996) , generate model syntax, and summarize results in a report. Package: r-cran-kfda Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernlab, r-cran-mass Filename: pool/dists/resolute/main/r-cran-kfda_1.0.0-1.ca2604.1_all.deb Size: 19944 MD5sum: 1aeed6e222373b894ef1c42866ec13e2 SHA1: 8a983449a04fa1ee6d4131f546daeca0da76bf00 SHA256: dac8bfe511980efe8067db7ff134443b78264b914aabd7aad692b328be158cdc SHA512: 5f18c5114c5b9ad96648f3619b05c6df768a4615ef6e15d71ce9de77c81c32d4d313e42bf83cfe4edcbc77f111d3048d85565070a59fb271dd475d490983a9a6 Homepage: https://cran.r-project.org/package=kfda Description: CRAN Package 'kfda' (Kernel Fisher Discriminant Analysis) Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA). There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions. In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly. This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) . Package: r-cran-kfigr Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-ggplot2, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-kfigr_1.2.1-1.ca2604.1_all.deb Size: 44670 MD5sum: 461f634a48df4835b171017876cb117f SHA1: e8a582afaf2c111b330717519b6c6ba28fbfe2fa SHA256: 5cffc42f6f10f09ed9e59c47a8b48149f34c520adbbbc8be28e9939d3717d5f3 SHA512: 160c2c6bb98826d68452e86447cffdc83ff6601c4a896f03410daa77d8a4ebfa504c661dd0296becbc8df5f336801bdab04a5aba1c1142fe4fb3fe976d034961 Homepage: https://cran.r-project.org/package=kfigr Description: CRAN Package 'kfigr' (Integrated Code Chunk Anchoring and Referencing for R MarkdownDocuments) A streamlined cross-referencing system for R Markdown documents generated with 'knitr'. R Markdown is an authoring format for generating dynamic content from R. 'kfigr' provides a hook for anchoring code chunks and a function to cross-reference document elements generated from said chunks, e.g. figures and tables. Package: r-cran-kfino Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-kfino_1.0.0-1.ca2604.1_all.deb Size: 875114 MD5sum: 05109c43c4a7f567c582b2db005fed33 SHA1: 98224de7568faf6b7d44290047f8ac2295692a02 SHA256: 38ba7624bc040bee0dda2559f80bd851c5686569e40bf262c2735d57605bd753 SHA512: 6356de257c8c808b9bd7867785adc163d687cf9721eb375277074105798c3b1c8c4a4691ab4140a9b1c20d17dc65edd9dd37facf9d8cfe017bddcf66c83d372f Homepage: https://cran.r-project.org/package=kfino Description: CRAN Package 'kfino' (Kalman Filter for Impulse Noised Outliers) A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: . Package: r-cran-kfpca Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kader, r-cran-pracma, r-cran-fdapace, r-cran-fda Filename: pool/dists/resolute/main/r-cran-kfpca_2.0-1.ca2604.1_all.deb Size: 87464 MD5sum: 010397c6e6606a941f47f25dcb252a4a SHA1: abf353b5b8033d52589029cbe27d9866d96f6ca8 SHA256: b0990b94a3bb306962c2fa629036c8a357a341bf8243d64b4605503666d7653d SHA512: 674783dc4e3ba6a639839ec703dcb0401d5602e17df6c6c43a9029deea30a8472a4dcb3ffc8edbba718284e66d4701b8784e7fa5ec9c958472e47d72eb549ed1 Homepage: https://cran.r-project.org/package=KFPCA Description: CRAN Package 'KFPCA' (Kendall Functional Principal Component Analysis) Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) . Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) . Package: r-cran-kfpls Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda Filename: pool/dists/resolute/main/r-cran-kfpls_1.0-1.ca2604.1_all.deb Size: 26778 MD5sum: e4eca09aa867adb8a091e2ba7ea21ba1 SHA1: bbd0ec427d194bfeb11650e02f1c7ade80c7240f SHA256: 7eb0e75d6db83b407125f80af8f3953323c502dc9d4c6a5e8b774ddaebb0e777 SHA512: 4e368c9666187974ec0e53130c311d1ddfd03731960257d9e2f506f7dbf584313a94fe90748a6f0fbb89c6fd032fbdb39140db7f54e0d07284f2c324d50a700c Homepage: https://cran.r-project.org/package=KFPLS Description: CRAN Package 'KFPLS' (Kernel Functional Partial Least Squares) Implementation for kernel functional partial least squares (KFPLS) method. KFPLS method is developed for functional nonlinear models, and the method does not require strict constraints for the nonlinear structures. The crucial function of this package is KFPLS(). Package: r-cran-kfre Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-r6 Suggests: r-cran-devtools, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-rprojroot, r-cran-spelling, r-cran-svglite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kfre_0.0.2-1.ca2604.1_all.deb Size: 113868 MD5sum: 74cbd03b63ec542aeca141eb7cee2112 SHA1: e38c6e649a36d00db304cbcc6806f2e99d6f2514 SHA256: 2b1a9225ce89ccd179d44151761a896bbff90806006bdb1a0f581940f40cd370 SHA512: daa294fa8aab52c155ccb6b97645b8a982193dfff60cf4b4215c4c5821fd0dca88a22041423a4207afaab5acd140c128e191aef412feddbd59ae78432bca76e1 Homepage: https://cran.r-project.org/package=kfre Description: CRAN Package 'kfre' (Kidney Failure Risk Equation (KFRE) Tools) Implements the Kidney Failure Risk Equation (KFRE; Tangri and colleagues (2011) ; Tangri and colleagues (2016) ) to compute 2- and 5-year kidney failure risk using 4-, 6-, and 8-variable models. Includes helpers to append risk columns to data frames, classify chronic kidney disease (CKD) stages and end-stage renal disease (ESRD) outcomes, and evaluate and plot model performance. Package: r-cran-kgc Architecture: all Version: 1.0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3595 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kgc_1.0.0.2-1.ca2604.1_all.deb Size: 2645640 MD5sum: 730298ccca46afa38e55c0ddafba8103 SHA1: 38e21414c84fe65edc691f88021da99c1a042f1a SHA256: 11558d0eb7c54c3b2a0e867d3912527dfda17a7fffe96f219a663385d034cf98 SHA512: 18b7a183252afa7be6744049582564d55a03a3fa177b2d85fc3dc456c238dcd7493326db177842251178c6408d25913a474afe024bc14d1d8738fba6a299309a Homepage: https://cran.r-project.org/package=kgc Description: CRAN Package 'kgc' (Koeppen-Geiger Climatic Zones) Aids in identifying the Koeppen-Geiger (KG) climatic zone for a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system to classify regions of the earth by their relative heat and humidity through the year, for the benefit of human health, plant and agriculture and other human activity [1]. This climate zone classification system, applicable to all of the earths surface, has continued to be developed by scientists up to the present day. Recently one of use (FZ) has published updated, higher accuracy KG climate zone definitions [2]. In this package we use these updated high-resolution maps as the data source [3]. We provide functions that return the KG climate zone for a given longitude and lattitude, or for a given United States zip code. In addition the CZUncertainty() function will check climate zones nearby to check if the given location is near a climate zone boundary. In addition an interactive shiny app is provided to define the KG climate zone for a given longitude and lattitude, or United States zip code. Digital data, as well as animated maps, showing the shift of the climate zones are provided on the following website . This work was supported by the DOE-EERE SunShot award DE-EE-0007140. [1] W. Koeppen, (2011) . [2] F. Rubel and M. Kottek, (2010) . [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) . Package: r-cran-kgen Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson, r-cran-reticulate, r-cran-checkmate, r-cran-data.table Suggests: r-cran-future, r-cran-progressr, r-cran-future.apply, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kgen_1.1.1-1.ca2604.1_all.deb Size: 72200 MD5sum: 52651455ffa171a974dde90bbfda060f SHA1: d32471aa15385835e7656a888587753542754e14 SHA256: 3f5c9da43af1558039ee1e473e2c566e039ed140bc94de78ab1baeb9db9c7782 SHA512: 18e25c16dc85709049161fa7a087e999db143b7bea16fdb979d00494fa830af4e312a1c58f8bcc3850c66265d3ba1fe5925f69c2165aa0dfe512ff8969d166c1 Homepage: https://cran.r-project.org/package=kgen Description: CRAN Package 'kgen' (A Tool for Calculating Stoichiometric Equilibrium Constants (Ks)for Seawater) A unified software package simultaneously implemented in 'Python', 'R', and 'Matlab' providing a uniform and internally-consistent way of calculating stoichiometric equilibrium constants in modern and palaeo seawater as a function of temperature, salinity, pressure and the concentration of magnesium, calcium, sulphate, and fluorine. Package: r-cran-kgode Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-pracma, r-cran-pspline, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-kgode_1.0.5-1.ca2604.1_all.deb Size: 368970 MD5sum: ba17ac1b8625cc643de61707a79ea0cd SHA1: a07b3b922d861490b80f99d38c48f09fcd558c4b SHA256: 98b39d15d5a3b53c0443e37c935cf0ce6280e7d519916df3a251f0c644a60d19 SHA512: 78c29dc11d65ca7a2a87164424010e326e308e04824efaab983ef58a7e05717d1594aab254538c475a4be38729d4d98a288d59d573695721cb9075ca7f069c60 Homepage: https://cran.r-project.org/package=KGode Description: CRAN Package 'KGode' (Kernel Based Gradient Matching for Parameter Inference inOrdinary Differential Equations) The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) and the warping algorithm proposed in Niu et al. (2017) are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping. Package: r-cran-kgp Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tibble Filename: pool/dists/resolute/main/r-cran-kgp_1.1.1-1.ca2604.1_all.deb Size: 306290 MD5sum: 5a55b2f3c522da572ccd0d2b8c1276a9 SHA1: 3c7609e935ad69a015aad807fecaa4371c3ee5b6 SHA256: 085af86d1d34794f25c93ab4efb81fcfda3ee4c14ddc5d02acab1f2409f08152 SHA512: 4d9b9fa36cbac0996e981b889b22b84e5d3f50f40ad13bb737eb4ea0836f8b6ed33087c170179248f4bcf49ded94ac94dadcc208a5a7ce91716ed084bbf41eae Homepage: https://cran.r-project.org/package=kgp Description: CRAN Package 'kgp' (1000 Genomes Project Metadata) Metadata about populations and data about samples from the 1000 Genomes Project, including the 2,504 samples sequenced for the Phase 3 release and the expanded collection of 3,202 samples with 602 additional trios. The data is described in Auton et al. (2015) and Byrska-Bishop et al. (2022) , and raw data is available at . See Turner (2022) for more details. Package: r-cran-kgraph Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1669 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-htmltools, r-cran-igraph, r-cran-magrittr, r-cran-opticskxi, r-cran-plyr, r-cran-proc, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-sgraph, r-cran-shiny Suggests: r-cran-bslib, r-cran-data.table, r-cran-dt, r-cran-knitr, r-cran-nlpembeds, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kgraph_1.2.0-1.ca2604.1_all.deb Size: 1177380 MD5sum: ab1064179314d76be867d9102adab153 SHA1: acb7e152c679d81d84aa57ff29c5749d028c75a0 SHA256: 3f770b4a24e931e0837ff12b408f4a2d9b655d777523e1bf15cd6d2892ce040c SHA512: 4664b52027d4ae229cb7d5ac3d2900bfd24d5c0a3e68c5a596b11d57a4623b53afbbcb4d41cf554244229c27339d3413b821bde8f600c0bfd850cc5a179f23cb Homepage: https://cran.r-project.org/package=kgraph Description: CRAN Package 'kgraph' (Knowledge Graphs Constructions and Visualizations) Knowledge graphs enable to efficiently visualize and gain insights into large-scale data analysis results, as p-values from multiple studies or embedding data matrices. The usual workflow is a user providing a data frame of association studies results and specifying target nodes, e.g. phenotypes, to visualize. The knowledge graph then shows all the features which are significantly associated with the phenotype, with the edges being proportional to the association scores. As the user adds several target nodes and grouping information about the nodes such as biological pathways, the construction of such graphs soon becomes complex. The 'kgraph' package aims to enable users to easily build such knowledge graphs, and provides two main features: first, to enable building a knowledge graph based on a data frame of concepts relationships, be it p-values or cosine similarities; second, to enable determining an appropriate cut-off on cosine similarities from a complete embedding matrix, to enable the building of a knowledge graph directly from an embedding matrix. The 'kgraph' package provides several display, layout and cut-off options, and has already proven useful to researchers to enable them to visualize large sets of p-value associations with various phenotypes, and to quickly be able to visualize embedding results. Two example datasets are provided to demonstrate these behaviors, and several live 'shiny' applications are hosted by the CELEHS laboratory and Parse Health, as the KESER Mental Health application based on Hong C. (2021) . Package: r-cran-kgschart Architecture: all Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-deepnet, r-cran-ggplot2, r-cran-gridextra, r-cran-magrittr, r-cran-matrixstats, r-cran-nnet, r-cran-png, r-cran-shiny, r-cran-stringr Suggests: r-cran-jsonlite, r-cran-shinyjs, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kgschart_1.3.5-1.ca2604.1_all.deb Size: 388498 MD5sum: 23225ad0a3b5324a6c3c9eb2cd5ce895 SHA1: 68a34e5144cadac7178050f1481dc4544504a128 SHA256: b9dfef2275497f27f3a092c77898f3ae64c133e4564bd481795c5f70e1a3662d SHA512: 899d4db86434595b06003f9e41d15bf2b89d0f2c165f5717384c5066b21905e5dd4625ee55736882746c65883ad95de05c7fee5d338e0fa133d72b8c040f5219 Homepage: https://cran.r-project.org/package=kgschart Description: CRAN Package 'kgschart' (KGS Rank Graph Parser) Restore underlining numeric data from rating history graph of KGS (an online platform of the game of go, ). A shiny application is also provided. Package: r-cran-khaos Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lhs, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-khaos_2.1.1-1.ca2604.1_all.deb Size: 211744 MD5sum: 254853b91f89d1edcee9adea15b47d87 SHA1: 0aa7f046ff03c9214505553f16e77df9c754270c SHA256: c144565d6fee336af84a7d74036ee7fd8be96a13af2fa1c6d31839d8b2c59fff SHA512: 0d6d74b8558b7a1fe8daed7b3a4727cf5d1ea5e90516fd9c6ff410b47137200947562cd95b69991095756767f8e111bc305ddc878507c459512fc6c5d2b26054 Homepage: https://cran.r-project.org/package=khaos Description: CRAN Package 'khaos' (Bayesian Sparse Polynomial Chaos Expansion) Implements Bayesian polynomial chaos expansion methods for surrogate modeling, uncertainty quantification, and sensitivity analysis. Includes sparse regression-based approaches and adaptive Bayesian models based on reversible-jump Markov chain Monte Carlo. Optional screening and basis-enrichment strategies are provided to improve scalability in moderate to high dimensions. Package: r-cran-khq Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-khq_0.2.0-1.ca2604.1_all.deb Size: 95754 MD5sum: 512300956df17c85678fdc77a12ba3b6 SHA1: 431f0beb50a6d4a6e99e322b8442192ae2bc6664 SHA256: a8af12650657e1200cc8a03312a5eccc6773408927caa54f780a768ab37d0f0a SHA512: 3d2c2748ec188ea7a5a87bd584e1eea9eedd40c02a52d44fe910477b8d73359512a594e4e027eebd9d83454d6e6169464ea9cbdb14909633b8d396c091d37f23 Homepage: https://cran.r-project.org/package=KHQ Description: CRAN Package 'KHQ' (Methods for Calculating 'KHQ' Scores and 'KHQ5D' Utility IndexScores) The King's Health Questionnaire (KHQ) is a disease-specific, self-administered questionnaire designed specific to assess the impact of Urinary Incontinence (UI) on Quality of Life. The questionnaire was developed by Kelleher and collaborators (1997) . It is a simple, acceptable and reliable measure to use in the clinical setting and a research tool that is useful in evaluating UI treatment outcomes. The KHQ five dimensions (KHQ5D) is a condition-specific preference-based measure developed by Brazier and collaborators (2008) . Although not as popular as the SF6D and EQ-5D , the KHQ5D measures health-related quality of life (HRQoL) specifically for UI, not general conditions like the others two instruments mentioned. The KHQ5D ca be used in the clinical and economic evaluation of health care. The subject self-rates their health in terms of five dimensions: Role Limitation (RL), Physical Limitations (PL), Social Limitations (SL), Emotions (E), and Sleep (S). Frequently the states on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. This package provides methods to calculate scores for each dimension of the KHQ; converts KHQ item scores to KHQ5D scores; and also calculates the utility index of the KHQ5D. Package: r-cran-khroma Architecture: all Version: 1.17.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4889 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fontquiver, r-cran-ggplot2, r-cran-ggraph, r-cran-knitr, r-cran-markdown, r-cran-rsvg, r-cran-scales, r-cran-spacesxyz, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-khroma_1.17.0-1.ca2604.1_all.deb Size: 3303044 MD5sum: 79404d4c58a629dd3ecab386233355fd SHA1: 33691fa4738b4f5079cd14b6adecd24a156c6144 SHA256: 8779e3d76b286df1f9f0f8ec0bb8f3bc96476ec2c8ae1600bee3493a6c718ab2 SHA512: 16d97eda5abe1fd77691448616d57d43e4454aee88ccab18ecd53ec8f947aaca7a67cdb488d450e953bf66a8ea3380789964e7580f32e00224e06c2f63872509 Homepage: https://cran.r-project.org/package=khroma Description: CRAN Package 'khroma' (Colour Schemes for Scientific Data Visualization) Color schemes ready for each type of data (qualitative, diverging or sequential), with colors that are distinct for all people, including color-blind readers. This package provides an implementation of Paul Tol (2018) and Fabio Crameri (2018) color schemes for use with 'graphics' or 'ggplot2'. It provides tools to simulate color-blindness and to test how well the colors of any palette are identifiable. Several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) are also implemented. Package: r-cran-kidney.epi Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kidney.epi_1.4.0-1.ca2604.1_all.deb Size: 538760 MD5sum: dfebe0c551fb366ac27aa152a5b10490 SHA1: 87e4d0d3592f7de67c5aa6b72ddb12149199515e SHA256: 11417b95f88dbb7af15d52390065df5a9126207af29594becff17455d185bc91 SHA512: 535bdb6587c6030476312d6264597c6e57fef2c870151df40930c2d4439afb51975fd156a890d2bd09819f6bb0baa326b3dbad2dd834e487598169ec10546276 Homepage: https://cran.r-project.org/package=kidney.epi Description: CRAN Package 'kidney.epi' (Kidney-Related Functions for Clinical and EpidemiologicalResearch) Contains kidney care oriented functions. Current version contains functions for calculation of: - Estimated glomerular filtration rate by CKD-EPI (2021 and 2009), MDRD, CKiD, FAS, EKFC, etc. - Kidney Donor Risk Index and Kidney Donor Profile Index for kidney transplant donors. - Citation: Bikbov B. kidney.epi: Kidney-Related Functions for Clinical and Epidemiological Research. Scientific-Tools.Org, . . Package: r-cran-kidsides Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-r.utils Suggests: r-cran-ggplot2, r-cran-dbplyr, r-cran-tidyr, r-cran-stringr, r-cran-ggthemes, r-cran-rlang, r-cran-ggrepel, r-cran-scales, r-cran-pacman, r-cran-dplyr, r-cran-rmarkdown, r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-dt, r-cran-gt, r-cran-lobstr, r-cran-prettyunits, r-cran-purrr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-kidsides_0.5.0-1.ca2604.1_all.deb Size: 24882 MD5sum: b3d1ca9c530ec32bf6a27dd205cb1310 SHA1: 2c4437aef0e2064dfd0596a4afa814280a08e460 SHA256: 50c03e94863229755d144f7fd139b9d9c1569dc197f9499016556606b3d031f7 SHA512: 9bc3d3c680b384e636bb914710ac4c7d09f65416e72cf0b1e6d398fdc9621e020460a7bfe3fb7d496396bf56e5589ce0692ff03faa00796d57c1bee418429acc Homepage: https://cran.r-project.org/package=kidsides Description: CRAN Package 'kidsides' (Download, Cache, and Connect to KidSIDES) Caches and then connects to a 'sqlite' database containing half a million pediatric drug safety signals. The database is part of a family of resources catalogued at . The database contains 17 tables where the description table provides a map between the fields the field's details. The database was created by Nicholas Giangreco during his PhD thesis which you can read in Giangreco (2022) . The observations are from the Food and Drug Administration's Adverse Event Reporting System. Generalized additive models estimated drug effects across child development stages for the occurrence of an adverse event when exposed to a drug compared to other drugs. Read more at the methods detailed in Giangreco (2022) . Package: r-cran-kifidi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kifidi_0.1.0-1.ca2604.1_all.deb Size: 22068 MD5sum: 87047a8ea467bff03c597a1e7a2dc8f2 SHA1: 6a9b785e46ae1da9d9d13f13c0f5f15c4711e905 SHA256: f21d57ec7814c682053d72b0ba387f3ee599af55d1d3a93aa5f96f32fce651d6 SHA512: 5ccb7eb75ead38f7710b86fd9428c4457049f959ebc32e226032635f073c0168333863e410a0ad8a5aaa8f7ff30810e1fa1f9908a731b0bfad2a136e22b03df9 Homepage: https://cran.r-project.org/package=Kifidi Description: CRAN Package 'Kifidi' (Summary Table and Means Plots) Optimized for handling complex datasets in environmental and ecological research, this package offers functionality that is not fully met by general-purpose packages. It provides two key functions, 'summarize_data()', which summarizes datasets, and 'plot_means()', which creates plots with error bars. The 'plot_means()' function incorporates error bars by default, allowing quick visualization of uncertainties, crucial in ecological studies. It also streamlines workflows for grouped datasets (e.g., by species or treatment), making it particularly user-friendly and reducing the complexity and time required for data summarization and visualization. Package: r-cran-kim Architecture: all Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-remotes Suggests: r-cran-boot, r-cran-ggplot2, r-cran-moments, r-cran-mass, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kim_0.6.4-1.ca2604.1_all.deb Size: 963788 MD5sum: 7d5eaf65fae879fcf4d652da459950a6 SHA1: 20da390ef45052e93d4e8a60f85b6d67e5c696ec SHA256: c6cc8eccecc7c4bc3dbc0f3d4a3a7459d3821547cd7710131197e429fac48e69 SHA512: f07cb23fd3d4c23c269b8f3b0d920bd59c37127611badcb9ca55bfa627f9e1c57c49d5478ec14c0c579306ef60a294c275f9247c015ea23af0b4d6333f8dfee5 Homepage: https://cran.r-project.org/package=kim Description: CRAN Package 'kim' (A Toolkit for Behavioral Scientists) A collection of functions for analyzing data typically collected or used by behavioral scientists. Examples of the functions include a function that compares groups in a factorial experimental design, a function that conducts two-way analysis of variance (ANOVA), and a function that cleans a data set generated by Qualtrics surveys. Some of the functions will require installing additional package(s). Such packages and other references are cited within the section describing the relevant functions. Many functions in this package rely heavily on these two popular R packages: Dowle et al. (2021) . Wickham et al. (2021) . Package: r-cran-kimisc Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-memoise, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kimisc_1.0.1-1.ca2604.1_all.deb Size: 120310 MD5sum: 9b0a00fd1d9416d9973be78c2464a274 SHA1: 29919f60de25e61ccf184f9c651b42da5dece729 SHA256: b22a9047cf2569dedfdd03a310e3f382483f7959da751e74361ba3dc153e9cbe SHA512: 9c2c86ddcd9020de8c97febe67679c9d682024c8d92199599619186aa962b245234e211c2774fc4c90ca152a07a26dd9fd582ec516f66c660f6b03a51a876651 Homepage: https://cran.r-project.org/package=kimisc Description: CRAN Package 'kimisc' (Kirill's Miscellaneous Functions) A collection of useful functions not found anywhere else, mainly for programming: Pretty intervals, generalized lagged differences, checking containment in an interval, and an alternative interface to assign(). Package: r-cran-kin.cohort Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-kin.cohort_0.7-1.ca2604.1_all.deb Size: 158320 MD5sum: 5f4f093b2c38a18c0544f5e678e26c07 SHA1: 1764edd0e094541ae03cf4009b5bb22d8b8f849e SHA256: 794c7074736b58d0b0b8bc5cb842d1f2d426ff93d34e10db34252e7d1fc49e20 SHA512: 05d07eda40bbb4c0cd58bf0da7ce556ef0c610d18c139cc2f88db0a0f94227561ac045445654af92f4cf6eb4eb1435d7583fd6fa5ff36ee646db8784ecb41251 Homepage: https://cran.r-project.org/package=kin.cohort Description: CRAN Package 'kin.cohort' (Analysis of Kin-Cohort Studies) Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are retrospectively built from relatives of probands for whom the genotype is known. Currently the method of moments and marginal maximum likelihood are implemented. Confidence intervals are calculated from bootstrap samples. Most of the code is a translation from previous 'MATLAB' code by N. Chatterjee. Package: r-cran-kindisperse Architecture: all Version: 0.10.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-readr, r-cran-shiny, r-cran-shinythemes, r-cran-ggrepel, r-cran-fitdistrplus, r-cran-laplacesdemon, r-cran-here, r-cran-tibble, r-cran-magrittr, r-cran-plotly, r-cran-dplyr, r-cran-rlang, r-cran-tidyselect, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kindisperse_0.10.2-1.ca2604.1_all.deb Size: 878952 MD5sum: 882b9851f3a4eb5f8c26951db12b0595 SHA1: 90341f4d5ee8f7e167b911df00d2d8e9ebb29085 SHA256: d2bdf0340a3decad1c81a0b5fe0596b046137e996ea5efda7315c631c55e0c72 SHA512: 9cd77988cf769d31ebfc4ee9858c0d802e189537f7dbd372c487ee6cde2331c80a1ee9f5970db0dd3c39f90443ec4c8bde3b8374f2cc56672af22e5b3148a8eb Homepage: https://cran.r-project.org/package=kindisperse Description: CRAN Package 'kindisperse' (Simulate and Estimate Close-Kin Dispersal Kernels) Functions for simulating and estimating kinship-related dispersal. Based on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. Hoffmann (2019) "A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito". Assumes an additive variance model of dispersal in two dimensions, compatible with Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, as well as the functions needed to estimate parent-offspring dispersal for simulated or empirical data, and to undertake sampling design for future field studies of dispersal. For ease of use an integrated Shiny app is also included. Package: r-cran-kindling Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 771 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-torch, r-cran-rlang, r-cran-cli, r-cran-glue, r-cran-vctrs, r-cran-parsnip, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-neuralnettools, r-cran-vip, r-cran-ggplot2, r-cran-tune, r-cran-dials, r-cran-hardhat, r-cran-lifecycle, r-cran-coro Suggests: r-cran-testthat, r-cran-magrittr, r-cran-box, r-cran-recipes, r-cran-workflows, r-cran-rsample, r-cran-yardstick, r-cran-mlbench, r-cran-modeldata, r-cran-knitr, r-cran-rmarkdown, r-cran-dicedesign, r-cran-lhs, r-cran-sfd, r-cran-covr Filename: pool/dists/resolute/main/r-cran-kindling_0.3.0-1.ca2604.1_all.deb Size: 577108 MD5sum: 6c7ea68bd5db31b150da71c59875064d SHA1: 42422973ec2d865d29109d4940a7725939b3a7ab SHA256: b7a448b1405c89ca9c84eb96a811d2b2909be36c8d8317eb833e7276afdec62c SHA512: e11556889e8111319a2d239c7a76e16ab7a4b812b88bf2c1986841c08f4756bde5a5ac74f07ba607f68cea7f201cc9444498c6613a14605044db69bc969abc78 Homepage: https://cran.r-project.org/package=kindling Description: CRAN Package 'kindling' (Higher-Level Interface of 'torch' Package to Auto-Train NeuralNetworks) Provides a higher-level interface to the 'torch' package for defining, training, and fine-tuning neural networks, including its depth, powered by code generation. This package supports few to several architectures, including feedforward (multi-layer perceptron) and recurrent neural networks (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)), while also reduces boilerplate 'torch' code while enabling seamless integration with 'torch'. The model methods to train neural networks from this package also bridges to titanic ML frameworks in R, namely 'tidymodels' ecosystem, which enables the 'parsnip' model specifications, workflows, recipes, and tuning tools. Package: r-cran-kinematics Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-kinematics_1.0.0-1.ca2604.1_all.deb Size: 671464 MD5sum: a289e22c8298580ff3b0b50a44f26ae2 SHA1: 94e6691de06e0c8c0c32d359bdd98f27846c4f1d SHA256: 1bc55b88fd577577f4902721b23d04cd59c40213ee5461f9fc4c49c68cfb05c8 SHA512: fc1681d6b767410ec4cff751227a0d626fd7ff964c532f2299b86698a5c6c2a73b23190320b02f5fed4ad115bfa34d8666e78c0e1825cee7ac72a237935934a7 Homepage: https://cran.r-project.org/package=kinematics Description: CRAN Package 'kinematics' (Studying Sampled Trajectories) Allows analyzing time series representing two-dimensional movements. 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Package: r-cran-kinesis Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aion, r-cran-arkhe, r-cran-bslib, r-cran-config, r-cran-dimensio, r-cran-gt, r-cran-isopleuros, r-cran-kairos, r-cran-khroma, r-cran-mirai, r-cran-nexus, r-cran-sass, r-cran-shiny, r-cran-tabula Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-kinesis_0.3.1-1.ca2604.1_all.deb Size: 609314 MD5sum: e62aab27f40917c8af161ef0c792eebe SHA1: 228504984ee0ab95926d1545aafc96c07734ab6d SHA256: f9ac8f01e0ea1c1015a7d9cdeb20d2faae587755504556f6005c543747c59c12 SHA512: 271adfef9cc581720f5710d270a1e80a542e25791959379093f582de5d7f6048e0548b474b9584bd1c4945f15d2c97b3efe8238fc7d6197d0cd3b7dc43b64207 Homepage: https://cran.r-project.org/package=kinesis Description: CRAN Package 'kinesis' ('shiny' Applications for the 'tesselle' Packages) A collection of 'shiny' applications for the 'tesselle' packages . This package provides applications for archaeological data analysis and visualization. These mainly, but not exclusively, include applications for chronological modelling (e.g. matrix seriation, aoristic analysis) and count data analysis (e.g. diversity measures, compositional data analysis). Package: r-cran-kinformr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kinformr_0.1.2-1.ca2604.1_all.deb Size: 457042 MD5sum: fa61f1209b0b8f565e7fdff74fa86f09 SHA1: e1f90c875d3a91fbbc214125a6b63cc2e36222ea SHA256: c4449c2fd8652772f1ec8d515bc11136dcb51f5e8e491cc49c5289acc23daf6a SHA512: fbd710cfa7e9c299d127063fa298f26172f4c8232aaa33347a20ccc0af1b2bd1e126ff6a6d9f0bca0c17d984ee03709a5d08396de8e37ba686fcbcf1325f03b5 Homepage: https://cran.r-project.org/package=KinformR Description: CRAN Package 'KinformR' (Relationship-Informed Pedigree and Variant Scoring) Comparative evaluation of families and candidate variants in rare-variant association studies. The package can be used for two methodologically overlapping but distinct purposes. First, the prior to any genetic or genomic evaluation, evaluation of relative detection power of pedigrees, can direct recruitment efforts by showing which individuals not yet sampled would be the most meaningful additions to a study. Second, after sequencing and analysis, variants based on association with disease status and familial relationships of individuals, aids in variant prioritization. Methodology is described in Nugent (2025) . Package: r-cran-kingcountyhouses Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-kingcountyhouses_0.1.0-1.ca2604.1_all.deb Size: 611620 MD5sum: b41dcc5f434124345ff0347c9306357b SHA1: 3e159d817b56775538c30f4591444fedc5c48b9c SHA256: 221c911f32fd72b18043f7bd876576125b9a580a183f1eb5520779346e9863ab SHA512: d66261204bec5ff445c8159932c4e4c21ec2deb58093723aa1cd279739f12d69864a230f4805d422dc48a32b9b7c576ded3ce318b7b65e681c37dcd8621c4034 Homepage: https://cran.r-project.org/package=KingCountyHouses Description: CRAN Package 'KingCountyHouses' (Data on House Sales in King County WA) Data on houses in and around Seattle WA are included. Basic characteristics are given along with sale prices. Package: r-cran-kinmixlite Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dnamixtureslite, r-cran-graven, r-cran-statnet.common, r-cran-grbase, r-cran-rsolnp, r-cran-numderiv, r-cran-matrix, r-cran-ribd, r-cran-pedtools Filename: pool/dists/resolute/main/r-cran-kinmixlite_2.2.1-1.ca2604.1_all.deb Size: 266964 MD5sum: 0efd9d9f34343a49dbab9280fcb2eabd SHA1: 79ba4782d0eb5b384b36d8c0eaaf9f1e9b8923c7 SHA256: c06e904595fb646f5f76f42cbfa68655eab088dafbb40a7bf2382834ff757891 SHA512: ba76033e38f905bcca7f27d2edc531e9f1b61b524b19434d9165402fe560f6067fd5526447eca4edba24070ce927e6a3f722ba3c87365eb03f80934fa4b7a85e Homepage: https://cran.r-project.org/package=KinMixLite Description: CRAN Package 'KinMixLite' (Inference About Relationships from DNA Mixtures) Methods for inference about/under complex relationships using peak height data from DNA mixtures: the most basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. This provides most of the functionality of the 'KinMix' package, but with some loss of efficiency and restriction on problem size, as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'. The package implements the methods introduced in Green, P. J. and Mortera, J. (2017) and Green, P. J. and Mortera, J. (2021) . Package: r-cran-kinship2 Architecture: all Version: 1.9.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 804 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-quadprog, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-kinship2_1.9.6.2-1.ca2604.1_all.deb Size: 504836 MD5sum: 325a8d783ccf3b3f173881d32c0eb8ae SHA1: 5454339a2955f7d936acc6975e245239dee68d98 SHA256: db94afe666806fa1863d425f103592931ad954d3eb0b24a5f48c1ed33c450e4b SHA512: 3c5866d60a7d2e09932d9166d99df6a97287e54ecb34846e42e138a97b7ce4bfe9daadfbd587662e7e78b31628e4a8f03c02934cf433407f73c06ff377fee4f5 Homepage: https://cran.r-project.org/package=kinship2 Description: CRAN Package 'kinship2' (Pedigree Functions) Routines to handle family data with a pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the pedigree object with various criteria, and kinship for the X chromosome. Package: r-cran-kinsimu Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kinsimu_0.1.3-1.ca2604.1_all.deb Size: 419276 MD5sum: 279c92680b30cd45ed4c0ec705074b9b SHA1: 3ac8dcad98eec1abffadb6c5914b316482e9f279 SHA256: dd1059cb7a3d98d171f011bfaa977e18a2f0313a412a1e41f3d584f985f94ade SHA512: 6a4976d246671e1aa2a986e64eba18bf1b9243f5a9d19af294e726bbc6bc8ac72ccddeace93aaa4c639910a8a0caf1f64c675373c0cd13fd3488b0b3d9fe4f95 Homepage: https://cran.r-project.org/package=KINSIMU Description: CRAN Package 'KINSIMU' (Panel Evaluation in Forensic Kinship Analysis) Evaluate specific panels in different aspects: i) Simulation tools related to pedigree researches; ii) calculation for systemic effectiveness indicators, such as probability of exclusion (PE). 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There are two classes of models here, designed for use with confined aquifers: (1) for sealed wells, based on the model of Kitagawa et al (2011, ), and (2) for open wells, based on the models of Cooper et al (1965, ), Hsieh et al (1987, ), Rojstaczer (1988, ), Liu et al (1989, ), and Wang et al (2018, ). Wang's solution is a special exception which allows for leakage out of the aquifer (semi-confined); it is equivalent to Hsieh's model when there is no leakage (the confined case). These models treat strain (or aquifer head) as an input to the physical system, and fluid-pressure (or water height) as the output. The applicable frequency band of these models is characteristic of seismic waves, atmospheric pressure fluctuations, and solid earth tides. Package: r-cran-kitesquare Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggh4x, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-kitesquare_0.0.2-1.ca2604.1_all.deb Size: 138070 MD5sum: 7b598f628430d8d364acf760a7844b8c SHA1: 2569609aef72d85a59f5e13033ff588ca35ed8af SHA256: 42d16ad6ab338beacc4f15f45ca1b70f394db14b6e5f69c5382d409dd83fd1c1 SHA512: 6d3916ca62c65ca143d1c62092d18d48dcc850ea9beb8f0a6477ab5295774f88c52ecf71a54d19595b6116e1b4b42d57010f4455ceb0866d5c14f24f19163556 Homepage: https://cran.r-project.org/package=kitesquare Description: CRAN Package 'kitesquare' (Visualize Contingency Tables Using Kite-Square Plots) Create a kite-square plot for contingency tables using 'ggplot2', to display their relevant quantities in a single figure (marginal, conditional, expected, observed, chi-squared). 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Package: r-cran-klar Architecture: all Version: 1.7-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-combinat, r-cran-questionr Suggests: r-cran-scatterplot3d, r-cran-som, r-cran-mlbench, r-cran-rpart, r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-klar_1.7-4-1.ca2604.1_all.deb Size: 567644 MD5sum: ea26437d2bde9259cb4f433e748fa2f7 SHA1: 53e1d435a3893c1c3ecaed57799512c5cf5f1c63 SHA256: c5e119e4f62cbe0dc00cba7467dfd2c371107b378f030c4bdca888c288a8da14 SHA512: 52fab20f9908f26bfbe59917ded8baf1b8e8720424350eff62378b6d1ced1ddf1f177f78b54074bd5fe0e13442fa87c7ccc4d977bacfb777684922dff103cf9a Homepage: https://cran.r-project.org/package=klaR Description: CRAN Package 'klaR' (Classification and Visualization) Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to 'svmlight' and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing. Package: r-cran-klassr Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tm, r-cran-httr, r-cran-jsonlite, r-cran-igraph Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-kableextra, r-cran-magrittr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-klassr_1.0.5-1.ca2604.1_all.deb Size: 288700 MD5sum: a8c8fa083c51686272baa038d5e75f99 SHA1: a9af643eee06820f5f79efb2b988ff29617a5db0 SHA256: f9bb687080d85564eddf438b6e52e744fb4548915836ff9a30bc2e1facf5d51e SHA512: 8db509524a2f3ed3d6dd235fa2c8fce31483f857415751a684450c17affcf81f13c29ad69a9861b59739fcbc6c324168efda60e1908f355ae58cbdfee226dfde Homepage: https://cran.r-project.org/package=klassR Description: CRAN Package 'klassR' (Classifications for Statistics Norway) Functions to search, retrieve, apply and update classification standards and code lists using Statistics Norway's API from the system 'KLASS'. Retrieves classifications by date with options to choose language, hierarchical level and formatting. Package: r-cran-klausur Architecture: all Version: 0.12-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable, r-cran-psych Filename: pool/dists/resolute/main/r-cran-klausur_0.12-14-1.ca2604.1_all.deb Size: 385602 MD5sum: 4b1e2d3cf4527d6bd4f6abefa0f38306 SHA1: f8925750fe749b9dd5f23bd10379e7daf6707af9 SHA256: d6021f51c63f0dab1bf2b975e21838c8ea79ded3aebea6b76ae60b0c8f00a4a0 SHA512: e580f7a52411156515d5c2f8e177a98d24ce25dafa357b1b1b3caa1682a8b21f7faa6f52546be7fb6daff1d297828e0b845727233d9a925db935937a32000716 Homepage: https://cran.r-project.org/package=klausuR Description: CRAN Package 'klausuR' (Multiple Choice Test Evaluation) A set of functions designed to quickly generate results of a multiple choice test. 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Package: r-cran-klexp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-klexp_1.0.0-1.ca2604.1_all.deb Size: 22348 MD5sum: c8bbd0437ff562e14dd4fb5053065a40 SHA1: 1cafb455284a413aa3759d190d0119396ea6c497 SHA256: c542863d4ebd6c270fdc2a630639174fb578cd8c60b41b5d230e1d430fea03a6 SHA512: daa49d2607242d2bb2a1f16ef0d4143a10d68891fba85ccd6fd1cf3ed5e1753b1eefbea13e698c41d67a4e4fe56797c990b4793dde286b2b6f77185ed9bc48d5 Homepage: https://cran.r-project.org/package=KLexp Description: CRAN Package 'KLexp' (Kernel_lasso Expansion) Provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) . 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The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2020) . Package: r-cran-klink Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forrel, r-cran-norstr, r-cran-gt, r-cran-openxlsx, r-cran-pedfamilias, r-cran-pedmut, r-cran-pedprobr, r-cran-pedtools, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyjs, r-cran-verbalisr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-klink_1.2.0-1.ca2604.1_all.deb Size: 705666 MD5sum: d365374b8da865f48eac1e6a820cc1c5 SHA1: d3ad3937c6e85119752e9262085fb05fc258063f SHA256: 76e69baa4d971734a2c9f7eb629a8195d7314281194bd2b4e145753141f43ebe SHA512: 8e07b474bb2bf22bc9ff3c71ed9a4300b5444d7830a5827d2362b5c1325aa5070e039bdf5a72dff1fba6cc48579dbba1f4f58d5be5a8641a8cfe48a5918fa12e Homepage: https://cran.r-project.org/package=KLINK Description: CRAN Package 'KLINK' (Kinship Analysis with Linked Markers) A 'shiny' application for forensic kinship testing, based on the 'pedsuite' R packages. 'KLINK' is closely aligned with the (non-R) software 'Familias' and 'FamLink', but offers several unique features, including visualisations and automated report generation. The calculation of likelihood ratios supports pairs of linked markers, and all common mutation models. Package: r-cran-klovan Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1933 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-dplyr, r-cran-fields, r-cran-ggally, r-cran-ggforce, r-cran-ggplot2, r-cran-ggrepel, r-cran-gstat, r-cran-metr, r-cran-pracma, r-cran-sp, r-cran-tibble, r-cran-magrittr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-sf, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-klovan_0.1.0-1.ca2604.1_all.deb Size: 1407160 MD5sum: 29fc3a6ce9d6ac9b5d50647cdfdec95a SHA1: cdd4343b32141d6bbf295802468545fdb5cdc1ac SHA256: 3535af3617a3394680295a14a4697f086c7b8e5cf51fec8c15e9d4c56667cc12 SHA512: 3db94389374947c7fe9275df03cb2bc1e37990b9685235f267ea57fe88ee17340dd395940b5196acb702ce757f55c066fab9f3f9c39b782a2ed0075fcd3d658f Homepage: https://cran.r-project.org/package=klovan Description: CRAN Package 'klovan' (Geostatistics Methods and Klovan Data) A comprehensive set of geostatistical, visual, and analytical methods, in conjunction with the expanded version of the acclaimed J.E. Klovan's mining dataset, are included in 'klovan'. This makes the package an excellent learning resource for Principal Component Analysis (PCA), Factor Analysis (FA), kriging, and other geostatistical techniques. Originally published in the 1976 book 'Geological Factor Analysis', the included mining dataset was assembled by Professor J. E. Klovan of the University of Calgary. Being one of the first applications of FA in the geosciences, this dataset has significant historical importance. As a well-regarded and published dataset, it is an excellent resource for demonstrating the capabilities of PCA, FA, kriging, and other geostatistical techniques in geosciences. For those interested in these methods, the 'klovan' datasets provide a valuable and illustrative resource. Note that some methods require the 'RGeostats' package. Please refer to the README or Additional_repositories for installation instructions. This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the Department of Energy's National Nuclear Security Administration under Award Number DE-NA0004104. Package: r-cran-klsh Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blink, r-cran-plyr, r-cran-rcpp, r-cran-stringi, r-cran-snowballc Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-klsh_0.1.0-1.ca2604.1_all.deb Size: 100090 MD5sum: b159b53df570f425d84e03b1a0306255 SHA1: 5861f969bc86d2533afb759e3630680998c44652 SHA256: ec4a894431660333802ba32c9b62c8636dcae1f3f8b319952a8f36a9932a9bfc SHA512: 2650f2b063ec69bd54e2d623e6fe86f0862d5e9b7f0da402d3324d8e875c9f7f5bc714757ab4083671c16819bd4a6d78d28d8cf8df85f8c313de197771a00e2e Homepage: https://cran.r-project.org/package=klsh Description: CRAN Package 'klsh' (Blocking for Record Linkage) An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) , which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette. Package: r-cran-klustr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-klustr_0.1.0-1.ca2604.1_all.deb Size: 203890 MD5sum: f408ae3bf8c50a9ae06bd80bf8641123 SHA1: a3931cbdd04c6bf47b22c9dd5d8cdfcac98753f3 SHA256: aebfbc62f50675f2c05a2f9289fa3074084c972eeacb6b218aa26bd16e205a43 SHA512: af98fd31ef5c50a4fcda380a6499cd2f10ca638374579a3685b54797aac3678a861a5a99336b279646f8c6eb4347dde728e5eedfcda43fc3a6e29bc0836e6e44 Homepage: https://cran.r-project.org/package=klustR Description: CRAN Package 'klustR' (D3 Dynamic Cluster Visualizations) Used to create dynamic, interactive 'D3.js' based parallel coordinates and principal component plots in 'R'. The plots make visualizing k-means or other clusters simple and informative. Package: r-cran-km.ci Architecture: all Version: 0.5-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-km.ci_0.5-6-1.ca2604.1_all.deb Size: 68266 MD5sum: f0199206d5389d90e88c7f6b7d9cbba1 SHA1: 5d7278bbece50ffda305eec0d63a59cd6d5b9b02 SHA256: 1b83f91850b58de2dfed2125864d92edc1f8aed2da615649cd2cf787407446a3 SHA512: d07bb1596cb56658510c18c730ee58ecbd86042e522044eb8b6244cc40541d1864c91ce5920959f465a02c4d72d2609d75ebdc5e342be9f0dd18c7b5186e13c8 Homepage: https://cran.r-project.org/package=km.ci Description: CRAN Package 'km.ci' (Confidence Intervals for the Kaplan-Meier Estimator) Computes various confidence intervals for the Kaplan-Meier estimator, namely: Peto's CI, Rothman CI, CI's based on Greenwood's variance, Thomas and Grunkemeier CI and the simultaneous confidence bands by Nair and Hall and Wellner. Package: r-cran-kmd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rann, r-cran-proxy, r-cran-mlpack, r-cran-boot, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-kmd_0.1.0-1.ca2604.1_all.deb Size: 43512 MD5sum: 95369da122737417f506d5b65d119bc5 SHA1: 3f08517a55eef1d7c3ecf94c514977e5d2e36db1 SHA256: 0efd9300cbd053bd5c8934c8400e3480464095a29dadb7addd88ab7b26fac275 SHA512: 5f275b3227b3cbfb420ec26554a85172e72b703555a21dcc0ff0b731be94143957bafbc36e34879c5525a8cebb95530f3a7bf497471a5b03e7d6e4386bae6225 Homepage: https://cran.r-project.org/package=KMD Description: CRAN Package 'KMD' (Kernel Measure of Multi-Sample Dissimilarity) Implementations of the kernel measure of multi-sample dissimilarity (KMD) between several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD measures the dissimilarity between multiple samples, based on the observations from them. It converges to the population quantity (depending on the kernel) which is between 0 and 1. A small value indicates the multiple samples are from the same distribution, and a large value indicates the corresponding distributions are different. The population quantity is 0 if and only if all distributions are the same, and 1 if and only if all distributions are mutually singular. The package also implements the tests based on KMD for H0: the M distributions are equal against H1: not all the distributions are equal. Both permutation test and asymptotic test are available. These tests are consistent against all alternatives where at least two samples have different distributions. For more details on KMD and the associated tests, see Huang, Z. and B. Sen (2022) . Package: r-cran-kmeans.knn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factoextra, r-cran-cluster, r-cran-ggplot2, r-cran-assertthat, r-cran-class, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kmeans.knn_0.1.0-1.ca2604.1_all.deb Size: 40328 MD5sum: 17db224effdec44d6d93936e5cb51fe4 SHA1: a3ceb261bb17f304ec151ccf33e4364d9c885abf SHA256: 6cc41fb770303594a34397d0a520c9b6ad243ac4e364caa0b17231c113a3497e SHA512: 0a4d975b335d3912526ff59c8555bd793e795ed9abc9736c90b5a418e81276544ee9baf74eacef641d356595cf3eb3392efd64b5e5c8c793d0858ba8717cfba8 Homepage: https://cran.r-project.org/package=KMEANS.KNN Description: CRAN Package 'KMEANS.KNN' (KMeans and KNN Clustering Package) Implementation of Kmeans clustering algorithm and a supervised KNN (K Nearest Neighbors) learning method. It allows users to perform unsupervised clustering and supervised classification on their datasets. Additional features include data normalization, imputation of missing values, and the choice of distance metric. The package also provides functions to determine the optimal number of clusters for Kmeans and the best k-value for KNN: knn_Function(), find_Knn_best_k(), KMEANS_FUNCTION(), and find_Kmeans_best_k(). Package: r-cran-kmed Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kmed_0.4.2-1.ca2604.1_all.deb Size: 268596 MD5sum: b6b9b3084cfc79b7cc026814bf7a534e SHA1: 1d12873a3de8307af41184bfd09ecf6f85facba7 SHA256: c8ccbee2f232556bd8a4aa7523ff19eda083080b0a065f09920f2387bf02a513 SHA512: c971627203e4d279535d74d72d4976bfa393028678ce260e972fa699b8d80742f1eb81b49254ee6b9c4e8bac8e4685fe4176512e1d7515a379142646b7bcb848 Homepage: https://cran.r-project.org/package=kmed Description: CRAN Package 'kmed' (Distance-Based k-Medoids) Algorithms of distance-based k-medoids clustering: simple and fast k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. Calculate distances for mixed variable data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and relative criteria. The internal criteria includes silhouette index and shadow values. The relative criterium applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages. The cluster result can be plotted in a marked barplot or pca biplot. Package: r-cran-kmedians Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-genieclust, r-cran-gmedian, r-cran-mvtnorm, r-cran-capushe, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-kmedians_2.2.0-1.ca2604.1_all.deb Size: 61104 MD5sum: 2473ecb3925ab42d4f45491e1f7259a2 SHA1: 717891bfd718d816674d0ef51abf9ac9a5df1a37 SHA256: a1ee11ae13589ec7b707829bdf59a50a35aac514dc3484129d9c9d0c820591b6 SHA512: 285651fe05774918d75f7b926cfdedd7791a941bf323d46aeec67811accdd404cf8d5a3066ac882be540205987a098ebc0b29a7b98aafb066c54f8add6d561c9 Homepage: https://cran.r-project.org/package=Kmedians Description: CRAN Package 'Kmedians' (K-Medians) Online, Semi-online, and Offline K-medians algorithms are given. For both methods, the algorithms can be initialized randomly or with the help of a robust hierarchical clustering. The number of clusters can be selected with the help of a penalized criterion. We provide functions to provide robust clustering. Function gen_K() enables to generate a sample of data following a contaminated Gaussian mixture. Functions Kmedians() and Kmeans() consists in a K-median and a K-means algorithms while Kplot() enables to produce graph for both methods. Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. . Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 . Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians" Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. . 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Package: r-cran-kmodr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kmodr_0.2.0-1.ca2604.1_all.deb Size: 24544 MD5sum: b6a5a49e4b85684e6a5ab8207ba9497a SHA1: 85692c2fd177f35ecad3be9603b73003ae9094e1 SHA256: b1c6d5136361a95ae29afbd18e8550857aadc2dfadac34b7d83c0f6b632abbf3 SHA512: 6fe92ad498f2bf599415c9c7918ff8cedb9c2a43aabd2b152f589fcf147462c08e6be2f362fc1cf3037873ea7331d09f4b83bb7415218c803e0ae5566aef4318 Homepage: https://cran.r-project.org/package=kmodR Description: CRAN Package 'kmodR' (K-Means with Simultaneous Outlier Detection) An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space. 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Package: r-cran-kmunicate Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-checkmate, r-cran-cowplot, r-cran-ggplot2, r-cran-pammtools, r-cran-tidyr Suggests: r-cran-broom, r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-simsurv, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-kmunicate_0.2.5-1.ca2604.1_all.deb Size: 2103246 MD5sum: 998f517c2b000ef1d2476465d4d9e948 SHA1: 747b01ede29d07ea8a4bf9437445b5caca8a0078 SHA256: ac753fbffd641a07dd40397509b338bbb8900d2307331097b9cef6b881ec74db SHA512: d64d1a5974dbfc22c58371fdf6e4456ba7fe40298f7a5e95a382f28109c333857271ba4720c6234d448d8aafaef8378e558a62fe54f3d14b5a624f1d7c462be7 Homepage: https://cran.r-project.org/package=KMunicate Description: CRAN Package 'KMunicate' (KMunicate-Style Kaplan–Meier Plots) Produce Kaplan–Meier plots in the style recommended following the KMunicate study by Morris et al. (2019) . The KMunicate style consists of Kaplan-Meier curves with confidence intervals to quantify uncertainty and an extended risk table (per treatment arm) depicting the number of study subjects at risk, events, and censored observations over time. The resulting plots are built using 'ggplot2' and can be further customised to a certain extent, including themes, fonts, and colour scales. Package: r-cran-knapsacksampling Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/resolute/main/r-cran-knapsacksampling_0.1.1-1.ca2604.1_all.deb Size: 27690 MD5sum: b81a30bdf4bc29f2e99776d5f4f7f031 SHA1: 94a7ca930c65a3482d70113e8f866b2136f4f356 SHA256: 690d6d403cd42d09a5914830f372f23566d235cb028135e183eefa8d9dd112fd SHA512: ca36879d9df6da1b409c8dd8943c118cb0a1d4a7b65018e832edfe0684f7acf9a0395002bb4464737b520c2c6f8529a76ca87515af3559280f5f7e4d6786a5a1 Homepage: https://cran.r-project.org/package=KnapsackSampling Description: CRAN Package 'KnapsackSampling' (Generate Feasible Samples of a Knapsack Problem) The sampl.mcmc function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints. Package: r-cran-kneearrower Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kneearrower_1.0.0-1.ca2604.1_all.deb Size: 51048 MD5sum: 250ff8469582d652951bef26fca59074 SHA1: b701dd9e6da43fc6f1c1e3d519d01f5555eaa935 SHA256: e0ad8ec415e92bf048409f5bafcd09a670eb3730c2c6bd29696741f772560f23 SHA512: 4269e1168b568ce06917ac8c716918497afa874af401a4501a620f90dc290ed36a3718ab9f5335d586c8f3d19f65e6fb3eb4b11dad5d8064cfeb28cf111795ab Homepage: https://cran.r-project.org/package=KneeArrower Description: CRAN Package 'KneeArrower' (Finds Cutoff Points on Knee Curves) Given a set of points around a knee curve, analyzes first and second derivatives to find knee points. Package: r-cran-knfi Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1815 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readxl, r-cran-stringr, r-cran-vegan, r-cran-tidyr, r-cran-biodiversityr, r-cran-data.table, r-cran-sf, r-cran-plotrix, r-cran-rlang, r-cran-cellranger, r-cran-broom, r-cran-ggplot2, r-cran-sp, r-cran-cowplot, r-cran-ggpubr, r-cran-drat, r-cran-purrr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-knfi_1.0.2-1.ca2604.1_all.deb Size: 1766562 MD5sum: fc6e55786d563fd63aea70ef0368b168 SHA1: 7053f419d81faf71cdf8109114f3d29d3f0599d3 SHA256: 3e55fa012af565d4e3ae0d244152c19fbbf75c86367a42f2955df21d059f50bf SHA512: 6c504f81e14629f397eab6c4b4e2a92bdfecbd57dfeebc44de1c0fbc266eed9d7261101425bc8ea296d63f36b488e55c0704037f2d14b6e7ded6085b68634763 Homepage: https://cran.r-project.org/package=knfi Description: CRAN Package 'knfi' (Analysis of Korean National Forest Inventory Database) Understanding the current status of forest resources is essential for monitoring changes in forest ecosystems and generating related statistics. In South Korea, the National Forest Inventory (NFI) surveys over 4,500 sample plots nationwide every five years and records 70 items, including forest stand, forest resource, and forest vegetation surveys. Many researchers use NFI as the primary data for research, such as biomass estimation or analyzing the importance value of each species over time and space, depending on the research purpose. However, the large volume of accumulated forest survey data from across the country can make it challenging to manage and utilize such a vast dataset. To address this issue, we developed an R package that efficiently handles large-scale NFI data across time and space. The package offers a comprehensive workflow for NFI data analysis. It starts with data processing, where read_nfi() function reconstructs NFI data according to the researcher's needs while performing basic integrity checks for data quality.Following this, the package provides analytical tools that operate on the verified data. These include functions like summary_nfi() for summary statistics, diversity_nfi() for biodiversity analysis, iv_nfi() for calculating species importance value, and biomass_nfi() and cwd_biomass_nfi() for biomass estimation. Finally, for visualization, the tsvis_nfi() function generates graphs and maps, allowing users to visualize forest ecosystem changes across various spatial and temporal scales. This integrated approach and its specialized functions can enhance the efficiency of processing and analyzing NFI data, providing researchers with insights into forest ecosystems. The NFI Excel files (.xlsx) are not included in the R package and must be downloaded separately. Users can access these NFI Excel files by visiting the Korea Forest Service Forestry Statistics Platform to download the annual NFI Excel files, which are bundled in .zip archives. Please note that this website is only available in Korean, and direct download links can be found in the notes section of the read_nfi() function. 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For more details see Schaefer (1954) , Pella and Tomlinson (1969) and MacCall (2002) . 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It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas. 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Small and simple package that adds immense convenience for the data professionals using 'Kobotoolbox'. Package: r-cran-kofdata Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-xts, r-cran-zoo Suggests: r-cran-tstools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kofdata_0.2.1-1.ca2604.1_all.deb Size: 47946 MD5sum: c33d765e730936540ff78085ed4b5d49 SHA1: fa631c084c6c177c156de06a555f1fb842b0770e SHA256: 6cfb953406b1bce310d8efbcb61986c437d796c9314e1266712550f52394302b SHA512: a8ac93e3bacff84c07d2fd892bc941ff99eb70f71adc3fd47190a9c4aee99ce559b7c439bb951808fa27ec8a6804313788328ebc7d3ebc5ede8fb9facfe5bf6d Homepage: https://cran.r-project.org/package=kofdata Description: CRAN Package 'kofdata' (Get Data from the 'KOF Datenservice' API) Read Swiss time series data from the 'KOF Data' API, . The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token. Package: r-cran-kofm Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensormiss, r-cran-mefm, r-cran-rspectra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kofm_1.1.1-1.ca2604.1_all.deb Size: 53736 MD5sum: 63239a151fbffa1192393c122a31c390 SHA1: daa5b2830957461f66c00a1eacfb4d17a951a0ff SHA256: ab427d17e762997821d773e47fa467b4bae4930d5668cc9f1f478dc8be9bc926 SHA512: ad2f34f16c149bc3d0ea0d87464b18b6660ae7b120a2e24c6a7cfc403c5aa9079021dbbb5f68771d13752aa10b933e3c887170d7b9ad0b8ce136a8b749ed6c81 Homepage: https://cran.r-project.org/package=KOFM Description: CRAN Package 'KOFM' (Test the Kronecker Product Structure in Tensor Factor Models) To test if a tensor time series following a Tucker-decomposition factor model has a Kronecker product structure. Supplementary functions for tensor reshape and its reversal are also included. Package: r-cran-kofnga Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory Filename: pool/dists/resolute/main/r-cran-kofnga_1.3-1.ca2604.1_all.deb Size: 45688 MD5sum: 523fd5a55c676923abfcec7d324712af SHA1: d1014722af5e73c0bd4bd828445bdf1f6ce9a501 SHA256: 5a29e87fed03761d9ec47710d2f56ca4f8d9696a8150967a722e73fb9afacde6 SHA512: 34b322a7fdf98425823837b901499174b483f5d117b83053eced438684ebce07d44770c56bab479145b5c0ab20ce4ada380fa11c3f49ac4879cbde02e4f5c959 Homepage: https://cran.r-project.org/package=kofnGA Description: CRAN Package 'kofnGA' (A Genetic Algorithm for Fixed-Size Subset Selection) Provides a function that uses a genetic algorithm to search for a subset of size k from the integers 1:n, such that a user-supplied objective function is minimized at that subset. The selection step is done by tournament selection based on ranks, and elitism may be used to retain a portion of the best solutions from one generation to the next. Population objective function values may optionally be evaluated in parallel. Package: r-cran-kogmwu Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pheatmap Filename: pool/dists/resolute/main/r-cran-kogmwu_1.2-1.ca2604.1_all.deb Size: 861698 MD5sum: 4ea930046b03fc4a681e19b7d1a1ed4b SHA1: aa1ff1e81c6c670242705af1190a4bac796ead4c SHA256: b068e233971c8dadb036e7d5edee984f2c24f7b64f1b8a0b2bd7a89a1f3a786d SHA512: a8474106f333ff04250206a8ef57ec5e7aff1004c3e914aa6f5d209c38dd1f41ea97b1f82ab8f757782e01acce62f021261b6033e39120dc1018c36e95491b5f Homepage: https://cran.r-project.org/package=KOGMWU Description: CRAN Package 'KOGMWU' (Functional Summary and Meta-Analysis of Gene Expression Data) Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed. Package: r-cran-kokudosuuchi Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kokudosuuchi_1.0.0-1.ca2604.1_all.deb Size: 377392 MD5sum: baa89f6650045e34ef4ad0eddf7cc0b3 SHA1: 9a516f6a35171bf623deb2c1d406af9ca3c99ec2 SHA256: 0ddacb26dcde04c9d3166e701a24706076ef27148711f93d906fd40981e7b0e3 SHA512: 8d9e66980efb068472b7ccb497fc14894fa2f9e699296f376c77d57b497f8282b07053d8f8c24b2dc35b7d734af706d4b19875a8e2b8856cfe0e04d26cf6ca83 Homepage: https://cran.r-project.org/package=kokudosuuchi Description: CRAN Package 'kokudosuuchi' (Utilities for 'Kokudo Suuchi') Provides utilities for 'Kokudo Suuchi', the GIS data service of the Japanese government. See for more information. Package: r-cran-kolaide Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-kolaide_0.0.1-1.ca2604.1_all.deb Size: 97312 MD5sum: 03ac56fa99de48c2ea0ad4a6683f40c5 SHA1: 39592cfc8f7f1a196a7e703f7f02bd835b86bc77 SHA256: e9497c57711ef8a88386fc1ae08347630bb6b3d12d8d23023cba8448dfc15078 SHA512: 718cf3f993ec52e49f41310a6b70499f6d64c0214354fea6de0bb1535cce0122f44fb7a4f0f442f0c8f6ec5cfeee8505b6a7522e19ee64c84ae0d117208675c2 Homepage: https://cran.r-project.org/package=KOLaide Description: CRAN Package 'KOLaide' (Pick and Plot Key Opinion Leaders from a Network GivenConstraints) Assists researchers in choosing Key Opinion Leaders (KOLs) in a network to help disseminate or encourage adoption of an innovation by other network members. Potential KOL teams are evaluated using the ABCDE framework (Neal et al., 2025 ). This framework which considers: (1) the team members' Availability, (2) the Breadth of the team's network coverage, (3) the Cost of recruiting a team of a given size, and (4) the Diversity of the team's members, (5) which are pooled into a single Evaluation score. Package: r-cran-kollar Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 899 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-zoo, r-cran-ggforce, r-cran-tidyr, r-cran-ggpubr, r-cran-jpeg, r-cran-patchwork, r-cran-shiny, r-cran-base64enc, r-cran-magick, r-cran-scales Filename: pool/dists/resolute/main/r-cran-kollar_1.1.4-1.ca2604.1_all.deb Size: 839898 MD5sum: c6f8e2c08735f5629e2bd81d6c9347d8 SHA1: bf12958f45edc78fea879f287cb42e4265f791ce SHA256: 4e65ebc3ce441040126d335740e1822eb1b2d33cddf3f50998ce01aa04777774 SHA512: 1ab581af95dfe539cb20af44baf0685f9a2e639b18509963e002909503aa5f60534866d4d003dba489beb5bde2fa5e94a437b57f7250873e8672c4395c320713 Homepage: https://cran.r-project.org/package=kollaR Description: CRAN Package 'kollaR' (Event Classification, Visualization and Analysis of Eye TrackingData) Functions for analysing eye tracking data, including event detection, visualizations and area of interest (AOI) based analyses. The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms. See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in Salvucci & Goldberg (2000) . Two-means clustering is described in Hessels et al. (2017), . The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010),. A documentation of the 'kollaR' can be found in Kleberg et al (2026) . Cite this paper when using 'kollaR' See a demonstration in the URL. Package: r-cran-komaletter Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1516 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-komaletter_0.5.0-1.ca2604.1_all.deb Size: 1210722 MD5sum: 7978b4a3a4115b437b2c9f5f06420ad4 SHA1: a76afc04907049212c59f31fa0edb0f5664ded75 SHA256: e14f23a8cb874a432dc4bea7ce09129d7ed2d22be877c219d5db74f0d3cabe7d SHA512: d7d666a2e5f14f1b6f86d86fbdf3905295f5a4e219a3e54d8f5395b44a00cf3705ffc1c19c5cee5ebfa38a9d69a1f81b650388d13d87663734497cd9cb08343d Homepage: https://cran.r-project.org/package=komaletter Description: CRAN Package 'komaletter' (Simply Beautiful PDF Letters from Markdown) Write beautiful yet customizable letters in R Markdown and directly obtain the finished PDF. Smooth generation of PDFs is realized by 'rmarkdown', the 'pandoc-letter' template and the 'KOMA-Script' letter class. 'KOMA-Script' provides enhanced replacements for the standard 'LaTeX' classes with emphasis on typography and versatility. 'KOMA-Script' is particularly useful for international writers as it handles various paper formats well, provides layouts for many common window envelope types (e.g. German, US, French, Japanese) and lets you define your own layouts. The package comes with a default letter layout based on 'DIN 5008B'. Package: r-cran-konfound Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-broom.mixed, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-cran-lavaan, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-lme4, r-cran-tibble, r-cran-ggrepel, r-cran-pbkrtest, r-cran-ppcor Suggests: r-cran-covr, r-cran-devtools, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-mice, r-cran-roxygen2, r-cran-testthat, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-konfound_1.0.3-1.ca2604.1_all.deb Size: 291270 MD5sum: a916483310e2498d0cf12c3693d6dacc SHA1: 6b0b688528a609b84364c0d27e8bc770ff35e224 SHA256: 0d13743e05472fc1f60064fa5ca2530e35078c4a0f13eb65c912403c64711417 SHA512: f8cb972ba48eec6381201277a97602da44a50176f31d75d2526d58749215c63a8fb98f3cc303b5dac8e8c23bc88d586870e2ea6837c762d3dba05aa405b924b9 Homepage: https://cran.r-project.org/package=konfound Description: CRAN Package 'konfound' (Quantify the Robustness of Causal Inferences) Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) and Frank et al. (2013) extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., the treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with nor which the treatment had no effect to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively. Package: r-cran-kor.addrlink Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringdist, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-kor.addrlink_1.0.1-1.ca2604.1_all.deb Size: 1340032 MD5sum: 517e46af63253f95ab46705e996cc2db SHA1: dd741b8817a51f7f1fee1f7c837da244cb719a66 SHA256: c9b6592a06eda005cf8b861f87bc254e3a92b0c2f71f8a8fc72bbe44f880bb3c SHA512: dc5bc9ad0144501a7654932b5414848074840467f9ffbfed887afc6d691e6c531aab8e82e8a1430b0646df899e3eae0f11a26ba3986ed59a85c0bc4c807f8de0 Homepage: https://cran.r-project.org/package=KOR.addrlink Description: CRAN Package 'KOR.addrlink' (Matching Address Data to Reference Index) Matches a data set with semi-structured address data, e.g., street and house number as a concatenated string, wrongly spelled street names or non-existing house numbers to a reference index. The methods are specifically designed for German municipalities ('KOR'-community) and German address schemes. Package: r-cran-korpus.lang.en Architecture: all Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-korpus, r-cran-sylly.en Filename: pool/dists/resolute/main/r-cran-korpus.lang.en_0.1-4-1.ca2604.1_all.deb Size: 21128 MD5sum: f711a94d703d1276e9fc9506e2dd6cd1 SHA1: 223f84b0f4f32ceaa0e1ebe157b2ef632b91456a SHA256: f243bdd687e2778d01ebb341bfdd830169dfea8278d1ef6a852293d7cfc96192 SHA512: 046f4debe7eed90a901f857567399793fcafc43a5821baa27cea437cbc7e49af02bd2e3a84aeb969b20bea17b18b775653803bba85897e5f5da819c132407e37 Homepage: https://cran.r-project.org/package=koRpus.lang.en Description: CRAN Package 'koRpus.lang.en' (Language Support for 'koRpus' Package: English) Adds support for the English language to the 'koRpus' package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (). Package: r-cran-korpus Architecture: all Version: 0.13-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sylly, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat, r-cran-tm, r-cran-snowballc, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-korpus.lang.en Filename: pool/dists/resolute/main/r-cran-korpus_0.13-9-1.ca2604.1_all.deb Size: 1277708 MD5sum: 4d5ab8194750bb5b608c0b3497b1eb96 SHA1: 2719acab2262716e10a3e7e1e9d1ce7ad1eb337d SHA256: d3dceecbd667acaa28d86f85796f9d85e884ca4c52a8a953f8a102c009c511ee SHA512: 3966ea5a0a0c5bdd4c109027249bf3c575291f31face7b55bc91539fc775ac4904023d905b42b0e658c507419de4f230d689741b6c8af51d33149b946d505bc1 Homepage: https://cran.r-project.org/package=koRpus Description: CRAN Package 'koRpus' (Text Analysis with Emphasis on POS Tagging, Readability, andLexical Diversity) A set of tools to analyze texts. Includes, amongst others, functions for automatic language detection, hyphenation, several indices of lexical diversity (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability (e.g., Flesch, SMOG, LIX, Dale-Chall). Basic import functions for language corpora are also provided, to enable frequency analyses (supports Celex and Leipzig Corpora Collection file formats) and measures like tf-idf. Note: For full functionality a local installation of TreeTagger is recommended. It is also recommended to not load this package directly, but by loading one of the available language support packages from the 'l10n' repository . 'koRpus' also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. To make full use of this feature, please install RKWard from (plugins are detected automatically). Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (). Package: r-cran-kosel Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-ordinalnet Filename: pool/dists/resolute/main/r-cran-kosel_0.0.1-1.ca2604.1_all.deb Size: 36344 MD5sum: 961010be1939b864dd91dd7b3346ba4e SHA1: 8599161a29da75ed94205d1460914c9a271b2cdd SHA256: ce12a653ce026cf5765d85e1c4eed283a146a2fb0e02a29d4eebff0d974c07d4 SHA512: fe042406be62bb4f3d3d875b3059525710a22349075f55b592be96514ed1063430ea420167499dfc46dd47ec1d979133a247173f32d76db70abf4492d2151c97 Homepage: https://cran.r-project.org/package=kosel Description: CRAN Package 'kosel' (Variable Selection by Revisited Knockoffs Procedures) Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) . Package: r-cran-kosis Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-kosis_0.0.1-1.ca2604.1_all.deb Size: 44208 MD5sum: bfd8abd43bc7f1a6a0bb89f5ce951781 SHA1: eba20d0e7916cac1e79ba823875ab002e4ae8a9f SHA256: a4f6bafcb47fda224240dc639ea541e9d5f05b796027f6453d6a35b3a8ac13af SHA512: 6271418f771124e0b2f27dd62c091b8d823b6fddbafa2ddae05e8c005ddcff4891182f2b23f97e6ff1ded3d40387a8002364c9b8a33975db426036c5e47bad58 Homepage: https://cran.r-project.org/package=kosis Description: CRAN Package 'kosis' (Korean Statistical Information Service (KOSIS)) API wrapper to download statistical information from the Korean Statistical Information Service (KOSIS) . Package: r-cran-kpart Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaps Filename: pool/dists/resolute/main/r-cran-kpart_1.2.2-1.ca2604.1_all.deb Size: 16860 MD5sum: 60b12a60462364fab135f48199568946 SHA1: b02af8a1be9fa06cea08796444435019c272fe01 SHA256: f6973b3e9c6092058f3d887d12a74c782062e24b3e90280ce1904fd108a32c8b SHA512: 76a69bbbf5aeccacbbd46d8925fe85f975480a5263867f835dde7e87cb15543b0c07d4a03e382c2a52673d05bfc80238d5e9056d5d235687ca8d64c8655e2169 Homepage: https://cran.r-project.org/package=Kpart Description: CRAN Package 'Kpart' (Cubic Spline Fitting with Knot Selection) Cubic spline fitting along with knot selection, includes support for additional variables. Package: r-cran-kpc Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-kernlab, r-cran-rann, r-cran-proxy, r-cran-mlpack Filename: pool/dists/resolute/main/r-cran-kpc_0.1.3-1.ca2604.1_all.deb Size: 95032 MD5sum: 7860fe2af078f7ba2f85b9806fdfaaa5 SHA1: fcb400ffc24883ceff9d1d3699cb0ea8f37b880b SHA256: b6d8abfd5d9b2129f6d7587b097e85f683e1e7cf31fc133b153b2eda0a2ed14b SHA512: 15d1d238d731129e7d61f5dfff363a744ce9304147cbd5b8f61225ce38c6f61aa9932d25ef6809b16c464e218afa3ecd324ce06b9813194a618a6e8720fb1fe9 Homepage: https://cran.r-project.org/package=KPC Description: CRAN Package 'KPC' (Kernel Partial Correlation Coefficient) Implementations of two empirical versions the kernel partial correlation (KPC) coefficient and the associated variable selection algorithms. KPC is a measure of the strength of conditional association between Y and Z given X, with X, Y, Z being random variables taking values in general topological spaces. As the name suggests, KPC is defined in terms of kernels on reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is consistent under very weak conditions. The other empirical estimator, defined using conditional mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm based on the RKHS based estimator. For more details on KPC, its empirical estimators and its application on variable selection, see Huang, Z., N. Deb, and B. Sen (2022). “Kernel partial correlation coefficient – a measure of conditional dependence” (URL listed below). When X is empty, KPC measures the unconditional dependence between Y and Z, which has been described in Deb, N., P. Ghosal, and B. Sen (2020), “Measuring association on topological spaces using kernels and geometric graphs” , and it is implemented in the functions KMAc() and Klin() in this package. The latter can be computed in near linear time. Package: r-cran-kpcaig Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-kernlab, r-cran-ggplot2, r-cran-progress, r-cran-viridis, r-cran-wallomicsdata Filename: pool/dists/resolute/main/r-cran-kpcaig_1.0.1-1.ca2604.1_all.deb Size: 46222 MD5sum: 2d008e3f3b619022fc19056cb475d243 SHA1: a16041c1f6bb21e2ae27914e72af9aa51e4fcffd SHA256: deee92be089e11c3c203ae013b006a721c73166aed2a54717112cc73cf1fbb57 SHA512: de81f10872b322b186fc3094672985db0838eb4b502d74653e7b75ac4cd2d469aae2184ac429b0a8177ad3b13dabd34b75930381375874c45b47df210117d894 Homepage: https://cran.r-project.org/package=kpcaIG Description: CRAN Package 'kpcaIG' (Variables Interpretability with Kernel PCA) The kernelized version of principal component analysis (KPCA) has proven to be a valid nonlinear alternative for tackling the nonlinearity of biological sample spaces. However, it poses new challenges in terms of the interpretability of the original variables. 'kpcaIG' aims to provide a tool to select the most relevant variables based on the kernel PCA representation of the data as in Briscik et al. (2023) . It also includes functions for 2D and 3D visualization of the original variables (as arrows) into the kernel principal components axes, highlighting the contribution of the most important ones. Package: r-cran-kpeaks Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kpeaks_1.1.0-1.ca2604.1_all.deb Size: 79224 MD5sum: 96012597862d05b33abe2c5584b613c2 SHA1: 159853e188e20854917dec1a5ba6a2ff85da387a SHA256: 037566c527beef1b61b4be020d30558048c98cd586db311f28ccecc12737f1e2 SHA512: 9a28869775f9bb2c9bab2f1840393b3011d938d667ff8e77a16a9604058c3c567f7393bb74c9ec03b781cddf79bcd44420c20eb83c9da7d66f9c6b134edc9019 Homepage: https://cran.r-project.org/package=kpeaks Description: CRAN Package 'kpeaks' (Determination of K Using Peak Counts of Features for Clustering) The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary, the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set. Package: r-cran-kpiwidget Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crosstalk, r-cran-htmlwidgets Suggests: r-cran-dplyr, r-cran-dt, r-cran-flexdashboard, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kpiwidget_0.1.1-1.ca2604.1_all.deb Size: 130578 MD5sum: adfab726a34f95693f9344e490bf610a SHA1: a82e87b868857a95a85419af0648ff034fb1d7c2 SHA256: 29dc0923a7ed8d24eda5591dbcf9407cd50add3ab96b34e97b0413f6f8f8e35a SHA512: 0f862e2670b19f77c0b57c72cce814833d786e79f181e75b47311743b82330b22558e483dc47a9b7289020ba7a67a9666cc44d97a5d7f3fa24d4cd5b127e1d52 Homepage: https://cran.r-project.org/package=kpiwidget Description: CRAN Package 'kpiwidget' (KPI Widgets for Quarto Dashboards with Crosstalk) Provides an easy way to create interactive KPI (key performance indicator) widgets for 'Quarto' dashboards using 'Crosstalk'. The package enables visualization of key metrics in a structured format, supporting interactive filtering and linking with other 'Crosstalk'-enabled components. Designed for use in 'Quarto' Dashboards. Package: r-cran-kpodclustr Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kpodclustr_1.1-1.ca2604.1_all.deb Size: 26200 MD5sum: ba9f89c4bdf30901e20a16703f9b2ca6 SHA1: 0b17c247f7ab03d23f6cb0060f902d55dbfb62f0 SHA256: 8b25362632d12d315951f2ce6ede3a0c29da23d35f0672317f7d675e093708c3 SHA512: d4d78408a91b719cb85cd3254c588faa1303ae6ee927ae2f213f388229beeb04ef472374c6fb21ac0a7f5108e4cbc47b5249458a9221f79e2defb2784aca7f68 Homepage: https://cran.r-project.org/package=kpodclustr Description: CRAN Package 'kpodclustr' (Method for Clustering Partially Observed Data) Software for k-means clustering of partially observed data from Chi, Chi, and Baraniuk (2016) . 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Package: r-cran-krakenr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr, r-cran-anytime, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-krakenr_1.0.0-1.ca2604.1_all.deb Size: 66646 MD5sum: f95fd1fa7cfe14a8c8b9b761e54ba292 SHA1: a08d378f5fbba374cbeac78b8fe88ab1c6f4178e SHA256: 66b23390e3476fdda15aeeb9ca7e0a741bee2aa105914c3c48be84ed80490eaf SHA512: cd6d6befe8dfdd561163b1a36630e418adf1f93e63e58b2424d6a72090721f753480f16899597a4bd85e4837151f1f5cf3713d59124df6c24c8d8d32cbbcebe0 Homepage: https://cran.r-project.org/package=KrakenR Description: CRAN Package 'KrakenR' (Comprehensive R Interface for Accessing Kraken CryptocurrencyExchange REST API) A comprehensive R interface to access data from the Kraken cryptocurrency exchange REST API . It allows users to retrieve various market data, such as asset information, trading pairs, and price data. The package is designed to facilitate efficient data access for analysis, strategy development, and monitoring of cryptocurrency market trends. Package: r-cran-kraljicmatrix Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kraljicmatrix_0.2.1-1.ca2604.1_all.deb Size: 168798 MD5sum: f1106d0f960121ff5e89e6999e5f6752 SHA1: b6c0d957f20b45e92c86a35f636a32de3f968a79 SHA256: d5cd6e446993ac8d3d4544fc75eb21f1046ac245a9acb82ad161ff969b2ba169 SHA512: 91bbc52a1c74ed2528466093a871129fe665951b5db0e5aada07ee7c006e0f7761d5485c61f2efc28f2f80ed836139222cd6ff5218404af2d77da65691826719 Homepage: https://cran.r-project.org/package=KraljicMatrix Description: CRAN Package 'KraljicMatrix' (A Quantified Implementation of the Kraljic Matrix) Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, ) for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and uses this information to place products and services within the Kraljic Matrix. Package: r-cran-kriens Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kriens_0.1-1.ca2604.1_all.deb Size: 22562 MD5sum: 729f052e0fb271298f6653da98bf648f SHA1: 4069f0ca115cfb34331d9c55f3cb9663d145a11f SHA256: 68f6574c0a3d565ebf69edc1b5138f2b6d957668b25413443b7351207cef5cf1 SHA512: f726f21f95e57fd2a8c6771096b5112da77094f68f126116ce751d1b2de468c34a9231458276ebb514e432b148e1e866d2da2a763eb1173e8015212cc0854481 Homepage: https://cran.r-project.org/package=kriens Description: CRAN Package 'kriens' (Continuation Passing Style Development) Provides basic functions for Continuation-Passing Style development. 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Package: r-cran-krippendorffsalpha Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pbapply, r-cran-spam, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-krippendorffsalpha_2.0-1.ca2604.1_all.deb Size: 407008 MD5sum: c17df98cfb80829c88fa9777b59dd8cf SHA1: 55ebe7637d9a03bee5aa5abb2be2bb572ab309be SHA256: 13e5bac3df5a3e8076c53f025ee118495b735538b538d02c501572d01974f05c SHA512: f2aaf126f95fb250dc70048534f5adc6146344b031f1e080619a76989a0362fa23c9ea2dfc10e8b8d980b225a2592a575fadc07a8db9fcee7ac4a9b07235686b Homepage: https://cran.r-project.org/package=krippendorffsalpha Description: CRAN Package 'krippendorffsalpha' (Measuring Agreement Using Krippendorff's Alpha Coefficient) Provides tools for applying Krippendorff's Alpha methodology . Both the customary methodology and Hughes' methodology are supported, the former being preferred for larger datasets, the latter for smaller datasets. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval estimation can be done in parallel for either methodology. 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Package: r-cran-krls Architecture: all Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-krls_1.1-0-1.ca2604.1_all.deb Size: 81110 MD5sum: 7dd7a237f958dacd27bc07dcaa9dd52c SHA1: 31810aaac4357001a937e241bf72ccaf34255d95 SHA256: f1648b05f38e15a314ac2550c2a9e86ef9d825482f47e8c88781590e20d707d6 SHA512: 01b6cef48b291805c2000bbbbf570186f99549d595a335036d49da27e2fbf8d09632e59e56fa4469b150d7a7a45da8dfbcf0810c40c6c9ff1ccdd2cf9f8b39e3 Homepage: https://cran.r-project.org/package=KRLS Description: CRAN Package 'KRLS' (Kernel-Based Regularized Least Squares) Implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y = f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014, ). Package: r-cran-krmm Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-kernlab, r-cran-cvtools, r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-krmm_1.0-1.ca2604.1_all.deb Size: 49790 MD5sum: a8d511b9a757712aaaafe2dcff7409c4 SHA1: 7a73e88faf160025a1e3d5f682edc14113e75fef SHA256: 55350c2010b6ae4dd77797ebadb09747b6f9f3f37f5c92369c8ab14b62588a45 SHA512: d45b248adcb85a5e6d84c76c2b61ada69c7ba77f4c290b4d807a182fffbccee8d85d3de50364f2f73df455e6a0eaf05ddfe80549d7a57128c464da3ea7e2e4be Homepage: https://cran.r-project.org/package=KRMM Description: CRAN Package 'KRMM' (Kernel Ridge Mixed Model) Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. . Package: r-cran-kronos Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-kronos_1.0.0-1.ca2604.1_all.deb Size: 297512 MD5sum: 01f8eef2a7ebf77784e81df08c52b13f SHA1: 8ee79397fa8b94b2de4a987881dc3491dccd0ad9 SHA256: 0c203415b2549c8750326ab65ac65e4842c58f5db614bb206412408b4aba0994 SHA512: 6ee362dedfea45e0f1bc752fb58c0848f5703d6a54df123bc7d6ec46d9c11c87073b790e8f9367e3f5c96aa8a7f819139e8eea212f439fe553f88622ae026b6a Homepage: https://cran.r-project.org/package=kronos Description: CRAN Package 'kronos' (Microbiome Oriented Circadian Rhythm Analysis Toolkit) The goal of 'kronos' is to provide an easy-to-use framework to analyse circadian or otherwise rhythmic data using the familiar R linear modelling syntax, while taking care of the trigonometry under the hood. 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The user selects "fixed" values for enough unknowns to leave as many variables as there are equations, which in most cases means the system is properly defined and a unique solution exists. The function, the fixed values and initial values for the remaining unknowns are fed to a nonlinear backsolver. The original version of "TK!Solver" , now a product of Universal Technical Systems () was the inspiration for this function. Package: r-cran-kuenm2 Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-enmpa, r-cran-foreach, r-cran-fproc, r-cran-glmnet, r-cran-mgcv, r-cran-mop, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-kuenm2_0.1.3-1.ca2604.1_all.deb Size: 3129944 MD5sum: 1854b556f2f1c2aae3101edbda0f3040 SHA1: ca55835007e48c375615ad04226d1bd17418a166 SHA256: 407a03e45d035cdeb5f001466179deadd4d1ce284131d128d9b4a999b1d2f78a SHA512: 11a3fc201bd9a5f68197136095ed8f94599c6642f47c0f43bd002c09658ede646dcc1c679f8fa4edefeb96875a603fe29cfc621425814dc3d51f3ce53dec61fb Homepage: https://cran.r-project.org/package=kuenm2 Description: CRAN Package 'kuenm2' (Detailed Development of Ecological Niche Models) A new set of tools to help with the development of detailed ecological niche models using multiple algorithms. Pre-modeling analyses and explorations can be done to prepare data. Model calibration (model selection) can be done by creating and testing models with several parameter combinations. Handy options for producing final models with transfers are included. Other tools to assess extrapolation risks and variability in model transfers are also available. Methodological and theoretical basis for the methods implemented here can be found in: Peterson et al. (2011) , Radosavljevic and Anderson (2014) , Peterson et al. (2018) , Cobos et al. (2019) , Alkishe et al. (2020) , Machado-Stredel et al. (2021) , Arias-Giraldo and Cobos (2024) , Cobos et al. (2024) . Package: r-cran-kuiper.2samp Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kuiper.2samp_1.0-1.ca2604.1_all.deb Size: 16084 MD5sum: e87fa456bd153badbee92122417bc0d0 SHA1: b46936d232e8f5d5ab2b54fc0bb74a7692af7231 SHA256: 9d6048827f7f4adfd63b41b6fd523deec6ce7a9e9faba98fdeb34b17caba1701 SHA512: ac33a4e90c877cb5de74e02a915799b943116cd6adbf774d74fa4918fdf9d06f6b85a758d36dd713fbaa1646a0f767632fa30f638508d44770e5ef2765d012b3 Homepage: https://cran.r-project.org/package=kuiper.2samp Description: CRAN Package 'kuiper.2samp' (Two-Sample Kuiper Test) This function performs the two-sample Kuiper test to assess the anomaly of continuous, one-dimensional probability distributions. References used for this method are (1). Kuiper, N. H. (1960). and (2). Paltani, S. (2004). . Package: r-cran-kurt Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom, r-cran-expm Filename: pool/dists/resolute/main/r-cran-kurt_1.1-1.ca2604.1_all.deb Size: 54834 MD5sum: ed0076553d8c25307fdcb51a52cc1acd SHA1: e88c41909c6c938f634eec9644530d6c01e305da SHA256: 141252ea51688133dfcff13afcb5fe8590f83522e4f15bfc42dd553ac55fb749 SHA512: 3439cd0e4026dde5c0b8febd8732032728430e1a3cdd568caf93c043b8710334d10bf3cf98ef9e32a168b42bf306a7adff2bb274ab4210934cc7c4f71a5f3cd1 Homepage: https://cran.r-project.org/package=Kurt Description: CRAN Package 'Kurt' (Performs Kurtosis-Based Statistical Analyses) Computes measures of multivariate kurtosis, matrices of fourth-order moments and cumulants, kurtosis-based projection pursuit. Franceschini, C. and Loperfido, N. (2018, ISBN:978-3-319-73905-2). "An Algorithm for Finding Projections with Extreme Kurtosis". Loperfido, N. (2017,ISSN:0024-3795). "A New Kurtosis Matrix, with Statistical Applications". Package: r-cran-kutils Architecture: all Version: 1.73-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-xtable, r-cran-plyr, r-cran-openxlsx, r-cran-runit Suggests: r-cran-rockchalk Filename: pool/dists/resolute/main/r-cran-kutils_1.73-1.ca2604.1_all.deb Size: 726280 MD5sum: f1624755fad9ddd16d12c06c4781e8af SHA1: 64800cb64c4fcd2bd601adcb52a30147a9965906 SHA256: f53e6408b667e6dd8ad8fe3f60fb85eba8f9b881c09ca8da129ad74303274a92 SHA512: 05e104c8957fecd3641d73ccd090a21fd6158993eb736f0999ec2f93dd44e8e0f8afdc49fdcebd7de2420dc0d08ec33db9b2df91ca5f3bda7c9954720c193bc3 Homepage: https://cran.r-project.org/package=kutils Description: CRAN Package 'kutils' (Project Management Tools) Tools for data importation, recoding, and inspection. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame. The main feature here is the systematic implementation of the "variable key" framework for data importation and recoding. We are eager to have community feedback about the variable key and the vignette about it. In version 1.7, the function 'semTable' is removed. It was deprecated since 1.67. That is provided in a separate package, 'semTable'. Package: r-cran-kuzco Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ellmer, r-cran-gt, r-cran-gtextras, r-cran-imager, r-cran-jsonlite, r-cran-magick, r-cran-ollamar, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-quarto, r-cran-shiny, r-cran-bslib, r-cran-rmarkdown, r-cran-purrr, r-cran-mirai, r-cran-tibble, r-cran-tictoc, r-cran-httr Filename: pool/dists/resolute/main/r-cran-kuzco_0.1.0-1.ca2604.1_all.deb Size: 991738 MD5sum: f8a1b5ef03d92148eddba7cc9806e0a2 SHA1: 6c846a8c741ea652583f13a0c6d01a8177561224 SHA256: 95fc3856056bc4cde62a70c88d9dbdafafaafbab818d33557c1d37b634e32d30 SHA512: f587b26d16fd5b94f7cfbf0f7faf2d411cf237bde90c2919844d7dcf1bf0237d420ddba4667bdaaf825fc91540e6db66ae8b9df95c791a03f4be3e31ded66083 Homepage: https://cran.r-project.org/package=kuzco Description: CRAN Package 'kuzco' (Computer Vision with Large Language Models) Make computer vision tasks approachable in R by leveraging Large Language Models. Providing fine-tuned prompts, boilerplate functions, and input/output helpers for common computer vision workflows, such as classifying and describing images. Functions are designed to take images as input and return structured data, helping users build practical applications with minimal code. Package: r-cran-kuzur Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2711 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-igraph, r-cran-tibble, r-cran-tidygraph Suggests: r-cran-g6r, r-cran-jsonlite, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-arrow Filename: pool/dists/resolute/main/r-cran-kuzur_0.2.3-1.ca2604.1_all.deb Size: 1741192 MD5sum: 9bc791fad95077f8059e3e1dcdc26a13 SHA1: 3f14c0bc43adb6fee39c1cbfc7975101ae1859aa SHA256: f79228507174b5fa9397cd068d3737e4c450ccff16d0e827c6215be4df37117e SHA512: 4be7dc67ba7019e07ab45f757a6642b0c69dfc7e5ae295428c612d0644ecda22addd1dbf9702081abcc4854362f685e8fb54a6ffc707e1e144f776c568056c08 Homepage: https://cran.r-project.org/package=kuzuR Description: CRAN Package 'kuzuR' (Interface to 'kuzu' Graph Database) Provides a high-performance 'R' interface to the 'kuzu' graph database. It uses the 'reticulate' package to wrap the official 'Python' client ('kuzu', 'pandas', and 'networkx'), allowing users to interact with 'kuzu' seamlessly from within 'R'. Key features include managing database connections, executing 'Cypher' queries, and efficiently loading data from 'R' data frames. It also provides seamless integration with the 'R' ecosystem by converting query results directly into popular 'R' data structures, including 'tibble', 'igraph', 'tidygraph', and 'g6R' objects, making 'kuzu's powerful graph computation capabilities readily available for data analysis and visualization workflows in 'R'. The 'kuzu' documentation can be found at . Package: r-cran-kvkapir Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-lifecycle, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-kvkapir_0.1.2-1.ca2604.1_all.deb Size: 116270 MD5sum: 630eacf40a600b8c4bc8a3639e75923c SHA1: 28df9d33ddcd707e848a0d258cd6ec5ae8cc6532 SHA256: 9e36b0e20bef9d242b42fd950f4ad34b7f9dde265c97cf8fac9614de745ac784 SHA512: d43e77ec63155105975dd475424c7fee4d48f852b82ab730a5167d770061d30e3d6f81ad63ba6990a11359b4854e86ac4db51698b83e381598b89cfdc9fcab8e Homepage: https://cran.r-project.org/package=kvkapiR Description: CRAN Package 'kvkapiR' (Interface to the Dutch Chamber of Commerce (KvK) API) Access business registration data from the Dutch Chamber of Commerce (Kamer van Koophandel, KvK) through their official API . Search for companies by name, location, or registration number. Retrieve detailed business profiles, establishment information, and company name histories. Built on 'httr2' for robust API interaction with automatic pagination, error handling, and usage tracking. Package: r-cran-kvr2 Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-insight, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kvr2_0.2.0-1.ca2604.1_all.deb Size: 187034 MD5sum: c043d2b9a187bb8c6e0b84942c86bb4f SHA1: ef57c59b147cafca107b3133dc746646205b3567 SHA256: 6026c1756e868182c9b276e262277c775db8a1736757e5551cd016927904f705 SHA512: d8fafa16cf4c4393a5188746b3815549d3099952c268bee83bd55236c260a5481a47e770687004898d67705df7c207c108c14706bb2c8b605125cfcd5c1fe283 Homepage: https://cran.r-project.org/package=kvr2 Description: CRAN Package 'kvr2' (Calculate and Compare Multiple Definitions of Coefficient ofDetermination) Calculate nine types of coefficients of determination (R-squared) based on the classification by Kvalseth (1985) . This package is designed for educational purposes to demonstrate how R-squared values can fluctuate depending on the choice of formula, particularly in power regression models or linear models without an intercept. By providing a comprehensive list of definitions, it helps users understand the mathematical sensitivity of goodness-of-fit indices. Package: r-cran-kwela Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kwela_1.0.0-1.ca2604.1_all.deb Size: 139408 MD5sum: b415fe3d85843da929c6aaa768f72c2b SHA1: 8931f1bc6f837f4297f51b70e34f367ade013ec9 SHA256: 2d448da3696b5cb52c0d8466a08f8e9327fada197ea24108ae3418998baa3d9d SHA512: 7ae53072570b96eb97683a63f986768b2a48688a850a7b46911985478b52d21f3365f994917a7d1d6941a970a7ede09f992ddd25dc03cd7bc6a4c4fe8aea0e32 Homepage: https://cran.r-project.org/package=KWELA Description: CRAN Package 'KWELA' (Hierarchical Adaptive 'RT-QuIC' Classification for ComplexMatrices) Extends 'RT-QuIC' (Real-Time Quaking-Induced Conversion) statistical analysis to complex environmental matrices through hierarchical adaptive classification. 'KWELA' is named after a deity of the Fore people of Papua New Guinea, among whom Kuru, a notable human prion disease, was identified. Implements a 6-layer architecture: hard gate biological constraints, per-well adaptive scoring, separation-aware combination, Youden-optimized cutoffs, replicate consensus, and matrix instability detection. Features dual-mode operation (diagnostic/research), auto-profile selection (Standard/Sensitive/Matrix-Robust), RAF integration for artifact detection, matrix-aware baseline correction, and multiple consensus rules. Methods include energy distance (Szekely and Rizzo (2013) ), CRPS (Gneiting and Raftery (2007) ), SSMD (Zhang (2007) ), and Jensen-Shannon divergence (Lin (1991) ). This package implements methodology currently under peer review; please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems ('Anthropic Claude' and 'OpenAI GPT') served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies. Package: r-cran-kzs Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3463 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-kzs_1.4.1-1.ca2604.1_all.deb Size: 3491924 MD5sum: a95a444db2d91e410d9e7b1c61edb9d7 SHA1: 76b108c92aca5a915bfd53bcc303821f26196a3a SHA256: 172f3eba88387a634eecb518f8024aa40e40ee302b95f3533fa13c6eaf86d910 SHA512: edff55f0a6c336bb4d2e4773a2414e65b1f49394f7083da9ce610676de477f41f5c3fb5a8d046b578d37a5e5a74344fb754ec9e5636147c930be4f1ea30da86a Homepage: https://cran.r-project.org/package=kzs Description: CRAN Package 'kzs' (Kolmogorov-Zurbenko Spatial Smoothing and Applications) A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise. Package: r-cran-l0cpt Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-l0cpt_0.2.0-1.ca2604.1_all.deb Size: 60446 MD5sum: 3bafef63001a879b22053ab618779c27 SHA1: 987c4cb6135c496e40b9c3b43dee0b6340a6762a SHA256: 6baab70d4706251e5193cd250502123d34bb2a297a9e0f45067592f6972c4653 SHA512: 163baf9268ecb75067658edbb724c2a84156ccf51bd241b17a38f10f412f44efb39b5a417c60c079ab261318445949cae8ae22fc1815ccf8f5d102e496eebe65 Homepage: https://cran.r-project.org/package=L0cpt Description: CRAN Package 'L0cpt' (Change Point Detection with L0 Penalty) Under an L0 penalty framework, a computationally efficient implementation of change point detection is developed. By integrating active set algorithms with warm start initialization, the package achieves linear-time complexity for solving change point detection problems. References: Wen et al. (2020) ; Zhu et al. (2020). Package: r-cran-l0tfinv Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-l0tfinv_0.1.0-1.ca2604.1_all.deb Size: 462328 MD5sum: e0d217ea305d5d8bdb1635429382550b SHA1: 41808fc4e22533e9d4cd1f7af621a7e8daf8ae92 SHA256: 73c4fe8f16dca303849bac24843d000e505a779d01b88fa96aea40ae4ff29263 SHA512: 659db4b62d55bad798ce593ba3bc8d7fc3efccf0cdb80f044e13a4bfc6419136d1d5f46b75ba1c4dff5086a3e14a29a78cce182ed66312c87cb1ae7909cc044e Homepage: https://cran.r-project.org/package=L0TFinv Description: CRAN Package 'L0TFinv' (A Splicing Approach to the Inverse Problem of L0 Trend Filtering) Trend filtering is a widely used nonparametric method for knot detection. This package provides an efficient solution for L0 trend filtering, avoiding the traditional methods of using Lagrange duality or Alternating Direction Method of Multipliers algorithms. It employ a splicing approach that minimizes L0-regularized sparse approximation by transforming the L0 trend filtering problem. The package excels in both efficiency and accuracy of trend estimation and changepoint detection in segmented functions. References: Wen et al. (2020) ; Zhu et al. (2020); Wen et al. (2023) . Package: r-cran-l1ball Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-l1ball_0.1.0-1.ca2604.1_all.deb Size: 33822 MD5sum: 1988882d71cfc722458180fb3d7ae8c3 SHA1: 77b13b45dbeeb3efd9eb86aa86d44b4d6a8dce9a SHA256: 3ae7c19ade9fdf7ceb11598f112585d3c58d1533565f15e07686e993f2e207ee SHA512: 4280450172c1c4a3619ad61c4e41d3a5494e6f7706bd4ca1d06646b0a084efcbae4c49f4c6e436ab7c73245ca8e3ef71ddfba881dc6ace5a803090fa1244af24 Homepage: https://cran.r-project.org/package=l1ball Description: CRAN Package 'l1ball' (L1-Ball Prior for Sparse Regression) Provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) . Package: r-cran-l1kdeconv Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mixtools, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-l1kdeconv_1.2.0-1.ca2604.1_all.deb Size: 40338 MD5sum: f7f02b81d7fe343ea7262676f38cd314 SHA1: 8e04b41184afc19a7c15839857992d98de4ff333 SHA256: fa4dfa25255dde409d45547306e5c6010d62c9ef37b6c7a2bf57ce6ce492ba8a SHA512: ed1cc25955d24048173c6f9656aacc4fb3483a98503ae975b41af3cf379d34f30b245a00990c280f659048b42ee49d7e0ce2c92704fac0efe4c1db60f5048232 Homepage: https://cran.r-project.org/package=l1kdeconv Description: CRAN Package 'l1kdeconv' (Deconvolution for LINCS L1000 Data) LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution. Package: r-cran-l1rotation Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-matrixstats, r-cran-pracma, r-cran-scales Suggests: r-cran-knitr, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-l1rotation_1.0.1-1.ca2604.1_all.deb Size: 467106 MD5sum: 9b418de67969679fc59da83748f6c703 SHA1: 16d9381e374fbd9b679551f0e716784ed3596a3c SHA256: 2b2522e2d03d20b4c68654c1bb15ab71c81ce9374233580cfdfef9a344d495f0 SHA512: bcc29e92250a753d5e92a5f6dd14bd17e84f5a1c7165fc1c204748b25f1c8181eaecfd7955c6c18f5c4f4c5e950c0ed5a8f57f43f57c342c8a2b9be6d7ccba6b Homepage: https://cran.r-project.org/package=l1rotation Description: CRAN Package 'l1rotation' (Identify Loading Vectors under Sparsity in Factor Models) Simplify the loading matrix in factor models using the l1 criterion as proposed in Freyaldenhoven (2025) . Given a data matrix, find the rotation of the loading matrix with the smallest l1-norm and/or test for the presence of local factors with main function local_factors(). 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Package: r-cran-labourmarketareas Architecture: all Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2099 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-sf, r-cran-data.table, r-cran-spdep, r-cran-tmap Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-labourmarketareas_3.4-1.ca2604.1_all.deb Size: 1209070 MD5sum: eb13acb7eb484de1fe3dda0fc7c6f293 SHA1: 0ff18cc9d221c3f2597728c0d24d7b375adc150a SHA256: 9b96b294b25e1f72ac8c8b12e3560091cca71e8b989fc28bf9e5e9049f89f6cb SHA512: 7ed3e506238eb49c0adc4906f55ef2b9ebf97ba7fbe052f210de60339e0be7a3282faee2022141254fbbf67b0132abb0a55b5e9f5af9d8eed069010db9dd7d53 Homepage: https://cran.r-project.org/package=LabourMarketAreas Description: CRAN Package 'LabourMarketAreas' (Identification, Tuning, Visualisation and Analysis of LabourMarket Areas) Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography. Package: r-cran-labrs Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-rcmdr Filename: pool/dists/resolute/main/r-cran-labrs_0.2.1-1.ca2604.1_all.deb Size: 351806 MD5sum: 76be09f5b06c9e0d7340a47ac4c515ff SHA1: 04e2a30fd8528607e3cf53db7b214ca329266414 SHA256: 7f2342e8f5d2f06d00008881524e3970d9bad967d1efccc7b899920991dd5408 SHA512: ff5840bc888417b6aad751d6e4ad6308ad2653bf00782de547035823bec71ab403bef3c723afe0d114da1643643eee8c72897084042602eeb6554106cc3ca537 Homepage: https://cran.r-project.org/package=LabRS Description: CRAN Package 'LabRS' (Laboratorio di Ricerca Sociale con R) Libreria di dati, scripts e funzioni che accompagna il libro "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale". Package: r-cran-labsimplex Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scatterplot3d, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-frf2 Filename: pool/dists/resolute/main/r-cran-labsimplex_0.1.2-1.ca2604.1_all.deb Size: 2831610 MD5sum: b76ae29edc6e3f21694bd44f60957065 SHA1: 4f845d5232acf6f650c53a60aebc026a067d38a4 SHA256: 4dc38a3818226e147ef1ddb6a5459690fc6cb7f37c3b227e6cd1a1a3d81d562b SHA512: 0808dc9bae8e33f8824bc4648576ed572d92a3be9741756b53a36f69892ef4147ab98dcfd9773da637f8116db68d5e8db3a96a12067c3cf25bfd96f83d1c401f Homepage: https://cran.r-project.org/package=labsimplex Description: CRAN Package 'labsimplex' (Simplex Optimization Algorithms for Laboratory and ManufacturingProcesses) Simplex optimization algorithms as firstly proposed by Spendley et al. (1962) and later modified by Nelder and Mead (1965) for laboratory and manufacturing processes. The package also provides tools for graphical representation of the simplexes and some example response surfaces that are useful in illustrating the optimization process. Package: r-cran-labstatr Architecture: all Version: 1.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-labstatr_1.0.13-1.ca2604.1_all.deb Size: 187452 MD5sum: 85b154fed4d72d7bcb9f2ddbf593c6a9 SHA1: 103e82cb820247f026efcb131eb4ed3e81524153 SHA256: 0ff9e00be413b6bca3f1ab477c60ea672f99520ca043294f6981093fd46e22ec SHA512: ef818978e42b84154b437427b158fa172b3e39c7fc8b9b927fc982fa16f229029ca069ffb351694111db088d102b9518f549e26666d79f5e77e58c69001dc3ef Homepage: https://cran.r-project.org/package=labstatR Description: CRAN Package 'labstatR' (Libreria Del Laboratorio Di Statistica Con R) Insieme di funzioni di supporto al volume "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. This package contains sets of functions defined in "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. Function names and docs are in italian as well. Package: r-cran-labstats Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-labstats_1.0.1-1.ca2604.1_all.deb Size: 83270 MD5sum: 827cc524be3f1b8b13628ddd67abadc3 SHA1: 21743a00878f6a5cb01312273f47308296a3244d SHA256: bd496277c05ea8b4ef148d5920da8d04b1c0092b5d6b1987ca2dc89f0a1411e5 SHA512: 8d0356fbbce54cb310b813e31b4494da62729db49b7dcbfa11ef5ef561e02f9336be0fb042bd7f9d4506ef88b80c726befae9f202c2902b69a8e8f9cca5dd6a0 Homepage: https://cran.r-project.org/package=labstats Description: CRAN Package 'labstats' (Data Sets for the Book "Experimental Design for LaboratoryBiologists") Contains data sets to accompany the book: Lazic SE (2016). "Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility". Cambridge University Press. Package: r-cran-lacrmr Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-janitor, r-cran-sjmisc, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-httptest, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-lacrmr_1.0.5-1.ca2604.1_all.deb Size: 230610 MD5sum: bb8918b76a69cc44a6c70ff3f170bd60 SHA1: 0c8589927e9eeea2feacf53387f911f155f4538e SHA256: 46c23c21af96ee9be7796a7954b434f3a9ed5a85e3e3f3485d98ee076cdbfe3c SHA512: a610e4498c8e57fad197e8147d5c674c6b9b5dd5ad89cfdb92ab98af0b801a3c641c599b66ba4a37e65c90e4f10cd6d1ea3642b20442f3d4bed6a2e16d956a29 Homepage: https://cran.r-project.org/package=lacrmr Description: CRAN Package 'lacrmr' (Connect to the 'Less Annoying CRM' API) Connect to the 'Less Annoying CRM' API with ease to get your crm data in a clean and tidy format. 'Less Annoying CRM' is a simple CRM built for small businesses, more information is available on their website . Package: r-cran-lactater Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-ggtext, r-cran-patchwork, r-cran-lubridate, r-cran-minpack.lm, r-cran-pracma, r-cran-rlang, r-cran-segmented, r-cran-stringr, r-cran-tidyr, r-cran-forcats Suggests: r-cran-bsplus, r-cran-covr, r-cran-datapasta, r-cran-glue, r-cran-knitr, r-cran-miniui, r-cran-rhandsontable, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lactater_0.2.0-1.ca2604.1_all.deb Size: 270960 MD5sum: 003d0eabe60eb9c77ef344b73d254954 SHA1: a42bcb12ab6e47f6b9897d3ba14c29f343df40f0 SHA256: 4f3f481813264bc4040af7b3dfbd1e7f6c418a73d44613c9540c9741056f1495 SHA512: a5b85ffe79283cd82edcdc8147f533423017fd9523f5e076e999e8ede8e8a7403b151eabeb873575538614d59bc89ea88ef57385f1259e4374fe092ed827e9ae Homepage: https://cran.r-project.org/package=lactater Description: CRAN Package 'lactater' (Tools for Analyzing Lactate Thresholds) Set of tools for analyzing lactate thresholds from a step incremental test to exhaustion. Easily analyze the methods Log-log, Onset of Blood Lactate Accumulation (OBLA), Baseline plus (Bsln+), Dmax, Lactate Turning Point (LTP), and Lactate / Intensity ratio (LTratio) in cycling, running, or swimming. Beaver WL, Wasserman K, Whipp BJ (1985) . Heck H, Mader A, Hess G, Mücke S, Müller R, Hollmann W (1985) . Kindermann W, Simon G, Keul J (1979) . Skinner JS, Mclellan TH (1980) . Berg A, Jakob E, Lehmann M, Dickhuth HH, Huber G, Keul J (1990) PMID 2408033. Zoladz JA, Rademaker AC, Sargeant AJ (1995) . Cheng B, Kuipers H, Snyder A, Keizer H, Jeukendrup A, Hesselink M (1992) . Bishop D, Jenkins DG, Mackinnon LT (1998) . Hughson RL, Weisiger KH, Swanson GD (1987) . Jamnick NA, Botella J, Pyne DB, Bishop DJ (2018) . Hofmann P, Tschakert G (2017) . Hofmann P, Pokan R, von Duvillard SP, Seibert FJ, Zweiker R, Schmid P (1997) . Pokan R, Hofmann P, Von Duvillard SP, et al. (1997) . Dickhuth H-H, Yin L, Niess A, et al. (1999) . Package: r-cran-lactcurvemodels Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-lactcurvemodels_0.1.5-1.ca2604.1_all.deb Size: 137542 MD5sum: 33cbf5cd203ff369e7bad1b0f2afb351 SHA1: 14a53688087f333a60686d019e7c675d4080e32c SHA256: e5280a4ffc0ff61610a47a8284800ee15ae9acf96634b2de626446763f95c186 SHA512: 4f33956ed745ef9f7b3531ca7f8da50ad82bbb8bf9db68ad221a0dfb064f5d6efda775e14d61826b22eec6c64ff26c4c9ff57f2090ff6c8800dee0181a1a2f8f Homepage: https://cran.r-project.org/package=LactCurveModels Description: CRAN Package 'LactCurveModels' (Lactation Curve Model Fitting for Dairy Animals) Fits up to 20 nonlinear lactation curve models to dairy animal milk yield data. Models fitted include exponential, polynomial, mixed logarithmic, inverse polynomial, and sigmoid families published between 1923 and 2000. Supports batch processing of multiple animals from a single CSV file, with flexible selection of animals and models. Produces per-animal parameter tables, goodness-of-fit metrics including R-squared (R2), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and a serial autocorrelation statistic, 15 diagnostic figures, and combined cross-animal comparison outputs. References: , , , , . Package: r-cran-lacunaritycovariance Architecture: all Version: 1.1-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-rcpproll, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.random Suggests: r-cran-cubature, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lacunaritycovariance_1.1-9-1.ca2604.1_all.deb Size: 440016 MD5sum: f73dd87a95591bcc6a33f84150d3a66f SHA1: 786ce486acdda5702e5e35038d4f7685c7bcc3ca SHA256: 8f83114791668cd0362094c6415881c4174388f0e36939e1cfad9e12ac5770cc SHA512: 1e7283a9a218ed0541f98ee284b1778752bb12a5619f927dbc219e83a87613af472b9302ca062cad1d3bab767caa8a1c3fd77f90e470b31bda78a4659463ce1f Homepage: https://cran.r-project.org/package=lacunaritycovariance Description: CRAN Package 'lacunaritycovariance' (Gliding Box Lacunarity and Other Metrics for 2D Random ClosedSets) Functions for estimating the gliding box lacunarity (GBL), covariance, and pair-correlation of a random closed set (RACS) in 2D from a binary coverage map (e.g. presence-absence land cover maps). Contains a number of newly-developed covariance-based estimators of GBL (Hingee et al., 2019) and balanced estimators, proposed by Picka (2000) , for covariance, centred covariance, and pair-correlation. Also contains methods for estimating contagion-like properties of RACS and simulating 2D Boolean models. Binary coverage maps are usually represented as raster images with pixel values of TRUE, FALSE or NA, with NA representing unobserved pixels. A demo for extracting such a binary map from a geospatial data format is provided. Binary maps may also be represented using polygonal sets as the foreground, however for most computations such maps are converted into raster images. The package is based on research conducted during the author's PhD studies. Package: r-cran-lad Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-lad_0.1.0-1.ca2604.1_all.deb Size: 1793894 MD5sum: d6addb94298a29ad306aee60e9d547f4 SHA1: e511d61afce85baaa4662ce02cd34386a2541d5a SHA256: 9fabcd93c5aa2d51e7d5c85b4fd8e81a7fccf24ac0c2fb11732a5259bdd35ddf SHA512: 8cd0f5494278553a62d0fe3e27a0d408cb163759a8b54ee7fd63355a344382ac99becfea66b6b88dbfa23f031ed049aa40c1c04b04e047cd43a6d65bd3b073ec Homepage: https://cran.r-project.org/package=LAD Description: CRAN Package 'LAD' (Derive Leaf Angle Distribution (LAD) from Measured LeafInclination Angles) Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) . 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Package: r-cran-ladderfuelsr Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1788 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gdata, r-cran-ggplot2, r-cran-magrittr, r-cran-segmented, r-cran-stringr, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ladderfuelsr_0.0.7-1.ca2604.1_all.deb Size: 717712 MD5sum: fa5f74cc8bd2f6a0b83484034c97ea8c SHA1: 2844ee2f3d8788db47fcd3e4e09082dd1d3ce0fd SHA256: d55662195059791db192ddeb5db4bd2170b5baff31a2aced14f37e499870d13a SHA512: af6a7d7f463ce9b88d4d67fb8a6eb3f14ef00c2796a634d3e37c5ab05b9613d6f6855f2b4565f699f88afc91888fac9e4f06957c9ea09c840d3eb09981cd3642 Homepage: https://cran.r-project.org/package=LadderFuelsR Description: CRAN Package 'LadderFuelsR' (Automated Tool for Vertical Fuel Continuity Analysis usingAirborne Laser Scanning Data) Set of tools for analyzing vertical fuel continuity at the tree level using Airborne Laser Scanning data. The workflow consisted of: 1) calculating the vertical height profiles of each segmented tree; 2) identifying gaps and fuel layers; 3) estimating the distance between fuel layers; and 4) retrieving the fuel layers base height and depth. Additionally, other functions recalculate previous metrics after considering distances greater than certain threshold. Moreover, the package calculates: i) the percentage of Leaf Area Density comprised in each fuel layer, ii) remove fuel layers with Leaf Area Density (LAD) percentage less than 10, and iii) recalculate the distances among the reminder ones. On the other hand, it identifies the crown base height (CBH) based on different criteria: the fuel layer with the highest LAD percentage and the fuel layers located at the largest- and at the last-distance. When there is only one fuel layer, it also identifies the CBH performing a segmented linear regression (breaking points) on the cumulative sum of LAD as a function of height. Finally, a collection of plotting functions is developed to represent: i) the initial gaps and fuel layers; ii) the fuels base height, depths and gaps with distances greater than certain threshold and, iii) the CBH based on different criteria. The methods implemented in this package are original and have not been published elsewhere. Package: r-cran-laeken Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-mass Filename: pool/dists/resolute/main/r-cran-laeken_0.5.3-1.ca2604.1_all.deb Size: 3102812 MD5sum: 3249b38f2caf4c8fb7af6e32ef4b4175 SHA1: f4a7b8b56af18a3e56e8f2e5a5f80c3a3ee44f62 SHA256: eea26f65c2eb484524e51aa3ce016a9172c3bdf81f2135fa8c58e63899462990 SHA512: ca518163ed09804f5a09e068745c7a7791a82dbf6d17a68ea2f6325d2fa22f5b9d8ce2af4828a0e593181385c348002f56428a3e0a0b0062933af60d6bcb45f5 Homepage: https://cran.r-project.org/package=laeken Description: CRAN Package 'laeken' (Estimation of Indicators on Social Exclusion and Poverty) Estimation of indicators on social exclusion and poverty, as well as Pareto tail modeling for empirical income distributions. 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Package: r-cran-lagosne Architecture: all Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1864 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rappdirs, r-cran-purrr, r-cran-magrittr, r-cran-sf, r-cran-curl, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-memoise, r-cran-rlang, r-cran-progress, r-cran-qs2, r-cran-xml2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-r.rsp, r-cran-printr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-lagosne_2.0.5-1.ca2604.1_all.deb Size: 568746 MD5sum: ec2afd064a8990b0176c66c52bcf17fb SHA1: 3cafd622012eff3016aae1341eaabf7d5b41e489 SHA256: e4d773871da7a78045469ec06275d6f07f6108e611a076d8f57ba99b9a10a2a5 SHA512: 2b1052790cf898d5a8720653a2ed8ad37d9f689ba768f74bfdd47b9da4fead7b8225b92760b79819fb1cc4ff947103a21d6ab571cb434b2694cd9b2231373dc4 Homepage: https://cran.r-project.org/package=LAGOSNE Description: CRAN Package 'LAGOSNE' (Interface to the Lake Multi-Scaled Geospatial and TemporalDatabase) Client for programmatic access to the Lake Multi-scaled Geospatial and Temporal database , with functions for accessing lake water quality and ecological context data for the US. Package: r-cran-lagsarlmtree Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-spatialreg, r-cran-formula Suggests: r-cran-strucchange Filename: pool/dists/resolute/main/r-cran-lagsarlmtree_1.0-2-1.ca2604.1_all.deb Size: 8655850 MD5sum: 60ebd24553502924bfe61efd038738fe SHA1: c7035ff3eb246b18fa504a3239dd350296803490 SHA256: 6dc5d06e10fc9e9882047c6cc5839581cd6093bd5002bb186bf5ea010f8a3932 SHA512: 132a9c173912e8a1b34acfda5fdddb3fa0ef9cf6ea7f654bda4c3f034e37cd5a9ecb747e8d94a647840d7a3ca305c60a5897646ef5fd2ce2e4bdc08b27355ed6 Homepage: https://cran.r-project.org/package=lagsarlmtree Description: CRAN Package 'lagsarlmtree' (Spatial Lag Model Trees) Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag, Wagner and Zeileis (2019) . Package: r-cran-lagsequential Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-lagsequential_0.1.1-1.ca2604.1_all.deb Size: 151724 MD5sum: a9c97e33bc06d78756112e850435a392 SHA1: 8fa35fd4c363cb7d876faedd69acbe919b302c65 SHA256: 0c80e81bd718566986a10afa337f632e1ef26f7871a3e6c7a5530ab37d6ca392 SHA512: b0c27e8bbbaa85609797588609899e2dd3906e9a557b64508f840096acd398b8ed6c710f331678abe4dfcf1927178964a0949ae5188987768f58ac6a6ab8dfd7 Homepage: https://cran.r-project.org/package=LagSequential Description: CRAN Package 'LagSequential' (Lag-Sequential Categorical Data Analysis) Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) , O'Connor (1999) , Wampold & Margolin (1982) , and Wampold (1995, ISBN:0-89391-919-5). Package: r-cran-lahman Architecture: all Version: 14.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7054 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-lattice, r-cran-ggplot2, r-cran-googlevis, r-cran-data.table, r-cran-vcd, r-cran-reshape2, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-plyr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-lahman_14.0-0-1.ca2604.1_all.deb Size: 6845874 MD5sum: 22586a007e1ea3b597697bafb181279a SHA1: b2b3b7da2a4d19b5e1727caed541efaaec32d681 SHA256: 9359635f68ae29636761301f08c5aa6885e847068cddb3e5ae2ed38e2319b6e4 SHA512: b9b0dcd0a64c02deeb0ab12ec6b70f6f6d20fc6ec8a31061654170933737bc67a9552b1db4992933b5c316437535626db5f5e9dea84051b27783a86f307080b1 Homepage: https://cran.r-project.org/package=Lahman Description: CRAN Package 'Lahman' (Sean 'Lahman' Baseball Database) Provides the tables from the 'Sean Lahman Baseball Database' as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2024, as recorded in the 2025 version of the database. Documentation examples show how many baseball questions can be investigated. Package: r-cran-lair Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-terra Filename: pool/dists/resolute/main/r-cran-lair_0.3.0-1.ca2604.1_all.deb Size: 647836 MD5sum: 032c79bf1de08b6986c0e148a5181e1e SHA1: 3f346f6781684917a8dc488f457f9b84e351d663 SHA256: 8efcd4a143c97e5437e535932277d7aba2594ff4479863563fd93033980af6eb SHA512: 61d4a048ec31cb0ddcbaedd82f5882f8f8089cee06358ace179b60c0df3a006dba8908c453b7199ba42466839c398a7e3463c0f6162983c84acf67a60083d942 Homepage: https://cran.r-project.org/package=LAIr Description: CRAN Package 'LAIr' (Converting NDVI to LAI of Field, Proximal and Satellite Data) Convert Leaf Area Index (LAI) from the Normalized Difference Vegetation Index (NDVI) using available equations from literature. Detailed description of conversion equations in Bajocco et al. 2022 . Package: r-cran-lakefetch Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-osmdata, r-cran-ggplot2 Suggests: r-cran-nhdplustools, r-cran-jsonlite, r-cran-shiny, r-cran-leaflet, r-cran-base64enc, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lakefetch_0.1.3-1.ca2604.1_all.deb Size: 307572 MD5sum: 36955a520af8b0a00e891d0fb154df25 SHA1: b2ecd9fb06314c843a061c600bec03d9cb2bb43c SHA256: c883be42face9dd5675009d0223ed6ceeead99bc3d603e09c693152f9fc3731f SHA512: 9a0685e1ff0732c4aec9af67c08a448831ed6c49e16ad11965bbe9316394c5dcd5d4bc56059068231a9347e76b593a4089f774084da40a2fe14c19a95b5eda35 Homepage: https://cran.r-project.org/package=lakefetch Description: CRAN Package 'lakefetch' (Calculate Fetch and Wave Exposure for Lake Sampling Points) Calculates fetch (open water distance) and wave exposure metrics for lake sampling points. Downloads lake boundaries from 'OpenStreetMap', calculates directional fetch using a ray-casting approach, and optionally integrates National Hydrography Dataset ('NHD') data for hydrological context including outlet and inlet locations. Can estimate lake depth from surface area using empirical relationships, and integrate historical weather data for cumulative wave energy calculations. Includes an optional interactive 'shiny' application for visualization. Package: r-cran-lakemorpho Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-geosphere, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-lakemorpho_1.3.2-1.ca2604.1_all.deb Size: 421782 MD5sum: 7f247bdc975e6e11114cfa048b445c15 SHA1: 6dc38fc6eadcdf4a22f8656bbbcb02be5ecc2abc SHA256: 35039b40f9bec6e5b6aba0ff8438db844df554ad026aab4c5896686b9f52427e SHA512: ccd8a2824c626780830aeb8c4986b2431643595a7ff1b53063f2ed263a2ae760a9c2f367a9795d7f6e9a771cd5f562b0cfc13f2fa5572025d7944c67d1154c2e Homepage: https://cran.r-project.org/package=lakemorpho Description: CRAN Package 'lakemorpho' (Lake Morphometry Metrics) Lake morphometry metrics are used by limnologists to understand, among other things, the ecological processes in a lake. Traditionally, these metrics are calculated by hand, with planimeters, and increasingly with commercial GIS products. All of these methods work; however, they are either outdated, difficult to reproduce, or require expensive licenses to use. The 'lakemorpho' package provides the tools to calculate a typical suite of these metrics from an input elevation model and lake polygon. The metrics currently supported are: fetch, major axis, minor axis, major/minor axis ratio, maximum length, maximum width, mean width, maximum depth, mean depth, shoreline development, shoreline length, surface area, and volume. Package: r-cran-laketemps Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-laketemps_0.5.1-1.ca2604.1_all.deb Size: 584768 MD5sum: c62a26d03d7d33beed621b3082feeaf0 SHA1: 7d2a3c0a667efd6c6a0df601cc5cbd0756327fe1 SHA256: bb5955b77ef54ce7cf5c7d52048b18718486e7a952380f9e4de2f6738c3baaf0 SHA512: 1e2f8602c664b7174bee430be5ac5c624a9a060e257b042f9b900dfb9b82e0e7bba41dc1983e13758949082538da4a437025736d6d4077c0d6b2b9809e0e21fc Homepage: https://cran.r-project.org/package=laketemps Description: CRAN Package 'laketemps' (Lake Temperatures Collected by Situ and Satellite Methods from1985-2009) Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. 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Package: r-cran-lakhesis Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-ca, r-cran-ggplot2, r-cran-rdpack, r-cran-shiny, r-cran-shinydashboard, r-cran-bslib Filename: pool/dists/resolute/main/r-cran-lakhesis_0.0.1-1.ca2604.1_all.deb Size: 127070 MD5sum: c8746bf5bf18b7c6080d472d6fbfcb03 SHA1: 6c09beb6aa0e6be1e7261a2dcc602bc8c6c5ee2a SHA256: c972189da32ac3ce98a8d658c0228561f99ffcd7fcef3d2c8c4d41568ae96db5 SHA512: cb3287a8e1216edd7054917d03e6c9e2db38f4372c52f37c8fc10fe4a556c6ee97c933f3e59fac3c777958833fcf5509953c4b214668169e952bd6625945ea64 Homepage: https://cran.r-project.org/package=lakhesis Description: CRAN Package 'lakhesis' (Consensus Seriation for Binary Data) Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. 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Package: r-cran-lancor Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-boot, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lancor_0.1.3-1.ca2604.1_all.deb Size: 50794 MD5sum: 01843e536411cfd025ebddd501b5f442 SHA1: b2b5ae3b6b05323a9846070e2c55b3def01dbcec SHA256: 7682a5d73c72b8331f388be62ca4f500ada726b4e5bf1ff3c327e056431a157a SHA512: a1076e4e80f4975750e45879ccf7aa9cb1017386156dad43e69965542d4a98f76cf3e9199a977d900504941f96d13762f82eff76c7d9033f4edfbe5c65094ff0 Homepage: https://cran.r-project.org/package=lancor Description: CRAN Package 'lancor' (Statistical Inference via Lancaster Correlation) Implementation of the methods described in Holzmann, Klar (2024) . Lancaster correlation is a correlation coefficient which equals the absolute value of the Pearson correlation for the bivariate normal distribution, and is equal to or slightly less than the maximum correlation coefficient for a variety of bivariate distributions. Rank and moment-based estimators and corresponding confidence intervals are implemented, as well as independence tests based on these statistics. Package: r-cran-landcomp Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-landcomp_0.0.5-1.ca2604.1_all.deb Size: 761004 MD5sum: 0bb8e20d300f17599848028e1b0aba7c SHA1: 603542bf1017b02594b1754ce2169ea59c7ec5f7 SHA256: 6bbb33cca8c4af0f4e5367ef45e2539217fe01df18b8e19968f1fd589ad6cb4a SHA512: e799a9791587fe8d0c6c9de030f3a31290c2903e1fde133baa1eae011e3a6d89cec04b8622296e6c866dd7a02a72e70fabc074450ffb106060ed968ab7552a43 Homepage: https://cran.r-project.org/package=LandComp Description: CRAN Package 'LandComp' (Analysing Landscape Composition and Structure at Multiple Scales) Changes of landscape diversity and structure can be detected soon if relying on landscape class combinations and analysing patterns at multiple scales. 'LandComp' provides such an opportunity, based on Juhász-Nagy's functions (Juhász-Nagy P, Podani J 1983 ). Functions can handle multilayered data. Requirements of the input: binary data contained by a regular square or hexagonal grid, and the grid should have projected coordinates. Package: r-cran-landest Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-landest_1.2-1.ca2604.1_all.deb Size: 274430 MD5sum: 96f74488502056f65dba040f1a503643 SHA1: f6911f381edce539d421f9e48e630cb6dde52f25 SHA256: 3a3e8dfc67feb88b0b514bab677af219196ddd3fd29214f72855b5b6191e7801 SHA512: 18a454cbdfdb5fb0617712318e110af8f7a83ffcd33ea1cefb19a1fcab08d8b23d9f4e337bd40d62ce3f48bf4158d55d8b1130bd6d0b60fe49ac31679f680ba6 Homepage: https://cran.r-project.org/package=landest Description: CRAN Package 'landest' (Landmark Estimation of Survival and Treatment Effect) Provides functions to estimate survival and a treatment effect using a landmark estimation approach. 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With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills. Package: r-cran-lareshiny Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-htmltools, r-cran-miniui, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets Suggests: r-cran-h2o, r-cran-lares Filename: pool/dists/resolute/main/r-cran-lareshiny_0.0.3-1.ca2604.1_all.deb Size: 88930 MD5sum: 66f3f150901e574ce0b23659adea517a SHA1: 9b10c46b624dcfbfb7d9d0bfc2a5d93af5bd0660 SHA256: 012c198a099406fb1fe0c4658da60fff5aa1585e2b9e58d0caf16a8ec82a815e SHA512: 24b3a92f07353874ecbd1a3f1162241771e9ebf9d2c3dfc6eda45f0d211552e4b483e071cd2f2597d78fadfc3924781509b472bf62f4815922475353632df86f Homepage: https://cran.r-project.org/package=lareshiny Description: CRAN Package 'lareshiny' (Lares 'shiny' Modules) Useful 'shiny' production-ready modules and helpers such as login window and visualization tools. Package: r-cran-larf Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula Filename: pool/dists/resolute/main/r-cran-larf_1.4-1.ca2604.1_all.deb Size: 202522 MD5sum: 4ff541040a5891e9e949d03b4535808c SHA1: 1ce8bf992be0184b8938184aaf8dc9264060f6b4 SHA256: 08f8b5fddb1f77abec5188e5d2a12e30d192293c2f4b222b3f1cb584fd9193c7 SHA512: 3173764ad39a60566934cd75612150ae0edcae602ae45c3e76f8e80a883340ae89caae082f047a9f6c0429bad7fba96571540d30b9aca7ca70dc3c1d1619b4e6 Homepage: https://cran.r-project.org/package=LARF Description: CRAN Package 'LARF' (Local Average Response Functions for Instrumental VariableEstimation of Treatment Effects) Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes. Package: r-cran-largevars Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-tibble, r-cran-data.table, r-cran-readr Filename: pool/dists/resolute/main/r-cran-largevars_1.0.3-1.ca2604.1_all.deb Size: 450468 MD5sum: 850d1c6c3ee8add597a4570d275285d1 SHA1: fb73dd147b1d5fc901c148a1f2cd889fe827f9bb SHA256: b6c64c917ffdecba0d4e38e730d3d1a4de3dfbea9da08319b8e7b1dd943c12a6 SHA512: 1f226f04352f06e76acbc4210540b4d263aeec4e57f2362817f4e3652bcdbec0f584a62f965ec14dd5d04afb0eb993c3976d3140b9964dd1f2f336e3694c67f5 Homepage: https://cran.r-project.org/package=Largevars Description: CRAN Package 'Largevars' (Testing Large VARs for the Presence of Cointegration) Conducts a cointegration test for high-dimensional vector autoregressions (VARs) of order k based on the large N,T asymptotics of Bykhovskaya and Gorin, 2022 (). The implemented test is a modification of the Johansen likelihood ratio test. In the absence of cointegration the test converges to the partial sum of the Airy-1 point process. This package contains simulated quantiles of the first ten partial sums of the Airy-1 point process that are precise up to the first three digits. Package: r-cran-lassopv Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lars Filename: pool/dists/resolute/main/r-cran-lassopv_0.2.0-1.ca2604.1_all.deb Size: 18090 MD5sum: ee68c767abd7b077877e16d058972f83 SHA1: f64d87108bf920ab620acf05af559ed2d6f048cc SHA256: 075b417d71ffa9ddd2c0d94f3ff2621b6fe06c94c813c7b885edd150f00d1dc5 SHA512: 83e7d7c0e4930aa99ae6307f74bd66a83ad9e2c7af3406806c77fcdc216c80de01328b7455874f0910ee714e8975f758af89d48da0b3928dbbdf762853ebdc98 Homepage: https://cran.r-project.org/package=lassopv Description: CRAN Package 'lassopv' (Nonparametric P-Value Estimation for Predictors in Lasso) Estimate the p-values for predictors x against target variable y in lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic. This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values. Package: r-cran-lassosir Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-lassosir_1.0-1.ca2604.1_all.deb Size: 30426 MD5sum: 281f66317bef3176386ad43e36028b32 SHA1: b2d18f9fc85fbaa0c302da1a01856d4008385aa4 SHA256: 20c6295775605d23ef275354b50774e44f5e75ff8e53ed2fa5c2f6ed8441e7ce SHA512: d169a955121c398c716a1eb08f076795a9d54868bcff938b330f2a0f310b4e8d7a9ce656c012990256ba8a970a4a2241c74bddcfe15f8efea72b28585af8e687 Homepage: https://cran.r-project.org/package=LassoSIR Description: CRAN Package 'LassoSIR' (Sparsed Sliced Inverse Regression via Lasso) Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2019) . The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models. Package: r-cran-latamverse Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-argentinapi, r-cran-brazildataapi, r-cran-chiledataapi, r-cran-colombiapi, r-cran-peruapis, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-latamverse_0.1.0-1.ca2604.1_all.deb Size: 138990 MD5sum: f40f8052040d44f10ad099487580cd4c SHA1: 4b760ca6d86f714ea18554d94a1f9d8086275a12 SHA256: b2b62ba6cd2b0c28000267fec4516ff35265283c3f9f1fafa1fa6bf41db79f0a SHA512: d297961144aa5bc9cda2c02b109c9b37c05c78bf2fae051a780822476446e3b43164c225b58f109b110b6395146a67d5502de656f77d3fe6c58d74f82ec5c1dd Homepage: https://cran.r-project.org/package=Latamverse Description: CRAN Package 'Latamverse' (Latin American Data via 'RESTful' APIs and Curated Datasets) Brings together a comprehensive collection of R packages providing access to API functions and curated datasets from Argentina, Brazil, Chile, Colombia, and Peru. Includes real-time and historical data through public 'RESTful' APIs ('Nager.Date', World Bank API, REST Countries API, and country-specific APIs) and extensive curated collections of open datasets covering economics, demographics, public health, environmental data, political indicators, social metrics, and cultural information. Designed to provide researchers, analysts, educators, and data scientists with centralized access to Latin American data sources, facilitating reproducible research, comparative analysis, and teaching applications focused on these five major Latin American countries. Included packages: - 'ArgentinAPI': API functions and curated datasets for Argentina covering exchange rates, inflation, political figures, national holidays and more. - 'BrazilDataAPI': API functions and curated datasets for Brazil covering postal codes, banks, economic indicators, holidays, company registrations and more. - 'ChileDataAPI': API functions and curated datasets for Chile covering financial indicators ('UF', UTM, Dollar, Euro, Yen, Copper, Bitcoin, 'IPSA' index), holidays and more. - 'ColombiAPI': API functions and curated datasets for Colombia covering geographic locations, cultural attractions, economic indicators, demographic data, national holidays and more. - 'PeruAPIs': API functions and curated datasets for Peru covering economic indicators, demographics, national holidays, administrative divisions, electoral data, biodiversity and more. For more information on the APIs, see: 'Nager.Date' , World Bank API , REST Countries API , 'ArgentinaDatos' API , 'BrasilAPI' , 'FINDIC' , and API-Colombia . Package: r-cran-latbias Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-psych, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-sf, r-cran-sp, r-cran-terra, r-cran-tidyr, r-cran-units Suggests: r-cran-elevatr, r-cran-knitr, r-cran-progress, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-latbias_1.0.0-1.ca2604.1_all.deb Size: 45590 MD5sum: 76e9e8038db332c8fa2a2e2fdf341272 SHA1: 1fb78957e4bb06a11f2b14040053ca91ac9ef705 SHA256: 36ecf0500ec28b1361257d31fa8256bb053ce9d8cd7ba9fb1682e982d0437a66 SHA512: 492198e3a28edb33e1927e35e623acffa412d5672491bb2496751de69d2b41cae40dcc6e455a3bc88b8bb3a74cc257a286b36fdf149f7051a1b76a003343a905 Homepage: https://cran.r-project.org/package=latbias Description: CRAN Package 'latbias' (Calculate the Latitudinal Bias Index) Studies that report shifts in species distributions may be biased by the shape of the study area. The main functionality of this package is to calculate the Latitudinal Bias Index (LBI) for any given shape. The LBI is bounded between +1 (100% probability to exclusively record latitudinal shifts, i.e., range shifts data sampled along a perfectly South-North oriented straight line) and -1 (100% probability to exclusively record longitudinal shifts, i.e., range shifts data sampled along a perfectly East-West oriented straight line). Package: r-cran-latcontrol Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan Filename: pool/dists/resolute/main/r-cran-latcontrol_0.1.1-1.ca2604.1_all.deb Size: 24374 MD5sum: 50ecbf79ad50b4b0a60837c9dc7f1dd5 SHA1: 6283cd077cbd3b254cb7574ec90108feeda7d390 SHA256: 3897cf44043f3a0a3b1c91814470925fe1d7912a9adc46aab9dcccc1b74a1db4 SHA512: be0f8bb3311065765f0d2ee10e9098fe036267ce2886406ac2591603fe17ee5c8143671530f04afdbbafdba9c39ab7156deaef02d810189bdf7dcf100bbceb10 Homepage: https://cran.r-project.org/package=latcontrol Description: CRAN Package 'latcontrol' (Evaluation of the Role of Control Variables in StructuralEquation Models) Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) . Package: r-cran-latdiag Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-ltm Filename: pool/dists/resolute/main/r-cran-latdiag_0.3-1.ca2604.1_all.deb Size: 233228 MD5sum: c86427a7e922a2d7960235dbece15d96 SHA1: b7047e69ad7d56ac3f83321267807805b8d1f8f9 SHA256: b0896572ac4424b405eceb9754757384f938f6b4a5037402b655d534709927f9 SHA512: 85509a5496e4a705cc1ae6aa54b4b88b1bf0f56462598621ef25063bc0e0be839a287d7560f729279a64c288b1538c147136180d41522895639b4cd1b783cc2f Homepage: https://cran.r-project.org/package=latdiag Description: CRAN Package 'latdiag' (Draws Diagrams Useful for Checking Latent Scales) A graph proposed by Rosenbaum is useful for checking some properties of various sorts of latent scale, this program generates commands to obtain the graph using 'dot' from 'graphviz'. Package: r-cran-latenetwork Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-statip Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-latenetwork_1.0.1-1.ca2604.1_all.deb Size: 92902 MD5sum: cc2866010fdc8f2f018139f52987ade9 SHA1: 415749eb61f06b243733f4650b081608fed96f81 SHA256: 24640cc342b460a3d28a76bcde9edd10f69b440f34a83acd5be1afc78b034a39 SHA512: 9ca6265330e716fc34ff3d11da7a1eb42f62f64fb9086caa1c64c52b5883340ebaa7e3b23f99ed0f1c58f62e9707e15cce65696ea4b0d356f65ee049fdc18dcb Homepage: https://cran.r-project.org/package=latenetwork Description: CRAN Package 'latenetwork' (Inference on LATEs under Network Interference of Unknown Form) Estimating causal parameters in the presence of treatment spillover is of great interest in statistics. This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form. The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect. The methods are developed by Hoshino and Yanagi (2023) . Package: r-cran-latent2likert Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-sn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-latent2likert_1.2.1-1.ca2604.1_all.deb Size: 319516 MD5sum: 6bad543fd22d7396ee0de3c5cebb2f65 SHA1: bc792cb453576a40cad69d0abfdc0e56e0b8360e SHA256: 9f995238bb15ce8f0b5634313f8b0154b96cc676a687b27e6e329b8de1d3310a SHA512: 6314138c11555d5f19c2f3a3aaab2248ee0b751cdb01764047ca38ac1dcabbd221fca2db6574d22cdd6860ef3f1eb755d3b227df8cceae0b3a439452f7696811 Homepage: https://cran.r-project.org/package=latent2likert Description: CRAN Package 'latent2likert' (Converting Latent Variables into Likert Scale Responses) Effectively simulates the discretization process inherent to Likert scales while minimizing distortion. It converts continuous latent variables into ordinal categories to generate Likert scale item responses. Particularly useful for accurately modeling and analyzing survey data that use Likert scales, especially when applying statistical techniques that require metric data. Package: r-cran-latentbma Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-knitr, r-cran-mnormt, r-cran-progress, r-cran-reshape2 Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-latentbma_0.1.3-1.ca2604.1_all.deb Size: 95922 MD5sum: 62dc3047aa65b48dd4eec802cdad68d9 SHA1: 52727e42e06e18551f2fb9e25c444da672683d6d SHA256: 0636490c24404829f40de486e7f4a2110fab6a360d5324c672ec7a141882f126 SHA512: d2b11de9cb4c43c07c1769e3731e30e26974de44ab09477f8e425c748a3e241253f54c73a7114178fd1a42a49c890d8350ed41d9aa804e08b3e075467063f8e2 Homepage: https://cran.r-project.org/package=LatentBMA Description: CRAN Package 'LatentBMA' (Bayesian Model Averaging for Univariate Link Latent GaussianModels) Bayesian model averaging (BMA) algorithms for univariate link latent Gaussian models (ULLGMs). For detailed information, refer to Steel M.F.J. & Zens G. (2024) "Model Uncertainty in Latent Gaussian Models with Univariate Link Function" . The package supports various g-priors and a beta-binomial prior on the model space. It also includes auxiliary functions for visualizing and tabulating BMA results. Currently, it offers an out-of-the-box solution for model averaging of Poisson log-normal (PLN) and binomial logistic-normal (BiL) models. The codebase is designed to be easily extendable to other likelihoods, priors, and link functions. Package: r-cran-latentfactor Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-car, r-cran-eganet, r-cran-fspe, r-cran-googledrive, r-cran-ineq, r-cran-lavaan, r-cran-matrix, r-cran-mlr, r-cran-mvtnorm, r-cran-psych, r-cran-rstudioapi, r-cran-xgboost Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-latentfactor_0.0.7-1.ca2604.1_all.deb Size: 279698 MD5sum: 66b812b3469dc9578440efb540fc016f SHA1: c50216668e91a4bae74c8ef4de22490ba066d629 SHA256: adde78f8bf9779efb7645fc308de1e6f99ac17079fac0af7933dae8d4300e37c SHA512: 5aad2a3a512e9ee3870bdca69bbee4ec9bd963ddf813f976ba44d5da7f9c66ac93b360af4e11067ce4399b2f9caf0c916727b4655c11a7570d67b9bf31e0a009 Homepage: https://cran.r-project.org/package=latentFactoR Description: CRAN Package 'latentFactoR' (Data Simulation Based on Latent Factors) Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) . Package: r-cran-laterality Architecture: all Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4 Filename: pool/dists/resolute/main/r-cran-laterality_0.9.5-1.ca2604.1_all.deb Size: 103440 MD5sum: 3c8d22ff6fb1ee93e955b154de1f8306 SHA1: 3005b6ac067be81f0474ec90fa1546f9d7112827 SHA256: 9516d893188d2f6f662aedbc56a7cd04a8bd87df39ec6626ea43320525d45459 SHA512: 70d715448e0b00e6204f163c0049de75938300dba7da86432960459825d6d991e6ce70f90a60fb256964be240ed24c52ce3b386489893c1ceeb4dff2293b77f7 Homepage: https://cran.r-project.org/package=Laterality Description: CRAN Package 'Laterality' (Functions to Calculate Common Laterality Statistics inPrimatology) Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates. 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It offers two core functions: first, lavaangui() launches a web application that allows users to specify models by drawing path diagrams, fitting them, assessing model fit, and more; second, plot_lavaan() creates interactive path diagrams from models specified in 'lavaan'. After customizing a diagram interactively, export_plot() saves it to a file, enabling reproducible scripts without sacrificing fine-grained control over appearance. Karch (2024) contains a tutorial. Package: r-cran-lavaanplot Architecture: all Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6604 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-diagrammer, r-cran-stringr, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-diagrammersvg, r-cran-rsvg, r-cran-png, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lavaanplot_0.8.1-1.ca2604.1_all.deb Size: 562210 MD5sum: a6219f00b601ebd65000de4067425eef SHA1: c273cbc113d4335a145e28dd6c01aed5239b8e5f SHA256: 60be4bde23ce0abfc33df27f26f277e4c653af9e85242971c400adf09796f92e SHA512: e53f0270824f63f7da65a1b2b4746f923c19c633f07ac3a9ec3630174bfab83344561f6f1290b51427f925c9f29132bba69649ac995ce084fa9483f79577355f Homepage: https://cran.r-project.org/package=lavaanPlot Description: CRAN Package 'lavaanPlot' (Path Diagrams for 'Lavaan' Models via 'DiagrammeR') Plots path diagrams from models in 'lavaan' using the plotting functionality from the 'DiagrammeR' package. 'DiagrammeR' provides nice path diagrams via 'Graphviz', and these functions make it easy to generate these diagrams from a 'lavaan' path model without having to write the DOT language graph specification. Package: r-cran-lavdiag Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggrepel, r-cran-igraph, r-cran-lavaan, r-cran-mgcv, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-vctrs, r-cran-visnetwork, r-cran-withr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lavdiag_0.1.0-1.ca2604.1_all.deb Size: 332150 MD5sum: abae852cc9a3e55630fcf7b0f3a2463f SHA1: d1b09ece8c7eb9b71187ab5f11f8510347115dd8 SHA256: 18efe8d3aa1627c089c75f9abd0a43f514e33262902307c7eaca6c6ceeaa515c SHA512: 0494a1680443f20190a5ee5a85e2cf829e5b4f13189dc7ab6847c6e5dfaa21998f536a47e145a52bf40cfeaf835ff08feaa1f12ad6950274a118ed3ef106c4f3 Homepage: https://cran.r-project.org/package=lavDiag Description: CRAN Package 'lavDiag' (Latent Variable Models Diagnostics) Diagnostics and visualization tools for latent variable models fitted with 'lavaan' (Rosseel, 2012 ). The package provides fast, parallel-safe factor-score prediction (lavPredict_parallel()), data augmentation with model predictions, residuals, delta-method standard errors and confidence intervals (augment()), and model-based latent grids for continuous, ordinal, or mixed indicators (prepare()). It offers item-level empirical versus model curve comparison using generalized additive models for both continuous and ordinal indicators (item_data(), item_plot()) via 'mgcv' (Wood, 2017, ISBN:9781498728331), residual diagnostics including residual correlation tables and plots (resid_cor(), resid_corrplot()) using 'corrplot' (Wei and Simko, 2021 ), and Q–Q checks of residual z-statistics (resid_qq()), optionally with non-overlapping labels from 'ggrepel' (Slowikowski, 2024 ). Heavy computations are parallelized via 'future'/'furrr' (Bengtsson, 2021 ; Vaughan and Dancho, 2018 ). Methods build on established literature and packages listed above. Package: r-cran-lavinteract Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-lavinteract_0.5.1-1.ca2604.1_all.deb Size: 246628 MD5sum: f6bd11ad25d946656d8acc5f7df1ccce SHA1: ca4be68340a6081d4593f6c0b07abf66448ed501 SHA256: 8f8e2b6889b06368632ecb8ca809b5f6942bc6c1a4291c3f9812fd9dd117d0ff SHA512: 1a84db095edea109e4272eac040789f95b726ae62fde6c6525b4dedb0d4658a517ae79aa31b97fbdae85e3bd7c35c4f130d1d7b37c63c5521043fcbdbdade09d Homepage: https://cran.r-project.org/package=lavinteract Description: CRAN Package 'lavinteract' (Post-Estimation Utilities for 'lavaan' Fitted Models) Companion toolbox for structural equation models fitted with 'lavaan'. 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Main goal is to process information within "Decision Support System" to come up with analysis or predictions. There are several utilities such as dynamic and adaptive risk management using reinforcement learning and even functions to generate predictions of price changes using pattern recognition deep regression learning. Summary of Methods used: Awesome H2O tutorials: , Market Type research of Van Tharp Institute: , Reinforcement Learning R package: . 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Package: r-cran-lcra Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-coda, r-cran-rjags Suggests: r-cran-r2winbugs, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-lcra_1.1.5-1.ca2604.1_all.deb Size: 121820 MD5sum: 73e6c201bdcdd575cf4168ec50e3c32a SHA1: 1a2ff1f1886d0e5adf018ca65724e3c3045fec34 SHA256: 1c052258e9bc81e14aba754a5727aca6f329503d8a9656ba29e635aaf663f2a8 SHA512: e4e9052c149a293ade5a8763454ca6019a75d3e5aae1eeb73a11d733b019534bc2f89819c69e5de35449ceb82f2e16ab731c24cc08a353a18ba22c049c336af5 Homepage: https://cran.r-project.org/package=lcra Description: CRAN Package 'lcra' (Bayesian Joint Latent Class and Regression Models) For fitting Bayesian joint latent class and regression models using Gibbs sampling. See the documentation for the model. The technical details of the model implemented here are described in Elliott, Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham, Belinda L., "Methods to account for uncertainty in latent class assignments when using latent classes as predictors in regression models, with application to acculturation strategy measures" (2020) In press at Epidemiology . 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Major features in the S&P500 index, such as regime identification, volatility clustering, and anti-correlation between return and volatility, can be extracted from HMM cleanly. Univariate symmetric lambda distribution is essentially a location-scale family of exponential power distribution. Such distribution is suitable for describing highly leptokurtic time series obtained from the financial market. It provides a theoretically solid foundation to explore such data where the normal distribution is not adequate. The HMM implementation follows closely the book: "Hidden Markov Models for Time Series", by Zucchini, MacDonald, Langrock (2016). 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This programmatic access facilitates researchers who are interested in performing batch queries in 1000 Genomes Project (2015) data using 'LDlink'. 'LDlink' is an interactive and powerful suite of web-based tools for querying germline variants in human population groups of interest. For more details, please see Machiela et al. (2015) . 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The package implements a four-stage workflow: data subset generation, functional form discovery, numerical parameter optimization, and multi-objective evaluation. It provides a high-level formula-style interface that abstracts and extends multiple discovery engines: genetic programming (via PySR), Reinforcement Learning with Monte Carlo Tree Search (via RSRM), and exhaustive generalized linear model search. 'leaf' extends these methods by enabling multi-view discovery, where functional structures are shared across groups while parameters are fitted locally, and by supporting the enforcement of domain-specific constraints, such as sign consistency across groups. The framework automatically handles data normalization, link functions, and back-transformation, ensuring that discovered symbolic equations remain interpretable and valid on the original data scale. Implements methods following ongoing work by the authors (2026, in preparation). 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Package: r-cran-leafpm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-leaflet, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-leafpm_0.1.0-1.ca2604.1_all.deb Size: 59072 MD5sum: f97d58930175ca08da210d0e7b78b150 SHA1: f1b3ca5ab0e16b3464f6e2617b6faa9441563765 SHA256: 0c96640bdb118d965513a8bc0576a3dddb3d4a7e1b1c176430613ed1ddf9dc8c SHA512: c03a0ce9183c00dcf4fadedb700f079672d6d07a98047bf8798750a9bc8f2ffa4dd99e068cc06cd1418a9d9ba8cf80cf77ab9d6c1d643022a9e7df493ecb5f0e Homepage: https://cran.r-project.org/package=leafpm Description: CRAN Package 'leafpm' (Leaflet Map Plugin for Drawing and Editing) A collection of tools for interactive manipulation of (spatial) data layers on leaflet web maps. 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Package: r-cran-leafpop Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-brew, r-cran-htmltools, r-cran-htmlwidgets, r-cran-sf, r-cran-svglite, r-cran-uuid Suggests: r-cran-lattice, r-cran-leaflet, r-cran-sp Filename: pool/dists/resolute/main/r-cran-leafpop_0.1.0-1.ca2604.1_all.deb Size: 1910534 MD5sum: 7766fd175cfc33841d2fd23907123839 SHA1: 559d9101e308a301e6560040b78aca58ce2e7724 SHA256: 273225860cc00ce652fc5e3979c8282670fff80e414bf1639a14f8165db7e9fe SHA512: 34761bb06afa0e7590451d89069bac614b2ce5a23ad479388bc7dd091f7c6555693b79027e734bd07aac08d5ead01187fbd2d1f21c75579123b5d14337cc20b3 Homepage: https://cran.r-project.org/package=leafpop Description: CRAN Package 'leafpop' (Include Tables, Images and Graphs in Leaflet Pop-Ups) Creates 'HTML' strings to embed tables, images or graphs in pop-ups of interactive maps created with packages like 'leaflet' or 'mapview'. 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Package: r-cran-leafstar Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-leafstar_1.0-1.ca2604.1_all.deb Size: 111162 MD5sum: 55b4cd7dfcffb6fe33c185b82c583fa9 SHA1: 5b39d2ff5dbd85df46d023ab85d1cce939ae7eff SHA256: e4e7396fcd3670fa166ac7413579a9fce50438e37b3bda24a622e615eaa67269 SHA512: deb69eba1777aa733e98b4f43814b91eed30ef3b2fabb13270ac1d7b804e82710bef81403810ddc130d6a434a9d0e45261ed168bd762291335c44a0ca7147d7b Homepage: https://cran.r-project.org/package=leafSTAR Description: CRAN Package 'leafSTAR' (Silhouette to Area Ratio of Tilted Surfaces) Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) in a few user-friendly R functions. All functions handle data obtained with 'Ahmes' 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes. Package: r-cran-leafsync Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet Filename: pool/dists/resolute/main/r-cran-leafsync_0.1.0-1.ca2604.1_all.deb Size: 841486 MD5sum: 990450fb5b1b481a5a82c4308116d861 SHA1: a24f8d2b41abefdb8980806de992052521925c76 SHA256: 4fa6e7523a7cedd2d0775f12bfd883925b559100fd07c2a51f8417f925e865d7 SHA512: 85e8a0ceed41f99f848c0c8d4ec8b0beaa627a6d337a4139219b90de198a888c8ca33fd94cbdf6459530959a73d964ef190198ff8dd47d336e8f92d623249344 Homepage: https://cran.r-project.org/package=leafsync Description: CRAN Package 'leafsync' (Small Multiples for Leaflet Web Maps) Create small multiples of several leaflet web maps with (optional) synchronised panning and zooming control. When syncing is enabled all maps respond to mouse actions on one map. 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Package: r-cran-learningstats Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-readxl, r-cran-haven, r-cran-readods Filename: pool/dists/resolute/main/r-cran-learningstats_0.1.0-1.ca2604.1_all.deb Size: 514564 MD5sum: a0029dbfc31c40be3c24191939a31600 SHA1: a1b37585c256bcb6196a45f4d76e30269036dc2f SHA256: 3ee7980b6b2f9db09a692d8d9e758d93b0df7389329b90e1988b7e4550e499a6 SHA512: f84757cbe092a2c4ba587cf80379ed257b244b78b37913dbe5f9178c5a8985f4f94bcd10b921f6c13e02b7c629147545268b3ee0f567fe63cfb291d80604f24b Homepage: https://cran.r-project.org/package=LearningStats Description: CRAN Package 'LearningStats' (Elemental Descriptive and Inferential Statistics) Provides tools to teach students elemental statistics. The main topics covered are descriptive statistics, probability models (discrete and continuous variables) and statistical inference (confidence intervals and hypothesis tests). One of the main advantages of this package is that allows the user to read quite a variety of types of data files with one unique command. Moreover it includes shortcuts to simple but up-to-now not in R descriptive features such a complete frequency table or an histogram with the optimal number of intervals. Related to model distributions (both discrete and continuous), the package allows the student to easy plot the mass/density function, distribution function and quantile function just detailing as input arguments the known population parameters. The inference related tools are basically confidence interval and hypothesis testing. Having defined independent commands for these two tools makes it easier for the student to understand what the software is performing, and it also helps the student to have a better knowledge on which specific tool they need to use in each situation. Moreover, the hypothesis testing commands provide not only the numeric result on the screen but also a very intuitive graph (which includes the statistic distribution, the observed value of the statistic, the rejection area and the p-value) that is very useful for the student to visualise the process. The regression section includes up to now, a simple linear model, with one single command the student can obtain the numeric summary as well as the corresponding diagram with the adjusted regression model and a legend with basic information (formula of the adjusted model and R-squared). 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Normalization and missing value imputation are commonly used techniques to address these issues and make the dataset suitable for further downstream analysis. This package provides an optimal combination of normalization and imputation methods for the dataset. The package utilizes three normalization methods and three imputation methods.The statistical evaluation measures named pooled co-efficient of variance, pooled estimate of variance and pooled median absolute deviation are used for selecting the best combination of normalization and imputation method for the given dataset. The user can also visualize the results by using various plots available in this package. The user can also perform the differential expression analysis between two sample groups with the function included in this package. The chosen three normalization methods, three imputation methods and three evaluation measures were chosen for this study based on the research papers published by Välikangas et al. (2016) , Jin et al. (2021) and Srivastava et al. (2023) .This work has published by Sakthivel et al. (2025) . 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Package: r-cran-lic Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lic_0.0.2-1.ca2604.1_all.deb Size: 269796 MD5sum: 4b921d084b8cde775c69c16a806de4af SHA1: 9e668a7f75297aa099c16044cdb1ca3a04827ad8 SHA256: 7a1ea6f157c1ca0db36202aa6b3dfcdc9033e541c7bf6b92611c62c9802d9989 SHA512: e8f9ab54268e94150681c1216ac88c3d069f7bb32d4c36f0dc4580f90737512e350779cd87227536b495bc3e9208cd4d9811ed606ff9b11020d346a2e516498e Homepage: https://cran.r-project.org/package=LIC Description: CRAN Package 'LIC' (The LIC Criterion for Optimal Subset Selection) The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2022) . Package: r-cran-licoread Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1239 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-licoread_0.3.0-1.ca2604.1_all.deb Size: 353814 MD5sum: add8d1259a698ed9ffd388cf4630d7ee SHA1: 425868c4e1eb3db857fef0dec30fa2a3f178aea3 SHA256: a848c24967acd499ede0905c8333c9bee8bf6bb04c88efcbcae606eae5696e59 SHA512: 9930402be63b0db3c00b6c975a136c37887f6c3e9f45ab19b529148e99223211e47e7074c12b0ececc68e91a27218305fcbddee0c215c0f5c953f882ae16ea24 Homepage: https://cran.r-project.org/package=licoread Description: CRAN Package 'licoread' (Reads Raw Files from Li-COR Gas Analyzers) Reads raw files from Li-COR gas analyzers and produces a dataframe that can directly be used with 'fluxible' . Package: r-cran-lidartree Architecture: all Version: 4.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1043 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-imager, r-cran-leaps, r-cran-gvlma, r-cran-car, r-cran-reldist, r-cran-lidr Suggests: r-cran-stars Filename: pool/dists/resolute/main/r-cran-lidartree_4.0.8-1.ca2604.1_all.deb Size: 1005878 MD5sum: e668ee3517e3adae033d88ecc756eb7c SHA1: 0c8087900ab1553b77ad96b5f8c46bd7f678cf04 SHA256: 3becf1e7dd10d4d5ac32266ab63cf329afdbd07eabe099714c2ece48f08c22a5 SHA512: e08a6e4f9d2daa122ef985de0db5b88353c8a7c57ce0d801caf67d89d50e7fe106628e624f61924b00793496cf067d7ce0c15451f8e6f1912fd9632173901f77 Homepage: https://cran.r-project.org/package=lidaRtRee Description: CRAN Package 'lidaRtRee' (Forest Analysis with Airborne Laser Scanning (LiDAR) Data) Provides functions for forest objects detection, structure metrics computation, model calibration and mapping with airborne laser scanning: co-registration of field plots (Monnet and Mermin (2014) ); tree detection (method 1 in Eysn et al. (2015) ) and segmentation; forest parameters estimation with the area-based approach: model calibration with ground reference, and maps export (Aussenac et al. (2023) ); extraction of both physical (gaps, edges, trees) and statistical features useful for e.g. habitat suitability modeling (Glad et al. (2020) ) and forest maturity mapping (Fuhr et al. (2022) ). Package: r-cran-lievens Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-lievens_0.0.1-1.ca2604.1_all.deb Size: 1549694 MD5sum: 91ba8f84e31a23a350a58e6f6fdd10a5 SHA1: e7491af04bb98f6e0ab83233c0db69f918224e5b SHA256: aacf76b01310c0853b8a60b8a483801683c4f4c9abb3b5e4f05482e878d9a04a SHA512: cc925467d90c8b959627fb2b8f11f52d0b78e38b268f4e27c02bd71659fe1fea58f5ed50cdf6f2dda0aa86016cb464f3a1037913967b64c645f7d398e0e6efcd Homepage: https://cran.r-project.org/package=lievens Description: CRAN Package 'lievens' (Real-Time PCR Data Sets by Lievens et al. (2012)) Real-time quantitative polymerase chain reaction (qPCR) data sets by Lievens et al. (2012) . Provides one single tabular tidy data set in long format, encompassing three dilution series, targeted against the soybean Lectin endogene. Each dilution series was assayed in one of the following PCR-efficiency-modifying conditions: no PCR inhibition, inhibition by isopropanol and inhibition by tannic acid. The inhibitors were co-diluted along with the dilution series. The co-dilution series consists of a five-point, five-fold serial dilution. For each concentration there are 18 replicates. Each amplification curve is 60 cycles long. Original raw data file is available at the Supplementary Data section at Nucleic Acids Research Online . Package: r-cran-lifecourse Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-traminer Filename: pool/dists/resolute/main/r-cran-lifecourse_2.0-1.ca2604.1_all.deb Size: 206202 MD5sum: 84444be86a4b4bd0b7b99f194403cca4 SHA1: f26d2f661f5297385b4491bd3f43c2dad1e290c0 SHA256: 3efa26751869b405f25c3d3fa029d6712fd8bc90e997b0c347415aee422c2fd0 SHA512: cd68826ce797fb658a7d941a91bb4caea1c6b6c2dfc2b79ef6b301bbba586626831f09b2088759579f96c8647b3bc40e231b3e99c261242c12bb4843cd34e9b0 Homepage: https://cran.r-project.org/package=lifecourse Description: CRAN Package 'lifecourse' (Quantification of Lifecourse Fluidity) Provides in built datasets and three functions. These functions are mobility_index, nonStanTest and linkedLives. 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Package: r-cran-lifehist Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-optimx, r-cran-bb Filename: pool/dists/resolute/main/r-cran-lifehist_1.0-1-1.ca2604.1_all.deb Size: 268344 MD5sum: 9c8a2350572f904efa64404fe7f33d99 SHA1: 58997ee13e697cd096df82c264e479768ee0ef88 SHA256: 77365a982c054b8a4b926dd3eeaddf7fa41662fba6aa9bbb8ad4ce316fb94da7 SHA512: 565f94bfbdaaebe6368f4f3a721a30200f9823dcdcd5bceda3979a8eca182264fbe268432d12072d7df0ebd43bebd2681b9a02533f1317aa8d5a9966712eb44c Homepage: https://cran.r-project.org/package=LifeHist Description: CRAN Package 'LifeHist' (Life History Models of Individuals) Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions. Package: r-cran-lifeinsurancecontracts Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifeinsurer Filename: pool/dists/resolute/main/r-cran-lifeinsurancecontracts_0.0.6-1.ca2604.1_all.deb Size: 8304 MD5sum: f831699fc8bed2c494672528fb4ffbd2 SHA1: 622aa3efeb1efcf40530a292c0eea3b44914b16e SHA256: b0c712fd0b40e53a0e3742de53980aec191b48f91f61c838040e81b0ad9081f2 SHA512: 9fcdf18058965a6bf0f97a26e1ff8e3a2d4ecbbbe27ece35f0176b1f2a4fdd9546c1b711fa44eef62b5cbb2f1782c96fe26cf7c2b81162c9edb2c23cfbf2d477 Homepage: https://cran.r-project.org/package=LifeInsuranceContracts Description: CRAN Package 'LifeInsuranceContracts' (Framework for Traditional Life Insurance Contracts) Use of this package is deprecated. It has been renamed to 'LifeInsureR'. Package: r-cran-lifeinsurer Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1538 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-mortalitytables, r-cran-objectproperties, r-cran-lubridate, r-cran-openxlsx, r-cran-dplyr, r-cran-scales, r-cran-abind, r-cran-stringr, r-cran-rlang, r-cran-rmarkdown, r-cran-kableextra, r-cran-pander, r-cran-tidyr Suggests: r-cran-knitr, r-cran-magrittr, r-cran-tibble, r-cran-testthat, r-cran-fs, r-cran-here, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-lifeinsurer_1.0.1-1.ca2604.1_all.deb Size: 794646 MD5sum: 3dacc6ad7076f81e1207b95a99eea3e4 SHA1: d786ab3f70a3adb681a9f0543a22c3abc9e55b79 SHA256: d8bbc9056c61400eb5705c54d7870c93c05b8ef798c007c058dc0f571a0160cf SHA512: 4cc212d107684ddfa4e240b6ddc3553374b6d821859b92511f1493a8d102d02e6e468e7cf4a8533651b54bf395c18e94909dda382777f00865a41dcb787dffe9 Homepage: https://cran.r-project.org/package=LifeInsureR Description: CRAN Package 'LifeInsureR' (Modelling Traditional Life Insurance Contracts) R6 classes to model traditional life insurance contracts like annuities, whole life insurances or endowments. Such life insurance contracts provide a guaranteed interest and are not directly linked to the performance of a particular investment vehicle, but they typically provide (discretionary) profit participation. This package provides a framework to model such contracts in a very generic (cash-flow-based) way and includes modelling profit participation schemes, dynamic increases or more general contract layers, as well as contract changes (like sum increases or premium waivers). All relevant quantities like premium decomposition, reserves and benefits over the whole contract period are calculated and potentially exported to 'Excel'. Mortality rates are given using the 'MortalityTables' package. Package: r-cran-lifemapr Architecture: all Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-shiny, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr, r-cran-leaflet.minicharts, r-cran-htmltools, r-cran-rlang, r-cran-rcurl, r-cran-fastmatch, r-cran-arrow Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-lifemapr_1.1.6-1.ca2604.1_all.deb Size: 2545034 MD5sum: e7a706896649b8c56eff4a622d7fd1b0 SHA1: d76c2d1937b219547d544d4ed25448189db3d493 SHA256: b1cb74471a5991932273011462d01af6a0f87aca47eefc7e288b20f571c0d014 SHA512: 2a493f98bee4b9baf0d3332816501f1ccc4946cb2dcbf3c7ce170531ceed59ed23b2084a83156c0d00c88482719a1f9c2361ef49dce622c8731f2df71667c4aa Homepage: https://cran.r-project.org/package=LifemapR Description: CRAN Package 'LifemapR' (Data Visualisation on 'Lifemap' Tree) Allow to visualise data on the NCBI phylogenetic tree as presented in Lifemap . It takes as input a dataframe with at least a "taxid" column containing NCBI format TaxIds and allows to draw multiple layers with different visualisation tools. Package: r-cran-lifer Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-maptiles, r-cran-readr, r-cran-rmarkdown, r-cran-stringr, r-cran-terra, r-cran-tidyterra Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lifer_1.0.3-1.ca2604.1_all.deb Size: 105292 MD5sum: 9cd1be1e763a8da3649002c1b67157c9 SHA1: cd2ed0fc811f4705420523ec03e3dc5567e08c8c SHA256: 64e9a35e24c10099e0e5f3b7f7059e51b76d18d72ac3decc25968d41896b8d97 SHA512: 162d249448700579fd639d8c74ed627a658668367de315289d41dad2ddd9507dd280d6933cc6f55774942ece61a9d2298bc80ee46de2933589a29f580a2426c8 Homepage: https://cran.r-project.org/package=lifeR Description: CRAN Package 'lifeR' (Identify Sites for Your Bird List) A suite of tools to use the 'eBird' database () and APIs to compare users' species lists to recent observations and create a report of the top sites to visit to see new species. Package: r-cran-lifertable Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-lifertable_1.0.1-1.ca2604.1_all.deb Size: 90608 MD5sum: afb4e01a34d00bf7b91be62ac2b3f0ad SHA1: 55ba9f540855993b79fca09d249816836ca5f71f SHA256: 6d81204845d12a8d0e59e65438a4242f52b99a8b33abf0f36a71b86bd5c59f02 SHA512: 724e91f2cfdafa3fe5dc0bf066dff2ef94907ba9d5995195be9db4467a84e8a50dc5d840de62a8d62aba3ab30267836c61c9c42639e630f43a3783f44e475396 Homepage: https://cran.r-project.org/package=Lifertable Description: CRAN Package 'Lifertable' (Life and Fertility Tables Specially for Insects) Life and Fertility Tables are appropriate to study the dynamics of arthropods populations. This package provides utilities for constructing Life Tables and Fertility Tables, related demographic parameters, and some simple graphs of interest. It also offers functions to transform the obtained data into a known format for better manipulation. In addition, two methods for obtaining the confidence interval are included. Package: r-cran-lifetablebuilder Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-ggplot2, r-cran-readxl, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lifetablebuilder_0.1.2-1.ca2604.1_all.deb Size: 18574 MD5sum: 56ef5b6d40c33314f0131d68efaf2c89 SHA1: ad53750b784d9e3b4f87168dcdb67a43582c6f06 SHA256: 9ceaff0fc975870371a6c30521ed4a4742330ac6a871d8e863e90eebe74b8cdb SHA512: 0c546f6dcc786f78e520e2ffae19f880cda4eddee9c42669764ad5a25fdbe9208ae9b4c37ff5384724339d7c78efd2e2c7d47481f1c79f21b407cd82a2222d66 Homepage: https://cran.r-project.org/package=LifeTableBuilder Description: CRAN Package 'LifeTableBuilder' (Interactive 'shiny' Application for Constructing Life Tables) Provides an interactive 'shiny' application to construct stage-structured life tables from tabular input data. 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The application computes age-specific survival and fertility functions and estimates key demographic parameters including the net reproductive rate, mean generation time, intrinsic rate of increase, finite rate of increase and doubling time. Optional confidence intervals can be obtained using percentile bootstrap or delete-1 jackknife resampling at the female level. Methods and definitions follow Stevens (2009) and Rossini et al. (2024) . 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The evidential approach used here is based on the book "Likelihood" by A.W.F. Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall (2011) , "Understanding Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), S. Glover & P. Dixon and others. This package accompanies "Evidence-Based Statistics" by P. Cahusac (2020, ISBN-13 : 978-1119549802) . 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It provides functions for organizing, visualizing, and summarizing MLE outcomes, streamlining statistical analysis workflows. By improving interpretation and facilitating model evaluation, it helps users gain deeper insights into parameter estimation and model fitting, making MLE result exploration more efficient and accessible. See Goffe et al. (1994) for details on MLE, and Canham and Uriarte (2006) for application of MLE using 'likelihood'. 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The stacked bar plot is the preferred method for presenting Likert results. Tabular results are also implemented along with density plots to assist researchers in determining whether Likert responses can be used quantitatively instead of qualitatively. See the likert(), summary.likert(), and plot.likert() functions to get started. 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Package: r-cran-lilikoi Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2088 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-caret, r-cran-dplyr, r-cran-gbm, r-cran-ggplot2, r-cran-glmnet, r-cran-h2o, r-bioc-impute, r-cran-infotheo, r-bioc-limma, r-bioc-m3c, r-cran-metrics, r-cran-mlmetrics, r-cran-princurve, r-bioc-pathview, r-cran-plyr, r-bioc-preprocesscore, r-cran-proc, r-bioc-rcy3, r-cran-reticulate, r-cran-reshape, r-cran-rweka, r-cran-scales, r-cran-stringr, r-cran-survminer, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-lilikoi_2.1.1-1.ca2604.1_all.deb Size: 1678962 MD5sum: c6e348f95aa16788473cc1d2fde1c780 SHA1: 2c62a58f0968c71d9578436eb483ab5d6f442783 SHA256: cc73bbc1cc7a6d663b0ac80ed02ed8d57d897219807d1ea544ec680659c1d613 SHA512: 60817626af545185fc74ab055f52ff685d85559ffbc853e9e884f3ab5ff58baab2f572ffe42c0e9c266634fd1c10f34a1137b21d82119ab1ec1503e752bfb2db Homepage: https://cran.r-project.org/package=lilikoi Description: CRAN Package 'lilikoi' (Metabolomics Personalized Pathway Analysis Tool) A comprehensive analysis tool for metabolomics data. It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton Jeff Xia Travers Ching, Xun Zhu, Lana X. Garmire (2018) . Package: r-cran-lillies Architecture: all Version: 0.2.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-knitr, r-cran-pracma, r-cran-progress, r-cran-rlang, r-cran-survival, r-cran-tidyr Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-lillies_0.2.12-1.ca2604.1_all.deb Size: 462994 MD5sum: 34270638d33c002c7521f69119a8165d SHA1: 9ca663b6a2e0f3fe2a1f6371aba554db7363cea1 SHA256: d5851fc78b1b399174bcd223958b50a586b0d3df0c57d9a50df9332bd38f44a4 SHA512: b5f83d604a948d0e84918aa43d58f43f9148f1278e787b88f2766cc556c4121ffaeece1361c43ead089713a01cf8a7cc703750de02e9cdcb8a57159d16e3b653 Homepage: https://cran.r-project.org/package=lillies Description: CRAN Package 'lillies' (Estimation of Life Years Lost) Estimation of life expectancy and Life Years Lost (LYL, or lillies for short) for a given population, for example those with a given disease or condition. 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Briefly, in an L-system a series of symbols in a string are replaced iteratively according to rules to give a more complex string. Eventually, the symbols are translated into turtle graphics for plotting. Wikipedia has a very good introduction: en.wikipedia.org/wiki/L-system This package provides basic functions for exploring L-systems. 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The 'lipidomeR' provides a streamlined pipeline for the systematic interpretation of the lipidome through publication-ready visualizations of regression models fitted on lipidomics data. With 'lipidomeR', associations between covariates and the lipidome can be interpreted systematically and intuitively through heatmaps, where lipids are categorized by the lipid class and are presented on two-dimensional maps organized by the lipid size and level of saturation. This way, the 'lipidomeR' helps you gain an immediate understanding of the multivariate patterns in the lipidome already at first glance. You can create lipidome-wide heatmaps of statistical associations, changes, differences, variation, or other lipid-specific values. The heatmaps are provided with publication-ready quality and the results behind the visualizations are based on rigorous statistical models. Package: r-cran-lipidomicsr Architecture: all Version: 0.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-broom, r-cran-car, r-cran-ggiraph, r-cran-rcompanion, r-cran-tidyr, r-cran-cowplot, r-cran-dplyr, r-cran-fmsb, r-cran-ggplot2, r-cran-ggplotify, r-cran-ggrepel, r-cran-pheatmap, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-scales, r-cran-stringr, r-cran-tidyselect, r-cran-ggforce, r-cran-ggsci Filename: pool/dists/resolute/main/r-cran-lipidomicsr_0.3.6-1.ca2604.1_all.deb Size: 152428 MD5sum: cbdb4b23deeb48b91a2ed83c7ccbd10d SHA1: 7a0b7beb8584d67d021064f09ffc057333b96608 SHA256: 7a6a2faf9f36ce76a090c782063ebf4a14d4b742cc051016e11ba832f2e3af93 SHA512: 4d4995e1e76a67782c1cf1d1676c2cbd0514a27d74dbf66858a92c80d33c147aa8f7d3fd288a9646646fe2af9746396eb8984c3a4f791093cb3abf53e5d29256 Homepage: https://cran.r-project.org/package=LipidomicsR Description: CRAN Package 'LipidomicsR' (Elegant Tools for Processing and Visualization of LipidomicsData) An elegant tool for processing and visualizing lipidomics data generated by mass spectrometry. 'LipidomicsR' simplifies channel and replicate handling while providing thorough lipid species annotation. Its visualization capabilities encompass principal components analysis plots, heatmaps, volcano plots, and radar plots, enabling concise data summarization and quality assessment. Additionally, it can generate bar plots and line plots to visualize the abundance of each lipid species. 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Package: r-cran-liteq Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dbi, r-cran-rappdirs, r-cran-rsqlite Suggests: r-cran-callr, r-cran-covr, r-cran-processx, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-liteq_1.1.0-1.ca2604.1_all.deb Size: 86810 MD5sum: acdab5a68f7ce2e6054fe630a565af83 SHA1: d54bc0eacfc661f4547303d194e5f3714e15dd3a SHA256: 7910a225ba037e9fcf6ace8ec9afdb1edb5419448d2e4ca9ae471a27cdaec130 SHA512: 9a7eb5cc38446d276d4a98f5dd5b2e38aa16e44be1478ce7389ac19ad4bb3ffd30524227a6eb9da9a40e5c61f72bf4e044d415a07f24b7302a056aeb862365bb Homepage: https://cran.r-project.org/package=liteq Description: CRAN Package 'liteq' (Lightweight Portable Message Queue Using 'SQLite') Temporary and permanent message queues for R. Built on top of 'SQLite' databases. 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Access to Web of Science and Scopus requires personal API keys, while PubMed can be queried without one. The optional deduplication functionality requires the package 'ASySD' available from . Package: r-cran-litriddle Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4910 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-stylo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-litriddle_1.0.0-1.ca2604.1_all.deb Size: 4796838 MD5sum: 2fa0086e9eb92d87223ae4b54b9c2014 SHA1: 87c786f45a9e7ce348d8db2593a5bd3b8b9e7b14 SHA256: 17405a4719b3659608f4770ab7897cf4e327b309413de69c0cc52ae233a35c15 SHA512: 38dee9040c872ae8c1398ae90e2d5f6263e9e8ee3bd6eac00a0e8c7485665bce81ca658563e323fb2d95c28a236dcecd4a949aa5f9ee19d24ea079e02862ff99 Homepage: https://cran.r-project.org/package=litRiddle Description: CRAN Package 'litRiddle' (Dataset and Tools to Research the Riddle of Literary Quality) Dataset and functions to explore quality of literary novels. 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Package: r-cran-liver Architecture: all Version: 1.29-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5891 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-class, r-cran-ggplot2 Suggests: r-cran-proc, r-cran-skimr, r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-mltools, r-cran-forcats Filename: pool/dists/resolute/main/r-cran-liver_1.29-1.ca2604.1_all.deb Size: 5106906 MD5sum: a542bc4944dd07e1eaf64bd520a3822c SHA1: bc773e4eba1f3ed6fc1fd217c0fe1b8c71e2acf6 SHA256: 6914862fc27f5306ca373d09c526661d8f19f4238d6936b58acdf7f40fbbdabe SHA512: 7430309673b49b63ddeb36817dfe8e238f33feb7a81c793f0a3aeead87b6bf0373b26de0e6847c9a7ff981d9a0c8dfb874418b05de2b0d6445e34bd28abff4de Homepage: https://cran.r-project.org/package=liver Description: CRAN Package 'liver' (Toolkit and Datasets for Data Science) Provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book , making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge. Package: r-cran-ljexm Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs, r-cran-pdftools, r-cran-rstudioapi, r-cran-webshot Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ljexm_1.0.5-1.ca2604.1_all.deb Size: 24668 MD5sum: 5a2a0b7daf2342d6f6d65acd554a4bad SHA1: 30312f52b175a4b3c8ffb7b582924ff687f3bebe SHA256: 1780fb4240d6e95b515fc0167f36a786e045e873cd0d05e6f394cc82c16df6ff SHA512: 227430700abd65a406be0b24ac535d067cef62d7e91ed60804e038c5b84cccbef13aea68152c570f01cded8470dea3da26c8e203fda3c502d50877d466ba1351 Homepage: https://cran.r-project.org/package=LJexm Description: CRAN Package 'LJexm' (Extract, Convert, and Merge 'pdf' Files from 'zip' Files) Extracts 'zip' files, converts 'Word', 'Excel', and 'html'/'htm' files to 'pdf' format. 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Package: r-cran-llm.api Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-llm.api_0.1.3-1.ca2604.1_all.deb Size: 142012 MD5sum: 7e894d72768ef531b83de3ca025121b1 SHA1: 25554e474157d72bc752617a5fcb66006028b101 SHA256: b1add61bdbacc4d2a1e73f397ba6931089069d66600d3dbc57e219b14fe38b33 SHA512: 5554593ce4a0dabada4ba6d9a205a9f05691228f7c094b1298a3a2d21fe5cac1e6ac71b4e9c263ad8082dd86893034859a2b5039e00e15d1132545d9e146bb98 Homepage: https://cran.r-project.org/package=llm.api Description: CRAN Package 'llm.api' (Minimal LLM Chat Interface) A minimal-dependency client for Large Language Model chat APIs. Supports 'OpenAI' , 'Anthropic' 'Claude' , 'Moonshot' 'Kimi' , 'Ollama' , and other 'OpenAI'-compatible endpoints. Includes an agent loop with tool use and a 'Model Context Protocol' client . API design is derived from the 'ellmer' package, reimplemented with only base R, 'curl', and 'jsonlite'. Package: r-cran-llm Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-stringr, r-cran-rweka, r-cran-survey, r-cran-reghelper, r-cran-scales Suggests: r-cran-mlbench Filename: pool/dists/resolute/main/r-cran-llm_1.1.0-1.ca2604.1_all.deb Size: 74588 MD5sum: 23862969b64fb4a5d87a397a447a0736 SHA1: 8ed1be881f9a60bf0e68e326ecb560543459482e SHA256: e4124a897d1e49a1a4231c5591effb75191cd3e93695866e61a235b542262c9e SHA512: 24f6dbd67f3122f105b140d95be4aa5591a675c9c510db26a910378325492aefd1361d63e2e721e3ba020c539d38800de76a48dd08e8488aea8bd84b0e2b224b Homepage: https://cran.r-project.org/package=LLM Description: CRAN Package 'LLM' (Logit Leaf Model Classifier for Binary Classification) Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) ). Package: r-cran-llmagentr Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-dbi, r-cran-rsqlite, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-officer, r-cran-purrr, r-cran-timetk, r-cran-pdftools, r-cran-parsnip, r-cran-recipes, r-cran-workflows, r-cran-rsample, r-cran-modeltime.ensemble, r-cran-modeltime, r-cran-xml2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-usethis, r-cran-prophet, r-cran-forcats, r-cran-kernlab, r-cran-xgboost, r-cran-xfun, r-cran-modeltime.resample, r-cran-tidymodels, r-cran-tibble, r-cran-lubridate, r-cran-tesseract, r-cran-rvest, r-cran-fastdummies, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-llmagentr_0.3.2-1.ca2604.1_all.deb Size: 1696958 MD5sum: e760ef7666154f6c447c6c119449a54b SHA1: 7e9ac211fd1763e4e6b61a3be1a0e7684a42c259 SHA256: 2e5e07ee7b43d0336083618ff94d20fe1d0cb9abade32060b494c095d0fd4aff SHA512: d8368320e86852acb5ba18a39d3783a7d68dbe6f85959eb321ffefb66d174fdadb237315ae656271c6caa15b08f0a22bd0678c47aa3b62785af9a23f9c8662c3 Homepage: https://cran.r-project.org/package=LLMAgentR Description: CRAN Package 'LLMAgentR' (Language Model Agents in R for AI Workflows and Research) Provides modular, graph-based agents powered by large language models (LLMs) for intelligent task execution in R. Supports structured workflows for tasks such as forecasting, data visualization, feature engineering, data wrangling, data cleaning, 'SQL', code generation, weather reporting, and research-driven question answering. Each agent performs iterative reasoning: recommending steps, generating R code, executing, debugging, and explaining results. Includes built-in support for packages such as 'tidymodels', 'modeltime', 'plotly', 'ggplot2', and 'prophet'. Designed for analysts, developers, and teams building intelligent, reproducible AI workflows in R. Compatible with LLM providers such as 'OpenAI', 'Anthropic', 'Groq', and 'Ollama'. Inspired by the Python package 'langagent'. Package: r-cran-llmclean Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httr2, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-llmclean_0.1.0-1.ca2604.1_all.deb Size: 117132 MD5sum: 114c8ecc9ce7bd96bd4c97057f465db8 SHA1: daad9c06f282fdd4fa95405d7cecdbf09dabd64e SHA256: 50002897f68f8af480062d223810eb7f8fc674ffc24674b6309279f410857bf1 SHA512: 67e55ef1016fdea86183f761901e83e7f56cfed91a4adfc55e6305fead111db917afa7ecd5d6009686bcf996b9b8a95928b293409510b8caa72d7ae8b2cdb168 Homepage: https://cran.r-project.org/package=llmclean Description: CRAN Package 'llmclean' ('LLM'-Assisted Data Cleaning with Multi-Provider Support) Detects and suggests fixes for semantic inconsistencies in data frames by calling large language models (LLMs) through a unified, provider-agnostic interface. Supported providers include 'OpenAI' ('GPT-4o', 'GPT-4o-mini'), 'Anthropic' ('Claude'), 'Google' ('Gemini'), 'Groq' (free-tier 'LLaMA' and 'Mixtral'), and local 'Ollama' models. The package identifies issues that rule-based tools cannot detect: abbreviation variants, typographic errors, case inconsistencies, and malformed values. Results are returned as tidy data frames with column, row index, detected value, issue type, suggested fix, and confidence score. An offline fallback using statistical and fuzzy-matching methods is provided for use without any API key. Interactive fix application with human review is supported via 'apply_fixes()'. Methods follow de Jonge and van der Loo (2013) and Chaudhuri et al. (2003) . Package: r-cran-llmcoder Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstudioapi, r-cran-httr2, r-cran-miniui, r-cran-shiny, r-cran-stringi, r-cran-stringr, r-cran-rlang, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-llmcoder_1.2.0-1.ca2604.1_all.deb Size: 176852 MD5sum: af812a8298e86c6e4212a9bf4b2a4f95 SHA1: cd69592ed6982da64e17e6aefe70c718aa85adeb SHA256: 5708a255f64ea9001e2e76e903c16e924fc1bae1ae67b2c101ca67d2c80c93d4 SHA512: 23514e5feeabc313469c6a5b5dab06585d4bf8603a2b9c8832ba69d99e79b592ed4f0362a3df372e94f967b0740db5c5a3dad8e6751b97772cfdaa93b0694bcf Homepage: https://cran.r-project.org/package=llmcoder Description: CRAN Package 'llmcoder' (LLM-Powered Code Generation, Error Fixing, and Chat for'RStudio') An 'RStudio' addin that integrates large language model (LLM) assistance directly into the code-editing workflow. Features include: (1) generate R code from inline comments; (2) obtain LLM-assisted fixes for console errors; (3) insert plain-English explanations of selected code blocks; (4) a multi-turn Chat Panel with session-context awareness (loaded packages, global objects, source editor contents, console history). Supports 'OpenAI', 'Anthropic' (Claude), 'DeepSeek', 'Groq', 'Together AI', 'OpenRouter', 'Ollama' (fully local, no API key required), and any 'OpenAI'-compatible custom endpoint (e.g. 'LM Studio', 'vLLM', 'llama.cpp'). Package: r-cran-llmflow Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-glue, r-cran-jsonlite, r-cran-jsonvalidate Suggests: r-cran-ellmer, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-llmflow_3.0.2-1.ca2604.1_all.deb Size: 163094 MD5sum: ba21926edb9a8844a4921ea4b9267b0b SHA1: 0ace68fbd6b4eb3a9796dfdf36b38d4f6e588692 SHA256: eb235f4751caca0ab1ec943cb1e7eef300859d4feea8cafe03b68e48dcbeccc1 SHA512: 75a925ce3038a06e94c1360d1faf404b0d7fb9171a899b2322735977d6f42b272858acd5f13912b19001aa11782ac1a82e11f99325a38e2b01692e5cb3b254a7 Homepage: https://cran.r-project.org/package=llmflow Description: CRAN Package 'llmflow' (Reasoning and Acting Workflow for Automated Data Analysis) Provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management. Package: r-cran-llmhelper Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyprompt Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-llmhelper_1.0.0-1.ca2604.1_all.deb Size: 89928 MD5sum: 67caa4f8eb766cd7c851e90ea6f36ea3 SHA1: 37776dac1f6e60c35a7aa80a9ec2bac47e3dec0c SHA256: 6a94d58c6e9289e4f62332e3ca29afe1217807f01629613bdf8a58995d4c504a SHA512: 9f249ee8769814d9f0fbde1667868f596caf10ba57cdd1f1b66cb20939f73897f182570cf9a6e66865c8c507a71eb743287494f6ae591ab1f85a28e339766dd0 Homepage: https://cran.r-project.org/package=llmhelper Description: CRAN Package 'llmhelper' (Unified Interface for Large Language Model Interactions) Provides a unified interface for interacting with Large Language Models (LLMs) through various providers including OpenAI , Ollama , and other OpenAI-compatible APIs. Features include automatic connection testing, max_tokens limit auto-adjustment, structured JSON responses with schema validation, interactive JSON schema generation, prompt templating, and comprehensive diagnostics. Package: r-cran-llming Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-quanteda, r-cran-stopwords, r-cran-stringi, r-cran-reticulate, r-cran-text, r-cran-dbscan, r-cran-pracma, r-cran-jsonlite, r-cran-matrix, r-cran-text2vec Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-llming_1.2.1-1.ca2604.1_all.deb Size: 72896 MD5sum: 3aa71a2faca8e297228983f73075e1f4 SHA1: 522c82f655bcce41a487e8f4c0e26dff6f163ac8 SHA256: 919d49d3d2c3a9dadfa40fa14de5277d4c6dc22972929f3d910f835200c4aa0b SHA512: be105ba08009f7bf42bd29cb6b46247bb473195d1b5fd27db2b6709204145881a148ef623f09254d3e9f00b25a5fd6caad62522477102e0c6924454a9596d8ea Homepage: https://cran.r-project.org/package=LLMing Description: CRAN Package 'LLMing' (Large Language Model (LLM) Tools for Psychological Text Analysis) A collection of large language model (LLM) text analysis methods designed with psychological data in mind. 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Package: r-cran-llmtranslate Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-dt, r-cran-readxl, r-cran-writexl, r-cran-openxlsx, r-cran-httr2, r-cran-jsonlite, r-cran-glue, r-cran-later, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-llmtranslate_0.3.0-1.ca2604.1_all.deb Size: 54468 MD5sum: bd0be2d466332ae8047fa6f8b06367f0 SHA1: 6f7017af229178d267d45e5a91dd72f2048c5fec SHA256: ba4043c73a1d05d854902d02a67ae214bb6c3d6aae139a3158a9a90e3376edda SHA512: b39c1bf6e79a36b94db159971e4f8fca7935af27434ea944868424272b0b649b31a9f2f1c714227eebc665ddba18a3023e925fa159add1abb02c4ae923be5c94 Homepage: https://cran.r-project.org/package=LLMTranslate Description: CRAN Package 'LLMTranslate' ('shiny' App for TRAPD/ISPOR Survey Translation with LLMs) A 'shiny' application to automate forward and back survey translation with optional reconciliation using large language models (LLMs). Supports both item-by-item and batch translation modes for optimal performance and context-aware translations. Handles multi-sheet Excel files and supports OpenAI (GPT), Google Gemini, and Anthropic Claude models. Follows the TRAPD (Translation, Review, Adjudication, Pretesting, Documentation) framework and ISPOR (International Society for Pharmacoeconomics and Outcomes Research) recommendations. See Harkness et al. (2010) and Wild et al. (2005) . Package: r-cran-llogistic Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-llogistic_1.0.3-1.ca2604.1_all.deb Size: 14240 MD5sum: 06e49a8dfac384ff58ee3ff540944528 SHA1: b1383d9649395ee7984792701cf31c1b6050d8d4 SHA256: c8c123b1a45e863840c5dea41a80064177a09723edf8aa46dd8b4fdef6cc0d5b SHA512: fd5624f87ec8eec9754eb42d4107501b9e4570f02d315b415b51690dcb77aa30ba74b6a306524086c906172eabd537fd1f8d642d50667875346f3709bef65fc6 Homepage: https://cran.r-project.org/package=llogistic Description: CRAN Package 'llogistic' (The L-Logistic Distribution) Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and phi. The parameter m is the median of the distribution. Package: r-cran-llsr Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve, r-cran-openxlsx, r-cran-digest, r-cran-svdialogs, r-cran-minpack.lm, r-cran-ggplot2, r-cran-dplyr, r-cran-nleqslv, r-cran-crayon Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-llsr_0.0.4-1.ca2604.1_all.deb Size: 454014 MD5sum: 04e908f3b9ea5a38d1b092586bddfe8b SHA1: 27a272ff8e780e61f5e299dab78e35b12029d788 SHA256: 09fc00e4d6d2a4c50b9bc62ee8694dc534fb3a17f68907dac1b607d0161e66c7 SHA512: 36f9150d89f6a6f26f72044d8bae06b15fa92955fbed67315fb33943c38d15884781f2a83df915394e7d8df1024e546d354df2b65da0bd09992b109df3b4de69 Homepage: https://cran.r-project.org/package=LLSR Description: CRAN Package 'LLSR' (Data Analysis of Liquid-Liquid Systems using R) Originally design to characterise Aqueous Two Phase Systems, LLSR provide a simple way to analyse experimental data and obtain phase diagram parameters, among other properties, systematically. The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research. Package: r-cran-lm.beta Architecture: all Version: 1.7-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-lm.beta_1.7-3-1.ca2604.1_all.deb Size: 249600 MD5sum: 16f930751b991ee3528b8b085302d532 SHA1: 0cbfc2333a7911b5969ea607507010f3db1e175b SHA256: 969c78b793eae21ed731dfe1015bc42f8e780d2bc654b7fc470708c7576c751f SHA512: 1234bf3fbec67e770a2df3bd7fa0e4cfe915216ca4e943976888c58394cc3254ee7f8d19b487be3465a7c5a062c034a7d22d80fae815d2ba7d3ec87223df08b1 Homepage: https://cran.r-project.org/package=lm.beta Description: CRAN Package 'lm.beta' (Add Standardized Regression Coefficients to Linear-Model-Objects) Adds standardized regression coefficients to objects created by 'lm'. Also extends the S3 methods 'print', 'summary' and 'coef' with additional boolean argument 'standardized' and provides 'xtable'-support. Package: r-cran-lmboot Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evd Filename: pool/dists/resolute/main/r-cran-lmboot_0.0.1-1.ca2604.1_all.deb Size: 72376 MD5sum: 56a983f37ef9b0d8493bb53fb3d8f2b0 SHA1: 40d388a5a889e93161c95228c1c07e9524b98d22 SHA256: 46bd10c4568135b0d3b72885fd062701cf15f5442b212f17149a836492e07ff7 SHA512: 2cba2972733e1b22a5e25e42cc1be72d95c91c8b9b1a8d067672025b5f544e713660569f8cdea524b79bf90bb408e7e358d44be544d4f8a7ae0fb44860425852 Homepage: https://cran.r-project.org/package=lmboot Description: CRAN Package 'lmboot' (Bootstrap in Linear Models) Various efficient and robust bootstrap methods are implemented for linear models with least squares estimation. Functions within this package allow users to create bootstrap sampling distributions for model parameters, test hypotheses about parameters, and visualize the bootstrap sampling or null distributions. Methods implemented for linear models include the wild bootstrap by Wu (1986) , the residual and paired bootstraps by Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by Quenouille (1956) , and the Bayesian bootstrap by Rubin (1981) . Package: r-cran-lmd Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emd, r-cran-ggplot2, r-cran-patchwork, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggformula, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lmd_1.2.1-1.ca2604.1_all.deb Size: 369428 MD5sum: f66063e09bd803a936b1945f73525257 SHA1: 311a4066c53c176ca57bfc66f9f516083d1063c7 SHA256: 1206bac83a0ee5330ce3372b894cef282c7100cadc78dacab2b733bf98ab827f SHA512: fa981c76713bdd9dfa4eec9fe15cefd7e1a1effe684cd4aa895897a6febbe1297009f56488244de6c95b1b4156a3cc5f01ea19c2fd7f21f724a01cffafcdc623 Homepage: https://cran.r-project.org/package=LMD Description: CRAN Package 'LMD' (A Self-Adaptive Approach for Demodulating Multi-Component Signal) Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) and the 'Python' library 'PyLMD'. Package: r-cran-lmdiallel Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multcomp, r-cran-plyr, r-cran-sommer, r-cran-enhancer, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-lmdiallel_1.0.2-1.ca2604.1_all.deb Size: 246400 MD5sum: c2ed6e3a470edb317805b14fea206d28 SHA1: d0ebaa13228201858160893fc5a2ab5c2b262b99 SHA256: e0744a03afcca748d5e641276d544b0d1054866cb8196327c247b0af54ec122d SHA512: fc0509d2bf1b4fe64524c650266d9b4366962fe4f885a87f90f2c64d7c2af6452dd63df271eb5b9135ee623297c5a9c9bdaa20d7e2d53f7dde1933a695ed62fb Homepage: https://cran.r-project.org/package=lmDiallel Description: CRAN Package 'lmDiallel' (Linear Fixed/Mixed Effects Models for Diallel Crosses) Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in Onofri et al (2020) . Package: r-cran-lmds Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dynutils, r-cran-irlba, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lmds_0.1.0-1.ca2604.1_all.deb Size: 161952 MD5sum: f331886a2e2dabeddf220d6346ffea1d SHA1: f14fc139fd929d7f09ec2c9240db552329360b83 SHA256: 7a633a1cf1e08e215cfe6ab1478918d6199c7e65d518d9a2567de4fe16ee849d SHA512: 7510f248b54dacc17f5517d54e384c06a00ddc9e181a27bb78374a7603a53b8eb8ce5621dda4d5f97a9e5202f205f9b01133f5b17325b03a9bff3ace3ce136de Homepage: https://cran.r-project.org/package=lmds Description: CRAN Package 'lmds' (Landmark Multi-Dimensional Scaling) A fast dimensionality reduction method scaleable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated. Package: r-cran-lme4breeding Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-crayon, r-cran-enhancer Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-orthopolynom, r-cran-mass, r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-lme4breeding_1.1.2-1.ca2604.1_all.deb Size: 364976 MD5sum: 7257a892eaf5c1dd55c980371a4631b6 SHA1: 5a4efd4fe62f522eef5b3cbe7eec063a53937eff SHA256: c602f8365843fac7305dc39639a1a9c7a8801bdf1ed90af5149b8466fa9c50f6 SHA512: 5e07e39258957668ec602c9bfc38dc95372ee6f215fe40447a9c557bfd7b89d022eb737c8b05fa5f3dde3a4aedbcbaae4c6d77ae5e56619267478129c13c392c Homepage: https://cran.r-project.org/package=lme4breeding Description: CRAN Package 'lme4breeding' (Breeding-Related Mixed-Effects Models) Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'. Package: r-cran-lme4gs Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix Suggests: r-cran-bglr Filename: pool/dists/resolute/main/r-cran-lme4gs_0.1-1.ca2604.1_all.deb Size: 350660 MD5sum: ee9989fdd592d4a02ffa938e2b4e6014 SHA1: 1ac144b5670dd87bd764a8257481f517b0d7d768 SHA256: 502a48244432c8044e63e14e21bdfa7503931b21d36fd19e227a30377333ef27 SHA512: c0016e674d73084156b9d12ebc621f7824b1c30eb09d57ffc295653e891b6f9f3c3d2c72f5ab8fc2890d866ef165b7fc68f8273e5e37960f9f178e4439fdec47 Homepage: https://cran.r-project.org/package=lme4GS Description: CRAN Package 'lme4GS' ('lme4' for Genomic Selection) Flexible functions that use 'lme4' as computational engine for fitting models used in Genomic Selection (GS). GS is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). The 'lme4' is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. The 'lme4GS' package is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance-covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data. For more details see Caamal-Pat et.al. (2021) . Package: r-cran-lmeinfo Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-scdhlm, r-cran-mlmrev, r-cran-cardata, r-cran-lme4, r-cran-matrix, r-cran-merderiv Filename: pool/dists/resolute/main/r-cran-lmeinfo_0.3.2-1.ca2604.1_all.deb Size: 137256 MD5sum: 4bcda1072dc825ee593f915f69f95de9 SHA1: cc4c802af1333036ec633b9db41100fc4c7d2ec0 SHA256: 51973e401c49b46580c94b0f845a2dd17de0dee4e2fd94558288a2b6d0fc06f8 SHA512: 8c1147db4cdb8d4de9bcac2857974bfc59d037627db6598b6081c67123061d0d95ebb0862b1f090552f7aaa25807bb1cd66c8b5cabc56ad3360d9948ec0bd31c Homepage: https://cran.r-project.org/package=lmeInfo Description: CRAN Package 'lmeInfo' (Information Matrices for 'lmeStruct' and 'glsStruct' Objects) Provides analytic derivatives and information matrices for fitted linear mixed effects (lme) models and generalized least squares (gls) models estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively. The package includes functions for estimating the sampling variance-covariance of variance component parameters using the inverse Fisher information. The variance components include the parameters of the random effects structure (for lme models), the variance structure, and the correlation structure. The expected and average forms of the Fisher information matrix are used in the calculations, and models estimated by full maximum likelihood or restricted maximum likelihood are supported. The package also includes a function for estimating standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) ) based on fitted lme or gls models. Package: r-cran-lmerconveniencefunctions Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-lcfdata, r-cran-fields, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-lmerconveniencefunctions_3.0-1.ca2604.1_all.deb Size: 278972 MD5sum: 81af4662951378d2a52d8e05868ee297 SHA1: 13bd3093ad9b150d2ee513d8e9bb20e19cafd57a SHA256: e162b18bd29d69598bbda6bf165002fa2d36d97acb791bc368aa90cc5abe1bf2 SHA512: c5929b6c39e99d92eb2d70a0c4efb965d2b32bb3434d1dfe48bfb4c2f159ba7775e06f003559ebc174eddc9d8a1b8978c7e755ea95080c140e964e4637696cb2 Homepage: https://cran.r-project.org/package=LMERConvenienceFunctions Description: CRAN Package 'LMERConvenienceFunctions' (Model Selection and Post-Hoc Analysis for (G)LMER Models) The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data. Package: r-cran-lmeresampler Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-matrix, r-cran-nlmeu, r-cran-ggplot2, r-cran-ggdist, r-cran-hlmdiag, r-cran-purrr, r-cran-forcats, r-cran-statmod, r-cran-tidyr, r-cran-magrittr, r-cran-tibble Suggests: r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-mlmrev, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-lmeresampler_0.2.4-1.ca2604.1_all.deb Size: 392360 MD5sum: bc4db9c066700ae80b82a3736c26bbef SHA1: a6fb009533fae170f5a1faeadb4ad1fdbd3a31a1 SHA256: 9f195e961e75d993b5c10cd5ba36e9ea22620ec7c7776f02b19f0b84aa46c29c SHA512: db6040691beb8321a37beb9213e078c7f94525fa5308fac1a767e0dfab86ee47aac1ccc0ffcfbc4cc229ce53aaa43e0f03ef0103fb9fe7decddc61a40e981e2c Homepage: https://cran.r-project.org/package=lmeresampler Description: CRAN Package 'lmeresampler' (Bootstrap Methods for Nested Linear Mixed-Effects Models) Bootstrap routines for nested linear mixed effects models fit using either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the parametric, residual, cases, random effect block (REB), and wild bootstrap procedures. An overview of these procedures can be found in Van der Leeden et al. (2008) , Carpenter, Goldstein & Rasbash (2003) , and Chambers & Chandra (2013) . Package: r-cran-lmerperm Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmertest Filename: pool/dists/resolute/main/r-cran-lmerperm_0.1.9-1.ca2604.1_all.deb Size: 21860 MD5sum: b5ec36cd44bf5f81fb10a182d9d87fcc SHA1: 4668da81e8e2f2cb777184109ab8a9be786b77c3 SHA256: f780012f0e009c601031a07a34466753f289031dff2ecde0368cc3bfd7d5ea2c SHA512: dfe8591fbd6061cfb8d142a04487056ef67808e3c1d9e4c4d040a424932ae3869183292818dcfa73272f42990d60df20769bf9c8c17d2c4c8d392c7b418ad969 Homepage: https://cran.r-project.org/package=lmerPerm Description: CRAN Package 'lmerPerm' (Perform Permutation Test on General Linear and Mixed LinearRegression) We provide a solution for performing permutation tests on linear and mixed linear regression models. It allows users to obtain accurate p-values without making distributional assumptions about the data. By generating a null distribution of the test statistics through repeated permutations of the response variable, permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023) ). In this early version, we focus on the permutation tests over observed t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null distribution of the test statistic through repeated permutations of the response variable, each observed t values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop criterion (Anscombe (1953) ) is adopted to force permutation to stop if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so, we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate p-values. Package: r-cran-lmertest Architecture: all Version: 3.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-numderiv, r-cran-mass, r-cran-ggplot2, r-cran-reformulas Suggests: r-cran-pbkrtest Filename: pool/dists/resolute/main/r-cran-lmertest_3.2-1-1.ca2604.1_all.deb Size: 467180 MD5sum: a23a0e8f2ffee300642f795a6fe6abe4 SHA1: e8febffce00b63ba4e2d93a3b44fe264db5c9be6 SHA256: 4f317cea84c8901b73e3af9e616f7c2b14667f24c697c7cc4244526bf6f5885d SHA512: dce3fd686dbcf05eb794a7f946efed844476f9328e3fda8dbfe52e2319691fd2d9ae9c764944ef40323dae42240efbf89ef66fbd8b86cc5f20433653d7fdf24d Homepage: https://cran.r-project.org/package=lmerTest Description: CRAN Package 'lmerTest' (Tests in Linear Mixed Effects Models) Provides p-values in type I, II or III anova and summary tables for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available. 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The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method. 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Several empirical datasets are also included. Package: r-cran-lmforc Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lmforc_1.0.0-1.ca2604.1_all.deb Size: 431128 MD5sum: 14c4f6ea8d68e9ad2433b19289d5c4ea SHA1: 76f982214eccb309d4d8f2c3d8c351237910ed31 SHA256: a3d68719433db0f6b9c71abd6ddd973e0d2d59f3b1eaa16130daee82b93ea985 SHA512: 85894dca0735350af444e88ed368442ff14556da540c568901c2a751b20a8b9a3807465e9f8897f96e7f3d0480a9067438e1643ab9d84f52c53ba2f6ae340608 Homepage: https://cran.r-project.org/package=lmForc Description: CRAN Package 'lmForc' (Linear Model Forecasting) Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast. 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Most of them are simple functions for simple tasks which can be done with coding, but may not be easy for occasional users of R. Most of the tasks addressed are those sometimes needed when using the 'manymome' package (Cheung and Cheung, 2023, ) and 'stdmod' package (Cheung, Cheung, Lau, Hui, and Vong, 2022, ). However, they can also be used in other scenarios. 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This package implements maximum likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms from Girolami and Calderhead (2011) and Nesterov (2009) ), a parametric bootstrap algorithm, and diagnostic plots for the model class. 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Package: r-cran-lmmpar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-mnormt, r-cran-plyr, r-cran-doparallel, r-cran-bigmemory Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lmmpar_0.1.0-1.ca2604.1_all.deb Size: 16914 MD5sum: 59d0a255ba2f40457844745651818851 SHA1: c248c5febf5c74e390ef9196c85cd0626befffc0 SHA256: ec76c62a76d2c593dfdfc7da4d14dfeced1544e9cc59325ddf5d5c4052509a62 SHA512: 810d6f9ee980879259a9c014861349a450b81ad1c29b55ebcbb39c4bdf77b14afcd0c37c1b074496f62b643b77b21dd5621c92850a14d8e02616558119947cf3 Homepage: https://cran.r-project.org/package=lmmpar Description: CRAN Package 'lmmpar' (Parallel Linear Mixed Model) Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence. Package: r-cran-lmmstar Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-lava, r-cran-matrix, r-cran-multcomp, r-cran-nlme, r-cran-numderiv, r-cran-rlang Suggests: r-cran-asht, r-cran-data.table, r-cran-ggh4x, r-cran-ggpubr, r-cran-lattice, r-cran-mvtnorm, r-cran-lme4, r-cran-lmertest, r-cran-mice, r-cran-nlmeu, r-cran-optimx, r-cran-pbapply, r-cran-psych, r-cran-publish, r-cran-qqtest, r-cran-r.rsp, r-cran-reshape2, r-cran-rmcorr, r-cran-scales, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lmmstar_1.1.0-1.ca2604.1_all.deb Size: 3579132 MD5sum: ffb8685fed4829ee98e5a706d2e44e51 SHA1: 3a5573dff7669631cd3d5cc7415c1356aea14a13 SHA256: 39e135289e5867f44210002194adc0670f184ab5c1d831a4d281e8674da4f197 SHA512: 020ef762ccf3d401b752679a2d8230b4691220feab045c6ed74d85cc85b2f3aec060289b9868eae6dfbc46c14b4748a9fd916ed7c73dfcfd7be754a4ddc27274 Homepage: https://cran.r-project.org/package=LMMstar Description: CRAN Package 'LMMstar' (Repeated Measurement Models for Discrete Times) Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values. Package: r-cran-lmodel2 Architecture: all Version: 1.7-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-lmodel2_1.7-4-1.ca2604.1_all.deb Size: 343530 MD5sum: 8ac8548224183f4abb48c38fd40e796a SHA1: 838e3d37f25460b72af9e22742be8b3d889f26c4 SHA256: 2036529a0f29407576ec9829f25abd03697daa26c467afe6bf39e84d02956a1c SHA512: 00a03efd7f447c044765666c7f8964d0ac7f904c70d55953a3461a59d7d5e2ef82b179f62d63d1a4c6808ca672b55a7b250297c892f872acecd07087f58970da Homepage: https://cran.r-project.org/package=lmodel2 Description: CRAN Package 'lmodel2' (Model II Regression) Computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA). Package: r-cran-lmofit Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2808 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmom, r-cran-pracma, r-cran-ggplot2, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-lmofit_0.1.7-1.ca2604.1_all.deb Size: 2491228 MD5sum: d6a197ca84e307f940dc37a8bb58fa08 SHA1: 164ce1b1f88831ff0bb3118395c78a7b2b0c07bf SHA256: 5e092db444619d9e581823465727f8ec5b4f9548b6aa133451848cd6114591c7 SHA512: 148f349ccc0a9d347111454e82d2fb8451ca377dd48ec048d6e0c5174b8ed56397d6b8dc3969f724a7ba3f80faf676f2f2e34e9005860f5a6365bf4bd919b955 Homepage: https://cran.r-project.org/package=LMoFit Description: CRAN Package 'LMoFit' (Advanced L-Moment Fitting of Distributions) A complete framework for frequency analysis is provided by 'LMoFit'. It has functions related to the determination of sample L-moments as in Hosking, J.R.M. (1990) , the fitting of various distributions as in Zaghloul et al. (2020) and Hosking, J.R.M. (2019) , besides plotting and manipulating L-space diagrams as in Papalexiou, S.M. & Koutsoyiannis, D. (2016) for two-shape parametric distributions on the L-moment ratio diagram. Additionally, the quantile, probability density, and cumulative probability functions of various distributions are provided in a user-friendly manner. Package: r-cran-lmomco Architecture: all Version: 2.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3759 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-goftest, r-cran-lmoments, r-cran-mass Suggests: r-cran-copbasic Filename: pool/dists/resolute/main/r-cran-lmomco_2.5.5-1.ca2604.1_all.deb Size: 3347414 MD5sum: 3bda49ff650ce6fb3dd8361c92a8da1b SHA1: a1254807d29af3efed431a9e26f85d71948470f1 SHA256: f5158ca0ef9295a3a785f9614d81aa50672a2b44922d1cf3c54255cc4d653ecd SHA512: bde7c8069e87e841b66ff1e3ede94717bd7e3d9a2889d524b6a39f35509859aae9ef611d868466672c6e64eed21e412c5ab1e4126d1c30536a626dee146e76c8 Homepage: https://cran.r-project.org/package=lmomco Description: CRAN Package 'lmomco' (L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments,and Many Distributions) Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L]. 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The implemented functions are able to manage time series with constant and/or missing values without stopping the execution with error messages. The package also contains tools to calculate several indices based on variability (e.g. 'SPI' , Standardized Precipitation Index, see and ) for multiple time series or spatially gridded values. 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Package: r-cran-lmviz Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3029 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinyjs, r-cran-lmtest, r-cran-mgcv, r-cran-mass, r-cran-scatterplot3d, r-cran-rgl, r-cran-car Filename: pool/dists/resolute/main/r-cran-lmviz_0.2.0-1.ca2604.1_all.deb Size: 2470160 MD5sum: d782da697d5c1630b25bbcb4602fae17 SHA1: c8146434591f0beb9371b3533eae52546af36c4e SHA256: dea68e3953d22cf84a1d8a635612b528de780b8183199d632fc0080ae4e214ff SHA512: 88899536f806b0b4ad82c2f7a4c603a881fe36531e32ceddef9ce5b651f4798362ab04eaa97670c034bc27e65fda0a244ecce62bee8c4def3c80ba5abc924fb5 Homepage: https://cran.r-project.org/package=lmviz Description: CRAN Package 'lmviz' (A Package to Visualize Linear Models Features and Play with Them) Contains a suite of shiny applications meant to explore linear model inference feature through simulation and games. 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Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han S., et al. (2019) . 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Supports variable person speed functions (intercept, trend, quadratic), and covariates for item and person (random) parameters. Data missing-by-design can be specified. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, . 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(2022) . Package: r-cran-loa Architecture: all Version: 0.3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1069 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-png, r-cran-rgooglemaps, r-cran-openstreetmap, r-cran-sp, r-cran-rcolorbrewer, r-cran-mgcv, r-cran-plyr Suggests: r-cran-sf Filename: pool/dists/resolute/main/r-cran-loa_0.3.1.1-1.ca2604.1_all.deb Size: 1020656 MD5sum: 9c18c904bac8efda5cb2b92c0ee41eac SHA1: 19e8ad99b299623c13618db2f63cc12ed7ca4be8 SHA256: 28199dc7475203fa2cae59befa50de086d85e30f3dd5453f296f744f2e8c4c99 SHA512: 8728b5816c8f980a37fc4b20b87ba4fc9dadad958e57161c5e5eaaebcb48e0c2dfa8e6262e943371f0812410455dd9a05823d68fb463518fd5e1e5d69acd23aa Homepage: https://cran.r-project.org/package=loa Description: CRAN Package 'loa' (Lattice Options and Add-Ins) Various plots and functions that make use of the lattice/trellis plotting framework. The plots, which include loaPlot(), loaMapPlot() and trianglePlot(), and use panelPal(), a function that extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel and panel-to-key synchronization/management. 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It allows for search terms such as keywords, time periods and entity names. It then attempts to clean, or at least flag, filings that could provide incorrect results when seeking to answer the question: How much is being spent on lobbying our Congress and the administration and what issues do they care about? 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Factors such as lobster density on ocean floor, their movement, trap saturation and bait shrinkage rate can be modeled. Details of the methods for modeling those processes can be found in: Addison and Bell (1997) . 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The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) . Package: r-cran-localice Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-checkmate Suggests: r-cran-covr, r-cran-h2o, r-cran-mlbench, r-cran-randomforest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-localice_0.1.1-1.ca2604.1_all.deb Size: 31456 MD5sum: cc756d126846dccca5d0f65fefcdf31d SHA1: bcbed4bebe01d294283e53036b35e5ccac23a91e SHA256: 5ff84253f3b4ef7b2f472cc3a1fb1a701a2fff11eccbcc6ca335726e6f8bf8e7 SHA512: 5f7d9b5f805a038f1e9041832a2bb071f83270c171f63d8bf0a2c20d078f23d54af58326a0eb8504a79cc0bda45dc642043dc1c3eae78ff7337878525dc30fc0 Homepage: https://cran.r-project.org/package=localICE Description: CRAN Package 'localICE' (Local Individual Conditional Expectation) Local Individual Conditional Expectation ('localICE') is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI). localICE is a model-agnostic XAI approach which provides three-dimensional local explanations for particular data instances. The approach is proposed in the master thesis of Martin Walter as an extension to ICE (see Reference). The three dimensions are the two features at the horizontal and vertical axes as well as the target represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. For classification models, the number of classes can be more than two and each class is added as a different color to the plot. The given instance is added to the plot as two dotted lines according to the feature values. The localICE-package can explain features of type factor and numeric of any machine learning model. Automatically supported machine learning packages are 'mlr', 'randomForest', 'caret' or all other with an S3 predict function. For further model types from other libraries, a predict function has to be provided as an argument in order to get access to the model. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) . 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Package: r-cran-logan Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pander, r-cran-psych, r-cran-foreign, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-stringr, r-cran-magrittr, r-cran-modules Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-logan_1.0.1-1.ca2604.1_all.deb Size: 759282 MD5sum: dccfe7b6ba14bb501939a970c0b7b421 SHA1: b4b7c29395cb797e9aaa164eb07c4c6b957270f2 SHA256: 42c1b28b2e8311330edb83b277db4ed04af8e7a68943789725b4d7fcfc090fd6 SHA512: a995496e1f2f924327b4fa609d53fced9dc3dd4abecc1079568f2a7b9405bc043c195866bd8734318d1be2a5f8d0bde3f389d3a9215c9c1fe632d93818dc82f8 Homepage: https://cran.r-project.org/package=LOGAN Description: CRAN Package 'LOGAN' (Log File Analysis in International Large-Scale Assessments) Enables users to handle the dataset cleaning for conducting specific analyses with the log files from two international educational assessments: the Programme for International Student Assessment (PISA, ) and the Programme for the International Assessment of Adult Competencies (PIAAC, ). An illustration of the analyses can be found on the LOGAN Shiny app () on your browser. 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Package: r-cran-logcondens Architecture: all Version: 2.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ks Filename: pool/dists/resolute/main/r-cran-logcondens_2.1.9-1.ca2604.1_all.deb Size: 575428 MD5sum: e680447881bb3ccffd012e09dc44fef5 SHA1: febb3ec77b79bd0bf689656602a507296f0489b5 SHA256: ba76dda770d34dd8807569081f15513b24b4c4e207dc8ab12a3045b70c918a4a SHA512: bb8ee8e23278feb37e188708437f402ff0c0d22faac0edea732d17138ab6287a9df6e1870a344de55dd0bdb281c95eca1bc2e7f6c853d7301b84edea20494dd7 Homepage: https://cran.r-project.org/package=logcondens Description: CRAN Package 'logcondens' (Estimate a Log-Concave Probability Density from Iid Observations) Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available. Package: r-cran-logcondiscr Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-cobs Filename: pool/dists/resolute/main/r-cran-logcondiscr_1.0.7-1.ca2604.1_all.deb Size: 87374 MD5sum: 7b76e559b7108ee9468269dab34e6323 SHA1: ecf9f050c7f1ef4d7c699b3fb272332c89380701 SHA256: 3d85143e9476c169440e9e50502efe410b96eaa24df29a3bfb51b3a20482e4f9 SHA512: 0a516ac60380791a11d53c512711be77dbc80467a8813c2dec9233ef3b7323766c728048739288e0960dc94ae55d9aa97e01e4c354d6daab7c5f750cb352ed86 Homepage: https://cran.r-project.org/package=logcondiscr Description: CRAN Package 'logcondiscr' (Estimate a Log-Concave Probability Mass Function from DiscreteI.i.d. Observations) Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k. 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Package: r-cran-logging Architecture: all Version: 0.10-111-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-logging_0.10-111-1.ca2604.1_all.deb Size: 162392 MD5sum: ef09c4d134050629014365178a810e4a SHA1: 38a3198fc4b8d500790b71790dbdda44fe6ea6ab SHA256: 2dd4feefb85ed81bffd74dd49fe66e6f3ca0a70d8bc8198e280cc652a8ead430 SHA512: 5199c4b8e1d2c7c909def993ab5fbb2b28b10a45b18f57b24892bbce17b6acd01788047f7897ef9be65a0f7cf491a877335e66d203e234e8e3c784b66dccc8ea Homepage: https://cran.r-project.org/package=logging Description: CRAN Package 'logging' (R Logging Package) Pure R implementation of the ubiquitous log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting. Package: r-cran-loggit Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-loggit_2.1.1-1.ca2604.1_all.deb Size: 59462 MD5sum: 0d24bffc74a9df7f8fe4f792e2de6ccc SHA1: d35f034f8c8513f9c59c91102030a42baae7d997 SHA256: fc20762ffa1e1366d3f9b79b9d816571979ca2b1249115880697d713e8c04b0c SHA512: 91f9337175cf1327b2d1db1be7599011bfb5fdffebe277df94729db84a4b4f85b9c774e394f866f995249f6df22d841b2775e704643f94a119a0add7c009e793 Homepage: https://cran.r-project.org/package=loggit Description: CRAN Package 'loggit' (Modern Logging for the R Ecosystem) An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary log-writing interfaces. 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Package: r-cran-logib Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-logib_0.2.1-1.ca2604.1_all.deb Size: 102346 MD5sum: 3445e66a67da20e7c11486b14829c30f SHA1: 3250dc35600b657ece6f561e99526c597f1b1df8 SHA256: 15ad216bd904266fa35974e42250e1a27e811082936c830bf65a8b190dc94463 SHA512: 160aabc9f16b4ce83be39d2efa4d641d7f6acb3b9497c75dee34fc30c1b1fba2e0da85ab6e506b6bcff92ddd863845070e79bf2ca40fa1d82eee22a664d8644c Homepage: https://cran.r-project.org/package=logib Description: CRAN Package 'logib' (Salary Analysis by the Swiss Federal Office for Gender Equality) Implementation of the Swiss Confederation's standard analysis model for salary analyses in R. The analysis is run at company-level and the model is intended for medium-sized and large companies. It can technically be used with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis. This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities. Package: r-cran-logibin Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-doparallel, r-cran-data.table, r-cran-foreach, r-cran-iterators Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-logibin_0.3-1.ca2604.1_all.deb Size: 76552 MD5sum: b6deb3ce414caa84201bb322bf463820 SHA1: c99900193b43abff8f1723d1237b0c1156053916 SHA256: 56eb7e5695d2da69bcddd34ac055cee971224fbb7aa4d7921f26f89af21adb27 SHA512: 64353ad3d17333877315491b95968a5db3acbe85b89509090c61165136d73e560996c5334d4e6aaff186af7530bc7eb2f072b308cdb4fdf0e502790e7de2e3f9 Homepage: https://cran.r-project.org/package=logiBin Description: CRAN Package 'logiBin' (Binning Variables to Use in Logistic Regression) Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) . 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Wolf, B.J., Slate, E.H., Hill, E.G. (2010) . Package: r-cran-logihist Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-popbio Filename: pool/dists/resolute/main/r-cran-logihist_1.1-1.ca2604.1_all.deb Size: 53666 MD5sum: 3791eef3af152db2ae23188b6a3f69de SHA1: db639e6adffbfa614de718990753616ba59a544b SHA256: 1727f3ce9ca7d5118db8ac1f6c0a6bbf048cc3d01229da318bf8bb3fbf273a55 SHA512: ded3ce41361a4f0057117aa74eb105252c57ebbbf2017ace999959894e808262b3dae849c88123621955472395058f6e4e221467223fcb357bd4cc6abd7c8228 Homepage: https://cran.r-project.org/package=logihist Description: CRAN Package 'logihist' (Combined Graphs for Logistic Regression) Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions. Package: r-cran-login Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cookies, r-cran-dbi, r-cran-digest, r-cran-emayili, r-cran-htmltools, r-cran-shiny, r-cran-shinybusy, r-cran-shinyjs, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-login_0.9.3-1.ca2604.1_all.deb Size: 640760 MD5sum: 55c1f5e8d9a22632fb492254541ca3c1 SHA1: b3fb4ae68af4c966a4c2939f21fe7cb967473078 SHA256: 15384f60bccfdad58d607f9fed97af2f082b6aa386af06e9aec6d29de7bc8041 SHA512: f5970d646bbab30e00846b5217d5abfc73b7a72c307e558489a1dbcccfec1f8f5db095b91a0f01f6de52e018eb84a08b3cdb3e664f06ed82d78d5d997d9ad8ce Homepage: https://cran.r-project.org/package=login Description: CRAN Package 'login' ('shiny' Login Module) Framework for adding authentication to 'shiny' applications. Provides flexibility as compared to other options for where user credentials are saved, allows users to create their own accounts, and password reset functionality. Bryer (2024) . Package: r-cran-logistic4p Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-logistic4p_1.6-1.ca2604.1_all.deb Size: 85956 MD5sum: 1de4788fade80204e7d1c06c315b6058 SHA1: 51c9d5fd52098c06e6f412e90b9e32faf141fb53 SHA256: b6bcbc690b44518ed522f67c0c5a8796788729600852dfa082653f23cdea50cd SHA512: 618585b4a3dd82c3b1a253fe3731c78e6be8a69fc894a289b36738e2d63b6117afb81215e895a1c0120141bc26ab2af0bcfd3c6bceb79f1460be2fe7e6eeb8ba Homepage: https://cran.r-project.org/package=logistic4p Description: CRAN Package 'logistic4p' (Logistic Regression with Misclassification in DependentVariables) Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables. Package: r-cran-logisticcopula Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brglm2, r-cran-vinecopula, r-cran-rvinecopulib, r-cran-igraph, r-cran-numderiv, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-logisticcopula_0.1.0-1.ca2604.1_all.deb Size: 165920 MD5sum: e41af7089987bee76117be8d12e13dc4 SHA1: c61bbbd11be078551da603972c29f01d645e8e46 SHA256: affeb8a3de242bfcb43602f50e8c83fa518dbad68cfcd8aac34cfa34f920869e SHA512: ed0ffd7aaf2eaa206609ab7200973ffd972feb6bcc6d1a1d075dfd249f0836e1efcc6a2d69a432897887f7114beb5610b88b0f7c50223e1d097078fed9e54eb9 Homepage: https://cran.r-project.org/package=LogisticCopula Description: CRAN Package 'LogisticCopula' (A Copula Based Extension of Logistic Regression) An implementation of a method of extending a logistic regression model beyond linear effects of the co-variates. The extension in is constructed by first equating the logistic regression model to a naive Bayes model where all the margins are specified to follow natural exponential distributions conditional on Y, that is, a model for Y given X that is specified through the distribution of X given Y, where the columns of X are assumed to be mutually independent conditional on Y. Subsequently, the model is expanded by adding vine - copulas to relax the assumption of mutual independence, where pair-copulas are added in a stage-wise, forward selection manner. Some heuristics are employed during the process of selecting edges, as well as the families of pair-copula models. After each component is added, the parameters are updated by a (smaller) number of gradient steps to maximise the likelihood. When the algorithm has stopped adding edges, based the criterion that a new edge should improve the likelihood more than k times the number new parameters, the parameters are updated with a larger number of gradient steps, or until convergence. Package: r-cran-logisticcurvefitting Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-logisticcurvefitting_0.1.0-1.ca2604.1_all.deb Size: 11082 MD5sum: 72522916902b5218761cd730dec776ac SHA1: 52d344e456fe507c245ff90fd7bc82492c487ea7 SHA256: f14a5e04c4c474c311a542c7a107ea325bc39972cbc4c7e3ac8d15ef26165222 SHA512: b10790a8506aac709463c378d921d228b04a44e39521ba205f02f3104bccb8476ed79b4fea88db7e1504ba74363528411cf4d400091625272e1ec052e9ceeb39 Homepage: https://cran.r-project.org/package=LogisticCurveFitting Description: CRAN Package 'LogisticCurveFitting' (Logistic Curve Fitting by Rhodes Method) A system for fitting Logistic Curve by Rhodes Method. Method for fitting logistic curve by Rhodes Method is described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-logisticensembles Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adabag, r-cran-arm, r-cran-brnn, r-cran-c50, r-cran-car, r-cran-caret, r-cran-corrplot, r-cran-cubist, r-cran-doparallel, r-cran-dplyr, r-cran-e1071, r-cran-gam, r-cran-gbm, r-cran-ggplot2, r-cran-ggplotify, r-cran-glmnet, r-cran-gridextra, r-cran-gt, r-cran-htmltools, r-cran-htmlwidgets, r-cran-ipred, r-cran-klar, r-cran-machineshop, r-cran-magrittr, r-cran-mass, r-cran-mda, r-cran-pls, r-cran-proc, r-cran-purrr, r-cran-randomforest, r-cran-ranger, r-cran-reactable, r-cran-readr, r-cran-rpart, r-cran-scales, r-cran-tidyr, r-cran-tree, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-logisticensembles_1.0.2-1.ca2604.1_all.deb Size: 2297766 MD5sum: ff069f9404d26fbce0deaa4bad709310 SHA1: e556455420303dbf53340984897d0ddcd8b90b4c SHA256: 72d72cbdb7fb4ad0a45a6d7383c3f7c176154718c8482d1aad8317c6c33cc710 SHA512: f5022033f40d1b99be758d28de93e6d0aaca487908987c5c58a8be3bc6adeceb0b3fe9793eeacc227d01f6d0ab5e148dd89a7d9f252734a64697b18177c0f1b4 Homepage: https://cran.r-project.org/package=LogisticEnsembles Description: CRAN Package 'LogisticEnsembles' (Automatically Runs 18 Logistic Models-14 Individual LogisticModels and 4 Ensembles of Models) Automatically returns results from 18 logistic models including 14 individual logistic models and 4 logistic ensembles of models. The package also returns 25 plots, 5 tables, and a summary report. The package automatically builds all 18 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data, such as sports or medical data. The package includes medical data (the Pima Indians data set), and information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver Operating Characteristic (ROC) curves for each of the 18 models. Package: r-cran-logisticrci Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-logisticrci_1.1-1.ca2604.1_all.deb Size: 42090 MD5sum: 0187c23ba254ccf1e8383532750def7a SHA1: 426575eb30c4eeca152f031403944629107c83b4 SHA256: 54d8ee0b7008182e0e58e2654539862b34f385042bae01e101a1326425136421 SHA512: 3b36ccb287660dcca94fd863331a175b86b4648119b7010c97aa4a7ffed4d028dc6b5850fd984dbe136c8b34cac608d8522c3a9052977f23f71313d1e1a4aeba Homepage: https://cran.r-project.org/package=LogisticRCI Description: CRAN Package 'LogisticRCI' (Linear and Logistic Regression-Based Reliable Change Index) Here we provide an implementation of the linear and logistic regression-based Reliable Change Index (RCI), to be used with lm and binomial glm model objects, respectively, following Moral et al. . The RCI function returns a score assumed to be approximately normally distributed, which is helpful to detect patients that may present cognitive decline. Package: r-cran-logisticrr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-logisticrr_0.3.0-1.ca2604.1_all.deb Size: 101788 MD5sum: a50ce1403da4d355c014ff6edc0b796b SHA1: c7364c87b178a5cb057d65967a92bda8f568244a SHA256: c9cabfba912b9aff6342703330cf4fb39dc11a26bf9271a1e97b779c90e3e9ae SHA512: ae30fc74ce27babbbd28dab996381c5a95bd31730f34dd66ec7d489f1b818a1dcb24addd54a26e48128358e5aeafec2606c7d90989b3b800c28321f88f3e9e0b Homepage: https://cran.r-project.org/package=logisticRR Description: CRAN Package 'logisticRR' (Adjusted Relative Risk from Logistic Regression) Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders. 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The proposed methods were developed in Escabias et al (2004) and Escabias et al (2005) . Package: r-cran-logitnorm Architecture: all Version: 0.8.39-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-logitnorm_0.8.39-1.ca2604.1_all.deb Size: 201200 MD5sum: f6fa3c5bf976c13664a3a98025b30738 SHA1: d9fa3575f3fb625c576397d2bea2422113764d6c SHA256: dc76ef449805b7814863ef5525efaf260c1009ef2096d71dfd978e2db220babc SHA512: 8bc99ecf47a9b89f2d192cc02ed8b70315e88d5366fd7fbac317064c214ced3af73f49de3dd8fe007217dd0c3a0d1ddb61cd4d8d79097d8fc0180be806d17f72 Homepage: https://cran.r-project.org/package=logitnorm Description: CRAN Package 'logitnorm' (Functions for the Logitnormal Distribution) Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters. 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Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) . More details can be found in Helveston (2023) . 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For complex survey data, models can be fitted from design objects from the 'survey' package. Currently supported models include UNIDIFF (Erikson & Goldthorpe, 1992), a.k.a. log-multiplicative layer effect model (Xie, 1992) , and several association models: Goodman (1979) row-column association models of the RC(M) and RC(M)-L families with one or several dimensions; two skew-symmetric association models proposed by Yamaguchi (1990) and by van der Heijden & Mooijaart (1995) Functions allow computing the intrinsic association coefficient (see Bouchet-Valat (2022) ) and the Altham (1970) index , including via the Bayes shrinkage estimator proposed by Zhou (2015) ; and the RAS/IPF/Deming-Stephan algorithm. 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Package: r-cran-logr Architecture: all Version: 1.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-common, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidylog, r-cran-dplyr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-logr_1.3.9-1.ca2604.1_all.deb Size: 300348 MD5sum: 67828a8198228fdbb799ca8c60b93f7c SHA1: ffa95dba001d851cb0854f6402c7fd8028e8c0f9 SHA256: 0cd1300627ddf4b1665557a63c4e4b4cdb824884780389fd9812e71bc8ff1b7d SHA512: 43ab65da09d084a47e563365698a5b84a6fcc267c28acb3195ed591b211d4422ccb32c69dd7506e2fb0dc67b49c7a007f51a2884d3d1c7ef61ae4fe5f33c7058 Homepage: https://cran.r-project.org/package=logr Description: CRAN Package 'logr' (Creates Log Files) Contains functions to help create log files. The package aims to overcome the difficulty of the base R sink() command. The log_print() function will print to both the console and the file log, without interfering in other write operations. Package: r-cran-logregequiv Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-logregequiv_0.1.5-1.ca2604.1_all.deb Size: 67164 MD5sum: 7feecfc9af9c53ab9e8cabd006d89d44 SHA1: 451ccda823ef0c971fa297c9237631ec6130250d SHA256: 79e523c2c68fcb61d1d56dc5eb2ecdfbae8f1bdbf9651ac15881da77acb623d8 SHA512: 558d468457ff34501e3cdd3296c868e80f4f0ecfe490e9fda420d5b3e794cf28869ed1508efc35b384df29925d056ab0e32cfdeabef20975e702de0aff24b479 Homepage: https://cran.r-project.org/package=LogRegEquiv Description: CRAN Package 'LogRegEquiv' (Logistic Regression Equivalence) Tools for assessing equivalence of similar Logistic Regression models. Package: r-cran-logrx Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-stringr, r-cran-sessioninfo, r-cran-stringi, r-cran-tibble, r-cran-digest, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-covr, r-cran-pkgdown, r-cran-tplyr, r-cran-haven, r-cran-lintr, r-cran-xml2, r-cran-here, r-cran-readr, r-cran-rstudioapi, r-cran-tidyselect, r-cran-renv, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-logrx_0.4.0-1.ca2604.1_all.deb Size: 271966 MD5sum: ca55818c2ceb3d4da72e5d2809da3e3c SHA1: 0927e1fbf9885993e6b17480853dc3b25816f807 SHA256: f59d6d9a447052e256504df5edfd9c5014d943bac115c8f5b22d81f475c76e06 SHA512: bb3a96cdefc1235e8bf12e92e8c04a8e5a55ca5db39241563292541333de8212e60cfbcf2a09702f92ea660a3e047f0cbd13ba30af4fb11e4c921121c89386b7 Homepage: https://cran.r-project.org/package=logrx Description: CRAN Package 'logrx' (A Logging Utility Focus on Clinical Trial Programming Workflows) A utility to facilitate the logging and review of R programs in clinical trial programming workflows. Package: r-cran-logrxaddin Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-logrx, r-cran-stringr, r-cran-miniui, r-cran-rstudioapi, r-cran-shiny, r-cran-waiter Filename: pool/dists/resolute/main/r-cran-logrxaddin_0.0.1-1.ca2604.1_all.deb Size: 16054 MD5sum: 06c244bfaa094b71dd1adb909cb96b43 SHA1: ed7be6d064a14c72c75effdde321e9d3ace92555 SHA256: 83cfa08634ca7c96c22ba384e5cd8ff15eaeb6aa95e1983a7ea23e92e8cba0da SHA512: 00d7cf6883823f6c77c5202994f1f2db624cb87ab74db999571c3ab7da3267a5600e2d5d3ddfb41414bb2614de469bac2662c34a01ccd8d975fa99eb04faedf9 Homepage: https://cran.r-project.org/package=logrxaddin Description: CRAN Package 'logrxaddin' (Addin for the 'logrx' Package) This is an extension package to 'logrx', which is a log creation program focused on Clinical Reporting within the Pharma Industry. This package enables a simple 'shiny-based' Add-in that provides a point and click interface to produce a log for a single program. Package: r-cran-lolr Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3755 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-abind, r-cran-mass, r-cran-irlba, r-cran-pls, r-cran-robust, r-cran-robustbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-randomforest, r-cran-latex2exp, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-lolr_2.1-1.ca2604.1_all.deb Size: 2661488 MD5sum: e2c3ffd575b5d1afa49e7bf99bc2d512 SHA1: 8ca39c6016ce5c795705133b3ce159abbc173bbf SHA256: 0c2eb847b74d5bc6958ed80d182cb2a63856d717cb6d8311bbb7d9bf2c604426 SHA512: 47aee634f09d958fdd6eae5da0a172c451baf788aebf0c07ffc21b577d48f171a07288bf8712cfc1b81502ebe69e41342c885d4d9345f93899000b012ed6ca9c Homepage: https://cran.r-project.org/package=lolR Description: CRAN Package 'lolR' (Linear Optimal Low-Rank Projection) Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) , we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications. Package: r-cran-lomb Architecture: all Version: 2.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2651 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-plotly, r-cran-pracma, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-lomb_2.5.0-1.ca2604.1_all.deb Size: 1590946 MD5sum: 06b366362a7175e7c7a10858bdb3c7a0 SHA1: bd9d34472564d238f88d8cb1aceb555a868e7ee1 SHA256: ccf051172af827e4d838c63842453ba3182ec782565ed1a33a14eb485dbb0923 SHA512: 4695c4144bf70f0897d8a1894cbaed801cc2c5960f3714a2d0a6c742e93381447a438b8dab51d457e8710efce8cad3c82213885dfe011a048c46542592c69ebb Homepage: https://cran.r-project.org/package=lomb Description: CRAN Package 'lomb' (Lomb-Scargle Periodogram) Computes the Lomb-Scargle Periodogram and actogram for evenly or unevenly sampled time series. Includes a randomization procedure to obtain exact p-values. Partially based on C original by Press et al. (Numerical Recipes) and the Python module Astropy. For more information see Ruf, T. (1999). The Lomb-Scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biological Rhythm Research, 30(2), 178-201. Package: r-cran-long2lstmarray Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-long2lstmarray_0.2.0-1.ca2604.1_all.deb Size: 54278 MD5sum: d9eec2e7969c6a212f7fa265e1cf1236 SHA1: 3e76fa396ebd40d57ad3026e4be6a3d2df4d40b1 SHA256: d062809e07666778b5fd77615d579cedf990cf2a885db3ee5481f73146c75f52 SHA512: f57c18aaa8c0b13579b6130b00b65ff14cce833709e0ea17f8dd939190c7dd040de16f8c0fee1075cf5b050928746b75831df0a32244b90ca12d91d9c5b7add9 Homepage: https://cran.r-project.org/package=long2lstmarray Description: CRAN Package 'long2lstmarray' (Longitudinal Dataframes into Arrays for Machine LearningTraining) An easy tool to transform 2D longitudinal data into 3D arrays suitable for Long short-term memory neural networks training. The array output can be used by the 'keras' package. Long short-term memory neural networks are described in: Hochreiter, S., & Schmidhuber, J. (1997) . Package: r-cran-longcart Architecture: all Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-rpart, r-cran-survival, r-cran-magic, r-cran-survminer, r-cran-formula Filename: pool/dists/resolute/main/r-cran-longcart_3.2-1.ca2604.1_all.deb Size: 314906 MD5sum: f982290e1ff6052fd1a739038d286a17 SHA1: 23bb0a02379b6fed6f36e8b6d21573ec5a57095e SHA256: d4ec4296753c635e2445ee1c531ef8c1905ea9d542d0656615d310c7b20244b3 SHA512: 43f06722e7c9e18f4044b9388be048acc2f6d58c04e5452f88086328680494cfdebe6c4d7ba780f4d5b4507c20998c451a7da2a166958d6c2847a7591922f96b Homepage: https://cran.r-project.org/package=LongCART Description: CRAN Package 'LongCART' (Recursive Partitioning for Longitudinal Data and Right CensoredData Using Baseline Covariates) Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial. 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'LongDat' is able to take in several data types as input, including count, proportion, binary, ordinal and continuous data. The output table contains p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy. Package: r-cran-longdecomphe Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-corpcor, r-cran-ggplot2, r-cran-patchwork, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-longdecomphe_0.1.0-1.ca2604.1_all.deb Size: 186842 MD5sum: 494c9e58682b65610298a61dd7f1d9b5 SHA1: 8b412c10bc3334dcf1b34fb4e75eb7a71bec2c93 SHA256: 40af168e8a0d29009966ac14e86763ccc545d8da62776397e02ee6442842ac88 SHA512: 15f249cb762d022020b7b2c0e4bb48d1bbf940543e364ecce9ade203e8a0e92520f2fe90eaa04ce54c1ab08b1a74af7f06771adebe16314eff27f1c6786844b8 Homepage: https://cran.r-project.org/package=LongDecompHE Description: CRAN Package 'LongDecompHE' (Longitudinal Decomposition of Health Expectancy by Age and Cause) Provides tools to decompose differences in cohort health expectancy (HE) by age and cause using longitudinal data. The package implements a novel longitudinal attribution method based on a semiparametric additive hazards model with time-dependent covariates, specifically designed to address interval censoring and semi-competing risks via a copula framework. The resulting age-cause-specific contributions to disability prevalence and death probability can be used to quantify and decompose differences in cohort HE between groups. The package supports stepwise replacement decomposition algorithms and is applicable to cohort-based health disparity research across diverse populations. Related methods include Sun et al. (2023) . Package: r-cran-longit Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aiccmodavg, r-cran-missforest, r-cran-r2jags, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-longit_0.1.0-1.ca2604.1_all.deb Size: 72354 MD5sum: 878801dfaf0cc87ee63d10a385b248f9 SHA1: deb819da93acb48c8102a92c330479b9eb3e99f6 SHA256: 63c3116a83a9adccd1032fa719914c2a160532ca73e68c07300d32fd689d9479 SHA512: 450f8dc1d0a677152923bb62d4d385e8b8d3cb668fcfa3e5fddc265158e229f4022cee94e236e1aa2827afe62c27b4ab2402a868e25638b2a63a8b2046b2f949 Homepage: https://cran.r-project.org/package=longit Description: CRAN Package 'longit' (High Dimensional Longitudinal Data Analysis Using MCMC) High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) . Package: r-cran-longitudinal Architecture: all Version: 1.1.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor Filename: pool/dists/resolute/main/r-cran-longitudinal_1.1.13-1.ca2604.1_all.deb Size: 81754 MD5sum: 64bcb0c39dbf1fd8d7b5feaf51c49ee2 SHA1: 5c365207b994422589b25ff102f672dffbf04c32 SHA256: 9f0f7258410645c9ceff4fafb1484442722da36951b889b94c8c28a6b6c377cb SHA512: 77c9e0768c728be670302562d162ebd191470032fe52a862ee0ef90b72386ed66a02886083f5e1ec1321be6cdf980824b127febd23bf419e1be332f96657636c Homepage: https://cran.r-project.org/package=longitudinal Description: CRAN Package 'longitudinal' (Analysis of Multiple Time Course Data) Contains general data structures and functions for longitudinal data with multiple variables, repeated measurements, and irregularly spaced time points. Also implements a shrinkage estimator of dynamical correlation and dynamical covariance. Package: r-cran-longitudinalanal Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-mass, r-cran-dlm Filename: pool/dists/resolute/main/r-cran-longitudinalanal_0.2-1.ca2604.1_all.deb Size: 60642 MD5sum: 26d17e250d77b42fb4a59d2694edc944 SHA1: 171a99cf1fa958f1d1a35ac24cd2e9d2f528e439 SHA256: 9cdb11cae250daec359877a41c52a1f1b94c290f73809c26588c0cf494d6640f SHA512: 3a3e39558dc9937d4c879e46d6e0af45c2a6a830076d0e4d7a2bc428604bd0b73f92b00d44081d6955ad7b648a06ab76ae393e5a12b62103c273dd691418266e Homepage: https://cran.r-project.org/package=longitudinalANAL Description: CRAN Package 'longitudinalANAL' (Longitudinal Data Analysis) Regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2023) and Liu, C. et al. (2023) . Package: r-cran-longitudinalcascade Architecture: all Version: 0.3.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-survival, r-cran-ggplot2, r-cran-tidyr, r-cran-zoo, r-cran-scales, r-cran-rlang, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-longitudinalcascade_0.3.2.6-1.ca2604.1_all.deb Size: 300508 MD5sum: fefec01f760f7fe8d21d6d446e8bcf23 SHA1: f662560763fd355c18f2607c0403a5baa8e9ac12 SHA256: 84a20ef6532894dcee8b948fb3c1dc0a5a59116c8a3814402945b92db61deb7a SHA512: ece02f625fbb0a9189120121b7e5437b4b05f8c9662bff8c64fd2fa7a1e4443f967eaefaef173772c0113f4935c8a73277f8934103b3caf21e518f46c492a68c Homepage: https://cran.r-project.org/package=longitudinalcascade Description: CRAN Package 'longitudinalcascade' (Longitudinal Cascade) Creates a series of sets of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests. Package: r-cran-longiturf Architecture: all Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-rpart, r-cran-mvtnorm, r-cran-latex2exp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-longiturf_0.9-1.ca2604.1_all.deb Size: 153874 MD5sum: bfa6a16a70d75aec7cb2ac96a6aca9ed SHA1: 8abcff5c3ab63b53c09e262f872b2e1e7601376b SHA256: 33b4074b66b7e189c5efec4e08d1b7d28cb1b18bbafc6dac27a6871fc2ab6417 SHA512: 5f487d55d5ffefcdc0e9903044656a483d4d923e961dbc154a4612de615854b2821ee750d3e05959094d91c55d2110bd793100e622947016229f7c415af72c0d Homepage: https://cran.r-project.org/package=LongituRF Description: CRAN Package 'LongituRF' (Random Forests for Longitudinal Data) Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) Random forests for high-dimensional longitudinal data. Package: r-cran-longke Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-bvls, r-cran-fdapace, r-cran-mvtnorm, r-cran-dplyr, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-longke_0.1.0-1.ca2604.1_all.deb Size: 55914 MD5sum: d99cc4be1181a71ed34a686cd6ae5733 SHA1: d7d69e45cdeb478295e774ee3ae5cb4c302fcd18 SHA256: f0b552e2ceb300fbee73daf22a58e6e2654990cbd343aa4ca30fac8adafea6f9 SHA512: db317014f7c76dae0a453a41aee8d55dab7afc085ef2881160d69a34a2802042b53a6b128633809a793b61ebc6fa0a84b173c09f588396cbf0a395154ca8207e Homepage: https://cran.r-project.org/package=longke Description: CRAN Package 'longke' (Nonparametric Predictive Model for Sparse and IrregularLongitudinal Data) The proposed method aims at predicting the longitudinal mean response trajectory by a kernel-based estimator. The kernel estimator is constructed by imposing weights based on subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. Users could also perform variable selections to derive functional predictors with predictive significance by the proposed multiplicative model with multivariate Gaussian kernels. 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Package: r-cran-loopanalyst Architecture: all Version: 1.2-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-loopanalyst_1.2-7-1.ca2604.1_all.deb Size: 223396 MD5sum: 1834752f7aff3fee737cd4d60469ae55 SHA1: 2b02f7aba054ec0d06604371acd9de261f579704 SHA256: 07415736f8b8ee824c5987083e8e6702ebdf463ec6ed6dd6c6367edbc6460fc4 SHA512: fe62f5ff11dce511bee51bb3968e846c79b7a80946ee7099e19a3bfe241f4de5a8b23d77d5271b67de6123d72d2085e8814a127007bc5373e1dc1c26f0f0bda1 Homepage: https://cran.r-project.org/package=LoopAnalyst Description: CRAN Package 'LoopAnalyst' (A Collection of Tools to Conduct Levins' Loop Analysis) Performs Levins' loop analysis of qualitatively-specified complex causal systems. 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Three main functions are provided in this package: (i) LASER(): it generates specially-designed artificial relevant samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii) rEB.proc(): performs empirical Bayes inference based on LASERs. The details can be found in Mukhopadhyay, S., and Wang, K (2021, ). Package: r-cran-lpsmooth Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpgraph, r-cran-lpbkg, r-cran-truncnorm, r-cran-nloptr, r-cran-hmisc, r-cran-orthopolynom, r-cran-polynom Filename: pool/dists/resolute/main/r-cran-lpsmooth_0.1.3-1.ca2604.1_all.deb Size: 83522 MD5sum: be883367880c60131eaa9cea32e9da21 SHA1: 9f5e55550ffd15d1ffa69d80c32ee760df2da232 SHA256: 9f17bfc53b6d0a0c85b836d3d05aaac5f869797181c184d38e00731e7901e9ff SHA512: 29bfa96d707a7c6e27785a6bf0a291a29abebb6d83163b3df3b3fd0f812c445eaeeec0b22f9e949abdaa014a8c9f28f695600479cd89581392f5f281359f9df4 Homepage: https://cran.r-project.org/package=LPsmooth Description: CRAN Package 'LPsmooth' (LP Smoothed Inference and Graphics) Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) . 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(Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung." Wien: facultas. (ISBN: 978-3-7089-1343-8, ) zur Verfügung. Package: r-cran-lsasim Architecture: all Version: 2.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-cli, r-cran-polycor Suggests: r-cran-testthat, r-cran-knitr, r-cran-formatr, r-cran-rmarkdown, r-cran-naepirtparams Filename: pool/dists/resolute/main/r-cran-lsasim_2.1.6-1.ca2604.1_all.deb Size: 523534 MD5sum: 84b98d59f93f1bd227313972c9c042b8 SHA1: d97e1041877944a20a25c55b2bd1662872d5b70b SHA256: 381d2010081464077a25c776d27d8e8af97b6eb375bdda0978ef71c6e873c31f SHA512: 7063ecae83a347a2c832b49b50d32798f8268935c79f0198c6f71822ea2c1b3ca786d30879d4ccb9560f7bf68f708b362c75fdd88bd0c9eaf62a934848d13513 Homepage: https://cran.r-project.org/package=lsasim Description: CRAN Package 'lsasim' (Functions to Facilitate the Simulation of Large Scale AssessmentData) Provides functions to simulate data from large-scale educational assessments, including background questionnaire data and cognitive item responses that adhere to a multiple-matrix sampled design. The theoretical foundation can be found on Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018) . Package: r-cran-lsbs Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ks, r-cran-numderiv, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-lsbs_0.1-1.ca2604.1_all.deb Size: 31242 MD5sum: 67aca7b1e58b24d6011148678cdad335 SHA1: 5fbee783adf95ae860bb0636f0eaf34145e7956e SHA256: 50e7c9e177bdde23c9fc280cccfcad8d8d84831e48fe0b8469149e6b4de818fa SHA512: 9d53b65f0bcecec1496ea6e1230bf999ff28c5b5afba9e10a234646e5ab335451013d6661b9570ebd88d3fcc9921bbae9e524fa599a4fe0bb70a81d5284784a9 Homepage: https://cran.r-project.org/package=lsbs Description: CRAN Package 'lsbs' (Bandwidth Selection for Level Sets and HDR Estimation) Bandwidth selection for kernel density estimators of 2-d level sets and highest density regions. It applies a plug-in strategy to estimate the asymptotic risk function and minimize to get the optimal bandwidth matrix. See Doss and Weng (2018) for more detail. Package: r-cran-lsd Architecture: all Version: 4.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-lsd_4.1-0-1.ca2604.1_all.deb Size: 248734 MD5sum: cb7c1c4020b19587035662e296539437 SHA1: 4c09c5db5de5b138f4e317efd2d8c689959758d7 SHA256: 6c585917f56707000bc9eb0aa2741af4063d0b932a0e8c5b0641e0b4cf010849 SHA512: 67be337c2695cdc615130c1b5589426f39837408b166022557693c6ba1eea1f99bf4108d522118d8a412f27b5966deb785ddf04e130b880c0787a0b8b20f781f Homepage: https://cran.r-project.org/package=LSD Description: CRAN Package 'LSD' (Lots of Superior Depictions) Create lots of colorful plots in a plethora of variations. Try the LSD demotour(). Package: r-cran-lsdbc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-lsdbc_0.1.0-1.ca2604.1_all.deb Size: 23868 MD5sum: 0ef332c636db942ed0d563a44223f58a SHA1: e48922a9115ff618cfad25afea856cc39e61eddc SHA256: 5eea703e91b09059e68e9ff19b82da40ebf8324869c29a0a9b022bd23f7abf0e SHA512: 372a6dd25370eed3771371e5600830e4ac2d30eb7e2842257aa7d12d73e56f7af576f5629e0f2a5235d02073c4fa18addfc1f205c071eb608cfa855f0b5c5c35 Homepage: https://cran.r-project.org/package=lsdbc Description: CRAN Package 'lsdbc' (Locally Scaled Density Based Clustering) Implementation of Locally Scaled Density Based Clustering (LSDBC) algorithm proposed by Bicici and Yuret (2007) . This package also contains some supporting functions such as betaCV() function and get_spectral() function. Package: r-cran-lsdinterface Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-abind, r-cran-paralleldist Filename: pool/dists/resolute/main/r-cran-lsdinterface_1.2.5-1.ca2604.1_all.deb Size: 187870 MD5sum: e0bccc28c92d2fd35d297f6e5dccd0a2 SHA1: 8ddfc5135c8ff45ca499a67a6f7bb0b23540565a SHA256: 1627f82a408f9ee4dc6d9b955f49eff353cbcc73059625d1db987bb16c868754 SHA512: 5c7cd7dfe539d74666fc61e76f386822075dae8e81550731ccb25c60ef2ec4b31b214c485f1d159a9327738fb294bad44b89460a465499581cd4a35a1ac05d94 Homepage: https://cran.r-project.org/package=LSDinterface Description: CRAN Package 'LSDinterface' (Interface Tools for 'LSD' Simulation Results Files) Interfaces 'R' with 'LSD' simulation models. Reads object-oriented data in results files (.res[.gz]) produced by 'LSD' and creates appropriate multi-dimensional arrays in 'R'. 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Package: r-cran-lsdirf Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-digest, r-cran-gplots, r-cran-abind, r-cran-partykit, r-cran-randomforest Suggests: r-cran-lsdinterface, r-cran-lsdsensitivity Filename: pool/dists/resolute/main/r-cran-lsdirf_0.1.4-1.ca2604.1_all.deb Size: 147654 MD5sum: 1fb4003542e0e2bd75e692fc92c56d09 SHA1: a68b65cdca3c21a5c8cda9a94c0c14b8157a1dae SHA256: 7972ed83277570114a87963bddb1d549b8a42a41b501f3ca91851ad032f0ba4d SHA512: 84e95116a354756f685abb3893f551beb4efaf739e08400361baa713c28280fabb1e47b3b678e6da75201e8f68a0e401df881a37242a7628d27fb51be9418ad1 Homepage: https://cran.r-project.org/package=LSDirf Description: CRAN Package 'LSDirf' (Impulse-Response Function Analysis for Agent-Based Models) Performing impulse-response function (IRF) analysis of relevant variables of agent-based simulation models, in particular for models described in 'LSD' format. Based on the data produced by the simulation model, it performs both linear and state-dependent IRF analysis, providing the tools required by the Counterfactual Monte Carlo (CMC) methodology (Amendola and Pereira (2024) ), including state identification and sensitivity. CMC proposes retrieving the causal effect of shocks by exploiting the opportunity to directly observe the counterfactual in a fully controlled experimental setup. 'LSD' (Laboratory for Simulation Development) is free software available at ). Package: r-cran-lsdsensitivity Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 738 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lsdinterface, r-cran-tseries, r-cran-ksamples, r-cran-diptest, r-cran-lawstat, r-cran-abind, r-cran-sensitivity, r-cran-car, r-cran-randtoolbox, r-cran-rgenoud, r-cran-dicekriging, r-cran-xml Suggests: r-cran-gplots, r-cran-rgl, r-cran-normalp Filename: pool/dists/resolute/main/r-cran-lsdsensitivity_1.3.2-1.ca2604.1_all.deb Size: 653758 MD5sum: 2c38236fdd13e3c8c55fbd7b5def0600 SHA1: 30e69c5e6ee41238b28c3f8dcf03cf13a7c7f5c8 SHA256: 6e3230e9ad596badbfabc9d9fc10daca5c23848dab35b4650c6a3fcfb4813dfa SHA512: c59cafcef4b0b37ca5231f2acca7874b4efe1508f709046db164d635504a338f4d15f128ca66feac4db7965cd599610914cc438b4be58c06308dbf9598091ec1 Homepage: https://cran.r-project.org/package=LSDsensitivity Description: CRAN Package 'LSDsensitivity' (Sensitivity Analysis Tools for 'LSD' Simulations) Tools for sensitivity analysis of 'LSD' simulation models. Reads object-oriented data produced by 'LSD' simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) ) is estimated using the simulation data to provide the data required by the Sobol decomposition. 'LSD' (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at ). Package: r-cran-lse Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-lse_1.0.0-1.ca2604.1_all.deb Size: 39120 MD5sum: 8bda2b86f9ecba929fed4f4dcb06cd8d SHA1: 8ad0b26ae5bc72305dfe59b3cc4df8e97daeebd4 SHA256: 1b8ff4d3637f64cf0db7e2cdf29fec5c2e9c01cb6ded6c329123c182f0f83e15 SHA512: c49d971cf10d6113ecf80a612dd9e94ada8c9042e371b7e3aaa78c46143cbd1377128a18943fb9304b8090ede33c48fe841a959ac887b73369d6c0737796dc6b Homepage: https://cran.r-project.org/package=LSE Description: CRAN Package 'LSE' (Constrained Least Squares and Generalized QR Factorization) The solution of equality constrained least squares problem (LSE) is given through four analytics methods (Generalized QR Factorization, Lagrange Multipliers, Direct Elimination and Null Space method). 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Package: r-cran-lsebootls Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-lsts, r-cran-tibble, r-cran-iterators, r-cran-rlecuyer Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lsebootls_0.1.0-1.ca2604.1_all.deb Size: 131310 MD5sum: 2e80f6415508d24ff9de98ea50ba66a1 SHA1: d62c085c7c46e4f1cf738a7b6ff8f11c84763c83 SHA256: 579ee17fab9aff232ce936c4f5960442d5ed16a1c7091674b11114c01fdcb1db SHA512: b3a14c6c075911ff9265c7af7297487adbe4f7eec3aa10cd254e296ab8f0ea0aa27a72a897f95938a28ba838005678e437541f404feacdf972b837cadd77d28b Homepage: https://cran.r-project.org/package=LSEbootLS Description: CRAN Package 'LSEbootLS' (Bootstrap Methods for Regression Models with Locally StationaryErrors) Implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis. Package: r-cran-lsl Architecture: all Version: 0.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-lavaan Filename: pool/dists/resolute/main/r-cran-lsl_0.5.6-1.ca2604.1_all.deb Size: 168226 MD5sum: e36150c4e1eabe06a5b227280c51f1e6 SHA1: d7ede9bf47ddd375daae563c19a0ff3944ce8a42 SHA256: 838e50fdf3408bf5079bde05db14df3736acb4a458213c076f432d7deb045449 SHA512: 6e933ea101b77700b6e0ee1e44956307b66849b01dba0343664d3543439445d49a897564f3f908ee1a4d5d76dd5b882e27bbf34ccee4abe8b7e4aa3120e58f78 Homepage: https://cran.r-project.org/package=lsl Description: CRAN Package 'lsl' (Latent Structure Learning) Fits structural equation modeling via penalized likelihood. Package: r-cran-lsm Architecture: all Version: 0.2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-lsm_0.2.1.5-1.ca2604.1_all.deb Size: 142610 MD5sum: c63a5bce50bd78275f38c955c325c39f SHA1: a260ebc227735fd0bb62b6b3156aac56362e82bd SHA256: d644f01f42aa8e7c18653fa13b19955898fc5e05fe177fbe2ecf11f4246c5911 SHA512: 95f1553cc8928a81a922bb31e7c93b078424b8651bbf54cf6940f39fbe2e25ebc0ddd4a28063b932d541d3ef3e947c751f0b1f1bb346646d7ae8e8588fc93afa Homepage: https://cran.r-project.org/package=lsm Description: CRAN Package 'lsm' (Estimation of the log Likelihood of the Saturated Model) When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K. Package: r-cran-lsmeans Architecture: all Version: 2.30-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emmeans Filename: pool/dists/resolute/main/r-cran-lsmeans_2.30-2-1.ca2604.1_all.deb Size: 39160 MD5sum: f9f88026c3148f53ce54d0dd09f65b4c SHA1: e61d2b06450fe95d3bdd8e56c6ccdcacaa3e1445 SHA256: 001a3671352b204e24dc01ce17b9a1b13fe8a122008d131cdc1feeef68d41e2d SHA512: 66beb2f8d8bdb4fdffe8bb5c71f4005a5bb272d3d796c5215effcf88b78f887dee54b0797f0f51e040aa1fa4856a356e3a749d020efca7bf6ecf208a3cf8f764 Homepage: https://cran.r-project.org/package=lsmeans Description: CRAN Package 'lsmeans' (Least-Squares Means) Obtain least-squares means for linear, generalized linear, and mixed models. Compute contrasts or linear functions of least-squares means, and comparisons of slopes. Plots and compact letter displays. Least-squares means were proposed in Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers", Tech Report ARS-20-8, USDA National Agricultural Library, and discussed further in Searle, Speed, and Milliken (1980) "Population marginal means in the linear model: An alternative to least squares means", The American Statistician 34(4), 216-221 . NOTE: lsmeans now relies primarily on code in the 'emmeans' package. 'lsmeans' will be archived in the near future. Package: r-cran-lsmontecarlo Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-fbasics Filename: pool/dists/resolute/main/r-cran-lsmontecarlo_1.0-1.ca2604.1_all.deb Size: 105428 MD5sum: 22bd6784c7648816aa38756c1c3a1c43 SHA1: 6db026a64daa91dae751f654a03712f806acb87a SHA256: af3e5a6969993aa920de5eaab44f3ba76fca744a2772352423abf449b9e39341 SHA512: 28a15eecca63cfdc63e2d74f0138159e2dc342154e8246c1a3d396959d5e0d4782eb96109059ced59278dbe1cef3acad9c6618d361f5973c3e8d996973fe1dc2 Homepage: https://cran.r-project.org/package=LSMonteCarlo Description: CRAN Package 'LSMonteCarlo' (American options pricing with Least Squares Monte Carlo method) The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution. Package: r-cran-lsmrealoptions Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-nfcp Filename: pool/dists/resolute/main/r-cran-lsmrealoptions_0.2.1-1.ca2604.1_all.deb Size: 130764 MD5sum: 933138af89debe2719b1c1ca0c7a35da SHA1: 092c2da63636015084204dac4d022ac33278d7a2 SHA256: 3bfe26f446642acac6c39f99c722ee29be3dffeaa9e5a03aa5ca897fd81ccef1 SHA512: e530c49b3b48d5c8746e14c00776bc7c376c9d7c81530995dcb88d2c102e862e31627d4839940c3a7b58220db03d24bce2066f7e28bc8944282ea125cd652ed8 Homepage: https://cran.r-project.org/package=LSMRealOptions Description: CRAN Package 'LSMRealOptions' (Value American and Real Options Through LSM Simulation) The least-squares Monte Carlo (LSM) simulation method is a popular method for the approximation of the value of early and multiple exercise options. 'LSMRealOptions' provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. 'LSMRealOptions' values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. 'LSMRealOptions' provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) . 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H., Thyholt, K., Næs, T. (2004) "A Comparison of Methods for Analysing Regression Models with Both Spectral and Designed Variables" Journal of Chemometrics, 18(10), 451--464, . 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Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.6. 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Over the past decade, remote sensing has become a key tool for monitoring and predicting environmental variables by using satellite data. This package presents the main applications in remote sensing for land surface monitoring and land cover mapping (soil, vegetation, water...). Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) . 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This paper presents an S-Plus/R program that implements a recently developed inference procedure (Jin, Lin and Ying, 2006) for the accelerated failure time model based on the least-squares principle. Package: r-cran-lst Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra Filename: pool/dists/resolute/main/r-cran-lst_2.0.0-1.ca2604.1_all.deb Size: 48734 MD5sum: 4917990033986411628ba1111ec3d090 SHA1: 389456a12618f740a04a16245ca246c8826072f1 SHA256: bb0ec7c236530edf849e5be852a74cb2e4c40feb8de904c57e40cc69b589cf8d SHA512: 3b1ec4ffbcd55372455b49119e1495e1f6c66a94f6051701a1aef98a18d568059dde6d4d9a590dc84ac7660ab5dba95da4138863127e2bff0dded05336f7ee7f Homepage: https://cran.r-project.org/package=LST Description: CRAN Package 'LST' (Land Surface Temperature Retrieval for Landsat 8) Calculates Land Surface Temperature from Landsat band 10 and 11. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). . Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). . Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). . Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). . On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). . Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). . Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. Skoković D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014). 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This approach can be used for single time-point situations (cross-sectional data) and multiple time-point situations (longitudinal data) to investigate how the covariates are associated with attribute mastery. For multiple time-point situations, the three-step approach of latent transition CDM with covariates allows researchers to assess changes in attribute mastery status and to evaluate the covariate effects on both the initial states and transition probabilities over time using latent logistic regression. Because stepwise approaches often yield biased estimates, correction for classification error probabilities (CEPs) is considered in this approach. The three-step approach for latent transition CDM with covariates involves the following steps: (1) fitting a CDM to the response data without covariates at each time point separately, (2) assigning examinees to latent states at each time point and computing the associated CEPs, and (3) estimating the latent transition CDM with the known CEPs and computing the regression coefficients. The method was proposed in Liang et al. (2023) and demonstrated using mental health data in Liang et al. (in press; annotated R code and data utilized in this example are available in Mendeley data) . 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The tool here presented carries out a series of instructions that harmonize the attributes in terms of name, meaning, and occurrence, while also introducing a series of new variables, instrumental to adding value to the product. Outputs include one harmonized table with all the years, and three separate geometries, corresponding to the theoretical point, the gps location where the measurement was made and the 250m east-facing transect. 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Supports three integration strategies: early, parallel, and serial. Provides model fitting and tuning, lasso-type regularization for exposure and omics feature selection, handling of missing data, including both sporadic and complete-case patterns, prediction, and g-computation for estimating causal effects of exposures, bootstrap inference for uncertainty estimation, and S3 summary and plot methods. For the multi-omics integration framework, see Jia (2024) . For the missing-data imputation mechanism, see Jia (2024) . 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We provide tools for calculating those moments and also performing decompositions into contributions from, for example, individual traits, environmental impacts, and luck (also called individual stochasticity). The functions included here are based on Snyder and Ellner (2024) , Cochran and Ellner (1992) , and Hernandez et al. (2024) . 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Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data. Package: r-cran-m3 Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ncdf4, r-cran-sf, r-cran-maps, r-cran-mapdata Filename: pool/dists/resolute/main/r-cran-m3_0.4-1.ca2604.1_all.deb Size: 2032606 MD5sum: b306e94a40de9241a8bb50424632a769 SHA1: c8fde5260b726a6fa12b5a1bdca4b41f5e89cb3b SHA256: 47a02ca1c6f0c65a608bf5d119c295b0bff186a1f0c619d129266457a482582b SHA512: e46b7d5adb945b2362c4a1e13598f313176f4d5ff20a0d01148b4ebf738fa535e123dee8302a95123fd0d69fe96bb25f723fdd9ba03c7b3c524ff6fada4d51ab Homepage: https://cran.r-project.org/package=M3 Description: CRAN Package 'M3' (Reading M3 Files) Provides functions to read in and manipulate air quality model output from Models3-formatted files. This format is used by the Community Multiscale Air Quality (CMAQ) model. Package: r-cran-m3jf Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-snftool, r-cran-dplyr, r-cran-intersim Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-m3jf_0.1.0-1.ca2604.1_all.deb Size: 85962 MD5sum: 3db2536791bbde5cbddaa78f3e9927fb SHA1: daf9b78a1d61ab881364e7fa05b645c54a6787f3 SHA256: 73bb52a3b0cc4ddfcb09ff8ff12adb8cf116343881ba04de7a6f2b50fbb9c3e1 SHA512: 5a261e51e6ca7b94b57274acab11cad8e68111f3cd565b29a1c6575ec00cd07a2a2c04802dca11178df96854ad14cc9a53da2e9ac7924b575339112c3d99daae Homepage: https://cran.r-project.org/package=M3JF Description: CRAN Package 'M3JF' (Multi-Modal Matrix Joint Factorization for IntegrativeMulti-Omics Data Analysis) Multi modality data matrices are factorized conjointly into the multiplication of a shared sub-matrix and multiple modality specific sub-matrices, group sparse constraint is applied to the shared sub-matrix to capture the homogeneous and heterogeneous information, respectively. Then the samples are classified by clustering the shared sub-matrix with kmeanspp(), a new version of kmeans() developed here to obtain concordant results. The package also provides the cluster number estimation by rotation cost. Moreover, cluster specific features could be retrieved using hypergeometric tests. Package: r-cran-m61r Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-m61r_0.1.0-1.ca2604.1_all.deb Size: 132926 MD5sum: e1ee53420bad3ee5a40290cacc364b91 SHA1: ac9d685a947c92dda73c6d00cf630159c0213034 SHA256: d43c1dfdfc1ed5b4b27fe855a039f15662a464336992caeba55b5988281635de SHA512: 7682281e623e71e86cf3467f4bd705ebba74da02bf022c5acd3570a914aa847c95aa16fd183e67dbb862e32391bdd741606142526ad958941be1361c4db2efb4 Homepage: https://cran.r-project.org/package=m61r Description: CRAN Package 'm61r' (Package About Data Manipulation in Pure Base R) A lightweight, dependency-free data engine for R that provides a grammar for tabular and time-series manipulation. Built entirely on Base R, 'm61r' offers a fluent, chainable API inspired by modern data tools while prioritizing memory efficiency and speed. It includes optimized versions of common data verbs such as filtering, mutation, grouped aggregation, and approximate temporal joins, making it an ideal choice for environments where external dependencies are restricted or where performance in pure R is required. Package: r-cran-maaper Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-genomeinfodb, r-bioc-rsamtools, r-bioc-iranges, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-maaper_1.1.1-1.ca2604.1_all.deb Size: 206998 MD5sum: a549c3496074821b22a837aa71c88b70 SHA1: 6ae524b689f790f743785d1b46d44c7c42506b34 SHA256: 2a73230641844fb0886d5b73fa7b074f300772847545e868dae3d9becc218694 SHA512: 17ca4be0c4e33f0693fd8cc625801a109c648e877e82f0e7a84d51132fb9b0e6d3fcf064fb19cc5fa233f4bcadde925c1ae58dbe60b2aa2996d4a1054b2de746 Homepage: https://cran.r-project.org/package=MAAPER Description: CRAN Package 'MAAPER' (Analysis of Alternative Polyadenylation Using 3' End-LinkedReads) A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) . Package: r-cran-maat Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testdesign, r-cran-readxl, r-cran-mass, r-cran-diagram Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-maat_1.1.1-1.ca2604.1_all.deb Size: 1603234 MD5sum: db9436a34dede87f3d33697e36c8ab61 SHA1: 8550e3d725fd8da252bb0cc6342bc838a5238c3f SHA256: 13e7efbe53ebb7ecad766e9e0fe8077be7f0e5dc777d08886739b1d871b06a59 SHA512: b72aff4ab5ce60353c8a2729d4c8601192dfdbafb459bea230f43e61eac5582d9e5f3456002fd5fd712ba5cef71c237e9aa38483caf28800174d914b749b81ef Homepage: https://cran.r-project.org/package=maat Description: CRAN Package 'maat' (Multiple Administrations Adaptive Testing) Provides an extension of the shadow-test approach to computerized adaptive testing (CAT) implemented in the 'TestDesign' package for the assessment framework involving multiple tests administered periodically throughout the year. This framework is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports multiple item pools vertically scaled and multiple phases (stages) of CAT within each test. Between phases and tests, transitioning from one item pool (and associated constraints) to another is allowed as deemed necessary to enhance the quality of measurement. Package: r-cran-mabacr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mabacr_0.1.0-1.ca2604.1_all.deb Size: 25462 MD5sum: c13c6190c3de0829727082618e4ff4dc SHA1: 3dade1fb2f77a737669ccd81f8c1126196f83a56 SHA256: e4978af2097340ef78e70a026bec8c9f7f267fa8e78ad2cb792b5c61e4f284e6 SHA512: 5607256b8deb835126935280ab6c0a4d459eb73b1060141ae8220eca486fc4112a9c6f764ff8809c8da8e4da211ee3295b252c07b850be4370bd105425a8916b Homepage: https://cran.r-project.org/package=mabacR Description: CRAN Package 'mabacR' (Assisting Decision Makers) Easy implementation of the MABAC multi-criteria decision method, that was introduced by Pamučar and Ćirović in the work entitled: "The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC)" - - which aimed to choose implements for logistics centers. This package receives data, preferably in a spreadsheet, reads it and applies the mathematical algorithms inherent to the MABAC method to generate a ranking with the optimal solution according to the established criteria, weights and type of criteria. The data will be normalized, weighted by the weights, the border area will be determined, the distances to this border area will be calculated and finally a ranking with the optimal option will be generated. Package: r-cran-macbehaviour Architecture: all Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-httr, r-cran-dplyr, r-cran-rjson Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-macbehaviour_1.2.8-1.ca2604.1_all.deb Size: 79964 MD5sum: 1be0bf82594d4a5d10082dceb86becfe SHA1: 27628a1a4cbd41899ebd41c3dff5e5da0a470537 SHA256: 28e4ced2a7948e9e953f11a9d9fbee443a17fbe9b9f3d895b6b6734ceb0f7c8a SHA512: fc9e5a9f481b138267457ca9d30793f55371bde149b229e719283126d0e0d3c6a3ea9e40a8606ca5952fc97f057cae7472d90acaed0fe447ac00a72c8b89d9cd Homepage: https://cran.r-project.org/package=MacBehaviour Description: CRAN Package 'MacBehaviour' (Behavioural Studies of Large Language Models) Efficient way to design and conduct psychological experiments for testing the performance of large language models. It simplifies the process of setting up experiments and data collection via language models’ API, facilitating a smooth workflow for researchers in the field of machine behaviour. Package: r-cran-macer Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rentrez, r-cran-ape, r-cran-httr, r-cran-ggplot2, r-cran-pbapply, r-cran-png Filename: pool/dists/resolute/main/r-cran-macer_0.2.1-1.ca2604.1_all.deb Size: 95170 MD5sum: d5019978ef8a8d89c380ca034e5bcb02 SHA1: b5ab13a2ae048f5f6feed83203151db70b0edbd2 SHA256: fe187d6d67e79653fb454292d15fde15f13f64f8158e431a5acc6527f0e78275 SHA512: 987d849ef7cd171279fb15851f2e73d868580241ee976992d4294c42dc235171bf1576b5ddc81c1867d9937bd83646749c0901b85b2363144cc4a1a30e657532 Homepage: https://cran.r-project.org/package=MACER Description: CRAN Package 'MACER' (Molecular Acquisition, Cleaning, and Evaluation in R 'MACER') To assist biological researchers in assembling taxonomically and marker focused molecular sequence data sets. 'MACER' accepts a list of genera as a user input and uses NCBI-GenBank and BOLD as resources to download and assemble molecular sequence datasets. These datasets are then assembled by marker, aligned, trimmed, and cleaned. The use of this package allows the publication of specific parameters to ensure reproducibility. The 'MACER' package has four core functions and an example run through using all of these functions can be found in the associated repository . 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The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures. Package: r-cran-maclogp Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bma, r-cran-plot.matrix, r-cran-rlist Filename: pool/dists/resolute/main/r-cran-maclogp_0.1.1-1.ca2604.1_all.deb Size: 82288 MD5sum: 31ea52e3ad4ea0e2a8f2ce9f4abefeba SHA1: 765831b2aeec8f7bd3391b5618cba08ded1b4535 SHA256: 4211c452dcc4fa5beea5b6ccaab10d27f5cfba4def26cd3070d396f7c736dcc1 SHA512: 01dbb7636e63a465ef252b67f3556c228099a3137999b060e36762f3eeaf9d1b419bf27195b62f85820dd1af83866b261a03aa6fa1b431115e8b80c94fa9eb5f Homepage: https://cran.r-project.org/package=maclogp Description: CRAN Package 'maclogp' (Measures of Uncertainty for Model Selection) Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) . The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package. Package: r-cran-macro Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-common, r-cran-fmtr, r-cran-crayon Suggests: r-cran-sassy, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-macro_0.1.6-1.ca2604.1_all.deb Size: 659966 MD5sum: 21d8ce480d38d7a294c33c6126134af5 SHA1: 4ff685200c7be21ace342e75d11e48363f60ea42 SHA256: 1f15372284f405bb0cd542aedfd0122b5b3438f51b921fac3dacc19247a97798 SHA512: 07209a77fb0e766947d19c09a36ae2e1e900887f59df2c8eba1433206c02c68dc25d90cd991805a60d8c9b9dbae08e735030b0d05ed35723eee0c0a8cbd74c1c Homepage: https://cran.r-project.org/package=macro Description: CRAN Package 'macro' (A Macro Language for 'R' Programs) A macro language for 'R' programs, which provides a macro facility similar to 'SAS®'. This package contains basic macro capabilities like defining macro variables, executing conditional logic, and defining macro functions. Package: r-cran-macrobiome Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-palinsol, r-cran-raster, r-cran-rnaturalearthdata, r-cran-sf, r-cran-strex, r-cran-terra Filename: pool/dists/resolute/main/r-cran-macrobiome_0.4.0-1.ca2604.1_all.deb Size: 1261504 MD5sum: 8b48b9686f6f1ba5004790537d212e12 SHA1: e19709ec0f7bd3ef1c1da71a8985fc7cc60f2683 SHA256: ebbacfcd8f30e27d36babc77454215367a5532f7a460d62ca38d1b0e67a6d2bd SHA512: 2c1cd567b5dc004258a8cdc0fe3d071bbd485c424aea711a5ecded22662fdaac8d5fb0577361b6eead1520810ad350b2575874c3775bf1e3bb2cafe9a194cae3 Homepage: https://cran.r-project.org/package=macroBiome Description: CRAN Package 'macroBiome' (A Tool for Mapping the Distribution of the Biomes and Bioclimate) Procedures for simulating biomes by equilibrium vegetation models, with a special focus on paleoenvironmental applications. Three widely used equilibrium biome models are currently implemented in the package: the Holdridge Life Zone (HLZ) system (Holdridge 1947, ), the Köppen-Geiger classification (KGC) system (Köppen 1936, ) and the BIOME model (Prentice et al. 1992, ). Three climatic forest-steppe models are also implemented. An approach for estimating monthly time series of relative sunshine duration from temperature and precipitation data (Yin 1999, ) is also adapted, allowing process-based biome models to be combined with high-resolution paleoclimate simulation datasets (e.g., CHELSA-TraCE21k v1.0 dataset: ). Package: r-cran-macrocol Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-httr, r-cran-lubridate, r-cran-readxl, r-cran-r.utils Filename: pool/dists/resolute/main/r-cran-macrocol_0.1.0-1.ca2604.1_all.deb Size: 40136 MD5sum: a56e5231dedc1dfee5e0059c8c550086 SHA1: 3311843823f9cb48bb4c3132eb7e9e14e399790a SHA256: d838ec53775753cb9d035965836c8f838fb5d41d73fd2a8a40c94bdec5e890e8 SHA512: 5e0b2b3966528aeeffae4c5620ea0b0737b18d0750b39ace44c0ab643e3c530784c56646f99fe5d15f80e52b8a8d516373b4b78bd02aeb282cd068d1d8686e95 Homepage: https://cran.r-project.org/package=macrocol Description: CRAN Package 'macrocol' (Colombian Macro-Financial Time Series Generator) This repository aims to contribute to the econometric models' production with Colombian data, by providing a set of web-scrapping functions of some of the main macro-financial indicators. All the sources are public and free, but the advantage of these functions is that they directly download and harmonize the information in R's environment. No need to import or download additional files. You only need an internet connection! Package: r-cran-macrosyntr Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-tidyr, r-cran-reshape2, r-cran-dplyr, r-cran-stringr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-macrosyntr_0.3.3-1.ca2604.1_all.deb Size: 2352070 MD5sum: 893bd82d56a8b55887ce884ea4696367 SHA1: c937ecd12af2fb9bca6549b35b22fb84ed771d61 SHA256: b56510645ddce1093e011ceecb542b669baa1b185f43f9736f587d3394ed8304 SHA512: db9303e62b94badf8e3f34a5ec3d20b71b80a149da4a8255309320c118832ed57b665fae5dc8631a8f85f68c3ab0770f1bdebb806103de5fecf5e8987291fea6 Homepage: https://cran.r-project.org/package=macrosyntR Description: CRAN Package 'macrosyntR' (Draw Ordered Oxford Grids and Chord Diagrams) Use standard genomics file format (BED) and a table of orthologs to illustrate synteny conservation at the genome-wide scale. Significantly conserved linkage groups are identified as described in Simakov et al. (2020) and displayed on an Oxford Grid (Edwards (1991) ) or a chord diagram as in Simakov et al. (2022) . The package provides a function that uses a network-based greedy algorithm to find communities (Clauset et al. (2004) ) and so automatically order the chromosomes on the plot to improve interpretability. Package: r-cran-macrozoobenthoswatera Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-macrozoobenthoswatera_0.1.0-1.ca2604.1_all.deb Size: 40994 MD5sum: 28b224ffd4a63734c944db5ba9d3e620 SHA1: be2d66d5ac310172e7d0b9b4c2e6f1001ea91454 SHA256: b537b2f69d8ebb4747afec760ff4cf3e1615a3a36da05ebcfb2439fccdb27884 SHA512: b47cee5a8ef6e3291b9e1e956ac5e9e8af146665d5b45c8f932b2951c082a06aa2158a3ca83b04f56a6ddb0d53a7044bd6e091c83e72fa71cd0432370dfa6645 Homepage: https://cran.r-project.org/package=MacroZooBenthosWaterA Description: CRAN Package 'MacroZooBenthosWaterA' (Fresh Water Quality Analysis Based on Macrozoobenthos) Includes functions for calculating basic indices of macrozoobenthos for water quality and is designed to provide researchers and environmental professionals with a comprehensive tool for evaluating the ecological health of aquatic ecosystems.The package is based on the following references: Paisley, M. F., Trigg, D. J. and Walley, W. J. (2014). Arslan, N., Salur, A., Kalyoncu, H. et al.(2016) . Hilsenhoff W.L. (1987). Hilsenhoff. W.L. (1988) Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B. (1999). Package: r-cran-maczic Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pscl, r-cran-sandwich, r-cran-survival, r-cran-mediation, r-cran-emplik, r-cran-bb, r-cran-mathjaxr Suggests: r-cran-suppdists Filename: pool/dists/resolute/main/r-cran-maczic_1.1.0-1.ca2604.1_all.deb Size: 231720 MD5sum: eefd77c58d7256c87bdd7baaf7599bbf SHA1: 7616402638d22d4b6d2244b03009592d2dc583cd SHA256: 1da5c68816030697753530ee6b21049bcd8cf11c0f3b5654852030dda0ac8f0b SHA512: 813a09752ae947d9b2e8436d5fb1e8f3356b8670efc1c785d6a3ffa2cd303709ef065ea3ffa9d118c07c2bed54bafcf97484f57507958247fe6ef764f62176e3 Homepage: https://cran.r-project.org/package=maczic Description: CRAN Package 'maczic' (Mediation Analysis for Count and Zero-Inflated Count Data) Performs causal mediation analysis for count and zero-inflated count data without or with a post-treatment confounder; calculates power to detect prespecified causal mediation effects, direct effects, and total effects; performs sensitivity analysis when there is a treatment- induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) . Implements Instrumental Variable (IV) method to estimate the controlled (natural) direct and mediation effects, and compute the bootstrap Confidence Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018) . This software was made possible by Grant R03DE028410 from the National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health. Package: r-cran-mad Architecture: all Version: 0.8-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-metafor, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-mad_0.8-3-1.ca2604.1_all.deb Size: 308764 MD5sum: 4d8dae839d8a4132123924411c882ebc SHA1: 12a75880c2908a6d58d6fc2f343fdb82049b8159 SHA256: ae1fe94c91902e85afbcfb5da0c66d4f173eec1d443535c1cde45757e993423e SHA512: 8d464b5db03225fe5a87e4bc98c03ea6779953d535e4ee5105ef163788da865c79777aa325a1218cdfa309822945ef798f2349d67cfa45fe3103d5ac9bc987ef Homepage: https://cran.r-project.org/package=MAd Description: CRAN Package 'MAd' (Meta-Analysis with Mean Differences) A collection of functions for conducting a meta-analysis with mean differences data. It uses recommended procedures as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). Package: r-cran-mada Architecture: all Version: 0.5.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-ellipse, r-cran-mvmeta, r-cran-metafor Filename: pool/dists/resolute/main/r-cran-mada_0.5.12-1.ca2604.1_all.deb Size: 488192 MD5sum: 882e74d5996da6d6ef5c38f98cae4306 SHA1: 68526ba1e3763f2d5839dce3ed1ce7328627d289 SHA256: e7824ed4a77f613d482a6daed0e28cc228024b374312c2aa0874ac267176a1fb SHA512: a0ce56a0dc878880a4534e6f84c2a21db6b9ef327557ec14003c7dabbd834da2fe8c8c833ec0767d5ea447e2c7dc04416073d38b4f1924baeefa118b5b0de471 Homepage: https://cran.r-project.org/package=mada Description: CRAN Package 'mada' (Meta-Analysis of Diagnostic Accuracy) Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted. 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Package: r-cran-maddison Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-maddison_0.2-1.ca2604.1_all.deb Size: 181780 MD5sum: 70e79f4fb256b6d20847d4bca3f2567d SHA1: d0ee2a1690af97fc5a224a778b2bee7979669493 SHA256: a27d768f7dc2af49c21455a3485248fa2447c5476f096e695fd166b83279a366 SHA512: b429f5995fc4d81d74901437d3a5136e58aa0d10e9d1644d80607fe406a3dfbe367f3a77ea5f573f1b10543809518a342e4ba390a459aa92957c5c51ef29134d Homepage: https://cran.r-project.org/package=maddison Description: CRAN Package 'maddison' (The Maddison Project Database) Contains the Maddison Project 2018 database, which provides estimates of GDP per capita for all countries in the world between AD 1 and 2016. See for more information. 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This project collates all the credible data on population and GDP for 169 countries, with some dating back to the year 1 of the current era. One function makes it easy to find the leaders for each year, allowing users to delete countries like OPEC with narrow economies to focus on technology leaders. Another function makes it easy to plot data for only selected countries or years. Another function makes it relatively easy to obtain references to the original sources, which must be cited per the copyright rules of the Maddison Project for different uses of their data. Package: r-cran-madgrad Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-madgrad_0.2.0-1.ca2604.1_all.deb Size: 35290 MD5sum: beae861250ce86687917b7521f2feabf SHA1: efbdf9dd4fd680372b7acdf1ea12ad361511ecd8 SHA256: 6c2056955ba7523f14ac2b411fae345d513f879f764991d97632f85b939662c6 SHA512: 2b028e8aa0f9bf611ad1c119b74b06e48faee08231f11bcddbc0832801cd5505585900b8c15e4269754b5398e9218a28d3c900a98e5505e9d17583d474b171a9 Homepage: https://cran.r-project.org/package=madgrad Description: CRAN Package 'madgrad' ('MADGRAD' Method for Stochastic Optimization) A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization algorithm. 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Package: r-cran-maditr Architecture: all Version: 0.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-magrittr Suggests: r-cran-knitr, r-cran-tinytest, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-maditr_0.8.7-1.ca2604.1_all.deb Size: 379662 MD5sum: d2afc32b1840e679755074a98edbced0 SHA1: 060890287fad9a71b8a29e4a1d0470ce6b016f51 SHA256: 5ff9b1ebdf7c34d168f82bad1874a437de4c9c8b0e709916d6464f703a50bb5d SHA512: 4805cf1badda895e93c88101832837a78aaf1adb8f61876031581e4fe910e24b9c670df101093296d4b6591ffa6b2a990d34479c66a61374335213c675391170 Homepage: https://cran.r-project.org/package=maditr Description: CRAN Package 'maditr' (Fast Data Aggregation, Modification, and Filtering with Pipesand 'data.table') Provides pipe-style interface for 'data.table'. Package preserves all 'data.table' features without significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data manipulation tasks. For example, you can write 'take(mtcars, mean(mpg), by = am)' for aggregation or 'let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional aggregation/modification. Additionally there are some conveniences such as automatic 'data.frame' conversion to 'data.table'. Package: r-cran-madness Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-expm Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-sharper, r-cran-sandwich, r-cran-formatr, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-madness_0.2.8-1.ca2604.1_all.deb Size: 1191222 MD5sum: 7f56a5d167e176e84029be1f4c646fb0 SHA1: 6d571e2c101bb7e5d43a8467c2263b0898b66bfb SHA256: 9084b5291619d0656151c89b04f34632c0752215b3f4520805ba0cf726c15c34 SHA512: afed15e25c6e5f3f3f31a43c1bb362c9cf45a7c51864bd66d507ddda225d67b4283045d2c32b7f3e3da30c1774144999eb6e34bb228d2d20d6121fdc99c829a7 Homepage: https://cran.r-project.org/package=madness Description: CRAN Package 'madness' (Automatic Differentiation of Multivariate Operations) An object that supports automatic differentiation of matrix- and multidimensional-valued functions with respect to multidimensional independent variables. 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Package: r-cran-madshapr Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-stringr, r-cran-crayon, r-cran-ggplot2, r-cran-lubridate, r-cran-janitor, r-cran-forcats, r-cran-knitr, r-cran-haven, r-cran-bookdown, r-cran-dt, r-cran-readr, r-cran-tidyr, r-cran-fs, r-cran-fabr Filename: pool/dists/resolute/main/r-cran-madshapr_2.0.0-1.ca2604.1_all.deb Size: 526568 MD5sum: 77d0b5eaa1658d095a5fbf480a8b2a39 SHA1: fde017587fe6c3a06092d8cf50b3e175833da44b SHA256: a170df821738c6b4860c24d396edcd69cd0010a559e132d5597b32d423534d83 SHA512: d9b1584e17471b80b88d5745bbd9adfe8cfb1a38194975d07bd842657bd001cae31bdc86c1920af8e8ffa999539cc087c66e11cf9bb3fcfbd1dd012beca954f5 Homepage: https://cran.r-project.org/package=madshapR Description: CRAN Package 'madshapR' (Functions to Support Data Management and Processing Using theMaelstrom Research Approach) Functions to support data cleaning, evaluation, and description, developed for integration with Maelstrom Research software tools. 'madshapR' provides functions primarily to evaluate and manipulate datasets and data dictionaries in preparation for data harmonization with the package 'Rmonize' and to facilitate integration and transfer between RStudio servers and secure Opal environments. 'madshapR' functions can be used independently but are optimized in conjunction with ‘Rmonize’ functions for streamlined and coherent harmonization processing. Package: r-cran-madsim Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-madsim_1.2.1-1.ca2604.1_all.deb Size: 238142 MD5sum: 2542a4037c69daa3abdc95a793917ebd SHA1: bbee09122b38a7068cee2812372cd9b08372535a SHA256: 215946e91f73dce2b351cfb898f9bf97df1b80bd9156fc087ba43f8707553487 SHA512: f4603d342b547f5d6fa5fefc71a49d42b1155d49c0b8309a77335d94b69ab1f2d4b3c201ddf7197f87151404a9b381469d44a27b2ca38192bd9139f020806f85 Homepage: https://cran.r-project.org/package=madsim Description: CRAN Package 'madsim' (A Flexible Microarray Data Simulation Model) This function allows to generate two biological conditions synthetic microarray dataset which has similar behavior to those currently observed with common platforms. User provides a subset of parameters. Available default parameters settings can be modified. Package: r-cran-madtests Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gld Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-madtests_0.1.1-1.ca2604.1_all.deb Size: 32946 MD5sum: 7a6891c0185d18e0b2ea4cceaec1efa5 SHA1: ae6f4b9d723d5da48d8e650f94f2ac71cfc7cd49 SHA256: 69645f9c10e0156816fdcb885fe447764120eaab8ac7df0c6950936addd0c902 SHA512: 713447bd35e1180cfaa8018276f880ca1bbb7dda930d6eb58ec6e29e50f5c886f333ff27b65a4c477c82845a6e68243ba7c49eec6eead701fd9cb3638d5dd232 Homepage: https://cran.r-project.org/package=MADtests Description: CRAN Package 'MADtests' (Hypothesis Tests and Confidence Intervals for Median AbsoluteDeviations) Conducts one- and two-sample hypothesis tests for median absolute deviations (mads) for robust inference of dispersion. Comparisons between two samples uses the ratio of mads. Confidence intervals are also computed. 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Organize, orchestrate, and monitor multiple pipelines in a single project. Use tags to decorate functions with scheduling parameters and configuration. 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Propensity Score Matching deals with this issue but current techniques are restricted to binary treatment variables. Moreover, they provide several solutions without providing a comprehensive framework on choosing the best model. The MAGMA R-package addresses these restrictions by offering nearest neighbor matching for two to four groups. It also includes the option to match data of a 2x2 design. In addition, MAGMA includes a framework for evaluating the post-matching balance. The package includes functions for the matching process and matching reporting. We provide a tutorial on MAGMA as vignette. More information on MAGMA can be found in Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022) . 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It can perform miscellaneous tasks such as gene set enrichment and test analyses, identifying gene symbols and building co-expression network. It can also estimate sample size for atleast two-fold expression change. The current version is its slenderized form for compatable and flexible implementation. Package: r-cran-maicchecks Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-ggplot2, r-cran-lpsolve, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-maicchecks_0.2.0-1.ca2604.1_all.deb Size: 98238 MD5sum: ea282121fc04fa6ac5d5a8487f64a8c1 SHA1: 677ba6420f00ac8e561b73f6223158094ea2f68b SHA256: 57a7b3a9265d3ee55509211754e7907f3b61ca6aab9ca1bf35be248e4f11a770 SHA512: 380a18af02d8ee3fc0ec1e73b1e4df937c7e8c3e002cc4f39f8a5cfa2caf934dc5b061ea6ca1471c49a033a97baecb1559a6412787cd825703c6609b5056bced Homepage: https://cran.r-project.org/package=maicChecks Description: CRAN Package 'maicChecks' (Exact Matching and Matching-Adjusted Indirect Comparison (MAIC)) The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2025)). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2021) .) Package: r-cran-maicplus Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1925 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-lubridate, r-cran-matrixstats, r-cran-mass, r-cran-boot, r-cran-stringr, r-cran-lmtest, r-cran-sandwich Suggests: r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-rmarkdown, r-cran-dplyr, r-cran-survminer, r-cran-flexsurv, r-cran-tibble, r-cran-vdiffr, r-cran-checkmate Filename: pool/dists/resolute/main/r-cran-maicplus_0.1.2-1.ca2604.1_all.deb Size: 1440340 MD5sum: 279157978a5dfbbf348f02371293cada SHA1: 6aba4634de7a4df3b5c7fb5927d5103e4a4657a5 SHA256: 7c61e7d7738489afa0e17697c114295034c6550f3025ccceea8da4cd2a25b082 SHA512: 04b2ca1b9a56c3bfdf3369e20f04f629339086ffb77bbb8761217f677b4f43ca47d09e5c74bb5cd03e14d6530dd19af9bf28f0ebebf9851cd16e614a54a31887 Homepage: https://cran.r-project.org/package=maicplus Description: CRAN Package 'maicplus' (Matching Adjusted Indirect Comparison) Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) . Package: r-cran-maictools Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-boot, r-cran-broom, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vim Suggests: r-cran-haven, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-maictools_0.1.1-1.ca2604.1_all.deb Size: 166186 MD5sum: 03c736c53b52da7ea61d2e9c53d08fd5 SHA1: a54d214b2bc217638511f437a7ab6b25c0be97e7 SHA256: 0a4986569414cb0ff7c5f42e8aa2c3355ecb756ce25c5347a2e52609eb6b7af2 SHA512: 2aa785ab9c98c6e7404f7b111d43bf827cd7a1b6567146236342101f985f39423577774a1f338bb49d5f8b3eb933a42add03f89b71f10857bca93a70bd7db659 Homepage: https://cran.r-project.org/package=MAICtools Description: CRAN Package 'MAICtools' (Performing Matched-Adjusted Indirect Comparisons (MAIC)) A generalised workflow for Matching-Adjusted Indirect Comparison (MAIC) analysis, which supports both anchored and non-anchored MAIC methods. In MAIC, unbiased trial outcome comparison is achieved by weighting the subject-level outcomes of the intervention trial so that the weighted aggregate measures of prognostic or effect-modifying variables match those of the comparator trial. Measurements supported include time-to-event (e.g., overall survival) and binary (e.g., objective tumor response). The method is described in Signorovitch et al. (2010) and Signorovitch et al. (2012) . Package: r-cran-maidr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-curl, r-cran-ggplot2, r-cran-ggplotify, r-cran-gridsvg, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-shiny, r-cran-xml2, r-cran-rlang, r-cran-r6 Suggests: r-cran-testthat, r-cran-lintr, r-cran-styler, r-cran-goodpractice, r-cran-cyclocomp, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-maidr_0.3.0-1.ca2604.1_all.deb Size: 2779236 MD5sum: c55348d348a561f1957df2993302cc39 SHA1: bca5dd2c2ac8d260b7bf85e9457c1403c7bf8a0c SHA256: b4fb83f2f5b1e11db2fa358e5b1b0afd533af11e43344efa786dea06b2f493d1 SHA512: 10d5e9544db686894984d9672b8a72476dc9d8e2bfb4b5dd9cf66cc3b5d36333a3ee58b02ab919fff79097077e6c37dfb352ad74706cbb3905e67d18d07a1a28 Homepage: https://cran.r-project.org/package=maidr Description: CRAN Package 'maidr' (Multimodal Access and Interactive Data Representation) Provides accessible, interactive visualizations through the 'MAIDR' (Multimodal Access and Interactive Data Representation) system. Converts 'ggplot2' and Base R plots into accessible HTML/SVG formats with keyboard navigation, screen reader support, and 'sonification' capabilities. Supports bar charts (simple, grouped, stacked), histograms, line plots, scatter plots, box plots, violin plots, heat maps, density/smooth curves, faceted plots, multi-panel layouts (including patchwork), and multi-layered plot combinations. Enables data exploration for users with visual impairments through multiple sensory modalities. For more details see the 'MAIDR' project . 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Methods are described in Merlo (2018) and Evans et al. (2018) . Automatically generates intersectional strata, fits analytical models, extracts statistics, and produces visualizations. Package: r-cran-mailchimpr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-mailchimpr_0.1.0-1.ca2604.1_all.deb Size: 23038 MD5sum: de0c2eb6646bb34121cb2dc7cdeaf21a SHA1: 0e68d6a4fe844ea7345e04fe0816a1931507185f SHA256: 29f46b50ac49755ea1accf86be4a9525b2855e15118701d576f15240c0bf1df0 SHA512: cbdeabd8f7edbbb9ac49e9e90a9eb25c25b59108d4b83d109d79fe1c8a730f029ef6d3f357fc59107ca5ed7581785c1e5e301a95d1d433b2312c9c9aeae527d1 Homepage: https://cran.r-project.org/package=mailchimpR Description: CRAN Package 'mailchimpR' (Get Mailchimp Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from Mailchimp using the 'Windsor.ai' API . 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Package: r-cran-mainexistingdatasets Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-glue, r-cran-golem, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-openxlsx, r-cran-pkgload, r-cran-processx, r-cran-rlang, r-cran-sf, r-cran-shiny, r-cran-spdata, r-cran-tidyr, r-cran-tmap, r-cran-tmaptools Suggests: r-bioc-bioccheck, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mainexistingdatasets_1.0.2-1.ca2604.1_all.deb Size: 54830 MD5sum: 32b6dc3e01fe4769ff899c031d8498b2 SHA1: f88b1f22d4d684cfb2638beb3e52a30138f6f1f4 SHA256: 40c21df40ea45c5b58a5b0d33d9a40c61327025950930c00d77deed15f5306a9 SHA512: 7a61fd7d2d9f9ff7c4636262401992bf393f03732d3d3d31853dad4021594b433eba6c9148167e0d1613e9f54e0c4e7240414630826b07ecb0920445eb069479 Homepage: https://cran.r-project.org/package=MainExistingDatasets Description: CRAN Package 'MainExistingDatasets' (Main Existing Human Datasets) Shiny for Open Science to visualize, share, and inventory the main existing human datasets for researchers. Package: r-cran-maive Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clubsandwich Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-varhandle Filename: pool/dists/resolute/main/r-cran-maive_0.2.4-1.ca2604.1_all.deb Size: 148054 MD5sum: 6b04ed3dedfe807c29ed41482428e36a SHA1: ab7cf2e959c783e87776a43c9dbe78f88ecf9efd SHA256: 080863bf229acb7af93e527e70a4758790a46b2ad60e9bb3734e6f371f680807 SHA512: 0c357b858049382cb285e49203ae421ea0293975534c8b75b7f9100b53c12522781f916325df686b14cdd9524f338a171c790a6eaec9ad43e831c2903afb5c93 Homepage: https://cran.r-project.org/package=MAIVE Description: CRAN Package 'MAIVE' (Meta Analysis Instrumental Variable Estimator) Meta-analysis traditionally assigns more weight to studies with lower standard errors, assuming higher precision. However, in observational research, precision must be estimated and is vulnerable to manipulation, such as p-hacking, to achieve statistical significance. This can lead to spurious precision, invalidating inverse-variance weighting and bias-correction methods like funnel plots. Common methods for addressing publication bias, including selection models, often fail or exacerbate the problem. This package introduces an instrumental variable approach to limit bias caused by spurious precision in meta-analysis. Methods are described in 'Irsova et al.' (2025) . Package: r-cran-majkmeans Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-majkmeans_0.1.0-1.ca2604.1_all.deb Size: 28792 MD5sum: 7d45f43302bfe27ad6eb15fc6463076e SHA1: 838db1f31e40f7bd5f449434fb626316a5707129 SHA256: 313cb5bf16fb1f7de9878fc18d5cd8b726079182625c3b6d30caf024068da22b SHA512: 9b5b65fd5c20f4e4701776dabe9eb5e377fe98b98884a5fb67128d91aa68fb431f32c82d7d755c356a89b8f719f040ab507a64affec991c6a81b8e1cda0c0efd Homepage: https://cran.r-project.org/package=MajKMeans Description: CRAN Package 'MajKMeans' (k-Means Algorithm with a Majorization-Minimization Method) A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) . The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Package: r-cran-majminkmeans Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-majminkmeans_0.1.0-1.ca2604.1_all.deb Size: 33146 MD5sum: 21af0e3a1f33fcbd3eb8d53d0fc23d54 SHA1: bc125a2dc4a7e724ffd5147bbcbdcd3cef2d4f0a SHA256: ce2a156c2560d106d305dafcd2f2106e8297dc8b973a38a5c8004e127cccd5e7 SHA512: 04f76f1333f57172946f73a0f02bbd0fda7331b4dcb7ca76069b2eec1ab5698e1080de9df0d2000f5e7540521008fa48915e7300b288da34a0f93f662fdec6c6 Homepage: https://cran.r-project.org/package=MajMinKmeans Description: CRAN Package 'MajMinKmeans' (k-Means Algorithm with a Majorization-Minimization Method) A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) . The two most important functions in package 'MajMinKmeans' are cluster_km() and cluster_MajKm(). Cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Another useful function is MajMinOptim(), which helps to find the optimum values of the Majorization-Minimization estimator. Package: r-cran-makedummies Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-makedummies_1.2.1-1.ca2604.1_all.deb Size: 17522 MD5sum: 961e928966e8789873e94c539952b754 SHA1: ac92c612446712be04e506dc2b3eb3d7f1abc1f8 SHA256: e60639b810924f50e05013266e9f48b0e63cc70a42382954a5ebbc130dcbc1f0 SHA512: 7602f09932b5a8c0adf8c275cb44d70b7459c6764e29d6f4be3bc8f19c880647e0717d8c732f3218f7981aee1629fd5dc682c3ee48865188ac301c341c3728db Homepage: https://cran.r-project.org/package=makedummies Description: CRAN Package 'makedummies' (Create Dummy Variables from Categorical Data) Create dummy variables from categorical data. This package can convert categorical data (factor and ordered) into dummy variables and handle multiple columns simultaneously. 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Package: r-cran-makemyprior Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3884 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-shiny, r-cran-shinyjs, r-cran-shinybs, r-cran-visnetwork, r-cran-rlang, r-cran-mass Suggests: r-cran-rstan, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-makemyprior_1.2.2-1.ca2604.1_all.deb Size: 1157730 MD5sum: d06c264e6c89c62f1eb239ddd0227906 SHA1: db7d7d43b9bf393bdb8a055ea925ae97b5c49ab4 SHA256: 041d371d361bc8fab73077d627fbbb2f53d0f8f7d6876da160e135e4b62b343f SHA512: f2a6792384aaaf4d1648ed173755ad84aecac58884bb576cd1e56c366a95f86aca788d69926416fa8ef6c439ff97febfcf5d4bfc5c7bf5dfa4da98de5b9bc942 Homepage: https://cran.r-project.org/package=makemyprior Description: CRAN Package 'makemyprior' (Intuitive Construction of Joint Priors for Variance Parameters) Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, ). 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Segments of the pipeline are specified in much the same way a 'Make' rule is, by declaring an executable recipe (which might be an R script), along with the corresponding targets and dependencies. When the entire pipeline is run through, only those recipes that need to be executed will be. Meanwhile, execution metadata is captured behind the scenes for later inspection. 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The test detects cointegration relationships in the presence of up to five structural breaks in the intercept and/or slope coefficients. Four different model specifications are supported: level shifts, level shifts with trend, regime shifts, and trend with regime shifts. The method is described in Maki (2012) "Tests for cointegration allowing for an unknown number of breaks" . Package: r-cran-makl Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-auc, r-cran-grplasso Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-makl_1.0.1-1.ca2604.1_all.deb Size: 31288 MD5sum: 5d846f2c70c683a292dd1619445b5c24 SHA1: 6f85f0af39b7dab4b3cf5ccd9ab8e4b583fa1e6e SHA256: 71215af4b1e32e7618d0e2e5354fe074ce65563d59f1cb277adfd428f0fa5441 SHA512: a9c82f065fa12b2d1631c0557ff70f7dc179424cabbba4e11c39eed136feb329fca7ad832425d360f51da3e62af1a1b162b8f269aadc02b593f264266efac471 Homepage: https://cran.r-project.org/package=MAKL Description: CRAN Package 'MAKL' (Multiple Approximate Kernel Learning (MAKL)) R package associated with the Multiple Approximate Kernel Learning (MAKL) algorithm proposed in . The algorithm fits multiple approximate kernel learning (MAKL) models that are fast, scalable and interpretable. Package: r-cran-malani Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-malani_1.0-1.ca2604.1_all.deb Size: 39172 MD5sum: 6495c12f1598784885d85304d56b0072 SHA1: a405fb695e37473edd921d3ca12cfb830b9b9a9f SHA256: 434475b962e4cde14bfde17f5fea4af5d9023f8702ed6d50c029897fb45dec8a SHA512: 0cc2619286e3be34d46d5d86db38bceb9aefeaee2bbd903572b5377ba5b3d6eb5972232337e4078210181d4adb7847b4a5e1dc3af8cf86f79db3b4cd210e678e Homepage: https://cran.r-project.org/package=malani Description: CRAN Package 'malani' (Machine Learning Assisted Network Inference) Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks. 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Package: r-cran-malaytextr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-malaytextr_0.1.3-1.ca2604.1_all.deb Size: 79584 MD5sum: b2e2e21d4251d802637e91ce22e06ee6 SHA1: d59c83bf2e5d0d5e21604ca73e79774acced1cb7 SHA256: 9af66dde5d058436426a4cf5528d23157d3a67718d04d72621433d04db22d8fc SHA512: f43986ef0a2c8f980f7da01bcaaa67404489793aa582a6781d8edb647b69a87918fa473c03d3931b506a841deea30725d5acd65d987288f214a247b0f0ea265d Homepage: https://cran.r-project.org/package=malaytextr Description: CRAN Package 'malaytextr' (Text Mining for Bahasa Malaysia) It is designed to work with text written in Bahasa Malaysia. We provide functions and data sets that will make working with Bahasa Malaysia text much easier. For word stemming in particular, we will look up the Malay words in a dictionary and then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah, Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce, Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi (2017) . This package includes a dictionary of Malay words that may be used to perform word stemming, a dataset of Malay stop words, a dataset of sentiment words and a dataset of normalized words. Package: r-cran-maldipickr Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2820 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-maldiquant, r-cran-readbrukerflexdata, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-coop, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-maldipickr_1.3.1-1.ca2604.1_all.deb Size: 1630930 MD5sum: c17d00645b3228b2c59905a1172760fc SHA1: b1e6df228b92524c09d5534139d3486a6a61b09a SHA256: 7419b3be2a2c42b9c674c88ccf5cde9dfa3f5117a3cc3c3fe05dd662ababfe5d SHA512: 8b26e7726828049855643388607a860f16d01195f00f17e17a7070cd945413740c8a5882d697a02ed210c9ef856e5c94b83a4d17ceef395850a361c1c25116f1 Homepage: https://cran.r-project.org/package=maldipickr Description: CRAN Package 'maldipickr' (Dereplicate and Cherry-Pick Mass Spectrometry Spectra) Convenient wrapper functions for the analysis of matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) spectra data in order to select only representative spectra (also called cherry-pick). The package covers the preprocessing and dereplication steps (based on Strejcek, Smrhova, Junkova and Uhlik (2018) ) needed to cluster MALDI-TOF spectra before the final cherry-picking step. It enables the easy exclusion of spectra and/or clusters to accommodate complex cherry-picking strategies. Alternatively, cherry-picking using taxonomic identification MALDI-TOF data is made easy with functions to import inconsistently formatted reports. 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Package: r-cran-maldirppa Architecture: all Version: 1.1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maldiquant, r-cran-signal, r-cran-robustbase, r-cran-lattice, r-cran-waveslim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-maldirppa_1.1.0-3-1.ca2604.1_all.deb Size: 1546966 MD5sum: 2f39cbb6aaf77e11e708aa281c1b2294 SHA1: ce9c434494d4fcbcd635dee6dfbb0ff3d571c410 SHA256: d6ca85ccd29f5c5487bbcc2b66cf6be0772b32725c53287656190bf7bd6db074 SHA512: 736b1f4917cee65c8a74539e8e1a5028395f1890d4312a684b379594a98914b841607110706473f29fdc7341d9cac943cc1083986edc1cdf44869cd782e345a0 Homepage: https://cran.r-project.org/package=MALDIrppa Description: CRAN Package 'MALDIrppa' (MALDI Mass Spectrometry Data Robust Pre-Processing and Analysis) Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) ). Package: r-cran-mall Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ellmer, r-cran-fs, r-cran-glue, r-cran-jsonlite, r-cran-ollamar, r-cran-rlang Suggests: r-cran-dbplyr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mall_0.2.0-1.ca2604.1_all.deb Size: 125146 MD5sum: f042f9fea0b8ffd36a1975848539c756 SHA1: 608f45a66790028217cd2faad452980716cbc884 SHA256: fbbbfec55ae5cd325e86b7c4df63583cfa33bad1a86d33418f7e9cc440dc76b6 SHA512: 7ae67294d4ecd9d4c2779dbba93c3b8b8db322346ce98164caa97b2cfbc1c30d8436b6a99bba0b57c712acad40021454e81f441d370f11d57ff693317930f874 Homepage: https://cran.r-project.org/package=mall Description: CRAN Package 'mall' (Run Multiple Large Language Model Predictions Against a Table,or Vectors) Run multiple 'Large Language Model' predictions against a table. The predictions run row-wise over a specified column. It works using a one-shot prompt, along with the current row's content. The prompt that is used will depend of the type of analysis needed. Package: r-cran-mallet Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4378 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mallet_1.3.0-1.ca2604.1_all.deb Size: 3955284 MD5sum: 43f82f9f64137150022c0c1950b8d0a9 SHA1: 8978971d9a1ff05c3ea7036eb84e17bcd18097e4 SHA256: 107cd4327e45380024cc819265d6e30248e7e3d85f5b049389945d045649d3b7 SHA512: 1cac8a10ba09f02b0724e75e26dbbaeb9436107eb414226ac7e2193e712c033d2dc2957982960f05de8ebe680258334d8b309ed0ea5b43ac93cf5db5b7bc4720 Homepage: https://cran.r-project.org/package=mallet Description: CRAN Package 'mallet' (An R Wrapper for the Java Mallet Topic Modeling Toolkit) An R interface for the Java Machine Learning for Language Toolkit (mallet) to estimate probabilistic topic models, such as Latent Dirichlet Allocation. We can use the R package to read textual data into mallet from R objects, run the Java implementation of mallet directly in R, and extract results as R objects. The Mallet toolkit has many functions, this wrapper focuses on the topic modeling sub-package written by David Mimno. The package uses the rJava package to connect to a JVM. Package: r-cran-malvinas Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-malvinas_0.1.0-1.ca2604.1_all.deb Size: 280214 MD5sum: febca1ea490525b2019c37325258f731 SHA1: a8a4dd3d6555b6ee930406110b4f5fb32e7bdfae SHA256: 9a9c2b997e6eab25a61d2252b255f0a9f4afe8447e449d5095f627d3ebc5db49 SHA512: 5d109341e4ff1df4978a08aa42151724a4826ff3bdedac27b17d4fde149e821090d3176cfa42b05c91aebc5a254bad5e27a0132bca505de9b5958cfd6d075100 Homepage: https://cran.r-project.org/package=malvinas Description: CRAN Package 'malvinas' (Islas Malvinas, Georgias Del Sur y Sándwich Del Sur) Data sets related to the Islas Malvinas /// Sets de datos relacionados a las Islas Malvinas - La Nación Argentina ratifica su legítima e imprescriptible soberanía sobre las islas Malvinas, Georgias del Sur y Sándwich del Sur y los espacios marítimos e insulares correspondientes, por ser parte integrante del territorio nacional. La recuperación de dichos territorios y el ejercicio pleno de la soberanía, respetando el modo de vida de sus habitantes y conforme a los principios del Derecho Internacional, constituyen un objetivo permanente e irrenunciable del pueblo argentino. 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Package: r-cran-managedcloudprovider Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dockerparallel, r-cran-adagio, r-cran-jsonlite Suggests: r-cran-markdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-readr Filename: pool/dists/resolute/main/r-cran-managedcloudprovider_1.0.0-1.ca2604.1_all.deb Size: 384854 MD5sum: e5a00735ee75fb8db8483f961b3483c3 SHA1: 699913a9b5f5a22febc596daf20cd9811ecbb0ac SHA256: 7efa625255562ca8e808acec66a5436def086af770bafc72c7777f3efbdf6db6 SHA512: c92b17030409a6b7bc6974b82f41e813609000b2988b125ff6a2508e69c2a6492238b57de568bf525713fc855415d24c95b1331272f1e820d511f314f3316f26 Homepage: https://cran.r-project.org/package=ManagedCloudProvider Description: CRAN Package 'ManagedCloudProvider' (Providing the Kubernetes-Like Functions for the Non-KubernetesCloud Service) Providing the kubernetes-like class 'ManagedCloudProvider' as a child class of the 'CloudProvider' class in the 'DockerParallel' package. The class is able to manage the cloud instance made by the non-kubernetes cloud service. For creating a provider for the non-kubernetes cloud service, the developer needs to define a reference class inherited from 'ManagedCloudProvider' and define the method for the generics runDockerWorkerContainers(), getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see the vignette in this package and . Package: r-cran-managelocalrepo Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-assertthat Filename: pool/dists/resolute/main/r-cran-managelocalrepo_0.1.5-1.ca2604.1_all.deb Size: 24588 MD5sum: b564aad88d515976f8e16e25a61c540b SHA1: a4084a2f21a1741b4ee9c0cd769e3e1cf5cc3641 SHA256: e161fc26c040e29ce08708bbda4e390cd8e9c28628f3de4ccf175942da1c3287 SHA512: e9d2d06c130a461cf2363693f6e2d04196137cb261c430b13fd441183b61e926e689c4d7f1a30c8d540d94722cbae576ef6cfe82fcd800f5ff0eec8b4ab20fcb Homepage: https://cran.r-project.org/package=managelocalrepo Description: CRAN Package 'managelocalrepo' (Manage a CRAN-Style Local Repository) This will allow easier management of a CRAN-style repository on local networks (i.e. not on CRAN). This might be necessary where hosted packages contain intellectual property owned by a corporation. Package: r-cran-mancie Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mancie_1.4-1.ca2604.1_all.deb Size: 572660 MD5sum: f14c58db9469ada12b3d1002aeddf938 SHA1: 4697510fcfb45e5fcbd3ca8901321aa7a734d8af SHA256: 0c67493ebc4e2efaacbab3aee3fd3756592a3b54d956bb3b1772c16d8656aa0c SHA512: e6bffd32650e2668668a7806e9e4b85724d93dde8d92b83cc2de767b4504743e16a70bb98a19d8f83bc515d3e21c45af06c43599db162902daf518bb3a3aaf90 Homepage: https://cran.r-project.org/package=MANCIE Description: CRAN Package 'MANCIE' (Matrix Analysis and Normalization by Concordant InformationEnhancement) High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. 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Package: r-cran-mand Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7422 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-msma, r-cran-oro.nifti, r-cran-oro.dicom, r-cran-imager, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mand_2.0-1.ca2604.1_all.deb Size: 994892 MD5sum: 6786a274a782bedaf4d19ee604c2e0fb SHA1: 5143f97f4699528d1d91826e79da902542e5c2eb SHA256: 24f523265fdbb4173d658e6eb1fa4eb37fdea00e32729bf0f14f4a927b8c7492 SHA512: 6d2600e13cc259a265ff58eb4b047cb8bb9fb80c146858ad1ce9492b4c8c0a69b7a74ca3a691ca1a21925fe0b9edba07c079f4937a8238a93b0158f7fe3089f1 Homepage: https://cran.r-project.org/package=mand Description: CRAN Package 'mand' (Multivariate Analysis for Neuroimaging Data) Several functions can be used to analyze neuroimaging data using multivariate methods based on the 'msma' package. The functions used in the book entitled "Multivariate Analysis for Neuroimaging Data" (2021, ISBN-13: 978-0367255329) are contained. Package: r-cran-mandalar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-mandalar_0.1.0-1.ca2604.1_all.deb Size: 49408 MD5sum: 17fb0378d1433ff21e91e7b072dbee3a SHA1: 351dcdf939d413c3b4a8c53db3708e1661ea0e27 SHA256: 2307682b65b98e59b9cd32d2ef81bc07761f14777e8db0792c3576fddc4331e1 SHA512: 73d0df7d44266d16c91599adc6e1cdb2d31a236deb3fe426cf8f017beb42000ff15a49463f2ac136031c9ab728a89b6c9d36c70f989df1b92dec6efebfb8182e Homepage: https://cran.r-project.org/package=MandalaR Description: CRAN Package 'MandalaR' (Building Mandalas from Parametric Equations of Classical Curves) Provides an algorithm for creating mandalas. From the perspective of classic mathematical curves and rigid movements on the plane, the package allows you to select curves and produce mandalas from the curve. The algorithm was developed based on the book by Alcoforado et. al. entitled "Art, Geometry and Mandalas with R" (2022) in press by the USP Open Books Portal. Package: r-cran-manet Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-combinat, r-cran-igraph, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-manet_2.0-1.ca2604.1_all.deb Size: 51016 MD5sum: 8c32eaf6988cb6469e3c94bdf7f0b002 SHA1: 6c5c30c3bb8a5c4b88ccf5b81865e06280f35bf7 SHA256: 641b15944783ced3959b3e591b2fc0d7d9d947bf1abf89a7613944175441833b SHA512: 3b5ea755b52db18d83272652ef187b5ccd9cd46ed32862f13c07d1626af4863cf7dfc050b414db6eb0c7e4af24a3acbbbf1027a5f6a374e44c964b63fe75d557 Homepage: https://cran.r-project.org/package=manet Description: CRAN Package 'manet' (Multiple Allocation Model for Actor-Event Networks) Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted. Package: r-cran-mangrove Architecture: all Version: 1.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kinship2 Filename: pool/dists/resolute/main/r-cran-mangrove_1.21-1.ca2604.1_all.deb Size: 344566 MD5sum: 034bda96b07eb8d6208ff7772d9918d8 SHA1: f6bb03a7b70c74813114298ee13d7b3e76ba5484 SHA256: 6e69d89dd44ab8f58218b2abfdb2fb8cdb3b887c34094dbff85b68a3e29136fa SHA512: e67a0fabef81c908e29f4128873b6938840c799f98db30c543b6ae52ea74903c3d99bff2d585f81282cc0968eb721caaf60beffcff17e70d2bd35dd61ebcf408 Homepage: https://cran.r-project.org/package=Mangrove Description: CRAN Package 'Mangrove' (Risk Prediction on Trees) Methods for performing genetic risk prediction from genotype data. You can use it to perform risk prediction for individuals, or for families with missing data. Package: r-cran-manhplot Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3517 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape2, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra Suggests: r-cran-r.utils, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-manhplot_1.1-1.ca2604.1_all.deb Size: 960634 MD5sum: f69f56c5085b46344510b900a8d47bdd SHA1: 0522ad3357e521437a3297a8e1ee4a8d5d0343cd SHA256: f8114047d0fb634a87686fd1b9a14a78c9d7a8edf72ead85a3c3be6475068d1f SHA512: cce033d3365005d7302ac0e7e6fedfea9005e10fc92a0a07663c1838f48626a6f6d509458a04bd2cf4d688a28c1685d13cedde55eaf438a2c7481f9b43a7d5eb Homepage: https://cran.r-project.org/package=manhplot Description: CRAN Package 'manhplot' (The Manhattan++ Plot) This plot integrates annotation into a manhattan plot. The plot is implemented as a heatmap, which is binned using -log10(p-value) and chromosome position. Annotation currently supported is minor allele frequency and gene function high impact variants. Package: r-cran-manifesto Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-pak, r-cran-tomledit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-manifesto_0.0.2-1.ca2604.1_all.deb Size: 139750 MD5sum: 04849abe7b97e5f9966c9f9d77cb6a19 SHA1: 12ab3136225e8a9587f8985f22c38ce9dc1a7256 SHA256: 8ceb48234b361aeb5c2aef084a0c5f11ff2dcca83a37cccf3d7d65453761f3e2 SHA512: f22b1613c533505622ab1358cda13b0fd7b7da1945de81a13cdb78db6a38f80fcda5afbc6089206e5aeff16b690b47c74a6ec0b389d68756c8a418e3fc631cf5 Homepage: https://cran.r-project.org/package=manifesto Description: CRAN Package 'manifesto' (Create Project Manifest Files) Provides 'TOML' representations of packages needed that must be installed to run a project. Includes functions to specify detailed installation functions, validate files, and to use a given file as the requirements for a project. Handles package installations, when necessary, via 'pak'. 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The Manifesto Project collects and analyses election programmes across time and space to measure the political preferences of parties. The Manifesto Corpus contains the collected and annotated election programmes in the Corpus format of the package 'tm' to enable easy use of text processing and text mining functionality. Specific functions for scaling of coded political texts are included. Package: r-cran-manipulate Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-manipulate_1.0.1-1.ca2604.1_all.deb Size: 45212 MD5sum: f0c5ac92fd68b6fa1e4b555bb213fdfa SHA1: 8ba96b4b25f5995b4703fa086072109c7f7d61b9 SHA256: 55a37bf1d17a3107a7eef3fd8923d3580fac2f031e3718cab5b9d3984199d8b5 SHA512: 2164a432679c307e15768f4ecd2798a64f80af8ca560f4668e1a57570285db07915930383864490518bc4b45b580919795f1178af452681bf7b721ced7e6da1c Homepage: https://cran.r-project.org/package=manipulate Description: CRAN Package 'manipulate' (Interactive Plots for RStudio) Interactive plotting functions for use within RStudio. The manipulate function accepts a plotting expression and a set of controls (e.g. slider, picker, checkbox, or button) which are used to dynamically change values within the expression. 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Package: r-cran-mannwhitneycopula Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-tidyverse, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-mannwhitneycopula_0.1.1-1.ca2604.1_all.deb Size: 65898 MD5sum: f8656b25d5af675669d443e461a7d9e6 SHA1: 9e8b5d47e2feea3a196013bdb3df579107ba48ac SHA256: d5b62e7eeddfb03d30d2b0da6bf3603a47c51c9de100615ab8605d8bc34a3b02 SHA512: 2836af8856e9cc2b82969f2c86550c824a7e796573933cc75b14d3663c822ce22cc73f845dc144a6e1a9d099e2abadf5333dc6153e3c6c6e3f6364b69fe44de8 Homepage: https://cran.r-project.org/package=MannWhitneyCopula Description: CRAN Package 'MannWhitneyCopula' (Computing Mann-Whitney Effect Based on Copulas) Computing the Mann-Whitney effect based on copula models. Estimation of the association parameter in survival copula models. A description of the underlying methods is described in Nakazono et al. (2024) and Nakazono et al. (accepted for publication in Statistical Papers). Package: r-cran-manorm2 Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5299 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-locfit, r-cran-scales, r-cran-statmod Suggests: r-cran-gplots, r-cran-desctools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-manorm2_1.2.2-1.ca2604.1_all.deb Size: 4000928 MD5sum: 9798492b84aa23286c14fbf6e394b6b5 SHA1: eb8675de6b50dff273a30f981b51c25487832dbc SHA256: d1c27c9f52bb8f2150c4e714587a008b3e0a17491ac0ee97cc0a9c4154d24828 SHA512: a26ff8e5a00a442e1283e14118fa2a7a0d1189fb28c4dae6eab9ca5ba0ea4c8e0d7f9dff84a6f4f2b6cb041b23044719c3ff6f0632071e4641fe3b9ec110d660 Homepage: https://cran.r-project.org/package=MAnorm2 Description: CRAN Package 'MAnorm2' (Tools for Normalizing and Comparing ChIP-seq Samples) Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the premier technology for profiling genome-wide localization of chromatin-binding proteins, including transcription factors and histones with various modifications. This package provides a robust method for normalizing ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable ChIP-seq signals across samples. Refer to Tu et al. (2021) and Chen et al. (2022) for associated statistical details. Package: r-cran-manova.rm Architecture: all Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1091 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-mass, r-cran-matrix, r-cran-magic, r-cran-plotrix, r-cran-ellipse, r-cran-multcomp, r-cran-data.table Suggests: r-cran-nparld, r-cran-knitr, r-cran-rmarkdown, r-cran-hsaur3, r-cran-tidyr, r-cran-ggplot2, r-cran-gfd, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-manova.rm_0.5.4-1.ca2604.1_all.deb Size: 522976 MD5sum: 9bc030cc9328a5fc79efc6e055c9a2d7 SHA1: f6e26a32cdbd1be9d238c35dbb700878e6467e0e SHA256: eb305798729a12bd23dcf38c21f2f3db534c57a35d994d94f80ded32501b2e2c SHA512: 3a57ff49f5e0732384e784a31bcb2bd51013746f9912d31fb24ced8f877d0fbcacf1203d87f7a99047232fbe100b4b0ebd96e4fde1bc0ba5a8f21be716821f8c Homepage: https://cran.r-project.org/package=MANOVA.RM Description: CRAN Package 'MANOVA.RM' (Resampling-Based Analysis of Multivariate Data and RepeatedMeasures Designs) Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) . Package: r-cran-mantaid Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biomart, r-cran-caret, r-cran-keras, r-cran-mlr3tuning, r-cran-mlr3, r-cran-ggplot2, r-cran-data.table, r-cran-magrittr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-ggcorrplot, r-cran-reshape2, r-cran-scutr, r-cran-paradox, r-cran-rcolorbrewer, r-cran-purrr, r-cran-dplyr Suggests: r-cran-mlr3hyperband, r-cran-mlr3learners, r-cran-ranger, r-cran-rpart, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mantaid_1.0.4-1.ca2604.1_all.deb Size: 274080 MD5sum: ec93fb377aee058f75ca88ee8a3d5d4e SHA1: 735bcf65e5fd3313e72a9f377ea863f02230b60d SHA256: ab2b375358ee595c31378e17ac4db80808abb48166f011e1d546b5fdd07bbc09 SHA512: cad1a7609e15efddb5b52eca97bb6a89b5005d78c594e85088f517bedc262eacb36821038bec54cb4716f19b9d9a1b8e0345539ba9125464c1b92346b22f4cfc Homepage: https://cran.r-project.org/package=MantaID Description: CRAN Package 'MantaID' (A Machine-Learning Based Tool to Automate the Identification ofBiological Database IDs) The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed 'MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The 'MantaID' model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. 'MantaID' supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and API were also developed for 'MantaID' to improve applicability. To our knowledge, 'MantaID' is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases. Package: r-cran-mantar Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-mathjaxr, r-cran-matrix, r-cran-glassofast Suggests: r-cran-numderiv, r-cran-mice, r-cran-lavaan, r-cran-qgraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mantar_0.2.0-1.ca2604.1_all.deb Size: 366826 MD5sum: 32ae0c060a9380a4a682eed71aea9dd3 SHA1: 89cf294c0975aecd87a2d211bd84d759f87fb1ab SHA256: f4057f2233c09c566b77f85fcae8ab86eb12f196f7f3c6923d7df4043572e00b SHA512: efe13147c24c59ad9cd271cab6317b0f327f3d296362660e73ab6bb813d456e2331c5b882cfbb4110b152b4d69790f575d3fcf7027597e49e6594a5ac116515e Homepage: https://cran.r-project.org/package=mantar Description: CRAN Package 'mantar' (Missingness Alleviation for Network Analysis) Provides functionality for estimating cross-sectional network structures representing partial correlations while accounting for missing data. Networks are estimated via neighborhood selection or regularization, with model selection guided by information criteria. Missing data can be handled primarily via multiple imputation or a maximum likelihood-based approach, as demonstrated by Nehler and Schultze (2025a) and Nehler and Schultze (2025b) . Deletion-based approaches are also available but play a secondary role. Package: r-cran-mantis Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown, r-cran-knitr, r-cran-reactable, r-cran-dplyr, r-cran-tidyr, r-cran-dygraphs, r-cran-xts, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-htmltools, r-cran-lubridate Suggests: r-cran-covr, r-cran-testthat, r-cran-codemetar, r-cran-vdiffr, r-cran-shiny, r-cran-diffviewer, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mantis_1.0.2-1.ca2604.1_all.deb Size: 1144262 MD5sum: 4be768476cf0cbfdf27dde4dfde5190d SHA1: 8f921bbe33c11b072e20327f4d1cfc6c68ecfa84 SHA256: 6b481ca2eb5636cd28a8a57966c92e24b6cf10eec33d79c16e1fdecd0fad5f3f SHA512: de57b9bf3994182905c105c8c8c93d7282a56c216d315c15650bb18f392f3134b2da5d90000001a9f2decbeba14cc0f5c570a9be8325c33493385753361430a0 Homepage: https://cran.r-project.org/package=mantis Description: CRAN Package 'mantis' (Multiple Time Series Scanner) Generate interactive html reports that enable quick visual review of multiple related time series stored in a data frame. For static datasets, this can help to identify any temporal artefacts that may affect the validity of subsequent analyses. For live data feeds, regularly scheduled reports can help to pro-actively identify data feed problems or unexpected trends that may require action. The reports are self-contained and shareable without a web server. Package: r-cran-manureshed Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-ggplot2, r-cran-tidyr, r-cran-jsonlite, r-cran-rlang, r-cran-magrittr, r-cran-scales, r-cran-igraph, r-cran-tigris Suggests: r-cran-nhdplustools, r-cran-rcolorbrewer, r-cran-cowplot, r-cran-ggpubr, r-cran-viridis, r-cran-testthat, r-cran-knitr, r-cran-ragg, r-cran-progress, r-cran-rmarkdown, r-cran-shiny, r-cran-shinydashboard, r-cran-leaflet, r-cran-plotly, r-cran-dt, r-cran-exactextractr, r-cran-ggrepel, r-cran-terra Filename: pool/dists/resolute/main/r-cran-manureshed_0.1.5-1.ca2604.1_all.deb Size: 615610 MD5sum: 6bf364621754130467a8292a3aa5dd9f SHA1: 7a2cef377d8989c50bc8021bde46f23ebb67f62f SHA256: 9838b5d5733c1e8f80587130c3adf8a37523b9d91961974dda94332dabad4150 SHA512: 46ff7f4650b3c10520e9996719f13eb1623b5d3586590c92ad55276d154b0e2cac73ab384aaa8c8ebcc496137157323da2a8c6a2dd9c5b35bcb583acc8b0c74e Homepage: https://cran.r-project.org/package=manureshed Description: CRAN Package 'manureshed' (Spatiotemporal Nutrient Balance Analysis Across Agricultural andMunicipal Systems) A comprehensive framework for analyzing agricultural nutrient balances across multiple spatial scales (county, 'HUC8', 'HUC2') with integration of wastewater treatment plant ('WWTP') effluent loads for both nitrogen and phosphorus. Supports classification of spatial units as nutrient sources, sinks, or balanced areas based on agricultural surplus and deficit calculations. Includes visualization tools, spatial transition probability analysis, and nutrient flow network mapping. Built-in datasets include agricultural nutrient balance data from the Nutrient Use Geographic Information System ('NuGIS'; The Fertilizer Institute and Plant Nutrition Canada, 1987-2016) and U.S. Environmental Protection Agency ('EPA') wastewater discharge data from the 'ECHO' Discharge Monitoring Report ('DMR') Loading Tool (2007-2016) . Data are downloaded on demand from the Open Science Framework ('OSF') repository to minimize package size while maintaining full functionality. The integrated 'manureshed' framework methodology is described in Akanbi et al. (2025) . Designed for nutrient management planning, environmental analysis, and circular economy research at watershed/administrative to national scales. This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. We thank Dr. Robert D. Sabo (U.S. Environmental Protection Agency) for his valuable contributions to the conceptual development and review of this work. We acknowledge Dr. Sheri Spiegal (U.S. Department of Agriculture–Agricultural Research Service) for foundational contributions to the manureshed classification framework (Spiegal et al. 2020) . Package: r-cran-manydata Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-messydates, r-cran-caret, r-cran-dtplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-remotes, r-cran-stringr, r-cran-text2vec, r-cran-tidyr Suggests: r-cran-testthat, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggvenndiagram, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-manydata_1.1.3-1.ca2604.1_all.deb Size: 1618638 MD5sum: 3db1304deb191734829ea8980febed8f SHA1: 1307103015b7e5325ebad44aa848b7aedb0c798c SHA256: 96a1180e74bdd1175095a5fdfa685a3f164644dc56e5ca32213b9272d25e2b65 SHA512: e545f5d8eafe52ebb5c59c817397672d6a7f844c7620298fdbf7eb7ed48b046511da47d17fff0931420c753e59011d49235c936d171344fc9002b502f42c43fa Homepage: https://cran.r-project.org/package=manydata Description: CRAN Package 'manydata' (Many Global Governance Datacubes) This is the core package offering a portal to the many packages universe. It includes functions to help researchers access, work across, and maintain ensembles of datasets on global governance called datacubes. Package: r-cran-manydist Architecture: all Version: 0.4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-entropy, r-cran-matrix, r-cran-fastdummies, r-cran-data.table, r-cran-philentropy, r-cran-cluster, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-forcats, r-cran-tibble, r-cran-magrittr, r-cran-fpc, r-cran-recipes, r-cran-rsample, r-cran-rfast, r-cran-readr, r-cran-distances Suggests: r-cran-palmerpenguins Filename: pool/dists/resolute/main/r-cran-manydist_0.4.9-1.ca2604.1_all.deb Size: 106854 MD5sum: bab0cb119067cebfc6852802e39e3b68 SHA1: 94666e1bc67160ff94837b4ca37aee1ef1539a99 SHA256: 9576a95e6c0169a0411c8d6fd634b40a6849bf142efb231157a75f91f18b2a35 SHA512: 6bf83d792a91d7b21b274076aa4376672f66870fc9c6da8d653de80b044196fd4f11f7707ec82899cf3c45a98ae7fe67d1314505c3910f842b631feb305574cb Homepage: https://cran.r-project.org/package=manydist Description: CRAN Package 'manydist' (Unbiased Distances for Mixed-Type Data) A comprehensive framework for calculating unbiased distances in datasets containing mixed-type variables (numerical and categorical). The package implements a general formulation that ensures multivariate additivity and commensurability, meaning that variables contribute equally to the overall distance regardless of their type, scale, or distribution. Supports multiple distance measures including Gower's distance, Euclidean distance, Manhattan distance, and various categorical variable distances such as simple matching, Eskin, occurrence frequency, and association-based distances. Provides tools for variable scaling (standard deviation, range, robust range, and principal component scaling), and handles both independent and association-based category dissimilarities. Implements methods to correct for biases that typically arise from different variable types, distributions, and number of categories. Particularly useful for cluster analysis, data visualization, and other distance-based methods when working with mixed data. Methods based on van de Velden et al. (2024) "Unbiased mixed variables distance". Package: r-cran-manyivsnets Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-aer, r-cran-lmtest, r-cran-sandwich, r-cran-magrittr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-knitr, r-cran-rtransferentropy, r-cran-tidyr, r-cran-viridis, r-cran-countrycode, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-manyivsnets_0.1.1-1.ca2604.1_all.deb Size: 163024 MD5sum: 5c7f6c228d9702880dcdb8d4cc798f73 SHA1: 3d5c8044b786ecfea7185ad207949856aa168027 SHA256: 9adfe6b9a1be1f12000b38d58e65e7679139ebb7e15f99daccef3a714a7deba1 SHA512: c9e211f1f7abca8b776bf2f7a2885d9b10f62e0b894f336230b4114339744b32c5fa7f235996df147bdb4fb13d56d5b1b008ecaba8adeb471a8201150006cd01 Homepage: https://cran.r-project.org/package=ManyIVsNets Description: CRAN Package 'ManyIVsNets' (Environmental Phillips Curve Analysis with Multiple InstrumentalVariables and Networks) Comprehensive toolkit for Environmental Phillips Curve analysis featuring multidimensional instrumental variable creation, transfer entropy causal discovery, network analysis, and state-of-the-art econometric methods. Implements geographic, technological, migration, geopolitical, financial, and natural risk instruments with robust diagnostics and visualization. Provides 24 different instrumental variable approaches with empirical validation. Methods based on Phillips (1958) , transfer entropy by Schreiber (2000) , and weak instrument tests by Stock and Yogo (2005) . Package: r-cran-manymodelr Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-stringr, r-cran-usethis, r-cran-testthat, r-cran-caret, r-cran-metrics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-manymodelr_0.4.0-1.ca2604.1_all.deb Size: 628560 MD5sum: cb250ae2914b8ce7093b9833dc00cbc5 SHA1: ad8badcc32d2d463d451269f477f1cffa410e4e7 SHA256: f0899a00b4256e1e90bb777fa3a966785b430ec13b3e86539bb9b523aeeb8d85 SHA512: 1ade19b992625e135b522d811deaad162960cc1f6c1d35e9698647e7111993521b7ac4312f3d4c4122080e0fc9532c520de5abc549765ffbb343d80c6f201027 Homepage: https://cran.r-project.org/package=manymodelr Description: CRAN Package 'manymodelr' (Build and Tune Several Models) Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) . Package: r-cran-manymome.table Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manymome, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-lavaan, r-cran-officer Filename: pool/dists/resolute/main/r-cran-manymome.table_0.4.0-1.ca2604.1_all.deb Size: 70944 MD5sum: e1a17e6e01f866793672567a6114163f SHA1: f5f18e8950023b9894df01e85927f68ad46d4682 SHA256: 09bffc8f9a2099e01a6e10e60b3e4719ab01dfa46177c21b99d0367dfbef403f SHA512: 43631bb4eb8ed25d62f3303b975017fac07923e6f44c7810737c25f6fe51ad660172f61a16247b71c08039d66c50797805cc53bfb1b7eef204313488e051b8d8 Homepage: https://cran.r-project.org/package=manymome.table Description: CRAN Package 'manymome.table' (Publication-Ready Tables for 'manymome' Results) Converts results from the 'manymome' package, presented in Cheung and Cheung (2023) , to publication-ready tables. Package: r-cran-manymome Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3540 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-boot, r-cran-pbapply, r-cran-ggplot2, r-cran-igraph, r-cran-mass, r-cran-lmhelprs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lavaan.mi, r-cran-amelia, r-cran-mice, r-cran-semplot, r-cran-semptools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-manymome_0.3.4-1.ca2604.1_all.deb Size: 2926300 MD5sum: 0f3e3583951227e7d9fc6d235414c237 SHA1: 8d41dde2d962c43a2b1e64c457e287c47e02bcb0 SHA256: f4c47fc7dbfacd31a5af0165e6dbaff53f8aa41da031b74d2da0d520ced41599 SHA512: fa1032aa478a3e98c976617799d5f825492a1e93f312446555562fcf70ea336b0e9cc8fe3933f519d564a15a3cbe91e7db75d292a74c234f804b6e81259dad97 Homepage: https://cran.r-project.org/package=manymome Description: CRAN Package 'manymome' (Mediation, Moderation and Moderated-Mediation After ModelFitting) Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax, using the approach presented in Cheung and Cheung (2024) . Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) and Cheung, Cheung, Lau, Hui, and Vong (2022) . Model fitting can be done by structural equation modeling using lavaan() or regression using lm(). Package: r-cran-manynet Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2809 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-igraph, r-cran-network, r-cran-pillar, r-cran-tidygraph Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-rsiena, r-cran-sna, r-cran-testthat, r-cran-tibble, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-manynet_2.0.1-1.ca2604.1_all.deb Size: 1927890 MD5sum: 99d97693e1316dbb97b2f0948247613e SHA1: 63a8c96c50b637e4068acf2ad553a8b6d872845c SHA256: be0ac6aa9eed802916ddba57e9af7e711388013bcec3c512d9f37b0960ceb757 SHA512: 0c0e2ac5a653df092f6baac76038f19656172e079f87432e679c87eed49124c422e2618f1a37b0c6c3986bdd21ee1bd03ee468e386eb12981a39e6ac0f9384b5 Homepage: https://cran.r-project.org/package=manynet Description: CRAN Package 'manynet' (Many Ways to Make, Modify, Mark, and Measure Myriad Networks) Many tools for making, modifying, marking, measuring, and motifs and memberships of many different types of networks. All functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, on directed, multiplex, multimodal, signed, and other networks. The package includes functions for importing and exporting, creating and generating networks, modifying networks and node and tie attributes, and describing networks with sensible defaults. Package: r-cran-manystates Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manydata, r-cran-knitr, r-cran-purrr, r-cran-stringi Suggests: r-cran-pointblank, r-cran-messydates, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-manystates_1.0.3-1.ca2604.1_all.deb Size: 318574 MD5sum: c9cbb184618bd15761337990e86059a5 SHA1: feddb72faa0f9a2ca974063f8731f319ddcced23 SHA256: 4078889b811de62e9d737e766160f171ae6c5dd89a344ca016b3d1600018cc33 SHA512: 95a7dd552188cb37931d9c105c9d9159dca363efeb537c2cc1d71e707fb1d4a38365a2aa997d6e31a8777f0f378fc851e11ac347601cd71d425409f2eeefbfa8 Homepage: https://cran.r-project.org/package=manystates Description: CRAN Package 'manystates' (Many Data on State and State-Like Actors in the InternationalSystem) Comprehensively identifying states and state-like actors is difficult. This package provides data on states and state-like entities in the international system across time. The package combines and cross-references several existing datasets consistent with the aims and functions of the manydata package. It also includes functions for identifying state references in text, and for generating fictional state names. Package: r-cran-manytests Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-manytests_1.2-1.ca2604.1_all.deb Size: 24402 MD5sum: 3fe062c7f511eaba0ab01327a3c708a6 SHA1: dda987089fa61d531e7f30e6c6601334cb45c919 SHA256: 8b989247196de3a556f62ea435f1d2b05ceae957e4f9c8a94187bfd3256320e2 SHA512: 169c7e96ba28112c18a66de131b6a718c09104f82cfb317fe520cb4097f2fbf4083b3d9549baf481405df431f467ac58bca165be3da054f125632f9652e58b23 Homepage: https://cran.r-project.org/package=ManyTests Description: CRAN Package 'ManyTests' (Multiple Testing Procedures of Cox (2011) and Wong and Cox(2007)) Performs the multiple testing procedures of Cox (2011) and Wong and Cox (2007) . Package: r-cran-maoea Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-nsga2r, r-cran-lhs, r-cran-nnet, r-cran-stringr, r-cran-randtoolbox, r-cran-e1071, r-cran-mass, r-cran-gtools, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-maoea_0.6.2-1.ca2604.1_all.deb Size: 190252 MD5sum: 4de34f74832cb5789f20a92a7a5eea87 SHA1: 6ca845a9d313a8e74c7978354a0e69467d242707 SHA256: b40f7dc5e097a6ee27ec014e26539c0bbdbb893a21ed1456036134ed517756b0 SHA512: e2990bc7fb391616b00744b07867c79e7227f4ba52e01e857d46e16be340687b37e1054f7ce03ddb20ae738a3cbf5d0c32c8aea2dbc48b9d50ba24f64cde7bcd Homepage: https://cran.r-project.org/package=MaOEA Description: CRAN Package 'MaOEA' (Many Objective Evolutionary Algorithm) A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' 'NSGA-III' 'MO-CMA-ES' The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) and 'WFG4'-'WFG9' from Huband, et al. (2005) . Package: r-cran-map2ncbi Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rentrez Filename: pool/dists/resolute/main/r-cran-map2ncbi_1.5-1.ca2604.1_all.deb Size: 83342 MD5sum: 420a69f72f7b99c3107a2dfd6ee785a0 SHA1: 6d004e1ad0a11adc3104e3abe5eefb0e5d9842a6 SHA256: 764dbf26a4ced7648a35ef80e8138caf7df0d1d1900b8f86137ba74174ca0ad5 SHA512: 5f6cc61f8592723ec7c3dcee3f513c1ff4253314feef5e1bfd4184f05dc1a041d2c6737ad868947213be3e0124900543000c6fde2bf1e942b03c8c6c7564ac27 Homepage: https://cran.r-project.org/package=Map2NCBI Description: CRAN Package 'Map2NCBI' (Mapping Markers to the Nearest Genomic Feature) Allows the user to generate a list of features (gene, pseudo, RNA, CDS, and/or UTR) directly from NCBI database for any species with a current build available. Option to save downloaded and formatted files is available, and the user can prioritize the feature list based on type and assembly builds present in the current build used. The user can then use the list of features generated or provide a list to map a set of markers (designed for SNP markers with a single base pair position available) to the closest feature based on the map build. This function does require map positions of the markers to be provided and the positions should be based on the build being queried through NCBI. Package: r-cran-map Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexmix, r-cran-matrix, r-cran-magrittr Suggests: r-cran-knitr, r-cran-proc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-map_1.0.0-1.ca2604.1_all.deb Size: 95320 MD5sum: fb720c51cf8192925c45714d476cd3b5 SHA1: 27a24b95f2f9a054012feb8efc48757fb2044665 SHA256: 80243a4c5a1f624b2d427f9fab2e491ca4fc181c3e4296a227aba334d428660d SHA512: af1a09f8162a96a3421d5eec6881ac2d212acdb2b83ed5ad9a64015edb568c868e9cb0fa5afa9ed6390c735a92e28c8feedb13bf66091cebbb3288974cc928cc Homepage: https://cran.r-project.org/package=MAP Description: CRAN Package 'MAP' (Multimodal Automated Phenotyping) Electronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. Towards that end, we developed an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). Specifically, our proposed method, called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent mixture models on aggregated ICD and NLP counts along with healthcare utilization. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no (See Katherine P. Liao, et al. (2019) .). Package: r-cran-mapa Architecture: all Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-rcolorbrewer, r-cran-smooth Filename: pool/dists/resolute/main/r-cran-mapa_2.0.7-1.ca2604.1_all.deb Size: 111248 MD5sum: a80df9057ce360f239f4129bba9ae054 SHA1: 7c8f41faa7202c6e5bd43d71de6d07c7d865f1ba SHA256: 6edc3039426139ac24024aaf2472600518c3fd7063e9a944a346dd8dd5bff532 SHA512: 27a519a64033d87f922e7353f0860879050621b48febfb5f8819e07fce3151c1dfaa1c7f3776ad0f975093cd28866ee82d23965232675a54ded44248d69d69f1 Homepage: https://cran.r-project.org/package=MAPA Description: CRAN Package 'MAPA' (Multiple Aggregation Prediction Algorithm) Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) . Package: r-cran-mapaccuracy Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mapaccuracy_0.1.2-1.ca2604.1_all.deb Size: 40794 MD5sum: bdc802bd66d098b61c0893313b5ccd26 SHA1: c1d3a046b981c1c407eeaa588edf219948a13ce2 SHA256: 5b9b9d9cdafe9180d1e27ace8d978450d4b137d24e0e1c545be174eb8342cb2d SHA512: 3675e81da266d448b4081b0ffe5fefd9ae4700f90e0e327a409e618eafd9f399957fe6d4e804252d7e4242926d2d5b142d45877c2ca976bcc89adce4d52d8740 Homepage: https://cran.r-project.org/package=mapaccuracy Description: CRAN Package 'mapaccuracy' (Unbiased Thematic Map Accuracy and Area) Unbiased estimators of overall and per-class thematic map accuracy and area published in Olofsson et al. (2014) and Stehman (2014) . Package: r-cran-mapbayr Architecture: all Version: 0.10.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mrgsolve, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lubridate, r-cran-minqa, r-cran-scales, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-mapbayr_0.10.2-1.ca2604.1_all.deb Size: 297376 MD5sum: 8c51a2b63907c689004102fcd10d603a SHA1: 7d14fd283f3eafa06e92087919bd6ae0fe5f4177 SHA256: 801922a221dbba281a0756ac99427c7779d38770a5d2e96892024a2f4b59e977 SHA512: 0bcfdf5b717c220132cdd87a8f774345714ac1483dd00b76fff95971cdc867c7b6a1f45f3b8ba710bb772197df2643a74f7b2b49a8598b9df1642b742100cb09 Homepage: https://cran.r-project.org/package=mapbayr Description: CRAN Package 'mapbayr' (MAP-Bayesian Estimation of PK Parameters) Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in 'mrgsolve', typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format. The performance of the package was described by Le Louedec et al (2021) . Package: r-cran-mapboxapi Architecture: all Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-jsonlite, r-cran-purrr, r-cran-curl, r-cran-dplyr, r-cran-tidyr, r-cran-aws.s3, r-cran-stringi, r-cran-slippymath, r-cran-protolite, r-cran-rlang, r-cran-geojsonsf, r-cran-magick, r-cran-leaflet, r-cran-units, r-cran-raster, r-cran-png, r-cran-jpeg, r-cran-htmltools Suggests: r-cran-ggspatial, r-cran-mapdeck, r-cran-tigris, r-cran-tidycensus, r-cran-tmap, r-cran-mapboxer, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mapboxapi_0.6.3-1.ca2604.1_all.deb Size: 264922 MD5sum: ee629aa8c014038618e89ea8cb31ef80 SHA1: d27ecfcd383078c1e2a002bb9a794ce57ecb67f8 SHA256: 2c4f2b69efee5823171d740d1a40bd62ab2a1bcf7e7cb04f03b96b3f19119322 SHA512: bf588d0de9ed0ff635b99986fd10db22b9d1ada77d244a2ae74f29989aef9c89cb215b582404cece47364b6e50357a1e446cbf59f786cddff82a563e417e3e66 Homepage: https://cran.r-project.org/package=mapboxapi Description: CRAN Package 'mapboxapi' (R Interface to 'Mapbox' Web Services) Includes support for 'Mapbox' Navigation APIs, including directions, isochrones, and route optimization; the Search API for forward and reverse geocoding; the Maps API for interacting with 'Mapbox' vector tilesets and visualizing 'Mapbox' maps in R; and 'Mapbox Tiling Service' and 'tippecanoe' for generating map tiles. See for more information about the 'Mapbox' APIs. Package: r-cran-mapboxer Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-htmlwidgets, r-cran-htmltools, r-cran-yaml, r-cran-purrr, r-cran-geojsonsf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-mapboxer_0.4.0-1.ca2604.1_all.deb Size: 698540 MD5sum: 01ce160bff6947b52eea4aefd845623e SHA1: 69a22553c82fe3f0df0eb9aeaf3b56993767f662 SHA256: 16326f61f96988fb6f80f4a6e02d03c62b051fde46b3edec2d581bc6dc8611f4 SHA512: a89f394b67df8f0abecd98ddb673c5bd34869f60df2a5f1aa314652ef30c26dd8ba901bc52f50c60a2713a1567eee340afecf2ddeb80b8abc436944258c5ef2b Homepage: https://cran.r-project.org/package=mapboxer Description: CRAN Package 'mapboxer' (An R Interface to 'Mapbox GL JS') Makes 'Mapbox GL JS' , an open source JavaScript library that uses WebGL to render interactive maps, available within R via the 'htmlwidgets' package. Visualizations can be used from the R console, in R Markdown documents and in Shiny apps. Package: r-cran-mapcan Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4824 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mapcan_0.0.1-1.ca2604.1_all.deb Size: 3963402 MD5sum: 1d45064d34d593ce14bc106e362fdad0 SHA1: 728d12332b4195486ba3891519db62526a24f091 SHA256: 10379bdf45ffd850f927440b472642bea07038ed84f185b76f19618300951089 SHA512: f8c91c78c2b9a306220d9e246e243ea56276c429482c92483a74305efc299b86423fab6d63bbd8034984fe70e62d7057d643df3ac7b12d728f7c3c704df3ba02 Homepage: https://cran.r-project.org/package=mapcan Description: CRAN Package 'mapcan' (Tools for Plotting Canadian Choropleth Maps and ChoroplethAlternatives) A variety of functions that make it easy to plot standard choropleth maps as well as choropleth alternatives in 'ggplot2'. Package: r-cran-mapchina Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-showtext Filename: pool/dists/resolute/main/r-cran-mapchina_0.1.0-1.ca2604.1_all.deb Size: 4342058 MD5sum: 9681212c3751a148805aa1c2b4f7c35d SHA1: e52950582ed0594b95e999e15a7514e49caee556 SHA256: 6a24ef387eb4342f48025cd297823d3d801f2967f8f9c16ac51937de0e1d823d SHA512: 877e92a34cd80cc6e64a233e8a361464fbf0287300a5df6b717bbd3d10f75fe8a6a89f97aa92c661a531b6d58b9633435e909b6efa6905b48312d102165b1d66 Homepage: https://cran.r-project.org/package=mapchina Description: CRAN Package 'mapchina' (China Administrative Divisions Geospatial Shapefile Data) Geospatial shapefile data of China administrative divisions to the county/district-level. Package: r-cran-mapctools Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5042 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-fastdummies, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-viridis, r-cran-scales, r-cran-purrr, r-cran-gridextra, r-cran-ggpubr, r-cran-tibble, r-cran-survey Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mapctools_0.1.0-1.ca2604.1_all.deb Size: 4742782 MD5sum: a98ba539b0bc23a7da2c7de61456845c SHA1: c573771e151a0aadbe0b4cfd8bdb7a11844f1404 SHA256: f0f9f05131fe8aaeacf7a82f21364bf371d6707a03e2e1f6162ededca437f33b SHA512: d8bd3e7f8f048f081093d68ae191f3ea12ff3d81ab3d394d6e6cff8eb0db132889ae37e992901b8e3e6b9e1a0898812e1e6226df00e97f9daa0e29dca8e50653 Homepage: https://cran.r-project.org/package=MAPCtools Description: CRAN Package 'MAPCtools' (Multivariate Age-Period-Cohort (MAPC) Modeling for Health Data) Bayesian multivariate age-period-cohort (MAPC) models for analyzing health data, with support for model fitting, visualization, stratification, and model comparison. Inference focuses on identifiable cross-strata differences, as described by Riebler and Held (2010) . Methods for handling complex survey data via the 'survey' package are included, as described in Mercer et al. (2014) . 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(2006), and Bai et al. (2020). Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders). 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Package: r-cran-mappingcalc Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-bslib, r-cran-ggplot2, r-cran-readxl, r-cran-haven, r-cran-writexl, r-cran-tibble Suggests: r-cran-dt, r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-mappingcalc_2.0.0-1.ca2604.1_all.deb Size: 245074 MD5sum: 140170fb4019adbe16bfbac91a7f753e SHA1: b8b99c8d9f54c67c97a102c26c76895fa49e5f09 SHA256: 41ce353751933e5e780fb44dd3901b7e66e76cadc3ee8166c19013c5fa32795e SHA512: 65615571b53a535864cbc246fad7e21636ab4881253e1155dc41779d588fd4d8090d911321f880c7c9446ee28984830555d30bfa825246ab82cbd27755e1c51e Homepage: https://cran.r-project.org/package=MappingCalc Description: CRAN Package 'MappingCalc' (Mapping Calculator for EQ-5D Utility Scores) Provides a 'shiny' web application to map scores from clinical instruments (PANSS, SQLS, WHODAS 2.0, PHQ-8, EQ-5D-5L) to preference-based EQ-5D-5L health utility values using validated regression-based and beta-mixture mapping algorithms developed from Singapore population studies. Intended for use in health economic evaluations and cost-utility analyses. Methods are based on: Abdin et al. (2019) , Seow et al. (2023) , Abdin et al. (2021) , Abdin et al. (2024) . 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Package: r-cran-marsannhybrid Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-neuralnet, r-cran-earth Filename: pool/dists/resolute/main/r-cran-marsannhybrid_0.1.0-1.ca2604.1_all.deb Size: 13294 MD5sum: 631c3478e92972b6f494ccb5f7778724 SHA1: 9c05d4ce39907bbcf48e7e7287138fb4878a91ee SHA256: 251381ec562439b8a97ed5b25100018f7fc0524519b8be9c01984eb4f84143f5 SHA512: 9bc3e55c196465738e9dd1f35221b0ab799715c37d9c0fe9dcd27832604816da2e558f544df10e013eb1b804ca10fa441c4262a351b7ecadb055afde99bdc65d Homepage: https://cran.r-project.org/package=MARSANNhybrid Description: CRAN Package 'MARSANNhybrid' (MARS Based ANN Hybrid Model) Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables. Package: r-cran-marsearth Architecture: all Version: 0.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-marsearth_0.0.0-1.ca2604.1_all.deb Size: 41802 MD5sum: fb2479fc04ffc0fccaa7b62fd2d302f9 SHA1: 98d9f640f7388ba5d736573460576c0676b63aaa SHA256: 25f07b35ac2ef4cb9287fb123a935f0089423029c396e410e14d953bf0865c4e SHA512: f5847248a285c8e9870930ae24f7d89391f05270608fbe361fc511300d1dc64c4d0fb4e5e458eb757ef035bf5272c448fa6b72a001c9bc1e0d423e5ba2249610 Homepage: https://cran.r-project.org/package=marsearth Description: CRAN Package 'marsearth' (Portable Mars Runtime Replay) Loads, validates, and replays portable 'mars' ModelSpec artifacts from R. The package provides helpers for constructing design matrices, generating predictions, and, when the companion runtime helper is available, fitting portable model specifications for cross-language replay. Package: r-cran-marsgwr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qpdf, r-cran-numbers, r-cran-earth Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-marsgwr_0.1.0-1.ca2604.1_all.deb Size: 28622 MD5sum: 8b9aa4d40cfdfebdaeb96f32d4c692e0 SHA1: 4bc7929553582e36e1b11c7da655f182aa7b6b5c SHA256: 331f5dbc2bf0196586c2acf716ef5942027c412051dc8f0070050d4112f444c9 SHA512: 51916f3eda98f6aa5403112daf87ab6cf663e75006a616e4c8dbbf41c5c959dc7db04073c947f5efd2b5d42ad1f8067759a54695aa090c3b50760bad028dbfcd Homepage: https://cran.r-project.org/package=MARSGWR Description: CRAN Package 'MARSGWR' (A Hybrid Spatial Model for Capturing Spatially VaryingRelationships Between Variables in the Data) It is a hybrid spatial model that combines the strength of two widely used regression models, MARS (Multivariate Adaptive Regression Splines) and GWR (Geographically Weighted Regression) to provide an effective approach for predicting a response variable at unknown locations. The MARS model is used in the first step of the development of a hybrid model to identify the most important predictor variables that assist in predicting the response variable. For method details see, Friedman, J.H. (1991). .The GWR model is then used to predict the response variable at testing locations based on these selected variables that account for spatial variations in the relationships between the variables. This hybrid model can improve the accuracy of the predictions compared to using an individual model alone.This developed hybrid spatial model can be useful particularly in cases where the relationship between the response variable and predictor variables is complex and non-linear, and varies across locations. Package: r-cran-marsrad Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-dt, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-marsrad_1.0.1-1.ca2604.1_all.deb Size: 152660 MD5sum: e66712635d81f0f5529aea47fbf6b1da SHA1: ebbe7d4ab05a1811ddc589d1e4eaa4d40ed91c93 SHA256: 446b95c1a18aaf2a74c2f884addc736415fe26fc0691fa632311f61559c589a2 SHA512: c039d814921b019bc01fdc467119f0b86759e936a2024523f987987996d8e33119aa7538f0e8267fbbe3810b4abdeb6e2cd7bb1861e885205b817fab57e08004 Homepage: https://cran.r-project.org/package=marsrad Description: CRAN Package 'marsrad' (Mars Solar Radiation) A set of functions to calculate solar irradiance and insolation on Mars horizontal and inclined surfaces. Based on NASA Technical Memoranda 102299, 103623, 105216, 106321, and 106700, i.e. the canonical Mars solar radiation papers. 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The package provides helpers for constructing design matrices, generating predictions, and, when the companion runtime helper is available, fitting portable model specifications for cross-language replay. 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MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at . 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Package: r-cran-massextra Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1001 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-demokde Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-visreg, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-massextra_1.2.2-1.ca2604.1_all.deb Size: 687646 MD5sum: cf176315292b99c66277191328c9fbcb SHA1: f546659abc1d31a3dcb9ed9efd9e4252dd48cd89 SHA256: e6d5d80dc638a1cd442a1f2e554b5c3ccaf8df5ba2322affc5163bc889c31515 SHA512: 096e05da6a137d293097aeb7e07c74ee3e5f3856f60ab141cd7305934db164ccf1c499047500228730421bce1f972853d0efdad7466d3be25642c0ed37e2fd2d Homepage: https://cran.r-project.org/package=MASSExtra Description: CRAN Package 'MASSExtra' (Some 'MASS' Enhancements) Some enhancements, extensions and additions to the facilities of the recommended 'MASS' package that are useful mainly for teaching purposes, with more convenient default settings and user interfaces. Key functions from 'MASS' are imported and re-exported to avoid masking conflicts. In addition we provide some additional functions mainly used to illustrate coding paradigms and techniques, such as Gramm-Schmidt orthogonalisation and generalised eigenvalue problems. 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Functions are developed to facilitate data formatting for the Water Quality Exchange Network and reporting of data quality objectives to state agencies. Quality control methods are from Massachusetts Department of Environmental Protection (2020) . 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Results are equivalent to those from 'Stata', and you can choose how to format your input data. Methods used are those described on page 56 the 'Stata' documentation for "Epitab - Tables for Epidemologists" . 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Package: r-cran-matchgate Architecture: all Version: 0.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-locpol Filename: pool/dists/resolute/main/r-cran-matchgate_0.0.10-1.ca2604.1_all.deb Size: 21928 MD5sum: 1dd4a40560788fca953abf2fd48ec99e SHA1: a85a44e884e436319f5ab855ee952cbe6d52366c SHA256: 15195033b1bb1aaf985f1bf29edf3fb09b8683c761e2dc0325b9a76f4aa78be1 SHA512: c71ad5690c3686d0a6db29c6aad3faca3917c61a4190a502fce28d11bb6d10bfcbbd4bc98d9e6610663efbdcb48d67b12293b4ed98e32f4bbcf022b7327e2133 Homepage: https://cran.r-project.org/package=MatchGATE Description: CRAN Package 'MatchGATE' (Estimate Group Average Treatment Effects with Matching) Two novel matching-based methods for estimating group average treatment effects (GATEs). 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For more details see Mallat and Zhang (1993) , Pati et al. (1993) , Elad (2010) and Różański (2024) . 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Package: r-cran-matchmaker Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-forcats, r-cran-cli Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-matchmaker_0.1.1-1.ca2604.1_all.deb Size: 55100 MD5sum: f4321c604e2fd26983566198d3056c05 SHA1: d3b716c82c367f6614430286561a9645e3357e28 SHA256: 0af899f02c9aa92caddacdcd72deafd4c93bde30cef366040ce916155f0e4fca SHA512: 9c4de8f7db75d101cc5194291ec796310ccaaa842f92268ab4b74cac1005bf033e4c43c9833d279c7a1af6f90bc9c59084715be023de4c058aeee80bb6204f14 Homepage: https://cran.r-project.org/package=matchmaker Description: CRAN Package 'matchmaker' (Flexible Dictionary-Based Cleaning) Provides flexible dictionary-based cleaning that allows users to specify implicit and explicit missing data, regular expressions for both data and columns, and global matches, while respecting ordering of factors. 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Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis. Details in Pimentel et al. (2018) . The optmatch package, which is useful for running many of the provided functions, may be downloaded from Github at if not available on CRAN. 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Conditions and return expressions are separated by '->' and multiple conditions can be associated with the same return expression using '|'. 'Match' also includes support for 'fallthrough'. The package also replicates the Result and Option enums from Rust. Package: r-cran-matchthem Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matchit, r-cran-mice, r-cran-rlang, r-cran-survey, r-cran-weightit Suggests: r-cran-amelia, r-cran-cobalt, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-matchthem_1.2.1-1.ca2604.1_all.deb Size: 606114 MD5sum: 156805b4d7fef4cca2d933cbe5f49cad SHA1: 3eefe84d99ba5cfa3d4c632c27847e4a23aee4a6 SHA256: ae1dcae6d701d96f529e2c569d087fd199227141f2e3d086419e0ae9a78ce529 SHA512: ba7c19535c070de6df3025b3df039c7fddb3a6841868e6129e3f9a197fc2867e1d5128a831acc2724684428d1abecc181f547c5d70cfb39d5cc833db2f126bbb Homepage: https://cran.r-project.org/package=MatchThem Description: CRAN Package 'MatchThem' (Matching and Weighting Multiply Imputed Datasets) Provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see for more details). 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Package: r-cran-matpow Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bigmemory Filename: pool/dists/resolute/main/r-cran-matpow_0.1.2-1.ca2604.1_all.deb Size: 359370 MD5sum: a5a0709bdd367ab41c414af916d3b508 SHA1: d55e7926606c1170e49fdc0b47cca1ae7bd3d2b0 SHA256: 03928c612ec492b9743b6e578984e7c45b67f50f09284f9d394c91c4c0c965f7 SHA512: 174f258248b3f0eea04aab3cf2766d1b5b888bc1bfb22097714d3dd64b922c797bebd1af4c525f077327b05da2105e39d2a98e8003f1a2df18d3fb5f62a44581 Homepage: https://cran.r-project.org/package=matpow Description: CRAN Package 'matpow' (Matrix Powers) A general framework for computing powers of matrices. A key feature is the capability for users to write callback functions, called after each iteration, thus enabling customization for specific applications. Diverse types of matrix classes/matrix multiplication are accommodated. If the multiplication type computes in parallel, then the package computation is also parallel. Package: r-cran-matrans Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-formatr, r-cran-glmnet, r-cran-mass, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-matrans_0.2.0-1.ca2604.1_all.deb Size: 155646 MD5sum: 9c3592bc3f51336c9f71189c14460d7e SHA1: fc2e44cb786ef8770c2e91bfbb0d635a22e07336 SHA256: a6988c7d9c210bb87ea37428ac931f023c5f08d311729d2121d3306158bc172e SHA512: f744cb58c9fa65c374adbd840fdcee417b7eb37e8c6629e3df216365aeedae46aa37eee690c87fe12aefb5c48ab957af375e89acbcea890d23fb7da2429a5989 Homepage: https://cran.r-project.org/package=matrans Description: CRAN Package 'matrans' (Model Averaging-Assisted Optimal Transfer Learning) Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) . Package: r-cran-matriks Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/resolute/main/r-cran-matriks_0.1.5-1.ca2604.1_all.deb Size: 355924 MD5sum: de89d77fc3571f25190c160ba17a77ba SHA1: 5e277eee8e7c913616fd5b5c753620939dc23d99 SHA256: 4272c117c09bee99c5f326d26063956994ce1718c07b7e94cda8afb526248674 SHA512: 0e74d04a64356cefb21a412e155f593480db497b1aa2a54e1dddc7660cd8330a68b681cbd916c4b01e42506e2e71a806f4585ecb3bf0b8f4a07f5d15d9e9b674 Homepage: https://cran.r-project.org/package=matRiks Description: CRAN Package 'matRiks' (Generates Raven-Like Matrices According to Rules) Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the 'DescTools' package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research. Package: r-cran-matrisk Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-sn, r-cran-dfoptim, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-matrisk_0.1.0-1.ca2604.1_all.deb Size: 37254 MD5sum: 8869f5bdcb6159e98124730706140483 SHA1: 1a17244bd15b93a2b2645da9b35ed48680355e03 SHA256: de18d5c53cbbc6ec09be73b1f2729b5bbdc9ef866cf7c34157b6eddd5d0fc933 SHA512: bc5800b079db66328d2b863d6e1ce13a2edb8399129ee9d2ad23239a376f2563dae3c21ba0e4c1aa65a2695a9ad41b6871819556c3adca754008d16f267994de Homepage: https://cran.r-project.org/package=matrisk Description: CRAN Package 'matrisk' (Macroeconomic-at-Risk) The Macroeconomics-at-Risk (MaR) approach is based on a two-step semi-parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) to reveal the vulnerability of economic growth to financial conditions, the MaR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers. Package: r-cran-matrixcalc Architecture: all Version: 1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-matrixcalc_1.0-6-1.ca2604.1_all.deb Size: 203638 MD5sum: d28e65961023d502f96ace6242133359 SHA1: d4a57db8fa70cc3ee6c7ff01ea75ee944741b89b SHA256: e95a634a414f6d9ca699e6acbe1ce54decba74976035f910ce20823882268da1 SHA512: 4fbc791c8989694d3810dce876638c37c4e5aefa8a52d21a6aa9fae2bcf1ac94c447d6f671b577aea7394bb1c62598040455889bd978eea7b473913b8d48c6b9 Homepage: https://cran.r-project.org/package=matrixcalc Description: CRAN Package 'matrixcalc' (Collection of Functions for Matrix Calculations) A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563. Package: r-cran-matrixcut Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-inflection Filename: pool/dists/resolute/main/r-cran-matrixcut_0.0.1-1.ca2604.1_all.deb Size: 62170 MD5sum: 4189c8d8aeaa6f9e467affb439b4c832 SHA1: 4ae9551b1413a8caa9d3e7c6744a7204834178d6 SHA256: 19ce7a65a6fe648486377e3a4e6a9761ccba5ee091915f567b07e63ccd7767cf SHA512: 43fd1f7e3b3794bbef06a7361b34dfcb93eba65685308d0fbeb9fa7860365d08878953d4c1baed38f3988bd8ca78b45ce010d0c21d9044720688a605457f59ae Homepage: https://cran.r-project.org/package=matrixcut Description: CRAN Package 'matrixcut' (Determines Clustering Threshold Based on Similarity Values) The user must supply a matrix filled with similarity values. The software will search for significant differences between similarity values at different hierarchical levels. The algorithm will return a Loess-smoothed plot of the similarity values along with the inflection point, if there are any. There is the option to search for an inflection point within a specified range. The package also has a function that will return the matrix components at a specified cutoff. References: Mullner. ; Cserhati, Carter. (2020, Journal of Creation 34(3):41-50), . 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Furthermore, the concept of biobanding, or grouping of athletes based on their biological development, instead of their chronological age, has been widely researched. The goal of this package is to help professionals working in the field of strength & conditioning and talent ID obtain common maturation metrics and as well as to quickly visualize this information via several plotting options. For the methods behind the computed maturation metrics implemented in this package refer to Khamis, H. J., & Roche, A. F. (1994) , Mirwald, R.L et al., (2002) and Cumming, Sean P. et al., (2017) . 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Can process and evaluate local risk aversion utilities for a set of indexes, compute utilities and weights for the whole decision tree defining the decision model and simulate weights employing Dirichlet distributions under addition constraints in weights. Also includes other rating analysis methods as for example the Colley, Offensive - Defensive ratings and the ranking aggregation with Borda count. Package: r-cran-mauricer Architecture: all Version: 2.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-beastier Suggests: r-cran-beautier, r-cran-curl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tracerer Filename: pool/dists/resolute/main/r-cran-mauricer_2.5.4-1.ca2604.1_all.deb Size: 70114 MD5sum: cd4583931d76265629fd17b1fc27bd0f SHA1: 08e0112f7380503c0c3192e7f46aa9a7fae7d7b6 SHA256: 7b4da5a5959a82485408c152cad5cc808945fcbd01d0cd0aa84a284b795d5a32 SHA512: 6f7893f095701115335d7ac7764c21d527c396a33bd259c3019b7f268862ab261b12d677bda35496bb62a8a3d61dda5d5d3f673fd48d483ec5114f2b922726e3 Homepage: https://cran.r-project.org/package=mauricer Description: CRAN Package 'mauricer' (Work with 'BEAST2' Packages) 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is commonly accompanied by 'BEAUti 2' (), which, among others, allows one to install 'BEAST2' package. This package allows to work with 'BEAST2' packages from 'R'. 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Eick, Jamie T. Bridgham, Douglas P. Anderson, Michael J. Harms, and Joseph W. Thornton (2017), "Robustness of Reconstructed Ancestral Protein Functions to Statistical Uncertainty" . 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The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo. Package: r-cran-maxent.ot Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-maxent.ot_1.0.0-1.ca2604.1_all.deb Size: 993766 MD5sum: 770c745b777b32e2f4597adce017fe67 SHA1: f136262d6e543816cde55d46b6a71639dff5445f SHA256: 714c946170efad67dcc065a8f40948cc3077b82ae8176819f37a8ec2e35f255e SHA512: 37c5a51872017e3e0c856d84a49025633bfeb94829fba68f31d85928c78a78b989bf7a675a2a74e4a58e888fc54004cb0b305ccf4d7f6f83218e49f11f5b3806 Homepage: https://cran.r-project.org/package=maxent.ot Description: CRAN Package 'maxent.ot' (Perform Phonological Analyses using Maximum Entropy OptimalityTheory) Fit Maximum Entropy Optimality Theory models to data sets, generate the predictions made by such models for novel data, and compare the fit of different models using a variety of metrics. The package is described in Mayer, C., Tan, A., Zuraw, K. (in press) . 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This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 ), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 ). Users of this package should be familiar with Maxent niche modelling. 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(2025) , Ahmed et al. (2023) , Ahmed et al. (2021) . 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Package: r-cran-maxnet Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-maxnet_0.1.4-1.ca2604.1_all.deb Size: 66062 MD5sum: 89748904c5c883d0fee6df21cf2787d7 SHA1: 394fddd1fb77a663b41b602e1c259ec6afc46143 SHA256: a0b1de206aa8fccfcd1bcee2a100a2db50ee345339d832c44c48095e2662acd1 SHA512: 9d4c68b98ac28af2567f14bdd5f4b5944724fc496f7bbf54da7b696fde6dd353b4b71e4499df6fd089409505b2daa355e17206d41b3157bebd61838c469a31a0 Homepage: https://cran.r-project.org/package=maxnet Description: CRAN Package 'maxnet' (Fitting 'Maxent' Species Distribution Models with 'glmnet') Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options. See the 'Maxent' website for more details. 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The method uses predictors of genotypic effects obtained from the fitting of mixed models. Its application is demonstrated with grapevine data, but is applicable to other species and breeding contexts. For more details see Surgy et al. (2025) . Package: r-cran-maxskew Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-maxskew_1.1-1.ca2604.1_all.deb Size: 35064 MD5sum: 093c9e8a249b99c53f16ba36a61d3751 SHA1: c7daf64e0ef55f6637ba2984f73bf626fe7fbc90 SHA256: 8a45d9edb651d7cb3992d18c4641beb32cfcafe0f61243c7a9629089015a6ff4 SHA512: b870c36ead7b18fe8ce267ca8701e7879ec6dbe415b18d9ed7cc03fcd62a5b49255229b6643f4c5cb21e4fb33223e8b59cada5a59d8b339662fcc96334c25124 Homepage: https://cran.r-project.org/package=MaxSkew Description: CRAN Package 'MaxSkew' (Orthogonal Data Projections with Maximal Skewness) It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on. 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Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or 'MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) and Iurii Nagornov (2023) . 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That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became more general and now contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from others, see for a detailed list of those that have contributed and other details. 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(2013) and adjusted in Robinson et al. (2017) . Designs using MBHdesign can: 1) accommodate, without substantial detrimental effects on spatial balance, legacy sites (Foster et al., 2017 ); 2) be based on points or transects (foster et al. 2020 and produce clustered samples (Foster et al. (in press). Additional information about the package use itself is given in Foster (2021) . Package: r-cran-mbir Architecture: all Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-effsize, r-cran-psych Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mbir_1.3.5-1.ca2604.1_all.deb Size: 437592 MD5sum: f0f273c255a3d66cab03e58558887ff1 SHA1: 7c47c456d72ddc774aa18c9874b214ebe2a77e35 SHA256: 02d37ae798c4fbcc9f6e44eee8f87c9d2842ec10df43748f8519aeb8e8fbf1b3 SHA512: 97a805529b82d44bff18a6aa3f12449dac6caeaf7d87f6d67f64dc21b10608f8b3c8b78b3d7067096ae819325c0bb6232980cdb6ea3bb161c2d051fcc705726f Homepage: https://cran.r-project.org/package=mbir Description: CRAN Package 'mbir' (Magnitude-Based Inferences) Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. 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They are very robust (29 or 50 percent breakdown point, respectively), and if no outliers are present, the estimators are very similar to OLS. 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See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. for more details. Package: r-cran-mbmixture Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-mbmixture_0.6-1.ca2604.1_all.deb Size: 45918 MD5sum: 3a64ef6708471016914948fc6938c36e SHA1: 338ce324ac83a359ed16d69ba71c2a75cd6efecd SHA256: e375f2647cb8ea3c3d1a950d7f5c2cd3cd5a82b4da18031a4dd0037ec2c8ff7e SHA512: 1c35f64159221e4527958d12d36bdaa7210134ed371084c8f2923afea431d58f3039c30780fb1129355bd4ec9b031ad14b4fad2c569c3fac8530542e94b61307 Homepage: https://cran.r-project.org/package=mbmixture Description: CRAN Package 'mbmixture' (Microbiome Mixture Analysis) Evaluate whether a microbiome sample is a mixture of two samples, by fitting a model for the number of read counts as a function of single nucleotide polymorphism (SNP) allele and the genotypes of two potential source samples. 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Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) . The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting. Package: r-cran-mbr Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplr, r-cran-mass, r-cran-matrix, r-cran-rfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mbr_0.0.1-1.ca2604.1_all.deb Size: 84520 MD5sum: 43afb4bdfa0bf4f80ef3868f872eeb21 SHA1: 5350165546ed28f6c3f297b3da788e7586895269 SHA256: 21a7898995ee249268155a771844845118f8508831d1c8613bd860a6c8285da9 SHA512: 224f7805ffb7a4fab42e73f2f62b7bca4686e577ed2db8213d52d0a1a56a47e3cc8f5a144e82af2feac872ed8ad208686317bbcd0f5fb4698964f7c1ebef02cd Homepage: https://cran.r-project.org/package=mbr Description: CRAN Package 'mbr' (Mass Balance Reconstruction) Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). 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Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses. The response dimension reduction is theoretically founded in Yoo and Cook (2008) . Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) and Yoo (2019) . The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace. 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Package: r-cran-mbsts Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kfas, r-cran-matrix, r-cran-pscl, r-cran-mcmcpack, r-cran-mass, r-cran-ggplot2, r-cran-reshape2, r-cran-bbmisc, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mbsts_3.0-1.ca2604.1_all.deb Size: 100216 MD5sum: e98baa44d371bc667f3e46a6e5914b58 SHA1: 53a9e26d4c3e8690d31aeb5fb4f22c08904998d2 SHA256: be341ed5fe77d805022f51c48b824d1b4a84a7db2ff11a3b9c45351a8b973efb SHA512: 511193e1b370fe7eb0deb63e3bf7e9d4e31a45b8d3082e3bf74c8333d5157f4904fc831bc047ff7babc1d622168a498e5da4ee1e3b784ae83cb2dfdc3d6d5dc7 Homepage: https://cran.r-project.org/package=mbsts Description: CRAN Package 'mbsts' (Multivariate Bayesian Structural Time Series) Tools for data analysis with multivariate Bayesian structural time series (MBSTS) models. Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component. Package: r-cran-mbx Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rstatix, r-cran-tibble, r-cran-fsa, r-cran-multcompview Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mbx_0.2.0-1.ca2604.1_all.deb Size: 100452 MD5sum: 50af9e39d425d91f6620b25f90d0dd76 SHA1: 69693e2b90a99b6ad7672cbfc182bad49722f503 SHA256: 57cc13cbff4a49b66c999a2ecf47e564bdb23e44c1bb4cebff1bdcb8210be0be SHA512: d59ae92c6cf9855c99b8995df83801c7a2b513da75a01459c147552bea59b3a6b9f3f50ed064823dfe98cb18c1749851b08e63175798aa8f98f4b08905c7c8bc Homepage: https://cran.r-project.org/package=mbX Description: CRAN Package 'mbX' (A Comprehensive Microbiome Data Processing Pipeline) Provides tools for cleaning, processing, and preparing microbiome sequencing data (e.g., 16S rRNA) for downstream analysis. Supports CSV, TXT, and Excel file formats. The main function, ezclean(), automates microbiome data transformation, including format validation, transposition, numeric conversion, and metadata integration. It also handles taxonomic levels efficiently, resolves duplicated taxa entries, and outputs a well-structured, analysis-ready dataset. The companion functions ezstat() run statistical tests and summarize results, while ezviz() produces publication-ready visualizations. Package: r-cran-mc2d Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1826 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-ggplot2, r-cran-ggpubr Suggests: r-cran-fitdistrplus, r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mc2d_0.2.1-1.ca2604.1_all.deb Size: 1409974 MD5sum: b928c3eb90c282cbb44d91727dea3544 SHA1: bde175e46ad8d18ef914407dfe36908c0bf56fec SHA256: d9b65076a1dde3468e5714b9614919f871844ac74ff9f8593b4fb9b86244e83f SHA512: 349b038cc9e8dba257675fd329e6e2a26bbb73c8b46d4a09e1f6da19247c9c8d085e64713fce43938e6ecd07f0ecf6b502796a546d8f5ce53fd842be0334a65d Homepage: https://cran.r-project.org/package=mc2d Description: CRAN Package 'mc2d' (Tools for Two-Dimensional Monte-Carlo Simulations) A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous). Package: r-cran-mcanalysis Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mcanalysis_0.1.0-1.ca2604.1_all.deb Size: 16264 MD5sum: 73db32434b6dbc7a55220846c9e10a6c SHA1: dce6763ad9483528ee1c82289329f753b8a996d1 SHA256: da9c7ede7af0b3febf119c3d86a0b37435a956ac57684da315fc213a4ed68fd0 SHA512: 4800fca5d9b7b2a5ba786156ccade2be3e1261cc75b453e5055900a455240b5d72611d5ed0985b465ab68bc568fa07a428b002915e0f2e4d1e89449acd2ce15d Homepage: https://cran.r-project.org/package=mcanalysis Description: CRAN Package 'mcanalysis' (Markov Chain Analysis for Structural Behaviour and Stability) Analyses the stability and structural behaviour of export and import patterns across multiple countries using a Markov chain modelling framework. Constructs transition probability matrices to quantify changes in trade shares between successive periods, thereby capturing persistence, structural shifts, and inter-country interdependence in trade performance. By iteratively generating expected trade distributions over time, the approach facilitates assessment of stability, long-run equilibrium tendencies, and comparative dynamics in longitudinal trade data, providing a rigorous tool for empirical analysis of export–import behaviour. Methodological foundations follow standard Markov chain theory as described in Gagniuc (2017) . Package: r-cran-mcauchyd Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-mass, r-cran-lifecycle, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mcauchyd_1.3.3-1.ca2604.1_all.deb Size: 60740 MD5sum: 0dcf8cb526e1d0063f6839d4d895c4f9 SHA1: 78a6736f44e7c7c1f3054ed563fef26bc3ac1755 SHA256: 61a8f4d146a586eb580d8d15378c2009cf441e181a76baf00e36fc4a441f39a2 SHA512: 8ce861fc3bf5b5500429614229adff4e9de6abed666e0e1bb59106db882e6b0b3720a10d8a5c70e64399c3466b15cbe4fd7f2b3822d07915f0ed84078658431b Homepage: https://cran.r-project.org/package=mcauchyd Description: CRAN Package 'mcauchyd' (Multivariate Cauchy Distribution; Kullback-Leibler Divergence) Distance between multivariate Cauchy distributions, as presented by N. Bouhlel and D. Rousseau (2022) . Manipulation of multivariate Cauchy distributions. Package: r-cran-mcavariants Architecture: all Version: 2.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-mcavariants_2.6.1-1.ca2604.1_all.deb Size: 91740 MD5sum: a0ce4b115a204b07462fcf68b2bd2eb3 SHA1: 744ce985fddb454a37d426ef99a528a01caaa97d SHA256: f397c0ea6f4bb7251af8deefd4a55fd5c6c719a5ea7dfc4f5e077d213d79eb5d SHA512: f8ba4468dcb398fb73e1204c680026368698a550d569cec70d50ccd2ff3c585d32ac5b504254d0afc2b268f27f26b97e87708f2adf2f4f80d79454d0ef351ae6 Homepage: https://cran.r-project.org/package=MCAvariants Description: CRAN Package 'MCAvariants' (Multiple Correspondence Analysis Variants) Provides two variants of multiple correspondence analysis (ca): multiple ca and ordered multiple ca via orthogonal polynomials of Emerson. Package: r-cran-mcb Architecture: all Version: 0.1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaps, r-cran-lars, r-cran-mass, r-cran-glmnet, r-cran-ncvreg, r-cran-smoothmest, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-flare, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mcb_0.1.15-1.ca2604.1_all.deb Size: 62256 MD5sum: 05058cb8e13863f18215245dd3bd0943 SHA1: 1d14b89aeaf3c6779de03fecab968f560b36a132 SHA256: 6235bb918fdeb728fdc52c8342e4170a54a8bb8ebf25dab40ac7857bff5a7ff2 SHA512: 0cb3742be117bdffe0685118a5d1076e4e401b2fda8102e6cce1008a79ceffd16e5e23c30542456ed74c7ce777d27f3c30a2cde09e2490b7db006dc3d486f4ab Homepage: https://cran.r-project.org/package=mcb Description: CRAN Package 'mcb' (Model Confidence Bounds) When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. . Besides, 'flare' is needed only you apply the SQRT or LAD method ('mcb' totally has 8 methods). Although 'flare' has been archived by CRAN, you can still get it in and the latest version is useful for 'mcb'. Package: r-cran-mcbackscattering Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mcbackscattering_0.1.1-1.ca2604.1_all.deb Size: 89174 MD5sum: da46c2cd53fbfc3a60cc71736460b754 SHA1: dcb6cbd0785bd495bf8cbe3b73464786c8a82c7c SHA256: abd1201e1380f3e089277efc0b2f0ed2d964ffdc3919b890f56f23c50d149d07 SHA512: 15c9b8dabf36f10f12869d22d6e88a1b0e539deab26f0372ce084b480260974df004d551edcd700d33a5ed6d36ee2b37b6821b7a2cd61091215d47c0a44970f5 Homepage: https://cran.r-project.org/package=MCBackscattering Description: CRAN Package 'MCBackscattering' (Monte Carlo Simulation for Surface Backscattering) Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous. Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile. You can find more information and validation in the Open Access paper. Laszlo Baranyai (2020) . Package: r-cran-mcbette Architecture: all Version: 1.15.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-babette, r-cran-beautier, r-cran-beastier, r-cran-curl, r-cran-devtools, r-cran-mauricer, r-cran-rmpfr, r-cran-testit, r-cran-txtplot Suggests: r-cran-ape, r-cran-ggplot2, r-cran-hunspell, r-cran-knitr, r-cran-lintr, r-cran-markdown, r-cran-nltt, r-cran-phangorn, r-cran-rappdirs, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tracerer Filename: pool/dists/resolute/main/r-cran-mcbette_1.15.3-1.ca2604.1_all.deb Size: 2036066 MD5sum: eed5a0849b16186a2fd58761dbac16a8 SHA1: 3b77506e09367805a434cd1c8c2c5863e8c000a4 SHA256: 825ec9c5d7ae4266f0876ad2e4a8ea338fffca14c46556fc44f1c405dea73538 SHA512: cdebe9b887f7f3b8200af826985c38051388a6c3a18c48153436843d102159bec507ace2ef6063bd0f6aa29ad44a5613d4d736a53e1301be08973751a1e301f3 Homepage: https://cran.r-project.org/package=mcbette Description: CRAN Package 'mcbette' (Model Comparison Using 'babette') 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'mcbette' allows to do a Bayesian model comparison over some site and clock models, using 'babette' (). Package: r-cran-mcbiopi Architecture: all Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mcbiopi_1.1.7-1.ca2604.1_all.deb Size: 31038 MD5sum: db9db61a58a9d3c5c6f1f8abffddff6b SHA1: b0ddaa9f19dadc3cac7ed51a7e34056ccc813828 SHA256: 6487efa14739f010b45fe31339a0e5c7315dda7034af693e291ac4e80ff2ea13 SHA512: 51a34dcfe9259b7b4b148739db4510f93e12b2145df8478914f35d4bf9e0cb0d15cda37c7349b4f8a405101a6e6e8d46f859f58e3abe2070ee56529ef713d520 Homepage: https://cran.r-project.org/package=mcbiopi Description: CRAN Package 'mcbiopi' (Matrix Computation Based Identification of Prime Implicants) Computes the prime implicants or a minimal disjunctive normal form for a logic expression presented by a truth table or a logic tree. Has been particularly developed for logic expressions resulting from a logic regression analysis, i.e. logic expressions typically consisting of up to 16 literals, where the prime implicants are typically composed of a maximum of 4 or 5 literals. Package: r-cran-mcboost Architecture: all Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-r6, r-cran-rmarkdown, r-cran-rpart, r-cran-glmnet Suggests: r-cran-curl, r-cran-lgr, r-cran-formattable, r-cran-tidyverse, r-cran-practools, r-cran-mlr3learners, r-cran-mlr3oml, r-cran-neuralnet, r-cran-paradox, r-cran-knitr, r-cran-ranger, r-cran-xgboost, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mcboost_0.4.4-1.ca2604.1_all.deb Size: 320672 MD5sum: 1740be6b420b6fc2392e4c9454183a6c SHA1: ef539a6f320ab35dd3d9ba2e7e6826ac3ce7c6f5 SHA256: 0a8e78f0f54be472e731e52aba6214b33701f24a7768e80ebd1fd4b325e34987 SHA512: c0b73f36b2e701dff32977de3620548bddca8775e047601d25472b6c4dd7ef53ff2037d1850307be63958a95212e9f08f9d5ffd4a0bd813ad60ba57768ed5932 Homepage: https://cran.r-project.org/package=mcboost Description: CRAN Package 'mcboost' (Multi-Calibration Boosting) Implements 'Multi-Calibration Boosting' (2018) and 'Multi-Accuracy Boosting' (2019) for the multi-calibration of a machine learning model's prediction. 'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations. Package: r-cran-mcca Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-rpart, r-cran-e1071, r-cran-mass, r-cran-proc, r-cran-caret Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-mcca_0.8.2-1.ca2604.1_all.deb Size: 123764 MD5sum: ea4e0e738b7fcd642fae4276d82050df SHA1: baa06630465536445715af00a29eb93ced89844e SHA256: db8eeba3a164bf8dbb00e4d6a21cc92d5f32ff9367173de46769d819748a8436 SHA512: 9ee018e740db53394c5acd2c1701dfe955ec8f406e5a6bb9960e53a5d417ceb2d6746263ac7a695e20a36d9f8e2b346a89a7f93c38acb0d4f4f88d1baae6d685 Homepage: https://cran.r-project.org/package=mcca Description: CRAN Package 'mcca' (Multi-Category Classification Accuracy) It contains six common multi-category classification accuracy evaluation measures. All of these measures could be found in Li and Ming (2019) . Specifically, Hypervolume Under Manifold (HUM), described in Li and Fine (2008) . Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li, Jiang and Fine (2013) . Polytomous Discrimination Index (PDI), described in Van Calster et al. (2012) . Li et al. (2018) . PDI with variance estimation using Dover et al. (2021) . We described all these above measures and our mcca package in Li, Gao and D'Agostino (2019) . Package: r-cran-mccf1 Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rocr Filename: pool/dists/resolute/main/r-cran-mccf1_1.1-1.ca2604.1_all.deb Size: 24600 MD5sum: 1b13392da31be523a05f0b71c65654b7 SHA1: 6c4423dd23681e3d338fea0ba5bc73656421a4b1 SHA256: 399f93a0d44d6a9bd73e8e27a1bc2342d90e47b62279e56743f3e208204edac2 SHA512: 7e0086ea39decd19bf9639b8eb7f2eb547c28023f6c477f30c0dcf8739087041b654b65a6de1482f4469ebb6d0a9cb1cd3d3f828918021d5819035c358f5c5ad Homepage: https://cran.r-project.org/package=mccf1 Description: CRAN Package 'mccf1' (Creates the MCC-F1 Curve and Calculates the MCC-F1 Metric andthe Best Threshold) The MCC-F1 analysis is a method to evaluate the performance of binary classifications. The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth. The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification. Package: r-cran-mccm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-polycor, r-cran-lavaan, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mccm_0.1.0-1.ca2604.1_all.deb Size: 184288 MD5sum: 106c641ba2d815608ab98ce1aeb82111 SHA1: b975971836ca9f3939ef9cd30cdfe22d6362d2e7 SHA256: 536d1a63a3957856e0cdcb012112b07046cb7970cec799d5338e4634965a1643 SHA512: 5f48ce777c6dffa028018144f75ac31e7e7ca3636ee6d16a9a19ae6db1d405315811308818609c3bde91be877e94fb56389925cd579ef28307cf9cc95349088b Homepage: https://cran.r-project.org/package=MCCM Description: CRAN Package 'MCCM' (Mixed Correlation Coefficient Matrix) The IRLS (Iteratively Reweighted Least Squares) and GMM (Generalized Method of Moments) methods are applied to estimate mixed correlation coefficient matrix (Pearson, Polyseries, Polychoric), which can be estimated in pairs or simultaneously. For more information see Peng Zhang and Ben Liu (2024) ; Ben Liu and Peng Zhang (2024) . Package: r-cran-mccmeiv Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-survival, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mccmeiv_2.1-1.ca2604.1_all.deb Size: 297398 MD5sum: b31284ff3721e3cab6fdd84a6407c6da SHA1: a9e9102984e0347d3e592c2ed8b49b974a7d37a4 SHA256: 435704e1324540d74743d6ebb45ec66621b1cde42d7e3c2aa5dfec82f0bc1e7c SHA512: 86194e6637e7f120d049cc74df95cc76cc65c70bea9ddd966746a9edea0df31e394fe119bda63120b6c88ee275fb046a3b99d69a7c1a678016e80a99d63a4dd4 Homepage: https://cran.r-project.org/package=mccmeiv Description: CRAN Package 'mccmeiv' (Analysis of Matched Case Control Data with a MismeasuredExposure that is Accompanied by Instrumental Variables) Applying the methodology from Manuel et al. to estimate parameters using a matched case control data with a mismeasured exposure variable that is accompanied by instrumental variables (Submitted). Package: r-cran-mccount Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cards, r-cran-cli, r-cran-cmprsk, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-boot, r-cran-ggplot2, r-cran-knitr, r-cran-matchit, r-cran-mstate, r-cran-optmatch, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-survival, r-cran-testthat, r-cran-weightit, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mccount_0.1.1-1.ca2604.1_all.deb Size: 1376870 MD5sum: 5ee37adf667454be2100372eb8e149a7 SHA1: 4fa29a482ae8452fdeb981b1e05d907b3c36fa30 SHA256: b4b34d4d3a4b17e80c21eea7dbee2b10106fdb2dc97832a795326eb45ab63e0c SHA512: 2a4626ed41e230d9cd232c7cd6bb51e60d90be1fd89b5f2c1f6b6a61f6d9f430b19849dc85ab357794d55aa6c17e0c451974718574f362f6a9cb2fbb39aec4ca Homepage: https://cran.r-project.org/package=mccount Description: CRAN Package 'mccount' (Estimate Recurrent Event Burden with Competing Risks) Calculates mean cumulative count (MCC) to estimate the expected cumulative number of recurrent events per person over time in the presence of competing risks and censoring. Implements both the Dong-Yasui equation method and sum of cumulative incidence method described in Dong, et al. (2015) . Supports inverse probability weighting for causal inference as outlined in Gaber, et al. (2023) . Provides S3 methods for printing, summarizing, plotting, and extracting results. Handles grouped analyses and integrates with 'ggplot2' for visualization. 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Package: r-cran-mcdabench Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4983 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-factoextra, r-cran-ggplot2, r-cran-gplots, r-cran-igraph, r-cran-monochromer, r-cran-networkd3 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mcdabench_1.1.1-1.ca2604.1_all.deb Size: 2140020 MD5sum: efeab63b0b58393bce0177cfe01920ba SHA1: 1c2183e43b1bee443ee414dec932a82294abc655 SHA256: dee9d30691633ec9340f91337a6277a770e4e67db3654fd624cc8e2b518d7e96 SHA512: 4686bd765afbab76fb5de7b21ffaab6fa352152432575c9bcc88cbc46ed7c0b81346261d3535802276664f0a64e8783021e2b6bfa3b2e8a09b12cb783f6ade6d Homepage: https://cran.r-project.org/package=mcdabench Description: CRAN Package 'mcdabench' (Benchmarking for Multi-Criteria Decision Analysis) Performs and benchmarks various Multi-Criteria Decision Analysis (MCDA) methods. MCDA is a decision-making framework used to evaluate and rank alternatives based on multiple conflicting criteria using normalization, weighting, and aggregation techniques. The package implements a wide range of MCDA methods including ARAS (Additive Ratio Assessment), AROMAN (Alternative Ranking Order Method Accounting for two-step Normalization), COCOSO (Combined Compromise Solution), CODAS (Combinative Distance-based Assessment), COPRAS (Complex Proportional Assessment), EDAS (Evaluation based on Distance from Average Solution), ELECTRE (Elimination and Choice Expressing Reality) family (I-IV), FUCA (Faire Un Choix Adequat), GRA (Grey Relational Analysis), MABAC (Multi-Attributive Border Approximation Area Comparison), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), MAUT (Multi-Attribute Utility Theory), MAVT (Multi-Attribute Value Theory), MEGAN (Multi-criteria Evaluation with Gradual-weighting and Aggregation of Normalized distance matrices), MOORA (Multi-Objective Optimization on the basis of Ratio Analysis), OCRA (Operational Competitiveness Rating Analysis), ORESTE (Organisation, Rangement Et Synthese De Donnees Relationnelles), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations I-VI), RAM (Root Assessment Method), ROV (Range of Value), SMART (Simple Multi-Attribute Rating Technique), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), WASPAS (Weighted Aggregated Sum Product Assessment), WPM (Weighted Product Model), and WSM (Weighted Sum Model). The package computes comparative evaluation measures including Spearman rank correlation (Spearman, 1904) , Salabun-Urbaniak's weight similarity index (Salabun and Urbaniak, 2020), Wilcoxon signed-rank test (Wilcoxon, 1945), and permutation- and bootstrap- based entropy difference tests for pairwise method comparisons using Jensen-Shannon divergence (Lin, 1991). It also provides sensitivity and stability analysis of MCDA results. Weight sensitivity analysis is implemented through deterministic and stochastic perturbation of criterion weights, and is also integrated as a built-in step within the MEGAN method framework (Cebeci, 2026). Package: r-cran-mcgf Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-sp Suggests: r-cran-testthat, r-cran-doparallel, r-cran-foreach, r-cran-knitr, r-cran-rmarkdown, r-cran-lubridate, r-cran-dplyr, r-cran-rsolnp Filename: pool/dists/resolute/main/r-cran-mcgf_1.1.1-1.ca2604.1_all.deb Size: 899984 MD5sum: 840399871b81c5d53c34e8cd019b610a SHA1: 016cbd4565914e5818a339900da93778e49b91f2 SHA256: 2bfa3123d07c04bb56126113b0e88a1f0fdf06f7514ce17c3d673a6b34820f99 SHA512: e30acb03f973a3c2d0ceab0ce6499f6b57cedbe202093a7abbd3ec1dc4ed1f1bfcc53d494f76ebff64dd8423d67bada2af9201d1f2bbb180b4120ffc91a240f3 Homepage: https://cran.r-project.org/package=mcgf Description: CRAN Package 'mcgf' (Markov Chain Gaussian Fields Simulation and Parameter Estimation) Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) . 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It combines the quantile estimate error-bounding approach of the Raftery and Lewis MCMC run length diagnostic `gibbsit` with the between verses within chain approach of the Gelman and Rubin MCMC convergence diagnostic. Package: r-cran-mchtest Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mchtest_1.0-3-1.ca2604.1_all.deb Size: 389606 MD5sum: 9f2eec3c60b23b0b105653dec4e70998 SHA1: 4ccfc4b3488317ebc27ab64da1178b3d8b5457fa SHA256: 2073485e4cf6b47db0eaef43caffb24ab03c08c226e20e6c563e057064d03c1f SHA512: a742821eda1c517a6cbd631c04957e2283278fb19554af72c10fb65563c3fd6ca1f08a2249e02f039657bb13231cbc18d5bb976623df0b267c2b5dcafb921e92 Homepage: https://cran.r-project.org/package=MChtest Description: CRAN Package 'MChtest' (Monte Carlo Hypothesis Tests with Sequential Stopping) Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 ) and a boundary proposed by Besag and Clifford, 1991 . Gives valid p-values and confidence intervals on p-values. Package: r-cran-mci Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mci_1.3.3-1.ca2604.1_all.deb Size: 296256 MD5sum: 9f3e8ec9c2f6bf557d5cba629b6352eb SHA1: e6e83fdfafb0eb63112901a672b5dcac312cc9ac SHA256: 2799b23b2f78eba09b8c6e0a8578d45e180e712b666cd2cfc1a8c334a6d22519 SHA512: 581f4eb891135527883c057a4924db918773805c141ddfae7a59308dc452eeacacc8d973d9dee8865ffb0e32eee8f06452d258ddbc075e83603ad2d009450e5d Homepage: https://cran.r-project.org/package=MCI Description: CRAN Package 'MCI' (Multiplicative Competitive Interaction (MCI) Model) Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. 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Bisson and Jiwei Zhao (2021) . Package: r-cran-mcl Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expm Filename: pool/dists/resolute/main/r-cran-mcl_1.0-1.ca2604.1_all.deb Size: 19174 MD5sum: 6c5b935bf4e77d7178c81c9032e83985 SHA1: 00fd785b29f34bdd1219fdf41897349ff80b8969 SHA256: 12ad9061fb0a825371a3bead9f74fb8d51f8230a30a1f39246e697c61f6d0fb6 SHA512: ad8157c32802fe8f7e5b04ae320a7934aba626ee5a3b6c94fe9f13a756d3a3729b34e46c2dcdbb3e8c2a128e2a3a488cacf695cb349715c509463257a1864d28 Homepage: https://cran.r-project.org/package=MCL Description: CRAN Package 'MCL' (Markov Cluster Algorithm) Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached. 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Package: r-cran-mclogit Architecture: all Version: 0.9.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-memisc, r-cran-nlme Suggests: r-cran-nnet, r-cran-ucminf, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mclogit_0.9.15-1.ca2604.1_all.deb Size: 328542 MD5sum: 27cf603c7af3eb6f7a2a4612af8846c4 SHA1: 1b7a1eeeaeb040621f1aade5e81ef4b368c2aa3d SHA256: e7381f9719b060fc61fcdaf8104d070614bfcb16dcd38b97f8ac832721b4ff30 SHA512: 986ec7120f95cec723975e6d5a0339f1877b3d7c0f6fb451ce2a3f4d97d65a16a53713d75ffb917f6d2c0e751a3c8da44c9528d4b8299b088e76e67549698fb2 Homepage: https://cran.r-project.org/package=mclogit Description: CRAN Package 'mclogit' (Multinomial Logit Models for Categorical Responses and DiscreteChoices) Provides estimators for multinomial logit models in their conditional logit (for discrete choices) and baseline logit variants (for categorical responses), optionally with overdispersion or random effects. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small. Package: r-cran-mcm Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-gee, r-cran-dplyr, r-cran-lme4, r-cran-stringr, r-cran-parameters Filename: pool/dists/resolute/main/r-cran-mcm_0.1.7-1.ca2604.1_all.deb Size: 1182574 MD5sum: 7966512ab22bd2d77e69bb5782a07c89 SHA1: ca534d7251d2907792c3fdbf0b55a69d0fb155fb SHA256: 637eb95e3630cb1279d6d332e6ca1f8d81f22b877b931a6edd10b14e9b5d9e01 SHA512: 5097234e5de97267f9ada6347fc7db05eb0a00b3ba7d40984275c4418657943c0f2c2624fad3d2cf9246465586d03d3bf0fa665c18b09fc166b8898ee2ed1085 Homepage: https://cran.r-project.org/package=MCM Description: CRAN Package 'MCM' (Estimating and Testing Intergenerational Social Mobility Effect) Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data. Package: r-cran-mcmapper Architecture: all Version: 0.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mcmapper_0.0.11-1.ca2604.1_all.deb Size: 62850 MD5sum: fdb1dd0e8f809b7aa706bbad22ee3fbe SHA1: 66ad728c5819e46bc877f41ab65c97c650228732 SHA256: 969895ba27e44f85188975021418e287495c5d570623b37772b473972e56d638 SHA512: eec0b7ab60220092cc9854279fdd01f8e64ab5f31207b31125a9841d7998d4d21ef121406512ba8cc91b6132e37b9c4095128566de8943fe7864c52aa6846323 Homepage: https://cran.r-project.org/package=mcmapper Description: CRAN Package 'mcmapper' (Mapping First Moment and C-Statistic to the Parameters ofDistributions for Risk) Provides a series of numerical methods for extracting parameters of distributions for risks based on knowing the expected value and c-statistics (e.g., from a published report on the performance of a risk prediction model). This package implements the methodology described in Sadatsafavi et al (2024) . The core of the package is mcmap(), which takes a pair of (mean, c-statistic) and the distribution type requested. This function provides a generic interface to more customized functions (mcmap_beta(), mcmap_logitnorm(), mcmap_probitnorm()) for specific distributions. Package: r-cran-mcmc.qpcr Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcglmm, r-cran-ggplot2, r-cran-coda Filename: pool/dists/resolute/main/r-cran-mcmc.qpcr_1.2.4-1.ca2604.1_all.deb Size: 179566 MD5sum: f91e9bb46aed1a1dcfaaff99b6c87324 SHA1: ff470a4168f6466e3f464d41f6d7737d6b57b08a SHA256: 6c21644b2f4f7301c17678e410c7d5976f63409d85a1f3b6fb87770e000f6bb4 SHA512: 2fda90a38f2876cdfbd9e9ff960f512017ee61905b7c61dc0623953984b75201f6d2c77dab108618b1685093d5dd82a60ac2ccc56f326bdab71ea20a63ab4ed0 Homepage: https://cran.r-project.org/package=MCMC.qpcr Description: CRAN Package 'MCMC.qpcr' (Bayesian Analysis of qRT-PCR Data) Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: . Package: r-cran-mcmc4extremes Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evir Filename: pool/dists/resolute/main/r-cran-mcmc4extremes_1.1-1.ca2604.1_all.deb Size: 147214 MD5sum: 3d5e20f647ba0dfae4726b1da0c5dd89 SHA1: 30d2633d06eb9223104019449bf1a022fa4ba99d SHA256: 38d20ccd0b24abd900a7f45bfe0e97efb87f320f6d28e09779e97cc8e2fad3e5 SHA512: 4b6c2a868f30d6a6fc475c2a7c88461c37092b19036153da5e7bd14eff8df20d00101e0410b63cf3cbd9732cce7ca98542a04fec90e808e98fb41e6573f55600 Homepage: https://cran.r-project.org/package=MCMC4Extremes Description: CRAN Package 'MCMC4Extremes' (Posterior Distribution of Extreme Value Models in R) Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots. Package: r-cran-mcmcderive Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-chk, r-cran-extras, r-cran-mcmcr, r-cran-nlist, r-cran-purrr, r-cran-universals Suggests: r-cran-coda, r-cran-covr, r-cran-doparallel, r-cran-plyr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mcmcderive_0.1.2-1.ca2604.1_all.deb Size: 32692 MD5sum: 99987e4f1b415b4a62ffdb3de88cc54f SHA1: 0d89aba7013b5960a2ead2ed798ce4f2290f72e3 SHA256: e80529b87d337ade7329715b14f1655165517f2f5b69d4ba13314446eadde499 SHA512: 51920d7bc834a68ea3819c06b3cf4df7765adf4c312ac20942b9d5d478d9b74eecd4be8f4dfd59ab0e42bbd18230be23b24b8f84fbd224d20347ecb76ad6d301 Homepage: https://cran.r-project.org/package=mcmcderive Description: CRAN Package 'mcmcderive' (Derive MCMC Parameters) Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples using R code. This allows Bayesian models to be fitted without the inclusion of derived parameters which add unnecessary clutter and slow model fitting. For more information on MCMC samples see Brooks et al. (2011) . Package: r-cran-mcmcensemble Architecture: all Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-progressr Suggests: r-cran-bayesplot, r-cran-coda, r-cran-mockery, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-mcmcensemble_3.2.0-1.ca2604.1_all.deb Size: 189492 MD5sum: c4f09f63b3a2dc24adf35d30c8b38c09 SHA1: 7ee8e0f16006fd67a529d5302cb68e7e85c20560 SHA256: 985c324269fb3418d821b99c6267a15c479a358ba76b88e7313ae3fd271e4038 SHA512: 59ee8bdffce45d6e613cca81c275a1e0d830008d1e96635ad849b892e2544345b0fc64acbd7bae440426a59664abf4389317c53a00dddd154e2d5d3bf7d5d3a2 Homepage: https://cran.r-project.org/package=mcmcensemble Description: CRAN Package 'mcmcensemble' (Ensemble Sampler for Affine-Invariant MCMC) Provides ensemble samplers for affine-invariant Monte Carlo Markov Chain, which allow a faster convergence for badly scaled estimation problems. Two samplers are proposed: the 'differential.evolution' sampler from ter Braak and Vrugt (2008) and the 'stretch' sampler from Goodman and Weare (2010) . Package: r-cran-mcmcr Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-chk, r-cran-coda, r-cran-extras, r-cran-generics, r-cran-lifecycle, r-cran-nlist, r-cran-purrr, r-cran-term, r-cran-tibble, r-cran-universals Suggests: r-cran-covr, r-cran-rlang, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mcmcr_0.6.2-1.ca2604.1_all.deb Size: 628400 MD5sum: cfee06484e954b2df0ededab59522ca5 SHA1: 390fc6064a17a9168ca3cda2d374542144fe37b3 SHA256: befd017a9fe84c4228f526e2e8fbced091560b04c9cbf3332f9f54c72f4fe3c4 SHA512: aa4b860cb4d17fa1d1496860347c6fcf2a5d17a1159fb2f8511bc0767da1be1850f29ef6a202a56aaf3adfb67daf1f97d914e96d2e252aec803a0f15af3d847d Homepage: https://cran.r-project.org/package=mcmcr Description: CRAN Package 'mcmcr' (Manipulate MCMC Samples) Functions and classes to store, manipulate and summarise Monte Carlo Markov Chain (MCMC) samples. For more information see Brooks et al. (2011) . Package: r-cran-mcmctreer Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-sn, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mcmctreer_1.1-1.ca2604.1_all.deb Size: 4350710 MD5sum: c131478f745b9b7824257030fb129d4a SHA1: bcb9e465472b9e8ffa701f2eed0a4d78637c0082 SHA256: 657ea9cce04e6db39fe13ed33908fd8453a1d31b5b9b14aca462f1b24d1601d9 SHA512: 57cdb0385cb26c29e6020fe54689e22e1f6031964d7fa4281830efae5ce4ddfb48d178a81b0dc00629a649d23635dceeb85e8bfb4a641cb1a5dca79cc01500a1 Homepage: https://cran.r-project.org/package=MCMCtreeR Description: CRAN Package 'MCMCtreeR' (Prepare MCMCtree Analyses and Plot Bayesian Divergence TimeAnalyses Estimates on Trees) Provides functions to prepare time priors for 'MCMCtree' analyses in the 'PAML' software from Yang (2007) and plot time-scaled phylogenies from any Bayesian divergence time analysis. Most time-calibrated node prior distributions require user-specified parameters. The package provides functions to refine these parameters, so that the resulting prior distributions accurately reflect confidence in known, usually fossil, time information. These functions also enable users to visualise distributions and write 'MCMCtree' ready input files. Additionally, the package supplies flexible functions to visualise age uncertainty on a plotted tree with using node bars, using branch widths proportional to the age uncertainty, or by plotting the full posterior distributions on nodes. Time-scaled phylogenetic plots can be visualised with absolute and geological timescales . All plotting functions are applicable with output from any Bayesian software, not just 'MCMCtree'. Package: r-cran-mcmcvis Architecture: all Version: 0.16.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rstan, r-cran-overlapping, r-cran-colorspace Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-posterior Filename: pool/dists/resolute/main/r-cran-mcmcvis_0.16.5-1.ca2604.1_all.deb Size: 3121904 MD5sum: a0fdf06823d1c10260da392e093da06a SHA1: 7497902983461ed1e90968165d45495929fa4eea SHA256: b7bc89c5fed8f1bcc76345fde28f2cd6277c3b5e7dabda1304fc67fd4abde9e8 SHA512: 54155aada115a0e412a9a648e1acb91202fe71a3442eeb0b25f4e1b4ddbaf50d667368821258ffeb0a57220341285f4fc7cefa14d3cc195d498c741eda642409 Homepage: https://cran.r-project.org/package=MCMCvis Description: CRAN Package 'MCMCvis' (Tools to Visualize, Manipulate, and Summarize MCMC Output) Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with Stan, NIMBLE, JAGS, and other software. Package: r-cran-mcmiso Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-future Filename: pool/dists/resolute/main/r-cran-mcmiso_0.2.0-1.ca2604.1_all.deb Size: 119078 MD5sum: 40248eb47d4ef4f22cb56826401d8a8e SHA1: 116607b0fd442bf7af9cca2be9930f2f47d04283 SHA256: 7088bae9b8ae92aad91de86483c3fa18f24ac85185422f54af760bb6a3ada344 SHA512: 55c8e009459af50d58e9f1ac50f29bc69bede723214052eec1527b5d5853da1ec7a8ff6ad0fea4722691b0372b4b34829a13bcecc02e37ea15aec53427db53f7 Homepage: https://cran.r-project.org/package=McMiso Description: CRAN Package 'McMiso' (Multicore Multivariable Isotonic Regression) Provides functions for isotonic regression and classification when there are multiple independent variables. The functions solve the optimization problem using a projective Bayes approach with recursive sequential update algorithms, and are useful for situations with a relatively large number of covariates. Supports binary outcomes via a Beta-Binomial conjugate model ('miso', 'PBclassifier') and continuous outcomes via a Normal-Inverse-Chi-Squared conjugate model ('misoN'). Parallel computing wrappers ('mcmiso', 'mcPBclassifier', 'mcmisoN') are provided that run the down-up and up-down algorithms simultaneously and return whichever finishes first. The estimation method follows the projective Bayes solution described in Cheung and Diaz (2023) . Package: r-cran-mcmodule Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mc2d, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-visnetwork, r-cran-igraph, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-mcmodule_1.2.0-1.ca2604.1_all.deb Size: 2551274 MD5sum: ba92c0b9f64d525c656692e892b9ece5 SHA1: c214e78b4329cf4dc8851f540ac6a7876e86dc59 SHA256: 7e9506f01c380413db80199c4f2f21b352f51fb6175cd6bf01bb2bae4031dd34 SHA512: 33e2a1f3d3e70288000a3c3325ef320fc96e06bd9bc33038daa039a4716cb630420520f6d0ef0bf67927be4a492f8e916a53e80694e1e58bf533aaa12ef648c7 Homepage: https://cran.r-project.org/package=mcmodule Description: CRAN Package 'mcmodule' (Modular Monte Carlo Risk Analysis) Framework for building modular Monte Carlo risk analysis models. It extends the capabilities of 'mc2d' to facilitate working with multiple risk pathways, variates and scenarios. It provides tools to organize risk analysis in independent flexible modules, perform multivariate Monte Carlo node operations, automate the creation of Monte Carlo nodes and visualise risk analysis models. For more details see Ciria (2025) . Package: r-cran-mcmsector Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-survey, r-cran-openxlsx, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-plyr, r-cran-stringr, r-cran-labelled, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-r2jags, r-cran-rmarkdown, r-cran-testthat, r-cran-mcmsupply Filename: pool/dists/resolute/main/r-cran-mcmsector_1.0.2-1.ca2604.1_all.deb Size: 235346 MD5sum: b0febacb2ad3e2e9c4abdfce8d4686f0 SHA1: 302b922589c959cd9ff52604976c66f13e8f088e SHA256: 1186147a9eb89294bb83672d79a396aea416c9ba6b262156e6f1f1a341dbffcb SHA512: 0116b0ad6bee880de9ce2e3eccfe9cd006629353489043dad820ab605344552aa0ddb2d2de9452a05ba38e5715912449f1d050430430d1aedf746b241747efe8 Homepage: https://cran.r-project.org/package=mcmsector Description: CRAN Package 'mcmsector' (Estimating Subnational Public and Private Contraceptive SupplyShares Over Time) Engaging the private sector in contraceptive method supply is critical for equitable, sustainable, and accessible healthcare systems. This package implements Bayesian hierarchical models to estimate public and private contraceptive supply shares over time at national and subnational levels, using Demographic and Health Survey (DHS) data. Penalized splines are used to track supply shares over time, and spatial correlation structures link national and subnational estimates in data- sparse settings. For more details see Comiskey (2025) . Package: r-cran-mcmst Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-ecr, r-cran-grapherator, r-cran-checkmate, r-cran-gtools, r-cran-ggplot2, r-cran-vegan, r-cran-qgraph, r-cran-viridis, r-cran-igraph Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-mcmst_1.1.1-1.ca2604.1_all.deb Size: 252244 MD5sum: b96ac044ad114fa07f288babf3d07685 SHA1: 4f75a46ab726332b73149beb7a4543b2f977ebca SHA256: ce94a73dc0fef95c183c2d4786924d28d279a2de335554884a66e5f71d01f6ca SHA512: d82f1a4ff6cd5ad24ad442ceec19980a0999ffeec0b1af81e7a12cf1c86f4388b8ea93e1c8b28fc12c34de4cf159c0ad11251c4126d857a52724bddbe194df5c Homepage: https://cran.r-project.org/package=mcMST Description: CRAN Package 'mcMST' (A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem) Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems. Package: r-cran-mcmsupply Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1049 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2jags, r-cran-magrittr, r-cran-foreach, r-cran-tidyverse, r-cran-tidybayes, r-cran-runjags, r-cran-rlang, r-cran-abind, r-cran-dplyr, r-cran-ggplot2, r-cran-plyr, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mcmsupply_1.1.1-1.ca2604.1_all.deb Size: 719280 MD5sum: 08b499ba8d585889a01a8adc7521ed42 SHA1: 7bdd14d67df0ddd7f31513397e313cf9c0d7d340 SHA256: 971415f5674d0918f5ed5fbcd785b27d6c8ed27b983520a696fc28a808cc9fa4 SHA512: c783b0560ef51ba7fd7cc36090c9744a9f8424fbff8154c612939fa3dacd609759202440be687a88c322e578d57cfee0d75f5cdd02dffed845980062b46cd87f Homepage: https://cran.r-project.org/package=mcmsupply Description: CRAN Package 'mcmsupply' (Estimating Public and Private Sector Contraceptive Market SupplyShares) Family Planning programs and initiatives typically use nationally representative surveys to estimate key indicators of a country’s family planning progress. However, in recent years, routinely collected family planning services data (Service Statistics) have been used as a supplementary data source to bridge gaps in the surveys. The use of service statistics comes with the caveat that adjustments need to be made for missing private sector contributions to the contraceptive method supply chain. Evaluating the supply source of modern contraceptives often relies on Demographic Health Surveys (DHS), where many countries do not have recent data beyond 2015/16. Fortunately, in the absence of recent surveys we can rely on statistical model-based estimates and projections to fill the knowledge gap. We present a Bayesian, hierarchical, penalized-spline model with multivariate-normal spline coefficients, to account for across method correlations, to produce country-specific,annual estimates for the proportion of modern contraceptive methods coming from the public and private sectors. This package provides a quick and convenient way for users to access the DHS modern contraceptive supply share data at national and subnational administration levels, estimate, evaluate and plot annual estimates with uncertainty for a sample of low- and middle-income countries. Methods for the estimation of method supply shares at the national level are described in Comiskey, Alkema, Cahill (2022) . 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Package: r-cran-mcoe Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggthemes, r-cran-googlesheets4, r-cran-keyring, r-cran-magick, r-cran-odbc, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mcoe_0.6.0-1.ca2604.1_all.deb Size: 2456050 MD5sum: ba4bf0ff799f5d20e5536d7d2a4cf3a3 SHA1: a819f9c4ae64ad0a1c8483161371e5d13a0c9157 SHA256: 6a24aaf581d838c4324bf7f22a49e2850c8ea500664f4c072f49607f9b846cfc SHA512: ace64d3f5b2207cd0ec36be52949cbf3ac051455dc010677d120861f3e1647964d2438cafbb4aba9d25b00e2342338da41fab96652ca160b362f85637de11a4b Homepage: https://cran.r-project.org/package=MCOE Description: CRAN Package 'MCOE' (Creates New Folders and Loads Standard Practices for MontereyCounty Office of Education) Basic Setup for Projects in R for Monterey County Office of Education. It contains functions often used in the analysis of education data in the county office including seeing if an item is not in a list, rounding in the manner the general public expects, including logos for districts, switching between district names and their county-district-school codes, accessing the local 'SQL' table and making thematically consistent graphs. 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A method for generation of multi-companion matrices with prespecified spectral properties is provided, as well as some utilities for periodically correlated and multivariate time series models. See Boshnakov (2002) and Boshnakov & Iqelan (2009) . 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The package provides tools to estimate marginalized count regression models for direct inference on the effect of covariates on the marginal mean of the outcome. The methods include the marginalized zero-inflated Poisson (MZIP) model described in Long et al. (2014) and the marginalized zero- and N-inflated binomial (MZNIB) model, which extends marginalized modeling to fractional count outcomes with boundary inflation at zero and the upper limit. Package: r-cran-mcp Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-rjags, r-cran-coda, r-cran-loo, r-cran-bayesplot, r-cran-tidybayes, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-tidyselect, r-cran-tibble, r-cran-stringr, r-cran-ggplot2, r-cran-patchwork, r-cran-rlang Suggests: r-cran-hexbin, r-cran-testthat, r-cran-purrr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mcp_0.3.4-1.ca2604.1_all.deb Size: 752886 MD5sum: 6ea7d86662b25f8f429028963371cf8d SHA1: 92b664388929d681f2cf33eeccc256745fc6e6c8 SHA256: fe2e5394c8e1ab3da7751a5845f2c432cac3869133a3367410f82af63553246c SHA512: 6dc8269fad3747bf427047f822519138286bdaf61522fb9ffeec463d9c09025dfb8595bad3c30151875453aea644c16f659b76c53cc07bb2d3a5d8e1fcb366f4 Homepage: https://cran.r-project.org/package=mcp Description: CRAN Package 'mcp' (Regression with Multiple Change Points) Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) and generalizes the approach described in Carlin, Gelfand, & Smith (1992) and Stephens (1994) . Package: r-cran-mcpan Architecture: all Version: 1.1-22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-multcomp, r-cran-magic, r-cran-mcmcpack, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-mcpan_1.1-22-1.ca2604.1_all.deb Size: 366464 MD5sum: 7d7ab66f29b2d4a1d5febda3982c9913 SHA1: 67da49e62209f1c5b9e48e774a779169a38ff3fb SHA256: 4e9d43518f1e513902d853cc4c5592e10debc21de7c31492015be7c333988aba SHA512: ab36254089ccff2b777623d5047870f79cab3acbf34ee157f8f466402735f9f37c480d76312cae2df3ae864f5aaa6d5d7d68ee1db5fc12e8dc784fb9d5449f8b Homepage: https://cran.r-project.org/package=MCPAN Description: CRAN Package 'MCPAN' (Multiple Comparisons Using Normal Approximation) Multiple contrast tests and simultaneous confidence intervals based on normal approximation. With implementations for binomial proportions in a 2xk setting (risk difference and odds ratio), poly-3-adjusted tumour rates, biodiversity indices (multinomial data) and expected values under lognormal assumption. Approximative power calculation for multiple contrast tests of binomial and Gaussian data. Package: r-cran-mcparalleldo Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.utils, r-cran-checkmate, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-mcparalleldo_1.1.0-1.ca2604.1_all.deb Size: 78224 MD5sum: deec96c1a49e286a64707eb218124a68 SHA1: 94f15724ca642f544e9bb1a0d2b9fcd99a220edc SHA256: 827e69f10ca31ecda8d16df9c8e65dc07b3df4a53334c91a92a35b52af5c9660 SHA512: ed4ec89ffd122cd2714d2961e547255323af4be354bcf085cf3ea4fc0901dc1da603d5ae331310b453434d7b96e36d9ee2c366f6070d3940e39536ee309bd19d Homepage: https://cran.r-project.org/package=mcparallelDo Description: CRAN Package 'mcparallelDo' (A Simplified Interface for Running Commands on ParallelProcesses) Provides a function that wraps mcparallel() and mccollect() from 'parallel' with temporary variables and a task handler. Wrapped in this way the results of an mcparallel() call can be returned to the R session when the fork is complete without explicitly issuing a specific mccollect() to retrieve the value. Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with a single call to mcparallelDoCheck(). Package: r-cran-mcpmod Architecture: all Version: 1.0-10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-mcpmod_1.0-10.1-1.ca2604.1_all.deb Size: 508266 MD5sum: fe1d0682dae9bf7fa69647a9d1ae211b SHA1: ece982fe9c7be90020d36055f6d27e65e7885f9a SHA256: f715a0869bde4fbbe0a8e7be173d7168d9b5f04978db9e442d3dd87988b1d146 SHA512: 7a8b995b48bbdd4ab3b4ad628bd064819ac655880df30c272cf1731d81cb1f138e0ab8f74d457b35d1dec277a5f7cb68da4462d2132d5c1dd407f41e7885eceb Homepage: https://cran.r-project.org/package=MCPMod Description: CRAN Package 'MCPMod' (Design and Analysis of Dose-Finding Studies) Implements a methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, ). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCP-Mod methodology. Please note: The 'MCPMod' package will not be further developed, all future development of the MCP-Mod methodology will be done in the 'DoseFinding' R-package. Package: r-cran-mcpmodbc Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dorng, r-cran-survival, r-cran-doparallel, r-cran-nleqslv, r-cran-foreach, r-cran-dosefinding, r-cran-dplyr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-mcpmodbc_1.1-1.ca2604.1_all.deb Size: 76718 MD5sum: b4e8529f4c37556acbda2099d31ebc78 SHA1: d22d9b44a336c8b9d74f6e821a7f914b50c5dc6e SHA256: ae3857f35cf657a39053a0c59f21f5b9de09c443295f5d352a4567f3a57ff07a SHA512: 2e06083aac549e996c1c8bb98a3273f66d613385330189bd357ddd2b07c3d6662b9f21ca87b02d8f70bc98efa2a4a7b6f444165e2c29d041cfa8b68bfecdc57c Homepage: https://cran.r-project.org/package=MCPModBC Description: CRAN Package 'MCPModBC' (Improved Inference in Multiple Comparison Procedure – Modelling) Implementation of Multiple Comparison Procedures with Modeling (MCP-Mod) procedure with bias-corrected estimators and second-order covariance matrices as described in Diniz, Gallardo and Magalhaes (2023) . Package: r-cran-mcpmodgeneral Architecture: all Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosefinding, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/resolute/main/r-cran-mcpmodgeneral_0.1-3-1.ca2604.1_all.deb Size: 237434 MD5sum: fa4d8177cc159a79f48b8fed9851d983 SHA1: 5d4574858fe19b71ee86d8b8e9d5d78b030c6b1b SHA256: c7c286904b8a0c22b96550a5bc4061b49ddd542b581fc275d4a0758db1085e42 SHA512: e9e1c0127e3f5e15e0cd47a73ada7af8da522c6ee4c7a47795e2d3dffd8ad3ccddddf0cba565875cb064138ae571072e6b75e6383bc30a45c4451c71c982cdab Homepage: https://cran.r-project.org/package=MCPModGeneral Description: CRAN Package 'MCPModGeneral' (A Supplement to the 'DoseFinding' Package for the General Case) Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach (MCP-Mod). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to provide or calculate the mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and passes them into the 'DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function (prepareGen()) will provide mu and S. The package currently handles power analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial data. The MCP-Mod procedure can also be applied to survival data, but power analysis is not available. Bretz, F., Pinheiro, J. C., and Branson, M. (2005) . Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997) . Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) . Package: r-cran-mcprofile Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-quadprog, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-markdown, r-cran-multcomp, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mcprofile_1.0-1-1.ca2604.1_all.deb Size: 193786 MD5sum: 346d197731633b2ed65d892deabc5948 SHA1: f9ee5e9def106c21fa3dfcaded3c4db32b71bdf8 SHA256: bf97c85c3ac717470d7fc66277b2809796242ac97c52d031f39c20816cc770d5 SHA512: 629aacca5194bba0953ef8465603241e6e8ab2bc8d377a94008f9c822001803c311e7b0f7701d4bc25ab6ab2168ce4a815fc5721c9e67e7dd2e12b4e8317f49a Homepage: https://cran.r-project.org/package=mcprofile Description: CRAN Package 'mcprofile' (Testing Generalized Linear Hypotheses for Generalized LinearModel Parameters by Profile Deviance) Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided. Package: r-cran-mcprogress Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-mcprogress_0.1.1-1.ca2604.1_all.deb Size: 377190 MD5sum: f0c16ae2220e03e9c64c443fd962134e SHA1: d88afcac084208b82046bdbb4616ca4b1843b337 SHA256: b1825e9c698c7cc981427bcbe54f3d927feb7c24ab6895942c6c4618c49722b2 SHA512: 1f93d28409fc29421936f79c6c84d1077bc2f730bac22cbd387a63b66df770f89fcc75bd3201d8f725e7922772a5f8b33ee42e5c1873c703efdeb64bf0dcb68a Homepage: https://cran.r-project.org/package=mcprogress Description: CRAN Package 'mcprogress' (Progress Bars and Messages for Parallel Processes) Tools for monitoring progress during parallel processing. Lightweight package which acts as a wrapper around mclapply() and adds a progress bar to it in 'RStudio' or 'Linux' environments. Simply replace your original call to mclapply() with pmclapply(). A progress bar can also be displayed during parallelisation via the 'foreach' package. Also included are functions to safely print messages (including error messages) from within parallelised code, which can be useful for debugging parallelised R code. Package: r-cran-mcptests Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smr, r-cran-writexl, r-cran-xtable, r-cran-foreach, r-cran-doparallel Suggests: r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-mcptests_1.0.1-1.ca2604.1_all.deb Size: 186932 MD5sum: 56ffe089316289aa86d06abf5e075ef7 SHA1: 0eebbf6f356adb06f0fbc639d6ba88ecae1723c0 SHA256: ec1140d188eef6e323b0813c4e4140cfb9b2751314f857a760cfca2e39a4edcd SHA512: a3d3ec66331d96ff2df855faba16553e4596feabe1d9fda9258484b255efc6da075b0bf177fc68e4302958b4c5259bdcd3d033ff19fec94e519abfafdc5b854d Homepage: https://cran.r-project.org/package=MCPtests Description: CRAN Package 'MCPtests' (Multiples Comparisons Procedures) Performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) , Batista (2016) , including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test. Package: r-cran-mcptools Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ellmer, r-cran-httpuv, r-cran-httr2, r-cran-jsonlite, r-cran-nanonext, r-cran-processx, r-cran-promises, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mcptools_0.2.1-1.ca2604.1_all.deb Size: 768140 MD5sum: 6b9dbe0f2897f737dc1095977fe199a9 SHA1: d911d963da7978beb6e449f04a1a7963f294f775 SHA256: 04d9eecf39bc3e72b862ae26c0485b6414b7fa82833bd0b31676857fe83ed9c4 SHA512: 578a824a4423948eb0a8206700e69f06c135b2d88f87f176a3fdb9715c8fd7f09aa54ccba191cbd860d6a387155cd092527dd34a2ee8247ed6c92e320d3d9d56 Homepage: https://cran.r-project.org/package=mcptools Description: CRAN Package 'mcptools' (Model Context Protocol Servers and Clients) Implements the Model Context Protocol (MCP). Users can start 'R'-based servers, serving functions as tools for large language models to call before responding to the user in MCP-compatible apps like 'Claude Desktop' and 'Claude Code', with options to run those tools inside of interactive 'R' sessions. On the other end, when 'R' is the client via the 'ellmer' package, users can register tools from third-party MCP servers to integrate additional context into chats. Package: r-cran-mcqanalysis Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mcqanalysis_0.1.0-1.ca2604.1_all.deb Size: 132664 MD5sum: cf848769f97d2eba03f6f337b546261f SHA1: c7f8397246ce2591a3fd51b58b58aeb25e90674f SHA256: da096cb67173f68b63c97b4a9a7e441f6227874393116a767d971cb86e70a1ac SHA512: 7c6a4cc4755c5241eb7b264019db53ced24369307450e73e91a4457ff72673cf001f3e114366735680e3921c588081c666eec5623b4fa1ccafef134a58245a82 Homepage: https://cran.r-project.org/package=mcqAnalysis Description: CRAN Package 'mcqAnalysis' (Classical Test Theory Item Analysis for Multiple-Choice Tests) A unified toolkit for classical test theory (CTT) item analysis of multiple-choice test data, including item difficulty (p-value), item discrimination (point-biserial correlation and upper-lower 27-percent discrimination index), per-distractor analysis (frequency, proportion, and discrimination), and Haladyna's distractor efficiency. A wrapper function returns a tidy 'mcq_analysis' object with print, plot (difficulty-discrimination scatter), and APA-style table methods for direct inclusion in journal manuscripts. Implemented in pure R with no compiled code and minimal dependencies. 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Most of the methods and algorithms refer to CLSI (Clinical & Laboratory Standards Institute) recommendations and NMPA (National Medical Products Administration) guidelines. In additional, relevant plots are constructed by 'ggplot2'. 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Package: r-cran-mcseqreplic Architecture: all Version: 1.0.0-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-traminer, r-cran-weightedcluster, r-cran-aricode, r-cran-doparallel, r-cran-foreach, r-cran-dosnow, r-cran-iterators, r-cran-vegan, r-cran-wcorr Filename: pool/dists/resolute/main/r-cran-mcseqreplic_1.0.0-1.ca2604.2_all.deb Size: 149720 MD5sum: ac407fc7e4915644b38e8692d1e437d7 SHA1: 818345646cb3b4e804ab28e7059a73a1c4c806b3 SHA256: ea2b3f20ad0d910ed82940ed903a9e486b0be2ae60766bc227aca77e108a3521 SHA512: 2f541f91e42f782a8890b93e005d5058654fa8c5103701b1b091dacd2e642e04be28d1bf9b93987ac128e619444f51b799fe84a7a2ea815e7052a51e9ec6b2f1 Homepage: https://cran.r-project.org/package=MCseqReplic Description: CRAN Package 'MCseqReplic' (Monte Carlo Simulations of Time Changes in Sequences) Generates replicated sets of sequences with Monte Carlo simulated timing changes and computes various indicators for evaluating effects of timing uncertainty on sequence analysis results. See Ritschard, G. and Liao, T.F. (2026): "Assessing the Impact of Timing Errors in Sequence Analysis". International Journal of Social Research Methodology . 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Isobel Claire Gormley and Thomas Brendan Murphy (2010) . 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Package: r-cran-med Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-med_0.1.0-1.ca2604.1_all.deb Size: 44710 MD5sum: 3f875b0e0745393ccc37fdfbf5e466fa SHA1: 70ac1b030dcf2b3120eb930a509cf0169edf60eb SHA256: 77685e52ee371b42625c03599bba3ca8410507e129a2ba143e20275456c93402 SHA512: ac6f1b028a3f52de1f99f802cb550394c2c28a492eb0ad05be02932ad0dc6b5d3c9e4272ddef31265d2126c23e2e10c38f9a1f222b9770b2770e8924d5a87c8d Homepage: https://cran.r-project.org/package=MED Description: CRAN Package 'MED' (Mediation by Tilted Balancing) Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) . Package: r-cran-meddatasets Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3513 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-meddatasets_0.1.0-1.ca2604.1_all.deb Size: 1833822 MD5sum: 001385803cc72088eef7a1731fc79e3f SHA1: e59e2e60c346017943167ccb2706fdf254a2b433 SHA256: a6c6a7546cba43a675f120abc04783da729615b9f1822d116354e79262dd5c42 SHA512: 489acf20c80b9076cf474bf9ef0afd60bbb8854d288fb9288ff9d119c1c2c57aab40fe22f4e41ce267e914a9733c2c3ba983b98d05376db36d119d6fba6df47a Homepage: https://cran.r-project.org/package=MedDataSets Description: CRAN Package 'MedDataSets' (Comprehensive Medical, Disease, Treatment, and Drug Datasets) Provides an extensive collection of datasets related to medicine, diseases, treatments, drugs, and public health. 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Here, three functions are given related to three new methods which will generate mating diallel cross designs (Hinkelmann and Kempthorne, 1963) or mating environmental (ME) designs along with design parameters, C matrix, eigenvalues (EVs), degree of fractionations (DF) and canonical efficiency factor (CEF). Another one function is added to check the properties of a given ME diallel cross design. Package: r-cran-medextractr Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stringr Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-medextractr_0.4.1-1.ca2604.1_all.deb Size: 1452586 MD5sum: 241701d2acf664de2c62092235d6d0c8 SHA1: c3ee1a31a886e0f1c630e2955dcf1e2b8548c75d SHA256: cfb23a3e573e37bd63fcc2b279f4728dc78ce67b20d20db4835c3b7b64efba0b SHA512: e0928601aa6ac2f9b79e3d739ba988da31b334b4bf2bcbacaf73a553b0bc248af73ba532a0e729a273debf10c909714adbe14be7ce47c85b436a6ef098a7af4f Homepage: https://cran.r-project.org/package=medExtractR Description: CRAN Package 'medExtractR' (Extraction of Medication Information from Clinical Text) Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) . The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition. 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This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) , Imai, Keele and Tingley (2010) , Imai, Tingley and Yamamoto (2013) , and Imai and Yamamoto (2013) . In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models. 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It numerically and graphically evaluates the sensitivity of causal mediation analysis results to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based methods. It is applicable to both randomized experiments and observational studies. 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Package: r-cran-medicaldata Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-learnr Filename: pool/dists/resolute/main/r-cran-medicaldata_0.2.0-1.ca2604.1_all.deb Size: 662330 MD5sum: 078dbf9d40ff66f9cd27989663cbbb59 SHA1: e635a4faa99c58c7199706cdbacd8d04dbf02673 SHA256: e43aa19a70e6e48d1a7b397dfbcc81f2bf22d2351026868351572ddfe13a605e SHA512: 51fe47f100a5ff334b148a5239e65513737f3618848aff3e64db50b64f0166fd007dd6a8d685eb15ef9f0a881939783f59308a0e6209d2a27de5badb8f52dbf9 Homepage: https://cran.r-project.org/package=medicaldata Description: CRAN Package 'medicaldata' (Data Package for Medical Datasets) Provides access to well-documented medical datasets for teaching. Featuring several from the Teaching of Statistics in the Health Sciences website , a few reconstructed datasets of historical significance in medical research, some reformatted and extended from existing R packages, and some data donations. Package: r-cran-medicare Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2164 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-maps, r-cran-magrittr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-medicare_0.2.1-1.ca2604.1_all.deb Size: 1024874 MD5sum: 94596eeb476d0d69cce75f88dc6c82e5 SHA1: dca0013076d058dfbe630e0d4ef3882e1a2b36d4 SHA256: 62b148de838fa5d7ed1b6d91342b6988d163588e2c6878d5516551328526fca0 SHA512: b4de0747c727c273e5a1db43d37dd6ff329cfe7beffbde602354a5fe54db4c71652a5d07f9f36dea2e38c4864d7efa91e26ee3cd811eb53f2e229b7da23de15b Homepage: https://cran.r-project.org/package=medicare Description: CRAN Package 'medicare' (Tools for Obtaining and Cleaning Medicare Public Use Files) Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See . Package: r-cran-meditations Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-meditations_1.0.1-1.ca2604.1_all.deb Size: 99980 MD5sum: 7aa7b41d42771fba7a8e594242aeccb5 SHA1: 49e23cabebef12b7951750b14d6b9ccd7b2203cb SHA256: ea161485a9394e3db247642617df9527506c299a3510561a683cd328753779f4 SHA512: b8a3d30774be6823c2cda237ac30859a0f9fe2805de54c3acf798ba15291b934242d4fa8b651393b348dfc673811dfc7344a9663fba4619c77fafa3d89acd5e0 Homepage: https://cran.r-project.org/package=meditations Description: CRAN Package 'meditations' (Prints a Random Quote from Marcus Aurelius' Book Meditations) Prints a random quote from Marcus Aurelius' book Meditations. Package: r-cran-medlea Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-tm, r-cran-patchwork, r-cran-wordcloud2 Filename: pool/dists/resolute/main/r-cran-medlea_1.0.2-1.ca2604.1_all.deb Size: 322716 MD5sum: a91ba07ddf78c18e7636150498811839 SHA1: a299d72b74b2c42d210239c0f57401fac5d0d746 SHA256: 858388afa7e5f182815745e4f17e97b1ccbf625dcf94f47cee960dc89cfb0c4c SHA512: 431b378de966c721a36b1326a05a2e581e227528a065c8ec8a235bf3760b3c87f0aa13b23d180e88feb8310077ede7d363503051fdeb8c27946f46f75256803e Homepage: https://cran.r-project.org/package=MedLEA Description: CRAN Package 'MedLEA' (Morphological and Structural Features of Medicinal Leaves) Contains a dataset of morphological and structural features of 'Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka. Package: r-cran-medparser Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-medparser_0.2.0-1.ca2604.1_all.deb Size: 20900 MD5sum: 5d60738e5dfa839e0868b1abbff5cd70 SHA1: e86ae66e00e923d993e720cdb401dab28fbd4cc4 SHA256: 9d16a272e39f99a6c2537503dc60bc31bafc6cd3694742bc7724b0fd2cb9e7ca SHA512: e41cc1d7aba7676628904c3e88237ba0247d5d87f04933b8330a0ffd2c41d9af10a1ff04645b79b8d3f49b2a56d632c72dd3aa40330af2d5b2f1374665c35129 Homepage: https://cran.r-project.org/package=medparser Description: CRAN Package 'medparser' (MedPC Text Parser) Parses information from text files with specific utility aimed at pulling information from Med Associate's (MPC) files. These functions allow for further analysis of MPC files. Package: r-cran-medrxivr Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1502 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-jsonlite, r-cran-httr, r-cran-stringr, r-cran-rlang, r-cran-bib2df, r-cran-tibble, r-cran-progress, r-cran-lubridate, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-kableextra, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-medrxivr_0.1.4-1.ca2604.1_all.deb Size: 972352 MD5sum: 6eb07d0e45d3131c1e52dcef333cb783 SHA1: 7411f78f18723ccb3fc6d41102f66e7303a0d074 SHA256: 148f8b033e3a8df3a44a977fafc1c1291ce572b119d6776309450aff6715894d SHA512: f8131270ac436f668fa1b08d4fd81eed9599591e94ae21972be84a9075de199488676d55440a998acc3647cbaf4ef1ee3461a286720f720e43842e2d86b6ca12 Homepage: https://cran.r-project.org/package=medrxivr Description: CRAN Package 'medrxivr' (Access and Search MedRxiv and BioRxiv Preprint Data) An increasingly important source of health-related bibliographic content are preprints - preliminary versions of research articles that have yet to undergo peer review. The two preprint repositories most relevant to health-related sciences are medRxiv and bioRxiv, both of which are operated by the Cold Spring Harbor Laboratory. 'medrxivr' provides programmatic access to the 'Cold Spring Harbour Laboratory (CSHL)' API , allowing users to easily download medRxiv and bioRxiv preprint metadata (e.g. title, abstract, publication date, author list, etc) into R. 'medrxivr' also provides functions to search the downloaded preprint records using regular expressions and Boolean logic, as well as helper functions that allow users to export their search results to a .BIB file for easy import to a reference manager and to download the full-text PDFs of preprints matching their search criteria. Package: r-cran-medscan Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hdmt, r-cran-locfdr, r-cran-qqman, r-bioc-qvalue, r-cran-fdrtool Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-medscan_1.0.2-1.ca2604.1_all.deb Size: 33756 MD5sum: 900ee125b901f2b4b1df127b9732b253 SHA1: 806e55637e8b1fa17e96ff52e87d34d8ec3cac64 SHA256: 24e7c8daa0ae0fa5235785dc0546b2d94a013e2e8c65611af279174c2cfa1496 SHA512: 3c4e1483ffb67aad6f69bdf31a4d1ec117ae4dfbb255e87d2e4cb2ba59b1d9a2eb28371f2daf80364747c610ee4bac4fb080e82a65daa656b1eb4c7416987f74 Homepage: https://cran.r-project.org/package=medScan Description: CRAN Package 'medScan' (Large Scale Single Mediator Hypothesis Testing) A collection of methods for large scale single mediator hypothesis testing. The six included methods for testing the mediation effect are Sobel's test, Max P test, joint significance test under the composite null hypothesis, high dimensional mediation testing, divide-aggregate composite null test, and Sobel's test under the composite null hypothesis. Du et al (2023) . Package: r-cran-medseq Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-matrixstats, r-cran-nnet, r-cran-seriation, r-cran-stringdist, r-cran-traminer, r-cran-weightedcluster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-medseq_1.4.2-1.ca2604.1_all.deb Size: 706494 MD5sum: da61a78b506b68c4cf8669580351c76e SHA1: f79aaf034588db6ec2227f71348455af98ec2b58 SHA256: 09484815cac188aa9d22a455a234df8da4723305d4461622f2b14266db3c041b SHA512: 7745e6d2cb0dacdaa3047ca1759be7c586f75b27cda2536fa38b5df5b128f779cf98bc120b3eed254d739464df5e679ffe3b9e69f20b4b1a5150757a1d899ad1 Homepage: https://cran.r-project.org/package=MEDseq Description: CRAN Package 'MEDseq' (Mixtures of Exponential-Distance Models with Covariates) Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) . A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided. Package: r-cran-medxr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-memoise Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-medxr_0.1.1-1.ca2604.1_all.deb Size: 313120 MD5sum: 013d607fefebb70225a739c781ad031a SHA1: 635c4d67a123292953b5c1ac21ed404380529736 SHA256: 8cf4ffdad5d185fc7e68e48005b374731be76e490236f4b7b1e13240417c7c4b SHA512: 03550732220e8cf305294a6a558fcd9517d77f4ec4b6c9513dc7522e16d15d137366c66b99e3446bd199be02c9b7cd16a759f8d8aa6ce2e275b0784ea895f9e7 Homepage: https://cran.r-project.org/package=MedxR Description: CRAN Package 'MedxR' (Access Drug Regulatory Data via FDA and Health Canada APIs) Provides functions to access drug regulatory data from public RESTful APIs including the 'FDA Open API' and the 'Health Canada Drug Product Database API', retrieving real-time or historical information on drug approvals, adverse events, recalls, and product details. Additionally, the package includes a curated collection of open datasets focused on drugs, pharmaceuticals, treatments, and clinical studies. These datasets cover diverse topics such as treatment dosages, pharmacological studies, placebo effects, drug reactions, misuses of pain relievers, and vaccine effectiveness. The package supports reproducible research and teaching in pharmacology, medicine, and healthcare by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'FDA API' and 'Health Canada API' . Package: r-cran-medzisc Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-betareg, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-medzisc_0.0.4-1.ca2604.1_all.deb Size: 284184 MD5sum: 6b02c2b4bad5f9c8e9c519d6e57e9a2e SHA1: 10c4d262da219815bfbeb393db1ba7dfd3a998ef SHA256: 0f685b76be851080a61d2626ad4d4dafaa410de8eef02b38bdf5c52f32fa09c1 SHA512: b46b910112990e4e9e75fa8c1ed24bcd6b60b829970c12c41be072d53490283a5ed1c2a71bb69ed7a3e1e94f9fbd2d75b1581fa21537c7925b60644f867cdb46 Homepage: https://cran.r-project.org/package=MedZIsc Description: CRAN Package 'MedZIsc' (Statistical Framework for Co-Mediators of Zero-InflatedSingle-Cell Data) A causal mediation framework for single-cell data that incorporates two key features ('MedZIsc', pronounced Magics): (1) zero-inflation using beta regression and (2) overdispersed expression counts using negative binomial regression. This approach also includes a screening step based on penalized and marginal models to handle high-dimensionality. Full methodological details are available in our recent preprint by Ahn S and Li Z (2025) . Package: r-cran-meerva Architecture: all Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-meerva_0.2-2-1.ca2604.1_all.deb Size: 328918 MD5sum: d68907c33e5448e3ea5487f2eed4ad0f SHA1: ec914cd6c4ad5338de93d91d750c670842c9253c SHA256: bf6ae20df49280b2f1ea8bc959ae4a6c62e0abf8199ecc3a204fa81000cd4e11 SHA512: 11e2042e29bcc53f1bfd544244ab7eac3d312a08b1311b930421d18592c292cb96f761c9bf97dbf08b8d7e92b8cc608a4bb3cb6cb59d56e26baf6abae0094be0 Homepage: https://cran.r-project.org/package=meerva Description: CRAN Package 'meerva' (Analysis of Data with Measurement Error Using a ValidationSubsample) Sometimes data for analysis are obtained using more convenient or less expensive means yielding "surrogate" variables for what could be obtained more accurately, albeit with less convenience; or less conveniently or at more expense yielding "reference" variables, thought of as being measured without error. Analysis of the surrogate variables measured with error generally yields biased estimates when the objective is to make inference about the reference variables. Often it is thought that ignoring the measurement error in surrogate variables only biases effects toward the null hypothesis, but this need not be the case. Measurement errors may bias parameter estimates either toward or away from the null hypothesis. If one has a data set with surrogate variable data from the full sample, and also reference variable data from a randomly selected subsample, then one can assess the bias introduced by measurement error in parameter estimation, and use this information to derive improved estimates based upon all available data. Formulaically these estimates based upon the reference variables from the validation subsample combined with the surrogate variables from the whole sample can be interpreted as starting with the estimate from reference variables in the validation subsample, and "augmenting" this with additional information from the surrogate variables. This suggests the term "augmented" estimate. The meerva package calculates these augmented estimates in the regression setting when there is a randomly selected subsample with both surrogate and reference variables. Measurement errors may be differential or non-differential, in any or all predictors (simultaneously) as well as outcome. The augmented estimates derive, in part, from the multivariate correlation between regression model parameter estimates from the reference variables and the surrogate variables, both from the validation subset. Because the validation subsample is chosen at random any biases imposed by measurement error, whether non-differential or differential, are reflected in this correlation and these correlations can be used to derive estimates for the reference variables using data from the whole sample. The main functions in the package are meerva.fit which calculates estimates for a dataset, and meerva.sim.block which simulates multiple datasets as described by the user, and analyzes these datasets, storing the regression coefficient estimates for inspection. The augmented estimates, as well as how measurement error may arise in practice, is described in more detail by Kremers WK (2021) and is an extension of the works by Chen Y-H, Chen H. (2000) , Chen Y-H. (2002) , Wang X, Wang Q (2015) and Tong J, Huang J, Chubak J, et al. (2020) . Package: r-cran-meetupr Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3071 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-countrycode, r-cran-cli, r-cran-clipr, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-rlist, r-cran-s7, r-cran-withr, r-cran-rstudioapi Suggests: r-cran-covr, r-cran-cyphr, r-cran-ggplot2, r-cran-ggwordcloud, r-cran-httpuv, r-cran-jose, r-cran-knitr, r-cran-openssl, r-cran-rmarkdown, r-cran-sodium, r-cran-testthat, r-cran-tidyr, r-cran-tidytext, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-meetupr_0.3.1-1.ca2604.1_all.deb Size: 511930 MD5sum: 60e66f5fbc0a67b4f6b0b63495ce5d6f SHA1: bc6cfe324a320fafa52d8012dbc3dbf2c5bcb0d7 SHA256: ca924e2ed5b74609e79e457db176f593d99d976d5dd539a12a40c169f0ca0a5c SHA512: 2f3674daae3fabe0a018fbd7b3f851123a10fa04183f9a19e4c0454c2cea73ceac21d32e1385ac410eb39ab104f67ca6c6ee6eb605fa5851ce34e4eb1d46930a Homepage: https://cran.r-project.org/package=meetupr Description: CRAN Package 'meetupr' (Access Meetup Data) Provides programmatic access to the 'Meetup' 'GraphQL' API (), enabling users to retrieve information about groups, events, and members from 'Meetup' (). Supports authentication via 'OAuth2' and includes functions for common queries and data manipulation tasks. Package: r-cran-mefa4 Architecture: all Version: 0.3-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-mefa Filename: pool/dists/resolute/main/r-cran-mefa4_0.3-12-1.ca2604.1_all.deb Size: 600930 MD5sum: 888827a112893a4ab402daecbce42932 SHA1: dc22c84db230378143af9722839d244e8cc3e1b1 SHA256: 7d752619c49c9c5f199c87a549d85f6f99b72fecccb2e4551640e3a67bceb50b SHA512: f141ed688924e60fd02b9a925d541e8f42de26732abe2b07218bf4db8e7e282ca27ead4a27b2119c426a80203921462b9f6f1ef073f04567a24007b5e3ee0b81 Homepage: https://cran.r-project.org/package=mefa4 Description: CRAN Package 'mefa4' (Multivariate Data Handling with S4 Classes and Sparse Matrices) An S4 update of the 'mefa' package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices. Package: r-cran-mefa Architecture: all Version: 3.2-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-vegan, r-cran-mefa4, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-mefa_3.2-10-1.ca2604.1_all.deb Size: 251700 MD5sum: a4f4edfabea91430b897eba75869a32a SHA1: 5245cc3e824cda8771ba66a1f2287784b4331f5e SHA256: 5cb864cd02315fdbe052bd9ef24dc71eda0ea8ebc4e051ae12308a3b45fb4957 SHA512: db213e71a88c3edf6218bfdb0e65e287783acffdc1ed3fea7579d7770e9a401925275609edd653e1d159f2ef01856dfff177440c725d6ac7b318d87c8bd404ea Homepage: https://cran.r-project.org/package=mefa Description: CRAN Package 'mefa' (Multivariate Data Handling in Ecology and Biogeography) A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples. Package: r-cran-mefm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensormiss Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mefm_0.1.1-1.ca2604.1_all.deb Size: 54810 MD5sum: 89d219ea44753a12a19559aa8a24444d SHA1: 12aeb1153a82b25e016cea5f3e60644480a72870 SHA256: 97cf7022037d915cf265952514ff74a24d710e8ed5be0cf3d440d2fde05d087b SHA512: d3a839b182399c3fe91d006edefa3140f2c2d2f30bcd35f8a02ec77b938557bbc3f6ffaf866238479e6154d77dbd24332f49729b272f17a949f3b70ffe78fae1 Homepage: https://cran.r-project.org/package=MEFM Description: CRAN Package 'MEFM' (Perform MEFM Estimation on Matrix Time Series) To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) . Estimation of traditional matrix factor models is also supported. Supplementary functions for testing MEFM over factor models are included. Package: r-cran-megatrees Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4238 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape Suggests: r-cran-piggyback, r-cran-testthat, r-cran-dplyr, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-megatrees_1.0.0-1.ca2604.1_all.deb Size: 4296392 MD5sum: be277ac0fe1eb54d24bc2f530d2807eb SHA1: 7107a683c00a8e0fa0b5bc211307d2144c1549f9 SHA256: cd0a16588dc9b97cb8031e2fe32b321648bd0372d9e91540dc5116368e7b5b5a SHA512: 8b7395c31a8c1056ee3dbe62d1c274f92f613e95a49446f5b37fc64709833c75816608a64092699546bccecb263c33410cb6c521555d6248aa10becde46b5a1a Homepage: https://cran.r-project.org/package=megatrees Description: CRAN Package 'megatrees' (Subsets of Randomly Selected Phylogenies from ExistingMega-Phylogenies) There are an increasing number of mega-phylogenies available nowadays, with many of them being sets of thousands of posterior distribution phylogenies. For ecological studies, we may need to randomly select many such posterior phylogenies to conduct analyses. This data package serves this purpose by providing a small number (100 or 50) of randomly selected posterior phylogenies (if available) so that we can readily use them for our downstream analyses without repeating the downloading and selecting processes. Package: r-cran-megb Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbm, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-megb_0.2-1.ca2604.1_all.deb Size: 43036 MD5sum: d12cb2a3d2f62e57e58affb0586a3bc1 SHA1: c29f18802e855c64c0741da8d3d64a6cf4806d58 SHA256: a29149ef1d57f0395b49dca01b05d63ce4dd2f29dd83757fabdf2c62558e4991 SHA512: 692ab85fdbb0fc4fdf45c3a4172d2c71d08146919b18b04d3225514e2a0380d47278c781ec752e9b4bdde338e0444f48b39f1cd9764433b50de326cab084afc0 Homepage: https://cran.r-project.org/package=MEGB Description: CRAN Package 'MEGB' (Gradient Boosting for Longitudinal Data) Gradient boosting is a powerful statistical learning method known for its ability to model complex relationships between predictors and outcomes while performing inherent variable selection. However, traditional gradient boosting methods lack flexibility in handling longitudinal data where within-subject correlations play a critical role. In this package, we propose a novel approach Mixed Effect Gradient Boosting ('MEGB'), designed specifically for high-dimensional longitudinal data. 'MEGB' incorporates a flexible semi-parametric model that embeds random effects within the gradient boosting framework, allowing it to account for within-individual covariance over time. Additionally, the method efficiently handles scenarios where the number of predictors greatly exceeds the number of observations (p>>n) making it particularly suitable for genomics data and other large-scale biomedical studies. Package: r-cran-meifly Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaps, r-cran-mass, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-meifly_0.3.1-1.ca2604.1_all.deb Size: 38678 MD5sum: 0afc899be356668609724e28f3e2bd36 SHA1: 6ab794000a91b87ba0d14ab5032d3da7095fad9d SHA256: b1f3b9b5c128ff5b31b44b8b5098331a3ab74ed3aaa3b2d13652de1bf1ca1a33 SHA512: eeec22034a20a272cd8e6ea760074e4989d0872d563e0499495008feff8c3c4ad9d9e561ee2378a3a9e82531fa47501b00a5a37aa47e462c839f5bd229f24be4 Homepage: https://cran.r-project.org/package=meifly Description: CRAN Package 'meifly' (Interactive Model Exploration using 'GGobi') Exploratory model analysis with . Fit and graphical explore ensembles of linear models. Package: r-cran-mekko Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mekko_0.1.0-1.ca2604.1_all.deb Size: 85492 MD5sum: cec7123dad9f05bf8f6c5ed2dbe1335c SHA1: 299f9dcf2c769d2080ca94a5ab47474b554f99eb SHA256: 940945e8d21fb0a27cc92dbf1be35dc923caa97a69787ba5a5ff6521fa79d56a SHA512: 913f1ccb1db1d96deddd1650c803439e3e0274a71bb6207c52a39a7a54f114dafec5109ded802306d5cd72d0708087276feed5d16b5677dfb54d28bbc2eacb0e Homepage: https://cran.r-project.org/package=mekko Description: CRAN Package 'mekko' (Variable Width Bar Charts: Bar Mekko) Create variable width bar charts i.e. "bar mekko" charts to include important quantitative context. Closely related to mosaic, spine (or spinogram), matrix, submarine, olympic, Mondrian or product plots and tree maps. Package: r-cran-melidosdata Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-hms, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-gt, r-cran-lightlogr, r-cran-testthat, r-cran-vroom Filename: pool/dists/resolute/main/r-cran-melidosdata_1.0.6-1.ca2604.1_all.deb Size: 1224066 MD5sum: 510aa4fb9daf03ee26aa883b20f8edb0 SHA1: 0cd888f08362029a7247537e44e5362bddd11599 SHA256: 0187dc3785be822196acfac169cd43a5be6c7893f75b208a221170d83835caba SHA512: 00241941be5baf063b9369f09d15c6f13aad077f6c47a244e8a220f95b77d0ee86463fbf2015fb8c40582ee4acc8367a73c954540f849e9e6d36a5c9f94add5e Homepage: https://cran.r-project.org/package=melidosData Description: CRAN Package 'melidosData' (Load Data from the MeLiDos Field Study) In the MeLiDos field study, personal light exposure data were collected in 9 sites, 7 countries, and 196 participants following the Guidolin et al. (2024) protocol. Data originate from wearable devices collecting personal light exposure at the eye level, chest, and the wrist. Questionnaires were collected via 'REDCap' and contain demographic information as well as chronotype, current conditions, sleep diaries, wear logs, and many more. This package makes loading the data from the respective repositories () into R a breeze. It further contains some quality of life functions for label handling and data from 'REDCap'. Package: r-cran-mem Architecture: all Version: 2.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sm, r-cran-boot, r-cran-rcolorbrewer, r-cran-mclust, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-rcpproll, r-cran-envstats Suggests: r-cran-magick Filename: pool/dists/resolute/main/r-cran-mem_2.19-1.ca2604.1_all.deb Size: 418460 MD5sum: c9bc1b6b550988bb298cf6d7b458ae36 SHA1: e9869d228cb2e18d0b11f26fbf32f69d3ed7a629 SHA256: 8a5ae888c4d97b42342314cf6ab713fe650e83f84ad8d763749895c17ea9aadd SHA512: e914fd042b1f5bd7c33106965192daa9f0046d32e323fe62f0bb28af3f577f8bc5b83c6d25b77c2e074b453a4f997306a680c0dcda863c07391476cf4034043c Homepage: https://cran.r-project.org/package=mem Description: CRAN Package 'mem' (The Moving Epidemic Method) The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) , , allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. Package: r-cran-memapp Architecture: all Version: 2.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-shinybs, r-cran-shinyjs, r-cran-rcolorbrewer, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-stringi, r-cran-dt, r-cran-formattable, r-cran-ggplot2, r-cran-plotly, r-cran-mem Suggests: r-cran-openxlsx, r-cran-readxl, r-cran-foreign, r-cran-haven, r-cran-readods, r-cran-rodbc, r-cran-magick Filename: pool/dists/resolute/main/r-cran-memapp_2.16-1.ca2604.1_all.deb Size: 158190 MD5sum: 15aef7a57547d3e9deada02c40b5e407 SHA1: 3e4f9463753d2f5497f83a719caae6298164c314 SHA256: 7d2372129147e7daebd664e80f52651f5f60bc846aa274f893909b0a92b7f324 SHA512: b8f31759c06ee968c43fe98b658d76e35c78c2cf34cab22b9c8bec4f89969ff267e97487360132d4e84ccfbc2aa732681d1c2815b34cb2e8fe619fc3a100852b Homepage: https://cran.r-project.org/package=memapp Description: CRAN Package 'memapp' (The Moving Epidemic Method Web Application) The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) , , allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. 'memapp' is a web application created in the Shiny framework for the 'mem' R package. Package: r-cran-meme Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3723 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridgraphics, r-cran-magick, r-cran-showtext, r-cran-sysfonts Suggests: r-cran-cowplot, r-cran-ggimage, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-shadowtext Filename: pool/dists/resolute/main/r-cran-meme_0.2.4-1.ca2604.1_all.deb Size: 2857208 MD5sum: 682a6acc07b6f7ec7542a104aad33765 SHA1: 966b6b2d800876335f87bf756cb4140cd40dde62 SHA256: 47e9671503db3ac0525bf07bd2e83e90af0161647aa28557dce071a2cee860ea SHA512: c64f1d0230e9ad354d99b3d5238f74fc3920a9e35759d8c43387d17be6b8c4cd5448871ddcc33b14d39ad03a35a0127679bc0a9ee2857bd44679e246da16837f Homepage: https://cran.r-project.org/package=meme Description: CRAN Package 'meme' (Create Meme) The word 'Meme' was originated from the book, 'The Selfish Gene', authored by Richard Dawkins (1976). It is a unit of culture that is passed from one generation to another and correlates to the gene, the unit of physical heredity. The internet memes are captioned photos that are intended to be funny, ridiculous. Memes behave like infectious viruses and travel from person to person quickly through social media. The 'meme' package allows users to make custom memes. Package: r-cran-memery Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-showtext, r-cran-sysfonts, r-cran-png, r-cran-jpeg, r-cran-cairo, r-cran-ggplot2, r-cran-cowplot, r-cran-magrittr, r-cran-purrr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinybs, r-cran-colourpicker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magick, r-cran-gifski Filename: pool/dists/resolute/main/r-cran-memery_0.6.0-1.ca2604.1_all.deb Size: 112366 MD5sum: 7454cd5b5efac1202a4ca62a0461f784 SHA1: 2e8aa4d83f1417d066c16614182ead83d2e7b212 SHA256: 2073aa1d560d7035a955be82d7936aba1cbeca70ea2aa1fc4719b2da8d319867 SHA512: 9c236d375b839b13e7e6a875fcb71c27f29dd879d51e103be8476d617d57dca8c28c8c711bcc005b8f252088748570902014758ab80478b8da4d4c618d0d165f Homepage: https://cran.r-project.org/package=memery Description: CRAN Package 'memery' (Internet Memes for Data Analysts) Generates internet memes that optionally include a superimposed inset plot and other atypical features, combining the visual impact of an attention-grabbing meme with graphic results of data analysis. The package differs from related packages that focus on imitating and reproducing standard memes. Some packages do this by interfacing with online meme generators whereas others achieve this natively. This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites. It reads images directly from local files or via URL and meme generation is done by the package. While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on allowing for non-standard meme layouts and hybrids of memes mixed with graphs. While this package can be used to make basic memes like an online meme generator would produce, it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting foreground graphs of data analysis results. The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes rather than restricting to the standard top and/or bottom text placement. This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels. The package offers a selection of templates for graph placement and appearance with respect to the underlying meme. Graph templates also permit additional template-specific customization. Animated gif support is provided but this is optional and functional only if the 'magick' package is installed. 'magick' is not required unless gif functionality is desired. Package: r-cran-memgene Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-gdistance, r-cran-raster, r-cran-sp, r-cran-vegan Suggests: r-cran-adegenet, r-cran-geosphere, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-memgene_1.0.3-1.ca2604.1_all.deb Size: 1392362 MD5sum: 0b92a3caf80b89dd20427ea2c911cd80 SHA1: 980687b9cad98b0b48d51f0e1c913204d74e6b72 SHA256: 6b749ca796cb29b58d16a1cd767e92ade12e973572291e39f9d810380cb10e01 SHA512: 091753d3d8b05b5c984aa12b95abed0e8143b7da6d9d522738a3d5ae2704fc9a1917f33a687a349ee664b996e798c4288401036233cfd300e9a3ce65a3ddb218 Homepage: https://cran.r-project.org/package=memgene Description: CRAN Package 'memgene' (Spatial Pattern Detection in Genetic Distance Data Using Moran'sEigenvector Maps) Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation. Has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation. Package: r-cran-memify Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-memify_0.1.1-1.ca2604.1_all.deb Size: 23918 MD5sum: 9a0dd328025a8af345f46b3562572acc SHA1: 05e6f48322064fdf02402ff5e73f53fae990addc SHA256: 2796c48cc7a10b5a86234a4891097854daeecf3725873d0b3b23e4dd9dba0897 SHA512: 7866cdf8092ac1f4027a6bb138db0c1a0c1fe9b15d1fd3432a0e790d9af8fff301eed91fb80cf9266291e5abbe9c978c6228936449acea4ec27ff54854267b7a Homepage: https://cran.r-project.org/package=memify Description: CRAN Package 'memify' (Constructing Functions That Keep State) A simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation. 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Package: r-cran-memoir Architecture: all Version: 1.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bookdown, r-cran-distill, r-cran-rmarkdown, r-cran-rmdformats, r-cran-usethis Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-memoir_1.3-1-1.ca2604.1_all.deb Size: 155842 MD5sum: fd3cf8d2601461fb328aded890c72cb3 SHA1: 765374de63ad13bbbd9b7d39eafa4f8f4b623df5 SHA256: 745faa7209a6f2d57bb9bce943e4562b7522018f737f5db31874f5f4ccbdb90b SHA512: cf489ef0c927403832564192a2ea450a4d465704f3cd85654b463160a9b716a147659d1cd9ecfaf759876fa8045d68e3fbf36882de763b03b0507f95b53a863b Homepage: https://cran.r-project.org/package=memoiR Description: CRAN Package 'memoiR' (R Markdown and Bookdown Templates to Publish Documents) Producing high-quality documents suitable for publication directly from R is made possible by the R Markdown ecosystem. 'memoiR' makes it easy. It provides templates to knit memoirs, articles and slideshows with helpers to publish the documents on GitHub Pages and activate continuous integration. Package: r-cran-memoise Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cachem Suggests: r-cran-digest, r-cran-aws.s3, r-cran-covr, r-cran-googleauthr, r-cran-googlecloudstorager, r-cran-httr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-memoise_2.0.1-1.ca2604.1_all.deb Size: 49256 MD5sum: a7a98a3d4bd16473f1947eb930e9dde6 SHA1: b66ec2c9f00d17ef447d6e069fdac91e706d7935 SHA256: 88594e98805d23095da40c89e180bc64f8f2cbe8e5e01e18125d4eea07b18322 SHA512: 11fea57ccbff55adc30a246cdb69f26174d5a943f699749d74f6256ef1b870d8e139cbdf53b3abaf4b7bad2fc11f0dcc22dcaa505e21ea6c90f10f36d7d454f3 Homepage: https://cran.r-project.org/package=memoise Description: CRAN Package 'memoise' ('Memoisation' of Functions) Cache the results of a function so that when you call it again with the same arguments it returns the previously computed value. Package: r-cran-memor Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-rmarkdown, r-cran-knitr Suggests: r-cran-kableextra, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-memor_0.2.3-1.ca2604.1_all.deb Size: 191426 MD5sum: af85dad86106602cb7268620aed84db5 SHA1: 885277dcc3232626e512bebc6900f6d668df6f1d SHA256: 8330fdfeaca942d58ae2554e4bdcccb7f02ddbc4f189b467e86b44e8b33c8134 SHA512: 494831a77d724af29450f96285ab2ad5c2d66ef548036e9d36ada99520a5ca1a5a080d704bf0d99ee5202bbac1f2855a506c9dfacc9428fea6fc63d9d49e3272 Homepage: https://cran.r-project.org/package=memor Description: CRAN Package 'memor' (A 'rmarkdown' Template that Can be Highly Customized) A 'rmarkdown' template that supports company logo, contact info, watermarks and more. Currently restricted to 'Latex'/'Markdown'; a similar 'HTML' theme will be added in the future. Package: r-cran-memoria Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ranger, r-cran-zoo, r-cran-rlang Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-memoria_1.1.0-1.ca2604.1_all.deb Size: 813352 MD5sum: 5f698220722e1c770fc8918ac18ede61 SHA1: f681995a7aea9e38fc8aa9b2e66e6c320746c5fc SHA256: 6dc11b8fa3535d67f89528e0d6bb4869386d4b945da732b504af8f42243440e0 SHA512: 3452544981507c2d17015904b06bd2f2a0093e9f7dca1bd8ceba42001d17f72750b9c01e6fa994acc12275d56a61d048b6f5998672a5d174af9c97ca53e1e875 Homepage: https://cran.r-project.org/package=memoria Description: CRAN Package 'memoria' (Quantifying Ecological Memory in Palaeoecological Datasets andOther Long Time-Series) Quantifies ecological memory in long time-series using Random Forest models ('Benito', 'Gil-Romera', and 'Birks' 2019 ) fitted with 'ranger' (Wright and Ziegler 2017 ). Ecological memory is assessed by modeling a response variable as a function of lagged predictors, distinguishing endogenous memory (lagged response) from exogenous memory (lagged environmental drivers). Designed for palaeoecological datasets and simulated pollen curves from 'virtualPollen', but applicable to any long time-series with environmental drivers and a biotic response. Package: r-cran-memss Architecture: all Version: 0.9-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-memss_0.9-4-1.ca2604.1_all.deb Size: 238740 MD5sum: b66b32c1febbad8de5aca9e7ee656a64 SHA1: 64ca4838561466d894e633f8a799425bd42925d8 SHA256: e20b6609512df98574bf9a576f6d851691b60988ea229130a451e34e5c35a25d SHA512: d3d3b18e9863079bda1f0b60bbc833e276a43887e0c9399733134b1ebb08f59187dabb1a379f23b4c4a06c4be3d088e7e2a17d8da9d46b0d10beff04da5afe51 Homepage: https://cran.r-project.org/package=MEMSS Description: CRAN Package 'MEMSS' (Data Sets from Mixed-Effects Models in S) Data sets and sample analyses from Pinheiro and Bates, "Mixed-effects Models in S and S-PLUS" (Springer, 2000). 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See Abrams and colleagues, 2021, . 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This material is partially based on work supported by the National Science Foundation under Grant Number 1460719. 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This package employs artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. This package makes it easier to use Large Language Models in your development environment by providing a chat-like interface, while also allowing you to inspect and execute the returned code. Package: r-cran-mergingtools Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2044 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-rlang, r-cran-mass Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-mergingtools_1.0.1-1.ca2604.1_all.deb Size: 721468 MD5sum: c6b341a86b5674ba67cf07f8f43e7c21 SHA1: 7e3884eea110c97b689a2e51d016aef8a2f83b98 SHA256: 4b764a74ea50249ae6e11abd54b2488e5a16262c3bff675ec6ac4326ae289a29 SHA512: f14cc5debf50ea93b0d91e4838d43eeb269c848022bbcc4ee412796c632f8693fed64c117ec3837ce4dba8e08e99cafd3ffeeed3c19f5b9acab4a29d193e8fd1 Homepage: https://cran.r-project.org/package=mergingTools Description: CRAN Package 'mergingTools' (Tools to Merge Hardware Event Monitors (HEMs) Coming fromSeparate Subexperiments into One Single Dataframe) Implementation of two tools to merge Hardware Event Monitors (HEMs) from different subexperiments. Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, . For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC '21). Association for Computing Machinery. . This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). Package: r-cran-mermboost Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-lme4, r-cran-stabs, r-cran-data.table, r-cran-mass, r-cran-matrix, r-cran-stringr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-mermboost_0.1.1-1.ca2604.1_all.deb Size: 134842 MD5sum: 063d8cafe2bd5c9f9dc786828d4f7626 SHA1: 9a6300edebeb67470c82464745c84da893754ead SHA256: 1b2dceccffcc036072affd707738b56d409d2e31e2795ea247858528d221f695 SHA512: e05ded7c28c07ca4a0445de9826da3b8467d179965e54cabb6db0e4a59800d6f3db4e4ee44c8ee46bdf1cc88a9cff167c1633c880a370b0e2b0d03c45dc67f92 Homepage: https://cran.r-project.org/package=mermboost Description: CRAN Package 'mermboost' (Gradient Boosting for Generalized Additive Mixed Models) Provides a novel framework to estimate mixed models via gradient boosting. 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Package: r-cran-metaanalyser Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggvis, r-cran-dt, r-cran-rstudioapi Suggests: r-cran-rmeta Filename: pool/dists/resolute/main/r-cran-metaanalyser_0.2.1-1.ca2604.1_all.deb Size: 41116 MD5sum: 1ba1991d10164227b9eb138f8e866683 SHA1: 2a172a123130be77d0bdc64bf21d6f499d8cad33 SHA256: 34666750b5a72a8012bd0cd1cd4dc423eaab867da0254a8632e33dc113b445e9 SHA512: 61b0eb34520de83ee524817ec19d5f0157d2a5f419e002cc36976a731fa0c9335264173380b831adeba462995edb9a5e9aacf3f9a6159073acba2d3e6b73b17c Homepage: https://cran.r-project.org/package=MetaAnalyser Description: CRAN Package 'MetaAnalyser' (An Interactive Visualisation of Meta-Analysis as a PhysicalWeighing Machine) An interactive application to visualise meta-analysis data as a physical weighing machine. 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See the packages 'PublicationBias', 'phacking', and 'multibiasmeta'. These package implement methods described in, respectively: Mathur & VanderWeele (2020) ; Mathur (2022) ; Mathur (2022) . 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The deconvolution part uses the algorithm described in Koh et al. (2009) . The alignment part is based on functions from the 'speaq' package, described in Beirnaert et al. (2018) and Vu et al. (2011) . A detailed description and evaluation of an early version of the package, 'MetaboDecon1D v0.2.2', can be found in Haeckl et al. (2021) . 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Package: r-cran-metabolic Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-magrittr, r-cran-usethis, r-cran-dplyr, r-cran-ggplot2, r-cran-ggfittext, r-cran-cli, r-cran-forcats, r-cran-ggimage, r-cran-patchwork, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-meta, r-cran-glue, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-here, r-cran-rd2roxygen, r-cran-kableextra, r-cran-fansi, r-cran-downloadthis, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-metabolic_0.1.2-1.ca2604.1_all.deb Size: 204978 MD5sum: 5524dd3e102e018eab549dc62de3e5ad SHA1: 4b94e9dd498e04b724578af8b419f54fa2d0ebe6 SHA256: b9dae86628b34e992f20d67d4b7333d6ea3f450a9b28e6bdf38ab54bf39b6043 SHA512: 6e733e5983449f71e011a4c37ab7d604ac69d9d249c79aa79ced383f20e403549a46571ae172ce78a4e2fa04b2217d498fd94574faa000e6676bc454e42493f6 Homepage: https://cran.r-project.org/package=metabolic Description: CRAN Package 'metabolic' (Datasets and Functions for Reproducing Meta-Analyses) Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. It contains all the data and functions to reproduce the analysis. "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis". Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) . Package: r-cran-metabolicsurv Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-superpc, r-cran-glmnet, r-cran-matrixstats, r-cran-survminer, r-cran-survival, r-cran-rms, r-cran-tidyr, r-cran-pls, r-cran-rdpack, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-metabolicsurv_1.1.2-1.ca2604.1_all.deb Size: 522488 MD5sum: e07fe4f97cb943ca22a07f38d2e8352e SHA1: 2ec54b3c928b60aabf136affdcc8360acdf1760a SHA256: 8e8d4521ef62746ec38444501fe38ec1ddad20305d665278b7261ec30abdf349 SHA512: e4d06fc4d70d93eb35b60cb0ce77fdcf0e5e0e67d46c0e359a5d9ef5b4b235fad588101a4c599f475dcdce52dc29b13839bd00a1adf5e206aedbb42d17ed0052 Homepage: https://cran.r-project.org/package=MetabolicSurv Description: CRAN Package 'MetabolicSurv' (A Biomarker Validation Approach for Classification andPredicting Survival Using Metabolomics Signature) An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) , the LASSO method by Robert Tibshirani (1998) , the elastic net approach by Hui Zou and Trevor Hastie (2005) . Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique. Package: r-cran-metabolicsyndrome Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-metabolicsyndrome_0.1.3-1.ca2604.1_all.deb Size: 14982 MD5sum: 6d3e320b95e40037a23dfb777861bdcc SHA1: 4845a311b03da9ccd22aaa40d50cc04cbe999fbc SHA256: 3fa11ed649e8620563a7b698d290de4540c6ee9b05ac85fffd97e74159a107c1 SHA512: e5bc6eb7a226a62ac5017e461cb8de5c5f3ae3519a0ae3cb3a43ac452663e138fcae7a07d1036072d346226b12905b4d4a3ddec758470fefecab9cea37e192e5 Homepage: https://cran.r-project.org/package=MetabolicSyndrome Description: CRAN Package 'MetabolicSyndrome' (Diagnosis of Metabolic Syndrome) The modified Adult Treatment Panel -III guidelines (ATP-III) proposed by American Heart Association (AHA) and National Heart, Lung and Blood Institute (NHLBI) are used widely for the clinical diagnosis of Metabolic Syndrome. The AHA-NHLBI criteria advise using parameters such as waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG) and high-density lipoprotein cholesterol (HDLC) for diagnosis of metabolic syndrome. Each parameter has to be interpreted based on the proposed cut-offs, making the diagnosis slightly complex and error-prone. This package is developed by incorporating the modified ATP-III guidelines, and it will aid in the easy and quick diagnosis of metabolic syndrome in busy healthcare settings and also for research purposes. The modified ATP-III-AHA-NHLBI criteria for the diagnosis is described by Grundy et al ., (2005) . Package: r-cran-metabolomicsbasics Architecture: all Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-c50, r-cran-caret, r-cran-e1071, r-cran-fs, r-cran-hirestec, r-cran-interpretmsspectrum, r-bioc-pcamethods, r-cran-plyr, r-cran-rpart, r-cran-rlang, r-cran-webchem Suggests: r-bioc-mixomics, r-bioc-ropls, r-bioc-xcms Filename: pool/dists/resolute/main/r-cran-metabolomicsbasics_1.4.7-1.ca2604.1_all.deb Size: 322212 MD5sum: 5d58e6c8c66d4fe07dda4ca6b03a4943 SHA1: 349a6aa4c3f1bce3a8722585784860c4aca044da SHA256: fd85337fe57dd4e1d129ad9f073df43adbbf55b0bae2249fe703e855de799f76 SHA512: 07040ca00105c1798bb4e30ce5e80398c3a3235751587b0dc4b4115fb37436405e2a5a56c62363c4e43c0707a7c5ab1f521bace65791ea4670a035321aba12b2 Homepage: https://cran.r-project.org/package=MetabolomicsBasics Description: CRAN Package 'MetabolomicsBasics' (Basic Functions to Investigate Metabolomics Data Matrices) A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication . Package: r-cran-metabolssmf Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-laplacesdemon, r-cran-nmf, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-lsei, r-cran-mclust, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caroline, r-cran-ggsci, r-cran-biocmanager, r-bioc-biobase Filename: pool/dists/resolute/main/r-cran-metabolssmf_0.1.0-1.ca2604.1_all.deb Size: 917204 MD5sum: a5170178827fa4d8157253dca17ee945 SHA1: c33ca5d4a8f8092202590954c91812fbc71ff1c8 SHA256: cf724c6ffdaab68a402cd338a22906c16188bab7a5db03e72ee8c047afef153a SHA512: 3444dead4408f9608a67f83e0afc65177f7ce631260ff56ecfa85365a4c8c17c903f0807c26e62d8bc8aba08d1e51f832d742775a49eafba980e9bba8bdedcc6 Homepage: https://cran.r-project.org/package=MetabolSSMF Description: CRAN Package 'MetabolSSMF' (Simplex-Structured Matrix Factorisation for MetabolomicsAnalysis) Provides a framework to perform soft clustering using simplex-structured matrix factorisation (SSMF). The package contains a set of functions for determining the optimal number of prototypes, the optimal algorithmic parameters, the estimation confidence intervals and the diversity of clusters. Abdolali, Maryam & Gillis, Nicolas (2020) . Package: r-cran-metabook Architecture: all Version: 0.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-meta, r-cran-netmeta, r-cran-metasens, r-cran-netdose Filename: pool/dists/resolute/main/r-cran-metabook_0.2-0-1.ca2604.1_all.deb Size: 177648 MD5sum: f0b8ce549e8b7737a155b66096cba501 SHA1: 54debb8282d1f261785bcbf45d7bb47020c10873 SHA256: f1a13d939fd9845310ed2058b94e8bf5c86c18bcd1faa0446559ffb2ff672f1a SHA512: fcf10e999eb929357f4ae0fdf63cd156c68e62f15b909481d779b8de6526bc527ceb1643a53b4806490170bac489d715037ad4df351e4cc5ce82068faa304519 Homepage: https://cran.r-project.org/package=metabook Description: CRAN Package 'metabook' (Data Sets and Code for "Meta-Analysis with R") Data sets and code supporting the second edition of "Meta-Analysis with R"; first edition: Schwarzer, Carpenter, and Rücker (2015) . Package: r-cran-metaboqc Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-metaboqc_1.1-1.ca2604.1_all.deb Size: 54292 MD5sum: 426cc69d43d09768c21d3ac7587a2b6c SHA1: 468a01def290edca5e6f6cb9a14777d53e4709cd SHA256: 454116be4b84fa66170416b4879421e6c0cdae5c14a33535cbaef480f86cbc1b SHA512: 49c429f5efd977d4260cad90b5dc57da0949b3f1c1db894ccd00aa4e3b8947f6e5a25dbc69472d7f024717ca72478bd0fba01c6c6222370730614808989fb618 Homepage: https://cran.r-project.org/package=MetaboQC Description: CRAN Package 'MetaboQC' (Normalize Metabolomic Data using QC Signal) Takes QC signal for each day and normalize metabolomic data that has been acquired in a certain period of time. At least three QC per day are required. Package: r-cran-metabup Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-partitions Filename: pool/dists/resolute/main/r-cran-metabup_0.1.3-1.ca2604.1_all.deb Size: 22370 MD5sum: f5e988767b4c68c66ea2493147d1d83c SHA1: 24d88373e3c37b0ab8545a9ed6d2cb2f44c17d53 SHA256: c33bbb2a3db6c73fffe60402b41f148718e89c338d7bbad2bcfc50e2a102cf11 SHA512: e80ff6d50e32c4e733af8ed8f2c7c3312f087f535225d3ed0f45fec5b4e89f32f6e036c43321dd6b2ea3c457fa582f65035f2561fce962cfc623c9911ee59bf7 Homepage: https://cran.r-project.org/package=metabup Description: CRAN Package 'metabup' (Bayesian Meta-Analysis Using Basic Uncertain Pooling) Contains functions that allow Bayesian meta-analysis (1) with binomial data, counts(y) and total counts (n) or, (2) with user-supplied point estimates and associated variances. Case (1) provides an analysis based on the logit transformation of the sample proportion. This methodology is also appropriate for combining data from sample surveys and related sources. The functions can calculate the corresponding similarity matrix. More details can be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022) , Evans and Sedransk (2001) , and Malec and Sedransk (1992) . Package: r-cran-metacluster Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factoextra, r-cran-cluster, r-cran-dbscan, r-cran-dplyr, r-cran-seqinr, r-bioc-biostrings Filename: pool/dists/resolute/main/r-cran-metacluster_0.1.1-1.ca2604.1_all.deb Size: 67594 MD5sum: 4dc988732796039be435611dae6da60f SHA1: f5c0ea5acf3f67395f36efc4141a49e09ccf5711 SHA256: 131b653437931311c595b0dd8ca5fde86b8be404cba1f841fa337fe02d58ee56 SHA512: 3316522bb83d2e58062dc8e795d43ec6baf3a8a166de9449f094ee563510552428934d897df907696468e91f2b49abd4a23ae13a3692b8c5a70b8f8ba88a87db Homepage: https://cran.r-project.org/package=metaCluster Description: CRAN Package 'metaCluster' (Metagenomic Clustering) Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning. Package: r-cran-metacom Architecture: all Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metacom_1.5.3-1.ca2604.1_all.deb Size: 63602 MD5sum: da29a4b67b7a77c6b386c27e052a17b4 SHA1: 73f2a2344deef2dcd479bda6f6f353bc9222a0a7 SHA256: e458d4318e235351f5ca70575c46132e2ee25e9dcb45c2aaee9cf8c8330f5757 SHA512: 321350d34dd0f5c7a3aa053f11d5eaf8ca1abeac0e74ee9ec537a2754ed82a57d3c8d1862c3bac9d958a604dc104ff4afe0dcb479dc7bc36103edd4ad702c96d Homepage: https://cran.r-project.org/package=metacom Description: CRAN Package 'metacom' (Analysis of the 'Elements of Metacommunity Structure') Functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson 2002 . The package also includes functions to visualize ecological networks, and to calculate modularity as a replacement to boundary clumping. Package: r-cran-metaconfoundr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-knitr, r-cran-metafor, r-cran-patchwork, r-cran-readr, r-cran-rio, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-metaconfoundr_0.1.2-1.ca2604.1_all.deb Size: 1783812 MD5sum: e150c52197765ea68e0fb605f4abb583 SHA1: 6c29111529f8c81da908f2b98e23a949b20aa1f9 SHA256: 190dac9c54fce4916dc93ad770a8667f9b9473935dc45fa08192034a8ae203f0 SHA512: c548ace994bab4581e94f068bb97edddf51a54da8dd786c7360482a251ef28d88d2565d2911ca6008d99762d0aa0e5c1a5e93bb3dbaea4d26c401fe282c68f4e Homepage: https://cran.r-project.org/package=metaconfoundr Description: CRAN Package 'metaconfoundr' (Visualize 'Confounder' Control in Meta-Analyses) Visualize 'confounder' control in meta-analysis. 'metaconfoundr' is an approach to evaluating bias in studies used in meta-analyses based on the causal inference framework. Study groups create a causal diagram displaying their assumptions about the scientific question. From this, they develop a list of important 'confounders'. Then, they evaluate whether studies controlled for these variables well. 'metaconfoundr' is a toolkit to facilitate this process and visualize the results as heat maps, traffic light plots, and more. 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Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size. Package: r-cran-metacor Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-officer, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metacor_1.2.1-1.ca2604.1_all.deb Size: 76716 MD5sum: 9c10ace23182db4693fc7f12c5c0c1ae SHA1: 701011ebf4a4b28b400af6f0b928d3b2da0c2df8 SHA256: 522430a2366a59a4ed7e94cb7c82f889d82e785e4df755d8bd905719b6280fb4 SHA512: d681938876a75367536d7d424a44e693535ad47e84d155e11e6ef79f5dbe5b2bfb699e07d9dd8fe67a91fcbcef53c0fdf634a02e6e0609bfbf36c62a1e3b51ea Homepage: https://cran.r-project.org/package=metacor Description: CRAN Package 'metacor' (Meta-Analytic Effect Size Calculation for Pre-Post Designs withCorrelation Imputation) Tools for the calculation of effect sizes (standardised mean difference) and mean difference in pre-post controlled studies, including robust imputation of missing variances (standard deviation of changes) and correlations (Pearson correlation coefficient). The main function 'metacor_dual()' implements several methods for imputing missing standard deviation of changes or Pearson correlation coefficient, and generates transparent imputation reports. Designed for meta-analyses with incomplete summary statistics. For details on the methods, see Higgins et al. (2023) and Fu et al. (2013). Package: r-cran-metacore Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metacore_0.3.0-1.ca2604.1_all.deb Size: 1281650 MD5sum: e3212c8d97ef4882c6149f9ad5899be7 SHA1: d9cff173e63d9be23a10d75713fc29ef7aa4c01c SHA256: f7777cf20fa1e7b523a329b600b2a80c947a01c7bf22a2259bf93bfce139a94e SHA512: 6eb766857eff44a1a70ee66541d8b0970c90ca036b1f9c36a27c9f761c19a3b546e60444e4f825f30cc821464e6537c7bc0bae7aafc488da3d52241be45bd8f0 Homepage: https://cran.r-project.org/package=metacore Description: CRAN Package 'metacore' (A Centralized Metadata Object Focus on Clinical Trial DataProgramming Workflows) Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow. Package: r-cran-metacycle Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gnm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-metacycle_1.2.1-1.ca2604.1_all.deb Size: 1028372 MD5sum: 946e6bf75bc2e3458d23dadc1e467d48 SHA1: 688532f7fd8a932a054235577e4a6d5ba7d07044 SHA256: aab0bea02c2f590bae5f68fac299e28065f147f2d799d3f84c74f3bac88d6929 SHA512: 5860dc7b1144f98249a08ac015ecd806107cee5c8b318619a1f431b3728401e3f8cd90a15afddb9ede282864ed7d612b9e44d474290edc4ca1f350fc7fa72223 Homepage: https://cran.r-project.org/package=MetaCycle Description: CRAN Package 'MetaCycle' (Evaluate Periodicity in Large Scale Data) There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, 'meta2d' is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, 'meta3d' is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual. Package: r-cran-metadat Architecture: all Version: 1.6-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-metafor, r-cran-numderiv, r-cran-biasedurn, r-cran-dfoptim, r-cran-igraph, r-cran-ape, r-cran-testthat, r-cran-digest, r-cran-lme4, r-cran-clubsandwich, r-cran-meta, r-cran-netmeta, r-cran-mvtnorm, r-cran-gridextra, r-cran-rms, r-cran-bayesmeta, r-cran-ellipse, r-cran-metasens, r-cran-mada Filename: pool/dists/resolute/main/r-cran-metadat_1.6-0-1.ca2604.1_all.deb Size: 1067410 MD5sum: 5078decbdc0286a7e0d42f435101e898 SHA1: c3d802171a83133c53a3945216d7f8e8ce3d9653 SHA256: f6fef8b11463f12104000322793172411589924fafdad5d47faf332748f4b027 SHA512: 8958add9141bc051ff7f06d4bd134af57e6c0a69f4ae09248edaa151e5663434bd6bc0b85920212c23684e226017da1cfcbf14b3fab4b4cbf71005d91ed9a0c2 Homepage: https://cran.r-project.org/package=metadat Description: CRAN Package 'metadat' (Meta-Analysis Datasets) A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses. Package: r-cran-metadeconfoundr Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-detectseparation, r-cran-lmtest, r-cran-foreach, r-cran-doparallel, r-cran-logger, r-cran-lme4, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-circlize, r-cran-dplyr, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-scales, r-cran-stringr Suggests: r-cran-pander, r-cran-knitr, r-cran-gridextra, r-cran-kableextra, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-metadeconfoundr_1.0.5-1.ca2604.1_all.deb Size: 884336 MD5sum: f4c2283cee39a89250f0805b6d45f3de SHA1: 5d58e460c7c694ccf301da52167a176e9e010921 SHA256: cdd89ba36e6fabb0f74e18170b9bcd5e2ef10b9eefb2f8e004e4976911ec14a9 SHA512: a426675aa4a0791c2f20243eb182efc72f7b2079269132969fc71e13178f12003446784714d6dfb9581f749bf53ac3b61adf8b45847386cbb8f070c28a444955 Homepage: https://cran.r-project.org/package=metadeconfoundR Description: CRAN Package 'metadeconfoundR' (Covariate-Sensitive Analysis of Cross-Sectional High-DimensionalData) Using non-parametric tests, naive associations between omics features and metadata in cross-sectional data-sets are detected. In a second step, confounding effects between metadata associated to the same omics feature are detected and labeled using nested post-hoc model comparison tests, as first described in Forslund, Chakaroun, Zimmermann-Kogadeeva, et al. (2021) . The generated output can be graphically summarized using the built-in plotting function. Package: r-cran-metadigitise Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick, r-cran-purrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-metadigitise_1.0.2-1.ca2604.1_all.deb Size: 431658 MD5sum: 1cb971a5017e7c580881ef3693f35c22 SHA1: 706ee4269d33609810260e0a06ab43eb54822632 SHA256: 2d56c7596e63c9aebe94f287086f6fbf50b51a7a116ecccae47bcf65fca6a64c SHA512: 66e273b5c46c4e5ad0adee269ba71093e83ca18cc1f3e5f0b2789412eca763a625c533e9a03d8160238435173c68ae2f6f3d137c36568abd94b7b034861d011c Homepage: https://cran.r-project.org/package=metaDigitise Description: CRAN Package 'metaDigitise' (Extract and Summarise Data from Published Figures) High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms. Package: r-cran-metadose Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-metafor, r-cran-rms, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-metadose_1.0.1-1.ca2604.1_all.deb Size: 33528 MD5sum: 714a77574d234a393628336de1868590 SHA1: 4aad63e4035c752fb9a0893f968e3c8b7755d33a SHA256: 68a73f67edeec9fe14c757de1235dd1805a5a19ec78d7113d59d0fd608bbe779 SHA512: 665a4a1590aa8e8e24c9631442b9d7facfb17bf3ee0e5da3075303c583880b7f4a0156eb40a342161503bc69263aad6a5d9509fab0bc65e0a35fa7bf21698c11 Homepage: https://cran.r-project.org/package=MetaDose Description: CRAN Package 'MetaDose' (Dose-Response Meta-Regression for Meta-Analysis) Conducting linear and nonlinear dose-response meta-regression using study-level summary data. It supports both continuous and binary outcomes and allows modeling of dose-effect relationships using linear trends or nonlinear restricted cubic splines. The package is designed to facilitate transparent, flexible, and reproducible dose-response meta-analyses, with built-in visualization of fitted dose-response curves. Package: r-cran-metadyn Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-matrix, r-cran-fitvarmxid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-simstatespace, r-cran-mass, r-cran-metasem, r-cran-expm Filename: pool/dists/resolute/main/r-cran-metadyn_1.0.1-1.ca2604.1_all.deb Size: 175152 MD5sum: d6e0c2f23ef1b470524447ccc4260444 SHA1: 39b611335fc62185df68c5d9afb0455886b16ecc SHA256: d52a86ef69a6b69150859b2d56e1183f641ff3a51c2cc351e7c8677163cd4d5b SHA512: e93d9e4a0b608f5e4bb9d36eb76a14028b358a4cf07c83f9497a264b1d31771d77d91d5fbce11f3b383146b23ab84802dcde53b450f77f5fa565bc63068103de Homepage: https://cran.r-project.org/package=metaDyn Description: CRAN Package 'metaDyn' (Multivariate Meta-Analysis of Dynamic Model Estimates) Fits fixed-, random-, or mixed-effects multivariate meta-analysis models using dynamic model estimates from each individual building on and extending Lee and Gates (2023) . Package: r-cran-metaensembler Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 980 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-caret, r-cran-ggplot2, r-cran-e1071, r-cran-gbm, r-cran-randomforest Suggests: r-cran-knitr, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-metaensembler_0.1.0-1.ca2604.1_all.deb Size: 418298 MD5sum: 93594a5aa84fcc71a6a336dfa6e00c8e SHA1: ccd809540d8af0c4ff62026b084cd41bdf6576e4 SHA256: f305d29796c8a429bddceafd408c31e381ce5712e161e54fe9c4d623db1086cf SHA512: 9c450d9d4c9df0a619ffbdba72559d73622c0abdfaee33d6cb3d8a22359cedbef243ade99904b887a060da607b31d366e0a92efe69b331b1db6416a330875e97 Homepage: https://cran.r-project.org/package=metaEnsembleR Description: CRAN Package 'metaEnsembleR' (Automated Intuitive Package for Meta-Ensemble Learning) Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems. Package: r-cran-metaentropy Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-cran-beeswarm, r-cran-knitr Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-metaentropy_1.3-1.ca2604.1_all.deb Size: 116054 MD5sum: f451a6351d8f7cb58eb89c8ae9de20ff SHA1: f01b73fd3f728ccdc72049a834e04be21674d024 SHA256: 760a6733fe5e66e7d90c999982089c9801645396b327e3841f0fc827c7695441 SHA512: 5d1280b69b91d91b8a5221ccc35f675b65f43839d2b53beb74eabbcc246af6b47af6a185eec14554c84325dc40e0d46e1215004c52e8032cd9347de20b5bc6a5 Homepage: https://cran.r-project.org/package=MetaEntropy Description: CRAN Package 'MetaEntropy' (Functional Shannon Entropy for Virome Mutational Analysis) Estimates Shannon entropy, per gene and per genomic position, associated with non-synonymous mutation frequencies in viral populations, such as wastewater samples. The package uses codon translations for functional insights. Each amino acid can be treated as an individual state, resulting in a 20-state entropy computation, or grouped into one of six physicochemical classes, adding further functional context. Provides normalized values (0-1 scale) to facilitate the direct comparison of different genomic positions or total functional entropy across multiple metagenomes. Designed to analyze mutational data using tabular 'Single Nucleotide Variant' (SNV) frequency tables generated by variant callers (e.g., 'iVar' or 'LoFreq'), operating independently of consensus sequence estimation and multiple sequence alignment. Package: r-cran-metafor Architecture: all Version: 5.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5673 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-metadat, r-cran-numderiv, r-cran-nlme, r-cran-mathjaxr, r-cran-pbapply, r-cran-digest Suggests: r-cran-lme4, r-cran-pracma, r-cran-minqa, r-cran-nloptr, r-cran-dfoptim, r-cran-ucminf, r-cran-lbfgsb3c, r-cran-subplex, r-cran-bb, r-cran-rsolnp, r-cran-alabama, r-cran-optimparallel, r-cran-optimx, r-cran-compquadform, r-cran-mvtnorm, r-cran-biasedurn, r-cran-epi, r-cran-survival, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-car, r-cran-multcomp, r-cran-gsl, r-cran-sp, r-cran-ape, r-cran-boot, r-cran-clubsandwich, r-cran-crayon, r-cran-r.rsp, r-cran-testthat, r-cran-rmarkdown, r-cran-wildmeta, r-cran-emmeans, r-cran-estmeansd, r-cran-metablue, r-cran-rstudioapi, r-cran-glmulti, r-cran-mumin, r-cran-mice, r-cran-amelia, r-cran-calculus Filename: pool/dists/resolute/main/r-cran-metafor_5.0-1-1.ca2604.1_all.deb Size: 5261266 MD5sum: ccab8a0b0537b64151dca62f05fa871b SHA1: cfe9e705b06aa60e56e32a24e744bcb68ab8b400 SHA256: 6aaeb085133025c7db8d9d69e3504d094ee7ea805bea752bcdd5bc96fd967b09 SHA512: e2c19253310d322eff50f9bfba8984f3b1bf9fc7e72405cd913ae3bf151579c504bc7db303def87a101879c5f786398f514dc23f7bca60d62ed236b72599aa71 Homepage: https://cran.r-project.org/package=metafor Description: CRAN Package 'metafor' (Meta-Analysis Package for R) A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) . Package: r-cran-metaforest Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-data.table, r-cran-metafor, r-cran-ranger, r-cran-metadat Suggests: r-cran-testthat, r-cran-caret, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-metaforest_0.1.5-1.ca2604.1_all.deb Size: 179150 MD5sum: b563c7ea4f8ea47e82435673d720a54f SHA1: a456f71e9620f778e6ba122919d8da3449f6403f SHA256: 8b77aa6a7c4374310b3a9e2c87850e4a2f6dcdf7afa0a5f4acf48b0d3f58368c SHA512: d5463db4d63a928f4ddce2ff2fca5f6b1a06e4efa0649ac98991465d335765d2c9dc11d36c28a5e5966bd698d0349710151c736de459aab17088e738ef5f1628 Homepage: https://cran.r-project.org/package=metaforest Description: CRAN Package 'metaforest' (Exploring Heterogeneity in Meta-Analysis using Random Forests) Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates (Van Lissa, 2020). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size. Package: r-cran-metafrontier Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-numderiv, r-cran-lpsolveapi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sfar, r-cran-frontier, r-cran-benchmarking, r-cran-plm, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-metafrontier_0.2.2-1.ca2604.1_all.deb Size: 327926 MD5sum: 49024900c721902d17a20a151b39778e SHA1: 266e5de2290ecff3f38a40754b77157c9e427ec9 SHA256: 7e493f8bf70145760e59f0f1b745411fddcd265418b1391bd4b57f474d31d32e SHA512: 7c128a39ab8c88a7446dd0a7b02f1b359fc10cdbc275d11778a1a3b6a5c482cf04acd9528b820fd362e5920084be942d32e219cb4ebda1928f89ac26af9f6b57 Homepage: https://cran.r-project.org/package=metafrontier Description: CRAN Package 'metafrontier' (Analysis of Metafrontier Models for Efficiency and Productivity) Implements metafrontier production function models for estimating technical efficiencies and technology gaps for firms operating under different technologies. Supports both stochastic frontier analysis (SFA) and data envelopment analysis (DEA) based metafrontiers. Includes the deterministic metafrontier of Battese, Rao, and O'Donnell (2004) , the stochastic metafrontier of Huang, Huang, and Liu (2014) , and the metafrontier Malmquist productivity index of O'Donnell, Rao, and Battese (2008) . Additional features include panel SFA with time-varying inefficiency, bootstrap confidence intervals for technology gap ratios, latent class metafrontier estimation via the EM algorithm, Murphy-Topel corrected standard errors, and 'ggplot2' visualisation methods. Package: r-cran-metafuse Architecture: all Version: 2.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-mass, r-cran-evd Filename: pool/dists/resolute/main/r-cran-metafuse_2.0-1-1.ca2604.1_all.deb Size: 66390 MD5sum: ddeb12f07a34c77f11a288e153186368 SHA1: 0263e5a05e3a99889ed071626cbe9857beca210d SHA256: 8dec9d45799bc7a4713f087475a8ca8ebe4dc7da92aa993776026864c9790fc9 SHA512: 01243ba0e635da9c4302f2d5af05e0aca3ddc37bdf7dc3d9cb858c5c994ac44efd29cf5de4050ba5667e5765d1b6c050448cf38a3ae9d3547d997bb1f2f2f7af Homepage: https://cran.r-project.org/package=metafuse Description: CRAN Package 'metafuse' (Fused Lasso Approach in Regression Coefficient Clustering) Fused lasso method to cluster and estimate regression coefficients of the same covariate across different data sets when a large number of independent data sets are combined. Package supports Gaussian, binomial, Poisson and Cox PH models. Package: r-cran-metagam Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ggplot2, r-cran-metafor, r-cran-rlang Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metagam_0.4.1-1.ca2604.1_all.deb Size: 593872 MD5sum: e7b4400fa5218919e5f4a80c3877ef5b SHA1: 99a9141f458a985728702aebaa4dfc066724de7c SHA256: 4eabab75e2078a9fb763607f73ec4625ec0f6df682ccc479ed44e83204b4569b SHA512: 47a519a39659b1bc18d78928f4bdfd1be14b9d994bb2b3d352eaf5a0cfc266f6ad1e7a0decb12a37dd883e4aa1bb74e344d9d63cbeb1af1672720da289fb2c3b Homepage: https://cran.r-project.org/package=metagam Description: CRAN Package 'metagam' (Meta-Analysis of Generalized Additive Models) Meta-analysis of generalized additive models and generalized additive mixed models. A typical use case is when data cannot be shared across locations, and an overall meta-analytic fit is sought. 'metagam' provides functionality for removing individual participant data from models computed using the 'mgcv' and 'gamm4' packages such that the model objects can be shared without exposing individual data. Furthermore, methods for meta-analysing these fits are provided. The implemented methods are described in Sorensen et al. (2020), , extending previous works by Schwartz and Zanobetti (2000) and Crippa et al. (2018) . Package: r-cran-metage Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-data.table, r-cran-dplyr, r-cran-emdbook, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-gplots, r-cran-ks, r-cran-purrr, r-cran-qqman, r-cran-rfast, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-viridis Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-metage_1.2.2-1.ca2604.1_all.deb Size: 4700180 MD5sum: 27832e119c745954234e4099bf1d0e9e SHA1: d13b0974e3a290532b8d20226ab8771990cc52ff SHA256: b901d3d6f17a87c9dcdf25c51143e6a4e92208794957ee5dc53e7f54691bfe95 SHA512: 669eb65960a3a7a299bbb2b57c1be46180f6d964aeae4d24910ea5e158f8aa1419ec1b356d1c7682199afba69b52692151f844a1ac1649e717b5a9b40d0f5e7d Homepage: https://cran.r-project.org/package=metaGE Description: CRAN Package 'metaGE' (Meta-Analysis for Detecting Genotype x Environment Associations) Provides functions to perform all steps of genome-wide association meta-analysis for studying Genotype x Environment interactions, from collecting the data to the manhattan plot. The procedure accounts for the potential correlation between studies. In addition to the Fixed and Random models, one can investigate the relationship between QTL effects and some qualitative or quantitative covariate via the test of contrast and the meta-regression, respectively. The methodology is available from: (De Walsche, A., et al. (2025) \doi{10.1371/journal.pgen.1011553}). Package: r-cran-metagear Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-metafor, r-cran-stringr Suggests: r-bioc-ebimage, r-cran-ape, r-cran-hexview, r-cran-rcurl, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-metagear_0.7-1.ca2604.1_all.deb Size: 942174 MD5sum: d768bc23b54dd045d2bbe762897d3999 SHA1: 0045a8095453d6c8fe917322c59b810e28730be4 SHA256: 2af8932c7b59fe0d2f1e00bd9a8e3f27453cc75d3f01ca2b1fabe0d8aea6bed3 SHA512: 0bc225171b56e0362583e865aa1496bb54b412209a01ca6095adf9516c69a3fe372fc58bb175803ceac9d0fbd34d232d68a3c0dd53e2a3e5b27d233c161735b2 Homepage: https://cran.r-project.org/package=metagear Description: CRAN Package 'metagear' (Comprehensive Research Synthesis Tools for Systematic Reviewsand Meta-Analysis) Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 . Package: r-cran-metaggr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metaggr_0.3.0-1.ca2604.1_all.deb Size: 331724 MD5sum: 2e2b61e52fa1a9c0e900d828681825ae SHA1: f9c62a67b27696ae9202480a8caeff8d350b86df SHA256: af8ca0b58cae15a259ed4a2eed549a1302137bcbd526e5ae0e8c8d08f2bf2318 SHA512: 7a045e781dd8ad2912df40368835b2380af2d59f9f2cbcafae17750d84c8c767ba292bb388217790a2ccfeed5bab78d60ec5f8841c29b0f6d8ab7cc130f59a8b Homepage: https://cran.r-project.org/package=metaggR Description: CRAN Package 'metaggR' (Calculate the Knowledge-Weighted Estimate) According to a phenomenon known as "the wisdom of the crowds," combining point estimates from multiple judges often provides a more accurate aggregate estimate than using a point estimate from a single judge. However, if the judges use shared information in their estimates, the simple average will over-emphasize this common component at the expense of the judges’ private information. Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions" proposes a procedure for calculating a weighted average of the judges’ individual estimates such that resulting aggregate estimate appropriately combines the judges' collective information within a single estimation problem. The authors use both simulation and data from six experimental studies to illustrate that the weighting procedure outperforms existing averaging-like methods, such as the equally weighted average, trimmed average, and median. This aggregate estimate -- know as "the knowledge-weighted estimate" -- inputs a) judges' estimates of a continuous outcome (E) and b) predictions of others' average estimate of this outcome (P). In this R-package, the function knowledge_weighted_estimate(E,P) implements the knowledge-weighted estimate. Its use is illustrated with a simple stylized example and on real-world experimental data. Package: r-cran-metagroup Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-meta, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-metagroup_1.0.2-1.ca2604.1_all.deb Size: 61992 MD5sum: 2ed3fea096853da79e5868061b068d81 SHA1: ce5f69411f4ad47d99d7edb12de6c92df5f35a0d SHA256: fa9756ba5fa045f7ed7c105329113dd1fe365ec619eec91e3c870a0e2a3c73a4 SHA512: d37c0091a2349aee67fccaea80c551129c6c4c3f39ff68e994b9d690fd1409e084119b2829a012b9bd3acded07b249efcb189d4a7e1ce72716b3810568097c7c Homepage: https://cran.r-project.org/package=metagroup Description: CRAN Package 'metagroup' (Meaningful Grouping of Studies in Meta-Analysis) Performs meaningful subgrouping in a meta-analysis. This is a two-step process; first, use the iterative grouping functions (e.g., mgbin(), mgcont() ) to partition studies into statistically homogeneous clusters based on their effect size data. Second, use the meaning() function to analyze these new subgroups and understand their composition based on study-level characteristics (e.g., country, setting). This approach helps to uncover hidden structures in meta-analytic data and provide a deeper interpretation of heterogeneity. Package: r-cran-metahelper Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-confintr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metahelper_1.0.0-1.ca2604.1_all.deb Size: 77194 MD5sum: 2c0e14c66332f493bdf52c4153007abf SHA1: 56ba97d3c431b075ba17e81c3a0bdc373ac0ff9f SHA256: 3d68d2eea29f2743e198963d713126c0d7423eece123a791e7c38a9de9b4582c SHA512: ea2711d2d9e9c16498f5148920c18d3f565c9c500e540037f7f89b14c416e05c3b1d5212202ce6b84143a21415e467ed5aef4c64871c32066f6bc57d7d67c2c2 Homepage: https://cran.r-project.org/package=metaHelper Description: CRAN Package 'metaHelper' (Transforms Statistical Measures Commonly Used for Meta-Analysis) Helps calculate statistical values commonly used in meta-analysis. It provides several methods to compute different forms of standardized mean differences, as well as other values such as standard errors and standard deviations. The methods used in this package are described in the following references: Altman D G, Bland J M. (2011) Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) Chinn S. (2000) Cochrane Handbook (2011) Cooper, H., Hedges, L. V., & Valentine, J. C. (2009) Cohen, J. (1977) Ellis, P.D. (2009) Goulet-Pelletier, J.-C., & Cousineau, D. (2018) Hedges, L. V. (1981) Hedges L. V., Olkin I. (1985) Murad M H, Wang Z, Zhu Y, Saadi S, Chu H, Lin L et al. (2023) Mayer M (2023) Stackoverflow (2014) Stackoverflow (2018) . Package: r-cran-metaheuristicopt Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-metaheuristicopt_2.0.0-1.ca2604.1_all.deb Size: 242548 MD5sum: d1b1cf885dfdefe1681fddc7f3f13630 SHA1: 1830599140c159b87bf041aa7447b65862b78b0e SHA256: a6a6b94c42c9f7846e7500616fd0ff21c58a7718630b8599c8057b64e9803b69 SHA512: 5dd97dd0c0518856cfb2cb15c89050349ac0c521231c87b2d22c573c317bdb45fcdfdd453005d2f45227e6a9dd7703602634f95539575cbfe0e2c0983a5fc3e7 Homepage: https://cran.r-project.org/package=metaheuristicOpt Description: CRAN Package 'metaheuristicOpt' (Metaheuristic for Optimization) An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 ), grey wolf optimizer (Mirjalili et al., 2014 ), dragonfly algorithm (Mirjalili, 2015 ), firefly algorithm (Yang, 2009 ), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 ), harmony search algorithm (Mahdavi et al., 2007 ), moth flame optimizer (Mirjalili, 2015 , sine cosine algorithm (Mirjalili, 2016 ), whale optimization algorithm (Mirjalili and Lewis, 2016 ), clonal selection algorithm (Castro, 2002 ), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013). Package: r-cran-metahunt Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quadprog, r-cran-dirichletreg, r-cran-withr Suggests: r-cran-grf, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metahunt_0.1.0-1.ca2604.1_all.deb Size: 511952 MD5sum: 519f9a870d068659c84083b10431820b SHA1: 6c847883eabb1e4ccbd2c26cedce7f80b2706dec SHA256: 52d313c7a684e84081c6400ebcb5fcaf9fdf2ea05b4fe07bbb12c60dc6af621b SHA512: c209f260432707724a7b0cfe0e6642ae9c97792d8932a082016e16a659080d4a03eccffbdaba1587af1491dea34753c9272d7431c6ba99a12aaded4fbc33e751 Homepage: https://cran.r-project.org/package=MetaHunt Description: CRAN Package 'MetaHunt' (Privacy-Preserving Meta-Analysis via Low-Rank Basis Hunting) Tools for privacy-preserving meta-analysis of function-valued quantities across heterogeneous studies. Implements the 'MetaHunt' pipeline, including the denoised functional Successive Projection Algorithm (d-fSPA) for basis hunting, constrained weight estimation, Dirichlet regression of weights on study-level covariates, target prediction, and split/cross conformal prediction intervals. Operates on aggregate-level function evaluations, so individual-level data from source studies are not required. Methodology described in Shi, Imai, and Zhang (2026) . Package: r-cran-metainc Architecture: all Version: 0.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta, r-cran-ggplot2, r-cran-confintr Suggests: r-cran-metafor Filename: pool/dists/resolute/main/r-cran-metainc_0.2-1-1.ca2604.1_all.deb Size: 532674 MD5sum: d8670c8f83a8f21a780f95160addda4b SHA1: 4c91729dfbeb184d817e8ed9da0212f768cfe592 SHA256: 54067faeb942b693a1005a34e95d019491dd3374a889508c2caae7b138cc579b SHA512: a214d100950189aff2d635d1f5577b7b6936f7d6ef20b3524a597b4bafd29de52a75c38c7e6510b9153f785794ec69e50c1725b5463e5d8b1121543cead606d3 Homepage: https://cran.r-project.org/package=metainc Description: CRAN Package 'metainc' (Assessment of Inconsistency in Meta-Analysis using DecisionThresholds) Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index. These indices quantify inconsistency taking into account outcome-level decision thresholds. Package: r-cran-metaintegration Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-corpcor, r-cran-mass, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-metaintegration_0.1.2-1.ca2604.1_all.deb Size: 79374 MD5sum: 2be19399a872e5c789e7c134ca27ebbd SHA1: 1e8a9e63e62f399e97793362b1a7d6853a92caf4 SHA256: 489323bda64a372f9c9e3ae95788c4c59627d6016c1805de619194886dd2fa9d SHA512: 8553b3f9ea6136054b7fc3ca55d02a4c842d37a51e6f6f5988b87785bc1e391cce16dd7f1264f59004594f1fa8acb92d0fd822c4bc5cdecd7a5b5be7171a1a14 Homepage: https://cran.r-project.org/package=MetaIntegration Description: CRAN Package 'MetaIntegration' (Ensemble Meta-Prediction Framework) An ensemble meta-prediction framework to integrate multiple regression models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) . A meta-analysis framework along with two weighted estimators as the ensemble of empirical Bayes estimators, which combines the estimates from the different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naive analysis of the internal data and other naive combinations of external estimators. Package: r-cran-metalandsim Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14637 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-e1071, r-cran-googlevis, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-sp, r-cran-minpack.lm, r-cran-zipfr, r-cran-coda, r-cran-terra, r-cran-knitr Suggests: r-cran-rastervis Filename: pool/dists/resolute/main/r-cran-metalandsim_2.0.0-1.ca2604.1_all.deb Size: 4099454 MD5sum: b965808bce880ed680f61c1112bcdec0 SHA1: 5726c0844cf8bf6d9ec5bc796771bcdfc3d967c5 SHA256: ba409eda269e7b5219b09faf4c5d20f18a230f2557cb7cec8ec229e9e69da171 SHA512: 4cb4e83604b66838e1c49d38d994f281f8e4e690e3c5e3ab5f4272efac642fec4842d4f104efe85721bce88f6c4752f40371c5a8e30b5a88a6949c4f25705eb1 Homepage: https://cran.r-project.org/package=MetaLandSim Description: CRAN Package 'MetaLandSim' (Landscape and Range Expansion Simulation) Tools to generate random landscape graphs, evaluate species occurrence in dynamic landscapes, simulate future landscape occupation and evaluate range expansion when new empty patches are available (e.g. as a result of climate change). References: Mestre, F., Canovas, F., Pita, R., Mira, A., Beja, P. (2016) ; Mestre, F., Risk, B., Mira, A., Beja, P., Pita, R. (2017) ; Mestre, F., Pita, R., Mira, A., Beja, P. (2020) . Package: r-cran-metalcor Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metalcor_1.0.0-1.ca2604.1_all.deb Size: 168182 MD5sum: 04302eeb717e8f4c3a8082b3a0b0be8a SHA1: b55ac6252b2dff2d9533fbdfadec3d6f7754346a SHA256: 3e2f31b95e56b6d84860ab8d43425e4d8540846c63ffbfcc5afe96ac500e3d4e SHA512: 812b852385f0675e46841a98f6b367c7026fb33c6f650e4dfd1565d8790e209e5d8aa38ed784cb825c4cd097c2018112cd5445c666845cb7064856a22232994a Homepage: https://cran.r-project.org/package=metalcor Description: CRAN Package 'metalcor' (Meta-Analysis of Correlated Genetic Association Studies) The main function performs meta-analysis of genetic association study summary statistics that may be correlated due to cryptic relatedness or other confounders, generalizing inverse variance weighted methods. The function that estimates the correlation structure is also provided standalone. Another key innovation, the estimation of the correlation parameter from the median product of correlated standard normal variables, is provided, as well as a complete set of functions for their underlying distribution: density, cumulative, quantile, and random deviates. Described in Tu and Ochoa (2025) . Package: r-cran-metalik Architecture: all Version: 0.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-metalik_0.44.0-1.ca2604.1_all.deb Size: 67732 MD5sum: 19d86684c3e5f27b057b6602c20b9d2f SHA1: 998c583a13e369ef2fb24403906f28e85f01042b SHA256: 67fe4b9061f9ea629be4ebe8884c36cd5528ce326ccd3e9f3a21efccc3b77c13 SHA512: 36d38f13c1b9ce54c722d14887d5fae9e76d6689d41f11437c0d7a6545874e50de492bcf298bb5bf5acd3e164416895985ce019520ed082a4e7986934f0790f7 Homepage: https://cran.r-project.org/package=metaLik Description: CRAN Package 'metaLik' (Likelihood Inference in Meta-Analysis and Meta-Regression Models) First- and higher-order likelihood inference in meta-analysis and meta-regression models. Package: r-cran-metalite.ae Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1976 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-metalite, r-cran-r2rtf Suggests: r-cran-desctools, r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-metalite.ae_0.1.3-1.ca2604.1_all.deb Size: 1024904 MD5sum: e942863cad0a6c0a6fd35e90e5d24e1e SHA1: 3bd376bd8290972ab53d502d07e21d72f93b4f50 SHA256: 672c7879bbf3f82d560c4144e92a4338f37d87ad8681837a3c92fc182550f59b SHA512: 001bb9ca9ad7b0b995a91fad77a1a8d6fff50d9bbb2a6f21a3421e1de161ec92991e57c139bb49be3e41b874d434d08c65c59f48d0ac848649355624c5da5a3d Homepage: https://cran.r-project.org/package=metalite.ae Description: CRAN Package 'metalite.ae' (Adverse Events Analysis Using 'metalite') Analyzes adverse events in clinical trials using the 'metalite' data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the adverse events analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) . Package: r-cran-metalite.sl Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-metalite, r-cran-metalite.ae, r-cran-r2rtf, r-cran-reactable, r-cran-stringr, r-cran-rlang, r-cran-plotly, r-cran-htmltools, r-cran-brew, r-cran-uuid Suggests: r-cran-dplyr, r-cran-knitr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-metalite.sl_0.1.1-1.ca2604.1_all.deb Size: 657870 MD5sum: ce9e44e6f980a4231382241102abf430 SHA1: 9a18c38135cca43714b7a83128db812015ee3c04 SHA256: c925d999cf31612ae6ac834f3b6a8e673540db119f6f00a936a869d4a40a6473 SHA512: 872e793d024a10ce3904f17f3402b6cafe4f580f0206e3d951c81891163e740817fb7e5f3fec4e123521cc3993f5ddd4327e326a1bec05b61d2b8d6113617f7c Homepage: https://cran.r-project.org/package=metalite.sl Description: CRAN Package 'metalite.sl' (Subject-Level Analysis Using 'metalite') Analyzes subject-level data in clinical trials using the 'metalite' data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the subject-level analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) . Package: r-cran-metalite.table1 Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metalite, r-cran-reactable, r-cran-htmltools, r-cran-r2rtf, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-table1, r-cran-devtools, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-metalite.table1_0.4.0-1.ca2604.1_all.deb Size: 34354 MD5sum: e6426f6b367744b37885626180fc6b80 SHA1: 4af96eb8f1d82da0463b5eb98121b831fba92611 SHA256: 0f8971ed569168849da41458faffa4fadd3e1fbf0d89fc91bfddcbefee26a04c SHA512: 470bb89c1c97b00fe80d574bb80fafad69964b5621455f3039b8b318bb8a5ef330934a1a2cb349e5250669d6dca92b8f6471302d4c9570fe626783c678f2696f Homepage: https://cran.r-project.org/package=metalite.table1 Description: CRAN Package 'metalite.table1' (Interactive Table of Descriptive Statistics in HTML) Create an interactive table of descriptive statistics in HTML. This table is typically used for exploratory analysis in a clinical study (referred to as 'Table 1'). 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Package: r-cran-metamer Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-progress Suggests: r-cran-shiny, r-cran-miniui, r-cran-testthat, r-cran-data.table, r-cran-covr, r-cran-sf Filename: pool/dists/resolute/main/r-cran-metamer_0.3.0-1.ca2604.1_all.deb Size: 213540 MD5sum: d736d56a980cd0ef04b270d71d2d83f6 SHA1: 382edca14fd0428849a360a622d1b8dc16472823 SHA256: 3aadce185b42a1b8e36da1b208f7f6f485bedb85cb6104f7ad991d45447bae06 SHA512: 784eb6682198f951e315b1a07a21e735707f630cba3466c095607d945b3c9007de10d1c56b6b6f79d71c0a321b917f7877850516747b29a21b0ac656a12a0618 Homepage: https://cran.r-project.org/package=metamer Description: CRAN Package 'metamer' (Create Data with Identical Statistics) Creates data with identical statistics (metamers) using an iterative algorithm proposed by Matejka & Fitzmaurice (2017) . 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(2007) , Stekhoven et al. (2012) , Tibshirani et al. (2017) , Troyanskaya et al. (2001) ), normalization (Bolstad et al. (2003) , Dieterle et al. (2006) , Zhao et al. (2020) ) transformation, centering and scaling (Van Den Berg et al. (2006) ) as well as statistical tests and plotting. 'metamorphr' introduces a tidy (Wickham et al. (2019) ) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with 'tidyverse' packages including 'dplyr' and 'ggplot2'. 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Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) , Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) , geometric adaptability index by Mohammadi & Amri (2008) , joint regression analysis by Eberhart & Russel (1966) , genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) , scale-adjusted coefficient of variation by Doring & Reckling (2018) , stability variance by Shukla (1972) , weighted average of absolute scores by Olivoto et al. (2019a) , and multi-trait stability index by Olivoto et al. (2019b) . Non-parametric methods includes superiority index by Lin & Binns (1988) , nonparametric measures of phenotypic stability by Huehn (1990) , TOP third statistic by Fox et al. (1990) . Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided. Package: r-cran-metanet Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-dplyr, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggrepel, r-cran-magrittr, r-cran-reshape2, r-cran-tibble, r-cran-pcutils, r-cran-rlang Suggests: r-cran-pheatmap, r-cran-vegan, r-cran-stringr, r-cran-foreach, r-cran-dosnow, r-cran-snow, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-hmisc, r-cran-gifski, r-cran-ggraph, r-cran-networkd3, r-cran-ggpmisc, r-bioc-ggtree, r-bioc-treeio, r-cran-circlize, r-cran-jsonify, r-cran-ggpubr, r-cran-corrplot, r-cran-philentropy, r-cran-spatstat.random, r-cran-spatstat.geom, r-cran-sf Filename: pool/dists/resolute/main/r-cran-metanet_0.3.1-1.ca2604.1_all.deb Size: 3881438 MD5sum: 9ee6afe75ae6cfe4694e8bfe5a952940 SHA1: 22d7eae66769b9e571506757e07f6b335108f2a4 SHA256: 6042f33dde7b42b113aec42684e14c2eb567bdbd0e74b12c112a41d3f429d876 SHA512: baa2ba697c8576849985f3917dac18a01baadcf8885300f056ab7bc7f80d4cee544f3168fd6479fd325191455c8c579c19060e17f33d85b0052b086c10981bc2 Homepage: https://cran.r-project.org/package=MetaNet Description: CRAN Package 'MetaNet' (Network Analysis for Omics Data) Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability. 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The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) , differential evolution Storn and Price (1997) , grey wolf optimizer Mirjalili et al. (2014) , secretary bird optimization Fu et al. (2024) , and Adam Kingma and Ba (2015) . Package: r-cran-metansue Architecture: all Version: 2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-metansue_2.6-1.ca2604.1_all.deb Size: 110120 MD5sum: 2251ee75971bb7c4452ea00774a89a32 SHA1: 4a57581fbb9dbd182f58e127c79eaa77a647d4ba SHA256: 09ae8becfa44096fd392eb29ff6cf17b808ecdcba946dc9362c9a1bb5faacd9f SHA512: c5ee47dad9c5f411e74170023e8275d9db1ca152a33b187cd67dff430e00e6c247bbcc12526823c118b00345bdb9b5d758834e7c43f1f9ccfc5a66cf6fcad44a Homepage: https://cran.r-project.org/package=metansue Description: CRAN Package 'metansue' (Meta-Analysis of Studies with Non-Statistically SignificantUnreported Effects) Novel method to unbiasedly include studies with Non-statistically Significant Unreported Effects (NSUEs) in a meta-analysis. First, the function calculates the interval where the unreported effects (e.g., t-values) should be according to the threshold of statistical significance used in each study. Afterward, the method uses maximum likelihood techniques to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance, and statistical significance bounds. Finally, it conducts a restricted-maximum likelihood random-effects meta-analysis separately for each set of imputations, and it performs estimations from these meta-analyses. Please read the reference in 'metansue' for details of the procedure. Package: r-cran-metap Architecture: all Version: 1.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rdpack, r-cran-tfisher, r-cran-mathjaxr, r-cran-qqconf, r-cran-mutoss Filename: pool/dists/resolute/main/r-cran-metap_1.14-1.ca2604.1_all.deb Size: 600590 MD5sum: 2b2f6da2b0a15701a1e6dc94f5ddefbf SHA1: 0436274425751c7f7865340ec4a16926baaf66b1 SHA256: f94581bd2a6aa5edd15607dd8fc16b624e3b4e574554fc82ca85234d1023d0dd SHA512: 031c0fde92b0a93badf8169c6ad0f4c831fb2b0168f77f35170345ae52687025e322725bc4001831ee513fe29c0d48bda65d85064cfa9e645df364f9012050f5 Homepage: https://cran.r-project.org/package=metap Description: CRAN Package 'metap' (Meta-Analysis of Significance Values) The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and routines for graphical display. Package: r-cran-metaphonebr Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metaphonebr_0.0.5-1.ca2604.1_all.deb Size: 40162 MD5sum: 6542a971e39f120eff3a391f5080c717 SHA1: 3add5ec8880e92734c2bac787e82741cb6dbb9f2 SHA256: cdfec64804a4cf117ab37a766402a936b528b10f27ae0889c4a8c2c5e473487c SHA512: 43c781b8f944b27370cda90364b2ad720db33a3699983f09767fe90e5bd98fc63e7d77c2aa6fd825c9cd8fe1cd366c6dbe41e49c291bd06528f1032b675203ba Homepage: https://cran.r-project.org/package=metaphonebr Description: CRAN Package 'metaphonebr' (Custom 'MetaphoneBR' Phonetic Encoding for Brazilian Names) Simplifies Brazilian names phonetically using a custom 'metaphoneBR' algorithm that preserves ending vowels. Useful for name matching processing preserving gender information carried generally by ending vowels in Portuguese. Mation (2025) . Package: r-cran-metaplot Architecture: all Version: 0.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-encode, r-cran-lattice, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-gridextra, r-cran-gtable, r-cran-ggplot2, r-cran-scales Suggests: r-cran-csv, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-metaplot_0.8.4-1.ca2604.1_all.deb Size: 367272 MD5sum: 3610d9923570da90c7fc62a83bfd2c62 SHA1: 23c65280dc43f561a8c0f2ea2bd501ab696873f6 SHA256: da4475da55be76f3abf91c5d9b88439cf9cb55bbedf186748b1e797acb7aa4b0 SHA512: 1d2ed0ead96f3efb8892bbe7d6b13a16d2fe33162d060623693ddf2827f2d7f8a66329ea80530f9621e7a1c732cd3539950932d952be625e58ff1f0fbd20bb13 Homepage: https://cran.r-project.org/package=metaplot Description: CRAN Package 'metaplot' (Data-Driven Plot Design) Designs plots in terms of core structure. See 'example(metaplot)'. Primary arguments are (unquoted) column names; order and type (numeric or not) dictate the resulting plot. Specify any y variables, x variable, any groups variable, and any conditioning variables to metaplot() to generate density plots, boxplots, mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' are honored, producing fully annotated plots with minimal effort. Attribute 'guide' is typically units, but may be encoded() to provide interpretations of categorical values (see '?encode'). Utility unpack() transforms scalar column attributes to row values and pack() does the reverse, supporting tool-neutral storage of metadata along with primary data. The package supports customizable aesthetics such as such as reference lines, unity lines, smooths, log transformation, and linear fits. The user may choose between trellis and ggplot output. Compact syntax and integrated metadata promote workflow scalability. Package: r-cran-metaplus Architecture: all Version: 1.0-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-metafor, r-cran-boot, r-cran-numderiv, r-cran-mass, r-cran-fastghquad, r-cran-lme4, r-cran-rfast Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-metaplus_1.0-8-1.ca2604.1_all.deb Size: 433508 MD5sum: e1e3e0a11fdb14ce50220ca38fbc1fec SHA1: 3aa00a953178ad5cbda7d21fc6e0621449e15fc4 SHA256: cb40d3e3e173c824a688fa491989ed824f63a3eea1c1706229223a497f2092d6 SHA512: 37e7c76dcaef61c2de21df492267825cdec7d8fcae491621e4e6d36ada399206e0589d5d485bc210e6d200d8b5944381ae0ba3071548266661558b3a4205eb6a Homepage: https://cran.r-project.org/package=metaplus Description: CRAN Package 'metaplus' (Robust Meta-Analysis and Meta-Regression) Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 or Baker and Jackson, 2008 ) or mixtures of normals (Beath, 2014 ). Package: r-cran-metapost Architecture: all Version: 1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridbezier Suggests: r-cran-grimport Filename: pool/dists/resolute/main/r-cran-metapost_1.0-6-1.ca2604.1_all.deb Size: 72634 MD5sum: 3c70597a8b99b5acd49890e3cc83c4ba SHA1: 480d1e11834f61a35b647b80c6d1efe42af4f1c7 SHA256: a6fe9f7a168cf3eaf33ab9dff32d39e8d186f05e5fc59a0d78dcf1e7ffd11aec SHA512: 3ca1237459bb9925a761dd689169724e88e31f5b7d31fa9d1f7a1052ca86081b982535cc1ef6c374a4d406ab844fe8b627fb9897ea241b370e34cd582f635c10 Homepage: https://cran.r-project.org/package=metapost Description: CRAN Package 'metapost' (Interface to 'MetaPost') Provides an interface to 'MetaPost' (Hobby, 1998) . There are functions to generate an R description of a 'MetaPost' curve, functions to generate 'MetaPost' code from an R description, functions to process 'MetaPost' code, and functions to read solved 'MetaPost' paths back into R. Package: r-cran-metapower Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-tidyr, r-cran-testthat, r-cran-rlang Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-metapower_0.2.2-1.ca2604.1_all.deb Size: 1447806 MD5sum: e73282d07eb6be3086a7ecf4cf1f9d9c SHA1: e72ba5a8f68e252a39ad5a02befcbba09a38bcff SHA256: 8fbe6914f69ce5fe97243dc5f1d0d6ef6f072310f69efd621b7da06c9e3f4d08 SHA512: 8f82b27e865fdc4d576819028c8ffc43d64191bbc5ed57385d2b1346869f7d126316f95fb694cde895391d9ba0c0cdc0b0c2566d7dd228491fe27b1bec358665 Homepage: https://cran.r-project.org/package=metapower Description: CRAN Package 'metapower' (Power Analysis for Meta-Analysis) A simple and effective tool for computing and visualizing statistical power for meta-analysis, including power analysis of main effects (Jackson & Turner, 2017), test of homogeneity (Pigott, 2012), subgroup analysis, and categorical moderator analysis (Hedges & Pigott, 2004). Package: r-cran-metapro Architecture: all Version: 1.5.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metap Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metapro_1.5.11-1.ca2604.1_all.deb Size: 24812 MD5sum: cc2aa62b9e51c1cbc4188d6ab5e820cc SHA1: 3a64021b235f8ccd23bedb3c9df64c58a7884fef SHA256: 84e2db94b68833f4d501aaa4a553c43bb15be2ebe09055fc574b8e74d6afde5c SHA512: 61ed162e95c7e304786154994196b8e5564e176083e112cdf9f2ac046279a395af2e3721a8bbca0c9ee6163b3d3af66b0dd1f0b454b3a77bb514e6f5ff0d7ccd Homepage: https://cran.r-project.org/package=metapro Description: CRAN Package 'metapro' (Robust P-Value Combination Methods) The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new method named weighted Fisher’s method we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method is useful for detecting partially associated features. Thus, the users can choose the function based on their purpose. Yoon et al. (2021) "Powerful p-value combination methods to detect incomplete association" . Package: r-cran-metaprotr Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-dendextend, r-cran-dplyr, r-cran-ggforce, r-cran-ggrepel, r-cran-reshape2, r-cran-stringr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-metaprotr_1.2.2-1.ca2604.1_all.deb Size: 1084676 MD5sum: 5917f15ac08d8b3c0758c1ef24e80637 SHA1: 111e4e051cdefac9143d6a4e6df0f6fcdce9dfab SHA256: 3cfff0efd2147aa3d3d70a5410d0d8c2dfa56f5ea099ef2772037ef175a23d71 SHA512: 064649b8e9cb3dc47cef4277c062080f3fad777610f25fd4ac1fb9855638cfba7127c7d831516fb9e76b7e27b379d099ea15090abcfc0f9dacba2fa85f86b976 Homepage: https://cran.r-project.org/package=metaprotr Description: CRAN Package 'metaprotr' (Metaproteomics Post-Processing Analysis) Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis. Package: r-cran-metaquant Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gld, r-cran-sld, r-cran-ggplot2, r-cran-plotly, r-cran-magrittr, r-cran-dplyr, r-cran-estmeansd, r-cran-metafor Filename: pool/dists/resolute/main/r-cran-metaquant_0.1.3-1.ca2604.1_all.deb Size: 126254 MD5sum: b38e1adaba585630afd98c271c65f42a SHA1: ba6b458a3b36febc5b8024dde6a86dc607a51fb0 SHA256: 1b978e22defd9bf490d5e6dce14ea3464b1c018c9e241aebeec990fdad1666bc SHA512: 5f3a06fcbfc734cafb885f35e7644f6e07e84bb1f7b39059bc6b8825d8ef90082a46115a1581e1e27c901576431f2e532ab345a2036cf00d56de6af7eb19e9cc Homepage: https://cran.r-project.org/package=metaquant Description: CRAN Package 'metaquant' (Meta-Analysis of Quantiles and Functions of Quantiles) Implements a novel density-based approach for estimating unknown parameters, distribution visualisations and meta-analyses of quantiles and ther functions. A detailed vignettes with example datasets and code to prepare data and analyses is available at . The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, ). Package: r-cran-metarnaseq Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-deseq2, r-cran-venndiagram Filename: pool/dists/resolute/main/r-cran-metarnaseq_1.0.8-1.ca2604.1_all.deb Size: 1272896 MD5sum: f4a5790a86e481e2ff794afeae744935 SHA1: 7bd28c667f88c991b0cef2ca9b484cc36570c0bf SHA256: 028910a1f2bd065d8ed66ed6588e04b61a849f5e75fdc7df20dd29c185931c5f SHA512: afa4a06b04c000de369265b29998670a19717b5e6b52f800e22c7cb1b68239b918a795d56f53d41d08751750bb16b5e655460cd51a5f57ef664bdd4b94d29ea5 Homepage: https://cran.r-project.org/package=metaRNASeq Description: CRAN Package 'metaRNASeq' (Meta-Analysis of RNA-Seq Data) Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments. Package: r-cran-metarvm Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-odin, r-cran-purrr, r-cran-r6, r-cran-tidyr, r-cran-yaml Suggests: r-cran-dde, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metarvm_2.1.0-1.ca2604.1_all.deb Size: 2564674 MD5sum: 344f38208366ff166b1d609df67c3a10 SHA1: 42d6955704706bbb7592ee5c5917a3f518996ef1 SHA256: 8454a73843170ef52691d6947beb1b3ad4a47ba2d5e06d87b9f9af96bc8cafbe SHA512: c33fc5516f783926eb97be2ec19b142a101b3ed9248f39b51604c746258d195b8200c594f08f124687c378fa734006f58f156317f2ca01ac44fa9cea3616b7d8 Homepage: https://cran.r-project.org/package=MetaRVM Description: CRAN Package 'MetaRVM' (Meta-Population Compartmental Model for Respiratory VirusDiseases) Simulates respiratory virus epidemics using meta-population compartmental models following Fadikar et. al. (2025) . 'MetaRVM' implements a stochastic SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) framework with demographic stratification by user provided attributes. It supports complex epidemiological scenarios including asymptomatic and presymptomatic transmission, hospitalization dynamics, vaccination schedules, and time-varying contact patterns via mixing matrices. Package: r-cran-metasdtreg Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ordinal, r-cran-maxlik, r-cran-truncnorm, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-metasdtreg_0.2.2-1.ca2604.1_all.deb Size: 108374 MD5sum: 7bd476045eb7db2bb793d406c28aab0e SHA1: 1a31b74e34341bda65999d0d7e3d4e607dc0e91f SHA256: 8dab901beaa818f4be81c0b5bd65336e846090406e172ca1d491697bbd0dfc5c SHA512: a6277bb525a9ea9add5491671e65e3ae5a0860b59b302b4540151265a9a2bc8784ee329c75695ceef92b49472e999fa0cd55e60b0d4397d0f34562f28a57ac3c Homepage: https://cran.r-project.org/package=metaSDTreg Description: CRAN Package 'metaSDTreg' (Regression Models for Meta Signal Detection Theory) Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. . Package: r-cran-metasem Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2938 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-matrix, r-cran-mass, r-cran-ellipse, r-cran-mvtnorm, r-cran-numderiv, r-cran-lavaan Suggests: r-cran-metafor, r-cran-semplot, r-cran-r.rsp, r-cran-testthat, r-cran-matrixcalc Filename: pool/dists/resolute/main/r-cran-metasem_1.5.0-1.ca2604.1_all.deb Size: 2094610 MD5sum: 8c838c71fd545961cd4d52ba7e1e3702 SHA1: d7b04dc07c9de4637c424837dff17ccff1517538 SHA256: fd70ecee80eb6af93dfada8c5d6ad992d7bdcd503c9761c6d10d0f630fecf403 SHA512: 112ba55fa559eeac06972c91b5efcfe60850a25b6417598f417a474bb5cbcca0930ebf9d64455030209f1e0a8ca20abac3a4838be2a89baaa443a0bf19c20dc7 Homepage: https://cran.r-project.org/package=metaSEM Description: CRAN Package 'metaSEM' (Meta-Analysis using Structural Equation Modeling) A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' and 'lavaan' packages. It also implements various procedures to perform meta-analytic structural equation modeling on the correlation and covariance matrices, see Cheung (2015) . Package: r-cran-metasens Architecture: all Version: 1.5-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta Filename: pool/dists/resolute/main/r-cran-metasens_1.5-3-1.ca2604.1_all.deb Size: 300456 MD5sum: 90b26a8c967c788b1ef1b94b9b6a6d3e SHA1: b7ce94df2abeb2f4d68c09b4a8c0dad3b1d8f078 SHA256: 5cd3dd35d0ef7bbe849d83203a93ced205ca52ad4512cb007034a5a043ecf5a3 SHA512: 9114f537c162eb4cb0d5802f6d1426c789ecf2a33e1f64a993498aae709a1b0b2575452e7ac86ab13ebda0d3794e7083a85034b069f90acffb0701565652c6c7 Homepage: https://cran.r-project.org/package=metasens Description: CRAN Package 'metasens' (Statistical Methods for Sensitivity Analysis in Meta-Analysis) The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) , Chapter 5 'Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) ; - limit meta-analysis by Rücker et al. (2011) ; - upper bound for outcome reporting bias by Copas & Jackson (2004) ; - imputation methods for missing binary data by Gamble & Hollis (2005) and Higgins et al. (2008) ; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) . Package: r-cran-metasnf Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5022 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-cluster, r-cran-data.table, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-mclust, r-cran-progressr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-snftool, r-cran-tibble, r-cran-tidyr Suggests: r-cran-circlize, r-bioc-complexheatmap, r-bioc-interactivecomplexheatmap, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggalluvial, r-cran-lifecycle, r-cran-dbscan Filename: pool/dists/resolute/main/r-cran-metasnf_2.1.2-1.ca2604.1_all.deb Size: 3164510 MD5sum: 689fd14cc4a215be255a4e59883a8abe SHA1: e3b8e78e4f45767421f671daec95b0593bc5bfd6 SHA256: f768fb88cd94c9e79abf476a8dfac21c824995b7994aa76c29334f3a5e8449cd SHA512: 02178ef8cd3935b191f7fbb462b31195b1fdb10c63ad36815253770ee6e996648dcabf5eb6ae6cf81e2b52d3dcfcf9838613f453faaa5606ef38c712850860ca Homepage: https://cran.r-project.org/package=metasnf Description: CRAN Package 'metasnf' (Meta Clustering with Similarity Network Fusion) Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in . SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in . Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning. Package: r-cran-metasplines Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-meta, r-cran-optimization Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-splines2 Filename: pool/dists/resolute/main/r-cran-metasplines_0.1.1-1.ca2604.1_all.deb Size: 93864 MD5sum: 8b54debe7f286af3721b2b3750e370a1 SHA1: 5a5f79e37e934fc1321b7efb82479c1f7a310fdf SHA256: e7bf21b069b05990b5f6a5a8ac6f10140c074c86e111d2000ea3301dac69f199 SHA512: d316232a5fe26ea1788e48fc2a5107f66b3fbe3afec4aa9030aa1ad685f7893373d4c6ef7e4097169e02d9bbae55db7ac694fde8c31b2a681941ff2c8cfbce3a Homepage: https://cran.r-project.org/package=metasplines Description: CRAN Package 'metasplines' (Pool Literature-Based and Individual Participant Data BasedSpline Estimates) Pooling estimates reported in meta-analyses (literature-based, LB) and estimates based on individual participant data (IPD) is not straight-forward as the details of the LB nonlinear function estimate are not usually reported. This package pools the nonlinear IPD dose-response estimates based on a natural cubic spline from lm or glm with the pointwise LB estimates and their estimated variances. Details will be presented in Härkänen, Tapanainen, Sares-Jäske, Männistö, Kaartinen and Paalanen (2026) "Novel pooling method for nonlinear cohort analysis and meta-analysis estimates: Predicting health outcomes based on climate-friendly diets" Epidemiology . Package: r-cran-metasubtract Architecture: all Version: 1.60-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-metasubtract_1.60-1.ca2604.1_all.deb Size: 179278 MD5sum: 793711cd87660e9c8ae56aa1a62a490f SHA1: 76bc955b9084d79a3bc338c69a65f960a9326f37 SHA256: 94abdc9e6af2d9f0220ed9277157a6508088f43eae2e32abea0a4cd1a9f5d37f SHA512: 3f4f942547a58ccfb5514890c7ea57b56abb596b46a8aa48ec7c4fcd74e57eb96db57da40fc24279d4914d5c3a7ac50092d3a43ade3f5b9316f9f23f5541d400 Homepage: https://cran.r-project.org/package=MetaSubtract Description: CRAN Package 'MetaSubtract' (Subtracting Summary Statistics of One or more Cohorts fromMeta-GWAS Results) If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. See for application: Nolte I.M. et al. (2017); . Package: r-cran-metasurvey Architecture: all Version: 0.0.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-jsonlite, r-cran-r6, r-cran-survey Suggests: r-cran-archive, r-cran-convey, r-cran-httr2, r-cran-haven, r-cran-openxlsx, r-cran-visnetwork, r-cran-roxygen2, r-cran-testthat, r-cran-tibble, r-cran-dplyr, r-cran-knitr, r-cran-foreign, r-cran-rmarkdown, r-cran-rio, r-cran-here, r-cran-gt, r-cran-magrittr, r-cran-shiny, r-cran-bslib, r-cran-bsicons, r-cran-htmltools, r-cran-xml2, r-cran-eph, r-cran-pnadcibge, r-cran-ipumsr Filename: pool/dists/resolute/main/r-cran-metasurvey_0.0.21-1.ca2604.1_all.deb Size: 2036214 MD5sum: e977af120ab7986956fd9219e32098ae SHA1: 2e1ae88fee3b0df7c0de09853e6c3a9cc6991b7e SHA256: 092799b8fc254cd0859b6bb3bba98b595c74d90900a08772457d4ce958b7dc3d SHA512: 35a4e74a60d9f054a4c27ab8684ef765d5b796dfb875897fe6d2512846f2db2d7f07c165a855b0c483ecc8a4c8ad7fd18de256203f4d8c608602a2e7b44829d1 Homepage: https://cran.r-project.org/package=metasurvey Description: CRAN Package 'metasurvey' (Reproducible Survey Data Processing with Step Pipelines) Provides a step-based pipeline for reproducible survey data processing, building on the 'survey' package for complex sampling designs. Supports rotating panels with bootstrap replicate weights, and provides a recipe system for sharing and reproducing data transformation workflows across survey editions. Package: r-cran-metasurvival Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-metasurvival_0.1.0-1.ca2604.1_all.deb Size: 53564 MD5sum: 7662b598cf6db44e6833fceb78d55814 SHA1: 7b4878ea297010f815ac6a159a37df8adb43ff8d SHA256: da71aed899b6e629a7235ffaa5c326300e2a1a7eea5ef11de00702634c6914b3 SHA512: 72a2e32d695a2075b97365aa4bb2193c8c6eb7b283874a04d5def544280266b6cf9bca5c6607f294bb11a39820e4e9976fb2f0967b7fe07beb4923400c81eb18 Homepage: https://cran.r-project.org/package=metaSurvival Description: CRAN Package 'metaSurvival' (Meta-Analysis of a Single Survival Curve) To assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity. Package: r-cran-metasvr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-hms Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metasvr_0.1.0-1.ca2604.1_all.deb Size: 140114 MD5sum: 500e8660084168a7a1c7161033ef2386 SHA1: 0823def36ee8c92b76ddeecb12594020fbace016 SHA256: 1b734fc077738cf0abaefa5faa5e687a36dfeefa71fc363e9cd2e0195309d6cb SHA512: 16d495247ac878b91bd22a61c63107b1f8ca02d3ff3867174ddce0ed699756dec5becfba3f33c3e1f5ccc3a2a1c1eeb1e168a7c92b445b22116848d5b354d551 Homepage: https://cran.r-project.org/package=metaSVR Description: CRAN Package 'metaSVR' (Support Vector Regression with Metaheuristic AlgorithmsOptimization) Provides a hybrid modeling framework combining Support Vector Regression (SVR) with metaheuristic optimization algorithms, including the Archimedes Optimization Algorithm (AO) (Hashim et al. (2021) ), Coot Bird Optimization (CBO) (Naruei & Keynia (2021) ), and their hybrid (AOCBO), as well as several others such as Harris Hawks Optimization (HHO) (Heidari et al. (2019) ), Gray Wolf Optimizer (GWO) (Mirjalili et al. (2014) ), Ant Lion Optimization (ALO) (Mirjalili (2015) ), and Enhanced Harris Hawk Optimization with Coot Bird Optimization (EHHOCBO) (Cui et al. (2023) ). The package enables automatic tuning of SVR hyperparameters (cost, gamma, and epsilon) to enhance prediction performance. Suitable for regression tasks in domains such as renewable energy forecasting and hourly data prediction. For more details about implementation and parameter bounds see: Setiawan et al. (2021) and Liu et al. (2018) . Package: r-cran-metatest Architecture: all Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-metatest_1.0-5-1.ca2604.1_all.deb Size: 39848 MD5sum: 05bd368e71ac7263f3c171917d6d652e SHA1: 3d186a0ed0d228f943a5a0da73705e837fa393c9 SHA256: 46682021c5f0d93abb90baff2025934919cf0b219cd4bbd4cf270a0d1f558294 SHA512: b664dbc1a3d329c1907587f2a35a17d66a4f2412e79b1cefc33d1f59bded1363ad40d7ec1247e076a0e7be0e584d4f99b552097b6e21059f2909bcb9e7ccb233 Homepage: https://cran.r-project.org/package=metatest Description: CRAN Package 'metatest' (Fit and Test Metaregression Models) Fits and tests meta regression models and generates a number of useful test statistics: next to t- and z-tests, the likelihood ratio, bartlett corrected likelihood ratio and permutation tests are performed on the model coefficients. Package: r-cran-metathis Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-knitr, r-cran-magrittr, r-cran-purrr Suggests: r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metathis_1.1.4-1.ca2604.1_all.deb Size: 208824 MD5sum: 70df84666d7dd8af5a8c5f5a3287a633 SHA1: ef6a0365c51259e1f08106de264975eabd947362 SHA256: 392b906d1b3ca585603c547a998dca25fe20e6d385654a6ac1da54a05f269176 SHA512: 9d9bfd7f83c1e5ddee2a4686654c135cd2ccccae35c9fb695b57aeee3f4fe48494d3f485939fe0ed5535718b8469a213443cf823996101d72487e14f991de24a Homepage: https://cran.r-project.org/package=metathis Description: CRAN Package 'metathis' (HTML Metadata Tags for 'R Markdown' and 'Shiny') Create meta tags for 'R Markdown' HTML documents and 'Shiny' apps for customized social media cards, for accessibility, and quality search engine indexing. 'metathis' currently supports HTML documents created with 'rmarkdown', 'shiny', 'xaringan', 'pagedown', 'bookdown', and 'flexdashboard'. Package: r-cran-metatools Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-metacore, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-haven, r-cran-pharmaversesdtm, r-cran-safetydata, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metatools_0.3.0-1.ca2604.1_all.deb Size: 325832 MD5sum: cff45fa28f0d522db8f250c2d2fb22ce SHA1: 1a308a6076da1cad0faa13f41e6a1bfe67083a0b SHA256: d94a773439464e62a4f914f053f13aab4b79d3ad939a641458e6d0f2e4c829fa SHA512: 3cb33d669c02f080d669a056ff860b94112f3caaf8dec097ce9b763889bbdedd18e652246355d5c67b6d2119b82a1d544de78ad39e20268ea09627ff2e5941f2 Homepage: https://cran.r-project.org/package=metatools Description: CRAN Package 'metatools' (Enable the Use of 'metacore' to Help Create and Check Dataset) Uses the metadata information stored in 'metacore' objects to check and build metadata associated columns. Package: r-cran-metaumbrella Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta, r-cran-metaconvert, r-cran-pwr, r-cran-powersurvepi, r-cran-readxl, r-cran-withr, r-cran-writexl, r-cran-xtable Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-spelling, r-cran-dt, r-cran-epir, r-cran-esc, r-cran-metafor Filename: pool/dists/resolute/main/r-cran-metaumbrella_1.1.0-1.ca2604.1_all.deb Size: 757532 MD5sum: 3499486f7a094f0e57f9ba92bfdd0683 SHA1: 8874f70e9375506c4991a60326a3c64d9bb8f174 SHA256: 00be3e001bc090a9fe5ee6508a4c735a9d7e57c751b9fb5c6c858606040222d6 SHA512: 7c39c41cbeee0674f6f994bc91c65f9b9233bc46b2b29b013adb053622e8aba8aef8aad8bcd024e4da79c28d00b1fc9c791788eb9d27f474494860d60e985ed6 Homepage: https://cran.r-project.org/package=metaumbrella Description: CRAN Package 'metaumbrella' (Umbrella Review Package for R) A comprehensive range of facilities to perform umbrella reviews with stratification of the evidence in R. The package accomplishes this aim by building on three core functions that: (i) automatically perform all required calculations in an umbrella review (including but not limited to meta-analyses), (ii) stratify evidence according to various classification criteria, and (iii) generate a visual representation of the results. Note that if you are not familiar with R, the core features of this package are available from a web browser (). Package: r-cran-metautility Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metafor, r-cran-metadat, r-cran-stringr, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-metautility_2.1.2-1.ca2604.1_all.deb Size: 258662 MD5sum: e31e78ca34abaae9dbc1f1bc39ce3641 SHA1: 5bcb2b11d3f44b3b00dc3d3b8f1f27decb38b136 SHA256: 3cc1853364ce1b736e347d29663c5fe0229900be46384f728bd41d80c5c0f3bc SHA512: 9e63b1cb53cbf62bdd02df8a7e3f274737e7d16a35a9d42a5ae0478a6a43b22fbf66c709ea161a21a3fbe85084f63df5c5d158ec5e9d924a969be8de897c931b Homepage: https://cran.r-project.org/package=MetaUtility Description: CRAN Package 'MetaUtility' (Utility Functions for Conducting and Interpreting Meta-Analyses) Contains functions to estimate the proportion of effects stronger than a threshold of scientific importance (function prop_stronger), to nonparametrically characterize the distribution of effects in a meta-analysis (calib_ests, pct_pval), to make effect size conversions (r_to_d, r_to_z, z_to_r, d_to_logRR), to compute and format inference in a meta-analysis (format_CI, format_stat, tau_CI), to scrape results from existing meta-analyses for re-analysis (scrape_meta, parse_CI_string, ci_to_var). Package: r-cran-metavcov Architecture: all Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-mixmeta, r-cran-metasem, r-cran-mvmeta, r-cran-mice Filename: pool/dists/resolute/main/r-cran-metavcov_2.1.5-1.ca2604.1_all.deb Size: 204126 MD5sum: e71fc722fb284c743e6650435af777c2 SHA1: f09bee84892c07fcfab6c1333f94d30c4f640d3a SHA256: 220a017b63f513534b8d4657a59fe84b8a04af0736ceae1a95f8c8952452ef73 SHA512: 7dc273d3a381622c9aa0f8a81b9fa8f4830974d1103c3807b44b90d7014ba3888c58cf9e85232c387bc6413d2b2801a5850ff8897d6a97bf6ddc01e80e40946e Homepage: https://cran.r-project.org/package=metavcov Description: CRAN Package 'metavcov' (Computing Variances and Covariances, Visualization and MissingData Solution for Multivariate Meta-Analysis) Collection of functions to compute within-study covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD). Package: r-cran-metaviz Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-nullabor, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-metafor, r-cran-gridextra, r-cran-ggpubr Suggests: r-cran-cairo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-metaviz_0.3.1-1.ca2604.1_all.deb Size: 1661678 MD5sum: b8a04ed582a70a0ba7fb960a1584dbaa SHA1: 7b8407891aefc91ad64878585579a925b91f9d6d SHA256: 4ce2aaa5d69685784f0f22d3b42e1b22da3ddfd94bfd6177608e2148c9654c7f SHA512: 3feebf2ee2826f660303b653362515d1d9143315961804db1a43f7e04972f3ca16a198ad80c4767e0e37018ed009b3cc9fbae96d82b77146105c67c8a377f774 Homepage: https://cran.r-project.org/package=metaviz Description: CRAN Package 'metaviz' (Forest Plots, Funnel Plots, and Visual Funnel Plot Inference forMeta-Analysis) A compilation of functions to create visually appealing and information-rich plots of meta-analytic data using 'ggplot2'. Currently allows to create forest plots, funnel plots, and many of their variants, such as rainforest plots, thick forest plots, additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities for visual inference with the funnel plot in the context of meta-analysis are provided. Package: r-cran-metawho Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metafor, r-cran-dplyr, r-cran-forestmodel, r-cran-magrittr, r-cran-purrr, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metawho_0.2.0-1.ca2604.1_all.deb Size: 123504 MD5sum: 3382c4bf1566ccae72be01d54ef9b6d2 SHA1: a1f743ff2e641c50758cd7f1db590206be54e983 SHA256: a227f5cbeec188915ff015137f56ffa5ef43783580a632b1f8ac1470db4fad41 SHA512: 7aa8eb1f93f62340bcdfdbb7cb0f3110a72c6bb3b97b3897adcf528ecc7cbc590fd2eb296cb8097bb9c078eccc815ee091cd20fd586a600edd8d13e4fbedc1c0 Homepage: https://cran.r-project.org/package=metawho Description: CRAN Package 'metawho' (Meta-Analytical Implementation to Identify Who Benefits Mostfrom Treatments) A tool for implementing so called 'deft' approach (see Fisher, David J., et al. 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Colorblind accessibility checked using the '{colorblindcheck} package by Jakub Nowosad'. 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(2020) ) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) , Sekulić et al. (2020) ) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data. 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The package 'meteoEVT' provides functions to calculate many energetic and vortical quantities, like potential vorticity, Bernoulli function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008, ], for given gridded data, like ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of atmospheric fronts. For this purpose, separate function are provided that allow the detection of fronts based on the thermic front parameter [Hewson, 1998, ], the F diagnostic [Parfitt et al., 2017, ] and the DSI [Mack et al., 2022, ]. 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DNA methylation occurs mainly through the addition of a methyl group to cytosines, for example to cytosines in a CpG dinucleotide context (CpG stands for a cytosine followed by a guanine). Tissue-specific methylation patterns lead to genomic regions with different characteristic methylation levels. E.g. in vertebrates CpG islands (regions with high CpG content) that are associated to promoter regions of expressed genes tend to be unmethylated. 'MethEvolSIM' is a model-based simulation software for the generation and modification of cytosine methylation patterns along a given tree, which can be a genealogy of cells within an organism, a coalescent tree of DNA sequences sampled from a population, or a species tree. The simulations are based on an extension of the model of Grosser & Metzler (2020) and allows for changes of the methylation states at single cytosine positions as well as simultaneous changes of methylation frequencies in genomic structures like CpG islands. 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Functions for calculating: reference evapotranspiration (ETref), extraterrestrial radiation (Ra), net radiation (Rn), saturation vapor pressure (satVP), global radiation (Rs), soil heat flux (G), daylight hours, and more. [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9). 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This process involves comparing missing rates of pooled plasma samples and biological samples. The functions assume a fixed injection order of samples where biological samples are randomized and processed between intermittent pooled plasma samples. By comparing patterns of missing data across injection order, metabolites that appear in blocks and are likely artifacts can be separated from metabolites that seem to have random dispersion of missing data. The two main metrics used are: 1. the number of consecutive blocks of samples with present data and 2. the correlation of missing rates between biological samples and flanking pooled plasma samples. 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For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes . Package: r-cran-metricminer Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-assertthat, r-cran-openssl, r-cran-gh, r-cran-getpass, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-tidyr, r-cran-googledrive, r-cran-googlesheets4, r-cran-janitor, r-cran-stringr, r-cran-magrittr, r-cran-rvest, r-cran-rprojroot, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-metricminer_1.0.1-1.ca2604.1_all.deb Size: 218688 MD5sum: 5fbe42738366c178bc9dfdc6d242b9ad SHA1: efeeb4ffe221d80378992cf0587f30a26d62cf21 SHA256: 5c8ac8fc6f9315eeb463d60f432bcc9a01614ea24d17ebdc49d8ad018015e8f1 SHA512: 165ba2791020a40c921f546d239e874ba76423eb56f01ba774227deca4fed146485c8a6ca4aad9a4bccce549f80532e0586aa1434cf0d610e7611817acec5bbf Homepage: https://cran.r-project.org/package=metricminer Description: CRAN Package 'metricminer' (Mine Metrics from Common Places on the Web) Mine metrics on common places on the web through the power of their APIs (application programming interfaces). 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(1997, ISBN: 978-0412997112). The package also contains a weighted version of generalized R-squared, see e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236). Furthermore, 'dplyr' chains are supported. 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Package: r-cran-metrology Architecture: all Version: 0.9-29-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-numderiv, r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-metrology_0.9-29-2-1.ca2604.1_all.deb Size: 827770 MD5sum: 0171ec797825bff0d2dc620be9bccceb SHA1: fc8179f13eb8e647a4206685c7632439bdbbd0e9 SHA256: 8cd25770906861f0ff748c3107cc4f5735c9c890b5b514ac2041573094050f57 SHA512: 9a59863c14f03521aa400385d1274da5e74964ebd083f41852b15f013f30346be5e3bca411b4af876a66e5460f9c30b5e8aee6ee72e36e52ee30d9a34a8746cd Homepage: https://cran.r-project.org/package=metRology Description: CRAN Package 'metRology' (Support for Metrological Applications) Provides classes and calculation and plotting functions for metrology applications, including measurement uncertainty estimation and inter-laboratory metrology comparison studies. 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Package: r-cran-metrosp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2514 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-quarto, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-metrosp_1.0.0-1.ca2604.1_all.deb Size: 1960280 MD5sum: 9c75d9c5251a5a51b164c324ac276601 SHA1: 6b59b6e28666132d6c53de4f76be37e5df33c2f1 SHA256: 432ff23d84ead9bd7e64a195a188aea94a9478e36a4652c212ce98eaf4736472 SHA512: 9887671be91a7de43336f6a6b5acd9731580c65941870bf2aadd72c98159f9a72ea98328b7c277682030b072c967e6e271437638d8df47b65b4d887a48eaf83b Homepage: https://cran.r-project.org/package=metrosp Description: CRAN Package 'metrosp' ('São Paulo' Metro Passenger Demand Data) Provides passenger demand data for the 'São Paulo' metro system, covering 2012 to 2025. 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Package: r-cran-metsizer Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-metabolanalyze, r-cran-rfast, r-cran-shiny, r-cran-shinythemes, r-cran-vroom Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-metsizer_2.0.0-1.ca2604.1_all.deb Size: 69662 MD5sum: 7e4dffa17b9525875a77e8b221cba5ac SHA1: b8c0172840837c70b98169ea81215edc1eadf487 SHA256: 12418c6f8011c3ff825c5010ac356dc7558da6f81858e67fc4309c84ff22702c SHA512: f7a37eb6bebea7a786e41a33641e23ce1f9478a8545eae0953b463b996cde8578e482e8fc8dffa7c5758c57c8f24d7a91135995a0ee23bca2a1260aad9963b49 Homepage: https://cran.r-project.org/package=MetSizeR Description: CRAN Package 'MetSizeR' (A Shiny App for Sample Size Estimation in MetabolomicExperiments) Provides a Shiny application to estimate the sample size required for a metabolomic experiment to achieve a desired statistical power. Estimation is possible with or without available data from a pilot study. Package: r-cran-metsyn Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-readr, r-cran-stringr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-metsyn_0.1.2-1.ca2604.1_all.deb Size: 28128 MD5sum: bb1161a3905c8666f339ddd1356d76c5 SHA1: a516b77d249ae81ce82167bae48768d4e181abb8 SHA256: 943d7066c7f4481332cb212f707b2915c82972da2b1b323b61ac868099741c6c SHA512: 7ff3f3b492642564cf8558b22a09ccc5ac64aa4c9490de82696c548ebd322ca1022725ed2f6f2adcc968d7cad432f7ee44f1f1b44779dd08397b4f2ad8d58abc Homepage: https://cran.r-project.org/package=metsyn Description: CRAN Package 'metsyn' (Interface with the Meteo France Synop Data API) Provides an interface with the Meteo France Synop data API (see for more information). The Meteo France Synop data are made of meteorological data recorded every three hours on 62 French meteorological stations. Package: r-cran-mevr Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-envstats, r-cran-foreach, r-cran-doparallel, r-cran-bamlss, r-cran-mgcv Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mevr_1.1.1-1.ca2604.1_all.deb Size: 127282 MD5sum: 6f563bf6c8114c29d66ce35fe7396bdc SHA1: 85f9ffd4f60e2f06f3612e48a7206b55ed232086 SHA256: 3fe9c37a3367214ebbd3b4f85c097ae6fdb0adbff78504f2afd3e3bf8e68d7c7 SHA512: 383ae5f0faa69286c892640dc967d579e9d0fbf752f2777c7c86bfa77c535a30421e48b16de72502375a9f8fb27422048d99d09dc782c761054b6642692bd711 Homepage: https://cran.r-project.org/package=mevr Description: CRAN Package 'mevr' (Fitting the Metastatistical Extreme Value Distribution MEVD) Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, ) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, ) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, ). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references. Package: r-cran-mexbrewer Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4037 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-mexbrewer_0.0.2-1.ca2604.1_all.deb Size: 3979406 MD5sum: 85eec54fa3ab67948a02a14b122c2267 SHA1: b581c98ba110bed4606c9f0a75391734a76926e3 SHA256: fc27b3fba9073506a762ca191c73818f2aa6197ebdf1752cf94ba5ed9e3f6044 SHA512: c3d7e49f146a0a1ffb7445f6a893b0ed78eea7d4badde5d2fcac186451205aedfc528d2f8376528eccd867f61e3edec5df9051455487c5ad87bc7ba7584db44a Homepage: https://cran.r-project.org/package=MexBrewer Description: CRAN Package 'MexBrewer' (Color Palettes Inspired by Works of Mexican Painters andMuralists) Color palettes inspired by the works of Mexican painters and muralists. The package includes functions that return vectors of colors and also functions to use color and fill scales in 'ggplot2' visualizations. Package: r-cran-mexicodataapi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 607 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mexicodataapi_0.2.0-1.ca2604.1_all.deb Size: 514382 MD5sum: 8ed17c5e09537c06a5c0cb39ecd99c0a SHA1: a955c20445caf027846b3c24e871d62a6fdb9a87 SHA256: a21e419e03600035a7139f50c5ea0671c543ab0a43b192ff135def72939ba638 SHA512: 5ffb88cbc5ba4aa626b956bd789453a0228f5f7068508bbf3c5d6927a8bfd2d98cc8d57c560ea978deb5a8e7b0081ee9e75f5e74e3dc84aa16fdfc0a1760d71d Homepage: https://cran.r-project.org/package=MexicoDataAPI Description: CRAN Package 'MexicoDataAPI' (Access Mexican Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'REST Countries API', 'World Bank API', and 'Nager.Date API', covering Mexico's economic indicators, population statistics, literacy rates, international geopolitical information and official public holidays. The package also includes curated datasets related to Mexico such as air quality monitoring stations, pollution zones, income surveys, postal abbreviations, election studies, forest productivity and demographic data by state. It supports research and analysis focused on Mexico by integrating reliable global APIs with structured national datasets drawn from open and academic sources. For more information on the APIs, see: 'REST Countries API' , 'World Bank API' , and 'Nager.Date API' . Package: r-cran-mexicolors Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mexicolors_0.2.0-1.ca2604.1_all.deb Size: 15202 MD5sum: 6231050f1a639417ceb36824d37fb7e4 SHA1: b0afac6a1565c3cf887f82dc9b65982a25795b3d SHA256: d3871b1afdcce53eabfa7b1b782f149f367d96fdc28906bb47b5f38a9f5b1363 SHA512: 333b4ef743b323a154ff55a207ed948be104b4bb882b4577dd53927b30686b7d5f056b0c24717cda668187c22f68107c5d4da511d393ceff820a4eae6557efb8 Homepage: https://cran.r-project.org/package=mexicolors Description: CRAN Package 'mexicolors' (Mexican Politics-Inspired Color Palette Generator) A color palette generator inspired by Mexican politics, with colors ranging from red on the left to gray in the middle and green on the right. Palette options range from only a few colors to several colors, but with discrete and continuous options to offer greatest flexibility to the user. This package allows for a range of applications, from mapping brief discrete scales (e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays including dozens of shades graded from red to green. Package: r-cran-mexplorer Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-bioc-qusage Filename: pool/dists/resolute/main/r-cran-mexplorer_1.0.0-1.ca2604.1_all.deb Size: 59492 MD5sum: 5869dcf540a2c91d0341acf1e369cc18 SHA1: defbdccfb41327b38c7efd79e75c94e36af7d914 SHA256: f27263b4997cafe9fbcfff9de6d4f8fd7b81a01cd30dbc3a61d3f69478f23fa4 SHA512: 7b437148065e7e2a2153bb4fff259ec4c97049e89f1cb81b8e897df32d52b904639f146ac14fb1460fb602d71cc2cc42343eeeb03b1fea03d94f519bbc89e2b4 Homepage: https://cran.r-project.org/package=mExplorer Description: CRAN Package 'mExplorer' (Identifying Master Gene Regulators from Gene Expression andDNA-Binding Data) The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 . Package: r-cran-mf.beta4 Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-devtools, r-cran-ggplot2, r-cran-dplyr, r-cran-ggpubr, r-cran-purrr, r-cran-patchwork, r-cran-tidyr, r-cran-lme4, r-cran-lmertest, r-cran-tidyverse, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mf.beta4_1.1.2-1.ca2604.1_all.deb Size: 1182984 MD5sum: 916c8a4bda6da7724c55831aaa088e3a SHA1: 2dd26d0315cc92509ab7f44f1bbb2de7a79a82b1 SHA256: f3f60656f730b34c61515bb500e527028709772985735ef07fc1ae60060092fe SHA512: 74bf6072e694671b21f59979afe5a3ab1118e91e123ff8d59db0db51fb12feaf551d6b78dfed796ab87677983f0749afb78cee5771f869d3e46912cdb7a28c3b Homepage: https://cran.r-project.org/package=MF.beta4 Description: CRAN Package 'MF.beta4' (Measuring Ecosystem Multi-Functionality and Its Decomposition) Provide simple functions to (i) compute a class of multi-functionality measures for a single ecosystem for given function weights, (ii) decompose gamma multi-functionality for pairs of ecosystems and K ecosystems (K can be greater than 2) into a within-ecosystem component (alpha multi-functionality) and an among-ecosystem component (beta multi-functionality). 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Related to the idea of specification curve analysis (Simonsohn, Simmons, and Nelson, 2020) . Includes data preprocessing, statistical testing, and visualization of results with confidence intervals. 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The package allows to compute functional diversity indices assessing the distribution of species (and of their dominance) in a given functional space for each assemblage and the overlap between assemblages in a given functional space, see: Chao et al. (2018) , Maire et al. (2015) , Mouillot et al. (2013) , Mouillot et al. (2014) , Ricotta and Szeidl (2009) . Graphical outputs are included. Visit the 'mFD' website for more information, documentation and examples. Package: r-cran-mfdb Architecture: all Version: 7.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-logging, r-cran-dbi, r-cran-duckdb, r-cran-getpass, r-cran-rlang Suggests: r-cran-dplyr, r-cran-dbplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsqlite, r-cran-unittest Filename: pool/dists/resolute/main/r-cran-mfdb_7.4-0-1.ca2604.1_all.deb Size: 616174 MD5sum: 0715b43186c78a2950e1128e3fa72af2 SHA1: 648b3436eac1065102e6e503c8408c52a87c3276 SHA256: 1a0ef4ec863adfd3c620313f8d41ecefddd4d023eb3f0ad9613d957b718efc12 SHA512: d40a999492fbc0f4a35e9a1c6a42746ec29e34f6c2dbf910db8653cefa0834ccb0f041682b07d1f96a6dc4888ed4e6a971d780ff6013fce65e94672b02be7e11 Homepage: https://cran.r-project.org/package=mfdb Description: CRAN Package 'mfdb' (MareFrame DB Querying Library) Creates and manages a PostgreSQL database suitable for storing fisheries data and aggregating ready for use within a Gadget model. See for more information. Package: r-cran-mfdfa Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numbers Filename: pool/dists/resolute/main/r-cran-mfdfa_1.1-1.ca2604.1_all.deb Size: 39838 MD5sum: 9912d8382af441e25cf987fb206b9981 SHA1: 0670117b068f53c7a4035c39689c1eb2d32b6fbc SHA256: b9b468e7eb92ff25748ccb20d3e493b1f4c5229d018f8d5908b0838c3f2e7c6c SHA512: 7ca5bf964085d8edebcd1f532d5db8642a62597e2a8d6f6aad72739d3bdfab2ebdcc2bd6548ba5b12cbb21ac1a9cfa27ed2d1a8dc079788785fbc484d0a908c4 Homepage: https://cran.r-project.org/package=MFDFA Description: CRAN Package 'MFDFA' (MultiFractal Detrended Fluctuation Analysis) Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. ( and ). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub. Package: r-cran-mfdp Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mfdp_0.2.2-1.ca2604.1_all.deb Size: 29388 MD5sum: e53626e93b3365408852e54185804752 SHA1: 6b4b0b6364499cf921fd264dea9e3cb3f50e37c2 SHA256: a5dc951fc08315607460cb1b12200838441586dc1a3a42d8f4bae4ef91440678 SHA512: 11daf10dfa54a6629a646a5ad7ab03ec0ad070ad01e7b682f1c992b5db72189bc27eb0c6e100d87bd13ba38a4c9389ece37db6e903384b6758b57320665e60f6 Homepage: https://cran.r-project.org/package=mFDP Description: CRAN Package 'mFDP' (Control of the Median of the FDP) Methods for controlling the median of the false discovery proportion (mFDP). Depending on the method, simultaneous or non-simultaneous inference is provided. The methods take a vector of p-values or test statistics as input. Package: r-cran-mff Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-xgboost, r-cran-lightgbm, r-cran-e1071, r-cran-ppclust, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mff_0.2.0-1.ca2604.1_all.deb Size: 66766 MD5sum: ae4cf47f57cdb0a39ce770e9a4686cb8 SHA1: 7e42c98d02ef20021258d8567de7bf0257153611 SHA256: f9af69deda69c8b086b4f7f62c2b500b6d937be88bf8d6dada21256b3a691759 SHA512: a731d05b04f5cb40b21f1b080786ed87bad10b756155a43be971b0e8169ca6da0a089203aa1f6aa8455fd9ac645dfe31fa4ffb69f4d03736b1d4f8e373e0c72e Homepage: https://cran.r-project.org/package=MFF Description: CRAN Package 'MFF' (Meta Fuzzy Functions) Implements Meta Fuzzy Functions (MFFs) for regression Tak and Ucan (2026) by aggregating predictions from multiple base learners using membership weights learned in the prediction space of validation set. The package supports fuzzy and crisp meta-ensemble structures via Fuzzy C-Means (FCM) Tak (2018) , Possibilistic FCM (PFCM) Tak (2021) , Gustafson–Kessel (GK) clustering, and k-means, and provides a workflow to (i) generate validation/test prediction matrices from common regression learners (linear and penalized regression via 'glmnet', random forests, gradient boosting with 'xgboost' and 'lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute membership-based weights, (iii) tune clustering-related hyperparameters (number of clusters/functions, fuzziness exponent, possibilistic regularization) via grid search on validation loss, and (iv) predict on new/test prediction matrices and evaluate performance using standard regression metrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables flexible, interpretable ensemble regression where different base models contribute to different meta components according to learned memberships. 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The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package. 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The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) for methodology and Chainarong Amornbunchornvej (2021) for software when referring to this package in publications. 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Key references are Royston and Altman (1994) and Royston and Sauerbrei (2008, ISBN:978-0-470-02842-1). In addition, it can model a sigmoid relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) . This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling. Package: r-cran-mfp Architecture: all Version: 1.5.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mfp_1.5.5.1-1.ca2604.1_all.deb Size: 143996 MD5sum: eadd2e69bbb4e9295eb7d102da91c54c SHA1: c6747e6b11b1af31226328a1cbf94883414d506b SHA256: 0b4aff29a600ad61414cf829f211f57d7b502fdab4a7c5cf3781a3e31804ce52 SHA512: a3e03c913ce4295367978755b8b9d8fdeca64345554aae13c8577fdeff5bd9543437e324ac9c86240a63958b6e84770446caec9f56cbd3bfb6b5569c740f53f5 Homepage: https://cran.r-project.org/package=mfp Description: CRAN Package 'mfp' (Multivariable Fractional Polynomials) Multivariable Fractional Polynomial algorithm for model-building. Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994. 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References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test for the detection of rate changes in renewal processes with varying variance . Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points . Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies . Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series . Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in time series. 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Package: r-cran-mggd Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rgl, r-cran-lifecycle, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mggd_1.3.3-1.ca2604.1_all.deb Size: 77708 MD5sum: 46b5ca2f857486cb6b9f34f44dfa4b8f SHA1: b478204d0d1de6be7a5f3ce9b6ff4e9629dff09c SHA256: 16269f4722fd4558920136c3fc17b7b5295d262be3a9c62869d439983781f261 SHA512: 509bc0f158682c083799c50da639c86628c4993e43a3734f3f3ea2bd4854f7f91d0feb2f377a2f03c6563508cd7aaa093bec320fa7340e45e5e578c0a44811e3 Homepage: https://cran.r-project.org/package=mggd Description: CRAN Package 'mggd' (Multivariate Generalised Gaussian Distribution; Kullback-LeiblerDivergence) Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) . Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) and Pascal, Bombrun, Tourneret and Berthoumieu (2013) ). 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Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported. Package: r-cran-mglmn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvabund, r-cran-snowfall Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mglmn_0.1.0-1.ca2604.1_all.deb Size: 49212 MD5sum: e140287fa9f5f5b0a61c89fbbd41b72a SHA1: c2aed3b2431fffb46537e116514b814fcd4f7854 SHA256: e824baffed9354eaee898809321127e9f91e0c1534742586653e899f1e3894b2 SHA512: 17f1edef92e92a3a8784a32c9c65fe5c8f6d373ab7f2b06dfa90ed35a3a2e44ff15d5a1badd072b9031475bf3083b1f8064bd83715da15a833d383180856bfc0 Homepage: https://cran.r-project.org/package=mglmn Description: CRAN Package 'mglmn' (Model Averaging for Multivariate GLM with Null Models) Tools for univariate and multivariate generalized linear models with model averaging and null model technique. Package: r-cran-mgm Architecture: all Version: 1.2-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-stringr, r-cran-hmisc, r-cran-qgraph, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mgm_1.2-15-1.ca2604.1_all.deb Size: 921718 MD5sum: e01073b98074e7ace66e8acb540786a0 SHA1: 1976d408f7b4a0b03350f52de9a49dc6dd40239e SHA256: 0b86e4b3336866195ab4b2d3526a96ddccb2624933300e5b1eefdfeaa012f0ce SHA512: fc13b4bdd3f99e1f488165df70abd9f56ae16733fbd184bd03ace2ede1e1bc3a3e0ba92760c9eef52a58ad9f2f08a0d9db9b7608fac53a3f732ad8be4a42dd0d Homepage: https://cran.r-project.org/package=mgm Description: CRAN Package 'mgm' (Estimating Time-Varying k-Order Mixed Graphical Models) Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) . Package: r-cran-mgms2 Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4670 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maldiquant, r-cran-maldiquantforeign Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mgms2_1.0.2-1.ca2604.1_all.deb Size: 3488336 MD5sum: ac893d0e8c8fd00e1f8d0c8bca80cfc3 SHA1: eab821a4ee066756aaecc5037db1877159220770 SHA256: 240e9d2901fdf34c7b5382b0bf6f5fd2099c5c5106f3ae26349036d7036ca5f3 SHA512: 4e0fce94e4b3044ea8f879f011013840398f31e86647c6618436132579a8bf482c07f36eff149a29ddc7eb7612355e29214b870e97b1ac5fcef574bb875ee0a5 Homepage: https://cran.r-project.org/package=MGMS2 Description: CRAN Package 'MGMS2' ('MGMS2' for Polymicrobial Samples) A glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. 'MGMS2' (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) "MALDIquant: a versatile R package for the analysis of mass spectrometry data." Package: r-cran-mgpsdk Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mgpsdk_1.0.0-1.ca2604.1_all.deb Size: 78686 MD5sum: 701528fe75d6fddc57a8f252edd18f74 SHA1: cf8f69956f3231a84740bd2828782aa54467a743 SHA256: 5ca1ada4074fc132f9ddbe2265c382deb50af07f99af925cddd7ae1c02e10db4 SHA512: 0eef9af32769db06135fb63dd2d042dfc7229679a0d3c51d642a5645eea6e21db839ca26619bd7c483d2ad5c6a3e4732d69fc7de5b38659f5edf5745ce15fdc1 Homepage: https://cran.r-project.org/package=MGPSDK Description: CRAN Package 'MGPSDK' (Interact with the Maxar 'MGP' Application Programming Interfaces) Provides an interface to the Maxar Geospatial Platform (MGP) Application Programming Interface. It facilitates imagery searches using the MGP Streaming Application Programming Interface via the Web Feature Service (WFS) method, and supports image downloads through Web Map Service (WMS) and Web Map Tile Service (WMTS) Open Geospatial Consortium (OGC) methods. Additionally, it integrates with the Maxar Geospatial Platform Basemaps Application Programming Interface for accessing Maxar basemaps imagery and seamlines. The package also offers seamless integration with the Maxar Geospatial Platform Discovery Application Programming Interface, allowing users to search, filter, and sort Maxar content, while retrieving detailed metadata in formats like SpatioTemporal Asset Catalog (STAC) and GeoJSON. Package: r-cran-mgpstreamingsdk Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mgpstreamingsdk_0.2.0-1.ca2604.1_all.deb Size: 60282 MD5sum: 661111c56fc016272025ac3f71e468b5 SHA1: b0327cfd653407b263404b493f0795d991f21fe2 SHA256: 521fc20804a9cd093399ed8e66e677943153e95ad94f894298bc633d632d95fc SHA512: a65b56bda600e06b205007dbd2479eb97f549b8d8fb240627802411fdffcae497d3f67a3c5cfcd464afbb9320d17f4a56f3f2887b41130db2261829419ea9d56 Homepage: https://cran.r-project.org/package=mgpStreamingSDK Description: CRAN Package 'mgpStreamingSDK' (Interact with the Maxar MGP Streaming API) This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming API. It can search for images using the WFS method. It can Download images using WMS WMTS. It can also Download a full resolution image. Package: r-cran-mgsub Architecture: all Version: 1.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mgsub_1.7.3-1.ca2604.1_all.deb Size: 37888 MD5sum: 0dafe2a612db171bd6e5b6cae26423a1 SHA1: 8de5c55a0e1e7026569c24a2e3dab4960300ab79 SHA256: 706539a0b16d55396b7fe5253d5581535b2898bfdb97eda35d35b019214f8630 SHA512: 1196befef04cd5d0d046cb4054351df602bffc1c717fbf975cb2f29dde3155a8d15d0d1392f66241530c97312fc0a30b433460c3fae93b740e3b7de3128c5bdd Homepage: https://cran.r-project.org/package=mgsub Description: CRAN Package 'mgsub' (Safe, Multiple, Simultaneous String Substitution) Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches). Package: r-cran-mgwnbr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/resolute/main/r-cran-mgwnbr_0.3.0-1.ca2604.1_all.deb Size: 110954 MD5sum: d1774e27fb68a9d04a0fcdf4d03ab054 SHA1: 528ef76fa11bfe1cfb5a7afd31a43d4312cdd13c SHA256: 1cae31799c07cc77636ea9dea5c61ead9b2ef937b9b07638d9efc3a345ded2a9 SHA512: 3f6e8c83b1a8d6ee6fbb486a0025526f0b04a8333fa53ad11950773268a9c4ea0ddaa8a82b8a9558d8a1d8cb0c32db512abf8bccae90c14827fafe351678fe5e Homepage: https://cran.r-project.org/package=mgwnbr Description: CRAN Package 'mgwnbr' (Multiscale Geographically Weighted Negative Binomial Regression) Fits a geographically weighted regression model with different scales for each covariate. Uses the negative binomial distribution as default, but also accepts the normal, Poisson, or logistic distributions. Can fit the global versions of each regression and also the geographically weighted alternatives with only one scale, since they are all particular cases of the multiscale approach. Hanchen Yu (2024). "Exploring Multiscale Geographically Weighted Negative Binomial Regression", Annals of the American Association of Geographers . Fotheringham AS, Yang W, Kang W (2017). "Multiscale Geographically Weighted Regression (MGWR)", Annals of the American Association of Geographers . Da Silva AR, Rodrigues TCV (2014). "Geographically Weighted Negative Binomial Regression - incorporating overdispersion", Statistics and Computing . Package: r-cran-mgwrhw Architecture: all Version: 1.1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spgwr, r-cran-sf, r-cran-psych, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mgwrhw_1.1.1.5-1.ca2604.1_all.deb Size: 936146 MD5sum: 634ff5ebf42854c628431743203e845f SHA1: b120af215084665746a77119d88c78d0f8135ae7 SHA256: 5c70cdc2b8a8133c40c6663168376e25c0e6377eb780947a2782f1ecff689a07 SHA512: 321098727a57327c38659b76475c3a1c9c3365a2288e9c287c973347a42fb992f9d35cfbc352e23b55ef3cd0b5bcc7430025aba375d6f3e9ee7c2c8f4457d9a8 Homepage: https://cran.r-project.org/package=mgwrhw Description: CRAN Package 'mgwrhw' (Displays GWR (Geographically Weighted Regression) and Mixed GWROutput and Map) Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196. . Package: r-cran-mhcnuggetsr Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rappdirs, r-cran-reticulate, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mhcnuggetsr_1.1-1.ca2604.1_all.deb Size: 125128 MD5sum: 5d94a408c8a5d0bd8f2eb870c4e5ee79 SHA1: c53822a67f3de1f19459bde1b9b0ed855920655f SHA256: a0bcc7c6f622e95688fecfa0adfb9b69bfbe16a18acc638e80ec9db22706eec5 SHA512: 29678cf6a6f55b556724870b9363e1b304ff3d4664bdd1c049765af81f3f8a952c998efe8d8f7cddfd1f3e738b67462bb9a65fced4a0bbaea0a650b04fa50c31 Homepage: https://cran.r-project.org/package=mhcnuggetsr Description: CRAN Package 'mhcnuggetsr' (Call MHCnuggets) MHCnuggets () is a Python tool to predict MHC class I and MHC class II epitopes. This package allows one to call MHCnuggets from R. Package: r-cran-mhctools Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-mhctools_1.6.0-1.ca2604.1_all.deb Size: 257474 MD5sum: 57f222840c6f769b9824b96cc4f40554 SHA1: 9082c38639f26419408d6d071b7b59df5382c393 SHA256: a652039dacfce9e043e2c2798773038d7af2321bfbce32f0f1cff0264b78dee6 SHA512: cf8fafe1eba4249f5fda91ae9ad785486b10efdcb088d5cf3ecaca0e72fba667f66f94868824b040fdfe9bd87bb1b3cd31cd63e15cb7765cf0053b003234e3cb Homepage: https://cran.r-project.org/package=MHCtools Description: CRAN Package 'MHCtools' (Analysis of MHC Data in Non-Model Species) Sixteen tools for bioinformatics processing and analysis of major histocompatibility complex (MHC) data. The functions are tailored for amplicon data sets that have been filtered using the dada2 method (for more information on dada2, visit ), but even other types of data sets can be analyzed. The ReplMatch() function matches replicates in data sets in order to evaluate genotyping success. The GetReplTable() and GetReplStats() functions perform such an evaluation. The CreateFas() function creates a fasta file with all the sequences in the data set. The CreateSamplesFas() function creates individual fasta files for each sample in the data set. The DistCalc() function calculates Grantham, Sandberg, or p-distances from pairwise comparisons of all sequences in a data set, and mean distances of all pairwise comparisons within each sample in a data set. The function additionally outputs five tables with physico-chemical z-descriptor values (based on Sandberg et al. 1998) for each amino acid position in all sequences in the data set. These tables may be useful for further downstream analyses, such as estimation of MHC supertypes. The BootKmeans() function is a wrapper for the kmeans() function of the 'stats' package, which allows for bootstrapping. Bootstrapping k-estimates may be desirable in data sets, where e.g. BIC- vs. k-values do not produce clear inflection points ("elbows"). BootKmeans() performs multiple runs of kmeans() and estimates optimal k-values based on a user-defined threshold of BIC reduction. The method is an automated and bootstrapped version of visually inspecting elbow plots of BIC- vs. k-values. The ClusterMatch() function is a tool for evaluating whether different k-means() clustering models identify similar clusters, and summarize bootstrap model stats as means for different estimated values of k. It is designed to take files produced by the BootKmeans() function as input, but other data can be analyzed if the descriptions of the required data formats are observed carefully. The SynDist() function analyses of synonymous variation among aligned protein-coding DNA sequences, that is, nucleotide substitutions that do not translate to changes in the amino acid sequences due to degeneracy of the genetic code. The SynDist() function calculates synonymous nucleotide changes per base and per codon in pairwise sequence comparisons, as well as mean synonymous variation among all pairwise comparisons of the sequences within each sample in a data set. The PapaDiv() function compares parent pairs in the data set and calculate their joint MHC diversity, taking into account sequence variants that occur in both parents. The HpltFind() function infers putative haplotypes from families in the data set. The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of the haplotype inference. The CreateHpltOccTable() function creates a binary (logical) haplotype-sequence occurrence matrix from the output of HpltFind(), for easy overview of which sequences are present in which haplotypes. The HpltMatch() function compares haplotypes to help identify overlapping and potentially identical types. The NestTablesXL() function translates the output from HpltFind() to an Excel workbook, that provides a convenient overview for evaluation and curating of the inferred putative haplotypes. Package: r-cran-mhda Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mhda_2.0-1.ca2604.1_all.deb Size: 49978 MD5sum: 2940226d4e7363c098f2d8ff4058c37f SHA1: 028bb0b7cc456f0e78a4977179e1800f140844d9 SHA256: 5eba860928fe53c2952e4742bffa283a8bdc4c0dce2aafa4a04d4c2f4deff98a SHA512: 929a011aa0bf810483a8edfecd13a2439fa4026ddaca76df7c712341d04131ccfdf6bab3d44bfb6955633850167f5d6c81bd9315e7504a032aaf2dd236e21a1a Homepage: https://cran.r-project.org/package=MHDA Description: CRAN Package 'MHDA' (Massive Hierarchically Data Analysis) Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories are demonstrated. Flexible and diverse applications of the function parameters make the data analyses powerful. Package: r-cran-mhg Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mhg_1.1-1.ca2604.1_all.deb Size: 34158 MD5sum: 92fcf01e4ecc22a487f1ef5a6f031e3f SHA1: 8f1990cf8066657fccae21832078cb2a11bea607 SHA256: 213413d3f5d064f3a9a73ba9d2b2340882e181bbb1b9c6924e353975c860579d SHA512: a3aaced580e345237c45f813f9d68e60e1ee457876f3a0b02dd56e0fe1bdbaf931b463177c3fa5b6191cb2e573dd10e6ebc245336ec8e6c97d9059413cf4322c Homepage: https://cran.r-project.org/package=mHG Description: CRAN Package 'mHG' (Minimum-Hypergeometric Test) Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa. Package: r-cran-mhqol Architecture: all Version: 0.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-assertthat, r-cran-tidyr, r-cran-shiny, r-cran-shinyalert, r-cran-dt, r-cran-writexl, r-cran-fmsb Suggests: r-cran-here, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mhqol_0.14.0-1.ca2604.1_all.deb Size: 98872 MD5sum: 9b5a2db9c86758e0aa8af34b33214634 SHA1: 458c848817eb3fea855795bef4efe4841a85021d SHA256: fe631d10ba90fa316c0fd545bf9bad5af7f8f189410415306b468cad9c7e853b SHA512: 3f9d0f520c00eeb1f58d6d96c901eeddfeecfc9eba8f5278c2e004a531cf45ae595986c93148532a1bc2e6fda014cf79843c2b83aeed88c80e7f186909a8f501 Homepage: https://cran.r-project.org/package=MHQoL Description: CRAN Package 'MHQoL' (Mental Health Quality of Life Toolkit) Transforms, calculates, and presents results from the Mental Health Quality of Life Questionnaire (MHQoL), a measure of health-related quality of life for individuals with mental health conditions. Provides scoring functions, summary statistics, and visualization tools to facilitate interpretation. For more details see van Krugten et al.(2022) . Package: r-cran-mhtboot Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mhtboot_1.3.3-1.ca2604.1_all.deb Size: 175634 MD5sum: b9355b54a60bc6d25497bd2e197420d2 SHA1: e49375affbbf1435b411da3a8eee9d6bdb60e2de SHA256: 6ba08509c0d9b719e6e7c43a44819213ee128f68318a47d225b6540707f415a8 SHA512: 69618435c6c1b8c6fe395742a004768622420d2c528dea77b7742564b3c48717aac2acc14001f434bcc257c1da28dcf839ae9a33294886ac243e51416aa3e0c8 Homepage: https://cran.r-project.org/package=mhtboot Description: CRAN Package 'mhtboot' (Multiple Hypothesis Test Based on Distribution of p Values) A framework for multiple hypothesis testing based on distribution of p values. It is well known that the p values come from different distribution for null and alternatives, in this package we provide functions to detect that change. We provide a method for using the change in distribution of p values as a way to detect the true signals in the data. Package: r-cran-mhtdiscrete Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mhtdiscrete_1.0.1-1.ca2604.1_all.deb Size: 82108 MD5sum: c42ac2ac651453d19f2748dcf10df62c SHA1: 626a9624f6896464733031c34c07e35a4e1835b1 SHA256: cf2f5f919c24d129f307e4155dfc9e432052ed9211cb5d05d66136785ec97b64 SHA512: 7a2d1e9e3fafdf84cbc6acc1c0d2fe05da43e490f5313c812c249534dd5ebfddbc6df3e39f19f464efad56e4ea77064b54b5c2538b6f10d7790e60036f6aa845 Homepage: https://cran.r-project.org/package=MHTdiscrete Description: CRAN Package 'MHTdiscrete' (Multiple Hypotheses Testing for Discrete Data) A comprehensive tool for almost all existing multiple testing methods for discrete data. The package also provides some novel multiple testing procedures controlling FWER/FDR for discrete data. Given discrete p-values and their domains, the [method].p.adjust function returns adjusted p-values, which can be used to compare with the nominal significant level alpha and make decisions. For users' convenience, the functions also provide the output option for printing decision rules. Package: r-cran-mhtrajectoryr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-mhtrajectoryr_1.0.1-1.ca2604.1_all.deb Size: 36108 MD5sum: 939394afacc772818c38987c6d07bba0 SHA1: cdae4d644bbf5bc6660d631536bb06ef6fdff4a9 SHA256: 86f0db5d05b91d2d56363049174aa209fec17614643fd8974355283ba02ffcf2 SHA512: 8a58f5092e82afeec7d973d66caf2d292fdfdd5a8da60410edf10fa04e7c046f9f9993aad865f03f696d6bf8c10d7e1762dbf54c02f70134f1b2041d16ce8497 Homepage: https://cran.r-project.org/package=MHTrajectoryR Description: CRAN Package 'MHTrajectoryR' (Bayesian Model Selection in Logistic Regression for theDetection of Adverse Drug Reactions) Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion. 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It is dedicated to dealing with multiple imputation for proteomics. 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Package: r-cran-miamaxent Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-e1071, r-cran-terra, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-sf, r-cran-disdat Filename: pool/dists/resolute/main/r-cran-miamaxent_1.4.1-1.ca2604.1_all.deb Size: 992658 MD5sum: 40d531b0e1e1d3fc45da98347d235369 SHA1: 5b02b5de28e959d48bd1ba3148441e02c1f3e91f SHA256: 3c83de2f5dda93eb9cbdbb9f2e410c40e7f81dc638aa3d906ae31be4f3edc195 SHA512: c65b8bf93ab2c1408a3ee1662019901099adc8020e79b620a99fe722e66fddcc82ac0f534e8c32594f85dde988cb3f9f7fcf68cff77330f2329a3decdf616c2f Homepage: https://cran.r-project.org/package=MIAmaxent Description: CRAN Package 'MIAmaxent' (A Modular, Integrated Approach to Maximum Entropy DistributionModeling) Tools for training, selecting, and evaluating maximum entropy (and standard logistic regression) distribution models. This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum- likelihood interpretation of maximum entropy modeling, and uses infinitely- weighted logistic regression for model fitting. The package is described in Vollering et al. (2019; ). Package: r-cran-miapack Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-nnet, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-miapack_0.1.0-1.ca2604.1_all.deb Size: 115564 MD5sum: b013950271d237ebb0ae36c3ca8748a1 SHA1: af07dbd799a68fe79cf73e12ad6c1d0d09025b6b SHA256: 75de8fec08cddb35f3eae17669036d1189e9ba77d9667ffdabc0016e14818c9b SHA512: f3029c458ba611e2a67e49083e4c79ef1dfb53129f610026db8bc30314c65e2f2c153a482adcbccfbc9091d0998cccae1155725e73c93ca61636695ec6a0e3a1 Homepage: https://cran.r-project.org/package=miapack Description: CRAN Package 'miapack' (Marginalization over Incomplete Auxiliaries) Implements methods to estimate conditional outcome means in settings with missingness-not-at-random and incomplete auxiliary variables. Specifically, this package implements the marginalization over incomplete auxiliaries (MIA) method. The package supports continuous and binary outcomes, and supports auxiliary variables that are normal, binary, and categorical. Package: r-cran-mic Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1084 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amr, r-cran-glue, r-cran-dplyr, r-cran-stringr, r-cran-rlang, r-cran-ggh4x, r-cran-ggplot2, r-cran-forcats, r-cran-purrr, r-cran-tibble Suggests: r-cran-testthat, r-cran-flextable, r-cran-lifecycle Filename: pool/dists/resolute/main/r-cran-mic_2.0.0-1.ca2604.1_all.deb Size: 853038 MD5sum: 13a9573bfaa0acf70b88bfeaaa0504f6 SHA1: 5a7de2c81ea162fc030c3584094f4d9f22e7b07c SHA256: b51d560fd7bbb5770b405c8ec140fb76215d913921d79ba10f58dd617bed23c5 SHA512: 8594a90a8f2a9cc2a52cd7f7d689f798bacd69f5a15edeed4468a0d55ca2bff2181db14ae2884e2fc988def82553912239683e7bb4b9415c2e50b6bb3c226380 Homepage: https://cran.r-project.org/package=MIC Description: CRAN Package 'MIC' (Analysis of Antimicrobial Minimum Inhibitory Concentration Data) Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data. Validate the results of an MIC experiment by comparing observed MIC values to a gold standard assay, in line with standards from the International Organization for Standardization (2021) . Package: r-cran-micar Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-micar_1.2.0-1.ca2604.1_all.deb Size: 146374 MD5sum: 7c0af2e46c82ab31a41e864125c01a0e SHA1: c3ed34c11e5977f5d6ec430fa173049aa6f9bf93 SHA256: f2f4cbcbe6cf994dbd189879248f8d039f150ac1d5d5e8a05f502a26729c49ee SHA512: b18a706e56d0401e6a7441250f65d05d98ff9785644f17ae4e50be7537f04b9fbac109299bc36f6d8fd4b6398c9b252b3e8c8fc0fbed78889155ca15f9ead313 Homepage: https://cran.r-project.org/package=micar Description: CRAN Package 'micar' ('Mica' Data Web Portal Client) 'Mica' is a server application used to create data web portals for large-scale epidemiological studies or multiple-study consortia. 'Mica' helps studies to provide scientifically robust data visibility and web presence without significant information technology effort. 'Mica' provides a structured description of consortia, studies, annotated and searchable data dictionaries, and data access request management. This 'Mica' client allows to perform data extraction for reporting purposes. Package: r-cran-micd Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcalg, r-cran-mice, r-bioc-rbgl, r-cran-rfast Suggests: r-cran-nnet, r-cran-ranger, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-micd_1.1.2-1.ca2604.1_all.deb Size: 161810 MD5sum: e313664cc577e2a9dc3dcc44ac520050 SHA1: ea69b1eb4454fe32dac93ee2949d78f4c73bafd1 SHA256: dc2f2a034861028787bf8806eab5a5b270506cbd3f311ae9f00babeb99c89747 SHA512: 7bacd744033cd8ecb578c7facfb9c76801d62ae3955ea1d30108358cc969f6091647302f42893628041181ee4e62a552835895a33a56b65e8c56ef70534ee8f9 Homepage: https://cran.r-project.org/package=micd Description: CRAN Package 'micd' (Multiple Imputation in Causal Graph Discovery) Modified functions of the package 'pcalg' and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) ; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) ; Witte J, Foraita R, Didelez V (2022) . Package: r-cran-miceafter Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-proc, r-cran-rms, r-cran-mice, r-cran-mitml, r-cran-mitools, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-car, r-cran-rlang, r-cran-magrittr Suggests: r-cran-foreign, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bookdown, r-cran-readr Filename: pool/dists/resolute/main/r-cran-miceafter_0.5.0-1.ca2604.1_all.deb Size: 313870 MD5sum: ceed0ca880a673df53292c972eabbdfb SHA1: 2d74335bcdf6433222561eb88e71ead5c503408e SHA256: c74e6805186ea75443530bae06de3c347933e73da74b6348c589ab4d38a2d810 SHA512: 76f7dca814232a902f935c85c8304c38340db45da8cfe93ea4aa8ec3ef11dd89dffb8e817ff4a80a5409d0cd06ea21eb9368a068b7619f229644b217fbcc7d5c Homepage: https://cran.r-project.org/package=miceafter Description: CRAN Package 'miceafter' (Data and Statistical Analyses after Multiple Imputation) Statistical Analyses and Pooling after Multiple Imputation. A large variety of repeated statistical analysis can be performed and finally pooled. Statistical analysis that are available are, among others, Levene's test, Odds and Risk Ratios, One sample proportions, difference between proportions and linear and logistic regression models. Functions can also be used in combination with the Pipe operator. More and more statistical analyses and pooling functions will be added over time. Heymans (2007) . Eekhout (2017) . Wiel (2009) . Marshall (2009) . Sidi (2021) . Lott (2018) . Grund (2021) . Package: r-cran-micecon Architecture: all Version: 0.6-20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-misctools, r-cran-plm Suggests: r-cran-ecdat, r-cran-systemfit Filename: pool/dists/resolute/main/r-cran-micecon_0.6-20-1.ca2604.1_all.deb Size: 221784 MD5sum: 9090acd8d49bba6028ff62c84cf920e3 SHA1: 00e6103dfaf91ba7f99e710e146edf9b87cc5acf SHA256: be26607f29ad6eb2dad79af9898c2e540e48d4e90737920eb896518fbd0a50ad SHA512: 0c3c3f3d7aa56733b6ad9ad5ed042286e838fe237129d340b1b464067d5a8da87ad6163e389240150fcddbbe9d009ae1a54f41491b53d51d20cb85a15580dc82 Homepage: https://cran.r-project.org/package=micEcon Description: CRAN Package 'micEcon' (Microeconomic Analysis and Modelling) Various tools for microeconomic analysis and microeconomic modelling, e.g. estimating quadratic, Cobb-Douglas and Translog functions, calculating partial derivatives and elasticities of these functions, and calculating Hessian matrices, checking curvature and preparing restrictions for imposing monotonicity of Translog functions. 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The econometric estimation can be done by the Kmenta approximation, or non-linear least-squares using various gradient-based or global optimisation algorithms. Some of these algorithms can constrain the parameters to certain ranges, e.g. economically meaningful values. Furthermore, the non-linear least-squares estimation can be combined with a grid-search for the rho-parameter(s). The estimation methods are described in Henningsen et al. (2021) . 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Package: r-cran-miceconindex Architecture: all Version: 0.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-misctools Suggests: r-cran-ecdat, r-cran-micecon Filename: pool/dists/resolute/main/r-cran-miceconindex_0.1-8-1.ca2604.1_all.deb Size: 19876 MD5sum: 90a70197c6eac42a27518cd8f609a245 SHA1: e7c9b51f06ba4e191c44572a60f22158f323dd86 SHA256: 1b888fbec2d22b98eee508750eafdc54865d06f8469e978087f41997466939a8 SHA512: 2a0aa8b803ff674063601750d4af09a40d046c6a476d85685dc180bb3408c994d5f515c754d23a59079a90c57b830c349a92d3edb3de4b27d473d41cb3e1d4c8 Homepage: https://cran.r-project.org/package=micEconIndex Description: CRAN Package 'micEconIndex' (Price and Quantity Indices) Tools for calculating Laspeyres, Paasche, and Fisher price and quantity indices. 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Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) , the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'. Package: r-cran-micer Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-micer_0.2.1-1.ca2604.1_all.deb Size: 59970 MD5sum: ff37661c7687dbb65853de49b3b2ef98 SHA1: d9f4778c4d087b1cfc8b9b52a5d642f75a0442c7 SHA256: 6bf016d1f5edb62aeabad29e8d5edbea330db5453971abbe18b1ec774540e49f SHA512: 210f3972948d7d7263e916e9987f306c678e4e6a5b7fccf0e578f36a6aae1afc86e7ec7c560e9c9c3b076f6490514548315a3c971eefde78934ed40772b72f00 Homepage: https://cran.r-project.org/package=micer Description: CRAN Package 'micer' (Map Image Classification Efficacy) Map image classification efficacy (MICE) adjusts the accuracy rate relative to a random classification baseline (Shao et al. (2021) and Tang et al. (2024)). Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results. Package: r-cran-miceranger Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3236 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger, r-cran-data.table, r-cran-fnn, r-cran-ggplot2, r-cran-crayon, r-cran-corrplot, r-cran-ggpubr, r-cran-desctools, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-miceranger_1.5.0-1.ca2604.1_all.deb Size: 967894 MD5sum: 4fd71c05f6bb95fc352a69c57aeda00f SHA1: c8e8b0c74a077867d64d766fd66d4f612b70a7ec SHA256: 036b835b74cdf8bffeb2601b13bd73b831de3f29481498df7019589a3437afa2 SHA512: 16477609d9f18a984ab19794d7a11f35498885662ff81cf407d867ea4ee66db86e11948d2baba264f0b877f7e88cd643841fcec8d9d000b34199f4f407bb6bbf Homepage: https://cran.r-project.org/package=miceRanger Description: CRAN Package 'miceRanger' (Multiple Imputation by Chained Equations with Random Forests) Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) . Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel. Package: r-cran-michelrodange Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-michelrodange_1.0.0-1.ca2604.1_all.deb Size: 155580 MD5sum: 7e0d94bff657f99f23d2323283d83173 SHA1: 3a629af9c93274177cef8140b388ff46d95d8832 SHA256: 32da772c53a0dc3448b7d1186f630ba53723a4796b8956dbe745db6d011df52a SHA512: a49c1988158b552fbc2dd08fdd4113f8626c9e8e09a12cde127c84b932643c52cbb6a15383818bfa6053a51a6c733077c5bb341dad9ad48540d56b6f13507f57 Homepage: https://cran.r-project.org/package=michelRodange Description: CRAN Package 'michelRodange' (The Works (in Luxembourguish) of Michel Rodange) Michel Rodange was a Luxembourguish writer and poet who lived in the 19th century. His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuß am Frack an a Ma'nsgrëßt"), but he also wrote many more works, including Rodange, Tockert (1928) ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the plain text versions of his works available on . Package: r-cran-miclust Architecture: all Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doby, r-cran-combinat, r-cran-flexclust, r-cran-irr, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-xtable, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-miclust_1.2.8-1.ca2604.1_all.deb Size: 165790 MD5sum: d0fab997c915f58965ddd8afa6c4ccca SHA1: ef65cdb58d7bb801e13d006a4a7771a1006171e5 SHA256: 1aeddead3f22ecf23fa9af17ae7e32b40afe13ced4f65be1eb40cb32a70e87ea SHA512: 0324f6d07e7f566c80b1e333c2101b1c323b0a2d8ddf644fd1622ac417e391f9e32416f1e732e5f6aef90244d141f884ad85cba3a65f1e1e35e3d91885e30dd0 Homepage: https://cran.r-project.org/package=miclust Description: CRAN Package 'miclust' (Multiple Imputation in Cluster Analysis) Implementation of a framework for cluster analysis with selection of the final number of clusters and an optional variable selection procedure. The package is designed to integrate the results of multiple imputed datasets while accounting for the uncertainty that the imputations introduce in the final results. In addition, the package can also be used for a cluster analysis of the complete cases of a single dataset. The package also includes specific methods to summarize and plot the results. The methods are described in Basagana et al. (2013) . Package: r-cran-micompr Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-biotools, r-cran-mvn, r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-micompr_1.3.0-1.ca2604.1_all.deb Size: 1684342 MD5sum: 2788bc5c9eb2f3e935b41d23cf46b991 SHA1: fdb7b572cd36f60329edea59a13b66eb4f837d68 SHA256: 5b953e106e11cb924153a14a9beaf170003dff956770bb127a695908ac88fb87 SHA512: 31246b3f373e2dc1ed7efa78a7bbba80db8467f21dbd74c7f9ff9a196967694786db1e8050ced8e4b4d609fbdaebf7ddf41fca572debc2378756b1f5a33ed4c2 Homepage: https://cran.r-project.org/package=micompr Description: CRAN Package 'micompr' (Multivariate Independent Comparison of Observations) A procedure for comparing multivariate samples associated with different groups. It uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. The procedure is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) . Package: r-cran-micoptcm Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nleqslv, r-cran-survival, r-cran-distr Filename: pool/dists/resolute/main/r-cran-micoptcm_1.1-1.ca2604.1_all.deb Size: 75750 MD5sum: 33fe8bbbe325565be50bf8f40bc249c4 SHA1: 3c2959417115842665d38cec39e4c627b0d97513 SHA256: 2940aa6fc9e41bcfd3a6494e41552d8cf272594abee2798251a2c653dcd08aab SHA512: 91915fac99a90a16a3e3a73c7d81f6c6ee257a639af7f60b5bf8abe9c3eba44f8ae04a2d6a642f66531b86a35e6c1786f87e3c596495e4f8da785e8f02378aa3 Homepage: https://cran.r-project.org/package=miCoPTCM Description: CRAN Package 'miCoPTCM' (Promotion Time Cure Model with Mis-Measured Covariates) Fits Semiparametric Promotion Time Cure Models, taking into account (using a corrected score approach or the SIMEX algorithm) or not the measurement error in the covariates, using a backfitting approach to maximize the likelihood. Package: r-cran-microbats Architecture: all Version: 0.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-microbats_0.1-1-1.ca2604.1_all.deb Size: 15720 MD5sum: c6b7bda8c84bda1316a3220622570350 SHA1: cdf98a73719055d46dd5738a9328836c3a023e44 SHA256: 529a92001f35904de6c7392fb39ab9d8dbbcc07dca862b6112a1cf9d6f4786c7 SHA512: b9035b1bf68f37fcf9e07d8733c04a81ff72f9137fde7f11453b2a3233fbf0d855143a3076364ab904ffa1cd2957357892331deaf0f8b11277568afd51b06bdb Homepage: https://cran.r-project.org/package=microbats Description: CRAN Package 'microbats' (An Implementation of Bat Algorithm in R) A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (, ). Package: r-cran-microbial Architecture: all Version: 0.0.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1067 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plyr, r-cran-magrittr, r-cran-broom, r-bioc-phyloseq, r-cran-vegan, r-cran-rlang, r-cran-ggplot2, r-cran-ggpubr, r-bioc-deseq2, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-rstatix, r-cran-tidyr, r-cran-phangorn, r-cran-randomforest, r-bioc-edger Suggests: r-cran-markdown, r-bioc-dada2, r-cran-rmarkdown, r-cran-knitr, r-bioc-biostrings, r-bioc-decipher, r-cran-mass, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-microbial_0.0.22-1.ca2604.1_all.deb Size: 945484 MD5sum: f67091061a880e6b75dad6890e29078f SHA1: 2da93e67d52535adb5dfe58af992a9e7e0fb4305 SHA256: 7f547321bf4ac6c5bf1ea39a68ed7d19e9372642fec8ddecc58e98abcec73215 SHA512: 24740b882645aba922fdd902270fa1189536b2615cc28ac99d762ec8a87c73ca91f68dde3adae1957254bf81c92875f3cec35fee9918719dd05d5740469db09a Homepage: https://cran.r-project.org/package=microbial Description: CRAN Package 'microbial' (Do 16s Data Analysis and Generate Figures) Provides functions to enhance the available statistical analysis procedures in R by providing simple functions to analysis and visualize the 16S rRNA data.Here we present a tutorial with minimum working examples to demonstrate usage and dependencies. Package: r-cran-microbialgrowth Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlstools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-microbialgrowth_1.0.0-1.ca2604.1_all.deb Size: 772372 MD5sum: 8171b795d9de50ebd47c3e7602697567 SHA1: 9756e9f0cac7e35c9fd2cdc57b8652d7d91e35f8 SHA256: 776e027dfb11319b3a1c30d8c1f64fab042718a893d0d7dba472b3294a89d8ec SHA512: ffba73261abcb6ea83d1ae6f9a6912695a873a3916c3db4da9916283bd7ac97ccaf1015398dc7e7795c735fcc7fd4abc35396da5ab6521a8a810a8e4c7f9d5f7 Homepage: https://cran.r-project.org/package=MicrobialGrowth Description: CRAN Package 'MicrobialGrowth' (Estimates Growth Parameters from Models and Plots the Curve) Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) ), Baranyi model (Baranyi and Roberts (1994) ), Rosso model (Rosso et al. (1993) ) and linear model (Dantigny (2005) ). Package: r-cran-microbiomemqc Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-vegan Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-microbiomemqc_1.0.2-1.ca2604.1_all.deb Size: 21150 MD5sum: 9b58d8aaa6939c4a0b7faea13e9f866f SHA1: 7a37a038dc6a551285f3a15176e6c4ceb0168efb SHA256: bc301e675631f4b9d28ebeeb4aad9e8a213f98fcaa940920dd2e3b5fb93f35bb SHA512: f25c46c07105571f08c5720c0a7607e7f2477d8ecc10a75b3f354b8b8bfde83dec5dbaa2852b2975f3e3b7da255e8c0091b2f97ceda0257ae6fe8dbd0c11ea07 Homepage: https://cran.r-project.org/package=microbiomeMQC Description: CRAN Package 'microbiomeMQC' (Calculate 4 Key Reporting Measures) Perform calculations for the WHO International Reference Reagents for the microbiome. Using strain, species or genera abundance tables generated through analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a reference reagent. This package will calculate measures of sensitivity, False positive relative abundance, diversity, and similarity based on mean average abundances with respect to the reference reagent. Package: r-cran-microbiomesurv Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 869 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-survminer, r-cran-glmnet, r-cran-superpc, r-cran-lmtest, r-cran-gplots, r-cran-tidyr, r-cran-dplyr, r-bioc-microbiome, r-cran-pls Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-microbiomesurv_0.1.0-1.ca2604.1_all.deb Size: 757976 MD5sum: 01e5c52cc6f31caf6cac55ec5efde9cd SHA1: d3f6753fadac2040dadff6931742025fcbc9521f SHA256: 0edb8d33734296d600b1c1884617c021bae2a10c28ebcb6808240fbda33597c8 SHA512: 73ace082dbbd3bcb5c058b11f94378b09e4066928bdad6d8b8e6d978c85d347438c92bce8715c4145dfd940449c5018b30253fc8b1beca610d94be6c6855c4d2 Homepage: https://cran.r-project.org/package=MicrobiomeSurv Description: CRAN Package 'MicrobiomeSurv' (Biomarker Validation for Microbiome-Based SurvivalClassification and Prediction) An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) , Elastic net approach by Hui Zou and Trevor Hastie (2005) , supervised principle component analysis of Wold Svante et al. (1987) , and supervised partial least squares analysis by Inge S. Helland . Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique. Package: r-cran-microbtisda Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5837 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cluster, r-cran-dplyr, r-cran-ggdendro, r-cran-ggplot2, r-cran-ggraph, r-cran-mass, r-cran-mgcv, r-cran-plyr, r-cran-pracma, r-cran-randomforest, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-vegan, r-cran-visnetwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-microbtisda_0.1.0-1.ca2604.1_all.deb Size: 5365524 MD5sum: baf8dd063cde4020c7ee259cc89cdf9a SHA1: 8c904c9cbda1f68d30e0cb3fdff9b32726a03e6c SHA256: f16beeb883d934e40b175bee547d36d2b5e33aa3a379a009aebf49d04ad8e45e SHA512: 835b434df56b6e6584e26484555f03f1268137d36e4eaf32b28a02b209314e9daab58cdeba846fcc7063900c5fa6c627e9568c23e2256b3da2577d6506c19ce5 Homepage: https://cran.r-project.org/package=MicrobTiSDA Description: CRAN Package 'MicrobTiSDA' (Microbiome Time-Series Data Analysis) Provides tools specifically designed for analyzing longitudinal microbiome data. This tool integrates seven functional modules, providing a systematic framework for microbiome time-series analysis. For more details on inferences involving interspecies interactions see Fisher (2014) . Details on this package are also described in an unpublished manuscript. Package: r-cran-microcran Architecture: all Version: 0.9.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-plumber, r-cran-assertthat, r-cran-mime, r-cran-xtable Suggests: r-cran-minicran, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-microcran_0.9.0-1-1.ca2604.1_all.deb Size: 73560 MD5sum: aacfb196f8f6dd84ca626376a86a07aa SHA1: 866014b0e415afcbdcebfb6e9089a1d780fb50fe SHA256: 594c111db87648f81caf676049e79789423d270d5ca807bc13257b987aae0898 SHA512: 6aefd2abd3debfcb40167e10f2bc74e723dee14ac68209251508cee89d5b75338fdfc2dd3062872c1b9d8e6f802779cb76668d1d616a02235c5caf779c1c0bbb Homepage: https://cran.r-project.org/package=microCRAN Description: CRAN Package 'microCRAN' (Hosting an Independent CRAN Repository) Stand-alone HTTP capable R-package repository, that fully supports R's install.packages() and available.packages(). It also contains API endpoints for end-users to add/update packages. This package can supplement 'miniCRAN', which has functions for maintaining a local (partial) copy of 'CRAN'. Current version is bare-minimum without any access-control or much security. Package: r-cran-microdatoses Architecture: all Version: 0.8.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr Filename: pool/dists/resolute/main/r-cran-microdatoses_0.8.15-1.ca2604.1_all.deb Size: 141834 MD5sum: 8eafaf08c2dfa556703d361832b06916 SHA1: e87a0063a274751ac21726d3dac317216735cf0f SHA256: 2992cfce1c4eebde6f9efae6ef6023a1bb946bbd377829ad10323fd9d4dc7fb1 SHA512: 593b170a4299b31c78c6d2230730140e7a277bad3762d021c7d007d21ae7febb1c0cdd79555f3723e38b7689a4cb94206487bff5551d1aa4132f7a660b5de30d Homepage: https://cran.r-project.org/package=MicroDatosEs Description: CRAN Package 'MicroDatosEs' (Utilities for Official Spanish Microdata) Provides utilities for reading and processing microdata from Spanish official statistics with R. Package: r-cran-microdiluter Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggthemes, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rstatix, r-cran-stringr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-microdiluter_1.0.1-1.ca2604.1_all.deb Size: 193800 MD5sum: 3561e514124f72f332c8e042f64c3d03 SHA1: 8ae4861cfa1227e9b33aa0da72b7347a3fb6fd70 SHA256: dc559b7f4c1bd5915e31a3626d328428f4a0cafc5f7647414a8a0fbab1673b69 SHA512: e40ef88daba5366cfff2408dbdd5495e9dd02221f435e2430b1372e8f497ca5f86e72866ceaaaf648951a1b23025be8e1624228023ed1d40bfc8ca0db41f88cf Homepage: https://cran.r-project.org/package=microdiluteR Description: CRAN Package 'microdiluteR' (Analysis of Broth Microdilution Assays) A framework for analyzing broth microdilution assays in various 96-well plate designs, visualizing results and providing descriptive and (simple) inferential statistics (i.e. summary statistics and sign test). The functions are designed to add metadata to 8 x 12 tables of absorption values, creating a tidy data frame. Users can choose between clean-up procedures via function parameters (which covers most cases) or user prompts (in cases with complex experimental designs). Users can also choose between two validation methods, i.e. exclusion of absorbance values above a certain threshold or manual exclusion of samples. A function for visual inspection of samples with their absorption values over time for certain group combinations helps with the decision. In addition, the package includes functions to subtract the background absorption (usually at time T0) and to calculate the growth performance compared to a baseline. Samples can be visually inspected with their absorption values displayed across time points for specific group combinations. Core functions of this package (i.e. background subtraction, sample validation and statistics) were inspired by the manual calculations that were applied in Tewes and Muller (2020) . Package: r-cran-microeco Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4294 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-ape, r-cran-vegan, r-cran-rlang, r-cran-data.table, r-cran-magrittr, r-cran-dplyr, r-cran-tibble, r-cran-scales, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-igraph, r-cran-lifecycle Suggests: r-cran-gunifrac, r-cran-mass, r-cran-ggpubr, r-cran-randomforest, r-cran-ggdendro, r-cran-ggrepel, r-cran-agricolae, r-cran-gridextra, r-cran-picante, r-cran-pheatmap, r-cran-rgexf, r-cran-mice, r-cran-ggally Filename: pool/dists/resolute/main/r-cran-microeco_2.2.0-1.ca2604.1_all.deb Size: 4170182 MD5sum: 386d0a77d81e5cc7bd2f4c3253383d9f SHA1: a99e2111b7ec12196ad210eec0cd42f79d67f78c SHA256: 15c61b7d26e05a28eef13087c5528166bd3d23dd6c9705ff67ad1c16b7cc38e6 SHA512: 95300fd115c031ca9583b57edf1bf25abd7196529c89d927fb6347eb60ba2144d892968ea3e2e6dfd0125ffec0c0cce83ce38801609e6c250ffbc62f0db80df9 Homepage: https://cran.r-project.org/package=microeco Description: CRAN Package 'microeco' (Microbial Community Ecology Data Analysis) A series of data analysis approaches for microbiome omics data based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis and functional analysis. Package: r-cran-microinverterdata Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-units Suggests: r-cran-httptest2, r-cran-knitr, r-cran-lubridate, r-cran-pins, r-cran-rmarkdown, r-cran-tibble, r-cran-tsibble, r-cran-imputets, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-microinverterdata_0.4.0-1.ca2604.1_all.deb Size: 887114 MD5sum: 970dd57944d5fed6511fd615669fea5c SHA1: 06386fcd98801004132e63617b8769529fcba65f SHA256: 8bb8fbc9164a16fefb077ccb0d16f55aeff6e8ee08fbc9567e9db568fdbf33f4 SHA512: ed43a9b97c7c33bbc1b669628772d9abafa9aed46427241afca5d2a57c1ca33544f91b71a83fcf98ecd0dde7e3fc4927699ccf791130272d82dc4dcf455fe569 Homepage: https://cran.r-project.org/package=microinverterdata Description: CRAN Package 'microinverterdata' (Collect your Microinverter Data) Collect and normalize local microinverter energy and power production data through off-cloud API requests. Currently supports 'APSystems', 'Enphase', and 'Fronius' microinverters. Package: r-cran-micromacromultilevel Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-micromacromultilevel_0.4.0-1.ca2604.1_all.deb Size: 47708 MD5sum: d01b0ffd46e31c77a7e9d96bd00f03a5 SHA1: 7051cc538f3289cb5b692bb8f1569aac2f871e37 SHA256: cbcb2ab2683989249d0f2f914cfd93ac1d54ab62a7554ee3201a746c3c9196b7 SHA512: ae34617c08d3f5b90cccaeda032e2f5a6e739fb665f2194d553c697ece6fe10332c7c3ddb36a4699a154c2e382487e6e947224ffc1a7c01caaceb1b8ccf7a76c Homepage: https://cran.r-project.org/package=MicroMacroMultilevel Description: CRAN Package 'MicroMacroMultilevel' (Micro-Macro Multilevel Modeling) Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to model micro-macro situations, wherein individual-level (micro) predictors (and other group-level predictors) are used to predict a group-level (macro) outcome variable in an unbiased way. Package: r-cran-micromap Architecture: all Version: 1.9.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1736 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-sp, r-cran-sf, r-cran-ggplot2 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-micromap_1.9.12-1.ca2604.1_all.deb Size: 1358510 MD5sum: 38b6e59b3e05ecf23c05a1e5a3fe0960 SHA1: 68b92dab95ece616b617ae7cc81ac791bd8a1991 SHA256: 7717eb00600e589fedffa2042e6dd7e5f309adee1c5d318bf523fe1f8b578696 SHA512: 22ea7809a6ad22a21a1a2f8a60497a9b312c4b8599a87f3086474d44fe19f580ac0acc2a5e0d3708289ba4c9ec928a5788ab8ce001c0ca2ce119c57678af9051 Homepage: https://cran.r-project.org/package=micromap Description: CRAN Package 'micromap' (Linked Micromap Plots) This group of functions simplifies the creation of linked micromap plots. Please see for additional details. Package: r-cran-micromapst Architecture: all Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3694 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-rcolorbrewer, r-cran-labeling, r-cran-sf, r-cran-spdep, r-cran-rmapshaper, r-cran-readxl, r-cran-writexl Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-micromapst_3.1.1-1.ca2604.1_all.deb Size: 3158076 MD5sum: ef5dc3af604331deac7bbb8a91529d1a SHA1: 776f1a78a48ac56c2096b363f3271db095dc1808 SHA256: e1803dc73f34df761252659add48a4cd544a2f039d6f3568222f438c7bcee61a SHA512: 7ac8a07c8eff13e53f12481037041a2fd304b2ab1588c5438cbe360f2b5f4bd479f28964a7eee4b9cf8cbbc970a690a67022c54baf1ee24be6c8ad6fc806c390 Homepage: https://cran.r-project.org/package=micromapST Description: CRAN Package 'micromapST' (Linked Micromap Plots for U. S. and Other Geographic Areas) Provides the users with the ability to quickly create linked micromap plots for a collection of geographic areas. Linked micromap plots are visualizations of geo-referenced data that link statistical graphics to an organized series of small maps or graphic images. The Help description contains examples of how to use the 'micromapST' function. Contained in this package are border group datasets to support creating linked micromap plots for the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, the 105 counties in the state of Kansas, the 62 counties of New York, the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas in China, the 218 administrative areas in the UK and Ireland (for testing only), the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa continent. A border group dataset contains the boundaries related to the data level areas, a second layer boundaries, a top or third layer boundary, a parameter list of run options, and a cross indexing table between area names, abbreviations, numeric identification and alias matching strings for the specific geographic area. By specifying a border group, the package create linked micromap plots for any geographic region. The user can create and provide their own border group dataset for any area beyond the areas contained within the package with the 'BuildBorderGroup' function. In April of 2022, it was announced that 'maptools', 'rgdal', and 'rgeos' R packages would be retired in middle to end of 2023 and removed from the CRAN libraries. The 'BuildBorderGroup' function was dependent on these packages. 'micromapST' functions were not impacted by the retired R packages. Upgrading of 'BuildBorderGroup' function was completed and released with version 3.0.0 on August 10, 2023 using the 'sf' R package. References: Carr and Pickle, Chapman and Hall/CRC, Visualizing Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson, and Carr (2015), micromapST: Exploring and Communicating Geospatial Patterns in US State Data., Journal of Statistical Software, 63(3), 1-25., . Copyrighted 2013, 2014, 2015, 2016, 2022, 2023, 2024, and 2025 by Carr, Pearson and Pickle. Package: r-cran-micromodal Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-bslib, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-micromodal_1.0.0-1.ca2604.1_all.deb Size: 28728 MD5sum: bb72122abc02383921090a21052783f6 SHA1: e6f8b483754ce96b4ad9e383ba2b5bad8d4e2302 SHA256: d72e508d4f53d56d3063f596991b6709f76f0227d9831d3241b078ea77a688d6 SHA512: eac3fc961ce821b0cb15ea474fa062b1d970ff75d595915a5ce53ac2dfe9d968a1ed9ee75053a9b3051723309213884b6f56604439aa673a5da464e56bfab5fc Homepage: https://cran.r-project.org/package=micromodal Description: CRAN Package 'micromodal' (Create Simple and Elegant Modal Dialogs in 'shiny') Enables you to create accessible modal dialogs, with confidence and with minimal configuration. Package: r-cran-microniche Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-microniche_1.0.0-1.ca2604.1_all.deb Size: 82032 MD5sum: a907edabb913bf982f509f71909fe44d SHA1: 782c57d68aa061a1f1428db1f4e367f2789b538b SHA256: eb4305402d16ae77ab7e3d8164fbd7ed8c883888bc6c98ba1bfe6e274783e520 SHA512: 5e3ebe01566c9dcca129e6d01369fe447ffdb3aa39b5c16b522f9f2f19126c7c14504f3a4928274cfff78fd8d3454141b5be00709854cc4e129494bf7075d3eb Homepage: https://cran.r-project.org/package=MicroNiche Description: CRAN Package 'MicroNiche' (Microbial Niche Measurements) Measures niche breadth and overlap of microbial taxa from large matrices. Niche breadth measurements include Levins' niche breadth (Bn) index, Hurlbert's Bn and Feinsinger's proportional similarity (PS) index. (Feinsinger, P., Spears, E.E., Poole, R.W. (1981) ). Niche overlap measurements include Levin's Overlap (Ludwig, J.A. and Reynolds, J.F. (1988, ISBN:0471832359)) and a Jaccard similarity index of Feinsinger's PS values between taxa pairs, as Proportional Overlap. Package: r-cran-micronutr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-micronutr_0.1.1-1.ca2604.1_all.deb Size: 431226 MD5sum: 0573793ce46ff4da258edc3e85279662 SHA1: 188f336def22c44670af50b60c0c533e6568b188 SHA256: 7fc0065469095b2cc7e65b2f0f0f5946eb767b21f7c49752876dd3d3479c7972 SHA512: 87753090905788c5da61697b85bf289e5074bf6bc756693339ef6e0f4ca04bc77a3c8b51a67b136a3126a2e0379ad1fea8e93aec98f34bf1c46adaf2d7a02804 Homepage: https://cran.r-project.org/package=micronutr Description: CRAN Package 'micronutr' (Determining Vitamin and Mineral Status of Populations) Vitamin and mineral deficiencies continue to be a significant public health problem. This is particularly critical in developing countries where deficiencies to vitamin A, iron, iodine, and other micronutrients lead to adverse health consequences. Cross-sectional surveys are helpful in answering questions related to the magnitude and distribution of deficiencies of selected vitamins and minerals. This package provides tools for calculating and determining select vitamin and mineral deficiencies based on World Health Organization (WHO) guidelines found at . Package: r-cran-micropan Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-microseq, r-cran-dplyr, r-cran-stringr, r-cran-igraph, r-cran-tibble, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-micropan_2.1-1.ca2604.1_all.deb Size: 1700912 MD5sum: 7db92cd1264084d34fb1424fd108ffdd SHA1: 7c9f5e8291eb9b1890823cde79be0e89ffeb7e9c SHA256: de1e879bea6466166fd5063025861489b62f219d06f79426370094885ac73b3c SHA512: 548bff52c902e9c0ea8e98e57e9c8e5c2e78eb0a781a6fd4f90f8f48c4b177104804dd27a9c8a9d1c7019ae42a204d62104bac2e637f4dc8ec3d28ec4dde5208 Homepage: https://cran.r-project.org/package=micropan Description: CRAN Package 'micropan' (Microbial Pan-Genome Analysis) A collection of functions for computations and visualizations of microbial pan-genomes. Package: r-cran-microplot Architecture: all Version: 1.0-47-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-hh, r-cran-lattice, r-cran-flextable, r-cran-officer, r-cran-ggplot2, r-cran-htmltools, r-cran-cowplot Suggests: r-cran-reshape2, r-cran-latticeextra, r-cran-xtable, r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-htmltable Filename: pool/dists/resolute/main/r-cran-microplot_1.0-47-1.ca2604.1_all.deb Size: 380470 MD5sum: 4e7de75640177e4ce52c4605727bf407 SHA1: 27e9c660521caa129a2518d6d07da3d0b574e7ef SHA256: d0f54cda93b0d12942e4b43bcd8c6cf644213d8a256c272a6ce5f38ad8a99814 SHA512: fcdc9f7054b2167acb3ac98eb6070466eae8e677fd300ec091c8ef26b49ecd3d38deb2d0f16939601138c4361f516f9b3983e3334aa23eacd4b9fe569c821a5b Homepage: https://cran.r-project.org/package=microplot Description: CRAN Package 'microplot' (Microplots (Sparklines) in 'LaTeX', 'Word', 'HTML', 'Excel') The microplot function writes a set of R graphics files to be used as microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word', or 'Excel' files. For 'LaTeX', we provide methods for the Hmisc::latex() generic function to construct 'latex' tabular environments which include the graphs. These can be used directly with the operating system 'pdflatex' or 'latex' command, or by using one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an intermediary. For 'MS Word', the msWord() function uses the 'flextable' package to construct 'Word' tables which include the graphs. There are several distinct approaches for constructing HTML files. The simplest is to use the msWord() function with argument filetype="html". Alternatively, use either 'Emacs org-mode' or the htmlTable::htmlTable() function to construct an 'HTML' file containing tables which include the graphs. See the documentation for our as.htmlimg() function. For 'Excel' use on 'Windows', the file examples/irisExcel.xls includes 'VBA' code which brings the individual panels into individual cells in the spreadsheet. Examples in the examples and demo subdirectories are shown with 'lattice' graphics, 'ggplot2' graphics, and 'base' graphics. Examples for 'LaTeX' include 'Sweave' (both 'LaTeX'-style and 'Noweb'-style), 'knitr', 'emacs org-mode', and 'rmarkdown' input files and their 'pdf' output files. Examples for 'HTML' include 'org-mode' and 'Rmd' input files and their webarchive 'HTML' output files. In addition, the as.orgtable() function can display a data.frame in an 'org-mode' document. The examples for 'MS Word' (with either filetype="docx" or filetype="html") work with all operating systems. The package does not require the installation of 'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files. Package: r-cran-micropop Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-visnetwork, r-cran-testthat Suggests: r-cran-rmarkdown, r-cran-r.rsp, r-cran-knitr, r-cran-webshot Filename: pool/dists/resolute/main/r-cran-micropop_1.6-1.ca2604.1_all.deb Size: 843146 MD5sum: 6ef8273c8c51ea39e94a46478079baed SHA1: 512fac0a12e5ba09fcbcbeaaeb0060d4509dd7eb SHA256: 8fc2466d5377cc3df631f1fd40c567491e747a45761c9c0d77cf4270d869dde3 SHA512: 8e90c9abe878c9259e5c4b2ef1e16ed9a176a41bf61409ccc486961e760885e1d84a62d66d4a2fdd9e9f8b43eb20ec3a59764c0e6831c50918ea79f05e9ae843 Homepage: https://cran.r-project.org/package=microPop Description: CRAN Package 'microPop' (Process-Based Modelling of Microbial Populations) Modelling interacting microbial populations - example applications include human gut microbiota, rumen microbiota and phytoplankton. Solves a system of ordinary differential equations to simulate microbial growth and resource uptake over time. This version contains network visualisation functions. Package: r-cran-microsoft365r Architecture: all Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azureauth, r-cran-azuregraph, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-vctrs, r-cran-mime Suggests: r-cran-openssl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-blastula, r-cran-emayili, r-cran-readr, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-microsoft365r_2.4.1-1.ca2604.1_all.deb Size: 775054 MD5sum: e901b3d5c534c06c7d161552d95fb302 SHA1: 52a742445e76962b1dcce3860c7c5cb6037b9b95 SHA256: ad033cd55385b636fd5435cf6093ac7d18797db92c2b41f404c3216bd6cd15a0 SHA512: 914c75fa0caa631e0849d619f4cf1fef644a21bfcc982b65b628b4dbbc13206f7890585fb3a3971ebb8abe4220d2061a38634f9e52764381a865e2838188a2ce Homepage: https://cran.r-project.org/package=Microsoft365R Description: CRAN Package 'Microsoft365R' (Interface to the 'Microsoft 365' Suite of Cloud Services) An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders. Package: r-cran-microsynth Architecture: all Version: 2.0.51-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-kernlab, r-cran-lowrankqp, r-cran-pracma Suggests: r-cran-mass, r-cran-xlsx, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-microsynth_2.0.51-1.ca2604.1_all.deb Size: 2134484 MD5sum: 4f2a44b9fa8a8734df5cbf57d1e2536f SHA1: 1b85382f615068984cfe4bf71096dba609aa773b SHA256: 499a23e90a86f98f1180aebfa976c546c8fb46fcff98d164343ac0b8a9908efa SHA512: f586f7968c31af2b2dd01c57f208853b0c12d2d4acd4684d0e6cd0fe540f4aa8097851a491c99bf657851952dfafc27e56f9fdc7a9f3783cdfcb62a356e2e702 Homepage: https://cran.r-project.org/package=microsynth Description: CRAN Package 'microsynth' (Synthetic Control Methods with Micro- And Meso-Level Data) A generalization of the 'Synth' package that is designed for data at a more granular level (e.g., micro-level). Provides functions to construct weights (including propensity score-type weights) and run analyses for synthetic control methods with micro- and meso-level data; see Robbins, Saunders, and Kilmer (2017) and Robbins and Davenport (2021) . Package: r-cran-micsim Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlecuyer, r-cran-snowfall Suggests: r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-micsim_3.0.0-1.ca2604.1_all.deb Size: 517566 MD5sum: 26f0fadf8693f7a1190f2763b8e604c9 SHA1: 7cf2a526c011377799b6ab66d381ac7148d9fe7e SHA256: cc6ac3150d44a8609402e5a8f87fff2dd53948739eb3c88c43d8155396a5862e SHA512: c3bd21527d1de374a0d17ccbb4b66e98e0bcc6692f68e2dd156058fee1297b90d6c02dde7f2e3b28f0f0ec18aa4639cf12a8aaf8be2ea35b87d5c8e0f1228544 Homepage: https://cran.r-project.org/package=MicSim Description: CRAN Package 'MicSim' (Performing Continuous-Time Microsimulation) This toolkit allows performing continuous-time microsimulation for a wide range of life science (demography, social sciences, epidemiology) applications. Individual life-courses are specified by a continuous-time multi-state model as described in Zinn (2014) . Package: r-cran-micss Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-micss_0.3.1-1.ca2604.1_all.deb Size: 167914 MD5sum: 8a8a749608419e10026bba1fc44eed6f SHA1: 3503f9c7f7b38ce83da61c25d12591556536d20e SHA256: 3257d95499564770b325d8383d6db5ae4c0e41860a9b09f44dc44d95794dcc24 SHA512: 9cca273e48660085b1ecb6c366a9e0683f9cd6e335b7f8f3167939630cea26eb18d6bdd6bdfb778514d000e29f0d8ee7344d89ac2c43da072834a29cea7c9950 Homepage: https://cran.r-project.org/package=micss Description: CRAN Package 'micss' (Modified Iterative Cumulative Sum of Squares Algorithm) Companion package of Carrion-i-Silvestre & Sansó (2026): "Testing for Constant Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified Iterative Cumulative Sum of Squares Algorithm, which is an extension of the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the unconditional variance of a time series controlling for the tail index of the underlying distribution. The fourth order moment is estimated non-parametrically to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). Critical values and p-values are generated using a Generalized Extreme Value distribution approach. References Carrion-i-Silvestre J.J & Sansó A (2026) . Inclan C & Tiao G.C (1994) , Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) . Package: r-cran-micvar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-micvar_0.1.0-1.ca2604.1_all.deb Size: 29654 MD5sum: b33bb2faf55025facbe561c9b5c470c3 SHA1: f3a183717aff93102d423462ca6fcd90c7494e52 SHA256: ceb11abb84182a645aec8e52a2b661477f598746ad44db6af9c2fe8c77088f8e SHA512: 5fd5d14aabc1cdece18517db8b31c091c311b39ed61e6a391005a4e027ef2c4274a03debb97655ed814b556329258bf4cd0eea7c37415d0bd3d1968980ba01f5 Homepage: https://cran.r-project.org/package=micvar Description: CRAN Package 'micvar' (Order Selection in Vector Autoregression by Mean SquareInformation Criteria) Implements order selection for Vector Autoregressive (VAR) models using the Mean Square Information Criterion (MIC). Unlike standard methods such as AIC and BIC, MIC is likelihood-free. This method consistently estimates VAR order and has robust performance under model misspecification. For more details, see Hellstern and Shojaie (2025) . Package: r-cran-midas2 Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-coda, r-cran-r2jags Filename: pool/dists/resolute/main/r-cran-midas2_1.1.0-1.ca2604.1_all.deb Size: 47760 MD5sum: 0146ab2b1487fc28b2a1a4137527b1ac SHA1: 570c00d3b06c8c2e594745d0aa4986ebd0fe9048 SHA256: 1e49578621c0ece62584768d3d6d73ecb39395c3bfd8171d5e025a96de971cea SHA512: e7d26c3ba99968e659c6e7310a2b9497e0a20acfc5e53a8e8a7a8e16127239c230ef571d571cb4318b6c320b1c852093c6c9f68f8dd5a831b51a51b3c7c7a786 Homepage: https://cran.r-project.org/package=midas2 Description: CRAN Package 'midas2' (Bayesian Platform Design with Subgroup EfficacyExploration(MIDAS-2)) The rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging, which necessitates innovative, integrated, and efficient trial designs(Yuan, Y., et al. (2016) ). MIDAS-2 package enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We used a regression model to characterize the efficacy pattern in subgroups. Information borrowing was applied through Bayesian hierarchical model to improve trial efficiency considering the limited sample size in subgroups(Cunanan, K. M., et al. (2019) ). MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion(Wathen, J. K., & Thall, P. F. (2017) ). Package: r-cran-midas Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-midas_1.0.1-1.ca2604.1_all.deb Size: 14472 MD5sum: 1938b75dd30972035b6e65cd008b06b0 SHA1: 336afde5da1c1a50575d3357d19805740d3afe3e SHA256: 87ea620be0e39d3a7b99938ef7d34b7cd446c6e7bc6ec1207bc718ea46ff0918 SHA512: c29cc82a87a87c2d29bd522e8276bd9cb192b7509df953657bcebef1ca9d702c8c88290ed93e54b17d10375c12ce811ccca3982202779245ef3bf28e77b1aa9a Homepage: https://cran.r-project.org/package=midas Description: CRAN Package 'midas' (Turn HTML 'Shiny') Contains functions for converting existing HTML/JavaScript source into equivalent 'shiny' functions. Bootstraps the process of making new 'shiny' functions by allowing us to turn HTML snippets directly into R functions. Package: r-cran-midasim Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-mass, r-cran-pracma, r-cran-scam Suggests: r-cran-vegan, r-bioc-phyloseq, r-cran-rmarkdown, r-cran-knitr, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-midasim_2.0-1.ca2604.1_all.deb Size: 240360 MD5sum: 1309a17c9012940ce7a7e2741ea7b4bd SHA1: aaf8b5e8ca8606c53f3ed1e6dd838f66b9e8c50b SHA256: 72e212ef7b5e99de742c672eead3cff35ecf1f2162476c3617ee8a2eed13db76 SHA512: 1cd93dc58f0808c68862888ff8648d98b54ebfe185eaa18c5f1cf8acb0726b86bb76c9400caae00c4f3e838a3ba64862b26a65c2afde973357f1cb8d081e157e Homepage: https://cran.r-project.org/package=MIDASim Description: CRAN Package 'MIDASim' (Simulating Realistic Microbiome Data using 'MIDASim') The 'MIDASim' package is a microbiome data simulator for generating realistic microbiome datasets by adapting a user-provided template. It supports the controlled introduction of experimental signals-such as shifts in taxon relative abundances, prevalence, and sample library sizes-to create distinct synthetic populations under diverse simulation scenarios. For more details, see He et al. (2024) . Package: r-cran-midasr Architecture: all Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich, r-cran-optimx, r-cran-quantreg, r-cran-mass, r-cran-numderiv, r-cran-matrix, r-cran-forecast, r-cran-zoo, r-cran-formula, r-cran-texreg Suggests: r-cran-testthat, r-cran-lubridate, r-cran-xts Filename: pool/dists/resolute/main/r-cran-midasr_0.9-1.ca2604.1_all.deb Size: 926838 MD5sum: b11fd160b5eba98f321daa6c84344bc9 SHA1: 848c654fc49c026b9eb037eef59f3f3042aa28d5 SHA256: 584685c73fedb40b509de092038810afbf962d6f590bb1ec2c28591c590950c6 SHA512: 962ec7289b3b6553f198892a37ccce67e4e04b11bdde91ecdf6d1d231b3d9a8faf57683a03a56654bb748971630c76ed26dcd82e9c638ad045ac8151381c8fbb Homepage: https://cran.r-project.org/package=midasr Description: CRAN Package 'midasr' (Mixed Data Sampling Regression) Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions. Package: r-cran-midastouch Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mice Filename: pool/dists/resolute/main/r-cran-midastouch_1.3-1.ca2604.1_all.deb Size: 22526 MD5sum: b32f6cca088f50fafe00e9bec818e94d SHA1: 1a67d3bc1a9065973444477eefd8d5a8c2e9b0a2 SHA256: 3fada8bd8fa19a6c4fac35b0b4abf55399e5b97a40cc0fccca77bd86c40a40a1 SHA512: 37789d4e5ce373e08672259fc671bcef2cff33fa1a4e3915ee6730a6cd484a2d3fc5788401275229920522e1bea6ce72deff88c14522df8818341a70809d4ddc Homepage: https://cran.r-project.org/package=midastouch Description: CRAN Package 'midastouch' (Multiple Imputation by Distance Aided Donor Selection) Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's MIDAS. Package: r-cran-midfieldr Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-wrapr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-midfieldr_1.0.3-1.ca2604.1_all.deb Size: 1151786 MD5sum: be2b3da0ec863a7f28bdba71fd11478a SHA1: c2aedf33d37de62ac8ae833d9ac0452b23be0768 SHA256: 89e092f40d55dbbf9cf9d815154f5897b142459bfba17d6221d21ec72e64aceb SHA512: 67bff175029a9cb1b2151cb2ca988f24e1f0374f35255fe8297884025c2c2148688ea2721f919dd4003ac8f205d5c880599ee31738d441cb4e756a304882a3ad Homepage: https://cran.r-project.org/package=midfieldr Description: CRAN Package 'midfieldr' (Tools and Methods for Working with MIDFIELD Data in 'R') Provides tools and demonstrates methods for working with individual undergraduate student-level records (registrar's data) in 'R'. Tools include filters for program codes, data sufficiency, and timely completion. Methods include gathering blocs of records, computing quantitative metrics such as graduation rate, and creating charts to visualize comparisons. 'midfieldr' interacts with practice data provided in 'midfielddata', an R data package available at . 'midfieldr' also interacts with the full MIDFIELD database for users who have access. As of the transfer of MIDFIELD to the American Society for Engineering Education in 2023, the development, expansion, and study of MIDFIELD has been supported by the National Science Foundation grants 0337629, 0646441, 0729596, 0734062, 0835914, 0935157, 0935058, 0969474, 1025171, 1129383, 1232740, 1329283, 1361058, 1545667, 2142087, 2141903, and 2152441. Package: r-cran-midi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3914 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-plotly, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-midi_0.1.0-1.ca2604.1_all.deb Size: 3931050 MD5sum: 8564d76402fe5448bedf766f07d7001d SHA1: b073045cf1be3f2cdabbab6f0f94bc9d2ca63a71 SHA256: e7173137a48bce9a4beb4bf4adc1c3fc81d67ab0a9af417502e8dd0bd16e24c4 SHA512: 0410cfe82f44435d78ff4534f660a2e39c2bb144898ed715e5b41fcec2b8308ab15dad39bd6408ca0ce567065bdefb729d192c5596c73f8380b61f4ccc29927d Homepage: https://cran.r-project.org/package=midi Description: CRAN Package 'midi' (Microstructure Information from Diffusion Imaging) An implementation of a taxonomy of models of restricted diffusion in biological tissues parametrized by the tissue geometry (axis, diameter, density, etc.). This is primarily used in the context of diffusion magnetic resonance (MR) imaging to model the MR signal attenuation in the presence of diffusion gradients. The goal is to provide tools to simulate the MR signal attenuation predicted by these models under different experimental conditions. The package feeds a companion 'shiny' app available at that serves as a graphical interface to the models and tools provided by the package. Models currently available are the ones in Neuman (1974) , Van Gelderen et al. (1994) , Stanisz et al. (1997) , Soderman & Jonsson (1995) and Callaghan (1995) . Package: r-cran-midn Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biasedurn Filename: pool/dists/resolute/main/r-cran-midn_1.0-1.ca2604.1_all.deb Size: 36110 MD5sum: 3a7f69b3d0d3089fff70fc2150a97386 SHA1: d75033f6a54412f14548ff52bbb059cc383e16c1 SHA256: a3a3b6a02b18cd6e05e6e35e80c06b0a8f4298f860284e52e6a9aba40f2b2fe2 SHA512: 8c7e47052bc20de797fbcd565cd3a551d39d3941480bd3e87ee75c77bfc75331062de454ba5c065bc8830b38621b2c04ad164b1c53588d6c08b154f6f1c80fd3 Homepage: https://cran.r-project.org/package=MIDN Description: CRAN Package 'MIDN' (Nearly Exact Sample Size Calculation for Exact PowerfulNonrandomized Tests for Differences Between BinomialProportions) Implementation of the mid-n algorithms presented in Wellek S (2015) Statistica Neerlandica 69, 358-373 for exact sample size calculation for superiority trials with binary outcome. Package: r-cran-midoc Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1081 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-blorr, r-cran-dagitty, r-cran-glue, r-cran-lifecycle, r-cran-mfp2, r-cran-mice, r-cran-rlang, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-midoc_1.0.0-1.ca2604.1_all.deb Size: 438120 MD5sum: a15e47f68ed9a6928cd41c0988a96bbf SHA1: 7160538b84d549118e0c81fc6d4eab4904deea03 SHA256: c3f1821c37c7c7ddb5e53c695a95e72102424b9cf578c532b352dbb79cc4c312 SHA512: 9306a9a9a923e3381ba8c9919c5f888dc92447146e722b484afe0d23804a3d392af51dbd77ac64ee4d5429f090730bacd7f20321cacd4e7bde597b01fc8066db Homepage: https://cran.r-project.org/package=midoc Description: CRAN Package 'midoc' (A Decision-Making System for Multiple Imputation) A guidance system for analysis with missing data. 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The tests are: Tukey Midrange ('TM' test), Student-Newman-Keuls Midrange ('SNKM' test), Means Grouping Midrange ('MGM' test) and Means Grouping Range ('MGR' test). The first two tests were published by Batista and Ferreira (2020) . The last two were published by Batista and Ferreira (2023) . Package: r-cran-miebl Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-miebl_0.1.0-1.ca2604.1_all.deb Size: 21750 MD5sum: b363c8a50b1f51e7ab3f06784c90d856 SHA1: 6f4b5a062d2980b6e46b0c2c41ded69bfcf3da05 SHA256: 33a7800c9304ba5d5d2040a10af12b33f9f3acd9e2c61f3abd6f8be025736343 SHA512: 866b12adab1b8629a5f34d1d52b40bd42e96a0b30b9da60b89b2474ca8ca61953be378c103866e1145dc7da10acbbd9e986fda2ee3172d34f41a3f894013d8a6 Homepage: https://cran.r-project.org/package=miebl Description: CRAN Package 'miebl' (Performance Criteria Modeler for Discrete Trial Training) Provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. 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Package: r-cran-miesmuschel Architecture: all Version: 0.0.4-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-paradox, r-cran-mlr3misc, r-cran-checkmate, r-cran-r6, r-cran-bbotk, r-cran-data.table, r-cran-matrixstats, r-cran-lgr Suggests: r-cran-tinytest, r-cran-mlr3tuning, r-cran-mlr3, r-cran-mlr3learners, r-cran-ranger, r-cran-xgboost, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-miesmuschel_0.0.4-3-1.ca2604.1_all.deb Size: 1535332 MD5sum: 9ffa54f8decf7336bb8df18f1f331dfa SHA1: 44e31741617e515eb57c74c8c6ea77a6e0d446ec SHA256: 2f55ed790c542393c1a055f89d94c9eb8136ab317f2d264e5a860c8d60adb398 SHA512: fea97b58b24bdc3291c3b55b1ceeba90664d62f55931b09734d650cb88c66a3806f999f97c3ab0af782a9dc02304dc41583252c34c4e19776fdbf991a9b2b620 Homepage: https://cran.r-project.org/package=miesmuschel Description: CRAN Package 'miesmuschel' (Mixed Integer Evolution Strategies) Evolutionary black box optimization algorithms building on the 'bbotk' package. 'miesmuschel' offers both ready-to-use optimization algorithms, as well as their fundamental building blocks that can be used to manually construct specialized optimization loops. The Mixed Integer Evolution Strategies as described by Li et al. (2013) can be implemented, as well as the multi-objective optimization algorithms NSGA-II by Deb, Pratap, Agarwal, and Meyarivan (2002) . Package: r-cran-mifa Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-dplyr, r-cran-checkmate Suggests: r-cran-psych, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-mifa_0.2.1-1.ca2604.1_all.deb Size: 173704 MD5sum: 93772c5126603878e1fc165c578cebac SHA1: 1fbd52392ccdbd05c9b987675abe1fa808bcab0d SHA256: a8d7c581b47366eb8b62386277c61889ec3086f89f4e15079b65e195e84a5026 SHA512: 6dd27b7cb7261a185c3f371216941bd6a2f25c75ccd38138a3ef53c0f3e414d95d1d0380e8ef746fe93ce221ebae9c38d6d6d277eeaa4cf32196a48f332ddbe2 Homepage: https://cran.r-project.org/package=mifa Description: CRAN Package 'mifa' (Multiple Imputation for Exploratory Factor Analysis) Impute the covariance matrix of incomplete data so that factor analysis can be performed. Imputations are made using multiple imputation by Multivariate Imputation with Chained Equations (MICE) and combined with Rubin's rules. Parametric Fieller confidence intervals and nonparametric bootstrap confidence intervals can be obtained for the variance explained by different numbers of principal components. The method is described in Nassiri et al. (2018) . Package: r-cran-migee Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-vim, r-cran-ggplot2, r-cran-lme4, r-cran-ggeffects, r-cran-dplyr, r-cran-readr, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-migee_0.1.0-1.ca2604.1_all.deb Size: 179218 MD5sum: f8a1690df897da6a7b3c6389a70c473d SHA1: e6967f1514f41a047757dc1e1804dec39d356a99 SHA256: 6002c1b56a7d3215c3e944f2f882085c4e8ddacae7bd7639636895326ec502b2 SHA512: 51169b1b041fff2c3edf6f06fcc2106e791738b4a6940b04d2fd3f976121cf862359414e5df1becd84505849b692e6054503f11079d84606c1734215f032d6c7 Homepage: https://cran.r-project.org/package=MIGEE Description: CRAN Package 'MIGEE' (Impute Missing Values and Fitting Linear Mixed Effect Model) Implements methods for estimating generalized estimating equations (GEE) with advanced options for flexible modeling and handling missing data. This package provides tools to fit and analyze GEE models for longitudinal data, allowing users to address missingness using a variety of imputation techniques. It supports both univariate and multivariate modeling, visualization of missing data patterns, and facilitates the transformation of data for efficient statistical analysis. Designed for researchers working with complex datasets, it ensures robust estimation and inference in longitudinal and clustered data settings. Package: r-cran-migest Architecture: all Version: 2.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-magrittr, r-cran-tibble, r-cran-forcats, r-cran-matrixstats, r-cran-migration.indices, r-cran-circlize, r-cran-mipfp Suggests: r-cran-spelling, r-cran-countrycode Filename: pool/dists/resolute/main/r-cran-migest_2.0.6-1.ca2604.1_all.deb Size: 372488 MD5sum: af28ed50b8bd47d10e78bfa75859cf13 SHA1: c729c5ddac53b8f8e8c7ca2e2ace317edb502770 SHA256: 3b9111e71a7253cfd45f08edef3a1024fd5b11fb6da37dff8171560a49e91da7 SHA512: e4c18f980d11b610a73f2ce64aa683473ac028b37ba41789c197c8b71671e430b28e3e6e67bba78e555d2e1e5885f8e25cbc23181c0fea2e30839f218e464010 Homepage: https://cran.r-project.org/package=migest Description: CRAN Package 'migest' (Tools for Estimating, Measuring and Working with Migration Data) Provides tools for estimating, measuring, and analyzing migration data. Designed to assist researchers and analysts in working effectively with migration data. Package: r-cran-mighty.metadata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-s7schema, r-cran-tibble, r-cran-yaml, r-cran-zephyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mighty.metadata_0.1.0-1.ca2604.1_all.deb Size: 235324 MD5sum: 9603113aaaa4f37203142210ab0a324e SHA1: b8a92480bcd436d8fe37a6ff48fd5036fb1b81c7 SHA256: 3df7fed921aba0256730651fe2dacb745b5852aa664f4d1d35c85a3e2c0c18af SHA512: 77895504c6902f1e62be6b5eca06b8cc65cbbd35d247f9c3a8042a4437e57ed85e4be2b71ae26ca56cbf3504d18319e84704a565d4019298558dff9c87b68288 Homepage: https://cran.r-project.org/package=mighty.metadata Description: CRAN Package 'mighty.metadata' (Manage 'CDISC' 'ADaM' Dataset Specifications in 'YAML' Format) Load, validate, and manipulate Clinical Data Interchange Standards Consortium ('CDISC') Analysis Data Model ('ADaM') dataset metadata stored as 'YAML' files. 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Package: r-cran-migraph Architecture: all Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4807 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manynet, r-cran-autograph, r-cran-netrics, r-cran-dplyr, r-cran-ergm, r-cran-future, r-cran-furrr, r-cran-generics, r-cran-knitr, r-cran-learnr, r-cran-purrr Suggests: r-cran-covr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-migraph_1.6.2-1.ca2604.1_all.deb Size: 3306370 MD5sum: 93bc1baca41f7204879442c79f0b7a52 SHA1: 85df1807d61f044a9dc8bad0f9bfc8d670ec9f9a SHA256: bc6297a9b39478dcba5c71f9c33c78e9f5d4605f180e7d406c4ee6442c804ae3 SHA512: 0cc0302ad97d59c31dd03209cbd0fc922fcddf6e85cc70a71921750c171db3b5b387b96f9b2b7ac1e60a6c15e1c64d6f4c26bf22012292f47a968dc90bf0838a Homepage: https://cran.r-project.org/package=migraph Description: CRAN Package 'migraph' (Inferential Methods for Multimodal and Other Networks) A set of tools for testing networks. It includes functions for univariate and multivariate conditional uniform graph and quadratic assignment procedure testing, and network regression. The package is a complement to 'Multimodal Political Networks' (2021, ISBN:9781108985000), and includes various datasets used in the book. Built on the 'manynet' package, all functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, and on one-mode and two-mode (bipartite) networks. Package: r-cran-migrate Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-cli, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-migrate_0.5.1-1.ca2604.1_all.deb Size: 172892 MD5sum: 208ee09dcf76ef12ccf1f58d842e2ded SHA1: dc9b0c524d7f7b2b999fea12435488e8850a9f23 SHA256: 4ca72aedd32886616286704c70186e79237d70a3fbfbe781123422236643ac55 SHA512: 63f0bdbceffb4d7e0c4bbcc15fe5d6532d89caf7014b4d91b27257379f677448705d481344d007c7a100c0c31e57e5eb162f4655e32488467ea6a3716db4054b Homepage: https://cran.r-project.org/package=migrate Description: CRAN Package 'migrate' (Create Credit State Migration (Transition) Matrices) Tools to help convert credit risk data at two timepoints into traditional credit state migration (aka, "transition") matrices. At a higher level, 'migrate' is intended to help an analyst understand how risk moved in their credit portfolio over a time interval. References to this methodology include: 1. Schuermann, T. (2008) . 2. Perederiy, V. (2017) . Package: r-cran-migration.indices Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-calibrate Filename: pool/dists/resolute/main/r-cran-migration.indices_0.3.1-1.ca2604.1_all.deb Size: 157708 MD5sum: c50c5313d7035459b8c7d2c85bd1b374 SHA1: 75bc51034bd3c653f3c74af50095a86295f76ea0 SHA256: b9cf454dbbe830fb5782cd581dc754751b795d6ebbda28806ca91031143b16c1 SHA512: 4ec959d99c43f22d0641c38b717a0b42ce9d50fc0188cda376a12dc6ef9adf7b60de9c804992abdde0249328d459d1b8966b4a4c780e3b46cd3ce028cb303f14 Homepage: https://cran.r-project.org/package=migration.indices Description: CRAN Package 'migration.indices' (Migration Indices) Calculate various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration. Package: r-cran-migrationdetectr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-migrationdetectr_0.1.1-1.ca2604.1_all.deb Size: 81236 MD5sum: af5838df29bfca1d938e3acc8af955d3 SHA1: e5c0c59235b916b4b61e747c3459904cfaa16361 SHA256: 67ce8454e91dc3940f735a8253fb54d6fc3197c53ef35266b2a5a8e743c35b5b SHA512: 8ad8a53cb52e47dbef8289c4e3b5a0b7e85d02dcc85775f7a8c6a1483c810bdeadee4129683ed7a92b5a3fd4672709ddb98cc79278b7d7c4afd9eb6e7c0f9b86 Homepage: https://cran.r-project.org/package=MigrationDetectR Description: CRAN Package 'MigrationDetectR' (Segment-Based Migration Detection Algorithm) Detection of migration events and segments of continuous residence based on irregular time series of location data as published in Chi et al. (2020) . Package: r-cran-migui Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gwidgets2, r-cran-mi, r-cran-arm Suggests: r-cran-foreign, r-cran-gwidgets2tcltk Filename: pool/dists/resolute/main/r-cran-migui_1.3-1.ca2604.1_all.deb Size: 75648 MD5sum: 46982518d6570685b403868f41dc9a50 SHA1: a967ba7656a3ff07e5b5ca397e175345c4a03934 SHA256: 8aa2970b28d6acda2d600862649f3d84bf811bf8b5c5c2aebda80b51c0fca71f SHA512: bd1925114566546d5da2fabdf95c0fb1c87f9107153b21372477d2ebc5b5e4356d11c78e0018d1fd0f782cae74407350f8421b3c0f692f49d601cbdf1cc31792 Homepage: https://cran.r-project.org/package=migui Description: CRAN Package 'migui' (Graphical User Interface to the 'mi' Package) This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data. Package: r-cran-miipw Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-spatstat, r-cran-mice, r-cran-matrix, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-miipw_0.1.2-1.ca2604.1_all.deb Size: 260992 MD5sum: 7a1ff001f69356464a0045ed4adf2cb2 SHA1: bd537be5137827a248d5de0b980de063db3e4f4a SHA256: e5cd7dd8ed05a0a29e397f7c6db8e09f9dd827f10c4c11531fca107c89840b84 SHA512: 39f05197fc4c74bb865528bbfa7141d41a7c19d70bc7e82c0f6feb3775d3204243f2985d42c191e271eddc3f53d4c2558d9d8f5ab6d18248b88a8734e6ac1a10 Homepage: https://cran.r-project.org/package=MIIPW Description: CRAN Package 'MIIPW' (IPW and Mean Score Methods for Time-Course Missing Data) Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007). Package: r-cran-miivefa Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-miivsem Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-mnormt, r-cran-lavaan, r-cran-mpsychor Filename: pool/dists/resolute/main/r-cran-miivefa_0.1.2-1.ca2604.1_all.deb Size: 57456 MD5sum: 0b5e4a3f701d2d7985c2b195aa5d1c3e SHA1: 9d9f6a98f4eb4739f2bb7cf6b12d28de897f2236 SHA256: 798b237b47abf16ff476102403d5d15be0d8d163b648b4e853dd6b4de3513db2 SHA512: 76184914649a8684725b3c15506b8ab1939ac566ba8f135439b2248e69125e06e2afb02c6cdcc6dc31ec067358662e4a21346718a0a38e2d82b3469a02cb60d9 Homepage: https://cran.r-project.org/package=MIIVefa Description: CRAN Package 'MIIVefa' (Exploratory Factor Analysis Using Model Implied InstrumentalVariables) Data-driven approach for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors. Package: r-cran-miivsem Architecture: all Version: 0.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-numderiv, r-cran-matrix, r-cran-car, r-cran-boot Filename: pool/dists/resolute/main/r-cran-miivsem_0.5.8-1.ca2604.1_all.deb Size: 279570 MD5sum: 6188c945044896106800ab9c3292956c SHA1: cf947e83f3c7c01ebd527ad287de6edfde809ca6 SHA256: 7fa692045a31ab6ae6bf1fddc0bb75c5e04babe5acc2171e77bc7737f015f5c5 SHA512: acd4f7693f2df5044ca71f2093e92a02ade4999b74561b1b93bdc82839a158d0a01277e246535cb0a2892fb1bc88ff251b8e403bae9eb1969d7198de3740720e Homepage: https://cran.r-project.org/package=MIIVsem Description: CRAN Package 'MIIVsem' (Model Implied Instrumental Variable (MIIV) Estimation ofStructural Equation Models) Functions for estimating structural equation models using instrumental variables. Package: r-cran-mikropml Architecture: all Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-e1071, r-cran-glmnet, r-cran-kernlab, r-cran-mlmetrics, r-cran-randomforest, r-cran-rlang, r-cran-rpart, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-tidyselect, r-bioc-treesummarizedexperiment, r-cran-xgboost Suggests: r-cran-assertthat, r-cran-dofuture, r-cran-forcats, r-cran-foreach, r-cran-furrr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-knitr, r-cran-progress, r-cran-progressr, r-cran-purrr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rsample, r-cran-styler, r-cran-testthat, r-cran-tidyr, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-mikropml_1.7.0-1.ca2604.1_all.deb Size: 2220694 MD5sum: d7615f2a01f7d0894c1b5abfcbc27ccf SHA1: 15d4976181ce0aea8f2cc9e8289121d9a67079de SHA256: d96bc971ecb5f9de0c6fc6417c3e5a163dee1665fc594d4d20c4ea1558c3768b SHA512: 2f6e2262553bfe821c077080a3f101d2b7be1825db6e6715dd2ad5cacbfb683d16bc48fc74f8f755ec4d71ea0a0f9fe834f65c7bf71fdcdea3f5456c61176b64 Homepage: https://cran.r-project.org/package=mikropml Description: CRAN Package 'mikropml' (User-Friendly R Package for Supervised Machine LearningPipelines) An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by Topçuoğlu et al. (2020) with reasonable default options for data preprocessing, hyperparameter tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website for more information, documentation, and examples. Package: r-cran-milag Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-minpack.lm, r-cran-nlsmicrobio Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-milag_1.0.5-1.ca2604.1_all.deb Size: 445282 MD5sum: 30621524fe4d8b4027c45c9e394b13f2 SHA1: 635270fc14e21beb29a8c22466ad48a18df71768 SHA256: 84c48bdae50f631189933820d9c9a92c414b03966b9a4760e1622711d7ff2fed SHA512: 311d3cd6239f2ff4cebdc3e2e9db9731b0bc315794425b212c80ce455a01fcc70f69acac5de75642c89cf4f0fc10d21720dd326592f57523eff129e9881e723e Homepage: https://cran.r-project.org/package=miLAG Description: CRAN Package 'miLAG' (Calculates Microbial Lag Duration (on the Population Level) fromProvided Growth Curve Data) Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions. It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022. Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301–307 . Package: r-cran-mildsvm Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-e1071, r-cran-kernlab, r-cran-magrittr, r-cran-mvtnorm, r-cran-pillar, r-cran-proc, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-matrix, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mildsvm_0.4.1-1.ca2604.1_all.deb Size: 452446 MD5sum: ea769dc4e3360e0ad89c84c4bcc923bb SHA1: 349da3ff535d168545e28cf11e51c859b27c1f66 SHA256: 31fb103df6c807765798fd8beb75c0179294fc45128707e2408435ee94344b9b SHA512: cfd0a560fc6b96b65d05522d0c14e98ef46a06402f44c09f2c40c9ba4dc246aafef28506e09bd15f613420750a48873b52c58fcda79fae87ec479ef1f1467a88 Homepage: https://cran.r-project.org/package=mildsvm Description: CRAN Package 'mildsvm' (Multiple-Instance Learning with Support Vector Machines) Weakly supervised (WS), multiple instance (MI) data lives in numerous interesting applications such as drug discovery, object detection, and tumor prediction on whole slide images. The 'mildsvm' package provides an easy way to learn from this data by training Support Vector Machine (SVM)-based classifiers. It also contains helpful functions for building and printing multiple instance data frames. The core methods from 'mildsvm' come from the following references: Kent and Yu (2024) ; Xiao, Liu, and Hao (2018) ; Muandet et al. (2012) ; Chu and Keerthi (2007) ; and Andrews et al. (2003) . Many functions use the 'Gurobi' optimization back-end to improve the optimization problem speed; the 'gurobi' R package and associated software can be downloaded from after obtaining a license. Package: r-cran-milineage Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-data.table, r-cran-geepack Filename: pool/dists/resolute/main/r-cran-milineage_2.1-1.ca2604.1_all.deb Size: 278728 MD5sum: d58731c7d4b10aa0d2a63cfbfcb2cdb2 SHA1: 0fb1c0faa00c5638091cf58da5758213c4323cf2 SHA256: 89368c9d790344b58e84414e3a93a351c65ab4ffecfd54a516d36e18743402c9 SHA512: 07c216eb4532eea9e4afb15522082036a5150553ed2a2faafaebcd02a67e70d895097742a1dd7690f3619de8d3710dfe15709db23dd3a5830b6c359d708fd185 Homepage: https://cran.r-project.org/package=miLineage Description: CRAN Package 'miLineage' (Association Tests for Microbial Lineages on a Taxonomic Tree) A variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted). Package: r-cran-mimdo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mimdo_0.1.0-1.ca2604.1_all.deb Size: 17152 MD5sum: d5f430ee3203d4ad82b9a7437cdf93a3 SHA1: aa6161ee1032661ebaabaec0cfbcc6bf35932958 SHA256: a96e1182e2937fdb4925cf427ead499a6a8ae69fe160b1655524f939f4c5f624 SHA512: b1ffe06b8bd4d092135c8533fb8c609a984391d49b5cafff00e7394057b302b043a0f34ac4cad70346c3fb43e7e8d0d79a31b1577e2d509f41e02eccf4d24aab Homepage: https://cran.r-project.org/package=mimdo Description: CRAN Package 'mimdo' (Multivariate Imputation by Mahalanobis Distance Optimization) Imputes missing values of an incomplete data matrix by minimizing the Mahalanobis distance of each sample from the overall mean [Labita, GJ.D. and Tubo, B.F. (2024) ]. Package: r-cran-mimer Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4034 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amr, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-testthat, r-cran-data.table, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-mimer_1.0.6-1.ca2604.1_all.deb Size: 362448 MD5sum: 9a93fb6e353f20329c5472c9a1b80ba9 SHA1: 1041b853c57d8213b0150dc604be65ef85f13f9d SHA256: 9451c81a3338e5da2b3588538bc6fbbaa4fa1b43ce261953537201afc77e849d SHA512: 4c0274b12990bf6febc53a88847a46e19c4f24608a726d52bb4b5c81614751c90972958cb9c921c473ef8467120d073f1935ecdd5fb5b37b58e623670be85229 Homepage: https://cran.r-project.org/package=MIMER Description: CRAN Package 'MIMER' (Data Wrangling for Antimicrobial Resistance Studies) Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC) dataset, a repository of freely accessible electronic health records. MIMER(MIMIC-enabled Research) package, offers a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes, particularly in antimicrobial resistance(AMR) studies. It simplifies complex data manipulation tasks, allowing researchers to focus on their primary inquiries without being bogged down by wrangling complexities. Package: r-cran-mimi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-softimpute, r-cran-factominer, r-cran-doparallel, r-cran-foreach, r-cran-data.table, r-cran-rarpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mimi_0.2.0-1.ca2604.1_all.deb Size: 342352 MD5sum: 3e0d8cf641125338606a872e485d4446 SHA1: 4a4a30c84eb285ef1a286a404497325340dc2511 SHA256: 86d3d38a2cd99618ef934d390de17d0d2a8286d1c84318c96d3994d48a4541c8 SHA512: 1cd0c3512804f0a85747661a887d714addfb57805909d0cc30844ae0ea4a080f3b4fcf8212f712b14d1ea70374886bc5e192615ef498ff5d1e6454a132de30f1 Homepage: https://cran.r-project.org/package=mimi Description: CRAN Package 'mimi' (Main Effects and Interactions in Mixed and Incomplete Data) Generalized low-rank models for mixed and incomplete data frames. The main function may be used for dimensionality reduction of imputation of numeric, binary and count data (simultaneously). Main effects such as column means, group effects, or effects of row-column side information (e.g. user/item attributes in recommendation system) may also be modelled in addition to the low-rank model. Geneviève Robin, Olga Klopp, Julie Josse, Éric Moulines, Robert Tibshirani (2018) . Package: r-cran-mimisbm Architecture: all Version: 0.0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockmodels Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mimisbm_0.0.1.3-1.ca2604.1_all.deb Size: 191458 MD5sum: bef928f31fee7641bea59f50bf747a5f SHA1: d0d986a230bdecc677e7023a2b3aa74fca6f1819 SHA256: 52477bd189b0f50b2276e815f4cba3b7779b256118910961279faaee5f403f47 SHA512: 0293970b47c47be0d45d39f248acb5de5bd31e8f0ca040fec176df9d2b3936e21fe74268f0fd9174f8b2d6ee6c9761666f6177dac16173d7b11a06be01596103 Homepage: https://cran.r-project.org/package=mimiSBM Description: CRAN Package 'mimiSBM' (Mixture of Multilayer Integrator Stochastic Block Models) Our approach uses a mixture of multilayer stochastic block models to group co-membership matrices with similar information into components and to partition observations into different clusters. See De Santiago (2023, ISBN: 978-2-87587-088-9). Package: r-cran-mimsunit Architecture: all Version: 0.11.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-catools, r-cran-tibble, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-plyr, r-cran-readr, r-cran-r.utils, r-cran-stringr, r-cran-xts, r-cran-signal, r-cran-dygraphs, r-cran-shiny, r-cran-rcolorbrewer, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-pkgdown, r-cran-gridextra, r-cran-remotes Filename: pool/dists/resolute/main/r-cran-mimsunit_0.11.3-1.ca2604.1_all.deb Size: 436106 MD5sum: 642b152c52350148c46226365cbea7ef SHA1: bdfe4baf469bfb87750c78810227a0f1f9352603 SHA256: 3c51c6dece83f80b75deeb48866051a3a112310fdc9be9370a4ee57414cde498 SHA512: ef85e0f9832d35c8c66060a6af3b380c73df779e26cfe19a2d1385aab2c3fb64f8a6b2970abac6121b35444bd05c7c0eaf3900cdffa9d1a4826eec497067df54 Homepage: https://cran.r-project.org/package=MIMSunit Description: CRAN Package 'MIMSunit' (Algorithm to Compute Monitor Independent Movement Summary Unit(MIMS-Unit)) The MIMS-unit algorithm is developed to compute Monitor Independent Movement Summary Unit, a measurement to summarize raw accelerometer data while ensuring harmonized results across different devices. It also includes scripts to reproduce results in the related publication (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) ). Package: r-cran-mimsy Architecture: all Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 684 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-lubridate, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-xfun Filename: pool/dists/resolute/main/r-cran-mimsy_0.6.5-1.ca2604.1_all.deb Size: 441120 MD5sum: c1dc8a667c32fabec5b5f5ba96e3b31f SHA1: dff3eccab8c746180156cb941197fa6d54aaf386 SHA256: de13d97f62cf69434662595d77c8962b2558341eb485cae48c91f1cc474b6d19 SHA512: e4cf0dedda8893d8eb500bb5f08b75215f9db084be95a4198b27ae104559ded5ffaffad59207469ed5911d37df85bcb92d49077a006138b18743d83efa1efa47 Homepage: https://cran.r-project.org/package=mimsy Description: CRAN Package 'mimsy' (Calculate MIMS Dissolved Gas Concentrations Without Getting aHeadache) Calculate dissolved gas concentrations from raw MIMS (Membrane Inlet Mass Spectrometer) signal data. Use mimsy() on a formatted CSV file to return dissolved gas concentrations (mg and microMole) of N2, O2, Ar based on gas solubility at temperature, pressure, and salinity. See references Benson and Krause (1984), Garcia and Gordon (1992), Stull (1947), and Hamme and Emerson (2004) for more information. Easily save the output to a nicely-formatted multi-tab 'Excel' workbook with mimsy.save(). Supports dual-temperature standard calibration for dual-bath MIMS setups. Package: r-cran-min2halfffd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-hrtlfmc, r-cran-shinybusy Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-min2halfffd_0.1.0-1.ca2604.1_all.deb Size: 186504 MD5sum: 81ccdc72a02213edbf820dec03fa6007 SHA1: 574d5a984b5a1d91181c994c2fb304e748bf199b SHA256: d317785e8a70174b588627aecf12637f3597809570d2d2b83fe9a0f29a3911d2 SHA512: 3f1ec0466c6c2c70baa72054eb7742ff400305bca133ecc4fd5af19caa4ba7dced97563cb8a62ad470e0dc04437e7474dd79b4dc1e9485076d821ff2123f3a6c Homepage: https://cran.r-project.org/package=min2HalfFFD Description: CRAN Package 'min2HalfFFD' (Minimally Changed Two-Level Half-Fractional Factorial Designs) In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) . This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface. Package: r-cran-minb Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pscl Filename: pool/dists/resolute/main/r-cran-minb_0.1.0-1.ca2604.1_all.deb Size: 48158 MD5sum: b798ba35861e54a952af98536a2e609e SHA1: 8e033796ec7588b4b9e67a39e72d6d2cec9c5c7d SHA256: 23b0f0f24dbbf5c0706e9656ec97b969c89242cd6c7be3901645da399691bd13 SHA512: ed374f22e01310145db53a87820014bfef0d63c284b326a304ca40c2a5bb92bb8ac4a309e6bd4980c49f18df2fd64369d17561dd023af8a2670e64a871039c8c Homepage: https://cran.r-project.org/package=minb Description: CRAN Package 'minb' (Multiple-Inflated Negative Binomial Model) Count data is prevalent and informative, with widespread application in many fields such as social psychology, personality, and public health. Classical statistical methods for the analysis of count outcomes are commonly variants of the log-linear model, including Poisson regression and Negative Binomial regression. However, a typical problem with count data modeling is inflation, in the sense that the counts are evidently accumulated on some integers. Such an inflation problem could distort the distribution of the observed counts, further bias estimation and increase error, making the classic methods infeasible. Traditional inflated value selection methods based on histogram inspection are easy to neglect true points and computationally expensive in addition. Therefore, we propose a multiple-inflated negative binomial model to handle count data modeling with multiple inflated values, achieving data-driven inflated value selection. The proposed approach provides simultaneous identification of important regression predictors on the target count response as well. More details about the proposed method are described in Li, Y., Wu, M., Wu, M., & Ma, S. (2023) . Package: r-cran-mind Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-matrix, r-cran-dplyr, r-cran-jwileymisc, r-cran-tm Filename: pool/dists/resolute/main/r-cran-mind_1.1.0-1.ca2604.1_all.deb Size: 755478 MD5sum: 2539cd63e2b9a1f902bf367587fbc51f SHA1: babf3fa51f84cde0df2f5c0f7572f5e9b74e7cb0 SHA256: 81b622e4597aeda6706060c22e9abaea1c95b88f8eaba64a17247b8e6153055f SHA512: c6d69f3b31100a6b3ff1b459420f1d2b28c6feaa553d99fc4bde24f5dc0e99fb82d86d35aa31aff93e47017b498a42d50fa97f6d2a1cbf3d9518a19e45ffbfe0 Homepage: https://cran.r-project.org/package=mind Description: CRAN Package 'mind' (Multivariate Model Based Inference for Domains) Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) . Package: r-cran-mindonstats Architecture: all Version: 0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mindonstats_0.11-1.ca2604.1_all.deb Size: 195526 MD5sum: 20c040fa4ca565feb9494b14be4c2708 SHA1: a3ea89621cca255da9ab00867e46683e8b38f168 SHA256: 31d71ab6a764afe4ac1dc2ec728f032034d488e5bd2f4f20bb6c744a65a64dac SHA512: 773238532442a4a996ee768a20cbdc390e8e799f7888b45445522b209677b9cc9075fcc1b65985b758671927d7cfe3f60d0a38f99c6376fca054ca5580c5bf3a Homepage: https://cran.r-project.org/package=MindOnStats Description: CRAN Package 'MindOnStats' (Data sets included in Utts and Heckard's Mind on Statistics) 66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv(). Package: r-cran-mindr Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3787 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-pdftools, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-mindr_1.4.1-1.ca2604.1_all.deb Size: 886684 MD5sum: 322d29834e05ad21b1ed47c2da5caf35 SHA1: c93d29f1619b1155fe52f509327be050918d653e SHA256: 8a7d101c04c6c87de0ab6b778d7f3d35e7e7e7d465c7b3af822925a4a55a3e30 SHA512: 4a55fbc5ffd1d57ef6ae433e0cf71047eb7a884a10a050e663dc61bfc33debcfcffff1133680381c1ec09bb0a2a5753386786b6376abf6be051fafa43f7c6dab Homepage: https://cran.r-project.org/package=mindr Description: CRAN Package 'mindr' (Generate Mind Maps) Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, directory structures, and other hierarchical structured documents into mind map widgets or 'Freemind' codes or 'Mermaid' mind map codes, and vice versa. 'Freemind' mind map ('.mm') files can be opened by or imported to common mind map software such as 'Freemind' (). 'Mermaid' mind map codes () can be directly embedded in documents. Package: r-cran-minecitrus Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-minecitrus_1.0.0-1.ca2604.1_all.deb Size: 4586284 MD5sum: 13190e0da940c0f873950691e14ce74c SHA1: c6d85f877a15d0144a5b7909b6bb7687ff45a429 SHA256: 88ca8898c65556757e3b1e3b40b5ae090651efde30fd59ce28e7f9f3eaae2e89 SHA512: 0639a8cf6b5a1d233cf71ee6bc85967d032f0a7e2fff2926f29407582eb900942c185f55972b4f6aa2e3ce1577eb384e9b062289d0a9076bbc8ad6bf4bbd2f05 Homepage: https://cran.r-project.org/package=mineCitrus Description: CRAN Package 'mineCitrus' (Extract and Analyze Median Molecule Intensity from 'citrus'Output) Citrus is a computational technique developed for the analysis of high dimensional cytometry data sets. This package extracts, statistically analyzes, and visualizes marker expression from 'citrus' data. This code was used to generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus, please see: Bruggner et al. (2014) . To download the 'citrus' package, please see . Package: r-cran-minedfind Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-minedfind_0.1.3-1.ca2604.1_all.deb Size: 46296 MD5sum: 0f33b52f660ec8d35a383f7a797850cd SHA1: c6b1c8727e8c9b7bb079eecd0484784e21b974ac SHA256: d4b378b820942b60fea8b9a3f81b9f7a72022cb55021c70491aa799326328650 SHA512: 838b82d835dcdbc1bb7ae717a37a9c2b3cc488c83b218bf47c171e1a4509de0fabdd9c44ea8ade5085487d702182919a3ce0e375f660ddbc7c092e76ad64fa51 Homepage: https://cran.r-project.org/package=MinEDfind Description: CRAN Package 'MinEDfind' (A Bayesian Design for Minimum Effective Dosing-Finding Trial) The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials. Package: r-cran-minesweeper Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gifski Filename: pool/dists/resolute/main/r-cran-minesweeper_1.0.1-1.ca2604.1_all.deb Size: 61016 MD5sum: 357957a2da99c39e4c763edb3e9e3f2b SHA1: 676296dc58a11a6395c80623287d74738a6b9af1 SHA256: b758718773247657471b93e93aa19e0cdceb42ce6486ba9235a97542946af27a SHA512: f75471996b4ca4fd2c8a86dedc9ec4c4ba85e711f9df9db07c093cdea375b7a489661ba2a413ed0a5e3b08cf1d54d74db09eea318dc32b12572cfb96d3069b7a Homepage: https://cran.r-project.org/package=minesweeper Description: CRAN Package 'minesweeper' (Play Minesweeper) Play and record games of minesweeper using a graphics device that supports event handling. Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by Crow P. (1997) . Package: r-cran-minesweepr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-complexheatmap, r-bioc-interactivecomplexheatmap, r-cran-dplyr, r-cran-gsignal, r-cran-mgc, r-cran-mmand, r-cran-hms, r-cran-rlang, r-cran-pals Filename: pool/dists/resolute/main/r-cran-minesweepr_0.1.1-1.ca2604.1_all.deb Size: 62816 MD5sum: 1fb112ca1fd27a1cf9328fdf2f04c2e0 SHA1: 6e69f739d0672e90c69c0e87127bd604f1fea934 SHA256: 039360493f64946383c7d0316ee2c612b802cc0c7751fc4fcecc416413c3ab21 SHA512: af81a1098c59ca45f6b06421d2b7255181aafe6f2759054e480390f1b417401f811104fb23f4fbc0517537887a19f6591359ea57c71e72b1929c9b1a48bcfeef Homepage: https://cran.r-project.org/package=mineSweepR Description: CRAN Package 'mineSweepR' (Mine Sweeper Game) This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`. Package: r-cran-minfactorial Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmc Filename: pool/dists/resolute/main/r-cran-minfactorial_0.1.0-1.ca2604.1_all.deb Size: 19358 MD5sum: 680fcf5c1af76491f8f88344a7711649 SHA1: e0e18561fcbed8eb551121630c1db732d348beaa SHA256: ca3915b2e940a408f3738deba958f87dc24a1df077ce9b1ab6b94cc9eb9ebcc8 SHA512: 4d61abfc35235d29850f975dd90fe76f7b12077c87526f7a72a3684dddf16d96029378bf02b3c3400fc8b7effac6842e33d832ff377ac11ddf5401ecdcea3cb2 Homepage: https://cran.r-project.org/package=minFactorial Description: CRAN Package 'minFactorial' (All Possible Minimally Changed Factorial Run Orders) In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017).This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign(). Package: r-cran-mini007 Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-diagrammer, r-cran-ellmer, r-cran-glue, r-cran-r6, r-cran-rlang, r-cran-uuid Filename: pool/dists/resolute/main/r-cran-mini007_0.3.0-1.ca2604.1_all.deb Size: 531364 MD5sum: afd2139d85f3326351aeccfc51a418ec SHA1: 2774a12bb806be66de0109d82b24a72e712ee46e SHA256: 489ec2ae34127b20319e9dd85dad8d54df7e2723c5f6119920cf7fe9cb183259 SHA512: d35e411529cab657b39b0bfd8c610a97e3e7606e8aacbd92e85a40a1313ca4141d456ab3446a7cc5463e880b32413375fa37b95d302471781c056cd0ed8d1b60 Homepage: https://cran.r-project.org/package=mini007 Description: CRAN Package 'mini007' (Lightweight Framework for Orchestrating Multi-Agent LargeLanguage Models) Provides tools for creating agents with persistent state using R6 classes and the 'ellmer' package . Tracks prompts, messages, and agent metadata for reproducible, multi-turn large language model sessions. 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The user specifies a set of desired packages, and 'miniCRAN' recursively reads the dependency tree for these packages, then downloads only this subset. The user can then install packages from this repository directly, rather than from CRAN. This is useful in production settings, e.g. server behind a firewall, or remote locations with slow (or zero) Internet access. Package: r-cran-minidown Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-katex, r-cran-knitr, r-cran-mime, r-cran-rmarkdown, r-cran-sass, r-cran-xfun Suggests: r-cran-callr, r-cran-dplyr, r-cran-purrr, r-cran-shiny, r-cran-testthat, r-cran-tidyr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-minidown_0.4.0-1.ca2604.1_all.deb Size: 85628 MD5sum: 430577af15dc573686af384e0aec698c SHA1: b35b4fa225ebb359e871369659c5d91e677eb596 SHA256: 03a18271ea9f67e0910fd2e5c53927c4d0293d61bbe957ae100007156c69bdb4 SHA512: b82dee13793b15ed4e829a0459e440401c63b5e6adb87ca00a7390767ff7a8e72a76714efa5372056af5d0a16d7865238703638fbd3b24cc5869ebe34e12aa02 Homepage: https://cran.r-project.org/package=minidown Description: CRAN Package 'minidown' (Create Simple Yet Powerful HTML Documents with Light Weight CSSFrameworks) Create minimal, responsive, and style-agnostic HTML documents with the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'. Powerful features include table of contents floating as a sidebar, folding codes and results, and more. Package: r-cran-minigui Architecture: all Version: 0.8-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-minigui_0.8-1-1.ca2604.1_all.deb Size: 49794 MD5sum: b59e72f83c9d0d88b85972bc9f85e10b SHA1: 31cd1a438da2656e56cf1cf0002d798d0559aa42 SHA256: fb128c84fbf10fe3e25ccd87c2bc29ca3733b8aeb9e9634a8b614ccbafedaf9c SHA512: f76790255b76872301480f1b406b9d5c1ec94bd6e6ab61ef317ebc856def1849601759bbc8f1028e759376d9a50227d5eb4eca71b648f7a31db0adecc5d10b58 Homepage: https://cran.r-project.org/package=miniGUI Description: CRAN Package 'miniGUI' (Tcl/Tk Quick and Simple Function GUI) Quick and simple Tcl/Tk Graphical User Interface to call functions. Also comprises a very simple experimental GUI framework. Package: r-cran-minimalistgodb Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1977 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-minimalistgodb_1.1.0-1.ca2604.1_all.deb Size: 1743216 MD5sum: dbdbf02d6bdb6c6b6013c50a593fec43 SHA1: 1c679b7e96b682db4525b9f21b777165c99dea53 SHA256: 6bba067fc4a66b50c136b1ad139f2133b35f6508acba8985acca8809d9aa3618 SHA512: ba9aa95fb667aa1c3f8aec49870064cffd6766091cebc94ff769e548d66a5c1c44ac6a3b2f933cf7138585074def57b64c6dcc4c521cfe5dd69ddbd01a4ba510 Homepage: https://cran.r-project.org/package=minimalistGODB Description: CRAN Package 'minimalistGODB' (Build a Minimalist Gene Ontology (GO) Database (GODB)) Normally building a GODB is fairly complicated, involving downloading multiple database files and using these to build e.g. a 'mySQL' database. Accessing this database is also complicated, involving an intimate knowledge of the database in order to construct reliable queries. Here we have a more modest goal, generating GOGOA3, which is a stripped down version of the GODB that was originally restricted to human genes as designated by the HUGO Gene Nomenclature Committee (HGNC) (see ). I have now added about two dozen additional species, namely all species represented on the Gene Ontology download page . This covers most of the model organisms that are commonly used in bio-medical and basic research (assuming that anyone still has a grant to do such research). This can be built in a matter of seconds from 2 easily downloaded files (see and ), and it can be queried by e.g. w<-which(GOGOA3[,"HGNC"] %in% hgncList) where GOGOA3 is a matrix representing the minimalist GODB and hgncList is a list of gene identifiers. This database will be used in my upcoming package 'GoMiner' which is based on my previous publication (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003)). Relevant .RData files are available from GitHub (). Package: r-cran-minimalrsd Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-minimalrsd_1.0.0-1.ca2604.1_all.deb Size: 26964 MD5sum: 0a85bdc2300509b22d2b72bff08f73a2 SHA1: 58d480368baf8270bee4c6e58382a684052a522b SHA256: a1044d7d5d8b420acee8d7a0b30b1e0119a008cc730cd86f545b36134f6f755b SHA512: 531629310475b6489c1c2e666036ff218b9dd0cf87cea05871431f820fab070f9114842ad77f55836a941be777610ca7476dd1b94244b1b843a37686e100e60b Homepage: https://cran.r-project.org/package=minimalRSD Description: CRAN Package 'minimalRSD' (Minimally Changed CCD and BBD) Generate central composite designs (CCD)with full as well as fractional factorial points (half replicate) and Box Behnken designs (BBD) with minimally changed run sequence. 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Package: r-cran-minimax Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-minimax_1.1.1-1.ca2604.1_all.deb Size: 19032 MD5sum: 360d51d0dc068fe8f5c7f6cc9544684e SHA1: 5b0dc6f2f6b050c2c8225a76c7c81266788eaa29 SHA256: a0c0f32d072dc312b60e79d69611027c0a3bba5f28ec1d5bb8d1c0beeced55bf SHA512: ec815a38940fd3d22e201101e5eedaaf29969893bb3cb25ef52368f4d6dc7c897872b90a66e0ef091f079c3113cb5a7df7603078261cbe302da8e516e385f73c Homepage: https://cran.r-project.org/package=minimax Description: CRAN Package 'minimax' (The Minimax Distribution Family) The minimax family of distributions is a two-parameter family like the beta family, but computationally a lot more tractible. Package: r-cran-minimeta Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-colourpicker, r-cran-rhandsontable, r-cran-metafor, r-cran-markdown, r-cran-writexls, r-cran-readxl, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-minimeta_0.3.2-1.ca2604.1_all.deb Size: 396120 MD5sum: df822c799d45cc9c2bb29cea1761066c SHA1: 37d0f5f6ccbeed966a3bedf1696aad62ba579094 SHA256: 7d50d48cbf3d99af8a7b91fe6bde8d6bfa44ff7ce1cee7e8eb9798d02500405e SHA512: 8afab6a0b50d8c4e3a881dd15d05f3f8978a71245b0c632dbddb1d949cf3d1a4ca4fb932f93801f88b587abf8154c67c83306a49e6040994b2c15ecb3c463c7e Homepage: https://cran.r-project.org/package=miniMeta Description: CRAN Package 'miniMeta' (Web Application to Run Meta-Analyses) Shiny web application to run meta-analyses. Essentially a graphical front-end to package 'meta' for R. Can be useful as an educational tool, and for quickly analyzing and sharing meta-analyses. Provides output to quickly fill in GRADE (Grading of Recommendations, Assessment, Development and Evaluations) Summary-of-Findings tables. Importantly, it allows further processing of the results inside R, in case more specific analyses are needed. Package: r-cran-minioclient Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs, r-cran-glue, r-cran-processx, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-testthat, r-cran-curl Filename: pool/dists/resolute/main/r-cran-minioclient_0.0.6-1.ca2604.1_all.deb Size: 66068 MD5sum: c8fa797c7572e89a011ac690ce70966a SHA1: b859f7edac8d24ab47f3490f1ca80afabe8771b9 SHA256: b2331765a2872d1a75eb7fd9fad6016d7e79d9c19f71418ad4506697a2dce1af SHA512: cfe37b096da1cf3d993bba2c8b8079ac86d1595c466dc5bcef35796a1d8e842224f52cd346d0773c1ad17f4994d3c731f52f2283ac1c52b557890f4fe6791450 Homepage: https://cran.r-project.org/package=minioclient Description: CRAN Package 'minioclient' (Interface to the 'MinIO' Client) An R interface to the 'MinIO' Client. The 'MinIO' Client ('mc') provides a modern alternative to UNIX commands like 'ls', 'cat', 'cp', 'mirror', 'diff', 'find' etc. It supports 'filesystems' and Amazon "S3" compatible cloud storage service ("AWS" Signature v2 and v4). This package provides convenience functions for installing the 'MinIO' client and running any operations, as described in the official documentation, . This package provides a flexible and high-performance alternative to 'aws.s3'. Package: r-cran-minipdf Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-minipdf_0.2.7-1.ca2604.1_all.deb Size: 2429056 MD5sum: 4913ab3154a2fb75c023f83346ab3cd5 SHA1: c7c4f38e1e6b05dcc44383a0e42a08379ece2d92 SHA256: 2ce6e358653fe2b1afafe8234a76efdea6080b4930bf42f62c4ad78c12c5a369 SHA512: 54b260761614c86bf5f4740108c245e88cc394e5035bbba2d49d7b3cdee2adae18d77cee22139fd7fcd73057597984197790254505127a06a09c04398453d0bb Homepage: https://cran.r-project.org/package=minipdf Description: CRAN Package 'minipdf' (PDF Document Creator) PDF is a standard file format for laying out text and images in documents. At its core, these documents are sequences of objects defined in plain text. This package allows for the creation of PDF documents at a very low level without any library or graphics device dependencies. Package: r-cran-minirand Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-minirand_0.1.3-1.ca2604.1_all.deb Size: 21816 MD5sum: 65f8a0eee97d2760ea3369a1bcedc851 SHA1: e5a2fb2961b8f6434a790a3019e2d1c8c96f0319 SHA256: 7311c4b088ffbbc85e3ad0b2d773e2a9487a87a6f88227da16b6db91e31156fe SHA512: 410d5a3ab45bab7a163217539fff40e5908a364130ed7bfdf02bac1a2c6ea37bed82cc544f9be70004805167c6a0feb3c3e2dff46f7772b50272bd44f1f657f5 Homepage: https://cran.r-project.org/package=Minirand Description: CRAN Package 'Minirand' (Minimization Randomization) Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms. Package: r-cran-miniui Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-miniui_0.1.2-1.ca2604.1_all.deb Size: 36432 MD5sum: 1d6fd150289370e7a7c0865031c28b3d SHA1: 2b9173cca496815b3596c7df231f6f9b0cc971e3 SHA256: d74832c30cb2e5d04090332b1b86b628f9bf36d7db25335756dc35f598ffd46d SHA512: 8e675eb30ac6d44d54ecfb84053f90d35b0d934b42d23f72d8c2d2e9b8eca2e0b0ce408e58cbd6cb3f9a4e9cbd69006fb8c3102e34eb70023380fd2dd3ce3247 Homepage: https://cran.r-project.org/package=miniUI Description: CRAN Package 'miniUI' (Shiny UI Widgets for Small Screens) Provides UI widget and layout functions for writing Shiny apps that work well on small screens. Package: r-cran-minorparties Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-quanteda, r-cran-quanteda.textmodels, r-cran-reticulate, r-cran-rlang, r-cran-spacyr, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-minorparties_1.0.0-1.ca2604.1_all.deb Size: 2380874 MD5sum: d56f1e1b7299e9522e4cff4a89ab92d7 SHA1: cc9bcdc4ff79bf5abfbb4e8dcf6511aabac0b87f SHA256: 231e9f18e6a8267c604ca4fce683b481a453dc76c4296e1d67771617d83d1bc3 SHA512: 45f554eaa1a51d6798ea41349b757f8a4865649cb207c4c2d33292373c411a6c5d89168aa17892f4a215ce7662a5a17e7cbb4dc780ef380914209097a9e1c873 Homepage: https://cran.r-project.org/package=minorparties Description: CRAN Package 'minorparties' (Quantitatively Analyze Minor Political Parties) Tools for calculating I-Scores, a simple way to measure how successful minor political parties are at influencing the major parties in their environment. I-Scores are designed to be a more comprehensive measurement of minor party success than vote share and legislative seats won, the current standard measurements, which do not reflect the strategies that most minor parties employ. The procedure leverages the Manifesto Project's NLP model to identify the issue areas that sentences discuss, see Burst et al. (2024) , and the Wordfish algorithm to estimate the relative positions that platforms take on those issue areas, see Slapin and Proksch (2008) . Package: r-cran-minque Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-minque_2.0.0-1.ca2604.1_all.deb Size: 226764 MD5sum: 13087af5afa0184388600cfe0f5ad115 SHA1: 9a0d6fc0bfac8018ecb0a138ec020452a12b1d68 SHA256: b9019d61c9b2312c0e6de6ecb74829736c304f1bcb9dfad10e8e68708ab8ae51 SHA512: 31d2b647365db6d8a1e85a3a74f098097a86d3b4f359baa69e7743583558907b56411164a1a9ddeff0be7b6341d801090cfbfda49a7a22fb6f9852e91a01d878 Homepage: https://cran.r-project.org/package=minque Description: CRAN Package 'minque' (Various Linear Mixed Model Analyses) This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures. 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Package: r-cran-minsnps Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3700 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocparallel, r-cran-data.table Suggests: r-cran-knitr, r-cran-testthat, r-cran-pkgdown, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/resolute/main/r-cran-minsnps_0.2.0-1.ca2604.1_all.deb Size: 356772 MD5sum: 835ce7317cc75cdb6d74a3aeca793815 SHA1: 01a80a3a0e23111251cb6a6d28c9eaf03d7eabda SHA256: 7f9bec3829f65eaba369a19bc86446009a9df7519653edc5a9881984aae95a4a SHA512: c655a41ab966220b23fb5336fbfeab020d050eb72846182e9630a3dcb767507e361b96f6dd5450907632ebaa6f7d1c8de0afb72be35e3b10c6ededef94768cd7 Homepage: https://cran.r-project.org/package=minSNPs Description: CRAN Package 'minSNPs' (Resolution-Optimised SNPs Searcher) This is a R implementation of "Minimum SNPs" software as described in "Price E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M." (2007) "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants." 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The package supports calculation of simple effects, overall effects, and interaction effects for use in factorial meta-analyses. See Gurevitch et al. (2000) , Morris et al. (2007) , Lajeunesse (2011) and Macartney et al. (2022) . 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While initially created for transfer RNA-derived small fragments (tRFs), this tool can be used for any genomic sequences including but not limited to: tRFs, microRNAs, etc. The detailed information can reference to Pliatsika V, Loher P, Telonis AG, Rigoutsos I (2016) . It can also be used to annotate tRFs. The detailed information can reference to Loher P, Telonis AG, Rigoutsos I (2017) . 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Built on 'nanonext' and 'NNG', its non-polling, event-driven architecture scales from a laptop to thousands of processes across high-performance computing clusters and cloud platforms. Features FIFO scheduling with task cancellation and bounded queues, promises for reactive programming, 'OpenTelemetry' distributed tracing, and custom serialization for cross-language data types. Package: r-cran-mirdd Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-amelia, r-cran-rdrobust Filename: pool/dists/resolute/main/r-cran-mirdd_0.2.4-1.ca2604.1_all.deb Size: 162030 MD5sum: aae51ad6f011555d935b8910d2b09145 SHA1: 229bc2de80d5a8e7bf8b4057d21bb11eae2eadba SHA256: 1e6ba0d732665f47b0fb0c1f87317797b840293c708b063d1848069343ba6b00 SHA512: 68735d4bc6b5add27acb6932c63d8cc79152f2520d92cd31a5557a776353958d2a74df981a840ce2b199b3e645543353d1f69924d2a0ff5c96afdc1961efff27 Homepage: https://cran.r-project.org/package=MIRDD Description: CRAN Package 'MIRDD' (Diagnostic Tool by Multiple Imputation for RegressionDiscontinuity Designs) Estimates average treatment effects at the cutoff based on sharp regression discontinuity designs (RDD) and multiple imputation regression discontinuity designs (MIRDD). It provides diagnostic tools for RDD by comparing results with those from MIRDD, as proposed in Takahashi (2023) . The package includes datasets from Takahashi (2023) and Takahashi (2026) . 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Package: r-cran-mirkat Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-compquadform, r-cran-quantreg, r-cran-gunifrac, r-cran-pearsonds, r-cran-lme4, r-cran-matrix, r-cran-permute, r-cran-mixtools, r-cran-survival Suggests: r-cran-knitr, r-cran-vegan, r-cran-rmarkdown, r-cran-magrittr, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-mirkat_1.2.3-1.ca2604.1_all.deb Size: 363530 MD5sum: 422a35add5e393acf154fda66e29fa1a SHA1: 27f985ac4c246337c0d8a34069b0ee18c4e03dff SHA256: 195760cd49c7243a788712fbd78689625e1b84e2e4c13e0bb0505eca7f18babe SHA512: eddbad44bb3987d0a9d11fd0200d3f6979df24f7db25c3296a0f3846c52faf25fe9894348346f5b165a712597b3c2b1109646f8ecc9209507c08430c63feb6ce Homepage: https://cran.r-project.org/package=MiRKAT Description: CRAN Package 'MiRKAT' (Microbiome Regression-Based Kernel Association Tests) Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels. The phenotype can be univariate continuous or binary (Zhao et al. (2015) ), survival outcomes (Plantinga et al. (2017) ), multivariate (Zhan et al. (2017) ) and structured phenotypes (Zhan et al. (2017) ). The package can also use robust regression (unpublished work) and integrated quantile regression (Wang et al. (2021) ). In each case, the microbiome community effect is modeled nonparametrically through a kernel function, which can incorporate phylogenetic tree information. Package: r-cran-mirnaqcd Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-proc, r-cran-qpdf Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mirnaqcd_1.1.3-1.ca2604.1_all.deb Size: 594248 MD5sum: ddfbcb39528f97d1b79d731753f8785c SHA1: 8905b38932608bea6ca3e67b87c8bf2dc334d9d7 SHA256: a71cf747e4cd9d1e2f782cd7219523fc9cf692e933ea71a84cff4d71b4839382 SHA512: cc56c0dca764578c35d95e684b9adf530f2c8b54229cbf8f58ff4bd8dd8c7b3b58e6bb5bee428e92bd64b01dee6b8db124f6fcb072515d5a415cb300ecfda8f7 Homepage: https://cran.r-project.org/package=MiRNAQCD Description: CRAN Package 'MiRNAQCD' (Micro-RNA Quality Control and Diagnosis) A complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 . The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 . Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information. Package: r-cran-mirrorselect Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yulab.utils Filename: pool/dists/resolute/main/r-cran-mirrorselect_0.0.3-1.ca2604.1_all.deb Size: 15414 MD5sum: a8fd3f6b4851f9aebe1b61710f5d017b SHA1: 0d1a357fc86c20a41bae726fb3e1bc434343175b SHA256: 475fd971fa8b96a3512fd97b4be55c2649a7df309434fc310ef9870e692ddc99 SHA512: 6f00d31eae05294d658f4b57d55eefb646b340ff187e7e5a0d177e7d8506738db45d3325dbae10f00343f75d8fb82095423dca22fc3ec88a06f6354d9881a309 Homepage: https://cran.r-project.org/package=mirrorselect Description: CRAN Package 'mirrorselect' (Test CRAN/Bioconductor Mirror Speed) Testing CRAN and Bioconductor mirror speed by recording download time of 'src/base/COPYING' (for CRAN) and 'packages/release/bioc/html/ggtree.html' (for Bioconductor). Package: r-cran-mirsea Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2028 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mirsea_1.1.1-1.ca2604.1_all.deb Size: 1351354 MD5sum: c355379ad1a62f64e62503a424c5493c SHA1: 5e8e565108b8abba899ccfe3ee39399dc0d827d2 SHA256: 86b29fd0cd6c420715cdf4233127cc82ef4c6e5a04c1c1924960e78dd06ee0e1 SHA512: 62071503651c4c1c57a097ba55ff8136a2bacf6582d04506053e481d4d83865ccabbe14d0ee54f14c3edcfbe8458a7b5ea7f83c135771f92a3ff8840a732dc63 Homepage: https://cran.r-project.org/package=MiRSEA Description: CRAN Package 'MiRSEA' ('MicroRNA' Set Enrichment Analysis) The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results. 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Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) . Package: r-cran-misc3d Architecture: all Version: 0.9-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rgl, r-cran-tkrplot, r-cran-mass, r-cran-maps, r-cran-lattice, r-cran-scatterplot3d Filename: pool/dists/resolute/main/r-cran-misc3d_0.9-2-1.ca2604.1_all.deb Size: 240500 MD5sum: 705fe062c6c9e6ffdbb14fcfcf6a66bf SHA1: 0b0f4c86d989d2d536904001f2442bf65f34de3f SHA256: 4698f28f8f7c8dae603f08ee3eb39c6edb392ba4fd3fdee28d85b72d414afbb2 SHA512: 88cf48c425861078807b3032ee644ffc6171b83294cefd3720e5505226a1156b4213d4b15621c888a1cd681eee195b3432112f3065a7861a5dc7038c2dbaec23 Homepage: https://cran.r-project.org/package=misc3d Description: CRAN Package 'misc3d' (Miscellaneous 3D Plots) A collection of miscellaneous 3d plots, including isosurfaces. 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'MiscMetabar' is mainly built on top of the 'phyloseq', 'dada2' and 'targets' R packages. It helps to build reproducible and robust bioinformatics pipelines in R. 'MiscMetabar' makes ecological analysis of alpha and beta-diversity easier, more reproducible and more powerful by integrating a large number of tools. Important features are described in Taudière A. (2023) . 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Package: r-cran-miselect Architecture: all Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mice, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-miselect_0.9.2-1.ca2604.1_all.deb Size: 145062 MD5sum: ca7904f9193f9ea9286ddd7514903df5 SHA1: d577cfbff5c9bf47adaf237a2e41c85b51d0bb4e SHA256: 217e6a801cdd5fb68788ab11bde1c44e0e22bf3f49872fd53391ce8de8d675ee SHA512: 86856fad03cca8f2805e063670888bf3508a9381b1362a020af0c74fd726731d30137ffe9b2068e429881d939659bb63db85784296461eefb4146168213d7915 Homepage: https://cran.r-project.org/package=miselect Description: CRAN Package 'miselect' (Variable Selection for Multiply Imputed Data) Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet) and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes, developed by Du et al (2022) . They, by construction, force selection of the same variables across multiply imputed data. 'miselect' also provides cross validated variants of these methods. 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Package: r-cran-mispr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mass, r-cran-penalized Filename: pool/dists/resolute/main/r-cran-mispr_1.0.0-1.ca2604.1_all.deb Size: 148624 MD5sum: f58a1c2cdb13a3ff2785e5119d13ff64 SHA1: 9cf4999442ae67267937fb0aa89dae371fc631f2 SHA256: 74135df10feb60082f90f7ad13f42b131a080a6cd1adbb4d354bcf6d132d22d0 SHA512: 6a17931807b8662c1b6496b1d2059a4abd8ba164875d6c33ede4b112ff17fce2e9a5af8d33a4da537b58ea70fcafd6ad1d8355e0920df9ea80f297c938acfc64 Homepage: https://cran.r-project.org/package=mispr Description: CRAN Package 'mispr' (Multiple Imputation with Sequential Penalized Regression) Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) . 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Package: r-cran-missalpha Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-deoptim, r-cran-nloptr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-missalpha_0.2.0-1.ca2604.1_all.deb Size: 80312 MD5sum: 747e7e59a21e0ca01f93b4bc33c89ce2 SHA1: f7fd30afb1fb6df7a5205ece0c5843254838e390 SHA256: f142c0e3ea93b35fb5a238d9788bc5c249be89564a2fcb18477c20ee373ca760 SHA512: 78359dc26a0b4d6406aad8e0795d66debdca29bb25619211ab87bed874def0f0e8cd8331280ea20e047dabbb742533d888b045ca86c055668b32430b7e5264cb Homepage: https://cran.r-project.org/package=missalpha Description: CRAN Package 'missalpha' (Find Range of Cronbach Alpha with a Dataset Including MissingData) Provides functions to calculate the minimum and maximum possible values of Cronbach's alpha when item-level missing data are present. Cronbach's alpha (Cronbach, 1951 ) is one of the most widely used measures of internal consistency in the social, behavioral, and medical sciences (Bland & Altman, 1997 ; Tavakol & Dennick, 2011 ). However, conventional implementations assume complete data, and listwise deletion is often applied when missingness occurs, which can lead to biased or overly optimistic reliability estimates (Enders, 2003 ). This package implements computational strategies including enumeration, Monte Carlo sampling, and optimization algorithms (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under arbitrary missing data patterns. The approach is motivated by Manski's partial identification framework and pessimistic bounding ideas from optimization literature. Package: r-cran-misscforest Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-misscforest_0.0.8-1.ca2604.1_all.deb Size: 22438 MD5sum: 1cfb144f7261dd0a98cd3b86808a20a2 SHA1: 8bf4bb8457d82ba06659e55deb88c655efd3f127 SHA256: 0bd5577a9a06b84bd4151d18e1e34033993d370a456417fde087696b02f496ac SHA512: b75e6b78a23e7c011c7bfbddb06249cbf07ca6642df4fa08e720d25b8482557f72bafd8fdc63e0a95d948718cded5040f1a0faac6fa9d5df8841d1b687d0f238 Homepage: https://cran.r-project.org/package=missCforest Description: CRAN Package 'missCforest' (Ensemble Conditional Trees for Missing Data Imputation) Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) ). Package: r-cran-misscompare Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amelia, r-cran-data.table, r-cran-dplyr, r-cran-ggdendro, r-cran-ggplot2, r-cran-hmisc, r-cran-ltm, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-mi, r-cran-mice, r-cran-missforest, r-cran-missmda, r-bioc-pcamethods, r-cran-plyr, r-cran-rlang, r-cran-tidyr, r-cran-vim Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-misscompare_1.0.3-1.ca2604.1_all.deb Size: 2727880 MD5sum: e1bf169887a059c4289567d7d53e5e75 SHA1: 6b57fadd8229f2faf826c92b12690bbb030fa079 SHA256: 9f34fa5e95292ed8e80d413a20e3c39ffdf0335f8cdfc907ed05f0887e62496c SHA512: d5351c43b4833f2f3510d30a950327b40faa23871c12d6c71b2fed8e13967eb41be0c9dc47543a6f5210fc1eac914c7f944ed632001b7ac6b38f7b17802246a3 Homepage: https://cran.r-project.org/package=missCompare Description: CRAN Package 'missCompare' (Intuitive Missing Data Imputation Framework) Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) ; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) ; 'missForest', described by Stekhoven and Buhlmann (2012) ; 'missMDA', described by Josse and Husson (2016) ; and 'pcaMethods', described by Stacklies et al. (2007) . The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance. Package: r-cran-missdiag Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-cobalt Suggests: r-cran-mice, r-cran-sbw, r-cran-ebal Filename: pool/dists/resolute/main/r-cran-missdiag_1.0.1-1.ca2604.1_all.deb Size: 145546 MD5sum: c1acf4a430b0ac3fe0b273cc02fb40d7 SHA1: 0d20f15bceb3a27f3a64cd8e2f71a47525982bb3 SHA256: 2e3b38a790a655defe2b0b1e11bb8438969b68ec855c142c9bb4d93d5e50e195 SHA512: 48902a1b33274d1789b1e9876edfe72796616ed7f80e6be6618ad3493e1dd237ab1f4b8ffb286798243fd3d5cbd262896151edc02ba50d09a2cdd92f53fd11ba Homepage: https://cran.r-project.org/package=missDiag Description: CRAN Package 'missDiag' (Comparing Observed and Imputed Values under MAR and MCAR) Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) . Package: r-cran-missforest Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-ranger, r-cran-foreach, r-cran-iterators, r-cran-itertools, r-cran-dorng, r-cran-rdpack Suggests: r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-missforest_1.6.1-1.ca2604.1_all.deb Size: 275170 MD5sum: 9e0d7f2defeb8550d0f2c5984ec0c5bb SHA1: af021a46dea07ab5a321edc84191ef72fd0cc3eb SHA256: 0829ea26fd52d9117d255f07c6eb80a4385b5087d45c2a26948ff12876564df2 SHA512: 86b3dc91d64022e99e5f0629c59ed6af25958dfb220b9ddb435a0ff07b56034ed2650a7fc90f30211ec92a3c116e7357f512f0734ce55198c0a9e9ed2da044db Homepage: https://cran.r-project.org/package=missForest Description: CRAN Package 'missForest' (Nonparametric Missing Value Imputation using Random Forest) The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest (via 'ranger' or 'randomForest') trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time. Package: r-cran-missforestpredict Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-missforestpredict_1.0.1-1.ca2604.1_all.deb Size: 217844 MD5sum: c426e1f8d19e2aa2e6f00d04dd90a295 SHA1: 3b3bf0c8755e14151aab072cca8fbb6c7f26268b SHA256: c82a147e6d33283677cb714d6d5a299b218f3b7b18bcf76e4a05a922f313d6f4 SHA512: c095eb14446c1a6dfef9c49fcd251e17611eb21b12eb32d888172739b5656c77b4f7fdecbc7fb1b4ef79aa4bab57d32998d8cfd6919d0551f2873593c4a48fa0 Homepage: https://cran.r-project.org/package=missForestPredict Description: CRAN Package 'missForestPredict' (Missing Value Imputation using Random Forest for PredictionSettings) Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) ) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings . Package: r-cran-missinghandle Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-imputets, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-missinghandle_0.1.1-1.ca2604.1_all.deb Size: 37160 MD5sum: 89477b68cf2f66e8b690af3863b16306 SHA1: 5657ebe4bed7a40280d90b69a78902115b066870 SHA256: 7b0798d484c9eb514284d15ba80c30d82d5a4dad339b196163cf3f869c68df71 SHA512: d236262c3af301523939d17d98651f0a2841593707f7c970d684cdca41fb3d8106416b90785d5fc33c6dbe1becc59b942592cbbce77463580bcc9f99812822a6 Homepage: https://cran.r-project.org/package=MissingHandle Description: CRAN Package 'MissingHandle' (Handles Missing Dates and Data and Converts into Weekly andMonthly from Daily) Many times, you will not find data for all dates. After first January, 2011 you may have next data on 20th January, 2011 and so on. Also available dates may have zero values. Try to gather all such kinds of data in different excel sheets of a single excel file. Every sheet will contain two columns (1st one is dates and second one is the data). After loading all the sheets into different elements of a list, using this you can fill the gaps for all the sheets and mark all the corresponding values as zeros. Here I am talking about daily data. Finally, it will combine all the filled results into one data frame (first column is date and other columns will be corresponding values of your sheets) and give one combined data frame. Number of columns in the data frame will be number of sheets plus one. Then imputation will be done. Daily to monthly and weekly conversion is also possible. More details can be found in Garai and others (2023) . Package: r-cran-missinghe Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggpubr, r-cran-ggmcmc, r-cran-ggthemes, r-cran-bcea, r-cran-ggplot2, r-cran-bayesplot, r-cran-r2jags, r-cran-loo, r-cran-coda, r-cran-mcmcr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-missinghe_1.6.0-1.ca2604.1_all.deb Size: 1493522 MD5sum: 990b2ebcd721d5c61ccc085461a33f69 SHA1: 4c4e9598ea20cf8378d16037c58b92497f9d339f SHA256: e5ff4282e192c3bc2f7dd5dbf9a729701108d03d476430f3cac893b3bcccf28c SHA512: dbf6d9e23c34aac0298bf5d88b7b898830c81aa130fcf3fd1727c5a90e41957bc097b151ccb69d20d684d94b906c848ab9841e4c507f5e85b5ed9008fc05d962 Homepage: https://cran.r-project.org/package=missingHE Description: CRAN Package 'missingHE' (Missing Outcome Data in Health Economic Evaluation) Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) , Molenberghs (2000) and Gabrio (2019) . Package: r-cran-missingplotlsd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-missingplotlsd_0.1.0-1.ca2604.1_all.deb Size: 13236 MD5sum: dc625bb231268b70bab75f8aae273856 SHA1: f6ecb7d819ae2ef30500c0209e102a0a0370c47d SHA256: 6337c277dabc26d21c842a6e58e03069505bdf2614ad207587a8623a65b45d41 SHA512: d01eba3674e7a965d058dfc4a1f1a3affc70b8228fa826c3f87873bce9c1d0e940001fc2f5c84a95342e511ed65aaf9b21444b96454aa5fdb321241fef3349cf Homepage: https://cran.r-project.org/package=MissingPlotLSD Description: CRAN Package 'MissingPlotLSD' (Missing Plot in LSD) A system for Analysis of LSD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-missingplotrbd Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-missingplotrbd_1.1.0-1.ca2604.1_all.deb Size: 11838 MD5sum: cbc4b51afc7da5e542a7f5d80102b212 SHA1: c2042c59b15fdd1df061c24096887e7400d1b3f0 SHA256: 77cc3d4cb71270d0efa564fb78e6321b2013f5c32f1ba524daad4c95b45c99fd SHA512: a57ac215c4e6ce24ca2d5344df110469defe9641274263ee6231121cf13f54b3aaf00bde84b67870fbf8be7863acab9b18f443658c2aefcdd583d085a7153597 Homepage: https://cran.r-project.org/package=MissingPlotRBD Description: CRAN Package 'MissingPlotRBD' (Missing Plot in RBD) A system for Analysis of RBD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-missmda Architecture: all Version: 1.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factominer, r-cran-ggplot2, r-cran-mice, r-cran-mvtnorm, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-missmda_1.21-1.ca2604.1_all.deb Size: 439524 MD5sum: b82fa2bd670ba75c4bbc34435984b948 SHA1: 1811a961f7de1480de7eee15259c5e08f73a5887 SHA256: 27c7772cd5b15fea402d5c4b4476face1cbd385df7193844bd4e23fa0f251cb3 SHA512: d1f6cd3e0330ac1adc7534496699030d33931df69af7b872ae66f5c39f39a8a1299508ca14976f5bd8a25916ec72a1b606137c2da912c1f4f2012aa4c01e774a Homepage: https://cran.r-project.org/package=missMDA Description: CRAN Package 'missMDA' (Handling Missing Values with Multivariate Data Analysis) Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA. Package: r-cran-missmech Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-missmech_1.0.4-1.ca2604.1_all.deb Size: 147880 MD5sum: 85edd248cde3ac331fa7cf4d7664517b SHA1: 7a16b40d73cc785a6052e40ba5c099c635709155 SHA256: 0a36d8f8d06af29ac352220cc8ac27ca4c034043533289fbf7c5c529231fb8b1 SHA512: 92de3b4ac4c5ca700775cd52564a640332363914271e51683e5d6995670719306a0831b575a0cc45aa22e37631e9d4cbd3a30b5c2a197055d73c7af3af0c023c Homepage: https://cran.r-project.org/package=MissMech Description: CRAN Package 'MissMech' (Testing Homoscedasticity, Multivariate Normality, and MissingCompletely at Random) To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)", Journal of Statistical Software, 56(6), 1-31. . Package: r-cran-missmethods Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-lpsolve, r-cran-norm, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-missmethods_0.4.0-1.ca2604.1_all.deb Size: 265138 MD5sum: cbcfedbc5ebc20117e881baa453033fa SHA1: ea5c50a09f7acadd9e031c7565f39b62279c77a6 SHA256: f8a29c0cf0c082d5cfef7a22400eca3e6b6a1f00456af1dc263a0a2bf44d080a SHA512: 45a4925d2ff9550b3d73bcb18088ac3609caf8d70642ab9f12abb72e75e9f176f40d1f5935e53a4a96e7df254774608df60f348274050b1ded22675d24cdc59f Homepage: https://cran.r-project.org/package=missMethods Description: CRAN Package 'missMethods' (Methods for Missing Data) Supply functions for the creation and handling of missing data as well as tools to evaluate missing data methods. Nearly all possibilities of generating missing data discussed by Santos et al. (2019) and some additional are implemented. Functions are supplied to compare parameter estimates and imputed values to true values to evaluate missing data methods. Evaluations of these types are done, for example, by Cetin-Berber et al. (2019) and Kim et al. (2005) . Package: r-cran-misspi Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lightgbm, r-cran-doparallel, r-cran-dosnow, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-sis, r-cran-plotly Suggests: r-cran-e1071, r-cran-neuralnet Filename: pool/dists/resolute/main/r-cran-misspi_0.1.1-1.ca2604.1_all.deb Size: 648218 MD5sum: 9cc4e1e9fd375f70a677b7670d521154 SHA1: 07e0b8f1c5d7fe692626d57619579b131d2581fc SHA256: 2a66aa718a7e58030a7467b3bc48ac4c15521a10611d4db076eeddfdd945d05c SHA512: 4ea5ba789f8beb79f2f94bf57fe4d419c327c08f4be069b004caa930039fadd80daeb49395f6cf3a4733694f773a6a3ce48a19c17e097f7e54b753d11f0f1520 Homepage: https://cran.r-project.org/package=misspi Description: CRAN Package 'misspi' (Missing Value Imputation in Parallel) A framework that boosts the imputation of 'missForest' by Stekhoven, D.J. and Bühlmann, P. (2012) by harnessing parallel processing and through the fast Gradient Boosted Decision Trees (GBDT) implementation 'LightGBM' by Ke, Guolin et al.(2017) . 'misspi' has the following main advantages: 1. Allows embrassingly parallel imputation on large scale data. 2. Accepts a variety of machine learning models as methods with friendly user portal. 3. Supports multiple initializations methods. 4. Supports early stopping that prohibits unnecessary iterations. Package: r-cran-missplot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-missplot_0.1.0-1.ca2604.1_all.deb Size: 17214 MD5sum: 2a58dae6539de33601e4028325d32719 SHA1: 7e7a4d0d67acd5e1f439a427c217be32055513d2 SHA256: 3724882fd08cbfd45c94d047cd92c84ca1d5aae60392bda18b1071d8cc50543a SHA512: d87f0498a510de0827651d6eb5c70ec7225c47bfdc9395943b4e0c0281cdb8bb31848b80cae0ade26f1796a369311d088a52d1ca3541a23f3f44ac6f960ead65 Homepage: https://cran.r-project.org/package=Missplot Description: CRAN Package 'Missplot' (Missing Plot Technique in Design of Experiment) A system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-misspls Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-plsrglm, r-cran-vim Suggests: r-cran-bcv, r-cran-knitr, r-cran-mlbench, r-cran-plsdof, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-misspls_0.2.1-1.ca2604.1_all.deb Size: 495068 MD5sum: 2e7d077e703d93bf8d6b74d5b85a6789 SHA1: d809ecc63da7910fd3ba8566b8acf2de1d06f014 SHA256: c4ed10476a4ee42dc0c9f7ed6447c0bf0a21afd370312039c5b6c2968576ec52 SHA512: ab88c6b774bc8852a80bed38979c694d2384e709c8a7e8c4b45572a4ef10ca0933619291e40ff7045ac28051715225099c201fdd2475442dda000facfde3d1ae Homepage: https://cran.r-project.org/package=missPLS Description: CRAN Package 'missPLS' (Methods and Reproducible Workflows for Partial Least Squareswith Missing Data) Methods-first tooling for reproducing and extending the partial least squares regression studies on incomplete data described in Nengsih et al. (2019) . The package provides simulation helpers, missingness generators, imputation wrappers, component-selection utilities, real-data diagnostics, and reproducible study orchestration for Nonlinear Iterative Partial Least Squares (NIPALS)-Partial Least Squares (PLS) workflows. Package: r-cran-missr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-norm, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-missr_1.0.1-1.ca2604.1_all.deb Size: 129722 MD5sum: e67392099a259eee5892938b62e434a8 SHA1: ba373ac0554b301e4cb6b8f705fab85044dbb8ae SHA256: dcbc8a804e8beac34acebc0824628cbe5c82557d56b60142918d92e5233403e5 SHA512: ee4d5de71082cd722067678cf2ef6334a6f664dd08608c2f8293c3c83248512a8e5d8a3a54cf2d28c26e121471e9925593f19ecd6157a5dbc7a1d083c3cfcad7 Homepage: https://cran.r-project.org/package=missr Description: CRAN Package 'missr' (Classify Missing Data as MCAR, MAR, or MNAR) Classify missing data as missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). This step is required before handling missing data (e.g. mean imputation) so that bias is not introduced. See Little (1988) for the statistical rationale for the methods used. Package: r-cran-missranger Architecture: all Version: 2.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-missranger_2.6.1-1.ca2604.1_all.deb Size: 89732 MD5sum: 9e8100b37782b53c0e1631c9ecb45a60 SHA1: dd574e62b277ba79566f60481962075dec2e1fb1 SHA256: 3735635aa2598a68c8dc1cba8378e372b8613cc54b76c463ecdf429f1969821d SHA512: 6959b946f8f2bf40807110d718e300ad06ab412e8cec53ef1689269ea87c6a34f3e0ea8ac22ca0d6bdc60282f591547e9bce459a16413ee348076f7517c5780a Homepage: https://cran.r-project.org/package=missRanger Description: CRAN Package 'missRanger' (Fast Imputation of Missing Values) Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) . Under the hood, it uses the lightning fast random forest package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow, e.g., to do multiple imputation when repeating the call to missRanger(). Out-of-sample application is supported as well. Package: r-cran-mistr Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-pinp Filename: pool/dists/resolute/main/r-cran-mistr_0.0.6-1.ca2604.1_all.deb Size: 1653678 MD5sum: aa62b2d7a742429278e547fe512cd2b0 SHA1: 02afb6afbd777e012da038ebfea26324a81d18dd SHA256: 328ee7b48c38e77dd85260904e9ed9ccfa6e8a4c5cf2c42bc9d30084f6ba1f1b SHA512: ab874eadb1148db7cb89a131bb4d93d434468face2085324bee54a1674c9450eeab1a7312676aece22472ea1e4e947793652e3c6508af493418d2816b6ea4ae3 Homepage: https://cran.r-project.org/package=mistr Description: CRAN Package 'mistr' (Mixture and Composite Distributions) A flexible computational framework for mixture distributions with the focus on the composite models. 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Yanagida') Miscellaneous functions for (1) data handling (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., bootstrap confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with 'Blimp' and 'Mplus'. 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Serial intervals measure the time between symptom onset in linked transmission pairs, while case reproduction numbers quantify how many secondary cases each infected individual generates over time. These parameters are essential for understanding transmission dynamics, evaluating control measures, and informing public health responses. The package implements the maximum likelihood framework from Vink et al. (2014) for serial interval estimation and the retrospective method from Wallinga & Lipsitch (2007) for reproduction number estimation. Originally developed for scabies transmission analysis but applicable to other infectious diseases including influenza, COVID-19, and emerging pathogens. Designed for epidemiologists, public health researchers, and infectious disease modelers working with outbreak surveillance data. 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Based on the Laplace-Inversion algorithm by Garrappa, R. (2015) . 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(1997) . 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Thermal decay curves derived from thermogravimetric analysis (TGA) are imported, modified, and then modelled in a three- or four- part mixture model using the Fraser-Suzuki function. The output is estimates for weights of pseudo-components corresponding to hemicellulose, cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, H. (2007) , Órfão, J. J. M. and Figueiredo, J. L. (2001) , and Yang, H. and Yan, R. and Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) . Package: r-cran-mixcure Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-boot, r-cran-flexsurv, r-cran-survey, r-cran-gam, r-cran-timereg Filename: pool/dists/resolute/main/r-cran-mixcure_2.0-1.ca2604.1_all.deb Size: 82880 MD5sum: 4704e21cdb28cd9c09a754b296f077c9 SHA1: 09b9fe056b6e5a9320b6943c1773e909af25b81b SHA256: f465ec236653ab9cc001e429dc84613f0f90b3b9d2a6740f96b44c04852a787a SHA512: 989adf11c13a24aef2724f9d80dd05e1a2993a96153aaceac296cfff0ce20c2b53b4432dd1993dcb76fbdbe7c0a5bf4bdff2e7124b74a4a128054d0210381ff8 Homepage: https://cran.r-project.org/package=mixcure Description: CRAN Package 'mixcure' (Mixture Cure Models) Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) . Package: r-cran-mixdist Architecture: all Version: 0.5-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mixdist_0.5-5-1.ca2604.1_all.deb Size: 170860 MD5sum: 092eecef4919ee5ff8937f93d36a4854 SHA1: 40bccb98b4e76ee5f90ec44fd9b7dfda2c7c3b5d SHA256: d753e713eb2d3d5539f910438fcafeb7d451a53e8ffa3eb43b31885208649262 SHA512: 1e986509a8608322c96ca2dc8e24b90a205a158a2a08886145e61f6a4113cfa727a2b2de6aca4e35cf65f0a5a196b9791af8943a515c20b6ec68a3ed0f3b0a2e Homepage: https://cran.r-project.org/package=mixdist Description: CRAN Package 'mixdist' (Finite Mixture Distribution Models) Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm. Package: r-cran-mixedbiastest Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-plm, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mixedbiastest_1.0.2-1.ca2604.1_all.deb Size: 47938 MD5sum: 8569dbfbf62847a56e5c60131d5bfa02 SHA1: 5ec024a865cf109e781190be30b712e6d6823c09 SHA256: 3abd781d2616e2d142498380cc202c4023c9a72d36ef5bca51bffff3e3df43b0 SHA512: 2545610c78898ed59bf539d8ad84f3d61b2c6e0c25535228fb18299d2ffa7528325e304e06d6e4617e0766ee3efffacd6da7624ab211098843edc56df3cf790f Homepage: https://cran.r-project.org/package=mixedbiastest Description: CRAN Package 'mixedbiastest' (Bias Diagnostic for Linear Mixed Models) Provides a function to perform bias diagnostics on linear mixed models fitted with lmer() from the 'lme4' package. Implements permutation tests for assessing the bias of fixed effects, as described in Karl and Zimmerman (2021) . Karl and Zimmerman (2020) provide R code for implementing the test using 'mvglmmRank' output. Development of this package was assisted by 'GPT o1-preview' for code structure and documentation. Package: r-cran-mixedfact Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mixedfact_0.1.1-1.ca2604.1_all.deb Size: 90956 MD5sum: be521b27bdb5eac62432ddae3a4ae412 SHA1: 07dde36a28c31bee6b057bc1b11e23312fb5810b SHA256: 2018faa4db5d026ac03359e0549fbc57139ee4713b02b5333d5e7cd1acb1ea88 SHA512: 17460c4ebe21c4686644c32e5211c40164a87ef59bb53647886422fa894c932366f18cbaff2a024f97684d4ba6ed763dcaae603d582a19d1018750d80096ad76 Homepage: https://cran.r-project.org/package=mixedfact Description: CRAN Package 'mixedfact' (Generate and Analyze Mixed-Level Blocked Factorial Designs) Generates blocked designs for mixed-level factorial experiments for a given block size. Internally, it uses finite-field based, collapsed, and heuristic methods to construct block structures that minimize confounding between block effects and factorial effects. The package creates the full treatment combination table, partitions runs into blocks, and computes detailed confounding diagnostics for main effects and two-factor interactions. It also checks orthogonal factorial structure (OFS) and computes efficiencies of factorial effects using the methods of Nair and Rao (1948) . When OFS is not satisfied but the design has equal treatment replications and equal block sizes, a general method based on the C-matrix and custom contrast vectors is used to compute efficiencies. The output includes the generated design, finite-field metadata, confounding summaries, OFS diagnostics, and efficiency results. Package: r-cran-mixediffusion Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-adaptmcmc Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mixediffusion_1.0.1-1.ca2604.1_all.deb Size: 230658 MD5sum: 569ba3fab9b114fbfe31fc81114f4c23 SHA1: 0f2ebe7ff437d618b0e291c80f82dd713b33e192 SHA256: 3d33a96d4951ef26989863946e9be09d64be3718b60e7e19b9d21cadbd85f17e SHA512: c34591a67f60192df5a5034751926719b28a7de65144318098a54eccd375f6385e4d74d3c29aced30b667ded530e40a8bafe19cc5d2919ea8d3e98245a00d2a4 Homepage: https://cran.r-project.org/package=mixediffusion Description: CRAN Package 'mixediffusion' (Mixed-Effects Diffusion Models with General Drift) Provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectation–maximization (EM) algorithms. The package supports Wiener and Ornstein–Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM. Package: r-cran-mixedlevelrsds Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tords, r-cran-frf2, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mixedlevelrsds_1.0.0-1.ca2604.1_all.deb Size: 183014 MD5sum: 1e35225caa1dcc0cbbceecbf54625caf SHA1: c179ed0615b4ae92973913571dcdee54dd1801c4 SHA256: e74d187dc6345d6fe14a55acd1c1acea080fbe01ef0d2edceb8bc082da25e909 SHA512: 7231b2fc7dec336aeddcf5749ea650bddb3d8049ee700b63ba1f5f01ed3a937f1a34c9fe529524b039d8f46a9f533b95f7d42c2ba4c382736cbb3867941fc0a8 Homepage: https://cran.r-project.org/package=MixedLevelRSDs Description: CRAN Package 'MixedLevelRSDs' (Mixed Level Response Surface Designs) Response Surface Designs (RSDs) involving factors not all at same levels are called Mixed Level RSDs (or Asymmetric RSDs). In many practical situations, RSDs with asymmetric levels will be more suitable as it explores more regions in the design space. (J.S. Mehta and M.N. Das (1968) . "Asymmetric rotatable designs and orthogonal transformations").This package contains function named ATORDs_I() for generating asymmetric third order rotatable designs (ATORDs) based on third order designs given by Das and Narasimham (1962). Function ATORDs_II() generates asymmetric third order rotatable designs developed using t-design of unequal set sizes, which are smaller in size as compared to design generated by function ATORDs_I(). In general, third order rotatable designs can be classified into two classes viz., designs that are suitable for sequential experimentation and designs for non-sequential experimentation. The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go (M. N. Das and V. Narasimham (1962) . "Construction of Rotatable Designs through Balanced Incomplete Block Designs"). ATORDs_I() and ATORDs_II() functions generate non-sequential asymmetric third order designs. Function named SeqTORD() generates symmetric sequential third order design in blocks and also gives G-efficiency of the given design. Function named Asymseq() generates asymmetric sequential third order designs in blocks (M. Hemavathi, Eldho Varghese, Shashi Shekhar and Seema Jaggi (2020) . "Sequential asymmetric third order rotatable designs (SATORDs)"). In response surface design, situations may arise in which some of the factors are qualitative in nature (Jyoti Divecha and Bharat Tarapara (2017) . "Small, balanced, efficient, optimal, and near rotatable response surface designs for factorial experiments asymmetrical in some quantitative, qualitative factors"). The Function named QualRSD() generates second order design with qualitative factors along with their D-efficiency and G-efficiency. The function named RotatabilityQ() calculates a measure of rotatability (measure Q, 0 <= Q <= 1) given by Draper and Pukelshiem(1990) for given a design based on a second order model, (Norman R. Draper and Friedrich Pukelsheim(1990) . "Another look at rotatability"). Package: r-cran-mixedlsr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-grpreg, r-cran-purrr, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-mixedlsr_0.1.0-1.ca2604.1_all.deb Size: 190328 MD5sum: 836d0422399a7177ee11fc4a807eedd8 SHA1: f7fa814f5069e34cdfca339a8c06dc93b50306a4 SHA256: e3f0acd9ea876061fb45cc535b69280c7da5c1527097ed8bad7afa1cc70e07c9 SHA512: 826930c496f072637318e972c84d4422e7dd904af2646b93ef0fe58857f5f7cbb802044859d785e346a349f3cc86c0673b515c977b062378f8b9b6098c787e0d Homepage: https://cran.r-project.org/package=mixedLSR Description: CRAN Package 'mixedLSR' (Mixed, Low-Rank, and Sparse Multivariate Regression onHigh-Dimensional Data) Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix. Package: r-cran-mixedpoisson Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gaussquad, r-cran-rmpfr, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mixedpoisson_2.0-1.ca2604.1_all.deb Size: 54226 MD5sum: 085ea87d6fdb1c444d4261b554dd4682 SHA1: ecde1928016a890b49b5e96cd6af260c3c6281f1 SHA256: 23de4b3a4b495d254870a7d4038a299aa5450b02bf806126924dc3c41e75d0db SHA512: 10c71d49004142878736568cbdd79945d7901a6f4663821d5cfb2257e2348368ef0cd9b3b5d7ad9639964dd33114b9be023d31afaf26b99a86d7f246ed6d7673 Homepage: https://cran.r-project.org/package=MixedPoisson Description: CRAN Package 'MixedPoisson' (Mixed Poisson Models) The estimation of the parameters in mixed Poisson models. Package: r-cran-mixedpsy Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beepr, r-cran-boot, r-cran-brglm, r-cran-lme4, r-cran-matrix, r-cran-mnormt, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-mixedpsy_1.3.0-1.ca2604.1_all.deb Size: 111864 MD5sum: 6aa7c1198d844519dd80489052324be1 SHA1: f0c9b1a886918889510cbb1c70f004f6068fdc96 SHA256: e75a01c892572eb06a11429869b12251a24473d05f52155d3295017b01cf3177 SHA512: c6ab318ce0035de8fc93a54a5eeec51cce4f38732b45da509385dac49a23de7ac7045e5bb5ea7123f25a1a602c1e7d7b8b70d86d468970e67f5de72529094911 Homepage: https://cran.r-project.org/package=MixedPsy Description: CRAN Package 'MixedPsy' (Statistical Tools for the Analysis of Psychophysical Data) Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets. For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012). Package: r-cran-mixedsde Architecture: all Version: 5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sde, r-cran-moments, r-cran-mass, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-mixedsde_5.0-1.ca2604.1_all.deb Size: 694994 MD5sum: ce4ab97ca7697e4ebdf041f93b9fb634 SHA1: 6b0768c51eeaf1f2838996a564f03dcb95b2c8c0 SHA256: 87c882b35417caa44612cadeacc89d6816cbea310fd0a12cffb5cf4d3b63f49d SHA512: 1cbb95c0d93250ab0092d77dcc05cf492706899ec5bffff77e4a8c58e7b48b000400774c0cf5e15d2b3aefa43c4e773fd18b67f447001fd9d677651be46f16ae Homepage: https://cran.r-project.org/package=mixedsde Description: CRAN Package 'mixedsde' (Estimation Methods for Stochastic Differential Mixed EffectsModels) Inference on stochastic differential models Ornstein-Uhlenbeck or Cox-Ingersoll-Ross, with one or two random effects in the drift function. Package: r-cran-mixedts Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mixedts_1.0.4-1.ca2604.1_all.deb Size: 213988 MD5sum: daff08101574440822c9bee878bab8a0 SHA1: d4c2870e346a1ba4a10eebafc80b6a9ad7f35d20 SHA256: 291dc3103fc832638c30fa7d142f744674c4374ebac6e45b7f02c34c6e5daf7a SHA512: 24e7591012d4200b218d757321200f058cc36dc7b05c4c7513766b35932775993c668503b6eaa2c502a7521fb7fe2582b8871daeb45f2118c3699ee86a1ccaf9 Homepage: https://cran.r-project.org/package=MixedTS Description: CRAN Package 'MixedTS' (Mixed Tempered Stable Distribution) We provide detailed functions for univariate Mixed Tempered Stable distribution. Package: r-cran-mixfim Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-mvtnorm, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-mixfim_1.1-1.ca2604.1_all.deb Size: 66284 MD5sum: a210e8253f3a932c124c55088a3a9538 SHA1: 8b110c3d35032211a76bbda8572d29d8f6724557 SHA256: 6b0aa3e67f881b8c58a8458e6caff2a6f2fdea4c30c080de3da361752bbd94bd SHA512: dd4dba415d7d44fbd9b31e4c1b3096ee0c8dfd10c4f5edef1f30a0278a523c4b1e2fe60ab3a9cea8db1d12689a60201212c5052f445493b3b309bfa660acee91 Homepage: https://cran.r-project.org/package=MIXFIM Description: CRAN Package 'MIXFIM' (Evaluation of the FIM in NLMEMs using MCMC) Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data. Package: r-cran-mixfrac Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mixfrac_1.0-1.ca2604.1_all.deb Size: 64674 MD5sum: 78cc0d3475b76e18ac0afb3128bff796 SHA1: 482a9c5d6f4b735c82a11218d5ad40db41423fae SHA256: 62c4f2adbba20005a8cce766daba28e488316665c5c736257f671c16d5e41266 SHA512: 900fa0d4ebe636414241b6790f24d8518fe42926e937f7069c2cbd924bebb39345fd1c47f0ee3f461a4f4f2bfef8502b860cad6ed79db79eaea0c2f591cc5d6f Homepage: https://cran.r-project.org/package=MixFrac Description: CRAN Package 'MixFrac' (Fractional Factorial Designs with Alias and Trend-Free Analysis) Constructs mixed-level and regular fractional factorial designs using coordinate-exchange optimization and automatic generator search. Design quality is evaluated with J2 and balance (H-hat) criteria, alias structures are computed via correlation-based chaining, and deterministic trend-free run orders can be produced following Coster (1993) . Mixed-level design construction follows the NONBPA approach of Pantoja-Pacheco et al. (2021) . Regular fraction identification follows Guo, Simpson and Pignatiello (2007) . Alias structure computation follows Rios-Lira et al.(2021) . Package: r-cran-mixghd Architecture: all Version: 2.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-bessel, r-cran-mvtnorm, r-cran-ghyp, r-cran-numderiv, r-cran-mixture, r-cran-e1071, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-mixghd_2.3.7-1.ca2604.1_all.deb Size: 311266 MD5sum: 093b46c895510b80de117f46f9cb49b4 SHA1: f8a942706e52c240516be7a14ce26d40c24e8f64 SHA256: e323ff559f91c43bc03489348697b8c62a2bbe65b4876b5c8c6d8a184df2a057 SHA512: 3efb82043846b48520e53bbc40efdf95e84369eb661bb335969f84ce6bfdbcd09903a3230ec6aa3a027e2b85457644a8d72191a6ac6b434cb976b73972eb78d8 Homepage: https://cran.r-project.org/package=MixGHD Description: CRAN Package 'MixGHD' (Model Based Clustering, Classification and Discriminant AnalysisUsing the Mixture of Generalized Hyperbolic Distributions) Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) ) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) ) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of multiple scaled generalized hyperbolic distributions, the 'cMSGHD' is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019). The paper related to the software can be found at . Package: r-cran-mixhvg Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-scran, r-cran-seurat, r-cran-matrix, r-bioc-singlecellexperiment, r-bioc-scuttle Filename: pool/dists/resolute/main/r-cran-mixhvg_1.0.1-1.ca2604.1_all.deb Size: 1343326 MD5sum: 9c8c7118215d9ecd99e5a4fd30c3858d SHA1: bda72de34bc6c0942c3d84c55a528947f440219a SHA256: d73ba6d11dd4269263bf8986e0970421320a0b503e33d4612437ac465ddd37bf SHA512: 51cf9d186e7ba8db90211a4c63df7c239d4c407291a348355a537c4d68575e87eec9a0b0aa5e17414adcf309fd65e5294a2cdecf5616ea5e42300013af36b772 Homepage: https://cran.r-project.org/package=mixhvg Description: CRAN Package 'mixhvg' (Mixture of Multiple Highly Variable Feature Selection Methods) Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, . Reference: . Package: r-cran-mixkernel Architecture: all Version: 0.9-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3396 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-mixomics, r-cran-ggplot2, r-cran-reticulate, r-cran-vegan, r-bioc-phyloseq, r-cran-corrplot, r-cran-psych, r-cran-quadprog, r-cran-ldrtools, r-cran-matrix, r-cran-markdown Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mixkernel_0.9-2-1.ca2604.1_all.deb Size: 2176002 MD5sum: 0587cc72f61b73b26ca7bb8525441531 SHA1: e3c9f239528d0fbab3f245855f3eba1edbb53dad SHA256: 2892219a045531dd4283bfef26381009e088eeaac6fb76f9f1fd0e8e1c8e039f SHA512: 35ef460988b8727e1be5bda6df3e1181e6ea51ef88d7a6f68a9f25c4be41499892ab0100e8ad7e3c76679db7bd9ff0e4ee0d99b61e5c2d0a44ded78815fc822d Homepage: https://cran.r-project.org/package=mixKernel Description: CRAN Package 'mixKernel' (Omics Data Integration Using Kernel Methods) Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view . A method to select (as well as funtions to display) important variables is also provided . Package: r-cran-mixlm Architecture: all Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-pls, r-cran-multcomp, r-cran-pracma, r-cran-leaps Suggests: r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-mixlm_1.4.3-1.ca2604.1_all.deb Size: 321668 MD5sum: 8e1c1078177aced7bd7c6abb1a08db26 SHA1: 787ed1deb3251c0319dc90944336e67dbe5a844f SHA256: 22fcb292801df20c7c0335cebec0ac4b5acfef22634d1abd8c72a292df2dd2eb SHA512: edec66f813ee31892fcdf863602f15e602363ca5b76dd5e9581b52ad4de040f2b0550f4a98011b7a2d81d48eeac3f8f6e5a6fbe7674c50e40e1293f7212ef38d Homepage: https://cran.r-project.org/package=mixlm Description: CRAN Package 'mixlm' (Mixed Model ANOVA and Statistics for Education) The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm() and glm(). 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Package: r-cran-mixmashnet Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgm, r-cran-igraph, r-cran-qgraph, r-cran-colorspace, r-cran-future.apply, r-cran-ggplot2, r-cran-eganet, r-cran-networktools, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-patchwork, r-cran-progressr Filename: pool/dists/resolute/main/r-cran-mixmashnet_1.0.0-1.ca2604.1_all.deb Size: 3043380 MD5sum: 90d8627d79b1ad916c028c20c2203b4e SHA1: f16c334f4a7f62f09d5a8995f06f32f037aeace5 SHA256: 2dbd30302a23ea6caf0ae37fd377f79db163c46a9637aff1d4b75a3501fdf1bb SHA512: 82ecff40dd8ab5a3eeb593c0973c35c810950ce5d632d780a024ea9a017dc864d8e2a3102cadc62a5ea4715beb76174dba0be4709125668d0fb57efb92639f48 Homepage: https://cran.r-project.org/package=MixMashNet Description: CRAN Package 'MixMashNet' (Tools for Multilayer and Single Layer Network Modeling) Estimation and bootstrap utilities for single layer and multilayer Mixed Graphical Models, including functions for centrality, bridge metrics, membership stability, and plotting (De Martino et al. (2026) ). Package: r-cran-mixmeta Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-metafor, r-cran-dosresmeta, r-cran-nlme, r-cran-mass, r-cran-dlnm Filename: pool/dists/resolute/main/r-cran-mixmeta_1.2.2-1.ca2604.1_all.deb Size: 428144 MD5sum: 5742a073b77c9805ee0406547fcb50c3 SHA1: aea3c78eeecff4b1e891b0f0d220e2f14e6b9b4b SHA256: 3a41029a9d7f332cd5df927d5fb6ee192abea94e683bd1e0e16eae7726d0c479 SHA512: d911d3faef68a8041229540076e4ad94b4795b9ff5506ae883111980a067a096e5d142896f1974588f19974e4cbe33602bc3041f522597ce32387938d39061c7 Homepage: https://cran.r-project.org/package=mixmeta Description: CRAN Package 'mixmeta' (An Extended Mixed-Effects Framework for Meta-Analysis) A collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models. Package: r-cran-mixoofa Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doofa, r-cran-crossdes, r-cran-mixexp, r-cran-combinat, r-cran-rsolnp Filename: pool/dists/resolute/main/r-cran-mixoofa_1.0-1.ca2604.1_all.deb Size: 54194 MD5sum: 598555772a4f1a3a8ef63e79f6272bf1 SHA1: abe2a9e422bdf2b864d358aae4cb13babf982c6d SHA256: 533108cbe4e92a67c0160c659397f079d3f93e6383fe16d770241eb801ee666f SHA512: 0c9dbd82076979de4af0caf3fe3974cad0dba6c0ce2ead61f445e2de52fe70e2a3d9611a50bdba5891a8b9206002e3f15fb1dec5e7da790a86af075661b19df9 Homepage: https://cran.r-project.org/package=mixOofA Description: CRAN Package 'mixOofA' (Design and Analysis of Order-of-Addition Mixture Experiments) A facility to generate various classes of fractional designs for order-of-addition experiments namely fractional order-of-additions orthogonal arrays, see Voelkel, Joseph G. (2019). "The design of order-of-addition experiments." Journal of Quality Technology 51:3, 230-241, . Provides facility to construct component orthogonal arrays, see Jian-Feng Yang, Fasheng Sun and Hongquan Xu (2020). "A Component Position Model, Analysis and Design for Order-of-Addition Experiments." Technometrics, . Supports generation of fractional designs for order-of-addition mixture experiments. Analysis of data from order-of-addition mixture experiments is also supported. Package: r-cran-mixopt Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-splitfngr Suggests: r-cran-contourfunctions, r-cran-gridextra, r-cran-lhs, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mixopt_0.1.3-1.ca2604.1_all.deb Size: 96096 MD5sum: 95979eab725f395b77fd2effdfa6c3ec SHA1: 8d23669b9f43cbf5573e40fdbe90b13a23106014 SHA256: ca8a5428bce9cf3c28499977d25ee55627b454fa097e5117d7ae8dd3ae847560 SHA512: 3560f932a203917ae32654ce8632cf76f0c6d9e990d960abf3bb153de9414bdefb9a8ebc4c7160e3e8e3e6fe364769e42eeb52220f652f0ffe7861f29f17dd0d Homepage: https://cran.r-project.org/package=mixopt Description: CRAN Package 'mixopt' (Mixed Variable Optimization) Mixed variable optimization for non-linear functions. Can optimize function whose inputs are a combination of continuous, ordered, and unordered variables. Package: r-cran-mixoptim Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-cran-desirability, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mixoptim_0.1.2-1.ca2604.1_all.deb Size: 93096 MD5sum: 5bb58afcd19915346a1fd8298dc66c53 SHA1: d4ab026cdd2c028af0a3642ed957a7a5b7a377b1 SHA256: 60c4d9269c42ccb384b8a520a477688312fc6bacc8ec351280462a429025932b SHA512: 44c76af29e35ef5388fd798ababdd380f6147a38615946766dc64d4646ebf9b3f2ac17aee3c40fdaaa24f6c8b36cdce554dfdfb43029e36beb503df2415cd6f9 Homepage: https://cran.r-project.org/package=MixOptim Description: CRAN Package 'MixOptim' (Mixture Optimization Algorithm) Simple tools to perform mixture optimization based on the 'desirability' package by Max Kuhn. It also provides a plot routine using 'ggplot2' and 'patchwork'. Package: r-cran-mixphm Architecture: all Version: 0.7-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-mixphm_0.7-2-1.ca2604.1_all.deb Size: 92966 MD5sum: c4904cff440ecbd1edecb05f578a9837 SHA1: f22d4ba5e10dc2fd389d94cecf997b52fa0240c1 SHA256: 425887ace19318c2c27bb87eb5aad9db3a0de3ac93f2c2977ac3930e00fe8d83 SHA512: 674b3612a353c8e4d5cfc3fcd1589523a7b911300fa4e59e8b07fc55d75376d37bd18ec4b8e9b96259def5230e07a5da3e5009aba5cfbf44649943d1cfdbe6e1 Homepage: https://cran.r-project.org/package=mixPHM Description: CRAN Package 'mixPHM' (Mixtures of Proportional Hazard Models) Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. 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Package: r-cran-mixpoissonreg Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4828 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-formula, r-cran-rfast, r-cran-dplyr, r-cran-gamlss.dist, r-cran-generics, r-cran-ggplot2, r-cran-gridextra, r-cran-lmtest, r-cran-magrittr, r-cran-statmod, r-cran-tibble, r-cran-rlang, r-cran-ggrepel, r-cran-gamlss Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-devtools, r-cran-bbreg, r-cran-testthat, r-cran-covr, r-cran-tidyr, r-cran-ggfortify, r-cran-broom Filename: pool/dists/resolute/main/r-cran-mixpoissonreg_1.0.0-1.ca2604.1_all.deb Size: 3029230 MD5sum: be8373a20cd7edb6c54c1a374a2a5149 SHA1: 938f852e0b4039552b861b87a9842700f076915a SHA256: 2546bf15f89e6d6145a3edea7dc606fce975c52c500334c258560b7b711d812d SHA512: f4cb3eb5064b318738f3f0c0c31f894683c6296e586d6adf9c4d60700ff8b39b783d282495ce3b807fe38a721a8686016b7d70ca039e931ad56d60c0d111bacc Homepage: https://cran.r-project.org/package=mixpoissonreg Description: CRAN Package 'mixpoissonreg' (Mixed Poisson Regression for Overdispersed Count Data) Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. 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Package: r-cran-mixpower Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-digest, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-mixpower_0.1.0-1.ca2604.1_all.deb Size: 201582 MD5sum: 84b12c2eabd3e685a9445567f6410cb1 SHA1: 35d923ec4ea6aff914eede533aa428c0929eb88d SHA256: 22b0ec2e4b930d5e5f5d2d4a09881d39e4ffb12330823effbf7269cb832c7cad SHA512: 2a4d97add714e098e04b4faf7c75fd91e05ac73be7cda7aa09b974e008245ad89df29c2c61a77de8d7eb9b6f3380a1abf8a0011430a1c01d771d29da4edc2fa8 Homepage: https://cran.r-project.org/package=mixpower Description: CRAN Package 'mixpower' (Simulation-Based Power Analysis for Mixed-Effects Models) A comprehensive, simulation-based toolkit for power and sample-size analysis for linear and generalized linear mixed-effects models (LMMs and GLMMs). Supports Gaussian, binomial, Poisson, and negative binomial families via 'lme4'; Wald and likelihood-ratio tests; multi-parameter sensitivity grids; power curves and minimum sample-size solvers; parallel evaluation with deterministic seeds; and full reproducibility (manifests, result bundling, and export to CSV/JSON). Delivers thorough diagnostics per run (failure rate, singular-fit rate, effective N) and publication-ready summary tables. References: Bates et al. (2015) "Fitting Linear Mixed-Effects Models Using lme4" ; Green and MacLeod (2016) "SIMR: an R package for power analysis of generalized linear mixed models by simulation" . Package: r-cran-mixraschtools Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mixraschtools_1.1.1-1.ca2604.1_all.deb Size: 216216 MD5sum: f5f76c024ae863d0cf0d264ff3e9ca95 SHA1: 07bb6c2b95bb01e43eb771d7d9e385a0f628dedf SHA256: 0579c8636ab5406ab8ee5e9b4119f815f347071611fc9edd7b12e183e177bcf5 SHA512: d5d0655401e01509f63fa398d3f8c80c8ce74ca8d6ab40942a8ad96e051173ae7ac9333f2bae28cce865d0c04a25379f3aa551d807878e2f6428a0297b4d4a4c Homepage: https://cran.r-project.org/package=mixRaschTools Description: CRAN Package 'mixRaschTools' (Plotting and Average Theta Functions for Multiple Class MixedRasch Models) Provides supplemental functions for the 'mixRasch' package (Willse, 2014), including a plotting function to compare item parameters for multiple class models and a function that provides average theta values for each class in a mixture model. Package: r-cran-mixrf Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-randomforest, r-cran-lme4, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-mixrf_1.0-1.ca2604.1_all.deb Size: 102176 MD5sum: 5aa6e80fdec2f1487f132f164e272939 SHA1: a7fe2addb09f99a22a24a443d8377c7a49d1651b SHA256: e7ec79251f774df6c72a561100dd3b169edfb37aa8778bc43ee2d15a5c236f47 SHA512: 98a6b7ccee63da5154cab9a29e7df72a7e9b8b6b6c57a2b2027c432aa1931ba4013ef0c847f0a565a786b69c86b200baf27493083b1130cb8729ea3b6c153316 Homepage: https://cran.r-project.org/package=MixRF Description: CRAN Package 'MixRF' (A Random-Forest-Based Approach for Imputing Clustered IncompleteData) It offers random-forest-based functions to impute clustered incomplete data. 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Package: r-cran-mixsemirob Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gofkernel, r-cran-mass, r-cran-mixtools, r-cran-mvtnorm, r-cran-rlab, r-cran-robustbase, r-cran-ucminf, r-cran-pracma, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mixsemirob_1.1.1-1.ca2604.1_all.deb Size: 493982 MD5sum: ab7b73ebf18f11082cbd81a731df19df SHA1: d85bb74a6ff908cfeb2edfecd27ef99e0068b3d7 SHA256: 1b397b14d4ce758b9bc31d996a635affe51c7afcd7daa82c9dee7ee6a9d68040 SHA512: feaa23e4f26a91681bc8e9e43c5076693bc9e26ac395eb729105234d7b30d7ecc2d4770df4ae492ef3901caf4d30124dc4da3ef9cfc8d9eb7a910f451d96e5c3 Homepage: https://cran.r-project.org/package=MixSemiRob Description: CRAN Package 'MixSemiRob' (Mixture Models: Parametric, Semiparametric, and Robust) Various functions are provided to estimate parametric mixture models (with Gaussian, t, Laplace, log-concave distributions, etc.) and non-parametric mixture models. 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Package: r-cran-mixsmsn Architecture: all Version: 1.1-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-mixsmsn_1.1-12-1.ca2604.1_all.deb Size: 211484 MD5sum: 79af7c30b42840c91761c58662782294 SHA1: 7b0654ebea97ce40a542469b9f8bb9a95dead2c9 SHA256: 70482a4332297b603442bb86818214c511e87a9e90242f33a6f31085c78bb3cf SHA512: e8c07329aa4316931b06d89d6267db43f24b93289b152bc58fe0288e0872e977878114b39145ab10927c39bcb01d6bb9707ecf4eb2ac1593bd82503800e344bd Homepage: https://cran.r-project.org/package=mixsmsn Description: CRAN Package 'mixsmsn' (Fitting Finite Mixture of Scale Mixture of Skew-NormalDistributions) Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) , Cabral, Lachos and Prates (2012) and Basso, Lachos, Cabral and Ghosh (2010) . Package: r-cran-mixspe Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mixspe_0.9.3-1.ca2604.1_all.deb Size: 105102 MD5sum: 7190dd1c027df076e2254bf4511eccf5 SHA1: 400d7d61e9f8673960f1c9a2c6df07612c034ecc SHA256: 059711d29247ecaaf9d879b3063134f01c15c7578fb17ddc71bd08358274e0ed SHA512: 08d826a9e781a69e03d8a6c0be3fddf319b6f940018c1af5552221211ca221ebc5f19aad15ba1b408c9c357d3a271c0b9e05dbb2148318813c5cded2674c9b7b Homepage: https://cran.r-project.org/package=mixSPE Description: CRAN Package 'mixSPE' (Mixtures of Power Exponential and Skew Power ExponentialDistributions for Use in Model-Based Clustering andClassification) Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew power exponential and power exponential distributions, respectively. A generalized expectation-maximization framework is used for parameter estimation. See citation() for how to cite. Package: r-cran-mixssg Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ars, r-cran-mass, r-cran-rootsolve Filename: pool/dists/resolute/main/r-cran-mixssg_2.1.1-1.ca2604.1_all.deb Size: 86016 MD5sum: dbe0820d21e96252ae80499814652feb SHA1: 7f399fdf5b41a39822c6d492b267d740dbb368b9 SHA256: da40ad62b645ceae5dce57a86e9e56237da0edadd5f15f7b9a36fce1866f6a3d SHA512: afd96247e104efdb0305b4f3a673e9dbd0976b588c374a91065ce89e7701fbd5b1db8ad6e484505746467fdbb56ae9fc8d921e333d1ff8b6a60af5b9c2891f41 Homepage: https://cran.r-project.org/package=mixSSG Description: CRAN Package 'mixSSG' (Clustering Using Mixtures of Sub Gaussian Stable Distributions) Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian stable distributions developed by Teimouri (2022) and estimating parameters of the symmetric stable distribution within the Bayesian framework. Package: r-cran-mixstable Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stabledist, r-cran-mixtools, r-cran-nortest, r-cran-openxlsx, r-cran-e1071, r-cran-jsonlite, r-cran-libstable4u, r-cran-mass Suggests: r-cran-ggplot2, r-cran-moments, r-cran-readxl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mixstable_0.1.0-1.ca2604.1_all.deb Size: 446746 MD5sum: d087ee6185b511b109aed00c08ed5761 SHA1: e69280025368ffe7eec076c6a4820bcbd8842c38 SHA256: 81cb829b2f2a88c6289a494400808fa492c1e82c7ec61b2eeda08eaf316ad495 SHA512: 61aee3ddc74507fc3cfe2dc5f720485d84e68776333cbdc615b4fca77cc9590f163023826a9168655797a9c553477aa017c49519f789018377deaf4ee8921cb0 Homepage: https://cran.r-project.org/package=MixStable Description: CRAN Package 'MixStable' (Parameter Estimation for Stable Distributions and Their Mixtures) Provides various functions for parameter estimation of one-dimensional stable distributions and their mixtures. It implements a diverse set of estimation methods, including quantile-based approaches, regression methods based on the empirical characteristic function (empirical, kernel, and recursive), and maximum likelihood estimation. For mixture models, it provides stochastic expectation–maximization (SEM) algorithms and Bayesian estimation methods using sampling and importance sampling to overcome the long burn-in period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes tools and statistical tests for analyzing whether a dataset follows a stable distribution. Some of the implemented methods are described in Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) . Package: r-cran-mixtox Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm Filename: pool/dists/resolute/main/r-cran-mixtox_1.5.0-1.ca2604.1_all.deb Size: 599254 MD5sum: edb12589e0001b718ba9cc8fe7dbfd41 SHA1: 0a74d3e3a8678ccdefea68ad3cca6b431a2ff07e SHA256: 5dcf74b73d73241a122410d77829afdc9eda2272878be58fb84569768d75f835 SHA512: ccf21360784e5c4c4f6e78d6b762a2ac8ee269cbc4fa8d5689b0ccc51894f5fbeae3ee620d90a979a91acea0ab01e48e976dbde2ab601be22110cf45b6de9e0c Homepage: https://cran.r-project.org/package=mixtox Description: CRAN Package 'mixtox' (Dose Response Curve Fitting and Mixture Toxicity Assessment) Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response data. Predicting mixture toxicity based on reference models such as 'concentration addition', 'independent action', and 'generalized concentration addition'. Package: r-cran-mixtree Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-treespace, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mixtree_0.0.1-1.ca2604.1_all.deb Size: 128294 MD5sum: be2246f3f24be430c741343601d8e21d SHA1: 70bed3c5919fd11fe3f5c3fbd775da5e4a54c722 SHA256: d45f07adead0cef851148f870fe6de5e087a44cee67c5c05c3075cd242e23cb6 SHA512: 469402a9f7259deac2b73e783292393ce7233a8d4ca1d12e28c0d822d3b3eca177d40d96a334ee51de8e14a98cd43c13ffd0fe12d004d34df148ba55a34cec29 Homepage: https://cran.r-project.org/package=mixtree Description: CRAN Package 'mixtree' (A Statistical Framework for Comparing Sets of Trees) Statistical framework for comparing sets of trees using hypothesis testing methods. Designed for transmission trees, phylogenetic trees, and directed acyclic graphs (DAGs), the package implements chi-squared tests to compare edge frequencies between sets and PERMANOVA to analyse topological dissimilarities with customisable distance metrics, following Anderson (2001) . Package: r-cran-mixtur Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mixtur_1.2.3-1.ca2604.1_all.deb Size: 469874 MD5sum: 458edae787aac9cc9e03edc089d56292 SHA1: cfa88ab0e918c66f639a65485fed1eaf8f7d960b SHA256: 6fafd55a30c0f4f37ce18a4b67848bd8af971cd1dddb9363aca356a0de4c04e7 SHA512: 3dbafdab28cec6712f3a7ea576da11bed3506b5c8542c70bcd29a43656ba8ae82e8543835aadc4472e0f70b4a2bc05b091c60a999dda7a54179596451f129d1d Homepage: https://cran.r-project.org/package=mixtur Description: CRAN Package 'mixtur' (Modelling Continuous Report Visual Short-Term Memory Studies) A set of utility functions for analysing and modelling data from continuous report short-term memory experiments using either the 2-component mixture model of Zhang and Luck (2008) or the 3-component mixture model of Bays et al. (2009) . Users are also able to simulate from these models. Package: r-cran-mixturemissing Architecture: all Version: 3.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mnormt, r-cran-cluster, r-cran-mass, r-cran-numderiv, r-cran-bessel, r-cran-mclust, r-cran-mice Filename: pool/dists/resolute/main/r-cran-mixturemissing_3.0.6-1.ca2604.1_all.deb Size: 282284 MD5sum: c2d975ae2e30e49a5e7a3474b9db9924 SHA1: a56cd4a327d7624e0474d53fdf31a9011658b566 SHA256: d992ec0b0d49e997bebf746f525db08a60c00570a17391afa8f08e16c33d48a1 SHA512: 8cf4c91a335261486b74c2ec09bd6d4877e5f23023abe33ab4bf046991c7340c82deae387eca372ac0d28efebf4604f4732e6cd8d537abd75eaaaeefae25c1aa Homepage: https://cran.r-project.org/package=MixtureMissing Description: CRAN Package 'MixtureMissing' (Robust and Flexible Model-Based Clustering for Data Sets withMissing Values at Random) Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random (Tong and Tortora, 2025, ). Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, ) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, ). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974. Package: r-cran-mixtwice Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-alabama, r-cran-ashr, r-cran-fdrtool, r-cran-iso Filename: pool/dists/resolute/main/r-cran-mixtwice_2.0-1.ca2604.1_all.deb Size: 2173228 MD5sum: 8755ba060e0e1f841ce04ff1767442e7 SHA1: 7defafab82802d4c74f92e279abd1cbe4dfa9e98 SHA256: 91f087fa2058a693e6a41043d0533ae968bd0762c5388215f450bef1f88d08bd SHA512: 5c68dda9a202669a7432578cc07fe4a4a0fb29209fb2caef6cf69ce1b36511dca64270f37b1cffcde28cddb1a0a677e759a1257ceb3c947b13a594e8b6d38c40 Homepage: https://cran.r-project.org/package=MixTwice Description: CRAN Package 'MixTwice' (Large-Scale Hypothesis Testing by Variance Mixing) Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) . Package: r-cran-mize Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-mize_0.2.5-1.ca2604.1_all.deb Size: 400616 MD5sum: e81446ee5e111917301233276077f6f5 SHA1: a9bf370caaf06311d7a277f5c63307c202031fd0 SHA256: a235c92122bd89909deabfd2ad95bff29ab37976f333117b29f54545ed198c15 SHA512: 5220801ec9f283424c26211297b086d63a85c7fe3fd0e3d795c790152cca867e0c9121312a2402bef64dd95611063068b71c41e0cc08fe65cdf71d415e2b06f5 Homepage: https://cran.r-project.org/package=mize Description: CRAN Package 'mize' (Unconstrained Numerical Optimization Algorithms) Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems. Package: r-cran-mkbo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-stringr, r-cran-rlang, r-cran-broom Filename: pool/dists/resolute/main/r-cran-mkbo_0.1.0-1.ca2604.1_all.deb Size: 371918 MD5sum: 090278f77a44f8b9e8c69a1c4a903572 SHA1: b78a7a6fc83aa2f6ea0b6f4c389070442cdc77b7 SHA256: 20de0bacd8443c0cf65522fa2a3cd8f8380363ae363ae105afc2a76fbec715c2 SHA512: 7de6edf81bffd4b531de5b5fcdbf66649063646612c2c18d4d5897efec7cf40bbe0697341fbddf7644310a14393e40bacb327d336597dd65ebed6b1a1b5f2084 Homepage: https://cran.r-project.org/package=mKBO Description: CRAN Package 'mKBO' (Multi-Group Kitagawa-Blinder-Oaxaca Decomposition) Provides multigroup Kitagawa-Blinder-Oaxaca ('mKBO') decompositions, that allow for more than two groups. Each group is compared to the sample average. For more details see Thaning and Nieuwenhuis (2025) . Package: r-cran-mkclass Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-mkclass_0.5-1.ca2604.1_all.deb Size: 152192 MD5sum: b26b43d429d46fb44c4aaca2d5b672d2 SHA1: d1b4c547ccfe7ce9bc499b5f43e7105163ade0ae SHA256: 0098e91dfcd422b859b990932ce494ec4e20a2669d3f6ae3149dc9bb450584f9 SHA512: 13fb4b53240018dffbafc44c533becb1dabbcff73c87868fda5205401759f47cca7ca956785470df25cc1939485cd626668c4b791ec0610d07a9256ff4d854fe Homepage: https://cran.r-project.org/package=MKclass Description: CRAN Package 'MKclass' (Statistical Classification) Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), ) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), ; Hosmer et al (1997), ). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), ). Package: r-cran-mkdescr Architecture: all Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mkdescr_0.9-1.ca2604.1_all.deb Size: 531346 MD5sum: 447ee4c1d20bd8f871730464d546ba5d SHA1: 7ba69c87cb5ba582cd85ab4ee7916b30c761c7e1 SHA256: aa4a6481548958831d3c6116062d1b5a730a018869b387da62d3f102850cb6aa SHA512: e46147b5fc14e8d2243a9ba188cbcaf56db36d401cef0bf7e44521d9834cc9b5a9fee49e90d2e39a71590a6cde9f5bfd56b98307bc5a7d7aebbd70f8419f03c5 Homepage: https://cran.r-project.org/package=MKdescr Description: CRAN Package 'MKdescr' (Descriptive Statistics) Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), ), robust coefficient of variation (CV) (Arachchige et al. (2019), ), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for 'ggplot2' (Wickham (2016), ISBN:978-3-319-24277-4). Package: r-cran-mkendall Architecture: all Version: 1.5-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mkendall_1.5-4-1.ca2604.1_all.deb Size: 23270 MD5sum: 3540f3699d26a674b8e9396cebdd9994 SHA1: da6474bd9781116fa8145d3b7233de2ed6d55c51 SHA256: 738297c330be8dabeebeeddba10fb48f54bbb32569dbe7d7100fe5c8ce01489b SHA512: 4595dc93c11944bde8dbf293f5fb6babfdc086a504f82c8ffc4c445e347cefde78014b04ab14ea52af325160e1ce794b1c6b42d57c87186fbca9e8d25a2b6552 Homepage: https://cran.r-project.org/package=MKendall Description: CRAN Package 'MKendall' (Matrix Kendall's Tau and Matrix Elliptical Factor Model) Large-scale matrix-variate data have been widely observed nowadays in various research areas such as finance, signal processing and medical imaging. Modelling matrix-valued data by matrix-elliptical family not only provides a flexible way to handle heavy-tail property and tail dependencies, but also maintains the intrinsic row and column structure of random matrices. We proposed a new tool named matrix Kendall's tau which is efficient for analyzing random elliptical matrices. By applying this new type of Kendell’s tau to the matrix elliptical factor model, we propose a Matrix-type Robust Two-Step (MRTS) method to estimate the loading and factor spaces. See the details in He at al. (2022) . In this package, we provide the algorithms for calculating sample matrix Kendall's tau, the MRTS method and the Matrix Kendall's tau Eigenvalue-Ratio (MKER) method which is used for determining the number of factors. Package: r-cran-mkin Architecture: all Version: 1.2.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3915 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-r6, r-cran-inline, r-cran-numderiv, r-cran-lmtest, r-cran-pkgbuild, r-cran-nlme, r-cran-saemix, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rbenchmark, r-cran-tikzdevice, r-cran-testthat, r-cran-rmarkdown, r-cran-covr, r-cran-vdiffr, r-cran-benchmarkme, r-cran-tibble, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-mkin_1.2.10-1.ca2604.1_all.deb Size: 2813838 MD5sum: 9b60dfaccddcbc247f4481fc2dc05cd3 SHA1: 3415bd435d0d49c64c76ec0b8dd4067b698206b4 SHA256: 91ec93e42dfe11532cda1d5aa4f425b6b9b5c5e695b9cd2e75a6bd6c034b844a SHA512: 28bb11cac101fea0894511d7efdd8b4688eb3682ce70741fe4382430bb730084ea82543a0f67f7b5a8f32a03201f5801cb1b7b0638370709e4729b01f2e96c3a Homepage: https://cran.r-project.org/package=mkin Description: CRAN Package 'mkin' (Kinetic Evaluation of Chemical Degradation Data) Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using automatically generated C functions. Non-constant errors can be taken into account using variance by variable or two-component error models . Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end . Please note that no warranty is implied for correctness of results or fitness for a particular purpose. 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(2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), ), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), ) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualizations: volcano plot, Bland-Altman plots (Bland and Altman (1986), ; Shieh (2018), ), mean difference plot (Boehning et al. (2008), ), plot of test statistic for permutation and bootstrap tests as well as objects of class htest. 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Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), ; Dobbin and Simon (2007), ) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), ; Thulin (2014), ), comparing two negative binomial rates (Zhu and Lakkis (2014), ), ANCOVA (Shieh (2020), ), reference ranges (Jennen-Steinmetz and Wellek (2005), ), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), ). Package: r-cran-mkssd Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mkssd_1.2-1.ca2604.1_all.deb Size: 22254 MD5sum: 3f230ba276720442183c13d78f42f956 SHA1: 8ee4d8509863e7c897a00168bae213ba5332474c SHA256: 1bdec22714c0425740958e4d7927f4b277eed9cdee84f95509807c035bafd1f1 SHA512: b55fce26c9037a94ba7bc3b0cbb8227ad1c654d3057b4909bd0c2c3167f5356da0ccca22842f0aa628d5f18f689b58f676509cde344665abb0c16c9f59569ff9 Homepage: https://cran.r-project.org/package=mkssd Description: CRAN Package 'mkssd' (Efficient Multi-Level k-Circulant Supersaturated Designs) Generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). 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It will contain datasets for supervised machine learning Jiang (2020) and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains. Package: r-cran-mldr.datasets Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3562 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mldr Filename: pool/dists/resolute/main/r-cran-mldr.datasets_0.4.2-1.ca2604.1_all.deb Size: 3405644 MD5sum: a46cfb5feb64027fef05afc3cc24e1f3 SHA1: eb404a250d795b2f019a18cecc8d1fdeb777572f SHA256: 1f58368fdae7c6766c6e1703e7ec894bddcb47aca443c3ff4bb7bcca616c620a SHA512: e79e225958948ee17eecf7c0e8e952ed853d62fec6269ffb01d511bb5dec428b34a48b57e7637e915fd974d07bebfbf8496dbeba1b52bed97ff0fb3d830ac299 Homepage: https://cran.r-project.org/package=mldr.datasets Description: CRAN Package 'mldr.datasets' (R Ultimate Multilabel Dataset Repository) Large collection of multilabel datasets along with the functions needed to export them to several formats, to make partitions, and to obtain bibliographic information. 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Package: r-cran-mldr Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-xml, r-cran-circlize Suggests: r-cran-proc, r-cran-knitr, r-cran-mldr.datasets, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mldr_0.4.3-1.ca2604.1_all.deb Size: 1375474 MD5sum: 59efd5b5b0cf90dc78f3284c9ad89a76 SHA1: 7581583c649cd25bcf045e08a0e2f35c7d9c2b50 SHA256: 73970662f369491a26b3ba3a68ee1fedf2e82a38b026b6dc65ad91f2d1bd3171 SHA512: 30ff0db5a791e2c4cdf8543d37c982c780df307e870ad214b84c8e75e9f9ca19571f93ba024c8a6b9b9a4431153461ffcbff7ec0e93d4d4f0d6ce288c5041c93 Homepage: https://cran.r-project.org/package=mldr Description: CRAN Package 'mldr' (Exploratory Data Analysis and Manipulation of Multi-Label DataSets) Exploratory data analysis and manipulation functions for multi- label data sets along with an interactive Shiny application to ease their use. Package: r-cran-mlds Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mlds_0.5.1-1.ca2604.1_all.deb Size: 548526 MD5sum: 79bb6ce7c872009bca50be5d099ff440 SHA1: 2b78f82c8dfe2a6febd88518f9288bdc463bc880 SHA256: 14422357dc075101e827571afdbdf2367a49a421fdeee14c13fd5b9ca42632f4 SHA512: a134cd60e6bafd88524f68b6d3c3a508c2722fea33e61c5a2a14f140ffbf2af10e377072f96fac5fac9d645adb8a3473ee2c79cbdcdeaa37d9e651bdd6d27cb4 Homepage: https://cran.r-project.org/package=MLDS Description: CRAN Package 'MLDS' (Maximum Likelihood Difference Scaling) Difference scaling is a method for scaling perceived supra-threshold differences. The package contains functions that allow the user to design and run a difference scaling experiment, to fit the resulting data by maximum likelihood and test the internal validity of the estimated scale. Package: r-cran-mle.tools Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fitdistrplus Filename: pool/dists/resolute/main/r-cran-mle.tools_1.0.0-1.ca2604.1_all.deb Size: 35556 MD5sum: 248d59d12c9fe2fbfb5275bf4a572bc9 SHA1: 7b0b78f6d72b844e9e720f869ec11029afbc6ac5 SHA256: 3d28d28f77263c205c4de4b1483c2e35dc5e81183cedeaf7945a9c7097cf50d4 SHA512: 5833f487593b33397f3e049623d4ddc8541ff0acf53a52f29b2eca43a2f5aff26d237d823fdfcda48d4304370a597f82554043eb71f1e7fb1f7551312152165f Homepage: https://cran.r-project.org/package=mle.tools Description: CRAN Package 'mle.tools' (Expected/Observed Fisher Information and Bias-Corrected MaximumLikelihood Estimate(s)) Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology. Package: r-cran-mle Architecture: all Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bgfd, r-cran-bivpois, r-cran-ccd, r-cran-compoissonreg, r-cran-compositional, r-cran-directional, r-cran-foldedt, r-cran-geppe, r-cran-gp, r-cran-mn, r-cran-rfast, r-cran-rfast2 Suggests: r-cran-mvcauchy, r-cran-rangen Filename: pool/dists/resolute/main/r-cran-mle_1.7-1.ca2604.1_all.deb Size: 184778 MD5sum: 31c4bad134c80ea23271e65a8e019bdb SHA1: 9cb9a1934019cb0fdf41e383b9d7f354f2d88e72 SHA256: 428580a65f240b02dbfee434ad8f9902b14417a538b960505897f94ea6ed4ee3 SHA512: e8888064a685b5b4b4473cf2908ba5412c7eec939ff6f769967105c5dc6a7a329c382a05c6c1029369de7b0a72dc60945c1dc1628fcc187c75ebb8be0d98559f Homepage: https://cran.r-project.org/package=MLE Description: CRAN Package 'MLE' (Maximum Likelihood Estimation of Various Univariate andMultivariate Distributions) Several functions for maximum likelihood estimation of various univariate and multivariate distributions. The list includes more than 100 functions for univariate continuous and discrete distributions, distributions that lie on the real line, the positive line, interval restricted, circular distributions. Further, multivariate continuous and discrete distributions, distributions for compositional and directional data, etc. Some references include Johnson N. L., Kotz S. and Balakrishnan N. (1994). "Continuous Univariate Distributions, Volume 1" , Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). "Univariate Discrete Distributions". and Mardia, K. V. and Jupp, P. E. (2000). "Directional Statistics". . Package: r-cran-mlearning Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-class, r-cran-nnet, r-cran-mass, r-cran-e1071, r-cran-randomforest, r-cran-ipred, r-cran-rpart Suggests: r-cran-mlbench, r-cran-rcolorbrewer, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-mlearning_1.2.1-1.ca2604.1_all.deb Size: 231032 MD5sum: 6ebc66a7d80299c05ce5cebf01b156e9 SHA1: d4843cd7fb3cae5de37ca6b36b3355fbc77b802d SHA256: 8041a5f4c0241ce675ac2f329bb270c6c05b9e409e30dd3b23bcde827e97aaa6 SHA512: 42da4985823646f6f194021207fff562338eb61b320de0740bc3180ba83aa430884f6c33b59e37ccd03e60078065cedf765c34e9886482b2f1cb88b2356a9d1d Homepage: https://cran.r-project.org/package=mlearning Description: CRAN Package 'mlearning' (Machine Learning Algorithms with Unified Interface and ConfusionMatrices) A unified interface is provided to various machine learning algorithms like linear or quadratic discriminant analysis, k-nearest neighbors, random forest, support vector machine, ... It allows to train, test, and apply cross-validation using similar functions and function arguments with a minimalist and clean, formula-based interface. Missing data are processed the same way as base and stats R functions for all algorithms, both in training and testing. Confusion matrices are also provided with a rich set of metrics calculated and a few specific plots. Package: r-cran-mlece Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nleqslv, r-cran-laplacesdemon, r-cran-sirt, r-cran-ggplot2, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-mlece_2.1.0-1.ca2604.1_all.deb Size: 111132 MD5sum: 44317046ee5b7dad07ad88e722595362 SHA1: ac7162f8294b2c3219ae7db930aaa6eba7763bbd SHA256: 0c12661907cd9dbad4758fb1fa5f26f1996ea6683400c3e6d2d595c6cecd4605 SHA512: eca1c16515fc84f02176c3fe6b2925804235fb0d02290ace6585cba602663d6adbd63c48a0839f2a942f32dc26d67a77ce37be6962e8efa9542d57a46f4d14e5 Homepage: https://cran.r-project.org/package=MLEce Description: CRAN Package 'MLEce' (Asymptotic Efficient Closed-Form Estimators for MultivariateDistributions) Asymptotic efficient closed-form estimators (MLEces) are provided in this package for three multivariate distributions(gamma, Weibull and Dirichlet) whose maximum likelihood estimators (MLEs) are not in closed forms. Closed-form estimators are strong consistent, and have the similar asymptotic normal distribution like MLEs. But the calculation of MLEces are much faster than the corresponding MLEs. Further details and explanations of MLEces can be found in. Jang, et al. (2023) . Kim, et al. (2023) . Package: r-cran-mlelod Architecture: all Version: 1.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mlelod_1.0.0.1-1.ca2604.1_all.deb Size: 15506 MD5sum: 082991740b0715da152eed62e0197ecc SHA1: d353858e1722fb43892b5a9d08112998c0f6d772 SHA256: e8d59883127a96229c3e6c0a1c8e38e387e85e9112a2c2151145646ad658b40d SHA512: 6e9997762bd5536eb25f78251e09192a519354f75b0867a953793dcc91ef18ce5c03be79386c47c5c7a3933c2d466d5be03a02fb88c09b04b39394b590bf5a6a Homepage: https://cran.r-project.org/package=mlelod Description: CRAN Package 'mlelod' (MLE for Normally Distributed Data Censored by Limit of Detection) Values below the limit of detection (LOD) are a problem in several fields of science, and there are numerous approaches for replacing the missing data. We present a new mathematical solution for maximum likelihood estimation that allows us to estimate the true values of the mean and standard deviation for normal distributions and is significantly faster than previous implementations. The article with the details was submitted to JSS and can be currently seen on . 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The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (JCGS, 2006). The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks. 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Hyperparameter optimization can be performed with Bayesian optimization (via 'rBayesianOptimization' ) and grid search. The optimized hyperparameters can be validated using k-fold cross-validation. Alternatively, hyperparameter optimization and validation can be performed with nested cross-validation. While 'mlexperiments' focuses on core wrappers for machine learning experiments, additional learner algorithms can be supplemented by inheriting from the provided learner base class. 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Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). . Package: r-cran-mlfdr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nmof Filename: pool/dists/resolute/main/r-cran-mlfdr_0.1.0-1.ca2604.1_all.deb Size: 35922 MD5sum: 3db35a58230696247fa8e38c8f1b1d89 SHA1: ee07b36c989a734dade9e6ceb2fbb028a6acff7b SHA256: 5b2824c26e1ae4907bbce1abe54a13a40f84ee99b24f648460062285f373d2bb SHA512: 837d0e2c7a5ce4afcab1c95a0432032b8c4f052e0cc30603d880e0cdc9bc7c9a753b3d24881452b02765023570965de230129ccd369e466a102eb0c7c447d137 Homepage: https://cran.r-project.org/package=MLFDR Description: CRAN Package 'MLFDR' (High Dimensional Mediation Analysis using Local False DiscoveryRates) Implements a high dimensional mediation analysis algorithm using Local False Discovery Rates. The methodology is described in Roy and Zhang (2024) . Package: r-cran-mlfit Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-dplyr, r-cran-hms, r-cran-kimisc, r-cran-matrix, r-cran-plyr, r-cran-tibble, r-cran-forcats, r-cran-rlang, r-cran-wrswor, r-cran-lifecycle Suggests: r-cran-covr, r-cran-testthat, r-cran-mass, r-cran-sampling, r-cran-xml, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-mlfit_0.5.3-1.ca2604.1_all.deb Size: 230296 MD5sum: 5b9085440972959d791b0596abf3440f SHA1: 9d15233fad3e7ddb95f627b0058dea92d90f2118 SHA256: 23ad07b6c3e4688a37503277c151544216db39f81e3dc2779a5696553cc068dd SHA512: 47b04a0e878b112a324ac11a54094156a65a81e00f2e645fb4fff5626d2b31a4f875bc5c2be9656f046edc353468bfbef869fe64bb507884bba6e7c75caee355 Homepage: https://cran.r-project.org/package=mlfit Description: CRAN Package 'mlfit' (Iterative Proportional Fitting Algorithms for Nested Structures) The Iterative Proportional Fitting (IPF) algorithm operates on count data. This package offers implementations for several algorithms that extend this to nested structures: 'parent' and 'child' items for both of which constraints can be provided. The fitting algorithms include Iterative Proportional Updating , Hierarchical IPF , Entropy Optimization , and Generalized Raking . Additionally, a number of replication methods is also provided such as 'Truncate, replicate, sample' . Package: r-cran-mlflow Architecture: all Version: 3.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-fs, r-cran-git2r, r-cran-glue, r-cran-httpuv, r-cran-httr, r-cran-ini, r-cran-jsonlite, r-cran-openssl, r-cran-processx, r-cran-purrr, r-cran-rlang, r-cran-swagger, r-cran-tibble, r-cran-withr, r-cran-yaml, r-cran-zeallot Suggests: r-cran-carrier, r-cran-covr, r-cran-h2o, r-cran-keras, r-cran-lintr, r-cran-sparklyr, r-cran-stringi, r-cran-testthat, r-cran-reticulate, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mlflow_3.10.1-1.ca2604.1_all.deb Size: 239600 MD5sum: 0932d1d39f65c202a90e18102e8a79e1 SHA1: 50cfa1c3a6587da19278998e3811af837041e24a SHA256: abe43731e73c39e78baff979ebc8a7d56b74a9185509850a6499570af17349b9 SHA512: b5abe8b8d8679762491636f61db242a4afa5f3bbb6863372aebb11e87eba842a7b7622496e776ccaff475991e37948694a5c769d44e5fe8b600182372e6b9611 Homepage: https://cran.r-project.org/package=mlflow Description: CRAN Package 'mlflow' (Interface to 'MLflow') R interface to 'MLflow', open source platform for the complete machine learning life cycle, see . This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models. Package: r-cran-mlfs Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brnn, r-cran-ranger, r-cran-reshape2, r-cran-pscl, r-cran-naivebayes, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-mlfs_0.4.3-1.ca2604.1_all.deb Size: 2028750 MD5sum: aea019892a9488f836cc925ff8b7bac8 SHA1: 5bbb021909f39f208e23240cf26a4315707591d6 SHA256: c20d3e9a12242a1c643b0698404d5d2c5b583f433140752ca121147d98b859b9 SHA512: 26bbe83dba8f6d8693b84d06e2ddb226b9c056918168abac6f5150504112372d9923b4dd1053eba5a962c7d456c77b0312d9ac866d8d01ab2d6c81fe6557a03e Homepage: https://cran.r-project.org/package=MLFS Description: CRAN Package 'MLFS' (Machine Learning Forest Simulator) Climate-sensitive, single-tree forest simulator based on data-driven machine learning. It simulates the main forest processes— radial growth, height growth, mortality, crown recession, regeneration, and harvesting—so users can assess stand development under climate and management scenarios. The height model is described by Skudnik and Jevšenak (2022) , the basal-area increment model by Jevšenak and Skudnik (2021) , and an overview of the MLFS package, workflow, and applications is provided by Jevšenak, Arnič, Krajnc, and Skudnik (2023), Ecological Informatics . Package: r-cran-mlgdata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mlgdata_0.1.0-1.ca2604.1_all.deb Size: 240840 MD5sum: 481826bb83ab5d21d8658d7af70322ce SHA1: 94bd36134707a29046be2be316704246e9a090e1 SHA256: 243ffc8bcfb4de54643e4c16bd5548a07c9432ad9e38464f673eabaccd86bf78 SHA512: d8e9c7a45d502ed46ce06622174d5a8d3fa389d492765e38e3e6de2865e6a8804cfccc3d228ce34bf2e8499269a792c40d5343b8c133060e093adfff469c5569 Homepage: https://cran.r-project.org/package=MLGdata Description: CRAN Package 'MLGdata' (Datasets for Use with Salvan, Sartori and Pace (2020)) Contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati". Package: r-cran-mlgl Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gglasso, r-cran-mass, r-cran-matrix, r-cran-fastcluster, r-cran-factominer, r-cran-paralleldist Filename: pool/dists/resolute/main/r-cran-mlgl_1.0.1-1.ca2604.1_all.deb Size: 192552 MD5sum: 3d665f8845495bb597a8c6b7fe25287f SHA1: c23da03ae3a42bddfd22cc86905f24049d72e4e4 SHA256: febd1111070e3dac2b81e9b8563e0c1b6ae8a6be5794bdfee07ca2a16a90856f SHA512: 8cd1dcf12fdfd0e9ba7f6b96fe6f924cf38e78089a2d44a1a6e46febe6a022a7e7e33ddb93da866c1e2e2eede55a941a4ed31f906eea195c899fb00fdb0efd76 Homepage: https://cran.r-project.org/package=MLGL Description: CRAN Package 'MLGL' (Multi-Layer Group-Lasso) It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) ). Package: r-cran-mlid Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-lme4 Suggests: r-cran-raster, r-cran-sp, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mlid_1.0.1-1.ca2604.1_all.deb Size: 2977462 MD5sum: d7242a745e2d56617f56764f9e7ed3b6 SHA1: 1abb04e587c5871cb72708574979775599e5af44 SHA256: b54e71202bf9e8795325b451160d32ef6202bfedb750113c9f1fffaba00b20f8 SHA512: 5e8f25fd6197793c8d2c3b98a0776b3ae67b6dcba1fa3ea4c2e2d0177222461da0aca863fb2ef3589ebbd289cce03f6683be53d903b6acb78864f286df0f1d5e Homepage: https://cran.r-project.org/package=MLID Description: CRAN Package 'MLID' (Multilevel Index of Dissimilarity) Tools and functions to fit a multilevel index of dissimilarity. Package: r-cran-mlim Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-h2o, r-cran-curl, r-cran-mice, r-cran-missranger, r-cran-memuse, r-cran-md.log Filename: pool/dists/resolute/main/r-cran-mlim_0.3.0-1.ca2604.1_all.deb Size: 897024 MD5sum: 273cfed90290290741d74672295635ad SHA1: 93af6a789196c94aa587b1c181c1c632a5da56d1 SHA256: 7388b6a0e32301806d7f97c772f7706ed6e9205d9508a8bea84f79a07102a54e SHA512: 1a16f3ed7b8baae66b53174ff2ba246e9e639ad9f6c5a30cb03557769dec85693a7430a78de7cac96191963a58907bbe813a27593d7be274dc66d2a7eaff7553 Homepage: https://cran.r-project.org/package=mlim Description: CRAN Package 'mlim' (Single and Multiple Imputation with Automated Machine Learning) Machine learning algorithms have been used for performing single missing data imputation and most recently, multiple imputations. However, this is the first attempt for using automated machine learning algorithms for performing both single and multiple imputation. Automated machine learning is a procedure for fine-tuning the model automatic, performing a random search for a model that results in less error, without overfitting the data. The main idea is to allow the model to set its own parameters for imputing each variable separately instead of setting fixed predefined parameters to impute all variables of the dataset. Using automated machine learning, the package fine-tunes an Elastic Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting, or Stacked Ensemble machine learning model (from one or a combination of other supported algorithms) for imputing the missing observations. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models. The multiple imputation is implemented via bootstrapping without letting the duplicated observations to harm the cross-validation procedure, which is the way imputed variables are evaluated. Most notably, the package implements automated procedure for handling imputing imbalanced data (class rarity problem), which happens when a factor variable has a level that is far more prevalent than the other(s). This is known to result in biased predictions, hence, biased imputation of missing data. However, the autobalancing procedure ensures that instead of focusing on maximizing accuracy (classification error) in imputing factor variables, a fairer procedure and imputation method is practiced. Package: r-cran-mllmcelltype Architecture: all Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1381 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-digest, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-seurat, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mllmcelltype_2.0.5-1.ca2604.1_all.deb Size: 698624 MD5sum: 1b2a70748ecb8b05524d73d603ef65f4 SHA1: 99410244be59252e35ead08289c6167c4b85d0b2 SHA256: 50605e3b00c41fce8300deb54e3350f99e724984a1c633083b7c396e1cd594f2 SHA512: e02fde324238b2711a45e75c42ae726897ffc36f46b54c82802be85862e118aee26b49db059171641d97c7a8ffbb088bbebcf877a8af637cabebe2f3287e12f3 Homepage: https://cran.r-project.org/package=mLLMCelltype Description: CRAN Package 'mLLMCelltype' (Cell Type Annotation Using Large Language Models) Automated cell type annotation for single-cell RNA sequencing data using consensus predictions from multiple large language models. Integrates with Seurat objects and provides uncertainty quantification for annotations. Supports various LLM providers including OpenAI, Anthropic, and Google. For details see Yang et al. (2025) . 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The package provides R6-based learners for the following algorithms: 'glmnet' , 'ranger' , 'xgboost' , and 'lightgbm' . These can be used directly with the 'mlexperiments' R package. Package: r-cran-mlma Architecture: all Version: 6.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-car, r-cran-abind, r-cran-coxme, r-cran-gplots, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mlma_6.3-1-1.ca2604.1_all.deb Size: 324660 MD5sum: 3807046c0cf9670b4beccfb4aac754f2 SHA1: a108d17bef59e66c7f45802e633e6e3294072e7f SHA256: 6bdb23159a3e2237bd028bdd2883d93acc7c08a17233387a9833430ba3c1b9ce SHA512: 796925e15636981da4f908d23fba60c154e027aea158ac128db30223b8b8fc01475f31abed6aaf30f3d15b0caf4c5636fc186691c3abbd2fe8d21042f8d082fb Homepage: https://cran.r-project.org/package=mlma Description: CRAN Package 'mlma' (Multilevel Mediation Analysis) Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072. 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Package: r-cran-mlmhelpr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-rdpack, r-cran-mathjaxr Suggests: r-cran-clubsandwich, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mlmhelpr_0.1.1-1.ca2604.1_all.deb Size: 166552 MD5sum: 283029464a9638950d12095bc5155e85 SHA1: 52b6af2146f7d6b7b8dfc3ca9ad1f8797297e5d8 SHA256: 2945daf7ee3bc2ef32b3727beca0bca5841342bedc9b490754aeecbf1ef0818c SHA512: a3eb2ddeea347955a9a1dc63909dbd430ed6d6c6e72f063edd9892bfa33976ddf4daa4c2149cd68e21a2930ded5e5b169aafe6036a6c47f6e95f5f026df3c940 Homepage: https://cran.r-project.org/package=mlmhelpr Description: CRAN Package 'mlmhelpr' (Multilevel/Mixed Model Helper Functions) A collection of miscellaneous helper function for running multilevel/mixed models in 'lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015). Package: r-cran-mlmi Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gsl, r-cran-norm, r-cran-cat, r-cran-mix, r-cran-matrix, r-cran-nlme Suggests: r-cran-bootimpute, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlmi_1.1.3-1.ca2604.1_all.deb Size: 103136 MD5sum: 065ab36c003ff0d1d295fd5c4ee74f83 SHA1: 1832688ca4be592289dee546c05bcb1a0bd67255 SHA256: ff77d6b4a83f150188de629d4929f98f46c04dddeac5d372a41b0b3248900965 SHA512: 843f3dfb255ac1a41aef3ee4282aa16d68c3d3de1692b6a612c7ea2118f22bd739f426011d460ff522bd0f5c9d0b7c81b2d8b7e072087ed50ef92b1349f51837 Homepage: https://cran.r-project.org/package=mlmi Description: CRAN Package 'mlmi' (Maximum Likelihood Multiple Imputation) Implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) . 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One can use information from all three methods or any combination of two of them. Estimates are based on Binomial model by Qu et al. (2013) and Kiihl et al. (2019) . 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The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC and p-value threshold, estimation of the effects of the SNPs in the selected model and graphical functions. Package: r-cran-mlmodels Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2534 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-hardhat, r-cran-insight, r-cran-marginaleffects, r-cran-mass, r-cran-matrixcalc, r-cran-maxlik, r-cran-rlang, r-cran-tibble Suggests: r-cran-boot, r-cran-dplyr, r-cran-e1071, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-wooldridge Filename: pool/dists/resolute/main/r-cran-mlmodels_0.1.2-1.ca2604.1_all.deb Size: 1707066 MD5sum: 99af0f161acb09b952da64f517381780 SHA1: f4d957e33744c887f0e5ab1cf70eea8ef967ab4e SHA256: fd44b0a5f7dade54bb6fe010cf47811fdfeac49057f5d767b6cbe7637f396014 SHA512: 9fb110fd76ad31e2caddfc11d0f893595ee59960f30f75f44163023b42f2ceae91eb007f543451f0f308bffee3a90f29f2f10bc61d1f37408205938fadef8104 Homepage: https://cran.r-project.org/package=mlmodels Description: CRAN Package 'mlmodels' (Maximum Likelihood Models and Tools for Estimation, Prediction,and Testing) Provides a collection of maximum likelihood estimators with a consistent S3 interface. Supported models include Gaussian (linear and log-normal), logit, probit, Poisson, negative binomial (NB1 and NB2), gamma, and beta regression. A distinctive feature is flexible modeling of the scale parameter (variance, dispersion, precision, or shape) alongside the location/mean parameters. The package offers unified predict() methods, multiple variance-covariance estimators (observed information, outer product of gradients, robust/Huber-White, cluster-robust, bootstrap, jackknife), and a full suite of hypothesis tests (Wald, likelihood ratio, information matrix, Vuong, overdispersion, and goodness-of-fit). It is fully compatible with 'marginaleffects' for post-estimation analysis. Methods implemented include Cameron and Trivedi (1990) , for Poisson overdispersion testing, Manjon and Martinez (2014) , for goodness-of-fit testing of count data models, Vuong (1989) , for non-nested likelihood ratio testing, and White (1982) , for information matrix tests. Package: r-cran-mlmoderator Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-mlmoderator_0.2.1-1.ca2604.1_all.deb Size: 193380 MD5sum: cd689ad216b3447128c7dcf72a39f17c SHA1: 53dcd9e840bf2bc5fa2180ffd1c5537facc90bd8 SHA256: d6db23d61b5ae2ea380b9f0c741949a5309a72ec4960b02ed7ee551fe0b108f8 SHA512: f2dd9f1c088b3e672889d4947f759f7f31b67edd3760bd5922603ea9dd47a1b538a3a0fc0f99f964871202f097243a88075ef6499cd0d770a4e468a554faae68 Homepage: https://cran.r-project.org/package=mlmoderator Description: CRAN Package 'mlmoderator' (Probing, Plotting, and Interpreting Multilevel InteractionEffects) Provides a unified workflow for probing, plotting, and assessing the robustness of cross-level interaction effects in two-level mixed-effects models fitted with 'lme4' (Bates et al., 2015) . Implements simple slopes analysis following Aiken and West (1991, ISBN:9780761907121), Johnson-Neyman intervals following Johnson and Fay (1950) and Bauer and Curran (2005) , and grand- or group-mean centering as described in Enders and Tofighi (2007) . Includes a slope variance decomposition that separates fixed-effect uncertainty from random-slope variance (tau11), a contour surface plot of predicted outcomes over the full predictor-by-moderator space, and robustness diagnostics comprising intraclass correlation coefficient shift analysis and leave-one-cluster-out (LOCO) stability checks. Designed for researchers in education, psychology, biostatistics, epidemiology, organizational science, and other fields where outcomes are clustered within higher-level units. 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The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) and Bell and 'McCaffrey' (2002) is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X). 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The software is described in Croissant (2020) and the underlying methods in Train (2009) . 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The niche preemption model is a widely used model in ecology and biodiversity studies. Package: r-cran-mlpugs Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-progress, r-cran-c50, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-mlpugs_0.2.0-1.ca2604.1_all.deb Size: 69392 MD5sum: af4690518563a58931e447042797f286 SHA1: 98439dacebeb5961860199fd52e6602ef72bbafb SHA256: 51ca98debecd4296d9b18dd08ed8d4574dd0a058547545fca529f11929bc52a1 SHA512: 90bc74e6af8ae0893f848bceea99c8b8dc9ccb5e447f5d0f3164b684a61bb99196e5c34383230afaa27f5f674e01c64bbcac0d5d840acead065c2e95c60eadf9 Homepage: https://cran.r-project.org/package=MLPUGS Description: CRAN Package 'MLPUGS' (Multi-Label Prediction Using Gibbs Sampling (and ClassifierChains)) An implementation of classifier chains (CC's) for multi-label prediction. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. The package can train a single set of CC's or train an ensemble of CC's -- in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications. Package: r-cran-mlpwr Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dicekriging, r-cran-digest, r-cran-ggplot2, r-cran-randtoolbox, r-cran-rlist, r-cran-rgenoud Suggests: r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-mirt, r-cran-pwr, r-cran-rmarkdown, r-cran-simr, r-cran-sn, r-cran-tidyr, r-cran-weightsvm Filename: pool/dists/resolute/main/r-cran-mlpwr_1.1.1-1.ca2604.1_all.deb Size: 1167416 MD5sum: 47bdcbf8275069b5de08ebc49e2a22ec SHA1: f3e3eb12ccd017aba6b5967ebd367f557eb60e82 SHA256: 748ef0d4064b5e75e4ba3ed6f0de23968d7e75b9f0f8fae1c51cd8b0e878721b SHA512: 95a941de67c20a375d92bfc0659fa9e740ef1ed0595cd9941906b1044d1e4cf1b6837b5f96396dbe3661d6be610bf97d608538c3ea7f7ace89ca271c4b3f39fe Homepage: https://cran.r-project.org/package=mlpwr Description: CRAN Package 'mlpwr' (A Power Analysis Toolbox to Find Cost-Efficient Study Designs) We implement a surrogate modeling algorithm to guide simulation-based sample size planning. The method is described in detail in our paper (Zimmer & Debelak (2023) ). It supports multiple study design parameters and optimization with respect to a cost function. It can find optimal designs that correspond to a desired statistical power or that fulfill a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023) ). 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This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. . Package: r-cran-mlr3 Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3558 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-evaluate, r-cran-future, r-cran-future.apply, r-cran-lgr, r-cran-mirai, r-cran-mlbench, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-parallelly, r-cran-palmerpenguins, r-cran-paradox, r-cran-uuid Suggests: r-cran-callr, r-cran-codetools, r-cran-future.callr, r-cran-mlr3data, r-cran-progressr, r-cran-remotes, r-cran-rhpcblasctl, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3_1.6.0-1.ca2604.1_all.deb Size: 2727326 MD5sum: 979adec5eedc01c64773cedf0ecb1174 SHA1: 8938e07a19723226f2fc21471403db2c0ed8047e SHA256: 8c1f49b49fbc393e583bb83059efa39c9aac89cf3e2f57290df5d1fc548fd744 SHA512: d7d1c5e14e4653b549a7153e9816df7228d9c1a607b9562ee50e17190524adc5bc0ef79e8cb42661b812873b8aaf3382bf7edd00bf65b8647050c7d892198dd0 Homepage: https://cran.r-project.org/package=mlr3 Description: CRAN Package 'mlr3' (Machine Learning in R - Next Generation) Efficient, object-oriented programming on the building blocks of machine learning. 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Package: r-cran-mlr3batchmark Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-batchtools, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-uuid Suggests: r-cran-renv, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3batchmark_0.2.2-1.ca2604.1_all.deb Size: 39016 MD5sum: 0dc957bf1662b21c8e5b2053a5c74d0e SHA1: fa8b4613144f3f277b402bf326378a1a4372587a SHA256: d6f9c91428b4e708652450d1c9b356426b6ee56237cbbc475d5def01de57c95b SHA512: 23f0c7e0751c1c386c1f1d962b6846ab454e799827af4771e074fef348979a998b495ba13154bcc1fa54fb12686c3b553eff208f27aea819d2916d405ce60fb8 Homepage: https://cran.r-project.org/package=mlr3batchmark Description: CRAN Package 'mlr3batchmark' (Batch Experiments for 'mlr3') Extends the 'mlr3' package with a connector to the package 'batchtools'. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters. Package: r-cran-mlr3benchmark Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-mlr3, r-cran-mlr3learners, r-cran-pmcmrplus, r-cran-rpart, r-cran-testthat, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mlr3benchmark_0.1.7-1.ca2604.1_all.deb Size: 160376 MD5sum: 672dc81efa34ce86ec4a019f7a6030f5 SHA1: 946dc01b703c4e41fedf720c5363ed663f360658 SHA256: b80ea5107f23f0669a3971d066fd289567ddc7efeb23ec1a65179375b4dcb25a SHA512: 8fd2b152d5177322c64c64412c47fa6dc209dffbcdf698e6a05e6db36baae10be60c4207a123618afe234db14695adfb46295d62965a92bd53a224f4a40b158c Homepage: https://cran.r-project.org/package=mlr3benchmark Description: CRAN Package 'mlr3benchmark' (Analysis and Visualisation of Benchmark Experiments) Implements methods for post-hoc analysis and visualisation of benchmark experiments, for 'mlr3' and beyond. Package: r-cran-mlr3cluster Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1527 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-backports, r-cran-checkmate, r-cran-cluster, r-cran-data.table, r-cran-fpc, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-apcluster, r-cran-clue, r-cran-clusterr, r-cran-clustmixtype, r-cran-dbscan, r-cran-e1071, r-cran-kernlab, r-cran-lpcm, r-cran-mclust, r-cran-mirai, r-cran-mlbench, r-cran-protoclust, r-cran-rweka, r-cran-stream, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3cluster_0.3.0-1.ca2604.1_all.deb Size: 1163922 MD5sum: 9bfa3300bd36ae2619c931289475c068 SHA1: bbfda6957ad787a83148555b7d6c877139d572e0 SHA256: f2c1ced4fb0b66e2b1be8e2a91e384a835ce3987889dc174ffb772422efb72b9 SHA512: 1fba90750ed58fac0ab78eead5d7d99268f8133172bb7350c5be32265727bb4551a1b8d7a2d6024de95d1b68e6b5259440729a38591d80eda86d808d8d54e24c Homepage: https://cran.r-project.org/package=mlr3cluster Description: CRAN Package 'mlr3cluster' (Cluster Extension for 'mlr3') Extends the 'mlr3' package with cluster analysis. Package: r-cran-mlr3data Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3246 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mlr3 Filename: pool/dists/resolute/main/r-cran-mlr3data_0.9.0-1.ca2604.1_all.deb Size: 3284234 MD5sum: 4e7c5d7de0548ce332b3f74c24bdec5c SHA1: 47a704a7cca980584b2f2a0c3bc6516db1870ef8 SHA256: 95646bcb47f86375a90fb332836fbad326a4d48f40ddc65a63543c3b1f81f9a0 SHA512: 0ca84e57c7bf2fb03b14fa0e7be59a4b2091defe4feada52fbfb85151fac3f93edf5d8a4e0341ff2c0997150c3be17f65094f1a7fe2fd3a87b071ad6c6b5d509 Homepage: https://cran.r-project.org/package=mlr3data Description: CRAN Package 'mlr3data' (Collection of Machine Learning Data Sets for 'mlr3') A small collection of interesting and educational machine learning data sets which are used as examples in the 'mlr3' book (), the use case gallery (), or in other examples. All data sets are properly preprocessed and ready to be analyzed by most machine learning algorithms. Data sets are automatically added to the dictionary of tasks if 'mlr3' is loaded. Package: r-cran-mlr3db Architecture: all Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-future, r-cran-future.apply, r-cran-future.callr, r-cran-hms, r-cran-lgr, r-cran-rsqlite, r-cran-rlang, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-mlr3db_0.7.2-1.ca2604.1_all.deb Size: 580146 MD5sum: 7350ffe6f778dc68f31039d98d391ffb SHA1: dac89f9b0189de17e8af90169f3eec34504c0d2a SHA256: fed094fae7561fde9700f2863dfcdcfc3223cfaaaec79ffe243df2ad536b5df9 SHA512: 362dc086dbb60245614c21cce5bba9aa0b38e108909d380a1774c259697146bd25827f25219c4f4f262d7123fcbace1feb081175817720d1ae468bb38a9cb61b Homepage: https://cran.r-project.org/package=mlr3db Description: CRAN Package 'mlr3db' (Data Base Backend for 'mlr3') Extends the 'mlr3' package with a backend to transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL', 'MariaDB', or 'PostgreSQL'. The package provides three additional backends: 'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases and also on Apache Parquet files. 'DataBackendPolars' operates on 'Polars' data frames. Package: r-cran-mlr3fairness Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-paradox, r-cran-r6, r-cran-rlang Suggests: r-cran-cccp, r-cran-cvxr, r-cran-pagedown, r-cran-fairml, r-cran-future, r-cran-iml, r-cran-kableextra, r-cran-knitr, r-cran-linprog, r-cran-lgr, r-cran-mlr3viz, r-cran-patchwork, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3fairness_0.4.0-1.ca2604.1_all.deb Size: 924850 MD5sum: 911b9d018af37a887c7afd0c89324476 SHA1: e291769ffbc6d3cc2d454beb6b03c93d9d5e9a37 SHA256: 8c1935ad7d9b44b5dc509ca15d008ba5fa0dcd8dd72eccc6d5c284a20fd0d464 SHA512: 39d22bce28888648726ad72730cf6345106b84aeb296aa009d09969831e8afe7b33cd634249c44f12da142e1324d9fa61d63514616a2d0b783c9efbcdf03b9a8 Homepage: https://cran.r-project.org/package=mlr3fairness Description: CRAN Package 'mlr3fairness' (Fairness Auditing and Debiasing for 'mlr3') Integrates fairness auditing and bias mitigation methods for the 'mlr3' ecosystem. This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as "Reweighing" described in 'Kamiran, Calders' (2012) and "Equalized Odds" described in 'Hardt et al.' (2016) . Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods. Package: r-cran-mlr3fda Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2317 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-tf Suggests: r-cran-fdboost, r-cran-lme4, r-cran-mboost, r-cran-rpart, r-cran-testthat, r-cran-tsfeatures, r-cran-wavelets, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mlr3fda_0.5.0-1.ca2604.1_all.deb Size: 2208270 MD5sum: 7e84a62a8d32a93ee2e11a0d3f75c60c SHA1: 5c840d5b8fa3c4e49ce64d74c4f280c8ca61b18e SHA256: 8a7ae4ea435a112d1c38e76f10054d1033549b010d2030f65291d5950f1a27cc SHA512: a430e801a0f0112c89d7ead76415570006559ace90551ce5595d0cfc6ae51457b3727fb8116ad6c0cf31fa2626719e6937dc30d19709cefbe0e03f3fa4d43797 Homepage: https://cran.r-project.org/package=mlr3fda Description: CRAN Package 'mlr3fda' (Extending 'mlr3' to Functional Data Analysis) Extends the 'mlr3' ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the 'tf' package. The package provides 'PipeOps' for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional 'PCA'. Package: r-cran-mlr3filters Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-boruta, r-cran-care, r-cran-caret, r-cran-carsurv, r-cran-fselectorrcpp, r-cran-knitr, r-cran-lgr, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-mlr3pipelines, r-cran-praznik, r-cran-rpart, r-cran-survival, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mlr3filters_0.9.1-1.ca2604.1_all.deb Size: 436520 MD5sum: bc00b0d4957f55302e7cd76d86e9ab7b SHA1: 6c3947c0d34c70dd18e75db42073300397dc25f2 SHA256: 82a569aa2d140b494b636080636689b620bdf314c5d5ef557deeac05ed75f5a7 SHA512: be855a1db03cae13150d322f391b7fa650ee39e9b8d808a6323e6278a73ca2c341a0c89634aa9c49dedd021dc9833c882ff755d592863393c44a4505ed62e3f6 Homepage: https://cran.r-project.org/package=mlr3filters Description: CRAN Package 'mlr3filters' (Filter Based Feature Selection for 'mlr3') Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported. Package: r-cran-mlr3fselect Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1142 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-bbotk, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-stabm Suggests: r-cran-e1071, r-cran-fastvoter, r-cran-genalg, r-cran-mirai, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-processx, r-cran-redux, r-cran-rpart, r-cran-rush, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3fselect_1.6.0-1.ca2604.1_all.deb Size: 818114 MD5sum: c3af3165e9c87ef299b1d7d74713cc57 SHA1: dbb17442d062a7714a1fcd0ae83af17a3fa4b35d SHA256: 80cc1b8682e77790565dd11a3e51cc4adde84787b74c050ee2c0ffa6ac42605f SHA512: ba9d506c51c12172a6e9383c7ef8f69e80bb0f939f0889c9f0f3209d77f0fec3a58a47cee42596e36c57290c501b3c8aa76763043020da090fedfc2c20c85f9e Homepage: https://cran.r-project.org/package=mlr3fselect Description: CRAN Package 'mlr3fselect' (Feature Selection for 'mlr3') Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling. Package: r-cran-mlr3hyperband Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3tuning, r-cran-bbotk, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-uuid Suggests: r-cran-emoa, r-cran-mirai, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-processx, r-cran-redux, r-cran-rpart, r-cran-rush, r-cran-testthat, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mlr3hyperband_1.1.0-1.ca2604.1_all.deb Size: 190838 MD5sum: 05593aca3f3809ea9981af3ed8885dda SHA1: dfc4a21ade532ed607ca54288c12c3b865dc9be2 SHA256: 0d8bd138e89c982e9e6d98cdaa763b0eced417efee37c9c1950020d75fcf60f0 SHA512: 23fb9a8cf483907d64371c6c2d4871a61290fece5fff82c3b4a0e8f41bc93ce4b27bce1aef0381eb2e89d3df823ea2b11db43342fc0d790148932ec6865e6648 Homepage: https://cran.r-project.org/package=mlr3hyperband Description: CRAN Package 'mlr3hyperband' (Hyperband for 'mlr3') Successive Halving (Jamieson and Talwalkar (2016) ) and Hyperband (Li et al. 2018 ) optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk. Package: r-cran-mlr3inferr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-future, r-cran-lgr, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-withr Suggests: r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3inferr_0.2.1-1.ca2604.1_all.deb Size: 315450 MD5sum: 352b77d7c9be01a3f3cbead06e024802 SHA1: afec52df149af794734f6c6ea4bad0f88d654b78 SHA256: 369cf31f8deb4e29931cf9bbd30142743ccfd6d58154f073e0ef5ed56e7ba724 SHA512: 0843fb09d34fc7b3272c1954705dec9491f942066172c2037a6300ff2aca0cf90ca11d85da17b74b5b6fa1f77af156f9802de610b90dc56fc9fc20a1ee4371ed Homepage: https://cran.r-project.org/package=mlr3inferr Description: CRAN Package 'mlr3inferr' (Inference on the Generalization Error) Confidence interval and resampling methods for inference on the generalization error. Package: r-cran-mlr3measures Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-mlr3misc, r-cran-prroc Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3measures_1.3.0-1.ca2604.1_all.deb Size: 341664 MD5sum: ce4417474c2f98338b7862cbbeff6dc0 SHA1: 793d193d5a282ad274db5fe04c4deaf8f624a21b SHA256: b8790383733bc3ff338c9d0d841988d49937f4c0ba7c19922eae69a7c7b8b1c7 SHA512: ce2170765cc06079ae76875268de3630c3d46edc73b381fb77ea239afb2a2ec9aafd6ab54542e6f19264ec1a0a8a07b8626ec36f82e3b8070df9802249480c2b Homepage: https://cran.r-project.org/package=mlr3measures Description: CRAN Package 'mlr3measures' (Performance Measures for 'mlr3') Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are. Package: r-cran-mlr3pipelines Architecture: all Version: 0.11.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-ggplot2, r-cran-glmnet, r-cran-igraph, r-cran-knitr, r-cran-lme4, r-cran-mlbench, r-cran-bbotk, r-cran-mlr3filters, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-nloptr, r-cran-quanteda, r-cran-rmarkdown, r-cran-rpart, r-cran-stopwords, r-cran-testthat, r-cran-visnetwork, r-cran-bestnormalize, r-cran-fastica, r-cran-kernlab, r-cran-smotefamily, r-cran-evaluate, r-cran-nmf, r-cran-mass, r-cran-gensa, r-cran-vtreat, r-cran-future, r-cran-htmlwidgets, r-cran-ranger, r-cran-themis, r-cran-dimred, r-cran-rspectra, r-cran-rann Filename: pool/dists/resolute/main/r-cran-mlr3pipelines_0.11.0-1.ca2604.1_all.deb Size: 2248806 MD5sum: 54556d2ddd870fb20cc59c668a835561 SHA1: 4923f1c4f208b11f52d6f31abdd495ccd64834f7 SHA256: 05ed8e4bb94017758da1ee231d02dc73a9516f2c4e285c1180467ab26b8f13f6 SHA512: 11d512ffaec50af4560237f61bbda22ed7ad1c5c468b0677881b47e519269336952de23c7a844499cca3083a79139d0d305dfd047323b3c23f33aace9b910f9f Homepage: https://cran.r-project.org/package=mlr3pipelines Description: CRAN Package 'mlr3pipelines' (Preprocessing Operators and Pipelines for 'mlr3') Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned. Package: r-cran-mlr3resampling Architecture: all Version: 2026.2.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1248 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-checkmate, r-cran-paradox, r-cran-mlr3, r-cran-mlr3misc, r-cran-pbdmpi Suggests: r-cran-ggplot2, r-cran-animint2, r-cran-mlr3tuning, r-cran-lgr, r-cran-future, r-cran-future.apply, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-nc, r-cran-rpart, r-cran-directlabels, r-cran-mlr3pipelines, r-cran-glmnet, r-cran-mlr3learners, r-cran-mlr3torch, r-cran-torch Filename: pool/dists/resolute/main/r-cran-mlr3resampling_2026.2.24-1.ca2604.1_all.deb Size: 759238 MD5sum: d6c12b190b08c243b959d1ef03c60126 SHA1: e08e00e4f88445a18b4d4122dba62f7978c308b9 SHA256: d919346dcc59829385e8f292cde49b9e6fa160cc398a286422cb7eed2edf31a6 SHA512: 4535a8ee67bc23c34ab612b4f1868f0ce6b2cc99be8f8b38f462a146b494575aa4a66911cd27608c7684d7fbc78fd2a0dc1f62de83047f38d30fdef720519cc3 Homepage: https://cran.r-project.org/package=mlr3resampling Description: CRAN Package 'mlr3resampling' (Resampling Algorithms for 'mlr3' Framework) A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set. Package: r-cran-mlr3shiny Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3measures, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlr3viz, r-cran-metrics, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets, r-cran-shinyalert, r-cran-data.table, r-cran-dt, r-cran-stringr, r-cran-plyr, r-cran-dplyr, r-cran-purrr, r-cran-patchwork, r-cran-ggparty, r-cran-ggally Suggests: r-cran-testthat, r-cran-shinytest, r-cran-devtools, r-cran-ranger, r-cran-e1071, r-cran-xgboost, r-cran-igraph, r-cran-readxl, r-cran-dalex, r-cran-dalextra, r-cran-bslib, r-cran-haven Filename: pool/dists/resolute/main/r-cran-mlr3shiny_0.5.0-1.ca2604.1_all.deb Size: 142770 MD5sum: 50797d0ebbcbfd0f62fc36d5a20ce5cd SHA1: c6c254c442de2da678cc6449538bf21349a5191d SHA256: 72368542d9ea11c2a18feaaf1da5886f4077b45f13b9853668813074e5df51f5 SHA512: ded5df188a5e34ca6646bd711924357f396b1d58adce53e442165589763ecb6d7ba4d9871cc6e368bc01295d1a4ebed252e08f2a781f03d2f8281b6da7b467ee Homepage: https://cran.r-project.org/package=mlr3shiny Description: CRAN Package 'mlr3shiny' (Machine Learning in 'shiny' with 'mlr3') A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework. Package: r-cran-mlr3spatial Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6, r-cran-sf, r-cran-terra Suggests: r-cran-bench, r-cran-future, r-cran-future.callr, r-cran-knitr, r-cran-mlr3learners, r-cran-paradox, r-cran-ranger, r-cran-raster, r-cran-rmarkdown, r-cran-rpart, r-cran-stars, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3spatial_0.6.1-1.ca2604.1_all.deb Size: 1915304 MD5sum: fa1fc9e2545154cf964cdc7f52a9f922 SHA1: 2fb96362f194a55e7df89a4dc82d1390e7cded44 SHA256: 7106d3e1b52ab29fafdb6a1ff73310f0aecc7da90030575cea3ba45c77189670 SHA512: 345333bb7451cda495def0ab191c6a497716a2b5c4166141788635486f76591e52c8c7f62c25a0477d5565db952ec5859e235184586fc701613241d6ee56ce9c Homepage: https://cran.r-project.org/package=mlr3spatial Description: CRAN Package 'mlr3spatial' (Support for Spatial Objects Within the 'mlr3' Ecosystem) Extends the 'mlr3' ML framework with methods for spatial objects. Data storage and prediction are supported for packages 'terra', 'raster' and 'stars'. Package: r-cran-mlr3spatiotempcv Architecture: all Version: 2.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-bbotk, r-cran-blockcv, r-cran-caret, r-cran-cast, r-cran-ggsci, r-cran-ggtext, r-cran-here, r-cran-knitr, r-cran-lgr, r-cran-mlr3filters, r-cran-mlr3pipelines, r-cran-mlr3spatial, r-cran-mlr3tuning, r-cran-patchwork, r-cran-plotly, r-cran-rmarkdown, r-cran-rpart, r-cran-sf, r-cran-sperrorest, r-cran-terra, r-cran-testthat, r-cran-twosamples, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mlr3spatiotempcv_2.3.4-1.ca2604.1_all.deb Size: 2366070 MD5sum: 2ce9c28eb0ed698b1b04934ac2eda445 SHA1: 61a2f8347e3f9383820c0dfb02c93864d18333fe SHA256: fb705e2ef2639f2738ee97944356a31dfc93b690fff1ae10a7827d4748ce5b1d SHA512: 7eb0e63bfbde79e762d68bd58eb59eb91caed0ab20107b0dc1b2f8f9665c228b2096248e40793a7df63dffb496c3f2e7b73cb7a126575fe9539d70eded21cee3 Homepage: https://cran.r-project.org/package=mlr3spatiotempcv Description: CRAN Package 'mlr3spatiotempcv' (Spatiotemporal Resampling Methods for 'mlr3') Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at . Package: r-cran-mlr3summary Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-future.apply, r-cran-mlr3, r-cran-mlr3misc Suggests: r-cran-fastshap, r-cran-iml, r-cran-mlr3fairness, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlr3tuning, r-cran-future, r-cran-ranger, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3summary_0.1.2-1.ca2604.1_all.deb Size: 93158 MD5sum: d2aa8b5fcc97a244733224e7d6561556 SHA1: cbb4de4e28c42081d310b6b7921fe13c5bf116fa SHA256: 8d819b40e11ce34e11a612ffcf04d6fc0bf73507c0f0f3bf7188f7df55733b08 SHA512: 0e67f1813dc10bd38ab46076aa7c3811bddf603067c16daa497e221bbbcf27c34fa92d294a6310546134ce51ceef0143d54b8704f5bfac60afbbf12642695dab Homepage: https://cran.r-project.org/package=mlr3summary Description: CRAN Package 'mlr3summary' (Model and Learner Summaries for 'mlr3') Concise and interpretable summaries for machine learning models and learners of the 'mlr3' ecosystem. The package takes inspiration from the summary function for (generalized) linear models but extends it to non-parametric machine learning models, based on generalization performance, model complexity, feature importances and effects, and fairness metrics. Package: r-cran-mlr3superlearner Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3learners, r-cran-checkmate, r-cran-lgr, r-cran-mlr3, r-cran-data.table, r-cran-purrr, r-cran-cli, r-cran-glmnet Suggests: r-cran-ranger, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlr3superlearner_0.1.2-1.ca2604.1_all.deb Size: 36458 MD5sum: ba35a22772bcc66103b3bc7968426200 SHA1: abffd4368fcf35ffb1e499e5605a8cd6f4409817 SHA256: e84ec71f1b02e89e74cdb8b32e0368cadd4d628286b0fa723a4b167388c5a11c SHA512: bb488f8bd8a644d98a35e158b40e0778f6a041f496aa6471e144982c7444a2c995a26ea7cb03f28317263931b78f506f172ad0d02c95092491dd84868d673cf6 Homepage: https://cran.r-project.org/package=mlr3superlearner Description: CRAN Package 'mlr3superlearner' (Super Learner Fitting and Prediction) An implementation of the Super Learner prediction algorithm from van der Laan, Polley, and Hubbard (2007) Installed-Size: 4670 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-torch, r-cran-backports, r-cran-cli, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-withr Suggests: r-cran-callr, r-cran-curl, r-cran-future, r-cran-ggplot2, r-cran-igraph, r-cran-jsonlite, r-cran-knitr, r-cran-mlr3tuning, r-cran-progress, r-cran-rmarkdown, r-cran-rpart, r-cran-viridis, r-cran-visnetwork, r-cran-testthat, r-cran-tibble, r-cran-tfevents, r-cran-torchvision, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-mlr3torch_0.3.3-1.ca2604.1_all.deb Size: 2812728 MD5sum: 379532b800ec46c02255720f320aa147 SHA1: e304443e61cec0fd311720af4cc4fdf2216eb074 SHA256: d5ab6191882ee950b281f850c90f6b773bd766cb30fef88a06dbdc25fc672938 SHA512: f53ddeb6f08ff6b1a61456b95d584596fe7dde31662ffe7bf006f2b273a3c7942c3b6a43a1fb341061b59ccbc86c21be1bae00974c9f8cea88319c2ba80311c2 Homepage: https://cran.r-project.org/package=mlr3torch Description: CRAN Package 'mlr3torch' (Deep Learning with 'mlr3') Deep Learning library that extends the mlr3 framework by building upon the 'torch' package. It allows to conveniently build, train, and evaluate deep learning models without having to worry about low level details. Custom architectures can be created using the graph language defined in 'mlr3pipelines'. Package: r-cran-mlr3tuning Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1326 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-paradox, r-cran-bbotk, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-adagio, r-cran-future, r-cran-gensa, r-cran-irace, r-cran-knitr, r-cran-mirai, r-cran-mlflow, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-nloptr, r-cran-processx, r-cran-redux, r-cran-rush, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mlr3tuning_1.6.0-1.ca2604.1_all.deb Size: 912968 MD5sum: 109cfda8ac625fc6040c5fb324f8d582 SHA1: 2c9fb09b984e8ff4c8b15b6f26c422e8742d7156 SHA256: 9240ff1d3fbfdf15fa73c3202080b897e44939ae95b03d36b7c118636611c612 SHA512: 65cc89f9f78114eb63b7bbdd0244a376d86c54275c0572f9ed7d5ed85f2603fc82ffcae3fa4fc1312648345d0565dba155f98f52779e73baea47e83f79dfd679 Homepage: https://cran.r-project.org/package=mlr3tuning Description: CRAN Package 'mlr3tuning' (Hyperparameter Optimization for 'mlr3') Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling. Package: r-cran-mlr3tuningspaces Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3tuning, r-cran-cli, r-cran-checkmate, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-e1071, r-cran-bbotk, r-cran-glmnet, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-ranger, r-cran-rpart, r-cran-testthat, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mlr3tuningspaces_0.6.0-1.ca2604.1_all.deb Size: 306442 MD5sum: 234e47117d092e234ade33a44c211682 SHA1: 52452c35564f4f370aeb619531c755b09d165c45 SHA256: 1377b645835772985278291f4c960b87f25990d5c4dd8c2a5f5e7b7795b3b788 SHA512: 2abf4308c846ced82ef44b4cbe3c4e7309264065ad474f12e2cef62c82296882a981df596f1212967b7790d5da99bf1c73a40178bc443696173351509098e487 Homepage: https://cran.r-project.org/package=mlr3tuningspaces Description: CRAN Package 'mlr3tuningspaces' (Search Spaces for 'mlr3') Collection of search spaces for hyperparameter optimization in the 'mlr3' ecosystem. It features ready-to-use search spaces for many popular machine learning algorithms. The search spaces are from scientific articles and work for a wide range of data sets. Package: r-cran-mlr3verse Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-bbotk, r-cran-data.table, r-cran-mlr3cluster, r-cran-mlr3data, r-cran-mlr3filters, r-cran-mlr3fselect, r-cran-mlr3hyperband, r-cran-mlr3inferr, r-cran-mlr3learners, r-cran-mlr3mbo, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-mlr3tuning, r-cran-mlr3tuningspaces, r-cran-mlr3viz, r-cran-paradox Suggests: r-cran-miesmuschel, r-cran-mlr3batchmark, r-cran-mlr3benchmark, r-cran-mlr3db, r-cran-mlr3fairness, r-cran-mlr3fda, r-cran-mlr3oml, r-cran-mlr3spatial, r-cran-mlr3spatiotempcv, r-cran-mlr3summary, r-cran-mlr3torch, r-cran-rush Filename: pool/dists/resolute/main/r-cran-mlr3verse_0.3.1-1.ca2604.1_all.deb Size: 31350 MD5sum: c0e07296450f4ee95465e69c2433c868 SHA1: d940fd98bb695d14f3498d7bf1c36953b6c5e626 SHA256: 70830ded594249b64fff1d883d82a94c00a3b2e8de2d7ca8852fc2bc0b8434ac SHA512: 61ffb66c079b3137c2d0f43d74590eaf12b9c15a894005f8f65764295a6949a8b2fe37b8639687f2dab7fb813924b912ee8a0ca2d366e2d6168c580bc08df9b2 Homepage: https://cran.r-project.org/package=mlr3verse Description: CRAN Package 'mlr3verse' (Easily Install and Load the 'mlr3' Package Family) The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at . Package: r-cran-mlr3viz Architecture: all Version: 0.11.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-scales, r-cran-viridis Suggests: r-cran-bbotk, r-cran-clue, r-cran-cluster, r-cran-ggally, r-cran-ggdendro, r-cran-ggfortify, r-cran-ggparty, r-cran-glmnet, r-cran-knitr, r-cran-lgr, r-cran-mlr3cluster, r-cran-mlr3filters, r-cran-mlr3fselect, r-cran-mlr3inferr, r-cran-mlr3learners, r-cran-mlr3tuning, r-cran-paradox, r-cran-partykit, r-cran-patchwork, r-cran-precrec, r-cran-ranger, r-cran-rpart, r-cran-testthat, r-cran-vdiffr, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mlr3viz_0.11.0-1.ca2604.1_all.deb Size: 346990 MD5sum: bf9ac6e4ce68035bb0842c934285aa73 SHA1: 98a09feaddd672eb50e96832035cbd1150a4a6d8 SHA256: b24f6c73abb6b18c28cccf7b2381811458a5a35660826f92c39444f770c9eaa1 SHA512: 0162502800a2813a525e98ede098a52345aafad531aafa3b7d46c066663c204ac6d2608745a3bfb2f1f9b6947b790a57739cddad3708dbad08e835c5607b86cd Homepage: https://cran.r-project.org/package=mlr3viz Description: CRAN Package 'mlr3viz' (Visualizations for 'mlr3') Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the 'autoplot()' generic of 'ggplot2'. The package draws plots with the 'viridis' color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves. Package: r-cran-mlrcpo Architecture: all Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-paramhelpers, r-cran-mlr, r-cran-bbmisc, r-cran-stringi, r-cran-checkmate, r-cran-backports Suggests: r-cran-care, r-cran-party, r-cran-rpart, r-cran-mlbench, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mrmre, r-cran-digest, r-cran-praznik, r-cran-randomforestsrc, r-cran-randomforest, r-cran-ranger, r-cran-rfast, r-cran-fselector, r-cran-fselectorrcpp, r-cran-e1071, r-cran-fnn, r-cran-lintr, r-cran-hmisc, r-cran-fastica, r-cran-rex Filename: pool/dists/resolute/main/r-cran-mlrcpo_0.3.8-1.ca2604.1_all.deb Size: 2110540 MD5sum: 2166a5bd9cb210faf70a81eb160ca1cf SHA1: ec336de41b34a5d5aed1f398b4f459dbeafe718c SHA256: 61cae433b577b62e5feeb152a666f2ec109328b62c0f2e92610aeaeed2475382 SHA512: 3322f283811c57c4859cc01980e2ec0f3d5a62580277ae2b82f4f7b2d74d91e8f569e89cb89eb1988f08e4f0929eb7103fa06a976331ba0b38c4a261753309b5 Homepage: https://cran.r-project.org/package=mlrCPO Description: CRAN Package 'mlrCPO' (Composable Preprocessing Operators and Pipelines for MachineLearning) Toolset that enriches 'mlr' with a diverse set of preprocessing operators. Composable Preprocessing Operators ("CPO"s) are first-class R objects that can be applied to data.frames and 'mlr' "Task"s to modify data, can be attached to 'mlr' "Learner"s to add preprocessing to machine learning algorithms, and can be composed to form preprocessing pipelines. Package: r-cran-mlrintermbo Architecture: all Version: 0.5.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-lhs, r-cran-callr, r-cran-bbotk, r-cran-mlr3tuning Suggests: r-cran-mlr, r-cran-paramhelpers, r-cran-testthat, r-cran-rgenoud, r-cran-dicekriging, r-cran-emoa, r-cran-cmaesr, r-cran-randomforest, r-cran-smoof, r-cran-lgr, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlrmbo, r-cran-ranger, r-cran-rpart, r-cran-mco Filename: pool/dists/resolute/main/r-cran-mlrintermbo_0.5.1-1-1.ca2604.1_all.deb Size: 151322 MD5sum: bf666c51295c7d3fd0d074a8f597f94b SHA1: 2f524d8da6adc0dfa9d6d8f5609affdbf9a3ce60 SHA256: 03a7761a433030bf1e469119f3b44071bd722ffdb263f3d64473bfbe27f4c34a SHA512: 9864336c1e6bcf123151996a776f2d5df24b941233fd17e871115e904abf79b35d4ce330fa946ab820f585462a8eb2c1ef35de503647253cb356dee344e05867 Homepage: https://cran.r-project.org/package=mlrintermbo Description: CRAN Package 'mlrintermbo' (Model-Based Optimization for 'mlr3' Through 'mlrMBO') The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization of general objective functions using the 'bbotk' black box optimization framework. The control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'. Package: r-cran-mlrpro Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-dplyr, r-cran-mass, r-cran-dgof Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlrpro_0.1.3-1.ca2604.1_all.deb Size: 40404 MD5sum: da5cbe18cd0d0cd393e5617ddf51c798 SHA1: 9c2325316e54979de839d4b515c2aa58bc822cbd SHA256: 61be6ce0766c57f582a1b36f19cba3a3e3dbee2a03cc09487afc2704c5774255 SHA512: abca3cd6066fdda3b83fec8d3bb2fe5254b95533de831f284ee35f9f1de8c0d82b1b5a0c271756e1f668d753011c44d06ec90a67e77eabb6b8e28d0fc3469228 Homepage: https://cran.r-project.org/package=mlrpro Description: CRAN Package 'mlrpro' (Stepwise Regression with Assumptions Checking) The stepwise regression with assumptions checking and the possible Box-Cox transformation. Package: r-cran-mls3 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-glmnet, r-cran-lightgbm, r-cran-ranger Suggests: r-cran-caret, r-cran-knitr, r-cran-randomforest, r-cran-kernlab Filename: pool/dists/resolute/main/r-cran-mls3_0.1.0-1.ca2604.1_all.deb Size: 100878 MD5sum: f2e766fbd0ea923f08d72a6a56160af8 SHA1: 6b3165235a12aeda182ef0c65da4bde726981093 SHA256: cdce8f168dc0f3dc0b125c769d27238a438929a1eae9d08aa561ac7afc1e7575 SHA512: fb7043062459782375a531a0427c5573bbc6adb9086f7569690c2ca88dc44eb07cb619539fae7508f8bc9450608a89fce59ff36348db0ead311c51056d1f35be Homepage: https://cran.r-project.org/package=mlS3 Description: CRAN Package 'mlS3' (Unified S3 Interface to Machine Learning Models) Provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from 'glmnet', 'lightgbm', 'ranger', 'e1071', and 'caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility. Package: r-cran-mlsjunkgen Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlsjunkgen_0.1.2-1.ca2604.1_all.deb Size: 81990 MD5sum: c9744a546ad00de9a77069eb9d607d6e SHA1: 159d18bd39eba6631d52dedcd0faffbd1203d7de SHA256: 0374e3ec24d19c83d641806cc39b69751dc33aa19e1f84752a24b11d33d85572 SHA512: e121613c0ff11c5878bc6a16340c5b0252e1a11aba9e3e852cd9322d3563508e2b5766fa21c50bc43bbf196a10c11af59bc71eb75a737d6d6d11e908e7dcad5f Homepage: https://cran.r-project.org/package=mlsjunkgen Description: CRAN Package 'mlsjunkgen' (Use the MLS Junk Generator Algorithm to Generate a Stream ofPseudo-Random Numbers) Generate a stream of pseudo-random numbers generated using the MLS Junk Generator algorithm. Functions exist to generate single pseudo-random numbers as well as a vector, data frame, or matrix of pseudo-random numbers. Package: r-cran-mlsp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsignal, r-cran-pls, r-cran-glmnet, r-cran-cubist, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-mlsp_0.1.0-1.ca2604.1_all.deb Size: 94458 MD5sum: 4f5542677e30aa6cda0f0bc3168c4996 SHA1: 1f62fe762d73efe61b77d57fa1228daa984dcf00 SHA256: 02cad10435a95cca70a701fffae622eb912198d0cedcf5ba1f127073117f3632 SHA512: de1944d58c7d16b5c3d1f90b22233b345f83e14d452e118abfe12bd5795dc3ed7033d809c968059dc82f09a7a398021e17cbbc8c208d772aa21076090be407d2 Homepage: https://cran.r-project.org/package=MLSP Description: CRAN Package 'MLSP' (Machine Learning Models for Soil Properties) Creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types. Package: r-cran-mlspatial Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-readxl, r-cran-dplyr, r-cran-ggplot2, r-cran-randomforest, r-cran-xgboost, r-cran-e1071, r-cran-caret, r-cran-tmap, r-cran-spdep, r-cran-ggpubr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-kernlab, r-cran-writexl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlspatial_0.1.1-1.ca2604.1_all.deb Size: 1564062 MD5sum: cbede357cfb4e2b6b356f895ba8f51d0 SHA1: ced015fef29713844ecac814d7bc492db8ae794d SHA256: b9527f153978c06238f39bd28a386f4ba3f1d8908b5c6d2b5c246be96f005128 SHA512: 9448448662de32ea4b973c09f349a8b1eadafeba1d44f6dcbfac779e89217289612ed00cb2561dbc72a024d5399a9feea555cd703bd2591479bf18b877e22adc Homepage: https://cran.r-project.org/package=mlspatial Description: CRAN Package 'mlspatial' (Machine Learning and Mapping for Spatial Epidemiology) Provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). 'Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model' and . It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. 'mlspatial' enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, 'XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions. Package: r-cran-mlstropalr Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-stringr, r-cran-opalr, r-cran-fabr, r-cran-madshapr Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mlstropalr_1.0.3-1.ca2604.1_all.deb Size: 71212 MD5sum: c8c18abc7f99855c3b07d3a881d8a0c4 SHA1: a7ccdf299f0f3710aa1fef17677c2f3ebaa6f922 SHA256: 4a5168ab72ab300075b5d8c5696b80b6178974cb5e2105d8a57e542de33eac94 SHA512: bc7d1009a3978664e871f176237eac0df8c0b6c03f7a19169c7d460b120784ca30a21cc2cfde1ad809f86fe6fa514638e25175a14f930261a7ece9cc47b492fa Homepage: https://cran.r-project.org/package=mlstrOpalr Description: CRAN Package 'mlstrOpalr' (Support Compatibility Between 'Maelstrom' R Packages and 'Opal'Environment) Functions to support compatibility between 'Maelstrom' R packages and 'Opal' environment. 'Opal' is the 'OBiBa' core database application for biobanks. It is used to build data repositories that integrates data collected from multiple sources. 'Opal Maelstrom' is a specific implementation of this software. This 'Opal' client is specifically designed to interact with 'Opal Maelstrom' distributions to perform operations on the R server side. The user must have adequate credentials. Please see for complete documentation. Package: r-cran-mlsurvlrnrs Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-kdry, r-cran-mlexperiments, r-cran-mllrnrs, r-cran-r6 Suggests: r-cran-glmnet, r-cran-lintr, r-cran-measures, r-cran-quarto, r-cran-ranger, r-cran-rbayesianoptimization, r-cran-rpart, r-cran-splittools, r-cran-survival, r-cran-testthat, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mlsurvlrnrs_0.0.8-1.ca2604.1_all.deb Size: 252838 MD5sum: a234bc7614c55568276f4db8a8fc0090 SHA1: 2418366be2e055a1d161a688c7128c02cc38899c SHA256: f01cd4e6f84400653e258e8e371afe784290476cad525865cf4a622dbd29e38c SHA512: cd99b7a13bd15ab9e36b87f0a04c863d5748dcc83cdc4fd3c87683ab656e235e0dcba3fde1a41cbf7531036b9d995dfbabe722bd3f378f91eefea3a1348348cc Homepage: https://cran.r-project.org/package=mlsurvlrnrs Description: CRAN Package 'mlsurvlrnrs' (R6-Based ML Survival Learners for 'mlexperiments') Enhances 'mlexperiments' with additional machine learning ('ML') learners for survival analysis. The package provides R6-based survival learners for the following algorithms: 'glmnet' , 'ranger' , 'xgboost' , and 'rpart' . These can be used directly with the 'mlexperiments' R package. Package: r-cran-mlt.docreg Architecture: all Version: 1.1-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlt, r-cran-numderiv, r-cran-eha, r-cran-multcomp, r-cran-lattice, r-cran-survival, r-cran-flexsurv, r-cran-truncreg Suggests: r-cran-variables, r-cran-basefun, r-cran-mass, r-cran-th.data, r-cran-knitr, r-cran-prodlim, r-cran-gridextra, r-cran-nnet, r-cran-mgcv, r-cran-hsaur3, r-cran-sandwich, r-cran-latticeextra, r-cran-colorspace, r-cran-matrix, r-cran-aer, r-cran-coin, r-cran-gamlss.data, r-cran-mlbench, r-cran-tram, r-cran-rms Filename: pool/dists/resolute/main/r-cran-mlt.docreg_1.1-12-1.ca2604.1_all.deb Size: 656230 MD5sum: 2982458f36ab8c9f5c8199f76158f573 SHA1: 7e97b59dc805e069886b47bcef6cbbcc5735ab2b SHA256: 3e3c77ac087e5a921a8791cbdb2703ba00bc6bd2b450c01856ea5b5233a7029c SHA512: 05fd9141a75a6a6e2e6d604a877ddc661ff24f0d86fb8fc1a863f1e224de9c767d504489aea41c62c2679f61f74857bd4dced43a4f4500c36bfe437336b1eda0 Homepage: https://cran.r-project.org/package=mlt.docreg Description: CRAN Package 'mlt.docreg' (Most Likely Transformations: Documentation and Regression Tests) Additional documentation, a package vignette and regression tests for package mlt. Package: r-cran-mltest Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mltest_1.0.3-1.ca2604.1_all.deb Size: 20642 MD5sum: c452a44e0073bb1c09a87d1032ee9452 SHA1: ef8ee39d4c567b43144008a0a29ee374fd5fb1b2 SHA256: d7c90ee7412afba02d4fef41e4887ff5a3df1d3d388896d7a7d6891e7cc52135 SHA512: b6a863dbe4b127f1d5260a8773ea96d9c9f8eb17c17e255d723769612a6c617eb4c12717f8df1cbb6260d40cafa792f3f9a77d767d9e53e1d167bbd8adbd3259 Homepage: https://cran.r-project.org/package=mltest Description: CRAN Package 'mltest' (Classification Evaluation Metrics) A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix. Package: r-cran-mltools Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mltools_0.3.5-1.ca2604.1_all.deb Size: 110604 MD5sum: 7cbe3a90ccf92a924b54afffb39ddae4 SHA1: cb4c7621c49523346d7f3a0ee4418ef5b2720d84 SHA256: f9916dbff6bff111eee9138d85d487606806e7177515c78d578a7af7cb33e1e0 SHA512: 9bb066dc6f351053fd63be054582c603d0337cde1b7b21a50a3b3b691da5cd9d2ca67caf761ac310b056abe634769ef9b57b5effb8ccf358d6c38ac4ca2ec56b Homepage: https://cran.r-project.org/package=mltools Description: CRAN Package 'mltools' (Machine Learning Tools) A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions. Package: r-cran-mlvar Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-arm, r-cran-qgraph, r-cran-dplyr, r-cran-clustergeneration, r-cran-mvtnorm, r-cran-corpcor, r-cran-abind, r-cran-mplusautomation, r-cran-graphicalvar, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-mlvar_0.6.1-1.ca2604.1_all.deb Size: 310404 MD5sum: 2f79078f7f316f5981154236981b3d34 SHA1: 8f42e7e762bd372bca7470b3c79581685e9d2c11 SHA256: 9b79db3b7b4d2fa53cb544166988204cd7e6d3c88747d588aceba35d9d944562 SHA512: ec714e5c108aebde2646d2f9434dc2c14a4dd64e9cbaf1a46c6bd3304e73b248f746ff5ed29b9f996aec6e9942b35f451dbd5f8a9687a6ba532dceeab576ccfc Homepage: https://cran.r-project.org/package=mlVAR Description: CRAN Package 'mlVAR' (Multi-Level Vector Autoregression) Estimates the multi-level vector autoregression model on time-series data. 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Package: r-cran-mlwrap Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 872 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-tidyr, r-cran-magrittr, r-cran-dials, r-cran-parsnip, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-glue, r-cran-innsight, r-cran-shapr, r-cran-diagrammer, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-sensitivity, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-patchwork, r-cran-cli, r-cran-scales Suggests: r-cran-testthat, r-cran-torch, r-cran-brulee, r-cran-ranger, r-cran-kernlab, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-mlwrap_0.4.0-1.ca2604.1_all.deb Size: 791378 MD5sum: 25f71b2d4f04d263b814960855ffb3f1 SHA1: bcc8e964a0bcdf15b3340f6df391ef702cd9d493 SHA256: 51802a1c2d44ca179231778c312913b7cb6731dcf68dea2c1186f47e67ba78e9 SHA512: 91434eb1c2f9f108946e74e7caf4d49fe13a9fc222b461d15ed456758f516c09fafeb1e5f12be28f4b4bee7fa2cd85ecdd04f11fc8474a4886a0a5a9dbb030cc Homepage: https://cran.r-project.org/package=MLwrap Description: CRAN Package 'MLwrap' (Machine Learning Modelling for Everyone) A minimal library specifically designed to make the estimation of Machine Learning (ML) techniques as easy and accessible as possible, particularly within the framework of the Knowledge Discovery in Databases (KDD) process in data mining. The package provides essential tools to structure and execute each stage of a predictive or classification modeling workflow, aligning closely with the fundamental steps of the KDD methodology, from data selection and preparation, through model building and tuning, to the interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and sensitivity_analysis(). It also includes global and pairwise interaction analysis based on Friedman’s H-statistic to support a more detailed interpretation of complex feature relationships.These steps correspond, respectively, to data preparation and transformation, model construction, hyperparameter optimization, and sensitivity analysis. The user can access comprehensive model evaluation results including fit assessment metrics, plots, predictions, and performance diagnostics for ML models implemented through 'Neural Networks', 'Random Forest', 'XGBoost' (Extreme Gradient Boosting), and 'Support Vector Machines' (SVM) algorithms. By streamlining these phases, 'MLwrap' aims to simplify the implementation of ML techniques, allowing analysts and data scientists to focus on extracting actionable insights and meaningful patterns from large datasets, in line with the objectives of the KDD process. Package: r-cran-mm2sdata Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mm2sdata_1.0.3-1.ca2604.1_all.deb Size: 4894754 MD5sum: c0cd34dce35bdeccbb46ce792401d026 SHA1: 881a88b27094f962036ad7df476071ff8241985f SHA256: e89bfb0bf45de741611d676510cfb9bcdf41920c20f9a964c7e1de651bfc992a SHA512: a2a881d2ee11bf907d19bbbb871d1855cdaa97c70b45f048effc4360270f05116dd0364db8046f43cfdf6ec7c77c7d0ce97c76dca9c4d52fbe1e053984e5590d Homepage: https://cran.r-project.org/package=MM2Sdata Description: CRAN Package 'MM2Sdata' (Gene Expression Datasets for the 'MM2S' Package) Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) . Package: r-cran-mm Architecture: all Version: 1.7-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magic, r-cran-abind, r-cran-quadform, r-cran-partitions, r-cran-oarray Filename: pool/dists/resolute/main/r-cran-mm_1.7-0-1.ca2604.1_all.deb Size: 457790 MD5sum: 91075ba42ff8c1c52a1144194136eb59 SHA1: 6d122d97ff084573162f948cce8d639bb453880a SHA256: 54e17182d3fc652dd051069335b35ba3e3fa24382ff95b2f87b01f2b8a9ea0fe SHA512: 3cc0b0057bbacba43deb9f4bfd7708931d5d178a3eaaa26e00cc58ccaa17173d5554e1bbf7e97420ea7fae7c094bd33c93496ac6c846ce43544cdc0cd52a6395 Homepage: https://cran.r-project.org/package=MM Description: CRAN Package 'MM' (The Multiplicative Multinomial Distribution) Various utilities for the Multiplicative Multinomial distribution. Package: r-cran-mma Architecture: all Version: 10.8-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbm, r-cran-survival, r-cran-car, r-cran-gplots, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mma_10.8-1-1.ca2604.1_all.deb Size: 927974 MD5sum: 6459ced729fa51c6353c19e6db8da6ee SHA1: 1a744e0ec7caf0cc49afec94f7338022205c2676 SHA256: 1195b591f8861cc4051f25f771d83a7769edf803e46237c27a461f738520f43e SHA512: 875b2ab3125ad499c49a2912305ec45e7bd24f7d387cd501cf3e733f7cb3f379b90900d380860fb6e4d7c66fd2621edb8877d21b22c37057203dd57a1546f136 Homepage: https://cran.r-project.org/package=mma Description: CRAN Package 'mma' (Multiple Mediation Analysis) Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23. Package: r-cran-mmabig Architecture: all Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mma, r-cran-survival, r-cran-car, r-cran-gplots Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mmabig_3.2-0-1.ca2604.1_all.deb Size: 178512 MD5sum: 345045f3c3ff5ff26747578ffe841e97 SHA1: 57313c8bae89e54ad0384ad4914023226e388a05 SHA256: 38026b7243c7774b299cb32aac4df42d2d4de9318ccf77319cafbddc9e3fe2e9 SHA512: 02bba4a52092a4503bb5d5f3a65ba5a3e643f5ded7300784aa8fb39d446673868724bc967cff792f8801ed490c148a7d8081e0cbd28d7458395f4472140c1021 Homepage: https://cran.r-project.org/package=mmabig Description: CRAN Package 'mmabig' (Multiple Mediation Analysis for Big Data Sets) Used for general multiple mediation analysis with big data sets. Package: r-cran-mmac Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mosaic, r-cran-mosaiccalc, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-mmac_1.0-1.ca2604.1_all.deb Size: 754258 MD5sum: d1db8f661087811bed385a517df5d582 SHA1: 13c1a057c33a22eea620bd0c5a579365e0e75dd8 SHA256: f3fb1caf5b5fe392d77ccb2927751b341dae025df40d1e7f65aeee75d71c0115 SHA512: 2ea00d62ce66c65e8f69fe6fafb45ef65eba940981816f9c620cf9e150e1c6e6929e3ba6554ec350e11981d953b6cc1f8120341a1c76dbeef7202caa45f037aa Homepage: https://cran.r-project.org/package=MMAC Description: CRAN Package 'MMAC' (Data for Mathematical Modeling and Applied Calculus) Contains the data sets for the first and second editions of the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The first edition of the book was published by Oxford University Press in 2018 with ISBN-13: 978-019882472. The second edition is expected to be published in January 2027. Package: r-cran-mmad Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mmad_2.0.1-1.ca2604.1_all.deb Size: 41154 MD5sum: 82f686b41fddf33be7c89f0ad6a04c68 SHA1: fb08c6e5154a56dcc5505f538a861a41bf32ca33 SHA256: 0f11865220fa6bc899f49b24a7cae622e8667ddb4696049d50a786c0b55d8123 SHA512: 43728c01199d26aff6a306e0fa37da85240d580a5b2e11f73272cdd7d28b4b42df3080db433e1d467b7ced8480690deb4cb0ba14520ed578f11abd730e0b97a8 Homepage: https://cran.r-project.org/package=MMAD Description: CRAN Package 'MMAD' (An R Package of Minorization-Maximization Algorithm via theAssembly--Decomposition Technology) The minorization-maximization (MM) algorithm is a powerful tool for maximizing nonconcave target function. However, for most existing MM algorithms, the surrogate function in the minorization step is constructed in a case-specific manner and requires manual programming. To address this limitation, we develop the R package MMAD, which systematically integrates the assembly--decomposition technology in the MM framework. This new package provides a comprehensive computational toolkit for one-stop inference of complex target functions, including function construction, evaluation, minorization and optimization via MM algorithm. By representing the target function through a hierarchical composition of assembly functions, we design a hierarchical algorithmic structure that supports both bottom-up operations (construction, evaluation) and top-down operation (minorization). Package: r-cran-mmaqshiny Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15863 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-cairo, r-cran-xts, r-cran-lubridate, r-cran-zoo, r-cran-catools, r-cran-ggplot2, r-cran-data.table, r-cran-dt, r-cran-dplyr, r-cran-leaflet, r-cran-stringr, r-cran-shiny, r-cran-xml, r-cran-shinyjs, r-cran-plotly Suggests: r-cran-testthat, r-cran-devtools, r-cran-usethis, r-cran-shinytest Filename: pool/dists/resolute/main/r-cran-mmaqshiny_1.0.0-1.ca2604.1_all.deb Size: 3242910 MD5sum: 4dfb09d010fe4fae37fe43d42ca0bf65 SHA1: 6d8286264f9e4ff5781214f4b54376ff1b1f1fe5 SHA256: 32da86881a9471b4bc42fe238cc38e40bd72f4bda0fd0953ce7dd211da49e124 SHA512: 4df245ea6625b0f515a7f1438dc4859b607bf0260b8fd630ed4583ef2e3c888359e7b01d41430c08e109d3c3ef82e6821e30e00df3aec6f0267d0b323c149238 Homepage: https://cran.r-project.org/package=mmaqshiny Description: CRAN Package 'mmaqshiny' (Explore Air-Quality Mobile-Monitoring Data) Mobile-monitoring or "sensors on a mobile platform", is an increasingly popular approach to measure high-resolution pollution data at the street level. Coupled with location data, spatial visualisation of air-quality parameters helps detect localized areas of high air-pollution, also called hotspots. In this approach, portable sensors are mounted on a vehicle and driven on predetermined routes to collect high frequency data (1 Hz). 'mmaqshiny' is for analysing, visualising and spatial mapping of high-resolution air-quality data collected by specific devices installed on a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate matter with size less than 2.5 microns), Black carbon mass concentrations (BC), ultra-fine particle number concentrations, carbon dioxide along with GPS coordinates and relative humidity (RH) data collected by popular portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It incorporates device specific cleaning and correction algorithms. RH correction is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) . Provision is given to add linear regression coefficients for correcting the PM2.5 data (if required). BC data will be cleaned for the vibration generated noise, by adopting the statistical procedure as explained in Apte et al., (2011) , followed by a loading correction as suggested by Ban-Weiss et al., (2009) . For the number concentration data, provision is given for dilution correction factor (if a diluter is used with CPC3007; default value is 1). The package joins the raw, cleaned and corrected data from the above said instruments and outputs as a downloadable csv file. Package: r-cran-mmarch.ac Architecture: all Version: 3.3.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-refund, r-cran-denseflmm, r-cran-dplyr, r-cran-xlsx, r-cran-survival, r-cran-tidyr, r-cran-zoo, r-cran-ineq, r-cran-cosinor, r-cran-cosinor2, r-cran-abind, r-cran-accelerometry, r-cran-actcr, r-cran-actfrag, r-cran-minpack.lm, r-cran-kableextra, r-cran-ggir Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mmarch.ac_3.3.4.0-1.ca2604.1_all.deb Size: 1085494 MD5sum: 7c869d9ae44e050283cf68fdc5647c94 SHA1: 1345539e2f527ef999825a20ca1c2db4ef87849b SHA256: 1fc4fbc9ed865737cdbbe7f5245137423a34995e8088ea4a2e492802ce74b952 SHA512: ac32ec54da67379e7f7033c4f03fce2c612101c417820917da6af3ca8e482b123bb500ab9d92742a78c3781bd26b7847b11495d6019df13c25d7732b2de46dbe Homepage: https://cran.r-project.org/package=mMARCH.AC Description: CRAN Package 'mMARCH.AC' (Processing of Accelerometry Data with 'GGIR' in mMARCH) Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package 'GGIR' is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with 'GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations. To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through 'GGIR' as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running 'GGIR' for accelerometer data. In module 1, all csv files in the 'GGIR' output directory were read, transformed and then merged. In module 2, the 'GGIR' output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44. Package: r-cran-mmb Architecture: all Version: 0.13.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-foreach, r-cran-doparallel Suggests: r-cran-devtools, r-cran-testthat, r-cran-covr, r-cran-e1071, r-cran-caret, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggpubr, r-cran-cowplot, r-cran-philentropy, r-cran-rtsne Filename: pool/dists/resolute/main/r-cran-mmb_0.13.3-1.ca2604.1_all.deb Size: 196636 MD5sum: 41a3bd4d9aea3cd97ce6a794d7fd5a16 SHA1: 83254fb330f9bc09a3fdf47764f468ec411aabba SHA256: 53152fdd67562d4ade2267561a1b22471e3fa4f4c11644dabd2a03570f878146 SHA512: 651d4f6864cdf06eb397c678b09878e1de80f6c9ff428ceea7b5766caeebdfec5542651e9303aaddec0f905712f3caffe4c817438de5343786f28127f004a11b Homepage: https://cran.r-project.org/package=mmb Description: CRAN Package 'mmb' (Arbitrary Dependency Mixed Multivariate Bayesian Models) Supports Bayesian models with full and partial (hence arbitrary) dependencies between random variables. Discrete and continuous variables are supported, and conditional joint probabilities and probability densities are estimated using Kernel Density Estimation (KDE). The full general form, which implements an extension to Bayes' theorem, as well as the simple form, which is just a Bayesian network, both support regression through segmentation and KDE and estimation of probability or relative likelihood of discrete or continuous target random variables. This package also provides true statistical distance measures based on Bayesian models. Furthermore, these measures can be facilitated on neighborhood searches, and to estimate the similarity and distance between data points. Related work is by Bayes (1763) and by Scutari (2010) . 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The methodology extends the marginal Cox model bias-correction framework of Wang et al. (2023) to the multi-state setting. Package: r-cran-mmc Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mmc_0.0.3-1.ca2604.1_all.deb Size: 30372 MD5sum: d0df7e3ad782987e5103e3f083cee6e0 SHA1: 37979c49a8383c24c43eb0f5c357600732cb0376 SHA256: 7b7fe097f988273993bdeb7cb351520dccc72e8c54b827d248c8cd785001cc05 SHA512: b1b32f015c3efbdd0b8869b2ef46a0a3f0d37bee155b73e21c87f2574e2a1baed164c7d5abc6f04f4964c1bba991b5770c6cb5a516418406fd5cae1a2e6c50f6 Homepage: https://cran.r-project.org/package=mmc Description: CRAN Package 'mmc' (Multivariate Measurement Error Correction) Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates. Package: r-cran-mmcards Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mmcards_0.1.1-1.ca2604.1_all.deb Size: 35882 MD5sum: 680c00bd79b3d89fd7c52c1f6debc142 SHA1: 405bacdb8e3f7b786009fc5124c72a43ed12fb41 SHA256: ac100e58106788c2955888b1d792f937f9c9d733da19a25bfabd46fe3bc8c84d SHA512: ac3053311d47d52bfb0598d3e370c9132a7207de9b13e521b7b6bb909c1e352137e3bc32a60d2f316c14888bfcbff3a1eb192752d5a6102711e586fff9d4aabb Homepage: https://cran.r-project.org/package=mmcards Description: CRAN Package 'mmcards' (Playing Cards Utility Functions) Early insights in probability theory were largely influenced by questions about gambling and games of chance, as noted by Blitzstein and Hwang (2019, ISBN:978-1138369917). In modern times, playing cards continue to serve as an effective teaching tool for probability, statistics, and even 'R' programming, as demonstrated by Grolemund (2014, ISBN:978-1449359010). The 'mmcards' package offers a collection of utility functions designed to aid in the creation, manipulation, and utilization of playing card decks in multiple formats. These include a standard 52-card deck, as well as alternative decks such as decks defined by custom anonymous functions and custom interleaved decks. Optimized for the development of educational 'shiny' applications, the package is particularly useful for teaching statistics and probability through card-based games. Functions include shuffle_deck(), which creates either a shuffled standard deck or a shuffled custom alternative deck; deal_card(), which takes a deck and returns a list object containing both the dealt card and the updated deck; and i_deck(), which adds image paths to card objects, further enriching the package's utility in the development of interactive 'shiny' application card games. Package: r-cran-mmcmcbayes Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6041 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-ksamples, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mmcmcbayes_0.2.0-1.ca2604.1_all.deb Size: 6149802 MD5sum: 414472b681eaeb37235d247e5c4dd2f5 SHA1: ad679a819feb74387aca3e045f83f53041e05619 SHA256: f287b865e67d34319705676812b710c136fe34428aebf43b4b22dd789f2b597c SHA512: b0604dbd2904dad245e7207d6dad4f1e5834a5aa913b2ee0ad79d55edfdf7e8233e4d8d009db7e79c16b1c010a70ac86ebc79af420c39d3f1899276f7946d89b Homepage: https://cran.r-project.org/package=mmcmcBayes Description: CRAN Package 'mmcmcBayes' (Multistage MCMC Method for Detecting DMRs) Implements differential methylation region (DMR) detection using a multistage Markov chain Monte Carlo (MCMC) algorithm based on the alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes the Anderson-Darling test stage, improves computational efficiency of the core ASGN and multistage MCMC routines, and adds convenience functions for summarizing and visualizing detected DMRs. The methodology is based on Yang (2025) . Package: r-cran-mmcsd Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rlist, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-simstudy, r-cran-kableextra, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-mmcsd_1.0.0-1.ca2604.1_all.deb Size: 231312 MD5sum: 3b967a38aff03b94ec9c77882eefd1a3 SHA1: 9a0b325c495a8ab262a3d063cdb9a4d9f6e9f492 SHA256: 305c8ed31f60e869b7f68492df812e4e6f225a06859ed9cd10b6dc99e2a493fb SHA512: 1e0964d94072eacfc7d535b028971db98e289849cf00e7173a44530c94655bf811999e0b83233f657ca47c181b817ddc66cb7a39cbc248bc37e385deb147af0c Homepage: https://cran.r-project.org/package=Mmcsd Description: CRAN Package 'Mmcsd' (Modeling Complex Longitudinal Data in a Quick and Easy Way) Matching longitudinal methodology models with complex sampling design. It fits fixed and random effects models and covariance structured models so far. It also provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." . Package: r-cran-mmd Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-plyr, r-cran-bigmemory Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mmd_1.0.0-1.ca2604.1_all.deb Size: 65198 MD5sum: 0d1432733f9bb4f81721f750090e55c4 SHA1: e04064d80cb751b4ada1a9b635681630d672de87 SHA256: e89a7433035aab5f96739d9bc5b4df80ee39577967c0ef012fa8fb866cd493a0 SHA512: 7f1e3828d4362f7dd27da168ac263ca4f9bd4cac842ec095cecb719fdcac82adcd8bff7057903d5c4aa1d1f4f5269f24ef0b232b354dac9a46caf46eba421cc7 Homepage: https://cran.r-project.org/package=MMD Description: CRAN Package 'MMD' (Minimal Multilocus Distance (MMD) for Source Attribution andLoci Selection) The aim of the package is two-fold: (i) To implement the MMD method for attribution of individuals to sources using the Hamming distance between multilocus genotypes. (ii) To select informative genetic markers based on information theory concepts (entropy, mutual information and redundancy). The package implements the functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to efficiently attribute individuals to source populations. Scientific Reports 10, 12124 (2020) . See more details and examples in the README file. 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Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) . 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Package: r-cran-mmeln Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mmeln_1.5-1.ca2604.1_all.deb Size: 128608 MD5sum: 630a4b2c821ef808d2ff8cc33114fb23 SHA1: 4d5672f5165c86e8b66b0754c9d87068ab677945 SHA256: 57111aec0d9f7408577e04ac9ca3d673a3c3fd302d340ee45edca9c0e281e756 SHA512: cd48e161486feab76c88f54cb89cf08bcb7a003b0f2d34747b5886c7f83301d6be86dcf1ccaeb34fa28967623a9d20f6dfb2f4a6aaf190e3280bbfcedd2fc17d Homepage: https://cran.r-project.org/package=mmeln Description: CRAN Package 'mmeln' (Estimation of Multinormal Mixture Distribution) Fit multivariate mixture of normal distribution using covariance structure. Package: r-cran-mmem Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-jointdiag, r-cran-lme4, r-cran-matrixcalc, r-cran-psych, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-mmem_0.1.1-1.ca2604.1_all.deb Size: 48640 MD5sum: fd2ec8dfce7840853de8c867189914db SHA1: bf02943a8c888478274a466d9b313535622e55ac SHA256: 3d04a6fd40ebfd5966ec6bf169f78e9c6c8f8ce2afcaf3daa9c9a9d7adb0ee77 SHA512: e5bd5d7f8daf766d41792174b1b28c481bc2da2c46dbef9caa7e6b2476f20a9526edac8fb9bfd8bd8e73a8f995aa384670d50a9beb9f74af429f947bbe39e771 Homepage: https://cran.r-project.org/package=MMeM Description: CRAN Package 'MMeM' (Multivariate Mixed Effects Model) Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. See Meyer (1985) and Wesolowska Janczarek (1984) . 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Can directly call the 'NIH HEAL MME Online Calculator' API or replicate API calculations on the user's local machine from the comfort of 'R'. Creation of the 'NIH HEAL MME Online Calculator' and the MME calculations implemented in this package are described in Adams MCB, Sward KA, Perkins ML, Hurley RW (2025) . Package: r-cran-mmibain Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2067 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bain, r-cran-broom, r-cran-car, r-cran-dt, r-cran-e1071, r-cran-ggplot2, r-cran-igraph, r-cran-lavaan, r-cran-mmcards, r-cran-psych, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mmibain_0.2.0-1.ca2604.1_all.deb Size: 1837838 MD5sum: a50d9fee01dd39327c3b8e200c0d07f5 SHA1: 7bf2bf306b27a934e94b1ba07a4e6ae8b4a0035f SHA256: d91a48e37b58f10f7052f76f5c4d31a188666a7ceebb6959209e1f19a2635242 SHA512: 80111827b42dae355d5235b6c19b1a9752451bfcaf1b1990474d685e957011d03b5f31cc4027cb5ab9519a20d4d0e0348af93695674a08f0ea2d591d0565b096 Homepage: https://cran.r-project.org/package=mmibain Description: CRAN Package 'mmibain' (Bayesian Informative Hypotheses Evaluation Web Applications) Researchers often have expectations about the relations between means of different groups or standardized regression coefficients; using informative hypothesis testing to incorporate these expectations into the analysis through order constraints increases statistical power Vanbrabant and Rosseel (2020) . Another valuable tool, the Bayes factor, can evaluate evidence for multiple hypotheses without concerns about multiple testing, and can be used in Bayesian updating Hoijtink, Mulder, van Lissa & Gu (2019) . The 'bain' R package enables informative hypothesis testing using the Bayes factor. The 'mmibain' package provides 'shiny' web applications based on 'bain'. The RepliCrisis() function launches a 'shiny' card game to simulate the evaluation of replication studies while the mmibain() function launches a 'shiny' application to fit Bayesian informative hypotheses evaluation models from 'bain'. 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The 'mmiCATs' package offers a suite of tools for working with CATs. The mmiCATs() function initiates a 'shiny' web application, facilitating the analysis of data utilizing CATs, as implemented in the cluster.im.glm() function from the 'clusterSEs' package. Additionally, the pwr_func_lmer() function is designed to simplify the process of conducting simulations to compare mixed effects models with CATs models. For educational purposes, the CloseCATs() function launches a 'shiny' application card game, aimed at enhancing users' understanding of the conditions under which CATs should be preferred over random intercept models. Package: r-cran-mminp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3666 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-omicspls Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-prettydoc, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mminp_0.1.0-1.ca2604.1_all.deb Size: 3629270 MD5sum: 89fd4392cff12a63296c52cff2c278d7 SHA1: 59c62b2d04eab748ea35523d6bc13e163f9e06cb SHA256: 6b100f353fa4e63bf3769af1bf2e91b0a03e6eca07c806285e7b9f118fa06639 SHA512: 0cf7748dd6346052ff56dce33eeae8d275b3dfa023f971a582897c5ba19489acdd2051c6817e9576fabf3961600083f67179e9e763dbd8bc94f96c7e0a0369d2 Homepage: https://cran.r-project.org/package=MMINP Description: CRAN Package 'MMINP' (Microbe-Metabolite Interactions-Based Metabolic ProfilesPredictor) Implements a computational framework to predict microbial community-based metabolic profiles with 'O2PLS' model. It provides procedures of model training and prediction. Paired microbiome and metabolome data are needed for modeling, and the trained model can be applied to predict metabolites of analogous environments using new microbial feature abundances. Package: r-cran-mmints Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-pool, r-cran-rpostgres, r-cran-shiny, r-cran-shinyauthr, r-cran-sodium Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mmints_0.2.0-1.ca2604.1_all.deb Size: 54514 MD5sum: 3e2f25932c2325a33e697b61ce30bd14 SHA1: a3c6b7f77de4e3706c041aa220a0be14f8b7ed2f SHA256: 279d7c2155118e86d87c1f6418714333e30f01bc82bea71c975c1776bfa83051 SHA512: 93e84ffc55ffd3b3da652d2d2668426f6b662339e2994184adda900803dfdbbbaa7ee28dfb95cbb7b8b629d350b75d8039e0fda0baf42de43bc0aa3811735e1c Homepage: https://cran.r-project.org/package=mmints Description: CRAN Package 'mmints' (Workflows for Building Web Applications) Sharing statistical methods or simulation frameworks through 'shiny' applications often requires workflows for handling data. To help save and display simulation results, the postgresUI() and postgresServer() functions in 'mmints' help with persistent data storage using a 'PostgreSQL' database. The 'mmints' package also offers data upload functionality through the csvUploadUI() and csvUploadServer() functions which allow users to upload data, view variables and their types, and edit variable types before fitting statistical models within the 'shiny' application. These tools aim to enhance efficiency and user interaction in 'shiny' based statistical and simulation applications. Package: r-cran-mmirestriktor Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2092 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-mass, r-cran-mmcards, r-cran-pool, r-cran-restriktor, r-cran-rpostgres, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mmirestriktor_0.3.1-1.ca2604.1_all.deb Size: 1859154 MD5sum: 6527e79117e07fe86d71c8d3ce871c4c SHA1: 91bde7403fb8fb9fc00b98a90270d535bf04947a SHA256: fe34f981168836b902ef6bab9f5f006368d8ec14800a9877dcd40a9a33f94f46 SHA512: 84465b8a0d3ead591af340628cd5bc9327beec26bca2c98d6074b0c9b30e345d2a1fc72cf419c33db0d8184a9b1abe225b56afac5ab2b5b80c5c02f3b8d5d283 Homepage: https://cran.r-project.org/package=mmirestriktor Description: CRAN Package 'mmirestriktor' (Informative Hypothesis Testing Web Applications) Offering enhanced statistical power compared to traditional hypothesis testing methods, informative hypothesis testing allows researchers to explicitly model their expectations regarding the relationships among parameters. An important software tool for this framework is 'restriktor'. The 'mmirestriktor' package provides 'shiny' web applications to implement some of the basic functionality of 'restriktor'. The mmirestriktor() function launches a 'shiny' application for fitting and analyzing models with constraints. The FbarCards() function launches a card game application which can help build intuition about informative hypothesis testing. The iht_interpreter() helps interpret informative hypothesis testing results based on guidelines in Vanbrabant and Rosseel (2020) . Package: r-cran-mmlr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-mmlr_0.2.0-1.ca2604.1_all.deb Size: 70160 MD5sum: 0e742e1c0280dcd000b7dd72cc2a55ac SHA1: 5debcd30197ab34eae374d3c0c3e819082aa5237 SHA256: 6d0e4bb64be11794f19f3c7f6e8c0a44d6d0faf95329b32bc966c8eb3d18b43d SHA512: 3b7afc74bff7cf73e4246a794e6e21707fdf4ce062b474d2b21092d6a497a38d2b95b2cece06677b6541cff4981dcb1c5b6944c56cc417fc3a18c53be22f0e3b Homepage: https://cran.r-project.org/package=MMLR Description: CRAN Package 'MMLR' (Fitting Markov-Modulated Linear Regression Models) A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space. Model is proposed by Alexander Andronov (2012) and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition. Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix. These models have significant differences in terms of the analytical approach. Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075. 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This software primarily concentrates on phylogenetic regression analysis, enabling implementation of tree transformation averaging and visualization functionality. Functions additionally support information theoretic approaches (Grueber, 2011 ; Garamszegi, 2011 ) such as model averaging and selection of phylogenetic models. Accessory functions are also implemented for coef standardization (Cade 2015), selection uncertainty, and variable importance (Burnham & Anderson 2000). There are other numerous functions for visualizing confounded variables, plotting phylogenetic trees, as well as reporting and exporting modeling results. Lastly, as challenges to ecology are inherently multifarious, and therefore often multi-dataset, this package features several functions to support the identification, interpolation, merging, and updating of missing data and outdated nomenclature. 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Package: r-cran-mmtsne Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mmtsne_0.1.0-1.ca2604.1_all.deb Size: 33772 MD5sum: 04c8cfd9578b1fde02160ec7d4fec225 SHA1: ed1e4676331c30a22cfa830178a94ff6598cfb85 SHA256: 97819d7618ff4b6476d555d859b218b6d83718e8f6a878600ce21ee07188498a SHA512: fe86265f71f9234f8505b753268f7d94712777aff18146befaa3f48994f728b4f854157ef39aa6ecaee9ae5e59c3136075ace0b507441cb886e76528fc7f4cbc Homepage: https://cran.r-project.org/package=mmtsne Description: CRAN Package 'mmtsne' (Multiple Maps t-SNE) An implementation of multiple maps t-distributed stochastic neighbor embedding (t-SNE). Multiple maps t-SNE is a method for projecting high-dimensional data into several low-dimensional maps such that non-metric space properties are better preserved than they would be by a single map. 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Including also, the estimation process by maximum likelihood method, for details see Fabio, L. C; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2023) and Fábio, L. C.; Villegas, C.; Mamun, A. S. M. A. and Carrasco, J. M. F. (2025) . 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In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) . 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It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at . 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The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) and Nordhausen and Oja (2011) . Package: r-cran-mnonr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-semtools Filename: pool/dists/resolute/main/r-cran-mnonr_1.0.1-1.ca2604.1_all.deb Size: 36762 MD5sum: c39b67f1215ebe4eb5c2e9674e10f8ff SHA1: 531197b8ed928f6287f4f6252f2cf6260ef875a8 SHA256: 1bd6627a6227595d0cae9b97a49702f4719132f28968dbfa20add3e48ba517c1 SHA512: bf07711f9cfc9ad4cc0f425ac02ac402c12edb89006a346ca3a6420d72bc53354ffad7f1b9a49d8c05dcf5b9b471212a3546da3864aa692d133fb850bd9d6233 Homepage: https://cran.r-project.org/package=mnonr Description: CRAN Package 'mnonr' (A Generator of Multivariate Non-Normal Random Numbers) A data generator of multivariate non-normal data in R. It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 ), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis. Package: r-cran-mnormtest Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpfr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-yaml, r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-pkgbuild, r-cran-covr, r-cran-usethis, r-cran-dt Filename: pool/dists/resolute/main/r-cran-mnormtest_1.1.1-1.ca2604.1_all.deb Size: 63568 MD5sum: 108e0954d094185ab0434c8f69f1d3ee SHA1: 330758857c57443c7a712bbd713ea39903f01ef9 SHA256: f17dcb3d18c2ee74e9e1c9c08018c19900e3a584a624cab58e3d6480f85cd7ff SHA512: f602f9d10b5939511d4a3beefbc950ff73189a76c7e37a6674e845d416394d15373da3601fdd62ecdf0e4e94dae2d6d6b418daa8f1d0de2f77a4de29c4e89e08 Homepage: https://cran.r-project.org/package=MNormTest Description: CRAN Package 'MNormTest' (Multivariate Normal Hypothesis Testing) Hypothesis testing of the parameters of multivariate normal distributions, including the testing of a single mean vector, two mean vectors, multiple mean vectors, a single covariance matrix, multiple covariance matrices, a mean and a covariance matrix simultaneously, and the testing of independence of multivariate normal random vectors. Huixuan, Gao (2005, ISBN:9787301078587), "Applied Multivariate Statistical Analysis". Package: r-cran-mnpplasmonr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mnpplasmonr_0.1.0-1.ca2604.1_all.deb Size: 14126 MD5sum: 9d13278e2d790562a09a2dc49a100497 SHA1: 163e21a9a0386c3f9f1af5cfc65b222c3fb0b64c SHA256: 2d1567e335eb095bb4653e62e04a4e99865817e21bfa50cfbdd3e14c86e95c41 SHA512: 15295b8f014a5999f20004555146c6f83f926712825d1334f7f9b6a82188d888183f8a6c307597b9dae987b4195dce236741078c65904547e8bcbe1e4288e5e8 Homepage: https://cran.r-project.org/package=mnpPlasmonR Description: CRAN Package 'mnpPlasmonR' (Optical Response of Metallic Nanoparticles (Drude + Rayleigh)) Computes dielectric response and optical cross-sections of metallic nanoparticles using Drude dielectric model and Rayleigh approximation. Package: r-cran-mns Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-mass, r-cran-glmnet, r-cran-mvtnorm, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-mns_1.0-1.ca2604.1_all.deb Size: 372768 MD5sum: 357942ab7fbf4577ffbfa6c3b2da820f SHA1: e3fddeb9df168483ff9267a2703daef9cb58898a SHA256: de2d25d85317032aee252b67372fbdca32e24959103f9303cd17fd1c28eef090 SHA512: f19f7d9f9d51eeca73cca1b44d4eafae7bf670f656acc9fd01a93e0361fdaf1eaf0ea30722e651716c229029d970baa5465aa6360f399336e128657bf397a8fa Homepage: https://cran.r-project.org/package=MNS Description: CRAN Package 'MNS' (Mixed Neighbourhood Selection) An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis. Package: r-cran-mnt Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-mnt_1.3-1.ca2604.1_all.deb Size: 224032 MD5sum: 7f5db69b45515188b269df0192894a25 SHA1: ac43c1d2c4b6930dedc68a0df599cdb624c676e9 SHA256: c0ce31eb8307be6141cf5ac7853f2a904e698d2ddc775d6f8c183cca41972ffa SHA512: fd2639797970d52357617ac550e3e41b324c577b6457613e23298b9c3e5288d92745512f55507f3dc31aedc6aedafaf8f9c13dd58b4de5b67ded7cdfc335f0dd Homepage: https://cran.r-project.org/package=mnt Description: CRAN Package 'mnt' (Affine Invariant Tests of Multivariate Normality) Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the 'energy' test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns). 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Associated publication: Pook et al. (2020) . 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The algorithm performs gene clustering using 'NSGA-II' as the underlying multi-objective evolutionary engine, together with Path-Relinking and Pareto Local Search as intensification and diversification strategies. Two versions of the Xie-Beni validity index are used as objective functions, one per distance matrix, so that prior biological knowledge can be incorporated through the second matrix. 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Package: r-cran-mockthat Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-pkgload, r-cran-curl, r-cran-jsonlite, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mockthat_0.2.8-1.ca2604.1_all.deb Size: 36904 MD5sum: b98108767a1fade73584f49055237739 SHA1: b6ee5e6f32729f2dcb253b29e10a760d7e6934f5 SHA256: 9e40fc005459977503df24838c30761254d2aeccb4457bd42dc52f10568e86ee SHA512: 7ca526e9cf923aba15cff6662faa0d509c9878ae1ddf6ddf46fb0962e67414e8a08aec5193b886d9cb1e49b8469b4853f58a1dfa93323412f2be3531c465c7a4 Homepage: https://cran.r-project.org/package=mockthat Description: CRAN Package 'mockthat' (Function Mocking for Unit Testing) With the deprecation of mocking capabilities shipped with 'testthat' as of 'edition 3' it is left to third-party packages to replace this functionality, which in some test-scenarios is essential in order to run unit tests in limited environments (such as no Internet connection). Mocking in this setting means temporarily substituting a function with a stub that acts in some sense like the original function (for example by serving a HTTP response that has been cached as a file). The only exported function 'with_mock()' is modeled after the eponymous 'testthat' function with the intention of providing a drop-in replacement. Package: r-cran-mod09nrt Architecture: all Version: 0.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mod09nrt_0.14-1.ca2604.1_all.deb Size: 33764 MD5sum: 67fd39010279a2cd1958dd47d6d47062 SHA1: d37096cac7d9227caeebc71c103b5e02a9359352 SHA256: 365d2ac76f655c6a40c774161e49ed0665e3c45258129bb65e5ac9127a3683d1 SHA512: 53a97cf7c2cdcfc6fb43dd7773bb53782b023bc066419f2f1ab64363b2090db2b08639fed7f03f7f83559005965f4d0024611be34fa4e915856df571915e0a28 Homepage: https://cran.r-project.org/package=mod09nrt Description: CRAN Package 'mod09nrt' (Extraction of Bands from MODIS Surface Reflectance Product MOD09NRT) Package for processing downloaded MODIS Surface reflectance Product HDF files. Specifically, MOD09 surface reflectance product files, and the associated MOD03 geolocation files (for MODIS-TERRA). The package will be most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath products; , and adds the directory with the MRTSwath executable to the default R PATH by editing ~/.Rprofile. Package: r-cran-mod2rm Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Filename: pool/dists/resolute/main/r-cran-mod2rm_0.2.1-1.ca2604.1_all.deb Size: 51488 MD5sum: 667f1ab37e880759a19e8353d8d7fd36 SHA1: a3aca9284dc88dd8ada405219f979a1f996b922b SHA256: 984e163a9a7088e8fb79ec0f5745ca922f7d4a4694a55d081aeddd825b17ba48 SHA512: b888a4c1a97e979d35e468c68cc7b350bb4688e8fb738ff57ebf72ee64b5036a3351c2fa1ed3dd88e2b91ba0bc5dcbda6518d86c1e466f86a2064ee88b29889a Homepage: https://cran.r-project.org/package=mod2rm Description: CRAN Package 'mod2rm' (Moderation Analysis for Two-Instance Repeated Measures Designs) Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" . Package: r-cran-mod Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-mod_0.1.3-1.ca2604.1_all.deb Size: 48864 MD5sum: dbc23ba11aff03ceec2760a578b0cc1c SHA1: 8c10c2328686f7a941016dbc11d3d070a2478a7a SHA256: e372e6dfe3a1ed6a4959d5816675199ae1b73dd1988922340573c337fef7467b SHA512: 743cc9d8701504bf4da366d4a0ad5b687b86b2c86f18dd1068e850da87bdbe9a2b79902be3ba202116c3bcf0283c5970b1aa96e986899863ee84cee848ab23de Homepage: https://cran.r-project.org/package=mod Description: CRAN Package 'mod' (Lightweight and Self-Contained Modules for Code Organization) Creates modules inline or from a file. Modules can contain any R object and be nested. Each module have their own scope and package "search path" that does not interfere with one another or the user's working environment. Package: r-cran-modacdc Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ccp, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-genieclust, r-cran-genio, r-cran-ggplot2, r-cran-partition, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cca Filename: pool/dists/resolute/main/r-cran-modacdc_2.0.1-1.ca2604.1_all.deb Size: 105982 MD5sum: f1877edd2faf5f4b66de7fcb40cca150 SHA1: 5f3463aea7685a630a6ed5429c6bcae899259642 SHA256: 4ac55c6e226800c070c58e598e8a74b42c1078d61a3f565eeec8bf1ee6b52603 SHA512: c7a5af6c3099136ae48b694bc3b158b53b7cf4232bdbf1b90e352a10030c41f528e81fac9e4e17f16961687861381bf05af9c8b2c0e43883046ba084f8a8a56f Homepage: https://cran.r-project.org/package=modACDC Description: CRAN Package 'modACDC' (Association of Covariance for Detecting DifferentialCo-Expression) A series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) . Package: r-cran-modalcens Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-modalcens_0.1.0-1.ca2604.1_all.deb Size: 36550 MD5sum: 94a619d60fa056758c0eb3d51a59f052 SHA1: 569b97d84dd4d71751a03a95ec43019c3c03d3d9 SHA256: 4cb89c897504000fbdd22c522ae71e47937f5a42529c9acd2ad3eee6e3288f7e SHA512: 7e8ad8c674fac6dd2c36b6f2eb93307265f9459e33d8f53b9cb18392747e698fddafc8f5be243b1bf3bda54232719020cc498f36d3db4d303d2e431c9c5baa42 Homepage: https://cran.r-project.org/package=ModalCens Description: CRAN Package 'ModalCens' (Parametric Modal Regression with Right Censoring) Implements parametric modal regression for continuous positive distributions of the exponential family under right censoring. Provides functions to link the conditional mode to a linear predictor using reparameterizations for Gamma, Beta, Weibull, and Inverse Gaussian families. Includes maximum likelihood estimation via numerical optimization, asymptotic inference based on the observed Fisher information matrix, and model diagnostics using randomized quantile residuals. Package: r-cran-modalclust Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-zoo, r-cran-class Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-modalclust_0.7-1.ca2604.1_all.deb Size: 411932 MD5sum: 53cc16ce9b6f257d15af8596cd5eca85 SHA1: 1fccfe8084a6d2b3d6f9e5ae5cac01ac0fe61fb4 SHA256: 847fbca9bebcf6b42e663f6a56e5bb62f1e6aad010ca6e69a1c1aaf1ca7c3c14 SHA512: 16dd6bfb43dfe825b6d090b625ad30e0152c95ac0ff555bd7f95617c53d76dcdc7fd0a768f9d4d42ed72b62fb5f54c3eca56e70af0cc0d1d10d9e390773a294e Homepage: https://cran.r-project.org/package=Modalclust Description: CRAN Package 'Modalclust' (Hierarchical Modal Clustering) Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors. These model-based non-parametric clustering techniques can extract clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default. Package: r-cran-modalforecast Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1177 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forecast, r-cran-ggplot2, r-cran-gridextra, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-modalforecast_0.1.0-1.ca2604.1_all.deb Size: 895210 MD5sum: 721a600023f7f46fc84f1651d9330f8f SHA1: 1477c41294dca6c8bda44ee143705c9f41617375 SHA256: 57afcad249a39176bd14200598eaca9d6a7406361dde88d2589553969b4ee54a SHA512: 2e0be9fcf36c07817cf6161f74da13a4b12a84f1f3638fff633aa93d92ad6c79f2705355db4aefe45958ca4a76ec60012da78cf6ea8fc6e007febc94f7d4ed76 Homepage: https://cran.r-project.org/package=ModalForecast Description: CRAN Package 'ModalForecast' (Parametric Modal ARIMA Models using the SKD Family) Implements parametric modal Autoregressive Integrated Moving Average (ARIMA) models utilizing the Skewed Distribution (SKD) family. Current distributions supported are the Skew-Normal, Skewed Student-t, and Skewed Laplace. The conditional mode is parameterized and optimized via maximum likelihood using analytical gradients. Includes comprehensive residual diagnostics, robustness options (heavy tails, asymmetry), robust parametric bootstrap prediction intervals, and classical asymptotic inference via the Fisher Information matrix. Methods are described in Galarza, C.E., Lachos, V.H., Cabral, C.R.B., & Castro, L.M. (2017) . Package: r-cran-modeest Architecture: all Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fbasics, r-cran-stable, r-cran-stabledist, r-cran-statip Suggests: r-cran-evd, r-cran-knitr, r-cran-mvtnorm, r-cran-testthat, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-modeest_2.4.0-1.ca2604.1_all.deb Size: 146480 MD5sum: c002a7b7eb701a9cb5c07dccc8ee30ff SHA1: 45d24e32e4f540d78369177f371872e429b684fb SHA256: adb7f7242b87db923e53587fb6254f6dd8d431fb857301431dfef23f142e9062 SHA512: dce21889581caa2435d97bd501f9d8ad40cb159ed467dc90f41e3d3de71ac8fd7eba1f2c72d40c4fb2a5cc527f819d0f5db2ca28facb30f9bd4daa54390ebd97 Homepage: https://cran.r-project.org/package=modeest Description: CRAN Package 'modeest' (Mode Estimation) Provides estimators of the mode of univariate data or univariate distributions. Package: r-cran-modehunt Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-modehunt_1.0.8-1.ca2604.1_all.deb Size: 106664 MD5sum: ac1dada4d1000f4f7f46c6136f8d5f32 SHA1: 9964da098f4efbd4cf4fcd60c5a8c3c92e531f2f SHA256: 419cc933de7242a7878328aa31c0a4f76963538af13c9d7d2787ca5c30d8777f SHA512: 9bd2268303fdcfebb1306837999e35342988d3d5907cff95487c6781b4f6ef69fc5d6206c5d20b32ddd41cc5e57bf367652a80a861d5e2d1723aff4ab30e5c0f Homepage: https://cran.r-project.org/package=modehunt Description: CRAN Package 'modehunt' (Multiscale Analysis for Density Functions) Given independent and identically distributed observations X(1), ..., X(n) from a density f, provides five methods to perform a multiscale analysis about f as well as the necessary critical values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive correction term Gamma in either method offers another two approaches which are more powerful on small scales and less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically equivalent to the first and was introduced in Rufibach and Walther (2010). Package: r-cran-model4you Architecture: all Version: 0.9-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-sandwich, r-cran-ggplot2, r-cran-formula, r-cran-gridextra, r-cran-survival, r-cran-rlang Suggests: r-cran-mvtnorm, r-cran-th.data, r-cran-psychotools, r-cran-strucchange, r-cran-plyr, r-cran-knitr, r-cran-ggbeeswarm, r-cran-mass Filename: pool/dists/resolute/main/r-cran-model4you_0.9-9-1.ca2604.1_all.deb Size: 152430 MD5sum: 888db74ceed34f60e8139f7618061467 SHA1: 6455368c4610c885b35a7107442960dc7f6434fa SHA256: bcef0d34e69ab985983356f03d2befa96601a78c3aa52877ff26a1b4639fbf3b SHA512: 68f06216728d89228e31da302c5d74f382ad6e50d66915432a0ed326d28a73ec1dea60e1fa93a5d271914866f661018aa92ec1ae0161fff9b33db41d82599238 Homepage: https://cran.r-project.org/package=model4you Description: CRAN Package 'model4you' (Stratified and Personalised Models Based on Model-Based Treesand Forests) Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) ; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) . Package: r-cran-modelbased Architecture: all Version: 0.15.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestestr, r-cran-datawizard, r-cran-insight, r-cran-parameters Suggests: r-cran-afex, r-cran-bh, r-cran-betareg, r-cran-boot, r-cran-bootes, r-cran-brglm2, r-cran-brms, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-coda, r-cran-collapse, r-cran-correlation, r-cran-coxme, r-cran-curl, r-cran-discovr, r-cran-easystats, r-cran-effectsize, r-cran-emmeans, r-cran-formula, r-cran-gamm4, r-cran-gganimate, r-cran-ggplot2, r-cran-glmmtmb, r-cran-httr2, r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-logspline, r-cran-mass, r-cran-matchit, r-cran-matrix, r-cran-marginaleffects, r-cran-mice, r-cran-mgcv, r-cran-mvtnorm, r-cran-nanoparquet, r-cran-nestedlogit, r-cran-nnet, r-cran-ordinal, r-cran-palmerpenguins, r-cran-performance, r-cran-patchwork, r-cran-pbkrtest, r-cran-poorman, r-cran-pscl, r-cran-rcppeigen, r-cran-rdatasets, r-cran-report, r-cran-rmarkdown, r-cran-rstanarm, r-cran-rtdists, r-cran-rwiener, r-cran-sandwich, r-cran-scales, r-cran-see, r-cran-survival, r-cran-testthat, r-cran-tinyplot, r-cran-tinytable, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-modelbased_0.15.0-1.ca2604.1_all.deb Size: 867774 MD5sum: 140b9c15a3d8f9069e7be297a788e3ad SHA1: 33a120e6b619a36cf8de42a1eb5dc826bdda1035 SHA256: 25d074a1af9e122a06979ea12aa7e82066debbb12ba30fa7279342b94f437cd2 SHA512: 733fd5a8f61284ea1bf76067b73d54abfbed2c61aae4dc0a3b614af17c356006156e07b3832836a2a904d17b05cef01ac22385fc56f98ce07eee41e1d7bf61a1 Homepage: https://cran.r-project.org/package=modelbased Description: CRAN Package 'modelbased' (Estimation of Model-Based Predictions, Contrasts and Means) Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, see 'insight::supported_models()'. 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See Pesigan, Cheung, Wu, Chang, and Leung (2026) for an introduction to the package. Package: r-cran-modelc Architecture: all Version: 1.0.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-modelc_1.0.0.0-1.ca2604.1_all.deb Size: 39192 MD5sum: c06424fcbefb54d561d0036111e0b32d SHA1: eb3aeb3e193272cfa8e52a4e60e350267e9c5250 SHA256: b29e8e804cd474841c7832cbcf63f14273090c72dadd9af80d32f6687e3432c3 SHA512: 520a01b50ee19fe64653c11f4d53d9abb3e95fc363d2781b619453056807a3c34b814eebdfbfdbfe289c72ed8c08d2d0d942dcb22c9ee3ef32aaab800b53ef1b Homepage: https://cran.r-project.org/package=modelc Description: CRAN Package 'modelc' (A Linear Model to 'SQL' Compiler) This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) and originating in Nelder & Wedderburn (1972) . The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported. 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Package: r-cran-modeldb Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidypredict Suggests: r-cran-covr, r-cran-dbi, r-cran-dbplyr, r-cran-knitr, r-cran-nycflights13, r-cran-rmarkdown, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-modeldb_0.3.1-1.ca2604.1_all.deb Size: 355798 MD5sum: 179946fbce6e003910bb59e89900ccd0 SHA1: 4627a3a94accf9022d3da135c923199c841e281d SHA256: 73fa4a9f23f2c8f6d2cd0a0ec30148b1b5ec810e16e8a73e59e4c71009e47031 SHA512: 5f04d8165d97aca5772e6d11055efef300bc0658a80780e852b3a57889020892d650f892a578a123825f6113e77b4a50141f12bcfebb583a0b974e3ac05ef1e0 Homepage: https://cran.r-project.org/package=modeldb Description: CRAN Package 'modeldb' (Fits Models Inside the Database) Uses 'dplyr' and 'tidyeval' to fit statistical models inside the database. It currently supports KMeans and linear regression models. Package: r-cran-modeldiagramr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-magrittr, r-cran-tibble, r-cran-gtools, r-cran-forcats, r-cran-nlme, r-cran-diagrammer Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-ggthemes, r-cran-lme4, r-cran-lmertest, r-cran-knitr, r-cran-viridis, r-cran-diagrammersvg, r-cran-webshot, r-cran-fieldhub, r-cran-rsvg Filename: pool/dists/resolute/main/r-cran-modeldiagramr_0.2.1-1.ca2604.1_all.deb Size: 98752 MD5sum: 0b8b8e00a85ce5266bf3400231c17fa0 SHA1: 15d2794fe70fa8f8501ec8a395268f6a30ae5e85 SHA256: 0c0f7cf6c4fecbdfaa9acee66f85462fd828d9672a3ebb744d5d5301d2e097f0 SHA512: a45ee5ef03362830864efaccbfb86be3a3484c565f09f45c769b96e0e650ff031c22f397f3b25b5dbc2252f1d13bcc08bd9b0df4f24a44d7f6a6e51995c2962b Homepage: https://cran.r-project.org/package=modeldiagramR Description: CRAN Package 'modeldiagramR' (Generate Model Diagrams for Linear Mixed Effect Models) Generates 'DiagrammeR' model diagrams for hierarchical linear mixed effects models. Details can be found in Linse (2026) . Package: r-cran-modeldown Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-auditor, r-cran-ggplot2, r-cran-whisker, r-cran-dt, r-cran-kableextra, r-cran-psych, r-cran-archivist, r-cran-svglite, r-cran-devtools, r-cran-breakdown, r-cran-drifter Suggests: r-cran-ranger, r-cran-testthat, r-cran-useful Filename: pool/dists/resolute/main/r-cran-modeldown_1.1-1.ca2604.1_all.deb Size: 48754 MD5sum: 676f6b4e5de121badf1a32fd27030168 SHA1: 6821fc70af8c26bec2e76576e90bc500d70b1429 SHA256: 2a559f935de161cb793147f8332b42f2de5e4d901a9715cea294918bd9b6dde6 SHA512: 93ae34980dcd9b70bdc1c2031b5f959bb7de8674e815e2acbec358d8fd18d441b320bd43f99dfac217f513b7a8dcfb8d1342ba3f5ce406257ac2d337ff93b79d Homepage: https://cran.r-project.org/package=modelDown Description: CRAN Package 'modelDown' (Make Static HTML Website for Predictive Models) Website generator with HTML summaries for predictive models. This package uses 'DALEX' explainers to describe global model behavior. We can see how well models behave (tabs: Model Performance, Auditor), how much each variable contributes to predictions (tabs: Variable Response) and which variables are the most important for a given model (tabs: Variable Importance). We can also compare Concept Drift for pairs of models (tabs: Drifter). Additionally, data available on the website can be easily recreated in current R session. Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 at Warsaw University of Technology, Faculty of Mathematics and Information Science. Package: r-cran-modelenv Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-modelenv_0.2.0-1.ca2604.1_all.deb Size: 100992 MD5sum: ab5b9292a75cf8f6b1cc3768dd8c89e6 SHA1: 9520a008940573542e98f9e307fca7d01a3d97df SHA256: 5cd8661c2fe87b4f871cfe14b78203cf454b5a589047d820382ff4919364a95f SHA512: 44dd59134a71e2ff20a5bbeb3c17f7071e1a977cd95127fdf5f6ddbb4449aaccbabb008e8a5aecd6f3be89e32fc0fa5028cafce27bb220bc2e488baf755ed0c8 Homepage: https://cran.r-project.org/package=modelenv Description: CRAN Package 'modelenv' (Provide Tools to Register Models for Use in 'tidymodels') An developer focused, low dependency package in 'tidymodels' that provides functions to register how models are to be used. 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These are generic tools, but we also include specific examples for many common classifiers. Package: r-cran-modelfree Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparsem, r-cran-polynomf Filename: pool/dists/resolute/main/r-cran-modelfree_1.2.1-1.ca2604.1_all.deb Size: 219924 MD5sum: e0f37db2b15bd287c1603922dfc418cf SHA1: 77f4509e7755bfd7bca650e832ce5e2d04664d63 SHA256: 51a4c3c2b10f31b6cb0f386e2d6559d0e96aa40c22c3b6825ee5d42b7b868200 SHA512: 1a92ece148727fe10e34add87b7c138626014c031cee8a142aa9b2678c8e598de7ce3c9bc936fe084e060e4fba6bf07c1c8f34e0115abd8fa3c14b29da7fd31d Homepage: https://cran.r-project.org/package=modelfree Description: CRAN Package 'modelfree' (Model-Free Estimation of a Psychometric Function) Local linear estimation of psychometric functions. 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Package: r-cran-modelmap Architecture: all Version: 3.4.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2650 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-raster, r-cran-mgcv, r-cran-corrplot, r-cran-fields, r-cran-handtill2001, r-cran-presenceabsence Suggests: r-cran-party, r-cran-quantregforest, r-cran-sf Filename: pool/dists/resolute/main/r-cran-modelmap_3.4.0.8-1.ca2604.1_all.deb Size: 1597886 MD5sum: f9e730b273fb7b7da9c56ce50813c9e8 SHA1: d52aeec95c444c41bd9c0a344b1677be94883697 SHA256: 3d97c9f823f8f5f3537956ab7fda8eff9c10de28ec8770a1d2af422cd826b0b2 SHA512: caf92416b0fc5d40fc963d1d1bd413e9d2553e977498f35b6b826eedc9320b6a9f14450465e9683c2eb423d9764b1df9d905ab54b25fc27a40d60309de34dc5c Homepage: https://cran.r-project.org/package=ModelMap Description: CRAN Package 'ModelMap' (Modeling and Map Production using Random Forest and RelatedStochastic Models) Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data. Package: r-cran-modelmatrixmodel Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-modelmatrixmodel_0.1.0-1.ca2604.1_all.deb Size: 31830 MD5sum: c6822808c5bb1d8e076d3aaa66d63dd9 SHA1: 1d8fe710265cff64e9586cd946a5d6489bab0312 SHA256: dbae5a6e87348fde99b4c23d48edd420d9966745fb63b16c237858dbbaddf98d SHA512: 31b26bfd4ec08c9097a6bbfb489f265959fd7dc14009c3ef82282fec4ea0f95d47ee5561e99bfe33eb9a63eb598fdc5342ed46b90ec6cc3677146dd6a630d70d Homepage: https://cran.r-project.org/package=ModelMatrixModel Description: CRAN Package 'ModelMatrixModel' (Create Model Matrix and Save the Transforming Parameters) The model.matrix() function in R is convenient for transforming training dataset for modeling. But it does not save any parameter used in transformation, so it is hard to apply the same transformation to test dataset or new dataset. This package is created to solve the problem. Package: r-cran-modelobj Architecture: all Version: 4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-modelobj_4.3-1.ca2604.1_all.deb Size: 521816 MD5sum: f73d29d01ddeac1d2f2058674369d411 SHA1: 1a3b7e80f67b9f8204934df1d157648390099aa0 SHA256: 8ad3783408aef233e13c792745952775d389535f0d3b9666335d935e8ce520c7 SHA512: e6b23dfcee5b2a57d07e827e6e3d8efad3f6aa27aff11ace6bfcbdb549e52b6e73fdcbac0d2260810fe9e27364f8ecd1949d0a1c08caccfc3cedcd38b7cb1758 Homepage: https://cran.r-project.org/package=modelObj Description: CRAN Package 'modelObj' (A Model Object Framework for Regression Analysis) A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions. In this way, a user can easily go from linear to non-linear models within the same package. 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Package: r-cran-modelscompete4 Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-boot, r-cran-ggplot2, r-cran-nonnest2, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-modelscompete4_0.2.6-1.ca2604.1_all.deb Size: 91514 MD5sum: 79344852d7349891bfebf8bb98f15020 SHA1: c0777299df0e0b80a7ca15d3f109a52815ba764f SHA256: 71c3ba118b44693d3de8a9372c895a4f070f4bda586ad91be8398f269e0a4bb4 SHA512: 576af5a9db8411078f29f5740abf921fa041fe50abb9d5d7c87ba0e4a79b6c31a8abe4b2534d2773ff7b379cc1c5f4ffa9cc2995ef59c6c1d8040197b4fee1b4 Homepage: https://cran.r-project.org/package=modelscompete4 Description: CRAN Package 'modelscompete4' (Compare Nested and Non-Nested Structural Equation Models) A comprehensive package for comparing multiple Structural Equation Models (SEM). Supports both nested and non-nested model comparisons, chi-square difference tests, and extraction of multiple fit indices including AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), CFI (Comparative Fit Index), TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation), and SRMR (Standardized Root Mean Square Residual). Built on top of the 'lavaan' package for seamless SEM model comparison workflows. The Vuong test (Vuong, 1989) for non-nested models is used as the statistical test. Package: r-cran-modelsse Architecture: all Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-delaporte Filename: pool/dists/resolute/main/r-cran-modelsse_0.1-3-1.ca2604.1_all.deb Size: 132942 MD5sum: 0a9756c0bf5194bcc75bbfda1c12a003 SHA1: 831252361e70d0d7538f453521ec54df4902053c SHA256: 41098f7cdf429f4159d1a9666b085f3788c840f550aa88593766c4bd730e4b95 SHA512: d70ff5940cab050839e7ca5788bb96bc4f623df80042fb635ff4e0f6347f7d67b5ee452e1912dac70f0d4b27374e08b4ac83736fa33353fcc67c5cf403057a3b Homepage: https://cran.r-project.org/package=modelSSE Description: CRAN Package 'modelSSE' (Modelling Infectious Disease Superspreading from Contact TracingData) Comprehensive analytical tools are provided to characterize infectious disease superspreading from contact tracing surveillance data. The underlying theoretical frameworks of this toolkit include branching process with transmission heterogeneity (Lloyd-Smith et al. (2005) ), case cluster size distribution (Nishiura et al. (2012) , Blumberg et al. (2014) , and Kucharski and Althaus (2015) ), and decomposition of reproduction number (Zhao et al. (2022) ). 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Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks. The main function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. It is possible to easily save the dashboard and share it with others. modelStudio facilitates the process of Interactive Explanatory Model Analysis introduced in Baniecki et al. (2023) . 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Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr' dynamic documents. Details can be found in Arel-Bundock (2022) . Package: r-cran-modeltests Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-purrr, r-cran-testthat, r-cran-tibble Suggests: r-cran-covr Filename: pool/dists/resolute/main/r-cran-modeltests_0.1.8-1.ca2604.1_all.deb Size: 72696 MD5sum: 4404eabcc2b2e364cfb44b0d85a84bf8 SHA1: 3e4fc74d214137e65a927cd316cbf486165478c0 SHA256: 9454137e4c8177dc242d23b5e495cc4f48394b33b16971e497941552b9ef1c48 SHA512: 5044229a731503821ac01371a93bf28a2b2ce4fc23fde531f215b979556d2ba57f0a15e22cc8606a9d2214ff6cb7cae128b4b32db9369fddf701fbe11d7ffec0 Homepage: https://cran.r-project.org/package=modeltests Description: CRAN Package 'modeltests' (Testing Infrastructure for Broom Model Generics) Provides a number of testthat tests that can be used to verify that tidy(), glance() and augment() methods meet consistent specifications. This allows methods for the same generic to be spread across multiple packages, since all of those packages can make the same guarantees to users about returned objects. Package: r-cran-modeltime.ensemble Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltime, r-cran-modeltime.resample, r-cran-tune, r-cran-rsample, r-cran-yardstick, r-cran-workflows, r-cran-recipes, r-cran-timetk, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-rlang, r-cran-cli, r-cran-generics, r-cran-magrittr, r-cran-tictoc, r-cran-doparallel, r-cran-foreach, r-cran-glmnet Suggests: r-cran-gt, r-cran-dials, r-cran-earth, r-cran-testthat, r-cran-tidymodels, r-cran-xgboost, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-modeltime.ensemble_1.1.0-1.ca2604.1_all.deb Size: 1457328 MD5sum: 141ec2de514b7c504413a4002d7069e0 SHA1: cd34c5edfff568b4771b47fc03725f12c20b19d9 SHA256: 5ed620ea0d7546a9cd4446ec56b0fb369b6c1da7b12f12a18e046cb51c5c8390 SHA512: 86ac2d1df40c2d4cb73c785b3b147671e04a7faedab50cc407fec7f820a9aa031079d9c87276d7f7132170d39c8696bcf6defdaaa5f37c945bd74016249497f0 Homepage: https://cran.r-project.org/package=modeltime.ensemble Description: CRAN Package 'modeltime.ensemble' (Ensemble Algorithms for Time Series Forecasting with Modeltime) A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability. Package: r-cran-modeltime.resample Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltime, r-cran-tune, r-cran-rsample, r-cran-workflows, r-cran-recipes, r-cran-yardstick, r-cran-timetk, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-ggplot2, r-cran-plotly, r-cran-cli, r-cran-magrittr, r-cran-rlang, r-cran-progressr, r-cran-tictoc, r-cran-hardhat, r-cran-withr Suggests: r-cran-testthat, r-cran-tidymodels, r-cran-tidyquant, r-cran-glmnet, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-modeltime.resample_0.3.0-1.ca2604.1_all.deb Size: 1799568 MD5sum: b3bd3a3a6a0b7cbfffaac33916991e9e SHA1: a313c1ec551a0f93cee8d67906ee24c74b0c7f2a SHA256: cb2f87252a23821105b5954e507a3f74acc475430c259e32e33a74435cdc0614 SHA512: ea9cc72503eab372c6a8fa3c47e30b45fdc7745acb46265b4d25a59e9f6db7fdfe7e263cd845e25f1b3b75bf882e5badc1ea609ba47663dc6c3c498cf023ba5c Homepage: https://cran.r-project.org/package=modeltime.resample Description: CRAN Package 'modeltime.resample' (Resampling Tools for Time Series Forecasting) A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis. Package: r-cran-modeltime Architecture: all Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3743 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stanheaders, r-cran-timetk, r-cran-parsnip, r-cran-dials, r-cran-yardstick, r-cran-workflows, r-cran-hardhat, r-cran-rlang, r-cran-glue, r-cran-plotly, r-cran-reactable, r-cran-gt, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-forcats, r-cran-scales, r-cran-janitor, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-magrittr, r-cran-forecast, r-cran-xgboost, r-cran-prophet, r-cran-cli, r-cran-tidymodels Suggests: r-cran-rstan, r-cran-slider, r-cran-sparklyr, r-cran-dorng, r-cran-workflowsets, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-lubridate, r-cran-testthat, r-cran-kernlab, r-cran-glmnet, r-cran-thief, r-cran-smooth, r-cran-greybox, r-cran-earth, r-cran-randomforest, r-cran-trelliscopejs, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-qpdf, r-cran-tsrepr, r-cran-future, r-cran-dofuture Filename: pool/dists/resolute/main/r-cran-modeltime_1.3.5-1.ca2604.1_all.deb Size: 3031848 MD5sum: 78582e21aa061750a0d0e4d034f40af3 SHA1: 3f62589478e5aa145cbdfa8aff09a39bfc61e6fa SHA256: 5a53cf53b0d4ff099294e5411cd9970cee3056ac01f6658c8543fd09b1f4b1bb SHA512: 14bb79002202777dfb2c15661b3c996ce85cca589841bf4f9f39d5e7a8a097baa3892786c51dcc6b1f84d869fb515db9b94834b868fa15e3974331d1c3bef4c4 Homepage: https://cran.r-project.org/package=modeltime Description: CRAN Package 'modeltime' (The Tidymodels Extension for Time Series Modeling) The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (). Refer to "Prophet: forecasting at scale" (.). Package: r-cran-modeltools Architecture: all Version: 0.2-24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-modeltools_0.2-24-1.ca2604.1_all.deb Size: 222516 MD5sum: 2cfc0a278e7375115b018e7e6acc81ac SHA1: d4211870560ada4e29b337948c2ca7207c6917e9 SHA256: e1be8781f0790d1df4d246e2bde046e6f8fbc68b83c5be200b312be7bc5bba73 SHA512: ac58d57535a1159500092c2d7a25e3a654bd3dbb6dd6d9e399bdf36ea37b9bfa0b305f1c0ab917e0ec3c2e34a4f160dc872749929b5e3a29d3aca971f03470a7 Homepage: https://cran.r-project.org/package=modeltools Description: CRAN Package 'modeltools' (Tools and Classes for Statistical Models) A collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future. The documentation is rather terse, but packages `coin' and `party' have some working examples. However, if you find the implemented ideas interesting we would be very interested in a discussion of this proposal. Contributions are more than welcome! Package: r-cran-modeltuning Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1789 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-progressr, r-cran-r6, r-cran-rlang Suggests: r-cran-matrix, r-cran-e1071, r-cran-future, r-cran-parallelly, r-cran-paws, r-cran-rsample, r-cran-rpart, r-cran-yardstick, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-modeltuning_0.1.3-1.ca2604.1_all.deb Size: 1630020 MD5sum: b2da8b870cde6f078cd2ec8762c15221 SHA1: 7019c43ce40dcc6abdca6da9b685249c8fe4b393 SHA256: 808c25175ab8f1d6185e18c5c15bd2b8dba59b7787f2fa7cbb8b09e19eece520 SHA512: 39690f6363a371ee385ef7ba66238dc5ca296c75726b45e59eecb7fd432f203f3df1a87f59c0ef7512bbea93863cac03b6786c5a750a0e8a0abdfac712a6d111 Homepage: https://cran.r-project.org/package=modeltuning Description: CRAN Package 'modeltuning' (Model Selection and Tuning Utilities) Provides a lightweight framework for model selection and hyperparameter tuning in R. 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Package: r-cran-modelwordcloud Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-modelwordcloud_0.1-1.ca2604.1_all.deb Size: 19372 MD5sum: cbdd5295d8cecccbc556b2124a88b8a1 SHA1: 2bb542f1e94ec6207d6b43a633db73701d9866c5 SHA256: 0334c9425a212798fc2ddfbedf9a1f5f989515bc1a377f3dd6813f23165194ec SHA512: b5b4655a490015729099ad765c70c744c753b262dc81cf9050b200e4c9d7860e317881e45a4a8867b33fdf50ccf208d8c181d1254c083bc64912bfb1c806b043 Homepage: https://cran.r-project.org/package=modelwordcloud Description: CRAN Package 'modelwordcloud' (Model Word Clouds) Makes a word cloud of text, sized by the frequency of the word, and colored either by user-specified colors or colored by the strength of the coefficient of that text derived from a regression model. Package: r-cran-moder Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-moder_0.2.1-1.ca2604.1_all.deb Size: 76984 MD5sum: efd0cd3867086f2ab90c737f8642b34e SHA1: 62113e37f6138bc87b8453eb89377c86665bb5cc SHA256: ef7773381bf5a10203c4fb760ad0ca57509b69d598b761263a46ec893df31c6a SHA512: 3bc57848326b3c7248924a0a4188c31c9bf4702ff9714d5afea2cf30ce87f47199fc539fda8ea8941fe1919af1bbe2d301894b7797fd54c40cdb2c4cd72e4ebd Homepage: https://cran.r-project.org/package=moder Description: CRAN Package 'moder' (Mode Estimation) Determines single or multiple modes (most frequent values). Checks if missing values make this impossible, and returns 'NA' in this case. Dependency-free source code. See Franzese and Iuliano (2019) . Package: r-cran-moderate.mediation Architecture: all Version: 0.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-cowplot, r-cran-distr, r-cran-ggplot2, r-cran-reshape2, r-cran-dosnow, r-cran-scales, r-cran-foreach, r-cran-earth Filename: pool/dists/resolute/main/r-cran-moderate.mediation_0.0.12-1.ca2604.1_all.deb Size: 246180 MD5sum: e24b926c894d9a0e93ae7eced21e5866 SHA1: c708c21e44ebea37209bb62691bb06edb2c84376 SHA256: fe401df5bb9b2f3ee5528a1735305cc2e6e85a55ebe0eb765ed600b897b9bca8 SHA512: 0532fe5c7a0457c1bf8fcc8e0a33956fa9c2a8c64e53cfd5073391683b2ca09dd5961337a1ce2e7652a89345b45b4523988d631ed621dcfa3838d8296babd2f8 Homepage: https://cran.r-project.org/package=moderate.mediation Description: CRAN Package 'moderate.mediation' (Causal Moderated Mediation Analysis) Causal moderated mediation analysis using the methods proposed by Qin and Wang (2023) . 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Package: r-cran-modernboot Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-future, r-cran-future.apply Suggests: r-cran-testthat, r-cran-covr, r-cran-pkgdown, r-cran-rhub Filename: pool/dists/resolute/main/r-cran-modernboot_0.1.1-1.ca2604.1_all.deb Size: 56018 MD5sum: b25aadb1f20277825381a80233661023 SHA1: 09841e6e09a6837e38d968c7ab2b3b7d93731317 SHA256: be055f27de4c1e829b07ae673dae2868a043a7f9cb6657fb633dbb3b79473549 SHA512: 3d6bea03b8d25723e595652840000b318ea5adb76a1faca96c43bb1c682fa844f20087e651673923a42d2d86652cde01d727744e1934f56760452e199f97370a Homepage: https://cran.r-project.org/package=modernBoot Description: CRAN Package 'modernBoot' (Modern Resampling Methods: Bootstraps, Wild, Block, Permutation,and Selection Guidance) Implements modern resampling and permutation methods for robust statistical inference without restrictive parametric assumptions. 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Package: r-cran-modest Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-rhandsontable, r-cran-shiny, r-cran-shinybs Filename: pool/dists/resolute/main/r-cran-modest_0.3-1-1.ca2604.1_all.deb Size: 92336 MD5sum: df1f8d7da118e3bba0e9d645d4251502 SHA1: 4f52518cf7a7ad8ae99a7cd399b28bbda2066f21 SHA256: 8cb9282d2f18ca854cbd0de95f18b0d9e07fdb788249be908785bf184d713b6f SHA512: dea8f95f56ad44e99ee2dc2e891eb00266637f439b7cd201dcb39509f8fb04d2532fa39797f6c49ffe2e55a8096d32a8565b17e50bce3901035baaad049b7b2c Homepage: https://cran.r-project.org/package=modest Description: CRAN Package 'modest' (Model-Based Dose-Escalation Trials) User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression. 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In the location case, one can thus obtain halfspace depth contours in two to six dimensions. Hallin, M., Paindaveine, D. and Šiman, M. (2010) Multivariate quantiles and multiple-output regression quantiles: from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669 For more references about the method, see Help pages. 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The focus is on the emergence of argument-marking systems (Dowty (1991) , Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) , Lestrade (2010) ), person indexing (Ariel 1999, Dahl (2000) , Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) , Jackendoff 2002, Arbib (2015) ) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) , Givon (1995) , Croft (2000), Saffran (2001) , Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) , Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). 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The concepts are modelled directly after the Monad typeclass in Haskell, but adapted for idiomatic use in R. Package: r-cran-monan Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-monan_1.1.0-1.ca2604.1_all.deb Size: 2398098 MD5sum: 0ec859d83881c82b48182530a3373155 SHA1: 4b5b9a18994a3952a083e356221db5e5df08a59c SHA256: bcb6c4a00f389e5fbb4c6d57bce0b9eac6210dc11c21f53618b28c4fe9c13010 SHA512: ff62361c5088198e27d17acffae170b206f596dc9fe8a2192b815731b222ab60d45fd85f2eedddf46be6a0f54b497a9ca40d59fdbf952c64b04d42c948faccf4 Homepage: https://cran.r-project.org/package=MoNAn Description: CRAN Package 'MoNAn' (Mobility Network Analysis) Implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. 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As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts). We named the package after the famous painter as the graphical output recalls Mondrian’s paintings. 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Package: r-cran-monitor Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3720 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tuner Suggests: r-cran-fftw, r-cran-rodbc, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-monitor_1.2-1.ca2604.1_all.deb Size: 3242908 MD5sum: 06e4fccd109d5bba3bff8535f1701773 SHA1: c8b0453ec32ef078fe82ffa5dfd1622953941f7e SHA256: d007d8505c14715357fa378ef540d3863a14ea93817a64145836fb6842a5e45c SHA512: 0333dc79e4afed0b21a132c2a48b6b0cfff50c5841d63e63dc5c0757932e7ad6089daf1e4c187b6638c00e2a2b98f0af7e43ea48be64dc189fb92cc210833743 Homepage: https://cran.r-project.org/package=monitoR Description: CRAN Package 'monitoR' (Acoustic Template Detection in R) Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database. 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The proposed guidelines facilitate transparent discussions between stakeholders focusing on the risks of erroneous decisions and what might be an acceptable trade-off between power and the false positive error rate. 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Functions that use isotonic regression in the first stage of binning process have an additional feature for correction of minimum percentage of observations and minimum target rate per bin. Additionally, monotonic trend can be identified based on raw data or, if known in advance, forced by functions' argument. Missing values and other possible special values are treated separately from so-called complete cases. 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It provides shiny-based user interface (UI) that is especially handy for less experienced 'R' users as well as for those who intend to perform quick scanning of numeric risk factors when building credit rating models. The additional functions implemented in 'monobinShiny' that do no exist in 'monobin' package are: descriptive statistics, special case and outliers imputation. The function descriptive statistics is exported and can be used in 'R' sessions independently from the user interface, while special case and outlier imputation functions are written to be used with shiny UI. 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Package: r-cran-monographar Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-png, r-cran-raster, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rpart, r-cran-sf, r-cran-shiny, r-cran-shinydashboard, r-cran-shinythemes, r-cran-shinywidgets, r-cran-sp, r-cran-terra Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-monographar_1.3.1-1.ca2604.1_all.deb Size: 678418 MD5sum: 495de57f4ae7e45e7f10ae1ae0c3695f SHA1: 557609bac39e91cd4ce545e8a32637e5863d1fe9 SHA256: b676b8225ab4cd02b820daa9258ac748bc3d4facf6ddf03d93e9b8baad0daf81 SHA512: e8d5ba2f511154a4a16423277ac318d418ccca30558f9976706c05b5c27e09446968e455f61627a7d24aede2e692ea8c75dcd10dbca89453b3b67203e271e227 Homepage: https://cran.r-project.org/package=monographaR Description: CRAN Package 'monographaR' (Taxonomic Monographs Tools) Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identification keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available. Package: r-cran-monophy Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phytools, r-cran-phangorn, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-testthat, r-cran-paleotree, r-cran-rmarkdown, r-cran-taxize Filename: pool/dists/resolute/main/r-cran-monophy_1.3.2-1.ca2604.1_all.deb Size: 325488 MD5sum: 06338b5ef5e89e85a439ac2f4198db1a SHA1: bcb87e8b2836935861e504ff99a477050163e47e SHA256: bbe02520a2b3c20f9f71ab64a2d16a18dedce381a40511406c763d2361b0bb25 SHA512: 5abc1f5c06696d0a5a46d1f51076726b8b47906f5f10d728ed98aa40be44b3de1015a8d0ed9ec746c8d2ce39572c2625f3ab4f8dea582e442ccee74b9b174452 Homepage: https://cran.r-project.org/package=MonoPhy Description: CRAN Package 'MonoPhy' (Explore Monophyly of Taxonomic Groups in a Phylogeny) Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) . Package: r-cran-monotonehazardratio Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-kernsmooth, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-monotonehazardratio_0.2.0-1.ca2604.1_all.deb Size: 36516 MD5sum: 4c60ca74ee5c32ba3cd6ea64214836f5 SHA1: 4dad9313ba5326dba64c257df55413201c30a717 SHA256: cc03f2f8c62cfad4658b8ced26245e2d3bc15a688d823d00f12fde675322e964 SHA512: b379a4bd1adc4f7d1a8b32b91ded30f507fea2d2371fe6a8675d04795e7d2f26657b664e8bc74f34cc8af58964ae45cf83bca7e34920c2a2ea4f6e0af55a3548 Homepage: https://cran.r-project.org/package=MonotoneHazardRatio Description: CRAN Package 'MonotoneHazardRatio' (Nonparametric Estimation and Inference of a Monotone HazardRatio Function) Nonparametric estimation and inference of a non-decreasing monotone hazard ratio from a right censored survival dataset. The estimator is based on a generalized Grenander typed estimator, and the inference procedure relies on direct plugin estimation of a first order derivative. More details please refer to the paper "Nonparametric inference under a monotone hazard ratio order" by Y. Wu and T. Westling (2023) . Package: r-cran-monotonicity Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmtest, r-cran-mass, r-cran-sandwich Suggests: r-cran-testthat, r-cran-xts Filename: pool/dists/resolute/main/r-cran-monotonicity_1.3.1-1.ca2604.1_all.deb Size: 106596 MD5sum: 8f13a2174306efdea048eb86eb576dda SHA1: be20415ceda7608fedc9a9801b22482b86d2fd27 SHA256: 24b610281b7a13360afb58d50e678548b364062058d6716c4fa2d33015eedacd SHA512: 72b30a940e8053b3fd10ebe4df1ddd08c94cd28d36e31ffa48d1af07d11eb99e0775e5328eda82378ebda389b96171a878a5b61c61ae7a36121e729173c7fd2e Homepage: https://cran.r-project.org/package=monotonicity Description: CRAN Package 'monotonicity' (Test for Monotonicity in Expected Asset Returns, Sorted byPortfolios) Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations. Package: r-cran-monte.carlo.se Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-monte.carlo.se_0.1.1-1.ca2604.1_all.deb Size: 172988 MD5sum: ad64759d4a471011ba1b3437dd1871e1 SHA1: b3e145496e08df6872d071c1d3aea6a23dc8a6bd SHA256: 17f19ff92d8d8f8cc8ae8cad5fbff6aa349d354944d7d72caf89f2f1d5fc6f5c SHA512: 54a73eb4a0be6cb48a8888c5dc5dd41c1e1d1ee7a2271f2d28c12ad082aba03a6f3bde0230c93d5269f7d9f9377ed0d7b22757355b4a0e31c0fd4e9cb2b20d84 Homepage: https://cran.r-project.org/package=Monte.Carlo.se Description: CRAN Package 'Monte.Carlo.se' (Monte Carlo Standard Errors) Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) . Package: r-cran-montecarlosem Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lavaan Filename: pool/dists/resolute/main/r-cran-montecarlosem_2.0.0-1.ca2604.1_all.deb Size: 72320 MD5sum: 1765a7e7b8a3b36ef604cc6444819ab8 SHA1: 341667449465bcbaeb96479480a71c1f60b26a8f SHA256: 607ee3624102f59c51e266d49944f585dc671d50b9bd4b3b7a30e2f3b167a239 SHA512: 869f29de0940df326a711c943a37bb8f8723f35b233d0c6021e9295b8f9ec4363b536755b3a48f7c53dc3ec08fb29e2872f9a232e775c2a81f2d08d316258d2c Homepage: https://cran.r-project.org/package=MonteCarloSEM Description: CRAN Package 'MonteCarloSEM' (Monte Carlo Simulation for Structural Equation Modeling) Provides tools to conduct Monte Carlo simulations under different conditions (e.g., varying sample size, data normality) for structural equation models (SEMs). Data can be simulated based on user-defined factor loadings and correlations, with optional non-normality added via Fleishman's power method (1978) . Once generated, models can be estimated using 'lavaan'. This package facilitates testing model performance across multiple simulation scenarios. When data generation is completed (or when generated data sets are given) model tests can also be run. Please cite as "Orçan, F. (2021). MonteCarloSEM An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713." Package: r-cran-moode Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-far, r-cran-progressr, r-cran-rdpack, r-cran-rlang Suggests: r-cran-dofuture, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-moode_1.1.0-1.ca2604.1_all.deb Size: 139804 MD5sum: 1799b532aed803f8bf4e8eb0ee91ec33 SHA1: 3bd3a3701b85a8a3c10c39c0b04b204da2a8d0b5 SHA256: dba373db4b44a0199343f84b77ca877cd6051afaf802dc871b9ff73a175153e7 SHA512: afe63aa0fb368712847c58153ea4879c6f510320565a4c33135db0da1c817b5c6aadb9008f0fd2c40361e44cb6612779065bc6a00c3a5cb3fd48a09d153076f1 Homepage: https://cran.r-project.org/package=MOODE Description: CRAN Package 'MOODE' (Multi-Objective Optimal Design of Experiments) Provides functionality to generate compound optimal designs for targeting the multiple experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. Designs can be found using point or coordinate exchange algorithms combining estimation, inference and lack-of-fit criteria that account for model inadequacy. Details and examples are given by Koutra et al. (2024) . Package: r-cran-moodef Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blastula, r-cran-dplyr, r-cran-glue, r-cran-magick, r-cran-readr, r-cran-readxl, r-cran-snakecase, r-cran-tibble, r-cran-tidyr, r-cran-xlsx, r-cran-xml2 Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-moodef_1.2.0-1.ca2604.1_all.deb Size: 421988 MD5sum: 7768a84b6c206f2e3be7a44743018487 SHA1: 723c7e8269c85a8d71457aa4bbe2733f42a33aa0 SHA256: f9b9e8c340a393a6a32a523805a11d4a018a65f46e618c148fa134edc338c1d0 SHA512: f1095b532acd2886ef3a8de397a1bf7f5425aeb57bea29e49d72150cd19be1a2d7a485cebc8d27bd4f7740acf47131d454f0970880e8e8a4bc266fa4a02fb2d9 Homepage: https://cran.r-project.org/package=moodef Description: CRAN Package 'moodef' (Defining 'Moodle' Elements from R) The main objective of this package is to support the definition of 'Moodle' elements taking advantage of the power that R offers. In this first version, it allows the definition of quizzes to be included in the question bank. Package: r-cran-moodlequiz Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1657 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-rmarkdown, r-cran-bookdown, r-cran-xfun, r-cran-yaml Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-moodlequiz_0.2.1-1.ca2604.1_all.deb Size: 1453802 MD5sum: f6a1055c77e4c465f955e94ca1c516c7 SHA1: cb8c1ca90f1beebabeabadc4f2ddc4c1b72fc0c7 SHA256: 81c875ca15363c5bd44b844ef4a94a94bbf5f99ad0790ca243309346f07b07a4 SHA512: f5efe17c5fa4a818d7258124afb9cd082b8f56f25896028b3c3fa859329f4f3603d0f36dd8657d418abf88cd46dc14d75aaceeca39cba775e03fce793eb1a157 Homepage: https://cran.r-project.org/package=moodlequiz Description: CRAN Package 'moodlequiz' (R Markdown format for 'Moodle' XML cloze quizzes) Enables the creation of 'Moodle' quiz questions using literate programming with R Markdown. This makes it easy to quickly create a quiz that can be randomly replicated with new datasets, questions, and options for answers. Package: r-cran-moodlequizr Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64, r-cran-mvtnorm, r-cran-shiny, r-cran-nmcalc Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-shinywidgets, r-cran-shinymatrix Filename: pool/dists/resolute/main/r-cran-moodlequizr_2.1.1-1.ca2604.1_all.deb Size: 279890 MD5sum: d97100be2a1b4427b6dfa1af2e4e5dfe SHA1: d68297b66b399b5380bd1946148e2b95d0a08c07 SHA256: 71de0ca038ee718658092d58b894a189fe1c87a0b100fd8fdce56e0477f32099 SHA512: 8ef710689ab07b2f122c41af1770fc085400636102837fc17047edd0596f485eacb03757447b2b5573d9becfa64887f78abb5d00f8be33a1987af5ccde69037d Homepage: https://cran.r-project.org/package=moodlequizR Description: CRAN Package 'moodlequizR' (Easily Create Fully Randomized 'Moodle' Test Questions) Routines to generate fully randomized 'moodle' quizzes. It also contains 15 examples and a 'shiny' app. Package: r-cran-moodler Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-ggplot2, r-cran-tidytext, r-cran-ggwordcloud, r-cran-stringr, r-cran-rlang, r-cran-scales, r-cran-rmariadb, r-cran-glue, r-cran-config, r-cran-anytime, r-cran-cli, r-cran-lifecycle, r-cran-rsqlite, r-cran-usethis, r-cran-rpostgres Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettyunits, r-cran-lubridate, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-moodler_1.0.5-1.ca2604.1_all.deb Size: 101448 MD5sum: b341ad7da872ebccaf121973d6c7208a SHA1: 75141a9fcd24b9a2fbcf605d32d421afc1e3dc04 SHA256: 2a37aed7189630b73906fdb309354175d97344d4bc60f45808813501537441c2 SHA512: 8eb88ad9544ec3deb69df3215c5f54ed11a4ddf4e7a4808bae85584f17b32f8ae0a24dfa3253e7d99cded97eadc091881d7e404d19e6dc0eb63639459fd1d382 Homepage: https://cran.r-project.org/package=moodleR Description: CRAN Package 'moodleR' (Helper Functions to Work with 'Moodle' Data) A collection of functions to connect to a 'Moodle' database, cache relevant tables locally and generate learning analytics. 'Moodle' is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit . Package: r-cran-moonbook Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1825 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-survival, r-cran-sjmisc, r-cran-stringr, r-cran-magrittr, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-sjlabelled, r-cran-ztable, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-moonbook_0.3.1-1.ca2604.1_all.deb Size: 1243210 MD5sum: 5f6a0a697eb4c57ac762f4938a1391b4 SHA1: fcbd080c01966da46dcb7f2c7da3833864c48d18 SHA256: b1a4e1011504e9cc3ac30b0a1e4dc06cacfe465f92673bcacddda02d904ff33c SHA512: b974a6e2ae09f2d83193790728a3be2e94700974507c6f7185c4592abbc1b25c5ee462924353612c779e7b10579b5f68c9d0eefc31d2ccd94543ee49562f47e1 Homepage: https://cran.r-project.org/package=moonBook Description: CRAN Package 'moonBook' (Functions and Datasets for the Book by Keon-Woong Moon) Several analysis-related functions for the book entitled "R statistics and graph for medical articles" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions. Package: r-cran-moonboot Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-moonboot_2.0.1-1.ca2604.1_all.deb Size: 64872 MD5sum: 38147633e6bff6574b594f1de6013de2 SHA1: c2f6b5e1ef53eb159ed780802cfb5605446bc2ac SHA256: 9d6a4f7f51cb2367c4806e492c7d9d671078e35346f3fa457a3fb1c931d80679 SHA512: 7f744daf57bb3c928d2a082a530d0836fd6dc365b76289de37d2aa8fbd9f417fc00a8fd4e3f0361af16f302e7266569da0220b48d8a7f87f9d4ad2d888e57790 Homepage: https://cran.r-project.org/package=moonboot Description: CRAN Package 'moonboot' (m-Out-of-n Bootstrap Functions) Functions and examples based on the m-out-of-n bootstrap suggested by Politis, D.N. and Romano, J.P. (1994) . Additionally there are functions to estimate the scaling factor tau and the subsampling size m. For a detailed description and a full list of references, see Dalitz, C. and Lögler, F. (2025) . Package: r-cran-moonlit Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-suncalc, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-moonlit_0.1.1-1.ca2604.1_all.deb Size: 912762 MD5sum: ed7643f08898e0e7dc2c32cc8d8828e9 SHA1: 08b5e10a1cd6a5ec310a30f9f4eb1aa9a90489fb SHA256: 76d5396671d88acb5e2e255fc923138da749b9374ca7060ba6609292caa59973 SHA512: d51359b12357e366c789a067327b92841ba0d4f1bc93285b1bc3e975308dc908171667961ee72756c1f8240d1953d56fbf351b3a15300c95a11e7e8f9ec080b0 Homepage: https://cran.r-project.org/package=moonlit Description: CRAN Package 'moonlit' (Predicting Moonlight Intensity for a Given Time and Location) Tools for predicting moonlight intensity on the ground based on the position of the moon, atmospheric conditions, and other factors. Provides functions to calculate moonlight intensity and related statistics for ecological and behavioral research, offering more accurate estimates than simple moon phase calculations. The underlying model is described in Smielak (2023) . Package: r-cran-mooplot Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-collapse, r-cran-matrixstats, r-cran-moocore Suggests: r-cran-extrafont, r-cran-viridislite, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mooplot_0.1.1-1.ca2604.1_all.deb Size: 165862 MD5sum: 3d71a0376f54a5034897a5beb01106b4 SHA1: 7f96b88fd0a0a1febf5eb1eb10ae8a1d5fe56183 SHA256: 468c811366a00a52fb11d6969350a8818dbe65ce2dc9858f9cdf20f593dbeb46 SHA512: 2ecb88f2f6fb9a7028179cf883095dcb6b788153d649aea1b0f26bd90603ffb84c4d76d8fd51a16ac7b2ab27c82c0c9c12632ec976e4e52f1ca968748503ba85 Homepage: https://cran.r-project.org/package=mooplot Description: CRAN Package 'mooplot' (Graphical Visualizations for Multi-Objective Optimization) Visualization of multi-dimensional data arising in multi-objective optimization, including plots of the empirical attainment function (EAF), M. López-Ibáñez, L. Paquete, and T. Stützle (2010) , and symmetric Vorob'ev expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) , among others. Package: r-cran-moose Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-moose_0.0.1-1.ca2604.1_all.deb Size: 14906 MD5sum: 5c7d7c678c6de12351dc463046d564ff SHA1: 6126a039f4c146c193006a8d6495723a104f45ec SHA256: 51e77240720542ebf901da56b931ccb761970fe760a38047a84b60ef86ff2ea7 SHA512: bdad2932108f4c82e5b4c11e1ad387b89ed6bb24bc09fdbc553f90b5be911c02abc5b1a7e99ea4e515d3db04d5adcd398db15bfe4efb1aac61f58d69c5797e6a Homepage: https://cran.r-project.org/package=moose Description: CRAN Package 'moose' (Mean Squared Out-of-Sample Error Projection) Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) . It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper. Package: r-cran-mopac Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-lubridate, r-cran-tibble, r-cran-readr, r-cran-hms Filename: pool/dists/resolute/main/r-cran-mopac_0.1.0-1.ca2604.1_all.deb Size: 1282860 MD5sum: 62d76425d3fb649d5d259d1b250bab0f SHA1: 4d6bac204bd362d85137fe2c98a9cbe19eb84641 SHA256: 7bc62f34687c8312310126b303cda986a28a663a123d2f5d9f649a6f5272a1c6 SHA512: 9a0d790149c6a3c000fadd1c492db4f8ee9484c9e1802faefa678a5b559267a730f063fd663b18be8fa08941ba8ebf5fcb3240e583479a8795254cdfd5f93b40 Homepage: https://cran.r-project.org/package=mopac Description: CRAN Package 'mopac' (Collection of Datasets Pertaining to Loop 1 "Mopac") Provides real & simulated datasets containing time-series traffic observations and additional information pertaining to Loop 1 "Mopac" located in Austin, Texas. Package: r-cran-moqa Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-gplots, r-cran-readr Filename: pool/dists/resolute/main/r-cran-moqa_2.0.0-1.ca2604.1_all.deb Size: 83300 MD5sum: cc31b22481f6be9188ce58f771d0d918 SHA1: 8f02ab5551db42205814312eb6f63106f67f8527 SHA256: 17c644f2f5376b3ccb6b62c29ad1f530f5c2412013773083e39d7284cd3c314a SHA512: c648bedf39d59215cfa725630d019b6b73f525863458e8d4845d9feeb448a88ba92ff1da81ea298a24b2c577321fc8b4c23ddac4295b68218a5a891a19a81f03 Homepage: https://cran.r-project.org/package=MOQA Description: CRAN Package 'MOQA' (Basic Quality Data Assurance for Epidemiological Research) With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) . . 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Package: r-cran-morestopwords Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4561 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cld2 Filename: pool/dists/resolute/main/r-cran-morestopwords_0.2.0-1.ca2604.1_all.deb Size: 729074 MD5sum: d77b67858745215708e9e5fa20c1f4f3 SHA1: 5d00247febc83b178a92d13736c635c547a2f2c7 SHA256: f6e325e40c691c1bd5da512353811fc98245745181a5fe9f692095a73a375a05 SHA512: 715862918a5f1145a22669503037c5975a4b8b4b70882e5df562b18a84e0d309c321d619c2f56db6d3871ec45238466ed0b92387de93f48d93ce03f9a94ee27f Homepage: https://cran.r-project.org/package=morestopwords Description: CRAN Package 'morestopwords' (All Stop Words in One Place) A standalone package combining several stop-word lists for 65 languages with a median of 329 stop words for language and over 1,000 entries for English, Breton, Latin, Slovenian, and Ancient Greek! The user automatically gets access to all the unique stop words contained in: the 'StopwordISO' repository; the 'Natural Language Toolkit' for 'python'; the 'Snowball' stop-word list; the R package 'quanteda'; the 'marimo' repository; the 'Perseus' project; and A. Berra's list of stop words for Ancient Greek and Latin. Package: r-cran-morph Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-reshape2, r-cran-igraph, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-morph_1.1.0-1.ca2604.1_all.deb Size: 77218 MD5sum: 94d6aba665f468edfd59e5c21dfb0bfa SHA1: 3fe25439af22673467c08c588fb3d0cc68d20025 SHA256: 9b1c203c4b3dda3ea46d59231430688d250b965729684448d16451a65f977059 SHA512: 1d07c45aefe881f6e74da65a44595ad51469b5295f69a198ee6e79fc0e1e0b4eb97c8caa57e2e6cbf12ca1c7e987b39e990a19853be11b40061e25511aab869d Homepage: https://cran.r-project.org/package=morph Description: CRAN Package 'morph' (3D Segmentation of Voxels into Morphologic Classes) Automatically segments a 3D array of voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been accepted for publication in the journal Landscape Ecology. Detailed references will be updated here once those are known. 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The morphemepiece algorithm uses a lookup table to determine the morpheme breakdown of words, and falls back on a modified wordpiece tokenization algorithm for words not found in the lookup table. Package: r-cran-morphomap Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4968 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arothron, r-cran-lattice, r-cran-mgcv, r-cran-rvcg, r-cran-morpho, r-cran-oce, r-cran-sp, r-cran-geometry, r-cran-rgl, r-cran-colorramps, r-cran-desctools Filename: pool/dists/resolute/main/r-cran-morphomap_1.5-1.ca2604.1_all.deb Size: 5046470 MD5sum: bb74cf0d4b57f8b494a77fe15803f0d8 SHA1: 10ca645a647ac12b93b74aafab6b14eb3b2c94d3 SHA256: ba81f367e6aae9b44aac19a82977fc27c3b85a6daff2d78f5d48e6be3a43977b SHA512: f61ee87be9e7c7a815a78f229ceafb2851e5af81d252e36ae1e62b29da78f10dcdecfbb64a3aa3417ad3f86e2f6ce285bd43bdf01868798323bc90d9056dd2ed Homepage: https://cran.r-project.org/package=morphomap Description: CRAN Package 'morphomap' (Morphometric Maps, Bone Landmarking and Cross Sectional Geometry) Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section. Package: r-cran-morphomenses Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dendextend, r-cran-geomorph, r-cran-cluster, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-morphomenses_1.0.3-1.ca2604.1_all.deb Size: 155516 MD5sum: 88e9c7807839586762b378d6d3fdde4c SHA1: f891f5e89f6a1b9d175dd3752df4eea8205202c7 SHA256: 5a23ff6fffe92c7a1ae9ccbfd03764a97743839628ae7cf4482174bbca7f189f SHA512: e8cc6aaece422ccc76b7392344a9622c56f66c0759d35e425d9fd56497e4afbb70567b782722bdeb8c7251d555c43091d0b082a5821e1492f8cbc541636d22af Homepage: https://cran.r-project.org/package=moRphomenses Description: CRAN Package 'moRphomenses' (Geometric Morphometric Tools to Align, Scale, and Compare"Shape" of Menstrual Cycle Hormones) Mitteroecker & Gunz (2009) describe how geometric morphometric methods allow researchers to quantify the size and shape of physical biological structures. We provide tools to extend geometric morphometric principles to the study of non-physical structures, hormone profiles, as outlined in Ehrlich et al (2021) . Easily transform daily measures into multivariate landmark-based data. Includes custom functions to apply multivariate methods for data exploration as well as hypothesis testing. Also includes 'shiny' web app to streamline data exploration. Developed to study menstrual cycle hormones but functions have been generalized and should be applicable to any biomarker over any time period. Package: r-cran-morphoregions Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1918 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-cluster, r-cran-scales, r-cran-ggplot2, r-cran-chk, r-cran-pbapply Suggests: r-cran-viridislite, r-cran-patchwork, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-morphoregions_0.1.0-1.ca2604.1_all.deb Size: 1300828 MD5sum: 62f1c02cacc2fe83fffc6801a887e079 SHA1: 8a972c33be13b0a8a442fb4406c43ac792b95e58 SHA256: c644a622b4e8f5357dc3991f60a56ebcc1077e2473c4afec3a581558bd1e3417 SHA512: 70a854169b5320cb81e8d854273dbd8ff8a71059c15c8ef2835e6962717830c41104366819a2029511225dfc39729bae72a95bab0c1c191e3d33d310c4db58f8 Homepage: https://cran.r-project.org/package=MorphoRegions Description: CRAN Package 'MorphoRegions' (Analysis of Regionalization Patterns in Serially HomologousStructures) Computes the optimal number of regions (or subdivisions) and their position in serial structures without a priori assumptions and to visualize the results. After reducing data dimensionality with the built-in function for data ordination, regions are fitted as segmented linear regressions along the serial structure. Every region boundary position and increasing number of regions are iteratively fitted and the best model (number of regions and boundary positions) is selected with an information criterion. This package expands on the previous 'regions' package (Jones et al., Science 2018) with improved computation and more fitting and plotting options. Package: r-cran-morphotools2 Architecture: all Version: 1.0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-candisc, r-cran-car, r-cran-class, r-cran-ellipse, r-cran-heplots, r-cran-mass, r-cran-plot3d, r-cran-statmatch, r-cran-vegan Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-morphotools2_1.0.2.1-1.ca2604.1_all.deb Size: 921524 MD5sum: 0c2407520b576a589351e052c02d97c1 SHA1: 2584bdc645d70a0933ef99c2b99f97e0aa8cb1fb SHA256: fc29e55ba1bc76461f6ee5d2952c633e1e9d37ceb6335aeb9a62552dd2f826a6 SHA512: 55a15dd94946f44aee2441ac0e01996eb1106950a8eac3f9fc77d04a57dd202294f8729257603adc38f2d8eb1cbd00c33b7bea705ffa67616d742f51bc3552ad Homepage: https://cran.r-project.org/package=MorphoTools2 Description: CRAN Package 'MorphoTools2' (Multivariate Morphometric Analysis) Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Šlenker et al. 2022. Package: r-cran-morphsim Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-fossilsim, r-cran-phangorn Suggests: r-cran-treesim, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-morphsim_1.2.0-1.ca2604.1_all.deb Size: 279698 MD5sum: 11d760e15df9e4b05423b06e86e668b5 SHA1: 7d425eace724a002986c3d645c49bbf75f5e8167 SHA256: cef2e17ce22afe4a4bb40fb567f67e4e4eed7a7b4e95a3044100089e06ee0dbb SHA512: cf4fd220d4238160ca42a1758b9d153cebad64e638288ab7687b17c22f1d6326722684ec535d865396ae254184c822b33230018f3e4ceca22a60087564aa75d6 Homepage: https://cran.r-project.org/package=MorphSim Description: CRAN Package 'MorphSim' (Simulate Discrete Character Data along Phylogenetic Trees) Tools to simulate morphological traits along phylogenetic trees with branch lengths representing evolutionary distance or time. Includes functions for visualizing evolutionary processes along trees and within morphological character matrices. 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The world's second oldest ongoing agricultural experiment, the Morrow Plots measure the impact of crop rotation and fertility treatments on corn yields. The data includes planting information and annual yield measures for corn grown continuously and in rotation with other crops, in treated and untreated soil. Package: r-cran-morsedr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-rjags Suggests: r-cran-ggally, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-morsedr_0.1.2-1.ca2604.1_all.deb Size: 922738 MD5sum: 32688e32711efebf68a90bf08171946e SHA1: bf2c0e7f9be7ba8a4e9b079e360dc280c63922a7 SHA256: 76e13ec0c68a27dc1427639170a574b347a8581fa37af183ce78b42e6458bd41 SHA512: f3f5c35e5853b2d7c8962a44754b935372773fdfdb381c6804c87b4438da67e04b050def3ab7443112f954c52b74a1404c2f8932881d156a657ce93b9c91dd56 Homepage: https://cran.r-project.org/package=morseDR Description: CRAN Package 'morseDR' (Bayesian Inference of Binary, Count and Continuous Data inToxicology) Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of several type of toxicological data. 'binary' (e.g., survival, mobility), 'count' (e.g., reproduction) and 'continuous' (e.g., growth as length, weight). Estimation procedures can be used without a deep knowledge of their underlying probabilistic model or inference methods. Rather, they were designed to behave as well as possible without requiring a user to provide values for some obscure parameters. That said, models can also be used as a first step to tailor new models for more specific situations. Package: r-cran-mort Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2056 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mort_0.0.1-1.ca2604.1_all.deb Size: 1701336 MD5sum: 9b55259b3bae125b05990ee5c02f99e4 SHA1: 28e8766c287669b7e541cf21db3acedc9d4641c8 SHA256: efc7a396ced1c2fd00e8fda495c6ab09390389c2cb995a35ed8af932dd85ab8d SHA512: 05cc54d96838273d18c7cfb709d6aee394feb3647bd903274529fe1c4e669aa948e313228f85889819dc3fd28d868e9ab02e0a60b8ece81979a1c3ac13f80caa Homepage: https://cran.r-project.org/package=mort Description: CRAN Package 'mort' (Identifying Potential Mortalities and Expelled Tags in AquaticAcoustic Telemetry Arrays) A toolkit for identifying potential mortalities and expelled tags in aquatic acoustic telemetry arrays. Designed for arrays with non-overlapping receivers. Package: r-cran-mortaar Architecture: all Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rdpack, r-cran-reshape2, r-cran-tibble, r-cran-rlang, r-cran-flexsurv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-mortaar_1.1.8-1.ca2604.1_all.deb Size: 907560 MD5sum: e75ff0b31ccf33c2c05822b9c0de1e6a SHA1: 77b50b00586979df4fe69e8438830f81ba00cf14 SHA256: 68f7c55cb66971ceef45c0c8eee85caed8a52df644c62c75afc0607ae0ee8ed5 SHA512: a9480fb55a12b38bf31161110ba69809d85621bf17b33781a584573dfdfff999713cbb3a3d0be0dd0f46b1cc59c83d6bd471a8cfd4d7b062138c007312dc3af0 Homepage: https://cran.r-project.org/package=mortAAR Description: CRAN Package 'mortAAR' (Analysis of Archaeological Mortality Data) A collection of functions for the analysis of archaeological mortality data (on the topic see e.g. Chamberlain 2006 ). It takes demographic data in different formats and displays the result in a standard life table as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population). It also checks for possible biases in the age structure and applies corrections to life tables. Package: r-cran-mortalitygaps Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-mass, r-cran-crch, r-cran-pbapply, r-cran-rdpack Suggests: r-cran-mortalitylaws, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-mortalitygaps_1.0.7-1.ca2604.1_all.deb Size: 379716 MD5sum: 950d7f53975b148cd24ccce570165bd2 SHA1: 0c55ad9700425cd52daf9e97b6c0e67c0d367996 SHA256: 3d61333e97c293c6967e9118266cec5d84671b39b58925854b1873144a63de15 SHA512: 6dd785c70d1f6de0098b78fa7305b549debfcf83a4f8756dc8340d3061615f2f34b7b0b9168652beb50f0d59121e7d4165c35b17db0398f4071f0a57b514835f Homepage: https://cran.r-project.org/package=MortalityGaps Description: CRAN Package 'MortalityGaps' (The Double-Gap Life Expectancy Forecasting Model) Life expectancy is highly correlated over time among countries and between males and females. These associations can be used to improve forecasts. Here we have implemented a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We named this method the Double-Gap model. For a detailed description of the method see Pascariu et al. (2018). . Package: r-cran-mortalitylaws Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcurl, r-cran-pbapply, r-cran-tidyr, r-cran-rvest, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mortalitylaws_2.2.0-1.ca2604.1_all.deb Size: 559894 MD5sum: b2afa949d3c595d009f2a89fa05b0b59 SHA1: 5d5d4414dfb1bcd6cd794fb8b593bbbe8b14cd50 SHA256: a155c00ced3b5707da234d2d9f7c483bd139cb38233d9b1b9e42360db610ba95 SHA512: 82edd37a40f164d8feca6025350278a254f3bab79c097cd48c84347883d4105efe98a48c24bfaad1e75a14a729ce0f46f63a0ea75124d830b53f48356a057ed5 Homepage: https://cran.r-project.org/package=MortalityLaws Description: CRAN Package 'MortalityLaws' (Parametric Mortality Models, Life Tables and HMD) Fit the most popular human mortality 'laws', and construct full and abridge life tables given various input indices. A mortality law is a parametric function that describes the dying-out process of individuals in a population during a significant portion of their life spans. For a comprehensive review of the most important mortality laws see Tabeau (2001) . Practical functions for downloading data from various human mortality databases are provided as well. Package: r-cran-mortalitytables Architecture: all Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-pracma Suggests: r-cran-knitr, r-cran-tidyverse, r-cran-reshape2, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mortalitytables_2.0.5-1.ca2604.1_all.deb Size: 1235546 MD5sum: a9ded5bb4764c1b46c281217166abfe7 SHA1: ca2039c87939252bcf6aa78307291f1c21669c66 SHA256: 015c2a100d9f0c3872e560972df219fcf8b13db0dac53d19888afd91c17fb23e SHA512: b1e5d580cec9f238186d7c0dfea46d37095475b0910cf8e457775c4bca72339e80fc2e6e8d3cb8eba7a8326f3f7a1edbb62f39eb36b7c68c5e2f69dc6f1aab86 Homepage: https://cran.r-project.org/package=MortalityTables Description: CRAN Package 'MortalityTables' (A Framework for Various Types of Mortality / Life Tables) Classes to implement, analyze and plot cohort life tables for actuarial calculations. Birth-year dependent cohort mortality tables using a yearly trend to extrapolate from a base year are implemented, as well as period life table, cohort life tables using an age shift, and merged life tables. Additionally, several data sets from various countries are included to provide widely-used tables out of the box. Package: r-cran-mortar Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-gert, r-cran-glue, r-cran-purrr, r-cran-r.utils, r-cran-rlang, r-cran-usethis Suggests: r-cran-knitr, r-cran-renv, r-cran-rmarkdown, r-cran-stringr, r-cran-tarchetypes, r-cran-targets, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mortar_0.4.0-1.ca2604.1_all.deb Size: 66930 MD5sum: d961ef0214d59404afd53d8a34bcf805 SHA1: 88077d3ecffd3308e6cea9553688494a06369473 SHA256: 3af636d74282e372bb27b3543b5f3a9b28ccea0ef8d75ac81be6247819ec0619 SHA512: 916c414b2bf318511263e952fda66b4eefea7fbcd686908507037f51792a4073e40b526206a7396cfbc5d2bd3b044ece3d0777fd3796d8b45c229fd91821c550 Homepage: https://cran.r-project.org/package=mortar Description: CRAN Package 'mortar' (Standardize Data Science Workflows) Helper functions to standardizes common workflows in the USGS Data Science Community of Practice to produce more robust, reproducible pipelines. It contains helper functions to standardize (1) the organization of project repositories and (2) the creation ofpipelines from the 'targets' R Package (Landau et al. (2026) ), using the DS CoP best practices. We draw upon community developed best practices as well as certain USGS-specific requirements. See Shrycock et al. (2023) for examples of these USGS requirements. Package: r-cran-mortsoa Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-mortsoa_0.1.0-1.ca2604.1_all.deb Size: 87796 MD5sum: 61200363afc12f2f00224f0aecb631eb SHA1: 378eb0adb301705afb164ec62f82dce3607ff9af SHA256: 8a0b159338baf039b788e03b4f073bee94db3b74c2760d0e6880da79b03c2e72 SHA512: 6610e9fd4c69cbfc38a7b9614c197d23f72961b968a7560abd27d54d236496e7789518dff546e1e0b529fd62e9580b86d5902b71e28b74b699c789867e9bcf3e Homepage: https://cran.r-project.org/package=mortSOA Description: CRAN Package 'mortSOA' (Obtain Data from the Society of Actuaries 'Mortality and OtherRate Tables' Site) The Society of Actuaries (SOA) provides an extensive online database called 'Mortality and Other Rate Tables' ('MORT') at . This database contains mortality, lapse, and valuation tables that cover a variety of product types and nations. Users of the database can download any tables in 'Excel', 'CSV', or 'XML' formats. This package provides convenience functions that read 'XML' formats from the database and return R objects. Package: r-cran-mos Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hypergeo2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-moments Filename: pool/dists/resolute/main/r-cran-mos_0.1.3-1.ca2604.1_all.deb Size: 123648 MD5sum: 36b6b68b57ab8d614df8af1867001511 SHA1: 0f963fda2bb0cd7f1d883fa5dc444ab678393423 SHA256: 775e4d371bb6ffccaaea7eda4cbcad9a190b9b71e4f02c5f0977cc9ed47792d3 SHA512: 812c8b02cd3c674b947b91868fe305559a67caf8fc7636f4526582efba36949039c679f5ad3f2fbac2a7e404f5d5db5a5ea2f75231b5a4ad7c2faab615f205e1 Homepage: https://cran.r-project.org/package=mos Description: CRAN Package 'mos' (Simulation and Moment Computation for Order Statistics) Provides a comprehensive set of tools for working with order statistics, including functions for simulating order statistics, censored samples (Type I and Type II), and record values from various continuous distributions. Additionally, it offers functions to compute moments (mean, variance, skewness, kurtosis) of order statistics for several continuous distributions. These tools assist researchers and statisticians in understanding and analyzing the properties of order statistics and related data. The methods and algorithms implemented in this package are based on several published works, including Ahsanullah et al (2013, ISBN:9789491216831), Arnold and Balakrishnan (2012, ISBN:1461236444), Harter and Balakrishnan (1996, ISBN:9780849394522), Balakrishnan and Sandhu (1995) , Genç (2012) , Makouei et al (2021) and Nagaraja (2013) . Package: r-cran-mosaic Architecture: all Version: 1.10.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3875 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-lattice, r-cran-ggformula, r-cran-mosaicdata, r-cran-matrix, r-cran-mosaiccore, r-cran-ggplot2, r-cran-rlang, r-cran-purrr, r-cran-mass, r-cran-tidyr Suggests: r-cran-ggstance, r-cran-ggridges, r-cran-vdiffr, r-cran-lubridate, r-cran-magrittr, r-cran-nhanes, r-cran-rcurl, r-cran-sp, r-cran-vcd, r-cran-testthat, r-cran-knitr, r-cran-mapproj, r-cran-rgl, r-cran-rmarkdown, r-cran-covr, r-cran-formatr, r-cran-palmerpenguins, r-cran-ggrepel, r-cran-readr, r-cran-ggdendro, r-cran-gridextra, r-cran-latticeextra, r-cran-glue, r-cran-broom, r-cran-leaflet Filename: pool/dists/resolute/main/r-cran-mosaic_1.10.2-1.ca2604.1_all.deb Size: 3072884 MD5sum: 0ecbbe550da036eb25981dcb77b31a4c SHA1: f827801451b2afc3e334365a8eb7ec0a7a59d322 SHA256: 476410dec9823cb8527b69867a46a8ab973796542b96ce9779e8fcaf8ff1f5ec SHA512: 50455db065ee10d874fd060e03602c57790c5749d8297b13da09fdc8422e730221ba79620db208d6661e19f2c305c47d0336f72b3a6bfa39c111942ae4c79f2c Homepage: https://cran.r-project.org/package=mosaic Description: CRAN Package 'mosaic' (Project MOSAIC Statistics and Mathematics Teaching Utilities) Data sets and utilities from Project MOSAIC () used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all. Package: r-cran-mosaiccalc Architecture: all Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-calculus, r-cran-deriv, r-cran-dplyr, r-cran-ggformula, r-cran-ggplot2, r-cran-glue, r-cran-mass, r-cran-matrix, r-cran-metr, r-cran-mosaiccore, r-cran-mosaic, r-cran-orthopolynom, r-cran-rlang, r-cran-ryacas, r-cran-sp, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-cubature, r-cran-plotly, r-cran-mosaicdata Filename: pool/dists/resolute/main/r-cran-mosaiccalc_0.6.4-1.ca2604.1_all.deb Size: 2735380 MD5sum: b8b1b564a486b9fb1cd9c16b805800b0 SHA1: 90f8bc8d0fffb8b11cc52917c5404ee6619f78d0 SHA256: e83638e6c2a24ee3f73a136072aa363db5475a1c1f402ae6349fc7ab005ed500 SHA512: 964b60efe894cbe072b7a2e0f073b52ea4117230d6141b0010d0662bae11699be2024a34c7c0f772e3cdb195f9a5e5ffaba2e91ec891930615e89c4d3afdaa68 Homepage: https://cran.r-project.org/package=mosaicCalc Description: CRAN Package 'mosaicCalc' (R-Language Based Calculus Operations for Teaching) Software to support the introductory *MOSAIC Calculus* textbook ), one of many data- and modeling-oriented educational resources developed by Project MOSAIC (). Provides symbolic and numerical differentiation and integration, as well as support for applied linear algebra (for data science), and differential equations/dynamics. Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc. The software is suitable for general use, but intended mainly for teaching calculus. Package: r-cran-mosaiccore Architecture: all Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-mass Suggests: r-cran-mosaicdata, r-cran-mosaic, r-cran-ggformula, r-cran-nhanes, r-cran-testthat, r-cran-mosaiccalc Filename: pool/dists/resolute/main/r-cran-mosaiccore_0.9.5-1.ca2604.1_all.deb Size: 194538 MD5sum: f553880b5961e3e55ee8601809ecadd8 SHA1: c997b446f681af12c737d9582baae071c041e231 SHA256: 37e0ab836c24bd00c49d3cb6486595dfb7bb308de99a4993600edcfa04e763ef SHA512: 2edce939a16120891efe1245706e03f5c50f1d1427be23072a1b461f66ceaac8af77d4216dfdeccc11d23fb4edf15172092abdd2dd870db82736f37e4f9658c0 Homepage: https://cran.r-project.org/package=mosaicCore Description: CRAN Package 'mosaicCore' (Common Utilities for Other MOSAIC-Family Packages) Common utilities used in other MOSAIC-family packages are collected here. Package: r-cran-mosaicdata Architecture: all Version: 0.20.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1698 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-mosaic, r-cran-reshape2, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-ggformula Filename: pool/dists/resolute/main/r-cran-mosaicdata_0.20.4-1.ca2604.1_all.deb Size: 1599082 MD5sum: 0a3b16bfba0d8be8e0c64080c1ae526f SHA1: d73c175574a1250fb5151f50956e8786b66c79b8 SHA256: 2bb4a698f32dee5d6b72ab663b39b0fdcf5d1a545aaec5a2b31ffa1106b15e28 SHA512: 85546b9da2ae9ab23a82ed9cccbb94ec4c746d13bb0bcc98e743c478756920cb58c41247d41a1394caec606207b9bea54c94d5c5258f52e119841fbeb89e0587 Homepage: https://cran.r-project.org/package=mosaicData Description: CRAN Package 'mosaicData' (Project MOSAIC Data Sets) Data sets from Project MOSAIC () used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all. Package: r-cran-mosalloc Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ecosolver, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mosalloc_1.2.5-1.ca2604.1_all.deb Size: 177706 MD5sum: 98d94d17772159f622ccb71995e8cd4c SHA1: ca7533b3c64a1733037259dbd996375a9d70ff90 SHA256: 7d5626d27ddff9749463f4faa7210714f9cf65f946da4457ecb8437c0b0ea229 SHA512: cc4a0dd49d2f90c8859291a25b76ede31f8027a2dc41c4c43a8739e9181831d83d7ba2792fdcee3de765f85527a41a0108104f71871d7f5a3ba52a2f6a69a3e5 Homepage: https://cran.r-project.org/package=MOSAlloc Description: CRAN Package 'MOSAlloc' (Constraint Multiobjective Sample Allocation) Provides a framework for multipurpose optimal resource allocation in survey sampling, extending the classical optimal allocation principles introduced by Tschuprow (1923) and Neyman (1934) to multidomain and multivariate allocation problems. The primary method mosalloc() allows for the consideration of precision and cost constraints at the subpopulation level while minimizing either a vector of sampling errors or survey costs across a broad range of optimal sample allocation problems. The approach supports both single- and multistage designs. For single-stage stratified random sampling, the mosallocSTRS() function offers a user- friendly interface. Sensitivity analysis is supported through the problem's dual variables, which are naturally obtained via the internal use of the Embedded Conic Solver from the 'ECOSolveR' package. See Willems (2025, ) for a detailed description of the theory behind 'MOSAlloc'. Package: r-cran-mosclust Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-clusterv Filename: pool/dists/resolute/main/r-cran-mosclust_1.0.2-1.ca2604.1_all.deb Size: 366138 MD5sum: 9e0aba14364feade2a28a88996d9f97c SHA1: 36760f7e7222941f67462216873527ef171b395a SHA256: 617abea720495dc3e31a847987b3dc7ea7c3c45f88ef5e59bb6b59b1b7d6bfba SHA512: 3fc35be959e1f4e87dffe29322ee8cdba3e43eb6bb1d7b2e20445eefcd1cb21d8f9bbc9d6c800ee6951f738bb99d995c9b03cb8bb5ff19801c4e8a7fa37867cf Homepage: https://cran.r-project.org/package=mosclust Description: CRAN Package 'mosclust' (Model Order Selection for Clustering) Stability based methods for model order selection in clustering problems (Valentini, G (2007), ). Using multiple perturbations of the data the stability of clustering solutions is assessed. Different perturbations may be used: resampling techniques, random projections and noise injection. Stability measures for the estimate of clustering solutions and statistical tests to assess their significance are provided. Package: r-cran-mosemiind Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mosemiind_0.1.2-1.ca2604.1_all.deb Size: 16534 MD5sum: 75518fb2f80200bdbc55a4001b43a4e0 SHA1: 861102015d6c5b611b852b6ecc86295cd07b8a13 SHA256: 3b3e5889ec951150c20c2afeccaadac3fcd451cec588b5089142fae28b8d747e SHA512: 37d16670658abeb7b8c7a1e037873547695bb993198e0ac0c0bce885123f394147afeb9984dc95154a8d002be50e465adda8a87844c2bbfb2fdfd84face99ca6 Homepage: https://cran.r-project.org/package=MOsemiind Description: CRAN Package 'MOsemiind' (Marshall-Olkin Shock Models with Semi-Independent Time) Provides tools for analyzing Marshall-Olkin shock models semi-independent time. It includes interactive 'shiny' applications for exploring copula-based dependence structures, along with functions for modeling and visualization. The methods are based on Mijanovic and Popovic (2024, submitted) "An R package for Marshall-Olkin shock models with semi-independent times." Package: r-cran-mosqcontrol Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-assertthat, r-cran-pracma, r-cran-nlcoptim, r-cran-nloptr, r-cran-sfsmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mosqcontrol_0.1.0-1.ca2604.1_all.deb Size: 317040 MD5sum: b61b63ac3195c9ce1420fabc338df47e SHA1: 9b78d721b72bec29cd7d74c99e2e601d2e37ec4b SHA256: 8739ca0ae7e69c384689197d3d05aa368225d280a13b3d583a107608bc9ce11c SHA512: ccb247ea74839769fb9a9fb83e699292ba41d68b9527e5d92124e5bb3d12c4866235e5931660316876cd1ff9972fc9b954e32e17844230d2c2a5658235ea3f1e Homepage: https://cran.r-project.org/package=mosqcontrol Description: CRAN Package 'mosqcontrol' (Mosquito Control Resource Optimization) This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments. Package: r-cran-most Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-most_0.1.2-1.ca2604.1_all.deb Size: 60776 MD5sum: 6bd1f98e698870807ae510f8431b724f SHA1: fd15e1f6591818ddb143c6ba7e662861cd89270f SHA256: 26db0d332b5dcb971df124e0bd07ceb3c0bab8bf60583e29f38000ddbedee1c5 SHA512: 50e5b5da245fc5d3532e289658044c6621cdb0e1d854cf277fb1c7efd7f9587b929c1dd1279f4c24bc3fef7cdee4a6dd07fd83b3008442ce719f0919e43f2d0f Homepage: https://cran.r-project.org/package=MOST Description: CRAN Package 'MOST' (Multiphase Optimization Strategy) Provides functions similar to the 'SAS' macros previously provided to accompany Collins, Dziak, and Li (2009) and Dziak, Nahum-Shani, and Collins (2012) , papers which outline practical benefits and challenges of factorial and fractional factorial experiments for scientists interested in developing biological and/or behavioral interventions, especially in the context of the multiphase optimization strategy (see Collins, Kugler & Gwadz 2016) . The package currently contains three functions. First, RelativeCosts1() draws a graph of the relative cost of complete and reduced factorial designs versus other alternatives. Second, RandomAssignmentGenerator() returns a dataframe which contains a list of random numbers that can be used to conveniently assign participants to conditions in an experiment with many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect size, or required sample size of a factorial or fractional factorial experiment, for main effects or interactions, given several possible choices of effect size metric, and allowing pretests and clustering. Package: r-cran-mote Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-car, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mote_1.2.2-1.ca2604.1_all.deb Size: 326272 MD5sum: a4bb901aca9c1220bb114976215e16c1 SHA1: 333f5463549ea201391d208b364ff2a35503f0c0 SHA256: b76a93e0094cb0ae9ff19b9126e8691fdb7c5664f5191d5356e3d7b9c0496a86 SHA512: 1d13b326944e06d6d572e0a0aa880375d017ece390c7ba3f2f1cb36ce170b55d0d4c2793e06a27451778eed6ecd0007200fe3cc12eac65033ade1056c868af34 Homepage: https://cran.r-project.org/package=MOTE Description: CRAN Package 'MOTE' (Effect Size and Confidence Interval Calculator) Measure of the Effect ('MOTE') is an effect size calculator, including a wide variety of effect sizes in the mean differences family (all versions of d) and the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central confidence intervals for each effect size, relevant test statistics, and output for reporting in APA Style (American Psychological Association, 2010, ) with 'LaTeX'. In research, an over-reliance on p-values may conceal the fact that a study is under-powered (Halsey, Curran-Everett, Vowler, & Drummond, 2015 ). A test may be statistically significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 ). Although the American Psychological Association has long advocated for the inclusion of effect sizes (Wilkinson & American Psychological Association Task Force on Statistical Inference, 1999 ), the vast majority of peer-reviewed, published academic studies stop short of reporting effect sizes and confidence intervals (Cumming, 2013, ). 'MOTE' simplifies the use and interpretation of effect sizes and confidence intervals. Package: r-cran-motherduck Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-assertthat, r-cran-cli, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-glue, r-cran-purrr, r-cran-stringr, r-cran-httr2, r-cran-rlang, r-cran-janitor, r-cran-tibble Suggests: r-cran-testthat, r-cran-quarto, r-cran-contoso, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-motherduck_0.2.1-1.ca2604.1_all.deb Size: 2322098 MD5sum: edd8ae37e2caf44caf2deb623599d8d0 SHA1: 91c89681436e694fe22ae3b07b420c87b333c050 SHA256: 723ef07ec148f3e52f858c234f6f0d5969a7ce00110aefe57150e2e50d2fe326 SHA512: c2b5b3bfb3e7a76096e8aea271568b1eb3e7f9b53d95d6d6005b6153cdfc8493e6c90a0261400b137527395039fd93678f7807a31009fcbb0822a061e61e3164 Homepage: https://cran.r-project.org/package=motherduck Description: CRAN Package 'motherduck' (Utilities for Managing a 'Motherduck' Database) Provides helper functions, metadata utilities, and workflows for administering and managing databases on the 'Motherduck' cloud platform. Some features require a 'Motherduck' account (). Package: r-cran-motifcluster Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-rspectra Suggests: r-cran-covr, r-cran-knitr, r-cran-mclust, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-motifcluster_0.2.3-1.ca2604.1_all.deb Size: 449148 MD5sum: 488a76fd2754cdd180219f1f1791b225 SHA1: 02a7bf42a015fd4251fc1461f2b4df6c1b24d14f SHA256: 4a191a34503e8d19e7773d6917f580785d7435cc18731f51baed171609835699 SHA512: 6818beb667186026a53e8df3453d0153608b8c59af085777faf437bb193c2d9d137709d0cb01ad0bf19ea45442de0ab89de05a0549e82753bd303919c2946465 Homepage: https://cran.r-project.org/package=motifcluster Description: CRAN Package 'motifcluster' (Motif-Based Spectral Clustering of Weighted Directed Networks) Tools for spectral clustering of weighted directed networks using motif adjacency matrices. Methods perform well on large and sparse networks, and random sampling methods for generating weighted directed networks are also provided. Based on methodology detailed in Underwood, Elliott and Cucuringu (2020) . Package: r-cran-motifr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2370 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-intergraph, r-cran-network, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-reticulate, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidygraph Suggests: r-cran-ergm, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-motifr_1.0.0-1.ca2604.1_all.deb Size: 607700 MD5sum: 41ba6bd8d64d292133925f365b142954 SHA1: 19a9b325337a2e314324f2370156e8762b0f6269 SHA256: db02b3ce7e163b569d4b1c599c1d305a263818a555cdf8951a5f03cf2f3763b1 SHA512: 9284838cb32bd87f13ceb93276de45fd54e4550f5cd85058a74639107ec2a81b35440f1cae960a33e781eed2592654b1b4e9cdef31e8971cbcbd78139b97c0e9 Homepage: https://cran.r-project.org/package=motifr Description: CRAN Package 'motifr' (Motif Analysis in Multi-Level Networks) Tools for motif analysis in multi-level networks. Multi-level networks combine multiple networks in one, e.g. social-ecological networks. Motifs are small configurations of nodes and edges (subgraphs) occurring in networks. 'motifr' can visualize multi-level networks, count multi-level network motifs and compare motif occurrences to baseline models. It also identifies contributions of existing or potential edges to motifs to find critical or missing edges. The package is in many parts an R wrapper for the excellent 'SESMotifAnalyser' 'Python' package written by Tim Seppelt. Package: r-cran-motorneuron Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2737 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dygraphs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-motorneuron_1.0.0-1.ca2604.1_all.deb Size: 229210 MD5sum: 9bdbbc5103df601ea2c2afff5f002a29 SHA1: f0da45eaf10ea28f346629c268149918f64cd80c SHA256: cffe57b97c495fdccd9421391af08d6e753a1bca3d2736c2d0e936bb0eb8cd9f SHA512: 66c59179f9de874e4a2eaed9b64042e480d5b66dad95e9714838cac55eb1063423c92f2b3ade89321633f4d15f52c5ae9c90f3045975c8cb82043c4b47b96f22 Homepage: https://cran.r-project.org/package=motoRneuron Description: CRAN Package 'motoRneuron' (Analyzing Paired Neuron Discharge Times for Time-DomainSynchronization) The temporal relationship between motor neurons can offer explanations for neural strategies. We combined functions to reduce neuron action potential discharge data and analyze it for short-term, time-domain synchronization. Even more so, motoRneuron combines most available methods for the determining cross correlation histogram peaks and most available indices for calculating synchronization into simple functions. See Nordstrom, Fuglevand, and Enoka (1992) for a more thorough introduction. Package: r-cran-moult Architecture: all Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-moult_2.3.1-1.ca2604.1_all.deb Size: 679566 MD5sum: 38fab3bd7c4b4ea5ae1679caebc5075a SHA1: e8235ebd56e2b61a45409777aba228c8704e9f63 SHA256: 7f85eb35081bd8c3ce2cca39af0f7e6e85f837c1773cbcb2cddad39c29dd9ff3 SHA512: 1b8a4ed65426f73589bc587a7b63428fccce074526e6b6f9a31ff03385c79b886b014148b0e726655dcdf29cdc4ece8da85d756bd6abda78313cb331b87661b4 Homepage: https://cran.r-project.org/package=moult Description: CRAN Package 'moult' (Models for Analysing Moult in Birds) Functions to estimate start and duration of moult from moult data, based on models developed in Underhill and Zucchini (1988, 1990). Package: r-cran-mountainplot Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-latticeextra, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mountainplot_1.4-1.ca2604.1_all.deb Size: 110678 MD5sum: 71f14b9a1ef920d4f7e7bf58f65ec360 SHA1: 93dd6c18ff461b7173cd665f23d9a382e7c28cef SHA256: 76fb2b27ee28e40db77493b3adb8eaf111f4bfe8f85dd9894f6f2da9d37ce608 SHA512: 8c3c696485218f1d028534d0fab5615511e2b813f7dd1252fabe281f7424a3e3b4765805f92f5f843b076855bbff775ac82910dbb5c19a915429b2e981a74885 Homepage: https://cran.r-project.org/package=mountainplot Description: CRAN Package 'mountainplot' (Mountain Plots, Folded Empirical Cumulative Distribution Plots) Lattice functions for drawing folded empirical cumulative distribution plots, or mountain plots. A mountain plot is similar to an empirical CDF plot, except that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at the right side. See Monti (1995) . Package: r-cran-mousetrajectory Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-signal Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/resolute/main/r-cran-mousetrajectory_0.2.1-1.ca2604.1_all.deb Size: 88684 MD5sum: 725b5fc35631790a1a22a06617bc194a SHA1: 0b7c0e6032a8862db46466ead7d5819fc8657085 SHA256: 000f5b09b05806d063945ea52abb90898da62e2ba2e2820532b16ef5ccdf2267 SHA512: 4ec8c7b46a66e9ce9bc1620bc6a0093c423cf6b3c2092b3f0b9a6e6dbaa8f53539ea94801e8447034e399f73d3e4e4692af00f8bbe1c437e5b1de64f2f3dd354 Homepage: https://cran.r-project.org/package=mousetRajectory Description: CRAN Package 'mousetRajectory' (Mouse Trajectory Analyses for Behavioural Scientists) Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) . Package: r-cran-move2 Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-sf, r-cran-rlang, r-cran-units, r-cran-tidyselect, r-cran-dplyr, r-cran-tibble, r-cran-vroom, r-cran-cli, r-cran-vctrs, r-cran-bit64 Suggests: r-cran-knitr, r-cran-askpass, r-cran-digest, r-cran-keyring, r-cran-xml2, r-cran-curl, r-cran-magrittr, r-cran-purrr, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown, r-cran-lwgeom, r-cran-s2, r-cran-move, r-cran-raster, r-cran-withr, r-cran-lubridate, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-circular, r-cran-tidyr, r-cran-gganimate, r-cran-prettymapr, r-cran-gifski, r-cran-ggspatial Filename: pool/dists/resolute/main/r-cran-move2_0.5.0-1.ca2604.1_all.deb Size: 3286402 MD5sum: 6eb17b78a59657101f4eed054f73548c SHA1: 3ad4b62dbc86e75ab5564e3122594d7d8905d900 SHA256: 09cf9e6ae2884267511a18d81c3fa0ebbe367a43da5e298853421fc761c0df10 SHA512: 5c59c1e56c8ccb4ca70ca29eb952eb4cdf158fa4c10ce8a94fe23a2ee47927e36977b5aae2cf2ad7a1f47611c98ffcc4088c15943b19f877f39c51f171435857 Homepage: https://cran.r-project.org/package=move2 Description: CRAN Package 'move2' (Processing and Analysing Animal Trajectories) Tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from 'movebank' and files is facilitated. Package: r-cran-movecost Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 980 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chron, r-cran-gdistance, r-cran-elevatr, r-cran-matrix, r-cran-raster, r-cran-sf, r-cran-sp, r-cran-terra Filename: pool/dists/resolute/main/r-cran-movecost_2.2-1.ca2604.1_all.deb Size: 962564 MD5sum: f8d6cd6bd2f0a4559a7a3b3c67a074e5 SHA1: 964ecc1f998c5dd96a93db61b4461607ea851c3d SHA256: ff1d5767dff038644c1c3d274c4497aa86d1366486e865f2142bbd2f86382280 SHA512: ba428d161804fa38da2108e675f4f250b51f6def39db1325e34b488500329c529a0ef18d9bb30119d6e080674253b02cb33c3b99742197f52db12860d91fc476 Homepage: https://cran.r-project.org/package=movecost Description: CRAN Package 'movecost' (Calculation of Slope-Dependant Accumulated Cost Surface,Least-Cost Paths, Least-Cost Corridors, Least-Cost NetworksRelated to Human Movement Across the Landscape) Provides the facility to calculate non-isotropic accumulated cost surface, least-cost paths, least-cost corridors, least-cost networks using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) . Package: r-cran-movedesign Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4036 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-bsplus, r-cran-combinat, r-cran-config, r-cran-crayon, r-cran-ctmm, r-cran-data.table, r-cran-dplyr, r-cran-fontawesome, r-cran-gdtools, r-cran-ggiraph, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggtext, r-cran-golem, r-cran-gsl, r-cran-lubridate, r-cran-parsedate, r-cran-quarto, r-cran-reactable, r-cran-rintrojs, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyalert, r-cran-shinybusy, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-stringr, r-cran-terra, r-cran-tidyr, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2 Filename: pool/dists/resolute/main/r-cran-movedesign_0.3.2-1.ca2604.1_all.deb Size: 3431800 MD5sum: a75140be3cd1be7e163524b914228c24 SHA1: 9b020cf0d4c6e3696dc6ec86d69a2c84db187caf SHA256: 7fb52450ee9a05f193b2ff1ecc833f58aab457f8c40eef44a0546483649ac8bc SHA512: 4f45744cb99a8324bd53c62abc26583391d5bc7ece388c86869b184bfc2f37f39bd7acfe5f0836ef3c03738574ba72863e669e30db46c11be1cba8bd4aaff69c Homepage: https://cran.r-project.org/package=movedesign Description: CRAN Package 'movedesign' (Study Design Toolbox for Movement Ecology Studies) Toolbox and 'shiny' application to help researchers design movement ecology studies, focusing on two key objectives: estimating home range areas, and estimating fine-scale movement behavior, specifically speed and distance traveled. It provides interactive simulations and methodological guidance to support study planning and decision-making. The application is described in Silva et al. (2023) . Package: r-cran-moveez Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2823 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-biplotez, r-cran-gganimate, r-cran-ggplot2, r-cran-gpabin Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-tibble, r-cran-scales, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-moveez_1.2.0-1.ca2604.1_all.deb Size: 1989434 MD5sum: 26dea852895c53fd937f74ab949edd0d SHA1: eafa5ad5a9aff9bd356bcdee3c567b0eeac49bf3 SHA256: e8dbff29fd27f9508db150407661fd3ca4678045528e5ebb978c7d7a6dc174aa SHA512: 831a3978b9d8e624fcaa02bafaefe48faeec3c5e0fcfde2c49dde16a7b87c613430a4255fde45db7f154181db3e3553f151b2043a137db343b0d998f2e301b42 Homepage: https://cran.r-project.org/package=moveEZ Description: CRAN Package 'moveEZ' (Animated Biplots) Create animated biplots that enables dynamic visualisation of temporal or sequential changes in multivariate data by animating a single biplot across the levels of a time variable. It builds on objects from the 'biplotEZ' package, Lubbe S, le Roux N, Nienkemper-Swanepoel J, Ganey R, Buys R, Adams Z, Manefeldt P (2024) , allowing users to create animated biplots that reveal how both samples and variables evolve over time. Package: r-cran-movegroup Architecture: all Version: 2024.03.05-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2759 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-beepr, r-cran-dplyr, r-cran-ggmap, r-cran-knitr, r-cran-lubridate, r-cran-magick, r-cran-move, r-cran-purrr, r-cran-raster, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stars, r-cran-starsextra, r-cran-stringr, r-cran-terra, r-cran-tidyr, r-cran-tidyselect, r-cran-viridis Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-movegroup_2024.03.05-1.ca2604.1_all.deb Size: 2108308 MD5sum: 9709d2ab75d3680cee24825b21d8ac8b SHA1: cb170b28920f5302d9e8ce093c81eb77d87caea7 SHA256: d509f68f9a2a930134cca09977b635d1a2b1b2b63a54128e8598277e09a1b838 SHA512: 9d16fb2acf58b5b286662faa92fb32800b80372dbbd48d0ed2cf442d004d30627cea170653572a834c0583a9544906a7f94013b7de25bf51e514a9584762877a Homepage: https://cran.r-project.org/package=movegroup Description: CRAN Package 'movegroup' (Visualizing and Quantifying Space Use Data for Groups of Animals) Offers an easy and automated way to scale up individual-level space use analysis to that of groups. Contains a function from the 'move' package to calculate a dynamic Brownian bridge movement model from movement data for individual animals, as well as functions to visualize and quantify space use for individuals aggregated in groups. Originally written with passive acoustic telemetry in mind, this package also provides functionality to account for unbalanced acoustic receiver array designs, and satellite tag data. Package: r-cran-movementsync Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-hms, r-cran-igraph, r-cran-lmtest, r-cran-osfr, r-cran-rlang, r-cran-scales, r-cran-signal, r-cran-tidyr, r-cran-waveletcomp, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-movementsync_0.1.5-1.ca2604.1_all.deb Size: 1959300 MD5sum: 3dd58951ded15bd0786cf452fdda13cd SHA1: 06a11c80ab1c66f908468ed1af9d38505c0aae3c SHA256: 4a3ab0a2b37f4241d3fc8a7db510b49522e9ec9873b10457676b262cd5735c45 SHA512: 68fc964a7cec197d8eacb8057427ff3d85dc1254de721502d27743097d25410d804720427b121a256fcc3f5f99a401dad7d31520ad8b3810560f3559a11a74fb Homepage: https://cran.r-project.org/package=movementsync Description: CRAN Package 'movementsync' (Analysis and Visualisation of Musical Audio and Video MovementSynchrony Data) Analysis and visualisation of synchrony, interaction, and joint movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and Tarsitani (2021) , while example analyses can be found in Clayton, Jakubowski, and Eerola (2019) . Additionally, wavelet analysis techniques have been applied to examine movement-related musical interactions, as shown in Eerola et al. (2018) . Package: r-cran-movieroc Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-animation, r-cran-intrval, r-cran-gtools, r-cran-e1071, r-cran-robustbase, r-cran-rsolnp, r-cran-ks, r-cran-zoo Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-movieroc_0.1.2-1.ca2604.1_all.deb Size: 2069088 MD5sum: 02dfdc3f73d39a6caf76fa702fe34f06 SHA1: e7cccebfd1e4d9437fc1c372ad08fd01e48a33bd SHA256: 9d0e881d15b157f76f4db84bc46c95245ece0b76aa9348478eef735d913ec72e SHA512: 63bf0687181da45854a1e86a70b6ceb139db13f09a9084a2a2bb1e82770bdef632c07c5e2d0313cfcb01cf487802b4543365368025e770a0ab28b71e4c317704 Homepage: https://cran.r-project.org/package=movieROC Description: CRAN Package 'movieROC' (Visualizing the Decision Rules Underlying Binary Classification) Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC() function); - transforming the marker by a proper function trying to improve the classification performance (hROC() function); - when dealing with multivariate markers, considering a proper transformation to univariate space trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function). The classification regions behind each point of the ROC curve are displayed in both static graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or videos (movieROC() function). Package: r-cran-mpae Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-rcmdrmisc Suggests: r-cran-car, r-cran-gbm, r-cran-leaps, r-cran-lmtest, r-cran-glmnet, r-cran-mgcv, r-cran-np, r-cran-neuralnettools, r-cran-pdp, r-cran-vivid, r-cran-plot3d, r-cran-appliedpredictivemodeling, r-cran-islr Filename: pool/dists/resolute/main/r-cran-mpae_0.1.2-1.ca2604.1_all.deb Size: 122886 MD5sum: e3cc7e79ff07e8beca21be78639dcc20 SHA1: ba27e465b83829dcb6fe7b3cc254cec1927b2430 SHA256: 9ceb637f35dd9f12278a5dc9f97b406e8082176a88772172167d0c5313a19412 SHA512: 057b070d1018390cb830ef43e641de8f01ad4f56562adf66d534bfa176cfb4bbf27ee944ebed674e44f59336e0ad4c7825380545d57a8b65a6e352198db8d0e1 Homepage: https://cran.r-project.org/package=mpae Description: CRAN Package 'mpae' (Metodos Predictivos de Aprendizaje Estadistico (StatisticalLearning Predictive Methods)) Functions and datasets used in the book: Fernandez-Casal, R., Costa, J. and Oviedo-de la Fuente, M. (2024) "Metodos predictivos de aprendizaje estadistico" . Package: r-cran-mpathr Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-readr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mpathr_1.0.4-1.ca2604.1_all.deb Size: 300220 MD5sum: cbeed977e1812371ec88bbf567352fa5 SHA1: a1bd6152bf44fde14f8e9a73e42bf8c518daf9fe SHA256: 8bad71e9c0d1cd096adb3e71eb542787d3a148426fe64d725d03985b72d0b725 SHA512: c612edd7f3aa473bb4ab2d4d0f62582a5aac1d5d71c2274e3255da2a51973929ac456524a0ceddc5b41432982c36c0c1f7c6d6ab75c98800f7be06e271a784d3 Homepage: https://cran.r-project.org/package=mpathr Description: CRAN Package 'mpathr' (Easily Handling Data from the ‘m-Path’ Platform) Provides tools for importing and cleaning Experience Sampling Method (ESM) data collected via the 'm-Path' platform. The goal is to provide with a few utility functions to be able to read and perform some common operations in ESM data collected through the 'm-Path' platform (). Functions include raw data handling, format standardization, and basic data checks, as well as to calculate the response rate in data from ESM studies. Package: r-cran-mpathsenser Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4882 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-furrr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-rsqlite, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cli, r-cran-curl, r-cran-ggplot2, r-cran-httr, r-cran-kableextra, r-cran-knitr, r-cran-lintr, r-cran-progressr, r-cran-rmarkdown, r-cran-rvest, r-cran-sodium, r-cran-spelling, r-cran-testthat, r-cran-vroom Filename: pool/dists/resolute/main/r-cran-mpathsenser_1.2.4-1.ca2604.1_all.deb Size: 3980236 MD5sum: 7f40c31a3380e4d6bab6e65d95e2ffeb SHA1: 842796152e6c021153e60828309f49e088699fdc SHA256: 276344af2df0d1093f1daacc2f147d15c7bdaec1b2223e56cef63b3979ed078b SHA512: f51f2c956603f57a6051db743a40f29847f5334627c53f15ed5f253d4c430c2f79c4c0a391af7b5ac140baca4d025a4bee775ebe219e14622f5f417f42ddb118 Homepage: https://cran.r-project.org/package=mpathsenser Description: CRAN Package 'mpathsenser' (Process and Analyse Data from m-Path Sense) Overcomes one of the major challenges in mobile (passive) sensing, namely being able to pre-process the raw data that comes from a mobile sensing app, specifically 'm-Path Sense' . The main task of 'mpathsenser' is therefore to read 'm-Path Sense' JSON files into a database and provide several convenience functions to aid in data processing. Package: r-cran-mpci Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mpci_1.0.7-1.ca2604.1_all.deb Size: 44012 MD5sum: 62595737d528bb8986bd148bd5705050 SHA1: c0cad7a55e4f9549669ce24ec53fb909c02098ca SHA256: 076683fcc2d1b624527799b0aabec15e11619f932be487bd23c72ccde622c3b3 SHA512: 0fbeac950eaf7efd110e74ce60b06c9cc8fc4167af26bb54366dd3e4f34f97da08c434552e6a44c7f28347a90bc65affb12eaacad3b500a76a0fb6e8def4494b Homepage: https://cran.r-project.org/package=MPCI Description: CRAN Package 'MPCI' (Multivariate Process Capability Indices (MPCI)) It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included. Package: r-cran-mpdir Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-mpdir_0.3-1.ca2604.1_all.deb Size: 392320 MD5sum: 5917e74449075c47347e6b216ff355fa SHA1: 1b6d762e31f8261a6f9dc6c04f038dd5ac1ade75 SHA256: 30b3a11189b72f78524013263d692386315729dbddfa5797b7584fb8b4e45892 SHA512: 801846e00a5b3aa0ada77d8a612662c12531c5a4b7f231d87979e48e8ba5bafeba374a802cb00540ca199dcb57bfc631b503cf0ca97d98b32c907065d5ded526 Homepage: https://cran.r-project.org/package=MPDiR Description: CRAN Package 'MPDiR' (Data Sets and Scripts for Modeling Psychophysical Data in R) Data sets and scripts for Modeling Psychophysical Data in R (Springer). Package: r-cran-mpge Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mpge_1.0.1-1.ca2604.1_all.deb Size: 78522 MD5sum: 9add99d8047f3a96a148d121ea45c4d7 SHA1: df7f39d23a0cdb84416a5698ef465290148c79e6 SHA256: ba52af6928d2d866b9f85d379130ec1cb9ec493c7b2a5a74d91a1b88e897347b SHA512: 1e42deffce60ce20c4bb002dd83cf3650561849702a188fcec2124d7f0108816319f0fbee4d3a3b29d1bba7e578e6cfb568d134857ad3a9c0e61431815c73470 Homepage: https://cran.r-project.org/package=MPGE Description: CRAN Package 'MPGE' (A Two-Step Approach to Testing Overall Effect ofGene-Environment Interaction for Multiple Phenotypes) Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, T Haldar, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) . Package: r-cran-mpi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-doparallel, r-cran-foreach, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-mpi_0.1.0-1.ca2604.1_all.deb Size: 37648 MD5sum: 547a9d7e14da59ec256aacbcd7117c97 SHA1: c40f90d0aa13b8c12bb69e88bc1264d5758d963e SHA256: a3556d31bfc382548082a10890847e7525904177bfb950eb89a32ab1aa137b95 SHA512: 0ad47b06c9e75bfd57cb7c8b0977b1857cf50dad15b9b4d72b963d48aa48b99bb18ad888a681af693293172eea84d2eb9e5a9841b818825f34b1251ff807fc9d Homepage: https://cran.r-project.org/package=MPI Description: CRAN Package 'MPI' (Computation of Multidimensional Poverty Index (MPI)) Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold. The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator. Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) for The Alkire-Foster methodology. Package: r-cran-mpindex Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-jsonlite, r-cran-purrr, r-cran-tibble, r-cran-openxlsx Suggests: r-cran-rlang, r-cran-testthat, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-gt Filename: pool/dists/resolute/main/r-cran-mpindex_0.2.1-1.ca2604.1_all.deb Size: 134170 MD5sum: a808242a90726f002d698f3e49da1e35 SHA1: beecbabbb895ee4de0e4f7fab7e6789161c59328 SHA256: 1c845c0ee4412d8d06fb663e026e59f584f2e0cbcb40cbddd28b0b4c380b0b06 SHA512: 7acd79cdfb172be25e184db4ae62262ca3dd9e1e8423357b9709a094c1e19ce52e576656f3ec9bca2e9b7e793a151b98a12eacb2df03bf2af3d7b4201b8af612 Homepage: https://cran.r-project.org/package=mpindex Description: CRAN Package 'mpindex' (Multidimensional Poverty Index (MPI)) A set of easy-to-use functions for computing the Multidimensional Poverty Index (MPI). Package: r-cran-mpitbr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1647 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-survey Suggests: r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-stringi, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-mpitbr_1.0.1-1.ca2604.1_all.deb Size: 1454282 MD5sum: 9ce214278136e024251a130456a5e37a SHA1: d2ded5d600d978ad7a8b199a723f986a7641d316 SHA256: 37e2f9617ad6f66cff53e8fc5dc6c1a11d747e101d0907cc080fbbfb864ed8e9 SHA512: 2e411237a230059ea2d9d34e725b00b8136ff35b488ea4d7f74fe1c6efbe298362709c41fa53d8b72d8a643520050d6b22bcc62b43a8e246bc994ea6e4f81dae Homepage: https://cran.r-project.org/package=mpitbR Description: CRAN Package 'mpitbR' (Calculate Alkire-Foster Multidimensional Poverty Measures) Estimate Multidimensional Poverty Indices disaggregated by population subgroups based on the Alkire and Foster method (2011) . This includes the calculation of standard errors and confidence intervals. Other partial indices such as incidence, intensity and indicator-specific measures as well as intertemporal changes analysis can also be estimated. The standard errors and confidence intervals are calculated considering the complex survey design. Package: r-cran-mplot Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaps, r-cran-foreach, r-cran-bestglm, r-cran-doparallel, r-cran-dorng, r-cran-plyr, r-cran-shinydashboard, r-cran-shiny, r-cran-glmnet, r-cran-googlevis, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-mvoutlier, r-cran-glmulti, r-cran-rmarkdown, r-cran-dt, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mplot_1.0.6-1.ca2604.1_all.deb Size: 203590 MD5sum: acb48653aa8765fe53a7c541dff63ee4 SHA1: 52da26559c466380771895a3ed19be0c1d970d2f SHA256: 3f09775128030b9e5e9e146e71a32f8d4173697af227fbddb0c713d594b17b7d SHA512: 5b58bcaf2e26245a19f0dff4c85911863292892f3657e18b9b3a2211f164f330e0a090535af0dd469aa4e92df01dbcece109a172e0b1430941906585b6cc5b23 Homepage: https://cran.r-project.org/package=mplot Description: CRAN Package 'mplot' (Graphical Model Stability and Variable Selection Procedures) Model stability and variable inclusion plots [Mueller and Welsh (2010, ); Murray, Heritier and Mueller (2013, )] as well as the adaptive fence [Jiang et al. (2008, ); Jiang et al. (2009, )] for linear and generalised linear models. Package: r-cran-mplusautomation Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3386 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-plyr, r-cran-gsubfn, r-cran-coda, r-cran-xtable, r-cran-lattice, r-cran-texreg, r-cran-pander, r-cran-digest, r-cran-ggplot2, r-cran-data.table, r-cran-fastdummies, r-cran-checkmate Suggests: r-bioc-rhdf5, r-cran-relimp, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mplusautomation_1.2-1.ca2604.1_all.deb Size: 2499684 MD5sum: c362c2721b929e532fd59d7124c1e558 SHA1: f8989a6b060ea3c7f388606e6f551f8b5a0102a7 SHA256: 31cd6f9682b5f3e0c8309e83f1a08275907a89bc7c5d7bf79334f720a41c9432 SHA512: 9bf633f7cd565330a72713ac06deef01de391424d561ff219e68bebe50947c7ae5e4023536c60a0337a7485a9ec9d19313f572c9cf311ba666dbe0a901169a88 Homepage: https://cran.r-project.org/package=MplusAutomation Description: CRAN Package 'MplusAutomation' (An R Package for Facilitating Large-Scale Latent VariableAnalyses in Mplus) Leverages the R language to automate latent variable model estimation and interpretation using 'Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics. Package: r-cran-mpluslgm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mplusautomation, r-cran-magrittr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-glue Filename: pool/dists/resolute/main/r-cran-mpluslgm_1.0.0-1.ca2604.1_all.deb Size: 91810 MD5sum: 5331f6b0c61d3a0cb3e811625d26b0e7 SHA1: 539e8d19e0b435a116ef861d68c5674c3e242cff SHA256: c4c3cac15ec0c0385f923bc41f9997da639254742a3f2b006a2db8ec7f3a3228 SHA512: 268eddf256462a209611ed7253db846e9260e3552f0a57da81d5b27dea3b22f818fbe6c5793e5b875543a19173517663af61e747113c305d7947e420cd957522 Homepage: https://cran.r-project.org/package=MplusLGM Description: CRAN Package 'MplusLGM' (Automate Latent Growth Mixture Modelling in 'Mplus') Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in 'Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). The package builds upon the capabilities of the 'MplusAutomation' package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. “MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.” Structural Equation Modeling, 25(4), 621–638. The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). “An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.” Advances in Life Course Research, 43, Article 100323. . Package: r-cran-mplusparallel.automation Architecture: all Version: 0.0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mplusautomation, r-cran-dplyr, r-cran-furrr, r-cran-future Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mplusparallel.automation_0.0.1.1-1.ca2604.1_all.deb Size: 39268 MD5sum: b4a9d98b12c2cb2ccb88d17295a2ded1 SHA1: fc04c2460b1e149e03cd48c816f9ac6dec216540 SHA256: 46c1d7e78cd57f85270b57ed1570728ba8c2f802ea8eb234b588bf2cb35df69a SHA512: 5f26703d3a85b5a6e9d128850c4f93e85a6ccaa774c560739bfba6b0937fe4982b81f9715bca77a56257e8ede62802ce73487d6e54881474ca2fecc9e71e2a7e Homepage: https://cran.r-project.org/package=mplusParallel.automation Description: CRAN Package 'mplusParallel.automation' (Parallel Processing Automation for 'Mplus') Offers automation tools to parallelize 'Mplus' operations when using 'R' for data generation. 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Package: r-cran-mpmaggregate Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expm Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-rcompadre, r-cran-kableextra, r-cran-rphylopic, r-cran-collidr, r-cran-png Filename: pool/dists/resolute/main/r-cran-mpmaggregate_0.2.5-1.ca2604.1_all.deb Size: 430546 MD5sum: b878ab34d87205c463910037029fabfa SHA1: 7c09726d3bc47d41e9da0504b4bf73d53c75ebd0 SHA256: 918ef5e4e711ca5f0a01380c8fe6bad9ab2771f5bfd703a5cd0035f12201ed1b SHA512: f3fe1f31b7e3970367c23783db107d0c25cd3b7c4facc308a1318b433c0d698c4119bd1f3e3e1c7e3d29f874f766d711845dcf0ee7f38ae685edc7cb54c6b5cd Homepage: https://cran.r-project.org/package=mpmaggregate Description: CRAN Package 'mpmaggregate' (Aggregate Matrix Population Models) Aggregates matrix population models (MPMs) in both the lambda (stable growth rate) and R0 (net reproductive rate) frameworks, including standard and elasticity-consistent aggregators. Standard aggregation in the lambda framework maintains consistent lambda and stable stage distribution, while standard aggregation in the R0 framework maintains consistent R0 and cohort stable stage distribution. Elasticity-consistent aggregators maintain these same consistencies with respect to the chosen framework and additionally preserve consistent reproductive values in the lambda framework and cohort reproductive values in the R0 framework. Aggregation can take the form of general-to-general MPM (mpm_aggregate) or Leslie-to-Leslie MPM (leslie_aggregate). 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Users can set up a data generative model that preserves dependence structures among predictors given existing data (continuous, binary, or ordinal). Users can also generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This package includes several statistical models common in environmental mixtures studies. For more details and tutorials, see Nguyen et al. (2022) . Package: r-cran-mppr Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-matrix, r-cran-nlme, r-cran-qtl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mppr_1.5.0-1.ca2604.1_all.deb Size: 2039796 MD5sum: b3d40666b934bb409c3d6d243f312646 SHA1: 9ecd52ffd33485606cd8acbb4bae5fbfab8b5d77 SHA256: acc33a6a8c2eb84cafb32496ed697952a659f7a71db0f1503df539b18152ef1f SHA512: ba71a9563283e1c41592c836736c536dd0678f1d8e92a3925cfd9e5d97746a2cd2f621fb25897491934636c68d6aad9a32c5ef044ab4e87322bfc810ad187b00 Homepage: https://cran.r-project.org/package=mppR Description: CRAN Package 'mppR' (Multi-Parent Population QTL Analysis) Analysis of experimental multi-parent populations to detect regions of the genome (called quantitative trait loci, QTLs) influencing phenotypic traits measured in unique and multiple environments. The population must be composed of crosses between a set of at least three parents (e.g. factorial design, 'diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) , in Garin, Malosetti and van Eeuwijk (2020) , and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) . 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Package: r-cran-mps Architecture: all Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1853 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mps_2.3.1-1.ca2604.1_all.deb Size: 1287994 MD5sum: 80ba5299abbfd76c2b8c42510ca86cac SHA1: d8a17c9ad2df00e9526b8a385398dd1604a7a760 SHA256: b0ee960b0e1be8153b1953fde8bd54cc7d84256387ae54bfe03f19f329718dcf SHA512: 01e1fdacf1a247472e0895f2cbfbc5bb94dba21de5d705137b8cb3953e6699fb56a96c3b597b65ffb32351c0c30c4cffafa03b5047445b335936b1c71e14b921 Homepage: https://cran.r-project.org/package=MPS Description: CRAN Package 'MPS' (Estimating Through the Maximum Product Spacing Approach) Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in . 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Version 0.1.0 includes thirteen series covering the United States, United Kingdom, and Australia: for the US, the policy news shock of Nakamura and Steinsson (2018) , the orthogonalised surprise of Bauer and Swanson (2023) , the target and path factors of the Swanson (2021) extension of Gurkaynak, Sack, and Swanson (2005), the pure monetary policy and central bank information shocks of Jarocinski and Karadi (2020) , the informationally-robust shock of Miranda-Agrippino and Ricco (2021) , and the shadow federal funds rate of Wu and Xia (2016) ; for the UK, the UK Monetary Policy Event-Study Database of Braun, Miranda-Agrippino, and Saha (2025) , the high-frequency surprise of Cesa-Bianchi, Thwaites, and Vicondoa (2020) , and the narrative shock of Cloyne and Hurtgen (2016) ; for Australia, the three-component RBA surprise of Hambur and Haque (2023) and the credit-spread-augmented RBA narrative shock of Beckers (2020). 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Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) and Hasler and Hothorn (2008) . Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances. 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Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. . 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Users can supply a tidy model, and 'mrIML' automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) and Fountain-Jones et al. (2024) . 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Reader performance metrics are provided for area under and expected utility of ROC curves, likelihood ratio of positive or negative tests, and sensitivity and specificity. ROC curves can be estimated empirically or with binormal or binormal likelihood-ratio models. Statistical comparisons of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and the unified framework of Hillis (2005) . The ANOVA can be conducted with data from a full factorial, nested, or partially paired study design; with random or fixed readers or cases; and covariances estimated with the DeLong method, jackknifing, or an unbiased method. Smith and Hillis (2020) . Package: r-cran-mrmcbinary Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-survival Filename: pool/dists/resolute/main/r-cran-mrmcbinary_1.0.6-1.ca2604.1_all.deb Size: 107524 MD5sum: 89c7c2a95c23ae952834be9d495bfc7c SHA1: a5c5972608beb68720b3b35ba50bf103e1986571 SHA256: 198ab1daa13244c763cadea73d2d6afc08132144b0302bd00a68949aaee61e6a SHA512: ec43e6a8c52507205774089a4373e88c550036e7305b10ea2de6a44901897b5226059a5425377da6871c416bd3b3bcd2f9c07ea0b04bdbb8dfad970c7815866f Homepage: https://cran.r-project.org/package=MRMCbinary Description: CRAN Package 'MRMCbinary' (Multi-Reader Multi-Case Analysis of Binary Diagnostic Tests) Implements methods for comparing sensitivities and specificities in balanced (or fully crossed) multi-reader multi-case (MRMC) studies with binary diagnostic test results. It implements conditional logistic regression and provides score tests equivalent to Cochran's Q test (which corresponds to McNemar's test when comparing two modalities only). The methodology is based on Lee et al. (2026) . 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The function can calculate sample sizes where the endpoint of interest in the study is either ROC AUC (Area-Under-the-Receiver-Operating-Characteristics-Curve) or sensitivity. The package can also return sample sizes for studies expected to have clustering effect (e.g.- multiple pulmonary nodules per patient). All calculations assume that the study design is fully crossed (paired-reader, paired-case) where each reader reads/interprets each case and that there are two interventions/imaging-modalities/techniques in the study. In addition to MRMC, it can also be used to estimate sample sizes for standalone studies where sensitivity or AUC are the primary endpoints. The methods implemented are based on the methods described in Zhou et.al. (2011) and Obuchowski (2000) . 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In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information. 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This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) , Badsha, Martin and Fu (2021) , Kvamme and Badsha, et al. (2025) . 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Package: r-cran-mrpstrata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-progress, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mrpstrata_0.1.0-1.ca2604.1_all.deb Size: 755178 MD5sum: 8f4e27fc09bc61fb06d2dd5aca48b66b SHA1: d7b6e566c2147535e8312ff03f941461fd6eec0c SHA256: 27378bdb7e902af463c6f4af5449d32cf2706654744a3da1ac6fad84bffbd762 SHA512: 83063e39e172ad7765e4be50d05a0490dbbda39845fe745d148461cc836854a086ba0fd4ab94b85af1315f2277e92d656068ba610c61acf5272f4e43e54e2533 Homepage: https://cran.r-project.org/package=mrPStrata Description: CRAN Package 'mrPStrata' (Multiply Robust Estimation in Causal Survival Analysis withTreatment Noncompliance) Provides multiply robust estimators of principal survival causal effects among always-takers, compliers, and never-takers in studies with treatment noncompliance. The methods are based on Cheng et al. (2026) . Package: r-cran-mrqol Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mrqol_1.0.1-1.ca2604.1_all.deb Size: 35438 MD5sum: 2f3c2abef7d4e25c3e20331c28498d0e SHA1: ee2a00a5030816a84789b9a95404ccf39719a929 SHA256: eded34cd7b294be32d7f683caee32771e9a43ac3bb7da7e65058d0ac652bdb52 SHA512: a81aafb9b49f7d9d68f44896a3e3f891e00d54c3e65dbc71a67e5d0e7197d77c966b9de893280e46e080619454afb684413dc966ac7714d3339d4f3e9ec36d8e Homepage: https://cran.r-project.org/package=MRQoL Description: CRAN Package 'MRQoL' (Minimal Clinically Important Difference and Response ShiftEffect for Health-Related Quality of Life) To calculate the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method. Package: r-cran-mrreg Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-mrreg_0.1.6-1.ca2604.1_all.deb Size: 117180 MD5sum: 865cc54b00b8ebc9aca78b9bff92e4af SHA1: dccde826b6e0428fa7fb382d66351d8d59f080a3 SHA256: b984aa58ba8af3250252f688b71fdbbaade96f456bdca2808392b6e20cf05add SHA512: cab21bdb635f1f94fe31dc2fabfb418fafbf08b3cf0708ee91f9fb63e7dbf4acaff35063e17acb986cec4e55e11e54d0b63b9d65b235376409e95427e1b201af Homepage: https://cran.r-project.org/package=MRReg Description: CRAN Package 'MRReg' (MDL Multiresolution Linear Regression Framework) We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q). We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2021) . Package: r-cran-mrregression Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mrregression_1.0.0-1.ca2604.1_all.deb Size: 243788 MD5sum: 11cac313fe2a4b6029c002b2fdf61b73 SHA1: bccf24db9ea35de55dd51d1cfc025bc660506f6e SHA256: e904bbd7b524827332123e94930662b526b96c35d3aced3b6bd2498f132592e2 SHA512: 270a1b8dc362ff9e2fafd39e3e2a7b02b2e252cb1678a62609bfb6054297a04ea2e9c66ad8eb2ff3f263d45cc57cb52ec5e7d87ab121a384b622c7434f10a22e Homepage: https://cran.r-project.org/package=mrregression Description: CRAN Package 'mrregression' (Regression Analysis for Very Large Data Sets via Merge andReduce) Frequentist and Bayesian linear regression for large data sets. Useful when the data does not fit into memory (for both frequentist and Bayesian regression), to make running time manageable (mainly for Bayesian regression), and to reduce the total running time because of reduced or less severe memory-spillover into the virtual memory. This is an implementation of Merge & Reduce for linear regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). 'Streaming statistical models via Merge & Reduce'. International Journal of Data Science and Analytics, 1-17, . Package: r-cran-mrstdcrt Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geepack, r-cran-lme4, r-cran-nlme, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mrstdcrt_0.1.1-1.ca2604.1_all.deb Size: 183712 MD5sum: 4456abd384fbdefd474a5e82de0478e7 SHA1: f85853da1c43fb4d45f29c9e7cc274a8154f89fc SHA256: 49356ba7e4383b39148f3978da3a87d3ab3965945aa99732c65126cb55bebe45 SHA512: 4711ad232d31bb3bc960e57be2203565a9017c9f4bf874dd242aae8d4fdc31992c275872c2e782bf6be9c9a83add9f6dc18f49121af12120bf738c8623c2d5fd Homepage: https://cran.r-project.org/package=MRStdCRT Description: CRAN Package 'MRStdCRT' (Model-Robust Standardization in Cluster-Randomized Trials) Implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) . 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The working model may be fit using linear mixed models for continuous outcomes or generalized estimating equations and generalized linear mixed models for binary outcomes. Period inclusion for aggregation is determined automatically: only periods with both treated and control clusters are included in the construction of the marginal means and treatment effect contrasts. See Fang et al. (2025) . 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Supports four types of analyses: (1) proximal causal excursion effects, including weighted and centered least squares (WCLS) for continuous proximal outcomes by Boruvka et al. (2018) and the estimator for marginal excursion effect (EMEE) for binary proximal outcomes by Qian et al. (2021) ; (2) distal causal excursion effects (DCEE) for continuous distal outcomes using a two-stage estimator by Qian (2025) ; (3) mediated causal excursion effects (MCEE) for continuous distal outcomes, estimating natural direct and indirect excursion effects in the presence of time-varying mediators by Qian (2025) ; and (4) standardized proximal effect size estimation for continuous proximal outcomes, generalizing the approach in Luers et al. (2019) to allow adjustment for baseline and time-varying covariates for improved efficiency. 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For method details see Hsiao C, Wan SK (2014). , Hansen BE (2007). , Elliott G, Gargano A, Timmermann A (2013). . 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The 'msig' package provides you with powerful, easy-to-use and flexible query functions for the 'MsigDB' database. There are 2 query modes in the 'msig' package: online query and local query. Both queries contain 2 steps: gene set name and gene. The online search is divided into 2 modes: registered search and non-registered browse. For registered search, email that you registered should be provided. Local queries can be made from local database, which can be updated by msig_update() function. Package: r-cran-msigdbr Architecture: all Version: 26.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-babelgene, r-cran-curl, r-cran-dplyr, r-cran-lifecycle, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-msigdbr_26.1.0-1.ca2604.1_all.deb Size: 33560 MD5sum: 98cce0e3646a0a760117cfd5cd881cdc SHA1: 0f078b19545ef123d2ff5441dccc0cc991362dd6 SHA256: 041bd3ffab0f074ee5b1ea14767d14188fe97b18fffefacb949285c3e041cc68 SHA512: e757576be9d8e3e3f77c338a889d3b7126bd1602e482df3e0447277003a2350b67f0ea02239217456ac03013ed5958c7d0e7e2dcf1aa021e0bfc433cb0d20576 Homepage: https://cran.r-project.org/package=msigdbr Description: CRAN Package 'msigdbr' (MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format) Provides the 'Molecular Signatures Database' (MSigDB) gene sets typically used with the 'Gene Set Enrichment Analysis' (GSEA) software (Subramanian et al. 2005 , Liberzon et al. 2015 , Castanza et al. 2023 ) as an R data frame. The package includes the human genes as listed in MSigDB as well as the corresponding symbols and IDs for frequently studied model organisms such as mouse, rat, pig, fly, and yeast. 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Generates plots similar to those used previously in Alexandrov et al. (2020) and Rozen et al. (2026). 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However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields. The segmentation(), lambda_estimator() functions are the main functions of this package. Package: r-cran-msigtools Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-philentropy, r-cran-quadprog, r-cran-sets Suggests: r-cran-cosmicsig, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-msigtools_1.0.7-1.ca2604.1_all.deb Size: 67938 MD5sum: de594456dedc7653a4cfc6e4d5104f2c SHA1: 6cdf0a52b3b01aac06464669562c9c1217c579b3 SHA256: b656e7b86106ea356b915dc185b91c59d1f51bd7e938cf78f0afda2d4eb02c91 SHA512: 0d82239e8bb1cb08dfbda31775aebd6a732f73f6905d87b9c70b323283bc705410f65c130d311dfc4db10e8d4322c5f6e9e7f9af4b2b953ac5da96f161f4b0b6 Homepage: https://cran.r-project.org/package=mSigTools Description: CRAN Package 'mSigTools' (Mutational Signature Analysis Tools) Utility functions for mutational signature analysis as described in Alexandrov, L. B. (2020) . This package provides two groups of functions. One is for dealing with mutational signature "exposures" (i.e. the counts of mutations in a sample that are due to each mutational signature). The other group of functions is for matching or comparing sets of mutational signatures. 'mSigTools' stands for mutational Signature analysis Tools. Package: r-cran-msir Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-msir_1.4-1.ca2604.1_all.deb Size: 500170 MD5sum: b31ae1b6a02a9107e360f5c5a7d0cb40 SHA1: 91585942bfe8c1a429471597c39d9c2d7b697ade SHA256: 42a87e0ad648921f929ca3316987954d6ade2d79bb1cbdac5e1908f4ad442d74 SHA512: b406a1122354a9110b144eec1064745088f82b915fcf9f5ccaf34de675e9f8092bbf5c3cfe4bda12013a3fdfdb7ec3ce111a5caa972945fd59b5680fe49824f7 Homepage: https://cran.r-project.org/package=msir Description: CRAN Package 'msir' (Model-Based Sliced Inverse Regression) An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression. 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If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided. 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Package: r-cran-msmgoptimizer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-readxl, r-cran-dplyr, r-cran-dt, r-cran-waiter, r-cran-htmltools, r-cran-zip Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-msmgoptimizer_0.1.0-1.ca2604.1_all.deb Size: 1460716 MD5sum: 56007ecc0013f202fb438b4cdf4a2a49 SHA1: 91b5199305f85c2c969f8e2f36f9dc1bf22b2c6e SHA256: ebde8bffd5a137f81a638ec0610c5be027cb1b9ebf49436d7eda7b5b7ebedb64 SHA512: 2241da197d38ff392bead935a5d92771e2c8455351b3c0c199cbffadd170b31d645c4e28cb17946b3cd612098e105573e5d9c00f091e741a6be16fea542ff7f1 Homepage: https://cran.r-project.org/package=MSMGOptimizer Description: CRAN Package 'MSMGOptimizer' (Mine Sustainability Modeling Group (MSMG) 'SimaPro' CSVOptimizer) A 'Shiny' application for converting 'Excel'-based Life Cycle Inventory (LCI) data into 'SimaPro' CSV (Comma-Separated Values) format for use in Life Cycle Assessment (LCA) modeling. Developed by the Mine Sustainability Modeling Group (MSMG) at Missouri University of Science and Technology under NSF (National Science Foundation) funding (Award No. 2219086). See Pizzol (2022) for the original 'Python' implementation that inspired this tool. 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For more details, see McCurdy (2025), "Introduction to Data Science with R" . Package: r-cran-msmwra Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-msmwra_1.5-1.ca2604.1_all.deb Size: 29828 MD5sum: b4e1c40287eb02890c0fa04da2be0e04 SHA1: 95b07774365a5075bb69cae7fe7738b50d1fdf43 SHA256: e395c981b0dc96087711bee6f3a2d7d45b21d22c474c972a6c516847c014056a SHA512: c0dc581a0afb7b2b5a1d86aaa0f088b6e8c0c619ccc339df1a7e4639d4204780f867988decfd24a2101fddf0b5da22006397db79ddc7df7569f2633fdb45cd66 Homepage: https://cran.r-project.org/package=MSMwRA Description: CRAN Package 'MSMwRA' (Multivariate Statistical Methods with R Applications) Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler". 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This also serves as a companion package for the STAT 571: Multivariate Analysis course offered by the Department of Statistics at the University of Illinois at Urbana-Champaign ('UIUC'). Package: r-cran-msoutcomes Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-msoutcomes_0.2.1-1.ca2604.1_all.deb Size: 119834 MD5sum: ffe57811216bf04610f556097acb933a SHA1: d6e26934dfbae058f58a942f572dbb2a5e282795 SHA256: 780cbc2488e7defa75725a898a83726a8c9f16521fe6d72e9156e8afb762b010 SHA512: 4e0bf01f32ebb69ee4e88d65e169d7e703869c184a2352c18ee6bf4b3c176166336986dced473710adc402dabf150d3371a989ec3bb153072b993544a3c584a1 Homepage: https://cran.r-project.org/package=MSoutcomes Description: CRAN Package 'MSoutcomes' (CORe Multiple Sclerosis Outcomes Toolkit) Enable operationalized evaluation of disease outcomes in multiple sclerosis. ‘MSoutcomes’ requires longitudinally recorded clinical data structured in long format. 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Offers filtering of decision variables through item–module eligibility and the application of explicit bounds to simplify the MILP model and accelerate the optimization process. Supports bottom up, top down, and hybrid assembly strategies; enemy-item and enemy-stimulus exclusions; stimulus all in/all out or partial selection; anchor item/stimulus specification; and item exposure control. Accommodates both single-objective and multi-objective optimization ('weighted sum', 'maximin', 'capped maximin', 'minimax', and 'goal programming'). Enables simultaneous assembly of multiple panels with item and stimulus content balancing and exposure control. Provides analytical evaluation of assembled MST performance within seconds. Includes tools for diagnosing infeasible optimization models by systematically identifying sources of infeasibility and reformulating models with slack variables to restore feasibility.Methods implemented in this package build on established work in optimal test assembly (van der Linden, 2005 ), item-set constrained test assembly (van der Linden, 2000 ), hybrid assembly (Xiong, 2018 ), recursion-based analytic methods (Lim et al., 2021 ), and classification evaluation (Rudner, 2000 ; Rudner, 2005 ). 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Quantitative data is imported and normalized and thermal behavior is modeled at the protein level. Methods exist for normalization, modeling, visualization, and export of results. For a general introduction to MS-based thermal profiling, see Savitski et al. (2014) . 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See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2015) . 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Package: r-cran-mtlgmm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-caret, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-mtlgmm_0.1.0-1.ca2604.1_all.deb Size: 111120 MD5sum: 95bd0d4c597ca9143b205d75bf8926f0 SHA1: 9323c0f940a9d6a854ab6b409e0e2800bbae1ad9 SHA256: cfff46f0f77fdf5e89d408db67e914351937ef54b9edbaebacd8e5e73285d120 SHA512: a6e76d5628d6eee002d5b6ddfca106772d1474b9d08ee61292fff84faaca3795fef82ca13d395d0ed3d9ddf65fbf82a398e67476fb49ee742cd8273b7b0cb45a Homepage: https://cran.r-project.org/package=mtlgmm Description: CRAN Package 'mtlgmm' (Unsupervised Multi-Task and Transfer Learning on GaussianMixture Models) Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) . 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An implementation of methodologies proposed in our paper: Li Xing, Mary L Lesperance, Xuekui Zhang. (2019) Bioinformatics, "Simultaneous prediction of multiple outcomes using revised stacking algorithms" . 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(2024) . The TCC are defined using a rule based on the smallest worthwhile difference (SWD). Using the defined TCC, the NMA estimates (i.e., treatment effects and standard errors) are first transformed into treatment preferences, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). These treatment preferences are then synthesized using a probabilistic ranking model, which estimates the latent ability parameter of each treatment and produces the final treatment hierarchy. This parameter represents each treatments ability to outperform all the other competing treatments in the network. Here the terms ability to outperform indicates the propensity of each treatment to yield clinically important and beneficial effects when compared to all the other treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list. 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Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region. 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This package allows users to search for globally threatened tree species within the andean montane forests, including cloud forests and seasonal (wet) forests above 1500 m a.s.l. 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Package: r-cran-muckrock Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2466 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-muckrock_0.1.0-1.ca2604.1_all.deb Size: 2470660 MD5sum: 1243aebab323e7501087fc1535182c53 SHA1: 39f35450a08156f56aa55af4e3696d5c14d93dc2 SHA256: 49de290a6950d677d0aded961f7722165f9fc433f30e21bbc8f409abcc0cd397 SHA512: 22ad11bfa876bb96970747f991d206314bc492b768a751bc40fb1596b444e4aa6ac1ee14f862b0d6573b5ada461795a7b83442782485a06a5dd1e100e6738aed Homepage: https://cran.r-project.org/package=muckrock Description: CRAN Package 'muckrock' (Data on Freedom of Information Act Requests) A data package containing public domain information on requests made by the 'MuckRock' (https://www.muckrock.com/) project under the United States Freedom of Information Act. 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Structure in Political Beliefs: A New Model for Stochastic Unfolding with Application to European Party Activists, and W.J.Post (1992). Nonparametric Unfolding Models: A Latent Structure Approach). The package implements MUDFOLD (Multiple UniDimensional unFOLDing), an iterative item selection algorithm that constructs unfolding scales from dichotomous preferential-choice data without explicitly assuming a parametric form of the item response functions. Scale diagnostics from Post(1992) and estimates for the person locations proposed by Johnson(2006) and Van Schuur(1984) are also available. This model can be seen as the unfolding variant of Mokken(1971) scaling method. 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The details of the method are available in Carter et al (2010) . 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See Li et al. (2024) for details. 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(2001) is both applied for differences and extended to ratios of means. A related single-step procedure is also available. Package: r-cran-multfisher Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-multfisher_1.1-1.ca2604.1_all.deb Size: 80808 MD5sum: b168342b214ff5854b5d9c2ad9670ef6 SHA1: 65fc612b4cb1cc27ce76165ff134885db459cb9d SHA256: 6bea623d8690d010afc58ddbb757d780e9c82df9b9a7988f273f04435d14fb42 SHA512: 13708c3324fc490340a86d76d7630141312607bfeb6af91c9d53b9df81d5bb2b024b59f8878258846eb0e4ee1b840ca714635667afe59460f7d1a844fac6870c Homepage: https://cran.r-project.org/package=multfisher Description: CRAN Package 'multfisher' (Optimal Exact Tests for Multiple Binary Endpoints) Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints. The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple testing procedures. Package: r-cran-multiactionbutton Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-multiactionbutton_1.0.0-1.ca2604.1_all.deb Size: 335944 MD5sum: 2f5c9cd46da8809c5f4c1aeb5cc74f09 SHA1: be7b7e2df1e95290b7846e6de56b952f74181b4d SHA256: e777d5734bea2268ac86c903c2c23810336996e1fff76b44977c16e32ba230f8 SHA512: d69d0ff61abc3db3ec379a0cc9715493e394b8633801e0e2645722b78cb32cdd8ce1646c2af677597dfd3b75a279668a453a5a067e955b7c741ff3ff71421b7d Homepage: https://cran.r-project.org/package=multiActionButton Description: CRAN Package 'multiActionButton' (Multi Action Button for 'Shiny' Applications) Provides a multi action button for usage in 'shiny' applications. Package: r-cran-multiapply Architecture: all Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multiapply_2.1.5-1.ca2604.1_all.deb Size: 54086 MD5sum: 69cf232ad2929dd5a730000929546b51 SHA1: 56d0558e9dd61334619df7cf46e8aa56f663a8c4 SHA256: 1361329512428dd09d4fe1960f0618bdb3e32528ac044e640d8c8c8c7e2acece SHA512: 7356bce33a30336efc4b7f5b6802b252ef1f18782f8f5b68e49c417a17ae422999d3ee3af73d82cf3cc740252d1aa983bd9b561479605cdde8b17c92131ca8dc Homepage: https://cran.r-project.org/package=multiApply Description: CRAN Package 'multiApply' (Apply Functions to Multiple Multidimensional Arrays or Vectors) The base apply function and its variants, as well as the related functions in the 'plyr' package, typically apply user-defined functions to a single argument (or a list of vectorized arguments in the case of mapply). The 'multiApply' package extends this paradigm with its only function, Apply, which efficiently applies functions taking one or a list of multiple unidimensional or multidimensional arrays (or combinations thereof) as input. The input arrays can have different numbers of dimensions as well as different dimension lengths, and the applied function can return one or a list of unidimensional or multidimensional arrays as output. This saves development time by preventing the R user from writing often error-prone and memory-inefficient loops dealing with multiple complex arrays. Also, a remarkable feature of Apply is the transparent use of multi-core through its parameter 'ncores'. In contrast to the base apply function, this package suggests the use of 'target dimensions' as opposite to the 'margins' for specifying the dimensions relevant to the function to be applied. Package: r-cran-multiassetoptions Architecture: all Version: 0.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-multiassetoptions_0.1-2-1.ca2604.1_all.deb Size: 155312 MD5sum: 26247f87d8fc236eddb9b132fd0572b1 SHA1: 20bc03f3f864c4ef2c70a952c1e7312c60c77b2e SHA256: 41241b6cb4a8bf56d694a4afa8ece40d77491cbd06d3b4fcfbf9c930ca416683 SHA512: 130680d5e12cdf354a54ce0cece2af9fd926e3eac57664f3f1001ea5af4de8f8b7bac2d6ffdcda45cd73b7ff178982c6ef0f33d3dad0896ca546ab117734d963 Homepage: https://cran.r-project.org/package=multiAssetOptions Description: CRAN Package 'multiAssetOptions' (Finite Difference Method for Multi-Asset Option Valuation) Efficient finite difference method for valuing European and American multi-asset options. Package: r-cran-multiatsm Architecture: all Version: 1.5.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-ggplot2, r-cran-hablar, r-cran-magic, r-cran-pracma Suggests: r-cran-readxl, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-kableextra, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-multiatsm_1.5.1-1-1.ca2604.1_all.deb Size: 2814948 MD5sum: 3d7fb39710d46dbcb203933d35524b2d SHA1: 43f725de9ddff7266b774018a1be307928ee6de4 SHA256: 8cc8d1337ec61ead6db4c3515de9ee2e992a5953cacd91a1d80856a472ce9714 SHA512: 0941483713fae4cbdecdd2e2a8978b3d73c54d077c39eea9c0a1d1aad1548375dd218228cc8261c130e2f39c05253294e7fab407c9b8f763916726b190e65584 Homepage: https://cran.r-project.org/package=MultiATSM Description: CRAN Package 'MultiATSM' (Multicountry Term Structure of Interest Rates Models) Package for estimating, analyzing, and forecasting multi-country macro-finance affine term structure models (ATSMs). All setups build on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) . Multicountry extensions by Jotikasthira, Le, and Lundblad (2015, JFE) , Candelon and Moura (2023, EM) , and Candelon and Moura (2024, JFEC) are also available. The package also provides tools for bias correction as in Bauer Rudebusch and Wu (2012, JBES) , bootstrap analysis, and several graphical/numerical outputs. Package: r-cran-multibias Architecture: all Version: 1.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4788 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-rlang, r-cran-broom, r-cran-purrr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-multibias_1.7.3-1.ca2604.1_all.deb Size: 2894516 MD5sum: 41cf9be27f934df11ab1f4bba93ee05d SHA1: c91f92f7cb22954a8b7c3a1b5c392e62c1f16d46 SHA256: b1f97ae0d9008e54c5e48b017ebcb8029bd39c17da32a195891f0ba61874bcbf SHA512: 77e42530905b40fa17fda8d3596751b8114624a7efde746284a4a54fb8c8653ec0fdc120816d86b691857d77f9fd3f3c5167c03732a782e18e9376b47e95c6d2 Homepage: https://cran.r-project.org/package=multibias Description: CRAN Package 'multibias' (Multiple Bias Analysis in Causal Inference) Quantify exposure-outcome causal effects with adjustment for multiple biases. The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the combination of inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) . 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This package conducts sensitivity analyses for the joint effects of these biases (per Mathur (2022) ). These sensitivity analyses address two questions: (1) For a given severity of internal bias across studies and of publication bias, how much could the results change?; and (2) For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount? Package: r-cran-multibiplotgui Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-tkrplot, r-cran-tcltk2, r-cran-shapes, r-cran-plotrix, r-cran-mass, r-cran-matrix, r-cran-cluster, r-cran-dendroextras Filename: pool/dists/resolute/main/r-cran-multibiplotgui_1.1-1.ca2604.1_all.deb Size: 214306 MD5sum: 7f34f5f58773f3cd34ef195bca8f8069 SHA1: 44cc7f30d0497ac50b7e17b5405546b21ecccf98 SHA256: 3c1e1d6c8bbe6df63026bc22ffcb8fb34ab8bd2ff17d2bc62759e9de53170d3b SHA512: eb4c9db62d0848d9ffac63d0de868ab0e94b3acbf41585e4d40e5d07a462993b36b568a313574419a75a2934d5c837f0e4fee2f0412722ffa394191618eb9a21 Homepage: https://cran.r-project.org/package=multibiplotGUI Description: CRAN Package 'multibiplotGUI' (Multibiplot Analysis in R) Provides a GUI with which users can construct and interact with Multibiplot Analysis. Package: r-cran-multibreaker Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multibreaker_0.1.0-1.ca2604.1_all.deb Size: 62710 MD5sum: b12aa473a443e5a44e7e3666fd0efc96 SHA1: 5090ad91352f36dca703cf55e7b4110f878b5b78 SHA256: 4970eb6f5c98c8220c1cab7077c48a769dcfebae026cb2856e858203ada13058 SHA512: 4e72ee8346a98bd3bf83681d409eb4db738acb4c31a1377f181f151c570b90c7a0e77ef5ad22228470a6ee88a75c1e4ea7655f0fe9c9864fc3cd0d8a02066aa8 Homepage: https://cran.r-project.org/package=multibreakeR Description: CRAN Package 'multibreakeR' (Tests for a Structural Change in Multivariate Time Series) Flexible implementation of a structural change point detection algorithm for multivariate time series. It authorizes inclusion of trends, exogenous variables, and break test on the intercept or on the full vector autoregression system. Bai, Lumsdaine, and Stock (1998) . Package: r-cran-multica Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bitops, r-cran-multcomp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multica_1.2.0-1.ca2604.1_all.deb Size: 51212 MD5sum: af11cd304d83be232dcf399e2f5e97d4 SHA1: a9e1315577b5678a47a310ac70a1afff438d94fa SHA256: f8bbc04c3baf4050f44b6384d4e1636efc94d43db62d4a448072834334eeb142 SHA512: 6d7c7943db00a1c40d4b6ff782f597a928a70b37f7d6cf1360a26e227274b6cf0d8b189003581b8a160f975ace66bf20cef70480c6818c21657b3468c30f6807 Homepage: https://cran.r-project.org/package=multiCA Description: CRAN Package 'multiCA' (Multinomial Cochran-Armitage Trend Test) Implements a generalization of the Cochran-Armitage trend test to multinomial data. In addition to an overall test, multiple testing adjusted p-values for trend in individual outcomes and power calculation is available. Package: r-cran-multicastr Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-multicastr_2.0.0-1.ca2604.1_all.deb Size: 41682 MD5sum: 871db030a4e5416c74d5556f51bfe1d8 SHA1: 77e1922e7ed8c916e89072a2f919c60788a5cda2 SHA256: 6e763dc8370130852b2118077b08972620fa61f96b46df33cab5bb72caac3d70 SHA512: 63db003f52d7f727d7051ec7965f2c4fdbeb14570c25c6ce0047b413abf51afedaaa512df5adad5989dad61354e24be5a240255d67ed40d9590283fe52e6fdec Homepage: https://cran.r-project.org/package=multicastR Description: CRAN Package 'multicastR' (A Companion to the Multi-CAST Collection) Provides a basic interface for accessing annotation data from the Multi-CAST collection, a database of spoken natural language texts edited by Geoffrey Haig and Stefan Schnell. The collection draws from a diverse set of languages and has been annotated across multiple levels. Annotation data is downloaded on request from the servers of the University of Bamberg. See the Multi-CAST website for more information and a list of related publications. Package: r-cran-multicca Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-geigen, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multicca_0.1.0-1.ca2604.1_all.deb Size: 80398 MD5sum: f4ef0c5e7bea4cdd60b80f77d49c142f SHA1: 608b3204a659d65a4d514ef61294a09129cd6e85 SHA256: 7f2f12be73bb5950391d1ce0e3ca66eea108dab9bd94a2acde3065ce90cda79d SHA512: 90aa94ff83658f8376f1e8eb886051679bd15ef10f061c84ca971751575e905d1e84629ad8868d009d199064a9538e8ff5d898f0ff8846cb37a60ae5f58aad16 Homepage: https://cran.r-project.org/package=multiCCA Description: CRAN Package 'multiCCA' (Multiple Canonical Correlation Analysis (Kernel and Functional)) Implements methods for multiple canonical correlation analysis (CCA) for more than two data blocks, with a focus on multivariate repeated measures and functional data. The package provides two approaches: (i) multiple kernel CCA, which embeds each data block into a reproducing kernel Hilbert space to capture nonlinear dependencies, and (ii) multiple functional CCA, which represents repeated measurements as smooth functions and performs analysis in a Hilbert space framework. Both approaches are formulated via covariance operators and solved as generalized eigenvalue problems with regularization to ensure numerical stability. The methods allow estimation of canonical variables, generalized canonical correlations, and low-dimensional representations for exploratory analysis and visualization of dependence structures across multiple feature sets. The implementation follows the framework developed in Górecki, Krzyśko, Gnettner and Kokoszka (2025) . Package: r-cran-multichull Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-plotly, r-cran-shiny, r-cran-shinythemes, r-cran-dt Filename: pool/dists/resolute/main/r-cran-multichull_3.0.1-1.ca2604.1_all.deb Size: 76424 MD5sum: c0c21b6ac11648864ab21c12a392fbac SHA1: 217c0fc993bf061d3678ebbf1bc2581051fb83f4 SHA256: 1019b8f5e675aca275c54bb0360b2ca91d70618cf8134c58da9fd3fb54c2a78c SHA512: 470554779d590b09bdb56be0d485b632a40e43d3cf9c8e94755205e7f71f208c3077a996a9c4a0488c9338ac4dfa3297664fd7522d95d09ebc9ef2f12ce6c314 Homepage: https://cran.r-project.org/package=multichull Description: CRAN Package 'multichull' (A Generic Convex-Hull-Based Model Selection Method) Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) , determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values. Package: r-cran-multiclasspairs Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2709 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger, r-cran-boruta, r-cran-dunn.test, r-cran-caret, r-cran-e1071, r-cran-rdist Suggests: r-cran-biocmanager, r-bioc-biobase, r-bioc-switchbox, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-leukemiaseset, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-multiclasspairs_0.4.3-1.ca2604.1_all.deb Size: 1286752 MD5sum: 0c1113f207dc13b27e4d2d9b174d7363 SHA1: ac58acde244c9631618022b2b6df55d1d5f014fe SHA256: 0b86f1385e18450112ca321d37f3fda0091c5ba9b27e93a63c4d31f532634ee6 SHA512: 42eecafc9a8b281d5ca7fc8259312e00741d432cda11a8e0ed2c623d2b7e41e3c18d5e899f65aac61931460f1a6ddf8fe3e02acc64cc1a8ff85f71c900cb373d Homepage: https://cran.r-project.org/package=multiclassPairs Description: CRAN Package 'multiclassPairs' (Build MultiClass Pair-Based Classifiers using TSPs or RF) A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret. For complex problems RF performs better. Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...). Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model. We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) . Package: r-cran-multiclassroc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-proc Filename: pool/dists/resolute/main/r-cran-multiclassroc_0.1.0-1.ca2604.1_all.deb Size: 15714 MD5sum: be08d1e3c2c1c2e44c63910c08ed1e0a SHA1: 042b863a83522255e3475ac5d71918adbf8f70c1 SHA256: ba2fab34bf06510157b0a1fa366174ea08bf906e501227d950bbacf4022296e3 SHA512: a140fd2ede3b1cdfa13e99c0b4660c97cfbdf96e99c9d79b8dd1ced67f7f25e8a134651f62aed56c3a51a8ae48f43756edc694b40fd3b5dd4f4d7719fde88852 Homepage: https://cran.r-project.org/package=MultiClassROC Description: CRAN Package 'MultiClassROC' (ROC Curves for Multi-Class Analysis) Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029). Package: r-cran-multicmp Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-multicmp_1.1-1.ca2604.1_all.deb Size: 33356 MD5sum: df10d2fc2a7a5c41535b512ba474aeae SHA1: 9eba7545655c0a44b86263955cfc7a520943abf0 SHA256: 0f41bbe91c5816e0aa1e398692b1bfcc5621af5d54dc83e73439a47b36c9eeda SHA512: fa35e41415493c81edb5ebd826d7b98bba1a379913a654dc45d9891be5db859dc25f42d4cbf904afc1faac4073668ab4ecfcdb0e7dfedf3141d2f19f2b5bbec5 Homepage: https://cran.r-project.org/package=multicmp Description: CRAN Package 'multicmp' (Flexible Modeling of Multivariate Count Data via theMultivariate Conway-Maxwell-Poisson Distribution) A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) . Future development will extend the package to higher-dimensional data. Package: r-cran-multicoll Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-multicoll_2.0-1.ca2604.1_all.deb Size: 75154 MD5sum: 14ce3bb3239c2f197457ed7b74456887 SHA1: 0a25e8d4cfe994f728430cdf9b02728a746ce2dc SHA256: 2a595e6d614223d6d96b952d4ba76f637c0a1609d799a4de547de283977184f3 SHA512: 9ae9f19213f9b5f5e071b17a3da4f4d48b6e338ad45ad6d60e278b2cae8952459d5bd0630dc6093321d5a22c1494d28940b4b2244d26d8451ee5f1e51a6b21c0 Homepage: https://cran.r-project.org/package=multiColl Description: CRAN Package 'multiColl' (Collinearity Detection in a Multiple Linear Regression Model) The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed. Package: r-cran-multid Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glmnet, r-cran-proc, r-cran-lavaan, r-cran-emmeans, r-cran-lme4, r-cran-quantreg, r-cran-lmertest, r-cran-ggpubr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-overlapping, r-cran-rio Filename: pool/dists/resolute/main/r-cran-multid_1.0.2-1.ca2604.1_all.deb Size: 226146 MD5sum: b9d8e7f85261c2d11a374817e43e93a7 SHA1: d50405a4625ec59a7ca5ab6a423dbd5b5edea9e5 SHA256: 9257fc621a1e270dbffe407ee65ae0b456cbab5f858ae04e32b3c9d16b86cac2 SHA512: 68f2eba98b1555d1a04cfd2d6ab0566dc19a9227be2e4cc7025e1f7fdd00c61679e814d78bed35b4e07611ed5e91d1de40102b5038ebdf465224239d4d7659fb Homepage: https://cran.r-project.org/package=multid Description: CRAN Package 'multid' (Multivariate Difference Between Two Groups) Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Ilmarinen et al. (2023) . Deconstructing difference score correlations (e.g., gender-equality paradox), see Ilmarinen & Lönnqvist (2024) . Includes also tools that help in understanding difference score reliability, conditional intra-class correlations, tail-dependency, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891. Package: r-cran-multideggs Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3015 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-knitr, r-cran-mass, r-cran-magrittr, r-cran-pbapply, r-cran-pbmcapply, r-cran-rmarkdown, r-cran-sfsmisc, r-cran-shiny, r-cran-shinydashboard, r-cran-visnetwork Suggests: r-cran-kernlab, r-cran-nestedcv, r-cran-pls, r-bioc-qvalue, r-cran-randomforest, r-cran-ranger, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multideggs_1.2.1-1.ca2604.1_all.deb Size: 1183706 MD5sum: 5c05b1779cfdf9533b5ac0c5b39130dd SHA1: 228516ee7141ef777a04eca07cf65ef5b58e818c SHA256: 47ae3f73581f541b72e82694a63983ff938c909094fb1c59a3d133987cac1d9a SHA512: 75fe711d2061b7f1928e31d0af02ed7dba736b192d225eb578fb29594872a6932259f2af48474dda501cc56a222503f16fb11195835905c81523656f363c3ab2 Homepage: https://cran.r-project.org/package=multiDEGGs Description: CRAN Package 'multiDEGGs' (Multi-Omic Differentially Expressed Gene-Gene Pairs) Performs multi-omic differential network analysis by revealing differential interactions between molecular entities (genes, proteins, transcription factors, or other biomolecules) across the omic datasets provided. For each omic dataset, a differential network is constructed where links represent statistically significant differential interactions between entities. These networks are then integrated into a comprehensive visualization using distinct colors to distinguish interactions from different omic layers. This unified display allows interactive exploration of cross-omic patterns, such as differential interactions present at both transcript and protein levels. For each link, users can access differential statistical significance metrics (p values or adjusted p values, calculated via robust or traditional linear regression with interaction term) and differential regression plots. The methods implemented in this package are described in Sciacca et al. (2023) . Package: r-cran-multideploy Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gh, r-cran-base64enc, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multideploy_0.1.0-1.ca2604.1_all.deb Size: 73092 MD5sum: d2d72580f54a64cbb7d9aec5270e91e1 SHA1: 34140e2bd65151738323dd4962d223fbdc0c7aa4 SHA256: d9255c081f4e98fbbfaaa3247c5ce254f5f20b7df4e5a895f64ed9edde2f93e2 SHA512: 6394ad84bcf31a4dfa4d205c170ae1174f73fc38474ec10f3c0b8e2dee2494d77e3f6453eaf9a4512efeff6fd7b4b41741b0c3cf6d4f880636906e7f85664628 Homepage: https://cran.r-project.org/package=multideploy Description: CRAN Package 'multideploy' (Deploy File Changes Across Multiple 'GitHub' Repositories) Deploy file changes across multiple 'GitHub' repositories using the 'GitHub' 'Web API' . Allows synchronizing common files, Continuous Integration ('CI') workflows, or configurations across many repositories with a single command. Package: r-cran-multidimbio Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lme4, r-bioc-pcamethods, r-cran-misc3d, r-cran-mass, r-cran-rcolorbrewer, r-cran-gridgraphics Filename: pool/dists/resolute/main/r-cran-multidimbio_1.2.5-1.ca2604.1_all.deb Size: 143412 MD5sum: 345c8e15cda35ee58e779179cd763daf SHA1: 828637c49efe003a46f8412516de914456bf7de3 SHA256: ef2db227f6d08479eaa14b892979b378a91f9ea3cf313be6457bacc419561d83 SHA512: 8dbf838923aebf55856fef4a1682d9f8271f012ef5f08c70066d90a438656cf9d46f16add4827dc52923d676361ac14632b2c3f2896b697b3e2f9cb6a7696977 Homepage: https://cran.r-project.org/package=multiDimBio Description: CRAN Package 'multiDimBio' (Multivariate Analysis and Visualization for Biological Data) Code to support a systems biology research program from inception through publication. The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations. The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques. As a result, we encourage suggested additions to the package, both methodological and graphical. Package: r-cran-multidiscreterng Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-genord, r-cran-matrix, r-cran-multiord, r-cran-matrixcalc, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multidiscreterng_0.1.0-1.ca2604.1_all.deb Size: 109570 MD5sum: 477be019ab12d848713ef1baae6b84af SHA1: 0e943d216abe99885ea927f4073c1dd2ab92b717 SHA256: 467ec5045d6308ef0087942c15ffc993dcc1a3fdf66907d4f8dc45445d13ea60 SHA512: b0ceda5406aefe28ac5e1747e028589c137db62886d7c6da663191398241e797960dc95288248216fd55df292fb04fb585a0ada92ec9c66c45cd9bd70baec3f1 Homepage: https://cran.r-project.org/package=MultiDiscreteRNG Description: CRAN Package 'MultiDiscreteRNG' (Generate Multivariate Discrete Data) Generate multivariate discrete data with generalized Poisson, negative binomial and binomial marginal distributions using user-specified distribution parameters and a target correlation matrix. The method is described in Cheng and Demirtas (2026) . 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The algorithm finds the optimal Pareto front and, if requested, selects a possible symmetrical design on it. The symmetrical design is selected based on two techniques: minimum distance with the Utopia point or the 'TOPSIS' approach. 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Package: r-cran-multifamm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2881 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fundata, r-cran-mfpca, r-cran-mgcv, r-cran-sparseflmm, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-multifamm_0.1.1-1.ca2604.1_all.deb Size: 2789488 MD5sum: 31cd47011fa4d43ee11a27f52dbdd8a4 SHA1: 0b2f8cf19fdc9a7503806045552762acf6c35354 SHA256: 77c79f2821f58065888830174220384d00308f78a4a0beabc1ceb3aa4d18ddbd SHA512: 9b91c2f20b07347e23000285718043f40a387a354835d53bff8d59d1fc489501af475ac2da4d5434d477062e3202e0ddd1d094d5f1b3a6d7df081ecb2a6fc5fc Homepage: https://cran.r-project.org/package=multifamm Description: CRAN Package 'multifamm' (Multivariate Functional Additive Mixed Models) An implementation for multivariate functional additive mixed models (multiFAMM), see Volkmann et al. (2021, ). It builds on developed methods for univariate sparse functional regression models and multivariate functional principal component analysis. This package contains the function to run a multiFAMM and some convenience functions useful when working with large models. An additional package on GitHub contains more convenience functions to reproduce the analyses of the corresponding paper (). Package: r-cran-multifanova Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-mass, r-cran-foreach, r-cran-matrix, r-cran-gfdmcv, r-cran-fda Filename: pool/dists/resolute/main/r-cran-multifanova_0.1.0-1.ca2604.1_all.deb Size: 46502 MD5sum: 14796f9fb8d465bb212969d7c6c729ca SHA1: 71dec02c12320a3e3feb1039c38d04269e00bc61 SHA256: 2dc376179155e52bfae373709a2bab137e4be0db81f1809048406ddf84345bf2 SHA512: 4e8e91f103a7a68390f6e4b029ff0581f756f9d26005c34b52eebd60f2ad03b9f451d60260eef1b779f53486069d201648ed0d2716046729927fd93cd033ee83 Homepage: https://cran.r-project.org/package=multiFANOVA Description: CRAN Package 'multiFANOVA' (Multiple Contrast Tests for Functional Data) The provided package implements multiple contrast tests for functional data (Munko et al., 2023, ). These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data. Package: r-cran-multifear Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-reshape2, r-cran-tibble, r-cran-ggplot2, r-cran-effsize, r-cran-nlme, r-cran-bayesfactor, r-cran-bayestestr, r-cran-broom, r-cran-effectsize, r-cran-esc, r-cran-forestplot, r-cran-bootstrap, r-cran-fastdummies, r-cran-rlang, r-cran-plyr, r-cran-maditr, r-cran-car Suggests: r-cran-gridextra, r-cran-vctrs, r-cran-tidyselect, r-cran-tidyr, r-cran-ggraph, r-cran-testthat, r-cran-cowplot, r-cran-covr, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multifear_0.1.5-1.ca2604.1_all.deb Size: 535842 MD5sum: 9a1085a79e3fea9bb0d05e304b871da9 SHA1: 5d441d80fea1bb20c5df9b84cf4285076118d9e8 SHA256: 939295577a8ea0616b74e65f85d999faa4a8c9dc0e400b116f5ab9ec41770048 SHA512: 75d03f6ae1e2f2b0d1529dea4f976fc5f346c46db68d3fb455a4549f5b8de928e48bf65086284541ccf2e489bfce9afa6173ef4211a1c4b1ca6c7c87234b91b8 Homepage: https://cran.r-project.org/package=multifear Description: CRAN Package 'multifear' (Multiverse Analyses for Conditioning Data) A suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos (2022) . Since November 2025, this package contains code from the 'ez' R package (Copyright (c) 2016-11-01, Michael A. Lawrence ), originally distributed under the 'GPL' (equal and above 2) license. Package: r-cran-multifunc Architecture: all Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2470 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-broom, r-cran-mass, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-forcats, r-cran-tidyr, r-cran-gridextra, r-cran-knitr, r-cran-patchwork, r-cran-car Filename: pool/dists/resolute/main/r-cran-multifunc_0.9.4-1.ca2604.1_all.deb Size: 1717788 MD5sum: fc7251484c51bc420c98603a32e1a455 SHA1: da133c57fffed97247854a03279776ae858a3f53 SHA256: c7bf74ba105f15c0808ddabe174c1b42c396ffdba42734c26c8cbecf801f0011 SHA512: 3d85948f2fe53ec547eb146d7aa3324b4782bcbe7a445523b5246c37d7317c29f9688b8ba5bcbd715f1184a659e9ca38b56edcdad770364e3256240e12bd64df Homepage: https://cran.r-project.org/package=multifunc Description: CRAN Package 'multifunc' (Analysis of Ecological Drivers on Ecosystem Multifunctionality) Methods for the analysis of how ecological drivers affect the multifunctionality of an ecosystem based on methods of Byrnes et al. 2016 and Byrnes et al. 2022 . 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The minimum number of data points required to fit a grey model is four observations. This package fits Grey model of First order and One Variable, i.e., GM (1,1) for multivariate time series data and returns the parameters of the model, model evaluation criteria and h-step ahead forecast values for each of the time series variables. For method details see, Akay, D. and Atak, M. (2007) , Hsu, L. and Wang, C. (2007).. Package: r-cran-multigroup.vaccine Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-shiny, r-cran-bslib, r-cran-htmltools, r-cran-socialmixr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multigroup.vaccine_0.1.1-1.ca2604.1_all.deb Size: 457978 MD5sum: 76ab8360d5474943bc0607c52c20ba44 SHA1: bd99de87a7ca7a9d5b92a713f3dd44ce45c08c20 SHA256: d6ad48eb260a30e4db4b121da9be12f743e356bfe66f01340f6dd7301acc4a0c SHA512: e8d2b6004c6704b5bf344257d6bcd86b21e902c89056f998f2dd96025372e155a5a01ab6ad96322f295ae67b4214abf8cebbf9286f27424bde3712cbb16e1f10 Homepage: https://cran.r-project.org/package=multigroup.vaccine Description: CRAN Package 'multigroup.vaccine' (Analyze Outbreak Models of Multi-Group Populations withVaccination) Model infectious disease dynamics in populations with multiple subgroups having different vaccination rates, transmission characteristics, and contact patterns. Calculate final and intermediate outbreak sizes, form age-structured contact models with automatic fetching of U.S. census data, and explore vaccination scenarios with an interactive 'shiny' dashboard for a model with two subgroups, as described in Nguyen et al. (2024) and Duong et al. (2026) . 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The first technique called multigroup PCA (mgPCA) this multivariate exploration approach that has the idea of considering the structure of groups and / or different types of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality Reduction (MDR) it is another multivariate exploration method that is based on projections. In addition, a method called Single Dimension Exploration (SDE) was incorporated for to analyze the exploration of the data. It could help us in a better way to observe the behavior of the multigroup data with certain variables of interest. Package: r-cran-multigroupsequential Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-hommel Filename: pool/dists/resolute/main/r-cran-multigroupsequential_1.1.0-1.ca2604.1_all.deb Size: 79134 MD5sum: 6eb8beff95646e06d11c771c94d05834 SHA1: 563f9498785cab39b2f43ca4a93f85398ebf8c9e SHA256: f98d447126867f0161a07b099bd1b6ed5aa78c7f14712c7899c0cf759108d1a6 SHA512: 130c84f79919f99e6494e3ba6c1fb4219f0ecc6ae277d217c005f35f6b95bb92362cfd022700133ccf3e4526856abe79d5c4425feb22eadf0b14a542c3b1fcfa Homepage: https://cran.r-project.org/package=MultiGroupSequential Description: CRAN Package 'MultiGroupSequential' (Group-Sequential Procedures with Multiple Hypotheses) It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The 'MultiGroupSequential' package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) and the graphical procedure proposed by Maurer and Bretz (2013) . Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013). 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The files can be compressed and various filters can be deployed before joining. Compiles only under Unix. Package: r-cran-multikink Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-quantreg, r-cran-gam, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multikink_0.2.0-1.ca2604.1_all.deb Size: 83552 MD5sum: 6d97e6a9ba88261da15f32bcab3907a9 SHA1: c20c9b7d5bed7c7d91f1a2f436dff258e44ee37c SHA256: 7fb9386a363b51746535a366c501aa054134c816780fd21f353e11ca60aa78e9 SHA512: c2f7667608f04d2771c112dd7382b5d488cf82b27d7df3ef37546476ea6b4b1e292c0876337904388ba914d625ee41e5cd4a7358da377a2d674955d3e3a19baa Homepage: https://cran.r-project.org/package=MultiKink Description: CRAN Package 'MultiKink' (Estimation and Inference for Multi-Kink Quantile Regression) Estimation and inference for multiple kink quantile regression for longitudinal data and the i.i.d data. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. For longitudinal data, we also develop the GEE estimator to incorporate the within-subject correlations. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, ``Wei Zhong, Chuang Wan and Wenyang Zhang (2022). Estimation and inference for multikink quantile regression, JBES'' and ``Chuang Wan, Wei Zhong, Wenyang Zhang and Changliang Zou (2022). Multi-kink quantile regression for longitudinal data with application to progesterone data analysis, Biometrics". Package: r-cran-multilandr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4963 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-ggally, r-cran-ggplot2, r-cran-tidyterra, r-cran-gridextra, r-cran-landscapemetrics, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multilandr_1.0.0-1.ca2604.1_all.deb Size: 4651382 MD5sum: 5594c68cd551f3cb2dfe6af105ff2bd6 SHA1: 9f764df2f48a2881f816a057f041b147c6e92722 SHA256: ee9c6ecd1e97be82e0971499570870f843a10a35de3efe900c0f6478073868cd SHA512: 308c2d87831fefcfd6ec124b2789ab8052cd6f356d8a966b081475916d96c18127787d865d693a7c95a5a74d9aa58c08a387adaba0c5a73a6fa205a1e53f0eb4 Homepage: https://cran.r-project.org/package=multilandr Description: CRAN Package 'multilandr' (Landscape Analysis at Multiple Spatial Scales) Provides a tidy workflow for landscape-scale analysis. 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For the calculation of transitive indexes, the EKS (Coelli et al., 2005 ; Rao et al., 2002 ) and Minimum spanning tree (Hill, 2004 ) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas. 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Compute multilevel compositional data and perform log-ratio transforms at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2025) , Le, Dumuid, Stanford, and Wiley (2025) . Package: r-cran-multilevelmediation Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-furrr, r-cran-future, r-cran-matrixcalc, r-cran-mcmcpack, r-cran-nlme, r-cran-parallelly, r-cran-tidyr, r-cran-brms, r-cran-posterior, r-cran-glmmtmb Suggests: r-cran-testthat, r-cran-boot, r-cran-bh Filename: pool/dists/resolute/main/r-cran-multilevelmediation_0.4.1-1.ca2604.1_all.deb Size: 158482 MD5sum: 9dc8e0a2ea4cdeb75036a57ced70bcba SHA1: 712b6dfd4274808a1e67dbf5ad7f27dbfb382d86 SHA256: 41353231680e82cb4b795a5d052127aaee06cefc052d68d0c214d8a3b9b4c38f SHA512: afa2def4fc9e1d30fb67debcaacd1b96b6a2dedc593f1128d810408df22c135712de9f326d1040980381ca4fb567b1c78ff40534bd82a833e997a8386b276a31 Homepage: https://cran.r-project.org/package=multilevelmediation Description: CRAN Package 'multilevelmediation' (Utility Functions for Multilevel Mediation Analysis) The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and Miočević (2024) . Support for Bayesian estimation using 'brms' comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented. Package: r-cran-multilevelmod Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parsnip, r-cran-dplyr, r-cran-lme4, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-withr Suggests: r-cran-covr, r-cran-gee, r-cran-ggplot2, r-cran-knitr, r-cran-nlme, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidymodels Filename: pool/dists/resolute/main/r-cran-multilevelmod_1.0.0-1.ca2604.1_all.deb Size: 197856 MD5sum: 3999ef8ad05df86a1978f297c2b1540a SHA1: bba5abc05156c8ee2a8dcd5d45d3fe8e540bef53 SHA256: f290e5bfbab3e018bbf334b1ec58581bd89a25ae9420e9d5143784e7733cb6fe SHA512: 76ac65b5a9ba5f876864b175b1cbc650ba03df317ab6c4029f94a020abbc634bc02f2cf604a32bfded7d38b38bddcc536c09338dc1da58c6c0fe96f3419937df Homepage: https://cran.r-project.org/package=multilevelmod Description: CRAN Package 'multilevelmod' (Model Wrappers for Multi-Level Models) Bindings for hierarchical regression models for use with the 'parsnip' package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) , and mixed-effect models (Pinheiro and Bates) . Package: r-cran-multileveloptimalbayes Architecture: all Version: 0.0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multileveloptimalbayes_0.0.4.0-1.ca2604.1_all.deb Size: 126116 MD5sum: 1d02d722ee98407c5617b220be219849 SHA1: 6d23374559051e23c50bcfc2a907f9f20957139e SHA256: 72a0ad5815bbc96c300bdd159a09f1e2f36e7685a5e1b00d226d92ecdb9aeb09 SHA512: 2472c569c6c9caaff5fa82e22f2041481c54984d81deebf0ac09a6bc63da077756b6ece97bd278773eaa20352470e2c2d166d1272fddd2c99311f236546ed4fd Homepage: https://cran.r-project.org/package=MultiLevelOptimalBayes Description: CRAN Package 'MultiLevelOptimalBayes' (Regularized Bayesian Estimator for Two-Level Latent VariableModels) Implements a regularized Bayesian estimator that optimizes the estimation of between-group coefficients for multilevel latent variable models by minimizing mean squared error (MSE) and balancing variance and bias. The package provides more reliable estimates in scenarios with limited data, offering a robust solution for accurate parameter estimation in two-level latent variable models. It is designed for researchers in psychology, education, and related fields who face challenges in estimating between-group effects under small sample sizes and low intraclass correlation coefficients. The package includes comprehensive S3 methods for result objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(), dim(), length(), names(), and update() for enhanced usability and integration with standard R workflows. Dashuk et al. (2025a) derived the optimal regularized Bayesian estimator; Dashuk et al. (2025b) extended it to the multivariate case; and Luedtke et al. (2008) formalized the two-level latent variable framework. Package: r-cran-multilevelpsa Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xtable, r-cran-mass, r-cran-party, r-cran-plyr, r-cran-psagraphics, r-cran-psych, r-cran-reshape Suggests: r-cran-knitr, r-cran-matchit, r-cran-mice, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multilevelpsa_1.3.1-1.ca2604.1_all.deb Size: 2946142 MD5sum: ae97e17712fc8e858fd968e575c0bffd SHA1: 854a32233e4448f77a12e4fc2035ee5569d002af SHA256: 02a016b5e98341537808d8d87fa78e94cd74c2e99f5000dd889f7d87df413c3a SHA512: 73d51b4598d3c3fc3f287148ce4f50b23a704d5cd016ef566d156747e1b0a1fd58b603aa8eed8fc757710242bba5cd26f9418f5ea54cd3951a1a10ce23fcb2c2 Homepage: https://cran.r-project.org/package=multilevelPSA Description: CRAN Package 'multilevelPSA' (Multilevel Propensity Score Analysis) Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) . Package: r-cran-multileveltools Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-lmertest, r-cran-data.table, r-cran-nlme, r-cran-extraoperators, r-cran-jwileymisc, r-cran-ggplot2, r-cran-ggpubr, r-cran-scales, r-cran-lavaan, r-cran-zoo, r-cran-brms, r-cran-testthat, r-cran-reformulas Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multileveltools_0.2.1-1.ca2604.1_all.deb Size: 1373926 MD5sum: ff68db6ad3451626a21060dabbf3ec93 SHA1: fa9a895d9a1969c3536ed6c72976186f760b5043 SHA256: 9c6a2df1a8a5d85f51baafbb5164bf47b5e88a05376ab92e8bf77b5545effb56 SHA512: 5700c2e1055ae3bf04498647a71be24597964862e8bf43f2b265d70c43cd5e4a0be56a3d963c05042a6114d309b1e91a4eceb351dc6d09abe1d3daf992265b80 Homepage: https://cran.r-project.org/package=multilevelTools Description: CRAN Package 'multilevelTools' (Multilevel and Mixed Effects Model Diagnostics and Effect Sizes) Effect sizes, diagnostics and performance metrics for multilevel and mixed effects models. Includes marginal and conditional 'R2' estimates for linear mixed effects models based on Johnson (2014) . Package: r-cran-multilinguer Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sys, r-cran-rappdirs, r-cran-usethis, r-cran-askpass Filename: pool/dists/resolute/main/r-cran-multilinguer_0.2.4-1.ca2604.1_all.deb Size: 618636 MD5sum: 5c3e622589f3e92af58b72124ccdb751 SHA1: 6bad0bf1ca792af4721efb96878953b461b81670 SHA256: 75666a37fefb2bb9750b8dab13711a03881e0aad0020365744652d6dafe078bc SHA512: b6a5fb4f7988b5669ea3be7eede4a32f4ed3aba789619a5a03ef5315e9ae0a0fc0df30ce0d5c35b438621c4c6c14f10c39574343d2105c21c21ef284814d83f3 Homepage: https://cran.r-project.org/package=multilinguer Description: CRAN Package 'multilinguer' (Gentle Language Installer for R User) Provides install functions of other languages such as 'java', 'python'. Package: r-cran-multimarker Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncnorm, r-cran-ordinalnet Filename: pool/dists/resolute/main/r-cran-multimarker_1.0.1-1.ca2604.1_all.deb Size: 74136 MD5sum: ffe8a9dc76e9b8c8d8161c115b7906f8 SHA1: f89bff23221e96b7f311143aa04c4aaf225b8f22 SHA256: f7b85a9a27a94bc8b54684ccc3a669e5c27d790e5d30cad34f68533acf648dfe SHA512: 67e9f9f3f0a009c94f7747e4acd6850a45dd6696f1279f724123ffdbbda2f91be05f505493480cba64b4986e5dbbf79040633228bfb1f20ba77c3ec9e0fc9909 Homepage: https://cran.r-project.org/package=multiMarker Description: CRAN Package 'multiMarker' (Latent Variable Model to Infer Food Intake from MultipleBiomarkers) A latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates inference on food intake from biomarker data alone, along with the associated uncertainty. Details are in D'Angelo, et al. (2020) . Package: r-cran-multimedia Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1639 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-glmnetutils, r-cran-ranger, r-cran-tidyselect, r-cran-mass, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-cli, r-cran-dplyr, r-cran-fansi, r-cran-formula.tools, r-cran-ggplot2, r-cran-glue, r-cran-minilnm, r-cran-patchwork, r-bioc-phyloseq, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-tidygraph, r-cran-tidyr Suggests: r-cran-compositions, r-cran-ggdist, r-cran-ggraph, r-cran-ggrepel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-vroom Filename: pool/dists/resolute/main/r-cran-multimedia_0.2.0-1.ca2604.1_all.deb Size: 1209414 MD5sum: 3104e0df8e17684bc3b9ba80b10ca7f4 SHA1: 1f6e590f3e585f5df9850f1ecf32a76edc8bc19e SHA256: 54e6140617abe68ce72eb98b19ae6416e56782c724fc40446df99de978f43e55 SHA512: 76f7315508b786f2dd5e92cbb1ebab9902ebe863988de7de75be8d523a27838678fa3e4130d4bd7c72eb8c64a20ca2da09c30852b1f2250e272358056609626e Homepage: https://cran.r-project.org/package=multimedia Description: CRAN Package 'multimedia' (Multimodal Mediation Analysis) Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" . Package: r-cran-multimediate Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rmutil, r-cran-mass, r-cran-mvtnorm, r-cran-timereg Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multimediate_0.1.6-1.ca2604.1_all.deb Size: 331694 MD5sum: eb8a8f69efa8bbd9f94445a42fefc33b SHA1: 3591e16790207be625d2560c5f68833c966ce251 SHA256: 7e9b2f357c0db58aabd845d8624b00e659e7dddbeeaedb86bc9ab8dcec807feb SHA512: 896b305d6d56dde16c86b2ba8df0bea6f1bdcbaca518f893b57b44d6bde3a6d13dd333e66b681c46b761e801aade2042405bba65ba4bf2a6085d2a740721a455 Homepage: https://cran.r-project.org/package=multimediate Description: CRAN Package 'multimediate' (Causal Mediation Analysis in Presence of Multiple MediatorsUncausally Related) Estimates key quantities in causal mediation analysis - including average causal mediation effects (indirect effects), average direct effects, total effects, and proportions mediated - in the presence of multiple uncausally related mediators. Methods are described by Jerolon et al., (2021) and extended to accommodate survival outcomes as described by Domingo-Relloso et al., (2024) . Package: r-cran-multimix Architecture: all Version: 1.0-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-multimix_1.0-10-1.ca2604.1_all.deb Size: 138384 MD5sum: 8e91e982bd6b5e25d9dfac48e9dcb0be SHA1: 3bd86b99d2b286433d96f549c75b1fe390635536 SHA256: d672cf1b066624f44c48b07c1d0e657ab20c8fd5f956acfc9bdc020fcd844279 SHA512: 7c3c257c634ad119722127aa9d7274c2cc130867cd98899dcaf036cd3c118295784f07a75d72fa6a0bdbf0f36e0ce917281b8474cfdb14e9a221623fe58a234f Homepage: https://cran.r-project.org/package=multimix Description: CRAN Package 'multimix' (Fit Mixture Models Using the Expectation Maximisation (EM)Algorithm) A set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1--22) to take a finite mixture model approach to clustering. The package is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. The method is described in Hunt, L. and Jorgensen, M. (1999) Australian & New Zealand Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429--440. Package: r-cran-multimodtest Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1881 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-mass, r-cran-sis, r-cran-glmnet, r-cran-ncvreg, r-cran-mbess, r-cran-survival, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-multimodtest_1.0-1.ca2604.1_all.deb Size: 1873858 MD5sum: a7bf38b58ac8e47696db4cb140b5b0d2 SHA1: 752aa4159b848f454e7caba6b0a00238c6bdcb2e SHA256: 749b7abe87f1fa05d3eba57dffaec5d64f2ee989ea979ba88cd007ae1cab3e30 SHA512: 3b7152d65b73a93a82d4ba53742b581cc904b18241c413fc93070add25d03d0b129c77f545cc944f9d589d4965c71a00fcaafd893566775fc5236bf04e63b848 Homepage: https://cran.r-project.org/package=multiModTest Description: CRAN Package 'multiModTest' (Information Assessment for Individual Modalities in MultimodalRegression Models) Provides methods for quantifying the information gain contributed by individual modalities in multimodal regression models. Information gain is measured using Expected Relative Entropy (ERE) or pseudo-R² metrics, with corresponding p-values and confidence intervals. Currently supports linear and logistic regression models with plans for extension to additional Generalized Linear Models and Cox proportional hazard model. Package: r-cran-multimolang Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-multimolang_0.1.1-1.ca2604.1_all.deb Size: 214498 MD5sum: 80e9e5459a247c52f12af4ec0f04a9df SHA1: d4d19fd0b858936a1390e9d07a9433352be38caf SHA256: f6d79ac40bedc7a5c9b816f229be89d6beb5525d50e90ad39b33f498204b7d9b SHA512: 7e96967c7aed69768c0b4453dc393e007215d900d7797f6b381aa95720ea182a52252eb91b31391b4f7da56afa26acc67c063a70255fe69ed141e10adb23d03e Homepage: https://cran.r-project.org/package=multimolang Description: CRAN Package 'multimolang' ('multimolang': Multimodal Language Analysis) Process 'OpenPose' human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA 'NewsScape' archive. Package: r-cran-multimorbidity Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-stringr, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr, r-cran-sqldf Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-multimorbidity_0.5.1-1.ca2604.1_all.deb Size: 288268 MD5sum: 28e88ef6ef985a77b578765347d20c66 SHA1: 76013230e62539bc5bf5122f39c0a0a8e6b423a5 SHA256: 8d40a88d25b3e0f8e70c8c0e5d0fdde1f6314e7690c8354011098702f33b26bd SHA512: 2b74045ff86766fcf8a9490fb8cf43950c3624ce71431cf98fcf1a4fa1400b4a79054da4023fa34701643fc68ff88d768184e467809fabb7f050cbbe375be153 Homepage: https://cran.r-project.org/package=multimorbidity Description: CRAN Package 'multimorbidity' (Harmonizing Various Comorbidity, Multimorbidity, and FrailtyMeasures) Identifying comorbidities, frailty, and multimorbidity in claims and administrative data is often a duplicative process. The functions contained in this package are meant to first prepare the data to a format acceptable by all other packages, then provide a uniform and simple approach to generate comorbidity and multimorbidity metrics based on these claims data. The package is ever evolving to include new metrics, and is always looking for new measures to include. The citations used in this package include the following publications: Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) , Brian J Moore, Susan White, Raynard Washington, et al. (2017) , Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) , Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) , Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) , Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) , Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) , Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) , Martin Fortin, José Almirall, and Kathryn Nicholson (2017). Package: r-cran-multiness Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-multiness_1.0.2-1.ca2604.1_all.deb Size: 373230 MD5sum: 0b6b75f9ce05c076ea825be271828e54 SHA1: d5bc8ddbb18f942dfa06420443a0eda5b99862a8 SHA256: e983dbf3ce1051a7dd564ddad0269a16691bb36fa17d9142038f4f7eb522ce09 SHA512: c9500189da36d7b3cf6e1bba4e7b00ed99a6990de4156f432b22e67c1b92acb2ca23ef49e6b1b12d267c679fbaba9d39097c9dc291acd206a96bcb92b201b3bc Homepage: https://cran.r-project.org/package=multiness Description: CRAN Package 'multiness' (MULTIplex NEtworks with Shared Structure) Model fitting and simulation for Gaussian and logistic inner product MultiNeSS models for multiplex networks. The package implements a convex fitting algorithm with fully adaptive parameter tuning, including options for edge cross-validation. For more details see MacDonald et al. (2020). Package: r-cran-multinmix Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-clustergeneration, r-cran-mvtnorm, r-cran-extradistr, r-cran-rstan, r-cran-abind, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multinmix_0.1.0-1.ca2604.1_all.deb Size: 177812 MD5sum: 1bd763a6f6a18220b0b0eb7b77905afe SHA1: 369c8d72d9976257eb48eb7da5c6e5b8be0436b0 SHA256: 75b75b2fcb564ccf7b6d3da2c1b78dec3af51e51c8e11a2a9bf3929d36e93405 SHA512: d6790793fe7f82f6811ba6944e3f18a9092fee5dcbef9fc659b3ea39e5d15bd56159969541cb1b9518d8fc8b031c3e1606e0e6c207c3e51d6b6614b8c67bdacd Homepage: https://cran.r-project.org/package=MultiNMix Description: CRAN Package 'MultiNMix' (Multi-Species N-Mixture (MNM) Models with 'nimble') Simulating data and fitting multi-species N-mixture models using 'nimble'. Includes features for handling zero-inflation and temporal correlation, Bayesian inference, model diagnostics, parameter estimation, and predictive checks. Designed for ecological studies with zero-altered or time-series data. Mimnagh, N., Parnell, A., Prado, E., & Moral, R. A. (2022) . Royle, J. A. (2004) . 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Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 90:366-369 (1995). The method is an R translation of the SAS code implemented by May and Johnson in their paper: May, W.L. and W.D. Johnson. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000). Paper and code available at . 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Package: r-cran-multiobjectivemdp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-linprog, r-cran-lintools, r-cran-nsga2r, r-cran-pracma, r-cran-prodlim Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multiobjectivemdp_1.0.0-1.ca2604.1_all.deb Size: 222090 MD5sum: 755a821523eeebc2a28a25c6ac6d3d9c SHA1: 3a0940a1c1b2803b425339fe9eadb6cc690b0243 SHA256: 91d99ceb5163505ff43e8dae93799733cc2bcfd2977cc09be9544ebf1ac14582 SHA512: d2cda9b485e3a6046fc1771a3ebfc994b9cec3555e0e3eaa8453e12edb8279c91e6a02d9193f0d8b0a4916d105d5b5196ce46af473f744fd016c2f15072b2a80 Homepage: https://cran.r-project.org/package=multiobjectiveMDP Description: CRAN Package 'multiobjectiveMDP' (Solution Methods for Multi-Objective Markov Decision Processes) Compendium of the most representative algorithms in print---vector-valued dynamic programming, linear programming, policy iteration, the weighting factor approach---for solving multi-objective Markov decision processes, with or without reward discount, over a finite or infinite horizon. Mifrani, A. (2024) ; Mifrani, A. & Noll, D. ; Wakuta, K. (1995) . 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Flexible functional forms are supported using natural cubic splines ('splines'), B-splines ('splines'), and GAM smooths ('mgcv'). Supports two-way and nested clustering via 'lme4', automatic knot selection by AIC or BIC, multilevel R-squared decomposition (Nakagawa-Schielzeth marginal and conditional R-squared with level-specific variance partitioning), a postestimation suite returning first and second derivatives with confidence bands, turning points and inflection regions, and a model comparison workflow contrasting linear, polynomial, and spline fits by AIC, BIC, and likelihood-ratio tests. Cluster heterogeneity in nonlinear effects is supported via random-slope spline terms. 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The package is primary written for the course Multivariate analysis and for the course Computer intensive methods at the masters program of Applied Statistics at University of Ljubljana. Package: r-cran-multivariateanalysis Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcamixdata, r-cran-magrittr, r-cran-nbclust, r-cran-factoextra, r-cran-gridextra, r-cran-rstudioapi, r-cran-candisc, r-cran-biotools, r-cran-corrplot, r-cran-ggdendro, r-cran-plotly, r-cran-crayon, r-cran-ecodist, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-multivariateanalysis_0.5.1-1.ca2604.1_all.deb Size: 573956 MD5sum: 704dca41b6ba8de0a023b25e94e9d4cb SHA1: 7f9a592380638216319e53d406867fe4c48cb80c SHA256: 189bb77968e65d33e2b4787077b90f04f28b07ef1287b880c01b932f94a7829b SHA512: 3b28beeac0bd93df6dde1879e3c384a9b74246ef7136004c4f9aa17f40a490476797f6361e58e16bd1d6147d9b73f8c860b5e0c38fb591d60c487913efeffc9e Homepage: https://cran.r-project.org/package=MultivariateAnalysis Description: CRAN Package 'MultivariateAnalysis' (Pacote Para Analise Multivariada) Package with multivariate analysis methodologies for experiment evaluation. The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA, principal components, canonical variables, etc. (Pacote com metodologias de analise multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes principais, variaveis canonicas, etc.) Package: r-cran-multivariatetrendanalysis Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-resample, r-cran-vgam, r-cran-zoo Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-multivariatetrendanalysis_0.1.3-1.ca2604.1_all.deb Size: 61786 MD5sum: c7a97bf131de3968858d5bcaae8119d8 SHA1: 24a320ec7184f5dfeb43b901d28056adfd652f92 SHA256: 16ea5c49682cec5cf7bfbf1c7d6d23fdcf21588348026c85846783631e25d549 SHA512: 2ab1cdc78b249b72ecbe2d93a9400675a56b28b6b2f4685a560826d890eceb4d2545719696654eab6626459ce645f23e07e75a9f01acd1b01400a9fc3cac390a Homepage: https://cran.r-project.org/package=MultivariateTrendAnalysis Description: CRAN Package 'MultivariateTrendAnalysis' (Univariate and Multivariate Trend Testing) With foundations on the work by Goutali and Chebana (2024) , this package contains various univariate and multivariate trend tests. The main functions regard the Multivariate Dependence Trend and Multivariate Overall Trend tests as proposed by Goutali and Chebana (2024), as well as a plotting function that proves useful as a summary and complement of the tests. Although many packages and methods carry univariate tests, the Mann-Kendall and Spearman's rho test implementations are included in the package with an adapted version to hydrological formulation (e.g. as in Rao and Hamed 1998 or Chebana 2022 ). For better understanding of the example use of the functions, three datasets are included. These are synthetic data and shouldn't be used beyond that purpose. Package: r-cran-multivarious Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1884 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-chk, r-cran-glmnet, r-cran-corpcor, r-cran-matrix, r-cran-rsvd, r-cran-svd, r-cran-pls, r-cran-irlba, r-cran-rspectra, r-cran-proxy, r-cran-matrixstats, r-cran-ggplot2, r-cran-ggrepel, r-cran-future.apply, r-cran-tibble, r-cran-dplyr, r-cran-crayon, r-cran-mass, r-cran-cli, r-cran-withr, r-cran-assertthat, r-cran-future, r-cran-geigen, r-cran-primme, r-cran-gparotation, r-cran-lifecycle Suggests: r-cran-covr, r-cran-randomforest, r-cran-testthat, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-multivarious_0.3.1-1.ca2604.1_all.deb Size: 1087828 MD5sum: 7686be6d2e62bf9f80e8e3dd675717c4 SHA1: 9ea6b13aa18974c129e554f8bf4aabf055a18bfd SHA256: aacd553fa39a63d4c1e69fe22237e8cb847d052cec397729715941adcd14a57d SHA512: 4c7a9acf906e31c56e17a5e5dd3e00aac795d98581642077ad79fbd83583f0ca0b64745b5f2c46286534980135d035e63bb5d2e3e7ddeefdf5a52d60e6b6ba0f Homepage: https://cran.r-project.org/package=multivarious Description: CRAN Package 'multivarious' (Extensible Data Structures for Multivariate Analysis) Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks. Package: r-cran-multivarmi Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-binordnonnor, r-cran-corrtoolbox, r-cran-corpcor, r-cran-matrix, r-cran-moments, r-cran-norm, r-cran-poisnonnor Suggests: r-cran-poisbinordnonnor Filename: pool/dists/resolute/main/r-cran-multivarmi_1.0-1.ca2604.1_all.deb Size: 60694 MD5sum: f2e797f2dfc64d1eccd732795c4280f0 SHA1: ca3784ff30ceb8b60d2ddd1f63ec77660d2eb4fd SHA256: c847d579512abbc1f2550263bf99a1af81c65ddd612a15980923dc92c8aa94e8 SHA512: 6aae9f67dffdc38da55beeaaec51fb3a4c9747f6f73ec35a14f62b7b3867719b1caabafae10b3a1d7e2038cf909fea554571a746ab93d745a3ece9697d4b006f Homepage: https://cran.r-project.org/package=MultiVarMI Description: CRAN Package 'MultiVarMI' (Multiple Imputation for Multivariate Data) Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) . Package: r-cran-multivator Architecture: all Version: 1.1-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3652 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emulator, r-cran-mvtnorm, r-cran-mathjaxr Suggests: r-cran-abind Filename: pool/dists/resolute/main/r-cran-multivator_1.1-11-1.ca2604.1_all.deb Size: 3461734 MD5sum: f0bc5b4c29372112cd72258267417364 SHA1: b38c1974d61a03a503ef68f50b6a5fbd53416c9c SHA256: 1daf0fc4ed2c9fe07075503f35dc3eab56f1a8e4e9e5c516ccb0b6fed1058df4 SHA512: d67796567457223b93268d94d47cd443f59ec064ad77492c9d59f1b96c9c845937ba96339d7be0de1e349ee6714edee9db366317ce62927d87c41d53a0bc39a1 Homepage: https://cran.r-project.org/package=multivator Description: CRAN Package 'multivator' (A Multivariate Emulator) A multivariate generalization of the emulator package. Package: r-cran-multiverse Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4344 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-r6, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-tidyselect, r-cran-formatr, r-cran-collections, r-cran-evaluate, r-cran-rstudioapi, r-cran-berryfunctions, r-cran-furrr, r-cran-styler, r-cran-distributional, r-cran-jsonlite, r-cran-readr Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-highr, r-cran-rmarkdown, r-cran-covr, r-cran-broom, r-cran-boot, r-cran-gganimate, r-cran-gifski, r-cran-forcats, r-cran-stringr, r-cran-cowplot, r-cran-tidybayes, r-cran-png, r-cran-stringi, r-cran-modelr, r-cran-future Filename: pool/dists/resolute/main/r-cran-multiverse_0.6.2-1.ca2604.1_all.deb Size: 2612644 MD5sum: c4862612d2c225c6c4840dfccc3112a7 SHA1: 46585004080cf3fe918b67c403036f9c9e32cee3 SHA256: a5260fd5b928aecced7560e8ad606fe0444b1c6dcecfd65dac129f955be1010e SHA512: 96183a376b315bbbfbe3c702225af7dd00071983b6c721cc677e87a2cb0680c03e54b6f72cd34c9d546b1f73af1627f68d75ce250913e4bad3a11465f5312996 Homepage: https://cran.r-project.org/package=multiverse Description: CRAN Package 'multiverse' (Create 'multiverse analysis' in R) Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016) to show the robustness of statistical inference. 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Package: r-cran-multiwave Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal Filename: pool/dists/resolute/main/r-cran-multiwave_2.0-1.ca2604.1_all.deb Size: 993788 MD5sum: 912618e43170886978d038ef392fd54e SHA1: dc9fd8bfbf2ea92215955925d0fb146111e7104e SHA256: e6798e840f6f7c9c3acc38584eefa675d23bc8593d191e23ba381d2820b821d9 SHA512: e399d4210eb255efe6e0c909032246e13d30cb102e46bcbeeb9909e0d498e88579baeb2519029e414f22c7dbb5b66bbf260399e3066597c31a41edf815cb76ea Homepage: https://cran.r-project.org/package=multiwave Description: CRAN Package 'multiwave' (Estimation of Multivariate Long-Memory Models Parameters) Computation of an estimation of the long-memory parameters and the long-run covariance matrix using a multivariate model (Lobato (1999) ; Shimotsu (2007) ). 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Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis. 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Package: r-cran-multregcmp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-mvnfast, r-cran-progress, r-cran-bayesplot, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-multregcmp_0.1.0-1.ca2604.1_all.deb Size: 117780 MD5sum: 6e172f6524e17441056a64cc46398140 SHA1: 9e855e34905acc65de1d2a4922bf029ea1e4d7f7 SHA256: ba10d761b5826c4a2aefa4f277f6d922214ee7790e07e100cba8436fe203c70e SHA512: 3a2bf5fb2b3d33d5792910936669bd9c377c963f9f7af6c05456c9d3b4348f3344b38d20ac14a5141a843bf0b084039d2796c20afdafbb2683e25c23108cbb42 Homepage: https://cran.r-project.org/package=MultRegCMP Description: CRAN Package 'MultRegCMP' (Bayesian Multivariate Conway-Maxwell-Poisson Regression Modelfor Correlated Count Data) Fits a Bayesian Regression Model for multivariate count data. 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Package: r-cran-musclesynergies Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-gridextra, r-cran-plyr, r-cran-proxy, r-cran-reshape2, r-cran-signal, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-musclesynergies_1.2.5-1.ca2604.1_all.deb Size: 1455940 MD5sum: 5bb2703a690887dad4e7217a65b599bb SHA1: 94e4d141c99e3a2eb6e7e0f35579c78d181b027f SHA256: 06d69fd996ad6ef246257d369949a6d226cb57c6ad82fdf9f1857e6835be363c SHA512: f032a73850c4dc970eadc35c0e884be6516ebc5cba34b26f5a3bd305d016421985dc1ff89e88c7d859ff253055b05bc7c0c53f5cf58dde9f9a27b1c7c9054d45 Homepage: https://cran.r-project.org/package=musclesyneRgies Description: CRAN Package 'musclesyneRgies' (Extract Muscle Synergies from Electromyography) Provides a framework to factorise electromyography (EMG) data. 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Package: r-cran-music Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-audio, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-music_0.1.2-1.ca2604.1_all.deb Size: 70990 MD5sum: de2575b3f0e3b3042dc0f6137edf7c66 SHA1: e4e6e4c0a332d3579517c49774a8d5eed8817bd3 SHA256: f14c3128ddebf3716cff443d80e2c4441fe7af4796599414e0f0c2fcb02e2724 SHA512: 3947d65c5af9988d6d81db28b131f94bcea778ad8be3cffddd9ef61e8e7eb3be5db3783aecdc63ef168a66aba408dc0cc21beaff61fa9f67f190707b5796eccc Homepage: https://cran.r-project.org/package=music Description: CRAN Package 'music' (Learn and Experiment with Music Theory) An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. 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The initials MCT in the package title refer to the article's title: "Modal Color Theory." Offers support for conventional musical pitch class set theory as developed by Forte (1973, ISBN: 9780300016109) and David Lewin (1987, ISBN: 9780300034936), as well as for the continuous geometries of Callender, Quinn, & Tymoczko (2008) . Identifies structural properties of scales and calculates derived values (sign vector, color number, brightness ratio, etc.). Creates plots such as "brightness graphs" which visualize these properties. Package: r-cran-musicnmr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2518 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seewave Filename: pool/dists/resolute/main/r-cran-musicnmr_1.0-1.ca2604.1_all.deb Size: 2535396 MD5sum: 68679a2ba146a50fa77d1e54d96aeeb1 SHA1: 31e5be646c4d012492b011425afb81ad48378f8c SHA256: 239e3a2a882bde4bb85fc985d30c588a6171ef0d50d47a60719ec928572f201d SHA512: 7d1b65a87bac9b8b7627867a64291be7ccfeb7b8fbdfa46c5518160dbc6533f1f07b1bc8d612d06a47e36c562046ee0596680a2f98274e707628987b058c7699 Homepage: https://cran.r-project.org/package=musicNMR Description: CRAN Package 'musicNMR' (Conversion of Nuclear Magnetic Resonance Spectra in Audio Files) A collection of functions for converting and visualization the free induction decay of mono dimensional nuclear magnetic resonance (NMR) spectra into an audio file. It facilitates the conversion of Bruker datasets in files WAV. The sound of NMR signals could provide an alternative to the current representation of the individual metabolic fingerprint and supply equally significant information. The package includes also NMR spectra of the urine samples provided by four healthy donors. Based on Cacciatore S, Saccenti E, Piccioli M. Hypothesis: the sound of the individual metabolic phenotype? Acoustic detection of NMR experiments. OMICS. 2015;19(3):147-56. . 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Several classes are defined for basic musical objects such as note pitch, note duration, note, measure and score. Moreover, sonification utilities functions are provided, e.g. to map data into musical attributes such as pitch, loudness or duration. A typical sonification workflow hence looks like: get data; map them to musical attributes; create and write the 'musicXML' score, which can then be further processed using specialized music software (e.g. 'MuseScore', 'GuitarPro', etc.). Examples can be found in the blog , the presentation by Renard and Le Bescond (2022, ) or the poster by Renard et al. (2023, ). Package: r-cran-mutationtypes Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertions, r-cran-cli, r-cran-data.table Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mutationtypes_0.0.1-1.ca2604.1_all.deb Size: 73970 MD5sum: 1f15393d7e1ce1da8e91d734bea99453 SHA1: 45a31ed2c3db60f40b2475d6bf8e3d44177fff43 SHA256: b61e522b07223d85062e5cf7d05694a7961dfb351abe68f0593e4f49d6728d6e SHA512: f84860ad2d93baaad8d8bfae3603582666be7f57b6eaa1439443eb8e6aae1926a89c6f1cb417395c61be28e9ef0e7f1cf905f0c382cbd0bef310b0fe668e0b44 Homepage: https://cran.r-project.org/package=mutationtypes Description: CRAN Package 'mutationtypes' (Validate and Convert Mutational Impacts Using Standard GenomicDictionaries) Check concordance of a vector of mutation impacts with standard dictionaries such as Sequence Ontology (SO) , Mutation Annotation Format (MAF) or Prediction and Annotation of Variant Effects (PAVE) . 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Types: Classification and regression. Methods: Partial Least Squares, Random Forest ans Elastic Net Data structures: Paired and unpaired Validation: repeated double cross-validation (Westerhuis et al. (2008), Filzmoser et al. (2009)) Variable selection: Performed internally, through tuning in the inner cross-validation loop. 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References include: Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press. 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Package: r-cran-mvmise Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mvmise_1.0-1.ca2604.1_all.deb Size: 67658 MD5sum: 14837732d7eda52d0ea6a51a028b9644 SHA1: 55342d779741ddabe8ac9f244abc225ccab8f860 SHA256: 75dffbeaedbea2dcde9422b0f1b36f17a13e8e3bd754666684815445661cebee SHA512: 52e73013fa8774c3bdc0c5be7377bb37c869e4d877b8c5ea5241b724e123d14fbf52c68a617f106833e63a179cccca64044d05812f82c372fb5eac8d7e894965 Homepage: https://cran.r-project.org/package=mvMISE Description: CRAN Package 'mvMISE' (A General Framework of Multivariate Mixed-Effects SelectionModels) Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) . The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models: 1) mvMISE_b() allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) mvMISE_e() allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix. Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness. Package: r-cran-mvmonitoring Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4575 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lazyeval, r-cran-plyr, r-cran-rlang, r-cran-xts, r-cran-zoo, r-cran-robustbase Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mvmonitoring_0.2.4-1.ca2604.1_all.deb Size: 2812592 MD5sum: 2e0207c526bb90032947858098c15d44 SHA1: b3d8aa556a1cf35335591490d1d84fa32fb96bd8 SHA256: 781abd37f9eeed04f536ee1cd1d1f49a8a90d4151d51f377511aff889549ab43 SHA512: 1e192f6526b7486dfa5e0260fe2aec82459730d0ad76ac69271ee3e9d324e98d334f0b7553f24edefcc6f3b4a529c16da978f956120cc970984489f57a136bf8 Homepage: https://cran.r-project.org/package=mvMonitoring Description: CRAN Package 'mvMonitoring' (Multi-State Adaptive Dynamic Principal Component Analysis forMultivariate Process Monitoring) Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data generated from a continuous-time multivariate industrial or natural process. 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Package: r-cran-mvn Architecture: all Version: 6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-moments, r-cran-mass, r-cran-boot, r-cran-car, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-ggplot2, r-cran-viridis, r-cran-cli, r-cran-energy, r-cran-plotly, r-cran-mice Suggests: r-cran-dt, r-cran-bslib, r-cran-future, r-cran-haven, r-cran-jsonlite, r-cran-readxl, r-cran-shiny, r-cran-yaml, r-cran-promises, r-cran-testthat, r-cran-zip Filename: pool/dists/resolute/main/r-cran-mvn_6.3-1.ca2604.1_all.deb Size: 265842 MD5sum: c2f6d379072e73caff916d59214a4499 SHA1: cd376b4d3752d9aec40ca281a32740b2cb9c0cbd SHA256: 270a1c5b392c3ab20acd0085e8e81c97d8e1dd1222a6ce81198c389941b98f56 SHA512: 8a0cbd7a33bcceb16c93235461d852a037684019eff0fbe841a78a29e2dbb97063f94ad9d6b70b2b2ff83dfbf2bea4600bc5d13cdc4d38db8689cda086280ef7 Homepage: https://cran.r-project.org/package=MVN Description: CRAN Package 'MVN' (Multivariate Normality Tests) A comprehensive suite for assessing multivariate normality using six statistical tests (Mardia, Henze–Zirkler, Henze–Wagner, Royston, Doornik–Hansen, Energy). Also includes univariate diagnostics, bivariate density visualization, robust outlier detection, power transformations (e.g., Box–Cox, Yeo–Johnson), and imputation strategies ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported for selected tests to improve p-value accuracy in small samples. Diagnostic plots are available via both 'ggplot2' and interactive 'plotly' visualizations. See Korkmaz et al. (2014) . Package: r-cran-mvnbayesian Architecture: all Version: 0.0.8-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-plyr Suggests: r-cran-rgl, r-cran-rfast Filename: pool/dists/resolute/main/r-cran-mvnbayesian_0.0.8-11-1.ca2604.1_all.deb Size: 68120 MD5sum: 8722115142ee3dec9464adef15c071d6 SHA1: a9fe3206764b4b16836d7372a1313c11bcb66e95 SHA256: b6bee84fa51d760aa479633e302db406b5be107017cbc67fb88fa3285cfb837b SHA512: 7b62909543875c43b916caabde7bd391f63c1d6d371a7970f86b0c4351219fbd0c270c9822bfffa5e696a28827f63129afd3b73bbaa03a8a4c260fbf5d748887 Homepage: https://cran.r-project.org/package=MVNBayesian Description: CRAN Package 'MVNBayesian' (Bayesian Analysis Framework for MVN (Mixture) Distribution) Tools of Bayesian analysis framework using the method suggested by Berger (1985) for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution. Package: r-cran-mvnggrad Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 528 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mvnggrad_0.1.6-1.ca2604.1_all.deb Size: 412090 MD5sum: d487bb498dd3d0e4f428965211136dae SHA1: 7c1b48aa7ea26df4709fb7a581bd3676ca5d3ee0 SHA256: 6096ff10c036e45babf70cf6ccfcd8a7f093c938f216d06f17ec8e546eeb2e55 SHA512: 754b148cb34f9f20bdccffed61d67f9d7d0d686809e7ac52b79f008f78b064272aae5f9a681d41415e494243f1151e3638aa02a09f256b99602ae994efbcad58 Homepage: https://cran.r-project.org/package=mvngGrAd Description: CRAN Package 'mvngGrAd' (Moving Grid Adjustment in Plant Breeding Field Trials) Package for moving grid adjustment in plant breeding field trials. Package: r-cran-mvngmod Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-clustergeneration, r-cran-distributionutils, r-cran-matlib, r-cran-maxlik, r-cran-truncnorm, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-mvngmod_0.1.0-1.ca2604.1_all.deb Size: 146984 MD5sum: bb1322bafbe86b28497d1072e59e3cb4 SHA1: 877cd5aa357ab529a5c1442dfa82070479299f5b SHA256: a3946f889f8c75c3551bf4ff1d07e7637d924ff14ab3396fbb3953a4e3f324d7 SHA512: e6ea8295211f1cbd29abfabbf177f5d29e229c1b3bc4fbdb742b94dde1a80ec230f582e28670a04cd8a7001ebb8618e57585f3a6358b5c1d9c484850e5e42e1c Homepage: https://cran.r-project.org/package=MVNGmod Description: CRAN Package 'MVNGmod' (Matrix-Variate Non-Gaussian Linear Regression Models) An implementation of the expectation conditional maximization (ECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in 'gaad_res', and covariates in 'gaad_cov'. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) . Package: r-cran-mvnma Architecture: all Version: 0.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-meta, r-cran-netmeta, r-cran-r2jags, r-cran-coda, r-cran-dplyr, r-cran-magrittr, r-cran-matrixstats, r-cran-rlist, r-cran-ggplot2, r-cran-forcats Filename: pool/dists/resolute/main/r-cran-mvnma_0.1-0-1.ca2604.1_all.deb Size: 910762 MD5sum: 88a2b3aba3465c70b80653d2e16a5584 SHA1: 696df652fc7eb18ee2ebd185a54262f23fd296a2 SHA256: dae43951f9244f762d6b7d1b70dc9fcaec53dbe3296bc43aa038bb3b885d9106 SHA512: b1594f020236946ebf7fc870c4e50b7664f5117c31611c3ce531e349d3ed9aa3793d65a914c6f3f1782358f439b524f6892d50bab6c10a8566fc0f51b7530b36 Homepage: https://cran.r-project.org/package=mvnma Description: CRAN Package 'mvnma' (Multivariate Network Meta-Analysis using Bayesian Methods) Tools to conduct Bayesian multivariate network meta-analysis providing - the single correlation coefficient model by Efthimiou et al. (2015) ; - per-outcome treatment hierarchies using the surface under the cumulative ranking curve (SUCRA), the probability of best value, or median (or mean) ranks (Salanti et al., 2011) ; - across-outcomes benefit-risk assessment using the VišeKriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method (Opricovic & Tzeng, 2004) ; - convergence checks using trace plots, density plots, or the R-hat statistic; - forest plots of treatment estimates, scatter plots of per-outcome rankings, Hasse diagrams (Carlsen & Bruggemann, 2014) to visualize the partial order of the treatments across all outcomes. Package: r-cran-mvnormaltest Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-moments, r-cran-copula Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mvnormaltest_1.0.1-1.ca2604.1_all.deb Size: 123788 MD5sum: d504b08389f17fc2d656899d02d39f99 SHA1: afb527dba74f58bc7d77a93d444391ace5251f3b SHA256: 3f185e525757375277f2b208cbaf75a2f149d81d77cf3b86161e528737e5f1db SHA512: d3232c3ae90f0b28f7674f7e40e67c275ece733775f6537dd02846c7424ce1f44ded7e5808c5fd320102b1c1dad37310c4c0be45f27835a2019a9433123ac357 Homepage: https://cran.r-project.org/package=mvnormalTest Description: CRAN Package 'mvnormalTest' (Powerful Tests for Multivariate Normality) A simple informative powerful test (mvnTest()) for multivariate normality proposed by Zhou and Shao (2014) , which combines kurtosis with Shapiro-Wilk test that is easy for biomedical researchers to understand and easy to implement in all dimensions. This package also contains some other multivariate normality tests including Fattorini's FA test (faTest()), Mardia's skewness and kurtosis test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's test (msk()), Royston’s H test (msw()), and Villasenor-Alva and Gonzalez-Estrada's test (msw()). Empirical power calculation functions for these tests are also provided. In addition, this package includes some functions to generate several types of multivariate distributions mentioned in Zhou and Shao (2014). 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The motivation for these functions was to support students learning multivariate concepts and R - there may be other functions and packages better-suited to practical data analysis. For details about the ellipse methods see Johnson and Wichern (2007, ISBN:9780131877153). 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Package: r-cran-mvskmod Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-clustergeneration, r-cran-distributionutils, r-cran-matlib, r-cran-maxlik, r-cran-truncnorm, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-mvskmod_0.1.0-1.ca2604.1_all.deb Size: 147114 MD5sum: 24d8811848a975e8a256cd176eb767d7 SHA1: 5579fada87aa7af4c81a2dc8786b32f12cdc1435 SHA256: 1513581bd801dbf9c73bdcfa82dc8d5042f211a78b498162e622fbbf78f2e900 SHA512: 262a9a717c0a1771accd055b86353919852a6931f095a3d4633f27cfb4558f7233a631586bcfb5e0f4781f9c8e402ba833f24f721cc4705c4d3020ca971adc40 Homepage: https://cran.r-project.org/package=MVSKmod Description: CRAN Package 'MVSKmod' (Matrix-Variate Skew Linear Regression Models) An implementation of the alternating expectation conditional maximization (AECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res, and covariates in gaad_cov. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) . 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See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) and K. Bartoszek, and J. Tredgett Clarke, J. Fuentes-Gonzalez, V. Mitov, J. Pienaar, M. Piwczynski, R. Puchalka, K. Spalik, K. L. Voje (2024) . The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from . 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Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, . 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Implemented classifiers handle missing data and can take advantage of sparse data. 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If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. Also, one could face the model uncertainty for structural equation, as large micro dataset is commonly available nowadays. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case (naivereg). The package also incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection, logistic-regression instrumental variables estimator (LIVE, for dummy endogenous variable problem), double-selection plus instrumental variable estimator (DS-IV) and double selection plus logistic regression instrumental variable estimator (DS-LIVE), where the double selection methods are useful for high-dimensional structural equation models. 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Package: r-cran-nasaweather Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nasaweather_0.1.1-1.ca2604.1_all.deb Size: 422948 MD5sum: 34e77980446e1ca2e683a0e13098793a SHA1: 724185c289be96f9407ad9018cc5608be75bc055 SHA256: cc969eacb20cae0d0e776daf02d616f2b5a9e159489e7316fe735ded4f90df71 SHA512: db129818f4d9cf66464f454f72e56bfcb4e60df304a32b14383ee5e6a5616e609a54f3ae9cf0adf7165a20af364f2ff228ef33145f3db2e6b6b243b2168a974b Homepage: https://cran.r-project.org/package=nasaweather Description: CRAN Package 'nasaweather' (Collection of Datasets from the ASA 2006 Data Expo) Tidied data from the ASA 2006 data expo, as well as a number of useful other related data sets. 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It includes the covariate balancing propensity score proposed by Imai and Ratkovic (2014) , which uses covariate balancing conditions in propensity score estimation. The point estimate of the parameter of interest as well as coefficients for propensity score estimation and their uncertainty are produced using the M-estimation. The same functions can be used to estimate average outcomes in missing outcome cases. Package: r-cran-nb.mclust Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-nb.mclust_1.1.1-1.ca2604.1_all.deb Size: 38752 MD5sum: 54f5a98bbcb347172e6aead934edec27 SHA1: 41ad4e7ced49864b9bb073d70b889835658fe9df SHA256: 5f9ec96e1a18aef1a3c8858110ffc2848546b919663825e64bafe50bfe33da68 SHA512: 935da4b6d8a2d4975fd2d8df2b2eaed14c53fbf2307cfc8acf37eb980b0d4d22d468bcccb30429ea5e10e98581be5489c84b3a59a70d6f527a08bf52cee56ed0 Homepage: https://cran.r-project.org/package=NB.MClust Description: CRAN Package 'NB.MClust' (Negative Binomial Model-Based Clustering) Model-based clustering of high-dimensional non-negative data that follow Generalized Negative Binomial distribution. All functions in this package applies to either continuous or integer data. 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Package: r-cran-nbapalettes Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-nbapalettes_0.1.0-1.ca2604.1_all.deb Size: 118216 MD5sum: db8377dd0d77fc57a34d54d54f3704fb SHA1: 1000161beba142033c9a838ed5b8c40d80612765 SHA256: 808d5fd2e2c5bf82c9f190e144c21d229170e96cdee0071e75b8f390bdf84a98 SHA512: 817759c54d76b89d7dda9b96c349aa8ac6cafefa3a8a4d13683511e2b7e8b1a2833b8fb668884380278d16f1a0c351fa6779c436161c2161fd6ef3811b2d5b81 Homepage: https://cran.r-project.org/package=nbapalettes Description: CRAN Package 'nbapalettes' (An NBA Jersey Palette Generator) Palettes generated from NBA jersey colorways. Package: r-cran-nbbdesigns Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nbbdesigns_1.1.0-1.ca2604.1_all.deb Size: 66294 MD5sum: c5cde753534c5ed2e947eb4e1858e31a SHA1: 037bfd454c6ba12ae70a05f095cd7f4ae5aac47e SHA256: 28d9d482f07669897920d66ef9628c8379c27cd68ece4a9a824d9b74764884bb SHA512: adbe76261cb26c67c2294ba3baa50b2a8b27d696dcdb94ee2803ef673efc89c6d6a2f9d769596fdaa0e2385fe74d0d10626b98e97b11a0cb4ab9cd86bcb79673 Homepage: https://cran.r-project.org/package=NBBDesigns Description: CRAN Package 'NBBDesigns' (Neighbour Balanced Block Designs (NBBDesigns)) Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005). "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials. 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Functions are provided to calculate the density function, the distribution function and the quantile function of the convolutions in question given said evaluation methods. Functions for generating random deviates from negative binomial convolutions and for directly calculating the mean, variance, skewness, and excess kurtosis of said convolutions are also provided. 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The package provides tools to create, clean, process, and filter datasets and associated metadata. These utilities are intended to simplify reproducible data-preparation for future research. 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Also provides functions for making tables that compare observed and theoretical statistics. Package: r-cran-nbdesign Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pweall, r-cran-mass Filename: pool/dists/resolute/main/r-cran-nbdesign_2.0.0-1.ca2604.1_all.deb Size: 54752 MD5sum: 2f2e2492c2daf91c4dcab056a890ea27 SHA1: f883bbd889b000a8e13019859932f4afa8b3a1dc SHA256: da78f5af8b902f5e3a1b9e343eb63b9c30aa0e3f2a9122979d0fe41ccb1fcff6 SHA512: 7218f107a0b65ca7a93b69a882402a06a7bd9e5ce58187a1b15ac16c2ceef39a3f56e6b4f1c28daeb62e8f4187e5b9704f5c59ee363d40f9b42e71fc04c00f8f Homepage: https://cran.r-project.org/package=NBDesign Description: CRAN Package 'NBDesign' (Design and Monitoring of Clinical Trials with Negative BinomialEndpoint) Calculate various functions needed for design and monitoring clinical trials with negative binomial endpoint with variable follow-up. This version has a few changes compared to the previous version 1.0.0, including (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario. 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Package: r-cran-nblda Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-poiclaclu, r-bioc-sseq Filename: pool/dists/resolute/main/r-cran-nblda_1.0.1-1.ca2604.1_all.deb Size: 355466 MD5sum: ddad2fb5d96301c920a69d82df01b295 SHA1: e63a38f791247cd4da8bac7c10477ab2cfcb4b91 SHA256: 1c8b231e9291b76488fc406c7b51d4e672fda7962cf9b48b2767b899a47a22c6 SHA512: 181fd8afd0fc67c617ebf82d30cd974fc08560078d5ce814f98a2dba37d77fe9f4e7d41e5b5fc3b5d841a56ec626579491284f29a5a94323723c00ca7149ba6f Homepage: https://cran.r-project.org/package=NBLDA Description: CRAN Package 'NBLDA' (Negative Binomial Linear Discriminant Analysis) We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version. 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Runtime examples are provided in the package function as well as at . 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Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (2020) . 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For further details we refer the reader to the paper Gomtsyan (2023), . 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Package: r-cran-nca Architecture: all Version: 5.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gplots, r-cran-quantreg, r-cran-kernsmooth, r-cran-lpsolve, r-cran-ggplot2, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-plotly, r-cran-truncnorm, r-cran-dbi, r-cran-rsqlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nca_5.0.1-1.ca2604.1_all.deb Size: 389468 MD5sum: af97e216099b40e81752b12e862a6cff SHA1: e3969bf9f902e1753d75cfd0ac9e135019e2571b SHA256: ac2ae3adb2b1e64a33dc8fab1b4b97abbc532d171697de6353be23833445afae SHA512: c8db714849f7693b0f788daa26e1c7513599f24a11023cc4186da53521b47336db2f199bd87b44256f127c5f49ef7b882eda740ebac6d56d5c667bc76ff1cb58 Homepage: https://cran.r-project.org/package=NCA Description: CRAN Package 'NCA' (Necessary Condition Analysis) Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52) . NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations. (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g., an effort, a characteristic) is necessary but not sufficient for a (desired or undesired) level of y (e.g., good performance or disease). A quick start guide for using this package can be found here: or . 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The 'negligible' package provides functions that are useful for conducting negligible effect testing (also called equivalence testing). For example, there are functions for evaluating the equivalence of means or the presence of a negligible association (correlation or regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) . Beribisky, N., Davidson, H., Cribbie, R. A. (2019) . Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) . Mara, C. & Cribbie, R. A. (2017) . Counsell, A. & Cribbie, R. A. (2015) . van Wieringen, K. & Cribbie, R. A. (2014) . Goertzen, J. R. & Cribbie, R. A. (2010) . Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) . Package: r-cran-neighboot Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdstreeboot, r-cran-igraph, r-cran-rds, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-neighboot_1.0.1-1.ca2604.1_all.deb Size: 475926 MD5sum: c9fdc6ad9528873b7cd406bcce59b375 SHA1: 5a12deae657ad0d8519651960eea744ed81f29e1 SHA256: 6a7b1b1d48cfffc15b86ffe3ac1872c8249b7b23b8e8b48469fe6c279ad804bf SHA512: 4fd49f46d1ac0cc97e1b33849e5bc76f95873ae28ab199f831a87fbf4d4e819d37dc770becb4f947a830ba1b1183a04566b0ae365695faa335836a32983cde29 Homepage: https://cran.r-project.org/package=Neighboot Description: CRAN Package 'Neighboot' (Neighborhood Bootstrap Method for RDS) A bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty. 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These functions take a solution and return a slightly-modified copy of it, i.e. a neighbour. The package provides a function neighbourfun() that constructs such neighbourhood functions, based on parameters such as admissible ranges for elements in a solution. Supported are numeric and logical solutions. The algorithms were originally created for portfolio-optimisation applications, but can be used for other models as well. Several recipes for neighbour computations are taken from "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). 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Package: r-cran-nemsqar Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4960 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-nemsqar_1.2.0-1.ca2604.1_all.deb Size: 4800988 MD5sum: eb7bc98ace62437378a5b79d3340a2f0 SHA1: 3fefc14b252d964925eea661c111c4c40dce07a7 SHA256: 45e41c87cc65965de9a601064df8e2fcb699a89d54831808345e8bb8982cebbc SHA512: ed79a761f33b12806e6d7032181b2e7d008411282cd78219ff1c016d95af89e8f56cac89cedf82188e93576e6fa06e126dadd50bda1d1a8b3d19134be4aa0c7e Homepage: https://cran.r-project.org/package=nemsqar Description: CRAN Package 'nemsqar' (National Emergency Medical Service Quality Alliance MeasureCalculations) Designed to automate the calculation of Emergency Medical Service (EMS) quality metrics, 'nemsqar' implements measures defined by the National EMS Quality Alliance (NEMSQA). 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It then generates a control chart to assess processes and determine if any streams are out of control. Package: r-cran-neo2r Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-httr2 Filename: pool/dists/resolute/main/r-cran-neo2r_3.0.0-1.ca2604.1_all.deb Size: 52692 MD5sum: df17d5b741b517a2db3979b484939e2b SHA1: eb55d91ec57957a02a6ae7fd931182e537fa86cc SHA256: 90ed5f09974804f94b5aa71cd2eb5f6141fd893e1ed486ec0cc66267eeebe7a0 SHA512: af20bc329e5427b05e503a108862440147e93b9a1376e21760365926239dea29dda821f78e17b221d09cc9f58a85412748afa5aa740aed3b39613e8b63544609 Homepage: https://cran.r-project.org/package=neo2R Description: CRAN Package 'neo2R' (Neo4j to R) The aim of neo2R is to provide simple and low level connectors for querying neo4j graph databases (). 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Package: r-cran-neo4jshell Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-ssh, r-cran-sys, r-cran-fs, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-neo4jshell_0.1.2-1.ca2604.1_all.deb Size: 45598 MD5sum: 5aae4dd6cf37f90f5d47140f7b3781fa SHA1: cd521200cfdcf02530a55c7405294c6184f4f727 SHA256: d634d3294d5ea84f8b8345b1e2175076113d68a91357890c1ccfc6123ec8b161 SHA512: 72abd7fa13fb865039d5d0bd4d103afd9701215452ef80902769ad624ec0a9c1e1f18baf6551191489ab9b62e8e08f99e745a473abe43a2dc7bad86b20d228c3 Homepage: https://cran.r-project.org/package=neo4jshell Description: CRAN Package 'neo4jshell' (Querying and Managing 'Neo4J' Databases in 'R') Sends queries to a specified 'Neo4J' graph database, capturing results in a dataframe where appropriate. Other useful functions for the importing and management of data on the 'Neo4J' server and basic local server admin. 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It's modern in the sense it provides a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis and graph packages. It's flexible in the way it returns the results, by trying to stay as close as possible to the way 'Neo4J' returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the "heavy lifting" of data wrangling is not left to the user. Package: r-cran-neodistr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinythemes, r-cran-plotly, r-cran-brms, r-cran-rmpfr, r-cran-ggplot2, r-cran-shiny Suggests: r-cran-spelling, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bayesplot, r-cran-loo Filename: pool/dists/resolute/main/r-cran-neodistr_0.1.2-1.ca2604.1_all.deb Size: 4577524 MD5sum: 5cd1ba918ce36b2c729f972a39d4de60 SHA1: 84a60b16df7eb55c02eff43a6ff8be3d529d3f14 SHA256: f6d760e44747b5b012085d4d27b978f8d5d490a283d0be2c9ecd2b43fdf7fec8 SHA512: 2c2d945db35d79b33aca2b0dc43cbd4d1e040e4066e1cead050aca4c5ef16577eae117b6a6bbadfbd974bcc25474fcaeba0a91fc1f08becbea733e1ac8a9114f Homepage: https://cran.r-project.org/package=neodistr Description: CRAN Package 'neodistr' (Neo-Normal Distribution) Calculating the density, cumulative distribution, quantile, and random number of neo-normal distribution. It also interfaces with the 'brms' package, allowing the use of the neo-normal distribution as a custom family. This integration enables the application of various 'brms' formulas for neo-normal regression. Modified to be Stable as Normal from Burr (MSNBurr), Modified to be Stable as Normal from Burr-IIa (MSNBurr-IIa), Generalized of MSNBurr (GMSNBurr), Jones-Faddy Skew-t, Fernandez-Osiewalski-Steel Skew Exponential Power, and Jones Skew Exponential Power distributions are supported. References: Choir, A. S. (2020).Unpublished Dissertation, Iriawan, N. (2000).Unpublished Dissertation, Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) . Package: r-cran-neonos Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-curl, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-neonutilities Filename: pool/dists/resolute/main/r-cran-neonos_1.1.0-1.ca2604.1_all.deb Size: 132560 MD5sum: 959ffabe49ae46ff9f31c4a8ad7df5b1 SHA1: 465bcdf518093d3a2587cbf5bdff7962153fd967 SHA256: b600feaf68f93783ed745ec806a061cb83f6f53e1aad62617f3eddc6177bc517 SHA512: cc0b07e9ec38ea3682a390ed4c6c86b21f7fbad1a1b15fdc5650d2b62062ebc363b23b2b2f262f53242909ce8cba141bdcdb944712828b8e3bb8eb25564f35f4 Homepage: https://cran.r-project.org/package=neonOS Description: CRAN Package 'neonOS' (Basic Data Wrangling for NEON Observational Data) NEON observational data are provided via the NEON Data Portal and NEON API, and can be downloaded and reformatted by the 'neonUtilities' package. NEON observational data (human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and algal chemistry) are published in a format consisting of one or more tabular data files. This package provides tools for performing common operations on NEON observational data, including checking for duplicates and joining tables. 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Package: r-cran-nestedcv Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-future.apply, r-cran-ggplot2, r-cran-glmnet, r-cran-matrixstats, r-cran-matrixtests, r-cran-proc, r-cran-rfast, r-cran-rhpcblasctl, r-cran-rlang, r-cran-rocr Suggests: r-cran-boruta, r-cran-corelearn, r-cran-fastshap, r-cran-gbm, r-cran-ggbeeswarm, r-cran-ggpubr, r-cran-hsstan, r-cran-mda, r-cran-mlbench, r-cran-pbapply, r-cran-pls, r-cran-randomforest, r-cran-ranger, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-knitr, r-cran-superlearner Filename: pool/dists/resolute/main/r-cran-nestedcv_0.8.2-1.ca2604.1_all.deb Size: 2106012 MD5sum: a239c938793bafb6c2bc5c8aaf6ecaae SHA1: 1864a79d1b022adeaf1066b564598bad0c4ad7ec SHA256: 406e791d29f5e4d36766289b871da636b97503fee1b71b02260269f235206c86 SHA512: 781543af2ce6c62fbb6250252b448e9570b68466795b1b9960ffefe35b17d5fe85eb0a696a29e09fef144cea32660ff9f8132e6fe6380474ce426d5d9a121356 Homepage: https://cran.r-project.org/package=nestedcv Description: CRAN Package 'nestedcv' (Nested Cross-Validation with 'glmnet' and 'caret') Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package . Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) . Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) . Package: r-cran-nestedlogit Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1349 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-dplyr, r-cran-effects, r-cran-stringr, r-cran-tibble, r-cran-scales Suggests: r-cran-aer, r-cran-cardata, r-cran-geomtextpath, r-cran-ggplot2, r-cran-ggeffects, r-cran-here, r-cran-insight, r-cran-lobstr, r-cran-knitr, r-cran-nnet, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-see, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-mass, r-cran-vgam, r-cran-mlogit, r-cran-vcd Filename: pool/dists/resolute/main/r-cran-nestedlogit_0.4.0-1.ca2604.1_all.deb Size: 717558 MD5sum: 3a5faf4ab94f668c5a829a7007862863 SHA1: cf973d4cbf390fd206b8b5ce7b3f98ba3b2e7f12 SHA256: a830d406d4e7f2b08941f1fa061672a4c366f48d6fd79ec5e20a6e42d81505a2 SHA512: 7609f1c143ef14c4d4d275cfb1af770f792b036e3f10d512556ffcb8fb93ccf9a283a1947045772d41f1a084101b96174c23de501485e61aecc27b9f65d117c5 Homepage: https://cran.r-project.org/package=nestedLogit Description: CRAN Package 'nestedLogit' (Nested Dichotomy Logistic Regression Models) Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663. Package: r-cran-nestedmenu Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fontawesome, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jquerylib, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-nestedmenu_0.2.0-1.ca2604.1_all.deb Size: 61866 MD5sum: 06f2a41da1569c23bfd1c08e3fb5dd77 SHA1: f02f75c38a484dd898fa137172113a00f3eea572 SHA256: 813f4faef9ce1166af718b93d9d5036add719cf2adbf23c981f4f0fce1c1e413 SHA512: 8e2d302daacc7bfd3f017ee5a17d5f3c5a5108c8856363c253aff9e0682bc24c5e37c7e0b8bc477ede13b8553b44153afc2b9706e911d7bf26668994fb9789ec Homepage: https://cran.r-project.org/package=NestedMenu Description: CRAN Package 'NestedMenu' (A Nested Menu Widget for 'Shiny' Applications) Provides a nested menu widget for usage in 'Shiny' applications. This is useful for hierarchical choices (e.g. continent, country, city). Package: r-cran-nestedpp Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-nestedpp_0.2.0-1.ca2604.1_all.deb Size: 40458 MD5sum: 37e77154a6491a22aca642dcded54482 SHA1: df1cacaa57f94155fdae690ca2941a5eed4640db SHA256: 0f85069002c42890f770a2a58cd8352ed46383047b68c0debf3b8d5bb2878c9e SHA512: 0852d08073a4d04258937956e4b6247484c6c89572f2b84a64153869ec454a3a8ff9f450a89dee304bd44fc8baebdb853d112baea99a5a78b054f94743e7902a Homepage: https://cran.r-project.org/package=nestedpp Description: CRAN Package 'nestedpp' (Performance Profiles and Nested Performance Profiles) Library to plot performance profiles (Dolan and More (2002) ) and nested performance profiles (Hekmati and Mirhajianmoghadam (2019) ) for a given data frame. Package: r-cran-nestfs Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dgof, r-cran-proc Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nestfs_1.0.3-1.ca2604.1_all.deb Size: 249314 MD5sum: 7a06bb9ff8712d1621ee20e193f41c4a SHA1: 3d79119b3528f2671ad1d2faea4cf3b3f126b675 SHA256: c491378d888f01f7f17e592fbc2868fb4d6b979ea794bd805d55ccbc4118b4c2 SHA512: a8c2052f965d2bcf7a1ea0a0e8b0e8fadfacd96338d28baf5f9126ff91bd7bad3331065a3976762fdfe4dbd09265432001af05b1030251dca57a463442cf2f4e Homepage: https://cran.r-project.org/package=nestfs Description: CRAN Package 'nestfs' (Cross-Validated (Nested) Forward Selection) Implementation of forward selection based on cross-validated linear and logistic regression. Package: r-cran-nestimate Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-glasso, r-cran-data.table, r-cran-cluster, r-cran-scales Suggests: r-cran-testthat, r-cran-markovchain, r-cran-tna, r-cran-cograph, r-cran-igraph, r-cran-glmnet, r-cran-lavaan, r-cran-stringdist, r-cran-nnet, r-cran-isingfit, r-cran-bootnet, r-cran-gimme, r-cran-qgraph, r-cran-reticulate, r-cran-gridextra, r-cran-lme4, r-cran-corpcor, r-cran-mlvar, r-cran-mgm, r-cran-matrix, r-cran-networkcomparisontest, r-cran-arules, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-nestimate_0.4.3-1.ca2604.1_all.deb Size: 2417716 MD5sum: f9786478d243516ab33e364894b3319e SHA1: 24c4fa9ebdb1db0b2c793365d4853ffc7d4d2c1a SHA256: 99e0d1586ed02e0de15deb85ae626a435ac3bcddf37d3128e118f01ea5bf7551 SHA512: 19c51fad66abf4041496c258b4cfc0357fc7fcc348921109149636723931f7cfccaa264bac79e90d1b30f6b58fd75e2a73919d0f21b3ae6d052dc727ab9f2e9e Homepage: https://cran.r-project.org/package=Nestimate Description: CRAN Package 'Nestimate' (Network Estimation, Bootstrap, and Higher-Order Analysis) Estimate, compare, and analyze dynamic and psychological networks using a unified interface. Provides transition network analysis estimation (transition, frequency, co-occurrence, attention-weighted) Saqr et al. (2025) , psychological network methods (correlation, partial correlation, 'graphical lasso', 'Ising') Saqr, Beck, and Lopez-Pernas (2024) , and higher-order network methods including higher-order networks, higher-order network embedding, hyper-path anomaly, and multi-order generative model. Supports bootstrap inference, permutation testing, split-half reliability, centrality stability analysis, mixed Markov models, multi-cluster multi-layer networks and clustering. Package: r-cran-nestr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nestr_0.1.2-1.ca2604.1_all.deb Size: 31880 MD5sum: b0bbf1325348ec92fb9186137f880363 SHA1: 1a20e84f013253da5b1a03674636f0de66042636 SHA256: eba09ad33c1b5b3590e405fdf2a2963c92bc869df48f54db393551a713b05646 SHA512: a9f68ca405047c548ce4bc3312823a736b77c849ac5e6a7c9459305f15de66c4be06975d287f9abfdfa0b07622331954ab8c2383336c59f2f0ac6c5f77c7c6cc Homepage: https://cran.r-project.org/package=nestr Description: CRAN Package 'nestr' (Build Nesting or Hierarchical Structures) Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax. Package: r-cran-net4pg Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1091 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-bioc-graph, r-cran-magrittr, r-cran-matrix Suggests: r-bioc-biocstyle, r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-net4pg_0.1.2-1.ca2604.1_all.deb Size: 396278 MD5sum: d82db525b1573093028921ab55b21bf5 SHA1: 274a63d2fdebe381346daa697960f39beae972b6 SHA256: b8ad5645f4b710b95b459576557df51d74556256a3b86652927e7d254aa65d07 SHA512: f50487b3f9f011a3458ec31b6759ecd80f2000e649828ac1960103c119b82309881057cb8f6913fb8020f35490c4cea31adae121963912cee4ebf5434c0fb64e Homepage: https://cran.r-project.org/package=net4pg Description: CRAN Package 'net4pg' (Handle Ambiguity of Protein Identifications from ShotgunProteomics) In shotgun proteomics, shared peptides (i.e., peptides that might originate from different proteins sharing homology, from different proteoforms due to alternative mRNA splicing, post-translational modifications, proteolytic cleavages, and/or allelic variants) represent a major source of ambiguity in protein identifications. The 'net4pg' package allows to assess and handle ambiguity of protein identifications. It implements methods for two main applications. First, it allows to represent and quantify ambiguity of protein identifications by means of graph connected components (CCs). In graph theory, CCs are defined as the largest subgraphs in which any two vertices are connected to each other by a path and not connected to any other of the vertices in the supergraph. Here, proteins sharing one or more peptides are thus gathered in the same CC (multi-protein CC), while unambiguous protein identifications constitute CCs with a single protein vertex (single-protein CCs). Therefore, the proportion of single-protein CCs and the size of multi-protein CCs can be used to measure the level of ambiguity of protein identifications. The package implements a strategy to efficiently calculate graph connected components on large datasets and allows to visually inspect them. Secondly, the 'net4pg' package allows to exploit the increasing availability of matched transcriptomic and proteomic datasets to reduce ambiguity of protein identifications. More precisely, it implement a transcriptome-based filtering strategy fundamentally consisting in the removal of those proteins whose corresponding transcript is not expressed in the sample-matched transcriptome. The underlying assumption is that, according to the central dogma of biology, there can be no proteins without the corresponding transcript. Most importantly, the package allows to visually inspect the effect of the filtering on protein identifications and quantify ambiguity before and after filtering by means of graph connected components. As such, it constitutes a reproducible and transparent method to exploit transcriptome information to enhance protein identifications. All methods implemented in the 'net4pg' package are fully described in Fancello and Burger (2022) . 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Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods. 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These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) , Langendorf (2020) , and Langendorf and Goldberg (2019) . 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Package: r-cran-netcutter Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-poissonbinomial, r-cran-rlecuyer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-qpdf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-netcutter_0.3.1-1.ca2604.1_all.deb Size: 47758 MD5sum: 77a41049ef49f98d67e52f03928a5d9b SHA1: eb1a69887fe714b4428e2e78c6e3914bb69f8673 SHA256: 33896ffc43b05493a618015cda703757770a336d5ed5b8897268fe11cb273d15 SHA512: 9b83a7410697ff1729d02c85967968a15bc82c8730d64c84d4f7d7bcc5c7308f541b4bc5cd88977494494e027e9548e3d298d9e344a3e2f4efeb84c47d884af7 Homepage: https://cran.r-project.org/package=netcutter Description: CRAN Package 'netcutter' (Identification and Analysis of Co-Occurrence Networks) Implementation of the NetCutter algorithm described in Müller and Mancuso (2008) . The package identifies co-occurring terms in a list of containers. For example, it may be used to detect genes that co-occur across genomes. 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Package: r-cran-netexplorer Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-netexplorer_0.0.2-1.ca2604.1_all.deb Size: 289956 MD5sum: 1f9b28fe78ea518277676067b4b0c0b4 SHA1: e6865908b16e54bf44e23c0d9d68ff23477e33f1 SHA256: 389a572a2f2fffad32903352caa4c0c61ba58ebcb955e4723edbd7246252c99a SHA512: 2309c1689616076afbd89fe07a0ecf243e32b90b4be69d8824cf0de2ff3f10646f4d1cb2d0da7d65f685d3ecbf807609dcc898132d957fa97d00cca7b6285fbe Homepage: https://cran.r-project.org/package=NetExplorer Description: CRAN Package 'NetExplorer' (Network Explorer) Social network analysis has become an essential tool in the study of complex systems. 'NetExplorer' allows to visualize and explore complex systems. It is based on 'd3js' library that brings 1) Graphical user interface; 2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation. Package: r-cran-netgreg Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-huge, r-cran-glmnet, r-cran-dplyr, r-cran-plsgenomics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-netgreg_0.0.4-1.ca2604.1_all.deb Size: 19020 MD5sum: d072b7f3936327a70a0b6d1addce2ffc SHA1: f0205d6239930206d61e5701962f48581e938b02 SHA256: acb5c44e40d4f700931e4db2b900f898af415fc32a1cc3044e64b1821a3045a7 SHA512: 280f4033651520603bee83c0f1860cb07020b32850605dafb129d8511f2e98ddeb15bd8363f2c768d4497d8345ad804a8bf27400e49c819aa1164a2d83e96f03 Homepage: https://cran.r-project.org/package=NetGreg Description: CRAN Package 'NetGreg' (Network-Guided Penalized Regression (NetGreg)) A network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our publication by Ahn S and Oh EJ (2026) . Package: r-cran-netgwas Architecture: all Version: 1.14.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-qtl, r-cran-glasso, r-cran-mass, r-cran-huge, r-cran-tmvtnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-netgwas_1.14.5-1.ca2604.1_all.deb Size: 381606 MD5sum: 55d6b292a03042574d554a625f5ac751 SHA1: dfa6359b8c414614677fc9126e9d6e1f3757da45 SHA256: 567bc42a6414ab08b095370441df577ba1ffa17e58a672b5f1e15b65bfe5c519 SHA512: 1565d63d0dc44103129881dc473510bdc93340fd60612d0647614e8dccfd084ba69d42f43a2f3a7d97694fdfa0ca2dc3f2ab823b68b37189ed969ba9ab5beee3 Homepage: https://cran.r-project.org/package=netgwas Description: CRAN Package 'netgwas' (Network-Based Genome Wide Association Studies) A multi-core R package that contains a set of tools based on copula graphical models for accomplishing the three interrelated goals in genetics and genomics in an unified way: (1) linkage map construction, (2) constructing linkage disequilibrium networks, and (3) exploring high-dimensional genotype-phenotype network and genotype- phenotype-environment interactions networks. The 'netgwas' package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is considerably larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The 'netgwas' implements the methodological developments in Behrouzi and Wit (2017) and Behrouzi and Wit (2017) . Package: r-cran-netie Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-netie_1.0-1.ca2604.1_all.deb Size: 43498 MD5sum: e8f3caaf5998893ee0a3fbe360ec0f3f SHA1: 934dc617f1c1a2e26c9ba7c47a883be53192e171 SHA256: 13ff3a37bc1a63e3ad98f6cbd4f8e6992ced969c8e056bb07b7a22d1adbe5a01 SHA512: 4c879ee123689ac3f8f8bf7dfa0e9b9ed27e30e5f78e801fc38a8a1a6ca820d5b4077acff40497ecfd6124eb480b4732baef4aa46d107c889bdf5ee3a4d32196 Homepage: https://cran.r-project.org/package=netie Description: CRAN Package 'netie' (Antigen T Cell Interaction Estimation) The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) . Package: r-cran-netindices Architecture: all Version: 1.4.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-netindices_1.4.4.1-1.ca2604.1_all.deb Size: 138026 MD5sum: ee1f941bf97794baf9f4f86e57efcaf2 SHA1: b4e3af622bf535a53d987add163d157e4d3f7812 SHA256: c07373b4f3617242c0a86beb83fe462cdf2962ba194d41e584112e3a30fc1a13 SHA512: bf2fb573ec2613b96caaf26387c1f46a37ea4c8b0ff63364659940c6074da4ff55851149b157a922f14aca5821d06d635ff270a5081abfaf40efd66a7bd7cdc4 Homepage: https://cran.r-project.org/package=NetIndices Description: CRAN Package 'NetIndices' (Estimating Network Indices, Including Trophic Structure ofFoodwebs in R) Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs. Package: r-cran-netint Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-netint_1.0.1-1.ca2604.1_all.deb Size: 200270 MD5sum: 439acd6fa7c1ea6cfe4623cead327025 SHA1: 0f6997681e48fe1776e37ed9259fa89945087106 SHA256: 6f806faa344c2bd4fe78d7fc39e3fd931b7998e9dab2c61f07bb1ec586c23ec3 SHA512: 75278661adabbe437d3a41f897e873298105e5e43366ec2d618a515f0becf3fc909e9461ab3039a23b120e1ffb0b868743f1cad31efeaaa53578d13bfd05416f Homepage: https://cran.r-project.org/package=NetInt Description: CRAN Package 'NetInt' (Methods for Unweighted and Weighted Network Integration) Implementation of network integration approaches comprising unweighted and weighted integration methods. Unweighted integration is performed considering the average, per-edge average, maximum and minimum of networks edges. Weighted integration takes into account a weight for each network during the fusion process, where the weights express the ''predictiveness strength'' of each network considering a specific predictive task. Weights can be learned using a machine learning algorithm able to associate the weights to the assessment of the accuracy of the learning algorithm trained on the network itself. The implemented methods can be applied to effectively integrate different biological networks modelling a wide range of problems in bioinformatics (e.g. disease gene prioritization, protein function prediction, drug repurposing, clinical outcome prediction). Package: r-cran-netknitr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-shiny, r-cran-shinydashboard, r-cran-dplyr, r-cran-visnetwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-netknitr_0.2.1-1.ca2604.1_all.deb Size: 681430 MD5sum: e057653cc9671862ee3fe83fd436cb35 SHA1: dfc8e008c995fec775eb2fca35d00ce30be5a7a0 SHA256: ae7c19e6267f68e60926681ab1e60be02501744c0457a67c810179120fd62349 SHA512: 04cfaa9b2fe1a9fd05da6814124cf0680b78d721123bf6eb4aad6e994d3ce5a347d315917454a97e2329fa4d28ab2c22234a94ef01c96462f84f1a823a1b5ede Homepage: https://cran.r-project.org/package=netknitr Description: CRAN Package 'netknitr' (Knit Network Map for any Dataset) Designed to create interactive and visually compelling network maps using R Shiny. It allows users to quickly analyze CSV files and visualize complex relationships, structures, and connections within data by leveraging powerful network analysis libraries and dynamic web interfaces. Package: r-cran-netlogor Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-quickplot, r-cran-terra Suggests: r-cran-circstats, r-cran-covr, r-cran-knitr, r-cran-microbenchmark, r-cran-raster, r-cran-rmarkdown, r-cran-sf, r-cran-sp, r-cran-spades.core, r-cran-spades.tools, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-netlogor_1.0.6-1.ca2604.1_all.deb Size: 862344 MD5sum: 0ba8d79c05a0a8fbe3e2981d025bd3e8 SHA1: cd974d5c82f3d1d385ef3a88adaf2b26323cfa0b SHA256: 5980f7b16521b3b056f2dee2933caaf61a3dae4aabb7b6708dd608d8fa0f3ba4 SHA512: dad0b55f8e66148610f91358372ad7b38dbb908e3ad1548f95c90761b3b4353cc82d3a584def96e1f643d4174affc5074ea51ec5e380c062b36cdb168c093207 Homepage: https://cran.r-project.org/package=NetLogoR Description: CRAN Package 'NetLogoR' (Build and Run Spatially Explicit Agent-Based Models) Build and run spatially explicit agent-based models using only the R platform. 'NetLogoR' follows the same framework as the 'NetLogo' software (Wilensky (1999) ) and is a translation in R of the structure and functions of 'NetLogo'. 'NetLogoR' provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed 'NetLogo' framework, coupled with the versatility, power and massive resources of the R software. Examples of two models from the NetLogo software repository (Ants ) and Wolf-Sheep-Predation (), and a third, Butterfly, from Railsback and Grimm (2012) , all written using 'NetLogoR' are available. The 'NetLogo' code of the original version of these models is provided alongside. A programming guide inspired from the 'NetLogo' Programming Guide () and a dictionary of 'NetLogo' primitives () equivalences are also available. NOTE: To increment 'time', these functions can use a for loop or can be integrated with a discrete event simulator, such as 'SpaDES' (). Package: r-cran-netmap Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggnetwork, r-cran-igraph, r-cran-network, r-cran-rlang, r-cran-sf, r-cran-sna Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-netmap_0.1.4-1.ca2604.1_all.deb Size: 343334 MD5sum: c0443c704cb8501105abdd68d829f7d0 SHA1: 67c9cf87e9f98afb8358ba60734995b9b9e74361 SHA256: 1bd519e66dd0f5352d2dd241ac75f14b258320b2321d46b13d5d9f9479e881de SHA512: b79f7e106b787c1329867d3890846fa4f92a14a1e507c30a316282f9026fe1f7c657c1cdad686e8d2939536020b0b62656826d7d66d570dc073b10d61311e1a6 Homepage: https://cran.r-project.org/package=netmap Description: CRAN Package 'netmap' (Represent Network Objects on a Map) Represent 'network' or 'igraph' objects whose vertices can be represented by features in an 'sf' object as a network graph surmising a 'sf' plot. Fits into 'ggplot2' grammar. Package: r-cran-netmediate Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-btergm, r-cran-ergm, r-cran-tergm, r-cran-rsiena, r-cran-sna, r-cran-network, r-cran-ergmargins, r-cran-vgam, r-cran-plyr, r-cran-lme4, r-cran-plm, r-cran-gam, r-cran-intergraph Suggests: r-cran-matrix, r-cran-igraph, r-cran-relevent, r-cran-statnet, r-cran-statnet.common Filename: pool/dists/resolute/main/r-cran-netmediate_1.1.1-1.ca2604.1_all.deb Size: 304168 MD5sum: 514cb4b2f87330a710adad9b988e8a76 SHA1: 44ab2e1e971940a84c3caf88a4414e66bb404883 SHA256: 3bf4f2cee66cc5b9dac44c7c32acf4ec090c6ce60bf417a61c1c7521f2abed55 SHA512: 6d2c7ce197312585849cefc78080adca0574130cf3758729a5a789f96396f0297beeec90702bce591526e92d1d7c34deb6bffc6ca48c6db9ea440c476a165b00 Homepage: https://cran.r-project.org/package=netmediate Description: CRAN Package 'netmediate' (Micro-Macro Analysis for Social Networks) Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME). URL: . BugReports: . Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) . Snijders, Tom A. B., and Christian E. G. Steglich (2015) . Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) . Duxbury, Scott (2023) . Duxbury, Scott (2024) . Package: r-cran-netmem Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-usethis, r-cran-styler Filename: pool/dists/resolute/main/r-cran-netmem_1.0-3-1.ca2604.1_all.deb Size: 654188 MD5sum: fb3ffac0fbe79d0d3488a79ef5f7b64e SHA1: 5069565a7f57aa9be5faf9fa7322cfb9bf0d9b2e SHA256: 5845fe3c5ef42a8f04444ca6b57a83d4efbbb0f1a8fbb968768a6ca493925a13 SHA512: ec3117eed6ac6e703f1fb84d4d0cbeb9039d1bb936bc13a8b3838a2b380d320e5ed0167ad70d6c63d1b096a9573d21c092fb0ca0787b25681e858e3d327ef2ba Homepage: https://cran.r-project.org/package=netmem Description: CRAN Package 'netmem' (Social Network Measures using Matrices) Provides measures to describe and manipulate one-mode, two-mode, multiplex, and multilevel networks using matrix algebra. Implements functions for network centrality, cohesive subgroups, structural holes, similarity measures, path distances, signed networks, and random network generation. Supports ego-centric and whole-network analyses, including dyadic and triadic census, structural balance, and bipartite projections. Key references: Bonacich (1972) , Breiger (1974) , Kivelä et al. (2014) , Espinosa-Rada et al. (2024) . Package: r-cran-netmeta Architecture: all Version: 3.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta, r-cran-matrix, r-cran-metafor, r-cran-mass, r-cran-mvtnorm, r-cran-magic, r-cran-igraph, r-cran-ggplot2, r-cran-colorspace, r-cran-dplyr, r-cran-magrittr Suggests: r-bioc-rgraphviz, r-bioc-graph, r-cran-rgl, r-cran-gridextra, r-cran-tictoc, r-cran-writexl, r-cran-r.rsp, r-cran-cccp, r-cran-brglm2, r-cran-crossnma, r-cran-gemtc, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-netmeta_3.4-0-1.ca2604.1_all.deb Size: 2020140 MD5sum: 7898f4bee56f4f1b2549b66cacacd502 SHA1: 0a8652120fa70dda75504e60d6953bd7a5556a0d SHA256: 1c2725e287a60e0101a783a3e8a0c540644b540b43c7aae11ec397ee71d3e609 SHA512: ad2b933fe56ac4a83ba8a250b44f8b0685017b056ffe2d5bddf3c55fde3575a35ad8567622c10a56b02d2fa20ffac1ecc63f84ac2336cd39a68169fd77e07586 Homepage: https://cran.r-project.org/package=netmeta Description: CRAN Package 'netmeta' (Network Meta-Analysis using Frequentist Methods) A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) and supporting Schwarzer et al. (2015) , Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) ; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) ; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) , or penalised logistic regression (Evrenoglou et al., 2022) ; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) ; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) ; - split direct and indirect evidence to check consistency (Dias et al., 2010) , (Efthimiou et al., 2019) ; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) ; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) ; - measures characterizing the flow of evidence between two treatments by König et al. (2013) ; - automated drawing of network graphs described in Rücker & Schwarzer (2016) ; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) ; (Rücker & Schwarzer, 2017) ; - contribution matrix as described in Papakonstantinou et al. (2018) and Davies et al. (2022) ; - network meta-regression with a single continuous or binary covariate; - subgroup network meta-analysis. Package: r-cran-netmhc2pan Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-dplyr, r-cran-rappdirs, r-cran-readr, r-cran-seqinr, r-cran-stringr, r-cran-testit, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-netmhc2pan_1.3.2-1.ca2604.1_all.deb Size: 149876 MD5sum: f8aa73c265ed93d248ffd32a9f8f3189 SHA1: 33e20e4c19b6937fff7645a876f3a349cc6920d2 SHA256: f1e3c09c659fcbd79e33d83cd796fed142041aaf2f19a7efe712f8ad003e174c SHA512: b0d77d713db9bfde5271d120b3ce4054ee2e346bfb5e5b8477dad8a2070386c76944a50b05a14cf1fa953522a7701c7e6f5b12f6b8e676e82e8dd325711a34dd Homepage: https://cran.r-project.org/package=netmhc2pan Description: CRAN Package 'netmhc2pan' (Interface to 'NetMHCIIpan') The field of immunology benefits from software that can predict which peptide sequences trigger an immune response. 'NetMHCIIpan' is a such a tool: it predicts the binding strength of a short peptide to a Major Histocompatibility Complex class II (MHC-II) molecule. 'NetMHCIIpan' can be used from a web server at or from the command-line, using a local installation. This package allows to call 'NetMHCIIpan' from R. 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Package: r-cran-netrics Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1991 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manynet, r-cran-dplyr, r-cran-igraph Suggests: r-cran-autograph, r-cran-sna, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-netrics_0.2.1-1.ca2604.1_all.deb Size: 871486 MD5sum: 7c53a0b5e26e4977bb38cdc0d64d38b6 SHA1: 1cf903d347eddd2bcc2ce721e7df3c8a76cf8c04 SHA256: 67bb05c4fef42da9eb841ebee7b0abde36cb72c04fc8b722e9694ed125c4857b SHA512: 6e7a25ca136df9876c1dce83206ecac0ba2a341d1f5cbdbceabf9cff4f543e600bcaef7f03dd5315f1704152947060ee4055861297fae8d163d08b7df26bf35a Homepage: https://cran.r-project.org/package=netrics Description: CRAN Package 'netrics' (Many Ways to Measure and Classify Membership for Networks,Nodes, and Ties) Many tools for calculating network, node, or tie marks, measures, motifs and memberships of many different types of networks. Marks identify structural positions, measures quantify network properties, memberships classify nodes into groups, and motifs tabulate substructure participation. All functions operate with all classes of network data covered in 'manynet', and on directed, undirected, multiplex, multimodal, signed, and other networks. 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Measures such as the Largest Connected Component, the Relative Largest Connected Component, Proximity and Separation are calculated along with their statistical significance. Significance can be computed both using a degree-preserving randomization and non-degree preserving. 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Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2025) . Package: r-cran-netseg Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-scales Filename: pool/dists/resolute/main/r-cran-netseg_1.0-3-1.ca2604.1_all.deb Size: 300230 MD5sum: 68f3b8b6ae96c64e4f32c9ef03fc5657 SHA1: 0332866004c73591af36a09d115515e28066e130 SHA256: b579b81788d9237c876da7b39e204557d6d254db0fbe9a6ff012dd01865b626c SHA512: f249390db72f06e69cf7ebad516c612b448be87bc5d9e7a0850251be16dbc8920b641b9eff38e0beaf47094089b640d327f7fc624206bd7b26d43093c62f3cff Homepage: https://cran.r-project.org/package=netseg Description: CRAN Package 'netseg' (Measures of Network Segregation and Homophily) Segregation is a network-level property such that edges between predefined groups of vertices are relatively less likely. Network homophily is a individual-level tendency to form relations with people who are similar on some attribute (e.g. gender, music taste, social status, etc.). In general homophily leads to segregation, but segregation might arise without homophily. This package implements descriptive indices measuring homophily/segregation. It is a computational companion to Bojanowski & Corten (2014) . 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It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the 'best fit' on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) . Package: r-cran-netsimr Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown, r-cran-shiny, r-cran-shinybusy, r-cran-future.apply, r-cran-scales, r-cran-future, r-cran-dbi, r-cran-rmysql, r-cran-rodbc, r-cran-rpostgresql, r-cran-rsqlite, r-cran-plotly, r-cran-shinyjs, r-cran-mass, r-cran-fitdistrplus, r-cran-shinywidgets, r-cran-pareto Suggests: r-cran-knitr, r-cran-crch, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-netsimr_0.1.5-1.ca2604.1_all.deb Size: 353710 MD5sum: 785f9fb01a90dde5cf5e77398e486dbf SHA1: 9a98711cb596fb466e6a7a6401330f23d165bfd5 SHA256: fc966c3c72b448dae5ad46f37c878429b548cc4a9505067e991be38a6da7c587 SHA512: 21e4d5d452a9c0c485c713862389e5365eefd88ede2c1f46c41ade5120721af03d45bd69cbb0e10bbe1d4caddadbc492fe9bd82a8e4b02d90636ed229c070ad6 Homepage: https://cran.r-project.org/package=NetSimR Description: CRAN Package 'NetSimR' (Actuarial Functions for Non-Life Insurance Modelling) Assists actuaries and other insurance modellers in pricing, reserving and capital modelling for non-life insurance and reinsurance modelling. Provides functions that help model excess levels, capping and pure Incurred but not reported claims (pure IBNR). Includes capped mean, exposure curves and increased limit factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes mean, probability density function (pdf), cumulative probability function (cdf) and inverse cumulative probability function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes calculating pure IBNR exposure with LogNormal and Gamma distribution for reporting delay. Includes three shiny tools, one to simulate insurance claims applying reinsurance structures, fit generalised linear models and fit claims frequency or severity distributions. Methods used in the package refer to Free for All by Yiannis Parizas (2023) ; Escaping the triangle by Yiannis Parizas (2019) ; Take to excess by Yiannis Parizas (2019) . Package: r-cran-netstat Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1665 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-netstat_0.1.2-1.ca2604.1_all.deb Size: 1651450 MD5sum: 5f9ac2dfa05f329c954d83c27eda7c9e SHA1: bb97eda8320cdd3e307f4d4a60e34adb884e62c6 SHA256: d7cebfabcbce225da91a2d7240a5ceda0de868194f9cada6cf5acf42f44edfff SHA512: f20eeeab6f73171b0da960d6915c94bb778d13bc0b899dd0bf03e8dcec084d262b35299b9b1c7faf6315714c3220333f6dc62db176efb323d91a86fa78eebf8c Homepage: https://cran.r-project.org/package=netstat Description: CRAN Package 'netstat' (Retrieve Network Statistics Including Available TCP Ports) R interface for the 'netstat' command line utility used to retrieve and parse commonly used network statistics, including available and in-use transmission control protocol (TCP) ports. Primers offering technical background information on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" by Michael Kerrisk (2014) , and on the Microsoft website (2017) . Package: r-cran-netsubsamp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-matrix, r-cran-randnet, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-netsubsamp_1.0.0-1.ca2604.1_all.deb Size: 67702 MD5sum: 7fb690bfd07b25f6ac7131e37a672e91 SHA1: 449fbbe5df154c9946d764e92c019b3dc6f56bbe SHA256: 1e69f26f56cf118f54b1633e4bea7084935332ac8d95e39c25b85534c24bdd4e SHA512: 94bf6e2d13d96797cbfa01b93a6af425e2e80e962f740f64b945f2513e8f5d31851b1aa10f561593f915b35cb0202a7265059ae02f40559963c9a4c6d8c1f4de Homepage: https://cran.r-project.org/package=netsubsamp Description: CRAN Package 'netsubsamp' (Multivariate Inference of Network Moments by Subsampling) Implements node subsampling methods for multivariate inference on network moments (rescaled motif counts), including: uniform node subsampling to approximate the joint distribution of multiple network moments (Algorithm 1); externally sparsified moments for density-matched comparisons (Algorithm 2); and a two-sample test for unmatchable networks with unequal edge densities via a split-and-sparsify subsampling procedure (Algorithm 3). Built-in support for V-shape (2-star), triangle, and 3-star motifs, with a user-extensible interface for arbitrary additional motifs. Parallel execution is supported via 'doParallel' and 'foreach'. Based on Qi, Hua, Li and Zhou (2024) . Package: r-cran-nettskjemar Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-covr, r-cran-dplyr, r-cran-fs, r-cran-haven, r-cran-knitr, r-cran-labelled, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-vcr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-nettskjemar_1.0.4-1.ca2604.1_all.deb Size: 485654 MD5sum: be60ddd5a885be17345aab68c8eea620 SHA1: 11a3ffa078f862b2caad293f75a76abc49f3f901 SHA256: aef6583d6be11a19cafeb7373e2be4f93d0f781326c8ac4846dca6deff583db4 SHA512: 5e4a3929e8551cd58315933fd9516abfac4bf5920a05e7954071788039024608f4caaa1099a61803e3f4bab3ea865e9f09036275bd706215a7b698531161ad45 Homepage: https://cran.r-project.org/package=nettskjemar Description: CRAN Package 'nettskjemar' (Connect to the 'nettskjema.no' API of the University of Oslo) Enables users to retrieve data, meta-data, and codebooks from . The data from the API is richer than from the online data portal. This package is not developed by the University of Oslo IT. Mowinckel (2021) . Package: r-cran-netweaver Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-netweaver_0.0.6-1.ca2604.1_all.deb Size: 1558462 MD5sum: 90cb5929ac1787c7b70b99b722a47dc2 SHA1: c006af17cb01f58259661886f75137e6791049f4 SHA256: a93f95893744278bb07327ff22fe33d140fe6221a34b86f576481bfc9e25775d SHA512: 8ee109ad6bb2ca8d1ef121d974b11617f3f9e8ef9b098d4350d52b8481d805237e1b656cf91fd75a6b595e937ee09f666e19bf32527105fb10af9f9d33855e4c Homepage: https://cran.r-project.org/package=NetWeaver Description: CRAN Package 'NetWeaver' (Graphic Presentation of Complex Genomic and Network DataAnalysis) Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features. Package: r-cran-networkchange Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-ggplot2, r-cran-rmpfr, r-cran-abind, r-cran-mvtnorm, r-cran-tidyr, r-cran-igraph, r-cran-qgraph, r-cran-network, r-cran-mass, r-cran-rcolorbrewer, r-cran-ggrepel, r-cran-rlang, r-cran-ggally, r-cran-patchwork, r-cran-viridis Suggests: r-cran-sna, r-cran-lifecycle Filename: pool/dists/resolute/main/r-cran-networkchange_1.0.0-1.ca2604.1_all.deb Size: 1139518 MD5sum: aa10a0923c2beb98bf89321a96515801 SHA1: fd9ece402a99d6c137a1e475a0b3a2a5c98d599f SHA256: 40d30a9cf53f0f64084012de241f75c23000f06f8fc1d73fb1ba680d2748c8fa SHA512: 484269575ff25b73aac475ddc919cf1e0666fd6b872907a6dba59a468ca157d2cacd8879b014f6ee1334184f66007b61f87c2f07b8f1f6f2b5ed174437b7a63d Homepage: https://cran.r-project.org/package=NetworkChange Description: CRAN Package 'NetworkChange' (Bayesian Package for Network Changepoint Analysis) Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. This version includes performance optimizations with vectorized MCMC operations and modern ggplot2-based visualizations with colorblind-friendly palettes. Package: r-cran-networkcomparisontest Architecture: all Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-reshape2, r-cran-qgraph, r-cran-networktools, r-cran-isingfit Suggests: r-cran-bootnet, r-cran-isingsampler, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-networkcomparisontest_2.2.3-1.ca2604.1_all.deb Size: 62980 MD5sum: 76d632025b3d22c91e4e6cd93c3286d6 SHA1: f70a838c2b1b26bb6334be79ac788b7977fb37d7 SHA256: 0abe896cf7c1dbcdeee45e384083d84160f5af787ae00799e3ca75b91796bcd9 SHA512: e2a85fa050c515daad6f11de9d04cd5290e8bc635632ed1acd4334824a15bcd14a5999230687e750fdaa060f528b92046ea9a95f0d7af394c21a851d05b57495 Homepage: https://cran.r-project.org/package=NetworkComparisonTest Description: CRAN Package 'NetworkComparisonTest' (Statistical Comparison of Two Networks Based on SeveralInvariance Measures) This permutation based hypothesis test, suited for several types of data supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018), assesses the difference between two networks based on several invariance measures (network structure invariance, global strength invariance, edge invariance, several centrality measures, etc.). Network structures are estimated with l1-regularization. The Network Comparison Test is suited for comparison of independent (e.g., two different groups) and dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021), available from . Package: r-cran-networkcomparr Architecture: all Version: 0.0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-qgraph, r-cran-igraph, r-cran-reshape2, r-cran-networktools, r-cran-gdata Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-networkcomparr_0.0.0.9-1.ca2604.1_all.deb Size: 54364 MD5sum: 64f459f29237e8c5590c557fe5f97ac8 SHA1: 5c17c2bf08ca2208e741989ad04c9ef6c50b8284 SHA256: c2d176cbe13161d8a03ab10c2b3c2ee0b3d9f16580fcfbd290b60cee789143c3 SHA512: cc3ee9fd66e2144fcc867f0bdd14ff1d9f1331e46fc6d63df6c861ae693611c9692ae1f0122c39c3e940af7827aa9fe1a9b2cf8bdfe8bb520c7d1bf35602ac8d Homepage: https://cran.r-project.org/package=NetworkComparr Description: CRAN Package 'NetworkComparr' (Statistical Comparison of Networks) A permutation-based hypothesis test for statistical comparison of two networks based on the invariance measures of the R package 'NetworkComparisonTest' by van Borkulo et al. (2022), : network structure invariance, global strength invariance, edge invariance, and various centrality measures. Edgelists from dependent or independent samples are used as input. These edgelists are generated from concept maps and summed into two comparable group networks. The networks can be directed or undirected. Package: r-cran-networkd3 Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-htmltools, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-networkd3_0.4.1-1.ca2604.1_all.deb Size: 221334 MD5sum: 28829a1c099b8dd981852071a11424e9 SHA1: a3c9261e9eea1bee427dc9eb5fb85617a80b4b4c SHA256: 2649b2bf962a1ee5b202887e5efefc798a650a3cb648fd26729bda54905e68d5 SHA512: 7a82c2118ea9cda7d24af579fd33cc54f5a6c4ab86993741154a2375aa32aeea235f96a73a1a4c8d5b55036b62a79d64685cab282c22756f767f2d1f323e9207 Homepage: https://cran.r-project.org/package=networkD3 Description: CRAN Package 'networkD3' (D3 JavaScript Network Graphs from R) Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey graphs from 'R'. Package: r-cran-networkdynamicdata Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1603 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-networkdynamic, r-cran-network Filename: pool/dists/resolute/main/r-cran-networkdynamicdata_0.3.0-1.ca2604.1_all.deb Size: 1574560 MD5sum: 97ad9b7c42e9fb7ad91acbb81adf3a26 SHA1: e7b6c90ec6b54229b66283033ff5f027ff9aad6a SHA256: e9d61aef0eb12e19ee0a231671eb1f4ae2e82f0ed2c6795ea4e9e52a46500e75 SHA512: de4efa0fe7deb05fd08b2fcf4c0470bc37353b47fb5042fdd06a51a758c9a5e6f150f08f912dd4221d5a3e91155a6552dd23fb69fdff1a7f94e12f5258bb518f Homepage: https://cran.r-project.org/package=networkDynamicData Description: CRAN Package 'networkDynamicData' (Dynamic (Longitudinal) Network Datasets) A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects. Package: r-cran-networkextinction Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-igraph, r-cran-magrittr, r-cran-network, r-cran-scales, r-cran-sna, r-cran-tidyr, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-patchwork, r-cran-dosnow Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-networkextinction_1.0.3-1.ca2604.1_all.deb Size: 507166 MD5sum: 195d3e657d2e0163275f8d14e9a69fb2 SHA1: 2765e3a56a75effe97d4cfd1680ce2dcf8517e8a SHA256: 78267fc0b9043394edd07bd3c2a9e938c6a71a0d1e0420532fa1be6303b936cd SHA512: e27c6e80cb86161f39136fd643fded42820814e41844f3dfff729b83c0cff65151d147b78c5db34f3ce32e81654798ba007a9dea21b2157994eb85cc6585e00e Homepage: https://cran.r-project.org/package=NetworkExtinction Description: CRAN Package 'NetworkExtinction' (Extinction Simulation in Ecological Networks) Simulates the extinction of species in ecological networks and it analyzes its cascading effects, described in Dunne et al. (2002) . Package: r-cran-networklite Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-statnet.common, r-cran-tibble, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-networklite_1.1.0-1.ca2604.1_all.deb Size: 118146 MD5sum: 0f14f89f4fb6604a2f5d6918ac6403f7 SHA1: 6813f1150471b9fd7aa1759aeeefa0672e909dcd SHA256: 56dc50cd5d9ffcb010c3ab07cd647a4670eaf2c7a48b9313d84bdd99f384eec3 SHA512: c6c0e50c2dc50a6470e6f4320513a79c8235d9cc375bb3c2d7c99daa2f4fa0fd882bc306a8004804a01486af6a4a90db3e333d519f12c567a9daab0e80c3bdbf Homepage: https://cran.r-project.org/package=networkLite Description: CRAN Package 'networkLite' (An Simplified Implementation of the 'network' PackageFunctionality) An implementation of some of the core 'network' package functionality based on a simplified data structure that is faster in many research applications. This package is designed for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for binary and weighted, directed and undirected, bipartite and unipartite networks; no current support for multigraphs, hypergraphs, or loops. Package: r-cran-networkreg Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randnet, r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-networkreg_2.0-1.ca2604.1_all.deb Size: 44904 MD5sum: 8319a8ba76e570b342adc1d951e1b3f5 SHA1: 6ed2c7505bf5d1d8a405cddb81654d8babebff76 SHA256: 8e56eae40310860facbce54eb1e8e14b312cb35058b7c101763bac3fa516373d SHA512: bac74f2ce2c328953b05650374d16527c53eee782e4e1096760442faaa66dbe012bd1751205bfe5b37774425f62df3d747014a5551808db71427d8bca7ddb793 Homepage: https://cran.r-project.org/package=NetworkReg Description: CRAN Package 'NetworkReg' (Generalized Linear Regression Models on Network-Linked Data withStatistical Inference) Linear regression model and generalized linear models with nonparametric network effects on network-linked observations. The model is originally proposed by Le and Li (2022) and is assumed on observations that are connected by a network or similar relational data structure. A more recent work by Wang, Le and Li (2024) further extends the framework to generalized linear models. All these models are implemented in the current package. The model does not assume that the relational data or network structure to be precisely observed; thus, the method is provably robust to a certain level of perturbation of the network structure. The package contains the estimation and inference function for the model. Package: r-cran-networkriskmeasures Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-expm, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat, r-cran-igraph, r-cran-covr Filename: pool/dists/resolute/main/r-cran-networkriskmeasures_0.1.7-1.ca2604.1_all.deb Size: 88848 MD5sum: a26191d7f75d17bb94357d94455bcbb7 SHA1: 1ce258ee4dfc6a48205793a91472a018e6db97a3 SHA256: 0a602131c4023f5374c68e0bb302c3d62432dfe0f8c40e0a0a1b6838d525302c SHA512: a62da77bbbf472d84febbe015fcb6a813c8744917f7dcf45434a524ab3619491fd2044d9108e96ab3478cc1dfbefbfe8b1074ff8d008a9f24913837522ec7597 Homepage: https://cran.r-project.org/package=NetworkRiskMeasures Description: CRAN Package 'NetworkRiskMeasures' (Risk Measures for (Financial) Networks) Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity. Package: r-cran-networksem Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-latentnet, r-cran-sna, r-cran-influential, r-cran-lavaan, r-cran-network, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-networksem_0.4-1.ca2604.1_all.deb Size: 80164 MD5sum: 233d06e418c71314d96ed3a4035eac30 SHA1: e725030f03b29c807b0e05abf18bccaddb0a7415 SHA256: 2cf2706316e4b5ab8de3ac7a43106647782024d8ea40e67bb1696400c08e4b2e SHA512: 5ad39152c1a7e2823d973bc5411dc6e4e798dff1cc8520a97bcf36ba9026d3625087b1838274965cc0f23555d14b5b80dc47019f37b27c5b07fda32a92c1f9d9 Homepage: https://cran.r-project.org/package=networksem Description: CRAN Package 'networksem' (Network Structural Equation Modeling) Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at . Package: r-cran-networktoolbox Architecture: all Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-doparallel, r-cran-fdrtool, r-cran-foreach, r-cran-igraph, r-cran-isingfit, r-cran-mass, r-cran-pbapply, r-cran-ppcor, r-cran-psych, r-cran-pwr, r-cran-r.matlab, r-cran-qgraph Suggests: r-cran-googledrive Filename: pool/dists/resolute/main/r-cran-networktoolbox_1.4.4-1.ca2604.1_all.deb Size: 522884 MD5sum: d9962201fc3ee319b9988e1d6804355c SHA1: b831f597ae048aeafbbef8fa5b9406fac12144ef SHA256: c56f72a33b6a6a53c6540f5639857df025309b212de5987386d3359afac75e73 SHA512: e70cd4d78559f48122cb1249317972ad3ce1af4056b8e9bea8c2aeb12fddb2a2758a1936db8f9643fb30b787a665724431729aec61d7f8b24f214f884f71d435 Homepage: https://cran.r-project.org/package=NetworkToolbox Description: CRAN Package 'NetworkToolbox' (Methods and Measures for Brain, Cognitive, and PsychometricNetwork Analysis) Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 ), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 ), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 ). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis. Package: r-cran-networktools Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qgraph, r-cran-igraph, r-cran-reshape2, r-cran-ggplot2, r-cran-gridextra, r-cran-cocor, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-eigenmodel, r-cran-psych, r-cran-smacof, r-cran-wordcloud Suggests: r-cran-dplyr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-networktools_1.6.0-1.ca2604.1_all.deb Size: 130070 MD5sum: a16bbe55fb87dc15041b1ee4a00017ab SHA1: f2fa35c49a6ec3b75c8d92f5bda3227d3420e340 SHA256: dc295f9c0da2abf206c54ef89d3097009862f9aa63c7e1f164df89b70f11eb51 SHA512: b6f13600b19643dfafd22a6224864ff243bb219150c4b507d8654994a388f2a1ba1f3c037c8d2712c9dbccb7d382d24083e3e8d9a4b26b292f461b00b26e6a1e Homepage: https://cran.r-project.org/package=networktools Description: CRAN Package 'networktools' (Tools for Identifying Important Nodes in Networks) Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting. Package: r-cran-networktree Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1538 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-qgraph, r-cran-matrix, r-cran-mvtnorm, r-cran-formula, r-cran-gridbase, r-cran-reshape2 Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-fxregime, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-networktree_1.0.1-1.ca2604.1_all.deb Size: 1265960 MD5sum: 09ac90ca218598f6cb903f296bc92e9a SHA1: e177f0994cea667d37dca4deb7a7c6d9082e59ce SHA256: 8035c1a9ec53f6c2a414ce61a18ff42b8642c4eed358407fb4f32047e3716215 SHA512: c7ced1ba4ea83f16c39c3caa08ce343cc3e945487a563f2069909bb6832abdcc95a1822089079f915132a32dabfecd7ab4859ebc0765e884628eade0c495df8e Homepage: https://cran.r-project.org/package=networktree Description: CRAN Package 'networktree' (Recursive Partitioning of Network Models) Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) . Package: r-cran-neudist Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-goftest Filename: pool/dists/resolute/main/r-cran-neudist_1.0.1-1.ca2604.1_all.deb Size: 737422 MD5sum: e583a412a17f5bacae60dd9cee085055 SHA1: d78fc35bdc5a833553eec1fcf5811f7e3d39b4d9 SHA256: 7500b072343bd068b7833a89b84ccd467f653bd016d79126b3625d551bd2c7c9 SHA512: 231717cffaf53202218fc553fdd610285c4a1a72864ac185d4b8d9568ce9e19299cc5039fa67db795286f5cc48fb85343dc81601903878c25996a62bce8a3817 Homepage: https://cran.r-project.org/package=NeuDist Description: CRAN Package 'NeuDist' (Univariate Continuous Distributions with Model Diagnostics) Implements univariate continuous probability distributions and associated model diagnostics based on the Lindley, Logistic, Half-Cauchy, Half-Logistic, and Poisson families. Provides functions for probability density, cumulative distribution, quantile, and hazard evaluation, random variate generation, and diagnostic procedures including Q-Q and P-P plots, goodness-of-fit tests, and model selection criteria. Package: r-cran-neuralestimators Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 833 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliaconnector, r-cran-magrittr Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-ggplotify, r-cran-ggpubr, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-neuralestimators_0.2.1-1.ca2604.1_all.deb Size: 514384 MD5sum: 5e2fa47818a3c399d266273e9629d533 SHA1: bdd1e36721cc3f058a8f63bd9197e6e37f872ecf SHA256: 440d33b7d1f99ba5d2ca03cd0d60f0aca259ac077a8175adf33bcfcc0502a8cb SHA512: 4a9f27ed0749ce1a6f9773796a884deaf3c57143528d44d0ca64bd9f6b15814e6fb1a78d5e8d5deb58977d5484201660087ea2ee5a3b1254c16a30036d76b7a1 Homepage: https://cran.r-project.org/package=NeuralEstimators Description: CRAN Package 'NeuralEstimators' (Likelihood-Free Parameter Estimation using Neural Networks) An 'R' interface to the 'Julia' package 'NeuralEstimators.jl'. The package facilitates the user-friendly development of neural Bayes estimators, which are neural networks that map data to a point summary of the posterior distribution (Sainsbury-Dale et al., 2024, ). These estimators are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, inference from observed data can be made in a fraction of the time required by conventional approaches. The package also supports amortised Bayesian or frequentist inference using neural networks that approximate the posterior or likelihood-to-evidence ratio (Zammit-Mangion et al., 2025, Sec. 3.2, 5.2, ). The package accommodates any model for which simulation is feasible by allowing users to define models implicitly through simulated data. Package: r-cran-neuralgam Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-keras, r-cran-ggplot2, r-cran-magrittr, r-cran-reticulate, r-cran-formula.tools, r-cran-matrixstats, r-cran-patchwork, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat, r-cran-fs, r-cran-withr Filename: pool/dists/resolute/main/r-cran-neuralgam_2.0.1-1.ca2604.1_all.deb Size: 225976 MD5sum: 9e677b43be87d25d85939eef1aa1afe9 SHA1: 9f6ad7afd92d3b369ee6fc6c26ac6797514306c5 SHA256: fb5dfda8af9de0dec4dfe3b3fed3a4df41e3ba3a3948850e461d67eeebe2532e SHA512: 904f46e43ebb453d2606d52bd91ceeede10db13035d21f6203c4e836e9026a0e2f5cb6940a524bd14d971fa66642f8eb0edcd9b4e16a06d499d7a9f405a2a84e Homepage: https://cran.r-project.org/package=neuralGAM Description: CRAN Package 'neuralGAM' (Interpretable Neural Network Based on Generalized AdditiveModels) Neural Additive Model framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms. Package: r-cran-neuralnet Architecture: all Version: 1.44.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-deriv Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-neuralnet_1.44.2-1.ca2604.1_all.deb Size: 122812 MD5sum: 7cd0ca187f40a16993b74f95397fdd06 SHA1: 68b2b9e24b82a1679bb492db4de08679a380149b SHA256: ed605aac5e277e164f4861863d0b60cd29ad29a4be8d1ebd24a8c1ff34f23c4c SHA512: 6c3c7d5e03e682c512e3dbe4c0dafc0ecb2c7adc3a6855869d32f2e8c8c5bcbee3856ad05bf2c4d1ca25a6af66992fa4874d985cede2506f6f5502f76a0f749e Homepage: https://cran.r-project.org/package=neuralnet Description: CRAN Package 'neuralnet' (Training of Neural Networks) Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented. Package: r-cran-neuralnettools Architecture: all Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-nnet, r-cran-reshape2, r-cran-scales, r-cran-tidyr Suggests: r-cran-caret, r-cran-neuralnet, r-cran-rsnns, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-neuralnettools_1.5.3-1.ca2604.1_all.deb Size: 244250 MD5sum: 3a6b279e2c4faf176b178431f2051c68 SHA1: 541dac8af0913303aa0138cc2ad030b0fc4f5f1a SHA256: 9cbefac1252f189a957de58249d063405cac532320e1a26d797864bfc6dcc9e7 SHA512: 3b19cf2610dbe6a23d1cff88a48b28e12795d597195a72b267068cef525989937520f4743eece2a4c2cee318fae634e4cfd77cbe9e7f8f342dc177c9b1e4d86e Homepage: https://cran.r-project.org/package=NeuralNetTools Description: CRAN Package 'NeuralNetTools' (Visualization and Analysis Tools for Neural Networks) Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights. Package: r-cran-neuralsens Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-neuralnettools, r-cran-reshape2, r-cran-caret, r-cran-fastdummies, r-cran-stringr, r-cran-hmisc, r-cran-ggforce, r-cran-scales, r-cran-ggnewscale, r-cran-magrittr, r-cran-ggrepel, r-cran-ggbreak, r-cran-dplyr Suggests: r-cran-h2o, r-cran-rsnns, r-cran-nnet, r-cran-neuralnet, r-cran-plotly, r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-neuralsens_1.1.3-1.ca2604.1_all.deb Size: 439374 MD5sum: 39b8b310a8f5df8d73e8eee622131d41 SHA1: 2e93298d82fcb5d5fb88701cfe73cc610cd2d738 SHA256: 9f582b6f8844dce13e3a6d398fa2fe5338f6ae5784bd2cff90f1b0fa02a46f1a SHA512: 0fd9217f6f08f78d47b52218443695db22580679198395c533a1edd7605d291ab5dcafd2b01d993b66a2164c1cb10af0aca54b3299741ae6534704f31f5ecb29 Homepage: https://cran.r-project.org/package=NeuralSens Description: CRAN Package 'NeuralSens' (Sensitivity Analysis of Neural Networks) Analysis functions to quantify inputs importance in neural network models. Functions are available for calculating and plotting the inputs importance and obtaining the activation function of each neuron layer and its derivatives. The importance of a given input is defined as the distribution of the derivatives of the output with respect to that input in each training data point . Package: r-cran-neurobase Architecture: all Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oro.nifti, r-cran-abind, r-cran-matrixstats, r-cran-r.utils, r-cran-rnifti Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-reshape2, r-cran-httr, r-cran-covr, r-cran-brainr Filename: pool/dists/resolute/main/r-cran-neurobase_1.34.0-1.ca2604.1_all.deb Size: 1412258 MD5sum: 093390ed1c04d80a0e93a521a56a34f9 SHA1: 391eb8048fa87ce7e367da9a7ba78318f42ee64a SHA256: 85650915e3e1680efbabe18e3ca5208d7d0eb083f83bd0ae334550a3af8448c5 SHA512: e4fa13fe3512f1ee77f33f0b73ea083b47da433ea11e22e2304bdc9337d5b254a415c5522c82e8fef5231058ef48f4332873bb239a3a05cd7c2eb5edd30f77cf Homepage: https://cran.r-project.org/package=neurobase Description: CRAN Package 'neurobase' ('Neuroconductor' Base Package with Helper Functions for 'nifti'Objects) Base package for 'Neuroconductor', which includes many helper functions that interact with objects of class 'nifti', implemented by package 'oro.nifti', for reading/writing and also other manipulation functions. Package: r-cran-neuroblastoma Architecture: all Version: 2023.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8058 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-neuroblastoma_2023.9.3-1.ca2604.1_all.deb Size: 8193252 MD5sum: 8f2f08a06be8ee877adab9492b8845d8 SHA1: 5739a25944d97850cb2622b35000babf1f854167 SHA256: 0074a753c208a414d4459b6437a50a7644254995d3ba48ef8c2f485c3c683447 SHA512: c67462423717d5d67fe9b6828da28f6f42980c159e9176b9483a31c60979f5f73e9da2afbabf2ef9d92d058b23c9b0cfabdcec1b755a718148ff840ca5a91143 Homepage: https://cran.r-project.org/package=neuroblastoma Description: CRAN Package 'neuroblastoma' (Neuroblastoma Copy Number Profiles) Annotated neuroblastoma copy number profiles, a benchmark data set for change-point detection algorithms, as described by Hocking et al. . Package: r-cran-neurodatasets Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1512 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-neurodatasets_0.3.0-1.ca2604.1_all.deb Size: 989954 MD5sum: 78b2f2ad7bf41210a8d6aff9a1633ca3 SHA1: 45a3d1fc4ed9a158da26e2b92d69ac771cc23948 SHA256: be2f8a2b06a4f4b07e847d7e11a831511951cbb9af05586f553569f0320fa835 SHA512: e8b8f7cf3c18e4b5813ed8834e44fc258e61e9d0cf9c92af3e95d5fb4bd449a5aadee1b8172dc122808a833cb552b34a535b71b49468ee2d625c3ded4cf5cc0c Homepage: https://cran.r-project.org/package=NeuroDataSets Description: CRAN Package 'NeuroDataSets' (A Comprehensive Collection of Neuroscience and Brain-RelatedDatasets) Offers a rich and diverse collection of datasets focused on the brain, nervous system, and related disorders. The package includes clinical, experimental, neuroimaging, behavioral, cognitive, and simulated data on conditions such as Parkinson's disease, Alzheimer's disease, dementia, epilepsy, schizophrenia, autism spectrum disorder, attention deficit, hyperactivity disorder, Tourette's syndrome, traumatic brain injury, gliomas, migraines, headaches, sleep disorders, concussions, encephalitis, subarachnoid hemorrhage, and mental health conditions. Datasets cover structural and functional brain data, cross-sectional and longitudinal MRI imaging studies, neurotransmission, gene expression, cognitive performance, intelligence metrics, sleep deprivation effects, treatment outcomes, brain-body relationships across species, neurological injury patterns, and acupuncture interventions. Data sources include peer-reviewed studies, clinical trials, military health records, sports injury databases, and international comparative studies. Designed for researchers, neuroscientists, clinicians, psychologists, data scientists, and students, this package facilitates exploratory data analysis, statistical modeling, and hypothesis testing in neuroscience and neuroepidemiology. Package: r-cran-neurodecoder Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3461 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dosnow, r-cran-e1071, r-cran-forcats, r-cran-foreach, r-cran-ggplot2, r-cran-gridextra, r-cran-magrittr, r-cran-purrr, r-cran-r.matlab, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tictoc, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-neurodecoder_0.2.0-1.ca2604.1_all.deb Size: 1399322 MD5sum: 6507b6d37b10cc0f3d6c77c9cfe6cf43 SHA1: f950df55ffb16ca2c3bfc31ab7f7dd451ce10353 SHA256: c353fb097d4a49975ca75a45d092a9caea5716b6a2dd1676ae81e57c8a12ff89 SHA512: cb08fc3af7a6650d4342a63475d61f927fb8d7d79f2932da9d358de63ea44e3338b541cb3a28e84b726763db53d6538d08468bd4b01c3ea11d40b38aef0659b0 Homepage: https://cran.r-project.org/package=NeuroDecodeR Description: CRAN Package 'NeuroDecodeR' (Decode Information from Neural Activity) Neural decoding is method of analyzing neural data that uses a pattern classifiers to predict experimental conditions based on neural activity. 'NeuroDecodeR' is a system of objects that makes it easy to run neural decoding analyses. For more information on neural decoding see Meyers & Kreiman (2011) . Package: r-cran-neurohcp Architecture: all Version: 0.11.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-httr, r-cran-xml2, r-cran-base64enc, r-cran-aws.s3 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-neurohcp_0.11.0-1.ca2604.1_all.deb Size: 517690 MD5sum: 9ea8e53cf4893b5cc2b2e0336b51662f SHA1: 3319b24a41a123f000faf1f89ab1b22bcc18a7f3 SHA256: 30c64d7080540c7b26aec66ff3ea29dcedda77d80a7e36b9844575bbdcabb26a SHA512: c0ae212240b950f6d937925bdddcdcfaa314040a64061d420ebce82060854974a7347ceabbaae86619ae1e0d2610c5b03f925c6dd6d11e0981221359c3f0ba55 Homepage: https://cran.r-project.org/package=neurohcp Description: CRAN Package 'neurohcp' (Human 'Connectome' Project Interface) Downloads and reads data from Human 'Connectome' Project using Amazon Web Services ('AWS') 'S3' buckets. Package: r-cran-neuroimagene Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1080 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-dbi, r-cran-stringr, r-cran-ggseg, r-cran-sf, r-cran-rsqlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-neuroimagene_0.1.4-1.ca2604.1_all.deb Size: 812376 MD5sum: fdadaf88616629dcf8d8cbc0b30eaeed SHA1: b495dfc34b56d6d9a7dbf1d50afdc95e09dc34db SHA256: 60159029f931fea82c5d7671139b430a89b8897c5cbecd2f886a2f4998ae4e4c SHA512: cfc34bbe09c2d6c14f793f6fe2c2bf8301cda40eb69e6485917cdf9bcf1123d2097c9fddf9e8f3c416aba252d75fd152903cd23b58efbf8838e5201e76103c01 Homepage: https://cran.r-project.org/package=neuroimaGene Description: CRAN Package 'neuroimaGene' (Transcriptomic Atlas of Neuroimaging Derived Phenotypes) Contains functions to query and visualize the Neuroimaging features associated with genetically regulated gene expression (GReX). The primary utility, neuroimaGene(), relies on a list of user-defined genes and returns a table of neuroimaging features (NIDPs) associated with each gene. This resource is designed to assist in the interpretation of genome-wide and transcriptome-wide association studies that evaluate brain related traits. Bledsoe (2024) . In addition there are several visualization functions that generate summary plots and 2-dimensional visualizations of regional brain measures. Mowinckel (2020). Package: r-cran-neuromapr Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-gifti, r-cran-httr2, r-cran-igraph, r-cran-lifecycle, r-cran-rlang, r-cran-tibble, r-cran-withr Suggests: r-cran-ciftitools, r-cran-clue, r-cran-freesurferformats, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-rnifti, r-cran-rspectra, r-cran-testthat, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-neuromapr_0.2.2-1.ca2604.1_all.deb Size: 536314 MD5sum: b83d8bb13ba873460633be7e77028597 SHA1: 644a8eab38ab338188210f44d33cc0d22b8390dc SHA256: 0acf3d944f8ef7ce88601d089294907a47c1d79e9eca65c2b977f7e7fa57d66d SHA512: 33b187cd0bb58d0453f5032228c66f12b17756bedcd729669d609f73c9dbc5d1f17b29e52ee2bf98f678db59df79149f800e27801fa9b43141b5e29b0df3d360 Homepage: https://cran.r-project.org/package=neuromapr Description: CRAN Package 'neuromapr' (Spatial Null Models and Transforms for Brain Map Comparison) Implements spatial null models and coordinate-space transformations for statistical comparison of brain maps, following the framework described in Markello et al. (2022) . Provides variogram-matching surrogates (Burt et al. 2020), Moran spectral randomization (Wagner & Dray 2015), and spin-based permutation tests (Alexander-Bloch et al. 2018). Includes an R interface to the 'neuromaps' annotation registry for browsing, downloading, and comparing brain map annotations from the Open Science Framework ('OSF'). Integrates with 'ciftiTools' for coordinate-space transforms. Package: r-cran-neuromplex Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayeslogit, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-neuromplex_1.0-1-1.ca2604.1_all.deb Size: 125346 MD5sum: e5554810c111c4026de7a3f2472a99ea SHA1: c8bc80a71f6b17f471c9a8c77ede751a624084be SHA256: f7beb54801d536794fe357574457b8f748afc66e525add525f2806082efcaf3e SHA512: 28f21434320aa965684f347d6a4c63231ca9587f5540335b14d48bac9103dcefc3618d7721ddbab106131ffc83c3853a84ced8cb7948718ca91f71822fd36f70 Homepage: https://cran.r-project.org/package=neuromplex Description: CRAN Package 'neuromplex' (Neural Multiplexing Analysis) Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at . Package: r-cran-neuroscc Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2731 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-memisc, r-cran-oro.nifti, r-cran-tidyr Suggests: r-cran-fields, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rcmdcheck, r-cran-remotes, r-cran-rmarkdown, r-cran-roxygen2, r-cran-scales, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-neuroscc_1.0.2-1.ca2604.1_all.deb Size: 2308220 MD5sum: e58fc19fc4a68169f43649e67a49e4cc SHA1: e4d381b90025b04761748486510674b70df1a9d3 SHA256: b6e033d4f735c4f3ef53d347e08afb6571461450847efed8872f8fee04c337bb SHA512: e8127e60f3cce2ba5fee620cbeb849e8aa1fce4dde87beecfe4628d3320ead3edebf9fd456796f09a23959a75c949dfc59d36b543cbc585899afb7b85321bbde Homepage: https://cran.r-project.org/package=neuroSCC Description: CRAN Package 'neuroSCC' (Bridging Simultaneous Confidence Corridors and PET Neuroimaging) Tools for the structured processing of PET neuroimaging data in preparation for the estimation of Simultaneous Confidence Corridors (SCCs) for one-group, two-group, or single-patient vs group comparisons. The package facilitates PET image loading, data restructuring, integration into a Functional Data Analysis framework, contour extraction, identification of significant results, and performance evaluation. It bridges established packages (e.g., 'oro.nifti') with novel statistical methodologies (e.g., 'ImageSCC') and enables reproducible analysis pipelines, including comparison with Statistical Parametric Mapping ('SPM'). 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Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) . 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(2019) Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies, Genetic Epidemiology. . 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Package: r-cran-nhpoisson Architecture: all Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car Filename: pool/dists/resolute/main/r-cran-nhpoisson_3.4-1.ca2604.1_all.deb Size: 385002 MD5sum: d7ea8f986d85e192452ded42a5f1ded6 SHA1: bf06f75235ae26c94bc062bb37b3a68724de6fe2 SHA256: 7fc214fe8000ecd503e618a11aef7464355f659e14baa206f21ed175a50920b0 SHA512: aec5164b67b8e25a42f7a5e6e7015855aca78cb452498a56c4f419ac857da05d6ac5657a7493fec455f0ee87f922c3d8a258ba9e670f497a4993a4532b869e0c Homepage: https://cran.r-project.org/package=NHPoisson Description: CRAN Package 'NHPoisson' (Modelling and Validation of Non Homogeneous Poisson Processes) Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time. Package: r-cran-nhs.predict Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nhs.predict_1.4.0-1.ca2604.1_all.deb Size: 55336 MD5sum: eb532452fe8d904ad374c2aee2c8a14b SHA1: d745b275df16b11c3fef24f3fe0589a0d50a7eac SHA256: 97361ae8ff178481bb3f4323290d9c1b3903cc57712f5fe03b498605847fd5cc SHA512: aef4a3db3ad91f120ba246f94ebf0fe63f550369761c58cbddb25eb8ef097574c5cb350362523017d3a80ddc96df71ab94d7610d042496e174143b8c55f42a6c Homepage: https://cran.r-project.org/package=nhs.predict Description: CRAN Package 'nhs.predict' (Breast Cancer Survival and Therapy Benefits) Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using 'NHS Predict'. The time interval for the estimation can be set up to 15 years, with default at 10. 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The UK's National Health Service issues NHS numbers to all users of its services and this package implements functions for verifying that the numbers are valid according to the checksum scheme the NHS use. Numbers can be validated and checksums created. 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This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data. This package exists to support skills development in the NHS-R community: . 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More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings. Package: r-cran-nic Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1712 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-nic_0.0.2-1.ca2604.1_all.deb Size: 1602308 MD5sum: e82d625dd093e3caf091f839b2e12db7 SHA1: 6717208fffe18287184ac095d1fcd2ecb7ba0d4a SHA256: 434661db61af9b08277eb627a2aec9316794e9f94e9d65d04aaef387615bb0b1 SHA512: fe59f84e328672196362e9fd667aeeb45e5c16b3aadec78e824d064043335f6803ca5fdaeb82a6b6130a85ea6723383cb9921c4cb910a3c25cdc45c907a77814 Homepage: https://cran.r-project.org/package=nic Description: CRAN Package 'nic' (Nature Inspired Colours) Color palettes based on nature inspired colours in "Sri Lanka". 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Package: r-cran-nicherover Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-nicherover_1.1.2-1.ca2604.1_all.deb Size: 572342 MD5sum: 172366537515cf08a10f2334c158b1d1 SHA1: 331936056ae197a20bbc2ac18f510e0ddc8320c2 SHA256: 1297b0039536c1dcd60f9db3c69b62d954adabfab992092ac7565339b1874808 SHA512: cef09496ea9b27f652c41a369e9096dc6dd1ed71ab7c28413bb758b07f80cbbdad696690dd42e895d00968dfb686210780d6440a82663190833bfa0aec0f2590 Homepage: https://cran.r-project.org/package=nicheROVER Description: CRAN Package 'nicheROVER' (Niche Region and Niche Overlap Metrics for MultidimensionalEcological Niches) Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%). Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions. It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region. The article by Swanson et al. (2015) provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data. Package: r-cran-nichetools Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2024 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ellipse, r-cran-lifecycle, r-cran-nicherover, r-cran-purrr, r-cran-rlang, r-cran-siber, r-cran-tibble, r-cran-tidyr Suggests: r-cran-bayestestr, r-cran-ggplot2, r-cran-ggdist, r-cran-ggtext, r-cran-ggh4x, r-cran-janitor, r-cran-knitr, r-cran-patchwork, r-cran-rjags, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-nichetools_0.3.3-1.ca2604.1_all.deb Size: 1704956 MD5sum: d9d2bb369befbb88dec7e997c15f1552 SHA1: 917538275804fb59bd35ad19cd8da725d50af71a SHA256: 319608636126fa98fc26bf3b372d951616acfc903374e55e4959dea0c50297f4 SHA512: 94e8b00153bd89df13ae64c8320c50647d4edd6ca475ecbfab527a4f10dcdfdaa842033095a7386e0e621e01bc6e820b09fca3a8673c4f26f043055a94d9bcea Homepage: https://cran.r-project.org/package=nichetools Description: CRAN Package 'nichetools' (Complementary Package to 'nicheROVER' and 'SIBER') Provides functions complementary to packages 'nicheROVER' and 'SIBER' allowing the user to extract Bayesian estimates from data objects created by the packages 'nicheROVER' and 'SIBER'. Please see the following publications for detailed methods on 'nicheROVER' and 'SIBER' Hansen et al. (2015) , Jackson et al. (2011) , and Layman et al. (2007) , respectfully. 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The method proposed here for ancestral reconstruction of ecological niches characterizes species' niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risk of overestimation of amounts and rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, executing comparative phylogenetic analyses of ecological niches, and plotting for interpretations. Details on the theoretical background and methods used can be found in: Owens et al. (2020) , Peterson et al. (1999) , Soberón and Peterson (2005) , Peterson (2011) , Barve et al. (2011) , Machado-Stredel et al. (2021) , Owens et al. (2013) , Saupe et al. (2018) , and Cobos et al. (2021) . 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Scientific publication first using these tools Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764. 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Reference: Reddy T., Shkedy Z., van Rensburg C. J., Mwambi H., Debba P., Zuma K. and Manda, S. (2021). "Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach". BMC medical research methodology, 21(1), 1-11. . 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Package: r-cran-nlmixr2 Architecture: all Version: 5.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-purrr, r-cran-rstudioapi, r-cran-nlmixr2est, r-cran-nlmixr2extra, r-cran-rxode2, r-cran-lotri, r-cran-nlmixr2plot, r-cran-tibble, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-devtools, r-cran-ggplot2, r-cran-testthat, r-cran-n1qn1, r-cran-withr Filename: pool/dists/resolute/main/r-cran-nlmixr2_5.0.0-1.ca2604.1_all.deb Size: 1140544 MD5sum: 9a6f6571f58ea020a09d050482966f9a SHA1: 09856c5b6889dd08e35bd0f8ad20c7f43dc34115 SHA256: c5a178421705e5deefe1a2e0d6fb3e4b0844f1040c5d9651d56d42a3253272f2 SHA512: c4bebe0693b8d1b153794ae8a1549080fdd40c7659ce5ecefc34159b16aea854ba3ada7c8036791ecc484ee59ba5ae40fbe18cbfcc36d0c25eb7062dbb22413c Homepage: https://cran.r-project.org/package=nlmixr2 Description: CRAN Package 'nlmixr2' (Nonlinear Mixed Effects Models in Population PK/PD) Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 ). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ). Package: r-cran-nlmixr2auto Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlmixr2data, r-cran-nlmixr2, r-cran-nlmixr2est, r-cran-nlmixr2autoinit, r-cran-rxode2, r-cran-dplyr, r-cran-progressr, r-cran-processx, r-cran-withr, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-nlmixr2auto_1.0.0-1.ca2604.1_all.deb Size: 419386 MD5sum: 2ade81a476d12f6a65bf0c728b8c4d73 SHA1: ba5c0c22a3737dd5598eedc8b4e48ddad8a3edf5 SHA256: 1ca857bb3acc2fe561af5e02095796e2434a24ebc1d4785cc050dcf029115895 SHA512: 597621acc947ea0e684407bbd3ae995041d160a69d7721adeb05efe400bec30f1b82ea746e53fea6f51380d0a34612d089ee85a3a7e0fe253d64a674da50d9f1 Homepage: https://cran.r-project.org/package=nlmixr2auto Description: CRAN Package 'nlmixr2auto' (Automated Population Pharmacokinetic Modeling) Automated population pharmacokinetic modeling framework for data-driven initialisation, model evaluation, and metaheuristic optimization. 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Package: r-cran-nlmixr2autoinit Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlmixr2data, r-cran-nlmixr2, r-cran-nlmixr2est, r-cran-rxode2, r-cran-dplyr, r-cran-tidyr, r-cran-crayon, r-cran-purrr, r-cran-knitr, r-cran-magrittr, r-cran-progressr, r-cran-tibble, r-cran-vpc Filename: pool/dists/resolute/main/r-cran-nlmixr2autoinit_1.0.0-1.ca2604.1_all.deb Size: 401762 MD5sum: 4ded94ab43bb679598822bcfaf21ead9 SHA1: 9330ed3007430f98767d581dc3a7a2f70bbc3825 SHA256: 21361e679197c7688cb8feee2e7adc1e867691c73f06f97618aedec408dfd467 SHA512: 60d202ecdc6c1e8ad89fcb61f2bd6bdcf63b8f2a17fe54266fb3abdcebed153ff27306ca6703b9d83d5cde64122369e201cb2577d66530441a6e8cf18f86b262 Homepage: https://cran.r-project.org/package=nlmixr2autoinit Description: CRAN Package 'nlmixr2autoinit' (Automatic Generation of Initial Estimates for PopulationPharmacokinetic Modeling) Provides automated methods for generating initial parameter estimates in population pharmacokinetic modeling. The pipeline integrates adaptive single-point methods, naive pooled graphic approaches, noncompartmental analysis methods, and parameter sweeping across pharmacokinetic models. It estimates residual unexplained variability using either data-driven or fixed-fraction approaches and assigns pragmatic initial values for inter-individual variability. These strategies are designed to improve model robustness and convergence in 'nlmixr2' workflows. For more details see Huang Z, Fidler M, Lan M, Cheng IL, Kloprogge F, Standing JF (2025) . Package: r-cran-nlmixr2data Architecture: all Version: 2.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nlmixr2data_2.0.9-1.ca2604.1_all.deb Size: 787818 MD5sum: dcdd4eb380a82ec17bcd9e31970541ae SHA1: 3722985871938c026c314569697dfc27458354ea SHA256: 47c6d868c5cec160d0225912ae016b9bd62227611006d406a4eaa08bb5ae09c8 SHA512: a1fed6ce925d54a35ae7094c892a49de22bcdc182a88c77ac351d8390fdd7a3a27f789e2d8fd0baffdecb1dacc28f297e6f893db047ff7bb1293e1e474e2476d Homepage: https://cran.r-project.org/package=nlmixr2data Description: CRAN Package 'nlmixr2data' (Nonlinear Mixed Effects Models in Population PK/PD, Data) Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 ). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ). 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The models include (and plan to include) pharmacokinetic, pharmacodynamic, and disease models used in pharmacometrics. Where applicable, references for each model are included in the meta-data for each individual model. The package also includes model composition and modification functions to make model updates easier. 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References: Tianwei Yu (2018); Haodong Liu and others (2016); Kai Wang and others (2015); Tianwei Yu and others (2010). Package: r-cran-nlopt Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-nlopt_0.1.1-1.ca2604.1_all.deb Size: 31394 MD5sum: a21c0e5146eef09b3b14f9c4d338339a SHA1: 81bc6e82ecb8cb321724e8d8bb34c4ff9f7e1185 SHA256: d0f31449b6761cfb1294e199b78ceb183dd743e8fbed0a43042ee505c28c7c43 SHA512: 3c2256eb38e310fc9d71d82a515e0043685bba9e22d0fbca2a9f272c29fe09b58fbd246df27800d0a1a7a4e0374546725a6eda62b32d1ffdebc48a70bcdf35d8 Homepage: https://cran.r-project.org/package=nlopt Description: CRAN Package 'nlopt' (Call Optimization Solvers with .nl Files) The purpose of this library is to to call different optimization solvers (such as Gonzalez Rodriguez et al. 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In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the 'RcppAlgos' package and sparse matrices. Furthermore, the functions can be called on 'SQL' databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) . Package: r-cran-nlpsem Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-matrix, r-cran-nnet, r-cran-readr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-nlpsem_0.4-1.ca2604.1_all.deb Size: 5514990 MD5sum: 62c3e9c7958fea5a934df25c00713d54 SHA1: 34a925eb6f83c5a350bfc1092a84585801be2f2d SHA256: 3a0eeb4a4484210b99c20072439c1010a758094c2b4491d5781e2bc4013b94b2 SHA512: 9c6a5ea99cc6d22f643b3eea1908a686638d1b0b69c8f6cda20aa0a54de6e7bf5e836568e2904c39053bacaac6481b2186dca52f49c0fae714a0a727e7c90b41 Homepage: https://cran.r-project.org/package=nlpsem Description: CRAN Package 'nlpsem' (Nonlinear Longitudinal Process in Structural Equation Modeling) Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Liu (2025) . Package: r-cran-nlputils Architecture: all Version: 0.0-5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlp, r-cran-snowballc, r-cran-qdap Filename: pool/dists/resolute/main/r-cran-nlputils_0.0-5.1-1.ca2604.1_all.deb Size: 18876 MD5sum: 00a5c4c727da393ca2d8855aa54b1d50 SHA1: ee4fddc63d3be5b3b48ab4ebcfcc97908da3256f SHA256: e953ac94901088adb283886028e8647b87a76a09c4ab1135e3668c220a0901a6 SHA512: 0e5549bfc40f42c8dc50e2b2476bb30d9e374da723f864668a5cbca791bad886384b9015b31e50ef45971308119ba5e029ef2ff3927809435d8797901e469362 Homepage: https://cran.r-project.org/package=NLPutils Description: CRAN Package 'NLPutils' (Natural Language Processing Utilities) Utilities for Natural Language Processing. Package: r-cran-nlraa Architecture: all Version: 1.9.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-nlme Suggests: r-cran-bbmle, r-cran-car, r-cran-emmeans, r-cran-ggplot2, r-cran-lattice, r-cran-minpack.lm, r-cran-nistnls, r-cran-nlstools, r-cran-nls2, r-cran-rmarkdown, r-cran-segmented Filename: pool/dists/resolute/main/r-cran-nlraa_1.9.10-1.ca2604.1_all.deb Size: 3487436 MD5sum: 8890612fe150f38d52a9317484c79e6b SHA1: 08cca9bbfd592dce5b3238ceb1e54311af0f036c SHA256: 45ce3bb3cd79b780d6f9a7f7c5b3dbfb813a91dd4b77fbb6a61a825b647668be SHA512: aee5828ff3348e4efe205f5d9bcdc95f6c1faf0e4f61460117cf783d2080d6abd667a0ac81a7bd32a33421bb6019dab8240e6e4c88d9f61dd3f487b4be584609 Homepage: https://cran.r-project.org/package=nlraa Description: CRAN Package 'nlraa' (Nonlinear Regression for Agricultural Applications) Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) . There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) , a book chapter with similar material and a publication by Oddi et. al. (2019) in Ecology and Evolution . The function 'nlsLMList' uses 'nlsLM' for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples. 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'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using class objects. Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating 'NetLogo' experiments on remote high performance computing machines. In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system. 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Package: r-cran-nlsem Architecture: all Version: 0.8-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-orthopolynom, r-cran-nlme, r-cran-lavaan, r-cran-gaussquad, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-nlsem_0.8-1-1.ca2604.1_all.deb Size: 630478 MD5sum: 94574ef2dbd9caf7f081a5bf907d01e5 SHA1: a9dba892d01a8c389de1ce9495bcc8e4050d58a2 SHA256: 05d9c670d5eae43b2abcab455393e245dcccd3e43953e78844bb6412dab8bc6a SHA512: 68e6471287d6f63b5e9e69489ea65937008d836f5036241e3a3990ddce7d8ceadb9bddd60024cc2d4f3204ae333d7345c8f05565983f772019eb2c5ac70f1a48 Homepage: https://cran.r-project.org/package=nlsem Description: CRAN Package 'nlsem' (Fitting Structural Equation Mixture Models) Estimation of structural equation models with nonlinear effects and underlying nonnormal distributions. 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Linear problems are solved by dense QR decomposition from 'LAPACK' which can limit the size of treated problems. On the other side, we avoid condition number degradation which happens in classical quadratic programming approach. Inequality constraints treatment on each non linear iteration is based on 'NNLS' method (by Lawson and Hanson). We provide an original function 'lsi_ln' for solving linear least squares problem with inequality constraints in least norm sens. Thus if Jacobian of the problem is rank deficient a solution still can be provided. However, truncation errors are probable in this case. Equality constraints are treated by using a basis of Null-space. User defined function calculating residuals must return a list having residual vector (not their squared sum) and Jacobian. If Jacobian is not in the returned list, package 'numDeriv' is used to calculated finite difference version of Jacobian. The 'NLSIC' method was fist published in Sokol et al. (2012) . 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Rigorously checks final Nonmem datasets. Implemented in 'data.table', but easily integrated with 'base' and 'tidyverse'. Package: r-cran-nmfbin Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nmfbin_0.2.1-1.ca2604.1_all.deb Size: 29940 MD5sum: a54d6b17951ebd466319dcf8d9be19bc SHA1: 471363966a987feb1fc2a5aa2472d0360c525534 SHA256: 26c54c053230e2d702bd6927f5760dc0b8c5dd2046b055bd44724434ebc04d58 SHA512: 185a3df4991ac86091698f9a20c45fcdf46ea7d19e8662b71757150c9e0fda83a4757a0ca349b423116c9000a7ff7134015530febe811fbb497456efee0264b5 Homepage: https://cran.r-project.org/package=nmfbin Description: CRAN Package 'nmfbin' (Non-Negative Matrix Factorization for Binary Data) Factorize binary matrices into rank-k components using the logistic function in the updating process. See e.g. Tomé et al (2015) . 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Given an observation matrix and kernel covariates, it optimizes both a basis matrix and a parameter matrix. Notably, if the kernel matrix is an identity matrix, the method simplifies to standard NMF. Also provides NMF with Random Effects (NMF-RE) via nmfre(), which estimates a mixed-effects model combining covariate-driven scores with unit-specific random effects together with wild bootstrap inference, and NMF-based Structural Equation Modeling (NMF-SEM) via nmf.sem(), which fits a two-block input-output model for blind source separation and path analysis. References: Satoh (2025) ; Satoh (2025) ; Satoh (2025) ; Satoh (2026) ; Satoh (2026) . 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Package: r-cran-nmmipw Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lava, r-cran-nloptr, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-nmmipw_0.1.0-1.ca2604.1_all.deb Size: 43258 MD5sum: 3984d023b19c64fb5f34c3f5c13880a2 SHA1: 2018992c8174b64f6f87909c5431ebaa454f0758 SHA256: c9bbc32474b590f4b88fabe98672d024d67d355131ca6da5e1d35f068d3b9d6f SHA512: 9d3fad1cb8481fb8d99857c495fb96bf33ab2149245112f6a77ce76c3496fa95b9c370705f6822493d4cb14ce37144b7ac1125475dd860bc17b9059d3249e5cf Homepage: https://cran.r-project.org/package=NMMIPW Description: CRAN Package 'NMMIPW' (Inverse Probability Weighting under Non-Monotone Missing) We fit inverse probability weighting estimator and the augmented inverse probability weighting for non-monotone missing at random data. Package: r-cran-nmof Architecture: all Version: 2.11-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2921 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-pmwr, r-cran-runit, r-cran-rglpk, r-cran-datetimeutils, r-cran-openxlsx, r-cran-quadprog, r-cran-readxl, r-cran-tinytest, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-nmof_2.11-0-1.ca2604.1_all.deb Size: 2277074 MD5sum: a0a5cac416f96c904e846974773024a4 SHA1: f0cdc1ed699d426962692aa038214f974fe865b5 SHA256: 856433b96cdf0d7d6fb62ba6e60ba62bb103d76e9828f60d28deb079e9d94b5b SHA512: b2ba30b7620f43fc2ce161d9c9cd6738922bd74cb7dd88c72c2cef91fc9e7eb10c67cad57a2ae0fe36c4ff3c9dc0c84f07a750252b555995d02b67e5738cd382 Homepage: https://cran.r-project.org/package=NMOF Description: CRAN Package 'NMOF' (Numerical Methods and Optimization in Finance) Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. 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Package: r-cran-nmrphasing Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3067 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-baseline, r-bioc-massspecwavelet, r-cran-signal Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggpubr, r-cran-conflicted Filename: pool/dists/resolute/main/r-cran-nmrphasing_1.0.7-1.ca2604.1_all.deb Size: 1383174 MD5sum: 74681edda01fa543c8af642825a1c42e SHA1: f8d71a6c1d075df3a97e40ee6f36ba93c83b2ee4 SHA256: 60d6a6e039078d8b593279599659eb1fef87bfe8ff81dadcf122216fb16108ed SHA512: 2f9a566d0508f5659a677188fd3108e5b38a2b8da2f8938cd00c66d1bc3df55c98dafaa3645e56fa85ce1305fc531650a1df6e9db1b26897c6ad83a2a48c9ed1 Homepage: https://cran.r-project.org/package=NMRphasing Description: CRAN Package 'NMRphasing' (Phase Error Correction and Baseline Correction for OneDimensional ('1D') 'NMR' Data) There are three distinct approaches for phase error correction, they are: a single linear model with a choice of optimization functions, multiple linear models with optimization function choices and a shrinkage-based method. The methodology is based on our new algorithms and various references (Binczyk et al. (2015) ,Chen et al. (2002) , de Brouwer (2009) , Džakula (2000) , Ernst (1969) , Liland et al. (2010) ). 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Turns 'Nonmem' control streams into simulation control streams, executes them with specified simulation input data and returns the results. The simulation is performed by 'Nonmem', eliminating manual work and risks of re-implementation of models in other tools. 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Several functions are provided for data exploration, including functions for creating a subset of dataset, frequency tables and plots. Inference for order restricted dose- response data is performed by testing the significance of monotonic dose-response relationship, using Williams, Marcus, M, Modified M and Likelihood ratio tests. Several methods of multiplicity adjustment are also provided. Description of the methods can be found in . 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Additionally, provides functions for post-run processing of NONMEM output files, generating comprehensive PDF diagnostic reports including objective function value analysis, parameter estimates, prediction diagnostics, residual diagnostics, empirical Bayes estimate (EBE) analysis, input data summary, and individual pharmacokinetic parameter distributions. 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This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species' distribution (random, uniform or clumped patterns). Ludwig & Reynolds (1988, ISBN:0471832359). 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(2006) , and Hsu and Yu (2018) . Note that the current version can only impute for a situation with one missing covariate. 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(2024), "Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials", , Grohs P., Hornung, F., Jentzen, A. et al. (2023), "Space-time error estimates for deep neural network approximations for differential equations", , Jentzen A., Kuckuck B., von Wurstemberger, P. (2023), "Mathematical Introduction to Deep Learning Methods, Implementations, and Theory" . Our implementation is meant mainly as a pedagogical tool, and proof of concept. Faster implementations with deeper vectorizations may be made in future versions. 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The package also contains functions for generating patterns of segregation, association, uniformity in a multi-class setting (Ceyhan (2014) ), and various non-random labeling patterns for disease clustering in two dimensional cases (Ceyhan (2014) ), and for visualization of all these patterns for the two dimensional data. The tests are usually (asymptotic) normal z-tests or chi-square tests. 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Package: r-cran-node2vec Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-word2vec, r-cran-rlist, r-cran-dplyr, r-cran-vctrs, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-node2vec_0.1.0-1.ca2604.1_all.deb Size: 33152 MD5sum: 02889b601858679f07d4304bfd6f79e8 SHA1: a324a4a2817be351f4586689b63bf7ca8cf42d0d SHA256: 46aae839950aa4e7818d3f4c8489b716cd5a9d88896d0d60a52ae7620c123df9 SHA512: 7013e3ad660e0535b0a6f3b6d4fe76d76df7415a84692bab0e4f7249f6c556fe78f4e1b3fa5d1b4c7d0b756ad35f6242f1865efb850aa9bf687d0f911a697a77 Homepage: https://cran.r-project.org/package=node2vec Description: CRAN Package 'node2vec' (Algorithmic Framework for Representational Learning on Graphs) Given any graph, the 'node2vec' algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at . 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Package: r-cran-noia Architecture: all Version: 0.97.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-noia_0.97.3-1.ca2604.1_all.deb Size: 170706 MD5sum: d2957666f994018f3aec08282e556866 SHA1: ac4c9c3095ee55487bccc3bd75af1457b311f1b7 SHA256: c19704fc5a92d60904bc3e5f604052bf7f5028dc1a240a379d56e0a1ed951768 SHA512: 7bff4f835883abf9a5ecb2b0df55da0edd6c54eefcf965cddbda6c931735ff99a57fc510e90bed9a9c8e86d951486402023b6ed7f9f458a54587d86d8b85c64a Homepage: https://cran.r-project.org/package=noia Description: CRAN Package 'noia' (Implementation of the Natural and Orthogonal InterAction (NOIA)Model) The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008). Package: r-cran-noise Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-preprocesscore Filename: pool/dists/resolute/main/r-cran-noise_1.0.2-1.ca2604.1_all.deb Size: 812820 MD5sum: 94cec82153dfec2a65839fc41ffe3eb0 SHA1: c7ba1a124a31f28ce664741b994e095934a40b3a SHA256: bd9a9170a1028c9efdf7631dbcc5e327cec346356309ea5b0038a1031708c72a SHA512: 83ca389140a4ec8976a423285291e8ca5fe0506f608519c90a911d060b7d3ab027a8af9c3e59c0b27def6209e9b0b1be204ae889aea85e99c70cbbebd4a17f8c Homepage: https://cran.r-project.org/package=noise Description: CRAN Package 'noise' (Estimation of Intrinsic and Extrinsic Noise from Single-CellData) Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186. Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471. Package: r-cran-noisemodel Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-nnet, r-cran-e1071, r-cran-fnn, r-cran-classint, r-cran-ggplot2, r-cran-extdist, r-cran-lsr, r-cran-stringr, r-cran-rcolorbrewer, r-cran-rsnns, r-cran-c50 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-noisemodel_1.0.2-1.ca2604.1_all.deb Size: 708290 MD5sum: 2a8d37e4029bf3df25f2e7e60a7c99e9 SHA1: 453c1898786fe5bc695cc496f837e860ee2402ec SHA256: a8f4f8632239ca347ffbac6321ee26f3eb0ac646659c55389fb1b7f409bae726 SHA512: 3d50cc870b360b8f395c1f052adf4581f2ab330bca761470b71e4473e042b6ea47de2569d6f4d0f17a80a2d2a930def96ead065030a3ac06e03a528dd67df6a6 Homepage: https://cran.r-project.org/package=noisemodel Description: CRAN Package 'noisemodel' (Noise Models for Classification Datasets) Implementation of models for the controlled introduction of errors in classification datasets. 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Package: r-cran-noisyr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4467 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-bioc-preprocesscore, r-bioc-iranges, r-bioc-genomicranges, r-bioc-rsamtools, r-cran-philentropy, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-noisyr_1.0.0-1.ca2604.1_all.deb Size: 1879158 MD5sum: 661cacc16c1777df886418c2c3e48f63 SHA1: e774b6a15ead3f48c76bef88c04de6d392bf4cc4 SHA256: 27cd3c86d693d1ad9637a0d37ad85a48d26fc39898a7a98bcfa6eea5f9c78dde SHA512: d4d3990d63b89f41d37a319d1f235a945f751a644c7a1fe0a9207bf88f35175ea7e6d8c0622cbd45767118310bfacd885c300ac29864ae9c080b79ac60f3f9b0 Homepage: https://cran.r-project.org/package=noisyr Description: CRAN Package 'noisyr' (Noise Quantification in High Throughput Sequencing Output) Quantifies and removes technical noise from high-throughput sequencing data. 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Package: r-cran-noisysbm Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1636 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-ggplot2, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-noisysbm_0.1.4-1.ca2604.1_all.deb Size: 1449504 MD5sum: d28fef4cbda372d0e68b1e188316bd28 SHA1: 5141214b09c1671e7c56d968a4ef16c935dee1b0 SHA256: 850b357f5d1690b6f66fe2a887b5261055fe09eacbe36c40ea86daeec2e8a71e SHA512: 977391765e8901ef6088c6fb5279a143ee330c531694abcdaa69459dd40f3b11b46e15bce0aa3728a5ad34152d24bac2d8c88d7605a4c3883b6f3ec9045b256c Homepage: https://cran.r-project.org/package=noisySBM Description: CRAN Package 'noisySBM' (Noisy Stochastic Block Mode: Graph Inference by Multiple Testing) Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. 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Package: r-cran-nolock Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-rstudioapi, r-cran-stringr, r-cran-ncmisc Filename: pool/dists/resolute/main/r-cran-nolock_1.1.0-1.ca2604.1_all.deb Size: 28620 MD5sum: 72a7141eab34da6008b956eaa83176d6 SHA1: 6d7e6e53fe688ed21da99ad460e6eb90e4ec6806 SHA256: b1714793464a94afff72b7c40dbbf0694f39c8b870fa88a202d9260fa214b125 SHA512: 39e25bb48ec44ddff0b58bfd0371a600dbf8273de07d07704c0c3a8a3fb9adad7f0fec93161f2a722ec5e400a8b7e7f6d8a4fea5e72353d99b64bd2a1eea69d2 Homepage: https://cran.r-project.org/package=nolock Description: CRAN Package 'nolock' (Append 'WITH (NOLOCK)' to 'SQL' Queries, Get Packages in ActiveScript) Provides a suite of tools that can assist in enhancing the processing efficiency of 'SQL' and 'R' scripts. - The 'libr_unused()' retrieves a vector of package names that are called within an 'R' script but are never actually used in the script. - The 'libr_used()' retrieves a vector of package names actively utilized within an 'R' script; packages loaded using 'library()' but not actually used in the script will not be included. - The 'libr_called()' retrieves a vector of all package names which are called within an 'R' script. - 'nolock()' appends 'WITH (nolock)' to all tables in 'SQL' queries. This facilitates reading from databases in scenarios where non-blocking reads are preferable, such as in high-transaction environments. Package: r-cran-nombre Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fracture Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nombre_0.4.1-1.ca2604.1_all.deb Size: 404410 MD5sum: 145a1c807d41230d5dbf9f060e143bea SHA1: e24a658f3714f4f20c17332d3b6ca489859a3530 SHA256: b06466800d0b97f411b2335cd6fbc60bbc80ef854b1484c79e29b4c10eefe4c2 SHA512: ec8fa94b52caa7b5ad18d85f25fdac40c4e1420f4cc2d2837b9c1fef630ee7d60e21b99f6fd9dc90d4e650d0eb45f074733418c89378b8ca57623c7986ae9d8b Homepage: https://cran.r-project.org/package=nombre Description: CRAN Package 'nombre' (Number Names) Converts numeric vectors to character vectors of English number names. Provides conversion to cardinals, ordinals, numerators, and denominators. Supports negative and non-integer numbers. 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Detecta e corrige erros tipográficos mais comuns, simplifica opcionalmente termos sujeitos eventualmente a omissão em cadastros, e simplifica foneticamente suas palavras, aplicando variação própria do algoritmo metaphoneBR. (Cleans and simplifies person names to assist in database matching when unambiguous unique keys are unavailable. Detects and corrects common typos, optionally simplifies terms prone to omission in records, and applies phonetic simplification using a custom variation of the metaphoneBR algorithm.) Mation (2025) . 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Provides access to UK labour market statistics including census data, benefit claimant counts, and employment surveys. Supports automatic pagination, optional disk caching, spatial data via 'sf', and tidy data output. Independent implementation unaffiliated with ONS or Durham University. 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The package introduces centered frequency plots, in which nominal categories are ordered from the most frequent category at the center toward less frequent categories on both sides, facilitating the detection of distributional patterns such as uniformity, dominance, symmetry, skewness, and long-tail behavior. In addition, the package supports Pareto charts for the study of dominance and cumulative frequency structure in nominal data. The package is designed for exploratory data analysis and statistical teaching, offering visualizations that emphasize distributional form rather than arbitrary category ordering. Package: r-cran-nomnoml Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-png, r-cran-webshot2, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-v8, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shinytest, r-cran-covr, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-nomnoml_0.3.0-1.ca2604.1_all.deb Size: 240942 MD5sum: 4654d4f051adcf17abd28bb053f7f645 SHA1: e6feec86fdc2f6f5aacc4a2ab39e8e32d66c7405 SHA256: a78cba8bbb52b0630093644c3f15c6fff0fb5189c9568f30e66cb068e8a5afe8 SHA512: 52c91d0078338c07faa480dc1d2a71b22760e28c398cd628883e72ce6d11fab9a7bb68c0de96fe6a6b3b7bc1952fb7aaaabfd32556cf3b8a63538ec050808bb6 Homepage: https://cran.r-project.org/package=nomnoml Description: CRAN Package 'nomnoml' (Sassy 'UML' Diagrams) A tool for drawing sassy 'UML' (Unified Modeling Language) diagrams based on a simple syntax, see . 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The package, including a function of nomogramEx(), is to extract the polynomial equations to calculate the points of each variable, and the survival probability corresponding to the total points. 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However, it is not very easy to draw straight lines, read points and probabilities accurately. Even, it is hard for users to calculate total points and probabilities for all subjects. This package provides formula_rd() and formula_lp() functions to fit the formula of total points with raw data and linear predictors respectively by polynomial regression. Function points_cal() will help you calculate the total points. prob_cal() can be used to calculate the probabilities after lrm(), cph() or psm() regression. For more complex condition, interaction or restricted cubic spine, TotalPoints.rms() can be used. Package: r-cran-noncompart Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-noncompart_0.8.0-1.ca2604.1_all.deb Size: 244132 MD5sum: 51f9186b8734ba3bccf1864274afd8b8 SHA1: 4245beb4e20ddb70fe2005e0c201ce1f27cfdfe4 SHA256: 4bbcf320d9db7f499fd2214cbac63901e942d2d07cf7b1eb6b7d32bc66a233a8 SHA512: a131003c4df8facd91e6d68b451284ad9b737b515ca19bd755b063d5956b982ef0aa72161506984ab45dc33e06180ca0d82e53a720e0e8e6d390b16d3236c241 Homepage: https://cran.r-project.org/package=NonCompart Description: CRAN Package 'NonCompart' (Noncompartmental Analysis for Pharmacokinetic Data) Conduct a noncompartmental analysis with industrial strength. Some features are 1) Use of CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107). Package: r-cran-noncomplyr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-noncomplyr_1.0-1.ca2604.1_all.deb Size: 59182 MD5sum: 0ce61d0df8cef4a69f0a6a7c6a174494 SHA1: 7623f28d3eee1166a8b795fbeaec4ae06999962e SHA256: ec0a353865851eeae59b287b74604a1ccdd74d10c5a8a820479524d4ce6bd724 SHA512: a463e51da268512a4ba90f49d87349829cab982bff627d9b894cdc5b99e66a09e0b3edb46abb1f9a394814e3b028674dee62c44fe28093512f104395c05a73bc Homepage: https://cran.r-project.org/package=noncomplyR Description: CRAN Package 'noncomplyR' (Bayesian Analysis of Randomized Experiments with Non-Compliance) Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) . Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) . 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It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions. Package: r-cran-nonlineardid Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sandwich, r-cran-lmtest, r-cran-ggplot2 Suggests: r-cran-did, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-nonlineardid_0.2.0-1.ca2604.1_all.deb Size: 178660 MD5sum: a125a429f2f7a9ea4b826adc131ddd7c SHA1: 97c44b0d2a45e9fd989a9fff0b874cb62aae8650 SHA256: 9cd2ab4ad989a71b24462d1bee660affb519ba6f6bed726ebd3188b6ac634259 SHA512: 6d7d9b8b6f99dabbd43b5fe76a4de6d925042093dc62af6c451b246472a204e4849fd8a46b5ef62e00da660862558bd9df2b64329530a82698fe257eb1f1e21f Homepage: https://cran.r-project.org/package=NonlinearDiD Description: CRAN Package 'NonlinearDiD' (Staggered Difference-in-Differences with Nonlinear Outcomes) Supports staggered difference-in-differences designs with nonlinear outcomes for both panel and repeated cross-section data. Implements estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes, extending Callaway and Sant'Anna (2021) to settings with nonlinear outcome models such as logit, probit, and Poisson. For panel data, units are followed over time and 'idname' identifies repeated observations. For repeated cross-section data, observations are independent within each time period; 'idname' is optional and may identify survey records or households, but the estimator does not require the same units to appear across periods. Repeated cross-section estimation includes pooled quasi-maximum likelihood approaches motivated by Wooldridge (2023) , with optional weighting and clustered inference. Methods also draw on Roth and Sant'Anna (2023) and Sant'Anna and Zhao (2020) . Package: r-cran-nonlineardotplot Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nonlineardotplot_0.5.0-1.ca2604.1_all.deb Size: 50942 MD5sum: 02f5ba40a2bfb8e2db5cb9316641e15e SHA1: 545ea6265bcbafbd6d531ae9d1a5b7ba33684150 SHA256: 376f82240091605244ab95138d20b106f54228acbfb4b9c4a31f266bddc7db4c SHA512: 62207ca299cca225e44e76e9dfa5769300547e7f64c5b53bad581a9550bec1837c04fd4516a2f875a51f83deeb8a75ee99a707673b5e0afb74df8fcfbb0b8d8f Homepage: https://cran.r-project.org/package=nonLinearDotPlot Description: CRAN Package 'nonLinearDotPlot' (Non Linear Dot Plots) Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. . Package: r-cran-nonlinearicp Architecture: all Version: 0.1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-condindtests, r-cran-data.tree, r-cran-catools, r-cran-randomforest Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nonlinearicp_0.1.2.1-1.ca2604.1_all.deb Size: 59044 MD5sum: 2f4e2f642ea6fab1ccfcf85ffad965f9 SHA1: 317ee641f74e86996cc253f5f668b37765cd56d7 SHA256: 81161bb353ca5a101ab9d366dab1661385212d42fb969e674c6398a3ad41698b SHA512: 9f5ec9ae20265a803477fc17a63f16f7a6a7c6120e3641022ebb17321c9094e84c3c7cdfcc0d0484eee794db51c923019076ed726842cd7589b5e98579b6795a Homepage: https://cran.r-project.org/package=nonlinearICP Description: CRAN Package 'nonlinearICP' (Invariant Causal Prediction for Nonlinear Models) Performs 'nonlinear Invariant Causal Prediction' to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending 'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016), , to nonlinear settings. For more details, see C. Heinze-Deml, J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', . 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The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied. 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Includes tests of model distinguishability and of model fit that can be applied to both nested and non-nested models. Also includes functionality to obtain confidence intervals associated with AIC and BIC. This material is partially based on work supported by the National Science Foundation under Grant Number SES-1061334. 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See Tapan Nayak (1987) . Package: r-cran-nonpar Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nonpar_1.0.2-1.ca2604.1_all.deb Size: 41334 MD5sum: 1c710d755ea687b14b1d8e1007f7d319 SHA1: 05271b771e8c1bce3a9acd31429918dd3f80d3df SHA256: 6b506111484f9c8c365996518615451de12d7b39c1a284da38aa63de07ea0aac SHA512: ef26aa16a30b888ed276cef121909cce681bdd6857dd25daf6862fdebb77a275ae21adfbe047a5bf7cf75295db6c7fb81774f8ca4025f05423d77926d34b943d Homepage: https://cran.r-project.org/package=nonpar Description: CRAN Package 'nonpar' (A Collection of Nonparametric Hypothesis Tests) Contains the following 5 nonparametric hypothesis tests: The Sign Test, The 2 Sample Median Test, Miller's Jackknife Procedure, Cochran's Q Test, & The Stuart-Maxwell Test. 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This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations. 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This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute. Package: r-cran-nonpareil Architecture: all Version: 3.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nonpareil_3.5.3-1.ca2604.1_all.deb Size: 182030 MD5sum: bdde67387b130fad01549a30a87f76e8 SHA1: 8ad65e5d0d3f25a1abaed1b327486ccd6edbca3f SHA256: 90c586e02d41992ea27133e18bc52ae30ed13573b780d56b159fb038fcec64d3 SHA512: 29fc36fdb0e48b312d0b3d18947bf601306eab0974263cb37829907f88d4cf4968862d9117e0090f2fc465a2cde1287e14d8c7802920ebfa53f15cdc62b2c348 Homepage: https://cran.r-project.org/package=Nonpareil Description: CRAN Package 'Nonpareil' (Metagenome Coverage Estimation and Projections for 'Nonpareil') Plot, process, and analyze NPO files produced by 'Nonpareil' . 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The current implementation uses one lag of each series (first-order Granger causality setup). Methodology is based on Balcilar, Gupta, and Pierdzioch (2016a) and Balcilar et al. (2016) . 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This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) . 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The method provides a unified framework for analyzing trends in both independent and dependent data samples, making it a versatile tool for various study designs. The package allows for the evaluation of different trend alternatives, including two-sided (general trend), monotonic increasing, and monotonic decreasing trends. As a nonparametric procedure, it does not require the assumption of data normality, offering a robust alternative to parametric tests. 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A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample. 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The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" . 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Fitting to data by efficient ML (Maximum Likelihood) or traditional EM estimation. Package: r-cran-nord Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nord_1.0.0-1.ca2604.1_all.deb Size: 45440 MD5sum: 27c144e5506504322b042a77443d36e5 SHA1: 7cb1556cc25a77863a9876ade07977d9b57cdfb9 SHA256: 73a6a5ade52eaa0b933dca948536ef1904cd15c25de92a687ebeb289423762cf SHA512: 0fa8e3febbee0ac0138b13c7e59467261ebcf69b66b338768dde0f562e3025a7f127e2801d69a5729cdd234fcc9b5ad05ac357039b0f788b8f816efe68cc8acd Homepage: https://cran.r-project.org/package=nord Description: CRAN Package 'nord' (Arctic Ice Studio's Nord and Group of Seven Inspired ColourPalettes for 'ggplot2') Provides the Arctic Ice Studio's Nord and Group of Seven inspired colour palettes for use with 'ggplot2' via custom functions. 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Designed for multi-user web applications where minimal fetch latency and asynchronous writes are required. Individual statistical values ("cells") are stored in a gatekeeper schema with a sidecar table for arbitrary metadata dimensions, enabling deduplication across overlapping queries. 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(2011) . Performs normalization of raw crossing threshold values (CT) and also calculates relative variability metrics that can be used to assess the impact of normalization on variance. 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In Journal of Statistical Software, Vol. 12, Issue 4). 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However, when dealing with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964) provides an optimal transformation for non-normal variables. Yet, for large datasets of continuous variables, its application in current software programs is cumbersome with analysts having to take several steps to normalise each variable. We present an R package 'normalr' that enables researchers to make convenient optimal transformations of multiple variables in datasets. This R package enables users to quickly and accurately: (1) anchor all of their variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, (3) apply each unique exponent to its variable, (4) rescale resultant values to within their original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, kurtosis, and other inferential assessments of normality. 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It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature. 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Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) . 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Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages. Package: r-cran-nos Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gmp, r-cran-bipartite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nos_2.0.0-1.ca2604.1_all.deb Size: 69220 MD5sum: 6f64d045acac0936e16ac30822379eed SHA1: 412551821e4d5020b925301c0902b9fed993f9ce SHA256: 2129eda39d9c341ae773c1d6907c6d702735f33b09dc51357fd0f2ca65866105 SHA512: a2200b87a27779feac2b988fe86c5c6ec2454e227ab6b71dbbe9051237f1f230580d4d2d09aa7d947b4434357c6cc18629afa416e054aaaafb8da1eaa6cde797 Homepage: https://cran.r-project.org/package=nos Description: CRAN Package 'nos' (Compute Node Overlap and Segregation in Ecological Networks) Calculate NOS (node overlap and segregation) and the associated metrics described in Strona and Veech (2015) and Strona et al. (2018) . The functions provided in the package enable assessment of structural patterns ranging from complete node segregation to perfect nestedness in a variety of network types. In addition, they provide a measure of network modularity. 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Helps in selecting suitable continuity corrections for zero cells in multi-centre or meta-analysis studies. Also supports sensitivity analysis and can detect phenomena such as Simpson's paradox. The methodology is based on Subbiah and Srinivasan (2008) . Package: r-cran-nosoi Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-ggplot2, r-cran-ggnetwork, r-cran-intergraph, r-cran-viridis, r-cran-gifski, r-cran-png, r-cran-gganimate, r-cran-ape, r-cran-tidytree, r-bioc-treeio, r-bioc-ggtree, r-cran-magrittr, r-cran-dplyr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-nosoi_1.1.2-1.ca2604.1_all.deb Size: 718726 MD5sum: fe11cc9ffe1124adae76374f60fa7e2b SHA1: a671cfc216fefed6dd09e1e1b3d8e5fe27755361 SHA256: de7926c368df3cc3108d5e4d2e5a23b5bb521a45a216bdc2bf76a43cce3f069d SHA512: e6f27dfd18b2fa7b4508154b522941811d426daacb441701bc3c2b7a954de550df3664519f313ad8852cc1395918327e2cea67e47fee183a18792e417b0811a7 Homepage: https://cran.r-project.org/package=nosoi Description: CRAN Package 'nosoi' (A Forward Agent-Based Transmission Chain Simulator) The aim of 'nosoi' (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. 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Applied Vegetation Science, 24, e12548. 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Automate the following individual or batch processing: check local source packages; build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment. 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Package: r-cran-npancova Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-npancova_0.1.1-1.ca2604.1_all.deb Size: 82866 MD5sum: 5da1061ce502bb987c200f5152c79334 SHA1: d037049b8b2bd63ed677940779ed76ef5af20324 SHA256: 65759a7593656120e881e4d6b0c58b765e0a0f828d7107885ccb55d462da8d08 SHA512: 8ef3278ebe8741bff306d70dcdc88fdf141e05b3ad93dafcab7a98f862664791f73470714bca02cefe41dcf38b83c87e39691f7e65d6c37defd86026153dddd3 Homepage: https://cran.r-project.org/package=npANCOVA Description: CRAN Package 'npANCOVA' (Nonparametric ANCOVA Methods) Nonparametric methods for analysis of covariance (ANCOVA) are distribution-free and provide a flexible statistical framework for situations where the assumptions of parametric ANCOVA are violated or when the response variable is ordinal. This package implements several well-known nonparametric ANCOVA procedures, including Quade, Puri and Sen, McSweeney and Porter, Burnett and Barr, Hettmansperger and McKean, Shirley, and Puri-Sen-Harwell-Serlin. The package provides user-friendly functions to apply these methods in practice. These methods are described in Olejnik et al. (1985) and Harwell et al. (1988) . Package: r-cran-nparact Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-nparact_0.9.1-1.ca2604.1_all.deb Size: 101608 MD5sum: 890468d16ca1033b1729707c69127158 SHA1: 4a780bf2fa22994599558db11bdd90a2a0a6d403 SHA256: b2635390d27a1de1977769e317c2bdbfb759b629973649e579da0891e2e9c05e SHA512: 6c86cf096ab471551d750db7792d3e56260b5ec13e107b22afe31f1cc69c46b61a8c2cd372a1fa52b9efce02b3becc2c3d2766e4ba6928cab5734d422fda2935 Homepage: https://cran.r-project.org/package=nparACT Description: CRAN Package 'nparACT' (Non-Parametric Measures of Actigraphy Data) Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) and van Someren et al. (1999) . Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days. Package: r-cran-nparcomp Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multcomp, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-nparcomp_3.0-1.ca2604.1_all.deb Size: 223164 MD5sum: b267e43b68029670a40fa302fd1562cd SHA1: d54ae8ff96caef13098d5d5031bc06347842645e SHA256: 651b35a05cf771fc12c8d21694a3227a115aca3ad807c1c71161139e9b71a5da SHA512: 499605544c2e7c3a894a31059619fc229ddc28b0a18fe4176deb39ae7e796f4b9a2710464c99993ca7436b48a4b8f565d6dca311bdf312a60a7360138501a295 Homepage: https://cran.r-project.org/package=nparcomp Description: CRAN Package 'nparcomp' (Multiple Comparisons and Simultaneous Confidence Intervals) With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) for details. 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Nonparametric methods do not require distributional assumptions, and are applicable to a variety of data types (continuous, discrete, purely ordinal, and dichotomous). Such methods are also robust with respect to outliers and for small sample sizes. Package: r-cran-nparmd Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-matrixcalc, r-cran-mass, r-cran-gtools, r-cran-formula Filename: pool/dists/resolute/main/r-cran-nparmd_0.2.3-1.ca2604.1_all.deb Size: 49914 MD5sum: d69d64d91e920d4fa3918c8e1474022e SHA1: 319d487a9e2539fa7dc18d1cc5949ea03a465653 SHA256: 9cd527e6c45eb73fed550b2f7a4e4e7a2e9b29a843c81b852b01d7b4b2ef8cf9 SHA512: 7653ab96ec81c39220953c9b08e299c6e90c497bcbf1350249b426e16c9bbc3297066dad54eea0edf8308f1b973a81cc87dc69fa55dff5f1be158f311300bc4f Homepage: https://cran.r-project.org/package=nparMD Description: CRAN Package 'nparMD' (Nonparametric Analysis of Multivariate Data in Factorial Designs) Analysis of multivariate data with two-way completely randomized factorial design. The analysis is based on fully nonparametric, rank-based methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda, Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The package offers two functions performing the analysis, one for small and the other for large sample sizes. The underlying methodology is largely described in Bathke and Harrar (2016) and in Munzel and Brunner (2000) and in Kiefel and Bathke (2022) . Package: r-cran-nparsurv Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-th.data Filename: pool/dists/resolute/main/r-cran-nparsurv_0.1.0-1.ca2604.1_all.deb Size: 27970 MD5sum: fb391cdcecd18843d67cfdced29e7540 SHA1: 10d21909f99ef1ee5b518338857dd9568131a00c SHA256: 1508c27e9c2c3d97b40e452cfc20d7fd0fe67de5ef056372e0135daaa3398596 SHA512: 39ea5039726a2c05894548f11b5c5906184feeb9b61dfdfbfccbb242e408a60cffd8a690d7d3dda3a960e27e1839bf9d87293fe94477af9b1f196e0325be6508 Homepage: https://cran.r-project.org/package=nparsurv Description: CRAN Package 'nparsurv' (Nonparametric Tests for Main Effects, Simple Effects andInteraction Effect in a Factorial Design with Censored Data) Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables. Package: r-cran-npbbbdaefficiency Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-npbbbdaefficiency_0.1.0-1.ca2604.1_all.deb Size: 16312 MD5sum: 310482cda3b4d2566b366a15871b25a4 SHA1: 7a2a79c21d7459457c0986228f28164ae53a92ea SHA256: 577d58925053819ebaa00255adf240c3cc26aa66fc51f485b2c27c11071f5df6 SHA512: ef66de3fd9b5104dee288933ad5e798b3fe14e65886d4a1d337b98be11199184264d22b769507bba947b56e57555cf792cd8b2988b1379a6c2320508b0bc4d81 Homepage: https://cran.r-project.org/package=NPBBBDAefficiency Description: CRAN Package 'NPBBBDAefficiency' (A-Efficiency for Nested Partially Balanced Bipartite Block(NPBBB) Designs) Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) . This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design. Package: r-cran-npboottprm Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-fgarch, r-cran-ggplot2, r-cran-lmperm, r-cran-mass, r-cran-mkinfer, r-cran-mmints, r-cran-shiny, r-cran-shinythemes, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-npboottprm_0.3.2-1.ca2604.1_all.deb Size: 280790 MD5sum: f5300017dd87a564bad1de4c28cb8ef5 SHA1: 434cd288d37b50386416447ce54b526f138cd8ef SHA256: ed666f96bdad768785a4ae68d7002273e33e0a1d4e2655859a6089ba3c6c45e2 SHA512: e2d6feae6c3acb2c9e8c00783ad5347d22f752638e421102533e5d64f3e5f3bfdf6915eb9354d1e4742f16ccbcc2ed5c1fba822e8e250731bcfaffda1f27b0e6 Homepage: https://cran.r-project.org/package=npboottprm Description: CRAN Package 'npboottprm' (Nonparametric Bootstrap Test with Pooled Resampling) Addressing crucial research questions often necessitates a small sample size due to factors such as distinctive target populations, rarity of the event under study, time and cost constraints, ethical concerns, or group-level unit of analysis. Many readily available analytic methods, however, do not accommodate small sample sizes, and the choice of the best method can be unclear. The 'npboottprm' package enables the execution of nonparametric bootstrap tests with pooled resampling to help fill this gap. Grounded in the statistical methods for small sample size studies detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017) , the package facilitates a range of statistical tests, encompassing independent t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests. The nonparboot() function undertakes essential computations, yielding detailed outputs which include test statistics, effect sizes, confidence intervals, and bootstrap distributions. Further, 'npboottprm' incorporates an interactive 'shiny' web application, nonparboot_app(), offering intuitive, user-friendly data exploration. Package: r-cran-npboottprmfbar Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-fgarch, r-cran-lmperm, r-cran-mmints, r-cran-npboottprm, r-cran-restriktor, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-npboottprmfbar_0.2.0-1.ca2604.1_all.deb Size: 49674 MD5sum: ab4d8862ebde6c3b4b9551e2d0def88b SHA1: e7af33b5efc623311af56e597b35b0ed7e8be065 SHA256: f02f3572a0737a449bf55cab9b17dc39ef4a36c66a2c5a0beedaaaa9f5bdf307 SHA512: 79c9bb96400c6c54d121c6aef38c51666c3deb4b417c9d939efb468e77f8bd1bd5e0d793b8f80cce4c5ac762859ff3731cae4b14e0b6eba2166588cff4cb7c34 Homepage: https://cran.r-project.org/package=npboottprmFBar Description: CRAN Package 'npboottprmFBar' (Informative Nonparametric Bootstrap Test with Pooled Resampling) Sample sizes are often small due to hard to reach target populations, rare target events, time constraints, limited budgets, or ethical considerations. Two statistical methods with promising performance in small samples are the nonparametric bootstrap test with pooled resampling method, which is the focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) , and informative hypothesis testing, which is implemented in the 'restriktor' package. The 'npboottprmFBar' package uses the nonparametric bootstrap test with pooled resampling method to implement informative hypothesis testing. The bootFbar() function can be used to analyze data with this method and the persimon() function can be used to conduct performance simulations on type-one error and statistical power. Package: r-cran-npcd Architecture: all Version: 1.0-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-r.methodss3 Filename: pool/dists/resolute/main/r-cran-npcd_1.0-11-1.ca2604.1_all.deb Size: 140676 MD5sum: 1ed050a11b997797a6457b048c0ccbbf SHA1: 89f9e1b8b89b85edb66a51c744176bba72643f50 SHA256: 69b7569736235e9d9434d625c83a9477d081bf3efdb714a504de9063ac361dae SHA512: facd611938507f0068fa76df1e1a59b59e0e18a835740121bec3b38797eddcd876289a77d7d912eb40d5dd83feb4dd00cf89b7b02a3d69311cf9cd4ac555fb42 Homepage: https://cran.r-project.org/package=NPCD Description: CRAN Package 'NPCD' (Nonparametric Methods for Cognitive Diagnosis) An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model. Package: r-cran-npcdtools Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdina, r-cran-psych, r-cran-gtools, r-cran-matrix, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-npcdtools_1.1.0-1.ca2604.1_all.deb Size: 179580 MD5sum: e4bd9cf19d77d084b0d5461e6bd97759 SHA1: 91edb09a5ac52cab11d9aba66b10096b12208c56 SHA256: 7909aabf62011bfdbf00d81efd695af34b98f6e490cbb99a16a6066945fadaf2 SHA512: e1add4518f7cf8d7aa8af60eff964067404c2731df5e3c5fbf5bd8a69877f348839180750824939298dce420f7502d49caf4d51992290f2fa3ebb0b4573904d2 Homepage: https://cran.r-project.org/package=NPCDTools Description: CRAN Package 'NPCDTools' (The Nonparametric Classification Methods for Cognitive Diagnosis) Statistical tools for analyzing cognitive diagnosis (CD) data collected from small settings using the nonparametric classification (NPCD) framework. The core methods of the NPCD framework includes the nonparametric classification (NPC) method developed by Chiu and Douglas (2013) and the general NPC (GNPC) method developed by Chiu, Sun, and Bian (2018) and Chiu and Köhn (2019) . An extension of the NPCD framework included in the package is the nonparametric method for multiple-choice items (MC-NPC) developed by Wang, Chiu, and Koehn (2023) . Functions associated with various extensions concerning the evaluation, validation, and feasibility of the CD analysis are also provided. These topics include the completeness of Q-matrix, Q-matrix refinement method, as well as Q-matrix estimation. Package: r-cran-npclust Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-npclust_0.1.1-1.ca2604.1_all.deb Size: 70010 MD5sum: 88c5e6b8350c5047d74ae5d80f612da7 SHA1: e438c9441bf1e6419c45e869750fedb144affe8f SHA256: 2d88a9e140b747b51016aaa5f4a665ee119340451e308477012eca2c74b6c94f SHA512: 623b87db10ef7df0eaab4749ded6e6694dbcace634d66abe5b1452085a23bd7bff5fe56150c073196c7a7800b5f8e8368bce434739a7fafcc63b2c4e8f6c5fc9 Homepage: https://cran.r-project.org/package=npclust Description: CRAN Package 'npclust' (Nonparametric Tests for Incomplete Clustered Data) Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) and Harrar and Cui (2022) . Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals. Package: r-cran-npcox Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-progress Filename: pool/dists/resolute/main/r-cran-npcox_1.3-1.ca2604.1_all.deb Size: 104040 MD5sum: 8bfce46912d8c95453125c890375560a SHA1: 4bf73483a390e5cd6f442645b3db64c1cd2e96ba SHA256: 03874eadd11fab0c5b57b5d206438079925856fc9d5d20671d335c7862ff0cec SHA512: bb8af721494e75418408d80f78e62f145ebf37f5df0a8dd66b89673ee9832d5e4e0a0a14cf6cc7001e4897bbe03d3ed183350fc10cbf4cbf8c709f3c12851561 Homepage: https://cran.r-project.org/package=NPCox Description: CRAN Package 'NPCox' (Nonparametric and Semiparametric Proportional Hazards Model) An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) . 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Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021). 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For details, see Safari et al (2021) , Safari et al (2022) and Safari et al (2023) . 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To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers. 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Probability methods include simple random sampling, stratified sampling, systematic sampling, cluster sampling, and probability-proportional-to-size sampling. Non-probability methods include convenience, judgement-based, and quota sampling. Estimation functions cover means, totals, ratio estimators, regression estimators, and the unequal-probability estimator of Horvitz and Thompson (1952, ) for unequal-probability designs. Utilities support biomass, soil-loss, and carbon-stock estimation from field plots. Spatial extensions provide random, systematic, stratified, and raster-weighted sampling within geographic polygons using the 'sf' and 'terra' packages, with extraction of remote-sensing covariates at sample locations. Applications include forest inventory, soil erosion monitoring, watershed studies, and ecological field surveys. 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Package: r-cran-nsm3data Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nsm3 Filename: pool/dists/resolute/main/r-cran-nsm3data_0.1-1.ca2604.1_all.deb Size: 164228 MD5sum: 6583ea82d653eefb0b5ea58ba5846196 SHA1: 635985ca7827f5b4fad690c8106684a05e79a347 SHA256: 33bf1dc33edc3873de7666c5e7431294b4eec91ed28280c4d15630f17669ce66 SHA512: 02faf5dc821126548194c14fd591c2091ab366c728d11caeed67edcd8db72d1f457e1e81817ffb69a0f07df0fa82ed7310735da9eb5da7836cc2e7819e5b47a4 Homepage: https://cran.r-project.org/package=nsm3data Description: CRAN Package 'nsm3data' (Datasets to Accompany Hollander, Wolfe, and Chicken NSM3) Designed to add datasets which are used in the Nonparametric Statistical Methods textbook, 3rd edition. Package: r-cran-nsmm Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-copula, r-cran-dplyr, r-cran-envstats, r-cran-evd, r-cran-ggplot2, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-nsmm_0.1.2-1.ca2604.1_all.deb Size: 161876 MD5sum: 7d5545ad48890028c606b0bdfde50b9d SHA1: b08a4e32e2fd7d2c127dcabd26520eb407c727db SHA256: 1fb87f743ed8759886f4049c2ee17898ad98de5bfd71ee6fde31787068c6e21e SHA512: 850c5212bbaf64e3995f98ab7b8c31ed8ccae81d00c3e0a3969ee4a394e1df30ec7750fc10d22fb8fcde85859f6f211dde781ded0a8aad668b402cd6fe189c1e Homepage: https://cran.r-project.org/package=NSMM Description: CRAN Package 'NSMM' (Non-Stationary Multivariate (Copula-Based) Framework,Hydrological Applications) To account for non-stationary multivariate data, this package implements the framework including copula and marginal distributions. In addition to modeling and parameter estimations, it allows the computation and visualization of multivariate quantile curves for given events. This package is useful for a variety of disciplines such as finance, climatology and particularly for hydrological applications, where dependence structures and marginal parameters may vary over time. This framework, based on Chebana & Ouarda (2021) , integrates both multivariate and non-stationary aspects to be more accurate (e.g. for risk assessment) and more realistic (e.g. considering climate changes). 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Package: r-cran-nsp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2932 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/resolute/main/r-cran-nsp_1.0.0-1.ca2604.1_all.deb Size: 2968992 MD5sum: 67e2b62e07906fbfb3c4e9f2ffec4632 SHA1: f83c546cd7f00f0e0ce882b524fa5deb5866fd17 SHA256: c039ba9a52dc71b778054c55fcf8b72288dfaf26247fcdda480614c9c98e9e6e SHA512: d010214a210d543c5167b8090da939409dfd87daabb4ec2a1bfcdba9bfc0fc9a259d3a6aab3c57f5159d8966f9d15251e564a0049e76ede494a84769b1a821c9 Homepage: https://cran.r-project.org/package=nsp Description: CRAN Package 'nsp' (Inference for Multiple Change-Points in Linear Models) Implementation of Narrowest Significance Pursuit, a general and flexible methodology for automatically detecting localised regions in data sequences which each must contain a change-point (understood as an abrupt change in the parameters of an underlying linear model), at a prescribed global significance level. 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Package: r-cran-nspmix Architecture: all Version: 2.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lsei Filename: pool/dists/resolute/main/r-cran-nspmix_2.0-0-1.ca2604.1_all.deb Size: 281684 MD5sum: fc838801641a1b95253a084d67676627 SHA1: 7e0a0a20c22ecc0ccb654b712837a915601dbbe9 SHA256: 0687a4157b90739c52b0a7dec7f16d102b1bdffb505922aa79a721cbf69f9f14 SHA512: 0e3a4a6ebada3efc184cac5f51f63425603e841675d8e0ef5c9dfd25fc36e7e5bf71ea288dad30f8331bfc3adb58823140563d8f7f94cf7f1310737b2887df30 Homepage: https://cran.r-project.org/package=nspmix Description: CRAN Package 'nspmix' (Nonparametric and Semiparametric Mixture Estimation) Mainly for maximum likelihood estimation of nonparametric and semiparametric mixture models, but can also be used for fitting finite mixtures. The algorithms are developed in Wang (2007) and Wang (2010) . Package: r-cran-nsprcomp Architecture: all Version: 0.5.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-nsprcomp_0.5.1-2-1.ca2604.1_all.deb Size: 65384 MD5sum: efa345662e93c8b38cc2597584205078 SHA1: f41bda9597308477518847f150080540c0198ff4 SHA256: c1fc3bf69fe89ed2bb5fad0b095539770a485709c19bea6e24b7930140663d7a SHA512: ed4f215cfcdb03029ac33e4d521522e952110645a31026ef127fd13cf32b2b6019eaa02af1084ba57b63965b363ce93d070c16e647e97a9e77f369c080536f0d Homepage: https://cran.r-project.org/package=nsprcomp Description: CRAN Package 'nsprcomp' (Non-Negative and Sparse PCA) Two methods for performing a constrained principal component analysis (PCA), where non-negativity and/or sparsity constraints are enforced on the principal axes (PAs). The function 'nsprcomp' computes one principal component (PC) after the other. Each PA is optimized such that the corresponding PC has maximum additional variance not explained by the previous components. In contrast, the function 'nscumcomp' jointly computes all PCs such that the cumulative variance is maximal. Both functions have the same interface as the 'prcomp' function from the 'stats' package (plus some extra parameters), and both return the result of the analysis as an object of class 'nsprcomp', which inherits from 'prcomp'. See and Sigg et al. (2008) for more details. Package: r-cran-nsr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-bien, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-nsr_0.1.0-1.ca2604.1_all.deb Size: 43692 MD5sum: 503cf6d37760df4ab24e2eefc3451658 SHA1: 037714ab12e34c7121fbc5f6f57c0bd7d0790bd1 SHA256: 2e34116b3f3697333eff4699793e6897bb2f1783db097fa609b94a4ba67878cd SHA512: 1576712fdcccd87fad805ddc354d44ab561e96328e95d869fb31fd2f2456acf434d3cfae4a532eed1aa9162737bd677a6ae942a2fe26cb4e9add7b5db36de489 Homepage: https://cran.r-project.org/package=NSR Description: CRAN Package 'NSR' ('Native Status Resolver') Provides access to the 'Native Status Resolver' (NSR) API through R. The user supplies plant taxonomic names and political divisions and the package returns information about their likely native status (e.g., native, non-native,endemic), along with information on how those decisions were made. Package: r-cran-nsrfa Architecture: all Version: 0.7-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2978 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nsrfa_0.7-17-1.ca2604.1_all.deb Size: 2496766 MD5sum: 8df63aaa61b2d29f4bbbd0fad033d6a0 SHA1: 2f852a03184bf7a1f1da1180dd8493a0a2b9b1e9 SHA256: 10dc183ea974f2dd5e9e3e6a76c0bcf7dbbe0cee9d7757538a2209c9e1098f19 SHA512: 3a11f60b68ca4d3e736605d2470d266afaf6bf10d2daf598f8d6cfc05fab0bb54c3ee5be56cc16fa6d5eded160f7d43a3a803ea848654a188cd0c84239561ac1 Homepage: https://cran.r-project.org/package=nsRFA Description: CRAN Package 'nsRFA' (Non-Supervised Regional Frequency Analysis) A collection of statistical tools for objective (non-supervised) applications of the Regional Frequency Analysis methods in hydrology. The package refers to the index-value method and, more precisely, helps the hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions with similar growth curves; (3) fit distribution functions to the empirical regional growth curves. Most of the methods are those described in the Flood Estimation Handbook (Centre for Ecology & Hydrology, 1999, ISBN:9781906698003). Homogeneity tests from Hosking and Wallis (1993) and Viglione et al. (2007) are available. Package: r-cran-nsroc Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sde, r-cran-survival Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-nsroc_1.1-1.ca2604.1_all.deb Size: 206268 MD5sum: b74f9f07359320001e13679b371de56a SHA1: fc3e2fb04db91c049227e59ff44e4eba21bcb1e9 SHA256: 56c6068d7f979bf3f5c9549affc116b5a3cddfbc7e6063add5bef7fc47ee2803 SHA512: 826bb596703d781ebe01c3c5d762c1df1b9542d9ab09fabc62a534980ab03e5269582600180dcb42beb82dc7c7e6a25bcc327692ee33e3aa00bc7e2354a4c3e9 Homepage: https://cran.r-project.org/package=nsROC Description: CRAN Package 'nsROC' (Non-Standard ROC Curve Analysis) Tools for estimating Receiver Operating Characteristic (ROC) curves, building confidence bands, comparing several curves both for dependent and independent data, estimating the cumulative-dynamic ROC curve in presence of censored data, and performing meta-analysis studies, among others. Package: r-cran-nst Architecture: all Version: 3.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-permute, r-cran-ape, r-cran-bigmemory, r-cran-icamp, r-cran-dirichletreg Filename: pool/dists/resolute/main/r-cran-nst_3.1.10-1.ca2604.1_all.deb Size: 250046 MD5sum: 8b2b90d9f58441aaa5d4df7c8d8e43c3 SHA1: 80d0701355c5ac5e671221e4882d32f4b034ed63 SHA256: d6e5fa073ee85e3434ee861b916f2b1258e365f5c1b3d927df8e44f7bc8271cc SHA512: 7ea8782f7264c126caeb36dccf9a9f64150681014577faefdb32fd81ff76b609c64eba5b7918f12f9e8259de8141f72709beb53b6066bca1d7f579ae264440ae Homepage: https://cran.r-project.org/package=NST Description: CRAN Package 'NST' (Normalized Stochasticity Ratio) To estimate ecological stochasticity in community assembly. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) . NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 (Ning et al 2019) . A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) . SES is calculated as described in Kraft et al (2011) . RC is calculated as reported by Chase et al (2011) and Stegen et al (2013) . Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) . 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Provides functionality to iteratively decompose larger datasets using contextual variables or within-cluster sum of squares. See Tarun & Boutin (2018) and Tarun & Boutin (2018) for the original method and applications. 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See Atchadé M, Otodji T, and Djibril A (2024) and Atchadé M, Otodji T, Djibril A, and N'bouké M (2023) for details. Package: r-cran-ntranova Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ntranova_0.0.1-1.ca2604.1_all.deb Size: 20840 MD5sum: 27f9ed9e37be8a453e0fe505b3a8dc06 SHA1: 91cbd0643049a4519d0519881d381bd8d62801b6 SHA256: 2b4421ea08bbef7f5567da863343b6eb023f2ad0b9cecb0f8cce202b7aae1e14 SHA512: de46605362e238a5d80f0568134ef464f93b46b6489b0af411ebca0ffa2e7e009016032047e755222dd98818a98bc29a0332d7f548724c7b6380ae27febbb70c Homepage: https://cran.r-project.org/package=ntranova Description: CRAN Package 'ntranova' (Two Way Neutrosophic ANOVA) Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number ,D is the determinant part of the number and I is the indeterminacy part) using the neutrosophic two way anova test keeps the type I error low. This algorithm calculates the fisher statistics when we have a neutrosophic data, also tests two hypothesizes, first is to test differences between treatments, and second is to test differences between sectors. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) . Package: r-cran-nts Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dlm, r-cran-mass, r-cran-mswm, r-cran-rdpack, r-cran-tensor Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nts_1.1.3-1.ca2604.1_all.deb Size: 381838 MD5sum: 26d8167507ccb3c6d473d75e2c349dcd SHA1: 59a023232227e0db96ce11481c68860fc11ac3ed SHA256: f8c0c2e5cb2e1fcd0f6ab74738fafe9f14e0dd499c1dae8e6158d7892a7225df SHA512: 6c233ff83596068b62b3cd1eb44bced1702b11588bd32f850f1eb7f0b4eeb81acc749c9c6fd04370ef780bdd6b2c49767c8b0a4daa4e2d686b1ca7cb1c0b6be4 Homepage: https://cran.r-project.org/package=NTS Description: CRAN Package 'NTS' (Nonlinear Time Series Analysis) Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1). Package: r-cran-ntsdatasets Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ntsdatasets_0.2.0-1.ca2604.1_all.deb Size: 199288 MD5sum: de6f8a3e7f3475304291e3ac8b9bbc63 SHA1: d91ef429837527a63b7466e5414bc75db8e095f4 SHA256: edd0f5adc50887bd3b2b277cd7acf0d446323e900577b63db5bdedbf4960f341 SHA512: 8310d03bec4a0ff618e7c470b1b4ed6a4b8ecf68813e7d85bb6ca03829dc36197608fa6ae80bfbab21b012f2b8bc1ad3b2b44ba042d47600bc1ae0ace8172639 Homepage: https://cran.r-project.org/package=ntsDatasets Description: CRAN Package 'ntsDatasets' (Neutrosophic Data Sets) Provides a collection of datasets related to neutrosophic sets for statistical modeling and analysis. Package: r-cran-ntsdists Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ntsdists_2.1.1-1.ca2604.1_all.deb Size: 193742 MD5sum: 26f64dd57a041c98cc3998bdc9b7c19d SHA1: 71c9470362bc24523f705e8d4948bed40a97360a SHA256: c022d9929166121eef63906ac59e59d604d1da8d86601dcb92fe423f2db3fb41 SHA512: e2dadd114315122934995f80fa5971d4d36fe9986d880f31d9cb8e71edf23d16653d93d5e036314668bc59a9357c47380cba63d13c545881511c7dd50e087595 Homepage: https://cran.r-project.org/package=ntsDists Description: CRAN Package 'ntsDists' (Neutrosophic Distributions) Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) and Rao et al (2023) . Package: r-cran-ntss Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.random, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.univar, r-cran-spatstat.model, r-cran-ks, r-cran-get, r-cran-geor Filename: pool/dists/resolute/main/r-cran-ntss_0.1.3-1.ca2604.1_all.deb Size: 110464 MD5sum: 76c89ee700b7757f898146114627522c SHA1: 8aeadd3a48e03d1fcae150e49fc8861c59561bd0 SHA256: 2cd6e96d9038ffa34a57c5ee3abb30111b06deedfd027dc69f2cbcb6ef80a184 SHA512: 21a2818289dede41800dc9d957d72dfd9cfe9115275272d3fc78a683e918a3376d6b3e972087080ae2c568314832565bec8d7a1fdfb164c299ee07fd34719e00 Homepage: https://cran.r-project.org/package=NTSS Description: CRAN Package 'NTSS' (Nonparametric Tests in Spatial Statistics) Nonparametric test of independence between a pair of spatial objects (random fields, point processes) based on random shifts with torus or variance correction. See Mrkvička et al. (2021) , Dvořák et al. (2022) , Dvořák and Mrkvička (2024) . Package: r-cran-nu.learning Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-nu.learning_1.5-1.ca2604.1_all.deb Size: 2235136 MD5sum: 1073a557612b7283492ce4c778428c1e SHA1: 3251ef35ca7dfc63bb3941b7ad9bad41fb3c94bf SHA256: a6dbfaeed86409e917a2729788450c2255bee58138c514a6a28c4584b98fa493 SHA512: 4ea32ece54ba68d997984da5105ffb678e5599c6ad2a534bbefa8103fa38c339498ca0a125286c008bf4701897d1fbac51598bbf623d5897c1a1ec7f52d4b1f9 Homepage: https://cran.r-project.org/package=NU.Learning Description: CRAN Package 'NU.Learning' (Nonparametric and Unsupervised Learning from Cross-SectionalObservational Data) Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are best revealed by using Nonparametric and Unsupervised methods to "Design" the analysis of the given data ...rather than the collection of "designed data". Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences when X-Covariates are assumed to influence Treatment choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes. Obenchain and Young (2013) ; Obenchain, Young and Krstic (2019) . Package: r-cran-nucim Architecture: all Version: 1.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-ebimage, r-cran-bioimagetools, r-cran-fields, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-nucim_1.0.13-1.ca2604.1_all.deb Size: 211858 MD5sum: a50409c8a0af16e80906ac22477b5fdd SHA1: 89a51e9129ac72145eabaa5364eb938228a31a53 SHA256: 5d4706fbb6f896b2dc6e34cf088df808e4cb10b00e191888d78c897bfa3c067f SHA512: 7ef7d0da3606f61ea15fddead5361c3f1e21d2f8d1c708c72b2a7f37c1487fc8517a339e2d0e3887d1b770fcafd986d6d45a34c39b658e311076759c969bfc5d Homepage: https://cran.r-project.org/package=nucim Description: CRAN Package 'nucim' (Nucleome Imaging Toolbox) Tools for 4D nucleome imaging. Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) . 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Palettes can be used in base R or with 'ggplot2'. Package: r-cran-nueton Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-nueton_0.2.0-1.ca2604.1_all.deb Size: 71218 MD5sum: 6d5ed9fcb3fc58f83eb712b58778331e SHA1: 919dbbf2ae116334b929c6cfdd11639edad3a343 SHA256: aaaa1c367df9b4f60ba9357f7ff8d07db20c8a42841e2c0352e10cdf08fbf282 SHA512: 65b583db7c5a06d044d542769785c00677f38201f23f1ac84e657567ba99915ea5df8a32bb1bd26bf3e843eb5c221e26db831574a94683a7e42313f093068c2d Homepage: https://cran.r-project.org/package=NUETON Description: CRAN Package 'NUETON' (Nitrogen Use Efficiency Toolkit on Numerics) A comprehensive toolkit for calculating and visualizing Nitrogen Use Efficiency (NUE) indicators in agricultural research. The package implements 23 parameters categorized into fertilizer-based, plant-based, soil-based, isotope-based, ecology-based, and system-based indicators based on Congreves et al. (2021) . Key features include vectorized calculations for paired-plot experimental designs, batch processing capabilities for handling large datasets, and built-in visualization tools using 'ggplot2'. Designed to streamline the workflow from raw agronomic data to publication-ready metrics and plots. 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Accurate calculations are done using 'Richardson''s' extrapolation or, when applicable, a complex step derivative is available. A simple difference method is also provided. Simple difference is (usually) less accurate but is much quicker than 'Richardson''s' extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions. Package: r-cran-numericensembles Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubist, r-cran-metrics, r-cran-arm, r-cran-brnn, r-cran-broom, r-cran-car, r-cran-caret, r-cran-corrplot, r-cran-doparallel, r-cran-dplyr, r-cran-e1071, r-cran-earth, r-cran-gam, r-cran-gbm, r-cran-ggplot2, r-cran-glmnet, r-cran-gridextra, r-cran-htmltools, r-cran-htmlwidgets, r-cran-ipred, r-cran-leaps, r-cran-nnet, r-cran-olsrr, r-cran-pls, r-cran-purrr, r-cran-randomforest, r-cran-reactable, r-cran-readr, r-cran-rpart, r-cran-scales, r-cran-tidyr, r-cran-tree, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-numericensembles_1.2-1.ca2604.1_all.deb Size: 952930 MD5sum: 9fdde1250afdb4ad54881b886cec85d7 SHA1: a15ac4ba921378051c143713ec153ec4d5183b59 SHA256: b8501ca246e321a2d44c099c01dec2c2c7fd6371026f289ed9d39244ab93635b SHA512: 05fbac3f8d933b21a566adbd024cd9ae886e190590a7340c70e304615a3e3c725250f7fc88991cbc22f78736963c57bdbbefa701619e92dc295b4e103450af73 Homepage: https://cran.r-project.org/package=NumericEnsembles Description: CRAN Package 'NumericEnsembles' (Automatically Runs 18 Individual and 14 Ensembles of Models) Automatically runs 18 individual models and 14 ensembles on numeric data, for a total of 32 models. The package automatically returns complete results on all 32 models, 25 charts and six tables. The user simply provides the tidy data, and answers a few questions (for example, how many times would you like to resample the data). From there the package randomly splits the data into train, test and validation sets as the user requests (for example, train = 0.60, test = 0.20, validation = 0.20), fits each of models on the training data, makes predictions on the test and validation sets, measures root mean squared error (RMSE), removes features above a user-set level of Variance Inflation Factor, and has several optional features including scaling all numeric data, four different ways to handle strings in the data. Perhaps the most significant feature is the package's ability to make predictions using the 32 pre trained models on totally new (untrained) data if the user selects that feature. This feature alone represents a very effective solution to the issue of reproducibility of models in data science. The package can also randomly resample the data as many times as the user sets, thus giving more accurate results than a single run. The graphs provide many results that are not typically found. For example, the package automatically calculates the Kolmogorov-Smirnov test for each of the 32 models and plots a bar chart of the results, a bias bar chart of each of the 32 models, as well as several plots for exploratory data analysis (automatic histograms of the numeric data, automatic histograms of the numeric data). The package also automatically creates a summary report that can be both sorted and searched for each of the 32 models, including RMSE, bias, train RMSE, test RMSE, validation RMSE, overfitting and duration. The best results on the holdout data typically beat the best results in data science competitions and published results for the same data set. 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This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" . The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. 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Package: r-cran-obm Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-rjson, r-cran-jsonlite, r-cran-rpostgresql, r-cran-dbi Filename: pool/dists/resolute/main/r-cran-obm_2.1-1.ca2604.1_all.deb Size: 106044 MD5sum: 95e423e154f76b28b14781dd445bf13e SHA1: e42c21aebf246e7c077c99e8c171611cd613153d SHA256: 85b17240c08279b576d8d0a44ff33696fb0629f3f0dd5b6548a97d6e49a71488 SHA512: 5b687d9944452bab59556e57a806f156376d542ea140be99e0c5c9cfc6fc40f0d03f507e396fc07dda5b7e87dd3d336e023f10ef6faa1f8ff925dbe4aa0148ef Homepage: https://cran.r-project.org/package=obm Description: CRAN Package 'obm' (Interface to 'OpenBioMaps' Data and Services) Provides access to selected functions and data available through any 'OpenBioMaps' server instance. 'OpenBioMaps' is an open-source biodiversity data management platform designed for conservation professionals and researchers. 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Package: r-cran-obr Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-obr_0.2.5-1.ca2604.1_all.deb Size: 81182 MD5sum: c6ac872a95be95214131d89625521a67 SHA1: c95417b70b7c218b7811a5c84780705ba3247156 SHA256: 8ef6ca2d8fe37090a2dad1cc1639711a3ea7955841a359e152dccd1cec2586a7 SHA512: 0090606238232542f7f8612a1ebb17b3eae25f5e0ce5a92754f5763915d56c6d35760f9bdca2cffba058f26949a7c4cfe71523e2b564a82df4e07072551f3351 Homepage: https://cran.r-project.org/package=obr Description: CRAN Package 'obr' (Access 'Office for Budget Responsibility' Data) Provides clean, tidy access to data published by the 'Office for Budget Responsibility' ('OBR'), the UK's independent fiscal watchdog. 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Package: r-cran-obre Architecture: all Version: 0.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-obre_0.2-0-1.ca2604.1_all.deb Size: 99850 MD5sum: 5567f26f02be0f3dee6b61dcc9defffc SHA1: e01988e2bc4c006633fbeb676c1b477da2f1bdb0 SHA256: 0fef198ef6e4a61749e81038ff486430d1f2bbc66587b6aba4761bd421e2555f SHA512: 2e64eca128aded6ae6ad194f67513a22cbca1300565bf25e6e6708fda2d515199904c05b70dc8dfa004a143b275a3bdea2b991d96e8f57150bd726656a68d521 Homepage: https://cran.r-project.org/package=OBRE Description: CRAN Package 'OBRE' (Optimal B-Robust Estimator Tools) An implementation for computing Optimal B-Robust Estimators of two-parameter distribution. The procedure is composed of some equations that are evaluated alternatively until the solution is reached. Some tools for analyzing the estimates are included. 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Package: r-cran-observation Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-svdialogs Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-observation_0.3.0-1.ca2604.1_all.deb Size: 98694 MD5sum: 97694347f534ab596a39d8f04bbef43f SHA1: dd90cffd6cfb8790f354f57f0ec47b7fce33824a SHA256: cc1ad649011c00e54874bce7a7d23e9e028dcf9f71e07868d540a91d43ff2d00 SHA512: 592d94f1e38249b44f20aaab0b7d5b4be726de7bbfd556e7c4603958ba4e4eec8b64ea6786ebbf00839eca7051bbb21ade9217cf90e3826ad2714e250724864b Homepage: https://cran.r-project.org/package=Observation Description: CRAN Package 'Observation' (Collect and Process Physical Activity Direct Observation Data) Two-part system for first collecting then managing direct observation data, as described by Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) . Package: r-cran-observationalblocks Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-itos, r-cran-lpsolve Suggests: r-cran-dos2, r-cran-sensitivity2x2xk, r-cran-sensitivitymv, r-cran-weightedrank, r-cran-xtable, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-observationalblocks_1.0.0-1.ca2604.1_all.deb Size: 138922 MD5sum: 175a032caedf204cc60969cd050eecb5 SHA1: 895fc3fa9c156f1ef7c47c9036cfe26e0443d81d SHA256: aad30efb5f286c09209150ac561feafa3b7e3625a2c4caf51de08ef84dd161bc SHA512: 5794a7a685835e301276f1e69daeb521b2905fb9e5b341121b0f3051c47c5f5b3a9a156eacaa0ab0755d554cdd8fbc14acdacd1a026b6594df8e95e30c9e4dc7 Homepage: https://cran.r-project.org/package=observationalBlocks Description: CRAN Package 'observationalBlocks' (Block Designs for Observational Studies) Creates block designs of fixed size J with at least one treated and control unit per block. Blocks larger than pairs better distinguish effects caused by a treatment from unmeasured confounding in assignment of individuals to treatment. Somewhat counterintuitively, blocks larger than pairs can use more units while attaining better covariate balance and block homogeneity. A forthcoming manuscript by Brumberg and Rosenbaum details the design. Package: r-cran-obssens Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-obssens_1.4-1.ca2604.1_all.deb Size: 38922 MD5sum: 33ab0bfb0c6c9bae3a6e0c5a3459bc50 SHA1: 0a09c5ddf55b90a512de9e7df1aa4a3ec11e6464 SHA256: d3cdfce70d6dcd1a0a436fae08c050f22b7fc72727291fcb0845fd04c57018a2 SHA512: f3ee4ee5410122c769cd7904f8a00840b742f3d00608b480f808c4fd6d47bec6d2d4ab463f07a5464ff82abd4b8bb9e59e80781820de8bfb913e3fa8612fa8dd Homepage: https://cran.r-project.org/package=obsSens Description: CRAN Package 'obsSens' (Sensitivity Analysis for Observational Studies) Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor. If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions). Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change. This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models. Package: r-cran-occ Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-occ_1.2-1.ca2604.1_all.deb Size: 27710 MD5sum: 8c10d20d67765f56f0d95ef3b45e7c9a SHA1: f6da1c219c6c31c6a4949a7fa1c061466469c47c SHA256: 179ebab659a0554fb43f2f1c7760ab7189d24e12e38efb4b9cba0b91496cfe0a SHA512: ee7215a894bd44978f97222d627d3bb5eb219d37162fef9683b9a810d629a0cc374eeddc3a1f8c67ad8caf214daf2035eef7572c1e6c016e09c8079940cce408 Homepage: https://cran.r-project.org/package=occ Description: CRAN Package 'occ' (Estimation of PET Neuroreceptor Occupancies) Estimate the positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fitting methods include the simple 'reference region', 'ordinary least squares' (sometimes known as occupancy plot), and 'restricted maximum likelihood estimation'. 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Model fitting results can be used to evaluate and compare the effectiveness of species detection to find an efficient survey design. Reference: Fukaya et al. (2022) , Fukaya and Hasebe (2025) . 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The PDF, CDF, quantile functions, generation of random variates, and calculating the first four central moments of the distributions are implemented as described in O’Neill (2019) . 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(2021) and Schierholz, M., Gensicke, M., Tschersich, N., Kreuter, F. (2018) . Generate suggestions for occupational categories based on free text input, with pre-trained machine learning models in German and a ready-to-use shiny application provided for quick and easy data collection. Package: r-cran-ocd Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4448 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ocd_1.1-1.ca2604.1_all.deb Size: 4486536 MD5sum: a206b7856dbdf764e6a353cb80780c90 SHA1: a133361cdf87f3b829c4b56ed1185047d455d87a SHA256: abf252a0562fa9d14f9dd413943d9da8f4068fd722a3d4f51bd2f9154012dd03 SHA512: f0bd72c582668a1943e37a236813a8fb1aa5c4ab1c51c9c05c8963288669f5e303ca8ed43d29bcad28f6e16ea574be4bccdffc04539117eadf94f0c35c390295 Homepage: https://cran.r-project.org/package=ocd Description: CRAN Package 'ocd' (High-Dimensional Multiscale Online Changepoint Detection) Implements the algorithm in Chen, Wang and Samworth (2020) for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) , Xie and Siegmund (2013) and Chan (2017) . 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It includes functions to extract NetCDF data from the repository and code to visualize several physical and chemical parameters of the ocean. A Shiny app further allows interactive exploration of the data. The methods for data collecting and quality checks are described in several papers, which can be found here: . Package: r-cran-oceanic Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6641 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-broom, r-cran-ggplot2, r-cran-maps Filename: pool/dists/resolute/main/r-cran-oceanic_0.1.9-1.ca2604.1_all.deb Size: 6761994 MD5sum: 8d1253cc155a50715ac3b721ff756451 SHA1: b4d9e68f1c311d2d14bcad8d2188ca0c2870bd4c SHA256: f61741549cd8ad568485c0c8b32827c8a7f9545839709eba1ceefe1fefaf9466 SHA512: c9edabecb5df31f151a33da12971c0e5ad7c39d7786116fb85de77b981bd7bdb5037419b72e83a734c657ce9f329a43bea778339c12c2d67bd18d76cafec2afe Homepage: https://cran.r-project.org/package=oceanic Description: CRAN Package 'oceanic' (Location Identify Tool) Determine the sea area where the fishing boat operates. The latitude and longitude of geographic coordinates are used to match oceanic areas and economic sea areas. You can plot the distribution map with dotplot() function. Please refer to Flanders Marine Institute (2020) . 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Package: r-cran-oceanwaves Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-bspec, r-cran-signal Suggests: r-cran-scales, r-cran-oce, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-oceanwaves_0.2.0-1.ca2604.1_all.deb Size: 132466 MD5sum: 80d2b5e8fc9dbb2feadbf7e5243fb258 SHA1: 724f09af1351d682cb4cc5ba1c9ed9923409cb83 SHA256: 0176ce18e7dbf38173ed15bb80b9bdf479f0d29e24e0ab1413c7c8b2b9ce7138 SHA512: 5bdd8d8c98b00a0dffe54cee6af1570b18aff0017011864efb8402fa7151d8d6f628dce64855bad9ad82f3b7d15ffa5a79575d353f774313f1fc2317a2d9f9f3 Homepage: https://cran.r-project.org/package=oceanwaves Description: CRAN Package 'oceanwaves' (Ocean Wave Statistics) Calculate ocean wave height summary statistics and process data from bottom-mounted pressure sensor data loggers. Derived primarily from MATLAB functions provided by U. Neumeier at . Wave number calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) "Direct Solution of Wave Dispersion Equation", American Society of Civil Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, Vol 105, pp 457-459. Package: r-cran-ocecens Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ocecens_0.1.2-1.ca2604.1_all.deb Size: 89202 MD5sum: c5d469378d8642f36c931e85bcb4b1b5 SHA1: 9f42912d343b20f6633152e1feefe127dd6be64f SHA256: c1f7181eb33b530e7d9e077ba9b51f49ca39301e3619762b86faedb2a4f1268e SHA512: f5d3b2fa8942ad205ab56314253ac9f80fabbbd07ec15917dcaa78a08974d209772d991d3935f6762fb716df69753367af742427730ee830fd37eee8bd1ed227 Homepage: https://cran.r-project.org/package=oceCens Description: CRAN Package 'oceCens' (Ordered Composite Endpoints with Censoring) Estimates win ratio or Mann-Whitney parameter for two group comparisons using ordered composite endpoints with right censoring as described in Follmann, Fay, Hamasaki, and Evans (2020). 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Different functions enable users to grab the data they need at different sections in the case study, as well as download the whole case study repository. All the user needs to do is input the name of the case study being worked on. The package relies on the httr::GET() function to access files through the 'GitHub' API. The functions usethis::use_zip() and usethis::create_from_github() are used to clone and/or download the case study repositories. To cite an individual case study, please see the respective 'README' file at . . 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Package: r-cran-od Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfheaders, r-cran-vctrs Suggests: r-cran-covr, r-cran-knitr, r-cran-lwgeom, r-cran-nngeo, r-cran-rmarkdown, r-cran-sf, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-od_0.5.1-1.ca2604.1_all.deb Size: 1111992 MD5sum: 460798862ee250fc8b2ab24bcdf3db84 SHA1: 9b0b2c73bf52bb1acf0331e675490976e05c29eb SHA256: a453b7ac7b47a0639c6568009270a1bdda75572b4d67d4e526429bfa7b6a3773 SHA512: 9d71157de31490ce6028201a238e99869a88651fd3923fcb07ced842bfceb85bf1b6baff7227dcecb3d355a93b936f529ddfd0c013499c374dc563b33c7f13c3 Homepage: https://cran.r-project.org/package=od Description: CRAN Package 'od' (Manipulate and Map Origin-Destination Data) The aim of 'od' is to provide tools and example datasets for working with origin-destination ('OD') datasets of the type used to describe aggregate urban mobility patterns (Carey et al. 1981) . The package builds on functions for working with 'OD' data in the package 'stplanr', (Lovelace and Ellison 2018) with a focus on computational efficiency and support for the 'sf' class system (Pebesma 2018) . With few dependencies and a simple class system based on data frames, the package is intended to facilitate efficient analysis of 'OD' datasets and to provide a place for developing new functions. The package enables the creation and analysis of geographic entities representing large scale mobility patterns, from daily travel between zones in cities to migration between countries. Package: r-cran-odataquery Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-odataquery_0.5.3-1.ca2604.1_all.deb Size: 102134 MD5sum: ee6a9a3f08e995bece0c0f86b8b68133 SHA1: 46fdb614c29e78a5d6cd35ebd5d1ff120dcef367 SHA256: 10fe0838f531bb19b4b599be83c7de58e7e840d054dec8107dd898bae150f556 SHA512: 94de1712f1e33486d025b49de438d5aa70ee94ba0994acef67c10b87f274d6c1f4ccb58ba5b23363497a1eacb740998c623e41d9fea568cb5a3abfb2d9b40b29 Homepage: https://cran.r-project.org/package=ODataQuery Description: CRAN Package 'ODataQuery' (Querying on 'OData') Make querying on 'OData' easier. It exposes an 'ODataQuery' object that can be manipulated and provides features such as selection, filtering and ordering. Package: r-cran-odbc.resourcer Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-resourcer, r-cran-odbc, r-cran-dbi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-odbc.resourcer_1.0.0-1.ca2604.1_all.deb Size: 94438 MD5sum: 556292a383e6cb398d3798179efe6606 SHA1: 2ac257e96752afe89b8c876dbc64f646898ea02b SHA256: b580d768182fcbf701052d407b691bb5c49751dc1dcb2e179211eda98234f72c SHA512: d637fb6105f50348308d6872435994f5c411b3d7dd09d0985291a04fb0e4c17a9d4656b87ee289ca1f47dc2ad998e85e5af6c1e1937cce9a780dbb382086d0db Homepage: https://cran.r-project.org/package=odbc.resourcer Description: CRAN Package 'odbc.resourcer' (Open Database Connectivity Resource Resolver) A database resource that is accessible through the Open Database Connectivity ('ODBC') API. This package uses the Resource model, with URL "resolver" and "client", to dynamically discover and make accessible tables stored in a 'MS SQL Server' database. For more details see Marcon (2021) . Package: r-cran-odbr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-fs, r-cran-haven, r-cran-piggyback, r-cran-r.utils, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-odbr_0.1.1-1.ca2604.1_all.deb Size: 140296 MD5sum: 7f06f2bad9559f3e38c74b13c357b1c8 SHA1: feac34f1dcdad3fc9c62669eb592710fb7d5333b SHA256: fbc580867d5d53c5b3357002dbcb4dfb432c5db095d646a1be6f5f15cc324f54 SHA512: 2a71a10f1cb1c43f3149272be8b07a87fbab2d821264e4152bc269596b4b20f000939db9013c0d465d1087c1704a89a7be2311359e254c85d0720231e7179b24 Homepage: https://cran.r-project.org/package=odbr Description: CRAN Package 'odbr' (Download Data from Brazil's Origin Destination Surveys) Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years. Package: r-cran-oddnet Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fable, r-cran-fabletools, r-cran-igraph, r-cran-lookout, r-cran-pcapp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tsibble Suggests: r-cran-feasts, r-cran-knitr, r-cran-rmarkdown, r-cran-rtensor, r-cran-urca Filename: pool/dists/resolute/main/r-cran-oddnet_0.1.1-1.ca2604.1_all.deb Size: 202384 MD5sum: b3630f4ca9c1de2aaa1c72cb0f5c15cb SHA1: 1d941b2f54694ff0f6d6917350866f208fe1eebe SHA256: 3c95098cb45b33216977a84cee3abdfaa076e2a1c2c37484394eadf29263f5a7 SHA512: d4c796f29474c7861956a389f18412475872891b2581301314f9b04e401383c1a696c3cf3e5a53f932d4e8320301acf3d3d235127bb783fb900e1e043feeee62 Homepage: https://cran.r-project.org/package=oddnet Description: CRAN Package 'oddnet' (Anomaly Detection in Temporal Networks) Anomaly detection in dynamic, temporal networks. The package 'oddnet' uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) . Package: r-cran-odds.converter Architecture: all Version: 1.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-odds.converter_1.4.8-1.ca2604.1_all.deb Size: 71720 MD5sum: a9ed93bee8b80da9755158bca9b3292b SHA1: 1e2d2ca9f920d3abcfe01854eb86f2c02af8bf49 SHA256: 9b6984b406c605d879102d87ec01d608eb83de30d2411686c9a0b82203f482e6 SHA512: d021ad2e9facdceed370c9f9dc74551f83925bf3232ffc7169df76714d389907bcaa0ecf67c990d3a961927acbf02e61e047a264397fa21803100eb6b856550e Homepage: https://cran.r-project.org/package=odds.converter Description: CRAN Package 'odds.converter' (Betting Odds Conversion) Conversion between the most common odds types for sports betting. Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw Probability are covered in this package. Package: r-cran-odds.n.ends Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-odds.n.ends_0.1.4-1.ca2604.1_all.deb Size: 27546 MD5sum: 28f032515ea66f3e57a17337a7d0d0d5 SHA1: ce07ae2e238a9e639697a6298ddaae91b419e931 SHA256: d1813c05d3def512198a7cabce9f66ea1b779d653a384fdc92cee64cd80dc7ba SHA512: a0676386705aa5d3abcbc2c471919b5cc512f060d894965238fb3e88399e1c74d61b868fb2a630384def0fbcbe0bb4c924b7700a4b6ff8830f8bebe34df61176 Homepage: https://cran.r-project.org/package=odds.n.ends Description: CRAN Package 'odds.n.ends' (Odds Ratios, Contingency Table, and Model Significance from aGeneralized Linear Model Object) Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity. Package: r-cran-oddsapir Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-rvest, r-cran-tidyr Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-ggplot2, r-cran-ggrepel, r-cran-gt, r-cran-knitr, r-cran-progressr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidyselect, r-cran-usethis, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-oddsapir_0.0.3-1.ca2604.1_all.deb Size: 66440 MD5sum: e9d6ba77d65203650c62d2b2334bc3e7 SHA1: 55e330184899737214481d2c1783938369e565c5 SHA256: 721fa1ecb5f86f617ffccf9025fad33f9b6b64868e9be406228256df2a54e1a0 SHA512: f1c1a46d6fed2829c2c25e64a5e8ff37d94740e84d209991281d070d23dfcb48902f2dfe0d87a773ab1afaea91b56b81cde22f425958563f86b91c0b0da0278d Homepage: https://cran.r-project.org/package=oddsapiR Description: CRAN Package 'oddsapiR' (Access Live Sports Odds from the Odds API) A utility to quickly obtain clean and tidy sports odds from The Odds API . Package: r-cran-oddsplotty Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-mlbench, r-cran-magrittr, r-cran-ggplot2, r-cran-tibble, r-cran-ggthemes, r-cran-e1071, r-cran-tidymodels, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-oddsplotty_1.0.2-1.ca2604.1_all.deb Size: 99518 MD5sum: 9b86e3aa2aba9984600abb23cf09540b SHA1: f334db36bebb65c63b32ed15086e528758ffd418 SHA256: b1038d52541e03dba2ec25ab1d8017e61bcc2d78ebf710a3465c86483c06a5c6 SHA512: 3e108f13729f1051bdf282437e021fc360a7a3f6392114588c387e56e3c7d2bbb8538581238f9df9624f2f8b40e108a055cb9b404a96fc6c83685dc9b26004a1 Homepage: https://cran.r-project.org/package=OddsPlotty Description: CRAN Package 'OddsPlotty' (Odds Plot to Visualise a Logistic Regression Model) Uses the outputs of a logistic regression model, from caret , to build an odds plot. This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model. Package: r-cran-oddsratio Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mgcv Suggests: r-cran-gam, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-oddsratio_2.0.2-1.ca2604.1_all.deb Size: 277062 MD5sum: e287a045583a9246a592ee949b8b0f5b SHA1: a73d290af788d70845c6b31caf4844701ea57402 SHA256: 3e1e15124ddda5574e9ab2b4c0dd7f62a12abcb0ee5e06a4c7bc36e4f30056fe SHA512: e5cb061247793a5dfa27b7e77c56548ca9349ef3a648327234a4fb127af58f945eed1fce967f24c58fee32599dbc016b8290b6485b42e61206b1d2a033d55578 Homepage: https://cran.r-project.org/package=oddsratio Description: CRAN Package 'oddsratio' (Odds Ratio Calculation for GAM(M)s & GLM(M)s) Simplified odds ratio calculation of GAM(M)s & GLM(M)s. Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals for further analyses. It helps to avoid false references of predictors and increments by specifying these parameters in a list instead of using 'exp(coef(model))' (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of the multiple 'predict()' calls of the chosen predictor while holding other predictors constant. Also, this package allows odds ratio calculation of percentage steps across the whole predictor distribution range for GAM(M)s. In both cases, confident intervals are returned additionally. Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot. Package: r-cran-oddstream Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcapp, r-cran-ggplot2, r-cran-ks, r-cran-mass, r-cran-rcpproll, r-cran-mgcv, r-cran-rcolorbrewer, r-cran-mvtsplot, r-cran-tibble, r-cran-reshape, r-cran-dplyr, r-cran-tidyr, r-cran-kernlab, r-cran-magrittr Suggests: r-cran-testthat, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-oddstream_0.5.0-1.ca2604.1_all.deb Size: 3279074 MD5sum: e005953980c5b3738ab55d69d91bf38b SHA1: 85ff93c13f970d01b771afce6b3f15e66fb0df2f SHA256: 487fcfa1381a87f5b3b802f08e5c88309dc0ba1abb82f0f99fb65a4e306692f3 SHA512: 3bdfc2efe6859ae91b2131e22840fe5f89a3cc57e591f5da421872d7037a65e90b9d448345d7c2d92793fc5aaa6bec3eeca825b8ce7b673c6dcb3ffc260ad92a Homepage: https://cran.r-project.org/package=oddstream Description: CRAN Package 'oddstream' (Outlier Detection in Data Streams) We proposes a framework that provides real time support for early detection of anomalous series within a large collection of streaming time series data. By definition, anomalies are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the system's typical behaviour using a representative sample of the typical behaviour of the system. An approach based on extreme value theory is used for this boundary prediction process. Then a sliding window is used to test for anomalous series within the newly arrived collection of series. Feature based representation of time series is used as the input to the model. To cope with concept drift, the forecast boundary for the system's typical behaviour is updated periodically. More details regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019) . Package: r-cran-odetector Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ppclust Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/resolute/main/r-cran-odetector_1.0.1-1.ca2604.1_all.deb Size: 225762 MD5sum: 4945f80b270c9c10b2d4a58630ea1705 SHA1: 27248a5f1d8656bc8ee5e576fe9594430cddcfd4 SHA256: 0a8ff63a010aa191ea1ea10668e1066e7b7539b07232c44e29455d128760f170 SHA512: 8a3affc575c1213f43533431c4520a5d636937d1695e3f9ec7a344275b20a436f91124be6b4bb272c97f65034d4c54093b5854f84b57224aabc48c6b41e7f67b Homepage: https://cran.r-project.org/package=odetector Description: CRAN Package 'odetector' (Outlier Detection Using Partitioning Clustering Algorithms) An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 ). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants. Package: r-cran-odiffr Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-magick, r-cran-png, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-odiffr_0.5.1-1.ca2604.1_all.deb Size: 148650 MD5sum: 8cd35a41893cdb4b3c54da8be9226bbb SHA1: d520b57e49130ea2c999ada27cacecd3f7afbceb SHA256: 6ef599756b5e7c782ea9d7457c25b4a1017a3d97c2bdd90592d785bbda4b4afc SHA512: d82148f42536988a5d4995574a37b6fd9eee30cc8ccf54a814df9437e602f7932ccb1ad9cb99bd8d5ce4641d769d03d702c4a233bf8fd1fd7fb494917d510d5b Homepage: https://cran.r-project.org/package=odiffr Description: CRAN Package 'odiffr' (Fast Pixel-by-Pixel Image Comparison Using 'odiff') R bindings to 'odiff', a blazing-fast pixel-by-pixel image comparison tool . Supports PNG, JPEG, WEBP, and TIFF with configurable thresholds, antialiasing detection, and region ignoring. Requires system installation of 'odiff'. Ideal for visual regression testing in automated workflows. Package: r-cran-odin Architecture: all Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cinterpolate, r-cran-desolve, r-cran-digest, r-cran-glue, r-cran-jsonlite, r-cran-ring, r-cran-withr Suggests: r-cran-dde, r-cran-jsonvalidate, r-cran-knitr, r-cran-mockery, r-cran-pkgbuild, r-cran-pkgload, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-odin_1.2.7-1.ca2604.1_all.deb Size: 1483008 MD5sum: a56b3e7928944ec010377a9d85b92c4d SHA1: 7f4644449041935e0b227bf34811d222e41ffd06 SHA256: b5586f7a44feaf868300ea0d4e1cd4a114dfbc6a88902ad65e4ac6bd49842e30 SHA512: 319c21bf2be4f5da054ce90892fb1cc57f6b6a3f5cb6b84800f720941faa8a0beed52c4c6862f1d66fde3b64cbe1b1f820869c44f1b6ebfb5394b7d7df941abd Homepage: https://cran.r-project.org/package=odin Description: CRAN Package 'odin' (ODE Generation and Integration) Generate systems of ordinary differential equations (ODE) and integrate them, using a domain specific language (DSL). The DSL uses R's syntax, but compiles to C in order to efficiently solve the system. A solver is not provided, but instead interfaces to the packages 'deSolve' and 'dde' are generated. With these, while solving the differential equations, no allocations are done and the calculations remain entirely in compiled code. Alternatively, a model can be transpiled to R for use in contexts where a C compiler is not present. After compilation, models can be inspected to return information about parameters and outputs, or intermediate values after calculations. 'odin' is not targeted at any particular domain and is suitable for any system that can be expressed primarily as mathematical expressions. Additional support is provided for working with delays (delay differential equations, DDE), using interpolated functions during interpolation, and for integrating quantities that represent arrays. Package: r-cran-odk Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsheet, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-odk_1.5-1.ca2604.1_all.deb Size: 153006 MD5sum: b89ae6a6b7c693db20993866733d4301 SHA1: 0a9d12d963a2699bcf7ad5a7b3750e1ced5c3dfe SHA256: c23485d910a39d6c28c0e703c11631e958c1506b877831676a2ea11d8357be5d SHA512: 02a83ccc77ab9f35348ec82a185f518777378e664c85fc4bde658699ad01fb2799c7f02133b1805f4a31ede14a929f148d340d9a9d0a9977620eb8dc05387b74 Homepage: https://cran.r-project.org/package=odk Description: CRAN Package 'odk' (Convert 'ODK' or 'XLSForm' to 'SPSS' Data Frame) After develop a 'ODK' frame, we can link the frame to 'Google Sheets' and collect data through 'Android' . This data uploaded to a 'Google sheets'. odk2spss() function help to convert the 'odk' frame into 'SPSS' frame. Also able to add downloaded 'Google sheets' data or read data from 'Google sheets' by using 'ODK' frame 'submission_url'. Package: r-cran-odmeans Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-ggplot2, r-cran-ggmap, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-odmeans_0.2.1-1.ca2604.1_all.deb Size: 3201176 MD5sum: f1b7c1cc7730d59e68a3ff97796c814d SHA1: 7dcb6d9c9f55ba63c665d36b6760a3c925b5a289 SHA256: 492e87e92e9006349beeec47752d7febb3c70cdff4f577a41428bfb7a94802ff SHA512: 1eea34c74bac66b2f73a9d2af93297e170c97f89116ca66b7b133a1678cc0bb9775f6d059c8aaef6ecc53050b6a8fd4ac9e91279e4e958d914cd50f203b39760 Homepage: https://cran.r-project.org/package=ODMeans Description: CRAN Package 'ODMeans' (OD-Means: k-Means for Origin-Destination) OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. Package: r-cran-odr Architecture: all Version: 1.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-odr_1.8.3-1.ca2604.1_all.deb Size: 573746 MD5sum: 3c9455a0800f5e61303802514b14ab66 SHA1: 67d8c6180e4bba68bdbf9f363335cec3ba3425f3 SHA256: 282c464bc1f5c6da667cfe95cd5df1aa91b6d5c715a8e24fcee52cae5b245a7c SHA512: 316ef45c8d90bd05043ef006474b2795411e97c77f66348fc80efda2d3f5dc8a954e6b5e8a9e27c272d12b0d40abfbdecf038e37be24d342c51b34365885e58d Homepage: https://cran.r-project.org/package=odr Description: CRAN Package 'odr' (Optimal Design and Statistical Power for Experimental StudiesInvestigating Main, Mediation, and Moderation Effects) Calculate the optimal sample size allocation that uses the minimum resources to achieve targeted statistical power in experiments. Perform power analyses with and without accommodating costs and budget. The designs cover single-level and multilevel experiments detecting main, mediation, and moderation effects (and some combinations). The references for the proposed methods include: (1) Shen, Z., & Kelcey, B. (2020). Optimal sample allocation under unequal costs in cluster-randomized trials. Journal of Educational and Behavioral Statistics, 45(4): 446-474. . (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for three-level multisite cluster-randomized trials. Journal of Research on Educational Effectiveness, 15 (1), 130-150. . (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in multisite randomized trials. The Journal of Experimental Education, 90(3), 693-711. . (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). Using ant colony optimization to identify optimal sample allocations in cluster-randomized trials. The Journal of Experimental Education, 93(1), 167-185. . (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and optimal design for randomized controlled trials investigating mediation effects. Psychological Methods. . (6) Champely, S. (2020). pwr: Basic functions for power analysis (Version 1.3-0) [Software]. Available from . Package: r-cran-ods Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-survival Filename: pool/dists/resolute/main/r-cran-ods_0.2.0-1.ca2604.1_all.deb Size: 248396 MD5sum: 58fcc37f05555149c24b19ced6b42324 SHA1: 46460ac3d4f6595aa4ac3880e0846f962f978a59 SHA256: 6295343463c719b8f891b824f8113cff7ce204e1abdafaf31d9086ea4acfde99 SHA512: e54a2a3668d7df6a4358b665f89157ab12d4a9222a6ab7aedb235edce8ae1baf230bcb3bc83a4c06fce41ce67c12607d24f3f8736be7630dd6129a014a89bcbd Homepage: https://cran.r-project.org/package=ODS Description: CRAN Package 'ODS' (Statistical Methods for Outcome-Dependent Sampling Designs) Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event outcome, and continuous outcome ODS design (Zhou et al. 2002) . Because ODS data has biased sampling nature, standard statistical analysis such as linear regression will lead to biases estimates of the population parameters. This package implements four statistical methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) . (3) Analyze a secondary outcome in continuous ODS design (Pan et al. 2018) . (4) An estimated likelihood method analyzing a secondary outcome in case-cohort data (Pan et al. 2017) . Package: r-cran-odt Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-partykit, r-cran-data.tree, r-cran-magick, r-cran-diagrammersvg, r-cran-diagrammer, r-cran-rsvg Suggests: r-cran-runit, r-cran-matrix, r-cran-rmarkdown, r-cran-robustbase, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-odt_1.0.0-1.ca2604.1_all.deb Size: 1338748 MD5sum: eec65d0beccc5e7786c107e43e214aaf SHA1: b40d68d57840e4dce41751f73cd0118a33eb5d82 SHA256: a9d0cd6e073eff7b1dc33b1aba4c08247657163ee6916856d7b6ca829fe666b2 SHA512: ead4eb8b50fe445f95eb5e89ad0e4cbd1ed87e5c4bed4b713e47df27bd8dba9247955616e709ede7aa4aba56f7cbb953de8e2840123fbefa56a0232b56580d53 Homepage: https://cran.r-project.org/package=ODT Description: CRAN Package 'ODT' (Optimal Decision Trees Algorithm) Implements a tree-based method specifically designed for personalized medicine applications. By using genomic and mutational data, 'ODT' efficiently identifies optimal drug recommendations tailored to individual patient profiles. The 'ODT' algorithm constructs decision trees that bifurcate at each node, selecting the most relevant markers (discrete or continuous) and corresponding treatments, thus ensuring that recommendations are both personalized and statistically robust. This iterative approach enhances therapeutic decision-making by refining treatment suggestions until a predefined group size is achieved. Moreover, the simplicity and interpretability of the resulting trees make the method accessible to healthcare professionals. Includes functions for training the decision tree, making predictions on new samples or patients, and visualizing the resulting tree. For detailed insights into the methodology, please refer to Gimeno et al. (2023) . Package: r-cran-odysseuscharacterizationmodule Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-databaseconnector, r-cran-jsonlite, r-cran-sqlrender Suggests: r-cran-andromeda, r-cran-eunomia, r-cran-featureextraction, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-odysseuscharacterizationmodule_0.0.1-1.ca2604.1_all.deb Size: 200284 MD5sum: 8bc4779a98610870f995948ad20d81ad SHA1: e37f6a3d95ab5c599a5c592bb14679587080b50b SHA256: 9de2fd36d786617256037d8b4863e227df8c2d73143fab9d24c96ae5e434a596 SHA512: 31bcbc35e4e932fa01504c9803c4c091c2d10e5e685999baa5a9f5583cf9731d4a17fa7047ccb68d815645c87d456fd9f3cc163efc93768fa81b6cdad220ace5 Homepage: https://cran.r-project.org/package=OdysseusCharacterizationModule Description: CRAN Package 'OdysseusCharacterizationModule' (Handy and Minimalistic Common Data Model Characterization) Extracts covariates from Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) domains using an R-only pipeline. Supports configurable temporal windows, domain-specific covariates for drug exposure, drug era (including Anatomical Therapeutic Chemical (ATC) groupings), condition occurrence, condition era, concept sets and cohorts. Methods are based on the Observational Health Data Sciences and Informatics (OHDSI) framework described in Hripcsak et al. (2015) and "The Book of OHDSI" OHDSI (2019, ISBN:978-1-7923-0589-8). Package: r-cran-odysseuspathwaymodule Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-databaseconnector, r-cran-sqlrender Suggests: r-cran-eunomia, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-odysseuspathwaymodule_0.0.1-1.ca2604.1_all.deb Size: 78982 MD5sum: 0333453e7f496622dbb5c293680ec0d6 SHA1: e10b44289bcb30aad4f2e81fe96b93a6f29bf44e SHA256: 0184465700a0fd782e4ae22bd2bd4660375e5d3ad302eb0236e6afd15644a51f SHA512: 1a2bdbcdc1b6e11ebd424ce23f07f421b3218f2494ec4d06e5c1d28cba7ec6eea72b3147ed72ac4b951d2b620a3807762eff177ec675c2f0eea9b1c6692cd14b Homepage: https://cran.r-project.org/package=OdysseusPathwayModule Description: CRAN Package 'OdysseusPathwayModule' (Cohort Pathway Analysis with Pre-Index Event Support) Provides cohort pathway analysis for Observational Medical Outcomes Partnership (OMOP) Common Data Model databases, including both standard (post-index) and pre-index pathway analyses. The pre-index analysis identifies sequences of events occurring in a lookback window before the target cohort index date. Built on the 'CohortPathways' analysis framework originally developed by Christopher Knoll and the Observational Health Data Sciences and Informatics community through 'WebAPI'. Methodological background and the originating implementation are described in . 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Package: r-cran-odyssey Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-rlang Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-odyssey_1.0.0-1.ca2604.1_all.deb Size: 78758 MD5sum: c8a116f8f52c1efaac4847176fac3302 SHA1: 473b6ebf0319a2b6168565c07788154340df65df SHA256: 2a234bb1b03a2eef560ebc504a9e0e0d38ad85f496f2925fbd5a4f6e4c3ccbc1 SHA512: 58c236f16735e7e95bca9ef9537f646151df42e8f6d43fe9f0d629d622fecbcc345220025888f6c67146618ff24f42bb205aedca2e2aab1fb7f76b4f8b3b7de4 Homepage: https://cran.r-project.org/package=odyssey Description: CRAN Package 'odyssey' (Interface to the HAL Open Archive API) An interface to the search API of 'HAL' , the French open archive for scholarly documents from all academic fields. This package provides programmatic access to the API and allows to search for records and download documents. Package: r-cran-oecd Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-readsdmx, r-cran-xml2 Suggests: r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-oecd_0.2.5-1.ca2604.1_all.deb Size: 25648 MD5sum: 2fff695bba00db57338a5da8686461fd SHA1: 6f7ca1e01e04f9f8b72888870adebab1175f8ed3 SHA256: 111b853740732b1578208deae77656cff2b6348298a78c79b8b6aefa0d23a076 SHA512: 357eec9612171e02781bf7d50d8ceed4196821db2cf91878ce0899cd463eb0fb49ef860a4537fcc2df60fb5337da27f14e7d50ad531b6c0b07cae6c5cdc6b001 Homepage: https://cran.r-project.org/package=OECD Description: CRAN Package 'OECD' (Search and Extract Data from the OECD) Search and extract data from the Organization for Economic Cooperation and Development (OECD). 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ODA data includes sovereign-level aid data such as key aggregates (DAC1), geographical distributions (DAC2A), project-level data (CRS), and multilateral contributions (Multisystem). 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Package: r-cran-oenokpm Architecture: all Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-openxlsx, r-cran-ggpubr, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-oenokpm_2.4.1-1.ca2604.1_all.deb Size: 89142 MD5sum: 14ad48dacfa8e5efbc67f9b3e885f513 SHA1: 14d40e731047959c87f23bec27c223effde6748c SHA256: e093eab0317d990105b8da37fd08da4407eb39cbbb1f5aa19f4374632c59e83b SHA512: 6d0a857f56d019a1293fb9c7c4c042a059e708f5530177326b72cb70aecf424f9c5417b97f34d5f71a15da387e8ab52c90f6bdf25c0f45acf81405a3a09c622f Homepage: https://cran.r-project.org/package=OenoKPM Description: CRAN Package 'OenoKPM' (Modeling the Kinetics of Carbon Dioxide Production in AlcoholicFermentation) Developed to help researchers who need to model the kinetics of carbon dioxide (CO2) production in alcoholic fermentation of wines, beers and other fermented products. The following models are available for modeling the carbon dioxide production curve as a function of time: 5PL, Gompertz and 4PL. This package has different functions, which applied can: perform the modeling of the data obtained in the fermentation and return the coefficients, analyze the model fit and return different statistical metrics, and calculate the kinetic parameters: Maximum production of carbon dioxide; Maximum rate of production of carbon dioxide; Moment in which maximum fermentation rate occurs; Duration of the latency phase for carbon dioxide production; Carbon dioxide produced until maximum fermentation rate occurs. In addition, a function that generates graphs with the observed and predicted data from the models, isolated and combined, is available. Gava, A., Borsato, D., & Ficagna, E. (2020)."Effect of mixture of fining agents on the fermentation kinetics of base wine for sparkling wine production: Use of methodology for modeling". . Package: r-cran-oews2020 Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4452 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-oews2020_1.0.0-1.ca2604.1_all.deb Size: 4523176 MD5sum: c622d6671fc0c87bad5f9c89059189d3 SHA1: 7da08fa4539c8f9a8b2e818316f2557a3f09f61c SHA256: 3c57df0b73cb30f2420c6becea88ebee3fad50627375bdd864d4c457d28e8d37 SHA512: 129f6b36da8d4e10387e2dd14cf6a0c83b42dcdab46ea93c39a32099837eaff6ca48308622e7558d9c86df7d1b6ad267a739553b5f59b384f88246d2df5d9897 Homepage: https://cran.r-project.org/package=oews2020 Description: CRAN Package 'oews2020' (May 2020 Occupational Employment and Wage Statistics) Contains data from the May 2020 Occupational Employment and Wage Statistics data release from the U.S. Bureau of Labor Statistics. The dataset covers employment and wages across occupations, industries, states, and at the national level. 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Package: r-cran-ofgem Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-mass, r-cran-forestplot Filename: pool/dists/resolute/main/r-cran-ofgem_1.0-1.ca2604.1_all.deb Size: 30562 MD5sum: f5d589a613f3d653e6e3ea3479dc6a94 SHA1: e0723dc4b78ce00257a1950a3eab5e2646af6462 SHA256: b6fbaa47ee34a9fc963e5570cc80c90c0fd5cf9b93e74714010e2957c016ddb5 SHA512: 8fe6ee5ff910469134e37959573437295cebc0cc00d17a8e96b0cab7dbf316ae08750f86748ec3efb80752b2a33e85e7324c4088b91701716016eb607f74ad4f Homepage: https://cran.r-project.org/package=ofGEM Description: CRAN Package 'ofGEM' (A Meta-Analysis Approach with Filtering for IdentifyingGene-Level Gene-Environment Interactions with GeneticAssociation Data) Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) . 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Package: r-cran-ofpetrial Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-sf, r-cran-lwgeom, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-ggpubr, r-cran-ggextra, r-cran-terra, r-cran-zip, r-cran-rmarkdown, r-cran-tmap, r-cran-magrittr, r-cran-dplyr, r-cran-bookdown, r-cran-leaflet Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-ofpetrial_0.1.3-1.ca2604.1_all.deb Size: 1988430 MD5sum: 86c3b1a5c2082a7024914d7423011dd6 SHA1: 53cdc6040ef23fbf3441fecd5f60429287fc6751 SHA256: 7ea9d729e723ccb52845e611bcc64007e27e32235065bf1bb81bc99a41769507 SHA512: 8a0996558aa16dd3727970808d0fdec87870eb588edf7e20eda23b42b05abce810efa8bc44e67fe95eba736d8fb8749e8f91a3db1b165e046374ae59587ce957 Homepage: https://cran.r-project.org/package=ofpetrial Description: CRAN Package 'ofpetrial' (Design on-Farm Precision Field Agronomic Trials) A comprehensive system for designing and implementing on-farm precision field agronomic trials. 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Package: r-cran-ohdsireportgenerator Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2928 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circer, r-cran-databaseconnector, r-cran-forestploter, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-gtextras, r-cran-kableextra, r-cran-parallellogger, r-cran-quarto, r-cran-rlang, r-cran-rmarkdown, r-cran-sqlrender, r-cran-tidyr Suggests: r-cran-htmltools, r-cran-knitr, r-cran-markdown, r-cran-resultmodelmanager, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ohdsireportgenerator_2.2.0-1.ca2604.1_all.deb Size: 2460254 MD5sum: f306fb68d72e42a52272424dee810538 SHA1: 8c54a05b2f7f9b2e0dfc7a0e71186dc5a13e7869 SHA256: 526cbf01cd8addc24b2740cef229abb8f77716d51a4d91dfcdf572a562ed3348 SHA512: d878a3f9d6e251bd2708b8e375e76ff00ec7eba7ec170a3e2bd55ce72e40c9b7087ae87f7df2d9df1a9b7ffc19edc42da307a10753dd036af3eb5457a84f2997 Homepage: https://cran.r-project.org/package=OhdsiReportGenerator Description: CRAN Package 'OhdsiReportGenerator' (Observational Health Data Sciences and Informatics ReportGenerator) Extract results into R from the Observational Health Data Sciences and Informatics result database (see ) and generate reports/presentations via 'quarto' that summarize results in HTML format. 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Package: r-cran-ohmmed Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvms, r-cran-ggmcmc, r-cran-ggplot2, r-cran-gridextra, r-cran-mistr, r-cran-scales, r-cran-vcd Filename: pool/dists/resolute/main/r-cran-ohmmed_1.0.2-1.ca2604.1_all.deb Size: 601046 MD5sum: 044860baf0b258542b41515aa15353e1 SHA1: 67447907578d089a67fc708122489ff15fe890da SHA256: 0310aa5883ac1c4a4b6b86bf3db9caaad2d10ac11b08764cd87a7f9c95634809 SHA512: 67600943dc0f1cc4e85ba59629e9092f39094b6de90d4d0b02c6bfd29126e606ea6b3ea0539c31fdb84769f1d22d553cb88c865775a92623e74942ea1bc955df Homepage: https://cran.r-project.org/package=oHMMed Description: CRAN Package 'oHMMed' (HMMs with Ordered Hidden States and Emission Densities) Inference using a class of Hidden Markov models (HMMs) called 'oHMMed'(ordered HMM with emission densities ): The 'oHMMed' algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. These are modelled as discrete hidden states; the observed data points are then realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are then inferred. Relevant for application to genomic sequences, time series, or any other sequence data with serial autocorrelation. 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Package: r-cran-ohsome Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr, r-cran-jsonlite, r-cran-readr, r-cran-sf Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-httptest, r-cran-janitor, r-cran-knitr, r-cran-mapview, r-cran-nominatimlite, r-cran-osmdata, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tmaptools, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ohsome_0.2.2-1.ca2604.1_all.deb Size: 1960338 MD5sum: 38e5d3e03123dfb0a37d20844becc87c SHA1: 16a3f7c7086e6dc8558846b72636baf880b5be52 SHA256: 03d91719992ad8b0206ea602f3a3118770583aca6828cd6ccf949f0e33a7f262 SHA512: ecefcaaa70469ceb37cfc25693c5bb20c19847b7859748346609fb4838566e54f5fdff65952871ae6301eebc30fa0078be682f9c273255d74c11228ca9359d4b Homepage: https://cran.r-project.org/package=ohsome Description: CRAN Package 'ohsome' (An 'ohsome API' Client) A client that grants access to the power of the 'ohsome API' from R. It lets you analyze the rich data source of the 'OpenStreetMap (OSM)' history. You can retrieve the geometry of 'OSM' data at specific points in time, and you can get aggregated statistics on the evolution of 'OSM' elements and specify your own temporal, spatial and/or thematic filters. Package: r-cran-ohun Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6933 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tuner, r-cran-warbler, r-cran-cli, r-cran-seewave, r-cran-fftw, r-cran-rlang, r-cran-sf, r-cran-igraph, r-cran-checkmate, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-viridis, r-cran-sim.diffproc, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ohun_1.0.4-1.ca2604.1_all.deb Size: 2794892 MD5sum: f0b2612da599a45f5e38b9441fb82f8f SHA1: d7e8657d1fa8598db3a0e13c1c72989d5ef99e51 SHA256: f6e3def714c18bc130581ed5e7b94f3ed3499d1ab251bf975cd0b02d6b2a1c3c SHA512: 8a1fbdcb829803e35320e8afbc91342e603e77ad663483ccc95a7a069d5e0de39aa258d9ab577fb64acea584ef5aed1e293086223a101dfe2371a58a1a3f7ddd Homepage: https://cran.r-project.org/package=ohun Description: CRAN Package 'ohun' (Optimizing Acoustic Signal Detection) Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. This package has been peer-reviewed by rOpenSci. Araya-Salas et al. (2022) . Package: r-cran-ohvbd Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-generics, r-cran-httr2, r-cran-lubridate, r-cran-rlang, r-cran-stringr, r-cran-terra, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-parsermd, r-cran-rgbif, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-testthat, r-cran-vcr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-ohvbd_1.0.1-1.ca2604.1_all.deb Size: 966924 MD5sum: aaa16a1b7fcd5a7f108a78f66407d56e SHA1: 25b25b23015ec92ced0590b2cf87c014b6e176f9 SHA256: 913da36a1197b3099731f82a3c1c75ce9e5eada6a16ce5dd43bd07aaa668214a SHA512: 702f2ecae67b464a21285093d3f1d77d5cd0f566176051462f1c6aaa3ed36037561a80ff759ccccf611bb998834fb25a61b0fd9e211a296dda7885e4786b4d68 Homepage: https://cran.r-project.org/package=ohvbd Description: CRAN Package 'ohvbd' (One Health VBD Hub) Interface with the One Health VBD (vector-borne disease) Hub and related repositories (VectorByte , GBIF and AREAdata ) directly to find, download, and subset vector-borne disease data. Package: r-cran-oii Architecture: all Version: 1.0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rapportools, r-cran-gmodels, r-cran-deducer Filename: pool/dists/resolute/main/r-cran-oii_1.0.2.1-1.ca2604.1_all.deb Size: 47470 MD5sum: 1710517f694858648396221f88df3a2a SHA1: 843e1dbb823ce3ea9f7cb89d944f72cc42333531 SHA256: b8c71f26c5da18fdc327fbbd2d555ebbf0afb5a413b73e1757ebe8b73728e167 SHA512: 61c0ad3b3c9a75904479534dd26a5410501d9c66f864c6fa7fd8279ab392c195330efb1d5b090bcb00ff2d68c899d71912182468fbe3790ea1ded36a27605b0a Homepage: https://cran.r-project.org/package=oii Description: CRAN Package 'oii' (Crosstab and Statistical Tests for OII MSc Stats Course) Provides simple crosstab output with optional statistics (e.g., Goodman-Kruskal Gamma, Somers' d, and Kendall's tau-b) as well as two-way and one-way tables. The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data. Package: r-cran-ojsr Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rvest, r-cran-tidyr, r-cran-urltools, r-cran-xml2, r-cran-purrr, r-cran-rlang, r-cran-rcurl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ojsr_0.1.5-1.ca2604.1_all.deb Size: 62158 MD5sum: 6e2ad21722ae7f5890d65b0a4dd65cf5 SHA1: 999779c50934937331c2f8eb9aa16982813052d6 SHA256: ab688d3bace4700fc9be51babacd23934d94881364a191ba1656167869626ee3 SHA512: 689b64530dcc9e34708fa6885112002ee2d19d5094fd6a15fae6346f6dfcc37dedd0f2d3b3ca14919b873d00284556c61f93a90dae265d8db7eba862961917c9 Homepage: https://cran.r-project.org/package=ojsr Description: CRAN Package 'ojsr' (Crawler and Data Scraper for Open Journal System ('OJS')) Crawler for 'OJS' pages and scraper for meta-data from articles. You can crawl 'OJS' archives, issues, articles, galleys, and search results. You can scrape articles metadata from their head tag in html, or from Open Archives Initiative ('OAI') records. Most of these functions rely on 'OJS' routing conventions (). Package: r-cran-okcolors Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-okcolors_0.1.0-1.ca2604.1_all.deb Size: 18002 MD5sum: 4f5fc8bd9037e6b653cc18afff426745 SHA1: d49593b88f4314e04e417aa20091d674fe8b4593 SHA256: dfb1f205f0d7fc5aa474b174bf9a5e53896098e00c34c35113da25eca818b36f SHA512: be8cfcb0231cffdf8f0367772ac648feccb623d721cd62ba88d1a747d893ff9924fefcbd1de7b7d1c716de2f83bb9cd1cff2ad54da640a89f629ab701cd9ba1e Homepage: https://cran.r-project.org/package=okcolors Description: CRAN Package 'okcolors' (A Set of Color Palettes Inspired by OK Go Music Videos for'ggplot2' in R) A collection of aesthetically appealing color palettes for effective data visualization with 'ggplot2'. Palettes support both discrete and continuous data. Package: r-cran-oknne Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn Filename: pool/dists/resolute/main/r-cran-oknne_1.0.1-1.ca2604.1_all.deb Size: 27190 MD5sum: da456d95e2b8a3cd6e7e75d7ea7ea702 SHA1: 52a9e0b7d792f5b0f4a95ea84def119400629425 SHA256: 66286d56061f292987589788b57aada07a965dcd58b63ddd6f416fd6c5ad1a93 SHA512: 54ccdd2deef776e09398f171cd3c48cbeb721e463866b6447ca572a4d8acc348b53bcd6e9c9b0d2b7a65513ba74d936484319a267ecfde657c4224ec491ef90a Homepage: https://cran.r-project.org/package=OkNNE Description: CRAN Package 'OkNNE' (A k-Nearest Neighbours Ensemble via Optimal Model Selection forRegression) Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) . Package: r-cran-okxapi Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-httr, r-cran-base64enc, r-cran-jsonlite, r-cran-websocket, r-cran-digest Filename: pool/dists/resolute/main/r-cran-okxapi_0.1.1-1.ca2604.1_all.deb Size: 242432 MD5sum: 7c9a11a78a4267fc205dbcb85bc2cc20 SHA1: 1c636afabc06651a13f51f09e6fc36daa748544b SHA256: 0c3a555eeb7dd343c2d39127e9641e8dcb7c89a42dc1e4d3bbd1f2dda3c1d2ce SHA512: 6a1cbefa78728f58512c3fa64e86c482a3e4339b30213225259ceb0073695a0f1ea17e8613c344ffa417c1a3b760958bbc222acbe24ce8b4a83db75f2ecae303 Homepage: https://cran.r-project.org/package=okxAPI Description: CRAN Package 'okxAPI' (An Unofficial Wrapper for 'okx exchange v5' API) An unofficial wrapper for 'okx exchange v5' API , including 'REST' API and 'WebSocket' API. Package: r-cran-okxr Architecture: all Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-httr, r-cran-jsonlite, r-cran-digest, r-cran-base64enc, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-okxr_0.4.5-1.ca2604.1_all.deb Size: 680062 MD5sum: 8f390818a725e890802caba654d4eeb1 SHA1: b83da80a987041c702d28778a1dba19630b5760c SHA256: 10ba3cfd5820781b640f5153fd2e365ce92cdeb30733aeb7ca2553d90f837374 SHA512: 76389b773890a036b9d55e07b23df368942e38905a65a5d90d6d80309ab772cda470f54396e47d66f621a061ddeddc182066bc4a5f603a3b9892da6d7706fda9 Homepage: https://cran.r-project.org/package=okxr Description: CRAN Package 'okxr' (R Interface to the 'OKX' REST API) Provides lightweight R wrappers for the 'OKX' REST API, covering endpoints for market data, trading, account management, asset balances, and copy trading. The upstream API reference is available at . Package: r-cran-olcpm Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-laplacesdemon, r-cran-rspectra Filename: pool/dists/resolute/main/r-cran-olcpm_0.1.2-1.ca2604.1_all.deb Size: 310646 MD5sum: ed4e39ddb382a8b2aa5f0ac460d1c4bc SHA1: 148e1ab038ad79990adc638ee12ebbd725c66bc1 SHA256: 0226bcdb9d9e3549281abbccb901d2d4fda92c32acd0061afb8de27fd9cc6c07 SHA512: cfa33483b8013e0c1dd831338421b8a4d54c038acb945641ea2b320342273618dbf599888a41b6c4d54ba453a2405b85175e421f08ee7df556b752e93f77a7f9 Homepage: https://cran.r-project.org/package=OLCPM Description: CRAN Package 'OLCPM' (Online Change Point Detection for Matrix-Valued Time Series) We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. This package also provides some simple functions for detecting and removing outliers, imputing missing entries and testing moments. See more details in He et al. (2021). Package: r-cran-oldr Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbw, r-cran-car, r-cran-withr, r-cran-tibble, r-cran-rmarkdown, r-cran-cli, r-cran-tinytex Suggests: r-cran-testthat, r-cran-covr, r-cran-diagrammer, r-cran-knitr, r-cran-kableextra, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-oldr_0.2.4-1.ca2604.1_all.deb Size: 1681496 MD5sum: 034259f0656ee292347e9ce4a5e16606 SHA1: e73ee548590ff5e2cfb2826b97abdd3edbd53212 SHA256: 34d630379dfb0f073326c7a2de4529ca0a396a2d5b3c36da0f07828b2f4cbe50 SHA512: 0ceda65371192f1ab737e96468ff75ba9442c12fa470da8b4e13a1eaacbdbe4b4be79b6eb720e8e7827ba9263c9f4c034c6949caf61da70ed570d15faee63426 Homepage: https://cran.r-project.org/package=oldr Description: CRAN Package 'oldr' (An Implementation of Rapid Assessment Method for Older People) An implementation of the Rapid Assessment Method for Older People or RAM-OP . It provides various functions that allow the user to design and plan the assessment and analyse the collected data. RAM-OP provides accurate and reliable estimates of the needs of older people. Package: r-cran-olinkanalyze Architecture: all Version: 5.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3040 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-data.table, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-forcats, r-cran-ggplot2, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-broom, r-cran-car, r-bioc-clusterprofiler, r-cran-curl, r-cran-emmeans, r-cran-ggplotify, r-cran-ggpubr, r-cran-ggrepel, r-cran-lme4, r-cran-lmertest, r-cran-msigdbr, r-cran-ordinal, r-cran-readxl, r-cran-pheatmap, r-cran-scales, r-cran-showtext, r-cran-sysfonts, r-cran-systemfonts, r-cran-testthat, r-cran-vdiffr, r-cran-withr, r-cran-writexl, r-cran-zip, r-cran-umap, r-cran-rstatix, r-cran-fsa, r-cran-kableextra, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-olinkanalyze_5.0.0-1.ca2604.1_all.deb Size: 2600554 MD5sum: c4e5bbaaa1102af682d596e4a9f2bcf1 SHA1: 3ce2848a7772c33a6a50fc8f264974fe92004c06 SHA256: 9908b2b54901999d78fa5c19509197b5f5cf186e351a662822dea4a8c28b7e58 SHA512: ce2900c40a46428a9529239dd09d3dd70a566c7f2f8a4dfed27864333ae93b226a767994600bf07eb7e09f4209536991078f38d4594b5a26dc5053d9cb2f638b Homepage: https://cran.r-project.org/package=OlinkAnalyze Description: CRAN Package 'OlinkAnalyze' (Facilitate Analysis of Proteomic Data from Olink) A collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform. Package: r-cran-ollamar Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-crayon, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ollamar_1.2.2-1.ca2604.1_all.deb Size: 615102 MD5sum: bde732527ed8d93d3061c7c36b3e1466 SHA1: 147dd4a86dce042f60bcb4d42ad070e81ae21f46 SHA256: 8b79ab2c9b5af4035cc12da6e92e670daa8c1ea300c02928e3d11c8c49de7044 SHA512: b2a222a526f9a9d25a81514ddb3db89c2714e0825b9bdbbf01058070152b2caab25782c13965fe93eb51646e20887558a91be349b5d74a63193c1621421118ef Homepage: https://cran.r-project.org/package=ollamar Description: CRAN Package 'ollamar' ('Ollama' Language Models) An interface to easily run local language models with 'Ollama' server and API endpoints (see for details). It lets you run open-source large language models locally on your machine. Package: r-cran-ollg Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ollg_1.0.0-1.ca2604.1_all.deb Size: 102294 MD5sum: 4189c80f226b1d9e5c85d0f4fe973d24 SHA1: 720408eedc3b29045aab1a38fff3f2a2ce105111 SHA256: ee220bb92fde0d5ee7434d62e1837b3916588607036d130fc28bd9fe531cb1c8 SHA512: ddd1841e69ec3a49644e2b99a8705acc88ab44d7e00cffa2983c15aaa58c1b0dde37898e1face58f098b2398e3dfda4d32a9678ca3b787cad07920700ae5c6f8 Homepage: https://cran.r-project.org/package=ollg Description: CRAN Package 'ollg' (Computes some Measures of OLL-G Family of Distributions) Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) for example. Package: r-cran-ollggamma Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggamma Filename: pool/dists/resolute/main/r-cran-ollggamma_1.0.2-1.ca2604.1_all.deb Size: 18192 MD5sum: dd25c81413eb743d099133b67e2436f8 SHA1: cd15585ff624c79ab23851ea90a464c2cd1e4493 SHA256: 15a115621c3e6c176e7e428b7c34764db16ccee594c82d3456e9eb71b616e4ec SHA512: f636b5d164698cd72495b3852cab2b8238f5ad43cfa7b145ca1fbdf69cd4a61c657aa9d1cd6fd220bc8ee751914683404c0a4eeb334979f4762729a0059eb0a9 Homepage: https://cran.r-project.org/package=ollggamma Description: CRAN Package 'ollggamma' (Odd Log-Logistic Generalized Gamma Probability Distribution) Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) . Package: r-cran-olr Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-readxl, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-olr_1.2-1.ca2604.1_all.deb Size: 66590 MD5sum: 13615ce998ab7256b2e1a76a9079b89e SHA1: 5c1e0d83c1e43f935f76957ef55982d605c7862f SHA256: 2f0be7736fa0f55bef096544e7957601d1b1bbb5fd5719ef2ee2df7646bed4ff SHA512: aa1653dac31e074bde8c81c186676affec8f667526600a7b794972b69b62f43e7ce7778b4446b5d7d64f549abe6b37189fc4b2dcfb54c8ee2cbeb9cc3f278698 Homepage: https://cran.r-project.org/package=olr Description: CRAN Package 'olr' (Optimal Linear Regression) The olr function systematically evaluates multiple linear regression models by exhaustively fitting all possible combinations of independent variables against the specified dependent variable. It selects the model that yields the highest adjusted R-squared (by default) or R-squared, depending on user preference. In model evaluation, both R-squared and adjusted R-squared are key metrics: R-squared measures the proportion of variance explained but tends to increase with the addition of predictors—regardless of relevance—potentially leading to overfitting. Adjusted R-squared compensates for this by penalizing model complexity, providing a more balanced view of fit quality. The goal of olr is to identify the most suitable model that captures the underlying structure of the data while avoiding unnecessary complexity. By comparing both metrics, it offers a robust evaluation framework that balances predictive power with model parsimony. Example Analogy: Imagine a gardener trying to understand what influences plant growth (the dependent variable). They might consider variables like sunlight, watering frequency, soil type, and nutrients (independent variables). Instead of manually guessing which combination works best, the olr function automatically tests every possible combination of predictors and identifies the most effective model—based on either the highest R-squared or adjusted R-squared value. This saves the user from trial-and-error modeling and highlights only the most meaningful variables for explaining the outcome. A Python version is also available at . Package: r-cran-olsengine Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-olsengine_1.0.0-1.ca2604.1_all.deb Size: 53964 MD5sum: e9a64bd2b6e04eba1b1ea509b6d295ad SHA1: 42868acda6ad19cb629fd0f09f27fb629b2799d6 SHA256: bb0041c2ed037c06f6014a8f95ea22e5a4b811a0b55aa2d8afa75484771e567c SHA512: 96bda986d6adb437f3b43af5498264620b3fe0af386f1a08472017199b5285814d47ea4940f08fe5c813b8e69fabfe1a68322ae35535fd92e0675c0703e5cfb5 Homepage: https://cran.r-project.org/package=OLSengine Description: CRAN Package 'OLSengine' (Transparent and Assisted Linear Modeling Engine) A transparent, modular, and base-R implemented statistical engine for linear regression (OLS), analysis of variance (ANOVA), and logistic regression (Logit). Designed under the principle of "assisted simplicity", it features an integrated methodological "customs" (Aduana) that automatically audits mathematical assumptions (e.g., multicollinearity, heteroskedasticity, normality, and perfect separation) and outputs publication-ready, APA-formatted tables. It deliberately avoids hidden heuristics and external dependencies, ensuring computational transparency and reproducibility for applied research. Package: r-cran-olsrr Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2544 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-ggplot2, r-cran-goftest, r-cran-gridextra, r-cran-nortest, r-cran-xplorerr Suggests: r-cran-covr, r-cran-descriptr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-olsrr_0.7.0-1.ca2604.1_all.deb Size: 2016568 MD5sum: dac5571e24bae3acc352a866b8f7613d SHA1: f08cf36c18c5cba18bea6f2b010871c72de72460 SHA256: e06f9f2fa1787c88e78ed2169b00c6101ecd22a082a1704d3f0ee8aedbb33271 SHA512: ecafe24fbc9c930cc61e28d267b82c95010ccfad831f88bbd35562a53656815de20b1512bf8e77c8eee5db921694b3c77eaa236f594441d26c88f5b447951689 Homepage: https://cran.r-project.org/package=olsrr Description: CRAN Package 'olsrr' (Tools for Building OLS Regression Models) Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures. Package: r-cran-olstrajr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-broom, r-cran-ggplot2, r-cran-purrr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-olstrajr_0.1.0-1.ca2604.1_all.deb Size: 208768 MD5sum: 20b07935c99638b25048c5fd5d503ff1 SHA1: d45b6aaa2b431d9b1c9f03197845b23ce3676cbf SHA256: 0473f159509fc9a294dba9bf12bce61b3a07021ef01da5da3528b8db50c1f007 SHA512: 18b5a75581e9169dc3e048d47fc803ac685291b9500d3943af2aaa5fe254ca8fc5e9618dab9b6fc938625b8cdd7b0dce5abe6085f8baf8af05b3bb52ef790863 Homepage: https://cran.r-project.org/package=OLStrajr Description: CRAN Package 'OLStrajr' (Ordinary Least Squares Trajectory Analysis) The 'OLStrajr' package provides comprehensive functions for ordinary least squares (OLS) trajectory analysis and case-by-case OLS regression as outlined in Carrig, Wirth, and Curran (2004) and Rogosa and Saner (1995) . It encompasses two primary functions, OLStraj() and cbc_lm(). The OLStraj() function simplifies the estimation of individual growth curves over time via OLS regression, with options for visualizing both group-level and individual-level growth trajectories and support for linear and quadratic models. The cbc_lm() function facilitates case-by-case OLS estimates and provides unbiased mean population intercept and slope estimators by averaging OLS intercepts and slopes across cases. It further offers standard error calculations across bootstrap replicates and computation of 95% confidence intervals based on empirical distributions from the resampling processes. Package: r-cran-olympicrshiny Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-golem, r-cran-shiny, r-cran-shinybusy, r-cran-shinythemes, r-cran-summarytools Filename: pool/dists/resolute/main/r-cran-olympicrshiny_1.0.2-1.ca2604.1_all.deb Size: 5138598 MD5sum: 3b7e4c03adae5e858260a4e9ddabc2c2 SHA1: f9eb9c37878301cc3931e89cb6472b60c3ec02c7 SHA256: 73b88658641724fc9cbfb1232e1a725ac0c90b9e3524ce32f586af1443030d47 SHA512: 5295948a525ef10b7f968a5aa94849635220b1481d24768ea54686ecdd663b69a1a7d98c0900b7931a3425853bddfcfdb35aa69067eb2f56712c9b090095cfe3 Homepage: https://cran.r-project.org/package=OlympicRshiny Description: CRAN Package 'OlympicRshiny' ('Shiny' Application for Olympic Data) 'Shiny' Application to visualize Olympic Data. From 1896 to 2016. Even Winter Olympics events are included. Data is from Kaggle at . Package: r-cran-omegag Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-omegag_1.0.1-1.ca2604.1_all.deb Size: 39278 MD5sum: 7b2c309880fbf6a9d9c441fd623d3715 SHA1: 220dff6c57e96636d83e5b8af1e7a4cbc4a5513b SHA256: 7ff69ae11a2b4f607df9f4215d73d8ea153479d3a5620ecf34c8653f4725a896 SHA512: 848c36733d829ef347b5e6f3310044bd4be3186af20d2e69c0ce1e4213dfe68032ce085fc990f6310c4f22f5eb2f55577a5dececce85a74d6e63791b67c59939 Homepage: https://cran.r-project.org/package=OmegaG Description: CRAN Package 'OmegaG' (Omega-Generic: Composite Reliability of MultidimensionalMeasures) It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) . The current version can only handle continuous data. Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC. Package: r-cran-omickriging Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-rocr, r-cran-irlba, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-omickriging_1.4.0-1.ca2604.1_all.deb Size: 1783746 MD5sum: 2d23a638c5b8e593ba7a7e379bc7d690 SHA1: 7d01b894adf8eae67d30952966fe5f63789a8a90 SHA256: 9acb8587d4b14c91e3aaaaf4bef0fe5e0842a833628e14a4c93d8951f479c9ed SHA512: 22b3c7a61f2ef638772fedf0f0b2676669e86dbbcb78cd89ddd9764bda8e73c571a2af01edd5d4aa5b1aee30a917785f3efcc1003e16b84fafba72c01bb10c11 Homepage: https://cran.r-project.org/package=OmicKriging Description: CRAN Package 'OmicKriging' (Poly-Omic Prediction of Complex TRaits) It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y. Package: r-cran-omicnavigator Architecture: all Version: 1.19.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite Suggests: r-cran-faviconplease, r-cran-ggplot2, r-cran-opencpu, r-cran-plotly, r-cran-tinytest, r-cran-ttdo, r-cran-upsetr Filename: pool/dists/resolute/main/r-cran-omicnavigator_1.19.0-1.ca2604.1_all.deb Size: 867894 MD5sum: 27f818e9c115a78235dbe5698332b9fd SHA1: 302c103362ef751e82c226dcc390ea10e8715eab SHA256: c2e3ba4a2e2edb767c5cf17fa455f6198bfdb3c804cfe64b622828fed8b625fc SHA512: d9792564529a9ec34615df17aa12dc8c3f8839ffc0ef0ddb42a20e44f483a4c47a4081c5560600208e0bfcaa7f988e9f240bca58259d5b11384a108a2ec230c6 Homepage: https://cran.r-project.org/package=OmicNavigator Description: CRAN Package 'OmicNavigator' (Open-Source Software for 'Omic' Data Analysis and Visualization) A tool for interactive exploration of the results from 'omics' experiments to facilitate novel discoveries from high-throughput biology. The software includes R functions for the 'bioinformatician' to deposit study metadata and the outputs from statistical analyses (e.g. differential expression, enrichment). These results are then exported to an interactive JavaScript dashboard that can be interrogated on the user's local machine or deployed online to be explored by collaborators. The dashboard includes 'sortable' tables, interactive plots including network visualization, and fine-grained filtering based on statistical significance. Package: r-cran-omicnetr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-mixomics Filename: pool/dists/resolute/main/r-cran-omicnetr_0.1.1-1.ca2604.1_all.deb Size: 1095450 MD5sum: e1d89f814adcf36fd40006a80b2cdd4d SHA1: a09ca16e5500c1f647ad6f03568dfad088b04f66 SHA256: cc641cf9f421df63d655caf3bd4b00cc0b502d32087a0a1157611533ac7aa6ec SHA512: 003e40785496ee15df45fc7aaac4c524b58e82a73be7133d5d962f4dee502813e60fe952911321901cbac7693d2c66049223ec548a8bdf2cf65f620fac90caac Homepage: https://cran.r-project.org/package=OmicNetR Description: CRAN Package 'OmicNetR' (Network-Based Integration of Multi-Omics Data Using Sparse CCA) Provides an end-to-end workflow for integrative analysis of two omics layers using sparse canonical correlation analysis (sCCA), including sample alignment, feature selection, network edge construction, and visualization of gene-metabolite relationships. The underlying methods are based on penalized matrix decomposition and sparse CCA (Witten, Tibshirani and Hastie (2009) ), with design principles inspired by multivariate integrative frameworks such as mixOmics (Rohart et al. (2017) ). Package: r-cran-omicsense Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-kernlab Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-omicsense_0.2.0-1.ca2604.1_all.deb Size: 128550 MD5sum: 2166596f463e774393e7d1d4782539e5 SHA1: 58eed8e82b03a21ec92174be9363dc7adc7a8654 SHA256: 527daca7214df9342d680801f7a356b925bcda9be9797969671498a64e6ab7b2 SHA512: 5cdc04df40f1a6cd379210d1213f8523c9486e7540f2d4f6cae1086778b6e2843664d2098761f0810ba6ba5e33765275a37fe047b46080cf88f4e7e2931b6256 Homepage: https://cran.r-project.org/package=OmicSense Description: CRAN Package 'OmicSense' (Biosensor Development using Omics Data) A method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data. Package: r-cran-omicspls Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-softimpute, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gplots Filename: pool/dists/resolute/main/r-cran-omicspls_2.1.0-1.ca2604.1_all.deb Size: 358732 MD5sum: 201a88b69dd2fa0e0bedb2fc477fa43c SHA1: bb12e2b5711a7966b3c98a12ad00fc6d91c4d658 SHA256: 1b5a6c5af061ec2998ec30cd00d9f6193cc97f8d39a22c8f43908daf79eff584 SHA512: 53eace1fc700899671bb351b98a5eda10a44b8d864d5b16939a606d93bdba35c1a55557d3115bc45a1ddc5ceb087e9cbdb85e5d3069edec0ab46905031aca6dc Homepage: https://cran.r-project.org/package=OmicsPLS Description: CRAN Package 'OmicsPLS' (Data Integration with Two-Way Orthogonal Partial Least Squares) Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset. The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data. It provides a rigorous and a faster alternative cross-validation method to select the number of components, as well as functions to report proportions of explained variation and to construct plots of the results. See the software article by el Bouhaddani et al (2018) , and Trygg and Wold (2003) . It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) and cross-validation for the degree of sparsity. Package: r-cran-omicsprepr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-omicsprepr_0.1.1-1.ca2604.1_all.deb Size: 39658 MD5sum: 4ab46dc2563cb66599e863ad93e34252 SHA1: 7c1901ead9a619034c1ba982cb18325607618151 SHA256: 8a5f109a736c62ff56eabf4a9120bb42b87417927945e384b3fd4aee8145d83c SHA512: f8b0b10b425bd5e8ca10ad923e3f3d7c895311f39d4d89ca1e6d8029b7e8df002fb8df0eefc22b7cca41458f5cbe9cb30760f9c8288315b4e6bfd88bc6aa5476 Homepage: https://cran.r-project.org/package=OmicsPrepR Description: CRAN Package 'OmicsPrepR' (Unified Preprocessing Toolkit for Proteomics and Metabolomics) Provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data. The package simplifies preprocessing through automated imputation, scaling, and principal component analysis (PCA)-based exploratory analysis, enabling researchers to prepare omics datasets efficiently for downstream statistical and machine learning analyses. Package: r-cran-omicsqc Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1917 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus, r-cran-lsa, r-cran-boutroslab.plotting.general Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-dplyr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-omicsqc_1.1.1-1.ca2604.1_all.deb Size: 1026330 MD5sum: 8634bb6eff6f00e897f32c92165610e0 SHA1: f19c8134ae93a48aeae5f81aeb830ea8003d010c SHA256: 808c63930a19768c50fed9fd9bb36fa857f56398825668af87bbda1720e8fd41 SHA512: 83ff99c5de4ab3508b832880ba02fd1656c093ae84f4705943618a377025f5571098e529b8a54034bef18a174014aa0d29c1dbf2a55b66e9820985befa3dc4e4 Homepage: https://cran.r-project.org/package=OmicsQC Description: CRAN Package 'OmicsQC' (Nominating Quality Control Outliers in Genomic Profiling Studies) A method that analyzes quality control metrics from multi-sample genomic sequencing studies and nominates poor quality samples for exclusion. Per sample quality control data are transformed into z-scores and aggregated. The distribution of aggregated z-scores are modelled using parametric distributions. The parameters of the optimal model, selected either by goodness-of-fit statistics or user-designation, are used for outlier nomination. Two implementations of the Cosine Similarity Outlier Detection algorithm are provided with flexible parameters for dataset customization. Package: r-cran-omicstools Architecture: all Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bs4dash, r-cran-cli, r-cran-config, r-cran-dbscan, r-cran-dplyr, r-cran-dt, r-cran-forcats, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggrepel, r-cran-ggsci, r-cran-ggvenn, r-cran-golem, r-cran-janitor, r-cran-magrittr, r-cran-matrixstats, r-cran-moments, r-cran-outliers, r-cran-pheatmap, r-cran-purrr, r-cran-rcolorbrewer, r-cran-readxl, r-cran-readr, r-cran-rlang, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-viridis Suggests: r-bioc-biobase, r-bioc-pvca, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-boruta, r-cran-randomforest, r-cran-caret, r-cran-recipes, r-cran-parsnip, r-cran-workflows, r-cran-rsample, r-cran-tune, r-cran-dials, r-cran-doparallel, r-cran-vip, r-cran-goplot, r-cran-openxlsx, r-cran-upsetr, r-cran-zip Filename: pool/dists/resolute/main/r-cran-omicstools_1.1.7-1.ca2604.1_all.deb Size: 606084 MD5sum: 5b7ddd53ff29dedab710a9e74f4eba60 SHA1: 235a7a0c08414c979151a4cd926003ddf25d038a SHA256: b433ff5d814e73ced97edae3876b208d3fa742afb1bac54c3414f03803308bdf SHA512: c0b53ed6146fc0afe0270f1dadcce3165ad41340b66e433ea86e556e4c1d05f76b29d3fe312e6d1c2dad19a9cb86a4368a84f044d723b04a1ba067e3c90f3399 Homepage: https://cran.r-project.org/package=omicsTools Description: CRAN Package 'omicsTools' (Omics Data Process Toolbox) Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. 'omicsTools' is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality control–based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) . Package: r-cran-omicwas Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-data.table, r-cran-dplyr, r-cran-ff, r-cran-glmnet, r-cran-magrittr, r-cran-mass, r-cran-matrixstats, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-omicwas_0.8.0-1.ca2604.1_all.deb Size: 4148524 MD5sum: 88f48ce42dd35ac358742fc3b7b5d635 SHA1: 3f40fa4961091806938a2f3c1b2e9b40842071cf SHA256: d9ff46d59e8741f3e6e480885f0f037c9c0142a0054690fc98f431525566bc71 SHA512: 7337b16af104c03d8662832466316c1bf302f50f13d4583c268f0217fc34f020caa5c80b9141881c0c4042e440e20365b7cca8c05e8079d97256cfb750f1a235 Homepage: https://cran.r-project.org/package=omicwas Description: CRAN Package 'omicwas' (Cell-Type-Specific Association Testing in Bulk Omics Experiments) In bulk epigenome/transcriptome experiments, molecular expression is measured in a tissue, which is a mixture of multiple types of cells. This package tests association of a disease/phenotype with a molecular marker for each cell type. The proportion of cell types in each sample needs to be given as input. The package is applicable to epigenome-wide association study (EWAS) and differential gene expression analysis. Takeuchi and Kato (submitted) "omicwas: cell-type-specific epigenome-wide and transcriptome association study". Package: r-cran-omisc Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-psych, r-cran-copula Suggests: r-cran-lavaan Filename: pool/dists/resolute/main/r-cran-omisc_0.1.5-1.ca2604.1_all.deb Size: 117318 MD5sum: 0d23389f273a8828be431705ac576ca7 SHA1: 272ffd2fed9c62c6b391f5a42522ec7e479c614b SHA256: 44e2f5c2316c36440cc1bf093f1e0f97ab19b33d640bc7d3801aec1970b005e8 SHA512: ebc4925085c9cadacbc607cb433a6dc5d528668359ec3dba8ebee53d031920fb9441d4474e39ed8ed16ec0471891dd2748cd544b03d561bb25a60830c9fa83ef Homepage: https://cran.r-project.org/package=Omisc Description: CRAN Package 'Omisc' (Univariate Bootstrapping and Other Things) Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) . Package: r-cran-omixvizr Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-data.table, r-cran-dplyr, r-cran-genpwr, r-cran-ggbreak, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-ggtext, r-cran-gridextra, r-cran-gtable, r-cran-lulab.utils, r-cran-magrittr, r-cran-matrix, r-cran-patchwork, r-cran-pheatmap, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-showtext, r-cran-stringr, r-cran-sysfonts, r-cran-systemfonts, r-cran-tibble Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-omixvizr_1.4.0-1.ca2604.1_all.deb Size: 146210 MD5sum: 6e034f1d8b2cfa25386ec2ff1953869f SHA1: a9ff9261fd1bf6c8c931de23f2a4a944594aa064 SHA256: 79780788eaf4a3a2b6b1e4115cc583e8b9b11e43bb37714eccb53d4a65e1ea28 SHA512: 51c21865da271b7c3419d1420a0a0e8d30d14d64b4ff667d2d901e9ca4de621229291b790e1a7f01e696697dd011ec174651c6a8472ce6c449ee909318f476fa Homepage: https://cran.r-project.org/package=omixVizR Description: CRAN Package 'omixVizR' (A Toolkit for Omics Data Visualization) Provides a suite of tools for the comprehensive visualization of multi-omics data, including genomics, transcriptomics, and proteomics. Offers user-friendly functions to generate publication-quality plots, thereby facilitating the exploration and interpretation of complex biological datasets. Supports seamless integration with popular R visualization frameworks and is well-suited for both exploratory data analysis and the presentation of final results. Key formats and methods are presented in Huang, S., et al. (2024) "The Born in Guangzhou Cohort Study enables generational genetic discoveries" . Package: r-cran-omnibus Architecture: all Version: 1.2.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-omnibus_1.2.15-1.ca2604.1_all.deb Size: 269788 MD5sum: a5d267929c32b44d451c21c6f0d2e31a SHA1: 17445f219bc2b60fc5722dfdf51890d683be3bb1 SHA256: d08f9d6627b1d0e26be76ca652c43eb6185eabd9d067fd793da20aa1be20c19a SHA512: 8e510d48caa13e80a10d75c6da8407323f3a10575f6a824a35a5e77073bdac88ad619db07a78cd7c453b4834fb858f70758e32b5ff295b3d86c1cb49e2d22154 Homepage: https://cran.r-project.org/package=omnibus Description: CRAN Package 'omnibus' (Helper Tools for Managing Data, Dates, Missing Values, and Text) An assortment of helper functions for managing data (e.g., rotating values in matrices by a user-defined angle, switching from row- to column-indexing), dates (e.g., intuiting year from messy date strings), handling missing values (e.g., removing elements/rows across multiple vectors or matrices if any have an NA), text (e.g., flushing reports to the console in real-time); and combining data frames with different schema (copying, filling, or concatenating columns or applying functions before combining). Package: r-cran-omnibusfisher Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-stringr, r-cran-survey Filename: pool/dists/resolute/main/r-cran-omnibusfisher_1.0-1.ca2604.1_all.deb Size: 91244 MD5sum: 67887c064c886fcb536bf69ee3aef964 SHA1: ff284101224851f8606c3b92aa1304d48a44f5eb SHA256: c67457f72e2ddefbf24e5c441c1dec4c3ad0c6afd3aef75fedb4bd0d95cc7376 SHA512: b037cb9b164a18da8309b6f417b12e62bb9f39a8d725831b038e91d3b748f66d799a1e1d2b008c5017b67d9807dd0928e64f1c90f32eeaa05e2979660c6970aa Homepage: https://cran.r-project.org/package=OmnibusFisher Description: CRAN Package 'OmnibusFisher' (A Modified Fisher’s Method to Test Overall Gene-Level Effect) The separate p-values of SNPs, RNA expressions and DNA methylations are calculated by KM regression. The correlation between different omics data are taken into account. This method can be applied to either samples with all three types of omics data or samples with two types. Package: r-cran-omock Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-clock, r-cran-omopgenerics, r-cran-purrr, r-cran-readr, r-cran-rlang Suggests: r-cran-cdmconnector, r-cran-dbi, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-patientprofiles, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-omock_0.6.2-1.ca2604.1_all.deb Size: 4100512 MD5sum: 821b009a081304737f010f7db99aa882 SHA1: 157b7d94df5aadca5d56ab9feb334b0c5a607262 SHA256: 26ca112d4bc1db861db8742eb1afaf0a4f2af096a1d8d9057814943c4bc776d5 SHA512: 3b75498ae0bb6cd05de4d8437f6a24b68a4cec4d8a92b53c90357c664625ee5e7206be5a202174d1336b920443ee32ad9801902126706f373bc08a5595afeac9 Homepage: https://cran.r-project.org/package=omock Description: CRAN Package 'omock' (Creation of Mock Observational Medical Outcomes PartnershipCommon Data Model) Creates mock data for testing and package development for the Observational Medical Outcomes Partnership common data model. The package offers functions crafted with pipeline-friendly implementation, enabling users to effortlessly include only the necessary tables for their testing needs. Package: r-cran-omopconstructor Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang Suggests: r-cran-bookdown, r-cran-cdmconnector, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-omopsketch, r-cran-rmarkdown, r-cran-rpostgres, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-visomopresults Filename: pool/dists/resolute/main/r-cran-omopconstructor_0.3.0-1.ca2604.1_all.deb Size: 717260 MD5sum: 0413e56206e7a17c111ce661a5fe42d6 SHA1: 610923f7d2668b8dec51a4647b51487ed3a7d711 SHA256: 96d7b42de4ad68291f19735b8e95f735a48fdea7a4ffc23fe735eb3a787ea189 SHA512: 301f5ec370ee5af19781ecae1fc5a4a1068c0b0ed490f07f3f3d5c48534476bf081a751403b6efb9ca1f91ffca715a55849689d4762c1452298f6e05a8cf9942 Homepage: https://cran.r-project.org/package=OmopConstructor Description: CRAN Package 'OmopConstructor' (Build Tables in the OMOP Common Data Model) Provides functionality to construct standardised tables from health care data formatted according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. The package includes tools to build key tables such as observation period and drug era, among others. Package: r-cran-omopgenerics Architecture: all Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbplyr, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-covr, r-cran-duckdb, r-cran-gt, r-cran-here, r-cran-jsonlite, r-cran-knitr, r-cran-omock, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-omopgenerics_1.3.7-1.ca2604.1_all.deb Size: 714994 MD5sum: 914e398bf31b772f6188c754c2e1aa35 SHA1: d35d5d6fc4c7e910a84714a3e0154b20f50ad5d5 SHA256: 836f6bc5284ed3b028dc5adc5142c16c4adc0d95733db61b5008097f393b438a SHA512: d8742398b9821abc7a832fa5d212b77a5fae73f6c481899faf08d804e80cbe84f0e4baa56d440634dae8eaab57f0cced1c67f2b10875e25e21914128c6daa2d8 Homepage: https://cran.r-project.org/package=omopgenerics Description: CRAN Package 'omopgenerics' (Methods and Classes for the OMOP Common Data Model) Provides definitions of core classes and methods used by analytic pipelines that query the OMOP (Observational Medical Outcomes Partnership) common data model. Package: r-cran-omophub Architecture: all Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-r6, r-cran-rlang, r-cran-cli, r-cran-tibble, r-cran-purrr, r-cran-glue, r-cran-checkmate Suggests: r-cran-testthat, r-cran-httptest2, r-cran-webmockr, r-cran-knitr, r-cran-rmarkdown, r-cran-keyring, r-cran-withr Filename: pool/dists/resolute/main/r-cran-omophub_1.7.0-1.ca2604.1_all.deb Size: 467122 MD5sum: 616edd163508c59d16e6e58c71348b93 SHA1: 471df87bddeb19e2fa41422f6d67d1c3e53f0e51 SHA256: 725cadc140ee9387d71e9b6ac39a2f87f7e96e49bc08dfae67e75889d925e7ad SHA512: 0cd828d6d71570ec94dac0ef5c1a7d3e92c1a37f99d0fd20b936a3ab6574657e70f8d4a186988025cd69478c8d9d8b4403c30ca1ce7365d6cf5ffb493bc11d11 Homepage: https://cran.r-project.org/package=omophub Description: CRAN Package 'omophub' (R Client for the 'OMOPHub' Medical Vocabulary API) Provides an R interface to the 'OMOPHub' API for accessing 'OHDSI ATHENA' standardized medical vocabularies. Supports concept search, semantic search using neural embeddings, concept similarity, vocabulary exploration, hierarchy navigation, relationship queries, concept mappings, and FHIR-to-OMOP concept resolution with automatic pagination. Package: r-cran-omoponspark Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr Suggests: r-cran-testthat, r-cran-omock, r-cran-knitr, r-cran-rmarkdown, r-cran-cdmconnector, r-cran-omopsketch, r-cran-odbc, r-cran-r6, r-cran-crayon, r-cran-sparklyr, r-cran-databaseconnector Filename: pool/dists/resolute/main/r-cran-omoponspark_0.1.0-1.ca2604.1_all.deb Size: 263092 MD5sum: e73cd7ae9fff9901d29bc41ab8757213 SHA1: 4faf40b689906243806933d146dac52dd56e5dfb SHA256: bc5a2f3e7d36a5fed57e855b00d864b70d019e918e14117dece47453d7627317 SHA512: 2bdc062486c31959e099c39fd2c0a1482632e47b26d45056a2d92b0dd215e74d1120c8205cb2b4f3f03869b973e302d692a686af723b8cc733b471a5de834f9b Homepage: https://cran.r-project.org/package=OmopOnSpark Description: CRAN Package 'OmopOnSpark' (Using a Common Data Model on 'Spark') Use health data in the Observational Medical Outcomes Partnership Common Data Model format in 'Spark'. Functionality includes creating all required tables and fields and creation of a single reference to the data. Native 'Spark' functionality is supported. Package: r-cran-omopsketch Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2905 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-cohortconstructor, r-cran-dplyr, r-cran-lifecycle, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-cohortcharacteristics, r-cran-dbi, r-cran-duckdb, r-cran-dt, r-cran-flextable, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-lubridate, r-cran-odbc, r-cran-omopviewer, r-cran-sortable, r-cran-reactable, r-cran-remotes, r-cran-rmarkdown, r-cran-rpostgres, r-cran-shinywidgets, r-cran-testthat, r-cran-withr, r-cran-omock, r-cran-covr, r-cran-ggplot2, r-cran-visomopresults, r-cran-devtools, r-cran-usethis, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-omopsketch_1.0.1-1.ca2604.1_all.deb Size: 2101712 MD5sum: 2d0af82f633b6c792821e4382982056b SHA1: 07e0014149c136c4e35ea3fb70072c4fdc377d6c SHA256: cf8146a4ac2da3759a869a9f30d8c0e2a579d9d883bbf41a4710748235feb8fc SHA512: 5f093765496fb09d4b9ec65a53db6d740569e336c535928bb90b1307863585cc04020f120efde357c52d91cf43c0a1482db089e0bb926039bcfdee66f1dd4c4e Homepage: https://cran.r-project.org/package=OmopSketch Description: CRAN Package 'OmopSketch' (Characterise Tables of an OMOP Common Data Model Instance) Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends. Package: r-cran-omopstudybuilder Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-omopgenerics, r-cran-rlang, r-cran-whisker Suggests: r-cran-dplyr, r-cran-gert, r-cran-getpass, r-cran-gh, r-cran-here, r-cran-knitr, r-cran-processx, r-cran-purrr, r-cran-renv, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-omopstudybuilder_0.1.1-1.ca2604.1_all.deb Size: 136464 MD5sum: fe5f24ded84674a94223133d93a0f2c0 SHA1: caddb2fce9ae0bed0adbd9bf3b1cf1899616986a SHA256: aa567ca6c6f8099b28b23496202dc9617e23186ae073960075ca9e898fcd98a7 SHA512: 4631df61bff377b69d219d8782e7c3cae19f4facc0799b50c9fe10dccd513dc36b3ed99a061290553c03fb363fab9f376bdd500f9371654889c1d0f61ed7eadb Homepage: https://cran.r-project.org/package=OmopStudyBuilder Description: CRAN Package 'OmopStudyBuilder' (Build Reproducible Network Studies for OMOP Common Data Model) Streamlines the setup and execution of network studies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Creates standardised project structures with template code, manages dependencies with 'renv', provides code review utilities, and supports containerised execution with 'Docker' for reproducible multi-site studies. Includes 'GitHub' integration for collaboration and version control. Package: r-cran-omopviewer Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-cli, r-cran-dplyr, r-cran-dt, r-cran-glue, r-cran-gt, r-cran-lifecycle, r-cran-markdown, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-snakecase, r-cran-stringr, r-cran-styler, r-cran-tidyr, r-cran-usethis, r-cran-visomopresults, r-cran-yaml Suggests: r-cran-brand.yml, r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-cohortcharacteristics, r-cran-cohortconstructor, r-cran-cohortsurvival, r-cran-cpp11, r-cran-devtools, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-drugutilisation, r-cran-duckdb, r-cran-ggplot2, r-cran-here, r-cran-htmlwidgets, r-cran-incidenceprevalence, r-cran-knitr, r-cran-measurementdiagnostics, r-cran-omock, r-cran-omopsketch, r-cran-patientprofiles, r-cran-plotly, r-cran-progress, r-cran-reactable, r-cran-renv, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsconnect, r-cran-rsvg, r-cran-shiny.fluent, r-cran-shinycssloaders, r-cran-shinytree, r-cran-shinywidgets, r-cran-sortable, r-cran-testthat, r-cran-webshot2, r-cran-zip Filename: pool/dists/resolute/main/r-cran-omopviewer_0.7.0-1.ca2604.1_all.deb Size: 600336 MD5sum: cbde17805a4f24993adc09cebf206247 SHA1: 9672d783b8d558e1e6b7a9817da69e828365ef1d SHA256: 90251b11a2218f82b2a3e3303d6bdd0144de53f0ff405250f98977266ca3445f SHA512: cb3332b04daeaf66ab52c97bd45a54ecd60a772fad0ad1acdc4e8cfddd17e4b04bec6976860e8f07f20967c2c0f2e175a4237944d78da271c485e167afc58c91 Homepage: https://cran.r-project.org/package=OmopViewer Description: CRAN Package 'OmopViewer' (Visualise OMOP Results using 'shiny' Applications) Visualise results obtained from analysing data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model using 'shiny' applications. Package: r-cran-ompr.roi Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-roi, r-cran-slam, r-cran-matrix, r-cran-ompr Suggests: r-cran-testthat, r-cran-magrittr, r-cran-roi.plugin.glpk Filename: pool/dists/resolute/main/r-cran-ompr.roi_1.0.2-1.ca2604.1_all.deb Size: 21778 MD5sum: d279e971cdee85ed07bb3821e4325765 SHA1: 5c102381f03ea8f2e01b2c362e03a6a95753d134 SHA256: 632d8e9d728adff2bae3964936443ca8c161eb0bd8805e24eabc6408c909dd5c SHA512: bc06a8caa4166eb1a8748e1cc5d2c0186c2aa54f1bcbbb649da731401e39597f72c6a95213a8ef546de72f285afc2dc1492b88e1116524dfb323ec7fdc7853ed Homepage: https://cran.r-project.org/package=ompr.roi Description: CRAN Package 'ompr.roi' (A Solver for 'ompr' that Uses the R Optimization Infrastructure('ROI')) A solver for 'ompr' based on the R Optimization Infrastructure ('ROI'). The package makes all solvers in 'ROI' available to solve 'ompr' models. Please see the 'ompr' website and package docs for more information and examples on how to use it. 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A suite of ancillary statistical tools are also provided, including: estimation of positive Poisson (PP) and zero-truncated negative binomial (ZTNB) models; marginal effects and their standard errors; diagnostic likelihood ratio and Wald tests; plotting; predicted counts and expected responses; and random variate generation. The models and tools, as well as four applications, are shown in Godwin, R. T. (2024). "One-inflated zero-truncated count regression models" arXiv preprint . Package: r-cran-onelogin Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-glue, r-cran-safer, r-cran-magrittr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-onelogin_0.2.0-1.ca2604.1_all.deb Size: 90178 MD5sum: 64a3eb318914b60c0c182baf0561f0af SHA1: f19653962aad8e3fc21e50ddf773645c5232dcfe SHA256: 4325a56f6e7e6a13c3c717b1ca9139f8f3fb9aa8ce45a2ec61463fabb6d66139 SHA512: 79e11d7dfea9d5aeea0fc64f1bd464c1ae5c53ca762f7df11786af53c2639122b3f78105811fc30384907b13bbbd1fb05890940ef7c7dbe59b3e326f7c55817b Homepage: https://cran.r-project.org/package=onelogin Description: CRAN Package 'onelogin' (Interact with the 'OneLogin' API) The identity provider ['OneLogin'] is used for authentication via Single Sign On (SSO). This package provides an R interface to their API. 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It is useful as a baseline for machine learning models and the rules are often helpful heuristics. 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When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003) and variation of the test for small sample sizes which is detailed in FD Liddell (1984) paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest. 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The package includes implementations of; the Sanderson and Windmeijer (2016) conditional F-statistic, the multiplicative structural mean model Hernán and Robins (2006) , and two-stage predictor substitution and two-stage residual inclusion estimators explained by Terza et al. (2008) . 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Input to the program should be binary(1/0) pathology data, where “0” may stand for negative and “1” for positive. Additional examples were given using the data from Rimm et al. (2017 ). 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Le Cam (1956) ) and can be computed faster than the maximum likelihood estimator for large observation samples, see e.g. Brouste et al. (2021) . 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(1989) , but is implemented in pure R. Package: r-cran-onmarg Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-readxl, r-cran-sf, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-onmarg_1.0.3-1.ca2604.1_all.deb Size: 35680 MD5sum: d5e5c8366cea872e38375805a1400b78 SHA1: d9dae2d296ccd12b191ab69209df63ddd705e29a SHA256: 7f7a2cf7cb5edae3f90eeec23a59716621f32b02e6f9b5f791dfdcb73656b663 SHA512: 2d8c554d6eb2d2fbaa15a052c0bf770d07b457d5b80a3ac1ad1d6402b511ce7a616162fba4fbb2a13ff6d8c200bca5757886545b008d77ede5e566886747f98b Homepage: https://cran.r-project.org/package=onmaRg Description: CRAN Package 'onmaRg' (Import Public Health Ontario's Ontario Marginalization Index) The Ontario Marginalization Index is a socioeconomic model that is built on Statistics Canada census data. 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Includes functions for visualising character annotations and creating simple queries using ontological relationships. 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It expands on the previous work of Tarasov et al. (2019) . The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps. Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest. 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This package provides a comprehensive toolbox for researchers and practitioners to design and analyze order-of-addition experiments. Detailed comparisons and summary of all statistical methods in this package can be found in Tsai (2026), "Order-of-addition experiments in R using OofAExp", Journal of Quality Technology (to appear). 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For more details see Fennell (2024) Handbook of UK Urban Tree Allometric Equations and Size Characteristics (Version 1.4). . Package: r-cran-openblender Architecture: all Version: 0.5.81-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-openblender_0.5.81-1.ca2604.1_all.deb Size: 42546 MD5sum: 4e82d017c82b641fccf1f1edc608e810 SHA1: c4ff6cf5fab5fd9e6bd440ab032bc9f4907fe655 SHA256: 5fdc799fbb284d67553d9884777a8861782e6eccc9f579e969217a3c0b11d3fe SHA512: ffb68fc95750a5cbfa9e5b2edc16541352b024e297a3b19053cfb5a7d91f0b711a8ca3a2e05507e140ecd2a40f13faa239e63e5a5d07c6a7839db43569462c5d Homepage: https://cran.r-project.org/package=openblender Description: CRAN Package 'openblender' (Request API Services) Interface to make HTTP requests to 'OpenBlender' API services. Go to for more information. Package: r-cran-opencage Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-dplyr, r-cran-lifecycle, r-cran-jsonlite, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-ratelimitr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-opencage_0.2.2-1.ca2604.1_all.deb Size: 148436 MD5sum: afad455f12a7617454579daa7d7f849d SHA1: 8ac2e623e3c3fade7c1f04027d441379be333ea4 SHA256: 074bb00defa8b38409bf9a55a2a2c2a6d89aeb252ec5849f0025ac77d2a28747 SHA512: bc83b09658bedf8eb49f57f74df5ffe04809fa00cd26b569fb393f68560b5e972cebf48df512d10460c180565385d008e98c1f1ab1bf060c6854b3337f63096c Homepage: https://cran.r-project.org/package=opencage Description: CRAN Package 'opencage' (Geocode with the OpenCage API) Geocode with the OpenCage API, either from place name to longitude and latitude (forward geocoding) or from longitude and latitude to the name and address of a location (reverse geocoding), see . Package: r-cran-opencast Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-cran-seqinr, r-cran-stringr, r-cran-tibble, r-cran-entropy, r-cran-ftrcool Suggests: r-cran-caret, r-cran-kernlab, r-cran-ranger, r-cran-xgboost, r-cran-gbm Filename: pool/dists/resolute/main/r-cran-opencast_0.1.1-1.ca2604.1_all.deb Size: 62806 MD5sum: f1c3a90b3c723f86462abc47590b09a9 SHA1: b4d2909e25eceb5dac7cc0263dc773e2c42cf967 SHA256: 573f64e2ee974b8772945a186e0f4621390a5f8aad65276012c8c93420126e13 SHA512: 7097edfe25033c33fb1d8db124334f8c4e0e1c87589524691e872af899bfab99248c705ba102a5c2fbb411f105af0f56608bd900b6aee9df23a059482aa904a8 Homepage: https://cran.r-project.org/package=OpEnCAST Description: CRAN Package 'OpEnCAST' (Optimized Ensemble Model for C and A Methylation Search in Plant) DNA methylation is an important epigenetic process that regulates gene activity through chemical modifications of DNA without changing its sequence. 'OpEnCAST' is a plant-specific ensemble-based prediction package that identifies 4mC, 5mC and 6mA methylation sites directly from DNA sequences. It combines multiple machine learning algorithms trained on monocot (Oryza sp.) and dicot (Arabidopsis sp.) reference models to deliver accurate predictions. This methodology is being inspired by the ensemble algorithm for methylation prediction developed by Wang et al. (2022) . Package: r-cran-opencis Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-haven, r-cran-magrittr, r-cran-rvest, r-cran-stringr, r-cran-memoise Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-opencis_0.1.1-1.ca2604.1_all.deb Size: 1153074 MD5sum: 9dbf7c43664ca7e3457cad94e0616f04 SHA1: 028bd9f7b0cde0df3eeebf6cb84ff7f071c914c4 SHA256: af7f6c3c5c3a8f15a1b28330bdb0c78b1f597a613acd97b6bea79040b6ec95d2 SHA512: cec57f2b46689bedfb5df67611bd0f0b28bd2a874df870aa01a779e7393f6b9cfdaf2c47a9ea396f7de4b8f7c658022ba8ab65388d1388687300f679d9e9105b Homepage: https://cran.r-project.org/package=opencis Description: CRAN Package 'opencis' (Import Data from Spanish Sociological Research Center (CIS)) Search and import data directly to R from the Spanish Sociological Research Center (CIS) . The CIS is a public institution that conducts electoral and sociological research studies on the Spanish society. The CIS has a large database of surveys that can be accessed through its website. The package includes functions to search for surveys, survey questions and timeseries, and import the data directly to R. Package: r-cran-opencpu Architecture: all Version: 2.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evaluate, r-cran-httpuv, r-cran-knitr, r-cran-jsonlite, r-cran-remotes, r-cran-sys, r-cran-webutils, r-cran-curl, r-cran-rappdirs, r-cran-rlang, r-cran-vctrs, r-cran-zip, r-cran-mime, r-cran-protolite, r-cran-brew, r-cran-openssl Suggests: r-cran-arrow, r-cran-unix, r-cran-haven, r-cran-pander, r-cran-r.rsp, r-cran-svglite Filename: pool/dists/resolute/main/r-cran-opencpu_2.2.14-1.ca2604.1_all.deb Size: 520234 MD5sum: af6b041f41576ad922a63be8d2823322 SHA1: 8d9473f9bea546269453b59374b340e21c473204 SHA256: 88cf98ecea0b07fa86682f67c768cf1366e4a01f5fff271657de356f1c4ffae2 SHA512: 1b28623f287d8348d7d229b7e038b1e67affce96e7bd44705b6fe55eded4eb288d15a8516eb95d09e84a947a898c18ee5d6d7891e58a86fea3a671fdde6ee3e3 Homepage: https://cran.r-project.org/package=opencpu Description: CRAN Package 'opencpu' (Producing and Reproducing Results) A system for embedded scientific computing and reproducible research with R. The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange with R. This provides a reliable and scalable foundation for statistical services or building R web applications. The OpenCPU server runs either as a single-user development server within the interactive R session, or as a multi-user Linux stack based on Apache2. The entire system is fully open source and permissively licensed. The OpenCPU website has detailed documentation and example apps. Package: r-cran-opendataformat Architecture: all Version: 2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-zip, r-cran-magrittr, r-cran-xml2, r-cran-data.table, r-cran-tibble, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-islr, r-cran-dplyr, r-cran-haven Filename: pool/dists/resolute/main/r-cran-opendataformat_2.2.2-1.ca2604.1_all.deb Size: 138174 MD5sum: bf1e0c63f9c05702ca01ba4fa69e1d3d SHA1: 8058208066ce7b843d3f0f574dc1772426378f78 SHA256: 6173c9bdfd9f9f51003b1eae4133ee2cebd5ff0acb398e74b24057779052e6b1 SHA512: e9e9bf548d39901105e9bd92da14d30ebce4c443da827b7297e12f8f45a0b1cfde2918f6b69fcac26e6d50110540b7d1fe34d2e063d0b95d15fdca0ec161c911 Homepage: https://cran.r-project.org/package=opendataformat Description: CRAN Package 'opendataformat' (Reading and Writing Open Data Format Files) The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see . Package: r-cran-opendatatoronto Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ckanr, r-cran-magrittr, r-cran-readxl, r-cran-sf, r-cran-tibble, r-cran-xml2, r-cran-curl Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggiraph, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-tidyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-opendatatoronto_0.1.6-1.ca2604.1_all.deb Size: 71354 MD5sum: 5789ae28bcec206b6e7b2715e832f657 SHA1: ab2d270611fc20510406a5e896e49e3622f4155d SHA256: ce3523644ca75dfb56b24fa05c6fb9509b529f4f6f996cae3bc08ee983c9c431 SHA512: bcbe060fb3801dd704cf55f3360d6c46094edfb204e0a9db6433523e5fec2247e402c93dbe4791dcdf8e6acbf84c4df1cb9833bc40f9cc72aa83cdedc4fd11f9 Homepage: https://cran.r-project.org/package=opendatatoronto Description: CRAN Package 'opendatatoronto' (Access the City of Toronto Open Data Portal) Access data from the "City of Toronto Open Data Portal" () directly from R. Package: r-cran-opendotar Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-dplyr, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-opendotar_0.1.4-1.ca2604.1_all.deb Size: 21756 MD5sum: e6e4eba4972762b210215cf168d92fd1 SHA1: 8cf8c7888b5eeabee1976e9fde999ca4ae27ed92 SHA256: 4378b0aa9ddc03db967ba2c16a3a2290c65ca3372541f1395c7424cdddbd9b01 SHA512: c0a62d3d7623255a851f170d98bc1c32fc7e6b82c7aa26cf72e97fd90e9b3a37127846e64d6f3aac546b4e1eb72059d2990371c5eee1a4400cd4a393c2ca6ffa Homepage: https://cran.r-project.org/package=opendotaR Description: CRAN Package 'opendotaR' (Interface for OpenDota API) Enables the usage of the OpenDota API from , get game lists, and download JSON's of parsed replays from the OpenDota API. Also has functionality to execute own code to extract the specific parts of the JSON file. Package: r-cran-openebgm Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-deoptim, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-openebgm_0.9.1-1.ca2604.1_all.deb Size: 428732 MD5sum: 52911310360ae3f5a6425a61b7dd1fa6 SHA1: 8468ad1ee5538cacfc07ba38024017cc0ade79a6 SHA256: b32199bb4b750e8b9a084b5f71ca5699d57508af385e4f2b9a2a88229d0eb00a SHA512: 4e2073665d4e0847c7be17ad16c2afca23c7ac92ff567515b150cedd45903f1225e161f0802dcb316992c2868554a9d6f8c96d69ebdd5a9939d79a63b43340b5 Homepage: https://cran.r-project.org/package=openEBGM Description: CRAN Package 'openEBGM' (EBGM Disproportionality Scores for Adverse Event Data Mining) An implementation of DuMouchel's (1999) Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the 'PhViD' package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the 'mederrRank' package. Package: r-cran-openeo Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr2, r-cran-r6, r-cran-lubridate, r-cran-base64enc, r-cran-sf, r-cran-irdisplay, r-cran-htmltools, r-cran-rlang Suggests: r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-stars, r-cran-pkgdown, r-cran-rmarkdown, r-cran-kableextra, r-cran-dt, r-cran-terra, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-openeo_1.4.1-1.ca2604.1_all.deb Size: 1815890 MD5sum: 0539eb2a61193646759e24146d117f48 SHA1: a58fb2c8b94189a0052bc55c6d424674a3550fb3 SHA256: 35c4e149798f41c136f4919c2a97f04d251efc22be633c1d7ba8c9e7a2de7f7e SHA512: f5f50b3cdf849f67419959c07ef060fcd0d13064ec543e379787230b97591779d1fe6ae66ef76592ebf59143c689c8ba691505bf06e1049ed31f562b37b79867 Homepage: https://cran.r-project.org/package=openeo Description: CRAN Package 'openeo' (Client Interface for 'openEO' Servers) Access data and processing functionalities of 'openEO' compliant back-ends in R. Package: r-cran-openesm Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-httr2, r-cran-jsonlite, r-cran-readr, r-cran-purrr, r-cran-dplyr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-openesm_0.2.0-1.ca2604.1_all.deb Size: 71468 MD5sum: 0097889a691fb973833e32c82f1fa527 SHA1: 46f00e809c535cc85077578406058f77f3b7c004 SHA256: 8bf4c4f99afcbd28a40505519e2394148fc0958fc47b10428f946bf6d5808393 SHA512: ff13af75072e6a98618fc43aadd495330bf3abb8e13090a851407864ee0fef39f081c52f3c181ba0e998ced42713fe606ccf9690142418c617e9279fdbe8ef2f Homepage: https://cran.r-project.org/package=openesm Description: CRAN Package 'openesm' (Access the Open Experience Sampling Method Database) Provides programmatic access to the Open Experience Sampling Method ('openESM') database (), a collection of harmonized experience sampling datasets. The package enables researchers to discover, download, and work with the datasets while ensuring proper citation and license compliance. Package: r-cran-openfda Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-checkmate, r-cran-purrr, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-openfda_0.1.0-1.ca2604.1_all.deb Size: 58918 MD5sum: 6ad1d5b51882980aa26fc7752c8fc4dd SHA1: 417b6cca8bb550041e6a1fe57ea9d7e2067a2287 SHA256: 782c4485d99e75b49ab2bb1d432220194ef6a5e4a37cbc162507aefa33bdf7c6 SHA512: 7dd96fb9c386828f888c8b6676099f6ea22662a54f1f9e21b12985d6ec67d686bad42050b2ea4b46ccce06dfd078d7a3cb020800dd00e891f5fd316c1cc1699c Homepage: https://cran.r-project.org/package=openFDA Description: CRAN Package 'openFDA' ('openFDA' API) The 'openFDA' API facilitates access to Federal Drug Agency (FDA) data on drugs, devices, foodstuffs, tobacco, and more with 'httr2'. This package makes the API easily accessible, returning objects which the user can convert to JSON data and parse. Kass-Hout TA, Xu Z, Mohebbi M et al. (2016) . Package: r-cran-opengraph Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rvest Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-opengraph_0.0.4-1.ca2604.1_all.deb Size: 44022 MD5sum: a0f2e294cc1691fbd445bc00b83d97dc SHA1: b3632bf668b06247598c08eff072b3790237203b SHA256: db36506627b891e90560e25bd94fcdc4772be54a39fd24422fbfd251f20c2538 SHA512: 09da1ccefbfe8464ec9927f219fc34a300aa9d1a7a1b21efc1b5fb673fd7056456c6859c4f12e30fc7f3df311d813ac4e03c6dbf3595b2d6a806d0243e041275 Homepage: https://cran.r-project.org/package=opengraph Description: CRAN Package 'opengraph' (Process Metadata from the 'Open Graph Protocol') Social media sites often embed cards when links are shared, based on metadata in the 'Open Graph Protocol' (). This supports extracting that metadata from a website. It further allows for the creation of tags to add to a website to support the 'Open Graph Protocol' and provides a list of the standard tags and their required properties. 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It is noteworthy that histone deacetylation and histone H3 lysine 27 trimethylation (H3K27me3) play a role in repressing transcription in eukaryotes. In contrast, histone acetylation (H3K9ac) and H3K4me3 have been inevitably linked to the stimulation of gene expression, which significantly influences plant development and plays a role in plant responses to biotic and abiotic stresses. To our knowledge this the first multiclass classifier for predicting histone modification in plants. . 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Kelly in the 1950s. Today, grids are used across various domains ranging from clinical psychology to marketing. The package contains functions to quantitatively analyze and visualize repertory grid data (e.g. 'Fransella', 'Bell', & 'Bannister', 2004, ISBN: 978-0-470-09080-0). The package is part of the The package is part of the project. 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Usually done by humans, automatic scoring using AI becomes more and more accurate. This package provides a simple interface to the 'Open Scoring' API , leading creativity scoring technology by Organiscak et al. (2023) . With it, you can score your own data directly from an R script. 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This package enables users to download predictor portfolio returns (over 200 cross-sectional predictors with multiple portfolio construction methods) and firm characteristics (over 200 characteristics replicated from the academic asset pricing literature). Center for Research in Security Prices (CRSP)-based variables such as Price, Size, and Short-term Reversal can be downloaded with a Wharton Research Data Services (WRDS, ) subscription. For a full list of what is available, see . Package: r-cran-openspecy Architecture: all Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1770 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-yaml, r-cran-catools, r-cran-hyperspec, r-cran-mmand, r-cran-plotly, r-cran-digest, r-cran-signal, r-cran-glmnet, r-cran-jpeg, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shinyjs, r-cran-shinywidgets, r-cran-bs4dash, r-cran-dplyr, r-cran-dt, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-openspecy_1.5.3-1.ca2604.1_all.deb Size: 1414940 MD5sum: 69fc84fed144342a59acb458b2498010 SHA1: 501d13e541a70db048bfacf9db1ecd05c4815702 SHA256: 922269101637fe7d7d848b9a40157bf31c8521fd786a74fcabb7d0f80c364d55 SHA512: 633df78ab96c24aad74d48526bf089454cc4af72267a1fbe9fe80007b514242e10b4b6442fd6f635a00a6e35776c3697f0f3d0f504a117c4ebcdb2f3af840a7e Homepage: https://cran.r-project.org/package=OpenSpecy Description: CRAN Package 'OpenSpecy' (Analyze, Process, Identify, and Share Raman and (FT)IR Spectra) Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2021, ). With read_any(), Open Specy provides a single function for reading individual, batch, or map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction and flattening, intensity conversions, wavenumber alignment, and min-max normalization. Spectra can be identified in batch using an onboard reference library (Cowger et al. 2020, ) using match_spec(). A Shiny app is available via run_app() or online at . Package: r-cran-openstreetmap Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rjava, r-cran-raster, r-cran-sp Filename: pool/dists/resolute/main/r-cran-openstreetmap_0.4.1-1.ca2604.1_all.deb Size: 2264280 MD5sum: cb5b02858d63d9981b5b5d6a16b950c7 SHA1: 5eb89463301e544a0611ffea3dd66fada8b44698 SHA256: 871c1ec0b3e454ba2d04fd24d51ff92d29242ae32254c55a56fc75c4f8e4ee8c SHA512: 70fb49820fdc6ccb151ca4dc9a3711a81b4ca9931b82e6d08e98a5a5513acb999c95762bcb882ad69bb64973f6e98fde8ffd64fa64556e5226012b5d899092ef Homepage: https://cran.r-project.org/package=OpenStreetMap Description: CRAN Package 'OpenStreetMap' (Access to Open Street Map Raster Images) Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite, and topographic map servers are directly supported. 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Package: r-cran-opentripplanner Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-geodist, r-cran-googlepolylines, r-cran-curl, r-cran-rjson, r-cran-purrr, r-cran-rcppsimdjson, r-cran-progressr, r-cran-sf, r-cran-sfheaders Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-terra, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-opentripplanner_0.5.2-1.ca2604.1_all.deb Size: 842744 MD5sum: 9b689ddea2abe98a2a52cd2217adcb8e SHA1: ce5546163fc4f44eec6a3b6c0223ef4f1ef7ae37 SHA256: be75b3d2f3a221bbc19d6faa31545e45c069d5002e6ac77845688046f4f42910 SHA512: b4cc33524f505828f469cf014fae71452a1577b4f814f20da4a0f1b3a39c4e07beab2ffa9b1a31040d7253392efbd90e8eef4e63877656f169f0e3ac7252c16b Homepage: https://cran.r-project.org/package=opentripplanner Description: CRAN Package 'opentripplanner' (Setup and connect to 'OpenTripPlanner') Setup and connect to 'OpenTripPlanner' (OTP) . OTP is an open source platform for multi-modal and multi-agency journey planning written in 'Java'. The package allows you to manage a local version or connect to remote OTP server to find walking, cycling, driving, or transit routes. This package has been peer-reviewed by rOpenSci (v. 0.2.0.0). Package: r-cran-openva Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5385 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-interva5, r-cran-insilicova, r-cran-interva4, r-cran-tariff, r-cran-ggplot2, r-cran-crayon, r-cran-cli, r-cran-rlang Suggests: r-cran-covr, r-cran-eava, r-cran-knitr, r-cran-nbc4va, r-cran-r.rsp, r-cran-testthat, r-cran-vacalibration Filename: pool/dists/resolute/main/r-cran-openva_1.2.0-1.ca2604.1_all.deb Size: 2289610 MD5sum: ba518912023f1311a9f851c9f7810188 SHA1: cfb9440c284aeeaeb6df407c98e109727147ad07 SHA256: 786e8e6d8db8906fb47bef980821b13e323976d1118b04ab890fc4c581d5da72 SHA512: 40a207daf21ea7fb4d16a02db8d97b98f44d7473582a17fc3744c1e5110b1877acd6f3611e78fe6493e6f927e536b43eb5633d35d8687f89af9a24a50ad9afb0 Homepage: https://cran.r-project.org/package=openVA Description: CRAN Package 'openVA' (Automated Method for Verbal Autopsy) Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include 'InterVA4' by Byass et al (2012) , 'InterVA5' by Byass at al (2019) , 'InSilicoVA' by McCormick et al (2016) , 'NBC' by Miasnikof et al (2015) , and a replication of 'Tariff' method by James et al (2011) and Serina, et al. (2015) . It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The 'NBC' method is implemented by the 'nbc4va' package that can be installed from . Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the 'Tariff' method. Package: r-cran-operator.tools Architecture: all Version: 1.6.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-operators, r-cran-magrittr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-operator.tools_1.6.3.1-1.ca2604.1_all.deb Size: 53854 MD5sum: f1b71c711e48a76c5451c252e142154b SHA1: cdc490421ad65d9f05b52620e3a03310d420bd50 SHA256: c85b41e6bf91f1b9bb053b5b4dbacdfa3b791184846593d7b545f85d477972e1 SHA512: e0693bdca439baffb0d76dbc72b2b19b0833bb14e76b02ea542fb22a5e382528df72fe88e743cfb630882fd2ee840798bf9975f3bce885c70ece4f12f8b2a804 Homepage: https://cran.r-project.org/package=operator.tools Description: CRAN Package 'operator.tools' (Utilities for Working with R's Operators) Provides a collection of utilities that allow programming with R's operators. 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Package: r-cran-opgmmassessment Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adaptgauss, r-cran-datavisualizations, r-cran-distributionoptimization, r-cran-cluster, r-cran-mixtools, r-cran-foreach, r-cran-rlang, r-cran-ggplot2, r-cran-catools, r-cran-dplyr, r-cran-mclust, r-cran-mixak, r-cran-multimode, r-cran-nbclust, r-cran-clusterr, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-opgmmassessment_0.4.1-1.ca2604.1_all.deb Size: 65432 MD5sum: 39bc23278e7452d7eea9c06e68078b23 SHA1: f6be7904615056062a64f4a7532f04bc9a2e73ab SHA256: 9e55e7746b25877ff8c12c697f3b0fb9de68c5536081214b8bd0f73cf50029e0 SHA512: 846923bdaf8d7e21eac5b0386de866f3f9527dd09df2e9c0cdf26ce6aaafb09212f3c1be653670812cf28cd3dcdcb5390a897e9a98ec552a7d3eb28234735b76 Homepage: https://cran.r-project.org/package=opGMMassessment Description: CRAN Package 'opGMMassessment' (Optimized Automated Gaussian Mixture Assessment) Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) . Package: r-cran-opi Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-rfast, r-cran-abind, r-cran-openssl Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-opi_3.1.0-1.ca2604.1_all.deb Size: 711600 MD5sum: 3e714fbc224ac87aafeb59600e71b607 SHA1: 8481ce7207019d79872e40132fe8b6f770afe494 SHA256: 6f17e7922213011aaf6373dea17533654b616fe8235f88df47a767161f5ea308 SHA512: c961cc772f45f992c0d401e36727e35bc296b4a4bdbc3d5689514e729405425be765c8533fde4c2c6b2054d5f204f28892757df412ffe0904140025ebedc03d6 Homepage: https://cran.r-project.org/package=OPI Description: CRAN Package 'OPI' (Open Perimetry Interface) Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters) first started as an open source project with support of Haag-Streit in 2010. It specifies basic functions that allow many visual field tests to be constructed. As of February 2022 it is fully implemented on the Haag-Streit Octopus 900 and 'CrewT ImoVifa' ('Topcon Tempo') with partial implementations on the Centervue Compass, Kowa AP 7000 and Android phones. It also has a cousin: the R package 'visualFields', which has tools for analysing and manipulating visual field data. Package: r-cran-opimputation Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfit, r-cran-caret, r-cran-abcanalysis, r-cran-ggplot2, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-missforest, r-cran-mice, r-cran-miceranger, r-cran-multius, r-cran-amelia, r-cran-mi, r-cran-reshape2, r-cran-datavisualizations, r-cran-abind, r-cran-cowplot, r-cran-twosamples, r-cran-ggh4x, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-opimputation_0.6-1.ca2604.1_all.deb Size: 143890 MD5sum: f289f211d08a71be982641fb74db9a46 SHA1: 6aa65648e3e2cd70af2b12fcbd30c6432e4ca641 SHA256: 48355cc027020958e07d0c5591ff6af0cdc09c741f09ddb2dd5ca28e665abf8f SHA512: 201fd749c4634f64d98907a6e1b4810deb6940f4c6a4c4dd92262b7b49b882a64965989a42be1ec24333660825ed82dfebe57cb3fd23f4cf7848fa276d1f61bb Homepage: https://cran.r-project.org/package=opImputation Description: CRAN Package 'opImputation' (Optimal Selection of Imputation Methods for Pain-RelatedNumerical Data) A model-agnostic framework for selecting dataset-specific imputation methods for missing values in numerical data related to pain. Lotsch J, Ultsch A (2025) "A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data" Canadian Journal of Pain (in minor revision). Package: r-cran-opinar Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-janitor, r-cran-rvest, r-cran-glue, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-magrittr, r-cran-assertthat, r-cran-rlang, r-cran-sjplot, r-cran-gt, r-cran-lubridate Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-opinar_1.0.0-1.ca2604.1_all.deb Size: 72040 MD5sum: 04cd73c1b8ede8755afeb984ba6d2dfb SHA1: 14c77253e09abc97fb00bd724b260c74fbdba8d9 SHA256: a2aaa24a2315f3a443eee757da3392f19eb51ffc508b5c97087928c4301b0912 SHA512: e01c0bb73086a38f7855f722f1958e127bee89733aeadf70a2aec931c6074a6d165a0924049b46643f048d79a17641ea5c56faea59126b3250ddccebaa275cd5 Homepage: https://cran.r-project.org/package=opinAr Description: CRAN Package 'opinAr' (Argentina's Public Opinion Toolbox) A toolbox for working with public opinion data from Argentina. It facilitates access to microdata and the calculation of indicators of the Trust in Government Index (ICG), prepared by the Torcuato Di Tella University. Although we will try to document everything possible in English, by its very nature Spanish will be the main language. El paquete fue pensado como una caja de herramientas para el trabajo con datos de opinión pública de Argentina. El mismo facilita el acceso a los microdatos y el cálculos de indicadores del Índice de Confianza en el Gobierno (ICG), elaborado por la Universidad Torcuato Di Tella. Package: r-cran-opl Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-pander, r-cran-randomforest, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-opl_1.0.2-1.ca2604.1_all.deb Size: 70396 MD5sum: 0f449a9ebbebda1484b5949da800ec4f SHA1: 5cefac29b1a43ca71c0a5be59c4fb4aab22eb9c8 SHA256: 3161cc58fb0f711f0ad91f18fb8aec13610cc6f3c36acee10833c64d70362cc0 SHA512: 94f6edf287623ed929e3a2e94c8c5d793c75932b966a9ebbe9a435502a613e535a32f2dfb51507e610d04a0d179d0520a3f7cf857157ffe0204248dfade178bc Homepage: https://cran.r-project.org/package=OPL Description: CRAN Package 'OPL' (Optimal Policy Learning) Provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based on data in order to maximize empirical welfare. Specifically, 'OPL' allows to find "treatment assignment rules" that maximize the overall welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. Documentation about 'OPL' is provided by several international articles via Athey et al (2021, ), Kitagawa et al (2018, ), Cerulli (2022, ), the paper by Cerulli (2021, ) and the book by Gareth et al (2013, ). Package: r-cran-opportunistic Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-hopbyhop, r-cran-endtoend Filename: pool/dists/resolute/main/r-cran-opportunistic_1.2-1.ca2604.1_all.deb Size: 28932 MD5sum: b761d3c20f9017521777f67b9acf4b6b SHA1: c71a7eee3fbfac9986d8074c9e8a5732b2222cc8 SHA256: a4e0ba508d21fcc6895e2a495e506147828bf8f4fd8ce80cc8a8091c0ce3d864 SHA512: f43bb3221329d346171ea41e053b895f95bc7fffbb4e3530351fcdbbb6d49f80cb7cbb91f4107aecdd5434be36eb64de34e8a3c84f93fa3731c96508d63212d9 Homepage: https://cran.r-project.org/package=Opportunistic Description: CRAN Package 'Opportunistic' (Routing Distribution, Broadcasts, Transmissions and Receptionsin an Opportunistic Network) Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations. Package: r-cran-optband Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lambertw Suggests: r-cran-survival, r-cran-km.ci, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-optband_0.2.2-1.ca2604.1_all.deb Size: 210354 MD5sum: 2646f997cf6d5987ec293e9c614a728f SHA1: 79038b7961687a4b8420ecda1930c070e471ddb5 SHA256: a96b1f137dfb6189d53eb3dd81a9e4cac132af99ff6c92ac56e9460894d37772 SHA512: 2df90d0c7e32a2b0ac29d59f941e9505ae7905db45d6c4f210094420eb81571b53d2e372dfff6b65a190567f15ab35f4ff8be6775309062bf55a112731072fd0 Homepage: https://cran.r-project.org/package=optband Description: CRAN Package 'optband' ('surv' Object Confidence Bands Optimized by Area) Given a certain coverage level, obtains simultaneous confidence bands for the survival and cumulative hazard functions such that the area between is minimized. Produces an approximate solution based on local time arguments. Package: r-cran-optbdmaeat Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-optbdmaeat_1.0.2-1.ca2604.1_all.deb Size: 128382 MD5sum: 23f3246110de678c600dd080804a6bed SHA1: 03b2739c21f6aa4fc631b86c7b029d60fd7987b7 SHA256: 6fefb9ae8de558cb91695d839f4886601ea113de63df82af8813bad802fc8448 SHA512: 3ecefa02a4fe8626c12e65fc4356fb1e4ea87a659c6d76a8433d4232d3f40d8c0127125393bbe211453f76d608b461c757e6c85bc7e8b3742ec6c9ce0766aeaa Homepage: https://cran.r-project.org/package=optbdmaeAT Description: CRAN Package 'optbdmaeAT' (Optimal Block Designs for Two-Colour cDNA Microarray Experiments) Computes A-, MV-, D- and E-optimal or near-optimal block designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The algorithms used in this package are based on the treatment exchange and array exchange algorithms of Debusho, Gemechu and Haines (2018) . The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly. Package: r-cran-optbinningr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-lintr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-optbinningr_0.2.1-1.ca2604.1_all.deb Size: 777722 MD5sum: 706ba894385dd00508be11764aa07e93 SHA1: 8de3a90ed50bd214920f2e842e716fd295aa217c SHA256: 43e994a7c0eec014723334ef1e2f92f6ce63932e9e49beb7feba61eddc20812b SHA512: a9995b2aa67c0c7c3075f852871993922bd3906a730ed7a7633533c2fa8fe5d86f0c8f9a4e51ab6eaf874123525b5379b8cbfac8e5eacd0f52d8483c09111d81 Homepage: https://cran.r-project.org/package=optbinningR Description: CRAN Package 'optbinningR' (Optimal Binning Methods for Predictive Modeling and Analytics) Native R tools for optimal binning workflows in predictive modeling. The package provides APIs for binary, multi-class and continuous targets, with multi-variable binning and scorecard workflows. Methods are informed by Navas-Palencia (2020) and Navas-Palencia (2021) . Package: r-cran-optbiomarker Architecture: all Version: 1.0-28-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpanel, r-cran-mass, r-cran-randomforest, r-cran-e1071, r-cran-ipred, r-cran-msm, r-cran-rgl, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-optbiomarker_1.0-28-1.ca2604.1_all.deb Size: 443350 MD5sum: 3ff88eaa4c72a8fd6c08ec957381d2e1 SHA1: a303610f559ee76c35c87cdde8af4707dc2ec81e SHA256: 9123f9695e6b62972d68e38602b2d6278f0116df011d82bbb3857abb3c58fd36 SHA512: ce4ef316f7ca9ae8343c071a7ae9982500dad73dd75227f2ac58e5d4841f986b14425b8969098e7b3b278ad7bfce73b1a68c61a02fdfd523ecafb8e8667140ff Homepage: https://cran.r-project.org/package=optBiomarker Description: CRAN Package 'optBiomarker' (Estimation of Optimal Number of Biomarkers for Two-GroupMicroarray Based Classifications at a Given Error ToleranceLevel for Various Classification Rules) Estimates optimal number of biomarkers for two-group classification based on microarray data. Package: r-cran-optconerrf Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-optconerrf_1.0.2-1.ca2604.1_all.deb Size: 320844 MD5sum: 7065847cdac98606b73224a6f82c81b6 SHA1: c8f48e9467754ba6f55a2309d48e87ad42dc8d27 SHA256: ac1e961f7d2cba9b78af77665be0cbeb3f74e55d865cb8d7a355d4be88fe9904 SHA512: b166d777b6ccff4910a17a655360a819e8e6563d7203bd0c90c5a9e30ef42c3ddfa8ca227daa2a38224bc2a82ed9054ed9d42ac8c85ea8e82c2629c8b62bef84 Homepage: https://cran.r-project.org/package=optconerrf Description: CRAN Package 'optconerrf' (Optimal Monotone Conditional Error Functions) Design and analysis of confirmatory adaptive clinical trials using the optimal conditional error framework according to Brannath and Bauer (2004) . An extension to the optimal conditional error function using interim estimates as described in Brannath and Dreher (2024) and functions to ensure that the resulting conditional error function is non-increasing are also available. Package: r-cran-optdesignslopeint Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-optdesignslopeint_1.1.1-1.ca2604.1_all.deb Size: 50666 MD5sum: aa4fbe52fff1c5c23c4b39c9c9e466b6 SHA1: 83462bed1e04f5baae671807229a5636add55148 SHA256: 594a14a72a732285fcadd4fef6edff76d6c32a272bc53c6950bf00191362a9f6 SHA512: 0016df22005fb13d096ec3492d2d49a8c93a266f91d230dd9fb0f373fb01e174bd9eea14868d44930a32c949a177cf27fdafa9d99f0ae0d8892d6334f92f6752 Homepage: https://cran.r-project.org/package=optDesignSlopeInt Description: CRAN Package 'optDesignSlopeInt' (Optimal Designs for Estimating the Slope Divided by theIntercept) Aids practitioners to optimally design experiments that measure the slope divided by the intercept and provides confidence intervals for the ratio. Package: r-cran-optecd Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-optecd_1.0.0-1.ca2604.1_all.deb Size: 13946 MD5sum: a9121afdc3a4ce324c9084df51961fa1 SHA1: 54ec376287588ecaae4b50c372d561f226d89261 SHA256: c19648e06eb2e9ff1654ae2961d78f3e7a6f6559fceef34ead3515c8dca9f312 SHA512: 76ef5a09686303fed30cf2ba48ffcee638fd5a0eae919f3afdb3c8ee097387ead175d3516884399155959524ec885e05525efebd988ac4cf55ec64cc82bb228b Homepage: https://cran.r-project.org/package=OPTeCD Description: CRAN Package 'OPTeCD' (Optimal Partial Tetra-Allele Cross Designs) Tetra-allele cross often referred as four-way cross or double cross or four-line cross are those type of mating designs in which every cross is obtained by mating amongst four inbred lines. A tetra-allele cross can be obtained by crossing the resultant of two unrelated diallel crosses. A common triallel cross involving four inbred lines A, B, C and D can be symbolically represented as (A X B) X (C X D) or (A, B, C, D) or (A B C D) etc. Tetra-allele cross can be broadly categorized as Complete Tetra-allele Cross (CTaC) and Partial Tetra-allele Crosses (PTaC). Rawlings and Cockerham (1962) firstly introduced and gave the method of analysis for tetra-allele cross hybrids using the analysis method of single cross hybrids under the assumption of no linkage. The set of all possible four-way mating between several genotypes (individuals, clones, homozygous lines, etc.) leads to a CTaC. If there are N number of inbred lines involved in a CTaC, the the total number of crosses, T = N*(N-1)*(N-2)*(N-3)/8. When more number of lines are to be considered, the total number of crosses in CTaC also increases. Thus, it is almost impossible for the investigator to carry out the experimentation with limited available resource material. This situation lies in taking a fraction of CTaC with certain underlying properties, known as PTaC. Package: r-cran-optedr Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-crayon, r-cran-cli, r-cran-dplyr, r-cran-nleqslv, r-cran-shiny Suggests: r-cran-testthat, r-cran-mockery, r-cran-markdown, r-cran-dt, r-cran-shinydashboard, r-cran-shinyalert, r-cran-plotly, r-cran-shinyjs, r-cran-orthopolynom, r-cran-magrittr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-optedr_2.2.0-1.ca2604.1_all.deb Size: 627580 MD5sum: 82d6dcdfa7d7776dc1f8b519a96deff2 SHA1: 89bdf78ca4fcfecf97ae89c5db0f10596e5a962b SHA256: 6c38d18a62f601d9d1379a7ee31fd8eee0deb7b4cb6c279f1e00c587024ea837 SHA512: b161bce234111d6c4758ab6cdd7d85d45654d6e2d0477de649ecf6f304ed046a9ab95458bc33f4c428efe5190b3a1c38ec70f0b7a06426328c7c3117f4c1ea23 Homepage: https://cran.r-project.org/package=optedr Description: CRAN Package 'optedr' (Calculating Optimal and D-Augmented Designs) Calculates D-, Ds-, A-, I- and L-optimal designs for non-linear models, via an implementation of the cocktail algorithm (Yu, 2011, ). Compares designs via their efficiency, and augments any design with a controlled efficiency. An efficient rounding function has been provided to transform approximate designs to exact designs. Package: r-cran-optextras Architecture: all Version: 2019-12.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-optextras_2019-12.4-1.ca2604.1_all.deb Size: 119474 MD5sum: 72ee2facf57546908084aea9b51c926d SHA1: baec9047c790ccb776bd7f34ab470f9cf14d51c4 SHA256: 0b3ca6bf0d4f8e6892f1c82665d69b01c109a9d5fc40ac4db662d29250f4ca36 SHA512: a00fea594eb7699414c01ca54930aa33436f1b83886d3402ee322724ce55d2b00d298d714bba1f8ba99f4b0d0e44c095c6d15d0d13467a03e1ed4d99a6d43489 Homepage: https://cran.r-project.org/package=optextras Description: CRAN Package 'optextras' (Tools to Support Optimization Possibly with Bounds and Masks) Tools to assist in safely applying user generated objective and derivative function to optimization programs. These are primarily function minimization methods with at most bounds and masks on the parameters. Provides a way to check the basic computation of objective functions that the user provides, along with proposed gradient and Hessian functions, as well as to wrap such functions to avoid failures when inadmissible parameters are provided. Check bounds and masks. Check scaling or optimality conditions. Perform an axial search to seek lower points on the objective function surface. Includes forward, central and backward gradient approximation codes. Package: r-cran-optholdoutsize Architecture: all Version: 0.1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3614 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-mnormt, r-cran-mvtnorm, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-optholdoutsize_0.1.0.2-1.ca2604.1_all.deb Size: 3330282 MD5sum: 24c359cb93df26e5103df23eacd8d607 SHA1: 83669d888293be0fafbe0f0766c418632daa2ab9 SHA256: 218b10e2acd4dcd9fd3ef3c5ec091ca8a45667846788aad3d14183fe0bc98dd7 SHA512: cfa76c49c114e8e88bc9e1516e3b0b037b7d8bf4140add65164f97f682d0ff23053c987beb820e2a15744cfe119e390ec15996bf149eb4e0db30d820a9b15cbb Homepage: https://cran.r-project.org/package=OptHoldoutSize Description: CRAN Package 'OptHoldoutSize' (Estimation of Optimal Size for a Holdout Set for Updating aPredictive Score) Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) (in Annals of Applied Statistics) for details of methods. Package: r-cran-optic Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-did, r-cran-dplyr, r-cran-future.apply, r-cran-lmtest, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-sandwich, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-optic_1.0.1-1.ca2604.1_all.deb Size: 249450 MD5sum: 873e70d98d4ed50dbcfa9f618c0d6247 SHA1: 1de7987bfb43d6a0a05bf618f2e34cfa2d3b9d92 SHA256: 58b7b4d945acdc71fba84fb1de8d45fb8d857e44d972808136b9ddf7a2a08cd7 SHA512: f8286a1ec89520da7cbc5dfa5873c99159c956fa836db7dae2d76d686acb2377ebee8e74a6ebadfcafd60e0a24775e53228d195361c4366c18926f99e8a336e5 Homepage: https://cran.r-project.org/package=optic Description: CRAN Package 'optic' (Simulation Tool for Causal Inference Using Longitudinal Data) Implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) and Griffin et al. (2022) for a description of our methods. Package: r-cran-optical Architecture: all Version: 1.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-optical_1.7.1-1.ca2604.1_all.deb Size: 469134 MD5sum: 7d4f91a3ba2fd1c14a8acec23648ca3a SHA1: 68b6e138867584fefaea8eeb988e9252c56f6c3f SHA256: 53787f591767c7eb5e87465f2f007b993997cc34a7f362fa8ea7fa43daa35afd SHA512: 57b8c6e45f4cb800ef38f59f3ed6eeac920d323b1c9cd69cd945029fabbe550b58df03f1c23318a0472ea0f57a718451b45b1dc3b8d8e01df384fa763e9c9179 Homepage: https://cran.r-project.org/package=optical Description: CRAN Package 'optical' (Optimal Item Calibration) The restricted optimal design method is implemented to optimally allocate a set of items that require calibration to a group of examinees. The optimization process is based on the method described in detail by Ul Hassan and Miller in their works published in (2019) and (2021) . To use the method, preliminary item characteristics must be provided as input. These characteristics can either be expert guesses or based on previous calibration with a small number of examinees. The item characteristics should be described in the form of parameters for an Item Response Theory (IRT) model. These models can include the Rasch model, the 2-parameter logistic model, the 3-parameter logistic model, or a mixture of these models. The output consists of a set of rules for each item that determine which examinees should be assigned to each item. The efficiency or gain achieved through the optimal design is quantified by comparing it to a random allocation. This comparison allows for an assessment of how much improvement or advantage is gained by using the optimal design approach. This work was supported by the Swedish Research Council (Vetenskapsrådet) Grant 2019-02706. Package: r-cran-opticskxi Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2904 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-rlang Suggests: r-cran-amap, r-cran-dbscan, r-cran-cowplot, r-cran-fastica, r-cran-fpc, r-cran-ggrepel, r-cran-gtable, r-cran-knitr, r-cran-plyr, r-cran-reshape2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-opticskxi_1.2.1-1.ca2604.1_all.deb Size: 2609382 MD5sum: c8450ba67518751d1d5b0aa836e6da51 SHA1: 3c46282fd3a14fac709875a52e3ae1330c971e38 SHA256: 9426507a02e55671aa9765623e0949f03aec0c45ae4f73229aa5fb56068f5bd4 SHA512: 3504011b0972c258485575c6f9fe191ab6a58de74b19fb5d3909a4a26d008a0050675eac3fc4d191480f57be410660e3fa0265da0cc742b64e7c39a6cf626d2f Homepage: https://cran.r-project.org/package=opticskxi Description: CRAN Package 'opticskxi' (OPTICS K-Xi Density-Based Clustering) Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) . A short video tutorial can be found at . Package: r-cran-opticut Architecture: all Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-mass, r-cran-pscl, r-cran-betareg, r-cran-resourceselection, r-cran-mefa4 Filename: pool/dists/resolute/main/r-cran-opticut_0.1-4-1.ca2604.1_all.deb Size: 403212 MD5sum: 41bc86a717f9e865f5030c5b89d9b802 SHA1: 214f2dfc38a6b7ef55b9099235cc9c0430aa3f58 SHA256: f1b2235f9017a7426b607e18d9a58743d2328fe260efcc4f8d1a354dee9cda66 SHA512: 6936d8e20ad334bb8e1d5a7d669454a4310d60abb9dce17fc379932a6a24cd7ab60d2d4dcf7b842f48c3e5427dee7748b15e4d4e31fe15fbc974f2ac9a4ea717 Homepage: https://cran.r-project.org/package=opticut Description: CRAN Package 'opticut' (Likelihood Based Optimal Partitioning and Indicator SpeciesAnalysis) Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) . The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations. Package: r-cran-optifunset Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-optifunset_1.0-1.ca2604.1_all.deb Size: 11180 MD5sum: d06eceda8785e8b9e0f534b92e60744d SHA1: 39503b31d86247dfbe96548ee207cf72380bffe4 SHA256: 3c6d65a499bf8ab896aef5cc8eb509a8bd62969b152c22a760ae8ef05f1f355e SHA512: bee97d81e36e13181d09e7e6c5651120b4c4355d05a60e224378c4debdae4ffadb24b41e462080ec2e0b43772d8af5fa3ae43e020d539c5ddf814c60e0856ff7 Homepage: https://cran.r-project.org/package=optifunset Description: CRAN Package 'optifunset' (Set Options if Unset) A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded. Package: r-cran-optim.functions Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lhs, r-cran-randtoolbox, r-cran-stringr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-optim.functions_0.1-1.ca2604.1_all.deb Size: 24988 MD5sum: c26834d46a87edc5101bfa2eb6221333 SHA1: e3a6186b17900339aa73f19fa559efd562bb7e77 SHA256: 1cd4dc537712c63180827bd2e4cdcc1c306b0d644eae11372c7cea84b198e21f SHA512: 512d54ffa274beb5f5376b7005f3942b1fe73a9c91966c4ef91667a715240398f587c904943b9d930131ab77092a2dba1fd18adffef4304fa00377e9c317ca7d Homepage: https://cran.r-project.org/package=optim.functions Description: CRAN Package 'optim.functions' (Standard Benchmark Optimization Functions) A set of standard benchmark optimization functions for R and a common interface to sample them. Package: r-cran-optimalcutpoints Architecture: all Version: 1.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-optimalcutpoints_1.1-5-1.ca2604.1_all.deb Size: 177730 MD5sum: 2a2d2d07ab05672b21f5e5e4237ce14e SHA1: 71e2befcb86b5afef4c74c4988800c2dd6718d5b SHA256: deac33f46e53c727fd9ea9d32d4b0ed9dfe8eaaea2a4e6cd4721de42237d9796 SHA512: bc0bacb4670e250166cad0084b942a4efa7c16b59e7d1bae1efc047de6ff8da7810706188a81a5df9e5b71bbced77ed139564024bad396758ae7c38e721a0a78 Homepage: https://cran.r-project.org/package=OptimalCutpoints Description: CRAN Package 'OptimalCutpoints' (Computing Optimal Cutpoints in Diagnostic Tests) Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained. Package: r-cran-optimaldesign Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lpsolve, r-cran-matrixstats, r-cran-matrixcalc, r-cran-plyr, r-cran-quadprog, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-optimaldesign_1.0.3-1.ca2604.1_all.deb Size: 379228 MD5sum: e49c8a4846f8ddd5afb5520fcec0bbc3 SHA1: 73be292818eadaea4c4632037d3b7162b224b392 SHA256: ac3a8b31728924775faaab23fbb42e6bdecaf59567de73b8760034eba339a412 SHA512: bce13a0dbb1bc6df6da9c3b58058102c70c912e1b2caf8e5cfce6a1d2c15bb27a06f2e6b304b9722f94ae6cf011bc562c17f012ca98c8a363c8f54b795c61d6a Homepage: https://cran.r-project.org/package=OptimalDesign Description: CRAN Package 'OptimalDesign' (A Toolbox for Computing Efficient Designs of Experiments) Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at and the file gurobi_inst.txt, respectively. Package: r-cran-optimall Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4574 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-tibble Suggests: r-cran-bslib, r-cran-diagrammer, r-cran-dt, r-cran-globals, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-shiny, r-cran-shinytest, r-cran-survey, r-cran-tidyr, r-cran-testthat, r-cran-webshot Filename: pool/dists/resolute/main/r-cran-optimall_1.4.0-1.ca2604.1_all.deb Size: 3178988 MD5sum: d059eb91ad581ce06345e9000f5d46e7 SHA1: 838af50c8ec55cb745e475f35377512913fd6a05 SHA256: 87de5027d7eaf33bae36b76f79b9d973b47d30c06964ce7239a72f230ed7efa6 SHA512: bf1aec85315e7c77438f8b7922621d6c60aa5bec36f4476415b5428976cb3b7f9fab3be2726fdb675769c15fc781a2ce0e18287504b45b2dbcd08d6831494bcd Homepage: https://cran.r-project.org/package=optimall Description: CRAN Package 'optimall' (Allocate Samples Among Strata) Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) or Wright (2012) allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits. A paper on this package was published in the Journal of Statistical Software . Package: r-cran-optimalrerandexpdesigns Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-momentchi2, r-cran-greedyexperimentaldesign Filename: pool/dists/resolute/main/r-cran-optimalrerandexpdesigns_1.1-1.ca2604.1_all.deb Size: 75680 MD5sum: 254fada1372ccf938d37808e4550eeef SHA1: 385f5dcdc97eaee4641c4287d6a3ff4420630754 SHA256: 0b28a9abd55c4b9f65fc197e72e8b846823c961be36c66d2eb3ac84425f13d88 SHA512: e7f7269581cbb1588c4808a2d94bc40390cb1a6df29699aeb6c271db4424c035675f17a0b3b71c6f08cdc90b8977412e974ca9f47f938c4b21514da3dbe5dd36 Homepage: https://cran.r-project.org/package=OptimalRerandExpDesigns Description: CRAN Package 'OptimalRerandExpDesigns' (Optimal Rerandomization Experimental Designs) This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed. Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices. Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments" Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) . Package: r-cran-optimalsurrogate Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-optimalsurrogate_1.0-1.ca2604.1_all.deb Size: 42936 MD5sum: 4b6b0b5c63f1f8574d28a067ea3d29b5 SHA1: 1ffed2b52568f84cf14ebbd7d104e5b42a112d83 SHA256: 0f0396c4627c7f7fdde95eff7e4d1123f12736626ff572320fe9ea8f5ce0b342 SHA512: a311c530fe54df062dbf99025b5d05f0851b658e2903d9237d36361bc3415c16829ec3f745ddab5dd7732f86b3f57c93fd8b57dc3c92f4d8937ecffd8920887d Homepage: https://cran.r-project.org/package=OptimalSurrogate Description: CRAN Package 'OptimalSurrogate' (Model Free Approach to Quantifying Surrogacy) Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) . Package: r-cran-optimalthreshold Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ars, r-cran-rjags, r-cran-hdinterval, r-cran-mgcv, r-cran-coda Filename: pool/dists/resolute/main/r-cran-optimalthreshold_1.0-1.ca2604.1_all.deb Size: 399002 MD5sum: 7c6c6292239f9cbad7063e1da331c902 SHA1: 891db4f073e95bdc73a4ebb32ee340dbace3ba33 SHA256: 73f2f65b183ba8a55509929ac23be452d82e04f4e7147d827c54dcd212269234 SHA512: 21d04a5937f2f9f504632017fe806a2c2e70d1edead77d40b725781f181e2f89140e3c2d0385c46a894683153bd55199ae52a6cb8e558320b495c5094930cbfe Homepage: https://cran.r-project.org/package=optimalThreshold Description: CRAN Package 'optimalThreshold' (Bayesian Methods for Optimal Threshold Estimation) Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) for diagnostic markers. Package: r-cran-optimaltiming Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mstate, r-cran-survival Filename: pool/dists/resolute/main/r-cran-optimaltiming_0.1.0-1.ca2604.1_all.deb Size: 76424 MD5sum: d1a5750e2df2aecb1816a72741998dd3 SHA1: cd8a45aa2ce02bcbe4b9ea7fea043dac2211a551 SHA256: d55e580ec2152de5c9d2eb760fa7924ab7436dc34f486466d9d9ac0dbda39407 SHA512: 801f865335ef97601a609c857a6ac421e9352cc8ed77313372f8492cc7d3ab9e09d11f95c6b1d7d3a6b1ce4d3b33073b1de35debd95a5c2f83b855c2096ee38a Homepage: https://cran.r-project.org/package=OptimalTiming Description: CRAN Package 'OptimalTiming' (Optimal Timing Identification) Identify the optimal timing for new treatment initiation during multiple state disease transition, including multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) . Package: r-cran-optimbase Architecture: all Version: 1.0-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-optimbase_1.0-10-1.ca2604.1_all.deb Size: 397654 MD5sum: 132c721f83f5614d3846fef99cd896fd SHA1: 6b123637655a08e11669ebedcd1d30961af1897e SHA256: 68735e2b3147c379da27590d6ef48fb1e9a6040883d29e6715ee8b25443a5627 SHA512: b0ea8b749ec9529bca5eac769a9a42a9eb813f996235f95d97c247576f0d7b98ea365e96823a3fb288838be930bc132bb073f3f5b36c50c814d463d968870ad5 Homepage: https://cran.r-project.org/package=optimbase Description: CRAN Package 'optimbase' (R Port of the 'Scilab' Optimbase Module) Provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc... Package: r-cran-optimcheck Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-quantreg, r-cran-mclust, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-optimcheck_1.0.1-1.ca2604.1_all.deb Size: 221498 MD5sum: 6ef71753de1cd4244abaa19c86853a03 SHA1: 0e3ac30ce8a8aa4bda9c938b9e0b371faf1efa9b SHA256: 879126a6f06392d812d1d3af0ee3db7446bce30568f2396326bf0ecf084682e4 SHA512: 72159de3f592b2599fc310894c3391c5dc2127a1028f1e1e2ad0180c9be748df43fb3c9249654a34ee77af8c14b47d0948e3c4a02774955cdb9b175b4165de9a Homepage: https://cran.r-project.org/package=optimCheck Description: CRAN Package 'optimCheck' (Graphical and Numerical Checks for Mode-Finding Routines) Tools for checking that the output of an optimization algorithm is indeed at a local mode of the objective function. This is accomplished graphically by calculating all one-dimensional "projection plots" of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed. The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines. Package: r-cran-optimflex Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-optimflex_0.1.6-1.ca2604.1_all.deb Size: 137940 MD5sum: 47cfe758429d8c929d64d02a6014a883 SHA1: ac61e5dbac2229a9b2d80ae477c2f649a3d15678 SHA256: 070fbe0be4d74c0bc3b3e72581d8fd50254acf6517127fa99a53c91db3d6b54e SHA512: 101bb85a910e1f0dca8b6ea3d4271264066c07e8a240f7f571c6bbf0f011ddcb096f4a240cc59dfc105c076ccb60c63d2978a980b4a78b8c4d611a4a94b0a0cf Homepage: https://cran.r-project.org/package=optimflex Description: CRAN Package 'optimflex' (Derivative-Based Optimization with User-Defined ConvergenceCriteria) Provides a derivative-based optimization framework that allows users to combine eight convergence criteria. Unlike standard optimization functions, this package includes a built-in mechanism to verify the positive definiteness of the Hessian matrix at the point of convergence. This additional check helps prevent the solver from falsely identifying non-optimal solutions, such as saddle points, as valid minima. Package: r-cran-optimg Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ucminf Filename: pool/dists/resolute/main/r-cran-optimg_0.1.2-1.ca2604.1_all.deb Size: 32432 MD5sum: 3948b6e7e1f5919ed61510b0a63a242e SHA1: f952ff55bd167a8b7ba63c595ee93b1f27fdf550 SHA256: 490be9c8ec137ca1f20ee1a3ff930a2b574bf81310303f75390ef3e6c20eb6f9 SHA512: 09cc6091aae344f37ca2fb707ca40004771b34d5d6ec8b737ab35cb2d9105c85e90125e342545b893ab72469683068e6ff0071f4e9ef33e5afa014d3c731789e Homepage: https://cran.r-project.org/package=optimg Description: CRAN Package 'optimg' (General-Purpose Gradient-Based Optimization) Provides general purpose tools for helping users to implement steepest gradient descent methods for function optimization; for details see Ruder (2016) . Currently, the Steepest 2-Groups Gradient Descent and the Adaptive Moment Estimation (Adam) are the methods implemented. Other methods will be implemented in the future. Package: r-cran-optimizer Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-lbfgsb3c, r-cran-numderiv, r-cran-oeli, r-cran-pracma, r-cran-r6, r-cran-testfunctions, r-cran-ucminf Suggests: r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-optimizer_1.3.0-1.ca2604.1_all.deb Size: 283012 MD5sum: 8873efd50d8104d20c090591099226b4 SHA1: 3e61d8b4960838eac05618672f7ec8b6488393e0 SHA256: 839821559fd1bd372c02544d3d6457fa96554f986f09d99765c59a625656c042 SHA512: 989b3eaa8fc6a8d02806512070e7ef3bb23f7616d75b0a94ebe3c08183a3aca6265217857cddd1620c076c55cdca422dda9512780e2d7b13dc0a3b33547ee9f8 Homepage: https://cran.r-project.org/package=optimizeR Description: CRAN Package 'optimizeR' (Unified Framework for Numerical Optimizers) Provides a unified object-oriented framework for numerical optimizers in R. Supports minimization and maximization with any optimizer, optimization over more than one function argument, computation time measurement, and time limits for long optimization tasks. Package: r-cran-optimizr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-progressr, r-cran-future.apply Suggests: r-cran-testthat, r-cran-dofuture Filename: pool/dists/resolute/main/r-cran-optimizr_1.0.1-1.ca2604.1_all.deb Size: 38084 MD5sum: c73eed8c2cde504b3d25d7d3b24aa5d8 SHA1: e88664ff7f7b39a52f9caecac2a2d456911c703a SHA256: 34445d6cef683133457c296a8e0394fd14a98870ae19d98d81905327e745f1ac SHA512: 78e88e30c4d62fff9bacf9843573494cbe0a1e3bb6e6be56e048a957f44626a89dbb1d3c87ccbcc40bae9a37bd9e79cbc2646c6dfc6b6647af90b9e692cdadcb Homepage: https://cran.r-project.org/package=optimizr Description: CRAN Package 'optimizr' (Further Numerical Optimization Algorithms) A collection of numerical optimization algorithms. One is a simple implementation of the primitive grid search algorithm, the other is an extension of the simulated annealing algorithm that can take custom boundaries into account. The methodology for this bounded simulated annealing algorithm is due to Haario and Saksman (1991), . Package: r-cran-optimlanduse Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolveapi, r-cran-tidyr, r-cran-dplyr, r-cran-future, r-cran-future.apply Suggests: r-cran-readxl, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-optimlanduse_1.2.1-1.ca2604.1_all.deb Size: 140178 MD5sum: 9ac78b7a0a4749e9b7456377fd0e5d52 SHA1: 9352e1fd88506e2b90b3ddd51c68f73abea2c5d6 SHA256: 9e50381e784443f28bb570dfad2a1a285dd15ed54c763587b893d3583efefe7f SHA512: 0c1b1016d1a8b8ccb8aee5654dc5aeeda3d2f752587eda92cae8c59760bed4dcdabd3010aae1d976d1f48bbe884abe24b77107bd269a1fa2c75838fb6d9efa4b Homepage: https://cran.r-project.org/package=optimLanduse Description: CRAN Package 'optimLanduse' (Robust Land-Use Optimization) Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) . Package: r-cran-optimmodel Architecture: all Version: 2.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-optimmodel_2.0-3-1.ca2604.1_all.deb Size: 223106 MD5sum: f32549a155a9bba9d80ddcfa91c08c61 SHA1: e86b0c7a31d86627c7be30bd10b9962fd2e152a2 SHA256: 99ad0694408ee6232bc05dab5d01aaa18fa7a11fbe3f5f3c0aca9d756c796832 SHA512: 3a280f13a9c0d0b3646af25f3e62e16e27a813b61b5a98e8e9e05dda07bfb0e4a55a0b4c27a31d8929657d081eaef073ae19200bc65a807ac5a2284d183190a6 Homepage: https://cran.r-project.org/package=OptimModel Description: CRAN Package 'OptimModel' (Perform Nonlinear Regression Using 'optim' as the OptimizationEngine) A wrapper for 'optim' for nonlinear regression problems; see Nocedal J and Write S (2006, ISBN: 978-0387-30303-1). Performs ordinary least squares (OLS), iterative re-weighted least squares (IRWLS), and maximum likelihood (MLE). Also includes the robust outlier detection (ROUT) algorithm; see Motulsky, H and Brown, R (2006) . Package: r-cran-optimos.prime Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyverse, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-optimos.prime_0.1.2-1.ca2604.1_all.deb Size: 52264 MD5sum: 082a8343f2bba754b6624d7ff5e776ee SHA1: 99c6391b76b6ef7873390ce0c788946772dfcb98 SHA256: ed2c72c87c0ac20067431115f49e3077358ea670831aa03380f588b51a5438d6 SHA512: f85faa29d5700d85fe00a20d97b257058ce48eccf1ddc6648d6210d6256b6aa7f6764d7736831db6ff5ed33c95e48c9006ff75627c31c081e4db4061b7457410 Homepage: https://cran.r-project.org/package=optimos.prime Description: CRAN Package 'optimos.prime' (Optimos Prime Helps Calculate Autoecological Data for BiologicalSpecies) Calculates autoecological data (optima and tolerance ranges) of a biological species given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa have unequal occurrences. See the detailed methodology by Birks et al. (1990) , and a case example by Potapova and Charles (2003) . Package: r-cran-optimparallel Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-r.rsp, r-cran-roxygen2, r-cran-spam, r-cran-microbenchmark, r-cran-testthat, r-cran-ggplot2, r-cran-numderiv, r-cran-lbfgsb3c Filename: pool/dists/resolute/main/r-cran-optimparallel_1.0-2-1.ca2604.1_all.deb Size: 200432 MD5sum: 7191e0df78f6f33c2f645b21af48e7a6 SHA1: eb718a42f656ab5a2acfced03f4333c50bb6a164 SHA256: e2da0fddfb99c43f2b2a834b18f59a51f216af8a49540dc462f05f7c4c711f2f SHA512: 2fad53cf126bf4552d099bf21dbf8f48f4d38d42553c2a6d8b947425e1f50c5361c52fdf931ce0c83b15e8aa5e959c8cee09abd8101c1f77583551668beb7293 Homepage: https://cran.r-project.org/package=optimParallel Description: CRAN Package 'optimParallel' (Parallel Version of the L-BFGS-B Optimization Method) Provides a parallel version of the L-BFGS-B method of optim(). 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Package: r-cran-optimsimplex Architecture: all Version: 1.0-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimbase Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-optimsimplex_1.0-8-1.ca2604.1_all.deb Size: 403356 MD5sum: 35c1f97bc37e8a173b358d9125822508 SHA1: 4829cd630c507221947852d602f2ba52e4195b99 SHA256: 1a1ba6c3646ecd9a19928b246298bf32e7246f4ba1aaed8c7b2c4de224a39fe1 SHA512: 8e5ec133bf208500d7992486651c7e25e4874d6e8418b83bcf615f082094ca9e4389d7e88d7742220de05e71649ad5e04e5b62c8febfebeb3940eb49e26cdcc2 Homepage: https://cran.r-project.org/package=optimsimplex Description: CRAN Package 'optimsimplex' (R Port of the 'Scilab' Optimsimplex Module) Provides a building block for optimization algorithms based on a simplex. The 'optimsimplex' package may be used in the following optimization methods: the simplex method of Spendley et al. (1962) , the method of Nelder and Mead (1965) , Box's algorithm for constrained optimization (1965) , the multi-dimensional search by Torczon (1989) , etc... Package: r-cran-optimstrat Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-mvtnorm, r-cran-cubature Filename: pool/dists/resolute/main/r-cran-optimstrat_2.4-1.ca2604.1_all.deb Size: 88242 MD5sum: 7da76b4cdbf3c54100a1a89ea61d5939 SHA1: 03167f455a9b7eaa0542f1a408292b46eca5eb75 SHA256: 3e81419e867a49ddb63e9915f2c1a1162d5bb9197c8eb5251118a51b32c1e1d2 SHA512: 8f298ce7f48eb91615ac8c8ce3bf43afabdf886bb38246d4ec60eef35606047d4eb63f7609a8d73b101d07e3ba8ef315e9148979d2ba14d7b989d331cc90f457 Homepage: https://cran.r-project.org/package=optimStrat Description: CRAN Package 'optimStrat' (Choosing the Sample Strategy) Intended to assist in the choice of the sampling strategy to implement in a survey. Package: r-cran-optimus Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvabund, r-cran-ordinal Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-optimus_0.2.0-1.ca2604.1_all.deb Size: 110370 MD5sum: 84d49086529be9537e5e5133851ef17b SHA1: a51f951865d876a2249d2cfc964aa5dd452ab38f SHA256: 72cc4ebc2184133600678a894aa0f8d373cc0b8e5f1bd6fd266ebeca7f4b8a42 SHA512: f7115cdd9745893e13fde4907eb271bfc9d7c336754a2467ca30f1339c204008f4463363cbc989fb1542515faa0ee98fb074aca8c9780650688372b902a70bf4 Homepage: https://cran.r-project.org/package=optimus Description: CRAN Package 'optimus' (Model Based Diagnostics for Multivariate Cluster Analysis) Assessment and diagnostics for comparing competing clustering solutions, using predictive models. 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Package: r-cran-optisolve Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-shapes, r-cran-alabama, r-cran-cccp, r-cran-nloptr, r-cran-mass, r-cran-plyr, r-cran-stringr, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-optisolve_1.0-1.ca2604.1_all.deb Size: 798582 MD5sum: e907575e4cc832592c93aa0ea056b431 SHA1: 54f28481a9155c01a7bac86765f3aae41453b561 SHA256: 1e42df46b83c0a72354654a1eb0c2e366abc84a8f1c55d334e1866da63cc6d2c SHA512: 260e63e54776345f6e474bd6986bb10207d442a5c6efdd7c0798a8b0b972f5ee04d9d7786c13524c07d79b07f9585e03caf65badd866371cd9be7d682a2af911 Homepage: https://cran.r-project.org/package=optiSolve Description: CRAN Package 'optiSolve' (Linear, Quadratic, and Rational Optimization) Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. 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Package: r-cran-optistock Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse, r-cran-shiny, r-cran-shinydashboard Filename: pool/dists/resolute/main/r-cran-optistock_0.0.2-1.ca2604.1_all.deb Size: 653430 MD5sum: dfac5c5364d41b878c5d39115a478392 SHA1: eed9c5921d9ac1fbda1e61e36f262e14cfdde4a2 SHA256: 667b3fe9727ff4859b44187829b1f75b232a47f135eca6ddb7ae1a4f4fdc063c SHA512: 917ab39866255c5b12fb36442bb274c936f07cfa97b5f21f457334eeef49b5b61a50965b641a5055f694dba3959a8fb942716e2f68d34941b9d0d7f2c151c160 Homepage: https://cran.r-project.org/package=optistock Description: CRAN Package 'optistock' (Determine Optimum Stocking Times Used in Fishery Enhancements) A collection of functions that aid in calculating the optimum time to stock hatchery reared fish into a body of water given the growth, mortality and cost of raising a particular number of individuals to a certain length. 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Methods build on classical and modern rank tests and two-stage/Group-Sequential designs, e.g., Park (2025) . Please see the package reference manual and vignettes for details. 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Package: r-cran-orbital Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-rlang Suggests: r-cran-arrow, r-cran-bonsai, r-cran-dbi, r-cran-dbplyr, r-cran-dtplyr, r-cran-duckdb, r-cran-earth, r-cran-embed, r-cran-glmnet, r-cran-glue, r-cran-gt, r-cran-hardhat, r-cran-jsonlite, r-cran-kknn, r-cran-knitr, r-cran-lightgbm, r-cran-modeldata, r-cran-parsnip, r-cran-partykit, r-cran-probably, r-cran-r6, r-cran-randomforest, r-cran-ranger, r-cran-recipes, r-cran-rmarkdown, r-cran-rpart, r-cran-rsqlite, r-cran-rstanarm, r-cran-sparklyr, r-cran-splines2, r-cran-tailor, r-cran-testthat, r-cran-themis, r-cran-tibble, r-cran-tidypredict, r-cran-workflows, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-orbital_0.5.1-1.ca2604.1_all.deb Size: 233366 MD5sum: 6136262aa06471d42b9a6915e9794372 SHA1: 6fb7306af2290e57730fb1daa47edabc44845fa1 SHA256: 3aeb74b925dd4183471833c23bd33c6011537eb41f9924240c9b77e8c3080840 SHA512: e63dddead7926e909a27c1a372dd0a105b72f1ef25f14dcd69419200ed199c8ca91ac07ef5f925b7317ba6569aa8a9f5a2861f28e4493a08a8e6050222862b39 Homepage: https://cran.r-project.org/package=orbital Description: CRAN Package 'orbital' (Predict with 'tidymodels' Workflows in Databases) Turn 'tidymodels' workflows into objects containing the sufficient sequential equations to perform predictions. These smaller objects allow for low dependency prediction locally or directly in databases. Package: r-cran-orcamentobr Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-orcamentobr_1.0.5-1.ca2604.1_all.deb Size: 60942 MD5sum: 77dc299af9c2821bfe74fe597ffc5caf SHA1: db83ad703f608245e7dab6ab3b6410a59d489cd3 SHA256: eead84650bec732f9cfd0494b1dfed2a8ef0daabcaf7dce5f8b2c92baf6c78fb SHA512: ffdd39179087b11614131b5f0859755b76f8058fa017b92fdcefe4614fe43a64cd2cbeece6d4b4ed87234c47deb648262bd211962f81db4c72853cc7e795a12c Homepage: https://cran.r-project.org/package=orcamentoBR Description: CRAN Package 'orcamentoBR' (Download Official Data on Brazil's Federal Budget) Allows users to download and analyze official data on Brazil's federal budget through the 'SPARQL' endpoint provided by the Integrated Budget and Planning System ('SIOP'). This package enables access to detailed information on budget allocations and expenditures of the federal government, making it easier to analyze and visualize these data. Technical information on the Brazilian federal budget is available (Portuguese only) at . The 'SIOP' endpoint is available at . Package: r-cran-orcidtr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-orcidtr_0.1.0-1.ca2604.1_all.deb Size: 182048 MD5sum: 161b2a7626e8f5f229c7824c41e86c0c SHA1: e8daef8fd3432588529bc0f9fdf0bdc7305adcb7 SHA256: ee5b0b006f5ebe963a5669ac2baccfc3e223423f3eb929db75cebcc54f5e8d31 SHA512: e70a23fa5f77d3e1c61582a3bd09d1f16891c4d9ed1bff41756e456dd3cd02d362d56533e30b99c2e8d9765bcd3d2a1407f4a2e7a6634b5ee5882cc8e5d874a8 Homepage: https://cran.r-project.org/package=orcidtr Description: CRAN Package 'orcidtr' (Retrieve Data from the ORCID Public API) Provides functions to retrieve public data from ORCID (Open Researcher and Contributor ID) records via the ORCID public API. Fetches employment history, education, works (publications, datasets, preprints), funding, peer review activities, and other public information. Returns data as structured data.table objects for easy analysis and manipulation. Replaces the discontinued 'rorcid' package with a modern, CRAN-compliant implementation. Package: r-cran-orclus Architecture: all Version: 0.2-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-orclus_0.2-6-1.ca2604.1_all.deb Size: 61170 MD5sum: 73d1bc414f2e6fed6fca471a089c2e69 SHA1: ca6218b813f780e6f5f79a0256e26c0525098352 SHA256: 7f7cf1be77e1f1cd21c04d197cd91838fae55a9d1e47884423c8aa59c6d6615f SHA512: 58ddbf64283553e785f52087905cb58709922d002591865f510249554528edfdb3646a0cc12aaa5505fecc284a2b5c6d65724930034a510acb4b0ad324b58f5a Homepage: https://cran.r-project.org/package=orclus Description: CRAN Package 'orclus' (Subspace Clustering Based on Arbitrarily Oriented ProjectedCluster Generation) Functions to perform subspace clustering and classification. Package: r-cran-orcme Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso Filename: pool/dists/resolute/main/r-cran-orcme_2.0.2-1.ca2604.1_all.deb Size: 129862 MD5sum: 53c28745b7164f3aa096a41681c378bb SHA1: 1b5bdbf1c2bc5aa2bc4e477b844324fc1cbdfee9 SHA256: 4798fbb06097e8c0cdb014eb77f0bf3795019d85852284f5cac02fbad47047d6 SHA512: 6a12122cb3050b235c6f016fe2e85d3296298bbed86d0b396d221827654fd8a87808b555562ab2cc4a065602f3d9ce0ba503a7c1ee540b3172872f6b97f21e90 Homepage: https://cran.r-project.org/package=ORCME Description: CRAN Package 'ORCME' (Order Restricted Clustering for Microarray Experiments) Provides clustering of genes with similar dose response (or time course) profiles. It implements the method described by Lin et al. (2012). Package: r-cran-ordbetareg Architecture: all Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7329 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-scales, r-cran-stringr, r-cran-abind, r-cran-checkmate, r-cran-insight, r-cran-rstantools Suggests: r-cran-rmarkdown, r-cran-quarto, r-cran-knitr, r-cran-gt, r-cran-modelsummary, r-cran-marginaleffects, r-cran-haven, r-cran-hmisc, r-cran-collapse, r-cran-ggthemes, r-cran-glmmtmb, r-cran-mice, r-cran-bayestestr, r-cran-gganimate, r-cran-transformr, r-cran-declaredesign Filename: pool/dists/resolute/main/r-cran-ordbetareg_0.8-1.ca2604.1_all.deb Size: 5574216 MD5sum: 5396095185494e35ba50252f61bac0ac SHA1: 0b0a4a213afd7ab76571e6b71b3555310472dc13 SHA256: 37d55ca1642743a963675f46b0df7235fdb438fe699a0fd5701b4912ab709f99 SHA512: 2f2cd1715b357b19eed2c77440f207fd5b8e183af90d42aef54d7b8843ce71db4d87274247d35eca0b839cc8781494d7151281a40fe889caa3547b5219432c8b Homepage: https://cran.r-project.org/package=ordbetareg Description: CRAN Package 'ordbetareg' (Ordered Beta Regression Models with 'brms') Implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as survey sliders, dose-response relationships and indexes. For more information, see Kubinec (2023) . The package is a front-end to the R package 'brms', which facilitates a range of regression specifications, including hierarchical, dynamic and multivariate modeling. Package: r-cran-ordcd Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-grbase, r-cran-mass, r-cran-bnlearn, r-cran-igraph, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-ordcd_1.1.2-1.ca2604.1_all.deb Size: 47078 MD5sum: 716e2c107614fdac3b43811e86b5e795 SHA1: fbec0ffddeb042b2a4c1815d484ec7522bf677ec SHA256: 583e069bf7a1482462f20468c13b836e9ac75b7867ac7c3342d32e581cec0ee6 SHA512: cc94d4e2f4fc3b7575eeed1ab55afd14962f312fd92b5ec124ee891e492c52d53fd44d4af64b92f3ae7e6e8bc98256ae44de6c834f7a720aabe6909a8938775e Homepage: https://cran.r-project.org/package=OrdCD Description: CRAN Package 'OrdCD' (Ordinal Causal Discovery) Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540". Package: r-cran-ordcrm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms Filename: pool/dists/resolute/main/r-cran-ordcrm_1.0.0-1.ca2604.1_all.deb Size: 154980 MD5sum: 1962a5377cb4b0cbfff13eab7a3c0f40 SHA1: 5c5d91180570a0907676e70d977ff3b61b5d7d25 SHA256: 1b23d22177e36737ee4c488831833bef9b1f13f4be428b52f38a03baa99d669f SHA512: a2ed297e6e3ed5bcbff39ba247e77734a266780913ec48cc756e54b72647acaa730d8ac816c4750197485c02d6d8ffdd36bf9ab55c81a7f2bfab4bad038bf359 Homepage: https://cran.r-project.org/package=ordcrm Description: CRAN Package 'ordcrm' (Likelihood-Based Continual Reassessment Method (CRM) DoseFinding Designs) Provides the setup and calculations needed to run a likelihood-based continual reassessment method (CRM) dose finding trial and performs simulations to assess design performance under various scenarios. 3 dose finding designs are included in this package: ordinal proportional odds model (POM) CRM, ordinal continuation ratio (CR) model CRM, and the binary 2-parameter logistic model CRM. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous dose levels, combining ordinal grades 0 and 1 into one category, and incorporate safety and/or stopping rules. For POM and CR model designs, ordinal toxicity grades are specified by common terminology criteria for adverse events (CTCAE) version 4.0. Function 'pseudodata' creates the necessary starting models for these 3 designs, and function 'nextdose' estimates the next dose to test in a cohort of patients for a target DLT rate. We also provide the function 'crmsimulations' to assess the performance of these 3 dose finding designs under various scenarios. Package: r-cran-orddisp Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-orddisp_2.1.2-1.ca2604.1_all.deb Size: 82594 MD5sum: 50abb83438b24dc7f857526d660a3e15 SHA1: b0b6f00bd4562174880c6de362b3c6df9f05b488 SHA256: e60d9d57d695a7c31bcc82e5eee2f1b7f65a277d2ba3d30b0b16255337877cae SHA512: f1cbbcba15816b3602654a9198d4751e449100926ebe9aabc1c647d084a199bddc44d5df1a2565ad9cb5a5957d25a3fc4afba26d3381609b4f17a180238730ae Homepage: https://cran.r-project.org/package=ordDisp Description: CRAN Package 'ordDisp' (Separating Location and Dispersion in Ordinal Regression Models) Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses. Package: r-cran-orderanalyzer Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyselect, r-cran-data.table, r-cran-dplyr, r-cran-matrixcalc, r-cran-quanteda, r-cran-rlist, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-digest, r-cran-lubridate Suggests: r-cran-pdftools, r-cran-tesseract, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-orderanalyzer_1.0.1-1.ca2604.1_all.deb Size: 366442 MD5sum: 27542eee6f816ac625cc1f79b130e2a5 SHA1: 20ac7b638a6a64fe3d8af03b78afaf1d5210b4c3 SHA256: 630e731cc6cde1975b71c267f105ca54d71d16af24182885a0d53623097b53c0 SHA512: 39a3af4fa7498fb389edf7426f95017a6674dad4417d85cbff0f05493e0075c86af777cc1de06c0deddea5454016e2c13e127e760b5d8ebc03542ac244bec633 Homepage: https://cran.r-project.org/package=orderanalyzer Description: CRAN Package 'orderanalyzer' (Extracting Order Position Tables from PDF-Based Order Documents) Functions for extracting text and tables from PDF-based order documents. It provides an n-gram-based approach for identifying the language of an order document. It furthermore uses R-package 'pdftools' to extract the text from an order document. In the case that the PDF document is only including an image (because it is scanned document), R package 'tesseract' is used for OCR. Furthermore, the package provides functionality for identifying and extracting order position tables in order documents based on a clustering approach. Package: r-cran-ordered Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parsnip, r-cran-cli, r-cran-dials, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-mass, r-cran-ordinalnet, r-cran-vgam, r-cran-rpartscore, r-cran-ordinalforest, r-cran-qsardata, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ordered_0.1.0-1.ca2604.1_all.deb Size: 97162 MD5sum: dd83258e3ef7090b72b6b996f57d2c86 SHA1: 51222f970ccd1592afe9679821f50a927780957b SHA256: 7b778ef50ccdf189d75b9db5fa38bd7d07364ef25b9701b5e90a9e8da4e580a0 SHA512: be553bebaf9b2840fa098701cf1783761d63cd7b0c8adfcfe5f81693cb0e4baae8db7a720b03b17dbfdb435e7c83c10629c50cc58ec9256a2bcd4d82ea7b43a5 Homepage: https://cran.r-project.org/package=ordered Description: CRAN Package 'ordered' ('parsnip' Engines and Wrappers for Ordinal Classification Models) Bindings, methods, and tuners for using ordinal classification models with the 'parsnip' and 'dials' packages. These include the regularized elastic net ordinal regression of Wurm, Hanlon, and Rathouz (2021) in 'ordinalNet', the ordinal classification trees of Galimberti, Soffritti, and Di Maso (2012) in 'rpartScore', and the latent variable ordinal forests of Hornung (2020) in 'ordinalForest'. Package: r-cran-ordering Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-na.tools Filename: pool/dists/resolute/main/r-cran-ordering_0.7.0-1.ca2604.1_all.deb Size: 22460 MD5sum: 06de736ddcb12ad7cc3ad9c93e15e89d SHA1: a3565146a723c23da2f7716c72edc36409a3bf18 SHA256: 91e2a1fce11fc860118f1d95094563d5079cc872ed25a92ec1abd8f61110c81c SHA512: 6b6d5f6dbb0c1404a3198db1fbc563718963adca4403529578c1998dd199da11861b70662b8261259cdd0bd1e8a8ca446056c9d07c28b247fdec3b4201a4ba81 Homepage: https://cran.r-project.org/package=ordering Description: CRAN Package 'ordering' (Test, Check, Verify, Investigate the Monotonic Properties ofVectors) Functions to test/check/verify/investigate the ordering of vectors. The 'is_[strictly_]*' family of functions test vectors for 'sorted', 'monotonic', 'increasing', 'decreasing' order; 'is_constant' and 'is_incremental' test for the degree of ordering. `ordering` provides a numeric indication of ordering -2 (strictly decreasing) to 2 (strictly increasing). Package: r-cran-orderly Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1381 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cli, r-cran-diffobj, r-cran-fs, r-cran-gert, r-cran-httr2, r-cran-jsonlite, r-cran-openssl, r-cran-pkgload, r-cran-rlang, r-cran-rstudioapi, r-cran-vctrs, r-cran-withr, r-cran-yaml Suggests: r-cran-dbi, r-cran-rsqlite, r-cran-callr, r-cran-jsonvalidate, r-cran-knitr, r-cran-mockery, r-cran-processx, r-cran-rmarkdown, r-cran-testthat, r-cran-webfakes Filename: pool/dists/resolute/main/r-cran-orderly_2.0.3-1.ca2604.1_all.deb Size: 884918 MD5sum: a24c9022eaafeb49f3e10a6463f7a210 SHA1: d4322e0f27bf84cbd228bbbb0cb2425cca48bd15 SHA256: cdb180846a5eae52077d8eade91f2c44290a49045f8cf3b50f0afb9b31637d33 SHA512: b9f34363209b9f93aac1becfd92d83e717e0fe92b0184bf5b01e39ccbb75a1cb7f8738d33c0ae61195dbb4217a20173e720144321d4bace847e4fb68dc07c60f Homepage: https://cran.r-project.org/package=orderly Description: CRAN Package 'orderly' (Lightweight Reproducible Reporting) Distributed reproducible computing framework, adopting ideas from git, docker and other software. By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans. Package: r-cran-orders Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-newdistns, r-cran-gamlss.dist, r-cran-actuar, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-orders_0.1.8-1.ca2604.1_all.deb Size: 129802 MD5sum: 4a4243d2a075f73c3acbcc4bc7039511 SHA1: 624dd4f262e39af69866ed317cd002e92d4db70e SHA256: 839edd8f8d981ba8206a489af4b69d24a17da7291e33ccf196cb5cc3cde462d7 SHA512: 8dea53e72fe152d71f9aaa5af51a3b7b218baafa559be324822ab88c185cf862b065c4412c8bfb1849abeea4de3aa260a89a0d2baa7a3040e0aa69189847eb6f Homepage: https://cran.r-project.org/package=orders Description: CRAN Package 'orders' (Sampling from k-th Order Statistics of New Families ofDistributions) Set of tools to generate samples of k-th order statistics and others quantities of interest from new families of distributions. The main references for this package are: C. Kleiber and S. Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag; Naradajah, S. and Rocha, R. (2016), and Stasinopoulos, M. and Rigby, R. (2015), . The families of distributions are: Benini distributions, Burr distributions, Dagum distributions, Feller-Pareto distributions, Generalized Pareto distributions, Inverse Pareto distributions, The Inverse Paralogistic distributions, Marshall-Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distributions, Skew normal type 1 distributions, Skew student t distributions, Singh-Maddala distributions, Sinh-Arcsinh distributions, Sichel distributions, Zero inflated Poisson distributions. Package: r-cran-orderstats Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-orderstats_0.1.0-1.ca2604.1_all.deb Size: 23636 MD5sum: 26f6353a2db6b034c2f57038cbc8a8b3 SHA1: 0a58a13398ce37a57fce07042f314473f4227e86 SHA256: df5c65678f176b17a0eece202ec2b41444e9138247b9ea38dc07b04e33a429f8 SHA512: 649f6a225efa6e5e7b8aa5119b31c963a2aedf0cbdce5b2ff23331b8c6b4b97f970fa99a59477dcb239312cbe038e7fda710b993d53d3668b18a51bca6be6ad0 Homepage: https://cran.r-project.org/package=orderstats Description: CRAN Package 'orderstats' (Efficiently Generates Random Order Statistic Variables) All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution. Package: r-cran-ordfacreg Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-eha, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ordfacreg_1.0.8-1.ca2604.1_all.deb Size: 74256 MD5sum: 0fed7337c6615e60a6ffe90b3a32a3b2 SHA1: 561267c892ac3248d1d6b1d884200cedbd134158 SHA256: 78c2338006ef35ddb13acb42bddb5872ca1282bc98d5bdb599290b4c86566cd8 SHA512: 3ed66dd63b1f9f36200c153820cbd7237608e523dc5d3c249429d7e57731e22aa570934f70ec710976f4bef5161a39800b28cdd3f518c6d3cd64937045cfea88 Homepage: https://cran.r-project.org/package=OrdFacReg Description: CRAN Package 'OrdFacReg' (Least Squares, Logistic, and Cox-Regression with OrderedPredictors) In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators. Package: r-cran-ordgam Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubicbsplines, r-cran-matrix, r-cran-mgcv, r-cran-marqlevalg, r-cran-sn, r-cran-mass, r-cran-numderiv, r-cran-ucminf Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ordgam_0.9.1-1.ca2604.1_all.deb Size: 1333798 MD5sum: 387800e5648efc25d4ea1ccc343dcb12 SHA1: c89ebe41da188361a5b5049050744d2d4d7d4c9f SHA256: 41bbf585973459b9b86f5a7f47b449dddd7795775649436bfc0950bd551f3ca9 SHA512: 0dd8ab88758b7760b5125549e67ab52d6537f2ff20d84104fddf46dc16b44e16c98ca28b69db61c6617062fc04a9db7827a44ce91f0932c6ce87ac8cf5d61660 Homepage: https://cran.r-project.org/package=ordgam Description: CRAN Package 'ordgam' (Additive Model for Ordinal Data using Laplace P-Splines) Additive proportional odds model for ordinal data using Laplace P-splines. The combination of Laplace approximations and P-splines enable fast and flexible inference in a Bayesian framework. Specific approximations are proposed to account for the asymmetry in the marginal posterior distributions of non-penalized parameters. For more details, see Lambert and Gressani (2023) ; Preprint: ). Package: r-cran-ordibreadth Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-ordibreadth_1.0-1.ca2604.1_all.deb Size: 73252 MD5sum: 7364e4f876a20ef6963800d9b77f4208 SHA1: 949aea16ad0e29c806f210382e330fefd39f5552 SHA256: fe3fd10cf8a19d8651bc34b72ef19078add18be2bb020298605f01861d4e68cd SHA512: 3cdfb4f21f6fcc3c98c490e71135defcdffe959db627cca2faafe19a050cbf96490642a908d2e57db26dea4238598d2da37da5a7b9ae8a718e9ffe2b305a6925 Homepage: https://cran.r-project.org/package=ordiBreadth Description: CRAN Package 'ordiBreadth' (Ordinated Diet Breadth) Calculates ordinated diet breadth with some plotting functions. Package: r-cran-ordinalbayes Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-deseq2, r-bioc-summarizedexperiment, r-cran-coda, r-cran-dclone, r-cran-runjags Suggests: r-cran-knitr, r-bioc-biobase, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ordinalbayes_0.1.2-1.ca2604.1_all.deb Size: 4323228 MD5sum: 1e1b0af3abc8a0e9a3b1635827b0a207 SHA1: 5edc950278520748a81ff2762dc77b5d382eab16 SHA256: f9259da09ffb71f80ef7280d6de1e31d5723db411fcb323b05e26796fc530d9b SHA512: c78894e8b91564e27705ba17fce229bc2765315bb81bdb63a39aa36a35685397f8d6d44224c5d60af5086778cf3b62b27818970b6fc2c7133f9ab3f4d7d2df79 Homepage: https://cran.r-project.org/package=ordinalbayes Description: CRAN Package 'ordinalbayes' (Bayesian Ordinal Regression for High-Dimensional Data) Provides a function for fitting various penalized Bayesian cumulative link ordinal response models when the number of parameters exceeds the sample size. These models have been described in Zhang and Archer (2021) . Package: r-cran-ordinalcont Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-deriv Filename: pool/dists/resolute/main/r-cran-ordinalcont_2.0.2-1.ca2604.1_all.deb Size: 219376 MD5sum: 9f2115517115c40bf6e6cb448952c312 SHA1: a09aff8cde60096576d8e9696c0f2ea52640ad55 SHA256: 1890adbe1f9618797b239edc5be67914cb5afa77e82fa5e065c33b0b6c879574 SHA512: 34ade70f923b439be7f7cc018be6f0b4862f16eb44c3a87aeb9141eaf2c397ac19992b57b0edeaa6af42c93c068f95b4f800a4227be638d52f450c2c3beb16b3 Homepage: https://cran.r-project.org/package=ordinalCont Description: CRAN Package 'ordinalCont' (Ordinal Regression Analysis for Continuous Scales) A regression framework for response variables which are continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects' perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent non-linearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function) connects the scale with an underlying continuous latent variable. The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. A variety of link functions are currently implemented. Such models are described in Manuguerra et al (2020) . Package: r-cran-ordinalgof Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mass, r-cran-vgam Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-ordinalgof_0.1.0-1.ca2604.1_all.deb Size: 23662 MD5sum: af365f5f228e5163c219b477dd6b7f93 SHA1: ebe77b474ee5d8b31f6c2340920903e94f64e0f4 SHA256: af475ba89f91ae2161483c532970329b167da2345e871048cc918055b08293f4 SHA512: e79d5137249b5b0ce811e4164e120f9d997ba6dad36eef92bf664a242afb7d3fb0607f83ffb9a288c64b61d12e4e98489b17103089bd99de6e7306c8ae0e2598 Homepage: https://cran.r-project.org/package=ordinalGOF Description: CRAN Package 'ordinalGOF' (Goodness-of-Fit Tests for Ordinal Regression Models) Provides goodness-of-fit tests for ordinal regression models, including the Fagerland-Hosmer ordinal test, reproducing same output as 'Stata'. Supports polr(), vglm(), and binary glm() models. See Fagerland and Hosmer (2013) and Fagerland and Hosmer (2017) for details. Package: r-cran-ordinallbm Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape2, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-ordinallbm_1.0-1.ca2604.1_all.deb Size: 55554 MD5sum: 8a6884018365c5bdf21d215aed30d77e SHA1: 805f65442e4eecdeb3fd8d63cb6b782d0fae0d36 SHA256: f768d3d2f699ee7c995fd3d50815b0bde4730900491a090c76ce85b4edd86c00 SHA512: 4469f319c27d397ea82a7db2bfe6d763af29e2e0afb79e5729b09ac40eaed1fcb81366b822458a993562b6512a4174b4af2f8366ad3f16f6cf0ce9a7da534813 Homepage: https://cran.r-project.org/package=ordinalLBM Description: CRAN Package 'ordinalLBM' (Co-Clustering of Ordinal Data via Latent Continuous RandomVariables) It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019). Package: r-cran-ordinalnet Architecture: all Version: 2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-mass, r-cran-glmnet, r-cran-penalized, r-cran-vgam, r-cran-rms Filename: pool/dists/resolute/main/r-cran-ordinalnet_2.14-1.ca2604.1_all.deb Size: 120520 MD5sum: 32e53ee3361e067a9f9df1a0f584840b SHA1: 4420722cc75d0fe0e9e6554a43d50365535f7451 SHA256: 3f20a132556ad8ec160c88d1ce5d19891b69b92ec0e46de0783b2363b1524b09 SHA512: 0adb2c12e168a7e7c814d49e1ab1f5f0ae363aac1d3d645c5a8493e049140642724cca79982bd8a64c1b42ddccd44c0f6c6d1216b6c9d58698d0be1740af368c Homepage: https://cran.r-project.org/package=ordinalNet Description: CRAN Package 'ordinalNet' (Penalized Ordinal Regression) Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) . Package: r-cran-ordinalrr Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-ordinalrr_1.1-1.ca2604.1_all.deb Size: 74522 MD5sum: 90542eda3de03b1fc88261c1502e160b SHA1: 12bd8f0b4c134388d6f476ac52f3319923001dfe SHA256: 38cf64c2508b0e801e59df73df3e4db34efc05f942d6071deeda85e5bb796c9f SHA512: 2a19e31f4d75ab4241fd9881daa10609d8b01d9c160c14a1b1a022f4ca34a3925b403891c75611ef272ca2cada888b3c7be21f0d27e705a2cf28d7384115abac Homepage: https://cran.r-project.org/package=ordinalRR Description: CRAN Package 'ordinalRR' (Analysis of Repeatability and Reproducibility Studies withOrdinal Measurements) Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper. Package: r-cran-ordinalsimr Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-bslib, r-cran-callr, r-cran-coin, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-rhandsontable, r-cran-rlang, r-cran-rms, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-pkgload, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-writexl Filename: pool/dists/resolute/main/r-cran-ordinalsimr_0.2.4-1.ca2604.1_all.deb Size: 1993294 MD5sum: 1df814dd75b1c4a98ee893eba52ccb6c SHA1: 27ce643fa3c631da394f2fc2f9ac31d7736d6390 SHA256: 9ec37c7008dda056625e4392ff3dffaf650f5fcff2301540fd74d92712d31377 SHA512: c05e5f4114e31778121aac0fca8f6e58f91c9f30bcb3d625fc658f43b34289c10231cc6e612dfeed3f2badebc0e94531745c4347d96388531460da6e6c4624c3 Homepage: https://cran.r-project.org/package=ordinalsimr Description: CRAN Package 'ordinalsimr' (Compare Ordinal Endpoints Using Simulations) Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included 'shiny' application allows users to simulate outcomes given different ordinal data distributions. Package: r-cran-ordinaltables Architecture: all Version: 1.0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ordinaltables_1.0.0.3-1.ca2604.1_all.deb Size: 987850 MD5sum: 2d4e5720d9d9ec9d1d39a4e0804846c1 SHA1: 114852d562938e42e06155c6d21bd66a169ef267 SHA256: bdf933afda9013cb9d1154331556699ae6504c721d6f9e4e5e466177524262e1 SHA512: d470eddfa0cec93b55fb463fe346aa3128fbcb8594bbee240b1546deddaea024855e808e585d47de38f47a266d8e586bfd006c78a56fd85de3409737499440bd Homepage: https://cran.r-project.org/package=ordinalTables Description: CRAN Package 'ordinalTables' (Fit Models to Two-Way Tables with Correlated Ordered ResponseCategories) Fit a variety of models to two-way tables with ordered categories. Most of the models are appropriate to apply to tables of that have correlated ordered response categories. There is a particular interest in rater data and models for rescore tables. Some utility functions (e.g., Cohen's kappa and weighted kappa) support more general work on rater agreement. Because the names of the models are very similar, the functions that implement them are organized by last name of the primary author of the article or book that suggested the model, with the name of the function beginning with that author's name and an underscore. This may make some models more difficult to locate if one doesn't have the original sources. The vignettes and tests can help to locate models of interest. For more dertaiils see the following references: Agresti, A. (1983) "A Simple Diagonals-Parameter Symmetry And Quasi-Symmetry Model", Agrestim A. (1983) "Testing Marginal Homogeneity for Ordinal Categorical Variables", Agresti, A. (1988) "A Model For Agreement Between Ratings On An Ordinal Scale", Agresti, A. (1989) "An Agreement Model With Kappa As Parameter", Agresti, A. (2010 ISBN:978-0470082898) "Analysis Of Ordinal Categorical Data", Bhapkar, V. P. (1966) "A Note On The Equivalence Of Two Test Criteria For Hypotheses In Categorical Data", Bhapkar, V. P. (1979) "On Tests Of Marginal Symmetry And Quasi-Symmetry In Two And Three-Dimensional Contingency Tables", Bowker, A. H. (1948) "A Test For Symmetry In Contingency Tables", Clayton, D. G. (1974) "Some Odds Ratio Statistics For The Analysis Of Ordered Categorical Data", Cliff, N. (1993) "Dominance Statistics: Ordinal Analyses To Answer Ordinal Questions", Cliff, N. (1996 ISBN:978-0805813333) "Ordinal Methods For Behavioral Data Analysis", Goodman, L. A. (1979) "Simple Models For The Analysis Of Association In Cross-Classifications Having Ordered Categories", Goodman, L. A. (1979) "Multiplicative Models For Square Contingency Tables With Ordered Categories", Ireland, C. T., Ku, H. H., & Kullback, S. (1969) "Symmetry And Marginal Homogeneity Of An r × r Contingency Table", Ishi-kuntz, M. (1994 ISBN:978-0803943766) "Ordinal Log-linear Models", McCullah, P. (1977) "A Logistic Model For Paired Comparisons With Ordered Categorical Data", McCullagh, P. (1978) A Class Of Parametric Models For The Analysis Of Square Contingency Tables With Ordered Categories", McCullagh, P. (1980) "Regression Models For Ordinal Data", Penn State: Eberly College of Science (undated) "Stat 504: Analysis of Discrete Data, 11. Advanced Topics I", Schuster, C. (2001) "Kappa As A Parameter Of A Symmetry Model For Rater Agreement", Shoukri, M. M. (2004 ISBN:978-1584883210). "Measures Of Interobserver Agreement", Stuart, A. (1953) "The Estimation Of And Comparison Of Strengths Of Association In Contingency Tables", Stuart, A. (1955) "A Test For Homogeneity Of The Marginal Distributions In A Two-Way Classification", von Eye, A., & Mun, E. Y. (2005 ISBN:978-0805849677) "Analyzing Rater Agreement: Manifest Variable Methods". Package: r-cran-ordmonreg Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ordmonreg_1.0.4-1.ca2604.1_all.deb Size: 85140 MD5sum: a3bc7bde5043adda4b7dbbc1a5331c76 SHA1: 96c5c0e298086613d8a46d6f5ec4e893032a3a11 SHA256: 8cf77018c9fb471263b54a076e01fe231273780253b35d5e938acdc5bff25ff6 SHA512: 2af86e26da67890969b5a17cd53f6a8a2c8138be9dad0ba56e74b8948532c19f4c4b4ce24f7e15086489631252b5a34f3be6c86cb51e4e0845cdd8735e67b468 Homepage: https://cran.r-project.org/package=OrdMonReg Description: CRAN Package 'OrdMonReg' (Compute Least Squares Estimates of One Bounded or Two OrderedIsotonic Regression Curves) We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm. Package: r-cran-ordnor Architecture: all Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor, r-cran-matrix, r-cran-genord Filename: pool/dists/resolute/main/r-cran-ordnor_2.2.3-1.ca2604.1_all.deb Size: 50560 MD5sum: 40227cd4c72d8082bf44bdf351689c7b SHA1: a045dfddfd93a8c869d64ef5d4502c6f5223d06e SHA256: 813e7bf8d2ea33db27bdef2c45af5c6b86ffe0b00f58407a5100a1adcb57581d SHA512: 2c297d3bc46de184e56974dc3b875f9c02f8c9c5117615aa995b431e43e061d4367a3300f42d20b1612a3f2da1e9e39e6414986872bef16f74c7816965ba5f33 Homepage: https://cran.r-project.org/package=OrdNor Description: CRAN Package 'OrdNor' (Concurrent Generation of Ordinal and Normal Data with GivenCorrelation Matrix and Marginal Distributions) Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2015) . Package: r-cran-ordpanel Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-flextable, r-cran-ggplot2, r-cran-consort, r-cran-patchwork, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ordpanel_0.1.1-1.ca2604.1_all.deb Size: 413768 MD5sum: 17cd12e7d31fa40c6f0118784eb8f7ec SHA1: 66e214c88ce87360d002f5288a3182c4b21627b8 SHA256: e2a96a2a7da7e76a51a89413d58d22ef4c78ceee99b3a2e23d41fff6556b8f3d SHA512: d82b16287a6ef7bdbbc1078ed09d476590fd3cb4ab18b4e2ee582dbfe4ebb8899da5e8de2feab0cab2644b97e8844aa2b6244846e990e78190fe94747b1e35cf Homepage: https://cran.r-project.org/package=ordPanel Description: CRAN Package 'ordPanel' (Ordered Panel) The ordered panel methodology (Zezulinski et al 2025 ) provides a structured framework for identifying and organizing sets of biomarkers, such as genetic variants, that distinguish between positive and negative subjects in a study when only a training cohort is available. This approach is particularly useful in situations where an independent validation cohort does not yet exist, rendering conventional performance metrics such as the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) inappropriate or potentially misleading. The methodology emphasizes transparent construction and evaluation of ordered signatures of biomarkers, allowing investigators to examine operating characteristics without establishing predictive performance. 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Greenacre (2010, ISBN:978-84-923846) shows how the most established of these, including principal components analysis, correspondence analysis, multidimensional scaling, factor analysis, and discriminant analysis, rely on eigen-decompositions or singular value decompositions of pre-processed numeric matrix data. These decompositions give rise to a set of shared coordinates along which the row and column elements can be measured. The overlay of their scatterplots on these axes, introduced by Gabriel (1971) , is called a biplot. 'ordr' provides inspection, extraction, manipulation, and visualization tools for several popular ordination classes supported by a set of recovery methods. It is inspired by and designed to integrate into 'Tidyverse' workflows provided by Wickham et al (2019) . 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It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the 'RStudio' Markers pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used for checking which packages are actually used in a project. Package: r-cran-orionz.g Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-orionz.g_1.0.1-1.ca2604.1_all.deb Size: 68478 MD5sum: 0c0967eeefea4fce7df5a53340fc5e22 SHA1: 9690467bf396ef8f06b3acd7d51107ee16795bdb SHA256: 77e4f4bb035a9168e3f6746a8b403fd2e1520b5352c8640a225c007ebd536fbe SHA512: 8c8c75427e2b399b51be219aff2ecd925defeaff9633c9c5370d28ce5acabc4da08fa157d85428145e3f6842393d559ea3002abe724e0a6ce032c32e57b1677e Homepage: https://cran.r-project.org/package=ORIONZ.G Description: CRAN Package 'ORIONZ.G' (EAP Scoring in Exploratory FA Solutions with CorrelatedResiduals) Obtaining Bayes Expected A Posteriori (EAP) individual score estimates based on linear and non-linear extended Exploratoy Factor Analysis solutions that include a correlated-residual structure. Package: r-cran-orisma Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-jsonlite, r-cran-magrittr, r-cran-pheatmap, r-cran-readr, r-cran-stringdist, r-cran-stringr, r-cran-synthesisr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-orisma_0.1.0-1.ca2604.1_all.deb Size: 390452 MD5sum: 6961662e700a53a42734eee3689aec12 SHA1: 8689dc890a897922730f9d84f44835f6ad26e16f SHA256: 2f9a21753a77de97cd551911093b1dd942a33edc4b3f74cc938057f189e704a6 SHA512: b58a838f1c59d739de3f6f3cd6c6b10f51ffc5f0fd492658da7bb1cb90ad7fe3452228a6ac99434f0890ea91d7f5291d1f701f5a7d97124732865341ef78e1de Homepage: https://cran.r-project.org/package=orisma Description: CRAN Package 'orisma' (Occupational Risk Integrated Systematic Mapping and Analysis) A complete pipeline for systematic bibliometric mapping of occupational health and safety (OHS) evidence. Starting from reference files exported from major bibliographic databases such as Web of Science, Scopus, PubMed, Dimensions, EBSCO, and others, 'orisma' automates ingestion, deduplication, relevance filtering, occupational risk category extraction, bibliometric analysis, and report generation. The package is related to bibliometric science mapping and evidence synthesis workflows described by Aria and Cuccurullo (2017) , Westgate (2019) , and Lajeunesse (2016) , but adds a domain-specific occupational safety and health layer. The package implements three original bibliometric indicators: (1) the Worker-Risk Disconnection Index (WRDI), measuring the proportion of studies that characterise an occupational risk without including direct worker exposure data; (2) the Risk Category Saturation Index (RCS), measuring the relative over- or under-representation of each risk category relative to a uniform baseline; and (3) the Material-Gap Profile (MGP), measuring the ratio between a material's known hazard potential and its coverage in the occupational health literature. Two additional preventive intelligence indicators are provided: (4) the Abstract Sufficiency Score (ASS, 0-5), a cumulative hierarchical index of the preventively useful information contained in an abstract; and (5) the Bridge Article Score (0-5), identifying studies that simultaneously address technology, hazardous agent, worker population, exposure measurement, and preventive recommendations. Risk categories are extracted using a built-in occupational risk dictionary of 58 categories anchored in ISO 45001:2018, INSST, NIOSH, and EU-OSHA frameworks, organised in six blocks: Safety, Industrial Hygiene, Ergonomics, Psychosociology, Biological Hazards, and Emerging Technologies. The dictionary is user-extensible. Outputs include bilingual HTML reports, occupational risk sheets, priority reading rankings, guided extraction matrices for systematic review, and reproducibility certificates with MD5 hashes. Package: r-cran-orkm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-orkm_1.0.0-1.ca2604.1_all.deb Size: 418872 MD5sum: 7a5b00e28a7f20531a9130a22788a676 SHA1: 258ca3dfaf531722c8a8aa4ef529755643d858f6 SHA256: db8029be00e7867db316675e7d771eb769e4ee319ba34ccce05e9af590e9782d SHA512: 7a72a30803ccb2c2cccfb8abffd56600d87d4043bd512f38a614158196fbd868135dd3f0362e2dd20382256ef444da2ba75b838fad4463566bc212efcbdda062 Homepage: https://cran.r-project.org/package=ORKM Description: CRAN Package 'ORKM' (The Online Regularized K-Means Clustering Algorithm) Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2024) . Package: r-cran-orloca.es Architecture: all Version: 5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-orloca Filename: pool/dists/resolute/main/r-cran-orloca.es_5.5-1.ca2604.1_all.deb Size: 93212 MD5sum: 8a7936fb7e89c860fbe6f8148e4c23f2 SHA1: 7922afc33fb9cabba5c6bad8575924c8dab11ee7 SHA256: af0e05f36601647145ca39f7143f444519013cfd94504ad487e6f73090860c95 SHA512: 5f1cb43edea2adc8e2b626abbb0f43db08354be0a0cb1bcefd420d92b2cac20ada3a45a91c7082db9e574efd6d8d70c3eb9a735a1bd859fa995614693c09a115 Homepage: https://cran.r-project.org/package=orloca.es Description: CRAN Package 'orloca.es' (Spanish version of orloca package. Modelos de localizacion eninvestigacion operativa) Help and demo in Spanish of the orloca package. Ayuda y demo en espanol del paquete orloca. Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. . Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo especifico Weiszfeld, vease "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. 355-386, 1937 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. . Package: r-cran-orloca Architecture: all Version: 5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-png, r-cran-ucminf, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-orloca.es, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-orloca_5.6-1.ca2604.1_all.deb Size: 417876 MD5sum: d22a1ab4936c67ad88b26ef05dd31c90 SHA1: 23559c3c896d6c44449c3a630e31b17b79bd4a9f SHA256: 92e3a5c88496d88219c5327cc3392b882909ff7053172310ee032c55ba0e5884 SHA512: 37a4d8219395e785e37d6b79f3b89058e9d31ab35b2babb2a8f15d34e40a721c44c890c8bf54080299479ac78ec0f79918c7bb19f36fd0864107f966ff7ee479 Homepage: https://cran.r-project.org/package=orloca Description: CRAN Package 'orloca' (Operations Research LOCational Analysis Models) Objects and methods to handle and solve the min-sum location problem, also known as Fermat-Weber problem. The min-sum location problem search for a point such that the weighted sum of the distances to the demand points are minimized. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. . General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. . Package: r-cran-ormplot Architecture: all Version: 0.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rms, r-cran-gtable Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/resolute/main/r-cran-ormplot_0.3.6-1.ca2604.1_all.deb Size: 394606 MD5sum: 2f5492914eeded4a260ee6bb6ef5c6ee SHA1: 9618af3f9cfe23faf7f0bbdf06e8efba1ee92c64 SHA256: b3d00e051b1f1a1c143933f743eb1b548f8836f536061422382dfd8b749e0750 SHA512: b296a9095acb8d984e56515f4b205f228fc4c3fe45869325e1b585f2ea3c15f5ef8086bfb6bd44f7d5e7612a7340f7b50b0859a26ccb8dfd2268f54fbc39ecec Homepage: https://cran.r-project.org/package=ormPlot Description: CRAN Package 'ormPlot' (Advanced Plotting of Ordinal Regression Models) An extension to the Regression Modeling Strategies package that facilitates plotting ordinal regression model predictions together with confidence intervals for each dependent variable level. It also adds a functionality to plot the model summary as a modifiable object. Package: r-cran-oro.dicom Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oro.nifti Suggests: r-cran-testthat, r-cran-hwriter Filename: pool/dists/resolute/main/r-cran-oro.dicom_0.5.3-1.ca2604.1_all.deb Size: 1665046 MD5sum: 20e4c4826add67a941db2864f1e09b50 SHA1: 931407bebea7a52a1afab633b2a82fa66843bfda SHA256: 1e552e4481693e31714050ec34b5797d8fadc903f8121cb8230dda30f8933f10 SHA512: 9f0021eb9b871b082c04d636e990ea8ec3a0bdf8228e4ff0dc17376d0622f8e1486f0af69b7a03fe633999e58a3f832d41e7a0180578173c87be7046d068ef66 Homepage: https://cran.r-project.org/package=oro.dicom Description: CRAN Package 'oro.dicom' (Rigorous - DICOM Input / Output) Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle. Package: r-cran-oro.nifti Architecture: all Version: 0.11.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bitops, r-cran-abind, r-cran-rnifti Suggests: r-cran-xml, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rticles Filename: pool/dists/resolute/main/r-cran-oro.nifti_0.11.4-1.ca2604.1_all.deb Size: 6055070 MD5sum: d82bb824f3bc9555c0aefb90a44a34aa SHA1: 0ccacabfbe9239a5670f00593658176140cc1af1 SHA256: 536cbdf5df4259b75855d78cb59222c6e5559d3b2a60d884b9b2860b821a6065 SHA512: 31c2b8934cbd5f24aa47aeb0b474f4365b69273fff4793eaf9224e80ade1ce41d3eac707f6b211695d95abaf94c6f03be78e9d87935a68429167fb549454bed3 Homepage: https://cran.r-project.org/package=oro.nifti Description: CRAN Package 'oro.nifti' (Rigorous - 'NIfTI' + 'ANALYZE' + 'AFNI' : Input / Output) Functions for the input/output and visualization of medical imaging data that follow either the 'ANALYZE', 'NIfTI' or 'AFNI' formats. This package is part of the Rigorous Analytics bundle. Package: r-cran-oro.pet Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oro.dicom, r-cran-oro.nifti, r-cran-minpack.lm, r-cran-msm Filename: pool/dists/resolute/main/r-cran-oro.pet_0.2.7-1.ca2604.1_all.deb Size: 72586 MD5sum: cb6d92245832f8b5fb64ec124c1de296 SHA1: a94b4d77cb683a4402de36ea98f11b93bba40eca SHA256: 300cf47f2d80c69fbb379e8ad6e64faade797a53d7479b6a8f2f4ba6f01607c1 SHA512: 7dbfaf307dbb2fb58f133e2c72d4f187c9a340efd917ed14ffc7fd916b3c1333df007c9b2d05cffcda962450d5f592df9d37083d2b3141bf54c0f0c2b92983b1 Homepage: https://cran.r-project.org/package=oro.pet Description: CRAN Package 'oro.pet' (Rigorous - Positron Emission Tomography) Image analysis techniques for positron emission tomography (PET) that form part of the Rigorous Analytics bundle. Package: r-cran-orscraper Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pdftools, r-cran-stringr, r-cran-readxl, r-cran-rentrez Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-mockery, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-orscraper_0.1.1-1.ca2604.1_all.deb Size: 336802 MD5sum: 57790f25c8f48168708e2f977048c69c SHA1: c12e3def092d0549c2658b317b930c60808fd675 SHA256: 74afb0c6b9e924698d2b33c6bb5705346fc5f7a5562362561adbbe0329992132 SHA512: 8e06b34ab7fd18bdc9ae481141a5264d38ef62eb24b4eb74379e4b679dbb072bbc90aa4f4aa9993192624d5865db0713fba235e5e97211a61cb2f82c3cebe16b Homepage: https://cran.r-project.org/package=ORscraper Description: CRAN Package 'ORscraper' (Extract Information from Clinical Reports from 'OncomineReporter' and NCBI 'ClinVar') Clinical reports generated by 'Oncomine Reporter' software contain critical data in unstructured PDF format, making manual extraction time-consuming and error-prone. 'ORscraper' provides a coherent suite of functions to automate this process, allowing researchers to parse reports, identify key biomarkers, extract genetic variant tables, and filter results. It also integrates with the NCBI 'ClinVar' API to enrich extracted data. Package: r-cran-orsifronts Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-orsifronts_0.2.0-1.ca2604.1_all.deb Size: 381370 MD5sum: e787ba5ecfd4c9680ef077935de541da SHA1: ab45706d1833673ef53feae42668c3d142203171 SHA256: f5c099f2f1f7e0fb59d9f85c10a6a19e749fa8460eb30b2b6b28779e58269a31 SHA512: 4c6c565552ac8c10851577b429e464713e8d7c3ddb03149261619a1ff03c6cb6a31b4f162b6bd7354daf4feb212a778a0324f1c06aefd0ea3d1c3f799259ffee Homepage: https://cran.r-project.org/package=orsifronts Description: CRAN Package 'orsifronts' (Southern Ocean Frontal Distributions (Orsi)) A data set package with the "Orsi" and "Park/Durand" fronts as 'SpatialLinesDataFrame' objects. The Orsi et al. (1995) fronts are published at the Southern Ocean Atlas Database Page, and the Park et al. (2019) fronts are published at the 'SEANOE' Altimetry-derived Antarctic Circumpolar Current fronts page, please see package CITATION for details. Package: r-cran-ort Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-imager Filename: pool/dists/resolute/main/r-cran-ort_0.1.0-1.ca2604.1_all.deb Size: 335644 MD5sum: 325383029526295a2ac973302b00ecdf SHA1: 8994a65a061dba7160c1c1478cd8ddeae781f451 SHA256: ec0382fa1e59c58b1e617abc3731e7030f4104867c13ee6e96d18612acaccf90 SHA512: e663f2887169817037141138e3cd6ba8a874ace308b870ae57016256b27dea32941b979e964e3e4be3491879ff7ae70d03675565af78ff0b045a4b10f4bce376 Homepage: https://cran.r-project.org/package=ort Description: CRAN Package 'ort' (Create a Data Frame Representation of an Image) Takes images, imported via 'imager', and converts them into a data frame that can be plotted to look like the imported image. This can be used for creating data that looks like a specific image. Additionally, images with color and alpha channels can be converted to grayscale in preparation for converting to the data frame format. Package: r-cran-orth.ord Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magic, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-orth.ord_1.0.1-1.ca2604.1_all.deb Size: 74238 MD5sum: f2259c79c25a57790643cefb0e8db602 SHA1: ae595f0e081f8908bf56007fe37da6ed4398991b SHA256: d8eb2500bdc7b8da6fcec4af9cc3ea6719d66ebb915d3b99e148f5c0e85d4a54 SHA512: 95db36833b84d42527375cd1707f9f284b23b90fde0339650307895d7ba52e1378ed272e817a00715407881dbf6c6b3880230207d05f9cc227a534b92a9e3bb9 Homepage: https://cran.r-project.org/package=ORTH.Ord Description: CRAN Package 'ORTH.Ord' (Alternating Logistic Regression with Orthogonalized Residualsfor Correlated Ordinal Outcomes) A modified version of alternating logistic regressions (ALR) with estimation based on orthogonalized residuals (ORTH) is implemented, which use paired estimating equations to jointly estimate parameters in marginal mean and within-association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). A finite-sample bias correction is provided to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and different bias-corrected variance estimators such as BC1, BC2, and BC3. Package: r-cran-orthanc Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-carrier, r-cran-clock, r-cran-digest, r-cran-glue, r-cran-fs, r-cran-httr2, r-cran-jsonlite, r-cran-mirai, r-cran-prettyunits, r-cran-purrr, r-cran-r6, r-cran-rlang Suggests: r-cran-png, r-cran-jpeg Filename: pool/dists/resolute/main/r-cran-orthanc_0.2.0-1.ca2604.1_all.deb Size: 3087224 MD5sum: bedac60a5019e6f69728ee43d5afe27f SHA1: 13b064179388b03a27b7fad551db2b2f7a1a9874 SHA256: 60563fa42ef3793df1f508ef72575287ee83d701ce0845ac47c61b5056eca356 SHA512: b4ea889770176c84adf54bdfc885cb1cfa9c33dfec2594eee0f0c1e6b017b0b70281c520eb0dcfc0ceef8fe723ebdbf7e988f3137949f926bb11d6881746c3de Homepage: https://cran.r-project.org/package=orthanc Description: CRAN Package 'orthanc' (Programmatic Interface to 'Orthanc' DICOM Servers) An R Interface to 'Orthanc' DICOM servers for medical imaging workflows. 'Orthanc' is a lightweight, open-source DICOM server that exposes a comprehensive REST API for managing, querying, retrieving, and modifying DICOM resources (). The goal of this package is to provide comprehensive and user-friendly access to the 'Orthanc' REST API, designed to align with idiomatic R workflows while preserving the structure and semantics of DICOM resources. 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Package: r-cran-orthopolynom Architecture: all Version: 1.0-6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom Filename: pool/dists/resolute/main/r-cran-orthopolynom_1.0-6.1-1.ca2604.1_all.deb Size: 339458 MD5sum: 60d06eec5f17b381c91ec93ffb366fe5 SHA1: 76593abfef384b168c1bb35df17b984d5717b1ae SHA256: 8b202da9dd8a5ddeb9dc03ccfcfa39ce6dd99778814198e145f44755ec293c45 SHA512: 33efeb303589c8d363f0bee87d6465a0474388748f7948cf35d0ca96fcc02e758b276a0ad3479db06820df089ed29cdb1114fd34a1680fdf81701c2eb503a54c Homepage: https://cran.r-project.org/package=orthopolynom Description: CRAN Package 'orthopolynom' (Collection of Functions for Orthogonal and OrthonormalPolynomials) A collection of functions to construct sets of orthogonal polynomials and their recurrence relations. Additional functions are provided to calculate the derivative, integral, value and roots of lists of polynomial objects. Package: r-cran-ortsc Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-googleauthr, r-cran-googlecloudvisionr Filename: pool/dists/resolute/main/r-cran-ortsc_1.0.0-1.ca2604.1_all.deb Size: 13598 MD5sum: 970171b83bc658f87ba0db4d640fa40e SHA1: cdd797256a5f157e92edff0180a44d03b93883f4 SHA256: 9966066145bff1476644174610148bc2c8c6099b84fbf1cd41176ee194a2f4d2 SHA512: 9beabf490903e6e2ea562a3612e0038e3fa07c6c803e36fc421e855897f725f227f93f7d8ddf12c6b229889c08f285325caf7acaa0c9f417e810e89c6b5aacce Homepage: https://cran.r-project.org/package=ORTSC Description: CRAN Package 'ORTSC' (Connects to Google Cloud API for Label Detection) Connects to Google cloud vision to perform label detection and repurpose this feature for image classification. Package: r-cran-oryzaprobe Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-oryzaprobe_0.1.0-1.ca2604.1_all.deb Size: 983792 MD5sum: 00bec5b20f8310e36162f7280c391496 SHA1: 088b8aca7605ded6922f28ef0d02b7f65f002d64 SHA256: 2d823627b42e6c4a5c257d6c4ae5c59a143892b9f9a2d6febd0e82a2f8682e0f SHA512: c617f0034dd4b5bf070c001b368218bb073b0110c2d1f987d29dc08086e1b7aad4debaadf73f5a71c2df88d9acdcb89527e12a50e993d1be2b88958d7286d9af Homepage: https://cran.r-project.org/package=OryzaProbe Description: CRAN Package 'OryzaProbe' (Rice Microarray Probe ID Conversion, from Probe ID to RAP-DB ID) Microarray probe ID is not convenient for further enrichment analysis and target gene selection. The package is created for the rice microarray probe ID conversion. This package can convert microarray probe ID from GPL6864 , GPL8852 , and GPL2025 platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research. For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes. Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID. Package: r-cran-osbng Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5010 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geos Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-osbng_0.2.0-1.ca2604.1_all.deb Size: 4207806 MD5sum: 88d2aa7f1fdac0bf60de7126abf39012 SHA1: 986bc2f6bcde697357d53cd86f921da96d8297cf SHA256: 39ba3ab9963917fb56f0b218c6d23d69f052de0d908071a3fb918e526581119e SHA512: 90f9f96af31422a469027c81b21704dac82b15578cb83e2b3a2931f4bb6e3a71818af20bc6d2195e8e67a590b33c7dfdca45181a835f83c77392655771b1629e Homepage: https://cran.r-project.org/package=osbng Description: CRAN Package 'osbng' (Geospatial Grid Indexing with the British National Grid) Offers a streamlined programmatic interface to Ordnance Survey's British National Grid (BNG) index system, enabling efficient spatial indexing and analysis based on grid references. 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Package: r-cran-oscars Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-oscars_0.1.2-1.ca2604.1_all.deb Size: 36628 MD5sum: 3326cecdf5ce80a326241590b52491d4 SHA1: 8c23b83dbbc19c976593d1df7e8755fe0492055b SHA256: d5089185f93d0abd694596723f0955d1386d1a54a7c274a5122bf1b67ba6233e SHA512: c529808b0f1590da1c1b012d7c1d14dbc67289bfcbe394ba0ec8c521bab58a6c2185c4a8b624e488287400c08ef3d7c4716734b540f0e6c1e36c0efd021efc6c Homepage: https://cran.r-project.org/package=OSCARS Description: CRAN Package 'OSCARS' (Global Bounded Optimization by the OSCARS-II Algorithm) A collection of general optimization routines based on variants of the One Side Cut Accelerated Random Search (OSCARS-II) algorithm (Price et al., 2020, ). The main function , 'oscars()', performs black-box optimization of a general (including nonsmooth or discontinuous) function subject to simple bounds on the unknowns. If all bounds are finite, oscars searches globally. The main method implements a stochastic direct search method and is derivative free. Testing shows the OSCARS-II algorithm usually finds extrema with fewer function evaluations than similar global derivative-free methods. Package: r-cran-oscillatorgenerator Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-oscillatorgenerator_0.1.0-1.ca2604.1_all.deb Size: 73634 MD5sum: 0272f95cd23d20f979d33350b9175009 SHA1: edfaa32ee3b990abe97d1e8188dc7c8cba3fdba0 SHA256: 5f35564532560ca734284a7bfd9181d98a1af0c8e101fb26a0bf8ee2b67ada05 SHA512: e1108e418681a5641a2e1c250d39a22681be097faa1b79a3bd12a9b901f5a97fac1a9d0e8324dd5f6a893f0a76f12bc919764b29adaf3a67d54076b5a3eb87da Homepage: https://cran.r-project.org/package=OscillatorGenerator Description: CRAN Package 'OscillatorGenerator' (Generation of Customizable, Discretized Time Series ofOscillating Species) The supplied code allows for the generation of discrete time series of oscillating species. 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Package: r-cran-oscv Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mc2d Filename: pool/dists/resolute/main/r-cran-oscv_1.0-1.ca2604.1_all.deb Size: 96052 MD5sum: 5b1469e1de0a38588afd253ac743281f SHA1: b59be67897a38f65958e2025ccfe1e2aa86f1f2f SHA256: 56d52c5db63600e70f11f74e9e50ce3f3742b2333b26dad89f2ac2552989bd75 SHA512: 7fc4257646fd2f8435a891a793591f10933f728edc663d439253a1d3435b2b9ad25d5c524eeb55859b419da1bf1875365a562ffdbfd9300dd0ffbce62ed25abe Homepage: https://cran.r-project.org/package=OSCV Description: CRAN Package 'OSCV' (One-Sided Cross-Validation) Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, . Package: r-cran-osd Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jade, r-cran-nnls Filename: pool/dists/resolute/main/r-cran-osd_0.1-1.ca2604.1_all.deb Size: 211596 MD5sum: 4797e5e49698cb52d6732672bb61b0a9 SHA1: 9625cf52826498569863f5383ef31975ce7f5404 SHA256: 1645b1a9537baa0b60edd21a6063067a1261fb67d192c018bc1f3f316b96418f SHA512: b7e7caba6ed69e6b6a1ef87c2a9de9039145da4d420c092bcf3ca307fc87717ce079c917e2cc8b85abd406247bc829eb7f3d082d8d5b2018e6fea637f198d76a Homepage: https://cran.r-project.org/package=osd Description: CRAN Package 'osd' (Orthogonal Signal Deconvolution for Spectra Deconvolution inGC-MS and GCxGC-MS Data) Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone. Package: r-cran-osdatahub Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-geos, r-cran-httr, r-cran-jsonlite Suggests: r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-osdatahub_0.3.0-1.ca2604.1_all.deb Size: 1287322 MD5sum: 97bb105883a081d035a5fa45afea9fcd SHA1: 105a0eaa656227815bf3afc42d71e6d41f865c69 SHA256: 7cfea9751e969e524cd51bd42b84e9389d76e673591033869f1497c352136d81 SHA512: c737f715f31e0ba1fbe48f33d6ef9cc7e37f1a96ca557f2df211083d1c0be1b851bdc5539c4a1457f832306f5deee080e48b8fdfbd35ee5b934836162caf8e5f Homepage: https://cran.r-project.org/package=osdatahub Description: CRAN Package 'osdatahub' (Easier Interaction with the Ordnance Survey Data Hub) Ordnance Survey ('OS') is the national mapping agency for Great Britain and produces a large variety of mapping and geospatial products. 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Package: r-cran-osdesign Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1068 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-osdesign_1.8-1.ca2604.1_all.deb Size: 1058746 MD5sum: 3588d341493c6bddad9c10905b65a76a SHA1: c8307a7b86541703733c372d34b08fe595eabaa4 SHA256: d33be904c89591e64e75cd40c617e69cb8f334922bdd03de6ab7c03fe1fdf50c SHA512: 03ad968ea88541ad56f20562403e560eb4236baebffd8d916f8808e60972f82ff9db98be50a4df5bd11661f96353ecc77de5f4d105a4e7ee21fff2f0efb7a400 Homepage: https://cran.r-project.org/package=osDesign Description: CRAN Package 'osDesign' (Design, Planning and Analysis of Observational Studies) A suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011). Package: r-cran-osdr Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-osdr_1.1.4-1.ca2604.1_all.deb Size: 30906 MD5sum: 00e06cecf0dd5c4be325458cde5c1423 SHA1: 7708f9a9ed7691d3cfa0d9b4299cee232977627e SHA256: f7beacf8ce8e7d2d213b6fd678729b5c0e136ef49f34f365ca8b5c9263c35021 SHA512: dffc1da1926cfddb335f6c47a3bdc0d1b46e7aa108a0557468e4e34b5a805121340f93341a962194b7e3c24421c68531867e30c14f8f7c02dc6063da91d5755f Homepage: https://cran.r-project.org/package=OSDR Description: CRAN Package 'OSDR' (Finds an Optimal System of Distinct Representatives) Provides routines for finding an Optimal System of Distinct Representatives (OSDR), as defined by D.Gale (1968) . 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Package: r-cran-osldecomposition Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-luminescence, r-cran-deoptim, r-cran-minpack.lm, r-cran-gridextra, r-cran-ggplot2, r-cran-scales, r-cran-ggpubr, r-cran-rmarkdown Suggests: r-cran-kableextra, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-osldecomposition_1.2-1.ca2604.1_all.deb Size: 601468 MD5sum: 8d394627732645763c06d953cb91ad2e SHA1: 27605105ef447348aa6ff0e1107e5f2a094c20fe SHA256: a601710ac4dd156818c165400060f3691c11eaad1c168eb009d25696df670ec6 SHA512: fea75a338cb4bf3ff593e718b6249059cf1d76153b83e49c1ed42787a46d9152bfe433b480f031f37751a37d58c0de8b591e741f22af0f7764025acf564817c2 Homepage: https://cran.r-project.org/package=OSLdecomposition Description: CRAN Package 'OSLdecomposition' (Signal Component Analysis for Optically Stimulated Luminescence) Function library for the identification and separation of exponentially decaying signal components in continuous-wave optically stimulated luminescence measurements. A special emphasis is laid on luminescence dating with quartz, which is known for systematic errors due to signal components with unequal physical behaviour. Also, this package enables an easy to use signal decomposition of data sets imported and analysed with the R package 'Luminescence'. This includes the optional automatic creation of HTML reports. Further information and tutorials can be found at . Package: r-cran-osmapir Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 736 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-xml2 Suggests: r-cran-httptest2, r-cran-httpuv, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-osmapir_0.2.5-1.ca2604.1_all.deb Size: 484836 MD5sum: 761dd5d47d1688997b6ba96c4798578f SHA1: fcd5a6b383e26f983272342e00fecd06b8753576 SHA256: 74dad0b9d770dd484c65d6db2b69796dfc352cbff170fd2c7a2c9eee80791ec5 SHA512: 1d91d2c341d519078947c594a253a4a7b5b6bd1e5e049b1aaee89eb96d8f816ffa4518cd25af31907e21166f2c405e9ec085195de7156ee505a79a750a51bed9 Homepage: https://cran.r-project.org/package=osmapiR Description: CRAN Package 'osmapiR' ('OpenStreetMap' API) Interface to 'OpenStreetMap API' for fetching and saving data from/to the 'OpenStreetMap' database (). Package: r-cran-osmclass Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-collapse, r-cran-data.table, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-osmclass_0.1.5-1.ca2604.1_all.deb Size: 264918 MD5sum: 50cd071fbb23db27a3c226a3f9f54010 SHA1: 944e190e524501f86120a56613a0cfc07c66bded SHA256: 08debea9c3fc7e1eaa95908adc17206fe526ddea6580afbe2439d87fd47b9131 SHA512: 45445b019fd8d0d0b160d843cc0f15ca8d9a1ff4a686eada6149dd2c8a4b4a7a34ff939851dd02fe5a8a85d4be571c95b4e2bab7c5533616bb330618465aab3c Homepage: https://cran.r-project.org/package=osmclass Description: CRAN Package 'osmclass' (Classify Open Street Map Features) Classify Open Street Map (OSM) features into meaningful functional or analytical categories. Designed for OSM PBF files, e.g. from imported as spatial data frames. A classification consists of a list of categories that are related to certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main osm_classify() function returns a classification data table giving, for each feature, the primary and alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. The package also contains a classification of OSM features by economic function/significance, following Krantz (2023) . Package: r-cran-osmextract Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-osmextract_0.6.0-1.ca2604.1_all.deb Size: 5030754 MD5sum: 3960a4e014891a0e87c966ede22ef658 SHA1: a9ac7acfcdcb77616a3692f72a92a3f6b5377a28 SHA256: fd1ef2eca480304791e1dfa4f476b4f45c75000e526ff0704724da95a44955be SHA512: 806e6d7338c2baeb85a67af638945ba92969f678f19a71599aaa98014d517be7992382571edb81dee3c838612bee4ad39213935ddc0eb15109a530fe9b78617e Homepage: https://cran.r-project.org/package=osmextract Description: CRAN Package 'osmextract' (Download and Import Open Street Map Data Extracts) Match, download, convert and import Open Street Map data extracts obtained from several providers. Package: r-cran-osmscale Architecture: all Version: 0.5.23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openstreetmap, r-cran-berryfunctions, r-cran-sf, r-cran-pbapply Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-osmscale_0.5.23-1.ca2604.1_all.deb Size: 95634 MD5sum: b28350c4780536eaf29f5764b3946ec2 SHA1: e4416d112b501a2984dd638dc220007359a4649e SHA256: b6499834acfd749443bf570a0c7fb8def97804b37535e8c60e834620e0503b03 SHA512: f125ea856f1fedf8bff2c89ba0b081f41488c8a86cfa45ea84d2fdea103a9a0a62fd90d1ea3978fc108b3912734aa19960c80f808e747f2a705340013578277a Homepage: https://cran.r-project.org/package=OSMscale Description: CRAN Package 'OSMscale' (Add a Scale Bar to 'OpenStreetMap' Plots) Functionality to handle and project lat-long coordinates, easily download background maps and add a correct scale bar to 'OpenStreetMap' plots in any map projection. Package: r-cran-osnmtf Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-osnmtf_0.1.0-1.ca2604.1_all.deb Size: 215074 MD5sum: 277d38c57239b8d32cb0a230b93e5fa7 SHA1: 7a53b596a6d215fe4e586077da7a7df0a9c4e125 SHA256: a5f597378a70802df71001810664d5e288aa4dd423bb7bf60bc37589048ed5c7 SHA512: 83d2e9ea740671045bed31d195fa3c9d08632bc58c4778a782d4119daaa13a10afc36b67a9a80b8e54222a3e9420beca1cf74c0bae7d37bba90cb5a3131214f4 Homepage: https://cran.r-project.org/package=OSNMTF Description: CRAN Package 'OSNMTF' (Orthogonal Sparse Non-Negative Matrix Tri-Factorization) A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC. Package: r-cran-osrm.backend Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-processx, r-cran-ps, r-cran-viridislite Suggests: r-cran-dt, r-cran-knitr, r-cran-mapgl, r-cran-osrm, r-cran-rmarkdown, r-cran-sf, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-osrm.backend_0.3.1-1.ca2604.1_all.deb Size: 962278 MD5sum: efe1c0a2c87cbad257c15ee587456367 SHA1: 444dac4a1e4edeee61b0ed6ef7debb42dd44f099 SHA256: fe42507c6dfa8bfaba635b90ec9c3acf94814e9d7ee0b8d865db2409f5780768 SHA512: 8d97301d84a37de93eb8d34c3822f341b8b125e6198a030850233aa392b3f28947606de7b356bc14c98765df1acc0af938bac9b208a658cb45a23e4996c8371a Homepage: https://cran.r-project.org/package=osrm.backend Description: CRAN Package 'osrm.backend' (Bindings for 'Open Source Routing Machine') Install and control 'Open Source Routing Machine' ('OSRM') backend executables to prepare routing data and run/stop a local 'OSRM' server. For computations with the running server use the 'osrm' R package (). Package: r-cran-osrm Architecture: all Version: 5.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppsimdjson, r-cran-curl, r-cran-mapiso, r-cran-googlepolylines, r-cran-sf Suggests: r-cran-mapsf, r-cran-tinytest, r-cran-covr, r-cran-terra Filename: pool/dists/resolute/main/r-cran-osrm_5.0.0-1.ca2604.1_all.deb Size: 473626 MD5sum: 8400cfded39a8f24e0aaaafa428a1888 SHA1: bef13e3c16d9b551dbdb68316d8aa1eea3f78592 SHA256: 0648b3fe92191977da59b249b3e1d035e2583f0ad61c651518db5d1c4e289005 SHA512: 919a0fff0d889cee93cdf987aba6e97aeafe4f72ac264069c9750680fad90cfbf6d27a2a7bc9aa09a9f244a48f0da148cdc958d632bb5ed62fd64ad8c4507d60 Homepage: https://cran.r-project.org/package=osrm Description: CRAN Package 'osrm' (Interface Between R and the OpenStreetMap-Based Routing ServiceOSRM) An interface between R and the 'OSRM' API. 'OSRM' is a routing service based on 'OpenStreetMap' data. See for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance). Package: r-cran-osrmr Architecture: all Version: 0.1.36-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-bitops, r-cran-rjson, r-cran-r.utils, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-osrmr_0.1.36-1.ca2604.1_all.deb Size: 88880 MD5sum: d0371a5634308a56bdfcdc4f73b6f5e3 SHA1: 7b9a5fd092605ae6ba123dd293b9d9c979460450 SHA256: db104fd57f7faf3f70854124a1b2b62990c04ebfc69e21ac1fd1816fa2376094 SHA512: da86bad8efb609cbe21681cc2c18762cf6ef22533d6a7b1773f494c710f8f22c95126b1b69dd1cb3a64c47a667afa4ba1342484fcb20ed660f05bd8ee9e261b2 Homepage: https://cran.r-project.org/package=osrmr Description: CRAN Package 'osrmr' (Wrapper for the 'OSRM' API) Wrapper around the 'Open Source Routing Machine (OSRM)' API . 'osrmr' works with API versions 4 and 5 and can handle servers that run locally as well as the 'OSRM' webserver. Package: r-cran-ossanma Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlcoptim, r-cran-deoptimr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-netmeta Filename: pool/dists/resolute/main/r-cran-ossanma_0.1.2-1.ca2604.1_all.deb Size: 36372 MD5sum: a5c404dc5b50b2cd679853fae441e78f SHA1: 5a7496dae7a01deed149bc9f77130270c6eaf595 SHA256: 649808f2d68c23ac435aaa85c81010cdfde102f8c9a23b4d521b62b35e68925a SHA512: 7a31d622ff2b9e2ff4e48453740c93e4841ef81b75e08f284df424a0af89a46571d85c81ec2132c3664a75d884d685cb934b4d184886bea921549201e4482e35 Homepage: https://cran.r-project.org/package=OssaNMA Description: CRAN Package 'OssaNMA' (Optimal Sample Size and Allocation with a Network Meta-Analysis) A system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power. Package: r-cran-ossurvival Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ossurvival_1.0-1.ca2604.1_all.deb Size: 85564 MD5sum: 3ce520ed112be613de7746b7e1ba53e1 SHA1: db1741455208e752cbd25d0991bc49cb591d79be SHA256: 09c46efbff21fa669b9b94e0734d15c5aa8f797b74e3d459dc71f35a12a85c07 SHA512: 246cb776fdcd1365025995db1b8dbb919992cd5d804f48375734d1f86edf44260ecf63513aa1d4544131cfbd1f4953d7ef0794ab2fadad5b7694e99910980bad Homepage: https://cran.r-project.org/package=OSsurvival Description: CRAN Package 'OSsurvival' (Assessing Surrogacy with a Censored Outcome) Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) . Package: r-cran-ostats Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc, r-cran-matrixstats, r-cran-circular, r-cran-hypervolume, r-cran-ggplot2, r-cran-gridextra, r-cran-viridis, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ostats_0.2.0-1.ca2604.1_all.deb Size: 575268 MD5sum: 531eea8f18bd0bf0ab9efe59533bd6e5 SHA1: f3bce0e0e06ffe86003749b9eebe05a0bffaacd7 SHA256: 3c4c1956d62182d89abb05184e3cd824e0707d4f71ace65034c1e3bec689aefe SHA512: 0e85afc03dc8b203d9418c92f384f32ea12e134b821a4f32699421d8f17d3be486ae9b457e9d05c8b49bd3e5a9bf606352272e1d51022a09a8ef4a2b5a53bd48 Homepage: https://cran.r-project.org/package=Ostats Description: CRAN Package 'Ostats' (O-Stats, or Pairwise Community-Level Niche Overlap Statistics) O-statistics, or overlap statistics, measure the degree of community-level trait overlap. They are estimated by fitting nonparametric kernel density functions to each species’ trait distribution and calculating their areas of overlap. For instance, the median pairwise overlap for a community is calculated by first determining the overlap of each species pair in trait space, and then taking the median overlap of each species pair in a community. This median overlap value is called the O-statistic (O for overlap). The Ostats() function calculates separate univariate overlap statistics for each trait, while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. O-statistics can be evaluated against null models to obtain standardized effect sizes. 'Ostats' is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale drivers of biodiversity across the National Ecological Observatory Network (NEON)." For more information on this project, see the Macrosystems Biodiversity Website (). Calculation of O-statistics is described in Read et al. (2018) , and a teaching module for introducing the underlying biological concepts at an undergraduate level is described in Grady et al. (2018) . Package: r-cran-osum Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-foreign, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-osum_0.1.0-1.ca2604.1_all.deb Size: 429290 MD5sum: b234f89df8addf60fbade9c3d023760b SHA1: abb153ee69fa05ba28e7ecef7a8147bef56b9d2b SHA256: d448252d6af7ba991b23b7b93e860bcab62e3bf9d46d88946b347789213661c9 SHA512: a9c4a6e7353844509c13a75d9acb923f1ab3f74775609761757d07535afed8766c73ebe1ada8cdd67476e1b08d1c51801e0fee98c92fcd94c3d5d69a1a3ed115 Homepage: https://cran.r-project.org/package=osum Description: CRAN Package 'osum' (Provide Summary Information About R Objects) Inspired by 'S-PLUS' function objects.summary(), provides a function with the same name that returns data class, storage mode, mode, type, dimension, and size information for R objects in the specified environment. Various filtering and sorting options are also proposed. Package: r-cran-otargen Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ghql, r-cran-cli, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-httr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-otargen_2.0.0-1.ca2604.1_all.deb Size: 901830 MD5sum: 24633ad316ebda31b16c65f8d999a8fc SHA1: ac8d8a036f12ed12941a14345e534e6d958bec88 SHA256: 995d5db45b241c85c36a9bbdda1142d032be8cd71d326b31021b489faf33bcf9 SHA512: 99f3bac555c14967f81b0c7ee33645d74dddd94c4956d62b18c202f49bf7a3712729b0e05b0fd246c9532c452ab9c1a9e004c82b4afa406a98eacbf8bb810ad6 Homepage: https://cran.r-project.org/package=otargen Description: CRAN Package 'otargen' (Access Open Target) Interact seamlessly with Open Target GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of gene, disease, drug, and genetic data. For more information about the Open Target API (). Package: r-cran-otbsegm Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-terra, r-cran-link2gi Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-otbsegm_0.1.2-1.ca2604.1_all.deb Size: 1577608 MD5sum: 51fbafc59e8b63b83b5ad5b995faa64a SHA1: 49654851fb08c7798c7a78b4a30d509bd8b2b6ee SHA256: 0bfaab0692e44671b763c3c6205d0fb95570e9d962649e06a90471f40efcb13a SHA512: 14943b80ffdb37cabae5b92c36acdb23a8198cdd1a9805ac8c1aa930e775720351f18cde64c0ff224cd550d6917f36c8fceb8fdd834802a457db6eb128fbf93f Homepage: https://cran.r-project.org/package=OTBsegm Description: CRAN Package 'OTBsegm' (Apply Unsupervised Segmentation Algorithms from 'OTB') Apply unsupervised segmentation algorithms included in 'Orfeo ToolBox' software (), such as mean shift or watershed segmentation. Package: r-cran-ote Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-ote_1.0.1-1.ca2604.1_all.deb Size: 86324 MD5sum: 206b56392c3d91a0d3638cbc3a5cbfef SHA1: 1ec7f48839a686e58a19ec8f729d7bdf28693f82 SHA256: b6117018799c87d6d0f8b50ec41b6c645588230934569269c1afbbb762de6679 SHA512: 94a4af32f91736bccbff9af6bd9e2f4ea0497fd9d5e444a59f7999586168e4d2ccb2bfd01155af730cc7e26a5c02862b052893d78d5cb575163d64ee40f6d91c Homepage: https://cran.r-project.org/package=OTE Description: CRAN Package 'OTE' (Optimal Trees Ensembles for Regression, Classification and ClassMembership Probability Estimation) Functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) ) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) ) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data. Package: r-cran-otel Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-cli, r-cran-glue, r-cran-jsonlite, r-cran-otelsdk, r-cran-processx, r-cran-shiny, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-otel_0.2.0-1.ca2604.1_all.deb Size: 275258 MD5sum: 9b6411324f0d3e22efd5a9cfff60d2a9 SHA1: 084927b0ac10ab9ccf8f55e72b1d068784d7c9ea SHA256: 4211a38ed8c22d60957b181aee1ed036f8447624ab216d025bef9e43c0d87e8e SHA512: e54a7ee7da894fd000a0f281815ea2692f7fc679bdec99c2655825f7599ec64f9883d83d62d05835a23386bf902952833490a5487e42ff1820c4735e734808e5 Homepage: https://cran.r-project.org/package=otel Description: CRAN Package 'otel' (OpenTelemetry R API) High-quality, ubiquitous, and portable telemetry to enable effective observability. OpenTelemetry is a collection of tools, APIs, and SDKs used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior. This package implements the OpenTelemetry API: . Use this package as a dependency if you want to instrument your R package for OpenTelemetry. 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Perform a two-sample test on multivariate data using these limiting distributions and binning. 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From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed. Package: r-cran-otrimle Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-foreach, r-cran-doparallel, r-cran-robustbase, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-otrimle_2.0-1.ca2604.1_all.deb Size: 189556 MD5sum: a5f558bfbb4172227b6807b289d41786 SHA1: 850cf117da1ddd5dce89b4b51c229608b3ea65dc SHA256: 27d5bfdb4d52f2a1a98bc5388c567c2e723a90e2681c7f705ded61655ab509b6 SHA512: 8653ed50254ca0d7b015e18ba7b2bc9d0029738d46a8414490b14fc5c4c4b2d8e369e4876e14694e93898ec225773889ebced5efab5cb121e6da340b0abdea90 Homepage: https://cran.r-project.org/package=otrimle Description: CRAN Package 'otrimle' (Robust Model-Based Clustering) Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. 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Package: r-cran-outliermbc Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clusterr, r-cran-dbscan, r-cran-flexcwm, r-cran-ggplot2, r-cran-mixture, r-cran-mvtnorm, r-cran-spatstat.univar Filename: pool/dists/resolute/main/r-cran-outliermbc_0.0.1-1.ca2604.1_all.deb Size: 391670 MD5sum: 11c95ece8577b3ae34715bb379f61692 SHA1: 7b608fb0fecde544f28cdf3d1dbb54c67266b924 SHA256: 0997097a98bc6440295aafc0901bb374591d52619f754cb8f1737482c03d7fe0 SHA512: e1f0db661b0ee9ced223515e9f8c3aec1a184fba4ebab2711ec3d166d42feffee5905ac2f21fabf61672b8b704e7e0656b0e7195c768e01502bb526372ed84c3 Homepage: https://cran.r-project.org/package=outlierMBC Description: CRAN Package 'outlierMBC' (Sequential Outlier Identification for Model-Based Clustering) Sequential outlier identification for Gaussian mixture models using the distribution of Mahalanobis distances. 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Package: r-cran-outliers.ts.oga Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-forecast, r-cran-future, r-cran-future.apply, r-cran-gsarima, r-cran-parallelly, r-cran-robust Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-outliers.ts.oga_1.1.2-1.ca2604.1_all.deb Size: 594134 MD5sum: c8c0649a3e97f431e7a5f703e8c72f52 SHA1: 483e9a04f6ff38edc2ce5c27c9576c2271d95d3d SHA256: 9c682621281d54fb9b2fd69c44c0e764172f40f1d99d2609010aed61ed2134c7 SHA512: cf946056f14d9ba6e40e306e21e6442e92266dc91fba1f7647aebe038590696d79861c37341dc03cb3537fa2fd9582bb70cca16edbaf9dc6c5c467654fa3efcb Homepage: https://cran.r-project.org/package=outliers.ts.oga Description: CRAN Package 'outliers.ts.oga' (Efficient Outlier Detection for Large Time Series Databases) Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. 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Package: r-cran-outliers Architecture: all Version: 0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-outliers_0.15-1.ca2604.1_all.deb Size: 83146 MD5sum: f60f6b39b05e7619900fa56c4b139478 SHA1: 0188faca9492a85dd13c9fe61137b9f7fdbb87cb SHA256: d3c972e352582c69d61aebd368dbbca3a084d719078185a32e1ebd5f3268e96e SHA512: 75a8e74a876a632af527960361b3615b1bd35432f400cc38ace2caa07b75613becfb9a2e91b9fb7921e7e8ee8f4b3286bc29aea368621b1a427a44044b3592fa Homepage: https://cran.r-project.org/package=outliers Description: CRAN Package 'outliers' (Tests for Outliers) A collection of some tests commonly used for identifying outliers. 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O3 plots are described in Unwin(2019) . The available methods are HDoutliers() from the package 'HDoutliers', FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'. 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See Remiro‐Azócar A, Heath A, Baio G (2022) "Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data", Res. Synth. Methods, 1–31. ISSN 1759-2879, . 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Beaulieu et al (2012). Package: r-cran-ovbsa Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lmtest, r-cran-tidyr Suggests: r-cran-sensemakr Filename: pool/dists/resolute/main/r-cran-ovbsa_2.0.0-1.ca2604.1_all.deb Size: 71736 MD5sum: 1e7c2fe09779fb552ac275145877a97c SHA1: 7e7fb6d84915a55a2dc2b3f46f87714390400f0f SHA256: 24a110f8a276a93af50438df6c28d5c616d2aad4ce87e5d3ee4f58b1af0ec499 SHA512: cabf90bf56025a5e8c5d8b26d738fbe23bd946824493a117097e921ae0ae0e550b4a5e22e2e5a67fd2edb912b443da8c8afbcfaf46f5816a0d8b68ac38e633c7 Homepage: https://cran.r-project.org/package=ovbsa Description: CRAN Package 'ovbsa' (Sensitivity Analysis of Omitted Variable Bias) Conduct sensitivity analysis of omitted variable bias in linear econometric models using the methodology presented in Basu (2025) . Package: r-cran-overdisp Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-overdisp_0.1.2-1.ca2604.1_all.deb Size: 17168 MD5sum: 58fd7ad24701224d438c169c2b2f0883 SHA1: 97953ab79e3bc00c46c0327e062d0e8d883ea272 SHA256: 1832ca68ba55b17368da85fdb07f9d2c71f20bc70873d3298cb4f7af369d243c SHA512: 4b3db90ca55998f05b56fcfacf356d671796817502d909cd27cdf55b1d683fafd71ffd91be849ed2895f9a8d9a6db1190505b26f97f15e64eb7d9ea30d8be6c7 Homepage: https://cran.r-project.org/package=overdisp Description: CRAN Package 'overdisp' (Overdispersion in Count Data Multiple Regression Analysis) Detection of overdispersion in count data for multiple regression analysis. Log-linear count data regression is one of the most popular techniques for predictive modeling where there is a non-negative discrete quantitative dependent variable. In order to ensure the inferences from the use of count data models are appropriate, researchers may choose between the estimation of a Poisson model and a negative binomial model, and the correct decision for prediction from a count data estimation is directly linked to the existence of overdispersion of the dependent variable, conditional to the explanatory variables. Based on the studies of Cameron and Trivedi (1990) and Cameron and Trivedi (2013, ISBN:978-1107667273), the overdisp() command is a contribution to researchers, providing a fast and secure solution for the detection of overdispersion in count data. Another advantage is that the installation of other packages is unnecessary, since the command runs in the basic R language. 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Package: r-cran-overlapptest Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.geom Suggests: r-cran-sf Filename: pool/dists/resolute/main/r-cran-overlapptest_1.4-1.ca2604.1_all.deb Size: 980162 MD5sum: 7bb691ddb976bedded5cf294d30406b2 SHA1: f28e9188179e61a10f5b43f71b1307fae07defca SHA256: 910cacb4878c06ce4d91a5b54ce00e3148262e8101771d8cf76ea2e8faced138 SHA512: dffce6da22cd056960cd10d80e9e3da4ab378acd8526309034ebf8353dfceb8a0efb12ec8869d98dd027f194cc666e2e0fe39b67175a7cc2e9e65f23068c6567 Homepage: https://cran.r-project.org/package=overlapptest Description: CRAN Package 'overlapptest' (Test Overlapping of Polygons Against Random Rotation) Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) . 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This may be useful in applications where users need to mark regions on the plot for further input or processing. Package: r-cran-overturemapsr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-overturemapsr_0.1.0-1.ca2604.1_all.deb Size: 23442 MD5sum: 04bea6a2871a7606278fdd81c22a2eb6 SHA1: 4b27d90abf3848bf8ca6b049ed48ee3ba00643db SHA256: b2b819c827539582dfb2585c06d77dc3735f7a588dba279d59ce5728a5281b70 SHA512: 8ec536ee52d29683f1d655e11788cd7976a05c705d930b140bd7b30af680e788c20decb9de1e2386650ccbac7374a3dda6443a0d79f557ce77cc22a7c5e06d13 Homepage: https://cran.r-project.org/package=overturemapsr Description: CRAN Package 'overturemapsr' (Download Overture Maps Data in R) Overture Maps offers free and open geospatial map data sourced from various providers and standardized to a common schema. 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Getting an easy overview of a data set by displaying and visualizing sample information in different tables (e.g., time and scope conditions). The package also provides publishable 'LaTeX' code to present the sample information. Package: r-cran-ovl.ci Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ks, r-cran-matrix, r-cran-mixtools Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ovl.ci_0.1.1-1.ca2604.1_all.deb Size: 177906 MD5sum: ffd6baac77d7074d5fc88b8f97271563 SHA1: 48d6cf62271588624f46367486402bc6cde2801d SHA256: 956b4a5af4f5190c8f55cefe89ef1f72de3f83a0898725d9dc7b61bdc8a01b91 SHA512: 8be888afd90b3a913e9924492ffa81d7f5a8985f383c497b95df0324f2a7e1d0d8a5447cc9140c01919e8c297ed6767f9c4055799e9b8fea71d6473633e95311 Homepage: https://cran.r-project.org/package=OVL.CI Description: CRAN Package 'OVL.CI' (Inference on the Overlap Coefficient) Provides functions to construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) as well as extensions for multimodal distributions proposed by Alcaraz-Peñalba, A., Franco-Pereira, A., and Pardo, M.C. (2025) . 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The package supports live and/or batch evaluation workflows across multiple providers ('OpenAI', 'Anthropic', 'Google Gemini', 'Together AI', and locally-hosted 'Ollama' models), includes bias-tested prompt templates and a flexible template registry, and offers tools for constructing forward and reversed comparison sets to analyze consistency and positional bias. Results can be modeled using Bradley–Terry (1952) or Elo rating methods to derive writing quality scores. For information on the method of pairwise comparisons, see Thurstone (1927) and Heldsinger & Humphry (2010) . For information on Elo ratings, see Clark et al. (2018) . 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The package allows users to download environmental variables from global datasets (e.g., WorldClim, ESA WorldCover, Nighttime Lights), and to compute spatial and landscape metrics using a hexagonal grid system based on the H3 spatial index. It is useful for ecological modeling, biodiversity studies, and spatial data processing in landscape ecology. Fick and Hijmans (2017) . Zanaga et al. (2022) . Uber Technologies Inc. (2022) "H3: Hexagonal hierarchical spatial index". Package: r-cran-pakpc2017 Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pakpc2017_1.0.0-1.ca2604.1_all.deb Size: 1481984 MD5sum: c81aaa590ef8ca71f211a2befa026986 SHA1: d48ec263b07550a2294156e0eaaedf5c8a5aaa90 SHA256: 652f10c988e2d7197323ed15357928677c72cc9f1d7a964d2bb209fa97363d6d SHA512: 1e7300a8b4cbf7c7fef660103b7b34bef398bea9fd9ca1a4d33acbf7879aa7c316eaa05e77dbb8ffcf54363f5a32cb368934b608e81746fbc66485b77c533b18 Homepage: https://cran.r-project.org/package=PakPC2017 Description: CRAN Package 'PakPC2017' (Pakistan Population Census 2017) Provides data sets and functions for exploration of Pakistan Population Census 2017 (). 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Package: r-cran-pakpmics2014ch Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-pakpmics2014ch_0.1.0-1.ca2604.1_all.deb Size: 3199448 MD5sum: 59d4e9b6acad50a17c83b2769bd841e3 SHA1: a64c6785b366b17031b1b7df53c4298e94611c4d SHA256: d9b811bc2063b864dfa71120539c914ad04054ed0ebe56744c4d84eeaba0e1ac SHA512: 29bb9078ed00522cbfe436ead12b33188fee0538a04ef4c1f119e689fdd45af39721ccbf2f0db1a72b0370facb818db37c1d4dffa9abcefabfb6164deefa449b Homepage: https://cran.r-project.org/package=PakPMICS2014Ch Description: CRAN Package 'PakPMICS2014Ch' (Multiple Indicator Cluster Survey (MICS) 2014 ChildQuestionnaire Data for Punjab, Pakistan) Provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (). Package: r-cran-pakpmics2014hh Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-pakpmics2014hh_0.1.0-1.ca2604.1_all.deb Size: 2515716 MD5sum: dec7143acf6c6c63c5bc0f4431d18ca0 SHA1: 56af4c7d228e59484a616c3462108bc59fd0e900 SHA256: 88a3e80f8404bfdb431dec7bda4f30b24a09a58086187ae67b47827ee21827f8 SHA512: e6776ce78d476e982ee98cd49c0aa5c6d359421220636e0b568f5608bc3894053db4c513519df746dfefaff85523b24e53a602bc1f6c920cffa055475ae73fb2 Homepage: https://cran.r-project.org/package=PakPMICS2014HH Description: CRAN Package 'PakPMICS2014HH' (Multiple Indicator Cluster Survey (MICS) 2014 HouseholdQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (). Package: r-cran-pakpmics2014hl Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-pakpmics2014hl_0.1.1-1.ca2604.1_all.deb Size: 3323578 MD5sum: 70a716a2331ac171f42d05c8eaa29f8d SHA1: 746afb78ef84047fdef2af75fbefd6d62b721ffa SHA256: 1d179046f2760abec2559357f454e6913cf826ff9a509a8c8614fda75beab3f5 SHA512: 538a1d943f468439892f954a2e5cd92ae846296871d1a2e5ff6cf968bf546ea139beea55ab748a61f4ae9ef0246ec6571b911e753540934dab8ef0729d3efc40 Homepage: https://cran.r-project.org/package=PakPMICS2014HL Description: CRAN Package 'PakPMICS2014HL' (Multiple Indicator Cluster Survey (MICS) 2014 Household ListingQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Household Listing questionnaire data for Punjab, Pakistan. Package: r-cran-pakpmics2014wm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2883 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-pakpmics2014wm_0.1.1-1.ca2604.1_all.deb Size: 2917184 MD5sum: 6fe074db2dabe8639d828d71d5696c94 SHA1: 534963f936e687806bc586b711f954721462df1f SHA256: b39fd3c50e8e308f1e7e7ab284870fd2712147839842dac3ec2f614a8c190f56 SHA512: 5a965abfbfae70a37993d95047e132ff14c0332bb2e191d97671c8315ddbfe8629474d495091e4046ed0d0f3ed6a9d1aee8b07a1ec1050f31c2889558fabc06e Homepage: https://cran.r-project.org/package=PakPMICS2014Wm Description: CRAN Package 'PakPMICS2014Wm' (Multiple Indicator Cluster Survey (MICS) 2014 WomenQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan. Package: r-cran-pakpmics2018 Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1954 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pakpmics2018_1.2.0-1.ca2604.1_all.deb Size: 1804196 MD5sum: 162eacef353131e3da8d88f7fa8b5b34 SHA1: 7c1b6acb828a9067b56851494bc8f452b4cc87b5 SHA256: 738b0ff487d552671460257166cc0471deae01352f2eea0efabaf6f93c4183fd SHA512: 13908113dc554d882bb62b424ee15686a6ea26111aa368ff944570a782d8e4c892f42ede8707d5d0c433d9bf2077d8657244b27fb978efe640365d8836e319fd Homepage: https://cran.r-project.org/package=PakPMICS2018 Description: CRAN Package 'PakPMICS2018' (Multiple Indicator Cluster Survey (MICS) 2017-18 Data forPunjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household. Package: r-cran-pakpmics2018bh Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3891 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pakpmics2018bh_0.1.0-1.ca2604.1_all.deb Size: 3814078 MD5sum: c6f9edf7c8d360800103d5e89ad302c8 SHA1: 762728afd4fc05a9f0d7bfe52de341b7f42d3cbc SHA256: a85bc1375861372e00be0ce84453784718a73adf34aacee495fc6327316dcb3b SHA512: 021fc26859663ccd4184807c0f0ea26b20681f26c1423a0547c83e4973e54e00f83c79f8f8145bec3e28931f220317b52d92da712005af0f10ad56b1308d1ca8 Homepage: https://cran.r-project.org/package=PakPMICS2018bh Description: CRAN Package 'PakPMICS2018bh' (Multiple Indicator Cluster Survey (MICS) 2017-18 Birth Historyof Children Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018fs Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pakpmics2018fs_0.1.0-1.ca2604.1_all.deb Size: 3539472 MD5sum: f31899ee0a71eac46fd93757c4b3930f SHA1: 87877e91d817bb39d32a9411132de1a56e96e4fb SHA256: e681bfd04ccf67b4c258f718406c9e66437ffd1f31524d8fab8d46193edc4d17 SHA512: 97b061da2dd4d474fb0a2be570c4b9de428284ee78df59b882bee71602cae8ba676da640f508ccc6ad8cf9e010f6995c3a482561d281acacea41a355556cf0e8 Homepage: https://cran.r-project.org/package=PakPMICS2018fs Description: CRAN Package 'PakPMICS2018fs' (Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age5-17 Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018hh Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4779 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pakpmics2018hh_0.1.0-1.ca2604.1_all.deb Size: 4637546 MD5sum: 9fb878801eb2c6a4c275e8874b02be3d SHA1: 43a9885509ed01423b4f7d5924dc147aa9236b5c SHA256: 334d99a534f759b683b2d64c43aa361935cfcbc6384b2cd64bf2d9cd2eb3da06 SHA512: 2827344e8147654b492b98c1e0f0be62d005611a2bda8285511319f33d3d1c9e34b0dcea52330eaf733279f698a34c9ac7ae12eac7b5c8c9477e407e6aca3c28 Homepage: https://cran.r-project.org/package=PakPMICS2018hh Description: CRAN Package 'PakPMICS2018hh' (Multiple Indicator Cluster Survey (MICS) 2017-18 HouseholdQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018mm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3553 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pakpmics2018mm_0.1.0-1.ca2604.1_all.deb Size: 3434484 MD5sum: bc80a67daf1d98eb6bd6ec7bd1bf0218 SHA1: fe87fae7b5276cbff6ca68cf119fea4d4844e23b SHA256: f53f98fea733cdd81c613177597c8b52a9f3e63a21d91a5bebe6c217a9105f31 SHA512: eb6043c9b2969f6fac4ef1cf33e915ada1cf259b8db57ed97a3732e7e0513f2ca1cbc34f471a6592236a49b444a35e638b095d4004e4f8da1362f17ffb0ad22a Homepage: https://cran.r-project.org/package=PakPMICS2018mm Description: CRAN Package 'PakPMICS2018mm' (Multiple Indicator Cluster Survey (MICS) 2017-18 MaternalMortality Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). 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The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). 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'pakret' provides a minimalist API that generates preformatted citations for 'R' and 'R' packages, and adds their references to a '.bib' file directly from within your document. Package: r-cran-palaeosig Architecture: all Version: 2.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-teachingdemos, r-cran-rioja, r-cran-mgcv, r-cran-mass, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-forcats, r-cran-assertr, r-cran-vegan, r-cran-rlang, r-cran-ggrepel, r-cran-tidyr, r-cran-purrr Suggests: r-cran-units, r-cran-knitr, r-cran-rmarkdown, r-cran-gstat, r-cran-sf, r-cran-analogue, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-palaeosig_2.1-4-1.ca2604.1_all.deb Size: 442938 MD5sum: 82ec77aaba655a3cc729d3e0af779bc6 SHA1: 26cc7eb71819d59ae2a28460fcc18083f2256a87 SHA256: 4544cd6922797530a7f026e086c9723cef6353a43c9f7ad9715bbea6a9f8c5a9 SHA512: 305726920cd452df747b97a34dbdb92c048e51b80a2ae90b2a744b1fcb153cb0322bdbabb08c48387c4225ff1bfa77ed317a6133fc3d8106d9305139bf5c4a3d Homepage: https://cran.r-project.org/package=palaeoSig Description: CRAN Package 'palaeoSig' (Significance Tests for Palaeoenvironmental Reconstructions) Several tests of quantitative palaeoenvironmental reconstructions from microfossil assemblages, including the null model tests of the statistically significant of reconstructions developed by Telford and Birks (2011) , and tests of the effect of spatial autocorrelation on transfer function model performance using methods from Telford and Birks (2009) and Trachsel and Telford (2016) . Age-depth models with generalized mixed-effect regression from Heegaard et al (2005) are also included. Package: r-cran-palaeoverse Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2788 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-ape, r-cran-sf, r-cran-stringdist, r-cran-geosphere, r-cran-h3jsr, r-cran-httr, r-cran-pbapply, r-cran-lifecycle Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr, r-cran-paleotree, r-cran-phytools, r-cran-covr Filename: pool/dists/resolute/main/r-cran-palaeoverse_1.4.0-1.ca2604.1_all.deb Size: 2071842 MD5sum: 7bc8e1c7ab9b3fb9de95082b5bdd2559 SHA1: 414a2ff2860736995b0647e6e765b9d5554158fb SHA256: 4e40e933a940cb9cbbe26cc9fbc5eb0d58c93e7bfc0cf7d3311b022b73cc25e1 SHA512: 58a5d135d4fdac402130498a860b1b6c70119e005094e8f35bf7e293d413a3c803652e9a1f1f4b9c411a58a7353d0bc39f00d8fc29faefc18cac1b2cc68fe8f5 Homepage: https://cran.r-project.org/package=palaeoverse Description: CRAN Package 'palaeoverse' (Prepare and Explore Data for Palaeobiological Analyses) Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales ) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) . Package: r-cran-palasso Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-survival Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-remotes, r-cran-proc, r-bioc-edger, r-cran-ashr Filename: pool/dists/resolute/main/r-cran-palasso_1.0.0-1.ca2604.1_all.deb Size: 175248 MD5sum: af0fda47f6954f37bd324d98068f8941 SHA1: 32cb5fd8656a54aae6b982ab4e893012aba06514 SHA256: f347f74c9febf87aa80dcb0c40e0bab4192346f3da287fbacad1493d733e08b9 SHA512: d8352f90348fac1027fe7eae76a951fed6ac7e1e2a15dc542146ee5daeed41075d11cbfd8f93bf07121a7243aaa571f38ebd4a718bf2a304eeedd236a88d30de Homepage: https://cran.r-project.org/package=palasso Description: CRAN Package 'palasso' (Sparse Regression with Paired Covariates) Implements sparse regression with paired covariates (). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr () and CorShrink () from GitHub (see README). Package: r-cran-pald Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pald_0.0.5-1.ca2604.1_all.deb Size: 812044 MD5sum: 786c5696f63f911199c1139a0959cb70 SHA1: 6dbf05a7a47ed90fad8fba7e7c898639cd55f645 SHA256: 1dd1501b946a23f083747e3550d7430398e64305a3f3c870bf7485d3a4a223ad SHA512: 5483caaeeb8787dcf1ea01221647a5ecd9a14981af061eeff77c3b648ca5f9a85d17a038ef807811b9f6f708d9acbe19510c960021064c88b3ab4ff75823563e Homepage: https://cran.r-project.org/package=pald Description: CRAN Package 'pald' (Partitioned Local Depth for Community Structure in Data) Implementation of the Partitioned Local Depth (PaLD) approach which provides a measure of local depth and the cohesion of a point to another which (together with a universal threshold for distinguishing strong and weak ties) may be used to reveal local and global structure in data, based on methods described in Berenhaut, Moore, and Melvin (2022) . No extraneous inputs, distributional assumptions, iterative procedures nor optimization criteria are employed. This package includes functions for computing local depths and cohesion as well as flexible functions for plotting community networks and displays of cohesion against distance. Package: r-cran-paleoam Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-paleoam_1.0.1-1.ca2604.1_all.deb Size: 156226 MD5sum: 3dae6660afa4af82c9ba7e521bc40e4a SHA1: b6ef2e35e3d4204f4f3dd58de115d0d4ebb7980d SHA256: 8497e6c66457c7514be846f14624f896d8834b171ce374805de5a7e41f6d3d4b SHA512: 3d1aeeaaa9202bbeb4e228e2fcd9838607005ae7dc3b7a85e555ad8fa43c5966bda22fab419bdb36a9a48c4472899d50705c76a2436a06b9075db7a7b7ee6d02 Homepage: https://cran.r-project.org/package=paleoAM Description: CRAN Package 'paleoAM' (Simulating Assemblage Models of Abundance for the Fossil Record) Provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages. Package: r-cran-paleobiodb Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-maps, r-cran-terra Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-paleobiodb_1.0.1-1.ca2604.1_all.deb Size: 445484 MD5sum: ca35a983fd72958ecf4d14a45ef3de80 SHA1: da1c4edce0136cfe201cd59451ec11bf9b904df4 SHA256: c846b53e3db1c0c310420a3f5c09ac3ae806dd6b92821b990387546cb503acaf SHA512: 7ce64dde04f91afe886510786676720da3e9350ebc23ac1fbf9875d7fa1c69bb7ee4890cd94d33265b6dc9cebd0c8ed97681cb7be9c0204d12210e1d88e90f11 Homepage: https://cran.r-project.org/package=paleobioDB Description: CRAN Package 'paleobioDB' (Download and Process Data from the Paleobiology Database) Includes functions to wrap most endpoints of the 'PaleobioDB' API and to visualize and process the obtained fossil data. The API documentation for the Paleobiology Database can be found at . Package: r-cran-paleobuddy Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3395 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ape, r-cran-fitdistrplus, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-paleobuddy_1.1.0-1.ca2604.1_all.deb Size: 1705564 MD5sum: 1c5ad593097621b8badb9e94e4ca1560 SHA1: f904538b0d1b8909a46f028a4813784345e4831e SHA256: eebdfb14762ca8cc524340bc3135a1f9a6f690f5e0a4db38a05bfeebbddc147d SHA512: efc7f02980e24a4d31723f02167781ef506c6a2304f76e4d33495c9e859212085884a4f31e227649b97586af3fbe299dbe4994914dfc907c54ae734a81abca9f Homepage: https://cran.r-project.org/package=paleobuddy Description: CRAN Package 'paleobuddy' (Simulating Diversification Dynamics) Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like 'paleotree' and 'APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, 'paleobuddy' can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in 'RPANDA': Morlon H. et al (2016) . Package: r-cran-paleodiv Architecture: all Version: 0.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-stringr Suggests: r-cran-testthat, r-cran-strap, r-cran-divdyn, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-paleodiv_0.4.6-1.ca2604.1_all.deb Size: 748842 MD5sum: c9a5af7620d2ec0cba0285a91cae34e9 SHA1: 37e2a27baeabfd4b7797738c7cc485e6eaa6f915 SHA256: 188be95813223fae789617e40c73198a13c146cbf613612aef6be40bb06808cf SHA512: 7f0e54626d9de78dd4ef9ec6dfedcf93883fc5e4df643e72a063660d954adb98ba8833b0c647bba29a8733576d2824ca29f76c00e4a51133732fce9d91153c6f Homepage: https://cran.r-project.org/package=paleoDiv Description: CRAN Package 'paleoDiv' (Extracting and Visualizing Paleobiodiversity) Contains various tools for conveniently downloading and editing taxon-specific datasets from the Paleobiology Database , extracting information on abundance, temporal distribution of subtaxa and taxonomic diversity through deep time, and visualizing these data in relation to phylogeny and stratigraphy. Package: r-cran-paleomorph Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-abind, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-paleomorph_0.1.4-1.ca2604.1_all.deb Size: 64332 MD5sum: 60ee451eb3177fc418a6cb31d6b753fe SHA1: d97d6aeaeece49582fab717eb03120ac999ebcd5 SHA256: 2062eccfa0a9206cf7a7678bd8ecc3355fbf669f9fd78423d55ac47d2eda40cb SHA512: 487a330fd6edf42481aec85ac0bc57906adf4b1ada5f625578ae15227ca8b7bb03b6c6f8e2e9c9f500ff8d8fc0f18fd1dad235f414bf9e8f2dabd535438dd124 Homepage: https://cran.r-project.org/package=paleomorph Description: CRAN Package 'paleomorph' (Geometric Morphometric Tools for Paleobiology) Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses. Package: r-cran-paleopop Architecture: all Version: 2.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-poems, r-cran-r6, r-cran-sf, r-cran-trend Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-dplyr, r-cran-raster Filename: pool/dists/resolute/main/r-cran-paleopop_2.1.7-1.ca2604.1_all.deb Size: 3996942 MD5sum: e7b4cbfd9c149a280c2cbf74449fc056 SHA1: e864c3f59e6971c50f19454e95d2cf97b4ce2f4e SHA256: 82247783a72ab2e9a68a5b806d0a8e6d05d54b3b959dcf7f9df57c4fee763247 SHA512: f648a8f739551ffae84f5f804d88c58227048feb788e59caed9a2e60833aeec28e8291dfdfc75c03824c2f0d904a0a0acd85c517d1d10bbc97aae2217966e419 Homepage: https://cran.r-project.org/package=paleopop Description: CRAN Package 'paleopop' (Pattern-Oriented Modeling Framework for Coupled Niche-PopulationPaleo-Climatic Models) This extension of the poems pattern-oriented modeling (POM) framework provides a collection of modules and functions customized for paleontological time-scales, and optimized for single-generation transitions and large populations, across multiple generations. Package: r-cran-paleotree Architecture: all Version: 3.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1586 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-phytools, r-cran-jsonlite, r-cran-png, r-cran-rcurl Suggests: r-cran-spelling, r-cran-testthat, r-cran-curl Filename: pool/dists/resolute/main/r-cran-paleotree_3.4.7-1.ca2604.1_all.deb Size: 1528662 MD5sum: eda9f57205eba2f6c9ee44f7971440ff SHA1: 91f3ee1b9c8932ec330b839a50dd224e9426c114 SHA256: 39809e9faaeb69104e88ee798f9f92bc09bd0a01ecb3ec8fcbc7dc686105068e SHA512: c12a085f3a49121b3b74b48b88bce7694ded1fdf2d14681b9005f274fe4aaf888f86c3be07d637f772751128a142dfd94f39efb7222ad9d1a2bc5cfa9afd66d3 Homepage: https://cran.r-project.org/package=paleotree Description: CRAN Package 'paleotree' (Paleontological and Phylogenetic Analyses of Evolution) Provides tools for transforming, a posteriori time-scaling, and modifying phylogenies containing extinct (i.e. fossil) lineages. In particular, most users are interested in the functions timePaleoPhy, bin_timePaleoPhy, cal3TimePaleoPhy and bin_cal3TimePaleoPhy, which date cladograms of fossil taxa using stratigraphic data. This package also contains a large number of likelihood functions for estimating sampling and diversification rates from different types of data available from the fossil record (e.g. range data, occurrence data, etc). paleotree users can also simulate diversification and sampling in the fossil record using the function simFossilRecord, which is a detailed simulator for branching birth-death-sampling processes composed of discrete taxonomic units arranged in ancestor-descendant relationships. Users can use simFossilRecord to simulate diversification in incompletely sampled fossil records, under various models of morphological differentiation (i.e. the various patterns by which morphotaxa originate from one another), and with time-dependent, longevity-dependent and/or diversity-dependent rates of diversification, extinction and sampling. Additional functions allow users to translate simulated ancestor-descendant data from simFossilRecord into standard time-scaled phylogenies or unscaled cladograms that reflect the relationships among taxon units. Package: r-cran-paleots Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mnormt, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-paleots_0.6.2-1.ca2604.1_all.deb Size: 579222 MD5sum: bb334a1b9975e81cf4ad835a532726be SHA1: d76a2a0930255905611dcb9f47efe7f38feb85ac SHA256: ec7b76e469219762e6737098d2e30fc8e7ae9936649b2e6df1ec16718e3cceda SHA512: c287a8e04bba947bcb62b3ff9726d44afa53aee4e6c69e571e84a9d34f1e0e5e891bb9e66d091c402177065061e5fe8d073dd1a57a219b1c9e5e4679400a1669 Homepage: https://cran.r-project.org/package=paleoTS Description: CRAN Package 'paleoTS' (Analyze Paleontological Time-Series) Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts). Package: r-cran-palette Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-pillar, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-palette_0.0.3-1.ca2604.1_all.deb Size: 121758 MD5sum: 13ee7e657a06164e5a570be570fb4af9 SHA1: 6b6e25631cdb6ff249cf2db8e07498f7b85e126e SHA256: a8a89791a030cc2ef5de7b6ec6777de33a7b75eca3fcb1f42cf85dba204dd3e6 SHA512: 42567f60745f3ee9f2ff60107dba71ed1553befc39af6be5508420daf06fd92d95cfb014704dd54f3adfbec4073454fb47ad821a65e43f24ec543ab27f1b33fd Homepage: https://cran.r-project.org/package=palette Description: CRAN Package 'palette' (Color Scheme Helpers) Hexadecimal codes are typically used to represent colors in R. Connecting these codes to their colors requires practice or memorization. 'palette' provides a 'vctrs' class for working with color palettes, including printing and plotting functions. The goal of the class is to place visual representations of color palettes directly on or, at least, next to their corresponding character representations. Palette extensions also are provided for data frames using 'pillar'. Package: r-cran-paletteer Architecture: all Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-prismatic, r-cran-rematch2, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggthemes, r-cran-harrypotter, r-cran-knitr, r-cran-oompabase, r-cran-palr, r-cran-pals, r-cran-rmarkdown, r-cran-scico, r-cran-testthat, r-cran-vdiffr, r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-paletteer_1.7.0-1.ca2604.1_all.deb Size: 473800 MD5sum: c13f357dfda40a1ee8885435eb690455 SHA1: b987b558ba98ab0aace451ef398f0bd9be9a81a7 SHA256: 10a84e51e09e2b852a7ce9005feceb1e8b3d8d6aba94123d91047017f542b901 SHA512: 7d978562bf5774a16f74ddf30ed85f58ffbc915c57db6a20dd6228d0aa733a29c229c388ef0a399b5f17dff45aca2c2c36bea8a3b5b69ff5dcf920b5bc7ed135 Homepage: https://cran.r-project.org/package=paletteer Description: CRAN Package 'paletteer' (Comprehensive Collection of Color Palettes) The choices of color palettes in R can be quite overwhelming with palettes spread over many packages with many different API's. This packages aims to collect all color palettes across the R ecosystem under the same package with a streamlined API. Package: r-cran-paletteknife Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-paletteknife_0.4.2-1.ca2604.1_all.deb Size: 58214 MD5sum: 75de673ff203296d53e5859bf6026c17 SHA1: 0337cda8e214ba2b4ffbba5fbf987f2843e2f74d SHA256: 6bf7f79781a7d2cd837ae79cc715a5f235631e8ca8758f0ae93d9c26a13689a5 SHA512: b57cd64051cc3f11e2daa4cb6afc095b3e41266c216036c824c5a11e4010aa569a79fc5bfa92763ad494c65cd8996a942bf618dd2151796ab984de1cd77bb6f0 Homepage: https://cran.r-project.org/package=paletteknife Description: CRAN Package 'paletteknife' (Create Colour Scales and Legend from Continuous or CategoricalVectors) Streamlines the steps for adding colour scales and associated legends when working with base R graphics, especially for interactive use. Popular palettes are included and pretty legends produced when mapping a large variety of vector classes to a colour scale. An additional helper for adding axes and grid lines complements the base::plot() work flow. Package: r-cran-palettephines Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-dplyr, r-cran-sfdep, r-cran-tidyr, r-cran-roroph, r-cran-rnaturalearth, r-cran-rnaturalearthdata Filename: pool/dists/resolute/main/r-cran-palettephines_0.1.3-1.ca2604.1_all.deb Size: 54564 MD5sum: 1cd865869ff640e549c460b453c655e3 SHA1: 0241c81a7c87f8471160888293a63f154649016e SHA256: 987606541ec24ee56f52a2fcd351f7c775d4a82eb3c6d0385a97fc152750ddad SHA512: bf8cb55c6bb6bd651310258631bcf39a0de4ac8a03c61ab9f407f531c74176bc7b4b9b009ace8b9619ba7cb582e989dc1f9fc32026777e5ca3d80d04dd87b172 Homepage: https://cran.r-project.org/package=palettephines Description: CRAN Package 'palettephines' (Analytical Color Palettes for Philippine Phenology) Provides specialized color palettes representing phenological transitions and biological lifecycles within Philippine landscapes. Rather than abstract gradients, these scales are anchored to topologically grounded states such as agricultural maturity, seasonal vegetation shifts, and environmental readiness. Palettes are indexed against the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale (Meier, 2023) for terrestrial vegetation and the Reef Health Index (RHI) framework (McField and Kramer, 2007) for marine ecosystems. This ensures scientific interoperability across archipelagic spatial models, aligning with global standards for ecological state-transition modeling (Schwartz, 2013) . Package: r-cran-palettes Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 988 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-cli, r-cran-pillar, r-cran-rlang, r-cran-purrr, r-cran-prismatic, r-cran-farver, r-cran-ggplot2, r-cran-scales, r-cran-tibble Suggests: r-cran-pkgdown, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-colorspace, r-cran-gt, r-cran-biscale, r-cran-sf, r-cran-patchwork, r-cran-metbrewer, r-cran-nord, r-cran-pnwcolors, r-cran-viridislite, r-cran-covr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-palettes_0.2.2-1.ca2604.1_all.deb Size: 613018 MD5sum: 5de2d69216c9baf39e8782b11979dcee SHA1: 65a553e83ec55614747d3ab39df85d70fc0f9b1b SHA256: 4c005e40981e8a5075cd054097a44f86c210704a494dea0f80d3e85947693701 SHA512: e19e19a8728a03623f781f6e5da9fce5b9e6d97d3d355f221c59ed1344e212734126c3b478533215abcacb94e9959916a8536ee1597fac350a1b568b8b88dc59 Homepage: https://cran.r-project.org/package=palettes Description: CRAN Package 'palettes' (Methods for Colour Vectors and Colour Palettes) Provides a comprehensive library for colour vectors and colour palettes using a new family of colour classes (palettes_colour and palettes_palette) that always print as hex codes with colour previews. Capabilities include: formatting, casting and coercion, extraction and updating of components, plotting, colour mixing arithmetic, and colour interpolation. Package: r-cran-palettesforr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-palettesforr_0.1.2-1.ca2604.1_all.deb Size: 124818 MD5sum: b3b06253502345e4c12a9d7661aa4c09 SHA1: 2aeb41b58572743a4082cba5df790dac73964bc0 SHA256: 043cb3ca88b1064f0a099a0a22a207e67e9cdc7ca10b353e157428ecbf19b60a SHA512: efe911e87604bba8b9d6554e408b59c7b9f23234b2637bc5bc58a5eea31c3b58568b45da38b155f0717313b9c883e96593666ee06cd9e560846610da3357743c Homepage: https://cran.r-project.org/package=palettesForR Description: CRAN Package 'palettesForR' (GPL Palettes Copied from 'Gimp' and 'Inkscape') A set of palettes imported from 'Gimp' distributed under GPL3 (), and 'Inkscape' distributed under GPL2 (). Package: r-cran-palettetown Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-palettetown_0.1.1-1.ca2604.1_all.deb Size: 51536 MD5sum: 88c3d4c13cb8ca263af25830a42cf113 SHA1: 53fedc86807f55873088155a69b5a0b64250860e SHA256: f7cc4d2fb9dcb2929b7b3f0b8c367133986ef8cc6f774cb7584f08d74cb18f88 SHA512: bbcc190896d85e61f7e93ae2c52da1ed71c3444d5341f0b51405cd9d9dd649833a039dfe11b91e5ee093c59937ee0d4200dbbc4a91b6de8c668821d1fb4aecc2 Homepage: https://cran.r-project.org/package=palettetown Description: CRAN Package 'palettetown' (Use Pokemon Inspired Colour Palettes) Use Pokemon(R) inspired palettes with additional 'ggplot2' scales. Palettes are the colours in each Pokemon's sprite, ordered by how common they are in the image. The first 386 Pokemon are currently provided. Package: r-cran-palimpsestr Architecture: all Version: 0.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2263 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-ggplot2, r-cran-viridis, r-cran-ggrepel, r-cran-plotly, r-cran-dbi, r-cran-rsqlite, r-cran-rpostgres Filename: pool/dists/resolute/main/r-cran-palimpsestr_0.10.0-1.ca2604.1_all.deb Size: 1621158 MD5sum: 517bdd1ce33e752fccdc77409029a78a SHA1: 05500eccc5b419e09d4c00e795dcaa32c5a2ab66 SHA256: 9793b86fa415d89f416a4820c81630d44643e42b61d7aafa5ea7ca1651f09ca6 SHA512: e965497a1319f30195ba65f6676cbba2ea68e973eaf3e28f0559caaf8af0f46eec7b61385be723f89fc4bde37b1f324c3997decae3c4cda822b00d5ae5475593 Homepage: https://cran.r-project.org/package=palimpsestr Description: CRAN Package 'palimpsestr' (Probabilistic Decomposition of Archaeological Palimpsests) Probabilistic framework for the analysis of archaeological palimpsests based on the Stratigraphic Entanglement Field (SEF). Integrates spatial proximity, stratigraphic depth, chronological overlap, and cultural similarity to estimate latent depositional phases via diagonal Gaussian mixture Expectation-Maximisation (EM). Provides the Stratigraphic Entanglement Index (SEI), Excavation Stratigraphic Energy (ESE), and Palimpsest Dissolution Index (PDI) for quantifying depositional coherence, detecting intrusive finds, and measuring palimpsest formation. Includes simulation, diagnostics, phase-count selection, publication-quality plots, and Geographic Information System (GIS) export via 'sf'. Methods are described in Cocca (2026) . Package: r-cran-palinsol Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4394 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gsl Filename: pool/dists/resolute/main/r-cran-palinsol_1.0-1.ca2604.1_all.deb Size: 4347550 MD5sum: 2bbbf3e6864e119aea5690476622f65a SHA1: a16becbdd2845d8b8035119c70d7cfaef551e16f SHA256: ecf401ee278343d9b1123b010425c600b2a13313ca2e9f12ea77d8aeb9d95984 SHA512: c003dcb179249c937646d95e839f51daa3fa5d0351e3d32096abd91736235c37f9a8f479627e88e0567eabe53a42c8d4fcdf9c2737ebfc8e302bf7cef2a500d7 Homepage: https://cran.r-project.org/package=palinsol Description: CRAN Package 'palinsol' (Insolation for Palaeoclimate Studies) R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means and for all latitudes, and polar night dates. Package: r-cran-palmerpenguins Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3218 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-recipes Filename: pool/dists/resolute/main/r-cran-palmerpenguins_0.1.1-1.ca2604.1_all.deb Size: 2972064 MD5sum: dba25a3a93f7ab272c32f673a4b4bb7c SHA1: 3ce7eb6d6bb154eec82c1a5ff9de6475d8535dc6 SHA256: a3f58bce52ac744f3397c984eb2f8e4c9d8e1687672cc7cbae0cc5cbe24cb9b3 SHA512: 1fb96e4315e9b8ef0c1f65ec2a1b147c64221e44ebc706a673dbd21e2ec57d12011ed47c493441fa2219a22eb38f0ebd38ed08549f5d0448ead082ebefafa98b Homepage: https://cran.r-project.org/package=palmerpenguins Description: CRAN Package 'palmerpenguins' (Palmer Archipelago (Antarctica) Penguin Data) Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program. Package: r-cran-palmr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-palmr_0.2.0-1.ca2604.1_all.deb Size: 57122 MD5sum: 315ac37d7de8442d95399321f667bb4d SHA1: 055f7bddb395d10df95cda78511922f34ae7c21c SHA256: 036a5132e5e7734549f4381cde6561d14a4b7eef3a2f87365430911f752e88bf SHA512: 453dd28bdd3a993eff707f25ee8865fca461d49967b56c6e903fb37751e4fba11beb14af050d4dc49ffe44a2bb446375e080c56c05defcf0fbbacb8eacd72c99 Homepage: https://cran.r-project.org/package=PaLMr Description: CRAN Package 'PaLMr' (Interface for 'Google Pathways Language Model 2 (PaLM 2)') 'Google Pathways Language Model 2 (PaLM 2)' as a coding and writing assistant designed for 'R'. With a range of functions, including natural language processing and coding optimization, to assist 'R' developers in simplifying tedious coding tasks and content searching. Package: r-cran-palmtree Architecture: all Version: 0.9-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-formula Suggests: r-cran-mvtnorm, r-cran-psychotools Filename: pool/dists/resolute/main/r-cran-palmtree_0.9-1-1.ca2604.1_all.deb Size: 33690 MD5sum: a3a98602e303843f2737574a83fb0917 SHA1: ff8d2a032c471c3b2f6d1a4de4a4b0cbfbfd4031 SHA256: cf904e60950fc71d470ebe6d88063f09d55f4fb5e0a0f1888f3f65544ffcef91 SHA512: 148bb786db811d1b0b3d00c1b8eae6a4be26999976ad3c1b9ddf9a3a377264ad71cf34e9b7a6f7c472b54800a38bdcef6e9f71b9f3d5375b15da7ae2957ec450 Homepage: https://cran.r-project.org/package=palmtree Description: CRAN Package 'palmtree' (Partially Additive (Generalized) Linear Model Trees) This is an implementation of model-based trees with global model parameters (PALM trees). The PALM tree algorithm is an extension to the MOB algorithm (implemented in the 'partykit' package), where some parameters are fixed across all groups. Details about the method can be found in Seibold, Hothorn, Zeileis (2016) . The package offers coef(), logLik(), plot(), and predict() functions for PALM trees. Package: r-cran-palr Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-raster, r-cran-testthat, r-cran-covr, r-cran-stars, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-palr_0.4.0-1.ca2604.1_all.deb Size: 225688 MD5sum: 36c61bb231a5ce6870a6e50ba1239e7b SHA1: a6c8899755c31d44cc4e0046eb288a20c7786e8b SHA256: db31dd1ba1edde55acc31483cb24cc811252d581aa8ba9b4d147e304c53e49e0 SHA512: 44c58d711a6afcab2638093d89dc857916baf8a482fca1a898d4055804d9cc1484c8a28aca84245ffd3f990e778b94c42b5f5bddcf7be93acfc41e74563a1a57 Homepage: https://cran.r-project.org/package=palr Description: CRAN Package 'palr' (Colour Palettes for Data) Colour palettes for data, based on some well known public data sets. Includes helper functions to map absolute values to known palettes, and capture the work of image colour mapping as raster data sets. Package: r-cran-pals Architecture: all Version: 1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-dichromat, r-cran-mapproj, r-cran-maps Suggests: r-cran-classint, r-cran-ggplot2, r-cran-knitr, r-cran-latticeextra, r-cran-reshape2, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pals_1.10-1.ca2604.1_all.deb Size: 1842840 MD5sum: eb0b85c7ebee556a5123eaeed28b0e1e SHA1: af1427cd693cf1382073c7b53b7c69e078af496e SHA256: f3b1db4a763e1f5b9ca26f36b98976df01ba95d74593eec1645fbe2fba281623 SHA512: 8eed0ea2e7bdad90c5bdc2599972b0bbcdd6b12ef7d6b7ca06dfed7db4a69c19651428250e08f4fbebd6b65678a07491111eee519f5e8a7c82e26275fa258d49 Homepage: https://cran.r-project.org/package=pals Description: CRAN Package 'pals' (Color Palettes, Colormaps, and Tools to Evaluate Them) A comprehensive collection of color palettes, colormaps, and tools to evaluate them. See Kovesi (2015) . Package: r-cran-pam Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-data.table, r-cran-ggplot2, r-cran-minpack.lm, r-cran-cowplot, r-cran-gridextra, r-cran-ggthemes, r-cran-metrics Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pam_2.2.1-1.ca2604.1_all.deb Size: 186146 MD5sum: ee679c0d984a12e1918ee90cef472121 SHA1: dfa6cff4f6ebb62d5a4437c99fcf3bfbc21efd2a SHA256: 308876215384088e5bd3340ff069448a46def1997fb253a05cceeb316d62f698 SHA512: e3debbd59613b4dd0f92b79f2bc7b72918a4b2c6f1dcc579698ef364e17bd6428ca9d1092f93bf8913a993b70f57e8b03585fa9cb18cac76710d4aa1193a66f1 Homepage: https://cran.r-project.org/package=pam Description: CRAN Package 'pam' (Fast and Efficient Processing of PAM Data) Processing Chlorophyll Fluorescence & P700 Absorbance data. Four models are provided for the regression of Pi curves, which can be compared with each other in order to select the most suitable model for the data set. Control plots ensure the successful verification of each regression. Bundled output of alpha, ETRmax, Ik etc. enables fast and reliable further processing of the data. Package: r-cran-pambinaries Architecture: all Version: 1.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2977 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pambinaries_1.9.3-1.ca2604.1_all.deb Size: 1489184 MD5sum: a7d470f5b1a26b4b44eff9c3bd7f6c37 SHA1: 905a71f47208616118f559bbb9c17429d90d9394 SHA256: 244631d6273ecc640c222bda757177a4b946fbf1a36acf1bb793b7cf2c3acb4b SHA512: d67b88dc3a0120a3acfb26103e0c1c95b8d1657024aa69d6a1e80e25164ca5be8d71e60a0837fb623f5e126ec1bfe8fb4b0113cc7f89cd02eccab1a5933d9245 Homepage: https://cran.r-project.org/package=PamBinaries Description: CRAN Package 'PamBinaries' (Read and Process 'Pamguard' Binary Data) Functions for easily reading and processing binary data files created by 'Pamguard' (). All functions for directly reading the binary data files are based on 'MATLAB' code written by Michael Oswald. Package: r-cran-pameasures Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pameasures_0.1.0-1.ca2604.1_all.deb Size: 30422 MD5sum: 223d9a74aff09fbe48b9924aa725980b SHA1: f3482f65103cc5ac62b213bd74a8d2dadf2eb786 SHA256: bc84cc7d22a5507db20517d726bb11bea17621fc5f182a393b112d481d7439ce SHA512: a58635e5e524dfcff4c5c9b30a6ebdb4934ba79217b3ee04e1b4f2c05ba8b4b68ea34b211512712697a3badaef16812b4136afc39df549a9ab55252f135f73f3 Homepage: https://cran.r-project.org/package=PAmeasures Description: CRAN Package 'PAmeasures' (Prediction and Accuracy Measures for Nonlinear Models and forRight-Censored Time-to-Event Data) We propose a pair of summary measures for the predictive power of a prediction function based on a regression model. The regression model can be linear or nonlinear, parametric, semi-parametric, or nonparametric, and correctly specified or mis-specified. The first measure, R-squared, is an extension of the classical R-squared statistic for a linear model, quantifying the prediction function's ability to capture the variability of the response. The second measure, L-squared, quantifies the prediction function's bias for predicting the mean regression function. When used together, they give a complete summary of the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) for more details. Package: r-cran-pamhm Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-heatmapflex, r-cran-cluster, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-readxl, r-cran-readmore, r-cran-plyr, r-cran-robusthd Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pamhm_0.1.2-1.ca2604.1_all.deb Size: 262774 MD5sum: 3e9c707aec7b63571e2a62467720ff59 SHA1: 6dc061ad262f04acbb20203dbbaf596870a1affe SHA256: 48307d9e048839d860e36f2e1faa15b92dce56f59dc37f47534f2e99ab0c2e7d SHA512: 96abc1c140f8cc1cc56d631a84e77a7c841bcce6018e7f4650d6ea4a39bd3d29f4feea373e2bc56b13384c126a41443b41ebeed3f8a28f6ca2f785ce435a2631 Homepage: https://cran.r-project.org/package=PAMhm Description: CRAN Package 'PAMhm' (Generate Heatmaps Based on Partitioning Around Medoids (PAM)) Data are partitioned (clustered) into k clusters "around medoids", which is a more robust version of K-means implemented in the function pam() in the 'cluster' package. The PAM algorithm is described in Kaufman and Rousseeuw (1990) . Please refer to the pam() function documentation for more references. Clustered data is plotted as a split heatmap allowing visualisation of representative "group-clusters" (medoids) in the data as separated fractions of the graph while those "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering. Package: r-cran-pamm Architecture: all Version: 1.122-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmertest, r-cran-lattice, r-cran-mvtnorm, r-cran-lme4 Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-pamm_1.122-1.ca2604.1_all.deb Size: 86972 MD5sum: 5b7e1305d8eb6b2b43ef86b53cb3e25b SHA1: a6ff49048fffe434444c5c35fe2514e8ba7bdfcb SHA256: db31b008edfc3ea225f4abcd273204b00b062cb3ce6eac8ece4046eb912382a0 SHA512: 8555dff7ea0af0d5a83a149edb1b18a89f6e31e890feee665012f1a44cdc0fab1579af91e6e76a8ddc2b462f5bd9fb9a0b87513b0b85d6bca0f59803935c7283 Homepage: https://cran.r-project.org/package=pamm Description: CRAN Package 'pamm' (Power Analysis for Random Effects in Mixed Models) Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" and "lmerTest" packages. Package: r-cran-pammtools Architecture: all Version: 0.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-survival, r-cran-checkmate, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-lazyeval, r-cran-formula, r-cran-mvtnorm, r-cran-pec, r-cran-vctrs, r-cran-scam Suggests: r-cran-testthat, r-cran-mstate, r-cran-broom Filename: pool/dists/resolute/main/r-cran-pammtools_0.7.4-1.ca2604.1_all.deb Size: 773168 MD5sum: c64d8c6631a6a293c6a1074bfab89be7 SHA1: 1cb94b5f5191ee340e5bc585d66dff50df855ae9 SHA256: 46e04995b45fb9f18f4632ce9f8bca01e4b02b5aa6913ab37f0cf9e807a14ca3 SHA512: 5837f45ef286b79f11c9f057691517c9f31fa1a6eafd31f87a18f863c3c917a017be6dbf7a192dcc9aa53405ba7b5e9ea087d0aa64d505bdc08eed5c43e0e431 Homepage: https://cran.r-project.org/package=pammtools Description: CRAN Package 'pammtools' (Piece-Wise Exponential Additive Mixed Modeling Tools forSurvival Analysis) The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) ) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization. Package: r-cran-pampal Architecture: all Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-pambinaries, r-cran-pammisc, r-cran-tuner, r-cran-seewave, r-cran-gam, r-cran-data.table, r-cran-rsqlite, r-cran-purrr, r-cran-signal, r-cran-tidyr, r-cran-ggplot2, r-cran-knitr, r-cran-xml2, r-cran-rlang, r-cran-reticulate, r-cran-lubridate, r-cran-geosphere, r-cran-shiny, r-cran-future.apply Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pampal_1.5.2-1.ca2604.1_all.deb Size: 1205334 MD5sum: dcb98f6aaf769ffff155f7df7baa3025 SHA1: de2f264d9904f69318e57e87281d26641dc1c03d SHA256: b1dee000f8bcee9364db805259eb656cfd7b752f8603e8dfa8721faadd8199e5 SHA512: fe0c6e84119121c3b54b9bfff58c48096cde59889aa2209557d4c0ccf0a28abaf9a3bfdd3cd0a8d4abde33cab4585a0a5a419e6283d1fa3f3af49ee75914b780 Homepage: https://cran.r-project.org/package=PAMpal Description: CRAN Package 'PAMpal' (Load and Process Passive Acoustic Data) Tools for loading and processing passive acoustic data. Read in data that has been processed in 'Pamguard' (), apply a suite processing functions, and export data for reports or external modeling tools. Parameter calculations implement methods by Oswald et al (2007) , Griffiths et al (2020) and Baumann-Pickering et al (2010) . Package: r-cran-pamr Architecture: all Version: 1.57-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-survival Filename: pool/dists/resolute/main/r-cran-pamr_1.57-1.ca2604.1_all.deb Size: 664208 MD5sum: 85edce729207e12b717b15ea06a02b76 SHA1: ad2161d4ffcd1bec23bf57607848a06cc0f86fb2 SHA256: dbc40b481016dfcbd1e625e428b813038f188805247ce34e979a8cc6489b4cc2 SHA512: f4695fa97e4329e3497faa8474792863902aa11e857b3aba938265f97e4f48b0ebac7dfc459d23cd98b6d562b4fed4dfebe05aaccc1cebff44be7c4bc0dda4de Homepage: https://cran.r-project.org/package=pamr Description: CRAN Package 'pamr' (Pam: Prediction Analysis for Microarrays) Some functions for sample classification in microarrays. Package: r-cran-pams Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smacof Suggests: r-cran-lmtest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pams_0.1.0-1.ca2604.1_all.deb Size: 44014 MD5sum: 7e0248a42aa641874148994b7919cf04 SHA1: 35b87a0209e588172700cdd02c4df3d00702e10c SHA256: 6c1ce52faee2c5fb978a298ed6ecb869b6d03cfeb286f500905c3d5b8f921709 SHA512: 31f4928d0edff43fabd0b272053fd64f1e11a9e0b971db373d7b8a191d64fe60f63a76b50b31dfc748926267914bd5bc1efb654654126401e40d1838501bae2d Homepage: https://cran.r-project.org/package=pams Description: CRAN Package 'pams' (Profile Analysis via Multidimensional Scaling) Implements Profile Analysis via Multidimensional Scaling (PAMS) for the identification of population-level core response profiles from cross-sectional and longitudinal person-score data. Each person profile is decomposed into a level component (the person mean) and a pattern component (ipsatized subscores). PAMS uses nonmetric multidimensional scaling via the SMACOF algorithm to identify a small number of core profiles that represent the central response patterns in a sample of any size. Bootstrap standard errors and bias-corrected and accelerated (BCa) confidence intervals for individual core profile coordinates are estimated, enabling significance testing of coordinates that is not available in other profile analysis methods such as cluster profile analysis or latent profile analysis. Person-level weights, R-squared values, and correlations with core profiles are also estimated, allowing individual profiles to be interpreted in terms of the core profile structure. PAMS can be applied to both cross-sectional data and longitudinal data, where core trajectory profiles describe how response patterns change over time. Methods are described in Kim and Kim (2024) , de Leeuw and Mair (2009) , and Kruskal (1964) . 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There are tools for downloading AIS (automatic identification system) data from Marine Cadastre , connecting AIS data to GPS coordinates, plotting summaries of various soundscape measurements, and downloading relevant environmental variables (wind, swell height) from the National Center for Atmospheric Research data server . Most tools were developed to work well with output from 'Triton' software, but can be adapted to work with any similar measurements. 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(2019) . Package: r-cran-pandemics Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pandemics_0.1.0-1.ca2604.1_all.deb Size: 82058 MD5sum: e12f9e0ccae02e73ac002510df13b734 SHA1: 564401ae6ca64026eb5467da9162bde1c8e879e0 SHA256: a0dce3eee41ceb1f6e4d8759bfdb2bbdc82105f77019056a0cfc796c2f984279 SHA512: 09b86f26a26f9efe3f9525bfccca80535f940d9d0ec6fcd97aa944c79213116061a8f414a7c87643680498c136e382765c28d66618e74e756d7c1defefff9f36 Homepage: https://cran.r-project.org/package=pandemics Description: CRAN Package 'pandemics' (Monitoring a Developing Pandemic with Available Data) Full dynamic system to describe and forecast the spread and the severity of a developing pandemic, based on available data. These data are number of infections, hospitalizations, deaths and recoveries notified each day. The system consists of three transitions, infection-infection, infection-hospital and hospital-death/recovery. The intensities of these transitions are dynamic and estimated using non-parametric local linear estimators. The package can be used to provide forecasts and survival indicators such as the median time spent in hospital and the probability that a patient who has been in hospital for a number of days can leave it alive. Methods are described in Gámiz, Mammen, Martínez-Miranda, and Nielsen (2024) and . Package: r-cran-pandemonium Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3967 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tourr, r-cran-tibble, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-dendextend, r-cran-fpc, r-cran-shiny, r-cran-shinythemes, r-cran-shinyfeedback, r-cran-dt, r-cran-tidyr, r-cran-tidyselect, r-cran-magrittr, r-cran-detourr, r-cran-crosstalk, r-cran-rtsne, r-cran-plotly, r-cran-alphahull, r-cran-uwot, r-cran-ggpcp, r-cran-rlang, r-cran-viridis Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-vim Filename: pool/dists/resolute/main/r-cran-pandemonium_1.0.0-1.ca2604.1_all.deb Size: 2958966 MD5sum: 8c8f1dc57dddba238d25b2d2db7fca84 SHA1: e00c8aa71d5c1815586f4b4f5c3fb1a2bb92ea84 SHA256: 7ed76670411087efd03a1bcaad776f5aff10753304ebfabc49393ccd10c1fffe SHA512: 76745accb0fa76ad9ffdc5cacc3c90ab1c39a8f37c6da32a3dd2cf8c5bf68cf9746fafd176f3b1e7e37fc8c48c7880173bfcf7e4de3c29cbb4c8decb740491a5 Homepage: https://cran.r-project.org/package=pandemonium Description: CRAN Package 'pandemonium' (High Dimensional Analysis in Linked Spaces) A 'shiny' GUI that performs high dimensional cluster analysis. This tool performs data preparation, clustering and visualisation within a dynamic GUI. With interactive methods allowing the user to change settings all without having to to leave the GUI. An earlier version of this package was described in Laa and Valencia (2022) . Package: r-cran-pandoc Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs, r-cran-rappdirs, r-cran-rlang Suggests: r-cran-covr, r-cran-gh, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-pandoc_0.2.0-1.ca2604.1_all.deb Size: 150940 MD5sum: 4d41f02cda229fc4909cbefe6f367b12 SHA1: 615bd2fa0ac980fec3b270478248adceb77c8fff SHA256: 4431555d8b49042bbd5e14cbff29d1f53de485c3c20d2ee3b24a9ac92a47edf8 SHA512: e9722efc3b497e66d04f526e66b78f672a221d328999abe148887da618542d9eed26461e8e456b8d400cf3abe057d954874cbf02abd0d8c5830e0be35f77be01 Homepage: https://cran.r-project.org/package=pandoc Description: CRAN Package 'pandoc' (Manage and Run Universal Converter 'Pandoc' from 'R') Provides a set of tools to install, manage and run several 'Pandoc' versions. Package: r-cran-pandocfilters Architecture: all Version: 0.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pandocfilters_0.1-6-1.ca2604.1_all.deb Size: 247912 MD5sum: e52ac9e5607e8e6b477990428631e4b1 SHA1: 479916dd5235d997184566cb726d2de98348744f SHA256: 69bb9819cea65c321530fe5331b80f914046d5690c03dfb3246663b4d54680ee SHA512: 137fe9f7e0ed203e25d740a470c58b0640aa5348e61d00885fadf0a3aba31979c2ac0297e1808e2554a79bf69dbcebffc35e56d8ec803b96a06ebf2b56846133 Homepage: https://cran.r-project.org/package=pandocfilters Description: CRAN Package 'pandocfilters' (Pandoc Filters for R) The document converter 'pandoc' is widely used in the R community. One feature of 'pandoc' is that it can produce and consume JSON-formatted abstract syntax trees (AST). This allows to transform a given source document into JSON-formatted AST, alter it by so called filters and pass the altered JSON-formatted AST back to 'pandoc'. This package provides functions which allow to write such filters in native R code. Although this package is inspired by the Python package 'pandocfilters' , it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a report generator. Since 'pandocfilters' inherits most of it's functionality from 'pandoc' it can create documents in many formats (for more information see ) but is also bound to the same limitations as 'pandoc'. Package: r-cran-paneldesc Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-paneldesc_0.1.1-1.ca2604.1_all.deb Size: 262586 MD5sum: 0e72983c712f0068cc90958cbf3f7506 SHA1: 33b799ec31155f678c77840b486f1c50bbdbc659 SHA256: 2b4b064e1d867aab0d520d55ddd5266a77e2cc7017d927eda017419652098341 SHA512: 9fda7aef2a4b02f1f8062dc015cad29bdcfb87d21a8b33261c304365feea1e4966997339ce7efa6cf7c88cc4bc74cc37f9634dec50e4e57e16ae7478549cc8cd Homepage: https://cran.r-project.org/package=paneldesc Description: CRAN Package 'paneldesc' (Descriptive Analysis and Visualization for Panel Data) Provides a comprehensive set of tools for describing and visualizing panel data structures, as well as for summarizing and visualizing variables within a panel data context. Package: r-cran-panelhetero Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-ggplot2, r-cran-kernsmooth, r-cran-rearrangement Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-panelhetero_1.0.1-1.ca2604.1_all.deb Size: 228178 MD5sum: fcf4bd5d4c46ee52c3557278a9ce97d5 SHA1: 9f343a9500976810cb8ceb0e861ac21e4eb45364 SHA256: 22e84ca5ea54883b62caa31a83ff7f2427deae8218b560f52f810295ec6167c3 SHA512: 1fb81e5125c928d67e63c6b97b0c25f015e577548cd4f8ddf6b1a181bc139759763d5a9ca3641d023223cc78e9ec2aa7ab8ab8f2c625f19b89a4cfd689ef60a7 Homepage: https://cran.r-project.org/package=panelhetero Description: CRAN Package 'panelhetero' (Panel Data Analysis with Heterogeneous Dynamics) Understanding the dynamics of potentially heterogeneous variables is important in statistical applications. This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis. The methods are developed by Okui and Yanagi (2019) and Okui and Yanagi (2020) . Package: r-cran-panelr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-crayon, r-cran-dplyr, r-cran-formula, r-cran-ggplot2, r-cran-jtools, r-cran-lmertest, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-reformulas, r-cran-vctrs Suggests: r-cran-aer, r-cran-brms, r-cran-broom.mixed, r-cran-car, r-cran-clubsandwich, r-cran-geepack, r-cran-generics, r-cran-nlme, r-cran-plm, r-cran-sandwich, r-cran-skimr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-panelr_1.0.1-1.ca2604.1_all.deb Size: 943666 MD5sum: 38b59b383e5867574ca04bac88c4e9d5 SHA1: 506c4756242168cb4dba7cf17df3ab95114a8760 SHA256: 5026744bfa4d1f020a92cd3594cbd5911def479b9c89654f296f8340603efc63 SHA512: 76a0050d645968594538a064a020a57918652ac9bdc9b57ac26915fbe0bac74f3acf41b39de93512db32a9b503cd758e498b7f9fa63563f2e55a497d880df7e8 Homepage: https://cran.r-project.org/package=panelr Description: CRAN Package 'panelr' (Regression Models and Utilities for Repeated Measures and PanelData) Provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 ; Bell & Jones, 2015 ). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 ) and Bayesian estimation is (optionally) supported via 'Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 ) as well as a generalized linear model extension thereof using GEE. Package: r-cran-panelsummary Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fixest, r-cran-kableextra, r-cran-modelsummary, r-cran-rlang, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-gt, r-cran-knitr, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-panelsummary_0.1.3-1.ca2604.1_all.deb Size: 681442 MD5sum: f70f82d6e5a31d9cdc26c5edfd433200 SHA1: d97b120fc8164b69680997a7510fb76d0fac4163 SHA256: be6e9d7c3db6726b047dd32f316cf1d54f5c9178a695112b8be13a2758de90f1 SHA512: 21f28eeb35c352d3e4dee6247e1200a867394c0344bfa5be4112d1516361a9f9bd11fd8892d9504be04fc0131c8bf7898f1ecc72dc820c29cba2dbc4a5cfb24c Homepage: https://cran.r-project.org/package=panelsummary Description: CRAN Package 'panelsummary' (Create Publication-Ready Regression Tables with Panels) Create an automated regression table that is well-suited for models that are estimated with multiple dependent variables. 'panelsummary' extends 'modelsummary' (Arel-Bundock, V. (2022) ) by allowing regression tables to be split into multiple sections with a simple function call. Utilize familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars, coef_map, coef_omit, coef_rename, gof_map, and gof_omit from 'modelsummary' to clean the table, and additionally, add a row for the mean of the dependent variable without external manipulation. Package: r-cran-panelsur Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-formula.tools, r-cran-plm, r-cran-matlib, r-cran-fastmatrix Filename: pool/dists/resolute/main/r-cran-panelsur_0.1.0-1.ca2604.1_all.deb Size: 73094 MD5sum: 8c8b26fe17fdfe13271eaf75b90ee12a SHA1: 29ccf51a1c142109ef27a61f19b9e9b4b3711031 SHA256: f5e5972568ecb044f7197bc8f3eaa80436187eef7994b58f22c9b67d4e1e7de3 SHA512: 9fdd3d2a55b2161b1be8cb519c04af568cc5413126fc4ff08576909e7db79e06417343e6727629965ce3cd29359e6db68be6666c394892d0da46005a89b337b2 Homepage: https://cran.r-project.org/package=panelSUR Description: CRAN Package 'panelSUR' (Two-Way Error Component SUR Systems Estimation on UnbalancedPanel Data) Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression (SUR) systems on unbalanced panel in the one/two-way cases also taking into account the possibility of cross equation restrictions. Methodological details can be found in Biørn (2004) and Platoni, Sckokai, Moro (2012) . Package: r-cran-paneltests Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-plm, r-cran-zoo, r-cran-quantreg Filename: pool/dists/resolute/main/r-cran-paneltests_1.0.5-1.ca2604.1_all.deb Size: 172320 MD5sum: ea09ed2044f867db06ead51540b5455e SHA1: 79fd7eba502a7d7aa00c27d876b55a59e41724b6 SHA256: b6b1d44b12196285006a75327610c011523692a944383a3d7e1e0908b339f420 SHA512: b243be84b08270d562b33aa9eb5323afac0927f61cea51af4d144fdacef843a79db151dae8dc48e64f2a2a6d7dc4fc6c012e6df23c60ed78993047fa2eec6bb9 Homepage: https://cran.r-project.org/package=paneltests Description: CRAN Package 'paneltests' (Panel Data Pre-Testing and Diagnostic Suite) Pre-testing and diagnostic tools for panel data analysis. Researchers should run these tests before any panel regression to verify modelling assumptions. The package implements: (1) the Hsiao (2014, ) homogeneity F-tests (F1/F2/F3), Swamy (1970) parameter heterogeneity test, and Pesaran (2004) cross-sectional dependence test via xtpretest(); (2) missing-data detection, mechanism testing, and imputation for unbalanced panels via xtmispanel(); (3) quantile-regression cross-sectional dependence tests (T_tau and T-tilde_tau statistics) of Demetrescu, Hosseinkouchack and Rodrigues (2023) via xtcsdq(); and (4) the panel quantile-regression slope homogeneity S-hat and D-hat statistics of Galvao, Juhl, Montes-Rojas and Olmo (2017) via xtqsh(). Together these tests address three fundamental pre-testing questions: (i) are slopes homogeneous? (ii) is there cross-sectional dependence? and (iii) is the panel balanced and is missingness ignorable? Package: r-cran-paneltm Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-mass Filename: pool/dists/resolute/main/r-cran-paneltm_1.1-1.ca2604.1_all.deb Size: 142684 MD5sum: bb84c5366ad3de7f08e31491b90ba202 SHA1: 2f9bfe070c2a78078a3522ca8d724e921a3bcac5 SHA256: 4330e87627863b7f270aa158494a6928029be7eee4d39a7d5801f2d4f28f807d SHA512: f330124b872db2d565f56da17c42adb7c468bbde7e04423395a642cbf31775fcffd70bd347890a05e621d2fde84a4ec3c00ee87a3bc2ab49c843d1718b691a4f Homepage: https://cran.r-project.org/package=PanelTM Description: CRAN Package 'PanelTM' (Two- And Three-Way Dynamic Panel Threshold Regression Model forChange Point Detection) Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) ; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) ) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented. Package: r-cran-panelvar Architecture: all Version: 0.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-progress, r-cran-matrixcalc, r-cran-texreg, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-panelvar_0.5.6-1.ca2604.1_all.deb Size: 2582608 MD5sum: 17222e149867273e0978831fe9dda0b0 SHA1: aa4cd0252a21e4a06904a1efbfa908a38898ecd8 SHA256: 3d170aef8a9afe70a8356767611341970e33ad19f16800a09e63c31dbabc4cfb SHA512: 77440b4a33abfaa211c6a4dfd78535aa1fb4047f0605c5c31e2402e9f6da1da8bbe686b45224418a6d6bcc7c24f959d397db50f549426ce83214a8f85818cf99 Homepage: https://cran.r-project.org/package=panelvar Description: CRAN Package 'panelvar' (Panel Vector Autoregression) We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) , Arellano and Bond (1991) and the system GMM estimator by Blundell and Bond (1998) . We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions. Package: r-cran-panelview Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr Suggests: r-cran-testthat, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-panelview_1.3.1-1.ca2604.1_all.deb Size: 234998 MD5sum: d465689645c8d6125824f1eccae22a50 SHA1: 3555d458ebaf5c71b1a59e4d64e4b5a006e61a0b SHA256: 2d2c4f77568f090efb90db719ba3fefaf5c0820db1394aa8e0372ed76fb2813f SHA512: 33670f16e9f434e953a229a301e85990a7266d041bf872c74cd54d776847ac0a109b50ebc80b77deb5dfc4f7d12b6b61a17185edc73d18a3e537f42dbf0eec46 Homepage: https://cran.r-project.org/package=panelView Description: CRAN Package 'panelView' (Visualizing Panel Data) Visualizes panel data. It has four main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. For details, see . Package: r-cran-panelwranglr Architecture: all Version: 1.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-hmisc, r-cran-caret Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-panelwranglr_1.2.13-1.ca2604.1_all.deb Size: 40200 MD5sum: ac7f07604a6ada1332c918a17d78e1f6 SHA1: faee95821b45ac3eb8cc6853f3df501b3ba5b4ad SHA256: 13123ffbdfb018dbb8bb67bec5d005d185a4a6a4f1a7bafec583a3b7bf5527d1 SHA512: e31c35e770ebb2f6d00e7b9815a52fcb043298f8c0ee445c666d203eece3d21e1d12b9d85fd39ffa2f07d4954a6b2f3da7d60745933399daaa48cce93254b663 Homepage: https://cran.r-project.org/package=panelWranglR Description: CRAN Package 'panelWranglR' (Panel Data Wrangling Tools) Leading/lagging a panel, creating dummy variables, taking panel differences, looking for panel autocorrelations, and more. Implemented via a 'data.table' back end. Package: r-cran-pangaear Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-jsonlite, r-cran-xml2, r-cran-oai, r-cran-tibble, r-cran-hoardr, r-cran-png Suggests: r-cran-knitr, r-cran-testthat, r-cran-vcr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-pangaear_1.1.0-1.ca2604.1_all.deb Size: 89178 MD5sum: 20932a4755fb572ac9a231e19cf01108 SHA1: 9e7cd1351e7a2a3774096a542d751121205082ff SHA256: 6cd0b7aa716f5e898d77a14bbe616238d782e33e46006afccef53d473c06aad1 SHA512: 6be696a6c1d03e0005bcbd5d8f66299af836641204ec68c20fa8c662236e1d0472910e99d3ac834ea620cf6f27d77a4971cac4cc6031cd38affc3e6ce5e0cbf1 Homepage: https://cran.r-project.org/package=pangaear Description: CRAN Package 'pangaear' (Client for the 'Pangaea' Database) Tools to interact with the 'Pangaea' Database (), including functions for searching for data, fetching 'datasets' by 'dataset' 'ID', and working with the 'Pangaea' 'OAI-PMH' service. Package: r-cran-pangoling Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2669 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cachem, r-cran-data.table, r-cran-memoise, r-cran-reticulate, r-cran-rstudioapi, r-cran-tidyselect, r-cran-tidytable Suggests: r-cran-brms, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-covr Filename: pool/dists/resolute/main/r-cran-pangoling_1.0.3-1.ca2604.1_all.deb Size: 811962 MD5sum: 4b7777d3993c4a4cec63220c469f7662 SHA1: 188b0f89678671c5347675d7ef45b68aee78b6a5 SHA256: 257f094fe17ae532f5d4d6520d4943a7617799c61cbc565a580cc89b8f09a402 SHA512: 7052d5c563b694fc1a33edeebb06fd5cc44a1a9764011ccf71fba43f4be8da6b861e922f3e87be7b42ad3f86e83d4192cd9eb791c1231a842f9212fd8c50d1c4 Homepage: https://cran.r-project.org/package=pangoling Description: CRAN Package 'pangoling' (Access to Large Language Model Predictions) Provides access to word predictability estimates using large language models (LLMs) based on 'transformer' architectures via integration with the 'Hugging Face' ecosystem . The package interfaces with pre-trained neural networks and supports both causal/auto-regressive LLMs (e.g., 'GPT-2') and masked/bidirectional LLMs (e.g., 'BERT') to compute the probability of words, phrases, or tokens given their linguistic context. For details on GPT-2 and causal models, see Radford et al. (2019) , for details on BERT and masked models, see Devlin et al. (2019) . By enabling a straightforward estimation of word predictability, the package facilitates research in psycholinguistics, computational linguistics, and natural language processing (NLP). Package: r-cran-panjen Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-panjen_1.6-1.ca2604.1_all.deb Size: 73346 MD5sum: 04332585e5a3f62c790c30338d4a1c75 SHA1: 15cd2b207d66ea0ce97b0566970a0e68b006a319 SHA256: 41e5c12ae28b4a643b7fcfe890a4903fc0a43c660cb875f94c69292da70d2251 SHA512: 4ad110fd06f0b8746546d020d869d7c5bcb3a0e6917117f478858091b50ccdacdba13fff6a2a060c6d5ab74f2e8c5fe010b4181bf78a16b008bc30e29ee98ad1 Homepage: https://cran.r-project.org/package=PanJen Description: CRAN Package 'PanJen' (A Semi-Parametric Test for Specifying Functional Form) A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations. In contrast to the classic power-transformations, the methods in this package allows for theoretical driven user input and the possibility to compare with a non-parametric transformation. Package: r-cran-pannotator Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-colourpicker, r-cran-config, r-cran-configr, r-cran-dplyr, r-cran-exiftoolr, r-cran-geojsonsf, r-cran-ggplot2, r-cran-golem, r-cran-htmlwidgets, r-cran-jpeg, r-cran-jsonify, r-cran-jsonlite, r-cran-leaflet, r-cran-leafpm, r-cran-magrittr, r-cran-readr, r-cran-scales, r-cran-sf, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyhelper, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pannotator_1.0.1-1.ca2604.1_all.deb Size: 1835696 MD5sum: 488fe4c312e5030e308cef0c4ae59c54 SHA1: 839d4e3a4160c408a0c0c6b39a733686ae202926 SHA256: 31af150ade9d85bc830bca401be65b2c791b9f082ed137d38667e5d32b7668d2 SHA512: 40c5016409e065f0f0c5b827c6eb583771e187cacaca4f1347de78c5d7f555f2ad9db07d8770863d9f4d848c9d448ef89097c52bb14ab7aae1b7b1354a0c951a Homepage: https://cran.r-project.org/package=pannotator Description: CRAN Package 'pannotator' (Visualisation and Annotation of 360 Degree Imagery) Provides a customisable R 'shiny' app for immersively visualising, mapping and annotating panospheric (360 degree) imagery. The flexible interface allows annotation of any geocoded images using up to 4 user specified drop-down menus. The app uses 'leaflet' to render maps that display the geo-locations of images and Panellum , a lightweight panorama viewer for the web, to render images in virtual 360 degree viewing mode. Key functions include the ability to draw on & export parts of 360 images for downstream applications. Users can also draw polygons and points on map imagery related to the panoramic images and export them for further analysis. Downstream applications include using annotations to train Artificial Intelligence/Machine Learning (AI/ML) models and geospatial modelling and analysis of camera based survey data. Package: r-cran-panstarrs Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit64, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-jsonlite Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-panstarrs_0.2.3-1.ca2604.1_all.deb Size: 358016 MD5sum: 491a3c045c34c442dbe08d8488599a6e SHA1: bc13d258b700b61423e1904c373e0f7ac840df59 SHA256: 52b0ecbbdf6b3322aa04e769d46f9728e100d551571123c01945bd69e123250f SHA512: 427963091843748ed999e5f16bf8680210807f916d72aeeb9726b2e9e511c3cd08c6bd451b1dda266a8daf2995fb3ac0c98af751465603658ebc7da4bd51858f Homepage: https://cran.r-project.org/package=panstarrs Description: CRAN Package 'panstarrs' (Interface to the Pan-STARRS API) An interface to the API for 'Pan-STARRS1', a data archive of the PS1 wide-field astronomical survey. The package allows access to the PS1 catalog and to the PS1 images. (see for more information). You can use it to plan astronomical observations, make guidance pictures, find magnitudes in five broadband filters (g, r, i, z, y) and more. Package: r-cran-pantarhei Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1096 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-pantarhei_0.1.2-1.ca2604.1_all.deb Size: 552934 MD5sum: 1462ff24c1e73f802457ef8e7e9c6751 SHA1: b344704ba9b17076064e614e063670839fc6ce96 SHA256: b6fc173bee7bfc54586a9bc1373dfaa9f4fbfc226716e624fc37bff77114a9a0 SHA512: b1bfeb1b7df11a34a0791101df0a290edf95b1f6f1c1976705c14137186af10835222f77c3e39b02f13f6aeecb970c5c94946627f6f8720548e8cb11c22412bf Homepage: https://cran.r-project.org/package=PantaRhei Description: CRAN Package 'PantaRhei' (Plots Sankey Diagrams) Sankey diagrams are a powerfull and visually attractive way to visualize the flow of conservative substances through a system. They typically consists of a network of nodes, and fluxes between them, where the total balance in each internal node is 0, i.e. input equals output. Sankey diagrams are typically used to display energy systems, material flow accounts etc. Unlike so-called alluvial plots, Sankey diagrams also allow for cyclic flows: flows originating from a single node can, either direct or indirect, contribute to the input of that same node. This package, named after the Greek aphorism Panta Rhei (everything flows), provides functions to create publication-quality diagrams, using data in tables (or spread sheets) and a simple syntax. Package: r-cran-papaja Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2447 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tinylabels, r-cran-bookdown, r-cran-broom, r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-rmdfiltr, r-cran-yaml, r-cran-zip Suggests: r-cran-afex, r-cran-bayesfactor, r-cran-beeswarm, r-cran-boot, r-cran-car, r-cran-dplyr, r-cran-effectsize, r-cran-emmeans, r-cran-ggforce, r-cran-ggplot2, r-cran-latex2exp, r-cran-lme4, r-cran-lmertest, r-cran-mass, r-cran-multcomp, r-cran-nlme, r-cran-nnet, r-cran-r.rsp, r-cran-skimr, r-cran-spelling, r-cran-testthat, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-papaja_0.1.4-1.ca2604.1_all.deb Size: 1210598 MD5sum: cf3cbbfa7dcd74dff739c38463f30b95 SHA1: 17f6e0e2a1c5b76fd4c8f447ad6bc61f23d5091f SHA256: b0744bddae06ff373830f72230b82581c4caed5ec2f7cbaf0bd41c2da404acd6 SHA512: 7808896275540c4f8da1db184919b369d388000d6c93d9126c13c811de10d67a62a28b7560c925babc976305cafe4d8b7c3562126c3b4b807c1c52aa88113dc2 Homepage: https://cran.r-project.org/package=papaja Description: CRAN Package 'papaja' (Prepare American Psychological Association Journal Articles withR Markdown) Tools to create dynamic, submission-ready manuscripts, which conform to American Psychological Association manuscript guidelines. We provide R Markdown document formats for manuscripts (PDF and Word) and revision letters (PDF). Helper functions facilitate reporting statistical analyses or create publication-ready tables and plots. 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Package: r-cran-parabar Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-progress, r-cran-callr, r-cran-filelock Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-parabar_1.4.2-1.ca2604.1_all.deb Size: 1893430 MD5sum: a681af0a9d1ab83174c9d7ef5ec331f0 SHA1: c33fd40603c8d95beead65cd1dd4302487d09caa SHA256: 44a9589f997935844caa52d3d997768866a26b56d5d88fca590d0e2f264c4b0e SHA512: e19a739c352b6ac8b358b3013dc37078ebd5e6da2965a13b940a70114e5b1c2ffb42c247d9138db3c14d4acf75d7c8cdfa1ddc42f0bb5e648a71a7486c409a04 Homepage: https://cran.r-project.org/package=parabar Description: CRAN Package 'parabar' (Progress Bar for Parallel Tasks) A simple interface in the form of R6 classes for executing tasks in parallel, tracking their progress, and displaying accurate progress bars. Package: r-cran-parade Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-parade_0.1-1.ca2604.1_all.deb Size: 24352 MD5sum: b18299cec84b3d09e2dceaa928e3e04e SHA1: 41d8b81600a5a28d80c3acab80b31be7d18db7be SHA256: 26c17df0d07f5e58b05388be743dc7f2dc190caccb121e6e725c5df8c4cb560d SHA512: a89f1c71bf5022ad76cacfd96dd7c97de60d3ef20564917d94903e54279fca92e2c70491469a0474b2d43557e2cbcf189d011ad6581f51c282b7adb78c732de0 Homepage: https://cran.r-project.org/package=parade Description: CRAN Package 'parade' (Pen's Income Parades) Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities. Package: r-cran-paradox Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-rmarkdown, r-cran-mlr3learners, r-cran-e1071, r-cran-knitr, r-cran-lhs, r-cran-spacefillr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-paradox_1.0.1-1.ca2604.1_all.deb Size: 739164 MD5sum: 5cb4885f5c0ff6ac6796242baff51623 SHA1: 286ad822cd09664f22e1b9bafcbe16f8540b8f55 SHA256: 4347132199ea580093ef0d5b5562b5b12e09e3ded8cbe9609eed8ef882cc54ca SHA512: 7b36df9bdc252e973b12f4d747edf28740942c4f01be7aded9c351febe32edf6ef489ae325bcfd9d85c475637911f67f55e721e66f789dc92b7ee114ee927869 Homepage: https://cran.r-project.org/package=paradox Description: CRAN Package 'paradox' (Define and Work with Parameter Spaces for Complex Algorithms) Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes. Package: r-cran-parafac4microbiome Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositions, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-lifecycle, r-cran-magrittr, r-cran-multiway, r-cran-pracma, r-cran-rlang, r-cran-rtensor, r-cran-tidyr Suggests: r-cran-knitr, r-bioc-phyloseq, r-cran-rmarkdown, r-bioc-treesummarizedexperiment, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-withr, r-cran-nplstoolbox Filename: pool/dists/resolute/main/r-cran-parafac4microbiome_1.3.2-1.ca2604.1_all.deb Size: 2886764 MD5sum: 85487a2087006e9f61c0bcd0d27df4e9 SHA1: a5f9316ab8ab72bf0075a7cc5655b2f64c21cf77 SHA256: d5be918a1bf06ab66253fe8cea9a87de4790411fb45a7464bf373e6f9b566caf SHA512: 74f4561c56d5810eeb27c0c5943ea3af3406eb685b467a64d959ca196b3db2fb7a357c7d0c9ccc0090f32e2902f7577937726358384daf1be7ee7ee99701f9d8 Homepage: https://cran.r-project.org/package=parafac4microbiome Description: CRAN Package 'parafac4microbiome' (Parallel Factor Analysis Modelling of Longitudinal MicrobiomeData) Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) and Harshman (1970) . Package: r-cran-parallellogger Architecture: all Version: 3.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snow, r-cran-xml2, r-cran-jsonlite, r-cran-rstudioapi, r-cran-memuse Suggests: r-cran-sendmailr, r-cran-testthat, r-cran-shiny, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-parallellogger_3.5.1-1.ca2604.1_all.deb Size: 587742 MD5sum: 949e63a871d14d2429f0da868c129e51 SHA1: 15d01b9505faaab98436b6ba9927ea27c55e7813 SHA256: 7bb8f89ebd05dedc6c6e4343299d315f384b67e673decdee51aabeba26dfcf16 SHA512: 9d3fd6028ca9a85b1a0045bc8c9a97eae20730797855da616785311eb9ecda156ad5295935c6dc593eb178a6b19d8af821c80a2a22fa1d4b6ebb210c4270aa5d Homepage: https://cran.r-project.org/package=ParallelLogger Description: CRAN Package 'ParallelLogger' (Support for Parallel Computation, Logging, and FunctionAutomation) Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel). Package: r-cran-parallelmap Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate Suggests: r-cran-batchjobs, r-cran-batchtools, r-cran-data.table, r-cran-rmpi, r-cran-rpart, r-cran-snow, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-parallelmap_1.5.1-1.ca2604.1_all.deb Size: 108130 MD5sum: a0b2ff2f66f402839d7d320983c79058 SHA1: 76782f13cdee63045b8c7434b350ae915970509e SHA256: 76a0c884a9265128de4931883997536f37f2ada556e0df8fbe11232bfb741a58 SHA512: 3deaf03e8c312bebd3f3b52762983d8d139e4be9be2ddf42410e1c93531375cfde389fce12874d50dd5ae3df7873f0b673234e737cd4d3d4f31f431211433d99 Homepage: https://cran.r-project.org/package=parallelMap Description: CRAN Package 'parallelMap' (Unified Interface to Parallelization Back-Ends) Unified parallelization framework for multiple back-end, designed for internal package and interactive usage. The main operation is parallel mapping over lists. Supports 'local', 'multicore', 'mpi' and 'BatchJobs' mode. Allows tagging of the parallel operation with a level name that can be later selected by the user to switch on parallel execution for exactly this operation. Package: r-cran-parallelmcmccombine Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-parallelmcmccombine_2.0-1.ca2604.1_all.deb Size: 38510 MD5sum: a8f728e89314c6df55f1daef8b52e0bf SHA1: 0e10d298293586f42dea010cc3e43c8bab786c34 SHA256: 5d92e4a5f291817b26eee657328f06913b0d17bcb2daedd167f0405369e42adb SHA512: 20100995394c16769b3b33fe845b140e260c28e859651f4f0888ba8f6d56e61e02ad63722f8be1a410d5407253a5920e15358ad706712f98d5ac148430bd367c Homepage: https://cran.r-project.org/package=parallelMCMCcombine Description: CRAN Package 'parallelMCMCcombine' (Combining Subset MCMC Samples to Estimate a Posterior Density) See Miroshnikov and Conlon (2014) . Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets. Package: r-cran-parallelplot Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-testthat, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-parallelplot_0.4.0-1.ca2604.1_all.deb Size: 165356 MD5sum: 85e1219a989ad2bb829ceb69fb62b4f3 SHA1: d536c8d933a414ab24a9eaa076ca002b0935540e SHA256: 42d8f0937ea68c491473abb564f5b393d4238ee39d8a04122c389ec7b0bcb30e SHA512: 7d83275a98c52c1a7ca0560982ac92c60388b9274e259acef23d8d26faad166c131dd5d6f0d44739b0f9bc872bf89037051c38fd98ce636137d4ff6fd01d31c4 Homepage: https://cran.r-project.org/package=parallelPlot Description: CRAN Package 'parallelPlot' (`htmlwidget` for a Parallel Coordinates Plot) Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`. Package: r-cran-param2moment Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sn Filename: pool/dists/resolute/main/r-cran-param2moment_0.1.3-1.ca2604.1_all.deb Size: 160392 MD5sum: 1dd13e0735bba60110778bc091b46a20 SHA1: ef4e04ae97f21641f5a2d3b316396c01e294bdba SHA256: 8d4bded1e4ca599504710c12a939b4e72f8e21893318eb1cdfbfe755473059b5 SHA512: e3ce834054b730dc07208a3010ef6703b50ccbb82c6e2b2f7746677cd4d535e46ef0a7e0857aa9374c7aa3e57623a6131f60160eb8f117b093c71fa2fef0d962 Homepage: https://cran.r-project.org/package=param2moment Description: CRAN Package 'param2moment' (Raw, Central and Standardized Moments of ParametricDistributions) To calculate the raw, central and standardized moments from distribution parameters. To solve the distribution parameters based on user-provided mean, standard deviation, skewness and kurtosis. Normal, skew-normal, skew-t and Tukey g-&-h distributions are supported, for now. Package: r-cran-paramanova Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mlr3misc, r-cran-rlang, r-cran-rstatix, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-paramanova_0.1.4-1.ca2604.1_all.deb Size: 84340 MD5sum: 4e0b90cf6d346d16f806ec925c7c936a SHA1: 35c46f79546f0bc12404e1393fe7fa5f7082f2e7 SHA256: 5008c641975cb0c007466216dd05999a3592ed06e95635adfbebdd7b0705e614 SHA512: 08ef5bbebfa49fd597c1458b2245cc30cc335660626432ea46c17c453c57cfd0fe757a581ca56ff28b11d584814bbf8d05673e64557aa769961d68496448f4e9 Homepage: https://cran.r-project.org/package=ParamANOVA Description: CRAN Package 'ParamANOVA' (Fully Parameterizable ANOVA Tests) Allows the user to perform ANOVA tests (in a strict sense: continuous and normally-distributed Y variable and 1 or more factorial/categorical X variable(s)), with the possibility to specify the type of sum of squares (1, 2 or 3), the types of variables (Fixed or Random) and their relationships (crossed or nested) with the sole function of the package (FullyParamANOVA()). The resulting outputs are the same as in 'SAS' software. A dataset (Butterfly) to test the function is also joined. 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Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution). 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Package: r-cran-paramhetero Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass Filename: pool/dists/resolute/main/r-cran-paramhetero_1.0.0-1.ca2604.1_all.deb Size: 30714 MD5sum: 07268585cb14b67f0c466afc02d4d13a SHA1: 48276889c4f5d24b1ec224b4c68b25d93fe20bee SHA256: 45e21d3d7b397dce11a8da673f1d31ab98d53f864716203f77a8561e8858320f SHA512: 7751274dd94d9aee1559a0b7d940e286ccd1824f242e6841b5b4a99298b798dc853604e14f74f8da2b5fe52e0588ebc052fc327a65ea9562e923aceb2d1206cb Homepage: https://cran.r-project.org/package=paramhetero Description: CRAN Package 'paramhetero' (Numeric and Visual Comparisons of Heterogeneity in ParametricModels) Performs statistical tests to compare coefficients and residual variance across models. Also provides graphical methods for assessing heterogeneity in coefficients and residuals. Currently supports linear and generalized linear models. 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Package: r-cran-paramlink2 Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pedtools, r-cran-pedprobr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-paramlink2_1.0.6-1.ca2604.1_all.deb Size: 149990 MD5sum: ead4800207e3a996088faf79a6467eca SHA1: 2f0ed99d3cd7c003c9176f8bcafa14f72c592b6f SHA256: e76c8a3d67201c4f59ecca8a0d121617cf33fb27fc06125908fd17ef822f5ecf SHA512: 7bfeed5c6f5ccac2068e573c8b3960c04971c8909680813f4abffc60d974096ae14e9e4a514c5427207cb35d451618709a2ff62d64eebe4a00d93c84fc5a5152 Homepage: https://cran.r-project.org/package=paramlink2 Description: CRAN Package 'paramlink2' (Parametric Linkage Analysis) Parametric linkage analysis of monogenic traits in medical pedigrees. Features include singlepoint analysis, multipoint analysis via 'MERLIN' (Abecasis et al. (2002) ), visualisation of log of the odds (LOD) scores and summaries of linkage peaks. Disease models may be specified to accommodate phenocopies, reduced penetrance and liability classes. 'paramlink2' is part of the 'pedsuite' package ecosystem, presented in 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302). 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A suite of tools for analysing pedigrees with marker data, including parametric linkage analysis, forensic computations, relatedness analysis and marker simulations. The core of the package is an implementation of the Elston-Stewart algorithm for pedigree likelihoods, extended to allow mutations as well as complex inbreeding. Features for linkage analysis include singlepoint LOD scores, power analysis, and multipoint analysis (the latter through a wrapper to the 'MERLIN' software). Forensic applications include exclusion probabilities, genotype distributions and conditional simulations. Data from the 'Familias' software can be imported and analysed in 'paramlink'. Finally, 'paramlink' offers many utility functions for creating, manipulating and plotting pedigrees with or without marker data (the actual plotting is done by the 'kinship2' package). Package: r-cran-paramsim Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-foreach, r-cran-doparallel, r-cran-future, r-cran-tibble Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-paramsim_0.1.0-1.ca2604.1_all.deb Size: 34578 MD5sum: 4db4a597e967c987a9bb1a8b1326d3e1 SHA1: 6142019cf8970fa41d84c543204d6d0d21254666 SHA256: 58c712321a9a6f9758d53e7cd820066e6658474d1e0745857600cbf01798f2ae SHA512: adcb664113a4ebc04778b54089e8393abe122bcd41827b22f9bd52828a5b120ade9f30b88bf6a6c06803bb3f11d795c7e903b8f7e1ac340da0a6a9422acd7eb2 Homepage: https://cran.r-project.org/package=paramsim Description: CRAN Package 'paramsim' (Parameterized Simulation) This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters. 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Package: r-cran-parasiter Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-blakerci, r-cran-boot, r-cran-readr Filename: pool/dists/resolute/main/r-cran-parasiter_1.0-1.ca2604.1_all.deb Size: 94360 MD5sum: bd854ffe744c048ec9f92138dcb5661d SHA1: b698f96c2e5dc352cd893e09e2d5e7160e39a305 SHA256: a1842ccaed867ea22902810f4093961b933af2eab7791708fdc2c6ee1f2b5a0d SHA512: 127f3d90f69341a0e1e1c7ed556611df826d2228205544b46e5a7c565ee50b39fecbce9ccb6c451455de8e3fbad36f2b585c70bd0a2a9463aa36cd49f878f566 Homepage: https://cran.r-project.org/package=parasiteR Description: CRAN Package 'parasiteR' (A Theorical-Practical Approach to Parasitological Data Analysis) Standardizes and streamlines the processing of parasitological data by integrating descriptive analyses of parasite count distributions, automated calculation of parasitological indices and their dispersion measures, and intuitive visualizations for representing these metrics (Bush et al. 1997 , Reiczigel et al. 2019 ). Package: r-cran-parcats Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3928 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-easyalluvial, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-forcats, r-cran-magrittr, r-cran-tibble, r-cran-htmlwidgets, r-cran-stringr Suggests: r-cran-testthat, r-cran-covr, r-cran-randomforest, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-plotly, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-parcats_0.1.0-1.ca2604.1_all.deb Size: 1095244 MD5sum: b5e9d6f46053b1617b125d00345be144 SHA1: 3f6c003a97e64372ff6e49add61e72e7d92576af SHA256: 1684fad2432c6517c0f847f2ece08559077e5ca0466bbc880ffc1a146923369c SHA512: 9c965a94af0eb18372f967893a16fdc443fcf8f63ce16ad1800fa59847ba931f0e977e0a3922a5b2cf7be0ed13ccc3ae65ff122b283ac173e8d6cfcf64c1d918 Homepage: https://cran.r-project.org/package=parcats Description: CRAN Package 'parcats' (Interactive Parallel Categories Diagrams for 'easyalluvial') Complex graphical representations of data are best explored using interactive elements. 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You provide a function wrapper for your 'RSelenium' scraping routine with a set of inputs, and 'parsel' runs it in several browser instances. Chunked input processing as well as error catching and logging ensures seamless execution and minimal data loss, even when unforeseen 'RSelenium' errors occur. You can additionally build safe scraping functions with minimal coding by utilizing constructor functions that act as wrappers around 'RSelenium' methods. Package: r-cran-parserpdr Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-readr, r-cran-parallelly, r-cran-foreach, r-cran-future, r-cran-dofuture, r-cran-progressr Suggests: r-cran-testthat, r-cran-reticulate, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-parserpdr_1.1.2-1.ca2604.1_all.deb Size: 391936 MD5sum: 8e605c410cd61f31fba4815f75e31426 SHA1: 060afe713f7abf5b0fac223a2ff4bbba3a64f87c SHA256: c8c024ebec3f61fc0cb6449a8c38a4554f4e0eb9bb6e5202d8ed0dfb82cff947 SHA512: da8deacf6e77475afbcd3707c2298c901b8f06d4b57f7eab5816d3d3cce85caeab7bc186e1463e58accfc1f376430250aeca28ba392787c314e6e8d015e77243 Homepage: https://cran.r-project.org/package=parseRPDR Description: CRAN Package 'parseRPDR' (Parse and Manipulate Research Patient Data Registry ('RPDR')Text Queries) Functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R. The package also provides helper functions to manipulate data and execute common procedures such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database from the downloaded images. All functionalities are parallelized for fast and efficient analyses. Package: r-cran-parsim Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-parabar Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-parsim_0.3.1-1.ca2604.1_all.deb Size: 112800 MD5sum: 8198290ab53a5f9ed1e2e8de8fed59fa SHA1: fe4f9a4f2ceddfe830aa5973f705dc278f5c8894 SHA256: 2dafcf13b9d06e7e43c9ebdeb6f17f78a8881f75fe74dd451b1cde2584ccada0 SHA512: 619a56fda7afbaebfd703f85b626185d086f604d5efb8d10db9af98494ae7a02ab5876c8b37f4cc053e819f0d9bb24020c69cfadc0a6faee6a671984e46b1d5a Homepage: https://cran.r-project.org/package=parSim Description: CRAN Package 'parSim' (Parallel Simulator) Perform flexible simulation studies using one or multiple computer cores. The package is set up to be usable on high-performance clusters in addition to being run locally (i.e., see the package vignettes for more information). Package: r-cran-parsnip Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1798 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-globals, r-cran-glue, r-cran-hardhat, r-cran-lifecycle, r-cran-magrittr, r-cran-pillar, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-sparsevctrs, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-bench, r-cran-c50, r-cran-covr, r-cran-dials, r-cran-earth, r-cran-ggrepel, r-cran-keras, r-cran-keras3, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-liblinear, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-modeldata, r-cran-nlme, r-cran-prodlim, r-cran-ranger, r-cran-remotes, r-cran-rmarkdown, r-cran-rpart, r-cran-sparklyr, r-cran-survival, r-cran-tensorflow, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-parsnip_1.6.0-1.ca2604.1_all.deb Size: 1606986 MD5sum: eb8135fad205b1b58986101095fb867f SHA1: 0dade1ee42cde5b201eaa433e024e0bbfa3f8be0 SHA256: 0faf5e27f938982523400c9a73cc8daa3492838eb573cab16346b2d7d25dc91e SHA512: 64b769af37725cb3e0c8e5985402c1540fff3fe746ccddeb7c80b4af096412f18afed09228b6d8137de10f0464e5655c00857f92482c76bf3e9822cfa15ac0ea Homepage: https://cran.r-project.org/package=parsnip Description: CRAN Package 'parsnip' (A Common API to Modeling and Analysis Functions) A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', 'H2O', etc). 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The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) but considering the SMN family. 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This method follows the Frisch-Waugh-Lovell theorem, as explained in Lovell (2008) . Package: r-cran-partiallyoverlapping Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-partiallyoverlapping_2.0-1.ca2604.1_all.deb Size: 28748 MD5sum: 51dc7dddc4af58750afcc023357e702c SHA1: b50d0d290459e436cf58102ff92b90969e82ff21 SHA256: b840d6d335c61064754c320a6d9b93f083167357302400d513989ba042819e77 SHA512: c77ba89e1c8c5647aa9f058c28136936b0a4fe9df1638193c10c75d380afba3279f7fa77ee83ea4a02fe350d73b63ac92f678271501ca9a817e40b1a48eb72d1 Homepage: https://cran.r-project.org/package=Partiallyoverlapping Description: CRAN Package 'Partiallyoverlapping' (Partially Overlapping Samples Tests) Tests for a comparison of two partially overlapping samples. A comparison of means using the partially overlapping samples t-test: See Derrick, Russ, Toher and White (2017), Test statistics for the comparison of means for two samples which include both paired observations and independent observations, Journal of Modern Applied Statistical Methods, 16(1). A comparison of proportions using the partially overlapping samples z-test: See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics for comparing two proportions with partially overlapping samples. Journal of Applied Quantitative Methods, 10(3). Package: r-cran-partialtl Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-doubleml, r-cran-ncvreg Suggests: r-cran-lgr, r-cran-mlr3, r-cran-mlr3learners, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-partialtl_0.1.0-1.ca2604.1_all.deb Size: 48482 MD5sum: 37c225cef7a714b6c6f2e47540caa318 SHA1: 7d627e11b79e719af9f55eb9d818038a0ede2ee5 SHA256: e5a48330fc7182dc8fe0cf650b21ee7d2016a1536be5ccfb8dbd272209631cf6 SHA512: 663a9245f8b0c2d010248430b2be79f4c2545fbc6d2d27ca5f5b2bd4a59caac2634a67ffb101bd1b9b56d2f4038400fabd34a85716422140984fff411cd59a43 Homepage: https://cran.r-project.org/package=PartialTL Description: CRAN Package 'PartialTL' (Partial Transfer Learning for Causal Estimation) Implements partial transfer learning (PTL) for causal effect estimation using source and target data, with bootstrap-based source detection. Provides data generating processes and nuisance functions for simulation. Package: r-cran-particle.swarm.optimisation Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rgl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-particle.swarm.optimisation_1.0.1-1.ca2604.1_all.deb Size: 120488 MD5sum: 9d043db2953a1b2761a42c44486b7c4b SHA1: acb429aaa85c0775f6b349d72b2b8af0b604faa2 SHA256: 6394dfad422a520fe3eff223876ecdd8f601eb8b1a8a75f2dc52bc5ffe3368b5 SHA512: 55a7eb491b9482dae8a21514307457699563cb368316b184a6c826f9d2282d74bbd7bd6e5b8117254c2026ef4f619b2879da803a5855a98db50060f1ee9d489f Homepage: https://cran.r-project.org/package=particle.swarm.optimisation Description: CRAN Package 'particle.swarm.optimisation' (Optimisation with Particle Swarm Optimisation) A toolbox to create a particle swarm optimisation (PSO), the package contains two classes: the Particle and the Particle Swarm, this two class are used to run the PSO with methods to easily print, plot and save the result. Package: r-cran-partitionbefsp Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-partitionbefsp_1.0-1.ca2604.1_all.deb Size: 30862 MD5sum: 54d01b7829a70ce9be9212b267a3fd9d SHA1: ec7dc737f8e8debfc416cfa529910b902ccc8880 SHA256: 707136183f6d06a14e96d66ff7516c580b19083e443c17ce4a421454720cc470 SHA512: 3a3258f05722970e586fa4aa0d8e98d218c61b4d8dedc57a8c9d7c7a2bfb7ab4925c49eb0fd6218274d16c8abc805b252894ebc38b7bdda32993cdf6b2042e62 Homepage: https://cran.r-project.org/package=partitionBEFsp Description: CRAN Package 'partitionBEFsp' (Methods for Calculating the Loreau & Hector 2001 BEF Partition) A collection of functions that can be used to estimate selection and complementarity effects, sensu Loreau & Hector (2001) , even in cases where data are only available for a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the statistical corrections used here is available in Clark et al. (2019) . Package: r-cran-partitioncomparison Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-partitioncomparison_0.2.6-1.ca2604.1_all.deb Size: 221946 MD5sum: 9aea0082daeb4f59e33848251c5b5f13 SHA1: 3ce78dc1f9c11312d71576fe58bacd16a4f876bf SHA256: 62a16cd162ca34f3a36b82ee769b2e4a8e72f23b5f8f3ee2650126764c09bd7e SHA512: a285029d215aac4ac03a78217558b43548ca6aa3f947d99ae796945d7a06109ecd65b190446c990cebcb1bfa2ba73b791733c399f6a51aeeae2f0f6f5474b9ec Homepage: https://cran.r-project.org/package=partitionComparison Description: CRAN Package 'partitionComparison' (Implements Measures for the Comparison of Two Partitions) Provides several measures ((dis)similarity, distance/metric, correlation, entropy) for comparing two partitions of the same set of objects. The different measures can be assigned to three different classes: Pair comparison (containing the famous Jaccard and Rand indices), set based, and information theory based. Many of the implemented measures can be found in Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006) and Meila M (2007) . Partitions are represented by vectors of class labels which allow a straightforward integration with existing clustering algorithms (e.g. kmeans()). The package is mostly based on the S4 object system. Package: r-cran-partitionmetric Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-partitionmetric_1.1-1.ca2604.1_all.deb Size: 19294 MD5sum: 3fcf9fd820710ca083ff9426b15de4dd SHA1: 0f4fcdc82fa0b9ff4069eb49851e9f07950cfadb SHA256: 1d5c5b227204b9675b4ea169dad3e3485f271a26c74406fed3d366d8d2b93655 SHA512: 4daafe40c5e5f5a4a420526411cf8a881d5960989b5d8d2ecbe511255ccf0806c73fba2f69cb867c10cc2ab23ad8176fa9bd657169ef259900ff8843a698abdd Homepage: https://cran.r-project.org/package=partitionMetric Description: CRAN Package 'partitionMetric' (Compute a distance metric between two partitions of a set) partitionMetric computes a distance between two partitions of a set. Package: r-cran-partools Architecture: all Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-regtools, r-cran-data.table, r-cran-pdist Suggests: r-cran-rpart, r-cran-e1071, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-partools_1.1.7-1.ca2604.1_all.deb Size: 1242094 MD5sum: 3151882eaa78ce010f31103462428999 SHA1: a89aa10be0475efc573bf761b242a0f69d62c431 SHA256: 452d1b4a525cb072607359e0d50a3980b619e8c052032e2570dd8f26b0795742 SHA512: e1b4b67bf4953564364829124d2c48ffa769630ee3ec316dbf7ca51313703000ffba7c59dcc46acb9ef4270cb52336d2330ad464e0af03947fa73a162044034e Homepage: https://cran.r-project.org/package=partools Description: CRAN Package 'partools' (Tools for the 'Parallel' Package) Miscellaneous utilities for parallelizing large computations. Alternative to MapReduce. File splitting and distributed operations such as sort and aggregate. "Software Alchemy" method for parallelizing most statistical methods, presented in N. Matloff, Parallel Computation for Data Science, Chapman and Hall, 2015. Includes a debugging aid. 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Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support. 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Provides functions to scrape party infoboxes for color codes (HEX or HTML color names) and logo images. Includes integration with the Party Facts database for easy party lookups. Designed for political scientists and party researchers working with electoral and party data. For Party Facts, see Döring and Regel (2019) and Bederke, Döring, and Regel (2023) . 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Package: r-cran-path.analysis Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrr, r-cran-corrplot, r-cran-hmisc, r-cran-gplots, r-cran-mathjaxr, r-cran-pastecs, r-cran-diagrammer, r-bioc-complexheatmap, r-cran-metan Suggests: r-cran-car, r-cran-ggplot2, r-cran-devtools, r-cran-usethis, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-path.analysis_0.1-1.ca2604.1_all.deb Size: 750352 MD5sum: 0c9508228420aefc0f58bd823c3fab4c SHA1: fe92ffdd1b3f1b1f718af9a8ca0a76177a82a8cb SHA256: fcd62823e5e3174c1da7005aca220580a22df0a87832f8265c172dd2225c36ce SHA512: 804c671cfb2b7b427ea38e7647a07317fdc2c6d9feadd53b10ef2fbba38a98251b8e057bcaf64e8dc32a998eda71e7787be2395e43bb621b7046cae5380ed4f8 Homepage: https://cran.r-project.org/package=Path.Analysis Description: CRAN Package 'Path.Analysis' (Path Coefficient Analysis) Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the 'lavaan', 2)a sample of sequential path analysis in 'metan' suggested by Olivoto and Lúcio (2020) , 3)the simple 'PATHSAS' macro written in 'SAS' by Cramer et al. (1999) , and 4)the semPlot() function of 'OpenMx' as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a 'Minitab' macro developed by Arminian, A. in the paper by Arminian et al. (2008) . Package: r-cran-path.chain Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-stringi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-config, r-cran-yaml, r-cran-fs, r-cran-magrittr, r-cran-logger Filename: pool/dists/resolute/main/r-cran-path.chain_1.0.0-1.ca2604.1_all.deb Size: 194316 MD5sum: b5335ccb4fc3c56c146d05ac39254a86 SHA1: 68d60ed2c7671918fdf907318ea3c34b6e54649f SHA256: 48e69f3aee432c8791d057a3e38820c74645726cd47f2efc1b367296e2c10d28 SHA512: 0a46aced109ac7142786e9c9c6472e253f84344b63145ee5475f4adbe8486026affe0187479eb5f4f7e2fe44b7ce01cb0203a244ce08275c1ef392637724091a Homepage: https://cran.r-project.org/package=path.chain Description: CRAN Package 'path.chain' (Concise Structure for Chainable Paths) Provides path_chain class and functions, which facilitates loading and saving directory structure in YAML configuration files via 'config' package. The file structure you created during exploration can be transformed into legible section in the config file, and then easily loaded for further usage. Package: r-cran-pathfindr.data Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5188 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pathfindr.data_2.1.0-1.ca2604.1_all.deb Size: 5263568 MD5sum: 2f2dd40fdbcbbbfcf912df1f4fe81c23 SHA1: c3782fa3c3fdb9cde6a19c5b6bb1da40bef17486 SHA256: a6d28172b6f29a27e0e7612eb69cbedad215c205fd9a41c9b93ec1b5fbd985a5 SHA512: b7915a1c29161e1ff8f96a372aa1a06c3c7bc0de76a70dabf04f3654126cf6e47557cc5f393177aeddd79103cc7b4f17235f92fedd6ec62dff66a5d715b35d61 Homepage: https://cran.r-project.org/package=pathfindR.data Description: CRAN Package 'pathfindR.data' (Data Package for 'pathfindR') This is a data-only package, containing data needed to run the CRAN package 'pathfindR', a package for enrichment analysis utilizing active subnetworks. This package contains protein-protein interaction network data, data related to gene sets and example input/output data. Package: r-cran-pathfindr Architecture: all Version: 2.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pathfindr.data, r-cran-dbi, r-bioc-annotationdbi, r-cran-doparallel, r-cran-foreach, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggraph, r-cran-ggupset, r-cran-fpc, r-bioc-ggkegg, r-cran-httr, r-cran-igraph, r-cran-r.utils, r-cran-msigdbr, r-cran-knitr Suggests: r-bioc-org.hs.eg.db, r-cran-testthat, r-cran-covr, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-pathfindr_2.7.0-1.ca2604.1_all.deb Size: 1905714 MD5sum: 119cd296f3af60ef5e54ca4e4fa8ab47 SHA1: 614b013372607b134ea0262c49355adb08d3d239 SHA256: 899714df2e7a108a5c24d17cb4a32b13f8013b4563ceed3021a57aaffe047e03 SHA512: 65aa72cd5b1266ee3faff540d4d1bea7c4a8f1c37c8b654db8b32eb5ead2a927c90e99ae7fcf2a7c522ce905acec5116f8455e6ec3accccc9d12fb6016567aca Homepage: https://cran.r-project.org/package=pathfindR Description: CRAN Package 'pathfindR' (Enrichment Analysis Utilizing Active Subnetworks) Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. . Package: r-cran-pathlit Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-timeseries, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-pathlit_0.1.0-1.ca2604.1_all.deb Size: 31318 MD5sum: 3c2a7574227093d51c602956cfd1f5fa SHA1: e9717a147ec9da336e123645df9b84d8947ab718 SHA256: aa343dfb62d2141a8bdb620654959d316d8e81ebf6ff0ff29c26a655dcc42ca3 SHA512: e78f5979cce18893d53c3a574c7937a49ccb3196c6be1588928141da45eaedd4054576abe10426810417addc2a7a83ad95df80ba1b413104a6083f2d0cc936e9 Homepage: https://cran.r-project.org/package=pathlit Description: CRAN Package 'pathlit' (An SDK for the PathLit Engine) This wrapper houses PathLit API endpoints for R. The usage of these endpoints require the use of an API key which can be obtained at . Package: r-cran-pathmodelfit Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan Filename: pool/dists/resolute/main/r-cran-pathmodelfit_1.0.5-1.ca2604.1_all.deb Size: 27116 MD5sum: 1cd65983dff2b33be069eafe0a7cedeb SHA1: 0dcf2a1294df48830c16404b51932e0e9ec557a3 SHA256: 4b5e41a96cc21c147cf0eb36339e68228483b18c534500b89458dad520fd054c SHA512: 4766a35a3ceabba2030956c45ab8b44e18740f2d0b5911ca8e131f84735c0e90c9479a6114f6e0f16f4bf2a3386c3e7cf90b8e7c4a19dc66155ed0cd1a027b34 Homepage: https://cran.r-project.org/package=pathmodelfit Description: CRAN Package 'pathmodelfit' (Path Component Fit Indices for Latent Structural Equation Models) Functions for computing fit indices for evaluating the path component of latent variable structural equation models. Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated by Williams and O'Boyle (2011) and demonstrated by O'Boyle and Williams (2011) and Williams, O'Boyle, & Yu (2020) . Also included are fit indices described by Hancock and Mueller (2011) . Package: r-cran-paths Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bart, r-cran-boot, r-cran-gbm, r-cran-ggplot2, r-cran-metr, r-cran-twang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-paths_0.1.2-1.ca2604.1_all.deb Size: 125024 MD5sum: d5c4bcd33a734b8a80258fdfc1463b6c SHA1: 3401d50275575f9abc21f919eba506bed13dd710 SHA256: 6078d1a6a0536fd3afb97bdc2adfa25564fb6522efc4afe88d7522d50bf5a8a5 SHA512: 73dcab3621ed7da97e751ad54cd5f1972eba276757f8af87309c88080d996eb55762a0cb4216adba22c768e5fd8fa82fca5cafaedf45426b77bb54851ec67ee5 Homepage: https://cran.r-project.org/package=paths Description: CRAN Package 'paths' (An Imputation Approach to Estimating Path-Specific CausalEffects) In causal mediation analysis with multiple causally ordered mediators, a set of path-specific effects are identified under standard ignorability assumptions. This package implements an imputation approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis (Zhou and Yamamoto ). It contains two main functions: paths() for estimating path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified using the nonparametric bootstrap. Package: r-cran-pathviewr Architecture: all Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.matlab, r-cran-data.table, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-fancova, r-cran-purrr, r-cran-ggplot2, r-cran-tidyselect, r-cran-cowplot, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-anomalize, r-cran-covr Filename: pool/dists/resolute/main/r-cran-pathviewr_1.1.8-1.ca2604.1_all.deb Size: 2237092 MD5sum: 880777f12b65cd15cc6debbd9801dd30 SHA1: 0b845fff219392f1733eeecaeb3aa37baf4e7ef2 SHA256: 68589d6869f8ef451f1e393e5a39d339d5633cf36f1e0f8174f85bce1c58b525 SHA512: 15b5ca4348c19b4cf175341db3aee1d6114c92961d62b8e5595eb92eab28300c03351e87f6b3fa62f735b950bf4409d1342e64799c2fef0eee148e9c0af406f4 Homepage: https://cran.r-project.org/package=pathviewr Description: CRAN Package 'pathviewr' (Wrangle, Analyze, and Visualize Animal Movement Data) Tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's 'Motive', the Straw Lab's 'Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli. Package: r-cran-pathwayspace Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgraphspace, r-cran-scales, r-cran-rann, r-cran-igraph, r-cran-ggplot2, r-cran-ggrepel, r-cran-colorspace, r-cran-patchwork, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-reder Filename: pool/dists/resolute/main/r-cran-pathwayspace_1.2.0-1.ca2604.1_all.deb Size: 2794280 MD5sum: 98f0c44fb11a65824a6795a4e62f6064 SHA1: 9bed9e2393a6079db33dd4aeea723c8c2ad66306 SHA256: 058b438b13ad37267fe6915349f48c61cf6b603ee23dd9cf8ed3875d3044f1d7 SHA512: 8f9515af2d4fdf668bed5197f087bf2a1cbb8f73cc9fe6b854b7b6aa86450dc7b01f860feccc2f03a9619b4b77d6a512e0570587fb5b3ce49654fdebce910d95 Homepage: https://cran.r-project.org/package=PathwaySpace Description: CRAN Package 'PathwaySpace' (Spatial Projection of Network Signals along Geodesic Paths) For a given graph containing vertices, edges, and a signal associated with the vertices, the 'PathwaySpace' package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package uses a decay function to project these signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' has various applications, such as visualizing network data in a graphical format that highlights the relationships and signal strengths between vertices. By combining graph theory, signal processing, and visualization, 'PathwaySpace' provides a way of representing graph data on a continuous projection space. Based on methods introduced in Tercan et al. (2025) and Ellrott et al. (2025) . Package: r-cran-pathwayvote Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-harmonicmeanp, r-bioc-annotationdbi, r-bioc-clusterprofiler, r-cran-future, r-cran-furrr, r-cran-parallelly Suggests: r-bioc-go.db, r-bioc-org.hs.eg.db, r-bioc-reactome.db, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-pathwayvote_0.1.3-1.ca2604.1_all.deb Size: 77674 MD5sum: 937cbf78ef98668b308ee12c52c5fc9f SHA1: 28db0e07ac97b187597fccd160c2dd84ebdaeb9f SHA256: bbcbef38935b19ebaacbe93fc091c7e6a9cd062173374dd49a33a1d33f602619 SHA512: 08c35376b83e2c6b3f7ec7d8b2a1f46f0e9cb4116999f3c9db1a8979b0b0ffb25b03acb47210bb1986c2e36d8be772b956978967d855df1e1a8725bc35956e38 Homepage: https://cran.r-project.org/package=PathwayVote Description: CRAN Package 'PathwayVote' (Robust Pathway Enrichment for DNA Methylation Studies UsingEnsemble Voting) Performs pathway enrichment analysis using a voting-based framework that integrates CpG–gene regulatory information from expression quantitative trait methylation (eQTM) data. For a grid of top-ranked CpGs and filtering thresholds, gene sets are generated and refined using an entropy-based pruning strategy that balances information richness, stability, and probe bias correction. In particular, gene lists dominated by genes with disproportionately high numbers of CpG mappings are penalized to mitigate active probe bias—a common artifact in methylation data analysis. Enrichment results across parameter combinations are then aggregated using a voting scheme, prioritizing pathways that are consistently recovered under diverse settings and robust to parameter perturbations. Package: r-cran-patientgenerator Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1026 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-checkmate, r-cran-data.table, r-cran-dbi, r-cran-dplyr, r-cran-dt, r-cran-duckdb, r-cran-ellmer, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-r2d3, r-cran-r6, r-cran-shiny, r-cran-stringr, r-cran-testthat Suggests: r-cran-cdmconnector, r-cran-cohortcharacteristics, r-cran-cohortconstructor, r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testgenerator Filename: pool/dists/resolute/main/r-cran-patientgenerator_0.1.4-1.ca2604.1_all.deb Size: 637832 MD5sum: a4ad5685c086d3e45a0d0e398a0facf0 SHA1: 01797e92d7c13bce41f883307e0ebeee025c52a2 SHA256: 797ff15409e1f06376d4b82b971c6ff0239dccf40d98a211572656a81966bc94 SHA512: cff89a9d74b8a015a498767d3ad09fc638a589e2a76627b5b98a877ab4e47b19c6c3cf1602e5c692edc83d3846d17b5af21c6beebcad895b15dfeffa0bfaf14e Homepage: https://cran.r-project.org/package=PatientGenerator Description: CRAN Package 'PatientGenerator' (Generator of Synthetic Patient Data for the OMOP Common DataModel) Tools to generate synthetic patient-level test datasets in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Includes a chat-driven generator backed by large language models and an interactive 'shiny' designer for editing CDM test sets. Package: r-cran-patientlevelprediction Architecture: all Version: 6.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-andromeda, r-cran-cyclops, r-cran-databaseconnector, r-cran-digest, r-cran-dplyr, r-cran-featureextraction, r-cran-matrix, r-cran-memuse, r-cran-parallellogger, r-cran-proc, r-cran-prroc, r-cran-rlang, r-cran-sqlrender, r-cran-tidyr Suggests: r-cran-curl, r-cran-eunomia, r-cran-glmnet, r-cran-ggplot2, r-cran-gridextra, r-cran-iterativehardthresholding, r-cran-knitr, r-cran-lightgbm, r-cran-metrics, r-cran-mgcv, r-cran-ohdsishinyappbuilder, r-cran-pkgload, r-cran-polspline, r-cran-readr, r-cran-resourceselection, r-cran-resultmodelmanager, r-cran-reticulate, r-cran-rmarkdown, r-cran-rsqlite, r-cran-scoring, r-cran-survival, r-cran-survminer, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-patientlevelprediction_6.6.0-1.ca2604.1_all.deb Size: 2269746 MD5sum: b166c039ab638a1d9f4bdbadb3bbb1c0 SHA1: afaefdce49686531254659053f56f209f1b76203 SHA256: bb085b9c8807e90fd3d7333ce3636877e2b456f962cf1bef05c012be047b5dbc SHA512: bf4ed3af8e04a860975a573729d6d1f3b32103a0402103e83ecdb4a925225dc37a6213fa02e71f4112e1d9c498e15465f781b0335a2be0d2788638cc68484a7f Homepage: https://cran.r-project.org/package=PatientLevelPrediction Description: CRAN Package 'PatientLevelPrediction' (Develop Clinical Prediction Models Using the Common Data Model) A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) . Package: r-cran-patientprofiles Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3846 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-lifecycle, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-cohortconstructor, r-cran-covr, r-cran-dbi, r-cran-dbplyr, r-cran-dt, r-cran-duckdb, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-here, r-cran-hmisc, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-patchwork, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-withr Filename: pool/dists/resolute/main/r-cran-patientprofiles_1.5.0-1.ca2604.1_all.deb Size: 731342 MD5sum: a70ed734f27457cb7e85661f6f13d8c6 SHA1: b61c659006109e92b72be69edfd0e1a7954ac98b SHA256: 2500b66ed3eab19c5dd63adf27ec80c8c495a052f0d4d73324d7fb6158152024 SHA512: dca133dd33134302dc33e3f60f7013a4387953e532ffd7bde3e65e9bd93d1f58513ca080a707f34cf68d34dfbbe645d4fcb898d38d690bc4c452c7e45615e974 Homepage: https://cran.r-project.org/package=PatientProfiles Description: CRAN Package 'PatientProfiles' (Identify Characteristics of Patients in the OMOP Common DataModel) Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Package: r-cran-patientprofilesvis Architecture: all Version: 2.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7236 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clinutils, r-cran-ggplot2, r-cran-plyr, r-cran-cowplot, r-cran-reshape2, r-cran-knitr, r-cran-stringr, r-cran-gridextra, r-cran-scales Suggests: r-cran-pander, r-cran-shiny, r-cran-testthat, r-cran-rmarkdown, r-cran-gtable, r-cran-pdftools, r-cran-viridislite Filename: pool/dists/resolute/main/r-cran-patientprofilesvis_2.0.10-1.ca2604.1_all.deb Size: 3768394 MD5sum: 8f24893095a3e0c1faec30fe06e0f5ba SHA1: 8df84e5db368ac9cf6bf2052096d95d97f353e61 SHA256: c082e97851dbe76a23bec59e68494788b0cae4e956595085360b86770bdb083c SHA512: fcce4f567f58e0fd122568090fb1f840c39cd672ad2c99683535d799d1e9c84be53f77267b425237b66ed8d0b248e9512b610390194ceba93e23d7eca53851f8 Homepage: https://cran.r-project.org/package=patientProfilesVis Description: CRAN Package 'patientProfilesVis' (Visualization of Patient Profiles) Creation of patient profile visualizations for exploration, diagnostic or monitoring purposes during a clinical trial. These static visualizations display a patient-specific overview of the evolution during the trial time frame of parameters of interest (as laboratory, ECG, vital signs), presence of adverse events, exposure to a treatment; associated with metadata patient information, as demography, concomitant medication. The visualizations can be tailored for specific domain(s) or endpoint(s) of interest. Visualizations are exported into patient profile report(s) or can be embedded in custom report(s). Package: r-cran-patrick Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-patrick_0.3.1-1.ca2604.1_all.deb Size: 21794 MD5sum: 74392b47ea15ba86b24fdfa36d29d5ce SHA1: c5ba549bd263711743ce3b8c48cf7428daa0cb00 SHA256: 16c0eb028186a42bc38622a003b2c2cf742ca7c965fdaa72a347fc896e70707e SHA512: f9688db5258a8f025de90e4bbd5286d0c43d9665013e62716660986189b313e0343e7d5b75a3fe6adf355561d917b346be84dc14a39328b0801136965f8b6654 Homepage: https://cran.r-project.org/package=patrick Description: CRAN Package 'patrick' (Parameterized Unit Testing) This is an extension of the 'testthat' package that lets you add parameters to your unit tests. Parameterized unit tests are often easier to read and more reliable, since they follow the DNRY (do not repeat yourself) rule. Package: r-cran-pattern.checks Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pattern.checks_0.1.0-1.ca2604.1_all.deb Size: 23756 MD5sum: 5baa85667b37bbf391652e70b4060059 SHA1: de12a800b4a2f03649c0317f214f35a0e21a6e6a SHA256: 970794ce0f66f5d9c75653562a4c613e06d19ef6a91f75101910017ec88f6318 SHA512: 6e86f1460a8cf9ecb101a8df68809ad485ac58464664bbac75d20e4829c66e13133b18bdf97144cddd29ebe836bec549d644d755d1958f9121b34f255ae88a36 Homepage: https://cran.r-project.org/package=pattern.checks Description: CRAN Package 'pattern.checks' (Identifies Patterned Responses in Scales) Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c). Package: r-cran-patternator Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-plyr Filename: pool/dists/resolute/main/r-cran-patternator_0.1.0-1.ca2604.1_all.deb Size: 29926 MD5sum: e2560fadcc7d59d0a2375080c315e97a SHA1: 3fa03e727263d0defa4a6a0dc80bdb103ce7915b SHA256: d590bea86e2004b11e5bfa4749c05a3081ffedfffc192a706b4409477858eacc SHA512: 7baf9d07edddf733f52d7ed4785f71ef96a57dad1b45d88928647ca01fb1b2f56651b04ec11ccb1c975555687ca315bfe14d7c80ecc98a438770f8f8f003dc48 Homepage: https://cran.r-project.org/package=patternator Description: CRAN Package 'patternator' (Feature Extraction from Female Brown Anole Lizard DorsalPatterns) Provides a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from 'ImageJ', an open platform for scientific image analysis (see for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width. Package: r-cran-patterncausality Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d, r-cran-ggplot2, r-cran-reshape2, r-cran-ggthemes, r-cran-tidyr, r-cran-statebins, r-cran-ggrepel, r-cran-rcolorbrewer, r-cran-scales, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-lintr, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-patterncausality_0.2.4-1.ca2604.1_all.deb Size: 2956104 MD5sum: 6d32ceb661199c03fc9cee93616eefe7 SHA1: 62a7c1143101b3d2fc0c12c2026349f5bf3cb16b SHA256: d9d00ec8a506db5b5fc764ed93cd738135f8ecba21fea16a050db8c003156f4d SHA512: bc4e50b9c88e381b770fe8cac92764c7103d855fe8c971d022f1244cf94e9fc26cb7c3ecd58c95b7de31ad8533a77af68be5e5cfb06659beb1b59139d6cf138d Homepage: https://cran.r-project.org/package=patterncausality Description: CRAN Package 'patterncausality' (Pattern Causality Algorithm) A comprehensive package for detecting and analyzing causal relationships in complex systems using pattern-based approaches. Key features include state space reconstruction, pattern identification, and causality strength evaluation. 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Patternize defines homology between pattern positions across images either through fixed landmarks or image registration. Pattern identification is performed by categorizing the distribution of colors using RGB thresholds or image segmentation. Package: r-cran-patterns Architecture: all Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-cluster, r-cran-e1071, r-cran-gplots, r-cran-igraph, r-cran-lars, r-cran-lattice, r-bioc-limma, r-bioc-mfuzz, r-cran-movmf, r-cran-nnls, r-cran-plotrix, r-cran-selectboost, r-cran-tnet, r-cran-vgam, r-cran-wgcna Suggests: r-cran-animation, r-bioc-biobase, r-bioc-biomart, r-cran-c060, r-cran-cascadedata, r-cran-elasticnet, r-cran-glmnet, r-cran-knitr, r-cran-pixmap, r-cran-r.rsp, r-cran-rmarkdown, r-cran-spls, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-patterns_1.7-1.ca2604.1_all.deb Size: 1083230 MD5sum: 42c1e211f229e9aff0028d9acfdea477 SHA1: dbf0a7e79f4e3ac791447926741a9a0f998cf26c SHA256: 0d2dff35ce2c92442280d02c75fbbed32e21e364c344ed7d8c722fc4ed2703bc SHA512: cf7b4e05124136a9f3d26544ae4193977e41684a77312af01fb5d0aafe6870a36c834c347fe8ffffb264b0de349734d3bc741af57cba60f36a18a706cd45ca4e Homepage: https://cran.r-project.org/package=Patterns Description: CRAN Package 'Patterns' (Deciphering Biological Networks with Patterned HeterogeneousMeasurements) A modeling tool dedicated to biological network modeling (Bertrand and others 2020, ). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, ). Example of use with microarray or RNA-Seq data are provided. Package: r-cran-pavo Architecture: all Version: 2.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2089 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-future.apply, r-cran-geometry, r-cran-lightr, r-cran-magick, r-cran-farver, r-cran-plot3d, r-cran-progressr, r-cran-sf, r-cran-viridislite Suggests: r-cran-alphashape3d, r-cran-imager, r-cran-knitr, r-cran-mapproj, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-digest Filename: pool/dists/resolute/main/r-cran-pavo_2.9.0-1.ca2604.1_all.deb Size: 1893766 MD5sum: 66dac8c8463efac41a2cab97f3d0f9c0 SHA1: 16fa9db9b4bbf17d01c02ece0ffd0f8dbd98a6c1 SHA256: a029dc19902050251c3214066e1f84bea1951478ee827a2267c0a0864bb46023 SHA512: e1c5c8f31d80b081cb8d612c5b42dc0801b9088ed7d2cc9f594f1ad3077e9689be9a2a7b6099a02a16696477faf058da305e73644189758ff62bd651cf63f39f Homepage: https://cran.r-project.org/package=pavo Description: CRAN Package 'pavo' (Perceptual Analysis, Visualization and Organization of SpectralColour Data) A cohesive framework for the spectral and spatial analysis of colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) and Maia, Gruson, Endler & White (2019) . 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Package: r-cran-paws.analytics Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-paws.common Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-paws.analytics_0.9.0-1.ca2604.1_all.deb Size: 10233538 MD5sum: e1682f274c87ebfb7d932ca2d8dc3ea2 SHA1: 18ce4d4aed80d617a04c9a51bc36df7713f2add1 SHA256: f6ed969b13b63fc1d06653b4cbfe9635bffdd3d2d6ae0250d1d1a5ce3538062b SHA512: 5c82a172f732feac82b2ee52fac5dae013d0e7dc9c65319317a6cdfbe990af1f5efbcf7fe4f985fbabf3eea6e985955032f10077e45d2d2bb055276e3c614678 Homepage: https://cran.r-project.org/package=paws.analytics Description: CRAN Package 'paws.analytics' ('Amazon Web Services' Analytics Services) Interface to 'Amazon Web Services' 'analytics' services, including 'Elastic MapReduce' 'Hadoop' and 'Spark' big data service, 'Elasticsearch' search engine, and more . 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Package: r-cran-pbibd Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pbibd_1.4-1.ca2604.1_all.deb Size: 74642 MD5sum: 737f3ba4cce763e577580fe7f0d0e2b8 SHA1: a8b6743e3764b1939b772a024406f431d856cf4b SHA256: e83e26753fa69ebbc9cd5b158e3a4131c6d1bc98491195b145ac42c33fcc0505 SHA512: 60cb758f368dc246f3acac1797a797a652259711fb54e019d30a488faae24801e03b2c617b20f0e1842489f116b6ede8b14a06b48e459dbbc0f8c187263f7ffd Homepage: https://cran.r-project.org/package=PBIBD Description: CRAN Package 'PBIBD' (Partially Balanced Incomplete Block Designs) The PBIB designs are important type of incomplete block designs having wide area of their applications for example in agricultural experiments, in plant breeding, in sample surveys etc. This package constructs various series of PBIB designs and assists in checking all the necessary conditions of PBIB designs and the association scheme on which these designs are based on. It also assists in calculating the efficiencies of PBIB designs with any number of associate classes. The package also constructs Youden-m square designs which are Row-Column designs for the two-way elimination of heterogeneity. The incomplete columns of these Youden-m square designs constitute PBIB designs. With the present functionality, the package will be of immense importance for the researchers as it will help them to construct PBIB designs, to check if their PBIB designs and association scheme satisfy various necessary conditions for the existence, to calculate the efficiencies of PBIB designs based on any association scheme and to construct Youden-m square designs for the two-way elimination of heterogeneity. R. C. Bose and K. R. Nair (1939) . 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Implements parametric bootstrap test for generalized linear mixed models as implemented in 'lme4' and generalized linear models. The package is documented in the paper by Halekoh and Højsgaard, (2012, ). Please see 'citation("pbkrtest")' for citation details. 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Partial proportional odds models are supported, with flexible (non-)uniform association structures. Various logit types and parametrizations can be specified for both marginals and the association, including Dale’s model. The association structure can be regularized using polynomial-type penalty terms. Additive effects are modeled using P-splines. Standard methods such as summary(), residuals(), and predict() are available. Package: r-cran-pbm Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-pbm_1.2.1-1.ca2604.1_all.deb Size: 148698 MD5sum: becbe62f66f89cbaadeefa44917642b5 SHA1: 4b24c9c33c81f830a11eca683717d27e23363377 SHA256: e66fbf14131384e77219972dbe5e7368f59feb1099a4dbf96873b95ceb84f110 SHA512: 4e53e1d6cc77d2d0aad8227dac14a1143ac6e8a06a4021a76dd097ae66fba3420797fd751e32294f1929c680eac1cc4473250d47dcb6887dd96332662557a44b Homepage: https://cran.r-project.org/package=pbm Description: CRAN Package 'pbm' (Protein Binding Models) Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) . Hoang H. Nguyen et al. (2015) . After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression. Package: r-cran-pbnpa Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metarnaseq Filename: pool/dists/resolute/main/r-cran-pbnpa_0.0.3-1.ca2604.1_all.deb Size: 63508 MD5sum: e3d245315cf1db652bdb8fdde7c99f69 SHA1: 6f45eaa3779b81299d8ff73c7e61497b3e70dce1 SHA256: faa97e846bd6e6cf1a11eee3b0edf5dfcd3690b0afa8df27f2739d5301138ef7 SHA512: b7d753d6c1581063f862e6c07c17649996c7c319be5bc8d87cffdecdb5b6ac4b923d270a441eace2dc20fa13e15b16a3c0405168c51b6fd5f04115d47269feed Homepage: https://cran.r-project.org/package=PBNPA Description: CRAN Package 'PBNPA' (Permutation Based Non-Parametric Analysis of CRISPR Screen Data) Permutation based non-parametric analysis of CRISPR screen data. Details about this algorithm are published in the following paper published on BMC genomics, Jia et al. (2017) : A permutation-based non-parametric analysis of CRISPR screen data. Please cite this paper if you use this algorithm for your paper. 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Package: r-cran-pbs Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pbs_1.1-1.ca2604.1_all.deb Size: 19678 MD5sum: a1ac75c727c67d1c3deb06ecee16a782 SHA1: 3275ca805bc755f603fae97f64e193e9875ec875 SHA256: 7c44666153fde4fb7ed9c3623e7808aef655f8f869ef589c42df525fd1fb52d7 SHA512: 9b291feff4187a9415ec33a8117208ff9cb89bd078af6d93ff72b0777cb3dd16d1635008effd2fe7f5616a400e6c982d7a5fcb6f498c9c850f90e4c1335b92ef Homepage: https://cran.r-project.org/package=pbs Description: CRAN Package 'pbs' (Periodic B Splines) Periodic B Splines Basis Package: r-cran-pbsadmb Architecture: all Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3092 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbsmodelling Filename: pool/dists/resolute/main/r-cran-pbsadmb_1.1.6-1.ca2604.1_all.deb Size: 2835192 MD5sum: 9ffe5ba25dd9e3064693a0c6708aa417 SHA1: 4e2ce9e9be24b51e6334dc69f12c61c43e3c5a00 SHA256: fe8fea75813cb6d4fd61e5f43312b2f894061b69ea88188b9cb760c53f3a932c SHA512: 6693a7ae152def9c18ad61a3c329ed3b76d93d678c5f276788c1d6e2c9ff50fafc4dbd4700adc8aeede220735190f56a4445b3d8856ea5852f5efdcdfa84a523 Homepage: https://cran.r-project.org/package=PBSadmb Description: CRAN Package 'PBSadmb' (ADMB for R Using Scripts or GUI) A collection of software provides R support for 'ADMB' (Automatic Differentiation Model Builder) and a 'GUI' interface facilitates the conversion of 'ADMB' template code to 'C code' followed by compilation to a binary executable. Stand-alone functions can also be run by users not interested in clicking a 'GUI'. Package: r-cran-pbtdesigns Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-pbtdesigns_1.0.0-1.ca2604.1_all.deb Size: 26472 MD5sum: feb59338aedf3d1be63fe29a3da83810 SHA1: cd4638eca472e649b571e0d1762749aa1b829a0e SHA256: f1321f12319a6a117420afaee1f4b66e2c678436c350766790a4b59c29c924d0 SHA512: a2a190a883f0555ea84a98287741d3d8ded6d16de060c6d8e1a6f2f6b9708375cbd169c8a55d6375f50d694c24854aac3534a2fb6d483f54d985d1d78d21d8b5 Homepage: https://cran.r-project.org/package=PBtDesigns Description: CRAN Package 'PBtDesigns' (Partially Balanced t-Designs (PBtDesigns)) The t-designs represent a generalized class of balanced incomplete block designs in which the number of blocks in which any t-tuple of treatments (t >= 2) occur together is a constant. When the focus of an experiment lies in grading and selecting treatment subgroups, t-designs would be preferred over the conventional ones, as they have the additional advantage of t-tuple balance. t-designs can be advantageously used in identifying the best crop-livestock combination for a particular location in Integrated Farming Systems that will help in generating maximum profit. But as the number of components increases, the number of possible t-component combinations will also increase. Most often, combinations derived from specific components are only practically feasible, for example, in a specific locality, farmers may not be interested in keeping a pig or goat and hence combinations involving these may not be of any use in that locality. In such situations partially balanced t-designs with few selected combinations appearing in a constant number of blocks (while others not at all appearing) may be useful (Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2021)). Further, every location may not have the resources to form equally sized homogeneous blocks. Partially balanced t-designs with unequal block sizes (Damaraju Raghavarao & Bei Zhou (1998). Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2022)." Partially Balanced t-designs with unequal block sizes") prove to be more suitable for such situations.This package generates three series of partially balanced t-designs namely Series 1, Series 2 and Series 3. Series 1 and Series 2 are designs having equal block sizes and with treatment structures 4(t + 1) and a prime number, respectively. Series 3 consists of designs with unequal block sizes and with treatment structure n(n-1)/2. This package is based on the function named PBtD() for generating partially balanced t-designs along with their parameters, information matrices, average variance factors and canonical efficiency factors. Package: r-cran-pcadsc Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape2, r-cran-pander, r-cran-ggplot2, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-pcadsc_0.8.0-1.ca2604.1_all.deb Size: 60002 MD5sum: c017894428e5ad7ebcd4acbde26faa9f SHA1: 1871417f95847f6d7ddd9bd9efb756b5e4e93bff SHA256: dbf6ac3c9121f68f46066ef6179a9a003d9a33f0f344b4c62b735fc6d74adc7d SHA512: ea6d15f92c0b7198b801539f90e4ca6fa851e7dc492626e21237d5ee58e74cac4a2b717798909895cb78e9ed3db7099d1da1696eae200902804260e79c3a37c2 Homepage: https://cran.r-project.org/package=PCADSC Description: CRAN Package 'PCADSC' (Tools for Principal Component Analysis-Based Data StructureComparisons) A suite of non-parametric, visual tools for assessing differences in data structures for two datasets that contain different observations of the same variables. These tools are all based on Principal Component Analysis (PCA) and thus effectively address differences in the structures of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The angle plot presents the information loss when moving from the PCA decomposition of one dataset to the PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby presenting the relative weighting and importance of the variables from the original dataset. 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Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test. Package: r-cran-pcalls Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pcalls_1.0-1.ca2604.1_all.deb Size: 25516 MD5sum: 46341d6f4032b05662b12199c5c38155 SHA1: 94c59aeff410c5ce4c0e59d9279ca3ff7d6b822a SHA256: a0fbf303fdef3b49481a683eb746099f8cca7e0a832d5b6d8690e95ebfef2011 SHA512: 08117ab89e056c2233e473c61f457761794b821db87a1b1871a32a617efe3dacf3b164b7e2f73da8ebe31979217241154365be90f73653efc65a4aee5f42d804 Homepage: https://cran.r-project.org/package=pcalls Description: CRAN Package 'pcalls' (Pricing of Different Types of Call) Compute the price of different types of call using different methods. The types available are Vanilla European Calls, Vanilla American Calls and American Digital Calls. 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The 'PCAPAM50' package leverages principal component analysis and iterative 'PAM50' calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping. 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Package: r-cran-pcbn Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-igraph, r-cran-r2r, r-cran-vinecopula Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-data.tree, r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-cran-pcbn_0.1.1-1.ca2604.1_all.deb Size: 170930 MD5sum: d4c6882c9741d71521fcea9ac162641a SHA1: a209dc51f591fc9ac3b444b7879ed73e9706da83 SHA256: 0c7c77ae27141da03fbb4e59246cf3131219b7fc500a2dd9124be669ef5a76b7 SHA512: d9a8fb551309f19464cc27d33dbdaf645c745ceff0a36898b2a55d5082eaab7c0171fcd8805ebd1659bc6f852509e9290483d2172f0f229d6093f4548fb87d2c Homepage: https://cran.r-project.org/package=PCBN Description: CRAN Package 'PCBN' (Inference of Pair-Copula Bayesian Networks) Creates, fits and samples Pair-Copula Bayesian networks (PCBN) under some restrictions on the underlying Directed Acyclic Graph (DAG), that is, no active cycles nor interfering v-structures, following Derumigny, Horsman and Kurowicka (2025) . Package: r-cran-pcbs Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-ggrepel, r-cran-dplyr, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-pcbs_0.1.1-1.ca2604.1_all.deb Size: 1553842 MD5sum: 3a058d3faf3b4faf349534d030bb3cef SHA1: cf1224e8f5f4d94572a47fa76c33d5386142ca8f SHA256: 1d63bd784cfceb4ff48d1a9353f813ed1fb3ae9c2f9b23f384d1de9a08830899 SHA512: 2046743b83920ededb1c67ce39abdad5b057bf0b69a3dd493d66bcfad4464239689db32235692cdf478c9322a744522fd07feddebaaa41f576b9c05b8286d33c Homepage: https://cran.r-project.org/package=PCBS Description: CRAN Package 'PCBS' (Principal Component BiSulfite) A system for fast, accurate, and flexible whole genome bisulfite sequencing (WGBS) data analysis of two-condition comparisons. Principal Component BiSulfite, 'PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: . 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Package: r-cran-pcdimension Architecture: all Version: 1.1.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-classdiscovery, r-cran-oompabase, r-cran-kernlab, r-cran-changepoint, r-cran-cpm Suggests: r-cran-mass, r-cran-nfactors Filename: pool/dists/resolute/main/r-cran-pcdimension_1.1.14-1.ca2604.1_all.deb Size: 278578 MD5sum: 6c2c17f8dce5acc2d3e70895d3f18899 SHA1: 6f3d4ba560a379900f275609c24683572ba0c54f SHA256: f541e5fd58fb5b2637d5e4a1383b81bcdbafca6636c951ec20fdb42605b83df9 SHA512: 395d6a1be2d8abdd00a85f35b1199fbd224f4af8b68ccab31b5051e1a76e6efccf48fb825fafebdff80ab07e2ff761eb31b28e4aecc00f5167d134f3b4575c62 Homepage: https://cran.r-project.org/package=PCDimension Description: CRAN Package 'PCDimension' (Finding the Number of Significant Principal Components) Implements methods to automate the Auer-Gervini graphical Bayesian approach for determining the number of significant principal components. 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The package also has tools for generating points from these spatial patterns. The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011) ) and arc density (Ceyhan et al. (2006) ; Ceyhan et al. (2007) ). The PCD families considered are Arc-Slice PCDs, Proportional-Edge PCDs, and Central Similarity PCDs. 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(2010) with reconstruction of direct genetic effects. 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This package is developed for estimating and testing partial correlation graphs with prior information incorporated. Package: r-cran-pcgse Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmtstat, r-cran-mass Filename: pool/dists/resolute/main/r-cran-pcgse_0.5.0-1.ca2604.1_all.deb Size: 43302 MD5sum: 7c13869b0e67e1e605ac2f7e4b9c5891 SHA1: cd1fd0c7bd31aa7b475d58d08a06ae53a9cca0a6 SHA256: c8ddd2d974479a61f63d9cf6da7cbc11c145567ae629301458b3b2c336775f4f SHA512: 7f62581488cf28dae380291bf2f3465edd5984b37da0e9e08d8e2126c2ed141639379d1b325bf50718b0747a41cda734404e8e62d4876b9f70b84dd405753404 Homepage: https://cran.r-project.org/package=PCGSE Description: CRAN Package 'PCGSE' (Principal Component Gene Set Enrichment) Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data. Package: r-cran-pch Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-pch_2.2-1.ca2604.1_all.deb Size: 108314 MD5sum: 4937e525316103f72c030e788264af21 SHA1: a5115233303c88000275f1b2b8989cf5f4cd0a37 SHA256: 23af436823395d2c5b7d35afa36691f0c2b04a48d1ddfb73f1c47e38cba986f6 SHA512: 28fcb9762aa1098ce105ba1478d4ba99e70b17e6f791ce86248bd6863edb5c1105493387690307d18ebcd6ebe23fe16c65fed1061fcbddcd1a52c0089ae322d7 Homepage: https://cran.r-project.org/package=pch Description: CRAN Package 'pch' (Piecewise Constant Hazard Models for Censored and Truncated Data) Piecewise constant hazard models for survival data. The package allows for right-censored, left-truncated, and interval-censored data. Package: r-cran-pchc Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigstatsr, r-cran-bnlearn, r-cran-dcov, r-cran-foreach, r-cran-doparallel, r-cran-rangen, r-cran-rfast, r-cran-rfast2, r-cran-robustbase Suggests: r-cran-bigreadr, r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-cran-pchc_1.4-1.ca2604.1_all.deb Size: 229828 MD5sum: a623eff430c1b73e564c0691917e300c SHA1: 0fe7611004159ed85bb5a5eab441ab884010d2bd SHA256: 1e9a1471c1d6de1df346474d447f913b9cd49eade419bad6f81e8b84c6917e12 SHA512: e80cce3c3bfa3ebeeff5af304b3ab64130365424d48bba12a6646cf608345f6972fbe0e90cf58cdd7dc2a583dd43ae2c1aeebaca068626a61ff5edd845e65771 Homepage: https://cran.r-project.org/package=pchc Description: CRAN Package 'pchc' (Bayesian Network Learning with the PCHC and Related Algorithms) Bayesian network learning using the PCHC, FEDHC, MMHC and variants of these algorithms. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. The relevant papers are: a) Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics, 57(1): 341-367. . b) Tsagris M. (2022). "The FEDHC Bayesian Network Learning Algorithm". Mathematics 2022, 10(15): 2604. . c) Sevinc V. and Tsagris M. (2024). "On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms". . 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Its primary purpose is to form a foundation for other quality control packages to build on top of, by providing basic resources and functions. The indices belong to the field of statistical quality control, and quantify the degree to which a manufacturing process is able to create items that adhere to a certain standard of quality. For details see Montgomery, D. C. (2019, ISBN:978-1-119-39930-8). 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Package: r-cran-pcl Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pcl_1.0-1.ca2604.1_all.deb Size: 18480 MD5sum: 704b0c6ecac50199ffe88bceb2f73b74 SHA1: 1153be11bed1210aefe21f254e35530801e4f0a8 SHA256: 69f5bee5793e5bd9c079bbaf5443f53d0e6642ec3ba84492074d6f844028d0d1 SHA512: f5c8911369195e47a2ca80695587ef980d57062e9a5a9c7d73fccbaf00ecf22f0a00bf1bf9158fb82ef4c5fbb41b47ffcf08cb290802719922d662970c30026c Homepage: https://cran.r-project.org/package=PCL Description: CRAN Package 'PCL' (Proximal Causal Learning) We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 . Package: r-cran-pclassoreg Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-grpreg Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pclassoreg_1.0.0-1.ca2604.1_all.deb Size: 894736 MD5sum: 0675172aa9cb0ec36b305c8a4810dcb1 SHA1: 4cec21a77c3dac24b800fe39e0c37a06f4f57a94 SHA256: 29793d027eb18eb3d94a5f459f1c24728c10b17f59fd58453b69997b28c8ce07 SHA512: 564e1d22fe75a306145a3b9c0cd2beeb084697e895c4ee1a055c0395e9ef6fc6defba5067c6467274359edebbcff3ea98fe3ab0f47af51e0f5b20e2118b469a5 Homepage: https://cran.r-project.org/package=PCLassoReg Description: CRAN Package 'PCLassoReg' (Group Regression Models for Risk Protein Complex Identification) Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. 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The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2019) . The suggested geiger package can be obtained from CRAN's archive , suggested to take latest version. Otherwise its required code is present in the pcmabc package. The suggested distory package can be obtained from CRAN's archive , suggested to take latest version. 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Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site provides access to the documentation and other resources. 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Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions of Burdukiewicz et al. (2018) can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve. 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Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. Implements methods presented in Wolf et al. (2021) Wolf et al. (2020) and Gasdaska et al. (2019) . Package: r-cran-pcsteiner Architecture: all Version: 1.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pcsteiner_1.0.0.1-1.ca2604.1_all.deb Size: 222172 MD5sum: acc94015c516f513f7dd699f408676fe SHA1: 97c60aafea684464741dea8a2ce0551ba4b036ef SHA256: fe898f1b523cbe73a28735a1ded327c571a93abb24d58444d4081d11d20678a6 SHA512: 24fdcf13d57edc98dd06ad239e4e2618e628fa1f1bd40cb2958ef691d0731c291666d4a33ff01bac0fc6f6a4f93fd6fe8d4657f94cb79d840080d8c7645783b5 Homepage: https://cran.r-project.org/package=pcSteiner Description: CRAN Package 'pcSteiner' (Convenient Tool for Solving the Prize-Collecting Steiner TreeProblem) The Prize-Collecting Steiner Tree problem asks to find a subgraph connecting a given set of vertices with the most expensive nodes and least expensive edges. 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Package: r-cran-pctax Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1730 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcutils, r-cran-dplyr, r-cran-readr, r-cran-ggplot2, r-cran-vegan, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-ggrepel, r-cran-reshape2, r-cran-tibble, r-cran-ggpubr, r-cran-patchwork, r-cran-ggnewscale, r-cran-ade4, r-cran-scales Suggests: r-cran-picante, r-cran-httr, r-cran-nst, r-cran-permute, r-cran-aplot, r-cran-ggfun, r-cran-pheatmap, r-cran-mass, r-cran-rtsne, r-bioc-mixomics, r-cran-geosphere, r-bioc-phyloseq, r-cran-phyloseqgraphtest, r-cran-plotly, r-cran-umap, r-cran-hmisc, r-cran-minpack.lm, r-cran-bbmle, r-cran-snow, r-cran-foreach, r-cran-dosnow, r-cran-tidytree, r-bioc-ggtree, r-bioc-ggtreeextra, r-cran-vctrs, r-cran-zoo, r-cran-ape, r-bioc-deseq2, r-bioc-limma, r-bioc-aldex2, r-bioc-mfuzz, r-bioc-edger, r-cran-randomforest, r-cran-knitr, r-cran-rmarkdown, r-cran-metanet, r-cran-showtext, r-cran-jsonlite, r-cran-prettydoc, r-cran-readxl, r-cran-stringr, r-cran-ggextra, r-cran-clipr, r-cran-zetadiv, r-cran-ggforce, r-cran-gggenes, r-cran-mediation Filename: pool/dists/resolute/main/r-cran-pctax_0.1.7-1.ca2604.1_all.deb Size: 1613770 MD5sum: a2dcf826b53529595224494f3c45468d SHA1: 16747f5f2c64f147d4f30256329e9d34c7b884b4 SHA256: fb62581b469cc1db9aa373d4e0ca172752ea8521cce59fd8618f805be803a823 SHA512: b3c482489b9419fa9d498454e884e95cfd696eed12eecb92d662f6cacbba5000b0ba4bb7a3cba239fbde78bc1b11b0e85f685b318fe25f7f6a3d40e35d0c2771 Homepage: https://cran.r-project.org/package=pctax Description: CRAN Package 'pctax' (Professional Comprehensive Omics Data Analysis) Provides a comprehensive suite of tools for analyzing omics data. 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Package: r-cran-pcts Architecture: all Version: 0.15.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2337 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sarima, r-cran-matrix, r-cran-bb, r-cran-polynomf, r-cran-gbutils, r-cran-zoo, r-cran-xts, r-cran-lagged, r-cran-mcompanion, r-cran-rdpack, r-cran-lubridate Suggests: r-cran-testthat, r-cran-funitroots, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pcts_0.15.8-1.ca2604.1_all.deb Size: 1787132 MD5sum: 675f32fcaf81006dea7c17b3437965da SHA1: ca93218a25c759dec6ffddd7147ef6cd3c2a0035 SHA256: 9d2788aa95dc3008e88fb62a887439d161defa2957e495550c64342bd3e07eeb SHA512: 376b7084f8cd82786807dfb6624ddbdd43144c63d7ad10fc6a5d23589d3dd3c6c2d946183b580a5a096b56b04b1b1bf2fdf175bbe6997edb9b6c51a4e7c3e185 Homepage: https://cran.r-project.org/package=pcts Description: CRAN Package 'pcts' (Periodically Correlated and Periodically Integrated Time Series) Classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) , Boshnakov (1996) . Package: r-cran-pcutils Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2011 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-tidyr, r-cran-tibble, r-cran-rcolorbrewer Suggests: r-cran-agricolae, r-cran-clipr, r-cran-rlang, r-cran-biocmanager, r-cran-ggpubr, r-cran-kableextra, r-cran-htmlwidgets, r-cran-pagedown, r-cran-ggsci, r-cran-readr, r-cran-grimport2, r-cran-rsvg, r-cran-pmcmrplus, r-cran-nortest, r-cran-fitdistrplus, r-cran-ggalluvial, r-cran-ggspatial, r-cran-sf, r-cran-magick, r-cran-ggimage, r-cran-ggpmisc, r-cran-upsetr, r-cran-eulerr, r-cran-plotrix, r-cran-vegan, r-cran-circlize, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-htmltools, r-cran-leaflet, r-cran-relaimpo, r-cran-snow, r-cran-dosnow, r-cran-foreach, r-cran-stringr, r-cran-ggraph, r-cran-ggrepel, r-cran-treemap, r-cran-voronoitreemap, r-cran-devtools, r-cran-multcompview, r-cran-rio, r-cran-bookdown, r-cran-sysfonts, r-cran-showtext, r-cran-jsonlite, r-cran-httr, r-cran-r.proxy, r-cran-openssl, r-cran-styler, r-cran-lintr, r-cran-aplot, r-cran-ggbeeswarm, r-cran-ggvenndiagram, r-cran-gifski, r-cran-ggnewscale, r-cran-revtools Filename: pool/dists/resolute/main/r-cran-pcutils_0.2.8-1.ca2604.1_all.deb Size: 1949448 MD5sum: 458c226858a70c6887d361ded500c4df SHA1: 843c39fcc6ac1081f46274586fdcdbce99339ec8 SHA256: a1881acfd0288f1447c20daf46400fbea3c6d43e1a6b55df82ba3b597d174fc4 SHA512: 5a804d32d91b1c32c6ed50d61e73865085a6c09de9b317b1d896d3c80e40477c7e4946f5da58e776299ae8356430bcea344bb7f405fc83155b5464a9636b241f Homepage: https://cran.r-project.org/package=pcutils Description: CRAN Package 'pcutils' (Some Useful Functions for Statistics and Visualization) Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. 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Package: r-cran-pcv Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pcv_1.1.0-1.ca2604.1_all.deb Size: 477976 MD5sum: 3f49bcc3d9ed3a8e1d26702d4871a9cb SHA1: 5e8ae700dbbc4586b38fb63767438c625836c1d0 SHA256: 1abe88f98e74c2ea6736597954722a0ad6ec3680da7032febfc9a7bd0aeb6c8a SHA512: 7388a9883bc215389d8fcf0a2c7320301c88a95603a572c059d654d295c3ed5e0b9ae2cad83ba6dbc3f68cc22ae3263ea94fe596133f80c7631ec6864b667ad0 Homepage: https://cran.r-project.org/package=pcv Description: CRAN Package 'pcv' (Procrustes Cross-Validation) Implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) . 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Package: r-cran-pdcor Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcov, r-cran-rangen, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-pdcor_1.3-1.ca2604.1_all.deb Size: 27074 MD5sum: dcb655cff9349ed8a663f8df29857323 SHA1: 359623de71b4ee461edcf00081d57b4a169d91f5 SHA256: f5b68774687e7eda630a7ae0f1a7415faefef6fff8903e1275d76fcd0846bc33 SHA512: c96f1a7bcbee692fb6d4f5f794cfdac7999d2c2f823d08fd6f3c4b0b8c544b951ea7ac9a191a7a9a401a259a229c485c158f3036d62116af22b6d15911fef7a5 Homepage: https://cran.r-project.org/package=pdcor Description: CRAN Package 'pdcor' (Fast and Light-Weight Partial Distance Correlation) Fast and memory-less computation of the partial distance correlation for vectors and matrices. Permutation-based and asymptotic hypothesis testing for zero partial distance correlation are also performed. References include: Szekely G. J. and Rizzo M. L. (2014). "Partial distance correlation with methods for dissimilarities". The Annals Statistics, 42(6): 2382--2412. . Shen C., Panda S. and Vogelstein J. T. (2022). "The Chi-Square Test of Distance Correlation". Journal of Computational and Graphical Statistics, 31(1): 254--262. . Szekely G. J. and Rizzo M. L. (2023). "The Energy of Data and Distance Correlation". Chapman and Hall/CRC. . Kontemeniotis N., Vargiakakis R. and Tsagris M. (2025). On independence testing using the (partial) distance correlation. . 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(2015) "A simple new test for slope homogeneity in panel data models with interactive effects" , Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" , Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" , Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" , Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" . 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Package: r-cran-pdxtrees Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet, r-cran-infer, r-cran-moderndive, r-cran-readr, r-cran-ggplot2, r-cran-forcats Filename: pool/dists/resolute/main/r-cran-pdxtrees_0.5.1-1.ca2604.1_all.deb Size: 468664 MD5sum: 59f9f7de50c0589f97c18b94a09266e3 SHA1: 201a784ed0f0ac5421002dc25e1ec5c4fe8faf8a SHA256: 64bcb09e8b01c25bfdfda489230de4efa0d9b6d5fb842628cc2f9d2512bf1051 SHA512: fbb53e390c333da2c4163ece34c7746477e2a3788f6bb915b0f036c8af7412add4e0d215afdb4572bb5767b60870b44e7d3e336e040ae6dfe6b7643d2925fb9c Homepage: https://cran.r-project.org/package=pdxTrees Description: CRAN Package 'pdxTrees' (Data Package of Portland, Oregon Trees) An engaging collection of datasets from Portland Parks and Recreation. The city of Portland inventoried every tree in over 170 parks and along the streets in 96 neighborhoods. Package: r-cran-pdynmc Architecture: all Version: 0.9.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-matrix, r-cran-optimx, r-cran-rdpack Suggests: r-cran-pder, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pdynmc_0.9.12-1.ca2604.1_all.deb Size: 1132070 MD5sum: d4972eb7e22a83c33969acbe99a8f79d SHA1: fcdc9f77b20da8771f67730baabf490d2df55040 SHA256: 80c6b2162e5c219542287eddda8e0c3fab6b4b2116b8e7c59b461d679cb81c86 SHA512: 2cb3285b3b63ec4cac4aa9b5cf05a1f9d8438bf431e56d0fc24217885ac934aded6cd694c4ccede3e7b6a7a2cc2b8548b56b345dea3e96edb0db3a771fd71ce4 Homepage: https://cran.r-project.org/package=pdynmc Description: CRAN Package 'pdynmc' (Moment Condition Based Estimation of Linear Dynamic Panel DataModels) Linear dynamic panel data modeling based on linear and nonlinear moment conditions as proposed by Holtz-Eakin, Newey, and Rosen (1988) , Ahn and Schmidt (1995) , and Arellano and Bover (1995) . Estimation of the model parameters relies on the Generalized Method of Moments (GMM) and instrumental variables (IV) estimation, numerical optimization (when nonlinear moment conditions are employed) and the computation of closed form solutions (when estimation is based on linear moment conditions). One-step, two-step and iterated estimation is available. For inference and specification testing, Windmeijer (2005) and doubly corrected standard errors (Hwang, Kang, Lee, 2021 ) are available. Additionally, serial correlation tests, tests for overidentification, and Wald tests are provided. Functions for visualizing panel data structures and modeling results obtained from GMM estimation are also available. The plot methods include functions to plot unbalanced panel structure, coefficient ranges and coefficient paths across GMM iterations (the latter is implemented according to the plot shown in Hansen and Lee, 2021 ). For a more detailed description of the GMM-based functionality, please see Fritsch, Pua, Schnurbus (2021) . For more details on the IV-based estimation routines, see Fritsch, Pua, and Schnurbus (WP, 2024) and Han and Phillips (2010) . Package: r-cran-peacesciencer Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-geosphere, r-cran-tidyr, r-cran-stringr, r-cran-rlang, r-cran-stevemisc, r-cran-lifecycle, r-cran-isard Suggests: r-cran-countrycode, r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-peacesciencer_1.2.0-1.ca2604.1_all.deb Size: 4355094 MD5sum: 1060108384ee80a6b7f4a8e96616eaf1 SHA1: 8f60ba8b8a4822599388dc067b733a54e2af55c3 SHA256: 889249ed4017252d6d8842a786955eaf6196203111a60dbdabc72a410f7a5289 SHA512: a5d9fa7a456b48a500ec86ba584e58b657317b243837c74f073cb1cd36ab32552ea1816724d25a04a987698cbb4ec5f8a509c7407f9c6d13541336693a57c616 Homepage: https://cran.r-project.org/package=peacesciencer Description: CRAN Package 'peacesciencer' (Tools and Data for Quantitative Peace Science Research) These are useful tools and data sets for the study of quantitative peace science. The goal for this package is to include tools and data sets for doing original research that mimics well what a user would have to previously get from a software package that may not be well-sourced or well-supported. Those software bundles were useful the extent to which they encourage replications of long-standing analyses by starting the data-generating process from scratch. However, a lot of the functionality can be done relatively quickly and more transparently in the R programming language. 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The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series. Citation: Louca, Stilianos and Doebeli, Michael (2015) . 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So-called "copy-on-modify" behavior, characteristic of R, means that some expressions or functions may require an unexpectedly large amount of RAM overhead. For example, replacing a single value in a matrix duplicates that matrix in the back-end, making this task require twice as much RAM as that used by the matrix itself. This package makes it easy to monitor the total and peak RAM used so that developers can quickly identify and eliminate RAM hungry code. 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This distribution is relevant when estimating the velocity of processive motor proteins with random detachment. Package: r-cran-pearsonica Architecture: all Version: 1.2-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pearsonica_1.2-5-1.ca2604.1_all.deb Size: 36386 MD5sum: 14328d810b434e4a2fcccd083b2f186e SHA1: 3c3e6d42c047cb7287b45f9350c36d33adef132b SHA256: 0a08bdba25be26472b7b9b128320135c05b77dc70a59e7b484eb1050707e13d8 SHA512: 3a2cd908dd2fc3d519d2cefc14ab256c908e1c61c2721990e66bf3d4f8e3522e83d49317a6b78721cebb61c0ba0b42eb94640b1ccbd0548c54b7b12759154875 Homepage: https://cran.r-project.org/package=PearsonICA Description: CRAN Package 'PearsonICA' (Independent Component Analysis using Score Functions from thePearson System) The Pearson-ICA algorithm is a mutual information-based method for blind separation of statistically independent source signals. 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The approach trains predictive models on DEA-derived efficiency labels (Charnes et al., 1985) , enabling explainable artificial intelligence (XAI) workflows with global and local interpretability tools, including permutation importance (Molnar et al., 2018) , Shapley value explanations (Strumbelj & Kononenko, 2014) , and sensitivity analysis (Cortez, 2011) . The framework also supports probability-threshold peer selection and counterfactual improvement recommendations for benchmarking and policy evaluation. The probabilistic efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based Technical Efficiency Analysis through Machine Learning", in review for publication. Package: r-cran-pecan Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pecan_0.1.0-1.ca2604.1_all.deb Size: 136692 MD5sum: 6291eb032fa9817c7c8ff4b29a39d215 SHA1: d29628a1c348463e795b28315c2cb20b206bf83c SHA256: 6f6a8483fee6bb1a8ba1da6bab4b62ade3b4c09821d06cd355813c88a40669a8 SHA512: fd271435535a5c719eb01f2624ed232e4a70476e6b4e2007da301b0f6dd9bdf2e2f4be8cd53a18211aad8fb57a7a8985fffaa09655c8f6415fd154fae7627dda Homepage: https://cran.r-project.org/package=pecan Description: CRAN Package 'pecan' (Portfolio for Economic Complexity Analysis and Navigation) A portfolio of tools for economic complexity analysis and industrial upgrading navigation. The package implements essential measures in international trade and development economics, including the relative comparative advantage (RCA), economic complexity index (ECI) and product complexity index (PCI). It enables users to analyze export structures, explore product relatedness, and identify potential upgrading paths grounded in economic theory, following the framework in Hausmann et al. (2014) . 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Supports crossed, nested, and mixed (crossed-and-nested) random effects structures with any number of grouping factors. Mixed designs handle cases where grouping factors are simultaneously crossed with some variables and nested within others (e.g., photos nested within models, but both crossed with participants). Random slope variances are translated to the outcome scale using a variance decomposition approach, correctly accounting for predictor scaling and interaction terms. Both general and operative effect sizes are provided. Methods are based on Correll, Mellinger, McClelland, and Judd (2020) , Correll, Mellinger, and Pedersen (2022) , and Rights and Sterba (2019) . 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Tailor-made plots facilitate evaluation of the output. This package is part of the 'pedsuite' ecosystem for pedigree analysis. In particular, it imports 'pedprobr' for calculating pedigree likelihoods and 'forrel' for estimating pairwise relatedness. 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This package generates Mermaid syntax from a pedigree data frame to visualize a pedigree flowchart. The Mermaid syntax can be embedded in a Markdown or R Markdown file, or viewed on Mermaid editors and renderers. Links' shape, style, and orientation can be customized via function arguments, and nodes' shapes and styles can be customized via optional columns in the pedigree data frame. Package: r-cran-pedmut Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pedmut_0.9.1-1.ca2604.1_all.deb Size: 147748 MD5sum: 6a08141baae93a28fc84b36a7f1d955f SHA1: f11d4120e7c4f9fb7016214dcff99ae59fe2e0c2 SHA256: 16b4089768c1be364d475037e8914ed539d97a585a38e40c529792c332a28935 SHA512: 9ec7849a71a6bde3cc3fb96bb28ea9824ed90a6b543b8aea9ff1bd703d2eb61c3154bcb65f496414a37c76106a2682a481fc16f31fb55c047264da2467087897 Homepage: https://cran.r-project.org/package=pedmut Description: CRAN Package 'pedmut' (Mutation Models for Pedigree Likelihood Computations) A collection of functions for modelling mutations in pedigrees with marker data, as used e.g. in likelihood computations with microsatellite data. Implemented models include equal, proportional and stepwise models, as well as random models for experimental work, and custom models allowing the user to apply any valid mutation matrix. Allele lumping is done following the lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922. Package: r-cran-pedprobr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pedtools, r-cran-pedmut Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pedprobr_1.0.1-1.ca2604.1_all.deb Size: 194224 MD5sum: 95ae0171ff51ca92d064c3c5f2eb42a7 SHA1: 4751cfc568dbe02679b77f0e5417525dc13802cb SHA256: ab655c5f1e510e5c6df802ef1b2b3acc1a0cbc05ba4ae660651138a00c2f016e SHA512: 0a85192615df916e38f183f66f8fe41b8544bef7ed0f1407b8f4f2152982cb0adc2734cde93cbcd186768f691025b80cd93b3f00246647fb10dc14857f2f4aea Homepage: https://cran.r-project.org/package=pedprobr Description: CRAN Package 'pedprobr' (Probability Computations on Pedigrees) An implementation of the Elston-Stewart algorithm for calculating pedigree likelihoods given genetic marker data (Elston and Stewart (1971) ). The standard algorithm is extended to allow inbred founders. 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The data sources including NBS, FRED, Sina, Eastmoney and etc. It also provides quantitative functions for trading strategies based on the 'data.table', 'TTR', 'PerformanceAnalytics' and etc packages. Package: r-cran-pedsimulate Architecture: all Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pedsimulate_1.4.3-1.ca2604.1_all.deb Size: 66898 MD5sum: ca7cb9a978d295f723636c317aaafd61 SHA1: affb70fae557da84f2f651714e55e8a2e68ca4d7 SHA256: 1487a051b7c9a31f143d44973e4ab9774281f721ddeb57762971778f1298ad82 SHA512: 075b1b9905cd624d4ec79056a670beff13e2546ee00e4ede9bc38d4334cef139af07ac97b7afd4eabd40366a121b2203709a1d130f630864025835485f54d1e5 Homepage: https://cran.r-project.org/package=pedSimulate Description: CRAN Package 'pedSimulate' (Pedigree, Genetic Merit, Phenotype, and Genotype Simulation) Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Mrode, R. A. (2005) ; Nilforooshan, M.A. (2022) . 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A detailed presentation of the 'pedsuite' is given in the book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN: 9780128244302). Package: r-cran-pedtools Architecture: all Version: 2.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kinship2, r-cran-pedmut Suggests: r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pedtools_2.10.0-1.ca2604.1_all.deb Size: 832234 MD5sum: 2cdd66f71b728d882cfc40e734c9579a SHA1: d756288acdb9073506899185fa95d26e8a65f0d7 SHA256: 1db778bd4524794d70b8ce45e6ee1ff63ec03cf08ef89f82d34d1a289803f56e SHA512: 0d66aae719d77b2edc3ac6f84c834dee3bfd6320bc2c27f66c775e01243ba65f18e395c1c4a8c3cbd24b0d101157deb9a3d2be644fca310aac5cf6d7efbb1a05 Homepage: https://cran.r-project.org/package=pedtools Description: CRAN Package 'pedtools' (Creating and Working with Pedigrees and Marker Data) A comprehensive collection of tools for creating, manipulating and visualising pedigrees and genetic marker data. Pedigrees can be read from text files or created on the fly with built-in functions. A range of utilities enable modifications like adding or removing individuals, breaking loops, and merging pedigrees. An online tool for creating pedigrees interactively, based on 'pedtools', is available at . 'pedtools' is the hub of the 'pedsuite', a collection of packages for pedigree analysis. A detailed presentation of the 'pedsuite' is given in the book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302). Package: r-cran-pedtricks Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-kinship2, r-cran-tidyr, r-cran-matrix, r-cran-mvtnorm, r-cran-mcmcglmm, r-cran-nadiv, r-cran-genetics, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-pedtricks_0.5.0-1.ca2604.1_all.deb Size: 233208 MD5sum: 5c02b18c44d8ff5784f5ab619f24bd4e SHA1: 56e564a82edb5171a260a2ae7522be018f342742 SHA256: dca49753bb52512694b87ca02bb241d258f8064c6ffc2a7c2c38cdd6738c767c SHA512: 92228dbe3017b0f297795feae892d8eba70e47f934ad99c99ce2487a3abb3ed106e0826cc55ec367abe9aca63ab9c48d28a3e729454fd59368857f391abc6d10 Homepage: https://cran.r-project.org/package=pedtricks Description: CRAN Package 'pedtricks' (Visualize, Summarize and Simulate Data from Pedigrees) Sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for visualizing pedigrees. 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Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2025) . Package: r-cran-peip Architecture: all Version: 2.2-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bvls, r-cran-matrix, r-cran-rseis, r-cran-pracma, r-cran-geigen, r-cran-fields Filename: pool/dists/resolute/main/r-cran-peip_2.2-5-1.ca2604.1_all.deb Size: 177258 MD5sum: 52844383b72bbd2d8ae8e3024249f53b SHA1: d92b0122ecf1c8b9fd9aae84d84fe917259ad2ca SHA256: e9860f3d6ccca57531f90e464cde457c64efb31a311aa448078783af78a64563 SHA512: 7cf0c4d37e571ffaf98df4fb612282fed328236c1ac2f90292211798701ce7cc024b49d16d50ae41af88640a0bca63daf314418018d3217d99996e0d208d3806 Homepage: https://cran.r-project.org/package=PEIP Description: CRAN Package 'PEIP' (Geophysical Inverse Theory and Optimization) Several functions introduced in Aster et al.'s book on inverse theory. 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The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities. 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The package includes a dedicated 'pems' data class that manages many of the quality control, unit handling and data archiving issues that can hinder efforts to standardise 'PEMS' research. 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For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) . 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The smoothly clipped absolute deviation (SCAD), 'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic SCAD' (SCAD and 'L2-norm') penalties are available. The tuning parameters can be found using either a fixed grid or a interval search. 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See Signorelli (2024) and Signorelli et al. (2021) for details. Package: r-cran-penetrance Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1665 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-clipp, r-cran-mass, r-cran-kinship2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-ggplot2, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-penetrance_0.1.3-1.ca2604.1_all.deb Size: 1512418 MD5sum: 0330fc4a5a5479f8b8bd140713618d3a SHA1: 078a6fb2efd8b514234743650cda6c78caa332a5 SHA256: dddb87cc4b5483df06d373ba7ca91f4414231d0f3453b93e58dbebe8f12376da SHA512: dde2e4ea8c6e9eda8b52429d9347f33daeccac073eef5ab73de30bccf67fe83dacd7c17874d276a80204d3ac1d026a2dfe679e4f7fc7ed56a41cdac3835f4c5d Homepage: https://cran.r-project.org/package=penetrance Description: CRAN Package 'penetrance' (Methods for Penetrance Estimation in Family-Based Studies) Implements statistical methods for estimating disease penetrance in family-based studies. Penetrance refers to the probability of disease manifestation in individuals carrying specific genetic variants. The package provides tools for age-specific penetrance estimation, handling missing data, and accounting for ascertainment bias in family studies. Cite as: Kubista, N., Braun, D. & Parmigiani, G. (2024) . Package: r-cran-penfa Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-gjrm, r-cran-trust Suggests: r-cran-cartography, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-penfa_0.1.1-1.ca2604.1_all.deb Size: 533762 MD5sum: c38234f9d49ea9bf63a7049c9b145b03 SHA1: 5c040a1a7a8acca1313861b70507f10e23498877 SHA256: 8b634dd41556974cda3acc37a602aa1ea21b5c7cf102391b23dacd5b38b4800b SHA512: 69e30b67730fb0116aff9c6fb46cac5e809f0994dfa94ace615e7f54b1f6ec932b11e6f05ae7f60fb6066ffba4ec42e3f012a3e1973e2b2af3eea8d4e24e295c Homepage: https://cran.r-project.org/package=penfa Description: CRAN Package 'penfa' (Single- And Multiple-Group Penalized Factor Analysis) Fits single- and multiple-group penalized factor analysis models via a trust-region algorithm with integrated automatic multiple tuning parameter selection (Geminiani et al., 2021 ). Available penalties include lasso, adaptive lasso, scad, mcp, and ridge. Package: r-cran-penguinr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-penguinr_0.1.0-1.ca2604.1_all.deb Size: 136234 MD5sum: f07e50dcd2ac0d0cdda585635b8b5f3e SHA1: 5b8cadc1f7fe7b4116ec88bc95ba08f0fd46a4c2 SHA256: dba33ff8e6bae124d1e9dd33bbf86a94b13fdfd6b7921a1e8e73d997a2235830 SHA512: 2e0ecefafc0541851ec119efefd5ce0434f3ec7f34d1ad0dcef2b8b3d3fa8a2d624cf1acb3578b236ee8a5a8473e41ea2bfc61e801e469c634f608ea726251cc Homepage: https://cran.r-project.org/package=PenguinR Description: CRAN Package 'PenguinR' (A Comprehensive Collection of Penguin Datasets for StatisticalAnalysis and Experimental Design) Offers a comprehensive collection of penguin-related datasets suitable for descriptive statistics, hypothesis testing, and experimental design. Derived from open ecological and biological sources such as Palmer Station studies, the package integrates datasets covering adult morphology, clutch size, blood isotope composition, and heart rate. It is designed for researchers, students, and educators to explore statistical methods including ANOVA, regression, multivariate analysis, and design of experiments in an accessible and reproducible context. Package: r-cran-penic Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-matrix, r-cran-mass Filename: pool/dists/resolute/main/r-cran-penic_1.0.0-1.ca2604.1_all.deb Size: 34454 MD5sum: f05ef10e320ec791b06e62c5b9498383 SHA1: 24bc77ebc4d906df455e865d6cd5e63dc5bc9641 SHA256: 7717d34e9f904f28e6433c363416afcc8f156f89486d1e4f9d27cc48bdcf8c7c SHA512: 2a2461666a9eade53a2f213e003fd00c52b5cf9024aa8aeb2ebae9f57309d6e3ad9e24f703607fe87d203b1a0f1ec79519a7e8e2917a3790e1d8831f7f5dff34 Homepage: https://cran.r-project.org/package=PenIC Description: CRAN Package 'PenIC' (Semiparametric Regression Analysis of Interval-Censored Datausing Penalized Splines) Currently incorporate the generalized odds-rate model (a type of linear transformation model) for interval-censored data based on penalized monotonic B-Spline. More methods under other semiparametric models such as cure model or additive model will be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. (2019) . Package: r-cran-pensar Architecture: all Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-digest, r-cran-stringdist, r-cran-yaml Suggests: r-cran-jsonlite, r-cran-llm.api, r-cran-saber, r-cran-simplermarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-pensar_0.6.3-1.ca2604.1_all.deb Size: 381002 MD5sum: bf9fd2982c41b4e1ede415888221b70e SHA1: f2eb153f99959924fe13596301b510d4c16ebe34 SHA256: 9157911731928141d8ee5c41848cea95792ad965b91e4e5a527491ab057fbe5b SHA512: d97a3d8151d65ef4f20a8308dac1db6eb0fa846a157cc50941df07ff283688509bd6a8bac4aec3add0996b9baeb27a2ffbb61b568e063771c3dcd031b4642f92 Homepage: https://cran.r-project.org/package=pensar Description: CRAN Package 'pensar' (LLM Wiki Engine) Personal wiki engine with a large language model (LLM) as research assistant. Supports guided sessions through a 'Claude Code' skill bundle and autonomous research runs from R via autoresearch(). Results land in a structured vault of markdown pages with 'YAML' frontmatter and wikilinks, ready for hand-editing in your favourite editor alongside the LLM. Vaults are seeded with 'CLAUDE.md' and 'AGENTS.md' so 'Claude Code', 'Codex' , and other agents share the same operating instructions. Can adopt an existing 'Obsidian' vault in place via init_vault(adopt = TRUE). Package: r-cran-pensim Architecture: all Version: 1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2876 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-penalized, r-cran-mass Suggests: r-cran-survivalroc, r-cran-survival, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pensim_1.3.6-1.ca2604.1_all.deb Size: 2423836 MD5sum: 486b5dcbe911361d607adb633c1a0715 SHA1: 86b6d1b5e6ccced2d247e696bfe03dacbb736bcd SHA256: 58d0b00c483a1c7e3dd77193c78421e6b0b4b2a7af136e50b133cf98faf1ddd6 SHA512: 2b5887e917bd9f6330d1ef243b1ba8bdd601afe90d1430363d76ed04f05d8e2bc1dc7c4593855b980ef973e048d4032ed22b5e466f87724dd01841c2f3222210 Homepage: https://cran.r-project.org/package=pensim Description: CRAN Package 'pensim' (Simulation of High-Dimensional Data and Parallelized RepeatedPenalized Regression) Simulation of continuous, correlated high-dimensional data with time to event or binary response, and parallelized functions for Lasso, Ridge, and Elastic Net penalized regression with repeated starts and two-dimensional tuning of the Elastic Net. Package: r-cran-pensynth Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-clarabel, r-cran-cli, r-cran-lifecycle, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pensynth_0.8.2-1.ca2604.1_all.deb Size: 83712 MD5sum: afb9da1ab382ddf7ab35fd9f308ce8af SHA1: 0899b113958a51da087d5a36b7e04603a9adad40 SHA256: 1d278cfe786b01312817c4d7f30932a521b33f0a3f4eb5f32edcea4eaafde4b9 SHA512: 9486657b44d12f3946c1d29f38c6ff236e2d356d2a5695d376620ddeb397e8c40f8876e420d0b0849274ef86fd6f04f94083a9956ff6a6399244b5c2e51d3948 Homepage: https://cran.r-project.org/package=pensynth Description: CRAN Package 'pensynth' (Penalized Synthetic Control Estimation) Estimate penalized synthetic control models and perform hold-out validation to determine their penalty parameter. This method is based on the work by Abadie & L'Hour (2021) . Penalized synthetic controls smoothly interpolate between one-to-one matching and the synthetic control method. Package: r-cran-peopleanalytics Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-peopleanalytics_0.1.0-1.ca2604.1_all.deb Size: 123510 MD5sum: e92c76fce82103327d50587a84e8c9b3 SHA1: 9e52c248ce02886aecf574f33caf99cee07ccec1 SHA256: 6e40da9cbbc7d3a795aa60269486b82505304a5d4492e4818207f0affd2491f4 SHA512: 349ad7585342f809de8270319daddfd5f2c5e3f3ef3665f2c757282b14409c27e5488977f859a5b9756b4800d04006784b2f37d6f7bf3ce24aa2bafccecd4308 Homepage: https://cran.r-project.org/package=peopleanalytics Description: CRAN Package 'peopleanalytics' (Data Sets for Craig Starbuck's Book, "The Fundamentals of PeopleAnalytics: With Applications in R") Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R". Package: r-cran-peopleanalyticsdata Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-peopleanalyticsdata_0.2.1-1.ca2604.1_all.deb Size: 187646 MD5sum: 13245190ddc2e043699903cbcc03c8ad SHA1: fa01e722f3cf30c72df589fb04fb032f22f7ae51 SHA256: a8c1981a68912292970f65ec8583d097d0a83479158c2ce052646295e1db81b6 SHA512: b519d5ac5248db2e89eef3f88a81dbd0062dd2200cb08441150dc53f74e8b8541fae3aefb6150d49cfc60de2f3a8afc63eb6b7a2d2ddc706c4ef9fd916d7b3dd Homepage: https://cran.r-project.org/package=peopleanalyticsdata Description: CRAN Package 'peopleanalyticsdata' (Data Sets for Keith McNulty's Handbook of Regression Modeling inPeople Analytics) Data sets for statistical inference modeling related to People Analytics. Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' by Keith McNulty (2020). Package: r-cran-pep725 Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4947 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-dplyr, r-cran-mgcv, r-cran-patchwork, r-cran-purrr, r-cran-robustbase, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-rnaturalearth, r-cran-sf Suggests: r-cran-ggmap, r-cran-kendall, r-cran-knitr, r-cran-leaflet, r-cran-miniui, r-cran-nlme, r-cran-quantreg, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-pls, r-cran-shiny, r-cran-sp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pep725_1.1.0-1.ca2604.1_all.deb Size: 3384340 MD5sum: b60bb1fd1e82a96c919dbc14b256c5f8 SHA1: 68d86d2c5290fa46704c44414f70a61d22a221b8 SHA256: 0da141224d4b5595e1deaaf2c0533829340dd7ebd5d82e6f7543b2ebbf38aab8 SHA512: dbe56f237220e6e8f5e8fae74b63775d249e0ac390f41e487336949a9cbe1cde3dd4c31d48ba69cc854bf6b7fab35877acf1175311be6a8117df1a34dfef1c6a Homepage: https://cran.r-project.org/package=pep725 Description: CRAN Package 'pep725' (Pan-European Phenological Data Analysis) Provides a framework for quality-aware analysis of ground-based phenological data from the PEP725 Pan-European Phenology Database (Templ et al. (2018) ; Templ et al. (2026) ) and similar observation networks. Implements station-level data quality grading, outlier detection, phenological normals (climate baselines), anomaly detection, elevation and latitude gradient estimation with robust regression, spatial synchrony quantification, partial least squares (PLS) regression for identifying temperature-sensitive periods, and sequential Mann-Kendall trend analysis. Supports data import from PEP725 files, conversion of user-supplied data, and downloadable synthetic datasets for teaching without barriers of registration. All analysis outputs provide 'print', 'summary', and 'plot' methods. Interactive spatial visualization is available via 'leaflet'. Package: r-cran-pepdiff Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4459 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-readr, r-cran-ggplot2, r-cran-cowplot, r-cran-emmeans, r-cran-artool, r-cran-magrittr, r-cran-stringr, r-cran-forcats Suggests: r-bioc-complexheatmap, r-cran-upsetr, r-cran-rcolorbrewer, r-cran-viridis, r-cran-circlize, r-cran-factoextra, r-bioc-rankprod, r-cran-mkinfer, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pepdiff_1.0.0-1.ca2604.1_all.deb Size: 1639396 MD5sum: 172e1032d7a3cae4177b0c38e8773872 SHA1: 5c9a337a18e49f735aaf73a350637461a28a92aa SHA256: 6b74c9da61016cb305c46124d885b1d24e9b267e5ca0e1625147d8b32648d5d9 SHA512: c141e2558674a6abaead630ad637e22f1d4fe2123638851e6f650e1f7db109e8ec86b6e9d46b93cd17332e0ee14d26c8ede5a9bcef8080b7c3fea42106a18f39 Homepage: https://cran.r-project.org/package=pepdiff Description: CRAN Package 'pepdiff' (Differential Abundance Analysis for Phosphoproteomics Data) Provides tools for analyzing differential abundance in proteomics experiments. Implements S3 classes for data management and supports Generalized Linear Models (GLM; Nelder and Wedderburn (1972) ), Aligned Rank Transform (ART; Wobbrock et al. (2011) ), and pairwise test methods for statistical analysis. Includes visualization functions for Principal Component Analysis (PCA), volcano plots, and heatmaps. Package: r-cran-pepe Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-psych, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pepe_1.2.0-1.ca2604.1_all.deb Size: 876522 MD5sum: 229c97e7edec2454b652c025e2166d82 SHA1: a32960e77f1c24ed2c08f4699a6e725a28180962 SHA256: 13bde664924068d747924403bb5d4325491dab44d5d6de350eb2838e3e5ff0bd SHA512: e66cd0a5a14a0b186e8936fa7cd561f1444aa6a4d260a838b1d33fc861c7a59e148c3cfb1374908eacba80226192651562941e26674833994c310bb181e36305 Homepage: https://cran.r-project.org/package=pepe Description: CRAN Package 'pepe' (Data Manipulation) Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor. Package: r-cran-pepmapviz Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-stringr, r-cran-ggforce, r-cran-ggh4x, r-cran-ggnewscale, r-cran-data.table, r-cran-rlang, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-mzid, r-bioc-msnbase Filename: pool/dists/resolute/main/r-cran-pepmapviz_1.1.0-1.ca2604.1_all.deb Size: 446484 MD5sum: c3c9e90b74b7296902fc844ed218bed1 SHA1: 4ed2a1b69bd8f43e5006b1852ea427449001de75 SHA256: 7655b619ea68123a850d111f8596c4ddea8141dbd4fb1f99faab9fcf2e1a2458 SHA512: e6dab3966e1ae6351e2ed24353c59a8bb2558a71e3ab12c5b17a80a2c80fe6fa4fc9ab403e8b1fecc983ac1cf019fc0e5b212c8f8ecef610125100c43907882a Homepage: https://cran.r-project.org/package=PepMapViz Description: CRAN Package 'PepMapViz' (A Versatile Toolkit for Peptide Mapping, Visualization, andComparative Exploration) A versatile R visualization package that empowers researchers with comprehensive visualization tools for seamlessly mapping peptides to protein sequences, identifying distinct domains and regions of interest, accentuating mutations, and highlighting post-translational modifications, all while enabling comparisons across diverse experimental conditions. Potential applications of 'PepMapViz' include the visualization of cross-software mass spectrometry results at the peptide level for specific protein and domain details in a linearized format and post-translational modification coverage across different experimental conditions; unraveling insights into disease mechanisms. It also enables visualization of Major histocompatibility complex-presented peptide clusters in different antibody regions predicting immunogenicity in antibody drug development. Package: r-cran-peppwr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1657 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-purrr, r-cran-rcolorbrewer, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-univariateml Suggests: r-cran-bench, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-peppwr_0.1.0-1.ca2604.1_all.deb Size: 512986 MD5sum: 26beb0578960aecb38eac16ff047e902 SHA1: 8e663504837b2e8d2b484508ec6f42be058912d4 SHA256: 7cd8fe0bcacc5bd03f459a437a6393c4f653dc9d1f0acfd89c1e2fbad4a3dc20 SHA512: ca6d3da37e6692e93bfef5ebfdd08088e9f8acda7a3ec2ba0bd5bfd248c704c6b8bde4197ba69abf6a7668439246c37c5b9e194a23ee902bebd74808e082d003 Homepage: https://cran.r-project.org/package=peppwR Description: CRAN Package 'peppwR' (Power Analysis for Phosphopeptide Abundance Hypothesis Test) Estimate best fit distributions and do power analysis for hypothesis tests on phosphopeptide abundance data. Package: r-cran-pepr Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-stringr, r-cran-data.table, r-cran-rcurl, r-cran-httr2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-curl Filename: pool/dists/resolute/main/r-cran-pepr_0.6.0-1.ca2604.1_all.deb Size: 285468 MD5sum: fc901f4c1507584e4933cdcc3a8a9d3d SHA1: 8a8768a7e015c09f9707941c05ce66680994f3af SHA256: 2d65d9f8ea86f4493bcb97766163d48f62c5017a419fdc60f6aa34ade770cab7 SHA512: 0ff5d965c6baf5262cb83a36855320a192a997bfa50376c760871ed135583d4ebf56314df773651817efe1d8805faa8a4afb635e092c28b90daaf7ec002ae832 Homepage: https://cran.r-project.org/package=pepr Description: CRAN Package 'pepr' (Reading Portable Encapsulated Projects) A PEP, or Portable Encapsulated Project, is a dataset that subscribes to the PEP structure for organizing metadata. It is written using a simple YAML + CSV format, it is your one-stop solution to metadata management across data analysis environments. This package reads this standardized project configuration structure into R. Described in Sheffield et al. (2021) . Package: r-cran-pepsavims Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-elasticnet Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pepsavims_0.9.1-1.ca2604.1_all.deb Size: 3380118 MD5sum: fdae867baa61a8ea6900476effb31323 SHA1: 7478fa0d17d14aa2bab523bcb7a4370269324521 SHA256: 45117f9748d7393f01cdc7e01debdfafde60c22964fb60a15ed1df5d8ee58507 SHA512: 4210b934dc3ffc543b19c53618723069937bad03c98bff59571fb81d022abe1a6a971fc361062116124869d377e0dc8ad1a304b2c1d304188f8d543a15b32d1e Homepage: https://cran.r-project.org/package=PepSAVIms Description: CRAN Package 'PepSAVIms' (PepSAVI-MS Data Analysis) An implementation of the data processing and data analysis portion of a pipeline named the PepSAVI-MS which is currently under development by the Hicks laboratory at the University of North Carolina. The statistical analysis package presented herein provides a collection of software tools used to facilitate the prioritization of putative bioactive peptides from a complex biological matrix. Tools are provided to deconvolute mass spectrometry features into a single representation for each peptide charge state, filter compounds to include only those possibly contributing to the observed bioactivity, and prioritize these remaining compounds for those most likely contributing to each bioactivity data set. Package: r-cran-peptoolkit Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-peptides, r-cran-dplyr, r-cran-stringr, r-cran-caret Filename: pool/dists/resolute/main/r-cran-peptoolkit_0.0.1-1.ca2604.1_all.deb Size: 41360 MD5sum: b9eb3062884668556230d289d6ddfa44 SHA1: e37430fbbf7f8587baa9e26c2a88841e932bae09 SHA256: 7b48d68afd98f4a87f400f56d0ec17dc8d295fa952bac4d47a33ab3e561ad16d SHA512: 747265b05eec88a655cbbd80e94e0d03be73e9176814c14892c4ed197f29e39194380e97de79430a81d5de79cd54c36526ac114143e9758f1448c74121ae691e Homepage: https://cran.r-project.org/package=peptoolkit Description: CRAN Package 'peptoolkit' (A Toolkit for Using Peptide Sequences in Machine Learning) This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) ). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning. Package: r-cran-pequod Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-pequod_0.2.0-1.ca2604.1_all.deb Size: 32920 MD5sum: 066f56a727560c1ba8ef95d28b98bc83 SHA1: 4e6c0724628e0d955cebe4a295e1b770e1a0fc0a SHA256: 6486a1f66f14b167c9b9816858ee936525c1ddeec60c1f243eb01b02486c4155 SHA512: e69aa25b678ba88eb7e832b061a3ac141617ef6e9babeeccf52ff0d22828e015b15a12faedb74b6d91ba6a4ca30f87977beb4115164fc7ece7e0ee033e08cfa4 Homepage: https://cran.r-project.org/package=pequod Description: CRAN Package 'pequod' (Colour Palette for Reading and Code, Inspired by Moby-Dick) The Pequod colour palette, named after the whaler in Herman Melville's Moby-Dick. Provides the full Log base scale from warm paper (Log 50) to deep ink (Log 950), eight crew accent hues with light and dark variants, and 'ggplot2' scales for discrete and continuous mapping. Designed for long-form reading and code, with low saturation and a consistent earth- pigment register. Full design rationale and accessibility notes at . Package: r-cran-peramo Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-lme4, r-cran-parameters, r-cran-emmeans Suggests: r-cran-multcompview Filename: pool/dists/resolute/main/r-cran-peramo_0.1.5-1.ca2604.1_all.deb Size: 149544 MD5sum: 574bf6d1c5f415fc686782290ed2ce5e SHA1: 6094a5e686f205fbdccca2e53c11a3a1d7f848b1 SHA256: 6e8168e50da0ad9ca2f133ccddafd5d0fc6ac6ca81e148afcbf94f8a952e4f7a SHA512: ccceaca195e28a794c01afcacf39bae1039688d4545631c4533c717929d4f1767c7731f7ef6cd4703fc9a42e3a03c9b91c8cf3136d9bc233efcc6e7478d6c664 Homepage: https://cran.r-project.org/package=peramo Description: CRAN Package 'peramo' (Permutation Tests for Randomization Model) Perform permutation-based hypothesis testing for randomized experiments as suggested in Ludbrook & Dudley (1998) and Ernst (2004) , introduced in Pham et al. (2022) . Package: r-cran-perarma Architecture: all Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-gnm, r-cran-matlab, r-cran-matrix, r-cran-signal Filename: pool/dists/resolute/main/r-cran-perarma_1.7-1.ca2604.1_all.deb Size: 280130 MD5sum: 52fe10c81636033340ed056186cdcdd5 SHA1: 1368a503ca29bbe0d961a7f62a6cf4ed2071677d SHA256: 91c69eb9ff14cf93e38a57d4da4e022b109a3b4874db0ef5de30f74cb7a8f8be SHA512: 8d712315898c696f43336d541e7b75612e73da4005bb9edfb67ee9a7c1d135a967c3861cab672bd10213f1bd067f6b9986fbe50def8cd7243409fcca3011e1db Homepage: https://cran.r-project.org/package=perARMA Description: CRAN Package 'perARMA' (Periodic Time Series Analysis) Identification, model fitting and estimation for time series with periodic structure. Additionally, procedures for simulation of periodic processes and real data sets are included. Hurd, H. L., Miamee, A. G. (2007) Box, G. E. P., Jenkins, G. M., Reinsel, G. (1994) Brockwell, P. J., Davis, R. A. (1991, ISBN:978-1-4419-0319-8) Bretz, F., Hothorn, T., Westfall, P. (2010, ISBN: 9780429139543) Westfall, P. H., Young, S. S. (1993, ISBN:978-0-471-55761-6) Bloomfield, P., Hurd, H. L.,Lund, R. (1994) Dehay, D., Hurd, H. L. (1994, ISBN:0-7803-1023-3) Vecchia, A. (1985) Vecchia, A. (1985) Jones, R., Brelsford, W. (1967) Makagon, A. (1999) Sakai, H. (1989) Gladyshev, E. G. (1961) Ansley (1979) Hurd, H. L., Gerr, N. L. (1991) . Package: r-cran-perc Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-reshape2, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-perc_0.1.6-1.ca2604.1_all.deb Size: 136610 MD5sum: 7ea02aea4654d46f4cd6ec13fd272c2d SHA1: 22f590c68d39dc3c0f08c0491bb288547453f384 SHA256: e0ddff25fa1b52128b4bb35bfacaa6fa2c4cd0f88bad48134182316d1569dd73 SHA512: d67c7db6ac756256220e334ea58eeea021068b3531456c43bb8eaa394b2d4e5ea953659a366f2b21f6cecea2858d83167d452b7c4c81ce40d0de61eaa4d53f38 Homepage: https://cran.r-project.org/package=Perc Description: CRAN Package 'Perc' (Using Percolation and Conductance to Find Information FlowCertainty in a Direct Network) To find the certainty of dominance interactions with indirect interactions being considered. 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These statistics allow assessing whether individual response patterns to tests or questionnaires are (im)plausible given the other respondents in the sample or given a specified item response theory model. Some PFSs apply to dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs (personal biserial correlation, caution index, (normed) number of Guttman errors, agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include extensions of lz, U3, and (normed) number of Guttman errors. Package: r-cran-performance Architecture: all Version: 0.17.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3396 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestestr, r-cran-insight, r-cran-datawizard Suggests: r-cran-aer, r-cran-afex, r-cran-bayesfactor, r-cran-bayesplot, r-cran-betareg, r-cran-bigutilsr, r-cran-blavaan, r-cran-boot, r-cran-brms, r-cran-car, r-cran-cardata, r-cran-compquadform, r-cran-correlation, r-cran-cplm, r-cran-curl, r-cran-dagitty, r-cran-dbscan, r-cran-dharma, r-cran-discovr, r-cran-estimatr, r-cran-fixest, r-cran-flextable, r-cran-forecast, r-cran-ftextra, r-cran-gamm4, r-cran-ggdag, r-cran-glmmtmb, r-cran-gparotation, r-cran-hmisc, r-cran-httr2, r-cran-ics, r-cran-icsoutlier, r-cran-islr, r-cran-ivreg, r-cran-lavaan, r-cran-lme4, r-cran-lmtest, r-cran-loo, r-cran-mass, r-cran-matrix, r-cran-mclogit, r-cran-mclust, r-cran-metadat, r-cran-metafor, r-cran-mgcv, r-cran-mlogit, r-cran-modelbased, r-cran-multimode, r-cran-nestedlogit, r-cran-nlme, r-cran-nnet, r-cran-nonnest2, r-cran-ordinal, r-cran-parameters, r-cran-patchwork, r-cran-pscl, r-cran-psych, r-cran-psychtools, r-cran-quantreg, r-cran-qqplotr, r-cran-randomforest, r-cran-rcppeigen, r-cran-reformulas, r-cran-rempsyc, r-cran-rmarkdown, r-cran-rstanarm, r-cran-rstantools, r-cran-sandwich, r-cran-see, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-tweedie, r-cran-vgam, r-cran-withr Filename: pool/dists/resolute/main/r-cran-performance_0.17.0-1.ca2604.1_all.deb Size: 2871358 MD5sum: 717014e5c3320241402d761d384afed1 SHA1: 69d07c555833167e44da124fb8f272957b528c4b SHA256: 90dbb481f30c2f2b82abb69801b60ca0a0f7274d80d36b5e1d12debcca1c43db SHA512: e7d603c8383fed4e0eb9b4a758f7448a3cb968aedeb49ffbef4643aca7091bbaf93c4f265a3da3a419883d36e4e12c0b9f0accd7d27f90a370414c79fae41f7a Homepage: https://cran.r-project.org/package=performance Description: CRAN Package 'performance' (Assessment of Regression Models Performance) Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) ), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models. References: Lüdecke et al. (2021) . Package: r-cran-periodics Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-rms Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-periodics_0.5.0-1.ca2604.1_all.deb Size: 180296 MD5sum: 5c395194bb79cab52b27f3468e8df9c0 SHA1: 49b6aa29ac5606c0d12f0f394e5491d01ad763cc SHA256: f22c8db9cfee42531993a19fbfc75ad59ad218bd13acff5105ee6cb9ec5727cc SHA512: e46a451228dd84540379e6422fe00e14d6547484d2b55607a2160d47bf64611404c0c97840ebd9a8431239d86b9efb6ff53d135106ab6cb5e307e9253ff3d67c Homepage: https://cran.r-project.org/package=peRiodiCS Description: CRAN Package 'peRiodiCS' (Functions for Generating Periodic Curves) Functions for generating variants of curves: restricted cubic spline, periodic restricted cubic spline, periodic cubic spline. Periodic splines can be used to model data that has periodic nature / seasonality. Package: r-cran-periodictable Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-periodictable_0.1.2-1.ca2604.1_all.deb Size: 45144 MD5sum: f18972f10addafeccc27c0818bc8e6b7 SHA1: 0ac09e10ac4432b039cf17d739d2e96baaf0b407 SHA256: 44e6c09017a41a2e6c28807d7a338488df32ebeb68caad8e2f8a3a85d72bab52 SHA512: 26a082ad240c3afaad99a7afa7cc575ca6786bf18fb50b16863036ab03838e5c2a2e77a99f2238ce3a115cc840ee240bbb07d29069e08dfddb6486cceb3bcaac Homepage: https://cran.r-project.org/package=PeriodicTable Description: CRAN Package 'PeriodicTable' (Periodic Table of the Elements) Provides a dataset containing properties for chemical elements. Helper functions are also provided to access some atomic properties. Package: r-cran-periscope2 Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-bs4dash, r-cran-dt, r-cran-fresh, r-cran-lubridate, r-cran-reactable, r-cran-shinyfeedback, r-cran-shinywidgets, r-cran-writexl, r-cran-yaml, r-cran-lifecycle Suggests: r-cran-assertthat, r-cran-canvasxpress, r-cran-colourpicker, r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-miniui, r-cran-openxlsx, r-cran-openxlsx2, r-cran-rmarkdown, r-cran-shinyjs, r-cran-spelling, r-cran-testthat, r-cran-waiter Filename: pool/dists/resolute/main/r-cran-periscope2_0.3.0-1.ca2604.1_all.deb Size: 2180394 MD5sum: 7e7296b313fe0e707ba252e27903f07b SHA1: 871770851821b508aee7992021fda2f140059c99 SHA256: 2e2f2fa7240e7b6834e6b9283793132d8ade039346b8ae617ca86465147a4dfb SHA512: 5176d5aa99ea907c84ffbec787739afd78ef00de8d1601bdbd0db8625dfa9a03faa9585d5160732bf471779b1c45267b6d5520e3b888bd2291b5b25b44413bae Homepage: https://cran.r-project.org/package=periscope2 Description: CRAN Package 'periscope2' (Enterprise Streamlined 'shiny' Application Framework Using'bs4Dash') A framework for building enterprise, scalable and UI-standardized 'shiny' applications. 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Package: r-cran-perk Architecture: all Version: 0.0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bs4dash, r-cran-dt, r-cran-shiny, r-cran-tibble, r-cran-colourpicker, r-cran-config, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-golem, r-cran-magrittr, r-cran-plotly, r-cran-readr, r-cran-shinyjs, r-cran-shinywidgets, r-cran-tidyr, r-cran-viridis, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-perk_0.0.9.2-1.ca2604.1_all.deb Size: 1610274 MD5sum: 1f06594b0a82c6b0671b89ac9a9a8f69 SHA1: 191f93c36d303c2e82f726a7c99e8664ce8db9f9 SHA256: 67c49ebf91c896e400193bacfaf203cea23be7f1a8eddf8c4eebbc1e9665031f SHA512: 9edad22871ea236d1f96eb67738d1aaf65220f1c2272c93558a638973fc18bba33c7f57084802db4741c4847449a295afd6cdc9f25e96988ffd83b65ca679bfc Homepage: https://cran.r-project.org/package=PERK Description: CRAN Package 'PERK' (Predicting Environmental Concentration and Risk) A Shiny Web Application to predict and visualize concentrations of pharmaceuticals in the aqueous environment. Jagadeesan K., Barden R. and Kasprzyk-Hordern B. (2022) . Package: r-cran-perm Architecture: all Version: 1.0-0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-coin Filename: pool/dists/resolute/main/r-cran-perm_1.0-0.4-1.ca2604.1_all.deb Size: 87322 MD5sum: 86b85537f8c6ca5f88b89f68ad92b2df SHA1: 4b9d81f3209126a98e44b9d67952bddb700c79f6 SHA256: dc53aec98ca0275d246f68ac48111eef61333befd04fbbe2692cb1136f5843fe SHA512: 9bab22e4396d42f75137a6ebdb03cbdcf3a780662556df5fa04cae5931572e7afe2a5e5bd46d2c10e22b2106bbdcc81a974e7e49caedbd43ef29ad316752199b Homepage: https://cran.r-project.org/package=perm Description: CRAN Package 'perm' (Exact or Asymptotic Permutation Tests) Perform Exact or Asymptotic permutation tests [see Fay and Shaw ]. Package: r-cran-permalgo Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-permalgo_1.2-1.ca2604.1_all.deb Size: 30698 MD5sum: 905a45629efcd58484077ce073b36f14 SHA1: 736a9a7f2cab7fce1fafcac3efc83097afb2bdf9 SHA256: c85055f0705d80b5ef23ab3fd0984815b3b7a63d203e30b72237960b8363870e SHA512: 44ecf8cbde0a9b7cc60bfe0550e56de065ea7ef3bbaeb2d1680059c047fdb2a515d39c83418b11792f6d537d947869f8ca59a7e94e63bba27da45095c103d138 Homepage: https://cran.r-project.org/package=PermAlgo Description: CRAN Package 'PermAlgo' (Permutational Algorithm to Simulate Survival Data) This version of the permutational algorithm generates a dataset in which event and censoring times are conditional on an user-specified list of covariates, some or all of which are time-dependent. Package: r-cran-permanova Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-xtable, r-cran-mass, r-cran-scales, r-cran-deldir Filename: pool/dists/resolute/main/r-cran-permanova_0.2.0-1.ca2604.1_all.deb Size: 320286 MD5sum: 5377d427155c5a18560483f96cdb5ac6 SHA1: bd7bec07e389d87e36f826f2899af7875bd46ca0 SHA256: 3ddeeb885790006290410fb9a611fe439526c6a50468006dfd2d22c39a5eaaf2 SHA512: e7068f205b1df9d3f31c9e5510a889c848f0de6925a28508f7f77574e0bf1315cbf04860e7417cc5efd84e678e5dc6d4ff4760b4a15f49d341832d870948e8ae Homepage: https://cran.r-project.org/package=PERMANOVA Description: CRAN Package 'PERMANOVA' (Multivariate Analysis of Variance Based on Distances andPermutations) Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon. Package: r-cran-permat Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-permat_0.1.0-1.ca2604.1_all.deb Size: 32556 MD5sum: d6f6fb2cdd58042096bbc818bafd29c4 SHA1: 78166c3707ba91c51fda981030cca56fccd53db5 SHA256: ccf20576f2f4d9372cf90ae0ba954d333ed1e4b1f97c3e88b49689f4dac26526 SHA512: 93b80907deb70636237b9fbe2386684d8c4c9c1f72182179845e30ca5b062a2e668a92f00a64906eb81ab0090a4285d2feb39b973dd00c1cd36844410b8b7290 Homepage: https://cran.r-project.org/package=PerMat Description: CRAN Package 'PerMat' (Performance Metrics in Predictive Modeling) Performance metric provides different performance measures like mean squared error, root mean square error, mean absolute deviation, mean absolute percentage error etc. of a fitted model. These can provide a way for forecasters to quantitatively compare the performance of competing models. For method details see (i) Pankaj Das (2020) . Package: r-cran-permchacko Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-permchacko_1.0.1-1.ca2604.1_all.deb Size: 41802 MD5sum: cbdd6669d97c703668ae572c1b3d9960 SHA1: 6ba6392fa79d20c4f6daa4e3bbbd9f43d5412b6d SHA256: 035b0e304ab3604cf2d194b4202536c54001fc703e90f0c0db7c4ff423d3d9a9 SHA512: 3499cfcff4b4491687df6d99bf3c080062499f14344dc0269fd224636d783a7d0c90e24c5fbe6e4556ebfcd253a94d4187dd53630877ca8c1503b04f2aa98b4e Homepage: https://cran.r-project.org/package=permChacko Description: CRAN Package 'permChacko' (Chacko Test for Order-Restriction with Permutation) Implements an extension of the Chacko chi-square test for ordered vectors (Chacko, 1966, ). Our extension brings the Chacko test to the computer age by implementing a permutation test to offer a numeric estimate of the p-value, which is particularly useful when the analytic solution is not available. Package: r-cran-permcor Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-permcor_0.1.0-1.ca2604.1_all.deb Size: 43378 MD5sum: 64e92f485ddf20a9cdf49752021512dc SHA1: c73d820438756c8649c7b2d43d1b633d6ffcf406 SHA256: 93b6d5f47e127a81219f67a01b43dbd390ae90f11fcc803015316b70a57c10e0 SHA512: d3d41f0671f1544758012632a4ec7bace18139c8f727bbf9484d6864bd945c3feda82162a87b37f62eeb7cb638d035c8e184c34aa5187609a6359d19ac1cc107 Homepage: https://cran.r-project.org/package=PermCor Description: CRAN Package 'PermCor' (Robust Permutation Tests of Correlation Coefficients) Provides tools for statistical testing of correlation coefficients through robust permutation method and large sample approximation method. Tailored to different types of correlation coefficients including Pearson correlation coefficient, weighted Pearson correlation coefficient, Spearman correlation coefficient, and Lin's concordance correlation coefficient.The robust permutation test controls type I error under general scenarios when sample size is small and two variables are dependent but uncorrelated. The large sample approximation test generally controls type I error when the sample size is large (>200). Package: r-cran-permimp Architecture: all Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ipred, r-cran-party, r-cran-pbapply, r-cran-randomforest, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-permimp_1.1-0-1.ca2604.1_all.deb Size: 146624 MD5sum: 8a8f99cd8ee43b45440a7334ba3cd07a SHA1: a4b82696152f73365e83610a053261120747c2f2 SHA256: b353e10a2ff5a46fbce7036cf93c5ee8bd41b192ddfc0ce8b6e348800b08eff0 SHA512: 1cd269e02a2a7b814a9f37a6ebdebbd3d0b56b58fc0d9381f29191605c75b7356cfa702842ce81d9fe12a06abcd9178d8e6c7763a7f0fcb0f850ebd28b1b7fb5 Homepage: https://cran.r-project.org/package=permimp Description: CRAN Package 'permimp' (Conditional Permutation Importance) An add-on to the 'party' package, with a faster implementation of the partial-conditional permutation importance for random forests. The standard permutation importance is implemented exactly the same as in the 'party' package. The conditional permutation importance can be computed faster, with an option to be backward compatible to the 'party' implementation. The package is compatible with random forests fit using the 'party' and the 'randomForest' package. The methods are described in Strobl et al. (2007) and Debeer and Strobl (2020) . Package: r-cran-permrand Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-permrand_1.0.0-1.ca2604.1_all.deb Size: 390214 MD5sum: a085371b89d260bb066a1e027dce44e4 SHA1: 98ffbd6431fa31ff4cffacac98270b5b2b820911 SHA256: a2d0eddb896efbe486a3b7d0ebd1676dccd2a915723bbb0e0c2706124055b003 SHA512: 71a40f2f8eeded67decf364929bb1149657c1c760d2993872a7692e28c1d900d1930b55189033648835d147ead8ad75293b33cf0eb4a837aa902bcb63379788d Homepage: https://cran.r-project.org/package=permRand Description: CRAN Package 'permRand' (Permutation Randomization) Provides randomization using permutation for applications. To provide a Quality Control (QC) check, QC samples can be randomized within strata. A second function allows for the ability to ”switch” samples to meet set requirements and perform a certain amount of minimization on these switches. The functions are flexible for users by specifying strata size and number of QC samples per strata. The randomization meets the following requirements • QC sample requirements: QC samples not adjacent, QC samples from same mother must follow certain patterns. • Matched sample sets must be within a single strata, and next to each other. Package: r-cran-permubiome Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-dabestr, r-cran-gridextra, r-cran-matrix, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-permubiome_1.3.2-1.ca2604.1_all.deb Size: 59088 MD5sum: 6b611a90ef8b48d108016bc7a8f9e973 SHA1: 5f258d948f11e5ab5a8d53fa381e2b6908dc4105 SHA256: c8d82e1fb772b523439bc2a5db55237def77e04c4e8cb940afe59309221f2005 SHA512: e7c6025cca52382799a47e53baa7e5daf8308f0938766bb5d3e26dbcc20e6266f42ff404b4f468cf6e22069fa6f7f8faaf08f003b4b03011f41d0909f65c9518 Homepage: https://cran.r-project.org/package=permubiome Description: CRAN Package 'permubiome' (A Permutation Based Test for Biomarker Discovery in MicrobiomeData) The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with a prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions' or 'Anders'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the 'dabestr' (>=0.3.0) R package. Current v1.3.2 fixed bug with "Class" recognition and updated 'dabestr' functions. 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Methods include Taylor-Russell classification (Taylor and Russell, 1939, ), Brogden-Cronbach-Gleser monetary utility (Brogden, 1949, ), Schmidt-Hunter-Pearlman intervention utility (Schmidt and others, 1979, ), Sturman comprehensive cascade (Sturman, 2001, ), Thomas-Owen-Gunst multivariate classification (Thomas and others, 1977, ), compensatory versus multiple-hurdle simulation (Ock and Oswald, 2018, ), AUC-to-effect-size conversions (Salgado, 2018, ), Pareto frontiers for validity-diversity trade-offs, and Monte Carlo uncertainty propagation. 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Package: r-cran-persval Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmsb Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-persval_1.1.2-1.ca2604.1_all.deb Size: 72152 MD5sum: 1f949e94d2a66c9c1a273e61e771f2a8 SHA1: ad596978e9ce0b4edc78d3d430f1c1479be48544 SHA256: 5783ffbe2854980ab3a555488da5ea8136955ad320ba884e1b9a5012d3b6fd3a SHA512: 4c3deea2117790cd37e1eada559d4c59cdefc40777264de1c579f6e7de9ff8cfea791938c208448fdf74fa7811d6b2027d5d7c0866572bb4d5572aa5d6a15274 Homepage: https://cran.r-project.org/package=persval Description: CRAN Package 'persval' (Computing Personal Values Scores) Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. 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Package: r-cran-peruapis Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-peruapis_0.1.0-1.ca2604.1_all.deb Size: 491416 MD5sum: 11faaea0cd9fab2721e97a0725dede0b SHA1: ef9875fc9298bf76ca30c91b7f0f460c6850efcb SHA256: 0f404a8cf12aaff8bf040a040485855193d5e242e012c50dd306bd5de2d10a9e SHA512: 4e5cf8131b80605a37c9d1c0e5d60468363a36cf494aa81cd3b3cf3a0c81d1be715b48c833688c40bc42751ee7a8a454ba205a922da5b354107dbcd3cc24218c Homepage: https://cran.r-project.org/package=PeruAPIs Description: CRAN Package 'PeruAPIs' (Access Peruvian Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to Peru, such as holidays, economic indicators, and international demographic and geopolitical indicators. 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Package: r-cran-peruflorads43 Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-gt, r-cran-scales, r-cran-stringdist, r-cran-ggplot2, r-cran-forcats, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-peruflorads43_0.2.3-1.ca2604.1_all.deb Size: 1026852 MD5sum: 87ddba8a7860b9a80f6631a4a1f022da SHA1: 8529553f9bd3505ee87c1ce8239ad5def1473db3 SHA256: d308940c7b62893f9772d8b6ed68c8592b842cde1125f4019dff2a8762bfce12 SHA512: 5552a44485c892ee9bbcbfe11df04f9240478da8f0a84bfb74c692327de9a270f44109e404d0a4fb18efd6a2add314032d47e7a11fb25376b6d0f22bd3283a1b Homepage: https://cran.r-project.org/package=peruflorads43 Description: CRAN Package 'peruflorads43' (Check Threatened Plant Species Status Against Peru's SupremeDecree 043-2006-AG) Provides tools to match plant species names against the official threatened species list of Peru (Supreme Decree 043-2006-AG, 2006). 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Package: r-cran-perumammals Architecture: all Version: 0.0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-memoise Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-ggtext Filename: pool/dists/resolute/main/r-cran-perumammals_0.0.0.2-1.ca2604.1_all.deb Size: 500490 MD5sum: d9677c74fe50790625b8c42613aad704 SHA1: 413d60ec6ab63245cc885b111b3bc689441eb99f SHA256: 5b5e8bc8d176b9f00e49b589f5719b42670bdd112f34786122f1647b3a6989a5 SHA512: 4800454cf6e9f04919f3ad7b301d3d1c581207498a8af252f8e622793aef99082ae97b2d992714b10896793c7892c902a9eda3cec2e946fbf8e33f64587e96fd Homepage: https://cran.r-project.org/package=perumammals Description: CRAN Package 'perumammals' (Taxonomic Backbone and Name Validation Tools for Mammals of Peru) Provides a curated taxonomic backbone of mammal species recorded in Peru, based on the checklist published by Pacheco and collaborators (2021) . 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Package: r-cran-peruse Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang Suggests: r-cran-testthat, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-peruse_0.3.1-1.ca2604.1_all.deb Size: 91766 MD5sum: 4bec396f05bc65f4ca39ffd0e745529e SHA1: 673bee033b469c9546aee9e866bd8f78c36549da SHA256: d3cbaf4afe9724e46dd8be32eba5252918f4cc946eecad488ab2a9abc3148ede SHA512: a8563f58f7ff31b1f04c36751ac40a69825a4ff18b860c5e123822a3f0e097a0ef6dfcd85d5bca450d923b774996cd85c0f53c2a7af2bd7ef2019c01f4dc8813 Homepage: https://cran.r-project.org/package=peruse Description: CRAN Package 'peruse' (A Tidy API for Sequence Iteration and Set Comprehension) A friendly API for sequence iteration and set comprehension. Package: r-cran-perutimber Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-perutimber_0.1.0-1.ca2604.1_all.deb Size: 3656082 MD5sum: c3f0762bd4925975d807bb5dd79f5f11 SHA1: a20b01a886f98c44fa664bafdf9a3d44c9fad3bb SHA256: 4b3f43e4657a778a6a9d8e38e7c4944cc48b5d39d1a637d454e3c1651981cb50 SHA512: 4f5489052a486c8ae638faa25bccb75467037cc2d03b0045c207acc6102084dbd4334635163d0ab0cf3cf55fb0dd7b3ef4cb3bacb61cc4b0a6c4176fb38a23b2 Homepage: https://cran.r-project.org/package=perutimber Description: CRAN Package 'perutimber' (Catalogue of the Timber Forest Species of the Peruvian Amazon) Access the data of the 'Catalogue of the Timber Forest Species of the Peruvian Amazon' Vásquez Martínez, R., & Rojas Gonzáles, R.D.P.(2022). Package: r-cran-pervasive Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arules, r-cran-dplyr, r-cran-tibble, r-cran-psych Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-psychtools Filename: pool/dists/resolute/main/r-cran-pervasive_1.0-1.ca2604.1_all.deb Size: 80316 MD5sum: fd059d3a378f12995694f55e160581a7 SHA1: c7821ad9f3657a84358d0b7293c36af423b47c57 SHA256: 2f7c1b98352fae0bd5f07c8c2bb2824841fb5fc604983f7a003c0b7cbf030fc0 SHA512: c5e25c7b91a9b001480ba41a8eaa261ab1d8228690973ac08163c249cdb15d8040b83d1108493d81966c18cec09fa1b3ada7e9d713d48fadd5ea41f5f63b8144 Homepage: https://cran.r-project.org/package=pervasive Description: CRAN Package 'pervasive' (Pervasiveness Functions for Correlational Data) Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the 'arules' package by Hahsler et al. (2025) and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science. Package: r-cran-pesel Architecture: all Version: 0.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pesel_0.7.5-1.ca2604.1_all.deb Size: 27894 MD5sum: b6be5402c74255145ef5c3737e1324fd SHA1: 7154eed560a79f7e4c403eadb4625ba637c1babf SHA256: 16290665921f71d639ee815fbb9f70af39817c62eedd355b0693639537a69576 SHA512: f6cdcfa1ea0d10110d057c899949144608c17c95809adca337f3da6bbfac89ba1fc06ccc29c2d144fe37cc795b671e69b503f49602b82e3f096583bd32cb974b Homepage: https://cran.r-project.org/package=pesel Description: CRAN Package 'pesel' (Automatic Estimation of Number of Principal Components in PCA) Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse "Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood" (2017) . Package: r-cran-pesticideloadindicator Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-stringr, r-cran-magrittr, r-cran-rlang, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pesticideloadindicator_1.3.1-1.ca2604.1_all.deb Size: 91150 MD5sum: 33e380e32339b90508ab83347d5a54b4 SHA1: 09662c419fe442ac8d381b0a843455bad1055bc5 SHA256: f1f26192a464b185911dc61a050d28c41959dec6c17bab164d4978b920dfc3d9 SHA512: 31c8e02429d13571a405ac5555f8ad1ff4ffc6410e1da93dadf842306c3250a3d5cc8f566080fc73c13bba1b14025feafd3c36310e6f87a699cc9bd17eabbd4b Homepage: https://cran.r-project.org/package=PesticideLoadIndicator Description: CRAN Package 'PesticideLoadIndicator' (Computes Danish Pesticide Load Indicator) Computes the Danish Pesticide Load Indicator as described in Kudsk et al. 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Package: r-cran-pestr Architecture: all Version: 0.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-mockr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pestr_0.8.4-1.ca2604.1_all.deb Size: 328056 MD5sum: ea1ad9fdeb5e56a8bdb97767dbca8a92 SHA1: 7a9f19b6b5e4bb5c8dd0820f54f54780ca4f53ee SHA256: b505edb15c82d5c3997e49b41f4023f7f61c0becce4575161a09d3e7e591f81e SHA512: f941fae6ed295dd181e3603659f59bc746890ddcb0493455f5bdb5d11676b147e2b94a5fe7491fd3afe2a7e9ffdea76a110f746978c04955f388798ecf250c1d Homepage: https://cran.r-project.org/package=pestr Description: CRAN Package 'pestr' (Interface to Download Data on Pests and Hosts from 'EPPO') Set of tools to automatize extraction of data on pests from 'EPPO Data Services' and 'EPPO Global Database' and to put them into tables with human readable format. 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Included are functions for various purposes, including evaluating the accuracy of judgments and predictions, performing scoring of assessments, generating correlation matrices, conversion of data between various types, data management, psychometric evaluation, extensions related to latent variable modeling, various plotting capabilities, and other miscellaneous useful functions. By making the package available, we hope to make our methods reproducible and replicable by others and to help others perform their data processing and analysis methods more easily and efficiently. The codebase is provided in Petersen (2025) and on 'CRAN': . The package is described in "Principles of Psychological Assessment: With Applied Examples in R" (Petersen, 2024, 2025a) , , and in "Fantasy Football Analytics: Statistics, Prediction, and Empiricism Using R" (Petersen, 2025b). 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It includes functions to interpolate regular positions of points between landmarks, to discretize polylines into regular point positions, link distant observations to points and convert a bounding box in a spatial object. It also provides miscellaneous functions for field ecologists such as spatial statistics and inference on diversity indexes, writing data.frame with Chinese characters. 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SDTM dataset specifications are described in the CDISC SDTM implementation guide, accessible by creating a free account on . 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Package: r-cran-pharmr Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vegawidget, r-cran-reticulate, r-cran-cli Suggests: r-cran-testthat, r-cran-magrittr, r-cran-here, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pharmr_2.1.0-1.ca2604.1_all.deb Size: 643728 MD5sum: 5cf88e7aeff0fe107181928104ab4b1f SHA1: cb2df337a3eab6b532d0593765bbfceb864bf743 SHA256: 1ed87261c8e93b76961f99c66fdc0c40da20dd390c27c01c8bcdd908b34328f6 SHA512: edc184c345c8861140b16ff40bdca9ed4cb034007c802ec2e6d54c5a9801e5df05382417a7c59703c020d563bd879930b151fc243092999af630318af880100b Homepage: https://cran.r-project.org/package=pharmr Description: CRAN Package 'pharmr' (Interface to the 'Pharmpy' 'Pharmacometrics' Library) Interface to the 'Pharmpy' 'pharmacometrics' library. The 'Reticulate' package is used to interface Python from R. Package: r-cran-phase12designs Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trialr, r-cran-iso Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-phase12designs_0.3.1-1.ca2604.1_all.deb Size: 212722 MD5sum: 9b19ccda92fe6ee2ef706f90d808c3f1 SHA1: 952c77e401a8800e56628d63481ea45fb0eeec44 SHA256: 2611d3923a6e2418197612411f91b65fbe6d158232683b5a41ae290334cefc07 SHA512: 28deff095f36cdbb3d09724e8579a7256a6809f16f6a0014e14e736fa4d02b19758f7b8af1b7aa7510617ed1bd1c63a3b01a2d84a413d64e1c860f3afcc7d360 Homepage: https://cran.r-project.org/package=phase12designs Description: CRAN Package 'phase12designs' (Comprehensive Tools for Running Model-Assisted Phase I/II TrialSimulations) Provides a comprehensive set of tools to simulate, evaluate, and compare model-assisted designs for early-phase (Phase I/II) clinical trials, including: - BOIN12 (Bayesian optimal interval phase 1/11 trial design; Lin et al. (2020) ), - BOIN-ET (Takeda, K., Taguri, M., & Morita, S. (2018) ), - EffTox (Thall, P. F., & Cook, J. D. (2004) ), - Ji3+3 (Joint i3+3 design; Lin, X., & Ji, Y. (2020) ), - PRINTE (probability intervals of toxicity and efficacy design; Lin, X., & Ji, Y. (2021) ), - STEIN (simple toxicity and efficacy interval design; Lin, R., & Yin, G. (2017) ), - TEPI (toxicity and efficacy probability interval design; Li, D. H., Whitmore, J. B., Guo, W., & Ji, Y. (2017) ), - uTPI (utility-based toxicity Probability interval design; Shi, H., Lin, R., & Lin, X. (2024) ). Includes flexible simulation parameters that allow researchers to efficiently compute operating characteristics under various fixed and random trial scenarios and export the results. Package: r-cran-phase1prmd Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-rjags, r-cran-arrayhelpers, r-cran-mass, r-cran-reshape2, r-cran-plyr, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-gridextra, r-cran-kableextra, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-phase1prmd_1.0.2-1.ca2604.1_all.deb Size: 187206 MD5sum: ba222264f91145588ff1f540c73ba498 SHA1: df8ef0d006cf9613cfe42f77cd0d9bce01b4a5e2 SHA256: e329e696a957ac764f9d41918b3d510c6c1f952bbd8e5299a5c54d807967247d SHA512: a5c76fd7c03142b9d91cb42b3f8fde37f1a204140fd021997c377d6d72da13cf3861aaf63a249479594f13b0cb405c3a5035d5cd9e84ae591af14ccb7d112a0e Homepage: https://cran.r-project.org/package=phase1PRMD Description: CRAN Package 'phase1PRMD' (Personalized Repeated Measurement Design for Phase I ClinicalTrials) Implements Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints and longitudinal efficacy measure from multiple treatment cycles. The package provides flags to fit a variety of model-based phase I design, including 1 stage models with or without individualized dose modification, 3-stage models with or without individualized dose modification, etc. Functions are provided to recommend dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) . Package: r-cran-phase1rmd Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-boot, r-cran-rjags, r-cran-mvtnorm, r-cran-ggplot2, r-cran-arrayhelpers Suggests: r-bioc-biocparallel Filename: pool/dists/resolute/main/r-cran-phase1rmd_1.0.9-1.ca2604.1_all.deb Size: 228092 MD5sum: 92299e6e006c4549446b70e5d99b2dca SHA1: bfb8ee2531f2ccd76b30b54715666ba0bf717193 SHA256: 22c0c17341525c78f58c1b2d638345c95d669e203f5437542cde6e20242c869c SHA512: d30109e9202dbd72fe14e89b7619725b21047257396d5407ab9a36f0df15f30625b0296da28d18c575fc02a8924dfdda4d8887fb5f2bdc1159f39a22e05c7160 Homepage: https://cran.r-project.org/package=phase1RMD Description: CRAN Package 'phase1RMD' (Repeated Measurement Design for Phase I Clinical Trial) Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) . 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The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) , Lomb-Scargle periodogram: Lomb (1976) , Scargle (1982) and Ruf (1999) , and Autocorrelation: Eijzenbach et al. (1986) . Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) ) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) , Harrisingh method: Harrisingh et al. (2007) , and Stoleru method: Stoleru et al. (2004) . Rose plots and circular analysis are based on methods from - Batschelet (1981) and Zar (2010) . Package: r-cran-phasegmm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nleqslv Suggests: r-cran-extradistr Filename: pool/dists/resolute/main/r-cran-phasegmm_0.1.1-1.ca2604.1_all.deb Size: 118870 MD5sum: 20877fb490bad1953d52061fae51fa56 SHA1: dcd7286652741ce3b7a389647ea399ce7eb8206c SHA256: 1802825e67b09ed6d698114887b04e2b5a32cc8da51c1ff48a7453524b651580 SHA512: fcd9d550c78f706288f287a484c4989814f0791e3dfdd6c75e7f55ec819433bc27043b5cf62b4334586f6c6201b18b4379d436245bf641398219de87c334d36b Homepage: https://cran.r-project.org/package=PhaseGMM Description: CRAN Package 'PhaseGMM' (Phase-Function Based Estimation and Inference for LinearErrors-in-Variables (EIV) Models) Estimation and inference for coefficients of linear EIV models with symmetric measurement errors. The measurement errors can be homoscedastic or heteroscedastic, for the latter, replication for at least some observations needs to be available. The estimation method and asymptotic inference are based on a generalised method of moments framework, where the estimating equations are formed from (1) minimising the distance between the empirical phase function (normalised characteristic function) of the response and that of the linear combination of all the covariates at the estimates, and (2) minimising a corrected least-square discrepancy function. Specifically, for a linear EIV model with p error-prone and q error-free covariates, if replicates are available, the GMM approach is based on a 2(p+q) estimating equations if some replicates are available and based on p+2q estimating equations if no replicate is available. The details of the method are described in Nghiem and Potgieter (2020) and Nghiem and Potgieter (2025) . Package: r-cran-phasetyper Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expm, r-cran-igraph Suggests: r-cran-knitr, r-cran-partitions, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phasetyper_1.0.4-1.ca2604.1_all.deb Size: 786762 MD5sum: e4048ad59e516d71b507c359daa7f71a SHA1: c65dbc4294fbdeabfa51767719e7c9a3dc05c6f7 SHA256: 526725cc39753523fe4e5bf36d5d12a083cfb45e9aa87a1ffe0690ec29f9bd38 SHA512: bfbd90e62ba09a5d33e72ca6a3b153975148b7b7282851ba3945800d05b26c47f1ce4e1e352ce24925c97b6fb276881dc000e574c4e1c44ecc65e3a54d83be65 Homepage: https://cran.r-project.org/package=PhaseTypeR Description: CRAN Package 'PhaseTypeR' (General-Purpose Phase-Type Functions) General implementation of core function from phase-type theory. 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Package: r-cran-phecodemap Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3001 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-collapsibletree, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-golem, r-cran-plotly, r-cran-purrr, r-cran-readr, r-cran-rintrojs, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phecodemap_0.1.0-1.ca2604.1_all.deb Size: 2531350 MD5sum: 155764fc489aa2355d4a452ef79c0633 SHA1: be2e2d2f358736d6a65c196eee013278801f0a39 SHA256: 9afdc151da0d9d62d8150135c96ddca2a7e7c5c09e969dd5dac71884ff6c9533 SHA512: b795d5b9bd45788268ec7dab9defcb42a812a1c6ed83bcc83b67203cd7efe5348cd6c652728705307b567a682e68e7e3c21fa721c76639e7de697a526f19ad52 Homepage: https://cran.r-project.org/package=phecodemap Description: CRAN Package 'phecodemap' (Visualization for PheCode Mapping with ICD-9 and ICD-10-CM Codes) To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classification of Diseases (ICD). The same PheCode hierarchy is displayed in two ways: as a sunburst plot and as a tree. Package: r-cran-pheindicatormethods Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-pheindicatormethods_2.1.1-1.ca2604.1_all.deb Size: 491562 MD5sum: a496ba86fb69b6c713e50d049dca8c64 SHA1: 5c9b28431587b151cbc12345a4f2e39a4d4b3c6f SHA256: 9f69d895ef2949ed193075a1121f0c3470752ac55551f0f6bdd2b468568b39c4 SHA512: 6034765e680e6cfb165cea3c984459a54d8a6154a0b5e9039ae4ceb63533bcdb95a6859d1e1e09f83e6ff68cfd0c3fc2a6c68588703abee04c929c9f86463335 Homepage: https://cran.r-project.org/package=PHEindicatormethods Description: CRAN Package 'PHEindicatormethods' (Common Public Health Statistics and their Confidence Intervals) Functions to calculate commonly used public health statistics and their confidence intervals using methods approved for use in the production of Public Health England indicators such as those presented via Fingertips (). It provides functions for the generation of proportions, crude rates, means, directly standardised rates, indirectly standardised rates, standardised mortality ratios, slope and relative index of inequality and life expectancy. Statistical methods are referenced in the following publications. Breslow NE, Day NE (1987) . Dobson et al (1991) . Armitage P, Berry G (2002) . Wilson EB. (1927) . Altman DG et al (2000, ISBN: 978-0-727-91375-3). Chiang CL. (1968, ISBN: 978-0-882-75200-6). Newell C. (1994, ISBN: 978-0-898-62451-9). Eayres DP, Williams ES (2004) . Silcocks PBS et al (2001) . Low and Low (2004) . Fingertips Public Health Technical Guide: . Package: r-cran-phenesse Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-fitdistrplus Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phenesse_0.1.3-1.ca2604.1_all.deb Size: 47348 MD5sum: 21725c2972c19bcb510884921ba483bc SHA1: e6fe537d905287ec86c87af3c981d0d11602a667 SHA256: 182ba9b0b5a3882ecc41c8dc4f7498ee2c778106e3be5212c6d6d244e603da1e SHA512: 684d900314b2ce483e413f85cfa18a7f70929ee338f7600569c4595d1dbd67edf0050946f0115570ec15ceabb8affba3b54fe3ed72494b7d937b8bd8ad6da368 Homepage: https://cran.r-project.org/package=phenesse Description: CRAN Package 'phenesse' (Estimate Phenological Metrics using Presence-Only Data) Generates Weibull-parameterized estimates of phenology for any percentile of a distribution using the framework established in Cooke (1979) . Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020) . Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest. Package: r-cran-phenix Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ppcor, r-cran-suppdists Filename: pool/dists/resolute/main/r-cran-phenix_1.3.2-1.ca2604.1_all.deb Size: 85006 MD5sum: c26f9a46e3a81d29a0a5bb9af37c2ec0 SHA1: e6c23d63cb8b1f5389a4f8e8f3ffb776f6de1ed3 SHA256: 4e86df44e9955b96b4af6a6f4197ee5986f0037c4f9a657115482e3b623162d7 SHA512: dd3b0710ac8625664cbf4ace290bdb3d032f6fc59bd83670c7d9c143b2bb485bdd9086861e546ee0088cf89a145752c5b166448e72c23bb26d2effbbd281cbdf Homepage: https://cran.r-project.org/package=PHENIX Description: CRAN Package 'PHENIX' (Phenotypic Integration Index) Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) , bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices. Further details can be found in Torices & Muñoz-Pajares . Package: r-cran-phenmodel Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-reshape Filename: pool/dists/resolute/main/r-cran-phenmodel_1.0-1.ca2604.1_all.deb Size: 192838 MD5sum: cc1fd6b2173d2619c58998e20b897a09 SHA1: e9a8df5c5702a20ef833e20e9856c49ca4457bac SHA256: bb8200146577a8364c96b54a5f54c3c4e8958b00e5bd1edb61db9d8c0d4edf37 SHA512: 62f6ce4c7ffdb8b8e51dedeec55f823b675a1ec5e8fc98fe81d69a01c65e7e8d9b62c6e70b4575572d14489db4430cd18e92f2e4f50223bf4e04391bf187ced1 Homepage: https://cran.r-project.org/package=phenModel Description: CRAN Package 'phenModel' (Insect Phenology Model Evaluation Based on Daily Temperatures) Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) . A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour. Package: r-cran-phenocamr Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-changepoint, r-cran-httr, r-cran-zoo, r-cran-memoise, r-cran-daymetr, r-cran-modistools Suggests: r-cran-shiny, r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-leaflet, r-cran-plotly, r-cran-dt Filename: pool/dists/resolute/main/r-cran-phenocamr_1.1.5-1.ca2604.1_all.deb Size: 248890 MD5sum: 086e2b339e4bc850f95211f64af7bb33 SHA1: afa8494f5e849a40e925ca54ab65b017ce6f8d4f SHA256: 10cb686078520c52aeb014e308d50aecd66ee4292b5536605db5e273851daea6 SHA512: ba8eb0390482c77ed5768dc1ac632b3527ea116fe5a10750dc3a5ea217e0f9cd6dcb3d2009e216962e9839d01be114a1f390a5ba41f1148f048fa4bf1c1dcdf1 Homepage: https://cran.r-project.org/package=phenocamr Description: CRAN Package 'phenocamr' (Facilitates 'PhenoCam' Data Access and Time SeriesPost-Processing) Programmatic interface to the 'PhenoCam' web services (). Allows for easy downloading of 'PhenoCam' data directly to your R workspace or your computer and provides post-processing routines for consistent and easy timeseries outlier detection, smoothing and estimation of phenological transition dates. Methods for this package are described in detail in Hufkens et. al (2018) . Package: r-cran-phenocdm Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-phenocdm_0.1.3-1.ca2604.1_all.deb Size: 128976 MD5sum: fe86b821b5e3bcd9272370de3715e3c2 SHA1: 2b794f3060ba5ee37124021068b81b018ef766de SHA256: 4dea76cf34a31f3150f9b49a4bcce5fbc3c39d3a39a94baf12acaa66679f3a1a SHA512: 5b73710291ba103cb991eeb55b06ce677bd90e7e5dd2cfe2fa2fb1a76b5c806259ae8061f1e5a914b8528971d44242f3f0cb09b04f5fd72aad2d629c4535be56 Homepage: https://cran.r-project.org/package=phenoCDM Description: CRAN Package 'phenoCDM' (Continuous Development Models for Incremental Time-SeriesAnalysis) Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development. Package: r-cran-phenolocrop Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phenolocrop_0.0.4-1.ca2604.1_all.deb Size: 38300 MD5sum: 2093e0e06ef8d17fbc706367b03980fe SHA1: 5c0819061094703a60d5a96d32dd3a9e6990eb06 SHA256: 1ca9e05a93ee146741e5704902db79c2e50a81948fadc7fe05ef66aee9c1150b SHA512: 1736556d6c5361aec3355a67c61aa15899e48dd43c6245eb3f5570b66b77c8e9ae910af7b9274ad48a0cca24cdbbb46a4477d9f0b3334c845f2fcef3cf375e7a Homepage: https://cran.r-project.org/package=phenolocrop Description: CRAN Package 'phenolocrop' (Time-Series Models to the Crop Phenology) Fit a time-series model to a crop phenology data, such as time-series rice canopy height. This package returns the model parameters as the summary statistics of crop phenology, and these parameters will be useful to characterize the growth pattern of each cultivar and predict manually-measured traits, such as days to heading and biomass. Please see Taniguchi et al. (2022) and Taniguchi et al. (2025) for detail. This package has been designed for scientific use. Use for commercial purposes shall not be allowed. Package: r-cran-phenology Architecture: all Version: 2026.2.28-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-optimx, r-cran-helpersmg Suggests: r-cran-shiny, r-cran-fields, r-cran-progress, r-cran-car, r-cran-multirng, r-cran-nlwaldtest, r-cran-pbapply, r-cran-cranlogs Filename: pool/dists/resolute/main/r-cran-phenology_2026.2.28-1.ca2604.1_all.deb Size: 1298340 MD5sum: 132a1530db64eee3b740530c78ea3a1f SHA1: 3591154360617dc63b41ef9814b81f39fe012515 SHA256: ec2a4910ca4f4c3142598acf2751a946e46f0bb096516ebc825f3a72dea0de39 SHA512: 92245174dbb5baf2f9cb44411cfef979aa3f49ab3778a85e4969d5d75889e5e9c3f4c67cc1fbe6b6c122a9f5893f415010feaff2b64a8c61ebcca67b057fc142 Homepage: https://cran.r-project.org/package=phenology Description: CRAN Package 'phenology' (Tools to Manage a Parametric Function that Describes Phenologyand More) Functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) for the phenology function, Girondot, M. (2017) for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) for tag-loss estimate, Hancock J, ..., Girondot M (2019) for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) for aggregating several seasons. Package: r-cran-phenomap Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 901 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-phenex, r-cran-plyr, r-cran-stringr, r-cran-terra, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-phenomap_2.0.1-1.ca2604.1_all.deb Size: 741100 MD5sum: 93a29454835952f22e700ecaf49643ae SHA1: 2742fbee4d5c969f7c8e04b7d376f3622ff78804 SHA256: 3ea3e45df596e31a63baad8deb580dc60ddebf24482d94291f4438f03a693104 SHA512: f2e4d5e3dcc21a0c4b50eb13538453e923845402d798de37a4191169c4ccc7b8fc67ec51cbfe54eb123d119c5e4ccfd5bac0d68fbc77240576dc3385a03ee4c0 Homepage: https://cran.r-project.org/package=phenomap Description: CRAN Package 'phenomap' (Projecting Satellite-Derived Phenology in Space) This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) . Package: r-cran-phenopix Architecture: all Version: 2.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-plyr, r-cran-jpeg, r-cran-stringr, r-cran-strucchange, r-cran-foreach, r-cran-doparallel, r-cran-iterators, r-cran-gtools, r-cran-raster, r-cran-sp, r-cran-terra Filename: pool/dists/resolute/main/r-cran-phenopix_2.4.5-1.ca2604.1_all.deb Size: 725256 MD5sum: 064848928746635be3c13a62f4657b9c SHA1: d20846e160853401b2f1cadc46d19fc9e7e7fe64 SHA256: 6c1d4656c796ae05d643b57203a5085c88877bc0887920ddb52b600d1891e7b5 SHA512: c8093c6f2c7d0e439f8ec86d3b2bc0ec9d3ea45b04189f50a2060697e8b73c99e06c75c9d388256b96f0f3b9d0c344ce26846624f557d9db6b471210f15f0a36 Homepage: https://cran.r-project.org/package=phenopix Description: CRAN Package 'phenopix' (Process Digital Images of a Vegetation Cover) A collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages. Package: r-cran-phenorm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phenorm_0.1.0-1.ca2604.1_all.deb Size: 25144 MD5sum: c0d347791b62e25d6572c4a3fc9736a6 SHA1: f40e649c26c3f86548019f641dee724ba78f87bb SHA256: bf400937387c9789c45bfeaee187a366b88295e064c39127e92ff9a882c9b868 SHA512: e0353dc49ce84679f8b9666db686bc2030824a42bfce7ec94729686bc20369fb8f3899298e784145da0e62b8e1b845e031c5693e3661a646f5b4f2373383153c Homepage: https://cran.r-project.org/package=PheNorm Description: CRAN Package 'PheNorm' (Unsupervised Gold-Standard Label Free Phenotyping Algorithm forEHR Data) The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) . Package: r-cran-phenospectra Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-writexl, r-cran-dplyr, r-cran-tidyr, r-cran-data.table, r-cran-lubridate, r-cran-openxlsx, r-cran-broom, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phenospectra_0.1.0-1.ca2604.1_all.deb Size: 483392 MD5sum: ec3b5cfc300e099629da6a01a0a3698d SHA1: add7d1c2822141f01208cdad6111be35e17ac3aa SHA256: d011ef3e8559da43f94f09a5f9b588995839b60f975fcdb864d6cada34b1c009 SHA512: d840c2ac4e15fe6cbc6c04c2764e25ac25d6dd1c73449f8f723a6ed379a093c82023e388e105038107b465069614ef0ae85d11ed8d8ad129f5c3880aabebc0dc Homepage: https://cran.r-project.org/package=PhenoSpectra Description: CRAN Package 'PhenoSpectra' (Multispectral Data Analysis and Visualization) Provides tools for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) and Saric et al. (2022) for related work. Package: r-cran-phenotype Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-phenotype_0.1.0-1.ca2604.1_all.deb Size: 37556 MD5sum: c9c824dd19b17fdd7b1268d028864c85 SHA1: 0ce5be265e557113e361700a51f786d5e03a7813 SHA256: da423ad340186052ff946a6bd7099cd8a95e0047b760a9a020b8878c12f31063 SHA512: 380ba900208cdfe11cabc943f16b6d7d7442007c5628e4e0aac3fe45f0508c402ad42256c3127defdd21632616f7eca08005be2e00ed363f6674dc1fe644655a Homepage: https://cran.r-project.org/package=Phenotype Description: CRAN Package 'Phenotype' (A Tool for Phenotypic Data Processing) Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) . Package: r-cran-phenotyper Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-codelistgenerator, r-cran-cohortcharacteristics, r-cran-cohortconstructor, r-cran-dplyr, r-cran-drugutilisation, r-cran-incidenceprevalence, r-cran-measurementdiagnostics, r-cran-omopgenerics, r-cran-omopsketch, r-cran-patientprofiles, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-vctrs Suggests: r-cran-cdmconnector, r-cran-duckdb, r-cran-dbi, r-cran-gt, r-cran-omock, r-cran-testthat, r-cran-knitr, r-cran-glue, r-cran-rpostgres, r-cran-ggplot2, r-cran-stringr, r-cran-shiny, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-reactable, r-cran-rsvg, r-cran-sortable, r-cran-shinycssloaders, r-cran-here, r-cran-dt, r-cran-bslib, r-cran-shinywidgets, r-cran-plotly, r-cran-tidyr, r-cran-scales, r-cran-usethis, r-cran-rmarkdown, r-cran-cohortsurvival, r-cran-ellmer, r-cran-htmltools, r-cran-visomopresults, r-cran-rsconnect, r-cran-cpp11, r-cran-progress, r-cran-qs2, r-cran-lubridate, r-cran-systemfonts, r-cran-officer, r-cran-fs, r-cran-omopconstructor Filename: pool/dists/resolute/main/r-cran-phenotyper_0.4.0-1.ca2604.1_all.deb Size: 605864 MD5sum: fd46e9bb2f4bfb6f0033d6ccdfca7c66 SHA1: 49aa23a11bda0d0fbff03d74c2594e1e346275a4 SHA256: a03ed088684829010a91db500494bc8af4f47b7f4eb181092d53538d44ef569b SHA512: 71cf5ad7c9f88e5e15c2b622372defd6b2c808088f7c6b2623d79e0015f605c043ccf1863b93c4da6fa737d47bc12b4da89aa29cf0f9a940326ebbc54d8b3cc7 Homepage: https://cran.r-project.org/package=PhenotypeR Description: CRAN Package 'PhenotypeR' (Assess Study Cohorts Using a Common Data Model) Phenotype study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research. 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The method, described in Guilmineau et al (2025) is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented. Package: r-cran-phoenix Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-qwraps2, r-cran-reticulate, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-phoenix_1.1.3-1.ca2604.1_all.deb Size: 266640 MD5sum: 4ab594f12d351f58d8afc2365533cb7e SHA1: 5e76fb91b987b8b2290344a1438a1dd90dcf076d SHA256: 13f3210fbe8e10f8af43074bbef41c67062aec92ef784be57c1b8d8e248a2bfa SHA512: 851fd7dd0a45f6902d4452f906b2653988e78b8a2bf165fd0d2454d21bb2d306adde14d2964fc485246d4a58017e1574bf9abf0343406d9fe8aed739ca338c61 Homepage: https://cran.r-project.org/package=phoenix Description: CRAN Package 'phoenix' (The Phoenix Pediatric Sepsis and Septic Shock Criteria) Implementation of the Phoenix and Phoenix-8 Sepsis Criteria as described in "Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock" by Sanchez-Pinto, Bennett, DeWitt, Russell et al. (2024) (Drs. Sanchez-Pinto and Bennett contributed equally to this manuscript; Dr. DeWitt and Mr. Russell contributed equally to the manuscript), "International Consensus Criteria for Pediatric Sepsis and Septic Shock" by Schlapbach, Watson, Sorce, Argent, et al. (2024) (Drs Schlapbach, Watson, Sorce, and Argent contributed equally) and the application note "phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria" by DeWitt, Russell, Rebull, Sanchez-Pinto, and Bennett (2024) . Package: r-cran-phonenumber Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phonenumber_0.2.3-1.ca2604.1_all.deb Size: 186440 MD5sum: b279d64704a6e4e8129918b02661cfc5 SHA1: d72e9ab1daa818bb46903486082d02dda5ec8fa5 SHA256: b77d2698a7f0424635c14163701b1c55b1c69171005431bb504298c255193ea4 SHA512: fba09ae869612e4c93950b3715360d677fdd69fbd41b2f3d756e0ec2f53d40382ff62017507539d6ac2e18f521b9b7e3cec3ea708f9db6412b6f3c8fcc255a4d Homepage: https://cran.r-project.org/package=phonenumber Description: CRAN Package 'phonenumber' (Convert Letters to Numbers and Back as on a Telephone Keypad) Convert English letters to numbers or numbers to English letters as on a telephone keypad. When converting letters to numbers, a character vector is returned with "A," "B," or "C" becoming 2, "D," "E", or "F" becoming 3, etc. When converting numbers to letters, a character vector is returned with multiple elements (i.e., "2" becomes a vector of "A," "B," and "C"). Package: r-cran-phonetisr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-unicode Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-phonetisr_0.1.0-1.ca2604.1_all.deb Size: 87268 MD5sum: 676612f46fdb2eea04e5b48f7f13701e SHA1: d278b9f207cd8e7280f7c00f409a667483c6fc50 SHA256: fee5a309f3f18043ca7d3127802099afb16e914d5c9286b2b233cd2cfbef6e23 SHA512: 618f726a75e62b8c00f0cde256a5d096835d5048a4aefd6dab8da2e41089690cc47d8b89c531d01c8162df747aceeac57ce2e22b6f22ce2f8e8ec22fa207a354 Homepage: https://cran.r-project.org/package=phonetisr Description: CRAN Package 'phonetisr' (A Naive IPA Tokeniser) It provides users with functions to parse International Phonetic Alphabet (IPA) transcriptions into individual phones (tokenisation) based on default IPA symbols and optional user specified multi-character phones. The tokenised transcriptions can be used for obtaining counts of phones or for searching for words matching phonetic patterns. Package: r-cran-phonevalidator Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phonevalidator_1.0.1-1.ca2604.1_all.deb Size: 11432 MD5sum: 6d39e6d49f7c1a5df1d9596fb95609a6 SHA1: e28ab1c4274ca8e400cafe1d08b2b7f00e68f5bc SHA256: a9f04e10b442cafa6c7bc4618f341eda90c1a47e1437937570e105605d631dd3 SHA512: 2c4cbc77e721fb20da52d80c7778e6dcb41cfdf02b416e8401cd31d995bf75f75032b4cbbca68c41e6fc1dc18b69859e39bddefa0cdbdbe681c95b0a192d1a1a Homepage: https://cran.r-project.org/package=PhoneValidator Description: CRAN Package 'PhoneValidator' (Client for 'GenderAPI.io' Phone Number Validation and FormatterAPI) Provides an interface to the 'GenderAPI.io' Phone Number Validation & Formatter API () for validating international phone numbers, detecting number type (mobile, landline, Voice over Internet Protocol (VoIP)), retrieving region and country metadata, and formatting numbers to E.164 or national format. Designed to simplify integration into R workflows for data validation, Customer Relationship Management (CRM) data cleaning, and analytics tasks. Full documentation is available at . Package: r-cran-phonfieldwork Architecture: all Version: 0.0.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tuner, r-cran-phontools, r-cran-rmarkdown, r-cran-xml2, r-cran-readr, r-cran-mime Suggests: r-cran-knitr, r-cran-tidyr, r-cran-dplyr, r-cran-dt, r-cran-lingtypology, r-cran-testthat, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-phonfieldwork_0.0.17-1.ca2604.1_all.deb Size: 981602 MD5sum: 2f81ab274593330f1ec7b2db12eddda1 SHA1: 95610eb76b56590e41b055131956df6f0aaee8e6 SHA256: 4a99fccf604c425199e6d76b974e5799227b714d1d1c12b1727e57adf8c8fbb8 SHA512: f60d12aac6b9fe7741afbd4d38c1502cb8d359912d8da8a389333941f10fbb544dcef9e215e7ac0caf605fca55bf8f7ddf2abbf127b3aa52366f4dccf7cba350 Homepage: https://cran.r-project.org/package=phonfieldwork Description: CRAN Package 'phonfieldwork' (Linguistic Phonetic Fieldwork Tools) There are a lot of different typical tasks that have to be solved during phonetic research and experiments. This includes creating a presentation that will contain all stimuli, renaming and concatenating multiple sound files recorded during a session, automatic annotation in 'Praat' TextGrids (this is one of the sound annotation standards provided by 'Praat' software, see Boersma & Weenink 2020 ), creating an html table with annotations and spectrograms, and converting multiple formats ('Praat' TextGrid, 'ELAN', 'EXMARaLDA', 'Audacity', subtitles '.srt', and 'FLEx' flextext). All of these tasks can be solved by a mixture of different tools (any programming language has programs for automatic renaming, and Praat contains scripts for concatenating and renaming files, etc.). 'phonfieldwork' provides a functionality that will make it easier to solve those tasks independently of any additional tools. You can also compare the functionality with other packages: 'rPraat' , 'textgRid' . 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Package: r-cran-phontools Architecture: all Version: 0.2-2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-phontools_0.2-2.2-1.ca2604.1_all.deb Size: 456254 MD5sum: 70b7a78889dea1fae5b0068de878686f SHA1: 5ce45692a4c06e6df42398a3d014ccd608f396f8 SHA256: 2f0f76a8c4dd1568c2c30c676c3625e49acea585b30c6ecc3b43aa3729a52b7d SHA512: 3e97bb324146184152bac198f2c55b97609fc39378d2daed7cc25ffd80e32244b12008be19846f74b5d0970d296c99f740e43afe2aab96dc2caad2b69c8d5c97 Homepage: https://cran.r-project.org/package=phonTools Description: CRAN Package 'phonTools' (Tools for Phonetic and Acoustic Analyses) Contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files. 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Plastic sheets and films; photography filters; theatrical gels; machine-vision filters; various types of window glass; optical glass and some laboratory plastics and glassware. Spectral reflectance data for frequently encountered materials. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . 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Facilitates the setup of local 'photon' instances to enable offline geocoding. Package: r-cran-photosynq Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-getpass Filename: pool/dists/resolute/main/r-cran-photosynq_0.2.3-1.ca2604.1_all.deb Size: 41824 MD5sum: b0fef39b318e25861625c355ac6c5d43 SHA1: 8d49b47d2c97d2a3c021da8340b31b4ce326e1fc SHA256: 33dc205bac3dd39ddf6116b2834195f7ac4556b039951dacd9f58b1e22a8a05f SHA512: dd5155e581109adc0833b41d6ea13894e728239c35b800fac43ba5a41fc2dc5235cb7e4357cb90a316fc38c473b1e324880a01ba3328b345866a9d46af23acee Homepage: https://cran.r-project.org/package=PhotosynQ Description: CRAN Package 'PhotosynQ' (Connect to PhotosynQ) Connect R to the PhotosynQ platform (). It allows to login and logout, as well as receive project information and project data. Further it transforms the received JSON objects into a data frame, which can be used for the final data analysis. Package: r-cran-photosynthesis Architecture: all Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-units, r-cran-checkmate, r-cran-crayon, r-cran-dplyr, r-cran-furrr, r-cran-glue, r-cran-gunit, r-cran-lifecycle, r-cran-magrittr, r-cran-nlme, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tealeaves Suggests: r-cran-brms, r-cran-broom, r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-photosynthesis_2.1.5-1.ca2604.1_all.deb Size: 1237958 MD5sum: ca2aeb0c5d6a7b64a4ff182bbfa65c50 SHA1: 6b085b6e58f9ac25ccc961f8438ce2f73d9c9723 SHA256: e06c81faf20178707963db57697c59558529a64ef65930fe0cbe5b031ab925b1 SHA512: 177dcd9e033909ce34d23a79187a527fd21fcb211f1e3af2b6f36ef615f31510d8db5d611130c0acb897cbc359cef856c282ed76295230fd2e28199bddf6e215 Homepage: https://cran.r-project.org/package=photosynthesis Description: CRAN Package 'photosynthesis' (Tools for Plant Ecophysiology & Modeling) Contains modeling and analytical tools for plant ecophysiology. MODELING: Simulate C3 photosynthesis using the Farquhar, von Caemmerer, Berry (1980) model as described in Buckley and Diaz-Espejo (2015) . It uses units to ensure that parameters are properly specified and transformed before calculations. Temperature response functions get automatically "baked" into all parameters based on leaf temperature following Bernacchi et al. (2002) . The package includes boundary layer, cuticular, stomatal, and mesophyll conductances to CO2, which each can vary on the upper and lower portions of the leaf. Use straightforward functions to simulate photosynthesis over environmental gradients such as Photosynthetic Photon Flux Density (PPFD) and leaf temperature, or over trait gradients such as CO2 conductance or photochemistry. ANALYTICAL TOOLS: Fit ACi (Farquhar et al. (1980) ) and AQ curves (Marshall & Biscoe (1980) ), temperature responses (Heskel et al. (2016) ; Kruse et al. (2008) , Medlyn et al. (2002) , Hobbs et al. (2013) ), respiration in the light (Kok (1956) , Walker & Ort (2015) , Yin et al. (2009) , Yin et al. (2011) ), mesophyll conductance (Harley et al. (1992) ), pressure-volume curves (Koide et al. (2000) , Sack et al. (2003) , Tyree et al. (1972) ), hydraulic vulnerability curves (Ogle et al. (2009) , Pammenter et al. (1998) ), and tools for running sensitivity analyses particularly for variables with uncertainty (e.g. g_mc(), gamma_star(), R_d()). Package: r-cran-photosynthesislrc Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-photosynthesislrc_1.0.6-1.ca2604.1_all.deb Size: 163990 MD5sum: 9151317ecc8f21258ee8394cc6dfb9c4 SHA1: b9a4a9ca8af043204165cdda1c4c3a3178213b4f SHA256: f5f351f21bcdb27d1463fc84899f7eb1fa557d4df922263114c853446a1484e7 SHA512: 3add5b09781e1b847e196c87267f29124e2b3777154c41bd176331d4bf57c6ca44f2a3022c53664c22397ac2f02aef9d9d5f2272c91de890c4516f7232aa3f0d Homepage: https://cran.r-project.org/package=photosynthesisLRC Description: CRAN Package 'photosynthesisLRC' (Nonlinear Least Squares Models for Photosynthetic Light Response) Provides functions for modeling, comparing, and visualizing photosynthetic light response curves using established mechanistic and empirical models like the rectangular hyperbola Michaelis-Menton based models ((eq1 (Baly (1935) )) (eq2 (Kaipiainenn (2009) )) (eq3 (Smith (1936) ))), hyperbolic tangent based models ((eq4 (Jassby & Platt (1976) )) (eq5 (Abe et al. (2009) ))), the non-rectangular hyperbola model (eq6 (Prioul & Chartier (1977) )), exponential based models ((eq8 (Webb et al. (1974) )), (eq9 (Prado & de Moraes (1997) ))), and finally the Ye model (eq11 (Ye (2007) )). Each of these nonlinear least squares models are commonly used to express photosynthetic response under changing light conditions and has been well supported in the literature, but distinctions in each mathematical model represent moderately different assumptions about physiology and trait relationships which ultimately produce different calculated functional trait values. These models were all thoughtfully discussed and curated by Lobo et al. (2013) to express the importance of selecting an appropriate model for analysis, and methods were established in Davis et al. (in review) to evaluate the impact of analytical choice in phylogenetic analysis of the function-valued traits. Gas exchange data on 28 wild sunflower species from Davis et al.are included as an example data set here. Package: r-cran-phrases Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-phrases_0.1-1.ca2604.1_all.deb Size: 41944 MD5sum: 2eba9b9d6e0c86986da51f45e1ffc6d5 SHA1: 2d71bab1dd185222d794d5a798740daecb2e7ee7 SHA256: 0f52aee3f1b63dbc4c8dc9b58c17105e2f163a792cb2156ee7c9540fa60b521c SHA512: 6437c347e418ddb05a45aa69af96966eab714292fa082b3b4c770315bcaa39e0f8f86712aac844240979cddbcb16ff52bb5c9669f84f9de780a6d9e5ebe18249 Homepage: https://cran.r-project.org/package=phrases Description: CRAN Package 'phrases' (Phrasal Verbs in English Club Website) Contains all phrasal verbs listed in as data frame. Useful for educational purpose as well as for text mining. 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Package: r-cran-phyloraster Architecture: all Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-purrr, r-cran-sesraster, r-cran-terra Suggests: r-cran-knitr, r-cran-maps, r-cran-phylobase, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phyloraster_2.3.0-1.ca2604.1_all.deb Size: 613402 MD5sum: 11501e138af63e991fe73a8c4b1dad1f SHA1: d42f9a100d587e81020da88c797e7f9eb2a3b2ed SHA256: 5458a915a08a98a4b31ad31cfb71124113c175bd1bbb7ad506d3b012c842c7e5 SHA512: e6a275795ed2b4fff6c612be88b25bf7ae6f16b957f252997c1fa39bc76058d67bff9fb94bbf477213949a8bd23709214e16a98450f602c8f5784a94cc6e9ccb Homepage: https://cran.r-project.org/package=phyloraster Description: CRAN Package 'phyloraster' (Evolutionary Diversity Metrics for Raster Data) Phylogenetic Diversity (PD, Faith 1992), Evolutionary Distinctiveness (ED, Isaac et al. 2007), Phylogenetic Endemism (PE, Rosauer et al. 2009; Laffan et al. 2016), and Weighted Endemism (WE, Laffan et al. 2016) for presence-absence raster. Faith, D. P. (1992) Isaac, N. J. et al. (2007) Laffan, S. W. et al. (2016) Rosauer, D. et al. (2009) . Package: r-cran-phyloregion Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-matrix, r-cran-betapart, r-cran-colorspace, r-cran-igraph, r-cran-clustmixtype, r-cran-maptpx, r-cran-terra, r-cran-vegan, r-cran-predicts, r-cran-smoothr Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-mapproj, r-cran-survival, r-cran-rjava, r-bioc-phyloseq, r-cran-v8 Filename: pool/dists/resolute/main/r-cran-phyloregion_1.0.9-1.ca2604.1_all.deb Size: 1502940 MD5sum: b66e9576c13f454889fa74515af85740 SHA1: dc728b36ded31a547a6aadbbc5bd1981abe645aa SHA256: 751100f95e66ccc56bf7ee915ea44874bbaf46f39ca39975902d5858b1b1a84b SHA512: 37514ca34a8b6724420659e337e8ed72cf00b146842c37d6abeefdba27d0eea56ed7cd9f97ed4a85ce406f72642018ddf7bb710a96911c2c21a251bdbc36b3dd Homepage: https://cran.r-project.org/package=phyloregion Description: CRAN Package 'phyloregion' (Biogeographic Regionalization and Macroecology) Computational infrastructure for biogeography, community ecology, and biodiversity conservation (Daru et al. 2020) . It is based on the methods described in Daru et al. (2020) . The original conceptual work is described in Daru et al. (2017) on patterns and processes of biogeographical regionalization. Additionally, the package contains fast and efficient functions to compute more standard conservation measures such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness and global endangerment, as well as compositional turnover (e.g., beta diversity). Package: r-cran-phylosamp Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2510 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phylosamp_1.0.1-1.ca2604.1_all.deb Size: 1115460 MD5sum: edc7c782bafa649f120e6990497fad63 SHA1: 7ec0c20afb4924e2432ffb68ce1537aac05fabc4 SHA256: fc11b790da00cc6f44f07e2e128572b0d0cd3b5dd9be99eb0a277004627ec875 SHA512: 102b59385407b4bbd1cea3d293ec6b5f6cc4af5188401b5e6052b26e765eb59f1dcf4eeb438883f6abe83f5aa3fba65188faa3d1dc9987fa78338547ceaf457a Homepage: https://cran.r-project.org/package=phylosamp Description: CRAN Package 'phylosamp' (Sample Size Calculations for Molecular and Phylogenetic Studies) Implements novel tools for estimating sample sizes needed for phylogenetic studies, including studies focused on estimating the probability of true pathogen transmission between two cases given phylogenetic linkage and studies focused on tracking pathogen variants at a population level. Methods described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, DiPrete, and Lessler (2023). Package: r-cran-phyloseqgraphtest Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-bioc-phyloseq, r-cran-ggnetwork, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-phyloseqgraphtest_0.1.1-1.ca2604.1_all.deb Size: 469044 MD5sum: 9425c689db8567ce305cddc43383efb4 SHA1: 839e128ca15be536ce71b011e534222b47702b90 SHA256: 929fdf459ca7fa37c38dfa55448ed216b9880ba27730cd8f71d1527dbd8f63f6 SHA512: 7b521961c776b6fc529b4d7471242508779efa3ef2f22cb372458af8a11b9c9509bd2f4e7ef6815c352b4fb63f65e69c6c952ca6ec0c4d5abd94215f98b741ac Homepage: https://cran.r-project.org/package=phyloseqGraphTest Description: CRAN Package 'phyloseqGraphTest' (Graph-Based Permutation Tests for Microbiome Data) Provides functions for graph-based multiple-sample testing and visualization of microbiome data, in particular data stored in 'phyloseq' objects. The tests are based on those described in Friedman and Rafsky (1979) , and the tests are described in more detail in Callahan et al. (2016) . Package: r-cran-phylosignaldb Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-castor, r-cran-cluster, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phylosignaldb_0.4.2-1.ca2604.1_all.deb Size: 65964 MD5sum: 56a4b11881ed99c5a0836ab354493c1a SHA1: 63e52e4a93fd7cd488b2cc33d3492724ba927dde SHA256: cc7d5f142d287d50c4d73ada99321e9ec2bdc7a9b4b145ab8c92a326ee5da818 SHA512: 97ec18f3098f2a5dcff30a542f44b8a1e3a2040913cf5da6b9d7afb22aa897e17e3ca23fef9abcbe0180af38616875dda6ab1af1152685c5f9ace248ef323ef1 Homepage: https://cran.r-project.org/package=phylosignalDB Description: CRAN Package 'phylosignalDB' (Explore Phylogenetic Signals Using Distance-Based Methods) A unified method, called M statistic, is provided for detecting phylogenetic signals in continuous traits, discrete traits, and multi-trait combinations. Blomberg and Garland (2002) provided a widely accepted statistical definition of the phylogenetic signal, which is the "tendency for related species to resemble each other more than they resemble species drawn at random from the tree". The M statistic strictly adheres to the definition of phylogenetic signal, formulating an index and developing a method of testing in strict accordance with the definition, instead of relying on correlation analysis or evolutionary models. The novel method equivalently expressed the textual definition of the phylogenetic signal as an inequality equation of the phylogenetic and trait distances and constructed the M statistic. The M statistic implemented in this package is based on the methodology described in Yao and Yuan (2025) . If you use this method in your research, please cite the paper. Package: r-cran-phylospatial Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2771 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-sf, r-cran-terra, r-cran-vegan Suggests: r-cran-canaper, r-cran-furrr, r-cran-future, r-cran-testthat, r-cran-betapart, r-cran-knitr, r-cran-rmarkdown, r-cran-tmap, r-cran-magrittr, r-cran-hillr, r-cran-picante, r-cran-phytools, r-cran-nullcat, r-cran-paralleldist Filename: pool/dists/resolute/main/r-cran-phylospatial_1.4.0-1.ca2604.1_all.deb Size: 2120434 MD5sum: 167dc85681288ef04a62aa7d98121246 SHA1: c4dfa8f2fd510995fdc7acbe481b4fc810f4e32c SHA256: 4e3d85b10a488d9ef05b1f4bfeef550e448e68caa64f678f4e3bef3c20e60423 SHA512: 35703574cc57394fd7c5ae163078317d621b9382ea8f54abe0fe6e5506d9ceddc4eb285b8db483ffb2f15ba6e6da31a5143194c6352e3d24fbbe01c028e19d26 Homepage: https://cran.r-project.org/package=phylospatial Description: CRAN Package 'phylospatial' (Spatial Phylogenetic Analysis) Analyze spatial phylogenetic diversity patterns. Use your data on an evolutionary tree and geographic distributions of the terminal taxa to compute diversity and endemism metrics, test significance with null model randomization, analyze community turnover and biotic regionalization, and perform spatial conservation prioritizations. All functions support quantitative community data in addition to binary data. Package: r-cran-phylotate Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ape Filename: pool/dists/resolute/main/r-cran-phylotate_1.3-1.ca2604.1_all.deb Size: 62144 MD5sum: 7a30d7e587d89c43c857baeb6d0e927d SHA1: ce293d21e16fe85640ae46761e2eac77e2ac655e SHA256: 4f5768d574b9fd39d60bc0a99390e01a238f0f03c4cb9f364a6f6260ceadc598 SHA512: d3e6de0dd553570ace490c74c956f0f5f008c19c01b46b4e88939284a5a928b4edc098008a9176a8032287c301b76000abf7eae905f6bde6819d95169d778223 Homepage: https://cran.r-project.org/package=phylotate Description: CRAN Package 'phylotate' (Phylogenies with Annotations) Functions to read and write APE-compatible phylogenetic trees in NEXUS and Newick formats, while preserving annotations. Package: r-cran-phylotop Architecture: all Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-igraph, r-cran-nhpoisson, r-cran-phylobase Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-phylotop_2.1.3-1.ca2604.1_all.deb Size: 131436 MD5sum: d80a328b159fc04e358e9ffa076634dc SHA1: 94641f6f881fa9e227bf151af9311a0804e71535 SHA256: 46ddb63e0ccabbfc9adee6244411ec1be3739652be36b85c44a4ad0044e8b076 SHA512: 73204769ec57bf490fb7d0b5ca72c78a277e66839af2202554c1947f90ec5c1fe963c2d7635d47c06dee383690b2053ce7c8a10d641022c9c83f04059b747fc4 Homepage: https://cran.r-project.org/package=phyloTop Description: CRAN Package 'phyloTop' (Calculating Topological Properties of Phylogenies) Tools for calculating and viewing topological properties of phylogenetic trees. Package: r-cran-phymapnet Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-gunifrac, r-cran-compositions Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-phymapnet_0.1.2-1.ca2604.1_all.deb Size: 32346 MD5sum: b879924e244b25809ba4785fefd63854 SHA1: 5ddd48bbaf230c5dc137632d74bc808c9b32c037 SHA256: 7dd57c84f0a230fdba664312527b45c32e2fd1f276a4bc6d5d7b93a69f93970d SHA512: 8d3908ce7a16c44823eed630c506a0b453811c59c78987c319da9703ff8653a0ac481608e4d13534c567b2f63ac3ba22e32cd5df769f50e05f21c1bc7a4260f8 Homepage: https://cran.r-project.org/package=phymapnet Description: CRAN Package 'phymapnet' (Phylogeny-Guided Bayesian Microbial Network Inference) Implements a phylogeny-aware Bayesian graphical modeling framework for microbial network inference using a shrinkage precision estimator guided by a phylogenetic kernel, with optional hyperparameter-ensemble edge reliability analysis. Package: r-cran-phyreg Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-phyreg_1.0.2-1.ca2604.1_all.deb Size: 133702 MD5sum: f8e759f7e6064aafb37b763e7156fb90 SHA1: 8caba3cbf67e21bd842bee3f45b2a98cd71e34f1 SHA256: 02a062cfa207d0b11c2f84118dd2b214f8c6b70f02ee01983f0e2e4c8d43d63e SHA512: b720fcb9fc80fa3bf4ecc147d55085db741703ff8ad6e7f056895fd81dc0159ec0377d1a2298d6b5ef87feac4c526d174572c2b87ba1257e4fd2513f534b68d4 Homepage: https://cran.r-project.org/package=phyreg Description: CRAN Package 'phyreg' (The Phylogenetic Regression of Grafen (1989)) Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true sequence of binary splits). Package: r-cran-physactbedrest Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chron, r-cran-stringr, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-physactbedrest_1.1-1.ca2604.1_all.deb Size: 42042 MD5sum: 0df13af683ebfaa1f300c658149edf80 SHA1: a9313e144e462b3a743c7f35c79c1a14a8bccd73 SHA256: 5716872a49aa702bcc03b55cad431d7a735b96586d12286acc6bdf5826027dc8 SHA512: ade6ce2ae34e0d4d02a50f159ff955bebeb19515e68bc77f0d17fe4bc7dba0b716bcc880562ee630ba90a5ba9538c8120c619616a2fb29a1d4d75a37346d74ef Homepage: https://cran.r-project.org/package=PhysActBedRest Description: CRAN Package 'PhysActBedRest' (Marks Periods of 'Bedrest' in Actigraph Accelerometer Data) Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active. The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively. The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package. Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation. Package: r-cran-physicalactivity Architecture: all Version: 0.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2626 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rsqlite, r-cran-dbi, r-cran-data.table, r-cran-e1071, r-cran-keras, r-cran-randomforest, r-cran-reticulate, r-cran-rms Filename: pool/dists/resolute/main/r-cran-physicalactivity_0.2-4-1.ca2604.1_all.deb Size: 2482206 MD5sum: 2dc4f3724633a9b6a51128367e64d44b SHA1: 88681564155f8dd2ae15c1fe0746de165ea4de9f SHA256: 1166b5079f4915cb8032751433964431a6521d92d45726361667f30a3d252d3f SHA512: bcc9133ed9a1c66453b3138b526ad440925e16f828a396a14705a0060cbe9ab2b26f93355b150e435f0007de02d48a60a5ae0df505f57e6b855a2a8c3a9b2277 Homepage: https://cran.r-project.org/package=PhysicalActivity Description: CRAN Package 'PhysicalActivity' (Process Accelerometer Data for Physical Activity Measurement) It provides a function "wearingMarking" for classification of monitor wear and nonwear time intervals in accelerometer data collected to assess physical activity. The package also contains functions for making plot for accelerometer data and obtaining the summary of various information including daily monitor wear time and the mean monitor wear time during valid days. "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail; "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete days and removing low activity days; "markPAI" can categorize physical activity intensity level based on user-defined cut-points of accelerometer counts. It also supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions. Package: r-cran-physioindexr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-physioindexr_0.1.0-1.ca2604.1_all.deb Size: 89046 MD5sum: 5f86239b5efc3ff17bbceb1f0e4afeca SHA1: 60e490bf6de5f2562dfee4e409bcd4699472feed SHA256: 8637bb8925e9d116207a928acb016706327db1b3e44390f22882804cd3428858 SHA512: 88ce07d853e16619d378a7412424514ae6945e36a12a067183dc5cbe5231273ede1c266e075d2ee736c8614fce71a0221050e7569421f80edff86fd0976a7152 Homepage: https://cran.r-project.org/package=PhysioIndexR Description: CRAN Package 'PhysioIndexR' (Physiological and Stress Indices for Crop Evaluation) Crop production systems are increasingly challenged by climate variability, resource limitations, and biotic–abiotic stresses. In this context, stress tolerance indices and physiological trait estimators are essential tools to identify stable and superior genotypes, quantify yield stability under stress versus non-stress conditions, and understand plant adaptive responses. The 'PhysioIndexR' package provides a unified framework to compute commonly used stress indices, physiological traits, and derived metrics that are critical in crop improvement, crop physiology, and other agricultural sciences. The package includes functions to calculate classical stress tolerance indices (See Lamba et al., 2023; ) such as Tolerance (TOL), Stress Tolerance Index (STI), Stress Susceptibility Percentage Index (SSPI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Mean Relative Performance (MRP), and Percent Yield Reduction (PYR), along with a convenience wrapper all_indices() that returns all indices simultaneously. The function mfvst_from_indices() integrates these indices into a composite stress score using direction-aware membership values (0–1 scaling) and also averaging, facilitating genotype ranking and selection (See Vinu et al., 2025; ). The package also implements two novel composite functions: WMFVST(), which computes the Weighted Mean Membership Function Value for Stress Tolerance, and WASI(), which computes the Weighted Average Stress Index, both derived from membership function values (MFV) and raw stress index values, respectively. Beyond stress indices, the package provides functions for key physiological traits relevant to sugarcane and other crops: bmap() computes biomass accumulation and partitioning between leaf, cane/shoot, and root fractions. chl() estimates total chlorophyll content from Soil-Plant Analysis Development (SPAD) and Chlorophyll Content Index (CCI) values using validated quadratic models particularly for sugarcane (See Krishnapriya et al., 2020; ). ctd() calculates canopy temperature depression (CTD) from ambient and canopy temperatures, an important indicator of transpiration efficiency. growth() computes key growth analysis parameters, including Leaf Area Index (LAI), Net Assimilation Rate (NAR), and Crop Growth Rate (CGR) across crop growth stages (See Watson, 1958; ). ranking() provides flexible ranking utilities for genotype performance with multiple tie-handling and NA-placement options. Through these tools, the package enables researchers to: (i) quantify crop responses to stress environments, (ii) partition physiological components of yield, (iii) integrate multiple indices into composite metrics for genotype evaluation, and (iv) facilitate informed decision making in breeding pipelines, and plant physiology experiments. By combining physiology-based traits with quantitative stress indices, 'PhysioIndexR' supports comprehensive crop evaluation and helps researchers identify multi-stress-resilient superior genotypes, thereby contributing to genetic improvement and ensuring sustainable production of food, fuel, and fibre in the era of limited resources and climate change. Package: r-cran-physortr Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-phytools, r-cran-ape Filename: pool/dists/resolute/main/r-cran-physortr_1.0.9-1.ca2604.1_all.deb Size: 50580 MD5sum: 724d4af9792d1858b220ee5a6a55d011 SHA1: 747ee6090b9b2b1d2ddde07ad4fec01b7c4ce2e4 SHA256: 12ec375d8388752067b2374541d21545dc543b2e8eb5d9c14c31963cb720c3c1 SHA512: 1044deaf2bc1b75eac3480e50e6a872663ed06773406a40c9694330490bc2b90a5fcc047e6ed8ffb578a82b730ab4810d38c07134b62acb02ccab640aef3bc3c Homepage: https://cran.r-project.org/package=PhySortR Description: CRAN Package 'PhySortR' (A Fast, Flexible Tool for Sorting Phylogenetic Trees) Screens and sorts phylogenetic trees in both traditional and extended Newick format. Allows for the fast and flexible screening (within a tree) of Exclusive clades that comprise only the target taxa and/or Non- Exclusive clades that includes a defined portion of non-target taxa. Package: r-cran-phytoclass Architecture: all Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bestnormalize, r-cran-dynamictreecut, r-cran-ggplot2, r-cran-rcppml, r-cran-tidyr, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-quarto, r-cran-here Filename: pool/dists/resolute/main/r-cran-phytoclass_2.3.1-1.ca2604.1_all.deb Size: 423528 MD5sum: 88c79020edc45ef98ec569e48a3d4f47 SHA1: 49464e619a2d9c9a1192d0482fa0ba7e71846070 SHA256: e45cda0e299b9c2ed1ba49d074f8857f5d03ee88b49bd5b2fa222fadd8cdf899 SHA512: a88443208a8e2ea7af286b75815eae4000be844f56ae380104dcaa107f74b579fbda506ef171eed69cbc0eb67f63fd6871b36b544c089c8a8fd28fa5c50aa48a Homepage: https://cran.r-project.org/package=phytoclass Description: CRAN Package 'phytoclass' (Estimate Chla Concentrations of Phytoplankton Groups) Determine the chlorophyll a (Chl a) concentrations of different phytoplankton groups based on their pigment biomarkers. The method uses non-negative matrix factorisation and simulated annealing to minimise error between the observed and estimated values of pigment concentrations (Hayward et al. (2023) ). The approach is similar to the widely used 'CHEMTAX' program (Mackey et al. 1996) , but is more straightforward, accurate, and not reliant on initial guesses for the pigment to Chl a ratios for phytoplankton groups. Package: r-cran-phytoin Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-packcircles, r-cran-biomass, r-cran-scales Suggests: r-cran-httr2, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-phytoin_0.2.0-1.ca2604.1_all.deb Size: 143758 MD5sum: 5fc36fab581eed3a2137bb224e833d41 SHA1: 7d804bf9f8684c12faf21afe3efb5dadcb5abed1 SHA256: ac6d3f77f611c69d698ab64e7115501026d23b4294e2169910bb5a921cc67afd SHA512: 577116388db398839d5b6f8a4284ef6e5c8b3e289049f3fc05e98d02dbb1fe94112abc429c2ae77314dece1fca4709a411264595789e0071d8d17694530e0923 Homepage: https://cran.r-project.org/package=PhytoIn Description: CRAN Package 'PhytoIn' (Vegetation Analysis and Forest Inventory) Provides functions and example datasets for phytosociological analysis, forest inventory, biomass and carbon estimation, and visualization of vegetation data. Includes functions to compute structural parameters [phytoparam(), summary.param(), stats()], estimate above-ground biomass and carbon [AGB()], stratify wood volume by diameter at breast height (DBH) classes [stratvol()], generate collector and rarefaction curves [collector.curve(), rarefaction()], and visualize basal areas on quadrat maps [BAplot(), including rectangular plots and individual coordinates]. Several example datasets are provided to demonstrate the functionality of these tools. For more details see FAO (1981, ISBN:92-5-101132-X) "Manual of forest inventory", IBGE (2012, ISBN:9788524042720) "Manual técnico da vegetação brasileira" and Heringer et al. (2020) "Phytosociology in R: A routine to estimate phytosociological parameters" . Package: r-cran-phytools Architecture: all Version: 2.5-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2943 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-maps, r-cran-clustergeneration, r-cran-coda, r-cran-combinat, r-cran-deoptim, r-cran-doparallel, r-cran-expm, r-cran-foreach, r-cran-mass, r-cran-mnormt, r-cran-nlme, r-cran-numderiv, r-cran-optimparallel, r-cran-phangorn, r-cran-scatterplot3d Suggests: r-cran-animation, r-cran-geiger, r-cran-plotrix, r-cran-rcolorbrewer, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-phytools_2.5-2-1.ca2604.1_all.deb Size: 2840344 MD5sum: 28f359ccf26651eacd9ca47204656a9b SHA1: 1cda8b55c99f327d121884eb2b61c623a4efdc23 SHA256: a5236b2d9a73177839feae12d90310267b38c3cb5e6b48af1f845120a826a79a SHA512: 222acb8f4ef64d88b2da31dd98a1146d8a7fde4bebcbf9072baa27da4665855e742698847c5dc16c0c6e9948bfa76f96c0ebf7b95eef37304c01a19085b0cac4 Homepage: https://cran.r-project.org/package=phytools Description: CRAN Package 'phytools' (Phylogenetic Tools for Comparative Biology (and Other Things)) A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research. Package: r-cran-phytosanitarycalculator Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-acceptancesampling, r-cran-htmltools, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-devtools, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-phytosanitarycalculator_1.1.3-1.ca2604.1_all.deb Size: 40126 MD5sum: b02589d7236bf09751f45c33c9229fde SHA1: 4aa65791ce6f594887deae5f30aeda9e6dafbc58 SHA256: c0e30fafa6543aaeb45fdd3da712ecaea5cc2a6f991b6606e82be05e9ebeb2a4 SHA512: 819c2492a8f728d6ac2321363b3f0b3337a1259cd787756746a58d58fbd7fc0aed1b1060f2525333bc99ea5c6279528b923867a79587c58c1b2c53efc4c3422c Homepage: https://cran.r-project.org/package=PhytosanitaryCalculator Description: CRAN Package 'PhytosanitaryCalculator' (Phytosanitary Calculator for Inspection Plans Based on Risks) A 'Shiny' application for calculating phytosanitary inspection plans based on risks. It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling). Package: r-cran-piar Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gpindex, r-cran-matrix Suggests: r-cran-data.tree, r-cran-knitr, r-cran-rmarkdown, r-cran-sps, r-cran-testthat, r-cran-treemap Filename: pool/dists/resolute/main/r-cran-piar_0.9.0-1.ca2604.1_all.deb Size: 357742 MD5sum: f295a0e47e615beb39c8f2d8c365608a SHA1: 1710f5270329c12049cc9bc00792f47219f07f09 SHA256: 6e78ad38c1dc14d537670b27b2cc5ee972e1aaa89ab6ca30f784e686e7121845 SHA512: 796324e5542359f76eafd14acc0425956d6160707ccf83ce444ae553bde4d05c957d7778266edabc5e964b4c904df10e36175c657bbd8c7e53363aa646fb98ba Homepage: https://cran.r-project.org/package=piar Description: CRAN Package 'piar' (Price Index Aggregation) Most price indexes are made with a two-step procedure, where period-over-period elementary indexes are first calculated for a collection of elementary aggregates at each point in time, and then aggregated according to a price index aggregation structure. 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Package: r-cran-pic Architecture: all Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-conicfit, r-cran-data.table, r-cran-dbscan, r-cran-dplyr, r-cran-foreach, r-cran-magrittr, r-cran-sf, r-cran-tictoc Suggests: r-cran-dt, r-cran-fs, r-cran-ggplot2, r-cran-later, r-cran-plotly, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-pic_1.2.7-1.ca2604.1_all.deb Size: 417160 MD5sum: 7bc6507d047a3d9f1248adb1aaf0bfa1 SHA1: b674d05c3623106b393f507a43b4c0b4d71ca6f4 SHA256: 3df282a69fb737705d8b5c13c1d8fb3e42a3cb915f8051ddc4f7f751cdeaf41d SHA512: c3850c40fd368e2085ab726b05f96816e3065614a87444832f1aed24ff76c278d28925bf765336f6b85a5523ed88d80a41d48bf4f22526da0f53ce9eed6ceb64 Homepage: https://cran.r-project.org/package=PiC Description: CRAN Package 'PiC' (Pointcloud Interactive Computation) Provides advanced algorithms for analyzing pointcloud data from terrestrial laser scanner in forestry applications. 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Package: r-cran-picbayes Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 672 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-mcmcpack, r-cran-survival Filename: pool/dists/resolute/main/r-cran-picbayes_1.0-1.ca2604.1_all.deb Size: 647636 MD5sum: dba92dbfc4cba03fa8f6dd03439985ad SHA1: db825e84f7040bb1d5243cbcdb813ee7e4c271f1 SHA256: 457be263d436c61ea436c7cd1ffc903bdea03396a124f924a0245b00322ce9c0 SHA512: 2549b7b583c05294f6ab9fade417c7f470d0c9ab65ee7123b1db7f801d961a5ac8b739c6f978f905ea9c5d96532d8929a03eb55a2a4647ebd92aac6fad8da162 Homepage: https://cran.r-project.org/package=PICBayes Description: CRAN Package 'PICBayes' (Bayesian Models for Partly Interval-Censored Data) Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. 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Package: r-cran-picclip Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-base64enc, r-cran-stringr, r-cran-htmltools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-picclip_0.1.0-1.ca2604.1_all.deb Size: 16502 MD5sum: 220337e4813769df0e894e4a1a3a408c SHA1: 7a002c78abccdf0fdd09d196db0235fe7c961b90 SHA256: 3e9603243b27d9dabc690fd4e80d548ad2f2095532b02804742e8c78c3cccb47 SHA512: c218debb8f79cefd318f207aebca1ffc99f10a0d8edd6aa736b1c75d0a0b1ea1fda8768002c6ed46655509fd4fb25dcec75177824fcb35178d2ad86462418c2a Homepage: https://cran.r-project.org/package=picClip Description: CRAN Package 'picClip' (Paste Box Input for 'Shiny') Provides a 'Shiny' input widget, pasteBoxInput, that allows users to paste images directly into a 'Shiny' application. 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Package: r-cran-pickmax Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-pickmax_0.1.0-1.ca2604.1_all.deb Size: 15308 MD5sum: 96db54115b7f49bc19e4cfc2ee200b2a SHA1: ab22257b92e0d3e81afbe6864ec0ee059607535c SHA256: 6382bcb78f066f649d21ec55d9e773592ab44a28e614465dde0c08aa5d4f2322 SHA512: 013cc7af8a9ff259f3f3c9431cfd8e43183dba8c1439344872fabf28087c89520dd1201490acecbe10c0f84dd3852e0b45d09267c67d64bc0fb3670f9c12a19b Homepage: https://cran.r-project.org/package=pickmax Description: CRAN Package 'pickmax' (Split and Coalesce Duplicated Records) Deduplicates datasets by retaining the most complete and informative records. Identifies duplicated entries based on a specified key column, calculates completeness scores for each row, and compares values within groups. 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Package: r-cran-picr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-picr_1.0.1-1.ca2604.1_all.deb Size: 43228 MD5sum: 043ef3530097b6ed6a49f0ff739de95a SHA1: 8b0d682944b684077a1704c82175b52f9767582f SHA256: 596fa0b037aa22e15d87c29ec6ead7c6dc5b0f307e7a884ce41b62d9d1a3a2b9 SHA512: fab0a653edf5159cd7cbf803993ecc6e5df42f3edde737f213abd97e7d7628bcf2056df401b6a0f51bf5c124269aa908c00e5dff7fbff9ef3d454f9d3a7c165a Homepage: https://cran.r-project.org/package=picR Description: CRAN Package 'picR' (Predictive Information Criteria for Model Selection) Computation of predictive information criteria (PIC) from select model object classes for model selection in predictive contexts. In contrast to the more widely used Akaike Information Criterion (AIC), which are derived under the assumption that target(s) of prediction (i.e. validation data) are independently and identically distributed to the fitting data, the PIC are derived under less restrictive assumptions and thus generalize AIC to the more practically relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity". Package: r-cran-pid Architecture: all Version: 0.65-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2008 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-png, r-cran-frf2, r-cran-doe.base, r-cran-frf2.catlg128 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-pid_0.65-1.ca2604.1_all.deb Size: 875092 MD5sum: 5d229c07d21924e27ce15c8ecdba22ee SHA1: 45ffd2d88d0d8c7df3bdb511ab8fb93a061df3e6 SHA256: bc9721ceb8e4d260ea3b44078bc64d96a2740951c441883c6891e3e73046db1f SHA512: 2e38c8cfd7084636777579d2dd7fd95e3f2b3ede1e1660f368cf57e973598b4c8175e70c62ab3377d925018e624bdc7ca2f6d5beede99e13a7c072bde291ae0c Homepage: https://cran.r-project.org/package=pid Description: CRAN Package 'pid' (Process Improvement using Data) A collection of scripts and data files for the statistics text: "Process Improvement using Data" and the online course "Experimentation for Improvement" found on Coursera. 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See the paper, Wang, Yang, Li, and Wang (2021) . Package: r-cran-piecemaker Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-stringi, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-piecemaker_1.0.2-1.ca2604.1_all.deb Size: 41192 MD5sum: a02db1ada78decfee9d9a795d7196894 SHA1: 642588880aebf5405e3b8734a16f9dd0644cd861 SHA256: b3483a752b331521430d19b80a9c3a5e6f5939a9e163a51a222732266deaf064 SHA512: 775dd8e515e6921774704974d410053e7e30cc463b7baa85e1eb676493fef3e7056d648c2fb183e3b6ac78f711fd2f0647c57be312656a8fc1236eb2416c1b6c Homepage: https://cran.r-project.org/package=piecemaker Description: CRAN Package 'piecemaker' (Tools for Preparing Text for Tokenizers) Tokenizers break text into pieces that are more usable by machine learning models. 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Package: r-cran-pii Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-uuid Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pii_1.3.0-1.ca2604.1_all.deb Size: 20010 MD5sum: 19781e1a6aa45141f83a80cd15e496d0 SHA1: 09e8cc373095d1d4c7bcf7b26018147662dffd7d SHA256: 41dff09125757ad91b9b5c83550399c38bce532b907ede2ca49b9fa82c322c6c SHA512: cde2d53fb7b9371a64322366a9944d51543eeaf18417789c25a169242ab3170ac7ea4ff0dee09865b2c8a128fc927e206eb227c92f75fde7c09bbc3b7aa6ac50 Homepage: https://cran.r-project.org/package=pii Description: CRAN Package 'pii' (Search Data Frames for Personally Identifiable Information) Check a data frame for personal information, including names, location, disability status, and geo-coordinates. 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Package: r-cran-pinference Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pinference_0.2.6-1.ca2604.1_all.deb Size: 40156 MD5sum: 80f66868fde842b0c0478d610197abf8 SHA1: ead93019c346b74dc0c80f09c95f1c60d5faf0af SHA256: 229e6368d4d1f81a4fba0da4165b1acaa848b43716c7837a963bda6de6f4e529 SHA512: 8e01039b0daa167e95605591160c8c85ad92b58944f90254fcf04af9e6d99a2240fca11e686f53f802c8cc4afe9263694945017699bd333268d46f8d0c9a7a21 Homepage: https://cran.r-project.org/package=Pinference Description: CRAN Package 'Pinference' (Probability Inference for Propositional Logic) Implementation of T. 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Package: r-cran-pinfsc50 Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4234 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pinfsc50_1.3.0-1.ca2604.1_all.deb Size: 3438314 MD5sum: 3f380b1f789de3daf39a4a1e96ae448c SHA1: 937d96a59227fc4334d2818416546b7e353f7193 SHA256: f0aa26dbf19797e9fa49f530a035080cce5596d4e0216e1be8de927d210634ec SHA512: b477e8ae60f219c96367c082a3b0c5b7183204481282437652d3a0866404ce515aa9f6ae4b4998afc06ace039d9edce378750f0996bab8402710a9e36d51a08c Homepage: https://cran.r-project.org/package=pinfsc50 Description: CRAN Package 'pinfsc50' (Sequence ('FASTA'), Annotation ('GFF') and Variants ('VCF') for17 Samples of 'P. Infestans" and 1 'P. Mirabilis') Genomic data for the plant pathogen "Phytophthora infestans." It includes a variant file ('VCF'), a sequence file ('FASTA') and an annotation file ('GFF'). 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Package: r-cran-pins Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 984 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-fs, r-cran-generics, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rappdirs, r-cran-rlang, r-cran-tibble, r-cran-whisker, r-cran-withr, r-cran-yaml Suggests: r-cran-archive, r-cran-arrow, r-cran-azurestor, r-cran-covr, r-cran-data.table, r-cran-filelock, r-cran-gitcreds, r-cran-googlecloudstorager, r-cran-googledrive, r-cran-httr2, r-cran-ids, r-cran-knitr, r-cran-microsoft365r, r-cran-mime, r-cran-mockery, r-cran-nanoparquet, r-cran-openssl, r-cran-paws.storage, r-cran-qs2, r-cran-r.utils, r-cran-rmarkdown, r-cran-rsconnect, r-cran-shiny, r-cran-sodium, r-cran-testthat, r-cran-webfakes, r-cran-xml2, r-cran-zip Filename: pool/dists/resolute/main/r-cran-pins_1.4.2-1.ca2604.1_all.deb Size: 679030 MD5sum: 5d9ca3b64668f9e2050e0951c9e16b00 SHA1: a33223ab6358a4a718307a704b66a2fba09b4012 SHA256: ada9cec5b53ab41f688ef4b392b19b15feea8396491f9df300121a70345f35b1 SHA512: f28bc6fb20c5b6ad3635cffed87cbbafd13da9e3436e3e1685ed16a626e49c9d2addbd24504e6575479b386d98f7646116cc3267fbf14cad8ebfa4405d41ee14 Homepage: https://cran.r-project.org/package=pins Description: CRAN Package 'pins' (Pin, Discover, and Share Resources) Publish data sets, models, and other R objects, making it easy to share them across projects and with your colleagues. 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The pharmacokinetics model explains that how the drug concentration change as the drug moves through the different compartments of the body. For pharmacokinetic modeling and analysis, it is essential to understand the basic pharmacokinetic parameters. All parameters are considered, but only some of parameters are used in the model. Therefore, we need to convert the estimated parameters to the other parameters after fitting the specific pharmacokinetic model. This package is developed to help this converting work. For more detailed explanation of pharmacokinetic parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001"; "Benet and Zia-Amirhosseini" (1995) ; "Mould and Upton" (2012) ; "Mould and Upton" (2013) . 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Create a package-verse, or meta package, by supplying a custom name for the collection of packages and the vector of desired package names to include– and optionally supply a destination directory, an indicator of whether to keep the created package directory, and/or a vector of verbs implement via the 'usethis' package. Package: r-cran-pklmtest Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger Filename: pool/dists/resolute/main/r-cran-pklmtest_1.0.1-1.ca2604.1_all.deb Size: 22198 MD5sum: 6d8ececa7ffc83597cf00ad3afa68b80 SHA1: 10f2f99134081a1d00598bba928c020abca146bc SHA256: 661a1350ac37896bfb466e58f9be518c465759a8d140b249e553c21065bdc067 SHA512: 4d4832da597c146929eb9cda87fa0b4824a7df56e0b43271b60e040465f2443b774c032d96070e7ec0f1895bf4e2bfcff60941ebf19602ea47bc08bcde596490 Homepage: https://cran.r-project.org/package=PKLMtest Description: CRAN Package 'PKLMtest' (Classification Based MCAR Test) Implementation of a KL-based (Kullback-Leibler) test for MCAR (Missing Completely At Random) in the context of missing data as introduced in Michel et al. (2021) . 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Downloads and caches spatial data at country, province, district, and tehsil levels as 'sf' objects compatible with the 'tidyverse' and geospatial ecosystem. Includes utilities for geographic dictionary lookup, coordinate reference system selection, spatial measurement, and neighbour structure construction for use with 'spdep', 'ggplot2', 'leaflet', and related packages. Package: r-cran-pkmon Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pkmon_1.1-1.ca2604.1_all.deb Size: 68396 MD5sum: 08a2f30911c08e5b93d86c241a00c22a SHA1: 7dd06dbd075a88101dce5d004913ee3158e856c6 SHA256: db860b2150b2322869c243ade1cf38d828895fdd924ae1e2e375433faf2712ce SHA512: ead8cb174c922a0e534bf509bdac5f54878f2e7b27d58a83551647485078b35c8ecdc7d4e2dc0b88baf4423632eff6e3ad6ac34c1fc14692a676bedeabb3a6fe Homepage: https://cran.r-project.org/package=pkmon Description: CRAN Package 'pkmon' (Least-Squares Estimator under k-Monotony Constraint for DiscreteFunctions) We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) . The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) , and from mixtures of splines. Package: r-cran-pknca Architecture: all Version: 0.12.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3056 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-digest, r-cran-nlme, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-covr, r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-labeling, r-cran-pander, r-cran-pmxtools, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-units, r-cran-withr Filename: pool/dists/resolute/main/r-cran-pknca_0.12.1-1.ca2604.1_all.deb Size: 1180750 MD5sum: 4cedc1d64b8aed578fcb2fca9c1a15c3 SHA1: 8bcea88e1a52410553a986be6f1f90eeb1cbebb1 SHA256: 3ccd070d3ea16d7a94ab9c1c0070f785cb65efae9816af5f7b96a8f14456febc SHA512: 33ca3a79466499d9981f646fb5666bb55c6f6a05acb341b377db9d8c842f8d11f41f36d33ebc20c5f9fc5b5f90a59804045df1172ca12ec3dc7b768b72784d48 Homepage: https://cran.r-project.org/package=PKNCA Description: CRAN Package 'PKNCA' (Perform Pharmacokinetic Non-Compartmental Analysis) Compute standard Non-Compartmental Analysis (NCA) parameters for typical pharmacokinetic analyses and summarize them. Package: r-cran-pkpd.release Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-gridextra, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pkpd.release_0.1.0-1.ca2604.1_all.deb Size: 268392 MD5sum: 3db6ca2230cb7d2b5198a8e255a7dbfd SHA1: 2723f835e9ebc64a8815b1568b5bd23204468871 SHA256: b8da6898cdf557b6a8741efacae578f8ee79c4303ac128963765e50ec6917986 SHA512: db3209acdd4980d54cedcfc19d5ebd7f8f2297582e882ad9c2b23fa23632df2d1955a7f2a55c0d94c27100bab4570d6310610d52f0e4f6bb3976ed8917504d20 Homepage: https://cran.r-project.org/package=pkpd.Release Description: CRAN Package 'pkpd.Release' (Model Fitting and Simulation for Drug Release Kinetics and PK/PD) Provides a comprehensive framework for model fitting and simulation of drug release kinetics, pharmacokinetics (PK), and pharmacodynamics (PD). The package implements widely used mechanistic and empirical models for in vitro drug release, including zero-order, first-order, Higuchi, Korsmeyer-Peppas, Hixson-Crowell, and Weibull models. Pharmacokinetic functionality includes linear and nonlinear functions for one- and two-compartment models for intravenous bolus and oral administration, Michaelis-Menten kinetics, and non-compartmental analysis (NCA). Pharmacodynamic and dose-response modeling is supported through Emax-based models, including stimulatory (sigmoid Emax) and inhibitory (sigmoid Imax) Hill models, four- and five-parameter logistic models, as well as median toxic dose (TD50) and lethal dose (LD50) models. The package is intended to support parameter estimation, simulation, and model comparison in pharmaceutical research, drug development, and pharmacometrics education. For more details, see Gabrielsson & Weiner (2000) , Holford & Sheiner (1981) , and Manlapaz (2025) . Package: r-cran-pkpdindex Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pkpdindex_0.2.0-1.ca2604.1_all.deb Size: 31684 MD5sum: 2a38dec40d940259eb5038697c06216a SHA1: de4d9fc2db9e416751cbabc825dba3725e20b4f2 SHA256: 7decda1627daa377472c44a6e6fd8514faa51c357459fcfce2acc6d3fc7f3e63 SHA512: 3033613b0c65e470c996122ec410bf258e58c806b8a3239342d0a52da2d17d2b3fef7e62be43fc1dc114c3882b8dc8248c1fa35f109f22e77f726276a2cca55c Homepage: https://cran.r-project.org/package=PKPDindex Description: CRAN Package 'PKPDindex' (Optimal PK/PD Index Finder) Fits Emax models to pharmacokinetic/pharmacodynamic (PK/PD) data, estimate key parameters, and visualise model fits for multiple PK/PD indices. Methods are described in Macdougall J (2006) , Spiess AN, Neumeyer N (2010) , and Burnham KP, Anderson DR (2004) . Package: r-cran-pkr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-binr, r-cran-forestplot, r-cran-rtf Filename: pool/dists/resolute/main/r-cran-pkr_0.1.3-1.ca2604.1_all.deb Size: 369740 MD5sum: ab2c57e4f277234467512597d60f1df8 SHA1: 148925d7ce9a587f1926fc30d98d2800ce6acca5 SHA256: 288a0024e1e08b2ed459bebeb08bafb11ed08dcf0c1fa9b2d805c21987d148d7 SHA512: 505b69d32ba377aa735e9bb05d573103587f2de7df98b02c673c6cee6f545d085cc3163297f3fec5ba1b50497b4d76b3b5c63c9604b18212149048cbfbae4645 Homepage: https://cran.r-project.org/package=pkr Description: CRAN Package 'pkr' (Pharmacokinetics in R) Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software. Some features are 1) CDISC SDTM terms 2) Automatic slope selection with the same criterion of WinNonlin(R) 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107). Package: r-cran-pks Architecture: all Version: 0.7-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sets Suggests: r-cran-relations, r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-cran-pks_0.7-0-1.ca2604.1_all.deb Size: 483514 MD5sum: 34370b8ac4bea4a357961a1d242ff811 SHA1: e5a39e8955bf1d23dceaa777c0a45a10801cc436 SHA256: ca143de76e33e5c6f2346a6f4cd5b0d29d6086432f0daeab0a75cae37396f3e6 SHA512: c77c022da1afa3e7a0a044f3099767e90b008e6d69fa23ca143a884f9dca262d4332eb5bde2ef12718e4df6557df3a584d63090296634a1985d6c307b1357b90 Homepage: https://cran.r-project.org/package=pks Description: CRAN Package 'pks' (Probabilistic Knowledge Structures) Fitting and testing probabilistic knowledge structures, especially the basic local independence model (BLIM, Doignon & Flamagne, 1999) and the simple learning model (SLM), using the minimum discrepancy maximum likelihood (MDML) method (Heller & Wickelmaier, 2013 ). Package: r-cran-pksea Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2875 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pksea_0.0.1-1.ca2604.1_all.deb Size: 2583986 MD5sum: 783e8db5db37eaf307ab667a96fee1e5 SHA1: a1efbbc5f0d860415ec7002cdb208adaf4a73eae SHA256: 5095a7b5e8f77f5a7194b0fe800762180a976e10127ffc9c9e51f08cef98e118 SHA512: 136c763eb6bd81bd7eb7fc252947476a548bade7a2ecc010a8bab14d8984759c67bee5df3bcea894f8a79608c3eef3f798dc84272317a5a6fc18406869bb1682 Homepage: https://cran.r-project.org/package=pKSEA Description: CRAN Package 'pKSEA' (Prediction-Based Kinase-Substrate Enrichment Analysis) A tool for inferring kinase activity changes from phosphoproteomics data. 'pKSEA' uses kinase-substrate prediction scores to weight observed changes in phosphopeptide abundance to calculate a phosphopeptide-level contribution score, then sums up these contribution scores by kinase to obtain a phosphoproteome-level kinase activity change score (KAC score). 'pKSEA' then assesses the significance of changes in predicted substrate abundances for each kinase using permutation testing. This results in a permutation score (pKSEA significance score) reflecting the likelihood of a similarly high or low KAC from random chance, which can then be interpreted in an analogous manner to an empirically calculated p-value. 'pKSEA' contains default databases of kinase-substrate predictions from 'NetworKIN' (NetworKINPred_db) Horn, et. al (2014) and of known kinase-substrate links from 'PhosphoSitePlus' (KSEAdb) Hornbeck PV, et. al (2015) . Package: r-cran-pksensi Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-desolve, r-cran-foreach, r-cran-doparallel Suggests: r-cran-covr, r-cran-knitr, r-cran-httk, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pksensi_1.2.3-1.ca2604.1_all.deb Size: 237050 MD5sum: f8d75ea18ccdbd4b931bc3e1d8f5e8fd SHA1: 22353cb1d031ced9b43c670f494349a4e6421efd SHA256: a86eb97848c9150508ff2e9366b1d90839fa7bc747ddda92862efe23a1007239 SHA512: 7128e18710ed070c1aa91885b37731490c6e6f98ff0044db096b42d2013d9dbc23fd0ddcb0ef2c2b3205fb4c744f8f530b791e93a6a49c782cdc2ccb6e845749 Homepage: https://cran.r-project.org/package=pksensi Description: CRAN Package 'pksensi' (Global Sensitivity Analysis in Physiologically Based KineticModeling) Applying the global sensitivity analysis workflow to investigate the parameter uncertainty and sensitivity in physiologically based kinetic (PK) models, especially the physiologically based pharmacokinetic/toxicokinetic model with multivariate outputs. The package also provides some functions to check the convergence and sensitivity of model parameters. The workflow was first mentioned in Hsieh et al., (2018) , then further refined (Hsieh et al., 2020 ). Package: r-cran-pl94171 Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-stringr, r-cran-readr, r-cran-dplyr, r-cran-tinytiger, r-cran-sf, r-cran-withr, r-cran-foreign Suggests: r-cran-testthat, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-pl94171_1.2.1-1.ca2604.1_all.deb Size: 318618 MD5sum: e4b1f69ec37059613e97e07d47f7584b SHA1: 553d6212b91cc9af449590c8ec7a2b4c791282ea SHA256: d8f766607c21649ae36c095a3ee0ba3e47b5ae26a7d8d750cbb53265a076a089 SHA512: 94bc1bfa2e662083b98f46bfa741a1547294df20d9cec8925824af28e79662be7291728782b06395457d2d16276c64a15e4f55aabd9208e92174bac77d127d1d Homepage: https://cran.r-project.org/package=PL94171 Description: CRAN Package 'PL94171' (Tabulate P.L. 94-171 Redistricting Data Summary Files) Tools to process legacy format summary redistricting data files produced by the United States Census Bureau pursuant to P.L. 94-171. These files are generally available earlier but are difficult to work with as-is. Package: r-cran-placematchr Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-stringdist, r-cran-data.table, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-placematchr_0.2.4-1.ca2604.1_all.deb Size: 1557916 MD5sum: da00d99df454f17c56b7e7fd1be1f711 SHA1: 464ecf7e8744afbf00fa6f2cee2829a6769ad142 SHA256: b7738169e7945642667f72deb7dfa610e7a304d9f9b644e7e8eae6daba85ca5a SHA512: 7e56198d7063a768a0f0c300abf98ed85090e6b05b122cd450b6438ca867cf332b9cc476fa8844adb91cd58f33c3a4cb95b260491df001bf10c0bf2bffd45c46 Homepage: https://cran.r-project.org/package=placematchr Description: CRAN Package 'placematchr' (Normalize and Match City Names to NUTS Regions) Normalizes city names for EEA countries and matches them to NUTS 3 regions using provided crosswalks. Features include comprehensive normalization rules, cascading matching logic (Exact NUTS -> Exact LAU -> Fuzzy), and single-source data synthesis. The package implements the NUTS classification as described in the NUTS methodology (Eurostat (2021) ). Package: r-cran-plackettluce Architecture: all Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-igraph, r-cran-matrix, r-cran-matrixstats, r-cran-partykit, r-cran-psychotools, r-cran-psychotree, r-cran-r6, r-cran-rspectra, r-cran-qvcalc, r-cran-sandwich Suggests: r-cran-bayesmallows, r-cran-bradleyterry2, r-cran-plmix, r-cran-rologit, r-cran-statrank, r-cran-bookdown, r-cran-covr, r-cran-hyper2, r-cran-kableextra, r-cran-knitr, r-cran-lbfgs, r-cran-gnm, r-cran-pmr, r-cran-rmarkdown, r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plackettluce_0.4.5-1.ca2604.1_all.deb Size: 971168 MD5sum: bbc77fd966469c8e47bde7a396118bfc SHA1: fa9af497ee5f2278203b950116a118935db2fab0 SHA256: 916c02de2ad89c0bb24fa12b354d2e56d84451d3f4e686da4b0dfa950b9af446 SHA512: 8e1c5580074e6f85ee7105aa9eba988d24cff4c1e4c38971c9cb861250c0310a18b6f9a5a8fc8ef8abcbbf3517a6ee0741ab40cf8dbc1d9bd1c820bcf2caf491 Homepage: https://cran.r-project.org/package=PlackettLuce Description: CRAN Package 'PlackettLuce' (Plackett-Luce Models for Rankings) Functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings may be handled by adding pseudo-rankings with a hypothetical item. Optionally, a multivariate normal prior may be set on the log-worth parameters and ranker reliabilities may be incorporated as proposed by Raman and Joachims (2014) . Maximum a posteriori estimation is used when priors are set. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for further details. Package: r-cran-plainview Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-lattice, r-cran-png, r-cran-raster, r-cran-viridislite Suggests: r-cran-shiny, r-cran-sf, r-cran-sp Filename: pool/dists/resolute/main/r-cran-plainview_0.2.2-1.ca2604.1_all.deb Size: 660272 MD5sum: ada1c16fa975b39c8ec16844a50d3182 SHA1: b7b5474874adf75142f1d74fa0653aece47e28bb SHA256: ccdd304407681ca5bcec6891a08ef96bbb7dac1204aa5367542fb860baede0bd SHA512: 31e548555c63fa9a670b08e86e2311f43457a84a9a19a656f740509b6e0009d543157c401519b76d12f4aa21ac11af9347f26304937220dff980ba40513e0c47 Homepage: https://cran.r-project.org/package=plainview Description: CRAN Package 'plainview' (Plot Raster Images Interactively on a Plain HTML Canvas) Provides methods for plotting potentially large (raster) images interactively on a plain HTML canvas. In contrast to package 'mapview' data are plotted without background map, but data can be projected to any spatial coordinate reference system. Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick' (from package 'raster') as well as 'png' files located on disk. Interactivity includes zooming, panning, and mouse location information. In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots are created (similar to 'raster::plotRGB' - but interactive). Package: r-cran-plan Architecture: all Version: 0.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plan_0.4-5-1.ca2604.1_all.deb Size: 309716 MD5sum: 521140a545be577e326eb25aef80bce8 SHA1: 87dc40677c9b51a6cf5ad322a25578e6d30e4190 SHA256: 94c96a3a3e932722070f6f187ac4a32af3e5ae1a12d2245ebfdc74edbfe3e9c3 SHA512: 612a7712d88f105c4dc1f14c313119c692f78315913115cf8e8c7da99b65960577065b4ec452f4a7c05ca7e717ad51a52f04de89b01c3c4a0416742158a70125 Homepage: https://cran.r-project.org/package=plan Description: CRAN Package 'plan' (Tools for Project Planning) Supports the creation of 'burndown' charts and 'gantt' diagrams. Package: r-cran-planesmuestra Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-planesmuestra_0.1-1.ca2604.1_all.deb Size: 69206 MD5sum: 3fe56ccd460aace675cdc9e7e876c033 SHA1: 6bb4c2849ea734bd615cfcdbde7c494edd66305a SHA256: b7bbffd300c30b46082508dd953008788561a4c0134e4ea9a4dd4a95b772e22a SHA512: 391ae2720f771c453873496cbe8276fdbcb9d693b06f080ed82a1b83b833125616def7e03fd483af1278bfa6affe648c4e10d4e78985153d04b9dc62c6db6fab Homepage: https://cran.r-project.org/package=Planesmuestra Description: CRAN Package 'Planesmuestra' (Functions for Calculating Dodge Romig, MIL STD 105E and MIL STD414 Acceptance Sampling Plan) Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution. Package: r-cran-planetnicfi Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-data.table, r-cran-glue, r-cran-terra Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-planetnicfi_1.0.5-1.ca2604.1_all.deb Size: 1988362 MD5sum: 269736c79e659e35ed112b0a1f374fdf SHA1: 580404f08bda8d4b139e09d7a412de6e5617c53b SHA256: e8f05a886a02e2117cba8ea1faad1b02dd81c424b5120ceaa661b673ec8668f6 SHA512: 148085bfc820515b9090bd9b1a700fcea8db48ed31e51c3adc5de41b56455c4e6c32ffd43c2917e0422525702436a0118918ab34147f8b5614ba54769eec15c0 Homepage: https://cran.r-project.org/package=PlanetNICFI Description: CRAN Package 'PlanetNICFI' (Processing of the 'Planet NICFI' Satellite Imagery) It includes functions to download and process the 'Planet NICFI' (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API . 'GDAL' (library for raster and vector geospatial data formats) and 'aria2c' (paralleled download utility) must be installed and configured in the user's Operating System. 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Allows for upload of plans from block assignment files and shape files. For shapes in memory, such as from 'sf' or 'redist', it processes them to save and upload. Includes tools for tidying responses and saving output from the website. 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Functionalities encompass estimation chlorophyll content according to Arnon (1949) , determination water potential of Polyethylene glycol(PEG)6000 as in Michel and Kaufmann (1973) and functions related to estimation of yield related indices like Abiotic tolerance index as given by Moosavi et al.(2008), Geometric mean productivity (GMP) by Fernandez (1992) , Golden Mean by Moradi et al.(2012), HAM by Schneider et al.(1997),MPI and TOL by Hossain etal., (1990), RDI by Fischer et al. (1979),SSI by Fisher et al.(1978), STI by Fernandez (1993),YSI by Bouslama & Schapaugh (1984), Yield index by Gavuzzi et al.(1997). 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More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) . 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Package: r-cran-plausibounds Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-mass, r-cran-magrittr, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-foreach, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plausibounds_1.0.1-1.ca2604.1_all.deb Size: 135312 MD5sum: 4ade2d9d225689d5ccfc515bb8e8bbd0 SHA1: ffa8a250f6d8d3599bddebcafad899f5bad462ca SHA256: 28579c213e6bd813644878dd5dcb7cd1774811a77e64e619c0f792e9e1d9f4b3 SHA512: 889a97af83c605fe7bff2fa6bfd792fc453d9bd8273fd5ca0af0528d5475c8dde89069c5d6d627ee04d13ac6f3512479bd5b1db3dd185d3fa6a7b50e56c43ecb Homepage: https://cran.r-project.org/package=plausibounds Description: CRAN Package 'plausibounds' (Plausible Bounds for Treatment Path Estimates) Enhances dynamic effect plots as suggested in Freyaldenhoven and Hansen (2026) . Data-driven smoothing delivers a smooth estimated path with potentially improved point estimation properties and confidence regions covering a surrogate that can be substantially tighter than conventional pointwise or uniform bands. Package: r-cran-plavaan Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plavaan_0.0.1-1.ca2604.1_all.deb Size: 61174 MD5sum: 4a45e9a6cfe2d7f746b17b9332f97f04 SHA1: 6e84b913cd68da38846176e4dcd03b8a7baa0466 SHA256: 26321f84461cbb80d327bbc9ffe83937daf8212462fbdcd5ee3c4dabf041bb4b SHA512: 0854277f71611d5dc8cc3b6360c83edb9399177bd7e64d946b1c9be76c53b6e1ed22c24d8da46aab76308bec2e3f6edb0a5b55966c8c6b7db7cc2796a48ce90c Homepage: https://cran.r-project.org/package=plavaan Description: CRAN Package 'plavaan' (Penalized Estimation for Latent Variable Models with 'lavaan') Extends the popular 'lavaan' package by adding penalized estimation capabilities. It supports penalty on individual parameters as well as the difference between parameters. Package: r-cran-player Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-and, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-glue, r-cran-nnet, r-cran-plu, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-stringr, r-cran-twenty48, r-cran-withr Suggests: r-cran-job, r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-player_0.1.0-1.ca2604.1_all.deb Size: 3427920 MD5sum: aab76c16615284f554c41532a2f3b063 SHA1: d9c8d84bbd68b1631bb7d1fc66944097e5bb5d71 SHA256: 2e70750e56d6dd23b7da4bc640fb5b06097d76586242d44e6815e495f4d117c4 SHA512: a3e2d9fa50c5cb273e82dec312a6dff8c1cf26e0aa38642c5dbc2874b6f49fcbea4926018721ff9b41ac273adeb79468d6612dcd2d1a1c3e23466301e7491c7a Homepage: https://cran.r-project.org/package=player Description: CRAN Package 'player' (Play Games in the Console) Games that can be played in the R console. Includes coin flip, hangman, jumble, magic 8 ball, poker, rock paper scissors, shut the box, spelling bee, and 2048. Package: r-cran-playerchart Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggtext, r-cran-magrittr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-playerchart_1.0.0-1.ca2604.1_all.deb Size: 18970 MD5sum: c7f1edca877efbdca9602f56104a7abb SHA1: b26780e73197357028745afdac8a261f5ba5016f SHA256: ffc13e11e11c5bf75351449d147474edd23217ddf0edc7e3c94fdb7fbe916d1e SHA512: 31d1e3446400b5ee44f0dd472e28a27f45244bf29ffa9eb4a5746ca571451fde0b954e43d23cfe3bac5641fb618fc929b99bf14006780230965881ea43fbfaa7 Homepage: https://cran.r-project.org/package=PlayerChart Description: CRAN Package 'PlayerChart' (Generate Pizza Chart: Player Stats 0-100) Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: 'data', a dataframe containing player data for any sports; 'player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; 'name_col', specifying the name of the column in the dataframe that contains the player names; and 'player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title. Package: r-cran-pldamixture Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-pldamixture_0.1.1-1.ca2604.1_all.deb Size: 125496 MD5sum: f956bc92ed28671982300eb9c8af2008 SHA1: a3a634e61a50f6200a7030d7a05f0a82017ba581 SHA256: 3af74121197c098c6285d7242fca572dee526d00f8d8c706c4a4903378758715 SHA512: e58364a431a0c0620166e8fb2e30b0a34afd571d3d4d72649084e10bf88b7c2a206b068ffcda9cb6a5f59a379bfe8c0ce6ecb7fc5acf97910a911428b54e6f49 Homepage: https://cran.r-project.org/package=pldamixture Description: CRAN Package 'pldamixture' (Post-Linkage Data Analysis Based on Mixture Modelling) Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the 'General Framework for Regression with Mismatched Data' developed by Slawski et al. (2023) . The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches). Package: r-cran-plde Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-plde_0.1.2-1.ca2604.1_all.deb Size: 43920 MD5sum: 10919e3bf22deca6212af325051e6a29 SHA1: 30b0b11626ae075d2a7367207867c693d0c19e21 SHA256: 06d0b5994f2752b90fe7c066f5f7110e30d1e5b6d3321a010d104b930e20eeb4 SHA512: 0e60bca409c5cb91efede8838c4ecddd176bca7ed973bbf74a16602e9d3a68241560cc959be3890cac237978636e36b71383e93aca0920da0c1fc40b91be470a Homepage: https://cran.r-project.org/package=plde Description: CRAN Package 'plde' (Penalized Log-Density Estimation Using Legendre Polynomials) We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC). Package: r-cran-pleio Architecture: all Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-matrix Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pleio_1.9-1.ca2604.1_all.deb Size: 434128 MD5sum: f64aa9a63f42b2c1c334bef5a4e9d69d SHA1: bc512c10bb30268f6e8477424cfcef957391ad94 SHA256: 75d3c47287c6a6de5a93cdfb9f91b2206ba5e3c0454d43c8608cc437a5da61b6 SHA512: a0448071053f5dbf8814522be8823298251a308979f8448dbb382a5a513dd2ebed7a1593b8a242f41d7227200e3e5995e75eb4edb16580dc99f892d2136480a8 Homepage: https://cran.r-project.org/package=pleio Description: CRAN Package 'pleio' (Pleiotropy Test for Multiple Traits on a Genetic Marker) Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker. Package: r-cran-pleioh2g Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-rlang, r-cran-mvtnorm, r-cran-fs, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-gdata, r-cran-glue, r-cran-purrr, r-cran-tibble, r-cran-vroom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pleioh2g_0.1.2-1.ca2604.1_all.deb Size: 3443968 MD5sum: aaf26061850c4d0cc3f9b8c680de5022 SHA1: afb213fe47985bde0dd2017c6ef71f795e8f8114 SHA256: f7892915c1384d2d949b6146447e90dac28fe47db14e5dd16b7860397e2af30f SHA512: 289546faf531899740fa35ca39a63e88b6268ccc1a4f15ae4deedf722599ecf620c0f2496b0a0b946c01df4cc5ea25f1bfffe14e3f428e0657c2271c319e2857 Homepage: https://cran.r-project.org/package=pleioh2g Description: CRAN Package 'pleioh2g' (Estimation of Pleiotropic Heritability from Genome-WideAssociation Studies (GWAS) Summary Statistics) Provides tools to compute unbiased pleiotropic heritability estimates of complex diseases from genome-wide association studies (GWAS) summary statistics. We estimate pleiotropic heritability from GWAS summary statistics by estimating the proportion of variance explained from an estimated genetic correlation matrix (Bulik-Sullivan et al. 2015 ) and employing a Monte-Carlo bias correction procedure to account for sampling noise in genetic correlation estimates. Package: r-cran-plelma Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlogit, r-cran-dfidx Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plelma_0.2.2-1.ca2604.1_all.deb Size: 225758 MD5sum: 7810a34a1c839fefb85068a7bb2e3d21 SHA1: e83292a759cd130b202807d7d145bdbde37a1493 SHA256: 3fcab7b1340d5b3679cc730f9552dcc7523897654c7fe4fdcf23fc6289e827f5 SHA512: 880ef8a14f8ce0c77162f8333f51f96ab38863b2bd9da4ec5868288d52f10af2b7e23e58f2122602ff4a49cdc5b116bb17da287f258ddf971604b3899d3406d1 Homepage: https://cran.r-project.org/package=pleLMA Description: CRAN Package 'pleLMA' (Pseudo-Likelihood Estimation of Log-Multiplicative AssociationModels) Log-multiplicative association models (LMA) are models for cross-classifications of categorical variables where interactions are represented by products of category scale values and an association parameter. Maximum likelihood estimation (MLE) fails for moderate to large numbers of categorical variables. The 'pleLMA' package overcomes this limitation of MLE by using pseudo-likelihood estimation to fit the models to small or large cross-classifications dichotomous or multi-category variables. Originally proposed by Besag (1974, ), pseudo-likelihood estimation takes large complex models and breaks it down into smaller ones. Rather than maximizing the likelihood of the joint distribution of all the variables, a pseudo-likelihood function, which is the product likelihoods from conditional distributions, is maximized. LMA models can be derived from a number of different frameworks including (but not limited to) graphical models and uni-dimensional and multi-dimensional item response theory models. More details about the models and estimation can be found in the vignette. Package: r-cran-plexi Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-igraph, r-cran-keras, r-cran-ggraph, r-cran-ggplot2, r-cran-aggregation Filename: pool/dists/resolute/main/r-cran-plexi_1.0.0-1.ca2604.1_all.deb Size: 90432 MD5sum: a99ff2791dac13f335a4475d30484cfa SHA1: aec562c1c3d00f512e1b06697bbb91dc7b61eae8 SHA256: 438a58814f6b937274cf50f7a814d8adfc5fad5175a2661645b43c37999d8d21 SHA512: 8fb78f6b813fca7c95bf83d41d1b6cfb3402e10d3331e326ccb4fc72af514d174f13c7ed6d1ae2c6a38512933378b67eea50507ff363cda53a8b8658df89a643 Homepage: https://cran.r-project.org/package=PLEXI Description: CRAN Package 'PLEXI' (Multiplex Network Analysis) Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) . Package: r-cran-plfma Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-tkrplot, r-bioc-limma, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-plfma_2.0-1.ca2604.1_all.deb Size: 133532 MD5sum: 9a36a477aa72a1149f29b79d3a036430 SHA1: e257aba58c9a64f1c2477b618dbe8970e4acbfe1 SHA256: e59b88783f4e8477836cd3e9521f678ac3311490f5d3f50f28b586598945985c SHA512: 2183c0c28f8de86dde20969d9b99f455168f3785ba8202d376d6bdbd9178d5c12421f829d4796c4098e39bc4a453339b225a8e08340d72a5b5a073652421e373 Homepage: https://cran.r-project.org/package=plfMA Description: CRAN Package 'plfMA' (A GUI to View, Design and Export Various Graphs of Data) Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image. 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Specificity, sensitivity, area under the curve and ROC curve are provided. Package: r-cran-plink Architecture: all Version: 1.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1761 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-plink_1.5-1-1.ca2604.1_all.deb Size: 1202272 MD5sum: d68794a938ea532beda8178957469c6b SHA1: e240b3f69552918be41d765edd09d06eafc7e4ac SHA256: 686ff306fcb8ce4b5a990331fc3a9beb5796f818f442da4e4b9ed85e15fd2868 SHA512: a6116cdb0fceeec9c776a46dd49de7e8e9f6ef90f57859cc8024f2628e1399a36c2376ed527f330588ef532bfbb0c13777c7d8b95d50246f4357d528b25b4661 Homepage: https://cran.r-project.org/package=plink Description: CRAN Package 'plink' (IRT Separate Calibration Linking Methods) Item response theory based methods are used to compute linking constants and conduct chain linking of unidimensional or multidimensional tests for multiple groups under a common item design. The unidimensional methods include the Mean/Mean, Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous (1PL, 2PL and 3PL) and/or polytomous (graded response, partial credit/generalized partial credit, nominal, and multiple-choice model) items. The multidimensional methods include the least squares method and extensions of the Haebara and Stocking-Lord method using single or multiple dilation parameters for multidimensional extensions of all the unidimensional dichotomous and polytomous item response models. The package also includes functions for importing item and/or ability parameters from common IRT software, conducting IRT true score and observed score equating, and plotting item response curves/surfaces, vector plots, information plots, and comparison plots for examining parameter drift. Package: r-cran-plinkfile Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-plinkfile_0.2.1-1.ca2604.1_all.deb Size: 601564 MD5sum: 1c1006790b518b143fe16a00d75c06a6 SHA1: b9921c364307b05c66b256503b94884853048b9a SHA256: 63b78ebe12256f182e7984a43fd085546bfeab3b2a2bbee2aaaf936ade8c27f2 SHA512: 29d34e690dc4c2002da3e85d34ad053faa2b1a487c3c670a47cdc9c2f8908ce3c442e04d25ff72e4f3caeaa355e83aae15ad5fe06ec89c9c5c68898419245b42 Homepage: https://cran.r-project.org/package=plinkFile Description: CRAN Package 'plinkFile' ('PLINK' (and 'GCTA') File Helpers) Reads/write binary genotype file compatible with 'PLINK' into/from a R matrix; traverse genotype data one windows of variants at a time, like apply() or a for loop; reads/writes genotype relatedness/kinship matrices created by 'PLINK' or 'GCTA' into/from a R square matrix. It is best used for bringing data produced by 'PLINK' and 'GCTA' into R workflow. Package: r-cran-plinkqc Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optparse, r-cran-data.table, r-cran-r.utils, r-cran-ggplot2, r-cran-ggrepel, r-cran-cowplot, r-cran-upsetr, r-cran-dplyr, r-cran-igraph, r-cran-sys, r-cran-randomforest, r-cran-tidyr Suggests: r-cran-testthat, r-cran-mockery, r-cran-formatr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-plinkqc_1.1.0-1.ca2604.1_all.deb Size: 2297160 MD5sum: 56f90f18fb4de1d18440e131c22e0167 SHA1: 5cc96ee3bb74060ca1d94c7e6fdc81e7023f89ef SHA256: bff7d85cabee1a1d239d679afc63e50b91c12ecd2c0c4240ef084366e6519633 SHA512: efdfe19434299c3a7fbe7911a0285f04e2e6fb4f2d5455316281ad5d84f0fd7bdfa25e25718d4ca4a8cd73e1e24091af1dfca545e2712466e50772c928960f0c Homepage: https://cran.r-project.org/package=plinkQC Description: CRAN Package 'plinkQC' (Genotype Quality Control with 'PLINK') Genotyping arrays enable the direct measurement of an individuals genotype at thousands of markers. 'plinkQC' facilitates genotype quality control for genetic association studies as described by Anderson and colleagues (2010) . It makes 'PLINK' basic statistics (e.g. missing genotyping rates per individual, allele frequencies per genetic marker) and relationship functions accessible from 'R' and generates a per-individual and per-marker quality control report. Individuals and markers that fail the quality control can subsequently be removed to generate a new, clean dataset. Removal of individuals based on relationship status is optimised to retain as many individuals as possible in the study. Additionally, there is a trained classifier to predict genomic ancestry of human samples. Package: r-cran-plis Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-plis_1.2-1.ca2604.1_all.deb Size: 53470 MD5sum: f84e694919212c3591decbedd54efd9c SHA1: 7ee9fd49fcac0fa2556dcc09ec96f242d6864bd0 SHA256: 26075d9a65726b71d441853efa7dd3304890ad94416ad183d44e6f2a25425fa3 SHA512: 1af9f5681651caa7c4e63637d6fccb5141dafb3df9bab3c1896ae5b3e90d2d0228faf8db7c9be164520a45f4fbf2007299e917bf3240d31e2746e22eda626e40 Homepage: https://cran.r-project.org/package=PLIS Description: CRAN Package 'PLIS' (Multiplicity Control using Pooled LIS Statistic) A multiple testing procedure for testing several groups of hypotheses is implemented. Linear dependency among the hypotheses within the same group is modeled by using hidden Markov Models. It is noted that a smaller p value does not necessarily imply more significance due to the dependency. A typical application is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency. See Wei Z, Sun W, Wang K, Hakonarson H (2009) for more details. Package: r-cran-plm Architecture: all Version: 2.6-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-bdsmatrix, r-cran-collapse, r-cran-zoo, r-cran-nlme, r-cran-sandwich, r-cran-lattice, r-cran-lmtest, r-cran-maxlik, r-cran-rdpack, r-cran-formula Suggests: r-cran-aer, r-cran-car, r-cran-statmod, r-cran-urca, r-cran-pder, r-cran-texreg, r-cran-knitr, r-cran-rmarkdown, r-cran-fixest, r-cran-lfe Filename: pool/dists/resolute/main/r-cran-plm_2.6-7-1.ca2604.1_all.deb Size: 1819594 MD5sum: c3090c80c3a820a4c484813330bc156d SHA1: 142f7db7d362f3f073c4e1abee599f91d16cc90b SHA256: 3d3a50c17f19470f3ab94b8440ddc95527c8690f57b76b47ef7a1afd10e9ee65 SHA512: 79ce1e9bf56c4552f727a3b511c12b7fc0acc4b6ce0e7cdd3f5df8da0ac3c97edf1e09f067f32446301b62734ab8df517a007e25b53b7aebafa025303ff7e6d6 Homepage: https://cran.r-project.org/package=plm Description: CRAN Package 'plm' (Linear Models for Panel Data) A set of estimators for models and (robust) covariance matrices, and tests for panel data econometrics, including within/fixed effects, random effects, between, first-difference, nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models, panel generalized method of moments (GMM) and general FGLS models, mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators with common factors, variable coefficients and limited dependent variables models. Test functions include model specification, serial correlation, cross-sectional dependence, panel unit root and panel Granger (non-)causality. Typical references are general econometrics text books such as Baltagi (2021), Econometric Analysis of Panel Data (), Hsiao (2014), Analysis of Panel Data (), and Croissant and Millo (2018), Panel Data Econometrics with R (). Package: r-cran-plmixed Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-numderiv, r-cran-optimx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-irtoys Filename: pool/dists/resolute/main/r-cran-plmixed_0.1.8-1.ca2604.1_all.deb Size: 1181876 MD5sum: f8f2654a9dc5932923ee22b16972f559 SHA1: 913e70ce740dc7e766c75042b388f945f3c38271 SHA256: 3cc2dfcfd7275be2de0691f86241ac46fa168bc5ae1cb245a09ac26313694f63 SHA512: bfe5b78c83b1c089bc0dba44636fb62c92556f2d8f1edde879df9c6ccc8cf46db7cc6bcb8bcc841f2321c2c966f2e30ee1af3fe94103263b7d9d1b226d4d247c Homepage: https://cran.r-project.org/package=PLmixed Description: CRAN Package 'PLmixed' (Estimate (Generalized) Linear Mixed Models with FactorStructures) Utilizes the 'lme4' and 'optimx' packages (previously the optim() function from 'stats') to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) and Rockwood and Jeon (2019) . Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models. Package: r-cran-plnr Architecture: all Version: 2025.11.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-digest, r-cran-fs, r-cran-foreach, r-cran-glue, r-cran-pbmcapply, r-cran-purrr, r-cran-r6, r-cran-tidyr, r-cran-usethis, r-cran-uuid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-progressr, r-cran-ggplot2, r-cran-readxl, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-plnr_2025.11.22-1.ca2604.1_all.deb Size: 321946 MD5sum: 932a0db96b80329cfb65d4e9c782396d SHA1: 5377f78771522e7c2b1cdb2094d10f231cb89d8f SHA256: 0dc079affd310efa0eae9ef7d5c87de8d1a6effd9e31e052b6ed9322b1328dc1 SHA512: e1c79bc0513bb8a85ddc3bfa62848224d381d973d4a71d3d9182fdd712c7df8931e608cfb60e0678b637ad0422f91ac99ca943b4b34a0b157f35686560670fcb Homepage: https://cran.r-project.org/package=plnr Description: CRAN Package 'plnr' (A Framework for Planning and Executing Analyses) A comprehensive framework for planning and executing analyses in R. It provides a structured approach to running the same function multiple times with different arguments, executing multiple functions on the same datasets, and creating systematic analyses across multiple strata or variables. The framework is particularly useful for applying the same analysis across multiple strata (e.g., locations, age groups), running statistical methods on multiple variables (e.g., exposures, outcomes), generating multiple tables or graphs for reports, and creating systematic surveillance analyses. Key features include efficient data management, structured analysis planning, flexible execution options, built-in debugging tools, and hash-based caching. 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Package: r-cran-plotdap Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5715 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cmocean, r-cran-dplyr, r-cran-gganimate, r-cran-ggnewscale, r-cran-ggplot2, r-cran-isoband, r-cran-lazyeval, r-cran-lubridate, r-cran-magrittr, r-cran-mapdata, r-cran-maps, r-cran-raster, r-cran-rerddap, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-tidyr, r-cran-viridis Suggests: r-cran-cairo, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plotdap_1.2.0-1.ca2604.1_all.deb Size: 4190460 MD5sum: 55ef004f61f7e6676d35341805d29a0e SHA1: 258a7809b804499f8dca15ecf2cff115546cc9f2 SHA256: 2ef0d260b0d04d262d501a859d0debea0abc7e947f74dc7b8783721cb5cd921f SHA512: 7cab2903d757b6d48ce8bd2df7de55f45ccdebd5230f6e0c5a29688e54b7154c2fa0de0713766a674a6c56bf360893d36c2cf4798b5232cbff715147952b198b Homepage: https://cran.r-project.org/package=plotdap Description: CRAN Package 'plotdap' (Easily Visualize Data from 'ERDDAP™' Servers via the 'rerddap'Package) Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command, using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data, map projections and continental outlines. Optionally the data can be animated through time using the 'gganmiate' package. Package: r-cran-plotftir Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2799 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-scales Suggests: r-cran-testthat, r-cran-withr, r-cran-ggplot2, r-cran-gghighlight, r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-ir, r-cran-chemospec, r-cran-r.utils, r-cran-readjdx Filename: pool/dists/resolute/main/r-cran-plotftir_1.2.1-1.ca2604.1_all.deb Size: 1914868 MD5sum: 290e55e0ce008ee3d8dc30cb8c176834 SHA1: 22497c9c29c37289e30353241595095758f7a628 SHA256: dbce94a7905f1fd02db415a6bb9b9205c03555dbbac73e7ea6ed9cf53326cdab SHA512: f90ddfb641186cc95869c84c41efce7dc2a3f73b68531357465f3ad15e8751b5c131f96e14fc99894a1710b6aaa123b435d161f55a8aaef1383ab17c76130133 Homepage: https://cran.r-project.org/package=PlotFTIR Description: CRAN Package 'PlotFTIR' (Plot FTIR Spectra) The goal of 'PlotFTIR' is to easily and quickly kick-start the production of journal-quality Fourier Transform Infra-Red (FTIR) spectral plots in R using 'ggplot2'. The produced plots can be published directly or further modified by 'ggplot2' functions. L'objectif de 'PlotFTIR' est de démarrer facilement et rapidement la production des tracés spectraux de spectroscopie infrarouge à transformée de Fourier (IRTF) de qualité journal dans R à l'aide de 'ggplot2'. Les tracés produits peuvent être publiés directement ou modifiés davantage par les fonctions 'ggplot2'. Package: r-cran-plotfunctions Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1558 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-sp Filename: pool/dists/resolute/main/r-cran-plotfunctions_1.5-1.ca2604.1_all.deb Size: 1147874 MD5sum: ca6ffa644c6bed2d958e62963fb96f7c SHA1: 066691a9d293f0d20f84a3454254fc9c6344f8c9 SHA256: f9da2aa2dc3c86b8d0ed8cb53d2c9d00e99b317aa2e7cece504c335ca7fffc44 SHA512: bfb752b6d74fa39779c931fa5bcc84e3eb5f52c4d1283fc73c44ba28ab9711bf17332532a20fd5b197e86808327b40563ffb49bdee014e66aa422ccaedfcddc2 Homepage: https://cran.r-project.org/package=plotfunctions Description: CRAN Package 'plotfunctions' (Various Functions to Facilitate Visualization of Data andAnalysis) When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions. Package: r-cran-plotgmm Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wesanderson, r-cran-amerika, r-cran-ggplot2 Suggests: r-cran-mixtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plotgmm_0.2.2-1.ca2604.1_all.deb Size: 21496 MD5sum: 71b73ee5a15922e8c6cb47d4862d6d89 SHA1: b5a982f1328e5732c95f112699e778c42f36c383 SHA256: 4b49509ddccbe118fec4b21865ab3536ff5602507dcc40b2e8976d4ccd1a7aaf SHA512: 14ebfb234fe96d36e9c4a7112722fb3753c3a827ef2e39f74777943e1682c8cf7d1c938bacbfde2b30636df6803003d1e1502349942f49a12af5bc5c104bc962 Homepage: https://cran.r-project.org/package=plotGMM Description: CRAN Package 'plotGMM' (Tools for Visualizing Gaussian Mixture Models) The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), including both densities and overlaying mixture weight component curves from the fit GMM. The package also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying the "fun" argument within "stat_function" in a ggplot2 object. Package: r-cran-plotlsirm Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-scales Suggests: r-cran-plotly, r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-plotlsirm_0.1.3-1.ca2604.1_all.deb Size: 164094 MD5sum: 17b4fc7deca65cdcc75b010f25338d4e SHA1: e0eb638bea01c5ba1fd5e214792396a3971bc883 SHA256: 56dcf33bddde13e8780efdeeab30244741e7e7ad0e6d8972f46de8d0ec32e08f SHA512: 61012693a4d283fbf0752a75c81351864603fa7fe07aa990ce8a7164979dbc5b256a4e3c609508ce80231f850af86e1ecfbb2b54934a4e6e9f65d1c839ab408d Homepage: https://cran.r-project.org/package=plotlsirm Description: CRAN Package 'plotlsirm' (Plot Toolkit for Latent Space Item Response Models) Provides publication‑quality and interactive plots for exploring the posterior output of Latent Space Item Response Models, including Posterior Interaction Profiles, radar charts, 2‑D latent maps, and item‑similarity heat maps. The methods implemented in this package are based on work by Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) . Package: r-cran-plotluck Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-quantreg, r-cran-scales, r-cran-plyr, r-cran-hexbin, r-cran-rcolorbrewer, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-ggplot2movies, r-cran-mgcv, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown, r-cran-gapminder Filename: pool/dists/resolute/main/r-cran-plotluck_1.1.1-1.ca2604.1_all.deb Size: 510410 MD5sum: f15930afeab1dc8d814647e5ff46ac55 SHA1: 68a5a7ea333eb74d5aea814a36cf85574a88675c SHA256: 8e8c5cde45d8b698b989290e275eb900d8e66d3ece4b523270fcab991b39dafe SHA512: 8ab1256f3a7f1339d9399ebeda054a8e9856109ab089eff45403bb408dd7f3b965cff9030ae82a3884f493f4dd87e0acf071e4a78f3fe7ae87feffcd9a9e00a7 Homepage: https://cran.r-project.org/package=plotluck Description: CRAN Package 'plotluck' ('ggplot2' Version of "I'm Feeling Lucky!") Examines the characteristics of a data frame and a formula to automatically choose the most suitable type of plot out of the following supported options: scatter, violin, box, bar, density, hexagon bin, spine plot, and heat map. The aim of the package is to let the user focus on what to plot, rather than on the "how" during exploratory data analysis. It also automates handling of observation weights, logarithmic axis scaling, reordering of factor levels, and overlaying smoothing curves and median lines. Plots are drawn using 'ggplot2'. Package: r-cran-plotly Architecture: all Version: 4.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-digest, r-cran-viridislite, r-cran-base64enc, r-cran-htmltools, r-cran-htmlwidgets, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-vctrs, r-cran-tibble, r-cran-lazyeval, r-cran-rlang, r-cran-crosstalk, r-cran-purrr, r-cran-data.table, r-cran-promises Suggests: r-cran-mass, r-cran-maps, r-cran-hexbin, r-cran-ggthemes, r-cran-ggally, r-cran-ggalluvial, r-cran-testthat, r-cran-knitr, r-cran-shiny, r-cran-shinytest2, r-cran-curl, r-cran-rmarkdown, r-cran-cairo, r-cran-broom, r-cran-webshot, r-cran-listviewer, r-cran-dendextend, r-cran-sf, r-cran-png, r-cran-irdisplay, r-cran-processx, r-cran-plotlygeoassets, r-cran-forcats, r-cran-withr, r-cran-palmerpenguins, r-cran-rversions, r-cran-reticulate, r-cran-rsvg, r-cran-ggridges Filename: pool/dists/resolute/main/r-cran-plotly_4.12.0-1.ca2604.1_all.deb Size: 3563152 MD5sum: b77b94e8a87e6dd3ce35ebd8fa0f98bd SHA1: 864887e3b317cbb37db9710f6df8c922c3e7e428 SHA256: b073e39f09a71ed71746b6a24e0d050276a545baf719deb3cba509ddabe8a8db SHA512: ed1f839b6f4b414e043b6f18a96cbecb55ef263abc9641c123071dbccd4c823ceb94700aff77980e0b6195d2ed4f68560bf51a9e666b9118b3c0604e4625d7a5 Homepage: https://cran.r-project.org/package=plotly Description: CRAN Package 'plotly' (Create Interactive Web Graphics via 'plotly.js') Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics. Package: r-cran-plotlygeoassets Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4051 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-plotlygeoassets_0.0.2-1.ca2604.1_all.deb Size: 803092 MD5sum: 64747a8ac20f4e4aedd3a21bcd85d0ed SHA1: fd96287d071b085d46c852cece75a79fe1ff35e2 SHA256: 7d3b563582c05c839bb65ee7475119b5210ffe707ec70a99e6aa3d6895c4a000 SHA512: 88638da2eadf1cdb969435b3a13b7428084f658088aac5b8e0b9d0d42c790b321ec03aefdacd5609f07487ca4cdb8d8c5ec90be8a123b84c36fc9b6f8b5c613a Homepage: https://cran.r-project.org/package=plotlyGeoAssets Description: CRAN Package 'plotlyGeoAssets' (Render 'Plotly' Maps without an Internet Connection) Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection. Package: r-cran-plotmcmc Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-gplots, r-cran-lattice Suggests: r-cran-gdata Filename: pool/dists/resolute/main/r-cran-plotmcmc_2.0.1-1.ca2604.1_all.deb Size: 1027458 MD5sum: b602bdad2a87133e340ffe845ce0897b SHA1: fd6cfe01d2158a2614bda8234ace670480ca00fa SHA256: 94dff54a9722e6b918c904ab131c3618d057e31409964fdb6f3bd34dd8be9468 SHA512: e0ee250876c381c2f2abab13e23d6a4b691b296a6f603b1c9cddb5323f5291cb1e5a83d072b5996a7d27db2817ceff49c4af718f638e9016a50b1d6013171707 Homepage: https://cran.r-project.org/package=plotMCMC Description: CRAN Package 'plotMCMC' (MCMC Diagnostic Plots) Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the 'coda' and 'lattice' packages, and make it easy to adjust graphical details. Package: r-cran-plotmelm Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-interactiontest Filename: pool/dists/resolute/main/r-cran-plotmelm_0.1.5-1.ca2604.1_all.deb Size: 23904 MD5sum: 766a1aa3e32ec85fc350c8ed731ab2a5 SHA1: 5acaabac2df9cf6efcd965ac762e204612598639 SHA256: 6f7da36a838ac7f34c247104c16f8098c94091741063eb9f22e3432045926fe2 SHA512: d59d8fa0a8d9b2f61e6ed54e0fb540e0036ac5fb56dfc0a0a7bca6bc20f02c935efa56bd256961768eefef6a42bc7bb8a3d9b0bbf1feecefda3562c4d461f8ef Homepage: https://cran.r-project.org/package=plotMElm Description: CRAN Package 'plotMElm' (Plot Marginal Effects from Linear Models) Plot marginal effects for interactions estimated from linear models. Package: r-cran-plotmm Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wesanderson, r-cran-amerika, r-cran-ggplot2, r-cran-mixtools, r-cran-emcluster, r-cran-flexmix Suggests: r-cran-testthat, r-cran-dplyr, r-cran-patchwork, r-cran-survival, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-plotmm_0.1.2-1.ca2604.1_all.deb Size: 49878 MD5sum: d36e3c531662ae09247257afffaf3f00 SHA1: 2bf2968a55dcb5073f304b4e17c17315b637e987 SHA256: e16a2c978366a253e94fc74677d2ba76c7927ad97d4f44d6db7f1cbebb187b89 SHA512: 661e219b50b8c10f4ac7c6cc1e4a3dc89e33eae8486ae08e88223932f5f2fa37ccf0ffae10199ebd94560ebbeac4b6402503c94cea88c11926b577d318f3645d Homepage: https://cran.r-project.org/package=plotmm Description: CRAN Package 'plotmm' (Tidy Tools for Visualizing Mixture Models) The main function, plot_mm(), is used for (gg)plotting output from mixture models, including both densities and overlaying mixture weight component curves from the fit models in line with the tidy principles. The package includes several additional functions for added plot customization. Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each in active dev. Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, logistic regressions, linear regressions, and Poisson regressions. Package: r-cran-plotmo Architecture: all Version: 3.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-plotrix Suggests: r-cran-c50, r-cran-earth, r-cran-gbm, r-cran-glmnet, r-cran-glmnetutils, r-cran-mass, r-cran-mlr, r-cran-neuralnet, r-cran-partykit, r-cran-pre, r-cran-rpart, r-cran-rpart.plot Filename: pool/dists/resolute/main/r-cran-plotmo_3.7.0-1.ca2604.1_all.deb Size: 1648400 MD5sum: 25e1547d218a47d072acbe8f74359a15 SHA1: 37cb55f010a0b1b09ea0e50be3d7e1d829034323 SHA256: efcc45f1c301b831b6bc0c7427e156f7992229260c23e030a2996af8f82ca57c SHA512: cc5c9b7269ee4f9678be5cab9c13fe762f4b27feb3a076be6be6ee4ad7252726a6503ab09527ef5562de46a35e936ae20c57658d908b30976d03886a2faeca5e Homepage: https://cran.r-project.org/package=plotmo Description: CRAN Package 'plotmo' (Plot a Model's Residuals, Response, and Partial Dependence Plots) Plot model surfaces for a wide variety of models using partial dependence plots and other techniques. Also plot model residuals and other information on the model. Package: r-cran-plotnormtest Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-ggplot2, r-cran-matrix, r-cran-matrixextra, r-cran-mass, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plotnormtest_1.0.1-1.ca2604.1_all.deb Size: 834202 MD5sum: 38378de8b43c78522953219984067047 SHA1: 0797d417888a8787853b596d9db2645890d91101 SHA256: 6ed928e85c8b28b1f25104828df6fd81bb73618de5703928591277e3d11eeba8 SHA512: c35ea4bda33fe8210f7a9219bf84c57ec92392496e76caca94693b90ccba2bd4ffb873f4ba72f29742651ee5a76cc4be46686301af1a33e6205e8ec0ac824187 Homepage: https://cran.r-project.org/package=PlotNormTest Description: CRAN Package 'PlotNormTest' (Graphical Univariate/Multivariate Assessments for NormalityAssumption) Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis, . Package: r-cran-plotor Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-callr, r-cran-car, r-cran-cli, r-cran-detectseparation, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-gtextras, r-cran-janitor, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-here, r-cran-knitr, r-cran-labelled, r-cran-magrittr, r-cran-mass, r-cran-medicaldata, r-cran-nhanes, r-cran-r4hcr, r-cran-rmarkdown, r-cran-svglite, r-cran-testthat, r-cran-vdiffr, r-cran-webshot2 Filename: pool/dists/resolute/main/r-cran-plotor_1.0.0-1.ca2604.1_all.deb Size: 4758916 MD5sum: a2d9ad231a5d92979cd53f7f8bb4924c SHA1: cdcb9eac80aac27d45e63782ce894d2a1907f702 SHA256: 0f3d5aa4834ecfc99b3f3eb029998e94a053311dd39056218161b0643f5ffafa SHA512: 1c6d0ebcacaf7d1762b7fffa38fe416185940d7070a30f043af449f21bafb1c03075d4a8a89517e78819347cd5013ea45c19f6c2f2be81c8002a8d3072212c5c Homepage: https://cran.r-project.org/package=plotor Description: CRAN Package 'plotor' (Odds Ratio Tools for Logistic Regression) Produces odds ratio analyses with comprehensive reporting tools. 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Package: r-cran-plrmodels Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-plrmodels_1.4-1.ca2604.1_all.deb Size: 297504 MD5sum: 9cd384699eed7edac1f7d07a9bb88c37 SHA1: ea388dbe1baa14153ba3afba8c8fa88c2d79695a SHA256: 73b0090b3bf07a5ad9458fc225b7ff860b2fda92c43e5e022cc2045d8e25c975 SHA512: 6fc046aec2b86f84c0f9113d29d8e5aa3e2beca4fabff506e2aab6fbf4e8a433d22c291299bba7e925ea3e83074766ee3822232f79efd0a9aac5378000fb66ed Homepage: https://cran.r-project.org/package=PLRModels Description: CRAN Package 'PLRModels' (Statistical Inference in Partial Linear Regression Models) Contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. 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Package: r-cran-plsdepot Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-factominer, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plsdepot_0.3.1-1.ca2604.1_all.deb Size: 199856 MD5sum: 77279be80ff4f46ce9aecd4faf490d8f SHA1: def5d6dece0c7bdd81d8d8f8842b028ee7decc3c SHA256: d08e1eab6f613ff7d2972f2e031296459458ce560a51b7e2174ed0fa7618ea09 SHA512: 4cbaadecca85a38dccda931cb544808f6ec7fd893390ed3169b4bbd259889b37b33790d75d8779c6c518a9eb5dfa0c1f1a6a0ddd99d903fa6c9d4502dbc9a772 Homepage: https://cran.r-project.org/package=plsdepot Description: CRAN Package 'plsdepot' (Partial Least Squares (PLS) Data Analysis Methods) Different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) 'La Regression PLS: Theorie et Pratique' by Michel Tenenhaus. 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Package: r-cran-plsgenomics Architecture: all Version: 1.5-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1685 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-boot, r-cran-reshape2, r-cran-plyr, r-cran-fields, r-cran-rhpcblasctl Filename: pool/dists/resolute/main/r-cran-plsgenomics_1.5-3-1.ca2604.1_all.deb Size: 1657048 MD5sum: 5a4eb089db7d3f919153549693454eba SHA1: 630a4940757da120fde04a61fa2524fd5ca3f979 SHA256: 013c756c529e61b7fb6ab32f4c26d9660194d1f01849017fb5dd8012d9700d0d SHA512: 849686ce0a2a6a8a70627adea9e758ef48490064042fadd2f62786f9fc51f90b906990491ca6055c6c225a23fafaa1a0077c3c7c52387ae7180db419b0fb8962 Homepage: https://cran.r-project.org/package=plsgenomics Description: CRAN Package 'plsgenomics' (PLS Analyses for Genomics) Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS. Package: r-cran-plsmmlasso Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-hdi, r-cran-mass, r-cran-mvtnorm, r-cran-rlang, r-cran-scalreg Filename: pool/dists/resolute/main/r-cran-plsmmlasso_1.1.0-1.ca2604.1_all.deb Size: 695800 MD5sum: 1997867c5bc8c19c2ff280d433133978 SHA1: 1d86d171decbe160af7ce81b39d7bb63cc4c987c SHA256: 66a0ccb63e74a8ef216e8d265d8c371d1d6f36da224383a1d021dbfd8438feac SHA512: 1ecf7e5667d4e10f3ae3decfa680509c8e240dad0c293084ec85596d253862460f197c1692cdd8caad24bf8d1bf6bb8eeb844775dd4c5c755eba83832573d4d1 Homepage: https://cran.r-project.org/package=plsmmLasso Description: CRAN Package 'plsmmLasso' (Variable Selection and Inference for Partial SemiparametricLinear Mixed-Effects Model) Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach. Package: r-cran-plsmod Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parsnip, r-cran-dplyr, r-cran-generics, r-cran-magrittr, r-bioc-mixomics, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-covr, r-cran-modeldata, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plsmod_1.0.0-1.ca2604.1_all.deb Size: 39700 MD5sum: c723baa10486065c1b9b4d28bf19f920 SHA1: 4cc8805801e8f5307405d1e956bdb4f0a2887f01 SHA256: 8272306d87febf976cc7fece976dbab78c0782a22ba73aa5c3c6977a825a8096 SHA512: fa0b6dd67904cdda67ad6d3507196853d9f2e2c3cdf3ffa9e2794e15bf5fb62f6fd81f916c9d3eb262473973d70d1aa9b1619312bdb40a36bf671a21b584ef05 Homepage: https://cran.r-project.org/package=plsmod Description: CRAN Package 'plsmod' (Model Wrappers for Projection Methods) Bindings for additional regression models for use with the 'parsnip' package, including ordinary and spare partial least squares models for regression and classification (Rohart et al (2017) ). Package: r-cran-plsmselect Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glmnet, r-cran-mgcv, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-plsmselect_0.2.0-1.ca2604.1_all.deb Size: 149446 MD5sum: 562f93409cf0e9658303500613255b14 SHA1: 404d7710293330cf3bda406697704f45a66bce2e SHA256: 86b456b046f2264b10a1f06bbbf91961d28e135c34c9690f491c79dd39b0fe79 SHA512: 16624506ce62e69bcf1813923e44665b887e2d809a655881e7dfbf4b1d4035fa42b2376da27e356fdd12df16d27efb52443096973734c62b0a53bd10090de4ea Homepage: https://cran.r-project.org/package=plsmselect Description: CRAN Package 'plsmselect' (Linear and Smooth Predictor Modelling with Penalisation andVariable Selection) Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model). Package: r-cran-plspm Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tester, r-cran-turner, r-cran-diagram, r-cran-shape, r-cran-amap Suggests: r-cran-factominer, r-cran-ggplot2, r-cran-reshape, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plspm_0.6.0-1.ca2604.1_all.deb Size: 688952 MD5sum: 4be6c7705cce8b18498dfea2605cdd89 SHA1: 1dbee6fee3018b759fc2839f44b2ca1eb3573df5 SHA256: 3e5e30df3bafd2e22643f9137299082b755e5080062f7bdf01a0729195b678ac SHA512: 1f1bd3cc6bc29fda658b7d8089b45e46bc850ba3e5d124f16bdbae2177a011ce49889330321996d5abf037736609725f23e38b71c5ca437737900783375a656c Homepage: https://cran.r-project.org/package=plspm Description: CRAN Package 'plspm' (Partial Least Squares Path Modeling (PLS-PM)) Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) , analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) . Package: r-cran-plsrbeta Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-boot, r-cran-formula, r-cran-mass, r-cran-plsrglm, r-cran-betareg Suggests: r-cran-bipartite, r-cran-knitr, r-cran-markdown, r-cran-plotrix, r-cran-pls, r-cran-plsdof, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plsrbeta_0.3.3-1.ca2604.1_all.deb Size: 3979288 MD5sum: 8c97220334e662c672787b2080977e01 SHA1: 2a2f8882476d5de43a1e203dec1b603a55027277 SHA256: cbe5d8836c1dade3db48571f9cde0e163e1ddebab419ca5510b6ec315b11f6b0 SHA512: d287236f5f206688b7f8c710e0da3c50479492c2e5d4e82f1011178f3d37da8c6107803ead4c6b5fddab65682f5b43ba1f9df981fb436fd20fb90dba4818cc43 Homepage: https://cran.r-project.org/package=plsRbeta Description: CRAN Package 'plsRbeta' (Partial Least Squares Regression for Beta Regression Models) Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, ) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available. Package: r-cran-plsrcox Architecture: all Version: 1.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-plsrglm, r-cran-lars, r-cran-pls, r-cran-kernlab, r-bioc-mixomics, r-cran-risksetroc, r-bioc-survcomp, r-cran-survauc, r-cran-rms Suggests: r-cran-survivalroc, r-cran-plsdof, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plsrcox_1.8.2-1.ca2604.1_all.deb Size: 2307638 MD5sum: 8719c5ade11a0c956488a3cfba37d480 SHA1: 22f49fa6a47bb15174f4ab9c3e13c3f3160bdc82 SHA256: 0a66b4785587c136f36cd4055e7c75a1bc94ca69b79754bdb17da17f4536c406 SHA512: cbfccbb078a88a9840f9aa398320c9d7b80704ed33bd1aacaba5a11ddd3360d414a284b081c440a8023d0878e3f1f693ba3753282e3c1dd9db175862f5e99909 Homepage: https://cran.r-project.org/package=plsRcox Description: CRAN Package 'plsRcox' (Partial Least Squares Regression for Cox Models and RelatedTechniques) Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings , Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in , Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data. Package: r-cran-plssem Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modsem, r-cran-lme4, r-cran-lavaan, r-cran-stringr, r-cran-purrr, r-cran-matrixstats, r-cran-rfast, r-cran-collapse, r-cran-mvnfast, r-cran-reformulas, r-cran-fnn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mice, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-plssem_0.1.1-1.ca2604.1_all.deb Size: 1708558 MD5sum: fe46736e70d4384952ff0f94b52960e4 SHA1: 01c7840c2a0721bedc97a06474e18f2e81cc3a35 SHA256: 4b82497c5308f3fd2f2b54e350144bcfccb95113cad875abc42eee7d5b8518bc SHA512: 6c3041e45e876e18a1f2d11e49d54b890434aef3e60bb563029071d613ab7eba565b46748592f7b516814032ddaf5d0e8352c205ad5998cb1232c23e9e0b9660 Homepage: https://cran.r-project.org/package=plssem Description: CRAN Package 'plssem' (Complex Partial Least Squares Structural Equation Modeling) Estimate complex Structural Equation Models (SEMs) by fitting Partial Least Squares Structural Equation Modeling (PLS-SEM) and Partial Least Squares consistent Structural Equation Modeling (PLSc-SEM) specifications that handle categorical data, non-linear relations, and multilevel structures. The implementation follows Lohmöller (1989) for the classic PLS-SEM algorithm, Dijkstra and Henseler (2015) for consistent PLSc-SEM, Dijkstra et al., (2014) for nonlinear PLSc-SEM, and Schuberth, Henseler, Dijkstra (2018) for ordinal PLS-SEM and PLSc-SEM. Additional extensions are under development. The MC-OrdPLSc algorithm, used to handle ordinal interaction models is detailed in Slupphaug et al., (2026). References: Lohmöller, J.-B. (1989, ISBN:9783790803002). "Latent Variable Path Modeling with Partial Least Squares." Dijkstra, T. K., & Henseler, J. (2015). . "Consistent partial least squares path modeling." Dijkstra, T. K., & Schermelleh-Engel, K. (2014). . "Consistent partial least squares for nonlinear structural equation models." Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). . "Partial least squares path modeling using ordinal categorical indicators." Slupphaug, K. Mehmetoglu, M. & Mittner, M. (2026). . "Consistent Estimates from Biased Estimators: Monte-Carlo Consistent Partial Least Squares for Latent Interaction Models with Ordinal Indicators." Package: r-cran-plsvarsel Architecture: all Version: 0.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-genalg, r-cran-mvtnorm, r-cran-bdsmatrix, r-cran-mass, r-cran-progress, r-cran-praznik Suggests: r-cran-rmpi Filename: pool/dists/resolute/main/r-cran-plsvarsel_0.10.0-1.ca2604.1_all.deb Size: 283982 MD5sum: ba77a8c2153eb000ef084996b43fee39 SHA1: 53af91d9696e45908e8557f56ef96e4a2d287fa3 SHA256: 7e8420239bd02b91f39a9c134680f2e9c149040846589d966bff8410cf1c8265 SHA512: d8f9f62a0589a36756fdb5273ea6ad5a63d80eeb3ddea7a263a2a74ff1f11a7b89ae6c49015ec2925b1ee4e57c23cdc27c31ed4e72fb5e4c8a6f55b8e9c920b7 Homepage: https://cran.r-project.org/package=plsVarSel Description: CRAN Package 'plsVarSel' (Variable Selection in Partial Least Squares) Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported. 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Package: r-cran-plumber2 Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-fiery, r-cran-fireproof, r-cran-firesafety, r-cran-firesale, r-cran-firestorm, r-cran-fs, r-cran-jsonlite, r-cran-promises, r-cran-r6, r-cran-ragg, r-cran-rapidoc, r-cran-readr, r-cran-reqres, r-cran-rlang, r-cran-routr, r-cran-roxygen2, r-cran-stringi, r-cran-svglite, r-cran-webutils, r-cran-yaml Suggests: r-cran-arrow, r-cran-callr, r-cran-geojsonsf, r-cran-htmlwidgets, r-cran-later, r-cran-mirai, r-cran-nanoparquet, r-cran-quarto, r-cran-redoc, r-cran-rmarkdown, r-cran-shiny, r-cran-storr, r-cran-swagger, r-cran-testthat, r-cran-tomledit Filename: pool/dists/resolute/main/r-cran-plumber2_0.2.0-1.ca2604.1_all.deb Size: 2414920 MD5sum: 04de94d2997a6b1bc2649c68bff15448 SHA1: f950e4b8f68540a7573396354633b8caf6f6af6a SHA256: 222ba80a816612383942e077a61d13c716885c83033bcf4f31a7c1e3a6320cf7 SHA512: a99a04be85574e00549d65c00b01dcd5dac63f08cb57ae881b2e001c304f6384236b5742fc46d347184261972c9dae801457d3952bf64edec0a569e5d612430b Homepage: https://cran.r-project.org/package=plumber2 Description: CRAN Package 'plumber2' (Easy and Powerful Web Servers) Automatically create a web server from annotated 'R' files or by building it up programmatically. 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Package: r-cran-plumber Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-httpuv, r-cran-jsonlite, r-cran-lifecycle, r-cran-magrittr, r-cran-mime, r-cran-promises, r-cran-r6, r-cran-rlang, r-cran-sodium, r-cran-stringi, r-cran-swagger, r-cran-webutils Suggests: r-cran-arrow, r-cran-base64enc, r-cran-coro, r-cran-future, r-cran-geojsonsf, r-cran-htmlwidgets, r-cran-later, r-cran-ragg, r-cran-rapidoc, r-cran-readr, r-cran-readxl, r-cran-redoc, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-sf, r-cran-spelling, r-cran-svglite, r-cran-testthat, r-cran-visnetwork, r-cran-withr, r-cran-writexl, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-plumber_1.3.3-1.ca2604.1_all.deb Size: 1083492 MD5sum: 026cb0197da7ad06ebbc90cab5d5e58b SHA1: 2e7330855740975feb7254e4cd3499a3e34e2f6b SHA256: a1d6ce049e904c4585978bed17f030cd827bac49e364c7eb468fa9400de214b7 SHA512: a19338183b1f9c9b70be2e432dfb41e7fd0e9fa32eb803078754db9e7e7c8b057a6844ebf631fe642f417210badf9c0bab34266eaf99820988595b7ced3a843e Homepage: https://cran.r-project.org/package=plumber Description: CRAN Package 'plumber' (An API Generator for R) Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions. Package: r-cran-plumberdeploy Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-analogsea, r-cran-ssh, r-cran-jsonlite, r-cran-lifecycle Filename: pool/dists/resolute/main/r-cran-plumberdeploy_0.2.1-1.ca2604.1_all.deb Size: 52148 MD5sum: 57b33b4adb6d3c6c3d72c176d326c3e7 SHA1: eb86813d941907417f38a52314a71f8b0b066a37 SHA256: a5065902571d10ab88d6a89ba93675d09afeac153b785e9b793846a329836b1c SHA512: 93f74165493a32f75da9ab09ee3e9f5603e55c646cd62246753975b35f273ee30970c3c409a83213bcc1af83e7310672ab584f53046316165bb1463572893db3 Homepage: https://cran.r-project.org/package=plumberDeploy Description: CRAN Package 'plumberDeploy' (Plumber Deployment) Gives the ability to automatically deploy a plumber API from R functions on 'DigitalOcean' and other cloud-based servers. Package: r-cran-plumbertableau Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2018 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plumber, r-cran-magrittr, r-cran-curl, r-cran-httpuv, r-cran-jsonlite, r-cran-later, r-cran-promises, r-cran-rlang, r-cran-htmltools, r-cran-debugme, r-cran-stringi, r-cran-markdown, r-cran-urltools, r-cran-httr, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-plumbertableau_0.1.1-1.ca2604.1_all.deb Size: 1392924 MD5sum: 9dc22c62c483af5c6c8a7341b5eb4245 SHA1: c37270a24903da0742b8e843772eb93d73725aa0 SHA256: 88482acb820743d4ceee4f0de0a5e60bb4a7efbb0b8e535ed11f0eb727ba51f4 SHA512: ed82162375551c3dfe732f59313bfde20aef7cbbb8259bffd0375071d8fc5a016bc731fcccc12d7bda694dafad43712ee85a09630aa6799a1ba9a0184c4ba2c8 Homepage: https://cran.r-project.org/package=plumbertableau Description: CRAN Package 'plumbertableau' (Turn 'Plumber' APIs into 'Tableau' Extensions) Build 'Plumber' APIs that can be used in 'Tableau' workbooks. Annotations in R comments allow APIs to conform to the 'Tableau Analytics Extension' specification, so that R code can be used to power 'Tableau' workbooks. Package: r-cran-plumbr Architecture: all Version: 0.6.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-objectsignals Suggests: r-cran-plyr, r-cran-testthat, r-cran-mass Filename: pool/dists/resolute/main/r-cran-plumbr_0.6.10-1.ca2604.1_all.deb Size: 346296 MD5sum: 561884bee5c022b0cf42425029738b20 SHA1: 89220f161284c19f6f4ccc39efe8ef9f11c25f08 SHA256: bf34cd23a75c2ddd193af856dee2d775d5976ed62d0d45b9b3284d18220888a6 SHA512: ca2123f446092594e5d88f16c7881e7a5d18e94d70971c31b55df3a3da4b9b3de0029bcb4f836a3d65d5ac25f5e3fd7ff98f10912a48db3b3bfd5d4c149b7656 Homepage: https://cran.r-project.org/package=plumbr Description: CRAN Package 'plumbr' (Mutable and Dynamic Data Models) The base R data.frame, like any vector, is copied upon modification. This behavior is at odds with that of GUIs and interactive graphics. To rectify this, plumbr provides a mutable, dynamic tabular data model. Models may be chained together to form the complex plumbing necessary for sophisticated graphical interfaces. Also included is a general framework for linking datasets; an typical use case would be a linked brush. 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'plume' provides easy-to-use and flexible tools for inserting author data in 'YAML' as well as generating author and contribution lists (among others) as strings from tabular data. Package: r-cran-pluscode2 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Filename: pool/dists/resolute/main/r-cran-pluscode2_0.1.0-1.ca2604.1_all.deb Size: 26090 MD5sum: 02c549d909ebaa79ecf2250de499c9e1 SHA1: 73734b097325712641e9a9f08c7c833f2315d01a SHA256: 2e482968100a898cb4c9220c4319a2f5c482e6a72f0179fdc5f6f0eed8f659c7 SHA512: 87120fb207a3eeb474c98ad5affbcc7b680139b0bd727b1ca72db9b8d274dd6b5e5088eb7d4b6659d37108a241ecb7c472f96f557eb368c668fbeb7b934c9f76 Homepage: https://cran.r-project.org/package=plusCode2 Description: CRAN Package 'plusCode2' (Coordinates to 'Plus Code' Conversion Tool) Generates 'Plus Code' of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by 'Google Inc.' present in the repository . For details about 'Plus Code', visit or . Package: r-cran-pluscode Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-pluscode_0.1.0-1.ca2604.1_all.deb Size: 63502 MD5sum: 0d60653800fd9de57a150b8b968a248b SHA1: bf384d65b9a552e647efeb9e2e9220d83a3a6949 SHA256: df884dcd722a29a2c2e237f31c232fdd05a2e7f2dffd2ccc33d26e1ab48ea542 SHA512: 7a10d33640a91313a1f85947790ba44e304d9f67ab04b56e75b9e26511cdfc23662241546ce05d606dfbc34611e16b689d4018a7f1eefb6f9da339cadd2dd09b Homepage: https://cran.r-project.org/package=pluscode Description: CRAN Package 'pluscode' (Encoder for Google 'Pluscodes') Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'. Package: r-cran-plutor Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-baizer, r-cran-cli, r-cran-dplyr, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggsci, r-cran-magrittr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-repr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-svglite, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-plutor_0.1.0-1.ca2604.1_all.deb Size: 518816 MD5sum: f8a322561d49c9201f9784a0e1182d1a SHA1: 38fb94e39cdb056f934f7e9ffedf32800021a121 SHA256: 03ad60e41ccb55b622d845862a9cb7d2beebc1e63f2a69fffd5de32f27b975d8 SHA512: b624ff456fdeeb7e377d068e1a61f5cf303908947e703c50710be48f4a92cc5c5dd3b0430584bd6f651b21562d92c3a19cac797145e40fb935abfb46c53d053b Homepage: https://cran.r-project.org/package=plutor Description: CRAN Package 'plutor' (Useful Functions for Visualization) In ancient Roman mythology, 'Pluto' was the ruler of the underworld and presides over the afterlife. 'Pluto' was frequently conflated with 'Plutus', the god of wealth, because mineral wealth was found underground. When plotting with R, you try once, twice, practice again and again, and finally you get a pretty figure you want. It's a 'plot tour', a tour about repetition and reward. Hope 'plutor' helps you on the tour! Package: r-cran-pm3 Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tableone Filename: pool/dists/resolute/main/r-cran-pm3_0.2.0-1.ca2604.1_all.deb Size: 28282 MD5sum: f9d8135880fffd36accf045c3028926f SHA1: 1b02d468611b6af9b59b78d36ac51ac5068513ee SHA256: 661701cfa3ce14f40c5eee8cc953a1d63ff86355ac126120307840df1433e722 SHA512: e1cc6ccd4345a979d8e760dcb6f7f5f22f78b075a8c57e6c09caccf3aa6c2db602ed049d1db254bcd26b9edba83d326d46dd7859c907492e2e4cbd7aed9f1f24 Homepage: https://cran.r-project.org/package=pm3 Description: CRAN Package 'pm3' (Propensity Score Matching for Unordered 3-Group Data) You can use this program for 3 sets of categorical data for propensity score matching. Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. The matching will make the differences in the baseline data smaller. This method was described by Alvaro Fuentes (2022) . Package: r-cran-pmc Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geiger, r-cran-ggplot2, r-cran-ouch, r-cran-tidyr, r-cran-phytools Suggests: r-cran-covr, r-cran-gridextra, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pmc_1.0.6-1.ca2604.1_all.deb Size: 126456 MD5sum: d19dcec9499efa7c586da8ce4012a3a8 SHA1: 5f8ada7756b62af044c2a8a8d08f3bfce1a82424 SHA256: 9619dfb5e4a3393ca3aa2e9efb4ec6f3d3391804d3bf9e1f4225467fef997dff SHA512: 2c154b24ab6a7da5289ad119e9eab0ca3f0c57acb7df9f750b7609b77ed71a7136fb10931cb20a4a02446a189ee8f7f22f4346ac8e079c1b605b2b8135685f49 Homepage: https://cran.r-project.org/package=pmc Description: CRAN Package 'pmc' (Phylogenetic Monte Carlo) Monte Carlo based model choice for applied phylogenetics of continuous traits. Method described in Carl Boettiger, Graham Coop, Peter Ralph (2012) Is your phylogeny informative? Measuring the power of comparative methods, Evolution 66 (7) 2240-51. . Package: r-cran-pmcalibration Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-mass, r-cran-mgcv, r-cran-survival, r-cran-pbapply Suggests: r-cran-rmarkdown, r-cran-data.table, r-cran-ggplot2, r-cran-rms, r-cran-simsurv Filename: pool/dists/resolute/main/r-cran-pmcalibration_0.2.0-1.ca2604.1_all.deb Size: 362944 MD5sum: ca09b8fa1127e42d991cb42b98716e69 SHA1: e85e227cf00bd7aed2a55475ffd9815bc85bf25e SHA256: 43fc5fbb950496b252b6a633a3e68280e4be3115c4a949682afad5a57801f887 SHA512: ec002303d567c8007e6f6e778a7434c0c3b75ba7a44023e411ee39b915d6e281799ea78577fc6a44b8de5fc2e93f2c475216bfe286bfe125be3a173edbdee1b7 Homepage: https://cran.r-project.org/package=pmcalibration Description: CRAN Package 'pmcalibration' (Calibration Curves for Clinical Prediction Models) Fit calibrations curves for clinical prediction models and calculate several associated metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model should align with observed probabilities. Calibration curves relate predicted probabilities (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 'pmcalibration' allows users to choose between several smoothers (regression splines, generalized additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. See Van Calster et al. (2016) ; Austin and Steyerberg (2019) ; Austin et al. (2020) . Package: r-cran-pmcmr Architecture: all Version: 4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-multcompview Filename: pool/dists/resolute/main/r-cran-pmcmr_4.4-1.ca2604.1_all.deb Size: 53012 MD5sum: 1ce50b01ff92d1e19bcefe53f9d88ec0 SHA1: 8494d0a3d326d6a83b2b05fdfee144ba0c7cc07a SHA256: 2df445605a8c86160754a16fef80a898fec2cd86b42a0304b93c5be4a0eb7f6a SHA512: 579cd8095a0c16759f93d25bb445aa879059d89fe1633e755b793e76e0f1c7cf400f2c1a107f5fd51fc529457c4f98f9052940c64407ae9d84fac332a68d43dd Homepage: https://cran.r-project.org/package=PMCMR Description: CRAN Package 'PMCMR' (Calculate Pairwise Multiple Comparisons of Mean Rank Sums) Note, that the 'PMCMR' package is superset by the novel 'PMCMRplus' package. The 'PMCMRplus' package contains all functions from 'PMCMR' and many more parametric and non-parametric multiple comparison procedures, one-factorial trend tests, as well as improved method functions, such as print, summary and plot. The 'PMCMR' package is no longer maintained, but kept for compatibility of reverse depending packages for some time. Package: r-cran-pmd Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4558 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-igraph, r-cran-envigcms Suggests: r-cran-knitr, r-cran-shiny, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pmd_0.2.7-1.ca2604.1_all.deb Size: 3723758 MD5sum: 18a40b12df77e1db5e63113d357cfa3b SHA1: a4104397f301ed543c7b2ae8688f1c99dd12502d SHA256: 43f19741fe1a59b6253f1e16dd44ac62f8b9b2d9c0930ec4c81279f3d52ff273 SHA512: fa27a5dc9157698d1484b8017217b70445f291173eefb03867120381e1601a432c3997955b7e91b8e9de9e15926ac2d62f14d8bc2e6c5cba478d082c96d40f26 Homepage: https://cran.r-project.org/package=pmd Description: CRAN Package 'pmd' (Paired Mass Distance Analysis for GC/LC-MS Based Non-TargetedAnalysis and Reactomics Analysis) Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) and PMD based reactomics analysis proposed in Yu and Petrick (2020) for gas/liquid chromatography–mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. Reactomics analysis could also be performed to build PMD network, assign sources and make biomarker reaction discovery. GUIs for PMD analysis is also included as 'shiny' applications. Package: r-cran-pmetar Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-lubridate, r-cran-magrittr, r-cran-rcurl, r-cran-tidyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-pmetar_0.7.0-1.ca2604.1_all.deb Size: 1449914 MD5sum: f9dd5f1ce9d5e065fc2dc645f1c2ea8d SHA1: df2a2f1bc050a57385b4966529310238b02f8778 SHA256: 52c7cdbacd355743557e947e079274a3d43ecfd51b04808dd6e31b18d70e95f0 SHA512: cac9a5d028f595db5732ba057fcd8a8d6c009635655ac9651247f3d72c3b1cc9e7d857b464f3404e3bf9ad0db80b6341b3393897dc8015884346c207bd27f7e6 Homepage: https://cran.r-project.org/package=pmetar Description: CRAN Package 'pmetar' (Processing METAR Weather Reports) Allows to download current and historical METAR weather reports extract and parse basic parameters and present main weather information. Current reports are downloaded from Aviation Weather Center and historical reports from Iowa Environmental Mesonet web page of Iowa State University ASOS-AWOS-METAR . Package: r-cran-pmev Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-zoo, r-cran-rlang, r-cran-ggplot2, r-cran-scales, r-cran-vdiffr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pmev_0.1.2-1.ca2604.1_all.deb Size: 113456 MD5sum: 83fab0a43c5a47799af60db251f6571b SHA1: eaf16e2b8f2356064a902d27e0d1469c2b7a161e SHA256: eacf57677e347fdc66685ca24927d60678c9bc908d786093455aa12b036c355b SHA512: be8ae6c87bd7b7d5b783543e74d15328229da02995559e7213fe5eefb57aebfd05c6edff220d80e85c6d1286d4273924df663dbf938c4a09ecae55e8fd4981b2 Homepage: https://cran.r-project.org/package=pmev Description: CRAN Package 'pmev' (Calculates Earned Value for a Project Schedule) Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)). Package: r-cran-pmevapotranspiration Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pmevapotranspiration_0.1.0-1.ca2604.1_all.deb Size: 15868 MD5sum: 6c4d9b513db134c371f5e314496e8d8d SHA1: df13d33260f9b9bec7ad87cf9ca7a2c9e21a00a5 SHA256: 98a3c8c5aaa3fe0b7db7bc3d8dc6fe090baa93c20440bbf571e03977ca535a05 SHA512: 6f83d3a07a7dab95902e41aa0ed0237539e7d94b31894ec2e0afcb0e80c6c30f8c32901ab2871d670f47855fde5b9c789f7016c2c540ec2d98ddf659acb2c9ce Homepage: https://cran.r-project.org/package=PMEvapotranspiration Description: CRAN Package 'PMEvapotranspiration' (Calculation of the Penman-Monteith Evapotranspiration usingWeather Variables) The Food and Agriculture Organization-56 Penman-Monteith is one of the important method for estimating evapotranspiration from vegetated land areas. This package helps to calculate reference evapotranspiration using the weather variables collected from weather station. Evapotranspiration is the process of water transfer from the land surface to the atmosphere through evaporation from soil and other surfaces and transpiration from plants. The package aims to support agricultural, hydrological, and environmental research by offering accurate and accessible reference evapotranspiration calculation. This package has been developed using concept of Córdova et al. (2015) and Debnath et al. (2015) . 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The package provides a small set of reliable, efficient and convenient tools for processing and analysing trade/portfolio data. The manual provides all the details; it is available from . Examples and descriptions of new features are provided at . 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Package: r-cran-pmxcv Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pmxcv_0.0.2-1.ca2604.1_all.deb Size: 28222 MD5sum: 99e66dbaba8aa99d1b3e7a6d66ae1ef1 SHA1: 17dfacb376714bf4bace12328d70f50acf4bc705 SHA256: 8b951cc12172604a353dc1ec3a06e2a28d4cbefacbd461206decca5d271f32dc SHA512: 989d4457ef1d428f3e02af133fb34a8f27e359d4e1f011b61617661c775d80ec1812a137e2ad71bec7c8946d86b60a9f691ae94f53a20a0efe574e35a363e906 Homepage: https://cran.r-project.org/package=pmxcv Description: CRAN Package 'pmxcv' (Integration-Based Coefficients of Variation) Estimate coefficient of variation percent (CV%) for any arbitrary distribution, including some built-in estimates for commonly-used transformations in pharmacometrics. Methods are described in various sources, but applied here as summarized in: Prybylski, (2024) . Package: r-cran-pmxnode Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-ggplot2, r-cran-checkmate Suggests: r-cran-rxode2, r-cran-nlmixr2, r-cran-withr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pmxnode_0.1.0-1.ca2604.1_all.deb Size: 1000982 MD5sum: aee057b1cc49bbcf8560a64e7ee2e45b SHA1: 39ce39e734cffc8a39c17c966aa342ebd7f51fa7 SHA256: 08e488ce3bc725f87114d4695db141b36690471bfa1f4ea544c9abb1a0d7d6a4 SHA512: 64adb7d42584fba5ca3785425c2c0f6d7981c4a5045eabcd6456589d346dc4fb1b3fa38b1e66ee5ea4be3bb3e6c08077b1058bb0dbd50161e1fdeeb1113ce91b Homepage: https://cran.r-project.org/package=pmxNODE Description: CRAN Package 'pmxNODE' (Application of NODEs in 'Monolix', 'NONMEM', and 'nlmixr2') An easy-to-use tool for implementing Neural Ordinary Differential Equations (NODEs) in pharmacometric software such as 'Monolix', 'NONMEM', and 'nlmixr2', see Bräm et al. (2024) and Bräm et al. (2025) . The main functionality is to automatically generate structural model code describing computations within a neural network. Additionally, parameters and software settings can be initialized automatically. For using these additional functionalities with 'Monolix', 'pmxNODE' interfaces with 'MonolixSuite' via the 'lixoftConnectors' package. The 'lixoftConnectors' package is distributed with 'MonolixSuite' () and is not available from public repositories. Package: r-cran-pmxpartab Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-table1, r-cran-data.table, r-cran-htmltools, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-yaml, r-cran-linpk, r-cran-survival Filename: pool/dists/resolute/main/r-cran-pmxpartab_0.5.0-1.ca2604.1_all.deb Size: 92616 MD5sum: ad0fb1bb4c85d0cd4346f63dedd8382e SHA1: 87f89d30992f02c3e5cb014522b8f2153c01a192 SHA256: 8ee47dc27e5a206b27cd807199c056c26b83c933a796a637bb192a81a3f98456 SHA512: 1fe269c3743463cc3e36744a498c8ca9e9dc0ce08d0853ad443b92eb1c3f48a2267cb42a3d8c0edf8dd8455e1c0a1bae324a77833590aa281a7a14ddee39e77e Homepage: https://cran.r-project.org/package=pmxpartab Description: CRAN Package 'pmxpartab' (Parameter Tables for PMx Analyses) Generate nicely formatted HTML tables to display estimation results for pharmacometric models. Package: r-cran-pmxtools Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-patchwork, r-cran-ggplot2, r-cran-chron, r-cran-xml2, r-cran-dplyr, r-cran-tibble, r-cran-ggdist, r-cran-scales, r-cran-mass, r-cran-stringr, r-cran-pknca, r-cran-magrittr, r-cran-data.tree Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pmxtools_1.5-1.ca2604.1_all.deb Size: 1027214 MD5sum: 70d0f6e40d38189da2d12d959e3798d9 SHA1: befeb78ef712f5ed2ced6c557c4c55dde9c18ace SHA256: 79b5aab3e5cb4e900d8f0747f8e43d764dee87693ac4e502bee78df8a082067e SHA512: 0e315282ee36c15fc60516bc753060b016e488436f7dfb4977eca0cad3f225ae3dcc18284c38f684ab724b9da790eb5b5537c376a59abd7492ea0cdd3cd58f52 Homepage: https://cran.r-project.org/package=pmxTools Description: CRAN Package 'pmxTools' (Pharmacometric and Pharmacokinetic Toolkit) Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) ); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN). Package: r-cran-pnadcibge Architecture: all Version: 0.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-projmgr, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-survey, r-cran-tibble, r-cran-timedate Suggests: r-cran-convey, r-cran-sipdibge, r-cran-srvyr Filename: pool/dists/resolute/main/r-cran-pnadcibge_0.7.5-1.ca2604.1_all.deb Size: 215226 MD5sum: 1e45d6066ecf86db162832f31dc55950 SHA1: 93624492ce3e975b20c6102b0e3d76121e5e2b48 SHA256: 40c340659a2cce254df6e85f3e46e0d0e65303b25349eeae4998f9e065db0d87 SHA512: b8c651a95c40a9b4d716414b2d4dc76e992d73ea78c20982889e13ecda67b7a1cd7259630b2260ae7a52c4ea2a46adecf080475265886096df9d345b96b5748d Homepage: https://cran.r-project.org/package=PNADcIBGE Description: CRAN Package 'PNADcIBGE' (Downloading, Reading and Analyzing PNADC Microdata) Provides tools for downloading, reading and analyzing the Continuous National Household Sample Survey - PNADC, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website . Further analysis must be made using package 'survey'. Package: r-cran-pnadcperiods Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-checkmate, r-cran-sidrar, r-cran-lubridate Suggests: r-cran-dplyr, r-cran-fst, r-cran-haven, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-scales Filename: pool/dists/resolute/main/r-cran-pnadcperiods_0.1.2-1.ca2604.1_all.deb Size: 2348730 MD5sum: 3174c729da249b5060b2cf335f8a1c4a SHA1: c380d8a012613c66397b8f6995063baabcb0fb04 SHA256: de8c94e115fca94860e02b57cf5b260cfa2831f65d5f345f052071461dc024c0 SHA512: ad344ba86232e554f0e9ebb0cc0b7670ca93bcf0fd63a1c5bc6d60cc09e0a5e0e0b9b54e708bbe62389418999dc2df00831e49cf44b7dae05e743ee9622ffae2 Homepage: https://cran.r-project.org/package=PNADCperiods Description: CRAN Package 'PNADCperiods' (Identify Reference Periods in Brazil's PNADC Survey Data) Identifies reference periods (months, fortnights, and weeks) in Brazil's quarterly PNADC (Pesquisa Nacional por Amostra de Domicilios Continua) survey data and computes calibrated weights for sub-quarterly analysis. The core algorithm uses IBGE (Instituto Brasileiro de Geografia e Estatistica) 'Parada Tecnica' (technical break) rules combined with respondent birthdates to determine which temporal period each survey observation refers to. Period identification follows a nested hierarchy enforced by construction: fortnights require months, weeks require fortnights. Achieves approximately 97% monthly determination rate with the full series (2012-2025). Strict fortnight and week rates are approximately 9% and 3% respectively, as they cannot leverage cross-quarter panel aggregation. Experimental strategies (probabilistic assignment and UPA (Primary Sampling Unit) aggregation) further improve these determination rates. The package provides adaptive hierarchical weight calibration (4/2/1 cell levels for month/fortnight/week) with period-specific smoothing to produce survey weights calibrated to SIDRA (Sistema IBGE de Recuperacao Automatica) population totals. Also includes a SIDRA mensalization module that converts 86+ official rolling quarter series from the IBGE SIDRA API (Application Programming Interface) into exact monthly estimates, without requiring access to microdata. Hecksher (2020) . Package: r-cran-pnar Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-nloptr, r-cran-rangen, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-pnar_1.8-1.ca2604.1_all.deb Size: 278892 MD5sum: 78c0bd43a21f9f2161d6df74134f91e3 SHA1: 39649e9e3c7517c9b549b131b5258592a343499a SHA256: 493f88c62ecd406ca8263b755b1c23aa969cdde3ace4b7adb0154638ff1f8082 SHA512: a2c09e32b21febccc568f4e4dbae3b3323d1368b4b5871f44e00c59c7f3da444576ab9e43f5fee7637dc32a52ebf642d484c7dc62eee10df7185c74e89596134 Homepage: https://cran.r-project.org/package=PNAR Description: CRAN Package 'PNAR' (Poisson Network Autoregressive Models) Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. . Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. . Armillotta, M., Tsagris, M. and Fokianos, K. (2023). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. . Package: r-cran-pnc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4469 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phytools, r-cran-geiger Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pnc_0.1.0-1.ca2604.1_all.deb Size: 4136242 MD5sum: 200d1a64381f4b4457f24b0c580e8f8a SHA1: 3f44ce6b58bfd83e56fde2dab2e99076d33f9f28 SHA256: 1b8ec1991fc0ba573d037bead110a2e205f422377fee393d52234311ba26f83b SHA512: 67aa02d6e585c26da933fc81b259d3e331c42c3d9982b5d505469815407d7d6e8c19d973692b4625b8d942a209fb96f02d8c4199cf361e4bf389e04aeea0889a Homepage: https://cran.r-project.org/package=PNC Description: CRAN Package 'PNC' (Phylogenetic Niche Conservatism Analysis for EcologicalCommunities) Provides functions for testing phylogenetic niche conservatism, a key prerequisite in community assembly studies. The package integrates global functional trait data across major taxonomic groups and implements methods such as Pagel's Lambda and Blomberg's K to quantify phylogenetic signals in ecological communities. Methods are described in Münkemüller et al. (2012) . 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The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering. Package: r-cran-pnd Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-numderiv, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pnd_0.1.2-1.ca2604.1_all.deb Size: 502656 MD5sum: 2031c6f9b02a2125ebd9fd68040a3098 SHA1: 6e861e5e0849608db29c2cfa9df33e02430f493f SHA256: 0eaabe29fe45f070c5af769b33cab4da2027cd9759647c8d2b861b6a151df5e4 SHA512: 2c1dfa40a760bb76c102cbbe52d14c0785dc43be9106bc4214f1e5e9fd1908dbe7fa8724c0f549b34a188ed17fedf973d44c30090335baf397273f631a8d7f73 Homepage: https://cran.r-project.org/package=pnd Description: CRAN Package 'pnd' (Parallel Numerical Derivatives, Gradients, Jacobians, andHessians of Arbitrary Accuracy Order) Numerical derivatives through finite-difference approximations can be calculated using the 'pnd' package with parallel capabilities and optimal step-size selection to improve accuracy. These functions facilitate efficient computation of derivatives, gradients, Jacobians, and Hessians, allowing for more evaluations to reduce the mathematical and machine errors. Designed for compatibility with the 'numDeriv' package, which has not received updates in several years, it introduces advanced features such as computing derivatives of arbitrary order, improving the accuracy of Hessian approximations by avoiding repeated differencing, and parallelising slow functions on Windows, Mac, and Linux. 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The data must be downloaded from the official website . Further analysis must be made using package 'survey'. Package: r-cran-pnt Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3963 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pnt_0.1.0-1.ca2604.1_all.deb Size: 1000112 MD5sum: 646d5fd741c0704eae468a69f1169d29 SHA1: 7360e0c69e6e7d19ca2dce08204148bd3eb6e5bd SHA256: 9938ca26b736a4b906ead5ad1c23a2ec13b4f764619acd39f7bc73d5d7098ca7 SHA512: 589b9a2e7bc93432e795a72532506f5fb6e84ff26aa125ecf257a9f8100f152ea0035d47e230fef591c8d9e90fbdd513a3439f0e3d66ca4dc859c38fe2a7e672 Homepage: https://cran.r-project.org/package=PnT Description: CRAN Package 'PnT' (Peak Finder) This program contains a function to find the peaks and troughs of a data set. It filters the set of peaks to remove noise based on the expected height and expected slope of a peak. Peaks that are too short (caused by random noise), or too shallow (part of the background data) are filtered out. 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Then transforms this into an optimisation problem, allowing both Nash and Optimal flows to be solved by nonlinear optimisation. See and Knight and Harper (2013) for more information. 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It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models. Package: r-cran-pocrm Architecture: all Version: 0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dfcrm, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-pocrm_0.13-1.ca2604.1_all.deb Size: 35000 MD5sum: f35d90803c8e4eba49f99d515bb27d1f SHA1: 36379d4b0d4991dcc7f5c219f783394a7520de69 SHA256: 5676e053db6af3283872ae300123468cf1a42032d3759f16397310eba53cabae SHA512: 8f54eb9017fe64d17322ac354de765172a237bb26f9f4091b0feb43f025fc90a27b205d09c8c41bcbde527fc8dbedb55ff367ce03cee8dcdd49893ff07fe573d Homepage: https://cran.r-project.org/package=pocrm Description: CRAN Package 'pocrm' (Dose Finding in Drug Combination Phase I Trials Using PO-CRM) Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user. Package: r-cran-pod Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pod_1.2.0-1.ca2604.1_all.deb Size: 172598 MD5sum: 00f75d414eea0acd8703ce226383d0d3 SHA1: 9a30b46dc2b44e2b661a61769ea4796195fa4aa9 SHA256: d8691986f472da362150d5b4ffb404255444e1a493887194e4f1e6a523abd07b SHA512: 44ea63ffa52b3862862140533425fbb113f702c31f6bdd21d498d0d5d17aa746e67d7f8f561d7ec4f104f63661df2ba33884240adcc128d6dd0acfab1f1327f9 Homepage: https://cran.r-project.org/package=POD Description: CRAN Package 'POD' (Probability of Detection for Qualitative PCR Methods) This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified. This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' . The modelling itself has been described by Uhlig et al. (2015) . Package: r-cran-podes Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-podes_0.1.0-1.ca2604.1_all.deb Size: 212402 MD5sum: 4e3dcc1c16c6651e35c6e5e482d6b511 SHA1: 30cee63a7011cb479d65f3f37dc4f9b20ecd8e79 SHA256: 38c68d233af26efc9a3038020b37d6f9d07da9716324113cec1803ed6236a13a SHA512: 75432c995da2a944db6bbb7b81e92252e8f883a7b41338d8759effd9ed8a762bce0f9c5ff428f142604dd459b78a5907df64aa346b55bc04c144392f64cd38ae Homepage: https://cran.r-project.org/package=PODES Description: CRAN Package 'PODES' (Village Potential Statistics of Indonesia) Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. 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The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, ); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, ), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes. Package: r-cran-poet Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-poet_2.0-1.ca2604.1_all.deb Size: 31164 MD5sum: 84fa51546bc11d7158dcb4383c209090 SHA1: 419b8f9ba76adce20fa06b2c8b70ffe0da2a9e89 SHA256: 1e119d5a73b19aed21c3f56c5f440a765bb61f549421599cd250fe46c2e56a4c SHA512: 8162ccd5e45c75fcd91adf8d474ef0701ac6b3caca25fe3a1ec096410a84e97c8d0b66b5128c23ba2ccb96555c69bdcda46926e42be6fd1c45d0bb2fd98ccde9 Homepage: https://cran.r-project.org/package=POET Description: CRAN Package 'POET' (Principal Orthogonal ComplEment Thresholding (POET) Method) Estimate large covariance matrices in approximate factor models by thresholding principal orthogonal complements. Package: r-cran-pogit Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-logistf, r-cran-plyr Suggests: r-cran-count Filename: pool/dists/resolute/main/r-cran-pogit_1.3.0-1.ca2604.1_all.deb Size: 338894 MD5sum: 0d9d534516f26074f1e534aa7b152aad SHA1: 5a1a63454a254996bfdd3138d2b0185365aae6d0 SHA256: 7be585195121f3ee25fb34a40fd743ae4a2393cf5c18c588c2b5541168922496 SHA512: 8cbca8dc7d953d851d2a28f8aa98aacd1552859171a4bd075166bb31b04fba07fce86a6e0b62b80e402f9de3e624d34b565aa159b9c5d7e9016b6f0db72040f8 Homepage: https://cran.r-project.org/package=pogit Description: CRAN Package 'pogit' (Bayesian Variable Selection for a Poisson-Logistic Model) Bayesian variable selection for regression models of under-reported count data as well as for (overdispersed) Poisson, negative binomal and binomial logit regression models using spike and slab priors. Package: r-cran-pogromcydanych Architecture: all Version: 1.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-smarterpoland Filename: pool/dists/resolute/main/r-cran-pogromcydanych_1.7.1-1.ca2604.1_all.deb Size: 4204948 MD5sum: f3237c55134915e145d21eaec3fec949 SHA1: b1923c2ecc965ed44fa2833fab887ca1afbda7fa SHA256: 239caa40f320a5a6f82df987bf5497732d9b7a459bedfefecf59069cc556fa5d SHA512: f4663b66756a1223132b1a4aa43c1f38fcf9294056f3a15e7be14cd49c3c46ea27b07ded96e94628ee75a886d2e8a15cdb805f1bc79dc5f00bfc561188063013 Homepage: https://cran.r-project.org/package=PogromcyDanych Description: CRAN Package 'PogromcyDanych' (DataCrunchers (PogromcyDanych) is the Massive Online Open Coursethat Brings R and Statistics to the People) The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment. Package: r-cran-poiclaclu Architecture: all Version: 1.0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-poiclaclu_1.0.2.1-1.ca2604.1_all.deb Size: 62118 MD5sum: f1dadccaf987a01b5895a80fe31a671a SHA1: f96d24270b2b7934dd73e17edac513561c59f874 SHA256: 164c6df61fa519d960406bd0e4c270ac55683f2a52267281179a25e2c6d23eb3 SHA512: 5da9d0d804c5c0c1861301f4a28eb619d7f7764e53d721dec7084f3b5f79bf8c421ac4e0802403f345f6563b7af6b84d0ef321101f79b12d7839233b220750c9 Homepage: https://cran.r-project.org/package=PoiClaClu Description: CRAN Package 'PoiClaClu' (Classification and Clustering of Sequencing Data Based on aPoisson Model) Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518. 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Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M., Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) . 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Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions. Many other workflows are available including an information management workflow, where the aim is to record, collect, and generate useful information on data tables. Package: r-cran-pointdensityp Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-pointdensityp_0.3.5-1.ca2604.1_all.deb Size: 3361730 MD5sum: 9c1ffec465b5bf59135d64441e60ec92 SHA1: 131a109709b5656afe81169e4ac0ce804fbd7812 SHA256: fa75a2a9eabfe0ab7cac956ed88fc25210e0c5f48929bff5e52ec69277495ad0 SHA512: b21f0989683641e3f2006a588b6c4848be26166a1c3f5a364306b325395e92d75001b178143ede821eb8f076a0c698ea6f1bad030184e439818dbaa43104d5cf Homepage: https://cran.r-project.org/package=pointdensityP Description: CRAN Package 'pointdensityP' (Point Density for Geospatial Data) The function pointdensity returns a density count and the temporal average for every point in the original list. The dataframe returned includes four columns: lat, lon, count, and date_avg. The "lat" column is the original latitude data; the "lon" column is the original longitude data; the "count" is the density count of the number of points within a radius of radius*grid_size (the neighborhood); and the date_avg column includes the average date of each point in the neighborhood. Package: r-cran-pointedsdms Architecture: all Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2299 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-inlabru, r-cran-r6, r-cran-terra, r-cran-ggplot2, r-cran-fmesher, r-cran-raster, r-cran-sp, r-cran-r.devices, r-cran-blockcv, r-cran-fnn, r-cran-dplyr, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-sn, r-cran-rastervis, r-cran-ggmap, r-cran-rcolorbrewer, r-cran-cowplot, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-spocc, r-cran-covr Filename: pool/dists/resolute/main/r-cran-pointedsdms_2.1.5-1.ca2604.1_all.deb Size: 2013192 MD5sum: 1743d26cc96c902063058b41c4c44d09 SHA1: b7d27f043d617d3bdb8a58aa990557e392532b8c SHA256: 627449a7c3c6212f2e5a5cd67af7fed9d4c2e21007c6e685d7d3b556089fe132 SHA512: ea8ee1c62ed164c0422f6b763a96744d5e1195d886abe77f09a59aed44e0519a22fed0aeee3fb996a050c8fa8e8d0c9e64652599ccabc3be7537ad82507421e4 Homepage: https://cran.r-project.org/package=PointedSDMs Description: CRAN Package 'PointedSDMs' (Fit Models Derived from Point Processes to Species Distributionsusing 'inlabru') Integrated species distribution modeling is a rising field in quantitative ecology thanks to significant rises in the quantity of data available, increases in computational speed and the proven benefits of using such models. Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. We therefore introduce the R package 'PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them. There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions. An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) . 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Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible. The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the European Centre for Medium-Range Weather Forecasts (ECMWF, ) and GDP growth Greenbook data by the US Federal Reserve. See Schmidt, Katzfuss and Gneiting (2015) for more details on the identification and estimation of a directive behind a point forecast. 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Package: r-cran-poly4at Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1507 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-sf, r-cran-leaflet, r-cran-geojsonsf, r-cran-httr, r-cran-jsonlite, r-cran-shinydashboard, r-cran-dt, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-poly4at_1.0.2-1.ca2604.1_all.deb Size: 1456012 MD5sum: 312943a952bb9279a2fe06c64f80f098 SHA1: 25f832440e2428b0e27557c81b17f43f9c6498fd SHA256: 91f9a83a8d91a0de6027afc6ab09d644363fabafe1757a39d8a87b732827672f SHA512: 239515d93b9772013be112008992e86b73877d1fed2566905341017663cf9d8eed7af494eba73e0d434e32da12dadeccd199ce3cece59793b87db41f7c295311 Homepage: https://cran.r-project.org/package=Poly4AT Description: CRAN Package 'Poly4AT' (Access 'INVEKOS' API for Field Polygons) A 'shiny' app that allows to access and use the 'INVEKOS' API for field polygons in Austria. 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More information on the implementation can be found at Conrad J. Burden (2014) . 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A particular practical application of the polycross method occurs in the production of a synthetic variety resulting from cross-pollinated plants. Laying out these experiments in appropriate designs, known as polycross designs, would not only save experimental resources but also gather more information from the experiment. Different experimental situations may arise in polycross nurseries which may be requiring different polycross designs (Varghese et. al. (2015) . " Experimental designs for open pollination in polycross trials"). This package contains a function named PD() which generates nine types of polycross designs suitable for various experimental situations. 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Package: r-cran-polyhaplotyper Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 470 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-polyhaplotyper_1.0.1-1.ca2604.1_all.deb Size: 362352 MD5sum: b5f7f3282fd755b917c81d237720966b SHA1: ed22301814ab4ebf9dbde4d71783190c411d1e72 SHA256: 8bcb0e006d73104cb284338f369efaaf407dcf0a25705ee8f08cce23de42718f SHA512: f1d8766b04b56cabc10875ed31717f927853860637cf492c9827dec71c77f5060cb86826b08f65f9143b26579eb57819be31bb4a3ffe621e1ec1709478c70653 Homepage: https://cran.r-project.org/package=PolyHaplotyper Description: CRAN Package 'PolyHaplotyper' (Assignment of Haplotypes Based on SNP Dosages in Diploids andPolyploids) Infer the genetic composition of individuals in terms of haplotype dosages for a haploblock, based on bi-allelic marker dosages, for any ploidy level. Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data. Submitted to BMC Bioinformatics (2021). 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Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) . Since version 1.1.0, both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (2021) . Package: r-cran-polymatching Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optmatch, r-cran-ggplot2, r-cran-gridextra, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-vgam, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-polymatching_1.0.1-1.ca2604.1_all.deb Size: 72190 MD5sum: c2e466988cee6a63f54585ce91171cae SHA1: 4e25619fb39b92f943b978b2596acd55ef58ad90 SHA256: 513a5d122f665add36290a6c2928677a28feadf620dbcf454c30ab52d3007075 SHA512: f59c96c39ec37b07f48068fe26cacab04577370642f088ccfad3fc4057288e8d3b5b04f40329a5c880a916be28afcfd9268a766fa85f2ae064bd9737048cdab3 Homepage: https://cran.r-project.org/package=polymatching Description: CRAN Package 'polymatching' (A Matching Algorithm for Designs with Multiple Groups) Includes functions implementing the conditionally optimal matching algorithm, which can be used to generate matched samples in designs with multiple groups. 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It is based on the package 'polynom' and uses a lot of its methods to implement matrix operations. This package includes 3 methods of triangularization of polynomial matrices: Extended Euclidean algorithm which is most classical but numerically unstable; Sylvester algorithm based on LQ decomposition; Interpolation algorithm is based on LQ decomposition and Newton interpolation. Both methods are described in D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization, IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) and in Salah Labhalla, Henri Lombardi & Roger Marlin, Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux, Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) . Package: r-cran-polymigr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-polymigr_0.1.0-1.ca2604.1_all.deb Size: 27002 MD5sum: b35d7ee2427921a1e5f0b4f1f6040e57 SHA1: ade8e8a5d408bd1e4fb8a651cbeea1721330e060 SHA256: 0aa3b7f52cf61420c1a7ce13e4947729eac9fbe1a63dfcdf2eff6be56070ce2a SHA512: 1a392788d213bb2d00edc97d5f3a69ea1de319641533f17543147097e7beb7460cc40129528e5f7c25c7d1835d0a122da71b7f76436b7220016d935b02642e42 Homepage: https://cran.r-project.org/package=PolyMigR Description: CRAN Package 'PolyMigR' (Analysis of Polyphenol Migration from Packaging Films) The gradual release of active substances from packaging can enhance food preservation by maintaining high concentrations of polyphenols and antioxidants for a period of 72 hrs. To assess the effectiveness of packaging materials that serve as carriers for antioxidants, it is crucial to model the diffusivity of the active agents. Understanding this diffusivity helps evaluate the packaging's capacity to prolong the shelf life of food items. The process of migration, which encompasses diffusion, dissolution, and reaching equilibrium, facilitates the transfer of low molecular weight compounds from the packaging into food simulants. The rate at which these active compounds are released from the packaging is typically analysed using food simulants under conditions outlined in European food packaging regulations (Ramos et al., 2014). Package: r-cran-polynom Architecture: all Version: 1.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 901 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-polynom_1.4-1-1.ca2604.1_all.deb Size: 383806 MD5sum: 8a01585d690e521b6a4292ff4c319514 SHA1: dc6b2ac16a547910985e7d56eb6b94efee4a31a9 SHA256: 29799c1c51fc8dd681906a1828ae14351c8a51825053cea4dd5738873ef9423d SHA512: 80d54f0ad248755620a7ad32ec23e71bcd586e02f45e9f28b3788cd3c67c16ae36d624dbc33ba7bf9e07c4325ebda95195b04c9fa052bc7f4ef87dfaa62af717 Homepage: https://cran.r-project.org/package=polynom Description: CRAN Package 'polynom' (A Collection of Functions to Implement a Class for UnivariatePolynomial Manipulations) A collection of functions to implement a class for univariate polynomial manipulations. Package: r-cran-polypatex Architecture: all Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-polypatex_0.9.2-1.ca2604.1_all.deb Size: 484620 MD5sum: 5ec36fceed144368941b3eed498e7ae0 SHA1: f0e1ad3f9287e118cd6b614920ff10bab58a78a8 SHA256: f364bc1d55dd11d3b259338d0542fff0d23dde50f2a79e8a91fc3ade21765585 SHA512: aeaa82ba0514839d61c297f2b8419f2cab072c9e7eb29087ddb7eeaf2ff61ee23e6ef19faf0878396638794313318feeceba747a91676db0299fc8fd86ddc137 Homepage: https://cran.r-project.org/package=PolyPatEx Description: CRAN Package 'PolyPatEx' (Paternity Exclusion in Autopolyploid Species) Functions to perform paternity exclusion via allele matching, in autopolyploid species having ploidy 4, 6, or 8. The marker data used can be genotype data (copy numbers known) or 'allelic phenotype data' (copy numbers not known). Package: r-cran-polypharmacy Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-itertools, r-cran-lubridate, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-polypharmacy_1.0.0-1.ca2604.1_all.deb Size: 230396 MD5sum: d28955f121a3740dc4adc5c30fe3ea57 SHA1: 6f0a2a6856e42f963110261f656b9cf35af380b5 SHA256: 06a25c9b0e2cfe08defd93af8ad245c2e351b4cfbd41d33a8cfc6c70fe67cf5f SHA512: 92c738c4384ea2a07c6250acf4409239525e797bc9d941b59428050bcce41d4020e58bd937b31b38316d79a04345d4c6f63701d7fed31fb4a95ad2a0f0bf9d75 Homepage: https://cran.r-project.org/package=polypharmacy Description: CRAN Package 'polypharmacy' (Calculate Several Polypharmacy Indicators) Analyse prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature. Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997) . Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a) . Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S., Standring, P., Fiore, L., ... Gagnon, D.R. (2005) . Hovstadius, B., Astrand, B., & Petersson, G. (2009) . Hovstadius, B., Astrand, B., & Petersson, G. (2010) . Kennerfalk, A., Ruigómez, A., Wallander, M.-A., Wilhelmsen, L., & Johansson, S. (2002) . Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017) . Narayan, S. W., & Nishtala, P. S. (2015) . Nishtala, P. S., & Salahudeen, M. S. (2015) . Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., & Kwon, J. W. (2016) . Veehof, L., Stewart, R., Haaijer-Ruskamp, F., & Jong, B. M. (2000) . 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The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) . Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) . Gates, K. M., & Molenaar, P. C. M. (2012) . Lutkepohl, H. (2007, ISBN:3540262393). 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Package: r-cran-pooledcohort Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue Suggests: r-cran-testthat, r-cran-covr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-pooledcohort_0.0.2-1.ca2604.1_all.deb Size: 85288 MD5sum: 1af5ef1aa1198c478670723dc6a8ca4b SHA1: 70a8be5bb0c3f1ae154b1102471b490077c279fb SHA256: b63a7f0b2380c2df468a8a6461bca362a0a3fde30dea7cb92137a6d9d1b61c78 SHA512: b5d2f0faf4353abd8cb7b1fa036be062586cd52ec791ba1d46b043c170781c17befa1e79f0cdc2977c5310527eb918b02133a340eab483f365f84bfe25ba2394 Homepage: https://cran.r-project.org/package=PooledCohort Description: CRAN Package 'PooledCohort' (Predicted Risk for CVD using Pooled Cohort Equations, PREVENTEquations, and Other Contemporary CVD Risk Calculators) The 2017 American College of Cardiology and American Heart Association blood pressure guideline recommends using 10-year predicted atherosclerotic cardiovascular disease risk to guide the decision to initiate or intensify antihypertensive medication. The guideline recommends using the Pooled Cohort risk prediction equations to predict 10-year atherosclerotic cardiovascular disease risk. This package implements the original Pooled Cohort risk prediction equations and also incorporates updated versions based on more contemporary data and statistical methods. Package: r-cran-pooledmeangroup Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pooledmeangroup_1.0-1.ca2604.1_all.deb Size: 90716 MD5sum: 94176dce44490dd9a56b4007b37eb110 SHA1: 0ceaf922d99cfcc57c8c3ca02ab4378f329f4562 SHA256: c53ecd58c399204d29b99291166ae65698a93ef79388266f008037e2f48b6232 SHA512: 5d2042df307b90192edf817f0a796b5388992951e3ff489b92743d42f615d218958c1febb54f856e7207a5412aa653f403395fa7688343b44610e103afb6a44c Homepage: https://cran.r-project.org/package=PooledMeanGroup Description: CRAN Package 'PooledMeanGroup' (Pooled Mean Group Estimation of Dynamic Heterogenous Panels) Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) . Package: r-cran-pooledpeaks Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-fragman, r-cran-magrittr, r-cran-pdftools, r-cran-qpdf, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-plyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pooledpeaks_1.2.2-1.ca2604.1_all.deb Size: 1814778 MD5sum: 6b8bb8b6f98626e33d754cd5485aba74 SHA1: 13e1e09b15a26525172921412054815fb5e22986 SHA256: afc51e31173cfe3ba1d6ae93a96675c39ced0b5704383fe856351cf47002f760 SHA512: 1524b969d53313a414d52f691960b560e8055b3734255ef75f3f2fd3f840d3b56b72141607a16c0047b1049f1fae1f281005ddb09f914b766b06b0dca5575c86 Homepage: https://cran.r-project.org/package=pooledpeaks Description: CRAN Package 'pooledpeaks' (Genetic Analysis of Pooled Samples) Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) , Jost (2008) , Nei (1973) , Foulley et al. (2006) , Chao et al. (2008) . Package: r-cran-poolhelper Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-metrics, r-cran-scrm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-poolhelper_1.1.0-1.ca2604.1_all.deb Size: 308944 MD5sum: d42fd4ac75551bb2b6ad74f4700c6f12 SHA1: a1011213b77c71997ac7df1a1041f39e2af9865c SHA256: 715456c24a0dce1446a5b7b1880951622dd10e48a5be2956cce675e7320d564c SHA512: e5cb76ee0b749a9e65886beeb02f90720e614d6b6989116e43f27f1c4262bd283e6dea266abe27f88498ef1ef5c23577f63986e5f57b26c8cd2997e650d7d281 Homepage: https://cran.r-project.org/package=poolHelper Description: CRAN Package 'poolHelper' (Simulates Pooled Sequencing Genetic Data) Simulates pooled sequencing data under a variety of conditions. Also allows for the evaluation of the average absolute difference between allele frequencies computed from genotypes and those computed from pooled data. Carvalho et al., (2022) . Package: r-cran-poolr Architecture: all Version: 1.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-poolr_1.2-0-1.ca2604.1_all.deb Size: 256152 MD5sum: f3faf884ca3734f5e739af25502e1b89 SHA1: 014e5a0dd2977d4574e6680e8545fcbc82827e95 SHA256: 09899506f220e9bfd127599431fba3ca1731f09542807bae09a155c3d2fd6caf SHA512: d103b8404eb31de22a3edf7d2e87333486a3e578ff13e34cf00529d91150678a4420e665805c15f0e39ad29c0444d4836d4fac77ddd0a1f7d339bfee07663322 Homepage: https://cran.r-project.org/package=poolr Description: CRAN Package 'poolr' (Methods for Pooling P-Values from (Dependent) Tests) Functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) . Package: r-cran-poolvim Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-empiricalbrownsmethod, r-cran-hmisc, r-cran-ranger Filename: pool/dists/resolute/main/r-cran-poolvim_1.0.0-1.ca2604.1_all.deb Size: 26564 MD5sum: 512b09f481b362053819b5470f2575d7 SHA1: 797de3a82bad507001daf3439969056996c90bda SHA256: 9f58f5b2a0aca4761b046590f8089757500305344c59a24f572f9cc95ff57fee SHA512: f60c3c030912e72fd280572e12a4cfce9a52408ebe8dd0a59dd080796907c15809afb8d24688d3ee8c90981b0d13739ff4b9d4cc5bd4df65d7eb30bc9631ed48 Homepage: https://cran.r-project.org/package=poolVIM Description: CRAN Package 'poolVIM' (Gene-Based Association Tests using the Actual Impurity Reduction(AIR) Variable Importance) Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in and will be published soon. 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Package: r-cran-pop Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-mass Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pop_0.1-1.ca2604.1_all.deb Size: 118138 MD5sum: 3d2198d0eeb9b3a85031a6ed0aac7c38 SHA1: eba4949d5cfed93db53063e7723fef8760eb8081 SHA256: c7e2e793d92273dff538e5955732a973265169cde29c47314bd1164453416a51 SHA512: fe711d2009b69a68332e6f4aa6e1e3d93fb65c21e049f5b2b22938465ecc1416a66b6a1f74739a02d9314445facd56487d99e1b12f155c9df607c00a912eebc0 Homepage: https://cran.r-project.org/package=pop Description: CRAN Package 'pop' (A Flexible Syntax for Population Dynamic Modelling) Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways. Package: r-cran-popbayes Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2003 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2jags, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-popbayes_1.3-1.ca2604.1_all.deb Size: 1342032 MD5sum: 84dc9c63ea24cdeb6fe11262f28f6da7 SHA1: e35aa74bd1f154d41e0f21662bc0f0295b3da4a3 SHA256: 32fa841a521ee8da1679c764816ee3757073e8299681e04bc1b167de9557ef29 SHA512: 8babfdf47c439baa762f2b210e4e015033b979f319987288991d9aef21de8710c1708522540a532ed57a3f0e6ce80e9a14cb37f6c8226e99ecc9751d8e43da9d Homepage: https://cran.r-project.org/package=popbayes Description: CRAN Package 'popbayes' (Bayesian Model to Estimate Population Trends from Counts Series) Infers the trends of one or several animal populations over time from series of counts. It does so by accounting for count precision (provided or inferred based on expert knowledge, e.g. guesstimates), smoothing the population rate of increase over time, and accounting for the maximum demographic potential of species. Inference is carried out in a Bayesian framework. This work is part of the FRB-CESAB working group AfroBioDrivers . Package: r-cran-popbio Architecture: all Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-popbio_2.8-1.ca2604.1_all.deb Size: 300588 MD5sum: af92bd0bbfc2b2c4efc7871b50968d62 SHA1: e6a5cedbfa51699b9f5c5751bfa806f7480976d0 SHA256: 79f73e50d643046111be392b3df2b875ff23a135ddb76cfefe7399e3a8c28456 SHA512: 96958c7d3b98c5e3a2130644e424ddd9bdd5441dc79daf725dab4ebce0b58ee87f23fbfd87173ae12691e3a621153b36ce86c9306455b06d4b3ec3992879d692 Homepage: https://cran.r-project.org/package=popbio Description: CRAN Package 'popbio' (Construction and Analysis of Matrix Population Models) Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002). Package: r-cran-popcomm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2716 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-matrix, r-cran-ggplot2, r-cran-ggpubr, r-cran-pheatmap, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-scales, r-cran-igraph, r-cran-pbmcapply, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-popcomm_1.0.0-1.ca2604.1_all.deb Size: 2709324 MD5sum: 68cb9fabc72dbccaf0572ed12a8a7013 SHA1: 5b37ab10f0e9700993db9fda3aaf1d1cf835d253 SHA256: 3dfbaff7dee11a1bd682a05bd5cc0d2842ede6dc967e3bc318a3571c7d1a5751 SHA512: 09535c3a7b4bead2cfd6824a6f7e652695689b80e8bc1c32138ef6ab79537d6fd41b63422537ab78b9a9f9fa6f1a1e21d3550ff544c3054ed768893b9aa49ca8 Homepage: https://cran.r-project.org/package=PopComm Description: CRAN Package 'PopComm' (Population-Level Cell-Cell Communication Analysis Tools) Facilitates population-level analysis of ligand-receptor (LR) interactions using large-scale single-cell transcriptomic data. Identifies significant LR pairs and quantifies their interactions through correlation-based filtering and projection score computations. Designed for large-sample single-cell studies, the package employs statistical modeling, including linear regression, to investigate LR relationships between cell types. It provides a systematic framework for understanding cell-cell communication, uncovering regulatory interactions and signaling mechanisms. Offers tools for LR pair-level, sample-level, and differential interaction analyses, with comprehensive visualization support to aid biological interpretation. The methodology is described in a manuscript currently under review and will be referenced here once published or publicly available. 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Includes population projection, indices of short- and long-term population size and growth, perturbation analysis, convergence to stability or stationarity, and diagnostic and manipulation tools. Package: r-cran-popdesign Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-knitr, r-cran-magick Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-popdesign_1.1.0-1.ca2604.1_all.deb Size: 377576 MD5sum: 5f6939d5de6b263357118faf8140b2cf SHA1: 0ecee8e8f943e8f91f472a10f6f963ca879015c4 SHA256: 706b02b4bfb0b1849b79bcf1a8ef03cd0400fa5a8793ee8701b19ad8c6268dcf SHA512: df2c87967e24dd750a050ba5f07652a9431242a0c37b87bfe5f8e9d9c9232aee833dd8957ee38f0e01a8e25edc81267d2c5eb896564ad45bc9019c32a561f988 Homepage: https://cran.r-project.org/package=PoPdesign Description: CRAN Package 'PoPdesign' (Posterior Predictive (PoP) Design for Phase I Clinical Trials) The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses. Package: r-cran-poped Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4548 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-mvtnorm, r-cran-dplyr, r-cran-codetools, r-cran-magrittr, r-cran-boot, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-gtools Suggests: r-cran-testthat, r-cran-hmisc, r-cran-nlme, r-cran-ga, r-cran-desolve, r-cran-rcpp, r-cran-shiny, r-cran-rhandsontable, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-covr, r-cran-devtools, r-cran-mrgsolve Filename: pool/dists/resolute/main/r-cran-poped_0.7.0-1.ca2604.1_all.deb Size: 2575292 MD5sum: 0c26a5f7c3ed3342360a2da5205845ad SHA1: 2b0950e2700217d8a32bef09c02a99bda9ba7767 SHA256: 08694e09a2fdbc9a45fa75cf6e23af53e064ab275e560e82bcbfb696c729585d SHA512: 209fd116ee69db07ef1d4915574175cf0e0aaf8e42d65c178fa94cbb0ffbe7607921aaedb32d07f932cd851100ac945e59a5eec362121ec06557d058f19e042f Homepage: https://cran.r-project.org/package=PopED Description: CRAN Package 'PopED' (Population (and Individual) Optimal Experimental Design) Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) , and Foracchia et al. (2004) . Package: r-cran-popepi Architecture: all Version: 0.4.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2681 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-epi, r-cran-survival Suggests: r-cran-covr, r-cran-knitr, r-cran-mstate, r-cran-relsurv, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-popepi_0.4.14-1.ca2604.1_all.deb Size: 1488882 MD5sum: 6ceb479f9b5608db7a5ca0a25a71f543 SHA1: 652832e7a15a50611cf54d465fd85953c4a4fb61 SHA256: 2697b7cc95a0d5d1941c9ec946290548ec96f48583452133e21de6a2057b86eb SHA512: 3fff084a81567c45b009dcbbea3db64b74595dca09672a134567491c4f8fd056fb7bc1b868e254ac872c880a3c29a23b806a67a13cea57db2fbcfaac056bb300 Homepage: https://cran.r-project.org/package=popEpi Description: CRAN Package 'popEpi' (Functions for Epidemiological Analysis using Population Data) Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) ), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) ) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') and other computations achieved using 'data.table'. 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'PopGenHelpR' uses vcf, 'geno' (012), and csv files to generate output. Package: r-cran-popgenr Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-popgenr_0.2-1.ca2604.1_all.deb Size: 56154 MD5sum: 7be9bbf9a6aa95f47f92d41d023682dc SHA1: 9aeff09e913b1aa8143d745bd0eec8e902e57c5f SHA256: 43a09623fe2a58d3b632cb2667829e2c6f7a84e6aa6d13c3b508cdf23e84d674 SHA512: 9261e1ecd3d9d7dd59f1ec3dd10269a02dfb40f930650d2d084cb610442421095e24f2bc011b362924d60750454d6007039554052f3ab3d9aafb86a2acf86445 Homepage: https://cran.r-project.org/package=popgenr Description: CRAN Package 'popgenr' (Accompaniment to Population Genetics with R: An Introduction forLife Scientists) Provides several data sets and functions to accompany the book "Population Genetics with R: An Introduction for Life Scientists" (2021, ISBN:9780198829546). 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Method described in Ochoa and Storey (2021) . 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In the selection of ARIMA models, the most traditional methods such as correlograms or others, do not usually cover many alternatives to define the number of coefficients to be estimated in the model, which represents an estimation method that is not the best. The popstudy package contains several tools for statistical analysis in demography and time series based in Shryock research (Shryock et. al. (1980) ). 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Package: r-cran-popvar Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bglr, r-cran-qtl, r-cran-rrblup Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-popvar_1.3.2-1.ca2604.1_all.deb Size: 317850 MD5sum: 7a00cd3edd97526ec93430eb8861759b SHA1: 48c4a3532d7dce42ad05b6e3ce7c1dec4cbcc118 SHA256: a44a9c6b1225a8b08988a0cc041b4845571c3161ebc13cec17f86d521f4c2beb SHA512: ec0d9a748335820bb93ee365e6ac6cc8fffd0205ce39f32760c06f9ddac80c7c81f91d0ed51a5cbf7feadb32e29d9d956bf6375b9de4b1b1bf889c08d5859898 Homepage: https://cran.r-project.org/package=PopVar Description: CRAN Package 'PopVar' (Genomic Breeding Tools: Genetic Variance Prediction andCross-Validation) The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, ). A dataset 'think_barley.rda' is included for reference and examples. Package: r-cran-portalhacienda Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-timetk, r-cran-lubridate, r-cran-xts, r-cran-httr, r-cran-tibble, r-cran-magrittr, r-cran-zoo, r-cran-curl, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-portalhacienda_0.1.8-1.ca2604.1_all.deb Size: 190806 MD5sum: d181c503fedb54bbcb772cc75bbdc413 SHA1: 07cad6959d92c02ae3872ab82098410da67bf7a7 SHA256: e913554ba4a94ebb5660e0b08d65ee16baf52a92ef300b7db51d4a92d98257a2 SHA512: d64c7b37f147b1d78be5855cea80a410e2b9f72cf404c7b8bb0b6bab1fd27a74cba43eafa3f28b48638317e3874e3525c90286938df6bc0364154cbb355c0d4e Homepage: https://cran.r-project.org/package=PortalHacienda Description: CRAN Package 'PortalHacienda' (Acceder Con R a Los Datos Del Portal De Hacienda) Obtener listado de datos, acceder y extender series del Portal de Datos de Hacienda.Las proyecciones se realizan con 'forecast', Hyndman RJ, Khandakar Y (2008) . Search, download and forecast time-series from the Ministry of Economy of Argentina. Forecasts are built with the 'forecast' package, Hyndman RJ, Khandakar Y (2008) . 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Intended for researchers and practitioners to backtest a set of different portfolios, as well as by a course instructor to assess the students in their portfolio design in a fully automated and convenient manner, with results conveniently formatted in tables and plots. Each portfolio design is easily defined as a function that takes as input a window of the stock prices and outputs the portfolio weights. Multiple portfolios can be easily specified as a list of functions or as files in a folder. Multiple datasets can be conveniently extracted randomly from different markets, different time periods, and different subsets of the stock universe. The results can be later assessed and ranked with tables based on a number of performance criteria (e.g., expected return, volatility, Sharpe ratio, drawdown, turnover rate, return on investment, computational time, etc.), as well as plotted in a number of ways with nice barplots and boxplots. See Chapter 8 (Portfolio Backtesting) of the book: Daniel P. Palomar, "Portfolio Optimization: Theory and Application", Cambridge University Press, 2025. 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This enables the sequential use of practically any significance test, as long as the underlying data can be simulated in advance to a reasonable approximation. Lukács (2022) . Package: r-cran-postcard Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-deriv, r-cran-dplyr, r-cran-earth, r-cran-generics, r-cran-gggrid, r-cran-ggplot2, r-cran-options, r-cran-parsnip, r-cran-rlang, r-cran-rsample, r-cran-scales, r-cran-stringr, r-cran-tune, r-cran-workflowsets, r-cran-xgboost, r-cran-yardstick Suggests: r-cran-knitr, r-cran-liblinear, r-cran-mass, r-cran-ranger, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-postcard_1.1.0-1.ca2604.1_all.deb Size: 215968 MD5sum: dfa69e929bfa37c215964c05223086e0 SHA1: a176c6c08e97d611d01bc496117060f44d0bebe3 SHA256: 9144f837c230f3d9157936d2a702e214d712bb88a3f0cee2c1413d973063b678 SHA512: 1247bfc07dc1aea0799357ea70bc543abc6c1a35b383a7c87e6fd27cb58d55b64ef76dd1dc498c6a6b1fbb31b24e49cbf8bfa0c638245e5af893dd4e89aa51dd Homepage: https://cran.r-project.org/package=postcard Description: CRAN Package 'postcard' (Estimating Marginal Effects with Prognostic Covariate Adjustment) Conduct power analyses and inference of marginal effects. Uses plug-in estimation and influence functions to perform robust inference, optionally leveraging historical data to increase precision with prognostic covariate adjustment. The methods are described in Højbjerre-Frandsen et al. (2025) . Package: r-cran-postcards Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown, r-cran-rstudioapi Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-postcards_0.2.3-1.ca2604.1_all.deb Size: 3150730 MD5sum: ac5d268e0b885398fddac36297c975e1 SHA1: 8173083900f6401d4caa431bdf72bfc8b1370ccf SHA256: b045f88e4765cd9babe06ba9492f1cade1160ad9b74c0b931a4af33964fdacce SHA512: 335f24fc722dff64950c40656169cfe19bdcf5cfe0087e59d6afa07b1b11bf737c6c305a463fdc3db5a59d748abe5a6431c41a93e84b8fbf5df4e0598f792cd5 Homepage: https://cran.r-project.org/package=postcards Description: CRAN Package 'postcards' (Create Beautiful, Simple Personal Websites) A collection of R Markdown templates for creating simple and easy to personalize single page websites. Package: r-cran-postcodesior Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr Suggests: r-cran-knitr, r-cran-dplyr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-postcodesior_0.3.1-1.ca2604.1_all.deb Size: 104276 MD5sum: 01601aa8f976ea89784fd4ea758aaf7a SHA1: 8df5cb3eaa31c897db5d60967c6e1d7fe3a95271 SHA256: c699682dc4c3be8d8c138a52e8730e6079a83c4f95998ebfe33d917c230496ee SHA512: 224fca52bc1c698857df33986a19e07c209d35616527f073758fff07502495e7273c335710cac60800498767e7cae572fe6eead13bc3fd67040d240b382d600f Homepage: https://cran.r-project.org/package=PostcodesioR Description: CRAN Package 'PostcodesioR' (API Wrapper Around 'Postcodes.io') Free UK geocoding using data from Office for National Statistics. It is using several functions to get information about post codes, outward codes, reverse geocoding, nearest post codes/outward codes, validation, or randomly generate a post code. API wrapper around . Package: r-cran-postdoc Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-katex, r-cran-prismjs, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-postdoc_1.4.2-1.ca2604.1_all.deb Size: 36356 MD5sum: 9a63b19cc8cc3bb38be2a277dfc37941 SHA1: 5c4b8e57fbf67b99583b161bd08410e53d6552c7 SHA256: d55b3c11e640fffbef17567e439b7bc8a53b751068e954187cfa3c29328047f2 SHA512: 15acbecf7f6d31b31769a25c533f4ad19355a4c74e21d6ef90d2bc45c48a53be2ae21da81b62e8aa94100e0c6d4648ac608cf87b29fcb3836bb63e1163e7967f Homepage: https://cran.r-project.org/package=postdoc Description: CRAN Package 'postdoc' (Minimal and Uncluttered Package Documentation) Generates simple and beautiful one-page HTML reference manuals with package documentation. Math rendering and syntax highlighting are done server-side in R such that no JavaScript libraries are needed in the browser, which makes the documentation portable and fast to load. Package: r-cran-posterior Architecture: all Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-checkmate, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-tensora, r-cran-pillar, r-cran-distributional, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-caret, r-cran-gbm, r-cran-randomforest, r-cran-e1071, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-ggplot2, r-cran-ggdist, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-posterior_1.7.0-1.ca2604.1_all.deb Size: 980620 MD5sum: d0e47feefeb3d95cecff3699c7a0fa3b SHA1: 18aac3d8239c8dd77f5211893fa8d1973eada717 SHA256: 5f9a07c17d1d873f0927795255e00d277d3fd26e4ec681e5a6bf89ea84451f4e SHA512: 8b2a51fe5de9f6f11d9a75e137bbbc8649b2b983fff3f7967fc6e3b30220aed13d95b59135d4e0b93c2556cf47414f13bf1e4c83f5f247bacbb9314e14985e16 Homepage: https://cran.r-project.org/package=posterior Description: CRAN Package 'posterior' (Tools for Working with Posterior Distributions) Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) . Package: r-cran-posteriorbootstrap Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mass Suggests: r-cran-bh, r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-lintr, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstan, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-posteriorbootstrap_0.1.2-1.ca2604.1_all.deb Size: 517696 MD5sum: 54bf0014f6d16feac0829e67c2a75f2c SHA1: 464211870c4bcd38d694a8f7f417e814fd8a4d33 SHA256: 439cf41704bbf95eb8cc427973e5f9778303c936923b7cfe2bcf9d835178bea7 SHA512: eb1454bdf7f8b6cbc6fd13b541d32cb7b669eaf61a3225d9cfef5ca5b5380686408dc0c568a8461de20c799f1578a15a5c2152afdc5ef78f6050f3e2f00dde6f Homepage: https://cran.r-project.org/package=PosteriorBootstrap Description: CRAN Package 'PosteriorBootstrap' (Non-Parametric Sampling with Parallel Monte Carlo) An implementation of a non-parametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), "Nonparametric learning from Bayesian models with randomized objective functions" . Package: r-cran-postggir Architecture: all Version: 2.4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1691 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-refund, r-cran-denseflmm, r-cran-dplyr, r-cran-xlsx, r-cran-survival, r-cran-tidyr, r-cran-zoo, r-cran-ineq, r-cran-cosinor, r-cran-cosinor2, r-cran-abind, r-cran-accelerometry, r-cran-actcr, r-cran-actfrag, r-cran-minpack.lm, r-cran-kableextra, r-cran-ggir Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-postggir_2.4.0.2-1.ca2604.1_all.deb Size: 1015416 MD5sum: 4ea9291b3b70e90fbc2e4de416b1e24a SHA1: c8f78b9855a7daef6e5e627deb750110bc1d21f6 SHA256: 5a16293a0456355db3146bf77adebc853d51bf28192fb1093d5ceddc27d04e3e SHA512: 319bad75e81eba35f7bef12eeaaea895cfa21f9a33efbcfb017ade70d78cda6c7cf72c1933499dab133d8ac2e31c988c3ccb61f506118b23d4574bf526f79563 Homepage: https://cran.r-project.org/package=postGGIR Description: CRAN Package 'postGGIR' (Data Processing after Running 'GGIR' for Accelerometer Data) Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Package: r-cran-posthoc Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multcomp Suggests: r-cran-xtable, r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-posthoc_0.1.3-1.ca2604.1_all.deb Size: 206604 MD5sum: 441d9a59a82584a69a14ead76e3aca55 SHA1: d3c30e666ff6fb79b9d16ed621c762df84d4068d SHA256: f1df9428a6da1f889f313265c82c3582d928caf92b407e04c3b9fe4934f4593e SHA512: b4f078ce22614e82b35389556f81c001575f40752e2714482c989f87c730f62bcb000b1122b285064544ae256b3f9e43745f2ec65addf0fb945f3acd59da9c5e Homepage: https://cran.r-project.org/package=postHoc Description: CRAN Package 'postHoc' (Tools for Post-Hoc Analysis) Implements a range of facilities for post-hoc analysis and summarizing linear models, generalized linear models and generalized linear mixed models, including grouping and clustering via pairwise comparisons using graph representations and efficient algorithms for finding maximal cliques of a graph. 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Package: r-cran-postinfectious Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-postinfectious_0.1.0-1.ca2604.1_all.deb Size: 25326 MD5sum: 9ccb35600053e634b25d62e16c4f8fde SHA1: 144979ba1e56aa3a5c3134fc8db528e42ed0ff05 SHA256: 4b92f4b8d001098bdde0b261807586fef8a191b3d6cb66b348032dfa75c975e3 SHA512: 32a6a20e071d84299acfeaa7d658fc6e72f6b644850fc9150acd94f1b6d9ce8b64c4a8c8bf003a2020cea2ad91e02a6b880e15536904fe145a5914a60de41592 Homepage: https://cran.r-project.org/package=postinfectious Description: CRAN Package 'postinfectious' (Estimating the Incubation Period Distribution of Post-InfectiousSyndrome) Functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome. Package: r-cran-postlightmercury Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-crul, r-cran-purrr, r-cran-jsonlite, r-cran-rvest, r-cran-xml2 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-postlightmercury_1.2-1.ca2604.1_all.deb Size: 16068 MD5sum: db2039a879c63bafcb7dbe4f5894b167 SHA1: 77e06bdb7b86d7a39064dae00c1d1d3709e4156a SHA256: 507d9f22dfebb78221117b2a950baefc380b505e89d066cd70466cbf79979b52 SHA512: 752242c4b5f3be38e29699db6ed2c31c1e7653e8857d179ad4f8febaede1e6a1105e463909f73f3bbcf11388c21fc114abbbe615880679eb7ba377951c669c87 Homepage: https://cran.r-project.org/package=postlightmercury Description: CRAN Package 'postlightmercury' (Parses Web Pages using Postlight Mercury) This is a wrapper for the Mercury Parser API. The Mercury Parser is a single API endpoint that takes a URL and gives you back the content reliably and easily. With just one API request, Mercury takes any web article and returns only the relevant content — headline, author, body text, relevant images and more — free from any clutter. It’s reliable, easy-to-use and free. See the webpage here: . Package: r-cran-postlogic Architecture: all Version: 0.1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-postlogic_0.1.0.1-1.ca2604.1_all.deb Size: 14036 MD5sum: 7c83ac2c0372c479b9f85fad24eb1d56 SHA1: 1da79fecd8cbc89d27dadbd333b9753b0ef012a7 SHA256: 744e91e2458fc1f0bea64627b3ee6e8f3e19d648dd9f4b796126d64fef3eb41d SHA512: 599c27bdeabe8ae4eb77806883988ae89d3d744b0b4d4640863de71d572b48a53969ff9d7a42a5967977d53c402120639bf82fcbbf7999bf2cb12e75a310b58c Homepage: https://cran.r-project.org/package=postlogic Description: CRAN Package 'postlogic' (Infix and Postfix Logic Operators) Provides adds postfix and infix logic operators for if, then, unless, and otherwise. Package: r-cran-postm Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1639 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-ape Suggests: r-bioc-multtest, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-postm_1.4-1.ca2604.1_all.deb Size: 1397860 MD5sum: 923123bbed3380de3680b9e8a5686903 SHA1: 693668ba0a4d6ce3fe7bf9d8fc343b9936dd818e SHA256: 175f364fd3e68fd54bb383dfc724a3433593681bf0a1a3496a707d10d7a5a043 SHA512: da3f9e2c6c9b3e547b35e664fe2d255745f3efa6c79af2c5064595337b4c8c8bbdda4ad14ca553a6550b68059a77401d800f802430db076581a984f63eb883ff Homepage: https://cran.r-project.org/package=POSTm Description: CRAN Package 'POSTm' (Phylogeny-Guided OTU-Specific Association Test for MicrobiomeData) Implements the Phylogeny-Guided Microbiome OTU-Specific Association Test method, which boosts the testing power by adaptively borrowing information from phylogenetically close OTUs (operational taxonomic units) of the target OTU. This method is built on a kernel machine regression framework and allows for flexible modeling of complex microbiome effects, adjustments for covariates, and can accommodate both continuous and binary outcomes. Package: r-cran-postpack Architecture: all Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-mcmcse, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-r2winbugs, r-cran-r2jags, r-cran-r2openbugs, r-cran-nimble, r-cran-rjags, r-cran-jagsui Filename: pool/dists/resolute/main/r-cran-postpack_0.5.4-1.ca2604.1_all.deb Size: 514360 MD5sum: 777d2aaade409a66f83ecd7c210eb957 SHA1: a69edd4131579c0774e8277e245611b5881dc6b6 SHA256: cd66720099ee39b65f4807d61fa69f1caa60d134ce82cadee700251e5fc15cb9 SHA512: bc7bfc2cdb2aabbca40cea7a1248f0309a1dc462c69e039a3453e37743642ff3a15db9e1fb1d4741d721d14a4aafde947a485a499474a3d09e732db69684bc9c Homepage: https://cran.r-project.org/package=postpack Description: CRAN Package 'postpack' (Utilities for Processing Posterior Samples Stored in'mcmc.lists') The aim of 'postpack' is to provide the infrastructure for a standardized workflow for 'mcmc.list' objects. These objects can be used to store output from models fitted with Bayesian inference using 'JAGS', 'WinBUGS', 'OpenBUGS', 'NIMBLE', 'Stan', or even custom MCMC algorithms. Although the 'coda' R package provides some methods for these objects, it is somewhat limited in easily performing post-processing tasks for specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time for a particular question or figure. Thus, many 'postpack' functions support performing tasks on a subset of nodes, where the subset is specified with regular expressions. The functions in 'postpack' streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting. Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment of model sensitivity to changes in assumptions. Package: r-cran-potential Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-mapiso, r-cran-doparallel, r-cran-foreach Suggests: r-cran-covr, r-cran-lwgeom, r-cran-eurostat, r-cran-giscor, r-cran-mapsf, r-cran-knitr, r-cran-tinytest, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-potential_0.2.0-1.ca2604.1_all.deb Size: 1304176 MD5sum: 2aa39480c7e97c3a6c6e534d0cab6e0a SHA1: 6a8b9e3142a11906f792088a9fbcdff9bc054384 SHA256: ced78dac65ac88d3e024f32e15982871938ce7b50c6ad7d91f9797dbbfe675bc SHA512: 8381b00c1f112a5cfd7fa737c35edaff56764447bd926842ca8a9fd312a1438ff5cb2d9ba3b4f8262fdbf47a7a655dedf4d667eea3e3542220b3f05716b01282 Homepage: https://cran.r-project.org/package=potential Description: CRAN Package 'potential' (Implementation of the Potential Model) Provides functions to compute the potential model as defined by Stewart (1941) . Several options are available to customize the model, such as the possibility to fine-tune the distance friction functions or to use custom distance matrices. Some computations are parallelized to improve their efficiency. Package: r-cran-poth Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr, r-cran-netmeta, r-cran-mass Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-poth_0.3-0-1.ca2604.1_all.deb Size: 100382 MD5sum: 1e6594a6a217fe40b2b6eece8b8dac2d SHA1: 73693cf8c776d9e959ea0e7c278bafca4d04da7c SHA256: 608c255059718bd82eea5f258c0ada4e8e176ed4f35cd0901f3042d5a16fcba8 SHA512: 7cb2b39f6eecb83897b1aec075af2fdcd2bcd5c92486cf5425a97cfc9bcdd1e86962f25a32c840401233121f3fb63a9cee196a97e3478b83e02667152c8d0e51 Homepage: https://cran.r-project.org/package=poth Description: CRAN Package 'poth' (Precision of Treatment Hierarchy (POTH)) Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and best k treatments POTH can also be calculated to improve interpretation of treatment hierarchies. 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Package: r-cran-power.transform Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rlang, r-cran-nloptr Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-power.transform_1.0.4-1.ca2604.1_all.deb Size: 518160 MD5sum: c73b46db771dbd8d3b89a9132f974971 SHA1: 424996b121f939a723861f7f29fc3da6f8201889 SHA256: cfbdafad6ab5d417e8751aefaedaa6bb9578a25f813a48735a03ef2f6f8ac4ec SHA512: a470706d20112b7e892ef1a9f39bdb93a528453e5e7dfd52b2e85df72f93fae4b92852b1d1b2582d6040eeee7f125a39303fb9eb3572095e9a0b81a12874bd0d Homepage: https://cran.r-project.org/package=power.transform Description: CRAN Package 'power.transform' (Location and Scale Invariant Power Transformations) Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations allow for transforming variables with distributions distant from 0 to normality. 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Package: r-cran-powerbal Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-scales, r-cran-phytools, r-cran-treebalance, r-cran-r.utils, r-cran-memoise Suggests: r-cran-testthat, r-cran-diversitree Filename: pool/dists/resolute/main/r-cran-powerbal_0.1.0-1.ca2604.1_all.deb Size: 261528 MD5sum: 7186233a6a038c130d428749bbc2288a SHA1: ca5c687ec5bb92fc706843d06d22ed361d76d87d SHA256: 3ff7c9fe71c47fe953e4d06ea100d76a47f971286ab653e46b84faa1c97a0daa SHA512: fa78bcfc72baf732d98a61bf4426cf4ef845fc5295f08d7660d8ceb9cc03680b8267b45f25ed4e0d853755e40a0c73ca4808fb8cc2430fff5a25b1bed7e0fab3 Homepage: https://cran.r-project.org/package=poweRbal Description: CRAN Package 'poweRbal' (Phylogenetic Tree Models and the Power of Tree Shape Statistics) The first goal of this package is to provide a multitude of tree models, i.e., functions that generate rooted binary trees with a given number of leaves. 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Package: r-cran-powerbir Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-httr, r-cran-azureauth Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-powerbir_0.1.0-1.ca2604.1_all.deb Size: 91598 MD5sum: 6c0de32f37e5b72c20f981f17e0aca83 SHA1: 6a494d3858d8f74f9c37a22fae79f200bbc043fd SHA256: 056c326e9a8657c822c5aab11c5762509a317af2cd82cb10503e9503cd151971 SHA512: 2ce05f9a783b1c24f0b5a3b68b47af8f5cb752746902d235cde1d0f7afff72f9c488e261cff540e560b848e52274c474fe3c1a742a5619c91ddbab28148b69d0 Homepage: https://cran.r-project.org/package=powerbiR Description: CRAN Package 'powerbiR' (An Interface to the 'Power BI REST APIs') Makes it easy to push data to 'Power BI' using R and the 'Power BI REST APIs' (see ). 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Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) "brms: An R Package for Bayesian Multilevel Models using Stan". Package: r-cran-powerbydesign Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-gdata, r-cran-mass, r-cran-reshape2, r-cran-stringr, r-cran-plyr, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-powerbydesign_1.0.5-1.ca2604.1_all.deb Size: 32070 MD5sum: 6e5a994313e1258529d2f581a219ecaf SHA1: aa5857113471ce73fa07c3f1f273069eca9ada97 SHA256: b29cc3f8ffdd61c6630305e2d033ef4ab033215de15ace00ec8a3fdf9bf898eb SHA512: 9e573dec64597a2111dac6a04ac8e76241abf83ffd25374097b8621b6bf9b63be1b0e8453b21f00850d9556e6ef8808598cfcb3ffda18e4bb7bd36f7fc56c409 Homepage: https://cran.r-project.org/package=powerbydesign Description: CRAN Package 'powerbydesign' (Power Estimates for ANOVA Designs) Functions for bootstrapping the power of ANOVA designs based on estimated means and standard deviations of the conditions. Please refer to the documentation of the boot.power.anova() function for further details. Package: r-cran-powercomprisk Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-powercomprisk_1.0.1-1.ca2604.1_all.deb Size: 16422 MD5sum: 870e9a9783502fe41152cf193c73471b SHA1: 09bb33c862e6bc68f1aae6f92047a6491b4705e3 SHA256: d3b17479d6f9a5776ec404a4019bcd8ba4f42025a94510c8f28f7db69c88a276 SHA512: 93ed955fd34e4940150be75e9459ac8e680a1cd408dda4ab23bb9adc7fdfa6733d71408287c674935714bbfcab1ef33082fc2f54858b6d884aa2e462277c0602 Homepage: https://cran.r-project.org/package=powerCompRisk Description: CRAN Package 'powerCompRisk' (Power Analysis Tool for Joint Testing Hazards with CompetingRisks Data) A power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data. 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See Shui et al. (2025) for method details. Package: r-cran-powergrid Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-future.apply, r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-powergrid_0.5.0-1.ca2604.1_all.deb Size: 369076 MD5sum: 5a76b928bde65db56693fb8249ded86a SHA1: d133f25424d4465d4efc9aee2d299b240432bf4f SHA256: 61dd24c26166cd816b2fb11bb3f14807229f88377639642b88d1405077399847 SHA512: 60fcca86fc0f5e2c3df902f562ea2e478fcf42e27349821f2b535b05dd89d77854d3e4fef4350d6648eaec4d52291980ccdd8ff72afa08b64ce9fa1824d2baef Homepage: https://cran.r-project.org/package=powergrid Description: CRAN Package 'powergrid' (Power Analysis Across a Grid of Assumptions) Evaluate a function across a grid of parameters. The function may be evaluated once, or many times for simulation. Parallel computing is facilitated. Utilities aim at performing analyses of power and sample size, allowing for easy search of minimum n (or min/max of any other parameter) to achieve a desired minimal level of power (or maximum of any other objective). Plotting functions are included that present the dependency of n and power in relation to further assumptions. 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This includes power calculation for four two-step screening and testing procedures. It can also calculate power for GxE and GxG without any screening. Package: r-cran-powerhe Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-powerhe_1.0.1-1.ca2604.1_all.deb Size: 48812 MD5sum: 1725fe6122bb34110cf31912b9300d14 SHA1: cdecc3efeb24024968208c910b4fc72cf3d81422 SHA256: 9c761d2b0350ad27f3cd9e65b15c54b819cd88be89d424a2f6874a2022b58e15 SHA512: 4855b9d80ed9af37d80c0c9d30a8f305b6948297f135bb0664f2c4b0057d7b38348bbf0b4a0607f608037a77896edc4d0dd6953d4084f0ac3c5293fcdafd670c Homepage: https://cran.r-project.org/package=powerHE Description: CRAN Package 'powerHE' (Power and Sample Size Calculations with Hierarchical Endpoints) Calculate sample size or power for hierarchical endpoints. The package can handle any type of outcomes (binary, continuous, count, ordinal, time-to-event) and any number of such endpoints. It allows users to calculate sample size with a given power or to calculate power with a given sample size for hypothesis testing based on win ratios, win odds, net benefit, or DOOR (desirability of outcome ranking) as treatment effect between two groups for hierarchical endpoints. The methods of this package are described further in the paper by Barnhart, H. X. et al. (2024, ). 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Package: r-cran-powerlaw Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3737 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-covr, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-powerlaw_1.0.0-1.ca2604.1_all.deb Size: 3476806 MD5sum: 201318ab90053b89754c24e19b477cac SHA1: 5ee1d7df0207febb86c4d94e7b0d885b578f3d14 SHA256: d9deab932b4ab50004ed83c003a025ac2b17e0454da4aa395f12a32a2fb10644 SHA512: c0b5b184c5b0812b91da93b3d857d77a59824bef0c5f13e4b13ec286603f915a65da0fb437d9837849cce2ed81f8bfab6ecac2dee9006e5fbe3e3a4af0811233 Homepage: https://cran.r-project.org/package=poweRlaw Description: CRAN Package 'poweRlaw' (Analysis of Heavy Tailed Distributions) An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. 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Package: r-cran-powerly Architecture: all Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2586 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-splines2, r-cran-quadprog, r-cran-bootnet, r-cran-qgraph, r-cran-parabar, r-cran-ggplot2, r-cran-rlang, r-cran-mvtnorm, r-cran-patchwork Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-powerly_1.10.0-1.ca2604.1_all.deb Size: 2144926 MD5sum: ada3d2435f807505d2c9e498a0805df4 SHA1: 9622260894b91c659f417920a70f80ae7bd32521 SHA256: 5ab95dfa03b45c8152f490485f8a431814776ee64ff8ff7a577a39f0d00a6891 SHA512: d36e8b9f08266744b4edece64ffc606440a0d93c41ab0acb768ef2418c52e882871392b9d4432067e0f62458bc7417b9f4810c5c4ac3545c6d70a78c87e972c0 Homepage: https://cran.r-project.org/package=powerly Description: CRAN Package 'powerly' (Sample Size Analysis for Psychological Networks and More) An implementation of the sample size computation method for network models proposed by Constantin et al. (2023) . The implementation takes the form of a three-step recursive algorithm designed to find an optimal sample size given a model specification and a performance measure of interest. It starts with a Monte Carlo simulation step for computing the performance measure and a statistic at various sample sizes selected from an initial sample size range. It continues with a monotone curve-fitting step for interpolating the statistic across the entire sample size range. The final step employs stratified bootstrapping to quantify the uncertainty around the fitted curve. Package: r-cran-powermediation Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-powermediation_0.3.4-1.ca2604.1_all.deb Size: 207368 MD5sum: 31d6e527325327a4eb4dff058f687f59 SHA1: e68597f0f576cc32203ce3c47936d7103283152f SHA256: bc9c376d02a742cfd478301d40b13bb2a427ecdaf5e6a7ec541daf71e0beab23 SHA512: bb1486244f5308ad572702eea4bd50c54d4870d9c976337b967219038128e92e074e7f91fb6a2c217e90e7bfefbec2ccc02e94047857b2491b989266dc19d9c4 Homepage: https://cran.r-project.org/package=powerMediation Description: CRAN Package 'powerMediation' (Power/Sample Size Calculation for Mediation Analysis) Functions to calculate power and sample size for testing (1) mediation effects; (2) the slope in a simple linear regression; (3) odds ratio in a simple logistic regression; (4) mean change for longitudinal study with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in a simple Poisson regression. Package: r-cran-powernlsem Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-crayon, r-cran-lavaan, r-cran-mvtnorm, r-cran-numderiv, r-cran-pbapply, r-cran-rlang, r-cran-stringr Suggests: r-cran-knitr, r-cran-mplusautomation, r-cran-rmarkdown, r-cran-semtools, r-cran-simsem Filename: pool/dists/resolute/main/r-cran-powernlsem_0.1.2-1.ca2604.1_all.deb Size: 647242 MD5sum: 52561d0981f6fdff634ea5f80c341850 SHA1: 32f0b4db4b878818bcf5a8e62b556546b6f16f64 SHA256: f487daebc21027ce92460e616895f16c2719159b9196356b9e6d1509d1745c4f SHA512: bc736abf3aa3a8b1e61d58247218ea6153201e200b0c68a6221f9b9283aac8258abbc0d6cc556c608c71ab897a560a4ea6dbf0114cc95129d1dc139d058915ee Homepage: https://cran.r-project.org/package=powerNLSEM Description: CRAN Package 'powerNLSEM' (Simulation-Based Power Estimation (MSPE) for Nonlinear SEM) Model-implied simulation-based power estimation (MSPE) for nonlinear (and linear) SEM, path analysis and regression analysis. A theoretical framework is used to approximate the relation between power and sample size for given type I error rates and effect sizes. The package offers an adaptive search algorithm to find the optimal N for given effect sizes and type I error rates. Plots can be used to visualize the power relation to N for different parameters of interest (POI). Theoretical justifications are given in Irmer et al. (2024a) and detailed description are given in Irmer et al. (2024b) . Package: r-cran-powerpkg Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-powerpkg_1.6-1.ca2604.1_all.deb Size: 55598 MD5sum: f38d821185a3d5f06cf5e64c147eaea4 SHA1: 59b3cbd9c421ebeaee51fb0fccc9aa8bd7e1ab8b SHA256: c7d95488fdb5c11c47002d2d0f8c722a8727207f888e90187d0d1938040b5d06 SHA512: 065ae616c1b45786936d9194409c591b83efa8ae65efc94f65d5c895370113f8bdb444f5e2dfb718a49ab6cf2ae5bdc3d5ba1dc4b3d36fb0ae42af2f8e073249 Homepage: https://cran.r-project.org/package=powerpkg Description: CRAN Package 'powerpkg' (Power Analyses for the Affected Sib Pair and the TDT Design) There are two main functions: (1) To estimate the power of testing for linkage using an affected sib pair design, as a function of the recurrence risk ratios. We will use analytical power formulae as implemented in R. These are based on a Mathematica notebook created by Martin Farrall. (2) To examine how the power of the transmission disequilibrium test (TDT) depends on the disease allele frequency, the marker allele frequency, the strength of the linkage disequilibrium, and the magnitude of the genetic effect. We will use an R program that implements the power formulae of Abel and Muller-Myhsok (1998). These formulae allow one to quickly compute power of the TDT approach under a variety of different conditions. This R program was modeled on Martin Farrall's Mathematica notebook. Package: r-cran-powerpls Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositions, r-cran-fksum, r-cran-nipals, r-cran-mass, r-cran-foreach, r-cran-simukde, r-cran-ks, r-cran-mvtnorm, r-cran-proc, r-cran-caret Filename: pool/dists/resolute/main/r-cran-powerpls_0.2.1-1.ca2604.1_all.deb Size: 203788 MD5sum: 9b6662ccf7e754222ee4d25f51b62737 SHA1: 49f057e5c06e945435b0f42f654c5936a1d986b0 SHA256: 15ffbbf4191297d84748583f1259e2761f220165e3c7b7b719f3ed35564ed903 SHA512: 6df07870d781450a15f1357ff29a1160596a0ea21a4797882def608ce040f6013bc204dc1d8223f7351e0e22f4ac93bf05b911602252b715582224074e3c398e Homepage: https://cran.r-project.org/package=powerPLS Description: CRAN Package 'powerPLS' (Power Analysis for PLS Classification) It estimates power and sample size for Partial Least Squares-based methods described in Andreella, et al., (2024), . Package: r-cran-powerprior Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-laplacesdemon, r-cran-ggplot2, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-dt, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-powerprior_1.0.0-1.ca2604.1_all.deb Size: 105722 MD5sum: ac94135e03745e7a327c487e980281b3 SHA1: e01f8e9e5f0902a8887ddc3b29e39fbdafebb86a SHA256: 10c77d1b0ded516fddad62983d7395af36fed1138e762cb091b2a249a6727a38 SHA512: 9a2d46c619c61690f87a887ef46beff815b857ea0e6682993ac09fd2d28951c40878dd176bf7681804244a7ae0bd7295eac0dec06d331b68b09c0ce1858b99ef Homepage: https://cran.r-project.org/package=powerprior Description: CRAN Package 'powerprior' (Conjugate Power Priors for Bayesian Analysis of Normal Data) Implements conjugate power priors for efficient Bayesian analysis of normal data. Power priors allow principled incorporation of historical information while controlling the degree of borrowing through a discounting parameter (Ibrahim and Chen (2000) ). This package provides closed-form conjugate representations for both univariate and multivariate normal data using Normal-Inverse-Chi-squared and Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling. The conjugate framework builds upon standard Bayesian methods described in Gelman et al. (2013, ISBN:978-1439840955). Package: r-cran-powersdi Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmom, r-cran-lubridate, r-cran-nasapower Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-powersdi_1.0.0-1.ca2604.1_all.deb Size: 479104 MD5sum: 599f4b27e0473906ea77767b5c8c43bc SHA1: 99eadb6542265636243c63af276323c8b860a17c SHA256: b39d2c6aafc3913c207a4ff31426b2e6f4f032334030f248077819b8986ac042 SHA512: d5d19c37e4938896efd6cf98c4825f256ba5e27eb16d0ff21541dfee429dea033bd9556407ed334877bd3f79fd2089d10f4820168f250180bf4b9ecc5235d31a Homepage: https://cran.r-project.org/package=PowerSDI Description: CRAN Package 'PowerSDI' (Calculate Standardised Drought Indices Using NASA POWER Data) A set of functions designed to calculate the standardised precipitation and standardised precipitation evapotranspiration indices using NASA POWER data as described in Blain et al. 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Package: r-cran-powersurvepi Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-powersurvepi_0.1.5-1.ca2604.1_all.deb Size: 207124 MD5sum: 6853db21f89ffd7590aeb5fcbf6ca638 SHA1: 1dc9b2e967c87d9c4aa9fa74b3371f1ee843348d SHA256: 88cb99cd6c33644538a3a42d8ebd50587b7c747a39a04eb7a490284f3c712784 SHA512: 5cc8d56d42999ee5e5db7d431e51e9336517c77aa602358a71cbac1fac82b713831c41b8960f624b50bf4eb44cd7db72a25c82db8ea11b3413feb89cc557bca1 Homepage: https://cran.r-project.org/package=powerSurvEpi Description: CRAN Package 'powerSurvEpi' (Power and Sample Size Calculation for Survival Analysis ofEpidemiological Studies) Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study. Package: r-cran-powertools Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-powertost, r-cran-hmisc, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-powertools_1.0.0-1.ca2604.1_all.deb Size: 342958 MD5sum: d219a6c8156105fbf54a3f814ea63eb6 SHA1: 7c1bf64f5433823d50bdf69bce020083c9f69787 SHA256: 34066d43b44662eb3908dab662d55aeab17a54a345d39f2511e8976591957dc5 SHA512: b8fe85f58e263d09b3d31412fdad7117c34fcfd967bd07a50eefa37601114e312a86d31be9594ca9bb5097106b7edea9093be75f1c5be0e1c30e0b6e60174e69 Homepage: https://cran.r-project.org/package=powertools Description: CRAN Package 'powertools' (Power and Sample Size Tools) Power and sample size calculations for a variety of study designs and outcomes. Methods include t tests, ANOVA (including tests for interactions, simple effects and contrasts), proportions, categorical data (chi-square tests and proportional odds), linear, logistic and Poisson regression, alternative and coprimary endpoints, power for confidence intervals, correlation coefficient tests, cluster randomized trials, individually randomized group treatment trials, multisite trials, treatment-by-covariate interaction effects and nonparametric tests of location. Utilities are provided for computing various effect sizes. Companion package to the book "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622). Further resources available at . Package: r-cran-powertost Architecture: all Version: 1.5-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-cubature Suggests: r-cran-crossdes, r-cran-knitr, r-cran-rmarkdown, r-cran-tufte, r-cran-emmeans Filename: pool/dists/resolute/main/r-cran-powertost_1.5-7-1.ca2604.1_all.deb Size: 1676748 MD5sum: a5e8e9a2d5c5ec52592bec0f7759b4f9 SHA1: ecfe33fa44b972fbc892c942e804cd343f65613c SHA256: 225577e140ee8e55921eb6badfe87f00758e9594e790ed9bd1eaca3cf13bec42 SHA512: 7b48a8cc91c5cf636da7e0c095f73c5e4a535ed3588abb555a6d8ecfcb890fa6dd143f962907a201edb81212f7531d979c82fb1372c0c560784457c71d7250fd Homepage: https://cran.r-project.org/package=PowerTOST Description: CRAN Package 'PowerTOST' (Power and Sample Size for (Bio)Equivalence Studies) Contains functions to calculate power and sample size for various study designs used in bioequivalence studies. Use known.designs() to see the designs supported. Power and sample size can be obtained based on different methods, amongst them prominently the TOST procedure (two one-sided t-tests). See README and NEWS for further information. Package: r-cran-powriclpm Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-future.apply, r-cran-lavaan, r-cran-lifecycle, r-cran-progressr, r-cran-rlang, r-cran-ggplot2, r-cran-future Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-powriclpm_0.2.1-1.ca2604.1_all.deb Size: 237246 MD5sum: 4ce6de84c12570aa9e90d3803d7c4fd8 SHA1: 582aa9b7caccf24c8a53764c1b42eb1a43674073 SHA256: 83df5438ba5a5d9d97feef82b33522327479c5c8e6f4e2b9fafb0cb83ef573d3 SHA512: 948e139fe19957e09ffa64adb7d5e68efca584d3c94f4d89ad8b35bf9a6fbc6b55bfa1b7512bc152a4b0cf370242ad1d0d2855a0fa07d2b9e40108b5dd2d77f0 Homepage: https://cran.r-project.org/package=powRICLPM Description: CRAN Package 'powRICLPM' (Perform Power Analysis for the RI-CLPM and STARTS Model) Perform user-friendly power analyses for the random intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait autoregressive trait state (STARTS) model. The strategy as proposed by Mulder (2023) is implemented. Extensions include the use of parameter constraints over time, bounded estimation, generation of data with skewness and kurtosis, and the option to setup the power analysis for Mplus. Package: r-cran-powrpriori Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-lme4, r-cran-lmertest, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-powrpriori_0.2.0-1.ca2604.1_all.deb Size: 673726 MD5sum: 740fbeebb12bcc1c099ac83c6b25061c SHA1: da5d9996e337b9499d5e65a8bb3e8b4d94af9756 SHA256: 017f8538ea8ef98fb9a41e04dd445b66d3824c4de1a892d86ca34ed7a898b548 SHA512: 973934a53d09ec2509ab3132417372dfe2a24ad5179a18beb9fa18c27d15dd34f1a06e7b5179371a190c7e4a9b77aa1413be4020132715d679d551c7c55db15f Homepage: https://cran.r-project.org/package=PowRPriori Description: CRAN Package 'PowRPriori' (Power Analysis via Data Simulation for (Generalized) LinearMixed Effects Models) Conduct a priori power analyses via Monte-Carlo style data simulation for linear and generalized linear mixed-effects models (LMMs/GLMMs). Provides a user-friendly workflow with helper functions to easily define fixed and random effects as well as diagnostic functions to evaluate the adequacy of the results of the power analysis. Package: r-cran-ppbigdata Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-datanugget, r-cran-mass, r-cran-dplyr, r-cran-magrittr, r-cran-weights, r-cran-rstiefel, r-cran-gtools, r-cran-tourr, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-ppbigdata_1.0.0-1.ca2604.1_all.deb Size: 139894 MD5sum: 5fe24aa23f857330a021fc2236c569b6 SHA1: 8aa421afb40a33acdc01bbb3307d4a0f2aa3c524 SHA256: b818d2054d467488f68057496554a4f2c13ee774f6b364d8566d68eee73d57ca SHA512: 26798424b1c7888dd145575b0e3cc2e174e8f175a0d68183ece148e093dbd4195d5807d57d78d81d03ef01e7092a9726a573cfeebb7781ec042ac4bd603fb7e1 Homepage: https://cran.r-project.org/package=PPbigdata Description: CRAN Package 'PPbigdata' (Projection Pursuit for Big Data Based on Data Nuggets) Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data based on data nuggets. Reference papers: [1] Beavers et al., (2024) . [2] Duan, Y., Cabrera, J., & Emir, B. (2023). "A New Projection Pursuit Index for Big Data." . Package: r-cran-ppcdt Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ppcdt_0.2.0-1.ca2604.1_all.deb Size: 18698 MD5sum: 8749affe15e5ed5ececdef31c3afc34a SHA1: 6857b3790333779862bbb6188431d08ffc2a1104 SHA256: d33bcfa0d6288e89b87ed0b5e9e11e1cabb0d7c6133b8a69f5aca615323223ef SHA512: b66972082ab87e5c0284e6295e1e024bfe8b21a4545d423e22499cb845eda697822c7e399cf405ef4426724e5c5d78765dba91625565825600e8b989e0fe84d1 Homepage: https://cran.r-project.org/package=PPCDT Description: CRAN Package 'PPCDT' (An Optimal Subset Selection for Distributed Hypothesis Testing) In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) . Package: r-cran-ppci Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2567 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rarpack Filename: pool/dists/resolute/main/r-cran-ppci_0.1.5-1.ca2604.1_all.deb Size: 2514476 MD5sum: f3b82831af760a59254d908c0a4421c3 SHA1: 7766b8fc1b82644035d1fde2072beef5e9e8e81a SHA256: ff673ff68273be2fa66de05b3f562e709a27772024145dbd95657a322a5221e0 SHA512: a9d4b921e6fce8e0a34befa5dad67b2f9667470bac395545e6517cf36a394e511744b343a4813492cc30a458513f2700df7b5bf6e7b0be0eda16f5c3bfe0c892 Homepage: https://cran.r-project.org/package=PPCI Description: CRAN Package 'PPCI' (Projection Pursuit for Cluster Identification) Implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) ; minimum normalised cut, Hofmeyr (2017) ; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) . Package: r-cran-ppclust Architecture: all Version: 1.1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-inaparc, r-cran-mass Suggests: r-cran-cluster, r-cran-factoextra, r-cran-fclust, r-cran-knitr, r-cran-rmarkdown, r-cran-vegclust Filename: pool/dists/resolute/main/r-cran-ppclust_1.1.0.1-1.ca2604.1_all.deb Size: 3411942 MD5sum: 69d4628dac49b14ce035d9919fe6cbe3 SHA1: 38d9bfdeae97960559f90ca66a0744c805c3dacf SHA256: 498baca174b401f34f19e81700b55b98a5eeba81c3e0d95133cfa3dd41cb99e0 SHA512: a781671262b51ce5db043ce470af4d167dcb01d7ddd58a38780b593cbb8997d0d4b880fecf5d43efbaef1e0bcd3ed0f0ce9388009c6800abdf5608b553e2a5e0 Homepage: https://cran.r-project.org/package=ppclust Description: CRAN Package 'ppclust' (Probabilistic and Possibilistic Cluster Analysis) Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) , Possibilistic C-Means (Krishnapuram & Keller, 1993) , Possibilistic Fuzzy C-Means (Pal et al, 2005) , Possibilistic Clustering Algorithm (Yang et al, 2006) , Possibilistic C-Means with Repulsion (Wachs et al, 2006) and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package. Package: r-cran-ppcor Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ppcor_1.1-1.ca2604.1_all.deb Size: 29416 MD5sum: 6e1c3a7d30b7ca9966d72dcb6afc9f49 SHA1: 5f839d4d970ed76c36cfb7cb99ed82c82a7f3918 SHA256: 793a4b75872cff177070719208ba6c70945c45f4c4bce2136d7a77d9241c3eff SHA512: 868e3be870abaad8ae0066fa9799d52df8fc23c9a60dacdde5c7e1d16cffde50757781d84f217ae7a9a3708c5dc49a86203d4646c31f40f001beef8089a7365b Homepage: https://cran.r-project.org/package=ppcor Description: CRAN Package 'ppcor' (Partial and Semi-Partial (Part) Correlation) Calculates partial and semi-partial (part) correlations along with p-value. Package: r-cran-ppcspatial Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1852 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet, r-cran-magrittr, r-cran-pakpc2017, r-cran-scales, r-cran-shiny, r-cran-tidyr, r-cran-tmap Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ppcspatial_0.3.0-1.ca2604.1_all.deb Size: 1839360 MD5sum: 9a3247970820c574bfaf301e2f4fdc8e SHA1: b402744da26c01a0313ffb057dd48014428ddf20 SHA256: 98d25a826234ae537d5940511b7b4d4ef3dbed8ce8091ce08d6c6eec48fd9bbb SHA512: 1ac1652a68c48d345105eb93a5c311a57c18d7875b1b483835e8c98c72724e3cf635c175a3fa60b4521895a4f5a0df74e0a003b2a6827dfd8a28126b6d859231 Homepage: https://cran.r-project.org/package=ppcSpatial Description: CRAN Package 'ppcSpatial' (Spatial Analysis of Pakistan Population Census) Spatial Analysis for exploration of Pakistan Population Census 2017 (). It uses data from R package 'PakPC2017'. Package: r-cran-ppdiag Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-ppdiag_0.1.1-1.ca2604.1_all.deb Size: 265462 MD5sum: a0ab0aceb022b141e761da6edcc953c9 SHA1: 800d5c9b48ec8b83bf614050c6ce1be85d536d84 SHA256: cab67ad2f50049a80a7e832e23e8408fd04ebc2de8876ac95bb35fb8cad9fb4b SHA512: b1363f2b0535350c05292cf10dcdb02f2f659d73897c2ae5d370d73ecdff27f56c6217331216deee94f23b353d87f0fbc63ba3e6ff4dd98682626b7d08d50c89 Homepage: https://cran.r-project.org/package=ppdiag Description: CRAN Package 'ppdiag' (Diagnosis and Visualizations Tools for Temporal Point Processes) A suite of diagnostic tools for univariate point processes. 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Package: r-cran-ppendemic Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3480 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ppendemic_0.2.1-1.ca2604.1_all.deb Size: 3330662 MD5sum: c111ebf7c9272ac65d95efc926b80880 SHA1: 7f34b033993db0045f174d9703f7d3f2a1ab5d06 SHA256: 795cca9f7ab95899f12f32c58f1705cd4f02c3c4728eca3eaa3564cc45da793c SHA512: 747d06b031b9a44d8cee61566c70451de253d0ed42534f2e8e4b7f37156f387099e96b8439abe36d63a2f9e5e12ba18597017a869cfa425e80d84b4be5d55b67 Homepage: https://cran.r-project.org/package=ppendemic Description: CRAN Package 'ppendemic' (A Glimpse at the Diversity of Peru's Endemic Plants) Introducing a novel and updated database showcasing Peru's endemic plants. This meticulously compiled and revised botanical collection encompasses a remarkable assemblage of over 7,898 distinct species. The data for this resource was sourced from the work of Govaerts, R., Nic Lughadha, E., Black, N. et al., titled 'The World Checklist of Vascular Plants: A continuously updated resource for exploring global plant diversity', published in Sci Data 8, 215 (2021) . Package: r-cran-ppgam Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-evgam Filename: pool/dists/resolute/main/r-cran-ppgam_1.0.2-1.ca2604.1_all.deb Size: 130154 MD5sum: 0c99285a06edcda4d501df3ec937451e SHA1: 764dd62127ce70b5404b4a23ab909e6d5bd3c7bc SHA256: e7bebd40f3446757cbe3ec563ab73698c0ffdc1f6fc098471d792fc492565f09 SHA512: 6968850dc599208416e14c73523261eecc7e58edc3fc727d997db42ed1129ea79f1bb8c71ad6c56a5b4ac37548073d71be7f732849ea5504bc2cbdb5bea98dfb Homepage: https://cran.r-project.org/package=ppgam Description: CRAN Package 'ppgam' (Generalised Additive Point Process Models) Methods for fitting point processes with parameters of generalised additive model (GAM) form are provided. For an introduction to point processes see Cox, D.R & Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) , and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) . 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The user can specify various models of trait evolution or estimate the best fit model, include fossils, use one or multiple phylogenies for inference, and make animations of shifting suitable habitat through time. This model was first used in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and Rivera et al (2020). Lawing and Polly (2011) "Pleistocene climate, phylogeny and climate envelope models: An integrative approach to better understand species' response to climate change" Lawing et al (2016) "Including fossils in phylogenetic climate reconstructions: A deep time perspective on the climatic niche evolution and diversification of spiny lizards (Sceloporus)" Rivera et al (2020) "Reconstructing historical shifts in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.". Package: r-cran-ppitables Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-stringr, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ppitables_0.6.1-1.ca2604.1_all.deb Size: 3138132 MD5sum: be56e55980e8630d7c11dd343e7a57dd SHA1: 0a3e23b8e47f659b25abf3da5ba62eaeaee7f8be SHA256: eda374c337cb62d5678c714fda0785138dc96af6d0277c43b6b32207c5e73658 SHA512: 75c19d5fe04a6e21b22deca662d6ff5f405ce8a03703f23c1f54d7006ef46a58912b00879b009a330c40d665f48ce6bd178d03c62d87203bf2f278317107d142 Homepage: https://cran.r-project.org/package=ppitables Description: CRAN Package 'ppitables' (Lookup Tables to Generate Poverty Likelihoods and Rates usingthe Poverty Probability Index (PPI)) The Poverty Probability Index (PPI) is a poverty measurement tool for organizations and businesses with a mission to serve the poor. The PPI is statistically-sound, yet simple to use: the answers to 10 questions about a household's characteristics and asset ownership are scored to compute the likelihood that the household is living below the poverty line - or above by only a narrow margin. This package contains country-specific lookup data tables used as reference to determine the poverty likelihood of a household based on their score from the country-specific PPI questionnaire. These lookup tables have been extracted from documentation of the PPI found at and managed by Innovations for Poverty Action . Package: r-cran-ppks Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-ppks_1.0-1.ca2604.1_all.deb Size: 16886 MD5sum: fe3c2ee121ae5aeb6b2217136f5b435a SHA1: e86a8144486344b8f2bb901e6fce33eb78443048 SHA256: f1ad347cfb6828a042d53b40d83db619bffc312d88230e98cc3bec29310ff94a SHA512: 77d3d67df6c682b1b99eed9730a258a521f2ae526a28123afdc74f0ca63a1aeb1b4f13323888d8b1fe4f6a9d4f3b228a8abfad1799592c9a24dd7efa50b6b82e Homepage: https://cran.r-project.org/package=ppks Description: CRAN Package 'ppks' (Permutation Based Paired Kolmogorov-Smirnov Test) Permutation based Kolmogorov-Smirnov test for paired samples. The test was proposed by Wang W.S., Amsler C. and Schmidt, P. (2025) . Package: r-cran-pplot Architecture: all Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mbess Filename: pool/dists/resolute/main/r-cran-pplot_0.9-1.ca2604.1_all.deb Size: 27180 MD5sum: cbdffaac1a9e65578631b5b4b21b5bd7 SHA1: 9847e2994cfb1c25d87bd1816087fc4e984f5e17 SHA256: 8076c62534dcff4b253ca3cfa0bd6ecf9763168e97e636364f5554e71d7d2299 SHA512: b717ec23bdbc29cc7c2166766dbdc5ce2b79d168fe8c6d4340df15cc0b2a7944054a142178ef3d7a21f9675f8e25191990e886f00fbea8f79a91e32a7562df9a Homepage: https://cran.r-project.org/package=pplot Description: CRAN Package 'pplot' (Chronological and Ordered p-Plots for Empirical Data) Generates chronological and ordered p-plots for data vectors or vectors of p-values. The p-plot visualizes the evolution of the p-value of a significance test across the sampled data. It allows for assessing the consistency of the observed effects, for detecting the presence of potential moderator variables, and for estimating the influence of outlier values on the observed results. For non-significant findings, it can diagnose patterns indicative of underpowered study designs. The p-plot can thus either back the binary accept-vs-reject decision of common null-hypothesis significance tests, or it can qualify this decision and stimulate additional empirical work to arrive at more robust and replicable statistical inferences. Package: r-cran-ppls Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ppls_2.0.0-1.ca2604.1_all.deb Size: 359892 MD5sum: 468c795c8446bc5e2d1a4b11ed0f98de SHA1: 3dee88a6703415fa9b10f08bdcf83646b1852846 SHA256: 377027de27f278fed1ff525ceaed054f30cea091a7ac547091bf0c274b192dff SHA512: 0f428383194a8357591ba1d07d0e74eeb8ced9214378fe353fd7be3d9ff4086a9973976b1db90c6c0e8d8ed624ddaa1e64c3c2e051176aea0c2e3a2949b4bc8b Homepage: https://cran.r-project.org/package=ppls Description: CRAN Package 'ppls' (Penalized Partial Least Squares) Linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing. The method is described and applied to simulated and experimental data in Kraemer et al. (2008) . Package: r-cran-ppmf Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-censable, r-cran-dplyr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-zip Suggests: r-cran-roxygen2, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-ppmf_0.2.1-1.ca2604.1_all.deb Size: 87246 MD5sum: 80b1f87734fe909c92bbc68c6ed3193f SHA1: 62b2e064df05d09bab2dfaa0ae880d6e813cb8ee SHA256: a38307fa14d8917ee403f98e8dbba3dd18a2b8f4a3a8da1cbafbadeb73cc97a9 SHA512: 54d1381d87dca01a8a8fa73ba868c42772868705f6f3ec090157933df2bf45abcde4e660b34687fd222beadc7541d09b88541871fb02239b6953f58d259c80bd Homepage: https://cran.r-project.org/package=ppmf Description: CRAN Package 'ppmf' (Read Census Privacy Protected Microdata Files) Implements data processing described in to align modern differentially private data with formatting of older US Census data releases. The primary goal is to read in Census Privacy Protected Microdata Files data in a reproducible way. This includes tools for aggregating to relevant levels of geography by creating geographic identifiers which match the US Census Bureau's numbering. Additionally, there are tools for grouping race numeric identifiers into categories, consistent with OMB (Office of Management and Budget) classifications. Functions exist for downloading and linking to existing sources of privacy protected microdata. Package: r-cran-ppmhr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nleqslv Filename: pool/dists/resolute/main/r-cran-ppmhr_1.0-1.ca2604.1_all.deb Size: 147362 MD5sum: 46bf7521b73da2b456847d0213818411 SHA1: 47666aab077aa72821f561bf8c87e407db98aeaa SHA256: 2acd91dd0455496b9318762324bc32d3a211b5b1ca01060e37a4b82c6c3d535d SHA512: 7751fedac796e44742a3ddf540c5dbfd0da4c6342003efd3d115d2c72574af8c331a375bf60728bd0ec48cc709487c4670d61584878acef3f7c1d1733a3ee10c Homepage: https://cran.r-project.org/package=ppmHR Description: CRAN Package 'ppmHR' (Privacy-Protecting Hazard Ratio Estimation in Distributed DataNetworks) An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) . This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented. Package: r-cran-ppmlasso Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-data.table, r-cran-lattice, r-cran-plyr, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.geom Filename: pool/dists/resolute/main/r-cran-ppmlasso_1.5-1.ca2604.1_all.deb Size: 517940 MD5sum: a755d88e197b560859a570ff1d0f6e1a SHA1: 86959d4f294a4e41866e3bb4e8c41ebc3c681801 SHA256: c0ac5063cc8acf5cdadead0a45ba58945075d39d8ab5e382245cc060dc5fecba SHA512: 770921f4643cb1f565c3e4c49c51e0926e98401190290895e69b98461ce67015717d72f7daae54b3e98ae659023a059c047176cc699ee03241c483bbbe30ed35 Homepage: https://cran.r-project.org/package=ppmlasso Description: CRAN Package 'ppmlasso' (Point Process Models with LASSO-Type Penalties) Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) . Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff. Package: r-cran-ppqplan Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-ppqplan_1.1.0-1.ca2604.1_all.deb Size: 2126396 MD5sum: c6fbd8e3ffc6189f461ccf23c9283e9a SHA1: d45a91b6fc363391028bde47560ada6805d3a6f8 SHA256: b1acf8164fc24dc98db06202626d156108d115b59d68ce955d87af9cc657fdb9 SHA512: 0a4b11e8d54dcf4f3f22a13e20188216f1684419a795ea08ea5869b029dbf921c5c473b594610a424d29711b00d9dc26c3d716343fd715183a906e0d99002eca Homepage: https://cran.r-project.org/package=PPQplan Description: CRAN Package 'PPQplan' (Process Performance Qualification (PPQ) Plans in Chemistry,Manufacturing and Controls (CMC) Statistical Analysis) Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham. Package: r-cran-pprank Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pprank_0.1.1-1.ca2604.1_all.deb Size: 17474 MD5sum: 2f5af8237cbc14be7daaea53cd6dbe5f SHA1: b491cdec894c2f69a6a32b8a9aa9204000b294e8 SHA256: b5b00ddd81630bab6ad8b15a4d413aec907d6a1fb163ef756be391dc16546bb4 SHA512: 56168e745e7d2a6ffec452f43dd0ea535e3d54631546cad9a8ecb6632c212ca827af5e218ac48f725088a50154e976a8bbc83f16335d8aebebe4e0cefa47c831 Homepage: https://cran.r-project.org/package=ppRank Description: CRAN Package 'ppRank' (Classification of Algorithms) Implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions . Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values . Package: r-cran-pprep Architecture: all Version: 0.42.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hypergeo Suggests: r-cran-roxygen2, r-cran-tinytest, r-cran-cubature Filename: pool/dists/resolute/main/r-cran-pprep_0.42.3-1.ca2604.1_all.deb Size: 68370 MD5sum: af62bdfcc00dcbcd4f1ba77b38d8b7ff SHA1: fef0a4d87e3e67fe78c208c7e654e262f518f514 SHA256: 5f5d45af4dc3e33c5eb80496d1ee22caa9de0ece5f01063a12756900cdf5e880 SHA512: 6c5214bf6fabc200d5081d2a7cf31afd9e5347c12417aa154cd6d0f3b53f256388cbfece654df0ee9350a7c89bfe98dec2623f3f674451fb98b41fe9ccf0da70 Homepage: https://cran.r-project.org/package=ppRep Description: CRAN Package 'ppRep' (Analysis of Replication Studies using Power Priors) Provides functionality for Bayesian analysis of replication studies using power prior approaches (Pawel et al., 2023) . Package: r-cran-pps Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pps_1.0-1.ca2604.1_all.deb Size: 202200 MD5sum: c9b99237f9fdd65563e181152b189c5d SHA1: dc75b0dfdc1c45a352399a7f23f855b84b291624 SHA256: ef30771390f5a46a35dbc580bce8d7079520cbd4dc3633a30fafbb233d570895 SHA512: 1af5110fbb3fad81efb45768ccb41a7e712aea3efd15513bf2a124ea55aac1bce2ca067f5ba138ec31b8c2357e815bd68479606220dc808f7b5d96c1055b5fc6 Homepage: https://cran.r-project.org/package=pps Description: CRAN Package 'pps' (PPS Sampling) Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references. Package: r-cran-ppsbm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast, r-cran-clue, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-ppsbm_1.0.0-1.ca2604.1_all.deb Size: 205240 MD5sum: e4723719c2f9adca9f08802a95c1d3c0 SHA1: 1d86eb3cbcebdfce7fa93a97b36e63d6cea65705 SHA256: f0241ab5fdd724147b38e60b9ae90c820aef146351cad60286df775963cb90a3 SHA512: ca3d1ef162c215c5552ccefe927c98d9135bf980fd9a79063252d9146ab2aaba89acaa4255adf65f148e522b5b9eb9c56a82462818c1a0af9c28fb0bc0d71790 Homepage: https://cran.r-project.org/package=ppsbm Description: CRAN Package 'ppsbm' (Clustering in Longitudinal Networks) Stochastic block model used for dynamic graphs represented by Poisson processes. To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion. Y. Baraud and L. Birgé (2009). . C. Biernacki, G. Celeux and G. Govaert (2000). . M. Corneli, P. Latouche and F. Rossi (2016). . J.-J. Daudin, F. Picard and S. Robin (2008). . A. P. Dempster, N. M. Laird and D. B. Rubin (1977). . G. Grégoire (1993). . L. Hubert and P. Arabie (1985). . M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). . C. Matias, T. Rebafka and F. Villers (2018). . C. Matias and S. Robin (2014). . H. Ramlau-Hansen (1983). . P. Reynaud-Bouret (2006). . Package: r-cran-ppseq Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-furrr, r-cran-ggplot2, r-cran-plotly, r-cran-purrr, r-cran-tibble, r-cran-patchwork, r-cran-tidyr Suggests: r-cran-covr, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ppseq_0.2.5-1.ca2604.1_all.deb Size: 1412214 MD5sum: e5ace415db379dd243b053fb923c8217 SHA1: 423f5e435bb6044c82a32f1cc94fb53bdb94d409 SHA256: 2dc58ba295fabaf05b0dc2a688fee78cd211a54d3c5bad6feaf0c25640022d1a SHA512: e0b29e18419f85a4de7f68c28ef37a4fc16321760feed136402e861459f5bbaa1e6ea2e350bc0579c45d0a11d2f1f36057df14b22080382d2779f2a30d379ffd Homepage: https://cran.r-project.org/package=ppseq Description: CRAN Package 'ppseq' (Design Clinical Trials using Sequential Predictive ProbabilityMonitoring) Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs. Package: r-cran-ppsr Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-parsnip, r-cran-rpart, r-cran-withr, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ppsr_0.0.5-1.ca2604.1_all.deb Size: 401948 MD5sum: decb2df7db2ff2c34ed957d74e56194a SHA1: cc6b92e96f9c35cf146519de266102b69467e452 SHA256: b45f30a710ef29f2492f3fc5325213409c4cd78bf3ff3e9b2a3e8371329832ce SHA512: dab3b63e699b1dd2d93a0c70d416818860e63fdfbf4f7421493483033efde7b9520c6b5ba09336282a7048999a522678446c14ed1b82d940e90bb937335ad557 Homepage: https://cran.r-project.org/package=ppsr Description: CRAN Package 'ppsr' (Predictive Power Score) The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). PPS can be useful for data exploration purposes, in the same way correlation analysis is. For more information on PPS, see . Package: r-cran-pptcirc Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pptcirc_0.2.3-1.ca2604.1_all.deb Size: 148184 MD5sum: 3abc940cf2d8aa3a1958bc7054f9f303 SHA1: 1b0965779f0d5108f695f5995213711e2e30d715 SHA256: a1c54bf9f132b41e8e45c4ef11a66a30068f79f902c9afb2c495db642e7aa503 SHA512: 14cade4557b0f81d50fb3507756d0aeaf4ba20a46c373f09b51988829b2ed6bcbed56a61b10797e9d7ec35e878026bcaca9565b12732dbc743533c6bd281346d Homepage: https://cran.r-project.org/package=PPTcirc Description: CRAN Package 'PPTcirc' (Projected Polya Tree for Circular Data) Provides functionality for the prior and posterior projected Polya tree for the analysis of circular data (Nieto-Barajas and Nunez-Antonio (2019) ). Package: r-cran-ppwdeming Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ppwdeming_2.1.0-1.ca2604.1_all.deb Size: 92620 MD5sum: bc52e70e1a001e0098f9fb6e061c2f61 SHA1: 8ef8a20b67de3b7cb306445ff74d5d284a7079dd SHA256: f333bbdae33ad772ccb3c03846e7825b9228147b1dfaa4b33d6a4b7e9b2e9064 SHA512: 38662512e16ea16f7843aa48bf362665a722fb3fcfde4706301efa875cfc773252d4bf0718a5b84bfceb6a6f9970f9bcd28a0540d1580c21944681ee9d536b93 Homepage: https://cran.r-project.org/package=ppwdeming Description: CRAN Package 'ppwdeming' (Precision Profile Weighted Deming Regression) Weighted Deming regression, also known as 'errors-in-variable' regression, is applied with suitable weights. Weights are modeled via a precision profile; thus the methods implemented here are referred to as precision profile weighted Deming (PWD) regression. The package covers two settings – one where the precision profiles are known either from external studies or from adequate replication of the X and Y readings, and one in which there is a plausible functional form for the precision profiles but the exact (unknown) function must be estimated from the (generally singlicate) readings. The function set includes tools for: estimated standard errors (via jackknifing); standardized-residual analysis function with regression diagnostic tools for normality, linearity and constant variance; and an outlier analysis identifying significant outliers for closer investigation. The following reference provides further information on mathematical derivations and applications. Hawkins, D.M., and J.J. Kraker (2026). 'Precision Profile Weighted Deming Regression for Methods Comparison'. The Journal of Applied Laboratory Medicine 11, 379-392 . Package: r-cran-ppweibull Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pracma, r-cran-segmented, r-cran-msm, r-cran-nloptr Filename: pool/dists/resolute/main/r-cran-ppweibull_1.0-1.ca2604.1_all.deb Size: 75040 MD5sum: 47a38300f518d93ace7a357232a6a588 SHA1: 71b5a32e577f504392fc388901a8692882c4ad2d SHA256: 285c627a42c79a405338354d4bf1223236255b48c3258fa2ffef7d5e14b1c16d SHA512: 95847f6a1a40752eb42f6739cfba592f7cbeef2ee973adea5d339817dd7d94b658e41f38421b89a126000feedf0084e67ea7c4d1eb619678e7a63aa1eb2b99f6 Homepage: https://cran.r-project.org/package=ppweibull Description: CRAN Package 'ppweibull' (Piecewise Lifetime Models) Provides functions for estimation and data generation for several piecewise lifetime distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) for methodological details. Package: r-cran-pqa Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pqa_1.0.0-1.ca2604.1_all.deb Size: 69400 MD5sum: c0443fa5bbc48870571fe678c03f8856 SHA1: 1c3113363c51e255ca49a47f2b0c8bc9103f4f6f SHA256: 4983bde140189b829e3c9bed03e228930e166c94bfe831ea34d3106385610b4d SHA512: 8a769779ceb190013c1fa68acc472e6c93654f777e609d26a5855f8233c5c286c792e132894911cfe33786ef7c7fe12d881c2116cbb1e0142c4d164ea795f1a8 Homepage: https://cran.r-project.org/package=PQA Description: CRAN Package 'PQA' (Perform the Pearson-Quetelet Analysis on Two-Way ContingencyTables) Tools to perform Pearson-Quetelet analysis on two-way contingency tables. The package computes absolute and relative frequencies, Quetelet indices, Pearson-Quetelet decomposition, apex tables, and chi-square summaries for interpreting associations between categorical variables. Package: r-cran-pql Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pql_0.1.0-1.ca2604.1_all.deb Size: 31902 MD5sum: dd03d8a3dbff67f44af6d98f9966ef13 SHA1: 01052e35cebd8263960a11289788d378804c1109 SHA256: c38d58a259eb5178c9ca5511637897306b120c3350c5381599c120f6999bbb27 SHA512: c2d2ea017575270c3d8020bec4f6ec8369d4e9eda6c0dc464e478ed4837bd3c6d917178f7daa1345aeb12ea6ebc11169fccf9f75329cc0e95e3de3cc2b42f5ad Homepage: https://cran.r-project.org/package=pql Description: CRAN Package 'pql' (A Partitioned Quasi-Likelihood for Distributed StatisticalInference) In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) . Package: r-cran-pqtldata Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4033 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-rdpack Suggests: r-cran-dplyr, r-bioc-ensdb.hsapiens.v75, r-bioc-ensembldb, r-bioc-iranges, r-bioc-org.hs.eg.db, r-bioc-s4vectors, r-cran-venndiagram Filename: pool/dists/resolute/main/r-cran-pqtldata_0.6-1.ca2604.1_all.deb Size: 2157974 MD5sum: 9ae8609374bcf0e76e392494631444ba SHA1: bc3dde3da8c150dbbc2415fd7edb4720572c9764 SHA256: d8a76c03a24cc9e74c31d77e644e87ae0b7461acabf88a69bc765d62353b18cd SHA512: 4692e6a1da66b5ecc1da218e5b2cae68f079a2a81de4df87955f8dfc824ef06cc088342de71431b8693b08a728df077b13e0c00bea57b1ac05420fba700c3007 Homepage: https://cran.r-project.org/package=pQTLdata Description: CRAN Package 'pQTLdata' (A Collection of Proteome Panels and Metadata) It aggregates protein panel data and metadata for protein quantitative trait locus (pQTL) analysis using 'pQTLtools' (). The package includes data from affinity-based panels such as 'Olink' () and 'SomaScan' (), as well as mass spectrometry-based panels from 'CellCarta' (), 'Seer' () and 'SWATH-MS' (). The metadata encompasses updated annotations and publication details. Package: r-cran-pra Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1928 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mc2d, r-cran-minpack.lm Suggests: r-cran-base64enc, r-cran-bslib, r-cran-ellmer, r-cran-ggplot2, r-cran-htmltools, r-cran-jsonlite, r-cran-knitr, r-cran-ragnar, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinychat, r-cran-mockr, r-cran-testthat, r-cran-vitals, r-cran-withr Filename: pool/dists/resolute/main/r-cran-pra_0.4.0-1.ca2604.1_all.deb Size: 1339600 MD5sum: b8af71745ff564401fc863cca7522c9f SHA1: 315139dceb6d187548ccfc1cf6f69d6a23e49138 SHA256: 815f69f1029f6150ce08ed9c6d64b253ecf3e69037a05f67c826d905dbb7bd7e SHA512: 5ca6070d71fc64acb765a3fc7dd873e7c41773c1444ff88fd8c58bb4546e4a1dd72fce124f9d2d096447574b56cb84b5305bd875e02e5eb1f3b56d7303afd36c Homepage: https://cran.r-project.org/package=PRA Description: CRAN Package 'PRA' (Project Risk Analysis) Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, Learning Curves, Bayesian Methods, and more. Package: r-cran-praatpicture Architecture: all Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-av, r-cran-bslib, r-cran-crayon, r-cran-emur, r-cran-gifski, r-cran-gsignal, r-cran-ipa, r-cran-multitaper, r-cran-phontools, r-cran-rpraat, r-cran-rstudioapi, r-cran-shiny, r-cran-shinyjs, r-cran-soundgen, r-cran-tuner, r-cran-wrassp, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-praatpicture_1.8.0-1.ca2604.1_all.deb Size: 1159780 MD5sum: 18bee25e1ac14682bfddd3314b4b1abc SHA1: 4b933a6c15525c0068f8c220822c12e115403a25 SHA256: ab6275664e50fa8a771e3bd4684af40fc97eb6b50f75d4a3dbbb6bc51a7fac33 SHA512: 7a672e68fd1f79f19c64e9f60df189f595558a5ae9c083c4a5bb68685b01f4d4db0e3095cd0e7bd68b2cf55f80ae1906996454ae38b3509716d00259b38ddd63 Homepage: https://cran.r-project.org/package=praatpicture Description: CRAN Package 'praatpicture' ('Praat Picture' Style Plots of Acoustic Data) Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in 'Praat' using either derived signals generated directly in R with 'wrassp' or imported derived signals from 'Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of flexibility. Also provides options for embedding audio in figures and animating figures. Package: r-cran-prabclus Architecture: all Version: 2.3-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mclust Suggests: r-cran-spdep, r-cran-spatialreg, r-cran-bootstrap, r-cran-foreign, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-prabclus_2.3-5-1.ca2604.1_all.deb Size: 472202 MD5sum: b6cc65dd08639f4d30996d3560d4b960 SHA1: d36f3e4b6076b77af5fbbcdc25bc47b589c32afa SHA256: c845ec987636fd781fa0f9565ab69667d85dde14c32930196d9d7fa75577fb6c SHA512: cee9d5995fce2c9bdaa2a49f7128fb31412513230d14276785a4c03f653192d3c93cff910e7e865656944d629e12bd07dc72a1c1815f12e8badaaf7ae7099df0 Homepage: https://cran.r-project.org/package=prabclus Description: CRAN Package 'prabclus' (Functions for Clustering and Testing of Presence-Absence,Abundance and Multilocus Genetic Data) Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview. Package: r-cran-pracma Architecture: all Version: 2.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1898 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nlcoptim, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-pracma_2.4.6-1.ca2604.1_all.deb Size: 1711300 MD5sum: 797c4dabca0840633c4c8a784ba4ce08 SHA1: d9dc44369451d7028907899f9a7cb7572a021499 SHA256: 29896bcf3ae03c2b247588117e7bfdacfa9e2c87f9c3dc9e3f5116f278fae4a0 SHA512: 9ffedeb7103b3be18d0e6cb6181f8befd7a635db26dc09d29f05d6135f3aff243b6c754ae6894ec021c76491ff49f4cf0eb67cdb32303f2d093863d2b59bbf5c Homepage: https://cran.r-project.org/package=pracma Description: CRAN Package 'pracma' (Practical Numerical Math Functions) Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting. Package: r-cran-pracpac Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-glue, r-cran-fs, r-cran-rprojroot, r-cran-renv, r-cran-pkgbuild Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-pracpac_0.2.0-1.ca2604.1_all.deb Size: 322904 MD5sum: 7ae2409ad1d411cfa7fcf690f74fccd5 SHA1: ce01b4b80bec1c3d9e6e784edf6cba273085888a SHA256: eab34fd37e0b77c108b16bae63062bf8d2e851b93edc3a2509dce13dc0089409 SHA512: b5d0ffe6a065972b477ef98b0b7562101faae90b5f68e288f66e34b9259eeb6abfd0daec58aabccb0126712eafb7d9f103d574932c6be64214fb79f8919a0043 Homepage: https://cran.r-project.org/package=pracpac Description: CRAN Package 'pracpac' (Practical 'R' Packaging in 'Docker') Streamline the creation of 'Docker' images with 'R' packages and dependencies embedded. The 'pracpac' package provides a 'usethis'-like interface to creating Dockerfiles with dependencies managed by 'renv'. The 'pracpac' functionality is described in Nagraj and Turner (2023) . Package: r-cran-practicalequidesign Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-numderiv, r-cran-temporal, r-cran-tidyr Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-practicalequidesign_0.0.3-1.ca2604.1_all.deb Size: 60846 MD5sum: b43ac6cb2d02e6b17319d7206e3e61ae SHA1: 17af563e12186be8bc7b2fc9ce29852acb54e9f9 SHA256: 03b6a3837c7d5b31ba1c1eea9c33556fc7c413ee24625b63fcf8c039f42c8772 SHA512: b0bc1ba2de1a8aca7042ad338ce4ce2034470ff722618cb165ccafc9f33db4c2c8ae83d461215aa7229a82550d17db2b392a58fc173217735602810b536d544f Homepage: https://cran.r-project.org/package=PracticalEquiDesign Description: CRAN Package 'PracticalEquiDesign' (Design of Practical Equivalence Trials) Sample size calculations for practical equivalence trial design with a time to event endpoint. Package: r-cran-practicalsigni Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-np, r-cran-generalcorr, r-cran-xtable, r-cran-shapleyvalue, r-cran-nns, r-cran-randomforest, r-cran-hypergeo Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-practicalsigni_0.1.2-1.ca2604.1_all.deb Size: 496644 MD5sum: bb3ca336bdabea92508fcff00f986aa2 SHA1: 145e8a56b0f339fdda59c81076b2e61b2cfca658 SHA256: 03ff4ccfd795ae964f0d19f17cf43204c5aaaf7246fb2e4073cfd0e241a16244 SHA512: 3cc690abe59f949c4fd2901b92ac5242219da0cf42f75b138c699e25abf8fcfc006f824967eccf24df69ea74a083796370edc78f0e4ae8e7892ad692ec0f0443 Homepage: https://cran.r-project.org/package=practicalSigni Description: CRAN Package 'practicalSigni' (Practical Significance Ranking of Regressors and Exact t Density) Consider a possibly nonlinear nonparametric regression with p regressors. We provide evaluations by 13 methods to rank regressors by their practical significance or importance using various methods, including machine learning tools. Comprehensive methods are as follows. m6=Generalized partial correlation coefficient or GPCC by Vinod (2021) and Vinod (2022). m7= a generalization of psychologists' effect size incorporating nonlinearity and many variables. m8= local linear partial (dy/dxi) using the 'np' package for kernel regressions. m9= partial (dy/dxi) using the 'NNS' package. m10= importance measure using the 'NNS' boost function. m11= Shapley Value measure of importance (cooperative game theory). m12 and m13= two versions of the random forest algorithm. Taraldsen's exact density for sampling distribution of correlations added. Package: r-cran-practools Architecture: all Version: 1.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5004 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-mass, r-cran-usmap Suggests: r-cran-doby, r-cran-foreign, r-cran-kableextra, r-cran-knitr, r-cran-lpsolve, r-cran-markdown, r-cran-plyr, r-cran-pps, r-cran-rcpp, r-cran-reshape, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sampling, r-cran-samplingbook, r-cran-sp, r-cran-survey Filename: pool/dists/resolute/main/r-cran-practools_1.7.5-1.ca2604.1_all.deb Size: 3882952 MD5sum: 79b661ecce10bdc852dafa0a8f4956ad SHA1: 6b5fb22fdece4fe50d136b7af2a2dd4138f850ee SHA256: 8d24ce9a8e5ddef0141de1c5c44b542c906fe46c3a299fbbdac8367b80d36320 SHA512: 869b6b040fd1a58b5886958bc21bc4d236c036f64e07a80648bdea966d0087d2625bef21e69857e05d47ca91b0c9b5f6501109b776c44739d80412756cb153ef Homepage: https://cran.r-project.org/package=PracTools Description: CRAN Package 'PracTools' (Designing and Weighting Survey Samples) Functions and datasets to support Valliant, Dever, and Kreuter (2018), , "Practical Tools for Designing and Weighting Survey Samples". Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs, sample sizes in two-phase designs, and a stopping rule for ending data collection. A number of example data sets are included. Package: r-cran-prais Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich, r-cran-pcse Filename: pool/dists/resolute/main/r-cran-prais_1.1.4-1.ca2604.1_all.deb Size: 71302 MD5sum: 0aba65dcd3638feeb03db03474140054 SHA1: 1f439177c6686fb3ecfe37d12f944c93e222d9ad SHA256: 0de175178ba25bcd95af6543cf4d1b87cd0059173406a4dde0195a2adaadf8b4 SHA512: d450eb39dfa3d7e14f6a9507fcb95c6ffa05b05db850009b4e07c74eb3de84686211e174539ee4d6a2cd5c533f56cc7f543af0c084eaf4e946862d584936559b Homepage: https://cran.r-project.org/package=prais Description: CRAN Package 'prais' (Prais-Winsten Estimator for AR(1) Serial Correlation) The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained. Package: r-cran-praise Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-praise_1.0.0-1.ca2604.1_all.deb Size: 17826 MD5sum: aa4ffe3336c26060fae2fbb9035145a7 SHA1: 309b1235bc2753ec65c0fbed3a74c5b274dedab8 SHA256: 87b29c0d4e239d2fdd29a2baefd10d4d3c03a3feefd23a19bb2703736a436856 SHA512: ee0fdf32c1316c639117c25602481a7ff2186b031d8ce1a9cb1325b3445e4e36bca13d3c68415eef0b797d4b6acb42a9aa3e7d987201d7c28d6c215ecb014a95 Homepage: https://cran.r-project.org/package=praise Description: CRAN Package 'praise' (Praise Users) Build friendly R packages that praise their users if they have done something good, or they just need it to feel better. Package: r-cran-prakriti Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1967 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-prakriti_0.1.4-1.ca2604.1_all.deb Size: 1374666 MD5sum: 043719a26b94c4aec51635251ae49708 SHA1: a625a1e5c17c8e9f8cfc080a48567e8cd82cf21f SHA256: c5fd60f92614c9da83b8ff6c87be202fead1d6434be833dee550d4ce1cb0b55c SHA512: 3e8679c07ce778abc0f6acb593145a38bad852e7a20a842f5a353a4c14c45d54dc2225590a2ebabb79f177bb1d921833c3d1de77bde2b5ca9c52a7ad1cee822e Homepage: https://cran.r-project.org/package=prakriti Description: CRAN Package 'prakriti' (Color Palettes Inspired by India's Natural Landscapes) Curated color palettes drawn from India's natural beauty - Himalayan snow, Thar dunes, Kerala backwaters, Andaman reefs, Spiti's cold desert, Kashmir's autumn chinar, and more. Provides discrete and continuous palettes with first-class 'ggplot2' integration through scale_color_prakriti() and scale_fill_prakriti(), plus base graphics helpers for displaying palettes. Package: r-cran-prana Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-robustbase, r-bioc-minet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-prana_1.0.6-1.ca2604.1_all.deb Size: 115564 MD5sum: 2229b6d219e96a2334ca8e025f783152 SHA1: 9c226ed0f21c7fbfa7c7c126917031d107de07b0 SHA256: a4907b1afa0aa1308ce5440d6db15980ec5d315618f170c796a930262866e05e SHA512: edb50dff1ee11cbbca2033f1be5d4235eb5bb02a2e738f72c3c8c6229dc30e804a107b0ca5570d08d58d2f92c98be03c62ff49dd61124ae09e817bfcdb7a53e8 Homepage: https://cran.r-project.org/package=PRANA Description: CRAN Package 'PRANA' (Pseudo-Value Regression Approach for Network Analysis (PRANA)) A novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) . Package: r-cran-prbmsdesigns Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-prbmsdesigns_1.0.1-1.ca2604.1_all.deb Size: 85292 MD5sum: d8c65148f6edeb4adeb3da163d8100fc SHA1: b8a951b09c7ab57ff35853109e816cbda7acfbac SHA256: f91ce373214360d16af917fd95b1d956d2d5327bbbbd6031b5849a2f644deb51 SHA512: fe40876be3ad2c2785c3ab1cfadca81f3606c9f8d2c704c811ae769e09567704e0a525f1f7a01ea95135ee18005ac090cf222f811fde3de4b58c4ada6ff09377 Homepage: https://cran.r-project.org/package=PRBMSdesigns Description: CRAN Package 'PRBMSdesigns' (Partially Residual Balanced Multi-Session Designs) Provides functions for generating novel partially residual balanced multi-session designs. These designs arrange products over sessions and periods under partial balance restrictions and compute canonical efficiency factors for direct and residual (carryover) effects. For general background on PRBMS and related crossover design literature, see Aggarwal and Jha (2006) and Fardos et al. (2023) . Package: r-cran-prcr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-tibble, r-cran-irr, r-cran-lpsolve, r-cran-purrr, r-cran-class, r-cran-forcats, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-prcr_0.2.1-1.ca2604.1_all.deb Size: 305934 MD5sum: 71ba85b1b0afe93c734de5726a93fb97 SHA1: 06ed8e3aaa7ccf5bbcd9a2d519564beed78c4a15 SHA256: ae5a50016ea511da25c5e25c87a9911c8e0ca90069503811f1b05ba4e491214b SHA512: adaebeae903320ac9369ca9560ff5e1ec6a875c61ca1b537739103c1305af6af14ee1f561682295ec538f45ce8a0fb559a7b596fd44705c8f59bd171399e98c8 Homepage: https://cran.r-project.org/package=prcr Description: CRAN Package 'prcr' (Person-Centered Analysis) Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) , hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure. Package: r-cran-pre Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-earth, r-cran-formula, r-cran-glmnet, r-cran-partykit, r-cran-rpart, r-cran-stringr, r-cran-survival, r-cran-matrix, r-cran-matrixmodels Suggests: r-cran-interp, r-cran-doparallel, r-cran-foreach, r-cran-glmertree, r-cran-mlbench, r-cran-testthat, r-cran-mboost, r-cran-ggplot2, r-cran-caret, r-cran-proc, r-cran-knitr, r-cran-rmarkdown, r-cran-mice, r-cran-shape, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-pre_1.0.8-1.ca2604.1_all.deb Size: 1064284 MD5sum: 914753a215ed4353e12dc13a2131f086 SHA1: f44b7f79369dee2fff3bf18bae4bbb79dd0f5ccb SHA256: e56664055620216183e1b992c13472f28e038ed078ede3b14ea288ed16cb2ec4 SHA512: 5da29a69b72d8b684713154fd8d2cf9d9eff8b7592f052222f0b4ea047521ed3fcb53b5246cb280b450c37539f45f1c68db8aa78b83f985583e784bad06af8a2 Homepage: https://cran.r-project.org/package=pre Description: CRAN Package 'pre' (Prediction Rule Ensembles) Derives prediction rule ensembles (PREs). Largely follows the procedure for deriving PREs as described in Friedman & Popescu (2008; ), with adjustments and improvements described in Fokkema (2020; ) and Fokkema & Strobl (2020; ). The main function pre() derives prediction rule ensembles consisting of rules and/or linear terms for continuous, binary, count, multinomial, survival and multivariate continuous responses. Function gpe() derives generalized prediction ensembles, consisting of rules, hinge and linear functions of the predictor variables. Package: r-cran-precipe Architecture: all Version: 3.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-openair, r-cran-raster, r-cran-scales, r-cran-twc Suggests: r-cran-cowplot, r-cran-foreach, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-precipe_3.0.3-1.ca2604.1_all.deb Size: 2213682 MD5sum: 9683749373c591199499994e572cd5db SHA1: f2b6d67de598f784e1b84cf2f0aa809c4d01aa56 SHA256: 559acad8252f532c955cf06a1571637c115c681e8cfa7cfea6abb2cfffb20976 SHA512: bc8d54707592b580f2837e271b57af111f21763ee601a6f558698131e108e9c4175cdd35798268dfba4ef55ecdc421743c588baab967cf3e94c79f6c8029b5d1 Homepage: https://cran.r-project.org/package=pRecipe Description: CRAN Package 'pRecipe' (Precipitation R Recipes) An open-access tool/framework to download, validate, visualize, and analyze multi-source precipitation data. 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Package: r-cran-precisely Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3075 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinythemes, r-cran-tidyr Suggests: r-cran-covr, r-cran-ggrepel, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-precisely_0.1.2-1.ca2604.1_all.deb Size: 2322104 MD5sum: bb0f15c20799b33c65f23db985ab908e SHA1: 9ceafc5303406078338f9d9bf628834b9346cdd4 SHA256: 5740cb3e2a5191746efbbf752c5938126f7a8bb4e2e40ec1743a6c7fdda86529 SHA512: 5f20b005890bb02127b1363dca965118080c0598f277ea11e9a4c98d1c2f0cb6a45a38109ab70b98d5a7766c0f4b1c467336b9d0cf8df9f518c2415884685db3 Homepage: https://cran.r-project.org/package=precisely Description: CRAN Package 'precisely' (Estimate Sample Size Based on Precision Rather than Power) Estimate sample size based on precision rather than power. 'precisely' is a study planning tool to calculate sample size based on precision. Power calculations are focused on whether or not an estimate will be statistically significant; calculations of precision are based on the same principles as power calculation but turn the focus to the width of the confidence interval. 'precisely' is based on the work of 'Rothman and Greenland' (2018). Package: r-cran-preciseplacement Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-preciseplacement_0.2.0-1.ca2604.1_all.deb Size: 299388 MD5sum: a2b54c5d014b6042fbd133daa25cea14 SHA1: 83d5cb2bb152b49e9e0fe6cba5df777a355a83a6 SHA256: 9a6eb71c25058d4a591d0c1d120dc245bfb103b8db372a6102691de9a1405338 SHA512: 7ed405b186f892fff8a5ccf59da25de70d286e1b8c761124cda77d4c6e05cfec4e0774921598026adb1550bb92b9ebdae7bb01b36452c48403edaaf5b1b8993e Homepage: https://cran.r-project.org/package=precisePlacement Description: CRAN Package 'precisePlacement' (Suite of Functions to Help Get Plot Elements Exactly Where YouWant Them) Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par(). Package: r-cran-precmed Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gbm, r-cran-gam, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-mgcv, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-survival, r-cran-randomforestsrc Filename: pool/dists/resolute/main/r-cran-precmed_1.1.0-1.ca2604.1_all.deb Size: 1006798 MD5sum: 5f56e98153d7d813e85875eadf1e6018 SHA1: 31b156f3567befc742fffb339b3766143af40c42 SHA256: 165626f54096ff22977f1485370f05d26f053f22656d06f65208e7f5673a3212 SHA512: 3f2affcc056866a12dd3be1f43075fc586524c304471f186f8e712b1e362776e4c0818abf57a86dddf8f4ffabf1d6b93bcb920211b2a1d4d63b3ab51b9bfdeae Homepage: https://cran.r-project.org/package=precmed Description: CRAN Package 'precmed' (Precision Medicine) A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) . 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This package provides git hooks for common tasks like formatting files with 'styler' or spell checking as well as wrapper functions to access the 'pre-commit' executable. Package: r-cran-precviasbr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-rlang Suggests: r-cran-sf Filename: pool/dists/resolute/main/r-cran-precviasbr_0.1.0-1.ca2604.1_all.deb Size: 201766 MD5sum: c9b90d6e65db0e4119ef4f6400282b0a SHA1: 9fd60bf21357dc02d66e37522f0f71affd2a34fa SHA256: 7f08024a03aea3cdd9030da5c538022763b962baa5926f96e5118950720055ac SHA512: d95d92d1f9def51983fbc3953c3f6a3bd058ff977728b28b062e3c857a5499cd1a7f972dd7c42cb93294e8320ad32951f76708184a12d51b0aeb8eb061effce5 Homepage: https://cran.r-project.org/package=precviasBR Description: CRAN Package 'precviasBR' (Spatial Data of Road Precariousness in Brazil) Fornece acesso eficiente à malha espacial de precariedade viária brasileira. O pacote realiza o download em cache e a leitura otimizada (via Apache Arrow) de arquivos Parquet particionados, contendo o cruzamento de variáveis de infraestrutura do Entorno do Censo Demográfico 2022 (IBGE) com a malha viária aberta do Overture Maps. [English] Provides efficient access to the spatial network of road precariousness in Brazil. The package performs cached downloads and optimized reading (via Apache Arrow) of partitioned Parquet files. These files contain the intersection of infrastructure variables from the 2022 Demographic Census (IBGE) with the open street network from Overture Maps. Methodology and datasets are detailed in Passos (2026) . Package: r-cran-predfairness Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-caret Filename: pool/dists/resolute/main/r-cran-predfairness_0.1.0-1.ca2604.1_all.deb Size: 327296 MD5sum: 45e616515cbacb9beea428426d5df94e SHA1: 8bc2c996e7c1684fc079bfa5ed70bc9effcc3e6f SHA256: f451d3bec8c18aa396f8f53e61f6036123117f22797cd32f779aac93afeeac08 SHA512: 067dec7a5efda78dc4de5b6fff0dc50e0a15068a4c5b13acd7d7652638ad20c87e8c884b64a7c334c99ec58eea7ac787ec04e69975d946464dc7b56581982f4c Homepage: https://cran.r-project.org/package=predfairness Description: CRAN Package 'predfairness' (Discrimination Mitigation for Machine Learning Models) Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package is described by Kamiran, Karim and Zhang (2012) . Package: r-cran-predhy.gui Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-data.table, r-cran-dt, r-cran-predhy, r-cran-bglr, r-cran-pls, r-cran-glmnet, r-cran-xgboost, r-cran-lightgbm, r-cran-foreach, r-cran-doparallel, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-predhy.gui_2.1.1-1.ca2604.1_all.deb Size: 2307568 MD5sum: 1cf5272216d8049e7604caba26fef4d8 SHA1: 174ca39385dd5335dfa8dd242765b325ce23fafe SHA256: 2852f4ef58658cee6e4e0ceb28502fc87eb20fcc972f8727cd262f2ad9d17467 SHA512: 55cc75e3f914df24ddb7ef9076148d12113f591f424f1dd3bd173c24d582748eaa312cfb719fef7ae21395fc960d3712e75bccac1edd1fc58de101049af4f48e Homepage: https://cran.r-project.org/package=predhy.GUI Description: CRAN Package 'predhy.GUI' (Genomic Prediction of Hybrid Performance with Graphical UserInterface) Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM. GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) ; Xu S (2017) ). A complete manual for this package is provided in the manual folder of the package installation directory. You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI"). Package: r-cran-predhy Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bglr, r-cran-pls, r-cran-glmnet, r-cran-xgboost, r-cran-lightgbm, r-cran-foreach, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-predhy_2.1.2-1.ca2604.1_all.deb Size: 1262842 MD5sum: c0c43a4e1434c74c0b479c2df2f23987 SHA1: 7d0ac976fa828b9bbfd724aaab898e6335b68582 SHA256: 9d5a67bd398079f6e87036f86a4a85d8711baf67b174e4574d5380ff24d3657d SHA512: f9c75987461a72f1c1c10ff6f6daefaf242ebc01a3c61abd16959eb9ed43d18e91682d2a0016300ec78ea71b3eec72868fc09d1beb6dee912b7ba1fba4be305b Homepage: https://cran.r-project.org/package=predhy Description: CRAN Package 'predhy' (Genomic Prediction of Hybrid Performance) Performs genomic prediction of hybrid performance using eight statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) ; Xu Y et al (2021) ). Package: r-cran-predict3d Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rgl, r-cran-dplyr, r-cran-ggiraphextra, r-cran-modelr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-magrittr, r-cran-reshape2, r-cran-plyr, r-cran-tidyr Suggests: r-cran-moonbook, r-cran-th.data, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-predict3d_0.1.6-1.ca2604.1_all.deb Size: 548470 MD5sum: a70a2cd790fa3649a62b757412567cee SHA1: 33f5b895b8b8b97e01da7506f33f43351779e6d9 SHA256: 654c5631388a59b6cbe59f4da817b70ee4cc6db4f0fe2b340aef0b8ce9467787 SHA512: 74410cede04053e76db5d47a50bbe4232615577379ab95125dfe68e0b1350898a80353fca73041d585835e5410df2fafadc51b47828e8762e5f941f74e2253ed Homepage: https://cran.r-project.org/package=predict3d Description: CRAN Package 'predict3d' (Draw Three Dimensional Predict Plot Using Package 'rgl') Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'. Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess). Package: r-cran-predictabel Architecture: all Version: 1.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-rocr, r-cran-pbsmodelling, r-cran-lazyeval Filename: pool/dists/resolute/main/r-cran-predictabel_1.2-4-1.ca2604.1_all.deb Size: 177338 MD5sum: 9ede69efd9b33b1ddccc6936b5502af2 SHA1: 2ec8910c2c7852dfc1da1adc5d8deba448d3d8d7 SHA256: f55fa17cdf3d821c7966e4a4808564506c730b4a750a0fc63936ae822f738144 SHA512: 830571c5e15af25a67b3613ce3b0db76b71757f43be7a629128135c4d0f50dbd4a8650e104c4db88cb4410e7106a0223d473dd26265da33cb0eefa26ecd5cb7a Homepage: https://cran.r-project.org/package=PredictABEL Description: CRAN Package 'PredictABEL' (Assessment of Risk Prediction Models) We included functions to assess the performance of risk models. The package contains functions for the various measures that are used in empirical studies, including univariate and multivariate odds ratios (OR) of the predictors, the c-statistic (or area under the receiver operating characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test, reclassification table, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Also included are functions to create plots, such as risk distributions, ROC curves, calibration plot, discrimination box plot and predictiveness curves. In addition to functions to assess the performance of risk models, the package includes functions to obtain weighted and unweighted risk scores as well as predicted risks using logistic regression analysis. These logistic regression functions are specifically written for models that include genetic variables, but they can also be applied to models that are based on non-genetic risk factors only. Finally, the package includes function to construct a simulated dataset with genotypes, genetic risks, and disease status for a hypothetical population, which is used for the evaluation of genetic risk models. Package: r-cran-prediction Architecture: all Version: 0.3.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-prediction_0.3.18-1.ca2604.1_all.deb Size: 231346 MD5sum: 843a128005a925f7b36b179a6e176398 SHA1: 22ad3d810d972ee623d10228b3f0cd28305b3578 SHA256: c378a8e45e3a3ff66bb9339b73bc39b5cda0135f30cdca2d2867161c897b5573 SHA512: f0e57b6e60193cf77170ea2c3d7eb0260c00e1b962cabfe902f0934131ef49ca72b0619eda883d1d48fb4b4f6f34ae1723c5e42458a972092f9a18e0dfd47df2 Homepage: https://cran.r-project.org/package=prediction Description: CRAN Package 'prediction' (Tidy, Type-Safe 'prediction()' Methods) A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' . The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing. Package: r-cran-predictioninterval Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mbess, r-cran-mass, r-cran-pbapply Filename: pool/dists/resolute/main/r-cran-predictioninterval_1.0.0-1.ca2604.1_all.deb Size: 61044 MD5sum: d28e139e869a95da6959c9d71760b33a SHA1: 28f47d5955f1899149509a48ff8be448b33fb9a8 SHA256: 3ffcb53ff6e5e052a8ec47c715ffc723581e918d453b9a1755e1308a941fd38e SHA512: 6d67d3e1fe55faa6e668f17da85054944898b5b5b1e8372eccec3b14ed00ed3242b85dee1a25812100c1c284e04afd164c8f9f03280ee2b2c31b02a76b44e8b4 Homepage: https://cran.r-project.org/package=predictionInterval Description: CRAN Package 'predictionInterval' (Prediction Interval Functions for Assessing Replication StudyResults) A common problem faced by journal reviewers and authors is the question of whether the results of a replication study are consistent with the original published study. One solution to this problem is to examine the effect size from the original study and generate the range of effect sizes that could reasonably be obtained (due to random sampling) in a replication attempt (i.e., calculate a prediction interval). This package has functions that calculate the prediction interval for the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean. 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Features different preprocessing methods to homogenize variance and to remove trend and seasonality. Also has the potential to bring together different predictive models to make comparatives. Features ARIMA and Data Mining Regression models (using caret). Package: r-cran-pref Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jpeg Filename: pool/dists/resolute/main/r-cran-pref_0.4.0-1.ca2604.1_all.deb Size: 159598 MD5sum: db3a2cabca6a69bcf5f694249d3c7f60 SHA1: ac3a280471ed5e6e07b01df63cfadde4fde08a0f SHA256: 9f6d2779a7c01b7e7d0e163f556050eb3b288f017e604795df1845d1007e4788 SHA512: 796ea2e6a0b451b9346da660c784141fe142fbe029feb7fe792e266cb97da5ce954246170e759afc9df428ed26da473ae59e4733b126fe9ac1aa2503bff6de5c Homepage: https://cran.r-project.org/package=pref Description: CRAN Package 'pref' (Preference Voting with Explanatory Graphics) Implements the Single Transferable Vote (STV) electoral system, with clear explanatory graphics. The core function stv() uses Meek's method, the purest expression of the simple principles of STV, but which requires electronic counting. It can handle votes expressing equal preferences for subsets of the candidates. A function stv.wig() implementing the Weighted Inclusive Gregory method, as used in Scottish council elections, is also provided, and with the same options, as described in the manual. The required vote data format is as an R list: a function pref.data() is provided to transform some commonly used data formats into this format. References for methodology: Hill, Wichmann and Woodall (1987) , Hill, David (2006) , Mollison, Denis (2023) , (see also the package manual pref_pkg_manual.pdf). Package: r-cran-prefer Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmc, r-cran-entropy Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-prefer_0.1.3-1.ca2604.1_all.deb Size: 160368 MD5sum: 554975bf422ca6f2dc38010088ecb414 SHA1: 7818105c75920e0af89bf3c8f13f6271b67dca03 SHA256: 444dee7fc36428e509a5891965de865b65a7b8ea32494e1bc74547a041f02c96 SHA512: ddd0ba55a001d1e97164f5b27b06938dd1987e791f6190ce822add0b3a4f1c060d1dd62351d8b302086ca65dae1afe8c4b3d1c0c173b45163f94ef3c5cb56c30 Homepage: https://cran.r-project.org/package=prefeR Description: CRAN Package 'prefeR' (R Package for Pairwise Preference Elicitation) Allows users to derive multi-objective weights from pairwise comparisons, which research shows is more repeatable, transparent, and intuitive other techniques. These weights can be rank existing alternatives or to define a multi-objective utility function for optimization. Package: r-cran-preference Architecture: all Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-preference_1.1.6-1.ca2604.1_all.deb Size: 147910 MD5sum: 5883801674eabd1cab3565e2c44e8f01 SHA1: 6d98c3e87414d04d460a9be2aa560964ac15d26e SHA256: bcca22839e5b7250bf0cfd32fc1d4422bfd36a675cb6c5ef29ba997b84ba9961 SHA512: c77298df993b425f0f08bbd9bc8864ae25be40ac4f53627d27f484852c41c8a795a4bc5cc9724890dac268678b4a2c48ec9711362bb2a8f2d1f2241b16252e97 Homepage: https://cran.r-project.org/package=preference Description: CRAN Package 'preference' (2-Stage Preference Trial Design and Analysis) Design and analyze two-stage randomized trials with a continuous outcome measure. The package contains functions to compute the required sample size needed to detect a given preference, treatment, and selection effect; alternatively, the package contains functions that can report the study power given a fixed sample size. Finally, analysis functions are provided to test each effect using either summary data (i.e. means, variances) or raw study data . 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See Nicholas Mattei and Toby Walsh "PrefLib: A Library of Preference Data" (2013) . 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The package provides methods to visualise the preference distribution of one contest with bar charts and pairwise comparisons of two contestants, as well as methods to visualise multiple contests through 2D and high-dimensional simplex plots both statically and interactively. HD simplex displays are implemented via projection methods using the 'tourr' and 'detourr' packages, enabling dynamic exploration of high-dimensional preference structure. For more details on HD simplex projection, see Wickham et al. (2011) . Package: r-cran-pregnancy Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-anytime, r-cran-cli, r-cran-dplyr, r-cran-lubridate, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-pregnancy_0.2.1-1.ca2604.1_all.deb Size: 104948 MD5sum: f40d5a5828efb12f62e3382a2422f98f SHA1: a06e50d74d397f1edb69d1531ed4882a736ecdca SHA256: c39259d219c2010a628ba89ef78c209ece5c0938f8caa16b96dcf3e5759f09ef SHA512: e828480c58184adb6c1a8cea64839df2d53dca6bf0105f8b61c9eade7136a8d65947683e5407d44dd8d7aa0ab8512138bee438037f0f0543dacc78a0ec328b90 Homepage: https://cran.r-project.org/package=pregnancy Description: CRAN Package 'pregnancy' (Calculate and Track Dates and Medications During Pregnancy) Provides functionality for calculating pregnancy-related dates and tracking medications during pregnancy and fertility treatment. Calculates due dates from various starting points including last menstrual period and IVF (In Vitro Fertilisation) transfer dates, determines pregnancy progress on any given date, and identifies when specific pregnancy weeks are reached. Includes medication tracking capabilities for individuals undergoing fertility treatment or during pregnancy, allowing users to monitor remaining doses and quantities needed over specified time periods. Designed for those tracking their own pregnancies or supporting partners through the process, making use of options to personalise output messages. For details on due date calculations, see . 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Add in external HTML document within 'rmarkdown' rendered HTML doc. Package: r-cran-prenoms Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4095 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-prenoms_0.0.1-1.ca2604.1_all.deb Size: 4142156 MD5sum: 1ea289a7d3c34913ddc36a133a0a1c73 SHA1: 8df084c236c9f15b234dbef55051344ba4a4a202 SHA256: 83d2b5dc5b3c1d70f64947a83f6bbebc31a6687b38b62b4d809ba00aa2ef47df SHA512: b871c0173abb4bbbef688f5439320d2619b2355ddd73b6fc793741796f0465257136e75ec038dab81473f4de50ea713f7ff44b0a75081b186cf1377e585da7cd Homepage: https://cran.r-project.org/package=prenoms Description: CRAN Package 'prenoms' (Names Given to Babies in Quebec Between 1980 and 2020) A database containing the names of the babies born in Quebec between 1980 and 2020. Package: r-cran-prepdat Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-reshape2, r-cran-psych Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-prepdat_1.0.8-1.ca2604.1_all.deb Size: 117144 MD5sum: f9baa8134c27a7f8c01dfef52206fead SHA1: 6a64d1cd72fba30efedb85e063eadc8b1c9633b5 SHA256: 53fb0ff7ea9d97692d49f0d087e49617128482d24b7142d66a0a5dc04a5d8ed4 SHA512: 64b8a7c2421f87d5d2bbc3e675b3953a9aa04f1374e44e4201a67ee232fb2eb460e8f5cf60b725dee2c6a29f100c766affe8a26c44557409ad015020c4c16973 Homepage: https://cran.r-project.org/package=prepdat Description: CRAN Package 'prepdat' (Preparing Experimental Data for Statistical Analysis) Prepares data for statistical analysis (e.g., analysis of variance ;ANOVA) by enabling the user to easily and quickly merge (using the file_merge() function) raw data files into one merged table and then aggregate the merged table (using the prep() function) into a finalized table while keeping track and summarizing every step of the preparation. The finalized table contains several possibilities for dependent measures of the dependent variable. Most suitable when measuring variables in an interval or ratio scale (e.g., reaction-times) and/or discrete values such as accuracy. Main functions included are file_merge() and prep(). The file_merge() function vertically merges individual data files (in a long format) in which each line is a single observation to one single dataset. The prep() function aggregates the single dataset according to any combination of grouping variables (i.e., between-subjects and within-subjects independent variables, respectively), and returns a data frame with a number of dependent measures for further analysis for each cell according to the combination of provided grouping variables. Dependent measures for each cell include among others means before and after rejecting all values according to a flexible standard deviation criteria, number of rejected values according to the flexible standard deviation criteria, proportions of rejected values according to the flexible standard deviation criteria, number of values before rejection, means after rejecting values according to procedures described in Van Selst & Jolicoeur (1994; suitable when measuring reaction-times), standard deviations, medians, means according to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data frame prep() returns can also be exported as a txt file to be used for statistical analysis in other statistical programs. 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For such situations, partially replicated (p-Rep) designs have wide application potential as only a proportion of the test lines are replicated at each environment. A collection of several utility functions related to p-Rep designs have been developed. Here, the package contains six functions for a complete stepwise analytical study of these designs. Five functions pRep1(), pRep2(), pRep3(), pRep4() and pRep5(), are used to generate five new series of p-Rep designs and also compute average variance factors and canonical efficiency factors of generated designs. A fourth function NCEV() is used to generate incidence matrix (N), information matrix (C), canonical efficiency factor (E) and average variance factor (V). This function is general in nature and can be used for studying the characterization properties of any block design. A construction procedure for p-Rep designs was given by Williams et al.(2011) which was tedious and time consuming. Here, in this package, five different methods have been given to generate p-Rep designs easily. Package: r-cran-prepkit Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-prepkit_0.1.1-1.ca2604.1_all.deb Size: 458688 MD5sum: 2a46f196a3874098cc6e68334e51b194 SHA1: 5d0b2dc3c135cb66b974a2a8f45502926f3d91fb SHA256: 3306d9c7322cf522c259b480559a15ca0a6d360e3b96098e18e68c9c07ee29bd SHA512: bb4ef8c00ab920cb92d29701dda6fea4be86c01f6872a34caeeae60fbce8b3b1e89d23e38f6240c543ac5172debc5762d258085eca973cf8d93b93bb1fbcb8e9 Homepage: https://cran.r-project.org/package=prepkit Description: CRAN Package 'prepkit' (Data Normalization and Transformation) Provides functions for data normalization and transformation in preprocessing stages. Implements scaling methods (min-max, Z-score, L2 normalization) and power transformations (Box-Cox, Yeo-Johnson). Box-Cox transformation is described in Box and Cox (1964) , Yeo-Johnson transformation in Yeo and Johnson (2000) . 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Package: r-cran-preseqr Architecture: all Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom Filename: pool/dists/resolute/main/r-cran-preseqr_4.0.0-1.ca2604.1_all.deb Size: 227280 MD5sum: 7d99fc3eb5e2fe439c18b59a5ef3a50a SHA1: 05621ca7053f1ddc6d4883936d42d0744a408b78 SHA256: f322e9a5dc9dbc46de9695a7aa90a9e118104382adfe2377828079a1ff3d40de SHA512: b78ff67b8c8d9d81952989272ec011744b82777c2aeca57835ac0b0e13af726dbe8f0f586a1f215d6b892ee653741bac1ba304934544e9a896de1087dcae25fb Homepage: https://cran.r-project.org/package=preseqR Description: CRAN Package 'preseqR' (Predicting Species Accumulation Curves) Originally as an R version of Preseq , the package has extended its functionality to predict the r-species accumulation curve (r-SAC), which is the number of species represented at least r times as a function of the sampling effort. 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Package: r-cran-presiduals Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-formula, r-cran-rms, r-cran-sparsem Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-presiduals_1.0-2-1.ca2604.1_all.deb Size: 230544 MD5sum: e319c5a7434348c4eaf89488658d25b2 SHA1: 31a550186f8170c7c306b8c906fbf11d47a93936 SHA256: 5dfc9de5668bdb9a05789079897faa5c6da31f0fc2db8c0dd161dac6d15d3faf SHA512: 110a7efdb8eade2b954843db3616adbf7c805c40b60aaaaeb132e74422d418dd9279937120ad7d419329ae167184d2efdcbd21629be60b77b388aab0b42628aa Homepage: https://cran.r-project.org/package=PResiduals Description: CRAN Package 'PResiduals' (Probability-Scale Residuals and Residual Correlations) Computes probability-scale residuals and residual correlations for continuous, ordinal, binary, count, and time-to-event data Qi Liu, Bryan Shepherd, Chun Li (2020) . Package: r-cran-presize Architecture: all Version: 0.3.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 650 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kappasize, r-cran-shiny Suggests: r-cran-binom, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-hmisc, r-cran-knitr, r-cran-magrittr, r-cran-markdown, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-shinytest, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-presize_0.3.11-1.ca2604.1_all.deb Size: 408428 MD5sum: d34cb42cd5ed5b5fd3d193e1e90f2ce3 SHA1: efced3095a72b825ceb1d3e45f0b1ca2364650ce SHA256: 38bd1c35744709a548bfaff8aeb5b80eacb2aa5773cd49988c719ee22a00487d SHA512: 0bc8756a9adda33aefa21949dabf0aa6f37a097e8e5b3ddf12713a0227f45505234099e4ae590f1bb36dee31269535f6f765ff7cb6074fb82b24f88c257f6656 Homepage: https://cran.r-project.org/package=presize Description: CRAN Package 'presize' (Precision Based Sample Size Calculation) Bland (2009) recommended to base study sizes on the width of the confidence interval rather the power of a statistical test. The goal of 'presize' is to provide functions for such precision based sample size calculations. For a given sample size, the functions will return the precision (width of the confidence interval), and vice versa. Package: r-cran-presmtp Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survpresmooth, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-presmtp_1.1.0-1.ca2604.1_all.deb Size: 56714 MD5sum: f9871596615ddc08e42d71b2afae8500 SHA1: 96e203eb6af15494fdd720b82ee056938d69ea7a SHA256: d38b21d08a70f6f94f9c489c2c7f39976e92bad99cb756d3e5551e803a7dce9f SHA512: a96db1a4af58fbc6123a4d775d70f2ba8bd5e8213a8d449b29a02d3a9d5a9796be474e5336c0c6bf1a23e32be21b7394edfd978e510fcc89afb127162e40457b Homepage: https://cran.r-project.org/package=presmTP Description: CRAN Package 'presmTP' (Methods for Transition Probabilities) Provides a function for estimating the transition probabilities in an illness-death model. The transition probabilities can be estimated from the unsmoothed landmark estimators developed by de Una-Alvarez and Meira-Machado (2015) . Presmoothed estimates can also be obtained through the use of a parametric family of binary regression curves, such as logit, probit or cauchit. The additive logistic regression model and nonparametric regression are also alternatives which have been implemented. The idea behind the presmoothed landmark estimators is to use the presmoothing techniques developed by Cao et al. (2005) in the landmark estimation of the transition probabilities. Package: r-cran-presspurt Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-presspurt_1.0.2-1.ca2604.1_all.deb Size: 185694 MD5sum: 885c16f9e77f770179ef4f449b51a7e9 SHA1: 7ae269dfb66a6fb0c4574c81a86c4f87e00ca74c SHA256: 9709d4733e3e188ec2b0df5ba36c50aaf32b046828cb95d07b5e866dfb7e8759 SHA512: 1ff05f2b152f039cef0179df4c37c20618509b3f88d4ccbb6b75b9ce7c910a9e38b7c2555eee1328159485407a9ef58b28aad9872f517c32c17f885e1cbba784 Homepage: https://cran.r-project.org/package=PressPurt Description: CRAN Package 'PressPurt' (Indeterminacy of Networks via Press Perturbations) This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) . Package: r-cran-pressure Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10711 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-dplyr, r-cran-gdistance, r-cran-ggmap, r-cran-ggplot2, r-cran-magick, r-cran-magrittr, r-cran-morpho, r-cran-pracma, r-cran-raster, r-cran-rdist, r-cran-readxl, r-cran-rvcg, r-cran-scales, r-cran-sf, r-cran-stringr, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pressure_0.2.7-1.ca2604.1_all.deb Size: 805270 MD5sum: 1b2fde365382733478bd3d892a52af3a SHA1: 6fefea72d45db2e9dac15322494a4dda253be142 SHA256: 7004fa6e72f4bd5f883a1b0ed053c8e846f3eb9e9f6c387e2151122e186cb69e SHA512: 4d87b86ce222612f0a4e4c8c52da24084dd4b1a809839e625d5da7e8b020fbbac79e13a233b5e920ceec49eafee2858f278d0e599184f9466d287615f38495e8 Homepage: https://cran.r-project.org/package=pressuRe Description: CRAN Package 'pressuRe' (Imports, Processes, and Visualizes Biomechanical Pressure Data) Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) , Lee et al., (2014) , van der Zward et al., (2014) , Najafi et al., (2010) , Cavanagh and Rodgers (1987) . Package: r-cran-pretest Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pretest_0.2-1.ca2604.1_all.deb Size: 41998 MD5sum: 2a2136c36d7341e734cd1a240fc0dbe9 SHA1: fce2a5f51cd67434eec7c12ca0e9d0b73bb4368f SHA256: d64f0e4c4c42545c020e189f20fee0d43def9972a77bb465db2b08903243328a SHA512: c363d62d245cc1e09237d210dcaee25f32014d0b1e782c51e4d80d9e4ac462cbe3cdde26ea19f73a82d053f595888c37dab18b24d30b073d1a8ee453a0b0fa49 Homepage: https://cran.r-project.org/package=pretest Description: CRAN Package 'pretest' (A Novel Approach to Predictive Accuracy Testing in NestedEnvironments) This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) . 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Guidelines usually comes from the American Heart Association (AHA), American College of Cardiology (ACC) or European Society of Cardiology (ESC). Examples of PTP scores that comes from studies are the 2020 Winther et al. basic, Risk Factor-weighted Clinical Likelihood (RF-CL) and Coronary Artery Calcium Score-weighted Clinical Likelihood (CACS-CL) models , 2019 Reeh et al. basic and clinical models and 2017 Fordyce et al. PROMISE Minimal-Risk Tool . As diagnosis of CAD involves a costly and invasive coronary angiography procedure for patients, having a reliable PTP for CAD helps doctors to make better decisions during patient management. This ensures high risk patients can be diagnosed and treated early for CAD while avoiding unnecessary testing for low risk patients. 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This package provides index number methods for such price comparisons (e.g., The World Bank, 2011, ). Moreover, it contains functions for sampling and characterizing price data. 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Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020). Package: r-cran-pricesensitivitymeter Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pricesensitivitymeter_1.3.3-1.ca2604.1_all.deb Size: 229482 MD5sum: a00bb98d5ba3e5e7c3cd45a286918840 SHA1: f24fda2863b5ed926dc536dcaf9fa38ba6fc1393 SHA256: 8f5f80455ddd546f219932ccdabbfdd6b0549ccbbe4fd3175abe667dbf9bffef SHA512: 2302788ccbafa87741f0b1dfb4634d9b161c7493102e9e78b61ce7e1cadc8a01cc86a681d607003781dda29d9680d447d343df976d5db44389eeb9bbed4dcb1b Homepage: https://cran.r-project.org/package=pricesensitivitymeter Description: CRAN Package 'pricesensitivitymeter' (Van Westendorp Price Sensitivity Meter Analysis) An implementation of the van Westendorp Price Sensitivity Meter in R, which is a survey-based approach to analyze consumer price preferences and sensitivity (van Westendorp 1976, isbn:9789283100386). Package: r-cran-pridit Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pridit_1.1.0-1.ca2604.1_all.deb Size: 34706 MD5sum: c6365b5ac5641413949fe3b3da111b32 SHA1: 83ac1d895558fde586edf7e13078db853801dea6 SHA256: 8443fbf8452036b6e2a7b0d670a4ab52b6c1f309a4ca46c33e03c8611a671396 SHA512: 5aea133bad5dac2f747518ddbbdcbc4b8e4ef97ca4e72f4bd5fd3fbda9377f333641fbc570bade7ab7e4f53d1ceec987c3bc27724d08e77ed3fca32c8e3e2e15 Homepage: https://cran.r-project.org/package=pridit Description: CRAN Package 'pridit' (Principal Component Analysis Applied to Ridit Scoring) Implements the 'PRIDIT' (Principal Component Analysis applied to 'RIDITs') scoring system described in Brockett et al. (2002) . 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Package: r-cran-prim Architecture: all Version: 1.0.23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-scales, r-cran-plot3d Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/resolute/main/r-cran-prim_1.0.23-1.ca2604.1_all.deb Size: 375414 MD5sum: 9915febed9109979a4e8a9ceb055fa33 SHA1: 294889fe37db9640c0849275b42669ec5f078e6f SHA256: 06fc08497fbae3e78e81b63cdf2dfda0ff1751cf387fefefecc5fdd72e721775 SHA512: 1ddaeb56d19c5a292a61b8778fba21f20bdabe8c06e957c9e394d81b51b42fe6fee087109830399cd9b37c4174a6fa47e03c724d415a6b4c8b3e7df075f0d211 Homepage: https://cran.r-project.org/package=prim Description: CRAN Package 'prim' (Patient Rule Induction Method (PRIM)) Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data. Package: r-cran-primarycensored Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-bookdown, r-cran-dplyr, r-cran-fitdistrplus, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-primarycensored_1.4.0-1.ca2604.1_all.deb Size: 732286 MD5sum: 7adaf628e2527add4cec99009673ca3f SHA1: d8806586b7cd6e03d5de0aca0fe5dcab6bd32252 SHA256: 5f038cc91a038140e6b73f016e686d7b00743bb5bff2fe4fd350b66861a37e74 SHA512: e29f5a0240894591e2c136c4a08223f26c6fe879747da146e9e0580212751c01328b4458c177415552baf512b7f5968375ad8044a877fec6fdc739a6b862c573 Homepage: https://cran.r-project.org/package=primarycensored Description: CRAN Package 'primarycensored' (Primary Event Censored Distributions) Provides functions for working with primary event censored distributions and 'Stan' implementations for use in Bayesian modeling. Primary event censored distributions are useful for modeling delayed reporting scenarios in epidemiology and other fields (Charniga et al. (2024) ). It also provides support for arbitrary delay distributions, a range of common primary distributions, and allows for truncation and secondary event censoring to be accounted for (Park et al. (2024) ). A subset of common distributions also have analytical solutions implemented, allowing for faster computation. In addition, it provides multiple methods for fitting primary event censored distributions to data via optional dependencies. Package: r-cran-primate Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caroline Suggests: r-cran-rjdbc Filename: pool/dists/resolute/main/r-cran-primate_0.2.0-1.ca2604.1_all.deb Size: 635486 MD5sum: 7df39cea647fa65e128d0f3f8653a29c SHA1: 17fc832e8528d9c1bb02e3ec350b7477c8b6ff05 SHA256: d59e3bfceb75655dade740e4863dacd35565ad10b8a3b44be3820b51be7cd3ad SHA512: 18b5ac000dd13e641acad3c0d781d0134193bb67b6f6058cc2392970a2ec8437affb8e64e1f09cdc373c0d72af2f49d62b5dd182e8ed4223cf49b7c90dffbacd Homepage: https://cran.r-project.org/package=primate Description: CRAN Package 'primate' (Tools and Methods for Primatological Data Science) Data from All the World's Primates relational SQL database and other tabular datasets are made available via drivers and connection functions. Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs. Package: r-cran-primefactr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-primefactr_0.1.1-1.ca2604.1_all.deb Size: 20340 MD5sum: e4ca8f9f5fac27ced60fbb5e63390ed4 SHA1: 0e06fd7c68d3720ff36732779237acc0a536110b SHA256: 26f921db6ced0b4905ae81280ee8767c1079dedcc403ed6dfeea1e395fdcd90c SHA512: 7615909c4e8d04a84f41ceef4feefe0b526f99b9f1c09b469cc670264da60894e788ba64ba9fad453c36e0fdbb66a41e5b2f1b0fd74cb408ef6040ed50e9202b Homepage: https://cran.r-project.org/package=primefactr Description: CRAN Package 'primefactr' (Use Prime Factorization for Computations) Use Prime Factorization for simplifying computations, for instance for ratios of large factorials. Package: r-cran-primepca Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-softimpute, r-cran-matrix, r-cran-mass Filename: pool/dists/resolute/main/r-cran-primepca_1.2-1.ca2604.1_all.deb Size: 35858 MD5sum: 0501499447d3b509f3c33c25798ad40a SHA1: 9fb2f7abf7937f49ca53682002edc4c13a791959 SHA256: 0d2cdcbfb0ef5f1a34e2c8162845d4539974ef8134ef46fbb3d25ccc5f37c687 SHA512: 0673982006f7c3dec7213b349167edbe9b14673f8743f87ae16122d230b488beb54c2edd8a8a668418795987be82ac9d2cb00481aa639308a1b7f46bf594bc85 Homepage: https://cran.r-project.org/package=primePCA Description: CRAN Package 'primePCA' (Projected Refinement for Imputation of Missing Entries in PCA) Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. . Package: r-cran-primer Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-bbmle, r-cran-bipartite, r-cran-cowplot, r-cran-data.table, r-cran-diagram, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-gdata, r-cran-igraph, r-cran-lavaan, r-cran-magrittr, r-cran-nlme, r-cran-vegan, r-cran-reshape2, r-cran-rarpack, r-cran-rsvg Filename: pool/dists/resolute/main/r-cran-primer_1.2.0-1.ca2604.1_all.deb Size: 338370 MD5sum: 266a16588e3b91edc4904632b585e375 SHA1: 9845a55edf24bd38a153cd98164e9f57cd264ed3 SHA256: 02b2f211c5c970b930af23b45085b7252c09b0233bcaf5ed43b67d7925fe09fe SHA512: 66cb23e42bce49da094992dfc5e07deef7f367ebf09ca33b97e53a0e3563f8b463139faef4ea536cab3f39bb5fb8b926af036748a7366893b51510c1d5b9eaee Homepage: https://cran.r-project.org/package=primer Description: CRAN Package 'primer' (Functions and Data for the Book, a Primer of Ecology with R) Functions are primarily functions for systems of ordinary differential equations, difference equations, and eigenanalysis and projection of demographic matrices; data are for examples. Package: r-cran-prindt Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-party, r-cran-splitstackshape, r-cran-stringr, r-cran-gdata Filename: pool/dists/resolute/main/r-cran-prindt_2.0.2-1.ca2604.1_all.deb Size: 413740 MD5sum: 16191db13f748b04e4d22f7a261c23a6 SHA1: b8edda2b34ce488002fee67473595ff253253588 SHA256: cbb64fb4e9dae0040b601fc118a861c6f13d4c36a459f8775f74f12c83c41ad4 SHA512: 817b7ecbee1640fe0d1d5f5d63bcd7e33a33138e9e3689338dd2b543f3706aea77856109bbc2a412d5db8195b94f4a4ba728385aef06f9b24bf72371c371a994 Homepage: https://cran.r-project.org/package=PrInDT Description: CRAN Package 'PrInDT' (Prediction and Interpretation in Decision Trees forClassification and Regression) Optimization of conditional inference trees from the package 'party' for classification and regression. For optimization, the model space is searched for the best tree on the full sample by means of repeated subsampling. Restrictions are allowed so that only trees are accepted which do not include pre-specified uninterpretable split results (cf. Weihs & Buschfeld, 2021a). The function PrInDT() represents the basic resampling loop for 2-class classification (cf. Weihs & Buschfeld, 2021a). The function RePrInDT() (repeated PrInDT()) allows for repeated applications of PrInDT() for different percentages of the observations of the large and the small classes (cf. Weihs & Buschfeld, 2021c). The function NesPrInDT() (nested PrInDT()) allows for an extra layer of subsampling for a specific factor variable (cf. Weihs & Buschfeld, 2021b). The functions PrInDTMulev() and PrInDTMulab() deal with multilevel and multilabel classification. In addition to these PrInDT() variants for classification, the function PrInDTreg() has been developed for regression problems. Finally, the function PostPrInDT() allows for a posterior analysis of the distribution of a specified variable in the terminal nodes of a given tree. In version 2, additionally structured sampling is implemented in functions PrInDTCstruc() and PrInDTRstruc(). In these functions, repeated measurements data can be analyzed, too. Moreover, multilabel 2-stage versions of classification and regression trees are implemented in functions C2SPrInDT() and R2SPrInDT() as well as interdependent multilabel models in functions SimCPrInDT() and SimRPrInDT(). Finally, for mixtures of classification and regression models functions Mix2SPrInDT() and SimMixPrInDT() are implemented. Most of these extensions of PrInDT are described in Buschfeld & Weihs (2025Fc). References: -- Buschfeld, S., Weihs, C. (2025Fc) "Optimizing decision trees for the analysis of World Englishes and sociolinguistic data", Cambridge Elements. -- Weihs, C., Buschfeld, S. (2021a) "Combining Prediction and Interpretation in Decision Trees (PrInDT) - a Linguistic Example" ; -- Weihs, C., Buschfeld, S. (2021b) "NesPrInDT: Nested undersampling in PrInDT" ; -- Weihs, C., Buschfeld, S. (2021c) "Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles" . 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Package: r-cran-pro Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pro_0.1.1-1.ca2604.1_all.deb Size: 22058 MD5sum: a2a7e809aad6f5e6f086524ac3f5b537 SHA1: 9da4147bfa0d82882ee5801a71ba45399d38b1e7 SHA256: 283eed7fe21223e57b6f45f27912c4a8945d79a1121aabde80cac76533d8c0ff SHA512: 470a4c5edb21b6d18357aa4f478f253eeab2d73e40a28e27420ce781dc57bff609fd2c8a26093913a9513d775092765c41b8ed55c040e572c95d8988160cc138 Homepage: https://cran.r-project.org/package=pro Description: CRAN Package 'pro' (Point-Process Response Model for Optogenetics) Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments. 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Coissac Eric and Gonindard-Melodelima Christelle (2019) . 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The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index. Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for multilateral or multitemporal comparison. Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are only to be used for binary comparison. All indices can also be decomposed into different components, providing insightful information on the sources of productivity and profitability changes. In the use of Malmquist productivity index, the technological change index can be further decomposed into bias technological change components. The package also allows to prohibit technological regression (negative technological change). In the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont and Lowe indices, it is furthermore possible to rule out technological change. Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as an option, depending on the user's computer configuration. All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and several assumptions regarding returns to scale are available. All DEA linear programs are implemented using 'lp_solve'. 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The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar(), cli::cli_progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beepr::beep(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. 'lapply(...) |> futurize()' and 'purrr::map(...) |> futurize()', which uses future.apply::future_lapply() and furrr::future_map() internally. The package is compatible with Shiny applications. Package: r-cran-projections Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-incidence, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-outbreaks, r-cran-magrittr, r-cran-distcrete, r-cran-svglite, r-cran-epiestim Filename: pool/dists/resolute/main/r-cran-projections_0.6.1-1.ca2604.1_all.deb Size: 781802 MD5sum: e33d64fc43235eed8bbd919b487d7623 SHA1: a5a88ab3a1c26f27edd0d288578d288369ddb39e SHA256: 2207519a548a6a7e06b1a08276f9427273dae6bbdec752b0c2cf1e103a430de1 SHA512: 150f407e7345a606d0b3061f5f61855743345d4d6af02ea93e699db26e058e18a73f59e22449afb2018660f4cc9432ae33224259e571e72d7d187dafd6b9ebd3 Homepage: https://cran.r-project.org/package=projections Description: CRAN Package 'projections' (Project Future Case Incidence) Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) . The package provides the S3 class 'projections' which extends 'matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods. Package: r-cran-projectlsa Architecture: all Version: 0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-colourpicker, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ggiraph, r-cran-ggplot2, r-cran-glca, r-cran-haven, r-cran-kableextra, r-cran-knitr, r-cran-lavaan, r-cran-magick, r-cran-mclust, r-cran-mirt, r-cran-officer, r-cran-plotly, r-cran-polca, r-cran-psych, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-semplot, r-cran-semptools, r-cran-shiny, r-cran-shinywidgets, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidylpa, r-cran-tidyverse, r-cran-viridislite Suggests: r-cran-pkgdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-projectlsa_0.0.9-1.ca2604.1_all.deb Size: 136008 MD5sum: 2ca251343d4b10de2341de60fd9c8e42 SHA1: a03ceb74e59ba364f86f059476bc0621ce5f1d02 SHA256: e765b8210dc77806913fe139ad4e30d3ec1bd6354f0972bc1af01cfdeb5086ae SHA512: 77777fb0f9b74e478db8412ae9d1189a5673c3e2ae74b55d3f013285c5c3185672e403eea4d476aabc0c12ae4ea30c5c882bf840b9c79718d137c5b65246eab8 Homepage: https://cran.r-project.org/package=projectLSA Description: CRAN Package 'projectLSA' (Shiny Application for Latent Structure Analysis with a GraphicalUser Interface) Provides an interactive Shiny-based toolkit for conducting latent structure analyses, including Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Latent Trait Analysis (LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The implementation is grounded in established methodological frameworks: LPA is supported through 'tidyLPA' (Rosenberg et al., 2018) , LCA through 'poLCA' (Linzer & Lewis, 2011) & 'glca' (Kim & Kim, 2024) , LTA/IRT via 'mirt' (Chalmers, 2012) , and EFA via 'psych' (Revelle, 2025). SEM and CFA functionalities build upon the 'lavaan' framework (Rosseel, 2012) . Users can upload datasets or use built-in examples, fit models, compare fit indices, visualize results, and export outputs without programming. Package: r-cran-projectmanagement Architecture: all Version: 2.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-lpsolveapi, r-cran-triangle, r-cran-tuvalues, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-projectmanagement_2.1.4-1.ca2604.1_all.deb Size: 163422 MD5sum: 28fb234babfaf9df070020bcb08022c2 SHA1: 34d8febb6c7374295e19bca0d4a345fb6b66ecfa SHA256: 2f86763b03b207a21aafaca90740d86719e586bfc5346146b99042f91686cee3 SHA512: 2b2b5f6b10a5eb3242c04085e16e3621208ad53c73b9597203ecbdf48e0293bfb572b4d5dd39fcb7244e5985d0d1a4c05206d4cbb481e8384d1b01b5eb2d3dda Homepage: https://cran.r-project.org/package=ProjectManagement Description: CRAN Package 'ProjectManagement' (Management of Deterministic and Stochastic Projects) Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) ). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) ). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out. 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Creates a project folder with a single command, containing subdirectories for specific components, templates for manuscripts, and so on. 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The package implements estimation of marginal means (MMs) and average marginal component effects (AMCEs), with corrections for measurement error. Methods include profile-level and choice-level estimators, bias correction using intra-respondent reliability (IRR), and visualization utilities. For details on the methodology, see Clayton, Horiuchi, Kaufman, King, and Komisarchik (2025) . Package: r-cran-proliferativeindex Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1825 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-proliferativeindex_1.0.1-1.ca2604.1_all.deb Size: 1760148 MD5sum: 47f0413eb35fa72d3de8c056fd76fc79 SHA1: 60bf4c6ae7425d9af1e90c5b73a54683f0fcc728 SHA256: 4dff2cca257d76a625fca5773cd9e636ffc9e598b1882cf196125546e0337772 SHA512: f0707e4de62db26531cea6661bad95f2d543cfc25c51fc8ba5d9f3ca937142adbc21b58eb492cc49b6771c46fc3d34ad8d60261b85e1f5108a2a1510b555f69b Homepage: https://cran.r-project.org/package=ProliferativeIndex Description: CRAN Package 'ProliferativeIndex' (Calculates and Analyzes the Proliferative Index) Provides functions for calculating and analyzing the proliferative index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, et al. bioRxiv, 2016 . 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(1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) , and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) . 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Asynchronous programming is useful for allowing a single R process to orchestrate multiple tasks in the background while also attending to something else. Semantics are similar to 'JavaScript' promises, but with a syntax that is idiomatic R. 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Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) ), differential expression analysis (Ritchie et. al. (2015) ), and machine learning-based modeling (Kuhn (2008) ). Package: r-cran-promote Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-promote_1.1.1-1.ca2604.1_all.deb Size: 60620 MD5sum: e8981b08a0b3d36429c8e399b1772db8 SHA1: c58a5d68a3e17dda343aa73c708803cdd67d46b1 SHA256: 89be67df107af8f9271abe7080a2697c85e16f4b43794cd362dded3d41bcb928 SHA512: e270cfffd8adcdf547f111f95029c977a9193e24895044ee416f5f5e35644ec2704c024555941f5c4212314af1169e43b24234624d9081566a3812e32699b214 Homepage: https://cran.r-project.org/package=promote Description: CRAN Package 'promote' (Client for the 'Alteryx Promote' API) Deploy, maintain, and invoke predictive models using the 'Alteryx Promote' REST API. 'Alteryx Promote' is available at the URL: . 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After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) and some prespecified variables (e.g. start of a month). Package: r-cran-prompt Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-callr, r-cran-gert, r-cran-mockery, r-cran-pkgload, r-cran-ps, r-cran-r6, r-cran-rstudioapi, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-prompt_1.0.2-1.ca2604.1_all.deb Size: 201274 MD5sum: 7023b1528f4881890a263ed460c0ac0e SHA1: 02a06f288d4cd6ec8efaff4b3e95665526da30a7 SHA256: 501233be354e248f76fce1e099a3f8fee5a97d6204dcd45559c5c0915d0f964f SHA512: 8494269ad3d5b4d0619cb5eb42450dac1814ba3078950757a36cf642528fdb1b296f80dc24a16927cd77c79e11fba870e4ae92c914528b2b0e4eb9520ba4c78b Homepage: https://cran.r-project.org/package=prompt Description: CRAN Package 'prompt' (Dynamic 'R' Prompt) Set the 'R' prompt dynamically, from a function. The package contains some examples to include various useful dynamic information in the prompt: the status of the last command (success or failure); the amount of memory allocated by the current 'R' process; the name of the R package(s) loaded by 'pkgload' and/or 'devtools'; various 'git' information: the name of the active branch, whether it is dirty, if it needs pushes pulls. You can also create your own prompt if you don't like the predefined examples. 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Package: r-cran-propcis Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-propcis_0.3-0-1.ca2604.1_all.deb Size: 96182 MD5sum: ffaf94ecae0fce8278fac30951fdf9f9 SHA1: 7a1daaa39d5180a11f7c0283e5b6f5c934341676 SHA256: 9fb13d53b312a49a51cc2f25ebb381dcbf6118b2c7e569069fc850d9871773f2 SHA512: 7475a03f483f68284b46c5b951d87f49debe3bc2ee4f7ad567adee17752b9ef1bb2f326f01968f1f9e17a210ca9df5cf8598a3865425381f0cc6615792f8aaf4 Homepage: https://cran.r-project.org/package=PropCIs Description: CRAN Package 'PropCIs' (Various Confidence Interval Methods for Proportions) Computes two-sample confidence intervals for single, paired and independent proportions. 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It uses explicit specification of clusters, blocks and treatment allocations to furnish probability of assignment-based weights targeting any of several average treatment effect parameters, and for standard error calculations reflecting these design parameters. For covariance adjustment of its Hajek and (one-way) fixed effects estimates, it enables offsetting the outcome against predictions from a dedicated covariance model, with standard error calculations propagating error as appropriate from the covariance model. 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Package: r-cran-proporz Architecture: all Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-shiny, r-cran-shinymatrix, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-proporz_1.5.2-1.ca2604.1_all.deb Size: 238480 MD5sum: 32a79d9edc7bf49efadefd9bc4513ac2 SHA1: 540927eac372767649ab152db3845486813993b6 SHA256: f1524aee9c70c444c83cd97945a50759849ee7188da72be3a8070e59d5874a7f SHA512: 0d61b9e330184349bf9de1d6c450285de6d1532de905b72799252c7441d3370965def41ea5f09aaca5db20ee33f8e3a55b7a6937a907c9b31c2e92b14ccb6569 Homepage: https://cran.r-project.org/package=proporz Description: CRAN Package 'proporz' (Proportional Apportionment) Calculate seat apportionment for legislative bodies with various methods. The algorithms include divisor or highest averages methods (e.g. Jefferson, Webster or Adams), largest remainder methods and biproportional apportionment. Gaffke, N. & Pukelsheim, F. (2008) Oelbermann, K. F. (2016) . Package: r-cran-propoverlap Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4758 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase Filename: pool/dists/resolute/main/r-cran-propoverlap_1.0-1.ca2604.1_all.deb Size: 4837814 MD5sum: 5dd55bba5cce8df094cff34574322d67 SHA1: 1dae49da1a3db348bc85ef4ba5d3efe2f8be00fe SHA256: f5c3bab2dff02b11860cbb6832b86008b1fd94ec40a1fa5944efbf5a2bb65e80 SHA512: d14fabf5990b9b6072510c91b0d67a617148c9bbbfdeeb386e518c9b833015b102a74b4986f318665405b072a756c1561b218725391cfe648f16e25e706066cd Homepage: https://cran.r-project.org/package=propOverlap Description: CRAN Package 'propOverlap' (Feature (gene) selection based on the Proportional OverlappingScores) A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced. Package: r-cran-propscrrand Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-propscrrand_1.1.2-1.ca2604.1_all.deb Size: 35766 MD5sum: 0520e73bc17196449b2825de30cc6668 SHA1: bd31c6a04ef441452af12218a6909964c04a0376 SHA256: 4f5bf22071bfa3e4b179d4b06e5fb0571f1beb9af2e59ce65e06e6a29b65d742 SHA512: 2c73bf1e3584e595c132911d45ce0391461a0e6867af8501221a7bc439b1d9bef73bdbc3bd39f07fb01aac15d91376bf61afef54a096bda48ffa4196c1df389c Homepage: https://cran.r-project.org/package=PropScrRand Description: CRAN Package 'PropScrRand' (Propensity Score Methods for Assigning Treatment in RandomizedTrials) Contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization. Currently only supports trials comparing two groups. Package: r-cran-propubbills Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-propubbills_0.1-1.ca2604.1_all.deb Size: 23108 MD5sum: 330874f91e6e4e1984d2b0f776815081 SHA1: 837bdc4047ce2bff1771fbcd3857cad0c3c4e96a SHA256: 265c1f6cb835803ae3164dfda010f248a52ea9ad3fbcd30802a6b32d90f1cbdd SHA512: 7b2880e407cdeb3ff98fadd8af561bd45fb1435b6b066a7687ab2b5f67dfe8359b73698bb09b40906b09b4cc3ce16851d99a19f3ae84b1d0e406920940f03af3 Homepage: https://cran.r-project.org/package=proPubBills Description: CRAN Package 'proPubBills' ('ProPublica' U.S. Congress Bills API Wrapper) An API wrapper around the 'ProPublica' API for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the 'RPublica' package because it is for the 'ProPublica' U.S. Congress data API, and the 'RPublica' package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs. Package: r-cran-propublicar Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-httr, r-cran-config, r-cran-lubridate Suggests: r-cran-testthat, r-cran-httptest, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-propublicar_1.1.4-1.ca2604.1_all.deb Size: 290054 MD5sum: 4597d21b9250df7c9d29994ef301da55 SHA1: f99d86b162563b8f3d487166ea172cc235595ac2 SHA256: 01458259a228070ffbdd0826ae4668667ca3f9013db3d6da09fd592dd2d27cd6 SHA512: dde458c7ccca373c52eeafb8dbd79bd404573a7d508006f3857c20eaebc39f1282af1c32e9f7cff7e60518ba2eb6c582e5f1e801856dd4d8d5e036b9c9f63d86 Homepage: https://cran.r-project.org/package=ProPublicaR Description: CRAN Package 'ProPublicaR' (Access Functions for ProPublica's APIs) Provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs. The Congress API provides near real-time access to legislative data from the House of Representatives, the Senate and the Library of Congress. The Campaign Finance API provides data from United States Federal Election Commission filings and other sources. The API covers summary information for candidates and committees, as well as certain types of itemized data. For more information about these APIs go to: . Package: r-cran-proreg Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmsb, r-cran-car, r-cran-rcolorbrewer, r-cran-matrixcalc, r-cran-rootsolve, r-cran-numderiv, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-proreg_1.3.2-1.ca2604.1_all.deb Size: 170812 MD5sum: 957b13d3d3d9ae626b9abcd1fcf49fc0 SHA1: 170dcf461e6bb267bdabb0110e9959fd908f638b SHA256: 8b120887f4941597e32e69657b722c7ed6c8abace816235f33ea04d825b323b9 SHA512: 558d9099a0253767f0c3f49e3e624cf551ba49dad13e016715ae81607ff8d32ac562bb10874e0f255e3201138881514dc04bd6aa701d62f11cafd4bdd36213de Homepage: https://cran.r-project.org/package=PROreg Description: CRAN Package 'PROreg' (Patient Reported Outcomes Regression Analysis) It offers a wide variety of techniques, such as graphics, recoding, or regression models, for a comprehensive analysis of patient-reported outcomes (PRO). Especially novel is the broad range of regression models based on the beta-binomial distribution useful for analyzing binomial data with over-dispersion in cross-sectional, longitudinal, or multidimensional response studies (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2019) ). Package: r-cran-proscorer Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proscorertools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-proscorer_0.0.4-1.ca2604.1_all.deb Size: 105272 MD5sum: 6da8dc83ff8f4ad6222e5f3a9fe9efd9 SHA1: 1d46f1135c9f6bfa6d55c054add15087ee017a37 SHA256: 307a5f6b56b4ccc62cfe87d519229de5e3b81c0178b640ca606462b07a147f21 SHA512: 9e340c4c7da806c4252fbbe242c0ee4f6b5159bc158c688da781478b349106808e2a6f4fa2d30cf8ed2f01f65d8b1b3f47d79a327b8ba28c98cb75b709f3b6ad Homepage: https://cran.r-project.org/package=PROscorer Description: CRAN Package 'PROscorer' (Functions to Score Commonly-Used Patient-Reported Outcome (PRO)Measures and Other Psychometric Instruments) An extensible repository of accurate, up-to-date functions to score commonly used patient-reported outcome (PRO), quality of life (QOL), and other psychometric and psychological measures. 'PROscorer', together with the 'PROscorerTools' package, is a system to facilitate the incorporation of PRO measures into research studies and clinical settings in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote best practices for scoring PRO and PRO-like measures in research. The 'PROscorer' Instrument Descriptions vignette contains descriptions of each instrument scored by 'PROscorer', complete with references. These instrument descriptions are suitable for inclusion in formal study protocol documents, grant proposals, and manuscript Method sections. Each 'PROscorer' function is composed of helper functions from the 'PROscorerTools' package, and users are encouraged to contribute new functions to 'PROscorer'. More scoring functions are currently in development and will be added in future updates. Package: r-cran-proscorertools Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-proscorertools_0.0.4-1.ca2604.1_all.deb Size: 78846 MD5sum: e22aac058e92cddf73f33b48c5e41096 SHA1: 1d76aa1df465b092a51592a50259cb5b44186330 SHA256: e181d300e54ae84757a8e5c0d6c49dcd8ddced593183b3e6b4547ef1f0a14487 SHA512: 69737542be322e3aa5414d126be24adc87baf654cd502cc637258f8f538e82feaece1ae9fc363f598f2e253830f0bef961cd756c2a1a3a5f7c40f9c8af8df870 Homepage: https://cran.r-project.org/package=PROscorerTools Description: CRAN Package 'PROscorerTools' (Tools to Score Patient-Reported Outcome (PRO) and OtherPsychometric Measures) Provides a reliable and flexible toolbox to score patient-reported outcome (PRO), Quality of Life (QOL), and other psychometric measures. The guiding philosophy is that scoring errors can be eliminated by using a limited number of well-tested, well-behaved functions to score PRO-like measures. The workhorse of the package is the 'scoreScale' function, which can be used to score most single-scale measures. It can reverse code items that need to be reversed before scoring and pro-rate scores for missing item data. Currently, three different types of scores can be output: summed item scores, mean item scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' functions can be used to write new functions that score more complex measures. In fact, 'PROscorerTools' functions are the building blocks of the scoring functions in the 'PROscorer' package (which is a repository of functions that score specific commonly-used instruments). Users are encouraged to use 'PROscorerTools' to write scoring functions for their favorite PRO-like instruments, and to submit these functions for inclusion in 'PROscorer' (a tutorial vignette will be added soon). The long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to provide an easy-to-use system to facilitate the incorporation of PRO measures into research studies in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote "best practices" for scoring and describing PRO-like measures in research. Package: r-cran-prosgpv Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3716 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-brglm2, r-cran-mass, r-cran-survival Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-prosgpv_1.0.0-1.ca2604.1_all.deb Size: 2340896 MD5sum: 365b3458cd8d9b5d1035cb71190d8659 SHA1: 830c2c9412061c8e93a555f53dca2ea061fb52ca SHA256: ff052409d1021bf6aa87de547f194410ad5b49abca41fc4d374cf6242ad7c4c6 SHA512: 82a9945bd4f331c5a4bc2b4cdf838f008e332b93cce75e07930f3c6d903fd1912328131dbaf231e862b4c7b477940a4e332349f6c0e3ccae49a2eff7671a4064 Homepage: https://cran.r-project.org/package=ProSGPV Description: CRAN Package 'ProSGPV' (Penalized Regression with Second-Generation P-Values) Implementation of penalized regression with second-generation p-values for variable selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details can be found at Zuo et al. (2021) . Package: r-cran-prosper Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-prosper_0.3.3-1.ca2604.1_all.deb Size: 269632 MD5sum: b3f56034bee17b7d416f2f612d7b9382 SHA1: 6062d65a577f21fb657d5772e50ee589d389e3ea SHA256: 25c34f3438b49150522306e84a6297a173dad2c790e7554b4d84e60cfb8e2268 SHA512: 0231f74c0c284f9681f137414cac707fc95fe93f77122e9cf3aaaa53e449116bf99b11019e97456dd9bb24b7e0159c1c0867ee36d4bd7a22e90e5d81ba02e5ff Homepage: https://cran.r-project.org/package=PROSPER Description: CRAN Package 'PROSPER' (Simulation of Weed Population Dynamics) An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015). Renton, M, Diggle, A, Manalil, S and Powles, S (2011) Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.) Package: r-cran-prosportsdraftdata Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4989 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-prosportsdraftdata_1.0.3-1.ca2604.1_all.deb Size: 2559172 MD5sum: 58311d624bef89dffd9502642d082176 SHA1: 8c81d6a1bcac9322b1101ee37ec9dd97e518a8e7 SHA256: b8bef3452d9d016b28ab04a0c6aab694c152feda861ed83f7e60f1b7664915a0 SHA512: f65dd1b5a64144aab7424f7d700d156a127c7144181769d6dfdb732ec08ec6acb7c3e0cba7a50d37951d9df310d22fa5872cb11ae557a83263558e857d8f1a65 Homepage: https://cran.r-project.org/package=ProSportsDraftData Description: CRAN Package 'ProSportsDraftData' (Professional Sports Draft Data) We provide comprehensive draft data for major professional sports leagues, including the National Football League (NFL), National Basketball Association (NBA), and National Hockey League (NHL). It offers access to both historical and current draft data, allowing for detailed analysis and research on player biases and player performance. The package is useful for sports fans and researchers interested in identifying biases and trends within scouting reports. Created by web scraping data from leading websites that cover professional sports player scouting reports, the package allows users to filter and summarize data for analytical purposes. For further details on the methods used, please refer to Wickham (2022) "rvest: Easily Harvest (Scrape) Web Pages" and Harrison (2023) "RSelenium: R Bindings for Selenium WebDriver" . Package: r-cran-protag Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rcolorbrewer Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-protag_1.0.0-1.ca2604.1_all.deb Size: 59272 MD5sum: 18f0d38fc73881059dc61b1946c594e5 SHA1: 214f89f3427f62d52595276a8158834311944d50 SHA256: 6b133697d3bf6a2c964919de983052b5eb3fcc413bd889f77299bc7389151d7c SHA512: 524936abe8beeee1e6b51a9edd4b3a61fcec4976da3e9ef32da97b83bfa45ac82279df50ddfae075de974d1f058097eef512ca05ff51bd3b2472eda3437aa524 Homepage: https://cran.r-project.org/package=protag Description: CRAN Package 'protag' (Search Tagged Peptides & Draw Highlighted Mass Spectra) In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a “pair”, those with equal masses as a “match”, and all the other peaks as a “mismatch”.For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) ; Elizabeth Chang (2016) . Package: r-cran-protein8k Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-magick, r-cran-dplyr, r-cran-gridextra, r-cran-ggplot2, r-cran-rjson, r-cran-rlang, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-protein8k_0.0.2-1.ca2604.1_all.deb Size: 2218334 MD5sum: 39579c90abfb4bcc231a2678a9a5d354 SHA1: 5fb0e98be822824e3d917bb3909c78bd98df36c8 SHA256: a6de87528558aa8592308218d915670248dd59d4f69b6a0b72c5705bb8a9c58a SHA512: e947458972dcee445b4f0b82bd7b216896a4059398ea25ca453200a979a822f5db074223b7a869cf979b32707a6e60b434f56a7d0664eb789d757decd7064c93 Homepage: https://cran.r-project.org/package=protein8k Description: CRAN Package 'protein8k' (Perform Analysis and Create Visualizations of Proteins) Read Protein Data Bank (PDB) files, performs its analysis, and presents the result using different visualization types including 3D. The package also has additional capability for handling Virus Report data from the National Center for Biotechnology Information (NCBI) database. Nature Structural Biology 10, 980 (2003) . US National Library of Medicine (2021) . Package: r-cran-proteinpca Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/resolute/main/r-cran-proteinpca_0.1.1-1.ca2604.1_all.deb Size: 18384 MD5sum: a2794eefcccd8dffa1b4a30971ac2691 SHA1: 90897dde75e483548a3396c2a4db959edbd567f8 SHA256: 151c3a38a64935cdc3ae3e30b0d04a7721cf6375e9db1dc673c40dfae2510c1d SHA512: aedc53ee530cbfb0479025846f97819bd205c91c34ec89e1b964be98b70c90cfb7971eb08839ed94a99a24869888659f0ba16916c4b1890a36bd3e49799a5a57 Homepage: https://cran.r-project.org/package=ProteinPCA Description: CRAN Package 'ProteinPCA' (Principal Component Analysis (PCA) Tool on Protein ExpressionData) Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) , Gabriel (1971) , Zhang et al. (2024) , and Anandan et al. (2022) . Package: r-cran-proteobayes Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-extradistr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-proteobayes_1.0.0-1.ca2604.1_all.deb Size: 142506 MD5sum: a4c4f71eb15cc3c81cc4e5629f212f50 SHA1: ef80ae00b4959114d4954b866aab60b5c2ce4c92 SHA256: 6cf2fb28759553cfa5c2f10dcddfbfaf79c7b9d6abf9f98136d6c9d995c5e318 SHA512: bdd2d6f3cd41e82a3af3bd8c09017a9c3d031ad699f87bd9a3adc5a889ccf728364a25571e9ff8e06674b06de83c11fa3b2086ab996e6b892781881fe2f14e50 Homepage: https://cran.r-project.org/package=ProteoBayes Description: CRAN Package 'ProteoBayes' (Bayesian Statistical Tools for Quantitative Proteomics) Bayesian toolbox for quantitative proteomics. In particular, this package provides functions to generate synthetic datasets, execute Bayesian differential analysis methods, and display results as, described in the associated article Marie Chion and Arthur Leroy (2023) . Package: r-cran-proteomicscv Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-proteomicscv_0.4.0-1.ca2604.1_all.deb Size: 11850 MD5sum: fc4db491b5b50fd17e54dba6a7db0189 SHA1: b27f5f2487cb631327e4867c54c10575b361a72a SHA256: 59e4cf0e6ae8f6148903558e01895afcb921e17765ab7d3b7438c26f25909fb5 SHA512: fe311c7835a2a10937beb3de2cb2bc75c52e8e46d6a5497e5bc9e68a6ca25f4f6ff6558adc595cdac0ad77634a5329d772679bde4cd9059443cfc55ca8ef50a0 Homepage: https://cran.r-project.org/package=proteomicsCV Description: CRAN Package 'proteomicsCV' (Calculates the Percentage CV for Mass Spectrometry-BasedProteomics Data) Calculates the percentage coefficient of variation (CV) for mass spectrometry-based proteomic data. The CV can be calculated with the traditional formula for raw (non log transformed) intensity data, or log transformed data. Package: r-cran-proteus Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-abind, r-cran-ggplot2, r-cran-ggthemes, r-cran-readr, r-cran-stringr, r-cran-lubridate, r-cran-narray, r-cran-fancova, r-cran-imputets, r-cran-modeest, r-cran-scales, r-cran-tictoc, r-cran-torch, r-cran-moments, r-cran-dplyr, r-cran-greybox, r-cran-furrr, r-cran-future Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-proteus_1.1.5-1.ca2604.1_all.deb Size: 232274 MD5sum: afebd255bd2fe567b190037ec610f2c7 SHA1: b94b1a5de8c12f9588c4d1762fa638deec378ecd SHA256: a0eb57bdfa3ec1c0963866ed400c69e22b0ca3839809ab17fbe96de3765bc309 SHA512: 8990ea44f0af01eed850ddd04ffe0531a7b36b142002aafafd16b8217fa1be2f1ddb289b5467c48c7c0884d0a2fac1a5cced620045f82d8b63db63f801878e3b Homepage: https://cran.r-project.org/package=proteus Description: CRAN Package 'proteus' (Multiform Seq2Seq Model for Time-Feature Analysis) Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable. Package: r-cran-prothmm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-phontools Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-prothmm_0.1.1-1.ca2604.1_all.deb Size: 180934 MD5sum: cf5a986f7f7a1c7ae384517239942126 SHA1: 2ac38a34fc57d79bfdddb5f6348ff7f004471492 SHA256: fc2fb6539b2f8e50dfc4240fa5b04a3a51ec819670738437bfffda5d8128d97f SHA512: e2053d24bf95aa521ccfa06172b1f2f033831feb9bcb0d8b32217b6252b63983dcfc14825dcd19958f6dc5b327f6c941441639c14658e2f6394cfe9c143e703c Homepage: https://cran.r-project.org/package=protHMM Description: CRAN Package 'protHMM' (Protein Feature Extraction from Profile Hidden Markov Models) Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012) and 'HMMer' Eddy (2011) . Features calculated by this package can be used for protein fold classification, protein structural class prediction, sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features extracted are found in Song et al. (2018) , Jin & Zhu (2021) , Lyons et al. (2015) and Saini et al. (2015) . Package: r-cran-proto Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-proto_1.0.0-1.ca2604.1_all.deb Size: 464740 MD5sum: c80d6af6ce5e7d44cb83459565ff07ac SHA1: 1605af24962ba461eafe75658d2af409e0fece85 SHA256: 96e1cadf1f2dde97f8b34f503c3cd9a0bac1c01452b6f2e3dbf2de7741663bf3 SHA512: fde02514d2489cfbada36c1a3c349b995867fdbdc5b85b3889869f8b5e0dfdf49361b62e2e637dc608039349163c355ff456b8dd67f913fcd6ad70abdcf45598 Homepage: https://cran.r-project.org/package=proto Description: CRAN Package 'proto' (Prototype Object-Based Programming) An object oriented system using object-based, also called prototype-based, rather than class-based object oriented ideas. Package: r-cran-proton Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest Filename: pool/dists/resolute/main/r-cran-proton_1.0-1.ca2604.1_all.deb Size: 447308 MD5sum: ffec8733e609809a42655902e7e481e1 SHA1: 77d2144345de7c199f552e8cba67c9ffc3765443 SHA256: be563ae3d1b6a9fbeca9bbefd22f5a59a9170925584586536bcf998f7084e0b2 SHA512: ec4a7b5034b423b0bf06ff060f15202ee07572aad21c74da9e6efb95ea13e4e9f1022f0fef0b1db81f64b762c4f8e774d56353d851b5c61e75e4c26b5efc342a Homepage: https://cran.r-project.org/package=proton Description: CRAN Package 'proton' (The Proton Game) 'The Proton Game' is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. You have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. The knowledge of dplyr is not required but may be very helpful. This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw. It's a part of Beta and Bit series. You will find more about the Beta and Bit series at http://biecek.pl/BetaBit. Package: r-cran-protoshiny Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-dynamictreecut, r-cran-protoclust, r-cran-rare, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-protoshiny_0.1.1-1.ca2604.1_all.deb Size: 1429260 MD5sum: 3e8899f4d86c7fb8e4799fa39d25b23a SHA1: 0bac64dd5ed1f02316c2d7202e3b3a63b37d9b25 SHA256: 92ab6293a75eae743c9639f517ac54112e743dca8f15dc7dc0302ddf43ce1c46 SHA512: 360c8690a766cffe777526f074b357c47f8c4fecd72864220d5a40c583b3d1615b890d616fc494bdb1bfb29611f81f5078ac56eb7a50e11ba4d887df0c5fd9d7 Homepage: https://cran.r-project.org/package=protoshiny Description: CRAN Package 'protoshiny' (Interactive Dendrograms for Visualizing Hierarchical Clusterswith Prototypes) Shiny app to interactively visualize hierarchical clustering with prototypes. For details on hierarchical clustering with prototypes, see Bien and Tibshirani (2011) . This package currently launches the application. Package: r-cran-protr Architecture: all Version: 1.7-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3217 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biostrings, r-bioc-gosemsim, r-cran-foreach, r-cran-doparallel, r-bioc-org.hs.eg.db Filename: pool/dists/resolute/main/r-cran-protr_1.7-5-1.ca2604.1_all.deb Size: 1558024 MD5sum: ced688851197cb349076ebd98aadfdb3 SHA1: 16435eeb0cbc529fc927def8a9993c1ac0524a31 SHA256: 2cc90075e3fbe92ee9a8d7d0dbcb45815c1dc5cd6b2e241211e8a1e46d467e29 SHA512: fd98938587d593ec8a395f1e9609d3591cac9beceeb181df0a768c8384843647dfcdc77e25bc6b58913f6874ab448672f3b82b40a8b906f2a76b574a92991fe7 Homepage: https://cran.r-project.org/package=protr Description: CRAN Package 'protr' (Generating Various Numerical Representation Schemes for ProteinSequences) Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) . For full functionality, the software 'ncbi-blast+' is needed, see for more information. Package: r-cran-protrackr Architecture: all Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-audio, r-cran-lattice, r-cran-signal, r-cran-tune Suggests: r-cran-amigaffh Filename: pool/dists/resolute/main/r-cran-protrackr_0.4.4-1.ca2604.1_all.deb Size: 910956 MD5sum: 5662b26d4c5325706e0587838f56b3f9 SHA1: 157679244c564994bcdfbe48237ebc2aaa4ffba7 SHA256: b156550c06189a20f3738fab4d91a24f44230500f91230ad09dd7ea0c0428b6c SHA512: 91e038485922506a0fd134134b5e26695667f61b50836e479d998b9d3e07237d4af39f8123b9693ae970b1bc2b06f76cfc441efdbab4e7b798616d7ac84889aa Homepage: https://cran.r-project.org/package=ProTrackR Description: CRAN Package 'ProTrackR' (Manipulate and Play 'ProTracker' Modules) 'ProTracker' is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play 'ProTracker' module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of 'ProTracker' and the Commodore Amiga alive. Package: r-cran-protti Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-data.table, r-cran-janitor, r-cran-progress, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-forcats, r-cran-tibble, r-cran-plotly, r-cran-ggrepel, r-cran-curl, r-cran-readr, r-cran-lifecycle, r-cran-httr, r-cran-r.utils Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-r3dmol, r-bioc-proda, r-bioc-limma, r-cran-dendextend, r-cran-pheatmap, r-cran-heatmaply, r-cran-furrr, r-cran-future, r-cran-seriation, r-cran-drc, r-cran-igraph, r-cran-stringi, r-bioc-stringdb, r-cran-iq, r-cran-scales, r-cran-farver, r-cran-ggforce, r-cran-xml2, r-cran-jsonlite, r-cran-missforest Filename: pool/dists/resolute/main/r-cran-protti_1.0.0-1.ca2604.1_all.deb Size: 1744446 MD5sum: 24458924cd19fb237ff311bcaeb69e91 SHA1: 26ca48748a9580504fd7d66c3a551d136317cffc SHA256: 66a1fdcb9bf61142097921ec34e68581b1d9d1d5b28dd46bea4430cb9fb9539e SHA512: 8b096e9675c39eba08ac34e804764f762aead0cd5c33de7663012fdc73e2accc1d4dd963326e5d99de0071ef1f72fb5bb89fdd1ed55de1dfd7b7cc116add2e61 Homepage: https://cran.r-project.org/package=protti Description: CRAN Package 'protti' (Bottom-Up Proteomics and LiP-MS Quality Control and DataAnalysis Tools) Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) ) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions. Package: r-cran-proustr Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3028 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-tokenizers, r-cran-snowballc, r-cran-attempt Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-proustr_0.4.0-1.ca2604.1_all.deb Size: 2969766 MD5sum: 1f77023188f19b28f608e99f477029fc SHA1: b83e5664af0d43e54b1abf7381f75020961e814f SHA256: ad636643d1d074d0bb68c276d957284b75ba53e070a1a14ef7e716ecf9914a83 SHA512: 1039d40a2fa389bcf1d285f917c2671f6b51aa9822db3ddd2c601bdbbbffd060960670f955efee339b474d45c7a8c6389b76bca0c2ad53cafd2c6d7e929b2a5f Homepage: https://cran.r-project.org/package=proustr Description: CRAN Package 'proustr' (Tools for Natural Language Processing in French) Tools for Natural Language Processing in French and texts from Marcel Proust's collection "A La Recherche Du Temps Perdu". The novels contained in this collection are "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve". Package: r-cran-provdebugr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-provgraphr, r-cran-provparser, r-cran-textutils Suggests: r-cran-knitr, r-cran-rdtlite, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-provdebugr_1.0.1-1.ca2604.1_all.deb Size: 148252 MD5sum: 111b6654ea33f61f65af1f72c60ad85a SHA1: 2e04ba4c572e84b14f806c6fdb9646f9a5310625 SHA256: d64e6a6e2143aa06e5ab6f6e8185fa3fff88d9a323439b8d83fdb0b2f8b46dbe SHA512: d87e4608b4155dea041556d4cb24cbcfd870177518a863fb4bf9e3cca3752dafc03132130abeccd6d7f27ba00af3129baaaf6988e1c4fbe9f7f839ddfc5b089d Homepage: https://cran.r-project.org/package=provDebugR Description: CRAN Package 'provDebugR' (A Time-Travelling Debugger) Uses provenance post-execution to help the user understand and debug their script by providing functions to look at intermediate steps and data values, their forwards and backwards lineage, and to understand the steps leading up to warning and error messages. 'provDebugR' uses provenance produced by 'rdtLite' (available on CRAN), stored in PROV-JSON format. Package: r-cran-provenance Architecture: all Version: 4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-transport, r-cran-t4transport Filename: pool/dists/resolute/main/r-cran-provenance_4.4-1.ca2604.1_all.deb Size: 833350 MD5sum: 3e0a10125d29939db3d19a8c96b4e50b SHA1: 4df4dde98bfa43397bbff90ad98a3bd11c6b8a64 SHA256: ea88704f159843305ff16a09ed500630ff0074fdcc3af4c4edfcd0edf880022d SHA512: 8ba4fb7a8a699fbc307098897ab04bfa34139726450b889bd879b6300c8b91f22fb9e4b6e2df6cd6a96f119fcb393af163c0bf692537147c870f10ca9f2cd1ca Homepage: https://cran.r-project.org/package=provenance Description: CRAN Package 'provenance' (Statistical Toolbox for Sedimentary Provenance Analysis) Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R. Package: r-cran-proverbs Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-crayon, r-cran-httr2, r-cran-glue, r-cran-lubridate, r-cran-purrr, r-cran-rvest, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-proverbs_0.4.0-1.ca2604.1_all.deb Size: 321300 MD5sum: 0dfb8259725a4ae5cb51e951e5aed2d2 SHA1: 1842da689f0558ba88766e275cfc3c49a252517e SHA256: bdffe5986e02dc75d2ba308e8bf58970487cf80bf4bdcfa04375f8085d2b9109 SHA512: f39cb52e44096f9813fab77fab0833a2e2d607d89a96e93f3ca590372dda33f0e40738ef7dcac62fcab6b59ad61fadfff6aa56b29fa07c11e8f09c48ded2d542 Homepage: https://cran.r-project.org/package=proverbs Description: CRAN Package 'proverbs' (Print a Daily Bible Proverb to Console) A simple package to grab a Bible proverb corresponding to the day of the month. Package: r-cran-provexplainr Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-provparser, r-cran-diffobj, r-cran-digest, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-provexplainr_1.1.1-1.ca2604.1_all.deb Size: 69404 MD5sum: 64e84968ad0bc0f94c14b2dce6974033 SHA1: fe54fff8083f68c0d813c75a8594863b67ca5c5f SHA256: 5bbd3600ac96a5ef91b3ff8fcdc6f76281316e7c0ea9673d166727abd01f8e81 SHA512: 8b25699bc1fe36d70246ff118241d5637403c47529f2e7faa67f743d2940f5a6edfc8001bdd8ad2a0541ae472a672277c27af7c69e069a121f6a66e4b963f641 Homepage: https://cran.r-project.org/package=provExplainR Description: CRAN Package 'provExplainR' (Compare Provenance Collections to Explain Changed Script Outputs) Inspects provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output created by the execution of a script, and find differences between two provenance collections. Factors under examination included the hardware and software used to execute the script, versions of attached libraries, use of global variables, modified inputs and outputs, and changes in main and sourced scripts. Based on detected changes, 'provExplainR' can be used to study how these factors affect the behavior of the script and generate a promising diagnosis of the causes of different script results. More information about 'rdtLite' and associated tools is available at and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . Package: r-cran-provgraphr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-provparser Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-provgraphr_1.0.1-1.ca2604.1_all.deb Size: 111180 MD5sum: 48c74d5b0424aa1d625836492f212ae3 SHA1: c4045ee4301d44f9742cc417337029ac16743786 SHA256: 5e8c51e0cbcd76e5c134c4e8275f857585a5a34fe06fe7c528990373e3265a16 SHA512: 4fc3cd747229cd563adc8a6bb7ae61e08c67be6a42b9eac9af8c84fc7f9c3625244770778378432b0e50b154db5fd3d7b59ab97dca0723714f57def692434b77 Homepage: https://cran.r-project.org/package=provGraphR Description: CRAN Package 'provGraphR' (Creates Adjacency Matrices for Lineage Searches) Creates and manages a provenance graph corresponding to the provenance created by the 'rdtLite' package, which collects provenance from R scripts. 'rdtLite' is available on CRAN. The provenance format is an extension of the W3C PROV JSON format (). The extended JSON provenance format is described in . Package: r-cran-provolleyballr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-janitor, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-selenider Suggests: r-cran-chromote, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-provolleyballr_0.1.0-1.ca2604.1_all.deb Size: 431588 MD5sum: b9f35be3e4b9e5a4d51bf2ac34629008 SHA1: 93892f3ee3579eca8b7a8a9987059d3883b1c453 SHA256: b2290d54872e1fc30c05fc500ecee30906f5f0e871fd93f776d9cab5b3701ce7 SHA512: da34d52d8e7a1b9cc05bdfbdaf25084e8ccca70339f68092c06977b4236a417db2edcd31288ce43d20c68033438d22bc8a94acb3708f9c5aa8b5fecf13d8b6ab Homepage: https://cran.r-project.org/package=provolleyballr Description: CRAN Package 'provolleyballr' (Extract Data from US Women's Professional Volleyball Websites) Tools for scraping match statistics and player data from the Athletes Unlimited (UA) website , the League One Volleyball website , and the Major League (MLV) website . Package: r-cran-provparser Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-provparser_1.0-1.ca2604.1_all.deb Size: 151766 MD5sum: 4099777175d84b5654554d22764f5721 SHA1: fcf77dad83bae519a0cf9224914130f3a502dd02 SHA256: 21a479cca1704610103c22dde13aff99fa00d9bea7c5cca3d946b5bb63bd3cd4 SHA512: f4342a98b3b5bcd4dff539b4b9b9f85d602db82b0860daadc342a5c27395eb1871942095a08ea4477851166820703c828bd41a47bc77f5463a6420d06fc46f3c Homepage: https://cran.r-project.org/package=provParseR Description: CRAN Package 'provParseR' (Pulls Information from Prov.Json Files) R functions to access provenance information collected by 'rdt' or 'rdtLite'. The information is stored inside a 'ProvInfo' object and can be accessed through a collection of functions that will return the requested data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in . Package: r-cran-provsummarizer Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-provparser Suggests: r-cran-digest, r-cran-knitr, r-cran-rdtlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-provsummarizer_1.5.1-1.ca2604.1_all.deb Size: 53714 MD5sum: 98cc6473697752f542cd0cc57e39683f SHA1: 909f6216269ca8b941e5b1542d1076fe5c0a4ae6 SHA256: 82399f38921190a834f29a05e7a234ee188abbdfff037e44478cd1b90085cbb7 SHA512: 968c6cdb586b4e8f7b466d19a9959556b423e484a7b7cf7a1be3b664b76264e9fa68bed6d6670290bd21ed2b16826fc06354cf1251614124fd0f681177bd6213 Homepage: https://cran.r-project.org/package=provSummarizeR Description: CRAN Package 'provSummarizeR' (Summarizes Provenance Related to Inputs and Outputs of a Scriptor Console Commands) Reads the provenance collected by the 'rdtLite' or 'rdt' packages, or other tools providing compatible PROV JSON output, created by the execution of a script or a console session, and provides a human-readable summary identifying the input and output files, the scripts used (if any), errors and warnings produced, and the environment in which it was executed. It can also optionally package all the files into a zip file. The exact format of the PROV JSON file created by 'rdtLite' and 'rdt' is described in . More information about 'rdtLite' and associated tools is available at and Lerner, Boose, and Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . Package: r-cran-provtracer Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-provparser Suggests: r-cran-digest, r-cran-knitr, r-cran-rdtlite, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-provtracer_1.0-1.ca2604.1_all.deb Size: 42760 MD5sum: f7533464caaed04e0f52cd9e85d93147 SHA1: 708dfe340723d04d024d25c53b5d5c9f050113d6 SHA256: a7ab4889341d653a1d933e48fddffb6b044fd905bbcb8ba2af2420e8a38fef67 SHA512: 4d5ac53107a841aa886e4ec83d4e9df1a89eefa0ddc7a201e06747a4ab2cc5eab888a2c7069c1c4d0a036726046027f77cb34f932cf5cfc2b52e4135e6a0ec61 Homepage: https://cran.r-project.org/package=provTraceR Description: CRAN Package 'provTraceR' (Uses Provenance to Trace File Lineage for One or more R Scripts) Uses provenance collected by 'rdtLite' package or comparable tool to display information about input files, output files, and exchanged files for a single R script or a series of R scripts. Package: r-cran-provviz Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3481 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rdtlite Filename: pool/dists/resolute/main/r-cran-provviz_1.0.9-1.ca2604.1_all.deb Size: 3127130 MD5sum: 6005d53b973da9af5003dd8e30ebe95b SHA1: 09a2125d4ebf708e0718476c3ecd35da3935ba4e SHA256: 53ed2e5e486ce737c115a8551c1f6852b9e6e3395f3ebf12e3fed16fc430eb9d SHA512: 15d02c8dc10091fac1014733acde3957bc002860aa26df110ed02f8a6caf3cea15294a3320fd8d936b1cb23dd91272a1e32de1e3c817a70f00487a6bcf6256c4 Homepage: https://cran.r-project.org/package=provViz Description: CRAN Package 'provViz' (Provenance Visualizer) Displays provenance graphically for provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in . More information about rdtLite and associated tools is available at and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . Package: r-cran-proxirr Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-proxirr_0.5-1.ca2604.1_all.deb Size: 55654 MD5sum: 2e19bf5219070968b3ece7695bd1cce6 SHA1: cdfe51f94acf528c07e23650d787bb325ea917e2 SHA256: 5d7ab7ef6567003b8b73171b69ae38e2bfdfb7135b07e5c21f3da1ec1775e531 SHA512: dc6fe09d48f74d774d7808a84b52b1f6faa99b33804bc7c166cbe5f2db8d9e37a3aa1e207c8623f6cbe8caa815c2b6b2c88e030720ccbfab16fbb6805845757d Homepage: https://cran.r-project.org/package=proxirr Description: CRAN Package 'proxirr' (Alpha, Beta and Gamma Proximity to Irreplaceability) Functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability. The methods for Alpha and Beta irreplaceability were first described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping Global Irreplaceability. Conservation Biology 2021;1-11. . 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The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters. Package: r-cran-prozor Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ahocorasicktrie, r-cran-docopt, r-cran-matrix, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-seqinr, r-cran-stringr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-prozor_0.3.1-1.ca2604.1_all.deb Size: 3017304 MD5sum: 9ce95acbc571b7be662ebcdfe9be9068 SHA1: 6c8c17b890fed547df721cdeff2e05dabecab6a7 SHA256: 0190989314fe642b591c217a9e522cecdb383acb0b51a05cd5066879517d1e6c SHA512: 55751cb184d768fa4ed7c874844f01fa2b1351c8f7c53713d15c8c47a7b8f2f1751afb933e81e3e262edd4abbc2a874e87472407eff9206862a26c0a30dc98be Homepage: https://cran.r-project.org/package=prozor Description: CRAN Package 'prozor' (Minimal Protein Set Explaining Peptide Spectrum Matches) Determine minimal protein set explaining peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants. Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions. 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Package: r-cran-prroc Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-rocr Filename: pool/dists/resolute/main/r-cran-prroc_1.4-1.ca2604.1_all.deb Size: 574964 MD5sum: 80eb8a097dc57574757f84fa87c47ba1 SHA1: 6586315600261439202094381d80820ba25fbd6f SHA256: 5e7966f3062bcf4a24e26644fac47403ecdc5b6e453c180849f8397b3f8523da SHA512: 2636b7ba310e07d4caceaa3e00eaa7b2fee451422a7f1385a330ac5f41115efc74028e936fcd8616b384e2793ab7efd0660ab2fbd1cab8e2092b4909068c8f7e Homepage: https://cran.r-project.org/package=PRROC Description: CRAN Package 'PRROC' (Precision-Recall and ROC Curves for Weighted and Unweighted Data) Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) ; Keilwagen et al. (2014) ; Grau et al. (2015) . 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Data queries, constructed as 'R' expressions, are evaluated using the non-standard evaluation framework provided by 'rlang' and file-backing is powered by the fast and efficient 'fst' package. 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(2020) . Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) and Grunstra et al. (2020) . Package: r-cran-przewodnik Architecture: all Version: 0.16.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pogromcydanych, r-cran-pbimisc Filename: pool/dists/resolute/main/r-cran-przewodnik_0.16.12-1.ca2604.1_all.deb Size: 1582594 MD5sum: 147d73bfa3b3931d458efddbd138c36f SHA1: df6dd09520b89402e86e0034a78f69d6df3d12d6 SHA256: 6cdc667adbf74fa396cad19415365934ede30252d0934cdc06c7170350a775ff SHA512: 1789b00b052713b97ed1260c88cdee4cc5e9470c0a09f41d75251c867ebb2eef182d7e447b4007700c8d9768de2afc6bdd7a1efb563c6170e60bc8d4e2fdb67e Homepage: https://cran.r-project.org/package=Przewodnik Description: CRAN Package 'Przewodnik' (Datasets and Functions Used in the Book 'Przewodnik po PakiecieR') Data sets and functions used in the polish book "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). See more at . Among others you will find here data about housing prices, cancer patients, running times and many others. Package: r-cran-psaboot Architecture: all Version: 1.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-psagraphics, r-cran-ggthemes, r-cran-matching, r-cran-matchit, r-cran-modeltools, r-cran-party, r-cran-psych, r-cran-reshape2, r-cran-rpart, r-cran-trimatch Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-psaboot_1.3.9-1.ca2604.1_all.deb Size: 3542658 MD5sum: db930e29cdf9d3c43849fa3217fb92f3 SHA1: e9d71f866e55e193a3fb5a5ed711f91d51e13ebe SHA256: 6f62c6aa190bd56b4f7d32c30e96cca57236d4d52ecbab52d8734f87550d68e9 SHA512: 82e1d4e2bbb4a07ab7c605c4573f177d6b9119cc7fe947a052c34388b4f68f243334f84cd47ddebe8073308ce5fcfa595d3eee644cfbaa0fc44bd1eaae59835b Homepage: https://cran.r-project.org/package=PSAboot Description: CRAN Package 'PSAboot' (Bootstrapping for Propensity Score Analysis) It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) . The functions in this package facilitate bootstrapping for propensity score analysis (PSA). By default, bootstrapping using two classification tree methods (using 'rpart' and 'ctree' functions), two matching methods (using 'Matching' and 'MatchIt' packages), and stratification with logistic regression. A framework is described for users to implement additional propensity score methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results. Package: r-cran-psagraphics Architecture: all Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-psagraphics_2.1.3-1.ca2604.1_all.deb Size: 259470 MD5sum: f414cd1d844b34807b5bb64d41281ebb SHA1: 86d2c1e134c8ab1488fd3e9b73137c690a930fb5 SHA256: a1cf34c472253a29463b3934a492c92af9321310e398d3de8c34313c61045398 SHA512: dd15ab5b1a30ca21911cff25fb4b3a1f64dcaef7461981bbcbc789442146e20205548a9db14156702df891a7941aff97f18dba9d48488831490cb23f6bf7c7a7 Homepage: https://cran.r-project.org/package=PSAgraphics Description: CRAN Package 'PSAgraphics' (Propensity Score Analysis Graphics) A collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate. Package: r-cran-psawr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-psawr_0.1.0-1.ca2604.1_all.deb Size: 51978 MD5sum: 925ed167d48943dd40d9c145cbc56782 SHA1: 798f101948123ec8fecdaafd3d26b19f7cb3e154 SHA256: 3b813caec111df0755ed6c11232f45c9db6907ff53b6fd4fa70a810962eb079e SHA512: c8722e8b2bc612bf4eb8605932a13862a8a08a987b096e3508f121f98215456f8acae1f11fe92567f8b727a5a41082ac483151c1c6573f9088f96a60fd6fe19a Homepage: https://cran.r-project.org/package=PSAWR Description: CRAN Package 'PSAWR' ('Pushshift' API Wrapper for 'Reddit' Submission and CommentSearch) Connects to the API of to search for 'Reddit' comments and submissions. Package: r-cran-psborrow2 Architecture: all Version: 0.0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4668 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-glue, r-cran-posterior, r-cran-generics, r-cran-matrix, r-cran-mvtnorm, r-cran-future, r-cran-simsurv Suggests: r-cran-survival, r-cran-flexsurv, r-cran-eha, r-cran-testthat, r-cran-usethis, r-cran-vdiffr, r-cran-tibble, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-bayesplot, r-cran-matrixcalc, r-cran-weightit, r-cran-matchit, r-cran-bayesppd, r-cran-ggsurvfit, r-cran-gbm, r-cran-ggplot2, r-cran-cobalt, r-cran-table1, r-cran-gt, r-cran-gtsummary Filename: pool/dists/resolute/main/r-cran-psborrow2_0.0.5.1-1.ca2604.1_all.deb Size: 2687786 MD5sum: ece8610c975a31ee3a36dcab689dfde8 SHA1: 170479a09643e91026403987fc90a7572229012e SHA256: 4122f7735fa91255d06c3af471fee3d0e899a1836025b3c0be342b74969c6c5c SHA512: fece7afe7ce3b3528a605056618cfa22e21dc43cc6c028dc37d39b41557609f195e7bbda4b5db4c2107d0e213684bc84e8599d42f5af7b8b77475e00aa30c96d Homepage: https://cran.r-project.org/package=psborrow2 Description: CRAN Package 'psborrow2' (Bayesian Dynamic Borrowing Analysis and Simulation) Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 . There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from . Package: r-cran-psborrow Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-rjags, r-cran-mvtnorm, r-cran-ggplot2, r-cran-foreach, r-cran-doparallel, r-cran-matchit, r-cran-survival, r-cran-futile.logger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-matrix, r-cran-assertthat, r-cran-pkgload, r-cran-flexsurv, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-psborrow_0.2.4-1.ca2604.1_all.deb Size: 410622 MD5sum: 3ac3121a310b53693d0b5c2297c305cd SHA1: ea7e75385e067a98f602f55f236627d540554e80 SHA256: 1f667d022687c083b0b9de3daaacfabb3835f8ce09538193786ef267523f2622 SHA512: 3ca1bde70b8e029ba7ad95f965e7465c5004e57b2ac3bed22e72365c0c7944122449b9bf1c8244496f92fcc282ec9177c1ea0aa2ab5ee097c648077a908e70a8 Homepage: https://cran.r-project.org/package=psborrow Description: CRAN Package 'psborrow' (Bayesian Dynamic Borrowing with Propensity Score) A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) that combines propensity score and Bayesian dynamic borrowing methods. Package: r-cran-psc Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2612 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-mvtnorm, r-cran-enrichwith, r-cran-flexsurv, r-cran-survminer, r-cran-gtsummary, r-cran-rcolorbrewer, r-cran-ggpubr, r-cran-posterior, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-psc_2.0.1-1.ca2604.1_all.deb Size: 2278216 MD5sum: 994c91bc8d4768f2369b3a8ec8f99988 SHA1: d01af6a1627c2d2aee7884c2ecec8762a4a9c781 SHA256: ff9e454e5d02c00ce7facc421a58c6240e36e85d500eab15989d4bf7574a5bcc SHA512: 83478d6aa40f5085e3187f9be898b0fbc26090457d1ff26bb836c7bb4c8dfcbca7021ecb2def04637fe4d80754cfc6cfb99f8b77cb30befd617d2d3a277de3d7 Homepage: https://cran.r-project.org/package=psc Description: CRAN Package 'psc' (Personalised Synthetic Controls) Allows the comparison of data cohorts (DC) against a Counter Factual Model (CFM) and measures the difference in terms of an efficacy parameter. Allows the application of Personalised Synthetic Controls. 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Both tumor-normal paired and tumor-only analyses are supported. Package: r-cran-pscdesign Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-psc, r-cran-s7 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pscdesign_1.0.0-1.ca2604.1_all.deb Size: 1945428 MD5sum: 108143291461c07f4e0a69d5045d0c54 SHA1: df5899e34996ab0528c7f5ed80e4642527e09b03 SHA256: efd7e123c94371734f9c3a8d8d17cc19f071ba01ecf152cdb3b9d6bd5f20891a SHA512: 7cbd00abb72665d1de9d94a6014253b5840f666417f1f9e029608a39fc9880181a7a5e935d30be7d91da49ef58cebbef56e7577bea4438c9e47f6605f1a244b2 Homepage: https://cran.r-project.org/package=pscDesign Description: CRAN Package 'pscDesign' (Study Design for Personalised Synthetic Controls) Tools for the design of prospective studies using Personalised Synthetic Controls. Can be used in either single arm or randomised studies. Package: r-cran-pscore Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-lavaan, r-cran-jwileymisc Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pscore_0.4.1-1.ca2604.1_all.deb Size: 451776 MD5sum: d52aac9a8ae74c905b1b73de46ea40a7 SHA1: f51fae71d00b741fa51921d286ec13c8d0c51f8a SHA256: d18274246e20d5d988309325ec2d5d9247bd05623ed690645dd30fc271431a19 SHA512: caa6e6e082ddb520f002b8641c208f460b36e29ea0dc00f7933a40a315c9f889f4e0e545c2028c2abeea50c79d0d87cf817b12c61c05018a7d4102004266523d Homepage: https://cran.r-project.org/package=pscore Description: CRAN Package 'pscore' (Standardizing Physiological Composite Risk Endpoints) Provides a number of functions to simplify and automate the scoring, comparison, and evaluation of different ways of creating composites of data. It is particularly aimed at facilitating the creation of physiological composites of metabolic syndrome symptom score (MetSSS) and allostatic load (AL). Provides a wrapper to calculate the MetSSS on new data using the Healthy Hearts formula. Package: r-cran-pscr Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pracma, r-cran-vgam Suggests: r-cran-mstate Filename: pool/dists/resolute/main/r-cran-pscr_1.1-1.ca2604.1_all.deb Size: 39714 MD5sum: 559fa1d0c4ef3152a63faa81978ff036 SHA1: 138dff168ae7c420e299e9fea275f0370f147558 SHA256: 2730947bea86c550dfefff419de7c3aedf3871885c304cf429a62c9a2e6c8ae9 SHA512: 066918a5384c8d72516761282db929541a9c904f512801adf980843e8755ada999215d7e3e2b2055cc8a94e1064fef5a5115acd6bf7679217870d842add13c16 Homepage: https://cran.r-project.org/package=PScr Description: CRAN Package 'PScr' (Estimation for the Power Series Cure Rate Model) Provide estimation for particular cases of the power series cure rate model . For the distribution of the concurrent causes the alternative models are the Poisson, logarithmic, negative binomial and Bernoulli (which are includes in the original work), the polylogarithm model and the Flory-Schulz . The estimation procedure is based on the EM algorithm discussed in . For the distribution of the time-to-event the alternative models are slash half-normal, Weibull, gamma and Birnbaum-Saunders distributions. Package: r-cran-psdistr Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-psdistr_0.0.1-1.ca2604.1_all.deb Size: 90216 MD5sum: 36101a1d546c78cdd6706fa5517f9edb SHA1: 61a63b603b800c2055ae6d33cda28eb8be87bfe5 SHA256: b65e528254bef168d7267cd39cba544fcbcecf388d97f578c64f85f1d4a63e52 SHA512: fd53b3281a99803cd53a5fea3f153aaf8e67f59bc3acbc4403fd6b96c64c62411b75664fdd56a62baba932f41d582ec87286b2193fed869968bc2fc39ab9671a Homepage: https://cran.r-project.org/package=PSDistr Description: CRAN Package 'PSDistr' (Distributions Derived from Normal Distribution) Presentation of distributions such as: two-piece power normal (TPPN), plasticizing component (PC), DS normal (DSN), expnormal (EN), Sulewski plasticizing component (SPC), easily changeable kurtosis (ECK) distributions. Density, distribution function, quantile function and random generation are presented. For details on this method see: Sulewski (2019) , Sulewski (2021) , Sulewski (2021) , Sulewski (2022) <"New members of the Johnson family of probability dis-tributions: properties and application">, Sulewski, Volodin (2022) , Sulewski (2023) . Package: r-cran-psdr Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-devtools, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-psdr_1.0.3-1.ca2604.1_all.deb Size: 222814 MD5sum: 41c4f34abbb48a162d9dcc37bf5649b8 SHA1: e48cbf632ef3613df30c22a3e5d4478fdd32f487 SHA256: 9bb8e08a4a93f58787a568dddaaeb3df12d49147c5cfa241ce86a2c177f90c6f SHA512: f456aee00ed1618d917e1721132c08045d164c3c6d83fabc568fc0c6b6158e816d940937ab8c39f93a214ded22eecc04112f6dabdc1687cca613b97c49459d81 Homepage: https://cran.r-project.org/package=psdr Description: CRAN Package 'psdr' (Use Time Series to Generate and Compare Power Spectral Density) Functions that allow you to generate and compare power spectral density (PSD) plots given time series data. Fast Fourier Transform (FFT) is used to take a time series data, analyze the oscillations, and then output the frequencies of these oscillations in the time series in the form of a PSD plot.Thus given a time series, the dominant frequencies in the time series can be identified. Additional functions in this package allow the dominant frequencies of multiple groups of time series to be compared with each other. To see example usage with the main functions of this package, please visit this site: . The mathematical operations used to generate the PSDs are described in these sites: . . Package: r-cran-pseudo Architecture: all Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kmsurv, r-cran-geepack Filename: pool/dists/resolute/main/r-cran-pseudo_1.4.3-1.ca2604.1_all.deb Size: 43444 MD5sum: 30c47f52b6cedae7f7d005ee87efa9a1 SHA1: d0f91225b913377c369773c2ab896165185d1a94 SHA256: d89b9114199005859e35b80e47391168df55d08d23373c033059f6c9bbc5c24f SHA512: 41a17d730b025ed416fa51e4210798c63b112f6fd471631cff6d9a0e9fcb52ab836cad8edf83f4fe145d9880722033cfb2967ed9335015ebb5907d07b7bda5a6 Homepage: https://cran.r-project.org/package=pseudo Description: CRAN Package 'pseudo' (Computes Pseudo-Observations for Modeling) Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean, survival function based on the Kaplan-Meier estimator see Klein et al. (2008) . 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Parallel processing is supported. Package: r-cran-pseval Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-printr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pseval_1.3.3-1.ca2604.1_all.deb Size: 267492 MD5sum: 1a8498cbe896ba919e06a93ec73063ef SHA1: f578602e6ef360d4fac1d6e206714e746191afe9 SHA256: 4a1079e2bed2ca2a9e71786442f46626ba29cc0eea1bbb7736f7462c31ccd1df SHA512: d8c82d78f29c63c12f3f4016e144d1ce558fafeb843ab3cb2a983d4e6ce3116e3b9fa0ccf4f746a1036e49972dfb69b0ef8dcf54c2d658cf35e1537a6134661e Homepage: https://cran.r-project.org/package=pseval Description: CRAN Package 'pseval' (Methods for Evaluating Principal Surrogates of TreatmentResponse) Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing. Package: r-cran-psf Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-cluster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-psf_0.5-1.ca2604.1_all.deb Size: 110274 MD5sum: f521ce437be6eac43845d8c5add2e616 SHA1: 723e4e1498916500e544eb691ec9de0e287e2409 SHA256: 9843a76dced668c1779e8737a30af253eb8963351b189d38448952efbd5449f0 SHA512: 547a10e0609c040e2b824baf37afd55c395e6d44b0af698d1458fbbf0feb4b0115cfa97fcb897afa67f0672dabf95f4779147647eb482dfae75b957a70be47ee Homepage: https://cran.r-project.org/package=PSF Description: CRAN Package 'PSF' (Forecasting of Univariate Time Series Using the PatternSequence-Based Forecasting (PSF) Algorithm) Pattern Sequence Based Forecasting (PSF) takes univariate time series data as input and assist to forecast its future values. This algorithm forecasts the behavior of time series based on similarity of pattern sequences. Initially, clustering is done with the labeling of samples from database. The labels associated with samples are then used for forecasting the future behaviour of time series data. The further technical details and references regarding PSF are discussed in Vignette. Package: r-cran-psfmi Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-norm, r-cran-survival, r-cran-mitools, r-cran-proc, r-cran-rms, r-cran-magrittr, r-cran-rsample, r-cran-mice, r-cran-mitml, r-cran-cvauc, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-lme4, r-cran-car Suggests: r-cran-foreign, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bookdown, r-cran-readr, r-cran-gtools, r-cran-covr Filename: pool/dists/resolute/main/r-cran-psfmi_1.4.0-1.ca2604.1_all.deb Size: 893262 MD5sum: f87821ab23fda36f0e2b8da6d066e661 SHA1: 8e637487b3244c42ddfa24e5971a5a6f28ae4ea4 SHA256: ce6e1ae6d19beabc80d88be3d9b23de2b58d8563f0a60c4786f38453e7162641 SHA512: ab29dc7e9c64e3574d9fc008640869f94c5a1db60ac1bbb951eca1bcf29c21b51e67f6acc25c35aced779467f82336147e108c2b842d07601ba2f0e743a8ae92 Homepage: https://cran.r-project.org/package=psfmi Description: CRAN Package 'psfmi' (Prediction Model Pooling, Selection and Performance EvaluationAcross Multiply Imputed Datasets) Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) . Wiel (2009) . Marshall (2009) . Package: r-cran-psgc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11284 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-psgc_0.1.0-1.ca2604.1_all.deb Size: 6083890 MD5sum: 4f6010ba705898d5e4ca0f3efcdba4d4 SHA1: 1fab0edf3a9e6f02e186e4cea7d71f924a782b6a SHA256: fdf2cad96e53fbb78d29d7fa3b97b201d7665e99b54ae388dc912380a145e77e SHA512: a9836164421f9ca66bf991276c65ddab5ed4aaab12d6dbedd455fdfe46a3252a373db9cf27909b5467a73fdd952a84204c21d8b4c7a43443d492e25c3981d0b7 Homepage: https://cran.r-project.org/package=psgc Description: CRAN Package 'psgc' (Philippine Standard Geographic Code) Provides access to the Philippine Standard Geographic Code (PSGC), an official classification system for geographic areas in the Philippines published by the Philippine Statistics Authority (PSA). Includes area names, geographic levels (Region, Province, City, Municipality, Sub-Municipality, and Barangay), and census population figures across multiple PSA publication releases. Offers utilities to look up individual codes, filter by geographic level, track code changes across releases via a built-in crosswalk, and retrieve population data in long or wide format. Package: r-cran-psgoft Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-moments Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-psgoft_0.0.1-1.ca2604.1_all.deb Size: 27410 MD5sum: 6327df937cb7904f3fce441f1dc3daeb SHA1: 4760166de9262813e263ea235b8edfd376fb8ebe SHA256: c7c707dbcf8b8e3b552f15bba836a462a68ebe0b6d14efbaa8cdb3dfb45fed37 SHA512: 255425da76f2d5658fdd906c5c1e796d0ce99db0d07cd9d2771a8501eb75095995d3d44c29169896a6f08840dce27994f6d6674414ef56c04954bea151781847 Homepage: https://cran.r-project.org/package=PSGoft Description: CRAN Package 'PSGoft' (Modified Lilliefors Goodness-of-Fit Normality Test) Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) . Package: r-cran-psharmonize Architecture: all Version: 0.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-rmarkdown, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-psharmonize_0.3.6-1.ca2604.1_all.deb Size: 804984 MD5sum: b0224f0bcde4bbebda0b36d9c856d74f SHA1: 4dc9ddb899d2d8f9cca6297100f9167c99da8cf0 SHA256: 42f832dcbeaaf2a0a897a81be67f8b06630adf64da7ab52035a9a8f1d173efb8 SHA512: 587cb7c1b4e33e3c415913f60d88a75bab9022449b83458fd5cac800f39270f779c61a0ed2cffcd8f96884f5395cc906731f525e5438334c5424ac37ae007f92 Homepage: https://cran.r-project.org/package=psHarmonize Description: CRAN Package 'psHarmonize' (Creates a Harmonized Dataset Based on a Set of Instructions) Functions which facilitate harmonization of data from multiple different datasets. Data harmonization involves taking data sources with differing values, creating coding instructions to create a harmonized set of values, then making those data modifications. 'psHarmonize' will assist with data modification once the harmonization instructions are written. Coding instructions are written by the user to create a "harmonization sheet". This sheet catalogs variable names, domains (e.g. clinical, behavioral, outcomes), provides R code instructions for mapping or conversion of data, specifies the variable name in the harmonized data set, and tracks notes. The package will then harmonize the source datasets according to the harmonization sheet to create a harmonized dataset. Once harmonization is finished, the package also has functions that will create descriptive statistics using 'RMarkdown'. Data Harmonization guidelines have been described by Fortier I, Raina P, Van den Heuvel ER, et al. (2017) . Additional details of our R package have been described by Stephen JJ, Carolan P, Krefman AE, et al. (2024) . Package: r-cran-psica Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-gridbase, r-cran-randomforest, r-cran-rpart, r-cran-partykit, r-cran-party, r-cran-bayestree Filename: pool/dists/resolute/main/r-cran-psica_1.0.2-1.ca2604.1_all.deb Size: 59472 MD5sum: 897984638ab45d57c4a6e4ba75cd20ff SHA1: a3ad5c2d301bb08a6ce46b7dadc09eb1b1d90a07 SHA256: b43d69fb7855f0e950dbdd5f175d39c8d28b14c14c4490436b23d224b026fbfe SHA512: 50051f16e4a1a2186d221488b40ca1dc7414d73f49ad8608644cc9f4aec1f1f389d71c7d6141d1582decf4c01c8c5c9d273795f567e16df70f0eb0b68984cf0c Homepage: https://cran.r-project.org/package=psica Description: CRAN Package 'psica' (Decision Tree Analysis for Probabilistic Subgroup Identificationwith Multiple Treatments) In the situation when multiple alternative treatments or interventions available, different population groups may respond differently to different treatments. This package implements a method that discovers the population subgroups in which a certain treatment has a better effect than the other alternative treatments. This is done by first estimating the treatment effect for a given treatment and its uncertainty by computing random forests, and the resulting model is summarized by a decision tree in which the probabilities that the given treatment is best for a given subgroup is shown in the corresponding terminal node of the tree. Package: r-cran-psidr Architecture: all Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2863 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcurl, r-cran-foreign, r-cran-sascii, r-cran-openxlsx, r-cran-futile.logger Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-psidr_2.3-1.ca2604.1_all.deb Size: 1638134 MD5sum: ac0a4179c8b8168ebf758aeaa61bc36f SHA1: 38975c9b52bb2ef5f59e32e5a20ab14363ca7f78 SHA256: 89dd003dccfec96bf538caf394b838902ddd4a8b6e2edf66bd36627f3c6d4236 SHA512: a101aba197d6da52bcaad554d8dfb07115cda5953b1fcc598aaf8c2a4a296ed1fd4528a52f4ea29530966bb3331dd94942a48f635aad2a5d1687fa4d01dc934f Homepage: https://cran.r-project.org/package=psidR Description: CRAN Package 'psidR' (Build Panel Data Sets from PSID Raw Data) Makes it easy to build panel data in wide format from Panel Survey of Income Dynamics (PSID) delivered raw data. Downloads data directly from the PSID server using the 'SAScii' package. 'psidR' takes care of merging data from each wave onto a cross-period index file, so that individuals can be followed over time. The user must specify which years they are interested in, and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each year). The package offers helper functions to retrieve variable names from different waves. There are different panel data designs and sample subsetting criteria implemented ("SRC", "SEO", "immigrant" and "latino" samples). More information about the PSID can be obtained at . Package: r-cran-psidread Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-asciisetupreader, r-cran-dplyr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-diagrammer, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-psidread_1.0.6-1.ca2604.1_all.deb Size: 2414962 MD5sum: 6440879cc548609c135ad7e7dc83ae9d SHA1: b4e2bfdc7d7684149b4412d64bae237e4e1a623a SHA256: ac738b68136f4c470c29b7a223a875763e976a8c73e04bc2ebfbaccc5d71460e SHA512: e36f6aa68ba8a58916085d3c01f2bb5549301f9fd42e179670fd6116e3965b5df6d0bb3c6f80fff36fb1e3a797bd107d2bf6570a03cef2bbeabe03ef24fe8be5 Homepage: https://cran.r-project.org/package=psidread Description: CRAN Package 'psidread' (Streamline Building Panel Data from Panel Study of IncomeDynamics ('PSID') Raw Files) Streamline the management, creation, and formatting of panel data from the Panel Study of Income Dynamics ('PSID') using this user-friendly tool. Simply define variable names and input code book details directly from the 'PSID' official website, and this toolbox will efficiently facilitate the data preparation process, transforming raw 'PSID' files into a well-organized format ready for further analysis. Package: r-cran-psim Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-matrixstats, r-cran-magrittr, r-cran-tidyverse Suggests: r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-psim_0.1.0-1.ca2604.1_all.deb Size: 13810 MD5sum: 4fbddf40000bde88c33d5ff941ea6a36 SHA1: 9ea9dfab4f14ae13ec43585193eb0430c3daec5e SHA256: bd0bed5bc4afc1e8fad992669c95c20479ffbd947a762a83010c1ef0ef566999 SHA512: 2bbde8902454aad4639682b4ecc197c948ac075dcaf4732fe1d33dd961c1dcaee9cb64c91aa974c5e659225c717f216db7555d502467d9e0656963820851b2ef Homepage: https://cran.r-project.org/package=PSIM Description: CRAN Package 'PSIM' (Preference Selection Index Method (PSIM)) The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) . 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These tests are: the modular test and the logarithmic minimum test. For details on this method see: Sulewski (2017) , Sulewski (2018) , Sulewski (2019) , Sulewski (2021) . 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For more details see Klein et al. (2021) . Package: r-cran-pslm2015 Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-pslm2015_0.2.0-1.ca2604.1_all.deb Size: 4350602 MD5sum: 1b210a7c4bc655042bb90b90bda845e2 SHA1: 46250df9a5100607a8ee822d855356c49f2c829d SHA256: 48f7812c44e5f04cae6e22c728fb10028058f4d1bb68e250cbfdd9f6bf0c6f19 SHA512: e3b047a763b0d184bb6556d8ba393e864766f5c7359b07738aab3ca5fd6a3b54bc55a128b702367f2b12f8b8dd0e2aa4209def7629e61bcf6efa754fad28c26b Homepage: https://cran.r-project.org/package=PSLM2015 Description: CRAN Package 'PSLM2015' (Pakistan Social and Living Standards Measurement Survey 2014-15) Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (). Package: r-cran-psm3mkv Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 839 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-dplyr, r-cran-flexsurv, r-cran-ggplot2, r-cran-pharmaverseadam, r-cran-purrr, r-cran-rlang, r-cran-simplicialcubature, r-cran-survival, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-boot, r-cran-covr, r-cran-ggsci, r-cran-hmdhfdplus, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-psm3mkv_0.3.2-1.ca2604.1_all.deb Size: 636604 MD5sum: fc2795acd9a52e872585b0eb8a3f6596 SHA1: 396dee8ad195be93587d6fd7a65358db71973200 SHA256: 1cf3f3afdbb2c0b4cd2611254d693265bfd961a8af34417f9a0c2613a8d89394 SHA512: 15f08bd36f3d35eca720e3c972028c05632a7eefec83f453d78e7b52116bbfdfc5bcc02add1f3363e569e9422bd12a2e6e3cc50d6edbc889baa1a70f471e30ec Homepage: https://cran.r-project.org/package=psm3mkv Description: CRAN Package 'psm3mkv' (Evaluate Partitioned Survival and State Transition Models) Fits and evaluates three-state partitioned survival analyses (PartSAs) and Markov models (clock forward or clock reset) to progression and overall survival data typically collected in oncology clinical trials. These model structures are typically considered in cost-effectiveness modeling in advanced/metastatic cancer indications. Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy. Package: r-cran-psme Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mgcv, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-psme_1.0.0-1.ca2604.1_all.deb Size: 35798 MD5sum: def0de0b4a88042edcf9e1894c70d49a SHA1: ac1dda09852a8199d5584337c186e3857ec3e725 SHA256: 482bc91e69ba5cea4e8ab7655baf3014b4e199b2b12b16f7f9351143d0cbf6c4 SHA512: 37c535e7fe454f4b2b189b06440659c7551ae85bf0fc3de36e0fcebe388e055dcbb92840f0da7e0d3bc3ffda77a8c2f78ab67362f0557089cabf533caac62a6b Homepage: https://cran.r-project.org/package=psme Description: CRAN Package 'psme' (Penalized Splines Mixed-Effects Models) Fit penalized splines mixed-effects models (a special case of additive models) for large longitudinal datasets. The package includes a psme() function that (1) relies on package 'mgcv' for constructing population and subject smooth functions as penalized splines, (2) transforms the constructed additive model to a linear mixed-effects model, (3) exploits package 'lme4' for model estimation and (4) backtransforms the estimated linear mixed-effects model to the additive model for interpretation and visualization. See Pedersen et al. (2019) and Bates et al. (2015) for an introduction. Unlike the gamm() function in 'mgcv', the psme() function is fast and memory-efficient, able to handle datasets with millions of observations. Package: r-cran-psminer Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bupar, r-cran-dplyr, r-cran-data.table, r-cran-forcats, r-cran-ggplot2, r-cran-tidyr, r-cran-rlang, r-cran-cli, r-cran-glue, r-cran-stringi Suggests: r-cran-knitr, r-cran-eventdatar, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-edear Filename: pool/dists/resolute/main/r-cran-psminer_0.1.1-1.ca2604.1_all.deb Size: 122286 MD5sum: 41a10b0a1079cf9e550592e44690feb7 SHA1: 28c140a9663bd6472e20203e08fcd66140cf5db6 SHA256: 9d693b07f470800b920b98c6cca4918bc498d2c98eb497b845a104234838fceb SHA512: 5ead4b087af3965825c143c39e9c7f2f65d4e4a59ba0cec0a34a5c3a1c260a453cb31dae5614bffdc8b066d06362f2fea58309f8309619446acd6c9ffd8013c1 Homepage: https://cran.r-project.org/package=psmineR Description: CRAN Package 'psmineR' (Performance Spectrum Miner for Event Data) Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) . 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Package: r-cran-psor Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-superlearner, r-cran-caret, r-cran-dplyr, r-cran-geex, r-cran-magrittr, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-psor_0.1.0-1.ca2604.1_all.deb Size: 126558 MD5sum: c4268af2714dc4231b8fe120ee8548a1 SHA1: 26005cdb5077bf849826caa18d1e3fd6a7447586 SHA256: 09318b1534da1ab515c518e35c6bc40b1b940f53857924e0c2f392ef0d3d352f SHA512: 5010a9330339f6dd5bbffb1f4d5ca7ade0d4c1e8c400114918b9426a4548b470e999ef9cc486c4be0c14b47769710637450c1212dce507a57aaa9debc4b5c629 Homepage: https://cran.r-project.org/package=PSor Description: CRAN Package 'PSor' (Semiparametric Principal Stratification Analysis BeyondMonotonicity) Estimates principal causal effects under principal stratification using a margin-free, conditional odds ratio sensitivity parameter. 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Package: r-cran-pspatreg Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1610 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ameshousing, r-cran-dplyr, r-cran-fields, r-cran-ggplot2, r-cran-mba, r-cran-mass, r-cran-minqa, r-cran-matrix, r-cran-numderiv, r-cran-plm, r-cran-rdpack, r-cran-sf, r-cran-spatialreg, r-cran-spdep, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pspatreg_1.1.2-1.ca2604.1_all.deb Size: 1096158 MD5sum: a719c6c81b8f8baae5312ac7162916f7 SHA1: ca895ba357fb51d44a49aa8a0bada223d14ef419 SHA256: 2beb01e801cc2e7f096519642fe387d674d0003e381d917bb64c3f3387f8ad37 SHA512: 136bc3c4f2e8d68e377ce52e65f2793176a9f0057c11cc5cd31a47eb9f30b24f41d3309f77ef71ae7eed4d219e82d502b31e9ae775eacb1460f085c3e27fdff8 Homepage: https://cran.r-project.org/package=pspatreg Description: CRAN Package 'pspatreg' (Spatial and Spatio-Temporal Semiparametric Regression Modelswith Spatial Lags) Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), ; Rodriguez-Alvarez, M.X. et al. (2015) and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) . Package: r-cran-pspower Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pspower_2.0.0-1.ca2604.1_all.deb Size: 256926 MD5sum: c62bdf2e624fea716b04a101853332f1 SHA1: bfe35905d3581f48fb027b337c714d925e98343b SHA256: 919f2f0913dcf4431eb28bdbc3e300f4c174ab4a4908acfcc30b1bcd457fb47d SHA512: c4eedd1164c610f1586a44eb93c693b65a4170d9a1f67eae8ddf3690ac22e8132603bd4e7583aa69298c1af7cccb1fc8696fb094049fc41773f43f5e74aecc19 Homepage: https://cran.r-project.org/package=PSpower Description: CRAN Package 'PSpower' (Sample Size and Power for Propensity Score Weighted Estimators) Computes sample size and power for causal inference studies that use propensity score (PS) weighting. Supports continuous, binary, and time-to-event (survival) outcomes under four estimands: average treatment effect (ATE), average treatment effect on the treated (ATT), average treatment effect on the controls (ATC), and average treatment effect on the overlap population (ATO). For continuous and binary outcomes, the asymptotic variance of the Hajek inverse probability weighting estimator is derived under a logit-normal propensity score model, approximated by a Beta distribution matched through the Bhattacharyya overlap coefficient. For survival outcomes, the asymptotic variance of the propensity-score- weighted partial likelihood estimator is used for randomized trials and observational studies. The Schoenfeld formula is also available for randomized trial settings. Package: r-cran-psre Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-car, r-cran-nortest, r-cran-marginaleffects, r-cran-ggplot2, r-cran-boot, r-cran-cowplot, r-cran-fancova, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-tibble, r-cran-sm, r-cran-multcomp, r-cran-rlang, r-cran-ggrepel, r-cran-mgcv, r-cran-viztest Suggests: r-cran-ggeffects, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-psre_0.4-1.ca2604.1_all.deb Size: 630208 MD5sum: 24fdc3855284ed99430a219295db0f40 SHA1: f08c0b5280a2c8516ba7c3bd7aff782a959facd4 SHA256: 64dee4551a7100b61a4f7f6e3bd8a8deb36d99e9802afa96e46cf2c9a264255e SHA512: f9fcdfb99ca73ea9f68192ad065a455aba024779fc051bfd007d34ef6b62ba2214e8eafd0a2c263a453d8414b7335a23878112b078417c261e71805807283123 Homepage: https://cran.r-project.org/package=psre Description: CRAN Package 'psre' (Presenting Statistical Results Effectively) Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800). Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay. Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book. Package: r-cran-psricalc Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-psricalc_1.0.0-1.ca2604.1_all.deb Size: 15844 MD5sum: 4469eb8b0a1fdcb00ffbf164962ede66 SHA1: 2c3cfe2f852b41384b7cb48335ced774afc24321 SHA256: 939bb90c3d53c91f3c22d6432aeb175f7f723614bda5f43d05fd7ae0a8acb28f SHA512: 5e1d80757d8e25843b748c95d44b6ddc71713f7f055a4140b565bb4a1241fb7651ec7a76962cb5690dfeb9bc7647132a7b9efbb3f648bf8efbf9f7a4df9ceaaa Homepage: https://cran.r-project.org/package=PSRICalc Description: CRAN Package 'PSRICalc' (Plant Stress Response Index Calculator) Calculate Plant Stress Response Index (PSRI) from time-series germination data with optional radicle vigor integration. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) . Provides clean, direct PSRI calculations suitable for agricultural research and statistical analysis. Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach. Package: r-cran-psricalcsm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-psricalcsm_1.0.0-1.ca2604.1_all.deb Size: 46630 MD5sum: d9182fbc4c53b7fecba17300455ecf35 SHA1: acc645acca7b177659da8891c942be91b260f0ca SHA256: 330ecfc0e077c4f6543bb9fb346327334f4ee2672e56b71ca6bd7d8e1d1b946d SHA512: 085cb37e17884b7972ad2f899dc5e56f498a6aebcb44ee5aede2c01b280abef5cf57e670c8fc30777af04ab7ee286f30f398c7c5af4d9db0006f097a8ba32847 Homepage: https://cran.r-project.org/package=PSRICalcSM Description: CRAN Package 'PSRICalcSM' (Plant Stress Response Index Calculator - Softmax Method) Implements the softmax aggregation method for calculating Plant Stress Response Index (PSRI) from time-series germination data under environmental stressors including prions, xenobiotics, osmotic stress, heavy metals, and chemical contaminants. Provides zero-robust PSRI computation through adaptive softmax weighting of germination components (Maximum Stress-adjusted Germination, Maximum Rate of Germination, complementary Mean Time to Germination, and Radicle Vigor Score), eliminating the zero-collapse failure mode of the geometric mean approach implemented in 'PSRICalc'. Includes perplexity-based temperature parameter calibration and modular component functions for transparent germination analysis. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) . Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems (Anthropic Claude and OpenAI GPT) served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies. Package: r-cran-pss.health Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-easypower, r-cran-envstats, r-cran-epir, r-cran-ggplot2, r-cran-icc.sample.size, r-cran-kappasize, r-cran-longpower, r-cran-plotly, r-cran-powermediation, r-cran-powersurvepi, r-cran-presize, r-cran-proc, r-cran-pwr, r-cran-pwr2, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfeedback, r-cran-shinyhelper, r-cran-writexl Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mess, r-cran-webpower Filename: pool/dists/resolute/main/r-cran-pss.health_1.1.5-1.ca2604.1_all.deb Size: 823316 MD5sum: f196e4691633ae9407bd532027e3cbd8 SHA1: 0c657c222cbe942917ecc6d3d9f6ff3d3db00b46 SHA256: e0dd5db74092b812be06c2b146f2a7d776f2fcafb0857a52c18d0503a482384a SHA512: 3297d4db471a3854887f507d7fa0bc0d9bf30c101047d527f149d921d409378d639e2f673fc6af6ed6ad3b76160400b1e2cfbc123a55e765a6591040d98419f6 Homepage: https://cran.r-project.org/package=PSS.Health Description: CRAN Package 'PSS.Health' (Power and Sample Size for Health Researchers via Shiny) Power and Sample Size for Health Researchers is a Shiny application that brings together a series of functions related to sample size and power calculations for common analysis in the healthcare field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, regression coefficients (linear, logistic, gamma, and Cox), linear mixed model, Cronbach's alpha, interobserver agreement, intraclass correlation coefficients, limit of agreement on Bland-Altman plots, area under the curve, sensitivity and specificity incorporating the prevalence of disease. You can also use the online version at . Package: r-cran-pssim Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pssim_0.1.0-1.ca2604.1_all.deb Size: 54430 MD5sum: 028b55ac0fa258776f8180351fd75496 SHA1: af02463013b1756f41fa45aeed3cb339b5419a21 SHA256: a02bb12bd4bad42439731566bd8173f35eed0a41439b2f32d2cc1c53d759edac SHA512: c97cc539674ccfee4f46f1663dbb9d89902e02e5d4f7778dc044d92b15841e2384b64f50fbe81092071518eada978f47f4f482f7a4c9a80b47e6bd064b8b0abe Homepage: https://cran.r-project.org/package=PSSIM Description: CRAN Package 'PSSIM' (Test of Independence & Image Structural Similarity Measure PSSIM) Test-based Image structural similarity measure and test of independence. This package implements the key functions of two tasks: (1) computing image structural similarity measure PSSIM of Wang, Maldonado and Silwal (2011) ; and (2) test of independence between a response and a covariate in presence of heteroscedastic treatment effects proposed by Wang, Tolos, and Wang (2010) . Package: r-cran-pssmcool Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3078 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-infotheo, r-cran-phontools, r-cran-dtt Suggests: r-cran-testthat, r-cran-spelling, r-cran-waveslim, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pssmcool_0.2.4-1.ca2604.1_all.deb Size: 1541622 MD5sum: 19dcab31bc104a1206521f449083dce6 SHA1: 74dfed76be716830490cd512e2c7fd2b879d36a2 SHA256: 719cd94ddf56270bfa33d73dfad78841a36147a05ea7e45b5d036e61d3bac9ef SHA512: 7d88b77b24f788983adf3c058c3c87b1deaca550f7c4fdceea65428126ee3a429876999ae02e94eaf811a866ad3988c20a6f4028cf10db7c353ded4292def8bb Homepage: https://cran.r-project.org/package=PSSMCOOL Description: CRAN Package 'PSSMCOOL' (Features Extracted from Position Specific Scoring Matrix (PSSM)) Returns almost all features that has been extracted from Position Specific Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) and 20 columns produced by 'PSI-BLAST' which is a program to produce PSSM Matrix from multiple sequence alignment of proteins see for mor details. some of these features are described in Zahiri, J., et al.(2013) , Saini, H., et al.(2016) , Ding, S., et al.(2014) , Cheng, C.W., et al.(2008) , Juan, E.Y., et al.(2009) . Package: r-cran-pssmooth Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-osdesign, r-cran-np, r-cran-chngpt, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pssmooth_1.0.3-1.ca2604.1_all.deb Size: 109982 MD5sum: 0cff013c21cb6cdaa04301cb5705e09f SHA1: 57aea2fc51ff1c80bbea57baa585e50613eb9b96 SHA256: 6219b6610c5cb18175a9ed21eac54db43452f222ca6208c3ca16d30f1621bc26 SHA512: c2de8468f921463a584dbf46d9b3235a17fae983b33376a2361c691ff264192e131549be2434f0da19aa6b27aa16e50007877876693a2b3482ad64d8c3c06481 Homepage: https://cran.r-project.org/package=pssmooth Description: CRAN Package 'pssmooth' (Flexible and Efficient Evaluation of PrincipalSurrogates/Treatment Effect Modifiers) Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 . The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results. Package: r-cran-pssurvival Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2894 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-cowplot Suggests: r-cran-nnet, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pssurvival_0.2.0-1.ca2604.1_all.deb Size: 1712946 MD5sum: 4154f4437a771c9e4281f13c23e4bf70 SHA1: 1009c960d7189724936fc217c715bfbfc9ecb6c0 SHA256: 5c057b45f11083db9dd9ce10bf3f4710ea288fd1336b7a141e790302312afa8c SHA512: 08a4f71a1f3f48f77eea1a3c861e95f3971dba5e667e7fdd665915c3e15d47961de0de09f1e28fef720d3ae07b4ae8397b039411d66ec8c16f70838f2b4fac47 Homepage: https://cran.r-project.org/package=PSsurvival Description: CRAN Package 'PSsurvival' (Propensity Score Methods for Survival Analysis) Implements propensity score weighting methods for estimating counterfactual survival functions, marginal hazard ratios, and weighted Kaplan-Meier and cumulative risk curves in observational studies with time-to-event outcomes. Supports binary and multiple treatment groups with inverse probability of treatment weighting (IPW), overlap weighting (OW), and average treatment effect on the treated (ATT). Includes symmetric trimming (Crump extension) for extreme propensity scores. Variance estimation via analytical M-estimation or bootstrap. Methods based on Li et al. (2018) , Li & Li (2019) , and Cheng et al. (2022) . Package: r-cran-pstat Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pstat_1.2-1.ca2604.1_all.deb Size: 107584 MD5sum: 116e2947147cbcc0d5292d0339c06b77 SHA1: 1842bf7c5c6c5eb9fc3af56d50ae9914be7f9f2f SHA256: baa44b37984f6cbd01f191a0ff45ea10fc215da921a3cbfcbc398a5efe54d749 SHA512: 5b510db531711554d4279816fcbb5b146ffdd2a46f161fffafbdd5c769a9f0c5969408ccd6e265e35942d6c6fec96200e5e6ab38bdbd9f41ba31968ea3b70c2e Homepage: https://cran.r-project.org/package=Pstat Description: CRAN Package 'Pstat' (Assessing Pst Statistics) Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation). Package: r-cran-pstest Architecture: all Version: 0.1.3.900-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmx, r-cran-mass Filename: pool/dists/resolute/main/r-cran-pstest_0.1.3.900-1.ca2604.1_all.deb Size: 30994 MD5sum: ec8f28f1012820a5e0937faa5ce35664 SHA1: fffdd526bfc6c98ff5d4cac91ef553183baa3c5a SHA256: b36535d6dc10a2b6eaf444fa02c7e0452d2a859dd295bdf60928f89644f52f9f SHA512: 7ac1252ea7589b7c1f49ce2209982584cec6942771cc539eaac31b8aba0940a9e27545c620f5cab890ffe36144d590b42f5e816e9d61e325bb46b4dae9b64907 Homepage: https://cran.r-project.org/package=pstest Description: CRAN Package 'pstest' (Specification Tests for Parametric Propensity Score Models) The propensity score is one of the most widely used tools in studying the causal effect of a treatment, intervention, or policy. Given that the propensity score is usually unknown, it has to be estimated, implying that the reliability of many treatment effect estimators depends on the correct specification of the (parametric) propensity score. This package implements the data-driven nonparametric diagnostic tools for detecting propensity score misspecification proposed by Sant'Anna and Song (2019) . Package: r-cran-pstr Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-r6, r-cran-knitr, r-cran-cli, r-cran-ggplot2, r-cran-plotly, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-snowfall, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pstr_2.0.0-1.ca2604.1_all.deb Size: 492166 MD5sum: 17fcbf96907e316c0f6be55ef3a7a038 SHA1: 823ada4c3cf0582c302f78b28f9820f04214ee45 SHA256: 84a9a68df23d1f3566ac3498705655192364bbef8ed1ba68cfed406064974e66 SHA512: 22a59f56097fcf5eb346bb4fa6f40ffd4e5c9835382501669bff94c29af515043821ed65d2393b9d890820cab44885cfd476164ac5d00f6829b5156ae0aaef5f Homepage: https://cran.r-project.org/package=PSTR Description: CRAN Package 'PSTR' (Panel Smooth Transition Regression Modelling) Implements the Panel Smooth Transition Regression (PSTR) framework for nonlinear panel data modelling. The modelling procedure consists of three stages: Specification, Estimation and Evaluation. The package provides tools for model specification testing, to do PSTR model estimation, and to do model evaluation. The implemented tests allow for cluster dependence and are heteroskedasticity-consistent. The wild bootstrap and wild cluster bootstrap tests are also implemented. Parallel computation (as an option) is implemented in some functions, especially the bootstrap tests. The package supports parallel computation, which is useful for large-scale bootstrap procedures. Package: r-cran-pstrata Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rstan, r-cran-lme4, r-cran-reformulas, r-cran-purrr, r-cran-stringr Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-pstrata_1.0.1-1.ca2604.1_all.deb Size: 207940 MD5sum: 7fd1b882ebb55a5d73c2561cc7edbe7e SHA1: a4fead21fce3379a2d85122d4cecc68fbc39b728 SHA256: 6a141e8f964cd1efed9b78ca13b783336025344ebd93919708064969c19e7afa SHA512: 58da505c23f6f498a00a0264953dcc458acd701e2e5c9dfed15ba824e3a7d74f92d1c5daaf47576da39c7e0cd9ba5502e9977ad339315887f8d1b59762120a3e Homepage: https://cran.r-project.org/package=PStrata Description: CRAN Package 'PStrata' (Principal Stratification Analysis in R) Estimating causal effects in the presence of post-treatment confounding using principal stratification. 'PStrata' allows for customized monotonicity assumptions and exclusion restriction assumptions, with automatic full Bayesian inference supported by 'Stan'. The main workflow is PStrataModel() to specify the model, fit() to run MCMC sampling, estimate() to extract potential outcomes, and contrast() to compute causal effects. Visualization tools are provided for diagnosis and interpretation. See Liu and Li (2023) for details. Package: r-cran-psvmsdr Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-psvmsdr_3.0.1-1.ca2604.1_all.deb Size: 126898 MD5sum: fab0747aa144e1dc3b46424116bd0c60 SHA1: 4f5334b1a5fa0d83b04144f58181ef4df311b52f SHA256: eb09352331642d4f025d22e6555c0aa172aed8cb1fd741bc6d42a30d20b956e4 SHA512: 5c600b55d1b14b9f80d26bd15be9c9d28b1d577fadf8cd35732035bdb8cd73b7c807d32bd4b255cd643cc673adad0c4360f0cb64376692ee62d9118298e9d81e Homepage: https://cran.r-project.org/package=psvmSDR Description: CRAN Package 'psvmSDR' (Unified Principal Sufficient Dimension Reduction Package) A unified and user-friendly framework for applying the principal sufficient dimension reduction methods for both linear and nonlinear cases. The package has an extendable power by varying loss functions for the support vector machine, even for an user-defined arbitrary function, unless those are convex and differentiable everywhere over the support (Li et al. (2011) ). Also, it provides a real-time sufficient dimension reduction update procedure using the principal least squares support vector machine (Artemiou et al. (2021) ). Package: r-cran-psw Architecture: all Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-psw_1.1-3-1.ca2604.1_all.deb Size: 128856 MD5sum: 15e3d2dd359d44394f361858ca00c53a SHA1: b96dc4ac49c5e8f8a35ef8eed62e2e55d8772379 SHA256: 2a881cb92e2c5f886e46aafdd71e18fd70674154e534d4d5c317a58e2f2daeba SHA512: 8a627353cf7cca38f7c22a3306a67ed13176a70cb5ac458f1ac28f5405de19bb55a5b7edf5b12494b31d0a98ed6d252b68d37d2611036a95f73a550ef544ab34 Homepage: https://cran.r-project.org/package=PSW Description: CRAN Package 'PSW' (Propensity Score Weighting Methods for Dichotomous Treatments) Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) , Lunceford and Davidian (2004) , Li and Greene (2013) , and Li et al (2016) . 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The package reads downloaded study folders, parses questionnaire structure, optionally merges demographic exports from CloudResearch or Prolific, and produces summary overviews of responses and completion times. It also provides helper functions to extract and aggregate experiment measures and survey variables, and to export results to spreadsheet files for further analysis and archiving. See Stoet (2017) for the 'PsyToolkit' platform. 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This plethora of definitions and instructions necessitates unequivocal reference to specific definitions and instructions in empirical and secondary research. This package implements a human- and machine-readable standard for specifying construct definitions and instructions for measurement and qualitative research based on 'YAML'. This standard facilitates systematic unequivocal reference to specific construct definitions and corresponding instructions in a decentralized manner (i.e. without requiring central curation; Peters (2020) ). Package: r-cran-ptable Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-flexdashboard, r-cran-ggplot2, r-cran-nloptr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ptable_1.0.0-1.ca2604.1_all.deb Size: 229220 MD5sum: a21a0b0b70e9f63a6af728e52d23b05f SHA1: fded0772d1a2a0ee79f9f730e884c7eed756b708 SHA256: a245e28c5d8c8326f6966bb85180ae06094ae13fcf423cbd9ce650f1027bfed3 SHA512: a30a42de5608b82b61eb8d256bbb7ec04d4256d960925691de1239552468140debfcd1663e562068372cd0c9390df3ad7d6d17c8431cf614bc9af0a9be1a84d0 Homepage: https://cran.r-project.org/package=ptable Description: CRAN Package 'ptable' (Generation of Perturbation Tables for the Cell-Key Method) Tabular data from statistical institutes and agencies are mostly confidential and must be protected prior to publications. The cell-key method is a post-tabular Statistical Disclosure Control perturbation technique that adds random noise to tabular data. The statistical properties of the perturbations are defined by some noise probability distributions - also referred to as perturbation tables. This tool can be used to create the perturbation tables based on a maximum entropy approach as described for example in Giessing (2016) . The perturbation tables created can finally be used to apply a cell-key method to frequency count or magnitude tables. 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This is an extension for the 'TITAN2' package . 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Likelihood approximation based on adaptive Gauss Hermite quadrature rule. 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Package: r-cran-ptprocess Architecture: all Version: 3.3-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ptprocess_3.3-17-1.ca2604.1_all.deb Size: 277928 MD5sum: 6c6ebdd8506f385689a801d508fceede SHA1: 08fcfb104a4d2c49383cde2cc51f24a69d25f30f SHA256: 6d9c275e5073cc25dae713b5ad86a56dd777007d61d54b22f5a2af51ae244f51 SHA512: b56c52a78120b776c6568523070c134cbcbbd569a4e81ed2b854e7edc01d0d50a2f3db490dbaa1c18d70cf0a29d75e5a647936def386acf162719157c3b28d86 Homepage: https://cran.r-project.org/package=PtProcess Description: CRAN Package 'PtProcess' (Time Dependent Point Process Modelling) Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log". Package: r-cran-ptsddiag Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-modelr Suggests: r-cran-dt, r-cran-knitr, r-cran-lattice, r-cran-psych, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ptsddiag_0.1.0-1.ca2604.1_all.deb Size: 367706 MD5sum: a5ca0964b6c4490859569814efdf934e SHA1: b4b9238c546de514510fa789f729e3494b121c48 SHA256: 494c595b92f5857cb1f450ace96eabd4f48a836e298a5f350b1f1018cdc5f300 SHA512: 568105a6a0e7f317aeb5282963be87b627e8fbe5f9bd88422fae5acda75260260966a7f0b003084b462b03108a992eae76b263a9a4b6ac21414602786a49391f Homepage: https://cran.r-project.org/package=PTSDdiag Description: CRAN Package 'PTSDdiag' (Optimize PTSD Diagnostic Criteria) Provides tools for analyzing and optimizing PTSD (Post-Traumatic Stress Disorder) diagnostic criteria using PCL-5 (PTSD Checklist for DSM-5) data. 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This package implements the results presented in Prass, T.S.; Pumi, G.; Taufemback, C.G. and Carlos, J.H. (2025). "Positive time series regression models: theoretical and computational aspects". Computational Statistics 40, 1185–1215. . Package: r-cran-pttstability Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bayesiantools Filename: pool/dists/resolute/main/r-cran-pttstability_1.4-1.ca2604.1_all.deb Size: 116290 MD5sum: 674fd26e98d0e8f149331a8158d8bf6e SHA1: 003706fffbf6668b43eaa6f8b398b00c33d66dc7 SHA256: 8792dc8599f408c286e21f60503da3237aaa4fe9c98524d789287a833d30e101 SHA512: 0fb9e404e72ad6cf25c4148d8fbe8b8d57c160ea5a9752616a207a0f1ff8822f16591f9d8bba47236a4344c06aa576dd6d1288e8107b4acfc55e0ebbf50fb75f Homepage: https://cran.r-project.org/package=pttstability Description: CRAN Package 'pttstability' (Particle-Takens Stability) Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) in Ecosphere. These can be used to estimate the parameters of a stochastic state space model (i.e. a model where a time series is observed with error). The goal of this package is to estimate the variability around a deterministic process, both in terms of observation error - i.e. variability due to imperfect observations that does not influence system state - and in terms of process noise - i.e. stochastic variation in the actual state of the process. Unlike classical methods for estimating variability, this package does not necessarily assume that the deterministic state is fixed (i.e. a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated (e.g. stochastic fluctuations during predator-prey dynamics). 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The main format method allows researchers to display measures (including data.frame) that respect the established conventions in the precision teaching community (i.e., prefixed multiplication or division symbol, displayed number <= 1). Basic multiplication and division methods are allowed and other useful functions are provided for creating, converting or inverting precision teaching measures. For more details, see Pennypacker, Gutierrez and Lindsley (2003, ISBN: 1-881317-13-7). 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Retrieve information on stops, routes, disruptions, departures, and more. Package: r-cran-ptwikiwords Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 869 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ptwikiwords_0.0.3-1.ca2604.1_all.deb Size: 853466 MD5sum: 27cd537a709e3636842bd8d9c7863110 SHA1: bcbf08bc73ade9d714e687a94ad3ce41ab7fa498 SHA256: 07ad7d681760121d99bc274f7486e0c851717ee2294d60506cae1f3c098cd782 SHA512: c87837b120aeb44ca6d36b61b1b6e3ec6d4d2598616f717e5fd609c96719c0bf8fbf335b2402bd8333f758784356a7353246713c22aff22843c5272544e1eea6 Homepage: https://cran.r-project.org/package=ptwikiwords Description: CRAN Package 'ptwikiwords' (Words Used in Portuguese Wikipedia) Contains a dataset of words used in 15.000 randomly extracted pages from the Portuguese Wikipedia (). 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Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format. Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) . 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Package: r-cran-public.ctn0094data Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1468 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-broom, r-cran-conflicted, r-cran-diagrammer, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggthemes, r-cran-gt, r-cran-haven, r-cran-infer, r-cran-janitor, r-cran-knitr, r-cran-kableextra, r-cran-magrittr, r-cran-pkgdown, r-cran-plyr, r-cran-psych, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rum, r-cran-scales, r-cran-stringr, r-cran-table1, r-cran-tibble, r-cran-tidyr, r-cran-tidyverse, r-cran-testthat, r-cran-vcdextra, r-cran-xfun Filename: pool/dists/resolute/main/r-cran-public.ctn0094data_1.1.0-1.ca2604.1_all.deb Size: 1269614 MD5sum: 743dc9df59c4140768d030ab9df52a91 SHA1: 036dc0614cbba2894fb58b1759b1e930ac9ccede SHA256: 48939546f61a319c0140b94cfef46de6da242f516e3d375ad1ceae2ca83b62ac SHA512: e0382e9fbef94ab936152eaa2b460d001514739246133d20d4160fbd047883bd04feb5604b5cc641039267ab4e9482b08b371bb797a2a78dc8af40671766f939 Homepage: https://cran.r-project.org/package=public.ctn0094data Description: CRAN Package 'public.ctn0094data' (De-Identified Data from CTN-0094) These are harmonized datasets produced as part of the Clinical Trials Network (CTN) protocol number 0094. This is a US National Institute of Drug Abuse (NIDA) funded project; to learn more go to . These are datasets which have the data harmonized from CTN-0027 (), CTN-0030 (), and CTN-0051 (). 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This 'public.ctn0094data' package contains harmonized datasets from some of the National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN) projects. Specifically, the CTN-0094 project is to harmonize and de-identify clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for opioid use disorder. This current version is built from 'public.ctn0094data' v. 1.0.6. Package: r-cran-publicationbias Architecture: all Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-lifecycle, r-cran-metabias, r-cran-metafor, r-cran-rdpack, r-cran-rlang, r-cran-robumeta Suggests: r-cran-purrr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-publicationbias_2.4.0-1.ca2604.1_all.deb Size: 70200 MD5sum: d79a82a0ef6383d09cee5f617e51dd06 SHA1: eecfba2f1905e6294e73529da71f66ec5117126d SHA256: 23f74575b022e561a68e424ca924bf555aa3f8d493f1542cf0d5f6dfdd2a3558 SHA512: ca5df768f9388643dc6164c8cf238e65723fe44efc7e686250e53b32ce32044fcc2deefeec7be7891c5a4422a82b6a510a12c8022f0a1cd97114eb2154c119d8 Homepage: https://cran.r-project.org/package=PublicationBias Description: CRAN Package 'PublicationBias' (Sensitivity Analysis for Publication Bias in Meta-Analyses) Performs sensitivity analysis for publication bias in meta-analyses (per Mathur & VanderWeele, 2020 []). 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It provides 1) predefined data-generating mechanisms from the literature, 2) functions for running meta-analytic methods on simulated data, 3) pre-simulated datasets and pre-computed results for reproducible benchmarks, 4) tools for visualizing and comparing method performance. 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Package: r-cran-pubmatrixr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1321 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-pheatmap, r-cran-readods, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pubmatrixr_1.0.0-1.ca2604.1_all.deb Size: 723018 MD5sum: 7cbcff11f7c7db0092111b39922c0b36 SHA1: 261f2615b071349162a011a88e837e4d65ad2a42 SHA256: f1117ef5b7402823a68cbb4fc2ace13ffb5fa55bce3f77f3f3d1e028b09ed05d SHA512: 2878bc15b4cee77a434ad17eb1e87ed7f61511f2a5ae1a1d54c040ba9534d89604845e604fb73a7c9e198ba2842dcfc2729495489b46e36f768931d1a97ea8fc Homepage: https://cran.r-project.org/package=PubMatrixR Description: CRAN Package 'PubMatrixR' (PubMed Pairwise Co-Occurrence Matrix Construction andVisualization) Queries the 'NCBI' (National Center for Biotechnology Information) Entrez 'E-utilities' API to count pairwise co-occurrences between two sets of terms in 'PubMed' or 'PubMed Central'. It returns a matrix-like data frame of publication counts and can export hyperlink-enabled results in CSV or ODS format. The package also provides heatmap helpers for exploratory visualization of overlap patterns. Based on the method described in Becker et al. (2003) "PubMatrix: a tool for multiplex literature mining" . Package: r-cran-pubmed.miner Architecture: all Version: 1.0.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcurl, r-cran-xml, r-cran-boot, r-cran-r2html, r-cran-rjsonio Filename: pool/dists/resolute/main/r-cran-pubmed.miner_1.0.21-1.ca2604.1_all.deb Size: 1253818 MD5sum: 93ceb3addad57bdce6e0bd12985d8c6b SHA1: db9de5c2b364ef9e58d9ac71f6406fe46b5cd710 SHA256: 12c56fce3bff938f662d85dd0fb7573cee435bc1e1301fc8a7cebf1b6aa1ebf6 SHA512: d445532d51f04f4b5ac822ea7d843919edd0ab0184e32aa19720da4cbcdd6cead17fb34e13fcdbb974e255c8b0c98593b45311a78934909538794afb6bf6d03b Homepage: https://cran.r-project.org/package=pubmed.mineR Description: CRAN Package 'pubmed.mineR' (Text Mining of PubMed Abstracts) Text mining of PubMed Abstracts (text and XML) from . 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The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus) is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) A text file is generated with the 'PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term), a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates. 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Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) . Package: r-cran-pumilior Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-rcurl Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pumilior_1.3.1-1.ca2604.1_all.deb Size: 36006 MD5sum: d74b664e6cd519f0e65e7ffde31e9c66 SHA1: ed54a5c3b72660217e72914bb836e9effba452df SHA256: f1be05be1f4fe801bcf1c9a6dced9657948db80afb50048899be33413e3123bf SHA512: 5e559a316c0602fe2a5f5f3dff97242b8d6e13ca19f73ed925a2c41bc90f5e4a5116662ef317fcf36290db71aa379d7482c69664d52d111a991c00dd46d585fd Homepage: https://cran.r-project.org/package=pumilioR Description: CRAN Package 'pumilioR' (Pumilio in R) R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/). 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Adsorption kinetics is used for characterizing the rate of solute adsorption and the time necessary for the adsorption process. Adsorption kinetics offers vital information on adsorption rate, adsorbent performance in response time, and mass transfer processes. In addition, diffusion models are included in the package as solute diffusion affects the adsorption kinetic experiments. This package consists of 20 adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo Second Order (PSO), Elovich, and Weber-Morris model (commonly called the intraparticle model) stated by Plazinski et al. (2009) . This package also contains a summary function where the statistical errors of each model are ranked for a more straightforward determination of the best fit model. 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Package: r-cran-pvaluefunctions Architecture: all Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-zipfr, r-cran-pracma, r-cran-gsl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-pvaluefunctions_1.6.3-1.ca2604.1_all.deb Size: 1342052 MD5sum: c74b8dba6c8e8c039ed0d2f5e22eb960 SHA1: 5a4c5fdc80b12bf02adca70701040294c249dea5 SHA256: 4694eb05cabc6b52240395517bbaeee9740c1eef2d9ab1b9912a47c5d4738e07 SHA512: 7f84fc660ed5224a9589d3a1f3c52a1680bacc2eb43176795699002a407071aff9beae8974c4cd984803f96e6076b141f5c282e57ec88899ab27162ee43e88e8 Homepage: https://cran.r-project.org/package=pvaluefunctions Description: CRAN Package 'pvaluefunctions' (Creates and Plots P-Value Functions, S-Value Functions,Confidence Distributions and Confidence Densities) Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) ; Poole C. (1987) ; Schweder T, Hjort NL. (2002) ; Bender R, Berg G, Zeeb H. (2005) ; Singh K, Xie M, Strawderman WE. (2007) ; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) ; Greenland S. (2019) and Rafi Z, Greenland S. (2020) . Package: r-cran-pvars Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-svars, r-cran-clue, r-cran-copula, r-cran-deoptim, r-cran-expm, r-cran-ggplot2, r-cran-mass, r-cran-pbapply, r-cran-reshape2, r-cran-scales, r-cran-steadyica, r-cran-vars Suggests: r-cran-ggfortify, r-cran-ggpubr, r-cran-knitr, r-cran-plm, r-cran-rcolorbrewer, r-cran-testthat, r-cran-tikzdevice, r-cran-urca Filename: pool/dists/resolute/main/r-cran-pvars_1.1.1-1.ca2604.1_all.deb Size: 3171596 MD5sum: bccfcfb8d539c8a313c328c348876cac SHA1: ae7485deb05b2e7763d00192ec8ec5fa9112d675 SHA256: 5fe65ba8aa2db43b6dbae361a20298e8947a287b38bea3d6b5ce4583b93b951e SHA512: 51a4175d0e6ba47698f57a0ae7d0765e0254c62d514f4fb529da98ae4b83cc0899776e36a797157adbf2e5ef182deeef14c2fae710822851c1fc123d06ecbded Homepage: https://cran.r-project.org/package=pvars Description: CRAN Package 'pvars' (VAR Modeling for Heterogeneous Panels) Implements (1) panel cointegration rank tests, (2) estimators for panel vector autoregressive (VAR) models, and (3) identification methods for panel structural vector autoregressive (SVAR) models as described in the accompanying vignette. The implemented functions allow to account for cross-sectional dependence and for structural breaks in the deterministic terms of the VAR processes. Among the large set of functions, particularly noteworthy are those that implement (1) the correlation-augmented inverse normal test on the cointegration rank by Arsova and Oersal (2021, ), (2) the two-step estimator for pooled cointegrating vectors by Breitung (2005, ), and (3) the pooled identification based on independent component analysis by Herwartz and Wang (2024, ). 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From the progression of the curves turgor loss point, osmotic potential, apoplastic fraction as well as minimum conductance and stomatal closure can be derived. Methods adapted from Bartlett, Scoffoni, Sack (2012) and Sack, Scoffoni, PrometheusWikiContributors (2011) . 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Methods based on Warwicker and Rebennack (2025) "Efficient continuous piecewise linear regression for linearising univariate non-linear functions" . 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In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers. Package: r-cran-pwr2ppl Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-mass, r-cran-dplyr, r-cran-tidyr, r-cran-nlme, r-cran-phia, r-cran-afex, r-cran-mbess, r-cran-lavaan, r-cran-semtools, r-cran-quantreg, r-cran-broom, r-cran-lmtest, r-cran-lmperm, r-cran-pls Filename: pool/dists/resolute/main/r-cran-pwr2ppl_0.6.0-1.ca2604.1_all.deb Size: 471858 MD5sum: d8f14471e39bdd4dadd1631aebea0be9 SHA1: b49e0b3f3853fdaecac6ef1afd7118d45d9700d3 SHA256: 4eaede1328956f07960b01c5a0248078ec261fc952a361cc3499e5c18f82e961 SHA512: 16069c98b9077b55a0254673f5d6cc12b09a2aac742d9e8d8b548db1e98aa7ab979c8fedbb42501c5f152821202689cded13f15ae6fdbe4783e025b1096c872b Homepage: https://cran.r-project.org/package=pwr2ppl Description: CRAN Package 'pwr2ppl' (Power Analyses for Common Designs (Power to the People)) Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) . 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Package: r-cran-pwrab Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pwrab_0.1.0-1.ca2604.1_all.deb Size: 24670 MD5sum: e7cd6485a864619ea00f43ee63ce92de SHA1: 0856c5149ff715c864a55dd73dfc4e988369dfc9 SHA256: ec67d664ecc9f50b1be6741c50fd5a5be014262a69377aef013b95464ef519e0 SHA512: 3e9470379686f3212deb57db2bf3a08f3f4244b7a4691a74f02dd8d08cd6fab4040220537c9d45aaf81afb2835ac7f34fb9fc82700746325090ae5007b515c7b Homepage: https://cran.r-project.org/package=pwrAB Description: CRAN Package 'pwrAB' (Power Analysis for AB Testing) Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test, which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are unequal, which is frequently the case in AB testing. 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All of these procedures involve control of some summary of the distribution of the FDP, e.g. the proportion of discoveries which are false in a given experiment. The most widely known of these, the BH-FDR procedure, controls the FDR which is the mean of the FDP. A lesser known procedure, due to Lehmann and Romano, controls the FDX, or probability that the FDP exceeds a user provided threshold. This is less conservative than FWE control procedures but much more conservative than the BH-FDR proceudre. This package and the references supporting it introduce a new procedure for controlling the FDX which we call the BH-FDX procedure. This procedure iteratively identifies, given alpha and lower threshold delta, an alpha* less than alpha at which BH-FDR guarantees FDX control. This uses asymptotic approximation and is only slightly more conservative than the BH-FDR procedure. Likewise, we can think of the power in multiple testing experiments in terms of a summary of the distribution of the True Positive Proportion (TPP), the portion of tests truly non-null distributed that are called significant. The package will compute power, sample size or any other missing parameter required for power defined as (i) the mean of the TPP (average power) or (ii) the probability that the TPP exceeds a given value, lambda, (TPX power) via asymptotic approximation. All supplied theoretical results are also obtainable via simulation. The suggested approach is to narrow in on a design via the theoretical approaches and then make final adjustments/verify the results by simulation. The theoretical results are described in Izmirlian, G (2020) Statistics and Probability letters, "", and an applied paper describing the methodology with a simulation study is in preparation. See citation("pwrFDR"). 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Package: r-cran-pysd2r Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-reticulate, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pysd2r_0.1.0-1.ca2604.1_all.deb Size: 176400 MD5sum: c7adc932973ab750acd92b6b9b339942 SHA1: 0f35bf8172fb2feec086e1846a49cfe6b26a256f SHA256: 036c86ab42707597187c0c4c38380c1a2a02b585e4b387678496f5763bc5fc1c SHA512: 6e25f9551d896ac2d1036872e36acc9f2f358c418dbe102942cc54ab20d4541f02b5f2e458def2980864073a67b634d0595448c7bc2aacc81ae38c3a6f0cbe75 Homepage: https://cran.r-project.org/package=pysd2r Description: CRAN Package 'pysd2r' (API to 'Python' Library 'pysd') Using the R package 'reticulate', this package creates an interface to the 'pysd' toolset. The package provides an R interface to a number of 'pysd' functions, and can read files in 'Vensim' 'mdl' format, and 'xmile' format. The resulting simulations are returned as a 'tibble', and from that the results can be processed using 'dplyr' and 'ggplot2'. The package has been tested using 'python3'. Package: r-cran-pysparklyr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-lifecycle, r-cran-processx, r-cran-purrr, r-cran-reticulate, r-cran-rlang, r-cran-rstudioapi, r-cran-sparklyr, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-uuid, r-cran-withr, r-cran-connectcreds Suggests: r-cran-vcr, r-cran-crayon, r-cran-r6, r-cran-testthat, r-cran-tibble, r-cran-rsconnect, r-cran-rsample, r-cran-workflows, r-cran-tune, r-cran-parsnip, r-cran-dials, r-cran-tailor, r-cran-recipes Filename: pool/dists/resolute/main/r-cran-pysparklyr_0.2.1-1.ca2604.1_all.deb Size: 319192 MD5sum: 9db6a3018becc26aa2ac2256620f7018 SHA1: c6184fcd2e2e51931f9732ffb52c8a8a7117f19d SHA256: 242fdc241445068d75996899f3a759d776c0855759d93790d996eadfe5fb6072 SHA512: dc5863422458eef04ac12245c268b84a950438c04c89deaba579b95e88cd16836537420bb2e01eea0dc4b40277d5bfbea2e9127f5358e6d99df58f124edc0472 Homepage: https://cran.r-project.org/package=pysparklyr Description: CRAN Package 'pysparklyr' (Provides a 'PySpark' Back-End for the 'sparklyr' Package) It enables 'sparklyr' to integrate with 'Spark Connect', and 'Databricks Connect' by providing a wrapper over the 'PySpark' 'python' library. Package: r-cran-pzfx Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 869 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pzfx_0.3.1-1.ca2604.1_all.deb Size: 616596 MD5sum: b826e7cddb9b024cdf35a6d44fa777b3 SHA1: b31311883b39740cd8723bce4b653d1ca9d9a78d SHA256: b64483cd768c47e49f04119f85c3aa75108b940a4be1febe5ca26d809505df2c SHA512: 8edce2fe755b96960dbba6fd5d7389aa7afc197e177a10fc9f53e5c33c9fcc4fecdeab7247e871147b67f288c4e0d21813c3020fca771932d3f2a1e2c6281d6d Homepage: https://cran.r-project.org/package=pzfx Description: CRAN Package 'pzfx' (Read and Write 'GraphPad Prism' Files) Read and write 'GraphPad Prism' '.pzfx' files in R. Package: r-cran-q2q Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-q2q_0.1.2-1.ca2604.1_all.deb Size: 152070 MD5sum: 37f089049a8e0451bfedd88faa715232 SHA1: 764ef1b6bedf98adb0371501c52a1f19847d664f SHA256: 59dea5f45e57d30c3db8ba2b364dafe7f92eb2704e602224adb6e02409e4cdd1 SHA512: 948a0045697c178f1d8ebf5a0f19bd08d303bb2db03979aa55a18a136ce221cc29733bfabbabfe1fb7866eea4b5293aebee4089d0dbd2f82481ce67e23fb5888 Homepage: https://cran.r-project.org/package=Q2q Description: CRAN Package 'Q2q' (Interpolating Age-Specific Mortality Rates at All Ages) Mortality rates are typically provided in an abridged format, i.e., by age groups 0, [1, 5], [5, 10]', '[10, 15]', and so on. Some applications necessitate a detailed (single) age description. 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The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) . Package: r-cran-qardlr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qardlr_1.0.1-1.ca2604.1_all.deb Size: 160194 MD5sum: bd308ed414e5bd2a0961bca8dad2ca2f SHA1: 952455535beacdf47b410d4a07088c3968ec7b51 SHA256: 5d447661a1cc8a02ae494e1af9b3a1d4aac3c7b33e0c7fd2d90c4f9620780584 SHA512: df1985652b820bcf8c09bd02f6f971321c574f65607515748c708b67b44921a050b75fb2031ccca13ab80a5621c4f61c1f844c744727b0db8577e209e7b4d19d Homepage: https://cran.r-project.org/package=qardlr Description: CRAN Package 'qardlr' (Quantile Autoregressive Distributed Lag Model) Implements the Quantile Autoregressive Distributed Lag (QARDL) model of Cho, Kim and Shin (2015) . 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Package: r-cran-qcauchyreg Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg Filename: pool/dists/resolute/main/r-cran-qcauchyreg_1.0-1.ca2604.1_all.deb Size: 99642 MD5sum: 09c342b86e92d24d7a4ccf34140b77c7 SHA1: cae7f262d91f443d8689f1e6b37d39c0aa458c72 SHA256: 115a9b4def90012a47c7d370432c5205bb7825d0c4641c2decb94918bb5dacfb SHA512: f7d9c22364ab925e5d0b7d15610e51e2c8ac052dd16fd253a449774d22bca1a0bb4240a1d6d8e2588eb2806dd40b03519ca1fa92aefb4af0e3bbfed99b3d574c Homepage: https://cran.r-project.org/package=qcauchyreg Description: CRAN Package 'qcauchyreg' (Quantile Regression Quasi-Cauchy) Quasi-Cauchy quantile regression, proposed by de Oliveira, Ospina, Leiva, Figueroa-Zuniga and Castro (2023) . 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QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. Supported are 'CBA' implementations from 'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR', 'FOIL2' and 'PRM' implementations from 'arulesCBA' package and 'SBRL' implementation from the 'sbrl' package. The result of the post-processing is an ordered CBA-like rule list. 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Package: r-cran-qcpm Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-csem, r-cran-broom Filename: pool/dists/resolute/main/r-cran-qcpm_0.4-1.ca2604.1_all.deb Size: 113046 MD5sum: dd156ac099f5e24c64ddb595fe05c01b SHA1: 959b96a71dbd51a2ba8e17da4e4e92a1461c16e4 SHA256: 18de954906003dc073ce1d8a6a1c13ce0454cb622b4454b667d17445d961d063 SHA512: ac7724146d2e3c25b8188ce9d70b6c146f84f03e68edd324fb0a2e01ee12009b3f817aac60cf731f8dfd8ee7033ba4da98ac312e3fd336878133199b66e60429 Homepage: https://cran.r-project.org/package=qcpm Description: CRAN Package 'qcpm' (Quantile Composite Path Modeling) Implements the Quantile Composite-based Path Modeling approach (Davino and Vinzi, 2016 ; Dolce et al., 2021 ). 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Apart from integrating different R packages devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, 'X-bar', 'S', 'R', 'p', 'np', 'c', 'u', 'EWMA' and 'CUSUM'. In addition, it includes functions to perform multivariate control charts such as 'Hotelling T2', 'MEWMA' and 'MCUSUM'. As representative feature, multivariate nonparametric alternatives based on data depth are implemented in this package: 'r', 'Q' and 'S' control charts. In addition, Phase I and II control charts for functional data are included. This package also allows the estimation of the most complete set of capability indices from first to fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots. See Flores et al. (2021) . Package: r-cran-qcrlscr Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2900 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-qcrlscr_0.1.3-1.ca2604.1_all.deb Size: 2863006 MD5sum: b718a85012e4471926b0f623f15f64b8 SHA1: 8f43eae999c30f33208e904668b4796e0a445143 SHA256: 3c772074fc888083ac698c3ca7a9e2e614d7cf265d45ad3a4066842e0ccf7d47 SHA512: 9dad33c103463e0dda0e450f6c44ff4d85ee05a660cd8e238fbcb73105c12af3e52e9561c3961e63cd373a30075dd960aa150a95d5225588ab327abf4d511cdc Homepage: https://cran.r-project.org/package=qcrlscR Description: CRAN Package 'qcrlscR' (Quality Control–based Robust LOESS Signal Correction) An R implementation of quality control–based robust LOESS(local polynomial regression fitting) signal correction for metabolomics data analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011) . The optimisation of LOESS's span parameter using generalized cross-validation (GCV) is provided as an option. In addition to signal correction, 'qcrlscR' includes some utility functions like batch shifting and data filtering. Package: r-cran-qcsimulator Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-qcsimulator_0.0.1-1.ca2604.1_all.deb Size: 124952 MD5sum: 0ea0d31ba2945aa9084d1d5c3db2cfaa SHA1: f862c38cf442b65663b142d75b1a83fac4d93abd SHA256: 9619f5c5a7aef605f7378841ca0891c59fbe26ac3d7cb60e533e676da98287f5 SHA512: a0ce1033c35637a5a15931fa01c309b8dc1cccc9af60eea6361253f28472bed61e96709a704e04c4d5f6551dba850d450e3fffbd39cebf8032ba944c9a11e590 Homepage: https://cran.r-project.org/package=QCSimulator Description: CRAN Package 'QCSimulator' (A 5-Qubit Quantum Computing Simulator) Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2 qubit quantum gates. 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Package: r-cran-qcv Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-qcv_1.0-1.ca2604.1_all.deb Size: 33662 MD5sum: c94f289dd89a2bf5dbd2f628a7d12547 SHA1: 327fcda6457b0855eaae3429f15033e4ed557909 SHA256: 795715bfe56b28ceb2c7fab76e4f3b40e014e0181c323b186e678f4f602c80cd SHA512: 8bfa0f6b62dbb811124e064ed7c34b0afe7e51175da38c89bb99e54cefb607dd236556b92456c3e35abf44175867e11ac980dbf980ecc07eb1a93cdae86fca5c Homepage: https://cran.r-project.org/package=qcv Description: CRAN Package 'qcv' (Quantifying Construct Validity) Primarily, the 'qcv' package computes key indices related to the Quantifying Construct Validity procedure (QCV; Westen & Rosenthal, 2003 ; see also Furr & Heuckeroth, in press). 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Package: r-cran-qdap Architecture: all Version: 2.4.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qdapdictionaries, r-cran-qdapregex, r-cran-qdaptools, r-cran-rcolorbrewer, r-cran-chron, r-cran-dplyr, r-cran-gender, r-cran-ggplot2, r-cran-gridextra, r-cran-igraph, r-cran-nlp, r-cran-opennlp, r-cran-openxlsx, r-cran-plotrix, r-cran-rcurl, r-cran-reshape2, r-cran-scales, r-cran-stringdist, r-cran-tidyr, r-cran-tm, r-cran-venneuler, r-cran-wordcloud, r-cran-xml Suggests: r-cran-korpus, r-cran-knitr, r-cran-lda, r-cran-proxy, r-cran-stringi, r-cran-snowballc, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qdap_2.4.6.1-1.ca2604.1_all.deb Size: 3535022 MD5sum: a6f7a4d0cd305ff053fc2ab6b759213f SHA1: 4611a0e711abf835dafc14689f58153ac0ad31a6 SHA256: 439aa2c51e15102c5fbef62cd6df649df3076d4a1393fdae5301bdec75537119 SHA512: 20a9b3e227df75e5bdac6f7737757323804493a260f29e50332dd5bf03edfa1c1cd9e77f636bc979f5645ab64ba8710aa39d243ee90cd506f536caed37ee8c51 Homepage: https://cran.r-project.org/package=qdap Description: CRAN Package 'qdap' (Bridging the Gap Between Qualitative Data and QuantitativeAnalysis) Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing. Package: r-cran-qdapdictionaries Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2201 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-qdapdictionaries_1.0.7-1.ca2604.1_all.deb Size: 2152370 MD5sum: 9d10953a5cd3371ad2eadc5fb617de98 SHA1: f23b6f05b3d875609091faf72fb9c647bae10a9d SHA256: fea94f2f6fe49767d2f167974e757f24f5988f2c2bcd095f997e3418f6817b0e SHA512: 5836316d25b8a1fbb07822fb7c01ec14a2d983a732fc9ef24fa4640f395ad909c79b941028c0d8b6321028a76eeb3ccff4cc677e0272dcb6f72cec05dec477f0 Homepage: https://cran.r-project.org/package=qdapDictionaries Description: CRAN Package 'qdapDictionaries' (Dictionaries and Word Lists for the 'qdap' Package) A collection of text analysis dictionaries and word lists for use with the 'qdap' package. 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Tools include removal/extraction/replacement of abbreviations, dates, dollar amounts, email addresses, hash tags, numbers, percentages, citations, person tags, phone numbers, times, and zip codes. Package: r-cran-qdaptools Architecture: all Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chron, r-cran-data.table, r-cran-rcurl, r-cran-xml Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qdaptools_1.3.7-1.ca2604.1_all.deb Size: 133016 MD5sum: 07194889392f39d435a5659204539dfe SHA1: 6a6aaf825334905e4690f48a957a5cbf04b1b877 SHA256: f0480bcd03f20b3e945f56b2e2072cdfe24597e7ab44237df45227f50c5ccd88 SHA512: e9e16050819208d1784cfe801874b10906af4507c52f5c55e96e622b8973b9d3c877c2424ea2a7410e82c9ae37b0c8c684c06149115484d9e85f7053686590ff Homepage: https://cran.r-project.org/package=qdapTools Description: CRAN Package 'qdapTools' (Tools for the 'qdap' Package) A collection of tools associated with the 'qdap' package that may be useful outside of the context of text analysis. Package: r-cran-qdcomparison Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-qdcomparison_3.0-1.ca2604.1_all.deb Size: 64820 MD5sum: 978056e65cd6d3181070c43c25619962 SHA1: c28204835c3d4630dbe53d89b39b5cb4e75ba85a SHA256: cbd29a825b4e790a5cf9951e9b811a4119fd5dbbc6fcef13eaefe3846b580e38 SHA512: 5c871ed61803553cbaa4a0c5fa8d1e44f469d4e57215ef206f571c3b0e8a63453956469bd9554504a9e7b84d731daa18204136ccdac11af094c0f54ad085024f Homepage: https://cran.r-project.org/package=QDComparison Description: CRAN Package 'QDComparison' (Modern Nonparametric Tools for Two-Sample Quantile andDistribution Comparisons) Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report). Package: r-cran-qdea Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-doby, r-cran-highs, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qdea_1.0.0-1.ca2604.1_all.deb Size: 199544 MD5sum: 94ef49c10277483ee0960f6d39208e21 SHA1: 92b00f157df081e3018f8804f30e7a4a1052dce4 SHA256: 84866612332f990ba3b2fbd75ea825ad7d9816367f7f1a3ed80c61c3817afbae SHA512: e41370682af52c41a37cf5194289b667774d70ab539e0e783b8bf08d0890357b078830eec8ec13aa8f51d9da36de02c8f548deef7076e6299c97f4b206d9b55d Homepage: https://cran.r-project.org/package=qDEA Description: CRAN Package 'qDEA' (Quantile Data Envelopment Analysis) R implementation of Quantile Data Envelopment Analysis. The package 'qDEA' allows a user specified proportion of observations to lie external to a given Decision Making Units's (DMU's)reference hyperplane. 'qDEA' can be used to detect and address influential outliers or to implement quantile benchmarking, as discussed in Atwood and Shaik (2020). Quantile benchmarking is accomplished by using heuristic procedures to find a DMU's closest input-output projection point in a specified direction while allowing a specified proportion of observations to lie external to the projected point's hyperplane. The 'qDEA' package accommodates standard (DEA) and quantile DEA estimation, returns to scale CRS(constant),VRS(variable),DRS(decreasing) or IRS(increasing), the use of directional vectors, bias correction through subsample bootstrapping and subsample size selection procedures. The user can also recover each DMU's reference DMUs and external DMUs if desired. The implemented procedures are based on discussions in: Atwood and Shaik (2020) Atwood and Shaik (2018) Walden and Atwood (2023) Walden and Atwood (2025) . Package: r-cran-qdiabetes Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4461 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-qdiabetes_1.0-2-1.ca2604.1_all.deb Size: 4459018 MD5sum: 742aad42ec6b96feac2459aa1d6d0c50 SHA1: 97c287b21b409dcef11cd15ff79b77ee73202e2e SHA256: 3515223dd3949117a648899ed50c4010033dd09f3d1e6ef779ccdf98a0af38d0 SHA512: ad3989f4e3a664a406ac48037c499dcb2e2ee319aa66e20c99487c424d22bac9033673b6358c98929d1ca1b2e659633796d9ca708b04d7104d3cf1b42d976adf Homepage: https://cran.r-project.org/package=QDiabetes Description: CRAN Package 'QDiabetes' (Type 2 Diabetes Risk Calculator) Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'. Package: r-cran-qdm Architecture: all Version: 0.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-qdm_0.1-0-1.ca2604.1_all.deb Size: 781336 MD5sum: a16f6d6ffa6d47bae4a425ebc819a7b2 SHA1: bbde2dc44252478ab9a8bdc6b71bb3760f038963 SHA256: 4ecf576dbaeb2b74c301b48721ba8f0164889b42bf52403a959633ad07befa0b SHA512: 51608457ddde08f5d27011230bcf9f3da60a04eacdb274c16a31a8ba7b1a8b595299c7f845fe5a4231f3d1a932cff0b7138c244ed266e3b4110804b9c7f13e6a Homepage: https://cran.r-project.org/package=qdm Description: CRAN Package 'qdm' (Fitting a Quadrilateral Dissimilarity Model to Same-DifferentJudgments) This package provides different specifications of a Quadrilateral Dissimilarity Model which can be used to fit same-different judgments in order to get a predicted matrix that satisfies regular minimality [Colonius & Dzhafarov, 2006, Measurement and representations of sensations, Erlbaum]. From such a matrix, Fechnerian distances can be computed. Package: r-cran-qeml Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3782 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-regtools, r-cran-gtools, r-cran-rmarkdown, r-cran-tufte, r-cran-grf, r-cran-gbm, r-cran-toweranna, r-cran-tm, r-cran-rpart, r-cran-rpart.plot, r-cran-partools, r-cran-foci Suggests: r-cran-knitr, r-cran-partykit, r-cran-randomforest, r-cran-ranger, r-cran-e1071, r-cran-jousboost, r-cran-lightgbm, r-cran-keras, r-cran-neuralnet, r-cran-polyreg, r-cran-glmnet, r-cran-umap, r-cran-reticulate, r-cran-party, r-cran-proc, r-cran-xgboost, r-cran-rocr, r-cran-autoimage, r-cran-deepnet, r-cran-ncvreg, r-cran-uwot, r-cran-cdparcoord Filename: pool/dists/resolute/main/r-cran-qeml_1.1-1.ca2604.1_all.deb Size: 1742750 MD5sum: 8b9085c2a32ecac4559b66dc24bf8479 SHA1: 511bc4c110a4a04a0d73eefb454b0f07924e063d SHA256: fc5378982824dda8e5c254f00e554f363d0b24cf1503db010ec6307d498da564 SHA512: e3fb3b744f7e5c0b7c38bb63715f0472623681d75be3af5e14337ae3e37f9f161b95befae8f0d172aab4460f5b19ccaede3b36872a547ec09964cd917a3683ee Homepage: https://cran.r-project.org/package=qeML Description: CRAN Package 'qeML' (Quick and Easy Machine Learning Tools) The letters 'qe' in the package title stand for "quick and easy," alluding to the convenience goal of the package. We bring together a variety of machine learning (ML) tools from standard R packages, providing wrappers with a simple, convenient, and uniform interface. Package: r-cran-qfasar Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qfasar_1.2.1-1.ca2604.1_all.deb Size: 283650 MD5sum: dbfcdd1dfb8b8b9258e21e4d5416eac3 SHA1: 78b22bcf9e3e60f553beb7b0940781a255f3b20f SHA256: 0ca4c589ee55339d954f65d037841c275d699616746c5eed6defade7ee00e5f5 SHA512: b3546bfa4536b145416e62459444f66bc2e8dba745190d618356832ba8ab4a5b78714eb13a6c86bc7fd0e7862df069e2f0ba339a80c588b977714e09c24ac3b6 Homepage: https://cran.r-project.org/package=qfasar Description: CRAN Package 'qfasar' (Quantitative Fatty Acid Signature Analysis in R) An implementation of Quantitative Fatty Acid Signature Analysis (QFASA) in R. QFASA is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing the composition of fatty acids within lipids. Signature data from at least one predator and from samples of all potential prey types are required. Calibration coefficients, which adjust for the differential metabolism of individual fatty acids by predators, are also required. Given those data inputs, a predator signature is modeled as a mixture of prey signatures and its diet estimate is obtained as the mixture that minimizes a measure of distance between the observed and modeled signatures. A variety of estimation options and simulation capabilities are implemented. Please refer to the vignette for additional details and references. Package: r-cran-qfrm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-qfrm_1.0.1-1.ca2604.1_all.deb Size: 251784 MD5sum: 3e58bad8ac50db3eabadb23243e78641 SHA1: 55356e81df3392f7aa7ce3a15c228e234ee23a39 SHA256: 7ee848b2d4ee9469227e0c3c785c29f52daddf59e46a6551a0efe51190e365a4 SHA512: 7d07c9a5105a83c0b9a9fb58e57aad24dd0e8f9a9c770cd515bec814b63b0c83fdda4c7f4c026a6a9b288d45dd9560676d3cf286315f2182d0eda6110f8348bd Homepage: https://cran.r-project.org/package=QFRM Description: CRAN Package 'QFRM' (Pricing of Vanilla and Exotic Option Contracts) Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015. Package: r-cran-qga Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1375 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-qga_1.0-1.ca2604.1_all.deb Size: 443480 MD5sum: 2df8d51f89939d49c55c268ce6956f1c SHA1: 43e36ca7f9f2dbaf0a5ee561d179076540b40de8 SHA256: 79b88d14002a65f4b0316e48b379264b79c118554731dc81ba955acd2ce52f55 SHA512: f966436bb9f9cc149be795745e1d5006aaf8eb884f25b991d57add06036071db1afcf4c1227fb0e650dedf3687b6c07418223d528fe001051fc85b822a919e24 Homepage: https://cran.r-project.org/package=QGA Description: CRAN Package 'QGA' (Quantum Genetic Algorithm) Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra (). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization. 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Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) , Prisoner's Dilemma (J. Orlin Grabbe 2005) , Two Person Duel (Flitney and Abbott 2004) , Battle of the Sexes (Nawaz and Toor 2004) , Hawk and Dove Game (Nawaz and Toor 2010) , Newcomb's Paradox (Piotrowski and Sladkowski 2002) and Monty Hall Problem (Flitney and Abbott 2002) . Package: r-cran-qgarch Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qgarch_0.1.0-1.ca2604.1_all.deb Size: 104668 MD5sum: a66f6d3a9aca74df03c403ab2ecbf604 SHA1: 08778a0fd7578074ae7d8b2fc8b4f67e9396a76f SHA256: 48bd9ab3278e13e7d5b5be8194ae59a87bbc48511ee5c70d726a18069820fa13 SHA512: 434abfa6d239d5dcaa74db8b5451145a1828ffbb42090e632d16fd3ddccf617671422ea042da4c29d1ab1574d44f0efb7b4a7aaa21ba8d0faf2fd865ec50f9d9 Homepage: https://cran.r-project.org/package=qgarch Description: CRAN Package 'qgarch' (Quadratic GARCH-in-Mean Models for Volatility Feedback) Fits quadratic generalized autoregressive conditional heteroskedasticity-in-mean (QGARCH-M) models motivated by Campbell and Hentschel (1992). The package supports models with lambda fixed at zero, lambda restricted to a function of the remaining parameters, lambda estimated freely, and a threshold extension with state-dependent lambda. It also provides tools for starting values, estimation, forecasting, likelihood-ratio testing, moment diagnostics, and replication with the included monthly U.S. stock market dataset. 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Effect measure modification in this method is a way to assess how the effect of the mixture varies by a binary, categorical or continuous variable. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; . Package: r-cran-qgglmm Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature Filename: pool/dists/resolute/main/r-cran-qgglmm_0.8.0-1.ca2604.1_all.deb Size: 466252 MD5sum: e5d75bf6326b5be9b79f303e1cfbd407 SHA1: 544a6cb3ceee1f14c8374c69b744ea7a0ae52ea7 SHA256: 711f976564f2e0da861fcfa2582d1ac17f27df338989c39b2df79ab058381e23 SHA512: b21d67c42e6481d6f85dad86fa6b16017d604d23ab9f18f36475b5e7b07d3129196f8429c2de2485cef44363a2996e2f9b616bb4c2124f87ae39136609b6bbb8 Homepage: https://cran.r-project.org/package=QGglmm Description: CRAN Package 'QGglmm' (Estimate Quantitative Genetics Parameters from GeneralisedLinear Mixed Models) Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance. 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Package: r-cran-qhot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-qhot_0.1.0-1.ca2604.1_all.deb Size: 31898 MD5sum: 8e0c1f3348d22d8e7e36e0a799c4690f SHA1: f282a5d8d6e0df47a1c57586d7a0d1c65f9f5b1e SHA256: b872a444fdfba05760c4cf474d02a02a1d2c4c4ab13f6fafa395af58aca2ae49 SHA512: 49b4a31bc345a8d0ace7811d9291f4629bb0f203f6275fc961bf7632e8b7260193efc3210bf01c679bebb1168a9c856ee2592f8d890fa0b2ac1f2988ec0d06d7 Homepage: https://cran.r-project.org/package=QHOT Description: CRAN Package 'QHOT' (QTL Hotspot Detection) This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs. Package: r-cran-qi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-qi_0.1.0-1.ca2604.1_all.deb Size: 23430 MD5sum: 717179cce3e45ed28512b15f16bb84f7 SHA1: 164996cc5c485f97050aaa9ba8315a0bd0ea61ce SHA256: 6166621e16d112fecadbd5df5fbb1a6b4af3a2a9a7027b828c7fbc74fe7096ac SHA512: 191b84ab0fed0dba69331637c44fc6eb6b8d1c1a064bc5276abcbfec8cbecf40ba435c8102df1c885c796a40da27c81bc6e3bbd08a3a9d109e0d81837dee76df Homepage: https://cran.r-project.org/package=QI Description: CRAN Package 'QI' (Quantity-Intensity Relationship of Soil Potassium) The quantity-intensity (Q/I) relationships, first introduced by Beckett (1964), can be employed to assess the K supplying capacity of different soils based on solid-solution exchange equilibria. 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Package: r-cran-qicharts2 Architecture: all Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-qicharts2_0.8.1-1.ca2604.1_all.deb Size: 1054824 MD5sum: 853a5d633976f8bf22ff8636793278ac SHA1: 535b5cfbe3a9ed6c3997ce7ee57f104455606c3e SHA256: b6b13916b9c32d77a15078d1f8bf76e453a135fa4b96e492fef87203864eac24 SHA512: 61d6c5443cf256aa65cff0343b7972538340a49009f1cf591491d5fd2255d5290905ca653f257d2cb95f779a6b6091858056fd6f6a91d86c8a90344048f62ae3 Homepage: https://cran.r-project.org/package=qicharts2 Description: CRAN Package 'qicharts2' (Quality Improvement Charts) Functions for making run charts, Shewhart control charts and Pareto charts for continuous quality improvement. 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The main function, qic(), creates run and control charts and has a simple interface with a rich set of options to control data analysis and plotting, including options for automatic data aggregation by subgroups, easy analysis of before-and-after data, exclusion of one or more data points from analysis, and splitting charts into sequential time periods. Missing values and empty subgroups are handled gracefully. 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Package: r-cran-qlearning Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-qlearning_0.1.1-1.ca2604.1_all.deb Size: 23782 MD5sum: 222fed777a9ce7c92787754115c5b4d9 SHA1: 03f02afe5deeb1ccde240d9340859ee49cd68f75 SHA256: c52f944970873ac04363bc9abb8b84951559fb5b2c31c823ca6394eb41a285c8 SHA512: 42b57dd68f6b90fdc7fc538d4947b012c3027b49dbed80a4f272df8950197a1a4cfcd5cb8b32d51c8c1c8418b4f16936a5cbf0fc37b3c63dd0de649c97ff9a92 Homepage: https://cran.r-project.org/package=QLearning Description: CRAN Package 'QLearning' (Reinforcement Learning using the Q Learning Algorithm) Implements Q-Learning, a model-free form of reinforcement learning, described in work by Strehl, Li, Wiewiora, Langford & Littman (2006) . 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References: Pavía and Lledó (2022) . Pavía and Lledó (2023) . Pavía and Lledó (2025) . Acknowledgements: The authors wish to thank Conselleria de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257; CIAICO/2024/031), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el calculo de productos de vida') for supporting this research. Package: r-cran-qmap Architecture: all Version: 1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus Filename: pool/dists/resolute/main/r-cran-qmap_1.0-6-1.ca2604.1_all.deb Size: 328984 MD5sum: d2e0529a2e8a6d72e80a10ffe3e56292 SHA1: 9d489e9883f29438381beb6ad01a25c410e7adc2 SHA256: 5638460be83544ce770cf72931a2c9575effa7836d9ba33cf7c6bc1538d89d8f SHA512: 37d7d95e6c3a360e490b4c037a2d2d88dad375495f6278302c63a7e7c6598ce4ca8f87ae52a8d70b973315d8f68dd95d0ecce28f7b7c59834bfafbbfd5e80b9c Homepage: https://cran.r-project.org/package=qmap Description: CRAN Package 'qmap' (Statistical Transformations for Post-Processing Climate ModelOutput) Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping. 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For each profile, data imputations and adjustments are made as necessary and basic parameters are estimated. Supports single dose, multi-dose, and multi-subject data. Supports steady-state calculations and various routes of drug administration. See ?qpNCA and vignettes. Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and Gibaldi and Perrier (1982, ISBN:978-0824710422). Package: r-cran-qpraentry Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsplus, r-cran-dplyr, r-cran-dt, r-cran-eurostat, r-cran-ggiraph, r-cran-ggplot2, r-cran-giscor, r-cran-memoise, r-cran-purrr, r-cran-sf, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinywidgets, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qpraentry_0.1.1-1.ca2604.1_all.deb Size: 630534 MD5sum: c26815d140b6d34d4b2497dcc9138113 SHA1: b3933df192cc3679ec2187ae9524c76db19ede4b SHA256: 2555fb024a76e884b4b86eac342c2e0691dc991bd3946b21ffecd949fe67e588 SHA512: 86718f339cfad2ef7919d17e85c8ca02a8219e1d8f39bc7acb3d1e5860504b5240331b3430fd7333870da2babcb64f8c95a3bf925dd9b6858dc592b4ecd1444b Homepage: https://cran.r-project.org/package=qPRAentry Description: CRAN Package 'qPRAentry' (Quantitative Pest Risk Assessment at the Entry Step) Supports risk assessors in performing the entry step of the quantitative Pest Risk Assessment. It allows the estimation of the amount of a plant pest entering a risk assessment area (in terms of founder populations) through the calculation of the imported commodities that could be potential pathways of pest entry, and the development of a pathway model. Two 'Shiny' apps based on the functionalities of the package are included, that simplify the process of assessing the risk of entry of plant pests. The approach is based on the work of the European Food Safety Authority (EFSA PLH Panel et al., 2018) . Package: r-cran-qqman Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-calibrate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qqman_0.1.9-1.ca2604.1_all.deb Size: 1106710 MD5sum: 46ecfc6994081c02450a7a90683e1c60 SHA1: b6b9d67af5bd4e42eef6d7b74fa6ac40cca8fac7 SHA256: 2b511021623c74de48f08e8d712509e962c35f5fe40e50cd6665add90c38dc8c SHA512: c89cfa77f91cc1e4d3593210d5d1c24fa57543cf272e8933658aefe3bd5e6f43bc73c556d413251f2bb8f6130e2cbb54733d571481bf28224bdcedf31239c6cd Homepage: https://cran.r-project.org/package=qqman Description: CRAN Package 'qqman' (Q-Q and Manhattan Plots for GWAS Data) Create Q-Q and manhattan plots for GWAS data from PLINK results. Package: r-cran-qqplotr Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-robustbase, r-cran-mass, r-cran-opdisdownsampling, r-cran-qqconf Suggests: r-cran-shiny, r-cran-devtools, r-cran-lattice, r-cran-shinybs, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qqplotr_0.0.7-1.ca2604.1_all.deb Size: 1089794 MD5sum: a1af2cdb99d2e44e9b9b0249f87ad65b SHA1: 04a84230a32c6a2dbde307cb4b32d853f447e726 SHA256: 7f9a6bf76e917217a10a73f8a4f65e840dd4733daa43e76a8efffec6ee51b63d SHA512: f1c436cbcf6adf2093d9811b9fc1d86030cfe4a5126ac907a5bcc8c5fa904b56bc4c1f691495ce3bbc103462c55848d8382a092b82fbbc03f45de3bee98f6558 Homepage: https://cran.r-project.org/package=qqplotr Description: CRAN Package 'qqplotr' (Quantile-Quantile Plot Extensions for 'ggplot2') Extensions of 'ggplot2' Q-Q plot functionalities. Package: r-cran-qqreflimits Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qqreflimits_1.0.3-1.ca2604.1_all.deb Size: 58272 MD5sum: affb2cf32b70e83995cfed7db39c54af SHA1: ab4cc1ea74f6086689ab39cd28cf81eac6dfa7ea SHA256: 1b219eb0524cde312d6bc28e4edbe041b40cb6b858c09b1935c32fd0cc783c72 SHA512: dd54603c2c3aec27fd6741ca3a95b27445fcef000069d684d5964e494759b17c80275fef08498ae58f83209af38b21ec22faf06881e1c8498600ee20bfd3b2de Homepage: https://cran.r-project.org/package=QQreflimits Description: CRAN Package 'QQreflimits' (Reference Limits using QQ Methodology) A collection of routines for finding reference limits using, where appropriate, QQ methodology. All use a data vector X of cases from the reference population. The default is to get the central 95% reference range of the population, namely the 2.5 and 97.5 percentile, with optional adjustment of the range. Along with the reference limits, we want confidence intervals which, for historical reasons, are typically at 90% confidence. A full analysis provides six numbers: – the upper and the lower reference limits, and - each of their confidence intervals. For application details, see Hawkins and Esquivel (2024) . Package: r-cran-qqtest Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qqtest_1.2.0-1.ca2604.1_all.deb Size: 219870 MD5sum: 291e999850f5adc101066cd5aeca6d7f SHA1: 8c4b053e0ff55bc90cd4f298fc91d1a33f7ce004 SHA256: 9eb570df05de7328fb9478d6ce62d6158ecea791c305c94ab489f80c1610bf50 SHA512: 71a4367af8e2dd769ce7f2a2bacae08deaa0ed836996ce44e35c6bb89605a848432a0262fd165b767fe116f3d8d51a7a61f5657b8476321f6d9af438cc957d54 Homepage: https://cran.r-project.org/package=qqtest Description: CRAN Package 'qqtest' (Self Calibrating Quantile-Quantile Plots for Visual Testing) Provides the function qqtest which incorporates uncertainty in its qqplot display(s) so that the user might have a better sense of the evidence against the specified distributional hypothesis. qqtest draws a quantile quantile plot for visually assessing whether the data come from a test distribution that has been defined in one of many ways. The vertical axis plots the data quantiles, the horizontal those of a test distribution. The default behaviour generates 1000 samples from the test distribution and overlays the plot with shaded pointwise interval estimates for the ordered quantiles from the test distribution. A small number of independently generated exemplar quantile plots can also be overlaid. Both the interval estimates and the exemplars provide different comparative information to assess the evidence provided by the qqplot for or against the hypothesis that the data come from the test distribution (default is normal or gaussian). Finally, a visual test of significance (a lineup plot) can also be displayed to test the null hypothesis that the data come from the test distribution. Package: r-cran-qqvases Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-qqvases_1.0.0-1.ca2604.1_all.deb Size: 56734 MD5sum: 4e887d301842689524d6dd6255134587 SHA1: f2bd3c4b0f83fe6061b40a4b16eb8ca75f709fcc SHA256: c356266be1dcdd3a6bfce7935cf5e25902f7f22787bd27c935d0ffcdffd8b211 SHA512: 787926419f476e6bdcdbb541946b573cf77c725132aa29727be9436f9f3855560d66e7486b640e1cb2222d54e73cb06bf8e80ac6bd5ed4d0f777d1f5437d1925 Homepage: https://cran.r-project.org/package=qqvases Description: CRAN Package 'qqvases' (Animated Normal Quantile-Quantile Plots) Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy. The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions. The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot. The package draws QQ plots for several distributions, either as samples or continuous functions. Package: r-cran-qr.break Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-sparsem Filename: pool/dists/resolute/main/r-cran-qr.break_1.0.2-1.ca2604.1_all.deb Size: 179870 MD5sum: 96ffa4a79286edb8bef3991c4057b055 SHA1: 49c915f6a1c59a008a307a2a025910ba986128b1 SHA256: b50cb6b98f80d7a3c1652b27a9f44eca85a49d20e4e2e279c0629acb6902e5aa SHA512: 245d839f6a800573e6888ab81e0524d8bda5698078c6d09f8c9ea3cdef50d1b1650c972a641b88aa597bd66432bb9efaa8c0e8757e756675a136b5d34c49e0fb Homepage: https://cran.r-project.org/package=QR.break Description: CRAN Package 'QR.break' (Structural Breaks in Quantile Regression) Methods for detecting structural breaks, determining the number of breaks, and estimating break locations in linear quantile regression, using one or multiple quantiles, based on Qu (2008) and Oka and Qu (2011). Applicable to both time series and repeated cross-sectional data. The main function is rq.break(). References for detailed theoretical and empirical explanations: (1) Qu, Z. (2008). "Testing for Structural Change in Regression Quantiles." Journal of Econometrics, 146(1), 170-184 (2) Oka, T., and Qu, Z. (2011). "Estimating Structural Changes in Regression Quantiles." Journal of Econometrics, 162(2), 248-267 . Package: r-cran-qra Architecture: all Version: 0.2.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-knitr, r-cran-rmarkdown, r-cran-lme4, r-cran-ggplot2 Suggests: r-cran-fitodbod, r-cran-vgam, r-cran-glmmtmb, r-cran-gamlss, r-cran-prettydoc, r-cran-dharma, r-cran-kableextra, r-cran-plotrix, r-cran-dfoptim, r-cran-optimx, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-qra_0.2.8.1-1.ca2604.1_all.deb Size: 1538216 MD5sum: 092219c06e5251d0c4153a6f62dff53c SHA1: a1d0e5b3e5c3f819c08da009055a63f3cdb27615 SHA256: 6827ca713175154b30f9dbfed831852a3e60ba6831da413553b37bfccd4e7172 SHA512: 397f460af7bb1f288bf5b104fea956f5da27d61729f6aebf666a9988d7405b98a1842c63b72d49c9c613433e3e663cc3742b3f23382bae1075dbe9fd0767228b Homepage: https://cran.r-project.org/package=qra Description: CRAN Package 'qra' (Quantal Response Analysis for Dose-Mortality Data) Functions are provided that implement the use of the Fieller's formula methodology, for calculating a confidence interval for a ratio of (commonly, correlated) means. See Fieller (1954) . Here, the application of primary interest is to studies of insect mortality response to increasing doses of a fumigant, or, e.g., to time in coolstorage. The formula is used to calculate a confidence interval for the dose or time required to achieve a specified mortality proportion, commonly 0.5 or 0.99. Vignettes demonstrate link functions that may be considered, checks on fitted models, and alternative choices of error family. Note in particular the betabinomial error family. See also Maindonald, Waddell, and Petry (2001) . 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This allows to generate fast, free to use and privacy friendly QR codes. 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Package: r-cran-qtl2fst Architecture: all Version: 0.30-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fst, r-cran-qtl2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qtl2fst_0.30-1.ca2604.1_all.deb Size: 92914 MD5sum: 4a8a9c8e265bdb65cde976043e50c06f SHA1: 7ebaf471a08079ec62479e8f7d6fcdd7e6947c3e SHA256: 8590a057d4f96b05fa9c90949bb89f0f269b49263723cb818e6ea0db8c9b74ce SHA512: e5a58a3f1ab2b9225aa4dad86c15d62c69e86c9660e6533d01ad19b8a54dccf2d7eb1f849c03d3b159a79ae03120252c760d237dce12e0124797c51657e50dcc Homepage: https://cran.r-project.org/package=qtl2fst Description: CRAN Package 'qtl2fst' (Database Storage of Genotype Probabilities for QTL Mapping) Uses the 'fst' package to store genotype probabilities on disk for the 'qtl2' package. These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time. Package: r-cran-qtl2pattern Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2037 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-ggplot2, r-cran-assertthat, r-cran-qtl2, r-cran-qtl2fst, r-cran-fst, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-qtl2ggplot Filename: pool/dists/resolute/main/r-cran-qtl2pattern_1.2.1-1.ca2604.1_all.deb Size: 1115286 MD5sum: aefdc70de9322730359aec6d551dbbc9 SHA1: 2cff90600f630894478633206246cc0d327c2c95 SHA256: 9a72a1e5d80e7d1eb8810edef21ada70e86626ab8a7b5fce237616fc096aa459 SHA512: d3e29208a84f901df10053fad2586c32d6a2a55dfc549cb13bf650f6fc0ac0a25915b4540e1c9e0c49e81c148101920265821ea2613197a6bd07aa4986f141a2 Homepage: https://cran.r-project.org/package=qtl2pattern Description: CRAN Package 'qtl2pattern' (Pattern Support for 'qtl2' Package) Routines in 'qtl2' to study allele patterns in quantitative trait loci (QTL) mapping over a chromosome. Useful in crosses with more than two alleles to identify how sets of alleles, genetically different strands at the same locus, have different response levels. Plots show profiles over a chromosome. Can handle multiple traits together. See . Package: r-cran-qtlbook Architecture: all Version: 0.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-qtl Filename: pool/dists/resolute/main/r-cran-qtlbook_0.20-1.ca2604.1_all.deb Size: 187354 MD5sum: c9353678c98e49cb19344827e155f346 SHA1: a30dde0700f54a33f6af3c638643f883c89eb298 SHA256: 4b87cc4a69e9751150f909ea8663e341fa2e664a4e7dc1db6acdb07b2e6ebd60 SHA512: 59234479f95f4b823db8fa253939f88d4e1fdec8162d803f8f20c40ffbe47d9d3ad9a7c05727493dc6c52e45be7b86333036322118dcbb703691bb3649993dc9 Homepage: https://cran.r-project.org/package=qtlbook Description: CRAN Package 'qtlbook' (Datasets for the R/qtl Book) Datasets for the book, A Guide to QTL Mapping with R/qtl. Broman and Sen (2009) . Package: r-cran-qtlc Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tiff, r-cran-rgl, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-qtlc_1.0-1.ca2604.1_all.deb Size: 158484 MD5sum: 852b4795fabac903337c3e97de87265a SHA1: 995f04ffb6106a18547ef380590a49e2b48aba46 SHA256: 680b2fe8c40b70204de9c92273848e41331cfb2e6969eaffeca16881fe6666d8 SHA512: c4ea65a486fa909376bfff6bec194db03ce51775bda6ad732225464450d5bd6d0aaf4f04fbec66ab6afd0178482655f9763f63196489d9799209fa862065943c Homepage: https://cran.r-project.org/package=qtlc Description: CRAN Package 'qtlc' (Densitometric Analysis of Thin-Layer Chromatography Plates) Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates. Package: r-cran-qtlcharts Architecture: all Version: 0.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1839 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qtl, r-cran-htmlwidgets Suggests: r-cran-htmltools, r-cran-jsonlite, r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qtlcharts_0.18-1.ca2604.1_all.deb Size: 700880 MD5sum: f581744d8c9295ba534f8d87268182dc SHA1: 2701d5456a2966bc0a250e6228cfe6dce394f20c SHA256: 2569b96f24e545a7159eaa14ba13ef8a87a1125ad89024691a3b37747ccae342 SHA512: 7ae8b4b6ed8f4a0d1d2a4e140c70dea9e12c7126e09aaa46f44acd2bb264acf016ca48fb172953140b6bfab63d56af5c3bbfd71e32db5b39b31f5489573b44e4 Homepage: https://cran.r-project.org/package=qtlcharts Description: CRAN Package 'qtlcharts' (Interactive Graphics for QTL Experiments) Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits. Broman (2015) . Package: r-cran-qtldesign Architecture: all Version: 0.953-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-qtl Filename: pool/dists/resolute/main/r-cran-qtldesign_0.953-1.ca2604.1_all.deb Size: 99362 MD5sum: 914043452f0d770158a5656e60e71fa3 SHA1: f466f2dbe1d5b355e36d17ad2c2116ec193fb696 SHA256: b1161863f4bceed4207e414e2daeab8c8adaf0e4053b2257b48c176774bc7079 SHA512: 62b7d89a50585c21ec7499e0a2912f59846b0bbf698b41abfeb2649f5ba2b7208506b269efc75539065585e87fcea79fd0edfc8c5ce147843c7ce6f556ca8e98 Homepage: https://cran.r-project.org/package=qtlDesign Description: CRAN Package 'qtlDesign' (Design of QTL (Quantitative Trait Locus) Experiments) Design of QTL (quantitative trait locus) experiments involves choosing which strains to cross, the type of cross, genotyping strategies, phenotyping strategies, and the number of progeny to raise and phenotype. This package provides tools to help make such choices. Sen and others (2007) . Package: r-cran-qtlemm Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-qtlemm_3.1.0-1.ca2604.1_all.deb Size: 2423378 MD5sum: ba0837fd71c42177d3559ac1b0db6b33 SHA1: f95c274c0ed56dc4a0b297530971174f8119df88 SHA256: e8578baa309ef9be95259f5751f4df6239de31d0d4dea32f13357b475e3fa5fc SHA512: f6f879fe946eb2294c7561315a5fbc0c7ad0711e6e39fa4cd385430409f9aff15f1b4ab9cb46bb17ea6f749565ea528c881c3daf16787057144ee529a8e6e52a Homepage: https://cran.r-project.org/package=QTLEMM Description: CRAN Package 'QTLEMM' (QTL EM Algorithm Mapping and Hotspots Detection) For QTL mapping, this package comprises several functions designed to execute diverse tasks, such as simulating or analyzing data, calculating significance thresholds, and visualizing QTL mapping results. The single-QTL or multiple-QTL method, which enables the fitting and comparison of various statistical models, is employed to analyze the data for estimating QTL parameters. The models encompass linear regression, permutation tests, normal mixture models, and truncated normal mixture models. The Gaussian stochastic process is utilized to compute significance thresholds for QTL detection on a genetic linkage map within experimental populations. Two types of data, complete genotyping, and selective genotyping data from various experimental populations, including backcross, F2, recombinant inbred (RI) populations, and advanced intercrossed (AI) populations, are considered in the QTL mapping analysis. For QTL hotspot detection, statistical methods can be developed based on either utilizing individual-level data or summarized data. We have proposed a statistical framework capable of handling both individual-level data and summarized QTL data for QTL hotspot detection. Our statistical framework can overcome the underestimation of thresholds resulting from ignoring the correlation structure among traits. Additionally, it can identify different types of hotspots with minimal computational cost during the detection process. Here, we endeavor to furnish the R codes for our QTL mapping and hotspot detection methods, intended for general use in genes, genomics, and genetics studies. The QTL mapping methods for the complete and selective genotyping designs are based on the multiple interval mapping (MIM) model proposed by Kao, C.-H. , Z.-B. Zeng and R. D. Teasdale (1999) and H.-I Lee, H.-A. Ho and C.-H. Kao (2014) , respectively. The QTL hotspot detection analysis is based on the method by Wu, P.-Y., M.-.H. Yang, and C.-H. Kao (2021) . Package: r-cran-qtlhot Architecture: all Version: 1.2.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qtl, r-cran-mnormt, r-cran-corpcor, r-cran-broman Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qtlhot_1.2.10-1.ca2604.1_all.deb Size: 1742784 MD5sum: 50055eed61f7aa3095b18cb5dc238838 SHA1: 3e0f6a94351ebbcf83199e3d8bf0cd7ca15bfbf5 SHA256: b48bd1183d59d02fbc228cfdfb6046dc0669b21a1718fd1da44aceab1e4abee7 SHA512: 2a277453ec2290c99bf9519083dc3ed6287e90b303dcd79f2ec3662604b6989cc40f28f7b92b3a272d9182466408e883bd7aa58ec0fd1136a68244993b28721d Homepage: https://cran.r-project.org/package=qtlhot Description: CRAN Package 'qtlhot' (Inference for QTL Hotspots) Functions to infer co-mapping trait hotspots and causal models. Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. . Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013) Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013. . Package: r-cran-qtlnet Architecture: all Version: 1.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qtl, r-cran-igraph, r-cran-sem, r-bioc-graph, r-cran-pcalg Filename: pool/dists/resolute/main/r-cran-qtlnet_1.5.4-1.ca2604.1_all.deb Size: 3144340 MD5sum: 092e67abe3e38be0979bbb6dc970dd76 SHA1: a55d235b13ba8f15eaa7e1929392c76be4d0b89c SHA256: c261e9867c3e938c1a78c84c2a7d3cbd3f92c0a88d8806526baf81e5dc2609a0 SHA512: 71997697bdb1754e60ad362be216e1d09602bea24a3a9c78f1900954925662d15e74cf610dc048593b25de58b498b72a6d991fe916963e9e2bce481fe0265363 Homepage: https://cran.r-project.org/package=qtlnet Description: CRAN Package 'qtlnet' (Causal Inference of QTL Networks) Functions to Simultaneously Infer Causal Graphs and Genetic Architecture. Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by a few genetic loci (QTL). Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010) Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Annals of Applied Statistics 4: 320-339. . Package: r-cran-qtocen Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rgenoud, r-cran-quantreg, r-cran-rdpack, r-cran-matrixmodels Suggests: r-cran-stringr, r-cran-testthat, r-cran-faraway, r-cran-quantoptr, r-cran-survminer Filename: pool/dists/resolute/main/r-cran-qtocen_0.1.1-1.ca2604.1_all.deb Size: 132668 MD5sum: 715fc2fff200946aa1320af0dd435891 SHA1: e013b9ed266e21a39169daca2f536ce6e6415e79 SHA256: 3fa455aa662e521bf07d3b402b10f22f6464c3bf6333f4ebffaa46dcce185e4d SHA512: 990e99752743c61853ced65ff8b9a0e3f5eaaf325eaac24c31576acd1d6d9bf36de292e8f8672d77c25c7f75d006838ea87a248e354dc0ec599f7b4329cb19ac Homepage: https://cran.r-project.org/package=QTOCen Description: CRAN Package 'QTOCen' (Quantile-Optimal Treatment Regimes with Censored Data) Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature. Package: r-cran-qtwacademic Architecture: all Version: 2022.12.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2456 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qtwacademic_2022.12.13-1.ca2604.1_all.deb Size: 1694536 MD5sum: a9e4c2b49d1dbef214a42418e26ce0dd SHA1: e0a591241c7956e1dffc732ecfba4b739925466e SHA256: 7dad3116b80c9e9fb3ea42d52b01803a2978893b0294cd365df0d678525f5c3a SHA512: 9a3eab625253880949fb55b157a6ce66383a85214c896f3ace4eefcd38f6150140862afa871cfee6db75ad199d47546317bb9145e5fdf38f3aed48fef91ae74a Homepage: https://cran.r-project.org/package=qtwAcademic Description: CRAN Package 'qtwAcademic' ('Quarto' Website Templates for Academics) Provides three 'Quarto' website templates as an R project, which are commonly used by academics. Templates for personal websites and course/workshop websites are included, as well as a template with minimal content for customization. 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The functions 'quadmesh' or 'triangmesh' produce a continuous surface as a 'mesh3d' object as used by the 'rgl' package. This is used for plotting raster data in 3D (optionally with texture), and allows the application of a map projection without data loss and many processing applications that are restricted by inflexible regular grid rasters. There are discrete forms of these continuous surfaces available with 'dquadmesh' and 'dtriangmesh' functions. 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Package: r-cran-quadraticsd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-quadraticsd_0.1.0-1.ca2604.1_all.deb Size: 22420 MD5sum: 562a9cdc06052250b0580f73381698da SHA1: dd83620a493c3c008877574332156b9f9821a390 SHA256: d26d0a966b115e22ad5506bd00c04ef18b5ad119b4e24596c2374fc2353e7c3f SHA512: 94cde74743fed62a01f40e5f5cd3cb21a0f41555fcf4209c7edfffc4cd132962d9f709ed6c2914e48fdf98dc2eb3a047c118cf6cb8b9a1dd01a7693f13428fe3 Homepage: https://cran.r-project.org/package=quadraticSD Description: CRAN Package 'quadraticSD' (Visualizing the SD using a Quadratic Curve) Given a dataset, the user is invited to utilize the Empirical Cumulative Distribution Function (ECDF) to guess interactively the mean and the mean deviation. Thereafter, using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) and visualize the standard deviation (SD). For details, see Sarkar and Rashid (2019), Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10. Package: r-cran-quadroot Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-quadroot_0.2.1-1.ca2604.1_all.deb Size: 15172 MD5sum: b8fa4ef7bb093f73e5a9b91bd45f00e4 SHA1: 154c9c6b119f0aa68e230e285f8c9eb64ea5eef8 SHA256: 52a743722f70fc5d0483e897cc02ce0c0cae6373cf87270af36490fa2ec4d0e6 SHA512: 86848e1465aadf3e191630b73f3f6897c6c71cf4178fd413805d22b29f31005e62e1cea0f8b1bffb9b19152931102b7d793f5be6631821261c70ce8f5bcfdbc4 Homepage: https://cran.r-project.org/package=QuadRoot Description: CRAN Package 'QuadRoot' (Quadratic Root for any Quadratic Equation) It will assist the user to find simple quadratic roots from any quadratic equation. 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(2018) , compare the performance with linear models, and construct networks with partial derivatives. Package: r-cran-qualitycontrol Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-janitor, r-cran-openxlsx, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qualitycontrol_0.1.0-1.ca2604.1_all.deb Size: 62240 MD5sum: 78bb3064f2349073e85ac2d0e44c0853 SHA1: 7cf14e34fe80849bd6828bef6dc4ed8e1d1ccc48 SHA256: 98ec52a8a33064ed84eb5b831391d2342ca11e70532b9a45315d32d24eb5e833 SHA512: 1cac89c1220560364ae572ce1a5a64a3f5e25a43332e51b931d6df1c02a1ab5c0e700e7673d6e9f3765d192d6dc3f98b143c944afea3a13e668f0c8bd1c5a975 Homepage: https://cran.r-project.org/package=qualitycontrol Description: CRAN Package 'qualitycontrol' (Unified Framework for Data Quality Control) An easy framework to set a quality control workflow on a dataset. Includes a various range of functions that allow to establish an adaptable data quality control. Package: r-cran-qualitymeasure Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2546 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-doparallel, r-cran-lme4, r-cran-foreach, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qualitymeasure_2.0.1-1.ca2604.1_all.deb Size: 2203656 MD5sum: e3d22291a16c0190cc718bf02bbe7e56 SHA1: a35b99cf37d0c7043f62f0c2a2033f979e292fe5 SHA256: ddc5d6e69375d88d04ef135df2c8fba6b211c56bc88334817820e72dd59413d2 SHA512: f5b6221cdd58688dbde5290183da4f873849f6a76294eb6321776e47cdf485219648ce27c2006ab7634bf03de57feff0c0f74d1e019be260c210a1c28521be3c Homepage: https://cran.r-project.org/package=QualityMeasure Description: CRAN Package 'QualityMeasure' (Methods for Analyzing Quality Measure Performance) Quality of care is compared across accountable entities, including hospitals, provider groups, and insurance plans, using standardized quality measures. However, observed variations in quality measure performance might be the result of chance sampling or measurement errors. Contains functions for estimating the reliability of unadjusted and risk-standardized quality measures. Package: r-cran-quallmer.app Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quallmer, r-cran-shiny, r-cran-bslib, r-cran-dplyr, r-cran-tidyr, r-cran-irr, r-cran-htmltools, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-quallmer.app_0.1.0-1.ca2604.1_all.deb Size: 102186 MD5sum: 4b723306c173cf1196df9c53fce3e3c2 SHA1: beafdc4526f013c3f019662951af13be0db1e9fa SHA256: 7774be3a3cf5fb617cb8d414360e8ee1f966e10781a6e34a46d524cff1292e5e SHA512: 4af1d2d022d6132c353b4ef5722a894c481e3edb05e7be24b1e54d316a7c4bfc25f2ac9330ef2ebb596dc67a769e5531870e6c9c21506de71b7a5d2b51cec70d Homepage: https://cran.r-project.org/package=quallmer.app Description: CRAN Package 'quallmer.app' (Interactive Validation App for 'quallmer') Companion package to 'quallmer' providing an interactive 'shiny' application for manual coding, reviewing large language model (LLM) generated annotations, and computing inter-rater reliability metrics. Supports three modes: blind manual coding, LLM output validation, and agreement calculation. Computes standard reliability metrics including Krippendorff's alpha (Krippendorff 2019 ), Cohen's kappa, Fleiss' kappa (Fleiss 1971 ), intraclass correlation coefficient (ICC), and percent agreement for nominal, ordinal, interval, and ratio data. Also computes gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009 ). Package: r-cran-quallmer Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1799 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ellmer, r-cran-cli, r-cran-digest, r-cran-irr, r-cran-lifecycle, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-yardstick Suggests: r-cran-ggplot2, r-cran-janitor, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-mockery, r-cran-quanteda, r-cran-quanteda.tidy, r-cran-withr Filename: pool/dists/resolute/main/r-cran-quallmer_0.4.0-1.ca2604.1_all.deb Size: 1751620 MD5sum: 96f3525408aba29c4f3150596623309e SHA1: fa914fc1e1ed5a42b6fb32965cbc5ebb2364d258 SHA256: 1abe5e265df1f8da7cf495bdb7d3e74e5e4636c33d219195898396536af6a045 SHA512: 5b757eaa34112a45d541de8c95b08afcd54ca30b1a184428f1b0614acef2b6ea390ee77911ad7a4117096c4ac2cd236ded45fad1309909564f3571efac146d89 Homepage: https://cran.r-project.org/package=quallmer Description: CRAN Package 'quallmer' (Qualitative Analysis with Large Language Models) Tools for AI-assisted qualitative data coding using large language models ('LLMs') via the 'ellmer' package, supporting providers including 'OpenAI', 'Anthropic', 'Google', 'Azure', and local models via 'Ollama'. Provides a 'codebook'-based workflow for defining coding instructions and applying them to texts, images, and other data. Includes built-in 'codebooks' for common applications such as sentiment analysis and policy coding, and functions for creating custom 'codebooks' for specific research questions. Supports systematic replication across models and settings, computing inter-coder reliability statistics including Krippendorff's alpha (Krippendorff 2019, ) and Fleiss' kappa (Fleiss 1971, ), as well as gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009, ). Provides audit trail functionality for documenting coding workflows following Lincoln and Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness in qualitative research. Package: r-cran-qualmap Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-leaflet, r-cran-purrr, r-cran-rlang, r-cran-sf Suggests: r-cran-covr, r-cran-ggplot2, r-cran-testthat, r-cran-tigris, r-cran-tidycensus, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qualmap_0.2.2-1.ca2604.1_all.deb Size: 830550 MD5sum: b2939f29f5589f74ec331be6b31e0997 SHA1: fcb98aefc266c57a99f0f0392d96d9892c08e868 SHA256: ad08c0745d6969fe84374adefaa6ea2fc4cbfd4eebbcd085cb5b3fc09fec4ff5 SHA512: 22f8d3b586c3e2569e2ab5b5e5f4a799a7a95694b0aa5850cc947ae00aebc47b1ec8ed6bb188e8786bf4c5ed3e29d67a4c0b4d88f0a2d8f073fff17850462ec6 Homepage: https://cran.r-project.org/package=qualmap Description: CRAN Package 'qualmap' (Opinionated Approach for Digitizing Semi-Structured QualitativeGIS Data) Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and converting it to a simple features object. These tools are focused on data that are drawn on a map that contains some type of polygon features. For each area identified on the map, the id numbers of these polygons can be entered as vectors and transformed using qualmap. Package: r-cran-qualtrics Architecture: all Version: 3.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sjlabelled, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-withr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr, r-cran-webmockr Filename: pool/dists/resolute/main/r-cran-qualtrics_3.2.2-1.ca2604.1_all.deb Size: 257478 MD5sum: ae11ec6ae678cd43cbd25645e58af7c0 SHA1: ccd6cde013bcb2fe8f0a5cd265f1248bf85facb8 SHA256: 90a6d0ce164c79e9c8ab893305c22786db7f4c03ff9a01665a8dd883a7a1d3dd SHA512: a6f201f9f6fb30544023d59679a55c00bbd93dd21e4195251b67fb3f31273a3fec3196b4497e7d592b8719f1e167f3a8b281db760c39870fc8f700c4914339ad Homepage: https://cran.r-project.org/package=qualtRics Description: CRAN Package 'qualtRics' (Download 'Qualtrics' Survey Data) Provides functions to access survey results directly into R using the 'Qualtrics' API. 'Qualtrics' is an online survey and data collection software platform. See for more information about the 'Qualtrics' API. This package is community-maintained and is not officially supported by 'Qualtrics'. Package: r-cran-qualvar Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggally, r-cran-dt Filename: pool/dists/resolute/main/r-cran-qualvar_0.2.0-1.ca2604.1_all.deb Size: 198194 MD5sum: ffddd42db640a01e6ef15230c0b15e07 SHA1: 469f692600eac22f861a844c1a169e9a8b3be538 SHA256: abdd71b75826f28f46ded4a24fef3795cce94c462ec3fbf2431d25a296d2e2c1 SHA512: 894f619cb7bc3bb53372c21eec4d218ef1afc3ed21f4aaef24eb77f1afe48a1ce9568d05e37889ef1f6ffe09d2d4e827084e78266ad02b5602447c7e212abd41 Homepage: https://cran.r-project.org/package=qualvar Description: CRAN Package 'qualvar' (Implements Indices of Qualitative Variation Proposed by Wilcox(1973)) Implements indices of qualitative variation proposed by Wilcox (1973). 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It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) . Package: r-cran-quantbayes Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-quantbayes_0.1.0-1.ca2604.1_all.deb Size: 1315892 MD5sum: 2a3180b1606e19bf639968b2c2f1b2e0 SHA1: cebaebe2fadd9fe40c635fc2dceac0e6aec96013 SHA256: 24cf3beb74b3dbcad2ad8afd655fbe08fac5dce1d80aae48cbfc135b5aeb669b SHA512: da7dda6f6bd0785b8c966dd340c9379553a9cf4e5681f26abf943efae1f49015e0e6fc6a63c5b4d3aed9ac10156dc5cc81fa57a2fe4f9b80f7d6129fd49429ec Homepage: https://cran.r-project.org/package=quantbayes Description: CRAN Package 'quantbayes' (Bayesian Quantification of Evidence Sufficiency) Implements the Quantification Evidence Standard algorithm for computing Bayesian evidence sufficiency from binary evidence matrices. 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It covers fixed coupon assets, floating note assets, interest and cross currency swaps with different payment frequencies. Enables the calibration of spot, instantaneous forward and basis curves, making it a powerful tool for accurate and flexible bond valuation and curve generation. The valuation and calibration techniques presented here are consistent with industry standards and incorporates author's own calculations. Tuckman, B., Serrat, A. (2022, ISBN: 978-1-119-83555-4). 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See Fan and Gijbels (1996) and Perperoglou et al.(2019) . Package: r-cran-quantdates Architecture: all Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 528 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-quantdates_2.0.4-1.ca2604.1_all.deb Size: 201520 MD5sum: 7e1cdf7bfccc451bb565ab7ed5e7f5ad SHA1: 3a4f8fded5294dd4261c06a89a1711a18b767f44 SHA256: 6590cca913bed0b588dd9bc97d08362dd394c269a30cc1f3a3ed0b31ef7243f3 SHA512: 47805ab2c12e0c742440a6cf429519cde273a237d146fe71f56141da85f01eb7933c823660d2a5aa45ccc467d63a4e03b26b205211d4f8e739a256d5c51e1ab4 Homepage: https://cran.r-project.org/package=quantdates Description: CRAN Package 'quantdates' (Manipulate Dates for Finance) Functions to manipulate dates and count days for quantitative finance analysis. The 'quantdates' package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) regulations. 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Package: r-cran-quantilegh Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mixtools, r-cran-tclust, r-cran-fmx, r-cran-tukeygh77 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mixsmsn Filename: pool/dists/resolute/main/r-cran-quantilegh_0.1.8-1.ca2604.1_all.deb Size: 102004 MD5sum: c128c38fbb1e31e7cbc8c9411a5043e1 SHA1: 006243bafc4d46ae11c4fd1a219e431276f5f748 SHA256: b55a9b2f9b821b652351e036489c96ea2bf0ada8369cbe252df74095ad42a835 SHA512: 6ca28d01fa500c4e8962f7b293bf89dc79a58aa7e1881d45d52d0d98840e22a5515ec6a8eb742582007a20b8d9d2447b05cd4d4ced43be6449ba41442ef836a1 Homepage: https://cran.r-project.org/package=QuantileGH Description: CRAN Package 'QuantileGH' (Quantile Least Mahalanobis Distance Estimator for Tukey g-&-hMixture) Functions for simulation, estimation, and model selection of finite mixtures of Tukey g-and-h distributions. Package: r-cran-quantilegrader Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-quantilegrader_0.1.1-1.ca2604.1_all.deb Size: 72832 MD5sum: 5e1c785dae9387ee1614c8be55e50026 SHA1: 417d3b1a316d7cf455088a26b6c365e2fceac82c SHA256: d57ce651fdbc2fc628a3816f29819b81941b96bcbb60dbd86a57d9fb1d9e76a1 SHA512: 42ed3d20c2926602fecf224420a2b4aaa985f17fc8bbc036f679cc13b84a6204b3e19dc7229e5e8219b9e2ec11a7add1cd158c43ed1803f6225d2a83a4181e0b Homepage: https://cran.r-project.org/package=QuantileGradeR Description: CRAN Package 'QuantileGradeR' (Quantile-Adjusted Restaurant Grading) Implementation of the food safety restaurant grading system adopted by Public Health - Seattle & King County (see Ashwood, Z.C., Elias, B., and Ho. D.E. "Improving the Reliability of Food Safety Disclosure: A Quantile Adjusted Restaurant Grading System for Seattle-King County" (working paper)). As reported in the accompanying paper, this package allows jurisdictions to easily implement refinements that address common challenges with unadjusted grading systems. First, in contrast to unadjusted grading, where the most recent single routine inspection is the primary determinant of a grade, grading inputs are allowed to be flexible. For instance, it is straightforward to base the grade on average inspection scores across multiple inspection cycles. Second, the package can identify quantile cutoffs by inputting substantively meaningful regulatory thresholds (e.g., the proportion of establishments receiving sufficient violation points to warrant a return visit). Third, the quantile adjustment equalizes the proportion of establishments in a flexible number of grading categories (e.g., A/B/C) across areas (e.g., ZIP codes, inspector areas) to account for inspector differences. Fourth, the package implements a refined quantile adjustment that addresses two limitations with the stats::quantile() function when applied to inspection score datasets with large numbers of score ties. The quantile adjustment algorithm iterates over quantiles until, over all restaurants in all areas, grading proportions are within a tolerance of desired global proportions. In addition the package allows a modified definition of "quantile" from "Nearest Rank". Instead of requiring that at least p[1]% of restaurants receive the top grade and at least (p[1]+p[2])% of restaurants receive the top or second best grade for quantiles p, the algorithm searches for cutoffs so that as close as possible p[1]% of restaurants receive the top grade, and as close as possible to p[2]% of restaurants receive the second top grade. Package: r-cran-quantilenpci Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-quantilenpci_0.9.0-1.ca2604.1_all.deb Size: 25350 MD5sum: 1503c154e0b4f99baf5ecaa955bc7bb8 SHA1: bf1860ba2af264210fa2208fd9f02fa4b8c29f3a SHA256: 69a210207323bac3f9c35e49330869e8d67635eefa885d90e99dd070bef1846b SHA512: 8e877dc3ba6098e79288add683c3bbf66b0f6554e7aaa66c9f493e063d8a6b6182e548cadb76fd863325990729fba5d950a5aca34d23165e63360ca27e82d24f Homepage: https://cran.r-project.org/package=QuantileNPCI Description: CRAN Package 'QuantileNPCI' (Nonparametric Confidence Intervals for Quantiles) Based on Alan D. Hutson (1999) , "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353. Package: r-cran-quantileonquantile Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-htmlwidgets Filename: pool/dists/resolute/main/r-cran-quantileonquantile_1.0.3-1.ca2604.1_all.deb Size: 86988 MD5sum: 942fc95732770f823500b9fdf7dd60f7 SHA1: 10a4203082fbec3def813dbfdb4c1a7dcb231620 SHA256: 75c299b396fa6816a0af540d74097876119a24db10c30209483dc7fca30554d4 SHA512: 92b6a3e56d1d24bbfafc5871d5a89ed7bca77aaffa9e079a841d389f16b6ed2c23d74944a8514b175fd2628a1b00d314fab80933ae1dec37275a5d1bff1435d2 Homepage: https://cran.r-project.org/package=QuantileOnQuantile Description: CRAN Package 'QuantileOnQuantile' (Quantile-on-Quantile Regression Analysis) Implements the Quantile-on-Quantile (QQ) regression methodology developed by Sim and Zhou (2015) . QQ regression estimates the effect that quantiles of one variable have on quantiles of another, capturing the dependence between distributions. The package provides functions for QQ regression estimation, 3D surface visualization with 'MATLAB'-style color schemes ('Jet', 'Viridis', 'Plasma'), heatmaps, contour plots, and quantile correlation analysis. Uses 'quantreg' for quantile regression and 'plotly' for interactive visualizations. Particularly useful for examining relationships between financial variables, oil prices, and stock returns under different market conditions. Package: r-cran-quantilogram Architecture: all Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-np, r-cran-quantreg, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sparsem Filename: pool/dists/resolute/main/r-cran-quantilogram_3.1.1-1.ca2604.1_all.deb Size: 319362 MD5sum: f6c0ed568aa7b9f3d4ffe4e9563ed638 SHA1: 36b54794e7e6e3065d24cd1aa893fe1bbd2cc7cd SHA256: 5877796a6dec780670570b19a1f7dbb3eaef282198072fc93718d4dcea486ee2 SHA512: 81ca3c3156f2695010c59d15aa5ae396ce6f32d42f776e21b553908ee2a29c158ffeb239a15f71172c5f2973241f6e3c048113a0ca8da8d837c9a307f7e0bd88 Homepage: https://cran.r-project.org/package=quantilogram Description: CRAN Package 'quantilogram' (Cross-Quantilogram) Estimation and inference methods for the cross-quantilogram. The cross-quantilogram is a measure of nonlinear dependence between two variables, based on either unconditional or conditional quantile functions. It can be considered an extension of the correlogram, which is a correlation function over multiple lag periods that mainly focuses on linear dependency. One can use the cross-quantilogram to detect the presence of directional predictability from one time series to another. This package provides a statistical inference method based on the stationary bootstrap. For detailed theoretical and empirical explanations, see Linton and Whang (2007) for univariate time series analysis and Han, Linton, Oka and Whang (2016) for multivariate time series analysis. The full references for these key publications are as follows: (1) Linton, O., and Whang, Y. J. (2007). The quantilogram: with an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250-282 ; (2) Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270 . Package: r-cran-quantkriging Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hetgp, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-quantkriging_0.1.0-1.ca2604.1_all.deb Size: 65332 MD5sum: 5ff0e09bffd31d15e2e841b79a61d4e5 SHA1: 6d4e0e23e6fd8b2c8d05983b730dc7656f0ee23a SHA256: 60d904a542a0821e35f6a321907307ee11021a09bcc1d0723ac307f9ec0812ee SHA512: 789f8460452093324557e10b96e7685b636a9d64899a5405ac82666ecc152f8558bbdc0823572441f96bedbb9046bed76cef70d5a6198249087f73e4ebf6949a Homepage: https://cran.r-project.org/package=quantkriging Description: CRAN Package 'quantkriging' (Quantile Kriging for Stochastic Simulations with Replication) A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) . With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) it is now possible to apply quantile kriging to a wider class of problems. In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation. 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Package: r-cran-quantnorm Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1940 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggally, r-cran-ggplot2, r-cran-network, r-cran-pheatmap, r-cran-rgl, r-cran-sna Filename: pool/dists/resolute/main/r-cran-quantnorm_1.0.5-1.ca2604.1_all.deb Size: 1949914 MD5sum: e4bacb659160d29c65ffe1c0870483a8 SHA1: 4fb3f976a081dc64daa188681522b7cd9da41ed7 SHA256: 168caa802dc522fed24c27e3426e4b4b05f0491f93747f47e1844f3224611c15 SHA512: 65b82c900828957a26dce5c4aca1a9d4fda139eaca64b04f161529007940f12c992f09caa7c0cdb3a05e76a4abba21c9f0e288c7dda83dfbdbc13fbf0c3978a6 Homepage: https://cran.r-project.org/package=QuantNorm Description: CRAN Package 'QuantNorm' (Mitigating the Adverse Impact of Batch Effects in Sample PatternDetection) Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. 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Package: r-cran-quantreg.nonpar Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-mnormt, r-cran-fda, r-cran-rearrangement Filename: pool/dists/resolute/main/r-cran-quantreg.nonpar_1.0-1.ca2604.1_all.deb Size: 815108 MD5sum: 830a3f55ef166c6581fd15bb1cc6c4bd SHA1: 66dda32514715f8aa44ad77db77c2ba512fadef7 SHA256: 7195a3f5a1316e58e5709984b7d568a9da6db3b89fbf206197cbf2a014d42a92 SHA512: f940aad014b3b575c471add938f324cf6adf270f9c4a4a878957a766c5fdd9920cdc926a02742bce24caf2782c2f62780fc3b89a65cf4f64ef8659cd5d887dfe Homepage: https://cran.r-project.org/package=quantreg.nonpar Description: CRAN Package 'quantreg.nonpar' (Nonparametric Series Quantile Regression) Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods. Package: r-cran-quantreggrowth Architecture: all Version: 1.7-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-sparsem Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mgcv, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-quantreggrowth_1.7-2-1.ca2604.1_all.deb Size: 386708 MD5sum: d21c3739fc3a9ca936cb72440baedbfe SHA1: eea4284840b3595e10ab67185a81aac619fe9360 SHA256: 01c993143041089ac9ed2ae8dba8d323d3de1dc932170ee2fd3df6c1e5fb8667 SHA512: 50c47a28abd87174c80123b3ab100edc7d71c68fc23f064695cff1287f65a361eba9a18a7174232bff3d2843864eba581847d9cd05c18a6b45d8c65a419db118 Homepage: https://cran.r-project.org/package=quantregGrowth Description: CRAN Package 'quantregGrowth' (Non-Crossing Additive Regression Quantiles and Non-ParametricGrowth Charts) Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. Random intercepts and variable selection are allowed via the lasso penalties. The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) . Monotonicity and concavity constraints on the fitted curves are allowed, see Muggeo and others (2013) , and also or some code examples. Package: r-cran-quantsig Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-quantsig_0.1.0-1.ca2604.1_all.deb Size: 10598 MD5sum: f93bdf718a98f144a790f1cd15d396e9 SHA1: b110608937ebe2b94eca95a0e2c58eef11a1a487 SHA256: a3c3d8db03fbc741b29c21e71fb6c77bb70796e61612c6981f3921fcae809454 SHA512: 61412eb59ffc629a66d417b2ac11bf132898ef5e8cd6f8d628d2d7e932bc26556c05f2f8b7f518e9592342aa0ea4ca92c97e09f6112ca58f41760247aad7897d Homepage: https://cran.r-project.org/package=quantsig Description: CRAN Package 'quantsig' (Sigmoidal Quantile Function Estimator) A sigmoidal quantile function estimator based on a newly defined generalized expectile function. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. The package is based on the method introduced in Hutson (2024) . Package: r-cran-quantumops Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-quantumops_3.0.1-1.ca2604.1_all.deb Size: 264622 MD5sum: d12326d9c3261be7ef15dce0ef7a6264 SHA1: b3bdb80283d7df8aa5517f9702b2155085b4060f SHA256: 7a5cf89c24bd71566327e88f5a095eb9352984fa22c5413deb468ab567206015 SHA512: dad4919499b76d3b403f96a0928d70136e4a34fbfea81b9c38807621a9065c022b8f667a4de07db3831fe96dd4c4bd18246d6c2a0aa2783be59a1c2b28b90b18 Homepage: https://cran.r-project.org/package=QuantumOps Description: CRAN Package 'QuantumOps' (Performs Common Linear Algebra Operations Used in QuantumComputing and Implements Quantum Algorithms) Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help learn quantum mechanics and algorithms. Can create arbitrarily sized kets and bras and implements quantum gates, inner products, and tensor products. Creates arbitrarily controlled versions of all gates and can simulate complete or partial measurements of kets. Has functionality to convert functions into equivalent quantum gates and model quantum noise. Includes larger applications, such as Steane error correction , Quantum Fourier Transform and Shor's algorithm (Shor 1999), Grover's algorithm (1996), Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) , and a variational quantum classifier (Schuld 2018) . Can be used with the gridsynth algorithm to perform decomposition into the Clifford+T set. Package: r-cran-quaqcr Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-quaqcr_1.0.4-1.ca2604.1_all.deb Size: 869302 MD5sum: 8946c2a34934f3250006f6ce7ec3e4bb SHA1: f0481858741f149ecfc2c8fab796749f3b95c0b6 SHA256: 0e9caeb9d314bbbe9d51e79204454be570b071a9fc2e6326b1c0f268d18bdeb1 SHA512: 461b4398e85cefda6822e7b50722d88c3e10718bf4996ca555d3bcfb0ce1bc4ec211a22fa452b624ef3c8f6f31307ac416e91692c420e1143e4b97c5db8f3377 Homepage: https://cran.r-project.org/package=quaqcr Description: CRAN Package 'quaqcr' (Quick ATAC-Seq QC) A wrapper around the 'quaqc' program described in Tremblay and Questa (2024) . 'quaqc' allows for assay for transposase-accessible chromatin using sequencing (ATAC-seq) specific quality control and read filtering of next-generation sequencing (NGS) data with minimal processing time and extremely low memory overhead. Any number of samples can be processed, using multiple threads if desired. 'quaqc' outputs a comprehensive set of aligned read metrics, including alignment size, fragment size, percent duplicates, mapq scores, read depth, GC content, and others. Although designed for ATAC-seq data, 'quaqc' can also be used for other unspliced DNA sequencing experiments (such as chromatin immunoprecipitation sequencing, or ChIP-seq) as many of the metrics are related to general sequencing quality. This R package also provides additional utilities for custom analyses and plotting of 'quaqc' results. 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Package: r-cran-quartets Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2265 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-quartets_0.1.1-1.ca2604.1_all.deb Size: 2192136 MD5sum: a8d54a0ad937deffdc030ecf8e4f6c8e SHA1: 75c30d7d596bb78f786de9ae768b18bd265087c5 SHA256: a38dcf01aada08cc5e3bd05fc7b016f0c2e506fdaefc1063e7e2c3491a68331c SHA512: 8798502a3dde77149cac115ce8ae9553b4fdd918d1c5f578c74cc172f62fb73f361bf161d83aa3e95a4a172a6e95b21fe7561c3a0f7f59adb3904f1d7b9dc5c8 Homepage: https://cran.r-project.org/package=quartets Description: CRAN Package 'quartets' (Datasets to Help Teach Statistics) In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. 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Package: r-cran-quclu Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-quclu_1.0.1-1.ca2604.1_all.deb Size: 50102 MD5sum: e7ec023adb0a5f7672c60fdc7297a38c SHA1: 016dbe079ba6bc11dd0888f529c325b0cbfe736f SHA256: 58f2730cbb5fb0e75e7dd9afd4eae1538b6b824d9162191b1129785c4a482023 SHA512: c05a11503c7117462419f44b4e66ddada44cb8f1a7fa2ea49c49a5e818c09fa7895b08a276fe0c39eba9e3278457bb313ff645b58e2119ee3b081d400716b55c Homepage: https://cran.r-project.org/package=QuClu Description: CRAN Package 'QuClu' (Quantile-Based Clustering Algorithms) Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883 . 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Use it to drive reactive calculations, visualizations, downloads, and more. Package: r-cran-queryparser Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-queryparser_0.3.2-1.ca2604.1_all.deb Size: 213926 MD5sum: 3a8dacfe30d182624d925da1cdbda4de SHA1: 781f9c24571fb5a4d5a0391ec8cd95937f65acda SHA256: 77ccabaf64f79ba68c24d15973e687cedea5bc41ee61f0314fd8c01188e783df SHA512: 3f6b68564844b729914076fd4390e1473868da6fc180f59a29f3beee93339e493df79875d7bb383aa3f768af3926a7bd7b6ee4a3cba8911abc63d72f98a703c1 Homepage: https://cran.r-project.org/package=queryparser Description: CRAN Package 'queryparser' (Translate 'SQL' Queries into 'R' Expressions) Translate 'SQL' 'SELECT' statements into lists of 'R' expressions. Package: r-cran-queryup Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-xml2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-queryup_1.0.5-1.ca2604.1_all.deb Size: 70040 MD5sum: 8a613d00c0364ec5a9f863382af0c87d SHA1: c1149f80d61eb1465b3071d108021cb3221fa0c4 SHA256: c0496c25c4f3e5f57f04fe53df8da912a3c0465c703c4c97fadf3cf97b355643 SHA512: c77b41ae8bb5c87719b44ae7adaebd9264e48c34f3c70e4d1b86eddb97db520aa08546d28e5ef6306366fb1d6227a978aefa4ed5d2677a0a3f06a03b78ae5196 Homepage: https://cran.r-project.org/package=queryup Description: CRAN Package 'queryup' (Query the 'UniProtKB' REST API) Retrieve protein information from the 'UniProtKB' REST API (see ). Package: r-cran-quest Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-str2str, r-cran-abind, r-cran-checkmate, r-cran-plyr, r-cran-car, r-cran-psych, r-cran-boot, r-cran-mbess, r-cran-nlme, r-cran-lme4, r-cran-multilevel, r-cran-lavaan Suggests: r-cran-reshape, r-cran-psychtools, r-cran-lmeinfo, r-cran-semtools Filename: pool/dists/resolute/main/r-cran-quest_0.2.1-1.ca2604.1_all.deb Size: 727732 MD5sum: 244ce6376f6855f574cd0eb17bb91874 SHA1: 9d3e3526e8516b1bdda6134a54b128fcc5d2aa12 SHA256: 6f50e1a97797660014ffd48e570438afd9c4152a8c915354fa8807073bd747b5 SHA512: 1ce6be0401fd199d1e93ef3df4fc98b64568972185c2beab9d975ee5421d15e60b8c0445f8e3961788443e24363af73d9ec9aed653f2dbe3fbd6fb86e34dce10 Homepage: https://cran.r-project.org/package=quest Description: CRAN Package 'quest' (Prepare Questionnaire Data for Analysis) Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame. Package: r-cran-questionr Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-highr, r-cran-styler, r-cran-classint, r-cran-htmltools, r-cran-rlang, r-cran-labelled Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-janitor, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-survey, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-questionr_0.8.2-1.ca2604.1_all.deb Size: 3868664 MD5sum: 1c0dd3415f89413bb2c608d6fd119767 SHA1: e6bc19d16495eb1170b0c054fdfb7803d8463818 SHA256: 6c6b7b4e3dd57347ef622800ed47222c12657dc722bcd63ec87df5df836de795 SHA512: 2f13d04da6a8cb029d17b10b988fb1d32ce317fac2d0938b81619e2b30fed0b240cd79e087214327ea1bd30f5a58d8b262f55ef9bfaf77d099bb14e48fe29281 Homepage: https://cran.r-project.org/package=questionr Description: CRAN Package 'questionr' (Functions to Make Surveys Processing Easier) Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions. Package: r-cran-questr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-kernlab Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-questr_0.1.1-1.ca2604.1_all.deb Size: 136558 MD5sum: 5180c1d70256447a8d70650710090358 SHA1: e655af8df0607e498c369ad8c4ce3b60b097d4dd SHA256: c4d0079f5c3d6d3a15721fbfb3758c603c17df1e4702fc03285cadbc6e664469 SHA512: 9ca9dd93ddca2de925afbeb626d584e79c646a5946861d6f486e0f8fd60f2c0c6846e27975c2c5e893968e21e90e5fa54d80961f6c175f6d617630ce477c955d Homepage: https://cran.r-project.org/package=QuESTr Description: CRAN Package 'QuESTr' (Constructing Quantitative Environment Sensor usingTranscriptomes) A method for prediction of environmental conditions based on transcriptome data linked with the environmental gradients. 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Improve the quality and reproducibility of 'R' scripts. 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Package: r-cran-quicknmix Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-optimparallel, r-cran-doparallel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-quicknmix_1.1.1-1.ca2604.1_all.deb Size: 101804 MD5sum: 5e10697796829eb96e02641d20813319 SHA1: f0de1ecd0e25628f25b3a18b2b284b188baed40d SHA256: 18d05b8838b223db811f1ad7f59e007542e628377c91843e3aa32280648c0d15 SHA512: 1a8327703272a4fed6682e7508eb5801529ef9359711363dbad85f4d0c1945c5f18f5de269398a51b6348af75ac73e6224542b1179a0907f15cc66fba4a591c8 Homepage: https://cran.r-project.org/package=quickNmix Description: CRAN Package 'quickNmix' (Asymptotic N-Mixture Model Fitting) For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) . Asymptotic N-mixture models extend the work of Dail and Madsen (2011) , to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". Package: r-cran-quickoutlier Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbscan, r-cran-ggplot2, r-cran-isotree, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-quickoutlier_0.1.5-1.ca2604.1_all.deb Size: 1025756 MD5sum: e37009506444d7305a3b3291a518592a SHA1: 13a852991439fb99625a34fe566de24a73f299b0 SHA256: 27a5a8edfddcff15d95d16331d26ce4fc5e5765d7d9b976110a94a61a9d5c43d SHA512: ceb13d34764957852aaa23c2e7e842d818bb8bcc3c30a93fcb92968412154c291717d4f1b8f46763a76a498ae4e4e7553beb240f843ec583ba734503234ade4c Homepage: https://cran.r-project.org/package=quickOutlier Description: CRAN Package 'quickOutlier' (Detect and Treat Outliers in Data Mining) Implements a suite of tools for outlier detection and treatment in data mining. 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This package provides reusable tools and shortcuts for frequently used calculations and workflows. Package: r-cran-r.blip Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-bnlearn Filename: pool/dists/resolute/main/r-cran-r.blip_1.1-1.ca2604.1_all.deb Size: 1003462 MD5sum: 436f0cf03c596a7856bf8f7167d44d59 SHA1: 0262e0da948cfcdcefb976f04955e35f81801152 SHA256: c17d195bde9e66086c9bd5dfc8b1eb488bde0bff64b343f12accaebdc91a2cc2 SHA512: 60a67a9fd4285c5678c1009a81655f062afa06755676aa62d9b70cba1e8cb3efdbc7bf4aa15ddac1685ce262e055db4f8465b3ec456376ad2dab0fcbea0d6bf5 Homepage: https://cran.r-project.org/package=r.blip Description: CRAN Package 'r.blip' (Bayesian Network Learning Improved Project) Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the 'BIC' and the 'BDeu' score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) ; (2) general structure optimization - Mauro Scanagatta (2018) , Mauro Scanagatta (2018) ; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) ; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) . Distributed under the LGPL-3 by IDSIA. Package: r-cran-r.cache Architecture: all Version: 0.17.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.utils, r-cran-digest Filename: pool/dists/resolute/main/r-cran-r.cache_0.17.0-1.ca2604.1_all.deb Size: 111604 MD5sum: 53bcf83b3af93d69a038ec96289016f0 SHA1: eb07e7883e3c9209d9bf47f7b978ee19db3dd72f SHA256: 99e35043308c9101a7776bd913b17f55a0c8881bc54dddb3e8c2d3133fd3984c SHA512: 35d1cdceb7fe80649a11e686c829006ad9224390d02235d03f58106aa4962002bd99f39b15d80b8022b20aeb427de6be8c4ef5bc88ba8bf4e92d4faf3188cb13 Homepage: https://cran.r-project.org/package=R.cache Description: CRAN Package 'R.cache' (Fast and Light-Weight Caching (Memoization) of Objects andResults to Speed Up Computations) Memoization can be used to speed up repetitive and computational expensive function calls. 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See Jouan-Rimbaud Bouveresse and Rutledge (2024) , Boccard and Rutledge (2013) , and Puig-Castellví et al. (2021) . 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Package: r-cran-r.jive Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gplots, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-r.jive_2.4-1.ca2604.1_all.deb Size: 4037074 MD5sum: ba72f93f8af12306cea02ef2329f6d98 SHA1: b71e9e769eb48d035d53e258c3ff752bf6de3403 SHA256: 227f0a1c5a46b5ddcd4a0584a757f0157faf4734b325b84f3b5c24aea63cb7c8 SHA512: 7af08efb8d0c87a0d4017f5ae7346c741186f728b7e4b026a2d03551558604fc3cd7c87635976b463118e0869050a66220f163913e132a7a4f13cfe5ce7b4a3a Homepage: https://cran.r-project.org/package=r.jive Description: CRAN Package 'r.jive' (Perform JIVE Decomposition for Multi-Source Data) Performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) ]. 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Package: r-cran-r.matlab Architecture: all Version: 3.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.utils Suggests: r-cran-matrix, r-cran-sparsem Filename: pool/dists/resolute/main/r-cran-r.matlab_3.7.0-1.ca2604.1_all.deb Size: 272566 MD5sum: fd588cfa9836d73d452e7970c1890680 SHA1: 253881fcf3d1fae1a5d4f11562a67fa02eb32b17 SHA256: 6e6ac3179937fb7001a7dd48f971d22201780a26774fad0e22e7d5b1631a2287 SHA512: a99f3bf8c2c227f708e45ae187b5224b1c14197aa0fb250d59758077d83b3810942a2c4ba4f6781f8c86cf5ebf939c005b50c24b52ed05f06286e2984a2d12a6 Homepage: https://cran.r-project.org/package=R.matlab Description: CRAN Package 'R.matlab' (Read and Write MAT Files and Call MATLAB from Within R) Methods readMat() and writeMat() for reading and writing MAT files. 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Package: r-cran-r02pro Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr Filename: pool/dists/resolute/main/r-cran-r02pro_0.2-1.ca2604.1_all.deb Size: 854734 MD5sum: ef6828129fd6116ac8366d8d0c1f4bb2 SHA1: 9e8f8281f6d62dd5d1b9b7b462bf46d75ee93fc0 SHA256: 783934f76e75b1ceaee47b42913ee8d81d2c201918e61f5c12289e9ab55fe0b4 SHA512: 9ac608a604c3297ff311242275fb4b90580f1dbc7d3293574c9f4fe37099a78672dbae105cf1732c4fa6dc15438d932f3886fde7bcc9d63c013c9a7481958aea Homepage: https://cran.r-project.org/package=r02pro Description: CRAN Package 'r02pro' (R Programming: Zero to Pro) This is a companion package of the book "R Programming: Zero to Pro" . It contains the datasets used in the book and provides interactive exercises corresponding to the book. It covers a wide range of topics including visualization, data transformation, tidying data, data input and output. Package: r-cran-r0 Architecture: all Version: 1.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-r0_1.3-1-1.ca2604.1_all.deb Size: 247658 MD5sum: 465d60bd95c605bf05e370334f91f2a5 SHA1: 392defc101672d11b1076f3d1fee248d3b2d5e81 SHA256: f101b65417625c39ec69d9fef49fc6e11a68b23d96f1d251934985a5ae2f77aa SHA512: 20b5ee4b41d8848b757c2c0c05cc24ffa69e3920a9eea293636fb215e0c99805105ca2811bbcde645ddebac614e21e512756c04158e462b84c12fb42f0bb252a Homepage: https://cran.r-project.org/package=R0 Description: CRAN Package 'R0' (Estimation of R0 and Real-Time Reproduction Number fromEpidemics) Estimation of reproduction numbers for disease outbreak, based on incidence data. The R0 package implements several documented methods. It is therefore possible to compare estimations according to the methods used. Depending on the methods requested by user, basic reproduction number (commonly denoted as R0) or real-time reproduction number (referred to as R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs will give different graphs depending on the methods requested : basic reproductive number estimations will only show the epidemic curve (collected data) and an adjusted model, whereas real-time methods will also show the R(t) variations throughout the outbreak time period. Sensitivity analysis tools are also provided, and allow for investigating effects of varying Generation Time distribution or time window on estimates. Package: r-cran-r1magic Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-r1magic_0.3.4-1.ca2604.1_all.deb Size: 46902 MD5sum: 96ddcaa9904f7a7c583d14d04ae75693 SHA1: 41130cabc472739dfd04cb18e820002fca34d380 SHA256: d27d882a669db313dce0e6d0681fb30e457354dfa8128f1c2f863655caa2b375 SHA512: 91ae8a21d46aaa6779146ecdfef0bff9dfc4598bd3d8ef1e390dabb41c2542e8d0c16d17edc20da12b2fc854b497325d19696cc71d3eade134dacf96f04584a8 Homepage: https://cran.r-project.org/package=R1magic Description: CRAN Package 'R1magic' (Compressive Sampling: Sparse Signal Recovery Utilities) Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients. 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The measure can be used as a measure of predictive capability and therefore it can be adopted in model selection process. Rava, D. and Xu, R. (2020) . 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'R2BEAT' extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). 'R2BEAT' develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of 'ReGenesees', that can be installed from . Package: r-cran-r2country Architecture: all Version: 2.0.2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quickcode Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r2country_2.0.2.4.0-1.ca2604.1_all.deb Size: 348056 MD5sum: 90ec4a55a1dae00269ae64e2a7faf460 SHA1: 8656195e582314790bededacf2482a283ba27600 SHA256: 1a788bb7f4e86dd6119ccba672555a7036851d3298bd7f87a272d93e2690de5c SHA512: 0bd338ae976df3aca29e9d2351df568783b0268b11a7613960c7523c8dfc53e5bbd82758d4cf4d17a324cb729db9d1c586f274adbe85ee3b2d913714c83e7bfc Homepage: https://cran.r-project.org/package=r2country Description: CRAN Package 'r2country' (Country Data with Names, Capitals, Currencies, Populations,Time, Languages and so on) Obtain information about countries around the globe. Information for names, states, languages, time, capitals, currency and many more. 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Package: r-cran-r2d3 Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-jsonlite, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r6, r-cran-shiny, r-cran-shinytest, r-cran-testthat, r-cran-webshot Filename: pool/dists/resolute/main/r-cran-r2d3_0.2.6-1.ca2604.1_all.deb Size: 1618982 MD5sum: 6679e7befb12fe8a38748b68a0646da1 SHA1: 6c1ac9885b19065602d48dabe82967bb2365d238 SHA256: d5270d2a1406c0c5dd91288e111a1766e08ba14e0443a5785343c24fc700650d SHA512: 64257e2a8eff5acc167c078e2dd79818ad23dc68100a1cbc605547992792fd1b134ca1b9118ddd61e7210f032148f5a31341bfeab894e6c221017e7317b9e1bb Homepage: https://cran.r-project.org/package=r2d3 Description: CRAN Package 'r2d3' (Interface to 'D3' Visualizations) Suite of tools for using 'D3', a library for producing dynamic, interactive data visualizations. Supports translating objects into 'D3' friendly data structures, rendering 'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R packages. Package: r-cran-r2dictionary Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-rstudioapi Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-r2dictionary_0.3-1.ca2604.1_all.deb Size: 5196550 MD5sum: 5ff6cb8b612ac245fbdf3ae106f155d0 SHA1: 2321c8c9f440f69ecfda2e306014e971fc960e78 SHA256: a04f7c4dd6869dbe05af01d4ff59315afec9afbc8949900b2d69598fe2b49b7b SHA512: 2b93936afa76f77b0ba408ee7fb5c4d727658e4ab00598e286700a6a5b0063feece42ca13c5ed2403859fb653ebc59b4ed611a7ec64e49e21ec2d5e1bcabc0fe Homepage: https://cran.r-project.org/package=r2dictionary Description: CRAN Package 'r2dictionary' (A Mini-Dictionary for 'R', 'shiny' and 'rmarkdown' Documents) Despite the predominant use of R for data manipulation and various robust statistical calculations, in recent years, more people from various disciplines are beginning to use R for other purposes. In doing this seemlessly, further tools are needed users to easily and freely write in R for all kinds of purposes. The r2dictionary introduces a means for users to directly search for definitions of terms within the R environment. Package: r-cran-r2dii.analysis Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-r2dii.data, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-cli, r-cran-covr, r-cran-r2dii.match, r-cran-readr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-waldo, r-cran-withr Filename: pool/dists/resolute/main/r-cran-r2dii.analysis_0.5.3-1.ca2604.1_all.deb Size: 149794 MD5sum: da9b607ccf664a2228b71f37c589b4d1 SHA1: ec35380bdf66ee00d491e78ca1d1a7e1fc09357e SHA256: ddb73014879567d9dc1a3be2f5af5b022590c36108b197802b340e3c8ae3a14b SHA512: efbbe8e8e16c25911d04efc1e7e783b3ef1666011ca5f037acf8d273de1584a8800765aa6afb9878899227af7b8ad34da6424827a2fc5e0779b9c9c5c1e23b1a Homepage: https://cran.r-project.org/package=r2dii.analysis Description: CRAN Package 'r2dii.analysis' (Measure Climate Scenario Alignment of Corporate Loans) These tools help you to assess if a corporate lending portfolio aligns with climate goals. They summarize key climate indicators attributed to the portfolio (e.g. production, emission factors), and calculate alignment targets based on climate scenarios. They implement in R the last step of the free software 'PACTA' (Paris Agreement Capital Transition Assessment; ). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Package: r-cran-r2dii.data Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle Suggests: r-cran-charlatan, r-cran-covr, r-cran-readr, r-cran-rlang, r-cran-rmarkdown, r-cran-stringi, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r2dii.data_0.6.1-1.ca2604.1_all.deb Size: 336368 MD5sum: 8f2ff63331ca9a4cf5bf2ff4a278cbbc SHA1: 9b840b3a648da861daacc45cedf020db844f04d7 SHA256: 8661bf215584407402727ffc76f9940a4f0f03004f2517bc632343bb7fe210b6 SHA512: 84af952ba3c2eb4c3e8dfc033b63815568a34ac87a581d4ec5fe975a37b0c7c9f567a04f8db1fd61ffc3db334e2c0a4ef8c523741df23e40c1e2f3d48c0e570d Homepage: https://cran.r-project.org/package=r2dii.data Description: CRAN Package 'r2dii.data' (Datasets to Measure the Alignment of Corporate Loan Books withClimate Goals) These datasets support the implementation in R of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between corporate lending portfolios and climate scenarios (). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Because both financial institutions and market data providers keep their data private, this package provides fake, public data to enable the development and use of 'PACTA' in R. 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Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals. 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Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Package: r-cran-r2dt Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-plyr, r-cran-devfunc Filename: pool/dists/resolute/main/r-cran-r2dt_0.2-1.ca2604.1_all.deb Size: 70680 MD5sum: c59d89e6d4ba071b2e8f1a287e20982e SHA1: 000a048af73882b19319fe721eaf28276942d309 SHA256: de82cc081cc31f74e9aa2f3656237c2d85a5cb13c39e5149be22d78c94b288bd SHA512: cb8887863284518cf16282ff3640be31fe7e7a0cb5dd1d22657a214b10f0f94a11f5a40faf85e043e1a8c0933afcc6fc342751605f48e3f2260363ea113babb3 Homepage: https://cran.r-project.org/package=R2DT Description: CRAN Package 'R2DT' (Translation of Base R-Like Functions for 'data.table' Objects) Some heavily used base R functions are reconstructed to also be compliant to data.table objects. 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Package: r-cran-r2glmm Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-matrix, r-cran-pbkrtest, r-cran-ggplot2, r-cran-afex, r-cran-mass, r-cran-gridextra Suggests: r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-data.table, r-cran-dplyr, r-cran-lmertest Filename: pool/dists/resolute/main/r-cran-r2glmm_0.1.3-1.ca2604.1_all.deb Size: 91454 MD5sum: bfdbf528662b57cdd590c956d7a7e718 SHA1: 051faed3f8372a79efd367dc7ac2b85df93abf55 SHA256: 302efaf88b4a69cc776d0c535520b623d010b0335f19e75c10ba9202feed75fe SHA512: d1088cf5d45f5b47d7849baa99466a5f3f8b594d4c4d90491e05c57574677ca83c22ee88b60ebffa8e75c4e79987b473896da36587d9061e0c81360dc44b81f9 Homepage: https://cran.r-project.org/package=r2glmm Description: CRAN Package 'r2glmm' (Computes R Squared for Mixed (Multilevel) Models) The model R squared and semi-partial R squared for the linear and generalized linear mixed model (LMM and GLMM) are computed with confidence limits. The R squared measure from Edwards et.al (2008) is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. 2016 ). Three methods of computation are provided and described as follows. First, The Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest' package and the 'glmmPQL' function, the Kenward-Roger approach in the 'r2glmm' package is limited to the LMM. Second, The method introduced by Nakagawa and Schielzeth (2013) and later extended by Johnson (2014) . The 'r2glmm' package only computes marginal R squared for the LMM and does not generalize the statistic to the GLMM; however, confidence limits and semi-partial R squared for fixed effects are useful additions. Lastly, an approach using standardized generalized variance (SGV) can be used for covariance model selection. Package installation instructions can be found in the readme file. Package: r-cran-r2html Architecture: all Version: 2.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-boot, r-cran-survival, r-cran-cluster, r-cran-nlme, r-cran-rpart, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-r2html_2.3.4-1.ca2604.1_all.deb Size: 582254 MD5sum: df3429452ba4d839edd73bbf352b4c5f SHA1: 1dbc4991b240eeef0068714a586b0a2dcbe3ec85 SHA256: 314a5ed0725b13a7400313e65948e4bb975463e9263b0578be67578440c6e82b SHA512: c6b626b56d6c59a22e3388f0256f105ac40a9e1aea8be80549a4d8203d69324efed5424f77c60369cdbf07bb60e42e99dc69dabeab5a45744722ff7d28d41868 Homepage: https://cran.r-project.org/package=R2HTML Description: CRAN Package 'R2HTML' (HTML Exportation for R Objects) Includes HTML function and methods to write in an HTML file. Thus, making HTML reports is easy. Includes a function that allows redirection on the fly, which appears to be very useful for teaching purpose, as the student can keep a copy of the produced output to keep all that he did during the course. Package comes with a vignette describing how to write HTML reports for statistical analysis. Finally, a driver for 'Sweave' allows to parse HTML flat files containing R code and to automatically write the corresponding outputs (tables and graphs). Package: r-cran-r2jags Architecture: all Version: 0.8-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-abind, r-cran-coda, r-cran-r2winbugs, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r2jags_0.8-9-1.ca2604.1_all.deb Size: 107084 MD5sum: 8754da994ccbaaef01575fd58cefd06b SHA1: 6adbbe81130ea0539b79e7f93bc0faf4e8190a1c SHA256: 02366c752ab12ff4b45f30bc3147e6566a923ee96e3836705444a7b987f8b780 SHA512: 8da531b841af4bd586a5cb789c89954e7f2d7dfb62182d73040d4da3affd7e70a803166ce22c10e1fd85ecef36c9b672f64fb216de39f5759c6f1d48fad32a41 Homepage: https://cran.r-project.org/package=R2jags Description: CRAN Package 'R2jags' (Using R to Run 'JAGS') Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains. Package: r-cran-r2mlm Architecture: all Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-rockchalk, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-r2mlm_0.3.8-1.ca2604.1_all.deb Size: 407226 MD5sum: 81c72fa7706e0761302fe27692c54def SHA1: 8bdaf24c8f3dd8ebf3c43044da75302f970151af SHA256: 302867c0eb97e27a1c9121c4c19063fbce5d930c682bb34ed4b84cf92e9d3433 SHA512: b0e5cdcf5171abbc9c456e8edd48782b8cbda057347378d81c1596c795ab2be168bbe69557f838727dc7df855510cea47af1d666f0343d86a168323d147cb052 Homepage: https://cran.r-project.org/package=r2mlm Description: CRAN Package 'r2mlm' (R-Squared Measures for Multilevel Models) Generates both total- and level-specific R-squared measures from Rights and Sterba’s (2019) framework of R-squared measures for multilevel models with random intercepts and/or slopes, which is based on a complete decomposition of variance. Additionally generates graphical representations of these R-squared measures to allow visualizing and interpreting all measures in the framework together as an integrated set. This framework subsumes 10 previously-developed R-squared measures for multilevel models as special cases of 5 measures from the framework, and it also includes several newly-developed measures. Measures in the framework can be used to compute R-squared differences when comparing multilevel models (following procedures in Rights & Sterba (2020) ). Bootstrapped confidence intervals can also be calculated. To use the confidence interval functionality, download bootmlm from . Package: r-cran-r2mlwin Architecture: all Version: 0.8-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-foreign, r-cran-digest, r-cran-texreg, r-cran-foreach, r-cran-doparallel, r-cran-coda, r-cran-lattice, r-cran-memisc, r-cran-broom, r-cran-tibble Suggests: r-cran-doby, r-cran-car, r-cran-lmtest, r-cran-mitools, r-cran-reshape, r-cran-dorng, r-cran-r2winbugs, r-cran-r2openbugs Filename: pool/dists/resolute/main/r-cran-r2mlwin_0.8-10-1.ca2604.1_all.deb Size: 1732254 MD5sum: f5471c20b61303a6efb9c321acbd13fd SHA1: fc00075437c310bedb24d7e4c670274b9ebd3a0f SHA256: 7554aca237337a3c83114b0650172bdcf9e2cf5d72bbc497406da75cf84f5c00 SHA512: 94f7c98d994a82f0cbea885748306ee54b777d8f2ae7955541aa37413bdd578457bb0637749004c7b15332162b5965e22a7bda2a59e7a22bd4c1c7f0b27b5ca2 Homepage: https://cran.r-project.org/package=R2MLwiN Description: CRAN Package 'R2MLwiN' (Running 'MLwiN' from Within R) An R command interface to the 'MLwiN' multilevel modelling software package. 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Package: r-cran-r2resize Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-quickcode, r-cran-dt Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-r2symbols, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r2resize_2.0-1.ca2604.1_all.deb Size: 1368842 MD5sum: b8df1068a0e120b71d04b608d05085f4 SHA1: 4d2f9a5ab2df383c1ee40055e704a26c11610527 SHA256: 26dd7abdb733893a49fec7d081fab1b67e29a7ead9ac55f9942d111ac4d4cedb SHA512: 013f422ab2e16e05e6dcd01e15c15d1a5d89d54d0184c31001c85452b1bb45430b90b17f3ac308c2e246cffa8e9c4ffd42a2cee45617ee589a6432c692a55891 Homepage: https://cran.r-project.org/package=r2resize Description: CRAN Package 'r2resize' (In-Text Resize for Images, Tables and Fancy Resize Containers in'shiny', 'rmarkdown' and 'quarto' Documents) Offers a suite of tools designed to enhance the responsiveness and interactivity of web-based documents and applications created with R. It provides an automatic, configurable resizing toolbar that can be seamlessly integrated with HTML elements such as containers, images, and tables, allowing end-users to dynamically adjust their dimensions. Beyond the toolbar, the package includes a rich collection of flexible, expandable, and interactive container functionalities, such as highly customizable split-screen layouts (splitCard), versatile sizeable cards (sizeableCard), dynamic window-like elements (windowCard), visually engaging emphasis cards (empahsisCard), and sophisticated flexible and elastic card layouts (flexCard, elastiCard). Furthermore, it offers an elegant image viewer and resizer (shinyExpandImage) perfect for interactive galleries. r2resize is particularly well-suited for developers and data scientists looking to create modern, responsive, and user-friendly 'shiny' applications, 'markdown' reports, and 'quarto' documents that adapt gracefully to different screen sizes and user preferences, significantly improving the user experience. Package: r-cran-r2roc Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-r2roc_1.0.1-1.ca2604.1_all.deb Size: 455884 MD5sum: d4a914ee9cd1274775e695a4e7dc3cad SHA1: fc0e4c19396faab2cefcd180082d632768000415 SHA256: 814ec4456bf621c16fce6290e44c6829dc070a0511746217504cd59f4d596630 SHA512: dbf3b4a7bfd3aa00bf211b3c72998afade9c220bff7259f317e52961b225dc5ba3c1d2252379302041fa707bc97eab2be2ddce33dabe7eada4e9b7eae4c5f0ea Homepage: https://cran.r-project.org/package=R2ROC Description: CRAN Package 'R2ROC' (AUC Statistics) Area under the receiver operating characteristic curves (AUC) statistic for significance test. Variance and covariance of AUC values used to assess the 95% Confidence interval (CI) and p-value of the AUC difference for both nested and non-nested model. Package: r-cran-r2rtf Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-officer, r-cran-rmarkdown, r-cran-stringi, r-cran-testthat, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-r2rtf_1.3.0-1.ca2604.1_all.deb Size: 346434 MD5sum: 96d4b9243002456ec4cbb0ec614efe1f SHA1: 8372da4fb30962ca7d980c99c742cc706ea192ff SHA256: 21cff012f785f9687653ee4d3e0579cf567fb2338d476edba674c75e4eb6ff3e SHA512: e918b33a5d622a8863fc9cec20396f4e8598e2d949280f8a2d0100721d9b4d7d571b493811c811cf13fb3d1fe9271956046a4cf3d238411c04a1a4ebfe68203b Homepage: https://cran.r-project.org/package=r2rtf Description: CRAN Package 'r2rtf' (Easily Create Production-Ready Rich Text Format (RTF) Tables andFigures) Create production-ready Rich Text Format (RTF) tables and figures with flexible format. Package: r-cran-r2shortcode Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-r2shortcode_0.2-1.ca2604.1_all.deb Size: 49062 MD5sum: 28f2622276681098f61ec0731903a459 SHA1: e9162984b0a9473c09ce72ebb41d6feb03d189e8 SHA256: cfcdc0dda6ac29da3bb32fc5bd0e687e4f26959a28bdd1ed73138f48e987f626 SHA512: 278c669f6a338ed8a9b2b0ce1467cba0936f9caa47012358c529c1ec29fa0e571ac383f83dca69e7caaf7d6e9e0c3bdef7373e6e98fd474a96d98b97e8026263 Homepage: https://cran.r-project.org/package=r2shortcode Description: CRAN Package 'r2shortcode' (Shorten Function Names of Functions in Another Package andCreate an Index to Make Them Accessible) When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question. Package: r-cran-r2social Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-quickcode Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-r2social_1.2.1-1.ca2604.1_all.deb Size: 399870 MD5sum: 9e2fac20cfc2bdeeb0d07dd38c829e35 SHA1: efbfb790f1a90b6a9d2cb04f8dbfd1de6879e088 SHA256: 5107d71bf4864f6049877fea354276c6f64129a52d98789172fb03bdc075f94b SHA512: 9e66d7cd6a3b906f811a02756bded7234974679c46af64a4a190eb525420722481ea61314bcaf32f01202ead76de6d2908c657335f3ecd77eb7cee2d0d9b2fb0 Homepage: https://cran.r-project.org/package=r2social Description: CRAN Package 'r2social' (Seamless Integration of Sharing and Connect Buttons in Markdownand Apps) Implementation of 'JQuery' and 'CSS' styles to allow easy incorporation of various social media elements on a page. The elements include addition of share buttons or connect with us buttons or hyperlink buttons to 'Shiny' applications or dashboards and 'Rmarkdown' documents.Sharing capability on social media platforms including 'Facebook' , 'Linkedin' , 'X/Twitter' , 'Tumblr' , 'Pinterest' , 'Whatsapp' , 'Reddit' , 'Baidu' , 'Blogger' , 'Weibo' , 'Instagram' , 'Telegram' , 'Youtube' . Package: r-cran-r2spss Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-car Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-r2spss_0.3.2-1.ca2604.1_all.deb Size: 659038 MD5sum: 6de29bb363142019f7471860a5b21dcf SHA1: 119501c957577dab55fd379f897585ce947564a4 SHA256: cebc010075cf3ffc67fae40777558f20cab0df2b7b053b22a87a8843c0971af3 SHA512: 06e4cdb3cbe884717c27839f3814cd2aae7f163021028f480e1bd134169907cabe62e29fe90f68ce203b507cb1aa48ffd3dcae28c902f99f1984d997da25278f Homepage: https://cran.r-project.org/package=r2spss Description: CRAN Package 'r2spss' (Format R Output to Look Like SPSS) Create plots and LaTeX tables that look like SPSS output for use in teaching materials. Rather than copying-and-pasting SPSS output into documents, R code that mocks up SPSS output can be integrated directly into dynamic LaTeX documents with tools such as knitr. Functionality includes statistical techniques that are typically covered in introductory statistics classes: descriptive statistics, common hypothesis tests, ANOVA, and linear regression, as well as box plots, histograms, scatter plots, and line plots (including profile plots). Package: r-cran-r2stl Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-r2stl_1.0.3-1.ca2604.1_all.deb Size: 20062 MD5sum: 92c955757acb52f0fc1f84a6059af773 SHA1: 4e8a1fe2ef5cab2ac590b991a0a36dca99366ded SHA256: fa951a29f6617f548aa9dac386fd570fbc863cade68ca5ee1fb9aeeec19f600f SHA512: dcea1ffc767f12d8f1e4911d5a0c64d8984d926a6e173fca9d9e79adb60e59b09a209cb66b759c7ae44b028ebd5efce1cf6dc8ff5799867ee9a8cba0d856eae5 Homepage: https://cran.r-project.org/package=r2stl Description: CRAN Package 'r2stl' (Visualizing Data using a 3D Printer) Converts data to STL (stereolithography) files that can be used to feed a 3-dimensional printer. The 3-dimensional output from a function can be materialized into a solid surface in a plastic material, therefore allowing more detailed examination. There are many possible uses for this new tool, such as to examine mathematical expressions with very irregular shapes, to aid teaching people with impaired vision, to create raised relief maps from digital elevation maps (DEMs), to bridge the gap between mathematical tools and rapid prototyping, and many more. Ian Walker created the function r2stl() and Jose' Gama assembled the package. 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The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'. The suggested package 'BRugs' (only needed for function openbugs()) is only available from the CRAN archives, see . Package: r-cran-r311 Architecture: all Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble, r-cran-xml2, r-cran-xmlconvert Filename: pool/dists/resolute/main/r-cran-r311_0.4.4-1.ca2604.1_all.deb Size: 129794 MD5sum: 64aea70300d256f05b07627cc48db917 SHA1: 03d0c474f47e664bd50e1d2f612d637952dc1be3 SHA256: 5243d708b3ffb77522c5561c31496dd5a685777c552fe4ac98bdf5f48137fa6d SHA512: 213c00f6c821b1b0d01fdec69e2d555af84912d8d08880e954cb5f6a8a5cee1a888b3e8044dd6e195e48f9bd227abcb7738dca02f7052737be24a6f48b0f5ab4 Homepage: https://cran.r-project.org/package=r311 Description: CRAN Package 'r311' (Interface to the 'open311' Standard) Access and handle APIs that use the international 'open311' 'GeoReport v2' standard for civic issue tracking . Retrieve civic service types and request data. Select and add available 'open311' endpoints and jurisdictions. Implicitly supports custom queries and 'open311' extensions. Requires a minimal number of hard dependencies while still allowing the integration in common R formats ('xml2', 'tibble', 'sf'). 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Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package. 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Possibility to design tables and listings for reporting and also include R plots. 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Package: r-cran-r4goodpersonalfinances Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsicons, r-cran-bslib, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggtext, r-cran-gt, r-cran-glue, r-cran-prettycols, r-cran-scales, r-cran-shiny, r-cran-tidyr, r-cran-readr, r-cran-fs, r-cran-purrr, r-cran-stringr, r-cran-nloptr, r-cran-cli, r-cran-furrr, r-cran-future, r-cran-progressr, r-cran-lubridate, r-cran-memoise, r-cran-cachem, r-cran-rlang Suggests: r-cran-spelling, r-cran-tibble, r-cran-withr, r-cran-microbenchmark, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-r4goodpersonalfinances_1.2.0-1.ca2604.1_all.deb Size: 1816872 MD5sum: 52766411d0b1aa1cb353cdfb149ed503 SHA1: 158acb547c209ddbb6b2398b0c94435992d5275b SHA256: 8124658ac9d5a2e07e127c8509a0d58caa990b917415744028ab4480bcd9e797 SHA512: 19d1d395650aacd8d79ef3a8614586556f8dbf5cfef45c5eed0ed877ec6fac6a631a6ee22d1788819d5015fff0ccf87198773bb4e8107d6e258f22863ec6d37e Homepage: https://cran.r-project.org/package=R4GoodPersonalFinances Description: CRAN Package 'R4GoodPersonalFinances' (Make Optimal Financial Decisions) Make optimal decisions for your personal or household finances. Use tools and methods that are selected carefully to align with academic consensus, bridging the gap between theoretical knowledge and practical application. They help you find your own personalized optimal discretionary spending or optimal asset allocation, and prepare you for retirement or financial independence. The optimal solution to this problems is extremely complex, and we only have a single lifetime to get it right. Fortunately, we now have the user-friendly tools implemented, that integrate life-cycle models with single-period net-worth mean-variance optimization models. Those tools can be used by anyone who wants to see what highly-personalized optimal decisions can look like. For more details see: Idzorek T., Kaplan P. (2024, ISBN:9781952927379), Haghani V., White J. (2023, ISBN:9781119747918). Package: r-cran-r4googleads Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-r4googleads_0.1.1-1.ca2604.1_all.deb Size: 53722 MD5sum: 875cded3c6d633b6d89f72c471a994e1 SHA1: 14e78b8e5051b6b0537d150316adf8b9500926a5 SHA256: 32ed252e22296a307853d898ca9ee3b6e2143672e91b6ab7eb1618fe949ac1b9 SHA512: e86c676179ad80fe09261228d2056ae211fc4f5a262294cbe0b7e9a6d416e6363b8c074ec1b7fbe38be9d4bd0424180206145b1cf3a883aa65eac78d44d79fd3 Homepage: https://cran.r-project.org/package=r4googleads Description: CRAN Package 'r4googleads' ('Google Ads API' Interface) Interface for the 'Google Ads API'. 'Google Ads' is an online advertising service that enables advertisers to display advertising to web users (see for more information). Package: r-cran-r4hcr Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-irr, r-cran-mada, r-cran-meta, r-cran-metafor, r-cran-survival Filename: pool/dists/resolute/main/r-cran-r4hcr_0.1-1.ca2604.1_all.deb Size: 326438 MD5sum: cca43c4a6acf9824599f7d6bcd7c248a SHA1: 513e9806baabd8458a5f3cdcb7f106f1b855eff2 SHA256: 5b0327208028a72da76b837db38725c28aeeef44133d294011e3e15d5625053f SHA512: 13d1de316322a9063d01c0013a1fa1785af13577474303e81fad3e521e4d2bbe9ff536ef87f23f5e6d64f94e910310912148b4ffebcce404b03a65f6ea1761df Homepage: https://cran.r-project.org/package=R4HCR Description: CRAN Package 'R4HCR' (R for Health Care Research) A collection of datasets that accompany the forthcoming book "R for Health Care Research". Package: r-cran-r4lineups Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-here, r-cran-magick, r-cran-magrittr, r-cran-proc, r-cran-psych, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/resolute/main/r-cran-r4lineups_0.1.1-1.ca2604.1_all.deb Size: 529876 MD5sum: da00e8bd9d37480b22417539e70e6ccd SHA1: dd71800da92b6137b0d4f91842603d14738005cb SHA256: 9baa045b62eb6aadafaebdb310748f156639cb89e51c0f6dbee4473e002c853d SHA512: 36911b8f9aad58af6291c13d334b98ec91cf7c1b17d305751e77423cf8e1feaf74e04db1b8198dbd7baf33900123e1ad9c16fe9b120870c53fe315d998e5222b Homepage: https://cran.r-project.org/package=r4lineups Description: CRAN Package 'r4lineups' (Statistical Inference on Lineup Fairness) Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? 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It includes functions for quantifying disease, assessing spatial patterns, and modeling plant disease epidemics based on weather predictors. These tools are intended for teaching and research in plant disease epidemiology. Several functions are based on classical and contemporary methods, including those discussed in Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) . Package: r-cran-r4ss Architecture: all Version: 1.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-corpcor, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lifecycle, r-cran-stringr, r-cran-kableextra Suggests: r-cran-gtools, r-cran-gplots, r-cran-knitr, r-cran-maps, r-cran-pso, r-cran-testthat, r-cran-truncnorm, r-cran-rmarkdown, r-cran-shiny, r-cran-flextable, r-cran-reshape2, r-cran-ggpubr Filename: pool/dists/resolute/main/r-cran-r4ss_1.44.0-1.ca2604.1_all.deb Size: 2106392 MD5sum: 20796a6327227a3b5882293980f92340 SHA1: 189014bf7605cf81b68202397e8cc95cff9feeb2 SHA256: 6300fa575251006bcaf69c315ff44ae68a12e53593f290fa9a41039cef06e664 SHA512: 9bb18473b84d11fa521a67c0084f7f64e310e9b91b4057f831760d09f60e4ae2bfef909cfb7c887aa709becba1de897c9d6d67187419b06cd597da14b5c6aa62 Homepage: https://cran.r-project.org/package=r4ss Description: CRAN Package 'r4ss' (R Code for Stock Synthesis) A collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of '{r4ss}' is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.19.01, from April 2022). Package: r-cran-r4sub Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r4subcore, r-cran-r4subdata, r-cran-r4subprofile, r-cran-r4subrisk, r-cran-r4subscore, r-cran-r4subtrace, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r4sub_0.1.0-1.ca2604.1_all.deb Size: 29618 MD5sum: 17d3c5a88c12927aef96fde566e3f601 SHA1: 37e6720b3c234d7c5da1169094619a7992719f43 SHA256: 734c8675cbf862d91fb2f25f6cc56ca084aea56e5d4b39458f1a8e927be0831d SHA512: b0c17265737c325f23671fa064b0cbb0d430bea88f93c34102d40b7eb6830c2063cd4203aa2d41206b01d89f49453a54b7819583ff0161cbe7ae49f8bad4243e Homepage: https://cran.r-project.org/package=r4sub Description: CRAN Package 'r4sub' (Easily Install and Load the R4SUB Ecosystem) The 'r4sub' package is a meta-package that installs and loads core packages of the R4SUB (R for Regulatory Submission) clinical submission readiness ecosystem. Loading 'r4sub' attaches 'r4subcore', 'r4subtrace', 'r4subscore', 'r4subrisk', 'r4subdata', and 'r4subprofile'. Package: r-cran-r4subcore Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r4subcore_0.1.0-1.ca2604.1_all.deb Size: 71962 MD5sum: 9042762ccb72048cb54840045ba38e4d SHA1: 23bd09f2f3f34d3aca3713138fed6652a7c65c09 SHA256: 330900e46d14f961a0670fa120a488eb6aa47ba755a3a61e9535b3b342577236 SHA512: 40fedb8d3593b721f08341b9a48a944931bcf4296542f7b075b54dbe671a050ca94abb9779feac509b2d62d6abd341ae9f7ef9eaf807c40f77cd1a54560ec79b Homepage: https://cran.r-project.org/package=r4subcore Description: CRAN Package 'r4subcore' (Core Data Contracts, Parsers, and Scoring Primitives forClinical Submission Readiness) Foundational package in the R4SUB (R for Regulatory Submission) ecosystem. Defines the core evidence table schema, parsers, indicator abstractions, and scoring primitives needed to quantify clinical submission readiness. Provides a standardized contract for ingesting heterogeneous sources (validation outputs, metadata, traceability) into a single evidence framework. Package: r-cran-r4subdata Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r4subdata_0.1.1-1.ca2604.1_all.deb Size: 45830 MD5sum: 15fbd8ca054e4094b42bfadd1019ffce SHA1: 26476246c3b98b7613bac3a4c81ce6c633399c7c SHA256: 81491064a13da0c942eea8e007f9e4ea3fac88efb372416886ae57e3e7bc8457 SHA512: 557810f6092944c6956a3ea7c162937ecba0a8bd223d0008ff0a2809f5896fa3d5b65fa5fb6ded6ad3bf2ee6d87b9df17397c83ff0239739df62afe4936d0140 Homepage: https://cran.r-project.org/package=r4subdata Description: CRAN Package 'r4subdata' (Example Datasets for Clinical Submission Readiness) Provides realistic synthetic example datasets for the R4SUB (R for Regulatory Submission) ecosystem. Includes a pharma study evidence table, ADaM (Analysis Data Model) and SDTM (Study Data Tabulation Model) metadata following CDISC (Clinical Data Interchange Standards Consortium) conventions (), traceability mappings, a risk register based on ICH (International Council for Harmonisation) Q9 quality risk management principles (), and regulatory indicator definitions. Designed for demos, vignettes, and package testing. Package: r-cran-r4subprofile Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r4subcore, r-cran-rlang, r-cran-tibble Suggests: r-cran-r4subscore, r-cran-r4subrisk, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r4subprofile_0.1.0-1.ca2604.1_all.deb Size: 43254 MD5sum: 8aedcbf04b8dcb9856086c999623b743 SHA1: b2da779c725e24374c2c3def0015bc7373cacfc9 SHA256: 832ea4096ba6072f48439a790c48d5d14c37518e8ff948e4a162543dc77428dc SHA512: c30d33a83eeb4fce826fd9fb679881fc2524da561a1bab505cae69e4ce1de6cb4fcdff9db47a1b17663bbde4c5858f539545f13e2d9f29b21d6b89ffc1590135 Homepage: https://cran.r-project.org/package=r4subprofile Description: CRAN Package 'r4subprofile' (Regulatory Submission Profiles for Clinical Submission Readiness) Defines submission profiles per regulatory authority with authority-specific pillar weights, decision thresholds, indicator requirements, and risk configuration. Supports the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), Pharmaceuticals and Medical Devices Agency (PMDA), Health Canada, Therapeutic Goods Administration (TGA), and Medicines and Healthcare products Regulatory Agency (MHRA). Integrates with 'r4subscore' and 'r4subrisk' configuration systems. Package: r-cran-r4subrisk Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-r4subcore, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r4subrisk_0.1.0-1.ca2604.1_all.deb Size: 62600 MD5sum: b9790ee1a721c4048deb7b284074b0aa SHA1: 48d943dc56f3cd8e0952b090291e30533ee7ca86 SHA256: 8762bcd91cadd16b0a6bd8f933cdf531e940e63397c88ba706615d19f635a7e3 SHA512: ac3b96ef5a5a01f779478a59411bcb06a3fc0af7d33bfa92cf4cfc6dee3ed51e00e2ce2220ac53bde352c1e79f6430c1f12f85b4d7e482fb25ca0c5d17caac1f Homepage: https://cran.r-project.org/package=r4subrisk Description: CRAN Package 'r4subrisk' (Risk Quantification Engine for Clinical Submission Readiness) Quantifies submission risk using a Failure Modes and Effects Analysis (FMEA)-inspired framework (probability, impact, detectability). Builds risk registers from evidence, computes Risk Priority Numbers (RPN), classifies risk levels, and emits standardized R4SUB (R for Regulatory Submission) evidence table rows via 'r4subcore'. Supports risk mitigation tracking and trend analysis across submission milestones. Package: r-cran-r4subscore Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-r4subcore, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r4subscore_0.1.0-1.ca2604.1_all.deb Size: 44072 MD5sum: 933f910c777e9a7ca66bd175625c2448 SHA1: 99ce76d4eb978aebfa07f80830caac45090285eb SHA256: edafe17eeaa506f69ce6d77ec9b3450e411d647819d6b8c422ddf9fe9bd8e001 SHA512: 6f685e4824cb377213890fb54a1844b1a27a2a55221c5f0eab2a863d972c9182ac1980ac3f9f9c9ccdfe188af450ae3672703cecd324d5c9bd1d7356ccc1edef Homepage: https://cran.r-project.org/package=r4subscore Description: CRAN Package 'r4subscore' (Submission Confidence Index Engine) Converts standardized R4SUB (R for Regulatory Submission) evidence into indicator scores, pillar scores, and a Submission Confidence Index (SCI). Provides sensitivity analysis, explainability tables, and decision band classification to answer the question: are we ready for regulatory submission. Package: r-cran-r4subtrace Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-r4subcore, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-igraph, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r4subtrace_0.1.0-1.ca2604.1_all.deb Size: 51650 MD5sum: bbc821f2f677ed2fbbce76f3c494e022 SHA1: 752a2c80753eb58aa04901c4f55f54d16e2e9492 SHA256: 977181a06d7d069a416a8683752f77752bf4c1e9178c935079bd12e8b78ade13 SHA512: a00cff3dd59fee3fc91172b43d5283010310e034a0bbb5330544258f2f9386bb053dfbf9492152ecce4185aec03fd97e54ecc4c0a488cb3d99527ab96ea65dfa Homepage: https://cran.r-project.org/package=r4subtrace Description: CRAN Package 'r4subtrace' (Traceability Engine for Clinical Submission Readiness) Quantifies and explains end-to-end traceability between clinical submission artifacts (ADaM (Analysis Data Model) outputs, derivations, SDTM (Study Data Tabulation Model) sources, specs, code). Builds trace models from metadata and mapping sheets, computes trace levels, and emits standardized R4SUB (R for Regulatory Submission) evidence table rows via 'r4subcore'. Package: r-cran-r5r Architecture: all Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6155 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-isoband, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rjava, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-zip Suggests: r-cran-accessibility, r-cran-covr, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-gtfstools, r-cran-h3jsr, r-cran-interp, r-cran-knitr, r-cran-mapview, r-cran-patchwork, r-cran-rjavaenv, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r5r_2.4.0-1.ca2604.1_all.deb Size: 3281944 MD5sum: f56ad973dafd32621acaf549cf8c2ed5 SHA1: 28b1d948585f363a8ffa79974d8e815b8bf12d4a SHA256: 298ff908358847da4075e208ee0a7b4cf84a11f07a154b4dbbf5a6fd8620d412 SHA512: 7d38dc0e1109302547f286ab814cfaaa56b207ad7e592a49afa0c3dfef94e4ec5e01b7b63ea6091187e702b9cdd5b785369c266ef050bedaa4fa3ca60eeb1b44 Homepage: https://cran.r-project.org/package=r5r Description: CRAN Package 'r5r' (Rapid Realistic Routing with 'R5') Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using 'R5', the Rapid Realistic Routing on Real-world and Reimagined networks engine . The package allows users to generate detailed routing analysis or calculate travel time and monetary cost matrices using seamless parallel computing on top of the R5 Java machine. While R5 is developed by Conveyal, the package r5r is independently developed by a team at the Institute for Applied Economic Research (Ipea) with contributions from collaborators. Apart from the documentation in this package, users will find additional information on R5 documentation at . Although we try to keep new releases of r5r in synchrony with R5, the development of R5 follows Conveyal's independent update process. Hence, users should confirm the R5 version implied by the Conveyal user manual (see ) corresponds with the R5 version that r5r depends on. This version of r5r depends on R5 v7.1. Package: r-cran-r5rgui Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-mapgl, r-cran-r5r, r-cran-sf, r-cran-shiny Suggests: r-cran-mockery, r-cran-quarto, r-cran-rlang, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r5rgui_0.2.0-1.ca2604.1_all.deb Size: 125798 MD5sum: c88411fbd4a18fe6aa3363dccbe279d8 SHA1: 81c9f4d8d5f7e07570c92e7f27b053d2e30f535d SHA256: c724edbf2f35fd59d7731b97b8c4d9c38642342228795d75bcf742b703d5d6ae SHA512: 920988114c283cf83668f3e0999d4cc42fd4a647fc3d039f8790322b9bc83aa1b3d27a6e47021f88f05d9981d260ee3c97b247f1f40b4f8941cb97b2423c18ea Homepage: https://cran.r-project.org/package=r5rgui Description: CRAN Package 'r5rgui' (Graphical User Interface for 'r5r' Router) Interactively build and explore public transit routes with 'r5r' package via a graphical user interface in a 'shiny' app. The underlying routing methods are described in Pereira et al. (2021) . Package: r-cran-r62s3 Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-pkgdown, r-cran-testthat, r-cran-r6 Filename: pool/dists/resolute/main/r-cran-r62s3_1.4.1-1.ca2604.1_all.deb Size: 88132 MD5sum: 836529dd53d1ff3771638f5827bcce0a SHA1: 64ea754f87dcdbee9a7b56ccf487f7c401eb6d4a SHA256: c9d47ce440937a1447c300017efa260387894a8471742be45ee1f9dab75d3fd0 SHA512: ee9a5a47ed95bf2e4d4a1947861e440a91aedfcb0e4fb21cf9c69ffa82ce275e2222de64ef0bbea69d30a4b7a10b89ca45cca66a7838ab664a648e5e6329809c Homepage: https://cran.r-project.org/package=R62S3 Description: CRAN Package 'R62S3' (Automatic Method Generation from R6) After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects. Package: r-cran-r6 Architecture: all Version: 2.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lobstr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r6_2.6.1-1.ca2604.1_all.deb Size: 89890 MD5sum: 653da8d2bd9cc827a59b64c920e828a9 SHA1: a1ce66c4aab3ed7ca1f45eb9312bdf034f373181 SHA256: c3422b9db5d213f3a6bb974f4e556d186125a1d0ff9c09e7b1443349112b0165 SHA512: 21fe096183de40c7e93cf56718e06ce841566b747764ab8b9aa868c7597502ce10b48e3dcb8f4edc0fea5c07791c3c7f52457652741a343fda504949ad2e6560 Homepage: https://cran.r-project.org/package=R6 Description: CRAN Package 'R6' (Encapsulated Classes with Reference Semantics) Creates classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. These classes allow public and private members, and they support inheritance, even when the classes are defined in different packages. Package: r-cran-r6causal Architecture: all Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-causaleffect, r-cran-cfid, r-cran-data.table, r-cran-dosearch, r-cran-glue, r-cran-igraph, r-cran-r6, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qgraph, r-cran-sqldf Filename: pool/dists/resolute/main/r-cran-r6causal_0.8.3-1.ca2604.1_all.deb Size: 747878 MD5sum: 7cf5c49c8004491faa0ba48506df5c9a SHA1: 08c11d600de6a1cf41647a183100c13b4242e55b SHA256: 4d08f8a332733dfae4b2ec1b0bcc1b7fe249aaf5edd53e7e71459143adc1a91d SHA512: 837c32a173d75025340d307bd29ed26667cfe4bd7f30b4415c7c0fd568755c3a5043ff380c45e40835d32634472b66cec09b4b7a190a4932c0ef03e5a49c839c Homepage: https://cran.r-project.org/package=R6causal Description: CRAN Package 'R6causal' (R6 Class for Structural Causal Models) The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation. Package: r-cran-r6ds Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-r6ds_1.2.0-1.ca2604.1_all.deb Size: 313104 MD5sum: 70d34ec1517ac6ff1e5557d2200dad75 SHA1: 84af0d6e732622ada9e6f33f74ca470c0379feb3 SHA256: eb4141cf49cc0899e80bc0d5ad65768a92e9633486ac22eadc3d26dac30d059d SHA512: 51dd848d18087988ff85e72447f8e706beaddeee24b8b416e62f2692cc2ca2aedbdc2b8ce112522e2601e05fc02a8fd8599053c95c98c9ebb73871d271022505 Homepage: https://cran.r-project.org/package=R6DS Description: CRAN Package 'R6DS' (R6 Reference Class Based Data Structures) Provides reference classes implementing some useful data structures. The package implements these data structures by using the reference class R6. Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference. The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree. See for example for more information about the data structures. Package: r-cran-r6methods Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-rstudioapi, r-cran-miniui, r-cran-shiny, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-purrr Suggests: r-cran-r6, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-r6methods_0.1.0-1.ca2604.1_all.deb Size: 45532 MD5sum: 8a4fda6d9a5cc3a65ad5753ad62215e9 SHA1: 4eba00afcfbbbf6a3195115e928f383f6446d314 SHA256: c16897fa8154ede0c130a8f6f6293613d1c1dd8d0648ade887196e7802d05d12 SHA512: a9fdda1f45d77c36135ebbb94fc6893dbcc6dc6f4c46bd4926b5d742b76d9302b293ebcfd77807af813206f9a75ff899766d22ba9f5b841db59a204617354761 Homepage: https://cran.r-project.org/package=r6methods Description: CRAN Package 'r6methods' (Make Methods for R6 Classes) Generate boilerplate code for R6 classes. Given R6 class create getters and/or setters for selected class fields or use RStudio addins to insert methods straight into class definition. Package: r-cran-r6p Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-collections, r-cran-dplyr, r-cran-stringr, r-cran-r6, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-dbi, r-cran-rsqlite, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-r6p_0.4.0-1.ca2604.1_all.deb Size: 73820 MD5sum: 2d69a75d1c3f1e63eaf1ad01c2ff61b0 SHA1: 5b18146337d3f67594e7c65ed8337e542a477fed SHA256: 5aa2b991518d5cd6d57825d901e8ac5d041c463372e759e2f1be6f72bb06f6aa SHA512: df5c31ff9bf9a2390c51449048b8994492ec410b51356230e8859e4cdeb41511a81cbe0153f58f0f4780a2e20078b17095525d457574f02905967f27fa5534a9 Homepage: https://cran.r-project.org/package=R6P Description: CRAN Package 'R6P' (Design Patterns in R) Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. 'R6P' provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess. 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Based on the discontinued CRAN package 'qualitytools', this package refactors its original design by incorporating 'R6' object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using 'ggplot2' and 'plotly'. Built on 'tidyverse' principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science. Package: r-cran-ra4bayesmeta Architecture: all Version: 1.0-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesmeta Filename: pool/dists/resolute/main/r-cran-ra4bayesmeta_1.0-8-1.ca2604.1_all.deb Size: 214706 MD5sum: 6811985f5c2a937a0cd55d4983eda89c SHA1: 20b62a732f30014c04e9c6246a008e37254ec0cf SHA256: 8e391336be766e3acd566d18d43421f9fbe5a0d1fb6b7308c569a00f3b801589 SHA512: b2138558efb45430ca4230af2a6fea9ba62e1a97e4acb16a16b111ac9e5efbaacdf008619f530f41c13bb8b706e7360b8af852a11f76ea1127ca3f1660b146ee Homepage: https://cran.r-project.org/package=ra4bayesmeta Description: CRAN Package 'ra4bayesmeta' (Reference Analysis for Bayesian Meta-Analysis) Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) . 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For more details of the proposed method, please refer to Zhan et al. (2021) . 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Features include user permission management through a two-tier system of access panels and units, pluggable 'shiny' module for administrative interfaces, and support for multiple storage backends (local, 'AWS S3', 'Posit Connect'). The system enables fine-grained control over application features, with built-in audit trails and user management capabilities. Integrates seamlessly with 'Posit Connect's authentication system. Package: r-cran-racir Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-racir_2.0.0-1.ca2604.1_all.deb Size: 190260 MD5sum: 072169c2080202b83ee2d744b8dad208 SHA1: a405f6b7ca9e8f931e97ab7b7d4f211f7333f467 SHA256: bc355b8061f30682c4fb52eed7b26fa1450c7cb6e860cc87d1ac21bc6d2a1a56 SHA512: 3082a1dcb575fb5fbbc12432bfdeb843f14de70b4467b688692acbd5d29e1c15cbbe241bf1fbcea32374b14a18477f10ff9ad6e8211f2197ec29815855e78fff Homepage: https://cran.r-project.org/package=racir Description: CRAN Package 'racir' (Rapid A/Ci Response (RACiR) Data Analysis) Contains functions useful for reading in Licor 6800 files, correcting and analyzing rapid A/Ci response (RACiR) data. Requires some user interaction to adjust the calibration (empty chamber) data file to a useable range. 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Package: r-cran-ractivecampaign Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-pbapply, r-cran-stringr, r-cran-tidyr, r-cran-cli, r-cran-retry Filename: pool/dists/resolute/main/r-cran-ractivecampaign_0.6.0-1.ca2604.1_all.deb Size: 149738 MD5sum: 540f56f90ac508e276a1d52d01c788f0 SHA1: 48b1868697efcfd03b98231aa0c49e3fae966286 SHA256: da8f2807f527136aeac499703188b095633a0cd9187ae81e9d433fc394ea29d6 SHA512: 16a5400253a147d43fca2c80da4c56398777a2ee3cb5e8e994499fb6ba88ec24f690714abe4b4bb23669399850c95bc2041a714d1534d46478b8c206b581e230 Homepage: https://cran.r-project.org/package=ractivecampaign Description: CRAN Package 'ractivecampaign' (Loading Data from 'ActiveCampaign API v3') Interface for loading data from 'ActiveCampaign API v3' . Provide functions for getting data by deals, contacts, accounts, campaigns and messages. Package: r-cran-rada Architecture: all Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5745 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-ggplot2, r-cran-matrixstats, r-cran-reshape2, r-cran-lmertest, r-cran-e1071, r-cran-tidyr, r-cran-stringr, r-cran-dplyr, r-cran-hmisc, r-cran-gridextra, r-cran-forestplot, r-cran-knitr, r-cran-openxlsx, r-cran-car Suggests: r-cran-markdown Filename: pool/dists/resolute/main/r-cran-rada_1.1.9-1.ca2604.1_all.deb Size: 1668746 MD5sum: ef875e69405eee97cdeb288602450d62 SHA1: d0a674b0a436163b76a24894cbe36b0ad106afa3 SHA256: c81e7727c357bf8785df3bb28ca49cbeb0b841a17cbde69e1c7c23125df1d5e7 SHA512: 2d414f8c7f6d908bdffc634ec4f9d90a6da161dc9968590cf10d7cd066eff680913a32781d480981c8f6ceb7c1cc48cd9ab39040487ee2b18e0cb904d1d8b655 Homepage: https://cran.r-project.org/package=rADA Description: CRAN Package 'rADA' (Statistical Analysis and Cut-Point Determination of Immunoassays) Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. 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Rank abundance distributions are widely used in biology and ecology to describe species abundances, and are mathematically equivalent to complementary cumulative distribution functions (CCDFs) used in physics, linguistics, sociology, and other fields. The method is described in Saeedghalati et al. (2017) . 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Package: r-cran-radarchart Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-radarchart_0.3.1-1.ca2604.1_all.deb Size: 324246 MD5sum: 6f3fb07b4ea3665b02131a65f5fb2ec6 SHA1: bd9c28afc31a14b32c6bea5f48abd8d522853a40 SHA256: fca39dc10c9d1dbfd4eff596b538d8e4a0d00d206047c3b00133c9fee62d5ad0 SHA512: fb6f09048ebbb34d7bc1910a102f3399d20351a9526028445bbd105c17b26a8c1f6a08da7fb965035885aeb23c60930ca81665c27de3a2bc3d60470acc6c5c2f Homepage: https://cran.r-project.org/package=radarchart Description: CRAN Package 'radarchart' (Radar Chart from 'Chart.js') Create interactive radar charts using the 'Chart.js' 'JavaScript' library and the 'htmlwidgets' package. 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Package: r-cran-raddata Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4452 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-radsafer Filename: pool/dists/resolute/main/r-cran-raddata_1.0.2-1.ca2604.1_all.deb Size: 4518722 MD5sum: 4290610b809c2a8a423bb95a78089bfc SHA1: 6abf2f8825ce909ab645cdb79028625c8c3b5a57 SHA256: 79f30d770c3a34451c2abe23580c31ceac94584ed3ec86f10c20732182faed53 SHA512: e20bfbb9232cf3218a052b22369d9885fb53c6102e983581ddd516add58ac8050bb560138b1b4c6e272022915f3235dd548fba224870aa58ad54a9b9c38460f6 Homepage: https://cran.r-project.org/package=RadData Description: CRAN Package 'RadData' (Nuclear Decay Data for Dosimetric Calculations - ICRP 107) Nuclear Decay Data for Dosimetric Calculations from the International Commission on Radiological Protection from ICRP Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 . This is a database of the physical data needed in calculations of radionuclide-specific protection and operational quantities. The data is prescribed by the ICRP, the international authority on radiation dose standards, for estimating dose from the intake of or exposure to radionuclides in the workplace and the environment. The database contains information on the half-lives, decay chains, and yields and energies of radiations emitted in nuclear transformations of 1252 radionuclides of 97 elements. 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The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) ). 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This package was designed to facilitate an explicit pipeline for optimizing Stacks (Rochette et al., 2019) () parameters during de novo (without a reference genome) assembly and variant calling of restriction-enzyme associated DNA sequence (RADseq) data. The pipeline implemented here is based on the 2017 paper "Lost in Parameter Space" (Paris et al., 2017) () which establishes clear recommendations for optimizing the parameters 'm', 'M', and 'n', during the process of assembling loci. 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The package extends traditional radial coordinate visualization (RadViz) techniques to three-dimensional space, enabling enhanced exploration and analysis of high-dimensional datasets through interactive 3D plots. Zhu, Dai & Maitra (2022) . 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Output variables include hydraulic head and the discharge vector. Particle traces can be computed numerically in three dimensions. The underlying theory is described in Haitjema (1995) and references therein. 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Package: r-cran-rai Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-readr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rai_1.0.0-1.ca2604.1_all.deb Size: 83254 MD5sum: cb23795e9c8b5d4c39863fed00213f4e SHA1: aa0a5e2db13dfa6df7725dfb3e580cb2788578b4 SHA256: 85bd8399a4ebe5444b90ac13ad10bad83bad2d5d008e04b0e8f3770e23f0da9f SHA512: e47f4614baa7f0ed62c0ef593811fbc6f1985c737aad1b32709c73a129c76da6110e451a9b4484f0a10f37c1a29983c2f7413351096dced9390cd6fd3feb997a Homepage: https://cran.r-project.org/package=rai Description: CRAN Package 'rai' (Revisiting-Alpha-Investing for Polynomial Regression) A modified implementation of stepwise regression that greedily searches the space of interactions among features in order to build polynomial regression models. 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Supports multiple storm events, rainfall validation, and visualization for soil erosion modeling and hydrological analysis. Methods are based on Brown and Foster (1987) , Wischmeier and Smith (1978) "Predicting Rainfall Erosion Losses: A Guide to Conservation Planning" , and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)" (USDA Agriculture Handbook No. 703). 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Package: r-cran-rainfarmr Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rainfarmr_0.1-1.ca2604.1_all.deb Size: 70548 MD5sum: 454f805153c144ab3805c2f62c07e9eb SHA1: 258162be0720845b804355a543ae0dfde9d55ea2 SHA256: 43b71c2ff85c98178c28d879a4e748cdf910c61879d626de9fcbabf9eaf85da5 SHA512: 704b22a79533c280a7dc6c59b0538cac22aeb176fe1626a13441161afccdc5142a09830297eefaa5e1051bb14a829847562c7b570ad85855a7af8b0f54717a3a Homepage: https://cran.r-project.org/package=rainfarmr Description: CRAN Package 'rainfarmr' (Stochastic Precipitation Downscaling with the RainFARM Method) An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) ). Adapted for climate downscaling according to D'Onofrio et al. (2018) and for complex topography as in Terzago et al. (2018) . The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field. 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Such studies are, for example, international assessments like 'TIMSS', 'PIRLS' and 'PISA'. A graphical interface is available for the non-technical user.The package includes functions to covert the original data from 'SPSS' into 'R' data sets keeping the user-defined missing values, merge data from different respondents and/or countries, generate variable dictionaries, modify data, produce descriptive statistics (percentages, means, percentiles, benchmarks) and multivariate statistics (correlations, linear regression, binary logistic regression). The number of supported studies and analysis types will increase in future. For a general presentation of the package, see 'Mirazchiyski', 2021a (). For detailed technical aspects of the package, see 'Mirazchiyski', 2021b (). 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The charts can be included in 'Shiny' apps and R markdown documents, or viewed from the R console and 'RStudio' viewer. Based on the JavaScript library 'amCharts 4' and the R packages 'htmlwidgets' and 'reactR'. Currently available types of chart are: vertical and horizontal bar chart, radial bar chart, stacked bar chart, vertical and horizontal Dumbbell chart, line chart, scatter chart, range area chart, gauge chart, boxplot chart, pie chart, and 100% stacked bar chart. 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Package: r-cran-ramchoice Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ramchoice_2.2-1.ca2604.1_all.deb Size: 146880 MD5sum: e93eeeefa6c1b2d1572087b2eb1bb521 SHA1: fe2b3ca8f4ef20f4142ab604395aec57548e11af SHA256: 3785e30aed9c408a3b5b3e071d2124a4c2a415286a3ac340e546ba2cc300cafc SHA512: ddeaad217f60ee55414ebcad3821e3c665efe735bfe45ad910e9bac75edc477742d3fa120eb84a6a83d4198b57e467ec550f8ed019f4bea73929de450010c5de Homepage: https://cran.r-project.org/package=ramchoice Description: CRAN Package 'ramchoice' (Revealed Preference and Attention Analysis in Random LimitedAttention Models) It is widely documented in psychology, economics and other disciplines that socio-economic agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2020) and Cattaneo, Cheung, Ma, and Masatlioglu (2022) , which utilizes standard choice data to partially identify and estimate a decision maker's preference and attention. For inference, several simulation-based critical values are provided. Package: r-cran-rameritrade Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-urltools, r-cran-lubridate, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rameritrade_0.1.5-1.ca2604.1_all.deb Size: 81938 MD5sum: d9d85354c22b70867096d17f4439c495 SHA1: d4e0ead05d3673e7f611b0ab0c45479e234d931a SHA256: bc7394168b47018f50a3e18cf5bb7ec8719262a1c843fa6224d5fd43ee6b8105 SHA512: e73d1000257329f5506bea366c34fd2714e0a4703e750c7bc81dfd717c0219eeb9b9df77aff49c3c05720d7e4fd64e656bae0940c5921650ce8d418b450b6ad7 Homepage: https://cran.r-project.org/package=rameritrade Description: CRAN Package 'rameritrade' ('TD Ameritrade' API Interface for R) Use R to interface with the 'TD Ameritrade' API . Functions include authentication, trading, price requests, account information, and option chains. 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Package: r-cran-rampath Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-ellipse, r-cran-mass Filename: pool/dists/resolute/main/r-cran-rampath_0.5.1-1.ca2604.1_all.deb Size: 230194 MD5sum: 95c16873ec7051f15f4966550724f22c SHA1: 440614a4f3632372bc27e0b4f0350383afcfd14b SHA256: e2fdda5d11b114e954b607712c59738843147a78d1ad1b9f35433be74b191c6d SHA512: 204f1e0b0f8aa95f1eea1b68afcc3980b5b1a98857062a51503852cb0dd9a594552d0e1b527f6551469d3f1867a546b38bdb515ccec98ab8327612d634deace7 Homepage: https://cran.r-project.org/package=RAMpath Description: CRAN Package 'RAMpath' (Structural Equation Modeling Using the Reticular Action Model(RAM) Notation) We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models. Package: r-cran-ramps Architecture: all Version: 0.6.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-maps, r-cran-matrix, r-cran-nlme, r-cran-fields Filename: pool/dists/resolute/main/r-cran-ramps_0.6.18-1.ca2604.1_all.deb Size: 304470 MD5sum: 071edc25c927abfe7f4ab40315cad3e6 SHA1: e2f3957ecedc6c2def098978d3ac25690e6a3634 SHA256: f63ee5b34584192f6c09b44dcd5ced47ebaf95f9403ff1db51e527609685c76e SHA512: 6087197e727a4ca8021ba45a70b7de1043d0cef0914388e59a27032c50436e37ae975f8b0f2f0b9fd8519025a2bf1312df5d7c1682b77acc6c0d7178518d38c3 Homepage: https://cran.r-project.org/package=ramps Description: CRAN Package 'ramps' (Bayesian Geostatistical Modeling with RAMPS) Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples. Package performance is tuned for large spatial datasets. 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Package: r-cran-rando Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 517 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-rlang, r-cran-tibble Suggests: r-cran-spelling, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rando_0.2.0-1.ca2604.1_all.deb Size: 170982 MD5sum: 64cb9663f3f419281e276da065458e94 SHA1: b4ffe7d70361e9e0d4bd6c2a59deadecb531aa3d SHA256: 5e5405bebaf22f70426de77ff5a6f4c086d030577d224f5d3689593a1e3f4aa9 SHA512: c39f636f358000cfcf4077b003d027b62db087f195ec73001b9df3552809f41a9834febcfb6280bec9227a7f75670e2fb42667ce47b87f2602a00f134645ce3f Homepage: https://cran.r-project.org/package=rando Description: CRAN Package 'rando' (Context Aware Random Numbers) Provides random number generating functions that are much more context aware than the built-in functions. 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Package: r-cran-random.polychor.pa Architecture: all Version: 1.1.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-nfactors, r-cran-boot, r-cran-mass, r-cran-mvtnorm, r-cran-sfsmisc Filename: pool/dists/resolute/main/r-cran-random.polychor.pa_1.1.4-5-1.ca2604.1_all.deb Size: 88500 MD5sum: 661c2b9c454917826a82295f454ffe51 SHA1: 98466c11e408c2892ad267a885c6716a3043d72b SHA256: 0d9dba95d38028923b9445e79bd8f3e7dd6320e67d0a3dcd0784944a587223af SHA512: b8579f8485815369cfe9aabc12cce5cf4a498594a1b1cddc2475b684e8b2b839dcc167b5bc3dc105570f0f07906efab21c4ad7041c161c34d4ece88ee036cdcf Homepage: https://cran.r-project.org/package=random.polychor.pa Description: CRAN Package 'random.polychor.pa' (A Parallel Analysis with Polychoric Correlation Matrices) The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis. Also weighted correlation matrices may be considered for PA. Package: r-cran-random Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl Filename: pool/dists/resolute/main/r-cran-random_0.2.6-1.ca2604.1_all.deb Size: 463200 MD5sum: a539b6aeee1010c832ddd1e505be8be5 SHA1: 3c001f1f5575e2928a54cad0d215feb52ae990fc SHA256: 91b790e68f8a6e124fb24d7f8ce3980e37c9b6bcf9d305fc6c54095cb9a7d04f SHA512: b95998028e601507c40f7b392e18da44acae3063fb5ef62db12af6068c8ce66724251b8a695b5582605eff6eb846149f9c770193624bd449c23d97931c27389d Homepage: https://cran.r-project.org/package=random Description: CRAN Package 'random' (True Random Numbers using RANDOM.ORG) The true random number service provided by the RANDOM.ORG website created by Mads Haahr samples atmospheric noise via radio tuned to an unused broadcasting frequency together with a skew correction algorithm due to John von Neumann. More background is available in the included vignette based on an essay by Mads Haahr. In its current form, the package offers functions to retrieve random integers, randomized sequences and random strings. Package: r-cran-randomcolor Architecture: all Version: 1.1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-stringr, r-cran-v8, r-cran-scales, r-cran-rtsne, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-randomcolor_1.1.0.1-1.ca2604.1_all.deb Size: 26432 MD5sum: d95f3eb6894a96781b3a36a2d8750b63 SHA1: f9a4a414006db1675e5d3ba1561090a687b361e6 SHA256: aaf55965901a1205a18075c5cc017a6afb91c9e731b955c709b4ed0f6cf0c7b0 SHA512: 384dd298e6b232ca98404c1be03562c0ad0782ea4e32fe2fd731ec4d6698d59e17391e09f039c83e324351f60f0a5b899ee1f766454f5b1da8040c9d72be01c8 Homepage: https://cran.r-project.org/package=randomcoloR Description: CRAN Package 'randomcoloR' (Generate Attractive Random Colors) Simple methods to generate attractive random colors. The random colors are from a wrapper of 'randomColor.js' . 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Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) , Leo Breiman (2001) ). 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Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) . Package: r-cran-randomgaussiannb Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mlbench, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-randomgaussiannb_0.2.4-1.ca2604.1_all.deb Size: 40620 MD5sum: 89f1f7445895be65aee7706ba7fa64bf SHA1: 7417c0015f813f89e1a31739a257235468672efb SHA256: d6c681b8f9e7cee8c13b69a5fefbdd59b8b19b4906cd3672c245105a97f2d61d SHA512: 28e2fdedaee7856f29111debaf81e3e54932d3561ce9e1df253c4651ed2cedfa04bf31496f701b6f9478ea2d5981f9ab785c9568a4183edbf4859af894eb8149 Homepage: https://cran.r-project.org/package=RandomGaussianNB Description: CRAN Package 'RandomGaussianNB' (Randomized Feature and Bootstrap-Enhanced Gaussian Naive BayesClassifier) Provides an accessible and efficient implementation of a randomized feature and bootstrap-enhanced Gaussian naive Bayes classifier. The method combines stratified bootstrap resampling with random feature subsampling and aggregates predictions via posterior averaging. Support is provided for mixed-type predictors and parallel computation. Methods are described in Srisuradetchai (2025) "Posterior averaging with Gaussian naive Bayes and the R package RandomGaussianNB for big-data classification". Package: r-cran-randomglm Architecture: all Version: 1.10-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3556 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-foreach, r-cran-doparallel, r-cran-hmisc, r-cran-geometry, r-cran-survival, r-cran-matrixstats Filename: pool/dists/resolute/main/r-cran-randomglm_1.10-1-1.ca2604.1_all.deb Size: 3610140 MD5sum: 9ae96ae3f76c8f2129d78521bcea59c1 SHA1: 37be757b0155eb6db71734e64680354cd688c9f2 SHA256: e75ac223d9e0b1da306d4c783d0321f170acf7b4ce24844abfd20345bf7eeed3 SHA512: 382e819e21d2df566ab69881543517b37b601783fb1f6b3233c9cb1ba8eacba3f8696db4d687ed149bb85320d3c4c39a66d82c56d65f6d31807c6f12596d9534 Homepage: https://cran.r-project.org/package=randomGLM Description: CRAN Package 'randomGLM' (Random General Linear Model Prediction) A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in Song, Langfelder and Horvath (2013) . Package: r-cran-randomgodb Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-go.db Filename: pool/dists/resolute/main/r-cran-randomgodb_1.1-1.ca2604.1_all.deb Size: 405736 MD5sum: ed879a2fd095aba281b2ccad56447227 SHA1: e5c4d7a12164f80516eaf3b6dd5f1c7533f4b01b SHA256: 59d172006c1484e750052b1ae977376061e377513871437eeb71939862673a69 SHA512: 92e1cd0cc064548e679d60f304ac03dba5c0e6749f76ba79ab221979c45cad7aa74a0ba7e575f5639dea71f7b9221fb1be9f9c3ff816c962c7e66a606d13185e Homepage: https://cran.r-project.org/package=randomGODB Description: CRAN Package 'randomGODB' (Random GO Database) The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. The significance is traditionally determined by randomizing the input gene list to computing the false discovery rate (FDR) of the enrichment p-value for each category. We explore here the novel alternative of randomizing the GO database rather than the gene list. 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Package: r-cran-rangr Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-pbapply, r-cran-terra, r-cran-zoo Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rangr_1.0.9-1.ca2604.1_all.deb Size: 1597622 MD5sum: 21b6762de429decf23f47c952a933647 SHA1: 70e5c2b42ea6ea998bd27f35102c05fc14c2ccd1 SHA256: 36b6e2e61afa61e8d94368523101dfc9c9c56a38c42b1d373895cb8cfb92ca9a SHA512: d6eaf4f78c8f13c4d4fe03b3b9ada7f2b5a8c7bd4b1bb420df1027a1894c6f7acb83c46b7dc5236874502990561803f1b7933f441910189a402d72883ff20532 Homepage: https://cran.r-project.org/package=rangr Description: CRAN Package 'rangr' (Mechanistic Simulation of Species Range Dynamics) Integrates population dynamics and dispersal into a mechanistic virtual species simulator. 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Brunner, E., Bathke, A. and Konietschke, F. (2018) . Package: r-cran-rankhazard Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-rms Filename: pool/dists/resolute/main/r-cran-rankhazard_1.1.1-1.ca2604.1_all.deb Size: 75962 MD5sum: 9d9b204f342b65d0af852bd5cc21100f SHA1: b6e8ea9918b3a435f2fad1f0223203920b495073 SHA256: b59dcbfeff7899424c9cb347f268467e1e528a06fb4f1d4e2f6ebdf09662f9f0 SHA512: d6413e8d1f162a98f18cf75a5ad61a618b5e4d7d7b9eb0f7c91a210a1c4642c5709cd0a8b7f9cefce8319994e5b514d5e4e1cda9419450bf8bd5f53317185347 Homepage: https://cran.r-project.org/package=rankhazard Description: CRAN Package 'rankhazard' (Rank-Hazard Plots) Rank-hazard plots Karvanen and Harrell (2009) visualize the relative importance of covariates in a proportional hazards model. 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Companion package to "A Primer on Visualizations for Comparing Populations, Including the Issue of Overlapping Confidence Intervals" by Wright, Klein, and Wieczorek (2019) and "A Joint Confidence Region for an Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020) . 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Package: r-cran-rankpca Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret Filename: pool/dists/resolute/main/r-cran-rankpca_0.1.0-1.ca2604.1_all.deb Size: 16906 MD5sum: d2a7e472248609467f1a6b7c1dcde736 SHA1: 10c2aef08596ac2c86f1f342445333265929ff79 SHA256: 344025a90012d3ef6ed209d6cfc320b1f4856543df706aabd6ebe53ed956d2fb SHA512: 8f5ae886d1267bf3219a6a4a508cb1100e23fb803cc68ac3900e064b0b2dce7fd88114ccf821c771d11e482b6061697beefcf54067620aad242321a498c2364e Homepage: https://cran.r-project.org/package=RankPCA Description: CRAN Package 'RankPCA' (Rank of Variables Based on Principal Component Analysis forMixed Data Types) Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. 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You can also determine the ("special") card of highest rank of the remaining cards for that suit. At some point, you notice that a certain opponent discards that special card. What can you infer about his holding in that suit? A series of simulation studies are reported here that allows a quantitative inference based on the conditional probability, given that the opponent has the special card. The same procedure is also used for the conditional probability, given that the opponent does not have the special card. Package: r-cran-rankrate Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-isotone Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-ggplot2, r-cran-pander, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-rankrate_1.2.1-1.ca2604.1_all.deb Size: 284068 MD5sum: 3048766a1ec2900d639b46fbc2139d12 SHA1: 542151795ef8ba5c12f8ac73e00cd2dfbf259673 SHA256: f96d7c09d8e301744fb154eb71d7615fdfa6a1d821a8b42d4562a2c25fa4002d SHA512: 244423f9bb7c1e0b4ad013c1d1e74acdaa4045b43caa612e05d8bf43612145162e0b6fa7a5811d0735bcd3eb7ab80fe1846f0e1966294c9d2d89c7aebd628cff Homepage: https://cran.r-project.org/package=rankrate Description: CRAN Package 'rankrate' (Joint Statistical Models for Preference Learning with Rankingsand Ratings) Statistical tools for the Mallows-Binomial model, the first joint statistical model for preference learning for rankings and ratings. This project was supported by the National Science Foundation under Grant No. 2019901. Package: r-cran-rankresponse Architecture: all Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rankresponse_4.0.0-1.ca2604.1_all.deb Size: 42558 MD5sum: c31f440fe46b2e36f63acb08f7112b1b SHA1: 0ae00433dd014c0680d0bb3677b61397607aae9b SHA256: 22823288012d5c996bb9ede7de3b4004f4b157e699463dfec252d50e5d1a0a74 SHA512: b0d852dd3401f9e193d532a6663260e0d59ca57f4e5820eb1101f5c217ca7eee4313067b5153c852159acbae9e72d3e3a04877653d8849e1a8fa916f91e5e436 Homepage: https://cran.r-project.org/package=RankResponse Description: CRAN Package 'RankResponse' (Ranking Responses in a Single Response Question or a MultipleResponse Question) Methods for ranking responses of a single response question or a multiple response question are described in the two papers: 1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of Applied Statistics, 35, 465-474. 2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple Response Questions. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177, 191-208. . 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Discrimination metrics include Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), and difference in Area Under the Curves (AUCs), Brier scores and Brier skill. Plots include Risk Assessment Plots, Decision curves and Calibration plots. Methods are described in Pickering and Endre (2012) and Pencina et al. (2008) . Package: r-cran-raptor Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-raptor_1.0.1-1.ca2604.1_all.deb Size: 335872 MD5sum: 5e4a9f6402087199e1a1bd1bec367f74 SHA1: 4cc8806963f28020097516545d5b6a4fe0c5e6a9 SHA256: ea99d853535f7f7fd9877d33d07533b1b3dcdd18418dc73fcfef929cf3cb858b SHA512: 33b78e8375da83dea61c83f7af85e3377fa4c1c446ed292a105bdf0823f9d4883680616e33ff50cd6da2169e7743bbe0e6405d58ac2b27dc83f216fe44f5f2ce Homepage: https://cran.r-project.org/package=RAPTOR Description: CRAN Package 'RAPTOR' (Row and Position Tracheid Organizer) Performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets derived from thin sections of woody tissue. The package includes functions for visualisation, detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, e.g. as provided with ROXAS (von Arx & Carrer, 2014 ). The package has been validated for Picea abies, Larix Siberica, Pinus cembra and Pinus sylvestris. Package: r-cran-raqs Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2 Suggests: r-cran-data.table, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-raqs_1.0.2-1.ca2604.1_all.deb Size: 202468 MD5sum: c1ce824a7a08cd71275689fd97389973 SHA1: f7bd5ce9b9f3f67034113da0adfb422d5cd1c6c2 SHA256: 509071e7e5adc02bfad01802020cee4367dde17b0e9b4c54ce799adea5e9dd2d SHA512: a373c74b8299e6e524c76261e472db4b4cea9c301b9788f3078f7bdd19a955b85cc0ac1252e2965538eb19fea18108c84b413e9a056832dbead6d2958c8ef5b3 Homepage: https://cran.r-project.org/package=raqs Description: CRAN Package 'raqs' (Interface to the US EPA Air Quality System (AQS) API) Offers functions for fetching JSON data from the US EPA Air Quality System (AQS) API with options to comply with the API rate limits. See for details of the AQS API. Package: r-cran-raqsapi Architecture: all Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-covr, r-cran-devtools, r-cran-keyring, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-raqsapi_2.0.5-1.ca2604.1_all.deb Size: 694304 MD5sum: 5fa49b2d2ff9045ec5e7e8c4ee3c1f93 SHA1: acbdd62e274e5fcecbb9b139807e6ca307bf8088 SHA256: 4319696e5a9109a417fe97028df431f93020d0683bbaba4ef11160a659b92c4a SHA512: f58c1768f8339f7d01f3e6cf34bc0e2ec83f768740b95965fe83ae9d44692f63856dd5d889e6131d338f31665b57af6cadc1ca0017a41255e6feb3eb17b5a3ba Homepage: https://cran.r-project.org/package=RAQSAPI Description: CRAN Package 'RAQSAPI' (A Simple Interface to the US EPA Air Quality System Data MartAPI) Retrieve air monitoring data and associated metadata from the US Environmental Protection Agency's Air Quality System service using functions. See for details about the US EPA Data Mart API. Package: r-cran-raquifer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-magrittr, r-cran-dplyr, r-cran-pracma, r-cran-gsl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-raquifer_0.1.0-1.ca2604.1_all.deb Size: 113194 MD5sum: 7918dac9b2d74262fd6aad4912cd7292 SHA1: 914fd6eb12468346ba553d288d5a3bb8068cba0a SHA256: 96c2a7a3d33124589f0e6a6503ab54e3b4fc4fade53208d4aadb83e1b70bf924 SHA512: 5e4d38723db9d1ad8dbd37205b58e0f63e5f4801acdbb1023f9671a88aa7088a6984ef05b2e565d4c2d008f97a5122ccd25fd1019a6788819401437d0fb4ec13 Homepage: https://cran.r-project.org/package=Raquifer Description: CRAN Package 'Raquifer' (Estimate the Water Influx into Hydrocarbon Reservoirs) Generate a table of cumulative water influx into hydrocarbon reservoirs over time using un-steady and pseudo-steady state models. Van Everdingen, A. F. and Hurst, W. (1949) . Fetkovich, M. J. (1971) . Yildiz, T. and Khosravi, A. (2007) . Package: r-cran-rarecomb Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-arules, r-cran-dplyr, r-cran-pwr, r-cran-stringr, r-cran-tidyr, r-cran-reshape2, r-cran-sqldf Filename: pool/dists/resolute/main/r-cran-rarecomb_1.1-1.ca2604.1_all.deb Size: 253604 MD5sum: 57108df04bb54b7273bfdb57437db030 SHA1: 7e725c75c0aef3a1473b8b1118337602ab617eac SHA256: b998c86f648ae720fab771764fec99bbbdaf45d1b3aa74c5bbd5be39282a3eeb SHA512: 1bf807d2870c89f9d6803b6b0bef4b709942e8d3def95a619a9776ebbbe2051b4634302036526bd8b729f50207d1f60fea5b92541b0b25b7bc826fedfcde6355 Homepage: https://cran.r-project.org/package=RareComb Description: CRAN Package 'RareComb' (Combinatorial and Statistical Analyses of Rare Events) A custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. . Package: r-cran-rareflow Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-gganimate, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rareflow_0.1.0-1.ca2604.1_all.deb Size: 103318 MD5sum: 1b799e6fe6ce2ea40050fb13a90352ca SHA1: feb6fd677186d01fa41012ec6814a110823ffc73 SHA256: e1b2aa4d985af327560dc4d163febd9ee32a4348564f3caaa38552124f4a58d4 SHA512: a6d1fddfda325ba4fa9c5cbedaca2659b33b494d936f4278ef21ec1bf8895e83c725fd76591656690354195be8c5e6e73ce984494e1aac272082acb4993d1971 Homepage: https://cran.r-project.org/package=rareflow Description: CRAN Package 'rareflow' (Variational Flow-Based Inference for Rare Events and LargeDeviations) Variational flow-based methods for modeling rare events using Kullback–Leibler (KL) divergence, normalizing flows, Girsanov change of measure, and Freidlin–Wentzell action functionals. The package provides tools for rare-event inference, minimum-action paths, and quasi-potential computation in stochastic dynamical systems. Methods are based on Rezende and Mohamed (2015) , Girsanov (1960) , and Freidlin and Wentzell (2012, ISBN:978-0387955477). Package: r-cran-rarenmtests Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-rarenmtests_1.2-1.ca2604.1_all.deb Size: 116624 MD5sum: fabad5c52a84d9ec39e438f98c21d123 SHA1: 3caa53eba867e68148ee466758c112d3baac7964 SHA256: 6fb51dee6b563bc88611ad5294dc1330bdeba9eae8d5772253b9bb35df6be42d SHA512: bffcc0ef1ed4b056207a5e3397be0f8da68d0148c9ac2c527e1f35f9c3c7a48e4cc74552ff01e8de24a00afb064e5db41537d168e860f5851580cc305e2f5c8d Homepage: https://cran.r-project.org/package=rareNMtests Description: CRAN Package 'rareNMtests' (Ecological and Biogeographical Null Model Tests for ComparingRarefaction Curves) Randomization tests for the statistical comparison of i = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the i samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the i samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions. Functions are described in L. Cayuela, N.J. Gotelli & R.K. Colwell (2015) . Package: r-cran-rarestr Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rarestr_1.1.1-1.ca2604.1_all.deb Size: 65088 MD5sum: ea3e96b8101933d2e676651f47d69218 SHA1: ad1aab2e0d490bf07fcca57af24b487a571b8891 SHA256: bc663d3a387e921544b3268e2f3c22e8aeb0013280d127a642c6785fbd563d61 SHA512: 962fa946c2e10cd1ed5b950b663f74caa67c8677f723b077b9f354d287d3a7653aa91b5b92ad96a77e8a88e6ab4f4621f27eb333effc17ac872e259d71ed2926 Homepage: https://cran.r-project.org/package=rarestR Description: CRAN Package 'rarestR' (Rarefaction-Based Species Richness Estimator) Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators. Package: r-cran-raretrans Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1332 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-bslib, r-cran-dplyr, r-cran-formatr, r-cran-gridextra, r-cran-huxtable, r-cran-knitr, r-cran-popdemo, r-cran-popbio, r-cran-purrr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-raretrans_1.0.5-1.ca2604.1_all.deb Size: 639202 MD5sum: a0ba8ff84f6b13ab902ad32a6d439c8c SHA1: 12252b46e0833779bcda238c241e9efa4e06bf1b SHA256: 663518fffb91a256c98bf40e45a30c9abb2c52f64244420ee6bb5490a0a7ea89 SHA512: 433d93d49eb1d1ba0adbd26d6f69c7601f337894f17d2096f334765560684bbc6b46ccad82de2789617cdbc82b8f6505cefd8432f6fd27a163ff606d3f186d36 Homepage: https://cran.r-project.org/package=raretrans Description: CRAN Package 'raretrans' (Bayesian Priors for Matrix Population Models) Provides functions to correct biased transition and fertility estimates in population projection matrices caused by small sample sizes. Small or short-term studies frequently produce structural zeros (biologically possible transitions never observed) and structural ones (transitions estimated at 100% survival, stasis, or mortality that are biologically implausible). Both distort matrix structure and bias estimates of population growth. Implements a multinomial-Dirichlet Bayesian prior for transition probabilities and a Gamma-Poisson prior for reproduction, allowing analysts to incorporate prior biological knowledge and regularise estimates from rare or unobserved events. Includes functions to compute marginal posterior credible intervals for all transition probabilities (transition_CrI()), visualise those intervals as point-range plots (plot_transition_CrI()), and display the full posterior beta density for each matrix entry (plot_transition_density()). Methods are described in Tremblay et al. (2021) . Package: r-cran-rarfreq Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-latex2exp, r-cran-magrittr, r-cran-patchwork, r-cran-tidyr, r-cran-data.table, r-cran-reshape2, r-cran-rdpack Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rarfreq_0.1.5-1.ca2604.1_all.deb Size: 202142 MD5sum: b046f1e22a212bf5bef4fd3644b41bc2 SHA1: fb2345c015a0f55ea05104b4d2fa90798630b925 SHA256: 1f0d9497bd5ba8988460c126045bb289d08514b6557ff9620342ac8c773945af SHA512: 786b9a79a367017a399b4bed7426639df36a57afa004e8129a4bc2ead640e619ac0a7fe4e3cf0870d12ad467cafadad28294f1455680545b37b8640d2069473d Homepage: https://cran.r-project.org/package=RARfreq Description: CRAN Package 'RARfreq' (Response Adaptive Randomization with 'Frequentist' Approaches) Provides functions and command-line user interface to generate allocation sequence by response-adaptive randomization for clinical trials. The package currently supports two families of frequentist response-adaptive randomization procedures, Doubly Adaptive Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square (or 'ANOVA') hypothesis testing methods are also available in the package to facilitate the inference for treatment effect. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. For example, plots for relationship among assumed treatment effects, sample size, and power are provided. Five allocation functions for 'DBCD' and six addition rule functions for 'SEU' are implemented to target allocations such as 'Neyman', 'Rosenberger' Rosenberger et al. (2001) and 'Urn' allocations. Package: r-cran-rarity Architecture: all Version: 1.3-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rarity_1.3-8-1.ca2604.1_all.deb Size: 92368 MD5sum: c33092c65c052c98a9018fcdd388822b SHA1: acfb242d2242edf9e50fc836ab9328f4278e5fb1 SHA256: c9ea9bf9be96d3b679ef0efed1eea7940ce798cb3570d693a3c1b895b41ddf5a SHA512: 7ed0c62cffecd0d5402313dd024645337b2fe30ea9b376fc75fccafaeb7237846a04b9a54d401c13288182da9689229211204aeec777832d28999646146195a4 Homepage: https://cran.r-project.org/package=Rarity Description: CRAN Package 'Rarity' (Calculation of Rarity Indices for Species and Assemblages ofSpecies) Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, Insect. Conserv. Divers. 5:159-168 ; Leroy et al. 2013, Divers. Distrib. 19:794-803 ). Package: r-cran-rarms Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-rarms_1.0.0-1.ca2604.1_all.deb Size: 14740 MD5sum: 4e07c80a478fea878aea2d45d0cf5702 SHA1: 174e146ceaffddf2dfdcc8f51d9f79be5fcc1311 SHA256: b7b1245f13e9787e2175514399e2f04cca122d813d2c65899ce7dcc6b1ea9349 SHA512: 98cbf5d713e10af21a74b297f4783b0c37f28a37f1c70319113144e27abecae1521359f30ab90f976d6536804950e234e759cf5f6c014bf03640cce8bdc27b47 Homepage: https://cran.r-project.org/package=rarms Description: CRAN Package 'rarms' (Access Data from the USDA ARMS Data API) Interface to easily access data via the United States Department of Agriculture (USDA)'s Agricultural Resource Management Survey (ARMS) Data API . The downloaded data can be saved for later off-line use. Also provide relevant information and metadata for each of the input variables needed for sending the data inquery. Package: r-cran-rarpack Architecture: all Version: 0.11-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra Suggests: r-cran-matrix Filename: pool/dists/resolute/main/r-cran-rarpack_0.11-0-1.ca2604.1_all.deb Size: 29890 MD5sum: c5f69d6376fd41a7bc7c3f925365461e SHA1: a1ca6a99012068e95e2fa3859c45ea0de53331f9 SHA256: c84ef6565a10a948c569de5fd0604b482ed091e9cc9b38fe7b17307969dfc6c9 SHA512: 2eebf93cfc77893fd2dd0f5b782ee053bd98d26d24a0f498f8e56efcb4d0033ab5f01f1d00548dc3a60622447e40cf40760918204c9f022746d05bb6b7e76551 Homepage: https://cran.r-project.org/package=rARPACK Description: CRAN Package 'rARPACK' (Solvers for Large Scale Eigenvalue and SVD Problems) Previously an R wrapper of the 'ARPACK' library , and now a shell of the R package 'RSpectra', an R interface to the 'Spectra' library for solving large scale eigenvalue/vector problems. The current version of 'rARPACK' simply imports and exports the functions provided by 'RSpectra'. New users of 'rARPACK' are advised to switch to the 'RSpectra' package. Package: r-cran-rartrials Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pins, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rartrials_0.0.2-1.ca2604.1_all.deb Size: 640892 MD5sum: ba28b039eea9d1696b522737dd970b82 SHA1: 2938f4c4872056346cd841d600c2ce9dc7e5cd0a SHA256: 04eef981d376830c6fba8bd1459cb4699c171828df9d207f10d784b77829910d SHA512: 59875c2566f78ec151fcc91687484e08f1e5839c3eb9a0fddb513862169a08be2c6dac83fca1d05244c47d0be9bafa9d01f6814d0da0e597f5cc46ee06f9b62f Homepage: https://cran.r-project.org/package=RARtrials Description: CRAN Package 'RARtrials' (Response-Adaptive Randomization in Clinical Trials) Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) ), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) , Rosenberger and Lachin (2015) ) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) ) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) , Bello and Sabo (2016) ), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) ), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) ), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) ), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) ) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) , Williamson and Villar (2019) ). Package: r-cran-rasciidoc Architecture: all Version: 4.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-fritools, r-cran-gert, r-cran-highr, r-cran-knitr, r-cran-reticulate Suggests: r-cran-devtools, r-cran-pkgload, r-cran-rmarkdown, r-cran-rprojroot, r-cran-runit, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-rasciidoc_4.1.1-1.ca2604.1_all.deb Size: 103312 MD5sum: fe7d8d4fb312422f2401b7c9b4d48fe4 SHA1: a361c95d90fc5dd7cce62eae8ccf9ccc29d11f44 SHA256: 32df2f5301f28578eb5662bcc594d75aa803322434c3abb3cfcd77767b5dd475 SHA512: 76da855fb0218ad0f04076af2168d20acdb1e9e72c9263c1242b285e82bfc9bd223d02e0b5c8a600310338025c10cc69ab39b9e9a16f9322f09495d8556ce39f Homepage: https://cran.r-project.org/package=rasciidoc Description: CRAN Package 'rasciidoc' (Create Reports Using R and 'asciidoc') Inspired by Karl Broman`s reader on using 'knitr' with 'asciidoc' (), this is merely a wrapper to 'knitr' and 'asciidoc'. Package: r-cran-rasen Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-caret, r-cran-class, r-cran-doparallel, r-cran-e1071, r-cran-foreach, r-cran-nnet, r-cran-randomforest, r-cran-rpart, r-cran-ggplot2, r-cran-gridextra, r-cran-formatr, r-cran-fnn, r-cran-ranger, r-cran-kernelknn, r-cran-modelmetrics, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rasen_3.0.0-1.ca2604.1_all.deb Size: 4184620 MD5sum: 1b22b0a1c034ab31de9a7f376619f374 SHA1: c8b559d3be42e95c8e0507fff799d24d51130c5c SHA256: 18e976cc3cf7bc005216da8312aa484cfb095e3e702fc10cf9c51664806cb392 SHA512: a390ddf2d98459deb41687e3a80864eaa4aacfa9a5b51729f858d5bb57352723962acae418335e3402cc20e9df248c8d07935273b5d1652d6c36f15eac563814 Homepage: https://cran.r-project.org/package=RaSEn Description: CRAN Package 'RaSEn' (Random Subspace Ensemble Classification and Variable Screening) We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems. Package: r-cran-rashnu Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rashnu_0.1.2-1.ca2604.1_all.deb Size: 123658 MD5sum: 32b60926fd38b6220e9d3d7ac9927050 SHA1: a9c8ac452e08e93543738ca9125a48554156d8ef SHA256: 1ef6f09d70a9012fffe6907d71ad64ae127bd42ae8df662f507dd4db852897ea SHA512: 8f9d5ff88438d0efb33d881186215e1fb7b3d1a356f6cd814edbabf09155dac46782f3dc4b380b076fd5b23bcd13dc8ffb0909294b0b4262a3e5afd04cfacd48 Homepage: https://cran.r-project.org/package=rashnu Description: CRAN Package 'rashnu' (Balanced Sample Size and Power Calculation Tools) Implements sample size and power calculation methods with a focus on balance and fairness in study design, inspired by the Zoroastrian deity Rashnu, the judge who weighs truth. 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These algorithms offer a simple framework for the stratification of geographic space based on raster layers representing landscape factors and/or factor scales. The stratification process follows a hierarchical approach, which is based on first level units (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric techniques allow to measure the correspondence between the geographic space and the landscape configuration represented by the units. These correspondence metrics are useful to define sampling schemes and to model the spatial variability of environmental phenomena. The theoretical background of the algorithms and code examples are presented in Fuentes et al. (2022). . 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Layers are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR) with . The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from Hansen et al. (2013) and Beaudoin et al. (2017) ; and rasterized Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018. Users supply a polygon or point location in the province of BC, and 'rasterbc' will download the overlapping raster tiles hosted at FRDR, merging them as needed and returning the result in R as a 'SpatRaster' object. Metadata associated with these layers, and code for downloading them from their original sources can be found in the 'github' repository . 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For large rasters, the functions run from 5 to approximately 100 times faster than the 'raster' package functions they replace. The 'fasterize' package, on which one function in this package depends, includes an implementation of the scan line algorithm attributed to Wylie et al. (1967) . 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It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples. 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Trends in Ecology and Evolution, 37: 725-728. Package: r-cran-ratdat Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ratdat_1.1.0-1.ca2604.1_all.deb Size: 501886 MD5sum: 5d8cb0d34ec3c0d2e85bac273cbeb766 SHA1: 52816f0272ac55ac96ac1b98bcdf333aae7f882b SHA256: 53c7b6a914cbd17f22c0f3b32b9159e0360b62f283f0bed089bb57349ffb4e54 SHA512: 5f638f095b3a7089f429b144db181c2f92aa35ccaf239803984ee07a136ef7e169f7055525576f4e9d929bf7b12270a09707e362761e5ccc518eb9a6165006a9 Homepage: https://cran.r-project.org/package=ratdat Description: CRAN Package 'ratdat' (Portal Project Teaching Database) A simplified version of the Portal Project Database designed for teaching. It provides a real world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught. The full database (which should be used for research) is available at . 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Package: r-cran-rateratio.test Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rateratio.test_1.1-1.ca2604.1_all.deb Size: 129622 MD5sum: b4b933fe6bd301229e5e07d3a9399026 SHA1: 1b7f1663dbb6c17f6975f8e78aef5b440c95256a SHA256: 0bcd3c935166bf33d77a17541adce32d991e507e7c8dc0601198b1c3bc98d891 SHA512: 466f280236f979bec97ac4ae4d9619b908ebc584b4226c31a58e4cbdead79c72cb33a9221c4dff42b0f159b005a4529462d01008413ff05b92393c1c3adfeb9b Homepage: https://cran.r-project.org/package=rateratio.test Description: CRAN Package 'rateratio.test' (Exact Rate Ratio Test) Performs exact rate ratio tests. Package: r-cran-raters Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-raters_2.1.1-1.ca2604.1_all.deb Size: 52044 MD5sum: 5a61717852facba3f1730ba081594da6 SHA1: 6a2ab01103adb03ddf93a0cfa0fa9f6afb5cc762 SHA256: e9a15229df7502ebf04129970efde37c9b76a3d2ec1d747bdfe66df026161983 SHA512: 268e99d6b84e8a04560464f359577d51f6e200f7cc48919ddd35c11916e21de18c3aeb8ea3007099b5db813e4028e6576aa623086ff104bfed1d6df7ef2c75e2 Homepage: https://cran.r-project.org/package=raters Description: CRAN Package 'raters' (A Modification of Fleiss' Kappa in Case of Nominal and OrdinalVariables) The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data. Package: r-cran-ratesci Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1564 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ratesci_1.0.0-1.ca2604.1_all.deb Size: 1167530 MD5sum: ae569c11402cd5b6ad7bd152c9ee029b SHA1: ef9cd32c8583632cc74820e754e92f0b5b082458 SHA256: 287b278d4aa0750e174a45d856cab645f907521eb0a5693ecf722dc757551ee1 SHA512: 089509dcb674827dcee22ed4e3f467a96ff50ecc8b8fa42a6dcc6867036abb655d1d52182f404880832b36afc171f16bdf3d10095e1bfa0fa14d3c87732c9dd2 Homepage: https://cran.r-project.org/package=ratesci Description: CRAN Package 'ratesci' (Confidence Intervals and Tests for Comparisons of BinomialProportions or Poisson Rates) Computes confidence intervals for binomial or Poisson rates and their differences or ratios. Including the rate (or risk) difference ('RD') or rate ratio (or relative risk, 'RR') for binomial proportions or Poisson rates, and odds ratio ('OR', binomial only). Also confidence intervals for RD, RR or OR for paired binomial data, and estimation of a proportion from clustered binomial data. Includes skewness-corrected asymptotic score ('SCAS') methods, which have been developed in Laud (2017) from Miettinen and Nurminen (1985) and Gart and Nam (1988) , and in Laud (2025, under review) for paired proportions. The same score produces hypothesis tests that are improved versions of the non-inferiority test for binomial RD and RR by Farrington and Manning (1990) , or a generalisation of the McNemar test for paired data. The package also includes MOVER methods (Method Of Variance Estimates Recovery) for all contrasts, derived from the Newcombe method but with options to use equal-tailed intervals in place of the Wilson score method, and generalised for Bayesian applications incorporating prior information. So-called 'exact' methods for strictly conservative coverage are approximated using continuity adjustments, and the amount of adjustment can be selected to avoid over-conservative coverage. Also includes methods for stratified calculations (e.g. meta-analysis), either with fixed effect assumption (matching the CMH test) or incorporating stratum heterogeneity. Package: r-cran-ratest Architecture: all Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-quantreg Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ratest_0.1.10-1.ca2604.1_all.deb Size: 406246 MD5sum: f2f3540433220a5fcb190974ae997522 SHA1: 9d5e76e4792e65cc9621ea4cfbb5450f95156f6a SHA256: 9e1933d75737387b52a507084c24fea77a1b4d1e350e35a1e55a5b604c5f1039 SHA512: 1b98e3ed157674c0b7a1518b0169f6a061ccf952b460d40b75e4b156f8384555b96a45fd75db3f38d60bcd36ab31b22912c7da9dc47fc4b20a0c15de665f2ef2 Homepage: https://cran.r-project.org/package=RATest Description: CRAN Package 'RATest' (Randomization Tests) A collection of randomization tests, data sets and examples. The current version focuses on five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) . More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) . Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter. 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To do this 'Python' 'Boto3' Software Development Kit ('SDK') is used as a driver. Package: r-cran-ratingscalereduction Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-ratingscalereduction_1.4-1.ca2604.1_all.deb Size: 44332 MD5sum: 890d8cb7cfaa3177e86bde2741fe0b17 SHA1: 3ef26219e22b87629e591ac3616f95b885f1c188 SHA256: 9ff0d14c5d974bdfd96f588185f205a4b22783475aceee77d552e84fb2e1ba96 SHA512: b295e772799fb2a3664f66fc72f512a647d97d8b84a0aa818c4e1659e97327f2f6b57c7fffb94ae8ad7e007c80cdfbaabd8422453dbf6a41f250d8b1bfddd596 Homepage: https://cran.r-project.org/package=RatingScaleReduction Description: CRAN Package 'RatingScaleReduction' (Rating Scale Reduction Procedure) Describes a new procedure of reducing items in a rating scale called Rating Scale Reduction (RSR). The new stop criterion in RSR procedure is added (stop global max). The function order is replaced by sort.list. Package: r-cran-rationalexp Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rationalexp_0.2.2-1.ca2604.1_all.deb Size: 184614 MD5sum: 53f7896f514caa7003fdb6b310cf0856 SHA1: 36dd50c68e5a5f0395ffcf67db7a7cb53e17d29d SHA256: 104f10fa67298b7da0a99973dbd1ce7b394ee7da53f04948e2ad3a7ab2f869c3 SHA512: fb8f4023050ac5267bebc83a2321ef9faf0c0026297c8783abf3c50cc66f46af878651677dfa5b7c5bb3ba97c6a403c6ed05773a55c82771c93c2c8d4be0cd1d Homepage: https://cran.r-project.org/package=RationalExp Description: CRAN Package 'RationalExp' (Rationalizing Rational Expectations. Tests and Deviations) We implement a test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs from D'Haultfoeuille, Gaillac, and Maurel (2018) . This test can be used in cases where realizations and subjective beliefs are observed in two different datasets that cannot be matched, or when they are observed in the same dataset. The package also computes the estimator of the minimal deviations from rational expectations than can be rationalized by the data. Package: r-cran-rationalfun Architecture: all Version: 0.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom Filename: pool/dists/resolute/main/r-cran-rationalfun_0.1-1-1.ca2604.1_all.deb Size: 48286 MD5sum: df2ce73d77c448eeac57e049308dba72 SHA1: a53a1bcea3663242cce74ab39b400a62258165f7 SHA256: 18afabcba46a071f1cfbc3c71aeb7f6b48adbd4253fc8fc2d19330587e0cd8a1 SHA512: 80fe10bb14fffb3eacc01e910d97a6108dccec96b4adfc1435536303bf1d0391f37b929fced8c6c6b9d8b8f41492f53d23c0358136a3284556ad4774e5dbe5d9 Homepage: https://cran.r-project.org/package=rationalfun Description: CRAN Package 'rationalfun' (Manipulation of Rational Functions) Functions to manipulate rational functions, including basic arithmetic operators, derivatives, and integrals with EXPLICIT forms. Package: r-cran-ratios Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-ratios_1.2.0-1.ca2604.1_all.deb Size: 124284 MD5sum: 74a24c1bd7bd901528d1742b97b5e327 SHA1: 6c49895e9d42896302cfc3e77320bfdbaac827a2 SHA256: 000abd5342712ffd7b1b7c9344e3d17dea86e634b8a20f51b30ca9ad6576290b SHA512: 6ed7acb58aa87f58cceb324b4dc16f4da6abc81a0df05da37a6d8519a839c8d922766d23d7ebc044c3998348fee6efcf7a22e31e7b3407329a71ff451135cd88 Homepage: https://cran.r-project.org/package=ratios Description: CRAN Package 'ratios' (Calculating Ratios Between Two Data Sets and Correction forAdhering Particles on Plants) Calculation of ratios between two data sets containing environmental data like element concentrations by different methods. Additionally plant element concentrations can be corrected for adhering particles (soil, airborne dust). Package: r-cran-rato Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-igraph Suggests: r-cran-animation, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-rato_0.1.0-1.ca2604.1_all.deb Size: 54486 MD5sum: 7f1aefc4a94e6a463949a51d0124a775 SHA1: 154c68d155c36e59f6201d4c879dddafb40b26ab SHA256: 0036fe8e24bac09c937d2af717336af95563fc4706f74bd7fccb679772cf27b6 SHA512: bd358bde930a50e1bc791210a0472c0a1021760528422bd319a00a16326a5fc6b0d40e9228a07c8fb45c0571f5534addbcf15fe31ab167c287787eca205e63f7 Homepage: https://cran.r-project.org/package=Rato Description: CRAN Package 'Rato' (Resilience Analysis Toolkit (RATO)) Collection of tools for the analysis of the resilience of dynamic networks. Created as a classroom project. Package: r-cran-rattains Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-crul, r-cran-curl, r-cran-dplyr, r-cran-fauxpas, r-cran-fs, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-rlist, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-vcr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webmockr Filename: pool/dists/resolute/main/r-cran-rattains_1.1.0-1.ca2604.1_all.deb Size: 148646 MD5sum: 10cb6695e01a8f866bca4ba30b240208 SHA1: 4397fe035de0829a0c023c063db1e4085c16c305 SHA256: 62a9c8ff97ff2658f8f12eda16e2f163c8735eeb67c8fab768e7849ae8fa7d1f SHA512: 6949d53d154a791c5b7bedbe1ebb9d7419efdfb4cb609e015206f5912d7a48db907a4707a12bb956ccb3b8f853868bcb57af997549d1367546d62dff47767550 Homepage: https://cran.r-project.org/package=rATTAINS Description: CRAN Package 'rATTAINS' (Access EPA 'ATTAINS' Data) An R interface to United States Environmental Protection Agency (EPA) Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System ('ATTAINS') data. 'ATTAINS' is the EPA database used to track information provided by states about water quality assessments conducted under federal Clean Water Act requirements. ATTAINS information and API information is available at . Package: r-cran-rattle Architecture: all Version: 5.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-bitops, r-cran-ggplot2, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-xml, r-cran-rpart.plot Suggests: r-cran-ada, r-cran-amap, r-cran-arules, r-cran-arulesviz, r-cran-cba, r-cran-cluster, r-cran-colorspace, r-cran-corrplot, r-cran-descr, r-cran-doby, r-cran-e1071, r-cran-ellipse, r-cran-fbasics, r-cran-foreign, r-cran-fpc, r-cran-gdata, r-cran-ggdendro, r-cran-gplots, r-cran-gridextra, r-cran-gtools, r-cran-hmisc, r-cran-janitor, r-cran-kernlab, r-cran-matrix, r-cran-mice, r-cran-nnet, r-cran-party, r-cran-plyr, r-cran-psych, r-cran-randomforest, r-cran-rcolorbrewer, r-cran-readxl, r-cran-reshape, r-cran-rocr, r-cran-rpart, r-cran-scales, r-cran-snowballc, r-cran-survival, r-cran-tidyselect, r-cran-timedate, r-cran-tm, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-rattle_5.6.2-1.ca2604.1_all.deb Size: 7032020 MD5sum: 2b32fcc6dd51c9dc5ea13eac28b9e29d SHA1: e53a786f3cb2510427df9bbe00bc1e51c875369c SHA256: 1d394dd92fcfe78900661d8d25b74bda60b817e8079813cffd315a1142867538 SHA512: 9cedae069e75d9ca6bec2b242d16d6a8a3db56b15f84500089cc08e57a1c9bc56776b00bd05bc37a9aa7323ed4f3d7696df53a1cdf527fe76f5423be0529cb7b Homepage: https://cran.r-project.org/package=rattle Description: CRAN Package 'rattle' (R Data Science Supporting Rattle) The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. This package (v5.6.0) supports the companion graphical interface with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file transform and explore the data, and to build and evaluate models. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself. If you want to use the older Rattle implementing the GUI in RGtk2 (which is no longer available from CRAN) then please install the Rattle package v5.5.1. See rattle.togaware.com for instructions on installing the modern Rattle graphical user interface. Package: r-cran-ravecore Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2321 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bidsr, r-cran-data.table, r-cran-filearray, r-cran-fs, r-cran-ieegio, r-cran-jsonlite, r-cran-r6, r-cran-ravepipeline, r-cran-ravetools, r-cran-s7, r-cran-threebrain Suggests: r-cran-rpyants, r-cran-rpymat, r-cran-htmltools, r-cran-httpuv, r-cran-knitr, r-cran-plotly, r-cran-rnifti, r-cran-rniftyreg, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ravecore_0.1.1-1.ca2604.1_all.deb Size: 2127942 MD5sum: cc442a9f7ddbf02010d3213da58029db SHA1: e16d573ebdfff6b1528527393e8662b1b2efa356 SHA256: 4af3003ec2b304f27bf7df23b98948141130d27f803819170f77b10376830029 SHA512: 974af4adca73b771751fd58685117727f7ee9777b22b34e399c20652afa807904e19587dd114f90e0d5e53e981df669e00fe7bcbf7e3decabef964bbc2cbb0b0 Homepage: https://cran.r-project.org/package=ravecore Description: CRAN Package 'ravecore' (Core File Structures and Workflows for 'RAVE') Defines storage standard for Read, process, and analyze intracranial electroencephalography and deep-brain stimulation in 'RAVE', a reproducible framework for analysis and visualization of iEEG by Magnotti, Wang, and Beauchamp, (2020, ). Supports brain imaging data structure (BIDS) and native file structure to ingest signals from 'Matlab' data files, hierarchical data format 5 (HDF5), European data format (EDF), BrainVision core data format (BVCDF), or BlackRock Microsystem (NEV/NSx); process images in Neuroimaging informatics technology initiative (NIfTI) and 'FreeSurfer' formats, providing brain imaging normalization to template brain, facilitating 'threeBrain' package for comprehensive electrode localization via 'YAEL' (your advanced electrode localizer) by Wang, Magnotti, Zhang, and Beauchamp (2023, ). Package: r-cran-ravel Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-miniui, r-cran-rstudioapi, r-cran-shiny, r-cran-tibble Suggests: r-cran-keyring, r-cran-knitr, r-cran-lintr, r-cran-pkgdown, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ravel_0.1.1-1.ca2604.1_all.deb Size: 228994 MD5sum: 8940afba00bcad34b1d7707bc435e3f7 SHA1: 3695688e27b10a289492285174c930473c23147c SHA256: cb2600399bff54fd7e515c9067f560967f0562f2b74ae5ad002d6f42b669ce45 SHA512: edb4d33e6944eed564f7bee64f6d98505be278edd136a71c5d0e2a0d6680cb4efde0244a5687e26d4982208e1ddae041a128cdf0808af9941c62faf440ff1dd6 Homepage: https://cran.r-project.org/package=ravel Description: CRAN Package 'ravel' (AI Copilot for R Analysis Workflows in 'RStudio') An AI copilot for R users in 'RStudio' and Posit workflows with active-editor, workspace, object, console, plot, and git-aware context. 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Package: r-cran-ravelry Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ravelry_0.1.0-1.ca2604.1_all.deb Size: 102098 MD5sum: 666387bde858aaba2e7e5661d4de83a3 SHA1: 07648350064137815a17c64192269cc7d9a5a5e7 SHA256: 46a584e22d415062a8c0c1697c3097e85539519797eae393b8fdd14e0e2c7085 SHA512: 64e1ae7d84a7d6c88d9c87ede494b1f568cf37735f16a28901374200e58b516d1a5413f72c1c31723d6f2fc7544eb6e8889cfb55afbc05b3d90bcd6d96a2fea5 Homepage: https://cran.r-project.org/package=ravelRy Description: CRAN Package 'ravelRy' (An Interface to the 'Ravelry' API) Provides access to the 'Ravelry' API . An R wrapper for pulling data from 'Ravelry.com', an organizational tool for crocheters, knitters, spinners, and weavers. You can retrieve pattern, yarn, author, and shop information by search or by a given id. Package: r-cran-raven.rdf Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-raven.rdf_0.2.0-1.ca2604.1_all.deb Size: 58616 MD5sum: c5351fffe0f15bf78015e29ed33dd3a0 SHA1: ed65adfa2bb551eeac302a7d4be4e237ee314199 SHA256: 12aa22d77b7e765b0961842ea05834ebb92638aa34892d4758ae2fb9e48bb487 SHA512: 578017395cb4fb6581b391782660b75c0cb385de387e86fda12c7b29214cc34f03f746f7f80652bf617703f2671f561aeef86e81056acb3fb7a36c044f6272f6 Homepage: https://cran.r-project.org/package=raven.rdf Description: CRAN Package 'raven.rdf' (An R Interface for Raven DataFrames (Beta0)) Provides an I/O interface between R data.frames and Raven DataFrames. 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Package: r-cran-ravepipeline Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-callr, r-cran-cli, r-cran-digest, r-cran-fastmap, r-cran-future, r-cran-fst, r-cran-glue, r-cran-jsonlite, r-cran-knitr, r-cran-promises, r-cran-r6, r-cran-remotes, r-cran-rlang, r-cran-targets, r-cran-uuid, r-cran-yaml, r-cran-logger Suggests: r-cran-dipsaus, r-cran-filearray, r-cran-future.apply, r-cran-globals, r-cran-ieegio, r-cran-rpymat, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shidashi, r-cran-threebrain, r-cran-testthat, r-cran-visnetwork, r-cran-later, r-cran-shiny, r-cran-mirai, r-cran-distill Filename: pool/dists/resolute/main/r-cran-ravepipeline_0.0.3-1.ca2604.1_all.deb Size: 811452 MD5sum: 2557a61b028f038b64e6853a1bbd97ac SHA1: 46a7e7d1863de96bde347874131b791791e703b1 SHA256: 243881239db193b092a8b13577581ffec3dc98c1c935cff723b799597cf58c0a SHA512: 2921b7d8d5c1fde03df2d0c3f9da8365111888d83b1dc749b777b18d4f8cdebfa4f6800253e4afdee6411d012214536383dc8e1d35dbd6c2de12f9dd1bd4d471 Homepage: https://cran.r-project.org/package=ravepipeline Description: CRAN Package 'ravepipeline' (Reproducible Pipeline Infrastructure for Neuroscience) Defines the underlying pipeline structure for reproducible neuroscience, adopted by 'RAVE' (reproducible analysis and visualization of intracranial electroencephalography); provides high-level class definition to build, compile, set, execute, and share analysis pipelines. Both R and 'Python' are supported, with 'Markdown' and 'shiny' dashboard templates for extending and building customized pipelines. See the full documentations at ; to cite us, check out our paper by Magnotti, Wang, and Beauchamp (2020, ), or run citation("ravepipeline") for details. Package: r-cran-raw Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-actuar, r-cran-chainladder, r-cran-dplyr, r-cran-devtools, r-cran-fincal, r-cran-fitdistrplus, r-cran-forcats, r-cran-ggplot2, r-cran-insurancedata, r-cran-knitr, r-cran-lahman, r-cran-lubridate, r-cran-maps, r-cran-mondate, r-cran-nlme, r-cran-nycflights13, r-cran-purrr, r-cran-randomforest, r-cran-randomnames, r-cran-readr, r-cran-readxl, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tweedie, r-cran-xml Filename: pool/dists/resolute/main/r-cran-raw_0.1.8-1.ca2604.1_all.deb Size: 930902 MD5sum: afb9db2cc6ca7037987469a5482efced SHA1: 4e70dce6f1dd86f40197bd3f27edcc9f7338e940 SHA256: f701016b7aa4032a3b077ad7f235742bf2853f492b2cb7fce341d0f1c6fb8452 SHA512: 52f3a9c5ebae89928928f2b654438700924ffb0f95af3c7ee05e0834f19b8e24d57a5559f0f38e18737eac539050f72dfda0f031d90f493edc7377fed15cf800 Homepage: https://cran.r-project.org/package=raw Description: CRAN Package 'raw' (R Actuarial Workshops) In order to facilitate R instruction for actuaries, we have organized several sets of publicly available data of interest to non-life actuaries. 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(2011) . Publication: Kapsner et al. (2021) . Package: r-cran-rbin Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4776 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-knitr, r-cran-miniui, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-rbin_0.2.1-1.ca2604.1_all.deb Size: 453970 MD5sum: 4d154a1991bb76d8606d33a278adea48 SHA1: bbeb2ce83eafe11d18ebc66b7a4428b8bcb1fe19 SHA256: 97a615d906f3087b80736887893fa840ad7d8f2958b143e8e64098213d00e245 SHA512: bbad347fd3c8bf6ddd85f7c9579d14ee1618cca56b76f9dc9deab140106af32dcd1264444aa8b250f3a09dfd6c38e857f15648271d3ac5b1c8ba3607155cdd29 Homepage: https://cran.r-project.org/package=rbin Description: CRAN Package 'rbin' (Tools for Binning Data) Manually bin data using weight of evidence and information value. 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BMF is also known as boolean matrix decomposition (BMD) and was found to be an NP-hard (non-deterministic polynomial-time) problem. Currently implemented methods are 'Asso' Miettinen, Pauli and others (2008) , 'GreConD' R. Belohlavek, V. Vychodil (2010) , 'GreConDPlus' R. Belohlavek, V. Vychodil (2010) , 'topFiberM' A. Desouki, M. Roeder, A. Ngonga (2019) . 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It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 ), random mark permutation test (Crema et al 2016 ) and spatial permutation tests (Crema, Bevan, and Shennan 2017 ). 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The Catalog of fishes is the leading authority in fish taxonomy. Functions in the package allow users to search for fish taxa and valid names, retrieve taxonomic references, retrieve monthly taxonomic changes, obtain natural history collection information, and see the number of species by taxonomic group. For more information on the Catalog: Fricke, R., Eschmeyer, W. N. & R. van der Laan (eds) 2025. ESCHMEYER'S CATALOG OF FISHES . 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It leverages 'ggplot2' to create visually informative maps, displaying rectangles for each language and allowing for color-coding based on linguistic features. The package includes a built-in dataset of 56 languages and the template for their distribution and provides flexibility to customize the tile map's appearance. The default template can be modified via the ability to hide or rename languages. It's designed to be used with external data tables containing language information and features, offering a tool for visualizing the geographic distribution and linguistic characteristics of East Caucasian languages. Package: r-cran-rcausalegm Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcausalegm_0.3.3-1.ca2604.1_all.deb Size: 179224 MD5sum: ba13d1a071eee45fc0a80c708aee297a SHA1: 757c9dff119307904ca5cdfd94eb4c4aa1220c2f SHA256: 31aae6e0bae441afe08a9869c5796364d80dc566dba2533add257f390bb9a6ad SHA512: 2f868e30e9d459824c5e9e76e733fefacfce70ac360392ae9d6b3b892997f3ee7732d5d6cd3ff31ef7d7839dab4241f2603273c4d82c6cb0946d0e60e166079f Homepage: https://cran.r-project.org/package=RcausalEGM Description: CRAN Package 'RcausalEGM' (A General Causal Inference Framework by Encoding GenerativeModeling) CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) . 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The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at . Package: r-cran-rcba Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-arules, r-cran-r.utils, r-cran-tunepareto Filename: pool/dists/resolute/main/r-cran-rcba_0.4.3-1.ca2604.1_all.deb Size: 99634 MD5sum: 25f4785c0a89352022123ca26e33fc27 SHA1: 2f694b195bde286892f7c5b2ce68843c647a3387 SHA256: f388852d7154bca54d8d3ffaf1ea0623584cd6a3ac380656b956e0029726814b SHA512: 1994f4cb3369ed7154ea10d3356cf6a4bfb398afd3a1a2f8d693326960f751d88eedab98cad14a4d41a853a69695a02a0d2f38955c4a585eb701e537079040cf Homepage: https://cran.r-project.org/package=rCBA Description: CRAN Package 'rCBA' (CBA Classifier) Provides implementations of a classifier based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. The final classifier labels provided instances. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7). Package: r-cran-rcbalance Architecture: all Version: 1.8.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-plyr, r-cran-rlemon Suggests: r-cran-optmatch, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcbalance_1.8.8-1.ca2604.1_all.deb Size: 75790 MD5sum: 3355dd01f6ec2b6c5a01ed486500a5be SHA1: 80ded19625813abef0aeb67067a9ba13a4ca7a6c SHA256: 2debf1e6acfdda85c548e6b841fd67c9af0c64e967bced2886c740e244b3e131 SHA512: f8494eed4e962edb903d48f603a7b4a38508643776f10162795663516ee7b9918cb1a8d29b9c92c21ef0fccb1cc1be25f350b7d5cf5be58710668775fcfe9824 Homepage: https://cran.r-project.org/package=rcbalance Description: CRAN Package 'rcbalance' (Large, Sparse Optimal Matching with Refined Covariate Balance) Tools for large, sparse optimal matching of treated units and control units in observational studies. Provisions are made for refined covariate balance constraints, which include fine and near-fine balance as special cases. Matches are optimal in the sense that they are computed as solutions to network optimization problems rather than greedy algorithms. See Pimentel, et al.(2015) and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at . 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See J. Gu and R. Koenker (2020) . 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A close relative is the 'rcbalance' package. See Pimentel, et al.(2015) and Pimentel and Kelz (2020) . The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at . 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Package: r-cran-rcd3 Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rcd3_0.1.1-1.ca2604.1_all.deb Size: 28788 MD5sum: c61c4ac3bf766776ae0e346711c03bb0 SHA1: f6a1c6a1a564641227259f21d06d9b8804325865 SHA256: ac549d4bed147a14f31c85c72975410203af0782d8cc278ba67207c1f004bc34 SHA512: 956106e8a6dcba922b2f3c5177e49eb51136e8a53b6b1e747e57629ffd3b4470ee8b1caf88065a7daf00b6ea977a7a493025831042346a1221701adb1c6b182f Homepage: https://cran.r-project.org/package=rcd3 Description: CRAN Package 'rcd3' (Efficient Row-Column Designs for 3 Level Factorial Experimentsin 3 Rows) Provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) and Dash, S., Parsad, R., and Gupta, V. K. (2013) . Package: r-cran-rcdea Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-benchmarking Filename: pool/dists/resolute/main/r-cran-rcdea_1.0-1.ca2604.1_all.deb Size: 57196 MD5sum: c71fc878eb9a7ce38049e1dc56a2a424 SHA1: 56207a0647431e1e9481fc068fa66ceebcf87867 SHA256: e8e67f0040194660abf12393cf72c3240947abbb13dd1a19960047c22ad7fc04 SHA512: c928d0b46e907acbba6c5912e970eef71c4b53390b8c051d4f70e947a24d038462bb57479f5eb1f14847578fedc285300a7c732b5b5323a785e73b24581f72f9 Homepage: https://cran.r-project.org/package=rcDEA Description: CRAN Package 'rcDEA' (Robust and Conditional Data Envelopment Analysis (DEA)) With this package we provide an easy method to compute robust and conditional Data Envelopment Analysis (DEA), Free Disposal Hull (FDH) and Benefit of the Doubt (BOD) scores. The robust approach is based on the work of Cazals, Florens and Simar (2002) . The conditional approach is based on Daraio and Simar (2007) . Besides we provide graphs to help with the choice of m. We relay on the 'Benchmarking' package to compute the efficiency scores and on the 'np' package to compute non parametric estimation of similarity among units. Package: r-cran-rcdf Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-duckdb, r-cran-haven, r-cran-openxlsx, r-cran-fs, r-cran-zip, r-cran-glue, r-cran-openssl, r-cran-dplyr, r-cran-stringr, r-cran-jsonlite, r-cran-dbi, r-cran-rsqlite, r-cran-uuid, r-cran-lifecycle Suggests: r-cran-dbplyr, r-cran-rlang, r-cran-testthat, r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-withr, r-cran-gt Filename: pool/dists/resolute/main/r-cran-rcdf_0.1.5-1.ca2604.1_all.deb Size: 154934 MD5sum: 59303c72b9a129c0290c7cd91dc9da13 SHA1: a7bcc9996541da0f7541f66441673afb7df5d88f SHA256: 7d7358de420ede831de099144db11f25ee7a6d8178568fbcbe194bb5b0d7624c SHA512: 0a0ffccd15de86e2108ae6c4a424fa114ab1c23145524df9a627601d78a8b7760c6c2b8865c6c6fe24431eedae18c378652d461ea414e515301ca1326bb8694a Homepage: https://cran.r-project.org/package=rcdf Description: CRAN Package 'rcdf' (A Comprehensive Toolkit for Working with Encrypted Parquet Files) Utilities for reading, writing, and managing RCDF files, including encryption and decryption support. It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using AES and RSA encryptions. Package: r-cran-rcdk Architecture: all Version: 3.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcdklibs, r-cran-fingerprint, r-cran-rjava, r-cran-png, r-cran-iterators, r-cran-itertools Suggests: r-cran-xtable, r-cran-runit, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-rcdk_3.8.2-1.ca2604.1_all.deb Size: 561786 MD5sum: a0e5c5ba2b23dc3227323eae7e292290 SHA1: 28b77bd0390e7fa530b150643a2fc081fc865244 SHA256: 3d2810a7070842fc83a82aeb22698fa20014547c7e4a5039fa31ae6bf2dbe03e SHA512: 05b26266dae7e6a9e8072114b4405930aac37de39ee38e2e4456410a3da213edc7a740824d2d3a76c85afe381fa1cd798041d6d6a6152c24fbbd8b61f42222a0 Homepage: https://cran.r-project.org/package=rcdk Description: CRAN Package 'rcdk' (Interface to the 'CDK' Libraries) Allows the user to access functionality in the 'CDK', a Java framework for cheminformatics. This allows the user to load molecules, evaluate fingerprints, calculate molecular descriptors and so on. In addition, the 'CDK' API allows the user to view structures in 2D. Package: r-cran-rcdklibs Architecture: all Version: 2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20540 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Filename: pool/dists/resolute/main/r-cran-rcdklibs_2.9-1.ca2604.1_all.deb Size: 19255040 MD5sum: 8ad70cc34de97048336cab25082a09af SHA1: 1b03dc61d29d310d720009ef5c20c484987beabb SHA256: 818f5a04f638cf2951c56623ed59584c8793866ccd16b3541d624d6f6010be92 SHA512: 775fa131f9d906f49f7ef7ddbbdde75bee570c54c938f0a2cce42b0afa379fbfdd4babbce8cd33e71327df25ddd86d846e92232317b01af4d799c08cd4a6a23d Homepage: https://cran.r-project.org/package=rcdklibs Description: CRAN Package 'rcdklibs' (The CDK Libraries Packaged for R) An R interface to the Chemistry Development Kit, a Java library for chemoinformatics. Given the size of the library itself, this package is not expected to change very frequently. To make use of the CDK within R, it is suggested that you use the 'rcdk' package. Note that it is possible to directly interact with the CDK using 'rJava'. However 'rcdk' exposes functionality in a more idiomatic way. The CDK library itself is released as LGPL and the sources can be obtained from . Package: r-cran-rcdo Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-rlang Suggests: r-cran-glue, r-cran-knitr, r-cran-markdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcdo_0.3.2-1.ca2604.1_all.deb Size: 1450952 MD5sum: 5661f927a6c5f78d9739a23cde7c26ab SHA1: 66a09530d009b59b654f7f30c5484783d1b12f78 SHA256: 6cd2988077d4aecc2552625aa8efe4c836c434eee6dee4d913bfc6708904dcef SHA512: 5419a7d6dfe3a2fc090b6805964edd494653cc9bdcca7c05c6700f8702920d0bc518853289f322b6a87880ced5ce989ace0e0fea9dd2575b531bb439a79a983d Homepage: https://cran.r-project.org/package=rcdo Description: CRAN Package 'rcdo' (Wrapper of 'CDO' Operators) Provides a translation layer between 'R' and 'CDO' operators. Each operator is it's own function with documentation. Nested or piped functions will be translated into 'CDO' chains. Package: r-cran-rceim Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rceim_0.3-1.ca2604.1_all.deb Size: 1308806 MD5sum: e5d7e7986d387aef8f2d7f7e1fcd3b18 SHA1: 1bce320c04d8b8aab24da3dc1d23ef52807ec8a0 SHA256: 65be54e9b66bd4e9e4baa74361623ea64daea71cb421af4a8d07dcd64a989d2e SHA512: a0531f2cf4da61b34c4570c90acecf28c5a402564b0cdecce4d8b2d49024abc179d25190da831ae0c243175e2cc29f11f6329bf85887d1f766d65faa2b73b54e Homepage: https://cran.r-project.org/package=RCEIM Description: CRAN Package 'RCEIM' (R Cross Entropy Inspired Method for Optimization) An implementation of a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multi-core processing to enable optimization of time-consuming functions. Package: r-cran-rcens Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rcens_0.1.1-1.ca2604.1_all.deb Size: 48364 MD5sum: 57a3161655a75c14ea32e48696b40dfe SHA1: a52e0cffd1b6436831022d7071e55bb23ea0322a SHA256: 21a4c05eb7b90a86bc36e6ede0fc4626aa51af710023ae2021e5a8d5d59fc9d3 SHA512: af3893f5326b45e5524d35d69614a2e56a3d19978245997eff23e106ba8bb43d6a92dba82fd8174ac6a3a86b61c6f14c7289048dc0550e1c12ca10f8ce19284c Homepage: https://cran.r-project.org/package=rcens Description: CRAN Package 'rcens' (Generate Sample Censoring) Provides functions to generate censored samples of type I, II and III, from any random sample generator. It also supplies the option to create left and right censorship. Along with this, the generation of samples with interval censoring is in the testing phase, with two options of fixed length intervals and random lengths. Package: r-cran-rcensuspkg Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-httr2, r-cran-downloader, r-cran-jsonlite, r-cran-stringr, r-cran-purrr, r-cran-sf, r-cran-ggplotify, r-cran-gtable, r-cran-rplotterpkg Suggests: r-cran-knitr, r-cran-testthat, r-cran-usethis, r-cran-usmap, r-cran-vdiffr, r-cran-classint, r-cran-rcolorbrewer, r-cran-here, r-cran-withr Filename: pool/dists/resolute/main/r-cran-rcensuspkg_0.1.5-1.ca2604.1_all.deb Size: 239912 MD5sum: 38715740c1c95c73b970deba2211d1cb SHA1: c3cd75aba5587b484565a3a5472463ea69c3355b SHA256: 9e56821d68a2ff02f8449e90c24369066327ffa8f53fb4bec9f5ead92639ce1c SHA512: 9d3f46c4adb10c6fe267193cfaf67672694a0d10cd8d00c0e557144f681c8fbe5f6f6edeba44036b1c38666d989dd13ff0cb9feae991118202b358cbdef81d04 Homepage: https://cran.r-project.org/package=RcensusPkg Description: CRAN Package 'RcensusPkg' (Easily Access US Census Bureau Survey and Geographic Data) The key function 'get_vintage_data()' returns a dataframe and is the window into the Census Bureau API requiring just a dataset name, vintage(year), and vector of variable names for survey estimates/percentages. Other functions assist in searching for available datasets, geographies, group/variable concepts of interest. Also provided are functions to access and layer (via standard piping) displayable geometries for the US, states, counties, blocks/tracts, roads, landmarks, places, and bodies of water. Joining survey data with many of the geometry functions is built-in to produce choropleth maps. Package: r-cran-rcereal Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cpp11, r-cran-rcpp, r-cran-decor, r-cran-git2r, r-cran-httr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rcereal_1.3.2-1.ca2604.1_all.deb Size: 212858 MD5sum: 11e5e712787a84232312ce597936a03e SHA1: d90c9301c1c30dd3a376c5f9f74ae51c012b72b6 SHA256: fcbeb5b4090e912736265632090606730eb646002617669c120fd9b6d11077e5 SHA512: 7bac3d16bfa2a003525cd8ebc780cd884a5245d27f5a00c1ddc34c246b2192fc58bdf4127d7df9fc6b977a4e8e2a40b25423c361ff45cad90439baaddbafcb09 Homepage: https://cran.r-project.org/package=Rcereal Description: CRAN Package 'Rcereal' ("Cereal Headers for R and C++ Serialization") To facilitate using 'cereal' with R via 'cpp11' or 'Rcpp'. 'cereal' is a header-only C++11 serialization library. 'cereal' takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, 'XML', or 'JSON'. 'cereal' was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. Please see for more information. Package: r-cran-rcexttools Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-sqldf, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-rcexttools_0.1.1-1.ca2604.1_all.deb Size: 1213944 MD5sum: 2eacb42a428a05bb9bb3ece954a02813 SHA1: 56112aa8b577e0efae7c328dec7fdb76407d04d7 SHA256: 6496bbebca1be807e09fc275d4ce2c64df7e3b03e70e8cf3952e301109f32868 SHA512: 585433d25d60ee9508c90efe62610805201180c7e25bb635b24ec7ff8f0a283825cb84bf09c653c2399dbb80301a53eef33e9b0e3aafe15e40bb76bc70d49b08 Homepage: https://cran.r-project.org/package=RcextTools Description: CRAN Package 'RcextTools' (Analytical Procedures in Support of Brazilian Public SectorExternal Auditing) Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity. Package: r-cran-rcgls Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcurl, r-cran-ncdf4, r-cran-raster, r-cran-sp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcgls_1.0.3-1.ca2604.1_all.deb Size: 24570 MD5sum: 5b20f1386dd262f35961e84b314e7748 SHA1: ccfd3d3cbe585128b7c0a15a60e248106fc15641 SHA256: 00d195a91fd8fbb7c7adfba1b9d83a681561aea1e94448484dafd4d3f5e0fa53 SHA512: a2a479862b80783fe75f141e0c3f4c09790952259686bb366012b5ae022f7f8baf5cc428e09cfa456959f34823ae633a262022ab7d01b2f62d9976e274225fd4 Homepage: https://cran.r-project.org/package=RCGLS Description: CRAN Package 'RCGLS' (Download and Open Data Provided by the Copernicus Global LandService) Download and open manifest files provided by the Copernicus Global Land Service data . The manifest files are available at: . Also see: . Before you can download the data, you will first need to register to create a username and password. Package: r-cran-rchallenge Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rchallenge_1.3.4-1.ca2604.1_all.deb Size: 112544 MD5sum: 9a8a3caa23243b19e7a7a2ec9fe8fe40 SHA1: 83fdacbb099e950ac18c74c0f145ff88011a25fe SHA256: 43863838c2b98195957e5ee4b1031bd9a494efba48b5b812a544c794fc76cc8f SHA512: f8d6801ca751a8670b74e9a99bd5311deffe2f2f769f3366afacfe07da54b409a9cff2f5dd98ceab6cd4bf7856259089cda8cfaa7d0081aa5c0ceef33cbd80ec Homepage: https://cran.r-project.org/package=rchallenge Description: CRAN Package 'rchallenge' (A Simple Data Science Challenge System) A simple data science challenge system using R Markdown and 'Dropbox' . It requires no network configuration, does not depend on external platforms like e.g. 'Kaggle' and can be easily installed on a personal computer. Package: r-cran-rchasm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-envstats, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-ggstar, r-cran-magrittr, r-cran-matrixstats, r-cran-mclust, r-cran-rstatix, r-cran-sirt, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-extradistr Filename: pool/dists/resolute/main/r-cran-rchasm_1.0.1-1.ca2604.1_all.deb Size: 559042 MD5sum: 517898001a1e2b8ad2da3ff298841b3a SHA1: c9ee365dc14cb767809089bee08f2fd5a0cba435 SHA256: 0207ae398ba4031651ffb9a7426f354e8d7976be395ce0807111fdcf946d629c SHA512: 547283840e586138dfd76ac47d1c3351397b954b39f4639ef1888814ca1a6929037b5345d86da7b4f4a971f18a140d0d46c7ec86da2a714813be05954259e792 Homepage: https://cran.r-project.org/package=RChASM Description: CRAN Package 'RChASM' (Detection of Chromosomal Aneuploidies in Ancient DNA Studies) An R implementation of ChASM (Chromosomal Aneuploidy Screening Methodology): a statistically rigorous Bayesian approach for screening data sets for autosomal and sex chromosomal aneuploidies. This package takes as input the number of (deduplicated) reads mapping to chromosomes 1-22 and the X and Y chromosomes, and models these using a Dirichlet-multinomial distribution. From this, This package returns posterior probabilities of sex chromosomal karyotypes (XX, XY, XXY, XYY, XXX and X) and full autosomal aneuploidies (trisomy 13, trisomy 18 and trisomy 21). This package also returns two diagnostic statistics: (i) a posterior probability addressing whether contamination between XX and XY may explain the observed sex chromosomal aneuploidy, and (ii) a chi-squared statistic measuring whether the observed read counts are too divergent from the underlying distribution (and may represent abnormal sequencing/quality issues). Package: r-cran-rchea3 Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1807 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-rlang, r-cran-tidyselect, r-cran-writexl Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rchea3_0.2.0-1.ca2604.1_all.deb Size: 1052760 MD5sum: b175d1364ab17e5336a9abbd758f2267 SHA1: 255b3e13229a47a2e7c0a06bd66f0a952167c80d SHA256: 9924b9b70e9f075203779b9e6a45cb38e6df750210c82a3219baf64bb6c61ca8 SHA512: d4fa876322d8cc203a9027c5220fa96d2c59afbf3bdf2060324abd3863120543a3d1b1634fbfc7e43a5c7c96c0cd29d2df8271f19a50fce6ccfb77cbd60bda9c Homepage: https://cran.r-project.org/package=rChEA3 Description: CRAN Package 'rChEA3' (R Client for the 'ChEA3' Transcription Factor Enrichment API) Interface to the 'ChEA3' transcription factor enrichment API. 'ChEA3' integrates evidence from ChIP-seq, co-expression, and literature resources to prioritize transcription factors regulating a given set of genes. This package provides convenient R functions to query the API, retrieve ranked results across collections (including integrated scores), and standardize output for downstream analysis in R/Bioconductor workflows. See or Keenan (2019) for further details. Package: r-cran-rchemo Architecture: all Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fnn, r-cran-signal, r-cran-e1071 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rchemo_0.1-3-1.ca2604.1_all.deb Size: 5256090 MD5sum: 974cd3734bbcc4bcf76e57a443b04df1 SHA1: 6aeee3b1030a50baa152ab32194a3c06fcd9c184 SHA256: 15d19a2973cd54aa73165f5c886ec571aef7022e4458f6998baec173da88460a SHA512: b677a6c0806cd5c39cabda76482941b5c7cc86ed349c1fab0eaae726689223873be2ba500ed33191f3402f1f2b33dfe092bf9f0468df380a0d8224d3dac553b0 Homepage: https://cran.r-project.org/package=rchemo Description: CRAN Package 'rchemo' (Dimension Reduction, Regression and Discrimination forChemometrics) Data exploration and prediction with focus on high dimensional data and chemometrics. The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data. The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains. Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()). Package: r-cran-rcheology Architecture: all Version: 4.6.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1010 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcheology_4.6.0.0-1.ca2604.1_all.deb Size: 814764 MD5sum: 112fc86052744b1e58f8b1d29245f705 SHA1: b6f366af28df15fc81a98da2065f67aa38fd3cd2 SHA256: 72f50ce9bd96f9f9f6ce21e7c3e8882a083b993546d1fe8546aabd2a94b771d0 SHA512: de1f327974169d46299b5f0115a900d25db2e90272f73cefe3dd22110fd38472a27ce7eaef98f40db86d45b5cfdbb419ed15f6a09214fd6e0dd151d4b72bdb12 Homepage: https://cran.r-project.org/package=rcheology Description: CRAN Package 'rcheology' (Data on Base and Recommended Packages for Current and PreviousVersions of R) Provides a dataset of functions in all base and recommended packages of R versions 0.50 onwards. 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Package: r-cran-rcmdcheck Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-curl, r-cran-desc, r-cran-digest, r-cran-pkgbuild, r-cran-prettyunits, r-cran-r6, r-cran-rprojroot, r-cran-sessioninfo, r-cran-withr, r-cran-xopen Suggests: r-cran-covr, r-cran-knitr, r-cran-mockery, r-cran-processx, r-cran-ps, r-cran-rmarkdown, r-cran-svglite, r-cran-testthat, r-cran-webfakes Filename: pool/dists/resolute/main/r-cran-rcmdcheck_1.4.0-1.ca2604.1_all.deb Size: 170386 MD5sum: d18873ad54dcc151c7ce6913f37844fb SHA1: a8d6c7d3675a698328eb5de8acb7333449c6520f SHA256: a5e12e56749ce15ebe63406097c412a548949a690e0fcc2155a3e585a9ce7403 SHA512: a152c7f0cfe6e3c0672cfe0dec95e005754c983376de964e0562419c7aadf0e67441575adfdca67de1ce5dda17b4f76325d76f6334da39e60314fdcfa4cf6b0f Homepage: https://cran.r-project.org/package=rcmdcheck Description: CRAN Package 'rcmdcheck' (Run 'R CMD check' from 'R' and Capture Results) Run 'R CMD check' from 'R' and capture the results of the individual checks. 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Package: r-cran-rcmdrplugin.arnova Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2548 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.arnova_0.0.6-1.ca2604.1_all.deb Size: 1922412 MD5sum: acebd7f320237243a30177b639df317d SHA1: e5064273c96661372a0a123e70d33f05942f575a SHA256: e66e6aa3ec80e5afcd3687acaec9b2d062492e45388f18a5b4b0d3213d0f1282 SHA512: 6696e2060b5e2022646a584a396de936a2f7496e257adb110728879717d633c1431776626b8950c839b63a6fe4e4a8cca1bb06b0f972541a75d10e0f0ad08da6 Homepage: https://cran.r-project.org/package=RcmdrPlugin.aRnova Description: CRAN Package 'RcmdrPlugin.aRnova' (R Commander Plug-in for Repeated-Measures ANOVA) R Commander plug-in for repeated-measures and mixed-design ('split-plot') ANOVA. 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Package: r-cran-rcmdrplugin.bws1 Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crossdes, r-cran-support.bws, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.bws1_0.3-0-1.ca2604.1_all.deb Size: 115946 MD5sum: f368574359d53600f71d4a6faeffa95b SHA1: 872bebed8ce4a9ab131512982e0fe905c5267f8c SHA256: d17306f9655cdbc121f8f75f0311730793269a9c8cb4b7de2f9f4168204aa2be SHA512: 4157cced6db7249090ab626c5b2d2db8a3ce6f52a32a67cda96cdf7390207ea24dc6be470ee2738cda10f25f739b488a31df4a80966d3ac6cf09d15f6497f0e3 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS1 Description: CRAN Package 'RcmdrPlugin.BWS1' (R Commander Plug-in for Case 1 Best-Worst Scaling) Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details, refer to Aizaki and Fogarty (2023) . Package: r-cran-rcmdrplugin.bws2 Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-support.bws2, r-cran-support.ces, r-cran-survival, r-cran-rcmdr, r-cran-doe.base Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.bws2_0.3-0-1.ca2604.1_all.deb Size: 98468 MD5sum: b69d0b5a554c112983aabc04cc07cb3d SHA1: 1f538ee6e569af2f37c1bdd182bd83e6a0f5aaa3 SHA256: 8044a6a2afd3271d46ff427f4cb2f12924d126d1d5cbe37a6fd524c2275c9645 SHA512: 4f4e5bc32527e0ffb34c7c759411952a776f1119b6ff0954505c7fa15d5aa204791a0c7fac430bc8ac9c4f699cce4eabfe5e0bbfbf3979fe28acc25834e931f5 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS2 Description: CRAN Package 'RcmdrPlugin.BWS2' (R Commander Plug-in for Case 2 Best-Worst Scaling) Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) . Package: r-cran-rcmdrplugin.bws3 Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-support.bws3, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.bws3_0.3-1-1.ca2604.1_all.deb Size: 103660 MD5sum: 6b38c03b8a7a5ec4f87e2ff6593f989d SHA1: 5174ec0cd113839a867927782225b0804127ccab SHA256: 95009a063f90dd7b1f51847c8aba0ca387d3ce5cdda59e234d119b53692b9c03 SHA512: 18805ff1aa2f6f0342877cdf61a33edaa613455f63557227ba2a480be547d46fb3889b6a8ae0725f207c559bdbc12eb153808da95859f5156e0f8691db824313 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS3 Description: CRAN Package 'RcmdrPlugin.BWS3' (R Commander Plug-in for Case 3 Best-Worst Scaling) Adds menu items for case 3 (multi-profile) best-worst scaling (BWS3) to the R Commander. BWS3 is a question-based survey method that designs various combinations of attribute levels (profiles), asks respondents to select the best and worst profiles in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on BWS3, refer to Louviere et al. (2015) . Package: r-cran-rcmdrplugin.dccv Architecture: all Version: 0.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcchoice, r-cran-rcmdr, r-cran-isocodes Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.dccv_0.2-0-1.ca2604.1_all.deb Size: 66594 MD5sum: 9e8d3aaa25083dfda375faad172aab1c SHA1: 049726ea4700a37133111a44b5766b8d61c8465a SHA256: 8d648a0ff709091fd8d437f21b2d3b1876316da8351ac2e39ecc3331e5f8fefd SHA512: 12fcde73048a3d3e11273f236915dea775625ea87e9197362ce03dcc254761e6e2f2da0224ac0813ad86cf51c106a0f0fe6c44d520dbd1c0b1ca5056df2403a8 Homepage: https://cran.r-project.org/package=RcmdrPlugin.DCCV Description: CRAN Package 'RcmdrPlugin.DCCV' (R Commander Plug-in for Dichotomous Choice Contingent Valuation) Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals' preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) for DCCV. Package: r-cran-rcmdrplugin.dce Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.dce_0.3-1-1.ca2604.1_all.deb Size: 93452 MD5sum: be896d25a7fdf3920b984b63e48b3abc SHA1: 9a97bfeeff89e0c5b8790384b2f4c6f16f77b4dc SHA256: 7481e015267db797f718c782db6915b4af83e995122bed8aafd6b7d45ca04c2d SHA512: 605d3f3ce210a024a411410d9b57f328ac2c06cbc39fdc063f28cb6a102df39b59bf96e24b76e82f10aa2c3909bd0b3236bef0b6af8872333f183c5c74271557 Homepage: https://cran.r-project.org/package=RcmdrPlugin.DCE Description: CRAN Package 'RcmdrPlugin.DCE' (R Commander Plug-in for Discrete Choice Experiments) Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) . Package: r-cran-rcmdrplugin.depthtools Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-depthtools Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.depthtools_1.4-1.ca2604.1_all.deb Size: 71686 MD5sum: 3f7353cdc141adcc67863230de5cc736 SHA1: a1392a8171976d8e111c9e90d441456c1cd2e45c SHA256: d1280a49ecfb7bd1d16838dae0c7aef433c60c13b354a10c22ddaa7327774cb3 SHA512: 88518b361a2cb0bb9dad12801be6706523b7eeeaca42b6de223a852584abc492bec41c90b1a8a2b21060915a590d05a8977a5285f5e02ebbcb0b87759377d801 Homepage: https://cran.r-project.org/package=RcmdrPlugin.depthTools Description: CRAN Package 'RcmdrPlugin.depthTools' (R Commander Depth Tools Plug-in) We provide an Rcmdr plug-in based on the depthTools package, which implements different robust statistical tools for the description and analysis of gene expression data based on the Modified Band Depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. Package: r-cran-rcmdrplugin.doe Architecture: all Version: 0.12-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2357 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doe.base, r-cran-frf2, r-cran-doe.wrapper, r-cran-rcmdr, r-cran-rcmdrmisc Suggests: r-cran-frf2.catlg128 Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.doe_0.12-6-1.ca2604.1_all.deb Size: 2160830 MD5sum: 62f56334cef6160e776bd4b3d95ba18d SHA1: ec062d552168735a61bfeda52d0280e18723d04d SHA256: 76b5af6c88b45443e4ab95697a2d33db26def6c50a93b81408a5f785fcec3c15 SHA512: 2d338974fc86816b4ec39eded57f1b84ea0063ace726194839cee0f8217148736ff3bf10b9262cf2b7cb825c6e9652a31716443d0b4ab831008e333739cf8644 Homepage: https://cran.r-project.org/package=RcmdrPlugin.DoE Description: CRAN Package 'RcmdrPlugin.DoE' (R Commander Plugin for (Industrial) Design of Experiments) Provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus. Package: r-cran-rcmdrplugin.ebm Architecture: all Version: 1.0-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-epir, r-cran-abind Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.ebm_1.0-10-1.ca2604.1_all.deb Size: 60524 MD5sum: e2ddd56358327cfadd943dd6c9421544 SHA1: a3c9ca69d7a8a927f1aa3f3296b3e6326124ca6b SHA256: 65c71f1b0183871acc39cdef418231596eb0bd798b140e3469f08ee3992d61e1 SHA512: 0cd72a5ca09b907f2bbbb6d158cb45758eb48e8c4708528b7affb950643d84b4f8210508638e592d22027f9808567dd99bcdc6023572db3716d4707e836ff712 Homepage: https://cran.r-project.org/package=RcmdrPlugin.EBM Description: CRAN Package 'RcmdrPlugin.EBM' (Rcmdr Evidence Based Medicine Plug-in Package) Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...). Package: r-cran-rcmdrplugin.export Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-xtable, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.export_0.3-1-1.ca2604.1_all.deb Size: 70710 MD5sum: d3a94046bac22cf4b4bce132a4b0f6a9 SHA1: 90f6f4d45857bed53a37e68b99f9fc1f3b186a99 SHA256: 7429070eb07913bedcaa5aeef17a7b662504c8462e9b2611af641c0260c3c38a SHA512: 18b8ff884f5e8c7e5f2e386646481135bcddfe61e5381f08c0e0b48119ddb6ae590a48ecaf126a0dfd4a3748389cb6c09a6a2dffdafbe18c4ccdc1cc9fd9a248 Homepage: https://cran.r-project.org/package=RcmdrPlugin.Export Description: CRAN Package 'RcmdrPlugin.Export' (Export R Output to LaTeX or HTML) Export Rcmdr output to LaTeX or HTML code. The plug-in was originally intended to facilitate exporting Rcmdr output to formats other than ASCII text and to provide R novices with an easy-to-use, easy-to-access reference on exporting R objects to formats suited for printed output. The package documentation contains several pointers on creating reports, either by using conventional word processors or LaTeX/LyX. Package: r-cran-rcmdrplugin.ezr Architecture: all Version: 1.70-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-readstata13 Suggests: r-cran-abind, r-cran-aod, r-cran-aplpack, r-cran-brant, r-cran-car, r-cran-clinfun, r-cran-cmprsk, r-cran-consort, r-cran-foreign, r-cran-ggplot2, r-cran-lawstat, r-cran-meta, r-cran-metatest, r-cran-netmeta, r-cran-multcomp, r-cran-mvtnorm, r-cran-matching, r-cran-plotly, r-cran-proc, r-cran-survivalroc, r-cran-survrm2, r-cran-tableone, r-cran-readxl, r-cran-lmertest, r-cran-swimplot, r-cran-currentsurvival, r-cran-rstatix Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.ezr_1.70-1.ca2604.1_all.deb Size: 1427888 MD5sum: 0f14c26bffcf695333828dd7e8e35dfb SHA1: 8106bba8b8e8436dff6de7dd243461def72af2b9 SHA256: d59d2d5ffc74faa36109d6a45eb70077702b2a9076942be36ca750eed87f6d6e SHA512: d114165e1a388d9e0d073d879d7819da619ba1361f0e427beb5d0ddaa4cb97ca17e6b3e41bbdb1264ace194bf4220ca91f839a4fbd62da11483101930ca20152 Homepage: https://cran.r-project.org/package=RcmdrPlugin.EZR Description: CRAN Package 'RcmdrPlugin.EZR' (R Commander Plug-in for the EZR (Easy R) Package) EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles. Package: r-cran-rcmdrplugin.factominer Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factominer, r-cran-rcmdr Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.factominer_1.8-1.ca2604.1_all.deb Size: 492396 MD5sum: 4aca21d8508ea816166bfc2cd2a41eab SHA1: 514b941dbc62327f773b669e17d9d5a3c4ff83d0 SHA256: 827948a3abe2becf40b63b36376e6d185a161063cd650e5f5b7cfddebb31460f SHA512: da077588559aa1e2579284359e602a21cff37ae905663d5b52170caff9422a46d242c736511d8dc0e7925df3745e94067091ff06fb7f6c17c756120c65ab0cb6 Homepage: https://cran.r-project.org/package=RcmdrPlugin.FactoMineR Description: CRAN Package 'RcmdrPlugin.FactoMineR' (Graphical User Interface for FactoMineR) Rcmdr Plugin for the 'FactoMineR' package. 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Package: r-cran-rcmdrplugin.rmtcjags Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-runjags, r-cran-rmeta, r-cran-igraph, r-cran-coda, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.rmtcjags_1.0-2-1.ca2604.1_all.deb Size: 53974 MD5sum: d235431835a862e3f5c3e8844bd4fdf3 SHA1: d2013f70e1e87c008f359e1d6361f8223f3d3584 SHA256: f04de2d8ecfd1f7d705fa0ea3d517070a3c587704c370c1fe5fb9afc77bfbc11 SHA512: 15b887adcf3fc8d364c9de9e602bac448487a6565db8970ac412f594cfa25c877de2b5d4af1c3085b994758f022f792f0d5994caef8a0f5ae055e4ba562842f5 Homepage: https://cran.r-project.org/package=RcmdrPlugin.RMTCJags Description: CRAN Package 'RcmdrPlugin.RMTCJags' (R MTC Jags 'Rcmdr' Plugin) Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). 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Package: r-cran-rcmdrplugin.roc Architecture: all Version: 1.0-19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-proc, r-cran-resourceselection Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.roc_1.0-19-1.ca2604.1_all.deb Size: 170984 MD5sum: 7f080acee3ab6f6f3ef58220582dd6f3 SHA1: e64ad255e02148b774a08dfd6fd5d113e8482457 SHA256: 0eb5d20f6338de49557f7ad57c31957d34c444a1594a89167b3d10de479c3172 SHA512: 4ab548967536d87ba9e4128dee2a278805bad1f2d210bb7e4fa71e46db1599a5c8c1e4909ab615d89fe21ee3b04b97969189f56df6572066ef9f070470016e8f Homepage: https://cran.r-project.org/package=RcmdrPlugin.ROC Description: CRAN Package 'RcmdrPlugin.ROC' (Rcmdr Receiver Operator Characteristic Plug-in Package) Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC package. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection. Package: r-cran-rcmdrplugin.sos Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sos, r-cran-rcmdr, r-cran-tcltk2 Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.sos_0.3-0-1.ca2604.1_all.deb Size: 30146 MD5sum: 3749a79fe810c5e9b7c9364530b7588c SHA1: 462bba4e7d147753272a1add73ba71893eaa7e0d SHA256: 33432d3386cdae9463c1b30ca56ad7b7a1a1f57536dd5ae24fa1630a0fe41e90 SHA512: 9fc3e87a15cf5aec48a16e405cf62543fefce6571a78347a3cad86c1378f4885a6e5b836f6b3279bb8d293507e739a088aff03bea76b6c4b6de39c3699ad1d54 Homepage: https://cran.r-project.org/package=RcmdrPlugin.sos Description: CRAN Package 'RcmdrPlugin.sos' (Efficiently search the R help pages) Rcmdr interface to the 'sos' package. 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Package: r-cran-rcmdrplugin.teachingdemos Architecture: all Version: 1.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-teachingdemos, r-cran-rcmdr Suggests: r-cran-rgl, r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-rcmdrplugin.teachingdemos_1.2-0-1.ca2604.1_all.deb Size: 35708 MD5sum: ca3c4a6dc2204fca659f28e4ebf2816d SHA1: 5fea4dceb29984d8272ee032cb8d94a0f965ebc3 SHA256: 42b6082ec6b4b181b5139f0364228ab7e85f9107660ea58575cb63bafd38bad1 SHA512: 26a323771baa8f9309f0f7361e6efee92700172724ea674200db1d7c4e73121301faecfbec609144fe59c91a281098b07ebd69e50708376b6eb1d72d881499b3 Homepage: https://cran.r-project.org/package=RcmdrPlugin.TeachingDemos Description: CRAN Package 'RcmdrPlugin.TeachingDemos' (Rcmdr Teaching Demos Plug-in) Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes. 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Corpora can be imported from spreadsheet-like files, directories of raw text files, as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files. 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This process is difficult in 'R', as the raw data is very large and cannot be read into the R workspace. 'rcprd' utilises 'RSQLite' to create 'SQLite' databases which are stored on the hard disk. These are then queried to extract the required information for a cohort of interest, and create datasets ready for statistical analysis. The processes follow closely that from the 'rEHR' package, see Springate et al., (2017) . 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The package implements analysis algorithms scaled for city or regional crime analysis units. The package provides functions for kernel density estimation for crime heat maps, geocoding using the 'Google Maps' API, identification of repeat crime incidents, spatio-temporal map comparison across time intervals, time series analysis (forecasting and decomposition), detection of optimal parameters for the identification of near repeat incidents, and near repeat analysis with crime network linkage. 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Delineation entails the identification of corridor boundaries, segmentation of the corridor, and delineation of the river space using two-dimensional spatial information from street network data and digital elevation data in a projected CRS. The resulting delineation can be used to characterise spatial phenomena that can be related to the river as a central element. Package: r-cran-rcriteo Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcurl, r-cran-xml, r-cran-httr, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-rcriteo_1.0.2-1.ca2604.1_all.deb Size: 35710 MD5sum: 1fb84226f54542a7b4d5ca974b0eab29 SHA1: 31e4db15c42591531df18fbce195685c4a8fe1ce SHA256: 423f0ce0caf25a5046440ddb0037b65ba6c3ff41416a40daf1e3ce4e41c5d431 SHA512: 86d3f7896bf908119df15791afd959d072af1c7dc9752dd0003c8a5195c513b82e4b0e0622488398e3e4e1d5e8cd8909429b63417df4a383a3a635e678acc5a1 Homepage: https://cran.r-project.org/package=RCriteo Description: CRAN Package 'RCriteo' (Loading Criteo Data into R) Aims at loading Criteo online advertising campaign data into R. Criteo is an online advertising service that enables advertisers to display commercial ads to web users. The package provides an authentication process for R with the Criteo API . 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Here is the webpage of 'NanoString nCounter' where you can find detailed information about this platform . It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from 'NanoString nCounter' platform so that noise from various sources can be removed. 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The package enables the estimation of direct and spillover effects, conduct hypotheses tests, and conduct sample size calculation for two-stage randomized controlled trials. Package: r-cran-rct3 Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rct3_1.0.4-1.ca2604.1_all.deb Size: 34006 MD5sum: 743c208a0efb4b88bf55630f5d7872a0 SHA1: 25495618eb0697f767a066341f28d1f329f62070 SHA256: a4037d33851dae73e36e694c4c701ad388193952f5664a9893af18e62af2ab07 SHA512: 05831ea5743ce18aee3a2f5f4932ca13226077af3b008a35fde4772bb4973ab2500fd9b2596eb33d0a199c12a42f7394b048466abc5e68ad1ad28134a4313614 Homepage: https://cran.r-project.org/package=rct3 Description: CRAN Package 'rct3' (Predict Fish Year-Class Strength from Survey Data) Predict fish year-class strength by calibration regression analysis of multiple recruitment index series. 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Robust treatment assignment by strata/blocks, that handles misfits; Power calculations of the minimum detectable treatment effect or minimum populations; Balance tables of T-test of covariates; Balance Regression: (treatment ~ all x variables) with F-test of null model; Impact_evaluation: Impact evaluation regressions. This function gives you the option to include control_vars, fixed effect variables, cluster variables (for robust SE), multiple endogenous variables and multiple heterogeneous variables (to test treatment effect heterogeneity) summary_statistics: Function that creates a summary statistics table with statistics rank observations in n groups: Creates a factor variable with n groups. Each group has a min and max label attach to each category. Athey, Susan, and Guido W. Imbens (2017) . 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'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) . 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The classical framework is developed in Ando & Bai (2017) . The implementation within this package excludes the SCAD-penalty on the estimations of beta. This robust framework is developed in Boudt & Heyndels (2022) and is made robust against different kinds of outliers. The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along with their loadings. The number of groups and factors can be estimated if they are unknown. Package: r-cran-rcube Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-rcube_0.5-1.ca2604.1_all.deb Size: 66618 MD5sum: 638a16724f7418dd8776a12f40d6f7e5 SHA1: b8001ec258064b70da574127362ea27d0f44aa03 SHA256: e2402322d8a36f4dc3b82421bf2caae49c914faec118f72c36f964844cec13cb SHA512: d34f97144478ad580755456ded8cfdaaff5bbc02f37d6a79c27049b088658a11f5874a26d390d89e6305305225da0891b2bd00a5cfd1f2a064cc8e60fd34d043 Homepage: https://cran.r-project.org/package=rcube Description: CRAN Package 'rcube' (Simulations and Visualizations of Rubik's Cube (with Mods)) Provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). 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Package: r-cran-rcurvep Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-tidyselect, r-cran-boot, r-cran-tidyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-ggplot2, r-cran-rdpack, r-cran-rjava, r-cran-furrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tcpl, r-cran-future Filename: pool/dists/resolute/main/r-cran-rcurvep_1.3.2-1.ca2604.1_all.deb Size: 3874762 MD5sum: 40c03490f0db59e27200cb820e8702e8 SHA1: db6ff0d68ca0c2d459f052745a3a7ebda2de71ae SHA256: 13987ad293eb2cecc06a9a720e7b09476b58354dd08444d3846f3a925b7e40da SHA512: e5f28fe212515b8c56864ae5cf938df301fa2f8076d2aaac6a91f163259688a1dc1880fbd71d878e3ce0fcfdff0dc13fe06450ed5224a2bc3907536017674f30 Homepage: https://cran.r-project.org/package=Rcurvep Description: CRAN Package 'Rcurvep' (Concentration-Response Data Analysis using Curvep) An R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) and Sedykh A (2016) ). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. 3-parameter Hill equation from 'tcpl' package (Filer D et al., ) and 4-parameter Hill equation from Curve Class2 approach (Wang Y et al., ) are available. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. ). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD). 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Richard Reeve, et al. (2016) . Package: r-cran-rdkitpyr Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rdkitpyr_0.2.1-1.ca2604.1_all.deb Size: 79774 MD5sum: b113f1d09fd9a1951a18fe8502729d2b SHA1: 5aa2e0e59a8a4fc943f16713607e188a06448410 SHA256: e82ab809e97c4523ff668e298867ca3144ebbb143dc3a61efe9dbe0363b9f15e SHA512: 294bbc6990444c5c00fcf4e172b3f018db08097db7506ded5bed59c30b660b8b1a7d4f14a95fbab8e1bd49a9e4fe9761f836e15afb8a103fb4192c69d86919b9 Homepage: https://cran.r-project.org/package=rdkitpyr Description: CRAN Package 'rdkitpyr' (Task-Oriented Cheminformatics in R Using 'RDKit' via 'Python') A task-oriented R interface to the 'RDKit' library through its 'Python' API via 'reticulate'. The package offers high-level cheminformatics functionality, including molecule parsing, descriptor calculation, and fingerprint generation without replicating the native structure of 'RDKit'. 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(2022) . The learned cutoffs are guaranteed to perform no worse than the existing cutoffs in terms of overall outcomes. The 'rdlearn' package also includes features for visualizing the learned cutoffs relative to the baseline and conducting sensitivity analyses. 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See Cattaneo, Titiunik and Vazquez-Bare (2016) for further methodological details, and references. 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Package: r-cran-rdmulti Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rdrobust, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-rdmulti_2.0.0-1.ca2604.1_all.deb Size: 85772 MD5sum: d0962fa2c0851efce4ea6883d4ddcf61 SHA1: bbd40136e1c598b0a22836e007f46d0fa098b66e SHA256: 1352f4c78eb2101868f3834caae62258903cb9033a2b8b1913ac2c5186b6f4a8 SHA512: f1b0644ef7522f6af61052c39b0aa684ba8169dd1b574175e6586566f23f82b323c1352f3a5b5a5b1513f239091d3fd1a1d197c77db17674296285e6b0e618cb Homepage: https://cran.r-project.org/package=rdmulti Description: CRAN Package 'rdmulti' (Robust Local Polynomial Methods for RD Designs with MultipleCutoffs or Multiple Scores) The 'rdmulti' package implements estimation, inference, and graphical procedures for regression discontinuity (RD) designs with multiple cutoffs or multiple scores. rdmc() provides point estimation and robust bias-corrected inference for multi-cutoff designs, rdmcplot() provides data-driven RD plots for multi-cutoff designs, and rdms() provides point estimation and robust bias-corrected inference for multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) for further methodological details. 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The German National Library is the German central archival library, collecting, archiving, bibliographically classifying all German and German-language publications, foreign publications about Germany, translations of German works, and the works of German-speaking emigrants published abroad between 1933 and 1945. 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Use Shallalist (service discontinued), 'VirusTotal' (which provides access to lots of services) , 'DMOZ' , University Domain list , 'OpenAI' 'GPT' models, 'Anthropic' 'Claude' models, or validated machine learning classifiers based on 'Shallalist' data to learn about the kind of content hosted by a domain. Package: r-cran-rdota2 Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rdota2_0.1.6-1.ca2604.1_all.deb Size: 103610 MD5sum: ba30ac382cfa8e17b0878b98f32bee65 SHA1: a0d037a8182d01e4dd454ff6b25c55e54a8fe477 SHA256: 091d69d9d6bbd426df380af3849cc57bd86a1b5fda125604e048eb6547320880 SHA512: 755c9b789aed29fd68a2fc358aa2f41e3d3e2afcc1a3fbe505d7d17ae48c665d238306d3e1c3346ae59bc843238a96db4b99af5be2cc4f95293463ebd50a3997 Homepage: https://cran.r-project.org/package=RDota2 Description: CRAN Package 'RDota2' (An R Steam API Client for Valve's Dota2) An R API Client for Valve's Dota2. RDota2 can be easily used to connect to the Steam API and retrieve data for Valve's popular video game Dota2. You can find out more about Dota2 at . Package: r-cran-rdpack Architecture: all Version: 2.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rbibutils Suggests: r-cran-testthat, r-cran-rstudioapi, r-cran-rprojroot, r-cran-gbrd Filename: pool/dists/resolute/main/r-cran-rdpack_2.6.6-1.ca2604.1_all.deb Size: 620814 MD5sum: 243793f865798003e774ca26e65ad24c SHA1: 0b2e8fe432beb2adce5efb462748e006eae804d9 SHA256: 41d3364829dac50c34698c4ea400a63f77a730aaf728b8e75c0e89856c5bf38e SHA512: ba0ca13b5a1975cbdce243af227ae8e7ad08f86fa5128caa0395741d293a43dbe813b663fd3f21507150f3c023825b8ed1ccf4ec0d63a397a704b6cfbd7ded41 Homepage: https://cran.r-project.org/package=Rdpack Description: CRAN Package 'Rdpack' (Update and Manipulate Rd Documentation Objects) Functions for manipulation of R documentation objects, including functions reprompt() and ereprompt() for updating 'Rd' documentation for functions, methods and classes; 'Rd' macros for citations and import of references from 'bibtex' files for use in 'Rd' files and 'roxygen2' comments; 'Rd' macros for evaluating and inserting snippets of 'R' code and the results of its evaluation or creating graphics on the fly; and many functions for manipulation of references and Rd files. Package: r-cran-rdpower Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdrobust Filename: pool/dists/resolute/main/r-cran-rdpower_3.0-1.ca2604.1_all.deb Size: 98992 MD5sum: d5c640c79d39a2161b38d082771775dd SHA1: 71607e0c79597c8d1356f60a70eef88ece85b502 SHA256: 2d434f2169d399129afa50ca31572c468f4db29a52440992d5c944900bd10719 SHA512: e6b4fdf42df47e9271e499f957d77c913357239bc8daecc2290457f6fea275737e59f39fa78bdc607c58dd0f3b52859caa9488f0f60b847b5b2c2de8e341d567 Homepage: https://cran.r-project.org/package=rdpower Description: CRAN Package 'rdpower' (Power, Sample Size, and Minimum Detectable Effect Calculationsfor RD Designs) The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdpower' package provides tools to perform power, sample size, and minimum detectable effect calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power, and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) for further methodological details. Package: r-cran-rdracor Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-data.table, r-cran-xml2, r-cran-igraph, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rdracor_1.0.6-1.ca2604.1_all.deb Size: 447288 MD5sum: 14ac58b22e3fceaf0e04f5eb3745a72b SHA1: 20f42bf14bad25884c0547eaa73e88a16de2ed05 SHA256: 73226e153059ab1698d123ea238d42fdab3bee070bd6cf9e7a0c81d9566eb791 SHA512: 7661b3a6c046256d25bf7677c1af68f72c40eed9fe13136a624162839fcb5449fe06daf5ba7bde0908c5055aa9c1b933945586d74273344a30a87ad29095f005 Homepage: https://cran.r-project.org/package=rdracor Description: CRAN Package 'rdracor' (Access to the 'DraCor' API) Provide an interface for 'Drama Corpora Project' ('DraCor') API: . Package: r-cran-rdrobust Architecture: all Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-mass Suggests: r-cran-broom, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rdrobust_4.0.0-1.ca2604.1_all.deb Size: 382876 MD5sum: c20246d7e0feccca93f40bbbf8238f81 SHA1: c58254bb83196106d2430ebfcdb2357315ac62bd SHA256: 9076bbe6f586ae95fe03222fb2c6a3e61088c102db18faded1598004f6e39f8e SHA512: c31cac2c88afdbb21ebc367cd11e8821d5115dcd6b1d198cc8f98ad84a53ecbc594f1002ebdd1ddc8eb8420307a7c978ff6aa6df475e35444c595986d70fd75c Homepage: https://cran.r-project.org/package=rdrobust Description: CRAN Package 'rdrobust' (Robust Data-Driven Statistical Inference inRegression-Discontinuity Designs) Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots). Package: r-cran-rdrw Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-rdrw_1.0.2-1.ca2604.1_all.deb Size: 62672 MD5sum: a9edab777d384a2b27f7baf6aca1d50b SHA1: 1953db6449dd3a5f8f1a438643448f5cb84a34ea SHA256: 439390c1f06cbe5e8cc34ca833d8f40d3af89cd00ddee45dcb706d42a4df628f SHA512: 56a5586b6f148c830578b3fa49c844c846db4f333e765c008d728d131680ffa2868e0cb3961d45d3109583500249d03621742b866757d51b37d994ccb547fbf1 Homepage: https://cran.r-project.org/package=Rdrw Description: CRAN Package 'Rdrw' (Univariate and Multivariate Damped Random Walk Processes) We provide a toolbox to fit and simulate a univariate or multivariate damped random walk process that is also known as an Ornstein-Uhlenbeck process or a continuous-time autoregressive model of the first order, i.e., CAR(1) or CARMA(1, 0). This process is suitable for analyzing univariate or multivariate time series data with irregularly-spaced observation times and heteroscedastic measurement errors. When it comes to the multivariate case, the number of data points (measurements/observations) available at each observation time does not need to be the same, and the length of each time series can vary. The number of time series data sets that can be modeled simultaneously is limited to ten in this version of the package. We use Kalman-filtering to evaluate the resulting likelihood function, which leads to a scalable and efficient computation in finding maximum likelihood estimates of the model parameters or in drawing their posterior samples. Please pay attention to loading the data if this package is used for astronomical data analyses; see the details in the manual. Also see Hu and Tak (2020) . Package: r-cran-rdryad Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-curl, r-cran-tibble, r-cran-hoardr, r-cran-zip, r-cran-jsonlite, r-cran-mime Suggests: r-cran-testthat, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-rdryad_1.0.0-1.ca2604.1_all.deb Size: 83174 MD5sum: 7093c6a943aef36fde611387e2f57999 SHA1: e82a7da27c8d822731228ad761c916c73bfa623a SHA256: fd75b46396db232a355f3a3e91dca264cc12fe4640ec41c5161fcf1ec126aec3 SHA512: 304be2afdd2bd05e00f6700a21c15933a68384d3fb97c139a5011590cf437162fe1a123d9c2734f346e7bb0a269cac5a60baed73da820cb4a7f37af41460eb09 Homepage: https://cran.r-project.org/package=rdryad Description: CRAN Package 'rdryad' (Access for Dryad Web Services) Interface to the Dryad "Solr" API, their "OAI-PMH" service, and fetch datasets. Dryad () is a curated host of data underlying scientific publications. 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'rdss' includes datasets, helper functions, and plotting components to enable use and replication of the book. Package: r-cran-rdstagger Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-ggplot2, r-cran-rdrobust Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-rdstagger_0.1.0-1.ca2604.1_all.deb Size: 101358 MD5sum: da881a5e431b235a3db277d5225f554c SHA1: 0f077ad54152eeddb3c07b015acf2d2ad2fa7910 SHA256: 66b1d0762199021734f54dd04fa245499229c2a8a6cfd7c1bfbb0b3c84e75940 SHA512: 7a05c3ed2e885bf75ade6a8e9a37b4039f9dfcc570864b21bad769373a74f37935b38b3d51b0f0ca2e6e0600f6e27458f090a6f52450ad620953edc9c425410a Homepage: https://cran.r-project.org/package=rdstagger Description: CRAN Package 'rdstagger' (Staggered Regression Discontinuity with Network Interference) Implements a unified framework combining staggered difference-in-differences with regression discontinuity designs and network interference. Extends Callaway and Sant'Anna (2021) to settings where treatment assignment is determined by a running variable crossing a cutoff, adoption timing is heterogeneous across units, and spillover effects operate through a known network structure. Provides group-time average treatment effects (direct and spillover), aggregation schemes, bandwidth selection, and pre-treatment falsification tests. Package: r-cran-rdstreeboot Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rdstreeboot_1.0-1.ca2604.1_all.deb Size: 60792 MD5sum: d263eb5d85a92d5032e2c72fcc94e141 SHA1: 8640f540260b0fd923d553b1a970543c29e833a2 SHA256: 6a1bc5f5963dd1143a2bf32db9b0cb8d591bd75eff0062ec97c505a36363c725 SHA512: eb3df14e4c91ec39397c8c543e6e66bd93705fb3b0a59017bafd7e52e45b66c2d8df1c8e136d261cb6a1d1896728f95e232e2ecfcd361f8aa8a350247d5a001a Homepage: https://cran.r-project.org/package=RDStreeboot Description: CRAN Package 'RDStreeboot' (RDS Tree Bootstrap Method) A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process. Package: r-cran-rdta Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-rdta_1.0.1-1.ca2604.1_all.deb Size: 38450 MD5sum: e71245c4490fc8665a6a021eaa26031d SHA1: fd330e56ba9b3ae925c06765353ff57e426c84da SHA256: 9ed00479ed5b8a219bdf219947968125ef85673fb501e193eea2f0558c806fe9 SHA512: e2c4dbfc0768bf73e336ba1e817af07d5c1110e23c00e03159a54aa87a255be9f4696d08bb42263f2044a1335cffa6b658961090c4fa7197bac3322bb73d0ef0 Homepage: https://cran.r-project.org/package=Rdta Description: CRAN Package 'Rdta' (Data Transforming Augmentation for Linear Mixed Models) We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) . Package: r-cran-rdtlite Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-digest, r-cran-gtools, r-cran-jsonlite, r-cran-knitr, r-cran-provviz, r-cran-rlang, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-stringi, r-cran-xml Suggests: r-cran-ggplot2, r-cran-provsummarizer, r-cran-roxygen2, r-cran-testthat, r-cran-vroom Filename: pool/dists/resolute/main/r-cran-rdtlite_1.4-1.ca2604.1_all.deb Size: 334242 MD5sum: ddadd69b94a81ffda127d3f373737e07 SHA1: 3d1eb7e4f890bcc61503e2e9ef0b990b9cbddd77 SHA256: cec85f2ef26df1a616975a152ecbe42f7afc326b7441aa4fa6028e2106e9ecb9 SHA512: cd03320ac3415d61c5186d41c32a2c0ba086c3512a2a4f07303ccb7b5f49cd64fdc9c1ef8692259f7f58c346b708608f2fb90eb062d465999791434fa911724e Homepage: https://cran.r-project.org/package=rdtLite Description: CRAN Package 'rdtLite' (Provenance Collector) Defines functions that can be used to collect provenance as an 'R' script executes or during a console session. 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The 'DuckDB' C extension API and its 'htslib' dependency are compiled from vendored sources during package installation. James K Bonfield and co-authors (2021) . Package: r-cran-rduino Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-serial Filename: pool/dists/resolute/main/r-cran-rduino_0.1-1.ca2604.1_all.deb Size: 25868 MD5sum: 3f8b9d0e02aedd6b27aa1e902937c659 SHA1: 9c74f6431362793b20ef678ec1b5a1fb9a15e591 SHA256: 3cc1f7306ac1945cabff5220b884d77aa12f50ade2b19348c06cd1049cc7a091 SHA512: 9b6a19dc0acb0a0ac8543cc3059c0546907027a7b64faa492ffce816db326bf514074bd2ba1fa9283eb5e290c63ec34f6e597dfe6767732b9b87eaa4d3a9ebbe Homepage: https://cran.r-project.org/package=Rduino Description: CRAN Package 'Rduino' (A Microcontroller Interface) Functions for connecting to and interfacing with an 'Arduino' or similar device. 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References (citations in PubMed format in details of each function): Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) . Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) . Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) . Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) . Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) . Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) . Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) . Carlisle JB, Loadsman JA. (2017) . Carlisle JB. (2017) . 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Package: r-cran-recapr Architecture: all Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-recapr_0.4.4-1.ca2604.1_all.deb Size: 258648 MD5sum: a506a1eb18eb2191a57b0f0e2136c31b SHA1: 55e36630262e302c00715afb50f36724488f1e26 SHA256: 859c2957624ff1c750cdaf8c99a36fcfa37aa58d486682ec3be5c9b0b4158d57 SHA512: 7aa9782980da11abc1c44ef89f11634c043de034eb8bd44e260ee176a8b174d84505f27da8f7478fdf05915f278b696358d8817d2931af2e9373ef1cfedb4b42 Homepage: https://cran.r-project.org/package=recapr Description: CRAN Package 'recapr' (Two Event Mark-Recapture Experiment) Tools are provided for estimating, testing, and simulating abundance in a two-event (Petersen) mark-recapture experiment. Functions are given to calculate the Petersen, Chapman, and Bailey estimators and associated variances. However, the principal utility is a set of functions to simulate random draws from these estimators, and use these to conduct hypothesis tests and power calculations. Additionally, a set of functions are provided for generating confidence intervals via bootstrapping. Functions are also provided to test abundance estimator consistency under complete or partial stratification, and to calculate stratified or partially stratified estimators. Functions are also provided to calculate recommended sample sizes. Referenced methods can be found in Arnason et al. (1996) , Bailey (1951) , Bailey (1952) , Chapman (1951) NAID:20001644490, Cohen (1988) ISBN:0-12-179060-6, Darroch (1961) , and Robson and Regier (1964) . 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These methods are discussed in detail in VanderWeele and Padgett (2024) . The relative excess correlation matrices will, generally, have numerous negative entries even if all of the raw correlations between each pair of indicators are positive. The positive deviations of the relative excess correlation matrix entries help identify clusters of indicators that are more strongly related to one another, providing insights somewhat analogous to factor analysis, but without the need for rotations or decisions concerning the number of factors. A goal similar to exploratory/confirmatory factor analysis, but 'recmetrics' uses novel methods that do not rely on assumptions of latent variables or latent variable structures. Package: r-cran-recodeflow Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-sjlabelled, r-cran-stringr, r-cran-tidyr, r-cran-haven, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/resolute/main/r-cran-recodeflow_0.1.0-1.ca2604.1_all.deb Size: 135186 MD5sum: 5af2b8132abac0b549cc3c79c2aa3110 SHA1: 52c473db53bda1489e15c031ac1a63308c5f5dc2 SHA256: b2719bb4941114601787ab0501edd946430c093d468197bc7b8cf314fb839ba3 SHA512: aa093539636aefe08f7d08faa3fd2fa9dc35d43ef1eba38e3a24464718267aa79356654e81d2a13aaffe82b4e69a92863e60b113c3b17fcdd1d4f2765ccbd2da Homepage: https://cran.r-project.org/package=recodeflow Description: CRAN Package 'recodeflow' (Interface Functions for PMML Creation, and Data Recoding) Contains functions to interface with variables and variable details sheets, including recoding variables and converting them to PMML. 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It provides functions to create, modify and export data models in json format. It also allows importing models created with 'MySQL Workbench' (). These functions are accessible through a graphical user interface made with 'shiny'. Constraints such as types, keys, uniqueness and mandatory fields are automatically checked and corrected when editing a model. Finally, real data can be confronted to a model to check their compatibility. Package: r-cran-redas Architecture: all Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-car, r-cran-cluster, r-cran-foreign, r-cran-gparotation, r-cran-mass, r-cran-mclust, r-cran-psych, r-cran-usingr, r-cran-vcd Filename: pool/dists/resolute/main/r-cran-redas_0.9.4-1.ca2604.1_all.deb Size: 85790 MD5sum: b53d7348a03f0c82e65803c9d210ca6e SHA1: abae6ab5e22ad7150c3b04fee08b479ae04f99cb SHA256: 38d1ec66d238e3e58beb0933c1411fa43d108b024ac48572b844d7cdeaad09bd SHA512: cadbce2434e6b9b3b71ec479c5b3c04e382957dc5ab4a11f6b15dab3b7a0ecce6f03eb4d03f7529ce5ae17aa15b07c00ccf42e8dd3eea1e2e3be80e3d3f74354 Homepage: https://cran.r-project.org/package=REdaS Description: CRAN Package 'REdaS' (Companion Package to the Book 'R: Einführung durch angewandteStatistik') Provides functions used in the 'R: Einführung durch angewandte Statistik' (second edition). Package: r-cran-redbookperu Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 917 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-redbookperu_0.0.3-1.ca2604.1_all.deb Size: 874618 MD5sum: 2158dacbd0a961e2781d63f013b34c80 SHA1: 6ee4566f984c998addcc1a6ee5cf40ec8377ebfa SHA256: 792fc3312f18a3c74d8bda8ac751ba99f07b08291540491243b39ba3fe99f839 SHA512: c6e0eb178d567f2d19cfc9fa14caa82490bd66565c13b8d04a35f5cb39c0e8ea6695de202cbe02ba1d96c0ed186782e0be8dcda2d659d13d225a838c4afb1485 Homepage: https://cran.r-project.org/package=redbookperu Description: CRAN Package 'redbookperu' (Access and Analyze Data from the Red Book of Endemic Plants ofPeru) Provides access to and analysis of data from "The Red Book of Endemic Plants of Peru" (León, B., Roque, J., Ulloa, C., Jorgensen, P.M., Pitman, N., Cano, A. 2006) . This package offers comprehensive taxonomic, geographic, and conservation information about Peru's endemic plant species. It includes functions to verify species inclusion, obtain updated taxonomic details, and explore the dataset. Package: r-cran-redcapapi Architecture: all Version: 2.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3715 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-chron, r-cran-curl, r-cran-jsonlite, r-cran-labelvector, r-cran-lubridate, r-cran-mime, r-cran-shelter Suggests: r-cran-testthat, r-cran-hmisc, r-cran-mockery, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-redcapapi_2.12.0-1.ca2604.1_all.deb Size: 3267332 MD5sum: 3f5060bf54a68f8ab8b08d456f9fc4fa SHA1: cd8dcf0a886ad46915f35a66935f18373a3f3c87 SHA256: 495ebc8594d8b88c93b83adf1a70dbe95f27a8bcd913135377ed179295d59771 SHA512: 50f9e1cb62950a82cafc12ce169511fc9878ad9ef8a137630ecc33d44782466b3942dc94128bb6583ad8901ace1597f634f6f4f01bd6e59d68ff185920a9742d Homepage: https://cran.r-project.org/package=redcapAPI Description: CRAN Package 'redcapAPI' (Interface to 'REDCap') Access data stored in 'REDCap' databases using the Application Programming Interface (API). 'REDCap' (Research Electronic Data CAPture; , Harris, et al. (2009) , Harris, et al. (2019) ) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The API allows users to access data and project meta data (such as the data dictionary) from the web programmatically. The 'redcapAPI' package facilitates the process of accessing data with options to prepare an analysis-ready data set consistent with the definitions in a database's data dictionary. Package: r-cran-redcapcast Architecture: all Version: 26.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-redcapr, r-cran-tidyr, r-cran-tidyselect, r-cran-keyring, r-cran-purrr, r-cran-readr, r-cran-zip, r-cran-assertthat, r-cran-forcats, r-cran-vctrs, r-cran-gt, r-cran-bslib, r-cran-here, r-cran-glue, r-cran-gtsummary, r-cran-shiny, r-cran-haven, r-cran-openxlsx2, r-cran-readods Suggests: r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-cran-spelling, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-redcapcast_26.1.1-1.ca2604.1_all.deb Size: 265864 MD5sum: 929fd254c3059339c36fd25abefe955a SHA1: 0e79f15442fd69171964285d5ac20c00237a8da8 SHA256: 3df233f8261c7ce354ae7db0a3e2d0bf8704a52ff897ee4fcce931cfe91c62cd SHA512: fb0cf877b7146e53a4696477f569721e22b5fa183299f422ec8085854244d1a199436159c04451a3c406874892cee55066066dc15be0f102965faaa710461c57 Homepage: https://cran.r-project.org/package=REDCapCAST Description: CRAN Package 'REDCapCAST' (REDCap Metadata Casting and Castellated Data Handling) Casting metadata for REDCap database creation and handling of castellated data using repeated instruments and longitudinal projects in 'REDCap'. Keeps a focused data export approach, by allowing to only export required data from the database. Also for casting new REDCap databases based on datasets from other sources. Originally forked from the R part of 'REDCapRITS' by Paul Egeler. See . 'REDCap' (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources (Harris et al (2009) ; Harris et al (2019) ). Package: r-cran-redcapdm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-openxlsx, r-cran-purrr, r-cran-redcapr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-labelled, r-cran-stringi, r-cran-cli, r-cran-forcats, r-cran-lifecycle, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-redcapdm_1.0.1-1.ca2604.1_all.deb Size: 414618 MD5sum: e1acf9cf999eb9332a3fd6c6af5cc417 SHA1: 03a6597f176fc99e25f7332cf582c2a1e1af3d82 SHA256: e3bbf670a72fb2532401f13fe7d1a8c0993c314328014a9f4294a5947404aa38 SHA512: 4b283737d626a2891f0bc938f750bf16b942c75633a430d69bae15901791ff40961a7876ad635c8eb41f6b98c18a9cdf483cac1b0f375b2258011285f084a65e Homepage: https://cran.r-project.org/package=REDCapDM Description: CRAN Package 'REDCapDM' ('REDCap' Data Management) REDCap Data Management - 'REDCap' (Research Electronic Data CAPture; ) is a web application developed at Vanderbilt University, designed for creating and managing online surveys and databases and the REDCap API is an interface that allows external applications to connect to REDCap remotely, and is used to programmatically retrieve or modify project data or settings within REDCap, such as importing or exporting data. REDCapDM is an R package that allows users to manage data exported directly from REDCap or using an API connection. This package includes several functions designed for pre-processing data, generating reports of queries such as outliers or missing values, and following up on previously identified queries. Package: r-cran-redcapexporter Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-keyring, r-cran-lubridate, r-cran-rjson Suggests: r-cran-devtools, r-cran-fs, r-cran-getpass, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-qwraps2, r-cran-rmarkdown, r-cran-secret Filename: pool/dists/resolute/main/r-cran-redcapexporter_0.3.4-1.ca2604.1_all.deb Size: 122500 MD5sum: 8aea460d3aa46fec59045d6fda5f7d70 SHA1: 62d726d6e970b7bca715a54cf0ff0dea3164228e SHA256: 66ea180ea5f7fd45487df13cb9081293486c062d270a834ba0056f112ef721b2 SHA512: 98fa95eed248130d1d6f22c5dd0e1e49a7d753928220b855a25587da7a57d9f38c8f201d85fd38c913a3b527c9f5d97034b96a0a1f2e3e571b8083db6a17855b Homepage: https://cran.r-project.org/package=REDCapExporter Description: CRAN Package 'REDCapExporter' (Automated Construction of R Data Packages from REDCap Projects) Export all data, including metadata, from a REDCap (Research Electronic Data Capture) Project via the REDCap API . The exported (meta)data will be processed and formatted into a stand alone R data package which can be installed and shared between researchers. Several default reports are generated as vignettes in the resulting package. Package: r-cran-redcapr Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-spelling, r-cran-covr, r-cran-dbi, r-cran-kableextra, r-cran-knitr, r-cran-odbc, r-cran-purrr, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-testthat, r-cran-tidyselect, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-redcapr_1.6.0-1.ca2604.1_all.deb Size: 1240710 MD5sum: 26409e19f37acfbfa19720876a036cef SHA1: 9edfc5ffc442194e07a90ff04604180ec822049c SHA256: 4755101790136892e9250d7718b97f6ed906fdbc481cfc8a03ee09eb1b441669 SHA512: 74eb34b4188c080ec0577c4dc5bf30147da0ee16c1ec5641d151a34506f425c0da75c35b3a880719ca4cbd4c87412bd0c6fdfc2ac63af725b157e429a7f4ac74 Homepage: https://cran.r-project.org/package=REDCapR Description: CRAN Package 'REDCapR' (Interaction Between R and REDCap) Encapsulates functions to streamline calls from R to the REDCap API. REDCap (Research Electronic Data CAPture) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The Application Programming Interface (API) offers an avenue to access and modify data programmatically, improving the capacity for literate and reproducible programming. Package: r-cran-redcapsync Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3746 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-hoardr, r-cran-lubridate, r-cran-openxlsx, r-cran-r6, r-cran-readxl, r-cran-redcapapi, r-cran-redcapr, r-cran-skimr, r-cran-stringr Suggests: r-cran-keyring, r-cran-knitr, r-cran-listviewer, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-redcapsync_0.1.0-1.ca2604.1_all.deb Size: 2185768 MD5sum: 47b7b03dff488426c887cec75f445556 SHA1: d7a3986c159da846ec32728a079f328d0224bab2 SHA256: 78664550ad6fb2798040b1d7116852449fa9e74f70d0f1ff3e100b8b48724da8 SHA512: f430a2702f86f732c76ce672c87db795af6c3c61cc098dd7c873e5911b6bc3608a1aeb58d8fe3383f1a1ca1540eff4c49ff3d7d5565b3de44f302cfc93cde00f Homepage: https://cran.r-project.org/package=REDCapSync Description: CRAN Package 'REDCapSync' (Encapsulated 'REDCap' Projects for Synchronized Data Pipelines) Wraps dozens of 'REDCap' API endpoints into a standardized R6 object. Research Electronic Data Capture ('REDCap') is a survey and database web application software maintained by Vanderbilt University. It has a robust application programming interface (API) utilized by several R packages. 'REDCapSync' uses 'redcapAPI' and 'REDCapR' behind-the-scenes to retrieve all metadata, data, and log details for a project. To minimize unnecessary server calls, the interim 'REDCap' log is analyzed and used to only update necessary records. Furthermore, the user can define custom datasets that save to a directory. Those datasets continue to refresh when projects are synced. Having a secure, standardized, API-efficient, project-agnostic R object for 'REDCap' projects, streamlines downstream use in scripts, functions, and shiny applications. Package: r-cran-redcaptidier Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2977 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lobstr, r-cran-lubridate, r-cran-purrr, r-cran-redcapr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-formattable, r-cran-pillar, r-cran-vctrs, r-cran-readr, r-cran-forcats Suggests: r-cran-covr, r-cran-knitr, r-cran-labelled, r-cran-lintr, r-cran-openxlsx2, r-cran-prettyunits, r-cran-rmarkdown, r-cran-skimr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-redcaptidier_1.2.4-1.ca2604.1_all.deb Size: 1996566 MD5sum: acbf2721adbbb0f566d51e747c6f906e SHA1: 39af316152ca507f360552fe591989ed70cae9f6 SHA256: 1e3d6077c482b51d2a96f3e7b70e0dba7c18d94e27f24f674d82da0f4871dc66 SHA512: 9a71a2774714b5e4558a0424c178c1a1da20f24effcb86d56238d2f8917fe48b85442cbf0e81926d38158f82813bb0e8660986021d9d4d9429969214346e3685 Homepage: https://cran.r-project.org/package=REDCapTidieR Description: CRAN Package 'REDCapTidieR' (Extract 'REDCap' Databases into Tidy 'Tibble's) Convert 'REDCap' exports into tidy tables for easy handling of 'REDCap' repeat instruments and event arms. Package: r-cran-redcas Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-redcas_0.1.1-1.ca2604.1_all.deb Size: 368216 MD5sum: a1d7ce0a53489fad15754911905dbac5 SHA1: 08113b42b72ed9998a7577080f0aac1f3436c88b SHA256: 4aa5672b88f3097fa1e6671495a5016434cafcde32286bbf3b5b3f0b2785e61d SHA512: 01f4b6bd8b9b6b8ce908959a334c6892fc4dd4d05e8b531714b60d11a378e66ef0ee80fe2c6c623eebebe5cee088b3a11bfab002ea026ae0977794c4e083e3c2 Homepage: https://cran.r-project.org/package=redcas Description: CRAN Package 'redcas' (An Interface to the Computer Algebra System 'REDUCE') 'REDUCE' is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in 'LaTeX' format. 'REDUCE' is based on 'Lisp' and is available on the two dialects 'Portable Standard Lisp' ('PSL') and 'Codemist Standard Lisp' ('CSL'). The 'redcas' package provides an interface for executing arbitrary 'REDUCE' code interactively from 'R', returning output as character vectors. 'R' code and 'REDUCE' code can be interspersed. It also provides a specialized function for calling the 'REDUCE' feature for solving systems of equations, returning the output as an 'R' object designed for the purpose. A further specialized function uses 'REDUCE' features to generate 'LaTeX' output and post-processes this for direct use in 'LaTeX' documents, e.g. using 'Sweave'. Package: r-cran-redditadsr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-redditadsr_0.1.0-1.ca2604.1_all.deb Size: 22994 MD5sum: 3b0568e96824de72db17d47901c4cc17 SHA1: 5b61d48777e1997e2b3bf6ad6c7001aa10ffeb89 SHA256: 4da1cba9612fb88c653af2939c8bbab16be99ede5315fcf92210198e6de5f05c SHA512: 309c8f97482c86353860c1b41236fc03f03f3421a31b262e776fb06689e6c6a8e3abf696677e12e690f89e10cc6f03bf1def7b40e3b6e029db045b23f6fe349c Homepage: https://cran.r-project.org/package=redditadsR Description: CRAN Package 'redditadsR' (Get Reddit Ads Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from reddit Ads using the 'Windsor.ai' API . 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Package: r-cran-redeagroradar Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-magick, r-cran-shiny, r-cran-bslib Filename: pool/dists/resolute/main/r-cran-redeagroradar_0.1.1-1.ca2604.1_all.deb Size: 59730 MD5sum: bf6f0261eda4d8962d58e58a7557e08d SHA1: 3fbfe3ea1b585ae5cebbcf11f1e07fa625e3671d SHA256: ce8d28628f103e92bfa2d3f6c255a9609303494e54c08fde8e373c0c88d57894 SHA512: 4ca7a72b0e35958e6032fe08b6f52f9214c456e578552d86ccca675d315ba16bb45d2acf7cbdeb7e2b65af5ee09a0fc5b6f88501da51fbbe67b367d85131785a Homepage: https://cran.r-project.org/package=RedeAgroRadar Description: CRAN Package 'RedeAgroRadar' (Weather Radar Monitoring and 'Telegram' Alerts for AgricultureResearch) Provides tools to download, process, and analyze real-time meteorological radar images from Simepar (Paraná, Brazil) . Designed to support the 'Rede Agropesquisa' hydrological monitoring, it includes functions to detect rainfall intensity based on Red, Green, and Blue (RGB) color values within predefined circular study areas. Features automated integration with the 'Telegram Bot API' to send spatialized image alerts and an interactive 'shiny' dashboard for easy configuration and continuous weather tracking. Package: r-cran-redi Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-rlang, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-redi_1.0.0-1.ca2604.1_all.deb Size: 139830 MD5sum: a61dc1225404fcf1100d511d7cccbf22 SHA1: cf1df2483fb3ef8de65c97e36be60cfc771fcae6 SHA256: c7587193c38aabf73412d1f6da5aeba0640ed427f405ce59338cce6e0367bb2d SHA512: c7e6ad39c40a22466b75f9ebe78cddbdd667592b166c1480a44ed90abcedabc5e46645ce813439e9d08a5dd1ad6282fed7f4dd9713f7d3e07ead1b71102e35f4 Homepage: https://cran.r-project.org/package=REDI Description: CRAN Package 'REDI' (Robust Exponential Decreasing Index) Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) . The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way. Package: r-cran-redirection Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-redirection_1.0.1-1.ca2604.1_all.deb Size: 34070 MD5sum: 7bc9b9d865136bba565bd7a894adb285 SHA1: 87ca55d3b9de4a7052998796b630b4b667162879 SHA256: 22db7883ef7f87d3b74d6cb30591a5ac364365aef75681ef3a480c063407e58d SHA512: 9b04018a6207f012b9fe8defa0ed14c21a650b6eeb64f9bc1df643fb34b0d52379c3642c201d30fcb745a16e75617191ea5f19d7ac865697f9e773bacdbac58a Homepage: https://cran.r-project.org/package=ReDirection Description: CRAN Package 'ReDirection' (Predict Dominant Direction of Reactions of a Biochemical Network) Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. 'ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external. Package: r-cran-redisbasecontainer Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dockerparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-redisbasecontainer_1.0.1-1.ca2604.1_all.deb Size: 284598 MD5sum: 6bbbf4189097b202653a364595d8db4f SHA1: 3ea3a68b71d7e51a9ec384ce31ba6826ca2af103 SHA256: bfa6da5245c613e3bef0894cf75cf98f30e81aec0d3581112021bde521c158d7 SHA512: dfc1d42cef692a2d01f8d008310422db2b0f4eb84cad533808dceeea43871b678a1ba732ada498071aa850350f7d8dbe982a43debc85f96620cd473fe9f362f4 Homepage: https://cran.r-project.org/package=RedisBaseContainer Description: CRAN Package 'RedisBaseContainer' (The Container for the DockerParallel Package) Providing the container for the DockerParallel package. Package: r-cran-rediscover Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3761 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-poissonbinomial, r-cran-shiftconvolvepoibin, r-cran-matrixstats, r-bioc-maftools, r-cran-data.table, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-bioc-biocstyle, r-cran-dplyr, r-cran-kableextra, r-cran-magick, r-bioc-qvalue, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rediscover_0.3.3-1.ca2604.1_all.deb Size: 2758562 MD5sum: f168b0b32ff4c6bab3035aac2f3d497e SHA1: 26002d2c911b69a19d23fc09f3c72b7fe363aea0 SHA256: a4d8dd09d7c6ec925f2b3b180d79477c3a581f920f1608c5a37f9c94d7fae792 SHA512: 3f1153861a2cce59f0072318961da170432af21bca31ed5484164db2e0411506f66ba3e77eaf2e7a235ba344197fa5977b6383564b4d60603f0e603922f66d60 Homepage: https://cran.r-project.org/package=Rediscover Description: CRAN Package 'Rediscover' (Identify Mutually Exclusive Mutations) An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. ]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources. Package: r-cran-redistverse Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-redist, r-cran-redistmetrics, r-cran-geomander, r-cran-ggredist, r-cran-sf, r-cran-censable, r-cran-tinytiger, r-cran-easycensus, r-cran-pl94171, r-cran-alarmdata, r-cran-cli, r-cran-birdie, r-cran-baf Suggests: r-cran-wacolors, r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-redistverse_0.1.2-1.ca2604.1_all.deb Size: 101356 MD5sum: d665f0f90f00d2a2f6ff86a31132dfd7 SHA1: 91d13b9177b382f156b5379df905aa0cee5a6019 SHA256: b62fd36525c05c9656b3dadd7ecb18d9499f5f22875c6f978c7ce54bfa16db0d SHA512: f8e11e00affde77414ce1a4543ece7bc98393e69dbd4971625bd6716a3857d5339e8622dcf7f681046722ef054a511d4b22bfeb2f073f515975591a9990363c1 Homepage: https://cran.r-project.org/package=redistverse Description: CRAN Package 'redistverse' (Easily Install and Load Redistricting Software) Easy installation, loading, and control of packages for redistricting data downloading, spatial data processing, simulation, analysis, and visualization. This package makes it easy to install and load multiple 'redistverse' packages at once. The 'redistverse' is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see . Package: r-cran-redlist Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-magrittr, r-cran-rvest Suggests: r-cran-ggplot2, r-cran-httptest2, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-redlist_0.2.0-1.ca2604.1_all.deb Size: 1030024 MD5sum: eb271b8f29737986b1bae2bbea3457d1 SHA1: eba973d1eeaa9eec7d305f1d25bb5ecde41e948c SHA256: c6f38c7e4e79ec63c8966e050735b64f5d150e2d8f65c66bfca6d0d4692747ce SHA512: 42e0299e176de2deab03b2a957751ba488b22624c3ed8ba66e93d4ea6281fe0e1694a71c31b44673f99edbc61ebddadf0421a785515ea3a5b2051e4e07c9ea8a Homepage: https://cran.r-project.org/package=redlist Description: CRAN Package 'redlist' (Interface to the IUCN Red List Data) Provides an interface to access data from the International Union for Conservation of Nature (IUCN) Red List . It allows users to retrieve up-to-date information on species' conservation status, supporting biodiversity research and conservation efforts. Package: r-cran-redlistr Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-plyr, r-cran-sf, r-cran-terra Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-redlistr_1.0.4-1.ca2604.1_all.deb Size: 381112 MD5sum: 172d196043ed7fc73d17a80e4bfcf6a2 SHA1: e4fe51b472016be9ebe8407773137a691aa65a2f SHA256: 5629eb9929aa7905a5b77ace3cbbb01a075471f2bf06ac7d481bd4f0a62d8da5 SHA512: 7dace436099f6d6987cf62905514b82d20a30a79006ae466a1d3391f10bf3ff4c64497ae7b43965cc2bc9b98cbac0b6eda1f6c9ea10143e84a4aaa66f0444830 Homepage: https://cran.r-project.org/package=redlistr Description: CRAN Package 'redlistr' (Tools for the IUCN Red List of Ecosystems and Species) A toolbox created by members of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems Committee for Scientific Standards. Primarily, it is a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria. See the IUCN website for detailed guidelines, the criteria, publications and other information. Package: r-cran-redmonder Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-redmonder_0.2.0-1.ca2604.1_all.deb Size: 49136 MD5sum: 5c12ab2da1d1fad3c667b5c3e828472a SHA1: bb9a76740ddce9cf4cf8b62c6830037392977809 SHA256: e9966c7735649d8b9e85f598c0dcdd4e1499cdc64f572bd963196c79a0792a36 SHA512: e190504468bd84cc20d38d1b00862b0516b50601e4c97501d26eb92c02a0b5da7148db8892f666538930338d33995d01bb58269859d2765ad6d54ca2d3653abf Homepage: https://cran.r-project.org/package=Redmonder Description: CRAN Package 'Redmonder' (Microsoft(r)-Inspired Color Palettes) Provide color schemes for maps (and other graphics) based on the color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2. 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Package: r-cran-redquack Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-audio, r-cran-cli, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-duckdb, r-cran-httr2, r-cran-labelled, r-cran-readr, r-cran-rlang Suggests: r-cran-arrow, r-cran-keyring, r-cran-pak, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-redquack_0.3.0-1.ca2604.1_all.deb Size: 4121206 MD5sum: 0422c03a353f2c2a8e7c15da082fae83 SHA1: 21db59fc46182814a99d9dbc29c78221dd5e3588 SHA256: 36805bba026ca954467be6f06d188bdc68719517f82563e145a9403c9b706914 SHA512: b81d4917735a3666848b1eeaeacab0771dabb05eea4754b28a7afc6c36b20e5dadd3c1a6087170e750832fb2d1811f0144ef6cdb79ac5350ef4a9b7a02b279e8 Homepage: https://cran.r-project.org/package=redquack Description: CRAN Package 'redquack' (Transfer 'REDCap' Data to Database) Transfer 'REDCap' (Research Electronic Data Capture) data to a database, specifically optimized for 'DuckDB'. Processes data in chunks to handle large datasets without exceeding available memory. Features include data labeling, coded value conversion, and hearing a "quack" sound on success. Package: r-cran-reemtree Architecture: all Version: 0.90.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-rpart, r-cran-aer Filename: pool/dists/resolute/main/r-cran-reemtree_0.90.6-1.ca2604.1_all.deb Size: 68852 MD5sum: 8709196b85a6350ab1be9a189664e83f SHA1: 58c6fae3aeab643ca78b66875a6f9d28c85ada72 SHA256: 753647cdbc706d797341d39151cc6436af3978c7f38c9699106c957a7a900e0b SHA512: 523a4dc75fb6c94c97fd80217c0a5c047989fa86340904145767d0a183cd05f7c8fd25955dd658d4a03b86893a9652d18bed5907d59ca2315c48c18d0ef0ec89 Homepage: https://cran.r-project.org/package=REEMtree Description: CRAN Package 'REEMtree' (Regression Trees with Random Effects for Longitudinal (Panel)Data) A data mining approach for longitudinal and clustered data, which combines the structure of mixed effects model with tree-based estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM trees: a data mining approach for longitudinal and clustered data . Package: r-cran-ref.icar Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-spdep, r-cran-mvtnorm, r-cran-coda, r-cran-mcmcglmm, r-cran-rdpack, r-cran-pracma, r-cran-classint, r-cran-dplyr, r-cran-ggplot2, r-cran-gtools Suggests: r-cran-maps, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-rcrossref, r-cran-spdata, r-cran-formatr Filename: pool/dists/resolute/main/r-cran-ref.icar_2.0.2-1.ca2604.1_all.deb Size: 595056 MD5sum: 6467148545992291a8d234a84dc4aa88 SHA1: 1161bb710d0de17fc6ede0882dfe35d2ded86670 SHA256: 1b48e22c9de745d32b4d36366cd3580e0f008c6e2315102fc622c8e2e31a8964 SHA512: 20ef7077932800153807c4567adb9cca0513e8f2222b5e30ac0f8533ac6f510999ee5588774c6a69e0c7ca00b3d2e1ab2e8028849e195acdfc84db6059017204 Homepage: https://cran.r-project.org/package=ref.ICAR Description: CRAN Package 'ref.ICAR' (Objective Bayes Intrinsic Conditional Autoregressive Model forAreal Data) Implements an objective Bayes intrinsic conditional autoregressive prior. This model provides an objective Bayesian approach for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of spatial random effects. Package: r-cran-refa Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmultivar Filename: pool/dists/resolute/main/r-cran-refa_0.2.0-1.ca2604.1_all.deb Size: 40734 MD5sum: 5db8d1939da9859ce8c68bcbbd0cc5dc SHA1: a9e1c51677cd8667aa07fb315b848057158c28e0 SHA256: 321c6d1e696e72dd87ed0a1882cee69b98646e0b15a08ef312710d1b87dadb43 SHA512: 01fd0b53eecbc2930836a66ab054787f168208878052b3821efe839368f6883981e41109148216f743324f4430e8b0385bab9b0bc7a0ba8b6cfcb64da1b55fc7 Homepage: https://cran.r-project.org/package=REFA Description: CRAN Package 'REFA' (Robust Exponential Factor Analysis) A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) . Package: r-cran-refdb Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-readr, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-rentrez, r-cran-taxize, r-cran-xml2, r-cran-bioseq, r-cran-ape, r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-yaml, r-cran-rlang, r-cran-rmarkdown, r-cran-leaflet Suggests: r-cran-testthat, r-cran-covr, r-cran-dt, r-cran-knitr, r-cran-forcats Filename: pool/dists/resolute/main/r-cran-refdb_0.1.3-1.ca2604.1_all.deb Size: 688470 MD5sum: f591921dcb350de0627a731bcdcde114 SHA1: b612a75ef444a82d9075a6797005a15a127c1d0c SHA256: cc16399d33753cf5fdb8541ee2f61a44e6cd06ab836b9b649e25042399c40606 SHA512: d9528b2b9e7a980e19117109ccbd49d930e30705e3fda3a87a18d4a1e41e72c522e6117975d951ca2c71c32283ad8be8da12f3f627dc8bc7f454d18ae23e3f0a Homepage: https://cran.r-project.org/package=refdb Description: CRAN Package 'refdb' (A DNA Reference Library Manager) Reference database manager offering a set of functions to import, organize, clean, filter, audit and export reference genetic data. Provide functions to download sequence data from NCBI GenBank . Designed as an environment for semi-automatic and assisted construction of reference databases and to improve standardization and repeatability in barcoding and metabarcoding studies. Package: r-cran-refer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-elist, r-cran-matchr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-refer_0.1.0-1.ca2604.1_all.deb Size: 131520 MD5sum: 388fb1eaefcffaab2593537a6fbd7e76 SHA1: a7abf74c7c6a871f36518be7353cd99378c32177 SHA256: ebabf832ff84ac36ea4bfe5848b171806ccdbbaadc49e6fe604bcda21d840f5e SHA512: 9b4c8da9ef7789231f66adb20a12c6e71b2a06facd66b537e9ca2187bd30e6dcfdbd32140ba56c92cc44a8cde2870f18214b54b6645cfc0fb0325727d304f57a Homepage: https://cran.r-project.org/package=refer Description: CRAN Package 'refer' (Create Object References) Allows users to easily create references to R objects then 'dereference' when needed or modify in place without using reference classes, environments, or active bindings as workarounds. Users can also create expression references that allow subsets of any object to be referenced or expressions containing references to multiple objects. Package: r-cran-referenceintervals Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-extremevalues, r-cran-mass, r-cran-outliers Filename: pool/dists/resolute/main/r-cran-referenceintervals_1.3.1-1.ca2604.1_all.deb Size: 77492 MD5sum: 89eff3e23ee04c979ef7f67cdade2ff2 SHA1: 1f227e65948a5a438e4a067e82b0dec15d04cd7a SHA256: 82cc8640d31a9385feb8f5ffee2c6b60529618b71c8abb2f8a546dc655b37a38 SHA512: f35951c83bdbecdb8361009c155a72b1985cac5ac684dec54e3e53e3573e1a36c562f7c6240554079b2395ec3f978d41e42aaec72eca5f8aa6b39bb7dbdfe698 Homepage: https://cran.r-project.org/package=referenceIntervals Description: CRAN Package 'referenceIntervals' (Reference Intervals) This is a collection of tools to allow the medical professional to calculate appropriate reference ranges (intervals) with confidence intervals around the limits for diagnostic purposes. Package: r-cran-reffectivepred Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-config, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-reffectivepred_1.0.1-1.ca2604.1_all.deb Size: 110458 MD5sum: 611a841e5537bdf4f1ecc20292601872 SHA1: 19f6cb91f9a4850f4e37b7e16c9b29b0bf274492 SHA256: fbef074105ca2e53ec01be0650eccf84eef84ee6e31d4b819519e9415b495639 SHA512: 2c5e9fb2ab0d2dc418a2658950be4a9f3421f1ff819a7bc0e159e595a0de3377596745ad4a7f6e4c36bb2c5347c775dadfaa587d168b69d51aa066ebea2891e2 Homepage: https://cran.r-project.org/package=REffectivePred Description: CRAN Package 'REffectivePred' (Pandemic Prediction Model in an SIRS Framework) A suite of methods to fit and predict case count data using a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) model, based on an assumed specification of the effective reproduction number. The significance of this approach is that it relates epidemic progression to the average number of contacts of infected individuals, which decays as a function of the total susceptible fraction remaining in the population. The main functions are pred.curve(), which computes the epidemic curve for a set of parameters, and estimate.mle(), which finds the best fitting curve to observed data. The easiest way to pass arguments to the functions is via a config file, which contains input settings required for prediction, and the package offers two methods, navigate_to_config() which points the user to the configuration file, and re_predict() for starting the fit-predict process. The main model was published in Razvan G. Romanescu et al. . Package: r-cran-refiner Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5692 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ash, r-cran-future, r-cran-future.apply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-refiner_2.0.0-1.ca2604.1_all.deb Size: 4472084 MD5sum: bcecdfda8ff23e49da7a8d2a70688612 SHA1: 923a64b151694a760ef6015d15a5fdf0d43fe8e8 SHA256: 6e1436e8de341f9d928a71c2774da7d29735bdd033f510d26deb51d24610e084 SHA512: 48e48d9e276df3efaa1ed4dd6dbba81917381d218960d0568d3f342f19969deef52e7d96e21932059ca12b3299ed4e184be906b2973d241d4bf2874a320c33d2 Homepage: https://cran.r-project.org/package=refineR Description: CRAN Package 'refineR' (Reference Interval Estimation using Real-World Data) Indirect method for the estimation of reference intervals (RIs) using Real-World Data ('RWD') and methods for comparing and verifying RIs. Estimates RIs by applying advanced statistical methods to routine diagnostic test measurements, which include both pathological and non-pathological samples, to model the distribution of non-pathological samples. This distribution is then used to derive reference intervals and support RI verification, i.e., deciding if a specific RI is suitable for the local population. The package also provides functions for printing and plotting algorithm results. See ?refineR for a detailed description of features. Version 1.0 of the algorithm is described in 'Ammer et al. (2021)' . Additional guidance is in 'Ammer et al. (2023)' . The verification method is described in 'Beck et al. (2025)' . Package: r-cran-refitgaps Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-refitgaps_0.1.2-1.ca2604.1_all.deb Size: 108036 MD5sum: e6049b56e224855078025fc31ddff821 SHA1: 39562f6555dc21d002bf5c005ba13462f94a6ee0 SHA256: cd32a473abd4c8b0c6498c6da5a0df5f84d375394f79f859c51f93dcd1ef8593 SHA512: a7197cc8e819407c923a404581297d34267cbf84ab8f330db7a1c4318b6f42b82de9493abe446d5405e0c815e493f7577c3d135550f87c7c62b5c0d21f6e01e9 Homepage: https://cran.r-project.org/package=refitgaps Description: CRAN Package 'refitgaps' (Reduce the Number of Holes in the School Timetable) Reallocating the respective lessons by hours (respecting the constraints induced by the existence of coupled lessons) so that the total number of gaps is as small as possible. 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It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) . It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model. 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The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included. Package: r-cran-regspec Architecture: all Version: 2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-regspec_2.7-1.ca2604.1_all.deb Size: 77952 MD5sum: 0cb59f40aa866fb96e2fd49cc21522b6 SHA1: 8d2876eb5a0d88415b035b7b64f809bfbd60ca5d SHA256: 55f128d62fe011d352bfac558d6f8d6c47c5ae1f6294a922a0236c731a094523 SHA512: 168919eb88a0da22bb88212521fcda5f9849da0cde8069c81a155199e6263247ed86f58953dc8572d9e8470e30e357c40797fdb7d0660d70d8bff8568023c2ed Homepage: https://cran.r-project.org/package=regspec Description: CRAN Package 'regspec' (Non-Parametric Bayesian Spectrum Estimation for Multirate Data) Computes linear Bayesian spectral estimates from multirate data for second-order stationary time series. Provides credible intervals and methods for plotting various spectral estimates. Please see the paper `Should we sample a time series more frequently?' (doi below) for a full description of and motivation for the methodology. Package: r-cran-regsubseq Architecture: all Version: 0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-regsubseq_0.12-1.ca2604.1_all.deb Size: 1735980 MD5sum: b533547ebd857afa9c9d2cd3b9004118 SHA1: 728bde20f5255a72061de6883a2fbb25d34beaee SHA256: 4380d059d2f1f631efc74a0bc80c102bdffd852ac8c0f4a7e1cec9feb344c314 SHA512: cf5b3447e89096c0d9026a2b3287d6acfe3af1fe9e13756277cc43967900abbf368dbd3471a99776866a8d8869fc738a8f1e5f991484e1eb92c10f962dd2e203 Homepage: https://cran.r-project.org/package=regsubseq Description: CRAN Package 'regsubseq' (Detect and Test Regular Sequences and Subsequences) For a sequence of event occurence times, we are interested in finding subsequences in it that are too "regular". We define regular as being significantly different from a homogeneous Poisson process. The departure from the Poisson process is measured using a L1 distance. See Di and Perlman 2007 for more details. Package: r-cran-regtomean Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formattable, r-cran-effsize, r-cran-plotrix, r-cran-ggplot2, r-cran-htmlwidgets Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-regtomean_1.2.1-1.ca2604.1_all.deb Size: 50212 MD5sum: 5d4b6b0ab7ed48066c873aec563139b4 SHA1: 62e9da0ca7da0febc04fc326e401e894716e4a52 SHA256: 5dc3c39a699dd4d83244dcfce538177129299d84ed6caca28a5e50616d72d645 SHA512: 1d198d538f55de09f3b0678df803c7f3c32439439f9d5d6c4e6b665b4894af4d6e986ca508d8aac50f854afefd93d5221ac0a49659230656aeb928cdbcce259d Homepage: https://cran.r-project.org/package=regtomean Description: CRAN Package 'regtomean' (Regression Toward the Mean) In repeated measures studies with extreme large or small values it is common that the subjects measurements on average are closer to the mean of the basic population. Interpreting possible changes in the mean in such situations can lead to biased results since the values were not randomly selected, they come from truncated sampling. This method allows to estimate the range of means where treatment effects are likely to occur when regression toward the mean is present. Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.. Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant. 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Novel graphical methods for assessment of parametric models using nonparametric methods. One vs. All and All vs. All multiclass classification, optional class probabilities adjustment. Nonparametric regression (k-NN) for general dimension, local-linear option. Nonlinear regression with Eickert-White method for dealing with heteroscedasticity. Utilities for converting time series to rectangular form. Utilities for conversion between factors and indicator variables. Some code related to "Statistical Regression and Classification: from Linear Models to Machine Learning", N. Matloff, 2017, CRC, ISBN 9781498710916. 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Implementation of a clustering approach based on the k-means algorithm that can be used with any distance. In addition, implementation of the Hartigan and Wong method to accommodate alternative distance metrics. Both methods can operate with any distance measure, provided a suitable method is available to compute cluster centers under the chosen metric. Additionally, the k-medoids algorithm is implemented, offering a robust alternative for clustering without the need of computing cluster centers under the chosen metric. All three methods are designed to support Relative distances, Euclidean distances, and any user-defined distance functions. The Hartigan and Wong method is described in Hartigan and Wong (1979) and an explanation of the k-medoids algorithm can be found in Reynolds et al (2006) . 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Package: r-cran-reldist Architecture: all Version: 1.7-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-densestbayes Suggests: r-cran-locfit Filename: pool/dists/resolute/main/r-cran-reldist_1.7-2-1.ca2604.1_all.deb Size: 180708 MD5sum: c90b77f4aa61aaf89701eb0575751ad1 SHA1: fe35e78280ad9c92812a991d5df3008bec76753a SHA256: 6dd52bc7cbeae860eed91ae2926660b0e6a9ed15eeeea1de2e0d8d87480c86a8 SHA512: 6ebe9c22afc4e9240cf8022c9329539646c7665329304821af80e8469260891ca5d0ee1ce9a02a4091810ce89171252a2589f028244beb49c35b07aa6ea21639 Homepage: https://cran.r-project.org/package=reldist Description: CRAN Package 'reldist' (Relative Distribution Methods) Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789. 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This package prepares these files and also updates the versions according to the branches. It relies heavily on the 'desc' packages. 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All the above will allow the users to carry out deep studies on the results obtained in any type of electoral process. The methods are described in: Oñate, Pablo and Ocaña, Francisco A. (1999, ISBN:9788474762815); Ruiz Rodríguez, Leticia M. and Otero Felipe, Patricia (2011, ISBN:9788474766226). 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K., and Tibshirani, R., (2019) for details. 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See Dimitriadis, Gneiting, Jordan (2021) . Package: r-cran-reliabilitytheory Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-actuar, r-cran-combinat, r-cran-fraction, r-cran-igraph, r-cran-mcmc, r-cran-phasetype, r-cran-sfsmisc Suggests: r-cran-testthat, r-cran-reshape2, r-cran-ggplot2, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-reliabilitytheory_0.3.1-1.ca2604.1_all.deb Size: 271836 MD5sum: e9c9d51ff076069ac804f833ffd4eade SHA1: 337002775b9fe4050162a4babc1caba2d4405d13 SHA256: 44d91e29c505a5efae24ced60e948b7ff9a75ff2e1d4f04a821b1073d9bd7c49 SHA512: 9de041152f591b736d0846347b0183c916b80e836149a7ed03f5c38b1f1600efcb62269fe9d228f1c2cc57749566e5fe9cabb5cd9beddb119e85c8960f0ff0c6 Homepage: https://cran.r-project.org/package=ReliabilityTheory Description: CRAN Package 'ReliabilityTheory' (Structural Reliability Analysis) Perform structural reliability analysis, including computation and simulation with system signatures, Samaniego (2007) , and survival signatures, Coolen and Coolen-Maturi (2013) . Additionally supports parametric and topological inference given system lifetime data, Aslett (2012) . Package: r-cran-reliacoef Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-psych, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rcsdp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-reliacoef_1.0.1-1.ca2604.1_all.deb Size: 134518 MD5sum: ee6885f3b2b9d2578d3303ccf9cb633c SHA1: 7ec7ffbb0a42c4b9290eff80ace166191217583f SHA256: 12c4e860e6dc275015effe19c95fd889fc6e2bee29a811fd2454e4c0d0bf8a00 SHA512: bb510f602e7a622e178e66571349a24a4a5f2607a900beb917f44d58357fa73f1e1790a008064b4d9832e1b7444c68113f38cc9a7e99361a10cc51489314d10a Homepage: https://cran.r-project.org/package=reliacoef Description: CRAN Package 'reliacoef' (Unidimensional and Multidimensional Reliability Coefficients) Calculates and compares various reliability coefficients for unidimensional and multidimensional scales. Supported unidimensional estimators include coefficient alpha, congeneric reliability, the Gilmer-Feldt coefficient, Feldt's classical congeneric reliability, Hancock's H, Heise-Bohrnstedt's omega, Kaiser-Caffrey's alpha, and Ten Berge and Zegers' mu series. Multidimensional estimators include stratified alpha, maximal reliability, correlated factors reliability, second-order factor reliability, and bifactor reliability. See Cho (2021) , Cho (2024) , Cho (2025) . 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RGA models include the Duane (1962) , NHPP by Crow (1975) (No. AMSAATR138), Piecewise Weibull NHPP by Guo et al. (2010) , and Piecewise Weibull NHPP with Change Point Detection based on the 'segmented' package by Muggeo (2024) . Repairable systems functions include the Mean Cumulative Function (MCF) using the Nelson-Aalen estimator, parametric Power Law and Log-Linear NHPP models, and forecasting. Package: r-cran-relialearnr Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr, r-cran-reliagrowr, r-cran-weibullr, r-cran-weibullr.alt Suggests: r-cran-knitr, r-cran-mockery, r-cran-reliaplotr, r-cran-reliashiny, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-relialearnr_0.3-1.ca2604.1_all.deb Size: 501264 MD5sum: ff9f977d9eabbd9cb2d575e4820c581b SHA1: 39ea93c6b0e738b983783e3fc0a454cc266a2151 SHA256: 5d2ae2bfd7b0a73e0987209e5f745cdae8b585cb7010cb979ab0023ca86d7618 SHA512: 49a506e1fccbfb93c95ec137e058c18c4ec78966ce1164aebc7d946adfdf491458c2a267c07bd2eab52d628675611405ac3cac35356d7c7204e074e28863cef6 Homepage: https://cran.r-project.org/package=ReliaLearnR Description: CRAN Package 'ReliaLearnR' (Learning Modules for Reliability Analysis) Learning modules for reliability analysis including modules for Reliability, Availability, and Maintainability (RAM) Analysis, Life Data Analysis, and Reliability Testing. 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Package: r-cran-reliar Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-reliar_0.2-1.ca2604.1_all.deb Size: 580634 MD5sum: 16b2d866f56e2ae6551b3fb19f449924 SHA1: f5eb5d478f0e6cc8821ee68e06839497ec384e50 SHA256: 5153be3f89cd01b3f02c4e3649836c2437713131c256c4a2fc426d96297498f6 SHA512: ed57df43960f4097079794aef77c4eea3f33d519cb51d5365f87138e92c1206f71ce2c6b2e6dc4e73b42e0d20fb8e2b3ab61626ac32ee0d16a6d036eb6aed40d Homepage: https://cran.r-project.org/package=reliaR Description: CRAN Package 'reliaR' (Comprehensive Tools for some Probability Distributions) Provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling. 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The app provides an easy-to-use interface for performing reliability analysis using 'WeibullR' and 'ReliaGrowR' . 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Package: r-cran-relimppcr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-relaimpo, r-cran-rmisc, r-cran-caret, r-cran-ggplot2, r-cran-reshape2, r-cran-logger Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-relimppcr_0.3.0-1.ca2604.1_all.deb Size: 34974 MD5sum: 874125f1489418a75a91b6b16ddd843a SHA1: dfae7ef02e149730b5332a8ea8594a2e5778de66 SHA256: aaa236d6f3c038d269fa6b09be316390017b36798f974f288ef8e41b82493107 SHA512: 1220b3806c0fd050994d8f49a01df33bbf50a11b975d29a165f788a3645f4cf7e092ab68491b5006808c241fb989789134c5c68a20ed1012874bb72255e20538 Homepage: https://cran.r-project.org/package=RelimpPCR Description: CRAN Package 'RelimpPCR' (Relative Importance PCA Regression) Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) . Package: r-cran-relmix Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-familias, r-cran-flextable, r-cran-officer, r-cran-pedfamilias, r-cran-pedtools Suggests: r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-relmix_1.4.1-1.ca2604.1_all.deb Size: 260268 MD5sum: d7b20201818ac905d90c516b84723d3b SHA1: bc7be60890eaf422dba3441f98f4a0b6a88bfdaf SHA256: 173d8c77e9993906117a018e44d061b4611ed9d9b98110e81f471bc00c64d6d9 SHA512: db708ecac8f191ea29f9ced5287d4c8736c2531cf8c891c1c53fa33ce851216ae1844a05b61cc4d9aa24dc6f75f6067054d97f1596bb88fc1a633487c8a56226 Homepage: https://cran.r-project.org/package=relMix Description: CRAN Package 'relMix' (Relationship Inference for DNA Mixtures) Analysis of DNA mixtures involving relatives by computation of likelihood ratios that account for dropout and drop-in, mutations, silent alleles and population substructure. This is useful in kinship cases, like non-invasive prenatal paternity testing, where deductions about individuals' relationships rely on DNA mixtures, and in criminal cases where the contributors to a mixed DNA stain may be related. Relationships are represented by pedigrees and can include kinship between more than two individuals. The main function is relMix() and its graphical user interface relMixGUI(). The implementation and method is described in Dorum et al. (2017) , Hernandis et al. (2019) and Kaur et al. (2016) . Package: r-cran-remap Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-units Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-mgcv, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-remap_0.3.2-1.ca2604.1_all.deb Size: 572154 MD5sum: b27dde7a7b39650c2a02b2a963eba2c4 SHA1: 19decae6c164010ffb693360ffc72caa1315df31 SHA256: 1653f15236c0dfcc274c0e1d478e7ecc715466d519cce2176369b7343ec70c75 SHA512: b89fa404e4e855a4f83796e209b7d05b33751a3277279cad83d4aa04f5688a769e1e0120acf51befc0cd23955fdaaead17fb1b7c80538b44594cc58ba79d4cbe Homepage: https://cran.r-project.org/package=remap Description: CRAN Package 'remap' (Regional Spatial Modeling with Continuous Borders) Automatically creates separate regression models for different spatial regions. The prediction surface is smoothed using a regional border smoothing method. 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Package: r-cran-rematch Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rematch_2.0.0-1.ca2604.1_all.deb Size: 17732 MD5sum: 9ac881a7b537c4800796714d94d2d096 SHA1: 2e99e040f18167e7ed53f73652f3f603ebb8f393 SHA256: 5c32e4768c3e1231f0fa066d95dbd7cbdfc2f17f67f41f157980e132932ff6dc SHA512: ab6b63e521089c2c7938e4a8d86aa613adb35ac0b6dea93c980edab7f8d89ad240b92cc6dcc2aef93c642b57bd492a95839e1b9b8ebac405eb786ee58e1900f6 Homepage: https://cran.r-project.org/package=rematch Description: CRAN Package 'rematch' (Match Regular Expressions with a Nicer 'API') A small wrapper on 'regexpr' to extract the matches and captured groups from the match of a regular expression to a character vector. Package: r-cran-remedy Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-rstudioapi, r-cran-rematch2 Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-remedy_0.1.0-1.ca2604.1_all.deb Size: 99360 MD5sum: 5a099922c91549695db99d218efbbfc5 SHA1: df9fceb610bce19fb69e9fd7e1497878624ff02b SHA256: 4462c2875864bfa6c20cceba05bcf69ef8ec79b73471db18a38b16677d47b59f SHA512: 5058761796ddf9670895ddbc81d659156e4848ed07da1626e57792e454e04a8551a6a4a0ac80bf3bf760e51b28388c02e4bd31b96c237833e156c6b2ce12b917 Homepage: https://cran.r-project.org/package=remedy Description: CRAN Package 'remedy' ('RStudio' Addins to Simplify 'Markdown' Writing) An 'RStudio' addin providing shortcuts for writing in 'Markdown'. This package provides a series of functions that allow the user to be more efficient when using 'Markdown'. 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'ReMFPCA' is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, 'ReMFPCA' generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in 'ReMFPCA', please refer to Haghbin et al. (2023) . 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Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis. Package: r-cran-remixed Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-desolve, r-cran-rsmlx, r-cran-dosnow, r-cran-dplyr, r-cran-fastghquad, r-cran-ggplot2, r-cran-snow, r-cran-stringr, r-cran-rmpfr Filename: pool/dists/resolute/main/r-cran-remixed_1.1.2-1.ca2604.1_all.deb Size: 1033798 MD5sum: 48b21daf1de60c4624c16bf445004fee SHA1: 13539a4c663408167f4ccc34dfe823a899ea542b SHA256: 69781ac5b1b14495b1c52868def8f1c85e4b5026e512b8526b2967c7599b954d SHA512: 8d3858e6348d83c737adeb63949b7d67365d49135c6360d74ae54dceb9b2d570a1f67eb838fef9a47154e50b0017441665f8dcf36cd61f37a306e95022aab742 Homepage: https://cran.r-project.org/package=REMixed Description: CRAN Package 'REMixed' (Regularized Estimation in Mixed Effects Model) Implementation of an algorithm in two steps to estimate parameters of a model whose latent dynamics are inferred through latent processes, jointly regularized. This package uses 'Monolix' software (), which provide robust statistical method for non-linear mixed effects modeling. 'Monolix' must have been installed prior to use. Package: r-cran-remla Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gparotation, r-cran-geex Suggests: r-cran-knitr, r-cran-lavaan, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-remla_1.2.0-1.ca2604.1_all.deb Size: 95742 MD5sum: 893cd12c3889421abd21558f1c175119 SHA1: 799aed00d132defb930e3291f068e4bf1486ebff SHA256: ebfa323a265f22a7e2a8575356fd40167867e9835776423b103c46b49d1320c6 SHA512: 234ac3a361b2357361a6ae57f349c912ee566bd5a90e1d0bf154147da66ef46661fc6e35dcbf40cb54d2ca4da7ba576554fb17eb944d46584631b95dcb0434b8 Homepage: https://cran.r-project.org/package=REMLA Description: CRAN Package 'REMLA' (Robust Expectation-Maximization Estimation for Latent VariableModels) Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. However, this assumption is often violated in practice given that individuals may differ in their age, gender, socioeconomic status, and other factors that can affect their latent structure. The robust expectation maximization (REM) algorithm is a statistical method for estimating the parameters of a latent variable model in the presence of population heterogeneity as recommended by Nieser & Cochran (2023) . The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model. Package: r-cran-remm Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stream, r-cran-cluster, r-cran-clustergeneration, r-cran-mass, r-cran-proxy, r-cran-igraph Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-remm_1.2.1-1.ca2604.1_all.deb Size: 911606 MD5sum: 0793b6699849bf963f1fbbbf733383c7 SHA1: 90436b577c47920b8c832db6d6728bcaa140d61a SHA256: dc1a0bd2743b1f18a18141fad8c6c12d3ccf8e795f276b8922a535be9a6665f1 SHA512: 44475cb89ef1d2004110b0470a09e9d9a317e181f1048eca0ad027d1b1fef80223125831cb20cc08a0518c0555e73b107c2f55253e3afea7c3e512ce65fca399 Homepage: https://cran.r-project.org/package=rEMM Description: CRAN Package 'rEMM' (Extensible Markov Model for Modelling Temporal RelationshipsBetween Clusters) Implements TRACDS (Temporal Relationships between Clusters for Data Streams), a generalization of Extensible Markov Model (EMM). TRACDS adds a temporal or order model to data stream clustering by superimposing a dynamically adapting Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN data stream clustering). Development of this package was supported in part by NSF IIS-0948893 and R21HG005912 from the National Human Genome Research Institute. Hahsler and Dunham (2010) . Package: r-cran-remmy Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-remmy_0.1.0-1.ca2604.1_all.deb Size: 359818 MD5sum: a673778bf4635c8d1667b8acd9671a09 SHA1: 79215aa52f28c0c0980ece7cd7b0dbe58656e8f8 SHA256: 39a9be1fe2b2014f562e72e69ac78190eb10db67555c6a4f00b1be8d3664a053 SHA512: ee81d6469427e4a54ef6e7264668e3a3d1877883edae99e0cf220c3b4d4199ffacff954c1029428afc16bbba3c5fe210fe8f145202400bf7d7fa803aac969c45 Homepage: https://cran.r-project.org/package=remmy Description: CRAN Package 'remmy' (API Client for 'Lemmy') An HTTP API client for 'Lemmy' () in R. Code and documentation are generated from the official 'JavaScript' client source (). 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This package provides the 'install_*' functions in 'devtools'. Indeed most of the code was copied over from 'devtools'. Package: r-cran-rempsyc Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3703 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr Suggests: r-cran-flextable, r-cran-ggplot2, r-cran-effectsize, r-cran-performance, r-cran-insight, r-cran-correlation, r-cran-datawizard, r-cran-report, r-cran-modelbased, r-cran-see, r-cran-lmtest, r-cran-ggrepel, r-cran-boot, r-cran-bootes, r-cran-ggsignif, r-cran-qqplotr, r-cran-broom, r-cran-emmeans, r-cran-ggpubr, r-cran-interactions, r-cran-openxlsx2, r-cran-patchwork, r-cran-psych, r-cran-venndiagram, r-cran-rmisc, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rempsyc_0.2.0-1.ca2604.1_all.deb Size: 2176948 MD5sum: 038be31665bdd5bda74bb78b385c703b SHA1: 2fcef718c8f0f47b53d21ac91367199c95f44085 SHA256: c1e79886140edc3aaacb5b2f1a2734a1a4d9990bd58a4036d8b99877ec0cd689 SHA512: 1bf930c02398295ee3e1bef332511bb55a02438c709a507501be8dc45f095628c494d04a4ed963b46930e4e79dd51eba44801257560c550005686b2ab44a9213 Homepage: https://cran.r-project.org/package=rempsyc Description: CRAN Package 'rempsyc' (Convenience Functions for Psychology) Make your workflow faster and easier. Easily customizable plots (via 'ggplot2'), nice APA tables (following the style of the *American Psychological Association*) exportable to Word (via 'flextable'), easily run statistical tests or check assumptions, and automatize various other tasks. 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TNM stage is important in treatment decision-making and outcome predicting. The existing oropharyngeal Cancer (OPC) TNM stages have not made distinction of the two sub sites of Human papillomavirus positive (HPV+) and Human papillomavirus negative (HPV-) diseases. We developed novel criteria to assess performance of the TNM stage grouping schemes based on parametric modeling adjusting on important clinical factors. These criteria evaluate the TNM stage grouping scheme in five different measures: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance. The methods are described in Xu, W., et al. (2015) . 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Based on the book by Rencher and Christensen (2012, ISBN:9780470178966). Package: r-cran-renext Architecture: all Version: 3.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2010 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evd, r-cran-numderiv Suggests: r-cran-mass, r-cran-ismev, r-cran-xml Filename: pool/dists/resolute/main/r-cran-renext_3.1-5-1.ca2604.1_all.deb Size: 1901264 MD5sum: adb6339ab60c85ecc4a804a113f38787 SHA1: 9043dc457d73f4ff26813305b08cf292ac972434 SHA256: af633be0363ad3c3c309ef2a861ca41da78a647b1628f92a3f04448952566350 SHA512: 7f383b968a20d7e6b844262852cfa45800177c7a673e0aec21e6ba3c2c08427fdeb62cfd531df979967077179fc8cfe7461acb151b2cfce29184d192740ab8c7 Homepage: https://cran.r-project.org/package=Renext Description: CRAN Package 'Renext' (Renewal Method for Extreme Values Extrapolation) Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the excesses can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework. Package: r-cran-renpow Architecture: all Version: 0.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3241 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-renpow_0.1-1-1.ca2604.1_all.deb Size: 1889452 MD5sum: ece6ded840552e18b90b0e11665f65c5 SHA1: 1bd8a2a823e89dafb14f502cbba951a5fed2e926 SHA256: 7e1b4128c9365d59cf505ef99b3f189d9b0fb696dd0a7ee6664f3f772ba71f0e SHA512: 143f092d66c8e6b3601fc682f9db958f9c3dc40ebdf089381d258a095a40c144e56c6e4deb353f0afba1d7a956ed75d26b8abc8a43eb402d466c6890f15e21b5 Homepage: https://cran.r-project.org/package=renpow Description: CRAN Package 'renpow' (Renewable Power Systems and the Environment) Supports calculations and visualization for renewable power systems and the environment. Analysis and graphical tools for DC and AC circuits and their use in electric power systems. Analysis and graphical tools for thermodynamic cycles and heat engines, supporting efficiency calculations in coal-fired power plants, gas-fired power plants. Calculations of carbon emissions and atmospheric CO2 dynamics. Analysis of power flow and demand for the grid, as well as power models for microgrids and off-grid systems. Provides resource and power generation for hydro power, wind power, and solar power. Package: r-cran-rentrez Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rentrez_1.2.4-1.ca2604.1_all.deb Size: 121418 MD5sum: 76d0145ef92fbcff7dd326942001435a SHA1: f897339d426bd42059cdf7a7af133a878e64c491 SHA256: 156a0dabc44a2c738b84e7b09a4ed91938f11176f248908e7e6ac8bccb1fb11b SHA512: 4460d0d07332751491df7ec00e8e14fe814537de808ad8dd5d9adbfe4e1eec288406e73a7c8386d4fe83d9372c1532e5e265d007aff99ef1414238eb52f1c2e0 Homepage: https://cran.r-project.org/package=rentrez Description: CRAN Package 'rentrez' ('Entrez' in R) Provides an R interface to the NCBI's 'EUtils' API, allowing users to search databases like 'GenBank' and 'PubMed' , process the results of those searches and pull data into their R sessions. 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Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible. Package: r-cran-renvlp Architecture: all Version: 3.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-orthogonalsplinebasis, r-cran-pls, r-cran-matrixcalc, r-cran-matrix Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-renvlp_3.4.5-1.ca2604.1_all.deb Size: 804346 MD5sum: 7c3683bd27c1572e8d21e2bdd6d6c175 SHA1: 476755dd5a7510a81b89c32bc600de574c8d3544 SHA256: 2da59720dc6f34a2a406c3017027f26a06da18decf67494a1b00c816550ab019 SHA512: 637dbfe9414f072cec355e8c1f68c88fd0f6ad4837ce9aee393999d0fc5328b9452c27300d3c2abff9171fc3ff5654059ca7482f0773b1e80c36ca69b9215b21 Homepage: https://cran.r-project.org/package=Renvlp Description: CRAN Package 'Renvlp' (Computing Envelope Estimators) Provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. 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"bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session. Package: r-cran-renyiextropy Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-renyiextropy_0.4.0-1.ca2604.1_all.deb Size: 176578 MD5sum: f47996aa83d7d87134520917a8509d8b SHA1: 7973e18704570c2b3b51c8d71f2a0a5b910fd632 SHA256: a47225e104778d926804c017d1f214967d9e56179f17a99a4558b645862d99bf SHA512: f0bc7c20382dc7438151cc86b4cbf63623a03c595ebf12f1e94143c48f38340ed21a9d7d09fd540100ad27e9e6fdead6bbc487389852577625e8c5b8330fab80 Homepage: https://cran.r-project.org/package=RenyiExtropy Description: CRAN Package 'RenyiExtropy' (Entropy and Extropy Measures for Probability Distributions) Provides functions to compute Shannon entropy, Renyi entropy, Tsallis entropy, and related extropy measures for discrete probability distributions. 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Package: r-cran-repairdata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-repairdata_0.1.0-1.ca2604.1_all.deb Size: 1207448 MD5sum: 1984ff35f373f879131c19ad9ad8b87d SHA1: 8da8d5312a8ab80c5b337ffc93ae57cacf32d32e SHA256: 82a1ff1e84f10cc0a6ce18236a539b82623e865d676771931962a13952be5b75 SHA512: 2dca0da3f21ed35b8b9f52f0a3f413610e2d0da6f1a2293dc3fe17721c70790d19772a74a484c594d2c6437a6efea731011f3feafd4b2c5b6618e4fea31a8790 Homepage: https://cran.r-project.org/package=repairData Description: CRAN Package 'repairData' (Open Repair Alliance Datasets 2021) The complete data set of open repair data, full compliant with the Open Repair Data Standards (ORDS). It combines the datasets contributed by partner organizations of the Open Repair Alliance (ORA). Last updated: 2021-02-22. The package also contains via quests enriched datasets on batteries, printer, mobiles, and tablets. 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(2016) . Package: r-cran-replicationsuccess Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-replicationsuccess_1.3.3-1.ca2604.1_all.deb Size: 508622 MD5sum: ef7de76fc67ef51ba1ec3c94026787f9 SHA1: bf4a527f5aa7e0426cad11859beb509ecfd36e96 SHA256: 4e4d576a58ffa9fb8fc2e71b94616fcb5b0c73ddeae4867eb24dd510447d24ae SHA512: aa3ed978042e022c904e9ca0f502f19fa663fedf3d0a7e6bd70bfd6ad833e385f20c53d68c468193f82ab36e17564a106201b7b5cd5e4a2d8075c648d72ca6b0 Homepage: https://cran.r-project.org/package=ReplicationSuccess Description: CRAN Package 'ReplicationSuccess' (Design and Analysis of Replication Studies) Provides utilities for the design and analysis of replication studies. 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Package: r-cran-reporter.nih Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-crayon, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-tibble Suggests: r-cran-devtools, r-cran-ggplot2, r-cran-ggrepel, r-cran-knitr, r-cran-tinytex, r-cran-rmarkdown, r-cran-scales, r-cran-tufte, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-reporter.nih_0.1.4-1.ca2604.1_all.deb Size: 450878 MD5sum: d848057d4f081d788e4df6ff4348082d SHA1: f0bbe2177a3b9ceae3e179d23415e75ebd3c979c SHA256: f29d2c5da61762ff13b9194115714a84b7af6212d7a9c59f9110ffb9b049c0c3 SHA512: ca323bd59a1cee6f2b10f279f27a893ee10a1687a07481d9c649fb2f4937827c188c2d6a58914c5f647a845540434ab8b66a16eab1b7415e23dc6a87acb093b8 Homepage: https://cran.r-project.org/package=repoRter.nih Description: CRAN Package 'repoRter.nih' (R Interface to the 'NIH RePORTER Project' API) Methods to easily build requests in the non-standard JSON schema required by the National Institute of Health (NIH)'s 'RePORTER Project API' . 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Originally designed to create tables, listings, and figures for the pharmaceutical, biotechnology, and medical device industries, these reports are generalized enough that they could be used in any industry. Generates text, rich-text, PDF, HTML, and Microsoft Word file formats. The package specializes in printing wide and long tables with automatic page wrapping and splitting. Reports can be produced with a minimum of function calls, and without relying on other table packages. The package supports titles, footnotes, page header, page footers, spanning headers, page by variables, and automatic page numbering. Package: r-cran-reporterscore Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3935 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-pcutils, r-cran-scales, r-cran-ggnewscale, r-cran-ggrepel, r-cran-reshape2, r-cran-stringr, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plyr, r-cran-e1071, r-cran-factoextra, r-cran-snow, r-cran-dosnow, r-cran-pheatmap, r-cran-readr, r-cran-r.utils, r-bioc-keggrest, r-bioc-ggkegg, r-bioc-clusterprofiler, r-bioc-enrichplot, r-bioc-pathview, r-cran-gsa, r-cran-vegan, r-cran-metanet, r-cran-igraph, r-cran-ggraph, r-bioc-padog, r-bioc-safe, r-cran-rsea, r-bioc-gsva Filename: pool/dists/resolute/main/r-cran-reporterscore_0.2.5-1.ca2604.1_all.deb Size: 3875552 MD5sum: e743cfe7efe34d04c6a0abc9552b8ff9 SHA1: 7d718616eb131f3d4f1a30f01e9bd50fb1b5f0ee SHA256: b507aee1aed79a8236d6ecf7774aa9cf604587d26686baca520772a32f562c06 SHA512: fac6fb6f5cda570b1c2b72d637dbbf064d7efb18a34a9f77278ab0888a9e8f01987cdda85f42ece08f4511e2be8b5d75fbf5fc47791592642203ba481916dca1 Homepage: https://cran.r-project.org/package=ReporterScore Description: CRAN Package 'ReporterScore' (Generalized Reporter Score-Based Enrichment Analysis for OmicsData) Inspired by the classic 'RSA', we developed the improved 'Generalized Reporter Score-based Analysis (GRSA)' method, implemented in the R package 'ReporterScore', along with comprehensive visualization methods and pathway databases. 'GRSA' is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the 'GRSA' supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods. Package: r-cran-reportfactory Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rprojroot, r-cran-fs, r-cran-rmarkdown, r-cran-yaml, r-cran-callr, r-cran-rstudioapi, r-cran-knitr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-reportfactory_0.4.0-1.ca2604.1_all.deb Size: 52494 MD5sum: 57aa63c34243101e33e995391357b479 SHA1: eaf4b07e2e65665f350b622a1eccedfd98fbf41b SHA256: ad8b973c8b934e6aa712cbdde5fd72b3c992cffdd8a537d3cf6317adbbdbdaab SHA512: 3fb7d5a56c8eaf09a18e6a8b5765cd3768df94d746cc402451b120a8ee71781ad5b4f8714bff188913320653628a8817c6826d1d78e89b9f996f3838b6f4a7c0 Homepage: https://cran.r-project.org/package=reportfactory Description: CRAN Package 'reportfactory' (Lightweight Infrastructure for Handling Multiple R MarkdownDocuments) Provides an infrastructure for handling multiple R Markdown reports, including automated curation and time-stamping of outputs, parameterisation and provision of helper functions to manage dependencies. 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Package: r-cran-reportreg Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-reportreg_0.3.0-1.ca2604.1_all.deb Size: 16634 MD5sum: 55f8dd102620c6aa2b404418068d71c5 SHA1: c274d2c4438996158567b14673d24af17202602f SHA256: 18e0a493425a1565970446783ce5c83ea5f8abf7a2a9ddd2f65c119c467c2034 SHA512: e18d509520b727892e3e962424dfc4e117d1fc96a5e2f5fdd1ea5bebb80ca65bcff965a682735b6ab1f099bc6b2b965ed1b13d006a11c3ab8001c0979c71f038 Homepage: https://cran.r-project.org/package=reportReg Description: CRAN Package 'reportReg' (An Easy Way to Report Regression Analysis) Provides an easy way to report the results of regression analysis, including: 1. Proportional hazards regression from function 'coxph' of package 'survival'; 2. Conditional logistic regression from function 'clogit' of package 'survival'; 3. Ordered logistic regression from function 'polr' of package 'MASS'; 4. Binary logistic regression from function 'glm' of package 'stats'; 5. Linear regression from function 'lm' of package 'stats'; 6. Risk regression model for survival analysis with competing risks from function 'FGR' of package 'riskRegression'; 7. Multilevel model from function 'lme' of package 'nlme'. 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Package: r-cran-reportroc Architecture: all Version: 3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-vcd Filename: pool/dists/resolute/main/r-cran-reportroc_3.6-1.ca2604.1_all.deb Size: 29542 MD5sum: 834b92c57f4f903f2e30717da8b293a9 SHA1: 56997dcfe5517a1be97b8fb3492179f4053d0101 SHA256: 1256567db3bc3b182d30365d8117db56eb45a73ebd7d13d812b9e492d0d2fe69 SHA512: 254d5a432afdd668b509cdf16e232dd4bec5d4191eb8687b9892d5010ddd2ca3ac5c6fbfe50ce747ca561f72153f6c86d328042fff8d45724ad2d5e6729eb81e Homepage: https://cran.r-project.org/package=reportROC Description: CRAN Package 'reportROC' (An Easy Way to Report ROC Analysis) Provides an easy way to report the results of ROC analysis, including: 1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy), SEN (sensitivity), SPE (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), PPV (positive predictive value), NPV (negative predictive value), PPA (percentage of positive accordance), NPA (percentage of negative accordance), TPA (percentage of total accordance), KAPPA (kappa value). Package: r-cran-reportsubtotal Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-reportsubtotal_0.1.2-1.ca2604.1_all.deb Size: 34084 MD5sum: f8b081d450af8fafa758c23b38127f52 SHA1: 4abdefd6467a94ff3f4397fed4f6047f433b208a SHA256: 8de0a710947584030a9e4550610a721c6c855143484176c856a12741518e411f SHA512: 3e700d8b48ff18eb577b3a3a76125c674bf0637318b4352e7f59d7bb8e944b6de5a6d8454d4093d02a089dcf57396f88258a662c39c773d4efa399c58938b8a1 Homepage: https://cran.r-project.org/package=ReportSubtotal Description: CRAN Package 'ReportSubtotal' (Adds Subtotals to Data Reports) Adds subtotal rows / sections (a la the 'SAS' 'Proc Tabulate' All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. 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Package: r-cran-repplab Architecture: all Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2036 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-lattice, r-cran-ldrtools Suggests: r-cran-tourr, r-cran-amap Filename: pool/dists/resolute/main/r-cran-repplab_0.9.6-1.ca2604.1_all.deb Size: 1258958 MD5sum: b7fd54e379ee28ce60b4ee24891ff24b SHA1: a8a47a8c2e151dd0a4b1a58f7551bb6c82c48436 SHA256: b13ce907caca3b29f0260092b999ed095d53fcb8438b557a4d9cf71bce08a500 SHA512: 7cc4943c495b44e62bb85d9a0519e015a0dac714f137934e1e04340b11ed8bac9e810d2b7cf3d9b5faaabfb048481199c97d4b092de7b8e3db60acbc964a16b5 Homepage: https://cran.r-project.org/package=REPPlab Description: CRAN Package 'REPPlab' (R Interface to 'EPP-Lab', a Java Program for ExploratoryProjection Pursuit) An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for projection pursuit using genetic algorithms written by Alain Berro and S. Larabi Marie-Sainte and is included in the package. 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Package: r-cran-represent Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-represent_1.0.1-1.ca2604.1_all.deb Size: 47590 MD5sum: 6083e00ca69a795ebeb051d61bab7e0b SHA1: 1893cb215221e30b03115e1c827dab22c3816466 SHA256: 5b78dd0f0e9dba95050f922042337ac2cc056960b2eec2e8d378154f07e7fd5c SHA512: 412a0dbaabb2d632a70f99fad21cf96bdd9335d55a399ee66deb44255d5b254e53f6080a63e25cb63ecf00489788aa1bd44d089888165ab44eb372e1ada08795 Homepage: https://cran.r-project.org/package=represent Description: CRAN Package 'represent' (Determine How Representative Two Multidimensional Data Sets are) Compute the values of various parameters evaluating how similar two multidimensional datasets' structures are in multidimensional space, as described in: Jouan-Rimbaud, D., Massart, D. L., Saby, C. A., Puel, C. (1998), . The computed parameters evaluate three properties, namely, the direction of the data sets, the variance-covariance of the data points, and the location of the data sets' centroids. The package contains workhorse function jrparams(), as well as two helper functions Mboxtest() and JRsMahaldist(), and four example data sets. Package: r-cran-represtools Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-whisker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-represtools_0.1.3-1.ca2604.1_all.deb Size: 50074 MD5sum: e663335b44e5151944225fbe17806deb SHA1: 749c20dc94201314f57ad92ddf3c070ad021d356 SHA256: 54a20e6f470c06d59597f2a50bf625c7718e6ad2f3b39abbbc2942cc2c117031 SHA512: b023f27ef090a6cdf85135148a95c1de9df58f71f8ec1d7622c05444ef57cc784f27a20eb5a163fdf3adc8344cbb9cff2d04569e3bc3d1c2e53cfb8e121e588f Homepage: https://cran.r-project.org/package=represtools Description: CRAN Package 'represtools' (Reproducible Research Tools) Reproducible research tools automates the creation of an analysis directory structure and work flow. There are R markdown skeletons which encapsulate typical analytic work flow steps. Functions will create appropriate modules which may pass data from one step to another. Package: r-cran-reprex Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-clipr, r-cran-fs, r-cran-glue, r-cran-knitr, r-cran-lifecycle, r-cran-rlang, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-withr Suggests: r-cran-covr, r-cran-fortunes, r-cran-miniui, r-cran-rprojroot, r-cran-sessioninfo, r-cran-shiny, r-cran-spelling, r-cran-styler, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-reprex_2.1.1-1.ca2604.1_all.deb Size: 491452 MD5sum: 5fc5916656189dadc720e81831ce8ebe SHA1: 5c7633241ad22901af213e6ecc568c6d67ad3e21 SHA256: 10538afdcf051dc5ee811e46e624340caa8069ea64879c640817e63a58a57dc0 SHA512: 98a4e74fc56473bd6a4e62e669dc10bed783bb5a89483b20b8d2e845894ab7b8d7a55a1872c17da726cc4ef88a7e7c8f2047075275097bb4a6a0f5856cc8f7a8 Homepage: https://cran.r-project.org/package=reprex Description: CRAN Package 'reprex' (Prepare Reproducible Example Code via the Clipboard) Convenience wrapper that uses the 'rmarkdown' package to render small snippets of code to target formats that include both code and output. The goal is to encourage the sharing of small, reproducible, and runnable examples on code-oriented websites, such as and , or in email. The user's clipboard is the default source of input code and the default target for rendered output. 'reprex' also extracts clean, runnable R code from various common formats, such as copy/paste from an R session. 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Package: r-cran-reproducible Architecture: all Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1694 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-filelock, r-cran-fpcompare, r-cran-fs, r-cran-lobstr Suggests: r-cran-archive, r-cran-covr, r-cran-curl, r-cran-dbi, r-cran-future, r-cran-geodata, r-cran-glue, r-cran-googledrive, r-cran-httr, r-cran-httr2, r-cran-knitr, r-cran-parallelly, r-cran-qs2, r-cran-raster, r-cran-rcurl, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-r.utils, r-cran-rvest, r-cran-sf, r-cran-sp, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-reproducible_3.1.1-1.ca2604.1_all.deb Size: 1307462 MD5sum: 2eaa358d7f97e1fbcd171f2f20e5dee3 SHA1: 9bbc7ac496cbcd690aca9c5f89aeff87b9418e65 SHA256: e142d34b478316c8568d41dee76b36b56802b6af1871c57fc87aa40b7afccfe5 SHA512: 5d01627ae2c920088c7a36196f52e7b185e9fe0498e184c6ad68c3c2d9b41f8a0ca843dfe39b8b7807d061b56a1d7334fd9236d130cdfa09c54a95d99e2ee894 Homepage: https://cran.r-project.org/package=reproducible Description: CRAN Package 'reproducible' (Enhance Reproducibility of R Code) A collection of high-level, machine- and OS-independent tools for making reproducible and reusable content in R. The two workhorse functions are 'Cache()' and 'prepInputs()'. 'Cache()' allows for nested caching, is robust to environments and objects with environments (like functions), and deals with some classes of file-backed R objects e.g., from 'terra' and 'raster' packages. Both functions have been developed to be foundational components of data retrieval and processing in continuous workflow situations. In both functions, efforts are made to make the first and subsequent calls of functions have the same result, but faster at subsequent times by way of checksums and digesting. Several features are still under development, including cloud storage of cached objects allowing for sharing between users. Several advanced options are available, see '?reproducibleOptions()'. 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This is achieved by creating json files storing metadata about computational results. A comprehensive tutorial to the package is available as preprint by Brandmaier & Peikert (2024, ). Package: r-cran-reproj Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proj4, r-cran-crsmeta, r-cran-proj Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-reproj_0.7.0-1.ca2604.1_all.deb Size: 278958 MD5sum: 26805f84a6217ac2eee4d04fee4523b9 SHA1: ab3ce0bfeeb65f2a182e65b0dcb7d8548b9f74f3 SHA256: d71c3b28faef4cf3adfdf4d9a083bfd29000811f602ee2310b464f453244cb7c SHA512: 645e30d4cc4eaf48f3f0897c6c969d24cc3145c3df8f7f6215dd2a98ff183b67361ee1b4374da3e2c2e592d3c644d0837e92d6c8a53a9e40924644a6de4126cf Homepage: https://cran.r-project.org/package=reproj Description: CRAN Package 'reproj' (Coordinate System Transformations for Generic Map Data) Transform coordinates from a specified source to a specified target map projection. This uses the 'PROJ' library directly, by wrapping the 'PROJ' package which leverages 'libproj', otherwise the 'proj4' package. The 'reproj()' function is generic, methods may be added to remove the need for an explicit source definition. If 'proj4' is in use 'reproj()' handles the requirement for conversion of angular units where necessary. This is for use primarily to transform generic data formats and direct leverage of the underlying 'PROJ' library. (There are transformations that aren't possible with 'PROJ' and that are provided by the 'GDAL' library, a limitation which users of this package should be aware of.) The 'PROJ' library is available at . Package: r-cran-reproresearchr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-reproresearchr_0.1.1-1.ca2604.1_all.deb Size: 73156 MD5sum: e7ecb88f561d117298a25368e2be69ce SHA1: a34b44b8c8a9091925efab9f44c65f4dd0d637a4 SHA256: e265216d9ee5d75d88dec908f706815df82bbcd61ea6c85390c4021352d12c81 SHA512: f9fe50003b26dd83397ef7a4d3ad6c69afb2f75431ec61eda2e53131040e92bcd226e585d02a30a9db7c09c20d27f3cc661c4d23cdb76e37a099a6f3a7bba034 Homepage: https://cran.r-project.org/package=reproresearchR Description: CRAN Package 'reproresearchR' (Companion Package for 'Reproducible Research Using R') Provides teaching datasets and helper functions to support the open educational resource Martinez (2026) "Reproducible Research Using R" . 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Package: r-cran-reprostat Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-glmnet, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-reprostat_0.1.2-1.ca2604.1_all.deb Size: 127620 MD5sum: 2eecdac0d74802c22272d1afd204c314 SHA1: aeb0f9f0a28ba8a7cc35b2e6c0d2d59ec258e2ee SHA256: 214f5e23a0e7ba5fd0d6783e9893711257df7208c810bf1174aee0ca9c1bf9b5 SHA512: cfa50d726ef7e44edc6c8afe0d2ac9fe2d032266c1a5b3fd0040e618a1564262c5dad7e4a232506f41fef177c6c7e80cc264f04983cdaf63b9d2cd92cf24a59a Homepage: https://cran.r-project.org/package=ReproStat Description: CRAN Package 'ReproStat' (Reproducibility Diagnostics for Statistical Modeling) Tools for diagnosing the reproducibility of statistical model outputs under data perturbations. Implements bootstrap, subsampling, and noise-based perturbation schemes and computes coefficient stability, p-value stability, selection stability, prediction stability, and a composite reproducibility index on a 0 to 100 scale. Includes cross-validation ranking stability for model comparison and visualization utilities. Optional 'backends' support robust M-estimation ('MASS') and penalized regression ('glmnet'). Bootstrap perturbation follows 'Efron' and 'Tibshirani' (1993, ISBN:9780412042317); selection stability follows 'Meinshausen' and 'Buhlmann' (2010) ; reproducibility framework follows 'Peng' (2011) . Package: r-cran-reps Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-kfas, r-cran-stringr, r-cran-lmtest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-reps_1.1.1-1.ca2604.1_all.deb Size: 492110 MD5sum: e54301d5b9621d0aef7d48f94c67a9e7 SHA1: 93623b480b2f2f91d1bf194974028581b6e433c6 SHA256: 17fd71ba2d9ab8a7a8f460b7b67fae77cce030cc347abfbdeaf1b21604ea4237 SHA512: 05cac727b16679bf383904b806e0252ed2fcec6f05bb1107dab9eea9e7d0ccd6eea401aa00f94f8ca5cb247844adbfc9c59546d7ffe1aba30354a628a0b3adf5 Homepage: https://cran.r-project.org/package=REPS Description: CRAN Package 'REPS' (Hedonic and Multilateral Index Methods for Real Estate PriceStatistics) Compute price indices using various Hedonic and multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic Multilateral Time series re-estimation with splicing). The central function calculate_hedonic_index() offers a unified interface for running these methods on structured datasets. This package is designed to support index construction workflows across a wide range of domains — including but not limited to real estate — where quality-adjusted price comparisons over time are essential. The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands. The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024) . For broader methodological context, see Eurostat (2013, ISBN:978-92-79-25984-5, ). Package: r-cran-repsd Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-progress Suggests: r-cran-colordf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-repsd_1.0.1-1.ca2604.1_all.deb Size: 52112 MD5sum: ec1313b4412d956558ba4ecdd7d7ccdb SHA1: 4bb4f7e34ecaf745f32f95ad38e908dfb227c198 SHA256: c4a39e69050ef8e7075b78fd0b9ee8077d720fed1f7ef207c5dddfd6711cbd60 SHA512: 307203fb6c2d17047cd248e574c081211ef6e61bb1f167b3d48204f69c9544f3dc1cb980502fad62465aede16af50fd8766c5a6744d2b0bbb6692363a5d6cd1f Homepage: https://cran.r-project.org/package=repsd Description: CRAN Package 'repsd' (Root Expected Proportion Squared Difference for Detecting DIF) Root Expected Proportion Squared Difference (REPSD) is a nonparametric differential item functioning (DIF) method that (a) allows practitioners to explore for DIF related to small, fine-grained focal groups of examinees, and (b) compares the focal group directly to the composite group that will be used to develop the reported test score scale. Using your provided response matrix with a column that identifies focal group membership, this package provides the REPSD values, a simulated null distribution of possible REPSD values, and the simulated p-values identifying items possibly displaying DIF without requiring enormous sample sizes. 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See for more information. Package: r-cran-reptiledb.data Architecture: all Version: 0.0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr, r-cran-rvest, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-reptiledb.data_0.0.0.2-1.ca2604.1_all.deb Size: 763848 MD5sum: d90d94ccc1973e8f7d9ae13bd4905186 SHA1: 1ac9f3341adcc0b034af2fe53560c3c0c4c1cd83 SHA256: c29ab20be11c145ecfe80de6fd1c874e91dd3eefe3112d13cbfc83f4e221a187 SHA512: 0cdab3b810caa37f8f5b3cc3ba1ce24457a62ea6f8c18952e9f29def27bac1c973396a6e18a36a9384d46e75239808303f418df420db80facc1c9ab788b1aec0 Homepage: https://cran.r-project.org/package=reptiledb.data Description: CRAN Package 'reptiledb.data' (Reptile Database Data) Provides easy access to 'The Reptile Database', a comprehensive catalogue of all living reptile species and their classification. 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Package: r-cran-rerandpower Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-runuran Filename: pool/dists/resolute/main/r-cran-rerandpower_0.0.1-1.ca2604.1_all.deb Size: 31546 MD5sum: b067b4c545eadc210d7858c30e5c5ce5 SHA1: de5ec89c94cb6b615333e021bdd3bd2182b21b0c SHA256: 0daa4c181ae45e5f7166d6d64a92cdc309eb72c3ea1bf549f45927ff23990aed SHA512: f45856cb99150fc377b85698d215cae240000016041777744061ad381096af6ea8a20932b172a9e156136bf28b01c61b43994e76c7e3057cda971e31c2477ca2 Homepage: https://cran.r-project.org/package=rerandPower Description: CRAN Package 'rerandPower' (Power and Sample Size Calculations for Completely Randomized andRerandomized Experiments) Computes the power resulting from completely randomized and rerandomized experiments with two groups. 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Package: r-cran-rescomp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8090 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-desolve, r-cran-rlang, r-cran-cli, r-cran-glue Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-rescomp_1.0.0-1.ca2604.1_all.deb Size: 4517160 MD5sum: f49549163b6a2723162dfca176bc00d5 SHA1: dc877c38ec60b8778dac24f723b35860629f4774 SHA256: 76fae30dac27f0657ab49095d30d9ad3ebfb9260404e8c263efdf0897f585ecf SHA512: 9a6de8f09a821c93ba653687a9cbce931973c90cadff6523695b345517ab2ad3e6de5bffc40a0f039710b91b35be1e73e66645d2857e35f559fe5ca78b8f722f Homepage: https://cran.r-project.org/package=rescomp Description: CRAN Package 'rescomp' (Efficient Modelling of Resource Competition) Generate, simulate and visualise ODE models of consumer-resource interactions. 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Package: r-cran-resde Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deriv, r-cran-nlme Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-resde_1.1-1.ca2604.1_all.deb Size: 244524 MD5sum: 480856e4f85e843729b3a358accf91e0 SHA1: 4edd130bd856d88ffc12a28e4d4ce2cda5631f63 SHA256: 811873a761a890bb7712b72a47361f6f4fea04f324a7ef82865e62089faa4677 SHA512: 878b1e87d44eb883a2801f201ded328247abf55ce6ede4a6a43a99fb323a5fcd8d6e5f0a260a72c073d550af113766c85d4a74833c7460209a5b0d06a2d3fe3e Homepage: https://cran.r-project.org/package=resde Description: CRAN Package 'resde' (Estimation in Reducible Stochastic Differential Equations) Maximum likelihood estimation for univariate reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. 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Package: r-cran-reservoir Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-reservoir_1.1.5-1.ca2604.1_all.deb Size: 216246 MD5sum: 83f6720646d80daa6ea67e02e1158cce SHA1: 55205c527d875a048b3166812563d31319675782 SHA256: bae938299f283a07b3320400a1e5c8e1bbbb2aab65f39eea98368861738f1a5e SHA512: b47c54572d026ca8a407b663279ed91bed11eab65beb6fc1fb466b27528fcbc3eb67fe05ff04b160dfbd8895d24335c7932adaf4b3d4d1292dea0bd2eb05ddf8 Homepage: https://cran.r-project.org/package=reservoir Description: CRAN Package 'reservoir' (Tools for Analysis, Design, and Operation of Water SupplyStorages) Measure single-storage water supply system performance using resilience, reliability, and vulnerability metrics; assess storage-yield-reliability relationships; determine no-fail storage with sequent peak analysis; optimize release decisions for water supply, hydropower, and multi-objective reservoirs using deterministic and stochastic dynamic programming; generate inflow replicates using parametric and non-parametric models; evaluate inflow persistence using the Hurst coefficient. Package: r-cran-reservoirnet Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-testthat, r-cran-rlang, r-cran-ggplot2, r-cran-ggpubr, r-cran-janitor, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-covr, r-cran-kableextra, r-cran-slider, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-reservoirnet_0.3.0-1.ca2604.1_all.deb Size: 349982 MD5sum: 81e570912f0f2ff06b4402206d98e244 SHA1: 83b25749104a04d9c5dd6b2d1e2a49412e3da313 SHA256: dcd155cbfd9d70d8bebe081b47848ef3ed2c59e9fcc641d2171aac9094711c82 SHA512: 5e30fd80dd55a526629a4231613934b84a0b5d8b8f13d72fad7045b8a7a4368da82b29834c1747343abb7b1273f7091a1777bacd9aff9ff93fde27f760bc5d77 Homepage: https://cran.r-project.org/package=reservoirnet Description: CRAN Package 'reservoirnet' (Reservoir Computing and Echo State Networks) A simple user-friendly library based on the 'python' module 'reservoirpy'. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters. Finally, it includes several tutorials exploring time series forecasting, classification and hyperparameter tuning. For more information about 'reservoirpy', please see Trouvain et al. (2020) . This package was developed in the framework of the University of Bordeaux’s IdEx "Investments for the Future" program / RRI PHDS. Package: r-cran-reset Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-seurat, r-cran-seuratobject, r-cran-sctransform Filename: pool/dists/resolute/main/r-cran-reset_1.0.0-1.ca2604.1_all.deb Size: 356982 MD5sum: 021d6d38fc028b377aeb7937ad8268c9 SHA1: 9013a08dc0f35bb989af585c62e64e613a5dad18 SHA256: 884722c80090983d28f6717553e4da273e44d9295f9dd60520854e769ed94f4c SHA512: a995d507dcac034279daf5e1fd209144a2b7bd3c21fa1910431bbe8bb02fb97b3c137ef320676ff13b9c2eb2fef55bfae735ededcc792270999415526e7b0693 Homepage: https://cran.r-project.org/package=RESET Description: CRAN Package 'RESET' (Reconstruction Set Test) Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" . Package: r-cran-reshape Architecture: all Version: 0.8.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-reshape_0.8.10-1.ca2604.1_all.deb Size: 167520 MD5sum: 997ea99d55c3a5a06bd4908d039e2f58 SHA1: 9a0c0eb04632d0661198247596a9da1c467f1d0f SHA256: f825ee754665cbd0d9de54d97edf3cc622b3b12573998be27ecd3cbf6e23bd2d SHA512: 0a820da9772d624bb6e2345bf280100bb6becd55a176bda6b4cfcb628061d5d9a45d70a41259b0f5a6abd58ea59b5f74c7e18d1f937c987db28fbb0a8a88864d Homepage: https://cran.r-project.org/package=reshape Description: CRAN Package 'reshape' (Flexibly Reshape Data) Flexibly restructure and aggregate data using just two functions: melt and cast. 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It is described by a URL and credentials. This package proposes a Resource model with "resolver" and "client" classes to facilitate the access and the usage of the resources. 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Package: r-cran-respbibd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-respbibd_0.1.0-1.ca2604.1_all.deb Size: 31190 MD5sum: 74d9f9f09ecc860255e81d5933f91258 SHA1: b9ff11f88aacf57b1ec6785040e24fec3a8e32ef SHA256: e8176085a325de5e73f02f05ef33944fa2f54915ccbeb2d1a78986081f9939ac SHA512: 516ea383537bd4e2f102bec08fb6fe15c40fe1f7520eb6ab2e575dd14543f2730af0d24dca2c5000632c7b1e138988a50d1bc3f1af22789a410f33023279bae7 Homepage: https://cran.r-project.org/package=ResPBIBD Description: CRAN Package 'ResPBIBD' ("Resolvable Partially Balanced Incomplete Block Designs(PBIBDs)") A collection of several utility functions related to resolvable and affine resolvable Partially Balanced Incomplete Block Designs (PBIBDs), have been developed. In the class of resolvable designs, affine resolvable designs are said to be optimal, Bailey (1995) . Here, the package contains three functions to generate and study the characterization properties of these designs. Developed functions are named as PBIBD1(), PBIBD2() and PBIBD3(), in which first two functions are used to generate two new series of affine resolvable PBIBDs and last one is used to generate a new series of resolvable PBIBDs, respectively. In addition, these functions can also be used to generate design parameters (v, b, r and k), canonical efficiency factors, variance factor between associates and average variance factors of the generated designs. Here v is the number of treatments, b (= b1 + b2, in case of non-proper design) is the number of blocks, r is the number of replications and k (= k1 + k2; k1 is the size of b1 and k2 is the size of b2) is the block size. Package: r-cran-respirometry Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-birk, r-cran-dplyr, r-cran-lubridate, r-cran-marelac, r-cran-measurements, r-cran-minpack.lm, r-cran-plyr, r-cran-rlang, r-cran-seacarb, r-cran-segmented Filename: pool/dists/resolute/main/r-cran-respirometry_2.0.2-1.ca2604.1_all.deb Size: 528458 MD5sum: 000e897b1d3c7a2737511d958c3fbd72 SHA1: 714f8dc5cd6e183386bec8733e00398e16f5b846 SHA256: a148287278aff2456ca69667c5aa0851c24a6fece75daef57043f19784528b15 SHA512: a5cbf244e26e7e4876bd393aaf076b87f38df7b70c4a4042e579757b884c42b9a0265bc6c622156f0f00413fab4c4ffe612cc4093b9d278042e7e9c515abb7eb Homepage: https://cran.r-project.org/package=respirometry Description: CRAN Package 'respirometry' (Tools for Conducting and Analyzing Respirometry Experiments) Provides tools to enable the researcher to more precisely conduct respirometry experiments. Strong emphasis is on aquatic respirometry. Tools focus on helping the researcher setup and conduct experiments. Functions for analysis of resulting respirometry data are also provided. This package provides tools for intermittent, flow-through, and closed respirometry techniques. Package: r-cran-responsepatterns Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-responsepatterns_0.1.1-1.ca2604.1_all.deb Size: 111092 MD5sum: c9c9e05324fb9ab5ec2a570ac5dbcd1b SHA1: c1d20891a66af2ea1f34eb7a2d6080921f95e3c3 SHA256: 562c0901b7c1be22ff27aeb4e5c25c885e3c596ed41eb48149092be2bb0bc3a0 SHA512: 0d12e32dedb528fe3580c29eec17d926af4247f70783d23d50ef6013c1d4dc32b281ef52ea31bf17a6ff8ef55d9cba01429c21de3b3f3813d85e8636c5937370 Homepage: https://cran.r-project.org/package=responsePatterns Description: CRAN Package 'responsePatterns' (Screening for Careless Responding Patterns) Some survey participants tend to respond carelessly which complicates data analysis. This package provides functions that make it easier to explore responses and identify those that may be problematic. See Gottfried et al. (2022) for more information. Package: r-cran-respr Architecture: all Version: 2.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-marelac, r-cran-purrr, r-cran-roll, r-cran-segmented, r-cran-stringr, r-cran-xml2 Suggests: r-cran-respirometry, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-respr_2.3.4-1.ca2604.1_all.deb Size: 2112504 MD5sum: c8ed191bb5ed91e9d89d315584653f03 SHA1: 1e35d1b2133e20187cb39578910a0b1503221247 SHA256: 63c3eee1dbeae24d1596a0220a5c37b9a328dd0f46d0f8386f9f46a49d16c5b3 SHA512: 0098c93a6459fe663445d7a609eaa6cdd12800a0ac92b198df9099ecc49cb92a8549665442aaa291a1619b734b3c4614ac30c4b0417c12fa2b0c8a06a60fd6df Homepage: https://cran.r-project.org/package=respR Description: CRAN Package 'respR' (Import, Process, Analyse, and Calculate Rates from RespirometryData) Provides a structural, reproducible workflow for the processing and analysis of respirometry data. It contains analytical functions and utilities for working with oxygen time-series to determine respiration or oxygen production rates, and to make it easier to report and share analyses. See Harianto et al. 2019 . Package: r-cran-resquin Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-purrr, r-cran-rlang, r-cran-slider, r-cran-stringi, r-cran-tibble, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-resquin_0.1.1-1.ca2604.1_all.deb Size: 138802 MD5sum: 47a6c2be8a392d06bc430fe7d8d8a060 SHA1: 2fc8ccc8ad1d0f89d8d6d9df90b1da4f3e9be8f7 SHA256: b789995e1f559472886a1ac49acc92ec68c0708f5114392d5770b98aa8c7e0e7 SHA512: 15a04a8011097284d5453b898d861ac37d34822664d040df5ba012ffe6d9f15504bef02aa2663c52c0b7fab671efc27d8fa6adf1a9a211c63fcfd65e506a7274 Homepage: https://cran.r-project.org/package=resquin Description: CRAN Package 'resquin' (Response Quality Indicators for Survey Research) Calculate common survey data quality indicators for multi-item scales and matrix questions. Currently supports the calculation of response style indicators and response distribution indicators. For an overview on response quality indicators see Bhaktha N, Henning S, Clemens L (2024). 'Characterizing response quality in surveys with multi-item scales: A unified framework' . Package: r-cran-ress Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ress_1.3-1.ca2604.1_all.deb Size: 40134 MD5sum: a67bb2c5655e959684b5da36e04fa6ae SHA1: d100498595b4e96aab617cae12e45df0f33ede65 SHA256: d93fd40eceffe3b0120c905cfcc2e7e1d57257ef94c734e71bd8d51f7f0a2cf8 SHA512: 5ab89a5ddc899fd5455275a04a0776ccca6860cb9fcf3fddb109ce227fd5fe87054cadb39e9d05c391e525c6f48caa5be90c3e69c4855645a17ed8b80246f30c Homepage: https://cran.r-project.org/package=RESS Description: CRAN Package 'RESS' (Integrates R and Essentia) Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html. Package: r-cran-restatapi Architecture: all Version: 0.25.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rjson, r-cran-xml2 Suggests: r-cran-chron, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-remotes Filename: pool/dists/resolute/main/r-cran-restatapi_0.25.0-1.ca2604.1_all.deb Size: 233498 MD5sum: e296274f367fec142826d40346ae3a5c SHA1: 1c24ccdd5f70bec763508bf85dc586ed20946aab SHA256: a63a02926490c26bd72a3fcc3d7cc2602f4f5e36a7f7c7f6fde788ef4b9026f4 SHA512: 745b149ab76fc7ad2271ef58ffa7620edc335345b157479c2cfd67227aefefbdeb9cde91cffead545ce970d6d4f322f45e68b54160e88cdf84c24a41bf42f75e Homepage: https://cran.r-project.org/package=restatapi Description: CRAN Package 'restatapi' (Search and Retrieve Data from Eurostat Database) Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services () to search and download data from the Eurostat database using the SDMX standard. Package: r-cran-restatis Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-askpass, r-cran-httr2, r-cran-memoise, r-cran-readr, r-cran-tibble, r-cran-vctrs, r-cran-purrr Suggests: r-cran-httptest2, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat, r-cran-rvest, r-cran-usethis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-restatis_0.4.0-1.ca2604.1_all.deb Size: 530086 MD5sum: 7f978dfc8cf8ccf8764f5dd05b2d8dff SHA1: 08e3ffe804b07fdaf236f65ec0762fe449619850 SHA256: 22027be3f6dea96a39ae3e6afc153f9fb69d25788395d0f3165cdd0344bce787 SHA512: 8175a0654c071dd3a2e1609ccb47fd4f7a98f0829d44dced7070aa6fd051f0c5d6f7b98e0584d44c0a8f8ce8e0fa4a7e74fde923cb2099917eac524a22105049 Homepage: https://cran.r-project.org/package=restatis Description: CRAN Package 'restatis' (R Wrapper to Access a Wide Range of Germany's FederalStatistical System Databases Based on the GENESIS Web ServiceRESTful API of the German Federal Statistical Office(Statistisches Bundesamt/Destatis)) A RESTful API wrapper for accessing the main databases of Germany's Federal Statistical System. Supports data search functions, credential management, result caching, and handling remote background jobs for large datasets. Package: r-cran-restaurant Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-restaurant_0.1.0-1.ca2604.1_all.deb Size: 35730 MD5sum: 7e657614d23e2d046e461b57c65fc097 SHA1: 8c7fc7f0f0036502847c64d8b02f6e8e39d0c8c6 SHA256: d52a8386c82554686595c71f9a1c16c95b6d3497bf339ce94899ff8aa1960659 SHA512: 1a239b74275429a0fd4417f1f8e8cdedb7d887815c78d9340bd5f2d128c6c1014ca6923538d5635c38cf014529cfd4533d27b944420312572a9a69d8059f0fd3 Homepage: https://cran.r-project.org/package=restaurant Description: CRAN Package 'restaurant' (Restaurant Data for Entity Resolution) Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id. Package: r-cran-restez Architecture: all Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rentrez, r-cran-dbi, r-cran-curl, r-cran-cli, r-cran-crayon, r-cran-stringi, r-cran-duckdb, r-cran-fs, r-cran-assertthat, r-cran-ape Suggests: r-cran-sessioninfo, r-cran-testthat, r-cran-knitr, r-cran-r.utils, r-cran-rmarkdown, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-restez_2.1.5-1.ca2604.1_all.deb Size: 421870 MD5sum: 7f5bbf1b647298e45de7a6bfeb6f009d SHA1: 9b6caa775ec5a5c46f2eac315585fb532139d679 SHA256: 353247fe7dbd86755000c11f8f8944c9dc53445eba30f1b8f5269ecffe3a80a9 SHA512: e3219e676c3ea9edbccf5075be8a421e1dd5615de1fb47616f0830f288c2fea8954dd065c4192b34c73e3aa68793e2df70e656ed75ded9ee8d5918129046d5a4 Homepage: https://cran.r-project.org/package=restez Description: CRAN Package 'restez' (Create and Query a Local Copy of 'GenBank' in R) Download large sections of 'GenBank' and generate a local SQL-based database. A user can then query this database using 'restez' functions or through 'rentrez' wrappers. Package: r-cran-restimizeapi Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcurl, r-cran-rjsonio Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-restimizeapi_1.0.0-1.ca2604.1_all.deb Size: 40892 MD5sum: adda987847607097523a1fbea08fd878 SHA1: b1f716df3394205a57f6448af12fe1f02a2fc237 SHA256: 2e5aac2a6b656684f63d42212c1013baff5f5d5181cbc0c9e54a6e562725fffa SHA512: 62e963050bc154d8921015ddfa92e390cc26647d805cd9730144edaec9e85bbd0fe529646e8f4b96e7df000b51b5277b3d282b5a0ce2c7683191f1bd6eadb445 Homepage: https://cran.r-project.org/package=restimizeapi Description: CRAN Package 'restimizeapi' (Functions for Working with the 'www.estimize.com' Web Services) Provides the user with functions to develop their trading strategy, uncover actionable trading ideas, and monitor consensus shifts with crowdsourced earnings and economic estimate data directly from . Further information regarding the web services this package invokes can be found at . Package: r-cran-restk Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-restk_1.0.2-1.ca2604.1_all.deb Size: 33474 MD5sum: 31b5bae8f27d9e8d9a2458bd2f177694 SHA1: 7d68a526890272d74636b41888c57ab480c2f81e SHA256: cba8db6eccd3ad40be7d8a141a806b4390acaa14f5fe04c71f66639127dad3a1 SHA512: 6dcc1a26f8dd37e05c337a2598d2114f6b61af360b4039eb039291179b2611f86b0cb6b63c4afc3c715871167bd9a3bd8c7d8fbe48f40f387752ce406686d3b8 Homepage: https://cran.r-project.org/package=RESTK Description: CRAN Package 'RESTK' (An Implementation of the RESTK Algorithm) Implementation of the RESTK algorithm based on Markov's Inequality from Vilardell, Sergi, Serra, Isabel, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco J. and Del Castillo, J. (2022). "Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. . This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). Package: r-cran-restoptr Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rjava, r-cran-units, r-cran-assertthat, r-cran-magrittr, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-landscapemetrics, r-cran-vegan, r-cran-cluster, r-cran-ggthemes, r-cran-paletteer Filename: pool/dists/resolute/main/r-cran-restoptr_1.1.1-1.ca2604.1_all.deb Size: 3909790 MD5sum: 4d4a387aa07b1443d71a3311c6027fa1 SHA1: a105f9a98872572ae31d46fcc0509979672a1fb1 SHA256: 79fbe607741af1c4c5cdf4e4e59fa16c030de3dfee03999561c47139811e30ab SHA512: 5cddba7dfba54978cf7d261f5aa2dd080a7541b54edbc8e7c9fbd7db80dea84006f253a8ce25f30fedd789f5447af0d27610ca9fef470fa57b38412bdb461484 Homepage: https://cran.r-project.org/package=restoptr Description: CRAN Package 'restoptr' (Ecological Restoration Planning) Flexible framework for ecological restoration planning. It aims to identify priority areas for restoration efforts using optimization algorithms (based on Justeau-Allaire et al. 2021 ). Priority areas can be identified by maximizing landscape indices, such as the effective mesh size (Jaeger 2000 ), or the integral index of connectivity (Pascual-Hortal & Saura 2006 ). Additionally, constraints can be used to ensure that priority areas exhibit particular characteristics (e.g., ensure that particular places are not selected for restoration, ensure that priority areas form a single contiguous network). Furthermore, multiple near-optimal solutions can be generated to explore multiple options in restoration planning. The package leverages the 'Choco-solver' software to perform optimization using constraint programming (CP) techniques (). Package: r-cran-restorenet Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-xtable, r-cran-scales, r-cran-stringr, r-cran-ggplot2, r-cran-scatterpie, r-cran-rcolorbrewer Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-restorenet_1.0.1-1.ca2604.1_all.deb Size: 540262 MD5sum: 80dedc5e5a31c53d8e9dd7e6496f3799 SHA1: 14c80fe887d03c67285f5522f6dc97a6c690adb2 SHA256: e9520ee084d5be5b561939b424bc25788007d6398cd6ddc709331d6717b0cfe9 SHA512: 33a684d794b22b8fa7fe5b888ddf565e4887e8afa192ea54f0ffcba6dadb3bafab2b3b4989dbafa085f5a488a045d780e03b5074468c1aa4c7d569e938f91823 Homepage: https://cran.r-project.org/package=RestoreNet Description: CRAN Package 'RestoreNet' (Random-Effects Stochastic Reaction Networks) A random-effects stochastic model that allows quick detection of clonal dominance events from clonal tracking data collected in gene therapy studies. Starting from the Ito-type equation describing the dynamics of cells duplication, death and differentiation at clonal level, we first considered its local linear approximation as the base model. The parameters of the base model, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones. Although this assumption makes inference easier, in some cases it can be too restrictive and does not take into account possible scenarios of clonal dominance. Therefore we extended the base model by introducing random effects for the clones. In this extended formulation the dynamic parameters are estimated using a tailor-made expectation maximization algorithm. Further details on the methods can be found in L. Del Core et al., (2022) . 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When planning a study on a target species, the retentionmort_generation() function can be used to produce multiple synthetic mark-recapture datasets to anticipate the error associated with a planned field study to guide method development to reduce error. Similarly, if field data was already collected, the retentionmort() function can be used to predict the error from already generated data to adjust for user-based mortality and tag loss. The test_dataset_retentionmort() function will provide an example dataset of how data should be inputted into the function to run properly. Lastly, the retentionmort_figure() function can be used on any dataset generated from either model function to produce an 'rmarkdown' printout of preliminary analysis associated with the model, including summary statistics and figures. Methods and results pertaining to the formation of this package can be found in McCutcheon et al. (in review, "Predicting tagging-related mortality and tag loss during mark-recapture studies"). 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Hausman, Jeffrey (1978) . Allison, Paul (2009) . Neuhaus, J.M., and J. D. Kalbfleisch (1998) . 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Package: r-cran-rfdp Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rfdp_0.1.1-1.ca2604.1_all.deb Size: 14466 MD5sum: c0df6bdebdbd415765df1d683e79268e SHA1: cf52a53b52c418f201cfcf63a7b5c67c1e79b37f SHA256: f0b4053343dbe596c04b3816ed4b0e18bb5bc460f3fd0e8f577bcc30a54a5731 SHA512: af1ef378a0a1ddb3fdd6139d97f0b966f215660f46f7a0be4fb8199e7bdff7f815f16bb96fea2a996eab437b19c6adf2865b8e1e28af8755fcaf74588dd23e90 Homepage: https://cran.r-project.org/package=rFDP Description: CRAN Package 'rFDP' (Resampling-Based False Discovery Proportion Control) Methods for Resampling-based False Discovery Proportion control. A function is provided that provides simultaneous, multi-resolution False Discovery Exceedance (FDX) control as described in Hemerik (2025) . 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Package: r-cran-rfia Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dtplyr, r-cran-tidyr, r-cran-stringr, r-cran-sf, r-cran-data.table, r-cran-tidyselect, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gganimate, r-cran-r2jags, r-cran-coda, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rfia_1.1.3-1.ca2604.1_all.deb Size: 7225258 MD5sum: e8cf63532d79038b721e27aaf8284a89 SHA1: bca6ca81a66ee28ad53f405cad3371885421e6b2 SHA256: bb276272727fdea6039f0bd3a4e9e041444601af2c8bc7a33de5f9a2c8306cfd SHA512: 9a1d76c68fc85a68eeb1e31671f8f02d56b937018c7fdfac5894a1136298745fe8371b2abe539cade42c600c3d57e140e4c00ecbb92b9052f6aaed49ff83b65e Homepage: https://cran.r-project.org/package=rFIA Description: CRAN Package 'rFIA' (Estimation of Forest Variables using the FIA Database) The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) , and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates. Package: r-cran-rfieldclimate Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rfieldclimate_0.1.1-1.ca2604.1_all.deb Size: 31360 MD5sum: 9abbda2c0fca2a40defb33bf8129bba7 SHA1: 9e9949d872eb62f20b8b2d14a39284438dbfb113 SHA256: 67478ba7796a9b0ef8f4c22eca51076718b139398b2372903e36ab20b2dc9ff6 SHA512: 7629e9bd308486d417036cd034ca747f1b9498b536b924349faef5cdc849f73c158947e147539a5122a2121094c1c3c46ab87d6ae1e44bbcc2898925d803192a Homepage: https://cran.r-project.org/package=rfieldclimate Description: CRAN Package 'rfieldclimate' (Client for the 'FieldClimate' API) Provides functionality to interact with the 'FieldClimate' API . Package: r-cran-rfinterval Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rfinterval_1.0.0-1.ca2604.1_all.deb Size: 132434 MD5sum: 87048f4524bfab563360060bef3b47d6 SHA1: 2012d31a3c44269a63eb3d12597648ce96895aef SHA256: 8d1db7626e8f03002a81f74fa0327c5add153b204eb0af734bf6ab0c8a2219c7 SHA512: a0e276de4c5b2d5ff12ba1436c9971effcf23e5054ac3f5460f9f3a0febb6259623943108c52d44d4376564d0afe2bce0137ddeb879bf795efa73006e4440ee0 Homepage: https://cran.r-project.org/package=rfinterval Description: CRAN Package 'rfinterval' (Predictive Inference for Random Forests) An integrated package for constructing random forest prediction intervals using a fast implementation package 'ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) : the out-of-bag prediction interval, the split conformal method, and the quantile regression forest. Package: r-cran-rfishbase Architecture: all Version: 5.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2031 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-stringr, r-cran-purrr, r-cran-dplyr, r-cran-duckdbfs, r-cran-rlang, r-cran-magrittr, r-cran-memoise, r-cran-xml2 Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-covr, r-cran-spelling, r-cran-curl Filename: pool/dists/resolute/main/r-cran-rfishbase_5.0.3-1.ca2604.1_all.deb Size: 563836 MD5sum: aa4ae65821097fcdcaecfa7838ef4195 SHA1: 8045f355d6eb112da4c5233e15b5f15b71a9f3a1 SHA256: d294f04db6a2dbacfd1f166e0f700a8368767afea956a0d04f0410abbdb82e04 SHA512: 68ac61dc0abd5088c0b3a24e209e518d0c5fbd82c69a814bcc96e7b261d6bf61994563acd7d7ea916eae03427d0a814de74f33cbfe41ebc01ec01c0b41881fdd Homepage: https://cran.r-project.org/package=rfishbase Description: CRAN Package 'rfishbase' (R Interface to 'FishBase') A programmatic interface to 'FishBase', re-written based on an accompanying 'RESTful' API. 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This package is part of the 'rOpenSci' suite (http://ropensci.org). Package: r-cran-rflocalfdr.data Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13418 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rflocalfdr.data_0.0.3-1.ca2604.1_all.deb Size: 13705422 MD5sum: ee856b0b6b1255950367a206c4a816ff SHA1: c1729730cd6891848017ede9b53a4314949c30fa SHA256: 587928f11d45d95266d19ef760afa6d7f896e7eda0a408c3c071a2db1f56e8a0 SHA512: 26d5cddbf39f87a777538103f3c52d299344a878e563eadaebe617b1ec1534479ed7f43c21f40ddb5167aed29d0c9b53f11e23b37952e438517e950ade6ec6de Homepage: https://cran.r-project.org/package=RFlocalfdr.data Description: CRAN Package 'RFlocalfdr.data' (Data for the Vignette and Examples in 'RFlocalfdr') Data for the vignette and examples in 'RFlocalfdr'. Contains a dataset of 1103547 importance values, and the table of variables used in the random forest splits. 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Package: r-cran-rfm Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gganimate, r-cran-ggplot2, r-cran-magrittr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scales, r-cran-treemapify, r-cran-xplorerr Suggests: r-cran-cli, r-cran-covr, r-cran-dt, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-rmdformats, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-rfm_0.4.0-1.ca2604.1_all.deb Size: 1427130 MD5sum: 5eaa1a122dd8809d600220c479b2cdeb SHA1: d40958c34dd169e9e389d1ae1e8fdd9682186de5 SHA256: b91b4f1bcf8ebea497a1bd05b057c611dc8d03247cf28fdf6727d3339b0120ae SHA512: 1f7ed3bbc33ae4c9b777cb22598ece46a23b20eedf7807ae360f62bcf5a6b4aedb367012e946a985939e30daf5bc9b5e4e83c2e7cae66f64e7b96a3f7ecbb42f Homepage: https://cran.r-project.org/package=rfm Description: CRAN Package 'rfm' (Recency, Frequency and Monetary Value Analysis) Tools for RFM (recency, frequency and monetary value) analysis. Generate RFM score from both transaction and customer level data. Visualize the relationship between recency, frequency and monetary value using heatmap, histograms, bar charts and scatter plots. Includes a 'shiny' app for interactive segmentation. References: i. Blattberg R.C., Kim BD., Neslin S.A (2008) . Package: r-cran-rfmerge Architecture: all Version: 0.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-terra, r-cran-randomforest, r-cran-zoo, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rfmerge_0.3-3-1.ca2604.1_all.deb Size: 2119122 MD5sum: 344b059be8297acbd48fc44241c85a24 SHA1: 4f1e6d39c5848da134b8ea125b7b92de59dd1d8c SHA256: bc58dfd268da43e9d8134c32cc028e97ae2b50172d3d2a4b01c748a2296ccf1f SHA512: 753e68d901962f2943c09acb3fdecaa620e0bf35d536017d80854e3036d807d486e4d77dc0f1bd421010582dd6f0e17211de5bd617db9da643444e00e5ae286f Homepage: https://cran.r-project.org/package=RFmerge Description: CRAN Package 'RFmerge' (Merging of Satellite Datasets with Ground Observations usingRandom Forests) S3 implementation of the Random Forest MErging Procedure (RF-MEP), which combines two or more satellite-based datasets (e.g., precipitation products, topography) with ground observations to produce a new dataset with improved spatio-temporal distribution of the target field. In particular, this package was developed to merge different Satellite-based Rainfall Estimates (SREs) with measurements from rain gauges, in order to obtain a new precipitation dataset where the time series in the rain gauges are used to correct different types of errors present in the SREs. However, this package might be used to merge other hydrological/environmental gridded datasets with point observations. For details, see Baez-Villanueva et al. (2020) . Bugs / comments / questions / collaboration of any kind are very welcomed. Package: r-cran-rfmstate Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3664 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mstate, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rfmstate_0.1.2-1.ca2604.1_all.deb Size: 2185086 MD5sum: e7f41d9e96c9b7172b9984613ed84cf3 SHA1: 1dc03801e237cf07e5626860b92f067ed04eae11 SHA256: 1a133225cee8f1cbc061b26cc08fdb5334004ce4a62c91e8fe6a726932ca197f SHA512: bf4f2ade215dbf157c2f51d211e8a8cfd69c1172901c6a7df8e44d19bb3ee57ccbb03d8e32793cabb5a1a97fa395a62f39f4bbd61d49dee1e2922e2802fa6d5e Homepage: https://cran.r-project.org/package=RFmstate Description: CRAN Package 'RFmstate' (Random Forest-Based Multistate Survival Analysis) Fits cause-specific random survival forests for flexible multistate survival analysis with covariate-adjusted transition probabilities computed via product-integral. State transitions are modeled by random forests. Subject-specific transition probability matrices are assembled from predicted cumulative hazards using the product-integral formula. Also provides a standalone Aalen-Johansen nonparametric estimator as a covariate-free baseline. Supports arbitrary state spaces with any number of states (three or more) and any set of allowed transitions, applicable to clinical trials, disease progression, reliability engineering, and other domains where subjects move among discrete states over time. Provides per-transition feature importance, bias-variance diagnostics, and comprehensive visualizations. Handles right censoring and competing transitions. Methods are described in Ishwaran et al. (2008) for random survival forests, Putter et al. (2007) for multistate competing risks decomposition, and Aalen and Johansen (1978) for the nonparametric estimator. 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Package: r-cran-rfriend Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bestnormalize, r-cran-crayon, r-cran-dharma, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-magick, r-cran-magrittr, r-cran-multcomp, r-cran-multcompview, r-cran-mumin, r-cran-nortest, r-cran-pander, r-cran-png, r-cran-rlang, r-cran-rmarkdown, r-cran-rstatix, r-cran-rstudioapi, r-cran-stringr, r-cran-this.path, r-cran-tidyr, r-cran-writexl, r-cran-xfun Suggests: r-cran-mass, r-cran-nnet, r-cran-pbkrtest, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-rfriend_3.0.0-1.ca2604.1_all.deb Size: 1062980 MD5sum: ca0cb293176592f9067d366b261b39f6 SHA1: d12b7fc30d5849ced2df4f6ab5a18e305d7034f0 SHA256: d239d3ac447005d57c7c152b12f64a536504c96d0f0f0af26304601a934d8457 SHA512: 06cf7f2090b527d3af8faffaecb278f33030676834b4c62abfd4caed6f076fbefdc012e233aa5148d80a8092cb2452e453436fcb7b6255934dd6686cecb107aa Homepage: https://cran.r-project.org/package=rfriend Description: CRAN Package 'rfriend' (Provides Batch Functions and Visualisation for Basic StatisticalProcedures) Designed to streamline data analysis and statistical testing, reducing the length of R scripts while generating well-formatted outputs in 'pdf', 'Microsoft Word', and 'Microsoft Excel' formats. In essence, the package contains functions which are sophisticated wrappers around existing R functions that are called by using 'f_' (user f_riendly) prefix followed by the normal function name. This third version of the 'rfriend' package focuses primarily on data exploration, including tools for creating summary tables, f_summary(), summary figures, f_scan(), outlier detection and removal, f_outlier() and f_remove_outliers(), performing data transformations, f_boxcox() in part based on 'MASS/boxcox' and 'rcompanion', and f_bestNormalize() which wraps and extends functionality from the 'bestNormalize' package. Furthermore, 'rfriend' can automatically (or on request) generate visualizations such as boxplots, f_boxplot(), QQ-plots, f_qqnorm(), histograms f_hist(), and density plots f_density(). Additionally, the package includes several statistical test functions: f_aov(), f_chisq_test(), f_corplot(), f_kruskal_test(), f_lmer(), f_glm(), f_t_test(), f_wilcox_test(), for sequential testing and visualisation of the similar named 'stats' functions. These functions, except for f_chisq_test(), support testing multiple response variables and predictors, while also handling assumption checks, data transformations, and post hoc tests. Post hoc results are automatically summarized in a table using the compact letter display (cld) format for easy interpretation. The package also provides a function to do model comparison, f_model_comparison(), and several utility functions to simplify common R tasks. For example, f_clear() clears the workspace and restarts R with a single command; f_setwd() sets the working directory to match the directory of the current script; f_theme() quickly changes 'RStudio' themes; and f_factors() converts multiple columns of a data frame to factors, and much more. If you encounter any issues or have feature requests, please feel free to contact me via email. 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The main function of the package is rfvimptest(), which allows to test for the statistical significance of predictors in random forests using different (sequential) permutation test strategies [1]. The advantage of sequential over conventional permutation tests is that they are computationally considerably less intensive, as the sequential procedure is stopped as soon as there is sufficient evidence for either the null or the alternative hypothesis. Reference: [1] Hapfelmeier, A., Hornung, R. & Haller, B. (2023) Efficient permutation testing of variable importance measures by the example of random forests. Computational Statistics & Data Analysis 181:107689, . 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Package: r-cran-rgan Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-torch, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-rgan_0.1.1-1.ca2604.1_all.deb Size: 256850 MD5sum: 26d35ad552be3fc2ed43845d778b7a5e SHA1: d61e322ad8b87a340be195b88d4c0c7a3b90f0ce SHA256: 90bb5f6489fc7fbd7ac801e2903bc9317ea9e7ade56791110b2a24518a1d64f1 SHA512: abf970e9fde664e2ccd0b21f4966ff03942f78ed17e767ceae9018650bd05abae79430796ff7bde6b95e187bc84e09b9470420396a97d64d0cc739464eb7d103 Homepage: https://cran.r-project.org/package=RGAN Description: CRAN Package 'RGAN' (Generative Adversarial Nets (GAN) in R) An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 . A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available. Package: r-cran-rgap Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kfas, r-cran-zoo, r-cran-dlm, r-cran-openxlsx, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-rgap_0.1.1-1.ca2604.1_all.deb Size: 1687804 MD5sum: 254b3b11303a3c92e71de4a1e2d8c4b1 SHA1: 094d3142786b12e687f9bf2e1f62fc0ecfb5582c SHA256: 3899dc0084dee80d17add58efc833b1013c11cfd46d2b9429291d936e391d947 SHA512: e9433d060754e6e392fc80bb92f23806274dd4f6ed27dc004764b85b6eec4ededf6f2a90ab122ad2ea004ab4323edc7bca7308a1a5988ed92f16493078ab56f1 Homepage: https://cran.r-project.org/package=RGAP Description: CRAN Package 'RGAP' (Production Function Output Gap Estimation) The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. 'RGAP' facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) . To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. 'RGAP' features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the 'AMECO' database and automated model selection procedures. See the paper by Streicher (2022) for details. Package: r-cran-rgbif Architecture: all Version: 3.8.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1656 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-ggplot2, r-cran-crul, r-cran-data.table, r-cran-whisker, r-cran-magrittr, r-cran-jsonlite, r-cran-oai, r-cran-tibble, r-cran-lazyeval, r-cran-r6 Suggests: r-cran-testthat, r-cran-png, r-cran-terra, r-cran-magick, r-cran-protolite, r-cran-sf, r-cran-vcr, r-cran-knitr, r-cran-rmarkdown, r-cran-bit64 Filename: pool/dists/resolute/main/r-cran-rgbif_3.8.5-1.ca2604.1_all.deb Size: 1516600 MD5sum: b2d6315a3530d06b8a2996013092e58a SHA1: 9b92867b7e059893606d0671738cc30216bfc099 SHA256: da43bab64dc32c537474585b3af5252668b1c67b7194e69c66c86753a8806fd4 SHA512: 8213400ab846b50a627ebb81a7ba4ea308cd28c6b71305c9714c76d4a796ef09eb9ec98181966adb258b1658f4fe9ab49a7ce264d2f4f3e07b4c6332d9872d07 Homepage: https://cran.r-project.org/package=rgbif Description: CRAN Package 'rgbif' (Interface to the Global Biodiversity Information Facility API) A programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility (GBIF; ). GBIF is a database of species occurrence records from sources all over the globe. rgbif includes functions for searching for taxonomic names, retrieving information on data providers, getting species occurrence records, getting counts of occurrence records, and using the GBIF tile map service to make rasters summarizing huge amounts of data. Package: r-cran-rgbindices Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rgbindices_0.1.1-1.ca2604.1_all.deb Size: 1102828 MD5sum: 172e4cbf33b2b4e7c8035490de8db633 SHA1: e24fe664ec5ef9eefee8857be6ba009d6be6e26c SHA256: d580a520fcb01faf81baba21bdcdcc243b18f2209fded663c6562dd794b8bd40 SHA512: 5b01dd49c99b0e6dd0a50c5fe80077faa023d8e8c9b32386dc1e733f21c01f044b5b3b9878d98b46cb89c435521c22d5759933beb68c4371b5b62331065612de Homepage: https://cran.r-project.org/package=rgbIndices Description: CRAN Package 'rgbIndices' (RGB Visible Indices for Image Analysis) Computes RGB-based vegetation, color, and spectral indices from digital images for applications in agriculture, crop phenotyping, and remote sensing. The methods are based on digital image processing and plant phenotyping approaches (Singh et al. (2023) ). Package: r-cran-rgbp Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sn, r-cran-mnormt Filename: pool/dists/resolute/main/r-cran-rgbp_1.1.4-1.ca2604.1_all.deb Size: 197852 MD5sum: 5ecfbd1377f2018ec03cbbdb546f1864 SHA1: 108bcb21ec4283a9fc232a0a7fc8abeca2d511d1 SHA256: 9b9e7d28956e24128cb7e594ea4d921d7fa5bf33eda5ecfc5499737e5eb0d9b9 SHA512: 0fe3fffb6298382e09e9c312793ac2b8753a10dad58aaf91d32c7381bf72f853dbd3ecbe033bea670c481be73d8ed9b46c7ca96f9ce7708f583981c073862cfb Homepage: https://cran.r-project.org/package=Rgbp Description: CRAN Package 'Rgbp' (Hierarchical Modeling and Frequency Method Checking onOverdispersed Gaussian, Poisson, and Binomial Data) We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking. Package: r-cran-rgcca Architecture: all Version: 3.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 729 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-deriv, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-mass, r-cran-matrixstats, r-cran-pbapply, r-cran-rlang Suggests: r-cran-devtools, r-cran-factominer, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-rticles, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-rgcca_3.0.3-1.ca2604.1_all.deb Size: 616868 MD5sum: 6e722686c48682e3ea32b5e0860357ab SHA1: 0921bef06f4a3daa598c090fc6fb6c9e240ab209 SHA256: 55e3d868cb3aa73d10f68760b1f89a8287a698a337e9312e216fc72b85657111 SHA512: fbc3a2e3e09ba82fc5872379e87e38c955060eed5cdbec01d3a3f0179accaa768f2b13af6d9c759ee491b4c0f01c7049c912182c732539b748b8eb2f629524b3 Homepage: https://cran.r-project.org/package=RGCCA Description: CRAN Package 'RGCCA' (Regularized and Sparse Generalized Canonical CorrelationAnalysis for Multiblock Data) Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: to study the relationships between blocks and to identify subsets of variables of each block which are active in their relationships with the other blocks. This package allows to (i) run R/SGCCA and related methods, (ii) help the user to find out the optimal parameters for R/SGCCA such as regularization parameters (tau or sparsity), (iii) evaluate the stability of the RGCCA results and their significance, (iv) build predictive models from the R/SGCCA. (v) Generic print() and plot() functions apply to all these functionalities. Package: r-cran-rgcxgc Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rnetcdf, r-cran-ptw, r-cran-colorramps, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-prettydoc Filename: pool/dists/resolute/main/r-cran-rgcxgc_1.2.0-1.ca2604.1_all.deb Size: 3172072 MD5sum: 62135a3f96442dd40c330caa3c50c7be SHA1: 7f02057bd7066d3398ce1e57412f1e0b88e0149d SHA256: 69b7f5cb6470b4d8654e05b6ec022922a8e847b4b804ca7f46e14b957b7e651a SHA512: 7db301896892e89fc4ee8b62ef0968cbb258b669fb00c2ca2d08a57e43cca69567e834a6611239f46d3b6d6ab19a8bc7da62e73cbc4059e7bb2d1538dddda50d Homepage: https://cran.r-project.org/package=RGCxGC Description: CRAN Package 'RGCxGC' (Preprocessing and Multivariate Analysis of Bidimensional GasChromatography Data) Toolbox for chemometrics analysis of bidimensional gas chromatography data. This package import data for common scientific data format (NetCDF) and fold it to 2D chromatogram. Then, it can perform preprocessing and multivariate analysis. In the preprocessing algorithms, baseline correction, smoothing, and peak alignment are available. While in multivariate analysis, multiway principal component analysis is incorporated. Package: r-cran-rge Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixmodels, r-cran-coda Filename: pool/dists/resolute/main/r-cran-rge_1.0-1.ca2604.1_all.deb Size: 1585738 MD5sum: a80ac8884690a7a4c3a28f9a5135e57f SHA1: 3dcf6320c9740fd4bd76a4071299222ff501c412 SHA256: bc68969c9b14f261045ad671c07e335f309f9e4b1c375595a404f8c21985a716 SHA512: 46f4eeb6501db13ec60f9e845ec23ee2c27449d9960e0c6b53423764ddb1b13d65d4c3a8e78f0bc33886f489cfb40a6f1e86b292367593fcf33a17d7bb602d4e Homepage: https://cran.r-project.org/package=RGE Description: CRAN Package 'RGE' (Response from Genotype to Environment) Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico. Package: r-cran-rgee Architecture: all Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-rstudioapi, r-cran-leaflet, r-cran-magrittr, r-cran-jsonlite, r-cran-processx, r-cran-leafem, r-cran-crayon, r-cran-r6, r-cran-cli Suggests: r-cran-magick, r-cran-geojsonio, r-cran-sf, r-cran-stars, r-cran-googledrive, r-cran-gargle, r-cran-httr, r-cran-digest, r-cran-testthat, r-cran-future, r-cran-terra, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-png, r-cran-googlecloudstorager, r-cran-leaflet.extras2, r-cran-spelling, r-cran-raster Filename: pool/dists/resolute/main/r-cran-rgee_1.1.8-1.ca2604.1_all.deb Size: 2695520 MD5sum: 220eb269070cfd76770bf488d4dcf734 SHA1: 8e56f24962f5942e8b4464b70dd8a05a1ace28fc SHA256: 04adff6e90c17ae72e4122798ed17f20522eb5f7fb54c4a774a79a397631e4ac SHA512: ce2182f2d333dbf36a672f2e86f96461bb3a32da04e8241ed2b006e50d731347a3c04bdf4c54d4fc89e85537d07d7e4b7978bcb43f31d87d380f4b0abdbc2280 Homepage: https://cran.r-project.org/package=rgee Description: CRAN Package 'rgee' (R Bindings for Calling the 'Earth Engine' API) Earth Engine client library for R. All of the 'Earth Engine' API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See for further details. Package: r-cran-rgeedim Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-terra, r-cran-raster, r-cran-tinytest, r-cran-litedown Filename: pool/dists/resolute/main/r-cran-rgeedim_0.4.0-1.ca2604.1_all.deb Size: 362670 MD5sum: 952ccf5436dfefe9931638e18237cd6c SHA1: 7151e525c426043f4d9500919c327a3c8763ebe7 SHA256: d7e606b1e144d1289e4c96b9d48555254e965aa3eb7c5956ab0a0508f67885eb SHA512: 5bd87e09630c1475cbf32cf46a6700830667351884cce80e0759dce50eec303b702c7ec7580ff26ca52f2a30e600ad45ae6c63852d0fc43dea3e191b4d743073 Homepage: https://cran.r-project.org/package=rgeedim Description: CRAN Package 'rgeedim' (Search, Composite, and Download 'Google Earth Engine' Imagerywith the 'Python' Module 'geedim') Search, composite, and download 'Google Earth Engine' imagery with 'reticulate' bindings for the 'Python' module 'geedim' by Dugal Harris. Read the 'geedim' documentation here: . Wrapper functions are provided to make it more convenient to use 'geedim' to download images larger than the 'Google Earth Engine' size limit . By default the "High Volume" API endpoint is used to download data and this URL can be customized during initialization of the package. Package: r-cran-rgen Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rgen_0.0.1-1.ca2604.1_all.deb Size: 16442 MD5sum: 765b788f5b41f24af2188b2574b9085b SHA1: 200b8b3d954e6df6fd722ac55d6f5eb927944790 SHA256: ee176932c047976fc7b324e40e111defddd6a3cbc387524db720fcc027494209 SHA512: 40a4106ce6458a9168b1e642981ad5b5614a748373ea037aea1b4e0b3e0ea40273ed3d1acb98f62d0cedfa0dff0683cf28eade19a899490030307577c14de662 Homepage: https://cran.r-project.org/package=rgen Description: CRAN Package 'rgen' (Random Sampling Distribution C++ Routines for Armadillo) Provides popular sampling distributions C++ routines based in armadillo through a header file approach. Package: r-cran-rgendata Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rgendata_1.0-1.ca2604.1_all.deb Size: 32956 MD5sum: 0ad7a765a7569a145365576b015c02ed SHA1: 94e2c63211b9fc8750c9e0afa7b0f21797e7ae95 SHA256: 66617b1c349305b64704d01b6769eead9045513f06f2d29eb97c54552e97f470 SHA512: 8b8dca01cd966eef5736960f4bda8e7d17b9334699f3ba5dc1241f5ffa757deb504adafbb556f7b560674843d7e1ba585afcf73441d7e3b6a02ccab1119c643f Homepage: https://cran.r-project.org/package=RGenData Description: CRAN Package 'RGenData' (Generates Multivariate Nonnormal Data and Determines How ManyFactors to Retain) The GenDataSample() and GenDataPopulation() functions create, respectively, a sample or population of multivariate nonnormal data using methods described in Ruscio and Kaczetow (2008). Both of these functions call a FactorAnalysis() function to reproduce a correlation matrix. The EFACompData() function allows users to determine how many factors to retain in an exploratory factor analysis of an empirical data set using a method described in Ruscio and Roche (2012). The latter function uses populations of comparison data created by calling the GenDataPopulation() function. . . Package: r-cran-rgenerate Architecture: all Version: 1.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmawgen, r-cran-magrittr, r-cran-vars Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rgenerate_1.3.8-1.ca2604.1_all.deb Size: 3145298 MD5sum: 02dc62868979166de11185707c085140 SHA1: 9fa862011909eeaaeb6a13d4620ba6be7630f06a SHA256: 0f149f0a07392f9652970c2f7e5a27aabcde0929f66e6bddd090ba0486e25bca SHA512: e6f7ce2c2f86702a1137d056864408c0d1bf0abe3dc8ba6d734c04407a91c042572c0dd5e618b56a5b84550146941876063d890ca400606749bcbe7744d5d959 Homepage: https://cran.r-project.org/package=RGENERATE Description: CRAN Package 'RGENERATE' (Tools to Generate Vector Time Series) A method 'generate()' is implemented in this package for the random generation of vector time series according to models obtained by 'RMAWGEN', 'vars' or other packages. This package was created to generalize the algorithms of the 'RMAWGEN' package for the analysis and generation of any environmental vector time series. Package: r-cran-rgenerateprec Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4956 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-rgenerate, r-cran-blockmatrix, r-cran-matrix, r-cran-stringr, r-cran-rmawgen, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mapview, r-cran-sf, r-cran-lmom, r-cran-ggplot2, r-cran-reshape2, r-cran-refmanager Filename: pool/dists/resolute/main/r-cran-rgenerateprec_1.3.2-1.ca2604.1_all.deb Size: 4131056 MD5sum: c9ff56666a33cd66a355ed2457ecbe47 SHA1: 07e1065d0ba80771f11fac21db31b5e82f6d3dcc SHA256: 89f260b7699c5dddf620970ec9f515ef098d6869d3a5e0daa9dad64daef011b7 SHA512: f3e02674d3a792cf7ed8331ba626052aded79dafac0487058ecb01aa8a5c8162aeb09104ca54374748c1e80fe7cca5db1ec340795d8dc36e0cccee0fe4fe44d5 Homepage: https://cran.r-project.org/package=RGENERATEPREC Description: CRAN Package 'RGENERATEPREC' (Tools to Generate Daily-Precipitation Time Series) The method 'generate()' is extended for spatial multi-site stochastic generation of daily precipitation. It generates precipitation occurrence in several sites using logit regression (Generalized Linear Models) and the approach by D.S. Wilks (1998) . 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This package provides functions to simulate the RHawkes process with a given immigrant hazard rate function and offspring birth time density function, to compute the exact likelihood of a RHawkes process using the recursive algorithm proposed by Chen and Stindl (2018) , to compute the Rosenblatt residuals for goodness-of-fit assessment, and to predict future event times based on observed event times up to a given time. A function implementing the linear time RHawkes process likelihood approximation algorithm proposed in Stindl and Chen (2021) is also included. Package: r-cran-rhc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-permute, r-cran-lattice, r-cran-ade4, r-cran-geometry, r-cran-vegan, r-cran-fd, r-cran-randomforest, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-rhc_0.1.0-1.ca2604.1_all.deb Size: 2191094 MD5sum: 187e23f18b9623bf978c3d85feabaaf9 SHA1: 1e6e1d72735e57cfe05d64dd3dc9b806df28cdce SHA256: eac4da361a70b9395aa1fca65d932f0488685e277f2711d88d88566acb12e9b2 SHA512: eb05c037eb4cb0389682238786aaa71b5256510c72efc9a9fe248aabd7324ee168e239e9d11d5f4e534bb89a4c53a9b20d52a49b9c0f6353db049e989e0d5d00 Homepage: https://cran.r-project.org/package=RHC Description: CRAN Package 'RHC' (Rangeland Health and Condition) The evaluation criteria of rangeland health, condition and landscape function analysis based on species diversity and functional diversity of rangeland plant communities. 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This package contains functions designed to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), gene expression (GE), and covariate data. The novel concept follows the general K-means (Hartigan and Wong (1979) framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions. 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Package: r-cran-rhoneycomb Architecture: all Version: 2.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rhoneycomb_2.3.4-1.ca2604.1_all.deb Size: 156584 MD5sum: 0fc8c2bb2f4512f21d4d1b8ec608330b SHA1: 1992fab260d5f4b10309e74513d416a885839758 SHA256: 70ceb9a8f26841f8dc6de4d1443378f924b6cc53d956042d702ef25ac36fe0d4 SHA512: 77c2ce600b4c32428cb1d3974752f7424696f742718853504b008abdc7c6a128989dd8e6c52f09e467bcfe9571872f2dbd18b9f61631c0d6e5685c6299842bef Homepage: https://cran.r-project.org/package=rhoneycomb Description: CRAN Package 'rhoneycomb' (Analysis of Honeycomb Selection Designs) A useful statistical tool for the construction and analysis of Honeycomb Selection Designs. More information about this type of designs: Fasoula V. (2013) Fasoula V.A., and Tokatlidis I.S. (2012) Fasoulas A.C., and Fasoula V.A. (1995) Tokatlidis I. (2016) Tokatlidis I., and Vlachostergios D. (2016) . 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This package aims to provide a simple API to estimate and analyze them. The current implementation is based on Brillinger and Irizarry (1998) for estimating bispectrum or bicoherence, Lii and Helland (1981) for cross-bispectrum, and Kim and Powers (1979) for cross-bicoherence. 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Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty. To include any indirect method, a custom wrapper function needs to be provided. The package offers functions for generating the test sets, executing the indirect method and evaluating the results. See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) . 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Zhang (2025) . 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Zhang (2025) . Package: r-cran-ricci Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-calculus, r-cran-cli, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ryacas, r-cran-testthat, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-ricci_0.1.1-1.ca2604.1_all.deb Size: 166496 MD5sum: 39c9b9139b595cfcd6ae5e6ad7b2aebd SHA1: d0ddd7bde0fc18a585bb55c27c07e3c7683391e3 SHA256: 7eacd9e2d409a7b205a9b3acdb52da9f4fba4a78c047a99aebd1ad43674ceeab SHA512: e387e121307e9b9f95b8f2d2a07f67083b953e88e3805894d6e42a7f53f1649fa85190cc3f02ebef40850ddc07b3155098221a9a17dcb2c325052ca69f4f01c0 Homepage: https://cran.r-project.org/package=ricci Description: CRAN Package 'ricci' (Ricci Calculus) Provides a compact 'R' interface for performing tensor calculations. This is achieved by allowing (upper and lower) index labeling of arrays and making use of Ricci calculus conventions to implicitly trigger contractions and diagonal subsetting. Explicit tensor operations, such as addition, subtraction and multiplication of tensors via the standard operators, raising and lowering indices, taking symmetric or antisymmetric tensor parts, as well as the Kronecker product are available. Common tensors like the Kronecker delta, Levi Civita epsilon, certain metric tensors, the Christoffel symbols, the Riemann as well as Ricci tensors are provided. The covariant derivative of tensor fields with respect to any metric tensor can be evaluated. An effort was made to provide the user with useful error messages. Package: r-cran-rice Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rintcal, r-cran-rlang, r-cran-ggplot2, r-cran-maps Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8, r-cran-remotes, r-cran-sf, r-cran-flextable, r-cran-rnaturalearthdata, r-cran-rnaturalearth, r-cran-leaflet, r-cran-htmltools, r-cran-copernicusmarine, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rice_2.1.0-1.ca2604.1_all.deb Size: 1753816 MD5sum: 706a433734ea838a9cdd19cd83425639 SHA1: 31661367a98f8cfe6e50cd62ba5b70b0f12a3d41 SHA256: c6d14cc58715314a14d6d9d7b1590b531da6080f481a58f76b1b21fda1985660 SHA512: 1d6fd1a8a0248ffc5935591f26bdb6f4e33a27dd05e9e707bab873fa5dfdd97474a9394926e3073007cc0f2373a1df1fc8fa96fe86a591367f3c976e41789e45 Homepage: https://cran.r-project.org/package=rice Description: CRAN Package 'rice' (Radiocarbon Equations) Provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon-related timescales (cal BP, cal BC/AD, C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 ). Supporting publication: Blaauw, M., Reimer, P.J., in press. An open-source toolkit for radiocarbon dating and calibration. Radiocarbon. The methods follow long-established recommendations such as Stuiver and Polach (1977) and Reimer et al. (2004) . This package uses the calibration curves from the data package 'rintcal'. Package: r-cran-ricegeneann Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-riceidconverter Filename: pool/dists/resolute/main/r-cran-ricegeneann_1.0.2-1.ca2604.1_all.deb Size: 895668 MD5sum: 459b5f5dc5c703a4d94643616c57150c SHA1: c7b61eb79d9bf3fea9b67574b62a5772d40a96de SHA256: 9fb2b42405e7f25a4fbec5355581abf9c31c47cfe4f008b9c4afc253665ec8be SHA512: 0150b125e98ef0f553f60aa3bd6a9a3570b7baeef9d0c5d5a8e6d16a6807e1f79874b8118ae7b387143e98128f5c3ba83b2ad3fe76b0c98a07a81d6fceceaf15 Homepage: https://cran.r-project.org/package=ricegeneann Description: CRAN Package 'ricegeneann' (Gene Annotation of Rice (Oryza Sativa L.spp.japonica)) Gene annotation of rice (Oryza Sativa L.spp.japonica). The package is based on the annotation file from the website . 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Rice(Oryza sativa) has more than one form gene ID for the genome. The two main gene ID for rice genome are the RAP (The Rice Annotation Project, , and the MSU(The Rice Genome Annotation Project, . All RAP rice gene IDs are of the form Os##g####### as explained on the website . All MSU rice gene IDs are of the form LOC_Os##g##### as explained on the website . All SYMBOL rice gene IDs are the unique name on the NCBI(National Center for Biotechnology Information, . The TRANSCRIPTID, is the transcript id of rice, are of the form Os##t#######. The researchers usually need to converter between various IDs. Such as converter RAP to SYMBOLS for function searching on NCBI. There are a lot of websites with the function for converting RAP to MSU or MSU to RA, such as 'ID Converter' . But it is difficult to convert super multiple IDs on these websites. The package can convert all IDs between the three IDs (RAP, MSU and SYMBOL) regardless of the number. Package: r-cran-riceware Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-random Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-riceware_0.4-1.ca2604.1_all.deb Size: 396834 MD5sum: 9bc4449992b7757b24a8e910262e0ef9 SHA1: e9ce5751b6b64c8f501d0b47459c2fc478cb7dbc SHA256: 12ee23e6d74d4d86e9c267254f07456dedda3e997b7b632377a69551de7f0b3d SHA512: 5f986692c390e03bbb0ea057ef6a01f80c2ee59da3112fa7106d34f8840c7125d7158d9dd84296f98db407e1f0e310a97b8df23a3a1981ec91719ccf8fb5ce91 Homepage: https://cran.r-project.org/package=riceware Description: CRAN Package 'riceware' (A Diceware Passphrase Implementation) The Diceware method can be used to generate strong passphrases. In short, you roll a 6-faced dice 5 times in a row, the number obtained is matched against a dictionary of easily remembered words. 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Package: r-cran-rideogram Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-grimport2, r-cran-rsvg, r-cran-scales, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rideogram_0.2.2-1.ca2604.1_all.deb Size: 2632514 MD5sum: 98c6a53e33ad196b2269de9ee3b746d1 SHA1: e359dae0c2de499a42cd40f8e4c29905bf775864 SHA256: 728ad2ae0d63bc3529e771b08512ca808d53ed279dabd5b51cd20f5714cd454c SHA512: 173923ce385159f65fba37d754c57405c67073464961371cf1dcb88edf4748eb653f7aa8451e8c34f854000d1491528179ad54e8fd4ae01142833df318954ad9 Homepage: https://cran.r-project.org/package=RIdeogram Description: CRAN Package 'RIdeogram' (Drawing SVG Graphics to Visualize and Map Genome-Wide Data onIdiograms) For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. 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The package allows to regress the RIF on any number of covariates. Generic print, plot and summary functions are also provided. Reference: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2009) . "Unconditional Quantile Regressions.". 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The tree supports queries of intervals overlapping a single number or an interval (start, end). Intervals with same bounds but different names are treated as distinct intervals. Insertion of intervals is also allowed. Deletion of intervals is not implemented at this point. See Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars (2008). Computational Geometry: Algorithms and Applications, for a reference. 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Package: r-cran-rintrojs Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-rintrojs_0.3.4-1.ca2604.1_all.deb Size: 54788 MD5sum: 6a9c6796c48f868d83bc6c681d8a9aa4 SHA1: c07dd099822668a93a093c8e93ee32ea439a9f6a SHA256: 1c38124e123b1f7ab3f5aba24797f57f716818d3beaa5cfa614602bf4a27f7b3 SHA512: fc01119d6fd8452ea1dbc007414162cecc29d716c3cb0fad6eae5e80a5386a48ba9c02acb5f43b0396fabd171bf130fe49d3796fc8810ea78aa0704a08e57206 Homepage: https://cran.r-project.org/package=rintrojs Description: CRAN Package 'rintrojs' (Wrapper for the 'Intro.js' Library) A wrapper for the 'Intro.js' library (For more info: ). This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny' application. It supports both static introductions in the UI, and programmatic introductions from the server-side. Package: r-cran-rio Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-haven, r-cran-curl, r-cran-data.table, r-cran-readxl, r-cran-tibble, r-cran-writexl, r-cran-lifecycle, r-cran-r.utils, r-cran-readr Suggests: r-cran-bit64, r-cran-testthat, r-cran-knitr, r-cran-magrittr, r-cran-clipr, r-cran-fst, r-cran-hexview, r-cran-jsonlite, r-cran-pzfx, r-cran-readods, r-cran-rmarkdown, r-cran-rmatio, r-cran-xml2, r-cran-yaml, r-cran-arrow, r-cran-stringi, r-cran-withr, r-cran-nanoparquet, r-cran-qs2 Filename: pool/dists/resolute/main/r-cran-rio_1.3.0-1.ca2604.1_all.deb Size: 553336 MD5sum: d5740a4e04b1fe294f50699615bd2a13 SHA1: 115a1bcbf9d2be45dbaee2f11c4ff6251d2ce257 SHA256: ed419e1cfbfbc8b32b3ba85dee7649972714fd02a65ae0ee6caf9c09c49096e6 SHA512: d6dbe6a1ff67849b1e2ad3b10190e607a60dd056796655be0002c4cd013ffb5fb459dee909e3842f636bd9b37dd587c6d96ce7d16987b7ff564115ad724b4dd3 Homepage: https://cran.r-project.org/package=rio Description: CRAN Package 'rio' (A Swiss-Army Knife for Data I/O) Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data format from the file extension, reasonable defaults are used for data import and export, web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly, and fast import packages are used where appropriate. An additional convenience function, 'convert()', provides a simple method for converting between file types. Package: r-cran-rioplot Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggrepel, r-cran-mass Filename: pool/dists/resolute/main/r-cran-rioplot_1.1.2-1.ca2604.1_all.deb Size: 689858 MD5sum: 4659c5d055a6c52ae3d0171a4136a8e1 SHA1: 9dbb8a39e00b38a14500b24af41f6efb06f32201 SHA256: a55b7e15e05e071a02de0fe940750815f01cd828ff3cba7f23d86822c2ab7de7 SHA512: 4ad8e82b5ff7eecdaf70fc044c24bfc547436969df84f91ea45a52d68ed768ee1b8df3a13d2069c140bf08ecb2ca8ae907088fc6bbace4ec204848fcf4ae4f3f Homepage: https://cran.r-project.org/package=rioplot Description: CRAN Package 'rioplot' (Turn a Regression Model Inside Out) Turns regression models inside out. Functions decompose variances and coefficients for various regression model types. Functions also visualize regression model objects using techniques developed in Schoon, Melamed, and Breiger (2024) . Package: r-cran-ripc Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-countrycode, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-ripc_1.0.0-1.ca2604.1_all.deb Size: 157076 MD5sum: d41c22b047f036fe867d0e36e01c906b SHA1: 21cd4191490c711c6de217c18c9b8f4830a8223d SHA256: 27310e80b4d046ee1f597136fc079ac272193f54b3c01b2318c10b69cb931c98 SHA512: 13f77a2c6cdf0f425bf0edf3b4a5fd0802622197bf203279af38224873be2dd00e4e3c4fd9743f376526d57e07caaa6b344aa92d67f4d3961fca5b0ee7a7ec7e Homepage: https://cran.r-project.org/package=ripc Description: CRAN Package 'ripc' (Download and Tidy IPC and CH Data) Utilities to access Integrated Food Security Phase Classification (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are available for all of the 'IPC-CH' Public API () simplified and advanced endpoints to easily download the data in a clean and tidy format. Package: r-cran-rirods Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-jsonlite, r-cran-rappdirs, r-cran-testthat, r-cran-withr Suggests: r-cran-httptest2, r-cran-kableextra, r-cran-knitr, r-cran-purrr, r-cran-readr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-rirods_0.2.0-1.ca2604.1_all.deb Size: 203986 MD5sum: fefec5c5f2ef25191cbd622ea963bf7b SHA1: 2495dd6ec1aa69d1a026bfcd102e995c60f63438 SHA256: b71fad7e5d835fac292294aee48292fc99e69d5424005d7c09902ba85629489b SHA512: f0ade2a2fef216ed35ae96ef5dfb47612ef6695eddb2eab3b19302a5900b695e67b6ace0cfb9c928457d8e7d839adb54fcd2e8f4a3e09f242edbe933ee99830f Homepage: https://cran.r-project.org/package=rirods Description: CRAN Package 'rirods' (R Client for 'iRODS') The open sourced data management software 'Integrated Rule-Oriented Data System' ('iRODS') offers solutions for the whole data life cycle (). The loosely constructed and highly configurable architecture of 'iRODS' frees the user from strict formatting constraints and single-vendor solutions. This package provides an interface to the 'iRODS' HTTP API, allowing you to manage your data and metadata in 'iRODS' with R. Storage of annotated files and R objects in 'iRODS' ensures findability, accessibility, interoperability, and reusability of data. Package: r-cran-risca Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-relsurv, r-cran-reticulate, r-cran-tune, r-cran-nlme, r-cran-mass, r-cran-mvtnorm, r-cran-statmod, r-cran-doparallel, r-cran-foreach, r-cran-nnet, r-cran-kernlab, r-cran-glmnet, r-cran-caret, r-cran-superlearner, r-cran-rpart, r-cran-mosaic, r-cran-cubature Filename: pool/dists/resolute/main/r-cran-risca_1.0.7-1.ca2604.1_all.deb Size: 1364634 MD5sum: 8461f9bfc4eab92e1357c5122c065036 SHA1: c973e8c63e007476898cf4d2bb076d1e1c0b559b SHA256: d7c2ce787ebf51bd1e1150e9c92756476180b1a7fadca2a46737af62533412dd SHA512: 23e3842186beb277035d380a743b77b9043ebf78a96b5c1f9932e0cd6d5e84a3a4f06987a93152a506241e9cd8789212377e4f0815f48fb4a5bd948bc7757fb0 Homepage: https://cran.r-project.org/package=RISCA Description: CRAN Package 'RISCA' (Causal Inference and Prediction in Cohort-Based Analyses) Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data. Package: r-cran-rise Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rise_1.0.4-1.ca2604.1_all.deb Size: 18644 MD5sum: 0dc1049f4d10fe484a37d68f2e49608a SHA1: 235d572d5b69e244f4659c19e33c9338a1273c34 SHA256: d52f0d02386fa6b0091d9ef314e2aa124ddd4a4910c9d52d07fc6d95286e5f41 SHA512: 1f0c08c4541fd24ae4dc036be4502926007787ec52bc14f6d1ef8b61b1224ed9a38890d6c12804b6fcb6bf9c41ed571e67b8f1fc12e00fa096bc10eac3120fdf Homepage: https://cran.r-project.org/package=rise Description: CRAN Package 'rise' (Conduct RISE Analysis) Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) . Automates the process of identifying learning materials that are not effectively supporting student learning in technology-mediated courses by synthesizing information about access to course content and performance on assessments. Package: r-cran-risk.assessr Architecture: all Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-remotes, r-cran-callr, r-cran-checkmate, r-cran-covr, r-cran-desc, r-cran-dplyr, r-cran-fs, r-cran-pkgload, r-cran-purrr, r-cran-rmarkdown, r-cran-rcmdcheck, r-cran-rlang, r-cran-xml2, r-cran-stringr, r-cran-tidyr, r-cran-curl, r-cran-jsonlite, r-cran-memoise, r-cran-testthat, r-cran-biocmanager, r-cran-glue Suggests: r-cran-devtools, r-cran-forcats, r-cran-here, r-cran-htmltools, r-cran-htmlwidgets, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-openxlsx, r-cran-r6, r-cran-s7, r-cran-readr, r-cran-roxygen2, r-cran-testit, r-cran-tibble, r-cran-tidyselect, r-cran-withr, r-cran-mockery, r-cran-ggplot2, r-cran-dt Filename: pool/dists/resolute/main/r-cran-risk.assessr_3.1.1-1.ca2604.1_all.deb Size: 1921498 MD5sum: 33de395978872960cae15a2d0042ee12 SHA1: ed5442e0a7b14591f1ddfea3b611f56c89502671 SHA256: 2780474be64776e694653914b4dc11843116c922e87f4f86ee39f2a4ab3c125a SHA512: 636821f1a331d42a07ec4b1b00638e7faf4d5b57593b8558d1acb89284850c99e05fc1a6de85a528df01f75958bdcd42a415b2bb3226b2b966a62f016fb25c3c Homepage: https://cran.r-project.org/package=risk.assessr Description: CRAN Package 'risk.assessr' (Assessing Package Risk Metrics) A reliable and validated tool that captures detailed risk metrics such as R 'CMD' check, test coverage, traceability matrix, documentation, dependencies, reverse dependencies, suggested dependency analysis, repository data, and enhanced reporting for R packages that are local or stored on remote repositories such as GitHub, CRAN, and Bioconductor. Package: r-cran-risk Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-risk_1.0-1.ca2604.1_all.deb Size: 86358 MD5sum: 6315bed7535863bc04d19b848146df90 SHA1: 00269bcd23d0c9cf9dc62d015730d52f159d2f7d SHA256: bd9434877c3592575222b3e0025d557ab34fade1bcaa4b859142d4e45822fb25 SHA512: 34da8d8b2ef9fceb9bc834fe58a645eb8f2ab081a1190deedd1d83031e6fb55e4fab844276f6e7664128dc8ee6ef62a1671b464df1f35d67723a6f4f9005311e Homepage: https://cran.r-project.org/package=Risk Description: CRAN Package 'Risk' (Computes 26 Financial Risk Measures for Any ContinuousDistribution) Computes 26 financial risk measures for any continuous distribution. The 26 financial risk measures include value at risk, expected shortfall due to Artzner et al. (1999) , tail conditional median due to Kou et al. (2013) , expectiles due to Newey and Powell (1987) , beyond value at risk due to Longin (2001) , expected proportional shortfall due to Belzunce et al. (2012) , elementary risk measure due to Ahmadi-Javid (2012) , omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa due to Kaplan and Knowles (2004), Wang (1998)'s risk measures, Stone (1973)'s risk measures, Luce (1980)'s risk measures, Sarin (1987)'s risk measures, Bronshtein and Kurelenkova (2009)'s risk measures. Package: r-cran-riskclustr Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlogit, r-cran-stringr, r-cran-matrix Suggests: r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-dplyr, r-cran-knitr, r-cran-usethis, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-riskclustr_0.4.1-1.ca2604.1_all.deb Size: 385558 MD5sum: 1d37220a656b0844f6064a662ce75f32 SHA1: 591999e8f6e98d4a2d331021f293c0583fb3baf0 SHA256: c4f0153c74c8b7e63868eed954aeec23991b9ca35bd126662403e676cfb1925c SHA512: 5d805a90a3705dd6c180c811a2ff33b750cb051ede9e38c6f189b204f78bff66b914cb6f6951243b0951fc3e449ca555d895b1a401b245191ebc8e4291d2e9ee Homepage: https://cran.r-project.org/package=riskclustr Description: CRAN Package 'riskclustr' (Functions to Study Etiologic Heterogeneity) A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) . Package: r-cran-riskcommunicator Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1527 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-printr, r-cran-stringr, r-cran-formatr, r-cran-sandwich Filename: pool/dists/resolute/main/r-cran-riskcommunicator_1.0.1-1.ca2604.1_all.deb Size: 1091894 MD5sum: 44f440f672afb315c96f2cd77b865b7b SHA1: 9b27d90ffbb579835749af4b2c51b0668645638a SHA256: 63f6716e7317e18d1854dd425902da7d770f32babaecfb369d7b852faad4f643 SHA512: d36ca07e9ff0b5660b69e4769406761ff265bf4e7e156418ebd2c477b1f6664ca95eaea219b76fa797ba60a32417898cf8674bc1347af5c4e16be3fba32a9518 Homepage: https://cran.r-project.org/package=riskCommunicator Description: CRAN Package 'riskCommunicator' (G-Computation to Estimate Interpretable Epidemiological Effects) Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) and has been described in detail by Ahern, Hubbard, and Galea (2009) ; Snowden, Rose, and Mortimer (2011) ; and Westreich et al. (2012) . Package: r-cran-riskdiff Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-ggplot2 Suggests: r-cran-kableextra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-riskdiff_0.3.0-1.ca2604.1_all.deb Size: 411262 MD5sum: c1111dd29842f4c2fc880e76ea29455d SHA1: cfd4b24f48fb09ac68e876a9ece457f7ca159c65 SHA256: ecc665526f5795f22af1eda1d013eecccd0217cda9c4747012f91b80f55399df SHA512: e955aa8a73b5da62af548efd5b07ede0a404d5caccd18a7815f12957d05d9e409902d57c690a6399b26fdfecccdc48a363f5d52c3a7fa3a6edb9123833ee6906 Homepage: https://cran.r-project.org/package=riskdiff Description: CRAN Package 'riskdiff' (Risk Difference Estimation with Multiple Link Functions andInverse Probability of Treatment Weighting) Calculates risk differences (or prevalence differences for cross-sectional data) and Number Needed to Treat (NNT) using generalized linear models with automatic link function selection. Provides robust model fitting with fallback methods, support for stratification and adjustment variables, inverse probability of treatment weighting (IPTW) for causal inference with NNT calculations, and publication-ready output formatting. Handles model convergence issues gracefully and provides confidence intervals using multiple approaches. Methods are based on approaches described in Mark W. Donoghoe and Ian C. Marschner (2018) "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model" for robust GLM fitting, Peter C. Austin (2011) "An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies" for IPTW methods, and standard epidemiological methods for risk difference estimation as described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash (2008, ISBN:9780781755641) "Modern Epidemiology". Package: r-cran-riskmap Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-terra, r-cran-xtable, r-cran-spatialeco, r-cran-spatialsample, r-cran-deriv, r-cran-numderiv, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-sf, r-cran-sns, r-cran-stars Filename: pool/dists/resolute/main/r-cran-riskmap_1.0.0-1.ca2604.1_all.deb Size: 1067516 MD5sum: c00f4daa05f944744dfc47185b48db0a SHA1: 6784d1dfee861237d3a1011caf0752ea70239ebd SHA256: 3ad8b8622a886f643a038d8f5af7cf535bdca04f56b6483191dfb814971bd23a SHA512: 76cdfcb6fc6acac132eae319d6939c6c6f74cea934fbea89127fd7be75a0a32e0c64176ca28e45b019e5e88954abd79474b5511a984dac8d6f4118d262053ea0 Homepage: https://cran.r-project.org/package=RiskMap Description: CRAN Package 'RiskMap' (Geostatistical Modeling of Spatially Referenced Data) Geostatistical analysis of continuous and count data. Implements stationary Gaussian processes with Matérn correlation for spatial prediction, as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7). Package: r-cran-riskmetric Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-xml2, r-cran-httr, r-cran-curl, r-cran-urltools, r-cran-memoise, r-cran-biocmanager, r-cran-cranlogs, r-cran-covr, r-cran-vctrs, r-cran-pillar, r-cran-tibble, r-cran-pkgload, r-cran-devtools Suggests: r-cran-dplyr, r-cran-jsonlite, r-cran-knitr, r-cran-magrittr, r-cran-pkgbuild, r-cran-rmarkdown, r-cran-testthat, r-cran-webmockr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-riskmetric_0.2.7-1.ca2604.1_all.deb Size: 761874 MD5sum: ea9a9757c33449b380607988f559d656 SHA1: 1bb421b5d5c388860b26d4ed816244874415d39f SHA256: 99331528795a519e0e15e9ae470052613b8f828197f6e4fabbdce432e1a21042 SHA512: 3909ec5c1f91ba206464f4af37f5315d062ef0ab90d0946217778dc9d0d07e6bc986ed5bfddfd0f87283a531a2058980175bc71cb6478d4a49ef6e9e833cfd0b Homepage: https://cran.r-project.org/package=riskmetric Description: CRAN Package 'riskmetric' (Risk Metrics to Evaluating R Packages) Facilities for assessing R packages against a number of metrics to help quantify their robustness. Package: r-cran-riskportfolios Architecture: all Version: 2.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-quadprog, r-cran-nloptr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-riskportfolios_2.1.7-1.ca2604.1_all.deb Size: 98330 MD5sum: 3cd9b0c49ee602fd13b559cecacd9cf8 SHA1: 875da962fe242a6da90722b3f1f983e2fa7a328c SHA256: eadc33c6f0a18c65b59afac72c1819fc7dc02011ad286d0238dcfe934a8106a4 SHA512: 197c68b034dc82aedb84a4ca352aca372e4efe64966613e60f2d2864d464f83239db27fbfcd0c537c8afb990f58d8dea2831bd43911ccadad93bf8632e231308 Homepage: https://cran.r-project.org/package=RiskPortfolios Description: CRAN Package 'RiskPortfolios' (Computation of Risk-Based Portfolios) Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) and Ardia et al. (2017) . Package: r-cran-riskpredictclustdata Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gee, r-cran-hmisc, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-riskpredictclustdata_0.2.6-1.ca2604.1_all.deb Size: 87428 MD5sum: 17a988d2a834f6b60d364bc0c5b9d49f SHA1: de97c87ffc64d8ab5716c11d189246c734ca2492 SHA256: 8f572286073d22ac8ac6bd77e07b8ec0944eb3f08d792a38587d794ab1426c6d SHA512: 7ea654603bca2e9927b5b79a26303a5681538d8c4eeb262989e4a40972dc97fee4514584e05f19457a620008985f6d785e45c06a4eaa543ea36f17e5e5acfa58 Homepage: https://cran.r-project.org/package=riskPredictClustData Description: CRAN Package 'riskPredictClustData' (Assessing Risk Predictions for Clustered Data) Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) . Package: r-cran-risks Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-bcaboot, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-sandwich, r-cran-tibble, r-cran-tidyr Suggests: r-cran-addreg, r-cran-covr, r-cran-knitr, r-cran-logbin, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-risks_0.4.3-1.ca2604.1_all.deb Size: 1387444 MD5sum: c77775f9eebf6bb256120fa9d8526367 SHA1: e151ded64d1c7bfb28a9b6a1e0f903fbaca6a5e3 SHA256: 9875841204a8ede7171774a3cfc4c06ad530839de5d7634800b5dede475e83a1 SHA512: 60c1010e75034f4eccfbdb1b45b82a88dc2fe79b6a8e703526ef185d19dd6595bcbb22ba8d0fcb11a523e2c8ce99bb739d7f2c79868c4f91d88118845292086b Homepage: https://cran.r-project.org/package=risks Description: CRAN Package 'risks' (Estimate Risk Ratios and Risk Differences using Regression) Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, ), log-binomial (Poisson) models with empirical variance (Zou 2004, ), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, ), and others. Package: r-cran-riskscores Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-proc Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-riskscores_1.2.3-1.ca2604.1_all.deb Size: 168116 MD5sum: 4fc6d05613fd55d751391a6b60245562 SHA1: 4cfa643613ddae8b0de3ff89d08bb6c73e1ab285 SHA256: d95150522320fcbfd8036ec7bd497e352e77d41954947033ade4707de3f6817e SHA512: 235f2965d49433f8c60c98d30e3ecb60a9f79af8286b613bff8709330c17d36a87543039723658369a55cb5215ffd878ec4f1298a574601dbcdf891cd8304575 Homepage: https://cran.r-project.org/package=riskscores Description: CRAN Package 'riskscores' (Optimized Integer Risk Score Models) Implements an optimized approach to learning risk score models, where sparsity and integer constraints are integrated into the model-fitting process. Package: r-cran-riskscorescvd Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-pooledcohort Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-purrr, r-cran-testthat, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-riskscorescvd_0.3.1-1.ca2604.1_all.deb Size: 545448 MD5sum: 68f2a801e9bd281fed6e234fe4e1cb0e SHA1: e080131d75b3d705ba019f1f140eba707364c4b3 SHA256: b03eeef648e88989fdf3d64a8dff5828300eb512c7908587adc66aa0eceb3bfe SHA512: b4993eaa61ddac051c5ce3433ff27b3a7657dadb1cd6d6f1229ffef07c50c8c690304c457068f41c12b20d72393b3e9838c9cb1240dc615b8199f10dc39f4fd7 Homepage: https://cran.r-project.org/package=RiskScorescvd Description: CRAN Package 'RiskScorescvd' (Cardiovascular Risk Scores Calculator) A tool to calculate Cardiovascular Risk Scores in large data frames as published in Perez-Vicencio, et al (2024) . Cardiovascular risk scores are statistical tools used to assess an individual's likelihood of developing a cardiovascular disease based on various risk factors, such as age, gender, blood pressure, cholesterol levels, and smoking. Here we bring together the six most commonly used in the emergency department. Using 'RiskScorescvd', you can calculate all the risk scores in an extended dataset in seconds. PCE (ASCVD) described in Goff, et al (2013) . EDACS described in Mark DG, et al (2016) . GRACE described in Fox KA, et al (2006) . HEART is described in Mahler SA, et al (2017) . SCORE2/OP described in SCORE2 working group and ESC Cardiovascular risk collaboration (2021) . TIMI described in Antman EM, et al (2000) . SCORE2-Diabetes described in SCORE2-Diabetes working group and ESC Cardiovascular risk collaboration (2023) . SCORE2/OP with CKD add-on described in Kunihiro M et al (2022) . Package: r-cran-risksetroc Architecture: all Version: 1.0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mass Filename: pool/dists/resolute/main/r-cran-risksetroc_1.0.4.1-1.ca2604.1_all.deb Size: 86990 MD5sum: a19674c03fc5b2c714b5c4d18d3a3330 SHA1: a1831020bf13a65b6e62b2b1d89ad92290057e1a SHA256: 3427f8d6c04bd86bd884e885014194c9908b558bd9615ac27bdef28ba1f32538 SHA512: aba5056bb7699cf0d4a7d0cd6e6b7cce846e95ea347067cab65053e1b17b7a16536d533088e08794a4587024dfc042fd9552a6cdf816af872ac5623955fb5982 Homepage: https://cran.r-project.org/package=risksetROC Description: CRAN Package 'risksetROC' (Riskset ROC Curve Estimation from Censored Survival Data) Compute time-dependent Incident/dynamic accuracy measures (ROC curve, AUC, integrated AUC )from censored survival data under proportional or non-proportional hazard assumption of Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105). Package: r-cran-risksimul Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-runuran Filename: pool/dists/resolute/main/r-cran-risksimul_0.1.2-1.ca2604.1_all.deb Size: 64362 MD5sum: 99767020d65584e5612d0b39540b16f6 SHA1: 69866df10f383322101ae9f7cf3e22c1cd32ab91 SHA256: ae9909cbff035523621a1c447bed781b506c93695374301579cffb6ced839f61 SHA512: d92ede57cb6cece98a5f30630517e20e0be6869f4f02f1a595424a6cfce6ad3bae4ca66052389872fe0d1921e9071ac4bf8cbda26199185732385d060e7f2c1f Homepage: https://cran.r-project.org/package=riskSimul Description: CRAN Package 'riskSimul' (Risk Quantification for Stock Portfolios under the T-CopulaModel) Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals. Package: r-cran-riskyr Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4330 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-riskyr_0.5.0-1.ca2604.1_all.deb Size: 2735762 MD5sum: 35d0a1387937a309be632bb619d6ca25 SHA1: 5d2c020df73e57fd9fd10d60ba6c39e10e457f18 SHA256: 5faae4a1f071d19c2f4800619e23dac6d98c076e22d96690505e4d366e63b4a9 SHA512: d73bef3c8e8ad311f0e003c5d5fc02f919d58159359b87f45b39b66482184c64c5ec42ff23138d61ee3a285a3f31c005ba7cf7cf71a9abe5b7aa5dae1020eb2a Homepage: https://cran.r-project.org/package=riskyr Description: CRAN Package 'riskyr' (Rendering Risk Literacy more Transparent) Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent (see , for details). 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The name RISmed is a portmanteau of RIS (for Research Information Systems, a common tag format for bibliographic data) and PubMed. Package: r-cran-rita Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-rita_1.2.0-1.ca2604.1_all.deb Size: 83580 MD5sum: c4202c1ee6270cc22aa2b5da25bdf536 SHA1: a13d4b96be66c87446d9111c61c38766a0f9b593 SHA256: a25d79a3ab88ad8f564f57b8b4cd080898155dc5f485d8b4f82ab48105c68a4d SHA512: 95dc6ae6785742c21864f96bc3c9d67c49269bea9f2cf1ee2cb5664e9674031426cf28fdb029f7294d4f5237a766ea7dacf92d34fed7f64ad741e33cb68f02ad Homepage: https://cran.r-project.org/package=Rita Description: CRAN Package 'Rita' (Automated Transformations, Normality Testing, and Reporting) Automated performance of common transformations used to fulfill parametric assumptions of normality and identification of the best performing method for the user. Output for various normality tests (Thode, 2002) corresponding to the best performing method and a descriptive statistical report of the input data in its original units (5-number summary and mathematical moments) are also presented. Lastly, the Rankit, an empirical normal quantile transformation (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard use cases and facilitate adoption. . . Package: r-cran-ritalic Architecture: all Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4065 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-sf, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ritalic_0.12.0-1.ca2604.1_all.deb Size: 1600060 MD5sum: 0bd6b58a0e27b6b7527454917e985af3 SHA1: e765cdd08358e82b712d0ab5486ce36969dd67aa SHA256: 18018068cff110fa1ce9ea60912b720f9a76e6fa0f94d40e0a68fae542639d73 SHA512: 6b1e280384f18557ddc07d6e11f52d04f5439e5c8a1d061d5c1fee7d252baa0fcb4248d6568972967c850dc62fb02279e3045aea62e2cea5db5defbe4b8369d8 Homepage: https://cran.r-project.org/package=ritalic Description: CRAN Package 'ritalic' (Interface to the ITALIC Database of Lichen Biodiversity) A programmatic interface to the Web Service methods provided by ITALIC (). ITALIC is a database of lichen data in Italy and bordering European countries. 'ritalic' includes functions for retrieving information about lichen scientific names, geographic distribution, ecological data, morpho-functional traits and identification keys. More information about the data is available at . The API documentation is available at . 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Package: r-cran-ritis Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-solrium, r-cran-crul, r-cran-jsonlite, r-cran-data.table, r-cran-tibble Suggests: r-cran-testthat, r-cran-webmockr, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-ritis_1.0.0-1.ca2604.1_all.deb Size: 152316 MD5sum: 91de47a665f26268d688902f1c4ee0b4 SHA1: 1153347613c40b17b824a29bedf05c65bbe37882 SHA256: 4b725218dafe8faff130dd9fcaf791dbce14d1354f70f8237a9c60e036bbecc6 SHA512: 5feb2b55343598b2319dff3473bc05cc37b65414cf9da70d0d123d75dd95c82bed4920e36397304b42339b5a4f1098d6635e24eb6dbab69463ce44673e8cba53 Homepage: https://cran.r-project.org/package=ritis Description: CRAN Package 'ritis' (Integrated Taxonomic Information System Client) An interface to the Integrated Taxonomic Information System ('ITIS') (). Includes functions to work with the 'ITIS' REST API methods (), as well as the 'Solr' web service (). 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Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments. 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Package: r-cran-rkin Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ks, r-cran-sf, r-cran-ggplot2, r-cran-mass, r-cran-rcolorbrewer, r-cran-randomcolor, r-cran-shades, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rkin_1.0.4-1.ca2604.1_all.deb Size: 101298 MD5sum: 2cb88eb4f0971172c5aced5e477491d4 SHA1: f963bb8370c39a46ac5f3a372c3f4c27faa66f2e SHA256: 0537d7ffbcff2ae254ffe03278d0881f15b492c8ebd0865ce535578aacd9347a SHA512: 206ddaf54749e3cbd157e837549b12337495d2567e83a519561944d639d953395e45b69b7314fd5180d1c26d1ace04e3441c85310128a8b64b46907e99a11d62 Homepage: https://cran.r-project.org/package=rKIN Description: CRAN Package 'rKIN' ((Kernel) Isotope Niche Estimation) Applies methods used to estimate animal homerange, but instead of geospatial coordinates, we use isotopic coordinates. The estimation methods include: 1) 2-dimensional bivariate normal kernel utilization density estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon estimator, all applied to stable isotope data. Additionally, functions to determine niche area, polygon overlap between groups and levels (confidence contours) and plotting capabilities. Package: r-cran-rkmetrics Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rkmetrics_1.3-1.ca2604.1_all.deb Size: 96752 MD5sum: 5c661d047f13a0a41b84adef98473a69 SHA1: 4a1da5b57a5fe77688deaf42c48d9de2634254bd SHA256: 1b6dfb2ac6b8f0f0cb688c468465854060ba1dd2183ca526e4fae4764cde5429 SHA512: 63ec75b3f18af348bfccf7ebf64b6ee3b36e20b60d58302acaf85a4dd6562fc3ed46d56d8232585c4c22732e7d384f5336904295d47ecdd08714a13e8ea01688 Homepage: https://cran.r-project.org/package=RkMetrics Description: CRAN Package 'RkMetrics' (Hybrid Mortality Estimation) Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality. Package: r-cran-rkolada Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-ggplot2, r-cran-scales, r-cran-progress Filename: pool/dists/resolute/main/r-cran-rkolada_0.3.1-1.ca2604.1_all.deb Size: 690324 MD5sum: ab68584f513a138a738595422124eda7 SHA1: 15502cff03316f50a25ff628f31aebfc07076774 SHA256: 00c3bcbfbe7bb1a439af54faec6423533de9cf9c3372fb105ec35d5c0189274e SHA512: 8ee12a43883ab6b873b4dbec670e4389943ab7ae1b775ac86a81a4f75fe8eac69db5582647379e06275ec401574857d3b46a82a1869f4de5a82708ef8953c9d1 Homepage: https://cran.r-project.org/package=rKolada Description: CRAN Package 'rKolada' (Access Data from the 'Kolada' Database) Methods for downloading and processing data and metadata from 'Kolada', the official Swedish regions and municipalities database . Package: r-cran-rkomics Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 24871 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ape, r-cran-circlize, r-bioc-complexheatmap, r-cran-reshape2, r-cran-dplyr, r-cran-factoextra, r-cran-factominer, r-cran-ggpubr, r-cran-magrittr, r-cran-stringr Suggests: r-cran-viridis Filename: pool/dists/resolute/main/r-cran-rkomics_1.3-1.ca2604.1_all.deb Size: 1761898 MD5sum: aaecaa1fd327faa5d60f771294a8109a SHA1: 5a7842ca5c7ed9aade0894a9552a7d58fda634b7 SHA256: 4bdc08a038f57fe07d2aa87b7670545851b1068efd9c41798419e8b2bd9f4dc7 SHA512: 155a1834c9b0412e91986f36a26f5314f8c8d4239b90a897488505fae7bc046c5aa124af2313618cc21902defeffecaf456d1aed42e30edfcb0edef2c1590b56 Homepage: https://cran.r-project.org/package=rKOMICS Description: CRAN Package 'rKOMICS' (Minicircle Sequence Classes (MSC) Analyses) This is a analysis toolkit to streamline the analyses of minicircle sequence diversity in population-scale genome projects. rKOMICS is a user-friendly R package that has simple installation requirements and that is applicable to all 27 trypanosomatid genera. Once minicircle sequence alignments are generated, rKOMICS allows to examine, summarize and visualize minicircle sequence diversity within and between samples through the analyses of minicircle sequence clusters. We showcase the functionalities of the (r)KOMICS tool suite using a whole-genome sequencing dataset from a recently published study on the history of diversification of the Leishmania braziliensis species complex in Peru. Analyses of population diversity and structure highlighted differences in minicircle sequence richness and composition between Leishmania subspecies, and between subpopulations within subspecies. The rKOMICS package establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history. ***** !! WARNING: this package relies on dependencies from Bioconductor. For Mac users, this can generate errors when installing rKOMICS. Install Bioconductor and ComplexHeatmap at advance: install.packages("BiocManager"); BiocManager::install("ComplexHeatmap") *****. 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The corpus analysis platform 'KorAP' has been developed as a scientific tool to make potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo' or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify hypotheses and to find interesting patterns in real language use. The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code, as a programmatic alternative to the 'KorAP' web user-interface. You can learn more about 'KorAP' and use it directly on 'DeReKo' at . 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Gibbons, Christine Waternaux (1999) . Package: r-cran-rmat Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-rmat_0.2.0-1.ca2604.1_all.deb Size: 53982 MD5sum: 39f26a0af54eaca03e14808cab485a61 SHA1: 51ded7d3edc44f847afac966a82d4a112eaa23c8 SHA256: c82e9157814cbcfd6c8c9a4903133a15cc2a427ba3cd5ef3c20f014ae3146476 SHA512: 72d452fed6d7b94c0d3450451089efd210b313b39b924fc1247abc34d99cbd9c7e373af1d15db780803edf1f9afe7cb5fd85ff1701198dcd9a46c0ac9c161db3 Homepage: https://cran.r-project.org/package=RMAT Description: CRAN Package 'RMAT' (Random Matrix Analysis Toolkit) Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions. Package: r-cran-rmawgen Architecture: all Version: 1.3.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2006 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chron, r-cran-date, r-cran-vars, r-cran-matrix Suggests: r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-rmawgen_1.3.9.3-1.ca2604.1_all.deb Size: 1984204 MD5sum: 121383b8ef44b4733a382054ba9636b3 SHA1: 2686bd0e809148f86e9991a8b4a07ed722f8ba44 SHA256: bffd71142a3a6c351dbd00468fc1ac394ba7defe3c913c88b933bd8235ff0b10 SHA512: b62d42483ec628ca077e2fdbc4f3688d779a2aacf647835dcd77a636036148e9aa946fc0ab67ce86ec6ef03aa3403dafcaee6642dad7b9fd19672b76b3eb8d39 Homepage: https://cran.r-project.org/package=RMAWGEN Description: CRAN Package 'RMAWGEN' (Multi-Site Auto-Regressive Weather GENerator) S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. 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Package: r-cran-rmcda Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-fmsb, r-cran-lpsolve, r-cran-matlib, r-cran-nloptr, r-cran-matrixstats, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmcda_0.3-1.ca2604.1_all.deb Size: 248456 MD5sum: b82e703471e91faf8e74f71594e0917e SHA1: 0436085f535f85fa5dccf7ad6713e58e78b0105d SHA256: 134a222d2c0e641df32d35d423484179ff6a28d2ddf75281cd4cf6eae63fb2d5 SHA512: 0184c76b83e355a74bd682a20eb832d012e18616266d8ae5edf338fe87172aeac40eb62d94d82d80d4f132249326bb88e3d0cff0fb02153a9f0e39d2e8cdf143 Homepage: https://cran.r-project.org/package=RMCDA Description: CRAN Package 'RMCDA' (Multi-Criteria Decision Analysis) Supporting decision making involving multiple criteria. Annice Najafi, Shokoufeh Mirzaei (2025) RMCDA: The Comprehensive R Library for applying multi-criteria decision analysis methods, Volume 24, e100762 . Package: r-cran-rmcfs Architecture: all Version: 1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4940 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-yaml, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2, r-cran-dplyr, r-cran-stringi, r-cran-igraph, r-cran-data.table Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-rmcfs_1.3.6-1.ca2604.1_all.deb Size: 4685188 MD5sum: 5343084f173d4ecff7993f8d832c9eb7 SHA1: 2e2c027c90b5ba706a560b2ac7e341ab042907bf SHA256: de057a433e7866cbbb6f930cc1e413bee3cf3b12c8c3bc2f649116c021ce606c SHA512: 18c90587034dc888e0aad0e759c1b75990e2a8f3db1317128885313b416b8f9490929a00128f9bbb67cf4778eac1dfaa27137e1f4f85c28338c007f8d7deb869 Homepage: https://cran.r-project.org/package=rmcfs Description: CRAN Package 'rmcfs' (The MCFS-ID Algorithm for Feature Selection and InterdependencyDiscovery) MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) . Package: r-cran-rmcmc Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rlang, r-cran-withr Suggests: r-cran-knitr, r-cran-posterior, r-cran-progress, r-cran-ramcmc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rmcmc_0.1.2-1.ca2604.1_all.deb Size: 287604 MD5sum: e195f8a07520f6e85207633cc1619f86 SHA1: 4ade47ed8a1e6ebfe721294c4aff997e181fb060 SHA256: 7af58c443c138252b037ca25be496bcb89ab590661cdf39caec10181a632a564 SHA512: 32ee31dadb23682c9259e4a0a4c68a34fe3244b41485f866cde2eafe824ec4dc6bc4b0bc4e78ba917151b15c961328125094dbb47dae0eb74fd56584905e7c5c Homepage: https://cran.r-project.org/package=rmcmc Description: CRAN Package 'rmcmc' (Robust Markov Chain Monte Carlo Methods) Functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) , is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's R interface , available on GitHub , is installed, then 'BridgeStan' can be used to specify the target distribution to sample from. Package: r-cran-rmcorr Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plotrix, r-cran-lme4, r-cran-mertools, r-cran-pwr, r-cran-aiccmodavg, r-cran-pals, r-cran-testthat, r-cran-vdiffr, r-cran-corrplot, r-cran-cocor, r-cran-covr, r-cran-ggextra, r-cran-gglm, r-cran-dplyr, r-cran-esc, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-rmcorr_0.7.0-1.ca2604.1_all.deb Size: 1469068 MD5sum: 7dd77d0bf2ffd040d4b4e6ee2237270a SHA1: 7a1342897623457d18a94d4dc80753020eb9c4ec SHA256: 8a7e981ab4f5b33db8a2063c91b510628e9575d6662060ed6656b4d5d0a5972b SHA512: 2cffe1f7901b811175eae80d7b0f44fb127a220df74525927572c93966ac187ff1b73f9a30383efac1c0ba0fd278cdb89f8f19fc3c26945254377bca515c672e Homepage: https://cran.r-project.org/package=rmcorr Description: CRAN Package 'rmcorr' (Repeated Measures Correlation) Compute the repeated measures correlation, a statistical technique for determining the overall within-individual relationship among paired measures assessed on two or more occasions, first introduced by Bland and Altman (1995). 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Methods include the distribution of the product, Monte Carlo simulation, and bootstrap methods. It also performs the Model-Based Constrained Optimization (MBCO) procedure for hypothesis testing of indirect effects. References: Tofighi, D., and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692-700. ; Tofighi, D., and Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. Psychological Methods, 25(4), 496-515. ; Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. . 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Package: r-cran-rmfrac Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parallelly, r-cran-ggplot2, r-cran-zoo, r-cran-matrixstats, r-cran-proxy, r-cran-rlang, r-cran-foreach, r-cran-doparallel, r-cran-plotly, r-cran-shiny, r-cran-shinycssloaders, r-cran-fields Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rmfrac_1.0.0-1.ca2604.1_all.deb Size: 316234 MD5sum: e793d7c20cc2b60912d03cf8937dfbce SHA1: dbb2f5211fc34ca7073fcd50746952122eec6817 SHA256: ac3ca39d9798c2086f435dc281c6396f61da00d18dceef4e128076dbce893d73 SHA512: 45de0f18a2fee82747b11ff91312d902334977eb7364f30389c97f2ba168269e0b0bc32fa185cac40cf8a5d4f6d05a292ffea1351997138f5eec65ef1c252ab7 Homepage: https://cran.r-project.org/package=Rmfrac Description: CRAN Package 'Rmfrac' (Simulation and Statistical Analysis of Multifractional Processes) Simulation of several fractional and multifractional processes. Includes Brownian and fractional Brownian motions, bridges and Gaussian Haar-based multifractional processes (GHBMP). Implements the methods from Ayache, Olenko and Samarakoon (2026) for simulation of GHBMP. Estimation of Hurst functions and local fractal dimension. Clustering realisations based on the Hurst functions. Several functions to estimate and plot geometric statistics of the processes and time series. Provides a 'shiny' application for interactive use of the functions from the package. Package: r-cran-rmidas2 Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-processx, r-cran-rlang Suggests: r-cran-arrow, r-cran-jsonlite, r-cran-reticulate, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmidas2_0.1.1-1.ca2604.1_all.deb Size: 90364 MD5sum: 433658e00b2821f179accceabeb059d9 SHA1: a6f73168acd3269158de841713760750e8efcbde SHA256: cf97e822bd3edfcf919287a66541edbf0f322ea591e2f38fd93c316446a574a2 SHA512: 1429f11533c6ae07f936a3937a32ca9835f27eac62c5364ec1a52dfef26bed0db8220e5b7525fc3f02d8ab8bdc165b0986d8c4fa22f34f24218e8452384ccceb Homepage: https://cran.r-project.org/package=rMIDAS2 Description: CRAN Package 'rMIDAS2' (Multiple Imputation with 'MIDAS2' Denoising Autoencoders) Fits 'MIDAS' denoising autoencoder models for multiple imputation of missing data, generates multiply-imputed datasets, computes imputation means, and runs Rubin's rules regression analysis. 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Package: r-cran-rmidas Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mltools, r-cran-reticulate, r-cran-rappdirs, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmidas_1.0.1-1.ca2604.1_all.deb Size: 160182 MD5sum: 0e7b53ce09f837d86b84c04a1ffc97c6 SHA1: dc6e8e0227e038fb4a18a4025472579a85c14b98 SHA256: 16c036f573513d3f784e6ac58893ce3408b114a421f550cc351770eaa36e950d SHA512: e4a2f3fd4f77ffbd73e6523abe26450cfed12f87d56631d4d2a7173a9658f40e9aa55c1c9a133775158b8b29222b107ff479d3b8fb8d550138520e450dc3780b Homepage: https://cran.r-project.org/package=rMIDAS Description: CRAN Package 'rMIDAS' (Multiple Imputation with Denoising Autoencoders) A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; ). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) . This package is deprecated in favor of 'rMIDAS2'; it remains available for existing workflows but will receive only compatibility and documentation updates. Package: r-cran-rminer Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotrix, r-cran-lattice, r-cran-nnet, r-cran-kknn, r-cran-pls, r-cran-mass, r-cran-mda, r-cran-rpart, r-cran-randomforest, r-cran-adabag, r-cran-party, r-cran-cubist, r-cran-kernlab, r-cran-e1071, r-cran-glmnet, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-rminer_1.5.0-1.ca2604.1_all.deb Size: 928486 MD5sum: 8110bee91219963cec10db50f81fd9f6 SHA1: 1674919769dbeadffce2943f2f3a0e4fb3e47128 SHA256: 9eb4a5f6358eedeca5e85bdf6cc01369a7fb915286759ddef2e0b1f32691d153 SHA512: d796f887b3c51c2167c0294cc0af4f6e54e13eb99ab90276c04c85ce7679ee40435180165ae905cae56dd2c0efa7e6d73b63c81540b7b09561197be0c20eba31 Homepage: https://cran.r-project.org/package=rminer Description: CRAN Package 'rminer' (Machine Learning Classification and Regression Methods) Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version. 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The interface supports large-scale optimization of many kinds: Mixed-integer and continuous linear, second-order cone, exponential cone and power cone optimization, as well as continuous semidefinite optimization. Rmosek and the R-language are open-source projects. MOSEK is a proprietary product, but unrestricted trial and academic licenses are available. Package: r-cran-rmost Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rmost_1.0.1-1.ca2604.1_all.deb Size: 188818 MD5sum: e969532aa392181d1ed91a619a8125f4 SHA1: c5c4bbb81399ccc6d3d932c024a84c11c7df3edf SHA256: 324b15cfd6e6a5fadabc49fa6c423023df2821fd86c158f5f1979167a06418e8 SHA512: 1cfbf0beb03d6e77048ca53d909710d2decc1fc81fcf75433f8ed90039aab1532117ee426058436b61a23b348398c3691af2329acb845828ca09f666dbaacb17 Homepage: https://cran.r-project.org/package=rMOST Description: CRAN Package 'rMOST' (Estimates Pareto-Optimal Solution for Hiring with 3 Objectives) Estimates Pareto-optimal solution for personnel selection with 3 objectives using Normal Boundary Intersection (NBI) algorithm introduced by Das and Dennis (1998) . Takes predictor intercorrelations and predictor-objective relations as input and generates a series of solutions containing predictor weights as output. Accepts between 3 and 10 selection predictors. Maximum 2 objectives could be adverse impact objectives. Partially modeled after De Corte (2006) TROFSS Fortran program and updated from 'ParetoR' package described in Song et al. (2017) . For details, see Study 3 of Zhang et al. (2023). Package: r-cran-rmpw Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-mass Filename: pool/dists/resolute/main/r-cran-rmpw_0.0.6-1.ca2604.1_all.deb Size: 93356 MD5sum: cefc3b1235e652f366476d9225ce0d36 SHA1: 5c8bae9115b737ea619d78264a1368b4b0e46f4d SHA256: 2f1db70d2c088d7a6b51b1292cf0fd3034aabe40536148aa01d939eeb261c4ac SHA512: 0b4e3ac6cf819fb4836622a64f1d179f4eb1267186daedaef0f8e9270e58898a20e66d97df4fa5a307dab35c9fd41a2828133492c1a9f2f6e263abb940dfdbd2 Homepage: https://cran.r-project.org/package=rmpw Description: CRAN Package 'rmpw' (Causal Mediation Analysis Using Weighting Approach) We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) . It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) . Package: r-cran-rmsbma Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-matrix, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmsbma_0.1.2-1.ca2604.1_all.deb Size: 3246868 MD5sum: c28cca6952d9b5ca7d601cab427f4164 SHA1: c36995c01d7a3ba7ca950c09be6395d29ebcf7a7 SHA256: daa1a06829b16cf467187970ff2e31548574afc742ae08387b37b5f7f61d31e8 SHA512: 5e8e044d263e8cab854cc659b4cf4828007a4e289ceae4830546bb84ce71e5e999fa3826f4e669d35dc2a0f945254da42c69a8fc931c0e6dc8e3b7ba86331215 Homepage: https://cran.r-project.org/package=rmsBMA Description: CRAN Package 'rmsBMA' (Reduced Model Space Bayesian Model Averaging) Implements Bayesian model averaging for settings with many candidate regressors relative to the available sample size, including cases where the number of regressors exceeds the number of observations. By restricting attention to models with at most M regressors, the package supports reduced model space inference, thereby preserving degrees of freedom for estimation. It provides posterior summaries, Extreme Bounds Analysis, model selection procedures, joint inclusion measures, and graphical tools for exploring model probabilities, model size distributions, and coefficient distributions. The methodological approach follows Doppelhofer and Weeks (2009) . Package: r-cran-rmsd Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rmsd_0.1.1-1.ca2604.1_all.deb Size: 17098 MD5sum: 3687e3c7478e135ce3ad5bfdd79b4353 SHA1: f141e869d1096f47e07088dfeb7f8bcca05aec87 SHA256: b6f88ba0de93f8b639ad1af81e70c6c4d3ee93f59ea277f23688cf2bdaaace92 SHA512: 6c3b0f08b56c56a67eca1461e3878b8edace5056aeaa9294c23027616ee5b0f5bfe64248ef9cb06bd4a5f68698d5b7ee5d9019bf71b3e3fc112c930d9ea808f8 Homepage: https://cran.r-project.org/package=RMSD Description: CRAN Package 'RMSD' (Refined Modified Stahel-Donoho Estimators for Outlier Detection) A function for multivariate outlier detection named Modified Stahel-Donoho (MSD) estimators is contained. The function is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. The function is called the single core version in Wada & Tsubaki (2013) and evaluated with other methods in Wada, Kawano & Tsubaki (2020) . Package: r-cran-rmsdp Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rmsdp_0.1.1-1.ca2604.1_all.deb Size: 27346 MD5sum: af56c76eee93eacf3fc9438d8d716f85 SHA1: e42570c8a98ea47ec46b0a87bdc64659e997e9fe SHA256: dd3affbe06d2d5d198638c73ad8549ecc6f1445642e7b9b1092271cddf61bf06 SHA512: d5b6eec238df7ca2f638cad4360aa62ee5cea34f40bfff7a6c6078224094fec794709f7ebba90ce48bc1707f79239e56e7b6c0f1627c2fd9ad9d7c7822d6363a Homepage: https://cran.r-project.org/package=RMSDp Description: CRAN Package 'RMSDp' (Refined Modified Stahel-Donoho (MSD) Estimators for OutlierDetection (Parallel Version)) A parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package. The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in 'RMSD' package. See Wada and Tsubaki (2013) for the detail of the algorithm. Package: r-cran-rmsfact Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rmsfact_0.0.3-1.ca2604.1_all.deb Size: 20198 MD5sum: 49202670e353c85e3eac89102ad0c510 SHA1: 18b77555b1099e0edc9d6bfc734671a19575711b SHA256: dada8c5a665cc6e5c007c48952b9eb0cc169aa5127a35ed5a8012ca318b3c6ba SHA512: 3e87964f33b8888bab9de4243b66cc566ceaa33c9f50b9bfc2cb0c7cba1f998068da0ad595246dd4f68115e647cc3c26eac94fefd7e3b586e1fe932a54aa9758 Homepage: https://cran.r-project.org/package=rmsfact Description: CRAN Package 'rmsfact' (Amazing Random Facts About the World's Greatest Hacker) Display a randomly selected quote about Richard M. Stallman based on the collection in the 'GNU Octave' function 'fact()' which was aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site stallmanfacts.com (which is accessible only via ). Package: r-cran-rmsmd Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-ggplot2, r-cran-rlang, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-officer, r-cran-flextable, r-cran-dplyr, r-cran-testthat, r-cran-vdiffr, r-cran-hmisc Filename: pool/dists/resolute/main/r-cran-rmsmd_1.0.1-1.ca2604.1_all.deb Size: 395168 MD5sum: 643c80e1550b8d514bde124a01097463 SHA1: 8072122eed6dad496d77864cb2dd1179bd79bd62 SHA256: 38ed79ca362bf0f8ded7a4801499ef4bcf63f72d0305ce1d03977d0ca5033d3f SHA512: 00614aea516e579d8a55cef2e0aedef44e7505d7b48bc2b7e32428adff2aea436fd58268b5ff12e187680cc45cb9089fb65e5548a7b6fc56eb8bb79f2158a7cf Homepage: https://cran.r-project.org/package=rmsMD Description: CRAN Package 'rmsMD' (Output Results from 'rms' Models for Medical Journals) Provides streamlined functions for summarising and visualising regression models fitted with the 'rms' package, in the preferred format for medical journals. The 'modelsummary_rms()' function produces concise summaries for linear, logistic, and Cox regression models, including automatic handling of models containing restricted cubic spline (RCS) terms. The resulting summary dataframe can be easily converted into publication-ready documents using the 'flextable' and 'officer' packages. The 'ggrmsMD()' function creates clear and customizable plots ('ggplot2' objects) to visualise RCS terms. Package: r-cran-rmstbayespara Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-crayon, r-cran-loo, r-cran-rstan, r-cran-zipfr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rmstbayespara_0.1.0-1.ca2604.1_all.deb Size: 35690 MD5sum: b53fe06dfce2ff85ad79c702f1dfca8b SHA1: 554c9945b297e8090cf9d3bf72569dc899494daf SHA256: f66ce9c8e64876e965db3862255ed57c7fab7561eae2efe2e7a7a10fc11d75a8 SHA512: 95e463b97e826a8bf841a4018c205abddb45444296430c943e206126c14d01735cb65741fa0392ff014e67aea812be825b9bb1adba4e5de6a706984841cee354 Homepage: https://cran.r-project.org/package=rmstBayespara Description: CRAN Package 'rmstBayespara' (Bayesian Restricted Mean Survival Time for Cluster Effect) The parametric Bayes analysis for the restricted mean survival time (RMST) with cluster effect, as described in Hanada and Kojima (2024) . Bayes estimation with random-effect and frailty-effect can be applied to several parametric models useful in survival time analysis. The RMST under these parametric models can be computed from the obtained posterior samples. Package: r-cran-rmstcompsens Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-rmstcompsens_0.1.5-1.ca2604.1_all.deb Size: 36308 MD5sum: 0fc5d586fdc700fde4796f74a9a14979 SHA1: eac398658ed4f1f8fd80d1649bc5ce186342b047 SHA256: e6190c4dd5949c35376d4b37c01bb7aeb423374b863fd36085502af5db877820 SHA512: 2e05c71180993b2f2ab3a38d8c130c5691b1bcd497be6f3cd377267ff89232141a31b65aa2b63ee80cb0ed5280a9c7f91d56863816eb3eb05cc82fbc96cee82d Homepage: https://cran.r-project.org/package=rmstcompsens Description: CRAN Package 'rmstcompsens' (Comparing Restricted Mean Survival Time as Sensitivity Analysis) Performs two-sample comparisons using the restricted mean survival time (RMST) when survival curves end at different time points between groups. This package implements a sensitivity approach that allows the threshold timepoint tau to be specified after the longest survival time in the shorter survival group. Two kinds of between-group contrast estimators (the difference in RMST and the ratio of RMST) are computed: Uno et al(2014), Uno et al(2022), Ueno and Morita(2023). Package: r-cran-rmstpowerboost Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mgcv, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-future, r-cran-future.apply, r-cran-knitr Suggests: r-cran-roxygen2, r-cran-shiny, r-cran-shinyjs, r-cran-bslib, r-cran-dt, r-cran-plotly, r-cran-kableextra, r-cran-survminer, r-cran-rmarkdown, r-cran-mice, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-htmltools, r-cran-tinytex, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rmstpowerboost_1.0.3-1.ca2604.1_all.deb Size: 565224 MD5sum: cca2ce6fb786f1f1491c8ddcb9dd94fb SHA1: 7735fd15e4f0e5bce27915ca8b7f6131c60b35b7 SHA256: bc1ab97ae822e79e3b6abf05c3b45a3af4f8a6ffb195a4fcc61b447f1e7b8b21 SHA512: 51cd36de3f2ac7cbcd30b520e72b28efa6bccef7f4b9ad0b93e78600516d8f418490790f781b885dc75df65febdf3c613d411b617de020a2a54261b674556163 Homepage: https://cran.r-project.org/package=RMSTpowerBoost Description: CRAN Package 'RMSTpowerBoost' (Power and Sample Size for Restricted Mean Survival Time BasedClinical Trials) Tools for Restricted Mean Survival Time based study design and analysis planning. Provides power and sample size calculations for two-arm studies using direct modeling approaches from the literature, including semiparametric additive models, linear Inverse Probability Weighting based models from Wei (2014) , multiplicative stratified models from Wang (2019) , and covariate-dependent censoring methods from Wang (2018) . Package: r-cran-rmt4ds Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rmtstat, r-cran-lpsolve, r-cran-mpoly, r-cran-nleqslv, r-cran-pracma, r-cran-rarpack, r-cran-rootsolve, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmt4ds_0.0.1-1.ca2604.1_all.deb Size: 105816 MD5sum: 5026bed925a46838904d04b73dd5bc89 SHA1: 62f23d17482f4a6a132c1006ceb92436732695c0 SHA256: 0baedefccc6e3513ece52cfdfb3ca736e34a7fb779a6541ee3769947d759785b SHA512: 0f0ac533f4d913344d1e4a95eb2e646accc87a3df832fbb3562053bee0ae7273a9b96b51303496a39964743a8eba25287b9ca9db1c379100794ea8d9785d8d49 Homepage: https://cran.r-project.org/package=RMT4DS Description: CRAN Package 'RMT4DS' (Computation of Random Matrix Models) We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model. Package: r-cran-rmt Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmt_1.0-1.ca2604.1_all.deb Size: 120362 MD5sum: da06a2c1d3fb0ef0e6efee27c0d3cb77 SHA1: 4de769b7c6f47426780a010ec05745e4d52377c8 SHA256: 034678d5e66e59864f9ed042fc3fe26b47a3b40ac8ed8da47b35c4b3cd667d4c SHA512: ba6c674fce841cc827fbc6a6fb00a6039cac22eb686dc315d1e3466bea62db4a67d74509ae5df2816ebfb1113876ee0c18a436c5e8117f66f5f6c078c1f72c8c Homepage: https://cran.r-project.org/package=rmt Description: CRAN Package 'rmt' (Restricted Mean Time in Favor of Treatment) Contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment. Package: r-cran-rmthreshold Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-png Filename: pool/dists/resolute/main/r-cran-rmthreshold_1.1-1.ca2604.1_all.deb Size: 645176 MD5sum: 26cb9a7f37f0a71b6023ac38d8fad3b2 SHA1: ce1799328041e7bd2a2b8559bf2b22e713644f22 SHA256: e7b4d5a3293f1d85d96b8ebb91e50a603e6d3dc2040b11be9e4d9b6e1be0d702 SHA512: 3a6c6a9ea56e5b7207b4e81c26ea662851d82df4963f368a38393429bc405621db2c9d577c5e7e86e6f71665a341a750b0629c3c06ca46a0fc285cfd44db0df0 Homepage: https://cran.r-project.org/package=RMThreshold Description: CRAN Package 'RMThreshold' (Signal-Noise Separation in Random Matrices by using EigenvalueSpectrum Analysis) An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled. Package: r-cran-rmtl Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-psych, r-cran-corpcor, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmtl_1.0.0-1.ca2604.1_all.deb Size: 379566 MD5sum: 5de5519b9d2e3b6f1650244ca0a8d237 SHA1: 4c5a2f61ea6e45936cfd71c17ea7d9d0d17ebd67 SHA256: b2ef2ae76129f3c8e735d1bb964b07d6dccae207bec2ac54c7fcf54e9f98f22a SHA512: e1ae35547872bceb7b9ef87db09085c2e19592cd76fbb85cbe6df29e1067b75d4a8548b701f615c3f1cbf6141f1c5b91972f264d7140f4610aa1bd06f15e51ff Homepage: https://cran.r-project.org/package=RMTL Description: CRAN Package 'RMTL' (Regularized Multi-Task Learning) Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) . Package: r-cran-rmtstat Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rmtstat_0.3.1-1.ca2604.1_all.deb Size: 117358 MD5sum: ce66b285cb189909c174eaa8bd6438ae SHA1: f51337a0a56ad50a4ba7cb1df0af232e084f70f3 SHA256: d6d0da579e2793f3a47c1b7c24ed8c174e17d7620899c60e5dcf3dbb3e1decf6 SHA512: cac0a3f818d6066279c35bea7070a86336b6cb6338075d7d01fa5582c254bcd8aac76997aa489cf12687c70d8801490c4491e07028740d0fb4fc73add8a17af7 Homepage: https://cran.r-project.org/package=RMTstat Description: CRAN Package 'RMTstat' (Distributions, Statistics and Tests Derived from Random MatrixTheory) Functions for working with the Tracy-Widom laws and other distributions related to the eigenvalues of large Wishart matrices. The tables for computing the Tracy-Widom densities and distribution functions were computed by functions were computed by Momar Dieng's MATLAB package "RMLab". This package is part of a collaboration between Iain Johnstone, Zongming Ma, Patrick Perry, and Morteza Shahram. 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The reference paper can be found from the URL mentioned below. Ting Li, Zhongyuan Lyu, Chenyu Ren, Dong Xia (2023) . 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Retrieve currency exchange rates and gold prices data published by the National Bank of Poland in form of convenient R objects. 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Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time. We also provide functions to visualize these weather data on a map. There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user. 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The theoretical background of the method is described in Sato et al. (2021) . 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The theoretical background of the method is described in Sato et al. (2021) . 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Given expression estimates from any number of RNA-Seq samples and conditions it identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. Zambelli et al. (2018) . 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The details of the univariate part are explained in Demirtas (2017) , and the multivariate part is an extension of the correlated Poisson data generation routine that was introduced in Yahav and Shmueli (2012) . 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Package: r-cran-rnhanes Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-survey, r-cran-rvest, r-cran-xml2, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rnhanes_1.1.0-1.ca2604.1_all.deb Size: 123050 MD5sum: a56d82ef35f9da7426186f2751179782 SHA1: 6cd0d7f70dec0db5957f9fd8c0c5921646b3ef96 SHA256: 40edac1e0b8525bec50c78787f846a20ef31db3240f04040f345bd143445e4af SHA512: dffae121278a5a98980978887cc30598a39c21e56ad6d93511e73ff5399fdf4891b31df1a420c558a83fe66d8ab93c9ccf99b13acb8efe58388e1c1dbb65c36e Homepage: https://cran.r-project.org/package=RNHANES Description: CRAN Package 'RNHANES' (Facilitates Analysis of CDC NHANES Data) Tools for downloading and analyzing CDC NHANES data, with a focus on analytical laboratory data. 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The package covers core Bayesian one-stage models implemented in a systematic review with multiple interventions, including fixed-effect and random-effects network meta-analysis, meta-regression, evaluation of the consistency assumption via the node-splitting approach and the unrelated mean effects model (original and revised model proposed by Spineli, (2022) ), and sensitivity analysis (see Spineli et al., (2021) ). Missing participant outcome data are addressed in all models of the package (see Spineli, (2019) , Spineli et al., (2019) , Spineli, (2019) , and Spineli et al., (2021) ). The robustness to primary analysis results can also be investigated using a novel intuitive index (see Spineli et al., (2021) ). Methods to evaluate the transitivity assumption using trial dissimilarities and hierarchical clustering are provided (see Spineli, (2024) , and Spineli et al., (2025) ). A novel index to facilitate interpretation of local inconsistency is also available (see Spineli, (2024) ) The package also offers a rich, user-friendly visualisation toolkit that aids in appraising and interpreting the results thoroughly and preparing the manuscript for journal submission. The visualisation tools comprise the network plot, forest plots, panel of diagnostic plots, heatmaps on the extent of missing participant outcome data in the network, league heatmaps on estimation and prediction, rankograms, Bland-Altman plot, leverage plot, deviance scatterplot, heatmap of robustness, barplot of Kullback-Leibler divergence, heatmap of comparison dissimilarities and dendrogram of comparison clustering. The package also allows the user to export the results to an Excel file at the working directory. 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A non-negative matrix factorization factors non-negative matrix Y approximately as L R, for non-negative matrices L and R of reduced rank. This package supports such factorizations with weighted objective and regularization penalties. Allowable regularization penalties include L1 and L2 penalties on L and R, as well as non-orthogonality penalties. This package provides multiplicative update algorithms, which are a modification of the algorithm of Lee and Seung (2001) , as well as an additive update derived from that multiplicative update. See also Pav (2004) . 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Package: r-cran-robflreg Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-mass, r-cran-robustbase, r-cran-expm, r-cran-fda.usc, r-cran-goffda, r-cran-mvtnorm, r-cran-pcapp, r-cran-fields, r-cran-matrix, r-cran-cvtools, r-cran-quantreg Filename: pool/dists/resolute/main/r-cran-robflreg_1.3-1.ca2604.1_all.deb Size: 373464 MD5sum: edfee42c73ded239d8e47327559d327b SHA1: b7449d7d8bf3338dba2d5196dc7cd11f73e1ef47 SHA256: 9b02c841bed0caa47c0b6ba189373b382d32d4ef56bd5eaa47811ea7de61c975 SHA512: dafa35068b88f9db174c5ae7b6f56fc403c017968114329c90a754b57de02053a6ed7d12d2a8b1e5b1d0b5f643474c34994db885a12d08a114f36f93237cc84f Homepage: https://cran.r-project.org/package=robflreg Description: CRAN Package 'robflreg' (Robust Functional Linear Regression) Functions for implementing robust methods for functional linear regression. 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Package: r-cran-robin Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1429 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-ggplot2, r-cran-networkd3, r-cran-desctools, r-cran-fdatest, r-cran-gridextra, r-cran-spam, r-cran-qpdf, r-cran-matrix, r-cran-perturbr, r-bioc-biocparallel, r-cran-reshape2 Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robin_2.0.0-1.ca2604.1_all.deb Size: 904186 MD5sum: 1866f2ccae74ca9f7c86833d1d13154d SHA1: aab8ff92261ed37e1f045d6b62f28114b807ccac SHA256: 6abd4c31a6cfc514f64372767205dfa497437cd26a8106002f50ff9b917dbc1b SHA512: 66d683ae2419e1e19a553bc56c4d01127bba1216550588ffcf80e0a71e4ae0f9d79ef967640e9a76fe2d8d14dc30ad3af7232d874c21a60c92f77297f419a8b3 Homepage: https://cran.r-project.org/package=robin Description: CRAN Package 'robin' (ROBustness in Network) Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) . Package: r-cran-robincar2 Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-numderiv, r-cran-mass, r-cran-sandwich, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robincar2_0.2.2-1.ca2604.1_all.deb Size: 224982 MD5sum: 09c7754c73b55d83a252f9dd4b1e6626 SHA1: de2e35dca7d1da771131f451391a504f7bafa37c SHA256: 9939db603bd56100319fe88107bed933d3d811075b974c5ca7d4712593dfd9d0 SHA512: 55a26c4afb1ac3bef6d88b3427f06bde2b5fc3aa3af4f73d1c32497979a416703ec3d1a6a51ea76a6e4b5fa00ad4f57afab66a21fa57caf19312163820143000 Homepage: https://cran.r-project.org/package=RobinCar2 Description: CRAN Package 'RobinCar2' (ROBust INference for Covariate Adjustment in Randomized ClinicalTrials) Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized controlled trials. This package is trimmed to reduce the dependencies and validated to be used across industry. See "FDA's final guidance on covariate adjustment", Tsiatis (2008) , Bugni et al. (2018) , Ye, Shao, Yi, and Zhao (2023), Ye, Shao, and Yi (2022), Rosenblum and van der Laan (2010), Wang et al. (2021), Ye, Bannick, Yi, and Shao (2023), and Bannick, Shao, Liu, Du, Yi, and Ye (2024). 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Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) . Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) . Ting Ye, Jun Shao, Yanyao Yi (2023) . Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) . Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025) . Package: r-cran-robincid Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-numderiv, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robincid_1.0.0-1.ca2604.1_all.deb Size: 93302 MD5sum: ff358f471f20b37d2d9c37de770244c8 SHA1: 45d41dd3afeccb83cc557fa71442fd4bdccf7752 SHA256: bb2a7b7f528ede3128e6f9887569c3a36e0478012c10d124d7fab37e201d6a9f SHA512: 2ba67f690b7dcc0d93d1c5326359e93c965a31f20742f8de9af664716020963cea6fb31983e855dfc07a2818124611d282c06f04d178751a88ba9fc48bbe21b3 Homepage: https://cran.r-project.org/package=RobinCID Description: CRAN Package 'RobinCID' (Robust Inference in Complex Innovative Trial Design) Perform robust estimation and inference in platform trials and other master protocol trials. Yuhan Qian, Yifan Yi, Jun Shao, Yanyao Yi, Gregory Levin, Nicole Mayer-Hamblett, Patrick J. Heagerty, Ting Ye (2025) . Package: r-cran-robinhood Architecture: all Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-lubridate, r-cran-profvis, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-uuid Filename: pool/dists/resolute/main/r-cran-robinhood_1.7.0-1.ca2604.1_all.deb Size: 216586 MD5sum: d3d3e38feb059c3900d15c07715274e7 SHA1: 037b2b331ec0cc26b90c283b9f7f4f45da550a21 SHA256: 18661ba2428b33272795e21d7196db81e73fb63c083f6111893e423cfd249d1c SHA512: 2d27282d196cd7d9e5c8de4a2834d4be1de41292793f1dc7ee3457abf75bf8f6d573547f8399c8323c7966c2298bc60db16b89b792dd276e691791e13fe86f77 Homepage: https://cran.r-project.org/package=RobinHood Description: CRAN Package 'RobinHood' (Interface for the RobinHood.com No Commission Investing Platform) Execute API calls to the RobinHood investing platform. 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Package: r-cran-roblox Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-distrmod, r-cran-robastbase, r-cran-lattice, r-cran-rcolorbrewer, r-bioc-biobase, r-cran-randvar, r-cran-distr Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-roblox_1.2.3-1.ca2604.1_all.deb Size: 425620 MD5sum: af884fa1adb5dcb33d73a677db372e41 SHA1: 7426c76d402ad56bc787e143d29fb8760b60a8be SHA256: 31a5a0011efd0d75e1e8cb334088cbdfb70c9679db40eb387a8556f8f2322289 SHA512: 3f2da42576917feb16a62c4d8126d15cfcd16ec132853e6bdc5d72ec2331be4e8e4f48c969ed7bbbf741c16d0eda2e68f25625b5880ec587b210503b58fe7a19 Homepage: https://cran.r-project.org/package=RobLox Description: CRAN Package 'RobLox' (Optimally Robust Influence Curves and Estimators for Locationand Scale) Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005) ). Package: r-cran-robmed Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-robustbase, r-cran-boot, r-cran-quantreg, r-cran-sn Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robmed_1.3.0-1.ca2604.1_all.deb Size: 969710 MD5sum: f42c74dc7404feee587e8750617b325c SHA1: 0eaf788de4af9ffb85598e0896ba0019424f9c0c SHA256: a07846a18c2129c5fe9ab80ad039b297ffe2bc9d0677b0749bdff60765b1739c SHA512: 21606098bbf6ddb65e627984e236d57b5a77c6ebaa458588995722c71928c4daa95779976531ea7abd9de013537f1c1547e4ce045c2ecb7290e41e2ce3efdfee Homepage: https://cran.r-project.org/package=robmed Description: CRAN Package 'robmed' ((Robust) Mediation Analysis) Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; ), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) . Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) . Package: r-cran-robmedextra Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robmed, r-cran-shiny, r-cran-dt, r-cran-flextable, r-cran-ggplot2, r-cran-officer Filename: pool/dists/resolute/main/r-cran-robmedextra_0.1.1-1.ca2604.1_all.deb Size: 197074 MD5sum: 54111e962f87a5f2431dfec0e097eb6f SHA1: 84ddee02a17b2528c8698a42d42f78b2bdceffe4 SHA256: d1d29ff5d1f24c8778f336c57dfc93bc7334da482b95913c8d235e42003bf895 SHA512: d97b81706148f1f9e523196bec9193b53c2cfe83ef11e6c0614f1587f136598023902d9fcce282ba96ab74f6a8d839ecf001c697dde98a3d94031ceb6b598dd4 Homepage: https://cran.r-project.org/package=robmedExtra Description: CRAN Package 'robmedExtra' (Extra Functionality for (Robust) Mediation Analysis) This companion package extends the package 'robmed' (Alfons, Ates & Groenen, 2022b; ) in various ways. Most notably, it provides a graphical user interface for the robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; ) to make the method more accessible to less proficient 'R' users, as well as functions to export the results as a table in a 'Microsoft Word' or 'Microsoft Powerpoint' document, or as a 'LaTeX' table. Furthermore, the package contains a 'shiny' app to compare various bootstrap procedures for mediation analysis on simulated data. Package: r-cran-robmixreg Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexmix, r-cran-robustbase, r-cran-gtools, r-cran-mass, r-cran-robust, r-cran-lars, r-cran-dplyr, r-cran-rlang, r-cran-scales, r-cran-gplots, r-cran-rcolorbrewer, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-robmixreg_1.1.0-1.ca2604.1_all.deb Size: 2083054 MD5sum: f90cabe5089869f08b9b4d4c3b89d5e8 SHA1: 3f1136d34a1468eafb589f8e3f82fc5c3d1c8509 SHA256: 08869893d34ddc71c7d0fb9b61d98b85422cf951a587f3a2f4f9e330c55361a9 SHA512: 4dbed045aa212614885faee180ecebf5e396032177a5d9b8d8ecaf9d98c1ef9a5769e02e99cbddd1fe570a3a65f2d1436f7213739927212eb4b8aabaea13b87f Homepage: https://cran.r-project.org/package=RobMixReg Description: CRAN Package 'RobMixReg' (Robust Mixture Regression) Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine. This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. Reference: Chun Yu, Weixin Yao, Kun Chen (2017) . NeyKov N, Filzmoser P, Dimova R et al. (2007) . Bai X, Yao W. Boyer JE (2012) . Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) . Package: r-cran-robnptests Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1676 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-gtools, r-cran-robustbase, r-cran-statmod, r-cran-checkmate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-covr Filename: pool/dists/resolute/main/r-cran-robnptests_1.1.0-1.ca2604.1_all.deb Size: 488752 MD5sum: 44dde7dcb74ae52c5fe24c27ffc543a2 SHA1: 63bb6c95e76d0b608051d755b317ab9e2a5ff24c SHA256: 04be0b0aceb20c6e564099baaec6d8959fac5954c82eccd3eb133223b1ab64f7 SHA512: 61a357e8996f3ca5a8a958f709cf51f2e1853ce2c5760d04a4c46fed80cc4213edb5ca4d8d0a19feab352f23682df186aa0b8c82a3d1e993b39926dcb45218bd Homepage: https://cran.r-project.org/package=robnptests Description: CRAN Package 'robnptests' (Robust Nonparametric Two-Sample Tests for Location/Scale) Implementations of several robust nonparametric two-sample tests for location or scale differences. The test statistics are based on robust location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators as described in Fried & Dehling (2011) . The p-values can be computed via the permutation principle, the randomization principle, or by using the asymptotic distributions of the test statistics under the null hypothesis, which ensures (approximate) distribution independence of the test decision. To test for a difference in scale, we apply the tests for location difference to transformed observations; see Fried (2012) . Random noise on a small range can be added to the original observations in order to hold the significance level on data from discrete distributions. The location tests assume homoscedasticity and the scale tests require the location parameters to be zero. Package: r-cran-robomit Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plm, r-cran-dplyr, r-cran-ggplot2, r-cran-broom, r-cran-tidyr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robomit_1.0.7-1.ca2604.1_all.deb Size: 102242 MD5sum: 9dd69c8431c323ad8d002f434c1aecec SHA1: d1a495bd11490cbaf9df686202287fce19b222d7 SHA256: 2ba37ad0ac2b10d5343c7915b76636bb436d40b9ce296dface07fe9948925dab SHA512: 9663d2cbed5ddbc366e6ef35a56af261f0ede354cb8a8673150c7a5582144953258292bc4a1ea673675cb2f75fcc54d453eed01bbe22912c2debfbf241bc5409 Homepage: https://cran.r-project.org/package=robomit Description: CRAN Package 'robomit' (Robustness Checks for Omitted Variable Bias) Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). The 'robomit' package computes i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). The code is based on the 'psacalc' command in 'Stata'. Additionally, 'robomit' offers a set of sensitivity analysis and visualization functions. See Oster, E. 2019. . Additionally, see Diegert, P., Masten, M. A., & Poirier, A. (2022) for a recent discussion of the topic: . Package: r-cran-robosrmsmote Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrcov, r-cran-meanshiftr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-robosrmsmote_1.0.0-1.ca2604.1_all.deb Size: 39830 MD5sum: e8a66399ca6a9ff3163a804c746cb89c SHA1: 15d0fcc5d16392d3890f1d158eeb527a2660b746 SHA256: 12ee0089dbcdf8f70a2bdeb78e5e1736358672dc70414028f5f8a4f3affca515 SHA512: e43e318d99187c451d9d41c5a6cb878397f6f3d49f4b53de57975f583dd6ed4eaf22a16652e880320fb1021009cfa3f09715d364748afae7e88a1b470d34bd43 Homepage: https://cran.r-project.org/package=ROBOSRMSMOTE Description: CRAN Package 'ROBOSRMSMOTE' (Robust Oversampling with RM-SMOTE for Imbalanced Classification) Provides the ROBOSRMSMOTE (Robust Oversampling with RM-SMOTE) framework for imbalanced classification tasks. This package extends Mahalanobis distance-based oversampling techniques by integrating robust covariance estimators to better handle outliers and complex data distributions. The implemented methodology builds upon and significantly expands the RM-SMOTE algorithm originally proposed by Taban et al. (2025) . Package: r-cran-robotoolbox Architecture: all Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3394 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-rcppsimdjson, r-cran-data.table, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect, r-cran-tibble, r-cran-stringi, r-cran-glue, r-cran-dm, r-cran-labelled, r-cran-readr, r-cran-cli Suggests: r-cran-roxygen2, r-cran-devtools, r-cran-vcr, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-sf, r-cran-mapview Filename: pool/dists/resolute/main/r-cran-robotoolbox_1.6.2-1.ca2604.1_all.deb Size: 1067534 MD5sum: f9234363445185db29d56525866d486a SHA1: c5b596a6d26900b8f1384718831a3ad25356bbb5 SHA256: ae52f2dd13a823aa417e1f873351791d42c0f95cf806229bf6b5943d2c751e36 SHA512: 6db8490fc7651b82f6c54956d0635843bbff19e2a0cdb7d0eb9031cdbec05bb84b5a0955a6d6d2a7287a07049e4951f3cd9d1c3d610de60298e88fa77a9f7164 Homepage: https://cran.r-project.org/package=robotoolbox Description: CRAN Package 'robotoolbox' (Client for the 'KoboToolbox' API) Suite of utilities for accessing and manipulating data from the 'KoboToolbox' API. 'KoboToolbox' is a robust platform designed for field data collection in various disciplines. This package aims to simplify the process of fetching and handling data from the API. Detailed documentation for the 'KoboToolbox' API can be found at . Package: r-cran-robotstxt Architecture: all Version: 0.7.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-httr, r-cran-spiderbar, r-cran-future.apply, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat, r-cran-covr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-robotstxt_0.7.15-1.ca2604.1_all.deb Size: 192318 MD5sum: 5248e38ac762ceb6e66bd474852dd85c SHA1: 45710ee846e4d7cd648ce48d2ed6ed545cef19f7 SHA256: eb83cee44d5cdceb3102a638cd8a495e9daf02ec8856f4a44e634c6f9a0d9344 SHA512: 8ca7f9ec70ef2d93a96b3fbb767a8a739454e5c19ab386bbb3fc3c541d656d99dce653f9f064a486b37b360d135377ab54037bfc44f0bd0587487b121c1f81c0 Homepage: https://cran.r-project.org/package=robotstxt Description: CRAN Package 'robotstxt' (A 'robots.txt' Parser and 'Webbot'/'Spider'/'Crawler'Permissions Checker) Provides functions to download and parse 'robots.txt' files. 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Package: r-cran-robpc Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trimcluster Filename: pool/dists/resolute/main/r-cran-robpc_1.4-1.ca2604.1_all.deb Size: 13212 MD5sum: 8f50ad9040be78720909a72cc7af4596 SHA1: 2fe22fb4948999597929f0d309a1f64dd549fc2f SHA256: d4f02c4264fe3c5e9be79450e077ddb46f8911b51ad2074690d79cc317b908f7 SHA512: 02047c6b88645b611a8dbc23ef310a001d7d9d22f761817586cf4763983ef8a8d7892cb2d53f874f5d92e881af637f638edb236aca303feefd561ab1bfa4737e Homepage: https://cran.r-project.org/package=RobPC Description: CRAN Package 'RobPC' (Robust Panel Clustering Algorithm) Performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) , Cuesta-Albertos et al. (1997) . Package: r-cran-robper Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 925 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-quantreg, r-cran-bb, r-cran-rgenoud Filename: pool/dists/resolute/main/r-cran-robper_1.2.3-1.ca2604.1_all.deb Size: 785194 MD5sum: e06b3d2d9c08dba3d7123c750ca18a50 SHA1: 7a6421b4573c87f3e6faf38617b032afb3cdfcfe SHA256: 9eea5dfc65485d4195c44cda8f57c8c38ea46f9410d9cad4ebd0df5d4f3a308a SHA512: de4f717874961450f3ea17bf1737bb1c72883e9a766f8d24dd1f895c0828e333e34024c7b2a3c0a48f6cf70917b89a7b8ac7b29cbf63d028b66fe91ce307ea12 Homepage: https://cran.r-project.org/package=RobPer Description: CRAN Package 'RobPer' (Robust Periodogram and Periodicity Detection Methods) Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). 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Package: r-cran-robqda Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rfast, r-cran-rfast2 Suggests: r-cran-mvcauchy Filename: pool/dists/resolute/main/r-cran-robqda_1.0-1.ca2604.1_all.deb Size: 24042 MD5sum: 2ef80a17c7b548fee5054ffc291a3f9b SHA1: a0d0ad5b1b071ce1c724fc0ad38a48056a94027b SHA256: 6d8cdab3268649e99564dfea43c273e9b10d57f23c78ff67d24af8ef438552c4 SHA512: ab3ce1274587dcbc68f3bf5c09ac9ab973b4197918d6d0aa4ba802b283c5d2dc18ca4f7597fbf6360bbf02cc610d0b66f5ced557fd8f5b08d5abf327c7be4a94 Homepage: https://cran.r-project.org/package=robqda Description: CRAN Package 'robqda' (Robust Quadratic Discriminant Analysis) The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. . Package: r-cran-robratio Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-latex2exp, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-robratio_0.1.0-1.ca2604.1_all.deb Size: 73222 MD5sum: 218336801c3b02a69db0e71a2ec55213 SHA1: 5f1d9753ba2191532f88c6383d08ee27c960610f SHA256: 3bb26d8d69eba24ef269936dfcd8d768db4a89fa44564d5de15b0f8beeb24f77 SHA512: eeb030f408d7f3accbb4d1b50cd142fe3d447cb52946382001d7dc0103a204cf240056e78f8da9f76b5461bbebc4c4f399523c78abb0a0d630083bc30e9bdbdd Homepage: https://cran.r-project.org/package=robRatio Description: CRAN Package 'robRatio' (M-Estimators for Generalized Ratio and Linear Regression Models) Robust estimators for generalized ratio model (Wada, Sakashita and Tsubaki, 2021) and linear regression model by the IRLS(iterative reweighted least squares) algorithm are contained. Package: r-cran-robreg3s Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gse, r-cran-mass, r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-robreg3s_0.3-1-1.ca2604.1_all.deb Size: 47612 MD5sum: 21d966a8c1a78b9cffa3f0d99e5615b4 SHA1: 43167536797d98ea87d05d3aa12e28dc30a9d9db SHA256: 8ba732c958965dc3317639f5d8d708ba0be8a998dea6ff3b69f19b591b5adfff SHA512: 6a0aaa63d530969dab74703a45e760956f70b44f1163f8586c4d7db12073650d8699da8d404b50bc7f369316a82352e276e7bb93fd511891de443673b5ffe7b6 Homepage: https://cran.r-project.org/package=robreg3S Description: CRAN Package 'robreg3S' (Three-Step Regression and Inference for Cellwise and CasewiseContamination) Three-step regression and inference for cellwise and casewise contamination. 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Package: r-cran-robumeta Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-clubsandwich, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-robumeta_2.1-1.ca2604.1_all.deb Size: 518462 MD5sum: a7d023f0f1d3119b41fe1477e8e4a5e4 SHA1: 7d941226ffddfd3e92a2a965a770dfde39c2688e SHA256: e79b9676560c81561f15a655bdd34406a07c2d72e73e70baf680cb10e1935e0c SHA512: 8ab81bb8bf1291a9bff931a914d7f0ea833d17a35ffbb0f553207fb15157b04b22db0be2fe4d77d7f28df9f33509b665f0572b4b36375be3076e4d194344517f Homepage: https://cran.r-project.org/package=robumeta Description: CRAN Package 'robumeta' (Robust Variance Meta-Regression) Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. 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Package: r-cran-robustadaptivedecomposition Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-robustadaptivedecomposition_0.1.0-1.ca2604.1_all.deb Size: 12794 MD5sum: ccfbbd6a8ed8acb701ee50f5f3874017 SHA1: c567542f295782e326663d973b56dcb357286cd3 SHA256: 82f6901d71e6b77ac1e04ee001f5025579b8fb3f3aba3345bf2f87581186e887 SHA512: eacdac35202736a9058e3d5b6003f4a6cb1104ca7ad04bf1dee6107f2017821aabe012c3e69b5b46ca0678c2832ad3c58846ef7a2d3e59a6cdac6864ea96f82a Homepage: https://cran.r-project.org/package=RobustAdaptiveDecomposition Description: CRAN Package 'RobustAdaptiveDecomposition' (Decomposes a Univariate Time Series into Subcomponents) Provides a method to decompose a univariate time series into meaningful subcomponents for analysis and denoising. Package: r-cran-robustanova Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-peip, r-cran-optimbase Filename: pool/dists/resolute/main/r-cran-robustanova_0.3.0-1.ca2604.1_all.deb Size: 37334 MD5sum: 62c2147b6fb762cbe20184e3fa038964 SHA1: 0ef971d76ba188d23582bfeb6cc18c094f8e7986 SHA256: 4a50b666380e470c5ebf28994cfae87e5b7d9daebd966a7f9cfc5d6177ba0ac4 SHA512: 6c2a1e4ddb8b93103df4bf5bcade2f641d1900752f3b12c1cfda23c864b794388727d6828ecedbeb84f799e4faba462ae27e84d58e52d4d21d55d8c85123415a Homepage: https://cran.r-project.org/package=RobustANOVA Description: CRAN Package 'RobustANOVA' (Robust One-Way ANOVA Tests under Heteroscedasticity andNonnormality) Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) , parametric bootstrap test proposed by Krishnamoorthy et. al (2007) ; and generalized F test proposed by Weerahandi (1995) ;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) , . Package: r-cran-robustbetareg Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-rmpfr, r-cran-rstudioapi, r-cran-crayon, r-cran-pracma, r-cran-numderiv, r-cran-formula, r-cran-robustbase, r-cran-zoo, r-cran-bbmisc, r-cran-mass, r-cran-misctools, r-cran-matrix Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robustbetareg_0.3.1-1.ca2604.1_all.deb Size: 228950 MD5sum: 9614f682be343559f7b52294f225dbba SHA1: d8b6751ccd9b014568096e61dd351788d33c5a3f SHA256: 1dc78f95673d41b2647df642168c3189e02a44890bccb9653f81101d7391c899 SHA512: 5fe810dc87c3ee76b720c04330337237801dcec30fb421ec56cda70139566fa3f9295a0bd4afd84127030d15a369dc231020a94f270777b9f1e8ea5be94a91fa Homepage: https://cran.r-project.org/package=robustbetareg Description: CRAN Package 'robustbetareg' (Robust Beta Regression) Robust estimators for the beta regression, useful for modeling bounded continuous data. Currently, four types of robust estimators are supported. They depend on a tuning constant which may be fixed or selected by a data-driven algorithm also implemented in the package. Diagnostic tools associated with the fitted model, such as the residuals and goodness-of-fit statistics, are implemented. Robust Wald-type tests are available. More details about robust beta regression are described in Maluf et al. (2025) . Package: r-cran-robustbf Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-robustbf_0.2.0-1.ca2604.1_all.deb Size: 24942 MD5sum: 99cc04bfc7dd523e6ded602f94b3a10c SHA1: 4eeb570cc40b5b222f2a34e3cb3e02748af0f0e3 SHA256: 24113e0bf3e3ac29eec6cf993ddc01bbfdc0363f2abd33d2e1d423c652882827 SHA512: 5ffda57968e4ecacd001740343cc8edef3d34c9a30b9cb7e656933c48c85e06a69e3c87e2f1b4637f96527079e30b4b61dd2f96c34fc3b93cb839dd1a8c85c2c Homepage: https://cran.r-project.org/package=RobustBF Description: CRAN Package 'RobustBF' (Robust Solution to the Behrens-Fisher Problem) Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) and Donmez (2010) . Package: r-cran-robustda Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mclust, r-cran-rsolnp Filename: pool/dists/resolute/main/r-cran-robustda_1.2-1.ca2604.1_all.deb Size: 24064 MD5sum: cfe1ad87b7dc8a10f61d3f222b037ff4 SHA1: b7c44880e5a2a806e48e82d4589041016bd644c4 SHA256: f0f74114187789a69a74c3440eb97dbbfa7225a981a7d7ccbaf19bf487e56e6d SHA512: debc7d1285a7bf6c5d9a9087eab7bee8d8ae363c2617c9898219d1bd3e231ef3a511bc16ffa7d0f3b7c88b8311f2430099006f632f13a9a6642e99567c622c3c Homepage: https://cran.r-project.org/package=robustDA Description: CRAN Package 'robustDA' (Robust Mixture Discriminant Analysis) Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 , allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels. Package: r-cran-robustdif Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mirt, r-cran-lavaan Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robustdif_0.2.0-1.ca2604.1_all.deb Size: 109420 MD5sum: d749689c19dafd25397273340eeddf34 SHA1: 81441af13686ea4039f01252e58166855519320e SHA256: f9f30bb03e145a83d2273a1dd5db314c6bbd10c43897196e6897aa297c25e67e SHA512: 0519baf2ae2e0be04818d8036e1cb7e1bc710b87d4e2220e73330c5e88ef41f46d98ef5238d6af09af7518c7e0791eb45f317bf38de474ff18e772828e87f86f Homepage: https://cran.r-project.org/package=robustDIF Description: CRAN Package 'robustDIF' (Differential Item Functioning Using Robust Scaling) Provides tools for testing differential item functioning (DIF) and differential test functioning (DTF) in two-group item response theory models. The package estimates robust scaling parameters via iteratively reweighted least squares with Tukey's bisquare loss, and supports Wald-type tests of item-level and test-level differences from robust scaling parameters. Inputs can be supplied directly from model parameter/covariance objects or extracted from fitted 'mirt' and 'lavaan' models. Methods are described in Halpin (2022) . Package: r-cran-robustfa Architecture: all Version: 1.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1755 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrcov Suggests: r-cran-lattice, r-cran-cluster, r-cran-mclust, r-cran-mass, r-cran-ellipse, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robustfa_1.2-0-1.ca2604.1_all.deb Size: 1378454 MD5sum: c5fd1fef22d08f39e5e4e44e84d459b0 SHA1: 2e029df0ffbdde59d8bc2fd83d68b175c62537cf SHA256: 290abf72353b63f30780af37575d7823acfcb65e2d00ea3720803d8314fec7f2 SHA512: cf95569aa889d8b47ab86b606b74195affda3453c9f746a0c4a8d2e75068d3a60f75e7cff456f31deb20eae8eb6a266ce269416adbe7acd3167f0ed219daddcf Homepage: https://cran.r-project.org/package=robustfa Description: CRAN Package 'robustfa' (Object Oriented Solution for Robust Factor Analysis) Outliers virtually exist in any datasets of any application field. To avoid the impact of outliers, we need to use robust estimators. Classical estimators of multivariate mean and covariance matrix are the sample mean and the sample covariance matrix. Outliers will affect the sample mean and the sample covariance matrix, and thus they will affect the classical factor analysis which depends on the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, P. and Croux, C. (2003) ). So it is necessary to use the robust estimators of the sample mean and the sample covariance matrix. There are several robust estimators in the literature: Minimum Covariance Determinant estimator, Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, M, S, and Stahel-Donoho. The most direct way to make multivariate analysis more robust is to replace the sample mean and the sample covariance matrix of the classical estimators to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) ) (Todorov, V. and Filzmoser, P. (2009) ), which is our choice of robust factor analysis. We created an object oriented solution for robust factor analysis based on new S4 classes. Package: r-cran-robustflow Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-golem, r-cran-bslib, r-cran-ggplot2, r-cran-dt, r-cran-plotly, r-cran-scales, r-cran-htmltools, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-knitr, r-cran-covr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-robustflow_0.1.1-1.ca2604.1_all.deb Size: 129716 MD5sum: 22b31dce7a5dbcbb86cffbc45aa3fbc4 SHA1: 742a18d408bd445721af6d586ceed37c0b003d3b SHA256: c80b68f9a2505bd88ad607c3084ce231f53101d4ae4f031f363f6706cdaa3c0e SHA512: e74555e8b8b077f4dc384876e6ea89db9d3f1dea07cd6007a235e05aff25fb8f3ba670aedabf364b6f1e0c19082ee55dabad3a4930d94e4f38d5133f8ec16415 Homepage: https://cran.r-project.org/package=RobustFlow Description: CRAN Package 'RobustFlow' (Robustness and Drift Auditing for Longitudinal Decision Systems) Provides tools for constructing longitudinal decision paths, quantifying temporal drift, tracking subgroup disparity trajectories, and stress-testing longitudinal conclusions under hidden bias. Implements three signature metrics: the Drift Intensity Index (DII), which measures structural instability in transition dynamics using the Frobenius norm of consecutive transition matrix differences; the Bias Amplification Index (BAI), which quantifies whether group disparities widen or converge over time; and the Temporal Fragility Index (TFI), which estimates the minimum hidden-bias perturbation required to nullify a longitudinal trend conclusion. An interactive 'shiny' application supports exploratory analysis, visualization, and reproducible reporting. Methods are motivated by applications in educational and social science research, including the Early Childhood Longitudinal Study (ECLS). The DII is based on the Frobenius norm as described in Golub and Van Loan (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on disparity-trajectory methods discussed in Duncan and Murnane (2011, ISBN:9780871542731). Package: r-cran-robustgarch Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-nloptr, r-cran-rugarch, r-cran-zoo, r-cran-xts Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-pcra Filename: pool/dists/resolute/main/r-cran-robustgarch_0.4.2-1.ca2604.1_all.deb Size: 78424 MD5sum: 418ab598ed06f9e1e3faa0ddaa45f8a8 SHA1: 1522ed682d8c5cd700bd48f02a7595f03d7677a9 SHA256: 8e1027192d140f3516d573db07dd156a57936e8da5f1f38d78e6ec557f6a6fc6 SHA512: 2e1beb38c8ebd2ace9beb1dc89bd19c8935919a582b4d07548d84d8d2828e3ec8104bf7f6514943cb2edd58fff07346b708b886a6e0bae815fa4a62e01ba64cd Homepage: https://cran.r-project.org/package=robustGarch Description: CRAN Package 'robustGarch' (Robust Garch(1,1) Model) A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) . Package: r-cran-robustiv Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-matrix, r-cran-igraph, r-cran-intervals, r-cran-cvxr Filename: pool/dists/resolute/main/r-cran-robustiv_0.3.1-1.ca2604.1_all.deb Size: 130082 MD5sum: 08338f15f3d1ac4fcbade9aeaebad241 SHA1: 2024b47997df863566f19cc0a821f5fe936998ae SHA256: 7a655d559f1f05b201c9df277e43de77a49968bc396c3d118d5fafe8291de098 SHA512: 9f9501a629808345bfbf56dfd32f98dbab3e01c93d349e92df8a20969d04f65950050aa1bc54a3cf065a26a41ed4ca89f268d25dd8e4c3e3aba1df5c20e319dd Homepage: https://cran.r-project.org/package=RobustIV Description: CRAN Package 'RobustIV' (Robust Instrumental Variable Methods in Linear Models) Inference for the treatment effect with possibly invalid instrumental variables via TSHT ('Guo et al.' (2018) ) and SearchingSampling ('Guo' (2023) ), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.' (2018) ). Package: r-cran-robustlinearreg Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-robustlinearreg_1.2.0-1.ca2604.1_all.deb Size: 18230 MD5sum: e23ca97cbb2af61624cf98a02fc3e290 SHA1: e726c85efc557f867373118b94e389e83a5a1247 SHA256: 130d331d3ac0136285570fd7e4f8aece4455f49668f127400e6723437a5a7384 SHA512: 7bb2f37e462fd4329a7638d5a3656c3cb5b015cbf3a6cea7b722f514ca1a2fde8f5327c0bccdbedd94fb7222c7f2d4ee45991f2f0796888aa3585e16c4e30fd4 Homepage: https://cran.r-project.org/package=RobustLinearReg Description: CRAN Package 'RobustLinearReg' (Robust Linear Regressions) Provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) , Sen, P. K. (1968) . Package: r-cran-robustlm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-robustlm_0.1.0-1.ca2604.1_all.deb Size: 159898 MD5sum: e8e109088f1d81b09f69333aa3da3cde SHA1: 32ca5515fb847f063aa372bf69b6fc5f61d0d947 SHA256: 6b996aa5ddbe3782b7c2d9d34fc6c5f88981f903374f5f8b2f1d487fcda8293a SHA512: f4f23f2aad9487ffa095d91e794db6140cdbe0eddd5c33957f4fe669bb9fb2a9216cebd207842d88a826e881b5f92aaa38d61db8fccd93e1f529b21ef8988aea Homepage: https://cran.r-project.org/package=robustlm Description: CRAN Package 'robustlm' (Robust Variable Selection with Exponential Squared Loss) Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) . Users can enjoy the near optimal, consistent, and oracle properties of the procedures. Package: r-cran-robustmediate Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rlang, r-cran-scales, r-cran-broom Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-lme4, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-robustmediate_0.1.1-1.ca2604.1_all.deb Size: 227102 MD5sum: 560a0b87ae746b2a943c70368799395c SHA1: 359c63c62045d2e87d39015deeaa4f8214e588f1 SHA256: 9b5eb1c8c2bc6fe6fc2578c409adde20016773b023d79882804e65c7c6f9292c SHA512: 0a3482f0aac113afaa9fc5fddbb7fd38f454500c0703be9f9c1821811dc84fe9c945028246b5908c5a8e9ba9324380a73bd47dc575932ca9308a02c1c370cba7 Homepage: https://cran.r-project.org/package=RobustMediate Description: CRAN Package 'RobustMediate' (Causal Mediation Analysis with Diagnostics and SensitivityAnalysis) Provides tools for causal mediation analysis with continuous treatments using inverse probability weighting (IPW). Estimates natural direct and indirect effects over a user-defined treatment grid and supports flexible dose-response mediation analysis. Includes diagnostic procedures for assessing covariate balance in both treatment and mediator models using standardized mean differences. Implements pathway-specific extensions of the impact threshold for a confounding variable (ITCV; Frank, 2000 ) adapted to mediation settings. Provides joint sensitivity analysis combining E-values (VanderWeele and Ding, 2017 ) and violations of sequential ignorability (Imai, Keele, and Yamamoto, 2010 ). Additional utilities include visualization of dose-response mediation functions, robustness profiles, fragility summaries, and formatted outputs for applied research. Supports clustered data structures and multiple outcome families. Package: r-cran-robustmeta Architecture: all Version: 1.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metafor Filename: pool/dists/resolute/main/r-cran-robustmeta_1.2-1-1.ca2604.1_all.deb Size: 34404 MD5sum: 68ab487848ce3308cc525174a8b754b2 SHA1: 939b6a5981aaae7ddd20712ef8bfabc43a5700da SHA256: 81e1af9b1db4c37e4e498e908f763a47028b397ebd18f919c8e313c0560312bd SHA512: 7f969f79770f8afe0aab80384f3c053a05aecf93e24249469c835b32bb26cc27b3b2db30abe93256392c02918f10abd7c63aa08a0fe3a3a0d70a4f2d76cc78df Homepage: https://cran.r-project.org/package=robustmeta Description: CRAN Package 'robustmeta' (Robust Inference for Meta-Analysis with Influential OutlyingStudies) Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) , and the meta-analysis methods are developed by Noma et al. (2022) . Package: r-cran-robustmetrics Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-robustmetrics_0.1.1-1.ca2604.1_all.deb Size: 310640 MD5sum: 080a01b5f1d341a7c9e14fa636a1e710 SHA1: 63d9b0a593d4bca6631908fc33c9c74075e40e38 SHA256: 80b13301043f448c294b29d1fdca7dc69d534ed3ee83a250ce23586eee8fd3b7 SHA512: 0537d705e06622d6075b7eb43139540c29b9a0473234d738185e8e3bd461ffb790a390c5662c7f13a9a0b34d898446fddb4af294919f2b47f686bad0e569ee0b Homepage: https://cran.r-project.org/package=RobustMetrics Description: CRAN Package 'RobustMetrics' (Calculates Robust Performance Metrics for ImbalancedClassification Problems) Calculates robust Matthews Correlation Coefficient (MCC) and robust F-Beta Scores, as introduced by Holzmann and Klar (2024) . These performance metrics are designed for imbalanced classification problems. Plots the receiver operating characteristic curve (ROC curve) together with the recall / 1-precision curve. Package: r-cran-robustprediction Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mboost, r-cran-mlr, r-cran-ranger, r-cran-e1071, r-cran-proc Filename: pool/dists/resolute/main/r-cran-robustprediction_0.1.7-1.ca2604.1_all.deb Size: 299778 MD5sum: 219eaf5cbfe699860644abd2bc61becb SHA1: 633dad6f40c1d1b0996c29748de0073c50e3836a SHA256: bd7c6306fd4180a023d54fa4b2e351d96265796ad5980b57b1d4e43773e733d5 SHA512: 4c776fd4ffe8d02aaeebff9f6b5ba7e1daf8ca020b18fb7f70438ad6bc371abc9b63987a901643907122c012550b1be5e05b33a71cf591617ccd9654c78a8827 Homepage: https://cran.r-project.org/package=RobustPrediction Description: CRAN Package 'RobustPrediction' (Robust Tuning and Training for Cross-Source Prediction) Provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the 'RobustTuneC' method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" . Package: r-cran-robustrankaggreg Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-robustrankaggreg_1.2.1-1.ca2604.1_all.deb Size: 49928 MD5sum: cf21dd4b7342d38069441fefe1d7ea22 SHA1: 73fa55abbdbd140b9ec8d279c7d1aa9be7f5d1ff SHA256: 81539423f4e24cee8a0de6b1bd1104bf729a2814f67e4482dd0af912d5e5b1e2 SHA512: 67086f14a7525cc63957140bd9d5103b0717e264d82b9ee742bc37704dc89612a2d84cb33b8d924b6aa8cd0db41e8e2d7bd36d5e74d778cea813276766b2b25e Homepage: https://cran.r-project.org/package=RobustRankAggreg Description: CRAN Package 'RobustRankAggreg' (Methods for Robust Rank Aggregation) Methods for aggregating ranked lists, especially lists of genes. It implements the Robust Rank Aggregation Kolde et al (2012) and some other simple algorithms for the task. RRA method uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking. Package: r-cran-robustrao Architecture: all Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-gmp, r-cran-iterpc, r-cran-quadprog, r-cran-igraph, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-robustrao_1.0-5-1.ca2604.1_all.deb Size: 194844 MD5sum: 48c8482b4d0250e397b2e288d1b3db22 SHA1: 0bafe4245f207a0aa9104d13d44de1a162ae38df SHA256: cda0ea964c25c1a0a08ce902d41b7c576a6b8e92b5e09c2ef9cd1bc4b64b1b3c SHA512: 4cd719b25d1a5716a47eeac2a0b1052da5e3a81dfb3d4fb4918790128fb3530a62a662074408b0721efadf3a714e6ecd0aa71a2ea0d13afa9db9e3496b220d70 Homepage: https://cran.r-project.org/package=robustrao Description: CRAN Package 'robustrao' (An Extended Rao-Stirling Diversity Index to Handle Missing Data) A collection of functions to compute the Rao-Stirling diversity index (Porter and Rafols, 2009) and its extension to acknowledge missing data (i.e., uncategorized references) by calculating its interval of uncertainty using mathematical optimization as proposed in Calatrava et al. (2016) . The Rao-Stirling diversity index is a well-established bibliometric indicator to measure the interdisciplinarity of scientific publications. Apart from the obligatory dataset of publications with their respective references and a taxonomy of disciplines that categorizes references as well as a measure of similarity between the disciplines, the Rao-Stirling diversity index requires a complete categorization of all references of a publication into disciplines. Thus, it fails for a incomplete categorization; in this case, the robust extension has to be used, which encodes the uncertainty caused by missing bibliographic data as an uncertainty interval. Classification / ACM - 2012: Information systems ~ Similarity measures, Theory of computation ~ Quadratic programming, Applied computing ~ Digital libraries and archives. Package: r-cran-robustsur Architecture: all Version: 0.0-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-robreg3s, r-cran-matrix, r-cran-gse Suggests: r-cran-systemfit Filename: pool/dists/resolute/main/r-cran-robustsur_0.0-8-1.ca2604.1_all.deb Size: 46736 MD5sum: 2e19405e34ea7c0a44a3e3ed64a7aecb SHA1: f6f0b335a754e1cf11e713f870e7a5897776ab9b SHA256: f84ee642137b5f42e04b40482850916b8f5098a29de734c71c89a8660945704c SHA512: 3d3397f74906c59aad143b01a9e7ac28c079d0f0a52994b2bf9f66d9a417c7d8133801f53620b9ff6e2528f57328fa463e7685eed6256d29cbff1b56839cd1c8 Homepage: https://cran.r-project.org/package=robustsur Description: CRAN Package 'robustsur' (Robust Estimation for Seemingly Unrelated Regression Models) Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) . Package: r-cran-robustt2 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-ggplot2, r-cran-rrcov, r-cran-shiny, r-cran-ggrepel, r-cran-forcats, r-cran-matrix Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-robustt2_0.1.0-1.ca2604.1_all.deb Size: 95326 MD5sum: b5fd1fda7d685b5085fa9aa483a76ea9 SHA1: 8429f82f060d7a4b03ccbd54d096d43d30312483 SHA256: 13210eaae55751983216fe5b4ea1db43190ea62c83c3e860175c6d0ec6be6398 SHA512: f548691af36b7da87f77ccae8d01663306de19fd21507f6a6c9acbf9e0a939f9728035668a8d578d03d75accda0251b72441429ec1ebcc9eb20c24dd8dc23a66 Homepage: https://cran.r-project.org/package=robustT2 Description: CRAN Package 'robustT2' (Robust Hotelling-Type T² Control Chart Based on the Dual STATISApproach) Implements a robust multivariate control-chart methodology for batch-based industrial processes with multiple correlated variables using the Dual STATIS (Structuration des Tableaux A Trois Indices de la Statistique) framework. A robust compromise covariance matrix is constructed from Phase I batches with the Minimum Covariance Determinant (MCD) estimator, and a Hotelling-type T² statistic is applied for anomaly detection in Phase II. The package includes functions to simulate clean and contaminated batches, to compute both robust and classical Hotelling T² control charts, to visualize results via robust biplots, and to launch an interactive 'shiny' dashboard. An internal dataset (pharma_data) is provided for reproducibility. See Lavit, Escoufier, Sabatier and Traissac (1994) for the original STATIS methodology, and Rousseeuw and Van Driessen (1999) for the MCD estimator. Package: r-cran-robustx Architecture: all Version: 1.2-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase Suggests: r-cran-mass, r-cran-lattice, r-cran-pcapp Filename: pool/dists/resolute/main/r-cran-robustx_1.2-8-1.ca2604.1_all.deb Size: 128820 MD5sum: 2aa08508b3518bd8b7d172798b312bce SHA1: 20a219b07da686c2eaa72a91b734dd94718cadfe SHA256: e02321c3ace656ca661781a8239ffa5eef8cfb8286e793edf3392c3ceb7dac6e SHA512: 32fe2615a3058492196af908b5def1c3d599a489328a2acfe624b4cb99fa03ac8f303e91dd5c4490181014b1e6ee0c58dd822a21faae0060e17f566e1bd86d3a Homepage: https://cran.r-project.org/package=robustX Description: CRAN Package 'robustX' ('eXtra' / 'eXperimental' Functionality for Robust Statistics) Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary' Functionality for Robust Statistics. Hence methods which are not well established, often related to methods in package 'robustbase'. Amazingly, 'BACON()', originally by Billor, Hadi, and Velleman (2000) has become established in places. The "barrow wheel" `rbwheel()` is from Stahel and Mächler (2009) . Package: r-cran-robvis Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2097 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robvis_0.3.0-1.ca2604.1_all.deb Size: 1251728 MD5sum: 645cf6c735cc06ab0e212a756c9caff4 SHA1: d38f3374d0d2e071b8a378d1552efb44c4c795c3 SHA256: a0be7a0a60b6a534c11c3d1b26c4544dc848a916e0e49cb06e00759f529199fd SHA512: 7776fbf105e7adca76ef9149e60a9c1ee4c2a72479e51de5b1ac875cae85622ff068531c4eb1c10554fe8c7b6cdafc578394095794110b7f238f07878e57d2be Homepage: https://cran.r-project.org/package=robvis Description: CRAN Package 'robvis' (Visualize the Results of Risk-of-Bias (ROB) Assessments) Helps users in quickly visualizing risk-of-bias assessments performed as part of a systematic review. It allows users to create weighted bar-plots of the distribution of risk-of-bias judgments within each bias domain, in addition to traffic-light plots of the specific domain-level judgments for each study. The resulting figures are of publication quality and are formatted according the risk-of-bias assessment tool use to perform the assessments. Currently, the supported tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019) ), ROBINS-I (for non-randomised studies of interventions; Sterne et al (2016) ), and QUADAS-2 (for diagnostic accuracy studies; Whiting et al (2011) ). Package: r-cran-robyn Architecture: all Version: 3.12.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-ggridges, r-cran-glmnet, r-cran-jsonlite, r-cran-lares, r-cran-lubridate, r-cran-nloptr, r-cran-patchwork, r-cran-prophet, r-cran-reticulate, r-cran-stringr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-robyn_3.12.1-1.ca2604.1_all.deb Size: 1573180 MD5sum: 051de7f162610f86c0ed83c9a14011ce SHA1: 256928537bf39f3ee434e3de77735cc78b3a819e SHA256: 496011a46ab00bf1618865a0811232808020aff0ba277bfa067e1addec88a23f SHA512: e630cdda1e99e23f68675119b44ae5bb5ccbcfd0ec1b87e0b49a661ad7cfc50dd75724b3195c0121cd8582d15afd27c4ddba538e55ede0ae351f15de38ebc8c3 Homepage: https://cran.r-project.org/package=Robyn Description: CRAN Package 'Robyn' (Semi-Automated Marketing Mix Modeling (MMM) from Meta MarketingScience) Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation. Package: r-cran-rocaggregator Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-testthat, r-cran-mockery, r-cran-mockr, r-cran-knitr, r-cran-rmarkdown, r-cran-rocr, r-cran-proc, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-rocaggregator_1.0.1-1.ca2604.1_all.deb Size: 44074 MD5sum: 787bf4573cce70977f68b79aba4b12ab SHA1: 9155fba0fcd4831dae81b4fb834c9b92a78d9ada SHA256: 797a27da52d84e859eaf941dbee88ce69fd2144c97d06dc28eb51065a78eb150 SHA512: 6b375c206436a5bb977a5ed0191068adff47cb4550746d91e3a70198e935b1e8568ceff611f2a1c6dc4782960925af6f7a30297d5127cc1fea993bfc0ae886d2 Homepage: https://cran.r-project.org/package=ROCaggregator Description: CRAN Package 'ROCaggregator' (Aggregate Multiple ROC Curves into One Global ROC) Aggregates multiple Receiver Operating Characteristic (ROC) curves obtained from different sources into one global ROC. Additionally, it’s also possible to calculate the aggregated precision-recall (PR) curve. Package: r-cran-rocbc Architecture: all Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1779 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-clinfun, r-cran-splancs, r-cran-mvtnorm, r-cran-formattable, r-cran-mrmcaov, r-cran-proc Suggests: r-cran-knitr, r-cran-markdown, r-cran-matlab Filename: pool/dists/resolute/main/r-cran-rocbc_3.2.0-1.ca2604.1_all.deb Size: 993988 MD5sum: 573959e8c1e33c3c27c69c70cfa496a3 SHA1: a2194f0d499777c710ebfeb54cc33a8afe00ce37 SHA256: 8bd7d79a8b9b9467b7561cd28d88e0221d72990682717d5afb8501c706fb0eb2 SHA512: 276f4567598fafc2bae35bd465a5d9c134eb54d33b92ec1d4b02f3bde3160f19eeb0dd04922392dfa92ad274caf5650aa401860e4b4fb087646dfc3c5137ff70 Homepage: https://cran.r-project.org/package=rocbc Description: CRAN Package 'rocbc' (Statistical Inference for Box-Cox Based Receiver OperatingCharacteristic Curves) Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018)) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021)) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff. Package: r-cran-rocc Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocr Filename: pool/dists/resolute/main/r-cran-rocc_1.3-1.ca2604.1_all.deb Size: 37948 MD5sum: 375e77b12fa46c33fa65e1c1a7d8f5ab SHA1: a8d443fe688b7963bd062aefa619be6cba5e2b12 SHA256: bc4e9ca892d875bb49ab538980ed18bf6abddb221b1cb552e43f3932874bd4e6 SHA512: 13fb0229fb6cb397e9718696a7bef93233fd0ad77fcafc44ada6cc01895049166704bb56ffe8d9e32e7d1a3c5e6acd6b7e9e7ac61cfdd48e6d9f03dfb61df92b Homepage: https://cran.r-project.org/package=rocc Description: CRAN Package 'rocc' (ROC Based Classification) Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a meta-gene. The samples are ranked by their meta-gene value and the meta-gene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its meta-gene value relative to the threshold. In the first place, the package is aimed at two class problems in gene expression data, but might also apply to other problems. Package: r-cran-roccv Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-proc Filename: pool/dists/resolute/main/r-cran-roccv_1.2-1.ca2604.1_all.deb Size: 24772 MD5sum: 2afd7f62bc69ca01c44a94b94ac2fece SHA1: 9cb9528a5fd2890769e5a2a8e103ed1e0646abd0 SHA256: 363e5d82ebc5018164b80f3fe9090fb7eed4ed177aefdf4034544a3228db34c9 SHA512: a0338d9a14d49ce49550b8f418e8d2ca07bfcef9f6439c77e30d6babf86ee84e5ab5b840cb17d2b7fbc020579586c0fed77d6ee10d2ed8052576d13c3ec622db Homepage: https://cran.r-project.org/package=roccv Description: CRAN Package 'roccv' (ROC for Cross Validation Results) Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data. Package: r-cran-rocean Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ff Filename: pool/dists/resolute/main/r-cran-rocean_1.0-1.ca2604.1_all.deb Size: 45212 MD5sum: 74c8b680ecb84e53731be1ea4abd45f0 SHA1: c9864ce9ad803afb6c74d58f9822c78d5c90b091 SHA256: 06d3fbbe51c877efb41ae7605b0f6f7c14f5d62124ccca4ad448cc47657db632 SHA512: 2862a6f91d25fd6f6779c33d987df9966697c01d9da7ccff16083f8641495845f6f7c38c23246de0eb8e98fc657057e51c12b1e8a2d109fc111c4638bca2fb9e Homepage: https://cran.r-project.org/package=rOCEAN Description: CRAN Package 'rOCEAN' (Two-Way Feature Set Testing for Multi-Omics) For any two way feature-set from a pair of pre-processed omics data, 3 different true discovery proportions (TDP), namely pairwise-TDP, column-TDP and row-TDP are calculated. Due to embedded closed testing procedure, the choice of feature-sets can be changed infinite times and even after seeing the data without any change in type I error rate. For more details refer to Ebrahimpoor et al., (2024) . Package: r-cran-rocftp.mms Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rocftp.mms_1.0.1-1.ca2604.1_all.deb Size: 20982 MD5sum: 585d2714f610cca85b8176d237ba5f86 SHA1: c76628904bec996bba8267bed68bfced8d05ff2f SHA256: f8a5eb5811ce751ffa76b27ceffafcc42e5b0037655529c5b7e22aa59dfed343 SHA512: 3925f99a784d394da10488f544a5b533721b81418284a82d5fda7bf104894c00616f2d5c3c8d328b992eadad65cec2b85e45f330bd1843c1ef25b6aeec9a1c7f Homepage: https://cran.r-project.org/package=ROCFTP.MMS Description: CRAN Package 'ROCFTP.MMS' (Perfect Sampling) The algorithm provided in this package generates perfect sample for unimodal or multimodal posteriors. Read Once Coupling From The Past, with Metropolis-Multishift is used to generate a perfect sample for a given posterior density based on the two extreme starting paths, minimum and maximum of the most interest range of the posterior. It uses the monotone random operation of multishift coupler which allows to sandwich all of the state space in one point. It means both Markov Chains starting from the maximum and minimum will be coalesced. The generated sample is independent from the starting points. It is useful for mixture distributions too. The output of this function is a real value as an exact draw from the posterior distribution. Package: r-cran-rocit Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rocit_2.1.2-1.ca2604.1_all.deb Size: 281296 MD5sum: 479fa150ce07fce81bdec9522c532f30 SHA1: ec939a6482e9eaf885cb5acb709e2b74751d2ae6 SHA256: a264598c7ba7b5b8112b54c309c7456f56f42ccb958404df9288a0d1f037c28f SHA512: a7531cbb0c88ca5cf3062b2afad77d101c76d09800964b96f7dd69001ec3a22a84fd9fabad7abb7c89dee112d908c9d146e6765a27eff851640fee0c1c5f582b Homepage: https://cran.r-project.org/package=ROCit Description: CRAN Package 'ROCit' (Performance Assessment of Binary Classifier with Visualization) Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use. Package: r-cran-rock Architecture: all Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1688 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-ggplot2, r-cran-glue, r-cran-htmltools, r-cran-markdown, r-cran-purrr, r-cran-squids, r-cran-yaml, r-cran-yum Suggests: r-cran-covr, r-cran-googlesheets4, r-cran-haven, r-cran-justifier, r-cran-knitr, r-cran-limonaid, r-cran-openxlsx, r-cran-pdftools, r-cran-pkgdown, r-cran-preregr, r-cran-readxl, r-cran-rmarkdown, r-cran-rvest, r-cran-rsvg, r-cran-rstudioapi, r-cran-striprtf, r-cran-testthat, r-cran-writexl, r-cran-xlconnect, r-cran-xml2, r-cran-zip Filename: pool/dists/resolute/main/r-cran-rock_0.9.6-1.ca2604.1_all.deb Size: 1210180 MD5sum: ea7eee21fabd1f2200744bc3cbe57e92 SHA1: 47866115bbc3d505ce0fa2b3d0f70f94421effd9 SHA256: 57aa001196383269d9a3fecf4a272ab8178c5884cfd016a5b9c6cc2bc11b3432 SHA512: 0cc70709050162b943a302621c91893473276606c19fb9a4ba97b88ef7f37cb28477a17ae7fd556c97afba5e935e4d2e9ece6366f4accb7077d6a7716b575da6 Homepage: https://cran.r-project.org/package=rock Description: CRAN Package 'rock' (Reproducible Open Coding Kit) The Reproducible Open Coding Kit ('ROCK', and this package, 'rock') was developed to facilitate reproducible and open coding, specifically geared towards qualitative research methods. It was developed to be both human- and machine-readable, in the spirit of MarkDown and 'YAML'. The idea is that this makes it relatively easy to write other functions and packages to process 'ROCK' files. The 'rock' package contains functions for basic coding and analysis, such as collecting and showing coded fragments and prettifying sources, as well as a number of advanced analyses such as the Qualitative Network Approach and Qualitative/Unified Exploration of State Transitions. The 'ROCK' and this 'rock' package are described in the ROCK book (Zörgő & Peters, 2022; ), in Zörgő & Peters (2024) and Peters, Zörgő and van der Maas (2022) , and more information and tutorials are available at . Package: r-cran-rockchalk Architecture: all Version: 1.8.164-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2827 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-cardata, r-cran-mass, r-cran-kutils, r-cran-reformulas Suggests: r-cran-tables, r-cran-hmisc, r-cran-car, r-cran-mvtnorm, r-cran-scatterplot3d, r-cran-hh Filename: pool/dists/resolute/main/r-cran-rockchalk_1.8.164-1.ca2604.1_all.deb Size: 2380310 MD5sum: c69f133ec2e8cc03845423b4f9eb821f SHA1: f22b66b204ba78df5140a760bcb86c726d06b616 SHA256: 8e59958b1d8c3f9b529a96864ecc23b2f6e4516748535f869bf7645b26d29458 SHA512: d0c3e1a11c82db775eda168321292c50ba9205f35b1943b865b30e1f28f00aeed8e807cee3aae4934ceadc7dc21130c4045e01029604d9054cabde78f491eb08 Homepage: https://cran.r-project.org/package=rockchalk Description: CRAN Package 'rockchalk' (Regression Estimation and Presentation) A collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in . Includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette 'rockchalk' offers a fairly comprehensive overview. The vignette 'Rstyle' has advice about coding in R. The package title 'rockchalk' refers to our school motto, 'Rock Chalk Jayhawk, Go K.U.'. Package: r-cran-rocker Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-r6, r-cran-sodium Suggests: r-cran-covr, r-cran-crayon, r-cran-knitr, r-cran-rmariadb, r-cran-testthat, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-rocker_0.3.2-1.ca2604.1_all.deb Size: 348940 MD5sum: 5e84f71d51a355e7296a7bc2961e7f0d SHA1: 0c35e5583af1e92d1a4407c237d7688c214079a5 SHA256: 267eae28d46b3b3504b0b8d91aeda76bca3f063f0cf374f14ea9b5373663a18f SHA512: 29a53f1d41f268d87d9be7c13e07d3a4a5ea479cedf2b28969c156a5b7d49197c56f2c7f63d79cbb0c9918181f89636de30b78b92190fa5ccbad1161ce901d24 Homepage: https://cran.r-project.org/package=rocker Description: CRAN Package 'rocker' (Database Interface Class) 'R6' class interface for handling relational database connections using 'DBI' package as backend. The class allows handling of connections to e.g. PostgreSQL, MariaDB and SQLite. The purpose is having an intuitive object allowing straightforward handling of SQL databases. Package: r-cran-rocket Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rocket_1.0.3-1.ca2604.1_all.deb Size: 128672 MD5sum: a49fc47f74057528905e0e98c270b86f SHA1: 2f55e449645bed79eeb34c6fee3b431af94094fc SHA256: c45d59b05d8d3e05104de7630b3a2b3e0c85964b369577e8361f859f9731e770 SHA512: 6a5c74fad1ed3f51d88bc75908d416d645ecafb93ba17c526bcb82b5eeaaf805e9cf791bcd75589036d11e2ab8328703a33b36dc0f98a5dd35176206b6f0c33b Homepage: https://cran.r-project.org/package=ROCket Description: CRAN Package 'ROCket' (Simple and Fast ROC Curves) A set of functions for receiver operating characteristic (ROC) curve estimation and area under the curve (AUC) calculation. All functions are designed to work with aggregated data; nevertheless, they can also handle raw samples. In 'ROCket', we distinguish two types of ROC curve representations: 1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) are functions of a parameter (the score), 2) functions - TPR is a function of FPR. There are several ROC curve estimation methods available. An introduction to the mathematical background of the implemented methods (and much more) can be found in de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004). Package: r-cran-rockfab Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-bioc-ebimage Filename: pool/dists/resolute/main/r-cran-rockfab_1.2.1-1.ca2604.1_all.deb Size: 118768 MD5sum: 3e309e8e27c25c61eed029aa086e7ba7 SHA1: 598ce7c2add63fc4f3830f0096e307ffab7174a5 SHA256: 377924edd53ef83c81bad4ce72c0a083105a15820838d9b3eb7595991124ba6e SHA512: 50c2381b682464298b60c484decbf3cef6ea0de24e87cab9b698233b45ab69e4ffc7a02fc42b453c560438a7023a6a85bca4aa73e36ec3a24c9b70d2f90c2908 Homepage: https://cran.r-project.org/package=RockFab Description: CRAN Package 'RockFab' (Rock Fabric and Strain Analysis Tools) Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality. Package: r-cran-rockr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-mime, r-cran-progress Suggests: r-cran-knitr, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-rockr_1.0.0-1.ca2604.1_all.deb Size: 128176 MD5sum: d4e47fcf675d4d09c71b17ebf8101900 SHA1: 589f79e32d8601cb4afd1560685681f4bdacc31e SHA256: 9f77267a5f1ab1a79994d33f2f51f011061379db7497e2745fdc185bd210f9af SHA512: 29dd4968947cbc4f5d31b2bbaff241a9aff537f3efee2b018932bc7fdc083775f942bed8efdcb11e8d4cd852c4bf69bdc16bac7f66d14d1f35bd110dea537bff Homepage: https://cran.r-project.org/package=rockr Description: CRAN Package 'rockr' ('Rock' R Server Client) Connector to the REST API of a 'Rock' R server, to perform operations on a remote R server session, or administration tasks. See 'Rock' documentation at . Package: r-cran-rockx Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse, r-cran-magrittr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rockx_0.1.0-1.ca2604.1_all.deb Size: 43170 MD5sum: f8d14349f91cd8b7031458a1bc94947c SHA1: 085680f575aa157e2902ede344b4d7bb5df828c6 SHA256: 402626c6182946b9e908179a906ee0ed61a57179336bcd6d435395ce75016b66 SHA512: e5adac52e17405b22e75e0c729e1a31d9994f588c461649640856e4657018c4be9d5c29da1a64faa0c3307b162c3f27ad65133f806146fb4265307bff38465a0 Homepage: https://cran.r-project.org/package=rockx Description: CRAN Package 'rockx' (Easily Import Data from Your 'ODK-X Sync Endpoint') Provides helper functions for authenticating and retrieving data from your 'ODK-X Sync Endpoint'. This is an early release intended for testing and feedback. Package: r-cran-roclab Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-fastdummies, r-cran-kernlab, r-cran-pracma, r-cran-rsample, r-cran-dplyr, r-cran-caret, r-cran-proc Suggests: r-cran-mlbench, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-roclab_0.1.4-1.ca2604.1_all.deb Size: 243678 MD5sum: 99b1af671910e1264c3a798c2cde6562 SHA1: 140cedeb8e00156e421b0a2c06a674b94058b9a1 SHA256: b6a625b5e590cdc2dbed7afededd793f6813927e8cdac68ab41d62c68508f35e SHA512: 685e55feccbef7cc6bc3ac2a16bda9ab120dc9caea749bdba7a5af1646788c9eb08b6555066833db5fde0a25c6582730df9bd68dd1cc3f4a00f6a0559e9ee77e Homepage: https://cran.r-project.org/package=roclab Description: CRAN Package 'roclab' (ROC-Optimizing Binary Classifiers) Implements ROC (Receiver Operating Characteristic)–Optimizing Binary Classifiers, supporting both linear and kernel models. Both model types provide a variety of surrogate loss functions. In addition, linear models offer multiple regularization penalties, whereas kernel models support a range of kernel functions. Scalability for large datasets is achieved through approximation-based options, which accelerate training and make fitting feasible on large data. Utilities are provided for model training, prediction, and cross-validation. The implementation builds on the ROC-Optimizing Support Vector Machines. For more information, see Hernàndez-Orallo, José, et al. (2004) , presented in the ROC Analysis in AI Workshop (ROCAI-2004). Package: r-cran-roclang Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-stringr, r-cran-magrittr, r-cran-rlang, r-cran-roxygen2, r-cran-rex Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-roclang_0.2.3-1.ca2604.1_all.deb Size: 36644 MD5sum: c681870fd49e44f83308ae906304dfc2 SHA1: ebba5b81d14b5fe48b18fade2aad343f2195233f SHA256: 13b2358870b250e976ef594a7f0d492718fe17577b74dcab68cef73f930a112a SHA512: 3e9735beded337125b22171730d67400ab45ddfe81aebdc2e649374143972c3ee9b0e931e740f14e6b1fae3031ba4f71860561006f2585357fa10b12d51cbe98 Homepage: https://cran.r-project.org/package=roclang Description: CRAN Package 'roclang' (Functions for Diffusing Function Documentations into 'Roxygen'Comments) Efficient diffusing of content across function documentations. Sections, parameters or dot parameters are extracted from function documentations and turned into valid Rd character strings, which are ready to diffuse into the 'roxygen' comments of another function by inserting inline code. Package: r-cran-rocmodels Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-kedd, r-cran-dplyr, r-cran-survival, r-cran-nleqslv, r-cran-hdinterval, r-cran-rocit, r-cran-doparallel, r-cran-foreach, r-cran-pbivnorm, r-cran-nor1mix, r-cran-readr, r-cran-mass, r-cran-dorng Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rocmodels_1.0.0-1.ca2604.1_all.deb Size: 174138 MD5sum: abb6ae3330e6587f439a93d75ebb441c SHA1: 2b22e9cafaba673da30841948c764f62da67c4a6 SHA256: 8f8aadb8a68962b64de85b9b86bee1af1c0d5c0a6bc19fac709a0b4db02d3d01 SHA512: 5f62f1a27cff30b1a351e865c91b79d1e1eec9c31c095822e4803b77624c5c906c8663ec94f2e12fd42a0c32d9eca3de3dd9180ea2e661dbe44b60276324255f Homepage: https://cran.r-project.org/package=ROCModels Description: CRAN Package 'ROCModels' (ROC Models and AUC Estimation) The receiver operating characteristic (ROC) curve is one of the most widely used tools for evaluating diagnostic and prognostic biomarkers across diverse scientific fields, particularly in medicine. Despite its ubiquity, ROC estimation and testing methods differ substantially in their assumptions and resulting curve properties. This package provides a unified framework for constructing, visualizing, and comparing parametric, nonparametric, semiparametric, and Bayesian ROC curves. 'ROCModels' helps researchers identify and implement ROC inference methods most suitable for their data. See the accompanying vignette 'ROCModels_Package_Doc' for a detailed introduction. Alonzo, T. A., and Pepe, M. S. (2002) , Andrews, D. F., and Herzberg, A. M. (1985) , Bamber, D. (1975) , Cox, D. R. (1972) , Cox, D. R. (1975) , DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988) , Dorfman, D. D., and Alf, E. (1969) , Dorfman, D. D., Berbaum, K. S., and Metz, C. E. (1997) , Erkanli, A., Sung, L., and Stamey, J. D. (2006) , Faraggi, D., and Reiser, B. (2002) , Ghebremichael, M., and Habtemicael, S. (2018) , Ghebremichael, M., and Michael, H. (2024) , Ghebremichael, M., Michael, H., Tubbs, J., and Paintsil, E. (2019) , Gönen, M., and Heller, G. (2010) , Gopalakrishnan, V., Bose, E., Nair, U., Cheng, Y., and Ghebremichael, M. (2020) , Green, D. M., and Swets, J. A. (1966, ISBN:0471324205), Gu, J., and Ghosal, S. (2009) , Gu, Y., Ghosal, S., and Roy, A. (2008) , Guidoum, A. C. (2020) , , Guo, B. (2015) , Hanley, J. A., and McNeil, B. J. (1982) , Hsieh, F., and Turnbull, B. W. (1996) , Hussain, E. (2012) , Ishwaran, H., and James, L. F. (2002) , Jokiel-Rokita, A., and Topolnicki, R. (2020) , Krzanowski, W. J., and Hand, D. J. (2009) , Kundu, D., and Gupta, R. D. (2006) , Lloyd, C. J. (1998) , Lehmann, E. L. (1953) , Metz, C. E., Herman, B. A., and Shen, J. H. (1998) , Pepe, M. S. (2003) , Pundir, S., and Amala, R. (2014) , Silverman, B. W. (2018) , Yeo, I. K., and Johnson, R. A. (2000) , Zhou, X. H., McClish, D. K., and Obuchowski, N. A. (2009) , Zou, K. H., Hall, W. J., and Shapiro, D. E. (1997) . Package: r-cran-rocngo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rocngo_0.1.0-1.ca2604.1_all.deb Size: 236196 MD5sum: a135a773ef8898732cc4a34c25df72f8 SHA1: 6e35afd757171c01dfcc5277fe58cf5499b1b16f SHA256: 4983169d1129624c0a4746b40e6c4e1a71dda0401b15791642e982c3dbfd1747 SHA512: d1e167223b4dc534a2d859bc45b8cd25b7b67509604aee6400e0a6d53f754a3cb85f07e190ddb47a7a78522f635fcc69f84801c447392fad585b0748ca3ef065 Homepage: https://cran.r-project.org/package=ROCnGO Description: CRAN Package 'ROCnGO' (Fast Analysis of ROC Curves) A toolkit for analyzing classifier performance by using receiver operating characteristic (ROC) curves. Performance may be assessed on a single classifier or multiple ones simultaneously, making it suitable for comparisons. In addition, different metrics allow the evaluation of local performance when working within restricted ranges of sensitivity and specificity. For details on the different implementations, see McClish D. K. (1989) , Vivo J.-M., Franco M. and Vicari D. (2018) , Jiang Y., et al (1996) , Franco M. and Vivo J.-M. (2021) and Carrington, André M., et al (2020) . Package: r-cran-rocnit Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rocnit_1.0-1.ca2604.1_all.deb Size: 14346 MD5sum: b7c3c6ed47c9871c759a7657018ad73c SHA1: d69b601eb809987db214f8c24224fc191de74fac SHA256: 34c5fb55d5fb41aefb5f67baad7e4e57d8e715065c7d0be9866b02fe9055adc2 SHA512: 629b58b06d9a326c454a60fe9ee9f58b7dd4a3a1488ee24ca8eb9d231363a9a00abc9b9f82c075fe80dc46be56ab30dfded10409d6ef3f3e2359bccd8945d1c2 Homepage: https://cran.r-project.org/package=rocNIT Description: CRAN Package 'rocNIT' (Non-Inferiority Test for Paired ROC Curves) Non-inferiority test and diagnostic test are very important in clinical trails. This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test. Package: r-cran-rocnp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rocnp_0.1.0-1.ca2604.1_all.deb Size: 38098 MD5sum: 6c3bfc3e6291e3e2085ed9a1a276cf48 SHA1: b326e4fd1fd4b35f4c0c6247bf68a56feaf9ca6a SHA256: 69d5adcf99789958e70238ccdd78ca602745496bcaef3f2a1086eea9fbfff300 SHA512: cce4ab1fdd39254a50b65b3929eb9c8ad2c7a9b75d0c223954800a58a99eb8065ec63f10491e90ea48be048c1c01b6aab2d00647ae95c888fc511df1635d7252 Homepage: https://cran.r-project.org/package=rocnp Description: CRAN Package 'rocnp' (Work with Romanian Personal Numeric Codes PNC / CNP) A set of tools for working with Romanian personal numeric codes. The core is a validation function which applies several verification criteria to assess the validity of numeric codes. This is accompanied by functionality for extracting the different components of a personal numeric code. A personal numeric code is issued to all Romanian residents either at birth or when they obtain a residence permit. Package: r-cran-rocnreg Architecture: all Version: 1.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-np, r-cran-matrix, r-cran-moments, r-cran-nor1mix, r-cran-spatstat.univar, r-cran-lattice, r-cran-mass, r-cran-pbivnorm Filename: pool/dists/resolute/main/r-cran-rocnreg_1.0-9-1.ca2604.1_all.deb Size: 700606 MD5sum: 8071335db15851c6fe168f1d039b8056 SHA1: bf27b8658b097d02ddce3f4756942359e5bb625d SHA256: ab5f17c33b851416cdd19cfd8cea45dac57d315dd9bb5f0acf4a364ed2df3ebb SHA512: f948b7af8743330797c58d0d53a4c853181116ada06d74ba5a0d6ce1e13f30a181da3e9f2612a7fb14928f183cd163a025282cf1f9a6a6ff4967e59a31ee3532 Homepage: https://cran.r-project.org/package=ROCnReg Description: CRAN Package 'ROCnReg' (ROC Curve Inference with and without Covariates) Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) ; Faraggi, D. (2003) ; Gu, J. et al. (2008) ; Inacio de Carvalho, V. et al. (2013) ; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2022) ; Janes, H., and Pepe, M.S. (2009) ; Pepe, M.S. (1998) ; Rodriguez-Alvarez, M.X. et al. (2011a) ; Rodriguez-Alvarez, M.X. et al. (2011a) . Please see Rodriguez-Alvarez, M.X. and Inacio, V. (2021) for more details. Package: r-cran-rocpsych Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reportroc, r-cran-proc Filename: pool/dists/resolute/main/r-cran-rocpsych_1.4-1.ca2604.1_all.deb Size: 48096 MD5sum: de0be2e19c5d6cf006dd5c1f48793790 SHA1: 88952fc3d40e09034142aad2a77b1cdf207af2c2 SHA256: 4086d541f478b38018e2a3fff8e15e44f2e4aca02ed342857340ac7de31ddb41 SHA512: 81e0b3793a83dc6296efbd00b952abd13c8d17c362095f06a6ea9e60107565dea10c226b889ef94e8e8edd04a1bb2a76d8496620fe3b2b32088ff02c1f1e2a06 Homepage: https://cran.r-project.org/package=ROCpsych Description: CRAN Package 'ROCpsych' (Compute and Compare Diagnostic Test Statistics Across Groups) Functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) , (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) , and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables. Package: r-cran-rocr Architecture: all Version: 1.0-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gplots Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rocr_1.0-12-1.ca2604.1_all.deb Size: 462200 MD5sum: 38335d853f3387bc5f709f3881141039 SHA1: 0da221a8d526a6041fd1b6d5f1ca5dc797af6213 SHA256: eeda897bff6f67052013361c1599401cce1189e04ef07479dad93edf4e7dd606 SHA512: 785c5aacd855fc46c3bd7857bc8e9feb4781cafbb87d97ebe55d2b6c593042c6eac59a45f9c59f8ba6aac3a1a83a570f21ceeeecc7fcb3c9f16dde37509e68c9 Homepage: https://cran.r-project.org/package=ROCR Description: CRAN Package 'ROCR' (Visualizing the Performance of Scoring Classifiers) ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parameterized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parameterization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters. Package: r-cran-rocrater Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-jsonlite, r-cran-lifecycle, r-cran-zip Suggests: r-cran-fs, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rocrater_0.1.0-1.ca2604.1_all.deb Size: 209020 MD5sum: 1236952be5555c23c63bac06df2313bb SHA1: 3a6e7d31de96ada9b84eab816fb186058955f595 SHA256: bae08fc401e8a493e559307df0876855746d44e40ab35fcf0639035116ce43a9 SHA512: 87b3499d738eaf31fb2dd5d5261e96b22dda4e8866302669c97adf179f9dbb26c3c8ac5ddb44c84b2d1b15e26f0a1c4be583cd856d181ab5abb9dcd005ee7666 Homepage: https://cran.r-project.org/package=rocrateR Description: CRAN Package 'rocrateR' (Tools for Creating and Manipulating RO-Crates) Provides tools for creating, manipulating and reading Research Object Crates (RO-Crates), a lightweight approach to packaging research data with structured metadata. Includes utilities for metadata generation, entity management, validation and reading existing RO-Crates following the specification . 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Computes eROC curve and the corresponding AUC for imperfect reference standard. Package: r-cran-rocsi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass Filename: pool/dists/resolute/main/r-cran-rocsi_0.1.0-1.ca2604.1_all.deb Size: 62260 MD5sum: 40f9ccc682bf19fae8d6d75336d13bfd SHA1: 63639b6fceecc794f30ab055e1ba870e0d9eb760 SHA256: b2abc798597ef4a459599ad160dffca811c542e29222ece08e89160f7685f41b SHA512: d2344ef91a314206b399f0833688cf0baba825def176a1330d35ab23df0f14c8ca3189bde7487ac3f7c40c0df484e07362ab0aa42eb5b989b833a89f7e787f8e Homepage: https://cran.r-project.org/package=ROCSI Description: CRAN Package 'ROCSI' (Receiver Operating Characteristic Based Signature Identification) Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control). Package: r-cran-rocsurf Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rocsurf_0.1.1-1.ca2604.1_all.deb Size: 162818 MD5sum: 7df9e4ea714e6f1497655d0be16576f7 SHA1: a3b22ec794c8d2d793cddb3bf617089ff5feb9cc SHA256: ff639987a0165102907431fb8265efab8829b2dfbec7f86e8ade97f18b9495bd SHA512: 162a71b948ca6660843e5fe5b79bba22e888289a19fb5ea7567b7f2865a0d22dff8d152a333f0599c2b8852952f3907f43f6aab5a07995710c7b8cd0d94aef33 Homepage: https://cran.r-project.org/package=ROCsurf Description: CRAN Package 'ROCsurf' (ROC Surface Analysis Under the Three-Class Problems) Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the proposed distributions. 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Package: r-cran-rocsurvcomp Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 683 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-interval, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pwexp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rocsurvcomp_0.1.2-1.ca2604.1_all.deb Size: 501588 MD5sum: 76590bd0052f53a63cbc3fd3b21e6301 SHA1: bf1156f909ea9cb0f20a5bb1d3290014d8c5ac52 SHA256: 42f8f9d815ad662b4724922f09fcff2902e279336893a42ef34a56e174b4d013 SHA512: 5aff021e546cd9679966e06bb058baba7f2086e5c7e726410a352f066333d0e68924c1644864c84b2cac09a9f959b7079ef3d5fec9cd44c696552fc42ed171fb Homepage: https://cran.r-project.org/package=ROCsurvcomp Description: CRAN Package 'ROCsurvcomp' (ROC-Based Methods for Comparing Survival Distributions withRight, Left, and Doubly Censored Data) Implements nonparametric and semiparametric methods for comparing two survival distributions under non-proportional hazards (non-PH). The methods are based on the Receiver Operating Characteristic (ROC) curve length (Bantis et al. (2021) ) and the overlap coefficient (OVL) (Franco-Pereira et al. (2021) ), as well as a joint ROC length-OVL-based approach. These methods do not require prior knowledge of the underlying non-PH pattern and can accommodate right, left, and doubly censored data. 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The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) . 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Enables automated selection of the distribution families to be fit when smoothing ROC curves via the population probability density function estimation strategy described by Leeflang et al. (2008) , as well as generation of diagnostic performance and cutoff estimates from the resultant smoothed curves. 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Package: r-cran-roi.plugin.quadprog Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-roi, r-cran-slam Filename: pool/dists/resolute/main/r-cran-roi.plugin.quadprog_1.0-1-1.ca2604.1_all.deb Size: 17120 MD5sum: 40a80cc3eba8eaaecc80523ca6e7acac SHA1: bde365fa0db3c19985479a12b0ecad61296bd303 SHA256: 8d87f1a72282ee775c04c0d0cf7b4b539604f2c0ff1fb747139f5dd0d5c014b4 SHA512: b77761417cb2641487c557f7551c150c6b059c2217d24d9b1d9af210fbc3c80348b0e1169a59986593ce7d895e6a972458b51fae227c1e79141d08a6ec6b7d8e Homepage: https://cran.r-project.org/package=ROI.plugin.quadprog Description: CRAN Package 'ROI.plugin.quadprog' ('quadprog' Plug-in for the 'R' Optimization Infrastructure) Enhances the R Optimization Infrastructure ('ROI') package by registering the 'quadprog' solver. It allows for solving quadratic programming (QP) problems. Package: r-cran-roi.plugin.scs Architecture: all Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-slam, r-cran-roi, r-cran-scs Filename: pool/dists/resolute/main/r-cran-roi.plugin.scs_1.1-2-1.ca2604.1_all.deb Size: 36848 MD5sum: e4747391d381ee2df100883f846fd2fe SHA1: 35ec2780531ed28744f5b987efb788ba05acabc5 SHA256: 8d404a2e883b239cf117334ff34bfdebcdc26bf6e52c581d1d612a1e07845915 SHA512: 158167737e8fd87f2417971fdaea1c6043649333f6474f99bfc743b32bebb19f03cc81e6e3f17e90ccd5996548e1e82bb63bd49ac46d7f73fd5dd6c0ae3732a9 Homepage: https://cran.r-project.org/package=ROI.plugin.scs Description: CRAN Package 'ROI.plugin.scs' ('SCS' Plug-in for the 'R' Optimization Infrastructure) Enhances the 'R' Optimization Infrastructure ('ROI') package with the 'SCS' solver for solving convex cone problems. Package: r-cran-roi.plugin.symphony Architecture: all Version: 1.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-roi, r-cran-rsymphony, r-cran-slam Filename: pool/dists/resolute/main/r-cran-roi.plugin.symphony_1.0-0-1.ca2604.1_all.deb Size: 16322 MD5sum: 1d262cd0113319a58cf68c410dfeff95 SHA1: bdfa23138e5d411a01c6ffd31bab162bf72ba82f SHA256: d57f964eeaab25f8169fca2a4e66e66405c327645b04b911ffe14e93d95ff37b SHA512: 1d6b48f3300dcea518245b627183636c7d5fc5b72735f57a99c378f92c49ce75dc2c9debda097ec8aeeeab78baab102beb7efe3e007920a4e3fcc7d84cc3f796 Homepage: https://cran.r-project.org/package=ROI.plugin.symphony Description: CRAN Package 'ROI.plugin.symphony' ('SYMPHONY' Plug-in for the 'R' Optimization Interface) Enhances the R Optimization Infrastructure ('ROI') package by registering the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for solving mixed integer linear programming (MILP) problems as well as all variants/combinations of LP, IP. Package: r-cran-roi Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-registry, r-cran-slam, r-cran-checkmate Suggests: r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-roi_1.0-2-1.ca2604.1_all.deb Size: 472248 MD5sum: 5e0bc72e9e017303e099ce794d5863e9 SHA1: 1f9a761fc6412ad0b5af98b1ddd5744c458ad79b SHA256: 58cf6a9b87306e2fe83a12365eb31b4faf55a7eb4aa4866e059ac622120901a4 SHA512: 078c3f517f98053b54d6bf84b14495f2be25fd74a25977b5a1c377420af753efb9babed316165c2d10a90c09149e427fac501a45c4aff3d2ea93e5c2c272218a Homepage: https://cran.r-project.org/package=ROI Description: CRAN Package 'ROI' (R Optimization Infrastructure) The R Optimization Infrastructure ('ROI') is a sophisticated framework for handling optimization problems in R. 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Package: r-cran-rolap Architecture: all Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17649 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dm, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-snakecase, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-when, r-cran-xlsx Suggests: r-cran-dbi, r-cran-dbplyr, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-maps, r-cran-pander, r-cran-pivottabler, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rolap_2.5.2-1.ca2604.1_all.deb Size: 2342684 MD5sum: 11b96ddfbf79cdd2513025072f5c518a SHA1: c3e0b930a25d53434d9fd8f14cb23720959afe78 SHA256: 7e1bb134ce5e0302993df3ee7972902e93d228aac5b36d84b1fe072a0fedf17a SHA512: 21ae14826f7a1d956fbf6077ea1ccdc147264521bdb560d8f13eb8401fcd5bdbc3df057c4a1426972c7f0a4c6334c679bb6ac87ceb5cc903d833e197777b567b Homepage: https://cran.r-project.org/package=rolap Description: CRAN Package 'rolap' (Obtaining Star Databases from Flat Tables) Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. Frequently, the operations to be performed aim to transform a flat table into a ROLAP (Relational On-Line Analytical Processing) star database. The main objective of the package is to allow the definition of these transformations easily. The implementation of the multidimensional database obtained can be exported to work with multidimensional analysis tools on spreadsheets or relational databases. Package: r-cran-rollama Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4820 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-withr Suggests: r-cran-base64enc, r-cran-covr, r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-s7, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rollama_0.3.0-1.ca2604.1_all.deb Size: 3337882 MD5sum: cf7f29a1de1e0f30e818d3813f6791da SHA1: 883410d4b7cd5de4e3ff56782a44f49d1353f2da SHA256: eabc79092135c80ef44e7cfc1314f3ddb66476860d657b7c48c3834a27c913dc SHA512: 3747cee252e55d76d0dca53b6923ec31a10dd2c60653eaea8c81bbe83969e5da9818ae939c30d820ec469b1729aff4d25dd005f558d34d476250f7336253f05a Homepage: https://cran.r-project.org/package=rollama Description: CRAN Package 'rollama' (Communicate with 'Ollama' to Run Large Language Models Locally) Wraps the 'Ollama' API, which can be used to communicate with generative large language models locally. Package: r-cran-rollbar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr Filename: pool/dists/resolute/main/r-cran-rollbar_0.1.0-1.ca2604.1_all.deb Size: 22504 MD5sum: cdfd30253dbaf75970fe98a7b3d5bcbf SHA1: a25a0171dd79c4defb736644757d3fe63a7b3ecb SHA256: 729d9ebabdcc2fd64d733ac871ebecb7ade42e47be665ed7e565fef3bf06bf06 SHA512: cfd1ed4503b515f39238c68e5b66fb06f9ff080f798b95e4498eda6b22a6a79672eef0e1a6bf0a90174a05c489a19f497c671169056caf6a5ac2d30a5ca343e6 Homepage: https://cran.r-project.org/package=rollbar Description: CRAN Package 'rollbar' (Error Tracking and Logging) Reports errors and messages to Rollbar, the error tracking platform . Package: r-cran-rolldown Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-bookdown, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rolldown_0.2-1.ca2604.1_all.deb Size: 86254 MD5sum: 3f8dc678a8fe3125049fc217e5d59a0a SHA1: 02d5a3e5a22eeea11da18e82670f9c53281f8c1b SHA256: 3df294fae2338a9f7d4a88703315cb34e68d80526a5a6006b3ad37c3976b1ff5 SHA512: 7332393a694c7d28225db157489b62369f72d35d62e7cb419175561ae0c7d24950aa29be0585e349aa7f89ec96fb66591de78add69ecc2a0c8d76b9629bec13e Homepage: https://cran.r-project.org/package=rolldown Description: CRAN Package 'rolldown' (R Markdown Output Formats for Storytelling) R Markdown output formats based on JavaScript libraries such as 'Scrollama' () for storytelling. Package: r-cran-rollout Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom.mixed, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-pbapply, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-lme4, r-cran-lmertest, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-rollout_0.1.0-1.ca2604.1_all.deb Size: 87766 MD5sum: f29f46c39b093c4e95534b951b8bb8a8 SHA1: 05528c676e3ab434385bfdac036c4704300894e6 SHA256: db125b156b2a47697472d0267dc12aea8903f7f5e891e53850555945247a46b9 SHA512: 9a3785ff1c5cefe46bbf7a93af7bc6fd592b31a207cae7d6c9965e2b8f49921f1ebf1f43a50ce1b7bce96ed225e4b940e96ed44416f54291c09ff2651a9b464b Homepage: https://cran.r-project.org/package=rollout Description: CRAN Package 'rollout' (Tools for Designing, Simulating, and Analyzing ImplementationRollout Trials) Provides a unified framework for designing, simulating, and analyzing implementation rollout trials, including stepped wedge, sequential rollout, head-to-head, multi-condition, and rollout implementation optimization designs. The package enables users to flexibly specify rollout schedules, incorporate site-level and nested data structures, generate outcomes under rich hierarchical models, and evaluate analytic strategies through simulation-based power analysis. By separating data generation from model fitting, the tools support assessment of bias, Type I error, and robustness to model misspecification. The workflow integrates with standard mixed-effects modeling approaches and the tidyverse ecosystem, offering transparent and reproducible tools for implementation scientists and applied statisticians. Package: r-cran-rollup Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-sparklyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rollup_0.1.0-1.ca2604.1_all.deb Size: 322558 MD5sum: 3c643f0b5971087a89f2fa5323208680 SHA1: d9522e4fb9552133fc94ee328ee9652c04f79186 SHA256: 440950e3bcd1f1686acb996633a35b1f84e197757c2b3130272d6d7ed9e893be SHA512: e5b0d51b326c6091e58afad03ae85699672274b001d5b2f28c1b69853d430a27bfdb9ad2e779a76c677b668a0e5fb9e3b9c787a45f76224db1618de89c078c3c Homepage: https://cran.r-project.org/package=rollup Description: CRAN Package 'rollup' (A Tidy Grouping Set Aggregation) A Tidy implementation of 'grouping sets', 'rollup' and 'cube' - extensions of the 'group_by' clause that allow for computing multiple 'group_by' clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" . Package: r-cran-rolluptree Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rolluptree_0.4.1-1.ca2604.1_all.deb Size: 267856 MD5sum: 5910f5b71a36afb87c5f03a678b55007 SHA1: f6e315181fb1b65bcbe7c49aa8bbacf8a769b93c SHA256: d47d8c78c7fdacfc61ebdaa7cc6de433ab830436aef18b9c08822f5fa1871c51 SHA512: 7a04b6c403b4aab352eeeccf32b746ff8370197f6fcd8b9b9097cafc82f0bd0d6b057f22d5c9d8dbb553f5265535b2850b6c5677ba279b0115a03f30e9b5bdf4 Homepage: https://cran.r-project.org/package=rollupTree Description: CRAN Package 'rollupTree' (Perform Recursive Computations) Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the 'igraph' package of Csárdi, et al. (2006 ). Package: r-cran-roloc Architecture: all Version: 0.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-roloc_0.1-2-1.ca2604.1_all.deb Size: 115278 MD5sum: 1e591251f4f1b06d9ebed2845b8030e6 SHA1: cc2de37d61d61768adb3eb0f524fb210346551f9 SHA256: 98a24e6a8251083b7c0e992f0f5cee85993418a953a793264b8621c4dcaa0fcb SHA512: 276243505648feaebe6e2d2404cb53ef4b3fcf2c45c81abd1bd7eeb4e96e56c54e2cbdc37b2bf88c6af443639578353f6263c9d69dc6239e193b42678a315d0c Homepage: https://cran.r-project.org/package=roloc Description: CRAN Package 'roloc' (Convert Colour Specification to Colour Name) Functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion. Package: r-cran-rolocisccnbs Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6090 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-roloc, r-cran-colorspace Filename: pool/dists/resolute/main/r-cran-rolocisccnbs_0.1-1.ca2604.1_all.deb Size: 5273502 MD5sum: 5859c886395d04bf6dde549e2447323f SHA1: 12bffc6c7de45273d603f5c2182e261d13d04e6e SHA256: 79b1490c5f359994de2452a7e8a4e6035748736ded55bd5569e3c7d3fd1b00d3 SHA512: 0186cf07097129b0968b0b5449cbaf7cd0abe5bdb080c58cf140ee26dd88793fe8b728d1dd4eacc184e520397b80b1eb5350c0fbc38286f0467c24aa4612ae14 Homepage: https://cran.r-project.org/package=rolocISCCNBS Description: CRAN Package 'rolocISCCNBS' (A Colour List and Colour Metric Based on the ISCC-NBS System ofColor Designation) A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names. Package: r-cran-rologit Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-evd Filename: pool/dists/resolute/main/r-cran-rologit_0.1.3-1.ca2604.1_all.deb Size: 125778 MD5sum: ebf3390cbf0c21230125c9b204bcb479 SHA1: 7de4468a8e3d8b54b1067dc27e6e4e2a7e8268ed SHA256: 1c5059518b54b766ff4832bf4e575f0bc4635608108e0e29c41f1076e3eba230 SHA512: 2cf86a8212040d9debba25d9ffd2d166bb610655a32c876083037159ce43cf2be3e397e6816dfc009de921af7639b56df7a11128a14dcbaabadc6ed784e2a543 Homepage: https://cran.r-project.org/package=ROlogit Description: CRAN Package 'ROlogit' (Fit Rank-Ordered Logit (RO-Logit) Model) Implements the rank-ordered logit (RO-logit) model for stratified analysis of continuous outcomes introduced by Tan et al. (2017) . Model diagnostics based on the heuristic residuals and estimates in linear scales are available from the package, and outcomes with ties are supported. Package: r-cran-rolr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rolr_1.0.0-1.ca2604.1_all.deb Size: 47716 MD5sum: 9b3a5c31490e262aa036ee3c4112f12f SHA1: 62086a4d785e6f0b79d82b777305a90760047f79 SHA256: 45c2e10189849c65379201de97321a18563a00dee50b0aef356c528d0849923f SHA512: 32fe7ccb21967e543819e5499ad3e980b1393a5670c92a7323e89dd20f02dcaef64038728763b004e9f7525c7b9ab5bf4743e411327568a266b1af7962beab7a Homepage: https://cran.r-project.org/package=rolr Description: CRAN Package 'rolr' (Finding Optimal Three-Group Splits Based on a Survival Outcome) Provides fast procedures for exploring all pairs of cutpoints of a single covariate with respect to survival and determining optimal cutpoints using a hierarchical method and various ordered logrank tests. Package: r-cran-rolwinmulcor Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-zoo, r-cran-pracma, r-cran-colorspace, r-cran-scales Filename: pool/dists/resolute/main/r-cran-rolwinmulcor_1.2.0-1.ca2604.1_all.deb Size: 127446 MD5sum: aa5d65e768722f90de42feb443ccb21a SHA1: fa3bf9d62f4b7292509e97f1adc513c3a4637455 SHA256: 97eeae2a43caf1f091df709632dbb5185eb41d30ff97a24a8a2e82347e22e8c6 SHA512: 84a7e680480af0a8bacf28ff787c55722bc05fc5cad3bf3f449ccaa37bcecf3c12c720e54dc8e9cc72967c7398d14f02f194367e3e98af2384a56e1467280c10 Homepage: https://cran.r-project.org/package=RolWinMulCor Description: CRAN Package 'RolWinMulCor' (Subroutines to Estimate Rolling Window Multiple Correlation) Rolling Window Multiple Correlation ('RolWinMulCor') estimates the rolling (running) window correlation for the bi- and multi-variate cases between regular (sampled on identical time points) time series, with especial emphasis to ecological data although this can be applied to other kinds of data sets. 'RolWinMulCor' is based on the concept of rolling, running or sliding window and is useful to evaluate the evolution of correlation through time and time-scales. 'RolWinMulCor' contains six functions. The first two focus on the bi-variate case: (1) rolwincor_1win() and (2) rolwincor_heatmap(), which estimate the correlation coefficients and the their respective p-values for only one window-length (time-scale) and considering all possible window-lengths or a band of window-lengths, respectively. The second two functions: (3) rolwinmulcor_1win() and (4) rolwinmulcor_heatmap() are designed to analyze the multi-variate case, following the bi-variate case to visually display the results, but these two approaches are methodologically different. That is, the multi-variate case estimates the adjusted coefficients of determination instead of the correlation coefficients. The last two functions: (5) plot_1win() and (6) plot_heatmap() are used to represent graphically the outputs of the four aforementioned functions as simple plots or as heat maps. The functions contained in 'RolWinMulCor' are highly flexible since these contains several parameters to control the estimation of correlation and the features of the plot output, e.g. to remove the (linear) trend contained in the time series under analysis, to choose different p-value correction methods (which are used to address the multiple comparison problem) or to personalise the plot outputs. The 'RolWinMulCor' package also provides examples with synthetic and real-life ecological time series to exemplify its use. Methods derived from H. Abdi. (2007) , R. Telford (2013) , and J. M. Polanco-Martinez (2020) . Package: r-cran-rolwinwavcor Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-waveslim Filename: pool/dists/resolute/main/r-cran-rolwinwavcor_0.4.0-1.ca2604.1_all.deb Size: 95804 MD5sum: 9bd6b5d64e3684e3d4714ff0a7ce9861 SHA1: 26c74e696ba2635d570fd3387f62e9f7e93f8dfc SHA256: d8a5dfc154d77ef07920633e36e207cb84f604836f0aecc1454bb3f5c3b79c55 SHA512: 03c41da7d7810cfcd5421eb1d245e022bef9bbeb482ba30ea0d2aff208340e54a9ebef98b6ec47b4ddb9e1737753c3b16eb898d014b3e1c2e83ede35230fd2c1 Homepage: https://cran.r-project.org/package=RolWinWavCor Description: CRAN Package 'RolWinWavCor' (Estimate Rolling Window Wavelet Correlation Between Two TimeSeries) Estimates and plots as a heat map the rolling window wavelet correlation (RWWC) coefficients statistically significant (within the 95% CI) between two regular (evenly spaced) time series. 'RolWinWavCor' also plots at the same graphic the time series under study. The 'RolWinWavCor' was designed for financial time series, but this software can be used with other kinds of data (e.g., climatic, ecological, geological, etc). The functions contained in 'RolWinWavCor' are highly flexible since these contains some parameters to personalize the time series under analysis and the heat maps of the rolling window wavelet correlation coefficients. Moreover, we have also included a data set (named EU_stock_markets) that contains nine European stock market indices to exemplify the use of the functions contained in 'RolWinWavCor'. Methods derived from Polanco-Martínez et al (2018) ). Package: r-cran-romdb Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-httr, r-cran-stringr, r-cran-rodbc, r-cran-magick Filename: pool/dists/resolute/main/r-cran-romdb_0.1.0-1.ca2604.1_all.deb Size: 130386 MD5sum: 2a1f117d92c748743b7e827b0981968c SHA1: b0a66939391939dae87ee9004795cea29ecd7b7d SHA256: 6004dadba4c808314e84737255b45fb1968466c0c4392d9dded197a638094851 SHA512: 983f69e62dec0e7c81c06dcad18567a543b3ad1cb47454c7764503ff0a8bcf5db715bfb1edc786f51419b300ee7e25ccca1d228cb6e71f3bd3c3f8e93b0eb6c1 Homepage: https://cran.r-project.org/package=ROMDB Description: CRAN Package 'ROMDB' (Get 'OMDB' API Multiple Information) Load multiple movies, series, actors, directors etc from 'OMDB' API. More information in: . Package: r-cran-rome Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4030 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate, r-cran-stringr, r-cran-ggplot2, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-zip, r-cran-maps, r-cran-sp, r-cran-dplyr, r-cran-ggrepel, r-cran-magrittr, r-cran-geosphere, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-svdialogs, r-cran-shinyjs Filename: pool/dists/resolute/main/r-cran-rome_0.2.3-1.ca2604.1_all.deb Size: 3981946 MD5sum: d487ac56a95c949d5d08493ab3c7a558 SHA1: 5d5c81bf32fd4db3b0514369845d2b8e6880728b SHA256: 7c5b2154dc9f9cb68805b8fae4ad383d9bce31372fc65668d825b5734a3f12b8 SHA512: 736840753944424d2ab45df956be714bb61dde2ab079a57fba901918ee6c44ca497f03801ff83b6ba130bea6c58618bfe7ab2ca7e1cf7408e33385e74e2a5f6d Homepage: https://cran.r-project.org/package=RoME Description: CRAN Package 'RoME' (Multiple Checks on MEDITS Trawl Survey Data) Provides quality checks for MEDITS (International Bottom Trawl Survey in the Mediterranean) trawl survey exchange data tables (TA (Haul data), TB (Catch data), TC (Biological data), TE (Biological individual data), TL (Litter data)). The main function RoME() calls all check functions in a defined sequence to perform a complete quality control of TX (Generic exchange data) data, including header validation, controlled-vocabulary checks, cross-table consistency tests, and biological plausibility checks. No automatic correction is applied: the package detects errors, warns the user, and specifies the type of error to ease data correction. Checks can be run simultaneously on multi-year datasets. An embedded 'shiny' application is also provided via run_RoME_app(). References describing the methods: MEDITS Working Group (2017) . Package: r-cran-romeb Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-romeb_0.1.2-1.ca2604.1_all.deb Size: 58538 MD5sum: 0733877276f4cc797cdcca8f70fe7745 SHA1: facdf37873dd09a56ed36c251fb61f192dfd45fa SHA256: 3d4cc1c0522e32eca1f4875723851c6bed955422202ac473df884838472caaf0 SHA512: 20dabf70ddd57ff6990746adb7f35473025e9be4b5710c55473a5f80f3ae1a208ae9b347755835a51b4c8b1202f07918e243072d676b89f184be448ab9b72172 Homepage: https://cran.r-project.org/package=Romeb Description: CRAN Package 'Romeb' (Robust Median-Based Bayesian Growth Curve Modeling) Implements robust median-based Bayesian growth curve models that handle Missing Completely at Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) missing-data mechanisms, and allow auxiliary variables. Models are fitted via 'rjags' (interface to 'JAGS') and summarized with 'coda'. 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Package: r-cran-romney Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-psych Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-romney_0.1.0-1.ca2604.1_all.deb Size: 60344 MD5sum: cc9478432ef0360745300e3f947b53e1 SHA1: 459ca5d08137f037d8dd07fe1ed5f8c6b914bc8f SHA256: 768d97b4a38b3ca4346a861122be2f8ee203d78204e8aa439a84572a867ce5b9 SHA512: 730619bd9e18605ecf665e414d021caf7429e67c94821b90b9f71ce6fc1738358c0d35bef78d1a453aaa27b4358ddb215fb7150c323fd3d488a060e0d34f6bf1 Homepage: https://cran.r-project.org/package=Romney Description: CRAN Package 'Romney' (Classical Cultural Consensus Analysis) Implements classical cultural consensus analysis with formal, informal, and covariance agreement models, 'UCINET'-aligned minimum-residual factor extraction, competence estimation, and answer-key estimation. 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Package: r-cran-ronfig Architecture: all Version: 0.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-carrier, r-cran-cli Suggests: r-cran-litedown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ronfig_0.0.10-1.ca2604.1_all.deb Size: 29250 MD5sum: 3dff66792f5acbafea281f912f170b6b SHA1: d30def9bbc506c5863ec798ef55c72eacc21c072 SHA256: 449be7d6b511ecf19fa19fb35efc8cdcb326a55968271ba8a4c05906fc0b0f2d SHA512: 4001198ce698b05a7bbb39701e10131aa50932ea7e45a2d3cf4646aa3179d37b89c36f94419f214c9500f1431c6c6204fc187ec3ccf68a395b585eb3a81ff8d9 Homepage: https://cran.r-project.org/package=ronfig Description: CRAN Package 'ronfig' (Load Configuration Values) A simple approach to configuring R projects with different parameter values. Configurations are specified using a reduced subset of base R and parsed accordingly. Package: r-cran-roopsd Architecture: all Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-lmoments, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-roopsd_0.3.9-1.ca2604.1_all.deb Size: 465652 MD5sum: b61fbc25ce2f5f90dac978d244988e26 SHA1: 7371bd891091986be3577f8fc0e9e6666d75a754 SHA256: 8b3298920aac8d89fcdeeee9cc326d4d50e95ffc72710d3faef18d434c2a4bed SHA512: 9ef253ce64b63aa8a82e2b5a96bd9983361d210796e03f1e7c267323f4368e05df82def99aa90fc5af32ed6b377daa0d96b6b7daed5ebe128b73ce0b90bbfd3c Homepage: https://cran.r-project.org/package=ROOPSD Description: CRAN Package 'ROOPSD' (R Object Oriented Programming for Statistical Distribution) Statistical distribution in OOP (Object Oriented Programming) way. This package proposes a R6 class interface to classic statistical distribution, and new distributions can be easily added with the class AbstractDist. A useful point is the generic fit() method for each class, which uses a maximum likelihood estimation to find the parameters of a dataset, see, e.g. Hastie, T. and al (2009) . Furthermore, the rv_histogram class gives a non-parametric fit, with the same accessors that for the classic distribution. Finally, three random generators useful to build synthetic data are given: a multivariate normal generator, an orthogonal matrix generator, and a symmetric positive definite matrix generator, see Mezzadri, F. (2007) . Package: r-cran-root Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 791 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rpart, r-cran-gbm, r-cran-withr, r-cran-rpart.plot Suggests: r-cran-mlbench, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ragg Filename: pool/dists/resolute/main/r-cran-root_0.1.1-1.ca2604.1_all.deb Size: 571842 MD5sum: 8905f5d158ac19ed651b1a1e5d508d98 SHA1: 6870d7c37fd065537d9a3bedf2db9803270b1873 SHA256: dbe5e6aadacb30078d8aa6de113ca5aef1ea7e6d2f932deb5b127f38bbb23a9d SHA512: 14109a4852741520f2cfd9df7b7c938ba62197706d39e4d4783b4ae52665d6a6ac844780f229f516dfecf679dd674c80b743c7decee68779ef45b487e63c0743 Homepage: https://cran.r-project.org/package=ROOT Description: CRAN Package 'ROOT' (Rashomon Set of Optimal Trees) Implements a general framework for globally optimizing user-specified objective functionals over interpretable binary weight functions represented as sparse decision trees, called ROOT (Rashomon Set of Optimal Trees). It searches over candidate trees to construct a Rashomon set of near-optimal solutions and derives a summary tree highlighting stable patterns in the optimized weights. ROOT includes a built-in generalizability mode for identifying subgroups in trial settings for transportability analyses (Parikh et al. (2025) ). Package: r-cran-roots Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-animation, r-cran-rarpack, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-roots_1.0-1.ca2604.1_all.deb Size: 122976 MD5sum: a0ce2184c702aa1e211eefe301667fcc SHA1: 459bab11c041ceccf8aca1bdceffbcbbe0e50a63 SHA256: 717c55933730d4507b3ffdd06564e4414d220988d8d243e02e668d7dffd4b956 SHA512: ddab773600d7174eb377b1af334cdf0f7dc3bb6f9b8e09958ab203b1ca695a820480ce28d7cd0da0114bd029d98c38e6a94feee2cff914ba783bd63a182da2d7 Homepage: https://cran.r-project.org/package=roots Description: CRAN Package 'roots' (Reconstructing Ordered Ontogenic Trajectories) A set of tools to reconstruct ordered ontogenic trajectories from single cell RNAseq data. Package: r-cran-rootscanr Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-png, r-cran-abind Filename: pool/dists/resolute/main/r-cran-rootscanr_0.0.1-1.ca2604.1_all.deb Size: 182392 MD5sum: 9bb19c2206b76be95db346696e8d9016 SHA1: 76f26d967b56998fd7ee37e27f1f36120216480e SHA256: 5af77bab102f9fc057f56634f25c2b3cca41b325f8f317f0fb9dbc2533707879 SHA512: 5378d15c0a9195c68243084ca82a79554c65c3659a2e499362d02421c45ed2581c22350ef72bb1ff8ba1b0fc9aff7fda17db10ebe6d87a872407b5219790c6bd Homepage: https://cran.r-project.org/package=RootscanR Description: CRAN Package 'RootscanR' (Stitching and Analyzing Root Scans) Minirhizotrons are widely used to observe and explore roots and their growth. This package provides the means to stitch images and divide them into depth layers. Please note that this R package was developed alongside the following manuscript: Stitching root scans and extracting depth layer information -- a workflow and practical examples, S. Kersting, L. Knüver, and M. Fischer. The manuscript is currently in preparation and should be citet as soon as it is available. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM – Artificial Intelligence in Farming funded by the German Federal Ministry of Research, Technology and Space (No. 03WIR4805). Package: r-cran-rootsextremainflections Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iterators, r-cran-foreach, r-cran-doparallel, r-cran-inflection Filename: pool/dists/resolute/main/r-cran-rootsextremainflections_1.2.5-1.ca2604.1_all.deb Size: 175980 MD5sum: f370bc74f292c306632919d4913c18cd SHA1: 10e2e000be9343620c277f8e9705ee26bc5b4baa SHA256: 13ad1d44279b99611b571a5e2d36bf263567765772cdc8062c86013146049975 SHA512: b36f0c49197bd3610b067036b2eb7d08d0b891e5e584328d83af38f7ebdf122a7a9078a5cbbbec50a36cd72e5a1f81637b019ddc13c0ceba21eff869738d4190 Homepage: https://cran.r-project.org/package=RootsExtremaInflections Description: CRAN Package 'RootsExtremaInflections' (Finds Roots, Extrema and Inflection Points of a Curve) Implementation of Taylor Regression Estimator (TRE), Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE), Integration Extreme Finding Estimator (IEFE) and Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve . Christopoulos, DT (2019) . Christopoulos, DT (2016) . Christopoulos, DT (2016) . Christopoulos, DT (2014) . Package: r-cran-rope Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rope_1.0-1.ca2604.1_all.deb Size: 963202 MD5sum: 624d585486b20db45a0d0ee0c2e2a1d8 SHA1: 7acb8b8765c03e71b12c005bb3f374a6f0e3de9f SHA256: 6d429cd5736ff6f71b0820c4a08b9c0a442a64169e1d27c24ce8147e4c1dcacf SHA512: bf6d52e0050a38c9b7d8c78b704d64d58ba20e11df8a4128fd8ae1071606033366a7fdbb585a988608612ee168ca0d759a3fd276e5c219a263734a909298934b Homepage: https://cran.r-project.org/package=rope Description: CRAN Package 'rope' (Model Selection with FDR Control of Selected Variables) Selects one model with variable selection FDR controlled at a specified level. A q-value for each potential variable is also returned. The input, variable selection counts over many bootstraps for several levels of penalization, is modeled as coming from a beta-binomial mixture distribution. 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Package: r-cran-ropencvlite Architecture: all Version: 4.130.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ropencvlite_4.130.0-1.ca2604.1_all.deb Size: 63922 MD5sum: 50a9dc5f61468063923587d4068fb8f4 SHA1: 12bc9732bdd5dd9360c975d95e7a0a3bf3bb5788 SHA256: faba27cbb1d9ef05155aedefac3e87762f95b4b59b4f6d58a27ff1ead9f30e49 SHA512: e86323867ccc28addc09440399b84945133f3081de61fe8f36913563243711c684aec4af1b5d12de5e2eb586bb34f066295620f1a39f1f1f62fcf49eacc394cc Homepage: https://cran.r-project.org/package=ROpenCVLite Description: CRAN Package 'ROpenCVLite' (Helper Package for Installing 'OpenCV') Installs 'OpenCV' for use by other packages. 'OpenCV' is library of programming functions mainly aimed at real-time computer vision. 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Package: r-cran-ropensecretsapi Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjsonio, r-cran-rcurl Filename: pool/dists/resolute/main/r-cran-ropensecretsapi_1.0.1-1.ca2604.1_all.deb Size: 33704 MD5sum: 3c7826200868606c35e71c462ee50d01 SHA1: 03a7ef40607c808c6bd2f3eda4424110b034e15b SHA256: d9287a4575f0e304e752fc0ccefbb3516ca3a569c22e5d9b72ab437ad58153fd SHA512: 30732ce1fa3ec5ca5a04b8a9e12070a2c6f3ee747edec488adecd378299ac15679dae758c3629e734b1a996f3ac762ab5f95d3923e87de0ea4bcb789770c1e2a Homepage: https://cran.r-project.org/package=ropensecretsapi Description: CRAN Package 'ropensecretsapi' (R Package for the OpenSecrets.org API) An R package for the OpenSecrets.org web services API. Package: r-cran-ropenweathermap Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-rcurl, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-ropenweathermap_1.1-1.ca2604.1_all.deb Size: 19244 MD5sum: 118af47d2c5354dd3405fd0265377b16 SHA1: 1136428fd8a771e92197bfbe079584baef2391fd SHA256: 9731a2fd79586aa45d435d2519a11c6ee67a8ab3421e7e634ee7923aaaaa1a36 SHA512: acfc960275a2ea7cc9cb4a033d5d368f7fb8319c1f73a834ccacbd074f8394ddaa663df9179682817d345f080293046a0e81b368944ac870aa4a411dd221ce8a Homepage: https://cran.r-project.org/package=ROpenWeatherMap Description: CRAN Package 'ROpenWeatherMap' (R Interface to OpenWeatherMap API) OpenWeatherMap (OWM) is a service providing weather related data. This package can be used to access current weather data for one location or several locations. It can also be used to forecast weather for 5 days with data for every 3 hours. Package: r-cran-roperators Architecture: all Version: 1.3.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magrittr, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-prettydoc, r-cran-rvest Filename: pool/dists/resolute/main/r-cran-roperators_1.3.14-1.ca2604.1_all.deb Size: 176446 MD5sum: 8029f8911ae3fe7f5b3cb23fe30d1894 SHA1: abcf65fd9a5d9a118a19b1a1b3cfb80251fbd3e5 SHA256: 3941e0c5791ff442aeb97bcd10f8617e34931e23786762cf1fc2038a9f1c7892 SHA512: 39206c3246c5928a8809455a29f7c4b87eae8bc3e8e7c504ac833af7c204476f99bc145167bacb7f4476b092f08fb7574568b26ac4b7779821849235ec5fe7ab Homepage: https://cran.r-project.org/package=roperators Description: CRAN Package 'roperators' (Additional Operators to Help you Write Cleaner R Code) Provides string arithmetic, reassignment operators, logical operators that handle missing values, and extra logical operators such as floating point equality and all or nothing. The intent is to allow R users to write code that is easier to read, write, and maintain while providing a friendlier experience to new R users from other language backgrounds (such as 'Python') who are used to concepts such as x += 1 and 'foo' + 'bar'. Includes operators for not in, easy floating point comparisons, === equivalent, and SQL-like like operations (), etc. We also added in some extra helper functions, such as OS checks, pasting in Oxford comma format, and functions to get the first, last, nth, or most common element of a vector or word in a string. Package: r-cran-ropercenter Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rselenium, r-cran-dplyr, r-cran-foreign, r-cran-haven, r-cran-magrittr, r-cran-netstat, r-cran-purrr, r-cran-readr, r-cran-rio, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ropercenter_0.3.2-1.ca2604.1_all.deb Size: 1010148 MD5sum: f010b09877b81ecebfdbc066a5dfec1b SHA1: 361251f9aed35db9537d1fb3ff6c02ea0625fc16 SHA256: 189e5c90a8fe1b2f2b31dade3152299e8c2cb9bf609b600b40aae90cb109f0ce SHA512: 3acfdfc6ee969698f320dea4afb3ad8d128350c4cbdfea23acbe01cdeeea2542805506c8f4670caef338f150936517c35dcf14d4e7b9458d896fcf9b3767c05d Homepage: https://cran.r-project.org/package=ropercenter Description: CRAN Package 'ropercenter' (Reproducible Data Retrieval from the Roper Center Data Archive) Reproducible, programmatic retrieval of datasets from the Roper Center data archive. The Roper Center for Public Opinion Research maintains the largest archive of public opinion data in existence, but researchers using these datasets are caught in a bind. The Center's terms and conditions bar redistribution of downloaded datasets, but to ensure that one's work can be reproduced, assessed, and built upon by others, one must provide access to the raw data one employed. The `ropercenter` package cuts this knot by providing registered users with programmatic, reproducible access to Roper Center datasets from within R. Package: r-cran-roptest Architecture: all Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1915 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-distr, r-cran-distrex, r-cran-distrmod, r-cran-randvar, r-cran-robastbase, r-cran-startupmsg, r-cran-mass Suggests: r-cran-roblox Filename: pool/dists/resolute/main/r-cran-roptest_1.3.5-1.ca2604.1_all.deb Size: 1197580 MD5sum: 6e88b25bff74c09bd88354cf071b3d21 SHA1: 0dc2d34c74b869a8f1bb5f805d7500d34eaaecd1 SHA256: 225370ad0fa3916ebf8452d5a791b74548955b138db006e3c0be5e632252e59a SHA512: 78a80fbdc3d1afcdeba864cd6321b16162fb43af7fa688f9637fb010a4a9de889d50849f45679fa776ef77479a7ddf1856fa6d21b08f43422a0d3b48ea98b251 Homepage: https://cran.r-project.org/package=ROptEst Description: CRAN Package 'ROptEst' (Optimally Robust Estimation) R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), , and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), . Package: r-cran-roptimus Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3635 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iterators Filename: pool/dists/resolute/main/r-cran-roptimus_3.0.0-1.ca2604.1_all.deb Size: 1112094 MD5sum: e5a1818482125741d77093c94c04e448 SHA1: 466cbd8b6be3cfac9e6451a075ae834ed3206812 SHA256: 06f0e0c134b188aeaeeab4d3af26de744b7546d4886d1f201b26ff8289d63f06 SHA512: 6513e1b773f95ffb8348d11e880002c5cb55758e5b10bd43a7e68ef58f6862ad87193e193eaf4b2e9f73731a9d5eb313e2f3c2462806081873173c61543e6b6e Homepage: https://cran.r-project.org/package=ROptimus Description: CRAN Package 'ROptimus' (A Parallel General-Purpose Adaptive Optimisation Engine) A general-purpose optimisation engine that supports i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. (1953) , Hastings (1970) ] and Acceptance Ratio Simulated Annealing [Kirkpatrick et al. (1983) , Černý (1985) ] on multiple cores, and ii) Acceptance Ratio Replica Exchange Monte Carlo Optimisation. In each case, the system pseudo-temperature is dynamically adjusted such that the observed acceptance ratio is kept near to the desired (fixed or changing) acceptance ratio. Package: r-cran-roptions Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-ggplot2, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-roptions_1.0.3-1.ca2604.1_all.deb Size: 104762 MD5sum: 0db67b3cffecfa48477c89d929a480b4 SHA1: 0c7be04913b593be58e5ac7fd3d5df73ab9d8f9b SHA256: c62893a8aa775c274fee7c5231b7ccb6771bca51d9379a156af56984ff846830 SHA512: e775c1e132a6a5765a1dbdc74f6ed266090b2fd8cdfa1ed40a459e67138a62e4525c796113b479149192cb4a3236db868767973502a71eadbf51b6d7008c41da Homepage: https://cran.r-project.org/package=roptions Description: CRAN Package 'roptions' (Option Strategies and Valuation) Collection of tools to develop options strategies, value option contracts using the Black-Scholes-Merten option pricing model and calculate the option Greeks. Hull, John C. "Options, Futures, and Other Derivatives" (1997, ISBN:0-13-601589-1). Fischer Black, Myron Scholes (1973) "The Pricing of Options and Corporate Liabilities" . Package: r-cran-rorcid Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1343 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-httr, r-cran-fauxpas, r-cran-jsonlite, r-cran-xml2, r-cran-tibble, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rcrossref, r-cran-handlr, r-cran-httpuv, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-rorcid_0.7.0-1.ca2604.1_all.deb Size: 1320530 MD5sum: c46fea8e63b87c653d1415776cee147d SHA1: f73bdee1f6e4975a5e8251e7831c46a017308f15 SHA256: 8e2a09ec608cb2041d459e675f406bfb010994e90cb1b6aa31e99a150a9542b0 SHA512: fd9e071dabae320b2aa8a697261d4b716239ff787baef5853e5a2b0405af9dc8299712f9bd6360db36f7b1a2f0ccc437c8500f582feb20e1b116bc8acdb4703e Homepage: https://cran.r-project.org/package=rorcid Description: CRAN Package 'rorcid' (Interface to the 'Orcid.org' API) Client for the 'Orcid.org' API (). Functions included for searching for people, searching by 'DOI', and searching by 'Orcid' 'ID'. Package: r-cran-roroph Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archipelagoengine, r-cran-dplyr, r-cran-ggplot2, r-cran-sf, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-ggrepel, r-cran-ggspatial Filename: pool/dists/resolute/main/r-cran-roroph_0.1.1-1.ca2604.1_all.deb Size: 101602 MD5sum: 4be48293605001c2453922997190878a SHA1: 1550f8e05a38ce0ff97ef9054cb5d3a72f9906e9 SHA256: 4f2e9310688f302ab52f292b302f9ac01eed2104721bdcd7dc3c5d9f80604a4f SHA512: 93c03f172f14f00a1611eb661fdd6f9aac85bb329cf1bdb8c92eae5f5ab3ab11ec9583c0856eb7bd99b9c15f942d5f0fcdd3b1815b1616c6289b0c98fdffe60c Homepage: https://cran.r-project.org/package=roroph Description: CRAN Package 'roroph' (Philippine Roll-on/Roll-Off (RoRo) Connectivity and TransportData) Provides the first standardized dataset of the Philippines' Roll-on/Roll-off (RoRo) shipping network, reflecting the 2024-2026 operational state. It digitizes fragmented records from the Maritime Industry Authority (MARINA) and Philippine Ports Authority (PPA) into a unified framework for transport modeling. The package includes 108 bidirectional provincial links across the Western, Central, and Eastern Nautical Highways, complete with GADM-standardized naming, geospatial coordinates, and metrics such as distance, travel time, and vessel frequency. Methodology follows Anselin (1988, ISBN:9024737354) and LeSage and Pace (2009) for spatial weight construction. Data sources include "MARINA Inventory of RoRo Routes" and "PPA Port Statistics" . Designed to support research in economic geography and disaster-response logistics. Package: r-cran-rorqual.morpho Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-rorqual.morpho_0.1.1-1.ca2604.1_all.deb Size: 32496 MD5sum: 38fd69482ac863a5dff9d1ded7998611 SHA1: ddaa2f55972efaf8e6e3d5d7bd3433fa108009ca SHA256: e35a479c98f0e4ee0676a492d494c84a8b3231707c0749e440cfd0ad014e8d01 SHA512: 813842d75b6ecb9894d0e2cb05bed6503fc00843acff08aaf99f535637556387491034ff03da94304bc52d20ef294f3fa963fdc0871c7d0a4fe4b22e5b5c491c Homepage: https://cran.r-project.org/package=rorqual.morpho Description: CRAN Package 'rorqual.morpho' (Morphological Allometry of Rorquals) Predicts morphological parameters of rorquals (e.g. body mass, flipper length, maximum engulfment capacity) from body length using allometric equations from Kahane-Rapport and Goldbogen (2018) . Package: r-cran-rosario Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-magrittr, r-cran-purrr, r-cran-furrr, r-cran-future, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-ggplot2, r-cran-spelling, r-cran-knitr, r-cran-lubridate, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-rosario_0.1.1-1.ca2604.1_all.deb Size: 103666 MD5sum: 94f738d724734d65b9e2f9237e189364 SHA1: 949f855ac61e0069ea8ca7dcdab1d1f3813be8fc SHA256: e1a859b320ee0ad0e07529f6dcab69ea3d8c41d735ce5e2687cedad16967beaa SHA512: 85f95c56d42e2d759ce904d0dc6817f9d051ece2596aded919bd96b8ea379cdbabf48e6014ce5151884309f0e6e11beda3ca1f073447c403fde85a67c9b30f5a Homepage: https://cran.r-project.org/package=rosario Description: CRAN Package 'rosario' (A Null Model Algorithm to Analyze Cyclical Data in Ecology) Implements a null model analysis to quantify concurrent temporal niche overlap (i.e., activity or phenology) among biological identities (e.g., individuals, populations, species) using the Rosario randomization algorithm (Castro-Arellano et al. 2010) . Package: r-cran-rose Architecture: all Version: 0.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-nnet, r-cran-rpart, r-cran-tree Filename: pool/dists/resolute/main/r-cran-rose_0.0-4-1.ca2604.1_all.deb Size: 113782 MD5sum: 1b0596ac1f67dcd6c2a7353a2fa83403 SHA1: ed9d6610fe3c2f23c13eac7a19f124f758b519c9 SHA256: b1ea755b40f86726394d9f21cd15cf18c3b043596596e64a133d866b9a3f5a79 SHA512: 8736fcf143c40561c7d6eb18429614327111a5dff8615c7dcf5421f4807d3f62462a2fb75730146cbcf217f7332cdc8fcc4a710001d4dc5841af8c87894ae860 Homepage: https://cran.r-project.org/package=ROSE Description: CRAN Package 'ROSE' (Random Over-Sampling Examples) Functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods. Package: r-cran-rosenbrock Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-rosenbrock_0.1.0-1.ca2604.1_all.deb Size: 23188 MD5sum: f2a5b37e8fe5f2d66691f7efceceee9d SHA1: 3caed1d623d289be188614c7d3139da7c4efd729 SHA256: 0b18237c5c7b64529fdc260f624f2646891ce469787e7ee4ecf59a2e03a9188e SHA512: 8a062045e19932cc7e8dc4125b11326b7381f8bfdffc43a55677e49865ebe0e13c39be91a53ded5da210d5ee47a8fd7d56b7432dc457035991696d0c64625abe Homepage: https://cran.r-project.org/package=Rosenbrock Description: CRAN Package 'Rosenbrock' (Extended Rosenbrock-Type Densities for Markov Chain Monte Carlo(MCMC) Sampler Benchmarking) New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested and their performance accurately assessed. This requires densities that offer challenging properties to the novel sampling algorithms. One such popular problem is the Rosenbrock function. However, while its shape lends itself well to a benchmark problem, no codified multivariate expansion of the density exists. We have developed an extension to this class of distributions and supplied densities and direct sampler functions to assess the performance of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) . Package: r-cran-roserf Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-glmnet, r-cran-keras, r-cran-mgcv, r-cran-mlr, r-cran-paramhelpers, r-cran-ranger, r-cran-grf, r-cran-rpart, r-cran-tuneranger, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-roserf_0.1.0-1.ca2604.1_all.deb Size: 101710 MD5sum: 37bc7a9d39b380a1eac749ab12900fa8 SHA1: eb07c519b7e52bb759e66c571d7f6c1ff75853dc SHA256: fa19c1c4e70be28432d3d6286b40b742411056312d30fceba503bdf9e50c8641 SHA512: 7034225feccb9360bdfbdce2bb859d63324624519953b9943f593aced9c376c3f3ec770ce78ad7fe58e8b14d0ac7b0a88be1e33f256b6b098c11bab4c9de1c44 Homepage: https://cran.r-project.org/package=roseRF Description: CRAN Package 'roseRF' (ROSE Random Forests for Robust Semiparametric EfficientEstimation) ROSE (RObust Semiparametric Efficient) random forests for robust semiparametric efficient estimation in partially parametric models (containing generalised partially linear models). Details can be found in the paper by Young and Shah (2024) . 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Use to create basemaps quickly and add hillshade to vector-based maps. Package: r-cran-rosmium Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-geojsonsf, r-cran-processx, r-cran-sf Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rosmium_0.1.0-1.ca2604.1_all.deb Size: 914780 MD5sum: 78553fa212c39326430557fed385dd1f SHA1: 6c7f9f76b4dc786139e7b52af15838340f53344e SHA256: 513ad14a84d44ec185f7c32935167889aa9506c6ab21584a68c69160e0e9c6b5 SHA512: 5a182894d085c24d5e144b620f67811980872b2a4ee3009a4bc96207babd48dc5155508e76fd8117e398d821b44c25bac16122d074b7d29894675ff7be76d73e Homepage: https://cran.r-project.org/package=rosmium Description: CRAN Package 'rosmium' (Bindings for 'Osmium Tool') Allows one to use 'Osmium Tool' () from R. 'Osmium' is a multipurpose command line tool that enables one to manipulate and analyze OpenStreetMap files through several different commands. Currently, this package does not aim to offer functions that cover the entire 'Osmium' API, instead making available functions that wrap only a very limited set of its features. 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Package: r-cran-roxylint Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-roxylint_0.1.0-1.ca2604.1_all.deb Size: 52054 MD5sum: d7a9ae490325d83a82e38132f08bfe10 SHA1: 9188150f8b2c48bc628d6201981d500cce4d4b0a SHA256: 3936dd524f46909007134a7583d288411e39e8ce264228d38e3c6c4d8189be90 SHA512: b1ca380b29ee1ede25b33ea458aff41c7d082a9fd8bd1a49f0a0b8c770c85d4485e5465ce480e23c5416b8259a097658b7b39f9878502563af0da4e6dd85ecdb Homepage: https://cran.r-project.org/package=roxylint Description: CRAN Package 'roxylint' (Lint 'roxygen2'-Generated Documentation) Provides formatting linting to 'roxygen2' tags. Linters report 'roxygen2' tags that do not conform to a standard style. These linters can be a helpful check for building more consistent documentation and to provide reminders about best practices or checks for typos. Default linting suites are provided for common style guides such as the one followed by the 'tidyverse', though custom linters can be registered by other packages or be custom-tailored to a specific package. Package: r-cran-roxytest Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-roxygen2 Suggests: r-cran-testthat, r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-roxytest_0.0.2-1.ca2604.1_all.deb Size: 59370 MD5sum: c1b4044b50929f678f2e3f733a950216 SHA1: d4d31d76768290d5b8a35b4e4c65200955abe7fa SHA256: f2ca283e9b4484cad6590ef25e447339199505ecf75a5c96c8be3de122a5406c SHA512: f4d8dd096ed09e3998b7f68fac3672dc9b2d2e8747829a33daf5f6b94a3c8b9db79b8cba91eb86bfd7df3d1074103d4540adb65321bcf54a07db2e7b159c4a45 Homepage: https://cran.r-project.org/package=roxytest Description: CRAN Package 'roxytest' (Various Tests with 'roxygen2') Various tests as 'roxygen2' roclets: e.g. 'testthat' and 'tinytest' tests. Also other static analysis tools as checking parameter documentation consistency and others. Package: r-cran-roxytypes Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-roxygen2 Suggests: r-cran-mockery, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-roxytypes_0.1.2-1.ca2604.1_all.deb Size: 115550 MD5sum: b26379c924c3debba04c1b78d5468731 SHA1: 82e4aef42560f279443559ad2788d5737969ea1a SHA256: feed08b4f19be97be335830940c9de94d4bdc067e2fa02ad7c908630ad846e4c SHA512: e5d32da4f3dc2b216fe9c620112651f103fc93c7c2e9ccebc41dca5687e1c4428e562cc3f99b629bf0bf44963b81b1419701b3745fc59332b9bf2dba4df8fc5a Homepage: https://cran.r-project.org/package=roxytypes Description: CRAN Package 'roxytypes' (Typed Parameter Tags for Integration with 'roxygen2') Provides typed parameter documentation tags for integration with 'roxygen2'. 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Package: r-cran-rpaci Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-ggplot2, r-cran-tidyr, r-cran-ggpubr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rpaci_0.2.2-1.ca2604.1_all.deb Size: 1016324 MD5sum: 8d5d8b89700b13e241f59892d86823d3 SHA1: ba42633c10e50880411b3cf6a4c74954ead20731 SHA256: 9f23735c7aa166d81ea362b9d6cec70e947a45a2dcc530196c7547aa7e1e5504 SHA512: ee9d4ee6e921f2e65fbcfa5ac4f6d791a6587dbea03e21dcfd504ee111dfa68da602999245611ec6be98ba4119bc8c23306f3c83de93bf9768d2931875b4ca39 Homepage: https://cran.r-project.org/package=rPACI Description: CRAN Package 'rPACI' (Placido Analysis of Corneal Irregularity) Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)], which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)], [Ramos-Lopez et al. (2013)], and [Ramos-Lopez et al. (2011)]. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician. Package: r-cran-rpackedbar Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-data.table, r-cran-scales, r-cran-shiny Suggests: r-cran-covr, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-rpackedbar_0.2.2-1.ca2604.1_all.deb Size: 1104790 MD5sum: ee1f0837df2450f1f3f6c2120e1d4c55 SHA1: be57a2cb89c7e88c4c078cf27978e48db4e7bc86 SHA256: ffcce3440d3580d07981943a06acfbbc47c55338c1231e79690e6501dcd3ff23 SHA512: 331421df55dff8120f95f2879938ae2c21ad8177f0c0ce283c1ac2b82c967ab0503c3adfbb986e6f3e386b6f4c4d73bc11eca4a258d913ec6ef74a713c7797bd Homepage: https://cran.r-project.org/package=rPackedBar Description: CRAN Package 'rPackedBar' (Packed Bar Charts with 'plotly') Packed bar charts are a variation of treemaps for visualizing skewed data. The concept was introduced by Xan Gregg at 'JMP'. 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Package: r-cran-rpaex Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-agricolae Filename: pool/dists/resolute/main/r-cran-rpaex_1.0.5-1.ca2604.1_all.deb Size: 946024 MD5sum: bb9a44bc9df681693c5d05509ecae86c SHA1: f505dd1be0fe099bbbdd9c9969a50d1bde9c2dd7 SHA256: e4e7372f72945eb5540ba9c2e797c7a1301bd933fa3b4025f1df0d765c884179 SHA512: 6cd19b8d9f65b5e5044e87514379db3cac23aa7065e11fa1cd8e61d758223de6efea559a5fde84925aa7f117bb149a863d2e027629a25c75871a2af22054a455 Homepage: https://cran.r-project.org/package=rPAex Description: CRAN Package 'rPAex' (Automatic Detection of Experimental Unit in PrecisionAgriculture) A part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop. Package: r-cran-rpaleoclim Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-fs, r-cran-httr, r-cran-rlang, r-cran-terra Suggests: r-cran-knitr, r-cran-covr, r-cran-raster, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rpaleoclim_1.1.0-1.ca2604.1_all.deb Size: 491966 MD5sum: 02f206fed3d12025090c62d6d04731a4 SHA1: 45f05c4dc47716d8baf73fa438d9e209c00acc2d SHA256: 33badcdd35ea69a94566ef4c85bf36b51d1aea171f055d344bc36d542ef127f3 SHA512: 605a6873dee81a63f6240370a444d5c61aa7bfbd97ffda15eb69f6bb6f8a8f5fba327f91ec81c35c7c915d649e12403c094ba052872b835c306c3d580e0d2fee Homepage: https://cran.r-project.org/package=rpaleoclim Description: CRAN Package 'rpaleoclim' (Download Paleoclimate Data from 'PaleoClim') 'PaleoClim' (Brown et al. 2019, ) is a set of free, high resolution paleoclimate surfaces covering the whole globe. It includes data on surface temperature, precipitation and the standard bioclimatic variables commonly used in ecological modelling, derived from the 'HadCM3' general circulation model and downscaled to a spatial resolution of up to 2.5 minutes. Simulations are available for key time periods from the Late Holocene to mid-Pliocene. Data on current and Last Glacial Maximum climate is derived from 'CHELSA' (Karger et al. 2017, ) and reprocessed by 'PaleoClim' to match their format; it is available at up to 30 seconds resolution. This package provides a simple interface for downloading 'PaleoClim' data in R, with support for caching and filtering retrieved data by period, resolution, and geographic extent. Package: r-cran-rpandas Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rpandas_0.1.4-1.ca2604.1_all.deb Size: 97840 MD5sum: 78ed9eefb7e278354377c3eaafb3b14a SHA1: 2ccf8d767239175de6bb5a589de3322e1a3d74ee SHA256: 32b192a47b0a85604f40ac8719b6ee4999356e9aed7c11aec3f38b76b3048a7c SHA512: a2255e144d9a1d7b113b70811b2224f37b08913feb3f3ae3578acd606d1cf3e35c8342b1a8f56d7e1e21d21eeaf96803a4748c58ef5305964cf810ecc6f873f6 Homepage: https://cran.r-project.org/package=rPandas Description: CRAN Package 'rPandas' (Translating from R to Python's Pandas Package) Provides an R interface to Python's 'pandas' library using non-standard evaluation. 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Package: r-cran-rpeif Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-robstattm Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-performanceanalytics Filename: pool/dists/resolute/main/r-cran-rpeif_1.2.5-1.ca2604.1_all.deb Size: 483852 MD5sum: 25dc9ffcbaad5cdbce73e2889cdd8df1 SHA1: e32ef2475bfa7eaad88a0d791ea2e4b1761ac0c7 SHA256: cac99fd8cc4f08d3d20d2bf0221257a1b5425eb3770d5b134718ba55fafcae0e SHA512: ef31488677833be6766365719326fccd0f6c3179a08fbb11b2735b6aeb559f07b7cc1156ed6cbb5f1867ce98fbfb84c6953380beed56f9bf13fae7951065213a Homepage: https://cran.r-project.org/package=RPEIF Description: CRAN Package 'RPEIF' (Computation and Plots of Influence Functions for Risk andPerformance Measures) Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) , as well as in Zhang et al. 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Package: r-cran-rpese Architecture: all Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-boot, r-cran-rpeif, r-cran-rpeglmen, r-cran-robstattm Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-performanceanalytics Filename: pool/dists/resolute/main/r-cran-rpese_1.2.7-1.ca2604.1_all.deb Size: 290948 MD5sum: ca36cbe95fb5cc0ed08078be879e0436 SHA1: 283372daae2e69106b5bc1b1bc07b2ef17cb88d1 SHA256: 14ea66ea782a6df91169fddf91aaa7dfcbaf4f6f4443df142e829c26ed026c80 SHA512: 029f83a1d00a124180cfa42565bb9ad13a83b969a7f2700327a2e111d86ab9c616024e716fd746d449417bd2ce0fc54bda807ff157b62385b5db7d65d01fda20 Homepage: https://cran.r-project.org/package=RPESE Description: CRAN Package 'RPESE' (Estimates of Standard Errors for Risk and Performance Measures) Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2021) . 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Package: r-cran-rplec Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4004 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-pbapply, r-cran-purrr, r-cran-rpmm, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rplec_0.1.3-1.ca2604.1_all.deb Size: 3288684 MD5sum: 26bd8ba27fbcae861c2b51b89b6f5bda SHA1: 97221e5638056c86d1c492264b6e7744c878b1c9 SHA256: f8ca157115892ac78cce55de639c827323a9788b5703b54d4ea16f9d2baa28ce SHA512: 12360211d38fa9ccd092d388fd6ff18b0a7feac1acba4285e05b8e20948fbc4e5a3af0eabcd50969baeae511c860173f28f056d687ba41cfa31410c7238c0a6a Homepage: https://cran.r-project.org/package=rplec Description: CRAN Package 'rplec' (Placental Epigenetic Clock to Estimate Aging by DNA Methylation) Placental epigenetic clock to estimate aging based on gestational age using DNA methylation levels, so called placental epigenetic clock (PlEC). We developed a PlEC for the 2024 Placental Clock DREAM Challenge (). Our PlEC achieved the top performance based on an independent test set. PlEC can be used to identify accelerated/decelerated aging of placenta for understanding placental dysfunction-related conditions, e.g., great obstetrical syndromes including preeclampsia, fetal growth restriction, preterm labor, preterm premature rupture of the membranes, late spontaneous abortion, and placental abruption. Detailed methodologies and examples are documented in our vignette, available at . Package: r-cran-rplotengine Architecture: all Version: 1.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-rplotengine_1.0-9-1.ca2604.1_all.deb Size: 114008 MD5sum: d1578cfc2974088b35a0df59984adc67 SHA1: 778d0d70af31d4c9a5f07781137714199fd153cc SHA256: f32be2e1103ee7a6bacdad7bf95298cea5209613b374e81024fb84f39e940270 SHA512: 7a668fd34e855d27e94f6d51cf2a6f7834dfb7d43a6642882522b103685c1ec5aaa8bc5f58df4cca2fe6153925b07307bdcd4eda5a2a47144766ce47413bd3ce Homepage: https://cran.r-project.org/package=rplotengine Description: CRAN Package 'rplotengine' (R as a Plotting Engine) Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary: (1) The graph parameters file, which name is passed to the function 'rplotengine()'. The user can specify the titles, choose the type of the graph, graph output formats (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend, whether to show or not a grid at the background, etc. (2) The data to be plotted, which name is specified as a parameter ('data_filename') in the previous file. This data file has a tabulated format, with a single character (e.g. tab) between each column. Optionally, the file could include data columns for showing confidence intervals. Package: r-cran-rplotterpkg Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-rlang, r-cran-gt, r-cran-gtable, r-cran-glue, r-cran-purrr, r-cran-ggplotify, r-cran-aplpack Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-here, r-cran-socviz, r-cran-tsbox, r-cran-gapminder, r-cran-palmerpenguins, r-cran-spdata, r-cran-sf, r-cran-raster, r-cran-elevatr, r-cran-maps Filename: pool/dists/resolute/main/r-cran-rplotterpkg_0.1.5-1.ca2604.1_all.deb Size: 380842 MD5sum: c04b80cd7175da7a389190cdb86119e5 SHA1: 1666f038d0e0da85f8816b63e8e0c263bf8a575b SHA256: e8d713e11db662c2aef449fa4cb8536e4d0e8ba618d0ddac7dadd9487cc8584c SHA512: 3630e6abefc39491a04cfa1ef2b86fcf36a9ddf2fb3117156f1540095b525d71c7e11d9021944ffd1b190f17be625a7d609810b6e5d174fe3dd0b9aca45a4690 Homepage: https://cran.r-project.org/package=RplotterPkg Description: CRAN Package 'RplotterPkg' (R Plotting Functions Using 'ggplot2') Makes it easy to produce everyday 'ggplot2' charts in a functional way without an extensive "tree" implementation. The package includes over 15 functions for the production and arrangement of basic graphing. Package: r-cran-rpls Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcapp, r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-rpls_0.6.0-1.ca2604.1_all.deb Size: 43774 MD5sum: 45e0e37e89045c258bdcd9b2a999bf58 SHA1: b2ef6066b65da99dbbd45ec618293a0ae3940ba1 SHA256: d754182b750c998b80b71b671720e8be99ff7c6071ac53da981c89de88dcd4b9 SHA512: e9e32b642c6baf715693875a2d5094e8b96d8463de29870903568fa78d00d8df82e02b8b6e2aed6b00b6f2807cfc8cf211d665ddcb4b1c6d8b359ccde1a9ed4d Homepage: https://cran.r-project.org/package=rpls Description: CRAN Package 'rpls' (Robust Partial Least Squares) A robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator. Package: r-cran-rplum Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rintcal, r-cran-rice, r-cran-rbacon Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8 Filename: pool/dists/resolute/main/r-cran-rplum_1.0.0-1.ca2604.1_all.deb Size: 234318 MD5sum: 9856fd1a768488b849bb9b7ab6ced55f SHA1: 1777590005a3cda9e591a915011eccee1cb3e3fc SHA256: dbc9b6dee9cb8f5c39952d40e55041edf118cb5550d8cd25bae10d8fce51a700 SHA512: e7ffbba655d539c35edb292fbca42078d683e40e03e80991513b586d1284ffe4d026af84d5c05d263d31b0d09a9bb3b2c3729cf054fe3d3acc5a22223c81f5da Homepage: https://cran.r-project.org/package=rplum Description: CRAN Package 'rplum' (Bayesian Age-Depth Modelling of Cores Dated by Pb-210) An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for 210Pb-dated deposits using prior information. It can combine 210Pb, radiocarbon, and other dates in the chronologies. See Aquino et al. (2018) . Note that parts of the code underlying 'rplum' are derived from the 'rbacon' package by the same authors, and there remains a degree of overlap between the two packages. Package: r-cran-rpmg Architecture: all Version: 2.2-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rpmg_2.2-7-1.ca2604.1_all.deb Size: 216800 MD5sum: 14e2a5b36eebf6f7ce6db719bc07a4ff SHA1: 364245e3b0d31a48184223fd37e6128401c6ffde SHA256: 372526fbb277cfa9a2707551c10688c1c0d174b8ef03fe90731b1a2256d8fcb8 SHA512: a01f4d5414e091ac814fb93ccbf94fecf13d46f06534d8e135d7f410b8322f7bd1692f0e7df9db889a1b2c0815672cb8fcd683db6966ccb1a762f81b61eca54a Homepage: https://cran.r-project.org/package=RPMG Description: CRAN Package 'RPMG' (Graphical User Interface (GUI) for Interactive R AnalysisSessions) Really Poor Man's Graphical User Interface, used to create interactive R analysis sessions with simple R commands. Package: r-cran-rpmm Architecture: all Version: 1.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-rpmm_1.25-1.ca2604.1_all.deb Size: 268540 MD5sum: 82140dc6d6d888bcdfe5c44c0983bb94 SHA1: 418a25b512254a776a2788e536d82f86abcc7605 SHA256: 298701efc814c510feeeb0c192523dd3545a4534288a4d7db2bef57148d38fc2 SHA512: bb02779e490d825ab9b0d0e162a693542a9c4ee11cc66738aa675187cf190014fce55676995ec8967d0ec8857a278107752aeeecfc97e34b0b1fde7271caee26 Homepage: https://cran.r-project.org/package=RPMM Description: CRAN Package 'RPMM' (Recursively Partitioned Mixture Model) Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures. This is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchical clustering, but also similar to finite mixture models. Package: r-cran-rpmodel Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-rpmodel_1.2.3-1.ca2604.1_all.deb Size: 555770 MD5sum: d2d7146f7bcc4e2b5c80d56cfeed7a2f SHA1: ee6d460e5302f20b1cc3bf0a6a2ff57720b90731 SHA256: 8fdda3185433ee2619ce26a06b009f26fb38301687cc9b599973b73dee588bb3 SHA512: cf6c8e1b3068035a3a932298591df9a6c81785a573f2ad5507e74223145d0a58f0b5e2afa7824351888829a8136531488c4571978cbef4ac86d3a119f2d5e654 Homepage: https://cran.r-project.org/package=rpmodel Description: CRAN Package 'rpmodel' (P-Model) Implements the P-model (Stocker et al., 2020 ), predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis, assimilation, and dark respiration rates as a function of the environment (temperature, CO2, vapour pressure deficit, light, atmospheric pressure). Package: r-cran-rpnf Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rpnf_1.0.5-1.ca2604.1_all.deb Size: 107704 MD5sum: 50215c0264bb939538f4478905ed63ec SHA1: 54196efbb145ef4cb496d1a6be90ff62b5495ad5 SHA256: 402780dbab2b1c53efa1eb23ab00649f4e86a06d32773beb519c2f3bc92c6ea3 SHA512: 387fd80d496a5153496f3eb22340bc847f393fd34b96c3bdd9259dfe7792000d2fe35ef950c6a67f42489b88e5b410022471ed5d142d543bd57448691204f622 Homepage: https://cran.r-project.org/package=rpnf Description: CRAN Package 'rpnf' (Point and Figure Package) A set of functions to analyze and print the development of a commodity using the Point and Figure (P&F) approach. A P&F processor can be used to calculate daily statistics for the time series. These statistics can be used for deeper investigations as well as to create plots. Plots can be generated as well known X/O Plots in plain text format, and additionally in a more graphical format. Package: r-cran-rpoet Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-httr, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-rpoet_1.1.0-1.ca2604.1_all.deb Size: 18472 MD5sum: aeeae4323529003d35379e7ade577a75 SHA1: 299a3e42cd0dc03133d0fb800e07b255da5860a5 SHA256: 7ed9bd75610eeca6ec7916201d257d56d56997551685ff003ac94db37068ab9b SHA512: 6f9173776d2a9ca904886616c8a30c272ab4e2993c7b110365177be4fcf1b6ca27cabdfe00047cf1d54b496b90f7a5863c3d146f20ef6d4a1da48410e0ed2632 Homepage: https://cran.r-project.org/package=Rpoet Description: CRAN Package 'Rpoet' ('PoetryDB' API Wrapper) Wrapper for the 'PoetryDB' API that allows for interaction and data extraction from the database in an R interface. The 'PoetryDB' API is a database of poetry and poets implemented with 'MongoDB' to enable developers and poets to easily access one of the most comprehensive poetry databases currently available. Package: r-cran-rpointcloud Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3744 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tda, r-cran-classdiscovery, r-cran-classcomparison, r-cran-mercator, r-cran-rgl, r-cran-circlize Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-polychrome, r-cran-igraph, r-cran-ape, r-cran-pcdimension Filename: pool/dists/resolute/main/r-cran-rpointcloud_0.9.1-1.ca2604.1_all.deb Size: 2492480 MD5sum: f676f7ad3e94deebd18f96796938eeea SHA1: 969bcff451929701c8fbb0d4970b6a797b0ffdb9 SHA256: 46d49d50b43e6e0647a7b7994f62b11a3d7c11b309d6547ae608d45e8f1f293a SHA512: efabb0a2090b8211a508a63f5d70ae907fb5a0447037930ca6f8c5aecccfcbcc8dcba7117bf650f1df04d404e3ae73c6313658762d716334ff03cdd61d74a2bc Homepage: https://cran.r-project.org/package=RPointCloud Description: CRAN Package 'RPointCloud' (Visualizing Topological Loops and Voids) Visualizations to explain the results of a topological data analysis. The goal of topological data analysis is to identify persistent topological structures, such as loops (topological circles) and voids (topological spheres), in data sets. The output of an analysis using the 'TDA' package is a Rips diagram (named after the mathematician Eliyahu Rips). The goal of 'RPointCloud' is to fill in these holes in the data by providing tools to visualize the features that help explain the structures found in the Rips diagram. See McGee and colleagues (2024) . Package: r-cran-rportfolio Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-rportfolio_0.0.3-1.ca2604.1_all.deb Size: 55316 MD5sum: 078d767c874e774259ead685c20a7543 SHA1: 94e7c3f54a8b6b34b5c459900d9bc81976c2624b SHA256: e2d2562755eaf9a4fbcb4eb44bff520a7756e2fb933e731619cfcdb14f43d859 SHA512: 5afc5179aee9bd62185c90ab655ba6fe0d5f13b24c2aba4116e95ee515b14ea09137d7a97aa23e211b3ca960df3c4be50ba8975aec422536ab87bbcba9f9031a Homepage: https://cran.r-project.org/package=rportfolio Description: CRAN Package 'rportfolio' (Portfolio Theory) Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. This package uses portfolio theories to give investor tools to evaluate their portfolio performance. For more information see, Markowitz, H.M. (1952), . Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748]. Package: r-cran-rpostgis Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpostgresql, r-cran-dbi, r-cran-sf, r-cran-terra, r-cran-cli, r-cran-lifecycle Suggests: r-cran-rpostgres, r-cran-testthat, r-cran-sp, r-cran-raster Filename: pool/dists/resolute/main/r-cran-rpostgis_1.6.0-1.ca2604.1_all.deb Size: 283024 MD5sum: ae09d9cccc6661694384022f55cac100 SHA1: e62e518151199efafc9266c50771b10b2de5717c SHA256: 19042aeb2a83e475086ce61df84b65ee5eb681c28521609846ccc7d414ef0261 SHA512: 7f2380e25edeb31f02f5512c1554362f039daf0a99a59abfaf5fbdf3bc964d957fe036d515d321f8b6fc549fce2b7fbd925927b92bb72ced418bdb55fcc04752 Homepage: https://cran.r-project.org/package=rpostgis Description: CRAN Package 'rpostgis' (R Interface to a 'PostGIS' Database) Provides an interface between R and 'PostGIS'-enabled 'PostgreSQL' databases to transparently transfer spatial data. Both vector (points, lines, polygons) and raster data are supported in read and write modes. Also provides convenience functions to execute common procedures in 'PostgreSQL/PostGIS'. Package: r-cran-rpowersamplesize Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-ssanv Filename: pool/dists/resolute/main/r-cran-rpowersamplesize_1.0.2-1.ca2604.1_all.deb Size: 201378 MD5sum: de757849829600e1192dfeba3f699ebd SHA1: 68f271c6c6aa5b5db86ccf9fb52db7c3201d011f SHA256: 48c04e59c0dd7f68372e8098701d9e5caa976c0fb07121ddab0c8fee28e3224c SHA512: 5d276fc5314dc346475ee330be1f4a120be0a349131a7777949baabb9ac6da81c34e180f39e7ec29bf6602f2098eaf5d9265beafa0668ff2f76bba1638569261 Homepage: https://cran.r-project.org/package=rPowerSampleSize Description: CRAN Package 'rPowerSampleSize' (Sample Size Computations Controlling the Type-II GeneralizedFamily-Wise Error Rate) The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) . Package: r-cran-rppanalyzer Architecture: all Version: 1.4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-bioc-limma, r-cran-lattice, r-cran-gam, r-cran-gplots, r-cran-ggplot2, r-cran-hmisc, r-bioc-biobase Filename: pool/dists/resolute/main/r-cran-rppanalyzer_1.4.9-1.ca2604.1_all.deb Size: 1519366 MD5sum: 384eb7461351a8687b31d59c30ebe11a SHA1: 58b6c9e78ca7d06a4f948111f3d504899d6a5766 SHA256: badd8bcd86bba822360ba8cff083d066c88d8657b3898a7abf04ef1a6d82a0ba SHA512: 028eb45e6e2a4b13b86bc25fe0568238fc4a33b76488fbaf1c1ffeb2473218d15c46df36675f6f57a9b540ec84ed05466b83abb4fb9181fd645f60c1eb6ae383 Homepage: https://cran.r-project.org/package=RPPanalyzer Description: CRAN Package 'RPPanalyzer' (Reads, Annotates, and Normalizes Reverse Phase Protein ArrayData) Reads in sample description and slide description files and annotates the expression values taken from GenePix results files (text file format used by many microarray scanner and software providers). After normalization data can be visualized as boxplot, heatmap or dotplot. Package: r-cran-rppaspace Architecture: all Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1747 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-bmp, r-cran-jpeg, r-cran-tiff, r-cran-png, r-cran-imager, r-cran-cobs, r-cran-nlme, r-cran-robustbase, r-cran-mgcv, r-cran-sparsem, r-cran-quantreg, r-cran-timedate Suggests: r-cran-boot, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rppaspace_1.0.10-1.ca2604.1_all.deb Size: 1437568 MD5sum: 6022dbca005a7d80b9e18894bfcbd7d7 SHA1: c622aed59dfb1122cdfe683b1d8573f4fa99f24a SHA256: fd64dfe8be6ed13f5616d8437ef479ecc825753aecc3b4cd782e7aae1e7de935 SHA512: 171a1890b27cacff216aa81161cf77002c9e3bc68ae89c46ca00dead191d73a3e382c4c2ae05c6fe80cf90830aa1ba35454ff2d15863f5c7496be3264a3bd641 Homepage: https://cran.r-project.org/package=RPPASPACE Description: CRAN Package 'RPPASPACE' (Reverse-Phase Protein Array Super Position and ConcentrationEvaluation) Provides tools for the analysis of reverse-phase protein arrays (RPPAs), which are also known as 'tissue lysate arrays' or simply 'lysate arrays'. The package's primary purpose is to input a set of quantification files representing dilution series of samples and control points taken from scanned RPPA slides and determine a relative log concentration value for each valid dilution series present in each slide and provide graphical visualization of the input and output data and their relationships. Other optional features include generation of quality control scores for judging the quality of the input data, spatial adjustment of sample points based on controls added to the slides, and various types of normalization of calculated values across a set of slides. The package was derived from a previous package named SuperCurve. For a detailed description of data inputs and outputs, usage information, and a list of related papers describing methods used in the package please review the vignette 'Guide_to_RPPASPACE'. 'RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data'. Bioinformatics Nov 15;38(22):5131-5133. . Package: r-cran-rpraat Architecture: all Version: 1.3.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-readr, r-cran-dygraphs, r-cran-tuner Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rpraat_1.3.2-1-1.ca2604.1_all.deb Size: 745610 MD5sum: dcea105db345625d7720d898494f707b SHA1: abde2a17be4085184e4c07a379974e3f378a18b6 SHA256: 6e9162fcb30654aab0a7d45707b204ae35005710aaa4ce26d977e99c3b134806 SHA512: 54d3e3febb454fd12bd0bae82494c014f1474a8905724fb8f2431ff7236b99288acdaf0da84b2c884d5dd6b6b96bbe3b183965125708aa7d54c366c4f7121994 Homepage: https://cran.r-project.org/package=rPraat Description: CRAN Package 'rPraat' (Interface to Praat) Read, write and manipulate 'Praat' TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files . 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With just two lines of code, you can perform a regression analysis, visualize the results, and save the output. It is part of my make R easy project where one doesn't need to know how to use various packages in order to get results and makes it easily accessible to beginners. This is a part of my make R easy project. Help from 'ChatGPT' was taken. References were Wickham (2016) . 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Package: r-cran-rrepest Architecture: all Version: 1.6.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-flextable, r-cran-foreach, r-cran-labelled, r-cran-magrittr, r-cran-officer, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-rrepest_1.6.12-1.ca2604.1_all.deb Size: 954640 MD5sum: 08dcfd639f2d5d3bf9e788e0600080fe SHA1: 7e746a156f02c96c9656bdb2acd1469070e932c4 SHA256: 20e273f9f369bd6bb38a0ac0ed987a5e9624497e6433f1cbd2e40292c5498fd2 SHA512: 641b7c9e03b53dbda7431c2c601f6fd34c69545e78c511525ad1bae817d4dafb2a330da9990d9e17edc63d119fad4e8996c186ce2bc1fedf378983bab56e526d Homepage: https://cran.r-project.org/package=Rrepest Description: CRAN Package 'Rrepest' (An Analyzer of International Large Scale Assessments inEducation) An easy way to analyze international large-scale assessments and surveys in education or any other dataset that includes replicated weights (Balanced Repeated Replication (BRR) weights, Jackknife replicate weights,...) while also allowing for analysis with multiply imputed variables (plausible values). It supports the estimation of univariate statistics (e.g. mean, variance, standard deviation, quantiles), frequencies, correlation, linear regression and any other model already implemented in R that takes a data frame and weights as parameters. It also includes options to prepare the results for publication, following the table formatting standards of the Organization for Economic Cooperation and Development (OECD). Package: r-cran-rrgeo Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2920 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-pbapply, r-cran-rphylopars, r-cran-rrphylo, r-cran-dismo, r-cran-gtools, r-cran-terra, r-cran-adehabitatma, r-cran-ecospat, r-cran-foreach, r-cran-doparallel, r-cran-presenceabsence, r-cran-ade4, r-cran-sp, r-cran-sf, r-cran-scales, r-cran-ks, r-cran-leastcostpath, r-cran-dosnow, r-cran-biomod2 Suggests: r-cran-ggplot2, r-cran-cowplot, r-cran-openxlsx, r-cran-bchron, r-cran-curl, r-cran-rnaturalearth, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-knitr, r-cran-httr, r-cran-jsonlite, r-cran-adehabitaths Filename: pool/dists/resolute/main/r-cran-rrgeo_0.0.6-1.ca2604.1_all.deb Size: 2376772 MD5sum: 0ff9f53bc49803628e47fcc0c4e4e89a SHA1: ee04d756039fb2d61557e04a74e6b844816a8983 SHA256: 3d6d42d442d47a85c002163605083f2bafd1999e4278c6d3789066fd15ad1bb4 SHA512: 0090d4532d2db412a6f41cca670632a2110a5366184432388679c33994d2387c9c4c6385e0c6cb51135f555481543db40a756363f1121f15820c7d001f220d83 Homepage: https://cran.r-project.org/package=RRgeo Description: CRAN Package 'RRgeo' (Species Distribution Modelling for Rare Species) Performs species distribution modeling for rare species with unprecedented accuracy (Mondanaro et al., 2023 ) and finds the area of origin of species and past contact between them taking climatic variability in full consideration (Mondanaro et al., 2025 ). Package: r-cran-rriskdistributions Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mc2d, r-cran-eha, r-cran-msm, r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-rriskdistributions_2.1.2-1.ca2604.1_all.deb Size: 400714 MD5sum: 4d6c8dd0ef73e73f6ce6c1dd036243ab SHA1: 9a582f96a8329c224b510e23079bd5042debac97 SHA256: 7ffe9c1c54bd600801d8a04670ed949cf6e788e6c2930f4c0967fc71a01346ff SHA512: f1fa13487e5a1d73fd5f4a4dc75581daa1ec6fc2db5dd771d7f01b45225d373389c5b55b4d0a6a3a5db28ff1ca3c56da43d42d356977b6c2f18855528ab819fd Homepage: https://cran.r-project.org/package=rriskDistributions Description: CRAN Package 'rriskDistributions' (Fitting Distributions to Given Data or Known Quantiles) Collection of functions for fitting distributions to given data or by known quantiles. Two main functions fit.perc() and fit.cont() provide users a GUI that allows to choose a most appropriate distribution without any knowledge of the R syntax. Note, this package is a part of the 'rrisk' project. Package: r-cran-rrmlrfmc Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-rrmlrfmc_0.4.0-1.ca2604.1_all.deb Size: 70628 MD5sum: 19be88d86ef88b4d387b13fe9a97f3b9 SHA1: 4cc0f5d049d89a1be4c4f58fd0a4719767ba3b39 SHA256: 91d2b72dff90998d09271e5f24e5bad97f457981f31563094a65077d0f382714 SHA512: deb344041c21e9ad52a60328d22112a4b1107b7e88375ce553fa6deb7b27e119fdda7900cec9461ab06d7a09d3a0c6ee3035eea1532793bc80561a25bc55d2f1 Homepage: https://cran.r-project.org/package=RRMLRfMC Description: CRAN Package 'RRMLRfMC' (Reduced-Rank Multinomial Logistic Regression for Markov Chains) Fit the reduced-rank multinomial logistic regression model for Markov chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio (2021) in R. It combines the ideas of multinomial logistic regression in Markov chains and reduced-rank. It is very useful in a study where multi-states model is assumed and each transition among the states is controlled by a series of covariates. The key advantage is to reduce the number of parameters to be estimated. The final coefficients for all the covariates and the p-values for the interested covariates will be reported. The p-values for the whole coefficient matrix can be calculated by two bootstrap methods. Package: r-cran-rrmorph Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-morpho, r-cran-rgl, r-cran-rvcg, r-cran-rrphylo Suggests: r-cran-inflection, r-cran-ddpcr, r-cran-ape, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rrmorph_0.0.2-1.ca2604.1_all.deb Size: 1755734 MD5sum: fa882ada084414e07cfc5a51b462ee5a SHA1: ac95d0191ce3c34e9bcae935ae1be132c0cc713f SHA256: 31c54dd9ad2aa0a11512311fbc1022fdca5d4ede4d930a15eb1a63d7d9842981 SHA512: 19dbbbabfffdf02d3ee9f7cd953d9d53b4e288c5456583b69570aeac1ce86fa2e5c0d3a6f16db062e464f8d0574c561fb892674d32783bbe2ee68feb7613040e Homepage: https://cran.r-project.org/package=RRmorph Description: CRAN Package 'RRmorph' (3D Morphological Analyses with 'RRphylo') Combined with 'RRphylo', this package provides a powerful tool to analyse and visualise 3d models (surfaces and meshes) in a phylogenetically explicit context (Melchionna et al., 2024 ). Package: r-cran-rrna Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rrna_1.2-1.ca2604.1_all.deb Size: 120320 MD5sum: d7436126004d55900f34bcb65c270a29 SHA1: f30dd78688d1f8f80c45d25d6786df9d3eacfc4f SHA256: 541cfc42b897588cd467a541b7129ae1b911f9893952bf14db249f05ff81e65f SHA512: 177c09a2f1bf43d087d9404a74099404865e07d5a1fe270f00ae4320b3a7faebd4d4edbd811ec261d57ffd3fa7d05858d8785143f18c0d2b16761ce79335a74f Homepage: https://cran.r-project.org/package=RRNA Description: CRAN Package 'RRNA' (Secondary Structure Plotting for RNA) Functions for creating and manipulating RNA secondary structure plots. Package: r-cran-rroad Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-zoo, r-cran-biwavelet Filename: pool/dists/resolute/main/r-cran-rroad_0.0.5-1.ca2604.1_all.deb Size: 379544 MD5sum: 561223185db4a47553f52ac902645dd9 SHA1: 365e47bc574d401eebb6cf9c175e7ac4978a2f5c SHA256: af236ca14481f371d49ebe206d8da8376fb413ef18774bba8ab83a1644d5ced6 SHA512: 49b6056cad93728a4a839365f3f01ddc7f917acfc86b988307aa8cd18c87e874dc1d5a0f10a4bbabe839c3a40c8cdb648c5944da98f65280c3742c3222299a20 Homepage: https://cran.r-project.org/package=rroad Description: CRAN Package 'rroad' (Road Condition Analysis) Computation of the International Roughness Index (IRI) given a longitudinal road profile. The IRI can be calculated for a single road segment or for a sequence of segments with a fixed length (e. g. 100m). For the latter, an overlap of the segments can be selected. The IRI and likewise the algorithms for its determination are defined in Sayers, Michael W; Gillespie, Thomas D; Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) : establishing correlation and a calibration standard for measurements. World Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN 0-8213-0589-1) available from . 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For more details see Saluja, Parlak, and Mejia (2026+) . Package: r-cran-rrphylo Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emmeans, r-cran-ape, r-cran-phytools, r-cran-foreach, r-cran-doparallel Suggests: r-cran-phangorn, r-cran-rlist, r-cran-scales, r-cran-r.utils, r-cran-cluster, r-cran-rcolorbrewer, r-cran-nlme, r-cran-car, r-cran-smatr, r-cran-picante, r-cran-vegan, r-cran-ddpcr, r-cran-geomorph, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-plotrix, r-cran-pdftools, r-cran-rgl, r-cran-mvmorph, r-cran-ggplot2, r-cran-qpdf, r-cran-inflection, r-cran-rvcg, r-cran-morpho, r-cran-evolqg, r-cran-manipulate, r-cran-markdown, r-cran-rphylopars, r-cran-phylolm, r-cran-webshot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rrphylo_3.0.2-1.ca2604.1_all.deb Size: 3281286 MD5sum: a386ab9e38af9d8019d1e42d56357c24 SHA1: e2cab770731a63be54bb141c048532142e3a30be SHA256: 7eadfef3df210f2c8a2f781e8b4a40b37e9f8c74c5f1924d5f34cac3e507f484 SHA512: 92417907f72d16d77cb653289a0c23faa3924d8cb8e715e745ea1711e61bab12c830e1972e9eb1fa7bac69769c1efd50608ff1fb335b6b3b116e894e83d6fa18 Homepage: https://cran.r-project.org/package=RRphylo Description: CRAN Package 'RRphylo' (Phylogenetic Ridge Regression Methods for Comparative Studies) Functions for phylogenetic analysis (Castiglione et al., 2018 ). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence. Package: r-cran-rrpp Architecture: all Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1829 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-ggplot2, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-rrpp_2.1.2-1.ca2604.1_all.deb Size: 1539316 MD5sum: 77c95449c0d40b99a6afa66fc4786601 SHA1: 327afb920ed8f4841e06df19ce5be9f1ac4fd5ac SHA256: 29b04a7a7e9a0acc1467d4f9731648e14f726a161a2e6bca5936aa80fb1e53ec SHA512: 193f35f019fd000613ec4897e6591f60ed75e1e0eb92ec6c5d7623b0d916590da56251fa23184da182d82c23f604c440a26b8b1afda87d4dfede3ce2edf2bbd6 Homepage: https://cran.r-project.org/package=RRPP Description: CRAN Package 'RRPP' (Linear Model Evaluation with Randomized Residuals in aPermutation Procedure) Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well. Package: r-cran-rrr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-ggally Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readr Filename: pool/dists/resolute/main/r-cran-rrr_1.0.0-1.ca2604.1_all.deb Size: 1936180 MD5sum: 2abbef77afd7921ea3e6c68122c880cd SHA1: 2e3963a9949d4bf9d9b5fd0476ac0061bdb06794 SHA256: 73c2f320c36b0a6528e6457acac3e7c9f46a783d839e54b3816d7c40237f6151 SHA512: 3bf621ec2db5257e5c1d43016b5cc7ee4845d9eefbc9712aa582cbf213d22ad040bd5a416c1765d81e9cecbead56141fa1e5e21a664baa16cf1592246399cc25 Homepage: https://cran.r-project.org/package=rrr Description: CRAN Package 'rrr' (Reduced-Rank Regression) Reduced-rank regression, diagnostics and graphics. Package: r-cran-rrreg Architecture: all Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-lme4 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-rrreg_0.7.6-1.ca2604.1_all.deb Size: 878988 MD5sum: f4f0a111512798b355f7322fbc3a1b87 SHA1: bc665b07c2dd0113a7dcd00b1ccbd5ade688beec SHA256: e6c4a6862a486256a8a44c05c971d0a50d6a387dfe3a978c65eced2127b16bc7 SHA512: 72b95f8141dbcb691b8c7e68eb1dc84afc02f43a759220d1d05f76adb5be3eff156f77b620a2379eed1e3f5cb28e0f278043e65e62d1031767efeb04c1e9265d Homepage: https://cran.r-project.org/package=RRreg Description: CRAN Package 'RRreg' (Correlation and Regression Analyses for Randomized Response Data) Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63–69, ). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 1–29, ). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724–732, ). Package: r-cran-rrrr Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc, r-cran-expm, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm Suggests: r-cran-lazybar, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rrrr_1.1.1-1.ca2604.1_all.deb Size: 180108 MD5sum: 21253bdf9a03121614df98cb317b0109 SHA1: 72a7c25bef973d7c2ac6b0c0c58932aebaef88eb SHA256: d5104251169aa29e0b7c43653c2ec9550bb42d5d4c65053e2d059dab7db07a4c SHA512: 19c2e0c26c857d3528b3061fe53826acc8ddedca170949c32d1b4b848d7b0ad1801f6f43d0f79686237f54882887e262e2563ec052cbf2e1a0115d33756a3af9 Homepage: https://cran.r-project.org/package=RRRR Description: CRAN Package 'RRRR' (Online Robust Reduced-Rank Regression Estimation) Methods for estimating online robust reduced-rank regression. The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) . The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) . The description of the generic stochastic successive upper-bound minimisation method and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) . Package: r-cran-rrscale Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-nloptr, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-rrscale_1.0-1.ca2604.1_all.deb Size: 208750 MD5sum: edf26c154e2b42589eae3e959c2ee5ba SHA1: 66752bfaecdd678aa7b865033c717ee0671a4f64 SHA256: e710f333720b2ced3d43171f21dcf453ff4457012082bbc147fb1ebe2462a0a0 SHA512: 21b86273f8d1fb7bdda4d2e76194c3989fa9e0744a0fccb941634a634a50618edc46a6dad1326b60ceeec85c63d4a7a37fa95b8ea832d72811afe7e33b582fc5 Homepage: https://cran.r-project.org/package=rrscale Description: CRAN Package 'rrscale' (Robust Re-Scaling to Better Recover Latent Effects in Data) Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, . Package: r-cran-rrtable Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3095 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-ggplot2, r-cran-officer, r-cran-purrr, r-cran-flextable, r-cran-rvg, r-cran-magrittr, r-cran-devemf, r-cran-moonbook, r-cran-rmarkdown, r-cran-shiny, r-cran-editdata, r-cran-shinywidgets, r-cran-ggpubr, r-cran-rlang, r-cran-readr, r-cran-ztable Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-rrtable_0.3.0-1.ca2604.1_all.deb Size: 2208788 MD5sum: 0cab77bd7b58d8c8e7a0ebe628ddf951 SHA1: 2f024923bd8b047167d7fb34f1a616c9e19249ca SHA256: d5d086ae0613dbfa11a7accd75c4635a539f7882028b1db8b3bc6c4b05e5c6fc SHA512: c855fa16a50f2d930b285be56023e80db04c52bfee3e61afd302f3739d8f34ba219c9910da6383f8a61620bac3b991aeba50994ecc78337525fd788bfd1e14be Homepage: https://cran.r-project.org/package=rrtable Description: CRAN Package 'rrtable' (Reproducible Research with a Table of R Codes) Makes documents containing plots and tables from a table of R codes. Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code. In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications. Package: r-cran-rrtcs Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sampling, r-cran-samplingvarest Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-rrtcs_0.0.4-1.ca2604.1_all.deb Size: 478306 MD5sum: d0da8c0226eaf7f5db064146c702b5ae SHA1: 0ed4d7f0da3ffc838fca15b9c5047574b2ddebf8 SHA256: 6c2d456c1c342261ff0f32b203e6fca28aae9322b1d9ebb59f51b118b3a98cc1 SHA512: cace20b7e50d6516f5b643225f08395de56d2d4efa16cc36e123ad6b029bc547189d74223a5d0f15dfeb87c8a90125a8f67dc657e9a0eba51515e2014166ecdf Homepage: https://cran.r-project.org/package=RRTCS Description: CRAN Package 'RRTCS' (Randomized Response Techniques for Complex Surveys) Point and interval estimation of linear parameters with data obtained from complex surveys (including stratified and clustered samples) when randomization techniques are used. The randomized response technique was developed to obtain estimates that are more valid when studying sensitive topics. Estimators and variances for 14 randomized response methods for qualitative variables and 7 randomized response methods for quantitative variables are also implemented. In addition, some data sets from surveys with these randomization methods are included in the package. Package: r-cran-rsa Architecture: all Version: 0.10.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-ggplot2, r-cran-lattice, r-cran-plyr, r-cran-rcolorbrewer, r-cran-aplpack Suggests: r-cran-fields, r-cran-rgl, r-cran-qgraph, r-cran-tkrplot, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-rsa_0.10.8-1.ca2604.1_all.deb Size: 410242 MD5sum: 9a54f73f2fdbdf1b3695a038cac0cbfd SHA1: cde05948e819beaac8b1b70a60e97dc31a4699ec SHA256: dae0286ae1a6c812d9a6482988af2d9ef5edbb17a0d3ef445f99aa94f4e0df98 SHA512: 0e93e238dcb100f080a44f9639c83b689e6639510fab2830637b366fd2d41942db0cb62c65478ba7236ab0e19f2dd7980551e76f64ca54f8b486281bc0cc1285 Homepage: https://cran.r-project.org/package=RSA Description: CRAN Package 'RSA' (Response Surface Analysis) Advanced response surface analysis. The main function RSA computes and compares several nested polynomial regression models (full second- or third-order polynomial, shifted and rotated squared difference model, rising ridge surfaces, basic squared difference model, asymmetric or level-dependent congruence effect models). The package provides plotting functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. Calculates many surface parameters (a1 to a5, principal axes, stationary point, eigenvalues) and provides standard, robust, or bootstrapped standard errors and confidence intervals for them. Package: r-cran-rsadbe Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rsadbe_1.0-1.ca2604.1_all.deb Size: 113222 MD5sum: 4b58ebc3c76c461e4128d14b2db8f094 SHA1: 83808ebc6fc33ea8acd9401ee8c0a54542f22a36 SHA256: 6f19d127633654b19e208fbc6e03a157c41359adffddba0b2a700779256a2b6d SHA512: 9bf2c30f549f981b3dda9e766f51afafed8609117001484cab22e66f707c0e34bf072926f52bd3517cc1b516138fb9be6f6e56bf45939c53794ff051a0535ddd Homepage: https://cran.r-project.org/package=RSADBE Description: CRAN Package 'RSADBE' (Data related to the book "R Statistical Application Developmentby Example") The package contains all the data sets related to the book written by the maintainer of the package. Package: r-cran-rsafe Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-dendextend, r-cran-ggplot2, r-cran-ggpubr, r-cran-ingredients, r-cran-sets Suggests: r-cran-gbm, r-cran-knitr, r-cran-pander, r-cran-randomforest, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-rsafe_0.1.4-1.ca2604.1_all.deb Size: 287442 MD5sum: 06fa61403594a502bf036c732416f2c6 SHA1: b53ed0d63ce5ec59dfceb3544c200126113b05d8 SHA256: feda38a997f79310f0ae75cb5f6dedbed3d417e275d418deff51885fefccb131 SHA512: 4e5ee14a42ce2b5544530e0fc83353ede40cd0ee8126d78c048ed0f0745c9f8246e81add1273884895e64c27d18a06998b526193cf2f8506f31de0d4a2ee071b Homepage: https://cran.r-project.org/package=rSAFE Description: CRAN Package 'rSAFE' (Surrogate-Assisted Feature Extraction) Provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) . Package: r-cran-rsaga Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2501 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-plyr, r-cran-shapefiles, r-cran-magrittr, r-cran-stringr Suggests: r-cran-gam, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-rsaga_1.4.2-1.ca2604.1_all.deb Size: 2070412 MD5sum: 43a1560fbeaea4c6163643861bcc573b SHA1: e5600f5b5ea3f30249dffcd07a3b23fc9200e7cf SHA256: 74f5ea1f2432ff9df0f079ed9bdf1f626397b24afb5fcd111c3b905f1d06d934 SHA512: cdc9d10d1d56425b7b503acf872d249d7d5c30410de7a159ed2dc571f78a8fe0edbcc1a96b31425924df990455d6afa7614c97a3cc842434074a96966cc91796 Homepage: https://cran.r-project.org/package=RSAGA Description: CRAN Package 'RSAGA' (SAGA Geoprocessing and Terrain Analysis) Provides access to geocomputing and terrain analysis functions of the geographical information system (GIS) 'SAGA' (System for Automated Geoscientific Analyses) from within R by running the command line version of SAGA. This package furthermore provides several R functions for handling ASCII grids, including a flexible framework for applying local functions (including predict methods of fitted models) and focal functions to multiple grids. SAGA GIS is available under GPL-2 / LGPL-2 licences from . Package: r-cran-rsagacmd Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-sf, r-cran-terra, r-cran-stars, r-cran-foreign, r-cran-stringr, r-cran-rlang, r-cran-tibble, r-cran-processx, r-cran-rvest Suggests: r-cran-dplyr, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-rsagacmd_0.4.3-1.ca2604.1_all.deb Size: 823854 MD5sum: e7eccb5b1efbcab4bad7aeeb50d86daa SHA1: 01d3272a00de0e9f3f84efd66ad38dddbc8637c5 SHA256: e294919283d22ea79d64a88a914b68a4c3532366bc3d37100c9f2a894a337658 SHA512: 7e877f9587f1b6fb6e27cc3d490744162130a8932e4eaca5b2bbf2be4c85b7934baaeeda100becceb1b4224d2da19b6688f2422d107a38ef32473bcda0a6d4f1 Homepage: https://cran.r-project.org/package=Rsagacmd Description: CRAN Package 'Rsagacmd' (Linking R with the Open-Source 'SAGA-GIS' Software) Provides an R scripting interface to the open-source 'SAGA-GIS' (System for Automated Geoscientific Analyses Geographical Information System) software. 'Rsagacmd' dynamically generates R functions for every 'SAGA-GIS' geoprocessing tool based on the user's currently installed 'SAGA-GIS' version. These functions are contained within an S3 object and are accessed as a named list of libraries and tools. This structure facilitates an easier scripting experience by organizing the large number of 'SAGA-GIS' geoprocessing tools (>700) by their respective library. Interactive scripting can fully take advantage of code autocompletion tools (e.g. in 'RStudio'), allowing for each tools syntax to be quickly recognized. Furthermore, the most common types of spatial data (via the 'terra', 'sp', and 'sf' packages) along with non-spatial data are automatically passed from R to the 'SAGA-GIS' command line tool for geoprocessing operations, and the results are loaded as the appropriate R object. Outputs from individual 'SAGA-GIS' tools can also be chained using pipes from the 'magrittr' and 'dplyr' packages to combine complex geoprocessing operations together in a single statement. 'SAGA-GIS' is available under a GPLv2 / LGPLv2 licence from including Windows x86/x64 and macOS binaries. SAGA-GIS is also included in Debian/Ubuntu default software repositories. Rsagacmd has currently been tested on 'SAGA-GIS' versions from 2.3.1 to 9.5.1 on Windows, Linux and macOS. Package: r-cran-rsahmi Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blit, r-cran-cli, r-cran-rlang, r-bioc-shortread Filename: pool/dists/resolute/main/r-cran-rsahmi_0.0.2-1.ca2604.1_all.deb Size: 4717376 MD5sum: aad66b9138d72bfe9662897fa2f33687 SHA1: 6f7c54997094e9525e21de2fc9d4cf1ed56b19e1 SHA256: 0b12875f2cab587736eac84df805c4f03c59dac4f5262058eb7b38bab3e715ff SHA512: 905f7cec45f2fd8b027a6c651d979aa40fe1c2e253680455dd77216c208d5b3f23b4e617a8a3b2f0e782f3599a9cc8fd097802295b3d70c498895711794b29d5 Homepage: https://cran.r-project.org/package=rsahmi Description: CRAN Package 'rsahmi' (Single-Cell Analysis of Host-Microbiome Interactions) A computational resource designed to accurately detect microbial nucleic acids while filtering out contaminants and false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. For more details, see Ghaddar (2023) . This implementation leverages the 'polars' package for fast and systematic microbial signal recovery and denoising from host tissue genomic sequencing. Package: r-cran-rsample Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2668 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-furrr, r-cran-generics, r-cran-glue, r-cran-lifecycle, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-slider, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-broom, r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-testthat, r-cran-whisker, r-cran-withr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-rsample_1.3.2-1.ca2604.1_all.deb Size: 1665734 MD5sum: 3b03f04b15cef1da5e19a9b599271828 SHA1: 4b2ca2b195fd5af3a548478c843aa868367a65fb SHA256: 12bfcf369213d623c965ca8e9fbc258577f379d4077b3cd2ec5da68516ca0eea SHA512: 7042bfee0a85f0b5247e06deb1861ee1e85aa2e2c71af2be643cca6edbdb100fbedabcb24a977a4c8096b232d8192e9f582707cee742d8046b09f017f4ff2853 Homepage: https://cran.r-project.org/package=rsample Description: CRAN Package 'rsample' (General Resampling Infrastructure) Classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation). Package: r-cran-rsampling Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 862 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rsampling_0.1.1-1.ca2604.1_all.deb Size: 409526 MD5sum: a31dee913fb3cea381eea3607c188535 SHA1: c76df09290c7016648d5880c32b2d619cfb5901a SHA256: 31b1fb2827b1fc788539a0ab0b2f3cb37621a23a4a3b07629be496a6adc589ab SHA512: 68eb723af261c9d494b1cc07acb129145151bd2dcc0297ac7847bd8b11e40d91db0791c4bc244c5523bb038c6078a67dac6658da184aff913ec9cdcefa1fd21f Homepage: https://cran.r-project.org/package=Rsampling Description: CRAN Package 'Rsampling' (Ports the Workflow of "Resampling Stats" Add-in to R) Resampling Stats (http://www.resample.com) is an add-in for running randomization tests in Excel worksheets. The workflow is (1) to define a statistic of interest that can be calculated from a data table, (2) to randomize rows ad/or columns of a data table to simulate a null hypothesis and (3) and to score the value of the statistic from many randomizations. The relative frequency distribution of the statistic in the simulations is then used to infer the probability of the observed value be generated by the null process (probability of Type I error). This package intends to translate this logic for R for teaching purposes. Keeping the original workflow is favored over performance. Package: r-cran-rsat Architecture: all Version: 0.1.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-curl, r-cran-httr, r-cran-leafem, r-cran-leaflet, r-cran-rjson, r-cran-rvest, r-cran-tmap, r-cran-xml2, r-cran-zip, r-cran-rdpack, r-cran-fields, r-cran-calendr, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-sp, r-cran-raster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rsat_0.1.21-1.ca2604.1_all.deb Size: 2913090 MD5sum: 2894d19991c6554ff2da8eebdc143418 SHA1: 5554d270803734863f1150684ce621481f0d66f4 SHA256: 0e2fa919c249b47a5132a8b8c83fa777aaf8257392b2897e44954766a50ace16 SHA512: 43e173fee3bae2d0f68a4bb66f421c1c756a403adee411c9cd269260ec7695d4032f1573a95b5b8f607433f9ca799c604b8b52203b15142ce0c66ba25e80a283 Homepage: https://cran.r-project.org/package=rsat Description: CRAN Package 'rsat' (Dealing with Multiplatform Satellite Images) Downloading, customizing, and processing time series of satellite images for a region of interest. 'rsat' functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. 'rsat' also offers capabilities for customizing satellite images, such as tile mosaicking, image cropping and new variables computation. Finally, 'rsat' covers the processing, including cloud masking, compositing and gap-filling/smoothing time series of images (Militino et al., 2018 and Militino et al., 2019 ). Package: r-cran-rsatools Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-semtools, r-cran-rsa, r-cran-ggplot2, r-cran-lattice, r-cran-plyr, r-cran-rcolorbrewer, r-cran-aplpack, r-cran-mass Suggests: r-cran-fields, r-cran-rgl, r-cran-qgraph, r-cran-tkrplot, r-cran-testthat, r-cran-covr, r-cran-psych, r-cran-ggpubr Filename: pool/dists/resolute/main/r-cran-rsatools_0.1.2-1.ca2604.1_all.deb Size: 2696842 MD5sum: ae49c82149a2cbf5709cafc9a381262a SHA1: 63b4a4ae55de1f777d64f7a8cbe2bfc75e97fb63 SHA256: 37b7b6459a5e346bbe8fcb97e60fceaf9844692a98c6dbafd99c6d2e6423abd2 SHA512: c14faa61709a69f18039f3930775a732f2d98a3145eacd5757ad9ed342c0faa179db540e0e4dad05b039d6295e1a85de16898d75aadd4fc171306f9ad988f9ed Homepage: https://cran.r-project.org/package=RSAtools Description: CRAN Package 'RSAtools' (Advanced Response Surface Analysis) Provides tools for response surface analysis, using a comparative framework that identifies best-fitting solutions across 37 families of polynomials. Many of these tools are based upon and extend the 'RSA' package, by testing a larger scope of polynomials (+27 families), more diverse response surface probing techniques (+acceleration points), more plots (+line of congruence, +line of incongruence, both with extrema), and other useful functions for exporting results. Package: r-cran-rsatscan Architecture: all Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/resolute/main/r-cran-rsatscan_1.0.10-1.ca2604.1_all.deb Size: 135894 MD5sum: 9a87fd6d380af637d44580d937290028 SHA1: b189101fffa4c425352ce204d4e32ed687436ff8 SHA256: 5d47737decf14f82742e208cdc936c446146b92cd33186d97c26dea91f41aff9 SHA512: 7ab36df677854425ae39d3d127f1b4896fbdea24dd1c38ab78872785e91602292f6724ec15aed1197fae38a8a57286d9dba897b08e947e04cf59b4e4633c3ca1 Homepage: https://cran.r-project.org/package=rsatscan Description: CRAN Package 'rsatscan' (Tools for Running the External 'SaTScan' Software using RClasses and Methods) The 'SaTScan'(TM) software uses spatial and space-time scan statistics to detect and evaluate spatial and space-time clusters. 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The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) for the covariance-based method and Bolin and Kirchner (2020) for the operator-based rational approximation. These can be generated by the citation function in R. Package: r-cran-rspincalc Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rspincalc_1.0.2-1.ca2604.1_all.deb Size: 148344 MD5sum: 10fd5b0b44f5474b7505075c5eb50bc8 SHA1: ea8b53ffa4c15a14ec3b2796b2583df77d237d89 SHA256: aadc2f4d3b78150c48aa5f1b3b353da56d6aeb71a07480710db7fa9d80c3db71 SHA512: 593260a3900f6fa93b3688b4df376ee18135e85f62fac40d892862ae9dd89c484faec5120550a846b1d377697231a024f9e8397caabcf873917c96e55ca2fe67 Homepage: https://cran.r-project.org/package=RSpincalc Description: CRAN Package 'RSpincalc' (Conversion Between Attitude Representations of DCM, EulerAngles, Quaternions, and Euler Vectors) Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors. Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx). 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Package: r-cran-rspiro Architecture: all Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rspiro_0.5-1.ca2604.1_all.deb Size: 313908 MD5sum: c90a0978b4120263a3ffb3989c26addb SHA1: a90715bc2bd5081be140214641433a02128453ca SHA256: caafc0c279c3677c956db47d86bfd883b4ff1c59c26d413f954b096a48c47d73 SHA512: 8d72db7d5908db75cb19bfb00c373639c2285fa76e5ff5d20d7472f5182964f0faacb551dee24997c8cb9116aac21fc5b32852df2164a7188d0b528e2dc10e3b Homepage: https://cran.r-project.org/package=rspiro Description: CRAN Package 'rspiro' (Implementation of Spirometry Equations) Implementation of various spirometry equations in R, currently the GLI-2012 (Global Lung Initiative; Quanjer et al. 2012 ), the race-neutral GLI global 2022 (Global Lung Initiative; Bowerman et al. 2023 ), the NHANES3 (National Health and Nutrition Examination Survey; Hankinson et al. 1999 ) and the JRS 2014 (Japanese Respiratory Society; Kubota et al. 2014 ) equations. 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It gives an option to filter out non-mediators using variable selection methods. The original R package is directly related to the paper Yang et al (2021) "Estimation of mediation effect for high-dimensional omics mediators with application to the Framingham Heart Study" . The new version contains a choice of using cross-fitting, which is computationally faster. The details of the cross-fitting method are available in the paper Xu et al (2023) "Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting" . 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Package: r-cran-rstudiothemes Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2254 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-colorspace, r-cran-dplyr, r-cran-jsonlite, r-cran-rstudioapi, r-cran-sass, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-knitr, r-cran-quarto, r-cran-testthat, r-cran-uuid Filename: pool/dists/resolute/main/r-cran-rstudiothemes_1.1.1-1.ca2604.1_all.deb Size: 397582 MD5sum: 77016f041abbc9ae14c6940dc2666486 SHA1: 9ad45011a46e016056f27cd2f72a94cb1565482c SHA256: d575713f8f8c80b533b38e14f578904696dddd82fa52f7ac3b2f7ab6efdc0d62 SHA512: e963a1e41543490defa860c5d3720e838bb43838364a7b3d6d1210cc6f608ca9a36e024cfcfe18b278630be002dbd33af55ea4e20db7f26ef21d57900d9e9f82 Homepage: https://cran.r-project.org/package=rstudiothemes Description: CRAN Package 'rstudiothemes' (Create 'RStudio' Themes from Visual Studio Code, Positron and'TextMate' Themes) Create and install 'RStudio' themes derived from Visual Studio Code, Positron and 'TextMate' themes. Provides functions to convert between 'TextMate' and Visual Studio Code or Positron themes, as well as ports of several Visual Studio Code themes. Package: r-cran-rsubgroup Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2983 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-foreign Filename: pool/dists/resolute/main/r-cran-rsubgroup_1.1-1.ca2604.1_all.deb Size: 2657198 MD5sum: 91ebd99ce3b28c63375f1c61b2c9b0a0 SHA1: fe60a8e76d4fad4a27832be7e4184273f8d1261e SHA256: d0a4e93d175ce0a3b534405fc6278ab40ee9a0e246d57ed013e36bd3db6d0ba2 SHA512: 0e5034184e65551c994adb7f89b9edfd318242f10c62fcabd42b6a8d9046bdea7f562bfe36562666b1712c030a3ab725abdfb935c788db3fa09702468537e07a Homepage: https://cran.r-project.org/package=rsubgroup Description: CRAN Package 'rsubgroup' (Subgroup Discovery and Analytics) A collection of efficient and effective tools and algorithms for subgroup discovery and analytics. The package integrates an R interface to the org.vikamine.kernel library of the VIKAMINE system implementing subgroup discovery, pattern mining and analytics in Java. Package: r-cran-rsurface Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-rsm Filename: pool/dists/resolute/main/r-cran-rsurface_1.1.0-1.ca2604.1_all.deb Size: 31724 MD5sum: 20302a261ce65e8f092e66a992d8b8c7 SHA1: 3a0da375af1e9ebcc25f5278ec01aaf6978bfcad SHA256: 956656aed04ec2e2cb598a477986160c09e03ab6adb10127447999a57c184fa3 SHA512: 9580368a512412c64b2bf3e3f7e1eb36ed550fe0a3b566f7b76970e13b26a05ae93ec77573d38bbb5b2c105b67431ae27c9c702d36dc5492987713fb99a414bf Homepage: https://cran.r-project.org/package=rsurface Description: CRAN Package 'rsurface' (Design of Rotatable Central Composite Experiments and ResponseSurface Analysis) Produces tables with the level of replication (number of replicates) and the experimental uncoded values of the quantitative factors to be used for rotatable Central Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding variance of the predicted response according to Mead et al. (2012) design_ccd(), and analyzes CCD data with response surface methodology ccd_analysis(). A rotatable CCD provides values of the variance of the predicted response that are concentrically distributed around the average treatment combination used in the experimentation, which with uniform precision (implied by the use of several replicates at the average treatment combination) improves greatly the search and finding of an optimum response. These properties of a rotatable CCD represent undeniable advantages over the classical factorial design, as discussed by Panneton et al. (1999) and Mead et al. (2012) among others. Package: r-cran-rsurrogate Architecture: all Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-rsurrogate_3.2-1.ca2604.1_all.deb Size: 390530 MD5sum: 8d3f7d37c6bdedcf4bd52635c3b04c68 SHA1: 5e00c6da5fbbbd19e2f503e7626d8ecc5b408f91 SHA256: 454370698f34127f3af7cd66f08ac9c7c72cd74ef79795de699541c3846098fd SHA512: dd0b86924ab9f6bd8989ebe55a9f331e48f39d0d7cb4b1ae1869e31358f3f8749906833dcf0770637a9a7a1fc06c92c0dd953f9514140d5cfa56e5dbbde7b6df Homepage: https://cran.r-project.org/package=Rsurrogate Description: CRAN Package 'Rsurrogate' (Robust Estimation of the Proportion of Treatment EffectExplained by Surrogate Marker Information) Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker. 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The package 'rsurv' also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package 'rsurv' lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package 'rsurv' can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package 'rsurv' can be found in Demarqui (2024) . Package: r-cran-rsurveillance Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epitools, r-cran-epir, r-cran-mc2d Filename: pool/dists/resolute/main/r-cran-rsurveillance_0.2.1-1.ca2604.1_all.deb Size: 174970 MD5sum: 42714c476ec796a7642263052e538219 SHA1: d98f58622211c56bfacb269f1103a7c82637fa52 SHA256: 6dc714fd897d0e04a09e857df43fb91f411332fc0f919043b3c21ae279810e38 SHA512: 06fd420959ac57e52738c725462586d903c929d292559045fd97f09212ea980c024f8a9201877787c6220aa718aec5a5bc46087d3a1d47278e431f8dee35e620 Homepage: https://cran.r-project.org/package=RSurveillance Description: CRAN Package 'RSurveillance' (Design and Analysis of Disease Surveillance Activities) A range of functions for the design and analysis of disease surveillance activities. These functions were originally developed for animal health surveillance activities but can be equally applied to aquatic animal, wildlife, plant and human health surveillance activities. Utilities are included for sample size calculation and analysis of representative surveys for disease freedom, risk-based studies for disease freedom and for prevalence estimation. This package is based on Cameron A., Conraths F., Frohlich A., Schauer B., Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision making tools for implementing risk-based surveillance, Grant Number no. 310806, RISKSUR (). Many of the 'RSurveillance' functions are incorporated into the 'epitools' website: Sergeant, ESG, 2019. Epitools epidemiological calculators. Ausvet Pty Ltd. Available at: . Package: r-cran-rsurveycto Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-data.table, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-readxl, r-cran-rlang, r-cran-vctrs, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rsurveycto_0.2.6-1.ca2604.1_all.deb Size: 85986 MD5sum: 51deb11cada8f45667fda994f78afa04 SHA1: 861d05dcb7465b578715dfb0d046f9234cba4707 SHA256: 13d8bd7b21ef7119749daf6cb41fd3cc562cb1495f775b393244e6b4b2de6469 SHA512: ac5b583643e37262ef7be1f67d7a6017d5f3d88757429e2fcf6f738babc4ac70440261aec1ccbc34a37561796d27efed64dac40f5a481a416b38c259b679a5dc Homepage: https://cran.r-project.org/package=rsurveycto Description: CRAN Package 'rsurveycto' (Interact with Data on 'SurveyCTO') 'SurveyCTO' is a platform for mobile data collection in offline settings. The 'rsurveycto' R package uses the 'SurveyCTO' REST API to read datasets and forms from a 'SurveyCTO' server into R as 'data.table's and to download file attachments. The package also has limited support to write datasets to a server. Package: r-cran-rsurvstat Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-xml2, r-cran-stringr, r-cran-tibble, r-cran-httr, r-cran-curl, r-cran-whisker, r-cran-fs, r-cran-purrr, r-cran-tidyr, r-cran-cli, r-cran-locfit, r-cran-rlang, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rsurvstat_0.1.4-1.ca2604.1_all.deb Size: 1131884 MD5sum: 6c98a6b655054a0e570ee853185be1d1 SHA1: 60ee25801349787f84170fb8553d1bce631d9d92 SHA256: 94c4e568884d8b71e49538e601383a2c98c3aa6f890b634dd87b52b7e5fa5383 SHA512: 94bd2aa8db7773c333833264a6b590eb3cdab9ee6788f68ff94bae91e40038586ac6d7bbe99cbab6fd6ebc864e9aea0112a13c3b138b9e37f9dcc913d650a7b8 Homepage: https://cran.r-project.org/package=rsurvstat Description: CRAN Package 'rsurvstat' (Download Infectious Disease Data from 'SurvStat' (Robert KochInstitute)) Provides an interface to the 'SurvStat' web service from the Robert Koch Institute () allowing downloads of disease time series stratified by pathogen type and subtype, age, and geography from notifiable disease reports in Germany. Package: r-cran-rsvd Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3540 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rsvd_1.0.5-1.ca2604.1_all.deb Size: 3588232 MD5sum: 4a06d5f3daa50e89159ae1f691ade6a0 SHA1: 9dcc1961de6e6e92c9d15f8a01f1d5513787c4e6 SHA256: d39d04e0de2338bfb1f1ce7b37b79ebd12459d1a3e060970ad61f8ceb9559f16 SHA512: 6e3176bc62d927c703384cf5410b9675e6d313aa9d83c78263ac805110aee96a56eb1b3975206ee4499d96b3e9ce9eb7993e0b0b3b2ed0e10d6d9fdb91ce287e Homepage: https://cran.r-project.org/package=rsvd Description: CRAN Package 'rsvd' (Randomized Singular Value Decomposition) Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. Package: r-cran-rsyncrosim Architecture: all Version: 2.1.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-gtools, r-cran-processx, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-rcpp, r-cran-rmarkdown, r-cran-terra Filename: pool/dists/resolute/main/r-cran-rsyncrosim_2.1.13-1.ca2604.1_all.deb Size: 689864 MD5sum: 22310aeb17774e93153f3ead4316d87b SHA1: 5f42b620b765d789e074788254ab34902ea85e6e SHA256: 291c810a8d60e7bb2281714fa824fbe694a292f762b777308ba31b083f02238a SHA512: 8d288d4a22581f9ecfe662b333d2db27eb4d526fc6b4773357d6a1e30b1b9a28c567664e437393d85a7176c70f5b015eb0587a4c13bb6f9e1a379e9a633c73d0 Homepage: https://cran.r-project.org/package=rsyncrosim Description: CRAN Package 'rsyncrosim' (The R Interface to 'SyncroSim') 'SyncroSim' is a generalized framework for managing scenario-based datasets (). 'rsyncrosim' provides an interface to 'SyncroSim'. Simulation models can be added to 'SyncroSim' in order to transform these datasets, taking advantage of general features such as defining scenarios of model inputs, running Monte Carlo simulations, and summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 2.3.5 or higher (API documentation: ). Package: r-cran-rsyntax Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-tidyselect, r-cran-stringi, r-cran-digest, r-cran-rlang, r-cran-magrittr, r-cran-tokenbrowser, r-cran-base64enc, r-cran-png, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rsyntax_0.1.4-1.ca2604.1_all.deb Size: 418700 MD5sum: c9d55193c766a206c9176feb356e77e6 SHA1: 262c18159f430dad191a3fd44be6ce042cad2062 SHA256: b23bae3dc34c146709fdedd155b00ec5e1c79bcf4e866180f35ada0d621c3ca2 SHA512: fa6906621c4a379b9f918a1c25792eb020dadb5ebc5acfb1ee319391902a9b8496b59435cf2a6d395710f462ae5c614196fe006072986bce80f394c352a1a3bc Homepage: https://cran.r-project.org/package=rsyntax Description: CRAN Package 'rsyntax' (Extract Semantic Relations from Text by Querying and ReshapingSyntax) Various functions for querying and reshaping dependency trees, as for instance created with the 'spacyr' or 'udpipe' packages. This enables the automatic extraction of useful semantic relations from texts, such as quotes (who said what) and clauses (who did what). Method proposed in Van Atteveldt et al. (2017) . Package: r-cran-rsynthbio Architecture: all Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-getpass, r-cran-keyring, r-cran-jsonlite, r-cran-httr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-rsynthbio_4.1.0-1.ca2604.1_all.deb Size: 85974 MD5sum: 8f6f9d59d999d064ccaca8f80e20748a SHA1: 86c3bbe56d87c074f5147de7d3bd258c06eaa7c2 SHA256: 5f47d27bf3b1e52a0f53c75364403a22a8883e93570c724d216fcb8588788caf SHA512: 3bb84eb62a38acd8a9a1e7fb79d1a1a41b3a63f7a2fc3760affbc1a8de171a6fe0662f29d23f13e260cd837bd6338071cb537b1f3aae5a3ea3bfeb7b47cbf563 Homepage: https://cran.r-project.org/package=rsynthbio Description: CRAN Package 'rsynthbio' (Synthesize Bio API Wrapper) Access Synthesize Bio models from their API using this wrapper that provides a convenient interface to the Synthesize Bio API, allowing users to generate realistic gene expression data based on specified biological conditions. This package enables researchers to easily access AI-generated transcriptomic data for various modalities including bulk RNA-seq, single-cell RNA-seq, microarray data, and more. Package: r-cran-rt.test Architecture: all Version: 1.18.7.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rt.test_1.18.7.9-1.ca2604.1_all.deb Size: 601186 MD5sum: 0c8fc88b04f3b35735ea53da2c712cf9 SHA1: ff14eded7005a1c325cfe08fae47bce0c98b2fd9 SHA256: df1e925d01da4724b6c38744cab4bca2b03666665cfdd7a82d208f74ea3ebaed SHA512: bd35c7ecaf99c0cad7beb16917fae51ab1a1c4a49c7ebe420e07001bb52f0ba1425631d49fd8d5bea907e030c768bd2b0ff5fa9059bbb4d59e526a3542962774 Homepage: https://cran.r-project.org/package=rt.test Description: CRAN Package 'rt.test' (Robustified t-Test) Performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). 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Package: r-cran-rtemis Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-s7, r-cran-data.table, r-cran-future, r-cran-htmltools, r-cran-cli Suggests: r-cran-arrow, r-cran-bit64, r-cran-car, r-cran-colorspace, r-cran-dbi, r-cran-dbscan, r-cran-dendextend, r-cran-duckdb, r-cran-e1071, r-cran-farff, r-cran-fastica, r-cran-flexclust, r-cran-future.apply, r-cran-future.mirai, r-cran-futurize, r-cran-geosphere, r-cran-ggplot2, r-cran-glmnet, r-cran-geojsonio, r-cran-glue, r-cran-gsubfn, r-cran-haven, r-cran-heatmaply, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-leaflet, r-cran-leaps, r-cran-lightauc, r-cran-lightgbm, r-cran-matrixstats, r-cran-mgcv, r-cran-mice, r-cran-mirai, r-cran-missranger, r-cran-networkd3, r-cran-nmf, r-cran-openxlsx, r-cran-parallelly, r-cran-partykit, r-cran-plotly, r-cran-proc, r-cran-progressr, r-cran-psych, r-cran-pvclust, r-cran-ranger, r-cran-reactable, r-cran-readxl, r-cran-reticulate, r-cran-rocr, r-cran-rpart, r-cran-rtsne, r-cran-seqinr, r-cran-sf, r-cran-shapr, r-cran-survival, r-cran-tabnet, r-cran-threejs, r-cran-testthat, r-cran-tibble, r-cran-timedate, r-cran-toml, r-cran-torch, r-cran-uwot, r-cran-vegan, r-cran-vroom, r-cran-withr Filename: pool/dists/resolute/main/r-cran-rtemis_1.0.0-1.ca2604.1_all.deb Size: 3121516 MD5sum: 1a50a3ae672584e0da4128f44b8c2885 SHA1: 5659a52c2dae7d6b92c9f9e59a26b93771d61697 SHA256: f09501ef02ebb51390c33282434f2831fe672bb84a2d08a155c15db9aec64599 SHA512: 1955d2d348076a30ac171c80df2708aa2c3642f9c6de8b4d1cae929323ae21d7ab4ac6a5e3e136b89a3198f2195b333e5aaf0f5aa0ab0959d1e126f7bc1b4c05 Homepage: https://cran.r-project.org/package=rtemis Description: CRAN Package 'rtemis' (Machine Learning and Visualization) Machine learning and visualization package with an 'S7' backend featuring comprehensive type checking and validation, paired with an efficient functional user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and unsupervised learning. 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Package: r-cran-rtmpinv Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rclsp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rtmpinv_1.0.0-1.ca2604.1_all.deb Size: 40580 MD5sum: d4e4f02a32ea4ab4945f9dc7c7bfdcae SHA1: a6a50e1e50af528bc07ec19f41a4e41c85f1476c SHA256: cd0fe4117c7f5b9997e7328b1542f69ce0b024787ecd329d552e9291b2a0ba20 SHA512: c64dd233073e2af4efd230fa6fae593d86eea630b273b1f696c56082bbb965ba2956ee0f0320d7a3dd893d0f591ed8c8edbd9a0e792ea9180fd0ca0dd538e0ab Homepage: https://cran.r-project.org/package=rtmpinv Description: CRAN Package 'rtmpinv' (Tabular Matrix Problems via Pseudoinverse Estimation) The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. 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The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net). 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Scores are based on player-specific input probabilities (out, single, double, triple, walk, and homerun). Optional inputs include probability of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether a player is "fast" (e.g. the player could stretch home). These probabilities may be calculated from common player statistics that are publicly available on team's webpages. Scores are evaluated based on a nine-player lineup and may be used to compare lineups, evaluate base scenarios, and compare the offensive potential of individual players. Manuscript forthcoming. See Bukiet & Harold (1997) for implementation of discrete Markov chains. 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Package: r-cran-rvhpdt Architecture: all Version: 4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-rvhpdt_4.0-1.ca2604.1_all.deb Size: 89836 MD5sum: aaa65caec9439c6a03095c85543079ad SHA1: 63ab29b7398ad705bc1330bd3f659b241e536d85 SHA256: 835425f4e82d9611ebe9d1ed5d3379b8db4bd477b4f324d5e806da9b4876b113 SHA512: 90d9f8527ad4420922d41a9ce8aa1f60ad25b5cdc7ba228165d6f30c2a78d650240e180685c4946ead02fe0c7d0a0d1714a00e0c3e650ade9a1a52ee3121dcb3 Homepage: https://cran.r-project.org/package=rvHPDT Description: CRAN Package 'rvHPDT' (Calling Haplotype-Based and Variant-Based PedigreeDisequilibrium Test for Rare Variants in Pedigrees) To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping. Package: r-cran-rviennacl Architecture: all Version: 1.7.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4965 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rviennacl_1.7.1.8-1.ca2604.1_all.deb Size: 381756 MD5sum: 4e38a9a6c6669776212ddbf3c5c3d3fc SHA1: a457addb6617f99db379178cf062cf6b3221d8ac SHA256: 8b57a5833e7bf00d585595f64e48c456bb505bbaefb925767d6bd249d35e8555 SHA512: 25abcbafb892ff9404899ddaaedc44d7aa47af3bbb1e86f33bc60cf0d9e5bc7815a98020168de1a1e2a846e6515543f509189d301bae5066fdb6eeef9d04ffd5 Homepage: https://cran.r-project.org/package=RViennaCL Description: CRAN Package 'RViennaCL' ('ViennaCL' C++ Header Files) 'ViennaCL' is a free open-source linear algebra library for computations on many-core architectures (GPUs, MIC) and multi-core CPUs. 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Package: r-cran-rvif Architecture: all Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3630 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multicoll, r-cran-car Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rvif_3.2-1.ca2604.1_all.deb Size: 1270262 MD5sum: d9d2f9169076fe04c941bec601f91ca1 SHA1: ca82f7d7a42c0d8be5e5a9a3a7bfe9170e5b0c3b SHA256: 49da0e5ffe30c38e53bc8c20df0e9bf6c331dc48f4bf499d1b55f1f5fb6b91ba SHA512: b01028cce23fafdd9734d7359201ea11593abb5e9eb682185431d982dbcc2005e5bf01cd66b7440e24183724e2bd7de55dedf374bbdd3061817cf14455bdd053 Homepage: https://cran.r-project.org/package=rvif Description: CRAN Package 'rvif' (Collinearity Detection using Redefined Variance Inflation Factorand Graphical Methods) The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. This package is focused on determining whether or not the degree of approximate multicollinearity in a multiple linear regression model is of concern, meaning that it affects the statistical analysis (i.e. individual significance tests) of the model. This objective is achieved by using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., García C.B. and García J. (2018) , Salmerón, R., Rodríguez, A. and García C. (2020) , Salmerón, R., García, C.B, Rodríguez, A. and García, C. (2022) , Salmerón, R., García, C.B. and García, J. (2025) and Salmerón, R., García, C.B, García J. (2023, working paper) . You can also view the package vignette using 'browseVignettes("rvif")', the package website () using 'browseURL(system.file("docs/index.html", package = "rvif"))' or version control on GitHub (). 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On all platforms the package first honours a user-supplied 'VTK_DIR' environment variable. On macOS it then tries 'Homebrew', followed by 'pkg-config'. On Linux it tries 'pkg-config' and well-known system prefixes ('/usr', '/usr/local'). If no suitable system installation is found on macOS or Linux, pre-built static libraries are downloaded automatically from the package's GitHub releases. On Windows the package tries 'VTK_DIR', then 'Rtools45' 'pacman', then common 'MSYS2' prefixes, accepting both static ('.a') and shared ('.dll.a' import libs + DLLs) installations. When shared libraries are used, the VTK DLLs are staged in 'inst/vtk-dlls/' and an '.onLoad' hook prepends that directory to PATH via 'Sys.setenv()' when the package is loaded, and restored in '.onUnload()'. The pre-built fallback downloads static libraries by default; set 'VTK_LINK_TYPE=shared' before installation to download the DLL build instead. Note that on Windows the modules 'VTK_IONetCDF', 'VTK_IOHDF', 'VTK_GeovisCore', and 'VTK_RenderingCore' are disabled because 'netcdf' and 'libproj' are not available in the 'Rtools45' 'static.posix' sysroot. Downstream packages can declare 'Imports: rvtk' and obtain the correct compiler and linker flags at install time via rvtk::CppFlags() and rvtk::LdFlagsFile(). 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For more information see official documents . Package: r-cran-ryoutheria Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-rjsonio, r-cran-reshape2, r-cran-rcurl Suggests: r-cran-knitr, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-ryoutheria_1.0.3-1.ca2604.1_all.deb Size: 41062 MD5sum: 05dd36454ae41e22da2feb0c76b061fa SHA1: d1f8ab4bffc9252d34f96a892dc9649833e7dba3 SHA256: c237f1bd2e73557ff5e221e7923f065eb40878608a011cb935df677178c79811 SHA512: c233f8e7a1dcdff68d77c5ba79f613eed768ffbc62d0c1ee21061894da9fba754eff728685c251e65c2e7c620f8185627ee1eee6b03241d2cefd24ad51fcb042 Homepage: https://cran.r-project.org/package=rYoutheria Description: CRAN Package 'rYoutheria' (Access to the YouTheria Mammal Trait Database) A programmatic interface to web-services of YouTheria. YouTheria is an online database of mammalian trait data . 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The aim of the current package is to effectively distinguish the top-ranked genotypes in MET (Multi-Environmental Trials). For a detailed explanation of the process of obtaining "WAASB", "WAASBY" indices, and a "Y*WAASB" biplot, refer to the manual included in this package as well as the study by Olivoto & Lúcio (2020) . In this context, "WAASB" refers to the "Weighted Average of Absolute Scores" provided by Olivoto et al. (2019) , which quantifies the stability of genotypes across different environments using linear mixed-effect models. To run the package, you need to extract the "WAASB" and "WAASBY" coefficients using the 'metan' and apply them. This tool utilizes PCA (Principal Component Analysis) and differentiates the entries which may be genotypes, hybrids, varieties, etc using "WAASB", "WAASBY", and a combination of the specified trait and WAASB index. Package: r-cran-rzentra Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-rzentra_0.1.0-1.ca2604.1_all.deb Size: 28080 MD5sum: 1d2906f9d9f84c2430e769e9628a41b3 SHA1: b20254e444fa55e308935c3db806b7c19eef83fd SHA256: 59e91a3aada1a8d4b1f23cce2d4512d02aaf14be704ed6a548e658225b98475b SHA512: 0ccfa40a898d7b73d196825e1208b8c73bc6a4936bd192c5dc5a0c081fccc4bc391b51d3384ddbe51b540aeaa17c650d5c5995193a58c1b5c2364bcc6810e6cd Homepage: https://cran.r-project.org/package=rzentra Description: CRAN Package 'rzentra' (Client for the 'ZENTRA Cloud' API) Provides functionality to read settings, statuses and readings of weather stations from the 'ZENTRA Cloud' API . 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Intended for seasonal to decadal (s2d) climate forecast verification, but also applicable to other types of forecasts or general climate analysis. This package is specifically designed for comparing experimental and observational datasets. It provides functionality for data retrieval, post-processing, skill score computation against observations, and visualization. Compared to 's2dverification', 's2dv' is more compatible with the package 'startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The Climate Data Operators (CDO) version used in development is 1.9.8. Implements methods described in Wilks (2011) , DelSole and Tippett (2016) , Kharin et al. (2012) , Doblas-Reyes et al. (2003) . 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Package: r-cran-s3 Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-prettyunits, r-cran-fs, r-cran-rlang, r-cran-httr, r-cran-glue, r-cran-purrr, r-cran-dplyr Suggests: r-cran-aws.signature, r-cran-testthat, r-cran-digest, r-cran-withr Filename: pool/dists/resolute/main/r-cran-s3_1.1.0-1.ca2604.1_all.deb Size: 29494 MD5sum: f0accaba7d7fd4def6fc1e0f6bb44165 SHA1: f7f965cb31ffa28d365d38d2af3df2b357e1f7e1 SHA256: abcf7977619960fd65530d16ca80a620c8bad67d279a0a04bee0755d24ed6272 SHA512: 96dfb6c1820c7ce3a50347505f61d0d155c611f565d7d01b0d8baf1c0881413f29e14f8edccb5345d470a59bfc496f4baf7e8f66cf29a33232803a52eac4f990 Homepage: https://cran.r-project.org/package=s3 Description: CRAN Package 's3' (Download Files from 'AWS S3') Download files hosted on 'AWS S3' (Amazon Web Services Simple Storage Service; ) to a local directory based on their URI. Avoid downloading files that are already present locally. Allow for customization of where to store downloaded files. Package: r-cran-s3fs Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-r6, r-cran-data.table, r-cran-fs, r-cran-future, r-cran-future.apply, r-cran-lgr, r-cran-paws.storage Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-s3fs_0.1.7-1.ca2604.1_all.deb Size: 278448 MD5sum: bcfd5eb1b453fba512baee978767ed33 SHA1: 5eaa6a2c36427841391e740dd99f517e5bda0f1d SHA256: fae095d675034ebb1f90d56752c0c54d3e4097b7b95c46f17674af4bcafe7660 SHA512: caa633247d249acea52a2b3d98f7ce4dea1e011037f26fd75d6196a8f41debba8a6ef393e1cf0d00130a96474e6b80ee40f9ed5f0ca454f1f43183f3a33b147e Homepage: https://cran.r-project.org/package=s3fs Description: CRAN Package 's3fs' ('Amazon Web Service S3' File System) Access 'Amazon Web Service Simple Storage Service' ('S3') as if it were a file system. Interface based on the R package 'fs'. Package: r-cran-s3vs Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-survival, r-cran-mombf, r-cran-future.apply, r-cran-eha, r-cran-pec, r-cran-aftgee, r-cran-afthd Suggests: r-cran-rjags, r-cran-knitr, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-s3vs_1.0-1.ca2604.1_all.deb Size: 461394 MD5sum: caee1a63e01aa8ffaa24c839cdda3531 SHA1: 862b77959bfbccb2372422054c4435d523e4ce21 SHA256: b502e0852c6afcda1261b6b80625499b1fb037ed6426a1fbffdbaaf620ccd4a7 SHA512: 60225a74842d2d3a938bf282de5ae0a7e5371b56f3518ecd32c8089a8607e24e31569c55121a2e3b1b7c85463dbe5d0dcee22594ab24306543d6e3b167f75313 Homepage: https://cran.r-project.org/package=S3VS Description: CRAN Package 'S3VS' (Structured Screen-and-Select Variable Selection in Linear,Generalized Linear, and Survival Models) Performs variable selection using the structured screen-and-select (S3VS) framework in linear models, generalized linear models with binary data, and survival models such as the Cox model and accelerated failure time (AFT) model. Package: r-cran-s4dm Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-densratio, r-cran-flexclust, r-cran-geometry, r-cran-kernlab, r-cran-maxnet, r-cran-mvtnorm, r-cran-np, r-cran-proc, r-cran-robust, r-cran-rvinecopulib, r-cran-sf, r-cran-terra, r-cran-dplyr, r-cran-rdpack Suggests: r-cran-geodata, r-cran-bien, r-cran-ggplot2, r-cran-tidyterra, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-s4dm_0.0.2-1.ca2604.1_all.deb Size: 241072 MD5sum: 55e2b3b7f4eaf4e9b492672a009dc84f SHA1: a6c6dad61be22af1e75069999707dc4401cc4621 SHA256: 57f4119e6af328ea52bf2cb7ab46bb791315452bf94a9d80e070eeaa0e9b50f9 SHA512: 1d7d43c4ea24bb914b5d33cc804a06fd88e36a60dd4450c1ca064182a800eee96164ae34225be913946d2dd2142be58e6ec3fa2ad52ff6b70f587f35edd740dc Homepage: https://cran.r-project.org/package=S4DM Description: CRAN Package 'S4DM' (Small Sample Size Species Distribution Modeling) Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Maitner et al. (2026) , Drake and Richards (2018) , Drake (2015) , and Drake (2014) . Package: r-cran-s7contract Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-s7contract_0.1.0-1.ca2604.1_all.deb Size: 121608 MD5sum: 60fbd81910e0c7257b6bc806d216dab5 SHA1: bfad6267f6d62b683f703af4308356b033f00ba6 SHA256: 60eff93d0fc69dfaf46249ccbcf9484a13447497e74a768100cdac1005e090ac SHA512: b41781930640889da3ca66219ee67a627230dfb23e1dab0ce682374139258e22df03b8d80c2be7aa1ec7cd1327ccd45753db24646def5d6b4603705da250377b Homepage: https://cran.r-project.org/package=s7contract Description: CRAN Package 's7contract' ('Go'-Like Interfaces and 'Rust'-Like Traits with 'S7') Contract helpers built with 'S7' for expressing runtime protocols around ordinary 'S7' dispatch. Structural interfaces describe small sets of required 'S7' generics, while explicit traits record registered implementations with optional default methods and associated metadata. Optional runtime checks can validate argument and return specifications in contract-scoped evaluation. Package: r-cran-s7schema Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-s7, r-cran-v8, r-cran-yaml Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-s7schema_0.1.1-1.ca2604.1_all.deb Size: 202986 MD5sum: 3af8817db9c54f07f2fa7fe3a1b6d2c2 SHA1: 0fc528707bc8eb16192caed30f3d7270d321e8e6 SHA256: a1e022a6e5d1b7a0d2f34bf52ba23f4f216f6ef599f66d94e539601f66c5d076 SHA512: 6af03920d2dbbb63dc2e9db0cccac049d8a2d21b5e24a5a205679445bfe12a2663dfeaf17f69103f33cdef348df6e68d3ae4b594e65e7ae4b8f771406bb09737 Homepage: https://cran.r-project.org/package=S7schema Description: CRAN Package 'S7schema' ('S7' Framework for Schema-Validated YAML Configuration) Provides a generic framework for working with YAML (YAML Ain't Markup Language) configuration files. 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Package: r-cran-saascnv Architecture: all Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rann, r-bioc-dnacopy Filename: pool/dists/resolute/main/r-cran-saascnv_0.3.4-1.ca2604.1_all.deb Size: 992808 MD5sum: 532c08e994a7f383986f5a9eadb0029c SHA1: 373812cd5e19c12aab9556ad92867d2cacad8e4d SHA256: 880e3e0d2ffbf5566639fc074a047260a6d31068f27f5001f47a81fd830fbcdf SHA512: e8500efcd057fd1cffeb7c90efca9e6e9f0534cbbaeab99bf718e831d45c61c8a485cf04ae2196b098ab398327ba07649d6003b69b58534b0bed0a053b83d2a2 Homepage: https://cran.r-project.org/package=saasCNV Description: CRAN Package 'saasCNV' (Somatic Copy Number Alteration Analysis Using Sequencing and SNPArray Data) Perform joint segmentation on two signal dimensions derived from total read depth (intensity) and allele specific read depth (intensity) for whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data. Package: r-cran-saber Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-saber_0.7.1-1.ca2604.1_all.deb Size: 130370 MD5sum: 39df60e1371feba580de208e7bde5453 SHA1: 1c4f45c06322b57b932a5d89298ade8572007c77 SHA256: 8a1c79a3cd9a4b2bd56a35dc05878b9c9e50953d134c16037ce63bf279d808bd SHA512: 0c7df0c4cd0d37a7cccd0862e96ba00a3df975a0d8a06c4e34118cf7de08518be6ba7a6472d6ec19c1a914d173fd59e5ee30ff0c7b167b67390a69309620590b Homepage: https://cran.r-project.org/package=saber Description: CRAN Package 'saber' (Context Engineering for Large Language Model Agents) Context-engineering primitives for Artificial Intelligence (AI) coding agents working in R. Assembles agent context from memory and instruction files ('AGENTS.md', 'CLAUDE.md'), traces function call blast radius across projects, generates project briefings, parses source into Abstract Syntax Tree (AST) symbol indices, discovers dependency graphs, and introspects installed packages. Zero dependencies. Package: r-cran-sabre Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-entropy, r-cran-raster, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sabre_0.4.3-1.ca2604.1_all.deb Size: 631222 MD5sum: db0e40cd01d3fe6a923a19e5e87060f9 SHA1: f0e9a2373e7bd19a5bc8b2796f31ab90c53b1c01 SHA256: aaa7b43be1138a5512f4e05a40bda0ab546e60fda5b805d5e810c4ea606c6d41 SHA512: 907a46d1e82dd41a0718af4b48c4f193541e69f17020704fd967c4ffc6502c5018001eefae1fa161122dcb206c94ba925ad29a964690878d32373af4cd2b7710 Homepage: https://cran.r-project.org/package=sabre Description: CRAN Package 'sabre' (Spatial Association Between Regionalizations) Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure (Nowosad and Stepinski (2018) ). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) ). Package: r-cran-saccr Architecture: all Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-jsonlite, r-cran-trading Filename: pool/dists/resolute/main/r-cran-saccr_3.4-1.ca2604.1_all.deb Size: 112114 MD5sum: e7dd06ae8e2288022c917485814fbe39 SHA1: d59115f0ee3bf92cb01a67d5f50e724a514ff278 SHA256: fa5bed34dfb944c3db68507d38935d20c4c457cb1ed8eb71af8d4984a12ee0f1 SHA512: d3565d031b716d054e6becdf6f043e8481608fbb95a9b0015b2b34cdd3f3311b9d8150b27d9dba5e08c215e9efde839b6dfa95e1b1fdd3a3c938e71a0679e68c Homepage: https://cran.r-project.org/package=SACCR Description: CRAN Package 'SACCR' (SA Counterparty Credit Risk under CRR2) Computes the Exposure-At-Default based on the standardized approach of CRR2 (SA-CCR). The simplified version of SA-CCR has been included, as well as the OEM methodology. Multiple trade types of all the five major asset classes are being supported including the Other Exposure and, given the inheritance- based structure of the application, the addition of further trade types is straightforward. The application returns a list of trees per Counterparty and CSA after automatically separating the trades based on the Counterparty, the CSAs, the hedging sets, the netting sets and the risk factors. The basis and volatility transactions are also identified and treated in specific hedging sets whereby the corresponding penalty factors are applied. All the examples appearing on the regulatory papers (both for the margined and the unmargined workflow) have been implemented including the latest CRR2 developments. Package: r-cran-sad Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dualtrees, r-cran-emdist Filename: pool/dists/resolute/main/r-cran-sad_0.1.3-1.ca2604.1_all.deb Size: 825528 MD5sum: a4c71b0d5eac6454cb11c5d9904380b7 SHA1: ed66c1c5be415648e002f097596819d7e9142c24 SHA256: 164d30fc49dcdf2c41e260b79030437ea0bcb0b38568d5dec388ea25ff96e6f4 SHA512: cc4a2e511c25fc78385fe4c71b369d16c2c187a97151a59a6a373eab2d12012739bb6fd402ae0abae2691f897a97378c5d36d4c5dde0cd4abda4a96dd0cd5a1b Homepage: https://cran.r-project.org/package=sad Description: CRAN Package 'sad' (Verify the Scale, Anisotropy and Direction of Weather Forecasts) Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well. Package: r-cran-sadeg Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sadeg_1.0.0-1.ca2604.1_all.deb Size: 49996 MD5sum: d72a368d5924c6913198f44cb2530e6b SHA1: c8104bbf1862432fb4cc982417d218c57ef4201d SHA256: 866750f293ded302cf7f5c817b2f1a5d45cc620ece85a42b345f3a1ea789c47a SHA512: 28913d2321f0fa5ec200327dd3a8dea30ce9ef3b2cb41dc6d962a66399793d361012540e7ce686f442c144b4f97d6e894b0cf1ee705c61c5f981e19f7f20c5ad Homepage: https://cran.r-project.org/package=SADEG Description: CRAN Package 'SADEG' (Stability Analysis in Differentially Expressed Genes) We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level. Package: r-cran-sadisa Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-ddd Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sadisa_1.2-1.ca2604.1_all.deb Size: 129880 MD5sum: 0e3c42e7101fe04062709fcecc23ae18 SHA1: 4268d27f054aea0708e9c28db04c01dbbe246841 SHA256: 2af475429ebb9a92eb3028f8e69b8b1acf08cd6765742c97ad635b4b97e84285 SHA512: beedd0db255c8144d4a8932175fabd517d8246f83e33f5635a2a9b75a02be5141ffb57bde462cfe3dd1c7a5658fa7d080d273889f3ae4a2ec180eccbbd3f7ca2 Homepage: https://cran.r-project.org/package=SADISA Description: CRAN Package 'SADISA' (Species Abundance Distributions with Independent-SpeciesAssumption) Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution 8: 1506-1519 . Package: r-cran-sadists Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pdqutils, r-cran-hypergeo, r-cran-orthopolynom Suggests: r-cran-sharper, r-cran-shiny, r-cran-testthat, r-cran-ggplot2, r-cran-xtable, r-cran-formatr, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sadists_0.2.5-1.ca2604.1_all.deb Size: 473800 MD5sum: 535cef42c49ef7023e25579fd735f882 SHA1: a015d1a7273e1226ad7f59e2b70153516c1de422 SHA256: a85b6cb2bafc348f389453786102135e63a13242b325290b72f45df3f9648c72 SHA512: 467c5037c949ef1f0f33ab20e627817ea7425c52239ffe8f9102c0e683f0fecaff490df2ea421cbe85724f334677f215a02e3f20ddba2ce361e5c79e410bfc50 Homepage: https://cran.r-project.org/package=sadists Description: CRAN Package 'sadists' (Some Additional Distributions) Provides the density, distribution, quantile and generation functions of some obscure probability distributions, including the doubly non-central t, F, Beta, and Eta distributions; the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares; the product of non-central chi-squares to powers; the product of doubly non-central F variables; the product of independent normals. Package: r-cran-sae.projection Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidymodels, r-cran-survey, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-parsnip, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-bonsai, r-cran-ranger, r-cran-randomforest, r-cran-themis, r-cran-lightgbm, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sae.projection_0.1.5-1.ca2604.1_all.deb Size: 1450964 MD5sum: f9507a3f01d0f61566715cbac2aa6312 SHA1: fc630f022fac316c5b35ad19f0eeb10cd9a5531a SHA256: 72741a549cc9505851404777a88e4d270c765b3d9f63fedaabb74d644a09042e SHA512: ebdb0542e74f5a8165562e2760e9232a8e271ae107bff555d0f0fb57d9ddc530193b2403fa071944826d1337be683b78dd3a52b81b91f0bb272e260c3af03e43 Homepage: https://cran.r-project.org/package=sae.projection Description: CRAN Package 'sae.projection' (Small Area Estimation Using Model-Assisted Projection Method) Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) . Package: r-cran-sae.prop Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magic, r-cran-mass, r-cran-corpcor, r-cran-progress, r-cran-fpc Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sae.prop_0.1.2-1.ca2604.1_all.deb Size: 349484 MD5sum: dd6f7c6d31902c2f681ff6bba2b7895e SHA1: 7c4ad23a1daf8cce8ca0e3c435ffb5c3fa73be53 SHA256: db042d8343ba2d9fb1b909c504b4f62ff17aaa7372383e07e6a4730ac042526d SHA512: 37d639fd1e868f5a17b4af132bcd2156504ab96d3394d8182fcfd62135df5914dd0c7d5e7b87b86928c925b52d42e1ff6ee2a23c4a69ebddcf9757df2c777a0c Homepage: https://cran.r-project.org/package=sae.prop Description: CRAN Package 'sae.prop' (Small Area Estimation using Fay-Herriot Models with AdditiveLogistic Transformation) Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A . The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I , with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A . Package: r-cran-sae2 Architecture: all Version: 1.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survey Suggests: r-cran-sae Filename: pool/dists/resolute/main/r-cran-sae2_1.2-2-1.ca2604.1_all.deb Size: 146680 MD5sum: 66af3ef8e06a743ef8f1b1f6dc8be8e2 SHA1: 36029f84e62c5cd0a623a42ceed426f49d290acb SHA256: 8bcabad393d4f860aa69c7d1911aa73a38cb99fb3c816ff9b77505525726a728 SHA512: 89e68379f1b500eb73ce33c390d1d259a5be3064bd2f64850f50614d6564332f3371af592ae5702bbf3f2e4d19e91aaa6879a2d0a9e30222e92a682b5dc5ebdf Homepage: https://cran.r-project.org/package=sae2 Description: CRAN Package 'sae2' (Small Area Estimation: Time-Series Models) Time series area-level models for small area estimation. The package supplements the functionality of the sae package. Specifically, it includes EBLUP fitting of the Rao-Yu model in the original form without a spatial component. The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing the assumption of stationarity. Both univariate and multivariate applications are supported. Of particular note is the allowance for covariance of the area-level sample estimates over time, as encountered in rotating panel designs such as the U.S. National Crime Victimization Survey or present in a time-series of 5-year estimates from the American Community Survey. Key references to the methods include J.N.K. Rao and I. Molina (2015, ISBN:9781118735787), J.N.K. Rao and M. Yu (1994) , and R.E. Fay and R.A. Herriot (1979) . Package: r-cran-sae4health Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-gridextra, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet, r-cran-r6, r-cran-sf, r-cran-sp, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-summer, r-cran-surveyprev, r-cran-survey, r-cran-geodata, r-cran-bookdown, r-cran-markdown, r-cran-haven, r-cran-ggridges, r-cran-ggthemes, r-cran-rcolorbrewer, r-cran-viridislite, r-cran-scales, r-cran-patchwork, r-cran-leaflegend, r-cran-leafsync, r-cran-plotly, r-cran-readr, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sae4health_1.2.3-1.ca2604.1_all.deb Size: 1074276 MD5sum: 45f199078fc039cbfdfa4b6ffce69274 SHA1: d6b649979dd403a2878d711a64f7d0d559fbea7b SHA256: 76b025763326699ce509d39dda7822a6803d5ae1f0cb9bde991b385d0a7fedc3 SHA512: 031cabdfcff9ecd3604e208b5bc8a0ba78a2b2645dd012131e5f3d8935a7f8086cc2798196422f1e00dd8df1a8be953eac69284b94a7aaea790ae4fecc3f1c93 Homepage: https://cran.r-project.org/package=sae4health Description: CRAN Package 'sae4health' (Small Area Estimation for Key Health and Demographic Indicatorsfrom Household Surveys) Enables small area estimation (SAE) of health and demographic indicators in low- and middle-income countries (LMICs). It powers an R 'shiny' application for generating subnational estimates and prevalence maps of 150+ binary indicators from Demographic and Health Surveys (DHS). It builds on the SAE analysis workflow from the 'surveyPrev' package. For documentation, visit . Methodological details can be found at Wu et al. (2025) . Package: r-cran-sae Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-sae_1.3-1.ca2604.1_all.deb Size: 1177890 MD5sum: 01024a53fb2d34e8424e2efd23c4f889 SHA1: 94cb735ed4c5e05f247dc0354c8fa4101f847987 SHA256: 614496e5cb2b51d40571ae84a0d5d7ec4e586965ac8bc09b6626ee9a07ce87f8 SHA512: 2e68cb488683394cf34d7a6717414108c462f849093d9365629a2610399efaf7af917c5be3c4516536fda9b0407487261f0e2c6024fbe2a92b4f1eea6470854b Homepage: https://cran.r-project.org/package=sae Description: CRAN Package 'sae' (Small Area Estimation) Functions for small area estimation. Package: r-cran-saebest Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sae Filename: pool/dists/resolute/main/r-cran-saebest_0.1.0-1.ca2604.1_all.deb Size: 19064 MD5sum: a9c8f20a7a5963fc31abfea5795e731b SHA1: 610468fb20ee6985b8cab8b0a63d2326418f1d59 SHA256: 872dfd5750acf3ddaebf01d08c516617441c80db07ba7b6b79b32495d18679d7 SHA512: 30d7b63cc448ac609a053968dbd426cb530716cdb46e15937326d93c5e5ae193378b87ab08e85f6e99639e6e99ecd81d56350c0676147a0d8aef8c29069b0e65 Homepage: https://cran.r-project.org/package=saeBest Description: CRAN Package 'saeBest' (Selecting Auxiliary Variables in Small Area Estimation (SAE)Model) Select best combination of auxiliary variables with certain criterion. Package: r-cran-saebnocov Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-descr, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-saebnocov_0.1.0-1.ca2604.1_all.deb Size: 80428 MD5sum: 7b0e27387fca6dded7e82adbd260e7b6 SHA1: 55af88a2b54ef2676d002a2d2fc2d79f193831f9 SHA256: 8c3480093bceb1fe55b7946204256de4c3834ebb62da985c3682c906be1bda54 SHA512: 88c6e96c79da183e44ac60faf591157b4bac220432979676724b5797fa0f9c5608d7503de132b68965ef82748f297e9643cb73e03d11704394f68e0dc789e564 Homepage: https://cran.r-project.org/package=saebnocov Description: CRAN Package 'saebnocov' (Small Area Estimation using Empirical Bayes without AuxiliaryVariable) Estimates the parameter of small area in binary data without auxiliary variable using Empirical Bayes technique, mainly from Rao and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". This package provides another option of direct estimation using weight. This package also features alpha and beta parameter estimation on calculating process of small area. Those methods are Newton-Raphson and Moment which based on Wilcox (1979) and Kleinman (1973) . Package: r-cran-saeeb Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-count, r-cran-mass Filename: pool/dists/resolute/main/r-cran-saeeb_0.1.0-1.ca2604.1_all.deb Size: 33114 MD5sum: 62d58741898368f597606bd95565c3b9 SHA1: b1d955b719135c661eaece94e0c513400956478c SHA256: 88b300a4ffde581b655b548aa4d30972109fd4d65a17a9ab4fe697a4a74aa3e5 SHA512: 36ee50f4e7c0d762b750c01cdc63e6fffe4d346f3b15f844d97ea94e9c2cc9ca4ebc2db3955404af12f24d7b20d35d7a9b3bee7eeecdf72e2767d27f0108672f Homepage: https://cran.r-project.org/package=saeeb Description: CRAN Package 'saeeb' (Small Area Estimation for Count Data) Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) , the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) . Package: r-cran-saehb.me.beta Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-saehb.me.beta_1.1.0-1.ca2604.1_all.deb Size: 48930 MD5sum: 4e619603f094a6f2a83ae33d532ea4f8 SHA1: 89c3f0a78e09b9216225e968b6486da05b5d293b SHA256: 385ccfa5a512ee2b82e9837efde8a3470eea24e47ce7dfd8ac4b86133ccbaa1a SHA512: 64cb10999835ee190516c087bcea1e82377d91043e4b4275a8a33051893d592c776908104c64d9206de6a142841da9e1f7d955bbaeaab71b2f1259dd1bce33aa Homepage: https://cran.r-project.org/package=saeHB.ME.beta Description: CRAN Package 'saeHB.ME.beta' (SAE with Measurement Error using HB under Beta Distribution) Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The 'rjags' package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) , Ybarra and Sharon (2008) , and Ntzoufras (2009, ISBN-10: 1118210352). Package: r-cran-saehb.me Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-saehb.me_1.0.1-1.ca2604.1_all.deb Size: 58472 MD5sum: e0aad216b6bcbc5b683bb5cb8e2b8fe3 SHA1: f46b49cc8fb160d014a6de73c74ab390325427eb SHA256: 6f9555b015428e33b09e53da77fb0e8122f32bf8aef4004690d23df3b3285aa1 SHA512: 925afbddf24acade347d83edf1662bcf66a040bbcb84432ca9a8e38eecfb934683392341cd11c4b3f54bfdf5ddaf4924a7630275f55343ebb38903d0d1acf177 Homepage: https://cran.r-project.org/package=saeHB.ME Description: CRAN Package 'saeHB.ME' (Small Area Estimation with Measurement Error using HierarchicalBayesian Method) Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The 'rjags' package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) , Ybarra and Lohr (2008) , and Ntzoufras (2009, ISBN-10: 1118210352). Package: r-cran-saehb.panel.beta Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-rjags, r-cran-stringr Suggests: r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-saehb.panel.beta_0.1.5-1.ca2604.1_all.deb Size: 81334 MD5sum: 6b805f11822fbc7395d256f5e7424ab6 SHA1: ed7b6272626d8bce9044aa0aaf7034c02b84c2f3 SHA256: f62d681c2724ac93dcd94d254b763c1f490d6dbd45cc64273ee85fb7f52ec2cb SHA512: cd224732746e7147c041a11cc001740a547b963d480cb96db22ae8c52efa9c4a5110b605bac30a44a9a19c43891627b1eb7735e74f0b3a6176d761e94dd3da1a Homepage: https://cran.r-project.org/package=saeHB.panel.beta Description: CRAN Package 'saeHB.panel.beta' (Small Area Estimation using HB for Rao Yu Model under BetaDistribution) Several functions are provided for small area estimation at the area level using the hierarchical bayesian (HB) method with panel data under beta distribution for variable interest. This package also provides a dataset produced by data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators, which include the mean and the variation of the mean. For the reference, see Rao and Molina (2015, ISBN: 978-1-118-73578-7). Package: r-cran-saehb.panel Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-saehb.panel_0.1.1-1.ca2604.1_all.deb Size: 89946 MD5sum: e3a840a6ccdf1ca33ec405ae594136a8 SHA1: c89d86f6d92a8bb8c5dba39ce9aa10ae9f36e59d SHA256: ed5740a0366520edd4bb63831c52fb31560350e03716c6a4c2e054fc55fa2fd1 SHA512: 3ffd24ec6db061fc55d8723aa8c133c2b7a1a791103c6b20be47b6ca5fc5c73699bd7cdfeae4d5323a61205ef8422c220ea6f469b1a358a1278c9d87883e9ecc Homepage: https://cran.r-project.org/package=saeHB.panel Description: CRAN Package 'saeHB.panel' (Small Area Estimation using Hierarchical Bayesian Method for RaoYu Model) We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015). Package: r-cran-saehb.spatial Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-saehb.spatial_0.1.1-1.ca2604.1_all.deb Size: 44184 MD5sum: cb761aa80dced04d94decb76a0c570ff SHA1: 0a41526688a9ed0ae1410bdbdebae8eab0d855e9 SHA256: 5977b6ec2bb4e55f7518c92a1dc53715824d5470d145706b5376bf40c2526b7f SHA512: b8b24a3e820bbaa1ed28ae779a19a8f2ede979857a4f973b6a126142d8e01497b744b6b4e41b0d72fac0d64075430e77ce0a390516fc4d7042745559d2d74d67 Homepage: https://cran.r-project.org/package=saeHB.spatial Description: CRAN Package 'saeHB.spatial' (Small Area Estimation Hierarchical Bayes For Spatial Model) Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) . Package: r-cran-saehb.twofold Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-stringr, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-saehb.twofold_0.1.2-1.ca2604.1_all.deb Size: 108116 MD5sum: 6bcc000c9048a58a70123e0b5f2c2c78 SHA1: 9296626cd2b4e3932cd1498e0300f3dd46eee665 SHA256: 8878701c6e6f2fb30edfeaf98d56ce7d1909a9d2c1d7123aa5bfa69a00cce7e1 SHA512: 09e76233d68f4b2e2cef839107b3d52f83d767a4924bcd63535a3dcc137f31e370e519ed355654eab684152a108e652c2aaed79a9cf9543781c15b7cf0c38dfb Homepage: https://cran.r-project.org/package=saeHB.twofold Description: CRAN Package 'saeHB.twofold' (Hierarchical Bayes Twofold Subarea Level Model SAE) We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) , Torabi and Rao (2014) , Leyla Mohadjer et al.(2007) , and Erciulescu et al.(2019) . Package: r-cran-saehb.unit Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-coda, r-cran-dplyr, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-saehb.unit_0.1.0-1.ca2604.1_all.deb Size: 352428 MD5sum: 4915ff3e83207d9ba8e295e0dbdda960 SHA1: 941421755202d81f562f6fb55a229c42a4e2cbc6 SHA256: 5ed6a55314197a1c16b820fa641eb00f0fc5cca82cf941fca707f2d6f6e8dde2 SHA512: d313cc8c8de4a32a0ace6018418cbf4dc49a83d72833674412f4613b0109e0b0abd5e16c9e43d3466d2e9d5e1f6428a1838397e975eea105d66b9af84b9a9500 Homepage: https://cran.r-project.org/package=saeHB.unit Description: CRAN Package 'saeHB.unit' (Basic Unit Level Model using Hierarchical Bayesian Approach) Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) . Package: r-cran-saehb.zib Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-saehb.zib_0.1.1-1.ca2604.1_all.deb Size: 37370 MD5sum: 4f4150707b5af0af0fc0f7b7fb33714e SHA1: 2a7b9f0e730b1adedd223f31e0fdfa1aaee8654c SHA256: 148f3e78d4a71ef945fbfe08fc9a8eab7c0daf545aca1371f20bd9b44d6768fa SHA512: 08c9a1bc0d53c752a1cc240cc76cd98d4de3ed5dd5b7293afd4235b7eaa61df83ce103e64ad38a2f779dc44cc318b5a879ca41ae6835070cdc9e1589f55907af Homepage: https://cran.r-project.org/package=saeHB.ZIB Description: CRAN Package 'saeHB.ZIB' (Small Area Estimation using Hierarchical Bayesian under ZeroInflated Binomial Distribution) Provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for variables of interest. Some dataset produced by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. Package: r-cran-saehb Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nimble, r-cran-mass, r-cran-carbayesdata Filename: pool/dists/resolute/main/r-cran-saehb_0.2.3-1.ca2604.1_all.deb Size: 237044 MD5sum: df959f8e59fda51dd49a0df382aaa163 SHA1: b298db77b6588460a547bda06eff8d96d709bc8a SHA256: 6b58c1f5abb49b3f01dfc7657c2defae1ef6f33cbd4cc3c5223db36fd5f2f415 SHA512: 10d17c6af57bd9dfa3a4f977d70b51726d7bb58f4e17040ef5a880b88fc5cb07fe0a59dda2d03dde84c9eb1f945ddc3812eb4163199436269807437a86c0850a Homepage: https://cran.r-project.org/package=saeHB Description: CRAN Package 'saeHB' (Small Area Estimation using Hierarchical Bayesian Method) Provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) . Package: r-cran-saekernel Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-saekernel_0.1.1-1.ca2604.1_all.deb Size: 25534 MD5sum: 2e534fa06ff6293e7357156bdee666e6 SHA1: 1f7b3890786e6c45127f9be25ac64ba65eb0ba43 SHA256: 5b5b23bfa6953a023441166f11e53349d607307545eae81817dc043d5afa39ab SHA512: a75952e91e3f4684bc09e40070b9cb78b769a4a67f5c60bf53a1cf6a6a70939de4af4f59afb9d3beec5a6a39a7bc322c448f57224a74285484005cf77e9b0fa9 Homepage: https://cran.r-project.org/package=saekernel Description: CRAN Package 'saekernel' (Small Area Estimation Non-Parametric Based Nadaraya-WatsonKernel) Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) . Package: r-cran-saeme Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-saeme_1.3.1-1.ca2604.1_all.deb Size: 56140 MD5sum: 44894053501cd3a7e7b1ae872f48b469 SHA1: c7b14e157a1cad0e71491db55d8322491f0dbfa2 SHA256: 47746aecc4f72b403fddc3496e8507750f8e197e8a8ab9b52b2bdb1afc6abb93 SHA512: 2d23ab2fedb836345c5b305f6c0255f2e288d8c3c5b180a271768e1d72ee910ba3aa885bbb036106b5f3e657a5f870ccad15f934084954515a0cc6b36d758e67 Homepage: https://cran.r-project.org/package=saeME Description: CRAN Package 'saeME' (Small Area Estimation with Measurement Error) A set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) . Package: r-cran-saemix Architecture: all Version: 3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-npde, r-cran-gridextra, r-cran-ggplot2, r-cran-rlang, r-cran-mclust, r-cran-scales, r-cran-mass Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/resolute/main/r-cran-saemix_3.5-1.ca2604.1_all.deb Size: 3278632 MD5sum: 82fa438eba198737a9caca92e4919673 SHA1: 23db6b0929637b4c7dbddc9cc072acc42a876c2d SHA256: ada72e5377f4588cab256c8d6a3bae4f4ebee511191434abb201256d81e27f22 SHA512: a86549710c39f58400503cba4ef17c5c2365f23ac6f0f4a78eabfc84b2112f2e711b3c715b34b3b7a0ea81a545fce83c888a713077fe6c41667c44717fe6114c Homepage: https://cran.r-project.org/package=saemix Description: CRAN Package 'saemix' (Stochastic Approximation Expectation Maximization (SAEM)Algorithm) The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) ). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': . Package: r-cran-saens Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-saens_0.1.2-1.ca2604.1_all.deb Size: 284820 MD5sum: 157b325698d884e322ef9de04807cab8 SHA1: 9c39b31904bffd56c15f762691a300df56bddf32 SHA256: a4c446b0c1aa8b45373e155c22440ba8ba7052a77093e4b7279665eeac391bbd SHA512: d11f9d700ca4791b6a4de5d08ad230139e448e8a21ea64966782f5b87848ef915d76c9cd76f30f3996ccdc1440dbc2c68577c541e9a1e02f9289c4e6b1a91273 Homepage: https://cran.r-project.org/package=saens Description: CRAN Package 'saens' (Small Area Estimation with Cluster Information for Estimation ofNon-Sampled Areas) Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) . Package: r-cran-saepseudo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sae Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-saepseudo_0.1.0-1.ca2604.1_all.deb Size: 28430 MD5sum: 781345f424826ddccd5bdca77790457e SHA1: cb9943252b2a70f73b6766a2b48aba5de9f83d2e SHA256: fc632129be3d0f154cd1f77d8783dde0ecdbac365972bc861a3775514d106983 SHA512: 667e1d4acd1e971ab8ce677320434cbb786dfeec689aa8430146719280860c2fa6fd43aa328e2526bebf6470e055b99e46b116484c9886a718097911eb173e6f Homepage: https://cran.r-project.org/package=saePseudo Description: CRAN Package 'saePseudo' (Small Area Estimation using Averaging Pseudo Area Level Model) Provides function for small area estimation at area level using averaging pseudo area level model for variables of interest. A dataset produced by data generation is also provided. This package estimates small areas at the village level and then aggregates them to the sub-district, region, and provincial levels. Package: r-cran-saery Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-saery_2.0-1.ca2604.1_all.deb Size: 165210 MD5sum: 11a9b027079b4a5758006e93b5151ce8 SHA1: c975c2d232dac869e96be15e4e0cc9c61c59c207 SHA256: 6eda410a7ce5bd34cf02b1c8a900298d28470ef753fea105c6e097c37a1d5703 SHA512: a703dd88cc0d9ce5ba2c586f88f05ff188162777e9499e1cf4234d262f48df99e2988ebdadf0bda73cdd34fac5641664e24d0597ca8d395be306c8e0d4c8f9cf Homepage: https://cran.r-project.org/package=saery Description: CRAN Package 'saery' (Small Area Estimation for Rao and Yu Model) Functions to calculate EBLUPs (Empirical Best Linear Unbiased Predictor) estimators and their MSEs (Mean Squared Errors). Estimators are based on an area-level linear mixed model introduced by Rao and Yu (1994) . The REML (Residual Maximum Likelihood) method is used for fitting the model. Package: r-cran-saesim Architecture: all Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-functional, r-cran-ggplot2, r-cran-mass, r-cran-spdep, r-cran-parallelmap Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-saesim_0.13.0-1.ca2604.1_all.deb Size: 265424 MD5sum: 206c419f8d813bcfb5d5e32ea7054820 SHA1: 38f3f054cd3f9c8156cf1d9558008f212810aac5 SHA256: 9ade4a23833d5115474b497ff2abd04b6f1314d99c65a506fee791ebc17ad2b8 SHA512: 769e751d096edbc18aa7410446962dd80a1fe8bff0148f7dc9615b4c5910c9ce6c9b64ca046e42f8895485c6b8b2e1fa189808f891056e8da06c8f02acf99a52 Homepage: https://cran.r-project.org/package=saeSim Description: CRAN Package 'saeSim' (Simulation Tools for Small Area Estimation) Tools for the simulation of data in the context of small area estimation. Combine all steps of your simulation - from data generation over drawing samples to model fitting - in one object. This enables easy modification and combination of different scenarios. You can store your results in a folder or start the simulation in parallel. Package: r-cran-saetrafo Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2001 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-emdi, r-cran-sfsmisc, r-cran-parallelmap, r-cran-ggplot2, r-cran-moments, r-cran-openxlsx, r-cran-reshape2, r-cran-hlmdiag, r-cran-gridextra, r-cran-stringr, r-cran-readods, r-cran-rlang Suggests: r-cran-simframe, r-cran-sf, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-saetrafo_1.0.6-1.ca2604.1_all.deb Size: 1962934 MD5sum: 0e2d2229e3ee5f09dbf04ec1e302e55e SHA1: 21d302fb8ab1161ccac6b1906f75187295cf6fbf SHA256: 8213cbaf3829ef24ed354373424513e551442b1fa3bd3221c33eda79c2f73dbe SHA512: 2bb250271ad6c5b31b89ee5aecc896a8f60a53dc8896056183eb66b59c4fba9eee0a3dd52f117940244785d301374a76035e3dce2adb59ddcc6827e3f161e227 Homepage: https://cran.r-project.org/package=saeTrafo Description: CRAN Package 'saeTrafo' (Transformations for Unit-Level Small Area Models) The aim of this package is to offer new methodology for unit-level small area models under transformations and limited population auxiliary information. In addition to this new methodology, the widely used nested error regression model without transformations (see "An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data" by Battese, Harter and Fuller (1988) ) and its well-known uncertainty estimate (see "The estimation of the mean squared error of small-area estimators" by Prasad and Rao (1990) ) are provided. In this package, the log transformation and the data-driven log-shift transformation are provided. If a transformation is selected, an appropriate method is chosen depending on the respective input of the population data: Individual population data (see "Empirical best prediction under a nested error model with log transformation" by Molina and Martín (2018) ) but also aggregated population data (see "Estimating regional income indicators under transformations and access to limited population auxiliary information" by Würz, Schmid and Tzavidis ) can be entered. Especially under limited data access, new methodologies are provided in saeTrafo. Several options are available to assess the used model and to judge, present and export its results. For a detailed description of the package and the methods used see the corresponding vignette. Package: r-cran-saeval Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-ggplot2, r-cran-ggspatial Filename: pool/dists/resolute/main/r-cran-saeval_1.0.0-1.ca2604.1_all.deb Size: 1032350 MD5sum: ba6e3464e4fac6c2d620a14a324e5820 SHA1: b7ab9806c07a129addb7f1db746b0fc4b8d7a050 SHA256: 575ec188b586926b4dfa32eccf01e40f68cdfd42f72eafe374121fb55fe9e3d0 SHA512: 3ddf557ca8ee7298a75a9e31641723b0fad006933f9be6a2659f0e10cc8f9d3593b0a739c38e4fcdc44689cdc7a55bed207bc974f188e1f80904e75d1044eea8 Homepage: https://cran.r-project.org/package=SAEval Description: CRAN Package 'SAEval' (Small Area Estimation Evaluation) Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators. Package: r-cran-safari Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-catools, r-bioc-ebimage, r-cran-lattice, r-cran-png Filename: pool/dists/resolute/main/r-cran-safari_0.1.0-1.ca2604.1_all.deb Size: 60616 MD5sum: f689d64fd9162e89a9eacb2628a30524 SHA1: cd80755adce293b185abf5e7c01bdc6c41c95497 SHA256: 1f4626ff6612c559b25438bddbb3bb94bc504b34a73e9cf4c6e21fc4211bbff8 SHA512: fbcacdeba32bc84d039df432aeb4ac9c63fe3a138358741d4cb1671ed0f50a65170d282a3b110d137a89385a1bcc7278d9fe899a703b90e563e6967911bfd8eb Homepage: https://cran.r-project.org/package=SAFARI Description: CRAN Package 'SAFARI' (Shape Analysis for AI-Reconstructed Images) Provides functionality for image processing and shape analysis in the context of reconstructed medical images generated by deep learning-based methods or standard image processing algorithms and produced from different medical imaging types, such as X-ray, Computational Tomography (CT), Magnetic Resonance Imaging (MRI), and pathology imaging. Specifically, offers tools to segment regions of interest and to extract quantitative shape descriptors for applications in signal processing, statistical analysis and modeling, and machine learning. 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The functions can be used to design hypothesis tests in the prospective/randomised control trial setting or in the observational/retrospective setting. The resulting tests remain valid under both optional stopping and optional continuation. The current version includes safe t-tests and safe tests of two proportions. For details on the theory of safe tests, see Grunwald, de Heide and Koolen (2019) "Safe Testing" , for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with Unlimited Horizon" and Turner, Ly and Grunwald (2021) "Safe Tests and Always-Valid Confidence Intervals for contingency tables and beyond" for details on safe contingency table tests. Package: r-cran-safetensors Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-r6, r-cran-rlang Suggests: r-cran-testthat, r-cran-torch Filename: pool/dists/resolute/main/r-cran-safetensors_0.2.1-1.ca2604.1_all.deb Size: 68812 MD5sum: d30288dbb03cd58d0778d71847a07bd0 SHA1: b5c734081ee8b1055a8a85ba8492271a08256f86 SHA256: c8a6de48048eebdc216a54bca315f8b8374e93ccd9fbf98b77daef6e91fdd595 SHA512: a79bc91fe53c062cb75ad710b08d67cf577dd6ad87c931d770270e36864f5d2ba4d726dbbf3fe9bd53e61673a32c3ce664c4267697a09c93cfc59420ecd52fba Homepage: https://cran.r-project.org/package=safetensors Description: CRAN Package 'safetensors' (Safetensors File Format) A file format for storing tensors that is secure (doesn't allow for code execution), fast and simple to implement. 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Package: r-cran-safevote Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formattable, r-cran-knitr, r-cran-fields, r-cran-ggplot2, r-cran-data.table, r-cran-stringr, r-cran-forcats, r-cran-dplyr Suggests: r-cran-testthat, r-cran-vote, r-cran-stv Filename: pool/dists/resolute/main/r-cran-safevote_1.0.2-1.ca2604.1_all.deb Size: 739664 MD5sum: 8e5d99bb2f563507bf7e15cdf801225e SHA1: f57b760fd1f70ebaf921f0b3ea39da172bbfe574 SHA256: 944979c4038989233e046276a4cc6e52a06d758b3143ed2e2676beb535330752 SHA512: 1d345d7b652a24e48556f0bb26b1e3170dfc9fb819f26b5679431bb6cea16bf5206cfc539d5cc966b764d687a82ec7131ebf25c73d7f4e3e52c7f53e9ee8b1dc Homepage: https://cran.r-project.org/package=SafeVote Description: CRAN Package 'SafeVote' (Election Vote Counting with Safety Features) Fork of 'vote_2.3-2', Raftery et al. (2021) , with additional support for stochastic experimentation. Package: r-cran-sager Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-markdown, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-learnr, r-cran-spelling, r-cran-lattice, r-cran-spatstat.data, r-cran-matrix, r-cran-mgcv, r-cran-pmcmrplus, r-cran-devtools, r-cran-desctools, r-cran-ggally, r-cran-vgam, r-cran-car, r-cran-dplyr, r-cran-tibble, r-cran-ggiraphextra, r-cran-scales, r-cran-spatstat, r-cran-vcd, r-cran-factominer, r-cran-cluster, r-cran-caret, r-cran-corrplot, r-cran-actuar Filename: pool/dists/resolute/main/r-cran-sager_0.7.0-1.ca2604.1_all.deb Size: 499544 MD5sum: 1a497d1c71fefce53cc041b40cee122d SHA1: 84ba05d0e71bae0d6284cdcfe8d2cadbebeafd7a SHA256: a822a88a0a21fcee20a68487b1eb6c83ddb25c50f1e02c10c83e8c01a8c1bb22 SHA512: c67be9c4537ed77919327f1d3b02472b102e9bbb75d1c28ea1dff6117b66eefe3a270e3aa2ae4074f03211b6d059c3e40918f2614974f7e5776688a9ade2a3d7 Homepage: https://cran.r-project.org/package=sageR Description: CRAN Package 'sageR' (Applied Statistics for Economics and Management with R) Datasets and functions for the book "Statistiques pour l’économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). The first chapter of the book is dedicated to an introduction to statistics and their world. The second chapter deals with univariate exploratory statistics and graphics. The third chapter deals with bivariate and multivariate exploratory statistics and graphics. The fourth chapter is dedicated to data exploration with Principal Component Analysis. The fifth chapter is dedicated to data exploration with Correspondance Analysis. The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. The seventh chapter is dedicated to data exploration with automatic clustering. The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions. The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests. The tenth chapter is dedicated to an Gaussian linear model. The eleventh chapter is dedicated to an introduction to time series. The twelfth chapter is dedicated to an introduction to probit and logit models. Various example datasets are shipped with the package as well as some new functions. Package: r-cran-sagm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatrix, r-cran-gigrvg, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-sagm_1.0.0-1.ca2604.1_all.deb Size: 36218 MD5sum: eed04ec5ced96f6ec94b1c534024b925 SHA1: c0c2ee81a3858985ffa0a7e75231124885ce2da2 SHA256: 82e129fde8ee433e5d98ae76d25b8879947e842c5f2fd2eda2be156fca37e8d2 SHA512: 6b35cacbfa7dc4139708efed4da5d2c65b90261362b5336393d4f4d61ef9e44c9abd857c812e34c6bc81dabd968e0471d3f130c71f83d0f021f9dec33d8c41c8 Homepage: https://cran.r-project.org/package=SAGM Description: CRAN Package 'SAGM' (Spatial Autoregressive Graphical Model) Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2023) , and allows for the statistical modeling of asymmetric between-location effects, as well as within-location effects using spatial autoregressive graphical models. 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Package: r-cran-saive Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1871 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-crayon, r-cran-doparallel, r-cran-proxy, r-cran-rlang, r-cran-terra, r-cran-vsurf Suggests: r-cran-ranger, r-cran-testthat, r-cran-vdiffr, r-cran-whitebox Filename: pool/dists/resolute/main/r-cran-saive_1.0.6-1.ca2604.1_all.deb Size: 1337828 MD5sum: d69eddae08a2069e1eff0c5ee639528e SHA1: 2abdab21722433d15ef2ce28498bd89801c12920 SHA256: df0d72c2209ea622da91e38e7b5e7b76cade7a59018fc4660aed64e1bd7bbf38 SHA512: a0a7aa5eb3250a38807a8784e034ea5bbf2d1fb7aa7f8c54935c7ba7d366c4e17ee21b30e84276300c57d2a2bd371ebc056006db8bd4364de80b51f9f9cccd4e Homepage: https://cran.r-project.org/package=SAiVE Description: CRAN Package 'SAiVE' (Functions Used for SAiVE Group Research, Collaborations, andPublications) Holds functions developed by the University of Ottawa's SAiVE (Spatio-temporal Analysis of isotope Variations in the Environment) research group with the intention of facilitating the re-use of code, foster good code writing practices, and to allow others to benefit from the work done by the SAiVE group. 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Package: r-cran-salmonmse Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-msetool, r-cran-rtmb, r-cran-dplyr, r-cran-ggplot2, r-cran-gsl, r-cran-reshape2, r-cran-rlang, r-cran-rmarkdown Suggests: r-cran-ggrepel, r-cran-rstan, r-cran-scales, r-cran-testthat, r-cran-tmbstan Filename: pool/dists/resolute/main/r-cran-salmonmse_2.1.0-1.ca2604.1_all.deb Size: 858514 MD5sum: 3a8abc05d84a68e90eed5d0d7e923137 SHA1: 3b47faa6294ef78cd072773e1336350fa6f64504 SHA256: 8b302a4b856fc9c81798f3eb4eb8088088b70ed6ec399d749f35dec6ad01b445 SHA512: 4f84e03b6894c6b99908e633b1d965a5948f6854a4f8fe231b718117f44414ba925b4f82445bc0fbb0dfd4cf74f475812b09a74f9b8702b186be1bb6503a0c4c Homepage: https://cran.r-project.org/package=salmonMSE Description: CRAN Package 'salmonMSE' (Management Strategy Evaluation for Salmon Species) Simulation tools to evaluate the long-term effects of salmon management strategies, including a combination of habitat, harvest, and habitat actions. 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Package: r-cran-saltsampler Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-coda Filename: pool/dists/resolute/main/r-cran-saltsampler_1.1.0-1.ca2604.1_all.deb Size: 68304 MD5sum: 0849dff0c60de4382160e5a8c80815eb SHA1: bec37a9226e369391a774174c532ccd3f60167df SHA256: c820aacd95b922ad9fb45d8b22f4dffac11b69a27a01fcaf39326e600d3f38a4 SHA512: 20cdaeb9e6c3cabbe390305e81453910bcf5f5d2029f2587e7a12db698c34156e4d685651c5d20e99b7139bdde2a97ba11e9bd14f7d924612ee6be55c386e534 Homepage: https://cran.r-project.org/package=SALTSampler Description: CRAN Package 'SALTSampler' (Efficient Sampling on the Simplex) The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC) sampling of random variables on a simplex. A Self-Adjusting Logit Transform (SALT) proposal is used so that sampling is still efficient even in difficult cases, such as those in high dimensions or with parameters that differ by orders of magnitude. Special care is also taken to maintain accuracy even when some coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are included in the package, enabling easy assessment of the convergence and mixing of the chain within the constrained space. Package: r-cran-salty Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-purrr, r-cran-stringr Suggests: r-cran-charlatan, r-cran-testthat, r-cran-tibble, r-cran-covr Filename: pool/dists/resolute/main/r-cran-salty_0.1.2-1.ca2604.1_all.deb Size: 91234 MD5sum: f9c5cac4d348b76f2be75d5aef6f39ca SHA1: 440402265faed2c29ccedc6a07d37a56925ef3ee SHA256: a275a075521eb6419d1d7e0bad4b1509ea3af46f78384339f81742abc523aab7 SHA512: f3d43eeea7b96a247c59630255f21da54a1e43f4595d4279c324065b186cf6209253857da82b72f4d028f5655e0aecadc010b35039f6950b79254afc8a553112 Homepage: https://cran.r-project.org/package=salty Description: CRAN Package 'salty' (Turn Clean Data into Messy Data) Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc. Package: r-cran-samadb Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rmysql, r-cran-writexl, r-cran-data.table, r-cran-collapse Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-samadb_0.3.1-1.ca2604.1_all.deb Size: 69266 MD5sum: 37f47ad5ac23dc69e67fcc8252b13d48 SHA1: 5f4fe4e7f37d75daa09f9f2dde6d4cc4c9cc8265 SHA256: 5c41b5a152c69f79be73ade3b51bb9469eee353c652b3112bae509cfa39df7dd SHA512: 1e92a40cec787948a78ca047246cb7f688ba7609955631a7794058b3f4d3212c88045846030a481e6ace4c4762f9f6b916bb69c941233c052e0cc759018a333a Homepage: https://cran.r-project.org/package=samadb Description: CRAN Package 'samadb' (South Africa Macroeconomic Database API) An R API providing access to a relational database with macroeconomic time series data for South Africa, obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via the EconData platform and automated scraping of the SARB and STATSSA websites. The database is maintained at the Department of Economics at Stellenbosch University. Package: r-cran-samba Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimx, r-cran-survey Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-scales, r-cran-mass Filename: pool/dists/resolute/main/r-cran-samba_0.9.0-1.ca2604.1_all.deb Size: 224136 MD5sum: 3579b0c306a386270d7c596938e8be30 SHA1: b85a4ed20ae53c388f5beb63d6ac64391ba627f0 SHA256: 0eef9e11069f123e387509b732522a1a97d78166d452731e7b3fb3cf0a0b5c4b SHA512: 57aa4e80f0d0624bbdbfa79dda838cc1f57292dcbde3351ae0c03ff87d16558904c2725d2328e053217ce2493d26baf8079ec1512abc4944c901fb8f196aa9da Homepage: https://cran.r-project.org/package=SAMBA Description: CRAN Package 'SAMBA' (Selection and Misclassification Bias Adjustment for LogisticRegression Models) Health research using data from electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error for association tests. Here, the assumed target of inference is the relationship between binary disease status and predictors modeled using a logistic regression model. 'SAMBA' implements several methods for obtaining bias-corrected point estimates along with valid standard errors as proposed in Beesley and Mukherjee (2020) , currently under review. Package: r-cran-sambia Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-dplyr, r-cran-smotefamily, r-cran-e1071, r-cran-ranger, r-cran-proc, r-cran-fnn Filename: pool/dists/resolute/main/r-cran-sambia_0.1.0-1.ca2604.1_all.deb Size: 60034 MD5sum: 4bb04fc8d2676832fa805130ca94849a SHA1: 1246bbf76e7668b1b280c8ec8f6d6cd94767a4db SHA256: 91d2b718740f354f5dab8ccb3d023c7d1f0bef15031d48db8286e6105198e374 SHA512: c221f48a00433822fc535069beb3d6d0f89e79fc66c2de0bd5b7e5731e5e3b677bf324f6b94d3e9d409e2ec4012c7bca835e44b66b8883155dcea04f8ec2c36a Homepage: https://cran.r-project.org/package=sambia Description: CRAN Package 'sambia' (A Collection of Techniques Correcting for Sample Selection Bias) A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) . The methods may be used for further purposes where weighting and generation of new observations is needed. Package: r-cran-same Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-rjags, r-cran-coda, r-cran-extradistr, r-cran-survival, r-cran-ggplot2, r-cran-expint Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-same_0.1.0-1.ca2604.1_all.deb Size: 87770 MD5sum: 895ac6e95a9bb235d8aec09dfc971e8a SHA1: 82d7928c3f8c665e215f3d829f98dcf28bf8c753 SHA256: 1ffe420122b010d3f51af3c0b0bde55a207a7dc4879920f4d14a41b5071655c7 SHA512: c065eb14f01a8e45be8ae901e5a380e4e205ca8308003430d10e9d271a32d949e8c750f5462dc862ed4b53626566bf261dd7d92957664a9661f1b3a1f4013edb Homepage: https://cran.r-project.org/package=SAME Description: CRAN Package 'SAME' (Seamless Adaptive Multi-Arm Multi-Stage Enrichment) Design a Bayesian seamless multi-arm biomarker-enriched phase II/III design with the survival endpoint with allowing sample size re-estimation. James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) . Guosheng Yin, Nan Chen, J. Jack Lee (2018) . Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) . Package: r-cran-sameplot Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-ragg Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sameplot_0.1.0-1.ca2604.1_all.deb Size: 51834 MD5sum: e2c15492b4143c8bd04238954b9f2e6a SHA1: 414a098f1d34098088ad33b20cdbf04f4fdba742 SHA256: 91c3bd2e08ca81ea94a479164d970cbb51d714fc0f64555ed830c526eeebcbab SHA512: 1f2e194a414665f48e2b10a5bdabdd02e3397686948bec7f481cc7f4239c3572b41206f7d2d98f0e5f59e0bad8cc998fdcff43e70e167aa79de78d8cf4a9390b Homepage: https://cran.r-project.org/package=sameplot Description: CRAN Package 'sameplot' (Consistent Plot Rendering and Saving Across Interactive Sessionsand Reports) Renders plots to a temporary image using the ragg graphics device and returns knitr::include_graphics() output. Optionally saves the image to a specified path. 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Package: r-cran-sampbias Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-viridis, r-cran-terra, r-cran-sf, r-cran-rnaturalearth Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sampbias_2.0.0-1.ca2604.1_all.deb Size: 693708 MD5sum: ce97f4e06242073f0898c6a6ca0504d9 SHA1: fd8a1d37750465a81847d9c484b50c5fabd930c9 SHA256: e3d22a75d74d474eaffe63bdec4a977d1e43041ae68bb4a16ee3506921221266 SHA512: 3f5f6a0d3fd1844d4ee0d8e7000f345e5a32c95e3488ab02f77b5e00facdccbdfc0a0b8b53a2dc2339133688d931b4766c24ae2a6c9eb49374e3de0f22fb6ecf Homepage: https://cran.r-project.org/package=sampbias Description: CRAN Package 'sampbias' (Evaluating Geographic Sampling Bias in Biological Collections) Evaluating the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. Enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. Also available as 'shiny' app. The reference for the methodology is: Zizka et al. (2020) . Package: r-cran-sampcompr Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-hmisc, r-cran-lmtest, r-cran-magrittr, r-cran-psych, r-cran-purrr, r-cran-readr, r-cran-reshape2, r-cran-sandwich, r-cran-survey, r-cran-svrep, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-jtools, r-cran-stargazer Filename: pool/dists/resolute/main/r-cran-sampcompr_0.3.2-1.ca2604.1_all.deb Size: 606380 MD5sum: 7c938310012eb084943092a37b8c3313 SHA1: cef9e8020ffc0dfe8386dc12d7c04fc6da95072f SHA256: 60ef8f4a93e06615e6e85d756a0346abb8dc3b96eb0699cdf9271b7c2355b6d6 SHA512: 52ed3d46f12eba4265c7f462ac634d47cf994274e9f3cea525650fef9ff38d35ef440eccc69ec0e0c20584c3e2d4a30e62aef9af439a4b763951ead13d602b11 Homepage: https://cran.r-project.org/package=sampcompR Description: CRAN Package 'sampcompR' (Comparing and Visualizing Differences Between Surveys) Easily analyze and visualize differences between samples (e.g., benchmark comparisons, nonresponse comparisons in surveys) on three levels. The comparisons can be univariate, bivariate or multivariate. On univariate level the variables of interest of a survey and a comparison survey (i.e. benchmark) are compared, by calculating one of several difference measures (e.g., relative difference in mean), and an average difference between the surveys. On bivariate level a function can calculate significant differences in correlations for the surveys. And on multivariate levels a function can calculate significant differences in model coefficients between the surveys of comparison. All of those differences can be easily plotted and outputted as a table. For more detailed information on the methods and example use see Rohr, B., Silber, H., & Felderer, B. (2024). Comparing the Accuracy of Univariate, Bivariate, and Multivariate Estimates across Probability and Nonprobability Surveys with Population Benchmarks. Sociological Methodology . Package: r-cran-sampledatasets Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sampledatasets_0.1.0-1.ca2604.1_all.deb Size: 89344 MD5sum: 85e46fb36b7d4cd4ce09357e4f0d003e SHA1: 0b837294f9ada710c1aa369abb81c556dbb666ae SHA256: d394b131299ff95a2960fb73466f569f17db140b26012af008da5a82e08ffc5a SHA512: c003e5e487d60d0799e440c3485b2152af4f75e43b75f087e74f931ce15bfaf121bc220029572e23d2089076c5b15e9fd94970cf3939055f46a8f9f7826beb22 Homepage: https://cran.r-project.org/package=sampledatasets Description: CRAN Package 'sampledatasets' (A Collection of Sample Datasets) Provides a collection of sample datasets on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data. It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R. Package: r-cran-sampler Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-reshape, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-sampler_0.2.4-1.ca2604.1_all.deb Size: 261146 MD5sum: 0735ff415aed1db0b5642c4754ef8060 SHA1: 8bbc74974e2551284a36882196a54515149640f8 SHA256: 9b2ade0b1f166d7b2d029946a3d0475e8bc49521f404d5a5193db6dcaa34aa75 SHA512: 62cd450389ffb9b0c8da4a69d8b3fcadc2a08d4f05a9607f40dbea603382bb1c82844c764c04fbb67e7ec6f809f2f4ad084970eb0c80c838b70542211713aa04 Homepage: https://cran.r-project.org/package=sampler Description: CRAN Package 'sampler' (Sample Design, Drawing & Data Analysis Using Data Frames) Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs. Package: r-cran-sampleselection Architecture: all Version: 1.2-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-misctools, r-cran-systemfit, r-cran-formula, r-cran-vgam, r-cran-mvtnorm Suggests: r-cran-lmtest, r-cran-ecdat Filename: pool/dists/resolute/main/r-cran-sampleselection_1.2-14-1.ca2604.1_all.deb Size: 1592526 MD5sum: 594fb7abdef1f0428913ca43ce05cc98 SHA1: 3e54b6f21f7771a7ecf9113da5a22154d4d7e417 SHA256: e88fa6f81463034cb793a816fe6d8c90c298570b759377e72f58c966ec303613 SHA512: 4e6f285ec85ea1ec5bb5f97d0f8427a1ce2df0543a9e284b215bcf3fb93bb868991c3db661fed3649ad138f147407ce8d88cb8c210f12a514f85d76752b17731 Homepage: https://cran.r-project.org/package=sampleSelection Description: CRAN Package 'sampleSelection' (Sample Selection Models) Two-step and maximum likelihood estimation of Heckman-type sample selection models: standard sample selection models (Tobit-2), endogenous switching regression models (Tobit-5), sample selection models with binary dependent outcome variable, interval regression with sample selection (only ML estimation), and endogenous treatment effects models. These methods are described in the three vignettes that are included in this package and in econometric textbooks such as Greene (2011, Econometric Analysis, 7th edition, Pearson). Package: r-cran-samplesize4clinicaltrials Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samplesize4clinicaltrials_0.2.3-1.ca2604.1_all.deb Size: 18070 MD5sum: 12a2010de5522559816aacd1ac29b23d SHA1: 0dfe7f9d3a200503cea05a98d784f222e89b5347 SHA256: 91b5bcee296bede0bc8aabcb2453bf64e96b1c9334efd28f7a47beb45eece121 SHA512: 15cf79f0c41f413996731446b9e108696ba547bb199f2c63a6ccfa1bbb93ace25cfe7f80e2277686137b34f9e700e93aa87ace13d0308d44e7233eb3683a4858 Homepage: https://cran.r-project.org/package=SampleSize4ClinicalTrials Description: CRAN Package 'SampleSize4ClinicalTrials' (Sample Size Calculation for the Comparison of Means orProportions in Phase III Clinical Trials) There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals. Package: r-cran-samplesize4surveys Architecture: all Version: 4.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-teachingsampling, r-cran-timedate, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-samplesize4surveys_4.1.1-1.ca2604.1_all.deb Size: 1741844 MD5sum: 8d9dad29b55be80e22b66aa3f550ca43 SHA1: 178a7d509af3ed1e755fa33b84a3e32391d7248b SHA256: 6c2bbc2a5a4025efde4aa52ea1f1a6ae6020ab85d91b6a2aa2e702a0e4564ea9 SHA512: 1cf94f1e10fe5e68691f083327e93dcf8ed3b93a87e8991230e6175ad331fdcef39d848571c523a37be5f25a6e241b2ab73f4ccb89ea32256986973ba2ec68a7 Homepage: https://cran.r-project.org/package=samplesize4surveys Description: CRAN Package 'samplesize4surveys' (Sample Size Calculations for Complex Surveys) Computes the required sample size for estimation of totals, means and proportions under complex sampling designs. Package: r-cran-samplesize Architecture: all Version: 0.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samplesize_0.2-4-1.ca2604.1_all.deb Size: 23524 MD5sum: 721a049ef87a425a04b38389eaf38e15 SHA1: db75086ab8bf68a7434c96179f334f2fc7c02b9f SHA256: 66e031f70b9b4e2c9c516412fc63ffa2d90177e1307720572d6ccdf0c4ee7ef9 SHA512: e4fe2c573a3764dae9e988a8c080fe5c83cc2f654a7e19342c41ffb2c38b34caeb5fccf0272fbd6c724c2110fd9fc4a8d03360a98f26642b83fabb870110aa78 Homepage: https://cran.r-project.org/package=samplesize Description: CRAN Package 'samplesize' (Sample Size Calculation for Various t-Tests and Wilcoxon-Test) Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties. Package: r-cran-samplesizecalculator Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-dt, r-cran-bslib Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-samplesizecalculator_0.1.0-1.ca2604.1_all.deb Size: 197068 MD5sum: 57f2f65cc692668a013af1c3220abe25 SHA1: e32ab5ef3c3032a698f12f19044b1724722744ee SHA256: 88d06044c413517529d1bc1da31a2d93b313c7b431e1852eb3127c5d31f05e87 SHA512: 3bdc400e873c34ae619eea953cfead164df0b305acec418f76a391d111db0311e583df68c626435446a905cd44feb17d38c2911ed36c378d22ed7e5ff66329ed Homepage: https://cran.r-project.org/package=SampleSizeCalculator Description: CRAN Package 'SampleSizeCalculator' (Sample Size Calculator under Complex Survey Design) It helps in determination of sample size for estimating population mean or proportion under simple random sampling with or without replacement and stratified random sampling without replacement. When prior information on the population coefficient of variation (CV) is unavailable, then a preliminary sample is drawn to estimate the CV which is used to compute the final sample size. If the final size exceeds the preliminary sample size, then additional units are drawn; otherwise, the preliminary sample size is considered as final sample size. For stratified random sampling without replacement design, it also calculates the sample size in each stratum under different allocation methods for estimation of population mean and proportion based upon the availability of prior information on sizes of the strata, standard deviations of the strata and costs of drawing a sampling unit in the strata.For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" . Package: r-cran-samplesizecmh Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-desctools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-samplesizecmh_0.0.3-1.ca2604.1_all.deb Size: 74214 MD5sum: 2b61603cf874fff4acd7c833f9c72f5d SHA1: f5e80cb06d4179b5fb2abbce7315f98c1150b64a SHA256: 083d280d2d7b707953d3aad12df4371cd948e393359a111ea7edc3b5472e83b0 SHA512: fd4a761d8e7c8c1ebb4fb0134021ebee18cae0bffc02a6a81a3c03c0790e65f2d2953af9f3486dbbf6b834941e06a5172198dfeced0bb3dcbeca1ed578672a7e Homepage: https://cran.r-project.org/package=samplesizeCMH Description: CRAN Package 'samplesizeCMH' (Power and Sample Size Calculation for theCochran-Mantel-Haenszel Test) Calculates the power and sample size for Cochran-Mantel-Haenszel tests. There are also several helper functions for working with probability, odds, relative risk, and odds ratio values. Package: r-cran-samplesizediagnostics Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-samplesizediagnostics_0.1.1-1.ca2604.1_all.deb Size: 17580 MD5sum: f5c93c391a63fc480c448f3838624f56 SHA1: 20d2e8b1c27d1dca198aa66e7e44cc05e0b537ba SHA256: de29963dc7f527cd693b49c2ce4d2d18c4185f8fb31a8faec77e71f1a50c2223 SHA512: e57ea3fbf0afd1da2aaab93b89de5c7c5aa2daa50c039f7f2d1ab481cf013f754290d6e088eaf9a058a7a632f4e8ab5936b4aa7224f3d12bbd15e13f77427c6d Homepage: https://cran.r-project.org/package=SampleSizeDiagnostics Description: CRAN Package 'SampleSizeDiagnostics' (Choosing Sample Size for Evaluating a Diagnostic Test) Calculates the sample size needed for evaluating a diagnostic test based on sensitivity, specificity, prevalence, and desired precision. Based on Buderer (1996) . Package: r-cran-samplesizeestimator Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-samplesizeestimator_1.0.0-1.ca2604.1_all.deb Size: 67196 MD5sum: 9f19e12558043bb807b1d21502c4e181 SHA1: 71682d6d4a42e137c58bd83c1b838551d4fe86b6 SHA256: 28e807119cdd8940ee301fcfe8478b170c0270bfa81ab6c4c31f4ebbef6adcc9 SHA512: fd022901dae3fef2b50ea271307f119ff5ba840a3b33238fdad33df8a608b9db2fb2163c8a3284a97a00a36114cb041c91a8fbc11508a2880209b44f44b2e8c4 Homepage: https://cran.r-project.org/package=samplesizeestimator Description: CRAN Package 'samplesizeestimator' (Calculate Sample Size for Various Scenarios) Calculates sample size for various scenarios, such as sample size to estimate population proportion with stated absolute or relative precision, testing a single proportion with a reference value, to estimate the population mean with stated absolute or relative precision, testing single mean with a reference value and sample size for comparing two unpaired or independent means, comparing two paired means, the sample size For case control studies, estimating the odds ratio with stated precision, testing the odds ratio with a reference value, estimating relative risk with stated precision, testing relative risk with a reference value, testing a correlation coefficient with a specified value, etc. . Package: r-cran-samplesizelogisticcasecontrol Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-samplesizelogisticcasecontrol_2.0.2-1.ca2604.1_all.deb Size: 511198 MD5sum: 4d46f8010ffc6090de8b035c229608d1 SHA1: 514fdbdd0d069a30ed9ea60fcc6f395134652dac SHA256: 40eece50a0c525a366ab70599df7511190303e76167de68479df004d6f8de45d SHA512: 6d3bcbbf6e7f9657a5ed04fba163f4c417948043b58dd3173d8743d5b8192bf9584ab7a403ca5edd51d8e00a7c7d1aa71c92c6b9462f8a05bb71b10646990de4 Homepage: https://cran.r-project.org/package=samplesizelogisticcasecontrol Description: CRAN Package 'samplesizelogisticcasecontrol' (Sample Size and Power Calculations for Case-Control Studies) To determine sample size or power for case-control studies to be analyzed using logistic regression. Package: r-cran-samplesizemeans Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samplesizemeans_1.2.3-1.ca2604.1_all.deb Size: 194828 MD5sum: 0ba1a3c96ba7a9b54b2bc934773a9e35 SHA1: 2e64e701f4b78fc9a7448e4ca3ee53a765b810ee SHA256: 3925a08044aaeea02037bb2b3542976760a95df4cd22f72c030c9ea61396f745 SHA512: 46d3262bd23080ea9c62aa126d96bfd8077a0e844a5ec9acfb0b82d9ab51fe599bb2d543f936ac41938eb173ab38b0c66532d091734e8548dcacfc8266431cc8 Homepage: https://cran.r-project.org/package=SampleSizeMeans Description: CRAN Package 'SampleSizeMeans' (Sample Size Calculations for Normal Means) Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate a normal mean or the difference between two normal means. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of normal means are provided. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L. and Bélisle P. (1997) . Package: r-cran-samplesizeproportions Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samplesizeproportions_1.1.3-1.ca2604.1_all.deb Size: 90478 MD5sum: 492441608bb6b4470b983c21c617bc70 SHA1: d6cc3bccb513a1cb8a293943b450929942a70fac SHA256: 335ff76aa75fbe3a2a33fb8dbe821da7e27fa943655904d71f4ab2d60ff1e878 SHA512: d587bb33fb17dbd13dd81d533c56bac327f89e625f90e46a2d4a9e8ba0e5639251f5be735e4d336149d9f9775575e6ea863856c6281c2a8e0ab8d35acd5eac12 Homepage: https://cran.r-project.org/package=SampleSizeProportions Description: CRAN Package 'SampleSizeProportions' (Calculating Sample Size Requirements when Estimating theDifference Between Two Binomial Proportions) Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate the difference between two binomial proportions. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of binomial observations are provided. In all cases, estimation of the difference between two binomial proportions is considered. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L., du Berger R. and Bélisle P. (1997) . Package: r-cran-samplesizesinglearmsurvival Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-samplesizesinglearmsurvival_0.1.0-1.ca2604.1_all.deb Size: 21244 MD5sum: 68f5290d31e4b1fdb7511cafb0ebf1ab SHA1: f43ea62cb4a6e1d2b08ab21626be86e237b7c42d SHA256: e924aab7714cdbede7fdd5b6c333b18a8b2c6c3bfcb96d69398947652d178598 SHA512: 1efb1d993652af2786797c450ad45df749666ded02b33710bdd8d5c9cd09e7734ff8b932125fe59e01242a106d91b24ce95217efd2c622335f1872df68498922 Homepage: https://cran.r-project.org/package=SampleSizeSingleArmSurvival Description: CRAN Package 'SampleSizeSingleArmSurvival' (Calculate Sample Size for Single-Arm Survival Studies) Provides methods to calculate sample size for single-arm survival studies using the arcsine transformation, incorporating uniform accrual and exponential survival assumptions. Includes functionality for detailed numerical integration and simulation. This method is based on Nagashima et al. (2021) . Package: r-cran-samplevadir Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2590 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-splitstackshape Suggests: r-cran-haven, r-cran-rio Filename: pool/dists/resolute/main/r-cran-samplevadir_1.0.0-1.ca2604.1_all.deb Size: 2614672 MD5sum: 6123daa6b736f0f4ae9f41bfddc36687 SHA1: a446de2884f0d45d5ed73ff872dafeb3e6df2654 SHA256: 784315de8bbb53892d54493ba723797328759adc54610eb7f0eb71765200c17b SHA512: f2bf194964ccb906065d2d16a5b36a439e9e6dcc5f53cdccaf9a90eb14b6a6faf297a80a850871bfd7eaecd630b22a319f276fb42486999affb7768944feed84 Homepage: https://cran.r-project.org/package=sampleVADIR Description: CRAN Package 'sampleVADIR' (Draw Stratified Samples from the VADIR Database) Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) . Data from VADIR are not provided as part of this package. Package: r-cran-samplex Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-samplex_0.3.0-1.ca2604.1_all.deb Size: 31884 MD5sum: 6a1e9369f76a6e9a5f0dac8bf953af8c SHA1: 511b9b5e3ede1742d84a932fa7420f3014afd1f4 SHA256: 74379bc1e19acdedad45b50673008550b755e70eff19e121c62e1e0deaadbe1c SHA512: 737bf04285259a707fa044d3680374208617f0bb0cc0efc2efb7274ae2df052c5be6ab7c2aabfd8f9a1abe4748e5bde41ff6fb9ff738d71bd8e6a2e7f7b5e2c8 Homepage: https://cran.r-project.org/package=samplex Description: CRAN Package 'samplex' (Shiny Tool for Sample Size Calculation) An interactive 'shiny' application to assist in determining sample sizes for common survey designs such as 'simple random sampling', 'stratified sampling', and 'cluster sampling'. It includes formulas, helper calculators, and illustrative examples. Package: r-cran-samplezoo Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-samplezoo_1.2.1-1.ca2604.1_all.deb Size: 255402 MD5sum: 6bba8b6d2d397df76135925e4b03e19f SHA1: 6f6773fbccf0d9a64686c6675993ceea60a1b60b SHA256: 7b8c55aae608f5cb0756920c9a68a40c2fe2021af01eec0276fa0e4736bcb6b1 SHA512: b5dc6efd1bbd33e02f6784da473c5661436ff64bc7eb34a69d79e0d2fb12b8377e973b15bb89bb61ee1640c900759107d95f738e5c9108030e93e34b02284d69 Homepage: https://cran.r-project.org/package=samplezoo Description: CRAN Package 'samplezoo' (Generate Samples with a Variety of Probability Distributions) Simplifies the process of generating samples from a variety of probability distributions, allowing users to quickly create data frames for demonstrations, troubleshooting, or teaching purposes. Data is available in multiple sizes—small, medium, and large. For more information, refer to the package documentation. Package: r-cran-samplingbook Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pps, r-cran-sampling, r-cran-survey Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-samplingbook_1.2.4-1.ca2604.1_all.deb Size: 263042 MD5sum: 644d4a70192deed09ebb020ae06e899f SHA1: 8836d72086558314643305ace6d6039c3fa940e4 SHA256: 2709a803949a7dd8986a42c1f064e025e422ffd5aa4bf56d95b4528caf834753 SHA512: 41b4136ea3619eb28c0e264fa09cb8ab7cfc70129d11f92db28cb8c658228a61d5b2580d925357f069063a2a85c5aab8f7a1a9dcc53fe9cc15a177a65d036039 Homepage: https://cran.r-project.org/package=samplingbook Description: CRAN Package 'samplingbook' (Survey Sampling Procedures) Sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010). Package: r-cran-samplingdatacrt Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-samplingdatacrt_1.0-1.ca2604.1_all.deb Size: 347716 MD5sum: 938c10c1b3342b375792539437c1a6c5 SHA1: 382571420a86c1e0d9fe7224c2e0fc68e79182a5 SHA256: 23f00c86f33709d581dd280f455897990f4bc3b247186491868e2256755267c1 SHA512: 3ea96854c97d605d9c8f79da22381f8909f24cff420411f82a23a70ef80ebb12709119cb93dc20d21c7fc5131004fdd2f0f3761b1d383b0287ce945a568d715c Homepage: https://cran.r-project.org/package=samplingDataCRT Description: CRAN Package 'samplingDataCRT' (Sampling Data Within Different Study Designs for ClusterRandomized Trials) Package provides the possibility to sampling complete datasets from a normal distribution to simulate cluster randomized trails for different study designs. Package: r-cran-samplingin Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-sampling Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-samplingin_1.1.1-1.ca2604.1_all.deb Size: 277980 MD5sum: 1c84221fac3cc976ee129e9421452539 SHA1: d434c67e793cd23eb46931fd63d95294bb3a6ec7 SHA256: 742df15458675bd8f2b6eb94c89feed2925eeab3d17f920cbea0ba28d7c45781 SHA512: 460936f0d7cce38f5b605d41bd151b92eb981c4fa3bcde0b736e6d9e08a71689a93147860ef524ff2f84e5408c7c7a2881c6e45f65f911f1228b6204a7aec71e Homepage: https://cran.r-project.org/package=samplingin Description: CRAN Package 'samplingin' (Dynamic Survey Sampling Solutions) A robust solution employing the SRS (Simple Random Sampling), systematic and PPS (Probability Proportional to Size) sampling methods, ensuring a methodical and representative selection of data. Seamlessly allocate predetermined allocations to smaller levels. Package: r-cran-samplingr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-samplingr_1.0.1-1.ca2604.1_all.deb Size: 107146 MD5sum: 9af32e01eafebac6c821a867aeb3d867 SHA1: 7f1c61a78531f925bf18254ea801043ed28b2797 SHA256: 934569ec1a31ee1e16b59a148e0f90eb603e6441e04dea69f31114c8c7ddf0e1 SHA512: 34d56e116a8484bdda5a603790525fa0c40448b5201f0fd778a7a548214ee1475f34fd35a84159c6d1728925edc44888a622ab5012fe673bd2f5824c13ee5284 Homepage: https://cran.r-project.org/package=samplingR Description: CRAN Package 'samplingR' (Sampling and Estimation Methods) Functions to take samples of data, sample size estimation and getting useful estimators such as total, mean, proportion about its population using simple random, stratified, systematic and cluster sampling. Package: r-cran-samplingstrata Architecture: all Version: 1.5-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-memoise, r-cran-doparallel, r-cran-pbapply, r-cran-samplingbigdata, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formattable Filename: pool/dists/resolute/main/r-cran-samplingstrata_1.5-5-1.ca2604.1_all.deb Size: 1369610 MD5sum: b7bd5a485cdcc2c1cbea43101ee28dd1 SHA1: 7fde53fffc1701e48ee2609fd8d6296e3377da41 SHA256: 2c4dca7bff7fc534aba0961cffcdb31eff3c8b6dabcc99119c332a424d50ef12 SHA512: f160e2d25cfd2f1b8b18e4b8952fd6c92ff2840f82bfba9681e9a79cc5c63335d3041924efa4ae2099ec43fb0ae560344ede1a2db3109cdb282152a7d13079ac Homepage: https://cran.r-project.org/package=SamplingStrata Description: CRAN Package 'SamplingStrata' (Optimal Stratification of Sampling Frames for MultipurposeSampling Surveys) Tools for the optimization of stratified sampling design. It determines a stratification of a sampling frame that minimizes sample cost while satisfying precision constraints in a multivariate and multidomain context. The approach relies on a genetic algorithm; each candidate partition of the frame is an individual whose fitness is evaluated via the Bethel-Chromy allocation to meet target precisions. Functions support analysis of optimization results, labeling of the frame with new strata, and drawing a sample according to the optimal allocation. Algorithmic components adapt code from the 'genalg' package. See M. Ballin and G. Barcaroli (2020) "R package SamplingStrata: new developments and extension to Spatial Sampling" . Package: r-cran-samplrdata Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-samplrdata_1.0.0-1.ca2604.1_all.deb Size: 2712878 MD5sum: fdec8e464211114f67a5a093faa097da SHA1: 693a8827a102ff2b716523e93e14329ffe7ac19e SHA256: 42537399c5f5a96b266177d23f8b85b53e3a150c1e24f5bbe51bfef4f78b7c2d SHA512: 494734b13a9f7754be310c0998e071c895bd926958bc0e6fd00f73e9a6a29d5ebc6d4e7a1adc8b9202a3bbe00430d312586b0c6870b479d752ede085ca3759a2 Homepage: https://cran.r-project.org/package=samplrData Description: CRAN Package 'samplrData' (Datasets from the SAMPLING Project) Contains human behaviour datasets collected by the SAMPLING project (). 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For details, please refer to Yang et al. (2023) . 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For example, in some cases in healthcare, financial, or internet-security contexts, certain sub-classes are difficult to learn because they are underrepresented in training data. This 'R' package offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm ('SANSA'), which, in contrast to other solutions, introduces a novel “placement” parameter that can be tuned to adapt to each datasets unique manifestation of the imbalance. More information about the algorithm's parameters can be found at Nasir et al. (2022) . 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This approach is designed to accommodate asynchronous time sampling (i.e. different time points for different individuals), inter-individual variability, noisy measurements and large numbers of variables. Based on a smoothing splines functional model, 'santaR' is able to detect variables highlighting significantly different temporal trajectories between study groups. Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting options for non-specialist users. Package: r-cran-sanzo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sanzo_0.1.0-1.ca2604.1_all.deb Size: 117544 MD5sum: bdefc06dfd72f1132e986cff632f070d SHA1: ef879d0a8f130d6de6c416218a417fe9d11c1964 SHA256: 22e836a36d4ab4edcf9ef1c09b24081173330d175c29e16bb9fa9b5b92860903 SHA512: b4c5f923465a02598204c37609f408965749d1aa9b6d3b1f3a33249c9f00316c4b50c775badabd992e415c1d3d93a9ab31d81378fd8067ba0ae8f3dcf7f887e0 Homepage: https://cran.r-project.org/package=sanzo Description: CRAN Package 'sanzo' (Color Palettes Based on the Works of Sanzo Wada) Inspired by the art and color research of Sanzo Wada (1883-1967), his "Dictionary Of Color Combinations" (2011, ISBN:978-4861522475), and the interactive site by Dain M. Blodorn Kim , this package brings Wada's color combinations to R for easy use in data visualizations. This package honors 60 of Wada's color combinations: 20 duos, 20 trios, and 20 quads. Package: r-cran-sap Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsda Filename: pool/dists/resolute/main/r-cran-sap_1.0-1.ca2604.1_all.deb Size: 14122 MD5sum: a0226a1afc16c6e3a2ecb9cdb67058c3 SHA1: 18b1a70a0ac63ca330d0a483eb35df8502c98b81 SHA256: 25e7eca566c829c60b5e4489f630f4d269a21ffef7111dafba1db898a29b66ab SHA512: 78a4d75c64022b024b7302386d4c4e1bc3cffe06d02679421b76b2cc6fdf7212077c0484264742205e9b1c8a15f8553c3fb0573243b70adf6ce7c101d00c02f7 Homepage: https://cran.r-project.org/package=SAP Description: CRAN Package 'SAP' (Statistical Analysis and Programming) The Hypothesis tests for the means of independent or paired groups. This package investigates the normality assumption automatically. Then, it tests the hypothesis tests for two independent or paired group means by using parametric or non-parametric tests. It uses the Shapiro-Wilk test to test the normality assumption. For independent two groups, If data comes from the normal distribution, the package uses the Z or t-test according to whether variances are known. For paired groups, it uses paired t-test under normal data sets. If data does not come from the normal distribution, the package uses the Wilcoxon test for independent and paired cases. Package: r-cran-sapevom Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sapevom_0.2.0-1.ca2604.1_all.deb Size: 21454 MD5sum: 73d9cd859c5f28b84d01f910bc74846c SHA1: 1090e9436b74f8540965bb3fc74446d857b6f110 SHA256: d315a9abff005ced87e6d86ee54183a9d3d33e03043f6622f9a66c8c3db94c6d SHA512: aef02268c2571da22336be61b60d79895941443101e41e3f2cc5c89808638cd24b87041da65bee265f8c70cb6e172c2ec50a1dc9c1d2ac7272a559de6a03bcf8 Homepage: https://cran.r-project.org/package=sapevom Description: CRAN Package 'sapevom' (Group Ordinal Method for Multiple Criteria Decision-Making) Implementation of SAPEVO-M, a Group Ordinal Method for Multiple Criteria Decision-Making (MCDM). SAPEVO-M is an acronym for Simple Aggregation of Preferences Expressed by Ordinal Vectors Group Decision Making. This method provides alternatives ranking given decision makers' preferences: criteria preferences and alternatives preferences for each criterion.This method is described in Gomes et al. (2020) . 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Package: r-cran-sapo Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sapo_0.8.0-1.ca2604.1_all.deb Size: 97300 MD5sum: d4eba0a26ae6653374a191ae94a1a2b6 SHA1: 8a77af1d20fe31e2105b1efeb97682b95e760059 SHA256: c8c18524a46e36194bc33c238c3c18591532f1d7c0d1042672364853c601171c SHA512: e316c677b8201682d8b1627175feab02348911b914a0f2559d5a1d18ed742c67386d360877bba7ad10e629f071c9900864a593c5d035924b6adc6f4e54ce547d Homepage: https://cran.r-project.org/package=sapo Description: CRAN Package 'sapo' (Spatial Association of Different Types of Polygon) In ecology, spatial data is often represented using polygons. These polygons can represent a variety of spatial entities, such as ecological patches, animal home ranges, or gaps in the forest canopy. Researchers often need to determine if two spatial processes, represented by these polygons, are independent of each other. For instance, they might want to test if the home range of a particular animal species is influenced by the presence of a certain type of vegetation. To address this, Godoy et al. (2022) () developed conditional Monte Carlo tests. These tests are designed to assess spatial independence while taking into account the shape and size of the polygons. Package: r-cran-saqgetr Architecture: all Version: 0.2.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-lubridate, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-tidyr, r-cran-magrittr, r-cran-httr Suggests: r-cran-openair Filename: pool/dists/resolute/main/r-cran-saqgetr_0.2.21-1.ca2604.1_all.deb Size: 77696 MD5sum: 3018f5f68e649faf05cab5f2bcea60da SHA1: fe4b0ab51c9894656199d959fa5c34b44574f247 SHA256: e61b7ac7cfebfdc67dfceca0afc70fab0e444e94e0b7291e9dd19ae005b21922 SHA512: c0a02c38ed39213e60b1baa89fa930b7d7900cf252b58906ecb3c3f00f0401108c319339a840e57b5fe7b6dae8fffcb8f3a46b2f82c4b94e29af93fdeb168b22 Homepage: https://cran.r-project.org/package=saqgetr Description: CRAN Package 'saqgetr' (Import Air Quality Monitoring Data in a Fast and Easy Way) A collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. Air quality data are sourced from open and publicly accessible repositories and can be found in these locations: and . The web server space has been provided by Ricardo Energy & Environment. Package: r-cran-sara4r Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2556 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sara4r_0.1.0-1.ca2604.1_all.deb Size: 1986554 MD5sum: d635fb6728ea15bb76922f2119d03f25 SHA1: 5f669d17b7062c3761969787efcc192a17f3f7e4 SHA256: ffbb398217f58f366f6f283413c9c26fe946e1898d8558b0145bcd8e8320b164 SHA512: 70c7641c6ebef254f00f8fbf36aa35b13652d8835a7be59654445b30008abd1741fefd5598e57395970f59023bf5e7a1846333e0a999f77272144028021c67c8 Homepage: https://cran.r-project.org/package=sara4r Description: CRAN Package 'sara4r' (An R-GUI for Spatial Analysis of Surface Runoff using theNRCS-CN Method) A Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011). It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps. This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021) but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.). Package: r-cran-saros.base Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 936 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-rlang, r-cran-cli, r-cran-tidyselect, r-cran-dplyr, r-cran-tidyr, r-cran-glue, r-cran-stringi, r-cran-forcats, r-cran-fs, r-cran-yaml, r-cran-zip, r-cran-rstudioapi, r-cran-bcrypt Suggests: r-cran-covr, r-cran-haven, r-cran-srvyr, r-cran-readr, r-cran-purrr, r-cran-writexl, r-cran-webshot, r-cran-usethis, r-cran-quarto, r-cran-labelled, r-cran-testthat, r-cran-tibble, r-cran-withr, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-saros.base_1.2.0-1.ca2604.1_all.deb Size: 514246 MD5sum: 497d24dd687f6c3eeabbe5abd90ddcfb SHA1: bf8e041cb170ae3109e594ce9ec4549e3b0655a9 SHA256: 3a6221e84652c528de669824a844c1487b3ca2859ee54752527db59edbee95cc SHA512: 83ce7c959c83076d8168161aeea3fd40328e934f9614a714cbf45d71b31373bf7b4a7cf31caf3a9d4c7cc32b3ea1a1be13f75eb74ce3ce443a5b69f42d489726 Homepage: https://cran.r-project.org/package=saros.base Description: CRAN Package 'saros.base' (Base Tools for Semi-Automatic Reporting of Ordinary Surveys) Scaffold an entire web-based report using template chunks, based on a small chapter overview and a dataset. Highly adaptable with prefixes, suffixes, translations, etc. Also contains tools for password-protecting, e.g. for each organization's report on a website. Developed for the common case of a survey across multiple organizations/sites where each organization wants to obtain results for their organization compared with everyone else. See 'saros' () for tools used for authors in the drafted reports. Package: r-cran-saros Architecture: all Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-fs, r-cran-ggiraph, r-cran-ggplot2, r-cran-glue, r-cran-mschart, r-cran-officer, r-cran-rlang, r-cran-stringi, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-covr, r-cran-haven, r-cran-labelled, r-cran-pdftools, r-cran-quarto, r-cran-knitr, r-cran-readr, r-cran-scales, r-cran-spelling, r-cran-srvyr, r-cran-survey, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-withr, r-cran-writexl, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-saros_1.6.2-1.ca2604.1_all.deb Size: 677516 MD5sum: 68a098e07c5ce0239bde6b481345f963 SHA1: 8b8ae4d78ef8f1927e42036ca3a7be6d865aade7 SHA256: 40f3ded8b2d8c1d3ac481452ccadaba504808c6b290bf458e0325d1d7202abf9 SHA512: 5cb3621e83ba5611c75d3769927509abdbda60c791eb5fb7c6d40302b3822bd0018d8ab163dfc5ee56bd85baf9d265f857721f4fb143cf1a413b86c1780cbca2 Homepage: https://cran.r-project.org/package=saros Description: CRAN Package 'saros' (Semi-Automatic Reporting of Ordinary Surveys) Offers a systematic way for conditional reporting of figures and tables for many (and bivariate combinations of) variables, typically from survey data. Contains interactive 'ggiraph'-based () plotting functions and data frame-based summary tables (bivariate significance tests, frequencies/proportions, unique open ended responses, etc) with many arguments for customization, and extensions possible. Uses a global options() system for neatly reducing redundant code. Also contains tools for immediate saving of objects and returning a hashed link to the object, useful for creating download links to high resolution images upon rendering in 'Quarto'. Suitable for highly customized reports, primarily intended for survey research. Package: r-cran-sarp.compo Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-car Suggests: r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-sarp.compo_0.1.8-1.ca2604.1_all.deb Size: 246192 MD5sum: 9c8d365d068682ef310bee24edfb2992 SHA1: d9000a025cd8117486195dd2ffca499db7b470f9 SHA256: 8d8f94d8a295e18f50130ba68865bbe100b2897d765bba71d5df3f7a80575df6 SHA512: 7c758de2c1e565a1e3e4d4c9be27f570f0ddda6ca23ed7b863a2f904d85b50d73946db94e655336858e19f9fc2229eedd4b4edbfdc825d8fa9f2ab452b9f07a1 Homepage: https://cran.r-project.org/package=SARP.compo Description: CRAN Package 'SARP.compo' (Network-Based Interpretation of Changes in Compositional Data) Provides a set of functions to interpret changes in compositional data based on a network representation of all pairwise ratio comparisons: computation of all pairwise ratio, construction of a p-value matrix of all pairwise tests of these ratios between conditions, conversion of this matrix to a network. Package: r-cran-sarp.moodle Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-magick Suggests: r-cran-readods, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-sarp.moodle_1.2.3-1.ca2604.1_all.deb Size: 391316 MD5sum: 2c96061a94649dccd273cf82b8cf90e9 SHA1: 457532003e6fbcc93db059122bee8abb76a52ec8 SHA256: 250ffcee90816e794f0090ee379e8cb64966414d97dcf44049fce6eef5351b32 SHA512: c788a251719eca43ff1a4106cbde089a5c47d5254b3303f68fbb76923056572269535bfcba35c43d620e6c65750ada9d35fd700c1c1b1d446e156d2ec59c61fd Homepage: https://cran.r-project.org/package=SARP.moodle Description: CRAN Package 'SARP.moodle' (XML Output Functions for Easy Creation of Moodle Questions) Provides a set of basic functions for creating Moodle XML output files suited for importing questions in Moodle (a learning management system, see for more information). Package: r-cran-sarp.snowprofile.pyface Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sarp.snowprofile, r-cran-reticulate, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-sarp.snowprofile.pyface_0.4.3-1.ca2604.1_all.deb Size: 457430 MD5sum: 6e23dad666e7375d32af655727de5bd0 SHA1: 81c8fb8c861536353dfb66ce3ee90cc0a80e5c9f SHA256: 3a327ac7dd31c56b3ae1467727a58b8b28b56a170b705f8781f3059aae6141bb SHA512: 4ae3a68f1c6864cfefddb7871ec482dbcf466c386de5788123661cd09dc5e50a8d7bc5cf57e98d004a5d811b6a9b03f323450c366c30300c1c0d3f282c94c462 Homepage: https://cran.r-project.org/package=sarp.snowprofile.pyface Description: CRAN Package 'sarp.snowprofile.pyface' ('python' Modules from Snowpack and Avalanche Research) The development of post-processing functionality for simulated snow profiles by the snow and avalanche community is often done in 'python'. This package aims to make some of these tools accessible to 'R' users. Currently integrated modules contain functions to calculate dry snow layer instabilities in support of avalache hazard assessments following the publications of Richter, Schweizer, Rotach, and Van Herwijnen (2019) , and Mayer, Van Herwijnen, Techel, and Schweizer (2022) . Package: r-cran-sarp.snowprofile Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-sarp.snowprofile_1.4.1-1.ca2604.1_all.deb Size: 561046 MD5sum: 0fa47e7e652997f4aa048aacb4bda387 SHA1: 1341a96ae4a0919942d510a3de1695f3cfe25b3d SHA256: 0df68f76328626b2df9bebe29dbe85a41f6ff4c9db9c9712666400d716005c2f SHA512: eb61a8c2f549fdf719ecaf9a099d6f16024082d160ad063d6cc57b60e440f0e4bd11034713bb8e3e474988c338b7d2078f57acc2456118c834bd3ad234d43bc7 Homepage: https://cran.r-project.org/package=sarp.snowprofile Description: CRAN Package 'sarp.snowprofile' (Snow Profile Analysis for Snowpack and Avalanche Research) Analysis and plotting tools for snow profile data produced from manual snowpack observations and physical snowpack models. The functions in this package support snowpack and avalanche research by reading various formats of data (including CAAML, SMET, generic csv, and outputs from the snow cover model SNOWPACK), manipulate the data, and produce graphics such as stratigraphy and time series profiles. Package developed by the Simon Fraser University Avalanche Research Program . Graphics apply visualization concepts from Horton, Nowak, and Haegeli (2020, ). Package: r-cran-sars Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-crayon, r-cran-cli, r-cran-numderiv, r-cran-doparallel, r-cran-foreach, r-cran-aiccmodavg, r-cran-minpack.lm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-sars_2.1.1-1.ca2604.1_all.deb Size: 761166 MD5sum: 98bf9070259486ca7821553175b57896 SHA1: fb7e38a3109a255414c714af66b5307b5346585b SHA256: 16832f2853a1c5d357dde3590f41a58187c458a78dd95b575e78ee5fe15edaa7 SHA512: eceb494d141bb5b8cf4e9e91284142e1b8d29044ce7d4880dd7603ba2fbd52cc12cd2f68f336f4a074e2f55f9f8e032e4ced0eab8aa1ac8dbfefc898630df028 Homepage: https://cran.r-project.org/package=sars Description: CRAN Package 'sars' (Fit and Compare Species-Area Relationship Models UsingMultimodel Inference) Implements the basic elements of the multi-model inference paradigm for up to twenty species-area relationship models (SAR), using simple R list-objects and functions, as in Triantis et al. 2012 . The package is scalable and users can easily create their own model and data objects. Additional SAR related functions are provided. Package: r-cran-sas7bdat Architecture: all Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sas7bdat_0.8-1.ca2604.1_all.deb Size: 202860 MD5sum: 9dc687236362d371274c3aa9e3af033a SHA1: ff237310836ea32c89c26b94ee7854b8a51ad4dd SHA256: cc44768dcfbd585787cb8083108e15f46691e9f9453f0d1a02b92c9bb52a0d88 SHA512: 19c9edcea66c72e1ed320577b6dcd5a0f0cf4354baafd682ab5c5cfd0837b89efa127c4d4baff8e9400bea51e5c1dd4665a69fd7c60239b87921581e3621023f Homepage: https://cran.r-project.org/package=sas7bdat Description: CRAN Package 'sas7bdat' (sas7bdat Reverse Engineering Documentation) Documentation and prototypes for the earliest (circa 2010) open-source effort to reverse engineer the sas7bdat file format. The package includes a prototype reader for sas7bdat files. However, newer packages may contain more robust readers for sas7bdat files. Package: r-cran-sascii Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sascii_1.0.2-1.ca2604.1_all.deb Size: 43328 MD5sum: 44041bb157679b24a59ccf23364d6b48 SHA1: 2ae12d4295f2cc0fcf988a181f8bb45e33719f06 SHA256: de99f505f1cb2c93998665da3f0aeb6b95e903cdd16c68dba858db72dfe93f87 SHA512: 787c0c414b4fffeeac11080dfe9ea58eec61b7d2248d167cfec4b1648d929dbe1f3022027e6764be0e6dddf3818bbc2bef1596cd4eeea575c4cef5e41ead2b5f Homepage: https://cran.r-project.org/package=SAScii Description: CRAN Package 'SAScii' (Import ASCII Files Directly into R using Only a 'SAS' InputScript) Using any importation code designed for 'SAS' users to read ASCII files into 'sas7bdat' files, this package parses through the INPUT block of a '.sas' syntax file to design the parameters needed for a 'read.fwf()' function call. This allows the user to specify the location of the ASCII (often a '.dat') file and the location of the 'SAS' syntax file, and then load the data frame directly into R in just one step. Package: r-cran-sasdates Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sasdates_0.1.0-1.ca2604.1_all.deb Size: 19456 MD5sum: 0067df09602e90e4aa7d6438f8383fad SHA1: e02a0f866898dbf57bc27b41c597c77e12cfb8f3 SHA256: 49ec35b1f9cd0f9d2a2ee3b60a514f079fffc10c22c0cffdf0e69c2aff7d2867 SHA512: 295234c39e219bf61cdce88febba06be7e749d89a44f69ccde86f569400fdf5c4d9e4f6a9fa2134104755b139535d4518ead45363d530d946cb68be12e86c97f Homepage: https://cran.r-project.org/package=SASdates Description: CRAN Package 'SASdates' (Convert the Dates to 'SAS' Formats) Converts the dates to different 'SAS' date formats. In 'SAS' dates are a special case of numeric values. Each day is assigned a specific numeric value, starting from January 1, 1960. This date is assigned the date value 0, and the next date has a date value of 1 and so on. The previous days to this date are represented by -1 , -2 and so on. With this approach, 'SAS' can represent any date in the future or any date in the past. There are many date formats used in 'SAS' to represent date-time. Here, we try to develop functions which will convert the date to different 'SAS' date formats. Package: r-cran-sasif Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sasif_0.1.3-1.ca2604.1_all.deb Size: 34028 MD5sum: 1945f14a3f45f9b33234b78b09b3938b SHA1: 8eb94897794c4dc28d52ae037e4398009ec38aa9 SHA256: 5e4c4eae903ecdf31c1f66f2bf3bc3b1046fa0158f7e6f58cfd86f63febfbd69 SHA512: efc5466ac3588d6d2dbdfa5768777f2d569e0cafb4b94510343c8684dd754ddae18fba291a8e3fe2677169939ee2cb43b6f7955033677f2c3d5ba939775494b2 Homepage: https://cran.r-project.org/package=sasif Description: CRAN Package 'sasif' ('SAS' IF Style Data Step Logic for Data Tables) Provides 'SAS'-style IF/ELSE chains, independent IF rules, and DELETE logic for 'data.table', enabling clinical programmers to express Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM)-style derivations in familiar SAS-like syntax. Methods are informed by clinical data standards described in CDISC SDTM and ADaM implementation guides. See and . Package: r-cran-saslm Architecture: all Version: 0.10.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-saslm_0.10.8-1.ca2604.1_all.deb Size: 1183686 MD5sum: a0d849fb98cfb116b6fa042a567301c1 SHA1: 90ab3ba0276c93877ff7852a673af12f4561871d SHA256: 30cc2ccecdfa15120dcec44aa7fc504e718f9d57d14c8da3d3374d2cab39485d SHA512: e9ae84ab205fcdf62be25c6c7963596611988beef803923d8a24174ceb0b7222063037d2960ff47cf0ca3d050a761a43a4f1dc1e8441b767facbf590e6dfef66 Homepage: https://cran.r-project.org/package=sasLM Description: CRAN Package 'sasLM' ('SAS' Linear Model) This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0). Package: r-cran-sasmarkdown Architecture: all Version: 0.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-xfun Suggests: r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sasmarkdown_0.8.7-1.ca2604.1_all.deb Size: 100296 MD5sum: 8f489c309bae53ab2d546821b867dd01 SHA1: c7a9324681a67156a86334033235ad323d7a8962 SHA256: f1db0c60f54a187296c466cf60490e015b205e9a1cc3e4db5e805d956fcdc9e5 SHA512: e927a85c37921f578d185121d98f8a640abeeb9513aec7f3f355fd4d00738f1bf4684d73d2d35fbb5e79b0e264541a89ab7dd6b98ce1c2eaa1e802b7b8b88829 Homepage: https://cran.r-project.org/package=SASmarkdown Description: CRAN Package 'SASmarkdown' ('SAS' Markdown) Settings and functions to extend the 'knitr' 'SAS' engine. Package: r-cran-sasmixed Architecture: all Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-lme4, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-sasmixed_1.0-5-1.ca2604.1_all.deb Size: 330176 MD5sum: 64a86d260163b8bf0f422c933c873257 SHA1: 5197e3b1e5037dc4752c37cbf8c94deb62cee28a SHA256: e94d167536492867970045ed1144557ea6f0167d1366934c4307d07950203f8e SHA512: ad8dd525f5a2399e4857ef52ac2fcf7d42847b601674212864c1b98c84eb45cd81450bbfcabbb8d14ac50ec12ccab336ca608901359b4ae35d373c56af062f05 Homepage: https://cran.r-project.org/package=SASmixed Description: CRAN Package 'SASmixed' (Data Sets from "SAS System for Mixed Models) Data sets and sample lmer analyses corresponding to the examples in Littell, Milliken, Stroup and Wolfinger (1996), "SAS System for Mixed Models", SAS Institute. Package: r-cran-sasquatch Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-evaluate, r-cran-htmlwidgets, r-cran-knitr, r-cran-reticulate, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-curl, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-sasquatch_0.1.3-1.ca2604.1_all.deb Size: 1396608 MD5sum: 1111eceec6b77f4be895ba7f544e6685 SHA1: 75b8688875c7b9f2f74c3a5a51655d141e082d19 SHA256: 6c8107355eab1e20156f08ec33ff3de005ca8abee9c32858d27dff9848e43177 SHA512: e3f083793f386d141b47f846acdf14dcf9ccc473a4748b7bfb375083a910c59c11e5abb557f86ee7844307618d6ebac96f45100acb283f2a8cbf46394fc0f5ed Homepage: https://cran.r-project.org/package=sasquatch Description: CRAN Package 'sasquatch' (Use 'SAS', R, and 'quarto' Together) Use R and 'SAS' within reproducible multilingual 'quarto' documents. Run 'SAS' code blocks interactively, send data back and forth between 'SAS' and R, and render 'SAS' output within 'quarto' documents. 'SAS' connections are established through a combination of 'SASPy' and 'reticulate'. Package: r-cran-sasr Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-lifecycle, r-cran-reticulate Suggests: r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sasr_0.1.5-1.ca2604.1_all.deb Size: 363698 MD5sum: 7f0a9297ef5936f38a8059e5fa9f416b SHA1: 5c1c921c4b8025528cec4abbced7f7f5bbd66245 SHA256: 3231516f50c336862a262b5ea87dc6e0d14ca93782e79abb70627ecf123d8da4 SHA512: 3d5b1508fc47d32b2cc371185baef25b094a1c99eb7197e12beb7f745b7cafecd41a2e475ab56f11ad8f7347be9a9f29d6bf9b7db2c98d05e18fe34172ee6b5a Homepage: https://cran.r-project.org/package=sasr Description: CRAN Package 'sasr' ('SAS' Interface) Provides a 'SAS' interface, through 'SASPy'() and 'reticulate'(). This package helps you create 'SAS' sessions, execute 'SAS' code in remote 'SAS' servers, retrieve execution results and log, and exchange datasets between 'SAS' and 'R'. It also helps you to install 'SASPy' and create a configuration file for the connection. Please review the 'SASPy' license file as instructed so that you comply with its separate and independent license. Package: r-cran-sassy Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmtr, r-cran-common, r-cran-logr, r-cran-libr, r-cran-reporter, r-cran-procs, r-cran-macro Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidylog, r-cran-magrittr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-sassy_1.3.0-1.ca2604.1_all.deb Size: 1694408 MD5sum: c747015c9de2e0a04e198a3c2657e32b SHA1: c758fa902ece14e88a70e25e6aae0ea6036163a7 SHA256: 39607b89c9c8ca781a994e97b54c72c8a573d11387b814a0741ba0bc4c5af4a7 SHA512: d9cfe24c5f955f852567b27b098ba3a1c151ce1c3672b0a2aa45ac506d25740018c4f0c18787ca95bb635267c2c7894bd8c72b4682a171f0a158d495132340da Homepage: https://cran.r-project.org/package=sassy Description: CRAN Package 'sassy' (Makes 'R' Easier for Everyone) A meta-package that aims to make 'R' easier for everyone, especially programmers who have a background in 'SAS®' software. This set of packages brings many useful concepts to 'R', including data libraries, data dictionaries, formats and format catalogs, a data step, and a traceable log. The system also includes a package that replicates several commonly-used 'SAS®' procedures, like 'PROC FREQ', 'PROC MEANS', and 'PROC REG'. Package: r-cran-sate Architecture: all Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ellipse, r-cran-mass, r-cran-survey Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sate_3.1.1-1.ca2604.1_all.deb Size: 159080 MD5sum: f248087734c97b2d6b00a576c9ec5392 SHA1: ea056cc2152e953ca1d5b6efc8130a3250789b1e SHA256: 92d69409ccf2c63833ea9bbb9ff7a266de776c98bbf20786cd107e47bd173c12 SHA512: 92289903112d3fd0fbc8a3adf0e12ed537431f21748671ba7b1f257055005aeadb2cf823ab4462d4f28b4239675f1a8e97c7684345da53f375bd8ec925abcc7d Homepage: https://cran.r-project.org/package=sate Description: CRAN Package 'sate' (Scientific Analysis of Trial Errors (SATE)) Bundles functions used to analyze the harmfulness of trial errors in criminal trials. Functions in the Scientific Analysis of Trial Errors ('sate') package help users estimate the probability that a jury will find a defendant guilty given jurors' preferences for a guilty verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical trial conditions to conduct harmful error analysis. The conceptual framework is discussed by Barry Edwards, A Scientific Framework for Analyzing the Harmfulness of Trial Errors, UCLA Criminal Justice Law Review (2024) and Barry Edwards, If The Jury Only Knew: The Effect Of Omitted Mitigation Evidence On The Probability Of A Death Sentence, Virginia Journal of Social Policy & the Law (2025) . The relationship between individual jurors' verdict preferences and the probability that a jury returns a guilty verdict has been studied by Davis (1973) ; MacCoun & Kerr (1988) , and Devine et el. (2001) , among others. Package: r-cran-satin Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1070 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ncdf4, r-cran-maps, r-cran-pbsmapping, r-cran-splancs, r-cran-sp, r-cran-geosphere Filename: pool/dists/resolute/main/r-cran-satin_1.2.0-1.ca2604.1_all.deb Size: 1033022 MD5sum: df30a5dc2b6e74c15b31430177d6a83e SHA1: 6dbebd4cdd74db9bf4da0a511e6bf7f66b39384f SHA256: 5d99d51270f194d9d12b90f3e63d46d9bd2adc1d804d1cd60e5994ed5069140d SHA512: 52f5c7fd22e895bfe7f86b81390b7b100ec53351c70cdc817c20cccb94bfe3017e5898e7117bca3fd52a9e86857a073a7cbfc014f20fbdf350f0e9e229d680ac Homepage: https://cran.r-project.org/package=satin Description: CRAN Package 'satin' (Visualisation and Analysis of Ocean Data Derived from Satellites) With 'satin' functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done. Reading functions can import a user defined geographical extent of data stored in netCDF files. Currently supported ocean data sources include NASA's Oceancolor web page , sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS. Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others. Data sources specific for SST that can be imported too includes Pathfinder AVHRR and GHRSST . In addition, ocean productivity data produced by Oregon State University can also be handled previous conversion from HDF4 to HDF5 format. Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases , namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast. Package: r-cran-satres Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-snakecase, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-satres_1.1.1-1.ca2604.1_all.deb Size: 1767602 MD5sum: 2033df7a4d08d00481e95df84956ab47 SHA1: 2bda94d534622e2adf825479abb3139a69ccc0d0 SHA256: 84a4d2da480e67dbab58686a92ca08a7545faed93a8bfd81ab6a583aede53722 SHA512: a6a720a07eb80f0c4f700ecf9567464cf730257335ca2526ec7a3c8ff0be3e67ddd6c892c317ec1dd9199b0e28408abecf30fa701c44111f3b924e4460a90d86 Homepage: https://cran.r-project.org/package=satres Description: CRAN Package 'satres' (Grouping Satellite Bands by Spectral and Spatial Resolution) Given raster files directly downloaded from various websites, it generates a raster structure where it merges them if they are tiles of the same scene and classifies them according to their spectral and spatial resolution for easy access by name. Package: r-cran-saturncoefficient Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcorrelation, r-cran-projectionbasedclustering, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-saturncoefficient_1.6-1.ca2604.1_all.deb Size: 29860 MD5sum: 7d34223b2cd2565851900b6a4dad9791 SHA1: 11cd7c03ee2a9675a9c5568d4eae3c427b9771fc SHA256: bab2f25e063c377c8cf4c65b4d9ff0ac5e0cd8024407bce54e5f447e7c49d0a8 SHA512: 808e109682c505474ed446cf9c8fd2627e07f29196d09f8765375144c373339f960bc953eae9a351554b781862fcf99d1912c5e76388c8d947073965d1836145 Homepage: https://cran.r-project.org/package=SaturnCoefficient Description: CRAN Package 'SaturnCoefficient' (Statistical Evaluation of UMAP Dimensionality Reductions) A metric expressing the quality of a UMAP layout. This is a package that contains the Saturn_coefficient() function that reads an input matrix, its dimensionality reduction produced by UMAP, and evaluates the quality of this dimensionality reduction by producing a real value in the [0; 1] interval. We call this real value Saturn coefficient. A higher value means better dimensionality reduction; a lower value means worse dimensionality reduction. Reference: Davide Chicco et al. (February 2026), "The advantages of our proposed Saturn coefficient over continuity and trustworthiness for UMAP dimensionality reduction evaluation", PeerJ Computer Science 12:e3424 (pp. 1-30), . Package: r-cran-sautomata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sautomata_0.1.0-1.ca2604.1_all.deb Size: 39790 MD5sum: 22487630ac2d25074b2ee7f31dfbba38 SHA1: b5c81ca17ac0e4a73148bebfbde7601f14596209 SHA256: 5acd6061ac9da3809e832b5084aff03b73a72a8de1d1b9c0425b83d17acc49f6 SHA512: 9368d49136e047af6763a6aa538c30b6bfdbf055de6e7a394cc7b8ab895209f5522dc28ecdbcca700eb6b240144d42ab2fc3a60082aff6e7c732a446c0833532 Homepage: https://cran.r-project.org/package=SAutomata Description: CRAN Package 'SAutomata' (Inference and Learning in Stochastic Automata) Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) . 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It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022) for more details. 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Package: r-cran-scbursts Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytex Filename: pool/dists/resolute/main/r-cran-scbursts_1.6-1.ca2604.1_all.deb Size: 655042 MD5sum: c53be3612faa91a7d8d0e8893385c565 SHA1: bb7eea11c639324452599dc25a24349aed09bcfc SHA256: 5f0b8200551404f5eb66e355605575efbe255e2c60d9c7d2c8f437ef75138ce2 SHA512: 0032db66e99b6955b7d52ef341e7755379878852189d151fa4e38cef0a8edc85ab56bae62675d359fc1ae0e80ede4d3743b76fbfea8d606cabc6e212a8e061af Homepage: https://cran.r-project.org/package=scbursts Description: CRAN Package 'scbursts' (Single Channel Bursts Analysis) Provides tools to import and export from several existing pieces of ion-channel analysis software such as 'TAC', 'QUB', 'SCAN', and 'Clampfit', implements procedures such as dwell-time correction and defining bursts with a critical time, and provides tools for analysis of bursts, such as tools for sorting and plotting. Package: r-cran-sccatch Architecture: all Version: 3.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-progress, r-cran-reshape2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-prettydoc Filename: pool/dists/resolute/main/r-cran-sccatch_3.2.2-1.ca2604.1_all.deb Size: 2329900 MD5sum: ffb597880de5b0eb87cd4b76a95a77ff SHA1: 1a071ba4a1cf788b06a69ca5e216334532e59d15 SHA256: 2b80281f99abd088352e4b9c2a282443e3d95992efdd200136239a1705c938ff SHA512: df6ceef2d357d7228c73f7f241b411fb2e075297f8be4eb88f509ac5df165aa695aeb5141064a35e0bbdaf5a758cedd597b02f3fea8706bc2634714ceba5eb94 Homepage: https://cran.r-project.org/package=scCATCH Description: CRAN Package 'scCATCH' (Single Cell Cluster-Based Annotation Toolkit for CellularHeterogeneity) An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) for more details. Package: r-cran-sccca Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-cran-dplyr, r-cran-plyr, r-cran-scales, r-cran-hgnchelper, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-sccca_0.1.1-1.ca2604.1_all.deb Size: 60520 MD5sum: 398ef3ede2c8198a19f05462b9fc457d SHA1: ab7c7599b3b1d29ffed00b25125289712c2c2e4d SHA256: 248dc2dc5874eb99caf657d1827ba569ab28a2221a41c725a703ef060ec82aec SHA512: 3a7465d21ebaf325b65193f3c80c4c3254a45aa3cb5396cfb88b565c6728bdd4d493f6cde9de1ea79516d99fc7c180a1606996994fc72301e52471f1f554c4de Homepage: https://cran.r-project.org/package=sccca Description: CRAN Package 'sccca' (Single-Cell Correlation Based Cell Type Annotation) Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) ). Package: r-cran-sccic Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-wooldridge, r-cran-qte, r-cran-synth Filename: pool/dists/resolute/main/r-cran-sccic_0.1.1-1.ca2604.1_all.deb Size: 116098 MD5sum: b7660867b56d9f14558a5d0b2aeaafe0 SHA1: eb7152f7124ca1768a90e0fbf116200d7d323f6c SHA256: e6188d95eb5c0a42b5037605a4dc6ce40e041501ef8684542baef25776bca4ec SHA512: 3dbff6b508a63681d2c2cdbc2c404974da39678e09c32b3c633a858445505a57f4324fb5517ae2421647ad7188c35596a09f1f9a2820a1d1bc76142a8eab55d7 Homepage: https://cran.r-project.org/package=sccic Description: CRAN Package 'sccic' (Synthetic Control Changes-in-Changes Estimator) Implements the Changes-in-Changes (CIC) estimator of Athey and Imbens (2006) combined with synthetic control methods. Provides both the continuous CIC estimator (Theorem 3.1) and the discrete CIC estimator (Theorem 4.1) for integer-valued outcomes, with analytic and bootstrap inference. Also provides nonparametric estimation of the entire counterfactual distribution of outcomes for a treated group, allowing evaluation of average, quantile, and distributional treatment effects. Synthetic control weights are constructed via elastic net regularization to handle settings with many potential control units. Package: r-cran-sccr Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-sccr_2.1-1.ca2604.1_all.deb Size: 25794 MD5sum: 53c9da88f71da1e126cbfc5d6b7ed915 SHA1: ca2bea9e767a819ca24d2675c22bf67a39bfbefe SHA256: fd7b6730de8a63ef5efc54c57524e6f997f9f01c3a882adc6dd7bb6782a16826 SHA512: dea7a8ba6a69db5460626a1629536d6fee37b62735ded11fd839380d9269e244d9ef1f40fedf69fdb3b46d7f469f177ca63cc195c608c5bd26b1a6123d80c774 Homepage: https://cran.r-project.org/package=sccr Description: CRAN Package 'sccr' (The Self-Consistent, Competing Risks (SC-CR) Algorithms) The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added. Package: r-cran-sccs Architecture: all Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-corpcor, r-cran-fda, r-cran-r.methodss3, r-cran-gnm Filename: pool/dists/resolute/main/r-cran-sccs_1.7-1.ca2604.1_all.deb Size: 603348 MD5sum: 1bf54a8e21d66cc1f0fcb303ad8a2df0 SHA1: 0781f98c475cb881197cb2bd59d55736c0cd061a SHA256: 9db12f5308f876a0a68b9e7e40f0d5daa81a700a3983d40058b9bfb282ed1c47 SHA512: 2d7548639f6c0481d0fcad43e5a8ffbf48f558f78a53d8f54bc26bd8f7d2f1a43722cec6b25a042427a284d15ff2dafe4d7fff23898d80cf9541db12db994f45 Homepage: https://cran.r-project.org/package=SCCS Description: CRAN Package 'SCCS' (The Self-Controlled Case Series Method) Various self-controlled case series models used to investigate associations between time-varying exposures such as vaccines or other drugs or non drug exposures and an adverse event can be fitted. Detailed information on the self-controlled case series method and its extensions with more examples can be found in Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y. (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies: A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) and . Package: r-cran-sccustomize Architecture: all Version: 3.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2375 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seurat, r-cran-circlize, r-cran-cli, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-ggprism, r-cran-ggrastr, r-cran-ggrepel, r-cran-glue, r-cran-janitor, r-cran-lifecycle, r-cran-magrittr, r-cran-matrix, r-cran-mcprogress, r-cran-paletteer, r-cran-patchwork, r-cran-pbapply, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-scattermore, r-cran-seuratobject, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-bioc-biocfilecache, r-bioc-complexheatmap, r-bioc-dittoseq, r-bioc-dropletutils, r-cran-ggpubr, r-cran-hdf5r, r-cran-knitr, r-bioc-nebulosa, r-cran-remotes, r-cran-reticulate, r-cran-rliger, r-cran-rmarkdown, r-bioc-scuttle, r-cran-tidyselect, r-cran-qs2, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-sccustomize_3.3.0-1.ca2604.1_all.deb Size: 1775284 MD5sum: e9f674049e84243bee97c97334d6d8d1 SHA1: 43b2188079000ddf332ed223191454803895ed64 SHA256: b68650f16e7675aa0f49985ded74090a8eabb977a1df94b57c8c20e5df1fbb60 SHA512: fc5c48b8df7d7617ad82a900a8efaff3932d04965f11c1b8937d704ab108a22d6ef5cf3b5de92165f0ead3ee0618657e83847b46a54876b0854fc87d3fa801d3 Homepage: https://cran.r-project.org/package=scCustomize Description: CRAN Package 'scCustomize' (Custom Visualizations & Functions for Streamlined Analyses ofSingle Cell Sequencing) Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" RRID:SCR_024675. Package: r-cran-scda Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatialreg, r-cran-sp, r-cran-spdep, r-cran-rlang, r-cran-performance, r-cran-dplyr, r-cran-sf, r-cran-nbclust, r-cran-ggplot2, r-cran-ggspatial Suggests: r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-scda_0.0.2-1.ca2604.1_all.deb Size: 953530 MD5sum: 52261ac49c1694ac9033b11d24f4ef75 SHA1: 6ea640ddc0de65d5ee89d9bb90f9c4c00aa03d2d SHA256: 47599cb1d1ce34315e2ed9a2d1eaf2dde98736ad5841e7daa2ad62fa8a58f8e7 SHA512: 6e3485e17a509e0685c50cad8776e0cc0c2751eff8fa3e114ee41233a31af8e9e674303f9d7ce75622a824b86e74bd6cdfd59c997ee854c0b29d3902b843607d Homepage: https://cran.r-project.org/package=SCDA Description: CRAN Package 'SCDA' (Spatially-Clustered Data Analysis) Contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 . Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) . Package: r-cran-scdb Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-openssl, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-r6, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-magrittr Suggests: r-cran-callr, r-cran-conflicted, r-cran-devtools, r-cran-duckdb, r-cran-ggplot2, r-cran-here, r-cran-jsonlite, r-cran-knitr, r-cran-lintr, r-cran-microbenchmark, r-cran-odbc, r-cran-pak, r-cran-rmarkdown, r-cran-roxygen2, r-cran-pkgdown, r-cran-rpostgres, r-cran-rsqlite, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-tidyverse, r-cran-withr Filename: pool/dists/resolute/main/r-cran-scdb_0.6.1-1.ca2604.1_all.deb Size: 375512 MD5sum: be3b60b03697f75e20c37ea437e9f31c SHA1: 522b35ae72827799b614c56be6ba96a761251676 SHA256: c13f846eaab300ee95b235a8b434c4377a018d49c7f32b6e2b38afbb717a2cf8 SHA512: f4c89f5559a214f833ec457e8789c87b991b7b2d9312cf2b18471e22550f6c52de92c8bf93d66e4da65d61f15a84ccb0d9eb9ccfbfa62ddf5ddb51b328635526 Homepage: https://cran.r-project.org/package=SCDB Description: CRAN Package 'SCDB' (Easily Access and Maintain Time-Based Versioned Data(Slowly-Changing-Dimension)) A collection of functions that enable easy access and updating of a database of data over time. More specifically, the package facilitates type-2 history for data-warehouses and provides a number of Quality of life improvements for working on SQL databases with R. For reference see Ralph Kimball and Margy Ross (2013, ISBN 9781118530801). Package: r-cran-scdeco Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rjags, r-cran-msm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-scdeco_0.1.1-1.ca2604.1_all.deb Size: 83252 MD5sum: ebf3001241646307ef9f2a45374b5907 SHA1: aa4524ec0b61396fa1ce2d6aebc3697cfecf1ea3 SHA256: 9c3848b2edfb5d9d2c924d2c8d68b022110d4051b26109eb6b7b3b851033b19e SHA512: 9008b827ad772ab516c97eb39d41b8d33c849016f9a7b4ab594af402a2d19743cfa1fc41ebc3717d6e5229619b92564538ccbac14bad0bda00f4107170b39310 Homepage: https://cran.r-project.org/package=scDECO Description: CRAN Package 'scDECO' (Estimating Dynamic Correlation) Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) , while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) . Package: r-cran-scdeconr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-bioc-edger, r-cran-foreach, r-cran-ggplot2, r-cran-seurat, r-cran-data.table, r-cran-gtools, r-cran-harmony, r-cran-mass, r-cran-matrix, r-cran-nmf, r-cran-reshape2, r-bioc-biobase, r-cran-mgcv, r-bioc-glmgampoi Suggests: r-cran-fardeep, r-cran-reticulate, r-cran-plotly, r-bioc-deseq2, r-cran-knitr, r-bioc-limma, r-cran-nnls, r-bioc-preprocesscore, r-cran-rmarkdown, r-bioc-scater, r-bioc-scran, r-bioc-singlecellexperiment, r-cran-dofuture, r-cran-future, r-cran-sctransform, r-bioc-linnorm, r-bioc-spacexr Filename: pool/dists/resolute/main/r-cran-scdeconr_1.0.2-1.ca2604.1_all.deb Size: 1401330 MD5sum: 862f252b4d06c0e83ac2fadfc8c66600 SHA1: b9a3841026c7ad20a79c81ea42c46f7006d23b91 SHA256: a5ec034f011040bd107be5f2b8771ae36e57d93d6f2b57137e3a47735cac53ef SHA512: 5ae4e915d2090af110ed16a2b600d2debbaec5f4e298e298d777f96017db835a43fdcd9cd7944259b5b70ba44187cd65e994e6acd6df9fd7c4ab906f2d63b164 Homepage: https://cran.r-project.org/package=SCdeconR Description: CRAN Package 'SCdeconR' (Deconvolution of Bulk RNA-Seq Data using Single-Cell RNA-SeqData as Reference) Streamlined workflow from deconvolution of bulk RNA-seq data to downstream differential expression and gene-set enrichment analysis. Provide various visualization functions. Package: r-cran-scdensity Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2884 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scdensity_1.0.3-1.ca2604.1_all.deb Size: 1060400 MD5sum: 0ecbb6648ebade29594c96f0406f8008 SHA1: db1a34224151ba14ad545b7eceebd30a14e6c980 SHA256: fad099c605c2dd3026736442db89671b4da3e52b90a02598a1401f1debadfdb8 SHA512: f7db1714133d3dd16c1975d2a32d48f207f604da2252ef57db339e1442e0023ac4da0218c47667562ad3b1ae305a5f3fb865adedc1588134a452525cea02aef3 Homepage: https://cran.r-project.org/package=scdensity Description: CRAN Package 'scdensity' (Shape-Constrained Kernel Density Estimation) Implements methods for obtaining kernel density estimates subject to a variety of shape constraints (unimodality, bimodality, symmetry, tail monotonicity, bounds, and constraints on the number of inflection points). Enforcing constraints can eliminate unwanted waves or kinks in the estimate, which improves its subjective appearance and can also improve statistical performance. The main function scdensity() is very similar to the density() function in 'stats', allowing shape-restricted estimates to be obtained with little effort. The methods implemented in this package are described in Wolters and Braun (2017) , Wolters (2012) , and Hall and Huang (2002) . See the scdensity() help for for full citations. Package: r-cran-scdhlm Architecture: all Version: 0.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-readxl, r-cran-rlang, r-cran-dplyr, r-cran-tidyselect, r-cran-magrittr, r-cran-lmeinfo Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plyr, r-cran-boot, r-cran-shiny, r-cran-shinytest, r-cran-glue, r-cran-janitor, r-cran-rclipboard, r-cran-rvest, r-cran-testthat, r-cran-snow, r-cran-dosnow, r-cran-foreach, r-cran-iterators, r-cran-reshape, r-cran-rlecuyer Filename: pool/dists/resolute/main/r-cran-scdhlm_0.7.4-1.ca2604.1_all.deb Size: 846044 MD5sum: 4aa74eb3bd52cd365d67d8bb509b95ea SHA1: 7d909c75f8af9b1c792c9fb427f8698902ec7cc1 SHA256: fb8127b2bf5cf089c301fc15818fb942cd40fd46cda5c10c7cd5e2695a22719f SHA512: 935c82e6354db2e2f08531bc1f5d4288c4ed173e56f996f4bca5ab2743e6fe46dc9a057f1df0696143791d123265cb30762742c09fadcd9c05cc514c41082c68 Homepage: https://cran.r-project.org/package=scdhlm Description: CRAN Package 'scdhlm' (Estimating Hierarchical Linear Models for Single-Case Designs) Provides a set of tools for estimating hierarchical linear models and effect sizes based on data from single-case designs. Functions are provided for calculating standardized mean difference effect sizes that are directly comparable to standardized mean differences estimated from between-subjects randomized experiments, as described in Hedges, Pustejovsky, and Shadish (2012) ; Hedges, Pustejovsky, and Shadish (2013) ; Pustejovsky, Hedges, and Shadish (2014) ; and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) . Includes an interactive web interface. Package: r-cran-scdiffcom Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3440 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-bioc-delayedarray, r-cran-future, r-cran-future.apply, r-cran-lifecycle, r-cran-magrittr, r-cran-seurat Suggests: r-bioc-biomart, r-cran-covr, r-cran-dt, r-cran-ggplot2, r-bioc-gosemsim, r-cran-igraph, r-cran-kableextra, r-bioc-keggrest, r-cran-knitr, r-cran-ontologyindex, r-bioc-ontoproc, r-cran-pkgdown, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rmarkdown, r-bioc-rrvgo, r-cran-spelling, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-scdiffcom_1.2.0-1.ca2604.1_all.deb Size: 3291660 MD5sum: 2fbc244d0359c215e6a99bada72c9393 SHA1: 7e0e4319827f866ca6ea6b25aa55c99a43ce5bce SHA256: 028ea22be575be52ac8a278476aaf92dbdb99a116c4a92531e336b81dc2b0fda SHA512: bd0fdd826737e2e2cf12cb5edcb29e8114c329d89e5c7ab34d5098b3a675b1315661d02640736f4e06b96cb579c1a1d08ac63a031fa4f9179ceb163d5b7efa40 Homepage: https://cran.r-project.org/package=scDiffCom Description: CRAN Package 'scDiffCom' (Differential Analysis of Intercellular Communication fromscRNA-Seq Data) Analysis tools to investigate changes in intercellular communication from scRNA-seq data. Using a Seurat object as input, the package infers which cell-cell interactions are present in the dataset and how these interactions change between two conditions of interest (e.g. young vs old). It relies on an internal database of ligand-receptor interactions (available for human, mouse and rat) that have been gathered from several published studies. Detection and differential analyses rely on permutation tests. The package also contains several tools to perform over-representation analysis and visualize the results. See Lagger, C. et al. (2023) for a full description of the methodology. Package: r-cran-scdiftest Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich, r-cran-strucchange, r-cran-mirt, r-cran-zoo Suggests: r-cran-mvtnorm, r-cran-psychotree, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scdiftest_0.1.1-1.ca2604.1_all.deb Size: 43374 MD5sum: 39af30968d9792c44725a95690095512 SHA1: 694d2f233f3b70c0e33d7389166190c41a0fb713 SHA256: 13363d08db0039a9683e11122fa129b45945c590db2b4aba132111ee861de564 SHA512: 3ed28790f26a699f7da4675d0f6dc5dabd25c878b239d86908aa85143c924073bcb26ea42e63ed198cf1e812983dec56abc4f413c506579614abf3c3a5bcb7a2 Homepage: https://cran.r-project.org/package=scDIFtest Description: CRAN Package 'scDIFtest' (Item-Wise Score-Based DIF Detection) Detection of item-wise Differential Item Functioning (DIF) in fitted 'mirt', 'multipleGroup' or 'bfactor' models using score-based structural change tests. Under the hood the sctest() function from the 'strucchange' package is used. Package: r-cran-scdtb Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom.mixed, r-cran-dt, r-cran-ggplot2, r-cran-mass, r-cran-mmcards, r-cran-mmints, r-cran-nlme, r-cran-shiny, r-cran-shinythemes, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scdtb_0.2.0-1.ca2604.1_all.deb Size: 1883500 MD5sum: d10af53b5e3a9222f28a03a98f70cd1f SHA1: 0f484eb240140375527320d06c41987df021f128 SHA256: ee228ba6e7284d93b29d350b5b9c872747318bb6d051042be0996108b9832e67 SHA512: dc1bd67cc68c274f9c2c683185f644d873b30b6117acc609b9fb15a14c421c5c4d09fc93cd17c5db7535037ee76ff12b983a14a02461cd127baad687cbcf0737 Homepage: https://cran.r-project.org/package=scdtb Description: CRAN Package 'scdtb' (Single Case Design Tools) In some situations where researchers would like to demonstrate causal effects, it is hard to obtain a sample size that would allow for a well-powered randomized controlled trial. Single case designs are experimental designs that can be used to demonstrate causal effects with only one participant or with only a few participants. The 'scdtb' package provides a suite of tools for analyzing data from studies that use single case designs. The nap() function can be used to compute the nonoverlap of all pairs as outlined by the What Works Clearinghouse (2022) . The package also offers the mixed_model_analysis() and cross_lagged() functions which implement mixed effects models and cross lagged analyses as described in Maric & van der Werff (2020) . The randomization_test() function implements randomization tests based on methods presented in Onghena (2020) . The scdtb() 'shiny' application can be used to upload single case design data and access various 'scdtb' tools for plotting and analysis. Package: r-cran-sce Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sce_1.1.4-1.ca2604.1_all.deb Size: 219522 MD5sum: 9b3e28eb191ed0b2774226693295fda8 SHA1: e9ccfe3e0aa019e58909cb533ebad771a9ec1c59 SHA256: a6fff9a4b254bae7ab48036971cee6d7369c6fe412a5fb9da4b40ec58a54334a SHA512: 47912c37360247cff0545afd23a8216578174c14bb258296717e4f606aa8810d0573c3ae7de69a239addf605491d499c8e2a7ef7c067b6f3477468a1501f2b6d Homepage: https://cran.r-project.org/package=SCE Description: CRAN Package 'SCE' (Stepwise Clustered Ensemble) Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) . Package: r-cran-scenes Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-cookies, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-shiny Suggests: r-cran-covr, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scenes_0.1.0-1.ca2604.1_all.deb Size: 252066 MD5sum: 820f42073fd3c8ff2f493b5ee294033a SHA1: e3affd2affe3dd4c16b715ece47589ab1a77544d SHA256: 1f3d3e7e9206b32f4d008e3dcccf372e2fef00a39e796859c4a5b9708bf541cd SHA512: 413eaba407708c360f76562573161fef2102df7832f15733b31a71df5fd45a1a70dd9b14563603b8291ae5f9caef206b254e9ba60f725f3e8611c5e0a1227337 Homepage: https://cran.r-project.org/package=scenes Description: CRAN Package 'scenes' (Switch Between Alternative 'shiny' UIs) Sometimes it is useful to serve up alternative 'shiny' UIs depending on information passed in the request object, such as the value of a cookie or a query parameter. This packages facilitates such switches. Package: r-cran-scenfire Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-readr, r-cran-sf, r-cran-terra, r-cran-tidyr, r-cran-rlang, r-cran-stringr, r-cran-doparallel, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-scenfire_0.1.0-1.ca2604.1_all.deb Size: 68606 MD5sum: 320459defb459e89e39b109fb12429ba SHA1: f1f116975debd6daf9ff013f9ead19753dd3e28b SHA256: 951403aa0db1d61419ac212afb4aba752089821b8d11041e5aa502d8ea9d5825 SHA512: a9f9e1ebccf045986ec060a1943527a0cce30443bcc76a37199e29e6ac105d3a17527ee8d54322d5c040283ace6933aba434183856f1cc6af2c77cbfd22b85c6 Homepage: https://cran.r-project.org/package=scenfire Description: CRAN Package 'scenfire' (Post-Processing Algorithm for Integrating Wildfire Simulations) A specialized selection algorithm designed to align simulated fire perimeters with specific fire size distribution scenarios. The foundation of this approach lies in generating a vast collection of plausible simulated fires across a wide range of conditions, assuming a random pattern of ignition. The algorithm then assembles individual fire perimeters based on their specific probabilities of occurrence, e.g., determined by (i) the likelihood of ignition and (ii) the probability of particular fire-weather scenarios, including wind speed and direction. Implements the method presented in Rodrigues (2025a) . Demo data and code examples can be found in Rodrigues (2025b) . Package: r-cran-scent Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-entropy, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scent_0.0.1-1.ca2604.1_all.deb Size: 31610 MD5sum: 830ab138f9893557a63d3e40a4c23caf SHA1: 4d2b2bfc033e4dc27b760cddd09bec716298aafb SHA256: 6dc9ffbc5f9cec00e617ee9de0053028a7ce917d2f095d7c999c8262900c8a96 SHA512: dd4f267d42df22c57c02e39aa281e046b1e217ee25eb9f2091371ee7ccd694af219138e1fae729ba5ff22c431c051b52fa784ffcea97a13530bca4b96b30a83e Homepage: https://cran.r-project.org/package=SCEnt Description: CRAN Package 'SCEnt' (Single Cell Entropy Analysis of Gene Heterogeneity in CellPopulations) Analyse single cell RNA sequencing data using entropy to calculate heterogeneity and homogeneity of genes amongst the cell population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia and Ben D. MacArthur. Package: r-cran-scf Architecture: all Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1616 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-haven, r-cran-httr, r-cran-rlang, r-cran-survey, r-cran-quantreg Suggests: r-cran-dplyr, r-cran-hexbin, r-cran-kableextra, r-cran-knitr, r-cran-mitools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scf_1.0.10-1.ca2604.1_all.deb Size: 1518498 MD5sum: d4b1b61be58613274a964189ceb4d27a SHA1: d30d3887c3f2736677f87142a1321f35e1afab13 SHA256: 19dfa47a918ec7407ec643d91960d4507ccf447851c8741ef51941e9569aa861 SHA512: bac31d80842c2e694e11e86e01f47d3c49575d88fa9a3cab40e54cb8855114a9ac2e596f06af6ca621c40f43a16f7145b78d10ff88daec56d4c079c60c7f1f7f Homepage: https://cran.r-project.org/package=scf Description: CRAN Package 'scf' (Analyzing the Survey of Consumer Finances) Analyze public-use micro data from the Survey of Consumer Finances. Provides tools to download prepared data files, construct replicate-weighted multiply imputed survey designs, compute descriptive statistics and model estimates, and produce plots and tables. Methods follow design-based inference for complex surveys and pooling across multiple imputations. See the package website and the code book for background. Package: r-cran-scfmonitor Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1386 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-stringr, r-cran-tidyselect, r-cran-dplyr, r-cran-tibble, r-cran-ggplot2, r-cran-tidyr, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-scfmonitor_0.3.5-1.ca2604.1_all.deb Size: 396830 MD5sum: e9a84c37a4d68856911ea8d39df4636b SHA1: 650e69b3c079f31e9707398b1f811ae0e0567a3a SHA256: dc1439ce4026be33edbb33968549fdfbc6b1acc0efabe3fddc0094cbf93729f9 SHA512: 7551dcc9556d65f879332697c197f6a1341839e55174b5eb19c37d46a7b2f75eb02ab21423104b32f072b806c918335d3cf3bad30a20aba2a22f5cc5f1a13777 Homepage: https://cran.r-project.org/package=SCFMonitor Description: CRAN Package 'SCFMonitor' (Clear Monitor and Graphing Software Processing Gaussian .logFile) Self-Consistent Field(SCF) calculation method is one of the most important steps in the calculation methods of quantum chemistry. Ehrenreich, H., & Cohen, M. H. (1959). However, the most prevailing software in this area, 'Gaussian''s SCF convergence process is hard to monitor, especially while the job is still running, causing researchers difficulty in knowing whether the oscillation has started or not, wasting time and energy on useless configurations or abandoning the jobs that can actually work. M.J. Frisch, G.W. Trucks, H.B. Schlegel et al. (2016). 'SCFMonitor' enables 'Gaussian' quantum chemistry calculation software users to easily read the 'Gaussian' .log files and monitor the SCF convergence and geometry optimization process with little effort and clear, beautiful, and clean outputs. It can generate graphs using 'tidyverse' to let users check SCF convergence and geometry optimization processes in real-time. The software supports processing .log files remotely using with rbase::url(). This software is a suitcase for saving time and energy for the researchers, supporting multiple versions of 'Gaussian'. Package: r-cran-scgate Architecture: all Version: 1.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1943 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-bioc-ucell, r-cran-dplyr, r-cran-patchwork, r-cran-ggridges, r-cran-colorspace, r-cran-reshape2, r-cran-ggplot2, r-bioc-biocparallel Suggests: r-cran-ggparty, r-cran-partykit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-scgate_1.7.2-1.ca2604.1_all.deb Size: 1940844 MD5sum: 3142c775938fa1633889011f5e1da9b2 SHA1: 680b4880b81276daf76fa50d09b1bda2c10b5533 SHA256: d3e67b4ecb66f4cdf0bb67a01605ec66e85c5dd57801223845b2e1912495fb53 SHA512: 9556bc805159095f6fcd43e075d9795ff4e8924d55fe2036cb0a23974176d22bb9868c9a543c75dc06704af49a63dcfae706b33f19674337705a76e069a8c816 Homepage: https://cran.r-project.org/package=scGate Description: CRAN Package 'scGate' (Marker-Based Cell Type Purification for Single-Cell SequencingData) A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) . 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Covariates can also be grouped in themes. 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Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species. 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Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See for more information and documentation. 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Package: r-cran-scmappr Architecture: all Version: 1.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pheatmap, r-cran-seurat, r-bioc-gsva, r-cran-downloader, r-bioc-pcamethods, r-cran-gprofiler, r-cran-limsolve, r-cran-gprofiler2, r-cran-pbapply, r-cran-adapts, r-cran-reshape Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-scmappr_1.0.12-1.ca2604.1_all.deb Size: 825764 MD5sum: 8b2fe3f11952f4641891e46396a0abd8 SHA1: 2b0e4300dcf2a21a8129368ef3f59a0134e3530a SHA256: 20ca4532bf32c85eeb25eeeac2fcaf2e8732189b52a6e3612a1c6a387ce80ca0 SHA512: 3602cb9708991b950a8b475f9348979a7461c829da17a4850e1d8668b988aab4397288f9be3a1bb40c192a30bb7f40c065948eaa2143375009787b6a9750c1bb Homepage: https://cran.r-project.org/package=scMappR Description: CRAN Package 'scMappR' (Single Cell Mapper) The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. . Package: r-cran-scmetatraj Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-ggplot2, r-cran-uwot, r-cran-rann, r-cran-seurat, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-scmetatraj_0.1.1-1.ca2604.1_all.deb Size: 69406 MD5sum: d9e7567fadf317d38a54cc8604378990 SHA1: ee6d909bbbdce1d80341a8252b1e679e6f990829 SHA256: 282cf614335b27b56d7011daf6991a3edd37b5f58cd35bcde4c80e628145cde7 SHA512: 97db9eb6f7433afe79736d0bcbc9bfee334808ad556b598f153a95acd457979c4d227f2ea39fa49202a0f48af3aa619b74d7835bcd094a9254ddae997e613994 Homepage: https://cran.r-project.org/package=scMetaTraj Description: CRAN Package 'scMetaTraj' (Metabolic State Space and Trajectory Analysis for Single-CellData) Provides a framework for modeling cellular metabolic states and continuous metabolic trajectories from single-cell RNA-seq data using pathway-level scoring. 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Package: r-cran-scontomatch Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4926 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ontologyindex, r-cran-ontologyplot, r-cran-purrr Suggests: r-cran-knitr, r-cran-devtools, r-cran-seuratobject Filename: pool/dists/resolute/main/r-cran-scontomatch_0.1.1-1.ca2604.1_all.deb Size: 2790418 MD5sum: 646b3ea56299bfec3ea755afe8ea651e SHA1: d25fd4c748eda7663f2390f648c12e765c90ad89 SHA256: bd5f8a6da648d2bf866ed56460274385683d0981b258abd4233e08c4405ce930 SHA512: 63dc9de84b1fef34485acbff3de7d91cfb686608585ed9118070cb5de83690149bde9b83169abff47fac42d47f8ab06f7b004dedd2b1755bffe4fa6aa4001ffe Homepage: https://cran.r-project.org/package=scOntoMatch Description: CRAN Package 'scOntoMatch' (Aligning Ontology Annotation Across Single Cell Datasets with'scOntoMatch') Unequal granularity of cell type annotation makes it difficult to compare scRNA-seq datasets at scale. Leveraging the ontology system for defining cell type hierarchy, 'scOntoMatch' aims to align cell type annotations to make them comparable across studies. The alignment involves two core steps: first is to trim the cell type tree within each dataset so each cell type does not have descendants, and then map cell type labels cross-studies by direct matching and mapping descendants to ancestors. Various functions for plotting cell type trees and manipulating ontology terms are also provided. In the Single Cell Expression Atlas hosted at EBI, a compendium of datasets with curated ontology labels are great inputs to this package. 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Ethoscopes () are an open source/open hardware framework made of interconnected raspberry pis () designed to quantify the behaviour of multiple small animals in a distributed and real-time fashion. The default tracking algorithm records primary variables such as xy coordinates, dimensions and speed. This package is part of the rethomics framework . 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The score is assigned based on the the fraction of specific markers of the in 'vivo' stage that are conserved in the in 'vitro' clusters . 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Package: r-cran-score Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-msm Filename: pool/dists/resolute/main/r-cran-score_1.0.2-1.ca2604.1_all.deb Size: 37766 MD5sum: a8d03ed41f941b10974a406b6be05d62 SHA1: 4ddc801f0575ffab9e68004f503a663a0b093ed4 SHA256: e1b5eb73135e54a0715797b97e7878e55013355a4ed0de776b271c8212428576 SHA512: 444e5f5da00755682da4cb781819e1b7d789b8433eff92f8ab5a591167f72660790d129f927a1d8e494a39649f4f91a793fa70e32f99595f3b909891c8f2aea9 Homepage: https://cran.r-project.org/package=score Description: CRAN Package 'score' (A Package to Score Behavioral Questionnaires) Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' . Compares physical functional performance to the age- and gender-specific normal ranges. 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These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring. 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The functionalities defined are standard steps for any credit underwriting scorecard development, extensively used in financial domain. 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Acceptable input includes both one-dimensional and two-dimensional data for linear, logistic, functional, and spatial generalized least squares regression models. Functions are also available for constructing simultaneous confidence bands (SCBs) for these models. The definition of simultaneous confidence regions (SCRs) follows Sommerfeld et al. (2018) . Methods for estimating inverse regions, SCRs, and the nonparametric bootstrap are based on Ren et al. (2024) . Methods for constructing SCBs are described in Crainiceanu et al. (2024) and Telschow et al. (2022) . 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Package: r-cran-scorpion Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-irlba, r-cran-igraph, r-cran-rann, r-cran-matrix, r-cran-pbapply, r-cran-dplyr, r-cran-furrr, r-cran-future Suggests: r-cran-rhpcblasctl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scorpion_1.3.2-1.ca2604.1_all.deb Size: 2211842 MD5sum: 60ff8bbc474c74eb1d350d7f0f55d1e5 SHA1: 7c88856f90f0cfa4eb26423529a811abb8b2fd63 SHA256: d6d98ece902e6498df3835ad204512ea4d3b1a2e315ae4dda02901fa6af67d2f SHA512: c2daf967295b5f1c2b4159984869e6f2b7dfe6543604339add2442f1af01fa6c9a67aba80179bc8c5846a800ccf4bf28d962d8cabae5174a52e0a24af7855232 Homepage: https://cran.r-project.org/package=SCORPION Description: CRAN Package 'SCORPION' (Single Cell Oriented Reconstruction of PANDA IndividuallyOptimized Networks) Constructs cell-type–specific gene regulatory networks from single-cell RNA-sequencing data. 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Package: r-cran-scottknott Architecture: all Version: 1.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doby, r-cran-xtable Suggests: r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-scottknott_1.3-3-1.ca2604.1_all.deb Size: 150836 MD5sum: e10094f001f24a432ecba19cd27f41df SHA1: ad329fe83d8824d70f647ed040c76ab87eba340e SHA256: 63f08033579b24caae8165d09a1ae98b5717d25cc65491009e85890c793376b4 SHA512: 64c9254d81ea7ef50208d9271ad3b00e658fe0f3f0d65c6c7a2e74afb58153f53569da14e90417df0e24f073fea8b76a8f21007b934addb81eae6b97a1eb5e8f Homepage: https://cran.r-project.org/package=ScottKnott Description: CRAN Package 'ScottKnott' (The ScottKnott Clustering Algorithm) Perform the balanced (Scott and Knott, 1974) and unbalanced Scott & Knott algorithm. 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Unlike conventional pairwise co-expression analyses that rely on a single correlation metric, scPairs integrates 14 complementary metrics across five orthogonal evidence layers to compute a composite synergy score with optional permutation-based significance testing. The five evidence layers span cell-level co-expression (Pearson, Spearman, biweight midcorrelation, mutual information, ratio consistency), neighbourhood-aware smoothing (KNN-smoothed correlation, neighbourhood co-expression, cluster pseudo-bulk, cross-cell-type, neighbourhood synergy), prior biological knowledge (GO/KEGG co-annotation Jaccard, pathway bridge score), trans-cellular interaction, and spatial co-variation (Lee's L, co-location quotient). This multi-scale design enables researchers to move beyond simple co-expression towards a comprehensive characterisation of cooperative gene regulation at transcriptomic and spatial resolution. For more information, see the package documentation at . 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Package: r-cran-scpoem Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4427 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-tictoc, r-cran-matrix, r-cran-glmnet, r-cran-xgboost, r-cran-reticulate, r-cran-stringr, r-cran-magrittr, r-cran-sctenifoldnet, r-cran-vgam, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-monocle, r-bioc-cicero Filename: pool/dists/resolute/main/r-cran-scpoem_0.1.3-1.ca2604.1_all.deb Size: 4467722 MD5sum: 2423428e17b8e2808d280edfc9e450d7 SHA1: 438cceeaed0b460fd741f4b71b7b89944c5f46e9 SHA256: 79ced3d4160cc32b69c43f26577370fabc612aa4373712a55ad0f78dc4aa8ed8 SHA512: dcfb9ecaebff27e7ab99c43a2d363780e09cdfb58f2f9687cfe67bc6b1fd28fc8a0f1efe6e2fb29cab1473db03051bd6a12199323cc61318654a32d2af1510f6 Homepage: https://cran.r-project.org/package=scPOEM Description: CRAN Package 'scPOEM' (Single-Cell Meta-Path Based Omic Embedding) Provide a workflow to jointly embed chromatin accessibility peaks and expressed genes into a shared low-dimensional space using paired single-cell ATAC-seq (scATAC-seq) and single-cell RNA-seq (scRNA-seq) data. It integrates regulatory relationships among peak-peak interactions (via 'Cicero'), peak-gene interactions (via Lasso, random forest, and XGBoost), and gene-gene interactions (via principal component regression). With the input of paired scATAC-seq and scRNA-seq data matrices, it assigns a low-dimensional feature vector to each gene and peak. Additionally, it supports the reconstruction of gene-gene network with low-dimensional projections (via epsilon-NN) and then the comparison of the networks of two conditions through manifold alignment implemented in 'scTenifoldNet'. See for more details. Package: r-cran-scpoisson Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2078 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-glmpca, r-cran-seurat, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-matrix, r-cran-rdpack, r-cran-seuratobject, r-cran-wgcna, r-cran-broom, r-cran-matrixstats Suggests: r-cran-renv, r-cran-testthat, r-cran-vdiffr, r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-scpoisson_0.0.2-1.ca2604.1_all.deb Size: 1373144 MD5sum: 0ed3693eb4ef98175e3355d19f9351de SHA1: 1da50ddae3836d93370ff19d29c1f687e0d618ee SHA256: 55af5828e8bdf5ae089209a01259066afd54adc32c025fb98262e4f93c5722b7 SHA512: 72440eb4372eaa70911d14d3467b6269cd9c09fbaeb6c0c8926f23cbcf18468aaba2a978e69b34eab23537949c361e07ce224c037e84775d2c8f6f1a1ed93c2b Homepage: https://cran.r-project.org/package=scpoisson Description: CRAN Package 'scpoisson' (Single Cell Poisson Probability Paradigm) Useful to visualize the Poissoneity (an independent Poisson statistical framework, where each RNA measurement for each cell comes from its own independent Poisson distribution) of Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore cell clustering based on model departure as a novel data representation. Package: r-cran-scpropreg Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnsolve, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/resolute/main/r-cran-scpropreg_1.2-1.ca2604.1_all.deb Size: 40938 MD5sum: c620d95363836134296feda56a4d052c SHA1: 7d43774b2eef1440aeb957ac8e14021e068ff660 SHA256: 5555e182184503e61040a04cc4d1d9edf460b93b25bbcebf379b8f847751f47a SHA512: 47639883b1471b5a8139f983dec7765bd7a642afc70267e655ad28aeb03ac318181dc7bdb63d99a918ae3e3c7688d80a986e4173d6bf694224357bbd60afd191 Homepage: https://cran.r-project.org/package=scpropreg Description: CRAN Package 'scpropreg' (Simplicially Constrained Regression Models for Proportions) Simplicially constrained regression models for proportions in both sides. The constraint is always that the betas are non-negative and sum to 1. References: Iverson S.J.., Field C., Bowen W.D. and Blanchard W. (2004) "Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets". Ecological Monographs, 74(2): 211-235. . 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The package incorporates methods for estimating the Vapnik-Chervonenkis dimension (VCD) of a chosen algorithm, which can be used to estimate its sample complexity. Alternatively, we provide simulation methods to estimate sample complexity directly. For more details, see Carter, P & Choi, D (2024). "Learning from Noise: Applying Sample Complexity for Political Science Research" . 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Package: r-cran-scrnastat Architecture: all Version: 0.1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3754 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seurat, r-cran-ggplot2, r-cran-stringr, r-cran-clustree, r-cran-magrittr, r-cran-matrix, r-cran-dplyr, r-cran-patchwork, r-cran-colorspace, r-cran-dbi Filename: pool/dists/resolute/main/r-cran-scrnastat_0.1.1.2-1.ca2604.1_all.deb Size: 3634740 MD5sum: fd9ab8daaf7aeb50902dffcacdc0a3a7 SHA1: 0e60d669fed561d428456abeac336d10f74e1e6f SHA256: 60abf9c9be6e15718b3d78802c7bbde4bf066cffe96d0752e0a4541d9961aca8 SHA512: 81f95a63c0b32970be69c9ed0dbb5a8b9ce9710717aa897a48f96cac593a79006ee8c2dcb854110c0836616d92f2b95ff35db73e35395e223a0d28f4832bbaf2 Homepage: https://cran.r-project.org/package=scRNAstat Description: CRAN Package 'scRNAstat' (A Pipeline to Process Single Cell RNAseq Data) A pipeline that can process single or multiple Single Cell RNAseq samples primarily specializes in Clustering and Dimensionality Reduction. Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte, Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling them by biological names. Once users named each cluster, they can evaluate the quality of them again and find the de novo marker genes also. Package: r-cran-scrobbler Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-scrobbler_1.0.3-1.ca2604.1_all.deb Size: 35074 MD5sum: 137f0495b6d99fda576e26b864abab10 SHA1: 871ab2129ff4864d465520341f03faff89550f65 SHA256: be6926f0622e2fe0ad9ea7db00f277b0e1d98d4d6b8f90a9439f0c68ab6939ae SHA512: 3b8092f1b78ad965a9df2b71fa5c0a39f3afb46f3f074d528bc8c5ab9f4a80f7e85fbc270cac737ae0747aa709a3d7f781d38cc01433ea2e6e0b967adff6ed77 Homepage: https://cran.r-project.org/package=scrobbler Description: CRAN Package 'scrobbler' (Download 'Scrobbles' from 'Last.fm') 'Last.fm' is a music platform focussed on building a detailed profile of a users listening habits. 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See for more information. Package: r-cran-scroshi Architecture: all Version: 1.0.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-limma, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-uwot Filename: pool/dists/resolute/main/r-cran-scroshi_1.0.0.0-1.ca2604.1_all.deb Size: 761196 MD5sum: 7d1e997c73f19b0e8cc30fc5694eefc8 SHA1: fc4554835b4e5f8294680f36fabdcfd0e63e49aa SHA256: 6b83f75389679c760a4f2f18b4d3e803cfd440ecc3101b50588c870ac0b9888d SHA512: 29b68aef0310d0430d0b1f9efddc3cb87a878309e09ca5a0d877bee59a33fba76a35f9f3c608b2c50483ce87b4b91254e6b35b387d09a9655ecef3a1aff76d6d Homepage: https://cran.r-project.org/package=scROSHI Description: CRAN Package 'scROSHI' (Robust Supervised Hierarchical Identification of Single Cells) Identifying cell types based on expression profiles is a pillar of single cell analysis. 'scROSHI' identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) . Package: r-cran-scrt Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-scrt_1.3.1-1.ca2604.1_all.deb Size: 189944 MD5sum: 07913010ad912e2c9a19283b8ac63be3 SHA1: 30a6b8b2fa924f01f7ba6ac428a11f1a90a954a2 SHA256: 44de0ed14788ab8dbf473115cc931329eadf03898bdcc58c919063d765ef02a3 SHA512: 0139a42ed0891d633441660593a5e572c2828d93b388dcbf20580d226c63613271aed1286bb6aa4fcc10be5b5bcd3b149bd6dc16748315c9ab7d7331f6ff8104 Homepage: https://cran.r-project.org/package=SCRT Description: CRAN Package 'SCRT' (Single-Case Randomization Tests) Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) . 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The package also provides infrastructure for implementing new error detection techniques. 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Package: r-cran-scryr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-purrr, r-cran-tibble Suggests: r-cran-covr, r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scryr_1.0.0-1.ca2604.1_all.deb Size: 196814 MD5sum: a704576ea0936cf93da1e9df9ffafd73 SHA1: b502ed8c04ab7fa6d46b22e4d0fe0b13ae45a355 SHA256: 9b9759ec1730881942329d6d8b499872f92b94d145e1dffda18a24e68469c9ca SHA512: 385601b8d230f04c88a5e3231590d2ee1afb478dd6c049efc88f366f7a9d6794eb0da8f1ff663e82c421ffdf9dc45f3c67bffa105d885d2bc5fbb68be93f1734 Homepage: https://cran.r-project.org/package=scryr Description: CRAN Package 'scryr' (An Interface to the 'Scryfall' API) A simple, light, and robust interface between R and the 'Scryfall' card data API . Package: r-cran-scsorter Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-scsorter_0.0.2-1.ca2604.1_all.deb Size: 336968 MD5sum: db1db95f98016a0189343c80c59c481f SHA1: f677da50e6fce7fa4d2293c1ea13d2f280ed647e SHA256: 4330b8c5805368afa206a7b1df93e96eb7dda824fed9dd09dba544c94b4eea6a SHA512: 6798843021cf3c20a615fec22d8b493b2a50d1819d79f047613d13962a3c0b188c76313ad738008fce718ee5fe6a85fe3e1604d67f2660f5dc4833a262c368ce Homepage: https://cran.r-project.org/package=scSorter Description: CRAN Package 'scSorter' (Implementation of 'scSorter' Algorithm) Implements the algorithm described in Guo, H., and Li, J., "scSorter: assigning cells to known cell types according to known marker genes". Cluster cells to known cell types based on marker genes specified for each cell type. Package: r-cran-scspatialsim Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-spatstat.geom, r-cran-crayon, r-cran-ggpubr, r-cran-pbmcapply, r-cran-spatstat.random, r-cran-tidyr, r-cran-proxy Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spatialtime, r-cran-spdep Filename: pool/dists/resolute/main/r-cran-scspatialsim_0.1.4-1.ca2604.1_all.deb Size: 1515436 MD5sum: f9f2b249bbef4278155623936e0c38be SHA1: ed21b055803b795168778be2060e2d186f6ea21b SHA256: dc0bd6a7639c0819e0f245dae08ea0ae3f5b2b9114224a5754ae754b07f8c3bc SHA512: 3cd2a32a4742cd82b196bbb0248966f44546f49d472d17dedfc01a5fa21167bebc1529852afa273b160f5b930358d2f7d1d4008c8946ce4783570662488c169e Homepage: https://cran.r-project.org/package=scSpatialSIM Description: CRAN Package 'scSpatialSIM' (A Point Pattern Simulator for Spatial Cellular Data) Single cell resolution data has been valuable in learning about tissue microenvironments and interactions between cells or spots. This package allows for the simulation of this level of data, be it single cell or ‘spots’, in both a univariate (single metric or cell type) and bivariate (2 or more metrics or cell types) ways. As more technologies come to marker, more methods will be developed to derive spatial metrics from the data which will require a way to benchmark methods against each other. Additionally, as the field currently stands, there is not a gold standard method to be compared against. We set out to develop an R package that will allow users to simulate point patterns that can be biologically informed from different tissue domains, holes, and varying degrees of clustering/colocalization. The data can be exported as spatial files and a summary file (like 'HALO'). . Package: r-cran-scstability Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aricode, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-magrittr, r-cran-pcapp, r-cran-rlang, r-cran-rtsne, r-cran-seurat, r-cran-uwot, r-cran-vegan Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-bioc-scrnaseq, r-bioc-summarizedexperiment, r-cran-biocmanager, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scstability_1.0.3-1.ca2604.1_all.deb Size: 138708 MD5sum: e9dee86fd944ad31d974cc8b5b1335bb SHA1: 02f9ff7d303eeb2cadb997bc03e4a93190cd47dc SHA256: 1ae5ce744775246f6496e1610ddf31e873e506a9eba94377c4294ec7fe894de7 SHA512: 9eae9629c5526077eb5ffd14db390c2a654b64591b82d4d8f5eb459345a9b0f135d0a09ee5083cca6fccebd3ab76dc52749707c3a2c4c8c756e185f73219af40 Homepage: https://cran.r-project.org/package=scStability Description: CRAN Package 'scStability' (Measuring the Stability of Dimension Reduction and ClusterAssignment in scRNA-Seq Experiments) Provides functions for evaluating the stability of low-dimensional embeddings and cluster assignments in single‑cell RNA sequencing (scRNA‑seq) datasets. Starting from a principal component analysis (PCA) object, users can generate multiple replicates of t‑Distributed Stochastic Neighbor Embedding (t‑SNE) or Uniform Manifold Approximation and Projection (UMAP) embeddings. Embedding stability is quantified by computing pairwise Kendall’s Tau correlations across replicates and summarizing the distribution of correlation coefficients. In addition to dimensionality reduction, 'scStability' assesses clustering consistency using either Louvain or Leiden algorithms and calculating the Normalized Mutual Information (NMI) between all pairs of cluster assignments. For background on UMAP and t-SNE algorithms, see McInnes et al. (2020, ) and van der Maaten & Hinton (2008, ), respectively. Package: r-cran-sctenifoldknk Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-matrix, r-cran-mass, r-cran-sctenifoldnet, r-cran-cli, r-cran-enrichr, r-cran-igraph, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sctenifoldknk_1.0.3-1.ca2604.1_all.deb Size: 113932 MD5sum: eccb131e22fe4b828a80b57dd68860e1 SHA1: b667049469b850a0b89a7176024499046dc9ad68 SHA256: 4ebc31a8eca1a7f4d760f2ad143628c698e1bd0ded45687f6767c2b9eef054ad SHA512: c765708adab9ba3fdffcc4dc1847dd15c811a859f47843f927529f3649e46873e2889124a93062d3c275d8820df21a94ad57a185f0318abcfb30875f4ff10564 Homepage: https://cran.r-project.org/package=scTenifoldKnk Description: CRAN Package 'scTenifoldKnk' (In-Silico Knockout Experiments from Single-Cell Gene RegulatoryNetworks) A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells. Package: r-cran-sctenifoldnet Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-rspectra, r-cran-matrix, r-cran-mass, r-cran-rhpcblasctl Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sctenifoldnet_1.3-1.ca2604.1_all.deb Size: 83568 MD5sum: c958093be7b99accd828d3bbeffaae6d SHA1: 4d421d782288b07cbe0db38635024a433dfc88ea SHA256: d107a1349fd658c9edc65adbe4b12284699f3c238d8a3266f5027ab58fc7741d SHA512: 03c197064320850055880437f7aad2698e7eccd9ef5b69367ef9c57005ac08f40dc51a275e25fe345ab3e00c307d44763273721179ab9a56c90acc5325f3fe6f Homepage: https://cran.r-project.org/package=scTenifoldNet Description: CRAN Package 'scTenifoldNet' (Construct and Compare scGRN from Single-Cell Transcriptomic Data) A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See for more details. Package: r-cran-sctools Architecture: all Version: 0.3.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-ggplot2, r-cran-synth, r-cran-stringr, r-cran-cvtools, r-cran-furrr, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sctools_0.3.3.1-1.ca2604.1_all.deb Size: 1360262 MD5sum: ebb4775d6cf17d7e1183eba5e3216c6b SHA1: 063e740efb157d6e1ea2e6021052404babcde7f2 SHA256: 954766ae1839f7b024d6838d4c64822382412ac097148f9055074bb890dc0b07 SHA512: 0e453bbcb41e32e50dabdbbd6c6bf088562388e905996b911eb9f0db895eb2a85267a3004f6a6ff7ef0552d8dbea53974c0b12bfcaacc03533418d646a74f9cf Homepage: https://cran.r-project.org/package=SCtools Description: CRAN Package 'SCtools' (Extensions for Synthetic Controls Analysis) Extends the functionality of the package 'Synth' as detailed in Abadie, Diamond, and Hainmueller (2011) . Includes generating and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, and calculating average treatment effects for multiple treated units. Package: r-cran-scutr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smotefamily, r-cran-mclust Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scutr_0.2.0-1.ca2604.1_all.deb Size: 243994 MD5sum: 4be9284c2328ae36659a0da7bf85359f SHA1: 01b288d1d11be8c10d36395984a5a0e90df80561 SHA256: cd1bfa3c3a95edfaea531c3ca129309bd1c06eeead814d93fa82df98fa729a3e SHA512: 2d9f9316c3e1c5f42ee9674e6e74a49884cd76740fcb62ab45b0aa5bcfa86278f9d1d03081c50d00035f3b2b5e2476312741a6eda46b21eeee985c2618429fcc Homepage: https://cran.r-project.org/package=scutr Description: CRAN Package 'scutr' (Balancing Multiclass Datasets for Classification Tasks) Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) . Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package. Package: r-cran-scva Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggextra, r-cran-ggplot2, r-cran-plotly, r-cran-scales Filename: pool/dists/resolute/main/r-cran-scva_1.3.1-1.ca2604.1_all.deb Size: 229316 MD5sum: 948048767b242c6211b48e9ecee8e854 SHA1: 6578facaf36e97d124e598ac40d2ce12ec8d0133 SHA256: 087aa36498f54f3c25aaa664b9827cb7afe2c1cccad34fa6085147696d348350 SHA512: b00e187219b83f7d2cd6110f9412e312bf139c09164ba48a40ee5b17f066fcc5af9fc9bc9231e14fb2fc033e29ce25e8a68a3ee16aadf5ce7eb09add2d1c32fd Homepage: https://cran.r-project.org/package=SCVA Description: CRAN Package 'SCVA' (Single-Case Visual Analysis) Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) . The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend. Package: r-cran-sda Architecture: all Version: 1.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4003 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-entropy, r-cran-corpcor, r-cran-fdrtool Suggests: r-cran-crossval Filename: pool/dists/resolute/main/r-cran-sda_1.3.9-1.ca2604.1_all.deb Size: 4048102 MD5sum: efbdbdb1ef081bfb036464bad39a8374 SHA1: ac5fb940b37d46ff52052e290644ca870d0e4e4b SHA256: 99f1c8504e569cfdd6d87be6cda13f4909b88c83aef1c9818556dfecc7e30b14 SHA512: 48173ddfc9f841274ab964c523a541ad0444574c90e5707326d264d574117346da8a9fd8757aaf596f3ebd1a840bce8229f9e33b9d91f7f3387872dffedcd8cd Homepage: https://cran.r-project.org/package=sda Description: CRAN Package 'sda' (Shrinkage Discriminant Analysis and CAT Score Variable Selection) Provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with variable selection. The classifier is trained using James-Stein-type shrinkage estimators and predictor variables are ranked using correlation-adjusted t-scores (CAT scores). Variable selection error is controlled using false non-discovery rates or higher criticism. Package: r-cran-sdaa Architecture: all Version: 0.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survey, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-sdaa_0.1-5-1.ca2604.1_all.deb Size: 565920 MD5sum: b8f1825238136d375b0900fdac9db4e1 SHA1: f16bbc57c0b9f6103048d4c77916f6b34aa231b3 SHA256: 6af394527bea8bb046bfe7e5dbf06a0ede219a3e090ed232298c50854e507449 SHA512: 31d3627dd3fa0e8ee2d9e0585519df1b9057cbc13db9ae56a409552308174fa2a68ea9a1212a38b40123aea12d0e128fd0cc32eacaa8d3de9317939909133e86 Homepage: https://cran.r-project.org/package=SDaA Description: CRAN Package 'SDaA' (Sampling: Design and Analysis) Functions and Datasets from Lohr, S. (1999), Sampling: Design and Analysis, Duxbury. Package: r-cran-sdafilter Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-glasso, r-cran-poet, r-cran-selectiveinference Suggests: r-cran-testthat, r-cran-huge, r-cran-mass Filename: pool/dists/resolute/main/r-cran-sdafilter_1.0.1-1.ca2604.1_all.deb Size: 43326 MD5sum: b11a2781fb40e87fe5ce47211e0bd379 SHA1: 46ae3188938aad3571e207e1b6a42b7389bf117c SHA256: 6a4d612e142687d6a54ae7305bf2e86e22d82fd3125efb182dfbb374b62dc4c6 SHA512: abb48efb172ae62ff0b398a8f0130254dc5521c248fdf15c81dcc239ce1118a0db81813a4efc250e9603d6d723146d155ac9674cad0cf6f1018af9e505b9e5a2 Homepage: https://cran.r-project.org/package=sdafilter Description: CRAN Package 'sdafilter' (Symmetrized Data Aggregation) We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2023), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", . Some optional functionality uses the archived R packages ‘huge’ and ‘pfa’, which are not available from CRAN’s main repositories. Users who need this optional functionality can obtain them from the CRAN Archive as follows: ‘huge’ at ; ‘pfa’ at . Package: r-cran-sdam Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-grimport2, r-cran-multiplex Suggests: r-cran-httr, r-cran-rjson, r-cran-knitr, r-cran-rmarkdown, r-cran-multigraph Filename: pool/dists/resolute/main/r-cran-sdam_1.1.4-1.ca2604.1_all.deb Size: 2555656 MD5sum: 759474b1d27a589988c9c95e7715ff92 SHA1: 0f3f08fca67b07e026e8856796955cffc9bcaf6e SHA256: 13e55903228150acb2acccf8454bd70aa78178df0afef83b83059d11567abbda SHA512: b52c03d8640d1299db36b1a43875143d7cf34becb95981183446ba6ba8b2dce906e5156193be45dc0780a18fe93430bdf1a0fffd9b2f0109990bd9ee87c58da0 Homepage: https://cran.r-project.org/package=sdam Description: CRAN Package 'sdam' (Social Dynamics and Complexity in the Ancient Mediterranean) Provides digital tools for performing analyses within Social Dynamics and complexity in the Ancient Mediterranean (SDAM), which is a research group based at the Department of History and Classical Studies at Aarhus University. Package: r-cran-sdamr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-car Filename: pool/dists/resolute/main/r-cran-sdamr_0.2.0-1.ca2604.1_all.deb Size: 232448 MD5sum: 23124f86c3902921121218097df68d18 SHA1: d86cbdbd0956fe92a4aa70927374439b5df42edd SHA256: 9439441f218b3d5aae5b37dc86f8569ee934dda7a3d23a2d9480be8075b3f1da SHA512: 5a47bdc57582c4a98313203d7cd43631b4b7854335cb9de39e6880919dadf3f8562dc8a6788ae67ed1a37f406c555cbfbee57d5429873ed307c38af493efcfc0 Homepage: https://cran.r-project.org/package=sdamr Description: CRAN Package 'sdamr' (Statistics: Data Analysis and Modelling) Data sets and functions to support the books "Statistics: Data analysis and modelling" by Speekenbrink, M. (2021) and "An R companion to Statistics: data analysis and modelling" by Speekenbrink, M. (2021) . All datasets analysed in these books are provided in this package. In addition, the package provides functions to compute sample statistics (variance, standard deviation, mode), create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus tests and tests of individual contrasts. Package: r-cran-sdar Architecture: all Version: 0.9-55-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4656 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-linbin, r-cran-grimport2, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sdar_0.9-55-1.ca2604.1_all.deb Size: 1480084 MD5sum: 87df9860d1cf12176e3baba76e436c2e SHA1: b1f9df62a632488d83a789d80eff6a1c2302e0bf SHA256: 1874e77095f97a6912f87cd5357be8c8947e4e8c20e4674fed3adcd21cf09077 SHA512: 7debf03f3a7e28bc8293faccb108fb2928dc4113a4d8aed5ddab14326b0ad69c11ee0b7d233aa0c2dab6efb156406c508dc13c239653c95f1d2961c5e3952679 Homepage: https://cran.r-project.org/package=SDAR Description: CRAN Package 'SDAR' (Stratigraphic Data Analysis) A fast, consistent tool for plotting and facilitating the analysis of stratigraphic and sedimentological data. Taking advantage of the flexible plotting tools available in R, 'SDAR' uses stratigraphic and sedimentological data to produce detailed graphic logs for outcrop sections and borehole logs. These logs can include multiple features (e.g., bed thickness, lithology, samples, sedimentary structures, colors, fossil content, bioturbation index, gamma ray logs) (Johnson, 1992, ). Package: r-cran-sdaresources Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3166 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sdaresources_0.1.1-1.ca2604.1_all.deb Size: 3109192 MD5sum: 81315743b57316880b184525b0ddf8b8 SHA1: 57ae42efcbc2493c0a1149e10f49cd5e1100f0c5 SHA256: 7d2efe442cb3bdd1b473eb978e5e44dc16bd46fda4b25a0bf068b5c66459a635 SHA512: cac24a7214d4abae1508925df1c1dcfa6794333942cae4506ced9330d4ab1e78b52f950779e117471969306f9e19406bf6b2131470100ffdde6f78b39a3fc884 Homepage: https://cran.r-project.org/package=SDAResources Description: CRAN Package 'SDAResources' (Datasets and Functions for 'Sampling: Design and Analysis, 3rdEdition') Includes all the datasets of 'Sampling: Design and Analysis' (3rd edition by Sharon Lohr) in R format and additional functions for analyzing and graphing probability samples. 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They represent how systems change over time and are mathematically equivalent to ordinary differential equations. 'sdbuildR' (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. 'sdbuildR' simulates models in 'R' and 'Julia', where 'Julia' offers unit support and large-scale ensemble simulations. Additionally, 'sdbuildR' can import models created in 'Insight Maker' (). 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These tools help in checking descriptive statistics and models and in calculating extreme values that are not individual data. Also included is a simple function to create log files. The methods used here are described in the "Guidelines for the checking of output based on microdata research" by Bond, Brandt, and de Wolf (2015) . Package: r-cran-sdcnway Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 967 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-plyr, r-cran-dplyr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-sdcnway_1.0.1-1.ca2604.1_all.deb Size: 805524 MD5sum: 447efa6bb45529917e942e74bec62d98 SHA1: c216dbb243572d06bdfe16005a165a0bbd3cb572 SHA256: da6894ac8b4659feac67bf91f16a49a851f5e93f00047f40a6c658095764c571 SHA512: e596fc8c2f12bd1f9bf6368237d3881d2a60a6e8b15ee19f7589268def3e779ab9e853de4ba323192152edb4504804ed98bf2dbb1087f9cade64d3066a54b742 Homepage: https://cran.r-project.org/package=SDCNway Description: CRAN Package 'SDCNway' (Tools to Evaluate Disclosure Risk) Tools for calculating disclosure risk measures for microdata, including record-level and file-level measures. The record-level disclosure risk is estimated primarily using exhaustive tabulation. The file-level disclosure risk is estimated by fitting loglinear models on the observed sample counts in cells formed by key variables and their interactions. Funded by the National Center for Education Statistics. See Skinner and Shlomo (2008) for a description of the file-level risk measures and the loglinear model approach. 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(2002). , Bolós, V.J.; Benítez, R.; Coll-Serrano, V. (2024) . 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The time series are preprocessed with the discrete cosine transform and the variance stabilising transform to obtain an approximate Gaussian regression setting for the log-spectral density function. The test statistic is based on the squared L2 norm of the difference between the estimated log-spectral densities. The test returns the result, the statistic value, and the p-value. It also provides the estimated empirical quantile and null distribution under the hypothesis of equal spectral densities. An example using EEG data is included. For details see Nadin, Krivobokova, Enikeeva (2026), . Package: r-cran-sdgdetector Architecture: all Version: 2.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1989 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-ggplot2, r-cran-tidyr, r-cran-rnaturalearth, r-cran-scales, r-cran-magick Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sdgdetector_2.7.3-1.ca2604.1_all.deb Size: 1777484 MD5sum: 46fb62d9e76c09a35b9d98174d60b2a3 SHA1: 83ba9b24b3f970c1086b1809902a0b542def471e SHA256: 91f5d8ec65f61f956d566b99ccdb4f2bd2f411206468db1ee2e6616cf1a7b907 SHA512: 00c299d05750470c84079e022aa053a29cff088fc1075ac4fbf472246e018b85657bbfd3ec13f0d4e461631bc20480ed55a89b12988c65d2b146817afd0a0ad1 Homepage: https://cran.r-project.org/package=SDGdetector Description: CRAN Package 'SDGdetector' (Detect SDGs and Targets in Text) Identify 17 Sustainable Development Goals and associated 169 targets in text. 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The package supports Pareto-type and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such as wealth allocation and financial returns. It provides simulation tools for synthetic DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature ladders for parallel tempering, and posterior predictive evaluation metrics (e.g., R2, RMSE). The methodology is based on the scalable MCMC framework described in Guo et al. (2025). 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It considers parameters related to starch composition [total starch (TS), amylose/amylopectin ratio (Aratio), total amylose content (TAC), and total amylopectin content (TAPC)], starch digestibility [rapidly digestible starch (RDS), slowly digestible starch (SDS) and resistant starch (RS)], structural properties [relative crystallinity (RC)], non-starch components [total protein, total oil content (TOC), and total phenolic content (TPC)], and pasting behaviour [peak viscosity (PV), pasting temperature (PT), holding strength (HS), and final viscosity (FV)].The SDI is flexible and allows users to calculate the index using all parameters or only selected ones, depending on the data available. Users can also compute a starch-based SDI (using only starch-related parameters) or a principal component analysis (PCA)-based SDI, where weights are determined automatically from the data. Thus, the SDI provides a simple way to compare and rank crops or food samples based on their slow digestion potential. The package implements SDI(),starchSDI(), genSDI(), scoreSDI(), and pcaSDI() for estimating slow digestibility index using predefined weighted TOPSIS, starch-specific TOPSIS, user-defined weighted TOPSIS, score-based normalization, and PCA based approaches, respectively. The package has been developed using the algorithm of Pandey et al. (2026) . 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The package provides a set of functions for a complete analysis of integer-valued data, where the dependent variable is assumed to follow a modified SDL distribution. This regression model is useful for the analysis of integer-valued data and experimental studies in which paired discrete observations are collected. 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For more information, please check the following paper: Naimi, B., Araujo, M.B. (2016) . 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These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) ) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) ). 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The reusable algorithms concept in 'sdtm.oak' provides a framework for modular programming and can potentially automate the conversion of raw clinical data to SDTM through standardized SDTM specifications. SDTM is one of the required standards for data submission to the Food and Drug Administration (FDA) in the United States and Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. SDTM standards are implemented following the SDTM Implementation Guide as defined by CDISC . Package: r-cran-sdtm.terminology Architecture: all Version: 2025-3-25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2067 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-sdtm.terminology_2025-3-25-1.ca2604.1_all.deb Size: 1147044 MD5sum: a0f1ac3058c6bf42522bc6c9645e003c SHA1: 04970028d8ad670a4a9153e15ecab9394f0142c5 SHA256: 10690c4794430a30460267d9be8e9e809bdf9ec97c59e3fd600b6307e25929f2 SHA512: 465f0c9c97debac990e187e343b7daea205ba77318498b22fbd045ba4007b268cb9810c93805c026b1b5cdf14c789470d39d9c75ae4a79a1618817e572dcc628 Homepage: https://cran.r-project.org/package=sdtm.terminology Description: CRAN Package 'sdtm.terminology' (CDISC SDTM Controlled Terminology) Clinical Data Interchange Standards Consortium (CDISC) Standard Data Tabulation Model (SDTM) controlled terminology, 2025-03-25. Source: . Package: r-cran-sdtmchecks Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1331 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dt Filename: pool/dists/resolute/main/r-cran-sdtmchecks_1.0.0-1.ca2604.1_all.deb Size: 765120 MD5sum: 3b897f1737f943968fad1f7f63ce8ab3 SHA1: 054670ebdf6497a13f2287fb737ce57c9be3eb9c SHA256: deb53085cb53bfe4b56d0a36e0dc42b832ddb181440684d8812aa9d8c752077f SHA512: 270bd716dcb6bc24deba768520952aa44be94558bc78158d38bdf3fddb53bbc848b44723595f166d8a1a52684673ede03b5ecccaa46eb6e429ab510b25952566 Homepage: https://cran.r-project.org/package=sdtmchecks Description: CRAN Package 'sdtmchecks' (Data Quality Checks for Study Data Tabulation Model (SDTM)Datasets) A series of checks to identify common issues in Study Data Tabulation Model (SDTM) datasets. These checks are intended to be generalizable, actionable, and meaningful for analysis. Package: r-cran-sdtmval Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-haven, r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sdtmval_0.4.1-1.ca2604.1_all.deb Size: 143470 MD5sum: 3f45d9802a39b0d60dccc9dedab68f2e SHA1: dc74502eb90ce047b4bf262c2ebd7864298c1922 SHA256: 2551edcca2a29c451bf589972901ab3adb96245b253837171a8c184a8bff3d43 SHA512: eee32204b50d6e5b79ffe18c5632e02564bde3aa9595160a23875de2e766d88c8577aa7ab6777388d3a7118e691edd7ea5f5b7f9fcd516d9870b7650e18b2e6a Homepage: https://cran.r-project.org/package=sdtmval Description: CRAN Package 'sdtmval' (Validate SDTM Domains) Provides a set of tools to assist statistical programmers in validating Study Data Tabulation Model (SDTM) domain data sets. Statistical programmers are required to validate that a SDTM data set domain has been programmed correctly, per the SDTM Implementation Guide (SDTMIG) by 'CDISC' (), study specification, and study protocol using a process called double programming. Double programming involves two different programmers independently converting the raw electronic data cut (EDC) data into a SDTM domain data table and comparing their results to ensure accurate standardization of the data. One of these attempts is termed 'production' and the other 'validation'. Generally, production runs are the official programs for submittals and these are written in 'SAS'. Validation runs can be programmed in another language, in this case 'R'. Package: r-cran-se.eq Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-se.eq_1.0-1.ca2604.1_all.deb Size: 40218 MD5sum: 98b736e48f4e3a25ca5ee146c696a57b SHA1: eac69986d4af961abc411a6af38c5fcb136ef474 SHA256: afbc10b34a21ef6ed8e7ae972b015c62938390dce7acb906e7f7ec3805f794cd SHA512: 22873e8e1211f4598998a70a5bcb0fc2166583614724a925aa1100247137d4e6b3921ac013ba19243e0809fe47f415a0148a3f53569d6cb6e09fa221f22924ed Homepage: https://cran.r-project.org/package=SE.EQ Description: CRAN Package 'SE.EQ' (SE-Test for Equivalence) Implements the SE-test for equivalence according to Hoffelder et al. (2015) . The SE-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the sum of standardized differences between the expected values or in other words: the sum of effect sizes (SE) of all components of the two multivariate samples. The test is an asymptotically valid test for normally distributed data (see Hoffelder et al.,2015). The function SE.EQ() implements the SE-test for equivalence according to Hoffelder et al. (2015). The function SE.EQ.dissolution.profiles() implements a variant of the SE-test for equivalence for similarity analyses of dissolution profiles as mentioned in Suarez-Sharp et al.(2020) ). The equivalence margin used in SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ approach according to Hoffelder (2019) ) by means of a systematic shift in location of 10 [\% of label claim] of both dissolution profile populations. SE.EQ.dissolution.profiles() checks whether the weighted mean of the differences of the expected values of both dissolution profile populations is statistically significantly smaller than 10 [\% of label claim]. The weights are built up by the inverse variances. Package: r-cran-sea Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-kscorrect, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-sea_2.0.1-1.ca2604.1_all.deb Size: 7484978 MD5sum: 30b4df55f6e0389726bfe61365b56888 SHA1: 14907d7d00757677d3f50f3a35449f20c393aa61 SHA256: 19ac8191f4ae402491250e996800947ec6ae7328ec554b65d185e8679ccf080c SHA512: 36e6472c468427d98a4c6ac8ec8b9d5af37840c306f7541f51d0ebd848305815a65d3fe1ab2ce95849989c149e3c69de0314ccf5fc41670b80f2d12f7f914aff Homepage: https://cran.r-project.org/package=SEA Description: CRAN Package 'SEA' (Segregation Analysis) A few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple bi-parental segregation populations along with their parents are used to fit all the possible models and the best model of the trait for population phenotypic distributions is viewed as the model of the trait. There are fourteen types of population combinations available. Zhang Yuan-Ming, Gai Jun-Yi, Yang Yong-Hua (2003, ). Package: r-cran-seacarb Architecture: all Version: 3.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oce, r-cran-gsw, r-cran-solvesaphe Filename: pool/dists/resolute/main/r-cran-seacarb_3.3.4-1.ca2604.1_all.deb Size: 704806 MD5sum: 4745aa703357ef8221f0dd8ef41b3eb9 SHA1: df21110dba600085c1a967d18e122611d9df7164 SHA256: 763122533a1593bb7f98653598b24a8f26813b89a04d48956c2f77500c59b5fb SHA512: e4c2eeffb69d7774c8af3f910b1f4468242801ac636980eac02437b17499b9b32eab93181554d125bd2b7e7c5c8778e34c1001c372ebb473c988b12ff3ad4f7c Homepage: https://cran.r-project.org/package=seacarb Description: CRAN Package 'seacarb' (Seawater Carbonate Chemistry) Calculates parameters of the seawater carbonate system and assists in design of ocean acidification perturbation experiments. Package: r-cran-seagle Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-compquadform Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-seagle_1.0.1-1.ca2604.1_all.deb Size: 400338 MD5sum: 7b8da88fef9552730aa0ea8592c05da3 SHA1: 593d80e4354fcbd0971f42b41acdc543fd779ec1 SHA256: cb4605075c23051da4b70a785929d409272335eb2871c64035c0f32dddaeecc3 SHA512: 01275a2a7fbf525c8ebcd16e7f8dccebbebe94c5ca7d7c14b067563728755d2c011ebf3035a7bccb4d6cc54f484dd14e178f02c90776d091c64e9f717f2ff40d Homepage: https://cran.r-project.org/package=SEAGLE Description: CRAN Package 'SEAGLE' (Scalable Exact Algorithm for Large-Scale Set-BasedGene-Environment Interaction Tests) The explosion of biobank data offers immediate opportunities for gene-environment (GxE) interaction studies of complex diseases because of the large sample sizes and rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in GxE assessment, especially for set-based GxE variance component (VC) tests, a widely used strategy to boost overall GxE signals and to evaluate the joint GxE effect of multiple variants from a biologically meaningful unit (e.g., gene). We present 'SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based GxE tests, to permit GxE VC test scalable to biobank data. 'SEAGLE' employs modern matrix computations to achieve the same “exact” results as the original GxE VC tests, and does not impose additional assumptions nor relies on approximations. 'SEAGLE' can easily accommodate sample sizes in the order of 10^5, is implementable on standard laptops, and does not require specialized equipment. The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, Lin, Wang, Lee, Lu, and Tzeng. (2021+) . Package: r-cran-seagraphs Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2369 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfnetworks, r-cran-sf, r-cran-terra, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-leaflet.extras2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-seagraphs_0.1.3-1.ca2604.1_all.deb Size: 1506988 MD5sum: 0eeb7f02216c7ae721dc2fc60573de8f SHA1: 0ae4472129ceaff98f243684ac19539ea2cf3045 SHA256: 047bb9b6a81b4691f0e91e807a37bc04687c2be1dfa12835a621d6ca9dd64e98 SHA512: 8634fa30d62be57c2edf1a849632bfe4cfc8d41183737dff8c26e2ae06420c17b40fe5c5a1a1939fbafae335d4f402a28530fed03d57b3da33cea8f682d32eea Homepage: https://cran.r-project.org/package=SeaGraphs Description: CRAN Package 'SeaGraphs' (Sea Currents to Connectivity Transformation) Transformation of sea currents to connectivity data. Two files of horizontal and vertical currents flows are transformed into connectivity data in the form of 'sfnetwork', shapefile, edge list and adjacency matrix. An application example is shown at Nagkoulis et al. (2025) . Package: r-cran-seahors Architecture: all Version: 1.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-shinythemes, r-cran-shinyjs, r-cran-shinywidgets, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr, r-cran-plotly, r-cran-dt, r-cran-mass, r-cran-readxl, r-cran-raster, r-cran-stringr, r-cran-viridis, r-cran-rmarkdown, r-cran-htmlwidgets Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-seahors_1.9.0-1.ca2604.1_all.deb Size: 418652 MD5sum: e4375da8f9ace628b0d29ba41ca431c0 SHA1: 33e4eb7aa5669e0349aebd1ecfe6ed46f8c0bd02 SHA256: 0f18c56685cf139ac20390f8e7d7ab090ebbd0c9b2f19f97cd7fe4321c5d9d00 SHA512: 2e17cb46fad691f214308c47149e95cf4bb589dd265b2f9145e5f34ebfdea09bd634528686049b88080f98f503dadee07e5e74a53ef54f1c6f6414059559cef8 Homepage: https://cran.r-project.org/package=SEAHORS Description: CRAN Package 'SEAHORS' (Spatial Exploration of ArcHaeological Objects in R Shiny) An R 'Shiny' application dedicated to the intra-site spatial analysis of piece-plotted archaeological remains, making the two and three-dimensional spatial exploration of archaeological data as user-friendly as possible. Documentation about 'SEAHORS' is provided by the vignette included in this package and by the companion scientific paper: Royer, Discamps, Plutniak, Thomas (2023, PCI Archaeology, ). Package: r-cran-seairmobility Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-seairmobility_0.1.0-1.ca2604.1_all.deb Size: 70770 MD5sum: b8528daf2d9dfd293a1f7f5bf6789ea5 SHA1: 98f54d627b733241ee37a68040e5482a2e68134f SHA256: 862633dbd871d188818876194e50e311cafc104e1f4f7c5566429cdd6bd7ee4f SHA512: 567a8571d82f133967cbe92e5084c5f840367a19463b38b944c049f9c2bc91ffad208e3fc8b7a788cff16ba2aff3e35b5e2d375088ca6adf5bef2fb49a7d594d Homepage: https://cran.r-project.org/package=seairmobility Description: CRAN Package 'seairmobility' (Mobility-Based SEAIR Epidemic Models) Tools for simulating, analysing, and fitting mobility-based SEAIR (Susceptible-Exposed-Asymptomatic-Infectious-Recovered) compartmental epidemic models with heterogeneous individual mobility. Each individual carries a fixed mobility trait that scales susceptibility and infectiousness through a rank-one kernel, extending the mobility-based compartmental framework of Jiang et al. (2025) by adding a latent stage and a behavioural split between asymptomatic and symptomatic infectiousness. Provides a numerical solver for the underlying partial differential equation system, closed-form computation of the basic reproduction number R0 and the final epidemic size, and a parametric least-squares routine for recovering the mobility distribution from an observed aggregate symptomatic time series. Package: r-cran-sealasso Architecture: all Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lars Filename: pool/dists/resolute/main/r-cran-sealasso_0.1-3-1.ca2604.1_all.deb Size: 158288 MD5sum: eaaba5a0a1809346053a1419911a17a6 SHA1: dd1aa7efa6eaf0f7dc74957a7c3e99b2ef727bd8 SHA256: 2a9fd8067b213db0de1ab05bcb43c379e2923d299dbba0070c115e305b8eda4c SHA512: f598b594a957996c191066c8558c25924beba08a8c7808f923bfde07bfa7b0a877786a4dea59dc6135179b83e95526a647dad1d5beb08c03af308bfb0443fa3c Homepage: https://cran.r-project.org/package=sealasso Description: CRAN Package 'sealasso' (Standard Error Adjusted Adaptive Lasso) Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern. Package: r-cran-searchanalyzer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-ggplot2, r-cran-openxlsx, r-cran-stringdist, r-cran-lubridate, r-cran-digest Suggests: r-cran-testthat, r-cran-covr, r-cran-dplyr, r-cran-shiny, r-cran-shinydashboard, r-cran-flexdashboard, r-cran-plotly, r-cran-dt, r-cran-boot, r-cran-pwr, r-cran-progress, r-cran-rmarkdown, r-cran-knitr, r-cran-glue, r-cran-rentrez, r-cran-xml2, r-cran-readr, r-cran-jsonlite, r-cran-revtools, r-cran-tidyr, r-cran-patchwork, r-cran-metagear Filename: pool/dists/resolute/main/r-cran-searchanalyzer_0.1.0-1.ca2604.1_all.deb Size: 845032 MD5sum: 775ddf480cbf8279ef7d7141e8eeb92f SHA1: 7caf2c804ec8abd909bbe7ac1af16bf629ef8c93 SHA256: 60c991a3bc2c8f31b00ffdda917b2246fc4378e7d06fbacd68606f7ec3f6615b SHA512: c023094d299357f2a469f1f6048a2e66e97cdf524db68a96a8a0be295022dca4c13a6f033ca058419e3cd86ed4b02980fd09143c490cfd023059a9526d496a02 Homepage: https://cran.r-project.org/package=searchAnalyzeR Description: CRAN Package 'searchAnalyzeR' (Advanced Analytics and Testing Framework for Systematic ReviewSearch Strategies) Provides comprehensive analytics, reporting, and testing capabilities for systematic review search strategies. The package focuses on validating search performance, generating standardized 'PRISMA'-compliant reports, and ensuring reproducibility in evidence synthesis. Features include precision-recall analysis, cross-database performance comparison, benchmark validation against gold standards, sensitivity analysis, temporal coverage assessment, automated report generation, and statistical comparison of search strategies. Supports multiple export formats including 'CSV', 'Excel', 'RIS', 'BibTeX', and 'EndNote'. Includes tools for duplicate detection, search strategy optimization, cross-validation frameworks, meta-analysis of benchmark results, power analysis for study design, and reproducibility package creation. Optionally connects to 'PubMed' for direct database searching and real-time strategy comparison using the 'E-utilities' 'API'. Enhanced with bootstrap comparison methods, 'McNemar' test for strategy evaluation, and comprehensive visualization tools for performance assessment. Methods based on Manning et al. (2008) for information retrieval metrics, Moher et al. (2009) for 'PRISMA' guidelines, and Sampson et al. (2006) for systematic review search methodology. Package: r-cran-searcher Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-searcher_0.0.7-1.ca2604.1_all.deb Size: 51992 MD5sum: e6732a73f15547c39998c23534a5fa46 SHA1: 2e81fc171e4a904f93380ec7fc736915ba0334bc SHA256: 7b4a2adc6b149ce480b59ea2b365b170abbcf160f8800615d6174f666ace2aeb SHA512: 1db31921a15ec63c2fe7066d113e1cf5830cdcc47ca8dc7daa2f2930c8dda05112ed18da25c9a3b19c1bd97101b957972b073a4b164c41d99d0122f8dfc06fb3 Homepage: https://cran.r-project.org/package=searcher Description: CRAN Package 'searcher' (Query Search Interfaces) Provides a search interface to look up terms on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Ecosia', 'rseek', 'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'. Upon searching, a browser window will open with the aforementioned search results. Package: r-cran-sears Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boin Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sears_0.1.0-1.ca2604.1_all.deb Size: 43452 MD5sum: 993e51834f3d735caf77e65500106e8a SHA1: b07f846343d0815be569be2b9a782bb572b90b97 SHA256: 6a7f963b084f9f1f5f20c61d275c40b10b778e8d92b335e55c101e702a2212cd SHA512: 60526abb71a8dc1276dd7e5e9123dc4f0ac29c07704f7e1e7a977472d8ddc4588279f6aea448489ff22a9c69449858b11f01b82b8419cd2a86db003c79bda227 Homepage: https://cran.r-project.org/package=SEARS Description: CRAN Package 'SEARS' (Seamless Dose Escalation/Expansion with Adaptive RandomizationScheme) A seamless design that combines phase I dose escalation based on toxicity with phase II dose expansion and dose comparison based on efficacy. Package: r-cran-seasepi Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-ngspatial Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-seasepi_0.0.3-1.ca2604.1_all.deb Size: 132544 MD5sum: 705fdddade86894f9e8b4a2a8ecfdb9a SHA1: 16f50e6f594a659ce79462d8b25652b0a89c5295 SHA256: 13310aa9b0856f4bf412206ce52f28c500b5a44e09988e9a79ab698b419a8130 SHA512: cb339b903b9a46a536bdedbaa6254c2b280c0ba10140ed1205d1db7a1240ebd5da135788f9362dc03b83fd6e13b9299dff600904c5ded676047c8436dd8fad99 Homepage: https://cran.r-project.org/package=SeasEpi Description: CRAN Package 'SeasEpi' (Spatiotemporal Modeling of Seasonal Infectious Disease) Spatiotemporal individual-level model of seasonal infectious disease transmission within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model seasonal infectious disease transmission. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. In addition to model fitting and parameter estimation, the package offers functions for calculating AIC using real pandemic data and conducting simulation studies customized to user-specified model configurations. Package: r-cran-season Architecture: all Version: 0.3.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-survival, r-cran-coda, r-cran-stringr Suggests: r-cran-dlnm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-season_0.3.16-1.ca2604.1_all.deb Size: 511220 MD5sum: a7e27ebd11d950b4c386cdb21ca9b2eb SHA1: 2f44a469849db81d19abd93ae620d38dfc09fdee SHA256: 99c60c4dd00111adb120258dcaa0cc0ce764c56db5fcf842a4b948aa256ab475 SHA512: b9ea6ca995c72c2be8194ea33c6a174db8ae050b3597b340f324765b3fab2d7c05330592f2ed38cf620dd7f8dc0390acbca7e66b698c31c4fe679b85fcf6006b Homepage: https://cran.r-project.org/package=season Description: CRAN Package 'season' (Seasonal Analysis of Health Data) Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code. Package: r-cran-seasonal Architecture: all Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-x13binary Suggests: r-cran-seasonalview, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-seasonal_1.10.0-1.ca2604.1_all.deb Size: 554160 MD5sum: 81931ba6a40a91bbb44cefbc45a043e2 SHA1: bf1700dd54f148e427b7051af7873e417ceb99e6 SHA256: cfb69c5c8021c8023d5e8f712d229c82c74adbba4d3d1c894e57e7ef13b9b836 SHA512: 3d4c4a4d7a6d4f4b304b78c2424efe73a9291d1c499ca155aaa376dc81ead218ce635ab9bec975e4413179581fbcf7b15808d82ce7296471705841239a4f5fe4 Homepage: https://cran.r-project.org/package=seasonal Description: CRAN Package 'seasonal' (R Interface to X-13-ARIMA-SEATS) Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the 'seasonalview' package. Uses the X-13-binaries from the 'x13binary' package. Package: r-cran-seasonalityplot Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-quantmod, r-cran-dygraphs, r-cran-plotrix, r-cran-htmltools, r-cran-zoo, r-cran-lubridate, r-cran-crypto2, r-cran-ttr, r-cran-assertthat Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-seasonalityplot_1.3.1-1.ca2604.1_all.deb Size: 939558 MD5sum: da75efb8d34a1c44a1ec7f1b27c65918 SHA1: c179fffd98f95fa0345c04edd0cc4e6fe2fd573a SHA256: e04631f48134249439bb6d36f47b511eed70b0438b9528c548c0056347ac19d4 SHA512: 988fbe48807ba8ed2d1021ea190ab70e13b2496291fcf32618887131fe417439bf11a148b864179c0491ac539c4ec6cdc47bbec40cf904983c8cd747c4619ca2 Homepage: https://cran.r-project.org/package=seasonalityPlot Description: CRAN Package 'seasonalityPlot' (Seasonality Variation Plots of Stock Prices and Cryptocurrencies) The price action at any given time is determined by investor sentiment and market conditions. Although there is no established principle, over a long period of time, things often move with a certain periodicity. This is sometimes referred to as anomaly. The seasonPlot() function in this package calculates and visualizes the average value of price movements over a year for any given period. In addition, the monthly increase or decrease in price movement is represented with a colored background. This seasonPlot() function can use the same symbols as the 'quantmod' package (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc). Package: r-cran-seasonalview Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seasonal, r-cran-shinydashboard, r-cran-shiny, r-cran-dygraphs, r-cran-htmlwidgets, r-cran-openxlsx, r-cran-xtable, r-cran-xts, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-seasonalview_1.0.0-1.ca2604.1_all.deb Size: 88282 MD5sum: 34003196994af788ee6bbb27f64114e7 SHA1: b21c3a5f04613219b1ffb2d20205b1bfaaca95fa SHA256: d77c9ff14b2ec2ab82fdf5b19c0fd0182d2e676033c244ff32eb5f60dbffef18 SHA512: 7c2ca83b47bb5acf2454327beb4abb63d589114113766b18ff0462ecc9d45aff66b8bc2f8052ba50e53c9ccf7090b4f482e25a85bfaeca4643219868fe5e3b90 Homepage: https://cran.r-project.org/package=seasonalview Description: CRAN Package 'seasonalview' (Graphical User Interface for Seasonal Adjustment) A graphical user interface to the 'seasonal' package and 'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. Package: r-cran-seasonalytics Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seastests Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-seasonalytics_0.1.0-1.ca2604.1_all.deb Size: 16066 MD5sum: e38ed70f25c24701bf23caba08996a15 SHA1: 310d2489f6d1971ce151ff0de4724e0eb223ba41 SHA256: dd70d97ef61f0435a53a801ec273ead43f1490b49fb03332128eba7e0e64edb6 SHA512: abeae0d38ffde206db3c9484d033951d3d06fa64bf83b95d74b4f50bd983304d5002b311bfd931bddd9620c21aaecc95f9fa5a58758d2e99dea0ab70a8736955 Homepage: https://cran.r-project.org/package=seasonalytics Description: CRAN Package 'seasonalytics' (Compute Seasonality Index, Seasonalized and Deseaonalised theTime Series Data) The computation of a seasonal index is a fundamental step in time-series forecasting when the data exhibits seasonality. Specifically, a seasonal index quantifies — for each season (e.g. month, quarter, week) — the relative magnitude of the seasonal effect compared to the overall average level of the series. This package has been developed to compute seasonal index for time series data and it also seasonalise and desesaonalise the time series data. Package: r-cran-seasonder Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit64, r-cran-bitops, r-cran-constants, r-cran-data.table, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-pracma, r-cran-purrr, r-cran-rlang, r-cran-slider, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-uuid, r-cran-whisker, r-cran-yaml, r-cran-zoo Suggests: r-cran-here, r-cran-mockthat, r-cran-testthat, r-cran-openssl, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-seasonder_0.2.8-1.ca2604.1_all.deb Size: 4796134 MD5sum: 64d21b42010b2ae0d63012d9ca9380d9 SHA1: 28f161b6578e24c7a2d5aa1e30b74bc2b5eea406 SHA256: cc2a85bed419232e5132be9ef58787bcc0e8b142c9482f409459128a39eb33e5 SHA512: 5ee56af8031f85f852907211c36f7a198e8dc46c6d6b8a235c7ff27f0ea1c2ea768a79753356498a1b35688fe91b08e8ec63fef40e56acef327ab5b2b1c17972 Homepage: https://cran.r-project.org/package=SeaSondeR Description: CRAN Package 'SeaSondeR' (Radial Metrics from SeaSonde HF-Radar Data) Read CODAR's SeaSonde High-Frequency Radar spectra files, compute radial metrics, and generate plots for spectra and antenna pattern data. Implementation is based in technical manuals, publications and patents, please refer to the following documents for more information: Barrick and Lipa (1999) ; CODAR Ocean Sensors (2002) ; Lipa et al. (2006) ; Paolo et al. (2007) ; CODAR Ocean Sensors (2009a) ; CODAR Ocean Sensors (2009b) ; CODAR Ocean Sensors (2016a) ; CODAR Ocean Sensors (2016b) ; CODAR Ocean Sensors (2016c) ; Bushnell and Worthington (2022) . Package: r-cran-seastests Architecture: all Version: 0.15.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-seastests_0.15.4-1.ca2604.1_all.deb Size: 74110 MD5sum: 59f370d221862f76fd0c15cfe3786c9f SHA1: de3885a00788eef12bd9690dbdcd510802b9f93d SHA256: dd5585cc0936a8fde490f7f330a1f57755a0302aa2e5cd15323327affea24820 SHA512: 27e502a6598715c54f6f226db7a4d0e5127b0a155d54cd7cddf9917d5fa0d300197e998163d85684fb1e71f3907ee42e2539bafe872bf0c90cd4c6fb954d6264 Homepage: https://cran.r-project.org/package=seastests Description: CRAN Package 'seastests' (Seasonality Tests) An overall test for seasonality of a given time series in addition to a set of individual seasonality tests as described by Ollech and Webel (forthcoming): An overall seasonality test. Bundesbank Discussion Paper. Package: r-cran-seaval Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19674 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggplotify, r-cran-lifecycle, r-cran-maps, r-cran-ncdf4, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-scales, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-seaval_1.2.0-1.ca2604.1_all.deb Size: 3205358 MD5sum: 3475917be5b6d38f6deaef89785641ed SHA1: 825a8ea547dc3c7ef1e667df1bde20ac465d4ddf SHA256: d47b4d9e582130a9205e33d2b457e365bb264bd7421b279ad0e89f49ee4bcb72 SHA512: 9e61122a6776d89468b4fe5a2829e5af29658d2a241e5be3daeafbca689ff3f15dc7d3f356de8fca91f659fb5f7247718a87de3ec790c7bac85164593fa0cfaa Homepage: https://cran.r-project.org/package=SeaVal Description: CRAN Package 'SeaVal' (Validation of Seasonal Weather Forecasts) Provides tools for processing and evaluating seasonal weather forecasts, with an emphasis on tercile forecasts. We follow the World Meteorological Organization's "Guidance on Verification of Operational Seasonal Climate Forecasts", S.J.Mason (2018, ISBN: 978-92-63-11220-0, URL: ). The development was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869730 (CONFER). A comprehensive online tutorial is available at . Package: r-cran-sebr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fields, r-cran-gpgp, r-cran-knitr, r-cran-mass, r-cran-plyr, r-cran-quantreg, r-cran-rmarkdown, r-cran-spikeslabgam, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-sebr_1.1.0-1.ca2604.1_all.deb Size: 624748 MD5sum: a84d07d67448f99c1b182ab9683db19b SHA1: 445c4b22d599f3280db5be970933485396a32545 SHA256: d5167166a3515ef222d752b2c4c8e9952ed94dbfefaaf90f6b1a7341c2bf1fa4 SHA512: 481ef4b316493c78a40bc28c91ab179774cc0dd333eb22cea14ede31912948f244fb1e0ceda74595cbb88c2fa434f14b6f3b43a7c67948fd1735aeb673fda1c6 Homepage: https://cran.r-project.org/package=SeBR Description: CRAN Package 'SeBR' (Semiparametric Bayesian Regression Analysis) Monte Carlo sampling algorithms for semiparametric Bayesian regression analysis. These models feature a nonparametric (unknown) transformation of the data paired with widely-used regression models including linear regression, spline regression, quantile regression, and Gaussian processes. The transformation enables broader applicability of these key models, including for real-valued, positive, and compactly-supported data with challenging distributional features. The samplers prioritize computational scalability and, for most cases, Monte Carlo (not MCMC) sampling for greater efficiency. Details of the methods and algorithms are provided in Kowal and Wu (2024) . 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'sectorgap' features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output and a set of aggregate as well as subsector indicators. Estimation of the latent states and parameters is achieved using a simple Gibbs sampling procedure and various plotting options facilitate the assessment of the results. For details on the methodology and an illustrative example, see Streicher (2024) . 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Package: r-cran-sedproxy Architecture: all Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2966 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sedproxy_0.7.6-1.ca2604.1_all.deb Size: 2391300 MD5sum: 452c35c63da77ca2cff7ee66494db9fd SHA1: 71259ad4e918324cebf3c92a82f5652208a9f292 SHA256: 8cd2a4a8fb7e3c85bfcec8ec1c738fa5f95f047b63dba00c23ade3ed31c65b2e SHA512: 5a17a87d3067621383d12adf92741af89ea60622126d8216aceff18da7191b93c1385cf6a9af5a715977c5f705209a1b46f03d96e17bdd8131b6da2887ad833a Homepage: https://cran.r-project.org/package=sedproxy Description: CRAN Package 'sedproxy' (Simulation of Sediment Archived Climate Proxy Records) Proxy forward modelling for sediment archived climate proxies such as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, such as output from a climate model, and details of the sedimentation rate and sampling scheme of a sediment core. Sedproxy returns simulated proxy records. Implements the methods described in Dolman and Laepple (2018) . 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Color scales are based on . References: Lüdecke et al. (2021) . Package: r-cran-seeclickfixr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-rcurl Filename: pool/dists/resolute/main/r-cran-seeclickfixr_1.1.0-1.ca2604.1_all.deb Size: 51110 MD5sum: 8ba3bd044df7eda6e972d1bacb86d873 SHA1: 2c1f07ea950922cde884bccbcba927120963b31d SHA256: c1e1b9b7a06c9c870dcd7533fc7359e97aef892b23844975af52071ffe277c67 SHA512: 2da1e93a0a2781d0ee11be18ce3b7b43b9ea97db4116c14187c62c9a20863e1d66c596481fd83c3b8419ea203e93cbc53ed0548aeec619c37fcb34d85c2f2915 Homepage: https://cran.r-project.org/package=seeclickfixr Description: CRAN Package 'seeclickfixr' (Access Data from the SeeClickFix Web API) Provides a wrapper to access data from the SeeClickFix web API for R. SeeClickFix is a central platform employed by many cities that allows citizens to request their city's services. This package creates several functions to work with all the built-in calls to the SeeClickFix API. Allows users to download service request data from numerous locations in easy-to-use dataframe format manipulable in standard R functions. 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Package: r-cran-seedcca Architecture: all Version: 3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cca, r-cran-corpcor Filename: pool/dists/resolute/main/r-cran-seedcca_3.1-1.ca2604.1_all.deb Size: 357774 MD5sum: ba3476fe6272f86d11a34257bed41de4 SHA1: 849a4e8ef018c330a0ae4871a21be5c0851359c5 SHA256: 3c143235304fa0c1463e6790cf4d4a9cfbb08e9a336e124eafd3958957f7d1fb SHA512: 0ca8b214994b9bf3d1051c43e12366ea01dfc9943528fd32890e1c1a4f38b68b3f4863daa77ac921b2eb62061bb9cddf802a041cbd6d3beb70477dfcb502071e Homepage: https://cran.r-project.org/package=seedCCA Description: CRAN Package 'seedCCA' (Seeded Canonical Correlation Analysis) Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) \doi{10.1002/cem.2691}. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) \doi{10.1093/biomet/asm038} and Lee and Yoo (2014) \doi{10.1111/anzs.12057}. Then, the canonical correlation is finalized with the initially-reduced two sets of variables. Package: r-cran-seedimbibition Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-seedimbibition_0.1.0-1.ca2604.1_all.deb Size: 9954 MD5sum: 837bb51f6b68e413ad1d146047abd3c4 SHA1: ea09022a2b70af087df39d4f39f38f472bf21e0a SHA256: 6c9581eb174d5334bc33b05811cb64cb149ac38a65f4eaeea4e97df452f9f2e7 SHA512: 0da2f343f6a0c7ce86e40d5a491f962ba344942bafca5b921ae66bb6f864a42fc85a3869ba5ccd209ca1f32bd4635137d8cc0e3c67f6a7a4df903631164736fc Homepage: https://cran.r-project.org/package=SeedImbibition Description: CRAN Package 'SeedImbibition' (Seed Imbibition Percentage) Imbibition causes seeds to expand, which results in the seed coat or testa being broken. Seed germination begins with imbibition. Imbibition aids in the transport of water into the developing ovules. Imbibition is required during the first stages of root water absorption. Package: r-cran-seedmaker Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-seedmaker_1.0.0-1.ca2604.1_all.deb Size: 45068 MD5sum: 86ec98499413b08fb06cdc688a24c013 SHA1: 6aa9dafbd0e379136d003d8c5e55aadee82632c8 SHA256: 479d2f910bb33fdbe87059f14c8ef46811af94d73abb34e9e4cb395e5927a7bf SHA512: ed6ac85cb71628013bd0c037c8a3d279ee8f8eead6f838ce4454aec04cf155abf25c4e38568c1491607eddc1749b99505dbe64002cb6af129409ed96aa624c77 Homepage: https://cran.r-project.org/package=SeedMaker Description: CRAN Package 'SeedMaker' (Generate a Collection of Seeds from a Single Seed) A mechanism for easily generating and organizing a collection of seeds from a single seed, which may be subsequently used to ensure reproducibility in processes/pipelines that utilize multiple random components (e.g., trial simulation). 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Package: r-cran-seedvigorindex Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-seedvigorindex_0.1.0-1.ca2604.1_all.deb Size: 10118 MD5sum: d24eef8ff4d2343414b9d4578630f4bd SHA1: adc62e77d6790c93cefafd3548bba744096ac754 SHA256: cb127ec9795363857a16c6fa0609ef6e275b06842d0718e7197733a2ef67be1e SHA512: 313b23adf5d220774e3fd3cd156c5b6687f636b5c113e6f2af13bc20e520b8773aad996cd30623344bcd7f78925788952d3eb58ff2df0e3feb1b86b786fcc7b5 Homepage: https://cran.r-project.org/package=SeedVigorIndex Description: CRAN Package 'SeedVigorIndex' (Seed Vigor Index) Seed vigor is defined as the sum total of those properties of the seed which determine the level of activity and performance of the seed or seed lot during germination and seedling emergence. Testing for vigor becomes more important for carryover seeds, especially if seeds were stored under unknown conditions or under unfavorable storage conditions. Seed vigor testing is also used as indicator of the storage potential of a seed lot and in ranking various seed lots with different qualities. The vigour index is calculated using the equation given by (Ling et al. 2014) . 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Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at . 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This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) and Sueur (2018) . Package: r-cran-segcorr Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jointseg Filename: pool/dists/resolute/main/r-cran-segcorr_1.2-1.ca2604.1_all.deb Size: 308230 MD5sum: 11ec5a13da495dee65ea4a89eb24f840 SHA1: d4a62c9dade64196fc33359b079c187ea8d8d6f5 SHA256: df6c2e5c3c8763f29990c104262cd2e407b1ca9a9218a6aef3b148bd5fba34b6 SHA512: 67aa2e8be8981a2404a1320f49f34c36bdb427dbb63e5b24f42f4d187f1a1bf4d79cc5fb929d09406bf754c3840126f5b10f00d4730fb3ab38984fd2bab4d6d0 Homepage: https://cran.r-project.org/package=SegCorr Description: CRAN Package 'SegCorr' (Detecting Correlated Genomic Regions) Performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression. 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Package: r-cran-segmented Architecture: all Version: 2.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-segmented_2.2-1-1.ca2604.1_all.deb Size: 1426258 MD5sum: 80fd007aac5fec563aa8dbda4ace45fe SHA1: faff92d12ce3cb658f6c51f14a3deab4f4de6059 SHA256: 2f557452c2615793181fd54bab93f5beef5a539234dfd40cea8204a1d09eed3a SHA512: c1d0bf98737efd2be2579afb25a251e8a31ff7deb4afe816122935ae07d2b503b907ac0e093fa34467bc5e78cc5b07188cfd8e79da2719add74c6546418598ca Homepage: https://cran.r-project.org/package=segmented Description: CRAN Package 'segmented' (Regression Models with Break-Points / Change-Points Estimation(with Possibly Random Effects)) Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, ) and illustrated in Muggeo (2008, ). An approach for hypothesis testing is presented in Muggeo (2016, ), and interval estimation for the breakpoint is discussed in Muggeo (2017, ). Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, ). Estimation of piecewise-constant relationships and changepoints (mean-shift models) is discussed in Fasola et al. (2018, ). 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These metrics calculate the discrepancy between segmented and reference objects, and indicate the segmentation accuracy. For more details on choosing evaluation metrics, we suggest seeing Costa et al. (2018) and Jozdani et al. (2020) . 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The package is straightforward through three steps: (1) data ordering (function OrdData()), (2) split-moving-window analysis (function SMW()) and (3) piecewise redundancy analysis (function pwRDA()). Relevant references include Cornelius and Reynolds (1991) and Legendre and Legendre (2012, ISBN: 9780444538697). 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This implementation supports custom penetrance values and liability classes, and allows visualisations and robustness analysis as presented in Ratajska et al. (2023) . See also the online app 'shinyseg', , which offers interactive segregation analysis with many additional features (Carrizosa et al. (2024) ). 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Package: r-cran-sejong Architecture: all Version: 0.01-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sejong_0.01-1.ca2604.1_all.deb Size: 1616964 MD5sum: 43ce10f0ff42dab51ae4f513e6904788 SHA1: 9ec9976fd3e5c4ed04f5962a5bbbabd1304c7d01 SHA256: 0ed45ef62e5bc5b7e2a86c817329429c0d3a5ca1bd6ce0cee172513331a0d09d SHA512: 7afb946aa5b71b47b704c3c4db35a9904e55941b0e7c5bebd1ed90bf9655991a8fb465d09a35d90992bd295526d7fd8aed6e2195e71aebc02670b01e2266acdc Homepage: https://cran.r-project.org/package=Sejong Description: CRAN Package 'Sejong' (KoNLP static dictionaries and Sejong project resources) Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP Package: r-cran-selcorr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-selcorr_1.0-1.ca2604.1_all.deb Size: 20654 MD5sum: 2119c9f4292dd550bdef0dbf2cb2457b SHA1: 2e205ad2bed339186e645d02d8f4d9e488e2c630 SHA256: 4cc393f03f32a075ab1ff1769c49aad40177986fb4ef6ccab3a58d888b9af9aa SHA512: 13b42c02b88aae1e390e51dd9e83cca6ea7a643c9072e6c44ba95ec648fdb9c490396f706b1124713368e009ce09c8491491aa5f6f857a9be1c575d9f04b28bc Homepage: https://cran.r-project.org/package=selcorr Description: CRAN Package 'selcorr' (Post-Selection Inference for Generalized Linear Models) Calculates (unconditional) post-selection confidence intervals and p-values for the coefficients of (generalized) linear models. Package: r-cran-select Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-latticeextra, r-cran-lattice, r-cran-ade4 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-fd Filename: pool/dists/resolute/main/r-cran-select_1.6-1.ca2604.1_all.deb Size: 166366 MD5sum: 26b834501370203cc5813d95a1e82c29 SHA1: a04a91927dc194ec483a7fd2c0df319ac1643652 SHA256: 833d095c6aa58c5acfa5c867d67478c4e9094b32d01e83d225dee8b9395da2f7 SHA512: 1ca191df9847cc78a3139a82a39dde9170168c91b1a931228318e0ca52d6812db18bb47e4273bdc999778673a038a3e9cbc3cebcc865cdd9487207efd0b4012f Homepage: https://cran.r-project.org/package=Select Description: CRAN Package 'Select' (Determines Species Probabilities Based on Functional Traits) The objective of these functions is to derive a species assemblage that satisfies a functional trait profile. Restoring resilient ecosystems requires a flexible framework for selecting assemblages that are based on the functional traits of species. However, current trait-based models have been limited to algorithms that can only select species by optimising specific trait values, and could not elegantly accommodate the common desire among restoration ecologists to produce functionally diverse assemblages. We have solved this problem by applying a non-linear optimisation algorithm that optimises Rao Q, a closed-form functional trait diversity index that incorporates species abundances, subject to other linear constraints. This framework generalises previous models that only optimised the entropy of the community, and can optimise both functional diversity and entropy simultaneously. This package can also be used to generate experimental assemblages to test the effects of community-level traits on community dynamics and ecosystem function. The method is based on theory discussed in Laughlin (2014, Ecology Letters) and Laughlin et al. (2018, Methods in Ecology and Evolution). Package: r-cran-selectapref Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-selectapref_0.1.2-1.ca2604.1_all.deb Size: 15456 MD5sum: 4cc0f660aed2be561740652f0027fe30 SHA1: 63fc22cd44c3e83d686539c2a8152a068938dce8 SHA256: bbfa016bb24ad36a3686c99fd22dca798ae00179d278c058cc0dbd514fca05c2 SHA512: 681541971109fe0c070d5f29e259cf9ee9372642d45a5e275fad7608104a5f4e9dd2b6990b34e4ef8be416113e3de0be86e80b587de0e594f265c68ec7c3ecf3 Homepage: https://cran.r-project.org/package=selectapref Description: CRAN Package 'selectapref' (Analysis of Field and Laboratory Foraging) Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies. Package: r-cran-selectboost.fda Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-selectboost Suggests: r-cran-fdboost, r-cran-glmnet, r-cran-grpreg, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-sgl, r-cran-stabs, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-selectboost.fda_0.5.0-1.ca2604.1_all.deb Size: 672698 MD5sum: 6752ede506a84e16c23a3270b2652fdc SHA1: 56ece3a19bd1ff1b7070f5c846fe06ab6f8c742c SHA256: 3506b65cf3a63968bbbb5326a06054f25c3b8b8e239d2415e5727100d11e36b6 SHA512: 1b24a930e639841bce012894e3f35e7d1bbd3c2ce30f4b75c10f50fce9c9acd0b52af73b842147c854b1f3f3689dbe88664c101a1854a5e9bb098713bfdd30e3 Homepage: https://cran.r-project.org/package=SelectBoost.FDA Description: CRAN Package 'SelectBoost.FDA' (SelectBoost-Style Variable Selection for Functional DataAnalysis) Implements 'SelectBoost'-style variable selection workflows for functional data analysis. The package provides FDA-native design and preprocessing objects for raw curves, spline-basis expansions, Functional principal component analysis scores, and scalar covariates; grouped stability-selection routines based on repeated subject-level subsampling; multiple selector backends including lasso, group lasso, and sparse-group lasso; FDA-aware grouping functions and calibration helpers for 'SelectBoost'; method-comparison utilities; a formula interface; simulation, benchmarking, and validation helpers with mapped ground truth; targeted sensitivity-study utilities and shipped benchmark summaries for mean 'F1' comparisons between FDA-aware and plain 'SelectBoost' workflows; small example datasets; and an optional adapter to the native stability-selection interface from the 'FDboost' package. Package: r-cran-selectboost.quantile Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 717 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-movmf, r-cran-quantreg, r-cran-withr Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-selectboost.quantile_0.3.1-1.ca2604.1_all.deb Size: 464400 MD5sum: 3e7bc2f2876cff7482ab20b1198cb22f SHA1: 71fc7d33ec10ce00150cf327869e55547481df3e SHA256: 295c49a21f9b0f06fc5134e98a16167d010791732dddd4ddfe09d81d50092814 SHA512: 04501685d61ac81df780cc09f1f006dc260144ed33326642a176685d4ec9b5e2857c1fa0f9b9b58dd20db73a27746dff8a0624658332b15891a1a5f67d1566fa Homepage: https://cran.r-project.org/package=SelectBoost.quantile Description: CRAN Package 'SelectBoost.quantile' ('SelectBoost'-Style Variable Selection for Quantile Regression) A 'SelectBoost'-inspired workflow for sparse quantile regression. The package builds correlation neighborhoods, perturbs correlated predictors with a directional sampler inspired by the original 'SelectBoost' internals, refits penalized quantile regression models on the perturbed designs, and aggregates variable-selection frequencies across a path of correlation thresholds. Package: r-cran-selectboost Architecture: all Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2618 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lars, r-cran-glmnet, r-cran-igraph, r-cran-msgps, r-cran-rfast, r-cran-cascade, r-cran-varbvs, r-cran-spls, r-cran-abind Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-mixomics, r-cran-cascadedata, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-selectboost_2.3.0-1.ca2604.1_all.deb Size: 1879692 MD5sum: 54efe67ff4ac9b5c2b907686c5e18f7c SHA1: 80ee5de4ae6f10144c13611f90f27faaa2696c08 SHA256: d7e69237c2301e4aa5ff8ba28c4c57c9f50e3d1eae8f3d6237db57803d1f48b0 SHA512: 6dec36eec05bc26b91e72437d8e6e9f06a8ec915ecfff1f555acbfb4171d39ccebad5eaad8c5fbde12c6d1dfa926e79a026f0d934437a4c3c01d7411141022e3 Homepage: https://cran.r-project.org/package=SelectBoost Description: CRAN Package 'SelectBoost' (A General Algorithm to Enhance the Performance of VariableSelection Methods in Correlated Datasets) An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', ), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective. Package: r-cran-selectionbias Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-table1, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-selectionbias_2.1.0-1.ca2604.1_all.deb Size: 234938 MD5sum: b81d7752d9500e3adc6934f2d6bcdd47 SHA1: f496cb11e0f18fb7f977a219330449780d1ebe42 SHA256: c5cc85d8843284b5bd263df736b788a073476cc0b3cd34698b2b42c136621d01 SHA512: 6d5387bafbc38af292fa963dc9ef02c1e45ddee4032dbf2bf36d9b776e6db08ec98dfc69bed220f3070bafbf8f54d52a2058dffd53715bb4b1efac74731aa724 Homepage: https://cran.r-project.org/package=SelectionBias Description: CRAN Package 'SelectionBias' (Calculates Bounds for the Selection Bias for Binary Treatmentand Outcome Variables) Computes bounds and sensitivity parameters as part of sensitivity analysis for selection bias. Different bounds are provided: the SV (Smith and VanderWeele), sharp bounds, AF (assumption-free) bound, GAF (generalized AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity parameters for the SV, sharp, and GAF bounds assume an additional dependence structure in form of a generalized M-structure. The bounds can be calculated for any structure as long as the necessary assumptions hold. See Smith and VanderWeele (2019) , Zetterstrom, Sjölander, and Waernabum (2025) , Zetterstrom and Waernbaum (2022) , and Zetterstrom (2024) . Package: r-cran-selectiongain Architecture: all Version: 2.0.710-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-selectiongain_2.0.710-1.ca2604.1_all.deb Size: 229788 MD5sum: 21eac0b829fb81f7ec5d0459331e9e64 SHA1: a32f47f1f239409667a32793db8fee9454a3ef5d SHA256: cc03ea94c1270d3e9e6a05e93e09b4a4c55ca948c8d5ad812250b6d96966ee80 SHA512: 07338b882cdd4c156751980e739077b61b1837cb7d84fc98b5e48ac869ff12752c1744f84da17262dd8965012862875627e1485bae13f7ddb81eecf52243ee92 Homepage: https://cran.r-project.org/package=selectiongain Description: CRAN Package 'selectiongain' (A Tool for Calculation and Optimization of the Expected Gainfrom Multi-Stage Selection) Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage. Package: r-cran-selectmeta Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim Filename: pool/dists/resolute/main/r-cran-selectmeta_1.0.9-1.ca2604.1_all.deb Size: 89354 MD5sum: 97d8bb121afcdae4d85540f08e5deefa SHA1: 2144010f22ab03f4426009d55d667a43f015eee1 SHA256: f455297774074cfbe519326dc08ec31da99aba446e145393d5a6fe39235a340f SHA512: aa98abfeb9e107b66e8be9556f65a1bbd3de1632c15633637df0e3680efc808c582ea72e9def30b8c1eccc561f5f5ae1320a2f493f1f31c2991abcb334ddd839 Homepage: https://cran.r-project.org/package=selectMeta Description: CRAN Package 'selectMeta' (Estimation of Weight Functions in Meta Analysis) Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6). Package: r-cran-selectr Architecture: all Version: 0.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-r6 Suggests: r-cran-testthat, r-cran-xml, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-selectr_0.5-1-1.ca2604.1_all.deb Size: 479238 MD5sum: 7fc869beafabde4a07a8a79ed99c3188 SHA1: 0a7b6cc98b08d174a3b4a0523bbab46027b48572 SHA256: 82e2a6451076ea933e98bb3df9b06e40d9c02f22d704cb5b2a562c1806f4324b SHA512: cd9e7d2698c3749b6c5aa00aa8c588c7c05f90086b5f9101634e74ab1dbcc8cafe744d3bcdc0eb6da57f84e920d140a2bfb04832883353951c63bca7318161d2 Homepage: https://cran.r-project.org/package=selectr Description: CRAN Package 'selectr' (Translate CSS Selectors to XPath Expressions) Translates a CSS selector into an equivalent XPath expression. This allows us to use CSS selectors when working with the XML package as it can only evaluate XPath expressions. Also provided are convenience functions useful for using CSS selectors on XML nodes. This package is a port of the Python package 'cssselect' (). 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This package implements functionality to compute probability trees for two- and three-marker genotypes in the F2 to F7 selfing generations. The conditional probabilities are derived automatically and in symbolic form. The package also provides functionality to extract and evaluate the relevant probabilities. 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It uses mixed and random model least squares analysis to estimate the heritability of traits and genetic correlation between traits. The package uses the sire model as it is considered as random effect. The genetic and phenotypic (co)variances along with the relative economic values are used to construct the selection index for any number of traits. It also estimates the accuracy of the index and the genetic gain expected for different traits. Fisher (1936) . 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'SemanticDistance' computes two distinct metrics of cosine semantic distance (experiential and embedding). These values reflect pairwise cosine distance between different elements or chunks of a language sample. 'SemanticDistance' can process monologues (e.g., stories, ordered text), dialogues (e.g., conversation transcripts), word pairs arrayed in columns, and unordered word lists. Users specify options for how they wish to chunk distance calculations. These options include: rolling ngram-to-word distance (window of n-words to each new word), ngram-to-ngram distance (2-word chunk to the next 2-word chunk), pairwise distance between words arrayed in columns, matrix comparisons (i.e., all possible pairwise distances between words in an unordered list), turn-by-turn distance (talker to talker in a dialogue transcript). 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Package: r-cran-semblance Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-performanceanalytics, r-cran-desctools, r-cran-msos Suggests: r-cran-kernlab Filename: pool/dists/resolute/main/r-cran-semblance_1.1.0-1.ca2604.1_all.deb Size: 34124 MD5sum: 817571a43f4525c43026afda32efca76 SHA1: 05fef6a43629fa30b35d1d98340e1ca407b07292 SHA256: 4adcb5a5bfbc89062fee5b8789b37521122a7ed8e010ee4cea2e4fec9d9619fd SHA512: 0aa83825e4baa96f778f82bf5db637af67ea45e348fd4a98e3712a393a7d20152a5eddacb0c9de3318f119df8837bb354eaf28da2ebbb99bc4ad15dbeb1f7a99 Homepage: https://cran.r-project.org/package=Semblance Description: CRAN Package 'Semblance' (A Data-Driven Similarity Kernel on Probability Spaces) We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that feature’s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations. 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Support leave-one-out casewise sensitivity analysis presented by Pek and MacCallum (2011) and approximate casewise influence using scores and casewise likelihood. An introduction to the package can be found in Cheung and Lai (2026) . 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It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by 'spacyr'. For further reference, see: Stuhler (2022) . 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Package: r-cran-semiartificial Architecture: all Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corelearn, r-cran-rsnns, r-cran-mass, r-cran-nnet, r-cran-cluster, r-cran-fpc, r-cran-timedate, r-cran-robustbase, r-cran-ks, r-cran-logspline, r-cran-mcclust, r-cran-flexclust, r-cran-statmatch Filename: pool/dists/resolute/main/r-cran-semiartificial_2.4.1-1.ca2604.1_all.deb Size: 206984 MD5sum: 943b57b059f28cbab00c13202876a246 SHA1: 59b0094c7c64ad5249b3a263b856d8d1e92ccac3 SHA256: 21af18faad22005c65e0be41a498815fd1ef97341cc60e6f68e5ed7f640cc367 SHA512: 7a7301a13726e198851d5e7058d571131dea9cbd9450d217a65006a3f41fa948d1816c2b6a4142dabb75b93197b722a8de7954cef214c8039abe6eac99f7de49 Homepage: https://cran.r-project.org/package=semiArtificial Description: CRAN Package 'semiArtificial' (Generator of Semi-Artificial Data) Contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package 'RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests. Package: r-cran-semicmprskcoxmsm Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-twang, r-cran-fastghquad, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-semicmprskcoxmsm_0.2.0-1.ca2604.1_all.deb Size: 116718 MD5sum: 383ed55cbe18e242810b54db5cf60349 SHA1: 3486714a48a4305157bf688084ad1ec1ea2c77ee SHA256: 8d822e66d4c5ed0df8bea82b2b88ea0324579b27e248c7f51607c3db14100ecc SHA512: 982dc65b0d984be08354a33ba0997e1967f7929aaa393533b7bcb1131857abecbb9e2bfddd3145130c7f2de0cd425dda33c79fcaebd990ff49206fc27ada3191 Homepage: https://cran.r-project.org/package=semicmprskcoxmsm Description: CRAN Package 'semicmprskcoxmsm' (Use Inverse Probability Weighting to Estimate Treatment Effectfor Semi Competing Risks Data) Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) . Package: r-cran-semicontmanova Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-semicontmanova_0.2-1.ca2604.1_all.deb Size: 80336 MD5sum: 936eb5ea0ee501e8484c2c1b58d0bdc0 SHA1: 9e27bb229c85bd0f84387b68df791be906aa79db SHA256: d375f0737c05a325bb88416d92634946f29401b33db1dc7fb12460a300ba91d5 SHA512: b42d16d6dce76746acdcb2dcc93016622d3d36cdd83b086b856ccaee0fc49d0f2cc7806de3f5def840d98a873dc126d83eb3f48a7b87a78d6d6af433966ca3ca Homepage: https://cran.r-project.org/package=semicontMANOVA Description: CRAN Package 'semicontMANOVA' (Multivariate ANalysis of VAriance with Ridge Regularization forSemicontinuous High-Dimensional Data) Implements Multivariate ANalysis Of VAriance (MANOVA) parameters' inference and test with regularization for semicontinuous high-dimensional data. The method can be applied also in presence of low-dimensional data. The p-value can be obtained through asymptotic distribution or using a permutation procedure. The package gives also the possibility to simulate this type of data. Method is described in Elena Sabbioni, Claudio Agostinelli and Alessio Farcomeni (2025) A regularized MANOVA test for semicontinuous high-dimensional data. Biometrical Journal, 67:e70054. DOI , arXiv DOI . Package: r-cran-semid Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.oo, r-cran-r.methodss3, r-cran-igraph, r-cran-r.utils, r-cran-rje Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-semid_0.5.1-1.ca2604.1_all.deb Size: 379466 MD5sum: 6268d166b047e22d05ddbe0be772001d SHA1: 533e7b69dc71a1e513be48f3a8dd48fa27e45163 SHA256: ff327706b7129fe0cc3ed91aaa02d082dc0ca53864397dcc5ff8b1167fce74cd SHA512: 0f629d55caba2db3fb374cb071ca2ab5f6593cf316ba2b570a7c614678a5d59d8d6c4bc01e317487d9fc5927be765f3620fed7eae954e02bd391a924e5c9b4a7 Homepage: https://cran.r-project.org/package=SEMID Description: CRAN Package 'SEMID' (Identifiability of Linear Structural Equation Models) Provides routines to check identifiability of linear structural equation models and factor analysis models. The routines are based on the graphical representation of structural equation models. Package: r-cran-semiestimate Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-numderiv, r-cran-purrr, r-cran-rlang, r-cran-testthat, r-cran-bb, r-cran-nleqslv, r-cran-splines2 Filename: pool/dists/resolute/main/r-cran-semiestimate_1.1.3-1.ca2604.1_all.deb Size: 60500 MD5sum: d1aeb3eafb44d8de86759f21f81d664e SHA1: 19579aadb77470cdd8650b2360e2d18a6df6e227 SHA256: c9777829f0510fc5c24d830f38c92add47d9c6adbfec0531b5e789da5819e33d SHA512: af93c34e492c08020874c9feb75fd5255796c4733ff77493d5b726f018724ec8768e7e6ee30b9099a5eb7e26895b38d923b3be619ba1942ef88367e0979037d9 Homepage: https://cran.r-project.org/package=SemiEstimate Description: CRAN Package 'SemiEstimate' (Solve Semi-Parametric Estimation by Implicit Profiling) Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix. Package: r-cran-semimarkov Architecture: all Version: 1.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-mass, r-cran-rsolnp Filename: pool/dists/resolute/main/r-cran-semimarkov_1.4.6-1.ca2604.1_all.deb Size: 238044 MD5sum: 19f75917758081f0423998c590eb0e4c SHA1: 10cb84a5029cf6b28b3e25ba7d3cba8aa6a8d494 SHA256: c99987f95591746ef21fb731a4e5a8b40c2cd44a510ddf09686e8d550d19b014 SHA512: 9e932c3bd6eecffeab63c9772107161930a2525f47ab297fc16ac2be58115192cfac4666a3a54b5181cb0134461bed3046190a559ebdb1eda706e7169634dfe1 Homepage: https://cran.r-project.org/package=SemiMarkov Description: CRAN Package 'SemiMarkov' (Multi-States Semi-Markov Models) Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) . Package: r-cran-seminr Architecture: all Version: 2.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-glue, r-cran-diagrammer, r-cran-diagrammersvg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-webp Filename: pool/dists/resolute/main/r-cran-seminr_2.4.2-1.ca2604.1_all.deb Size: 1541158 MD5sum: 9b8c384049b00c3964d7a1b8becb53b9 SHA1: 98e97378143bf4da9afe4fd4737d75a5e19eb6fc SHA256: 56b45ffd32e1a71b5a69c6387e1ee14fefe365b51ad7f73b647ff41fe3cc78a0 SHA512: 9e0ea90ddbcb5ec01fbaceb208ea508d05dd1cffc4b18cece6edbb5a3ed9a289a2f83bc20da0e962134eef3d2a00eb3f5d1209b4968843508927af296577eca6 Homepage: https://cran.r-project.org/package=seminr Description: CRAN Package 'seminr' (Building and Estimating Structural Equation Models) A powerful, easy to use syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021 ). Package: r-cran-seminrextras Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2694 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seminr, r-cran-rpart Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-paran, r-cran-psych, r-cran-learnr Filename: pool/dists/resolute/main/r-cran-seminrextras_1.0.1-1.ca2604.1_all.deb Size: 832444 MD5sum: ab1bf584732762dc7fdbea2a8a415681 SHA1: f4c30e0b670e43e76bb21b2784900be8103d21e9 SHA256: 3c7f9fe409c08d8e1908cb46bcb2e6d30922dab6d030071874ce3cd3714519df SHA512: a2f6e50ac035f8005be273608268790c8a0556daee08dfd81f7166e660f92a2437c1db27dc82173f4a8c5968136864cf0284e06d0badd5b61fdef611b0b0ee30 Homepage: https://cran.r-project.org/package=seminrExtras Description: CRAN Package 'seminrExtras' (Conduct Additional Modeling and Analysis for 'seminr') Supplementary tools for evaluating and validating partial least squares structural equation models estimated with 'seminr'. Provides methods for predictive model assessment, importance-performance analysis with necessary condition testing, overfitting diagnostics, measurement model verification, mediator contribution analysis, unobserved heterogeneity detection via latent class and prediction-oriented segmentation, and congruence coefficient testing. All functions accept estimated 'seminr' model objects and return results with print, summary, and plot methods. Package: r-cran-semipar.depcens Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-copula, r-cran-foreach, r-cran-doparallel, r-cran-pbivnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-semipar.depcens_0.1.3-1.ca2604.1_all.deb Size: 105566 MD5sum: 31bd752016aae394599f7f5cdcba7a92 SHA1: 6b71ab821b93d2c914542bd179e6ce2bf4e70c4d SHA256: d1cbcbd7462b2576544ae6e3acfda1a00f6dbfb2ff0f6ee00b194e3b39f4b0d3 SHA512: b5aa2a6696d62d5404ebeea1ef7534c5e16b3b3ab737b8c3161ea976df8026983cfc61fb233fcafbf02eb7bce07029d8db24b76ea1a7ffd09beed7f535232900 Homepage: https://cran.r-project.org/package=SemiPar.depCens Description: CRAN Package 'SemiPar.depCens' (Copula Based Cox Proportional Hazards Models for DependentCensoring) Copula based Cox proportional hazards models for survival data subject to dependent censoring. This approach does not assume that the parameter defining the copula is known. The dependency parameter is estimated with other finite model parameters by maximizing a Pseudo likelihood function. The cumulative hazard function is estimated via estimating equations derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any parametric model that satisfies certain identifiability conditions could be used. Implemented methods are described in the article "Copula based Cox proportional hazards models for dependent censoring" by Deresa and Van Keilegom (2024) . Package: r-cran-semiparmf Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-spdep Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-semiparmf_1.0.0-1.ca2604.1_all.deb Size: 64794 MD5sum: a8848b76d7e2fa522b97850425ba8090 SHA1: 9c736e48ab62209ba33a9e31e01f3e367ce54b50 SHA256: 0c7c93192d2bc64fac37be3e2d207e614b6d7880fe67a23f8be2efdecc3ccc52 SHA512: 7b2ba707ce94e969162d3f72fe5eae9a87ccbe5b61cabbf337b4561e3e00ba9c853bb0f1aa54f25f7941846e1bc9d399e88542c986e3e6c546fa777a2b68a381 Homepage: https://cran.r-project.org/package=SemiparMF Description: CRAN Package 'SemiparMF' (Semiparametric Spatiotemporal Model with Mixed Frequencies) Fits a semiparametric spatiotemporal model for data with mixed frequencies, specifically where the response variable is observed at a lower frequency than some covariates. The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects, and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022) . Package: r-cran-semlbci Architecture: all Version: 0.11.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-nloptr, r-cran-mass, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-pbapply, r-cran-callr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-semlbci_0.11.5-1.ca2604.1_all.deb Size: 775222 MD5sum: 28821a24e0917042201a0a4e73786e6d SHA1: c1b89171f8b102e01ca006d80ee538ac03e7cc90 SHA256: 6f249c45f3c9114019ca105b9867c5a99f4e6169832a9a7c3d787d4ac9042bd4 SHA512: 0c5322304f3ee2592f779f5b50deb4e25d52f95cf1fb70e017ee33384aad8e8f2988cc4b5d495f39e72ca1f8637f422f5d0d29b538771913930f2a0cd2d3b7e5 Homepage: https://cran.r-project.org/package=semlbci Description: CRAN Package 'semlbci' (Likelihood-Based Confidence Interval in Structural EquationModels) Forms likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling, introduced in Cheung and Pesigan (2023) . Currently implements the algorithm illustrated by Pek and Wu (2018) , and supports the robust LBCI proposed by Falk (2018) . Package: r-cran-semlrtp Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-semlrtp_0.1.1-1.ca2604.1_all.deb Size: 537552 MD5sum: 1bc3ee43fab7e533ffda9d68e8d420c0 SHA1: 20fae392454676859842a728f5296e425001aa06 SHA256: 2d5d5968a2655f9e9ccb04ec7c269808a4f4130d07b7255ee8078d2df4b89d16 SHA512: 243cfb1c64e42b8ffa7e5ff42c5df62b6cfdc24e2f15353d9bc8c3c61b990d307d1a3753e87cc0f3db501078c99bbb69ee203df725879b649bf37391c865c834 Homepage: https://cran.r-project.org/package=semlrtp Description: CRAN Package 'semlrtp' (Likelihood Ratio Test P-Values for Structural Equation Models) Computes likelihood ratio test (LRT) p-values for free parameters in a structural equation model. Currently supports models fitted by the 'lavaan' package by Rosseel (2012) . Package: r-cran-semmcci Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-mice Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-psych, r-cran-amelia, r-cran-bmemlavaan Filename: pool/dists/resolute/main/r-cran-semmcci_1.1.5-1.ca2604.1_all.deb Size: 105212 MD5sum: 5f9b270d85babb9f5c9fd232492bd094 SHA1: cc3686805dff020f5327e52256c22894ec301435 SHA256: a3e36781afeb6939cb966ba1902e085db2e0dbb2decc83a899cc8ff966266904 SHA512: 9eb6c85d2ede3d5f20bbc1bd87b84d11b5682d1b773971c3ac349cb6c0ec791236bbb16c20c18f5a885054b02e9e3630db3f0b696b2f4f4455bfa1641037ba22 Homepage: https://cran.r-project.org/package=semmcci Description: CRAN Package 'semmcci' (Monte Carlo Confidence Intervals in Structural Equation Modeling) Monte Carlo confidence intervals for free and defined parameters in models fitted in the structural equation modeling package 'lavaan' can be generated using the 'semmcci' package. 'semmcci' has three main functions, namely, MC(), MCMI(), and MCStd(). The output of 'lavaan' is passed as the first argument to the MC() function or the MCMI() function to generate Monte Carlo confidence intervals. Monte Carlo confidence intervals for the standardized estimates can also be generated by passing the output of the MC() function or the MCMI() function to the MCStd() function. A description of the package and code examples are presented in Pesigan and Cheung (2024) . Package: r-cran-semmcmc Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-msm Filename: pool/dists/resolute/main/r-cran-semmcmc_0.0.6-1.ca2604.1_all.deb Size: 26984 MD5sum: de6b03db635da62bfbf9c734bedeed7d SHA1: 105b23b20f12c56961901e1754f8aef61e5651f6 SHA256: 0cbd19de84191cae14ba87823ec972330afbbaa73bb5cba940ecee987e4bb49e SHA512: 2367dac12a6f87c516c4d7fbd61d6c60a0bc5e88db32bd11b92bca6ba23d19087947e121f5ceb520b3f33e7b6f6733c22bd0d728a5609864ca81dc1c751fd589 Homepage: https://cran.r-project.org/package=semmcmc Description: CRAN Package 'semmcmc' (Bayesian Structural Equation Modeling in Multiple Omics DataIntegration) Provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) . This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms. Package: r-cran-semnar Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-lubridate, r-cran-parsedate, r-cran-leaflet, r-cran-urlshortener Suggests: r-cran-curl, r-cran-covr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-semnar_0.8.2-1.ca2604.1_all.deb Size: 81580 MD5sum: 3a9bf7331b4e4765265c4ae55debeb3c SHA1: 5c9b67bd5982651e52d425f29e870c1dbdd03c56 SHA256: dae7dec57bb1bfdeded5f4e57b998c83da1d544291f7559a9f55033e82468b59 SHA512: 88871bf3097d7de93024055938b19d79bc422a4674a0b2d9beb39c9147f63eb5ebd942a3df23b6a93d6256c4e6f4573307fd98e757949e34f111e9640b3dc2ce Homepage: https://cran.r-project.org/package=semnar Description: CRAN Package 'semnar' (Constructing and Interacting with Databases of Presentations) Provides methods for constructing and maintaining a database of presentations in R. The presentations are either ones that the user gives or gave or presentations at a particular event or event series. The package also provides a plot method for the interactive mapping of the presentations using 'leaflet' by grouping them according to country, city, year and other presentation attributes. The markers on the map come with popups providing presentation details (title, institution, event, links to materials and events, and so on). Package: r-cran-semnet Architecture: all Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2910 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-dplyr, r-cran-plyr, r-cran-magrittr, r-cran-ggplot2, r-cran-igraph, r-cran-qgraph, r-cran-scales, r-cran-car, r-cran-broom, r-cran-effects, r-cran-philentropy Suggests: r-cran-shiny, r-cran-shinyjs, r-cran-shinyalert, r-cran-shinymatrix, r-cran-shinybs, r-cran-animation, r-cran-r.matlab, r-cran-foreign, r-cran-readxl, r-cran-data.table, r-cran-networktoolbox, r-cran-semnetcleaner, r-cran-semnetdictionaries Filename: pool/dists/resolute/main/r-cran-semnet_1.4.5-1.ca2604.1_all.deb Size: 2615422 MD5sum: 3ffd5756290ebced063eabd8f08ed732 SHA1: 48b44161de481cac6d1872c84dcec238a0169be4 SHA256: b5f0fa913a975a6909a4e3cd9a70209c40a9a0e700704507e50d6b6651dcb5ff SHA512: 1d2fe606b0910a69c63379cbaa01883dcf6449ba85469b8241f0b370e3d4edb83357d303ee5f66df6901ee40f7300f3bcbbb7b51773fd2164a47693e6270fad7 Homepage: https://cran.r-project.org/package=SemNeT Description: CRAN Package 'SemNeT' (Methods and Measures for Semantic Network Analysis) Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 ), random walk simulation (Kenett & Austerweil, 2016 ), and a function to compute global network measures. Significance tests and plotting features are also implemented. Package: r-cran-semnetcleaner Architecture: all Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1759 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-semnetdictionaries, r-cran-foreign, r-cran-pbapply, r-cran-r.matlab, r-cran-readxl, r-cran-rstudioapi, r-cran-searcher, r-cran-shiny, r-cran-stringi, r-cran-stringdist Suggests: r-cran-dt, r-cran-hunspell, r-cran-htmltable, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-semnetcleaner_1.3.7-1.ca2604.1_all.deb Size: 596354 MD5sum: e5acf834980e25dd1f6df22fbbf3f00f SHA1: 6c8b61bfa7366e0fa9d8903c36314b49575263a6 SHA256: 98f29519bb378db55032c0236aa57e816d2b669eb2125c4a3dfc9b7dd1b820c4 SHA512: 5aa54a652677f2c5fce333d893237a4c48dbb1eb60cd05fad45d5ed83273b2ac694c727c60c9b4138b41c5714bf3299b679c24c0b622bf5516d395f636056a51 Homepage: https://cran.r-project.org/package=SemNetCleaner Description: CRAN Package 'SemNetCleaner' (An Automated Cleaning Tool for Semantic and Linguistic Data) Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible. Package: r-cran-semnetdictionaries Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3926 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-gridextra, r-cran-htmltable, r-cran-knitr, r-cran-markdown, r-cran-patchwork, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyalert, r-cran-shinyjs Filename: pool/dists/resolute/main/r-cran-semnetdictionaries_0.2.1-1.ca2604.1_all.deb Size: 3510800 MD5sum: 9f91d99ec8f926fce3fb072ed03fe3c7 SHA1: 96661dfd8bb95fe112d09980b185687dae643bad SHA256: 77bbc8690cc2ae53c68d243f9ec518d19b875b7c6a646fde3672eb82dc4e4954 SHA512: 88c2503b815cd021fe1c689fff25bd0a57cde7c9da6cc39ff085582694092a8337a275c51dbcc0727b00fb0a71be195147224613ab06f0a8b1855b224a2cc070 Homepage: https://cran.r-project.org/package=SemNetDictionaries Description: CRAN Package 'SemNetDictionaries' (Dictionaries for the 'SemNetCleaner' Package) Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to . Package: r-cran-semnova Architecture: all Version: 0.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-matrix, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-semnova_0.1-6-1.ca2604.1_all.deb Size: 114870 MD5sum: aefef7780065019d23269910d97ea3be SHA1: 846a60890794903596142c3b2a2306766ce68b4e SHA256: 1d5e9a4a4e5e45196a401499cee33ee26c3a81d9139ba89833f4ac6b6f234edc SHA512: e09135c6a9bfe313a0e129268d255ba4fd95c6ba733ac1f5cf2d4719f6b8df18b434e12ab054178ac0d82b9d4c3787dbea868638afdf1b5b862367c3f0aacda4 Homepage: https://cran.r-project.org/package=semnova Description: CRAN Package 'semnova' (Latent Repeated Measures ANOVA) Latent repeated measures ANOVA (L-RM-ANOVA) is a structural equation modeling based alternative to traditional repeated measures ANOVA. L-RM-ANOVA extends the latent growth components approach by Mayer et al. (2012) and introduces latent variables to repeated measures analysis. Package: r-cran-semplot Architecture: all Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qgraph, r-cran-lavaan, r-cran-sem, r-cran-plyr, r-cran-xml, r-cran-igraph, r-cran-lisreltor, r-cran-rockchalk, r-cran-colorspace, r-cran-corpcor, r-cran-openmx Suggests: r-cran-mplusautomation Filename: pool/dists/resolute/main/r-cran-semplot_1.1.8-1.ca2604.1_all.deb Size: 347048 MD5sum: e4d8d080fd332c4f4404a5327729c82c SHA1: e013858147fe2a718882a47cb5f82a07ced00ad1 SHA256: 18da1edec9ec5a01580091549d71f93a93baab2119c4aa0a5f1490bb58256c14 SHA512: bc60db9432c306308760e34368b0e1fd5423ee87c4cb9caaef9bcbc1fdbbebe061ee52ef9ef617976ca8ea747e16b99087a9408f41990ca4b20774277231df85 Homepage: https://cran.r-project.org/package=semPlot Description: CRAN Package 'semPlot' (Path Diagrams and Visual Analysis of Various SEM Packages'Output) Path diagrams and visual analysis of various SEM packages' output. Package: r-cran-sempower Architecture: all Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 958 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bookdown, r-cran-covsim, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-knitr, r-cran-lavaan, r-cran-mnonr, r-cran-progressr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sempower_2.1.3-1.ca2604.1_all.deb Size: 919450 MD5sum: 2876ea8e858623c22359240daf7a9275 SHA1: f200eb06afc45da64a126f712f9e78d9bab8ebc0 SHA256: f0912c3186fb5f5be3c17dd5bbfec79a5a8de27d4c8445a519ac508ac20354f7 SHA512: 525b9f3c806f18dffd1ff58831568040a870bf86f6ac1e538c4154a647a89d913bb080f9d1e5bb770249cf7f0c5567e2bdfe3daf8a64ffdf7c95346ee4b9a685 Homepage: https://cran.r-project.org/package=semPower Description: CRAN Package 'semPower' (Power Analyses for SEM) Provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM). Package: r-cran-semptools Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-rlang, r-cran-semplot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-semptools_0.3.3-1.ca2604.1_all.deb Size: 1085352 MD5sum: fa371b0ff17b0ad515c34a7fc6bf43b0 SHA1: 5e3c61b4d35dea7661049cc0467b2678a645805d SHA256: 4e563f775b38f5f205b825440684e731ee02e5b219c439e75c236aedfa16500e SHA512: 4e784bfaa73ae1e593d35b4dd0f8cc6752fe0f2357c1c3accbbe9c92851a37ca8db55f2d9036a4fc49bbf08302a50e53b64a8344c0513f35e8b7d40f5063ab18 Homepage: https://cran.r-project.org/package=semptools Description: CRAN Package 'semptools' (Customizing Structural Equation Modelling Plots) Most function focus on specific ways to customize a graph. They use a 'qgraph' output as the first argument, and return a modified 'qgraph' object. This allows the functions to be chained by a pipe operator. Package: r-cran-semsens Architecture: all Version: 1.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-semsens_1.5.5-1.ca2604.1_all.deb Size: 191736 MD5sum: 8a69fa1beecc757b91cc774aed7b696b SHA1: 6625410e2e396eacfb36565a6556e27849974868 SHA256: d64c3521152ad75834f1206d107518b797fa0cd0f719b624b3765229837d2f71 SHA512: c8cc40786c6fe57a74cb21da1d3c38c4224c6bff689faeb38d36b08ef1c1603eb8a85ca7f2ed2b208ac8f3318d431210ad85960e02fd7bbff1a7bfcf67f0a13f Homepage: https://cran.r-project.org/package=SEMsens Description: CRAN Package 'SEMsens' (A Tool for Sensitivity Analysis in Structural Equation Modeling) Perform sensitivity analysis in structural equation modeling using meta-heuristic optimization methods (e.g., ant colony optimization and others). The references for the proposed methods are: (1) Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (2022). (2) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017) ; (3) Fisk, C., Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. (2022). ; (4) Socha, K., & Dorigo, M. (2008) . We also thank Dr. Krzysztof Socha for sharing his research on ant colony optimization algorithm with continuous domains and associated R code, which provided the base for the development of this package. Package: r-cran-semsensitivity Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-dplyr, r-cran-r.utils, r-cran-semfindr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-semsensitivity_0.1.0-1.ca2604.1_all.deb Size: 167980 MD5sum: ffb09f06b44ae791f88e2a2c6147d6b9 SHA1: b7e62fee66f478df54cdb330d6fbd79e3852d61c SHA256: a2aae3903fcfe24d497d66f57ee359a366efcab7c0c8d0cc3c2cd2cbacae5c84 SHA512: 6dde81c3087e5cea5d49dfafe5413457d306abf2973592c1c1d0e69638be4f8aa4ec5830aa82fdac3baa5de53e6f84250615d5a43185790c7a707d2f6bdcc7b6 Homepage: https://cran.r-project.org/package=SEMsensitivity Description: CRAN Package 'SEMsensitivity' (SEM Sensitivity Analysis) Performs sensitivity analysis for Structural Equation Modeling (SEM). It determines which sample points need to be removed for the sign of a specific path in the SEM model to change, thus assessing the robustness of the model. Methodological manuscript in preparation. Package: r-cran-semsfa Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-np, r-cran-gamlss, r-cran-moments, r-cran-doparallel, r-cran-foreach, r-cran-iterators Filename: pool/dists/resolute/main/r-cran-semsfa_1.2-1.ca2604.1_all.deb Size: 47544 MD5sum: e46c9c0b2d571e62f3117403e97d035a SHA1: 03bb37ea2d9a3a183aaa6f7ed5f5da44703bba17 SHA256: a49d16b64f7bea0ca2aa6972bc86fc4565bf89e4f0fb74e5ff3cc307bd4e63a1 SHA512: e28c6759489af082b394c431aa2f46b91ccc98276c31cdcd5e53e240d7f0cb003c0a2aebcac729f00f34e82cb8024d3bb46bb12e2e0cae4d6f3bda7c529ad8e7 Homepage: https://cran.r-project.org/package=semsfa Description: CRAN Package 'semsfa' (Semiparametric Estimation of Stochastic Frontier Models) Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments. Package: r-cran-semtests Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-compquadform, r-cran-rspectra, r-cran-mass, r-cran-matrix Suggests: r-cran-covr, r-cran-testthat, r-cran-psych Filename: pool/dists/resolute/main/r-cran-semtests_0.7.1-1.ca2604.1_all.deb Size: 138918 MD5sum: 1d6b0ea806ca7c523fb4f75a2fb0f427 SHA1: 947c1e7d7ac9f1be3905cb9d0295f207d17d174c SHA256: 1933c4fb83e9f135aa5b8a90e1e65d153126f95e8c4cd24126c1fdeb994f5a43 SHA512: ab2e4317ab687aba4101fe81e9b26b68598fbe5b0455316bf1a0f45c7a44222fa41d78501986ed342a26005d55848e16752fd4ade1d2431a059231abc0a5302c Homepage: https://cran.r-project.org/package=semTests Description: CRAN Package 'semTests' (Goodness-of-Fit Testing for Structural Equation Models) Supports eigenvalue block-averaging p-values (Foldnes, Grønneberg, 2018) , penalized eigenvalue block-averaging p-values (Foldnes, Moss, Grønneberg, 2024) , penalized regression p-values (Foldnes, Moss, Grønneberg, 2024) , as well as traditional p-values such as Satorra-Bentler. All p-values can be calculated using unbiased or biased gamma estimates (Du, Bentler, 2022) and two choices of chi square statistics. Package: r-cran-semtools Architecture: all Version: 0.5-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-pbivnorm Suggests: r-cran-blavaan, r-cran-emmeans, r-cran-lavaan.mi, r-cran-mass, r-cran-mice, r-cran-mnormt, r-cran-restriktor, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-semtools_0.5-8-1.ca2604.1_all.deb Size: 2485358 MD5sum: ee32d6d403b222909974b5177c6b341c SHA1: 50658b28c040fc9e9f839063ab84e895cda4e941 SHA256: 8fb394745d2a224b9104e57ad88b4d8098bb39d7baf4edaf70764053114be69e SHA512: c0f95182219b5cf91b20c4d18ea4d2ce276d86870d999b0b4d795f74a7f17ead48d99051fc4e7c08f0197bcc1246f41ee6ff8371d0797867798a19d08f55f17e Homepage: https://cran.r-project.org/package=semTools Description: CRAN Package 'semTools' (Useful Tools for Structural Equation Modeling) Provides miscellaneous tools for structural equation modeling, many of which extend the 'lavaan' package. For example, latent interactions can be estimated using product indicators (Lin et al., 2010, ) and simple effects probed; analytical power analyses can be conducted (Jak et al., 2021, ); and scale reliability can be estimated based on estimated factor-model parameters. Package: r-cran-semtree Architecture: all Version: 0.9.23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-rpart, r-cran-rpart.plot, r-cran-lavaan, r-cran-cluster, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-strucchange, r-cran-sandwich, r-cran-zoo, r-cran-crayon, r-cran-clisymbols, r-cran-future.apply, r-cran-data.table, r-cran-expm, r-cran-gridbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-viridis, r-cran-mass, r-cran-psych, r-cran-psychtools, r-cran-testthat, r-cran-future, r-cran-ctsemomx Filename: pool/dists/resolute/main/r-cran-semtree_0.9.23-1.ca2604.1_all.deb Size: 782442 MD5sum: 7e16a5a6f788ded0c6162d78f9931201 SHA1: 652d30b20c5fc4cc0a6305a311cb0c74c53023ea SHA256: 80547ebb59189648c3081d61988d3dee5a23a95af9de3e2ab444ee41a488dc29 SHA512: 175f32388ec23a53635d9458b9cfea17f4788e11aa1495581eb3308e7c539d237b86989fbadf424542ada2e058454c813b13769e90ec3c7743a4e6879ca0f342 Homepage: https://cran.r-project.org/package=semtree Description: CRAN Package 'semtree' (Recursive Partitioning for Structural Equation Models) SEM Trees and SEM Forests -- an extension of model-based decision trees and forests to Structural Equation Models (SEM). SEM trees hierarchically split empirical data into homogeneous groups each sharing similar data patterns with respect to a SEM by recursively selecting optimal predictors of these differences. SEM forests are an extension of SEM trees. They are ensembles of SEM trees each built on a random sample of the original data. By aggregating over a forest, we obtain measures of variable importance that are more robust than measures from single trees. A description of the method was published by Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) and Arnold, Voelkle, & Brandmaier (2020) . 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Covers senators, legislative materials, committees, voting records, speeches, provisional measures, vetoes, and legislative agendas, returning results as tidy data frames ready for analysis. 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The package allows researchers to generate the set of recommended sensitivity summaries to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts) or survey contexts. 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The package is an umbrella of statistical and graphical methods that estimate and report different types of uncertainty in PCM: (i) Species Sampling uncertainty (sample size; influential species and clades). (ii) Phylogenetic uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty (intraspecific variation and measurement error). 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Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates. Zhang, B., Small, D. S. (2018). . Package: r-cran-sensitivitycasecontrol Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sensitivitycasecontrol_2.2-1.ca2604.1_all.deb Size: 51092 MD5sum: b002bd0e7ec1f2acfd1f8730647e2cbb SHA1: e41fb71edc044e07066a2f78c5553b8a8b4a58d0 SHA256: b976a8f9ec7c8b078b98ca104565ac28baaa8fab7b528a3556f19f037ed613c4 SHA512: 9d2aaa458c475007287adc0ead9d5745aa61572e59fbc80823150144bcd00b1cf56491df0c3a739306b8edc26f4851834b34e57b741a3c6de3bdc12f28ec96f2 Homepage: https://cran.r-project.org/package=SensitivityCaseControl Description: CRAN Package 'SensitivityCaseControl' (Sensitivity Analysis for Case-Control Studies) Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition. The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468). The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al. The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al. The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test. The other functions in the package are internal functions. 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For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) . Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function. Package: r-cran-sensitivitymv Architecture: all Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sensitivitymv_1.4.4-1.ca2604.1_all.deb Size: 76496 MD5sum: 924f127e9db5b7f02bb016b2c7fe02a1 SHA1: efc1b8454357dc2552ff08a7d263811791dbb073 SHA256: 7d7707a4a481ef2dd7e020ea2c86ef632bcf5bcdf964c8514ed6e3f2ccf86c41 SHA512: 4539c441b174735e75cb1f9c585624cabb1e7c3b13c5ab4dcf8f267b7686aafe89308d025c0bd6b8b478f84bb4c641ddb7c233002a59318357e05b7071c18002 Homepage: https://cran.r-project.org/package=sensitivitymv Description: CRAN Package 'sensitivitymv' (Sensitivity Analysis in Observational Studies) The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, . Package: r-cran-sensitivitymw Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sensitivitymw_2.1-1.ca2604.1_all.deb Size: 60144 MD5sum: 6ee34beccffac1778e59f43b74a2b67d SHA1: fff7d11ca429fe981ad24219880a2bd93d41d895 SHA256: a9d75d90cca59cd7f02d9515f174c4ced360fac634d8592a4a559484c03d78bb SHA512: c25ed8955d7a60705b95df5628ccc6457512ff8c7b6240454dcb3118d8dbb44878fb0de3ada13c7a0a114f74ef2040dfc3a79b5730dc5373e3b116a3ffed2533 Homepage: https://cran.r-project.org/package=sensitivitymw Description: CRAN Package 'sensitivitymw' (Sensitivity Analysis for Observational Studies Using WeightedM-Statistics) Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test). The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 . Package: r-cran-sensmap Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doby, r-cran-lattice, r-cran-factominer, r-cran-mcmcpack, r-cran-factoextra, r-cran-fields, r-cran-ggdendro, r-cran-glmulti, r-cran-mgcv, r-cran-plotly, r-cran-shiny, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sensmap_0.7-1.ca2604.1_all.deb Size: 120970 MD5sum: 3ce4e74139f11d48e9e1ad36d0a8e47d SHA1: ef0e062e6ad2769982d7d18fba5e665e2e2459a8 SHA256: 2fca197aa614fba0d62974f935137a774b03458574de00f978ec9b2de891c333 SHA512: bb7b179f12633f128f2fddb6fc173f143d74fbdc8f12dfbd976009c9da2ad93048d9d464a4f5f6b6277157c1170a2866bfe8efa53520a79b4d03fe0c7630848d Homepage: https://cran.r-project.org/package=SensMap Description: CRAN Package 'SensMap' (Sensory and Consumer Data Mapping) Provides Sensory and Consumer Data mapping and analysis . The mapping visualization is made available from several features : options in dimension reduction methods and prediction models ranging from linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm is available. A selection process of map stability is provided. A 'shiny' application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumer segmentation are also made available. Package: r-cran-sensmediation Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sensmediation_0.3.1-1.ca2604.1_all.deb Size: 233842 MD5sum: 57dc4da4a623b054ad721e069c0d2d5b SHA1: a0353d5121077fd6eff8d200db79f54a3f848dac SHA256: 50eaf6ab0b475254d82d74a657883d5ab89fb3ba5e77b97cd11d057f08d9bd36 SHA512: e525d602e3b8b31945f99e38cddbec0a81bdc0eeae2bde52bfa536aabf5fe477ecb259445913892a3b0963c13ddab73f697802a88e2b47210670dadfe1a10b3a Homepage: https://cran.r-project.org/package=sensmediation Description: CRAN Package 'sensmediation' (Parametric Estimation and Sensitivity Analysis of Direct andIndirect Effects) We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) and Lindmark (2022) . The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships. Package: r-cran-sensominer Architecture: all Version: 1.28-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-kernsmooth, r-cran-ggplot2, r-cran-reshape2, r-cran-algdesign, r-cran-gtools, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-sensominer_1.28-1.ca2604.1_all.deb Size: 996996 MD5sum: 745b37b8f674710bfe575cddb1c266bd SHA1: 8a6c5c4c681b08fc62be216a9ed4066af5e14536 SHA256: 44e09f34e4dd6a9a8b8f9c7f7f00566dac712237ed5ecfe8037f7d6d7d550c2b SHA512: 14650eace5da07c5dfb4b8c0ef1054e9f6892f55c7049d896b9725d4ab616fc7de2902cb90bbf5d17b3b194611ffb32dce30b9206539717af5e2f41646914c4d Homepage: https://cran.r-project.org/package=SensoMineR Description: CRAN Package 'SensoMineR' (Sensory Data Analysis) Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008). Package: r-cran-sensortowerr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-gt, r-cran-gtextras, r-cran-knitr, r-cran-pkgbuild, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-rhub, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sensortowerr_1.0.1-1.ca2604.1_all.deb Size: 746384 MD5sum: 7c5f273679e7520895521208d53f9c4a SHA1: 3da35f25098a53da793fa5869dbf69f87298359f SHA256: ad7f635d41626125785e99718be774b5bd18e870da96d1737c69891211b8ddfb SHA512: 3e7cf1b44b84a4277d6bd9c6238fd9008633576e139afc2dfff0f3568544feb073156626c0510c705559de855a1450a88fbcadcca056a49d4b182c7a4838410b Homepage: https://cran.r-project.org/package=sensortowerR Description: CRAN Package 'sensortowerR' (Interface to 'Sensor Tower' Mobile App Intelligence API) Interface to the 'Sensor Tower' API for mobile app analytics and market intelligence. Provides a small, consistent set of functions to retrieve app metadata, publisher information, download and revenue estimates, active user metrics, category rankings, aggregate game market denominators, and market trends. Four core verbs ('st_metrics', 'st_rankings', 'st_app'/'st_apps', 'st_filter') cover the common workflows with standardized parameters and tidyverse-friendly output. Supports both iOS and Android app ecosystems with unified data structures for cross-platform analysis. Package: r-cran-sensory Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-gtools, r-cran-mass Filename: pool/dists/resolute/main/r-cran-sensory_1.1-1.ca2604.1_all.deb Size: 77764 MD5sum: e98c1ed8e268f4900ed6b0396e3aced7 SHA1: 8f39379f61c1b30cabab6a305a7a5f939263290c SHA256: 9d0410e85f92fd2dd67394b3c3ffdc4767457e543a6fa29090cb07de070739a4 SHA512: 21e27e0b04068fe3d5af3df70904021cfa82affd6dc02879f78fecf7406d369d1e8d0e896ab28b921eca045ac3ab19935e8e3d8c0f40bf5a51c2ec1710675bbf Homepage: https://cran.r-project.org/package=sensory Description: CRAN Package 'sensory' (Simultaneous Model-Based Clustering and Imputation via aProgressive Expectation-Maximization Algorithm) Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously. Package: r-cran-sensr Architecture: all Version: 1.5-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1299 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multcomp, r-cran-mass, r-cran-numderiv Suggests: r-cran-ordinal, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sensr_1.5-3-1.ca2604.1_all.deb Size: 996114 MD5sum: 2a1e6442e8b7984a6b8e5866d04b1ed4 SHA1: d201377f8464f085c48ea30a6eee647bf0395b25 SHA256: 9af2c65c9784e88a4bffa7d4db0be6de2aaa6c40b8357b9aa06d5aa333e5641b SHA512: c9e3ceb3675fcd62f0417af014385d6f95b479db4ee75a3d3f919f48da8b61be64c8d4327e61616fa162135f60d734d0d9b33155a2900dfe56538920312ec8b1 Homepage: https://cran.r-project.org/package=sensR Description: CRAN Package 'sensR' (Thurstonian Models for Sensory Discrimination) Provides methods for sensory discrimination methods; duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and degree-of-difference. This enables the calculation of d-primes, standard errors of d-primes, sample size and power computations, and comparisons of different d-primes. Methods for profile likelihood confidence intervals and plotting are included. Most methods are described in Brockhoff, P.B. and Christensen, R.H.B. (2010) . Package: r-cran-sensrivastava Architecture: all Version: 2015.6.25.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-leaps, r-cran-car Filename: pool/dists/resolute/main/r-cran-sensrivastava_2015.6.25.1-1.ca2604.1_all.deb Size: 150076 MD5sum: 57bf33da9927279c51f17f78d9a5cc8e SHA1: 869b32b27438dab2649d7c872120395db159dda7 SHA256: e235ccea2666eaceb421d8e722551aecb96260dfb14b5ea003892e4d2f9c8e83 SHA512: 61fba085364e85f851871a87f61a58f97957a4e5677f62238469fcd8ada72a65cbe4ea1380d5db4517d0788eb117e7911ae91a5b91ef100d0f39dfebf1ed236c Homepage: https://cran.r-project.org/package=SenSrivastava Description: CRAN Package 'SenSrivastava' (Datasets from Sen & Srivastava) Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book. Package: r-cran-senstrat Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biasedurn, r-cran-mass Suggests: r-cran-sensitivitymw Filename: pool/dists/resolute/main/r-cran-senstrat_1.0.3-1.ca2604.1_all.deb Size: 91782 MD5sum: 1e27c0c104e55bda6d98eecdd6937e81 SHA1: 81c33cdb34779288460d4e3a497632d0cc19df9c SHA256: a2b4951ca4996d252cf5ac32e5282b158d6e497a21964ac1125a39c68f62cd07 SHA512: 6eb04c16462a01ab334ff5b3722a5f1f0c3b2e94fda0bc1e6f9304da237b6359e38c63ee1723dd7539077ce216dd384e599aac4a2cb4f7823961774507c8681c Homepage: https://cran.r-project.org/package=senstrat Description: CRAN Package 'senstrat' (Sensitivity Analysis for Stratified Observational Studies) Sensitivity analysis in unmatched observational studies, with or without strata. The main functions are sen2sample() and senstrat(). See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 . 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In addition to out-performing traditional, lexicon-based sentiment analysis (see ), it also allows the user to create embedding vectors for text which can be used in other analyses. GPU acceleration is supported on Windows and Linux. 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Package: r-cran-sentryr Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringr, r-cran-tibble, r-cran-uuid Suggests: r-cran-httptest, r-cran-mockery, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sentryr_1.1.2-1.ca2604.1_all.deb Size: 316924 MD5sum: bf97da364bd54b496b4f61849e3638b7 SHA1: cb147314df4415dcb1ec915bdf8c8e506e4fd598 SHA256: e4ddf9d14ccbe18947298314cbf0c20701daba0b9e8b441d8311bdd4d36cca52 SHA512: 316b2b1348e0ddd8027bf42cff096bcdf73dd72e8c5486ba58715f02ab7928e7b0afebc6fd78bd2fe5db527e7592cf3e9f4b9b57e1139be4bc017a795403ca42 Homepage: https://cran.r-project.org/package=sentryR Description: CRAN Package 'sentryR' (Send Errors and Messages to Sentry) Unofficial client for 'Sentry' , a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the 'Plumber' package. Package: r-cran-seofm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-seofm_0.1.0-1.ca2604.1_all.deb Size: 13346 MD5sum: 7632a1e85da6bb6529cf0696c9c4dfbe SHA1: 2a99ac07c5fb39a1129139eb69b350bdd9522e26 SHA256: dfd172770ad8e23e69bc2944eb9597a2d93b66924aad06b2d5f31a33519df50f SHA512: e4008a035ea1b6e30a13600680e72a5cfcd80dbed8761abc6c3d53030404104925833208a68ff201f08fec45baf5f99c2254ab2b42c2dfb8ee98135d629b7c15 Homepage: https://cran.r-project.org/package=SEofM Description: CRAN Package 'SEofM' (Standard Error of Measurement) To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation). The methods used in this package are referenced from Zoran B. Popović (2017) . Package: r-cran-sepa Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-writexl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sepa_0.1.0-1.ca2604.1_all.deb Size: 1756698 MD5sum: 5ba0d0b9273dc693e03bd07d0412e06c SHA1: 6a9dce25e44a6ad6958f1f6a4de992c6cf438b9a SHA256: 9c7c64018cee3aa02bd69c99563ab44e9c974db04605317cf4e8bc1fc5ac8716 SHA512: 81116bf42da5f2ed45cb8e2c0a8b66bf088d05ec8632ff8b74862ba7447c603b0c5002946fbde1b1de4acc472f4e07c93759e8309027a5a1206a63ca5e5f1edd Homepage: https://cran.r-project.org/package=SEPA Description: CRAN Package 'SEPA' (Segment Profile Extraction via Pattern Analysis) Implements the Segment Profile Extraction via Pattern Analysis method for row-mean-centered multivariate data. Core capabilities include SVD-based row-isometric biplot construction, bias-corrected and accelerated, and percentile bootstrap confidence intervals for domain coordinates and per-person direction cosines, Procrustes alignment of bootstrap replicates across planes, parallel analysis for dimensionality selection, and segment profile reconstruction in planes defined by pairs of singular dimensions. A synthetic Woodcock-Johnson IV look-alike dataset is provided for examples and testing. The method is described in Kim and Grochowalski (2019) . Package: r-cran-sepals Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sepals_0.1.0-1.ca2604.1_all.deb Size: 302620 MD5sum: 681fda2e78613e1c76a73984205621ab SHA1: dfb8644b39f0265e0ad90d73fd4a8ff5524c57c1 SHA256: 561797f8f94227427851311d4cb339e32e5c4f995cbf38f11e17786dd413b045 SHA512: b98f3ce57ff60ebb9623f635833faa79094131dac88c88aac3af42b5fc50b4b864f8d3b839b415429235c58849f011fb60559472e9bfd9d1dbea5a4cd785dc45 Homepage: https://cran.r-project.org/package=SEPaLS Description: CRAN Package 'SEPaLS' (Shrinkage for Extreme Partial Least-Squares (SEPaLS)) Regression context for the Partial Least Squares framework for Extreme values. Estimations of the Shrinkage for Extreme Partial Least-Squares (SEPaLS) estimators, an adaptation of the original Partial Least Squares (PLS) method tailored to the extreme-value framework. The SEPaLS project is a joint work by Stephane Girard, Hadrien Lorenzo and Julyan Arbel. R code to replicate the results of the paper is available at . Extremes within PLS was already studied by one of the authors, see M Bousebeta, G Enjolras, S Girard (2023) . Package: r-cran-separate Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-separate_0.3.2-1.ca2604.1_all.deb Size: 68810 MD5sum: 4029bbdc5f39166c12db43d44da36974 SHA1: dc2ce5ad2162e658dfce3eb6fb37c4e5b1c9f290 SHA256: 42d7f6f006de4b784a853b73bf061923ea540bf52ec419c92974c3ceeca46e99 SHA512: 53719b838bdde127bd561579634fcb1a063d2652a1be5020aa8656eec7ed6eceb4e3545c3e786e4e718a28d11b1632c5cf594d241b5a1acd3d052d0605980c1d Homepage: https://cran.r-project.org/package=sEparaTe Description: CRAN Package 'sEparaTe' (Maximum Likelihood Estimation and Likelihood Ratio TestFunctions for Separable Variance-Covariance Structures) Maximum likelihood estimation of the parameters of matrix and 3rd-order tensor normal distributions with unstructured factor variance covariance matrices, two procedures, and for unbiased modified likelihood ratio testing of simple and double separability for variance-covariance structures, two procedures. References: Dutilleul P. (1999) , Manceur AM, Dutilleul P. (2013) , and Manceur AM, Dutilleul P. (2013) . Package: r-cran-separationplot Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-hmisc, r-cran-mass, r-cran-foreign Filename: pool/dists/resolute/main/r-cran-separationplot_1.4-1.ca2604.1_all.deb Size: 35160 MD5sum: 9f10d3c72093b919f4847f092c5855bb SHA1: d597fbb60de68a02c5b695dbfaaa89bc5c597c42 SHA256: c56df1da91a9ca024fb184613141fa0720e9969ecfc543328a8c9fdd1626e61d SHA512: 01b96c322204d7f4572754b8d4b466ccf2a1fc00364679687d6d2c1dcf9a51c0cc68301f19c132d0b21b40ee87ea45b12ed4360f75a6aa548d7cc3964d73befa Homepage: https://cran.r-project.org/package=separationplot Description: CRAN Package 'separationplot' (Separation Plots) Visual representations of model fit or predictive success in the form of "separation plots." See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002. 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However, first and last appearance intervals have been updated to be consistent with stages of the International Geological Timescale. Functionality for generating a plot of Sepkoski's evolutionary fauna is also included. For specific details on the compendium see: Sepkoski, J. J. (2002). A compendium of fossil marine animal genera. Bulletins of American Paleontology, 363, pp. 1–560 (ISBN 0-87710-450-6). Access: . Package: r-cran-septest Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat, r-cran-dhsic, r-cran-fields, r-cran-get, r-cran-ggplot2, r-cran-mass, r-cran-patchwork, r-cran-reshape2, r-cran-scatterplot3d, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.model, r-cran-splancs Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-knitr, r-cran-spatstat, r-cran-stpp, r-cran-rcolorbrewer, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-septest_0.0.1-1.ca2604.1_all.deb Size: 224838 MD5sum: 8fc17d24cf7a4bcfeaa903bd4d398ef2 SHA1: 7c30f833c807fb7a568ca9634d04f9d4e13ea4de SHA256: cde608d7f6d1a26f45bd731723d174789040931482f2acc5c4d960d077bc530e SHA512: a9092ca2cc88ad0c3def65739d030606a9749f6498392852b783048c76693e8a5da912402924dd11035c895137b807363079b9c673d709d836fcd0fb09777689 Homepage: https://cran.r-project.org/package=SepTest Description: CRAN Package 'SepTest' (Tests for First-Order Separability in Spatio-Temporal PointProcesses) Provides statistical tools for testing first-order separability in spatio-temporal point processes, that is, assessing whether the spatio-temporal intensity function can be expressed as the product of spatial and temporal components. The package implements several hypothesis tests, including exact and asymptotic methods for Poisson and non-Poisson processes. Methods include global envelope tests, chi-squared type statistics, and a novel Hilbert-Schmidt independence criterion (HSIC) test, all with both block and pure permutation procedures. Simulation studies and real world examples, including the 2001 UK foot and mouth disease outbreak data, illustrate the utility of the proposed methods. The package contains all simulation studies and applications presented in Ghorbani et al. (2021) and Ghorbani et al. (2025) . Package: r-cran-seqalignr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot.matrix Filename: pool/dists/resolute/main/r-cran-seqalignr_0.1.1-1.ca2604.1_all.deb Size: 36178 MD5sum: 68a557b27d9936e830cc079655575979 SHA1: 36d5847dabf00d341a8948040939306e54f946e6 SHA256: cd69a9f8cc8ebd2db34f30f4d898316e79fe694fe475996ca7704b2afbc7455b SHA512: 297d8a8e4983cf4cdf014afaedc7de603ea5f01f1208e5e58701096e7d8f2818fe4bb5da95991c86e2d2932ff1ca5f95dbd3caf6c24f4bf8ce95c7d0c9ba0393 Homepage: https://cran.r-project.org/package=SeqAlignR Description: CRAN Package 'SeqAlignR' (Sequence Alignment and Visualization Tool) Computes the optimal alignment of two character sequences. Visualizes the result of the alignment in a matrix plot. Needleman, Saul B.; Wunsch, Christian D. (1970) "A general method applicable to the search for similarities in the amino acid sequence of two proteins" . Package: r-cran-seqalloc Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-seqalloc_1.0-1.ca2604.1_all.deb Size: 58006 MD5sum: 7f79b20a6c05b4875404bee4231e3157 SHA1: d741204ffa36a9e6dbd82add87e6a655558e27df SHA256: 8a9192aeaf4e901e9db28d4cb30c5632c15fc432740ee869c8d9d552463da821 SHA512: 881adb273bfe3d56d20ff242e844f2827ccf7cb82f04d8ab10b1bfd306d7295cf2d0400df7c4bb34c6032230c0cd1a74d5201cf994eb8d45ba1927da23c356a3 Homepage: https://cran.r-project.org/package=SeqAlloc Description: CRAN Package 'SeqAlloc' (Sequential Allocation for Prospective Experiments) Potential randomization schemes are prospectively evaluated when units are assigned to treatment arms upon entry into the experiment. The schemes are evaluated for balance on covariates and on predictability (i.e., how well could a site worker guess the treatment of the next unit enrolled). Package: r-cran-seqdesign Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-seqdesign_1.2-1.ca2604.1_all.deb Size: 423838 MD5sum: 71058fb676524beaf6768ce045f8e1db SHA1: 4b17e88981170c6fb1c991edba7177a797b5f4b9 SHA256: 60b6db7be4750f1c0408b81bdbd15fc3495e9aee88bd399727e3517f1480cbc9 SHA512: 4a7ffe9fdadef96f15561168a3beeb2c7ae84ba8110f2b4f3d84128a172781cb800dd162baa47a1a2aa80b4277690052edb3a229b3b9e66a3200886f5e4c3b75 Homepage: https://cran.r-project.org/package=seqDesign Description: CRAN Package 'seqDesign' (Simulation and Group Sequential Monitoring of RandomizedTwo-Stage Treatment Efficacy Trials with Time-to-EventEndpoints) A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design. Package: r-cran-seqfeatr Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-bioc-biostrings, r-cran-plotrix, r-cran-plyr, r-cran-phangorn, r-bioc-qvalue, r-bioc-widgettools, r-cran-calibrate, r-cran-ggplot2, r-cran-r2jags, r-cran-coda, r-cran-scales, r-cran-ape Suggests: r-cran-xtable Filename: pool/dists/resolute/main/r-cran-seqfeatr_0.3.3-1.ca2604.1_all.deb Size: 908798 MD5sum: 44d19b769ae05449eab4e60b0d9d430e SHA1: 849a810a7df8e3b7795fefabbcb1189aff541611 SHA256: 148cc33b098c14b2b0bfc051b46a6ef00c25da4331e59833a62800585efbe5a3 SHA512: a0ef3cdde60be8b6a6ebd0c8568365e698fdd44053413fb4925740a40a6d371be72427ec292e01d907d8ccb2291483593038182447bb8f7849c29477623bb6c4 Homepage: https://cran.r-project.org/package=SeqFeatR Description: CRAN Package 'SeqFeatR' (A Tool to Associate FASTA Sequences and Features) Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. 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Package: r-cran-seqgendiff Architecture: all Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-irlba, r-bioc-sva, r-cran-pdist, r-cran-matchingr, r-cran-clue Suggests: r-cran-covr, r-cran-testthat, r-bioc-summarizedexperiment, r-bioc-deseq2, r-cran-knitr, r-cran-rmarkdown, r-bioc-airway, r-bioc-limma, r-bioc-qvalue, r-bioc-edger Filename: pool/dists/resolute/main/r-cran-seqgendiff_1.2.4-1.ca2604.1_all.deb Size: 396902 MD5sum: 273e712f6b0fdf6e6b95b50d1da127d8 SHA1: 6c174756505a9bb39ccf680e83cbf7a40cfba437 SHA256: 9c8e2009e1e25d13d75f985075a26cf49d37ef92f688a6bfcd5aeb044c20a3de SHA512: 0d3673be9c973da8660c8be65b2a13ecdd8d50238387a908d507da2b2d8855c1bcbc7882147958e5bdedf063171921453e07fce7da95492f61f7cd0cbce8502c Homepage: https://cran.r-project.org/package=seqgendiff Description: CRAN Package 'seqgendiff' (RNA-Seq Generation/Modification for Simulation) Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) for details on the implemented methods. Package: r-cran-seqhandbook Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-traminer Suggests: r-cran-factominer, r-cran-descriptio, r-cran-rcolorbrewer, r-cran-traminerextras, r-cran-weightedcluster, r-cran-ade4, r-cran-cluster, r-cran-questionr, r-cran-rmdformats, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-seqhandbook_0.1.2-1.ca2604.1_all.deb Size: 78392 MD5sum: 7c391248733fecfbf98920c3ddceb2ed SHA1: 3efb62c1f3e3050967c08ed07cd9200d4fa99228 SHA256: d062a7ca36d7d1ed084d4828f4d9c1575124f6486f72eb147577223561beb2b0 SHA512: a3876a3c28751051ba4dbdc6953f199665c377f66dc6238f3271c0544a068e7b4328662fd5cf62497d6bfe24e6ada21623f7f253af6fa581563322206a66dd27 Homepage: https://cran.r-project.org/package=seqhandbook Description: CRAN Package 'seqhandbook' (Miscellaneous Tools for Sequence Analysis) It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) ), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) ), smoothing sequences for index plots (see Piccarreta (2012) ), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) ), symmetrical (or canonical) Partial Least Squares (see Bry (1996)). 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The function also matches the sequences with variables from other files such as the master files of persons (MP) and households (MH), and social origins (SO). It can also match with activity calendar data (CA). 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Package: r-cran-sfd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-cli, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sfd_0.1.0-1.ca2604.1_all.deb Size: 2449568 MD5sum: 7dad3a1275e39962194aff962005ab86 SHA1: 416a55ad61b4446195c6c76cd34bdbed0d7e7743 SHA256: f7c21a207ca91ea156ef476097f104cb5fc2dd1cb64bac1b83bdf745194ea6de SHA512: 6886f1648169c56983c52d69f08871cbb769006baa275e2854f752b0e24ac0438d3051bd410dd0403bfc6dd1fe9d0caf7192bec09a63524ebc2571217e2a3d21 Homepage: https://cran.r-project.org/package=sfd Description: CRAN Package 'sfd' (Space-Filling Design Library) A collection of pre-optimized space-filling designs, for up to ten parameters, is contained here. Functions are provided to access designs described by Husslage et al (2011) and Wang and Fang (2005) . 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The package provides tools to prepare data, fit honest newsvendor trees and forests, and obtain point and distributional predictions for demand decisions under uncertainty. 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Package: r-cran-sfislands Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4713 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-sf, r-cran-spdep, r-cran-stringr, r-cran-tidyr, r-cran-broom.mixed, r-cran-lifecycle Suggests: r-cran-mgcv, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sfislands_1.1.2-1.ca2604.1_all.deb Size: 2835620 MD5sum: ac1d44a1f88070c068fa1d1f0895a5b8 SHA1: 3753739840d170b800f57f3d1c7109e7ec619c0f SHA256: aad5d97c4103c1c8d94232f675d53bf0403172fefd1cabccd90d71cf3cf6d036 SHA512: f750822dd7fb8ac1735574dcdcd83b200fcff520f30ed744ae66a8b1ec1bf657d35a70e0cf41dede6aa197426721b0926c788cab4ad35835f539b463542bc0f1 Homepage: https://cran.r-project.org/package=sfislands Description: CRAN Package 'sfislands' (Streamlines the Process of Fitting Areal Spatial Models) Helpers for addressing the issue of disconnected spatial units. 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Package: r-cran-sfm Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-sopc, r-cran-matrixcalc, r-cran-sn, r-cran-psych Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-sfm_0.2.1-1.ca2604.1_all.deb Size: 1483614 MD5sum: 70007930a43a0b91a7c484fe5553af53 SHA1: 228e94d9cc7f4e6e3437e5f626ffa9543b02b099 SHA256: 17d889b7b3a0ee5a1f5d214a81ae86f130961cd1ea9fede0dd826aacc0e26342 SHA512: 28faa0a85081929f03121666f1d7fb6f4c5e15dcc9e84e66c64de2215d7d62690a588b3d198fa1200831dd5707a37455381fc6dee8dd3c744ebe011ab0d015e6 Homepage: https://cran.r-project.org/package=SFM Description: CRAN Package 'SFM' (A Package for Analyzing Skew Factor Models) Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Projected Principal Component (PPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix.The philosophy of the package is described in Guo G. (2023) . 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Package: r-cran-sfocds Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gtools Filename: pool/dists/resolute/main/r-cran-sfocds_1.2.0-1.ca2604.1_all.deb Size: 53098 MD5sum: 47d7b1d133fbac731e46764641e1c0ba SHA1: f36e1280b744d5945ac259cd208a0abfc4982a01 SHA256: c8979fa90ddf67eca7d7ca2ff156d410f86db59552aedc264591d4b621d24dcf SHA512: 3a7699e9d2566ab11a5cfad94bf70013bc6627fdb9c6aaeec28dd90277fa423db442ea51bb90b33cf543faae576169df2380efb4dd997c0b3037ad4e50006199 Homepage: https://cran.r-project.org/package=SFOCDs Description: CRAN Package 'SFOCDs' (Space Filling Optimal Covariate Designs) We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. 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For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole(). Package: r-cran-sft Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 994 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-suppdists Filename: pool/dists/resolute/main/r-cran-sft_2.4-1.ca2604.1_all.deb Size: 933288 MD5sum: c83edccbd4b196f2b66ba7d1a42be4b4 SHA1: bb6d9cd416d0b1bc5cdfdb26a8abb3d1febaf41d SHA256: 23432440fe4e491e9a6729b6694389754b1ce90a2acbea4a4a708284999444f5 SHA512: aa7b04bb854675abcc4653095386a654bc6acaacbd2f5162c0446b5244f67da899bc4d9a0c039ee84baff0f2e7ff4c70544e64f13dbee24c43266c6d82976f64 Homepage: https://cran.r-project.org/package=sft Description: CRAN Package 'sft' (Functions for Systems Factorial Technology Analysis of Data) A series of tools for analyzing Systems Factorial Technology data. This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions. Houpt, Blaha, McIntire, Havig, and Townsend (2013) provide a basic introduction to Systems Factorial Technology along with examples using the sft R package. 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(2009) . Tracking data are series of locations with at least 2-dimensional spatial coordinates (x,y), a time index (t), and individual identification (id) of the object being monitored; movement data are made of trajectories, i.e. the line representation of the path, composed by steps (the straight-line segments connecting successive locations). 'sftrack' is designed to handle movement of both living organisms and inanimate objects. 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Simplifies process of querying 'nomis' datasets and extracting desired datasets in dataframe format. Extracts area shapefiles at chosen resolution from 'Office for National Statistics Open Geography' . 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It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package. Package: r-cran-sgbj Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbj, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sgbj_0.1.1-1.ca2604.1_all.deb Size: 29456 MD5sum: 5dfadd6b6539749c5211402ac0cf4771 SHA1: aaae48622631e6d91a6f90bd2044b51343838284 SHA256: c1586e24dfcedd115a0efb49dff392ada97b7beea4004c44f0a019181e16cbc2 SHA512: 2c402358fd0bfe836ebb42b448fff3849e32be760276b6e67426b6954eb83a545e9e5a87145ae47ae34d8ec7d1807498446c7eba5189e59150ed39c09c3dd27d Homepage: https://cran.r-project.org/package=sGBJ Description: CRAN Package 'sGBJ' (Survival Extension of the Generalized Berk-Jones Test) Implements an extension of the Generalized Berk-Jones (GBJ) statistic for survival data, sGBJ. 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Package: r-cran-sgr Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-polycor Filename: pool/dists/resolute/main/r-cran-sgr_1.3.1-1.ca2604.1_all.deb Size: 83562 MD5sum: 3f2d0f60d4a12cd755cad7e0903ce357 SHA1: 783dd869e7f1a378689c1ed293c70fc4278674c3 SHA256: d95b7c4d8a65517a895453ef0177aa5e0a15ae05aeba3bb50bbc7e3c341dc23f SHA512: a8e2cddc5400f8e67e7ed8a2a14ef0c89510b66c07633119f49c32c03515c14749e40635886b094641f398e80ffcb72680d620d5bf06bb866275c3168ba47d8e Homepage: https://cran.r-project.org/package=sgr Description: CRAN Package 'sgr' (Sample Generation by Replacement) Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results. Package: r-cran-sgraph Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-stringi Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sgraph_1.1.0-1.ca2604.1_all.deb Size: 219920 MD5sum: fa54a8e5b67e499fe12c36935f99d3a5 SHA1: b1f6a49d52d9ec2644e03c45c6d1e831dae9b6f1 SHA256: d3dd2642b33deff07484241dba9f1c5d12c53b14c119c881b671546fbc5b6ceb SHA512: 00986c6d960ee0eea226e2546f920e873e7c90e80196761e5b4b191b84d75c20abe21d166d65e2a763ec69e42d13e1de6b4b6b5d417e3019f58f81b0c2daa30e Homepage: https://cran.r-project.org/package=sgraph Description: CRAN Package 'sgraph' (Network Visualization Using 'sigma.js') Interactive visualizations of graphs created with the 'igraph' package using a 'htmlwidgets' wrapper for the 'sigma.js' network visualization v2.4.0 , enabling to display several thousands of nodes. While several 'R' packages have been developed to interface 'sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the 'sigmaNet' package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic 'sigma.js' visualizations by overriding the underlying 'JavaScript' code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding 'sigma.js' code include toggling node visibility when clicked using a node attribute and highlighting specific edges. 'sigma.js' is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available. Package: r-cran-sgsr Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-sf, r-cran-terra, r-cran-tidyr, r-cran-clhs, r-cran-samplingbigdata, r-cran-balancedsampling, r-cran-spatstat.geom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rfast, r-cran-testthat, r-cran-doparallel, r-cran-dosnow, r-cran-snow, r-cran-foreach, r-cran-entropy, r-cran-roxygen2, r-cran-covr, r-cran-rann, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-sgsr_1.5.0-1.ca2604.1_all.deb Size: 2436776 MD5sum: ebdf46ea2a1a2d67a9be917d7b62f63b SHA1: cad7fa20707545dcc68e27ae03d2d85da5986e64 SHA256: 2c9bf18f582a0f6fa32d35f8c7fa8f5d3f2065736225f6dcadee87075c2e604c SHA512: bf1cd922477d6c342611d221f9d1f2526a7801d1565163f43c89991fdac784667625d587a2fe3fdd52cb433878453e974857275d38fa601e270616dd795892de Homepage: https://cran.r-project.org/package=sgsR Description: CRAN Package 'sgsR' (Structurally Guided Sampling) Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches. Package: r-cran-sgstar Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sgstar_0.1.2-1.ca2604.1_all.deb Size: 56192 MD5sum: 4d4764311be4a8d19724aab1535de475 SHA1: e6033fd150a89d55816f8a6c1fd3b508edd8d85f SHA256: 76444ea111559be3b26183667c9967cf8bf42711f8cb7919a3590f5b6cbdf515 SHA512: 01fc078873639481b0a11a62c2bd7924ee76ed0231a79bb5d791dbfb3a0408f29abfda7294adff0fd56b78205bebacee04cce8350df249c38768f5d6fb5eeb6f Homepage: https://cran.r-project.org/package=sgstar Description: CRAN Package 'sgstar' (Seasonal Generalized Space Time Autoregressive (S-GSTAR) Model) A set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016). Package: r-cran-sgt Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimx, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-sgt_2.0-1.ca2604.1_all.deb Size: 265324 MD5sum: 8cd05803d0596c27932061e6a3da1354 SHA1: 5caa33eaa8221859345d98c10cd1da97d49b6efd SHA256: 6cdb4c03698c75198e531428b2bebf67688f96eb66e79f538bb6410fac57a0c6 SHA512: 14dcf5cb31b88669ff9f2811fbfa0394fe0cd4cc7a5632755d9e915cc055f341b3aa0c7530c8d1518342c6dbab9c8d489cb41a0f9b75ea9b772d583221c3b3fa Homepage: https://cran.r-project.org/package=sgt Description: CRAN Package 'sgt' (Skewed Generalized T Distribution Tree) Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation. Package: r-cran-shades Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-shades_1.4.0-1.ca2604.1_all.deb Size: 87478 MD5sum: 1d7bf8769da7e560b11909680b329bcb SHA1: 1d652ae0ae71b5c711876ca6f4bebd14752034fe SHA256: 4c1c9ae981e4ba90a046cf5645d99491284ed373c7b58d42997e9efaa7a12d93 SHA512: 095b811ec35180fba42a2924d2d09b10246a2187b2d7f71d6d29df4de3344352322430ab55290899893116aac9895b3c326c3355ffd27ae06f84257771718b30 Homepage: https://cran.r-project.org/package=shades Description: CRAN Package 'shades' (Simple Colour Manipulation) Functions for easily manipulating colours, creating colour scales and calculating colour distances. Package: r-cran-shadowr Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rselenium Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shadowr_0.0.2-1.ca2604.1_all.deb Size: 358774 MD5sum: 905141558d732b005c98e7e89dd7cd5d SHA1: 05a538fb6de1c28c39d707efea1755c3d2de01d1 SHA256: b68eadf01e07317b4fbbe7f29570ac71cac216c8027fc48f4b99bf46196005c1 SHA512: 5682c1e015bcd4a689eb99005a8dd55f84b66703298d3c8199d1e4d989a72d083396e9ad345a7303e16e4975f0c8c32ca336ea30d9fcb6e1344bde36483f5d6e Homepage: https://cran.r-project.org/package=shadowr Description: CRAN Package 'shadowr' (Selenium Plugin to Manage Multi Level Shadow Elements on WebPage) Shadow Document Object Model is a web standard that offers component style and markup encapsulation. 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Package: r-cran-shakti Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-shakti_0.1.0-1.ca2604.1_all.deb Size: 24966 MD5sum: 4c74a795b5a0c06e3b8e0bdbad2c64d8 SHA1: 458d86fb01f0f1e0105c42cd72eed25a0d23157e SHA256: 915864eca009ae78dc01f6e85b8623abfe530bc1eb5f9447ebfe9eb357dc99b3 SHA512: dc522dbd6c61f35a0e31e65d6308fdd5f31faff0933866b6c87ee23f92758b697521a5f0fe5b3923b510d6d264ee078d97f264a5bf8116a58a97fc10acf14468 Homepage: https://cran.r-project.org/package=SHAKTI Description: CRAN Package 'SHAKTI' (Suite for Heat-Related Adsorption Knowledge and ThermodynamicInference) A comprehensive framework for quantifying the fundamental thermodynamic parameters of adsorption reactions—changes in the standard Gibbs free energy (delta G), enthalpy (delta H), and entropy (delta S)—is essential for understanding the spontaneity, heat effects, and molecular ordering associated with sorption processes. By analysing temperature-dependent equilibrium data, thermodynamic interpretation expands adsorption studies beyond conventional isotherm fitting, offering deeper insight into underlying mechanisms and surface–solute interactions. Such an approach typically involves evaluating equilibrium coefficients across multiple temperatures and non-temperature treatments, deriving thermodynamic parameters using established thermodynamic relationships, and determining delta G as a temperature-specific indicator of adsorption favourability. This analytical pathway is widely applicable across environmental science, soil science, chemistry, materials science, and engineering, where reliable assessment of sorption behaviour is critical for examining contaminant retention, nutrient dynamics, and the behaviour of natural and engineered surfaces. By focusing specifically on thermodynamic inference, this framework complements existing adsorption isotherm-fitting packages such as “AdIsMF” , and strengthens the scientific basis for interpreting adsorption energetics in both research and applied contexts. Details can be found in Roy et al. (2025) . Package: r-cran-shannon Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vares, r-cran-extradistr Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-shannon_0.2.0-1.ca2604.1_all.deb Size: 368812 MD5sum: cabbaed1d3da2dc6b76a783375255389 SHA1: 0ce99376a52792663170f121efd4f87ffbc735b7 SHA256: d212797dfe09765dc86214524981021aee2aef00080efe3a0f9a0871968caed4 SHA512: 0dc4f549c70c14a5f402b5c1e54f30cbbd92422c77ce6b78bae852df09c513a8647cc2720097edf4a510ed3d678a3226ab8c0a1354f2227a7b7728640695b0bc Homepage: https://cran.r-project.org/package=shannon Description: CRAN Package 'shannon' (Computation of Entropy Measures and Relative Loss) The functions allow for the numerical evaluation of some commonly used entropy measures, such as Shannon entropy, Rényi entropy, Havrda and Charvat entropy, and Arimoto entropy, at selected parametric values from several well-known and widely used probability distributions. Moreover, the functions also compute the relative loss of these entropies using the truncated distributions. Related works include: Awad, A. M., & Alawneh, A. J. (1987). Application of entropy to a life-time model. IMA Journal of Mathematical Control and Information, 4(2), 143-148. . Package: r-cran-shapboost Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost, r-cran-shapforxgboost, r-cran-caret, r-cran-matrix Suggests: r-cran-flare, r-cran-survival Filename: pool/dists/resolute/main/r-cran-shapboost_1.0.3-1.ca2604.1_all.deb Size: 178514 MD5sum: f9ff3002e22836b62fe621685514a801 SHA1: d536d68cf01e7fc70e946aee011a5c0b61e546a2 SHA256: 72d80357e9148ee3ae6bc1f867696dfe965e2f951b5d745ee4beda1fd3581125 SHA512: f498b8e085d7de688cfafa053f8407b41aa6c4b22c064310a6d5e327240671d8f41868225922e77adea041a31a06b9e0248102bcdeae53f26336ad2d9bba5256 Homepage: https://cran.r-project.org/package=SHAPBoost Description: CRAN Package 'SHAPBoost' (The SHAPBoost Feature Selection Algorithm) The implementation of SHAPBoost, a boosting-based feature selection technique that ranks features iteratively based on Shapley values. Package: r-cran-shapdoe Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-shapdoe_1.0.0-1.ca2604.1_all.deb Size: 55208 MD5sum: cf3529540e5662c95bb42718d5bd3737 SHA1: d9ac2cd57eaeafb46618229ed6fa0bbceb4a7274 SHA256: f67ec607ad93376ec42e00f71b43b81e5924b0d61bb851c53290b3430cbb405d SHA512: c4ce7411ad7e06ee01f55a995e7ce01704e1e601affb6d6f6211b45f5a4becdfe85ae2d77f96b0bbb652b0a8cf59624be5e0b34418a788ccf966d1e28ba4b852 Homepage: https://cran.r-project.org/package=ShapDoE Description: CRAN Package 'ShapDoE' (Approximation of the Shapley Values Based on ExperimentalDesigns) Estimating the Shapley values using the algorithm in the paper Liuqing Yang, Yongdao Zhou, Haoda Fu, Min-Qian Liu and Wei Zheng (2024) "Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs". You provide the data and define the value function, it retures the estimated Shapley values based on sampling methods or experimental designs. Package: r-cran-shape Architecture: all Version: 1.4.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-shape_1.4.6.1-1.ca2604.1_all.deb Size: 746488 MD5sum: 50cfd72458b3cde7a59645c4a8273cc3 SHA1: 6735e8998315259fdfd0cd35e8c46bc956784882 SHA256: c82f3de6c4bfa070308e312ff5b39003d90c902ff4a914f048082a2bc75509f4 SHA512: 9275573c80d27039fb223f4377fe3529ec37e7f5b57f22903451e73f987d59a7ec2971ad5fe92a3006893b76177c0150cda123fac25f3580d27a55ccd84f45d5 Homepage: https://cran.r-project.org/package=shape Description: CRAN Package 'shape' (Functions for Plotting Graphical Shapes, Colors) Functions for plotting graphical shapes such as ellipses, circles, cylinders, arrows, ... 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Package: r-cran-shapefiles Architecture: all Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign Filename: pool/dists/resolute/main/r-cran-shapefiles_0.7.2-1.ca2604.1_all.deb Size: 52026 MD5sum: fc7a1b97fe62db668a8d5bc4bb47dd44 SHA1: b0457c12e9ab8551463dcb840dc5f957ee631ffb SHA256: 960a7a981ecc2e72315115e15d45aeb0f4eeafa17fd8bd299d7639998268e1c2 SHA512: 7299c2ddf2c7929421a328ebe621a87b6e6ea70aee29ccf938582b77a9446174de5a6e4dcca8ea84a40c8988ded62a34e858c1f9a764fb962b82d4b9dd8e7b7e Homepage: https://cran.r-project.org/package=shapefiles Description: CRAN Package 'shapefiles' (Read and Write ESRI Shapefiles) Functions to read and write ESRI shapefiles. Package: r-cran-shapena Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-mice Filename: pool/dists/resolute/main/r-cran-shapena_0.0.2-1.ca2604.1_all.deb Size: 84044 MD5sum: ecab8dc37f0fd5d17e7e052ec901dc72 SHA1: 565534d03f7cf5d68690194d439a19cca8970ea0 SHA256: 0bd9b33d94574773eb0ab42636f72b28583e0cd2237e927e0252dce3e0a78deb SHA512: 50061bffcd3f20b8651a9d253464321369c24d92e0ae3beacb3e7d571391817074154b8009c20c7549c80f938220db87856f91623633927c5b9c6942fdc7b4b6 Homepage: https://cran.r-project.org/package=shapeNA Description: CRAN Package 'shapeNA' (M-Estimation of Shape for Data with Missing Values) M-estimators of location and shape following the power family (Frahm, Nordhausen, Oja (2020) ) are provided in the case of complete data and also when observations have missing values together with functions aiding their visualization. Package: r-cran-shapepattern Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-igraph, r-cran-terra, r-cran-landscapemetrics, r-cran-raster Filename: pool/dists/resolute/main/r-cran-shapepattern_3.1.0-1.ca2604.1_all.deb Size: 195974 MD5sum: 8278e75975d82a1cdb35070274408874 SHA1: 9964865c743dd127a1b392f0626c19abd1932709 SHA256: 4971fa1453b583f3269dc974aa8800a5acea05c4f2548b4422205923bc491a16 SHA512: 36eaef313babf2f781478e5347f128c69a75a4478aff8c9df2691d45151d6aa0e8677948cf8318384bd8fda708bb2a3f1fa63c5091294baa649a1530551b42c1 Homepage: https://cran.r-project.org/package=ShapePattern Description: CRAN Package 'ShapePattern' (Tools for Analyzing Shapes and Patterns) This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) , (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) , (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) , and (5) computing the porosity of raster patches . NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented. Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived. Package: r-cran-shaper Architecture: all Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotrix, r-cran-jpeg, r-cran-pixmap, r-cran-wavethresh, r-cran-vegan, r-cran-mass Filename: pool/dists/resolute/main/r-cran-shaper_1.0-2-1.ca2604.1_all.deb Size: 3625676 MD5sum: 10f3e42dadcf22bf3553dd82c0e77bfd SHA1: 06605f993cdb22fbeba685f543de07607be641bd SHA256: 978ccc11d1a386a90ce3a1dd3dcbc69f2e8ace61d7aa231b2f2f7007e741b491 SHA512: 9ad9c765b7d7eea6e14a1d4c98a473e67b856876e13d3c356765b3a6732ff61535363bfc2b94e2403a5743658d0d4e35a78dac601e7a3390cf7e1542acf9ac81 Homepage: https://cran.r-project.org/package=shapeR Description: CRAN Package 'shapeR' (Collection and Analysis of Otolith Shape Data) Studies otolith shape variation among fish populations. Otoliths are calcified structures found in the inner ear of teleost fish and their shape has been known to vary among several fish populations and stocks, making them very useful in taxonomy, species identification and to study geographic variations. The package extends previously described software used for otolith shape analysis by allowing the user to automatically extract closed contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000) , choose from conducting either a Fourier or wavelet see Gençay et al (2001) transform to the outlines and visualize the mean shape. The output of the package are independent Fourier or wavelet coefficients which can be directly imported into a wide range of statistical packages in R. The package might prove useful in studies of any two dimensional objects. Package: r-cran-shapes Architecture: all Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 994 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-scatterplot3d, r-cran-rgl, r-cran-mass, r-cran-fitdistrplus Filename: pool/dists/resolute/main/r-cran-shapes_1.2.8-1.ca2604.1_all.deb Size: 899892 MD5sum: e4e2a36085ce145423e02b96d92c6ee1 SHA1: b50b00bdc9193690862b1c0be4d455e6955393e5 SHA256: 7890cce8d5b7a0ab8aed204e862ee17339073501d44fbd6cdf9536c3c7286c18 SHA512: 8fa952d9acf542315732b0b4f77a3cb92bbccdbd664ec339d7453208e072a9e56201a1586c6e2d8bcce2d5a80abdd4c00bad05cb3a166b40fea401ba4a961817 Homepage: https://cran.r-project.org/package=shapes Description: CRAN Package 'shapes' (Statistical Shape Analysis) Routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons. Package: r-cran-shapeselectforest Architecture: all Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coneproj, r-cran-raster Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-shapeselectforest_1.7-1.ca2604.1_all.deb Size: 178944 MD5sum: e87f35b1dd88a1413fc312d43c83a7f6 SHA1: 193a55f8c7c7d1a5b7c06061702ef4afb53ed4b6 SHA256: 43cfce206a29238643325a9eff6f02de22d5a79319529cffece563a35a258426 SHA512: 6748e3fa97e67231ccc079201e3b8cd69d483d8cfc90e2294c970b955dd9491385955a3fc8e1d2361bf6ba6ef649991847f33b9690fe584de5f15c1a9e7f26ac Homepage: https://cran.r-project.org/package=ShapeSelectForest Description: CRAN Package 'ShapeSelectForest' (Shape Selection for Landsat Time Series of Forest Dynamics) Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site . By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery. Package: r-cran-shapforxgboost Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xgboost, r-cran-data.table, r-cran-ggforce, r-cran-ggextra, r-cran-rcolorbrewer, r-cran-ggpubr, r-cran-bbmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-here, r-cran-lightgbm Filename: pool/dists/resolute/main/r-cran-shapforxgboost_0.2.0-1.ca2604.1_all.deb Size: 803970 MD5sum: 889dfc106a6da20c8bed75e52c2495c5 SHA1: b4cbe6c2ee7a340dbcfe850858fdf42262e7e607 SHA256: 6c1d6456d3a44247927393f53ed4e974e95bd03510419f94073e96b05aa6de31 SHA512: ed90ec97f58971e0b86b3d105d048c9a257f14e4bf7b6b59eb8157094f079b17219daaf8ffc23e943ac9c1a979a855f1ff1e976901aa82c8dc10f9b837819bb4 Homepage: https://cran.r-project.org/package=SHAPforxgboost Description: CRAN Package 'SHAPforxgboost' (SHAP Plots for 'XGBoost') Aid in visual data investigations using SHAP (SHapley Additive exPlanation) visualization plots for 'XGBoost' and 'LightGBM'. It provides summary plot, dependence plot, interaction plot, and force plot and relies on the SHAP implementation provided by 'XGBoost' and 'LightGBM'. Package: r-cran-shapley Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-h2o, r-cran-curl, r-cran-pander Filename: pool/dists/resolute/main/r-cran-shapley_0.7.0-1.ca2604.1_all.deb Size: 805922 MD5sum: dcd58d8e6fced3f2705d32e240ccebca SHA1: bf8e307817d13015f81b7a2d3a810e643b2906a7 SHA256: 8c9d7600a60da0c43e197e50b68e4700cc202252c4b546603827ea600c30bb29 SHA512: faa21f9d8ac136d56da51f8cf55cc5229fa232352e624fb55ce1dfe4f6678376da9f31ca8f1f18f3bc3d72e35a416ad0f296f219da76d8c065f36c57d80db246 Homepage: https://cran.r-project.org/package=shapley Description: CRAN Package 'shapley' (Weighted Mean SHAP and CI for Robust Feature Assessment in MLGrid) This R package introduces Weighted Mean SHapley Additive exPlanations (WMSHAP), an innovative method for calculating SHAP values for a grid of fine-tuned base-learner machine learning models as well as stacked ensembles, a method not previously available due to the common reliance on single best-performing models. By integrating the weighted mean SHAP values from individual base-learners comprising the ensemble or individual base-learners in a tuning grid search, the package weights SHAP contributions according to each model's performance, assessed by multiple either R squared (for both regression and classification models). alternatively, this software also offers weighting SHAP values based on the area under the precision-recall curve (AUCPR), the area under the curve (AUC), and F2 measures for binary classifiers. It further extends this framework to implement weighted confidence intervals for weighted mean SHAP values, offering a more comprehensive and robust feature importance evaluation over a grid of machine learning models, instead of solely computing SHAP values for the best model. This methodology is particularly beneficial for addressing the severe class imbalance (class rarity) problem by providing a transparent, generalized measure of feature importance that mitigates the risk of reporting SHAP values for an overfitted or biased model and maintains robustness under severe class imbalance, where there is no universal criteria of identifying the absolute best model. Furthermore, the package implements hypothesis testing to ascertain the statistical significance of SHAP values for individual features, as well as comparative significance testing of SHAP contributions between features. Additionally, it tackles a critical gap in feature selection literature by presenting criteria for the automatic feature selection of the most important features across a grid of models or stacked ensembles, eliminating the need for arbitrary determination of the number of top features to be extracted. This utility is invaluable for researchers analyzing feature significance, particularly within severely imbalanced outcomes where conventional methods fall short. Moreover, it is also expected to report democratic feature importance across a grid of models, resulting in a more comprehensive and generalizable feature selection. The package further implements a novel method for visualizing SHAP values both at subject level and feature level as well as a plot for feature selection based on the weighted mean SHAP ratios. Package: r-cran-shapleyoutlier Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rdpack, r-cran-tibble, r-cran-tidyr, r-cran-robustbase, r-cran-forcats, r-cran-egg, r-cran-ggplot2, r-cran-gridextra, r-cran-rcolorbrewer, r-cran-magrittr Suggests: r-cran-cellwise, r-cran-robusthd, r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shapleyoutlier_0.1.2-1.ca2604.1_all.deb Size: 790078 MD5sum: 548e9366074fa83326f5f539e6b925ae SHA1: 8377b491dbe8f318cad791fb0a3f1314cebf79cf SHA256: 209e52d4a238552a8206957549991c7eef2e578f34e20ae76be54992fb1fc378 SHA512: 53b2f3b11ccad538ae710537128f960931213b397b3cf95e49566063c1c645312d9b11f40a5d6a46a185017ba1eadff35df865dc631068ce586da1ac9911d079 Homepage: https://cran.r-project.org/package=ShapleyOutlier Description: CRAN Package 'ShapleyOutlier' (Multivariate Outlier Explanations using Shapley Values andMahalanobis Distances) Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers. Includes implementations of methods described in Mayrhofer and Filzmoser (2023) . Package: r-cran-shapleyvalue Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-kableextra, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shapleyvalue_0.2.0-1.ca2604.1_all.deb Size: 21104 MD5sum: 47b4e54b1a7b5102ea7f96fb529cf5ff SHA1: 157f8602e056207f486d733ed9c47f9062083f85 SHA256: b73911872e6ef8ad4883f27c3325675e371b8885456a0c20f40ad0cdb3d45cf4 SHA512: 0fd165a0d65e971af6241bb3ee072536ef014d7e9511f1e704d4dcac9798b86ae556797ae6e1ac50033add547ae94159234b373e5d2b538db2dbb89e9e325073 Homepage: https://cran.r-project.org/package=ShapleyValue Description: CRAN Package 'ShapleyValue' (Shapley Value Regression for Relative Importance of Attributes) Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity. Package: r-cran-shapper Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-dalex, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-knitr, r-cran-randomforest, r-cran-rpart, r-cran-testthat, r-cran-markdown, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-shapper_0.1.3-1.ca2604.1_all.deb Size: 67892 MD5sum: 61645dd98a41cbdc8e1267e11b3568f8 SHA1: 2d2fe20586380f95fee1d55389491743b60aeeb1 SHA256: a93162335d17d498e308c7414e4c6c5a980bf7541e04d099d5f00b235374bcac SHA512: 6c7066ad2fb585d2f84e2b5b38fe3193b44c5ae825a472c5d39f2e66a59deca8178c72eb1892cfc52349dbfdb59b5a46240372a9fa5b4334167c609494c05975 Homepage: https://cran.r-project.org/package=shapper Description: CRAN Package 'shapper' (Wrapper of Python Library 'shap') Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'. Package: r-cran-shapviz Architecture: all Version: 0.10.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggfittext, r-cran-gggenes, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-rlang, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shapviz_0.10.3-1.ca2604.1_all.deb Size: 1911664 MD5sum: 25a105e4627a2f7071a2fa83a5331149 SHA1: 4b053af096dbf8d24d4a7199037923736dbe86fb SHA256: fd1390bc07adc61567d6d61c13e825253ed95d992b34978562bf2b79f0f24f22 SHA512: 04a9208b633376f11387a298f1cb432cd7f872717c3e25cd7e1753e5ac2610c8e4bfa34ab35f5a190c062a15fb31e3fc9746abc1ed74e6a76922ba8e60dcd30b Homepage: https://cran.r-project.org/package=shapviz Description: CRAN Package 'shapviz' (SHAP Visualizations) Visualizations for SHAP (SHapley Additive exPlanations), such as waterfall plots, force plots, various types of importance plots, dependence plots, and interaction plots. These plots act on a 'shapviz' object created from a matrix of SHAP values and a corresponding feature dataset. Wrappers for the R packages 'xgboost', 'lightgbm', 'fastshap', 'shapr', 'h2o', 'treeshap', 'DALEX', and 'kernelshap' are added for convenience. By separating visualization and computation, it is possible to display factor variables in graphs, even if the SHAP values are calculated by a model that requires numerical features. The plots are inspired by those provided by the 'shap' package in Python, but there is no dependency on it. Package: r-cran-shar Architecture: all Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.model, r-cran-spatstat.random, r-cran-terra Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-spatstat, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shar_2.3.1-1.ca2604.1_all.deb Size: 1236654 MD5sum: 9607cf422a671baf817e78e6acb6d2cb SHA1: d2f55904241e2e4dc6e32a0582f2d5f5228a40e8 SHA256: 2850ad0c28dd563d11cfcdae187712c62601208c2d18fd0986465c7bb22b8ff5 SHA512: 040d3f753b6ddeb9d89e985c894d41ba8c30becb1ad40a345e2c0e1a8b9dd6911d71627939209c114fec7e121cb97cc45a4ad9712f05cb8ca9832c44e1571d24 Homepage: https://cran.r-project.org/package=shar Description: CRAN Package 'shar' (Species-Habitat Associations) Analyse species-habitat associations in R. Therefore, information about the location of the species (as a point pattern) is needed together with environmental conditions (as a categorical raster). To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) and Harms et al. (2001) . Package: r-cran-shark4r Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-leaflet, r-cran-lifecycle, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stringi, r-cran-terra, r-cran-tidyr, r-cran-vroom, r-cran-worrms Suggests: r-cran-htmltools, r-cran-irfcb, r-cran-knitr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-skimr, r-cran-spelling, r-cran-shiny, r-cran-shinythemes, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shark4r_1.1.1-1.ca2604.1_all.deb Size: 1255788 MD5sum: 494a5927fa3e9f9c6351d1988b271476 SHA1: c7623b14fd2b1d7e37977e232f72743f588cb514 SHA256: 673eca78df18f4b922fadbff3054635f54dd9f2eeb6487c331a12415c0989240 SHA512: deb5d2da40587a0bd5910b4000a74e7359700ee62b3a66406949971b8ae43e81fe7bd876027851cb915ecfb886239ce441daeda4747d7614c9bfad9b35d99640 Homepage: https://cran.r-project.org/package=SHARK4R Description: CRAN Package 'SHARK4R' (Accessing and Validating Marine Environmental Data from 'SHARK'and Related Databases) Provides functions to retrieve, process, analyze, and quality-control marine physical, chemical, and biological data. The main focus is on Swedish monitoring data available through the 'SHARK' database , with additional API support for 'Nordic Microalgae' , 'Dyntaxa' , World Register of Marine Species ('WoRMS') , 'AlgaeBase' , OBIS 'xylookup' web service and Intergovernmental Oceanographic Commission (IOC) - UNESCO databases on harmful algae and toxins . Package: r-cran-sharkdemography Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-interp, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-popbio, r-cran-iterators, r-cran-dofuture, r-cran-doparallel, r-cran-foreach, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sharkdemography_1.1.0-1.ca2604.1_all.deb Size: 115784 MD5sum: 400eed7244a8171181ea1ab554aebe70 SHA1: e1e5b72b7fd269e0aa2e43a34bd3c67a449df41d SHA256: b1d88ced53274f6e404e9498370d572efb6e474d734a2f5782f79dae1c3772fe SHA512: e10f8795643f74911851d57ea7c53a23ba2b46c0604dc2afd128c7bc7735553a32c331cb1ae15107597fae0c1367114a6569ac1c7d55ba9f2f7c4c6197b98dea Homepage: https://cran.r-project.org/package=SharkDemography Description: CRAN Package 'SharkDemography' (Shark Demographic Analyses Using Leslie Matrix Models) Run Leslie Matrix models using Monte Carlo simulations for any specified shark species. This package was developed during the publication of Smart, JJ, White, WT, Baje, L, et al. (2020) "Can multi-species shark longline fisheries be managed sustainably using size limits? Theoretically, yes. Realistically, no".J Appl Ecol. 2020; 57; 1847–1860. . Package: r-cran-sharp Architecture: all Version: 1.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fake, r-cran-abind, r-cran-beepr, r-cran-future, r-cran-future.apply, r-cran-glassofast, r-cran-glmnet, r-cran-igraph, r-cran-mclust, r-cran-nloptr, r-cran-plotrix, r-cran-rdpack, r-cran-withr Suggests: r-cran-cluster, r-cran-corpcor, r-cran-dbscan, r-cran-elasticnet, r-cran-gglasso, r-bioc-mixomics, r-cran-nnet, r-cran-openmx, r-bioc-rcy3, r-cran-randomcolor, r-cran-rmarkdown, r-cran-rpart, r-cran-sgpls, r-cran-sparcl, r-cran-survival, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-sharp_1.4.8-1.ca2604.1_all.deb Size: 1460188 MD5sum: 3d47c4b91788af2f78c15ca403d36527 SHA1: 605ab69c6d6b138c4fa6d70c56d889317e14e72c SHA256: 5fd3eb4a0fd6e2a9fb9c6b7b7e12d39ca8beac595284e976cc4d7ee131be1053 SHA512: 98ee6d60329b730aa61eff4710ed652401d4f778f3048e0e29c9de94f507f11fc772e08f80227b367ce6f0087aa99be073b1555f3d60b1bdfcc40ed4de84c963 Homepage: https://cran.r-project.org/package=sharp Description: CRAN Package 'sharp' (Stability-enHanced Approaches using Resampling Procedures) In stability selection (N Meinshausen, P Bühlmann (2010) ) and consensus clustering (S Monti et al (2003) ), resampling techniques are used to enhance the reliability of the results. In this package (B Bodinier et al (2025) ), hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) and B Bodinier et al (2023b) ). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering. Package: r-cran-sharper Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3094 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc, r-cran-zoo, r-cran-epsiwal Suggests: r-cran-xtable, r-cran-xts, r-cran-timeseries, r-cran-quantmod, r-cran-mass, r-cran-ttr, r-cran-testthat, r-cran-sandwich, r-cran-txtplot, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sharper_1.4.0-1.ca2604.1_all.deb Size: 2646010 MD5sum: b4e6128209f7d0c903b210dc72f0b75d SHA1: b1105e0808484916bb12107d1db0119975572fc8 SHA256: 1d608a305efb1eb63f2f5c9e0e031a0061feed6526792c76b1b66d85a843a4e3 SHA512: dfe4627af0523ffb82a833e4d6a82261a57147db49b6c54d398426a6c3767f16ab3a6175fbd68f833a980b3fea2bc049256222fc7654551afc7b46172ed761d7 Homepage: https://cran.r-project.org/package=SharpeR Description: CRAN Package 'SharpeR' (Statistical Significance of the Sharpe Ratio) A collection of tools for analyzing significance of assets, funds, and trading strategies, based on the Sharpe ratio and overfit of the same. Provides density, distribution, quantile and random generation of the Sharpe ratio distribution based on normal returns, as well as the optimal Sharpe ratio over multiple assets. Computes confidence intervals on the Sharpe and provides a test of equality of Sharpe ratios based on the Delta method. The statistical foundations of the Sharpe can be found in the author's Short Sharpe Course . Package: r-cran-sharpr2 Architecture: all Version: 1.1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-sharpr2_1.1.1.0-1.ca2604.1_all.deb Size: 183714 MD5sum: e737d5017026dc1d4cdb3786d01dda24 SHA1: 129d43cf80e3e70604a03372b101168957018c05 SHA256: 970cc6f738da8e13ecda7ca1bdd5213ac898b74997a2c995ae1c96abc7474ede SHA512: ab59dc8c2effba6057a295ebd59ec5c33cecbd3e686c0d9014452ee972e528cdc2b94c6f0f258f0cde286ad143eb5c5cdf922066537fd15f3753e73566f91752 Homepage: https://cran.r-project.org/package=sharpr2 Description: CRAN Package 'sharpr2' (Estimating Regulatory Scores and Identifying ATAC-STARR Data) An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) . Package: r-cran-sharpshootr Architecture: all Version: 2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aqp, r-cran-ape, r-cran-soildb, r-cran-cluster, r-cran-lattice, r-cran-reshape2, r-cran-scales, r-cran-circular, r-cran-rcolorbrewer, r-cran-plyr, r-cran-digest, r-cran-e1071, r-cran-stringi, r-cran-curl Suggests: r-cran-mass, r-cran-spdep, r-cran-circlize, r-cran-rvest, r-cran-xml2, r-cran-terra, r-cran-raster, r-cran-exactextractr, r-cran-httr, r-cran-jsonlite, r-cran-igraph, r-cran-dendextend, r-cran-testthat, r-cran-latticeextra, r-cran-farver, r-cran-venn, r-cran-gower, r-cran-daymetr, r-cran-elevatr, r-cran-evapotranspiration, r-cran-zoo, r-cran-soiltaxonomy, r-cran-sf, r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-sharpshootr_2.5-1.ca2604.1_all.deb Size: 532310 MD5sum: 2446de8c54f693693e4ed7d2d8578bb0 SHA1: 88acfa1343c38d7abb5d5c6c89a2e2a658f98f77 SHA256: 0378898285f7d336c73696924a58c02ffdfdee5f378bea96f3e300b24eee67da SHA512: c54f19e496ab38b8a229f5329b7ef4cf4f9390435511c090f21fc58726c9e3834c703e5fecf2f7ba4128bcf346b5172b139486b157dc911c792f14d07b5a6568 Homepage: https://cran.r-project.org/package=sharpshootR Description: CRAN Package 'sharpshootR' (A Soil Survey Toolkit) A collection of data processing, visualization, and export functions to support soil survey operations. Many of the functions build on the `SoilProfileCollection` S4 class provided by the aqp package, extending baseline visualization to more elaborate depictions in the context of spatial and taxonomic data. While this package is primarily developed by and for the USDA-NRCS, in support of the National Cooperative Soil Survey, the authors strive for generalization sufficient to support any soil survey operation. Many of the included functions are used by the SoilWeb suite of websites and movile applications. These functions are provided here, with additional documentation, to enable others to replicate high quality versions of these figures for their own purposes. Package: r-cran-sharx Architecture: all Version: 1.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-dcmle, r-cran-dclone Filename: pool/dists/resolute/main/r-cran-sharx_1.0-7-1.ca2604.1_all.deb Size: 200862 MD5sum: f85c8a4c12d34c2a3f23331dd3728b14 SHA1: 82bdafeb03f9d8a97113f77cab910af9be82131e SHA256: 4ad4891f194059d0f38a4e1c637bfed048c7d0458a7ae7653de0b5714f579c91 SHA512: 9facd9b832989608c3afb81fa843cd464773e3f6e4ce6566a72fad5e21e83cdbb0df3fd134f99605181cf9c236551a497a1650d43ae633acbdedcb385e019109 Homepage: https://cran.r-project.org/package=sharx Description: CRAN Package 'sharx' (Models and Data Sets for the Study of Species-Area Relationships) Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) . Package: r-cran-shattering Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-pdist, r-cran-slam, r-cran-ryacas, r-cran-rmarkdown, r-cran-pracma, r-cran-e1071, r-cran-nmf Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shattering_1.0.7-1.ca2604.1_all.deb Size: 72198 MD5sum: fde779876dadffb2f3434519ee8d1906 SHA1: 9674bd39cb16f65297d2f91c5cc7ffae3ad2da3b SHA256: 72c2ed0afc1905c85072b489748ec681b6af90490921ac56a8cc4531e16aea72 SHA512: b909e42d1900ed687f1bd793db7bc0146c6329050125a916f1a3ead6960b38c3b12b2faea520b4d6717db77cea0f0eac926db510813419b502fb6f375208e2e0 Homepage: https://cran.r-project.org/package=shattering Description: CRAN Package 'shattering' (Estimate the Shattering Coefficient for a Particular Dataset) The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" ; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory". Package: r-cran-shazam Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-alakazam, r-cran-ape, r-cran-diptest, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-igraph, r-cran-iterators, r-cran-kernsmooth, r-cran-lazyeval, r-cran-mass, r-cran-progress, r-cran-rlang, r-cran-scales, r-cran-seqinr, r-cran-stringi, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-shazam_1.3.2-1.ca2604.1_all.deb Size: 2189770 MD5sum: bf40f1f0b6f9e8a1febf8492d1281222 SHA1: 4e83dfb2ab1abccebe5e4b02871e069e29054fc5 SHA256: 2765a729783f49ebc1478d11b42c93cfe93e81fec815b409d5473be29c6453e6 SHA512: 7a804d60c02a1463366c18131a0fd660834685e0ea6fef9d9c7260c2402ec54c107d7c4ec98cecea78142de710268baf007d4d978fdd0c9a73ae9e929983734d Homepage: https://cran.r-project.org/package=shazam Description: CRAN Package 'shazam' (Immunoglobulin Somatic Hypermutation Analysis) Provides a computational framework for analyzing mutations in immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of antigen-driven selection pressure, mutational load quantification, building of somatic hypermutation (SHM) models, and model-dependent distance calculations. Also includes empirically derived models of SHM for both mice and humans. Citations: Gupta and Vander Heiden, et al (2015) , Yaari, et al (2012) , Yaari, et al (2013) , Cui, et al (2016) . Package: r-cran-shelf Architecture: all Version: 1.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 902 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-ggextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-hmisc, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinymatrix, r-cran-sn, r-cran-survival, r-cran-survminer, r-cran-tidyr Suggests: r-cran-ggally, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-shelf_1.13.0-1.ca2604.1_all.deb Size: 701876 MD5sum: c693c7630bfc4d1df069895b4d6b91ce SHA1: d6ce2d7f94e77f115b6b8520c9dc2f23bc05c96d SHA256: deae603506251cde061f44bebdd3ef6f9c934c69847c1a671014f94857da0542 SHA512: 3f11e8ade0ca6d5e75126c8c7f059aa93bedb32907370901d6aa2360dfc17e94317e0895332d0706f8c89816e36dcb791afe1cad974b5b29d57a4ef3041e63ea Homepage: https://cran.r-project.org/package=SHELF Description: CRAN Package 'SHELF' (Tools to Support the Sheffield Elicitation Framework) Implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included. Package: r-cran-shellchron Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtop, r-cran-zoo, r-cran-ggplot2, r-cran-ggpubr, r-cran-tidyr, r-cran-scales, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shellchron_0.4.0-1.ca2604.1_all.deb Size: 522294 MD5sum: 9c54efbf1cf046f958447929e5abd8e9 SHA1: 37b9f1cdef04d2579d55a01b10453eaee752c440 SHA256: 2bb3da20557e9ad26c9515ac05cf31298bebd65d558ca139946138f2e5a3445f SHA512: 2bb660ae338cd70875125ba3211ef253ca69a0d975ce1bc719e82db291091ae5ba114f4bc119f55f78389f59f59dcbd9f69235d4cd7631f3fda35cbdee4f0bbb Homepage: https://cran.r-project.org/package=ShellChron Description: CRAN Package 'ShellChron' (Builds Chronologies from Oxygen Isotope Profiles in Shells) Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D) through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of the data (see Judd et al., (2018) ). This modeling is carried out along a sliding window through the data and yields estimates of the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell formation relative to the annual cycle with their uncertainties. All formulae in the package serve this purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the shell carbonate) through input parameters. Package: r-cran-shelltrace Architecture: all Version: 3.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2797 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xlsx, r-cran-bmp, r-cran-tiff Filename: pool/dists/resolute/main/r-cran-shelltrace_3.5.1-1.ca2604.1_all.deb Size: 2532396 MD5sum: 934804599fd07e0c919e51204a8851fb SHA1: bcff86f3474905273b680b766ce379b1ea9ae878 SHA256: a9929193d29a543320b18386373b500af0149edccd71cf84e0d91ae6c7e035c1 SHA512: ae337c77106981cf2b802d21beb5f5be280342cc5ad626838d47cc286209709b6860c9cdc252dec21f002fac723b6687ce0a08fa5b873d3d17bd382a429e1c10 Homepage: https://cran.r-project.org/package=shelltrace Description: CRAN Package 'shelltrace' (Bivalve Growth and Trace Element Accumulation Model) Contains all the formulae of the growth and trace element uptake model described in the equally-named Geoscientific Model Development paper (de Winter, 2017, ). The model takes as input a file with X- and Y-coordinates of digitized growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in the form of XLSX files. Package: r-cran-shelter Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-getpass, r-cran-yaml, r-cran-filelock, r-cran-rappdirs, r-cran-sodium Suggests: r-cran-testthat, r-cran-rstudioapi, r-cran-mockery, r-cran-keyring Filename: pool/dists/resolute/main/r-cran-shelter_0.2.1-1.ca2604.1_all.deb Size: 74800 MD5sum: 6f600e30c5512eb9edb07351d547b2b7 SHA1: 89cc4af1d0101e2f010682bcc2c05289d9fea25f SHA256: 3d799c80bdfee89af9a4242347868d85fe7bc8731af3e739c18cb5e0a64634fe SHA512: fb437817fb239d95f3600f5d7a0d2dd37c09cf4b41826f8602ea1a8a7813c9bbba3fc0ad33c7919e9c932a7ec223fdac34bd05f9a5ca212f531e5dcf51a069be Homepage: https://cran.r-project.org/package=shelter Description: CRAN Package 'shelter' (Support for Secure API Key Management) Secure handling of API keys can be difficult. This package provides secure convenience functions for entering / handling API keys and opening connections via inversion of control on those keys. Works seamlessly between production and developer environments. Package: r-cran-sherlock Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-forcats, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-cowplot, r-cran-scales, r-cran-ggh4x, r-cran-stringr, r-cran-plotly, r-cran-readr, r-cran-openxlsx, r-cran-purrr, r-cran-fs, r-cran-rstudioapi, r-cran-tidytext Suggests: r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-sherlock_0.7.0-1.ca2604.1_all.deb Size: 493944 MD5sum: 7bf1993ecd251249e84709847849e292 SHA1: 95c3f5679215d4118727d2d253d768752a471ae5 SHA256: 8922c0aae3f69ac05f34c61471b25a90bdff871b31f5d2508b41dab874fa9bd9 SHA512: 8b49425248676be1570e8b6467b5621f3397e78735dc82a3d6c7351a286922bf68eef5e064c9d835e6d44b1b05313cac0a59c2a4bf192b90aa3d5b5c7ee35879 Homepage: https://cran.r-project.org/package=sherlock Description: CRAN Package 'sherlock' (Graphical Displays for Structured Problem Solving and Diagnosis) Powerful graphical displays and statistical tools for structured problem solving and diagnosis. The functions of the 'sherlock' package are especially useful for applying the process of elimination as a problem diagnosis technique. The 'sherlock' package was designed to seamlessly work with the 'tidyverse' set of packages and provides a collection of graphical displays built on top of the 'ggplot' and 'plotly' packages, such as different kinds of small multiple plots as well as helper functions such as adding reference lines, normalizing observations, reading in data or saving analysis results in an Excel file. References: David Hartshorne (2019, ISBN: 978-1-5272-5139-7). Stefan H. Steiner, R. Jock MacKay (2005, ISBN: 0873896467). Package: r-cran-sherlockholmes Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qpdf, r-cran-stringr, r-cran-dpseg, r-cran-plotrix, r-cran-zoo, r-cran-stargazer, r-cran-textboxplacement, r-cran-plot.matrix, r-cran-devtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sherlockholmes_1.0.2-1.ca2604.1_all.deb Size: 2319118 MD5sum: 8b4b5299fb8d2bbe7fc83679f00d6868 SHA1: 615ec4115e0285ccace0079ccf57214df5d1a568 SHA256: c478b2ba12358c5e310a45fc704bdc773ee5f84b14e1182b56a31c51922e11a8 SHA512: fb30d163cb1b84dc11ebf70cceaac1bcaf016cf83175e2c6c8257f4fe94f2837b3522b796b5c4b844dd1155102aeaffeee251814240b8e0d82ab3ac6f22a1790 Homepage: https://cran.r-project.org/package=SherlockHolmes Description: CRAN Package 'SherlockHolmes' (Building a Concordance of Terms in a Series of Texts) Compute the frequency distribution of a search term in a series of texts. For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes' popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at . Package: r-cran-shewhartr Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1985 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-ggplot2, r-cran-broom, r-cran-slider Suggests: r-cran-patchwork, r-cran-plotly, r-cran-qcc, r-cran-lubridate, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-mass Filename: pool/dists/resolute/main/r-cran-shewhartr_1.3.0-1.ca2604.1_all.deb Size: 1336868 MD5sum: 529b6c8458e6d2eee2acdc4ea924836a SHA1: ccfc2a64cecb3ab9221b14c8eef849ca4c5f317a SHA256: 58c4bd08a7fec3beedfe581370f9b81e25f626b8a812b127256d3df445288d83 SHA512: 5576133251adc6c22d066e72e6c21d779987c03e743e0afa121bf0feef0413b75efc04f87ad936b7832e521977081922f7145f6dadd1bc3fdca2de96ffbd6924 Homepage: https://cran.r-project.org/package=shewhartr Description: CRAN Package 'shewhartr' (Statistical Process Control with Tidyverse-Native Workflows) A comprehensive toolkit for Statistical Process Control (SPC) that combines the rigor of classical Shewhart methodology with modern tidyverse-native interfaces. Provides classical control charts for variables (I-MR, Xbar-R, Xbar-S) and attributes (p, np, c, u), as well as regression-based control charts for processes with trend. Includes Nelson runs tests, Average Run Length (ARL) simulation, process capability indices with bootstrap confidence intervals, Box-Cox transformation guidance, and a clean Phase I / Phase II workflow. All chart objects integrate with broom via 'tidy', 'glance' and 'augment' methods. References: Shewhart (1931, ISBN:0-87389-076-0); Montgomery (2019, ISBN:978-1-119-39930-8); Nelson (1984) ; Woodall (2000) ; Box & Cox (1964) . Package: r-cran-shidashi Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-digest, r-cran-ellmer, r-cran-fastmap, r-cran-formatr, r-cran-httr2, r-cran-s7, r-cran-shiny, r-cran-shinychat, r-cran-yaml, r-cran-jsonlite, r-cran-htmlwidgets Suggests: r-cran-cli, r-cran-coro, r-cran-logger, r-cran-processx, r-cran-promises, r-cran-later, r-cran-rstudioapi, r-cran-htmltools, r-cran-httpuv, r-cran-ggplot2, r-cran-ggextra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shidashi_0.2.0-1.ca2604.1_all.deb Size: 1292888 MD5sum: eb220680bab407fb4eb3d022e5f19b9b SHA1: 159560965326063e3c7ef679987750d16e8bc6ff SHA256: bca9b8dcfa541b4f3ae45f86aed862b8840140170ed71225b0d509e9fa95e148 SHA512: aa7c13b99e158debff7692ed6666630a59c7480221c6fc5c11ad87cf35022f7a85226295f542629000bed4f26542082c8a8373bf4c8c466127ccc06d00d6e854 Homepage: https://cran.r-project.org/package=shidashi Description: CRAN Package 'shidashi' (A Shiny Dashboard Template Modular System with Chat Bot Support) A template dashboard system with AI agent integrated. 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Package: r-cran-shiftsharese Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-aer, r-cran-spelling, r-cran-formatr Filename: pool/dists/resolute/main/r-cran-shiftsharese_1.1.0-1.ca2604.1_all.deb Size: 1480468 MD5sum: 96749b6c17525bd3009d7fe823247bdf SHA1: 9721b430bccd2d2b9bd0380838fe5f445c05491a SHA256: 45406203c41adfb6bf2a0e08a593a32ae3f97d184b0e4f745bbed939ff327219 SHA512: 4a2767ac64917caa651dc732a6b088a622bb1a328b5af6e2bf8f7b9c4ee32bd2c3988c8a2d3d52eb6e4d9b074b48b95cd9c3b2b5653a016a9020040751beac93 Homepage: https://cran.r-project.org/package=ShiftShareSE Description: CRAN Package 'ShiftShareSE' (Inference in Regressions with Shift-Share Structure) Provides confidence intervals in least-squares regressions when the variable of interest has a shift-share structure, and in instrumental variables regressions when the instrument has a shift-share structure. 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Package: r-cran-shinipsum Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-attempt, r-cran-dt, r-cran-dygraphs, r-cran-ggplot2, r-cran-magrittr, r-cran-plotly Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinipsum_0.1.1-1.ca2604.1_all.deb Size: 355268 MD5sum: f49e57bab7c3eff96d4de9f5edb75e14 SHA1: a211609e258421ad0a023e2fc8b1e86465077a0c SHA256: 0450c82cdcea20d579fddaa92c55990d563cb20eb06b2ca452ae4ea729dc9d2d SHA512: 34ba3ebf1afd78464b5d17b450a93a4e92b7bfc31f1c35a5a43b0c353edc71f02611395b42d14c8fb690671a147740bb77255274edaf323ccdd05ad2e79fe788 Homepage: https://cran.r-project.org/package=shinipsum Description: CRAN Package 'shinipsum' (Lorem-Ipsum-Like Helpers for Fast Shiny Prototyping) Prototype your shiny apps quickly with these Lorem-Ipsum-like Helpers. Package: r-cran-shiny.blueprint Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-htmltools, r-cran-shiny, r-cran-shiny.react Suggests: r-cran-covr, r-cran-knitr, r-cran-lintr, r-cran-purrr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-shiny.router Filename: pool/dists/resolute/main/r-cran-shiny.blueprint_0.3.0-1.ca2604.1_all.deb Size: 657476 MD5sum: 3a8fbf9478e23c881c9bdb5ee548deec SHA1: 5c5f6996e6ab1a65da0514639c4ce0124a3db1aa SHA256: fb7f7c1cb5db2bb02ebaf36ec124e2220e15292d7e7dfece970e97d18d5c8229 SHA512: bb94cfef8e4408a9d068c68c0a630b62ec61596de37c7b2155a6d3c5e6ba8fc19a592c16d431a3daf6bc704f70b46890a78b04c658d65fdcace9a29e9801028f Homepage: https://cran.r-project.org/package=shiny.blueprint Description: CRAN Package 'shiny.blueprint' (Palantir's 'Blueprint' for 'Shiny' Apps) Easily use 'Blueprint', the popular 'React' library from Palantir, in your 'Shiny' app. 'Blueprint' provides a rich set of UI components for creating visually appealing applications and is optimized for building complex, data-dense web interfaces. This package provides most components from the underlying library, as well as special wrappers for some components to make it easy to use them in 'R' without writing 'JavaScript' code. Package: r-cran-shiny.destroy Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-rlang, r-cran-shiny Suggests: r-cran-bslib, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shiny.destroy_0.1.0-1.ca2604.1_all.deb Size: 309072 MD5sum: 0684ce01849c67118b2c2a08958a3385 SHA1: 31b38bfc833c4b37589e5b75544652908e61e22c SHA256: 1bc722eee4d3a6d3af6f5ebb37c7f16bc957da4bac3d2fb47f3e1afb4a557350 SHA512: 0b16b990a72e8fd9319ae1058bdbbe51311120b0b86e22e9bf386e51c5da97a31c5fcf72fb3fd756bd408c42357b0f3eb4bec41661deb3339ad48c603edecbe2 Homepage: https://cran.r-project.org/package=shiny.destroy Description: CRAN Package 'shiny.destroy' (Create Destroyable Modules in 'Shiny') Enables the complete removal of various 'Shiny' components, such as inputs, outputs and modules. 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It provides tools to create both default and customizable components for scenarios where data is absent or doesn't match user-defined filters. The package prioritizes user experience, ensuring clarity and consistency even when data is not available to display. Package: r-cran-shiny.exe Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-shiny, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shiny.exe_0.2.0-1.ca2604.1_all.deb Size: 23132 MD5sum: 14b76756bf38ce9d15fbe8abc1cc7aeb SHA1: 47db859098c1bb469dbab29dff4861dfdb7bf805 SHA256: 7261c7179ae7e31c353404ae1bfc5b3bb5aca9045d02276624716a6e831e8ec2 SHA512: c31b16486a1f7ff4533a962371059624f307f8695d1a80ce0c63b95ab1a6c745533198c4ec6ba7a7fca8a7961997de7c64162ee13a142bc541c8358f5c1392bd Homepage: https://cran.r-project.org/package=shiny.exe Description: CRAN Package 'shiny.exe' (Launch a Shiny Application without Opening R or RStudio) Launch an application by a simple click without opening R or RStudio. The package has 3 functions of which only one is essential in its use, `shiny.exe()`. It generates a script in the open shiny project then create a shortcut in the same folder that allows you to launch the app by clicking.If you set `host = 'public'`, the application will be launched on the public server to which you are connected. Thus, all other devices connected to the same server will be able to access the application through the link of your `IPv4` extended by the port. You can stop the application by leaving the terminal opened by the shortcut. Package: r-cran-shiny.fluent Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3434 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-purrr, r-cran-shiny, r-cran-shiny.react Suggests: r-cran-chromote, r-cran-covr, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-glue, r-cran-knitr, r-cran-leaflet, r-cran-mockery, r-cran-plotly, r-cran-rcmdcheck, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-sass, r-cran-shiny.i18n, r-cran-shiny.router, r-cran-shinyjs, r-cran-shinytest2, r-cran-sortable, r-cran-stringi, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-shiny.fluent_0.4.0-1.ca2604.1_all.deb Size: 1253914 MD5sum: aac1d968652546eafffddb4217ab36b4 SHA1: 3c3aadcebfa81fc8beb04a3dbdd9a0bc7e03ae9d SHA256: d693579b72a2683eb53b341bc30a00ee657acf580fd8ce4b4fd84062dc60b335 SHA512: 50e90aeb131d594d538bf91c7281897787e25bcb11e21f4f118f01152e586fe0b6fbe67e5e9e4ab3383136fc5b881930ea38ee96849f9c0d06fbc8e1771cd84e Homepage: https://cran.r-project.org/package=shiny.fluent Description: CRAN Package 'shiny.fluent' (Microsoft Fluent UI for Shiny Apps) A rich set of UI components for building Shiny applications, including inputs, containers, overlays, menus, and various utilities. 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Package: r-cran-shiny.ollama Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-bslib, r-cran-httr, r-cran-jsonlite, r-cran-markdown, r-cran-mockery Suggests: r-cran-testthat, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-shiny.ollama_0.1.1-1.ca2604.1_all.deb Size: 29328 MD5sum: 744a6a4add21478c49eece9910771903 SHA1: 32170b10f52cfa10a2003f0011c6a41421829cb3 SHA256: cbbcd0fd8fa8f36b457fb59e508dd704f62ed3a9103edd59cf99a4e2c8d7d3b3 SHA512: d7e0a12c289539ab41e1d7d92d609b6bd51aa2a63175c272b988cbb6f45adab055aac775e2b7daa6d4ad4fb3a048214009bf46668731622fae7fc026ef3090bf Homepage: https://cran.r-project.org/package=shiny.ollama Description: CRAN Package 'shiny.ollama' (R 'shiny' Interface for Chatting with Large Language ModelsOffline on Local with 'ollama') Chat with large language models on your machine without internet with complete privacy via 'ollama', powered by R 'shiny' interface. For more information on 'ollama', visit . 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Package: r-cran-shiny.semantic Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-semantic.assets, r-cran-shiny Suggests: r-cran-covr, r-cran-chromote, r-cran-dplyr, r-cran-dt, r-cran-gapminder, r-cran-knitr, r-cran-leaflet, r-cran-lintr, r-cran-markdown, r-cran-mockery, r-cran-plotly, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-testthat, r-cran-shinytest2, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-shiny.semantic_0.5.1-1.ca2604.1_all.deb Size: 2870262 MD5sum: 16a8a62bb7efb1d5e5241b5861355081 SHA1: a18817e9855183f218ba453379354a0e6c1575b2 SHA256: 46cf59735d3e2caa6d690fc6d40eadf820a6ee77163878eb2bbd1fe51959b574 SHA512: 0487450b042616ba70cce07dfe27fdb9ea9f25f6928e92cb955ba864016e94680ccb2bf66aab697d28e5b59b81b947d0da20ea88022154123c6010590b7b775d Homepage: https://cran.r-project.org/package=shiny.semantic Description: CRAN Package 'shiny.semantic' (Semantic UI Support for Shiny) Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. 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Package: r-cran-shiny.telemetry Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-htmltools, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-logger, r-cran-lubridate, r-cran-odbc, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-rsqlite, r-cran-shiny, r-cran-stringr, r-cran-tidyr Suggests: r-cran-box, r-cran-config, r-cran-dt, r-cran-knitr, r-cran-mongolite, r-cran-plotly, r-cran-plumber, r-cran-rcolorbrewer, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rpostgresql, r-cran-scales, r-cran-semantic.dashboard, r-cran-shiny.semantic, r-cran-shinyjs, r-cran-testthat, r-cran-timevis, r-cran-withr Filename: pool/dists/resolute/main/r-cran-shiny.telemetry_0.3.2-1.ca2604.1_all.deb Size: 1573446 MD5sum: b00e565274003dfc79544a111499662e SHA1: 3f8a5e90b2ef3b9d631e37acf788be0f32888514 SHA256: d9ad3c7319d57387a8d364819905c0c62e3a6de66a9d451541dfe9995883cb0d SHA512: 1b99a5e37d53cf58dfc9414a772f23a4929178eb092a1e9bb07e16298794e3fa9eb7ce90a1a739ff4cbc52c111b07fc1b18adfa1d7bd87cc5eaa50cfd395bc53 Homepage: https://cran.r-project.org/package=shiny.telemetry Description: CRAN Package 'shiny.telemetry' ('Shiny' App Usage Telemetry) Enables instrumentation of 'Shiny' apps for tracking user session events such as input changes, browser type, and session duration. 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Package: r-cran-shiny2docker Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-attachment, r-cran-cli, r-cran-dockerfiler, r-cran-here, r-cran-yesno Suggests: r-cran-knitr, r-cran-mockery, r-cran-renv, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shiny2docker_0.0.4-1.ca2604.1_all.deb Size: 35588 MD5sum: 492548d73d378bd31a9a62a18c31e5da SHA1: 94953a2a6ced605455691c9a6905eeadf69bb4a0 SHA256: 6b2e6d1ec240d6ffe282c1dd7ef60621ec0d21278dba72b3c816c5b63ec03e7f SHA512: 70664f42c832dc55a35bc349bd87082fda12fffcd8bdcde3738149cba688d05ee48444b40c1aa5a928c76b3d376f46aec27b5deeb571217c7733ccdde3553fe8 Homepage: https://cran.r-project.org/package=shiny2docker Description: CRAN Package 'shiny2docker' (Generate Dockerfiles for 'Shiny' Applications) Automates the creation of Dockerfiles for deploying 'Shiny' applications. 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Package: r-cran-shiny Architecture: all Version: 1.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9635 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-cachem, r-cran-cli, r-cran-commonmark, r-cran-fastmap, r-cran-fontawesome, r-cran-glue, r-cran-htmltools, r-cran-httpuv, r-cran-jsonlite, r-cran-later, r-cran-lifecycle, r-cran-mime, r-cran-otel, r-cran-promises, r-cran-r6, r-cran-rlang, r-cran-sourcetools, r-cran-withr, r-cran-xtable Suggests: r-cran-cairo, r-cran-coro, r-cran-dt, r-cran-dygraphs, r-cran-future, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-markdown, r-cran-mirai, r-cran-otelsdk, r-cran-ragg, r-cran-reactlog, r-cran-rmarkdown, r-cran-sass, r-cran-showtext, r-cran-testthat, r-cran-watcher, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-shiny_1.13.0-1.ca2604.1_all.deb Size: 3687930 MD5sum: 2184976603e796195237fff05aa3652e SHA1: ec54abe58d84f71843497b0a281da16ef2fe63a4 SHA256: 4d660a7d5c0bb9785bd06ec67fde02a8b1b337202f34dbc25d8898e82016b8e7 SHA512: c63dfc5ad307d301a99a6e8841efbec82c1caaa47c39da27260a8a376410c6c1b348a9c1e2a1b7f2e1c857f2e2099c21ff3aa5d198d8cffffbf01800f5c4cb74 Homepage: https://cran.r-project.org/package=shiny Description: CRAN Package 'shiny' (Web Application Framework for R) Makes it incredibly easy to build interactive web applications with R. 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Package: r-cran-shinyaframe Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1370 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-htmlwidgets, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-shinyaframe_1.0.1-1.ca2604.1_all.deb Size: 373832 MD5sum: 7570c4d609bc99c8b14eaa1187e1e947 SHA1: 435467e1131f637002fd1d59e1ccf22aaa518847 SHA256: 73a0a7ae38ab44646501ccad1778077055e0f890e36639dcd9cce18e60997e53 SHA512: c5f9bb2da056bb5e6480fc87844abc2e8e16f7ca16178ae4243c67c213cfb5a96090964588f01563810c173c350d796487c9d2d407000bc6e5d2f5a87ffe0732 Homepage: https://cran.r-project.org/package=shinyaframe Description: CRAN Package 'shinyaframe' ('WebVR' Data Visualizations with 'RStudio Shiny' and 'MozillaA-Frame') Make R data available in Web-based virtual reality experiences for immersive, cross-platform data visualizations. 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Package: r-cran-shinyalert Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-uuid Suggests: r-cran-colourpicker, r-cran-shinydisconnect Filename: pool/dists/resolute/main/r-cran-shinyalert_3.1.0-1.ca2604.1_all.deb Size: 442326 MD5sum: d66033b9150469639f4c8532cce08e10 SHA1: 1b904b9984c74f8e97e6ad6ef3b9ba81b42bebb1 SHA256: 36d8972c278a442d9d42e7ebd61fe911127bbbec5166a4f69c7e21489ab2a5f3 SHA512: 23e657711309779bee03992a66fde3e015c2805c80e1575ac7ab2b5edb63ee6a81f3da7c9e5cd7ad1e2726989897438a2a0a444de8b6cb65d915efcfa48e4876 Homepage: https://cran.r-project.org/package=shinyalert Description: CRAN Package 'shinyalert' (Easily Create Pretty Popup Messages (Modals) in 'Shiny') Easily create pretty popup messages (modals) in 'Shiny'. 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Package: r-cran-shinyauthr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinyjs, r-cran-dplyr, r-cran-rlang, r-cran-sodium, r-cran-glue Suggests: r-cran-dbi, r-cran-rsqlite, r-cran-lubridate, r-cran-shinydashboard, r-cran-testthat, r-cran-shinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-shinyauthr_1.0.0-1.ca2604.1_all.deb Size: 55260 MD5sum: 7659f0131aff064e734ceb0a4c588781 SHA1: 2e99903b596ffe2e466ce8697982aaad9d56940a SHA256: 61b9b8306f0936007b1768dd77aca257817a1f4b1bb5f25fbcfc6a7baa513c06 SHA512: c7f5a4a43b95f60ad77309cb18238de1431bb7166a94dca93c02848d6584024c3972d156d8b1fc6569008f65cec0a264495376d1ef660a8f91d39199913fa0f6 Homepage: https://cran.r-project.org/package=shinyauthr Description: CRAN Package 'shinyauthr' ('Shiny' Authentication Modules) Add in-app user authentication to 'shiny', allowing you to secure publicly hosted apps and build dynamic user interfaces from user information. Package: r-cran-shinybody Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-crosstalk Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-shinybody_0.1.3-1.ca2604.1_all.deb Size: 480250 MD5sum: a5bf67c988c45096922a99a672aae5ea SHA1: 7b8ddaa0ebe4e107f9fa755a5c95197cb1f9c789 SHA256: 0deba38ba06396e0edca05ba3965ab1c9324eff61a357fe0e6da54d852657ea5 SHA512: b1585a4eabe1765511d7d1f0d0469c43d98e16d5be60540891caedb3e5936f88beafd0f94857448dbba7050482bdbea6ab53a18543659957ac418d13ffcdd4bd Homepage: https://cran.r-project.org/package=shinybody Description: CRAN Package 'shinybody' (An Interactive Anatomography Widget for 'shiny') An 'htmlwidget' of the human body that allows you to hide/show and assign colors to 79 different body parts. 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It also functions as an input/output widget in a 'shiny' app. Package: r-cran-shinybrms Architecture: all Version: 1.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-brms, r-cran-rstan, r-cran-rlang Suggests: r-cran-posterior, r-cran-ggplot2, r-cran-shinystan, r-cran-callr, r-cran-rstanarm, r-cran-mass, r-cran-lme4, r-cran-testthat, r-cran-shinytest2 Filename: pool/dists/resolute/main/r-cran-shinybrms_1.8.1-1.ca2604.1_all.deb Size: 123330 MD5sum: 40e60cb74a4ed2bf764b74d7887e28be SHA1: 5cfa50de9839284520f0f46bc3fcf3175e8e8831 SHA256: 81aa63914f6b3c3e4a7decea25d70f48210de0e635ad1f8ae86069b6bf6e29ab SHA512: 4476a1a449395838fcc0b536265860a5d9dc4e1fdbc86d819a1c1e3efa943e8e422763fa5903fd1b0327659779371026706dbca73cf669aa6ac7aff2600ac7a4 Homepage: https://cran.r-project.org/package=shinybrms Description: CRAN Package 'shinybrms' (Graphical User Interface ('shiny' App) for 'brms') A graphical user interface (GUI) for fitting Bayesian regression models using the package 'brms' which in turn relies on 'Stan' (). 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Package: r-cran-shinychat Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-bslib, r-cran-cli, r-cran-coro, r-cran-ellmer, r-cran-fastmap, r-cran-htmltools, r-cran-jsonlite, r-cran-lifecycle, r-cran-promises, r-cran-rlang, r-cran-s7, r-cran-shiny Suggests: r-cran-knitr, r-cran-later, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-shinychat_0.3.0-1.ca2604.1_all.deb Size: 824128 MD5sum: c9a671104d0c0c3e2cbc86f57d5849f8 SHA1: 8aa60558fff3674162fb68303536195350256e06 SHA256: 2bbe6fa136a0eee25f73b112c58d2c8d7fc1f2f90ceeeed34896fecd74001ab3 SHA512: a535761e5ef4e0ccc271b1313eefecd6d6fee731feb1c426b322e3ad766c5d8adc40704b375c81a4a5e7d3fb840962259773be3a920204ae484c248ba1a854c1 Homepage: https://cran.r-project.org/package=shinychat Description: CRAN Package 'shinychat' (Chat UI Component for 'shiny') Provides a scrolling chat interface with multiline input, suitable for creating chatbot apps based on Large Language Models (LLMs). 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Package: r-cran-shinyclt Architecture: all Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-purrr, r-cran-shiny, r-cran-gamlss, r-cran-dplyr, r-cran-plotly, r-cran-future, r-cran-shinycssloaders, r-cran-waiter, r-cran-shinythemes, r-cran-shinywidgets, r-cran-cachem, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-shinyclt_0.9.4-1.ca2604.1_all.deb Size: 41636 MD5sum: cb4cdf68744de0bd544e3805a58cd187 SHA1: 15784c606c519355bb87bd31367ee7754a2fcf3e SHA256: 66efe43e81fc47603143f67a3531f350c94df59530bce7eeb6ef3f9ff6f30834 SHA512: b943300db09483a654f68c66f9344d5a54e62c4a9f663299cc319d412404e70d6010f3ef19ffbc8ca65815099be3c6bb960e362f53b7d7ca1243b14d37f718e2 Homepage: https://cran.r-project.org/package=shinyCLT Description: CRAN Package 'shinyCLT' (Central Limit Theorem 'shiny' Application) A 'shiny' application estimating the operating characteristics of the Student's t-test by Student (1908) , Welch's t-test by Welch (1947) , and Wilcoxon test by Wilcoxon (1945) in one-sample or two-sample cases, in settings defined by the user (conditional distribution, sample size per group, location parameter per group, nuisance parameter per group), using Monte Carlo simulations Malvin H. Kalos, Paula A. Whitlock (2008) . Package: r-cran-shinycohortbuilder Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2932 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-glue, r-cran-bslib, r-cran-jsonlite, r-cran-purrr, r-cran-ggplot2, r-cran-ggiraph, r-cran-htmltools, r-cran-shiny, r-cran-shinywidgets, r-cran-htmlwidgets, r-cran-dplyr, r-cran-cohortbuilder, r-cran-trycatchlog, r-cran-highr, r-cran-shinygizmo, r-cran-rlang, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-querybuilder, r-cran-shinyquerybuilder, r-cran-pkgload, r-cran-sass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinycohortbuilder_0.4.0-1.ca2604.1_all.deb Size: 1871628 MD5sum: c8da4324d6700ad4ec33327699984a57 SHA1: 1d38c995cccdabb27d56b91d1ee30b77e4253e54 SHA256: a20d44a18e837b77718e5ce44f64d317b915af582808984f22212413ae85aceb SHA512: 928d1590381caf9d6dc8c760c8efc7fa6c191bb7162fdc4bf7da54d4504bbe977d7bc348cba1982f4ed54a789cb9821d65084f141b7dc6152857a9d70aabdd99 Homepage: https://cran.r-project.org/package=shinyCohortBuilder Description: CRAN Package 'shinyCohortBuilder' (Modular Cohort-Building Framework for Analytical Dashboards) You can easily add advanced cohort-building component to your analytical dashboard or simple 'Shiny' app. Then you can instantly start building cohorts using multiple filters of different types, filtering datasets, and filtering steps. 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Package: r-cran-shinycox Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-survival Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-shinydashboard Filename: pool/dists/resolute/main/r-cran-shinycox_1.1.3-1.ca2604.1_all.deb Size: 111390 MD5sum: 4e54b9b35d50fd697f6e0508f36e4658 SHA1: 7549bd204620d4a45b2b14387d99039466978b48 SHA256: e26157a2123235a1b5913862388264c178af3d63b823315e8f42d738f4f76baa SHA512: 246245cd921d41a61d602facf9a6bc4525b4e6b9610106c5051cf781a3f891cf49235e010359bb23b73ce4cd7722fd29170f8a119d28f35497ada436277be5a9 Homepage: https://cran.r-project.org/package=shinyCox Description: CRAN Package 'shinyCox' (Create 'shiny' Applications for Cox Proportional Hazards Models) Takes one or more fitted Cox proportional hazards models and writes a 'shiny' application to a directory specified by the user. 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Package: r-cran-shinycroneditor Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets Suggests: r-cran-shiny Filename: pool/dists/resolute/main/r-cran-shinycroneditor_1.0.0-1.ca2604.1_all.deb Size: 34854 MD5sum: 544614ae00b21621f5806f5be097c05a SHA1: 2a39f848b5ff269a796cc900f619c19e15dc4e27 SHA256: b84bb632ccee756bb3314b9a23f00bd307cc066deca7abd2519946674dd28488 SHA512: 27ab4935da5eb54a7483e8912ee44481874762f83879dee8d7d3ea230fdb67f82845cee861742a4ac38019487aeef3b3aea35272d5b6935ffb4cd09fd7345ee0 Homepage: https://cran.r-project.org/package=shinycroneditor Description: CRAN Package 'shinycroneditor' ('shiny' Cron Expression Input Widget) A widget for 'shiny' apps to handle schedule expression input, using the 'cron-expression-input' JavaScript component. 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Package: r-cran-shinycyjs Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shinycyjs_1.0.0-1.ca2604.1_all.deb Size: 508010 MD5sum: 23ac15eb7883f6b43b71efe2fb3512fb SHA1: 45f122e6519b2c230dfb88d35025e13b8128ce6a SHA256: 9cf213b80f4db681009a902c63153f72c23085e3d21a01e9c8590508c7468b68 SHA512: 36b53cc4ddf9a7bc766bf1e48bc922a98ff400b9bf140c14053c6c8dbcf078a68c5a1eaa25e10bcfcead759f6442d2f03139544394630d8a16e7d6e45af6f54f Homepage: https://cran.r-project.org/package=shinyCyJS Description: CRAN Package 'shinyCyJS' (Create Interactive Network Visualizations in R and 'shiny') Create Interactive Graph (Network) Visualizations. 'shinyCyJS' can be used in 'Shiny' apps or viewed from 'Rstudio' Viewer. 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This is the same motivation as the 'shiny' package, but note that the development of 'shinylight' is not in any way linked to that of 'shiny' (beyond the use of the 'httpuv' package). You might prefer 'shinylight' to 'shiny' if you want a lighter weight deployment with easier horizontal scaling, or if you want to develop your front end yourself in JavaScript and HTML just using a lightweight remote procedure call interface to your R code on the server. Package: r-cran-shinylive Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-brio, r-cran-cli, r-cran-fs, r-cran-gh, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-pkgdepends, r-cran-rappdirs, r-cran-renv, r-cran-rlang, r-cran-whisker, r-cran-withr Suggests: r-cran-httpuv, r-cran-pkgcache, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinylive_0.4.1-1.ca2604.1_all.deb Size: 100878 MD5sum: 3f59834ee5f405e557192df4db3e0916 SHA1: 5e6448a4c8be17b1b4ca1bfffeccf8b4df37d405 SHA256: a66f167eeba928d9ee7c1510aebddea9d73753cfc569d508906ebe869544c735 SHA512: efd23f314e23dc5a27711d768d5b4b1d5e8f759850ae01d59f416a388db589d1d57329aeafc5ff37d8575b07401f41c8b258d0158702d03dbef0e02d31bb9a8f Homepage: https://cran.r-project.org/package=shinylive Description: CRAN Package 'shinylive' (Run 'shiny' Applications in the Browser) Exporting 'shiny' applications with 'shinylive' allows you to run them entirely in a web browser, without the need for a separate R server. The traditional way of deploying 'shiny' applications involves in a separate server and client: the server runs R and 'shiny', and clients connect via the web browser. When an application is deployed with 'shinylive', R and 'shiny' run in the web browser (via 'webR'): the browser is effectively both the client and server for the application. This allows for your 'shiny' application exported by 'shinylive' to be hosted by a static web server. Package: r-cran-shinyloadtest Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-httpuv, r-cran-jsonlite, r-cran-magrittr, r-cran-r6, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-svglite, r-cran-vroom, r-cran-websocket, r-cran-xml2 Suggests: r-cran-getpass, r-cran-glue, r-cran-gtable, r-cran-htmltools, r-cran-lubridate, r-cran-progress, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinyloadtest_1.2.1-1.ca2604.1_all.deb Size: 573686 MD5sum: 45a578543d5a66d059e0d3635c561e55 SHA1: e8d03ff5f0f44bdae7cb6ed315a82336dbef2f5a SHA256: cb9620d691f657167c478df21f1435044ee5e6f5c75c39eab8f995706faf9902 SHA512: 65a4967b6ffbdeb664c5059c1dc309452e3bece1fe5c86052986c5ff7a12eb253ab289eee7840f4cfbe96117e7ae282d30930cb78ac2314199adef7d0c0479ef Homepage: https://cran.r-project.org/package=shinyloadtest Description: CRAN Package 'shinyloadtest' (Load Test Shiny Applications) Assesses the number of concurrent users 'shiny' applications are capable of supporting, and for directing application changes in order to support a higher number of users. 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Package: r-cran-shinylogs Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-jsonlite, r-cran-data.table, r-cran-bit64, r-cran-nanotime, r-cran-digest, r-cran-anytime Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-dbi, r-cran-rsqlite, r-cran-googledrive Filename: pool/dists/resolute/main/r-cran-shinylogs_0.2.1-1.ca2604.1_all.deb Size: 119320 MD5sum: a6e2413dfce277a7dd489dc46b825a20 SHA1: bc705bb1634192bebf84802d23adcde2cd97ed33 SHA256: 1749a6ce598ab3d176b58800422558901faea625613f59ab1368f48fd73cc698 SHA512: 363f3659226b1c7402f62a971daf3998a8bd27307cffddab561aa1565868c6a66846ff11daf39b035013ce204c1d25f548c3b4dd7c853397f40762ff41363474 Homepage: https://cran.r-project.org/package=shinylogs Description: CRAN Package 'shinylogs' (Record Everything that Happens in a 'Shiny' Application) Track and record the use of applications and the user's interactions with 'Shiny' inputs. 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Package: r-cran-shinylottie Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite, r-cran-glue, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shinylottie_1.0.0-1.ca2604.1_all.deb Size: 91044 MD5sum: b8f02907960cbe0e014e8e9e15e89a88 SHA1: 081fc07ab21b207c1f342cd9c6bb108d530b5f72 SHA256: dbb94f7a88b69ac294392ab08fa037a6e7cfd4cac3299a62737b7707c60ebdcc SHA512: c950a8534786a377229ba45c4464efb607eacdf7fa91f08904b80a706a7d555a86a4d694b922ecf688e7611c233bf913cad34dcefad12b2f69f1b9accfc8b9e6 Homepage: https://cran.r-project.org/package=shinyLottie Description: CRAN Package 'shinyLottie' (Seamlessly Integrate 'Lottie' Animations into 'shiny'Applications) Easily integrate and control 'Lottie' animations within 'shiny' applications', without the need for idiosyncratic expression or use of 'JavaScript'. 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Package: r-cran-shinymaterial Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1653 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite, r-cran-sass Filename: pool/dists/resolute/main/r-cran-shinymaterial_1.2.0-1.ca2604.1_all.deb Size: 1152232 MD5sum: 97ce836477ae714f7e48f7a53a10b3f8 SHA1: b01e4e1cda906b2e20efd86ae5b1cf94831f1872 SHA256: cc8ca035d87b390a8d1999cea52802f256480c74db439b6d6f34ab516125aa94 SHA512: 3cac9ac8a1d0d0eac81551bcad0f0906d1beb17c6960e5e6996542e01b7edeb25d19041d5a85c5bf753569dba9ecfadded5ce138f2ba0299384d7f8e63bf00d8 Homepage: https://cran.r-project.org/package=shinymaterial Description: CRAN Package 'shinymaterial' (Implement Material Design in Shiny Applications) Allows shiny developers to incorporate UI elements based on Google's Material design. See for more information. 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Package: r-cran-shinymergely Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-shinyjqui, r-cran-shinythemes Filename: pool/dists/resolute/main/r-cran-shinymergely_0.2.0-1.ca2604.1_all.deb Size: 590908 MD5sum: d3027ff04f6840c31685385c78e85bbf SHA1: a13754be780c3c7fd14b81b3a3148d89ca05e1a2 SHA256: 483eae002b7878cd22ea4249967990c5edec6668e855bf7a546c2ebd6b323923 SHA512: efa6852ff3ea2be5ae2072e3cc48e250153ebbe9fea86015fdbc9f7084fee7ae4c348d00289e1e53bd2f9ce77bd0869ff2ebd55eb8f305986afdb1ac65be06d9 Homepage: https://cran.r-project.org/package=shinyMergely Description: CRAN Package 'shinyMergely' (Compare and Merge Two Files with a 'Shiny' App) A 'Shiny' app allowing to compare and merge two files, with syntax highlighting for several coding languages. Package: r-cran-shinymeta Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-fastmap, r-cran-fs, r-cran-htmltools, r-cran-rlang, r-cran-shiny, r-cran-sourcetools, r-cran-styler Suggests: r-cran-clipr, r-cran-cranlogs, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-shinyace, r-cran-stringr, r-cran-testthat, r-cran-xfun, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-shinymeta_0.2.2-1.ca2604.1_all.deb Size: 133884 MD5sum: e0056532a352d957097bf9a7e3dd3bff SHA1: 7ba630d133fc906f4cd94bf17a6228923165cb2d SHA256: f0eb7482112f01e1d0c99252ff4b2f4838afcfd9b1669dec5f4c8552a48e6a02 SHA512: fd71c875c0cdace50589a67f2b009f3c0281b05bb2a1251f5d72def7f1f184f0e308338797e827c0bf4371625151549a79ef0f65c9db9a251cc9ad31a0651eb2 Homepage: https://cran.r-project.org/package=shinymeta Description: CRAN Package 'shinymeta' (Export Domain Logic from Shiny using Meta-Programming) Provides tools for capturing logic in a Shiny app and exposing it as code that can be run outside of Shiny (e.g., from an R console). 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Package: r-cran-shinymgr Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dbi, r-cran-reactable, r-cran-renv, r-cran-rsqlite, r-cran-shinyjs, r-cran-shinydashboard Suggests: r-cran-fs, r-cran-learnr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinymgr_1.1.0-1.ca2604.1_all.deb Size: 2433724 MD5sum: 050d775849ef684c773556b3ac87fbc0 SHA1: 9fcdc633f980bb531c120fc7fc81a2c5a8523d2c SHA256: d649a94abba8e383f69c0ffc521cb01fe343d43d2612af76b917eaccc2e1b4e0 SHA512: 29a4a6821fe2cc2c797ca11435420122bea699dda4b17f6e2f0b3501bd63e0dd90cd0ed2a5a695ceab44a9f1a242b78a261458db682dd43a008b00e47fcef993 Homepage: https://cran.r-project.org/package=shinymgr Description: CRAN Package 'shinymgr' (A Framework for Building, Managing, and Stitching 'shiny'Modules into Reproducible Workflows) A unifying framework for managing and deploying 'shiny' applications that consist of modules, where an "app" is a tab-based workflow that guides a user step-by-step through an analysis. The 'shinymgr' app builder "stitches" 'shiny' modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Users of 'shinymgr' apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an R Markdown report for rapid reporting. In short, developers use the 'shinymgr' framework to write modules and seamlessly combine them into 'shiny' apps, and users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination. Package: r-cran-shinymixr Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1097 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-gridextra, r-cran-collapsibletree, r-cran-shinyace, r-cran-dt, r-cran-bs4dash, r-cran-shinywidgets, r-cran-stringi, r-cran-r3port, r-cran-whisker, r-cran-plotly, r-cran-patchwork, r-cran-shinyjs, r-cran-ps, r-cran-xfun, r-cran-fresh, r-cran-nlmixr2est, r-cran-magrittr, r-cran-rxode2, r-cran-cli Suggests: r-cran-xpose, r-cran-nlmixr2, r-cran-nlmixr2plot, r-cran-xpose.nlmixr2, r-cran-nlme, r-cran-testthat, r-cran-shinytest2, r-cran-knitr, r-cran-rmarkdown, r-cran-rlang, r-cran-miniui, r-cran-shinyfiles, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-shinymixr_0.5.3-1.ca2604.1_all.deb Size: 956038 MD5sum: b2e086527d0b0f32d3a961f1ef7d2b74 SHA1: 61f8fb8c28ea5ed41979489b872f0b395419bf69 SHA256: 23df6caa2117bf2d10ea964628f80f63fdd32ec8d228373efcce8612ceb2cdf7 SHA512: 5d28956b33efb5966299fb5899c352a4daed7d48b92ab6eb5e3615e432b5ce52d303d67c03ecf31ff667c3a60220b3eaaa469d6872b8246bdefa3de9924da23c Homepage: https://cran.r-project.org/package=shinyMixR Description: CRAN Package 'shinyMixR' (Interactive 'shiny' Dashboard for 'nlmixr2') An R shiny user interface for the 'nlmixr2' (Fidler et al (2019) ) package, designed to simplify the modeling process for users. 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Package: r-cran-shinyml Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-shiny, r-cran-argondash, r-cran-argonr, r-cran-shinyjs, r-cran-h2o, r-cran-shinywidgets, r-cran-dygraphs, r-cran-plotly, r-cran-sparklyr, r-cran-tidyr, r-cran-dt, r-cran-ggplot2, r-cran-shinycssloaders, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinyml_1.0.1-1.ca2604.1_all.deb Size: 159180 MD5sum: f22e56f2d4fc7b17057e0c546dfa0ce2 SHA1: 779ab8860205f9566a568c25519e23c2aab8137c SHA256: 805796636c49d0e4e77a9713594b4a90cb567427dc3740a97453368aa7990a2d SHA512: 0e847b6c2fff26f59c1186f6cd9d5973a3c746d32be5db5c7a6022d0ed80f7385389792028197e146fc849c7f39103dbd0fa62464683acc620bf75dd79e05f4e Homepage: https://cran.r-project.org/package=shinyML Description: CRAN Package 'shinyML' (Compare Supervised Machine Learning Models Using Shiny App) Implementation of a shiny app to easily compare supervised machine learning model performances. You provide the data and configure each model parameter directly on the shiny app. Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks. Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148). Package: r-cran-shinymobile Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4508 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-gplots, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cli, r-cran-testthat, r-cran-rstudioapi, r-cran-shinywidgets, r-cran-apexcharter, r-cran-ggplot2, r-cran-dplyr, r-cran-bslib, r-cran-shinytest2, r-cran-thematic Filename: pool/dists/resolute/main/r-cran-shinymobile_2.0.1-1.ca2604.1_all.deb Size: 1628304 MD5sum: c0e42c892e1b16f470dc5f9cb037c47b SHA1: 214d0e862faf91ad0aef7c7f352c483d7d9d0ee3 SHA256: 86572f8074c8d0aa0d851a519aa76079b23dac17a300f0afa3d09332bf5c16e9 SHA512: ad0f70729d09ffeae6f3ea7ca853fb484102c911ddf1242a8afa932c4ca5970de006e3dc3888769bb0f6db79d8f900fe68efeb78671be7196fd63347945942de Homepage: https://cran.r-project.org/package=shinyMobile Description: CRAN Package 'shinyMobile' (Mobile Ready 'shiny' Apps with Standalone Capabilities) Develop outstanding 'shiny' apps for 'iOS' and 'Android' as well as beautiful 'shiny' gadgets. 'shinyMobile' is built on top of the latest 'Framework7' template . Discover 14 new input widgets (sliders, vertical sliders, stepper, grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) combined with the power of server-side notifications such as alerts, modals, toasts, action sheets, sheets (and more) as well as 3 layouts (single, tabs and split). Package: r-cran-shinymodels Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1607 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-dt, r-cran-generics, r-cran-glue, r-cran-htmltools, r-cran-magrittr, r-cran-parsnip, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinydashboard, r-cran-tidyr, r-cran-tidyselect, r-cran-tune, r-cran-yardstick Suggests: r-cran-covr, r-cran-finetune, r-cran-knitr, r-cran-markdown, r-cran-modeldata, r-cran-rmarkdown, r-cran-shinytest, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-shinymodels_0.1.1-1.ca2604.1_all.deb Size: 1526498 MD5sum: d95ed18cc3c96eaced7d7a91b6d39cb7 SHA1: 8fb3a1076d97a762c18992696e243eb2b0f7cf11 SHA256: 3fda45af5b392c50939fbf38b96b55b23ad56f91c5e7c91df5282854debe599d SHA512: 45db5cd212187dc3505f1f1df4f56d93197548dbeb70f44aed498a4f25abeea38171f966f5a37f24f33a82c08d4eb9f3d07cde72e824e9ced7072e328942e37c Homepage: https://cran.r-project.org/package=shinymodels Description: CRAN Package 'shinymodels' (Interactive Assessments of Models) Launch a 'shiny' application for 'tidymodels' results. 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It is particularly well developed for 'JavaScript'. In addition to the 'Monaco' editor features, the app provides prettifiers and minifiers for multiple languages, 'SCSS' and 'TypeScript' compilers, code checking for 'C' and 'C++' (requires 'cppcheck'). Package: r-cran-shinymrp Architecture: all Version: 0.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsicons, r-cran-bslib, r-cran-checkmate, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-highcharter, r-cran-htmlwidgets, r-cran-httr2, r-cran-loo, r-cran-lubridate, r-cran-magrittr, r-cran-matrix, r-cran-patchwork, r-cran-posterior, r-cran-purrr, r-cran-qs2, r-cran-r6, r-cran-rcolorbrewer, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-stringr, r-cran-tidyr, r-cran-waiter Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinymrp_0.10.0-1.ca2604.1_all.deb Size: 2145006 MD5sum: 14abda458409ae1f87b17a9ce6031fa5 SHA1: 89b19d90a196d5432678ea00c4f031334b8c19ca SHA256: fe7b775451bce59df57da536ffabd4eead60d2a29cbca90689986ffc287fd306 SHA512: 28d76323a809c52f7532b49acd434a3e3c33e4749007bc8289c7ef7a940489847c24c006234f5c856cf539196def5ea902ba8112a0ab513c7790a5085534838a Homepage: https://cran.r-project.org/package=shinymrp Description: CRAN Package 'shinymrp' (Interface for Multilevel Regression and Poststratification) Dual interfaces, graphical and programmatic, designed for intuitive applications of Multilevel Regression and Poststratification (MRP). Users can apply the method to a variety of datasets, from electronic health records to sample survey data, through an end-to-end Bayesian data analysis workflow. The package provides robust tools for data cleaning, exploratory analysis, flexible model building, and insightful result visualization. For more details, see Si et al. (2020) and Si (2025) . Package: r-cran-shinynextui Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4691 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-shiny.react, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-shinytest2, r-cran-purrr, r-cran-thematic, r-cran-shiny.router, r-cran-roxy.shinylive Filename: pool/dists/resolute/main/r-cran-shinynextui_0.1.0-1.ca2604.1_all.deb Size: 1562290 MD5sum: 679a605092ba1da81188fa2d5df61f53 SHA1: a695dff3307e397d68e9e64fe247e6db58607574 SHA256: 6a459778026c2b50dea6cb5feae8665ac36b6daab2db06bd866809f4471e893d SHA512: a5906d34e90bccb95e54f90ff70367569380aa450f296c7e6fedd86fc8d4618004599a3242a44efbe76a97ca61e6220768958b57d0f3a7d19bb192b3b7f096c9 Homepage: https://cran.r-project.org/package=shinyNextUI Description: CRAN Package 'shinyNextUI' ('HeroUI' 'React' Template for 'shiny' Apps) A set of user interface components to create outstanding 'shiny' apps , with the power of 'React' 'JavaScript' . 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Package: r-cran-shinynorrrm Architecture: all Version: 0.8.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-pracma, r-cran-ternary Filename: pool/dists/resolute/main/r-cran-shinynorrrm_0.8.6-1.ca2604.1_all.deb Size: 1163122 MD5sum: 247ebf9409e1de1ab93d9090171ab830 SHA1: 9f229e1f8538bf34e755713bc87bf40a9cf195db SHA256: 803d69afa2a516de310a5bd49ebf256dbbd25b4b99790caf87fc8338bc125958 SHA512: 934daa52794e3a5618d76700e94f1fdca8c52383d26f00696ea3061fe7ddc51ff0a181d410fe8604462a06bd19d17df7917f268577149066a914d47fa0f4d1d2 Homepage: https://cran.r-project.org/package=shinyNORRRM Description: CRAN Package 'shinyNORRRM' (The Ultimate Igneous Norm) The computer program is an efficient igneous norm algorithm and rock classification system written in R but run as shiny app. Package: r-cran-shinynotes Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinyjs, r-cran-shiny, r-cran-shinywidgets, r-cran-dplyr, r-cran-dbi, r-cran-dbplyr, r-cran-rsqlite, r-cran-magrittr, r-cran-stringr, r-cran-markdown, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shinynotes_0.0.3-1.ca2604.1_all.deb Size: 90946 MD5sum: 58341cbdecdd0d708dbb38e64d93c2e3 SHA1: 0b92d640aace734df7ced156c44e84bcd9cdf83d SHA256: 49c63958504c8025b86a4d41b608c6e5117b150d774b11a3397d5040061623ca SHA512: 6727c16831cef2058fce7e446e83e1e125627363f0fc8fbf85b6825f6b63093b7679258779d6ffedeb5c188074d2f6ceb46c3826686a4b5a5f5d157f169ffb36 Homepage: https://cran.r-project.org/package=shinyNotes Description: CRAN Package 'shinyNotes' (Shiny Module for Taking Free-Form Notes) An enterprise-targeted scalable and customizable 'shiny' module providing an easy way to incorporate free-form note taking or discussion boards into applications. 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Package: r-cran-shinyoauth Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1842 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-r6, r-cran-rlang, r-cran-shiny, r-cran-jsonlite, r-cran-openssl, r-cran-httr2, r-cran-urltools, r-cran-cachem, r-cran-jose, r-cran-lifecycle, r-cran-cli, r-cran-htmltools, r-cran-otel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webfakes, r-cran-promises, r-cran-mirai, r-cran-future, r-cran-withr, r-cran-later, r-cran-callr, r-cran-chromote, r-cran-sodium, r-cran-shinytest2, r-cran-xml2, r-cran-otelsdk Filename: pool/dists/resolute/main/r-cran-shinyoauth_0.5.0-1.ca2604.1_all.deb Size: 1299274 MD5sum: b6774419f04c6a8145bef046f26efb35 SHA1: 227145531a0bb0545b6ef3bb6d2bbb3a8626334e SHA256: 30bf2b4cac924ea907ff27f226b41aaf2abfa2c40bc08555ab67d8c645419fff SHA512: 3fa00fe1b6c29d0e84a2fed00746ddec129da3c36b7b528d947020987d2984f0ba71b36e71f929a7836485aa1bbced8f479dcdbd14db57842f25e095c07581a3 Homepage: https://cran.r-project.org/package=shinyOAuth Description: CRAN Package 'shinyOAuth' (Provider-Agnostic OAuth Authentication for 'shiny' Applications) Provides a simple, configurable, provider-agnostic 'OAuth 2.0' and 'OpenID Connect' (OIDC) authentication framework for 'shiny' applications using 'S7' classes. Defines providers, clients, and tokens, as well as various supporting functions and a 'shiny' module. Features include cross-site request forgery (CSRF) protection, state encryption, 'Proof Key for Code Exchange' (PKCE) handling, validation of OIDC identity tokens (nonces, signatures, claims), automatic user info retrieval, asynchronous flows, and hooks for audit logging. Package: r-cran-shinyobjects Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-knitr, r-cran-magrittr, r-cran-pander, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-rstudioapi, r-cran-shiny, r-cran-stringr, r-cran-styler, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-mockery, r-cran-spelling, r-cran-covr Filename: pool/dists/resolute/main/r-cran-shinyobjects_0.2.0-1.ca2604.1_all.deb Size: 967724 MD5sum: 8ceceada88d4012bb6317849747327b3 SHA1: 33ffce08a0e12ab3c82de098cd92a504efb1bb63 SHA256: e1398c6fcb8e1aacbc1f6b6818d20c61361952d36a3c48a3cd6fe845ce1f7e9f SHA512: a1612aa76a1b91c505af0473ed4fc273b11cbae3e57a195717a0aa8ebc1de56023d8620dac7b30ecacda95f8e3db277d43f3d5b0128b85da750231ba3e631b4a Homepage: https://cran.r-project.org/package=shinyobjects Description: CRAN Package 'shinyobjects' (Access Reactive Data Interactively) Troubleshooting reactive data in 'shiny' can be difficult. 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Package: r-cran-shinypanel Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinyjs, r-cran-shiny, r-cran-shinybs, r-cran-htmltools, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-shinypanel_0.1.5-1.ca2604.1_all.deb Size: 42594 MD5sum: 7546a671c0bb3c277c2b17925f8dae0a SHA1: dc21ee06edea0e6a1d6af11bcb2d9c8333307f77 SHA256: 91848be38dc622509cf5eaf1c96b8cf86794481ecb0d44faee152ef8a7db3325 SHA512: 5af8ce4031ef1508a650f7764bffd4e89f8b79999aaf4805066a46d7f2c2f9f2699a2d8712f5feff6d90130a0a45283d6ed49cd50d8be7382d487eda27792c54 Homepage: https://cran.r-project.org/package=shinypanel Description: CRAN Package 'shinypanel' (Shiny Control Panel) Add shiny inputs with one or more inline buttons that grow and shrink with inputs. Also add tool tips to input buttons and styling and messages for input validation. Package: r-cran-shinypayload Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr, r-cran-styler, r-cran-roxygen2, r-cran-dt Filename: pool/dists/resolute/main/r-cran-shinypayload_0.1.0-1.ca2604.1_all.deb Size: 40002 MD5sum: 5bccbf7c3174bc3e71745fd2f21e63d7 SHA1: f02b1c04ee81b9c791ffa80ecf845f49c3353f13 SHA256: 861fdffd739c49ab8b980133bf617d89acbb05bfc37ebc4e093edb99307d5a83 SHA512: dd7ef282828a52d3dee14c04945fe88fff309e00658ee393e971dec47ea6aae287d66bb68b3aaa686d40eb820e0ff3d42a6f59e2902981c45c8f342b1712d468 Homepage: https://cran.r-project.org/package=shinypayload Description: CRAN Package 'shinypayload' (Accept POST Data and URL Parameters in 'shiny' (Same-PortIntegration)) Handle POST requests on a custom path (e.g., /ingress) inside the same 'shiny' HTTP server using user interface functions and HTTP responses. Expose latest payload as a reactive and provide helpers for query parameters. Package: r-cran-shinypivottabler Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pivottabler, r-cran-shiny, r-cran-openxlsx, r-cran-colourpicker, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-shinypivottabler_1.2-1.ca2604.1_all.deb Size: 796002 MD5sum: 23a696ef53f1b263a90df3d2e4b29e7d SHA1: 6a1debb9240f04be16f8f39c3258e29f5ab6cdb6 SHA256: 785c528769486a34ba59bf611a5d2357d84a8df353904139e0fde5e1141989d5 SHA512: 6c8d50020837bbcdba0ffe5d6978a71ac3a183c74da5b0516ef3c750e3f34aa9ae859e75fcf88bda4ad7540b414027aa50a40403e2396c08f7897936e7111a60 Homepage: https://cran.r-project.org/package=shinypivottabler Description: CRAN Package 'shinypivottabler' (Shiny Module to Create Pivot Tables) Shiny Module to create, visualize, customize and export Excel-like pivot table. 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Package: r-cran-shinyproxylogs Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinyproxylogs_0.1.0-1.ca2604.1_all.deb Size: 27080 MD5sum: 2db2b69927ffeb3a7aee70aefb7f78a3 SHA1: a426a2f917028a5d8d8848183146b7a2727c03a7 SHA256: 6cef3b8be5d2205d4b48313a062880ae22a3a740392f6948bd9b07e640ad2796 SHA512: 913cb9ee47d62a8ef701fb68d93e20eae4fd34065dec775c5135793d22d078b6474148468fcd9560245631414c98e0c78f2f216fb435acba629aac55f18be9c0 Homepage: https://cran.r-project.org/package=shinyproxyLogs Description: CRAN Package 'shinyproxyLogs' (Tools for Analyzing 'ShinyProxy' Containers Logs) Provides functions to parse and analyze logs generated by 'ShinyProxy' containers. 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Package: r-cran-shinyquerybuilder Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-querybuilder, r-cran-rlang, r-cran-r6, r-cran-magrittr, r-cran-jsonlite, r-cran-htmltools, r-cran-shiny, r-cran-glue, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shinyquerybuilder_0.1.0-1.ca2604.1_all.deb Size: 564672 MD5sum: 4fe36b8fa5cf2463f1a38e97b8bf7c96 SHA1: e050244397f37917d4c64ce9245b2a5e659a979e SHA256: 7a0f2f9917d97ac5e6d7e1164f4d3180b8a587ba282cd3121e47c180bd5f1fae SHA512: 66e3cabd030a8637818eff2ef47e577ac2598d97f29427981311d6fc783c03e7ae8cbb2dd8326d246b416705e64ef956542d6def865ad6cf035b2036edbafd57 Homepage: https://cran.r-project.org/package=shinyQueryBuilder Description: CRAN Package 'shinyQueryBuilder' (Construct Complex Filtering Queries in 'Shiny') Input widget that allows to construct complex filtering queries in 'Shiny'. It's a wrapper for 'JavaScript' library 'jQuery-QueryBuilder', check . 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Easily create quizzes from various pre-built question and choice types or create your own using 'htmltools' and 'shiny' packages as building blocks. Integrates with larger 'shiny' applications. Ideal for non-web-developers such as educators, data scientists, and anyone who wants to assess responses interactively in a small form factor. 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Package: r-cran-shinyrecipes Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-recipes, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-shiny, r-cran-miniui, r-cran-shinywidgets, r-cran-shinyglide, r-cran-sortable, r-cran-rstudioapi, r-cran-dt, r-cran-tidyr, r-cran-esquisse Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shinyrecipes_0.1.0-1.ca2604.1_all.deb Size: 77138 MD5sum: e9ef841127b08fafde73e3017de90f75 SHA1: d67b3ea317e4bd7b675bd637a8480afb06217438 SHA256: e4f64af2eee69caa79d3571295e8cf30dc96f741be5847d76502dca2331c3067 SHA512: d0073fd09d99b9437c384cbde905e94e41fab7afab4125c4b5bb5a3ec5c4e20990277dcd03a619f4bc919743cbd9e109b6923186a0d0ca4921fdb26531ea928b Homepage: https://cran.r-project.org/package=shinyrecipes Description: CRAN Package 'shinyrecipes' (Gadget to Use the Data Preprocessing 'recipes' PackageInteractively) This gadget allows you to use the 'recipes' package belonging to 'tidymodels' to carry out the data preprocessing tasks in an interactive way. Build your 'recipe' by dragging the variables, visually analyze your data to decide which steps to use, add those steps and preprocess your data. Package: r-cran-shinyreprex Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1516 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-s7, r-cran-constructive, r-cran-purrr, r-cran-rlang, r-cran-styler Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinyreprex_0.1.0-1.ca2604.1_all.deb Size: 1473674 MD5sum: edf63037c46d5aba5a06020901eece4c SHA1: 75a8312170c7c7cc05974c87a29f3726253f8b46 SHA256: 1b10cf2551174a47d404db2692a603daf43d2b9b1aa2f2f7de878d090bcb1f04 SHA512: 337a24531401a1025cee21755d8112acf1f21cf29d8757dc9c62894c6f88081738968cfb68dc40a083d5e6475a61d1da8f21c3d53e681b1050a7944ece188f53 Homepage: https://cran.r-project.org/package=shinyreprex Description: CRAN Package 'shinyreprex' (Reproducible Code for 'Shiny' Objects) Provides functionality to extract reactive expressions from a 'shiny' application and convert them into stand-alone R scripts. 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Package: r-cran-shinyrgl Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinyrgl_0.1.0-1.ca2604.1_all.deb Size: 22778 MD5sum: 89437f629619d1d84a6bde494dfbf39a SHA1: 8f24dbcb05bf96fcc245d440b4f5ad80b905176f SHA256: bfe19f2e390e751977096ed1e97f3116fc5f9195a4609d3695e59dced0ba0b9c SHA512: 60d2e69763df74f3251a60513339cf20afd0e9e511eb36f6453571cdebeb3d45dca08faf7259a584c5ad1a772d26c7c2deb8067ca204905ca5cf6d6d4de283dc Homepage: https://cran.r-project.org/package=shinyRGL Description: CRAN Package 'shinyRGL' (Shiny Wrappers for RGL) Shiny wrappers for the RGL package. This package exposes RGL's ability to export WebGL visualization in a shiny-friendly format. 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Forked from 'wallace'. Code is split into modules that are loaded and linked together automatically and each call one function. Guidance pages explain modules to users and flexible logging informs them of any errors. Options enable asynchronous operations, viewing of source code, interactive maps and data tables. Use to create complex analytical applications, following best practices in open science and software development. Includes functions for automating repetitive development tasks and an example application at run_shinyscholar() that requires install.packages("shinyscholar", dependencies = TRUE). A guide to developing applications can be found on the package website. Package: r-cran-shinyscreenshot Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-htmltools, r-cran-jsonlite, r-cran-shiny, r-cran-uuid Suggests: r-cran-timevis Filename: pool/dists/resolute/main/r-cran-shinyscreenshot_0.2.1-1.ca2604.1_all.deb Size: 285094 MD5sum: af1871d99e264a36539fc417dc2c9073 SHA1: 45e0d4cb1b108ce00c1debe5c75df5722072aa30 SHA256: 3bd2a0078d1ddf8fbd473a4a255e549bbeb6fb21093c4aa3b3dc81bb954c1007 SHA512: 0fa81cb0267af45ddfdce5994583cd60a683032fcd214496f6fb51c18e871b4ac73dc5c8ccfb3de5585fe4864a67c29eb8e4fd64c00d729ff76d08d745b3e2f2 Homepage: https://cran.r-project.org/package=shinyscreenshot Description: CRAN Package 'shinyscreenshot' (Capture Screenshots of Entire Pages or Parts of Pages in 'Shiny') Capture screenshots in 'Shiny' applications. 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Package: r-cran-shinyseo Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-yaml, r-cran-jsonlite Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinyseo_0.1.0-1.ca2604.1_all.deb Size: 25520 MD5sum: 970cdcfed5e6da9727339434ffffbc60 SHA1: e11dac2da5fe18748e865b352c097100beaba748 SHA256: d8b299eb185f8f0c638aec1a7763d30765e1e6e297bb0f6a280aec47fe70fc6d SHA512: f82c72e7a461fa6225bcf4840f7ab1a4eacc726179703cba1eca4f937b7d45a5f59f0498b867fe4556ae3a8379690a487da3ae7cddcc37854001c822688b7009 Homepage: https://cran.r-project.org/package=shinyseo Description: CRAN Package 'shinyseo' (Search Engine Optimization and Social Metadata Helpers for'Shiny' Apps) Utilities for injecting search engine optimization (SEO), Open Graph, Twitter, and schema.org metadata into 'Shiny' applications from YAML files or named lists. 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Package: r-cran-shinysir Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1081 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-shiny, r-cran-desolve Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinysir_0.1.2-1.ca2604.1_all.deb Size: 643044 MD5sum: 306bbeb9ac75e32f33e46a302aeb4e16 SHA1: fc0b7069a5188af74f7f53f169dd1a1428fe8ec6 SHA256: 4502806e1c95cc221110eed8e1f43bfc0a99f6fc4997a9f662efa0d03071a633 SHA512: 7c84442af3d85af56dd978395ab5c8bad9bd42801f77195f2108fc10d07173831f21e84b6af9a06c083fe13c55b27949e0585560e4bd97b2725e7431be9225aa Homepage: https://cran.r-project.org/package=shinySIR Description: CRAN Package 'shinySIR' (Interactive Plotting for Mathematical Models of InfectiousDisease Spread) Provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows 'shinySIR' to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) and Bjornstad (2018) . Package: r-cran-shinystan Architecture: all Version: 2.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-bayesplot, r-cran-colourpicker, r-cran-dt, r-cran-dygraphs, r-cran-ggplot2, r-cran-gridextra, r-cran-gtools, r-cran-markdown, r-cran-reshape2, r-cran-rstan, r-cran-shinyjs, r-cran-shinythemes, r-cran-threejs, r-cran-xtable, r-cran-xts Suggests: r-cran-coda, r-cran-knitr, r-cran-posterior, r-cran-rmarkdown, r-cran-rsconnect, r-cran-rstanarm, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinystan_2.7.0-1.ca2604.1_all.deb Size: 1316034 MD5sum: 560b2f4f4b64f9293ac616197afd0ac8 SHA1: 9b8f0510692f3701b0250fc0243b5c70c086d8c9 SHA256: e2ef8bd8f034581beafc680392efac6bd9d33d097ea0ee55f87c9f8c09454170 SHA512: 8c323d5c54d6a38ee41cc8614d540bd8e95a7899e2e716848b796cef07c316b9599d3b944bf945f6010df08ab8a2fe18a07650b06523c4e6e86bc85ad543d56b Homepage: https://cran.r-project.org/package=shinystan Description: CRAN Package 'shinystan' (Interactive Visual and Numerical Diagnostics and PosteriorAnalysis for Bayesian Models) A graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the 'Shiny' web application framework from 'RStudio' and works with the output of MCMC programs written in any programming language (and has extended functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' packages). Package: r-cran-shinystate Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-fs, r-cran-htmltools, r-cran-pins, r-cran-r6, r-cran-shiny Suggests: r-cran-bslib, r-cran-dt, r-cran-knitr, r-cran-lubridate, r-cran-rlang, r-cran-rmarkdown, r-cran-roxy.shinylive, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-shinystate_0.1.0-1.ca2604.1_all.deb Size: 433420 MD5sum: 36daf448443ab5e63757f0d09bc756f0 SHA1: d04771892043f7a897657a03041729da7d1c585f SHA256: 439983ac230e500038ec4cfcd1b96d27f2e943d1d09d9b6503a2ec4c2303073d SHA512: 8f370b595fa18bd91e033929d98d3b4a474c4c54ba6d3efab0f6c6dee7635c8d54ea8779872df5194a81163270d1953cdb52b6ad587817619f31dab6f364c3ec Homepage: https://cran.r-project.org/package=shinystate Description: CRAN Package 'shinystate' (Customization of Shiny Bookmarkable State) Enhance the bookmarkable state feature of 'shiny' with additional customization such as storage location and storage repositories leveraging the 'pins' package. Package: r-cran-shinystoreplus Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite, r-cran-htmltools, r-cran-shinywidgets Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/resolute/main/r-cran-shinystoreplus_1.6-1.ca2604.1_all.deb Size: 111814 MD5sum: 0ab2b3043d9b1065e5143c6552853443 SHA1: 5ceae3c3a21c3665d65601966a1cd64d31b6295e SHA256: 2a91cf8bd19c6e6da5c8ccc50a1cabb447e72517128c3f1d5391e4806c20b9b2 SHA512: 7500634954393c90dd547878cf1bd61584e0c81bda62a49dc72e8882b4bf402c1c0d9e26943bedd220bf4f6afd169b6651a6ee9b6a9aadab623f9feb1831f278 Homepage: https://cran.r-project.org/package=shinyStorePlus Description: CRAN Package 'shinyStorePlus' (Secure in-Browser and Database Storage for 'shiny' Inputs,Outputs, Views and User Likes) Store persistent and synchronized data from 'shiny' inputs within the browser. Refresh 'shiny' applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using 'Dexie.js' , a minimal wrapper for 'IndexedDB'. Transfer browser link parameters to 'shiny' input or output values. Store app visitor views, likes and followers. Package: r-cran-shinytempsignal Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-forecast, r-cran-ggplot2, r-cran-ggprism, r-cran-ggpmisc, r-bioc-ggtree, r-cran-golem, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets, r-bioc-treeio, r-cran-yulab.utils, r-cran-nlme Suggests: r-cran-attempt, r-cran-conflicted, r-cran-config, r-cran-glue, r-cran-htmltools, r-cran-knitr, r-cran-prettydoc, r-cran-processx, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinytempsignal_0.0.8-1.ca2604.1_all.deb Size: 804770 MD5sum: 809b448d6d0bbeb605924f07fb5b9caa SHA1: bb127d935c39ba101949c9920a7c1bef8784bf36 SHA256: 33ac3dbbbce700f719ee82a468e4f22e019a48edb47c3a370df42632209d8585 SHA512: 0c9482ec880815ffbe39f98349293daec71a54f0ba445e6184c4f4e14344d6f4fab253854cfaee820e1b80edf84e04b68824d7d17d78b9b97435ddacb115fb0f Homepage: https://cran.r-project.org/package=shinyTempSignal Description: CRAN Package 'shinyTempSignal' (Explore Temporal and Other Phylogenetic Signals) Sequences sampled at different time points can be used to infer molecular phylogenies on natural time scales, but if the sequences records inaccurate sampling times, that are not the actual sampling times, then it will affect the molecular phylogenetic analysis. This shiny application helps exploring temporal characteristics of the evolutionary trees through linear regression analysis and with the ability to identify and remove incorrect labels. The method was extended to support exploring other phylogenetic signals under strict and relaxed models. Package: r-cran-shinytest Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-callr, r-cran-crayon, r-cran-debugme, r-cran-digest, r-cran-htmlwidgets, r-cran-httpuv, r-cran-httr, r-cran-jsonlite, r-cran-parsedate, r-cran-pingr, r-cran-r6, r-cran-rematch, r-cran-rlang, r-cran-rstudioapi, r-cran-shiny, r-cran-testthat, r-cran-webdriver, r-cran-withr Suggests: r-cran-flexdashboard, r-cran-globals, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-shinytest_1.6.1-1.ca2604.1_all.deb Size: 399228 MD5sum: 903ad3e78d4cb85962545b9b706fc46d SHA1: 16010abbbf653a6d3071c67944cf386f8891e59f SHA256: 58090831611a3b7e22fd0e5f6d11d639e8c44881907dcc35b268e0ab111d9007 SHA512: e9e6f30463e11bb95f4e3294becde40a87573f253155d7926d870a885e96c8c59f32693c1f63fac8c5e6122c176f0866d1bd2b9ac6a36596d523f0aeb76945c1 Homepage: https://cran.r-project.org/package=shinytest Description: CRAN Package 'shinytest' (Test Shiny Apps) Please see the 'shinytest' to 'shinytest2' migration guide at . Package: r-cran-shinytester Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-shinytester_0.1.0-1.ca2604.1_all.deb Size: 24490 MD5sum: fb1aac9627676ae3bf43d2cc8c34aac7 SHA1: 7b7ef96ef969bec50820dd53fbc656514d91c4a9 SHA256: 07c5202e11d8f36c891130bdfdac048cc598471cad4ecab67181a9d10796ea5f SHA512: 631fc904f134fb42bf86986cd2758c6a3d54799ad448cff1458bf16faa9634c34d0495634d0406e06a59d14cf7af0d9c459873df99a0f796b818aa7972b477cc Homepage: https://cran.r-project.org/package=ShinyTester Description: CRAN Package 'ShinyTester' (Functions to Minimize Bonehead Moves While Working with 'shiny') It's my experience that working with 'shiny' is intuitive once you're into it, but can be quite daunting at first. Several common mistakes are fairly predictable, and therefore we can control for these. The functions in this package help match up the assets listed in the UI and the SERVER files, and Visualize the ad hoc structure of the 'shiny' App. Package: r-cran-shinytesters Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinytesters_0.1.0-1.ca2604.1_all.deb Size: 23828 MD5sum: 4c5075bccbd2d276859fbd9cceaea45a SHA1: d49ffd60d11e011bde2bf9727289fe869f1980d7 SHA256: 2aec014fdcf16a5c9a4c557edb69ed8159bcfd6525c273b787111bb235d5c4f0 SHA512: d8dbe987ce0464d662cc212eb56dfdae1399b90afde1a8c59f178debddc3190c7581d513ae6bcdacee8bd1b67b7ce42486e6422d4c3fc478134d8a1168ec6177 Homepage: https://cran.r-project.org/package=shinytesters Description: CRAN Package 'shinytesters' (Update 'Shiny' Inputs when using testServer()) Create mocked bindings to 'Shiny' update functions within test function calls to automatically update input values. The mocked bindings simulate the communication between the server and UI components of a 'Shiny' module in testServer(). Package: r-cran-shinythemes Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-shinythemes_1.2.0-1.ca2604.1_all.deb Size: 573502 MD5sum: 40cf9529c046b8084e25e8227296686d SHA1: 155c41841ca09cf74f28a6d4424e9aa4c80d1a9d SHA256: ae90906871f2579f727fbc194fff87a961b7f1cbce9261c66fccbf92ae928e8a SHA512: 70ff4d3c772a89ebe7678c4872072f95833945b09af619b7827ad3fbf04d1a95ac88901b889ec22110a10308f96be9398955860cc4ee90959cc724885210f511 Homepage: https://cran.r-project.org/package=shinythemes Description: CRAN Package 'shinythemes' (Themes for Shiny) Themes for use with Shiny. Includes several Bootstrap themes from , which are packaged for use with Shiny applications. Package: r-cran-shinytime Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny Suggests: r-cran-testthat, r-cran-spelling, r-cran-hms Filename: pool/dists/resolute/main/r-cran-shinytime_1.0.3-1.ca2604.1_all.deb Size: 42288 MD5sum: d1edbfc7c38b729700e210282adf07c7 SHA1: f8c68306baaaaab870a2cf6876dec22397bad07c SHA256: b70d459a59e963977ec509f16a1482a977d7beda1157b26f99abb4423ddffd3c SHA512: d3a38c88b978c750779f6dcc331e7c8a233fb447373f678bd976ed5a1497f4eb413ddac461fd060d0ade4986002dfc3701c38ed765e30d273fd35f4004646623 Homepage: https://cran.r-project.org/package=shinyTime Description: CRAN Package 'shinyTime' (A Time Input Widget for Shiny) Provides a time input widget for Shiny. This widget allows intuitive time input in the '[hh]:[mm]:[ss]' or '[hh]:[mm]' (24H) format by using a separate numeric input for each time component. 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Key features include countdown and count-up mode, multiple display formats (including simple seconds, minutes-seconds, hours-minutes-seconds, and minutes-seconds-centiseconds), ability to pause, resume, and reset the timer. 'shinytimer' widget can be particularly useful for creating interactive and time-sensitive applications, tracking session times, setting time limits for tasks or quizzes, and more. 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Package: r-cran-shinytree Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-stringr, r-cran-promises Suggests: r-cran-testthat, r-cran-shinytest, r-cran-data.tree Filename: pool/dists/resolute/main/r-cran-shinytree_0.3.1-1.ca2604.1_all.deb Size: 517228 MD5sum: 47bae7136d0f2868b3f143889e19a7e3 SHA1: 3a7372da08cf3e5f850bdfb7e402a53b8f6db937 SHA256: 8576e6e2c469f9d67090ebdc091dbf4fa4496fcc8c8f862e16ddd6e958d1d609 SHA512: 2e07f34b25edffd10b2b8e5d67319e24cfd43449721b677f1cf4d229ded613dffc596710ac342aa42b4fe5fd4708fb9e64e21519d2c78a5dcf64e0bf79aab02f Homepage: https://cran.r-project.org/package=shinyTree Description: CRAN Package 'shinyTree' (jsTree Bindings for Shiny) Exposes bindings to jsTree -- a JavaScript library that supports interactive trees -- to enable a rich, editable trees in Shiny. 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Package: r-cran-shinywgd Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3015 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinyalert, r-cran-stringr, r-cran-vroom, r-cran-fs, r-cran-tidyr, r-cran-data.table, r-cran-dplyr, r-cran-ape, r-cran-ks, r-cran-mclust, r-cran-htmltools, r-cran-seqinr, r-cran-httr, r-cran-jsonlite Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-argparse, r-cran-bslib, r-cran-bsplus, r-cran-english, r-cran-fontawesome, r-cran-igraph, r-cran-shinybs, r-cran-shinyfiles, r-cran-shinywidgets, r-cran-shinyjs, r-cran-stringi, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shinywgd_1.0.0-1.ca2604.1_all.deb Size: 891184 MD5sum: 6cbf292d19927312b5f4f3e715a9fb36 SHA1: 414556d263f00506dfe969df166775ec4df010a8 SHA256: 3eedd7037c8ef53a7b33f78a75f597f9a48a677338b860929694df6cd6f9a085 SHA512: 09e2a515b2c6950d147c8acd325025cd9f29ebc743138dc57b76e3a00153645db4b1d60a70cee56b754cd9e2659bcefdaefadf73974627f26a71a32322e05d74 Homepage: https://cran.r-project.org/package=shinyWGD Description: CRAN Package 'shinyWGD' ('Shiny' Application for Whole Genome Duplication Analysis) Provides a comprehensive 'Shiny' application for analyzing Whole Genome Duplication ('WGD') events. This package provides a user-friendly 'Shiny' web application for non-experienced researchers to prepare input data and execute command lines for several well-known 'WGD' analysis tools, including 'wgd', 'ksrates', 'i-ADHoRe', 'OrthoFinder', and 'Whale'. This package also provides the source code for experienced researchers to adjust and install the package to their own server. Key Features 1) Input Data Preparation This package allows users to conveniently upload and format their data, making it compatible with various 'WGD' analysis tools. 2) Command Line Generation This package automatically generates the necessary command lines for selected 'WGD' analysis tools, reducing manual errors and saving time. 3) Visualization This package offers interactive visualizations to explore and interpret 'WGD' results, facilitating in-depth 'WGD' analysis. 4) Comparative Genomics Users can study and compare 'WGD' events across different species, aiding in evolutionary and comparative genomics studies. 5) User-Friendly Interface This 'Shiny' web application provides an intuitive and accessible interface, making 'WGD' analysis accessible to researchers and 'bioinformaticians' of all levels. Package: r-cran-shinywidgets Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3641 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-sass, r-cran-shiny, r-cran-htmltools, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr, r-cran-ggplot2, r-cran-dt, r-cran-scales, r-cran-shinydashboard, r-cran-shinydashboardplus Filename: pool/dists/resolute/main/r-cran-shinywidgets_0.9.1-1.ca2604.1_all.deb Size: 1277416 MD5sum: 275c882d304ee13ee7601fad6865ee9e SHA1: 041ec6adb39575bb5c380cfc5e131e279ca7ef85 SHA256: 55244a32f7a74bb4d838320c2f01119a66ea2d0555ea22a7b59d2e510d5ec323 SHA512: 056fc6f306a6befa2653e08e602f98370cbd05ac401fce7139832f6de6ad7b451964e13e9d9eb68a412af9781e3640bbc207ca54be3cbc2342797b9d517db295 Homepage: https://cran.r-project.org/package=shinyWidgets Description: CRAN Package 'shinyWidgets' (Custom Inputs Widgets for Shiny) Collection of custom input controls and user interface components for 'Shiny' applications. Give your applications a unique and colorful style ! 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It also offers a scalable visualization scheme to present testing conclusions for large-scale dataset with a large number of dose-response curves. For more information, see Jin et al. (2026) . 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Package: r-cran-shortform Architecture: all Version: 0.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-stringr, r-cran-dosnow, r-cran-foreach, r-cran-rlang Suggests: r-cran-knitr, r-cran-mplusautomation, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shortform_0.5.8-1.ca2604.1_all.deb Size: 383260 MD5sum: a3c2c087fbcbf401267f9e7cff2d5349 SHA1: fa7af0d2601f9ec83545f45c84c492ffbc8044bd SHA256: 3ef594a18a2c6314b2a53c775137da4510397196ec7731f0768109e1c9c5a05c SHA512: e54f926147fe619cb2d596779d96d61fd10a80e9ebc6145a77dd6076d03e599bb3a57bb1af4b36a12b8c5b95b2c8c1877ff5806cb044fe846c16d7d0fb890c5b Homepage: https://cran.r-project.org/package=ShortForm Description: CRAN Package 'ShortForm' (Automatic Short Form Creation) Performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) for an applied example of the Tabu search. Package: r-cran-shortirt Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-mass, r-cran-rmarkdown, r-cran-sirt, r-cran-testthat, r-cran-v8 Filename: pool/dists/resolute/main/r-cran-shortirt_1.0.0-1.ca2604.1_all.deb Size: 104216 MD5sum: 540709b9f2bc5fb260bd9deaa4827f7e SHA1: e1d52672d1497e4945a322d850a7eab9cf6b0fa9 SHA256: 830ed7fea8921edb7a1cbad20deb547715a033a8546e521e4245e2f01a0383f2 SHA512: 5bea74af1eac61e20f3050494f0b8aef272ab0a7547c6b2ebd5c70a06096c2e03db9a6b9730c4a6a161da1bd787cfb7e01ba15590297c4820d809ea7fcef97f1 Homepage: https://cran.r-project.org/package=shortIRT Description: CRAN Package 'shortIRT' (Procedures Based on Item Response Theory Models for theDevelopment of Short Test Forms) Implement different Item Response Theory (IRT) based procedures for the development of static short test forms (STFs) from a test. Two main procedures are considered (Epifania, Anselmi & Robusto, 2022 ). The procedures differ in how the most informative items are selected for the inclusion in the STF, either by considering their item information functions without any reference to any specific latent trait level (benchmark procedure) or by considering their information with respect to specific latent trait levels, denoted as theta targets (theta target procedure). Three methods are implemented for the definition of the theta targets: (i) as the midpoints of equal intervals on the latent trait, (ii) as the centroids of the clusters obtained by clustering the latent trait, and (iii) as user-defined values. Importantly, the number of theta targets defines the number of items included in the STF. For further details on the procedure, please refer to Epifania, Anselmi & Robusto (2022) . 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Package: r-cran-shorts Architecture: all Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lambertw, r-cran-tidyr, r-cran-ggplot2, r-cran-minpack.lm, r-cran-purrr Filename: pool/dists/resolute/main/r-cran-shorts_3.2.0-1.ca2604.1_all.deb Size: 3797038 MD5sum: 7ffdb6f7a3b1f9a2b496678e6a35fcda SHA1: e05f1191f5f62156ee5b4a5fdf8a531313767b27 SHA256: 98b715d82166d29c9c05893599a979e9b002fa6110a012968b51548f0152c7a8 SHA512: 9801389a878d0e787b1992030ce24bec7a78ba99a77911cb17afcb772e843ccfdd07df8cd9e4b5145aaaf1fd66408ee645435e01756bbd1763ab7e26de8c7c5f Homepage: https://cran.r-project.org/package=shorts Description: CRAN Package 'shorts' (Short Sprints) Create short sprint acceleration-velocity (AVP) and force-velocity (FVP) profiles and predict kinematic and kinetic variables using the timing-gate split times, laser or radar gun data, tether devices data, as well as the data provided by the GPS and LPS monitoring systems. The modeling method utilized in this package is based on the works of Furusawa K, Hill AV, Parkinson JL (1927) , Greene PR. (1986) , Chelly SM, Denis C. (2001) , Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) , Samozino P. (2018) , Samozino P. and Peyrot N., et al (2022) , Clavel, P., et al (2023) , Jovanovic M. (2023) , and Jovanovic M., et al (2024) . Package: r-cran-shotgroups Architecture: all Version: 0.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2797 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-compquadform, r-cran-kernsmooth Suggests: r-cran-knitr, r-cran-coin, r-cran-robustbase, r-cran-energy, r-cran-mvoutlier, r-cran-shiny, r-cran-jsonlite, r-cran-interp, r-cran-mba, r-cran-bs4dash Filename: pool/dists/resolute/main/r-cran-shotgroups_0.8.4-1.ca2604.1_all.deb Size: 2064690 MD5sum: 695f2d1aa9e9248d3bc517aca8b81a88 SHA1: 656beb3652f4b7326466b1a9f02ffedc49082229 SHA256: 94ba7ef6cd70a6d89321497af544e5d649c7a40e17bd5bf37dd4618b14afc908 SHA512: 6290043d5a108de31efa82788ddd26d3fd9c20173ebe8aae3f4098e9e47b565142e4d3a0879af9efc315c92cadfc70d70f1917cd09f542e6f3a5da20caecf8e0 Homepage: https://cran.r-project.org/package=shotGroups Description: CRAN Package 'shotGroups' (Analyze Shot Group Data) Analyzes shooting data with respect to group shape, precision, and accuracy. This includes graphical methods, descriptive statistics, and inference tests using standard, but also non-parametric and robust statistical methods. Implements distributions for radial error in bivariate normal variables. Works with files exported by 'OnTarget PC/TDS', 'Silver Mountain' e-target, 'ShotMarker' e-target, 'SIUS' e-target, or 'Taran', as well as with custom data files in text format. Supports inference from range statistics such as extreme spread. Includes a set of web-based graphical user interfaces. 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The vector of parameters that defines the model can be estimated by maximum likelihood. A nonparametric estimator for the conditional density of the susceptible population is provided. For more details, see Piñeiro-Lamas (2024) (). Funding: This work, integrated into the framework of PERTE for Vanguard Health, has been co-financed by the Spanish Ministry of Science, Innovation and Universities with funds from the European Union NextGenerationEU, from the Recovery, Transformation and Resilience Plan (PRTR-C17.I1) and from the Autonomous Community of Galicia within the framework of the Biotechnology Plan Applied to Health. 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This model is then used with observational survey data to estimate population size, while accounting for uncertain detection. See Steinhorst and Samuel (1989). 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Implements spectral decomposition methods including wavelet multiresolution analysis via maximal overlap discrete wavelet transform, Percival and Walden (2000) , empirical mode decomposition for non-stationary signals, Huang et al. (1998) , and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) . Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) , for identifying structurally relevant predictors from high-dimensional candidate sets. Includes dynamic factor model estimation, principal component analysis with bootstrap significance testing, and automated technical interpretation of signal morphology and variance topology. 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This package provides functions for two subgroup identification methods based on penalized functions, both of which utilize factor model structures to adapt to data with cross-sectional dependency. The first method is the Subgroup Identification with Latent Factor Structure Method (SILFSM) we proposed. By employing Center-Augmented Regularization and factor structures, the SILFSM effectively eliminates data dependencies while identifying subgroups within datasets. For this model, we offer optimization functions based on two different methods: Coordinate Descent and our newly developed Difference of Convex-Alternating Direction Method of Multipliers (DC-ADMM) algorithms; the latter can be applied to cases where the distance function in Center-Augmented Regularization takes L1 and L2 forms. The other method is the Factor-Adjusted Pairwise Fusion Penalty (FA-PFP) model, which incorporates factor augmentation into the Pairwise Fusion Penalty (PFP) developed by Ma, S. and Huang, J. (2017) . Additionally, we provide a function for the Standard CAR (S-CAR) method, which does not consider the dependency and is for comparative analysis with other approaches. Furthermore, functions based on the Bayesian Information Criterion (BIC) of the SILFSM and the FA-PFP method are also included in 'SILFS' for selecting tuning parameters. For more details of Subgroup Identification with Latent Factor Structure Method, please refer to He et al. (2024) . Package: r-cran-silhouette Architecture: all Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-lifecycle Suggests: r-cran-proxy, r-cran-ppclust, r-cran-cluster, r-cran-factoextra, r-cran-drclust, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-silhouette_0.9.6-1.ca2604.1_all.deb Size: 411750 MD5sum: 4358c35941a93d0b45591ca5b383178f SHA1: b119c71a555448420ed1ee78693d1c99f3b19cc2 SHA256: 3b215d54c5aad4eaa90ef51019480c8da704b68ad9de1ce6ae1ff65b77c872ac SHA512: 03f96ada774c3b96bd7aec01aceda15c1d11f970dcfc75620e8b2f9bc2c49afa840765ffadf690fbb4460cb4532d2f0d1180738920249a485d14cb327e6cbc31 Homepage: https://cran.r-project.org/package=Silhouette Description: CRAN Package 'Silhouette' (Proximity Measure Based Diagnostics for Standard, Soft, andMulti-Way Clustering) Quantifies clustering quality by measuring both cohesion within clusters and separation between clusters. Implements advanced silhouette width computations for diverse clustering structures, including: simplified silhouette (Van der Laan et al., 2003) , Probability of Alternative Cluster normalization methods (Raymaekers & Rousseeuw, 2022) , fuzzy clustering and silhouette diagnostics using membership probabilities (Campello & Hruschka, 2006; Menardi, 2011; Bhat & Kiruthika, 2024) , , , and multi-way clustering extensions such as block and tensor clustering (Schepers et al., 2008; Bhat & Kiruthika, 2025) , . Provides tools for computation and visualization (Rousseeuw, 1987) to support robust and reproducible cluster diagnostics across standard, soft, and multi-way clustering settings. Package: r-cran-silicate Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gibble, r-cran-purrr, r-cran-rlang, r-cran-decido, r-cran-tibble, r-cran-unjoin, r-cran-magrittr, r-cran-gridbase, r-cran-crsmeta Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-sp, r-cran-testthat, r-cran-trip, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-silicate_0.7.1-1.ca2604.1_all.deb Size: 1984876 MD5sum: c366d74fe5ae6cd0695273748d12a073 SHA1: c86e8a574d11ba181e896f8c1797f8f68a518317 SHA256: 78044957dc6785bb3f3c74569b31b86a7e351a2f5ae7448bca2fa7f1bee918a4 SHA512: abfd4b45b8c9aa38ce465ebc80d03fa5723bf15b9fb97ced85508e2bc55f3c3d3d8465a044fd88d9816eadfed5eb3685bafb8b4b0fb32b5ddeb18410aaaedb0d Homepage: https://cran.r-project.org/package=silicate Description: CRAN Package 'silicate' (Common Forms for Complex Hierarchical and Relational DataStructures) Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex and provide intermediate forms that relate spatial data structures to this mathematical construct. Package: r-cran-sillyputty Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-thresher, r-cran-oompabase, r-cran-polychrome Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mercator, r-cran-umpire, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-sillyputty_0.4.2-1.ca2604.1_all.deb Size: 1176202 MD5sum: 14bd6b6b0595ccf25db2b1cc5ed69e8e SHA1: a405b24e8f90760642b41acc91c723c3557a264e SHA256: 38be391c9d09813f6b834bbfbdaab1c1c7c42418f3dbf0958db281f3fe0ea6c6 SHA512: 07bbe344c0048ee6463a2f96308d21a1a62049ac87276281b63fcad6ee065ecf556eb7b3c8f694889735553c6ccb1713e645163ef7af7fe0ac7e3953d5850f2f Homepage: https://cran.r-project.org/package=SillyPutty Description: CRAN Package 'SillyPutty' (Silly Putty Clustering) Implements a simple, novel clustering algorithm based on optimizing the silhouette width. See for details. Package: r-cran-silm Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scalreg, r-cran-glmnet, r-cran-hdi, r-cran-sis Filename: pool/dists/resolute/main/r-cran-silm_1.0.0-1.ca2604.1_all.deb Size: 30870 MD5sum: b7bee32e17d322fdef02e3510f98cc43 SHA1: a7369d67525d3e24c4be5b706d9d1c32a50e39e2 SHA256: 5bef67f2a7072576191131bec43432adb4679fa9aff8dc20786ed01e452f92a8 SHA512: b0c8372d86e71976f2fff80f079c2c574cedaef37abb12e1d8923a1ee25e151359bdce87d1a1fdd20b2a6a35406be4f7293111fd6f62f1e4738d3ce4f9527624 Homepage: https://cran.r-project.org/package=SILM Description: CRAN Package 'SILM' (Simultaneous Inference for Linear Models) Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) . Package: r-cran-silp Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lavaan, r-cran-mass, r-cran-purrr, r-cran-semtools, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-silp_1.0.3-1.ca2604.1_all.deb Size: 83614 MD5sum: 5fbce539bae98f0fc79850af40bb94d7 SHA1: 65cf8ede93372c88d5f5704d50c33da00dff0f2d SHA256: d225987c9b213a531ed5ef57715579caae15bbd6660447d60bf74eea1242c7a6 SHA512: df881896f1ffd4c86347ae1537b715e0d3fbac8f138013199287cfa2453984ddad75c480d09da468abd9c963748afb6e6605d422f9fbae26bf9b333b92b95002 Homepage: https://cran.r-project.org/package=silp Description: CRAN Package 'silp' (Conditional Process Analysis (CPA) via SEM Approach) Utilizes the Reliability-Adjusted Product Indicator (RAPI) method to estimate effects among latent variables, thus allowing for more precise definition and analysis of mediation and moderation models. Our simulation studies reveal that while 'silp' may exhibit instability with smaller sample sizes and lower reliability scores (e.g., N = 100, 'omega' = 0.7), implementing nearest positive definite matrix correction and bootstrap confidence interval estimation can significantly ameliorate this volatility. When these adjustments are applied, 'silp' achieves estimations akin in quality to those derived from LMS. In conclusion, the 'silp' package is a valuable tool for researchers seeking to explore complex relational structures between variables without resorting to commercial software. Cheung et al.(2021) Hsiao et al.(2018). Package: r-cran-silviculture Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-rlang, r-cran-s7 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-silviculture_0.2.0-1.ca2604.1_all.deb Size: 469938 MD5sum: bb0a917e4ade35466451c217d45b8fff SHA1: 6acb7857458efcba896e7570a3fd7ed4003cb9e9 SHA256: e6427625ac4c30de33eebf15cbab0865c0b4ec817adbc6cd3256a34277d7a435 SHA512: 5cac44d46681bfdd4019dfb93b49ea7f9d3b4ee6633f90b63a5611c8d9f413d22992bcae2eb6e16487873083a122909e00981cbf0c2bd33c7986aa04726c1748 Homepage: https://cran.r-project.org/package=silviculture Description: CRAN Package 'silviculture' (Utility Functions for Forest Inventory and Silviculture) Perform common dendrometry operations such as inventory preparing, and inventory data analysis. Package: r-cran-sim.ba Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chk, r-cran-cobalt, r-cran-ggplot2, r-cran-scales, r-cran-pbapply, r-cran-rlang, r-cran-survival Suggests: r-cran-matchit, r-cran-weightit, r-cran-openxlsx, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sim.ba_0.1.0-1.ca2604.1_all.deb Size: 227418 MD5sum: 0d2999efef7e07f3aad02bf98cd34339 SHA1: 5f794d7a0486d5843751425af8c7896c7d97225f SHA256: 2a54628130bb07d87acf6da3889edd9d73534d51fbdc79d95bee4e643b09e353 SHA512: 8e8fde46848edd68fd3eea8ffc2e49099789da2d4a7c9600ca0bf08beed4722a38d8d05ab231ad9cdf3a6eedeb67d881d25f7c3437084db29298ba73483a84b8 Homepage: https://cran.r-project.org/package=sim.BA Description: CRAN Package 'sim.BA' (Simulation-Based Bias Analysis for Observational Studies) Allows user to conduct a simulation based quantitative bias analysis using covariate structures generated with individual-level data to characterize the bias arising from unmeasured confounding. Users can specify their desired data generating mechanisms to simulate data and quantitatively summarize findings in an end-to-end application using this package. Package: r-cran-sim.diffproc Architecture: all Version: 5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deriv, r-cran-mass Suggests: r-cran-desolve, r-cran-knitr, r-cran-rgl, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-sm Filename: pool/dists/resolute/main/r-cran-sim.diffproc_5.0-1.ca2604.1_all.deb Size: 1468592 MD5sum: 2fdf936dd980da15a193fe48d37c5454 SHA1: 07b48cc749026004b37d0a4e006262c33fa9b052 SHA256: 44dbe02ad6672b733a7e42433760262c2449fd02bb9bedcddf6d712da79b6d0f SHA512: 1aa4efa32f9ff67130836235b34286f31dd99c2d51685de8331b70282f739fd517a767b6ff8b1fcc6812d838974f2f21aa614d41083e9278d7c9ba7c8dea9f91 Homepage: https://cran.r-project.org/package=Sim.DiffProc Description: CRAN Package 'Sim.DiffProc' (Simulation of Diffusion Processes) It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs . Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2. Package: r-cran-sim.plfn Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-distrib Filename: pool/dists/resolute/main/r-cran-sim.plfn_1.0-1.ca2604.1_all.deb Size: 50170 MD5sum: 88d1bc23d1b254c60aa861603c39a2a8 SHA1: ec1488d5a44e611d930ead60ff74c9c778a1c262 SHA256: 65c64aac55e77482717819232539d078977ccfbc54f8e8a434afb931acb52370 SHA512: bb9e308a1515db0bf1b74f53299cd52bc1a5ed43172aa2f9b58dbd855175f9cdae0651ca8d1fe20f3a113886e34d28232d5acecbe24cc63fcf149b24fef0145c Homepage: https://cran.r-project.org/package=Sim.PLFN Description: CRAN Package 'Sim.PLFN' (Simulation of Piecewise Linear Fuzzy Numbers) The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are: (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package. Package: r-cran-sim2dpredictr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rdpack, r-cran-spam, r-cran-tibble, r-cran-dplyr, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/resolute/main/r-cran-sim2dpredictr_0.1.1-1.ca2604.1_all.deb Size: 198400 MD5sum: 7cec5df450875a25c083ab7dd8742193 SHA1: 990e51410f5c6b6d8dcddf24c27d5bdd7a0c51bf SHA256: 161b31160f50158bf49937535fb7e8faefde82da72dd596a6fe1c0d898c2e4e2 SHA512: c44dcdf987868b0bcd265ec3b5100e9f2b840d0969f72003a4ac842a3f580b261e8b6ac1300bc632889f5fa336a11f7d4bbb74323c95e2f2aa8bf05b971f6270 Homepage: https://cran.r-project.org/package=sim2Dpredictr Description: CRAN Package 'sim2Dpredictr' (Simulate Outcomes Using Spatially Dependent Design Matrices) Provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) and Furrer and Sain (2010) ), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user. Package: r-cran-simaerep Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-forcats, r-cran-cowplot, r-cran-rcolorbrewer, r-cran-furrr, r-cran-progressr, r-cran-knitr, r-cran-tibble, r-cran-dbplyr, r-cran-glue Suggests: r-cran-testthat, r-cran-devtools, r-cran-pkgdown, r-cran-spelling, r-cran-haven, r-cran-vdiffr, r-cran-lintr, r-cran-dbi, r-cran-duckdb, r-cran-ggextra Filename: pool/dists/resolute/main/r-cran-simaerep_1.0.0-1.ca2604.1_all.deb Size: 619664 MD5sum: 3b66d38893b0fd786c73ad151eb352ec SHA1: 9534503f77b4be00e15692ff7bb6c759ce72654a SHA256: 1b9a065b022b7f9f119eba5f5963fc599caac6b7dbd6bbeaab2c065c5264b386 SHA512: c600fdabed4c9a4260551edefbb3a4839fd82e6f03a16853b863161dd63b74e801ef0a1ab6ecc7785ee2a13a5cb38702a04323acf90036a12bc1d5f47c7621c0 Homepage: https://cran.r-project.org/package=simaerep Description: CRAN Package 'simaerep' (Detect Clinical Trial Sites Over- or Under-Reporting ClinicalEvents) Monitoring reporting rates of subject-level clinical events (e.g. adverse events, protocol deviations) reported by clinical trial sites is an important aspect of risk-based quality monitoring strategy. Sites that are under-reporting or over-reporting events can be detected using bootstrap simulations during which patients are redistributed between sites. Site-specific distributions of event reporting rates are generated that are used to assign probabilities to the observed reporting rates. (Koneswarakantha 2024 ). 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In particular, the method is compatible with differentially private dataset, as long as the privacy mechanism is known. For more details, see Awan and Wang (2024), "Simulation-based, Finite-sample Inference for Privatized Data", . Package: r-cran-simbkmrdata Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-bkmr, r-cran-fields, r-cran-gt, r-cran-quarto, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-simbkmrdata_0.2.1-1.ca2604.1_all.deb Size: 586198 MD5sum: c64dc3d938f3f4fa74ef9a319b69e60f SHA1: f8834111f2441eb45e4a4c900ea194c712bba69c SHA256: 3045eba5aa1727f62bb4129c7cc1e76a68bba4bef208986f60c86cfbd6b7e3f0 SHA512: d9a2ab33deee7b5fca266acb94a012f082b3a9bf49b5ae5f46bc7a0fc1b5713d6b4edb0a1127c7772e7b7b2f4ab93247738c0cdb0b3ab2657a26f5349d27f992 Homepage: https://cran.r-project.org/package=simBKMRdata Description: CRAN Package 'simBKMRdata' (Helper Functions for Bayesian Kernel Machine Regression) Provides a suite of helper functions to support Bayesian Kernel Machine Regression (BKMR) analyses in environmental health research. 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Package: r-cran-simcat Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mirt, r-cran-mirtcat, r-cran-shiny, r-cran-shinycssloaders Filename: pool/dists/resolute/main/r-cran-simcat_1.0.1-1.ca2604.1_all.deb Size: 80544 MD5sum: 040c63ecc27de64069049a8d91423e2d SHA1: ed7ee3d24c339e4c27f6a4f6df5e6dd46b0778cc SHA256: f9eef65d35f75b03a313b59b73abd869e477e29f94cc141ba7c5ee6a2ef87d72 SHA512: 0c08911b06c0521d9d88ff75c4ac800490f22bb1a08006191e58eeec165c79103f864d9f46bc0d840bb565fec6089baeac4e74a4867c91b2cc17638281a471ad Homepage: https://cran.r-project.org/package=simCAT Description: CRAN Package 'simCAT' (Implements Computerized Adaptive Testing Simulations) Computerized Adaptive Testing simulations with dichotomous and polytomous items. Selects items with Maximum Fisher Information method or randomly, with or without constraints (content balancing and item exposure control). Evaluates the simulation results in terms of precision, item exposure, and test length. Inspired on Magis & Barrada (2017) . Package: r-cran-simcausal Architecture: all Version: 0.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-stringr, r-cran-r6, r-cran-assertthat, r-cran-matrix Suggests: r-cran-copula, r-cran-runit, r-cran-ltmle, r-cran-knitr, r-cran-ggplot2, r-cran-hmisc, r-cran-mvtnorm, r-cran-bindata Filename: pool/dists/resolute/main/r-cran-simcausal_0.5.7-1.ca2604.1_all.deb Size: 966768 MD5sum: df67fb6499b7c9079ba50db711b4584c SHA1: 0a9cf85f109888ef54d3f971fc2e94ff3c118fe7 SHA256: de3f7d7f9e189fd76094b469192a9c4fc6bbd4bb9062ac0f194b91fb5597d7eb SHA512: a4f18fda7b82c78deed26bbed2b42ff69e95bf660f9e385f664746ca14a227fd5dd6259688e8ed4f7992aef2d9fef63b305f094190836cd303bd2dfc3a9d671b Homepage: https://cran.r-project.org/package=simcausal Description: CRAN Package 'simcausal' (Simulating Longitudinal Data with Causal Inference Applications) A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples. Package: r-cran-simcomp Architecture: all Version: 3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-multcomp, r-cran-mratios Filename: pool/dists/resolute/main/r-cran-simcomp_3.6-1.ca2604.1_all.deb Size: 185820 MD5sum: fa19aae5e65cd806cbfcaa7dd9633d91 SHA1: 23018d88980385d7d08da337d788797b534043f1 SHA256: 8fca64ab9d30894403a8ce0ae7b311a401473e67ea829a0b3be3104d9f138d5e SHA512: 6521ee53c98ba21a6fbd56d1394b114c9273dfb403795310f79eb0fad502d200d9d106ae839322e5a2e2dff6b38f8239959f154191a5ab8d80623c3b47fc8aa2 Homepage: https://cran.r-project.org/package=SimComp Description: CRAN Package 'SimComp' (Simultaneous Comparisons for Multiple Endpoints) Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 ) or ratios (Hasler and Hothorn, 2012 ) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 ) or covariance matrices (Hasler, 2014 ). Package: r-cran-simcop Architecture: all Version: 0.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-simcop_0.7.4-1.ca2604.1_all.deb Size: 374574 MD5sum: dead35eb96df707c5a27f75b583773c6 SHA1: 3c05962078ae6c27430cda6397ba260e0651d496 SHA256: b5b656fb7f82683f741711dceb005324b299f254f723fd93af3e531f139558a8 SHA512: e8f70971e4e96aa45ca2a65aff8069b7ce2f8d9f722e4dc31648b57062cf54abad820c56439cf80c6881ce5cfea4042cabc29c9c7a1c0b01348c34bbc5078539 Homepage: https://cran.r-project.org/package=SimCop Description: CRAN Package 'SimCop' (Simulate from Arbitrary Copulae) Provides a framework to generating random variates from arbitrary multivariate copulae, while concentrating on (bivariate) extreme value copulae. Particularly useful if the multivariate copulae are not available in closed form. Detailed discussion of the methodologies used can be found in Tajvidi and Turlach (2018) . Package: r-cran-simcormultres Architecture: all Version: 1.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evd Suggests: r-cran-bookdown, r-cran-covr, r-cran-gee, r-cran-knitr, r-cran-multgee, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simcormultres_1.9.0-1.ca2604.1_all.deb Size: 578902 MD5sum: 48e4f66bcde672e5b0b533dd81e7cc2a SHA1: e628de62f906ca0d1f710e7570610c9067edb94c SHA256: 0d65c8d7f476866e8a39b42b42e22cb0dd395e82fcc5fd67094b8364848789d7 SHA512: 594b7679d6ca027fe6b7862f71152d24337e40d7246a40064aed97bed7b57cdc435f222ecf66b748b3a8634736076a272b52fec56a50062be113e8ef1eccc257 Homepage: https://cran.r-project.org/package=SimCorMultRes Description: CRAN Package 'SimCorMultRes' (Simulates Correlated Multinomial Responses) Simulates correlated multinomial responses conditional on a marginal model specification. Package: r-cran-simcorrmix Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4621 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-simmulticorrdata, r-cran-bb, r-cran-nleqslv, r-cran-mass, r-cran-mvtnorm, r-cran-matrix, r-cran-vgam, r-cran-triangle, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-printr, r-cran-bookdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simcorrmix_0.1.1-1.ca2604.1_all.deb Size: 852744 MD5sum: 550cabec57a77cc9d6fa48b1c70bd3b4 SHA1: a27868b84ff01a189d257e1e79596e5f58b34fcd SHA256: 91e992ee34654f68bb21ebf5c12452cfdb2bce6db932929b96570f5c25178a0a SHA512: f877216b0e6cc9eb9393b47ca273a5aa0ed29546edd6e25c550e1e36e4b0bad4057ac62fea907d995acffc26509e4a3e2089c79d47b045a63301e260aa3410f6 Homepage: https://cran.r-project.org/package=SimCorrMix Description: CRAN Package 'SimCorrMix' (Simulation of Correlated Data with Multiple Variable TypesIncluding Continuous and Count Mixture Distributions) Generate continuous (normal, non-normal, or mixture distributions), binary, ordinal, and count (regular or zero-inflated, Poisson or Negative Binomial) variables with a specified correlation matrix, or one continuous variable with a mixture distribution. This package can be used to simulate data sets that mimic real-world clinical or genetic data sets (i.e., plasmodes, as in Vaughan et al., 2009 ). The methods extend those found in the 'SimMultiCorrData' R package. Standard normal variables with an imposed intermediate correlation matrix are transformed to generate the desired distributions. Continuous variables are simulated using either Fleishman (1978)'s third order or Headrick (2002)'s fifth order polynomial transformation method (the power method transformation, PMT). Non-mixture distributions require the user to specify mean, variance, skewness, standardized kurtosis, and standardized fifth and sixth cumulants. Mixture distributions require these inputs for the component distributions plus the mixing probabilities. Simulation occurs at the component level for continuous mixture distributions. The target correlation matrix is specified in terms of correlations with components of continuous mixture variables. These components are transformed into the desired mixture variables using random multinomial variables based on the mixing probabilities. However, the package provides functions to approximate expected correlations with continuous mixture variables given target correlations with the components. Binary and ordinal variables are simulated using a modification of ordsample() in package 'GenOrd'. Count variables are simulated using the inverse CDF method. There are two simulation pathways which calculate intermediate correlations involving count variables differently. Correlation Method 1 adapts Yahav and Shmueli's 2012 method and performs best with large count variable means and positive correlations or small means and negative correlations. Correlation Method 2 adapts Barbiero and Ferrari's 2015 modification of the 'GenOrd' package and performs best under the opposite scenarios. The optional error loop may be used to improve the accuracy of the final correlation matrix. The package also contains functions to calculate the standardized cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables. Package: r-cran-simdag Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2640 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rfast, r-cran-rlang, r-cran-igraph, r-cran-dagitty, r-cran-ggdag Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-ggplot2, r-cran-ggforce, r-cran-mass, r-cran-covr, r-cran-foreach, r-cran-dosnow, r-cran-dorng, r-cran-simr, r-cran-rsurv, r-cran-survival Filename: pool/dists/resolute/main/r-cran-simdag_1.0.0-1.ca2604.1_all.deb Size: 1473472 MD5sum: 22466be24d3edd05d310619cdab03131 SHA1: 7d04fb8fa3881cd6b7361ade0dfb63b050c1b37e SHA256: c7ff05f9cf000c2c1399e452f24702b6a43997d0f0ded6b2e8762336898bda90 SHA512: 2e268309aafd6f3b827a5b1aebfdb18015678481168884fd118ecf6b8b7310e070a89b5f992db6f4c454c78aec65dfb5b88169755ad1698cbf5d7f24e7ce6be1 Homepage: https://cran.r-project.org/package=simDAG Description: CRAN Package 'simDAG' (Simulate Data from a (Time-Dependent) Causal DAG) Simulate complex data from a given directed acyclic graph and information about each individual node. Root nodes are simply sampled from the specified distribution. Child Nodes are simulated according to one of many implemented regressions, such as logistic regression, linear regression, poisson regression or any other function. Also includes a comprehensive framework for discrete-time simulation, discrete-event simulation, and networks-based simulation which can generate even more complex longitudinal and dependent data. For more details, see Robin Denz, Nina Timmesfeld (2026) . Package: r-cran-simdata Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-igraph Suggests: r-cran-doparallel, r-cran-dorng, r-cran-dplyr, r-cran-fitdistrplus, r-cran-forcats, r-cran-ggplot2, r-cran-ggally, r-cran-ggcorrplot, r-cran-knitr, r-cran-patchwork, r-cran-purrr, r-cran-reshape2, r-cran-rmarkdown, r-cran-nhanes, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simdata_0.4.1-1.ca2604.1_all.deb Size: 745348 MD5sum: bc6b42e78707bc0ad7bb12310ecdfaaf SHA1: 40ffc25df249ca26a14220072cd20d2ac68a60f4 SHA256: a0a6443bb293ae72bed876bdc9a12d60aa8024a5c72f62289f5c6766576c7a79 SHA512: c217c423d0f7cd02832b81791769d507769d0a1a395c995cad9df74a732f1dd656af08f8bf43f076bc40dae3716a9bfe929e981b8a9ab1e0dd10ce2d0a177ba3 Homepage: https://cran.r-project.org/package=simdata Description: CRAN Package 'simdata' (Generate Simulated Datasets) Generate simulated datasets from an initial underlying distribution and apply transformations to obtain realistic data. Implements the 'NORTA' (Normal-to-anything) approach from Cario and Nelson (1997) and other data generating mechanisms. Simple network visualization tools are provided to facilitate communicating the simulation setup. Package: r-cran-simdd Architecture: all Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-circstats, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simdd_1.1-2-1.ca2604.1_all.deb Size: 55038 MD5sum: 4ea90868f0cfb558466796b99b014a70 SHA1: c0f65873f1951b5aa1dd314faa774a381d1e3e86 SHA256: e0b8e58b135521c0be2ef48cf1362241d7909c52e8e2a915a8733d35878c70fb SHA512: 1dd62c514a7070a2fa9f2273d287ce7446330dfc99a040bc55afa76d53cf42e8978b28b64e5c8ebce77a2bc90fa037843c85c138cb080c6154d7ae153296a11d Homepage: https://cran.r-project.org/package=simdd Description: CRAN Package 'simdd' (Simulation of Fisher Bingham and Related DirectionalDistributions) Simulation methods for the Fisher Bingham distribution on the unit sphere, the matrix Bingham distribution on a Grassmann manifold, the matrix Fisher distribution on SO(3), and the bivariate von Mises sine model on the torus. The methods use an acceptance/rejection simulation algorithm for the Bingham distribution and are described fully by Kent, Ganeiber and Mardia (2018) . These methods supersede earlier MCMC simulation methods and are more general than earlier simulation methods. The methods can be slower in specific situations where there are existing non-MCMC simulation methods (see Section 8 of Kent, Ganeiber and Mardia (2018) for further details). Package: r-cran-simdesign Architecture: all Version: 2.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-parallelly, r-cran-dplyr, r-cran-sessioninfo, r-cran-beepr, r-cran-pbapply, r-cran-mirai, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-r.utils, r-cran-codetools, r-cran-clipr, r-cran-e1071, r-cran-qs2 Suggests: r-cran-snow, r-cran-knitr, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-shiny, r-cran-copula, r-cran-extradistr, r-cran-renv, r-cran-cli, r-cran-job, r-cran-future.batchtools, r-cran-frf2, r-cran-rmarkdown, r-cran-rpushbullet, r-cran-httr Filename: pool/dists/resolute/main/r-cran-simdesign_2.25-1.ca2604.1_all.deb Size: 1152950 MD5sum: 068d744206589281fd9f40e7a16f6bcd SHA1: 2c08426bfcd1ea1ea33a736c97e49c24d1d6d506 SHA256: 4610ff90d7132f920b8242130f583b7a89f25c6fbc043fa1c764edc15a3d0dec SHA512: 217a1eb8e6543f175d97a0583bdc32cd15d072588c4832408fa22ff9d38f18ddeddfb2a468d2bc841c5c45f9a64bc107e4462f6b94df94dc59202fb3d38cf9e9 Homepage: https://cran.r-project.org/package=SimDesign Description: CRAN Package 'SimDesign' (Structure for Organizing Monte Carlo Simulation Designs) Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) . For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) . Package: r-cran-simdissolution Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-alabama, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-simdissolution_0.1.0-1.ca2604.1_all.deb Size: 40554 MD5sum: fa204a8cdd10756195d1edfb6a1ba3b4 SHA1: 976ae2c55088740c959ecb8f746fec1a9d781b67 SHA256: 337743e2619746a9e8b24a0d5391aaa59bdee393fce2ded65d1c63c58a06115a SHA512: ea604e9d14eab5f78ae55cb13fb41b18b26e5131ffbbefde53b54461fba487229a7a53a504c26f7009fac07120ade2aa85d155cc709019f028e7fbb5919aad9b Homepage: https://cran.r-project.org/package=SimDissolution Description: CRAN Package 'SimDissolution' (Modeling and Assessing Similarity of Drug Dissolutions Profiles) Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) for details. Package: r-cran-simdistr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-simdistr_1.0.1-1.ca2604.1_all.deb Size: 23776 MD5sum: 7b2170afa891c651891599b2e91e46fa SHA1: 74feb6e479efa830f642494449b9fa9c8e1bda3d SHA256: 6c4332e61a75114f7c5d817f098e684b0cd35c2c797e7c00f788d72f51883319 SHA512: 4a8db1c9159b05a1cbcc1f7935ff9dfef5c0cb91c727461fa5790bf7b232f0601f210399c3d0535649d4d0ad94491e82acb730ef62e7e0646749815499e55978 Homepage: https://cran.r-project.org/package=simdistr Description: CRAN Package 'simdistr' (Assessment of Data Trial Distributions According to theCarlisle-Stouffer Method) Assessment of the distributions of baseline continuous and categorical variables in randomised trials. This method is based on the Carlisle-Stouffer method with Monte Carlo simulations. It calculates p-values for each trial baseline variable, as well as combined p-values for each trial - these p-values measure how compatible are distributions of trials baseline variables with random sampling. This package also allows for graphically plotting the cumulative frequencies of computed p-values. Please note that code was partly adapted from Carlisle JB, Loadsman JA. (2017) . Package: r-cran-simdnamixtures Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-naturalsort, r-cran-pedprobr, r-cran-xml2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-pedtools, r-cran-testthat, r-cran-readr, r-cran-readxl, r-cran-withr Filename: pool/dists/resolute/main/r-cran-simdnamixtures_1.1.2-1.ca2604.1_all.deb Size: 908126 MD5sum: a20f30f43c22362248d5e767962b77a3 SHA1: a3eaace6f7b16df235628c4cabb89d4c19db70dc SHA256: e1049c7b1224bc1af7fcf7d364837eeda0f1283e04cfa7ac2e5076ab56235590 SHA512: 90a888152e188a944f1932f7f368ce41862be4ad04e87392d516392ef720d29fb5d9d58e4d7d1bf5063bf3ae3682ac3ecceaa79e5198489397ba795bca9d5511 Homepage: https://cran.r-project.org/package=simDNAmixtures Description: CRAN Package 'simDNAmixtures' (Simulate Forensic DNA Mixtures) Mixed DNA profiles can be sampled according to models for probabilistic genotyping. Peak height variability is modelled using a log normal distribution or a gamma distribution. Sample contributors may be related according to a pedigree. Package: r-cran-simed Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 843 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstream, r-cran-shape Suggests: r-cran-magick Filename: pool/dists/resolute/main/r-cran-simed_2.0.2-1.ca2604.1_all.deb Size: 807790 MD5sum: a59cb076d9f68df7258a5a0c0897ebc5 SHA1: 399f808dc09e0d84ab120491047f826d8dbc3979 SHA256: 7698bcad79e605077fb153bfac614bc4d8300bd45548ff0134a96c3ff9a02cb6 SHA512: 1a0f03cb223fc1f7b4ab49b72ee5d175bd2088526053c835a1953ca5b26f60ab2e9324d91fe7757d70d54393711acc5946704aedbe37849f4a91de77d5666c73 Homepage: https://cran.r-project.org/package=simEd Description: CRAN Package 'simEd' (Simulation Education) Contains various functions to be used for simulation education, including simple Monte Carlo simulation functions, queueing simulation functions, variate generation functions capable of producing independent streams and antithetic variates, functions for illustrating random variate generation for various discrete and continuous distributions, and functions to compute time-persistent statistics. Also contains functions for visualizing: event-driven details of a single-server queue model; a Lehmer random number generator; variate generation via acceptance-rejection; and of generating a non-homogeneous Poisson process via thinning. Also contains two queueing data sets (one fabricated, one real-world) to facilitate input modeling. More details on the use of these functions can be found in Lawson and Leemis (2015) , in Kudlay, Lawson, and Leemis (2020) , and in Lawson and Leemis (2021) . Package: r-cran-simengine Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 936 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-pbapply, r-cran-data.table, r-cran-rlang, r-cran-mass Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-ggplot2, r-cran-sandwich Filename: pool/dists/resolute/main/r-cran-simengine_1.4.0-1.ca2604.1_all.deb Size: 655478 MD5sum: 17987de602a6d6d494ccd66f2f18d5a0 SHA1: 0db8544b21526c0c363e9e53abbea6c7b31ea767 SHA256: 00489974a53fe181135beb71595cd173ba7cb9948fa0f87be76086c585ac9922 SHA512: 80ea0fc1c6d065db219398c9afc04867e520c2a20f17c0fb00455a22bc455e6db77b5d88f58af243a9e1fbd272e275fe858779bbe74464c0122915992faeb9e1 Homepage: https://cran.r-project.org/package=SimEngine Description: CRAN Package 'SimEngine' (A Modular Framework for Statistical Simulations in R) An open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments.See full documentation at . 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These calculations main refer to Allen et al.(1998, ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and Liu et al.(2006) . 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It also combines the tests and returns a properly adjusted family-wise p value. Details can be found in . Package: r-cran-simhaz Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-simhaz_0.1-1.ca2604.1_all.deb Size: 86228 MD5sum: 3021fd0b58f2d187ebff52ec4167d376 SHA1: f37a1000c1dd3cf5c992add3d9a9489f48b4bd2a SHA256: ec1a016666afd3459fa7d5833c9f8bffd6624bf23fa24e374d12310cc8fc8e15 SHA512: 0de802e69b24b15aa4020f72d1949ce3dd5af9c5f1b1e9fbd92d5ee5c9166a9e9c2ca626a00ba1387a49e9c3b7e0b0a90bc0104e97fde2d70c38e730e63b83ef Homepage: https://cran.r-project.org/package=SimHaz Description: CRAN Package 'SimHaz' (Simulated Survival and Hazard Analysis for Time-DependentExposure) Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. 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Includes functions for constructing similarity matrices and conducting hypothesis testing. Users can use different similarity measures and define their own weighting schemes. For more details see Q Zhu, M Liu, Y Han, D Zhou (2025) . 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These tools include: fitting, selection and plotting distributions to model system effectiveness, transformation towards a prespecified expected value, proxy to fitting of copula models based on these distributions, and simulation of new evaluation data from these distributions and copula models. Package: r-cran-simitation Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-devtools, r-cran-markdown, r-cran-formatr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simitation_0.0.7-1.ca2604.1_all.deb Size: 233592 MD5sum: e670c9337d1c89a807f58af583c5d18e SHA1: e73a446b2f59369a09a7452e8d39e6c51d02c3f4 SHA256: a7ee06434c19fc6015d356adb65bea967c6df25bb3701ae2cb86973dcc8e0b23 SHA512: dbe8cc750fd2972c82d658a3a6d0aa5410333afa477755a995473c6cac6c1129e2eca8e7362ee95a26999184e1f4000cc969d3b871f061ac303eb7830000b960 Homepage: https://cran.r-project.org/package=simitation Description: CRAN Package 'simitation' (Simplified Simulations) Provides tools for generating and analyzing simulation studies. 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Package: r-cran-simits Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-arm, r-cran-ggplot2, r-cran-knitr, r-cran-plyr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-simits_0.1.1-1.ca2604.1_all.deb Size: 329380 MD5sum: 57cb838300e74682468032fa0869227e SHA1: 62b4931e51668e65ef7b39ab330687a5bc4466ca SHA256: 17ec3e580637680cd951d7673ce89cec9b798c310c5e6472f387563f0645d8f3 SHA512: a9a4475d0974c9cef3d8788355d6ea7a77dd25f72debca2835b0135e9b2463020897ff2e044729851ac2d9898d8c19138170168e77807a660609f413e9c8837a Homepage: https://cran.r-project.org/package=simITS Description: CRAN Package 'simITS' (Analysis via Simulation of Interrupted Time Series (ITS) Data) Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) . 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Package: r-cran-simmetric Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simmetric_0.1.1-1.ca2604.1_all.deb Size: 382064 MD5sum: 2717f51e70de3cdea4b45e06379dfcdf SHA1: 776f28f6b2e544e4a520237820238b991b7ae557 SHA256: e8c69008955aaaf78199d180f105ba773edc9c0b0ed019bbe65aeae64c15d7c7 SHA512: 5bf7fb83308d9de63af77398c6a7f4f6719852f370249c09c77d3d974439160fa46b85e4b23c0feea5cc2bd11664a6c6cce76bdd3b84624d1a9206151fb3ccd1 Homepage: https://cran.r-project.org/package=simMetric Description: CRAN Package 'simMetric' (Metrics (with Uncertainty) for Simulation Studies that EvaluateStatistical Methods) Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies. Package: r-cran-simml Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-simml_0.3.0-1.ca2604.1_all.deb Size: 68982 MD5sum: 45fde8e8c3ad5974e719ef0795d40fc5 SHA1: b4a9e9fe946b291cf624056a1b4622f0ee187a78 SHA256: 8343ceb2ee4deaa43e9b42b58a28aeeba49d4669bca416351b8655f2fa00cdd6 SHA512: 6e76e9deebc546d6dc8b6f307155b94ceac538e177384247e4fb4bbeed92addbad76ac4d0b5c61bad14c68f35a5ca6e67f6ec1814c7f8b0f5b704fd5b143cad2 Homepage: https://cran.r-project.org/package=simml Description: CRAN Package 'simml' (Single-Index Models with Multiple-Links) A major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) and Park, Petkova, Tarpey, and Ogden (2020) (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml(). 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It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order () or Headrick's fifth-order () polynomial transformation. Binary and ordinal variables are simulated using a modification of the ordsample() function from 'GenOrd'. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, ). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, ). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds. Package: r-cran-simnph Architecture: all Version: 0.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-simdesign, r-cran-survival, r-cran-minipch, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-nph, r-cran-nphrct, r-cran-car, r-cran-dplyr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-ldbounds, r-cran-ggplot2, r-cran-patchwork, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-simnph_0.5.8-1.ca2604.1_all.deb Size: 296506 MD5sum: cbe83d06674d06f5e8d255bea86fdb19 SHA1: fc176a4b62c948b3899d1e914caa2ddb704b5775 SHA256: 622fa8e9e12f7d76fdf84b29d6efcef3b4a7de1374469618103f99a0cb930758 SHA512: 17c9ae576d11a0ccb342e1bba86e31d32452ef2dda23e420b0e64a0add1c2a7fda7efcfaed31aaeb7d8a97ae91c5fe236cf36d9fb3281ca89314cc40a874bf01 Homepage: https://cran.r-project.org/package=SimNPH Description: CRAN Package 'SimNPH' (Simulate Non-Proportional Hazards) A toolkit for simulation studies concerning time-to-event endpoints with non-proportional hazards. 'SimNPH' encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the 'SimDesign' package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) . 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Dattner & Yaari (2018) . Dattner et al. (2017) . Dattner & Klaassen (2015) . 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Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) and Rimal, R., Almøy, T., Sæbø, S. (2018) . Package: r-cran-sims Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chk, r-cran-future.apply, r-cran-nlist, r-cran-yesno Suggests: r-cran-covr, r-cran-future, r-cran-knitr, r-cran-progressr, r-cran-rjags, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-sims_0.0.4-1.ca2604.1_all.deb Size: 63096 MD5sum: 6befc6a07e3d9a0679762241e6d6387a SHA1: de39f985dd15c4fdd9ff795cd90eca1673848402 SHA256: 2e53ec229381c3135f7da54cf8c620e055a6b4c8b020af2ff75de8c9b1d509da SHA512: 00107f7b0c41308deac00c6201860320f6857db46685ab3875b4c9ee52f478149b76fd524c005e09e0c75c7fba43d6b18901c31369d1cbffed6c0720337601a7 Homepage: https://cran.r-project.org/package=sims Description: CRAN Package 'sims' (Simulate Data from R or 'JAGS' Code) Generates data from R or 'JAGS' code for use in simulation studies. The data are returned as an 'nlist::nlists' object and/or saved to file as individual '.rds' files. Parallelization is implemented using the 'future' package. Progress is reported using the 'progressr' package. Package: r-cran-simsalapar Architecture: all Version: 1.0-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc, r-cran-gridbase, r-cran-colorspace Suggests: r-cran-lattice, r-cran-rmpi, r-cran-hmisc, r-cran-copula, r-cran-foreach, r-cran-doparallel, r-cran-fgarch, r-cran-robustbase, r-cran-codetools Filename: pool/dists/resolute/main/r-cran-simsalapar_1.0-13-1.ca2604.1_all.deb Size: 466592 MD5sum: a07a559a053105613fbd7f79f35577fa SHA1: bae6431dbc2cd2a533f05852ab3f883eb97a9a81 SHA256: 9ff0042fe828957e0e30a94a0b575a5afab7276e2f2ce69cc787885f5bb023ea SHA512: 2a838ff5132a0c17d9a1ef66a395ce8501f696bf1393fb6ef0b5f466c334ce2e7553019d0e090d60c6494fa586e0819bb460e86d8fb225cd835981c4ca37eade Homepage: https://cran.r-project.org/package=simsalapar Description: CRAN Package 'simsalapar' (Tools for Simulation Studies in Parallel) Tools for setting up ("design"), conducting, and evaluating large-scale simulation studies with graphics and tables, including parallel computations. Package: r-cran-simsem Architecture: all Version: 0.5-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-quantreg, r-cran-kernsmooth, r-cran-semtools, r-cran-lavaan.mi, r-cran-openmx, r-cran-copula Filename: pool/dists/resolute/main/r-cran-simsem_0.5-17-1.ca2604.1_all.deb Size: 1148214 MD5sum: c0640b59d00d7e3d1b7e1be4bedf9d29 SHA1: 07f8d7538ea3e9b3661c17fdd3971a43618d7576 SHA256: 18daba3e5da4fe973e95bd384040209df2ed862f2a2f50cc0cc2f4cd246727c1 SHA512: efbd91e0222b5c08f174e929ca37fbc8c5221beecdd36fd6cb7c4ef2a719fd5cf961809637ebfe15f9dcfb79c2b5152e17dd584f44e10afd54df1ca0fdb9c7b2 Homepage: https://cran.r-project.org/package=simsem Description: CRAN Package 'simsem' (SIMulated Structural Equation Modeling) Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning. Package: r-cran-simseq Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool Filename: pool/dists/resolute/main/r-cran-simseq_1.4.0-1.ca2604.1_all.deb Size: 4443222 MD5sum: 4bcde9105a38c5dff71ba066913f31b9 SHA1: a41cc8a18a4f22dc92bde2fab0d6e3a53ea199df SHA256: ff6e66a9e14f69726a6e8bc3abcb5e3fe2b611b8f61b9af72e67a2c50b267722 SHA512: 1379833a08ccb22d5ce5e28c869ff0fbcabb6df7859eb8e8d5e5e2bf0a8c6ff8125ae2c35eb7500f213ec65073056a9567cf23451899d3e1cffbed6551e32ed1 Homepage: https://cran.r-project.org/package=SimSeq Description: CRAN Package 'SimSeq' (Nonparametric Simulation of RNA-Seq Data) RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard. Package: r-cran-simsl Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-simsl_0.2.1-1.ca2604.1_all.deb Size: 217280 MD5sum: 89579fb884c6798fee5c273893da4e75 SHA1: 994ae8d4df02edf1a578fbc06610d6aaceef6392 SHA256: f897aa6d55b81ec7373880073c8cf2ec709cdb0a9be8d9024961b5653845e763 SHA512: e6d96f77f83f17494034dbbd6459acbce8092e61e414649c54fe0664925dde9d537bde4f721303579de9fe3711648a64d7bb226355d2baec891780300d5da92f Homepage: https://cran.r-project.org/package=simsl Description: CRAN Package 'simsl' (Single-Index Models with a Surface-Link) An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl(). Package: r-cran-simsst Architecture: all Version: 0.0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gamlss.dist, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-simsst_0.0.5.2-1.ca2604.1_all.deb Size: 56034 MD5sum: 7745aa88724124de52b0c08a526d21e5 SHA1: 35368f2640cd50191a7ae97be68d43a024d3690b SHA256: 880af7bcbba6bdf50f135e9097b89790d23abcc4191556e85ec8aae49ecd7f84 SHA512: 475ef85be04f62a7ed06425972825bbf9355086c194883fd71a5303a45abc0a891d1b67a01c64ee81c9ecb846f3b79946cef8284ffeae9fe6153dbcbc67999ee Homepage: https://cran.r-project.org/package=SimSST Description: CRAN Package 'SimSST' (Simulated Stop Signal Task Data) Stop signal task data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) , Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) , Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) . Package: r-cran-simstandard Architecture: all Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-mvtnorm, r-cran-tibble, r-cran-magrittr, r-cran-rlang, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-forcats, r-cran-stringr, r-cran-testthat, r-cran-covr, r-cran-badger Filename: pool/dists/resolute/main/r-cran-simstandard_0.6.3-1.ca2604.1_all.deb Size: 930882 MD5sum: 35dfd07f97583e9de77bc6f088555ee5 SHA1: 38ccf430e7e042291e3ce2c463fee11370b18080 SHA256: 6a67bf72bf194b643ce32532ec914bad54192958937b6d4d1548f6001e57e07c SHA512: 7915241b53563a6775acfe2e1c7f428613ce04cd4e2f4959bec00523e10cce54c1380eda1512dac6ccd1313b4df8bf4ba36b08bb39affae9411b15bd4257be68 Homepage: https://cran.r-project.org/package=simstandard Description: CRAN Package 'simstandard' (Generate Standardized Data) Creates simulated data from structural equation models with standardized loading. Data generation methods are described in Schneider (2013) . Package: r-cran-simsurv Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bb, r-cran-eha, r-cran-flexsurv, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-rstpm2, r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simsurv_1.0.1-1.ca2604.1_all.deb Size: 148874 MD5sum: 94928fd2d22e86894839e784d925ec85 SHA1: 42ee787af64706fb0ebefec5e0b1f3c364e056ad SHA256: 3d87aeb0ebbe6ba042a85343ecee65a6d02b1ef798ec6bdd35ce76722376cb9a SHA512: 56601a711a113738978fd8a59fead3312bf978daf483f16ffbd099a797c42536ce6ddf9ec847550fb2d81c2f2cb39b7ec95edd2ea0bf2171a9b113d51d15510d Homepage: https://cran.r-project.org/package=simsurv Description: CRAN Package 'simsurv' (Simulate Survival Data) Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper . Note that this package is modelled on the 'survsim' package available in the 'Stata' software (see Crowther and Lambert (2012) or Crowther and Lambert (2013) ). Package: r-cran-simsurvey Architecture: all Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-stars, r-cran-data.table, r-cran-progress, r-cran-doparallel, r-cran-foreach, r-cran-plotly, r-cran-rlang, r-cran-lifecycle, r-cran-matrix Suggests: r-cran-fields, r-cran-rmarkdown, r-cran-flexdashboard, r-cran-shiny, r-cran-crosstalk, r-cran-htmltools, r-cran-viridis, r-cran-lme4, r-cran-ggplot2, r-cran-inlaspacetime, r-cran-knitr, r-cran-bezier Filename: pool/dists/resolute/main/r-cran-simsurvey_0.1.8-1.ca2604.1_all.deb Size: 3626536 MD5sum: e9dc27c3d2de540cdcb64212a87ea52a SHA1: 64a278245d2d19532ae1af541cea60b05556471c SHA256: 826517a21626777ae7b151d4984ef1858c2933a439a34cf671a94430a8787178 SHA512: c7268cae7d7cfac61ea53834d072b7395e268f717448bebc0d73e1a2e4188fcb4e9ea48e1772fe94477364c5be83e11cc47a0c8ba4db9403a60c29f6b811c5b9 Homepage: https://cran.r-project.org/package=SimSurvey Description: CRAN Package 'SimSurvey' (Test Surveys by Simulating Spatially-Correlated Populations) Simulate age-structured populations that vary in space and time and explore the efficacy of a range of built-in or user-defined sampling protocols to reproduce the population parameters of the known population. (See Regular et al. (2020) for more details). Package: r-cran-simtablr Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1347 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-flextable, r-cran-lmtest, r-cran-openxlsx, r-cran-sandwich, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simtablr_1.2.0-1.ca2604.1_all.deb Size: 1222490 MD5sum: 9fe60838306c621173d206d778e9cef9 SHA1: 7ee3c7ed0f69be709db0b79c46838cdd2da04e6f SHA256: 4246985fa13b7bf6f56f6430c110df5c55ba03fb7d67e965f19cbe4b31012178 SHA512: 4fe09b9343f9691a195cd7f7a3fb7e765eeeb751c911eae26fb83c99be7074b188ea396a5bff08e45f9f0160f111190d16cd7d1de55c0e12f7312b967d8d4910 Homepage: https://cran.r-project.org/package=SimtablR Description: CRAN Package 'SimtablR' (Easy Publication-Ready Tables and Regression Analysis) Streamlines the creation of descriptive frequency tables ('Table 1'), diagnostic test accuracy evaluations (sensitivity, specificity, predictive values), and multi-outcome regression summaries. Features automatic tables, prevalence and odds ratio calculations, and seamless integration with 'flextable' for exporting results to 'Microsoft Word' and 'PowerPoint'. Package: r-cran-simtargetcov Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simtargetcov_1.0.1-1.ca2604.1_all.deb Size: 15480 MD5sum: 9834c28018becbfcffe25671b8ce6040 SHA1: aa2f826a8266a892b003bce29ea9d61306f7c7fc SHA256: 33a404047ab5d302ee6b43ffaab7a526e38c37e69b108f509e01e5527d2fb9e7 SHA512: 9faedfd1b203409de4bdca6c5534c2423bff7c10035f9a3721632ee2ce1ff6317a5915a45a8eb0b2615eb5119ce9f48b0da9098b04549a711aa4bd324425c6ac Homepage: https://cran.r-project.org/package=simTargetCov Description: CRAN Package 'simTargetCov' (Data Transformation or Simulation with Empirical CovarianceMatrix) Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) . Package: r-cran-simtimer Architecture: all Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-simtimer_4.0.0-1.ca2604.1_all.deb Size: 207838 MD5sum: 65852cc03b47dd9dc9d1f950b5b94cd7 SHA1: 1c8fa69f21379d6f7fb0061e489c16d1cf8e95ee SHA256: d07ee3c168dbeeddd3f2a2f4c649d0ffd9a1bf4e4c0e07552ac585e9bd35fa8d SHA512: 2d479dc7fd9753e5b017b09519f23fc083adc77f426bf355236fda2afe749938660ca6f32423b4c3aaff92345e7706aac92389fe3468b666449e51a70d6be71e Homepage: https://cran.r-project.org/package=simtimer Description: CRAN Package 'simtimer' (Datetimes as Integers for Discrete-Event Simulations) Handles datetimes as integers for the usage inside Discrete-Event Simulations (DES). The conversion is made using the internally generic function as.numeric() of the base package. DES is described in Simulation Modeling and Analysis by Averill Law and David Kelton (1999) . Package: r-cran-simtimevar Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-metafor, r-cran-mvtnorm, r-cran-icc, r-cran-misctools, r-cran-car, r-cran-plyr, r-cran-corpcor, r-cran-psych Filename: pool/dists/resolute/main/r-cran-simtimevar_1.0.0-1.ca2604.1_all.deb Size: 77768 MD5sum: 2600efc9446de5fe666ec5b16150dbf5 SHA1: 69f7b1ce11391ae8aaa6c2a738a418d7452cc49e SHA256: eb1d4e4653a63d7549a5d57129b00f44c84c07b4022720f72fd501683cfda94d SHA512: 528b953ac765c482e11a971b518ca0b046691b17b3cda6d41032c4637a55bd7156d06ff5f3cad34096bf861e052f225172214dd2ce15ee11df3b3bcf803dd647 Homepage: https://cran.r-project.org/package=SimTimeVar Description: CRAN Package 'SimTimeVar' (Simulate Longitudinal Dataset with Time-Varying CorrelatedCovariates) Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. for methodological details. Package: r-cran-simtool Architecture: all Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-boot, r-cran-broom, r-cran-rmarkdown, r-cran-tinytest, r-cran-lintr, r-cran-roxygen2, r-cran-covr Filename: pool/dists/resolute/main/r-cran-simtool_1.1.9-1.ca2604.1_all.deb Size: 321872 MD5sum: 88c13838eaae9ed2673c3f9affb9c0a7 SHA1: b1df63a36c7b4344d4b56ce8e8b012efb7561edc SHA256: 58fbee130fdeb82edd511926a63487e4c7d129890afcec3606007d15e22258cb SHA512: 6bd20d1c92f8562c81841277669ac892365b6c6ae67213b4a63ebaa54e9cdfef0b8e6953c91265cb968f7a16f204e0908f58a5cc85c64d8749739475d105a6fc Homepage: https://cran.r-project.org/package=simTool Description: CRAN Package 'simTool' (Conduct Simulation Studies with a Minimal Amount of Source Code) Tool for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with it. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results. Package: r-cran-simtrait Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-prroc Suggests: r-cran-popkin, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bnpsd, r-cran-bedmatrix Filename: pool/dists/resolute/main/r-cran-simtrait_1.1.3-1.ca2604.1_all.deb Size: 345948 MD5sum: 120c2979019588b174079aba5e7f85cd SHA1: 46fa21230e4b977045eb5de4226d5b2d39c2480f SHA256: 67896e4d7ad8ac4415899702f7c1c45109ba46621150a6905b26ff2f66b66c2f SHA512: 70bbc5b1c7974dde689b8b22320f05c41d4c4f5b4d5c22fcf91f111fab84426dd65cecff563f7dfaf37b672963c0a74cc97dd1ecb08c76e079ed9224a5f43b6c Homepage: https://cran.r-project.org/package=simtrait Description: CRAN Package 'simtrait' (Simulate Complex Traits from Genotypes) Simulate complex traits given a SNP genotype matrix and model parameters (the desired heritability, number of causal loci, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship for a real dataset). Emphasis on avoiding common biases due to the use of estimated allele frequencies. The code selects random loci to be causal, constructs coefficients for these loci and random independent non-genetic effects, and can optionally generate random group effects. Traits can follow three models: random coefficients, fixed effect sizes, and infinitesimal (multivariate normal). GWAS method benchmarking functions are also provided. Described in Yao and Ochoa (2022) . Package: r-cran-simuclustfactor Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simuclustfactor_0.0.3-1.ca2604.1_all.deb Size: 149474 MD5sum: b8d08982baf5a3f05fe7ae57fa0194e4 SHA1: e8de2d9bcb881b4401e789802c0e084b4849600a SHA256: fd7269d17c9fe7a89a7f06c1f6223edcdb4b388d2e33a99a417efef27558fbdd SHA512: b8c1f347d32f372a0f59ea7c1ab1f4d133bff76d93f72cf6361d0d4aa56ebf0b578b82f1fbbac942214f8bae0fa55a12f6d89a26a9fcd512b926e26a2e25e01e Homepage: https://cran.r-project.org/package=simuclustfactor Description: CRAN Package 'simuclustfactor' (Simultaneous Clustering and Factorial Decomposition of Three-WayDatasets) Implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) . Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) . Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) . Package: r-cran-simukde Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ks, r-cran-mvtnorm, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simukde_1.3.0-1.ca2604.1_all.deb Size: 47064 MD5sum: 54802454f8b1045babc0f74bdc6d5d45 SHA1: 1bebe95646e7de13023c25327048f8c6b9cc2de4 SHA256: ebf63d3d9898991424fac70d94c92ab1117d64ad5385c71ab542b9b64b028ece SHA512: 9480eaa412a8d9e7aad0e0bc6e270d46ab201f2d38bb88499785b86b91f1ff4c422ce073fb5024e41f37b9608245bca15808aa7d1826f3ca6a391d6fc094f0b7 Homepage: https://cran.r-project.org/package=simukde Description: CRAN Package 'simukde' (Simulation with Kernel Density Estimation) Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) , Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) . Package: r-cran-simulariatools Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-reticulate, r-cran-scales, r-cran-terra Suggests: r-cran-magick, r-cran-testthat, r-cran-openair, r-cran-sf Filename: pool/dists/resolute/main/r-cran-simulariatools_3.1.0-1.ca2604.1_all.deb Size: 968496 MD5sum: 9ef3330b2d6740923290e9b32789c20c SHA1: 12924a5486ccc96404c717cf302a2370677f6e47 SHA256: bf4df2b480c7a52abc95213b620ae8b24a62c76aec1788283c53baf568f13963 SHA512: b1eec8a3114ad51643b845399de626320f9f97a284556374ffa4e825b08a86c409fa51b99939eceaf34ee44321846f576561247846872a5b7bc1711d772eb97b Homepage: https://cran.r-project.org/package=simulariatools Description: CRAN Package 'simulariatools' (Simularia Tools for the Analysis of Air Pollution Data) A set of tools developed at Simularia for Simularia, to help preprocessing and post-processing of meteorological and air quality data. Package: r-cran-simulatedce Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-evd, r-cran-formula.tools, r-cran-ggplot2, r-cran-kableextra, r-cran-magrittr, r-cran-mixl, r-cran-psych, r-cran-purrr, r-cran-readr, r-cran-rmarkdown, r-cran-stringr, r-cran-tibble, r-cran-tictoc, r-cran-tidyr, r-cran-future, r-cran-furrr, r-cran-qs2, r-cran-data.table, r-cran-glue Suggests: r-cran-knitr, r-cran-testthat, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-simulatedce_0.3.2-1.ca2604.1_all.deb Size: 388412 MD5sum: 67f746cbdfb06638ce726d61bb22caba SHA1: 6e68138786178ad75049d4b13ca8ce33df5cd9ee SHA256: a8acc75ca99757e3df0e510a7699bd05fb8c081cc94dc9919866fd1c9b1b850f SHA512: 85a040675bd14b73da6a1a6d59ad7ab51490708050dc204109a391fba033a9482123308d10fd91469567bcc272854e5ea10dfceae7aa1a68d79b95970174ac4a Homepage: https://cran.r-project.org/package=simulateDCE Description: CRAN Package 'simulateDCE' (Simulate Data for Discrete Choice Experiments) Supports simulating choice experiment data for given designs. It helps to quickly test different designs against each other and compare the performance of new models. The goal of 'simulateDCE' is to make it easy to simulate choice experiment datasets using designs from 'NGENE', 'idefix' or 'spdesign'. You have to store the design file(s) in a sub-directory and need to specify certain parameters and the utility functions for the data generating process. For more details on choice experiments see Mariel et al. (2021) . Package: r-cran-simulator Architecture: all Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2028 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-digest, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-glmnet, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-simulator_0.2.5-1.ca2604.1_all.deb Size: 1372204 MD5sum: ad6853d5644a1dbc31fbbc6870995b54 SHA1: edffc32d505d0cb25ff7fa7730e696d792aa4971 SHA256: 0eadc0237efdeff38037d1e3a34fc55d73046e198a187c2113ec12fc97d258dd SHA512: 99dc5cc5579d82fb361f6c8a435635bb66bad5e88d2926610e1b837ac996669224c448b95b8280b5685b25957874f6285cd1157b958e05f13e53c3348cae5a0c Homepage: https://cran.r-project.org/package=simulator Description: CRAN Package 'simulator' (An Engine for Running Simulations) A framework for performing simulations such as those common in methodological statistics papers. The design principles of this package are described in greater depth in Bien, J. (2016) "The simulator: An Engine to Streamline Simulations," which is available at . Package: r-cran-simule Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-pcapp, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-simule_1.3.0-1.ca2604.1_all.deb Size: 376714 MD5sum: 327b24747f0e3fb71d43987f904466a4 SHA1: 5f31f5aedb4569f56ef1c881d4f1653113290d29 SHA256: b4a80eef763dca997f8cf3134b04b56730a06d590df0165c46a9c8d37c8342ee SHA512: bb65f46ee19799bc122b7867381a96b26fc3ff84f68278a7d68dcd56d63de56465d987bccf6bff5e9d94efefa9a8442834f71d32bf7bf2bc7ee44d8c1f1e030f Homepage: https://cran.r-project.org/package=simule Description: CRAN Package 'simule' (A Constrained L1 Minimization Approach for Estimating MultipleSparse Gaussian or Nonparanormal Graphical Models) This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) . Package: r-cran-simulist Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-english, r-cran-epiparameter, r-cran-grates, r-cran-randomnames, r-cran-rlang Suggests: r-cran-dplyr, r-cran-epicontacts, r-cran-ggplot2, r-cran-incidence2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-simulist_0.7.0-1.ca2604.1_all.deb Size: 1765738 MD5sum: fb049daa4d066e6dbde125d07b5e001c SHA1: c4cafd3049b06d196a11f0ca493ca7f5dba30f90 SHA256: 5488af023733aa2daa2d4b3a9cfa78767b74e2376132985f4ff2582750823796 SHA512: 9b8b20169b2f499ca63800da4f563f6c7aaae625166e7f8e706d7ba033ef1eb094a2414df6c0d3cdc0bb94092e59ddab579f7b9ebab222d59a7fe1f0d5a62fcf Homepage: https://cran.r-project.org/package=simulist Description: CRAN Package 'simulist' (Simulate Disease Outbreak Line List and Contacts Data) Tools to simulate realistic raw case data for an epidemic in the form of line lists and contacts using a branching process. Simulated outbreaks are parameterised with epidemiological parameters and can have age-structured populations, age-stratified hospitalisation and death risk and time-varying case fatality risk. Package: r-cran-simulmgf Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-simulmgf_0.1.1-1.ca2604.1_all.deb Size: 32052 MD5sum: 0dd9e5f640f4be004a2b9ffb24dbdb2f SHA1: d72d669409f30d6c6f0bfcf34890f39776ab18e7 SHA256: 8310c2a0b72ff0066b9606664328e1f8dc174510d686689204591270e46de15c SHA512: ca92f4d3703fa233f6e343db4d37d86f08cbb62a2ffeffa2cecd9ea364ba6c28acd65c1871601e8df1f0dbcaef41e41545d58f7690b245ce00fb506413207acd Homepage: https://cran.r-project.org/package=simulMGF Description: CRAN Package 'simulMGF' (Simulate SNP Matrix, Phenotype and Genotypic Effects) Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2), Sikorska et al., (2013) , or half-sib/full-sib SNP matrix from real or simulated parents SNP data, assuming mendelian segregation. Simulate phenotypic traits for real or simulated SNP data, controlled by a specific number of quantitative trait loci and their effects, sampled from a Normal or an Uniform distributions, assuming a pure additive model. This is useful for testing association and genomic prediction models or for educational purposes. Package: r-cran-simurg Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2368 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-dplyr, r-cran-fastdummies, r-cran-forcats, r-cran-ggplot2, r-cran-jsonlite, r-cran-mass, r-cran-philentropy, r-cran-purrr, r-cran-readr, r-cran-recipes, r-cran-rlang, r-cran-rxode2, r-cran-scales, r-cran-stringr, r-cran-synthpop, r-cran-sys, r-cran-tibble, r-cran-tidyr, r-cran-uwot, r-cran-zoo, r-cran-lhs, r-cran-sensitivity, r-cran-ppcor, r-cran-withr Suggests: r-cran-ggally, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simurg_0.2.0-1.ca2604.1_all.deb Size: 1374100 MD5sum: 65f81701e0a425f541e17640fba41f12 SHA1: c1a54b2fbe1e039c39624c018307689e621e747f SHA256: 948c4be3c14d084de9f852d9e8c6ee0618ad380a813f4729f385c3b44bceb24c SHA512: 47f79065f795cbb5615fcdea42f8daf6851f1298ca0029e6d399a1feccc26e23001eae762a5a39d35e4762ce1d00147586d2cb9ba2a2b4372c2c8251e33242e6 Homepage: https://cran.r-project.org/package=SimuRg Description: CRAN Package 'SimuRg' (Building, Fitting and Evaluating PK/PD Modeles) Provides a unified workflow for building, fitting using external engines, and evaluating ordinary differential equation (ODE)-based pharmacokinetic/pharmacodynamic (PK/PD) models. Supports generation of estimation scenarios and control files for external engines (e.g., 'Monolix'), simulation of models using 'rxode2', and creation of goodness-of-fit diagnostics. Includes tools for covariate modeling, virtual population design, and local and global sensitivity analyses. Package: r-cran-simvitd Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-simpleboot Filename: pool/dists/resolute/main/r-cran-simvitd_1.0.3-1.ca2604.1_all.deb Size: 588526 MD5sum: 6fcc0521ad0320315cc45207a23dd2fe SHA1: af160b63e2c9b2e637e78eccbb4b1581a73968c1 SHA256: 9196d45b86081160f868caa907d57fd16279011a257331e26af0eb8740ee587d SHA512: 72972e3973d669fac4babf1fb7a9ad131cb9e672bb8f08a065d9b3d75d1ccd1b50487b75dccbc81a5162a3a24d03040e5da235e45b4c7c6effad819b9f493cc6 Homepage: https://cran.r-project.org/package=SimVitD Description: CRAN Package 'SimVitD' (Simulation Tools for Planning Vitamin D Studies) Simulation tools for planning Vitamin D studies. Individual vitamin D status profiles are simulated, modelling population heterogeneity in trial arms. Exposures to infectious agents are generated, with infection depending on vitamin D status. Package: r-cran-sinaplot Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-sinaplot_1.1.0-1.ca2604.1_all.deb Size: 1156998 MD5sum: 5cf4b77190f5604a9e1eddef0fcc62a4 SHA1: 93854d4037f4c8ee9d537a2ca13501d4f0a9d17b SHA256: d1735cb6541746f5c1b8cce0471b62abc87f35c91aff3ced660de6e06114a412 SHA512: b018cad32f9ebd8751755544217869784f4c9f864f097ff418387b62e7cee37b6d65096836224fce8235ee7d6960ad1ce7791d8f60fe283e3c810744dda9d27f Homepage: https://cran.r-project.org/package=sinaplot Description: CRAN Package 'sinaplot' (An Enhanced Chart for Simple and Truthful Representation ofSingle Observations over Multiple Classes) The sinaplot is a data visualization chart suitable for plotting any single variable in a multiclass data set. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class. Package: r-cran-sinar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-sinar_0.1.0-1.ca2604.1_all.deb Size: 38800 MD5sum: 06b2de60bad4648a995f932e66aac6ef SHA1: 08f20ad940037257cba1e9b39f04fe236040cfa9 SHA256: 7b66d60d640167a0ddf762f43447299bab59fdaaeffcd454c4a540e49973da05 SHA512: 31725fdaa41b1d57a30c516f4e661d75688f4248609c42383ae97791ce93278602dec06caf7cc76f51858f5087cd4f64d091bb4502c15b21f42bb01ed2f9ee18 Homepage: https://cran.r-project.org/package=sinar Description: CRAN Package 'sinar' (Conditional Least Squared (CLS) Method for the Model SINAR(1,1)) Implementation of the Conditional Least Square (CLS) estimates and its covariance matrix for the first-order spatial integer-valued autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012) . Package: r-cran-sindyr Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-matrixstats, r-cran-igraph, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-sindyr_0.2.4-1.ca2604.1_all.deb Size: 43740 MD5sum: c16675a95fb9f56226809052d044f4d4 SHA1: 8e1684af25ef8c3def403aa4d6160371ac2a0944 SHA256: 8fae2b96476a0961077e4350440a28707cc2bf6c031adc743380be8c59cf345a SHA512: d002e6059657657ce0e3ce4ee916ec1d04ce798a75a0bad0d384054b0bbe549a7dd41501b29b0052b57d11605ecc9d9fd0b032f700676cc9bd66dd44f68c0674 Homepage: https://cran.r-project.org/package=sindyr Description: CRAN Package 'sindyr' (Sparse Identification of Nonlinear Dynamics) This implements the Brunton et al (2016; PNAS ) sparse identification algorithm for finding ordinary differential equations for a measured system from raw data (SINDy). The package includes a set of additional tools for working with raw data, with an emphasis on cognitive science applications (Dale and Bhat, 2018 ). See for examples and updates. Package: r-cran-sinew Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstudioapi, r-cran-sos, r-cran-stringi, r-cran-yaml, r-cran-crayon, r-cran-cli, r-cran-rematch2 Suggests: r-cran-rcmdcheck, r-cran-git2r, r-cran-shiny, r-cran-miniui, r-cran-withr, r-cran-usethis, r-cran-fs, r-cran-details, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sinew_0.4.0-1.ca2604.1_all.deb Size: 245646 MD5sum: 51528c3c093aae4a4e6e641a52bfd08a SHA1: 007df2ab8b0bddf8607d41db59d70cd1424a124c SHA256: aca517867dc0b3ff2e3ca9dbd8cdde7f9ece5f319e85c0ef3e01efec66fef3bb SHA512: 112cbae20501e73ff269f32052c3a77c1848c0c1dec76b6178359f721ecdfb0f5b81f3e03a34f87be499f2b3bc2b22497cd77cbedb22838a946cc1f55d75af6b Homepage: https://cran.r-project.org/package=sinew Description: CRAN Package 'sinew' (Package Development Documentation and Namespace Management) Manage package documentation and namespaces from the command line. Programmatically attach namespaces in R and Rmd script, populates 'Roxygen2' skeletons with information scraped from within functions and populate the Imports field of the DESCRIPTION file. Package: r-cran-singcar Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cholwishart, r-cran-mass, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-lme4, r-cran-lmertest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-singcar_0.1.5-1.ca2604.1_all.deb Size: 231594 MD5sum: 9465f1000ae07d3889725d305f33544b SHA1: 43f39cad07f29bca0874493feab5aa538444df70 SHA256: 29722ccff45dda5a1b4950b4e832538d47f1b3f4d84fd872d24883b0e284907e SHA512: 03d3f6cdfa177dd263423ab1c015c377c8871c991dcebe740925798580e9c88af59b155c0b9f4addb9c9f0a635fa39f2da56a0d17a329909413c465e1efd9b74 Homepage: https://cran.r-project.org/package=singcar Description: CRAN Package 'singcar' (Comparing Single Cases to Small Samples) When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) , Crawford and Garthwaite (2005) , Crawford and Garthwaite (2007) and Crawford, Garthwaite and Ryan (2011) . The package is also equipped with power calculators for each method. Package: r-cran-singlearmmrct Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-singlearmmrct_0.1.1-1.ca2604.1_all.deb Size: 269930 MD5sum: e6af5ed9d8af79a5d47f314ac4498800 SHA1: 109a223060195e43f4e3667e38cd43918d7e5d2d SHA256: df4593769d97d3d19f650cf76a535607cdf1eb970d71d0285a44ece6e60a011b SHA512: 86606fa1e011cf31289942da9bdb5ed644b073fb3b032b1f5134810687af09f903b1a78864c113cc680f104caf811a98b9d7723427c6286744bdb073b715d863 Homepage: https://cran.r-project.org/package=SingleArmMRCT Description: CRAN Package 'SingleArmMRCT' (Regional Consistency Probability for Single-Arm Multi-RegionalClinical Trials) Provides functions to calculate and visualise the Regional Consistency Probability (RCP) for single-arm multi-regional clinical trials (MRCTs) using the Effect Retention Approach (ERA). Six endpoint types are supported: continuous, binary, count (negative binomial), time-to-event via hazard ratio, milestone survival, and restricted mean survival time (RMST). For each endpoint, both a closed-form (or semi-analytical) solution and a Monte Carlo simulation approach are implemented. Two consistency evaluation methods are available: Method 1 (effect retention in Region 1 relative to the overall population) and Method 2 (simultaneous positive effect across all regions). Plotting functions generate faceted visualisations of RCP as a function of the regional allocation proportion, overlaying formula and simulation results for direct comparison. The methodology follows the Japanese MHLW guidelines for MRCTs. Abbreviations used: RCP (Regional Consistency Probability), MRCT (Multi-Regional Clinical Trial), RMST (Restricted Mean Survival Time), MHLW (Ministry of Health, Labour and Welfare). Package: r-cran-singlecasees Architecture: all Version: 0.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-spelling, r-cran-shiny, r-cran-shinytest2, r-cran-stringr, r-cran-rvest, r-cran-ggplot2, r-cran-purrrlyr, r-cran-testthat, r-cran-markdown, r-cran-knitr, r-cran-rmarkdown, r-cran-cleanrmd, r-cran-kendall, r-cran-kableextra, r-cran-covr, r-cran-readxl, r-cran-glue, r-cran-janitor, r-cran-rclipboard Filename: pool/dists/resolute/main/r-cran-singlecasees_0.7.4-1.ca2604.1_all.deb Size: 295714 MD5sum: e8fa353bbaa6d351da0bea94cdc10bec SHA1: 14aed0d036a99aaa7cc541e101421028ba9dc053 SHA256: c5aa812dbb85e7ce98fc40a29a8822b677381a149def6646415e1c6d005ae737 SHA512: e55aedafe3c516e8efe993a12f3b98a186e1b18cfc683ba1d2ab659f5ec8f34a2b151435ef9ce1d816bc73bda2468ebdd01310f365ca6bd636c90f98b6ecabd3 Homepage: https://cran.r-project.org/package=SingleCaseES Description: CRAN Package 'SingleCaseES' (A Calculator for Single-Case Effect Sizes) Provides R functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by Swan and Pustejovsky (2018) . Standard errors and confidence intervals (based on the assumption that the outcome measurements are mutually independent) are provided for the subset of effect sizes indices with known sampling distributions. Package: r-cran-singlecellcomplexheatmap Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-complexheatmap, r-cran-seurat, r-cran-dplyr, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-circlize, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-viridis, r-cran-devtools, r-cran-biocmanager, r-cran-ggsci, r-cran-seuratobject Filename: pool/dists/resolute/main/r-cran-singlecellcomplexheatmap_0.1.2-1.ca2604.1_all.deb Size: 656626 MD5sum: cab903e3d20f92c81b86a21712741934 SHA1: 1657d712b57bfdf0c6cd5cc7a9ba8fa239b8ca88 SHA256: 6264a1fb3ff236030d93ac965828b1ce9ef6b65164e7480a2288e311347764cf SHA512: 0ad4ee48f281f964bf62af4de20b279513436da1d350e6d45e6b09cd49131b1171931d6cd0e11682b5527dbf14cf05263b7db238e341d5071af98b53f8fdf3e9 Homepage: https://cran.r-project.org/package=SingleCellComplexHeatMap Description: CRAN Package 'SingleCellComplexHeatMap' (Complex Heatmaps for Single Cell Expression Data with DualInformation Display) Creates complex heatmaps for single cell RNA-seq data that simultaneously display gene expression levels (as color intensity) and expression percentages (as circle sizes). Supports gene grouping, cell type annotations, and time point comparisons. Built on top of 'ComplexHeatmap' and integrates with 'Seurat' objects. For more details see Gu (2022) and Hao (2024) . Package: r-cran-singlecellhaystack Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-cran-seuratobject, r-cran-cowplot, r-cran-wrswor, r-bioc-sparsematrixstats, r-bioc-complexheatmap, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-singlecellhaystack_1.0.3-1.ca2604.1_all.deb Size: 732362 MD5sum: a98743f57978962e37c51739bb226938 SHA1: ea7420ccafdb26d2e2a1f6963cd3f9763e4a6b9a SHA256: 94abd8fccdbcca0138c814119c63c12e1d17f1eb3d09bae01a3769be49b1ba12 SHA512: 42d2f9c028474be5a181c70ed286d113d06152e109fbe03deccffabdc1de932e59bc7761b265526885f1b9816bfb2316f404f43715807c8a7365774bad23fdba Homepage: https://cran.r-project.org/package=singleCellHaystack Description: CRAN Package 'singleCellHaystack' (A Universal Differential Expression Prediction Tool forSingle-Cell and Spatial Genomics Data) One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) and our update Vandenbon and Diez (Scientific Reports, 2023) . Package: r-cran-singlecellstat Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2570 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-matrix, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-singlecellstat_0.3.1-1.ca2604.1_all.deb Size: 2596104 MD5sum: 3997cd5b50abc1fae8b4e42c9abefffa SHA1: 166e8e24a075382b5603d0dd39a6870542b7d728 SHA256: 87234db09791f87363c50252f44dfc8224330316d7b3bd44e8d82c1b0a88e87e SHA512: c623c0a173d4981ba441a6642bea16a4bb44eba60d5a2b3fb7bb93f7971533dfae64725486356e1cef1f9669df80b5bde7b7f23008271c6a37cbd0632c6a4438 Homepage: https://cran.r-project.org/package=SingleCellStat Description: CRAN Package 'SingleCellStat' (A Toolkit for Statistical Analysis of Single-Cell Omics Data) A suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics). Package: r-cran-singlercapture Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2633 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lamw, r-cran-mathjaxr, r-cran-sandwich, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tinytest, r-cran-covr, r-cran-vgam, r-cran-lmtest Filename: pool/dists/resolute/main/r-cran-singlercapture_1.1.0-1.ca2604.1_all.deb Size: 1909790 MD5sum: 93f60556a6e2f5b40e7a1112b8002534 SHA1: de0aa280739a4e0fa9fcc630ab4203efcbdba242 SHA256: 41848fd2b92d54984e978972bba31a5384c20a1407372d1fc5d386c55062ab1c SHA512: 611263f35171940b08a3f575042851c070de2c0ae9ae789c7ed89cde2945a4993a7d891efb6c77bee1b7ea08ec416fa0ae93c052dc73edd341aaf173e013bb74 Homepage: https://cran.r-project.org/package=singleRcapture Description: CRAN Package 'singleRcapture' (Single-Source Capture-Recapture Models) Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's, Chao's and ratio-regression estimators. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) , Böhning and van der Heijden (2019) , Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) . Package: r-cran-sinib Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sinib_1.0.0-1.ca2604.1_all.deb Size: 25172 MD5sum: bf14987a41c86d1032c242b6e7bd2023 SHA1: 15e014527bfdda4a015ba38fd88209708667a575 SHA256: be0a628198b14530851ebd21a3cb21a8ba90cf9531f9d0ccc27c730eec441966 SHA512: 0e0ee91816bdb88e921aa82b642ac039f78d56da3d56ffbb7045864e4ff50f49e54a02157c47c205e2dbdbc84be41be134c3c2681950a9ddbcd0b49fa8b072fe Homepage: https://cran.r-project.org/package=sinib Description: CRAN Package 'sinib' (Sum of Independent Non-Identical Binomial Random Variables) Density, distribution function, quantile function and random generation for the sum of independent non-identical binomial distribution with parameters \code{size} and \code{prob}. Package: r-cran-sinrelef.ld Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs Filename: pool/dists/resolute/main/r-cran-sinrelef.ld_1.1.0-1.ca2604.1_all.deb Size: 86764 MD5sum: 91b83f7a001cf2d203240723cbcae525 SHA1: d04c6a4a5ff9b4f32ff20af26a5c60aabab64c51 SHA256: a09d46d55b4df85c2fd5c8934bf7992982e24d0ecf40e75883ab69f284685246 SHA512: 93a1a182c51f67da1cf3ee58bdc313f6dc15e07655317da0e79f5992b85180f93c3dcb4e9e21695b6e4116b5fc9ed2f8fb6429f65f3954e12266a3f24184f295 Homepage: https://cran.r-project.org/package=SINRELEF.LD Description: CRAN Package 'SINRELEF.LD' (Reliability and Relative Efficiency in Locally-DependentMeasures) Implements an approach aimed at assessing the accuracy and effectiveness of raw scores obtained in scales that contain locally dependent items. The program uses as input the calibration (structural) item estimates obtained from fitting extended unidimensional factor-analytic solutions in which the existing local dependencies are included. Measures of reliability (Omega) and information are proposed at three levels: (a) total score, (b) bivariate-doublet, and (c) item-by-item deletion, and are compared to those that would be obtained if all the items had been locally independent. All the implemented procedures can be obtained from: (a) linear factor-analytic solutions in which the item scores are treated as approximately continuous, and (b) non-linear solutions in which the item scores are treated as ordered-categorical. A detailed guide can be obtained at the following url. Package: r-cran-sip Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sip_0.1.0-1.ca2604.1_all.deb Size: 2445802 MD5sum: 9705b99bbfd2c15087b426b215eaa6f7 SHA1: bcf200335a201db4e8057a3f9321a4f8339eba52 SHA256: 0fee0b38a20764732d5cb9da9b0bbd28b80ae6f45bc514c2fc1c2ecb508c2712 SHA512: cb985a6b2b1062b5ededdd1a3625fa17f6512bff8e5740953f54edb46708a47f59e1ba85fb9f241554411260c4660f1ba0a3f0b30c16e8a767ce5ab52ffd0eae Homepage: https://cran.r-project.org/package=SIP Description: CRAN Package 'SIP' (Single-Iteration Permutation for Large-Scale Biobank Data) A single, phenome-wide permutation of large-scale biobank data. When a large number of phenotypes are analyzed in parallel, a single permutation across all phenotypes followed by genetic association analyses of the permuted data enables estimation of false discovery rates (FDRs) across the phenome. These FDR estimates provide a significance criterion for interpreting genetic associations in a biobank context. For the basic permutation of unrelated samples, this package takes a sample-by-variable file with ID, genotypic covariates, phenotypic covariates, and phenotypes as input. For data with related samples, it also takes a file with sample pair-wise identity-by-descent information. The function outputs a permuted sample-by-variable file ready for genome-wide association analysis. See Annis et al. (2021) for details. Package: r-cran-sipdibge Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-covidibge, r-cran-pnadcibge, r-cran-pndsibge, r-cran-pnsibge, r-cran-cli, r-cran-png, r-cran-purrr, r-cran-rstudioapi, r-cran-tibble Suggests: r-cran-convey, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-projmgr, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-srvyr, r-cran-survey, r-cran-timedate Filename: pool/dists/resolute/main/r-cran-sipdibge_0.2.1-1.ca2604.1_all.deb Size: 362634 MD5sum: e0322d68e0830655b8543375d89f637a SHA1: 13af76677ad14b2905dfc6141b743ecf649d0900 SHA256: d23f862ff0aa75c207baf07b63a6276698d65cdf3600da017f3fe8431a8da686 SHA512: b916a1ae17e0580c6c4c6dd27ffa59614a4c66287fd6211ef1ff4444235950b306d1ecad72ab05396a9f998a8c9dab58e264451cefc18ced9f2cb5d0d9701c1b Homepage: https://cran.r-project.org/package=SIPDIBGE Description: CRAN Package 'SIPDIBGE' (Collection of Household Survey Packages Conducted by IBGE) Provides access to packages developed for downloading, reading and analyzing microdata from household surveys in Integrated System of Household Surveys - SIPD conducted by Brazilian Institute of Geography and Statistics - IBGE. More information can be obtained from the official website . Package: r-cran-sipetool Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-convolutioner Filename: pool/dists/resolute/main/r-cran-sipetool_0.1.0-1.ca2604.1_all.deb Size: 236270 MD5sum: 275ac03619278f5dd8af3d8838e93bde SHA1: 07a9dd64d8b18658beaf5a758bf0f543b9d5213c SHA256: 6a0523dfa91d5e504dcd42eaa3ff843708cfe5efaf82ca4e5907c80de5f45e34 SHA512: 1b9440752bbb2c921508cc61e583c8ad1db288ecc30067256cf91f6025237605912a3d07c057b9ccbdbd39c301ee936334145e354c7dce70006b308a141c7da1 Homepage: https://cran.r-project.org/package=SIPETool Description: CRAN Package 'SIPETool' (SIFT-MS and CPET Data Processor) Processor for selected ion flow tube mass spectrometer (SIFT-MS) output file from breath analysis. It allows the filtering of the SIFT output file (i.e., variation over time of the target analyte concentration) and the following analysis for the determination of: maximum, average, and standard deviation value of target concentration measured at each exhalation, and the respiratory rate over the measurement. Additionally, it is possible to align the SIFT-MS data with other on-line techniques such as cardio pulmonary exercise test (CPET) for a comprehensive characterization of breath samples. Package: r-cran-siplab Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-siplab_1.6-1.ca2604.1_all.deb Size: 481412 MD5sum: 5286c73ac2ec584b325cde3d8a33603a SHA1: 264666c49d2302f469d574897a950902bf95d709 SHA256: b0dbc93f23dac1ffa29f7bf229aaf23892fbf8c3eadd29f58be38927343e3a66 SHA512: 2dfe367fa53ba815c2e6a9d879cba8cf2ff36566ba34ddea2ca62b503e591844ddea51dd73292b3c24f4721d2dcf31a1bc9e591b86669e9724a21ebcdc702718 Homepage: https://cran.r-project.org/package=siplab Description: CRAN Package 'siplab' (Spatial Individual-Plant Modelling) A platform for computing competition indices and experimenting with spatially explicit individual-based vegetation models. Package: r-cran-sirad Architecture: all Version: 2.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3066 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-raster Filename: pool/dists/resolute/main/r-cran-sirad_2.3-3-1.ca2604.1_all.deb Size: 3101022 MD5sum: ddd1fec43a5bfd748aa78156b92f89ba SHA1: 01d032aa5b212ae3cc1b0f490944bc4d39c3b59e SHA256: 0237b15594334759fb8579e8031aa14d7650de950c6697860314c2d0232a6b56 SHA512: 0ec7013c490e4e4a05091e796bedcdf52153e617b258eaa1a05c8db6bf975fd466e2a5edf3cc8883955653f2e67a0c72ce03ce2beeb40d857b14c245ed211b5a Homepage: https://cran.r-project.org/package=sirad Description: CRAN Package 'sirad' (Functions for Calculating Daily Solar Radiation andEvapotranspiration) Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included. Package: r-cran-sire Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-systemfit, r-cran-psych, r-cran-igraph, r-cran-matrixcalc, r-cran-mass, r-cran-numderiv, r-cran-matrix, r-cran-stringr, r-cran-rsolnp, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-sire_1.1.0-1.ca2604.1_all.deb Size: 53398 MD5sum: 2851c256bb447203d844cb9f3be13d17 SHA1: 49a6c4936c76488fa0c11d6f2ac6fb68f76dfe06 SHA256: 88eae1166e95441dc0673517dc85337ba5ac1099d8d7f3c56c6da567b1ddde24 SHA512: 65bff69e9270b5622282e5590e5564bee8d7a526d18dd1db2b0bef388e09ae62a7d7181f563c1bc0f3022cd4717c3a6a3473db50cc4f715569cfc1978cef7a80 Homepage: https://cran.r-project.org/package=SIRE Description: CRAN Package 'SIRE' (Finding Feedback Effects in SEM and Testing for TheirSignificance) Provides two main functionalities. 1 - Given a system of simultaneous equation, it decomposes the matrix of coefficients weighting the endogenous variables into three submatrices: one includes the subset of coefficients that have a causal nature in the model, two include the subset of coefficients that have a interdependent nature in the model, either at systematic level or induced by the correlation between error terms. 2 - Given a decomposed model, it tests for the significance of the interdependent relationships acting in the system, via Maximum likelihood and Wald test, which can be built starting from the function output. For theoretical reference see Faliva (1992) and Faliva and Zoia (1994) . Package: r-cran-siren Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-efa.mrfa, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-siren_1.0.6-1.ca2604.1_all.deb Size: 63320 MD5sum: 1c078117ae6b553c9bfb2f860f76c66b SHA1: 8a31a304b85097f4f996ff55a2831bdf55f04809 SHA256: 18b72bb58be96aef5df5af787412168222d46dfe4d660dd177d2208b6d413497 SHA512: b410662ab7203d3d96e9c37fde982088d9f4417b13eac056334369a25d5d4d6f1e11d065f6374859cf4bb45098a0ed9474727b4dfb699d9e3186639c66d40d85 Homepage: https://cran.r-project.org/package=siren Description: CRAN Package 'siren' (Hybrid FA-CFA for Controlling Acquiescence in RestrictedFactorial Solutions) Performs hybrid multi-stage factor analytic procedure for controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000 ). 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Package: r-cran-sisal Architecture: all Version: 0.49-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-lattice, r-cran-mgcv, r-cran-digest, r-cran-r.matlab, r-cran-r.methodss3 Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sisal_0.49-1.ca2604.1_all.deb Size: 639106 MD5sum: a314ebfeb0849409608d665c2c4015eb SHA1: d07b0e1dee4e5c764ad23502aa492663e223bdbe SHA256: 25c85ae4a68fe67943b560e363c2af3aaa087a3787a03b1b24aef731498fdbcd SHA512: 6e1625556f579849b9b59acf56064e522d893af1f4c10f68c7c671f67e34b9d14c81e363f91f006cce5764c32af717c735d52dea4032b3d5c00ddc691099a088 Homepage: https://cran.r-project.org/package=sisal Description: CRAN Package 'sisal' (Sequential Input Selection Algorithm) Implements the SISAL algorithm by Tikka and Hollmén. 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Methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in . Package: r-cran-sistec Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 942 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-openxlsx, r-cran-rlang, r-cran-shiny, r-cran-stringi, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sistec_0.2.0-1.ca2604.1_all.deb Size: 369114 MD5sum: 46de87019182dfeba2688d9e229ef7b5 SHA1: c20c2529f9633f61a5fb6bdf034b5d1a09bcd267 SHA256: 0eb085575e4b7ee9792cae9fb5e91e21e75078f97a49662b1508bfed7560f9ca SHA512: 99d403663dd9a563965ee76b2e3036108d4127f4c9c09f8dea458c1301de077593c16b8530ffe7d0a66d148768325acf83feacc89e252661e74711c90e920529 Homepage: https://cran.r-project.org/package=sistec Description: CRAN Package 'sistec' (Tools to Analyze 'Sistec' Datasets) The Brazilian system for diploma registration and validation on technical and superior courses are managing by 'Sistec' platform, see . This package provides tools for Brazilian institutions to update the student's registration and make data analysis about their situation, retention and drop out. Package: r-cran-sisti Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4786 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-sisti_0.0.1-1.ca2604.1_all.deb Size: 2973728 MD5sum: 99308ce7a2f9fd9a14e249d44e3f6ebf SHA1: 9fd4ad6b617f2e1fdad83b5aa23c1a14d870636d SHA256: af5ad877e1e9ec67513df90d7b6d2b91f0424244c266cea8849f0cd651673cf0 SHA512: d3791bbc2e8407c38b1c251794d57b2ebd5f010e610c494e917eed8c7741d4aa6e67488626c321c8c55bfa4889625a5774aa23a85ed97a593516fcdb6d972b28 Homepage: https://cran.r-project.org/package=sisti Description: CRAN Package 'sisti' (Real-Time PCR Data Sets by Sisti et al. (2010)) This data package contains four datasets of quantitative PCR (qPCR) amplification curves that were used as supplementary data in the research article by Sisti et al. (2010), . The primary dataset comprises a ten-fold dilution series spanning copy numbers from 3.14 × 10^7 to 3.14 × 10^2, with twelve replicates per concentration. These samples are based on a pGEM-T Promega plasmid containing a 104 bp fragment of the mitochondrial gene NADH dehydrogenase 1 (MT-ND1), amplified using the ND1/ND2 primer pair. The remaining three datasets contain qPCR results in the presence of specific PCR inhibitors: tannic acid, immunoglobulin G (IgG), and quercetin, respectively, to assess their effects on the amplification process. These datasets are useful for researchers interested in PCR kinetics. The original raw data file is available as Additional File 1: . 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Package: r-cran-sitrep Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-apyramid, r-cran-epidict, r-cran-epikit, r-cran-epitabulate Suggests: r-cran-anthro, r-cran-binom, r-cran-broom, r-cran-clipr, r-cran-covr, r-cran-dplyr, r-cran-flextable, r-cran-forcats, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggspatial, r-cran-glue, r-cran-gtsummary, r-cran-here, r-cran-janitor, r-cran-knitr, r-cran-labelled, r-cran-lubridate, r-cran-matchmaker, r-cran-pacman, r-cran-parsedate, r-cran-patchwork, r-cran-purrr, r-cran-rio, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-sessioninfo, r-cran-sf, r-cran-slider, r-cran-srvyr, r-cran-stringr, r-cran-summarytools, r-cran-survey, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tsibble, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-sitrep_0.4.1-1.ca2604.1_all.deb Size: 952072 MD5sum: ffa3f8674af29ff50caab7963647b1a3 SHA1: 4e71fb536217250db5ead34884faf3bf58d4107b SHA256: 0ed30b01980e72829b19004c81c81c19d4ec01c9194387fd1955dee6af1a023d SHA512: fb333e8e4dda983002b3bd2fcfaa3b310e01bd007c297ca926d55999d7deed8fa196e05ea2309b0bcdef1420fc91e9ebc16ce6051c05c2746bf6b55156c7e1d9 Homepage: https://cran.r-project.org/package=sitrep Description: CRAN Package 'sitrep' (Report Templates and Helper Functions for Applied Epidemiology) A meta-package that loads the complete sitrep ecosystem for applied epidemiology analysis. This package provides report templates and automatically loads companion packages, including 'epitabulate' (for epidemiological tables), 'epidict' (for data dictionaries), 'epikit' (for epidemiological utilities), and 'apyramid' (for age-sex pyramids). Simply load 'sitrep' to access all functions from the ecosystem. Package: r-cran-sivs Architecture: all Version: 0.2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-proc, r-cran-varhandle Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sivs_0.2.11-1.ca2604.1_all.deb Size: 203670 MD5sum: 4d03a1eca0e14641122a92caded24ab7 SHA1: 48eb6f02826f54bd7333bf7f914611f1360b8e2d SHA256: 852567479582c3c22b1946bf6dd42ffa63a9696a9b56a5d6ae81ab95f2a9e83e SHA512: 7ea7c7d74d71dccac7ec973d59309f6b28896d67faf6a64842b468e0d5279746ba74231d8d1e23626ee599790fe9988b2330b51007ae8eec8841953210e441c8 Homepage: https://cran.r-project.org/package=sivs Description: CRAN Package 'sivs' (Stable Iterative Variable Selection) An iterative feature selection method that internally utilizes various Machine Learning methods that have embedded feature reduction in order to shrink down the feature space into a small and yet robust set. Package: r-cran-sixsigma Architecture: all Version: 0.11.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-ggplot2, r-cran-reshape2, r-cran-nortest, r-cran-e1071, r-cran-scales, r-cran-testthat, r-cran-xtable Suggests: r-cran-covr Filename: pool/dists/resolute/main/r-cran-sixsigma_0.11.1-1.ca2604.1_all.deb Size: 605888 MD5sum: abd58b1f8266422196871621fcf4cddf SHA1: 3bb8d854353f21966493bed8c8c6278444df7e66 SHA256: b89570b941065db8d8b2c15840b5b0ae329e57a65dd2950e8d785f849ba00846 SHA512: 8588c5ca3840b61e77fc606ed3cc0c65f64e56d81b0130e10d849df030d98f94516b2bdafc7fe3358a3ff416b31124a4d7fabf4e857e7b8776decfa6f22a2ed7 Homepage: https://cran.r-project.org/package=SixSigma Description: CRAN Package 'SixSigma' (Six Sigma Tools for Quality Control and Improvement) Functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2] and "Quality Control with R" [ISBN 978-3-319-24046-6], are also included in the package. Package: r-cran-sixtyfour Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-paws, r-cran-paws.common, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-fs, r-cran-s3fs, r-cran-cli, r-cran-glue, r-cran-memoise, r-cran-uuid, r-cran-jsonlite, r-cran-curl, r-cran-tidyr, r-cran-clipr, r-cran-withr, r-cran-ipaddress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-dbi, r-cran-rpostgres, r-cran-rmariadb, r-cran-ggplot2, r-cran-lubridate, r-cran-testthat, r-cran-vcr, r-cran-webmockr Filename: pool/dists/resolute/main/r-cran-sixtyfour_0.2.0-1.ca2604.1_all.deb Size: 681036 MD5sum: acffbe47343dcf88cccf88e87a662805 SHA1: 8de5d887bec3508365aa52eb267b6689d7363f1e SHA256: 10c2449195b4e90c370fcd6996fc852a7fb50a8906d685576e19cbb41be4e44d SHA512: fcbb3128e0645ce8a91183491fd2d10ca97a7a2c74c7679b26c8316d87d3d69da69fd4a12766121fe3155c000e43692fcf426b7006acbb4ad76e25b670dfb8f7 Homepage: https://cran.r-project.org/package=sixtyfour Description: CRAN Package 'sixtyfour' (Humane Interface to Amazon Web Services) An opinionated interface to Amazon Web Services , with functions for interacting with 'IAM' (Identity and Access Management), 'S3' (Simple Storage Service), 'RDS' (Relational Data Service), Redshift, and Billing. Lower level functions ('aws_' prefix) are for do it yourself workflows, while higher level functions ('six_' prefix) automate common tasks. Package: r-cran-sizemat Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-matrixstats, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sizemat_1.1.2-1.ca2604.1_all.deb Size: 549870 MD5sum: 234a9158bc6f761c60acf5ad9b0c70f6 SHA1: 67578f7647fc8d001f242b65eaca6dbdc042b304 SHA256: d02cc0dd1be780faf60af007cc08b0410fadb31166ed9934130eae3b1f87ba62 SHA512: fd4e2950b818397d144c831e709501cbb8cac0dcdd5c12dc59376bf5102bd1b261c2c1c15c7338a9b61231289acb57afb87cae5fcd4b4e41ceeb3cc6a3043ec6 Homepage: https://cran.r-project.org/package=sizeMat Description: CRAN Package 'sizeMat' (Estimate Size at Sexual Maturity) Estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. 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Package: r-cran-sizer Architecture: all Version: 0.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-sizer_0.1-8-1.ca2604.1_all.deb Size: 81720 MD5sum: b5a3136bdc1cb6057858c05a70de2d61 SHA1: a48942fa1eb294032d54171bf2cf624a9d30a04a SHA256: e87a5b9d59133f45df431116b4b63321e36687f7ee11855af7f71bb22794502e SHA512: adb2034674dfbcaa415ab548b64317dfb66408c3755b8e0850d22484c7657a79396135d8cdca4a9c30b98b5b6b3d14460f78e628a47f9b66d21e52e44c14504d Homepage: https://cran.r-project.org/package=SiZer Description: CRAN Package 'SiZer' (Significant Zero Crossings) Calculates and plots the SiZer map for scatterplot data. 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Package: r-cran-sjlabelled Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-insight, r-cran-datawizard Suggests: r-cran-dplyr, r-cran-haven, r-cran-magrittr, r-cran-sjmisc, r-cran-sjplot, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-snakecase, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sjlabelled_1.2.0-1.ca2604.1_all.deb Size: 294078 MD5sum: efdd1b6da582c40cac844510ecb1d83a SHA1: 653a92da3111fcf84cf88a3f3f51b542e0663560 SHA256: 9195bfba8cc4373db6bd16c522b9c563434f5df20e707ba4418070d30180c5e3 SHA512: bdb441467eeaaa5800cfc7e8c28115ae6960e9fb5dbe8744221e0ff7624701122a76024cc2dcba28c35ac94e973ca39232acf801ef536386fbce0e6375ec75cd Homepage: https://cran.r-project.org/package=sjlabelled Description: CRAN Package 'sjlabelled' (Labelled Data Utility Functions) Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. 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Package: r-cran-sjmisc Architecture: all Version: 2.8.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-insight, r-cran-datawizard, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-sjlabelled, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-haven, r-cran-mice, r-cran-nnet, r-cran-sjplot, r-cran-sjstats, r-cran-knitr, r-cran-rmarkdown, r-cran-stringdist, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-sjmisc_2.8.11-1.ca2604.1_all.deb Size: 515042 MD5sum: d99e5599507969ea8027282cbe1cf711 SHA1: 30fad120ab953b0996ea0147857f08281d3e9694 SHA256: cff2ebe6016b557390092179426b3913f65e451c7bbe82450ec02edaa926b88c SHA512: 623aa0b923c40de35667b527d1a24878fb4cb04058fbdb8472d57c106350f8b4b865aa5d1be923f41db63929e119817a9d06ea2ce3328ec9b65dceb95af75cd3 Homepage: https://cran.r-project.org/package=sjmisc Description: CRAN Package 'sjmisc' (Data and Variable Transformation Functions) Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. 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Package: r-cran-sjplot Architecture: all Version: 2.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-datawizard, r-cran-dplyr, r-cran-ggeffects, r-cran-ggplot2, r-cran-knitr, r-cran-insight, r-cran-parameters, r-cran-performance, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-sjlabelled, r-cran-sjmisc, r-cran-sjstats, r-cran-tidyr Suggests: r-cran-brms, r-cran-car, r-cran-clubsandwich, r-cran-cluster, r-cran-cowplot, r-cran-effects, r-cran-haven, r-cran-gparotation, r-cran-ggrepel, r-cran-glmmtmb, r-cran-gridextra, r-cran-ggridges, r-cran-httr, r-cran-lme4, r-cran-mass, r-cran-nfactors, r-cran-pscl, r-cran-psych, r-cran-rmarkdown, r-cran-rstanarm, r-cran-sandwich, r-cran-survey, r-cran-tmb, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sjplot_2.9.0-1.ca2604.1_all.deb Size: 1397134 MD5sum: c925c4cb32d1e4a86b42d9375be8d5f4 SHA1: 462af8102f281b22e1d2f26d7ec7266e3f267fb3 SHA256: 92bcdb04cdc2896b22fd055fbd407cd92476feab2846d3bc0a5bd3af9684fad8 SHA512: 74fa422d1255ff01aab4f996d62812321096577900343bf918d1123cce165175453dfc3181215ed2c474be6307131c2307134c54cefb118822eae240013ad76c Homepage: https://cran.r-project.org/package=sjPlot Description: CRAN Package 'sjPlot' (Data Visualization for Statistics in Social Science) Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data. Package: r-cran-sjsdm Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-mvtnorm, r-cran-rstudioapi, r-cran-abind, r-cran-metrics, r-cran-mgcv, r-cran-cli, r-cran-crayon, r-cran-ggplot2, r-cran-checkmate, r-cran-mathjaxr, r-cran-beeswarm, r-cran-qgam, r-cran-scales, r-cran-viridis Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-iml, r-cran-fields Filename: pool/dists/resolute/main/r-cran-sjsdm_1.0.7-1.ca2604.1_all.deb Size: 1082072 MD5sum: 74e84625b623f2fb04cada1048fa1b4f SHA1: 2cc8977d9a98b3275674d0e754db1dcb45fb6e9b SHA256: 531acea54fb7ee6200c3389f5b76ac3f8300d8fa982e45049f203ebf89eea8d2 SHA512: a6fc91b094bcc34139b8b79e29dd087ebf636f545595076c17c1fd8391c3c559116cb1d6952a4183034d54167dcdff47b86054b0ebeec299f6a10027760da552 Homepage: https://cran.r-project.org/package=sjSDM Description: CRAN Package 'sjSDM' (Scalable Joint Species Distribution Modeling) A scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., . The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes. The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) , scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via 'PyTorch' and 'reticulate', which can be run on CPUs or GPUs. 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This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), or for which currently no functions available. Second, another focus lies on weighted variants of common statistical measures and tests like weighted standard error, mean, t-test, correlation, and more. Package: r-cran-sjtable2df Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-kableextra, r-cran-rlang, r-cran-rvest, r-cran-xml2 Suggests: r-cran-lintr, r-cran-lme4, r-cran-mlbench, r-cran-quarto, r-cran-sjplot, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sjtable2df_0.0.5-1.ca2604.1_all.deb Size: 37768 MD5sum: 1e394c0f69a46f4602b2dd899bb35216 SHA1: e0b58168a80335be554ecbe87f8796c18767222b SHA256: a869b3bb6690a5f2eeb7ff81dac4f6ae858b0709dfd7104d6cb4bff1fba78b17 SHA512: 964eb851ce00076a6bb3ae2af468a32307f6223ef1d142f1fb960c2f40e1aba3a74a34df136bf2692e17fd3bfe807b17575e96a6a717229be9217cb176091261 Homepage: https://cran.r-project.org/package=sjtable2df Description: CRAN Package 'sjtable2df' (Convert 'sjPlot' HTML-Tables to R 'data.frame') A small set of helper functions to convert 'sjPlot' HTML-tables to R data.frame objects / knitr::kable-tables. Package: r-cran-skater Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-purrr, r-cran-kinship2, r-cran-corrr, r-cran-tibble, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-skater_0.2.0-1.ca2604.1_all.deb Size: 455418 MD5sum: 9934dfb2aaf05fed521c6eaf919359a0 SHA1: e7ed0e459aad1eb601948a629659642054a7c57d SHA256: a69811816c2aabe72ddffa98a7a6f4d095ac4a0bc2ab4744210730b85ff48252 SHA512: 5807ddf0b9db442fb0535b55a0376b2f117e18b9f95b1cc64c48da0349bb708cbb2b02c5d734ac37fc8ea6d3d65fc7e0995841c74351ce9e5f5a9caa97fa753b Homepage: https://cran.r-project.org/package=skater Description: CRAN Package 'skater' (Utilities for SNP-Based Kinship Analysis) Utilities for single nucleotide polymorphism (SNP) based kinship analysis testing and evaluation. The 'skater' package contains functions for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing identity by descent (IBD) segment data. Package functions and methods are described in Turner et al. (2021) "skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation" . Package: r-cran-skedastic Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4187 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-broom, r-cran-pracma, r-cran-compquadform, r-cran-mass, r-cran-quadprog, r-cran-inflection, r-cran-rfast, r-cran-caret, r-cran-matrix, r-cran-quadprogxt, r-cran-slam, r-cran-roi, r-cran-osqp, r-cran-mgcv, r-cran-roi.plugin.qpoases Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-lmtest, r-cran-car, r-cran-tseries, r-cran-tibble, r-cran-testthat, r-cran-mlbench, r-cran-expm, r-cran-arrangements, r-cran-quantreg, r-cran-gmp, r-cran-rmpfr, r-cran-cubature, r-cran-mvtnorm, r-cran-lmboot, r-cran-sandwich, r-cran-cmna Filename: pool/dists/resolute/main/r-cran-skedastic_2.0.3-1.ca2604.1_all.deb Size: 4184258 MD5sum: 5fb42ba4e16255e84ad50df20ab49f6e SHA1: de691814097952c723579d5728e9d767690c155d SHA256: 577c730b296944955c4c9d3c3044c52df7ab5c5c8c84dc123185688881123846 SHA512: a71c0435233f83c6488a4ac501a0e7bb217af51c9917773347ff3273af21edeaae77ec78736cf5c8c2ad2bcdedbe0de320040392729141bfa4553f503a895bae Homepage: https://cran.r-project.org/package=skedastic Description: CRAN Package 'skedastic' (Handling Heteroskedasticity in the Linear Regression Model) Implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) . The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) . The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) . Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) . A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) , Ramsey's (1969) BAMSET Test , the tests of Bickel (1978) , Breusch and Pagan (1979) with and without the modification proposed by Koenker (1981) , Carapeto and Holt (2003) , Cook and Weisberg (1983) (including their graphical methods), Diblasi and Bowman (1997) , Dufour, Khalaf, Bernard, and Genest (2004) , Evans and King (1985) and Evans and King (1988) , Glejser (1969) as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) , Goldfeld and Quandt (1965) , Harrison and McCabe (1979) , Harvey (1976) , Honda (1989) , Horn (1981) , Li and Yao (2019) with and without the modification of Bai, Pan, and Yin (2016) , Rackauskas and Zuokas (2007) , Simonoff and Tsai (1994) with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) , Szroeter (1978) , Verbyla (1993) , White (1980) , Wilcox and Keselman (2006) , Yuce (2008) , and Zhou, Song, and Thompson (2015) . Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) , the conditional two-sided p-values of Kulinskaya (2008) , and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) , MacKinnon and White (1985) , Cribari-Neto (2004) , Cribari-Neto et al. (2007) , Cribari-Neto and da Silva (2011) , Aftab and Chang (2016) , and Li et al. (2017) . Package: r-cran-skeletalvis Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cowplot, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggrepel, r-cran-httr, r-cran-igraph, r-cran-miniui, r-cran-pbapply, r-cran-plotly, r-cran-rlang, r-cran-shiny, r-cran-tidyr, r-cran-visnetwork Suggests: r-cran-mockery, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-skeletalvis_0.1.2-1.ca2604.1_all.deb Size: 1295394 MD5sum: a102802091beab645ebcd84a49fcd483 SHA1: a953d9a992e81ec374c59d29c58b4da87bba2a80 SHA256: a11ebf0381015b0739aba3ffa1d82daaf01a19bb8c9770b3207b10d036c7e59b SHA512: 7203332c5e880cbce961e31464ba89321ca3ec719335c8999d6521ec52dd9bb1a5ae6b35d02feb2d415ddc85c8678df33c5f0596d39666d01673c1e07004d809 Homepage: https://cran.r-project.org/package=SkeletalVis Description: CRAN Package 'SkeletalVis' (Exploration and Visualisation of Skeletal Transcriptomics Data) Allows search and visualisation of a collection of uniformly processed skeletal transcriptomic datasets. Includes methods to identify datasets where genes of interest are differentially expressed and find datasets with a similar gene expression pattern to a query dataset Soul J, Hardingham TE, Boot-Handford RP, Schwartz JM (2019) . 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Package skeletons it creates are set up for pushing your package to 'GitHub' and using other hosted services for building and test automation. 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It implements an R-to-JavaScript transpiler and enables users to write JavaScript applications using the syntax of R. Package: r-cran-sketcher Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jpeg, r-cran-png, r-cran-readbitmap, r-cran-downloader, r-cran-imager, r-cran-magrittr, r-cran-stringr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sketcher_0.1.3-1.ca2604.1_all.deb Size: 1183032 MD5sum: b4e538f66da7352a7801b65d4264a8eb SHA1: d33f720b69a0cda409f24742e121c4f6595e3287 SHA256: 11378b50ca48cc31ac76cb38bc4fac85666bbf353cdf60c6766259028b1663c3 SHA512: 86b1415982dbae5ea2fcef04d292842ed8bdf46d92d53d23c41b7bdc4251c711f53113cbbb3353966510b0b9c149e3b428b6a3cafdd0934d21df63a8a65d1312 Homepage: https://cran.r-project.org/package=sketcher Description: CRAN Package 'sketcher' (Pencil Sketch Effect) An implementation of image processing effects that convert a photo into a line drawing image. For details, please refer to Tsuda, H. (2020). sketcher: An R package for converting a photo into a sketch style image. . 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Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control. Package: r-cran-skewhyperbolic Architecture: all Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-distributionutils, r-cran-generalizedhyperbolic Suggests: r-cran-runit Filename: pool/dists/resolute/main/r-cran-skewhyperbolic_0.4-2-1.ca2604.1_all.deb Size: 172188 MD5sum: 0943291d7c2ee8a0401bebde44acebee SHA1: 99deb25dbadd9b96ac79f11f1fe61894e1b7e105 SHA256: e74edba7e438a9d8a2faf3cb327762e4c72cdd4ef7fed88abe36827f68a9ef14 SHA512: 802081c9307454831f76458b6aa8e0f384c3fb2d5a35aa13e608f82d2e952d8e1a45df447725d11a46b6b2573587fda9c144048c7e269f84f11b1e918b790ea7 Homepage: https://cran.r-project.org/package=SkewHyperbolic Description: CRAN Package 'SkewHyperbolic' (The Skew Hyperbolic Student t-Distribution) Functions are provided for the density function, distribution function, quantiles and random number generation for the skew hyperbolic t-distribution. There are also functions that fit the distribution to data. There are functions for the mean, variance, skewness, kurtosis and mode of a given distribution and to calculate moments of any order about any centre. To assess goodness of fit, there are functions to generate a Q-Q plot, a P-P plot and a tail plot. Package: r-cran-skewlmm Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-future, r-cran-ggrepel, r-cran-haven, r-cran-mvtnorm, r-cran-nlme, r-cran-purrr, r-cran-furrr, r-cran-matrixcalc, r-cran-moments, r-cran-numderiv, r-cran-momtrunc, r-cran-truncatednormal Filename: pool/dists/resolute/main/r-cran-skewlmm_1.1.3-1.ca2604.1_all.deb Size: 1183436 MD5sum: 2fa8e79d84a42a8af5b0fd8c2c9d99ab SHA1: 29a87b57cb2842dee3cdf2fc28b889e428f5debc SHA256: c9d5c9776381ff6a130b79c9432e86b44768d5b0f765cb90177e72c554a62f4a SHA512: be0ac43b6423521bad985a4322229a61b336d555bf187719cc6836454ac34c701f85fd2902794945e18eae469272056968844bbf6bfb3d4076b8094cabe98f19 Homepage: https://cran.r-project.org/package=skewlmm Description: CRAN Package 'skewlmm' (Scale Mixture of Skew-Normal Linear Mixed Models) It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence . Package: r-cran-skewmlrm Architecture: all Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-moments, r-cran-clustergeneration, r-cran-doparallel, r-cran-mass, r-cran-mvtnorm, r-cran-matrixcalc Suggests: r-cran-sn Filename: pool/dists/resolute/main/r-cran-skewmlrm_1.7-1.ca2604.1_all.deb Size: 672028 MD5sum: ed6784f7eb3c171fecefd35ee58ba922 SHA1: 2d7929e1d889c87b163e61ecf04026ce0d236f06 SHA256: 04813b648fac6472c1d515830fdc6e03fd0dd71e774d794eb3305daa18ad22b1 SHA512: a6cdba29d5c001ad4133d9ab46b16d7152950afd684795261b25e414b3d6f2d6e6a88362e1d56e9712291cb8c1cb18644db0e188e43faf9fdceca4eb6030558b Homepage: https://cran.r-project.org/package=skewMLRM Description: CRAN Package 'skewMLRM' (Estimation for Scale-Shape Mixtures of Skew-Normal Distributions) Provide data generation and estimation tools for the multivariate scale mixtures of normal presented in Lange and Sinsheimer (1993) , the multivariate scale mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) , the multivariate skew scale mixtures of normal presented in Louredo, Zeller and Ferreira (2021) and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020) . Package: r-cran-skewsamp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-skewsamp_1.0.0-1.ca2604.1_all.deb Size: 74000 MD5sum: 307911001ab575b50aa898389d31f252 SHA1: 660696da7c8d6a706378bd7b3e962cc9945219ca SHA256: 56a360b972b99d1c45adc63300f3a97f4ca687f08102ef4d3f9a37d60070121f SHA512: 1028d390daa215e6b6e3fd4f10bcc0d7c092e9ad0e09c1c5c071fbe4992c82783efff4597b0ffdc6dcd30607f95133f19a3d95b959b520e3fd715b3eab133f08 Homepage: https://cran.r-project.org/package=skewsamp Description: CRAN Package 'skewsamp' (Estimate Sample Sizes for Group Comparisons with SkewedDistributions) Estimate necessary sample sizes for comparing the location of data from two groups or categories when the distribution of the data is skewed. The package offers a non-parametric method for a Wilcoxon Mann-Whitney test of location shift as well as methods for several generalized linear models, for instance, Gamma regression. Package: r-cran-skewt Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-skewt_1.0-1.ca2604.1_all.deb Size: 14476 MD5sum: 84129ff046573f3caf807d72466e6f7c SHA1: aee69f1a3c6ca30941deb4aa30691fd88651f222 SHA256: 18e09fb6b8c46710bd8728e6e34373e7d448f8d9ed5b6e1669d738b58d162f37 SHA512: 3d2fbf6610845964dc8d3cc2823a189fa42bf66e9e4988893cd65012bb6fd2992911621a947445e2e51071118aa66f386b868a8b9611a4eeb160c5512e83f98e Homepage: https://cran.r-project.org/package=skewt Description: CRAN Package 'skewt' (The Skewed Student-t Distribution) Density, distribution function, quantile function and random generation for the skewed t distribution of Fernandez and Steel. Package: r-cran-skewunit Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-skewunit_1.0-1.ca2604.1_all.deb Size: 60434 MD5sum: 1df720bf3ad8f290f1e36c0789741819 SHA1: 7de52200ebb6ea70af2210fa4d951c8cba1f8a3c SHA256: 316dcef8b79fcb9eef1a1e117ca12a1773d2e55409c22134dbb520018c4dd44d SHA512: 32550bb3d5f467bdbb1e242703a0403bb1bac977dad5359b0cba233d9f19cf61c51e624f204f683634e1ad145604b7c53826d256a8047370934c1c07a688b53b Homepage: https://cran.r-project.org/package=skewunit Description: CRAN Package 'skewunit' (Estimation and Other Tools for Skew-Unit Models) Provide estimation and data generation tools for the skew-unit family discussed based on Mukhopadhyay and Brani (1995) . The family contains extensions for popular distributions such as the ArcSin discussed in Arnold and Groeneveld (1980) , triangular, U-quadratic and Johnson-SB proposed in Cortina-Borja (2006) distributions, among others. 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Package: r-cran-slap Architecture: all Version: 2024.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-slap_2024.4.1-1.ca2604.1_all.deb Size: 436006 MD5sum: d701f70e30be4482c42206b7677185ee SHA1: 8ec6b9f9856faf904b651dbc0937e582b042087d SHA256: 752389e2ce9111a78bdf24e8fc026ae5109431c515f50abe3fb129c715a16e3c SHA512: 9891a594a0575a4c200abb713e4cf0d3706c287fb2cdfd8a71c380a74834b015fa8ea928b5764d0fef350acfd8c58ff07e979ca909ca04e4d64e8a75b775385c Homepage: https://cran.r-project.org/package=slap Description: CRAN Package 'slap' (Simplified Error Handling) Alternative to using withCallingHandlers() in the simple case of catch and rethrow. 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Package: r-cran-slcare Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-nnet, r-cran-reda, r-cran-rereg, r-cran-rlang, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-slcare_1.2.0-1.ca2604.1_all.deb Size: 76994 MD5sum: 0cc992b987a8866389d67fbf92b00738 SHA1: 89e9d8010577eeb6c483096b4b4aff7984211bd9 SHA256: 61200176c193a5f90eec288474e00b27358d6e3a1175a0497f71f13ae5fabb43 SHA512: 4548c584cd4794bac27ef3dae123b4604cb4570b6bddb7679488ba8505095fb2a06602bb457ee8e4ea0a00cf777c97dd27478cc75bdc6e746a97024278d6ceb4 Homepage: https://cran.r-project.org/package=SLCARE Description: CRAN Package 'SLCARE' (Semiparametric Latent Class Analysis of Recurrent Events) Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) . 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Package: r-cran-sldassay Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sldassay_1.8-1.ca2604.1_all.deb Size: 30604 MD5sum: 1c6a76c0a2e8603ad7f959669fa37a70 SHA1: b6ab13d6e4eec8ad31ff13f7d09585c660e7c252 SHA256: 4936fcf046898251751bc01d50df632228a2e361a0033a89d66a3d421903cdfd SHA512: d6c6075630869cb502a81ea43756dc9c6ebebf91224074e4a5a1b08fc5ce43cf8e915e94584bbc1aa6db5c8214bb39760514afc0d36079cf487492186245cdc0 Homepage: https://cran.r-project.org/package=SLDAssay Description: CRAN Package 'SLDAssay' (Software for Analyzing Limiting Dilution Assays) Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. 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However, such methods are resource intensive and often only used for final programme evaluation meaning results arrive too late for programme adaptation. SLEAC, which stands for Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage, is a low resource method designed specifically to address this limitation and is used regularly for monitoring, planning and importantly, timely improvement to programme quality, both for agency and Ministry of Health (MoH) led programmes. SLEAC is designed to complement the Semi-quantitative Evaluation of Access and Coverage (SQUEAC) method. This package provides functions for use in conducting a SLEAC assessment. Package: r-cran-sleekts Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sleekts_1.0.2-1.ca2604.1_all.deb Size: 19886 MD5sum: a8ca59e7b120f6b7bbfcb61eb7d84726 SHA1: fafc2769541dedaaf51845a6b87b2c736a3da1ad SHA256: 0fb34b2ce9c1e9c61dd19ab04966646090d8d3ada0f8dab01d3ee2978d6645c6 SHA512: eba321a423e03a5d50a864ccdd233fd0176f925f5b25909456ab9a19da23c83c2e2e1cc9738fbafbf678986d1a69bfce25c6fbe8ad71a2bca5c00c3ef3a469e9 Homepage: https://cran.r-project.org/package=sleekts Description: CRAN Package 'sleekts' (4253H, Twice Smoothing) Compute Time series Resistant Smooth 4253H, twice smoothing method. Package: r-cran-sleepcycles Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr, r-cran-stringr, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sleepcycles_1.1.4-1.ca2604.1_all.deb Size: 99616 MD5sum: 632a5a601d8c1ad4a4d2bf565fc96466 SHA1: f9538a5f590c6d51e648708472585df279bd9845 SHA256: 92f8a53797e22e228021724d07eae07ee64d6031cd5a20651815bed94af2c67d SHA512: c4aa950eab8c9761da6261f7b30fc9e0fc149de9adf45271a3fe293af3ea0dc60e8dfd3a1e3149cee2da3c544b447b39a8e641a477e45626488bd75da9e8cb7d Homepage: https://cran.r-project.org/package=SleepCycles Description: CRAN Package 'SleepCycles' (Sleep Cycle Detection) Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979) as described in Blume & Cajochen (2021) . Package: r-cran-sleepr Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-behavr, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sleepr_0.3.1-1.ca2604.1_all.deb Size: 42954 MD5sum: caf925b45c79e39ad8b1b35448defe0d SHA1: 5c31908a7181108db941588a71e2cc94c9ab803a SHA256: bafaae2d708a9915af1d6c5cca3e8b8c96b8dff16d018f78ca5761d0c28f48b4 SHA512: f1c9c1df4bd222e53083b231937d7a57a4447d6c3827f4a59c59f6122ecb50b64af8aad108bd7c9e697706a15ba1148fb11081d9949b1da21cefdee6e775d702 Homepage: https://cran.r-project.org/package=sleepr Description: CRAN Package 'sleepr' (Analyse Activity and Sleep Behaviour) Use behavioural variables to score activity and infer sleep from bouts of immobility. It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data. It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) ), activity classification for Ethoscopes (Geissmann et al. (2017) ) and a new algorithm to detect when animals are dead. Package: r-cran-sleepwalk Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jrc, r-cran-cowplot, r-cran-httpuv, r-cran-jsonlite, r-cran-scales, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-sleepwalk_0.3.2-1.ca2604.1_all.deb Size: 121978 MD5sum: a837be14b701a4000e461e03711caf7d SHA1: 3308ae67eea37295f3af460482aca450f2296ad7 SHA256: 0dbc54ef76aa5c4739fe3c914bd3148f22c7d20514b3c6ba58c1ec3ee8b76cb3 SHA512: c406390e7a0b626b77912737d1cc33648460e0430d49b16de1714c1c24a6e8c8025065f76c44a020dd86c3c17629f4b7a5c0bd7b0c835d3a06035ff5c850884e Homepage: https://cran.r-project.org/package=sleepwalk Description: CRAN Package 'sleepwalk' (Interactively Explore Dimension-Reduced Embeddings) A tool to interactively explore the embeddings created by dimension reduction methods such as Principal Components Analysis (PCA), Multidimensional Scaling (MDS), T-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) or any other. 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Population genetic models are then automatically executed by the 'SLiM' software by Haller et al. (2019) behind the scenes, using a custom built-in simulation 'SLiM' script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the 'SLiM' built-in back-end script, or using an efficient coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022) with a custom-built 'Python' script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the 'Python' module 'tskit' by Kelleher et al. (2019) . Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis. Package: r-cran-sleuth2 Architecture: all Version: 2.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5068 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-knitr, r-cran-mass, r-cran-agricolae, r-cran-car, r-cran-gmodels, r-cran-leaps, r-cran-mosaic Filename: pool/dists/resolute/main/r-cran-sleuth2_2.0-7-1.ca2604.1_all.deb Size: 3895102 MD5sum: bada5a76540f959ef661559bb85f4ddd SHA1: dbcac828f9ddcb0b49dfbd51ad42b2b89efa86b3 SHA256: a44189c8d64d2055956641ae97f3a8302a6376fae363fa2142ec0b8ec2d3bc01 SHA512: 40ced2beef8fef5d83af0bb4ce78af4e4e16b30df1021c92677afc14f1a3de694ec33af78eed50696a68b6a26131fccc3ad2779f95f234c6fa66a437301f2b3c Homepage: https://cran.r-project.org/package=Sleuth2 Description: CRAN Package 'Sleuth2' (Data Sets from Ramsey and Schafer's "Statistical Sleuth (2ndEd)") Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury. 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(2013), "The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)", Cengage Learning. Package: r-cran-slfpca Architecture: all Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-fdapace, r-cran-psych Filename: pool/dists/resolute/main/r-cran-slfpca_3.0-1.ca2604.1_all.deb Size: 44058 MD5sum: 16cee77daa5698497409f002994a5065 SHA1: 498cbd48c30e5ed554c7d45d049ef9489389e92a SHA256: de227049506bed529d6ac06246d68ec327c01e14b31cb4cc9f09bd58df18eb2c SHA512: d3bd1b3291902f354233663e2f78ec37e249f3784db54b7e8aeb83dd9c0b8ff96503ed6ca816760ae8021eeb7310c8fb9596bd9435663935fce9b3295d54d25b Homepage: https://cran.r-project.org/package=SLFPCA Description: CRAN Package 'SLFPCA' (Sparse Logistic Functional Principal Component Analysis) Implementation for sparse logistic functional principal component analysis (SLFPCA). SLFPCA is specifically developed for functional binary data, and the estimated eigenfunction can be strictly zero on some sub-intervals, which is helpful for interpretation. The crucial function of this package is SLFPCA(). Package: r-cran-slgf Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-numderiv Suggests: r-cran-knitr, r-cran-formatr, r-cran-rcrossref, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-slgf_2.0.0-1.ca2604.1_all.deb Size: 80862 MD5sum: 462ea92bedf588beaf366ca630102042 SHA1: e448c1348ec5a43631c0bb1ef5a4a9db52a7161f SHA256: 5b4a9089ee85044201a065f0ef4f43153a6bf5af49b4f5e1dab285f9289b0d6b SHA512: ac00ffe9c4177772b387984f1192c84017a5ca3a528767e64a3c762d1849a700a3a35c4169ff486677693716b25a97f75d24b31069f49f8ed6047cbbc97a8218 Homepage: https://cran.r-project.org/package=slgf Description: CRAN Package 'slgf' (Bayesian Model Selection with Suspected Latent Grouping Factors) Implements the Bayesian model selection method with suspected latent grouping factor methodology of Metzger and Franck (2020), . SLGF detects latent heteroscedasticity or group-based regression effects based on the levels of a user-specified categorical predictor. Package: r-cran-slic Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-laplacesdemon, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-slic_0.3-1.ca2604.1_all.deb Size: 32426 MD5sum: 1337f7715e5744753b667b250f050eda SHA1: b2cf098498be4aad2c0c4d4909f94152d83999f1 SHA256: c39e33aa4d7abf81c419288446b136c077187a2cb47e70648192141fc587ab74 SHA512: 4eefd824cb821307c0080b4bfefb73c5f7f5ca50e91b6fafbbb57e6d77073c1209012311934e23cf62297ade709ab3ca2421465f7089195a32395444f11427c2 Homepage: https://cran.r-project.org/package=SLIC Description: CRAN Package 'SLIC' (LIC for Distributed Skewed Regression) This comprehensive toolkit for skewed regression is designated as "SLIC" (The LIC for Distributed Skewed Regression Analysis). It is predicated on the assumption that the error term follows a skewed distribution, such as the Skew-Normal, Skew-t, or Skew-Laplace. The methodology and theoretical foundation of the package are described in Guo G.(2020) . 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Statistica Sinica, 1117-1130, . Package: r-cran-slick Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1656 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggrepel, r-cran-golem, r-cran-scales, r-cran-shiny, r-cran-tibble Suggests: r-cran-bookdown, r-cran-colourpicker, r-cran-colorspace, r-cran-cowplot, r-cran-esquisse, r-cran-flextable, r-cran-fresh, r-cran-httr, r-cran-kableextra, r-cran-knitr, r-cran-msetool, r-cran-openmse, r-cran-rcolorbrewer, r-cran-shiny.i18n, r-cran-shinyalert, r-cran-shinybs, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyhelper, r-cran-shinyjs, r-cran-shinywidgets, r-cran-rmarkdown, r-cran-testthat, r-cran-waiter Filename: pool/dists/resolute/main/r-cran-slick_1.0.1-1.ca2604.1_all.deb Size: 1294972 MD5sum: 788e8833fd43f4171c03b05359d15a1f SHA1: c905f31ebd13fd1ba755c6c38a89d191f7601647 SHA256: 6ee27106707f5cde83f3043fe63c2b518dd369d1a3beff4475eb4cc6b97b791c SHA512: 906fb0bc8e3ecdc1fc5ab3378c3ae4b0d20d669613121d4584399cd6f7c843a31610fc9430ab0500a12d7edbfad72a74d27cd150641a251c0c14438ad31a701e Homepage: https://cran.r-project.org/package=Slick Description: CRAN Package 'Slick' (Interactive Visualization of MSE Results) A framework for visualizing and exploring results of a Management Strategy Evaluation (MSE). 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Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) . 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Documentation about 'SlideCNA' is included in the the pre-print by Zhang et al. (2022, ). The package 'enrichR' (>= 3.0), conditionally used to annotate SlideCNA-determined clusters with gene ontology terms, can be installed at or with install_github("wjawaid/enrichR"). 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Package: r-cran-slim Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mass Suggests: r-cran-lme4, r-cran-jmcm, r-cran-gee, r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-slim_0.1.1-1.ca2604.1_all.deb Size: 321672 MD5sum: 657d42b1fa34914e8f9e9b130c973072 SHA1: 1fa758e492ff2122e178dd62284912e04aa9b680 SHA256: 28ee0db060f26754c10d13909083058ed3e9c7941ee8036f68d18172a5916d31 SHA512: 40106d49ea8e0e79aff11649b1ab3717252f612c5ad58563cfe38f1c5f22f18e0dcd52cffffa4ad67c2588e8305e3e51aa19899a95a60ac4344137326df243f7 Homepage: https://cran.r-project.org/package=slim Description: CRAN Package 'slim' (Singular Linear Models for Longitudinal Data) Fits singular linear models to longitudinal data. 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Jette and Tim Wickberg (2023) describe Slurm in detail. Package: r-cran-slurmr Architecture: all Version: 0.5-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-slurmr_0.5-4-1.ca2604.1_all.deb Size: 281518 MD5sum: 763c905bb78c18bde9dbffb9874d71e9 SHA1: 2044c06a4c2b2fa2b803b60188ba318cb99fd732 SHA256: 9063bc418d90438e06003e5c5c6369925953668e2835d370d6db58d01821f5eb SHA512: 0db1292b238cbc49d67f1a3515bab42aae8d296df54428c551376b1a8d22a317f1fcc68f5fbeb9df2456ebd910e03094ce8c372a6840147a944e42439f6f0783 Homepage: https://cran.r-project.org/package=slurmR Description: CRAN Package 'slurmR' (A Lightweight Wrapper for 'Slurm') 'Slurm', Simple Linux Utility for Resource Management , is a popular 'Linux' based software used to schedule jobs in 'HPC' (High Performance Computing) clusters. 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This package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different flexible MDS models. These are: Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459) with powers, Sammon mapping (Sammon, 1969, ) with ratio and interval optimal scaling, Multiscale MDS (Ramsay, 1977, ) with ratio and interval optimal scaling, s-stress MDS (ALSCAL; Takane, Young & De Leeuw, 1977, ) with ratio and interval optimal scaling, elastic scaling (McGee, 1966, ) with ratio and interval optimal scaling, r-stress MDS (De Leeuw, Groenen & Mair, 2016, ) with ratio, interval, splines and nonmetric optimal scaling, power-stress MDS (POST-MDS; Buja & Swayne, 2002 ) with ratio and interval optimal scaling, restricted power-stress (Rusch, Mair & Hornik, 2021, ) with ratio and interval optimal scaling, approximate power-stress with ratio optimal scaling (Rusch, Mair & Hornik, 2021, ), Box-Cox MDS (Chen & Buja, 2013, ), local MDS (Chen & Buja, 2009, ), curvilinear component analysis (Demartines & Herault, 1997, ), curvilinear distance analysis (Lee, Lendasse & Verleysen, 2004, ), nonlinear MDS with optimal dissimilarity powers functions (De Leeuw, 2024, ), sparsified (power) MDS and sparsified multidimensional (power) distance analysis aka extended curvilinear (power) component analysis and extended curvilinear (power) distance analysis (Rusch, 2024, ). 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Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources. Package: r-cran-smartmap Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-magrittr, r-cran-sf Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-leafsync, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-smartmap_0.2.0-1.ca2604.1_all.deb Size: 250270 MD5sum: 0f66c9db9c53fc7b8cacace1f186764a SHA1: 55a2351eb27f7dbf811649acab54ad9b79f8f55e SHA256: 2e787c7efd2b0b0386d993af00e8bab5038690857575c5c8440d7efa2478805b SHA512: c188b4a95c824f5d1006cc2eba649af1ce64fb655f4db00d36c34d8c7036dbdbb84a408bbfecd814ef736779214f036f3ec73498520a38ac5e8997e1372939df Homepage: https://cran.r-project.org/package=smartmap Description: CRAN Package 'smartmap' (Smartly Create Maps from R Objects) Preview spatial data as 'leaflet' maps with minimal effort. smartmap is optimized for interactive use and distinguishes itself from similar packages because it does not need real spatial ('sp' or 'sf') objects an input; instead, it tries to automatically coerce everything that looks like spatial data to sf objects or leaflet maps. It - for example - supports direct mapping of: a vector containing a single coordinate pair, a two column matrix, a data.frame with longitude and latitude columns, or the path or URL to a (possibly compressed) 'shapefile'. Package: r-cran-smartmeteranalytics Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-futile.logger, r-cran-fnn, r-cran-stinepack, r-cran-zoo Suggests: r-cran-stringr, r-cran-knitr, r-cran-rmarkdown, r-cran-rocr, r-cran-randomforest, r-cran-caret, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-smartmeteranalytics_1.1.1-1.ca2604.1_all.deb Size: 157382 MD5sum: 4acf9d563eb395513f88e80d4982dbd8 SHA1: 1f7f695812ef54eee7016feb1d33a109f8a0b180 SHA256: bb2395ade8d915bbb44549dc023f31c32a2d55b9b1df3434e34c589f9a94a6d2 SHA512: c88ce6c1f1ef64e7d72fe78889a6d271ba673ce5628d1508db603c65dc96ff15677233bf22724802fac140af3e2d7f95ad06b642598ec9093501a954e4b5a2d0 Homepage: https://cran.r-project.org/package=SmartMeterAnalytics Description: CRAN Package 'SmartMeterAnalytics' (Methods for Smart Meter Data Analysis) Methods for analysis of energy consumption data (electricity, gas, water) at different data measurement intervals. The package provides feature extraction methods and algorithms to prepare data for data mining and machine learning applications. Deatiled descriptions of the methods and their application can be found in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and Energy Retailing" and Hopf et al. (2016) "Enhancing energy efficiency in the residential sector with smart meter data analytics". Package: r-cran-smartp Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-covr, r-cran-sn, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-smartp_0.1.1-1.ca2604.1_all.deb Size: 51778 MD5sum: 985cef8dfa21a9b6600ad5eb04df1f84 SHA1: 7e140a3f26ac071afe9a2d86acb29cfce967274b SHA256: 1db4632b00a266097f174de6542e9807ac8521a1ba1b6f2e5cc808c08e138738 SHA512: de624fa52e3dc0a40bd3eb31ed07ebb76baf32ea3c2f29f4661a544e33f08a3e9a43f4b31df11bc7a89a86aa0915e2eac3a0f35d9778c55272ffe6e9af69762b Homepage: https://cran.r-project.org/package=SMARTp Description: CRAN Package 'SMARTp' (Sample Size for SMART Designs in Non-Surgical Periodontal Trials) Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) . Package: r-cran-smartsheetr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-memoise, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smartsheetr_0.1.0-1.ca2604.1_all.deb Size: 79426 MD5sum: 8f835f0c8b0e2bd78cf5fb0ae4f17bfb SHA1: 06c05f01b812e224d7f98c47e96e8de3beb0ec1f SHA256: bd05bbf4930b4462fb473715733ae27e150c2d17792b291f843ef64aa9d8a335 SHA512: 5b28018361bdce2c708e718f8f7fa2cb0e07565ac8e690b5d834ac4963a2895cdd0017da7a6116c4eab1c80683d51d4b343f111aac703da1f1407ba2a691226b Homepage: https://cran.r-project.org/package=smartsheetr Description: CRAN Package 'smartsheetr' (Access and Write 'Smartsheet' Data using the 'Smartsheet' API2.0) Interact with the 'Smartsheet' platform through the 'Smartsheet' API 2.0. . 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Package: r-cran-smartsizer Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smartsizer_1.0.3-1.ca2604.1_all.deb Size: 48568 MD5sum: 6f36aa74d1c2528684040d4e25e70449 SHA1: 41e905b1dd8a60f72eab9678de3145800b4c4d2f SHA256: a20fcc811f288d88a7363fa1a905991e7c7d8e0e09b13607c9d4bb68712e8e89 SHA512: ac927345df1328f6247b8a09ad1884cc0a25210feedc3474e91a01fd292984c0e2aa589bd3dcd6c5f05682a1714e8f69310db24d28459622a8dc25f2f9c6e4c3 Homepage: https://cran.r-project.org/package=smartsizer Description: CRAN Package 'smartsizer' (Power Analysis for a SMART Design) A set of tools for determining the necessary sample size in order to identify the optimal dynamic treatment regime in a sequential, multiple assignment, randomized trial (SMART). 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Package: r-cran-smatr Architecture: all Version: 3.4-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-smatr_3.4-8-1.ca2604.1_all.deb Size: 214746 MD5sum: 7d2962a5d2115e1215bacd6908cb570b SHA1: 600187d11ae9cf551372ec59349d73a4986b2aed SHA256: 1d0118628787c76bcdf95fa3855ee8eef27be44bfd1e12dfb10bbcb93cd4b758 SHA512: 805ae25db2cc23bdfb4de048e4ead69db5a57638022d20f0e69d9312aff5894caf246679a890173cf482a0207bc79741c62004b204c31bee13be116fe9adff05 Homepage: https://cran.r-project.org/package=smatr Description: CRAN Package 'smatr' ((Standardised) Major Axis Estimation and Testing Routines) Methods for fitting bivariate lines in allometry using the major axis (MA) or standardised major axis (SMA), and for making inferences about such lines. 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Package: r-cran-smbdata Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-smbdata_0.2.0-1.ca2604.1_all.deb Size: 133512 MD5sum: 3a4c75a486479c2ccf4646f4b6af1aea SHA1: 5d77f92a87f7b3d882cfde8103e924fc0b161ae3 SHA256: 48b8e6efc0dc342d4458603706e9d1dadd12e4c361437ee6317e82702e53302a SHA512: 9cc055f92c145603f233133d2eb1b507110645ea1aeb0442cc8358d0729db5a62c0044cc9ab90de66b58b16908c673eb5db8b73a7f2b976b2abf5fc6d4d129f2 Homepage: https://cran.r-project.org/package=smbdata Description: CRAN Package 'smbdata' (Data from "Statistical Methods in Biology") All data in the book "Statistical Methods in Biology" by Welham et al. (2015) with a corresponding documentation and illustrative analysis of the data. Package: r-cran-smbinning Architecture: all Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sqldf, r-cran-partykit, r-cran-formula, r-cran-gsubfn Filename: pool/dists/resolute/main/r-cran-smbinning_0.9-1.ca2604.1_all.deb Size: 315818 MD5sum: 49eec2445b46fa00687d8ec800b7ceb2 SHA1: 7e30532e48b34d3fc4011fdff6b7eba36b69412d SHA256: df639193eb888809f5617b0627c114cfdce40f2b164e5f920276cab5a3ae375a SHA512: f429382f37985dc9774721b0882ed905eb010caec604362546cc08537d2364836a08cdf6d983702e74f0c769158bba36234e2411512324d11ae7e805860edc9e Homepage: https://cran.r-project.org/package=smbinning Description: CRAN Package 'smbinning' (Scoring Modeling and Optimal Binning) A set of functions to build a scoring model from beginning to end, leading the user to follow an efficient and organized development process, reducing significantly the time spent on data exploration, variable selection, feature engineering, binning and model selection among other recurrent tasks. 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This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model. Package: r-cran-smcure Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-smcure_2.2-1.ca2604.1_all.deb Size: 50194 MD5sum: 862f9e55fd6a5b090a88898df546a369 SHA1: 5593647fa02c4e19fb29614bd06c2e45fab75fad SHA256: ce39bb483e78bd6935bb3772c51812e9512ea44cb1e0a9b541850ae1c990c5bc SHA512: f799197995467ae5bdd56724075c936e9cc3ca77c21d4796aaade44bb0a0d374ac1dc106c18009729722d23fb67c845d61c0251d2da84a6e5da77212ff941741 Homepage: https://cran.r-project.org/package=smcure Description: CRAN Package 'smcure' (Fit Semiparametric Mixture Cure Models) An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models. Package: r-cran-smd Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat, r-cran-stddiff, r-cran-tableone, r-cran-knitr, r-cran-dplyr, r-cran-purrr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-smd_0.8.0-1.ca2604.1_all.deb Size: 55628 MD5sum: a1401c0cdbb34ae349d2fe87071ca2d7 SHA1: 960594bfc8445fa633fb3fd327f55b074c97518e SHA256: 123afaa0d58dd4f8fe90ac711e6cadbabc1c34892764c7066d7cab15241f264d SHA512: 0121b3bcd28d97311ad320353755b42b97f7bd6cedf8388752be5faa7a5c049bf5365cba011595e82ee91ee62dd98bf4f4ec82fff083ea4dbbf95638e79c058e Homepage: https://cran.r-project.org/package=smd Description: CRAN Package 'smd' (Compute Standardized Mean Differences) Computes standardized mean differences and confidence intervals for multiple data types based on Yang, D., & Dalton, J. E. (2012) . 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Package: r-cran-smdi Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-caret, r-cran-dplyr, r-cran-fastdummies, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-hotelling, r-cran-lifecycle, r-cran-magrittr, r-cran-mice, r-cran-naniar, r-cran-proc, r-cran-randomforest, r-cran-stringr, r-cran-survival, r-cran-tableone, r-cran-tibble, r-cran-tidyr Suggests: r-cran-broom.helpers, r-cran-cardx, r-cran-gridextra, r-cran-gtsummary, r-cran-here, r-cran-knitr, r-cran-reactr, r-cran-reactable, r-cran-rmarkdown, r-cran-simsurv, r-cran-smd, r-cran-survminer, r-cran-usethis, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-smdi_0.3.2-1.ca2604.1_all.deb Size: 2242866 MD5sum: 582709e849ba16547380e42875f098a5 SHA1: 8ea8174a343d9f353f935c84d9ef573cf1db499e SHA256: 4b4a27c7e176976b999b537dfedee4fe82957839eb927ab2d5fd6b9ee791b62b SHA512: a9a8622f52bfaa8a8c75d09072327823d0be90a36f82ae5065c62e5a03ee914f52e6f89afbaa1e06ad7152352a7562b78c7ff1bf5a8bb67ca589eac5635dfafb Homepage: https://cran.r-project.org/package=smdi Description: CRAN Package 'smdi' (Perform Structural Missing Data Investigations) An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. . Package: r-cran-smdic Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4377 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-gsva, r-cran-samr, r-cran-e1071, r-bioc-preprocesscore, r-cran-pheatmap, r-bioc-maftools, r-cran-survival, r-cran-survminer, r-cran-mass, r-cran-pracma, r-cran-rcolorbrewer, r-cran-backports Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.utils Filename: pool/dists/resolute/main/r-cran-smdic_0.1.6-1.ca2604.1_all.deb Size: 3406384 MD5sum: 1105530d42b1777b821579c7d22e7941 SHA1: 020bafcc609650470129a68d8aca0db49f3ac7dc SHA256: d331d478e2cc5c15763bbfc52799bdd432d0800e41dcb50904c90b26f322c3c3 SHA512: cb4318e633c73ed34e5a2510668461cb00f0369f0f15822783b3d893c0c2a35fd046b8b9031650cefa459e3d3e15186c7da453055158f2b56c4c43d7c230ad37 Homepage: https://cran.r-project.org/package=SMDIC Description: CRAN Package 'SMDIC' (Identification of Somatic Mutation-Driven Immune Cells) A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status.. Package: r-cran-smdocker Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-paws.compute, r-cran-paws.developer.tools, r-cran-paws.machine.learning, r-cran-paws.management, r-cran-paws.storage, r-cran-paws.security.identity, r-cran-zip, r-cran-uuid Suggests: r-cran-covr, r-cran-crayon, r-cran-mockery, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smdocker_0.1.4-1.ca2604.1_all.deb Size: 67824 MD5sum: 0996b71ba659785f316a7f14305a1d22 SHA1: 866d461470b2930e2fc978acba1153e63b6f31b8 SHA256: 5603984cf4a593336c78f8a8aaa083f936b8b10034da6021cd4344f11c46a535 SHA512: 1bbff10458a2b72c5effda8416e65e90fc2415a9a0632c72f52ffbf6d1e8b71423579344794070066d6c4d0e5cc4d4bef67498ae75b3e8e2a41b18ed96d19a29 Homepage: https://cran.r-project.org/package=smdocker Description: CRAN Package 'smdocker' (Build 'Docker Images' in 'Amazon SageMaker Studio' using 'AmazonWeb Service CodeBuild') Allows users to easily build custom 'docker images' from 'Amazon Web Service Sagemaker' using 'Amazon Web Service CodeBuild' . Package: r-cran-smetlite Architecture: all Version: 0.2.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2561 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-stringr Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-smetlite_0.2.10-1.ca2604.1_all.deb Size: 363816 MD5sum: 76007dd8a95231ff028826f372af6b67 SHA1: c63287e95edb5bb5ffb4e6d177541fc6b045d160 SHA256: 346d02ce7372869e0a023ad82aab1305a8c1603086572c2d5e8c5e4c3c7c512e SHA512: 2c39f8c70057ff1a981a0ae76a4071011ad5a04a9a702d2e0e529bc7dff822140d1dc0fc5c717427bb9837c6ec2e05f47a3221540cd1b6df1c8b22169bc142c8 Homepage: https://cran.r-project.org/package=smetlite Description: CRAN Package 'smetlite' (Read and Write SMET Files) Simple class to hold contents of a SMET file as specified in Bavay (2021) . There numerical meteorological measurements are all based on MKS (SI) units and timestamp is standardized to UTC time. Package: r-cran-smfa Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1070 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfar Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-lmtest Filename: pool/dists/resolute/main/r-cran-smfa_1.0.0-1.ca2604.1_all.deb Size: 432960 MD5sum: 34b50e1637d2753981f1b1ff069f6971 SHA1: dcbade33aba2426b8b9f80f92a616c7f70051a32 SHA256: 048868ae62de85d2e0d8e177e3ad509291d72dcc86eeb6b2a5b6816b7b9ea64e SHA512: c698749d8f45e1540ad00377dd7aff6e11a5f6b41505bf67865c8249d30b363975e445d24bd62e67c6725f72136eb97bb9759d556fbdba060834652003e22f02 Homepage: https://cran.r-project.org/package=smfa Description: CRAN Package 'smfa' (Stochastic Metafrontier Analysis) Implements stochastic metafrontier analysis for productivity and performance benchmarking across firms operating under different technologies. Contains routines for the deterministic metafrontier envelope of O'Donnell et al. (2008) via linear and quadratic programming, and the stochastic metafrontier of Huang et al. (2014) . Also supports latent class stochastic metafrontier analysis and sample selection correction stochastic metafrontier models. Depends on the 'sfaR' package by Dakpo et al. (2023) . Package: r-cran-smfilter Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-smfilter_1.0.3-1.ca2604.1_all.deb Size: 169822 MD5sum: 9dca2296cfd5936452bfd9ab86bda497 SHA1: 2bb4a3ac9416448af51db0871220648c2f796bb8 SHA256: 203e9ced07d2e7ac5149a21dcf1a9a171d3ae8760b1e529637eb89f2cae04e8b SHA512: 554958786fc3f771102ed1f3014cb0eb1f0e70dd4e7f11edb18a8aadf91a1fad14b93326cec291704c6d4b431192b6b4eb960ba93678950a2d7aa3fce609ed49 Homepage: https://cran.r-project.org/package=SMFilter Description: CRAN Package 'SMFilter' (Filtering Algorithms for the State Space Models on the StiefelManifold) Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold. Package: r-cran-smicd Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ineq, r-cran-truncnorm, r-cran-lme4, r-cran-formula.tools, r-cran-mvtnorm, r-cran-hmisc, r-cran-laeken, r-cran-weights Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mlmrev, r-cran-r.rsp, r-cran-kernelheaping Filename: pool/dists/resolute/main/r-cran-smicd_1.1.5-1.ca2604.1_all.deb Size: 623064 MD5sum: f45bd3fea6e6dfbf5f9c5fdf5a0a0c56 SHA1: 15be9b158d6109ff7670c42169ac2fd1f75dce78 SHA256: 0dd3be745feeaffd3954543299acba338c195c78e9e6e499be34a186acbf43bf SHA512: e7cd7bab2fa493f34af9aa0bea65e4baab93da95c911bd161358fed4d7ed01e73024f9fb7496591737b46cc2dab4e38fc55f99d7f8342858ffb2328a4a77d8d3 Homepage: https://cran.r-project.org/package=smicd Description: CRAN Package 'smicd' (Statistical Methods for Interval-Censored Data) Functions that provide statistical methods for interval-censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval-censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval-censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Walter, P. (2019) ). Package: r-cran-smidm Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-extradistr Suggests: r-cran-testthat, r-cran-covr, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-hdrcde, r-cran-ggplot2, r-cran-scales Filename: pool/dists/resolute/main/r-cran-smidm_1.0-1.ca2604.1_all.deb Size: 154426 MD5sum: 98fd714a52c12c0c6fbda71f85344187 SHA1: 197a13e9d3205021374893627de06e6a8f202c4d SHA256: 1e65dcd030e0c3735b2fd028619af8a9d1b2b8766fa7f74b25f0e1d8a39b79b9 SHA512: af1d29a022416d8810ac8a30c20f6b8730b19e9659ed6df919b018f604e12358d27ddd3d28b0f445ef1e621f0f8a18ab7b852cc1f1117fbce9e78009227c8351 Homepage: https://cran.r-project.org/package=smidm Description: CRAN Package 'smidm' (Statistical Modelling for Infectious Disease Management) Statistical models for specific coronavirus disease 2019 use cases at German local health authorities. All models of Statistical modelling for infectious disease management 'smidm' are part of the decision support toolkit in the 'EsteR' project. More information is published in Sonja Jäckle, Rieke Alpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max Westphal "'EsteR' – A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities" (2022) and Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions" (2021) . Package: r-cran-smimodel Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cgaim, r-cran-conformalforecast, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-generics, r-cran-ggplot2, r-cran-gratia, r-cran-gtools, r-cran-matrix, r-cran-mgcv, r-cran-purrr, r-cran-roi, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-tsibble Suggests: r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smimodel_0.1.3-1.ca2604.1_all.deb Size: 572276 MD5sum: 67b83afb50f2680de6e2a2e4a64dc6af SHA1: 55b4e5c14a911e0cb0c0201ffbb878830939614b SHA256: 0b8fd26d11d049c045c7d613d46a148d17965aa86155a33a0651af88db1b84ae SHA512: 2c0475761607d19631991b7ea17e9070475a36da79b14aa28daa395b80cc00e0292e3be7a1450b2f04cd277b96060b6b3d7603590867c85e99a4d2365d91a631 Homepage: https://cran.r-project.org/package=smimodel Description: CRAN Package 'smimodel' (Sparse Multiple Index Models for Nonparametric Forecasting) Implements a general algorithm for estimating Sparse Multiple Index (SMI) models for nonparametric forecasting and prediction. Estimation of SMI models requires the Gurobi mixed integer programming (MIP) solver via the gurobi R package. To use this functionality, the Gurobi Optimizer must be installed, and a valid license obtained and activated from . The gurobi R package must then be installed and configured following the instructions at . The package also includes functions for fitting nonparametric additive models with backward elimination, group-wise additive index models, and projection pursuit regression models as benchmark comparison methods. In addition, it provides tools for generating prediction intervals to quantify uncertainty in point forecasts produced by the SMI model and benchmark models, using the classical block bootstrap and a new method called conformal bootstrap, which integrates block bootstrap with split conformal prediction. Package: r-cran-smirnov Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-smirnov_1.0-1-1.ca2604.1_all.deb Size: 11530 MD5sum: c4a170e57ee13c00e23b4b67a0820410 SHA1: 2a82782d80c58a37d780e3017eeef1591e9411cf SHA256: 5ae7b3c6a4560c3bbada8a47d577cb6ce7856718853ca17ce87a52b69fe653ca SHA512: 385a385e9dc279b87f828de41da24f0e845ee36f904ff29d749acaf00d507324df96cdcf49c4c84d38d6f30d8fb95ca244f778edeeaf6812ab947bf071cd12f4 Homepage: https://cran.r-project.org/package=smirnov Description: CRAN Package 'smirnov' (Provides two taxonomic coefficients from E. S. Smirnov"Taxonomic analysis" (1969) book) This tiny package contains one function smirnov() which calculates two scaled taxonomic coefficients, Txy (coefficient of similarity) and Txx (coefficient of originality). These two characteristics may be used for the analysis of similarities between any number of taxonomic groups, and also for assessing uniqueness of giving taxon. It is possible to use smirnov() output as a distance measure: convert it to distance by "as.dist(1 - smirnov(x))". Package: r-cran-smithwilsonyieldcurve Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smithwilsonyieldcurve_1.1.1-1.ca2604.1_all.deb Size: 189180 MD5sum: 9f12e77a11271545e15343a90ce9cf8d SHA1: edc223b8214094ba387fd5a082233124a8368c03 SHA256: d3017cc303edc62879be29eb957e796af373d0e1aaef0a342b595fbd8aa9b59a SHA512: 2107cf7b174685a082d110f3bd8d3f3df130966c8959c0b86efd6993f3cb2dbaa14bf09796cadb813fd0238475eb3ec52ab795247893d2af624fb25d6e615f13 Homepage: https://cran.r-project.org/package=SmithWilsonYieldCurve Description: CRAN Package 'SmithWilsonYieldCurve' (Smith-Wilson Yield Curve Construction) Constructs a yield curve by the Smith-Wilson method from a table of libor and swap rates. Now updated to take bond coupons and prices in the same table. Package: r-cran-smitidstruct Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sf, r-bioc-biostrings Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smitidstruct_0.0.5-1.ca2604.1_all.deb Size: 217852 MD5sum: aae5685d36f09d52d49d36280bd88435 SHA1: 89a465dbf55dc9257f1296cafc023a536e2d5178 SHA256: 456b9f2d2d9d8fd4d5da4f5bc8678d24d6ef6c35b8b32677d976e75d785a3b5d SHA512: fa0cf715fe9e6b815e7e81ba1b5e681c1358f5019060d324970fd33e2e831fef32254289b054020a73c03a6c02e3d848aa8ea45dc9512aade9ebf13adb809345 Homepage: https://cran.r-project.org/package=SMITIDstruct Description: CRAN Package 'SMITIDstruct' (Data Structure and Manipulations Tool for Host and ViralPopulation) Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID). It allow sequence-space-time host and viral population data storage, indexation and querying. Package: r-cran-smle Architecture: all Version: 2.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrixcalc, r-cran-mvnfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smle_2.2-3-1.ca2604.1_all.deb Size: 3215984 MD5sum: d362ca29007096ebaaaa549e8064affa SHA1: a2b8b86c49789dd00b326eba2911cd5a61838904 SHA256: 1a6ec76b3916d38456eda094cb86d4e55ae3f06b4009e107ad81710436df0ff8 SHA512: 7818f8df3271242323a90390ba7e34f24b611994bf1c5d421b983e86d9aabb89573feec921a033299aa853fd3735fb2cd24ada94fb009c307c5dce5a0d332001 Homepage: https://cran.r-project.org/package=SMLE Description: CRAN Package 'SMLE' (Joint Feature Screening via Sparse MLE) Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014) proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion. Zang, Xu, and Burkett (2025). Package: r-cran-smleph Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-splines2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-smleph_0.1.1-1.ca2604.1_all.deb Size: 27022 MD5sum: bf45febdd59a0b4274927d3215892647 SHA1: 1df59d25400d4b626b2739d9839e8d96c2d05606 SHA256: 5dd41f044fab2807a8aa2e0fc953f23377d3cd557128d5cede7661b0713c8183 SHA512: 508769f5f95d8e69b07baad56c64139b3b79511c6d1663dc62e15574b3322768e063c6c5e2730b87bd593b568244bb4c0bbdcfaf9cc33d799a0184aa15a86661 Homepage: https://cran.r-project.org/package=smlePH Description: CRAN Package 'smlePH' (Sieve Maximum Full Likelihood Estimation for the Right-CensoredProportional Hazards Model) Fitting the full likelihood proportional hazards model and extracting the residuals. Package: r-cran-smlmkalman Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdep, r-cran-pracma, r-cran-scales, r-cran-truncnorm Filename: pool/dists/resolute/main/r-cran-smlmkalman_0.1.1-1.ca2604.1_all.deb Size: 3019860 MD5sum: 5abe563843d084710f1be1241093070c SHA1: d1671cc37c4edec6b89691fa898c79ee2c2c5f45 SHA256: f757b8190daafbf8b97c9a66c98fb85de21cf38d7e16424bc9af80b690778ca8 SHA512: 55a89ffe6b785837653ce017522d5df7145aa7441e3493f3c1708a3f755c2851484bc5bdb5c611d7b1408500b5fc0f8eab708242f6fdbe54c44654a45794d11f Homepage: https://cran.r-project.org/package=smlmkalman Description: CRAN Package 'smlmkalman' (Generation and Tracking of Super-Resolution Filamentous Datasets) A pair of functions that allow for the generation and tracking of coordinate data clouds without a time dimension, primarily for use in super-resolution plant micro-tubule image segmentation. Package: r-cran-smloutliers Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-smloutliers_0.1-1.ca2604.1_all.deb Size: 17452 MD5sum: c423c39da6dc794d849542dd2f4840c9 SHA1: 292f42f33631065f5efc5644f13d70523d307b75 SHA256: 281a542c1ed7d26ca9c89e6a8a70bddff91f69ae1f3a550226d4d36c1eb17e28 SHA512: ead2509c1dbf827b983fa42e6f9a5ece68f531bc815ff4d111813c01d601fc9dc68b115bca6b8290e8e63fbafcfd93323fd1166261581d32cb9e9d058e138409 Homepage: https://cran.r-project.org/package=SMLoutliers Description: CRAN Package 'SMLoutliers' (Outlier Detection Using Statistical and Machine Learning Methods) Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see . Package: r-cran-smm Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seqinr, r-cran-discreteweibull Filename: pool/dists/resolute/main/r-cran-smm_1.0.2-1.ca2604.1_all.deb Size: 946488 MD5sum: 2667fbcb753d0389fc33d3146b06e5d2 SHA1: 84600f7da0ef1f940befe4066af6d0b3ad621fa6 SHA256: a1e1e6cf719c84e6ba3a73a998aafb77ee9adef876bbe80c31a23dc58f2a1bad SHA512: 90a93253c311b6f8d04d54b89afc283076821330d3a7e9233d94ab27857b221c970476d8cf5500988d32e43fa08afcae130544d60345dd37c2c87a2dd9248fb0 Homepage: https://cran.r-project.org/package=SMM Description: CRAN Package 'SMM' (Simulation and Estimation of Multi-State Discrete-TimeSemi-Markov and Markov Models) Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) , Barbu, V.S., Limnios, N. (2008) and Trevezas, S., Limnios, N. (2011) . Estimation and simulation of discrete-time k-th order Markov chains are also considered. Package: r-cran-smmal Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-splines2, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smmal_0.0.5-1.ca2604.1_all.deb Size: 1522628 MD5sum: 9debeaa7d9ce0ae04e2421cc7f3d6f55 SHA1: 487c4247c81440d231a224d93fcb3d82394ee94a SHA256: 4b6688f82c98fbad87c9b790ac7c507fa2b3fbfec4d77e8fad3a1c06d424cd06 SHA512: 59f7908dbce0fcf918f2d9530fa76aeadbe3762feade15cc748737701406a7fa7743ae1eddeaba9ef337d199a7643a28d54198841d50b2d510f8b9e5de0084c2 Homepage: https://cran.r-project.org/package=SMMAL Description: CRAN Package 'SMMAL' (Semi-Supervised Estimation of Average Treatment Effects) Provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) . A detailed vignette is included. Package: r-cran-smmt Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-xml, r-cran-tibble, r-cran-curl, r-cran-rvest, r-cran-xml2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-smmt_1.2.0-1.ca2604.1_all.deb Size: 206554 MD5sum: 8a749ae23b082824bc7df8f9e7d3f69f SHA1: 95b3df9d48855c124187df46ee53a4daef98b006 SHA256: 1c4be13955620748c81144af889b19594475ec962be40a5ee7d8647a8548b006 SHA512: b1338864b2e9efa4e86b3104b86869b245402d7a4ae1b133da5eb4e170a8479002981ef3b26e755f77a0c60fff2d8477b7b6e6e10f483130332714c8e87991fa Homepage: https://cran.r-project.org/package=SMMT Description: CRAN Package 'SMMT' (The Swiss Municipal Data Merger Tool Maps Municipalities OverTime) In Switzerland, the landscape of municipalities is changing rapidly mainly due to mergers. The Swiss Municipal Data Merger Tool automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state to municipalities of a new state. This functionality is helpful when working with datasets that are based on different spatial references. The package's idea and use case is discussed in the following article: . Package: r-cran-smncensreg Architecture: all Version: 3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-performanceanalytics Filename: pool/dists/resolute/main/r-cran-smncensreg_3.1-1.ca2604.1_all.deb Size: 88550 MD5sum: 2b7455481f830e1f099294d3949c77cd SHA1: 99f60f48492a05596030817955648c432a3cdb60 SHA256: a07568955f369981253daf6c5f4e2b86432b8e75c17bce4d2a7943bd26e4f62a SHA512: 41c7e955cf4505c5061e48918e07c204db97ceba48456447c9adb2bc79c523aba5827d32d6c1ab3fe04b9d5dd4f6360e528cc3bdfe5206849e8c4a41e4a501a3 Homepage: https://cran.r-project.org/package=SMNCensReg Description: CRAN Package 'SMNCensReg' (Fitting Univariate Censored Regression Model Under the Family ofScale Mixture of Normal Distributions) Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions. Package: r-cran-smnlmec Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstan, r-cran-stanheaders, r-cran-mass, r-cran-tmvtnorm, r-cran-mvtnorm, r-cran-mnormt, r-cran-laplacesdemon, r-cran-truncatednormal, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-smnlmec_1.0.2-1.ca2604.1_all.deb Size: 268238 MD5sum: 9da42931506a39cbe7ba3910bf5d1265 SHA1: 26202e96801d06f67ff191972ea9dd9417297432 SHA256: 3ebd397b1a780a80530d0cc03b86442ad36db68a3e9e71b74dae42ec91307345 SHA512: 33bb5af44b7ccf0dd6e069741e1c56bc678c5734b1656b354532fa27b1deeb68775c0cbe56a511e060136e20b8c1a33cfc2da5743c4057a603b08aa9b8d3b004 Homepage: https://cran.r-project.org/package=SMNlmec Description: CRAN Package 'SMNlmec' (Scale Mixture of Normal Distribution in Linear Mixed-EffectsModel) Bayesian analysis of censored linear mixed-effects models that replace Gaussian assumptions with a flexible class of distributions, such as the scale mixture of normal family distributions, considering a damped exponential correlation structure which was employed to account for within-subject autocorrelation among irregularly observed measures. For more details, see Kelin Zhong, Fernanda L. Schumacher, Luis M. Castro, Victor H. Lachos (2025) . Package: r-cran-smof Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-survival, r-cran-nloptr Filename: pool/dists/resolute/main/r-cran-smof_1.2.2-1.ca2604.1_all.deb Size: 90464 MD5sum: dcd33488e18c5a0eaf9cc3a285a6c2bb SHA1: bcb995f04c6ad1a900bc4cc041adee4ae5a47f93 SHA256: e7e0f566ea5ff2265eaa1eb7e20e0ed5889727f74b2194ed7d27a0f44b9f95ab SHA512: e15bd0ddba2ac5a0cc65825279c91b286540810ec70fb7323e8f6c5bd18695d2c403d564450930b72dde2f189f370d76f165aee28eea0e7123989aad92fb249c Homepage: https://cran.r-project.org/package=smof Description: CRAN Package 'smof' (Scoring Methodology for Ordered Factors) Starting from a given object representing a fitted model (within a certain set of model classes) whose (non-)linear predictor includes some ordered factor(s) among the explanatory variables, a new model is constructed and fitted where each named factor is replaced by a single numeric score, suitably chosen so that the new variable produces a fit comparable with the standard methodology based on a set of polynomial contrasts. Two variants of the present approach have been developed, one in each of the next references: Azzalini (2023) , (2024) . Package: r-cran-smoke Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1703 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-smoke_2.0.1-1.ca2604.1_all.deb Size: 1609232 MD5sum: 093a886b2020dad19fa30fadf356591d SHA1: abbb918e5a238ef6b89dbe83e43ea0b41f2a35c9 SHA256: ba88c5b66f63c2d3f39f67ab91826c1327cf66fc68f6b9f9752e6baa91f4b2e6 SHA512: d80ecce3ef6912eff20f51715ddd1243f6776735fee1b95bfd6757850f2b514b8dbcd1eab3048838f699461ebd48fc3f22190772b7562bbae9b3f9518339000c Homepage: https://cran.r-project.org/package=smoke Description: CRAN Package 'smoke' (Small Molecule Octet/BLI Kinetics Experiment) Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. Sun Q., Li X., et al (2020) . In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure. Package: r-cran-smoothedipw Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smoothedipw_0.1.0-1.ca2604.1_all.deb Size: 285502 MD5sum: d6a12562a5941f40ddd7e55e8e0a90b5 SHA1: c7e679bb090d33a06abdf1cae2f5dfd2991bd833 SHA256: f4e2ad11f05c10d732dee7d7d86f54d12b999dacfa82a19ae8b8398426555087 SHA512: a3fe930e0ae1989333e8e1ee6bbcd7621098d41802a3af6714aee2937f52ef7a9f004add62fc9ea0488f321cde99b1eff9a3b3f59ae50a0a6b07dbe347070deb Homepage: https://cran.r-project.org/package=smoothedIPW Description: CRAN Package 'smoothedIPW' (Time-Smoothed Inverse Probability Weighting for RepeatedlyMeasured Outcomes) Implements several methods to estimate effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. Specifically, the package implements the time-smoothed inverse probability weighted estimators described in McGrath et al. (2025) . Outcomes may be repeatedly, non-monotonically, informatively, and sparsely measured in the data source. The package also supports settings where outcomes are truncated by death, i.e. some individuals die during follow-up which renders the outcome of interest undefined at the follow-up time of interest. Package: r-cran-smoothedlasso Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-smoothedlasso_1.6-1.ca2604.1_all.deb Size: 56390 MD5sum: 064a71cf0d8cd63255ff737d34649217 SHA1: b65bc97d5cf50706f65b785f05c51c4167c19c1d SHA256: 9e9544c0c355a31c2203ac9ab38d4afd768052964af412b5bc6917001bd66876 SHA512: a68501a473e99ffdb16828d74259e480d12b1798793b1c1be84aca815456e288acb8850e419907ec705a200ec82dd8405b2ec8805d04d97eea1b69e3b657266f Homepage: https://cran.r-project.org/package=smoothedLasso Description: CRAN Package 'smoothedLasso' (A Framework to Smooth L1 Penalized Regression Operators usingNesterov Smoothing) We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) ), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) ). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided. Package: r-cran-smoothhr Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-smoothhr_1.0.5-1.ca2604.1_all.deb Size: 118738 MD5sum: bb608e9d5de85a222738f04ac120f29f SHA1: 504f59d79e3db9cb3e3bc14b71a272eea7b8f403 SHA256: 086c89b32307e625bd916e2b2ddb9c9b8139a667ce00228b2d97b79cb7e9297c SHA512: 1f49fbed479ed224bab726193b374d6178d1352ea20e2b896cc80d4d51c696991ece51d82a8cbe640d896ccbd84aaf66ca5054aa20949951a9b29d74c5146be9 Homepage: https://cran.r-project.org/package=smoothHR Description: CRAN Package 'smoothHR' (Smooth Hazard Ratio Curves Taking a Reference Value) Provides flexible hazard ratio curves allowing non-linear relationships between continuous predictors and survival. To better understand the effects that each continuous covariate has on the outcome, results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived. Package: r-cran-smoothic Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-numderiv, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-toordinal Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-smoothic_1.2.1-1.ca2604.1_all.deb Size: 345672 MD5sum: 65bb48e3de9615fbf0f87a33cb7201b7 SHA1: 4d5ed815953e7f505d725bbe07715a7ba5d5dbb2 SHA256: 4ffe870ba98db3e8e8c7a9af5bf312ccbd71eb3d1b6063ac9e659c9e0216ce83 SHA512: 29d9c9b5bdff72ffe12126113fb0982dfddffea06946c242d50efe7a1bcf35e99c6dd6a2f2ccbce58b770d415ee5813c89a39f8d5743bbe98e108d63b7a2a953 Homepage: https://cran.r-project.org/package=smoothic Description: CRAN Package 'smoothic' (Variable Selection Using a Smooth Information Criterion) Implementation of the SIC epsilon-telescope method, either using single or distributional (multiparameter) regression. Includes classical regression with normally distributed errors and robust regression, where the errors are from the Laplace distribution. The "smooth generalized normal distribution" is used, where the estimation of an additional shape parameter allows the user to move smoothly between both types of regression. See O'Neill and Burke (2022) "Robust Distributional Regression with Automatic Variable Selection" for more details. . This package also contains the data analyses from O'Neill and Burke (2023). "Variable selection using a smooth information criterion for distributional regression models". . Package: r-cran-smoothie Architecture: all Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spatialvx, r-cran-fields Filename: pool/dists/resolute/main/r-cran-smoothie_1.0-4-1.ca2604.1_all.deb Size: 59790 MD5sum: 06ffcfe71aef7b1b96fdfa7d2a3194cd SHA1: ff8f752e42254d079c1cf6ba258c4ca708a57243 SHA256: 586548d50f230c787f5e4f7f44e2b634b4586c34a28dbe64b165e1289d9c3889 SHA512: e3e5742b26cd58613e9b4c727cd06bd3001b9ec0c84c851fdbe52ebcf506bd2f911f0e936fa6d31b3bb16c41e8d486eac8a9e0272ba8f7a69aaf53c0bf9a5fce Homepage: https://cran.r-project.org/package=smoothie Description: CRAN Package 'smoothie' (Two-Dimensional Field Smoothing) Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, ). Package: r-cran-smoothmest Architecture: all Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-smoothmest_0.1-3-1.ca2604.1_all.deb Size: 59402 MD5sum: 1fed0e96331d4f5e82ee7db284343b2c SHA1: 86de190ef4e9048612c5323186ccece824e1eecc SHA256: 4f51eeefd4e6a33b368704d88990c7666d28f0e4f5cffb3dbbd93a9377532f02 SHA512: ba8397e3ae48aebade9abbc12842e734543fa2f6ff924efc9045b1594c02bacdb8de57a5ecea9d99de13ff54a0f1941a3fbd6fab8fb0d13886faa38b423da42a Homepage: https://cran.r-project.org/package=smoothmest Description: CRAN Package 'smoothmest' (Smoothed M-Estimators for 1-Dimensional Location) Some M-estimators for 1-dimensional location (Bisquare, ML for the Cauchy distribution, and the estimators from application of the smoothing principle introduced in Hampel, Hennig and Ronchetti (2011) to the above, the Huber M-estimator, and the median, main function is smoothm), and Pitman estimator. Package: r-cran-smoothpls Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3099 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cfda, r-cran-dplyr, r-cran-fda, r-cran-ggplot2, r-cran-mass, r-cran-mgcv, r-cran-pls, r-cran-pracma, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-future.apply, r-cran-future Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smoothpls_0.1.5-1.ca2604.1_all.deb Size: 1151614 MD5sum: f0140d61dc26c18bca5a369d120d838e SHA1: 1cf34e632e032585cf9c19e65384dee31fe107e2 SHA256: af5a017ae5a29a7945d8d843a94d30afbbdb17715ae2bf0b486a12476a345bf7 SHA512: cb78ec2235d8f84bb77287c083b3d2459e1f1e8709cf24b9379e39eb935cf3d5917d85161096033cb9d01011c6f20c63fafc34aeca3203b508919b5d3ac11a84 Homepage: https://cran.r-project.org/package=SmoothPLS Description: CRAN Package 'SmoothPLS' (Partial Least-Squares Algorithm for Categorical and ScalarFunctional Data) Performs the Partial Least-Squares ('PLS') algorithm for functional data through the concept of active area integration. This approach builds upon the basis expansion methods for functional 'PLS' regression described in Aguilera et al. (2010) . The package seamlessly handles both Scalar Functional Data ('SFD') and Categorical Functional Data ('CFD'), providing interpretable regression curves even for discrete state changes. It was developed during a PhD thesis between 'DECATHLON' and French research institute 'INRIA' 2022-2026. The 'SmoothPLS' method does not directly decompose the data into a basis; rather, it assumes the data is known as precisely as desired, and for every 'PLS' component, the weight functions are decomposed into the basis. For both single-state and multi-state 'CFD' as well as 'SFD', the algorithm is implemented for a scalar response. To provide a baseline, a naive 'PLS' method on time-value functions and standard Functional 'PLS' are also implemented. Package: r-cran-smoothr Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-units Suggests: r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown, r-cran-sp, r-cran-testthat, r-cran-terra Filename: pool/dists/resolute/main/r-cran-smoothr_1.3.0-1.ca2604.1_all.deb Size: 899114 MD5sum: 45c74087b8b6150365c3a9a9dd528903 SHA1: fe59e1922151d21f9961ef27b3eba0fc3de0a0ed SHA256: 75be5734405874600afd62429a073593189e965fc19542a8fcef21ee55c57c14 SHA512: 22610336ea9ce07067184362255eef6e9b34270fecc847a808ecb76f1ea1fe2231d17e2c5b298d6db9b6972a340695bb127826ed89fffc9507cc9ee6ab92ab51 Homepage: https://cran.r-project.org/package=smoothr Description: CRAN Package 'smoothr' (Smooth and Tidy Spatial Features) Tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. 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Package: r-cran-smoothroctime Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ks Suggests: r-cran-kmsurv, r-cran-lattice, r-cran-survival Filename: pool/dists/resolute/main/r-cran-smoothroctime_0.1.1-1.ca2604.1_all.deb Size: 46300 MD5sum: 8555ae6106fd8f2337e7dc8ff867de3b SHA1: c1fb8227b75ccb9c0225b4e150fe1609e69f9502 SHA256: 9ee6c087090b4d880b0ff38c0b243b544902155f23e146a8d14be99711d6ef1f SHA512: af710f5b24f6e75f5c5a667ca6cb63022602c7c0cd3d6e807e8ff85bfe53f74de331aa0d5cb7630b8017ec6f27ac20d26cb57bba6bc57002caff9c5f1c8e2ce9 Homepage: https://cran.r-project.org/package=smoothROCtime Description: CRAN Package 'smoothROCtime' (Smooth Time-Dependent ROC Curve Estimation) Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of the Marker and Time-to-event variables. 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The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0]. 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Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier. 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The learned status matrix can then be used for retrieval, clustering, and classification. Package: r-cran-snha Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-snha_0.1.3-1.ca2604.1_all.deb Size: 405436 MD5sum: 984c406de3add185d711528262b0f299 SHA1: 2d6bf08829d36f90cd8591311f117c909efdf892 SHA256: 4b6016e2b7c8d0abafcdd0d36eacb66036fa9c41eeebbc802a94c53452db9fce SHA512: 33f696140feaaed751d4d3a32043e11f418ec069a32f7e59f4e4f1f58c225a70a03d7c95df420c57feb9d995360579c141f060b9ddea5e39c94051610f31c895 Homepage: https://cran.r-project.org/package=snha Description: CRAN Package 'snha' (Creating Correlation Networks using St. Nicolas House Analysis) Create correlation networks using St. Nicolas House Analysis ('SNHA'). The package can be used for visualizing multivariate data similar to Principal Component Analysis or Multidimensional Scaling using a ranking approach. In contrast to 'MDS' and 'PCA', 'SNHA' uses a network approach to explore interacting variables. For details see 'Hermanussen et. al. 2021', . Package: r-cran-snirh.lab Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-cli Suggests: r-cran-sf, r-cran-curl, r-cran-testthat, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-snirh.lab_0.1.0-1.ca2604.1_all.deb Size: 93676 MD5sum: 6ee12494d1e6a58eb0198db10ad38ab4 SHA1: e372883fae111433fedf6caa4cd8e1b5dea900f5 SHA256: 9f349aa444ff8eaeb62d8df57612d9700c639d339935c543207ca8d631066976 SHA512: a54eb30e4352eb9986678f71aefca0084c093d81d5112a77896c36efff6fb0616e81ea06e52cbd3e351add6198e89f769035cc831876e55ff2fa719091c233eb Homepage: https://cran.r-project.org/package=snirh.lab Description: CRAN Package 'snirh.lab' (Convert Laboratory Water-Quality Data to 'SNIRH' Import Format) Convert laboratory data to the Portuguese Information System for Water Resources 'SNIRH' file format. 'SNIRH' is Portugal's national water resources information system . The package validates station data, converts parameters and units, and generates compliant output files for data submission. Package: r-cran-snma Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-geosphere, r-cran-igraph Suggests: r-cran-ggplot2, r-cran-ggnewscale, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-snma_0.1.5-1.ca2604.1_all.deb Size: 483818 MD5sum: 7b84623f2a3931982d4f06ae95a5c087 SHA1: 5de7b5f768086847d23d43177c5f3f65879c0592 SHA256: b6d441d92d02518c4946d51b2493fa67059272f1f9d29bfca1ab7bd1b7eac53c SHA512: 612613126ec517d635e49892c97f170914f67e75021af3c470a5f77183eba0dd2fc1e7981739fe134b831bcc6f23a1739fc062d0041edf732eaad4f2e66a5ed7 Homepage: https://cran.r-project.org/package=SNMA Description: CRAN Package 'SNMA' (Stream Network Movement Analyses) Calculating home ranges and movements of animals in complex stream environments is often challenging, and standard home range estimators do not apply. This package provides a series of tools for assessing movements in a stream network, such as calculating the total length of stream used, distances between points, and movement patterns over time. See Vignette for additional details. This package was originally released on 'GitHub' under the name 'SNM'. SNMA was developed for analyses in McKnight et al. (2025) which contains additional examples and information. Package: r-cran-snn Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-snn_1.1-1.ca2604.1_all.deb Size: 45200 MD5sum: 1e1d4924478d16c3de7c8de09e572438 SHA1: 0351b109f262afa3ea3eda781a7b1d768938cfae SHA256: 1e8b8020f1d07cb820b402483fe9d22819f9d9b67a094cd0fc904b979252d2fa SHA512: dbfa635abe0f6e91cff2a0036117e381266673b9b64688917f58980b87fac0c2b9b4e6d0d614aebdd4ce677b241adb57cfd34acdf8ff72394c3ebb49b7c0b761 Homepage: https://cran.r-project.org/package=snn Description: CRAN Package 'snn' (Stabilized Nearest Neighbor Classifier) Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. 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Package: r-cran-snotelr Architecture: all Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-httr, r-cran-rvest, r-cran-dplyr, r-cran-memoise Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-shinydashboard, r-cran-leaflet, r-cran-plotly, r-cran-dt Filename: pool/dists/resolute/main/r-cran-snotelr_1.5.2-1.ca2604.1_all.deb Size: 86598 MD5sum: 482cfdde404e671a4c046c257bbffbe4 SHA1: 9e0c7e2d7bef5b0fb0ea5d7da8361771f0902874 SHA256: 834c5b8f3d6ee0a402eb99fbe6628403e4a1284667178be74ea35e1a12c398fe SHA512: 54793c52c9590135bcf0a20d0288ace6859747f802cc8f6400472f317ed2aff41192cc086c3ae5c4ca4c9024f40965978875bc4beb6102fd1fd4134ae4c7627c Homepage: https://cran.r-project.org/package=snotelr Description: CRAN Package 'snotelr' (Calculate and Visualize 'SNOTEL' Snow Data and Seasonality) Programmatic interface to the 'SNOTEL' snow data (). 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Package: r-cran-snowdata Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra Filename: pool/dists/resolute/main/r-cran-snowdata_1.0.0-1.ca2604.1_all.deb Size: 29550 MD5sum: 5863c84d1ba11bf5de6f713b12408cea SHA1: 485b79e73637038efd48f1d018352d2fbff4f598 SHA256: 9787b5c5859e4ab37b5cf559e31635790573e3b13402eb29842e661488b66df2 SHA512: 963cef4c557ed2c00a74d7f3c3b36ba4076ae6790c35dc552910978a43a9fca0b9fb17e91ef5fd7081bf6acd01a776b91094b90c3248be02a4dc27e88e3a0844 Homepage: https://cran.r-project.org/package=SnowData Description: CRAN Package 'SnowData' (Historical Data from John Snow's 1854 Cholera Outbreak Map) Provides historical datasets related to John Snow's 1854 cholera outbreak study in London. Includes data on cholera cases, water pump locations, and the street layout, enabling analysis and visualisation of the outbreak. Package: r-cran-snowfall Architecture: all Version: 1.84-6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snow Suggests: r-cran-rmpi Filename: pool/dists/resolute/main/r-cran-snowfall_1.84-6.3-1.ca2604.1_all.deb Size: 248924 MD5sum: 048d80d3240d9e5643b99ae34f07e8b7 SHA1: ee88e055e1016ad364a6d4e7382d8c0d809af734 SHA256: 49153fc99129d0cb653f246a209e7630d318f54b14434c3daaf723772ad66697 SHA512: 35eb1a7bb21a755634b7128f2d61b24f83330a048ee669a47aa0241cf29fb50233f1af19059294798595c5de0f2e92fbac8d63ca03141827fd9322c80d8b4586 Homepage: https://cran.r-project.org/package=snowfall Description: CRAN Package 'snowfall' (Easier Cluster Computing (Based on 'snow')) Usability wrapper around snow for easier development of parallel R programs. This package offers e.g. extended error checks, and additional functions. All functions work in sequential mode, too, if no cluster is present or wished. 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Package: r-cran-snowflakeauth Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-jsonlite, r-cran-rcpptoml, r-cran-rlang, r-cran-jose, r-cran-openssl Suggests: r-cran-httpuv, r-cran-keyring, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-snowflakeauth_0.2.2-1.ca2604.1_all.deb Size: 67558 MD5sum: 21856c3863d45bf1c10e9f397ea947d4 SHA1: 7bdb2877c47058fa8036d807c65042214ccee4a9 SHA256: 4500ec855cb411a09d27dca18e64f3da7090225e8ce6cd9a2e2a5df0a64eba98 SHA512: 825fff8951a0675c078590d484cbb9952a98204ba473cfe90663c6901602437c9af1d6bd52958b294f456bd97bc3af1cc6f6bc6a919bccd0fcd510bb29def20b Homepage: https://cran.r-project.org/package=snowflakeauth Description: CRAN Package 'snowflakeauth' (Authentication Helpers for 'Snowflake') Authentication helpers for 'Snowflake'. It provides compatibility with authentication approaches supported by the 'Snowflake Connector for Python' and the 'Snowflake CLI' . Package: r-cran-snowflakes Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-snowflakes_1.0.0-1.ca2604.1_all.deb Size: 485258 MD5sum: 0dbcf3072b64be76bed0bad8d70f2842 SHA1: bbd2ad05d264b20d695efa0ab0f156966517a95d SHA256: 1e06b329d69b6aa13d9660a43ef949c15409555523d699f45af49505160cec65 SHA512: eedf54e7947db0d13d52c2eccf3467aba9e58531b9628484057bbe0d3dcf4265f298729971f9691c20082ce32b1d43fab381a810948f68651c118de7bdc0a16a Homepage: https://cran.r-project.org/package=snowflakes Description: CRAN Package 'snowflakes' (Random Snowflake Generator) The function generates and plots random snowflakes. Each snowflake is defined by a given diameter, width of the crystal, color, and random seed. Snowflakes are plotted in such way that they always remain round, no matter what the aspect ratio of the plot is. Snowflakes can be created using transparent colors, which creates a more interesting, somewhat realistic, image. Images of the snowflakes can be separately saved as svg files and used in websites as static or animated images. Package: r-cran-snowft Architecture: all Version: 1.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlecuyer, r-cran-snow Suggests: r-cran-rmpi Filename: pool/dists/resolute/main/r-cran-snowft_1.6-1-1.ca2604.1_all.deb Size: 76866 MD5sum: 56d9185edbf5f040d566130bfe7190bd SHA1: 5164e19a4fb37429ade5c28b18c4e27bbc566315 SHA256: 7aef29e5636b63a4ffb001d5d1424085af2a485f66aa89c6de58dc2c4839c65e SHA512: f29a90f0dbecbeeee221ffb203793a0fcec1f0f60f4e4567006e60bbe4fab0d5d470bb2cdb39759eaa40d5cbae17c5fb42e8f3669cb28b8c8223570e46c1d20b Homepage: https://cran.r-project.org/package=snowFT Description: CRAN Package 'snowFT' (Fault Tolerant Simple Network of Workstations) Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available. Package: r-cran-snowquery Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-reticulate, r-cran-rpostgres, r-cran-rsqlite, r-cran-dbi, r-cran-duckdb, r-cran-dplyr, r-cran-dbplyr Filename: pool/dists/resolute/main/r-cran-snowquery_1.3.0-1.ca2604.1_all.deb Size: 31762 MD5sum: 0fefa1aafed06ffff39f663a00735364 SHA1: 0a4cef99d1be6fae03c60e3e53ac80f663d7fa7a SHA256: 59174e4c0fe57244a6a4202dae537e77c479ae15ab426bc66e2f264707b2879a SHA512: 9a24277f840ffccec5c1731e4867afe1f87382454e4ca1e24e34a9d7017260a83711f567880f080b2d8ae68a9b24232274e29a4431c45be3afe7df659d55b193 Homepage: https://cran.r-project.org/package=snowquery Description: CRAN Package 'snowquery' (SQL Interface to 'Snowflake', 'Redshift', 'Postgres', 'SQLite',and 'DuckDB') Run 'SQL' queries across 'Snowflake', 'Amazon Redshift', 'PostgreSQL', 'SQLite', and 'DuckDB' from R with a single function. Optionally stream and cache large query results to a local 'DuckDB' database for efficient work with larger-than-memory datasets. Package: r-cran-snpaimer Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adegenet, r-cran-doparallel, r-cran-dplyr, r-cran-forcats, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-readr, r-cran-tidyr, r-cran-withr, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-snpaimer_2.1.1-1.ca2604.1_all.deb Size: 25064 MD5sum: 0740b367bd790de99591077125f210ec SHA1: 040fa1340d0fbad7adbd2daba626ca9a74add7ee SHA256: 71cae38c92bd5141e7318b1c0e4b46f07dc7cac113246a5f1d299469225a774a SHA512: 2fea2763c9e6ae3cf75a0b75513e8adc58a5d4a94bf97dfb0739db7873334688f0bbb873b775f5de4bec03956bf42ec9551150bab0dcf2f035426d060d566ff7 Homepage: https://cran.r-project.org/package=snpAIMeR Description: CRAN Package 'snpAIMeR' (Assess the Diagnostic Power of Genomic Marker Combinations) Population genetics package for designing diagnostic panels. Candidate markers, marker combinations, and different panel sizes are assessed for how well they can predict the source population of known samples. Requires a genotype file of candidate markers in STRUCTURE format. Methods for population cross-validation are described in Jombart (2008) . Package: r-cran-snpannotator Architecture: all Version: 1.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1786 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-xml2, r-cran-openxlsx, r-cran-progress, r-cran-ggplot2, r-cran-kableextra, r-cran-rmarkdown, r-cran-ini, r-cran-igraph, r-cran-png, r-cran-ggraph, r-cran-logger, r-cran-readr Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-snpannotator_1.4.6-1.ca2604.1_all.deb Size: 1738856 MD5sum: 3e84c46eaa204d7abcbb1a84f7ccf631 SHA1: 73ec93644a47710cf277220c511e4ad1dd67351e SHA256: f46eb0be001d12e177069176a1e204d00d67f21c6d1352b03b3ab455a474bb9a SHA512: 4b5f5ef531d82556d55f0cbc68535d8afe278d44c4e434cc1d8d94520f5290484b1e7c59087e5d462aba75f23bbbe7614152d190c15277d69ac09d7f2e7e26bc Homepage: https://cran.r-project.org/package=SNPannotator Description: CRAN Package 'SNPannotator' (Automated Functional Annotation of Genetic Variants and LinkedProxies) To automated functional annotation of genetic variants and linked proxies. Linked SNPs in moderate to high linkage disequilibrium (e.g. r2>0.50) with the corresponding index SNPs will be selected for further analysis. Package: r-cran-snpfiltr Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 984 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vcfr, r-cran-ggplot2, r-cran-rtsne, r-cran-cluster, r-cran-adegenet, r-cran-gridextra, r-cran-ggridges Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-snpfiltr_1.0.7-1.ca2604.1_all.deb Size: 659940 MD5sum: 62d02095d87fcaf959f26b173375f338 SHA1: 2084c13cafb309017a2121dd2a7ee5624c2f4509 SHA256: 4de216e736c8dc231e6a7b00faf937795d61243c5052375a1de4bccc425ed24d SHA512: 058c437ba1df676d95b25445f6f589c6492e76757fa6d79e921887b90a5f6ca2649d20fac431ed30cbca515f5b214ad082747d5f0708e8cb305fa03539a308fb Homepage: https://cran.r-project.org/package=SNPfiltR Description: CRAN Package 'SNPfiltR' (Interactively Filter SNP Datasets) Is designed to interactively and reproducibly visualize and filter SNP (single-nucleotide polymorphism) datasets. This R-based implementation of SNP and genotype filters facilitates an interactive and iterative SNP filtering pipeline, which can be documented reproducibly via 'rmarkdown'. 'SNPfiltR' contains functions for visualizing various quality and missing data metrics for a SNP dataset, and then filtering the dataset based on user specified cutoffs. All functions take 'vcfR' objects as input, which can easily be generated by reading standard vcf (variant call format) files into R using the R package 'vcfR' authored by Knaus and Grünwald (2017) . Each 'SNPfiltR' function can return a newly filtered 'vcfR' object, which can then be written to a local directory in standard vcf format using the 'vcfR' package, for downstream population genetic and phylogenetic analyses. Package: r-cran-snplinkage Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5673 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-gwastools, r-bioc-biomart, r-cran-cowplot, r-cran-data.table, r-bioc-gdsfmt, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtable, r-cran-knitr, r-cran-magrittr, r-cran-reshape2, r-bioc-snprelate Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-snplinkage_1.2.0-1.ca2604.1_all.deb Size: 4714024 MD5sum: 90876df1f48b0a3f0a006b85bdc0d061 SHA1: b9fc6963834ed96b7be97306878f587d8109cbe4 SHA256: eb1d5e9bc6c444e8dba6879ec79d93524e6e86657acee583bb894e9357bf138e SHA512: 249d5721488f3da90cb3f1c969e40c3b69e6f73c1cdd8a0100af50aa2778ce89ddf3e1aadb1acc2664b922905fe3bb2fb7d809ce348d9060832f36f1ebd8f44e Homepage: https://cran.r-project.org/package=snplinkage Description: CRAN Package 'snplinkage' (Single Nucleotide Polymorphisms Linkage DisequilibriumVisualizations) Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the 'GWASTools' package using the 'SNPRelate' package, and the plots are customizable 'ggplot2' and 'gtable' objects and are annotated using the 'biomaRt' package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) . Package: r-cran-snpls Architecture: all Version: 1.0.27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clickr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggrepel, r-cran-ks, r-cran-mass, r-cran-matrix, r-cran-pbapply Filename: pool/dists/resolute/main/r-cran-snpls_1.0.27-1.ca2604.1_all.deb Size: 90774 MD5sum: adde273c79706e921ba8e3daf4257d65 SHA1: 0ef1442d37ccc97d061de480aa1a7fb4acad9dd5 SHA256: 55040116756e76c363561cf65dbe84f6967bb7237f8e285c2beebc4505fa764c SHA512: f14d1dd85bf07ef1c8d70a9d4d4ae6733092de769c126b85792b10d39f0010977f34a06bd237b8b95fc1385c2497dd4db19d38cec3a3c0242eb9a0b69cac1036 Homepage: https://cran.r-project.org/package=sNPLS Description: CRAN Package 'sNPLS' (NPLS Regression with L1 Penalization) Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 ) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores. Package: r-cran-snqtl Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rarpack, r-cran-mass Filename: pool/dists/resolute/main/r-cran-snqtl_0.2-1.ca2604.1_all.deb Size: 202212 MD5sum: aa3645b76206adf74a24c906ba64705d SHA1: a99a1c0af5e0c34458219fad7edcd1cab7a7fde1 SHA256: 913e67fd95893527cbe35ec98fd9fe2c67c391ca635e7742cec585d8ae333b9e SHA512: 84f8be640426b66831c4b695904d1d28ffe0c5944c7ec391a0bd18a32f0f41aa78bc6f5f8a27a92c340bd9081cd84bb53708d13d3e71eb073fe9f5d72b3ec8ef Homepage: https://cran.r-project.org/package=snQTL Description: CRAN Package 'snQTL' (Spectral Network Quantitative Trait Loci (snQTL) Analysis) A spectral framework to map quantitative trait loci (QTLs) affecting joint differential networks of gene co-Expression. Test the equivalence among multiple biological networks via spectral statistics. See reference Hu, J., Weber, J. N., Fuess, L. E., Steinel, N. C., Bolnick, D. I., & Wang, M. (2025) . Package: r-cran-sns Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda, r-cran-numderiv Suggests: r-cran-regressionfactory, r-cran-mfusampler Filename: pool/dists/resolute/main/r-cran-sns_1.2.2-1.ca2604.1_all.deb Size: 542708 MD5sum: 3a2ad1965ed372382ea5576f08990623 SHA1: e0332a3460764820efcf0c29d23a5eb1adf28d03 SHA256: 67a6bcbf33d7ca11045a4042b2d2d1e3b503a0720015836f38199ba2d8d756bf SHA512: f65689bb24c48cbb55afa5b14c0bd1d48d62c0e59da8c2cd4a9453fe41f589e73decd9ddcab535a5ae7e8194fdc3b05c665e7d86325c62fb2bec3ae50f7a606b Homepage: https://cran.r-project.org/package=sns Description: CRAN Package 'sns' (Stochastic Newton Sampler (SNS)) Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive . Package: r-cran-snschart Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-snschart_1.4.0-1.ca2604.1_all.deb Size: 236530 MD5sum: 5a25eea2411c63ff3a38eb6ddb8f8e35 SHA1: 2cddc74c3d00aab28adc27c871bc90d54fc7016a SHA256: 58a3771778aee48c9df31e57b0d8b446159708f06e35ee60590c5c2472b74dd9 SHA512: 2fb324764e1d76c9751157badcb81c9ee421c206ec8b41d136b46e7df450754a9074f705a6ae3b3ae51f5300b88968639161a66612709ab6bdf01d79a65ef210 Homepage: https://cran.r-project.org/package=SNSchart Description: CRAN Package 'SNSchart' (Sequential Normal Scores in Statistical Process Management) The methods discussed in this package are new non-parametric methods based on sequential normal scores 'SNS' (Conover et al (2017) ), designed for sequences of observations, usually time series data, which may occur singly or in batches, and may be univariate or multivariate. These methods are designed to detect changes in the process, which may occur as changes in location (mean or median), changes in scale (standard deviation, or variance), or other changes of interest in the distribution of the observations, over the time observed. They usually apply to large data sets, so computations need to be simple enough to be done in a reasonable time on a computer, and easily updated as each new observation (or batch of observations) becomes available. Some examples and more detail in 'SNS' is presented in the work by Conover et al (2019) . Package: r-cran-snsequate Architecture: all Version: 1.3-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magic, r-cran-ake, r-cran-equate, r-cran-moments, r-cran-emdbook, r-cran-plyr, r-cran-statmod, r-cran-knitr, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-snsequate_1.3-5-1.ca2604.1_all.deb Size: 320002 MD5sum: e6f377ff6a3187d006b15af609f505fb SHA1: daa8e50fa8994f475d2112a43be2c547692f80e0 SHA256: 1a5ea366a1080c092b343bf28e34609b332b77495a1cf52bf4e9a1edfe0eae25 SHA512: 0ae84122d69eee4056cfd25f2f596e0c31e9fb79747dee01f0e7defb0be64ddc1dc531078ceea22093e39b8398f199d0e67e68585629c71c720de64e2137970a Homepage: https://cran.r-project.org/package=SNSequate Description: CRAN Package 'SNSequate' (Standard and Nonstandard Statistical Models and Methods for TestEquating) Contains functions to perform various models and methods for test equating (Kolen and Brennan, 2014 ; Gonzalez and Wiberg, 2017 ; von Davier et. al, 2004 ). It currently implements the traditional mean, linear and equipercentile equating methods. Both IRT observed-score and true-score equating are also supported, as well as the mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods. It also supports newest methods such that local equating, kernel equating (using Gaussian, logistic, Epanechnikov, uniform and adaptive kernels) with presmoothing, and IRT parameter linking methods based on asymmetric item characteristic functions. Functions to obtain both standard error of equating (SEE) and standard error of equating differences between two equating functions (SEED) are also implemented for the kernel method of equating. Package: r-cran-snsfdatasets Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-snsfdatasets_0.1.1-1.ca2604.1_all.deb Size: 22426 MD5sum: d5855273a0678ad1aed9b802075571b5 SHA1: f575e006e1433ddc380839c027983f408497eb0e SHA256: 2b157cbd6b980301f7eca1f3fea4a20607d2b8aef201c62426e72914c00ce056 SHA512: 2061136101d1082621b64274ffc550c754c63546cddce3a6be4f0fafad76562379d8b9f133e1c08281d2bbfed56e9efed78df79c12d2b743aabf8faffe93b4e3 Homepage: https://cran.r-project.org/package=SNSFdatasets Description: CRAN Package 'SNSFdatasets' (Download Datasets from the Swiss National Science Foundation(SNF, FNS, SNSF)) Download and read datasets from the Swiss National Science Foundation (SNF, FNS, SNSF; ). The package is lightweight and without dependencies. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. There are also utilities for comparing different versions of datasets, i.e. to report added, removed and changed entries. Package: r-cran-snsmart Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-envstats, r-cran-bayestestr, r-cran-condmvnorm, r-cran-cubature, r-cran-geepack, r-cran-hdinterval, r-cran-pracma, r-cran-rjags, r-cran-tidyr, r-cran-truncdist Suggests: r-cran-coda, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-snsmart_0.2.4-1.ca2604.1_all.deb Size: 155626 MD5sum: 087a76b86ffb40b129e22148ee18c623 SHA1: efa8612e11b810470d8b3071ed9065834cfd981e SHA256: 28470d1e7b3f5b20c9d115a5378d13e00ea6799c6af7fc97949d725711f5f429 SHA512: 318a94530571c951f1302361653c7a27412bb51ab6d1708f09e14e37f0ae07b7f0030b750ee03f9e33a2eeb360d9bdeb1eed329404a4812ead98bf1c5f04d131 Homepage: https://cran.r-project.org/package=snSMART Description: CRAN Package 'snSMART' (Small N Sequential Multiple Assignment Randomized Trial Methods) Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 . Package: r-cran-snvecr Architecture: all Version: 3.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1782 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-readr, r-cran-tidyselect, r-cran-rlang, r-cran-glue, r-cran-backports, r-cran-stringr Suggests: r-cran-astrochron, r-cran-ggplot2, r-cran-tidyr, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-withr, r-cran-curl, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-snvecr_3.10.1-1.ca2604.1_all.deb Size: 1330080 MD5sum: 6775ed191938f82da3a1490951f2c99b SHA1: bf938df081df35bb5357f8c78c01f3eaa0e73541 SHA256: c1e718c56b3652f329c24181cb9d07c8d767a22ea673b7767817bcb6ba3d5065 SHA512: fb99c3070cdef35f882320123e70c980e918d39ad5157b148187b28cd4b56f98497497966fa88cf77df871450e39868b5610a19a68bc293d33d028c01b0d839b Homepage: https://cran.r-project.org/package=snvecR Description: CRAN Package 'snvecR' (Calculate Earth’s Obliquity and Precession in the Past) Easily calculate precession and obliquity from an orbital solution (defaults to ZB18a from Zeebe and Lourens (2019) ) and assumed or reconstructed values for tidal dissipation (Td) and dynamical ellipticity (Ed). This is a translation and adaptation of the 'C'-code in the supplementary material to Zeebe and Lourens (2022) , with further details on the methodology described in Zeebe (2022) . The name of the 'C'-routine is 'snvec', which refers to the key units of computation: spin vector s and orbit normal vector n. Package: r-cran-snvlfdr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-snvlfdr_1.0.1-1.ca2604.1_all.deb Size: 109430 MD5sum: edce6a00ec0fd65d651ef4f55a323c4d SHA1: f17667b4da32b04b9ff8e706e02fbb04022cd094 SHA256: 4a8fa45d1205231fbc94fd06be764919b33977e7e18ab549af48db307e8f41b4 SHA512: 1ed9d61a3aa249a1f364574134f269a31575d10f503a95743d7249cf9ce527825e5a1bd07dbc05007108a9acd1f7843f3e490a876cc63c510d3e25c1ac68524a Homepage: https://cran.r-project.org/package=SNVLFDR Description: CRAN Package 'SNVLFDR' (Empirical Bayes Single Nucleotide Variant Calling) Identifies single nucleotide variants in next-generation sequencing data by estimating their local false discovery rates. For more details, see Karimnezhad, A. and Perkins, T. J. (2024) . Package: r-cran-soar Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-soar_1.0-1-1.ca2604.1_all.deb Size: 225278 MD5sum: 1695de456275ff8afeb41b551c66945e SHA1: 054522c9c2bbf0c7577ec0b8f2fcdc53d10b72db SHA256: 6b1a891b3f643f98401e09e43a1f037dc55ddc72c07fbbdc2533e90e4aee38b9 SHA512: ace60e4a5f4558ef08c7bf17ab5b7717bce8ee62d99cbc61fa546849bbfce78f85b052d8f0e5d8fc91a9c16bd94b9df06eec2fdee20f4f682db5307eb0e4a725 Homepage: https://cran.r-project.org/package=SOAR Description: CRAN Package 'SOAR' (Memory Management in R by Delayed Assignments) Allows objects to be stored on disc and automatically recalled into memory, as required, by delayed assignment. Package: r-cran-soas Architecture: all Version: 1.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doe.base, r-cran-combinat, r-cran-frf2, r-cran-igraph, r-cran-lhs, r-cran-conf.design, r-cran-sfsmisc, r-cran-partitions Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-soas_1.4-1-1.ca2604.1_all.deb Size: 366546 MD5sum: c72cdcbee88bd98979b5f163784b9ed4 SHA1: 1087c3571048bffa68647d9a5f804f1eae178e1a SHA256: 23ed213a65dd3d27bc2fb3e7bc84040480e1989f94c8fd8fcf66d408f2404c37 SHA512: bac8b696f3516f9f89c9a4b720faca0efee5ff2c30050dd0dfd632eb360d1dc36e28e570052e29f1fa1ce1cae2fe3b2664a10de2a50b2d627fb299512ef9111b Homepage: https://cran.r-project.org/package=SOAs Description: CRAN Package 'SOAs' (Creation of Stratum Orthogonal Arrays) Creates stratum orthogonal arrays (also known as strong orthogonal arrays). These are arrays with more levels per column than the typical orthogonal array, and whose low order projections behave like orthogonal arrays, when collapsing levels to coarser strata. Details are described in Groemping (2022) "A unifying implementation of stratum (aka strong) orthogonal arrays" . Package: r-cran-sobolnp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-np, r-cran-minqa, r-cran-pbmcapply Filename: pool/dists/resolute/main/r-cran-sobolnp_0.1.0-1.ca2604.1_all.deb Size: 25522 MD5sum: 7b6fbe5247d13fc1b5fc4dfb35a016b6 SHA1: 02fda9a4de15802fca21d2693a9ea270e4daa63d SHA256: 9bfa42345addd943223d00b1812ece73f48b03d6f43e7ef8131f14f8c6c6d0ac SHA512: f3145fa6d73036313d38f842e1ff0ae8f2341f3f5b9990c40c40c14a8ac80a9b7c87b16a9e84f275267c51addfa0de7e2540eade931c132f014034732ac6190a Homepage: https://cran.r-project.org/package=sobolnp Description: CRAN Package 'sobolnp' (Nonparametric Sobol Estimator with Bootstrap Bandwidth) Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solís, M. (2018) . Package: r-cran-socialdrift Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-igraph Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-socialdrift_0.1.0-1.ca2604.1_all.deb Size: 171342 MD5sum: 14bc67f5826aad02d594cd9647575080 SHA1: d8614e457b932bc5387122a5baed501ef1b9d58d SHA256: d8e3a60cebeb675db3a8bdf7ac599920b2f37f990959bd63e83b661bc3a26e78 SHA512: 7ba8a39b2e509ba3681fc95be8b4ee0a168c6924424d9da30f6eeb3a317c92ff33b37530d07fea114725510540c48075be21db8980af76a4384a55e2d3e76980 Homepage: https://cran.r-project.org/package=socialdrift Description: CRAN Package 'socialdrift' (Temporal Auditing of Social Interaction Networks) Tools for constructing, auditing, and visualizing temporal social interaction networks from event-log data. Supports graph construction from raw user-to-user interaction logs, longitudinal tracking of network structure, community dynamics, user role trajectories, and concentration of engagement over time. Designed for computational social science, platform analytics, and digital community health monitoring. Includes four longitudinal audit indices: the Network Drift Index ('NDI'), Community Fragmentation Index ('CFI'), Visibility Concentration Index ('VCI'), and Role Mobility Index ('RMI'). 'NDI', 'CFI', 'VCI', and 'RMI' are purpose-built composite scores for longitudinal platform auditing. Package: r-cran-socialh Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-socialh_0.1.1-1.ca2604.1_all.deb Size: 1003144 MD5sum: c6aab33634d3df158bdbd146e3e2da98 SHA1: 5dd6717912af264355c2ce154419aa84e66d54a5 SHA256: 8c649602973f06a23c52f007b0865a0c219f53b57921b9c499312e8ad1bd09a4 SHA512: e2a6180bb5009b45fca18291c1e02c13e5fd84157ccd0b7885047485b63437f7cccb677369e74e1e1ed2a417da2fde5b1bd7893b200f38a38db090193acf932b Homepage: https://cran.r-project.org/package=socialh Description: CRAN Package 'socialh' (Rank and Social Hierarchy for Gregarious Animals) Tools developed to facilitate the establishment of the rank and social hierarchy for gregarious animals by the Si method developed by Kondo & Hurnik (1990). It is also possible to determine the number of agonistic interactions between two individuals, sociometric and dyadics matrix from dataset obtained through electronic bins. In addition, it is possible plotting the results using a bar plot, box plot, and sociogram. Package: r-cran-socialmixr Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-countrycode, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-memoise, r-cran-purrr, r-cran-oai, r-cran-wpp2017, r-cran-xml2, r-cran-cli, r-cran-rlang Suggests: r-cran-contactsurveys, r-cran-ggplot2, r-cran-here, r-cran-knitr, r-cran-quarto, r-cran-reshape2, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-socialmixr_0.6.0-1.ca2604.1_all.deb Size: 897414 MD5sum: 4c198ebf086ded11ce6f010de1b3767f SHA1: d9672fd3e36a3b69cb58b8f6b8795707483a0649 SHA256: e0c03da41d96ad3d9abfeee6cb78e098fa3cb32c78c23d7b35c2d40d6bc9f61a SHA512: f46ab51b5e7e13ff6583cdbea5d6b9ccef5539572b77075de2f11fae131d5a1f5f413412a1a7f989351305c34c92dd796ab801be2717ee3304f3e326766df6c3 Homepage: https://cran.r-project.org/package=socialmixr Description: CRAN Package 'socialmixr' (Social Mixing Matrices for Infectious Disease Modelling) Methods for sampling contact matrices from diary data for use in infectious disease modelling, as discussed in Mossong et al. (2008) . Package: r-cran-socialranking Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1731 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-relations, r-cran-rlang, r-cran-rdpack Suggests: r-cran-clipr, r-cran-testthat, r-cran-xfun, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-partitions Filename: pool/dists/resolute/main/r-cran-socialranking_1.2.0-1.ca2604.1_all.deb Size: 771348 MD5sum: 8e348faa7edba30d73831c1aa6479138 SHA1: 526c214907ea8710134eb3335c629bb152c4c890 SHA256: 072ea9c17cd3d44a21d510c1a8d2900ae32ce303b5b63bc6a73598b94a52ecd0 SHA512: 8cd5184fb20031e266bd65735887522c66359abcef03b2022bd5d94d95f6d3d43852f772afdf547e83ee711e18c40193b13804d44dd8c4fd69d1601740e81874 Homepage: https://cran.r-project.org/package=socialranking Description: CRAN Package 'socialranking' (Social Ranking Solutions for Power Relations on Coalitions) The notion of power index has been widely used in literature to evaluate the influence of individual players (e.g., voters, political parties, nations, stockholders, etc.) involved in a collective decision situation like an electoral system, a parliament, a council, a management board, etc., where players may form coalitions. Traditionally this ranking is determined through numerical evaluation. More often than not however only ordinal data between coalitions is known. The package 'socialranking' offers a set of solutions to rank players based on a transitive ranking between coalitions, including through CP-Majority, ordinal Banzhaf or lexicographic excellence solution summarized by Tahar Allouche, Bruno Escoffier, Stefano Moretti and Meltem Öztürk (2020, ). Package: r-cran-socialrisk Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-socialrisk_0.5.1-1.ca2604.1_all.deb Size: 36394 MD5sum: f7949449576eceebdd16eccaea4f6001 SHA1: fef13fb35b41cb871f803f49f26f53d1e4ca507d SHA256: b7eda6f356dac01861a0558edbd1ebb4e35fc13282a5bd878c0b6492ecf341b0 SHA512: e9fa3ae6a3949b2179bcef10300b000e88de141e7787eafef911618241f2df22082cde59503c4b98fc9ad9255208a87c1ad26653606f25e9449d48a709fca0d9 Homepage: https://cran.r-project.org/package=socialrisk Description: CRAN Package 'socialrisk' (Identifying Patient Social Risk from Administrative Health CareData) Social risks are increasingly becoming a critical component of health care research. One of the most common ways to identify social needs is by using ICD-10-CM "Z-codes." This package identifies social risks using varying taxonomies of ICD-10-CM Z-codes from administrative health care data. The conceptual taxonomies come from: Centers for Medicare and Medicaid Services (2021) , Reidhead (2018) , A Arons, S DeSilvey, C Fichtenberg, L Gottlieb (2018) . Package: r-cran-socialsim Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-future, r-cran-future.apply Suggests: r-cran-rstan, r-cran-devtools, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-socialsim_0.1.9-1.ca2604.1_all.deb Size: 40444 MD5sum: 869c54be8259f5a4a38ace0d495e953b SHA1: d5d3c2cddc43faa960fe616bbb376f5e952b0dcf SHA256: 9ebf817cdb0a02608dc4559e933a0c91b8bf5143b05cfaca22843982c58e7bf5 SHA512: e352abde72e78d261849dfdae91fa2bc90cb49f4eddc35e940c1c724b306b785df0cbe3af9d9c9c8661d8d111315cacbba0e7f708a3a6add62d6ecb6ed05c36b Homepage: https://cran.r-project.org/package=socialSim Description: CRAN Package 'socialSim' (Simulate and Analyse Social Interaction Data) Provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan. Model fitting is performed via the 'rstan' package. Users can generate realistic interaction data where individual phenotypes influence and respond to those of their partners, with control over sampling design parameters such as the number of individuals, partners, and repeated dyads. The simulation framework allows flexible control over variation and correlation in mean trait values, social responsiveness, and social impact, making it suitable for research on interacting phenotypes and on direct and indirect genetic effects ('DGEs' and 'IGEs'). The package also includes functions to fit and compare alternative models of social effects, including impact–responsiveness, variance–partitioning, and trait-based models, and to summarise model performance in terms of bias and dispersion. For a more detailed description of the available models and impact–responsiveness, see the accompanying article Wijnhorst et al. (2026) . Package: r-cran-sociome Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mice, r-cran-psych, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidycensus, r-cran-tidyr Suggests: r-cran-uspopcenters, r-cran-cluster, r-cran-geosphere, r-cran-ggplot2, r-cran-sf, r-cran-testthat, r-cran-tigris, r-cran-units Filename: pool/dists/resolute/main/r-cran-sociome_3.0.0-1.ca2604.1_all.deb Size: 169568 MD5sum: 8dcaebe48e1564ba3b96b8da6e8624ce SHA1: ec294d4319c3bc13829b273c0f2fc37ca8b17a00 SHA256: d92f0d5735d05e94cc6f90bc25b072acdeccc05d37c193e920a9f4abe2f16685 SHA512: 68c56eb2984b65c2928c5b8e18aff75443a4a7290ed5f96ce0b1a46ddc629150053f1462e554587bee67adc3019cc6616f71ae7b5f1af38b1aca2a53c7324871 Homepage: https://cran.r-project.org/package=sociome Description: CRAN Package 'sociome' (Operationalizing Social Determinants of Health Data forResearchers) Accesses raw data via API and calculates social determinants of health measures for user-specified locations in the US, returning them in tidyverse- and sf-compatible data frames. 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Apuntes de clase interactivos. Package: r-cran-sofia Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-png Filename: pool/dists/resolute/main/r-cran-sofia_1.0-1.ca2604.1_all.deb Size: 185922 MD5sum: 9df47f018427ba05567a68fea8da0762 SHA1: 02105a6d2b9de336c3a596066b01ddff97eca258 SHA256: d21d209e7d30fdc3b577eaa4e7ae82ed8d8bdf41c5cdd9ce93f4ec8630628b7e SHA512: c0b467ab4124b533e7f1a057dbfa5101a4fb61af87b04c7d187d99fc5e061ceb055f324514ceb61de2f5d87d4450e87a4dabe05df3ef0766c7d2d83f43781cc7 Homepage: https://cran.r-project.org/package=SOFIA Description: CRAN Package 'SOFIA' (Making Sophisticated and Aesthetical Figures in R) Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. 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Package: r-cran-softwarerisk Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-ineq, r-cran-purrr, r-cran-scales, r-cran-tibble, r-cran-tidygraph, r-cran-rlang, r-cran-sensobol Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-softwarerisk_0.2.0-1.ca2604.1_all.deb Size: 1233430 MD5sum: c40227740df0aad41356cc0c619f07ec SHA1: 9379a76880e4c56a4f54ccc83b43cedb9e370280 SHA256: 1bcae94c215604961a3f8f899682a64dc73b43bf4beae6d1cdf3d300487580d7 SHA512: 87b89a535e9aa9c8aa2c60b1ae4d2310a8846f6c07863756f7601a7fa576829e6c5c43c4e472c6b9efae2a6239c3fd015061c31c17597067ca25503b48d29474 Homepage: https://cran.r-project.org/package=softwareRisk Description: CRAN Package 'softwareRisk' (Computation of Node and Path-Level Risk Scores in ScientificModels) It leverages the network-like architecture of scientific models together with software quality metrics to identify chains of function calls that are more prone to generating and propagating errors. It operates on tbl_graph objects representing call dependencies between functions (callers and callees) and computes risk scores for individual functions and for paths (sequences of function calls) based on cyclomatic complexity, in-degree and betweenness centrality. The package supports variance-based uncertainty and sensitivity analyses after Puy et al. (2022) to assess how risk scores change under alternative risk definitions. Package: r-cran-sohpie Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-dplyr, r-cran-fdrtool, r-cran-gtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sohpie_1.0.6-1.ca2604.1_all.deb Size: 152484 MD5sum: b4e70b4391b95891ff4c3cae2b992ee1 SHA1: 53a151a849853e57a6c3cd79dedc9acc412607db SHA256: 5d19b5373e8d5eb5c96f5c2c6951cc071244800e6a7863464348daf4dd950f57 SHA512: bd158eaff6b35afa6a6208cc2b97b67fba6c8481c58bfba3e3583f5d4cc11c2994021e61bfd18375d928d4bc68b3f71e6094b50fce48446681a6e508d5cbb423 Homepage: https://cran.r-project.org/package=SOHPIE Description: CRAN Package 'SOHPIE' (Statistical Approach via Pseudo-Value Information and Estimation) 'SOHPIE' (pronounced as SOFIE) is a novel pseudo-value regression approach for differential co-abundance network analysis of microbiome data, which can include additional clinical covariate in the model. The full methodological details can be found in Ahn S and Datta S (2023) . Package: r-cran-soil Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-mass, r-cran-brglm2 Filename: pool/dists/resolute/main/r-cran-soil_1.1-1.ca2604.1_all.deb Size: 41500 MD5sum: 07098b543e1d4bc6b3bdc566fcca900b SHA1: dc2cc9bd6d236229fd8068adda5f34b4a781fad8 SHA256: 5ddfb5c62b94cad512c9846210a54c8f1184de9536630a938d679495f19b2522 SHA512: 3324ca99891bde81a126cfa2d3821ad0e6cf3d00b2a6e5edafb4bddfc966bc8940363bc29992b8905ab12a8405d501b8257d230e69b5cb8fad4280fb41fd63ec Homepage: https://cran.r-project.org/package=SOIL Description: CRAN Package 'SOIL' (Sparsity Oriented Importance Learning) Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+). Package: r-cran-soilassessment Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2309 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-desolve, r-cran-e1071, r-cran-fuzzyahp, r-cran-googledrive, r-cran-hmisc, r-cran-nnet, r-cran-png, r-cran-randomforest, r-cran-raster, r-cran-soiltexture, r-cran-sp, r-cran-sf, r-cran-terra, r-cran-curl, r-cran-httr, r-cran-withr Suggests: r-cran-arm, r-cran-cubist, r-cran-aqp, r-cran-kernlab, r-cran-nlme, r-cran-ranger, r-cran-rpart, r-cran-plyr, r-cran-qrnn, r-cran-quantregforest Filename: pool/dists/resolute/main/r-cran-soilassessment_1.3.0-1.ca2604.1_all.deb Size: 2264110 MD5sum: e57386aedd82d667368bd3eb3716f4b7 SHA1: 8e5fe30a8d69dfcf461d92688700ff3a2b794a3d SHA256: 8d0baee4237418cd8801576905d805b054e8e159e3252e2269db5569689dbe01 SHA512: 888f214296b2a86da91c0d1a1803ffc38220d8e9369aa8e55a2d40216086bb4fc4d96d6f42ef3127801dc7a39931be2ac4dc63d8b2dc8dba0307d7e79e27df54 Homepage: https://cran.r-project.org/package=soilassessment Description: CRAN Package 'soilassessment' (Soil Health Assessment Models for Assessing Soil Conditions andSuitability) Soil health assessment builds information to improve decision in soil management. It facilitates assessment of soil conditions for crop suitability [such as those given by FAO ], groundwater recharge, fertility, erosion, salinization [], carbon sequestration, irrigation potential, and status of soil resources. Package: r-cran-soilchemistry Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-soilchemistry_0.1.0-1.ca2604.1_all.deb Size: 29740 MD5sum: 3413bc76aa55a50cf2854f9b9ec36387 SHA1: d3599ecf68a622b82f7d88daf2cf228dbcfb0737 SHA256: d8132e443d7447a814aa96f6a914660a6e85251c984e32da616b002e86fe04b6 SHA512: 6629ddd466318c27424fa7590b5c42f662c882833947aaaeb510d48bf35b0d587f55a6ac37a89ff07a55875d85763c21a0b254506340243170dd4019b9da489c Homepage: https://cran.r-project.org/package=soilchemistry Description: CRAN Package 'soilchemistry' (Computation of Properties Related to Soil Chemical Environmentand Nutrient Availability) Facilitates basic and equation-based analyses of some important soil properties related to soil chemical environment and nutrient availability to plants. Freundlich H (1907). . Datta SP, Bhadoria PBS (1999). ."Boron adsorption and desorption in some acid soils of West Bengal, India". Langmuir I (1918). "The adsorption of gases on plane surfaces of glass, mica, and platinum". Khasawneh FE (1971). "Solution ion activity and plant growth". Package: r-cran-soilconservation Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-soilconservation_1.0.1-1.ca2604.1_all.deb Size: 52856 MD5sum: 10a01bc02ba875ef191a2b9ef0d88aa4 SHA1: 6a2544c48b0fa97abfac247af70165c48789e56b SHA256: 5ac4bb63331c751f4c5522b5c0197f58d143f9dea5654d4fbb6824bb51634153 SHA512: 6f2ec452c73f128d77262b329cbc5de228fe31b5faea5dcd62fe5f172d5e846144a703b0e2c103c712e1195ad7dc2f1dee2391a85c158f64427fb935f01f1f7c Homepage: https://cran.r-project.org/package=SoilConservation Description: CRAN Package 'SoilConservation' (Soil and Water Conservation) Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and SoilLoss(). The rainfall erosivity factors can be calculated or estimated, and soil erodibility will be estimated by the equation extracted from the monograph. Soil loss will be estimated by the product of five factors (rainfall erosivity, soil erodibility, length and steepness slope, cover-management factor and support practice factor. In the future, additional functions can be included. This efforts to advance research in soil and water conservation, with fast and accurate results. Package: r-cran-soildb Architecture: all Version: 2.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-curl, r-cran-jsonlite Suggests: r-cran-aqp, r-cran-xml2, r-cran-httr, r-cran-rvest, r-cran-odbc, r-cran-rsqlite, r-cran-sf, r-cran-wk, r-cran-terra, r-cran-raster, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-soildb_2.9.1-1.ca2604.1_all.deb Size: 1533660 MD5sum: 3c514184d2176a76f415ed5cad0bd803 SHA1: a6a7a6787d4262a4bea4e5a0aec45c8ac9f9bb05 SHA256: 760498fd0ed865d49e7c56ff76daf021a9c57bcd1c1caa221560e74384e02c54 SHA512: 8b6541fe7b829b030e7896898b39e0eb56773925d7992989eb7c08bd63f26d3040e18b9a6428dc9a2499bec1b09df6ea01bee4959284dbf461316c6b50314276 Homepage: https://cran.r-project.org/package=soilDB Description: CRAN Package 'soilDB' (Soil Database Interface) A collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases. Package: r-cran-soilfda Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-soilfda_0.1.0-1.ca2604.1_all.deb Size: 17802 MD5sum: c570619796dc1acfdb57578077648f69 SHA1: 7d04f795702b2dbc5dfb89117a72207b15aacf0a SHA256: 5d345c2ca00ec13ae9092fc265bc9dc0853675d54a241cba1b22a74a15ad2a4a SHA512: f7dec48e2029c2c7632158fd190536c8421be90338f963e6019eebb821a651a604e7c6951e3a7571a7ba49efe0c95d795c8dadfda74ed6fbf047a7437f674c54 Homepage: https://cran.r-project.org/package=SoilFDA Description: CRAN Package 'SoilFDA' (Fractal Dimension Analysis of Soil Particle Size Distribution) Function for the computation of fractal dimension based on mass of soil particle size distribution by Tyler & Wheatcraft (1992) . It also provides functions for calculation of mean weight and geometric mean diameter of particle size distribution by Perfect et al. (1992) . Package: r-cran-soilflux Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3709 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-stringr, r-cran-reticulate, r-cran-tensorflow, r-cran-rlang Suggests: r-cran-keras3, r-cran-ggtern, r-cran-readxl, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-soilflux_0.1.5-1.ca2604.1_all.deb Size: 3667934 MD5sum: eff1fea0e9890b4ebb94ec62a81e57dd SHA1: 355587e1b7940af677af68e4d5ea6d6da27ae2de SHA256: 785ddbe461e0cf03bcd9d83d2f3ec15a3d76fcc00113a52126df0240bc45352c SHA512: b0aec8caff1df5016d902dbe85a4fbe4cb10140102d24cf617044b39021ab57d7aa971c7ce9d30440a7955b3524e9ede88c8abd3bfc5e38aeac8ef496cb5a209 Homepage: https://cran.r-project.org/package=soilFlux Description: CRAN Package 'soilFlux' (Physics-Informed Neural Networks for Soil Water Retention Curves) Implements a physics-informed one-dimensional convolutional neural network (CNN1D-PINN) for estimating the complete soil water retention curve (SWRC) as a continuous function of matric potential, from soil texture, organic carbon, bulk density, and depth. The network architecture ensures strict monotonic decrease of volumetric water content with increasing suction by construction, through cumulative integration of non-negative slope outputs (monotone integral architecture). Four physics-based residual constraints adapted from Norouzi et al. (2025) are embedded in the loss function: (S1) linearity at the dry end (pF in [5, 7.6]); (S2) non-negativity at pF = 6.2; (S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative in the saturated plateau region (pF in [-2, -0.3]). Includes tools for data preparation, model training, dense prediction, performance metrics, texture classification, and publication-quality visualisation. Package: r-cran-soilfoodwebs Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-diagram, r-cran-quadprog, r-cran-lpsolve, r-cran-rootsolve, r-cran-desolve Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-soilfoodwebs_1.0.2-1.ca2604.1_all.deb Size: 261190 MD5sum: 03aebc10c214bb6e68389e8db0f976b4 SHA1: 0a8b269b1de8bb8fcca5b617d21fe0678d8f3535 SHA256: 3231637658fa4098ae12435b56ce583e9f817c23763f9e29af27c772167da354 SHA512: 7f00939c89f0f9e0299200c52c8a0205437f02bdf571f17bc86cccac36fc0e7cf8175eef97fc9bacb4acf2f2f21e9b15f4a686156187fa2229c9d47e800ff297 Homepage: https://cran.r-project.org/package=soilfoodwebs Description: CRAN Package 'soilfoodwebs' (Soil Food Web Analysis) Analyzing soil food webs or any food web measured at equilibrium. The package calculates carbon and nitrogen fluxes and stability properties using methods described by Hunt et al. (1987) , de Ruiter et al. (1995) , Holtkamp et al. (2011) , and Buchkowski and Lindo (2021) . The package can also manipulate the structure of the food web as well as simulate food webs away from equilibrium and run decomposition experiments. Package: r-cran-soilfunctionality Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-soilfunctionality_0.1.0-1.ca2604.1_all.deb Size: 15316 MD5sum: 6b9d5f9354507948de44d160ad7bb9aa SHA1: 480d90702e2ce9e8532a41c7ccc8e8e7a6336ee1 SHA256: f2ef671562f4cdac653ab474030a978310b239fe7732465333f51d2a10ca7078 SHA512: 3363154ea94fbfd805c4b0df1f283a0c036383d42f100808f66f7138cfd905fe9bc745d6e50a9335bd3403207c14257c135c9acce2a64e95eb5ae1928371b6b9 Homepage: https://cran.r-project.org/package=SoilFunctionality Description: CRAN Package 'SoilFunctionality' (Soil Functionality Measurement) Generally, soil functionality is characterized by its capability to sustain microbial activity, nutritional element supply, structural stability and aid for crop production. Since soil functions can be linked to 80% of ecosystem services, conservation of degraded land should strive to restore not only the capacity of soil to sustain flora but also ecosystem provisions. The primary ecosystem services of soil are carbon sequestration, food or biomass production, provision of microbial habitat, nutrient recycling. However, the actual magnitude of soil functions provided by agricultural land uses has never been quantified. Nutrient supply capacity (NSC) is a measure of nutrient dynamics in restored land uses. Carbon accumulation proficiency (CAP) is a measure of ecosystem carbon sequestration. Biological activity index (BAI) is the average of responses of all enzyme activities in treated land over control/reference land. The CAP parameter investigates how land uses may affect carbon flows, retention, and sequestration. The CAP provides a signal for C cycles, flows, and the systems' relative operational supremacy. Package: r-cran-soilhyp Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-soilhyp_0.1.7-1.ca2604.1_all.deb Size: 229008 MD5sum: 646cd00f51b184d2132f5d70f0192683 SHA1: e375a06b8dd5303a9c97b74f571b8b0c38112401 SHA256: bcc22de489f5af28d71972dc0b6c43de90fd0df5d13e6d71af8a7c75689b5bb9 SHA512: 5800ac5509bfd83af42cf2d88139f945efb3afac17e0787080ef2933d5816ac27017bb98cc19adb8d6c87308ef9bd7c8677fefdfa4da616980bf6f576eac1ae7 Homepage: https://cran.r-project.org/package=SoilHyP Description: CRAN Package 'SoilHyP' (Soil Hydraulic Properties) Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7]. Main references: [1] van Genuchten (1980) , [2] Mualem (1976) , [3] Peters (2013) , [4] Iden and Durner (2013) , [5] Peters (2014) , [6] Wind G. P. (1966), [7] Peters and Durner (2008) and [8] Brooks and Corey (1964). Package: r-cran-soilhypfit Architecture: all Version: 0.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr, r-cran-snowfall, r-cran-mgcv, r-cran-quadprog, r-cran-rmpfr, r-cran-soilhyp Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-soilhypfit_0.1-8-1.ca2604.1_all.deb Size: 370868 MD5sum: 19bba1043889c33bf422e90d3aa457b6 SHA1: 7ab6fa5367d35330e1ccade2561420316d263e84 SHA256: 0427084e04e70f1c8657bbb73d6df82d038bb7c23c6ca008f9650bfa52dcc620 SHA512: 5ac372b10140c10bec7809b897c8dd40e512bab736706c72f5df6aad6d05c04f215ef1528b8451056007fb834e8f445d4806a4d20e77273238029edf3d5e27f5 Homepage: https://cran.r-project.org/package=soilhypfit Description: CRAN Package 'soilhypfit' (Modelling of Soil Water Retention and Hydraulic ConductivityData) Provides functions for efficiently estimating properties of the Van Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil water retention and hydraulic conductivity data by multi-response parameter estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) "Parameter estimation from multi-response data" ). Parameter estimation is simplified by exploiting the fact that residual and saturated water contents and saturated conductivity are conditionally linear parameters (Bates, D. M. and Watts, D. G. (1988) "Nonlinear Regression Analysis and Its Applications" ). Estimated parameters are optionally constrained by the evaporation characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020) "Physical Constraints for Improved Soil Hydraulic Parameter Estimation by Pedotransfer Functions" ) to ensure that the estimated parameters are physically valid. Common S3 methods and further utility functions allow to process, explore and visualise estimation results. Package: r-cran-soilkey Architecture: all Version: 0.9.97-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14157 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-yaml, r-cran-cli, r-cran-rlang, r-cran-withr Suggests: r-cran-aqp, r-cran-soiltaxonomy, r-cran-mpspline2, r-cran-terra, r-cran-foreign, r-cran-sf, r-cran-chromote, r-cran-munsellinterpol, r-cran-pls, r-cran-prospectr, r-cran-resemble, r-cran-ellmer, r-cran-httr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-pdftools, r-cran-magick, r-cran-shiny, r-cran-dt, r-cran-dbi, r-cran-rsqlite, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-soilkey_0.9.97-1.ca2604.1_all.deb Size: 7964166 MD5sum: aa8dee0269294388d0d23eb29f603be2 SHA1: 6438907e6812458e77c3a61bee104a55ffc2147c SHA256: 454f4c0c063c18ff69c89fa5e4f17722d19b3bd5aa2cabf270fbdfa84e6ea29d SHA512: b9581d871861f9037901d0f911d0782bda28adec8ee8a7d686a6a43564766c46d32861ce4685a2a6674710e93d9abcfcbe28e8db721d20ad0c8f05829646e72b Homepage: https://cran.r-project.org/package=soilKey Description: CRAN Package 'soilKey' (Automated Soil Profile Classification per 'WRB' 2022, 'SiBCS' 5and 'USDA' Soil Taxonomy 13) Implements deterministic classification keys for the World Reference Base for Soil Resources ('WRB') 2022, 4th edition (IUSS Working Group WRB, 2022, ISBN:979-8-9862451-1-9), the Brazilian System of Soil Classification ('SiBCS') 5th edition (Santos et al., 2018, ISBN:978-85-7035-800-4) and the United States Department of Agriculture ('USDA') Soil Taxonomy 13th edition (Soil Survey Staff, 2022, ). Provides a unified profile representation with explicit per-attribute provenance, multimodal extraction from field reports and photos via vision-language models (VLM), spatial priors from 'SoilGrids' (Poggio et al., 2021, ) and national soil maps, and gap-filling of soil attributes from visible-near-infrared (Vis-NIR) or mid-infrared (MIR) spectra via the Open Soil Spectral Library ('OSSL'; Safanelli et al., 2025, ). The taxonomic key itself is never delegated to a large language model (LLM); LLMs are restricted to schema-validated extraction. Each classification result reports a key trace, a provenance-aware evidence grade, and ambiguities that further measurement would resolve. Package: r-cran-soilmanager Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-ggplot2, r-cran-ggthemes, r-cran-tidyr, r-cran-tibble, r-cran-readxl, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-soilmanager_1.1.0-1.ca2604.1_all.deb Size: 859146 MD5sum: 1a127c866854b830f403952377b07a4c SHA1: c739f1fb2e1e1bdf15b7f632d82bef3648703744 SHA256: 60a3a61a37d6aa3514b29aac8961c24a387297c08be849384057b9efb8e720c7 SHA512: a45a649f0e7a8cedadb38b4388eaebb0a6827b4fe121e22ae986f2c2c7d610f39061f799c263b1775b04a5f8425f38634c0b73b84fcf9d06b7d9ed8947c7c0c3 Homepage: https://cran.r-project.org/package=SoilManageR Description: CRAN Package 'SoilManageR' (Calculate Soil Management Indicators for Agricultural PracticeAssessment) Calculate numerical agricultural soil management indicators from on a management timeline of an arable field. Currently, indicators for carbon (C) input into the soil system, soil tillage intensity rating (STIR), number of soil cover and living plant cover days, N fertilization and livestock intensity, and plant diversity are implemented. The functions can also be used independently of the management timeline to calculate some indicators. The package contains tables with reference information for the functions, as well as a '*.xlsx' template to collect the management data. Package: r-cran-soilphysics Architecture: all Version: 5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-mass, r-cran-shiny, r-cran-rhandsontable, r-cran-shinydashboard, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rpanel Filename: pool/dists/resolute/main/r-cran-soilphysics_5.1-1.ca2604.1_all.deb Size: 1767730 MD5sum: ae393149014666f0b0333b4ed107ecb5 SHA1: 87b8efcf6e99a2b4ee4f7edd063ea226648a6dfd SHA256: 94fee8bca142fd579f1d9c26f27b45733680d6ce0ed3e3e0efc88389dc803039 SHA512: 40ee151b54bc9a5d6d566b9dc331909578e24eeface094d9119b5bf30463a1143fb7b92a0b8719670bd3de473ef028d7e54a8ab9b701b783a853d8094e59def1 Homepage: https://cran.r-project.org/package=soilphysics Description: CRAN Package 'soilphysics' (Soil Physical Analysis) Basic and model-based soil physical analyses. Package: r-cran-soilr Architecture: all Version: 1.2.107-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3690 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-igraph, r-cran-assertthat, r-cran-expm, r-cran-sets, r-cran-purrr Suggests: r-cran-fme, r-cran-lattice, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-getopt, r-cran-tinytex Filename: pool/dists/resolute/main/r-cran-soilr_1.2.107-1.ca2604.1_all.deb Size: 1980192 MD5sum: 5211ebeb00a723b5ddba9c9325729f81 SHA1: 130d39e19614596a358a6ab908932011556cfbc0 SHA256: 699e4091f48e8f9d4ef265e528a6dbe4f9646fa0617af169f3cbc5f72a01d5de SHA512: 1328d1d22f0e10121219881705483b71fe02ca2a1a7028bbbd2c81f39a65c3f59d40d7e752127d48cbcc8711b61b245ad2f96bfc05ec5050f2059a5253977263 Homepage: https://cran.r-project.org/package=SoilR Description: CRAN Package 'SoilR' (Models of Soil Organic Matter Decomposition) Functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) and Sierra and others (2014) . Package: r-cran-soilsaltindex Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-soilsaltindex_0.1.0-1.ca2604.1_all.deb Size: 4398678 MD5sum: aa0f7f74903d9d62a47a638f9dc5425a SHA1: eb7abc6611b369b00919d6185a89f75cd9ec9498 SHA256: a6909ce2babd6074e12636dc772c26a0b11ced5ed6740c3072480a1f345bc1b0 SHA512: 1d488f3ad548bd3f5e1ade8641f062ae734b7794936c64e3e2ef64582a5a59724f0da5f0c26e184a7243955bf8ff76b48df70d65a06d1918bdad0d73657645b1 Homepage: https://cran.r-project.org/package=SoilSaltIndex Description: CRAN Package 'SoilSaltIndex' (Soil Salinity Indices Generation using Satellite Data) The developed function generates soil salinity indices using satellite data, utilizing multiple spectral bands such as Blue, Green, Red, Near-Infrared (NIR), and Shortwave Infrared (SWIR1, SWIR2). It computes 24 different salinity indices crucial for monitoring and analyzing salt-affected soils efficiently. For more details see, Rani, et al. (2022). . One of the key features of the developed function is its flexibility. Users can provide any combination of the required spectral bands, and the function will automatically calculate only the relevant indices based on the available data. This dynamic capability ensures that users can maximize the utility of their data without the need for all spectral bands, making the package versatile and user-friendly. Outputs are provided as GeoTIFF file format, facilitating easy integration with GIS workflows. Package: r-cran-soiltaxonomy Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-soildb, r-cran-ape, r-cran-data.tree Filename: pool/dists/resolute/main/r-cran-soiltaxonomy_0.2.8-1.ca2604.1_all.deb Size: 410018 MD5sum: f4a65285251c863e32c74f1d0670880d SHA1: f2422b286aa6c0b0de4961c006360f0de196361a SHA256: 8c437e9eac50f08bda0863bb59a4cdf2e9cec4634eb9bf7e4bf2265bf13c8ba0 SHA512: 7d92a2244ab866c79136456b558284418d30194e4e3f4c85664421b4963719988a0b5b6fd9112ec33b7c285d31615a37ff4db94776d6569d0621b82653c31811 Homepage: https://cran.r-project.org/package=SoilTaxonomy Description: CRAN Package 'SoilTaxonomy' (A System of Soil Classification for Making and Interpreting SoilSurveys) Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup. Package: r-cran-soiltestcorr Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3019 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-data.table, r-cran-ggplot2, r-cran-ggpp, r-cran-nlstools, r-cran-minpack.lm, r-cran-modelr, r-cran-nlraa, r-cran-aiccmodavg, r-cran-smatr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-soiltestcorr_2.2.1-1.ca2604.1_all.deb Size: 1507658 MD5sum: f1b7d78c278aaed9c5c43fe4c5597ab5 SHA1: 4c89440ae9232071a536c6da2ad9d1ebc3e43293 SHA256: 21f6faa5fbd19f83a9e3bba88eaf429f9364183d779a9da443133e48c6d51680 SHA512: d08444fe819d0b03364a5e49f75bdfcb71907cc1ec5b220ebbe7f37fefe16ae29374f4ba3c814cd092e3be90fc7f216ab17df3868fa6409d41c6bf849856823d Homepage: https://cran.r-project.org/package=soiltestcorr Description: CRAN Package 'soiltestcorr' (Soil Test Correlation and Calibration) A compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) ); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) ), 4) the linear-plateau regression (Anderson & Nelson (1975) ), 5) the quadratic-plateau regression (Bullock & Bullock (1994) ), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) ). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects. Package: r-cran-soiltesting Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-soiltesting_0.1.0-1.ca2604.1_all.deb Size: 48282 MD5sum: e062b15737b9c9952c795dc829e7ac76 SHA1: 905cd9fe51fc90a55bd6a3e4fe23e981719844ce SHA256: ac6852e1bde32ab4dfc7ba4dbca800943899fe061cccb4e64b3a3a80813124d4 SHA512: 438ceaacb8c57e193c030a989cbb65a2e94af39886f03bb140102a4e205d89030341e1c48ff86f2a0db66c9c4663f01d75966ed83e6f249fad7e68c073c1809e Homepage: https://cran.r-project.org/package=SoilTesting Description: CRAN Package 'SoilTesting' (Organic Carbon and Plant Available Nutrient Contents in Soil) Testing of soil for the contents of organic carbon, and available macro- and micro-nutrients is a crucial part of soil fertility assessment. This package computes some routinely tested soil properties viz. organic carbon (C), total nitrogen (N), available N, mineral N, available phosphorus (P), available potassium (K), available iron (Fe), available zinc (Zn), available manganese (Mn), available copper (Cu), and available nickel (Ni) in soil based on laboratory analysis data obtained by most commonly followed protocols. Besides, it can also draw standard curves based on absorption/emission vs. concentration data, and give out unknown concentrations from absorption/emission readings. Package: r-cran-soiltexture Architecture: all Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-mass Suggests: r-cran-xtable Filename: pool/dists/resolute/main/r-cran-soiltexture_1.5.3-1.ca2604.1_all.deb Size: 763342 MD5sum: eec5badc37963d45d48aa8d056811a03 SHA1: 6255ff41271416d11e98fefec759930e86968a90 SHA256: fa8c187fff591dda56bd8f7453449cc4a67ae84e29a8aef96c9c28cf8e87682e SHA512: fb1750e615ff594491a8ca8a39b6a6fbccaac908a265c37f9709251a6fac2606e208dfaa0193082cda4927ef25f8081f1987b8315fcf4518e1068a10a12b6cdb Homepage: https://cran.r-project.org/package=soiltexture Description: CRAN Package 'soiltexture' (Functions for Soil Texture Plot, Classification andTransformation) "The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui(). Package: r-cran-soiltillr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-soiltillr_0.1.0-1.ca2604.1_all.deb Size: 113046 MD5sum: 0f1954553d20089cc6b9f40a7884e5c9 SHA1: f3958313093c5910171dcf965976983b135cb512 SHA256: d878a948cab8c96813a5d440ae94943eff912d15b6bffcefe614b50ed72b608d SHA512: e77c20dd2de9a0ee85f90d8ba463c4013e627e0a74d481159e9bbe0a0774d901ccae225a26b54e2b2927b2a27dde96533284bcd0600f971833c6bec0f292f734 Homepage: https://cran.r-project.org/package=soiltillr Description: CRAN Package 'soiltillr' (Analyse Soil Tillage Depth and Erosion Over Time) Provides tools to record, validate, and analyse soil tillage depth and erosion across years and field treatments. Includes functions for year-wise tillage operation summaries, erosion depth tracking, compaction detection, soil loss estimation, and visualisation of temporal changes in tillage and erosion profiles. Methods follow Lal (2001) and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)" . Package: r-cran-soilvae Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prospectr, r-cran-pls Filename: pool/dists/resolute/main/r-cran-soilvae_0.1.9-1.ca2604.1_all.deb Size: 2310254 MD5sum: 4f6ddee82510a2f1405f7334dbd712b3 SHA1: 5fd1b5c5cd94418a22a55c4fae549122d71a446c SHA256: 2e3ef4e6dfaba961064b62c9aa93b06d039ded55d59c7bf290506b71740c0593 SHA512: 386d3738305bcd4c27c289c20ea97cba9549db42e16072d6c5d53df09edeff59b81790723a0189d725fe1976b48db9c5675f0e2d17d52f0f94e6cbb7d3816991 Homepage: https://cran.r-project.org/package=soilVAE Description: CRAN Package 'soilVAE' (Supervised Variational Autoencoder Regression via 'reticulate') Supervised latent-variable regression for high-dimensional predictors such as soil reflectance spectra. The model uses an encoder-decoder neural network with a stochastic Gaussian latent representation regularized by a Kullback-Leibler term, and a supervised prediction head trained jointly with the reconstruction objective. The implementation interfaces R with a 'Python' deep-learning backend and provides utilities for training, tuning, and prediction. Package: r-cran-soilwater Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-raster Filename: pool/dists/resolute/main/r-cran-soilwater_1.0.5-1.ca2604.1_all.deb Size: 35596 MD5sum: 429976d65c5fc99e52e564302b432940 SHA1: a4233d58d1ad9fd757ba20649b5c53f67a283bde SHA256: 138fbcb15cf1c54b44ee29609ac385762ed3ab8c40f5e10aee92eae8db78732b SHA512: dffcfa1bf7ef1d55223229c272a0b1663fd4f77af239e3b2e80726dedefc6a488bc5f33ec73e0ebae9412261b03dbc26cd9db80ca02405bc7c51c834b7e22a86 Homepage: https://cran.r-project.org/package=soilwater Description: CRAN Package 'soilwater' (Implementation of Parametric Formulas for Soil Water Retentionor Conductivity Curve) It implements parametric formulas of soil water retention or conductivity curve. At the moment, only Van Genuchten (for soil water retention curve) and Mualem (for hydraulic conductivity) were implemented. See reference (). Package: r-cran-sojourn.data Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nnet Filename: pool/dists/resolute/main/r-cran-sojourn.data_0.3.0-1.ca2604.1_all.deb Size: 47648 MD5sum: 2c2359ba0f030c98a371bb9da588e3e8 SHA1: 9b23d76c69fe8c763f1c86cc6141bb6744da6439 SHA256: e46a117f5e2ee3ee3375559dd08515b83e1dd96f66c6ba5dc71b277d3a21d769 SHA512: 5a7495a6d1d71f4843ee9c510257c529345e298cb2bd011b802a8063daf572e29672b667e7c0abcc57f18804508dab235f2e32b2e3bd93a9325bfeff4d322827 Homepage: https://cran.r-project.org/package=Sojourn.Data Description: CRAN Package 'Sojourn.Data' (Supporting Objects for Sojourn Accelerometer Methods) Stores objects (e.g. neural networks) that are needed for using Sojourn accelerometer methods. For more information, see Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014) , Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016) , and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) . Package: r-cran-sojourn Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-lubridate, r-cran-nnet, r-cran-pautilities, r-cran-rlang, r-cran-svdialogs, r-cran-zoo Suggests: r-cran-data.table, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sojourn_1.2.1-1.ca2604.1_all.deb Size: 372604 MD5sum: ccca9937804d3d564ae506cc85159964 SHA1: 315f41e3e739ff7c901612fcd980072607112786 SHA256: 4b496764d52e6ec3722548de59d847f62333c90d8e971e2d6771a6747b8f8de7 SHA512: a071fac7acfdc78f50756af5af0c6d4e8e565329b893c977fb43c344af89cc170a029d9ffe1e9a66c31871efa62bb5cd6d854402a61101daa178014b24a3bde5 Homepage: https://cran.r-project.org/package=Sojourn Description: CRAN Package 'Sojourn' (Apply Sojourn Methods for Processing ActiGraph AccelerometerData) Provides a simple way for utilizing Sojourn methods for accelerometer processing, as detailed in Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014) , Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016) , and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) . Package: r-cran-sokoban Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sokoban_0.1.0-1.ca2604.1_all.deb Size: 31594 MD5sum: aa750766e21ea8faa74c8742d71a0c49 SHA1: 4f54b70e751ccf15e2b8b70358e2afbcd555d3a4 SHA256: fb40e5942dfa476da5d61ce43881f74f9528fe073d173ca2bfce331db26a7577 SHA512: 37e3408f64b4448771e2dd3b9a56eb3a71f5eb26588c08e8d5c11f7d4fb6ae0ce6ee4fa8c037edc2183fae9316cb87529a40e47d2db8ae267fde98976c093985 Homepage: https://cran.r-project.org/package=sokoban Description: CRAN Package 'sokoban' (Sokoban Game) Interactively play a game of sokoban ,which has nine game levels.Sokoban is a type of transport puzzle, in which the player pushes boxes or crates around in a warehouse, trying to get them to storage locations. 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Different versions of the algorithm are implemented, for numeric and relational data and for contingency tables as described, respectively, in Kohonen (2001) , Olteanu & Villa-Vialaneix (2005) and Cottrell et al (2004) . The package also contains many plotting features (to help the user interpret the results), can handle (and impute) missing values and is delivered with a graphical user interface based on 'shiny'. 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Package: r-cran-somenv Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlist, r-cran-kohonen, r-cran-shiny, r-cran-dplyr, r-cran-plyr, r-cran-openair, r-cran-colourpicker, r-cran-shinycssloaders, r-cran-shinycustomloader Filename: pool/dists/resolute/main/r-cran-somenv_1.1.2-1.ca2604.1_all.deb Size: 150594 MD5sum: 6d1e3d9b763d32995aad6bec072946f7 SHA1: f4166541187c0c9e2af8b6b0177508a3a560e29a SHA256: 51e07436d2e5ce46db5cd3f8c2e715a571e7883f72129ad404098a2ec671f8a8 SHA512: 4d3146d92c3f5f66766f13fe57737b6cbc3cf88b052173c9a26c170fcdac9e647ae9c0d4f9a15f74382ab2047a382a89b0bf8d9dd0ad251f80e8bd5c45965611 Homepage: https://cran.r-project.org/package=SOMEnv Description: CRAN Package 'SOMEnv' (SOM Algorithm for the Analysis of Multivariate EnvironmentalData) Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) are provided. Package: r-cran-somhca Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-kohonen, r-cran-awesom, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-maptree, r-cran-fpc Filename: pool/dists/resolute/main/r-cran-somhca_0.4.0-1.ca2604.1_all.deb Size: 60626 MD5sum: f465924a5b7d26eb8f6a6f1207d951a2 SHA1: acc2090e45616e0edaa00bfdb7c0f65b77982610 SHA256: 1ae7a755b2981507aabc770ce6b1527afdae9da33ed330d1c7621fa4646e6234 SHA512: 481f31fa0e1c936ddb34808b28590c2eacad4bd707e1640febf4a66d127c5097e363974403ea31f0f1ef97f4a117bb410240662c9cd138be0f3bae975f10f1f3 Homepage: https://cran.r-project.org/package=somhca Description: CRAN Package 'somhca' (Self-Organising Maps Coupled with Hierarchical Cluster Analysis) Implements self-organising maps combined with hierarchical cluster analysis (SOM-HCA) for clustering and visualization of high-dimensional data. The package includes functions to estimate the optimal map size based on various quality measures and to generate a model using the selected dimensions. It also performs hierarchical clustering on the map nodes or other data to group similar units. Documentation about the SOM-HCA method is provided in Pastorelli et al. (2024) . 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Output depends on the method chosen. For the Molecular Mixing Model, a measurement of the fitting quality is given by its R-factor. For more details see: . Package: r-cran-sonar Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sonar_1.0.2-1.ca2604.1_all.deb Size: 203780 MD5sum: 46599239a881ba768a3c605683d6e065 SHA1: 9f62abc1192b1f50082d4596856a74611c02f56a SHA256: 13902acfc97a9a5b41f7514c01aeeef178cf63dae1a8e9feb23f03573c0e7458 SHA512: 8f483df39e2b13b6703e1219b086e7648a897dec90533ba9184400ad57bf1b23c7696cb6aab62e01af2f6d19980ee6fb4c89db334dbb9e79293bc6e2f5248788 Homepage: https://cran.r-project.org/package=sonar Description: CRAN Package 'sonar' (Fundamental Formulas for Sonar) Formulas for calculating sound velocity, water pressure, depth, density, absorption and sonar equations. Package: r-cran-songevo Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-geosphere, r-cran-lattice, r-cran-sp Suggests: r-cran-reshape2, r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-songevo_1.0.0-1.ca2604.1_all.deb Size: 619616 MD5sum: 8930ffcc246b88d6a373904b2f82acc7 SHA1: 5bc1c97d02232f9e8f766f9214b98e9bdb431c29 SHA256: 124528f56d2987a2d483b31547e84db972bea733a6370018de63005b3b7a9733 SHA512: 4091dfcc1bc8320e9d15a1f0512ea01704b2aecfaebbcf0d960bba5f121128c7f8948a30a735a967ad3736cd18a166047622c3879d9e7af808f36e5ff81c76b2 Homepage: https://cran.r-project.org/package=SongEvo Description: CRAN Package 'SongEvo' (An Individual-Based Model of Bird Song Evolution) Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 'SongEvo' is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing. Package: r-cran-soniclength Architecture: all Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-soniclength_1.4.7-1.ca2604.1_all.deb Size: 387094 MD5sum: 53da1e0b8e9ca87961b67af1ade21c67 SHA1: 6785746272399a512456dfbee7f5ca4b291638e9 SHA256: 018a6227748abdd0dd2aff312a0795de797b21a4286f65a1138e65f84e6b72b5 SHA512: 36fa37fcc4e590ad3a6511da81f51ed44e10de27847328434e99e4a50269500552d14ff9f35dd554328861e8b9dfcc0ef8d5b5774cd8d4d6a9adb0c27e3e00b1 Homepage: https://cran.r-project.org/package=sonicLength Description: CRAN Package 'sonicLength' (Estimating Abundance of Clones from DNA Fragmentation Data) Estimate the abundance of cell clones from the distribution of lengths of DNA fragments (as created by sonication, whence `sonicLength'). The algorithm in "Estimating abundances of retroviral insertion sites from DNA fragment length data" by Berry CC, Gillet NA, Melamed A, Gormley N, Bangham CR, Bushman FD. Bioinformatics; 2012 Mar 15;28(6):755-62 is implemented. The experimental setting and estimation details are described in detail there. Briefly, integration of new DNA in a host genome (due to retroviral infection or gene therapy) can be tracked using DNA sequencing, potentially allowing characterization of the abundance of individual cell clones bearing distinct integration sites. The locations of integration sites can be determined by fragmenting the host DNA (via sonication or fragmentase), breaking the newly integrated DNA at a known sequence, amplifying the fragments containing both host and integrated DNA, sequencing those amplicons, then mapping the host sequences to positions on the reference genome. The relative number of fragments containing a given position in the host genome estimates the relative abundance of cells hosting the corresponding integration site, but that number is not available and the count of amplicons per fragment varies widely. However, the expected number of distinct fragment lengths is a function of the abundance of cells hosting an integration site at a given position and a certain nuisance parameter. The algorithm implicitly estimates that function to estimate the relative abundance. Package: r-cran-sonicscrewdriver Architecture: all Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hms, r-cran-jsonlite, r-cran-mime, r-cran-rdpack, r-cran-seewave, r-cran-stringi, r-cran-suncalc, r-cran-tuner Suggests: r-cran-av, r-cran-covr, r-cran-devtools, r-cran-googlecloudstorager, r-cran-googlelanguager, r-cran-knitr, r-cran-pbapply, r-cran-plotrix, r-cran-reticulate, r-cran-rmarkdown, r-cran-soundecology, r-cran-spelling, r-cran-testthat, r-cran-waveletcomp Filename: pool/dists/resolute/main/r-cran-sonicscrewdriver_0.0.7-1.ca2604.1_all.deb Size: 2070612 MD5sum: 5115028b5f93604b7b2737cc761bdf05 SHA1: d07076bc8cb4ddbea850c9c9745c068302e3671a SHA256: a114f5a6680a7d7fbf7a099b836a601b38f12745a384efa0ceb1ca8ef92d3484 SHA512: 20d3ef5e4841bf8c91a308a93da91609b3b1cfc2ac531ada708715b625546a24ee245260094487d264045f6b0700409b1ef0cc851a0295f553638bd0c5f85116 Homepage: https://cran.r-project.org/package=sonicscrewdriver Description: CRAN Package 'sonicscrewdriver' (Bioacoustic Analysis and Publication Tools) Provides tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible. Package: r-cran-sonify Architecture: all Version: 0.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tune Filename: pool/dists/resolute/main/r-cran-sonify_0.0-1-1.ca2604.1_all.deb Size: 20492 MD5sum: 13bcdf4803ea59af00309655fe9beff7 SHA1: ba7ae7f6df3ac3f8093e87146de9c9b823bbad24 SHA256: f7832bb7f2f44205c50313e25085f12ce924c70e8684c65c57c1185bbf945406 SHA512: 8aa466b7f2a1c91c49a8225c001ea787166ec35c5515f085cf979c026b943485698530f9df0e726ddb7483d57f27b95235e25e6fbb2bfe749187be734b17ca4d Homepage: https://cran.r-project.org/package=sonify Description: CRAN Package 'sonify' (Data Sonification - Turning Data into Sound) Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired. Package: r-cran-sono Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-desctools, r-cran-ggplot2, r-cran-rdpack, r-cran-rje Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sono_1.2-1.ca2604.1_all.deb Size: 170758 MD5sum: d60124b4bf330e52d3ee30ad95f92610 SHA1: d52f8c40df763c0ecb4ced54d537007315d2e500 SHA256: f64dffffa10a916d5c35fbb13a4bec5f240b4fee90c0851de3574ff0e86b2993 SHA512: 7473106156f735aea8081db8a804898e478ccf0873d13588d5a101b684c57cb37b154f8da99b44f8595865438ddd0a1cb6182b53c686467f6ae775120b55143b Homepage: https://cran.r-project.org/package=SONO Description: CRAN Package 'SONO' (Scores of Nominal Outlyingness (SONO)) Computes scores of outlyingness for data sets consisting of nominal variables and includes various evaluation metrics for assessing performance of outlier identification algorithms producing scores of outlyingness. The scores of nominal outlyingness are computed based on the framework of Costa and Papatsouma (2025) . Package: r-cran-sooty Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2914 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-curl, r-cran-s7, r-cran-tibble Suggests: r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-sooty_0.6.1-1.ca2604.1_all.deb Size: 1450158 MD5sum: 4d1d9da08d27d3ca70c37442945827db SHA1: 4bb1e6df7e8d74b32abfc8eea7cc0d9f833d85dd SHA256: b82f891981ab18902be46004fd2063cfa2aa2f2ab5402cb674c88adfe9161027 SHA512: 9e9694f882a5e57a77fa66668ddfc7ca0912ac6fed9b7c8dada4443f3240913a65c97feaef1f7ac8aa1fd0d0e2bc23731f27f514e4e68df2cbaa12c104a7b113 Homepage: https://cran.r-project.org/package=sooty Description: CRAN Package 'sooty' (Data Source Catalogues Online for Southern Ocean EcosystemResearch) Obtains lists of files of remote sensing collections for Southern Ocean surface properties. Commonly used data sources of sea surface temperature, sea ice concentration, and altimetry products such as sea surface height and sea surface currents are cached in object storage on the Pawsey Supercomputing Research Centre facility. Patterns of working to retrieve data from these object storage catalogues are described. The catalogues include complete collections of datasets Reynolds et al. (2008) "NOAA Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1" , Spreen et al. (2008) "Artist Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice concentration" . In future releases helpers will be added to identify particular data collections and target specific dates for earth observation data for reading, as well as helpers to retrieve data set citation and provenance details. This work was supported by resources provided by the Pawsey Supercomputing Research Centre with funding from the Australian Government and the Government of Western Australia. This software was developed by the Integrated Digital East Antarctica program of the Australian Antarctic Division. Package: r-cran-sop Architecture: all Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-spats Filename: pool/dists/resolute/main/r-cran-sop_1.0-1-1.ca2604.1_all.deb Size: 172248 MD5sum: a6a7b35246814559c56672be54dfc655 SHA1: e2ef10183d6de9e7d2a82858e734b7e671111ae0 SHA256: 604f8ae134df756b0912fcde3be2cf806c416c7eecde18d1ab6b08a38f13c197 SHA512: c46c9b40952dcab89095e6de9608b476b5ac88224b4e1172d93097ac247caeffe1043d8546b58765464a3ef3779fab4fe52d4c164d6960bf98d68cf95b5350e5 Homepage: https://cran.r-project.org/package=SOP Description: CRAN Package 'SOP' (Generalised Additive P-Spline Regression Models Estimation) Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) ; Rodriguez-Alvarez et al. (2019) , and Eilers and Marx (1996) . 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The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly. Package: r-cran-soql Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-soql_0.1.1-1.ca2604.1_all.deb Size: 34124 MD5sum: 0e5de93db3e4aff6d9f77198668f3d1b SHA1: b992745d7dcf56e9ffce642bebc3a9c23d089ae6 SHA256: 5abfdfe4ee28f60f134b24bc4baa4caa99cb6bc42349cc17c2d6ba0cba2444d7 SHA512: 45cd6ef0876e4f1da18ad48daefca962328240c6b1758a8e0cad37f7e9dd2644487d2af4785b3c2e03125bb83af93e17a789b15d7bb47838dd65be357f71f118 Homepage: https://cran.r-project.org/package=soql Description: CRAN Package 'soql' (Helps Make Socrata Open Data API Calls) Used to construct the URLs and parameters of 'Socrata Open Data API' calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes. Package: r-cran-sor Architecture: all Version: 0.23.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-sor_0.23.1-1.ca2604.1_all.deb Size: 144638 MD5sum: aad25d54fe4a0d3f75c7c9c49cfd3186 SHA1: e5ccaa71bb278385d3ca1c12e7690632d3a23978 SHA256: 753cd3f9d7bb912fd52c1147e4e190837ec432b3cdb34ca1569c376ad1b7f953 SHA512: a3b5966314e30c7e2b474a042e7248d7b6fc1219f0421c22323109265fa017aa002f1cafcc181e53c91fe7fbd67a4a3e74f5272bb465f14eb9f17d22a4ac6fa8 Homepage: https://cran.r-project.org/package=SOR Description: CRAN Package 'SOR' (Estimation using Sequential Offsetted Regression) Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique. Includes support for binary, count, and continuous data. The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates. The second regression uses this estimated probability to calculate the mean population response given covariates. Package: r-cran-sorocs Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mcmcpack, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sorocs_0.1.0-1.ca2604.1_all.deb Size: 70784 MD5sum: 234a9b7f49a388e1fd819bc215df44eb SHA1: 4c93dc1da90fe61a968f8f8ef3a1bc516d927825 SHA256: 6c3326abc6a7d4b078e3e63bb2ebf1bf82dace20d46a6c6cfc93febceff12c64 SHA512: ccc4e707937587688bf6b5b753ddaaeb633761d99c1c9ed1d5531687a5b2661896bc423b517f0c8b60fc0e29e895e5cfb249f9cfc240845a1225319bb305308f Homepage: https://cran.r-project.org/package=sorocs Description: CRAN Package 'sorocs' (A Bayesian Semiparametric Approach to Correlated ROC Surfaces) A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. . 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The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) . 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Package: r-cran-sos Architecture: all Version: 2.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brew Suggests: r-cran-rodbc, r-cran-writexls, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sos_2.1-8-1.ca2604.1_all.deb Size: 173500 MD5sum: a9e9e204595d540aca12ca8930c7fdc6 SHA1: 8ba82d6fb6fe864c8301f1c4511ae7de818128d6 SHA256: aa0606d8d7bedb130ff9a4719ca83bfb922bf7e0b705c6bf44ba3ad439e10588 SHA512: b8b19154581bcd92f28777a850172d68d184c518c0d85e2175e10fd7ff94ddad69e03a9bff8a9030b5e19acf972001b552b1772a5ae0c5f19a64be78893a0ec5 Homepage: https://cran.r-project.org/package=sos Description: CRAN Package 'sos' (Search Contributed R Packages, Sort by Package) Search contributed R packages, sort by package. Package: r-cran-sotkanet Architecture: all Version: 0.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-lubridate, r-cran-refmanager, r-cran-digest, r-cran-frictionless, r-cran-httr2, r-cran-magrittr Suggests: r-cran-remotes, r-cran-ggplot2, r-cran-knitr, r-cran-testthat, r-cran-roxygen2, r-cran-markdown, r-cran-kableextra, r-cran-rmarkdown, r-cran-covr, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-sotkanet_0.10.1-1.ca2604.1_all.deb Size: 148876 MD5sum: 359cad5067c0baefc46befdf04ad87b2 SHA1: 7586ab09b4b390af03e9aef3af2822aa436ac23f SHA256: a536c1e0c03b50d725778661a61092eb2158566195df6d6a9d09830af93f4da2 SHA512: b0165fb752cefa6ad964f373d89b3b0afa8a38c52fe6e79ab88164ae34e3309f69e8269c91c60e373498b9be0a53c32c39b957eb1151af3084395d0ced932ff4 Homepage: https://cran.r-project.org/package=sotkanet Description: CRAN Package 'sotkanet' (Sotkanet Open Data Access and Analysis) Access statistical information on welfare and health in Finland from the Sotkanet open data portal . Package: r-cran-sotu Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3954 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sotu_1.0.4-1.ca2604.1_all.deb Size: 4014182 MD5sum: 0918403ce87d9d86b88c7546718ec76c SHA1: e39662b26bbc33ab5b2e31518856277b5af3890b SHA256: 7a2195e5f8062541e398c822b38f59ee809ef158d1e3563de9b40f773761f211 SHA512: 3c9a5e64f75680e65354c0270ff7f8eb5e6f9b0a55b400c711b17d6eccb303215b85374b1f8e8411483ca8994f80212a0177b8d3f3ce31507ff13e8b8aa2f0d4 Homepage: https://cran.r-project.org/package=sotu Description: CRAN Package 'sotu' (United States Presidential State of the Union Addresses) The President of the United States is constitutionally obligated to provide a report known as the 'State of the Union'. The report summarizes the current challenges facing the country and the president's upcoming legislative agenda. While historically the State of the Union was often a written document, in recent decades it has always taken the form of an oral address to a joint session of the United States Congress. This package provides the raw text from every such address with the intention of being used for meaningful examples of text analysis in R. The corpus is well suited to the task as it is historically important, includes material intended to be read and material intended to be spoken, and it falls in the public domain. As the corpus spans over two centuries it is also a good test of how well various methods hold up to the idiosyncrasies of historical texts. Associated data about each address, such as the year, president, party, and format, are also included. Package: r-cran-sound Architecture: all Version: 1.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sound_1.4.6-1.ca2604.1_all.deb Size: 148708 MD5sum: f3f038e8ae9f30c413381280f81bb50c SHA1: 1e950d1f9d68381de8ec9c002ef86cdf7a412f9d SHA256: 6e25d5587303393744e5ecf5ba10ce7f6a2758c1deda741058915c63d8a10558 SHA512: 3a45c8b582f09e215f8025637c20528e0e1358425a817bc596064fd56151fe3d3904f4406aff68268146183fa2a0287f1041213b805fd7c65d47fa1858bbed70 Homepage: https://cran.r-project.org/package=sound Description: CRAN Package 'sound' (A Sound Interface for R) Basic functions for dealing with wav files and sound samples. Package: r-cran-soundclass Architecture: all Version: 0.0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinybs, r-cran-htmltools, r-cran-seewave, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-rsqlite, r-cran-signal, r-cran-tuner, r-cran-zoo, r-cran-magrittr, r-cran-shinyfiles, r-cran-shiny, r-cran-generics, r-cran-keras, r-cran-shinyjs Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-soundclass_0.0.9.2-1.ca2604.1_all.deb Size: 133412 MD5sum: 6847609810de8cd73ae0d28ca257e29e SHA1: ddcc13390108c2bab7b433732e585fa31ec3c484 SHA256: 21d891d871ec96e5f617d5a073b63439123cae9f1be34c2accf263e28b7c8912 SHA512: 9d4c5ee21d384bccb72292a831954d275414d0523761e981d3ba432f77520cd4af1cfc05f7bcef2bcaabc1ce20ff952ed444dc1dc54f3afc744fe6ad7cf71fd0 Homepage: https://cran.r-project.org/package=soundClass Description: CRAN Package 'soundClass' (Sound Classification Using Convolutional Neural Networks) Provides an all-in-one solution for automatic classification of sound events using convolutional neural networks (CNN). The main purpose is to provide a sound classification workflow, from annotating sound events in recordings to training and automating model usage in real-life situations. Using the package requires a pre-compiled collection of recordings with sound events of interest and it can be employed for: 1) Annotation: create a database of annotated recordings, 2) Training: prepare train data from annotated recordings and fit CNN models, 3) Classification: automate the use of the fitted model for classifying new recordings. By using automatic feature selection and a user-friendly GUI for managing data and training/deploying models, this package is intended to be used by a broad audience as it does not require specific expertise in statistics, programming or sound analysis. Please refer to the vignette for further information. Gibb, R., et al. (2019) Mac Aodha, O., et al. (2018) Stowell, D., et al. (2019) LeCun, Y., et al. (2012) . Package: r-cran-soundecology Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-oce, r-cran-ineq, r-cran-vegan, r-cran-tuner, r-cran-seewave Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-soundecology_1.3.3-1.ca2604.1_all.deb Size: 824130 MD5sum: 16a5de72c9272a6f1f0cc781c7cee2d8 SHA1: e38d5d36eef86e4411b2943d132006b9aa898f9b SHA256: db5e7b61d377842d80500b019fb62e29025bd9f3ef0f5eb23f52a8ddfab23fa2 SHA512: e314a219bfad49fdc001bb580d37412028d5d4e37b01a97b63be90becdf295ed453f810cbe54b377ac4a890ad147bb6611e5a85321db50978793781cf026a6c6 Homepage: https://cran.r-project.org/package=soundecology Description: CRAN Package 'soundecology' (Soundscape Ecology) Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings. Package: r-cran-soundgen Architecture: all Version: 2.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tuner, r-cran-seewave, r-cran-zoo, r-cran-mvtnorm, r-cran-dtw, r-cran-phontools, r-cran-signal, r-cran-shiny, r-cran-shinyjs, r-cran-bslib, r-cran-foreach, r-cran-doparallel, r-cran-nonlineartseries, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-soundgen_2.9.0-1.ca2604.1_all.deb Size: 2038856 MD5sum: e4c91d184366f6a77d3bdd9dd368e87f SHA1: f2a7e974f11689ea64391100b498fc3d0f29bc47 SHA256: 82e88369603ef487c5ffda38447dda27118ec27109e1fd7b8faedbfa4454e29a SHA512: 4d884799669dedb1d893cfc69344036a9504cc01462c13825dbdb90762be636337ea2a8e1155a26261f15b340f07b8a813f9c8370dc855ff3a95e623f14e2bec Homepage: https://cran.r-project.org/package=soundgen Description: CRAN Package 'soundgen' (Sound Synthesis and Acoustic Analysis) Performs parametric synthesis of sounds with harmonic and noise components such as animal vocalizations or human voice. Also offers tools for audio manipulation and acoustic analysis, including pitch tracking, spectral analysis, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for synthesizing and annotating audio, manually correcting pitch contours, and measuring formant frequencies. Reference: Anikin (2019) . Package: r-cran-soundshape Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-geomorph, r-cran-plot3d, r-cran-reshape2, r-cran-seewave, r-cran-tuner, r-cran-stringr Suggests: r-cran-vegan, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-soundshape_1.3.2-1.ca2604.1_all.deb Size: 1012392 MD5sum: 010ab5212e59d139f3ad680d099a1716 SHA1: fb1564493090f28f281a668752c68c986dccbb4c SHA256: 7a300c4e6167f8c9b7754d63f7343ca47dd6d0361d07d932d719de01936dd54d SHA512: 6f32a67ffb78059c1b4ab2cd1a049429bd479d728a1b443ec4024f8bbf313afe11c857ed5ee24d0f697aea3b54427ef2eae04cf35398bab6f41153afb8c04b1f Homepage: https://cran.r-project.org/package=SoundShape Description: CRAN Package 'SoundShape' (Sound Waves Onto Morphometric Data) Implement a promising, and yet little explored protocol for bioacoustical analysis, the eigensound method by MacLeod, Krieger and Jones (2013) . Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (2021) and check 'SoundShape' repository on GitHub for news and updates . Package: r-cran-soupx Architecture: all Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5829 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-seurat Suggests: r-cran-knitr, r-cran-rstan, r-bioc-dropletutils, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/resolute/main/r-cran-soupx_1.6.2-1.ca2604.1_all.deb Size: 5088780 MD5sum: 6b8b4909b917f2e01c5024ef76e582e7 SHA1: 21ed708363d1b48d96fb75430d9069c056aec9ae SHA256: 515c26d6ba556b09257f3a603e5c25f77ab034081f70e14cdd57af6d62b23dc5 SHA512: 48211fa408b0bbd7f15d43cb0040162fb42fbd98324ae634a691b4d97f192fd98ccf0d05d6bb48f06d33c6bac83847b192c407e1fc53b517d8f19af5d8c6b09a Homepage: https://cran.r-project.org/package=SoupX Description: CRAN Package 'SoupX' (Single Cell mRNA Soup eXterminator) Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments. 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Package: r-cran-spades.core Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7091 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quickplot, r-cran-reproducible, r-cran-cli, r-cran-data.table, r-cran-fs, r-cran-igraph, r-cran-lobstr, r-cran-qs2, r-cran-require, r-cran-rlang, r-cran-terra, r-cran-whisker Suggests: r-cran-archive, r-cran-circstats, r-cran-codetools, r-cran-xml2, r-cran-xmlparsedata, r-cran-covr, r-cran-dbi, r-cran-diagrammer, r-cran-future, r-cran-future.callr, r-cran-ggplot2, r-cran-ggplotify, r-cran-gitcreds, r-cran-googledrive, r-cran-httr, r-cran-httr2, r-cran-knitr, r-cran-lattice, r-cran-lme4, r-cran-logging, r-cran-magrittr, r-cran-pkgload, r-cran-png, r-cran-rcolorbrewer, r-cran-raster, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rsqlite, r-cran-rstudioapi, r-cran-sp, r-cran-spades.tools, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-spades.core_3.1.0-1.ca2604.1_all.deb Size: 3697960 MD5sum: e2dcfb7aee0b18e16319bee83f921ee5 SHA1: 4dfc5d8ceffa5abb6ce7cfc2917f8cb4ec9acd1f SHA256: 779fbb6cfad7dedcd1c0e66f26c8925790d188affaeadd0ca32c6d4d5ecbf516 SHA512: 0a587ad12f6e32692cea2ed43243b9b7749880250a2a2876cf010c0e2a70293e10bca7b8b582d973eae21a7cdaf1e2d77a833af4a73bd6977563486c0f713aef Homepage: https://cran.r-project.org/package=SpaDES.core Description: CRAN Package 'SpaDES.core' (Core Utilities for Developing and Running Spatially ExplicitDiscrete Event Models) Provides the core framework for a discrete event system to implement a complete data-to-decisions, reproducible workflow. 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Package: r-cran-spades Architecture: all Version: 2.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quickplot, r-cran-reproducible, r-cran-spades.core, r-cran-spades.tools Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spades_2.0.11-1.ca2604.1_all.deb Size: 204098 MD5sum: 0ebe5904ba6936d9b513c3c8b25ff54c SHA1: b209df3acb4d026f91ec4cd3b6b5e32adf4ffc02 SHA256: e291998704e4e4e5d5b3ba160ea7fe0ef87a94d1978054c007f1ad12fe042d7f SHA512: c9351fd9581d1aa31266e6192c8b87218286bc0e1e46c6565d026bb9442659c6ce5bc1fa5670ae19cf683c3da9e4104b47d5af37d8c3035efa837efca3c354fe Homepage: https://cran.r-project.org/package=SpaDES Description: CRAN Package 'SpaDES' (Develop and Run Spatially Explicit Discrete Event SimulationModels) Metapackage for implementing a variety of event-based models, with a focus on spatially explicit models. 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Package: r-cran-spagmix Architecture: all Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-abind, r-cran-sparr, r-cran-mvtnorm, r-cran-spatstat.geom, r-cran-spatstat.random Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-spagmix_0.4-2-1.ca2604.1_all.deb Size: 141842 MD5sum: 62964db78940b674df38419721540eaa SHA1: 0354928a62936446a64413c323292af6ca70de45 SHA256: 057c40b0174e8c61fbd66b14e82775d0282de41a98d223ec7ea19e05257e1db9 SHA512: d143134b17e536bee8733d21899ed6985ebaa4e8620c433097fb581c6505a275aa4529b6a34c265341a66c6578c0ef1db4799d884e5d0a4e786eadec26cd015f Homepage: https://cran.r-project.org/package=spagmix Description: CRAN Package 'spagmix' (Artificial Spatial and Spatio-Temporal Densities on BoundedWindows) Simple utilities to design and generate density functions on bounded regions in space and space-time, and simulate independent, identically distributed data therefrom. See Davies & Lawson (2019) for example. Package: r-cran-spanish Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-xml2 Suggests: r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-spanish_0.4.2-1.ca2604.1_all.deb Size: 41306 MD5sum: ed9444a22f5671875ee130cc468dbd2b SHA1: f73dde9cf25cb92591e1e6733f03fec0f93d63f6 SHA256: b9a476de8c52668715ed0ab32deed2cf025f4305a3da2c90c31b7d4abf82fd37 SHA512: 42191603c37b97304cc7db0d8e58501ca9ded58dda997b444d29e3746b3c706470da4d34f7d0f0fcdb56e8750208aa5130cb4dbc07c0a1b422ecc6dbad689d9e Homepage: https://cran.r-project.org/package=spanish Description: CRAN Package 'spanish' (Translate Quantities from Strings to Integer and Back. MiscFunctions on Spanish Data) Character vector to numerical translation in Euros from Spanish spelled monetary quantities. 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Package: r-cran-spanishoddata Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dbi, r-cran-digest, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-memoise, r-cran-openssl, r-cran-parallelly, r-cran-paws.storage, r-cran-purrr, r-cran-rdpack, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-xml2 Suggests: r-cran-cli, r-cran-flowmapblue, r-cran-flowmapper, r-cran-furrr, r-cran-future, r-cran-future.mirai, r-cran-hexsticker, r-cran-mapspain, r-cran-quarto, r-cran-remotes, r-cran-scales, r-cran-testthat, r-cran-tidyverse, r-cran-withr Filename: pool/dists/resolute/main/r-cran-spanishoddata_0.2.4-1.ca2604.1_all.deb Size: 2741130 MD5sum: b7caa00a95342deb81bce5152d0b41d1 SHA1: 46dc168b9d9b4a6eee8db431a23a8d1eeb2bd363 SHA256: 8b8cdb4bb61804380fc74731a54a80739e999de25798c28842a561066be0640c SHA512: df7fea1f5b4c8fe769236f126c6603bd1889278fda507f80dfe380946c09c74a70cb0a72f5c06f9192d5930b9ffa82cc0c9c8a9126043bb736d00d25931c8a97 Homepage: https://cran.r-project.org/package=spanishoddata Description: CRAN Package 'spanishoddata' (Get Spanish Origin-Destination Data) Gain seamless access to origin-destination (OD) data from the Spanish Ministry of Transport, hosted at . 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Package: r-cran-sparklyr.nested Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparklyr, r-cran-jsonlite, r-cran-listviewer, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-reactr Filename: pool/dists/resolute/main/r-cran-sparklyr.nested_0.0.4-1.ca2604.1_all.deb Size: 49286 MD5sum: 16ced93357bdc1b90cc7481e15ebf610 SHA1: 5c84c2d44f7025e2f09e438b98d466654a128602 SHA256: 8d648822212ec1686983bcbf05cab882c1f46ead3920e6f9c96f828eadc8b170 SHA512: 5d9af6a01e761cec2377e79aab4912573ec325de4937181c3c5cf76e83668b4c688229d2a89b00c5b8570adc57b8e9106998abc731f564b9f378542dbe51ca99 Homepage: https://cran.r-project.org/package=sparklyr.nested Description: CRAN Package 'sparklyr.nested' (A 'sparklyr' Extension for Nested Data) A 'sparklyr' extension adding the capability to work easily with nested data. Package: r-cran-sparklyr Architecture: all Version: 1.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4750 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-generics, r-cran-globals, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-rstudioapi, r-cran-tidyr, r-cran-tidyselect, r-cran-uuid, r-cran-vctrs, r-cran-withr, r-cran-xml2 Suggests: r-cran-arrow, r-cran-broom, r-cran-diffobj, r-cran-foreach, r-cran-ggplot2, r-cran-iterators, r-cran-janeaustenr, r-cran-lahman, r-cran-mlbench, r-cran-nnet, r-cran-nycflights13, r-cran-r6, r-cran-r2d3, r-cran-rcurl, r-cran-reshape2, r-cran-shiny, r-cran-parsnip, r-cran-testthat, r-cran-rprojroot Filename: pool/dists/resolute/main/r-cran-sparklyr_1.9.4-1.ca2604.1_all.deb Size: 3804732 MD5sum: cbaf86db3f5fc30596461e0c458f3258 SHA1: 55bc78b3a0f616971aa35c9ce8073fc6da3173e1 SHA256: 54dcc99bdba910cc62a07eb50847e2d7c954b6605fb352410f93054a0d978b05 SHA512: 7c34b6853bc95e392be7b219bac5f3f3c56a8c235e349aff4a39a5898cc5e965aeade40ac3080f24523a01b199e52cc42c7d3d2a70fb2022c8cc3faf4464da10 Homepage: https://cran.r-project.org/package=sparklyr Description: CRAN Package 'sparklyr' (R Interface to Apache Spark) R interface to Apache Spark, a fast and general engine for big data processing, see . This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms. Package: r-cran-sparktex Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sparktex_0.1-1.ca2604.1_all.deb Size: 112142 MD5sum: 27fc9cbfb896b41349d057ae4557e6c6 SHA1: 28b5afe98f76d740cb42893514adcf424175130e SHA256: ea065bb96236773f0185f5183093d164bdbad4801fb2e6dcbc503cbbe314e783 SHA512: 8a19cd945ac6642a94adbd5f74944258e0d6ce07b3feff27edd16a752c75a83b06aff37833756e3b44fddbad5a4ca56b205e24af240f71599fe4b5c26f33dfea Homepage: https://cran.r-project.org/package=sparktex Description: CRAN Package 'sparktex' (Generate LaTeX sparklines in R) Generate syntax for use with the sparklines package for LaTeX. Package: r-cran-sparktf Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparklyr Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-sparktf_0.1.0-1.ca2604.1_all.deb Size: 19396 MD5sum: 53c1e9932ce97d8dc9e9f7a86d9623c5 SHA1: 7d61971f5d2bea9a47a9a16c7bf9054cdd56ff47 SHA256: ff5cee76d729a9106dd495ee870b936a26b73d5c79212074a8ef1cb515fa0036 SHA512: 9e1591cd2601caae9cf1561bb888a2c6bc24ebe4722da6c2f01b39286fd451c53e99d6110ee00c6f0572dc67de96d63e90c3225baeab7c4684b523734f6bfac7 Homepage: https://cran.r-project.org/package=sparktf Description: CRAN Package 'sparktf' (Interface for 'TensorFlow' 'TFRecord' Files with 'Apache Spark') A 'sparklyr' extension that enables reading and writing 'TensorFlow' TFRecord files via 'Apache Spark'. Package: r-cran-sparkxgb Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparklyr, r-cran-rlang, r-cran-magrittr, r-cran-vctrs, r-cran-fs Suggests: r-cran-dplyr, r-cran-purrr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-sparkxgb_0.2.1-1.ca2604.1_all.deb Size: 70556 MD5sum: 8625e8e6dd8840f7c6eb8899b88ad751 SHA1: fe4255dbe5a24c92040e623af63129ae0975f76d SHA256: 63b64f46318da35cbdf321c4fbca4ce04b5ed30abd87d21840a3c7112141f10e SHA512: 16aa2b57db87ba5811a56069887a9996ecb2dc11d877ff7f747f9dff7d48549c3323eb18f46b8cf1490b534e793156392ad1637bfeae6fed8a22f19f698ed88a Homepage: https://cran.r-project.org/package=sparkxgb Description: CRAN Package 'sparkxgb' (Interface for 'XGBoost' on 'Apache Spark') A 'sparklyr' extension that provides an R interface for 'XGBoost' on 'Apache Spark'. 'XGBoost' is an optimized distributed gradient boosting library. Package: r-cran-sparqlr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lintr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-styler, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparqlr_0.1.0-1.ca2604.1_all.deb Size: 64188 MD5sum: 2ee90f438ff726ca678a87470cfde282 SHA1: ba28c28b1386a4b542bdac79c9c4209f7ffb9e68 SHA256: 6add2caf3bdf1d4852234d2418d94612d34f89892800db784cb95171c34e004c SHA512: ecfb3f9c6a5e74f5d3ec8e95e15c79576a8efa84b1251f3b7c3110d47ffb9a9ed94f92f8a3a739ea38d06d557b727cd7d83653586dac755a05329ee28992c86f Homepage: https://cran.r-project.org/package=sparqlr Description: CRAN Package 'sparqlr' (A SPARQL Client for R) Provides a client for running SPARQL queries directly from R. SPARQL (short for SPARQL Protocol and RDF Query Language) is a query language used to retrieve and manipulate data stored in RDF (Resource Description Framework) format. Package: r-cran-sparr Architecture: all Version: 2.3-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.utils, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.random, r-cran-spatstat.univar, r-cran-doparallel, r-cran-foreach, r-cran-misc3d Suggests: r-cran-fftwtools Filename: pool/dists/resolute/main/r-cran-sparr_2.3-16-1.ca2604.1_all.deb Size: 623644 MD5sum: b8a9eb7de0ec6d569e73932b2c77f54c SHA1: cbf0beb5d7fa9dd03effca74b39e7216459581bc SHA256: ac826b76b5f339c7dc6a065f327da89f22f7d7475ae07533c3aa8065c6f36350 SHA512: a626ba76839facd68abf765d2b42f569e3619d898142c9fb4fb74bd0ea00917b0bd2a5c2e5a0790bac5c0e598e7baa11240adc96d2d3d3ee250c6612fa1fae52 Homepage: https://cran.r-project.org/package=sparr Description: CRAN Package 'sparr' (Spatial and Spatiotemporal Relative Risk) Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) . Package: r-cran-sparrafairness Architecture: all Version: 0.1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1552 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-ranger, r-cran-mvtnorm, r-cran-cvauc, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sparrafairness_0.1.0.0-1.ca2604.1_all.deb Size: 1480120 MD5sum: 5216c7dd4467440249d154171681a4a9 SHA1: d9cf07d4724beb22d9baa6de0bb4b240cfd5ba76 SHA256: 5004f588f12383834132db93be286ed6edd9883b9474ee4d8b72c9062fc187b1 SHA512: e2beb87e944837e2cacf241fb190855f0c516a33eb28b4e03d2e92cf7fb2ca7bddedce207e4b5bdb48ed5917b0f2985bddf6c158be026b5d15ca3c20cb389e64 Homepage: https://cran.r-project.org/package=SPARRAfairness Description: CRAN Package 'SPARRAfairness' (Analysis of Differential Behaviour of SPARRA Score AcrossDemographic Groups) The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset. Our manuscript can be found at: . Package: r-cran-sparrpowr Architecture: all Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1248 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-dorng, r-cran-fields, r-cran-foreach, r-cran-future, r-cran-iterators, r-cran-lifecycle, r-cran-sparr, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-terra Suggests: r-cran-geojsonsf, r-cran-ggmap, r-cran-ggplot2, r-cran-sf, r-cran-spatstat.data, r-cran-spelling, r-cran-testthat, r-cran-tidyterra, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-sparrpowr_0.2.9-1.ca2604.1_all.deb Size: 918408 MD5sum: 1aa41dd54d470a0e5d853fa953c73d36 SHA1: f92bab7fa971b7e5e131964fcbe02519f74d879b SHA256: aff6ac4cdc5e77f160285afb82e2ab1326a104966db14e259e9fa17035d567a0 SHA512: d1ecabd583a5df47901847230a824a4c7ad1e60512fb365dca02fce31ec3b718f5e192c23af4edf7c6cc8e2c97e5a18934d0edb81546b211dc94579e4334dd51 Homepage: https://cran.r-project.org/package=sparrpowR Description: CRAN Package 'sparrpowR' (Power Analysis to Detect Spatial Relative Risk Clusters) Calculate the statistical power to detect clusters using kernel-based spatial relative risk functions that are estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) . Details about kernel density estimation can be found in J. F. Bithell (1990) . More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) . Package: r-cran-sparsebiplots Architecture: all Version: 4.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-sparsepca Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparsebiplots_4.1.1-1.ca2604.1_all.deb Size: 50562 MD5sum: 2463c9c376f5f715df96c928c029b4a5 SHA1: 631b2e525059b8fd9bd2a457d7a6027e9211c43a SHA256: 0fa4555edac139444a5096e6cd3f78a6962f7fa8e3ef0b969318f96d3f5d680a SHA512: 54109f61a295c38314c103bb53cce2c152c3238b1d4c19a4f79780d2ce972c679b8f479a97c6989d936846bc0fdab3617eb87d9a3bff5c17a22286fcd79df30e Homepage: https://cran.r-project.org/package=SparseBiplots Description: CRAN Package 'SparseBiplots' ('HJ-Biplot' using Different Ways of Penalization Plotting with'ggplot2') The 'HJ-Biplot' is a multivariate method that represents high-dimensional data in a low-dimensional subspace, capturing most of the information’s variability in just a few dimensions. This package implements three new regularized versions of the HJ-Biplot: Ridge, LASSO, and Elastic Net. These versions introduce restrictions that shrink or zero-out variable weights to improve interpretability based on regularization theory. All methods provide graphical representations using 'ggplot2'. Package: r-cran-sparsecommunity Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rspectra Suggests: r-cran-irlba, r-cran-clue, r-cran-igraph, r-cran-igraphdata, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sparsecommunity_0.1.1-1.ca2604.1_all.deb Size: 112680 MD5sum: 6d0098cdafbbad71c6ccea966985f514 SHA1: 76577dbb1fe858fcb5e60b04e739ee8dd5ceea0e SHA256: da5b996c1e2138e7f2cece6f3ad2afbf3238ffba338571039e8102d4b8cf9e1f SHA512: 68b4d019d7b7882c650f44b0f2f16e6b6b23fcc65ff202f28f741d65661d928e9cd7723c85cb7eaebd3edacdf5de8925d4781fa2cd76537ac754cf690b1746a0 Homepage: https://cran.r-project.org/package=sparsecommunity Description: CRAN Package 'sparsecommunity' (Spectral Community Detection for Sparse Networks) Implements spectral clustering algorithms for community detection in sparse networks under the stochastic block model ('SBM') and degree-corrected stochastic block model ('DCSBM'), following the methods of Lei and Rinaldo (2015) . Provides a regularized normalized Laplacian embedding, spherical k-median clustering for 'DCSBM', standard k-means for 'SBM', simulation utilities for both models, and a misclustering rate evaluation metric. Also includes the 'NCAA' college football network of Girvan and Newman (2002) as a benchmark dataset, and the Bethe-Hessian community number estimator of Hwang (2023) . Package: r-cran-sparsecov Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mvnfast, r-cran-rfast, r-cran-sparsemvn Filename: pool/dists/resolute/main/r-cran-sparsecov_0.0.1-1.ca2604.1_all.deb Size: 39018 MD5sum: 6df0b75a00da174a0d18ac76dbeff29d SHA1: 12e482d00fdd69a36a014f15915105cc67302fc9 SHA256: 0c0c2b65ec12f9fd69bca3d80d097813236d09c9bf3e01f0cd4602e8ed48e231 SHA512: 12e7b835ac835f2f6eede52ac194c32bdb3934046988c18a6559b54eb7cd0500c83362b5ccbbf7f1d715a3f1dc956e117dc5e9efb805b16d6918abadaf6311ce Homepage: https://cran.r-project.org/package=sparseCov Description: CRAN Package 'sparseCov' (Sparse Covariance Estimation Based on Thresholding) A sparse covariance estimator based on different thresholding operators. Package: r-cran-sparsedc Architecture: all Version: 0.1.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4785 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sparsedc_0.1.17-1.ca2604.1_all.deb Size: 4818010 MD5sum: a06f222f49b9dd71d876ac91431359e2 SHA1: 5cf3906c127407ceae8c2582555bc6143c248689 SHA256: 151d9c0d409255dd34a6df566007804c29cc559db9c84b13e287edb1338886c3 SHA512: ea3060af285163e20f75f85b931e3a83179ca680271180d3e656e3c1c83fac447081e0df53246d4b0ef152b378ce9c2df2537728044f6dc659ade0ac691cc620 Homepage: https://cran.r-project.org/package=SparseDC Description: CRAN Package 'SparseDC' (Implementation of SparseDC Algorithm) Implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, . This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers. Package: r-cran-sparsediscrim Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bdsmatrix, r-cran-corpcor, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-rlang Suggests: r-cran-testthat, r-cran-mass, r-cran-covr, r-cran-modeldata, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-sparsediscrim_0.3.0-1.ca2604.1_all.deb Size: 342426 MD5sum: 63bca15524f9b474b273fbbbac66c0c1 SHA1: e32c429e50f5888f398f57798e783f4d1dc7362c SHA256: d62cc73ca50f75a6a91b4bcfb69b7481f5a4fbde049c824c32bc2e85a06ba130 SHA512: 6e21c2ecd32f04d1a6e197be3a95ea37622a772c65d207534f51449f672be370d628e2074214ac5bb9764578a618ef79f057af153fe8f58622a44969fcc650f5 Homepage: https://cran.r-project.org/package=sparsediscrim Description: CRAN Package 'sparsediscrim' (Sparse and Regularized Discriminant Analysis) A collection of sparse and regularized discriminant analysis methods intended for small-sample, high-dimensional data sets. The package features the High-Dimensional Regularized Discriminant Analysis classifier from Ramey et al. (2017) . Other classifiers include those from Dudoit et al. (2002) , Pang et al. (2009) , and Tong et al. (2012) . Package: r-cran-sparseeigen Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparseeigen_0.1.0-1.ca2604.1_all.deb Size: 326540 MD5sum: c5a99fb1b6f8b613a69b792a1e956ead SHA1: 6058e954d5f7c870c62dddbfcef304e13e49e313 SHA256: 460d5e6ffcfad71a41e66482da4a13209de2f359341b844632d7315b12d31999 SHA512: fd2f51387f619576993b4aa11eda7754fbfc0151e92a0e2da2c0ff0118ea4b4094f321a024a2a987d5547171d3651cc7abc44cef0df640f5a03879a10a7a48af Homepage: https://cran.r-project.org/package=sparseEigen Description: CRAN Package 'sparseEigen' (Computation of Sparse Eigenvectors of a Matrix) Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing . Package: r-cran-sparseflmm Architecture: all Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-refund, r-cran-mass, r-cran-matrix, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-sparseflmm_0.4.2-1.ca2604.1_all.deb Size: 384968 MD5sum: 5c0643391dcba5ed72b359fb73b6acd7 SHA1: 57c3a5ee9565bb504bff5d91ff5458f12bba749c SHA256: 027e8399ec584f5e15683a9d75944602912c15299d36f280773423b72d53b036 SHA512: 7fc6ce9ea74fe2b14e0a8552e2af95c23d9c9bae8c38967cbcddf1cae3ba320095b9ddc752df91bf87be9e957f54075f1ddd05fe9bf9683109db9838eedc1670 Homepage: https://cran.r-project.org/package=sparseFLMM Description: CRAN Package 'sparseFLMM' (Functional Linear Mixed Models for Irregularly or SparselySampled Data) Estimation of functional linear mixed models for irregularly or sparsely sampled data based on functional principal component analysis. Package: r-cran-sparsefunclust Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sparsefunclust_1.0.0-1.ca2604.1_all.deb Size: 129840 MD5sum: bb82a650b227550bb86f419160f59195 SHA1: edf714212a2b6c7575ce51f9aa21b9a75fee96f8 SHA256: d8f6a6e9ebc5556ee16e279a8fd96cd7f907e84a13b5c89c707f155dfc16d0aa SHA512: 27a3e45289abe102e3479ee6b42054c02461262bb082f16cb0d5c836524699b69b2be4200d100f9e347d96299553f1910f563aab998953835d8fba7089989fa3 Homepage: https://cran.r-project.org/package=SparseFunClust Description: CRAN Package 'SparseFunClust' (Sparse Functional Clustering) Provides a general framework for performing sparse functional clustering as originally described in Floriello and Vitelli (2017) , with the possibility of jointly handling data misalignment (see Vitelli, 2019, ). Package: r-cran-sparsegfm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gfm, r-cran-mass, r-cran-irlba Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparsegfm_0.1.0-1.ca2604.1_all.deb Size: 61754 MD5sum: 6daa368c46e269583117722de77e7022 SHA1: 87434e528850775050852f53b7f13c7ebe822571 SHA256: 4fbd0ea22a338f2b35d2920c7b49e1881685b133a64ed2c22ac173e579a38642 SHA512: 17049ee09e4efeb60e5c625c9407e76f651cc2cf540a003d06959dbda8285c8193d70045c4cf7bcd37244bc4fb5f3bd3f03705c57c9e5c08b95bdfd0085a25fb Homepage: https://cran.r-project.org/package=sparseGFM Description: CRAN Package 'sparseGFM' (Sparse Generalized Factor Models with Multiple Penalty Functions) Implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) , Fan and Li (2001) , and Zhang (2010) . Package: r-cran-sparsegrid Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-statmod, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-sparsegrid_0.8.2-1.ca2604.1_all.deb Size: 174142 MD5sum: 0edcf4a8de3bed89f22c9d5e63313b39 SHA1: a155e4b576df222de9a98ec97a9246eff5e5fd25 SHA256: 345fcf1ba54279fb1377f6e86498dce7e8037f6201666753094cacd288ae5be5 SHA512: b7026eb516856755f7890aa1901e50e9c8625b834e173e36b678bb78aa442cc2a425016253e768170adba85de15818ccc30b707882eeabde7fa6592a8084032f Homepage: https://cran.r-project.org/package=SparseGrid Description: CRAN Package 'SparseGrid' (Sparse grid integration in R) SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de Package: r-cran-sparseindextracking Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2961 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bookdown, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-xts Filename: pool/dists/resolute/main/r-cran-sparseindextracking_0.1.1-1.ca2604.1_all.deb Size: 2383894 MD5sum: cbc65a0735fa6135535d979a7542e55d SHA1: f826a44ebbf10f0f23c40e10d90ee8d7a685e20e SHA256: e0d778eed908e8a6f18ce6be15b86a08e07956632e603f8b8f77cba30f19fb8d SHA512: da5757e06f94c7876e17b8381a91f63f93ff18f03608e310e129d5b62575b0739587e9ea41402edcc25066e85114cf343c08e1a76effea4925228e673596c48b Homepage: https://cran.r-project.org/package=sparseIndexTracking Description: CRAN Package 'sparseIndexTracking' (Design of Portfolio of Stocks to Track an Index) Computation of sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation). The level of sparsity of the portfolios, i.e., the number of selected assets, is controlled through a regularization parameter. Different tracking measures are available, namely, the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking error (HETE), and Huber downside risk (HDR). See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. . Package: r-cran-sparselda Architecture: all Version: 0.1-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-elasticnet, r-cran-mass, r-cran-mda Filename: pool/dists/resolute/main/r-cran-sparselda_0.1-9-1.ca2604.1_all.deb Size: 380098 MD5sum: c61f619374519ff99c78cc32b1167cae SHA1: 793928b6ed8d23e18e8fa887788f5426b22f639f SHA256: 4fcb90e41f165a0c774b59c9db8197a20d35bd3af7b87a4104f3adfa5e9f8ead SHA512: 5aaa35240fd46f37255062b8757168ac7b73c147ed0e666b82778359987aefc7206fbbd4544dfbe1b001c54c302709fe831c6430958e40f9e6fad10e82845ccb Homepage: https://cran.r-project.org/package=sparseLDA Description: CRAN Package 'sparseLDA' (Sparse Discriminant Analysis) Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models. Package: r-cran-sparselink Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-proc, r-cran-mvtnorm, r-cran-spls, r-cran-xrnet Suggests: r-cran-knitr, r-cran-testthat, r-cran-remotes, r-cran-glmtrans, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sparselink_1.0.0-1.ca2604.1_all.deb Size: 170986 MD5sum: 8c7fcdd08f0f4a68277d0d36273ed82e SHA1: e5b20573b0d7b9b2938b39c3da7502143aec12c4 SHA256: 87083271999f37f8ba72272a844667c728fa3cbc03b4dcff329a255f18811a52 SHA512: 0a9951e6489b9749bb0351a76343a69d0464a0f3110f6523bc3feb85bec519cfe3e5eeff8d973319a4691fe9dc70d3b17bccdfeb6d129d56541429d675bc680d Homepage: https://cran.r-project.org/package=sparselink Description: CRAN Package 'sparselink' (Sparse Regression for Related Problems) Estimates sparse regression models (i.e., with few non-zero coefficients) in high-dimensional multi-task learning and transfer learning settings, as proposed by Rauschenberger et al. (2025) . Package: r-cran-sparselrmatrix Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rspectra Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparselrmatrix_0.1.0-1.ca2604.1_all.deb Size: 27376 MD5sum: ed38b716fa4e24d095e7994828cb0090 SHA1: 0a5b01e908c9ef2a23e7a81a232bcd80f4894e8f SHA256: 72ca714cabea31035cfe17e9bfa40134881b4127d30c595e4579fdc5e4ee2609 SHA512: bd5ee5ac6431fe7a4b4fa3d014d53a013250775ea12d1ce726539040819bafaa762fc8f07e13f3ec20b794992d425907cf947fd782d6e39b14953476b2e08e9e Homepage: https://cran.r-project.org/package=sparseLRMatrix Description: CRAN Package 'sparseLRMatrix' (Represent and Use Sparse + Low Rank Matrices) Provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects. Package: r-cran-sparsematest Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso Filename: pool/dists/resolute/main/r-cran-sparsematest_1.0.0-1.ca2604.1_all.deb Size: 41432 MD5sum: 89d34d43498dda74cd11abc655c9f806 SHA1: 8637d4acb8b8220e34e1e6cb98bfea93f4bfdb17 SHA256: 68605a5b6c8bab28e60b8063f4dfc6714c5ad27fd1f1028c8fb6f8d707a561e0 SHA512: 2eaacd90d62c240962153aa31dea009a62cf3988bddb181920ca68ae761892666e7754944e5d61c99566e7a7c1601a60d2907c380d29128c16b62763121b6200 Homepage: https://cran.r-project.org/package=sparseMatEst Description: CRAN Package 'sparseMatEst' (Sparse Matrix Estimation and Inference) The 'sparseMatEst' package provides functions for estimating sparse covariance and precision matrices with error control. A false positive rate is fixed corresponding to the probability of falsely including a matrix entry in the support of the estimator. It uses the binary search method outlined in Kashlak and Kong (2019) and in Kashlak (2019) . Package: r-cran-sparsemdc Architecture: all Version: 0.99.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dorng, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sparsemdc_0.99.5-1.ca2604.1_all.deb Size: 4829430 MD5sum: b7bd55d7f8e1eea84d31677c039c9fe6 SHA1: 78fb4684399bc0cd25a687b7a4cdcd05fa8ce158 SHA256: a885a5bdae0ac1fff6e52d8ccae95cb76cdd9fc728b595f8c80f8446d6d97ec0 SHA512: c71a0115ff5f1590ef5e8ac96965382bb961d63d098733b08ece60e4c43cd69b3aa826503e54211a2990a7c33a89e5b42d0bc8c138f0bed05dc0e9e27ca6acbb Homepage: https://cran.r-project.org/package=SparseMDC Description: CRAN Package 'SparseMDC' (Implementation of SparseMDC Algorithm) Implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers. Package: r-cran-sparsemse Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-rcapture Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sparsemse_2.0.1-1.ca2604.1_all.deb Size: 115758 MD5sum: eca98250ed44ce62786a02fcf2732679 SHA1: ca78ac4cdeae9147cdad57ce44dea40b81e27311 SHA256: 3da5a3035afdaef8c670b2cda8ffdc7405c031c5152f91a44da1ae9b79ac295e SHA512: 8b6b08362d9cc1987b413a36a1ff192f433d92cdf50f51c083b6becf9929dc6837dc1af85dc56ea2f147896551e864006581e5f8f83e0ad17ae8e47600882ebb Homepage: https://cran.r-project.org/package=SparseMSE Description: CRAN Package 'SparseMSE' ('Multiple Systems Estimation for Sparse Capture Data') Implements the routines and algorithms developed and analysed in "Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) . This package explicitly handles situations where there are pairs of lists which have no observed individuals in common. It deals correctly with parameters whose estimated values can be considered as being negative infinity. It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates. Package: r-cran-sparsemvn Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-forcats, r-cran-knitr, r-cran-bookdown, r-cran-kableextra, r-cran-testthat, r-cran-scales, r-cran-trustoptim Filename: pool/dists/resolute/main/r-cran-sparsemvn_0.2.2-1.ca2604.1_all.deb Size: 274930 MD5sum: f961880bc95b51c0d6296631741a6338 SHA1: 21aba9cf60e0d72c6348c73b6057da20f605532a SHA256: 906bf93fa00740e6eb4425b6447d2c6bed3e578199087162e1943b65949c52a6 SHA512: a7fed2186ac5da52bb558397f132c64ecd7c19b5041e8f61c02c6eb12dcdb70b6033950314a2be14ca00421386a9479b1a3bad8582e185a1029d6180d8f6a9ee Homepage: https://cran.r-project.org/package=sparseMVN Description: CRAN Package 'sparseMVN' (Multivariate Normal Functions for Sparse Covariance andPrecision Matrices) Computes multivariate normal (MVN) densities, and samples from MVN distributions, when the covariance or precision matrix is sparse. Package: r-cran-sparsepca Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsvd Filename: pool/dists/resolute/main/r-cran-sparsepca_0.1.2-1.ca2604.1_all.deb Size: 49944 MD5sum: 2122f468e37bbc60795b08156fa0ec17 SHA1: 256928431da108342a7c12401b1b206a79e67852 SHA256: b8fb67bf0f90ad633ad5a55f0fd68e846c68fb4507c847230536100e90fe5d1c SHA512: 6d6bf30b93b84c55455035725f9cf4f29f0aac03a8c632a7f2d6a943e093700988eb3673feaf1a38f321a1aaf39a264a65474058ce53817a7f4943f001f2ba68 Homepage: https://cran.r-project.org/package=sparsepca Description: CRAN Package 'sparsepca' (Sparse Principal Component Analysis (SPCA)) Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) . Package: r-cran-sparsepp Architecture: all Version: 1.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-sparsepp_1.22-1.ca2604.1_all.deb Size: 111842 MD5sum: f8c012f2b79505c81a427571a7e61a58 SHA1: 78c42b0a2c6f3d42b68c16c5062274881ca43cf2 SHA256: 96b761a3ff13c63c1924f26154c83645666b277b31ece0526d029a4fc1aedfb2 SHA512: b3864129813102f7141e30cde9b6ed5d3835ecd1d67ec0d2eb895d999d50804d99cec1ee248647a2728f276afd4e4640aa4923b7090f43b69b96d288c98d36c6 Homepage: https://cran.r-project.org/package=sparsepp Description: CRAN Package 'sparsepp' ('Rcpp' Interface to 'sparsepp') Provides interface to 'sparsepp' - fast, memory efficient hash map. It is derived from Google's excellent 'sparsehash' implementation. We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance). Package: r-cran-sparser Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ncvreg, r-cran-rlang, r-cran-magrittr, r-cran-dplyr, r-cran-recipes Suggests: r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-covr, r-cran-modeldata, r-cran-mass Filename: pool/dists/resolute/main/r-cran-sparser_0.3.2-1.ca2604.1_all.deb Size: 1665520 MD5sum: d658c4b258551d613063365dfdc7397d SHA1: d7215f1874b934b967fb69981599ec3396b67995 SHA256: b97147ff981215cb5a1edab684a1501b179b9fedf4d89b08977d9773f33d2e32 SHA512: ab9dd8ec11eff9bcf825e4dc8ec64ad48826ef29da2163fd0979ec36352b16ca51bd16a990817004b98fe20966415f645b16bb95638f20599fad22667c16ab49 Homepage: https://cran.r-project.org/package=sparseR Description: CRAN Package 'sparseR' (Variable Selection under Ranked Sparsity Principles forInteractions and Polynomials) An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) , ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods. Package: r-cran-sparsestep Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-sparsestep_1.0.1-1.ca2604.1_all.deb Size: 49846 MD5sum: 922dd91174ca5683545499768b641484 SHA1: 500fbfa8289bd0e63f16d1e6498cc36a13c8c5b8 SHA256: 8001780c7554f5a241754b07860047f9560e90800d5dba1fca7b333f4a9ec00d SHA512: aca43fcf49c5aa976e22c845eb9b480543ec633d4918ed2ca43efefa977fe8f92cd3cc540e347b88b74b0130c9a6f1350edbf60b8668deb5510e65eb7f351692 Homepage: https://cran.r-project.org/package=sparsestep Description: CRAN Package 'sparsestep' (SparseStep Regression) Implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) . In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter. Package: r-cran-sparsesurv Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2jags, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rjags, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-sparsesurv_0.1.1-1.ca2604.1_all.deb Size: 130650 MD5sum: f67383deb04a7db3b28ff40a160013f2 SHA1: 9d5c4440944db45bd6943e79fd4911865e6e6a80 SHA256: 6daf4f7402731847ac5269a5b7d8909e3772f80a0969bd3083f96854b8047bb8 SHA512: 327c3928df2b2f0009a24708811521beae1a52fa7640b4dc13f1f4e818903ae4b266ea1a247e4c5a44145bbd3b3dfb93ddf6d4d4ec4e6d177c7ccb6cd883ca1f Homepage: https://cran.r-project.org/package=sparsesurv Description: CRAN Package 'sparsesurv' (Forecasting and Early Outbreak Detection for Sparse Count Data) Functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics. Package: r-cran-sparsevar Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-ncvreg, r-cran-doparallel, r-cran-glmnet, r-cran-ggplot2, r-cran-reshape2, r-cran-mvtnorm, r-cran-corpcor, r-cran-checkmate, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sparsevar_1.0.0-1.ca2604.1_all.deb Size: 230864 MD5sum: e4c10ac88c27622d74c645f63c44d950 SHA1: 60e6e84d73ec23c933a25c0bbb1e3a416f4479fa SHA256: d6f961a0afd526c1a7e91bf25369d06f4d2e45d2417121eca6972cfc7ca32c22 SHA512: c3e91d34cfa471fbd51e6572c4e8348a83f2cb07f17e64faf74bf3b725d9b57b48db3226bbfbd121ea3f8e4d47dc7f616d9449cc50f390e07597c72e798d51f2 Homepage: https://cran.r-project.org/package=sparsevar Description: CRAN Package 'sparsevar' (Sparse VAR (Vector Autoregression) / VECM (Vector ErrorCorrection Model) Estimation) A wrapper for sparse VAR (Vector Autoregression) and VECM (Vector Error Correction Model) time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Basu and Michailidis (2015) . Package: r-cran-sparsevfc Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pdist, r-cran-purrr Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sparsevfc_0.1.2-1.ca2604.1_all.deb Size: 2782746 MD5sum: af67fe23d6121167784f93c8501c89d8 SHA1: fa7b05f383e26d00d5fa88f705d02f28942c03bd SHA256: dea141fdcc7da094c3db0b4fbdf0a3ba661a30ab26588773a375e14688f091c1 SHA512: 74702be929e0088b39e7ae35e7de26d103469b1bb27f0e7943e4d7231259d8aa18e7f11d015351793278d4b5f972bb68254fe9819c375ea51fd5eb1b5d34a47c Homepage: https://cran.r-project.org/package=SparseVFC Description: CRAN Package 'SparseVFC' (Sparse Vector Field Consensus for Vector Field Learning) The sparse vector field consensus (SparseVFC) algorithm (Ma et al., 2013 ) for robust vector field learning. Largely translated from the Matlab functions in . Package: r-cran-spartaas Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factominer, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-plotly, r-cran-stringr, r-cran-colorspace, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinyjqui, r-cran-fpc, r-cran-ggdendro, r-cran-htmltools, r-cran-htmlwidgets, r-cran-shinythemes, r-cran-explor, r-cran-shinywidgets, r-cran-scatterd3, r-cran-ks, r-cran-cluster, r-cran-leaflet, r-cran-ape, r-cran-mass, r-cran-ade4, r-cran-lmtest, r-cran-nor1mix, r-cran-shinycssloaders, r-cran-scales, r-cran-fastcluster, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/resolute/main/r-cran-spartaas_1.2.5-1.ca2604.1_all.deb Size: 2492916 MD5sum: ac1fc27c09d62503bc1d58c258e65038 SHA1: d62179cd402dc81596de5764ecb48414ba96dc6a SHA256: db7565d0d5cbcd166acfad958634ed59335a83ff1f305ca3d6df8e23b6c6390f SHA512: 43145317215d662b59d2ea606ad696939f048af31ed7ba0b8171e49c4564f033811fe8342f4d726faa74ec26b575388fd44a1ad3d7cce0b6a1984a0998ef01ca Homepage: https://cran.r-project.org/package=SPARTAAS Description: CRAN Package 'SPARTAAS' (Statistical Pattern Recognition and daTing using ArchaeologicalArtefacts assemblageS) Statistical pattern recognition and dating using archaeological artefacts assemblages. Package of statistical tools for archaeology. hclustcompro()/perioclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4). mapclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021) . seriograph(): Desachy Bruno (2004) . cerardat(): Bellanger Lise, Husi Philippe (2012) . Package: r-cran-spatemr Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-sphet, r-cran-matrix Suggests: r-cran-testthat, r-cran-sp, r-cran-spdep Filename: pool/dists/resolute/main/r-cran-spatemr_1.3.0-1.ca2604.1_all.deb Size: 69066 MD5sum: c1bc342de5dd2dbb24123f4235ab5da5 SHA1: d31a50fe0aefb2d6349f5bd54a18386a5640411b SHA256: 8d5e5d3c6403b57303dc547391130625c2fd70ea0b91b5e58ad7e86db26c158f SHA512: 5c6da3d30bded27e184f4ff1b3250bbf9ee90d41a88ca32acf29c48d2c5d95566acf4433ad4bde4485317ccdb229cb9eab0dfd3cc08748a5a8bc8b1fd318c8c8 Homepage: https://cran.r-project.org/package=spatemR Description: CRAN Package 'spatemR' (Generalized Spatial Autoregresive Models for Mean and Variance) Modeling spatial dependencies in dependent variables, extending traditional spatial regression approaches. It allows for the joint modeling of both the mean and the variance of the dependent variable, incorporating semiparametric effects in both models. Based on generalized additive models (GAM), the package enables the inclusion of non-parametric terms while maintaining the classical theoretical framework of spatial regression. Additionally, it implements the Generalized Spatial Autoregression (GSAR) model, which extends classical methods like logistic Spatial Autoregresive Models (SAR), probit Spatial Autoregresive Models (SAR), and Poisson Spatial Autoregresive Models (SAR), offering greater flexibility in modeling spatial dependencies and significantly improving computational efficiency and the statistical properties of the estimators. Related work includes: a) J.D. Toloza-Delgado, Melo O.O., Cruz N.A. (2024). "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices". . b) Cruz, N. A., Toloza-Delgado, J. D., Melo, O. O. (2024). "Generalized spatial autoregressive model". . Package: r-cran-spatentropy Architecture: all Version: 2.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.random Filename: pool/dists/resolute/main/r-cran-spatentropy_2.2-4-1.ca2604.1_all.deb Size: 452160 MD5sum: 5097fca20edc792af940f957ab573d37 SHA1: e9e9507908702cfced1c000811dbb3e999175932 SHA256: 4bba5f9391ce72d8cecb3e51a8002ba65fefea6dc17cd13e9fa1a4bd3d4dd4ad SHA512: f837d0ce52667f83c2c90a7de70e5aafb7214d343b96780bdc26da4370b65c4cfcc592ba35454a3f76e2f246d0bfb34491cfb1dc4ba8d8541be92fb0ed999193 Homepage: https://cran.r-project.org/package=SpatEntropy Description: CRAN Package 'SpatEntropy' (Spatial Entropy Measures) The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 ), Batty's spatial entropy (Batty, 1974 ), O'Neill's entropy (O'Neill et al., 1998 ), Li and Reynolds' contagion index (Li and Reynolds, 1993 ), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 ), Leibovici's entropy (Leibovici, 2009 ), Parresol and Edwards' entropy (Parresol and Edwards, 2014 ) and Altieri's entropy (Altieri et al., 2018, ). Full references for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated 'spatstat' package (>= 3.0-2). Package: r-cran-spatest Architecture: all Version: 3.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spatest_3.1.2-1.ca2604.1_all.deb Size: 120428 MD5sum: d35ba20bb8d44d051934ce01dffe0ef2 SHA1: bdd4f269736d4eae028fa5efa46de8d29f029e3b SHA256: d6af84d4e73ddb291ed47119f577e3b10efaf4e4806df1fbdc97d238a23efc06 SHA512: ff81b0ec1a5e47ee7a19dfdd4623c3b6e8ebeb229dc10f77a7dd24d20c580fedece7101f153a8992122a6486168dc5dd72b3e18b522e88cfa565faa49b54a777 Homepage: https://cran.r-project.org/package=SPAtest Description: CRAN Package 'SPAtest' (Score Test and Meta-Analysis Based on Saddlepoint Approximation) Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check . Package: r-cran-spatfd Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-dplyr, r-cran-proxy, r-cran-reshape, r-cran-gstat, r-cran-sp, r-cran-fda, r-cran-sf, r-cran-mass, r-cran-geor, r-cran-tidyr, r-cran-fda.usc Filename: pool/dists/resolute/main/r-cran-spatfd_0.0.1-1.ca2604.1_all.deb Size: 3739922 MD5sum: e9eea253d00eacddfd4ff6f60e28eff2 SHA1: 009548f147aa3f0b6414c9a5b620d430fedfe06c SHA256: 5cbbf7ae808f072688b18753d2cea849f7fb8c5da63e00bb8b5fc5b1ea989717 SHA512: 134f54809b4d29b3c0d19d1e3962b4700b0445dd4db5b942f15acdd59c0865cf4bbc7eb6cf00ef32ba747fa36f21f7c0df4e405d55a12ebb94004ccb13b4636e Homepage: https://cran.r-project.org/package=SpatFD Description: CRAN Package 'SpatFD' (Functional Geostatistics: Univariate and Multivariate FunctionalSpatial Prediction) Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. 'SpatFD' is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) , Bohorquez, M., Giraldo, R., and Mateu, J. (2016) , Bohorquez M., Giraldo R. and Mateu J. (2021) . Package: r-cran-spatgc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-spdep, r-cran-sf Filename: pool/dists/resolute/main/r-cran-spatgc_0.1.0-1.ca2604.1_all.deb Size: 56354 MD5sum: 61030c5d56d4827151e82b5597870f56 SHA1: b1f75957249b4ad8cda549fa5de3e48a584baa8b SHA256: 713f811478af57030a0164543436d917f73d8d62ee2022b4ec15f7178c528ab1 SHA512: a0b757f2546d80581346591080ac287e3539b92db4a6723fcc6cf89e64f0459c33f7388c34c8de13879ac35dc10fdb2aea0c84e62d1611795d948c5c9c38f94d Homepage: https://cran.r-project.org/package=SpatGC Description: CRAN Package 'SpatGC' (Bayesian Modeling of Spatial Count Data) Provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) . Package: r-cran-spatgeom Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-sf, r-cran-dplyr, r-cran-lwgeom, r-cran-cowplot, r-cran-purrr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spatgeom_0.3.0-1.ca2604.1_all.deb Size: 46462 MD5sum: b249c9a901dd1ecec10a60b3d62d4be7 SHA1: 8d9d83f22fb31ebaa5d0acb0ae45b5347dd7c4d9 SHA256: 189da8e4daab8f54ae50069f803d37d132d2ae9eef49192d6dbd801f1b3cd44d SHA512: 0d385f1f5e311e21ee642a504b8d263571d9c9684502cbd1d99a45eaea1bae6db7e70a4005aecccbd88ba1746332f5683332e998b38cf584a1b1e9b7ddb8baab Homepage: https://cran.r-project.org/package=spatgeom Description: CRAN Package 'spatgeom' (Geometric Spatial Point Analysis) The implementation to perform the geometric spatial point analysis developed in Hernández & Solís (2022) . It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the 'sf' package. The package has methods to print and plot the results. Package: r-cran-spatgrid Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spatgrid_0.1.0-1.ca2604.1_all.deb Size: 25134 MD5sum: 2c6896e2ca7244c93c7bf1c413b693ef SHA1: 014adcbfb470fee570ade65c8d4798e22fb46dfa SHA256: 2ea500768ff095710ecf2367176b0dd188163593e66d02d6e081de8be832e062 SHA512: 2def716f6ba541b01cdb37a10afeeda29e065b2eaa58dac6723325247d613259c04dc2e69ef8c4fba2c650906d86aa95bb79e1b9c523337d3869833fbc07a30f Homepage: https://cran.r-project.org/package=SpatGRID Description: CRAN Package 'SpatGRID' (Spatial Grid Generation from Longitude and Latitude List) The developed function is designed for the generation of spatial grids based on user-specified longitude and latitude coordinates. The function first validates the input longitude and latitude values, ensuring they fall within the appropriate geographic ranges. It then creates a polygon from the coordinates and determines the appropriate Universal Transverse Mercator zone based on the provided hemisphere and longitude values. Subsequently, transforming the input Shapefile to the Universal Transverse Mercator projection when necessary. Finally, a spatial grid is generated with the specified interval and saved as a Shapefile. For method details see, Brus,D.J.(2022).. The function takes into account crucial parameters such as the hemisphere (north or south), desired grid interval, and the output Shapefile path. The developed function is an efficient tool, simplifying the process of empty spatial grid generation for applications such as, geo-statistical analysis, digital soil mapping product generation, etc. Whether for environmental studies, urban planning, or any other geo-spatial analysis, this package caters to the diverse needs of users working with spatial data, enhancing the accessibility and ease of spatial data processing and visualization. Package: r-cran-spatialacc Architecture: all Version: 0.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/resolute/main/r-cran-spatialacc_0.1-6-1.ca2604.1_all.deb Size: 51160 MD5sum: 1d4ac93a4807d70b773fb0c6ca86fd4b SHA1: ee1adbba908ef6667350f2d88f8021d0e3c3fe6e SHA256: 721af71b8410d98abeb59c97b038e1196ffe48f6a45732232033b22d46f0f530 SHA512: e7b0fd9a1c29448caac29f2b04d72168de0252ea1385aed6170853e3b360c387fb491b704186f868f89f2a8ec74ccca45518428080c1aee5ffa2298897c5f873 Homepage: https://cran.r-project.org/package=SpatialAcc Description: CRAN Package 'SpatialAcc' (Spatial Accessibility Measures) Provides a set of spatial accessibility measures from a set of locations (demand) to another set of locations (supply). It aims, among others, to support research on spatial accessibility to health care facilities. Includes the locations and some characteristics of major public hospitals in Greece. Package: r-cran-spatialatomizer Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-sp, r-cran-sf, r-cran-spdep, r-cran-mass, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-reshape2, r-cran-coda, r-cran-biasedurn, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tigris Filename: pool/dists/resolute/main/r-cran-spatialatomizer_0.2.8-1.ca2604.1_all.deb Size: 4711692 MD5sum: ee0895b2786d3b54df903223dbd982a3 SHA1: 8059490fab74294d497c6338d53ae6039cdac030 SHA256: bfc174910946944d7bf5adb57dcca53747631f545a796759671688a5fb1fbee8 SHA512: 8337c3e38b6f5cbd1be7ef7d12e8a061540217f696a9c361015b52bdb9e7466626155ebed64561f41138df3de6da9f937df46c0bcfeb923574fee7bf8ff2ee3f Homepage: https://cran.r-project.org/package=spatialAtomizeR Description: CRAN Package 'spatialAtomizeR' (Spatial Analysis with Misaligned Data Using Atom-BasedRegression Models) Implements atom-based regression models (ABRM) for analyzing spatially misaligned data. Provides functions for simulating misaligned spatial data, preparing NIMBLE model inputs, running MCMC diagnostics, and providing results. All main functions return S3 objects with print(), summary(), and plot() methods for intuitive result exploration. Methods originally described in Mugglin et al. (2000) , further investigated in Trevisani & Gelfand (2013), and applied in Nethery et al. (2023) . Package: r-cran-spatialcatalogueviewer Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-leaflet.extras, r-cran-shiny, r-cran-shinythemes, r-cran-dt Filename: pool/dists/resolute/main/r-cran-spatialcatalogueviewer_0.2.1-1.ca2604.1_all.deb Size: 38304 MD5sum: cf42137d5bc4dbc8e33cff686b19ee93 SHA1: 071aab3952dd613ce4430836affe3668a9720ff5 SHA256: 1eb33db0b09e13e3f92a4e8d5aafd070a9e6b32ef21624d9f592ff01a0fe8faf SHA512: 544843030f5518e3fbe08b535c92022d1df83d2535635a2168b1f53cc6bc7f292146ff6e63a4e7b30cfa8654a7a7675712f0bf4ffd115c95a8093c743d733bca Homepage: https://cran.r-project.org/package=spatialCatalogueViewer Description: CRAN Package 'spatialCatalogueViewer' (A 'Shiny' Tool to Create Interactive Catalogues for GeospatialData) Seamlessly create interactive online catalogues for geospatial data. Items can be mapped as points or areas and retrieved using either a map or a dynamic table with search form and optional column filters. Package: r-cran-spatialcovariance Architecture: all Version: 0.6-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spatialcovariance_0.6-9-1.ca2604.1_all.deb Size: 95506 MD5sum: e452ebe9525be0e64b4215ae510b15e5 SHA1: 8c7d2ca9d98487d839c37e2f5ff7f5110bc274d2 SHA256: 75d7fe81d3567ff0168bff8df7c25ccc4cf388c60980e7f97299465d208ea192 SHA512: 2943368b153b80515cd5938285a9a9e1d6cb1d13c7c66903e22c0e3ef3fdd9a22271e73642403610ffd9fcd711dc6535cf929956af6e09856871937d1cf13cfe Homepage: https://cran.r-project.org/package=spatialCovariance Description: CRAN Package 'spatialCovariance' (Computation of Spatial Covariance Matrices for Data onRectangles) Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units. This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration. Package: r-cran-spatialdata Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4366 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-terra Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spatialdata_1.0.1-1.ca2604.1_all.deb Size: 4420670 MD5sum: 46b87b200c734a4232e51686bc5915b0 SHA1: 24bf0b3c442ad4225a10b512acaf9ef8b5ef933f SHA256: 4f6fdf1e1222da0f7ea4fdbdfbbd90710e9fe48701f4ca9f2adf7f28fb319b6f SHA512: 4e7bb24f7d09db00619439afb43d173ef3090da5066cabb436c2817ce68f42ad3ed606b7ff3782d616aa3a8d21533f6d91411cf7b185dae1abe08f8d3cef1409 Homepage: https://cran.r-project.org/package=spatialData Description: CRAN Package 'spatialData' (Spatial Datasets for Ecological Modeling) Provides spatial datasets ready to use for ecological modelling and raster companion data for prediction: Neanderthal presence during the Last Interglacial (Benito et al. 2017 ); Plant diversity metrics for the World's Ecoregions (Maestre et al. 2021 ); tree richness across the Americas (Benito et al. 2013 ); plant communities from the Sierra Nevada (Spain) with future climate scenarios (Benito et al. 2013 ); butterfly-plant interaction data from Sierra Nevada (Spain) (Benito et al. 2011 ); plant species occurrences in Andalusia (Spain) (Benito et al. 2014 ); presence of the plant Linaria nigricans and greenhouses (Benito et al. 2009 ); global NDVI and environmental predictors, and European oak species occurrences. All datasets include pre-processed environmental predictors ready for statistical modelling. Package: r-cran-spatialdownscaling Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-keras3, r-cran-magrittr, r-cran-rdpack, r-cran-raster, r-cran-abind Filename: pool/dists/resolute/main/r-cran-spatialdownscaling_0.1.2-1.ca2604.1_all.deb Size: 1781054 MD5sum: 5502098394ea316590b530489c11aefe SHA1: f4a641e421f8578fa07928ba1c9adf8911e63988 SHA256: 04a206b23d14bc8a43bae8686526000c51b44d8ca13803e34322109a765e6c6f SHA512: 705210f893cc1c1f62badd04d69d17a3d4fbe58de2b21ebeb829817b7e325b06e2bbbe7a9accc1b161c8870fac5d524373bf7819535418d329f3721244a2e8cb Homepage: https://cran.r-project.org/package=SpatialDownscaling Description: CRAN Package 'SpatialDownscaling' (Methods for Spatial Downscaling Using Deep Learning) The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 ) and UNet (Ronneberger et al., 2015 ), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 ). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) . Package: r-cran-spatialeco Architecture: all Version: 2.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2321 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-terra Suggests: r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spdep, r-cran-ks, r-cran-cluster, r-cran-readr, r-cran-rcurl, r-cran-rann, r-cran-rms, r-cran-yaimpute, r-cran-mgcv, r-cran-zyp, r-cran-spatialpack, r-cran-mass, r-cran-caret, r-cran-dplyr, r-cran-earth, r-cran-matrix, r-cran-gstat, r-cran-spatstat.data, r-cran-units, r-cran-sp, r-cran-stringr, r-cran-lwgeom, r-cran-geodata Filename: pool/dists/resolute/main/r-cran-spatialeco_2.0-5-1.ca2604.1_all.deb Size: 2157240 MD5sum: d4e46764b1ca596c646a575cdc886855 SHA1: b578fc1be6c7e01038af33ae4e57477a8951b5d5 SHA256: 6c28514d9779106dc2d2f9308f4e352ba943c380d8bcd7fa01ed917eb70c52fd SHA512: 2bf814ff3a98666f816363fa31446552b2f8e9ec0737d6e4c84611db00c4a343a4284b91d79f997e5e6b1952ff23e26bb930a1a7a9aecb2b927a97dcd856e4ea Homepage: https://cran.r-project.org/package=spatialEco Description: CRAN Package 'spatialEco' (Spatial Analysis and Modelling Utilities) Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics. Package: r-cran-spatialgraph Architecture: all Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-pracma, r-cran-sf, r-cran-shape, r-cran-sp, r-cran-splancs Filename: pool/dists/resolute/main/r-cran-spatialgraph_1.0-4-1.ca2604.1_all.deb Size: 124224 MD5sum: 03d182fc65b6e4f3112f5e08099aac3a SHA1: 969a78046343e1c71d0caed4133411be7fe8ae06 SHA256: 818b3b629364ac0d88f3097f0c615c01c3dfee091d0935b80e82ade14e8e7739 SHA512: a35c3b28e509729fa7097485f8337ee18250fc481e29dfe23035056b65e121f4130cb283b8b268ad5882a7edd14d7e001f5514329c9f312c6f23f2e4c493a625 Homepage: https://cran.r-project.org/package=SpatialGraph Description: CRAN Package 'SpatialGraph' (The SpatialGraph Class and Utilities) Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg(). Package: r-cran-spatialpop Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-qpdf, r-cran-numbers Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spatialpop_0.1.0-1.ca2604.1_all.deb Size: 22432 MD5sum: 17de4ed32b4d83def7fad7b8981c988c SHA1: 09a70242659586b3dcff817f95cc3b5e0c087120 SHA256: 407aade6305782507a2c89e9783aa5d47e09fa8c6dfbcc8eaec7f1293f69af06 SHA512: 6ceb325386a9f14f91972c3b4be67d96d947e0a832909dcda7060222a51e1feb733f68edebea1e065f8ee534d0c77fce5bacc7b66031c3069528ccb3ecdc4893 Homepage: https://cran.r-project.org/package=SpatialPOP Description: CRAN Package 'SpatialPOP' (Generation of Spatial Data with Spatially Varying ModelParameter) A spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).. This spatially generated data can be used to test different issues related to the statistical analysis of spatial data. This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables. Package: r-cran-spatialposition Architecture: all Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-isoband, r-cran-raster Suggests: r-cran-lwgeom, r-cran-doparallel, r-cran-foreach, r-cran-cartography, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spatialposition_2.1.3-1.ca2604.1_all.deb Size: 477782 MD5sum: 7fddd2be5079b31547271554ba14c603 SHA1: 0636ddf406b0ac8f7e92279b7717f4d3b7e58e36 SHA256: 455d02e1f666b53f20ed04f8d2b672da7801f588d173f5ced45deebf4435e100 SHA512: 417a3167b8319a81035799e52d118c9c95c52f057c58554369346b843c5fb4770752dcb8a4b5a723248d794e923fb1bec152afd4dfd70b470ebf6571b0ca6f46 Homepage: https://cran.r-project.org/package=SpatialPosition Description: CRAN Package 'SpatialPosition' (Spatial Position Models) Computes spatial position models: the potential model as defined by Stewart (1941) and catchment areas as defined by Reilly (1931) or Huff (1964) . Package: r-cran-spatialprobit Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-spdep, r-cran-spatialreg, r-cran-mvtnorm, r-cran-tmvtnorm Suggests: r-cran-runit, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spatialprobit_1.0.4-1.ca2604.1_all.deb Size: 272348 MD5sum: b18dddc4c68c26ffa1c6e41d5ba28162 SHA1: 2efc318e5806db5e5775dbe2c7bec367b1be4740 SHA256: 9ded651bc7da1729c949a97ba5090957151149f07b70e399f7a07e0c3e811091 SHA512: 8a3069f4e53ac11ac05cc37fb01e471b115f74daa3dd7b3099b7253ddea5fdd5bb352d5c78db4c74fec605eef3b3934493d57d05acec73e1f045112d6994ea6e Homepage: https://cran.r-project.org/package=spatialprobit Description: CRAN Package 'spatialprobit' (Spatial Probit Models) A collection of methods for the Bayesian estimation of Spatial Probit, Spatial Ordered Probit and Spatial Tobit Models. Original implementations from the works of 'LeSage and Pace' (2009, ISBN: 1420064258) were ported and adjusted for R, as described in 'Wilhelm and de Matos' (2013) . Package: r-cran-spatialrdd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-ggplot2, r-cran-rdrobust, r-cran-lmtest, r-cran-sandwich, r-cran-cowplot, r-cran-magrittr, r-cran-rlang, r-cran-broom Suggests: r-cran-knitr, r-cran-tmap, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-lfe, r-cran-stargazer Filename: pool/dists/resolute/main/r-cran-spatialrdd_0.1.0-1.ca2604.1_all.deb Size: 1676808 MD5sum: 09d9d28d212f4796fa1b869769c36759 SHA1: c0808f1023ca44ad30f2fd2911b70554f787d8bf SHA256: b3015b2e46fefd244cdbed15f9868948afd992477897855de08a620dea1fc154 SHA512: bd6559e00177e00b6efb55b88e044b7f3283e76c5dada8a568c729bd3c0899ef855d7a65862b91ff8b868db946c8bcc2e05efd9f07283e744ffe9c98dceca47f Homepage: https://cran.r-project.org/package=SpatialRDD Description: CRAN Package 'SpatialRDD' (Conduct Multiple Types of Geographic Regression DiscontinuityDesigns) Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on 'APIs' of external 'GIS' software ensures replicability and raises the standard for spatial RDDs. Package: r-cran-spatialreg.hp Architecture: all Version: 0.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatialreg, r-cran-ggplot2, r-cran-vegan, r-cran-spdep Filename: pool/dists/resolute/main/r-cran-spatialreg.hp_0.0-2-1.ca2604.1_all.deb Size: 45462 MD5sum: 9adb73464627a41ac16f7075110420fb SHA1: f75f795eba7c3d2239fc9daca026a6a2c9b4be9e SHA256: 7e1539fecde0470320c21b8c565382aaa402d0fe27c5b459631855585f4750b9 SHA512: 71a2b08d425bac486cd25fd12de85dbb6fa0744abfc1955b21a03da41fe05e5642a726256d97c9a0263349835e6b93dacf909d119c8e244c00360631f0c1520d Homepage: https://cran.r-project.org/package=spatialreg.hp Description: CRAN Package 'spatialreg.hp' (Hierarchical Partitioning of R2 for Spatial SimultaneousAutoregressive Model) Conducts hierarchical partitioning to calculate individual contributions of spatial and predictors (groups) towards total R2 for spatial simultaneous autoregressive model. Package: r-cran-spatialregimes Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdep, r-cran-quantreg, r-cran-gwmodel, r-cran-plm, r-cran-spatialreg Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-spatialregimes_1.2-1.ca2604.1_all.deb Size: 454014 MD5sum: 8979c2978decf61365e5ca6c01900c9a SHA1: c990d06b41daf1b4d28c514b3846c1c4188d94b2 SHA256: 1f02c52c761540cc66fe53c10f9cd506d77cc076f2fb37a41f43b786f079bd78 SHA512: 62299c2e34ce165c4c5486e238adf8e881f3c33504c515f3bbcf6ad507795ad2706d126a54836de46dd0effe3b9ebad4545201c59bd4b9bb6506356a0de49eda Homepage: https://cran.r-project.org/package=SpatialRegimes Description: CRAN Package 'SpatialRegimes' (Spatial Constrained Clusterwise Regression) A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process. Package: r-cran-spatialregroup Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-spdep, r-cran-igraph, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-spatialregroup_0.1.0-1.ca2604.1_all.deb Size: 54912 MD5sum: 638d12a25b120263106c7c16ecc7f71c SHA1: ebee92999c557f50987a0c6447c07b446ed42f53 SHA256: 37abd057a06a02986b7c24492a8c791e0c7f62c6d4cddc627491c7e94cf41236 SHA512: d833f3ff4925227089f1df21f3531f95440a5629704ad2525a1a9dd4baeb2168821a8fe73f60f876f2b17e958f903259be59954bb42ce925d67794833c4a6281 Homepage: https://cran.r-project.org/package=spatialRegroup Description: CRAN Package 'spatialRegroup' (Iterative Spatial Regrouping of Administrative Units byAttributive Affinity) Evaluates the statistical coherence of existing administrative partitions (e.g. inter-municipal groupings, districts) by identifying spatial units whose attributive profile is more similar to a neighbouring group than to their own. Border units are iteratively reassigned to the group they are most affine with, based on Euclidean or Mahalanobis distance computed on user-supplied numeric variables, with optional per-variable weighting and standardisation. Spatial contiguity is enforced throughout: isolated candidates are reintegrated into their original group, disconnected fragments are resolved, and empty groups are restored. Convergence is monitored via an eta-squared cohesion criterion. The resulting partition can be compared to the original administrative delineation using multilevel models, providing a quantitative measure of boundary inefficiency. Package: r-cran-spatialrf Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-foreach, r-cran-doparallel, r-cran-ranger, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-huxtable, r-cran-patchwork, r-cran-viridis Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-spatialrf_1.1.5-1.ca2604.1_all.deb Size: 6992070 MD5sum: a3435393a05bb9ec1c90aff5106d8d7f SHA1: 7f9dfeb24453bd979e272cb11d0d2ad1eff5ee5d SHA256: 9e4bb4af79b739f297795c92b28f6f36c87992cf9c93d9585894f049b95d7ee0 SHA512: 14c838380de0b6345d7710b67d5bdd1b34f93eadb668e627a5709f689c525267cfbaed5ad4198172b0dd992229b710e5dd73c791e393391fb332f6265ef01c35 Homepage: https://cran.r-project.org/package=spatialRF Description: CRAN Package 'spatialRF' (Easy Spatial Modeling with Random Forest) Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 ) or the RFsp approach (Hengl et al. ). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the 'ranger' package (Wright and Ziegler 2017 ). Package: r-cran-spatialromle Architecture: all Version: 0.1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spatialromle_0.1.1.1-1.ca2604.1_all.deb Size: 42368 MD5sum: 33e7d842d6ea917f80cb355318e7b2af SHA1: b812dd4bdf03c263fa0038985c42d21d353bc025 SHA256: 9bf3cbdafdad7068823cbb8a9fb22448f2fa8e8b0617ecd1d88cf10e5d1d77a6 SHA512: 22bf48dc0525d8f83933d9d88629e2e41668c85a0b8356976d35358bab7a6ce76dbde35346ff5a12f0fb6a7f49ea9ba588fc0fc05ae69b32b23b98e612d5112b Homepage: https://cran.r-project.org/package=SpatialRoMLE Description: CRAN Package 'SpatialRoMLE' (Robust Maximum Likelihood Estimation for Spatial Error Model) Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, ). Package: r-cran-spatialtime Architecture: all Version: 1.3.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-rcolorbrewer, r-cran-furrr, r-cran-future, r-cran-tidyselect, r-cran-crayon, r-cran-pbmcapply, r-cran-dixon, r-cran-tibble, r-cran-stringr Suggests: r-cran-knitr, r-cran-devtools, r-cran-rmarkdown, r-cran-testthat, r-cran-gridextra, r-cran-pheatmap Filename: pool/dists/resolute/main/r-cran-spatialtime_1.3.4-5-1.ca2604.1_all.deb Size: 3397180 MD5sum: 5fa40fa50712310a4ba6316530f7dd86 SHA1: 281ee2249cc23cc3eadf22239fc218a4c38ea525 SHA256: fae9e2ba0e13c9b363bb35d7d3293b06ed915a20e8bec6c75c8d850972c4d784 SHA512: 05d6f4c78240f607cc61a26f426a9d0d80248ecbdae637c659f93e13720bdcae127646578519a697ea00c86aafff50064c7bd99eb0eae1e8b369b459c0362394 Homepage: https://cran.r-project.org/package=spatialTIME Description: CRAN Package 'spatialTIME' (Spatial Analysis of Vectra Immunoflourescent Data) Visualization and analysis of Vectra Immunoflourescent data. Options for calculating both the univariate and bivariate Ripley's K are included. Calculations are performed using a permutation-based approach presented by Wilson et al. . Package: r-cran-spatialvs Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-fields Filename: pool/dists/resolute/main/r-cran-spatialvs_1.1-1.ca2604.1_all.deb Size: 343206 MD5sum: 2e4175b50c2fa03417fa63b19c6ea3a2 SHA1: 8e343ddb9c9f09ca2ae89962bd36f47a8c4d7d22 SHA256: b44b65d1f20eebcededb9fe065003ee62a26f4d3f31ab0733aa5e15c1f40a267 SHA512: 917291691eab3e60e2b93bd7d8e3e116ea3521186bb0c9dfaf48adc4e35671cd8daa88a3ebd72c0b25395d464fc7de2f5fc6a9777e5dfe3ca4d2efab1bd0f07a Homepage: https://cran.r-project.org/package=SpatialVS Description: CRAN Package 'SpatialVS' (Spatial Variable Selection) Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, ). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006. Package: r-cran-spatialvx Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3884 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-fields, r-cran-smoothie, r-cran-smatr, r-cran-turboem, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-distillery, r-cran-maps, r-cran-boot, r-cran-circstats, r-cran-fastcluster, r-cran-waveslim Filename: pool/dists/resolute/main/r-cran-spatialvx_1.0-3-1.ca2604.1_all.deb Size: 3886820 MD5sum: 929699cb333d483c3b0f49d86988da4c SHA1: 75d49876d105994a382683c79ae083f83dfdefae SHA256: 8f52a5bc9596f1c69d9e37a85cbeae3f0abbac60610a87ced376931655889123 SHA512: afd603c762455d32f466dfbbd128ef596526f2a26d98ce4cd18af4d0ba1f4c49c75e8f68dfbba0503f607f927cbcfd5708b3f4260c47f4347a563105cbc2831d Homepage: https://cran.r-project.org/package=SpatialVx Description: CRAN Package 'SpatialVx' (Spatial Forecast Verification) Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field. Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) and for a tutorial on some of the main functions available here, see Gilleland (2022) . Package: r-cran-spats Architecture: all Version: 1.0-20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fields, r-cran-spam, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-spats_1.0-20-1.ca2604.1_all.deb Size: 184130 MD5sum: 681c158e0398520860c1911037880bd3 SHA1: f538f1eb00c56c4a85b9d878f40d58d654af2d70 SHA256: 8da853e28c785f04b4b471a5e792dc66669c3e5f5711f21d9c8a4ed8262c3214 SHA512: 92558e8cf638ad1dc7b4d8c3ae6e66bb736a89b38247f1ca0bec0ed787757904412b873dbb306da137704336a2dec730a888d4dbb449532b0456eb7de74aca34 Homepage: https://cran.r-project.org/package=SpATS Description: CRAN Package 'SpATS' (Spatial Analysis of Field Trials with Splines) Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models. Package: r-cran-spatsoc Architecture: all Version: 0.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adehabitathr, r-cran-data.table, r-cran-igraph, r-cran-sf, r-cran-lwgeom, r-cran-circstats, r-cran-units, r-cran-rlang Suggests: r-cran-asnipe, r-cran-knitr, r-cran-markdown, r-cran-move2, r-cran-rmarkdown, r-cran-s2, r-cran-sftrack, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spatsoc_0.2.13-1.ca2604.1_all.deb Size: 1020142 MD5sum: 477f22a3c1c6f83fcfea667b29256942 SHA1: d85e6c7ce6d91deeaeaf033545f739d3fa7b7f50 SHA256: 207f3cf03186f2ae4fda9ca48b9eee6f98bb6865ad5553fe3841c7ec5494e064 SHA512: 97d2469613ecf06c78fa3645b283ed1c18e839dd2f9ffb3753d850debfd7cd4b0c677c3029d01afefc276f368f24c6240d48828f3233d8459918b7e0f0aada37 Homepage: https://cran.r-project.org/package=spatsoc Description: CRAN Package 'spatsoc' (Group Animal Relocation Data by Spatial and TemporalRelationship and Measure Intragroup Social Dynamics) Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) ). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks, and perform data-stream randomization methods suitable for GPS data. Also provides measures of intragroup social dynamics including distance and direction to leaders, centroids and nearest neighbours. Package: r-cran-spatstat.data Architecture: all Version: 3.1-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4276 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.utils, r-cran-matrix Suggests: r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet Filename: pool/dists/resolute/main/r-cran-spatstat.data_3.1-9-1.ca2604.1_all.deb Size: 4152034 MD5sum: 215625fc576d656775bc9f7a7d3411ee SHA1: 4ba6b9d11f24f03ca81093aeddf266702f83c4e3 SHA256: a6d859e513cec1661676ecece17da60b26bb79cdd2f5f70295380323fa515d15 SHA512: 903b055c156dff75dfe315e25661516c3003d3e5a9c5f3407018111284508924fcc3f93a7796bbfdfb6d2f421eaa1003ee226fceab4e95ccbf45fd087478b573 Homepage: https://cran.r-project.org/package=spatstat.data Description: CRAN Package 'spatstat.data' (Datasets for 'spatstat' Family) Contains all the datasets for the 'spatstat' family of packages. Package: r-cran-spatstat.gui Architecture: all Version: 3.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-rpanel, r-cran-spatstat.utils Filename: pool/dists/resolute/main/r-cran-spatstat.gui_3.1-0-1.ca2604.1_all.deb Size: 80736 MD5sum: cca8b828d849414e91b518780f66d5e5 SHA1: 808cf9870c4d91fd85c8ec282ff07ca9d6b2cfb7 SHA256: eff20064d0fbeb80f323f03542bbff9eeed0b0489e13c3f0b4a2af6d54835d4a SHA512: 13dcab4a49466c786844e6f324a2eaf4fc83f321c6f4c023e952b82dc46128869011e5d2ba02538a62aa506acc48e372ceb5fb8f7327bb0f4a43fc055240da1a Homepage: https://cran.r-project.org/package=spatstat.gui Description: CRAN Package 'spatstat.gui' (Interactive Graphics Functions for the 'spatstat' Package) Extension to the 'spatstat' package, containing interactive graphics capabilities. Package: r-cran-spatstat.local Architecture: all Version: 5.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.sparse, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat, r-cran-tensor, r-cran-spatstat.utils Filename: pool/dists/resolute/main/r-cran-spatstat.local_5.1-0-1.ca2604.1_all.deb Size: 349884 MD5sum: dbb98a401f6e764d66f953da8cc38285 SHA1: f5c4fa2aa698bf316b16ed6ea4e3139f410fe5e2 SHA256: be9cccfb1d1c51b9c5f620888c5e7e81c4b5daa3ba126f4cfe84f13a369a5435 SHA512: 83c854464f32a5637244baca33efc537d422f13789adfd566f32add083f6e5d86759f6678b9c8738c7f70833f07b569f973643e44546e2a999f7086a5bdcbcb6 Homepage: https://cran.r-project.org/package=spatstat.local Description: CRAN Package 'spatstat.local' (Extension to 'spatstat' for Local Composite Likelihood) Extension to the 'spatstat' package, enabling the user to fit point process models to point pattern data by local composite likelihood ('geographically weighted regression'). Package: r-cran-spatstat Architecture: all Version: 3.6-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5477 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat.utils Filename: pool/dists/resolute/main/r-cran-spatstat_3.6-0-1.ca2604.1_all.deb Size: 4332002 MD5sum: ecd634550f955db61da7da75f53f2607 SHA1: ac01e72721268ffea54e5192fca1e1b35eb2896f SHA256: dfb702ec183a7f35449b7dc010f422367bf7fe5c2630acac78c8426229636f10 SHA512: 28588a6a2e51d40f516a2c8925cad5c249d5a152a14ddda6e4039e881013fe574fe7f346f2d4bd02bb2d9f44cd22ea513530edce42aa16eacc4ce2e645cc9129 Homepage: https://cran.r-project.org/package=spatstat Description: CRAN Package 'spatstat' (Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests) Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Package: r-cran-spatsurv Architecture: all Version: 2.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-sp, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-raster, r-cran-iterators, r-cran-fields, r-cran-matrix, r-cran-stringr, r-cran-sf, r-cran-rcolorbrewer, r-cran-lubridate Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-spatsurv_2.0-1-1.ca2604.1_all.deb Size: 927066 MD5sum: 7adf94c0d968318c499c0245e3d0809e SHA1: d4da0af07137e342f9493857ba72a06ace282ecc SHA256: 87b6d06b404354a2a088bf0eecd89b18387d6bfe78d4402f0eba5521c4d16ca9 SHA512: 6ca9f20e58010f37e238be31bc2e33897fdc711bda8f2e06fbc39addcab34ecc7df0f73777622f61a2f8f5245878c319a01a2c38f173a58005cc29556cc6f71e Homepage: https://cran.r-project.org/package=spatsurv Description: CRAN Package 'spatsurv' (Bayesian Spatial Survival Analysis with Parametric ProportionalHazards Models) Bayesian inference for parametric proportional hazards spatial survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) . Package: r-cran-spb Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-spb_1.0-1.ca2604.1_all.deb Size: 36394 MD5sum: dcc66880401efd4f8a9f8539a05bd80e SHA1: 2947631dd8062778447bae4f5aa9724cea505aa1 SHA256: c1c839f0abf67accbb64b4de5a90d3fbda368a7b537f59fd411d7e0f0351e800 SHA512: 2eefee13746c1409581f4b6432887f7af0110bfda54199ab17a916d50dd56f413f677e7c3679ef00a594391a5db34f9031e7aa3a554f9af98bd637b04065df3f Homepage: https://cran.r-project.org/package=SPB Description: CRAN Package 'SPB' (Simple Progress Bars for Procedural Coding) Provides a simple progress bar to use for basic and advanced users that suits all those who prefer procedural programming. It is especially useful for integration into markdown files thanks to the progress bar's customisable appearance. Package: r-cran-spbabel Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 915 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sp, r-cran-tibble, r-cran-rlang, r-cran-pkgconfig Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-raster, r-cran-sf, r-cran-rmarkdown, r-cran-covr, r-cran-trip, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-spbabel_0.6.0-1.ca2604.1_all.deb Size: 848706 MD5sum: 0290bee1d8b271078a8fe6c7c4bbd377 SHA1: d23dcbe90eb530fde4a13e01132dd69ba03cfb80 SHA256: 99c0cdf68869522bcefd7996de362beda408a38579f43d26fdc43782c09d17ba SHA512: f587658b74970bea7c5e9a367568375a9995681867893feff547ad76face6bdb407a680f92b786a3b12e954a5b93ee457986274b1755928de0292e1d33e67c91 Homepage: https://cran.r-project.org/package=spbabel Description: CRAN Package 'spbabel' (Convert Spatial Data Using Tidy Tables) Tools to convert from specific formats to more general forms of spatial data. 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(2) local defect monitoring and diagnostics in stochastic textured surfaces, which was proposed by Bui and Apley (2018a) . (3) global change monitoring in the nature of stochastic textured surfaces, which was proposed by Bui and Apley (2018b) . (4) computation of dissimilarity matrix of stochastic textured surface images, which was proposed by Bui and Apley (2019b) . 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Package: r-cran-speck Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4079 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-magrittr, r-cran-matrix, r-cran-rsvd, r-cran-seurat Suggests: r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-seuratobject, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-speck_1.0.1-1.ca2604.1_all.deb Size: 4086296 MD5sum: b0aa8c6c997fe9aa3b384d3795d80795 SHA1: f6fb75c98667abe3084ded52c5ffab692d1e623f SHA256: fb6f2e1cf624a2fc190c64c67de24c6a5c9b30a3676927c9241e38fca01e5926 SHA512: a61d1259d2410122803e7cf10a896e1642e87baf238f23ab5ef5e22c8b384a0c1c6e596e6166fdf1af0011e1f54ee036b2858c2d092375acb402ede49d339547 Homepage: https://cran.r-project.org/package=SPECK Description: CRAN Package 'SPECK' (Receptor Abundance Estimation using Reduced Rank Reconstructionand Clustered Thresholding) Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) , Stuart et al., (2019) , Butler et al., (2018) and Satija et al., (2015) . Method for the RRR is further detailed in: Erichson et al., (2019) and Halko et al., (2009) . Clustering method is outlined in: Song et al., (2020) and Wang et al., (2011) . Package: r-cran-specr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cowplot, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggraph, r-cran-glue, r-cran-igraph, r-cran-lifecycle, r-cran-lme4, r-cran-magrittr, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-broom.mixed, r-cran-gapminder, r-cran-ggridges, r-cran-knitr, r-cran-lavaan, r-cran-testthat, r-cran-tidyverse, r-cran-performance, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-specr_1.0.0-1.ca2604.1_all.deb Size: 2344862 MD5sum: 1e7b64df0cc2cbead34f49cdb26e3777 SHA1: c0ee3b9d87439289550c9ca8ebd3c808c2a9d38d SHA256: 471e9611c9d0b865b3d4d10b1f03f1a2ba62d371acaa8fb1c71c07468c0c9984 SHA512: dc32874b5f77046fceeb59ceb8be1d50d07d00cd0f96566132d9294ae15b15c3c19120035c7fc2eb09b13baf63086c085b297730a72d9b52c4f3d8447082a0f7 Homepage: https://cran.r-project.org/package=specr Description: CRAN Package 'specr' (Conducting and Visualizing Specification Curve Analyses) Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2020, ) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, ) including functions to setup, run, evaluate, and plot all specifications. 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'spect' provides a simple interface consistent with commonly used R data analysis packages, such as 'caret', a variety of parameter options to help facilitate search automation, a high degree of transparency to the end-user - all intermediate data sets and parameters are made available for further analysis and useful, out-of-the-box visualizations of model performance. Methods for transforming survival data into discrete-time are adapted from the 'autosurv' package by Suresh et al., (2022) . Package: r-cran-spectacles Architecture: all Version: 0.5-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2821 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-reshape2, r-cran-stringr, r-cran-baseline, r-cran-signal, r-cran-epir Suggests: r-cran-caret, r-cran-pls, r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-spectacles_0.5-5-1.ca2604.1_all.deb Size: 2466044 MD5sum: 00d2a6e425ebd791435c242180c81571 SHA1: 146cc176753ee54012d1e53eae1ab2a38ba5df10 SHA256: 948a41d958d66d294e3e6e0869ca29afa7ef78eef6bc799f1c5626bcd3a7cb71 SHA512: 775b0b28092660d3709e3b30252f9dacb8bf2b3cb2ff84c3ae7a34cc6a3afc79f4feca88b1a7d18daebf1ef59af1d0316d71feaaed122602b90ae744f4056761 Homepage: https://cran.r-project.org/package=spectacles Description: CRAN Package 'spectacles' (Storing, Manipulating and Analysis Spectroscopy and AssociatedData) Stores and eases the manipulation of spectra and associated data, with dedicated classes for spatial and soil-related data. 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Package: r-cran-spectr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-lomb Suggests: r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spectr_1.0.1-1.ca2604.1_all.deb Size: 22718 MD5sum: 96b95c24078504ba7e74d690798033f4 SHA1: e86bf77adcc349d2e03b5cf5de1d41174db0a30d SHA256: 7c8afad7b089b65c24e4daf7844dc1d88c6b5bee6b2cb0506fb15c088c5aba57 SHA512: 66b8b207285c9859e8256e72b7bb2b33438de6503d44834a74ad47eeab0b6b1e8e1b7be6ee0f84de87398be0c7613452710a70fbe39392d05da263b07dc06f60 Homepage: https://cran.r-project.org/package=spectr Description: CRAN Package 'spectr' (Calculate the Periodogram of a Time-Course) Provides a consistent interface to use various methods to calculate the periodogram and estimate the period of a rhythmic time-course. Methods include Lomb-Scargle, fast Fourier transform, and three versions of the chi-square periodogram. See Tackenberg and Hughey (2021) . 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Fragmented and irregularly spaced data can be processed in almost all methods. Both, FFT as well as LOMB methods take multivariate data and return standardized PSD. For didactic reasons an analytical approach for deconvolution of noise spectra and sampling function is provided. A user friendly interface helps to interpret the results. Package: r-cran-spectralanomaly Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spectralanomaly_0.1.1-1.ca2604.1_all.deb Size: 85424 MD5sum: 01ab09cc7f0f1a1ef633776becf7d970 SHA1: 6afe22d9bb6a0a1b6b0652569df5843ca2e18724 SHA256: f349e6ae0e503dd3076614763f15aea3621e810a3b9064fe16b73c2a60b69256 SHA512: 4cb4d5280c63f0896de1db5daa9c264732da5c9d38591a18d788bbeb96a8bb6db6a29aa03c3196dc57c8eab68defc7d97e039ec349092f8935f844dd76ec77c6 Homepage: https://cran.r-project.org/package=spectralAnomaly Description: CRAN Package 'spectralAnomaly' (Detect Anomalies Using the Spectral Residual Algorithm) Apply the spectral residual algorithm to data, such as a time series, to detect anomalies. Anomaly scores can be used to determine outliers based upon a threshold or fed into more sophisticated prediction models. Methods are based upon "Time-Series Anomaly Detection Service at Microsoft", Ren, H., Xu, B., Wang, Y., et al., (2019) . Package: r-cran-spectralclmixed Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra, r-cran-cluster, r-cran-ggplot2, r-cran-ggally Filename: pool/dists/resolute/main/r-cran-spectralclmixed_1.0.2-1.ca2604.1_all.deb Size: 25402 MD5sum: baa4925bcb49a7f5924eb6855ac5b9bd SHA1: 8e171f42a5d904bc78957746d56d1ae2a19a6e73 SHA256: 1fa0b746221c5ac03f232b24ae266603c78d6dbe8c232002b335ad89b49c1c01 SHA512: eaa37f4fac68aa29401d2acc5196e06a0f0ca9341c160cb96c537606bf7af2ecb638874f66fd09c4406413fd8c4045c5b5d925b5787442cc0658681df1c55fb5 Homepage: https://cran.r-project.org/package=SpectralClMixed Description: CRAN Package 'SpectralClMixed' (Spectral Clustering for Mixed Type Data) Performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) . Package: r-cran-spectralgp Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spectralgp_1.3.4-1.ca2604.1_all.deb Size: 138512 MD5sum: 670a7e1e9bc32facfb59b061b85940e6 SHA1: a8f7d9f94f5d9d83c682b51d08e1253f3ca75a99 SHA256: c4d94c22044a68cbd586dd6aec72067c144b40c16c5ed5d5077ef48bc3a25be7 SHA512: e8d546132564959adf359e83594898d261da63077974b26de31f46e93fcbf219d5a9407647a5da819332150e40e5b8f84835b1f269905dfc4a3570ff618b80db Homepage: https://cran.r-project.org/package=spectralGP Description: CRAN Package 'spectralGP' (Approximate Gaussian Processes Using the Fourier Basis) Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers. Package: r-cran-spectralmap Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scatterplot3d, r-cran-fields Filename: pool/dists/resolute/main/r-cran-spectralmap_1.0-1.ca2604.1_all.deb Size: 14710 MD5sum: d2ba80763b4dbb3666342dfeecd68359 SHA1: adf8090958f2ee0a34172bd96ffb0059eb13d777 SHA256: fa07b15caf9e9d59e1cf660ca0c42006f0a3ac4e5529c32acc3854c3a2678cfa SHA512: 809a727d3546adb82e8521778b31e2810b36cd15256c1e044673921cc65ad718ae9e245175637b29ebe0201f3d028fad9fc363ce2d694514122036acd03d76c4 Homepage: https://cran.r-project.org/package=SpectralMap Description: CRAN Package 'SpectralMap' (Diffusion Map and Spectral Map) Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps. Package: r-cran-spectralr Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgee, r-cran-geojsonio, r-cran-sf, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-spectralr_0.1.4-1.ca2604.1_all.deb Size: 955006 MD5sum: a7a64b8cf7560f155e9ff76c375b488f SHA1: 36c05c20831105247b921eb1093106efc52ef02a SHA256: 6ac266961925c177d08811c6cf69e91e5a66b2dc98d64d377686e3268832589d SHA512: a60bfb5bfbc22c75e0842a5c70b3027cb637e4608eb69af65223ae3d9a99f53f9c044bda4bed5a6c5fbcae0815eaf03462e69a05a447478e21405a39576d2d3b Homepage: https://cran.r-project.org/package=spectralR Description: CRAN Package 'spectralR' (Obtain and Visualize Spectral Reflectance Data for Earth SurfacePolygons) Tools for obtaining, processing, and visualizing spectral reflectance data for the user-defined land or water surface classes for visual exploring in which wavelength the classes differ. Input should be a shapefile with polygons of surface classes (it might be different habitat types, crops, vegetation, etc.). The Sentinel-2 L2A satellite mission optical bands pixel data are obtained through the Google Earth Engine service () and used as a source of spectral data. Package: r-cran-spectran Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chromote, r-cran-colorspec, r-cran-cowplot, r-cran-dplyr, r-cran-gghighlight, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-ggtext, r-cran-gt, r-cran-htmltools, r-cran-magrittr, r-cran-openxlsx, r-cran-pagedown, r-cran-patchwork, r-cran-png, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyalert, r-cran-shinydashboard, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spacesxyz, r-cran-spscomps, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-waiter, r-cran-webshot2, r-cran-withr Suggests: r-cran-config, r-cran-here, r-cran-magick, r-cran-pkgload, r-cran-readxl, r-cran-rhub, r-cran-rmarkdown, r-cran-rsconnect Filename: pool/dists/resolute/main/r-cran-spectran_1.0.6-1.ca2604.1_all.deb Size: 2725698 MD5sum: 933f458d5a07d4288c1983c6cb8d7694 SHA1: 344f0e07f553d8180a22fe3b73e9ebe3df68376e SHA256: 4f6b40c8cc7924ace213fa24291d078f68f4865c1c8f1142bf9fe549bab16370 SHA512: 1823f03c01c9867b04f22ae673d6dd451ceddd531de613fd06ce86a6aa67894561686946e8fd8eddb72c6c0c06d3137f3a48d49425581f0145d5804429ce8af8 Homepage: https://cran.r-project.org/package=Spectran Description: CRAN Package 'Spectran' (Visual and Non-Visual Spectral Analysis of Light) Analyse light spectra for visual and non-visual (often called melanopic) needs, wrapped up in a Shiny App. 'Spectran' allows for the import of spectra in various CSV forms but also provides a wide range of example spectra and even the creation of own spectral power distributions. The goal of the app is to provide easy access and a visual overview of the spectral calculations underlying common parameters used in the field. It is thus ideal for educational purposes or the creation of presentation ready graphs in lighting research and application. 'Spectran' uses equations and action spectra described in CIE S026 (2018) , DIN/TS 5031-100 (2021) , and ISO/CIE 23539 (2023) . Package: r-cran-spectrolab Architecture: all Version: 0.0.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-covr, r-cran-tinytex, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spectrolab_0.0.19-1.ca2604.1_all.deb Size: 1722422 MD5sum: 19d43332f1d7f9bdc426837ee3b78521 SHA1: ef3b555d39ecc5ff5c53be0164490e3993cc8768 SHA256: add1efc001269f07129fbe238b9418fba6165babe3bc01e02befa02021baee67 SHA512: 0b8664e80aeaa6fa51eec517231c5052add67f2bfc0a1c7577e1c5ed588f06e5dfb5e35ba905beef59bd0d510eb2f5f312bfa73011a93ceb439cac8e9affcf99 Homepage: https://cran.r-project.org/package=spectrolab Description: CRAN Package 'spectrolab' (Class and Methods for Spectral Data) Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class spectra that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra. Package: r-cran-spectrum Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-clusterr, r-cran-rfast, r-cran-diptest Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-spectrum_1.1-1.ca2604.1_all.deb Size: 3536002 MD5sum: 46bc3fe64e797eb161f0923c5ce4b8dd SHA1: 0cec68cfdf32da49274a3d284f6c29bb364a92d5 SHA256: f7547d5c0c2cf0a6a5d068ca6142baf24a9e90e16a98c8c4bfd758a8f3a3d156 SHA512: 8f6f31f60aec148f5c9591bf3da0e942284094e2a737e6f64e73cad2d9b4c510611d174f84ac12f71e397c4b5c9030c15d97a62a12cf96a91515c40df860cf0d Homepage: https://cran.r-project.org/package=Spectrum Description: CRAN Package 'Spectrum' (Fast Adaptive Spectral Clustering for Single and Multi-View Data) A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K. Package: r-cran-spedecon Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-spedecon_0.1-1.ca2604.1_all.deb Size: 49666 MD5sum: 9342fd00b56a51cf880e6e7381f2ad68 SHA1: 3002d8dfee3b47feb860c4697d822498a84557c4 SHA256: e99f665d82d4b2f1b77d46ddfbe617d7243e5cad666d9e40b13ac50832130ca3 SHA512: 7a294133762756e609e7dfac86c0f2e1f02fbb9d88d6f6591f44aaf858cf5cb4bb6b01c40d76aa641b9fe2c12ef990db9eca67e62d7eddedc8dccb5d63e575dd Homepage: https://cran.r-project.org/package=spedecon Description: CRAN Package 'spedecon' (Smoothness-Penalized Deconvolution for Density Estimation UnderMeasurement Error) Implements the Smoothness-Penalized Deconvolution method for estimating a probability density under measurement error of Kent and Ruppert (2023) . The estimator is formed by computing a histogram of the error-contaminated data, and then finding an estimate that minimizes a reconstruction error plus a smoothness-inducing penalty term. The primary function, sped(), takes the data and error distribution, and returns the estimator as a function. Package: r-cran-spedinstabr Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2940 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-beanplot, r-cran-raster, r-cran-plotrix Filename: pool/dists/resolute/main/r-cran-spedinstabr_2.2-1.ca2604.1_all.deb Size: 2520564 MD5sum: d92980bb305fdd6a0c28db70f0e6d267 SHA1: 8de9dd037eedb471045f06508b76adfb18de7c87 SHA256: 11c32e39477db5d3f89da46c1934ee49433ee626619131c217d5d8de711f91a8 SHA512: c4c45b21785ca8aeff290dc2e96152039d3c4d731a33be6712e7dec275f0cdeeacc1167e1a2fe80affb43983b6fb166101a84c5f3447a761abbd93da9e23bd2d Homepage: https://cran.r-project.org/package=SPEDInstabR Description: CRAN Package 'SPEDInstabR' (Estimation of the Relative Importance of Factors AffectingSpecies Distribution Based on Stability Concept) From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent. Package: r-cran-speech Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tm, r-cran-tidyr, r-cran-pdftools, r-cran-rvest Filename: pool/dists/resolute/main/r-cran-speech_0.1.5-1.ca2604.1_all.deb Size: 3631568 MD5sum: 329421aae1ff52fbf912d420caa36b88 SHA1: 20636abc9ce150a30b7401dd89b99e078acc8b33 SHA256: 6a84ca08bc00ba34ce6ac336b08365d42dfd3dfd7702c3aed2b028bfdae216aa SHA512: ea30e09d4d3aa881dc389acf86ce42ab22548e5383f9cd86f32469443f9e50a88fc8350f4ecb6bc2877309c536c2c01a4099e46dd0f79cc67452e8d89645f0f4 Homepage: https://cran.r-project.org/package=speech Description: CRAN Package 'speech' (Legislative Speeches) Converts the floor speeches of Uruguayan legislators, extracted from the parliamentary minutes, to tidy data.frame where each observation is the intervention of a single legislator. 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Package: r-cran-speedglm Architecture: all Version: 0.3-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-biglm Filename: pool/dists/resolute/main/r-cran-speedglm_0.3-5-1.ca2604.1_all.deb Size: 190520 MD5sum: 411498c8d79343cbb3d83273963fc026 SHA1: 176a308fa0b79a56c2800f85e2669b76e86d627e SHA256: 4f1764ea46b3919c639f93b03006bc40960c4c8891e88f37b35ae146da7426b7 SHA512: 75e59221b6eecd3e639988045c3a33ea8bdcc6748ad4d9a6d08d81a372e824b68e2dd02cbc4e4c6761df8206551c53d68adca211ee97be3da57d2d3d117d9076 Homepage: https://cran.r-project.org/package=speedglm Description: CRAN Package 'speedglm' (Fitting Linear and Generalized Linear Models to Large Data Sets) Fitting linear models and generalized linear models to large data sets by updating algorithms, according to the method described in Enea (2009, ISBN: 9788861294257). Package: r-cran-speedybbt Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1575 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayeslogit, r-cran-matrix Suggests: r-cran-expm, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-speedybbt_1.0-1.ca2604.1_all.deb Size: 1468664 MD5sum: c28d6be30f16af9380f2da35b5aa6a90 SHA1: 61c4e38d736bea4c7f9b1dc456dd07b9c47239ae SHA256: 31fb2e21735d130069375405f03e84f484143353792806c30028bfde0751122e SHA512: d8147b0a67273d66f20a632c8b672e095fd3711cb8330dfa7a3f2d6c73202dd0d7a2126fdecfc70c37eb2fe11a695af8c4deb2ce0699bac17ba48ca388d44bec Homepage: https://cran.r-project.org/package=speedyBBT Description: CRAN Package 'speedyBBT' (Efficient Bayesian Inference for the Bradley--Terry Model) A suite of functions that allow a full, fast, and efficient Bayesian treatment of the Bradley--Terry model. 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Package: r-cran-speedycode Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-purrr Suggests: r-cran-labelled, r-cran-readroper Filename: pool/dists/resolute/main/r-cran-speedycode_0.3.0-1.ca2604.1_all.deb Size: 42874 MD5sum: 43325ade86abae4478a60134bfd70d0e SHA1: 51c4f851c5b4b42f43b142b4689f8f7e1120d97b SHA256: 03a3104d7393af486e1a587ec466076efe01f9178b61f30ca21060005917a4c3 SHA512: b59b7a3ae03461b3e5478ce4c0380346d2f3f3e2011f51b50a988d174f35c0e242b662ea0ca090c38d3fbfaaed40da6f7f4024f0ff0157645dc43904059837b6 Homepage: https://cran.r-project.org/package=speedycode Description: CRAN Package 'speedycode' (Automate Code for Adding Labels, Recoding and RenamingVariables, and Converting ASCII Files) Label, recode, rename, and convert datasets and ASCII files more efficiently. 'speedycode' automates the code necessary for labeling variables with the 'labelled' package, recoding and renaming variables with 'dplyr' syntax, and converting ASCII files with the 'readroper' package. Most functions require only the name of the dataset and the code will be automatically written. Some convenience functions useful for converting ASCII files are also included. Package: r-cran-speff2trial Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaps, r-cran-survival Filename: pool/dists/resolute/main/r-cran-speff2trial_1.0.5-1.ca2604.1_all.deb Size: 118578 MD5sum: 78d7038c62cec1e2d806dd6e35cf8dac SHA1: 5a6011c5dbf797dda73a756558e6dee71a46238d SHA256: b0ae4b9157547c6905be17157b7ff46761626d69ff238525128a5fb8cf3f1bbc SHA512: 2e09f4961e7795a81e3a313f3928d6411cbbb9ad15b06f2af9e3b0adcd7357f636177724bdc7e3f3cd04c9d5726f2dce44f81bc57eb648351ca09badaedffe05 Homepage: https://cran.r-project.org/package=speff2trial Description: CRAN Package 'speff2trial' (Semiparametric Efficient Estimation for a Two-Sample TreatmentEffect) Performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random. Package: r-cran-spei Architecture: all Version: 1.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmomco, r-cran-lmom, r-cran-tlmoments, r-cran-reshape, r-cran-ggplot2, r-cran-checkmate, r-cran-zoo, r-cran-lubridate Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spei_1.8.1-1.ca2604.1_all.deb Size: 209716 MD5sum: dc5dd6c8bed5ab7eaee3746095ec98a2 SHA1: d27bf6d7a66826217af93d9d62f53b7eec7ea648 SHA256: 34e3a3936d538899f1f9c13b1d4eb7f5377c0741a6ee4156efb4ef54f4575656 SHA512: 77186dcdc2b61f1fded75577e7a9933435e72feee74b92b27e2f75c3336ad52eadcab850f1ae41e661390fe55e0698ca36a9bb16c9709ef1fb68f926e130739f Homepage: https://cran.r-project.org/package=SPEI Description: CRAN Package 'SPEI' (Calculation of the Standardized Precipitation-EvapotranspirationIndex) A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI). 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The algorithm tries to find the spelling with maximum probability of intended correction out of all possible candidate corrections from the original word. 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Package: r-cran-sperich Architecture: all Version: 1.5-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-foreach, r-cran-raster Suggests: r-cran-lattice, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-sperich_1.5-9-1.ca2604.1_all.deb Size: 230530 MD5sum: 45a857b7760d98004d1a25768454c7bc SHA1: 81ddc51f376bf8616ffb756dfe67be07f8b48e2e SHA256: 921cb7ad80d74afa6712cbe5e848ddcd8855b49092087b1fbe085cb88d19a7d2 SHA512: 47511ca07725e102fec71c154aeec1e0daeccdf80b532ec1d33850e5fb69c4460852f2ac3fbe149c710c018683581f0cc267550a9d212028d5fbf8f9d3254edb Homepage: https://cran.r-project.org/package=sperich Description: CRAN Package 'sperich' (Auxiliary Functions to Estimate Centers of Biodiversity) Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity. Package: r-cran-sperrorest Architecture: all Version: 3.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1592 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-rocr, r-cran-stringr Suggests: r-cran-knitr, r-cran-mass, r-cran-nnet, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-sp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sperrorest_3.0.5-1.ca2604.1_all.deb Size: 1464702 MD5sum: c0693077ac04e31600f92db1b7626c4e SHA1: 477389afbad5f57616042d6ba820558fe8888268 SHA256: 83aa65c249736a0f8e4d7135703a739c7fcf61e1efa5f46f5dc4642524125b29 SHA512: cab15aa25efca325e6679928a311162754c972bd3de4c6a2e9805e03a922ed78035c3f8c3720ef26021f31f584ea1605da8df286b67eef0d9595f3c8dc9e94e9 Homepage: https://cran.r-project.org/package=sperrorest Description: CRAN Package 'sperrorest' (Perform Spatial Error Estimation and Variable ImportanceAssessment) Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap. Package: r-cran-spev Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spev_1.0.0-1.ca2604.1_all.deb Size: 15968 MD5sum: 2ea73f8f15fb998304fdf2f903fe1363 SHA1: 4433f04e7d618f4687cc6897a70df9fd73078103 SHA256: a7ba8c3fd87dfb9711eca56717849ab556a661e6bbf9034e33503c1fe729337e SHA512: 475eee40bf1bd1e070d608449d13ce3ec49bce1d6a30fd11df10a6b65a82976b317f16d720dbecaf51badb6e3a7395227e4df84ccb73bbd2561c477a5d8aa54f Homepage: https://cran.r-project.org/package=SPEV Description: CRAN Package 'SPEV' (Unsmoothed and Smoothed Penalized PCA using Nesterov Smoothing) We provide functionality to implement penalized PCA with an option to smooth the objective function using Nesterov smoothing. Two functions are available to compute a user-specified number of eigenvectors. The function unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has three parameters (the input matrix, the Lasso penalty, and the number of desired eigenvectors). The function smoothed_penalized_EV() computes a smoothed penalized PCA using the same parameters and additionally requires the specification of a smoothing parameter. Both functions return a matrix having the desired eigenvectors as columns. Package: r-cran-spex Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadmesh, r-cran-raster, r-cran-reproj, r-cran-sp, r-cran-crsmeta Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spex_0.7.1-1.ca2604.1_all.deb Size: 127284 MD5sum: 59382aff4e09bdcafcd7fee524903689 SHA1: 2c019c7210453728bbc4bbba5539d3621b8aa459 SHA256: 6d0fb8da9aeb0090c17ed9c6eebd828b4bb837db93688ded196b845ce9545cdb SHA512: bd6d8e83674f031aa8144d8133b85b364a6b7b6b8c8ae15ab0180fe36d35faf0fa55357eb544b1040e2e708ee209bdf5e5213b14732ad08851fd07c5bd99e8d4 Homepage: https://cran.r-project.org/package=spex Description: CRAN Package 'spex' (Spatial Extent Tools) Functions to produce a fully fledged 'geo-spatial' object extent as a 'SpatialPolygonsDataFrame'. Also included are functions to generate polygons from raster data using 'quadmesh' techniques, a round number buffered extent, and general spatial-extent and 'raster-like' extent helpers missing from the originating packages. Some latitude-based tools for polar maps are included. Package: r-cran-spfda Architecture: all Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-grpreg, r-cran-refund Filename: pool/dists/resolute/main/r-cran-spfda_0.9.2-1.ca2604.1_all.deb Size: 62026 MD5sum: 36bc89176bf6e9bf68e7fc060bcbd32f SHA1: dc01020898bb55bfbff0751b18b5c6b5f3b1b3b0 SHA256: 0afd466d9538efb77f7d9882f9e9a1f9afefb21dac4f167cecac6b69e333af97 SHA512: 8c097083c79d6101eb472700f49a98ac3c396e8a660f3bedc7b366bafa6af31bff365c3a164f32ca577da1b6d1e67b9bf2c113d06bfcf898d2c58bb7e538804b Homepage: https://cran.r-project.org/package=spfda Description: CRAN Package 'spfda' (Function-on-Scalar Regression with Group-Bridge Penalty) Implements a group-bridge penalized function-on-scalar regression model proposed by Wang et al. (2023) , to simultaneously estimate functional coefficient and recover the local sparsity. Package: r-cran-spfilter Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spfilter_2.2.0-1.ca2604.1_all.deb Size: 184824 MD5sum: 32525b523743ea0b96d99f86b498b16a SHA1: 1498f0c64c2e04b6cc30c0157303caa17f3c76da SHA256: 650bfb137f837b84d904b383590634b8382825098c5151ebbd5476354e7f5841 SHA512: 494257c9307b9897fe73af4185539868eb6b6c43e2ebe5409130055797251c8fdc5d92d5d7b4cda6ad6d01f8b65f4f4ec3154aed7e71e7dc0cfc662df4746072 Homepage: https://cran.r-project.org/package=spfilteR Description: CRAN Package 'spfilteR' (Semiparametric Spatial Filtering with Eigenvectors in(Generalized) Linear Models) Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models. Package: r-cran-spfsr Architecture: all Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-future, r-cran-tictoc, r-cran-mlr3pipelines, r-cran-mlr3learners, r-cran-ranger, r-cran-ggplot2, r-cran-lgr Suggests: r-cran-caret, r-cran-mass Filename: pool/dists/resolute/main/r-cran-spfsr_2.0.4-1.ca2604.1_all.deb Size: 75428 MD5sum: 617fdef6471db1181d525525f7d85833 SHA1: fe5c41084b45f32453ed1e12ba17b4a59520ea02 SHA256: 9430ad349447bfe050dbd8222b7d34a98606cbf9c7d57c2adfd791f23e5c49bb SHA512: 100e8dd3955191107190ddd6f254d1eb71976abed5b94d564aa72335c5a43bae2faa96a9c1dc204f7efb9b4a5e8595788e74ed95307f073416b1de4debb714a7 Homepage: https://cran.r-project.org/package=spFSR Description: CRAN Package 'spFSR' (Feature Selection and Ranking via Simultaneous PerturbationStochastic Approximation) An implementation of feature selection, weighting and ranking via simultaneous perturbation stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using some error measures such as mean squared error (for regression problems) and accuracy rate (for classification problems). Package: r-cran-spheredata Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spheredata_0.1.3-1.ca2604.1_all.deb Size: 185276 MD5sum: 39498090214b85888b5cc6c08a1eae5e SHA1: 3f311076c91114fd50f998ddf76be4ebdcf99e02 SHA256: e9310a3a4a7518e92a8fc32b0783c86a695b5d51d5cd17fdf4774a282b892f94 SHA512: 9459b1ab60cdb27b85cecef87656c485799bcbd80290c50592c383460465aa20d6de2efff63aadd180312011c987bedae0d93bb2817784dcc95a8c71aa74ea2d Homepage: https://cran.r-project.org/package=spheredata Description: CRAN Package 'spheredata' (Students' Performance Dataset in Physics Education Research(SPHERE)) A multidimensional dataset of students' performance assessment in high school physics. The SPHERE dataset was collected from 497 students in four public high schools specifically measuring their conceptual understanding, scientific ability, and attitude toward physics [see Santoso et al. (2024) ]. The data collection was conducted using some research based assessments established by the physics education research community. They include the Force Concept Inventory, the Force and Motion Conceptual Evaluation, the Rotational and Rolling Motion Conceptual Survey, the Fluid Mechanics Concept Inventory, the Mechanical Waves Conceptual Survey, the Thermal Concept Evaluation, the Survey of Thermodynamic Processes and First and Second Laws, the Scientific Abilities Assessment Rubrics, and the Colorado Learning Attitudes about Science Survey. Students' attributes related to gender, age, socioeconomic status, domicile, literacy, physics identity, and test results administered using teachers' developed items are also reported in this dataset. Package: r-cran-sphereml Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-spheredata, r-cran-lavaan, r-cran-semplot, r-cran-ctt, r-cran-mirt, r-cran-shinycssloaders, r-cran-fselectorrcpp, r-cran-randomforest, r-cran-caret, r-cran-catools, r-cran-proc, r-cran-ga, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-sphereml_0.1.1-1.ca2604.1_all.deb Size: 195254 MD5sum: 816fc2ae94ef898e0445101e075664c1 SHA1: e78964f84d2745446c1be4bf2282bc083bcc4176 SHA256: 1cd6ffdf4891061fe3714a7c5916a0cbfc6ff19a6a8729f233f76f4137307da8 SHA512: c3c55557d6ddebbcc2e93f80544ee58341add127241b14d3fee537ff7ce173c1b2e6a4ae9389008e6adfe7935102039215b83bed16f0e0af77ea3091b3a241a4 Homepage: https://cran.r-project.org/package=sphereML Description: CRAN Package 'sphereML' (Analyzing Students' Performance Dataset in Physics EducationResearch (SPHERE) using Machine Learning (ML)) A simple package facilitating ML based analysis for physics education research (PER) purposes. The implemented machine learning technique is random forest optimized by item response theory (IRT) for feature selection and genetic algorithm (GA) for hyperparameter tuning. The data analyzed here has been made available in the CRAN repository through the 'spheredata' package. The SPHERE stands for Students' Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at the high school curriculum. We follow the stream of multidimensional students' assessment as probed by some research based assessments in PER. The goal is to predict the students' performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables predicting students' performance on physics are also demonstrated. Package: r-cran-sphereoptimize Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sphereoptimize_0.1.1-1.ca2604.1_all.deb Size: 17276 MD5sum: eac28860f5623947f64f2687a495ba00 SHA1: 758fa343c5b9750abd465b3a6c41ba7f36a7c709 SHA256: 98a3de7cd3b2f7e478e840ae597ed3d940bc853092a04d42d5e3cc57aaced791 SHA512: 7685e35bcb6535dce6db4e64410bdf39485fcb25102f2d69d88076a575b29d7517dab5f3961c74a80739502a485e4e9ec245f47e85bd7243aa97727ebfbe4c0e Homepage: https://cran.r-project.org/package=SphereOptimize Description: CRAN Package 'SphereOptimize' (Optimization on a Unit Sphere) A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) . Package: r-cran-spherepc Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-rgl, r-cran-sphereplot Filename: pool/dists/resolute/main/r-cran-spherepc_0.1.7-1.ca2604.1_all.deb Size: 113732 MD5sum: 14c443cc63b8071f98830cd33a6707a1 SHA1: 4e8a6c0458a63dce37de8a83c5e1a35978b99c53 SHA256: 46542e79e5a8b54230963c16b9bb66d36103974b26f2f1e2ec54f31f5338f19b SHA512: be8276933d527cb2c0c0e29be51e7aadc0c771222c8f9912a454e65552758babef6d50337d6e264c33c2543010ae0e8562bc1047da6ea79c55fddc9572ba48ca Homepage: https://cran.r-project.org/package=spherepc Description: CRAN Package 'spherepc' (Spherical Principal Curves) Fitting dimension reduction methods to data lying on two-dimensional sphere. This package provides principal geodesic analysis, principal circle, principal curves proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally defined principal geodesics which is underway. The detailed procedures are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) . Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) . Package: r-cran-sphereplot Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-sphereplot_1.5.1-1.ca2604.1_all.deb Size: 42012 MD5sum: b24cd8b840819e14309f520f6ddcff5e SHA1: c247259dd9c734c571b8fbbd09c47221e5d3a75f SHA256: ce5d14835465e976388676222952a81e8b9c587f03fef8cf883ab795df916c40 SHA512: 7466026b6930dc1db07a0625cadbaa25488db9d5ecbc176d894d76961462914da614bb2fcd5aaba9a63a6ba2d2d1fee06198bc32ae7fb2a1559271ea463c6fc7 Homepage: https://cran.r-project.org/package=sphereplot Description: CRAN Package 'sphereplot' (Spherical Plotting) Various functions for creating spherical coordinate system plots via extensions to rgl. Package: r-cran-spheresmooth Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sphereplot, r-cran-rgl, r-cran-ggplot2, r-cran-rworldmap, r-cran-sf Filename: pool/dists/resolute/main/r-cran-spheresmooth_0.1.3-1.ca2604.1_all.deb Size: 218596 MD5sum: b6d925847005e1f18487aff60299c704 SHA1: 5411ac40318dcbe7c0b86a67f55282c65a2b6379 SHA256: 98210370555dc7a7b7161ef5236a013017b6af351d05a9e2246d4c901de63a6b SHA512: 394da47cf535c96fcc93b5a778956b30518afe890362d1c89222c3075f638bef0d0df2f3f5bd735fc84beb2dde3092fc06df17ff44dbb88b7ddb644335cc968d Homepage: https://cran.r-project.org/package=spheresmooth Description: CRAN Package 'spheresmooth' (Piecewise Geodesic Smoothing for Spherical Data) Fitting a smooth path to a given set of noisy spherical data observed at known time points. It implements a piecewise geodesic curve fitting method on the unit sphere based on a velocity-based penalization scheme. The proposed approach is implemented using the Riemannian block coordinate descent algorithm. To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J. K., & Koo, J. Y. (2023) for the case of order 1. Additionally, this package includes various functions necessary for handling spherical data. Package: r-cran-sphericalcubature Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-simplicialcubature, r-cran-mvmesh, r-cran-abind Filename: pool/dists/resolute/main/r-cran-sphericalcubature_1.5-1.ca2604.1_all.deb Size: 101850 MD5sum: b8101d27b470cb1b70d056dd9423c2ac SHA1: c7c9b4ab9b09be1ff981ff61ffd49ed1c1bf5a9c SHA256: 2de105c305d823af90f50ac58c1ccfc50c362f27cfcbe07d59e276355ef59180 SHA512: 696803c8be2d309230b3b5522e642f3dc8043ffee775bcc639c4631e80edf70c6b9b643f79c57e498560a647e4283edc9d20b702a9ef7d06110d3df993c78f2b Homepage: https://cran.r-project.org/package=SphericalCubature Description: CRAN Package 'SphericalCubature' (Numerical Integration over Spheres and Balls in n-Dimensions;Multivariate Polar Coordinates) Provides several methods to integrate functions over the unit sphere and ball in n-dimensional Euclidean space. Routines for converting to/from multivariate polar/spherical coordinates are also provided. Package: r-cran-sphet Architecture: all Version: 2.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-spatialreg, r-cran-spdep, r-cran-matrix, r-cran-sp, r-cran-mvtnorm, r-cran-stringr, r-cran-coda, r-cran-spdata, r-cran-sf Filename: pool/dists/resolute/main/r-cran-sphet_2.1-1-1.ca2604.1_all.deb Size: 542816 MD5sum: e50ded8ead0c1303d5d54b9d869ac6bb SHA1: 5874611ae1f35e0e1f5027bff0778ac23cc45e0b SHA256: 2234e9cba704f9b9a915da300ec09aaaccf41085c5ad26898db5591e5f795fe7 SHA512: 5a85c12433d44e1035d39b26de602bfb68b4203e75b3be2077226e984ef14a6a1cc7635f2a498e06d57c36b3937cef388916e2857311f59d2c283f1ae399d02d Homepage: https://cran.r-project.org/package=sphet Description: CRAN Package 'sphet' (Estimation of Spatial Autoregressive Models with and withoutHeteroskedastic Innovations) Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables. Package: r-cran-spicefp Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-stringr, r-cran-tidyr, r-cran-matrix, r-cran-genlasso, r-cran-purrr Suggests: r-cran-fields Filename: pool/dists/resolute/main/r-cran-spicefp_0.1.2-1.ca2604.1_all.deb Size: 800812 MD5sum: 1aeca15c264e6f74749202ea97a393fc SHA1: 31fd725b4164b0b08c11b9b08440ad9042837037 SHA256: 5971a9409d2f0d5c61192887e28b315a1f1a6c890fa5fea72e8206d359f5cca0 SHA512: e8d8e2fcfdaeb6668efc2cc131ea3e10e90f9c59e5f42d8374f632bafb21ec48d51555b19fee49a97159986e20ba7872e764ad4fc637769cf6df38eec1594203 Homepage: https://cran.r-project.org/package=SpiceFP Description: CRAN Package 'SpiceFP' (Sparse Method to Identify Joint Effects of Functional Predictors) A set of functions allowing to implement the 'SpiceFP' approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates. The methods in this package are describing in Girault Gnanguenon Guesse, Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021) "An exploratory penalized regression to identify combined effects of functional variables -Application to agri-environmental issues" . Package: r-cran-spichanges Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-lubridate, r-cran-mumin, r-cran-rlang, r-cran-spsutil, r-cran-brglm2, r-cran-zoo Suggests: r-cran-archive, r-cran-curl, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-knitr, r-cran-ncdf4, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-sf, r-cran-purrr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-spichanges_0.2.1-1.ca2604.1_all.deb Size: 403126 MD5sum: a8f549b2b7fb0d1c7e1916c7f8aad103 SHA1: 0015d13894a287fc1083d32359a34dae58562be9 SHA256: ea92e0e69924c7ba970057b7a7205fe5e451f60f471856c4bc5caa9de0a27f05 SHA512: 7572f0f7af741826b892e3061a6ad1b28d9e366536db4ddaa5db2f4e25e20517ba63f04bdbe8f6c740b68eab0e2ee6822fccd670b2145369b8fe3f5ce4dbf6f7 Homepage: https://cran.r-project.org/package=SPIChanges Description: CRAN Package 'SPIChanges' (Improves the Interpretation of the Standardized PrecipitationIndex Under Changing Climate Conditions) Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring. Package: r-cran-spicy Architecture: all Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2567 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-labelled, r-cran-rlang, r-cran-sandwich, r-cran-stringr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-broom, r-cran-clipr, r-cran-clubsandwich, r-cran-dt, r-cran-effectsize, r-cran-emmeans, r-cran-flextable, r-cran-gt, r-cran-haven, r-cran-htmltools, r-cran-knitr, r-cran-marginaleffects, r-cran-officer, r-cran-openxlsx2, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tinytable, r-cran-withr Filename: pool/dists/resolute/main/r-cran-spicy_0.12.0-1.ca2604.1_all.deb Size: 1747850 MD5sum: dc43552dec64c0afd4a2c0baa516f057 SHA1: ecfde457885bc34f9edff2381a7734279c20eac4 SHA256: b460a913293d271629c85a73f792848346753649704df5c8f838ec33c9bc8ee9 SHA512: 8c5285689e845e8165165e0ad15bc72edcba6586489614d74018a2094a97e0b1d8ef69ced532c4bc1456b4ca32f197b62a1333273959f842397c6682d617593e Homepage: https://cran.r-project.org/package=spicy Description: CRAN Package 'spicy' (Descriptive Statistics, Summary Tables, and Data ManagementTools) Provides tabulation, descriptive-summary, and variable-inspection tools for applied data analysis. Frequency tables and cross-tabulations with contingency-table association measures (Cramer's V, Phi, Goodman-Kruskal Gamma, Kendall's Tau-b, Somers' D, and others); categorical and continuous summary tables; regression coefficient tables for one or more 'lm' or 'glm' fits side by side; and outcome-by-group comparison tables from linear models with optional additive covariate adjustment. All table outputs follow APA conventions and expose 'broom'-compatible 'tidy()' / 'glance()' methods for downstream pipelines. Helpers cover interactive codebooks, variable-label extraction, clipboard export, and row-wise descriptive summaries. Package: r-cran-spider Architecture: all Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-pegas Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spider_1.5.2-1.ca2604.1_all.deb Size: 258484 MD5sum: 52fa137faa1d4583f1b0804a6367f5e3 SHA1: 714eb9e39fa846766524f7f06a8ff398b71897fa SHA256: d4c53b708e4326b7edb953289bb2a6e06f394e6e158c23762ed3265c26edd313 SHA512: 917f12bbaef9e95b88e4e61ebcfd838d2fe8d480f2c6b0c994f99c363b529632aa74b8ddf3f48453c7b5af29a4a5397d3040d2b6c83882f1d0087b4a20a74ddc Homepage: https://cran.r-project.org/package=spider Description: CRAN Package 'spider' (Species Identity and Evolution in R) Analysis of species limits and DNA barcoding data. 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Package: r-cran-spiga Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga Filename: pool/dists/resolute/main/r-cran-spiga_1.0.0-1.ca2604.1_all.deb Size: 68384 MD5sum: 10f02f0327edcfa0685f32adb7224bd4 SHA1: 00b8400594e0fd871eec14689e0d4e38111783be SHA256: 9bb8c8c6f4c516007cde6bf75e24f28e4361f8944d91b5b68c6c7911eeb986d3 SHA512: daf61e00c4761a263f7be164c0983866129cc1d07ca46992854440f16ee653a98064bc3d28f803cce3c1795113c74a53148490c3d1b588ad9198f75966f5747c Homepage: https://cran.r-project.org/package=SPIGA Description: CRAN Package 'SPIGA' (Compute SPI Index using the Methods Genetic Algorithm andMaximum Likelihood) Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx. Package: r-cran-spikes Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emdbook Filename: pool/dists/resolute/main/r-cran-spikes_1.1-1.ca2604.1_all.deb Size: 687194 MD5sum: 6b4e6e76c4de5c7b3f36a7f6d4c1dbbd SHA1: db0b7cbb54ec55f0ee0a2798b5213c7498ab0d1b SHA256: 1687a3daa1d1a91323a41c9c3301d11992ed83d021f5886107799cef735dcd3c SHA512: 7d52da57e8536f38f929cd5b303f67c2aee65c3f5a8ffa931800371b1094a3835279802eee186d380d24fa2579f5e78b1f9c33d7c0148c3762739cbefe1623e4 Homepage: https://cran.r-project.org/package=spikes Description: CRAN Package 'spikes' (Detecting Election Fraud from Irregularities in Vote-ShareDistributions) Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud. Package: r-cran-spina Architecture: all Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spina_4.1.0-1.ca2604.1_all.deb Size: 61036 MD5sum: 39961846de44ed4c4aae606df93492af SHA1: 175738ddceb49640d58bed330c54630b0630f056 SHA256: 148f6bc8f25e78c087b05d1a5456277b816a024e0a05863bf869ba3813979aae SHA512: a31eb5457f80a5fc2c82137f068a210ecf96d08d702dbdd0a97cb1fa35f4fb6fe5bea331140f43e0fa75f41eb677a0352f2ce2243b49b3353b070813b39bfe59 Homepage: https://cran.r-project.org/package=SPINA Description: CRAN Package 'SPINA' (Structure Parameter Inference Approach) Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) and Dietrich et al. (2016) . Package: r-cran-spinar Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spinar_0.2.0-1.ca2604.1_all.deb Size: 130540 MD5sum: 0e3a47ae607fce903460e8f60f90f620 SHA1: 79833c6ee82757dad264bc897a54cbfdffd4d734 SHA256: ed78b3b58b033b88399c55336288a66a38a17310f5aee0fd46a0182070263398 SHA512: 9272424f6f8b7256a5f92b096698110645e22cf2f3c918f20280f46e02b687350e626a398d1288a7d7a967e1c46a7003808fa728a4c128c9dd256a14e7f30f82 Homepage: https://cran.r-project.org/package=spINAR Description: CRAN Package 'spINAR' ((Semi)Parametric Estimation and Bootstrapping of INAR Models) Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. 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Further, contains functions for spatially corrected model accuracy measures. Package: r-cran-spinebil Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tourr, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tictoc, r-cran-cassowaryr, r-cran-rlang Suggests: r-cran-minerva, r-cran-testthat, r-cran-purrr, r-cran-furrr, r-cran-future, r-cran-quarto, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-spinebil_1.0.5-1.ca2604.1_all.deb Size: 2239324 MD5sum: 5a6c2c9dd54efeb8130b6c192fe9e48f SHA1: fb5858165e12a0da981a430689ab042b38faf157 SHA256: b767056f68dde394587087f8fa29e2ce1e102b366ce10b2a92c425967f047edc SHA512: 272d086f167e57f7650f2b3b37fb0620e39ac18f4e73bd1108fd8ed662707d790f2cce3d7c989acc72a9768583106dd859c004c3f2992e243616384bc186415a Homepage: https://cran.r-project.org/package=spinebil Description: CRAN Package 'spinebil' (Investigating New Projection Pursuit Index Functions) Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. 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Package: r-cran-spinifex Architecture: all Version: 0.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tourr, r-cran-ggplot2, r-cran-gganimate, r-cran-plotly, r-cran-shiny, r-cran-rdimtools, r-cran-magrittr Suggests: r-cran-mass, r-cran-ggrepel, r-cran-patchwork, r-cran-gifski, r-cran-hexbin, r-cran-htmlwidgets, r-cran-png, r-cran-dplyr, r-cran-knitr, r-cran-ggally, r-cran-testthat, r-cran-lifecycle, r-cran-covr, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-spinifex_0.3.10-1.ca2604.1_all.deb Size: 1187110 MD5sum: 7b9676d672b58e41154e1f2744a01424 SHA1: b5ab7220d792b32d3a9fc092d401b31cb4b9d20e SHA256: e7d00e9a63d4ffdc07b8a6232ab120473abbea4baa5c230790b042e0fb1e7cee SHA512: e9327320a6f3350484bf119ae87404c9f596b3397beab283630931a38b9b58034c628bedfe23471ce9691d0092eb9b470042910a70fe631cae4e5de5bade6f31 Homepage: https://cran.r-project.org/package=spinifex Description: CRAN Package 'spinifex' (Manual Tours, Manual Control of Dynamic Projections of NumericMultivariate Data) Data visualization tours animates linear projection of multivariate data as its basis (ie. orientation) changes. The 'spinifex' packages generates paths for manual tours by manipulating the contribution of a single variable at a time Cook & Buja (1997) . Other types of tours, such as grand (random walk) and guided (optimizing some objective function) are available in the 'tourr' package Wickham et al. . 'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 'plotly' graphics, and allows for exporting as an .html widget and as an .gif, respectively. This work is fully discussed in Spyrison & Cook (2020) . Package: r-cran-spinner Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-torch, r-cran-purrr, r-cran-ggplot2, r-cran-ggthemes, r-cran-tictoc, r-cran-readr, r-cran-lubridate, r-cran-rlist, r-cran-fastdummies, r-cran-entropy, r-cran-abind Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spinner_1.1.1-1.ca2604.1_all.deb Size: 188632 MD5sum: 2d19af0a780bc4ccb32654ad9f7e292f SHA1: 1952d3b9c5db5630d64f7a09bc546547239dbff6 SHA256: ddcc607ae4adf88b7c02634a60435d221f85fbf34cdb518f866304b70f1af658 SHA512: 67a564d470c212e3cc0ec7d94935d0f1d4bdcc0cfaf65213103cf16ee5ded47f012acd1d4e667e9770612ea7174e4bc46bc50e9b656283751e6705c12643b478 Homepage: https://cran.r-project.org/package=spinner Description: CRAN Package 'spinner' (An Implementation of Graph Net Architecture Based on 'torch') Proposes a 'torch' implementation of Graph Net architecture allowing different options for message passing and feature embedding. 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It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns. Package: r-cran-spiritr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-spiritr_0.1.1-1.ca2604.1_all.deb Size: 61960 MD5sum: 1f5ebfd52db7ee86f9478aa1d64efe14 SHA1: e7a9165fa06bfe2b8ce613717e89bdd151fe3ccc SHA256: 945a0145c9c6cb9c8d47cbb252990666c62231242a030625cfc589cc29fcf600 SHA512: e50c06c1e1b05719830e0647a0de8b08257e6646341e5d5137a34856a6754f396b38a0b658e510145470865f5a1207be313e5f250dae01c65a883229fda72c39 Homepage: https://cran.r-project.org/package=spiritR Description: CRAN Package 'spiritR' (Template for Clinical Trial Protocol) Contains an R Markdown template for a clinical trial protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol Items for Interventional Trials) statement outlines recommendations for a minimum set of elements to be addressed in a clinical trial protocol. Also contains functions to create a xml document from the template and upload it to clinicaltrials.gov for trial registration. 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Predictions are based on a minimum of required inputs: latitude, precipitation, air temperature, and cloudiness. Davis et al. (2017) . Package: r-cran-spldv Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-matrix, r-cran-maxlik, r-cran-sphet, r-cran-memisc, r-cran-car, r-cran-numderiv, r-cran-mass, r-cran-spatialreg Suggests: r-cran-spdep Filename: pool/dists/resolute/main/r-cran-spldv_0.1.3-1.ca2604.1_all.deb Size: 112112 MD5sum: 0ae3a56b5f5e41f806a4a2803f98bb8a SHA1: 6f870ceb3cc4ae1bfd3ea66215d6c2814e539827 SHA256: 3d5628a0a4776d9d10a1bb356b8a4f23b6e125ce7bd14387d73c896ce0d69759 SHA512: 9ca46e006cee35713b15e79c1cd558c35328825d1c301a04566151cb9169e5e53ed345e07f3838fdea6c2f64a6d772db1cdc8a464b59b42023197f2efb5362db Homepage: https://cran.r-project.org/package=spldv Description: CRAN Package 'spldv' (Spatial Models for Limited Dependent Variables) The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators . It supports one-step (Pinkse and Slade, 1998) and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) . It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) . Package: r-cran-splice Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2710 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-synthetic, r-cran-zoo, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-actuar Filename: pool/dists/resolute/main/r-cran-splice_1.1.2-1.ca2604.1_all.deb Size: 2058578 MD5sum: f21f61b9fd42285d0d7dcc635a6a0b34 SHA1: f2d818d271fb643c55b5b71c67bcb42d633a0854 SHA256: 59e55dab2a392db35231a3b4e7b6bab94db5166905b2f68ba1c50aef2389e386 SHA512: f465dd536ce5c1c0527e5e1e42ef7221fc78ed913e3fded522bee9d2837e97e26d6d7a26792d0c929f66e948e26f22a2702f815b11a9181163d59819ae8b36f0 Homepage: https://cran.r-project.org/package=SPLICE Description: CRAN Package 'SPLICE' (Synthetic Paid Loss and Incurred Cost Experience (SPLICE)Simulator) An extension to the individual claim simulator called 'SynthETIC' (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" . Package: r-cran-splinecox Architecture: all Version: 0.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-joint.cox Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-splinecox_0.0.8-1.ca2604.1_all.deb Size: 83616 MD5sum: 46082574f253093e405cc25705b87999 SHA1: daf4938fc49dec0b4115b48d9001aee5e45ec7c5 SHA256: f93082da33b8ce23d9ee0b6a28b64838e25a0f022d17f3b3a74b68b32d5e2a69 SHA512: 19ffe8a3443b63c9f418f6234d9def146cd8e76be04f3bc46c43b1fda1e75fede2e81aab666c2429052ec8c5abfcbd2627755eb5f4d6d350a35ed94e619aea0a Homepage: https://cran.r-project.org/package=splineCox Description: CRAN Package 'splineCox' (A Two-Stage Estimation Approach to Cox Regression Using M-SplineFunction) Implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters. Package: r-cran-splinemixmeta Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mgcv, r-cran-mixmeta Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-splinemixmeta_1.0.1-1.ca2604.1_all.deb Size: 92592 MD5sum: 2d281d8c99697cc963bf4c25c1cdc082 SHA1: 3959dbde4297d9a61a06a0800134b1972a1197e7 SHA256: e19adf9037fbd4158c0de8d21745f9b49ed28f92c3491423b7e53ac28f0d0338 SHA512: ff7e3de73245f545edbfee087e56f53b70e57c7a4a1bf4973e1ce14c425fc4a74d40b81a5ce08950d3ddd3eb911ba52020273d5df714ec483381792c1b109e23 Homepage: https://cran.r-project.org/package=splinemixmeta Description: CRAN Package 'splinemixmeta' (Additive Mixed Meta-Analysis with Spline Meta-Regression) Fit additive mixed meta-analysis (AMMA) models, extending the 'mixmeta' package to allow for spline-based meta-regression. 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Supports survey-weighted models ('svyglm', 'svycoxph') from the 'survey' package, interaction terms, and provides hazard ratio plots with histograms for survival analysis. Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models. Package: r-cran-splinetree Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart, r-cran-nlme, r-cran-mosaic, r-cran-ggplot2, r-cran-treeclust, r-cran-mclust Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-splinetree_0.2.0-1.ca2604.1_all.deb Size: 2427830 MD5sum: b329f19a310e3be6108e65a8de6c7d22 SHA1: 4b279c8ffb52c7ee86d4fd81570f9cc4cf643e17 SHA256: 29772cb4916589b9f8e62270cf40123c0441db140b62e71812b4ed7fce65bdc8 SHA512: 06c10879d27cb638ab7eb0d43af05f8d67c7e8b852b6f1b6da4ba91207e55a0bcaec75a1a2abad702496cfeaa94989b83c4cc19d7476c23ece67c420027c71d9 Homepage: https://cran.r-project.org/package=splinetree Description: CRAN Package 'splinetree' (Longitudinal Regression Trees and Forests) Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) . This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories. Package: r-cran-splinetrials Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-cli, r-cran-dplyr, r-cran-emmeans, r-cran-mmrm, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-splinetrials_0.1.1-1.ca2604.1_all.deb Size: 332024 MD5sum: 8c92d84b85bb7c8e151619637288a679 SHA1: ce9b884c7ebee44dfb4a5b8162b055b852710241 SHA256: fecd48478250077f25b65993a1e7b721af469f191029f9da838adca86addd65f SHA512: 683706699d2c5503137a7ce36091fe739a04429447ddaa7f609955caefee5f0a32185db436ddc41f4d06b755a05741fd7658876caa9d2a5e7e40024ed95b6ea2 Homepage: https://cran.r-project.org/package=splinetrials Description: CRAN Package 'splinetrials' (Facilitate Clinical Trials Analysis Using Natural Cubic Splines) Create mixed models with repeated measures using natural cubic splines applied to an observed continuous time variable, as described by Donohue et al. (2023) . Iterate through multiple covariance structure types until one converges. Categorize observed time according to scheduled visits. Perform subgroup analyses. Package: r-cran-splinets Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-splinets_1.5.1-1.ca2604.1_all.deb Size: 4099846 MD5sum: 8be7c915388af6c1140a91c8b15f0494 SHA1: 67aea534031995bbdd7963f9d6cf4a00620a47be SHA256: feb40ecedf25d8feae6c70434559c23d33aa2d482dcdc41f0d96f86dd252a0bf SHA512: f2f3b679b628cda0a453750aadcec4a36cf49f0bfec0da4c4e88dab6d9d9843f8d1301c742d3b158b25c4eacf196dd5ba478fc02370d6f56ab3788dcfea60530 Homepage: https://cran.r-project.org/package=Splinets Description: CRAN Package 'Splinets' (Functional Data Analysis using Splines and Orthogonal SplineBases) Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. Two cases of boundary conditions are considered: zero-boundary or periodic-boundary for all derivatives except the last. The periodical splines are represented graphically using polar coordinates. The B-splines and orthogonal bases of splines that reside on small total support are implemented. The orthogonal bases are referred to as 'splinets' and are utilized for functional data analysis. Random spline generator is implemented as well as all fundamental algebraic and calculus operations on splines. The optimal, in the least square sense, functional fit by 'splinets' to data consisting of sampled values of functions as well as splines build over another set of knots is obtained and used for functional data analysis. The S4-version of the object oriented R is used. , , . Package: r-cran-splitfngr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lbfgs Filename: pool/dists/resolute/main/r-cran-splitfngr_0.1.2-1.ca2604.1_all.deb Size: 24778 MD5sum: 77500d158b252623c3eda4f23556b7fa SHA1: 7bf9e205b9962eb0b19b460fef5fc782d8498fdc SHA256: 24a81394cba11056d4794d2ba802842a1ea6363eee2ec5f308df8bb80e426c69 SHA512: 92d1cc76ffa252b797a3c3dd39eb778e1f0ede1e7d792999a2708afc0ec1e637b6d1d4e16bbb11a0400c00d701f23a255d0556979cf2e7dd7cdde2226689e1a0 Homepage: https://cran.r-project.org/package=splitfngr Description: CRAN Package 'splitfngr' (Combined Evaluation and Split Access of Functions) Some R functions, such as optim(), require a function its gradient passed as separate arguments. When these are expensive to calculate it may be much faster to calculate the function (fn) and gradient (gr) together since they often share many calculations (chain rule). This package allows the user to pass in a single function that returns both the function and gradient, then splits (hence 'splitfngr') them so the results can be accessed separately. The functions provided allow this to be done with any number of functions/values, not just for functions and gradients. 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Validates dataset structure, detects sample-level overlap, derives deterministic split constraints, and produces a tool-agnostic split specification for leakage-aware evaluation workflows. 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The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, the option to sub-sample data, and bootstrapped confidence intervals. 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This proposed scheme relaxes the linear subspace constraint to its neighborhood, often known as variable splitting in optimization. Simulation experiments can be reproduced following the Vignette. 'Split Knockoffs' is first defined in Cao et al. (2021) . 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Package: r-cran-spm2 Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spm, r-cran-gstat, r-cran-sp, r-cran-randomforest, r-cran-gbm, r-cran-fields, r-cran-nlme, r-cran-glmnet, r-cran-e1071 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spm2_1.1.3-1.ca2604.1_all.deb Size: 372004 MD5sum: f378af9c71e2ea52a831cd8db64509c2 SHA1: 69a8339dee8f653142fdbf8419a523dbf7917793 SHA256: 46624bfadf70a8a72948b98e581ed2fce4744d44187831ad63b6aded3acb4364 SHA512: f9a0fdc6df642a13aecd4149a740923241efee1056855fad256869793ad058a6eac6312cc0220e660b1b5d0d3ead12cc11fb2e55f5b46c9f143d1e08d79c24f3 Homepage: https://cran.r-project.org/package=spm2 Description: CRAN Package 'spm2' (Spatial Predictive Modeling) An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. 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It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) Li, J., Potter, A., Huang, Z. and Heap, A. (2012) . 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Package: r-cran-spmixw Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spdep, r-cran-sf, r-cran-ggplot2, r-cran-broom Filename: pool/dists/resolute/main/r-cran-spmixw_0.2.2-1.ca2604.1_all.deb Size: 283760 MD5sum: a4273a9fe9998e20780f25ca002fd7c8 SHA1: c0dcb12e7ad921b4e32312c78a5336b8c5809b04 SHA256: 929b2fa33c557b05a531c90dc44d907552629f22a77b21b3f2bb44083880bcf6 SHA512: e4fbd3cbb35d5105ee1322fdf888af73a5191c74f009d8422d4f30ad614040df0a798b6bf81a3de2d44cbe5dc22094635639cb070ea55d33c54680924f2cd86a Homepage: https://cran.r-project.org/package=spmixW Description: CRAN Package 'spmixW' (Bayesian Spatial Panel Data Models with Convex Combinations ofWeight Matrices) Bayesian Markov chain Monte Carlo (MCMC) estimation of spatial panel data models including Spatial Autoregressive (SAR), Spatial Durbin Model (SDM), Spatial Error Model (SEM), Spatial Durbin Error Model (SDEM), and Spatial Lag of X (SLX) specifications with fixed effects. Supports convex combinations of multiple spatial weight matrices and Bayesian Model Averaging (BMA) over subsets of weight matrices. Implements the convex combination spatial weight matrix methodology of Debarsy and LeSage (2021) and the Bayesian spatial panel data models of LeSage and Pace (2009, ISBN:9781420064247). 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Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) . Package: r-cran-spmoran Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4801 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-fields, r-cran-vegan, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-spdep, r-cran-rarpack, r-cran-rcolorbrewer, r-cran-fnn Suggests: r-cran-r.rsp, r-cran-spdata Filename: pool/dists/resolute/main/r-cran-spmoran_0.3.3-1.ca2604.1_all.deb Size: 4779484 MD5sum: e79586bb6756633703b2165f2bb4baa3 SHA1: 3893ce44f24f2c30225a22a55983bc9403536b5f SHA256: de2331882ebffa75b29c3c685ae625efa7ad8eee58d99351ec05b4df55280487 SHA512: 1b822f239c9b908c08b4fdce6ec570008e0b059c1ac1ecd2dd8fb922470cddfb4183bb558d538ab1aed9dab3033267ba9b640ab69cfe8906839d0eed5fb25b05 Homepage: https://cran.r-project.org/package=spmoran Description: CRAN Package 'spmoran' (Fast Spatial and Spatio-Temporal Regression using MoranEigenvectors) A collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 ). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; ,), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 ), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, ). Package: r-cran-spnaf Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-sf, r-cran-spdep, r-cran-tidyr, r-cran-rlang, r-cran-deldir Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tmap Filename: pool/dists/resolute/main/r-cran-spnaf_1.1.0-1.ca2604.1_all.deb Size: 558810 MD5sum: b869930ae4fc4d22842a1eb030ba0ae6 SHA1: 0d513e82e94fcd797a89fdc156f42118cc77b4aa SHA256: d728916d92290f44e5f859a357cf26c7e95e7f12c284d4155e7f5d37811ddfc3 SHA512: 083effe9192797b2e6d23e97f23736c2ab9a27e29809a73991e0a674871e8e10a6ae0001bb4ebf2481c736eb912ebb358507105c434974f1c4d73a728f213517 Homepage: https://cran.r-project.org/package=spnaf Description: CRAN Package 'spnaf' (Spatial Network Autocorrelation for Flow Data) Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* proposed by 'Berglund' and 'Karlström' (1999) . The metric, an extended statistic of 'Getis/Ord' G ('Getis' and 'Ord' 1992) , detects a group of flows having similar traits in terms of directionality. You provide OD data and the associated polygon to get results with several parameters, some of which are defined by spdep package. Package: r-cran-spnmf Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nmf Filename: pool/dists/resolute/main/r-cran-spnmf_0.1.1-1.ca2604.1_all.deb Size: 257632 MD5sum: 060263ec421d20c8f29593edbc4a7736 SHA1: 4c54cb4ff02db517e6714f936a7128bfe80c72d3 SHA256: b611883aa4ece11f772eb98d0a328bb95e589b6d77c908623ebb1b1687704f6b SHA512: 42d46e43b6c478952230f20dd885042f7d4b0f0085d6fd08f226f656b772b1c6b5f47b31ee9db78cf099a3b62ea76c956e4cf04d56686944a51956055dec2e01 Homepage: https://cran.r-project.org/package=SpNMF Description: CRAN Package 'SpNMF' (Supervised NMF) Non-negative Matrix Factorization(NMF) is a powerful tool for identifying the key features of microbial communities and a dimension-reduction method. 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Includes functionality for retrieving species occurrence data, and combining those data. Package: r-cran-spoiler Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-shiny Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-spoiler_1.0.0-1.ca2604.1_all.deb Size: 12392 MD5sum: b9291a73e1acdf171c4da3ec81b99dfb SHA1: 0e90c7c5cbe1a61f4578b67b64e8985def651411 SHA256: 322e9b73ec7736aca58ba3ae42a472617545aa00bdf75155e125185ff97a8992 SHA512: 6e243f32e23b2ed6b6e4e5bbbf2f8fb5bbd02e9e66cb6456f02d60ed6a217df0b381d74fc19fa341e037f9a83b088d3ab374f88331689e80ee4877bfa6171a1e Homepage: https://cran.r-project.org/package=spoiler Description: CRAN Package 'spoiler' (Blur 'HTML' Elements in 'Shiny' Applications Using'Spoiler-Alert.js') It can be useful to temporarily hide some text or other HTML elements in 'Shiny' applications. 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'SPORTSCausal' (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on 'CausalImpact', as described in Brodersen et al. (2015) . 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Package: r-cran-spouse Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spouse_0.1.0-1.ca2604.1_all.deb Size: 17104 MD5sum: 1cc308b6c4a769220d24216006dd0df7 SHA1: 575238878b636ecda6fab8be1dee90f6c33e26e5 SHA256: b4b7d5b78c7e373afd59e262b18890e2ec721ac073b54538b99d6f2384a967cf SHA512: 8e320e1c39dd624bcd7821a403ab8e418230066ee7f67d98fe6f297348c16d1f941e04b53f875741bd9936eb88adaf50004ae7d587745ea88f4cdc38ac56852b Homepage: https://cran.r-project.org/package=SPOUSE Description: CRAN Package 'SPOUSE' (Scatter Plots Over-Viewed Using Summary Ellipses) Summary ellipses superimposed on a scatter plot contain all bi-variate summary statistics for regression analysis. Furthermore, the outer ellipse flags potential outliers. Multiple groups can be compared in terms of centers and spreads as illustrated in the examples. 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The package focuses exclusively on Monte Carlo simulation experiment variants of (expected) prospective power analyses, criterion analyses, compromise analyses, sensitivity analyses, and a priori/post-hoc analyses. The default simulation experiment functions defined within the package provide stochastic variants of the power analysis subroutines in G*Power 3.1 (Faul, Erdfelder, Buchner, and Lang, 2009) , along with various other parametric and non-parametric power analysis applications (e.g., mediation analyses) and support for Bayesian power analysis by way of Bayes factors or posterior probability evaluations. Additional functions for building empirical power curves, reanalyzing simulation information, and for increasing the precision of the resulting power estimates are also included, each of which utilize similar API structures. For further details see the associated publication in Chalmers (2025) . Package: r-cran-sppcomb Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nleqslv Filename: pool/dists/resolute/main/r-cran-sppcomb_0.1-1.ca2604.1_all.deb Size: 257422 MD5sum: 409da7707b3a4f7497fa52c294450105 SHA1: cdbd221abaa2aed6bd6b511b58b05988849b9650 SHA256: 55ae728db577f4dc6d4df069f3c929601bc67b1f988fc7949ec2bab6a4271568 SHA512: 946fbe127ec650c695467f1aa96bc7eb0307fbc97c1bacec0b446da5fed802150402809d91a0d6489db1a43c01a4586064816bb2cea4fd7c40c094ac4b1a6fd5 Homepage: https://cran.r-project.org/package=SPPcomb Description: CRAN Package 'SPPcomb' (Combining Different Spatial Datasets in Cancer Risk Estimation) We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. See Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford, Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical Association, 109:505, 11-23, . Package: r-cran-spphpr Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spphpr_1.1.5-1.ca2604.1_all.deb Size: 341586 MD5sum: 450d46dd24d2a11983e03c7442c8dc67 SHA1: 17a677ddd40d63136224d11eaee6e7239dc1361b SHA256: 1edb2f3c68f7f69fc1ce0ae6df484c07851e7a52f18439721bbd9d24c162c6ee SHA512: 0f2faf4d777be9f255392c533cd175876a16e0fa5f615d20caed0f7d4f3c3b0bbdf884fc53dbb0a583fa1ffd43b2eb852e562e8adb2c226f04a7b355bdec9b2f Homepage: https://cran.r-project.org/package=spphpr Description: CRAN Package 'spphpr' (Spring Phenological Prediction) Predicts the occurrence times (in day-of-year) of spring phenological events. Three methods, including the accumulated degree days (ADD) method, the accumulated days transferred to a standardized temperature (ADTS) method, and the accumulated developmental progress (ADP) method, were used. See Shi et al. (2017a) and Shi et al. (2017b) for details. Package: r-cran-sppop Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qpdf, r-cran-numbers Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sppop_0.1.0-1.ca2604.1_all.deb Size: 60658 MD5sum: 136fedcb105264b6ca98f0c8ecba3431 SHA1: ecac3b2488d9b2e750575f8dad53ba70e648c6bd SHA256: b6318be88f9b80f5460912b1d26b866a5ba2c33e782f758ed27643e2fe36e259 SHA512: b2c243573c17976c3b28300d9f8c60fc3f912cf2557c336730eececb3fccaaf1edc5063af74fd4376e235bbda742535e9fc88a23a2549337eeb99d5305fc4fbc Homepage: https://cran.r-project.org/package=SpPOP Description: CRAN Package 'SpPOP' (Generation of Spatial Population under Different Levels ofRelationships among Variables) The developed package can be used to generate a spatial population for different levels of relationships among the dependent and auxiliary variables along with spatially varying model parameters. A spatial layout is designed as a [0,k-1]x[0,k-1] square region on which observations are collected at (k x k) lattice points with a unit distance between any two neighbouring points along the horizontal and vertical axes. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).. The generated spatial population can be utilized in Geographically Weighted Regression model based analysis for studying the spatially varying relationships among the variables. Furthermore, various statistical analysis can be performed on this spatially generated data. Package: r-cran-spptrend Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3920 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-terra, r-cran-stringr, r-cran-tidyselect, r-cran-ggplot2, r-cran-patchwork, r-cran-sf Suggests: r-cran-testthat, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-spptrend_0.4-1.ca2604.1_all.deb Size: 2872754 MD5sum: 7848b6698778922609f03f75d38223ae SHA1: d3fb622c1553dce2c00b6903e73610aa26d63ec5 SHA256: 0474994b91df290bca6f86794caba13c93483a467407e2afcbe5b6eafb7d332a SHA512: aa8599dc158eafd245634e687f141d640b5efd9b12b248ec9d76ebce944c92c6a0890d1b89f7852ef707317ef135b45b9f272b3ef6c2e9f2729054ec4a8ac73c Homepage: https://cran.r-project.org/package=SppTrend Description: CRAN Package 'SppTrend' (Analyzing Linear Trends in Species Occurrence Data) Provides a methodology to analyze how species occurrences change over time, particularly in relation to spatial and thermal factors. It facilitates the development of explanatory hypotheses about the impact of environmental shifts on species by analyzing historical presence data that includes temporal and geographic information. Approach described in Lobo et al., 2023 . Package: r-cran-spqdep Architecture: all Version: 0.1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-gtools, r-cran-igraph, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-rsample, r-cran-sf, r-cran-sp, r-cran-spatialreg, r-cran-spdep, r-cran-tidyr, r-cran-units Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spqdep_0.1.3.6-1.ca2604.1_all.deb Size: 1316102 MD5sum: 871f9914d53b46ef5c731dc7850f5373 SHA1: 9409d5aac3a6c9136ac5d95e71e2da6c809946ff SHA256: d4696dbf5eccd51ad6dae00814d0f27e63f8e36ed08bb594caff82ba26e81c59 SHA512: c221566cb09fc0311819c374e46aa22b0cd15c4c319610d7ee8e5014ade0bc3dcb642b9943d88003fca4fbb817cc7a634e1c59e4a21d914b786ded78aebb840c Homepage: https://cran.r-project.org/package=spqdep Description: CRAN Package 'spqdep' (Testing for Spatial Independence of Cross-Sectional QualitativeData) Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in and . Package: r-cran-spreda Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-spreda_1.2-1.ca2604.1_all.deb Size: 858068 MD5sum: ad234dfa346fe2eeba327fc9c76f9e9c SHA1: 78f0dc80bb480de33da35fd0ce1e4d111a8971af SHA256: 666ae5b13c4d64e02fe5633a7f1ce4cee2bb6e956a4e67461fca846052afaedc SHA512: 5050aa2d290885a50563f39e7f341c16e21ab4b90cc8a627bfd36379b70c23482afe4573dee53e4aa7e3d6a3a230dab0ecf60f91a0c52ebba9ad9aebbfe02561 Homepage: https://cran.r-project.org/package=SPREDA Description: CRAN Package 'SPREDA' (Statistical Package for Reliability Data Analysis) The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged. Package: r-cran-spreg Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sn, r-cran-ucminf Filename: pool/dists/resolute/main/r-cran-spreg_1.0-1.ca2604.1_all.deb Size: 31168 MD5sum: 0820121145a34a3eb52e794c4c45c436 SHA1: e46a801493229589b79f906a55b1717d8d22c0e8 SHA256: 04910747499524757a797f9e16bb61de3d95416274827d5a86e1fe914f41e4af SHA512: 460dde4699777a146d9fceb1ecac3c6398de3f82113b3d35c2e1461df3816c68d9957ea582c5190ee175788d24240957033d9476bf665ab9b630614f19612636 Homepage: https://cran.r-project.org/package=SPreg Description: CRAN Package 'SPreg' (Bias Reduction in the Skew-Probit Model for a Binary Response) Provides a function for the estimation of parameters in a binary regression with the skew-probit link function. Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented, as described in DongHyuk Lee and Samiran Sinha (2019+) . Package: r-cran-spreval Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate, r-cran-interp Suggests: r-cran-fields, r-cran-knitr, r-cran-rmarkdown, r-cran-plotrix Filename: pool/dists/resolute/main/r-cran-spreval_1.1.0-1.ca2604.1_all.deb Size: 687132 MD5sum: 0efc005774da3ab901ba2156b93fd304 SHA1: e7f7b62b899d187b82e7f09767c3418713610f0b SHA256: a372411f5d48ca7e3927e3c55d859718791a05821261fd16a79f96b624327c1c SHA512: 1846afd1270ca96d7337ca86f132efcf41c8e088ade1f562ba61e54c7ad272ff24b5b8ead9d9fdec2abc6e0386ce2f26769ad26f41af7a28cd6fda11a65a404f Homepage: https://cran.r-project.org/package=spreval Description: CRAN Package 'spreval' (Evaluation of Sprinkler Irrigation Uniformity and Efficiency) Processing and analysis of field collected or simulated sprinkler system catch data (depths) to characterize irrigation uniformity and efficiency using standard and other measures. Standard measures include the Christiansen coefficient of uniformity (CU) as found in Christiansen, J.E.(1942, ISBN:0138779295, "Irrigation by Sprinkling"); and distribution uniformity (DU), potential efficiency of the low quarter (PELQ), and application efficiency of the low quarter (AELQ) that are implementations of measures of the same notation in Keller, J. and Merriam, J.L. (1978) "Farm Irrigation System Evaluation: A Guide for Management" . spreval::DU.lh is similar to spreval::DU but is the distribution uniformity of the low half instead of low quarter as in DU. spreval::PELQT is a version of spreval::PELQ adapted for traveling systems instead of lateral move or solid-set sprinkler systems. The function spreval::eff is analogous to the method used to compute application efficiency for furrow irrigation presented in Walker, W. and Skogerboe, G.V. (1987,ISBN:0138779295, "Surface Irrigation: Theory and Practice"),that uses piecewise integration of infiltrated depth compared against soil-moisture deficit (SMD), when the argument "target" is set equal to SMD. The other functions contained in the package provide graphical representation of sprinkler system uniformity, and other standard univariate parametric and non-parametric statistical measures as applied to sprinkler system catch depths. A sample data set of field test data spreval::catchcan (catch depths) is provided and is used in examples and vignettes. Agricultural systems emphasized, but this package can be used for landscape irrigation evaluation, and a landscape (turf) vignette is included as an example application. Package: r-cran-sprex Architecture: all Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-swfscmisc Filename: pool/dists/resolute/main/r-cran-sprex_1.4.3-1.ca2604.1_all.deb Size: 72976 MD5sum: 017d5f97f3ea76e92c51045ec721ba13 SHA1: e82c5db88d372eb2b4946eb4ad94a7e0b6b388e7 SHA256: c0473609182075ebeb3465233d21a733fdb875a7dbb90b6ffd96df1f3c4caf26 SHA512: ba499e3fb891f1383a0975d4e8425ffafda4bc442384ae06df569a22d3e3ad5c4b1378b523aed453dc13a3e8b8234ac40fabc5918913b2b2bbca3e399ede6ea3 Homepage: https://cran.r-project.org/package=sprex Description: CRAN Package 'sprex' (Species Richness and Extrapolation) Functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices. Package: r-cran-springpheno Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-springpheno_0.5.0-1.ca2604.1_all.deb Size: 149198 MD5sum: 3c3311c23bfd82c4b30fccc19e4a6527 SHA1: 3e3a6eb1be12fe6de5ec4843f7589a9ee68cd5fa SHA256: 42b123a6ccdae30bcf1b2009b0bc0af1341ed436a2f0f522db9cce833f93501d SHA512: 28db3f5cbd5655437c7a014fc45035a2a6ee50666319c3ce4bf34785a10f43b9604511b969014e6e889a93128d72ad29687bd35ffe1d7981836c91f67c41cffc Homepage: https://cran.r-project.org/package=springpheno Description: CRAN Package 'springpheno' (Spring Phenological Indices) Computes the extended spring indices (SI-x) and false spring exposure indices (FSEI). The SI-x indices are standard indices used for analysis in spring phenology studies. In addition, the FSEI is also from research on the climatology of false springs and adjusted to include an early and late false spring exposure index. The indices include the first leaf index, first bloom index, and false spring exposure indices, along with all calculations for all functions needed to calculate each index. The main function returns all indices, but each function can also be run separately. Allstadt et al. (2015) Ault et al. (2015) Peterson and Abatzoglou (2014) Schwarz et al. (2006) Schwarz et al. (2013) . Package: r-cran-springsteen Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-rlang Suggests: r-cran-tidytext, r-cran-dplyr, r-cran-lubridate, r-cran-readr, r-cran-stringr, r-cran-magrittr, r-cran-tidyselect Filename: pool/dists/resolute/main/r-cran-springsteen_0.1.0-1.ca2604.1_all.deb Size: 722530 MD5sum: 0c2d6f4a94c3396de67188d0f36f30a1 SHA1: 71334eecda49860b047fc0ffb5e89b934a2859ca SHA256: 286207a66890ed06166a4f40c53216763a7b04a05f877e02d45a5ab634f0fc64 SHA512: 1f8b741505b9db884e3bef81cc7c82b227481da7b06fc0e9b6c674cb367f440fbb66428559f77d806c01ca75069d1c9e69d958d91128d9006daa1db8dd0b6f2b Homepage: https://cran.r-project.org/package=spRingsteen Description: CRAN Package 'spRingsteen' (All Things Data and Springsteen) An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. Also includes all his song details such as lyrics and albums. Data extracted from: . Package: r-cran-sprt Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sprt_1.1.0-1.ca2604.1_all.deb Size: 34090 MD5sum: 6d5ea14a9aeaa3b9c49280c8bb6dfb52 SHA1: 23415cb0fc3e41d2809dbcaf9d1fb57432e3b83c SHA256: 89220ed643e9765d1acc7e876dcafcc801c615c6d955b904bc1ed225ea195ab2 SHA512: 75d1225d81292d39deb3ef7d50666db8bc0d0f562c5b447cc9eaa7b4f8aeccc2a02fb0f626c202b995d9e3248a7b87b312f951037e90219a5286fe64c68f3d3c Homepage: https://cran.r-project.org/package=SPRT Description: CRAN Package 'SPRT' (Sequential Probability Ratio Test (SPRT) Method) Provides functions to perform the Sequential Probability Ratio Test (SPRT) for hypothesis testing in Binomial, Poisson and Normal distributions. The package allows users to specify Type I and Type II error probabilities, decision thresholds, and compare null and alternative hypotheses sequentially as data accumulate. It includes visualization tools for plotting the likelihood ratio path and decision boundaries, making it easier to interpret results. The methods are based on Wald (1945) , who introduced the SPRT as one of the earliest and most powerful sequential analysis techniques. This package is useful in quality control, clinical trials, and other applications requiring early decision-making.The term 'SPRT' is an abbreviation and used intentionally. Package: r-cran-sprtt Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6298 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rappdirs, r-cran-dplyr, r-cran-mbess, r-cran-purrr, r-cran-glue, r-cran-ggplot2, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-gt, r-cran-ggtext, r-cran-scales, r-cran-piggyback Suggests: r-cran-testthat, r-cran-testthis, r-cran-withr, r-cran-xml2, r-cran-effsize, r-cran-effectsize, r-cran-vdiffr, r-cran-diffviewer Filename: pool/dists/resolute/main/r-cran-sprtt_0.3.1-1.ca2604.1_all.deb Size: 1647492 MD5sum: 47502908f2a4e039b0c5f292f3657e4a SHA1: f164bbf82f09a313a23c277bd36b99e1fe9516ec SHA256: 67098d7cd21c2a361154ab3eea0bcec1db897f59d0721a515e43bf77f42474da SHA512: f933274b82ded759931b8b65c08ee6948f887e40ca4002f1565e42aab58ed3973bdd082ff7e9aad75ef913b7aab795c8451500ee17aa4ced8ec7a8036cd0140e Homepage: https://cran.r-project.org/package=sprtt Description: CRAN Package 'sprtt' (Sequential Probability Ratio Tests Toolbox) A toolbox for Sequential Probability Ratio Tests (SPRT) based on Wald (1945) . SPRTs are applied during the sampling process, ideally after each observation, and at every stage return a decision to either continue sampling or terminate and accept one of the specified hypotheses. The `seq_ttest()` function performs one-sample, two-sample, and paired t-tests for one- and two-sided hypotheses (Schnuerch & Erdfelder (2019) ). The `seq_anova()` function performs a sequential one-way fixed effects ANOVA (Steinhilber et al. (2024) ). The `plan_sample_size()` function helps plan sequential studies by simulating required sample sizes across a range of effect sizes. For more information, see the vignettes browseVignettes(package = "sprtt") or the package website . Package: r-cran-sps Architecture: all Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kit, r-cran-knitr, r-cran-quarto, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sps_0.6.3-1.ca2604.1_all.deb Size: 261840 MD5sum: b02b3f2ce894bf40c24f7e294954d948 SHA1: 35f754534d9120c2371cfacf169fbf028c45d100 SHA256: 9e2a8f0cfe80128a32099d84965a2e1e1320fee71c15c847c016fa4a13be92aa SHA512: 33dca35d6a23597af2cf81564c69d24ce7203870c0e55e4d9db2c5469a5189bcc0e7702c80d4aa5f367df4c6790a5020a4714cdd9f5aa3834c348073afd37ca3 Homepage: https://cran.r-project.org/package=sps Description: CRAN Package 'sps' (Sequential Poisson Sampling) Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample designs by Rosén (1997, ), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, ). Package: r-cran-spscomps Architecture: all Version: 0.3.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-assertthat, r-cran-stringr, r-cran-glue, r-cran-magrittr, r-cran-shinytoastr, r-cran-shinyace, r-cran-htmltools, r-cran-r6, r-cran-crayon Suggests: r-cran-testthat, r-cran-shinyjqui, r-cran-spsutil Filename: pool/dists/resolute/main/r-cran-spscomps_0.3.4.0-1.ca2604.1_all.deb Size: 284780 MD5sum: 8ac28ad58a8800309c56466a91a4159c SHA1: 69dec9c5f08d92f42e7067bbaf8b2bc9ebdc2522 SHA256: 61dfe99749e430e88f960bfba0798580d737b9d22819d1c571b3040c8cf873b2 SHA512: 4ac8270eb68de098ec1bea3a5f87f971ce431cda2b89236fb90dfed025c7923f22f20885d4b4c3c78fb783e0f3ac741b0d6edc5a389251602733f4c7eb05f6ec Homepage: https://cran.r-project.org/package=spsComps Description: CRAN Package 'spsComps' ('systemPipeShiny' UI and Server Components) The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package. Package: r-cran-spselect Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tester, r-cran-magic, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-spselect_0.0.1-1.ca2604.1_all.deb Size: 92650 MD5sum: 14babcd6a487c51d187118e859ae23ab SHA1: b14b3aa9687e2b659e7386f001b8db42741ceb69 SHA256: 3f58028e49a0046dcbfc6e567e4955fc1091d119d452f874bba4f211a3d8c6c4 SHA512: d65972a6a9fab6f07b3117f7aeb8c4a09c5ffd4164d8d54087b4e227e786c52af80481f8b92340e6e5f95bb6167d65f127162ac6c784fef753450b972d26698c Homepage: https://cran.r-project.org/package=spselect Description: CRAN Package 'spselect' (Selecting Spatial Scale of Covariates in Regression Models) Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models. All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale. Package: r-cran-spsl Architecture: all Version: 0.1-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-spsl_0.1-9-1.ca2604.1_all.deb Size: 115022 MD5sum: 5c50548d705257d785ecef1ed927fca7 SHA1: 807a800422a3f4e946302e74b0f351f39f9a44a2 SHA256: c307022c5755b8c2cb635897db0ceb32859122a6195abf59c50a16b653e18518 SHA512: 6af56965b1f73fbda00c5722f126378031c08b71096278ad33ad87e6ea5911f927654fca613999dafba59e043c73258de27faa6d4bf523ca74ab6e6f85d64fde Homepage: https://cran.r-project.org/package=SPSL Description: CRAN Package 'SPSL' (Site Percolation on Square Lattices (SPSL)) Provides basic functionality for labeling iso- & anisotropic percolation clusters on 2D & 3D square lattices with various lattice sizes, occupation probabilities, von Neumann & Moore (1,d)-neighborhoods, and random variables weighting the percolation lattice sites. Package: r-cran-spsur Architecture: all Version: 1.0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-ggplot2, r-cran-gmodels, r-cran-gridextra, r-cran-mass, r-cran-matrix, r-cran-minqa, r-cran-numderiv, r-cran-rdpack, r-cran-rlang, r-cran-sparsemvn, r-cran-spatialreg, r-cran-spdep, r-cran-sphet Suggests: r-cran-bookdown, r-cran-dplyr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/resolute/main/r-cran-spsur_1.0.2.6-1.ca2604.1_all.deb Size: 1892490 MD5sum: 99092ea897e605dc744cf195869a3e38 SHA1: b65a724dee1472c749286abafe0e686caf65cc94 SHA256: 9f06cdc04b5f23d96f493a77ed21e33161abf58fa65941b29d358843139fdf3c SHA512: 2ebcadaa35558f54e41286fbe8b8ab306cb0205bd818fc8d84e0a8eed818d61921355f84ad84d3da4631b09f18fa54cea485a1a2b203f79af356959175eb3750 Homepage: https://cran.r-project.org/package=spsur Description: CRAN Package 'spsur' (Spatial Seemingly Unrelated Regression Models) A collection of functions to test and estimate Seemingly Unrelated Regression (usually called SUR) models, with spatial structure, by maximum likelihood and three-stage least squares. The package estimates the most common spatial specifications, that is, SUR with Spatial Lag of X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM). The methodology of these models can be found in next references Minguez, R., Lopez, F.A., and Mur, J. (2022) Mur, J., Lopez, F.A., and Herrera, M. (2010) Lopez, F.A., Mur, J., and Angulo, A. (2014) . Package: r-cran-spsurvey Architecture: all Version: 5.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2822 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-survey, r-cran-boot, r-cran-crossdes, r-cran-deldir, r-cran-lme4, r-cran-mass, r-cran-sampling, r-cran-units Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spsurvey_5.6.1-1.ca2604.1_all.deb Size: 1898696 MD5sum: 0832bfcd44682c34a62827208ed9d412 SHA1: 43d1b26e5a6a38785c8f16d4b2cd33b54ba7a2bd SHA256: 4555b318637ddf35008632aac46091749f05194c474f75abe751be4d499544e0 SHA512: 156c7d558c76bbe4479404f70778aeb9d58a8ba9514dc304ecf247121ea259877bc59eda752e10765ad51f90103e4b4a166a4b60359544bbe03debad8fbc7faa Homepage: https://cran.r-project.org/package=spsurvey Description: CRAN Package 'spsurvey' (Spatial Sampling Design and Analysis) A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) . Package: r-cran-spsutil Architecture: all Version: 0.2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-assertthat, r-cran-stringr, r-cran-glue, r-cran-magrittr, r-cran-crayon, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-spsutil_0.2.2.1-1.ca2604.1_all.deb Size: 127012 MD5sum: 84a4e5c8e1db28575a8f829573ee92cb SHA1: 261b593129e2d9258fa9a5539c6ee855be04404d SHA256: 1768f2071bc2e968c32004c0cb27a214278f1129cc6f101e15cb110f9b5095f5 SHA512: f369ac94f6a95ec85a6094ec7c4f6a0f7f3b6e1ecfaf9abeb105659f4a69af4a74691d4d3828cf97c31109980b06d5599f58d650684cc2f5267aad5b8e9783ea Homepage: https://cran.r-project.org/package=spsUtil Description: CRAN Package 'spsUtil' ('systemPipeShiny' Utility Functions) The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough. Package: r-cran-spthin Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spam, r-cran-fields, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-spthin_0.2.0-1.ca2604.1_all.deb Size: 94314 MD5sum: b3576315adbeddc91ae236ce5d79dd46 SHA1: 0267754e28dc78ce682358a9a0d96b6a3680ea34 SHA256: f3a318e911f4827b92e2af115ae6cdeaf03d51ff15a40ec443e466f4ea49d710 SHA512: b74dc7f88d41c43b4937743455259baf8ccd8b64f073c1ab712fc396af528a1f9b6fb5e814b4be64a2e0fa6e9f4f3c18bcb3899922c0a760fcc361034fccf851 Homepage: https://cran.r-project.org/package=spThin Description: CRAN Package 'spThin' (Functions for Spatial Thinning of Species Occurrence Records forUse in Ecological Models) A set of functions that can be used to spatially thin species occurrence data. The resulting thinned data can be used in ecological modeling, such as ecological niche modeling. Package: r-cran-sptotal Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1769 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-viridis Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-prettydoc, r-cran-tibble, r-cran-gstat Filename: pool/dists/resolute/main/r-cran-sptotal_1.0.1-1.ca2604.1_all.deb Size: 1152618 MD5sum: ad197731b5163be58093e27e7e86f88e SHA1: f6ac790bff43252d59706ebe9281ff48785ae320 SHA256: 5136514ce90fe9649f531aab30fa07628b9835d1f1acd80c1a126dda00c2daae SHA512: 586a6b88bfbdfe787c8ce57016ae9d2a2d39bd9a162d05c0b6c8885451c105ab4c366ef45fdb715865a75aac263bf6efa90940bfe91d02d2fa89e611902d3f55 Homepage: https://cran.r-project.org/package=sptotal Description: CRAN Package 'sptotal' (Predicting Totals and Weighted Sums from Spatial Data) Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) . The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package. Package: r-cran-spup Architecture: all Version: 1.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2362 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-magrittr, r-cran-mvtnorm, r-cran-purrr, r-cran-raster, r-cran-whisker Suggests: r-cran-dplyr, r-cran-ggally, r-cran-gridextra, r-cran-knitr, r-cran-png, r-cran-readr, r-cran-sp, r-cran-testthat, r-cran-sf Filename: pool/dists/resolute/main/r-cran-spup_1.4-0-1.ca2604.1_all.deb Size: 1709134 MD5sum: f808c13b79b9a4fc18501a101355ec25 SHA1: 897763a1477bf11ad7dde4c57e7b8b137839bef8 SHA256: dcc741be6e22cba59c7874c412977ceab54524b9136effb1c6613c79f708a951 SHA512: 25c0ca126d5592b615c34646b0bcee5b000efc96d4845fe0c6a6594e5b8cf037e3e8095f75170a20ef5a81944885d79a293850e68a8c760a71e7aa2724b95933 Homepage: https://cran.r-project.org/package=spup Description: CRAN Package 'spup' (Spatial Uncertainty Propagation Analysis) Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) and Brown and Heuvelink (2007) . The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally. Package: r-cran-spuriouscorrelations Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spuriouscorrelations_0.1-1.ca2604.1_all.deb Size: 84098 MD5sum: f9d592a969779dc1bc08d3292c76af12 SHA1: f209d7ce30b8831398b1669eeea515328bc9e831 SHA256: 3ee44e93638e47607ddddf2c89af426a129d2189b88e47d1d05601a1978c42bb SHA512: b33cc36c5c8371e22f74021c4ddaa8cf5e729eaad6143f874a1aaaaea15e3d05f13e4f0ba8ba75e10570bd250aa5b632624d08a15c004053f2c442b2d27f4863 Homepage: https://cran.r-project.org/package=spuriouscorrelations Description: CRAN Package 'spuriouscorrelations' (Datasets with Strong and Spurious Correlations) Provides datasets from Vigen (2015) rescued from the Internet Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that correlation is not causation. Package: r-cran-spuriousmemory Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fracdiff Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spuriousmemory_1.0.0-1.ca2604.1_all.deb Size: 74826 MD5sum: 6ba355349994900e298b7b4631dedf94 SHA1: 5838ce7ba08d81df356aabae2ccc2b5d446c8f9d SHA256: 168a03bd0eba7fda901f625ec28cf46ce4309a21cd70cb9a6cf7e849bccdf79b SHA512: 35f6cdabdb39adc8f9c72704dc1b711d361b4d2110d4536aeacf1cdcd1ffb87b9928bf39a17336d939d41e96db30a601a12fc3e4c2973313d577fecfa23e462e Homepage: https://cran.r-project.org/package=SpuriousMemory Description: CRAN Package 'SpuriousMemory' (Testing True Long Memory Against Spurious Long Memory) Implements a test for distinguishing between true long memory and spurious long memory. Reference: Qu, Z. (2011). "A Test Against Spurious Long Memory." Journal of Business & Economic Statistics, 29(3), 423–438. . Package: r-cran-spurs Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-spurs_2.0.3-1.ca2604.1_all.deb Size: 198204 MD5sum: a1d8e9e6a022a0bc1649fdb51d657a93 SHA1: 433316fd31e6be49011e2f6a0a45db3248f0f941 SHA256: 575a9a11ffc7ba8c29ac2764506f75bce5fb355c458a16fdf6b6866bef41ac63 SHA512: dbd607978e37e33f5b0b4f26bc0a3b564a0be3f2a6e1c30a8c82a3690a64f3655eeca24e64c6215de3e80ac801fcbe716295405c901d96639b856feb3bd5b9e5 Homepage: https://cran.r-project.org/package=spuRs Description: CRAN Package 'spuRs' (Functions and Datasets for "Introduction to ScientificProgramming and Simulation Using R") Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC. Package: r-cran-spyvsspy Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spyvsspy_0.1.1-1.ca2604.1_all.deb Size: 13514 MD5sum: 9c8c426a62b04468d59a790d90b65a8d SHA1: 7cf259843057dee500c85b1c2c9d76ad351a27cd SHA256: bfc5778a29a118ad9b9c8a80e58c6fdf340be8d583245a1c0b9c81fa5d52495e SHA512: ca40c6fa142cad47380c947d0993b27a9edd460bc2c66ee10b6598270da22ffbd193806936d476a980f644b0ab2a6f37da6816fbd8b2f544e6f194871061b1e5 Homepage: https://cran.r-project.org/package=SPYvsSPY Description: CRAN Package 'SPYvsSPY' (Spy vs. Spy Data) Data on the Spy vs. Spy comic strip of Mad magazine, created and written by Antonio Prohias. Package: r-cran-sqi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-matrixstats, r-cran-olsrr, r-cran-factominer Filename: pool/dists/resolute/main/r-cran-sqi_0.1.0-1.ca2604.1_all.deb Size: 86680 MD5sum: 1985b9375009614cbd481298d7d55981 SHA1: 98bd1a9a5a0b39d9e563b3a41565a35264a16358 SHA256: 2dad79c10af3a87445dad46796c6a17e09bde3d08f0c27d6a61cdede7f66ca8f SHA512: 872ca49bc9254699c9300c9b5407a06229873d41e6f3647c683ec1cdd9f9b93bc6220d81e18848fc335fef71526ef58d4bb06cdcede4fbd2f50b324d07287441 Homepage: https://cran.r-project.org/package=SQI Description: CRAN Package 'SQI' (Soil Quality Index) The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. 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Package: r-cran-sqipro Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-factominer, r-cran-factoextra, r-cran-rlang, r-cran-matrixstats, r-cran-glmnet, r-cran-car Suggests: r-cran-openxlsx, r-cran-readxl, r-cran-ggpubr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-covr Filename: pool/dists/resolute/main/r-cran-sqipro_0.1.0-1.ca2604.1_all.deb Size: 251526 MD5sum: 91defee307047c004550643bb14885cf SHA1: ceceb75d3fcd3057a488ab2103cd97a4a51c7c57 SHA256: a7202d1e9a9e383ee86145130d3a435dcbd4c79d9904b7738408666f02d9bbe1 SHA512: 37cc66fd7ebfc7003bb6dfceab487dbe3574d3b53896ee7d746451d3333874edeca26811c09e0ea1e9cfd03e14fafb62dedcb12e406c8f0f2e227e9212331a09 Homepage: https://cran.r-project.org/package=SQIpro Description: CRAN Package 'SQIpro' (Comprehensive Soil Quality Index Computation and Visualization) Provides a comprehensive, modular framework for computing the Soil Quality Index (SQI) using six established methods: Linear Scoring (Doran and Parkin, 1994, ), Regression-based (Masto et al., 2008, ), Principal Component Analysis-based (Andrews et al., 2004, ), Fuzzy Logic, Entropy Weighting (Shannon, 1948, ), and TOPSIS (Hwang and Yoon, 1981, ). Implements four variable scoring functions: more-is-better, less-is-better, optimum-value, and trapezoidal, following Karlen and Stott (1994, ). Includes automated Minimum Data Set selection via Principal Component Analysis with Variance Inflation Factor filtering (Kaiser, 1960, ), one-way ANOVA with Tukey HSD post-hoc tests, leave-one-out sensitivity analysis, and publication-quality visualization using 'ggplot2'. Package: r-cran-sql Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-arrow, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-sql_0.1.1-1.ca2604.1_all.deb Size: 16780 MD5sum: a612409b7b4cc4928144295e8755a80d SHA1: a849307a3317793bfa05db53101c21cd39b526ee SHA256: 277d7e0411c3df26584e974440c24e71f99d3711f3407f4d9a80c7b73a2f4ad2 SHA512: d3cc2f1a6053de202d3d39017549db6390056102aa60b54f8a6bc23144029b495b353a08ec101a3921359226fb1ce187eaec8fdba074fdd372f2890a6c075d75 Homepage: https://cran.r-project.org/package=SQL Description: CRAN Package 'SQL' (Executes 'SQL' Statements) Runs 'SQL' statements on in-memory data frames within a temporary in-memory 'duckdb' data base. Package: r-cran-sqlcaser Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sqlcaser_0.2.1-1.ca2604.1_all.deb Size: 41230 MD5sum: 91a91e007831f9cb3afc6e0ae1fbd608 SHA1: 77d83d9863d129a129629d1ff1e1f1c76a46447b SHA256: e2c15c92bc6393acafb8838cd87a84f26363096e1d414e5906ba807091d41c03 SHA512: f76a9b74a309bc8b2a33ba567e37f2723787698bb2da20942bd21a5c2162e584685c29d369875102ba934a14d7d1029a7fadb453289aa6e996e2297aa56a292b Homepage: https://cran.r-project.org/package=sqlcaser Description: CRAN Package 'sqlcaser' ('SQL' Case Statement Generator) Includes built-in methods for generating long 'SQL' CASE statements, and other 'SQL' statements that may otherwise be arduous to construct by hand.The generated statement can easily be concatenated to string literals to form queries to 'SQL'-like databases, such as when using the 'RODBC' package. 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Package: r-cran-sqldf Architecture: all Version: 0.4-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsubfn, r-cran-proto, r-cran-rsqlite, r-cran-dbi, r-cran-chron Suggests: r-cran-rh2, r-cran-rmysql, r-cran-rpostgresql, r-cran-svunit, r-cran-mass Filename: pool/dists/resolute/main/r-cran-sqldf_0.4-12-1.ca2604.1_all.deb Size: 76956 MD5sum: e737c1e243d24a6f601bb98ac3b69616 SHA1: 14250986589e6756cba2dee53df3852003a04bfb SHA256: 1652f63f86e76e04385afc17eec6677f4911c983b3eb22e20fc9f7a7a3500a2d SHA512: 5be56fbd2755a0fa89eeb832729ab12c92709fe4c1f3459214be5cb7a754e5e46a5e56544e5e6d273a5df336e58a57dc3013cea99a638c80e749729194ac6f18 Homepage: https://cran.r-project.org/package=sqldf Description: CRAN Package 'sqldf' (Manipulate R Data Frames Using SQL) The sqldf() function is typically passed a single argument which is an SQL select statement where the table names are ordinary R data frame names. sqldf() transparently sets up a database, imports the data frames into that database, performs the SQL select or other statement and returns the result using a heuristic to determine which class to assign to each column of the returned data frame. The sqldf() or read.csv.sql() functions can also be used to read filtered files into R even if the original files are larger than R itself can handle. 'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported. Package: r-cran-sqlfluffr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat, r-cran-glue, r-cran-rstudioapi, r-cran-withr Filename: pool/dists/resolute/main/r-cran-sqlfluffr_0.1.0-1.ca2604.1_all.deb Size: 55012 MD5sum: 681aa86980f82bfe2458d8e2d75e494b SHA1: 1f8190d4386b7d7ede39b030cae6642768b2704e SHA256: 62d7c30a1177ed999cb8bb64d06c644549fab856eb6563c7bf39b9e20d3f17af SHA512: 19533e34777c60b4159dc460c132af4bea513123f0667b6eb61730552c35896e32b54d93248d069f83573a0019bf503889a29a6d8494d843d7f7940c33a16249 Homepage: https://cran.r-project.org/package=sqlfluffr Description: CRAN Package 'sqlfluffr' (Wrapper to the 'SQL' Linter and Formatter 'sqlfluff') An R interface to the 'Python' 'sqlfluff' 'SQL' linter and formatter via the 'reticulate' package. 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'SQL' statements and queries may be interpolated with string literals. Execution of individual statements and queries may be controlled with keywords. Multiple connections may be defined with 'YAML' and accessed by name. 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Package: r-cran-sqliter Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-functional, r-cran-dbi, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-sqliter_0.1.0-1.ca2604.1_all.deb Size: 22008 MD5sum: c8d1056055905387109165f7a7d5ccdf SHA1: 3a84f8a924c84a0ea42e3d1d43876f0cfb1c8e63 SHA256: e6d8d3cdab78b31c92596d201c5f548e69e426035c10216a7d5394f4520c5d0f SHA512: 949428b714bd7bc9c6c3008e692873cab6cad0b63317e1d9f1137223036e1c9791af69c1cf6394e9925b28ccc494c9fc3b07c032345c4fe4c36ba7dc3d3d6dba Homepage: https://cran.r-project.org/package=sqliter Description: CRAN Package 'sqliter' (Connection wrapper to SQLite databases) sqliter helps users, mainly data munging practioneers, to organize their sql calls in a clean structure. It simplifies the process of extracting and transforming data into useful formats. Package: r-cran-sqliteutils Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsqlite, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sqliteutils_0.1.0-1.ca2604.1_all.deb Size: 16496 MD5sum: b49cba1e5f807720e20d9cfb76ac5a83 SHA1: 7aae9a94a0572745bb2e8e180d4016744ead575a SHA256: 9c1cf2677cc376b96d4eb8c0adaf13fb8e3f423e8749de36638172bd1129cc89 SHA512: 72d249ea8fa2c7871fb4f9ea056196ccfc5f5a1c6b2c0937ef3710dada0703095ad7e773f1d6fe0870af3c47e07e410ca38b52cfa2d544e76f7bd77a3ac19436 Homepage: https://cran.r-project.org/package=sqliteutils Description: CRAN Package 'sqliteutils' (Utility Functions for 'SQLite') A tool for working with 'SQLite' databases. 'SQLite' has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. 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Package: r-cran-sqlm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dbplyr, r-cran-dbi, r-cran-glue, r-cran-purrr, r-cran-s7, r-cran-mass, r-cran-broom, r-cran-tibble Suggests: r-cran-testthat, r-cran-duckdb, r-cran-orbital, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-sqlm_0.1.0-1.ca2604.1_all.deb Size: 49458 MD5sum: 02038bb3e70e99564d06c8a380d38de3 SHA1: f015de703e966928c2a0420a61807d1a19dd5db2 SHA256: 5fbdcf87b08dca8a67358b61d9215a40d0a03454328a4df92f20d6c9cc41c967 SHA512: d555678e1e8158ac3ee34642232f965b82ac2576aac0216d9e6aca89133953520fc92386c7ab8431ae28b26e21f4940b050460a07ee8a53990c30fa93b2f2e1d Homepage: https://cran.r-project.org/package=sqlm Description: CRAN Package 'sqlm' (SQL-Backed Linear Regression) Fits linear regression models on datasets residing in SQL databases without pulling data into R memory. 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Package: r-cran-sqlove Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-readr, r-cran-rjdbc, r-cran-odbc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sqlove_1.0.2-1.ca2604.1_all.deb Size: 24380 MD5sum: 96f980b7e066e433915ca8e56f1f68b7 SHA1: 03fe4a34476ed8a2e984fb95604d3ffc3df6009a SHA256: 2b492ebd82f67513cae0b15fcaf87417e6b9a775c29f68c62a9202ddb55ca0fb SHA512: 74609bd27d344e0bb2b6aae871a3a7e6d1f24f5695ffb8b539bd859613ffc29a03032d8cf7628ea9559d2ed378f810bc8d32db3247c7bec436b8d0c107288adc Homepage: https://cran.r-project.org/package=SQLove Description: CRAN Package 'SQLove' (Execute 'SQL' Scripts in 'R' Containing Multiple Queries) The nature of working with structured query language ('SQL') scripts efficiently often requires the creation of temporary tables and there are few clean and simple 'R' 'SQL' execution approaches that allow you to complete this kind of work with the 'R' environment. This package seeks to give 'SQL' implementations in 'R' a little love by deploying functions that allow you to deploy complex 'SQL' scripts within a typical 'R' workflow. Package: r-cran-sqlparser Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Filename: pool/dists/resolute/main/r-cran-sqlparser_0.1.0-1.ca2604.1_all.deb Size: 24484 MD5sum: 1aa3f58ea2f490c4df73dac4614ea769 SHA1: 55bbbdc161b6c9f54466ac5a7010aa685d5fd95a SHA256: ee86ddfd5c86b173ea7b214b4a817e895d4a9417c6572540d1ba46d3118511e1 SHA512: 7b32bb8d67540ae5862a338069724ca71976220c146fda4286a36748f0d629b450b696925dfe06a77744a49b0c6eefd91ff227daff94fa282fa5590c3c43aa48 Homepage: https://cran.r-project.org/package=sqlparseR Description: CRAN Package 'sqlparseR' (Wrapper for 'Python' Module 'sqlparse': Parse, Split, and Format'SQL') Wrapper for the non-validating 'SQL' parser 'Python' module 'sqlparse' . It allows parsing, splitting, and formatting 'SQL' statements. Package: r-cran-sqlq Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-chk, r-cran-dbi Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-cyclocomp, r-cran-lintr, r-cran-lgr, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-sqlq_1.0.1-1.ca2604.1_all.deb Size: 944490 MD5sum: e47838df7420d07a185b0a74f419911a SHA1: 0d6fc3c7c82f1243dfa0faa300cd3396dd1fd172 SHA256: 5dce64478f32e359a4af388b32a2ff79ac5427d71c45e38926805dba9fc1fa31 SHA512: 29151ad172b2dcf4f4a0151bd242a6dfa93b91decda26376d3735846d5605720507909a4808026a604e39ca469a741dc5500af97a864556def037c56a3e17f24 Homepage: https://cran.r-project.org/package=sqlq Description: CRAN Package 'sqlq' ('SQL' Query Builder) Allows to build complex 'SQL' (Structured Query Language) queries dynamically. Classes and/or factory functions are used to produce a syntax tree from which the final character string is generated. Strings and identifiers are automatically quoted using the right quotes, using either ANSI (American National Standards Institute) quoting or the quoting style of an existing database connector. Style can be configured to set uppercase/lowercase for keywords, remove unnecessary spaces, or omit optional keywords. Package: r-cran-sqlrender Architecture: all Version: 1.19.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-rlang, r-cran-checkmate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-shinydashboard Filename: pool/dists/resolute/main/r-cran-sqlrender_1.19.5-1.ca2604.1_all.deb Size: 457428 MD5sum: 01a7683a2052c3acf432bf7eaf8ca5c9 SHA1: d41babe54df230c949eec88dcb6a2b92dbe5bb66 SHA256: c92404016259016aac2c1654e9b2634eb5efc5ba991ebfd70562055cf1bb9c00 SHA512: 2ce8d331f277066c38e9f2709873325cfa3736279ff223dc562402bc2dda4a2b0a363d91e31856590f5cb101161fcda2e7ceb313dec27b9e126f1437925da5b1 Homepage: https://cran.r-project.org/package=SqlRender Description: CRAN Package 'SqlRender' (Rendering Parameterized SQL and Translation to Dialects) A rendering tool for parameterized SQL that also translates into different SQL dialects. These dialects include 'Microsoft SQL Server', 'Oracle', 'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', 'Snowflake', 'Azure Synapse Analytics Dedicated', 'Apache Spark', 'SQLite', and 'InterSystems IRIS'. Package: r-cran-sqlscore Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbplyr Suggests: r-cran-testthat, r-cran-arm, r-cran-glmnet, r-cran-mboost, r-cran-covr Filename: pool/dists/resolute/main/r-cran-sqlscore_0.1.4-1.ca2604.1_all.deb Size: 48268 MD5sum: d99bef901f172641252f2f351291d71f SHA1: 295648f7d793ef6d497a6acee770627c8018f3e1 SHA256: f1e89bcdfd95427064a2ba9f170ace869e8cd47f8057c750ec7054b1a5d87ed7 SHA512: 96482b791362bb832990ec2ceb4daeb9c22fec1b24bc292ae9be1625727892de0b07075daf6064e317e7fd5e006abef5ac1a8aeebb9cf098dd37a117f8986ff7 Homepage: https://cran.r-project.org/package=sqlscore Description: CRAN Package 'sqlscore' (Utilities for Generating SQL Queries from Model Objects) Provides utilities for generating SQL queries (particularly CREATE TABLE statements) from R model objects. The most important use case is generating SQL to score a generalized linear model or related model represented as an R object, in which case the package handles parsing formula operators and including the model's response function. Package: r-cran-sqlserverconnect Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-odbc, r-cran-pool, r-cran-cli Filename: pool/dists/resolute/main/r-cran-sqlserverconnect_0.1.0-1.ca2604.1_all.deb Size: 17612 MD5sum: eacd8c5145458d2edc983256ffc6a036 SHA1: 6017c9b72bcd53e4db924527035ef6ecf9a93127 SHA256: 805f423785c57128ef5d7a46cc4304ff04896792cf1acde3ecf66a3648033201 SHA512: c1608e41a529d5e0628029bc430caf9c498eaae5ee794d900a7c3fb8f88223359a803ffbc225bff1ad11d3c36cc577e15110a444d14759cd153ac5d84d61b2f5 Homepage: https://cran.r-project.org/package=sqlserverconnect Description: CRAN Package 'sqlserverconnect' (Simple Helpers for Connecting to 'SQL Server') Lightweight helpers for connecting to Microsoft 'SQL Server' using 'DBI', 'odbc', and 'pool'. Provides simple wrappers for building connection arguments, establishing connections, and safely disconnecting. Package: r-cran-sqlstrings Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs, r-cran-readr, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sqlstrings_1.0.0-1.ca2604.1_all.deb Size: 12656 MD5sum: d883adc497629d6da6026c6a7fede6a2 SHA1: 516d3ca5fc7b569f879f372831c62ac47a6c55c1 SHA256: e74cdbfccc17ec2393dd9077507fb63fbdb0501bbabf90a129dcac373ae9c6c0 SHA512: 7eb780235ad095bf14ca0907794155246f6e61c92f4dd0619dfa6133aebcefe3b1d0e91abad05a6b6b1bab619c0783cddaebfd8e57f36d9f576a1c9499d9258a Homepage: https://cran.r-project.org/package=sqlstrings Description: CRAN Package 'sqlstrings' (Map 'SQL' Code to R Lists) Provides a helper function, to bulk read 'SQL' code from separate files and load it into an 'R' list, where the list elements contain the individual statements and queries as strings. This works by annotating the 'SQL' code with a name comment, which also will be the name of the list element. Package: r-cran-sqltargets Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-fs, r-cran-glue, r-cran-jinjar, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tarchetypes, r-cran-targets, r-cran-withr Suggests: r-cran-testthat, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-sqltargets_0.3.0-1.ca2604.1_all.deb Size: 112840 MD5sum: ebad91f5358444e1252810fb381e8f24 SHA1: 76460c7934e6d385d7fc6960182207b706c5fc38 SHA256: fa252279ad815e83f0c36a499d217aa94e94a67f8c9a0d7f2adc4b6fcae961f4 SHA512: 9c5c8c980c4474f1a31a712b379815a4ef0c1f03f5ed48c6eb3e5f17ae82c8e62e14ccc737cbc03153b24f3ed1d42d3e5fa5633ab1b5e371746c2b4536503da5 Homepage: https://cran.r-project.org/package=sqltargets Description: CRAN Package 'sqltargets' ('Targets' Extension for 'SQL' Queries) Provides an extension for 'SQL' queries as separate file within 'targets' pipelines. The shorthand creates two targets, the query file and the query result. Package: r-cran-sqmtools Architecture: all Version: 1.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zip, r-cran-reshape2, r-cran-ggplot2, r-bioc-pathview, r-cran-data.table, r-bioc-biostrings Suggests: r-cran-vegan, r-cran-microeco, r-bioc-phyloseq Filename: pool/dists/resolute/main/r-cran-sqmtools_1.7.2-1.ca2604.1_all.deb Size: 3650066 MD5sum: fad2e6045cff5fe5f965a4d6b9a2aed9 SHA1: b230fb749da9bc24d7193e48595c78fe62a31918 SHA256: 82fc2fb7672b70651a677549fd5cb49902ea1a8bb7a6b96e574924be0285e1ba SHA512: 0fbd6dc8cfd215383770e98b037b94bb8c53240effd766237ee2008e454f50c0d84117a94ed22d0d6d1f9811146b40e7c3dadff86dec2f1a7b6ccdc7db0e04c6 Homepage: https://cran.r-project.org/package=SQMtools Description: CRAN Package 'SQMtools' (Analyze Results Generated by the 'SqueezeMeta' Pipeline) 'SqueezeMeta' is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (). This package provides functions loading 'SqueezeMeta' results into R, filtering them based on different criteria, and visualizing the results using basic plots. The 'SqueezeMeta' project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as 'vegan' or 'DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) . Package: r-cran-sqn Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-nor1mix Filename: pool/dists/resolute/main/r-cran-sqn_1.0.6-1.ca2604.1_all.deb Size: 106832 MD5sum: a914d6f2144915c92567e2f72e75a43f SHA1: 1e1d4e36467463938a0f64adae0a42ec85388852 SHA256: 7fc3d486f1a5e3440ab6372dc6c8f9d192c08ab170c227ca6ee5412bb2dfa5ff SHA512: 32801ef8016503d3a3f78174b81ef96952afeb876cd3fa4eb703a4fa3d13f053660cd3446f2d09e0de7c43778f1280c490ed6821a84de8250f9700e0196f18be Homepage: https://cran.r-project.org/package=SQN Description: CRAN Package 'SQN' (Subset Quantile Normalization) Normalization based a subset of negative control probes as described in 'Subset quantile normalization using negative control features'. Wu Z, Aryee MJ, J Comput Biol. 2010 Oct;17(10):1385-95 [PMID 20976876]. Package: r-cran-sqrl Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rodbc Filename: pool/dists/resolute/main/r-cran-sqrl_1.0.3-1.ca2604.1_all.deb Size: 395940 MD5sum: f25ed352288a6de8f86286fd18dd00d5 SHA1: 95056d8f28b5a41e54bf24349179abe087272ab9 SHA256: aafcae371e994cc1f646ae5417d2b4cde009d48d3af2a5a8551c31c4bdaa901d SHA512: 189797a293e3a006d9aea0283d23c65a58dc5d5b4ddf841c36d948690f16f571434a512db5cd08e31bab5e2fe3d31643ec77a23031bb81a23f4bc32c5debeb6c Homepage: https://cran.r-project.org/package=SQRL Description: CRAN Package 'SQRL' (Enhances Interaction with 'ODBC' Databases) Provides simple and powerful interfaces that facilitate interaction with 'ODBC' data sources. Each data source gets its own unique and dedicated interface, wrapped around 'RODBC'. Communication settings are remembered between queries, and are managed silently in the background. The interfaces support multi-statement 'SQL' scripts, which can be parameterised via metaprogramming structures and embedded 'R' expressions. Package: r-cran-squant Architecture: all Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-squant_1.1.7-1.ca2604.1_all.deb Size: 128344 MD5sum: 8e2bb91ca25543bdaa7b6156aa6eecb7 SHA1: abdec4d5af802917b96c0cb8cef85118cf2c6479 SHA256: 5bb7b695f72182123f5e1b9639d291ccf31363d924009cf89283d21b4f8bcff8 SHA512: 56b6a801f449b76a50ab2bc51c41768875e26f3a52702616cc89901fdfaade871da5baf9daada6119b30a4d21d9d43fa5a53a2af768b4a0e2d1af82cf874690a Homepage: https://cran.r-project.org/package=squant Description: CRAN Package 'squant' (Subgroup Identification Based on Quantitative Objectives) A subgroup identification method for precision medicine based on quantitative objectives. This method can handle continuous, binary and survival endpoint for both prognostic and predictive case. For the predictive case, the method aims at identifying a subgroup for which treatment is better than control by at least a pre-specified or auto-selected constant. For the prognostic case, the method aims at identifying a subgroup that is at least better than a pre-specified/auto-selected constant. The derived signature is a linear combination of predictors, and the selected subgroup are subjects with the signature > 0. The false discover rate when no true subgroup exists is controlled at a user-specified level. Package: r-cran-squarem Architecture: all Version: 2026.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-setrng, r-cran-interval Filename: pool/dists/resolute/main/r-cran-squarem_2026.1-1.ca2604.1_all.deb Size: 176864 MD5sum: afb219c98b7c7d07a52f0b967fad6382 SHA1: afc2a80bab42c84e32dda6947f7ee9f58f14f01f SHA256: 30394563a941813241eb286549ef742215d8172e7410efa5711634569c47f5d8 SHA512: f9a75fc4f69c059ab63508e8faf2f9d2fe6dca5e5a2dcfe8c09303c8b7ef820edd0af64b5b633eefcfd3af6189ff6cc40e7fdd7fa906eb7baf01f63ebb16312b Homepage: https://cran.r-project.org/package=SQUAREM Description: CRAN Package 'SQUAREM' (Squared Extrapolation Methods for Accelerating EM-Like MonotoneAlgorithms) Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mappings such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent scheme. A tutorial-style introduction is available in vignette("SQUAREM"). See also: Varadhan & Roland (2008) . Package: r-cran-squash Architecture: all Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-squash_1.0.9-1.ca2604.1_all.deb Size: 147806 MD5sum: c7c75723a3ffdd6f96b62ddea2a2bcce SHA1: 0597b1ba228752ac05127768a753459af313271f SHA256: 079837ff752a80446d70171aae6715120acdd3506c113f4a77b6e34c3087ff63 SHA512: 92471ff46cbd09c48373dc47b6e29deecc10a3f16a4a1c43dd49d426844e9776bcbf3a0f50dd4d213c7aef2ed2168e2bb2afd0bb456909c35aa9773a61bd5938 Homepage: https://cran.r-project.org/package=squash Description: CRAN Package 'squash' (Color-Based Plots for Multivariate Visualization) Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps. Lower-level functions map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more. Package: r-cran-squeakr Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8878 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggcorrplot, r-cran-ggeasy, r-cran-gghighlight, r-cran-ggplot2, r-cran-ggpubr, r-cran-googlesheets4, r-cran-mass, r-cran-plotly, r-cran-rcolorbrewer, r-cran-readxl, r-cran-report, r-cran-rlist, r-cran-rstatix, r-cran-shiny, r-cran-shinydashboard Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-squeakr_1.3.0-1.ca2604.1_all.deb Size: 4037606 MD5sum: 9f7c4cfed0fa2a78d397ed32e0ea1a64 SHA1: d2848ed6fd85288e95cfb0a83acf5e255ec21f60 SHA256: be19efed5ba0cbd43a9b6646c663515109b4e41821a106a5c77071449233ced8 SHA512: 06de40360c1763343bbfb694d5f8dc97f0a39910929e4caadf244c582f1cd1794fbd9ff5ec1976d0c27929bb1272e04e1354d16a8e046f0da0bf5f39658df41f Homepage: https://cran.r-project.org/package=SqueakR Description: CRAN Package 'SqueakR' (An Experiment Interface for 'DeepSqueak' Bioacoustics Research) Data processing and visualizations for rodent vocalizations exported from 'DeepSqueak'. 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Package: r-cran-squid Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinymatrix, r-cran-ggplot2, r-cran-plotly, r-cran-mass, r-cran-lme4, r-cran-arm, r-cran-data.table, r-cran-brms Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-squid_0.2.1-1.ca2604.1_all.deb Size: 3851076 MD5sum: 0abb05834636514ae462871788519fcb SHA1: bcc2c1d4b21102ca4e7f066ac5cf86b33306c1e7 SHA256: 66ded1ed2687c8238eb9bdf453c04b29846341596f84f9e741efbf43416a77d3 SHA512: 3f56fad145f0501086b0b63f237a0e097d89d6e07dbdb2baa370d5995161f0b5df346c78636112e700d59f0f4cbafb5d960bd446f79a9dcc00de38b6786edced Homepage: https://cran.r-project.org/package=squid Description: CRAN Package 'squid' (Statistical Quantification of Individual Differences) A simulation-based tool made to help researchers to become familiar with multilevel variations, and to build up sampling designs for their study. This tool has two main objectives: First, it provides an educational tool useful for students, teachers and researchers who want to learn to use mixed-effects models. Users can experience how the mixed-effects model framework can be used to understand distinct biological phenomena by interactively exploring simulated multilevel data. Second, it offers research opportunities to those who are already familiar with mixed-effects models, as it enables the generation of data sets that users may download and use for a range of simulation-based statistical analyses such as power and sensitivity analysis of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse, N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth, H. and Westneat, D.F. (2016) ]. Package: r-cran-squids Architecture: all Version: 25.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-squids_25.6.1-1.ca2604.1_all.deb Size: 228350 MD5sum: 8902d4dd12f6458c9d4f9c982405ef06 SHA1: ef55dba75d4a274de0de09ae223fc2e4baca8e44 SHA256: 2772a74f7838d34edc7db0a3b8ca4e42ee0a9f70a9e556be8e931ce1abe94359 SHA512: a176f5b348f0cdb18521941e27e622de76c7d50b492eb4b09fc0d0703c56d77e281eb6599eea2eb05c0be846e9de95f5b38adf5242081fbee4e310620891dd5b Homepage: https://cran.r-project.org/package=squids Description: CRAN Package 'squids' (Short Quasi-Unique Identifiers (SQUIDs)) It is often useful to produce short, quasi-unique identifiers (SQUIDs) without the benefit of a central authority to prevent duplication. Although Universally Unique Identifiers (UUIDs) provide for this, these are also unwieldy; for example, the most used UUID, version 4, is 36 characters long. SQUIDs are short (8 characters) at the expense of having more collisions, which can be mitigated by combining them with human-produced suffixes, yielding relatively brief, half human-readable, almost-unique identifiers (see for example the identifiers used for Decentralized Construct Taxonomies; Peters & Crutzen, 2024 ). SQUIDs are the number of centiseconds elapsed since the beginning of 1970 converted to a base 30 system. This package contains functions to produce SQUIDs as well as convert them back into dates and times. Package: r-cran-squire Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-galahad, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-squire_1.0.1-1.ca2604.1_all.deb Size: 44182 MD5sum: c1889613ceb504737ae1b42789c830a7 SHA1: 8a4a967b856cb8d095a421799c780c4ac4f5adc7 SHA256: e150a242f1e29c3bc44622c3ceb0d93e2372187552b6035cdfc9ca285da633e3 SHA512: 97115fbb1e00c7ff6a63aabbd2d00453515d3ad4659e7c14f661cd1c43d8fffa23ee4d649f055179e443f58bafd0a3f5aea65ab1aaaa744d21105f03c1c593b1 Homepage: https://cran.r-project.org/package=SQUIRE Description: CRAN Package 'SQUIRE' (Statistical Quality-Assured Integrated Response Estimation) Provides systematic geometry-adaptive parameter optimization with statistical validation for experimental biological data. Combines ANOVA-based validation with systematic constraint configuration testing (log-scale, positive domain, Euclidean) through T,P,E testing. Only proceeds with parameter optimization when statistically significant biological effects are detected, preventing over-fitting to noise. Uses 'GALAHAD' trust region methods with constraint projection from Conn et al. (2000) , ANOVA-based validation following Fisher (1925) , and effect size calculations per Cohen (1988, ISBN:0805802835). Designed for structured experimental data including kinetic curves, dose-response studies, and treatment comparisons where appropriate parameter constraints and statistical justification are important for meaningful biological interpretation. Developed at the Minnesota Center for Prion Research and Outreach at the University of Minnesota. 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SRCS: a technique for comparing multiple algorithms under several factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012. 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The main references for this package are Sarndal C.E., Swensson B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, "Model Assisted Survey Sampling." Springer-Verlag) Cardozo C.A, Paula G.A. and Vanegas L.H. (2022) "Generalized log-gamma additive partial linear mdoels with P-spline smoothing", Statistical Papers. Cardozo C.A and Alonso-Malaver C.E. (2022). "Semi-parametric model assisted estimation in finite populations." In preparation. 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Package: r-cran-srpi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-srpi_0.1.0-1.ca2604.1_all.deb Size: 21084 MD5sum: 3534a7060ebc6f3c589275fa4c5fde89 SHA1: 2eea1109cc16e1648ef7d9f2b8b835acb4f6467c SHA256: 1dcd8103b59fd9db31875d0634267d222907a1bc8a238db08982c8a4f18ef4da SHA512: 7ed2673fbb8a57221beee5be63d9ad2b6ce4fae48b09fd41ab904c802555d03d3a6c34e265e6a29efbfc08ec61b4206d550aa875eec117970c5a984e6860bd6e Homepage: https://cran.r-project.org/package=srpi Description: CRAN Package 'srpi' (Standardized Ranking Performance Index for Model Selection) Flexible implementation of the Standardized Ranking Performance Index (sRPI) for model selection based on multiple evaluation criteria. The package combines multiple statistical measures into a single index to provide an objective and robust ranking of models across calibration, validation, and combined scenarios. It supports evaluation of statistical, machine learning, and other predictive models using user-defined performance criteria. For more details see Aschonitis et al. (2019) and Singh et al. (2023) . Package: r-cran-srppp Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dm, r-cran-xml2, r-cran-tibble, r-cran-stringr, r-cran-dplyr, r-cran-cli, r-cran-data.tree, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-testthat, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-srppp_2.0.3-1.ca2604.1_all.deb Size: 2555082 MD5sum: 332e97fe12271338f909e33cf8b53241 SHA1: bb48b15f6b5baa358c2c0aa9daba17bce48bcb60 SHA256: 27157745b96d9c98a9d492fedd8c42a9f26d58b633312884a3c7f61e73c669bc SHA512: 0955884195d54f5b42c2a4fff89870ddbc655b8e53162af5639e412aaaa0d8ae1cd98674ce0f409809e17de0386a334fdb02c4513368be6271e11d7dc4fa3343 Homepage: https://cran.r-project.org/package=srppp Description: CRAN Package 'srppp' (Read the Swiss Register of Plant Protection Products) Generate data objects from XML versions of the Swiss Register of Plant Protection Products. An online version of the register can be accessed at . There is no guarantee of correspondence of the data read in using this package with that online version, or with the original registration documents. Also, the Federal Food Safety and Veterinary Office, coordinating the authorisation of plant protection products in Switzerland, does not answer requests regarding this package. Package: r-cran-srs Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-shiny, r-cran-dt, r-cran-shinycssloaders, r-cran-shinybusy Filename: pool/dists/resolute/main/r-cran-srs_0.2.3-1.ca2604.1_all.deb Size: 60638 MD5sum: 17563c72ad3727e5833de78c1f1a139d SHA1: 9519b8f5c8cebaae33ab57fe8863de5065f36369 SHA256: e867aedcb29ce20f68367e03b84375ae1edafa6089345477ac03102840f42fc4 SHA512: 0e65021e8d4cd44f43cdaa8b251a6cd7f7a8d5132fc3eaffdd4aee4d6511afac9138218b546ee60b6727fa6db03c950e545405f61d9fa5798be3aa08ab4b014a Homepage: https://cran.r-project.org/package=SRS Description: CRAN Package 'SRS' (Scaling with Ranked Subsampling) Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) for details. 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The SRscore is determined to evaluate and score genes on the basis of the consistency of the direction of their regulation (Up-regulation, Down-regulation, or No change) under stress conditions across multiple analyzed research projects. This package is based on the HN-score (score based on the ratio of gene expression between hypoxic and normoxic conditions) proposed by Tamura and Bono (2022) , and can calculate both the original method and an extended calculation method described in Fukuda et al. (2025) . 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In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers. 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If there is no prior information on the population CV, then a small preliminary sample of size is selected to estimate the population CV which is then used for determination of final sample size. If the final sample size is more than the preliminary sample size, then the preliminary sample is augmented by drawing additional units from the remaining population units so that the size of the augmented sample is equal to the final sample size. On the other hand, if the preliminary sample size is larger than the final sample size, then the preliminary sample is considered as the final sample. 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It estimates the Hazardous Concentration for x% of the species (HCx) from toxicity values that can be censored and provides various plotting options for a better understanding of the results. See our companion paper Kon Kam King et al. (2014) . Package: r-cran-ssddata Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chk, r-cran-dplyr, r-cran-rdpack Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ssddata_1.0.0-1.ca2604.1_all.deb Size: 118108 MD5sum: 34a2b39c8e83e2298ce145eab49a9852 SHA1: 2aef47f21711610bc25d1108c05325789487cf23 SHA256: 533b1ff68b43d4c6fe07a58538eb2d4c7cbf32ac3220c444507a91b33c6388be SHA512: b93d2d95c75cfb361f7d7b41814bc747f7b37698c22c78a03c54a0986393f84aca80c822d1f78dc17edfa8a755cd45d092fbcd0252d997bb9b189bd31b854072 Homepage: https://cran.r-project.org/package=ssddata Description: CRAN Package 'ssddata' (Species Sensitivity Distribution Data) Reference data sets of species sensitivities to compare the results of fitting species sensitivity distributions using software such as 'ssdtools' and 'Burrlioz'. 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Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface. Package: r-cran-ssdr Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-ssdr_1.2.0-1.ca2604.1_all.deb Size: 47680 MD5sum: 648c86691e0e1621e1b5c3613c751cf3 SHA1: c3ca39244d6816a86e8cac2edf8d6280835e42f5 SHA256: 797256a953d7c394df8c5a07dd01029a2f4ac081ef628b287e25cb56beb2872f SHA512: e0296662a73a3e0bfc6b0b54847d1edb43a3e47dce8146dfbc3fd497cd3b7c9fc425c7c228bee9e4544d80a17a9107c8b508543ff0c796f8e431f6df946a2b39 Homepage: https://cran.r-project.org/package=sSDR Description: CRAN Package 'sSDR' (Tools Developed for Structured Sufficient Dimension Reduction(sSDR)) Performs structured OLS (sOLS) and structured SIR (sSIR). 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The SSEBI is computed from albedo and TS–TA to estimate surface moisture and evaporative dynamics, providing a robust assessment of surface dryness while accounting for atmospheric variations. Based on Roerink, Su, and Menenti (2000) . Package: r-cran-sseparser Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-stringr Suggests: r-cran-jsonlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sseparser_0.1.0-1.ca2604.1_all.deb Size: 55364 MD5sum: d7bb1e5e546110f65588447f18717296 SHA1: a3998aaa4fff5987d9f9a64dc4f39c34b30a4bf4 SHA256: e983002e77c475769de38981f2a2f12ec426adb296817d8f5e1cf34eeb1e3a30 SHA512: d0e4cb2f5b350900ad19cc532235c68783efc5da7466bef7f26d8257174a04d2912c758dd87374bfd234b9b4a9e83223afc5800e49f7a0add1bf71449d43a238 Homepage: https://cran.r-project.org/package=SSEparser Description: CRAN Package 'SSEparser' (Parse Server-Sent Events) Functionality to parse server-sent events with a high-level interface that can be extended for custom applications. Package: r-cran-ssev Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pwr, r-cran-mess Filename: pool/dists/resolute/main/r-cran-ssev_0.1.0-1.ca2604.1_all.deb Size: 28748 MD5sum: c4f4322efbf6da82f0bf589fb93a32ab SHA1: 93cfdf28247ee863053d7a21b12daf8cd2dd4977 SHA256: b10a201c7062be6e4417b92fbedaa89cc739e3088a46816b8975f21a79179084 SHA512: 488347fb7cde7ffd1537afe3932c2e6071a217c4b0ecc2168927e52300dd2b9e4001f7651797d873d5d4496642b4b68951ff474a551648f506afa65471cf5563 Homepage: https://cran.r-project.org/package=ssev Description: CRAN Package 'ssev' (Sample Size Computation for Fixed N with Optimal Reward) Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. 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Package: r-cran-ssfit Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey Filename: pool/dists/resolute/main/r-cran-ssfit_1.2-1.ca2604.1_all.deb Size: 20150 MD5sum: c1bb220670c69d92e6a9cbf9c15d26d4 SHA1: 5b23784d689e8b4ce07bf47ba70767fec2aa8808 SHA256: 9e0832dd70b996d3fbefe4bdf3c8198df4b612a6a54296873818184566df081c SHA512: adf487193711af983171622dccdf26fbd75df9974ec93f820975228f2247e1ab2ed1193bd84d8150f61542f2ee79ea15fb4169b8c6cecf4788eb62591f9d7d23 Homepage: https://cran.r-project.org/package=ssfit Description: CRAN Package 'ssfit' (Fitting of Parametric Models using Summary Statistics) Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods. Package: r-cran-sshaarp Architecture: all Version: 2.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2890 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-gtools, r-cran-gmt, r-cran-desctools, r-cran-dplyr, r-cran-filesstrings, r-cran-purrr, r-cran-stringi, r-cran-hlatools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sshaarp_2.0.8-1.ca2604.1_all.deb Size: 1857374 MD5sum: 3e44389bd0a54e8a5504f3e900f19323 SHA1: 14697429463d9699149c4676b2d37f6510eaa09a SHA256: ffd121f67313850944d221a049c5c1a29967c2baed85ad8656d03f690b78432a SHA512: b6eec406a1c362b5336f090fcb41a36a9ee258ea18fbd4ecb2c4915460b555cf044d7fb9cfc8f3c02bd8c5cdafbb556cfaf569d3b7d4318f3e8f09f471692470 Homepage: https://cran.r-project.org/package=SSHAARP Description: CRAN Package 'SSHAARP' (Searching Shared HLA Amino Acid Residue Prevalence) Processes amino acid alignments produced by the 'IPD-IMGT/HLA (Immuno Polymorphism-ImMunoGeneTics/Human Leukocyte Antigen) Database' to identify user-defined amino acid residue motifs shared across HLA alleles, HLA alleles, or HLA haplotypes, and calculates frequencies based on HLA allele frequency data. 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Package: r-cran-ssize.fdr Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ssize.fdr_1.3-1.ca2604.1_all.deb Size: 83442 MD5sum: 9368beeb3af1c9ac1225a68cba5d69f8 SHA1: cfd2c1d8e643a2f883a90b3c2cd013eebe149928 SHA256: 3736788dbc5848209a1d477ba12876373d1ec76031037dd024683d4314d720d3 SHA512: a5f4d58011598bcf5e28d94754b40c0c6f0c5b33f388d2e2e820d9e84a08c54a54e1887808e9abdc479a29f6a5e4ace703e00f35fd2ce1a364e75a6344d4e60a Homepage: https://cran.r-project.org/package=ssize.fdr Description: CRAN Package 'ssize.fdr' (Sample Size Calculations for Microarray Experiments) Functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random. This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. Package: r-cran-ssizerna Architecture: all Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-bioc-biobase, r-bioc-edger, r-bioc-limma, r-bioc-qvalue, r-cran-ssize.fdr Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-ssizerna_1.3.3-1.ca2604.1_all.deb Size: 454928 MD5sum: 28e0fe9bb5b5f6762cc9d0e24e0c06a7 SHA1: d61f7c3c5eaae47aec7117b7d4140f272d9a4835 SHA256: 0366830b257298ca9448443162fec5cae0f288779a93e013a1d992e97359d793 SHA512: 0d01165941277633f450dfd0402113cba39e75e99fc459974585c8d66eaff2ecbc4f84e29baefc8f1288decf6a0c945c7f0debd0d19d4dbc58ef1ea6bed40680 Homepage: https://cran.r-project.org/package=ssizeRNA Description: CRAN Package 'ssizeRNA' (Sample Size Calculation for RNA-Seq Experimental Design) We propose a procedure for sample size calculation while controlling false discovery rate for RNA-seq experimental design. Our procedure depends on the Voom method proposed for RNA-seq data analysis by Law et al. (2014) and the sample size calculation method proposed for microarray experiments by Liu and Hwang (2007) . We develop a set of functions that calculates appropriate sample sizes for two-sample t-test for RNA-seq experiments with fixed or varied set of parameters. The outputs also contain a plot of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. To install this package, please use 'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. For R version 3.5 or greater, please use 'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'. Package: r-cran-sslfmm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrixstats Filename: pool/dists/resolute/main/r-cran-sslfmm_0.1.0-1.ca2604.1_all.deb Size: 111336 MD5sum: 708372d09da73beabe41676790dc9fd8 SHA1: 476a5f7c58754a3351ab3d910447d2a4424b196a SHA256: 247d9d766b8e989909c77fab90b3eac12c9aa0ee8795f45d6061cb83856998e8 SHA512: f00e2e2fa8f4390cec58f9b7cc21ffea7924c49523f9be534ff70b9c95dca9cef506b762db981c98fddaceca07d6f0a428f01258983ab12089fd2babec6032b0 Homepage: https://cran.r-project.org/package=SSLfmm Description: CRAN Package 'SSLfmm' (Semi-Supervised Learning under a Mixed-Missingness Mechanism inFinite Mixture Models) Implements a semi-supervised learning framework for finite mixture models under a mixed-missingness mechanism. The approach models both missing completely at random (MCAR) and entropy-based missing at random (MAR) processes using a logistic–entropy formulation. Estimation is carried out via an Expectation–-Conditional Maximisation (ECM) algorithm with robust initialisation routines for stable convergence. The methodology relates to the statistical perspective and informative missingness behaviour discussed in Ahfock and McLachlan (2020) and Ahfock and McLachlan (2023) . The package provides functions for data simulation, model estimation, prediction, and theoretical Bayes error evaluation for analysing partially labelled data under a mixed-missingness mechanism. Package: r-cran-ssm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ssm_1.0.1-1.ca2604.1_all.deb Size: 275760 MD5sum: 349478dc5276dfc825cf3d6634ab77b5 SHA1: 70f7374f755564e3d5059abd1eb85bf3e3a50626 SHA256: fae3a24626d828a2fcd8bc44c1c599fa1daaaf9b1ea1d07655557f58b67250d4 SHA512: 1ca04ed4585f0caed8b5d446037710919f4ec6f5f9b224222f759ff2513d6c40f88c0e853e33e0bef0b8f68a6de0da7d0f18bbf1f33d86866a27eca2a0b6de76 Homepage: https://cran.r-project.org/package=SSM Description: CRAN Package 'SSM' (Fit and Analyze Smooth Supersaturated Models) Creates an S4 class "SSM" and defines functions for fitting smooth supersaturated models, a polynomial model with spline-like behaviour. Functions are defined for the computation of Sobol indices for sensitivity analysis and plotting the main effects using FANOVA methods. It also implements the estimation of the SSM metamodel error using a GP model with a variety of defined correlation functions. Package: r-cran-ssmn Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mnormt, r-cran-moments, r-cran-truncdist, r-cran-sn Filename: pool/dists/resolute/main/r-cran-ssmn_1.1-1.ca2604.1_all.deb Size: 125380 MD5sum: 3421f1a4b276b274384398a06dd1850d SHA1: 8ef0022be7ad659c511042a3716bfc5c8da1187a SHA256: f1c87b33d54bf7ccc95d1109c24e06dee79e5313e43269b21f82a65b715b99ac SHA512: 32e81244426ff264e6511ae00c331f7d260688257c79f3c363d7fe20c21353f596613958c7211209b995eb8e01a70f3fd3681ada1898f7ac6915c6542e6688ae Homepage: https://cran.r-project.org/package=ssmn Description: CRAN Package 'ssmn' (Skew Scale Mixtures of Normal Distributions) Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions. Package: r-cran-ssmodels Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sn, r-cran-numderiv, r-cran-pracma, r-cran-misctools, r-cran-rdpack Suggests: r-cran-knitr, r-cran-testthat, r-cran-maxlik, r-cran-mvtnorm, r-cran-sampleselection, r-cran-kableextra, r-cran-kfigr, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-ssmodels_2.0.1-1.ca2604.1_all.deb Size: 1119654 MD5sum: 7d1bc89dcf1f146b298c84cf2563abcc SHA1: 155f655600562d0e774514d8eed71f4546a27cd8 SHA256: c50540acb489365729e5da26b636ca1ebf102d3e5f17b46a1b7f90b536c51429 SHA512: e4ec888703aa0055eab5f9cc3f8cd61eab30d6f7d759ed8d4f6a6416bbb63ff97454df53fadf8d74c13e6bd347340f6f3a11275d42200b43912dc5fee2ad65b8 Homepage: https://cran.r-project.org/package=ssmodels Description: CRAN Package 'ssmodels' (Sample Selection Models) In order to facilitate the adjustment of the sample selection models existing in the literature, we created the 'ssmodels' package. Our package allows the adjustment of the classic Heckman model (Heckman (1976), Heckman (1979) ), and the estimation of the parameters of this model via the maximum likelihood method and two-step method, in addition to the adjustment of the Heckman-t models introduced in the literature by Marchenko and Genton (2012) and the Heckman-Skew model introduced in the literature by Ogundimu and Hutton (2016) . We also implemented functions to adjust the generalized version of the Heckman model, introduced by Bastos, Barreto-Souza, and Genton (2021) , that allows the inclusion of covariables to the dispersion and correlation parameters, and a function to adjust the Heckman-BS model introduced by Bastos and Barreto-Souza (2020) that uses the Birnbaum-Saunders distribution as a joint distribution of the selection and primary regression variables. This package extends and complements existing R packages such as 'sampleSelection' (Toomet and Henningsen, 2008) and 'ssmrob' (Zhelonkin et al., 2016), providing additional robust and flexible sample selection models. Package: r-cran-ssmrob Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sampleselection, r-cran-robustbase, r-cran-mass Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-ssmrob_1.0-1.ca2604.1_all.deb Size: 252680 MD5sum: 42848922ca34a0886f8376032172c22c SHA1: fcff51def85524dba22623133b5cb42318fcefbb SHA256: b939191e6b371e28363b4a4ca688f6b752ee3a7d415bcfa33590ddd6ef1b58ed SHA512: a6bdba7674ee398c8222e1cb08837129a1a377b2a13e4280fca22feb2a058b38ddd78a28e1e36634c89eff47f84c9eb0261352b550d9e1ed6bb530c9c2fc2ead Homepage: https://cran.r-project.org/package=ssmrob Description: CRAN Package 'ssmrob' (Robust Estimation and Inference in Sample Selection Models) Package provides a set of tools for robust estimation and inference for models with sample selectivity and endogenous treatment model. For details, see Zhelonkin and Ronchetti (2021) . Package: r-cran-ssmsn Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/resolute/main/r-cran-ssmsn_0.2.0-1.ca2604.1_all.deb Size: 16464 MD5sum: 7f51fcd0172f59fe764a2f6ce6ee62d7 SHA1: d00c199712df5a2253cca2a0d4fb3bb67ddb95c1 SHA256: b7be952b7f15ddd1883e1d7dd40d4ee29d2bab83d2853fc23fe7a9d2b7f28bd8 SHA512: 90ce1b7289104a74a3fccd21d52cf6b115d3ebad38e92aab0e1e533500bd5ac52a40c4f867552bbbc0363a22db15a224b40d865061dc4a1deeb8bb1c117491e3 Homepage: https://cran.r-project.org/package=ssmsn Description: CRAN Package 'ssmsn' (Scale-Shape Mixtures of Skew-Normal Distributions) It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) . Package: r-cran-ssmutpa Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggridges, r-cran-igraph, r-cran-kernlab, r-bioc-maftools, r-cran-matrix, r-cran-nbclust, r-cran-pheatmap, r-cran-rcolorbrewer, r-cran-survival Suggests: r-cran-knitr, r-cran-qpdf, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ssmutpa_0.1.2-1.ca2604.1_all.deb Size: 1972628 MD5sum: 935eb673e5d9c64378b95992a7ff01ea SHA1: c032beffdb1c07837203844a252290debbbcb17f SHA256: 4419d5255728566cc6b595eee6dd89a760c2218e242d6fcf41dc9741541501db SHA512: 3a3fc34c3dd86fb1bc1ae1942199f7269a9b55d63f5a613edc0689ea031b6b0cd322cde0d21a1cc6be83130fc49d39087be9a2da8136d9c30ffa875e7f56b368 Homepage: https://cran.r-project.org/package=ssMutPA Description: CRAN Package 'ssMutPA' (Single-Sample Mutation-Based Pathway Analysis) A systematic bioinformatics tool to perform single-sample mutation-based pathway analysis by integrating somatic mutation data with the Protein-Protein Interaction (PPI) network. In this method, we use local and global weighted strategies to evaluate the effects of network genes from mutations according to the network topology and then calculate the mutation-based pathway enrichment score (ssMutPES) to reflect the accumulated effect of mutations of each pathway. Subsequently, the ssMutPES profiles are used for unsupervised spectral clustering to identify cancer subtypes. Package: r-cran-ssnbayes Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4961 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-dplyr, r-cran-rstan, r-cran-sf, r-cran-ssn2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ssnbayes_0.0.3-1.ca2604.1_all.deb Size: 814432 MD5sum: c2b40d47f436442674bbd7dd135705c3 SHA1: a560859f96b0553ffb1e6c745671df533daffa4c SHA256: 65747893470f0ffdcaf5cd71cfb2a849ddefa2df6345b788ae376b924d37aff2 SHA512: 58967946fcb6f1a3f897f6d2f094486f8b9b98bdae7efd0374271d6ecaa1f8e9ee16a5418ffcd982b6498349a5811d5fd43897acce773de5a54eade5432b8802 Homepage: https://cran.r-project.org/package=SSNbayes Description: CRAN Package 'SSNbayes' (Bayesian Spatio-Temporal Analysis in Stream Networks) Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". . In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods. Package: r-cran-ssnbler Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-pdist, r-cran-igraph, r-cran-rsqlite, r-cran-withr, r-cran-ssn2, r-cran-doparallel, r-cran-foreach Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ssnbler_1.1.1-1.ca2604.1_all.deb Size: 882422 MD5sum: 4a1778ad2447b899c25f4227107b172f SHA1: 763e456d13c2f8452f8c8b8cf5b1233794a0b4f2 SHA256: 44707e7cc7f5e9911fa1d48cfabdffeb49f076bee7e63862817a99b3aeabf3e0 SHA512: c6763778ca960c4791f6ed1727c71609e5b61a9e2f44f79eb192d3b7a31d0abe72f30a9389aec657fd56c81c85ba6221882f0f5a5f3f972042908b236ab310bb Homepage: https://cran.r-project.org/package=SSNbler Description: CRAN Package 'SSNbler' (Assemble 'SSN' Objects) Import, create and assemble data needed to fit spatial-statistical stream-network models using the 'SSN2' package for 'R'. Streams, observations, and prediction locations are represented as simple features and specific tools provided to define topological relationships between features; calculate the hydrologic distances (with flow-direction preserved) and the spatial additive function used to weight converging stream segments; and export the topological, spatial, and attribute information to an `SSN` (spatial stream network) object, which can be efficiently stored, accessed and analysed in 'R'. A detailed description of methods used to calculate and format the spatial data can be found in Peterson, E.E. and Ver Hoef, J.M., (2014) . Package: r-cran-ssp Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-sampling, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-ssp_1.1.0-1.ca2604.1_all.deb Size: 216034 MD5sum: bb7c3db349317f44990502a3cf74ccb1 SHA1: 8fa3eb647d000fc7dff893f9393add6990f8fee7 SHA256: c0a4ada73e3e1f4df9ab11981eab7e6f109ff3eb49705861200f5ef5d5cd0a10 SHA512: 6ab0eeeb2e8d76cdc65cdbb5f42fe073ce2b6ec3b88b9ffb5735757560b62ac09c3dd6cc332fef0601fcdb1939591f9edab672204a70f1a318590702ab54f7c3 Homepage: https://cran.r-project.org/package=SSP Description: CRAN Package 'SSP' (Simulated Sampling Procedure for Community Ecology) The Simulation-based Sampling Protocol (SSP) is an R package designed to estimate sampling effort in studies of ecological communities. It is based on the concept of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015, ) and the simulation of ecological data. The theoretical background is described in Guerra-Castro et al. (2020, ). Package: r-cran-ssplots Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-ssplots_0.1.2-1.ca2604.1_all.deb Size: 52804 MD5sum: 673dc0814849a098ff8e100d3dbb7169 SHA1: caee8d3a28ce2e199a2476a9feffde2cc3927633 SHA256: be24f1545f67b77b470ef166291b3f9d2ede5288e81caff485dfe0adcf0c8a04 SHA512: 6343b00da0939811522e8a2fba1beff7524e065f60c565be9f42bf7125ff06b3c113b658e56fbe454d2bf1efa9da3d701b2fb849c7d82ffd95ee0344ff1abe73 Homepage: https://cran.r-project.org/package=SSplots Description: CRAN Package 'SSplots' (Stock Status Plots (SSPs)) Pauly et al. (2008) created (and coined the name) 'Stock Status Plots' for a UNEP compendium on Large Marine Ecosystems(LMEs, Sherman and Hempel (2009)). Stock status plots are bivariate graphs summarizing the status (e.g., developing, fully exploited, overexploited, etc.), through time, of the multispecies fisheries of a fished area or ecosystem. This package contains three functions to generate stock status plots viz., SSplots_pauly() (as per the criteria proposed by Pauly et al.,2008), SSplots_kleisner() (as per the criteria proposed by Kleisner and Pauly (2011) and Kleisner et al. (2013) )and SSplots_EPI() (as per the criteria proposed by Jayasankar et al.,2021 ). Package: r-cran-sspm Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-mgcv, r-cran-units, r-cran-checkmate, r-cran-cli, r-cran-tibble, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr, r-cran-ggplot2, r-cran-ggforce, r-cran-lwgeom, r-cran-tweedie, r-cran-sfdct, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sspm_1.1.0-1.ca2604.1_all.deb Size: 2805954 MD5sum: 47a86f790995a25e67b84ab95a71a3a5 SHA1: 6cead7c4937414ff367e6fce744b0f83b4383806 SHA256: e7966ef8b193c8f109b642cf63e6448c6fa08423c9771a4d671ea9c8fc57b22f SHA512: 5def44bba6edcaec80efdfe9a5a0173b7d7d3d4d856850342d683648a218362496d5766f4b1d125ef564af782b99ff034cb813a66923ab1d0e20491e18070fca Homepage: https://cran.r-project.org/package=sspm Description: CRAN Package 'sspm' (Spatial Surplus Production Model Framework for Northern ShrimpPopulations) Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, ). Package: r-cran-ssr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-e1071 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tgp Filename: pool/dists/resolute/main/r-cran-ssr_0.1.1-1.ca2604.1_all.deb Size: 192846 MD5sum: 66567c8aaccd4bf1382b6772ea00cf0f SHA1: 40db19836e23eb19c6f298ecbabab52a3fe45507 SHA256: 6df43f6776680f0a7efc19765b84c02511bfa235795d19d7a058d85f6586a121 SHA512: 7825cca617859fb80a05a000232a89772e85946e2c56cc0764d02cc594636013516a5697e6595ce6d4279f37f42bb0dfa0a56678142ad2f3717cfc0be288094a Homepage: https://cran.r-project.org/package=ssr Description: CRAN Package 'ssr' (Semi-Supervised Regression Methods) An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) . Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions. Package: r-cran-ssra Architecture: all Version: 0.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shape, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-ssra_0.1-1-1.ca2604.1_all.deb Size: 103436 MD5sum: 9788257e85f4584bd5d8036d0a8c5e10 SHA1: 927ea9015a29734120075bcb1ecaa1c5584f15ab SHA256: c99078ca40cda97e2fbd0379378531c443efb5d17a46762d8f1456855383dc6b SHA512: f128822763e2c57230901c0e16322585dec719815d989b36321837761a0ab15f213965927c4e6af5384195df1587825c27a69c5c06a4e0f812c26dc5e1d45856 Homepage: https://cran.r-project.org/package=SSRA Description: CRAN Package 'SSRA' (Sakai Sequential Relation Analysis) 'Takea Semantic Structure Analysis' (TSSA) and 'Sakai Sequential Relation Analysis' (SSRA) for polytomous items. Package includes functions for generating a sequential relation table and a treegram to visualize the sequential relations between pairs of items. Package: r-cran-ssrm.logmer Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-sfsmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ssrm.logmer_0.1-1.ca2604.1_all.deb Size: 26088 MD5sum: 6325b33dabb22f20413c29152a49b4d1 SHA1: 8e05c2c235b1d556468aebc97219f237152f356c SHA256: d5e27588ef5119e9c04681067c1d957cca5e828775d567357461a711816b633d SHA512: ee4ee292563e13c323658eb348288d3f1fd46af6b45f2a9334931e3e32c07d3ca075c60e174e761291e820850e5bd4222b8438eecf1e51f6791db8a5b52d4d17 Homepage: https://cran.r-project.org/package=ssrm.logmer Description: CRAN Package 'ssrm.logmer' (Sample Size Determination for Longitudinal Designs with BinaryOutcome) Provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) . Package: r-cran-ssrmst Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-survrm2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ssrmst_0.1.1-1.ca2604.1_all.deb Size: 35992 MD5sum: 0a67a3520eae06c370895a9124cd96be SHA1: 3220b1dfc6beeaa2e9b35755184c5533903628ee SHA256: 6d451cb79ab288eb154fa1aa57b81ae1808981728cce3df565752efc759d9a74 SHA512: 07bc3d5d0ac3601f78ef8d4ba4a67ce118ee951f0a5d9305d0641a8509040d38ea7ace78d37cf7ad15837ec5eded083036927df19b7dda755e4ca1dc1926e513 Homepage: https://cran.r-project.org/package=SSRMST Description: CRAN Package 'SSRMST' (Sample Size Calculation using Restricted Mean Survival Time) Calculates the power and sample size based on the difference in Restricted Mean Survival Time. Package: r-cran-ssrn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-scanstatistics Filename: pool/dists/resolute/main/r-cran-ssrn_0.1.0-1.ca2604.1_all.deb Size: 618198 MD5sum: 13ba849065433adbf97d13401c7330ec SHA1: aee418bfb7edd231224df9dd434d3ba01ee10769 SHA256: 0128e343ab82784a353f3e45f59f1fa6582a3709c1dc7a6bf73eed490ff001dd SHA512: 4ee6ad5880860a20e6aead1988f2f1f2c74dd3e674d24592aad239ec74cc8f3ef40c924403427c0ba5c80edc3a17fec54130e014335b1a7fe85247b55f20fed8 Homepage: https://cran.r-project.org/package=ssrn Description: CRAN Package 'ssrn' (Scan Statistics for Railway Network) Implement the algorithm provided in scan for estimating the transmission route on railway network using passenger volume. 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Package: r-cran-sss Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-assertthat Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-spelling, r-cran-utf8 Filename: pool/dists/resolute/main/r-cran-sss_0.2.2-1.ca2604.1_all.deb Size: 94634 MD5sum: 9347f88a6cbc4acebfc91d4adf7838fd SHA1: 07df985d5a59098d251cb28dc56be5541bd139b9 SHA256: 85a9c93811ded230cc8412641639f20b7165da68e34f8c6abfd8878e48f1595e SHA512: e896383f41d4deb4a70eb8930b63584ea585fbd36610241f8ebe346517b205291004b470be38db591d0e9bb692bfaf5c2dc23588db47c6ede983d61643a5f279 Homepage: https://cran.r-project.org/package=sss Description: CRAN Package 'sss' (Import Files in the Triple-s (Standard Survey Structure) Format) Tools to import survey files in the '.sss' (triple-s) format. 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Package: r-cran-sssvcqr Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn, r-cran-igraph, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sssvcqr_0.0.4-1.ca2604.1_all.deb Size: 1433750 MD5sum: 82e205beabb5c8688d11cf93c4b4cf2d SHA1: da7b433aae56213215a3675448498b0fc06bf5f4 SHA256: 0648b65a058f76af31dd8344a51cae2fc5c63e1d7bda0bb1e218188981b0c0a8 SHA512: ca9f94677b6628165f98b2a661a7c7a0bf44e2ebe5a337a828e07a97b8c8f44993dce2c019df941c29e71f783ff095b4ba15edf03a39847b83cad42c67af1fa6 Homepage: https://cran.r-project.org/package=sssvcqr Description: CRAN Package 'sssvcqr' (Sparse-Smooth Spatially Varying Coefficient Quantile Regression) Implements sparse-smooth spatially varying coefficient quantile regression (SS-SVCQR), combining quantile regression of Koenker and Bassett (1978) , grouped variable selection of Yuan and Lin (2006) , graph regularization, and the alternating direction method of multipliers of Boyd et al. (2011) . The package provides graph-regularized estimation, spatially blocked cross-validation, prediction, diagnostics, and simulation helpers for global-local spatial quantile regression. Package: r-cran-sstack Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-dplyr, r-cran-doparallel Filename: pool/dists/resolute/main/r-cran-sstack_1.0.1-1.ca2604.1_all.deb Size: 659608 MD5sum: 1f235811fddb8df9e1aace2e3ad2994e SHA1: 4abd3bcda5f11befee165767533570aebd2f0345 SHA256: 3599c42389c7a50f9e82cc5d65549195f02cdecc8fdddee731c7e1dace173273 SHA512: 567acc231472d61f11f2278b29ce217f58f6cabd9365b7872b351cfc1c44196be252c53be50ced7d9d4a1e3a499d777ba0401aa2b9821e8caacad6091ebec4d2 Homepage: https://cran.r-project.org/package=Sstack Description: CRAN Package 'Sstack' (Bootstrap Stacking of Random Forest Models for HeterogeneousData) Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). 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Package: r-cran-sstn Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-sstn_1.0.1-1.ca2604.1_all.deb Size: 7251386 MD5sum: 22606ab85f8c2494f62912b6e737900f SHA1: 93770a18b55e58d48991b1fea29e14cb16788cf4 SHA256: ff42dbf3edddceebd133c392c75b32490e05d76c8838eed455b185595d8e5b5f SHA512: 7b4a60d8bb1e704f77cf52854705fa813f398b7ae89ad7f7e2516bf1ff6b6a1ed1603253137a45dcfb2566c9b11cddd1bcc787307a80de611fa3dadbd2428a87 Homepage: https://cran.r-project.org/package=sstn Description: CRAN Package 'sstn' (Self-Similarity Test for Normality) Implements the Self-Similarity Test for Normality (SSTN), a new statistical test designed to assess whether a given sample originates from a normal distribution. 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Package: r-cran-ssutil Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-broom, r-cran-mass, r-cran-gsdesign, r-cran-mvtnorm, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ssutil_1.0.0-1.ca2604.1_all.deb Size: 396266 MD5sum: 794138b85a95a56c711e0a26764ef969 SHA1: b4aa979d55f3e53264f97b73777637df90367691 SHA256: 7ea584e1ef4172cac8994b5d91b7916d0337328850b3de8da3102c1ecc3e0bd0 SHA512: d1a30a01d380a05c76e9d43c52755598f662b87506d79d15c00556b69256f9c3975e9d9678441d25a1d7a33a5a42ba0db073c6126f4a053a486ab3b8b34c1649 Homepage: https://cran.r-project.org/package=ssutil Description: CRAN Package 'ssutil' (Sample Size Calculation Tools) Functions for sample size estimation and simulation in clinical trials. Includes methods for selecting the best group using the Indifference-zone approach, as well as designs for non-inferiority, equivalence, and negative binomial models. For the sample size calculation for non-inferiority of vaccines, the approach is based on Fleming, Powers, and Huang (2021) . The Indifference-zone approach is based on Sobel and Huyett (1957) and Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9). Package: r-cran-ssvs Architecture: all Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-boomspikeslab, r-cran-checkmate, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-magrittr, r-cran-gridextra Suggests: r-cran-aer, r-cran-bslib, r-cran-foreign, r-cran-glue, r-cran-knitr, r-cran-mice, r-cran-psych, r-cran-reactable, r-cran-readxl, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ssvs_2.2.0-1.ca2604.1_all.deb Size: 168154 MD5sum: c38856b0ec53bb890e9d188128ffb89c SHA1: 3603bbae966d7640777a957c96f9c42e10e77407 SHA256: 4e2b52515b71be1df33cd39dcdfee77e645d3008408e728230d7378f53b2e2df SHA512: d73e84b8cecaa0796fcafbfd50e570a834b0a131195c83361c3223c369ad9dbeb7cf17f60c79bcf72458426200c3ad6426575950af777545c09d3489e3036ba9 Homepage: https://cran.r-project.org/package=SSVS Description: CRAN Package 'SSVS' (Functions for Stochastic Search Variable Selection (SSVS)) Functions for performing stochastic search variable selection (SSVS) for binary and continuous outcomes and visualizing the results. SSVS is a Bayesian variable selection method used to estimate the probability that individual predictors should be included in a regression model. Using MCMC estimation, the method samples thousands of regression models in order to characterize the model uncertainty regarding both the predictor set and the regression parameters. For details see Bainter, McCauley, Wager, and Losin (2020) Improving practices for selecting a subset of important predictors in psychology: An application to predicting pain, Advances in Methods and Practices in Psychological Science 3(1), 66-80 . Package: r-cran-ssw Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ssw_0.2.1-1.ca2604.1_all.deb Size: 265636 MD5sum: b141f6186e2c5424071c85418bfc1799 SHA1: 5a4f80c90368b30a1829d811360d00df9165a8b9 SHA256: d2b42f0f1e3add3c71ece4fc1edd179935e6baf1d5c833df776cdaa44ef37de4 SHA512: d615ef44a967291fc928c41cf37ef9e9942ce4d02fe59a61bcc64c29ae6b2a17c92aa6dbe9b0bcf24a2194a60cce510aabf7ab83772f596b25e9fb0e24d54556 Homepage: https://cran.r-project.org/package=ssw Description: CRAN Package 'ssw' (Striped Smith-Waterman Algorithm for Sequence Alignment usingSIMD) Provides an R interface for 'SSW' (Striped Smith-Waterman) via its 'Python' binding 'ssw-py'. 'SSW' is a fast 'C' and 'C++' implementation of the Smith-Waterman algorithm for pairwise sequence alignment using Single-Instruction-Multiple-Data (SIMD) instructions. 'SSW' enhances the standard algorithm by efficiently returning alignment information and suboptimal alignment scores. The core 'SSW' library offers performance improvements for various bioinformatics tasks, including protein database searches, short-read alignments, primary and split-read mapping, structural variant detection, and read-overlap graph generation. These features make 'SSW' particularly useful for genomic applications. Zhao et al. (2013) developed the original 'C' and 'C++' implementation. Package: r-cran-ssym Architecture: all Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gigrvg, r-cran-numderiv, r-cran-normalp, r-cran-formula, r-cran-survival, r-cran-sandwich Suggests: r-cran-nistnls, r-cran-gam, r-cran-sn, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ssym_1.5.8-1.ca2604.1_all.deb Size: 345054 MD5sum: 15e234a7630246c21ef0e18e51dc8425 SHA1: 7f119a35189838b8354c9666f8f80af39d1353ab SHA256: e5acd25512cd2473979e32992635f7e4db0a842c1db0265bc1d3a2e8c3fb9da4 SHA512: 6974a461f50e8b021f55b3077083681bf619ad42fc99be3c5855f74d56328438d1fd474b8aaecc102573624bca0737e90137eb48ac0bb7eabbae68b96a790451 Homepage: https://cran.r-project.org/package=ssym Description: CRAN Package 'ssym' (Fitting Semi-Parametric log-Symmetric Regression Models) Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions. Package: r-cran-st Architecture: all Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sda, r-cran-fdrtool, r-cran-corpcor Suggests: r-bioc-limma, r-cran-samr Filename: pool/dists/resolute/main/r-cran-st_1.2.7-1.ca2604.1_all.deb Size: 586386 MD5sum: f7cc9675045e735d73620b9874df1620 SHA1: 0fdf09df0426d9242c2df51bb33d4100c176e7c9 SHA256: ca8912c1a10af9b4651c21bde417ade8f38cb34b3dae6582de03040d1dbfc19d SHA512: 94e3b409754f17e7520cff0a189cf712be3e6f36a1fda79187a4b73b99b5bd880d7847d374ed2f6aa69fa8d84ef2713acb4e584239aafda5688ed21a7a340187 Homepage: https://cran.r-project.org/package=st Description: CRAN Package 'st' (Shrinkage t Statistic and Correlation-Adjusted t-Score) Implements the "shrinkage t" statistic introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described in Zuber and Strimmer (2009) . 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Package: r-cran-sta Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-geots, r-cran-foreach, r-cran-trend, r-cran-doparallel, r-cran-mapview, r-cran-rcolorbrewer Suggests: r-cran-sp Filename: pool/dists/resolute/main/r-cran-sta_0.1.7-1.ca2604.1_all.deb Size: 2046588 MD5sum: df123aa44aa7ef32bbc1d525f1d4c7df SHA1: 0f58c94d64bd7e8b002604db8e273621ee59ef2b SHA256: 41d2f6d4974c0b5f320c84b7cee80513b6f05b486f4387608e8a4edca123ff4c SHA512: 68dae62973469513bbebae01c5c794ac6a92ada9a4129973f4073bfa27e961bdee758c8be32fb58df34498d82c38c59d9df16a12ea46225224b47ddd5ec8172d Homepage: https://cran.r-project.org/package=sta Description: CRAN Package 'sta' (Seasonal Trend Analysis for Time Series Imagery in R) Efficiently estimate shape parameters of periodic time series imagery with which a statistical seasonal trend analysis (STA) is subsequently performed. 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Package: r-cran-staat1cho Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-readr, r-cran-rlang, r-cran-shiny Suggests: r-cran-bslib, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-staat1cho_0.1.0-1.ca2604.1_all.deb Size: 64958 MD5sum: 8e381c0309b094fed40ef1f1914494cb SHA1: 89a0d287bc9097924a85148736b89c43d1f3103c SHA256: 9c308425962d005011751f14727460506a3624380dc489712f1273432b88c2ea SHA512: b62c06f61057b959aeaef0462685b3797037b8f3a6e40448890b6233bc19ba32525740622d2a8c82bc29b77fb5b99a3f3248e45798d8859c4e3a958b6825af64 Homepage: https://cran.r-project.org/package=staat1cho Description: CRAN Package 'staat1cho' (Study Indicators Based on Dutch Higher Education Data (1CHO)) Calculates enrolment, graduation, dropout, and programme-switch indicators from the Dutch higher education registration data (1CHO) supplied by DUO. Includes an interactive 'Shiny' dashboard for exploring results. Package: r-cran-stabiliser Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-dplyr, r-cran-bigstep, r-cran-rsample, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-ggplot2, r-cran-broom, r-cran-caret, r-cran-ncvreg, r-cran-hmisc, r-cran-expss, r-cran-lme4, r-cran-matrixstats, r-cran-recipes, r-cran-lmertest, r-cran-furrr, r-cran-future Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-markdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-stabiliser_1.0.7-1.ca2604.1_all.deb Size: 177456 MD5sum: c659ae9631cd62c75944464c8841037e SHA1: b29c3669d330def4a653c035309f18674f3e6b8e SHA256: 532f7d4e0c588ab1616ef909a7a76e98790098208fc8e7c1083dd5c2ad506549 SHA512: 92725606922014923d207f87240c228f5ed168f5e683d7b3a292557e334a24c5111ce74799463cfb37c423c3b56eb3d70a232f19c61d81431561c824bba1e4b1 Homepage: https://cran.r-project.org/package=stabiliser Description: CRAN Package 'stabiliser' (Stabilising Variable Selection) A stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) , Meinshausen and Buhlmann (2010) . Package: r-cran-stability Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggfortify, r-cran-lme4, r-cran-magrittr, r-cran-matrixstats, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-stability_0.6.0-1.ca2604.1_all.deb Size: 125668 MD5sum: 2b18bf493260154dc95e3b27c4c17812 SHA1: 9d6e4ea9bc9d0afe4e0ecf661ff0b45f66fbb3b9 SHA256: 418a1e48063298a41dde88eed9883f24eabc8d2d75031dc82e624e3074903627 SHA512: 2d7d28baf4faa7d8b77751f1bf31cbfe4854674e17324947bd9a8619be2e2cbc24b77bc3a453fd9c6276ac1ded797b0b176198b5d598661811f24a88a54ccb8a Homepage: https://cran.r-project.org/package=stability Description: CRAN Package 'stability' (Stability Analysis of Genotype by Environment Interaction (GEI)) Provides functionalities for performing stability analysis of genotype by environment interaction (GEI) to identify superior and stable genotypes across diverse environments. It implements Eberhart and Russell’s ANOVA method (1966)(), Finlay and Wilkinson’s Joint Linear Regression method (1963) (), Wricke’s Ecovalence (1962, 1964), Shukla’s stability variance parameter (1972) (), Kang’s simultaneous selection for high yield and stability (1991) (), Additive Main Effects and Multiplicative Interaction (AMMI) method and Genotype plus Genotypes by Environment (GGE) Interaction methods. Package: r-cran-stabilityapp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-gridextra, r-cran-patchwork, r-cran-stability, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboardplus Suggests: r-cran-shinytest2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stabilityapp_0.1.0-1.ca2604.1_all.deb Size: 984330 MD5sum: 9d713b7b07d217d4e8be8bcf9cb4362f SHA1: c5c8dc529bb1d7a982a46976cc144fc4d6f36998 SHA256: c5e0d1b5c87d89465548175b56b7df867d57ef45528d4e8907195d1364655111 SHA512: 8231a455ef156e1c3f9fafb2cc2f03024f4ec631524fd324f2d14cea974efca4694127c78cca5f12ed09c7f1bf264e8c120a9f0186b3467b07122ca5c3e1a0ed Homepage: https://cran.r-project.org/package=StabilityApp Description: CRAN Package 'StabilityApp' (Stability Analysis App for GEI in Multi-Environment Trials) Provides tools for Genotype by Environment Interaction (GEI) analysis, using statistical models and visualizations to assess genotype performance across environments. It helps researchers explore interaction effects, stability, and adaptability in multi-environment trials, identifying the best-performing genotypes in different conditions. Which Win Where! Package: r-cran-stabilizedregression Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-r6, r-cran-glmnet, r-cran-corpcor, r-cran-ggplot2, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-stabilizedregression_1.1-1.ca2604.1_all.deb Size: 134412 MD5sum: 77e93d8a0912afbaf85f2f748ad20761 SHA1: 530c126d0ebe938d1143031f01260ffa6228777a SHA256: ba64bf3cda883566eca2be971bea76469b5f7acd2aabd2fb449c0979496f0dc6 SHA512: 1fb47a9a40ebf1c8a387949b336b8d9b965dd5fbc05132816e9d598bfe1993d7719265a7f1817e381dc49aa6905ca8dbf2142b875b5507b5e5e99f94f8c8ab27 Homepage: https://cran.r-project.org/package=StabilizedRegression Description: CRAN Package 'StabilizedRegression' (Stabilizing Regression and Variable Selection) Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) . The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors. Package: r-cran-stabilo Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-stabilo_0.1.1-1.ca2604.1_all.deb Size: 44954 MD5sum: 4c226b7c6be4e4c7dbfd58d048754ef9 SHA1: f4a3df1b6178a552edc9b31d8c136c16927d78b4 SHA256: 1678b5e0ff72db9b393011fe97d9ce5a9f0dc3e5f3a1a2dda2a3644ccf3a2e7c SHA512: eacaeef12ef75b7f1a8ece957c35c65a2994c9eb4c6be4c6787d70343828e8d7d79c22bbd53dacff6cf1b323d43d0f7cb5a87e69a9a55948c8efb51ec6277cdb Homepage: https://cran.r-project.org/package=stabilo Description: CRAN Package 'stabilo' (Stabilometric Signal Quantification) Functions for stabilometric signal quantification. The input is a data frame containing the x, y coordinates of the center-of-pressure displacement. Jose Magalhaes de Oliveira (2017) "Statokinesigram normalization method"; T E Prieto, J B Myklebust, R G Hoffmann, E G Lovett, B M Myklebust (1996) "Measures of postural steadiness: Differences between healthy young and elderly adults"; L F Oliveira et al (1996) "Calculation of area of stabilometric signals using principal component analisys". Package: r-cran-stabledist Architecture: all Version: 0.7-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-fbasics, r-cran-fmstable, r-cran-runit, r-cran-rmpfr, r-cran-libstable4u Filename: pool/dists/resolute/main/r-cran-stabledist_0.7-2-1.ca2604.1_all.deb Size: 77080 MD5sum: 90f33ea13ba28ea933e926b80a5fd74b SHA1: 29ea6ab36f5b4d3854df6bd32a2a84f999c52013 SHA256: a31c982486a8bcfb7be102f85b0f3d9713160bcf4c5528ff19e2a5a5bf49b05f SHA512: d498cbde442c2c4632a293a59d30639efbe89b8028a1f2147f0bce08992ff0dee6979e4e661a51b8bb3117f60aac9e80fb9ec6a8c5aa260fc82e69bf7353802b Homepage: https://cran.r-project.org/package=stabledist Description: CRAN Package 'stabledist' (Stable Distribution Functions) Density, Probability and Quantile functions, and random number generation for (skew) stable distributions, using the parametrizations of Nolan. Package: r-cran-stableestim Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-fbasics, r-cran-mass, r-cran-stabledist, r-cran-rdpack Suggests: r-cran-testthat, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-stableestim_2.4-1.ca2604.1_all.deb Size: 425154 MD5sum: 034c058c9baaae04b3cb464f82a2174a SHA1: 5d58633204344204c84144dcbc33a1da3e6e56a2 SHA256: 1fc61a9df6debaca8157448f39d06a6b93373b51838b6ffd29591c02e8bf7a47 SHA512: 6745a064da235decb5c8261d59ffdccb93d1bb274c5474a478fd577d7f35581a1449b8374d205b14babb6e8f47f0799cd2395a6fe44abd934472df83bbe553b9 Homepage: https://cran.r-project.org/package=StableEstim Description: CRAN Package 'StableEstim' (Estimate the Four Parameters of Stable Laws using DifferentMethods) Estimate the four parameters of stable laws using maximum likelihood method, generalised method of moments with finite and continuum number of points, iterative Koutrouvelis regression and Kogon-McCulloch method. The asymptotic properties of the estimators (covariance matrix, confidence intervals) are also provided. Package: r-cran-stablegr Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcse, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-stablegr_1.2-1.ca2604.1_all.deb Size: 61722 MD5sum: f1e1fda5af2a9bd4215c301e82178ff3 SHA1: ca5293f93a44918c4917318567276fb803ff0077 SHA256: a46c9bc1d5bfdd001559f9a3680d78545be3ba8dc078f9b0a444491b4a3cfd79 SHA512: a9ddf58addad6fc9a55fe3a106333e01b244fba593648216aed8a2cd280ed39a905f35f471c669b4e30ac9282f4a3e3aecf5c5f5c7c94a30067b8b577ca40f5b Homepage: https://cran.r-project.org/package=stableGR Description: CRAN Package 'stableGR' (A Stable Gelman-Rubin Diagnostic for Markov Chain Monte Carlo) Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and Knudson (2018) . Package: r-cran-stablelearner Architecture: all Version: 0.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-e1071, r-cran-partykit, r-cran-party, r-cran-randomforest, r-cran-ranger Suggests: r-cran-formula, r-cran-nnet, r-cran-rpart, r-cran-evtree, r-cran-rchallenge, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stablelearner_0.1-7-1.ca2604.1_all.deb Size: 392950 MD5sum: 31debed0c77f93bc910c61432b7b0f84 SHA1: cb3fb47c1109267aff9b672c461bca2781a52b04 SHA256: 9558b5cc3358ee41c9eae27a72d53fea1c9998d42bf9939234ca4ce4af150457 SHA512: 9478c2caa7d53a1ae395ce33deb3c27e1733c5245bf9bd4fe2e13a313e6f843ceff13a9bfb5c17449c1af3a9dde24a243045d0f6504332c35ee97ab46fc11c9e Homepage: https://cran.r-project.org/package=stablelearner Description: CRAN Package 'stablelearner' (Stability Assessment of Statistical Learning Methods) Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning. Package: r-cran-stablepopulation Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx Filename: pool/dists/resolute/main/r-cran-stablepopulation_1.0.3-1.ca2604.1_all.deb Size: 27960 MD5sum: 7f819b67c932cf134358967ab809029e SHA1: 30f85bbc93b0f96add68e67110903fd092fa1097 SHA256: 2bb6a1d7d653414fe774398c4193298f8cf57e3bb590edb028673afdc8033d02 SHA512: 3b1ca8225c61ef1418f32a61483e20432833ea285666e20c1e3243de3b6a13cb1896704bbb2edaab27e3236d71b905b31850824fa9cf6c58798ee9aa924e12be Homepage: https://cran.r-project.org/package=StablePopulation Description: CRAN Package 'StablePopulation' (Calculates Alpha for a Stable Population) Provides tools to calculate the alpha parameter of the Weibull distribution, given beta and the age-specific fertility of a species, so that the population remains stable and stationary. Methods are inspired by "Survival profiles from linear models versus Weibull models: Estimating stable and stationary population structures for Pleistocene large mammals" (Martín-González et al. 2019) . Package: r-cran-stablespec Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggm, r-cran-matrixcalc, r-cran-sem, r-cran-nsga2r, r-bioc-graph, r-bioc-rgraphviz, r-cran-polycor, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stablespec_0.3.0-1.ca2604.1_all.deb Size: 147474 MD5sum: ee42a64bc9168d55e427a2ab2875c501 SHA1: e770bebcad4eea86d5c0c17d922b0ab8ebe78b1d SHA256: ab1d4b614197770a6968e51bb292f523843616fb2ccf88a844135fedd552b710 SHA512: b2acb9dc0876d63792049bcb1341ad8b98a41c37a84084806504776d22d58ca8b45feeec38bb12fed1f92c77e0aa83c283ceefa5bb0b2819fb7eabc45bb107d8 Homepage: https://cran.r-project.org/package=stablespec Description: CRAN Package 'stablespec' (Stable Specification Search in Structural Equation Models) An exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequently) and parsimonious and use those to infer a causal model. Package: r-cran-stabm Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-matrix Suggests: r-cran-cowplot, r-cran-ggdendro, r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-mlbench, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stabm_1.2.2-1.ca2604.1_all.deb Size: 477764 MD5sum: 5356801a1fb133d8c0243ef7e1f3f499 SHA1: d1c605f076ce23a0958b44c13f225fa1cb3c64ed SHA256: 10270847f5e65938b9c3561f61253c318c53d3ad619fa29f2c0199ac3b7b5e4b SHA512: 0f42a8a812f1e618a5e547b70276671f8ebc7678b4fe212266e1c2dfca1967c3e798f37f515bea981d0581962666a736238776bcda23c3b70a66571a7222a7ca Homepage: https://cran.r-project.org/package=stabm Description: CRAN Package 'stabm' (Stability Measures for Feature Selection) An implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available, see Bommert (2020) . Package: r-cran-stabs Architecture: all Version: 0.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-glmnet, r-cran-lars, r-cran-mboost, r-cran-gamboostlss, r-cran-th.data, r-cran-hdi, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stabs_0.7-1-1.ca2604.1_all.deb Size: 144808 MD5sum: 214716bb9cf4171e412c720e6617f3ac SHA1: cca3c9b8964088065094eb5b8dd5e56d62ae25a8 SHA256: 64d65a95bd5e056e800822feaf3ba2e65954d9ed39c3b94cae26b2227efb0803 SHA512: 4ac49d127696ea5f6c844cbddfea4e83dcb96e6eb3724608c64070aa637591260610d660e32a7e25dc99b4f8633096a849b842897e610216dcf99643f0c40b84 Homepage: https://cran.r-project.org/package=stabs Description: CRAN Package 'stabs' (Stability Selection with Error Control) Resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010, ) and complementary pairs stability selection with improved error bounds (Shah & Samworth, 2013, ) are implemented. The package can be combined with arbitrary user specified variable selection approaches. Package: r-cran-staccuracy Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-autogam, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-staccuracy_0.2.2-1.ca2604.1_all.deb Size: 67432 MD5sum: 6112e870215b1475f55fd63af1136045 SHA1: 87e59711a6eb0c4496d8219a299fc5578b6388f9 SHA256: d3e3bd581304254024f341280aa80457331d437fd80f36cbdad0e27348511583 SHA512: bd83e6b73b56a7bf032f5e965e74e67154efb627a8a23af05a71f7b2ee773dcb8f8ba36f6438a785fda6d1fd5f57f763bacdc942e7382e8cb24fa422e0f22459 Homepage: https://cran.r-project.org/package=staccuracy Description: CRAN Package 'staccuracy' (Standardized Accuracy and Other Model Performance Metrics) Standardized accuracy (staccuracy) is a framework for expressing accuracy scores such that 50% represents a reference level of performance and 100% is a perfect prediction. The 'staccuracy' package provides tools for creating staccuracy functions as well as some recommended staccuracy measures. It also provides functions for some classic performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUCROC), as well as their winsorized versions when applicable. Package: r-cran-stackgbm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 887 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-progress, r-cran-rlang Suggests: r-cran-knitr, r-cran-lightgbm, r-cran-msaenet, r-cran-rmarkdown, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-stackgbm_0.1.0-1.ca2604.1_all.deb Size: 377640 MD5sum: 5de0ff7ed76d1b2905eca642f9d12fc4 SHA1: eb3780045575b3782f7401fbd391ff61be8acaea SHA256: f43ad3f950caa95c273fb5840cc29c8de895204c6b756780ce13b93860b0b92f SHA512: b5e380cc851681149c9c5d71e2e413bbaf858eb4288f57d39bcebc907e519ea87c1a35d3459bf30c36a05e98810041e48f7c73a8e12fe3f852ccd54ef07ec68d Homepage: https://cran.r-project.org/package=stackgbm Description: CRAN Package 'stackgbm' (Stacked Gradient Boosting Machines) A minimalist implementation of model stacking by Wolpert (1992) for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness. Package: r-cran-stackimpute Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich, r-cran-zoo, r-cran-mice, r-cran-dplyr, r-cran-mass, r-cran-magrittr, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stackimpute_0.1.0-1.ca2604.1_all.deb Size: 2069146 MD5sum: 45feb8598b8b7191602dea5d9d989046 SHA1: 7328fcc9141c6568f6e87f8cfb19783e7be05773 SHA256: 5b1c152de7e27fb17542795d0b213bf02134a5baa7b229373eb57de51d4db961 SHA512: 7e43a12c33243f1148ca4463e7dd704889fb7b33aabc6895c5271734ac222be1700c7ac82768317c57502a69b63f1f115cc1b72b5f763d364d3b2be285f64a73 Homepage: https://cran.r-project.org/package=StackImpute Description: CRAN Package 'StackImpute' (Tools for Analysis of Stacked Multiple Imputations) Provides methods for inference using stacked multiple imputations augmented with weights. The vignette provides example R code for implementation in general multiple imputation settings. For additional details about the estimation algorithm, we refer the reader to Beesley, Lauren J and Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple imputation incorporating the substantive model” , and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random missingness through imputation stacking” . Package: r-cran-stacking Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret Filename: pool/dists/resolute/main/r-cran-stacking_0.2.1-1.ca2604.1_all.deb Size: 56592 MD5sum: 190e5705c5f9b03ae0595ebaed4daa3a SHA1: 8054579efd3a4b1ad8622c28ae96457c4bcbf839 SHA256: c10477760fa8a061c54ab22ff7f5616f5de80e61fc5ac5ac102b72ca29a3a355 SHA512: dfc33045874c74d72453cfc7c765c98101a91dc77380382850a27b072b9d4f2c524f2bd7ffd358d6597f5611d8348749f7e3a1e1d759e4715561d1319cf10fbc Homepage: https://cran.r-project.org/package=stacking Description: CRAN Package 'stacking' (Building Predictive Models with Stacking) Building predictive models with stacking which is a type of ensemble learning. Learners can be specified from those implemented in 'caret'. For more information of the package, see Nukui and Onogi (2023) . Package: r-cran-stacks Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2000 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-butcher, r-cran-cli, r-cran-dplyr, r-cran-foreach, r-cran-furrr, r-cran-future, r-cran-generics, r-cran-ggplot2, r-cran-glmnet, r-cran-glue, r-cran-parsnip, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs, r-cran-workflows Suggests: r-cran-covr, r-cran-h2o, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-modeldata, r-cran-nnet, r-cran-ranger, r-cran-rmarkdown, r-cran-testthat, r-cran-workflowsets, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-stacks_1.1.1-1.ca2604.1_all.deb Size: 1693186 MD5sum: 038c6cac8ca3f1886d1c023dca55bf46 SHA1: 8d12101f7da1e055773d557fa76284a4c049999d SHA256: 5ea187b47d6611d3267be00d47aee4dab1249eb42072833288131b6281b88ec9 SHA512: 332589d983753e7608bf5732902566565d85daf66c1b9f52c8399c151d7ca830fe7510725e702312e37260d16fe06324595571562bbacc8e7e49a9be877106f5 Homepage: https://cran.r-project.org/package=stacks Description: CRAN Package 'stacks' (Tidy Model Stacking) Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking. Package: r-cran-stacomirtools Architecture: all Version: 0.6.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rodbc, r-cran-dbi, r-cran-pool, r-cran-xtable, r-cran-rpostgres Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-stacomirtools_0.6.0.1-1.ca2604.1_all.deb Size: 123072 MD5sum: 5ce7701ad51d59092c099f93abdc2e03 SHA1: a05b6e66f83def7e7426930f6d3e6c0b9cf19f35 SHA256: a92ab4ebc1b2fef34358f25bb0ae24fb5416b2fec83db4e2a2c804ba41b6a474 SHA512: be1d6ac1e6b3f6ac887d52f180219c45ff28d71d58517b2516092595a82310fea932970c731318aeb31c40fb654558eddaf4bbabe224df9acd52fc5074bc8495 Homepage: https://cran.r-project.org/package=stacomirtools Description: CRAN Package 'stacomirtools' (Connection Class for Package stacomiR) S4 class wrappers for the 'ODBC' and Pool DBI connection, also provides some utilities to paste small datasets to clipboard, rename columns. It is used by the package 'stacomiR' for connections to the database. Development versions of 'stacomiR' are available in R-forge. Package: r-cran-stacr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstac, r-cran-tibble, r-cran-cli Suggests: r-cran-gdalcubes, r-cran-jsonlite, r-cran-leaflet, r-cran-sf, r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stacr_0.1.0-1.ca2604.1_all.deb Size: 59234 MD5sum: 21e96650ad2b4c860ee766816ad5b864 SHA1: 36a952d02ddf15d92705385e656c842c0c169e9c SHA256: 4af2a702a419a29af29ca65aeced52ecf3b60c11f76916495129f640f7c60d97 SHA512: cadb4e3e7025c9f8633cc82133f9689015817c2a68b0cf2575e17fa7dd627486f065979814a5f3a9e1899850159de04bcd6f0ad4267fdd0f4d378f73e95734ed Homepage: https://cran.r-project.org/package=stacr Description: CRAN Package 'stacr' (Tidy 'STAC' Workflows for R) Wraps the 'rstac' package with a pipe-friendly, tidy API. All results return 'tibbles' instead of nested lists. Ships with a catalog registry of known 'STAC' endpoints including Planetary Computer, Earth Search, and 'USGS', while supporting any 'STAC' API URL. Package: r-cran-stagedtrees Architecture: all Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-bnlearn, r-cran-covr, r-cran-clue, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-stagedtrees_2.3.0-1.ca2604.1_all.deb Size: 771546 MD5sum: bdea7ae8f6d4d3e510fbbc4abff3d535 SHA1: 37ad65a8744ba1e84fb5ae29ff72bd4d998cf0a8 SHA256: 3f7d1a36eb7a81e8acd4381e8b3a0f0ac5382b84232da35aa1a3db0bee19d913 SHA512: accc6d50562d4a286da91963253f89783310a4238453e9764124a52c944d16384ae81741ea51d1ec15457281afea21c514da67f950901d87fd79bbd4513d9101 Homepage: https://cran.r-project.org/package=stagedtrees Description: CRAN Package 'stagedtrees' (Staged Event Trees) Creates and fits staged event tree probability models, which are probabilistic graphical models capable of representing asymmetric conditional independence statements for categorical variables. Includes functions to create, plot and fit staged event trees from data, as well as many efficient structure learning algorithms. References: Carli F, Leonelli M, Riccomagno E, Varando G (2022). . Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604). Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) . Thwaites P. A., Smith, J. Q. (2017) . Barclay L. M., Hutton J. L. and Smith J. Q. (2013) . Smith J. Q. and Anderson P. E. (2008) . Package: r-cran-stagepop Architecture: all Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-pbsddesolve Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-stagepop_1.1-2-1.ca2604.1_all.deb Size: 586836 MD5sum: 08b83d19dea852db890d65a931ea2f92 SHA1: b91dee47202763c8fe08ab8ef12d61280fc11f31 SHA256: c8b0b9bf69920070c46f25aaee02cccc80cf18bc9c1c413f2216cc1d4b68abd6 SHA512: dd315de780654b0a40bfa39f2a7749d1964ef920a7b63922aebf23c348ba0f903b5dfda2977bfa2f82398ae79ac50cfc2cfc159935925855bcae557d36751364 Homepage: https://cran.r-project.org/package=stagePop Description: CRAN Package 'stagePop' (Modelling the Population Dynamics of a Stage-Structured Speciesin Continuous Time) Provides facilities to implement and run population models of stage-structured species... 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The package implements a version of an approach proposed by Sun and Abraham (2021) to estimate cohort- and time-since-treatment specific difference-in-differences parameters, and it provides convenience functions both for specifying the model and for flexibly aggregating coefficients to answer a variety of research questions. Package: r-cran-stagsynth Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3960 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stagsynth_0.1.0-1.ca2604.1_all.deb Size: 1034036 MD5sum: f7ff5cf544f780a8e5cbb75bed890709 SHA1: cd5f3b4fb513380cd38762a67c8e676d74371503 SHA256: 461105f1fdee8ab0d8a1ad307e981ad2b1a7277bd136cbce9335d756d7edbad9 SHA512: 1eebb15f2a21067cd02d07743ad736f9c22342060b74221fe55c8d43b7059caa004a9dbdf4c92ab987b580c8218b759c9940e0393216672f1a40641ddbca4947 Homepage: https://cran.r-project.org/package=stagsynth Description: CRAN Package 'stagsynth' (Staggered Synthetic Control Estimation and Inference) Implements the Staggered Synthetic Control (SSC) method for estimating treatment effects in panel data with staggered adoption, as proposed by Cao, Lu, and Wu (2020) . Constructs synthetic control weights via constrained quadratic programming, estimates heterogeneous treatment effects and event-time average treatment effects on the treated (ATT), and provides placebo-in-time confidence intervals and p-values. Package: r-cran-stakeholderanalysis Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-stakeholderanalysis_1.2-1.ca2604.1_all.deb Size: 94206 MD5sum: 28845a78223ba72ce5aefc62ae94dfea SHA1: 42e316218568acfe46a1f2f5196b0b9cae549084 SHA256: f33ee4a4a7526db819a997bb9393a34b04b85abbc0ea1c4af641535a9fa569ce SHA512: 33ca4b64985945fe69d07feb33324416fdf6e90a639a5c7609fa3c4e525ada1f837966a37106fa8e9464b4dc7e1683cd9d8b215bb9860e6b63412def10fae931 Homepage: https://cran.r-project.org/package=StakeholderAnalysis Description: CRAN Package 'StakeholderAnalysis' (Measuring Stakeholder Influence) Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. Hester, P.T., & Adams, K.M. (2013) . 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(2010) in mixed and single ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects allowing integration with other packages such adegenet. Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. for the appropriate citation and user manual. Thank you in advance. Package: r-cran-stampr Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-spdep, r-cran-dplyr, r-cran-rlang, r-cran-lwgeom, r-cran-geosphere Filename: pool/dists/resolute/main/r-cran-stampr_0.3.1-1.ca2604.1_all.deb Size: 594786 MD5sum: 128de08428518ad5541aabea7c17cf72 SHA1: adbfd85e0dad993093f2f4a7d2f40880fa13e82d SHA256: 22fe37a30db86b39b2388157981fc29cc31bd1dc7bf725160c4e1c835783fc8d SHA512: 41e83bc2591aa7199f706f4a4ae4e0586e38a122b8c3d9526ce2cb8e0bcb351602c4e4c06972bc41fe0965d730e4ba24b1cce797ae00119caa47b3f080dfd1e6 Homepage: https://cran.r-project.org/package=stampr Description: CRAN Package 'stampr' (Spatial Temporal Analysis of Moving Polygons) Perform spatial temporal analysis of moving polygons; a longstanding analysis problem in Geographic Information Systems. Facilitates directional analysis, distance analysis, and some other simple functionality for examining spatial-temporal patterns of moving polygons. Package: r-cran-stand Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-stand_2.0-1.ca2604.1_all.deb Size: 208540 MD5sum: f7906bff27b8a3a2a76fe87253074038 SHA1: 4f9a55830cf884a33b04f845f6b547a67d0aae03 SHA256: 184f0fc3b743f672c0f5279652c7c13e904712d02b3d45d9ff164a6e98fe8afe SHA512: 7f32c67090c1ed631187708d51db54741b7e65316b58eb32c04f79d7496c4122d3c26037b3fc6c8f6f98dbec7e86c7c62969ed4ccac6843a0139fb9a41a97936 Homepage: https://cran.r-project.org/package=STAND Description: CRAN Package 'STAND' (Statistical Analysis of Non-Detects) Provides functions for the analysis of occupational and environmental data with non-detects. Maximum likelihood (ML) methods for censored log-normal data and non-parametric methods based on the product limit estimate (PLE) for left censored data are used to calculate all of the statistics recommended by the American Industrial Hygiene Association (AIHA) for the complete data case. Functions for the analysis of complete samples using exact methods are also provided for the lognormal model. Revised from 2007-11-05 'survfit~1'. Package: r-cran-standardize Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 940 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-mass, r-cran-stringr Suggests: r-cran-afex, r-cran-emmeans, r-cran-knitr, r-cran-lmertest, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-standardize_0.2.2-1.ca2604.1_all.deb Size: 348022 MD5sum: 45f035eeac91f08c0a5c01fcb408725f SHA1: 9822608a62cd4f9b41dced702f0657677a821169 SHA256: 81b3fd7a24035ea94acab0335bd659bcccefc82e49d60821a36eca3f3f99b5a9 SHA512: f2f01a098c8891c6d40b9d781c2bf8d50c68322dd30e68fdbd3701945aef22d073b5731acad3c7c801fa5998c552dcb6a44e6b65d3ccf1a3088e2bf1624ac080 Homepage: https://cran.r-project.org/package=standardize Description: CRAN Package 'standardize' (Tools for Standardizing Variables for Regression in R) Tools which allow regression variables to be placed on similar scales, offering computational benefits as well as easing interpretation of regression output. Package: r-cran-standardlastprofile Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7614 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lifecycle Suggests: r-cran-covr, r-cran-httr2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-standardlastprofile_1.1.0-1.ca2604.1_all.deb Size: 5988992 MD5sum: 51b7afe3aeb0eefd455e91f2ee28e059 SHA1: dabd8587eaebbc206b0351f8c4d7f8146441ded5 SHA256: f6d5e1d05c9c6636ab7e2ae000e7ef6317c110c507b477c08fa7b5bcfdd73d0e SHA512: 1344bdbbdf6bcd262fd00b44afa2eb0dd71c7763d2fd0608cebb2f0e5c4fb5340cf24c937dbbed0af0b9096dfd4a49e32955be728e0e785402f685de0cd3b8a5 Homepage: https://cran.r-project.org/package=standardlastprofile Description: CRAN Package 'standardlastprofile' (BDEW Standard Load Profiles for Electricity) Provides representative standard load profiles (SLPs) for electricity published by the German Association of Energy and Water Industries (BDEW Bundesverband der Energie- und Wasserwirtschaft e.V.) in a tidy format. Covers the 1999 profiles — households (H0), commerce (G0–G6), and agriculture (L0–L2) — and the updated 2025 profiles (H25, G25, L25, P25, S25), which additionally represent households with photovoltaic systems and battery storage. Also provides an interface for generating a standard load profile over a user-defined date range. The 1999 data and methodology are described in VDEW (1999), "Repräsentative VDEW-Lastprofile", . The generation algorithm is described in VDEW (2000), "Anwendung der Repräsentativen VDEW-Lastprofile step-by-step", . The 2025 profiles are described in BDEW (2025), "Standardlastprofile Strom", . 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Package: r-cran-starling Architecture: all Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-janitor, r-cran-stringr, r-cran-tidyr, r-cran-reclin2, r-cran-datawizard, r-cran-digest, r-cran-rlang, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-gtsummary, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-starling_0.6.5-1.ca2604.1_all.deb Size: 148124 MD5sum: 0832ead2a4a8d2bb2ff3a9d15b60e1dd SHA1: 0482ca868c478c8302bc04363508615824c1d86e SHA256: 95e903ef965ded69df10958542f3e5d68870b5f68e6bddbcc84781508b003483 SHA512: ec8dad1af6d2d0fc83003137f55606dc2f981d7d40b5ee82de40121e8dbde277aff896b71b074a5f0e8f0e46265d73fed9c8e27603d29c24835bf424050c67d0 Homepage: https://cran.r-project.org/package=starling Description: CRAN Package 'starling' (Link Infectious Disease Cases to Vaccination and HospitalizationRecords) Facilitates probabilistic record linkage between infectious disease surveillance datasets (notifiable disease registers, outbreak line-lists), vaccination registries, and hospitalization records using methods based on Fellegi and Sunter (1969) and Sayers et al. (2016) . The package provides core functions for data preparation, linkage, and analysis: clean_the_nest() standardizes variable names and formats across heterogeneous datasets; murmuration() performs machine learning-based record linkage using blocking variables and similarity metrics; molting() deidentifies datasets for secure sharing; homing() re-identifies previously deidentified datasets; plumage() identifies and categorizes comorbidities; and preening() creates analysis-ready variables including age categories and temporal groupings. Designed for epidemiological research linking acute and post-acute disease outcomes to vaccination status and healthcare utilization. Supports multiple linkage scenarios including case-to-vaccination, case-to-hospitalization, and event-based vaccination status determination (e.g., outbreak attendees, flight passengers, exposure site visitors). Package: r-cran-starm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-starm_0.1.0-1.ca2604.1_all.deb Size: 209504 MD5sum: 3cdc8311ad57b04a816ab154518d9389 SHA1: 38801eb785c8d138777e9e4082776ad3be89606a SHA256: 0a3982e2a68191ac91d5805a31f9b7ddf57f20b262d089675f447b3005245af8 SHA512: 1e70e66a53b3fb8ed181327d42ca2031f26cef1ae402d10e942f033e5080eb683f87b4a8fa091e510ce37cdd651d707c9b0797edc1e4588d25b14118e47b70e2 Homepage: https://cran.r-project.org/package=starm Description: CRAN Package 'starm' (Spatio-Temporal Autologistic Regression Model) Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. , using a grid of binary variables to estimate the spread of a disease on the grid over the years. 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Package: r-cran-starry Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-car, r-cran-corrr, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-stringr, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-starry_0.1.2-1.ca2604.1_all.deb Size: 178776 MD5sum: b10a4c99654f10b12000a0008ef433cd SHA1: c5705b2af16e228357683b0c7bb010adbcded12e SHA256: 43c2edb552f14caf4a3b01f1ce16986756efe00feec7ab6deb544bc689ea3c33 SHA512: 3bc094ca032cc85464887098435f62287c744781e40bb7a9600cb40052461c094e3c8bfe2f6bf01898c5910e86863c41049aae592ef938864c4fbf5232c2cf62 Homepage: https://cran.r-project.org/package=starry Description: CRAN Package 'starry' (Explore Data with Plots and Tables) Provides modular functions and applications for quickly generating plots and tables. 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Package: r-cran-stars Architecture: all Version: 0.7-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-sf, r-cran-classint, r-cran-rlang, r-cran-units Suggests: r-cran-cairo, r-cran-cftime, r-cran-openstreetmap, r-cran-rnetcdf, r-cran-clue, r-cran-covr, r-cran-cubble, r-cran-cubelyr, r-cran-digest, r-cran-dplyr, r-cran-exactextractr, r-cran-fnn, r-cran-future.apply, r-cran-ggforce, r-cran-ggplot2, r-cran-ggthemes, r-cran-gstat, r-cran-httr, r-cran-jsonlite, r-cran-knitr, r-cran-lwgeom, r-cran-maps, r-cran-mapdata, r-cran-ncdfcf, r-cran-ncdfgeom, r-cran-ncmeta, r-cran-pbapply, r-cran-plm, r-cran-randomforest, r-cran-raster, r-cran-rmarkdown, r-cran-sp, r-cran-spacetime, r-cran-spatstat, r-cran-spatstat.geom, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-viridis, r-cran-xts, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-stars_0.7-2-1.ca2604.1_all.deb Size: 4380436 MD5sum: 81e77af122e9c187683adf34ba329576 SHA1: 3f9fd3f2334718f6c8888a15d5fb879b905dfa67 SHA256: a61d0e1705f5816a466361d9e8c6333bc6dbdb4aa070767528ad9a7f50ba6360 SHA512: 101f5d45cf6ee08e048b6287e6d7b54f107ef75c7d7d6ea1b5bb25276f8d494aad64b42ae5215821b770d8f9ede0c96dcfc989acde82a27797144c99d9fc80b4 Homepage: https://cran.r-project.org/package=stars Description: CRAN Package 'stars' (Spatiotemporal Arrays, Raster and Vector Data Cubes) Reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL' bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'. Package: r-cran-starschemar Architecture: all Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-starschemar_1.2.5-1.ca2604.1_all.deb Size: 864768 MD5sum: d262c5adafce25f0f59e73a0f0e173b6 SHA1: 664b4fbe095e40fee00e69e1b8238230e99a3f0f SHA256: 34550876ea013011ea8decf3d338d8b7548109eb9f81eca8b6c6f97d276dc63b SHA512: acbfb55618e771ead2a071cc095c7117865dee7c380aa4cf1fe438f3f348c51a8c7b9a6e609fb20356d08b11bed2fd63015c898f36619fe7b8250d10b841613f Homepage: https://cran.r-project.org/package=starschemar Description: CRAN Package 'starschemar' (Obtaining Stars from Flat Tables) Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. Frequently, the operations to be performed aim to transform a flat table into a star schema. Transformations can be carried out using professional extract, transform and load tools or tools intended for data transformation for end users. With the tools mentioned, this transformation can be carried out, but it requires a lot of work. The main objective of this package is to define transformations that allow obtaining stars from flat tables easily. In addition, it includes basic data cleaning, dimension enrichment, incremental data refresh and query operations, adapted to this context. Package: r-cran-starstileserver Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-leaflet, r-cran-plumber, r-cran-png, r-cran-rlang, r-cran-sf, r-cran-stars, r-cran-units, r-cran-assertthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet.extras, r-cran-webshot, r-cran-mapview, r-cran-callr, r-cran-magick, r-cran-shiny, r-cran-dplyr, r-cran-magrittr, r-cran-abind, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-starstileserver_0.1.1-1.ca2604.1_all.deb Size: 1003468 MD5sum: 2127692191db9ad3ba2b73a1bf8fda2e SHA1: 33cb995d301d2a558ebbc9f1c2cbba58ba08936c SHA256: b83f9167f96e0b54e2a273c077e83fe4046c4e138e0fe151182b9dbb3207a2fa SHA512: 50b0d7abf270a34b3c795f3d1c48ed03c0ea99fde0076edf9adb7c65f015e603f64c0ba4097296ee59661c528bde71c65eb106ce7294b5c2a90c56e8c36be6a0 Homepage: https://cran.r-project.org/package=starsTileServer Description: CRAN Package 'starsTileServer' (A Dynamic Tile Server for R) Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. 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Package: r-cran-startr Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2743 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-bigmemory, r-cran-future, r-cran-multiapply, r-cran-easyncdf, r-cran-s2dv, r-cran-climprojdiags, r-cran-stringr Suggests: r-cran-testthat, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-startr_3.0.0-1.ca2604.1_all.deb Size: 1100026 MD5sum: 98014a2e60cd0db4083b6e2c084b9709 SHA1: 86c62bddca99574020b303d2f58be8b044753667 SHA256: 77ce8406a8b93dbbf53169d2beaf39aa45b97472ece8f8cd6414265eb247fe79 SHA512: 69456c68db362c86d410528093d01e94affd722f213776068372e0e3e5eacaffc38d3f6911b12ca4e25f4c188d03639b2196bf655d57c5befe60a003238057bc Homepage: https://cran.r-project.org/package=startR Description: CRAN Package 'startR' (Automatically Retrieve Multidimensional Distributed Data Sets) Automatically fetch, transform and arrange subsets of multidimensional data sets (collections of files) stored in local and/or remote file systems or servers, using multicore capabilities where possible. 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Package: r-cran-startup Architecture: all Version: 0.23.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-commonmark Filename: pool/dists/resolute/main/r-cran-startup_0.23.0-1.ca2604.1_all.deb Size: 220524 MD5sum: f9ffe1405d9beeb67659347e8152a448 SHA1: de9dc2e8686fddf8becef3f078978bf10f627d73 SHA256: 27789e9ddf32bb7479d0357745990d7d84ff2926bd4db7785f6538f151f1afb5 SHA512: fe7f2654257f661d2c8930dc9c1ecd97e8f3d52bbee2f812140f7b2de37780cdf4998878387b1b68dd931d50f0f460907506f515cc1e76b68df9bf9e40eed5e2 Homepage: https://cran.r-project.org/package=startup Description: CRAN Package 'startup' (Friendly R Startup Configuration) Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files. This makes it possible to keep private / secret environment variables separate from other environment variables. 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Package: r-cran-starvars Architecture: all Version: 1.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ks, r-cran-zoo, r-cran-dosnow, r-cran-foreach, r-cran-matrixcalc, r-cran-optimparallel, r-cran-vars, r-cran-xts, r-cran-lessr, r-cran-quantmod Filename: pool/dists/resolute/main/r-cran-starvars_1.1.11-1.ca2604.1_all.deb Size: 372706 MD5sum: c23fc65755a471178957a9122b084eb9 SHA1: f1c14d1f253ca808a1229a7b390d9d1cc26b3826 SHA256: ed35edfc31a0c3242f46cb10182e58381444c80ef5b99f05370ba13d07f758f1 SHA512: 8fecd8f4c7510dd154cb4c5f4b56b5faa3e3c3520ae4b7e03606ce79942880bd6943bb28367a4e2ae3bbc69314f7885e0747296a422925110df79392f556bc5e Homepage: https://cran.r-project.org/package=starvars Description: CRAN Package 'starvars' (Vector Logistic Smooth Transition Models Estimation andPrediction) Allows the user to estimate a vector logistic smooth transition autoregressive model via maximum log-likelihood or nonlinear least squares. 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Package: r-cran-statafrikr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 662 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-haven, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-survey, r-cran-tibble, r-cran-tidyr Suggests: r-cran-flextable, r-cran-httr2, r-cran-jsonlite, r-cran-officer, r-cran-openxlsx2, r-cran-sf, r-cran-srvyr, r-cran-testthat, r-cran-rmarkdown, r-cran-withr, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-statafrikr_0.1.0-1.ca2604.1_all.deb Size: 422906 MD5sum: 6b6e082e22d983e8543254e8b3e94e51 SHA1: fcb60da8fe5f85f0da691a8dd06ac3623658569e SHA256: adee943aef7c4a73e37a57026c0e890c05935b330a752b573f1271ada9a87c66 SHA512: a32a30a9d7406b336b756d6258d0ff5fb700e0c5415150691d1c514cb67d08eb21ac331cc5c93a0f9a888dede814cb806281d2b628076e65ae3662bf245bdd9e Homepage: https://cran.r-project.org/package=statAfrikR Description: CRAN Package 'statAfrikR' (Statistical Tools for African National Statistics Institutes) A comprehensive statistical toolbox for National Statistics Institutes (INS) in Africa. Provides functions for survey data import ('KoboToolbox', 'ODK', 'CSPro', 'Excel', 'Stata', 'SPSS'), data processing and validation, weighted statistical analysis (descriptive statistics, cross-tabulations, regression, Human Development Index (HDI), Multidimensional Poverty Index (MPI) following Alkire and Foster (2011) , inequalities), visualization (age pyramids, thematic maps, official charts) and dissemination ('SDMX' export, 'DDI' metadata, anonymization, Word/PDF reports). Designed to work in resource-constrained environments, offline and in French. 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Supports descriptive statistics, t-tests, z-tests, chi-square tests, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), two-way ANOVA with simple effects, Multivariate Analysis of Variance (MANOVA), robust and cluster-robust regression using Heteroscedasticity-Consistent (HC) standard errors, post-hoc pairwise comparisons, homoskedasticity and heteroscedasticity diagnostics including the Non-Constant Variance (NCV) test, proportion tests, and multilevel mixed-effects models with intraclass correlation coefficients (ICC) and model-comparison tables. Output can be directed to the console, Microsoft Word (via 'officer' and 'flextable'), or LaTeX. For APA style guidelines see American Psychological Association (2020, ISBN:978-1-4338-3216-1). 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It checks whether your p-values match their accompanying test statistic and degrees of freedom. statcheck searches for null-hypothesis significance test (NHST) in APA style (e.g., t(28) = 2.2, p < .05). It recalculates the p-value using the reported test statistic and degrees of freedom. If the reported and computed p-values don't match, statcheck will flag the result as an error. If the reported p-value is statistically significant and the recomputed one is not, or vice versa, the result will be flagged as a decision error. You can use statcheck directly on a string of text, but you can also scan a PDF or HTML file, or even a folder of PDF and/or HTML files. Statcheck needs an external program to convert PDF to text: Xpdf. Instructions on where and how to download this program, how to install statcheck, and more details on what statcheck can and cannot do can be found in the online manual: . You can find a point-and-click web interface to scan PDF or HTML or DOCX articles on . 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Package: r-cran-stepcam Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-fd, r-cran-geometry, r-cran-gtools Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stepcam_1.2.3-1.ca2604.1_all.deb Size: 187248 MD5sum: 9bfbe11fef18a0ca0e8f3a73bac81521 SHA1: a8a23837582d4e0c2ba8be49e683dbe8edfe54d7 SHA256: 0159574f051f559289e9f5a8cd7a98f050002128b4f3854d402e18d87b559187 SHA512: c7630694a5562ba4573e7d9cfdb63b7db89951d67fadd19e8b7ba526748cb46fdff63d22a9a1df4dc950f68e0ad93c4df7892f7a59baa6c52bd7df2ca4d9eb32 Homepage: https://cran.r-project.org/package=STEPCAM Description: CRAN Package 'STEPCAM' (ABC-SMC Inference of STEPCAM) Collection of model estimation, and model plotting functions related to the STEPCAM family of community assembly models. 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Package: r-cran-stepgbm Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spm, r-cran-steprf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-stepgbm_1.0.1-1.ca2604.1_all.deb Size: 30266 MD5sum: 502c4a32de82aee3bff54e610dc8038c SHA1: ac9f7ee95b0ce2843aaaeece9544ebbac2ed0001 SHA256: 3e9b98ff2f94abc45e2069d1107bf9a993b5c079e7be758ce3d55aa567824369 SHA512: 4d7af65aed1e9b21e3960272caa5bf50e8dd2817f986f48253a38e021dc93dce9485f7fa206d36fdf3f41c2dcccd8c76b71d35ea368c0c11c5efbb620e3e8909 Homepage: https://cran.r-project.org/package=stepgbm Description: CRAN Package 'stepgbm' (Stepwise Variable Selection for Generalized Boosted RegressionModeling) An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) . Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). . Package: r-cran-stepgwr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qpdf, r-cran-numbers, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stepgwr_0.1.0-1.ca2604.1_all.deb Size: 27576 MD5sum: 9d2437b830ca133f478f4b7810e8b2a8 SHA1: 181e0f482e1d80d62957ffe54661448e0bd1654b SHA256: d97027587dc6dc8b7d2f70698935e809fd6c2c1b7b7a3ffcdf8815ab9a082847 SHA512: c68d80fa6f87065029811fc19b430f1435f8f81ca6e62fbb8980c6269525a933cf148ea4882c531f72732f9c4aab75bc923f6ca08222744369dd31bf7c979485 Homepage: https://cran.r-project.org/package=StepGWR Description: CRAN Package 'StepGWR' (A Hybrid Spatial Model for Prediction and Capturing SpatialVariation in the Data) It is a hybrid spatial model that combines the variable selection capabilities of stepwise regression methods with the predictive power of the Geographically Weighted Regression(GWR) model.The developed hybrid model follows a two-step approach where the stepwise variable selection method is applied first to identify the subset of predictors that have the most significant impact on the response variable, and then a GWR model is fitted using those selected variables for spatial prediction at test or unknown locations. For method details,see Leung, Y., Mei, C. L. and Zhang, W. X. (2000)..This hybrid spatial model aims to improve the accuracy and interpretability of GWR predictions by selecting a subset of relevant variables through a stepwise selection process.This approach is particularly useful for modeling spatially varying relationships and improving the accuracy of spatial predictions. Package: r-cran-stepjglm Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsq Filename: pool/dists/resolute/main/r-cran-stepjglm_0.0.1-1.ca2604.1_all.deb Size: 65136 MD5sum: a803a6061209eb99b39de021fee44829 SHA1: 56dbfbb03fb8553e66d3d4c51e1e24d5fbc709af SHA256: 6d6494bd6904c639413dde1e0263df329b2861173163ed8ce6bdc08a6028ba36 SHA512: 61e0f0812ac591546114eb18b07ce6d77f2bbcb732c5bb5928a6e42fa0a920f58ac892df2398db91257cfed9d16cf32058689708d73214455f3f0344b66eac91 Homepage: https://cran.r-project.org/package=stepjglm Description: CRAN Package 'stepjglm' (Variable Selection for Joint Modeling of Mean and Dispersion) A Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. 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Package: r-cran-steppenal Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm, r-cran-proc, r-cran-dfoptim, r-cran-caret Filename: pool/dists/resolute/main/r-cran-steppenal_0.2-1.ca2604.1_all.deb Size: 80044 MD5sum: 9917bf18dbc5eebcb4a6c57d49f1dc12 SHA1: db9a613d096df1b0b384e09e7dc4cd5ab3394cba SHA256: c6f32a58af8853c8b68d9f3cdc7986b38270e3947fce2f0db9f64144d2ba7d8c SHA512: 90863291f211bfc885ff305b0acf85e6ee1fbc9eb573ae3148eab972908bb7dd34bbf352020a7deb8632822f0d84d78fec4fca0e7269ec5b8a66b85bc3a7baa3 Homepage: https://cran.r-project.org/package=stepPenal Description: CRAN Package 'stepPenal' (Stepwise Forward Variable Selection in Penalized Regression) Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms. Package: r-cran-stepreg Architecture: all Version: 1.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4793 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-mass, r-cran-survival, r-cran-flextable, r-cran-proc, r-cran-survauc Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-kableextra, r-cran-cowplot, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stepreg_1.6.5-1.ca2604.1_all.deb Size: 3010048 MD5sum: 1dbc9346af56cae3c1de7b5616a633bb SHA1: 3048edf6a5a6f36b3d48079f64ec074a45012805 SHA256: bbd9fe64044850688fbd54169b342703802c65958a7983523cf9def6ed326476 SHA512: abadc6145a93a0eae91b57a948ee62a57452638ed30ef727598daee38bcf573230e1b782718244dfcad5729d96460d595fff897b5a7222b7f2bb52cd14a18093 Homepage: https://cran.r-project.org/package=StepReg Description: CRAN Package 'StepReg' (Stepwise Regression Analysis) Stepwise regression is a statistical technique used for model selection. This package streamlines stepwise regression analysis by supporting multiple regression types(linear, Cox, logistic, Poisson, Gamma, and negative binomial), incorporating popular selection strategies(forward, backward, bidirectional, and subset), and offering essential metrics. It enables users to apply multiple selection strategies and metrics in a single function call, visualize variable selection processes, and export results in various formats. StepReg offers a data-splitting option to address potential issues with invalid statistical inference and a randomized forward selection option to avoid overfitting. We validated StepReg's accuracy using public datasets within the SAS software environment. For an interactive web interface, users can install the companion 'StepRegShiny' package. Package: r-cran-stepregshiny Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stepreg, r-cran-shiny, r-cran-dt, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-dplyr, r-cran-ggplot2, r-cran-stringr, r-cran-flextable, r-cran-cowplot, r-cran-ggcorrplot, r-cran-tidyr, r-cran-summarytools, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-survival Filename: pool/dists/resolute/main/r-cran-stepregshiny_1.6.1-1.ca2604.1_all.deb Size: 800522 MD5sum: 8f1d13eb4c0e6dc26bd9971930191f77 SHA1: b931ce3333e0e9c70a0e12ac684bbbd26893316b SHA256: ce37ee706b071086506e30100435572b882e1b8658d40520eab5238e26b96dc6 SHA512: f12a678fb1f554847b3116e22916556fea208b68e4a53101e25e07113dfa5871e661335b778f7b45d39693ccb7a10a5202a8bdf9b205ccb70b291c304c779d73 Homepage: https://cran.r-project.org/package=StepRegShiny Description: CRAN Package 'StepRegShiny' (Graphical User Interface for 'StepReg') A web-based 'shiny' interface for the 'StepReg' package enables stepwise regression analysis across linear, generalized linear (including logistic, Poisson, Gamma, and negative binomial), and Cox models. It supports forward, backward, bidirectional, and best-subset selection under a range of criteria. The package also supports stepwise regression to multivariate settings, allowing multiple dependent variables to be modeled simultaneously. Users can explore and combine multiple selection strategies and criteria to optimize model selection. For enhanced robustness, the package offers optional randomized forward selection to reduce overfitting, and a data-splitting workflow for more reliable post-selection inference. Additional features include logging and visualization of the selection process, as well as the ability to export results in common formats. Package: r-cran-steprf Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spm, r-cran-randomforest, r-cran-spm2, r-cran-psy Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-steprf_1.0.2-1.ca2604.1_all.deb Size: 51244 MD5sum: 2f92b56993cd91b5c91ca3d27d6a7ab9 SHA1: d0140cd80eb88ec1de61ecb42a5e8dd51af0fae5 SHA256: 268a82911ef15ca2eddf6b553c8c5dc78231796bfb07c1c255a371ce9aacde4a SHA512: a08fdbae4b705b43c4ea1c9c9cf62efc4a6c47301f7feb9154c24ed468025c329cd40d60ac2e852efb68d6a842f8d4f41ee0322a18904c38961dcfb4f93f53b3 Homepage: https://cran.r-project.org/package=steprf Description: CRAN Package 'steprf' (Stepwise Predictive Variable Selection for Random Forest) An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) . Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). . Package: r-cran-stepssurvey Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1351 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ggplot2, r-cran-glue, r-cran-haven, r-cran-janitor, r-cran-patchwork, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-survey Suggests: r-cran-knitr, r-cran-remotes, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-stepssurvey_0.1.0-1.ca2604.1_all.deb Size: 679792 MD5sum: 396ae0af71200e63d624dd2b5dff2d66 SHA1: 5d0d48e6d1f0443bd7ea8ec2ea6a7c0dc0020785 SHA256: 133202e40fdc0b4aa7401d1353a61b457a16a7f95c65fa765f9695b1584aca2d SHA512: 24eebc186a03279f65a59d204f7ea4cf11c68558f90768e108802a67cf1b683dbbcf0fc9a1897fff64d7cd99e865b6fd666237e028af890e9c75b3967dfd88bf Homepage: https://cran.r-project.org/package=stepssurvey Description: CRAN Package 'stepssurvey' (Analyse WHO STEPS Survey Data) Provides a complete analysis pipeline for the WHO STEPwise Approach to NCD Risk Factor Surveillance (STEPS) as described in Riley et al. (2016) . Imports raw survey data ('CSV', 'Excel', 'Stata', 'SPSS'), applies WHO-standard cleaning and recoding, sets up complex survey designs, computes all standard NCD indicators (tobacco, alcohol, diet, physical activity, anthropometry, blood pressure, biochemical), and generates publication-ready tables, visualisations, and 'Word'/'HTML' reports (fact sheet, data book, country report). Package: r-cran-stevedata Architecture: all Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4453 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stevedata_1.8.0-1.ca2604.1_all.deb Size: 4488340 MD5sum: 7bcebabc47eea27ee33602f48991a2ee SHA1: 6446f1406435677b6078f5bef0af7ccacbf4ae7c SHA256: fce292024a9da0e5e23b6bcc4be99d5f608b3d0ac39c4e5eee5fae84578f87bd SHA512: e5cc935d5a75a6fb00458dfa726a3bd834c991b9d7ee3c2bea9768987ff6a3a7909e51ccca48cc709833aa6dcbdaeaa954fc78ee82bba4e8bc1456b54b502c52 Homepage: https://cran.r-project.org/package=stevedata Description: CRAN Package 'stevedata' (Steve's Toy Data for Teaching About a Variety of Methodological,Social, and Political Topics) This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy. 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Rather than using system calls to interact with a docker client, using the API directly means that we can receive richer information from docker. The interface in the package is automatically generated using the 'OpenAPI' (a.k.a., 'swagger') specification, and all return values are checked in order to make them type stable. Package: r-cran-stevemisc Architecture: all Version: 1.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1083 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-labelled, r-cran-arm, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-lme4, r-cran-rlang, r-cran-forcats, r-cran-stringr, r-cran-httr, r-cran-rmarkdown, r-cran-tidyr Suggests: r-cran-knitr, r-cran-dbi, r-cran-rsqlite, r-cran-dbplyr Filename: pool/dists/resolute/main/r-cran-stevemisc_1.9.0-1.ca2604.1_all.deb Size: 946332 MD5sum: 80da1ae9f2d9a9b4567103d57c2ab08d SHA1: 2474fe79b079d2b636fcb112602fac645b5e6d21 SHA256: eb9080f35aac920481c585bc81f1c896d12082fe28d41d88dbdb1fbd1eb647c6 SHA512: 9460e813917ecad7c45704da10a6233ddf86fda380f138c2862f4b58be90af4307e289a3c8739ee816ec7cd77407dc92bd6bd35d87d8dc9e1ad995daecfd3bcd Homepage: https://cran.r-project.org/package=stevemisc Description: CRAN Package 'stevemisc' (Steve's Miscellaneous Functions) These are miscellaneous functions that I find useful for my research and teaching. The contents include themes for plots, functions for simulating quantities of interest from regression models, functions for simulating various forms of fake data for instructional/research purposes, and many more. All told, the functions provided here are broadly useful for data organization, data presentation, data recoding, and data simulation. 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These are useful for all things academic and professional, if you are using 'R Markdown' for things like your CV or your articles and manuscripts. 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I believe these themes and theme elements are aesthetically pleasing, both for pedagogical instruction and for the presentation of applied statistical research to a wide audience. These themes imply routine use of easily obtained/free fonts, simple forms of which are included in this package. Package: r-cran-stfts Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stfts_0.1.0-1.ca2604.1_all.deb Size: 56618 MD5sum: 33c27eabdbe5bad07585f0559154ed05 SHA1: e55e4ee6b8ea342762c8d9c203ae13faf14b7259 SHA256: c52acdd6c3f5059b420876fb89a5915880f23c777260252b47cc64afdb639fc2 SHA512: 6a45147e557f11bfdc37385d1227e984d12cb2c75f82acc5fd7aef52b7929a79cd117e3a76045ef454ee0625a533231eff53f7bcecc23b896102529199963e34 Homepage: https://cran.r-project.org/package=STFTS Description: CRAN Package 'STFTS' (Statistical Tests for Functional Time Series) A collection of statistical hypothesis tests of functional time series. 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The framework suggests the need to investigate for the presence and nature of any space-time dependencies in the data. It proposes a workflow that creates and refines an initial space-time GAM and includes tools to create and evaluate multiple model forms. The workflow sequence is to: i) Prepare the data by lengthening it to have a single location and time variables for each observation. ii) Create all possible space and/or time models in which each predictor is specified in different ways in smooths. iii) Evaluate each model via their AIC value and pick the best one. iv) Create the final model. v) Calculate the varying coefficient estimates to quantify how the relationships between the target and predictor variables vary over space, time or space-time. vi) Create maps, time series plots etc. The number of knots used in each smooth can be specified directly or iteratively increased. This is illustrated with a climate point dataset of the dry rain forest in South America. This builds on work in Comber et al (2024) and Comber et al (2004) . 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'STICr' (pronounced "sticker") includes functions for tidying, calibrating, classifying, and doing quality checks on data from STIC sensors. Some package functionality is described in Wheeler/Zipper et al. (2023) . 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This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) and the Cairns-Blake-Dowd (2006) models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations. 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'STMr' package allow users to estimate individual reps-max relationships, implement various progression tables, and create numerous set and rep schemes. The 'STMr' package is originally created as a tool to help writing Jovanović M. (2020) Strength Training Manual . 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Package: r-cran-stochlab Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-logr, r-cran-magrittr, r-cran-msm, r-cran-pracma, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-stochlab_1.1.2-1.ca2604.1_all.deb Size: 6877176 MD5sum: 9f6079f71851ded8787d902b3251c49c SHA1: f9ea4d71fee827a3c0d1d050226df82852126eb2 SHA256: 46ab7f4700f47cf174a855f1d5d30fd9bf285bc0857516e57b60a8d36356a93e SHA512: e344faf75e139c4dc05b100b17806a4e3d3663893c84a1fa0e318ff67b09e31e9f9745884f4d605e1da41a0012ae03bb5a67d279ab13bde8fa3701d36d6f7db7 Homepage: https://cran.r-project.org/package=stochLAB Description: CRAN Package 'stochLAB' (Stochastic Collision Risk Model) Collision Risk Models for avian fauna (seabird and migratory birds) at offshore wind farms. The base deterministic model is derived from Band (2012) . This was further expanded on by Masden (2015) and code used here is heavily derived from this work with input from Dr A. Cook at the British Trust for Ornithology. These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, many of the species included in the stochastic collision risk models can also be found in the North Atlantic in the United States and Canada, and could be applied there. Package: r-cran-stockanalyst Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-stockanalyst_1.0.1-1.ca2604.1_all.deb Size: 184180 MD5sum: 7d981c62192e3d4b5749abb88df06b80 SHA1: d6f0196c416d957b0d8893822a4dd3417ffe0875 SHA256: 92d1f666b592834868237557414b4ca7423ee3487def97af8e355009ee2a1cc1 SHA512: d81951aa5ad3066725688211a441b292475a1fd9aa34e607e291f10d2bc6d7a00e904616a61b9f5af9fad511c09316d8df115bfa321dd3449529f9b375908613 Homepage: https://cran.r-project.org/package=stockAnalyst Description: CRAN Package 'stockAnalyst' (Equity Valuation using Methods of Fundamental Analysis) Methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (“Equity Asset Valuation (4th Edition)”, 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614). Package: r-cran-stockdistfit Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1523 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fgarch, r-cran-fbasics, r-cran-fitdistrplus, r-cran-xts, r-cran-magrittr, r-cran-zoo, r-cran-quantmod, r-cran-ghyp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stockdistfit_1.0.0-1.ca2604.1_all.deb Size: 840966 MD5sum: 2b6193bdaf0fb21a0bc52887400b161f SHA1: d6fee27ecda3e2098cdfe350024d2c146ea0c4ce SHA256: 277edee85c32200e9b29f89dcff271b080342d66f0de9d7c8dcbe45d7494d8bd SHA512: b1815b9bc404cc23bfef24afc9a7809d7ce5c042eac2af74dcacc440ec4726a9495f4a5448af76164a6f34d0a551d31e7998b4d27cc484f3c3eeb1e39b208980 Homepage: https://cran.r-project.org/package=StockDistFit Description: CRAN Package 'StockDistFit' (Fit Stock Price Distributions) The 'StockDistFit' package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) and Benth et al. (2008) . Package: r-cran-stodom Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-ggplot2, r-cran-pracma, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stodom_0.0.1-1.ca2604.1_all.deb Size: 49836 MD5sum: 699426fcc79a44ba93fcea030915d62d SHA1: 388d943a17c97ea052132364ea5abc12fc62713e SHA256: e54d4e5468e2ab0addb0a3883574eff91a068c3b96528dfc9f04787114c10ed2 SHA512: 7752b7164031c54c9948dc26c1dd3507428dd24ce964c674b6ed208f6ff7aa71272091c26e1056cfa690a021cb2c2853f830c7a49e8ea12beb0f3c49707292d1 Homepage: https://cran.r-project.org/package=stodom Description: CRAN Package 'stodom' (Estimating Consistent Tests for Stochastic Dominance) Stochastic dominance tests help ranking different distributions. The package implements the consistent test for stochastic dominance by Barrett and Donald (2003) . Specifically, it implements Barrett and Donald's Kolmogorov-Smirnov type tests for first- and second-order stochastic dominance based on bootstrapping 2 and 1. Package: r-cran-stoichcalc Architecture: all Version: 1.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-stoichcalc_1.1-5-1.ca2604.1_all.deb Size: 37988 MD5sum: 1ce303dd49d2ff5ff5d1ee280b266cbe SHA1: 90190a49e4d2fed27a82e5009eabb2b6ec1c999a SHA256: 49b458a74a0785ca6f63929f501e92558fa9f792d8ab77b9310ce11e08bca599 SHA512: e7e873f4fa84d21a039291a0c5705664a641e3405f23016f903df812e48b4ee83053edc68e1e7d274cb2238effe503b6871c621ddd676933becf98c59c203713 Homepage: https://cran.r-project.org/package=stoichcalc Description: CRAN Package 'stoichcalc' (R Functions for Solving Stoichiometric Equations) Given a list of substance compositions, a list of substances involved in a process, and a list of constraints in addition to mass conservation of elementary constituents, the package contains functions to build the substance composition matrix, to analyze the uniqueness of process stoichiometry, and to calculate stoichiometric coefficients if process stoichiometry is unique. (See Reichert, P. and Schuwirth, N., A generic framework for deriving process stoichiometry in enviromental models, Environmental Modelling and Software 25, 1241-1251, 2010 for more details.) Package: r-cran-stoichutilities Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-purrr, r-cran-magrittr, r-cran-lubridate, r-cran-sf, r-cran-rlang, r-cran-units, r-cran-jsonlite Suggests: r-cran-tidyverse, r-cran-maps, r-cran-knitr, r-cran-rmarkdown, r-cran-gt, r-cran-glue, r-cran-gluedown, r-cran-kableextra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stoichutilities_1.0.2-1.ca2604.1_all.deb Size: 285152 MD5sum: b54fceee4b4caaee55aba8ac5552d0ea SHA1: 3f24a0f978bfe42e41b434eeb524019bac0fbe90 SHA256: 9c58ad5fe98351e0994bb5c1d4db9e3a03a79ce85ae9c38e644fc437d5c2f1f7 SHA512: cebdfdc43f9a028af4bd70067b982c2c96b997402fb7baa4b4f717b12efd8e43806364b3cde21ee9a1f45c96094a2383ea197ce2868b1cf0bf18fa7d1d0ced31 Homepage: https://cran.r-project.org/package=stoichUtilities Description: CRAN Package 'stoichUtilities' (User Tools for Accessing the STOICH Project Database) User tools for working with The STOICH (Stoichiometric Traits of Organisms in their Chemical Habitats) Project database . This package is designed to aid in data discovery, filtering, pairing water samples with organism samples, and merging data tables to assist users in preparing data for analyses. For additional examples see "Additional Examples" and the readme file at . Package: r-cran-stokes Architecture: all Version: 1.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-permutations, r-cran-partitions, r-cran-disordr, r-cran-spray Suggests: r-cran-knitr, r-cran-deriv, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown, r-cran-quadform, r-cran-magrittr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-stokes_1.2-3-1.ca2604.1_all.deb Size: 330678 MD5sum: 632be8fcdc62f336a785fee5e478fa62 SHA1: d46a944cb8e11351021c9c4bae135a158198fcc4 SHA256: 77e3a754fa917e1481c468f9cb1b5ceb66506077a8407e483799eef1de7418e1 SHA512: b619495e183a12b89a51e20a2bb3b52a0e2cef4df1e02ff9aaf1487e7676b63afec3c822137c06aa79efdf85934ed5a42d237cf3df8ef08bd409ff34f79d02f2 Homepage: https://cran.r-project.org/package=stokes Description: CRAN Package 'stokes' (The Exterior Calculus) Provides functionality for working with tensors, alternating forms, wedge products, Stokes's theorem, and related concepts from the exterior calculus. Uses 'disordR' discipline (Hankin, 2022, ). The canonical reference would be M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds". To cite the package in publications please use Hankin (2022) . Package: r-cran-stopes Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-cvtools, r-cran-glmnet, r-cran-changepoint Filename: pool/dists/resolute/main/r-cran-stopes_0.2-1.ca2604.1_all.deb Size: 44444 MD5sum: d9166564cf0628fbb03fed9390d58122 SHA1: f3fc108b22491ffcc861a0a0a5e1d2cedbaf63d7 SHA256: 93d4431ce6841efb179534fb3e1beb0a68be0ab3d8c1f1ea37c9ba8abd60dad3 SHA512: b522a1108517cf0f297ee4c6c760172c1f8c925071eadbb93e3d653693ba7ba7b76ebae26b4e0e4677a46636f05b2f307ca11bbdb2008118e555f3bb9a618f7a Homepage: https://cran.r-project.org/package=STOPES Description: CRAN Package 'STOPES' (Selection Threshold Optimized Empirically via Splitting) Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models). Package: r-cran-stopmotion Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick, r-cran-checkmate Suggests: r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-stopmotion_0.1.0-1.ca2604.1_all.deb Size: 574000 MD5sum: 6d597f2440b928f020d5c8453dce21e0 SHA1: 2da62677d35865c1e06b754cd66b7b24b3774da6 SHA256: df9b49d72b26b52d1b855c475878a869b6c8a32bdc736f69a0a257768b2be01d SHA512: fe1af478a5d300735421bca2e0486a046c96485ec0f843fe4b7346de42776e1de0debbc5a85d3c5b37f0edb226c115c5723b7bd006db904772fecc808b13d155 Homepage: https://cran.r-project.org/package=stopmotion Description: CRAN Package 'stopmotion' (Build Stop Motion Animations from Image Sequences) A pipeline-friendly toolkit for assembling stop motion animations from sequences of still images. Provides functions to read image directories, restructure frame sequences (duplicate, splice, arrange), apply per-frame pixel transformations (rotate, wiggle, flip, flop, blur, scale, crop, trim, border, background), and export the result as a GIF. All transformation functions accept a 'frames' argument to target any subset of frames, bridging the gap between 'magick' functions that operate on an entire image stack and fine-grained stop motion editing. Image processing is performed via 'ImageMagick Studio LLC' (2024) . Package: r-cran-stopp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1806 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-mass, r-cran-fields, r-cran-optimx, r-cran-plot3d, r-cran-sparr, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat.random, r-cran-splancs, r-cran-spatstat.model, r-cran-spatstat.utils, r-cran-stlnpp, r-cran-stpp, r-cran-mgcv, r-cran-spatstat.univar Filename: pool/dists/resolute/main/r-cran-stopp_1.0.0-1.ca2604.1_all.deb Size: 1777776 MD5sum: bfd5fa1902ef2653408cd8c429de3dc1 SHA1: 4d78635837b912ae171117a1c179c7d513963ed8 SHA256: a4dcdb41e7db3f39a123d331946dc5bb7d2c0a03deda4221ecee403a0adc5edf SHA512: 5bdf594d2b429f685a4ef1f1e8f7171320fb33676f83d5dea86979b3e9171a71cbd4bf269276200c4cc00e16b9ea3c6d422578d2314c54a437f17f8c1e96d6d7 Homepage: https://cran.r-project.org/package=stopp Description: CRAN Package 'stopp' (Spatio-Temporal Point Pattern Methods, Model Fitting,Diagnostics, Simulation, Local Tests) Toolbox for different kinds of spatio-temporal analyses to be performed on observed point patterns, following the growing stream of literature on point process theory. This R package implements functions to perform different kinds of analyses on point processes, proposed in the papers (Siino, Adelfio, and Mateu 2018; Siino et al. 2018; Adelfio et al. 2020; D’Angelo, Adelfio, and Mateu 2021; D’Angelo, Adelfio, and Mateu 2022; D’Angelo, Adelfio, and Mateu 2023). The main topics include modeling, statistical inference, and simulation issues on spatio-temporal point processes on Euclidean space and linear networks. Version 1.0.0 has been updated for accompanying the journal publication D Angelo and Adelfio 2025 . Package: r-cran-stoppingrule Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-matrixstats Filename: pool/dists/resolute/main/r-cran-stoppingrule_0.6-1.ca2604.1_all.deb Size: 166370 MD5sum: 309fabf17c4cb62ef64175a29fafcf66 SHA1: 1b2d68fef9bb39f78cc3e174e2933406c847489c SHA256: f5de58665744ec8d2cdb7095c5a9ffe59521aec75966721e9fe5ec40eb616e6e SHA512: 1b9a8db78447d648cc14da7e9c24c9db54e64a81027442b3fb764f6b7e3900ddac5274ad9ebeaa2e7f4a2339fb5be8d63b3003bdbfa394a01eb98a0f827231fb Homepage: https://cran.r-project.org/package=stoppingrule Description: CRAN Package 'stoppingrule' (Create and Evaluate Stopping Rules for Safety Monitoring) Provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies. Package: r-cran-stops Architecture: all Version: 1.9-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smacofx, r-cran-acepack, r-cran-clue, r-cran-cmaes, r-cran-cordillera, r-cran-dfoptim, r-cran-energy, r-cran-minerva, r-cran-nloptr, r-cran-pomp, r-cran-pso, r-cran-registry, r-cran-scagnostics, r-cran-smacof, r-cran-tgp, r-cran-vegan Suggests: r-cran-r.rsp, r-cran-diceoptim, r-cran-dicekriging Filename: pool/dists/resolute/main/r-cran-stops_1.9-1-1.ca2604.1_all.deb Size: 702230 MD5sum: 7a3f7151994494131036d2934794f840 SHA1: 3ad5ac39b0e742ddb16b2d1a7694fff4d4a057f9 SHA256: 1142a2ecbacd2219338049e7e55f57a0b3260cc6448f337a950ec989413be634 SHA512: 3cc003c2d5de04073326ea81c51c92c045f8b11ebd1680d42c9d7b622ee3abb540172892d84b0e50fa116133a2d25f9a5c5b2d6df550a8f1042480bd58be7f0f Homepage: https://cran.r-project.org/package=stops Description: CRAN Package 'stops' (Structure Optimized Proximity Scaling) Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices. Hyperparameter optimization can be done with a number of techniques but we recommend either Bayesian optimization or particle swarm. For using "Kriging", users need to install a version of the archived 'DiceOptim' R package. Package: r-cran-stopwords Architecture: all Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-isocodes Suggests: r-cran-covr, r-cran-quanteda, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stopwords_2.3-1.ca2604.1_all.deb Size: 226070 MD5sum: 30576b58f621d2ed0e87b44b40de4b7b SHA1: 35f8537b7986757b9d24e3328b4290523c7cfd3e SHA256: 2ecbae88b5a512a62455550ac190d56abfe0f7125a58c0ff0dbd7cf51e0d1647 SHA512: e2ac1de1a8addc859df50260073611ab464aae7b8512083c554ab7e140e82eb406802b63244ddab415adac7fb1287171e9b84029839e3e9fc32eeabea47d786c Homepage: https://cran.r-project.org/package=stopwords Description: CRAN Package 'stopwords' (Multilingual Stopword Lists) Provides multiple sources of stopwords, for use in text analysis and natural language processing. Package: r-cran-storm Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-permute, r-cran-rjson Filename: pool/dists/resolute/main/r-cran-storm_1.2-1.ca2604.1_all.deb Size: 138948 MD5sum: 9bf217824f7381865f34e2075a769f99 SHA1: 53e03ce27b93d1a563ce1171f8d4d1f465e0edfe SHA256: 48ff8b14d51243a29091f000df404e93c308f248c298901f24f0d7e75e396279 SHA512: ec09d82d7274eb1dce515f61411969c245fe65595166e817aa5ce497be741abafb4435d4365e8540fe20b23d04f2bbb41bad116e086c299e21520231c742e45b Homepage: https://cran.r-project.org/package=Storm Description: CRAN Package 'Storm' (Write Storm Bolts in R using the Storm Multi-Language Protocol) Storm is a distributed real-time computation system. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing real-time computation. Storm includes a "Multi-Language" (or "Multilang") Protocol to allow implementation of Bolts and Spouts in languages other than Java. This R extension provides implementations of utility functions to allow an application developer to focus on application-specific functionality rather than Storm/R communications plumbing. Package: r-cran-stortingscrape Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rvest, r-cran-httr2, r-cran-stringr Suggests: r-cran-magick, r-cran-rmarkdown, r-cran-knitr, r-cran-pscl Filename: pool/dists/resolute/main/r-cran-stortingscrape_0.4.1-1.ca2604.1_all.deb Size: 411304 MD5sum: 2dee685d1fabd5c15073b3b939f142a9 SHA1: 30ee05d04f2a4f91afd9de263395e37fa651a4c9 SHA256: 8c3f8b7ca8ccf78b32e514d27facf1b671e0677b9b427b4fd628d20f90ee47b7 SHA512: 3163f586e3280b23e5c71ad48a6dc1e766297f8f12e43b9fcf5604a000e7bb14e151fabec48bb9071c9aad05c29f4a616dd0295ab42c5d9626c7a88c9fc0dbdc Homepage: https://cran.r-project.org/package=stortingscrape Description: CRAN Package 'stortingscrape' (Access Data from the Norwegian Parliament API) Functions for retrieving general and specific data from the Norwegian Parliament, through the Norwegian Parliament API at . Package: r-cran-storywranglr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tibble, r-cran-urltools Filename: pool/dists/resolute/main/r-cran-storywranglr_0.2.0-1.ca2604.1_all.deb Size: 54090 MD5sum: d340c7d2098f396f34dd419397c5bdf9 SHA1: 45e7df954283832347646ec13ec3a99f49bf5202 SHA256: a3edf95e0511bf732dbf034ee59f5cd141d01031ebeb10721252131bf3c9927b SHA512: 41eee64eb1089c9f7b8f24513308d63c0532e6a118e0b9916de53d0d548dda25215e4834905fbf366c8aa95fc102d3e5854e0cc09a457a8cc5a802f6e89715e7 Homepage: https://cran.r-project.org/package=storywranglr Description: CRAN Package 'storywranglr' (Explore Twitter Trends with the 'Storywrangler' API) An interface to explore trends in Twitter data using the 'Storywrangler' Application Programming Interface (API), which can be found here: . Package: r-cran-stpga Architecture: all Version: 5.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algdesign, r-cran-scales, r-cran-scatterplot3d, r-cran-emoa Suggests: r-cran-r.rsp, r-cran-emmreml, r-cran-quadprog, r-cran-usingr, r-cran-glmnet, r-cran-leaps, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-stpga_5.2.1-1.ca2604.1_all.deb Size: 535738 MD5sum: a4de11c8b6bd3f2810f8bc8dc5875477 SHA1: 50ead091a56034ea44962894132461943270dc00 SHA256: 6822a883812a2c8bf11ba162420de53504128f75393d60ab4ec4ef2c64567722 SHA512: 43d724b672718b260f04eaef0aefb67cdef7a96c189b256412eac0552a3dbdb30a4da109316dc9f0777974f1c06692e05f67de7308a36896eee7ea3ac2b8f88d Homepage: https://cran.r-project.org/package=STPGA Description: CRAN Package 'STPGA' (Selection of Training Populations by Genetic Algorithm) Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems. Package: r-cran-stplanr Architecture: all Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2962 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-geosphere, r-cran-httr, r-cran-jsonlite, r-cran-lwgeom, r-cran-magrittr, r-cran-nabor, r-cran-od, r-cran-pbapply, r-cran-rcpp, r-cran-rlang, r-cran-sf, r-cran-sfheaders Suggests: r-cran-cyclestreets, r-cran-dodgr, r-cran-geodist, r-cran-igraph, r-cran-knitr, r-cran-leaflet, r-cran-mapsapi, r-cran-opentripplanner, r-cran-osrm, r-cran-pct, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/resolute/main/r-cran-stplanr_1.2.3-1.ca2604.1_all.deb Size: 2127890 MD5sum: e32960d32801eabd10f2417224733188 SHA1: 6c8804596fc64b9d95646bebcb572634bf36d7fc SHA256: 1dcd31d8d2c09e22051706d68d71e19df873860ed15a8577b37a6a2114738195 SHA512: bee4811170b2c037d14fe705020e5d38a5a2a730a940664bf3172745c4431230b54271d7c00ce350d1cc450c0a69ce4e5f5d95932eebcfa3a66929b2514f63d1 Homepage: https://cran.r-project.org/package=stplanr Description: CRAN Package 'stplanr' (Sustainable Transport Planning) Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) , but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) . The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as (Desjardins et al. 2021) ; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) and the 'OD jittering' method described in Lovelace et al. (2022) . Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) , and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) . Package: r-cran-stppsim Architecture: all Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-splancs, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-sf, r-cran-sp, r-cran-ks, r-cran-terra, r-cran-raster, r-cran-simriv, r-cran-data.table, r-cran-tibble, r-cran-stringr, r-cran-lubridate, r-cran-spatstat.geom, r-cran-sparr, r-cran-chron, r-cran-ggplot2, r-cran-geosphere, r-cran-leaflet, r-cran-cowplot, r-cran-gstat, r-cran-otusummary, r-cran-progressr, r-cran-future.apply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stppsim_1.3.4-1.ca2604.1_all.deb Size: 1439874 MD5sum: c96a0720230c248b4c495b2efd620227 SHA1: faf0fb58d6b54521dea5c8c845e5430d445068c3 SHA256: 41b31c26bba87aa7e6c470774c3e9ca2d75dc801a0c4cc287bfb7ff79287287d SHA512: 9475c84f3a529432e33dc94729cf5e535d70111d007a982632574e1b43c15f67695d4cf3ac5f9d6122789213c551d71653e63e2e1a08f9b702c8fbd70e87cf1b Homepage: https://cran.r-project.org/package=stppSim Description: CRAN Package 'stppSim' (Spatiotemporal Point Patterns Simulation) Generates artificial point patterns marked by their spatial and temporal signatures. The resulting point cloud may exhibit inherent interactions between both signatures. The simulation integrates microsimulation (Holm, E., (2017)) and agent-based models (Bonabeau, E., (2002)), beginning with the configuration of movement characteristics for the specified agents (referred to as 'walkers') and their interactions within the simulation environment. These interactions (Quaglietta, L. and Porto, M., (2019)) result in specific spatiotemporal patterns that can be visualized, analyzed, and used for various analytical purposes. Given the growing scarcity of detailed spatiotemporal data across many domains, this package provides an alternative data source for applications in social and life sciences. Package: r-cran-str2str Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-checkmate, r-cran-plyr, r-cran-reshape Filename: pool/dists/resolute/main/r-cran-str2str_1.0.0-1.ca2604.1_all.deb Size: 343796 MD5sum: e5a6952ad9730447c3966ce0eec6a892 SHA1: f506f543fe4c852ed9a359e685080894fbed1297 SHA256: 64ec41fc610e08dadeece6af0b3e78d4cb9b559b55132f887a0049196ff8a1bd SHA512: fb9e9402c71ba572d9ba06595f24967a9358b9b7d263425c7e09217e65fb7a4def8c57bc66a2496c856e975747680d566604259936fad58bcce99f266b6cfb7e Homepage: https://cran.r-project.org/package=str2str Description: CRAN Package 'str2str' (Convert R Objects from One Structure to Another) Offers a suite of functions for converting to and from (atomic) vectors, matrices, data.frames, and (3D+) arrays as well as lists of these objects. 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Package: r-cran-str Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5473 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-forecast, r-cran-matrix, r-cran-quantreg, r-cran-sparsem Suggests: r-cran-demography, r-cran-doparallel, r-cran-knitr, r-cran-markdown, r-cran-rgl, r-cran-rmarkdown, r-cran-seasonal, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-str_0.7.1-1.ca2604.1_all.deb Size: 4974472 MD5sum: b3353c509ffa71ff7d2a1426113a95dc SHA1: 35b8b144eecbf4758c22d15eec1446a2d08234d1 SHA256: 1761300f0df808be89faba800b584e722fad5203fef88e6d7c5440d18a5334c3 SHA512: 5b1b64c8c0e08e0e952d9df466ee6fa4502ae0c00be3f6e68fff2936546255fed6318167de22af1eb9081a866f2b86e64d923a42eee3533bd8cb7651355fb9b5 Homepage: https://cran.r-project.org/package=stR Description: CRAN Package 'stR' (Seasonal Trend Decomposition Using Regression) Methods for decomposing seasonal data: STR (a Seasonal-Trend time series decomposition procedure based on Regression) and Robust STR. 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Simulated portfolios optimize exposure to an input signal subject to constraints such as position size and factor exposure. For background see L. Chincarini and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management". 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Package: r-cran-strata.maxcombo Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-strata.maxcombo_0.0.1-1.ca2604.1_all.deb Size: 29996 MD5sum: c91184bafbd862eb5909b601d130581e SHA1: 39b7c87f223fcca43dc67a5db7f4184aa6c12c83 SHA256: 6f797f67b5c36dba78b6e5e39c08621771665e46ec4f195384b47ebbd7ae3fa4 SHA512: 86fadcd3ec643321d3c48a99bdb5f0c3343544b91fa058c1e7148f1f550a3e099f6e438b796514d91ffb4f87c4df9fa105f8fc79b969611bfaa829687332a39f Homepage: https://cran.r-project.org/package=strata.MaxCombo Description: CRAN Package 'strata.MaxCombo' (Stratified Max-Combo Test) Non-proportional hazard (NPH) is commonly observed in immuno-oncology studies, where the survival curves of the treatment and control groups show delayed separation. To properly account for NPH, several statistical methods have been developed. 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Package: r-cran-stratamatch Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-hmisc, r-cran-magrittr, r-cran-rlang, r-cran-survival Suggests: r-cran-knitr, r-cran-optmatch, r-cran-rmarkdown, r-cran-testthat, r-cran-glmnet, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-stratamatch_0.1.9-1.ca2604.1_all.deb Size: 796374 MD5sum: 5cf182d43fde20bd0c5502c6279ff9cc SHA1: 9618ffcae4ae7e7d048f7de656444a701a24456e SHA256: 7d464bf501df8d9dfd05d47ef83e85e99211e1d5cfb387d1a28945cff6f13346 SHA512: 84b1813bd3e4fc79d76ee43104f0738df1244cacc31a46e9435fb6a7ebaf960d2c83b81b7cb4e0d0d38559a1d94e45d0410d38857dc6ebf185d23402cab4f0a8 Homepage: https://cran.r-project.org/package=stratamatch Description: CRAN Package 'stratamatch' (Stratification and Matching for Large Observational Data Sets) A pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) ) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata. Package: r-cran-stratastats Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-gt Filename: pool/dists/resolute/main/r-cran-stratastats_0.2-1.ca2604.1_all.deb Size: 32408 MD5sum: cf4db242f90d0c9c0dd18066555fddb8 SHA1: 057a96654f4d46cc0ea04b8b7f704f3dcc073f2b SHA256: 389a3c55c83b60801d7cebf0c632996933d7b2fb6cf8907eaa769242b246d70d SHA512: 3c9665f8394aa53f696e8f77e08c1ff366eea3077018fa450dd0ad3189d9c2d52056a91375e304df44c7911d9438beedce15f2e8223316585316809036d45b6a Homepage: https://cran.r-project.org/package=stratastats Description: CRAN Package 'stratastats' (Stratified Analysis of 2x2 Contingency Tables) Offers a comprehensive approach for analysing stratified 2x2 contingency tables. It facilitates the calculation of odds ratios, 95% confidence intervals, and conducts chi-squared, Cochran-Mantel-Haenszel, Mantel-Haenszel, and Breslow-Day-Tarone tests. The package is particularly useful in fields like epidemiology and social sciences where stratified analysis is essential. The package also provides interpretative insights into the results, aiding in the understanding of statistical outcomes. Package: r-cran-stratbr Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rglpk, r-cran-snowfall, r-cran-stratification Filename: pool/dists/resolute/main/r-cran-stratbr_1.2-1.ca2604.1_all.deb Size: 31512 MD5sum: 388e5a205e55c7899a7b2622bda9e793 SHA1: 40220612fd640ccb945ec9bbf248a984953590f8 SHA256: aff9127724bc2c2cb2589df1195d8e68b786ca9affdc17620614429ee8204ef8 SHA512: d290b8af2165e48b104e43f58acdf303daa8e5d8806b83e34d158a9f5983df0b18dae221ead0499efd7090d9540587d8a74e60e21be0e64e74701bed5d1e4a0e Homepage: https://cran.r-project.org/package=stratbr Description: CRAN Package 'stratbr' (Optimal Stratification in Stratified Sampling) An Optimization Algorithm Applied to Stratification Problem.This function aims at constructing optimal strata with an optimization algorithm based on a global optimisation technique called Biased Random Key Genetic Algorithms. Package: r-cran-stratcols Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stratigrapher Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-stratcols_1.0.0-1.ca2604.1_all.deb Size: 86252 MD5sum: 1a77a6921456caa02cc713bc4fd16fbf SHA1: bd55e1ef4fc77740dd3a1d25129ec27f333cf0a8 SHA256: 84c318854e7846e7eda3e803be673ee0b286f35b162ecf51576a823e5038b256 SHA512: e63181b8fc26feb01fc809afd4d7b974dd0bae4d8b5b5f5cfd634a2d3012e2d4eb851a4fad36bc35999a94c8e487dcabaf5269e423b66bc5c4378280c1c7a78e Homepage: https://cran.r-project.org/package=stratcols Description: CRAN Package 'stratcols' (Stratigraphic Columns and Order Metrics) Quantify stratigraphic disorder using the metrics defined by Burgess (2016) . Contains a range of utility tools to construct and manipulate stratigraphic columns. Package: r-cran-strategicplayers Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-strategicplayers_1.1-1.ca2604.1_all.deb Size: 19218 MD5sum: 32c1b97574e6e645b87bc2181470ef6e SHA1: bd5c5046dfdff16e2227c42bec0574d04706eeb2 SHA256: aeecb482bf36ad1a2dae2e672c2cebd982282bf5166f521fdc05172226f137e6 SHA512: 5cbeae5f4a09ad0cc3cc0522fd1d39826bdb574d48d21fa6649b90adaa2174a4f14d1e26c74c833b5269f909f1ff8cead61ae48f1365d3239e5715f1cf2918e6 Homepage: https://cran.r-project.org/package=strategicplayers Description: CRAN Package 'strategicplayers' (Strategic Players) Identifies individuals in a social network who should be the intervention subjects for a network intervention in which you have a group of targets, a group of avoiders, and a group that is neither. Package: r-cran-strategy Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-xts Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-strategy_1.0.1-1.ca2604.1_all.deb Size: 477010 MD5sum: fccc97d33541d623ba7d300ffdeb4f9d SHA1: aead721676a54a6d48059b4e676b6d04c18bdd3d SHA256: 3bbfb6abb30b1ac97635d4cebbaadaaa9031b21367ba447691c39bbd33e546f7 SHA512: 689044f1b689370e7fd2432b1d57c478bac693749f461b7fd9b624b495c21ea646f0628c1331b121e9bb9150e9042741edd316a202b108a20ffb6f4a38e20271 Homepage: https://cran.r-project.org/package=Strategy Description: CRAN Package 'Strategy' (Generic Framework to Analyze Trading Strategies) Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches. The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies. Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor. Package: r-cran-stratifiedbalancing Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-stratifiedbalancing_0.3.0-1.ca2604.1_all.deb Size: 49774 MD5sum: faaf821170cb7cc67239b812176d0613 SHA1: fe1f6e9cddcaa4b9ffdd79ab0fbc657767097fab SHA256: 25456c24c894462be2b23aa09e1d6f2129a3648f356a2cf185c973505415f534 SHA512: d88bb0d91ed6285da13d15925d09b461a3f378502f3fe983d2bbf0cd440a6efe3c4185469742d2ed0ea0556eedb4eb86547953b9298062bfd4aac7e1785da4ac Homepage: https://cran.r-project.org/package=StratifiedBalancing Description: CRAN Package 'StratifiedBalancing' (Stratified Covariate Balancing) Performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) . Package: r-cran-stratifiedmedicine Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-partykit, r-cran-ranger, r-cran-survival, r-cran-glmnet, r-cran-ggplot2, r-cran-ggparty, r-cran-mvtnorm, r-cran-coin, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-bart, r-cran-proc, r-cran-survrm2, r-cran-th.data, r-cran-sandwich, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stratifiedmedicine_1.0.7-1.ca2604.1_all.deb Size: 793994 MD5sum: 1b151e75c2fc46f6d6324613f7b85a18 SHA1: 35bf0b16b6bb8f80c61f67499f0d79996d15d81f SHA256: 6028305cde086d98f754d49eedad902c07ef0637e2752e8fe1426b05df1a44db SHA512: aaa8b5f00297ad31185ff47ef3f711791ce551fd10c5c7eec08811a595c9aeaee2857218f2e6ffdf27aeea5484e33a8d3db23756b3fc9be19e969773ec5cb2e6 Homepage: https://cran.r-project.org/package=StratifiedMedicine Description: CRAN Package 'StratifiedMedicine' (Stratified Medicine) A toolkit for stratified medicine, subgroup identification, and precision medicine. Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate models (counterfactual patient-level quantities, such as the conditional average treatment effect), (3) subgroup identification models (find subsets of patients with similar treatment effects), and (4) treatment effect estimation and inference (for the overall population and discovered subgroups). These tools can be customized and are directly used in PRISM (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019 ). Package: r-cran-stratifiedrf Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-c50, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-stratifiedrf_0.2.2-1.ca2604.1_all.deb Size: 49024 MD5sum: 6add0e50ad2f60fa25754299c0d38552 SHA1: 80c9c5f65f449e00c47d91eaea599f666e2dfb04 SHA256: c86fdaf4c97d21294c926083619aa8fc4750d43a50184aef3347870aeef76c03 SHA512: fa874c7247e2352ef2e89f599b8066a80c7ee93ab98560266ed7f7016b1d10f35f241f36f0a09aaa828eba309e43845e5bc80a8f4c88377f388db769e2856676 Homepage: https://cran.r-project.org/package=StratifiedRF Description: CRAN Package 'StratifiedRF' (Builds Trees by Sampling Variables in Groups) Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees. Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options. As it's built with C5.0 trees, it works only for classification (not for regression). Package: r-cran-stratifiedyh Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stratifiedyh_0.1.0-1.ca2604.1_all.deb Size: 24464 MD5sum: d0151b6e1e082d611bc51ef727f58f37 SHA1: dd2f9ef6d1f60b5535be9496b6520b758981a059 SHA256: b74fd3476669509ce203a5e8f218dfda9f0294e893aaf0ebe31aec722758ee7e SHA512: 251a3a5a23ac41694f72f316b2712287d77bcd168c7bdec986ac3773b1a4fdeb3f454260d27eee73fabc30025587fe86464decdd8b4e1fc639a2dafc3d326bc5 Homepage: https://cran.r-project.org/package=stratifiedyh Description: CRAN Package 'stratifiedyh' (Stratified Sampling and Labeling of Data in R) Provides functions for stratified sampling and assigning custom labels to data, ensuring randomness within groups. The package supports various sampling methods such as stratified, cluster, and systematic sampling. It allows users to apply transformations and customize the sampling process. This package can be useful for statistical analysis and data preparation tasks. Package: r-cran-stratigrapher Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-dplyr, r-cran-stringr, r-cran-shiny, r-cran-diagram, r-cran-reshape Suggests: r-cran-astrochron, r-cran-rfoc, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-stratigrapher_1.3.1-1.ca2604.1_all.deb Size: 1215690 MD5sum: e26ea8b0a88f3a0f7e4be0982bc4125f SHA1: e7c53cf2a29b6d68b697efbbb8555bde1bbc6eee SHA256: c4b2b0917317da91dd90c93512836f5d605f57e519cc7cba82cb9fc5871e47ae SHA512: f317913899c2d125e5a27bd5138f926c9df61ba512510e46498b2bff794d08aeb22836334c7c3e81b09ac2d3e194911da586c5efe2f421c5c375be4e806c3005 Homepage: https://cran.r-project.org/package=StratigrapheR Description: CRAN Package 'StratigrapheR' (Integrated Stratigraphy) Includes bases for litholog generation: graphical functions based on R base graphics, interval management functions and svg importation functions among others. Also include stereographic projection functions, and other functions made to deal with large datasets while keeping options to get into the details of the data. When using for publication please cite Sebastien Wouters, Anne-Christine Da Silva, Frederic Boulvain and Xavier Devleeschouwer, 2021. The R Journal 13:2, 153-178. The palaeomagnetism functions are based on: Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press. ; Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge University Press, 289 pp.; Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205, . Package: r-cran-stratpal Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admtools, r-cran-paleots Suggests: r-cran-ape, r-cran-fossilsim, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stratpal_0.7.1-1.ca2604.1_all.deb Size: 724216 MD5sum: 624e046933f35562816dc8a2bc01aa52 SHA1: b39e6427c3e1a21d45666f5c67940c4ea6abd947 SHA256: 8d36d774bb87410ac52e9875cb30d12b556524acbd7d5c77aaec00ace9571e6e SHA512: 1ad000864577505e835c45e9a933ba1db1c875fe61cc46e6dce52f8b3c5a77dcf968999bc8d99c89de86418eadcf6625c6e7265014439473aafbfbf010d7cdea Homepage: https://cran.r-project.org/package=StratPal Description: CRAN Package 'StratPal' (Stratigraphic Paleobiology Modeling Pipelines) The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation. Package: r-cran-stratsel Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-memisc, r-cran-formula, r-cran-mnormt, r-cran-pbivnorm Filename: pool/dists/resolute/main/r-cran-stratsel_1.4-1.ca2604.1_all.deb Size: 147950 MD5sum: f8cfd487ea0b3c0dd2a14a7db2104a83 SHA1: 6ab28e9545cd4a5e677d6355599b962acc511ae6 SHA256: 947984141969f1a50ca085195994eab5666cd2a344b3d1faf1d039dd39a55f4f SHA512: 7be683b9514bb47483db0c10bac3cdff9b3fa86e02ffc9e26dd1a333f5df4ba16c17c4a96ae6f3571c82ff34ef106d4b7a2ae2d25d54e524d4575ba397e7b3c2 Homepage: https://cran.r-project.org/package=StratSel Description: CRAN Package 'StratSel' (Strategic Selection Estimator) Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets. Package: r-cran-stratus Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-igraph, r-cran-gmp, r-cran-ggplot2, r-bioc-ggtree, r-cran-rcppalgos Filename: pool/dists/resolute/main/r-cran-stratus_1.1.2-1.ca2604.1_all.deb Size: 82772 MD5sum: 3a8f1c6ffc82c3b6b81a25df446b227c SHA1: 68549d1d4d4bca5d031f66fdea912726017be56d SHA256: dbe0da3b8fa19df550c5c9f9650ab4f9f67ec9e7f693272a30bbbe62873b45fd SHA512: 7aeba0faa7a0269bfa084b504a0fa39a6ad28ea7937d622d6130dbd6703338df312131df61b363c2efe62f3812e6e917bb174a34cea415d645713711c0bdf989 Homepage: https://cran.r-project.org/package=STraTUS Description: CRAN Package 'STraTUS' (Enumeration and Uniform Sampling of Transmission Trees for aKnown Phylogeny) For a single, known pathogen phylogeny, provides functions for enumeration of the set of compatible epidemic transmission trees, and for uniform sampling from that set. Optional arguments allow for incomplete sampling with a known number of missing individuals, multiple sampling, and known infection time limits. Always assumed are a complete transmission bottleneck and no superinfection or reinfection. See Hall and Colijn (2019) for methodology. Package: r-cran-straweib Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-straweib_1.1-1.ca2604.1_all.deb Size: 77274 MD5sum: 50678cb5c65759383ea50a014c543e08 SHA1: 54161e839e1cb0769cdccec9382068e50cb7d10e SHA256: eb7138f534660f211a08839466ef68a3488ceaa9f6c48920aff4ca28f44931fe SHA512: d1c955de5c2bc188199c6a86fcc709bee7614594f085e1233db36e2aaba43fbd01939714d92f39c0e94c57ad7d9e55a2ee6ba0c627bf2a0cbd789219b67d7dd7 Homepage: https://cran.r-project.org/package=straweib Description: CRAN Package 'straweib' (Stratified Weibull Regression Model) The main function is icweib(), which fits a stratified Weibull proportional hazards model for left censored, right censored, interval censored, and non-censored survival data. We parameterize the Weibull regression model so that it allows a stratum-specific baseline hazard function, but where the effects of other covariates are assumed to be constant across strata. Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and Raji Balasubramanian (2014) for more details. Package: r-cran-stray Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-colorspace, r-cran-pcapp, r-cran-ks Filename: pool/dists/resolute/main/r-cran-stray_0.1.1-1.ca2604.1_all.deb Size: 415414 MD5sum: ab71313ad9936633e30290e95de1eecf SHA1: 073213674f46fed51738df7846f3c06fd899c575 SHA256: 1a4e8bf4e72e5d7399da8d103dde2df0cd6ea3e858ff01b6c9664688ce17d2e8 SHA512: 8e6477916ae8aa45d1e99655feecaa6db34a7bfce9cdc784d94cdd0610f138c0af8d7423f75b94cc4c3f6386ae6f2ce277287b96176a01b359a668007c871f43 Homepage: https://cran.r-project.org/package=stray Description: CRAN Package 'stray' (Anomaly Detection in High Dimensional and Temporal Data) This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. This package implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) for detecting anomalies in high-dimensional data that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation. 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Seurat's normalization method is described in: Hao et al., (2021) , Stuart et al., (2019) , Butler et al., (2018) and Satija et al., (2015) . Method for reduced rank reconstruction and rank-k selection is detailed in: Javaid et al., (2022) . Gene set scoring procedure is described in: Frost et al., (2020) . Clustering method is outlined in: Song et al., (2020) and Wang et al., (2011) . 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Package: r-cran-stringr Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-rlang, r-cran-stringi, r-cran-vctrs Suggests: r-cran-covr, r-cran-dplyr, r-cran-gt, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-stringr_1.6.0-1.ca2604.1_all.deb Size: 308114 MD5sum: 0493a3969162dd33955d3d0a32e7eb14 SHA1: 92dd0457da96bb2aa379893fa97db0af1b268df6 SHA256: 02e964a36e6504eb9eb4c827324f77c612c3f5d2fc19a8dd93022461c6de8e70 SHA512: 49011fc521986265c3b4bf4cb9df0cc7b412aa98ad3bcb0c678e04dd8abc3785e06c577d608f037678d6ab8574499ffa3c9cfeb3e6380cfab4389675faac1eaa Homepage: https://cran.r-project.org/package=stringr Description: CRAN Package 'stringr' (Simple, Consistent Wrappers for Common String Operations) A consistent, simple and easy to use set of wrappers around the fantastic 'stringi' package. All function and argument names (and positions) are consistent, all functions deal with "NA"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another. Package: r-cran-stringstatic Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-stringstatic_0.1.2-1.ca2604.1_all.deb Size: 442578 MD5sum: 9b23b10a7650d6a9fde0d209cdb17fa9 SHA1: d628fd58f0f15eca7f77ffb4fab379e4572a06a7 SHA256: 2dae40dba0d787c42d0c56767cd5e62b959ec1e70312be73d0031743ca4ac756 SHA512: 257c37e9d3b232edc766b943c9cbd245b28a8788cebb79e644a52ee1753e1e28a00be76015a003274f9a359cb7a73aea0951437b7e55f584ba1e4741fcaacef7 Homepage: https://cran.r-project.org/package=stringstatic Description: CRAN Package 'stringstatic' (Dependency-Free String Operations) Provides drop-in replacements for functions from the 'stringr' package, with the same user interface. These functions have no external dependencies and can be copied directly into your package code using the 'staticimports' package. Package: r-cran-stringx Architecture: all Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi Suggests: r-cran-realtest Filename: pool/dists/resolute/main/r-cran-stringx_0.2.9-1.ca2604.1_all.deb Size: 224978 MD5sum: f30d70180e44e4f45d29a52bc540ce08 SHA1: 2dfef4a56ec25cfcd9405f87be994732e694f119 SHA256: b8db080bf3fc5a16cb00cad0780e174d7b7a0f2fecd597b8403960f1c210b2f6 SHA512: c0c94e083542f2224175a60d167739ff04b0112672919eaa4410cafccf439c532d838fa607284528739958063fe9e36837908b06faca626bc65c9308418ddef9 Homepage: https://cran.r-project.org/package=stringx Description: CRAN Package 'stringx' (Replacements for Base String Functions Powered by 'stringi') English is the native language for only 5% of the World population. Also, only 17% of us can understand this text. Moreover, the Latin alphabet is the main one for merely 36% of the total. The early computer era, now a very long time ago, was dominated by the US. Due to the proliferation of the internet, smartphones, social media, and other technologies and communication platforms, this is no longer the case. This package replaces base R string functions (such as grep(), tolower(), sprintf(), and strptime()) with ones that fully support the Unicode standards related to natural language and date-time processing. It also fixes some long-standing inconsistencies, and introduces some new, useful features. Thanks to 'ICU' (International Components for Unicode) and 'stringi', they are fast, reliable, and portable across different platforms. Package: r-cran-strip Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlist Suggests: r-cran-caret, r-cran-e1071, r-cran-knitr, r-cran-randomforest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-strip_1.0.0-1.ca2604.1_all.deb Size: 30282 MD5sum: e9b64010f4973efc2d8c26364a948c17 SHA1: a058dce8df8faa12fda1cb5d93bd9168a4ff13a2 SHA256: d045c1635e804c7a43c895eb16e5e86362dfe2db8da0f447d590326ee628af41 SHA512: 9495508fa7eb7543c7e5ea3d372100b840ebbc160aef77e5ab93bfea589a5319ac96378de27bb5f7cadcc1d77d3dba934961755b885e5e5e8256dedba9fb0452 Homepage: https://cran.r-project.org/package=strip Description: CRAN Package 'strip' (Lighten your R Model Outputs) The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc. Package: r-cran-stripless Architecture: all Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-faraway, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-stripless_1.0-3-1.ca2604.1_all.deb Size: 632730 MD5sum: f2f234387fb224649b66e31c5bc9e087 SHA1: a0c88f4f08a3064c69c5be8707d3cd35c9c5aa7a SHA256: 0a1b3073060cb94d218e9df04edca12cfbdfd9485d6c88e2a25441aceeb521b7 SHA512: 2a7d496075c793f767a43c86e8e3a5804f1e3d68fe08ba9f861957219ddb1a6e34f9c4a0162829a857c833aa3c82b86ceda6e1dde65110b1500be867beabe0da Homepage: https://cran.r-project.org/package=stripless Description: CRAN Package 'stripless' (Structured Trellis Displays Without Strips for Lattice Graphics) For making Trellis-type conditioning plots without strip labels. This is useful for displaying the structure of results from factorial designs and other studies when many conditioning variables would clutter the display with layers of redundant strip labels. Settings of the variables are encoded by layout and spacing in the trellis array and decoded by a separate legend. The functionality is implemented by a single S3 generic strucplot() function that is a wrapper for the Lattice package's xyplot() function. This allows access to all Lattice graphics capabilities in the usual way. Package: r-cran-stroke Architecture: all Version: 25.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-calendar, r-cran-dplyr, r-cran-ggplot2, r-cran-gtsummary, r-cran-lubridate, r-cran-mass, r-cran-rankinplot, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-here, r-cran-spelling, r-cran-usethis, r-cran-pak, r-cran-roxygen2, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-stroke_25.9.2-1.ca2604.1_all.deb Size: 295138 MD5sum: e4ef0ee1e9d4ca463be690330ebb0c05 SHA1: c5d7f287d8621be192c09fd798d8db7826fed457 SHA256: 9f72305a3b82df20e135027b52e434748634c9fe48c1b84011283a43d7c175ba SHA512: e2dff1812427093c8e940a66a99bdacf88a0ce107927e9f241c225c80af4e95ccb88b53a76791035b89a2fca1b6ec36de10946ffa3d4f91acc8a2bdbda7e09f2 Homepage: https://cran.r-project.org/package=stRoke Description: CRAN Package 'stRoke' (Clinical Stroke Research) A collection of tools for clinical trial data management and analysis in research and teaching. The package is mainly collected for personal use, but any use beyond that is encouraged. This package has migrated functions from 'agdamsbo/daDoctoR', and new functions has been added. Version follows months and year. See NEWS/Changelog for release notes. This package includes sampled data from the TALOS trial (Kraglund et al (2018) ). The win_prob() function is based on work by Zou et al (2022) . The age_calc() function is based on work by Becker (2020) . Package: r-cran-stroupglmm Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aod, r-cran-broom.mixed, r-cran-car, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-lattice, r-cran-lmertest, r-cran-magrittr, r-cran-mass, r-cran-mutoss, r-cran-nlme, r-cran-parameters, r-cran-phia, r-cran-scatterplot3d, r-cran-survey Filename: pool/dists/resolute/main/r-cran-stroupglmm_0.3.0-1.ca2604.1_all.deb Size: 141786 MD5sum: 36792d45eb60b1c9f72a75f55d7d0536 SHA1: 75060bf294153b2017d741b3589f3d8c78e339db SHA256: 0220da29ef6c5700135f180a131ae9f9023d03d6accbfedb89d1a9406c4defb7 SHA512: 4d4f9a62d049b05340b4ade174f04d697c2fc9f7d3fdc72bcc4d6e8e1264c7f0078cf8a363e9d122be78046ca4ef67914bb09970d65eba30c16b84fd305d8bf8 Homepage: https://cran.r-project.org/package=StroupGLMM Description: CRAN Package 'StroupGLMM' (R Codes and Datasets for Generalized Linear Mixed Models: ModernConcepts, Methods and Applications by Walter W. Stroup) R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press. Package: r-cran-strs Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-strs_0.1.0-1.ca2604.1_all.deb Size: 85816 MD5sum: 0c3916dbb3c4aff5bf8239de2cc504e3 SHA1: 93fb11a84304b61481973dbf492896cdaad90fd8 SHA256: 2aa0b70070d15ea43bf54f4238fd708435b7f8ee3d0fabebb7373bd281dc1bc3 SHA512: 7632d22f549e209407bba13ea97bfd47ece0b851949de039d5975eb0a7a42f5180c9b58c9f8d382b1966744fc2759847b402dae6b3ae29be23bae40adc903afc Homepage: https://cran.r-project.org/package=strs Description: CRAN Package 'strs' ('Python' Style String Functions) A comprehensive set of string manipulation functions based on those found in 'Python' without relying on 'reticulate'. It provides functions that intend to (1) make it easier for users familiar with 'Python' to work with strings, (2) reduce the complexity often associated with string operations, (3) and enable users to write more readable and maintainable code that manipulates strings. Package: r-cran-structree Architecture: all Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-lme4, r-cran-penalized Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-structree_1.1.7-1.ca2604.1_all.deb Size: 328844 MD5sum: 4d5c4f1f45f207c9d5d06d5fd98fd11a SHA1: 06582cc32266818d91661b1fb3a20aa4b2a6c4b3 SHA256: 778865740e5bc9d8a0c90a4e92f7da4b0fa0e4604bddbff2985892fab48b0e72 SHA512: 570877a65be8bdd4b3bb2ff577b2ead48d4a01b1833b320df7b03df9e9f57e2d6536304cc8959957e79be04d3b8ceb463b3b3be4db7a3acb59b4ceaf09cf0135 Homepage: https://cran.r-project.org/package=structree Description: CRAN Package 'structree' (Tree-Structured Clustering) Tree-structured modelling of categorical predictors (Tutz and Berger (2018), ) or measurement units (Berger and Tutz (2018), ). Package: r-cran-structssi Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-multtest, r-cran-igraph, r-cran-jsonlite, r-cran-ggplot2, r-bioc-phyloseq, r-cran-reshape2 Suggests: r-cran-ape, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-structssi_1.2.1-1.ca2604.1_all.deb Size: 290884 MD5sum: 7919a9bb0ae86d468005042c3d05fbb3 SHA1: 7d18f747708bbb20f4e9d510a12a587beec7ebd6 SHA256: 02f6e96607226dc6316b8ad11856a469807b661288606c2aa5a93343c9a70d22 SHA512: 3d82750b90cfaa195a752d7d55177c99b02cd4ec073ade82073c31de01212e638a43a4cf2ed9b165751617841e7f21f61da39c5d766c4e91dfa47acf80a061ea Homepage: https://cran.r-project.org/package=structSSI Description: CRAN Package 'structSSI' (Multiple Testing for Hypotheses with Hierarchical or GroupStructure) Performs multiple testing corrections that take specific structure of hypotheses into account, as described in Sankaran & Holmes (2014) . Package: r-cran-structuraldecompose Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-segmented, r-cran-strucchange Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-structuraldecompose_0.1.1-1.ca2604.1_all.deb Size: 111600 MD5sum: d0860cf9d09dd2217ac047542f4bbba2 SHA1: 35481d86b29a0c7122b83f21971d02389f9f2491 SHA256: 8aff6c35402b8b08b93f54c23f7cf19a9d30fec4cadd067848d1750cc35597b1 SHA512: 0cbce2949376fdd6e5531695ad28cc4f00cd3e0818fe7b410e5e84b7f555e39d8792d48737d8d7b217e229c6f877012c0c16a0c0d31e72dd03887cb5cb1bc30e Homepage: https://cran.r-project.org/package=StructuralDecompose Description: CRAN Package 'StructuralDecompose' (Decomposes a Level Shifted Time Series) Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to 'lowess' for smoothing and 'strucchange' for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection. Package: r-cran-structuremc Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc Filename: pool/dists/resolute/main/r-cran-structuremc_1.0-1.ca2604.1_all.deb Size: 17156 MD5sum: 9cd5ddfb78b54bec2002f68a5ac0f517 SHA1: 4bc8208cc56508059f5b362584167c32749ad460 SHA256: d78c8cf79902b4e8ff9fd831a6f66e6030452c249c4998fdf90b9c7d66525dae SHA512: c2f50fc24e057cde0dddd0478353e2e0a85fcbe523febcd2191bf07f3a391ac8c72d5c9e495e9327c307f40ce617077dc9565dfd0b26e3c6c50741a08f6e24cf Homepage: https://cran.r-project.org/package=StructureMC Description: CRAN Package 'StructureMC' (Structured Matrix Completion) Provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) ]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21. Package: r-cran-strvalidator Architecture: all Version: 2.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3741 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-gridextra, r-cran-gtable, r-cran-plyr, r-cran-scales, r-cran-data.table, r-cran-dt, r-cran-dplyr, r-cran-plotly, r-cran-mass Suggests: r-cran-resourceselection, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-strvalidator_2.4.2-1.ca2604.1_all.deb Size: 2233550 MD5sum: 73f5f887b715a884728f32bef0aac157 SHA1: e06715779abb31a6b1971e32ffe852f5860099db SHA256: 4b0786bc21b2fcf0c49979a61224875e4b011e77725aec725fd80eec72a2f275 SHA512: b326d21ccc2246d4e7ba476003325b8a082188c06bca73d66def8282b48080b2f4340a45851daefaa5337dafdbce446f74a02a9e11896cc29634738d19f38dd7 Homepage: https://cran.r-project.org/package=strvalidator Description: CRAN Package 'strvalidator' (Process Control and Validation of Forensic STR Kits) An open source platform for validation and process control. Tools to analyze data from internal validation of forensic short tandem repeat (STR) kits are provided. The tools are developed to provide the necessary data to conform with guidelines for internal validation issued by the European Network of Forensic Science Institutes (ENFSI) DNA Working Group, and the Scientific Working Group on DNA Analysis Methods (SWGDAM). A front-end graphical user interface is provided. More information about each function can be found in the respective help documentation. 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Not only is simulation broadly informative as to what these various test statistics do and what are their plausible values, simulation provides more flexibility for assessing unit root by way of different thresholds or different hypothesized distributions. 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Package: r-cran-subincomer Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-countrycode, r-cran-curl, r-cran-dplyr, r-cran-rlang, r-cran-sf, r-cran-tidygeocoder, r-cran-zip Suggests: r-cran-fixest, r-cran-ggplot2, r-cran-ggtext, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-subincomer_0.5.0-1.ca2604.1_all.deb Size: 522714 MD5sum: 00c6b150beaa46d8930f728b6a81050d SHA1: 4d4a98aeb424be1efaabe11feb158726f8608002 SHA256: c4893cd6349ca88fd29c40bb1d02556f68046c6a9560511408a091fda439b363 SHA512: c1aea8827da91076597e7660471213334602cdcf92e5f5743ef2b2ccff6bd2aa3e7bc7313dbcb3e9b30b014c67620c87728053f45c0adc3c0be1aad5e5d356a4 Homepage: https://cran.r-project.org/package=subincomeR Description: CRAN Package 'subincomeR' (Access to Global Sub-National Income Data) Provides access to granular sub-national income data from the MCC-PIK Database Of Sub-national Economic Output (DOSE). 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Package: r-cran-submax Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-sensitivityfull Filename: pool/dists/resolute/main/r-cran-submax_1.1.5-1.ca2604.1_all.deb Size: 84410 MD5sum: 32c7a8e9b51e1e430092614844db9c26 SHA1: 8fe4254aaa67bff661910b2252cc3a8b813c59f2 SHA256: 99283f6331677a7105f70369c8023479cb2529f311911f920b0fc1b9e10a7124 SHA512: 642fc9850bd91b77003a30ed0d0fe47c553a333275ef6bf1c1c4b64f7ab0fd885580efc79658f91cf611132dd1e6573c80c732e1ff1950302ae1e3587561dc7d Homepage: https://cran.r-project.org/package=submax Description: CRAN Package 'submax' (Effect Modification in Observational Studies Using the SubmaxMethod) Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates. The submax or subgroup-maximum method of Lee et al. (2018) does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution. Package: r-cran-submitr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2334 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-readr, r-cran-yaml Suggests: r-cran-containr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-toolero, r-cran-withr Filename: pool/dists/resolute/main/r-cran-submitr_0.1.0-1.ca2604.1_all.deb Size: 1916118 MD5sum: 1436f751dc3e1d0d9b19893c613cfef9 SHA1: 7861e12aee421807016e2ed5bf7880ed223c514e SHA256: 078990b4a15c991c3d5622738e2fdc171de1920c024df8cd41ca1f1d4005fb3f SHA512: 40b6e701ee7a70dc1f3883eac6f62d0acf0e52c11b1951008acbc38894496979ab9a01dbaff1066067a9ce6f97f529347bc2184a14134c4bcc1cfae66e0179a3 Homepage: https://cran.r-project.org/package=submitr Description: CRAN Package 'submitr' (Scaffold and Submit Computational Jobs to HTC Schedulers) Provides scaffolding tools to help researchers prepare and submit computational jobs to high-throughput computing (HTC) schedulers. Generates the files required to run containerized R analyses on 'HTCondor', including submit files and executable scripts, and wraps the system commands needed to stage files, submit jobs, monitor status, and retrieve results from a CHTC submit node. Provides 'htc_config()' for managing connection details and SSH connection reuse guidance. Works naturally alongside 'containr' for container image management and 'toolero' for dataset splitting and project scaffolding. Package: r-cran-subniche Architecture: all Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-polyclip, r-cran-wordcloud Suggests: r-cran-adegraphics, r-cran-ape, r-cran-circstats, r-cran-deldir, r-cran-lattice, r-cran-mass, r-cran-pixmap, r-cran-spdep, r-cran-splancs, r-cran-waveslim Filename: pool/dists/resolute/main/r-cran-subniche_1.6-1.ca2604.1_all.deb Size: 187196 MD5sum: 86fbf4bfd40c361266ee0b26c1e9a5f2 SHA1: 265a1ea5701e9b1153912b7e42c6aa6e4cfae6e0 SHA256: 95d88115bcf13cf663e6f789bee125d0308ab0a53d644910cb4ecf45a030fa11 SHA512: a79f5adf47a996a7f9d15726ff4e147e132bdee70731c4052e0548fff43cabaad243cc9306c1bfacfc4ea8a73a78441d3596a3e1ca513a230250c8767d10de2e Homepage: https://cran.r-project.org/package=subniche Description: CRAN Package 'subniche' (Within Outlying Mean Indexes: Refining the 'OMI' Analysis) Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz 'et al.' (2017) . Package: r-cran-subscore Architecture: all Version: 3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ctt, r-cran-irtoys, r-cran-sirt, r-cran-ltm, r-cran-cocor, r-cran-boot Filename: pool/dists/resolute/main/r-cran-subscore_3.3-1.ca2604.1_all.deb Size: 110190 MD5sum: 9783bc92d644ba9f2e8f2316a120664f SHA1: e210e3546d1be7d480eea1761b04829d6080180f SHA256: 648b46070fbf4a69c5124976c22e24785ece9bc057112d232a0acb3a58c7a99a SHA512: ed9cce401a206ee165cba8bc8600ea965d225fbd2f19dac5b022148a805a6991a221242bb918df93ce2454866dafc15d6398da216d175f7ff0e9fb6b799f009f Homepage: https://cran.r-project.org/package=subscore Description: CRAN Package 'subscore' (Computing Subscores in Classical Test Theory and Item ResponseTheory) Functions for computing test subscores using different methods in both classical test theory (CTT) and item response theory (IRT). This package enables three types of subscoring methods within the framework of CTT and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) , (2) Haberman's subscoring methods (Haberman, 2008) , and (3) Yen's objective performance index (OPI; Yen, 1987) . It also includes functions to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to examine whether test subscores are of added value. In addition, the package includes a function to assess the local independence assumption of IRT with Yen's Q3 statistic (Yen, 1984 ; Yen, 1993 ). Package: r-cran-subscreen Architecture: all Version: 4.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-stringr, r-cran-shiny, r-cran-dt, r-cran-shinyjs, r-cran-bsplus, r-cran-colourpicker, r-cran-dplyr, r-cran-ranger, r-cran-shinywidgets Suggests: r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-subscreen_4.0.1-1.ca2604.1_all.deb Size: 1944616 MD5sum: fd82f1ab730938affdad96377f9c9524 SHA1: badae02ce6e62c20c22812439b26570f8b4af64e SHA256: c26631906c42d141cf28168d543525a0821a585adadb6251359ee2cb64833d63 SHA512: 473c7982a3d0eb9821a4e6483b9d93e1bcb36d763656f52f3f19a2fd5a1d084e8089b0bf78d51df7e354dc18b884c14d2536b63938606e783c92fbbe24445f8c Homepage: https://cran.r-project.org/package=subscreen Description: CRAN Package 'subscreen' (Systematic Screening of Study Data for Subgroup Effects) Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation. The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup. The diagram may show thousands of them. The position of the dot in the diagram is determined by the sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore, the diagram shows the line of no effect and the overall study results. For small subgroups, which are found on the left side of the plot, larger random deviations from the mean study effect are expected, while for larger subgroups only small deviations from the study mean can be expected to be chance findings. So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups. 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The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease reverse association score based on subpathway, and visualization of results (Castelo (2013) ; Han et al (2019) ; Lamb and Justin (2006) ). Please cite using . Package: r-cran-subvis Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-bioc-biostrings Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-subvis_2.0.2-1.ca2604.1_all.deb Size: 50238 MD5sum: 6d4fdc00ee06e201e29cb48df7156b96 SHA1: f5b3194a396723d1cafea09a66815643e3a72fa6 SHA256: c1d777350727c3c0947887d28b0e419baaaab00b041e1eba463e9b83c53ef2b6 SHA512: 55ad5e0713af13236c61aae09d827d9871e9e2988d6128260e7e69b767a3a4b593384092c02181c33f18bc237449b1704d4c4a53a7c7cfe15f859bab0b6cf6eb Homepage: https://cran.r-project.org/package=SubVis Description: CRAN Package 'SubVis' (Visual Exploration of Protein Alignments Resulting from MultipleSubstitution Matrices) Substitution matrices are important parameters in protein alignment algorithms. 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Package: r-cran-sudachir Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-reticulate, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sudachir_0.1.0-1.ca2604.1_all.deb Size: 79140 MD5sum: 4ab36835d32efc9373c4fc90ba20cd96 SHA1: b50dc43b7e05e17c5144f40465baa6ba148574a5 SHA256: d68bce390e5746d917d83c7aa2b76b16bb995f321dd2440bd954f64aa080e1fa SHA512: 57a80d828fbb0f65ab235fc33f89574e2660f51478d568838c56f5b35646e9eaeddaf06bdd0dc118fe7d36907a5cc3182d47fca1bdf932667492e636624c336c Homepage: https://cran.r-project.org/package=sudachir Description: CRAN Package 'sudachir' (R Interface to 'Sudachi') Interface to 'Sudachi' , a Japanese morphological analyzer. This is a port of what is available in Python. Package: r-cran-sudoku Architecture: all Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-sudoku_2.8-1.ca2604.1_all.deb Size: 50876 MD5sum: 865f1fc3bd7ad8dd5a6e8cde81e6ea89 SHA1: b1a9fef63d6eec34eeac00161561259771a1014a SHA256: 7daf802abd5633d601b69d1d8849b016e6492f9c8e37c2ed0492cc8420433bbd SHA512: 7ae19aac91d852360afd5ecec80d458ffa5f1b1e841cd6eaa16f925aca2d3d51337cff679c0a0f8be1079968b5aec08ffc7d8021f5021c85197becf43a94ada4 Homepage: https://cran.r-project.org/package=sudoku Description: CRAN Package 'sudoku' (Sudoku Puzzle Generator and Solver) Generates, plays, and solves Sudoku puzzles. The GUI playSudoku() needs package "tkrplot" if you are not on Windows. 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The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints. 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Some very important functions related to row-column designs as well as block designs along with basic functions are included in this package. Package: r-cran-suessr Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-suessr_0.1.6-1.ca2604.1_all.deb Size: 721000 MD5sum: 87b48aef957560e4719617bfe47074b0 SHA1: 38e66f2d0a22937769d7e18a795297120ef700c5 SHA256: 24292163532715795fc3b49fd194521d9ca226d5bc80dc8034621915d934c166 SHA512: eaa1272464012408c18bdf4f6f7561feedbfbc32e54b01fc964ca0bc77ff51fa50c609e738ec2ebacf35a59a8f65707824d6b4e22c2d0ba8c85afcf23ef5aef4 Homepage: https://cran.r-project.org/package=SuessR Description: CRAN Package 'SuessR' (Suess and Laws Corrections for Marine Stable Carbon Isotope Data) Generates region-specific Suess and Laws corrections for stable carbon isotope data from marine organisms collected between 1850 and 2023. 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See the Journal of Statistical Software reference: Zhang, H. S., Cook, D., Laa, U., Langrené, N., & Menéndez, P. (2024) . The manuscript for this package is currently under preparation and can be found on GitHub at . 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Here, Suns-Voc curves can be constructed with outdoor time-series I-V curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to be measured in the lab. Time series of four different power loss modes can be calculated based on obtained Isc-Voc curves. This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0008172. Jennifer L. Braid is supported by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy administered by the Oak Ridge Institute for Science and Education (ORISE) for the DOE. ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-SC0014664. [1] Wang, M. et al, 2018. . [2] Walters et al, 2018 . [3] Guo, S. et al, 2016. . 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Package: r-cran-superb Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-mass, r-cran-lsr, r-cran-plyr, r-cran-ggplot2, r-cran-stringr, r-cran-foreign, r-cran-reshape2, r-cran-shiny, r-cran-shinybs, r-cran-rrapply Suggests: r-cran-psych, r-cran-rlang, r-cran-dplyr, r-cran-gridextra, r-cran-emojifont, r-cran-fmultivar, r-cran-knitr, r-cran-lattice, r-cran-boot, r-cran-png, r-cran-rmarkdown, r-cran-rstatix, r-cran-rcolorbrewer, r-cran-sadists, r-cran-scales, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-superb_1.0.1-1.ca2604.1_all.deb Size: 1641202 MD5sum: 84ceb4bbfe52b5fc136168dc84ee39a9 SHA1: 90bc6cbb0b3192ce2de58382ae33ce02adcf2815 SHA256: ece6ac9ea691c5a4f68353e7b5addb71d8ad0b556a336912a8d46015b214fa59 SHA512: d36240020e5f4d311e20a7da24a8215def021600f191f7f2668a683dfc7e2b459a77c7158d6c8549d71206d5d5e6f8b2c213e264a9414b6ae29345e5cc16c155 Homepage: https://cran.r-project.org/package=superb Description: CRAN Package 'superb' (Summary Plots with Adjusted Error Bars) Computes standard error and confidence interval of various descriptive statistics under various designs and sampling schemes. The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) or Cousineau (2017) for a review as well as Cousineau (2005) , Morey (2008) , Baguley (2012) , Cousineau & Laurencelle (2016) , Cousineau & O'Brien (2014) , Calderini & Harding for specific references. The documentation is available at . Package: r-cran-supercell Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3766 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rann, r-cran-corpcor, r-cran-weights, r-cran-hmisc, r-cran-matrix, r-cran-matrixstats, r-cran-plyr, r-cran-irlba, r-cran-patchwork, r-cran-ggplot2, r-cran-umap, r-cran-entropy, r-cran-rtsne, r-cran-dbscan, r-cran-scales, r-cran-plotfunctions, r-cran-proxy, r-cran-rlang Suggests: r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-cowplot, r-bioc-scater, r-cran-seurat, r-cran-knitr, r-cran-rmarkdown, r-cran-remotes, r-bioc-bluster, r-cran-weightedcluster, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-supercell_1.1-1.ca2604.1_all.deb Size: 3471792 MD5sum: b0a66f29ee7bfaa26dd7938404fcfedb SHA1: d74f3efe2127699cc4b6c154379c6cd9b719025e SHA256: f811da605ec72ab5c1f7b2b39b93e3250af2b01c39501c8218bcc9a08f163aa3 SHA512: 4e83091d4fedfeff15a2d79b352698ab208c536f0e94eb0e1356001de3e06422ce5be4d4daf035dbf12b1bb7caecd8aa708d155023836200829095a64be61568 Homepage: https://cran.r-project.org/package=SuperCell Description: CRAN Package 'SuperCell' (Simplification of scRNA-Seq Data by Merging Together SimilarCells) Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells. 'SuperCell' uses 'velocyto.R' for RNA velocity and 'WeightedCluster' for weighted clustering on metacells. We also recommend installing 'scater' Bioconductor package . Package: r-cran-supercompress Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn Filename: pool/dists/resolute/main/r-cran-supercompress_1.1-1.ca2604.1_all.deb Size: 21522 MD5sum: 7a454d9491705fbf504f551227b3fc3c SHA1: 8bd9e0deb2fde006c14ecaa38950e38a3ac67e5a SHA256: 428cee78b1b06ed9801699f98c0d87c75e39c933bce75930e1e1c381cf12af9b SHA512: 32524b6e18b8ee1c0417f3fd7ac235cb4e1b778d97fe09770b01d66bbf9aaff0c4bcc3a43e60773620bf4a045c8d34480b5b2557a2c6be8965ff281f002b4540 Homepage: https://cran.r-project.org/package=supercompress Description: CRAN Package 'supercompress' (Supervised Compression of Big Data) A supervised compression method that incorporates the response for reducing big data to a carefully selected subset. Please see Joseph and Mak (2021) . This research is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646. Package: r-cran-superdiag Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda Filename: pool/dists/resolute/main/r-cran-superdiag_2.0-1.ca2604.1_all.deb Size: 2325992 MD5sum: 2479f9d82b11d538acf75c61e2c9ffe4 SHA1: 721ff15a5f05ffa9d24ce50bf9bedd51162d80ef SHA256: 95deab514af25835626b4204b87d92d7a792d420cc289a4adb878f5f59da813e SHA512: be0a23a45b20a19c0f86c0393bf33504fcb54efb52a7e6fce5b4abcbdce2244d6c83a436ea57aede56bc0cc83a880e59a99fee53c591017af42ba378aa26b095 Homepage: https://cran.r-project.org/package=superdiag Description: CRAN Package 'superdiag' (A Comprehensive Test Suite for Testing Markov ChainNonconvergence) The 'superdiag' package provides a comprehensive test suite for testing Markov Chain nonconvergence. It integrates five standard empirical MCMC convergence diagnostics (Gelman-Rubin, Geweke, Heidelberger-Welch, Raftery-Lewis, and Hellinger distance) and plotting functions for trace plots and density histograms. The functions of the package can be used to present all diagnostic statistics and graphs at once for conveniently checking MCMC nonconvergence. Package: r-cran-superheat Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gtable, r-cran-magrittr, r-cran-plyr, r-cran-scales, r-cran-ggdendro Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-superheat_0.1.0-1.ca2604.1_all.deb Size: 119616 MD5sum: c22b3bc2bef83f7f6a77eecbc37d6343 SHA1: b89d153f4faaaf5b2be5a0c088a471c87fd96271 SHA256: 649d3b7aeab0b14e436916b08bc20c4c2e0b097ce4dd9533adaa238242fdadc0 SHA512: 58d8090142a1f6dc7db469e050b8050af16938af5f6ca9e51e8b869098a77d1b317056ee2b69aefb5254cb67d3401397eb0e392248551b57924608e098e6ff55 Homepage: https://cran.r-project.org/package=superheat Description: CRAN Package 'superheat' (A Graphical Tool for Exploring Complex Datasets Using Heatmaps) A system for generating extendable and customizable heatmaps for exploring complex datasets, including big data and data with multiple data types. Package: r-cran-superlearner Architecture: all Version: 2.0-40-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnls, r-cran-gam, r-cran-cvauc Suggests: r-cran-arm, r-cran-bartmachine, r-cran-biglasso, r-cran-bigmemory, r-cran-caret, r-cran-class, r-cran-devtools, r-cran-e1071, r-cran-earth, r-cran-gbm, r-bioc-genefilter, r-cran-ggplot2, r-cran-glmnet, r-cran-ipred, r-cran-kernelknn, r-cran-kernlab, r-cran-knitr, r-cran-lattice, r-cran-logicreg, r-cran-mass, r-cran-mlbench, r-cran-nloptr, r-cran-nnet, r-cran-party, r-cran-polspline, r-cran-prettydoc, r-cran-quadprog, r-cran-randomforest, r-cran-ranger, r-cran-rhpcblasctl, r-cran-rocr, r-cran-rmarkdown, r-cran-rpart, r-cran-sis, r-cran-speedglm, r-cran-spls, r-bioc-sva, r-cran-testthat, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-superlearner_2.0-40-1.ca2604.1_all.deb Size: 569904 MD5sum: 0a13fbb45a118a53daca3a7594f16077 SHA1: 51a1f1fbd279ed0f0d201612d2405911a9a5d867 SHA256: e4c8e407aad560e4190dabb5ef4afe342ef3dcb9e86dd983a9f8bc4603afa773 SHA512: a76bb4280bfeef629265494cec8b954ac4b15a39845cba3fd5f8fe4d01f75a672922b22a553d5e9143ca9bc78ec2a3e877a20a0f19434f028560ce68f1b09766 Homepage: https://cran.r-project.org/package=SuperLearner Description: CRAN Package 'SuperLearner' (Super Learner Prediction) Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner. 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This includes proportional reduction in error and formatting to improve ease the transition between the book and R. Package: r-cran-superpc Architecture: all Version: 1.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-superpc_1.12-1.ca2604.1_all.deb Size: 290204 MD5sum: 86beaae5d72d90ec817903bdf2159019 SHA1: fc4019b6fadd2bddbc1496411ab0e3f52a137b17 SHA256: fb4b87bc28db6c1e89ddc427d3a92558742dc06a680ee4e662b0b2542a3841b2 SHA512: 1c47be19e9f7e35f78e99f0093c2936d337b32fe00c229e3b020dcb7e30e2065f3d6a15fa13585b2f6a2cccf91030376b8d13323002e44e97af2b97f2fb4bb0e Homepage: https://cran.r-project.org/package=superpc Description: CRAN Package 'superpc' (Supervised Principal Components) Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies. Package: r-cran-superpower Architecture: all Version: 0.2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2303 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-afex, r-cran-emmeans, r-cran-ggplot2, r-cran-reshape2, r-cran-dplyr, r-cran-magrittr, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pwr, r-cran-testthat, r-cran-covr, r-cran-jmvcore, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-superpower_0.2.4.1-1.ca2604.1_all.deb Size: 1440484 MD5sum: 1ae5229bb40c2ed37b4e10a062f217b7 SHA1: dcd684bba8280f0dbb482634d0c1d95e0ff7a4b9 SHA256: 5013fc8ba421da35a4181ba01c9153eb0d59cd60399903918405f783afc8578a SHA512: fa8c7df9dd6550c86a816003005849075a291521abb85d8019c3ba4f8d60bb8fc2df42702167fdf3f8ecc3e666a937b43ba7f1fee82b191f32f1945b3c590f59 Homepage: https://cran.r-project.org/package=Superpower Description: CRAN Package 'Superpower' (Simulation-Based Power Analysis for Factorial Designs) Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". . 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Implements density and cumulative compound Poisson discrete distribution functions (Kremer et al. (2021) ), as well as functions to calculate infectious disease outbreak statistics given epidemiological parameters on individual-level transmission; including the probability of an outbreak becoming an epidemic/extinct (Kucharski et al. (2020) ), or the cluster size statistics, e.g. what proportion of cases cause X\% of transmission (Lloyd-Smith et al. (2005) ). 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The package fits convex combinations of parametric, semiparametric, and machine learning survival learners by minimizing cross-validated risk using inverse probability of censoring weighting (IPCW). It provides tools for automated hyperparameter grid search, high-dimensional variable screening, and evaluation of prediction performance using metrics such as the Brier score, Uno's C-index, and time-dependent area under the curve (AUC). Additional utilities support model interpretation for survival ensembles, including Shapley additive explanations (SHAP), and estimation of covariate-adjusted restricted mean survival time (RMST) contrasts. The methodology is related to treatment-specific survival curve estimation using machine learning described by Westling et al. (2024) , and the unified ensemble framework described in Lyu et al. (2026) . Package: r-cran-supervisedprim Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-prim Suggests: r-cran-kernlab, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-supervisedprim_2.0.0-1.ca2604.1_all.deb Size: 19136 MD5sum: 12499af11ff4870471ee85908f551e3a SHA1: f8eac4da937c266eefab2d56a6efd4055479a4d8 SHA256: c58b12962dc66da49f6336ceb1985efbe318a6d437d8b81b4e303d507563c570 SHA512: f790b3c5a9b66ae43a5e3ce82b6cb16126573213358724955ba0453b4d66dcb002648931a0afb0ee4beb1c2d653d2a0f26940351d2348c8a1d25f16035736009 Homepage: https://cran.r-project.org/package=supervisedPRIM Description: CRAN Package 'supervisedPRIM' (Supervised Classification Learning and Prediction using PatientRule Induction Method (PRIM)) The Patient Rule Induction Method (PRIM) is typically used for "bump hunting" data mining to identify regions with abnormally high concentrations of data with large or small values. This package extends this methodology so that it can be applied to binary classification problems and used for prediction. Package: r-cran-supmz Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-supmz_0.2.0-1.ca2604.1_all.deb Size: 25112 MD5sum: 57898116436eeb65f4336cd9004c73e2 SHA1: 6dc31ee41d36975dcaf556c1c344fc8b4b37822d SHA256: 179d2085deb9dfae57a013648a93d0ea387ac161cbdce0a114d3c06d1e40d7c2 SHA512: 9124b0a2ad2df7944f67ea66470920ad531e413b0216aeee62572a1d49d0a5ba673cc91c9431b907eec5a77603be5e83d7667d55e30d7071d64efa85ee2014d2 Homepage: https://cran.r-project.org/package=SupMZ Description: CRAN Package 'SupMZ' (Detecting Structural Change with Heteroskedasticity) Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (). Package: r-cran-support.bws2 Architecture: all Version: 0.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-doe.base, r-cran-survival Filename: pool/dists/resolute/main/r-cran-support.bws2_0.4-0-1.ca2604.1_all.deb Size: 108746 MD5sum: f4ef95733aec7c09e09222c44cc8928c SHA1: 2db2f00f0560c2d5778f88918ec7aa0fe4c652e8 SHA256: 1c906f9840981792fd7a938dc984386b43599b2f2be8a8067b14cbc9b4b38c74 SHA512: 57cd8a0e91db186c19ae67037ee640c6ef0d0c6933b70e8682441c2562ded21c5aae108d7635dcce76a7a280174a12864bc0e881cac06000405f85780f1913ff Homepage: https://cran.r-project.org/package=support.BWS2 Description: CRAN Package 'support.BWS2' (Tools for Case 2 Best-Worst Scaling) Provides three basic functions that support an implementation of Case 2 (profile case) best-worst scaling. The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores. For details, see Aizaki and Fogarty (2019) . Package: r-cran-support.bws3 Architecture: all Version: 0.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-crossdes, r-cran-doe.base, r-cran-support.ces, r-cran-survival Filename: pool/dists/resolute/main/r-cran-support.bws3_0.2-1-1.ca2604.1_all.deb Size: 73626 MD5sum: f4881da58bba4dc55e9c021613725035 SHA1: 6615ab4e4b5303a7c0fcc6d89addd84ab6828430 SHA256: 34c5a52ae0b149059a19974078335b6cc0051e1465d011929a2e28a106cb4f05 SHA512: 4ef7f7247e9ecb6f1f0d9ed3f76990452044fb4d6b3ad0050ff60714aa66404e30eb111ca300adf0b28c3bda24cc8c7c6987f07643ede7d821877b82ba474a98 Homepage: https://cran.r-project.org/package=support.BWS3 Description: CRAN Package 'support.BWS3' (Tools for Case 3 Best-Worst Scaling) Provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) . Package: r-cran-support.bws Architecture: all Version: 0.4-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-doe.base, r-cran-crossdes, r-cran-survival, r-cran-mlogit, r-cran-gmnl, r-cran-apollo Filename: pool/dists/resolute/main/r-cran-support.bws_0.4-6-1.ca2604.1_all.deb Size: 127404 MD5sum: 63aeb07c2fdc0a84473e791e60687174 SHA1: 8458426e75cd474fd99d6841d166e6fff35fe735 SHA256: 6fbf7a418610faf62980bc88f50b747e4d7e64c83be2f1997bb71035930acc64 SHA512: 0db7e5c8d0224e8bc05cf4af7e325c2be5a94a7bcc4bb5bf19a41978e35b5c4c2a49ed5dba9a6bbab1efed6ea66bf1f67f29fc0a24d00a80cb8b67a5293a0381 Homepage: https://cran.r-project.org/package=support.BWS Description: CRAN Package 'support.BWS' (Tools for Case 1 Best-Worst Scaling) Provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. See Louviere et al. (2015) for details on best-worst scaling, and Aizaki and Fogarty (2023) for the package. Package: r-cran-support.ces Architecture: all Version: 0.7-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doe.base, r-cran-mass, r-cran-simex Suggests: r-cran-survival, r-cran-mded Filename: pool/dists/resolute/main/r-cran-support.ces_0.7-0-1.ca2604.1_all.deb Size: 130778 MD5sum: d83a422c419a41dcc68edc88aa7fcca0 SHA1: d4a8cd809ea55ffc43e158d84b59c67df053e6a0 SHA256: aa0131fd12a7bad74e617b4c8e0632f30cb3832f8e0a90d09547abb34a54d71e SHA512: eb75245c351b08a1519d1f4eeb80495ff63fb429dfde6e19fdd93bd9ac72166f7f610f73f40a67f2b5db651c9519c50e197822b05526471b6327e74131b2a7d3 Homepage: https://cran.r-project.org/package=support.CEs Description: CRAN Package 'support.CEs' (Basic Functions for Supporting an Implementation of ChoiceExperiments) Provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) for details on CEs, and Aizaki (2012) for the package. Package: r-cran-supportr Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-dplyr, r-cran-ggplot2, r-cran-gh, r-cran-googledrive, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-vegan Suggests: r-cran-ape, r-cran-devtools, r-cran-knitr, r-cran-palmerpenguins, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-supportr_1.6.0-1.ca2604.1_all.deb Size: 412460 MD5sum: b782ae615bd0737221213567978f053c SHA1: 420b39e5f37e8371bebb7ce78faa65e07242efdc SHA256: d3bf20d989aaf03dfefa9fc53c95eb556be7ef868245fff93ae186c4411d8b29 SHA512: 3b45815c9dc01890edd75a46ca8044274bd50a9b8b2b283c239a14cb5f48233448393e848f57f2164ed43636a0f93cd9d7510e0a21dad3a92357df75b5f39ae7 Homepage: https://cran.r-project.org/package=supportR Description: CRAN Package 'supportR' (Support Functions for Wrangling and Visualization) Suite of helper functions for data wrangling and visualization. The only theme for these functions is that they tend towards simple, short, and narrowly-scoped. These functions are built for tasks that often recur but are not large enough in scope to warrant an ecosystem of interdependent functions. Package: r-cran-sur Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr Filename: pool/dists/resolute/main/r-cran-sur_1.0.4-1.ca2604.1_all.deb Size: 189352 MD5sum: cd138896f4dd569685985501cf23f3af SHA1: 3cc499b02bbd2c01cf5f4797f605a0ef9437c5d2 SHA256: baa241d139cd9c38fac2ef878fd9a69b77affb43e674709e309b3f4524c5fc69 SHA512: 86b1190f6d663918ac02808b990f6c89b7435cfca54fd43c11c1f2755f8ebd4dc0e7034972583cce7e6afd045d3fcc5ed4abca3af14b084d8238cb60cc1b7b97 Homepage: https://cran.r-project.org/package=sur Description: CRAN Package 'sur' (Companion to "Statistics Using R: An Integrative Approach") Access to the datasets and many of the functions used in "Statistics Using R: An Integrative Approach". These datasets include a subset of the National Education Longitudinal Study, the Framingham Heart Study, as well as several simulated datasets used in the examples throughout the textbook. The functions included in the package reproduce some of the functionality of 'Stata' that is not directly available in 'R'. The package also contains a tutorial on basic data frame management, including how to handle missing data. Package: r-cran-sure Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-goftest, r-cran-gridextra Suggests: r-cran-mass, r-cran-ordinal, r-cran-rms, r-cran-testthat, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-sure_0.2.0-1.ca2604.1_all.deb Size: 140764 MD5sum: f8e91a0be63fb33e2926459a168e58a4 SHA1: 233f3fa707fd77b2411dad7dde5f23a8d50d7614 SHA256: c665cf1ed1ecab1f9a687233980fc791a7a8540d0202537f1bbd98bfd9e01ce1 SHA512: 60731d2132e720971c5a7d05abb49d7f5ce2313e40d65354a228bd7056a78bc0d4b0524b9e47ad6c950026792a6f51502bf2af6605362b4d4f965a8668c4530f Homepage: https://cran.r-project.org/package=sure Description: CRAN Package 'sure' (Surrogate Residuals for Ordinal and General Regression Models) An implementation of the surrogate approach to residuals and diagnostics for ordinal and general regression models; for details, see Liu and Zhang (2017) . These residuals can be used to construct standard residual plots for model diagnostics (e.g., residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). The package also provides an 'autoplot' function for producing standard diagnostic plots using 'ggplot2' graphics. The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. Support for binary regression models using the standard 'glm' function is also available. Package: r-cran-surf.vs Architecture: all Version: 1.1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-dplyr Suggests: r-cran-foreach, r-cran-doparallel, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-surf.vs_1.1.0.1-1.ca2604.1_all.deb Size: 95972 MD5sum: aa7922e5907288a7eb0ee983eee0006a SHA1: 33f10e8e3b1b657e563fed68b3708930bbd75e15 SHA256: 033254ee745a8debad9de98cbad0abd747f36a8eaa89b0e11f7cb73fc164cd6e SHA512: c73a8f6c4e40b61c7055916e5b96b895cea666960b4bf638070412ca179e8ce2ae0628c44a7dbbae4a39b1c1130d3eb8dbf95a9db9a275ab9c4ba893ff15762b Homepage: https://cran.r-project.org/package=SuRF.vs Description: CRAN Package 'SuRF.vs' (Subsampling Ranking Forward Selection (SuRF)) Performs variable selection based on subsampling, ranking forward selection. Details of the method are published in Lihui Liu, Hong Gu, Johan Van Limbergen, Toby Kenney (2020) SuRF: A new method for sparse variable selection, with application in microbiome data analysis Statistics in Medicine 40 897-919 . Xo is the matrix of predictor variables. y is the response variable. Currently only binary responses using logistic regression are supported. X is a matrix of additional predictors which should be scaled to have sum 1 prior to analysis. fold is the number of folds for cross-validation. Alpha is the parameter for the elastic net method used in the subsampling procedure: the default value of 1 corresponds to LASSO. prop is the proportion of variables to remove in the each subsample. weights indicates whether observations should be weighted by class size. When the class sizes are unbalanced, weighting observations can improve results. B is the number of subsamples to use for ranking the variables. C is the number of permutations to use for estimating the critical value of the null distribution. If the 'doParallel' package is installed, the function can be run in parallel by setting ncores to the number of threads to use. If the default value of 1 is used, or if the 'doParallel' package is not installed, the function does not run in parallel. display.progress indicates whether the function should display messages indicating its progress. family is a family variable for the glm() fitting. Note that the 'glmnet' package does not permit the use of nonstandard link functions, so will always use the default link function. However, the glm() fitting will use the specified link. The default is binomial with logistic regression, because this is a common use case. pval is the p-value for inclusion of a variable in the model. Under the null case, the number of false positives will be geometrically distributed with this as probability of success, so if this parameter is set to p, the expected number of false positives should be p/(1-p). Package: r-cran-surface.analytics Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-effectsize, r-cran-factominer, r-cran-factoextra, r-cran-randomforest, r-cran-multcompview Suggests: r-cran-testthat, r-cran-scales Filename: pool/dists/resolute/main/r-cran-surface.analytics_0.1.0-1.ca2604.1_all.deb Size: 42332 MD5sum: bd33c39b2449044c4091745b5fc032e0 SHA1: cd9612a7e5edfbc5ac718c1bf6c1febfa1e25d5a SHA256: 7d019a5cf1e38a332cc8454c42b3fae65800dfef1dc73be5879a324736561277 SHA512: b85409928c34d1d2d9a3f8566b362d28cd135122d778bc4086638549777e1c0da3f280eeaae5dafadb232217125b0383a3c427de6634af4869290c83ca2bf1af Homepage: https://cran.r-project.org/package=suRface.analytics Description: CRAN Package 'suRface.analytics' (Statistical Analysis and Visualization of Surface-EngineeredMaterial Properties) A collection of functions for statistical and multivariate analysis of surface-related data, with a focus on antimicrobial activity and omniphobicity. Designed to support materials scientists and researchers in exploring structure–function relationships in surface-engineered materials through reproducible and interpretable workflows. For more details, see Li et al. (2021) , and Kwon et al. (2020) . Package: r-cran-surface Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-ouch, r-cran-mass, r-cran-geiger, r-cran-phytools Suggests: r-cran-igraph Filename: pool/dists/resolute/main/r-cran-surface_0.6-1.ca2604.1_all.deb Size: 343676 MD5sum: f8f5d1c7d84a9eda8aac9a23acc540d8 SHA1: 6b5a1a71f0c01efda4e09057db1a4771e546bc10 SHA256: 30f603a1458182c792e75a596aff31f1f11e67cccd0f45fcb8f10581262111d9 SHA512: ea713b592552eec2a38f96151205a683b7ba762c9eb5294f970f4a4c996b5b6b67aad3fb5652f3288a4dc9a74ba63811d32490052c654aa264a464022ae86fd4 Homepage: https://cran.r-project.org/package=surface Description: CRAN Package 'surface' (Fitting Hansen Models to Investigate Convergent Evolution) This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the 'ouch' methodology of Butler and King (2004) . The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) . Package: r-cran-surfacetortoise Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-gstat, r-cran-sf Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surfacetortoise_2.0.1-1.ca2604.1_all.deb Size: 41298 MD5sum: 1f1b0eff615f74408eb1b2ab133707cb SHA1: 56d644f9e92cfae649f0789351371c13e6ee296e SHA256: 4d1a2714afb57d6fa2fc236417c93867a60755fcd18068d297017748162cbbbb SHA512: 2c688195659669564537164ff8d9c0b39b9cc0d7de74e1687262be35d3191bec47883fafc92f4f5eb89672e66a92efb735dc82d7488e0b008630c03c3e347d28 Homepage: https://cran.r-project.org/package=SurfaceTortoise Description: CRAN Package 'SurfaceTortoise' (Find Optimal Sampling Locations Based on Spatial Covariate(s)) Create sampling designs using the surface reconstruction algorithm. Original method by: Olsson, D. 2002. A method to optimize soil sampling from ancillary data. Poster presenterad at: NJF seminar no. 336, Implementation of Precision Farming in Practical Agriculture, 10-12 June 2002, Skara, Sweden. Package: r-cran-surprisalanalysis Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2561 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matlib, r-cran-shiny, r-cran-ggplot2, r-cran-shinythemes, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-patchwork, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pheatmap, r-cran-peakram, r-cran-data.table, r-cran-biocmanager, r-bioc-clusterprofiler, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-httpuv Filename: pool/dists/resolute/main/r-cran-surprisalanalysis_3.0.1-1.ca2604.1_all.deb Size: 2343800 MD5sum: 17f1e1699ad822ca7ce67c12dbb1986c SHA1: 6f78f409330e7db933db85bbb312f77b3a4e60f3 SHA256: ffdc806ece99fc27a5c9081f1f9ce1628c0e11b4a5779f2fb1f08b2f1d175647 SHA512: e92b0db736d4f31fa2f2efe1c3b6de24184788c98f6c7dc41734e155569c5f4d7b22e76ef5b4e80b8691d03632a7850ae4706dd68aacde3c72e70c53049a8503 Homepage: https://cran.r-project.org/package=SurprisalAnalysis Description: CRAN Package 'SurprisalAnalysis' (Information Theoretic Analysis of Gene Expression Data) Implements Surprisal analysis for gene expression data such as RNA-seq or microarray experiments. Surprisal analysis is an information-theoretic method that decomposes gene expression data into a baseline state and constraint-associated deviations, capturing coordinated gene expression patterns under different biological conditions. References: Kravchenko-Balasha N. et al. (2014) . Zadran S. et al. (2014) . Su Y. et al. (2019) . Bogaert K. A. et al. (2018) . Package: r-cran-surreal Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-png Suggests: r-cran-bmp, r-cran-bslib, r-cran-jpeg, r-cran-rsvg, r-cran-shiny, r-cran-tiff Filename: pool/dists/resolute/main/r-cran-surreal_0.0.2-1.ca2604.1_all.deb Size: 1054802 MD5sum: 47474647f7b6c5f5b253086f7fce443b SHA1: d1e24e93fb547d416a597ef7e569e3489b2f19f6 SHA256: dd7a82421a37c3f411c36a56b60a4c9bc73132298a729ae3eb79a748c16ef648 SHA512: e3f55734d902051765e59e6b4a901e0829a724254478ac1c0bf301393051542676b2433c3b5ed492cefd564c4d753a056cd6684c50cc8ae0510ff487a8194270 Homepage: https://cran.r-project.org/package=surreal Description: CRAN Package 'surreal' (Create Datasets with Hidden Images in Residual Plots) Implements the "Residual (Sur)Realism" algorithm described by Stefanski (2007) to generate datasets that reveal hidden images or messages in their residual plots. It offers both predefined datasets and tools to embed custom text or images into residual structures. Allowing users to create intriguing visual demonstrations for teaching model diagnostics. Package: r-cran-surrogate Architecture: all Version: 3.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2387 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lattice, r-cran-latticeextra, r-cran-survival, r-cran-nlme, r-cran-lme4, r-cran-logistf, r-cran-rms, r-cran-ks, r-cran-extradistr, r-cran-pbapply, r-cran-flexsurv, r-cran-rvinecopulib, r-cran-maxlik, r-cran-purrr, r-cran-mbess, r-cran-tidyr, r-cran-dplyr, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-copula, r-cran-fnn, r-cran-mgcv, r-cran-testthat, r-cran-vdiffr, r-cran-fitdistrplus, r-cran-kdecopula, r-cran-cubature, r-cran-mvtnorm, r-cran-withr, r-cran-stringr, r-cran-ggplot2, r-cran-sn, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-surrogate_3.4.1-1.ca2604.1_all.deb Size: 2221880 MD5sum: 7e6351f065ba691c52e4f7620b5b5edd SHA1: a0610c790822656cf35875c8dc1a757a2b7813cc SHA256: 62f742c5ab61c32278e3e4730a5d4113bb30d9799357891fcd6f734d39934729 SHA512: cb086b86663007b9e9da5c538624d9b05887b6f4e88379151b7bb154849d237778893dd5518706c0d2da2eac0673b6835a5189b8e22980bcae694520f248a24d Homepage: https://cran.r-project.org/package=Surrogate Description: CRAN Package 'Surrogate' (Evaluation of Surrogate Endpoints in Clinical Trials) In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine. Package: r-cran-surrogateoutcome Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-surrogateoutcome_1.2-1.ca2604.1_all.deb Size: 174166 MD5sum: c310a6a98b3b86f72c905527b7528d50 SHA1: ee2dbbb53f6c2e76ca6dad6464534e77297d6f8e SHA256: e4412688d352875a24ba62f96e150a429a27791ebe101529452556bc7c4cd6fd SHA512: 3354e3467aeff39e3b50bcb0c67b3a478b6d5a8d54c82b2764c99bb9bf960226abf6581c2401fc960c9ce3ddc995d9599e5d0716627a49c0f72cac61dc197a56 Homepage: https://cran.r-project.org/package=SurrogateOutcome Description: CRAN Package 'SurrogateOutcome' (Estimation of the Proportion of Treatment Effect Explained bySurrogate Outcome Information) Estimates the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" . A tutorial for this package can be found at . Package: r-cran-surrogateparadoxtest Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-monotonicitytest Filename: pool/dists/resolute/main/r-cran-surrogateparadoxtest_2.0-1.ca2604.1_all.deb Size: 50428 MD5sum: 9db6b8305996b2a5dfaff134d1cd1930 SHA1: 6e40e982f5f31d76729be6898c7b3e71215bf13d SHA256: 7e35ed30fb58593398ec30a1652f5a321295e918127cae0869b82ec71a866cc9 SHA512: c31bc1f1f6f5327acc6b2c0da4c9b72429fc932659b72b8358840fa057e5fa7a5cf4bfa9ba6f7a968d7e06f58a66fd6bbe54180d0f8eb7a078b174307fb2f496 Homepage: https://cran.r-project.org/package=SurrogateParadoxTest Description: CRAN Package 'SurrogateParadoxTest' (Empirical Testing of Surrogate Paradox Assumptions) Provides functions to nonparametrically assess assumptions necessary to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of regression functions, and non-negative residual treatment effects. More details are available in Hsiao et al 2025 (under review). A tutorial for this package can be found at . Package: r-cran-surrogaterank Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-pbmcapply Filename: pool/dists/resolute/main/r-cran-surrogaterank_2.2-1.ca2604.1_all.deb Size: 472022 MD5sum: 1645b4fa5c677ffb54f39b5205bd1aa4 SHA1: f76254cf5821c3aa61aa082d9068c27c7a4c050f SHA256: 57c881f751b908abe4a1117b9944e0e9e786e21d84bd3e06e343fe23b7727788 SHA512: 791e4782846ffbc34c928a81b41da9cbfc8c9cce10d14192a2689e9075ea3dcea51c4bda9c8253cd831c2ffd29712b6b2f138954b46809079241c94e4c9eed9f Homepage: https://cran.r-project.org/package=SurrogateRank Description: CRAN Package 'SurrogateRank' (Rank-Based Test to Evaluate a Surrogate Marker) Uses a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. Details are described in Parast et al (2024) and Hughes A et al (2025) . A tutorial for this package can be found at and a Shiny App implementing the package can be found at . 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More details are available in: Parast, L. and Bartroff, J (2024) . A tutorial for this package can be found at . A Shiny App implementing the methods can be found at . Package: r-cran-surrogatetest Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-surrogatetest_1.3-1.ca2604.1_all.deb Size: 159102 MD5sum: 2e608eb839286e78a750226d388a504b SHA1: 63e552e10915608dc9095ad6a628161ee5843371 SHA256: 69e1f713b63e274191c4484f953c2425275d6a92839c7b113c651c235a121023 SHA512: 107a3c137f639c2eaab2303ce664b339ab780e918be6d4ca6bb6b570ec416dfd23aa492aaa62d1eb5bdefc394142a2e164427340858cf356e65095c1d7766e80 Homepage: https://cran.r-project.org/package=SurrogateTest Description: CRAN Package 'SurrogateTest' (Early Testing for a Treatment Effect using Surrogate MarkerInformation) Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. . 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The approaches implemented are: (1) the two-step approach (Burzykowski et al, 2001) with a copula model (Clayton, Plackett, Hougaard) at the first step and either a linear regression of log-hazard ratios at the second step (either adjusted or not for measurement error); (2) mixed proportional hazard models estimated via mixed Poisson GLM (Rotolo et al, 2017 ). 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It includes semiparametric, parametric, tree-based, ensemble, boosting, kernel, and deep-learning survival learners, together with benchmarking, scoring, calibration, and model-agnostic interpretation utilities. Representative methodological anchors include Cox (1972) , Royston and Parmar (2002) , Ishwaran et al. (2008) , Jaeger et al. (2019) , Harrell et al. (1982) , Graf et al. (1999) , Friedman (2001) , Apley and Zhu (2020) , and Lundberg and Lee (2017) , and other related methods for survival modeling, prediction, and interpretation. Package: r-cran-survawkmt2 Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-survawkmt2_1.0.1-1.ca2604.1_all.deb Size: 29358 MD5sum: 3fd9784f2204e328cd605645d606b588 SHA1: 0539cf378ab603766f915f8780c9e4015db033e9 SHA256: 0a585c77a6eac74217bf9b3fabed432a21b4e5ef0c3b165c8ab116e51bac619b SHA512: f62fbd29c1569fc7eb67f2c032ec9ad8bef6588cdc4607ba6c1d2355ed5c52bd738821744939f4feb6676ac5bf65886c6ee5426ff53e3b064cc5843918db9f99 Homepage: https://cran.r-project.org/package=survAWKMT2 Description: CRAN Package 'survAWKMT2' (Two-Sample Tests Based on Differences of Kaplan-Meier Curves) Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves. 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(2007) , Blanche, Dartigues, and Jacqmin-Gadda (2013) , Blanche, Latouche, and Viallon (2013) , Harrell et al. (1982) , Peto and Peto (1972) , Schemper (1992) , and Uno et al. (2011) . 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Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" , where the method is described as Ensemble 1. 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Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation. Package: r-cran-survdnn Architecture: all Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-survival, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-rsample, r-cran-cli, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-survdnn_0.7.6-1.ca2604.1_all.deb Size: 136544 MD5sum: b51d14d28e08efbe5d3371cd4f41386f SHA1: 2e65ea62a31b2e1724fbd3e8f5ba43720e462986 SHA256: f3628ded4afb663b1874541c96661bc86e39a294f2ee079220bf3c2c660ed20c SHA512: 2eeff6a45c2639eb36a74b2c9efa725b0c5219a4130bf4ffb0a634f95574e6b26dd6ae4152d88c03a10ee0dfe89ea3bc445a234a8f0a2d5b0c23ff8c8df532f8 Homepage: https://cran.r-project.org/package=survdnn Description: CRAN Package 'survdnn' (Deep Neural Networks for Survival Analysis with R 'torch') Provides deep learning models for right-censored survival data using the 'torch' backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) . Package: r-cran-survdt Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-survdt_0.9.0-1.ca2604.1_all.deb Size: 409066 MD5sum: d17d448d91ff4450c388ae59bf8173cd SHA1: ccf2b8fcfe1c3ad14e29c8b9b80c715fcdf729ec SHA256: 61fa47b47c306f2499a368179e501782d07c654df41c72b95c1e3b6f59aa2317 SHA512: 09a848e4b9e5dc291ac022a4e588c0e98fd6f2150d9f64a53cc5faca9d2fd94c6be567de46e987e8e762ddab1b69f447a92a262556e12c44be051f41a02d5163 Homepage: https://cran.r-project.org/package=survdt Description: CRAN Package 'survdt' (Improved Methods for Survival Analysis under Double Truncation) Contains existing and novel methods for nonparametric analysis and Cox regression analysis with doubly truncated survival data, as described in Vazquez and Xie (2025) . Includes survival curves and hazard estimates through nonparametric maximum likelihood estimation, various tests for detecting group differences or non-ignorable sampling bias, and inverse probability weighted Cox regression with several options of nonparametric weights. Also implements diagnostics for key modeling assumptions such as quasi-independent truncation and the positivity assumption. Closed-form standard errors are available for all estimates, i.e. bootstrapping is not required. Package: r-cran-surveltest Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-nloptr, r-cran-plyr, r-cran-survival Filename: pool/dists/resolute/main/r-cran-surveltest_2.0.1-1.ca2604.1_all.deb Size: 178382 MD5sum: e2a3cf9fab654431ea7773c56538fe21 SHA1: 93349a00b25b41789badc9f7499057b0782376b9 SHA256: d93db954b83306c038448ed8c2e046bd67ed93722e6a7b4a14c7ea71d3abfe1e SHA512: f6aac37bee6f34b29e29e100f3c6d07e0210eb57ca4cac29ddee0c451ca17bd59dddf556285f152d45fb88e60d5ab35b509e4a4a0963de39594695dfec4fe8ff Homepage: https://cran.r-project.org/package=survELtest Description: CRAN Package 'survELtest' (Comparing Multiple Survival Functions with Crossing Hazards) Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions. Package: r-cran-survex Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-ggplot2, r-cran-kernelshap, r-cran-pec, r-cran-survival, r-cran-patchwork Suggests: r-cran-censored, r-cran-covr, r-cran-flexsurv, r-cran-gbm, r-cran-generics, r-cran-glmnet, r-cran-ingredients, r-cran-knitr, r-cran-mboost, r-cran-parsnip, r-cran-progressr, r-cran-randomforestsrc, r-cran-ranger, r-cran-reticulate, r-cran-rmarkdown, r-cran-rms, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-survex_1.2.0-1.ca2604.1_all.deb Size: 1190142 MD5sum: 3ccf562a759446c99b93ec7fb013aab7 SHA1: 0473846a140635b192f47049ac2ff62ca3617c40 SHA256: 641d26a1b0801b31277d1a194d649856c2a35d102f2f7382c04eb20f2424e6da SHA512: eeef88de177f1277d3e1bae4383b8b2d3facfa37faab38060afe57d9c73b4a2fa71abf0ff110c623f1bda5f70819a98359915a4022329db80f314ebead3faf54 Homepage: https://cran.r-project.org/package=survex Description: CRAN Package 'survex' (Explainable Machine Learning in Survival Analysis) Survival analysis models are commonly used in medicine and other areas. Many of them are too complex to be interpreted by human. Exploration and explanation is needed, but standard methods do not give a broad enough picture. 'survex' provides easy-to-apply methods for explaining survival models, both complex black-boxes and simpler statistical models. They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023) , SurvLIME described in Kovalev et al., (2020) as well as extensions of existing ones described in Biecek et al., (2021) . Package: r-cran-survexp.fr Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-writexls Filename: pool/dists/resolute/main/r-cran-survexp.fr_1.2-1.ca2604.1_all.deb Size: 83038 MD5sum: f56030e847f2d14ce02725d98895dd66 SHA1: 6f365f387b7f740625af194533c8742da971124b SHA256: f424dbefde7d175f64518564f2105eab9201114dd930eb6634e393a38a4108e8 SHA512: 57aee93371cbcde6087ec8682894f15d02f2f91f18acf0bdc732f967f223a3c9c19142de9f849d0001e4672c1fa86b76c289592aa5285836a814ce054ec8c16c Homepage: https://cran.r-project.org/package=survexp.fr Description: CRAN Package 'survexp.fr' (Relative Survival, AER and SMR Based on French Death Rates) It computes Relative survival, AER and SMR based on French death rates. Package: r-cran-surveycc Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-candisc, r-cran-survey Filename: pool/dists/resolute/main/r-cran-surveycc_0.2.1-1.ca2604.1_all.deb Size: 154790 MD5sum: 174dc97fd6c743fd4d91dee20b98d79d SHA1: 09b580d4a93b9970f6ddb7c0f3ed6f4e326e9a73 SHA256: b911caec11f648e0b6a0594e6dc3009399d5997420f495f28ec8e005497f8639 SHA512: 0f7b2df94602331f06e13ff01e14fe82c534bb48d21075d0d0281e330e2e1dcc8c186f0916df79a0d3a333a8584fb05abd8d1e377c72d88a0d13d364faf9f6b5 Homepage: https://cran.r-project.org/package=SurveyCC Description: CRAN Package 'SurveyCC' (Canonical Correlation for Survey Data) Performs canonical correlation for survey data, including multiple tests of significance for secondary canonical correlations. A key feature of this package is that it incorporates survey data structure directly in a novel test of significance via a sequence of simple linear regression models on the canonical variates. See reference - Cruz-Cano, Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" The R Journal under review. Package: r-cran-surveycore Architecture: all Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-rlang, r-cran-tidyselect, r-cran-cli, r-cran-tibble, r-cran-dplyr, r-cran-marginaleffects, r-cran-pbivnorm Suggests: r-cran-testthat, r-cran-withr, r-cran-survey, r-cran-survival, r-cran-srvyr, r-cran-haven, r-cran-lifecycle, r-cran-broom, r-cran-polycor, r-cran-jtools, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-surveycore_0.8.3-1.ca2604.1_all.deb Size: 5720172 MD5sum: 5a256a1eaec71d3b0b5714c4a0c0a0d2 SHA1: 885d1c60978fe1f4735f38111cecf6e62e24bc03 SHA256: 9f0b0c97978b52c31bc2c0689844ca7641ee29e0c654e7e30ca4fe42c221f2ba SHA512: 9d1e4259f1f4e1c0e07ac682e83b89ea5d807c6d8f80806be62d23ce6ae3f804bd485bf4f085d046c92d8e502aa8669ad93379421c3b84a78b65facbe9e32996 Homepage: https://cran.r-project.org/package=surveycore Description: CRAN Package 'surveycore' (Core Survey Analysis Infrastructure) Provides 'S7'-based infrastructure for survey analysis. Supports Taylor series, replicate weight, and two-phase designs following the methods in 'Lumley' (2004) . Includes design-based estimators such as means, frequencies, and regression models, with weighted 'polychoric' and 'polyserial' correlation following 'Mannan' (2025) . A metadata system automatically preserves 'haven'-style variable labels, value labels, and question-preface attributes through all operations. Uses a 'tidyselect' interface for design specification. Package: r-cran-surveycv Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-magrittr Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-islr, r-cran-knitr, r-cran-rmarkdown, r-cran-rpms, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surveycv_0.2.0-1.ca2604.1_all.deb Size: 738706 MD5sum: 282ed88c06ca71ee7851bf6e144175c9 SHA1: b3e1690b22e67a12eaa1133bd729125c40dcfb64 SHA256: b814af29ca353e685c1dc9168060f589cb8044a92c0e44f8fab5fff67ae982e3 SHA512: 355cafc7fd06c05d6352df09475d8361bf2cbe2cc4ce0a415afdc746ed899d7a442940e4b304d2181fdd3174a2b1080ebd5d781896c47254ade70719b74a9f6d Homepage: https://cran.r-project.org/package=surveyCV Description: CRAN Package 'surveyCV' (Cross Validation Based on Survey Design) Functions to generate K-fold cross validation (CV) folds and CV test error estimates that take into account how a survey dataset's sampling design was constructed (SRS, clustering, stratification, and/or unequal sampling weights). You can input linear and logistic regression models, along with data and a type of survey design in order to get an output that can help you determine which model best fits the data using K-fold cross validation. Our paper on "K-Fold Cross-Validation for Complex Sample Surveys" by Wieczorek, Guerin, and McMahon (2022) explains why differing how we take folds based on survey design is useful. Package: r-cran-surveydata Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-magrittr, r-cran-purrr, r-cran-ggplot2, r-cran-scales, r-cran-tidyr, r-cran-dt, r-cran-assertthat Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-covr, r-cran-rprojroot, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-surveydata_0.2.8-1.ca2604.1_all.deb Size: 372552 MD5sum: af013ec2658310f7680549e6eca20bdf SHA1: 3f378c81b70bf93b08ce85aa6976ba126527754a SHA256: b681d4738ec94627aae1505b106dcdb795bb64ba381ff72ab3be176e184a817e SHA512: 00fab45853fb8c869a3dc7a4f4fc49b5e0f85ba2f497f32106a74a20cdab231146f7d6047142057230b79e15de74bfc026c418ebaa1723d3859cf8fd26445d0b Homepage: https://cran.r-project.org/package=surveydata Description: CRAN Package 'surveydata' (Tools to Work with Survey Data) Data obtained from surveys contains information not only about the survey responses, but also the survey metadata, e.g. the original survey questions and the answer options. The 'surveydata' package makes it easy to keep track of this metadata, and to easily extract columns with specific questions. Package: r-cran-surveydefense Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-flextable Suggests: r-cran-officer Filename: pool/dists/resolute/main/r-cran-surveydefense_0.2.0-1.ca2604.1_all.deb Size: 44402 MD5sum: bf00354e13db6181a791ed4faf1ff1db SHA1: 8a04ecbfead76d710ba5684f56453243eac3ccb0 SHA256: e9521e01136b42e7f3a1e912239dad79c8ca22464e422529176b4d7a1db94c68 SHA512: 63dffd398ffe0ca8d6f30b27857cdd24202c86aa589542c1da52af77f2045a9d0ba76b1e51faa1ba93111a012d981d8c7e97ff9461fb6eb673fc81b6780c1563 Homepage: https://cran.r-project.org/package=SurveyDefense Description: CRAN Package 'SurveyDefense' (Survey Defense Tool) This tool is designed to analyze up to 5 Fraud Detection Questions integrated into a survey, focusing on potential fraudulent participants to clean the survey dataset from potential fraud. Fraud Detection Questions and further information available at . Package: r-cran-surveydown Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dotenv, r-cran-fs, r-cran-htmltools, r-cran-jsonlite, r-cran-markdown, r-cran-miniui, r-cran-pool, r-cran-quarto, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rstudioapi, r-cran-rvest, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-xml2, r-cran-yaml Suggests: r-cran-glue, r-cran-knitr, r-cran-leaflet, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surveydown_1.0.1-1.ca2604.1_all.deb Size: 897572 MD5sum: 287737f6c6533b7dcc943b4d1596276d SHA1: 2d0c1c3258e2fcaf89bc6a4ecc8ba9f743346258 SHA256: 28f12c5ebf9b3be9f6eef7673b677b4ed659835e3c00ad6dad4d5d4239d9a430 SHA512: 9a583c508f2fd751083f188af126d971f8b2b5ad2b5eb3d5f0b5162850afdcc032afc8702852d0b2118560b1a12e7dea68b4fff76a7e213e5044350429a14d1b Homepage: https://cran.r-project.org/package=surveydown Description: CRAN Package 'surveydown' (Markdown-Based Programmable Surveys Using 'Quarto' and 'shiny') Generate programmable surveys using markdown and R code chunks. Surveys are composed of two files: a survey.qmd 'Quarto' file defining the survey content (pages, questions, etc), and an app.R file defining a 'shiny' app with global settings (libraries, database configuration, etc.) and server configuration options (e.g., conditional skipping / display, etc.). Survey data collected from respondents is stored in a 'PostgreSQL' database. Features include controls for conditional skip logic (skip to a page based on an answer to a question), conditional display logic (display a question based on an answer to a question), a customizable progress bar, and a wide variety of question types, including multiple choice (single choice and multiple choices), select, text, numeric, multiple choice buttons, text area, and dates. Because the surveys render into a 'shiny' app, designers can also leverage the reactive capabilities of 'shiny' to create dynamic and interactive surveys. Package: r-cran-surveyexplorer Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggupset, r-cran-gt, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surveyexplorer_0.2.0-1.ca2604.1_all.deb Size: 572740 MD5sum: 3be69a983eaa988935341be9fcce028b SHA1: 9a9ad9b8e2bbdb7d640ac362459b579bdedb77cb SHA256: d4af8f56d9c61bd680e99b3e28bb25dad55c964aa9a6864192635a124aff5e18 SHA512: ae41872ab39b569df7497d3a94287b78a46d99ba116c7eae256f9c9147e5eb8e9ecb8a6602cccd91869c70bb09c24d9abdbc0ea113edb808410362534bac7441 Homepage: https://cran.r-project.org/package=surveyexplorer Description: CRAN Package 'surveyexplorer' (Quickly Explore Complex Survey Data) Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data. Package: r-cran-surveyprev Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5834 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-ggplot2, r-cran-rdhs, r-cran-summer, r-cran-dplyr, r-cran-labelled, r-cran-sjlabelled, r-cran-naniar, r-cran-raster, r-cran-sp, r-cran-spdep, r-cran-stringr, r-cran-tidyverse, r-cran-data.table, r-cran-sf, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-kableextra, r-cran-geodata, r-cran-patchwork, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-surveyprev_1.0.0-1.ca2604.1_all.deb Size: 4970366 MD5sum: f66a5e1c77c5ba3924033d1be257d447 SHA1: 4aa021d7518fe12219ddeb82ff53a1344141b1b2 SHA256: ad800cf84c1237f55532f6accb9020d0ff927d1b5087be1246b43551dc125bf0 SHA512: ef2b641d313c2ce565f6eef252f0b9c082dbe5d42f0d200322ac12b59b90e149618e4c55bb78f77c64c4f8114fb7c790ae7b2214c4d83bf5991a43975d0e5eed Homepage: https://cran.r-project.org/package=surveyPrev Description: CRAN Package 'surveyPrev' (Mapping the Prevalence of Binary Indicators using Survey Data inSmall Areas) Provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) and Wakefield et al. (2020) . Package: r-cran-surveysearch Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surveysearch_0.1.0-1.ca2604.1_all.deb Size: 23792 MD5sum: 0ec2afb81d8ae19842eb3cb6d55a5633 SHA1: 8073ae4468b4967124c2cea4e21b15caec04c335 SHA256: 3343facd408c4a56cbaa5033b633c60d171464c2bf16bd4bcc6c1d23725890f7 SHA512: f25b528a0c52e3a9e231ca32af4423095b91432046d0e345d0f98b8438270055554239eec5f89f79115049bd053d230146b32c4d7743b13542f9a801b34938ea Homepage: https://cran.r-project.org/package=surveysearch Description: CRAN Package 'surveysearch' (Search and Examine Variables Across Survey Datasets) Search for variables across multiple survey datasets, examine variable properties (labels, values, missingness), and explore variable context within datasets. Useful for navigating complex survey data with many variables and understanding variable relationships and metadata. Package: r-cran-surveysimr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-moments Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surveysimr_0.1.0-1.ca2604.1_all.deb Size: 38894 MD5sum: d8c9ae1275ce1e61b9d3b55df8ac9e92 SHA1: 665e28b2a0e2753bd55ca3e7c6aa06fb422eaaae SHA256: 355cbeb29796d070421574c8140bacceb0fe5eb9faf6b942b76618384634e5f3 SHA512: 8c3f6613df263414020c28710b899fe786975125934370d8e894e6ac43b0e7e1e693348f8f21b020ebae6561c5bcef34a119133f5215270a35720c7c56851a51 Homepage: https://cran.r-project.org/package=surveySimR Description: CRAN Package 'surveySimR' (Estimation of Population Total under Complex Sampling Design) Sample surveys use scientific methods to draw inferences about population parameters by observing a representative part of the population, called sample. The SRSWOR (Simple Random Sampling Without Replacement) is one of the most widely used probability sampling designs, wherein every unit has an equal chance of being selected and units are not repeated.This function draws multiple SRSWOR samples from a finite population and estimates the population parameter i.e. total of HT, Ratio, and Regression estimators. Repeated simulations (e.g., 500 times) are used to assess and compare estimators using metrics such as percent relative bias (%RB), percent relative root means square error (%RRMSE).For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" . Package: r-cran-surveystat Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surveystat_1.0.3-1.ca2604.1_all.deb Size: 70394 MD5sum: 51b98f8183e956dd9c826aa5af5f6cfa SHA1: 39f586131392c6650fa2ad0618d0c17127a9df49 SHA256: b407da4d9091cfad59629066efef2e1fe0e867fddd68e8e4073e81a99fabbcf0 SHA512: d806df772243e2fd4669a7e2775c278fee4db69bc14d21cabf4913b8af7cb9fb474831ad2db1f8a0beea1e323ffd95a344420b2157a323947299f3f7d08fe1d0 Homepage: https://cran.r-project.org/package=SurveyStat Description: CRAN Package 'SurveyStat' (Survey Data Cleaning, Weighting and Analysis) Provides utilities for cleaning survey data, computing weights, and performing descriptive statistical analysis. Methods follow Lohr (2019, ISBN:978-0367272454) "Sampling: Design and Analysis" and Lumley (2010) . Package: r-cran-surveytable Architecture: all Version: 0.9.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-magrittr, r-cran-glue, r-cran-survey, r-cran-huxtable Suggests: r-cran-gt, r-cran-kableextra, r-cran-openxlsx2, r-cran-mschart, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-surveytable_0.9.10-1.ca2604.1_all.deb Size: 694606 MD5sum: f7a1d35cbb4105af1b8b50db0d7dabb9 SHA1: 65080281e09b1c778032a457000441bfafc987eb SHA256: 691472d4827219be68fc5f35adf2eebec0679af4174ac1885a8f099f0cafafa9 SHA512: 817fef843f9a93f0cc1a10a5ec72e5a52f074f0492897ce03de53fec49c386b99cec0a062a982d71d15733fcccbe729068f2bac1532bfde4aae332a2c10f5635 Homepage: https://cran.r-project.org/package=surveytable Description: CRAN Package 'surveytable' (Streamlining Complex Survey Estimation and ReliabilityAssessment in R) Short and understandable commands that generate tabulated, formatted, and rounded survey estimates. Mostly a wrapper for the 'survey' package (Lumley (2004) ) that identifies low-precision estimates using the National Center for Health Statistics (NCHS) presentation standards (Parker et al. (2017) , Parker et al. (2023) ). Package: r-cran-surveytidy Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-haven, r-cran-rlang, r-cran-s7, r-cran-surveycore, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-withr Suggests: r-cran-covr, r-cran-mockery, r-cran-pkgdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-surveytidy_0.6.0-1.ca2604.1_all.deb Size: 469192 MD5sum: 7c2242e39e8951128aa764d23ce8c284 SHA1: a336edf67122e2ccba440b0b9a4c6e5fcc197d69 SHA256: cd1cf496bc323414c21c48d1009e377a2e9f472c7f41898400ee8df7397b0a91 SHA512: 178157898561821c96cda8ddf8efd8e07f3b687c3732c44fa508ad44d2fee9d5dfe1e1acd0668f0ec156f057ebace1386bfb8ab1e4bf513813f95eb7588eb65a Homepage: https://cran.r-project.org/package=surveytidy Description: CRAN Package 'surveytidy' (Tidy 'dplyr'/'tidyr' Verbs for Survey Design Objects) Provides 'dplyr' and 'tidyr' verbs, survey-aware recoding helpers, and row-wise statistics for survey design objects created with the 'surveycore' package. filter() uses domain estimation to preserve variance estimation validity; other verbs preserve design variables and metadata automatically. Also supports survey_collection objects for applying the same operation across a list of surveys. Package: r-cran-survgme Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ncvreg, r-cran-glmnet, r-cran-survival, r-cran-ahaz, r-cran-ggally, r-cran-network, r-cran-sna, r-cran-scales Filename: pool/dists/resolute/main/r-cran-survgme_0.1.0-1.ca2604.1_all.deb Size: 72014 MD5sum: bf3729cc92bc9e4f8aa038e86cb3b580 SHA1: 855a5571074139ae6ff2057725846c1a1854e6a8 SHA256: 24b6ac99f206bb09c728aaff7996204b4326862654ddafbd7a5e94dbafe723de SHA512: 1e965f7e72f324688d461bfa9a28a01232797f58ed82bb7105ab2229e3765297cce9026f2eaa83aba34004dc60d7cb342c35a62fb2678fcddbac3a9b2777c323 Homepage: https://cran.r-project.org/package=SurvGME Description: CRAN Package 'SurvGME' (Analysis of Survival Data under Graphical and Measurement ErrorModels) The estimation method proposed by Chen and Yi (2021) is extended to the analysis of survival data, accommodating commonly used survival models while accounting for measurement error and network structures among covariates. Package: r-cran-survhe Architecture: all Version: 2.0.51-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-dplyr, r-cran-ggplot2, r-cran-rms, r-cran-xlsx, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rstan, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-survhe_2.0.51-1.ca2604.1_all.deb Size: 300212 MD5sum: 9e37c1d4cfd8f98f0b90b4104370c255 SHA1: bcbf8bb0663e359b696e819962bc46035cf71fff SHA256: cb963a0b6de608de6f66112909beacc4b3e6faab9322a82e265bd8e210a85a88 SHA512: 0882a9b0f588d86073c75d0160e925986a41f0bf7f55c673599b0ca53c594c23c7bfaceb2b97be9bd0c92b429455f454c29bb7fbcdda7e462da48ba978b5787d Homepage: https://cran.r-project.org/package=survHE Description: CRAN Package 'survHE' (Survival Analysis in Health Economic Evaluation) Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed from using 'install.packages("survHEhmc", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))' and 'install.packages("survHEinla", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))' respectively. 'survHEinla' is based on the package INLA, which is available for download at . The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). . Package: r-cran-survhidim Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rdpack, r-cran-readr, r-cran-tidyverse, r-cran-glmnet, r-cran-factoextra, r-cran-igraph, r-cran-useful Filename: pool/dists/resolute/main/r-cran-survhidim_0.1.1-1.ca2604.1_all.deb Size: 445102 MD5sum: d8694e6588295b17b4909b24b164ff41 SHA1: e18316dee2868c63aaae12cac8bf412acdaa9436 SHA256: c47b0ed1d5d3a8dd4ef94a0b891e271946e772ad8f895445faa14e5dfff87b8f SHA512: 330b525c563caf5097182deb19c64ea03ca4ff26016be354f46466de5498a3f1478f49666b3558b2e12947790817fef96cae8c0631b2626b520e8fa99e49b37e Homepage: https://cran.r-project.org/package=SurvHiDim Description: CRAN Package 'SurvHiDim' (High Dimensional Survival Data Analysis) High dimensional time to events data analysis with variable selection technique. Currently support LASSO, clustering and Bonferroni's correction. Package: r-cran-survimchd Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-r2jags, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-survimchd_0.1.2-1.ca2604.1_all.deb Size: 406148 MD5sum: fd324d8ac73d7eacc99cf71d2b768b23 SHA1: 54f5523a8918c615286873d94944b60c2b0cc291 SHA256: d062b0dc2808fb1748b4d6390ab1682376582104cdc9c686f046d4670ac953c0 SHA512: 9357deb10a61abc5bdfdf24030ce037785dc5beaef48699254a3c41aef989c5485ded7459dc73e555185189e520745cb8cd7b9f3e05a033dd3721110b780152d Homepage: https://cran.r-project.org/package=SurviMChd Description: CRAN Package 'SurviMChd' (High Dimensional Survival Data Analysis with Markov Chain MonteCarlo) High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). Currently supports frailty data analysis. Allows for Weibull and Exponential distribution. Includes function for interval censored data. Package: r-cran-survimpute Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-vgam, r-cran-mass Suggests: r-cran-mitools Filename: pool/dists/resolute/main/r-cran-survimpute_0.1.0-1.ca2604.1_all.deb Size: 65906 MD5sum: 74bbda7d78e5e6a6a9bbf34298c38c2a SHA1: 792ada533909949fcdd493ee10308709d0790ad9 SHA256: f04fa55793a966e176ee14e0593cf1afef3961eaedfb98fd8face0836c6c84ca SHA512: d80c59553300a1f91f075dd14d598f320350983e3f38405ddc01674196ff58bed29112f326c49f40f432a7cb6d56f42277d2867b356bb8808336f1c370c1526e Homepage: https://cran.r-project.org/package=SurvImpute Description: CRAN Package 'SurvImpute' (Multiple Imputation for Missing Covariates in Time-to-Event Data) Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication. Package: r-cran-survinger Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-mass, r-cran-matrix, r-cran-nloptr, r-cran-survey, r-cran-tidyr, r-cran-vctrs, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-survinger_0.1.1-1.ca2604.1_all.deb Size: 589856 MD5sum: 19b02925092e4c0c65d8b087c892d75f SHA1: 5947052a5c41e6ddda62382dad80f292b4d2ba4a SHA256: 1f663a96dffed642ab051835494719f9cbfdf51c460d8c6e4c5b29c0fb967340 SHA512: 401c39cc8db6fb37ab87959d100d05bc9fe42bf47f024406a5e570e6b26bae20744e8ac3d42c9271a35860442b31f411b70d27542cdeca3065bd218c7868f7e4 Homepage: https://cran.r-project.org/package=survinger Description: CRAN Package 'survinger' (Design-Adjusted Inference for Pathogen Lineage Surveillance) Provides tools for optimizing sequencing resource allocation and estimating pathogen lineage prevalence under real-world genomic surveillance conditions. Implements constrained allocation optimization for limited sequencing capacity across multiple regions and sample sources. Includes Horvitz-Thompson and post-stratified estimators that account for unequal sequencing rates, delay-adjusted nowcasting for right-censored reporting data, and combined design-weighted delay-corrected inference with uncertainty propagation. Package: r-cran-surviv Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-rootsolve Filename: pool/dists/resolute/main/r-cran-surviv_0.1.3-1.ca2604.1_all.deb Size: 234268 MD5sum: b0e8dc9a8e6dd04e5a6dc75923ecab21 SHA1: 5480041ddc3955c58670c3dbb043422eef5a041f SHA256: fdce23a86b45fa3a3fe114b178c2afff2aacdcf08834b30ceec6eb7092afcb22 SHA512: ff25eaf5197b055a4cd724d1791a8eb9c6c621352f3d6b06e2d107f7d691dfa85fd5f5504cf1384518b5fbf348d6bd911a1099bacc1717737a87b26c429d917b Homepage: https://cran.r-project.org/package=surviv Description: CRAN Package 'surviv' (Flexible Instrumental Variable Methods for Survival Analysis) Instrumental variable analysis methods for causal inference on survival data based on the Cox model allowing for various treatment and effect types, including orthogonality method-of-moments instrumental variable estimation for the Cox model, two-stage residual inclusion Cox estimation with frailty, sequential trial emulation, sequential Cox analyses, and sequential two-stage residual inclusion Cox analyses. Methodological background includes MacKenzie et al. (2014) , Martinez-Camblor et al. (2019) , Martinez-Camblor et al. (2019) , Gran et al. (2010) , and Keogh et al. (2023) . Package: r-cran-survivalmpl Architecture: all Version: 0.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-mass Filename: pool/dists/resolute/main/r-cran-survivalmpl_0.2-4-1.ca2604.1_all.deb Size: 143726 MD5sum: c59bc71bb0c804bac0c873394cad7ffa SHA1: fbbfbb5cf2ef2f84d6cadda00720d27648ad9c1b SHA256: ce89e9b5398de6829bb3fcd85358c9157a939047adcfd2dff77cd6e4fdd1fd2f SHA512: 85954528fd2fdaad793bc27c9bf08c7ae2f39d021b59059cd3735ec897996392124e1473d83c30699f3ab0d2c477251a7c6fd57e0f82780dfe070a1a907ace60 Homepage: https://cran.r-project.org/package=survivalMPL Description: CRAN Package 'survivalMPL' (Penalised Maximum Likelihood for Survival Analysis Models) Estimate the regression coefficients and the baseline hazard of proportional hazard Cox models with left, right or interval censored survival data using maximum penalised likelihood. A 'non-parametric' smooth estimate of the baseline hazard function is provided. Package: r-cran-survivalplann Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-nnet, r-cran-risca Filename: pool/dists/resolute/main/r-cran-survivalplann_0.4-1.ca2604.1_all.deb Size: 267620 MD5sum: a1730a352c43e0ecf9a141695edf69d1 SHA1: 8bd9d6ef8b986f09f05db238bac6754c9dc87c99 SHA256: 1f185ea0caffd804da284334bd6c7b46b5b6c5ee55521105dc5d06ed7dc914c4 SHA512: ff7749ec7799f07534fd780a16a7b778ab7b46d7239f056074a06d7ee69bedfd19e035a7a1e16a0f0a2db7aceb5bb974088c77fa966b36079b8452e183e88277 Homepage: https://cran.r-project.org/package=survivalPLANN Description: CRAN Package 'survivalPLANN' (Neural Networks to Predict Survival) Several functions and S3 methods to predict survival by using neural networks. We implemented Partial Logistic Artificial Neural Networks (PLANN) as proposed by Biganzoli et al. (1998) . Package: r-cran-survivalsl Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-date, r-cran-mass, r-cran-glmnet, r-cran-caret, r-cran-flexsurv, r-cran-randomforestsrc, r-cran-hdnom, r-cran-survivalplann, r-cran-dplyr, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-survivalsl_1.0-1.ca2604.1_all.deb Size: 389170 MD5sum: dedd0a95b047ca79059760e81c85805a SHA1: 245de385e9962dbf50f68cf3fd1401f2bb0de188 SHA256: a0c863e0a4ada9834bc2386899bc38c6373a378cdb654601a4d070b61c8ea0fc SHA512: a9d7188d7379a7e07cc1564c2e3378727844c94e3492e260e3ad190af1f5b2316fcfa4fd73ce5597fbb232774dbdf356c5d648c72922ea5471a2f93ebf24b732 Homepage: https://cran.r-project.org/package=survivalSL Description: CRAN Package 'survivalSL' (Super Learner for Survival Prediction from Censored Data) Several functions and S3 methods to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities. Package: r-cran-survivalsurrogate Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-glue, r-cran-mlr3, r-cran-purrr, r-cran-sparsem, r-cran-rbeta2009, r-cran-data.table, r-cran-rpart Suggests: r-cran-mlr3learners Filename: pool/dists/resolute/main/r-cran-survivalsurrogate_1.1-1.ca2604.1_all.deb Size: 173334 MD5sum: 2434e196bcb5f86247ecd2e67a5965f4 SHA1: a1bf21aa058aaab75bc5e58c32b846191df3582d SHA256: 037a63aa8043c54b9aa278bf03344098bd6d49e70233b6512ea7caa69ff7b31a SHA512: dd965a5ffa1cd8904f85861553982b59cff3bdc546941c6bde8ed6904581c44e27e711517b94e99d87952a8f0cf476f7d970ff30c3ec96ab03c549b6bb4afd84 Homepage: https://cran.r-project.org/package=survivalsurrogate Description: CRAN Package 'survivalsurrogate' (Evaluate a Longitudinal Surrogate with a Censored Outcome) Provides influence function-based methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting, with plug-in and targeted maximum likelihood estimation options. Details are described in: Agniel D and Parast L (2025). "Robust Evaluation of Longitudinal Surrogate Markers with Censored Data." Journal of the Royal Statistical Society: Series B . A tutorial for this package can be found at and a Shiny App implementing the package can be found at . Package: r-cran-survivalsvm Architecture: all Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pracma, r-cran-kernlab, r-cran-matrix, r-cran-hmisc Suggests: r-cran-testthat, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-survivalsvm_0.0.6-1.ca2604.1_all.deb Size: 294914 MD5sum: 90c385135c84ca36e309e99cec53731f SHA1: 05e99c86a862ba5b8ebf9496ccb9f71c33966091 SHA256: 1e87e1b84a861d95d59f0ca34970b0160ec528e982a2d7c09bf600dd3fe69929 SHA512: df686a03502757d72a53fb34d140855443df08cf51e6fbb86717c759ded19b44498ee139dab6e6f47582a5f9bc2ebbc00031d4517dd7db1defbefd17b280c02e Homepage: https://cran.r-project.org/package=survivalsvm Description: CRAN Package 'survivalsvm' (Survival Support Vector Analysis) Performs support vectors analysis for data sets with survival outcome. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in the same model. Package: r-cran-survivaltests Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-weibullness, r-cran-ggplot2 Suggests: r-cran-survival, r-cran-arules Filename: pool/dists/resolute/main/r-cran-survivaltests_1.0-1.ca2604.1_all.deb Size: 51846 MD5sum: c73ad867cac48c661c5b8045c1dda68a SHA1: 6a9901717fb6bfcb31836e6c8a5e4339ccebb12f SHA256: 4d02308a5f5c0488d46cc103a87078e59cf777d445210d111742d9b944619c01 SHA512: fa2047ab9abf7de378253177c2361add7e5067b31eece57ddc04cb867c99738dcf4593f487b7b50dd9121806544295cd3587f2c56fa8156594292e2d8a1478bf Homepage: https://cran.r-project.org/package=SurvivalTests Description: CRAN Package 'SurvivalTests' (Survival Tests for One-Way Layout) Performs survival analysis for one-way layout. The package includes the generalized test for survival ANOVA (Tsui and Weerahandi (1989) and (Weerahandi, 2004; ISBN:978-0471470175)). 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Split from the 'survival' package since the vignettes were getting large. Also, since 'survival' is a recommended package it cannot make use of other packages outside of base+recommended (e.g. 'rmarkdown'). 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Package: r-cran-survlong Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-survlong_1.5-1.ca2604.1_all.deb Size: 108852 MD5sum: 1ba75ffebf62ff3a4a1e0b6eba012e3e SHA1: 366374a17dd7be09c880d8b9d72238997fb802ff SHA256: 284f543412cfb3b4d7efdd79b98938cb6b20879278eaa18a205a91c5439c5f18 SHA512: 43575c48692b3ffd63f63ecbb264277e6bb5072c1d19f98569ea192eb1f23a82232c84acca2732800ac5be801d5e81b85cc67e65d54e25b107b9c7a5dbcaf4ee Homepage: https://cran.r-project.org/package=SurvLong Description: CRAN Package 'SurvLong' (Analysis of Proportional Hazards Model with Sparse LongitudinalCovariates) Provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) . Package: r-cran-survma Architecture: all Version: 1.6.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-maxlik, r-cran-pec, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-survma_1.6.8-1.ca2604.1_all.deb Size: 80734 MD5sum: af76a5cb6dba8eeeb3160458ed262c68 SHA1: d0003771632d074c43d094541d5ae3402da347c2 SHA256: b4f7b646f258ee5824648019ba8e9d755e389029250ca6023334d0b859e27dff SHA512: b53320c64dec8af518f53ba5292c3e4e7acc22ef6b81084f510d8ad028d238f56803a4a507dbdd67322401bfef36d59f2050957f2916c204f238b85966aa6b15 Homepage: https://cran.r-project.org/package=SurvMA Description: CRAN Package 'SurvMA' (Model Averaging Prediction of Personalized SurvivalProbabilities) Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) . Package: r-cran-survmetrics Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survminer, r-cran-survival, r-cran-mass, r-cran-pec Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-ggplot2, r-cran-ggpubr, r-cran-randomforestsrc, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-survmetrics_0.5.1-1.ca2604.1_all.deb Size: 98898 MD5sum: 9e27cc482c66900e20aa9605c72db50b SHA1: 9de64d78fee1da9ec8624f88ac8f0870f0f797e9 SHA256: bea28213b2224a003c780057bcfa73340479b51336e0ff8f4c2af76eb8705e75 SHA512: a5c6d2d8144b5680b46978c08379506831ca591122a681d4361c554a70ef11d8dfc953078a13088ec23e584500d81f7be7a9093dcae7861228ddce641928394c Homepage: https://cran.r-project.org/package=SurvMetrics Description: CRAN Package 'SurvMetrics' (Predictive Evaluation Metrics in Survival Analysis) An implementation of popular evaluation metrics that are commonly used in survival prediction including Concordance Index, Brier Score, Integrated Brier Score, Integrated Square Error, Integrated Absolute Error and Mean Absolute Error. For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) ) , (Moradian H, Larocque D and Bellavance F (2017) ), (Hanpu Zhou, Hong Wang, Sizheng Wang and Yi Zou (2023) ) for different evaluation metrics. 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We propose to use multiple imputation (MI) introduced by Robin (1987) to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods. 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Other functions are also available to plot adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions. Package: r-cran-survmixer Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-survmixer_1.3-1.ca2604.1_all.deb Size: 47250 MD5sum: 9643edb2ac674689472094f3b12ab0c0 SHA1: 6f07541984dfa3172e7b3363fda2e2344fd9a4ec SHA256: c912681edd391e02ef8ce6628cf8f274da1087b74c53a06d3a2fd45696d72294 SHA512: 749809b78f3da81434923ccf7808efbbfd26169f7f82a2516864567c287c6fbaaa1906e4326b688fadeb643ce1c312129511d78c4be366656bfd239b576f94eb Homepage: https://cran.r-project.org/package=survmixer Description: CRAN Package 'survmixer' (Design of Clinical Trials with Survival Endpoints Based onBinary Responses) Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in Bofill, Shen & Gómez (2021) . 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(2010) proposed a Bayesian approach using complicated hierarchical models. Besides, frequentist approaches have been alternative standard methods for the statistical analyses of network meta-analysis, and the methodology has been well established. We proposed an easy-to-implement method for the network meta-analysis based on the frequentist framework in Noma and Maruo (2025) . This package involves some convenient functions to implement the simple synthesis method. 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Random values for the distributions are generated using the method described by Bender (2003) and Leemis (1987) in Operations Research, 35(6), 892–894. 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Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) . Package: r-cran-survregvb Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-invgamma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-survival Filename: pool/dists/resolute/main/r-cran-survregvb_0.0.2-1.ca2604.1_all.deb Size: 139086 MD5sum: ba84ea3b43cc9bbfd5cb8ab62ea5fcb6 SHA1: 410b9ce97fc8595594be0ded57ef9fdbe8128c11 SHA256: f8016d1be4eeeec67417edbfaff273f03a736e2a4cb39e298cf110173cf668d8 SHA512: 7c8768f5dbbe0f7f1a340ac1ece6989cf62370b267669ee03a2c06d25cbd8cc7a3235a9aa66e494f1cc733eb74be5feb96e1ead0839507e31f65d9afee2ccf76 Homepage: https://cran.r-project.org/package=survregVB Description: CRAN Package 'survregVB' (Variational Bayesian Analysis of Survival Data) Implements Bayesian inference in accelerated failure time (AFT) models for right-censored survival times assuming a log-logistic distribution. Details of the variational Bayes algorithms, with and without shared frailty, are described in Xian et al. (2024) and Xian et al. (2024) , respectively. 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Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures. 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When the sample size is less than 50 per group, it has been shown that there is non-negligible inflation of the type I error rate in the commonly used asymptotic test for the RMST comparison. Generally, permutation tests can be useful in such a situation. However, when we apply the permutation test for the RMST comparison, particularly in small sample situations, there are some cases where the survival function in either group cannot be defined due to censoring in the permutation process. Horiguchi and Uno (2020) have examined six workable solutions to handle this numerical issue. It performs permutation tests with implementation of the six methods outlined in the paper when the numerical issue arises during the permutation process. The result of the asymptotic test is also provided for a reference. 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Huang, R., Xu, R. and Dulai, P.S.(2020) . Package: r-cran-survsim Architecture: all Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eha, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-survsim_1.1.8-1.ca2604.1_all.deb Size: 125926 MD5sum: 878c7c08194b0a13d0ef15d52abd8d40 SHA1: 6dae07580f47e85c2980ebb85146c697ecd9d61e SHA256: 70c2b92973c1095772b86100753e4692cd6ac72f06ced4b5cb770b7bb55655c6 SHA512: 8a9ce1010c56fd7570bfa941200a708395639e4059d9359cd1c6b79c49ed8caaa247c9e6ee5f817f43ba7b58d5d3ac11a3983fc00b70e58092d40be25a573d2c Homepage: https://cran.r-project.org/package=survsim Description: CRAN Package 'survsim' (Simulation of Simple and Complex Survival Data) Simulation of simple and complex survival data including recurrent and multiple events and competing risks. See Moriña D, Navarro A. (2014) and Moriña D, Navarro A. (2017) . 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In case that the survival and truncation times are quasi-independent, the estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2018) , Shen (2010) , Martin and Betensky (2005) . When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) . Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert for questions regarding function coxDT and Yidan Shi for questions regarding function coxDTdep. Package: r-cran-susenas Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-susenas_0.1.0-1.ca2604.1_all.deb Size: 1247092 MD5sum: 7242d5713e730fd8b7b3f9fa29e3f395 SHA1: b63c9ad36fac4d5fbdfe0289c3c8d372965d5684 SHA256: fd89ea271334f4b2c6a9a9f8aa41ca8417279254ef347efe8b8eb2e3e3d115ad SHA512: a8d64aa7689109ddf5144449084c1f9875738da49d4987b17d88321d2af43150e152b8b69a82508d22a4d687f508b48dc3e0baaec1c920511336137db1fae2af Homepage: https://cran.r-project.org/package=SUSENAS Description: CRAN Package 'SUSENAS' (National Socio-Economic Survey Data Collection Indonesia) Survey to collect data about the social and economic conditions of Indonesian society. 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To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021). 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Provides three methods for estimation, namely outcome modeling and two factorizations of inverse probability weighting. Under stronger assumptions, these methods estimate the causal population average treatment effect. Salerno et al., (2024) . 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Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors as found in Li and Valliant (Surv. Meth., 2009, 35(1), pp. 15-24; Jnl. of Off. Stat., 2011, 27(1), pp. 99-119; Jnl. of Off. Stat., 2015, 31(1), pp. 61-75); Liao and Valliant (Surv. Meth., 2012, 38(1), pp. 53-62; Surv. Meth., 2012, 38(2), pp. 189-202). Variance inflation factors and condition indexes are also computed for some general linear models as described in Liao (U. Maryland thesis, 2010). 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As described in Capanu (2015) , the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots. Package: r-cran-svyroc Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-svyvarsel Filename: pool/dists/resolute/main/r-cran-svyroc_1.1.0-1.ca2604.1_all.deb Size: 425750 MD5sum: d93c2bb085a6d2127824b371cc98d3c1 SHA1: 17aa5d29e25444809d5005f5ee35e37c08adab3d SHA256: 08d00a79af813b53cdfd2a37d1b42f593173fef3f23cada56100b0a510ccf64a SHA512: 27b9eff733bb6651941155c23d1911d379a860da219d25d1702aa49ddc380e0cc1af31768620257d3c8daf98db268f4d4ffcc5610df0159b5d82ca734c5fa2cb Homepage: https://cran.r-project.org/package=svyROC Description: CRAN Package 'svyROC' (Estimation of the ROC Curve and the AUC for Complex Survey Data) Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) ; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) ; A. Iparragirre, I. Barrio (2024) . Package: r-cran-svyvarsel Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 741 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-svyvarsel_1.0.1-1.ca2604.1_all.deb Size: 716114 MD5sum: 6c123d16420a8c5232340cffa2ce5005 SHA1: ac42b7bd4efa1c96b28675ef87e8cf5e148bd4db SHA256: a24d1390bab6d41a5427da29ebef19d55dfe37254cbfc482fa37d57e5ceaec08 SHA512: d0d0854c7af57f9936a09cdf36bd5da9fa0fe675fe704d3c38e45cb7e33530e50e8ef498c6140a5369180fc67fdf2a4819c8ce2377d5ef08150a279b7a74dde4 Homepage: https://cran.r-project.org/package=svyVarSel Description: CRAN Package 'svyVarSel' (Variable Selection for Complex Survey Data) Fit design-based linear and logistic elastic nets with complex survey data considering the sampling design when defining training and test sets using replicate weights. Methods implemented in this package are described in: A. Iparragirre, T. Lumley, I. Barrio, I. Arostegui (2024) . Package: r-cran-svyvgam Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam, r-cran-survey Suggests: r-cran-pscl, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-svyvgam_1.3-1.ca2604.1_all.deb Size: 76006 MD5sum: d7d9b1b30e52a24f0e60b7d597a02efb SHA1: e77653dc8e6913533f1455ebb2d596566424e870 SHA256: 5c9a26a988785d3ed975d5a2a7cebbbeb0df6643c7badd3e52e7e0cab0b4ba24 SHA512: cd5a42df1eef0554d1e8da9d4cc5a341219f64d25ecb6cd25a4ad4a5706e01a361e8f7056018df16267eb039068ea3499d9e0c972578c4eea5420e4a6756c672 Homepage: https://cran.r-project.org/package=svyVGAM Description: CRAN Package 'svyVGAM' (Design-Based Inference in Vector Generalised Linear Models) Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package. Package: r-cran-svyweight Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-gdata Suggests: r-cran-dplyr, r-cran-srvyr, r-cran-testthat, r-cran-mice Filename: pool/dists/resolute/main/r-cran-svyweight_0.1.1-1.ca2604.1_all.deb Size: 100018 MD5sum: a9771d960543fbdc4499bf98ee6c9246 SHA1: dab29b3e08c2c8849191f48194d901aaf84d720c SHA256: 4db04d215e484ea8c0ffdf47ec1c7ba5dad1e87e5ad2154208a5a4132ded2d7c SHA512: 4070446aa93b81a5c414d0d4983e82d3b4d041813b237f5d32cc3167ce864e52b77422ee31482904f34c71e95049325b8ffb66043c83da9ac68eec7a32c688ac Homepage: https://cran.r-project.org/package=svyweight Description: CRAN Package 'svyweight' (Quick and Flexible Survey Weighting) Quickly and flexibly calculates weights for survey data, in order to correct for survey non-response or other sampling issues. Uses rake weighting, a common technique also know as rim weighting or iterative proportional fitting. This technique allows for weighting on multiple variables, even when the interlocked distribution of the two variables is not known. Interacts with Thomas Lumley's 'survey' package, as described in Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking to the base weighting functionality in 'survey.' 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Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) . 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This package works on top of the 'caret' package and proceeds in a forward-step manner. More specifically, it builds and tests learners starting from very few attributes until it includes a maximal number of attributes by increasing the number of attributes at each step. Hence, for each fixed number of attributes, the algorithm tests various (randomly selected) learners and picks those with the best performance in terms of training error. Throughout, the algorithm uses the information coming from the best learners at the previous step to build and test learners in the following step. In the end, it outputs a set of strong low-dimensional learners. 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Package: r-cran-swaprinc Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-broom.mixed, r-cran-dplyr, r-cran-gifi, r-cran-lme4, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-swaprinc_1.0.1-1.ca2604.1_all.deb Size: 43430 MD5sum: d39d3c9a739b0c614ec3485945ae9b62 SHA1: d18d44d93d0faba74acfa6c845123fe758f245fc SHA256: fd581c9534736e6a929494040dd7344eae293626499c729a8e708c06ceb60a2b SHA512: 5b1aa9d8de65960e73c1f78b101c4508537a6690a6a3260753e021245346071cbf3c217924a830a758722a3e67ac7c05983e4b82c5bc0b953b264414da9c5a2b Homepage: https://cran.r-project.org/package=swaprinc Description: CRAN Package 'swaprinc' (Swap Principal Components into Regression Models) Obtaining accurate and stable estimates of regression coefficients can be challenging when the suggested statistical model has issues related to multicollinearity, convergence, or overfitting. One solution is to use principal component analysis (PCA) results in the regression, as discussed in Chan and Park (2005) . The swaprinc() package streamlines comparisons between a raw regression model with the full set of raw independent variables and a principal component regression model where principal components are estimated on a subset of the independent variables, then swapped into the regression model in place of those variables. The swaprinc() function compares one raw regression model to one principal component regression model, while the compswap() function compares one raw regression model to many principal component regression models. Package functions include parameters to center, scale, and undo centering and scaling, as described by Harvey and Hansen (2022) . Additionally, the package supports using Gifi methods to extract principal components from categorical variables, as outlined by Rossiter (2021) . Package: r-cran-swarm Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-splancs, r-cran-geosphere, r-cran-lubridate, r-cran-mass Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-swarm_0.6.0-1.ca2604.1_all.deb Size: 214422 MD5sum: 445656dd00383906a6633663dd12de70 SHA1: b2a971e4c06731391a80c1601a5b0a90d21fe371 SHA256: da9e3dc49bb91877e459878546b5407326bd28b1bc4ecdcc11ba7b75e85ed21e SHA512: 8c99ec9c4908a60027a6592d8c38809e5cb9c0b982aab660ce27586595c9fd7106fe587cc7dc2e5b20bb2879abfdacb949f7db589ce92c782392051827e561f4 Homepage: https://cran.r-project.org/package=swaRm Description: CRAN Package 'swaRm' (Processing Collective Movement Data) Function library for processing collective movement data (e.g. fish schools, ungulate herds, baboon troops) collected from GPS trackers or computer vision tracking software. 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The swarm space concept, the metrics and data sets included are described in: Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King Andrew J. (2023) . Package: r-cran-swash Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-sf, r-cran-spdep, r-cran-zoo, r-cran-strucchange Filename: pool/dists/resolute/main/r-cran-swash_2.0.0-1.ca2604.1_all.deb Size: 5575218 MD5sum: e2d9e7bedf1fe058a7e0a2ab40f163d3 SHA1: 56643c79014fe4d91e3f66e4ddaa725701138475 SHA256: 717ee7f494d059657579d821edea30bd82b7f6cf357799fa546e1a38bd35b271 SHA512: 16f37a014c4e368a06174d1aa407c62f19c3f56022749a81187c56ed12ef68807f64353761213e671d757d0ecda0e734c9ec4bf80f054a3d4f9f5e41eebb968a Homepage: https://cran.r-project.org/package=swash Description: CRAN Package 'swash' (Health Geography Toolbox for Model-Based Analysis of InfectionsPanel Data) Within epidemic outbreaks, infections grow and decline differently between regions, and the velocity of spatial spread differs between countries. 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Package: r-cran-swcecon Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-swcecon_0.1.0-1.ca2604.1_all.deb Size: 146328 MD5sum: dc1eec4fa3847ab6658dac4955e36c67 SHA1: c358a9581d0a4c1cd52218865fd98e026b9ad887 SHA256: 7899816e984e0963df6177714c75f8923f0ea9b6fa9230fe06991f64fad963dc SHA512: 2a8f9838f2dbc4b6326ed4bc7eb3a389c804e4c2767be3c36067234cf06235e76dc45ad8c7c8f0b1abc0abdfde2e91afc0d2e17c761b728ee81050378f18502f Homepage: https://cran.r-project.org/package=swcEcon Description: CRAN Package 'swcEcon' (Economic Analysis of Soil and Water Conservation Measures inWatersheds) Provides functions and benchmark datasets for the economic appraisal of soil and water conservation (SWC) measures in watershed development projects. Implements benefit-cost ratio (BCR), net present value (NPV), internal rate of return (IRR) via the bisection method of Brent (1973, ISBN:9780130223715), modified BCR, marginal rate of return using the CIMMYT (1988, ISBN:9686127127) method, payback period, soil loss economic valuation via the Universal Soil Loss Equation of Wischmeier and Smith (1978, ISBN:0160016258), groundwater recharge valuation, employment generation ratio, sensitivity analysis, switching value analysis, and Monte Carlo simulation. Six datasets are included: state-wise BCR benchmarks from NABARD (2019) watershed evaluations, USLE erodibility parameters for Indian soil orders from NBSS and LUP, rainfall erosivity for twenty Indian districts from IMD data, SWC unit cost norms from PMKSY-WDC (GoI 2015), and two hypothetical datasets for illustration. Methods follow Gittinger (1982, ISBN:9780801825439) and Squire and van der Tak (1975, ISBN:9780801816697). Package: r-cran-swcrtdesign Architecture: all Version: 4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-lmertest, r-cran-glmmtmb Filename: pool/dists/resolute/main/r-cran-swcrtdesign_4.1-1.ca2604.1_all.deb Size: 226864 MD5sum: a7838aacfacef85a967736fcef836ffa SHA1: 1dd3a37ce57e3a545f5b403467931ed5fb9cc0ca SHA256: 7c39a65289ce6c0b991863d72ba85d06d9f26003e38c5a35e420e33d32a8e434 SHA512: 4055e570f6cf84c3f90530b29ebe2f312dfbe0d4985b8e6d8c92d5522c4e608e7ced7ff41f7586d6dc22ca6c33b2e752fadc94aa8b11f032ef97c8f5002f26e9 Homepage: https://cran.r-project.org/package=swCRTdesign Description: CRAN Package 'swCRTdesign' (Stepped Wedge Cluster Randomized Trial (SW CRT) Design) A set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional or cohort sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191). 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These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices. For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986) , Singh, D., Basak, P., Kumar, R. and Ahmad, T. (2023) . 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Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (). 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Package: r-cran-sym.arma Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sym.arma_1.0-1.ca2604.1_all.deb Size: 138236 MD5sum: 1a2f651687d47dd7c7e2b01b3087c250 SHA1: ffca3988edaa37d3814b1ff85238d90f16f1c7ec SHA256: 856ecbf7dbadba9604912961f76c7f4dc40135173fe19c525078d907b0174885 SHA512: 9b11a6d699596447ccfbbe464bdf58a465d94d5f6f58a6057ab9d741d188c584cc5fd66f9856d572e61db8e8b64d24268b74ad435570ece77b25da0c450c256c Homepage: https://cran.r-project.org/package=sym.arma Description: CRAN Package 'sym.arma' (Autoregressive and Moving Average Symmetric Models) Functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), . Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2. 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(Milligan, G.W., Cooper, M.C. (1985) , Breiman, L. (1996), , Hubert, L., Arabie, P. (1985), , Ichino, M., & Yaguchi, H. (1994), , Rand, W.M. (1971) , Breckenridge, J.N. (2000) , Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) , Dudek, A. (2007), ). 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The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone. 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Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) and Jiang, Z., Sharma, A., & Johnson, F. (2020) associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) . 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Package: r-cran-synthesizer Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-simplermarkdown Filename: pool/dists/resolute/main/r-cran-synthesizer_0.6.0-1.ca2604.1_all.deb Size: 124624 MD5sum: 4bd105b824fe359f3588717d98cd91ca SHA1: 22d9b442c80928d1ea275b4daba51223e1b9f4c9 SHA256: e438059f6fac2e53533abdb9b8a1fde55779dff443e26264e41ebf8730fc0715 SHA512: 5af9b48c6079ee485fae69a2d477e0abcfc9ca2116439f615353bb3dfa4a5e2c4174cc16ab6e2ad4943e0e9c8d979b1fb246bd968fbebb5c9b86756e9cf6b7a6 Homepage: https://cran.r-project.org/package=synthesizer Description: CRAN Package 'synthesizer' (Fast, Robust, and High-Quality Synthetic Data Generation with aTuneable Privacy-Utility Trade-Off) Synthesize numeric, categorical, mixed and time series data. Data circumstances including mixed (or zero-inflated) distributions and missing data patterns are reproduced in the synthetic data. A single parameter allows balancing between high-quality synthetic data that represents correlations of the original data and lower quality but more privacy safe synthetic data without correlations. Tuning can be done per variable or for the whole dataset. Package: r-cran-synthetic Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4975 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-actuar Filename: pool/dists/resolute/main/r-cran-synthetic_1.1.1-1.ca2604.1_all.deb Size: 4275768 MD5sum: 9f02e07130889debb69e486fbfa11127 SHA1: 825346fc914d6faee0511fadd5d0347fbd97077a SHA256: 447f9aeea484af4cffa5cae252fa4328b9d25c56116544597b221311818eb755 SHA512: e5869634bdb7002b7a694d09bf6db4c664aac45aec06b20bf16027241139a775f8d5764ab45da8743f7c6165021e0ed38b170ef6dbdc814666a5dce780e559ff Homepage: https://cran.r-project.org/package=SynthETIC Description: CRAN Package 'SynthETIC' (Synthetic Experience Tracking Insurance Claims) Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" . Package: r-cran-syntheticdata Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-syntheticdata_0.1.0-1.ca2604.1_all.deb Size: 186572 MD5sum: 0bef2becff1394f94923cbd52eb4018c SHA1: 02ae3402c9e943a42154bd4ea0777f9fe798168a SHA256: 1fdd8c99908a834fe5897b03534c051a41aaab72241d02f06ca2e1981c1fd701 SHA512: 1dbb20fbcce8e5805974eb2b27024f8e8fa3dd9b15c0da8f339d594bc22dc1cedae9fc8f2cd1672f2dbd6cc33c53c032f1f4ddebef92f6b966e33b01bd1a46f8 Homepage: https://cran.r-project.org/package=syntheticdata Description: CRAN Package 'syntheticdata' (Synthetic Clinical Data Generation and Privacy-PreservingValidation) Generates synthetic clinical datasets that preserve statistical properties while reducing re-identification risk. Implements Gaussian copula simulation, bootstrap with noise injection, and Laplace noise perturbation, with built-in utility and privacy validation metrics. Useful for privacy-aware data sharing in multi-site clinical research. Validates synthetic data quality via distributional similarity (Kolmogorov-Smirnov), discriminative accuracy (real-vs-synthetic classifier), and nearest-neighbor privacy ratio. Methods described in Jordon et al. (2022) and Snoke et al. (2018) . Package: r-cran-synthpop Architecture: all Version: 1.9-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2098 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-nnet, r-cran-ggplot2, r-cran-rpart, r-cran-party, r-cran-foreign, r-cran-plyr, r-cran-proto, r-cran-polspline, r-cran-randomforest, r-cran-ranger, r-cran-classint, r-cran-mipfp, r-cran-survival, r-cran-stringr, r-cran-rmutil, r-cran-broman, r-cran-forcats Filename: pool/dists/resolute/main/r-cran-synthpop_1.9-2-1.ca2604.1_all.deb Size: 1815962 MD5sum: 29528651a0d52bd5e2a0aabb31a60db8 SHA1: f032af58aafef6937b799855c8c21038805e9b41 SHA256: f335603f792c176a6c00a0438e1526cf9e6b1896c8f338f69baff59b6f0df0cf SHA512: f0b31b87c4326f55f125a3b3e15c590e616f82a1ab1fcbe678e002f338cd4cfc31fa8633032e66c646e66bed2497fe384dbff1fdc6e5464ffcaa7fd967b8478d Homepage: https://cran.r-project.org/package=synthpop Description: CRAN Package 'synthpop' (Generating Synthetic Versions of Sensitive Microdata forStatistical Disclosure Control) A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) . Functions to assess identity and attribute disclosure for the original and for the synthetic data are included in the package, and their use is illustrated in a vignette on disclosure (Practical Privacy Metrics for Synthetic Data). Package: r-cran-synthreturn Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-data.table, r-cran-quadprog, r-cran-mirai Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-synthreturn_1.0.0-1.ca2604.1_all.deb Size: 309688 MD5sum: 0770d8f8baf91d686bab956600c85000 SHA1: d070bcf90f5b0586c1579069d06f55114e81fd7a SHA256: 8a069d940d6972704d07af53319233a62b1d6092c2030f1f2e05d4d6c69e2f4d SHA512: f97732f45383547fc2c96e26cb422f2cf58dc5b55ab4a463734ee3c30ab1722fc012caa5bcfee09103b25fb97cd3969e31531b0ad62430d7e22a48d9ea86971a Homepage: https://cran.r-project.org/package=synthReturn Description: CRAN Package 'synthReturn' (Synthetic Matching Method for Returns) Implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) , building on the original approach of Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) , to estimate the cumulative treatment effect of an event on treated firms’ stock returns. Package: r-cran-synthtools Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rdpack Suggests: r-cran-synthpop, r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-synthtools_1.0.1-1.ca2604.1_all.deb Size: 100824 MD5sum: 528790f621932d8eba764c3e81a86750 SHA1: a4aa570e375cf8faefd2810c01bb2dc00c25132e SHA256: 53de69c466ab2d88cb59c0dcd263636a86c2f14eae5bfe0fdcdf4f9c73deaf31 SHA512: 6bd183bf3f6f192cceead9f984b685b85939d4412a9918c4fbc61ebbe35d37ebabed5d1957fcbdc7bb808b81a9fcf444c9ba7918e3873443450786b748d9d0c0 Homepage: https://cran.r-project.org/package=SynthTools Description: CRAN Package 'SynthTools' (Tools and Tests for Experiments with Partially Synthetic DataSets) A set of functions to support experimentation in the utility of partially synthetic data sets. All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) . Package: r-cran-syrup Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bench, r-cran-callr, r-cran-dplyr, r-cran-ps, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-syrup_0.1.4-1.ca2604.1_all.deb Size: 110320 MD5sum: f6e5d6364ea40eca513213b601a7fcbb SHA1: 4fba9cead405f35b2b75d9f863f9470118630af1 SHA256: fe5668b1b6dee1d62151ad1a62b88eadb9d187ef2ed2fc0417bf721eff78d764 SHA512: 83e38b4526755182fef2999eb0a653b8cd6bbc8479450d3e97c174dc25cbd2afa6e0e24da11747088574428b39f7c7341a00fa7fb8534f902641560cd1e3e26a Homepage: https://cran.r-project.org/package=syrup Description: CRAN Package 'syrup' (Measure Memory and CPU Usage for Parallel R Code) Measures memory and CPU usage of R code by regularly taking snapshots of calls to the system command 'ps'. The package provides an entry point (albeit coarse) to profile usage of system resources by R code run in parallel. 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To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs. 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The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) ). This package is the 'R' analogy to the 'Matlab' code published by Flood & Korenko in above-mentioned paper. Package: r-cran-sysid Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal, r-cran-tframe, r-cran-ggplot2, r-cran-reshape2, r-cran-polynom, r-cran-bitops, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-sysid_1.0.5-1.ca2604.1_all.deb Size: 636982 MD5sum: 5d01e1125e20853e74074360a1d4c1ad SHA1: 146f78a1ab40c306d8a83e4d58f155574d4336a3 SHA256: f603ef83bc63b820152202db73ec0d4843fb6559169357284d4c49e1285432a3 SHA512: e7eec54b206c64fd2e66bc5341dfb67eb575076f22ff4445f85244ecea3b335a56290fc4ef2063152d12ab866c38813a798c16dcf218500183fe029d2938df61 Homepage: https://cran.r-project.org/package=sysid Description: CRAN Package 'sysid' (System Identification in R) Provides functions for constructing mathematical models of dynamical systems from measured input-output data. Package: r-cran-syslognet Architecture: all Version: 0.1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-syslognet_0.1.2.1-1.ca2604.1_all.deb Size: 11378 MD5sum: 4a215b01af61c1537d86330dc8c097b1 SHA1: d9439379070d89e76dd3f38bf24806b9780b8200 SHA256: c8a6125f2a5904fa98f53c323de9ed40c402765032965439b9a71bf0f1a9f390 SHA512: df6d454653c500e97a2dafceac557b99b3174326e0f11df923b44610e6bf3290fc0778adcc799a1c60077bf1c46fe19689a17369b93b372bfbcc8082dba52a75 Homepage: https://cran.r-project.org/package=syslognet Description: CRAN Package 'syslognet' (Send Log Messages to Remote 'syslog' Server) Send 'syslog' protocol messages to a remote 'syslog' server specified by host name and TCP network port. Package: r-cran-sysrecon Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-ggplot2, r-bioc-ggtree, r-cran-magrittr, r-cran-patchwork, r-cran-plyr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-snowballc, r-cran-stringr, r-cran-tm Filename: pool/dists/resolute/main/r-cran-sysrecon_0.1.3-1.ca2604.1_all.deb Size: 197310 MD5sum: 496f430fa1d064d96b4c5a79da46ded8 SHA1: f3e9703e9c83c32e808ab564bf4e63755be12379 SHA256: 6edf48b52f44b88eb9ec8e39fb475b73db13bddf77bfa7630d57eb7c82d95747 SHA512: d4b255aa1438f4d37e4df781c2d6de1001a9a679365a2628088282400cd0e0ddba4193e6c722d0cf400ef5842d9af90429da090e27ee5d7b8e5da6745d120114 Homepage: https://cran.r-project.org/package=Sysrecon Description: CRAN Package 'Sysrecon' (Systematical Metabolic Reconstruction) In the past decade, genome-scale metabolic reconstructions have widely been used to comprehend the systems biology of metabolic pathways within an organism. Different GSMs are constructed using various techniques that require distinct steps, but the input data, information conversion and software tools are neither concisely defined nor mathematically or programmatically formulated in a context-specific manner.The tool that quantitatively and qualitatively specifies each reconstruction steps and can generate a template list of reconstruction steps dynamically selected from a reconstruction step reservoir, constructed based on all available published papers. Package: r-cran-systemfit Architecture: all Version: 1.1-30-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-car, r-cran-lmtest, r-cran-sandwich, r-cran-mass Suggests: r-cran-knitr, r-cran-plm, r-cran-sem Filename: pool/dists/resolute/main/r-cran-systemfit_1.1-30-1.ca2604.1_all.deb Size: 599302 MD5sum: 43b6bb3123be2a6717cdd4814e696246 SHA1: e9bef99e1816e2394955760db998c264fe0fa744 SHA256: 7726f1289974fa782c12d2614f470c98f181d403c829872b8058cc03ab687a70 SHA512: 3150b198833be1dee4107701b7c89ba3e92a719a491a82a86a33c672669f0010261eb9ce4d4d63b05c04acf978a88794ec66c27b405a8f07ed20cc69ecdc8021 Homepage: https://cran.r-project.org/package=systemfit Description: CRAN Package 'systemfit' (Estimating Systems of Simultaneous Equations) Econometric estimation of simultaneous systems of linear and nonlinear equations using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS) as suggested, e.g., by Zellner (1962) , Zellner and Theil (1962) , and Schmidt (1990) . Package: r-cran-systemicr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2585 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-quantreg, r-cran-xts Filename: pool/dists/resolute/main/r-cran-systemicr_0.1.0-1.ca2604.1_all.deb Size: 2598462 MD5sum: c98cecfd232941c4f20473bf3e70fe53 SHA1: 4605477e9ab456503dfd7ec197ebdd01cd88b5d2 SHA256: d1a25ede829a8d517f4252b9afaf9716bddd576446094b3da421a94d0be073ba SHA512: 606867b300173abc5248bdd9c599a0fb7dc75446bfcfdd6fdbe9a46fbee3b018281dcf74a4ade1ff9223e66b328a6e636c75476bb2d04d1b5f6b02554b222338 Homepage: https://cran.r-project.org/package=SystemicR Description: CRAN Package 'SystemicR' (Monitoring Systemic Risk) The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) and Billio, Getmansky, Lo and Pelizzon (2012) . Package: r-cran-syt Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-partitions Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-syt_0.5.0-1.ca2604.1_all.deb Size: 164716 MD5sum: e316e12155440fe4d4e0daf4c72b3691 SHA1: 3ed27d36cecdab97cacc6e2a9fa60f6cfb754948 SHA256: fdad3488973fb0e569ecfcd3e4736bbbd34b101a5c0dad836d02f62e6bc29ace SHA512: eaa714b5f63fc47e527e65fc8b090597fd183f8d44b4ca414ccc509329c3058f7001ace98544eefe340fc83691d4411877dca0741ea4a3a9810059fb7e650066 Homepage: https://cran.r-project.org/package=syt Description: CRAN Package 'syt' (Young Tableaux) Deals with Young tableaux (field of combinatorics). For standard Young tabeaux, performs enumeration, counting, random generation, the Robinson-Schensted correspondence, and conversion to and from paths on the Young lattice. Also performs enumeration and counting of semistandard Young tableaux, enumeration of skew semistandard Young tableaux, enumeration of Gelfand-Tsetlin patterns, and computation of Kostka numbers. 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Implemented dictionaries include "syuzhet" (default) developed in the Nebraska Literary Lab "afinn" developed by Finn Årup Nielsen, "bing" developed by Minqing Hu and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the "get_sentiment" function. The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization. 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Package: r-cran-tabler Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-dt, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tabler_0.1.0-1.ca2604.1_all.deb Size: 3488362 MD5sum: 051dafdbf513c881aafab83047188bba SHA1: b9e5e125d6da6213d02216a8f3ead621f9ecb074 SHA256: 0ae2c4caec32097e219a518836ce8dc2454f440f2384847a5585e100d703b4ad SHA512: 11bde898a40ac6fce9885514ad556a4a9ba9387712a488383f5aae80e15315fd9a4c52338cc8b3d5ef823136ad84d7018150052e8c1fafed2fc3c5c9112f9f6b Homepage: https://cran.r-project.org/package=tabler Description: CRAN Package 'tabler' (Create Dashboards with 'Tabler' and 'Shiny') Provides functions to build interactive dashboards combining the 'Tabler UI Kit' with 'Shiny', making it easy to create professional-looking web applications. 'Tabler' is fully responsive and compatible with all modern browsers. 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Package: r-cran-tabnet Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coro, r-cran-data.tree, r-cran-dials, r-cran-dplyr, r-cran-ggplot2, r-cran-hardhat, r-cran-magrittr, r-cran-matrix, r-cran-parsnip, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-torch, r-cran-tune, r-cran-vctrs, r-cran-withr, r-cran-zeallot Suggests: r-cran-cli, r-cran-knitr, r-cran-modeldata, r-cran-patchwork, r-cran-quarto, r-cran-recipes, r-cran-rmarkdown, r-cran-rsample, r-cran-spelling, r-cran-testthat, r-cran-tidymodels, r-cran-tidyverse, r-cran-vip, r-cran-visdat, r-cran-workflows, r-cran-xgboost, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-tabnet_0.8.0-1.ca2604.1_all.deb Size: 4014704 MD5sum: a463c11d4b2f5e70a3632ed69c606147 SHA1: 4ddd1bf2ad2931988cd1d9a85a8f55393b168152 SHA256: c302b6a032095619c8051aa097b57e1f92da7ce968a639008f623c0f0192c0a2 SHA512: 0a53e576e72cfa6c800c140d924dc76e08bc26dd8d2b99a7a2fbc96e88618ccc2dec3c3009762c77e539b12e94eee784c50397c1e7f04349d85346a2040cdc80 Homepage: https://cran.r-project.org/package=tabnet Description: CRAN Package 'tabnet' (Fit 'TabNet' Models for Classification and Regression) Implements the 'TabNet' model by Sercan O. Arik et al. (2019) with 'Coherent Hierarchical Multi-label Classification Networks' by Giunchiglia et al. and provides a consistent interface for fitting and creating predictions. It's also fully compatible with the 'tidymodels' ecosystem. Package: r-cran-taboolar Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-taboolar_0.1.0-1.ca2604.1_all.deb Size: 22986 MD5sum: 43c982a7b9e879048690bdc01224d916 SHA1: 77ed17399ac97501b21f7b738c33cdc51903d8b7 SHA256: 7f59f084bbb31639c7abbce59fd4c5d4f347c6a9f4b8620eebf3fc992746d4cd SHA512: 02950fef84b875b38da4e8b47d27ff81f2702af6cb5e1d78944b3f48ead423674a2e4ffb02caf84db4ced3e8846da373c77e2182f0522826c5bd1a07d489da9a Homepage: https://cran.r-project.org/package=taboolaR Description: CRAN Package 'taboolaR' (Get Data from 'Taboola' via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from 'Taboola' using the 'Windsor.ai' API . Package: r-cran-tabpfn Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-hardhat, r-cran-purrr, r-cran-reticulate, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-modeldata, r-cran-recipes, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tabpfn_0.2.0-1.ca2604.1_all.deb Size: 251988 MD5sum: 914ff5d61b7e397ca7d06c8d519f396d SHA1: 7e06c744ce634a7dd137c70f608bfcd8ac095657 SHA256: 0293871d1b7525a1ee0124e10a8a22fab871ea156f74d7f810d30bd800016337 SHA512: 6f1144ba436da81b743795a7d5f80f41012160901bd9626b3997c7dadcec1e7bd112c7d2fe6805f19d6ad0479b97d4050ced413b94e592ec938217188030ba43 Homepage: https://cran.r-project.org/package=tabpfn Description: CRAN Package 'tabpfn' (Prior-Data Fitted Network Foundational Model for Tabular Data) Provides a consistent API for classification and regression models based on the 'TabPFN' model of Hollmann et al. (2025), "Accurate predictions on small data with a tabular foundation model," Nature, 637(8045) . The calculations are served via 'Python' to train and predict the model. Package: r-cran-tabr Architecture: all Version: 0.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1334 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-fansi, r-cran-gridextra, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-png, r-cran-rmarkdown, r-cran-testthat, r-cran-tuner Filename: pool/dists/resolute/main/r-cran-tabr_0.5.5-1.ca2604.1_all.deb Size: 861172 MD5sum: e245d304d26c8e88d930549b856e7419 SHA1: 098af1a66211990d798000228555cd58ea4c024c SHA256: 842d8bd260acce79c785531143b9b659342ac2397599bcd26630939f2c61f2f6 SHA512: 362d1120eefc39eb3af44d870f02b72cf236a03467951da65afa72660c5bda1d6cc01d8fac6c1d61edaea8a43320089c7c82bcfc7c6a2518e7e6462ce98257be Homepage: https://cran.r-project.org/package=tabr Description: CRAN Package 'tabr' (Music Notation Syntax, Manipulation, Analysis and Transcriptionin R) Provides a music notation syntax and a collection of music programming functions for generating, manipulating, organizing, and analyzing musical information in R. Music syntax can be entered directly in character strings, for example to quickly transcribe short pieces of music. The package contains functions for directly performing various mathematical, logical and organizational operations and musical transformations on special object classes that facilitate working with music data and notation. The same music data can be organized in tidy data frames for a familiar and powerful approach to the analysis of large amounts of structured music data. Functions are available for mapping seamlessly between these formats and their representations of musical information. The package also provides an API to 'LilyPond' () for transcribing musical representations in R into tablature ("tabs") and sheet music. 'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. The package generates 'LilyPond' files from R code and can pass them to the 'LilyPond' command line interface to be rendered into sheet music PDF files or inserted into R markdown documents. The package offers nominal MIDI file output support in conjunction with rendering sheet music. The package can read MIDI files and attempts to structure the MIDI data to integrate as best as possible with the data structures and functionality found throughout the package. 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Package: r-cran-tabshiftr Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-tidyselect, r-cran-testthat, r-cran-crayon, r-cran-purrr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-readr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-tabshiftr_0.4.1-1.ca2604.1_all.deb Size: 621246 MD5sum: 136f607df03fe73636eacc8a224e53b0 SHA1: dca929dd515c2ece4da18e069ca8bdab56761951 SHA256: f3ed69f4dd8839f8ea4dd8979d2c3893bb0d659b455036c8327f607952ffcbaa SHA512: 9beefd95975ae52610b621d29952d00213ebf23553d514dca50473990e0b86f5397366de561d1e54b908c7c256f80c87d21e95eaf7123fe88cce286e96e061e9 Homepage: https://cran.r-project.org/package=tabshiftr Description: CRAN Package 'tabshiftr' (Reshape Disorganised Messy Data) Helps the user to build and register schema descriptions of disorganised (messy) tables. Disorganised tables are tables that are not in a topologically coherent form, where packages such as 'tidyr' could be used for reshaping. The schema description documents the arrangement of input tables and is used to reshape them into a standardised (tidy) output format. Package: r-cran-tabstats Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-tibble, r-cran-vctrs Suggests: r-cran-broom, r-cran-rstatix, r-cran-glue, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-fansi, r-cran-crayon, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-tabstats_0.1.0-1.ca2604.1_all.deb Size: 355208 MD5sum: 75693bb33d04c2908c4998da03d89480 SHA1: 45e6d0c7fc4f52ca6714e6b25cdf98bbb0ffdf45 SHA256: baa6745de6d07b986c2745c93e63e9ea923bbf514d51c083b81155d04d41e861 SHA512: aee1b9e1e56dffbf3e7844d2f33e7572f33f7939a24dee9c890b90971599b0cd60fe5e2573a19a18095cc29b8e3c41335cbf65e9cd6a00cecf4dc77afaf1c2f0 Homepage: https://cran.r-project.org/package=tabstats Description: CRAN Package 'tabstats' (A Lightweight Toolkit for Displaying Customizable Tables) A lightweight toolkit that provides functions for printing tables from input data in the R console or terminal with customizable formatting. 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This tool can reduce time and effort in data extraction processes in fields like investigative journalism. It allows for automatic and manual table extraction, the latter facilitated through a 'Shiny' interface, enabling manual areas selection\ with a computer mouse for data retrieval. Package: r-cran-tabularaster Architecture: all Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fasterize, r-cran-magrittr, r-cran-raster, r-cran-silicate, r-cran-spatstat.geom, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-tabularaster_0.7.2-1.ca2604.1_all.deb Size: 1958226 MD5sum: cd2fd4fa8fb1ae9a23e9728add1da82d SHA1: 25ad43820adf59d079d3971c3845c0b9103c648d SHA256: ad86af629b74feaeb3291f664af66b9a1d27f6c0fbb42902f7fa00ca62da8b1f SHA512: 3d1eb9e9d2a9dc4c20617936761d2159e6a06e25155d4fbc5d5878314a795f9e103a49c0dd1330bef9f1fd132d82e1edf3324ef33dc287610a2f3806ed340274 Homepage: https://cran.r-project.org/package=tabularaster Description: CRAN Package 'tabularaster' (Tidy Tools for 'Raster' Data) Facilities to work with vector and raster data in efficient repeatable and systematic work flow. Missing functionality in existing packages is included here to allow extraction from raster data with 'simple features' and 'Spatial' types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use. Package: r-cran-tabularmaps Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggforce, r-cran-ggplot2, r-cran-purrr, r-cran-rlang Suggests: r-cran-countrycode, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tabularmaps_0.1.0-1.ca2604.1_all.deb Size: 203166 MD5sum: 240230c9c1b43c5bab9f3c8db6991a0b SHA1: 64655c7a00047a5d819c80df61319f7f4020f76e SHA256: 2cd79e9ead752529fa89fa8b2f25ea6055c22294f012c9083af7bc524ba1b468 SHA512: 47cf150e6d4e57aa7463cd4e46ea88c0c6bd56218364890ee1eaaed53a159e9af6fb9cef8960b7b9da393e34a5bcc191bd21915141d0ac6e474bca2ebba5383e Homepage: https://cran.r-project.org/package=tabularmaps Description: CRAN Package 'tabularmaps' (Create Tile-Grid Geographical Maps) The 'tabularmap' is one of the visualization methods for efficiently displaying data consisting of multiple elements by tiling them. When dealing with geospatial, it corrects for differences in visibility between areas. Package: r-cran-tabulator Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-data.table, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-tabulator_1.0.0-1.ca2604.1_all.deb Size: 24130 MD5sum: b2abf5447ab1528316a5a1edb44fcf44 SHA1: a162baeb482fcdd743185e5e752f3b89345f7550 SHA256: 0f1ed33f45f6402ed976686a04236759a1df0b7738f45ddb6bfe78c6d3c140e7 SHA512: f3efd0504383e40748079c9e7eac7101e3cfd2a5c2d67e417a33be51c2c8a6380ba524e3810c02440d100e677866c829f610b99e106b9828411eef070568d175 Homepage: https://cran.r-project.org/package=tabulator Description: CRAN Package 'tabulator' (Efficient Tabulation with Stata-Like Output) Efficient tabulation with Stata-like output. 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This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve). 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The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) . 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Rapid and visual insights into the nature, timing and quality of time-aligned annotations in conversational corpora. For more details, see Dingemanse et al., (2022) . 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Package: r-cran-tandem Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tandem_1.0.3-1.ca2604.1_all.deb Size: 95874 MD5sum: d69fc891ddb1c6a1335502e346459c81 SHA1: ced33d7ff3e94580d34fc4e69a457cb14c23b865 SHA256: a4dc568d0a66719631105c786aaa88543bf42936fda50c5216a9dd16f64997f5 SHA512: 0f180d4511b2eacd0124608ed1640326518e5b6360d6280865f1fd608f81a683480795f1f69cb8bec128064b52e70c4c54c8483a75e661ee1cab3f03715f34fe Homepage: https://cran.r-project.org/package=TANDEM Description: CRAN Package 'TANDEM' (A Two-Stage Approach to Maximize Interpretability of DrugResponse Models Based on Multiple Molecular Data Types) A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. 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Package: r-cran-tangram.pipe Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tangram.pipe_1.1.2-1.ca2604.1_all.deb Size: 186212 MD5sum: f68db90502a1d2658024df530f6a3d5d SHA1: f3bd416f8c1ef8e4f483c663938525aa32a70d67 SHA256: 34259ab1bba4d2d995b831785c3562196189f37095df9d3c292dae333aeef809 SHA512: dc335d0ead466ccacbeb7c876e60cadb657f966694e37a7cf104a46767b8d0fffcdc5917356ce4949ca6f714cfbe6e1ad216298428681fd9dd7ccaea2f3485ca Homepage: https://cran.r-project.org/package=tangram.pipe Description: CRAN Package 'tangram.pipe' (Row-by-Row Table Building) Builds tables with customizable rows. Users can specify the type of data to use for each row, as well as how to handle missing data and the types of comparison tests to run on the table columns. Package: r-cran-tangram Architecture: all Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 926 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-magrittr, r-cran-knitr, r-cran-stringi, r-cran-stringr, r-cran-base64enc, r-cran-digest, r-cran-htmltools Suggests: r-cran-testthat, r-cran-rms, r-cran-rmarkdown, r-cran-hmisc, r-cran-sandwich, r-cran-dplyr, r-cran-matching, r-cran-epitools Filename: pool/dists/resolute/main/r-cran-tangram_0.8.3-1.ca2604.1_all.deb Size: 792224 MD5sum: 5170521fae510a35c3226929aa15949e SHA1: 0e7ab02cedddb495831bdcab02b796538a6bd9a8 SHA256: 0b41fc59735659a0f0bfd93552900287a10a28595ec1b0891516df41abc6dd06 SHA512: 0e35b4c5de84126060d448a95ede828aa050c5624bf3bf0cbe9f8527f771031eea5e42b298526322564f4e9c85c0d6b5e5eeb28bd531a044fca1ef2a2d7dd7e2 Homepage: https://cran.r-project.org/package=tangram Description: CRAN Package 'tangram' (The Grammar of Tables) Provides an extensible formula system to quickly and easily create production quality tables. The processing steps are a formula parser, statistical content generation from data as defined by formula, followed by rendering into a table. Each step of the processing is separate and user definable thus creating a set of composable building blocks for highly customizable table generation. A user is not limited by any of the choices of the package creator other than the formula grammar. For example, one could chose to add a different S3 rendering function and output a format not provided in the default package, or possibly one would rather have Gini coefficients for their statistical content in a resulting table. Routines to achieve New England Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are provided. The package contains rendering for HTML5, Rmarkdown and an indexing format for use in tracing and tracking are provided. Package: r-cran-taper Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-taper_0.5.3-1.ca2604.1_all.deb Size: 341240 MD5sum: a04d540be530385c7dc83b7dcb976405 SHA1: 7f843bc119da835ab2b0d69f683b8bcfb6a8b1e8 SHA256: b9b04672003554d7ec5ae046e2eebc33b058181e01098476a41b99f265e07857 SHA512: fc3ce2973da182494eb0f0f5f405a8803ff2ff0a2b5c4bce016a8c70ed0fbb093bfc6661e2179ea37f4ddc328b4c1aa82d2facbb40f525fab498509f6ac446e4 Homepage: https://cran.r-project.org/package=TapeR Description: CRAN Package 'TapeR' (Flexible Tree Taper Curves Based on Semiparametric Mixed Models) Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree heights are not measured, methods for estimating additional variance in volume predictions resulting from uncertainties in tree height models (tariffs) are provided. The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013) . Package: r-cran-tapkee Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-rgl, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-tapkee_1.2-1.ca2604.1_all.deb Size: 224462 MD5sum: 8001dabe1b9ccd93c84b09d36d454741 SHA1: dff3069a16af85cd4e6765696c976673853ae5e0 SHA256: afc1725aa6624edc07065c8835437fc039d9db7775eabd98fc29fd9897c643c2 SHA512: 2e1a4d3e3fe44c8ecae0ffc3421d54bf4deb15d1a05c66d1d517e49e878195eff8e626dc3d2dbdcbe2c7fa27926502b12c09005a5aeaaa2ea3f426b975388298 Homepage: https://cran.r-project.org/package=tapkee Description: CRAN Package 'tapkee' (Wrapper for 'tapkee' Dimension Reduction Library) Wrapper for using 'tapkee' command line utility, it allows to run it from inside R and catch the results for further analysis and plotting. 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Package: r-cran-tapnet Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-bipartite, r-cran-mpsem, r-cran-phytools, r-cran-vegan Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-tapnet_0.6-1.ca2604.1_all.deb Size: 281838 MD5sum: f621d90a8aafaec2d952f63925fa0b02 SHA1: e4f62823409b30c37c0baf04be06ece2c5c7e630 SHA256: 975ca600ef2f10d2e19cfa3546d87a750b686c407a3805e890572d19e9a53807 SHA512: 71d35b942d38c3d7d0e169023672f3b37c783ea48cd33e7b77abfbfcb4f8295b4a9a75e8cf31507d0a48cace5dd5c6ded3228d9d6863baca202b3ec751a4029c Homepage: https://cran.r-project.org/package=tapnet Description: CRAN Package 'tapnet' (Trait Matching and Abundance for Predicting Bipartite Networks) Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press. Package: r-cran-tar Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-tar_1.0-1.ca2604.1_all.deb Size: 157272 MD5sum: 8cdce886780e2c95e37ea4e2137d67c0 SHA1: 3f155c4660476786f2595e11d756644c07d4224c SHA256: 658ab581555ab39a7ef6d4ef5705a22dd26a9bd69839e2622a516fcab362afec SHA512: 180059b660683abe78c35c2031dde64f34c95be72bfc8c712db1b393eab2e853cf0350f8c6e2ee28c19590ec350b9db43857cb4a689266089ac419c0f099b312 Homepage: https://cran.r-project.org/package=TAR Description: CRAN Package 'TAR' (Bayesian Modeling of Autoregressive Threshold Time Series Models) Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data . Package: r-cran-tarchetypes Architecture: all Version: 0.14.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-rlang, r-cran-secretbase, r-cran-targets, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-withr Suggests: r-cran-curl, r-cran-knitr, r-cran-nanoparquet, r-cran-parsermd, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-tarchetypes_0.14.1-1.ca2604.1_all.deb Size: 947462 MD5sum: 0f022c6afd067f395dea0202bbc9e434 SHA1: d086ee1819b9b8d979ad1be69cb48ed1436febee SHA256: c61ebf2f7d642dd96d5757bc31c78d58968b4f2fa29b54662d8b7ca4170789c1 SHA512: 14975cc40a81570556204c4e2073ed28bae6e17c786b804af771a19967aa12e1a492d63d6a75920500f41fc94506ee37e09e8525302dc516a15532accf37d5c8 Homepage: https://cran.r-project.org/package=tarchetypes Description: CRAN Package 'tarchetypes' (Archetypes for Targets) Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'targets' R package. by Will Landau (2018) . Package: r-cran-targets Architecture: all Version: 1.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2923 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64url, r-cran-callr, r-cran-cli, r-cran-codetools, r-cran-data.table, r-cran-igraph, r-cran-knitr, r-cran-prettyunits, r-cran-ps, r-cran-r6, r-cran-rlang, r-cran-secretbase, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-yaml Suggests: r-cran-autometric, r-cran-bslib, r-cran-clustermq, r-cran-crew, r-cran-curl, r-cran-dt, r-cran-dplyr, r-cran-fst, r-cran-future, r-cran-future.batchtools, r-cran-future.callr, r-cran-gargle, r-cran-googlecloudstorager, r-cran-gt, r-cran-keras, r-cran-markdown, r-cran-nanonext, r-cran-rmarkdown, r-cran-parallelly, r-cran-paws.common, r-cran-paws.storage, r-cran-pkgload, r-cran-processx, r-cran-qs2, r-cran-reprex, r-cran-rstudioapi, r-cran-r.utils, r-cran-shiny, r-cran-shinybusy, r-cran-shinywidgets, r-cran-tarchetypes, r-cran-testthat, r-cran-torch, r-cran-usethis, r-cran-visnetwork Filename: pool/dists/resolute/main/r-cran-targets_1.12.0-1.ca2604.1_all.deb Size: 2367334 MD5sum: c9ceb8f90857e54d0549c585dad626d4 SHA1: a0cc1fd7df61cac8b66458ac6e4697f64ae260e9 SHA256: e1143954b8ba44503b1b3b6bfb94bc64a36aa76c5c4401fe9be76b27820c4114 SHA512: 48a574c8ab413dea93f09cb0e509e0c39fa27ab9facd30c8c086c4c8315967a04a40b74b90ddbacbe900c51abf58c4b9602007edf493b30ee444cd375377c37b Homepage: https://cran.r-project.org/package=targets Description: CRAN Package 'targets' (Dynamic Function-Oriented 'Make'-Like Declarative Pipelines) Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, ). Package: r-cran-tariff Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tariff_1.0.5-1.ca2604.1_all.deb Size: 55574 MD5sum: f9c06ddaa772a6757c01750cea8f977f SHA1: 753c3e8fab34c0eb4d98675e9ad6c1567f4b3b04 SHA256: a3e622726bba17b2ff353fa7b0f36f49f29569863767c10295e13170c17b4a88 SHA512: 079a1fdc8cf8013a749aa563021904ab9986fb3f2b0afe2fea91623afc0171210b01bbe594389aaf1cac3b353af310f205f527d7c6d009d2cef25ba908f03e05 Homepage: https://cran.r-project.org/package=Tariff Description: CRAN Package 'Tariff' (Replicate Tariff Method for Verbal Autopsy) Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) and later refined as Tariff 2.0 in Serina, et al. (2015) . Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME. Package: r-cran-tashiny Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-igraph, r-cran-tm, r-cran-snowballc, r-cran-dplyr, r-cran-wordcloud2 Filename: pool/dists/resolute/main/r-cran-tashiny_0.1.0-1.ca2604.1_all.deb Size: 71198 MD5sum: ea2e2a78b2199ba242abcddc2f949f8e SHA1: c0765e02afd72787aaef668c2bdfa2f809d1f1df SHA256: 6e28cfc06de3dd92a20016a83c1ce07666b677e7822e3825708852c0593aca9c SHA512: f2fb284aa14110f1b99e0ff6ede617439089145aba0f2acb8a983af1d962159ec6b6af59c70b1a09aaab24cbdccec4075f80628267f5178572a58ab2ec457b08 Homepage: https://cran.r-project.org/package=TAShiny Description: CRAN Package 'TAShiny' ('Text Analyzer Shiny') Interactive shiny application for working with textmining and text analytics. Various visualizations are provided. Package: r-cran-tashu Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-dplyr, r-cran-randomforest, r-cran-plyr, r-cran-reshape2, r-cran-rcolorbrewer, r-cran-drat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tashu_0.1.1-1.ca2604.1_all.deb Size: 84292 MD5sum: a779d54d693f69df97b20ce21dbc2760 SHA1: 0ddc8cd9715d670dd8c29f0fe20ec70c331a214e SHA256: 7506c516b32acf41f985b30f9e80c226820d4c471c1a66a46e288a1de4c742d6 SHA512: bcef733dddabc0ed9e624f654961e2aa7b734092d42e702d82d135769bc4e43b3d9ec16136050cf223eeba62a6f7220c15ed9e5c96a9c7eb0af922e0b5e99dca Homepage: https://cran.r-project.org/package=tashu Description: CRAN Package 'tashu' (Analysis and Prediction of Bicycle Rental Amount) Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions. Functions on this package interacts with data on 'tashudata' package, a 'drat' repository. 'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. To install this data package, see the instructions at . top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths. daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental. create_train_dataset(), create_test_dataset() is data processing function for prediction. Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset. Users can make random-forest prediction model by using create_train_model() and predict amount of bicycle rental in 2015 by using predict_bike_rental(). Package: r-cran-taskqueue Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-settings, r-cran-stringr, r-cran-rpostgres, r-cran-ggplot2, r-cran-whisker, r-cran-rlang, r-cran-shiny, r-cran-dbi, r-cran-ssh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-taskqueue_0.2.0-1.ca2604.1_all.deb Size: 174220 MD5sum: 857a87579d8bcff20799a14efba28c17 SHA1: a9335ca4a9612cc7fb3185c4a1ca39bd7a12688b SHA256: 07024da02b281e9669b7fdf5860ccd69d1972ca9bc7433caac931f10b09645a3 SHA512: e5d1440a376e50a164616c94c3d782d60570d5f631fcd52ccb0628222bc319aae47457de231c0ee589aaf5aaf7d49cba9cf0c842dc7f3a7b0cf403639782f31f Homepage: https://cran.r-project.org/package=taskqueue Description: CRAN Package 'taskqueue' (Task Queue for Parallel Computing Based on PostgreSQL) Implements a task queue system for asynchronous parallel computing using 'PostgreSQL' as a backend. 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Therefore, because of the little use due to the reasons already mentioned, making pie charts (and related) in R is not straightforward, so other functions are needed to simplify things. In this R package there are useful functions to make 'tasty' pie charts immediately by exploiting the many cool templates provided. Package: r-cran-tastyr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1962 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tastyr_0.1.0-1.ca2604.1_all.deb Size: 1968486 MD5sum: ac04380fdcc734faca86265e80c1984e SHA1: 3adba8e156b7290cf5a136305df79008f98eb23f SHA256: 16e0ffac860ed2e39c0c3940b68bb6a416561162e0c74b2124f267e4e210f19a SHA512: 001a83800bbc106df249850a197c4502c00d3381c70fa03938a1ed10c7099d1955ad8fa07f7bfc5bea3e50080f67e557992b74c4f9cd355df82a7c875e63f2c8 Homepage: https://cran.r-project.org/package=tastyR Description: CRAN Package 'tastyR' (Recipe Data from 'Allrecipes.com') A collection of recipe datasets scraped from , containing two complementary datasets: 'allrecipes' with 14,426 general recipes, and 'cuisines' with 2,218 recipes categorized by country of origin. 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Package: r-cran-tatest Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tatest_1.0-1.ca2604.1_all.deb Size: 347602 MD5sum: aff49359e6e9683f16ff1148346fa8ad SHA1: c6a2f1d8f74f33ffc6a95f4c158f8b916befead0 SHA256: 837e79c9fbfb79502e3ce784415d5128b80618e093cd40d83c8d76227eb55c96 SHA512: b7d944cdf1f4a9986a5b585a6ca37d0af6dc808d2235fbbf9ccac0e52fa8161fd5bcb67e35e7114f8c1967f081ef15f53b4189be26566dceb9d87ee39f64b503 Homepage: https://cran.r-project.org/package=tatest Description: CRAN Package 'tatest' (Two-Group Ta-Test) The ta-test is a modified two-sample or two-group t-test of Gosset (1908). 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Package: r-cran-tauprocess Architecture: all Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-tauprocess_2.1.3-1.ca2604.1_all.deb Size: 59056 MD5sum: 8a8443fee74327a4ea45698d95879b44 SHA1: 6179258d2d228975b6044792910200adfeb551c0 SHA256: ebdc434846e09fcf7eb81fb45c430845ba9a3025807f86781325bb6967e32987 SHA512: a1c7c5f6bee151afade64ebbf1b8254feed9c6cdb250a1c3586f557d2d15acf02cb566b988c848b14a54f38bb94307744584ff83aa43f075df0e6ce2412f7d64 Homepage: https://cran.r-project.org/package=tauProcess Description: CRAN Package 'tauProcess' (Tau Measure with Right-Censored Data) A clinically meaningful measures of treatment effects for right-censored data are provided, based on the concept of Kendall's tau, along with the corresponding inference procedures. 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Package: r-cran-tauturri Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-magrittr, r-cran-plyr, r-cran-purrr, r-cran-tibble Suggests: r-cran-covr, r-cran-dplyr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-tauturri_0.3.0-1.ca2604.1_all.deb Size: 317078 MD5sum: 2e79d4ef54f4f1f0eadd97c58ce432a5 SHA1: 782ae89f77f8a5d1988ca9c34c87486d6a197b46 SHA256: 373aef33b958c56064db76b0bce2d8e9fbf8cfa4350219ba6ec94784666fb7d5 SHA512: d95e9f59fc5c9260ffef9b188350bd1dcad75613fa25e4d0881e50fbf7d87088e0628c1fcc55199c72c59a0e9f175e4a3f56c23915a02a87299a0476bf8c425a Homepage: https://cran.r-project.org/package=tauturri Description: CRAN Package 'tauturri' (Get Data Out of 'Tautulli' (Formerly 'PlexPy')) 'Tautulli' () is a monitoring application for 'Plex' Media Servers () which collects a lot of data about media items and server usage such as play counts. This package interacts with the 'Tautulli' API of any specified server to get said data into R. The 'Tautulli' API documentation is available at . Package: r-cran-taxa Architecture: all Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-rlang, r-cran-stringr, r-cran-crayon, r-cran-pillar, r-cran-viridislite, r-cran-cli Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-taxa_0.4.4-1.ca2604.1_all.deb Size: 371646 MD5sum: 02bddad4a01c28f84dfd15f7e9ebb5e6 SHA1: 2a05a72848ec85519dc374a78c95c4ffd00c5b99 SHA256: b9d4045e2a8110556502298d214eeefba60eb0d7d34fa1a47d12bc072a87ff84 SHA512: d605696a209380ef5e74458c5a3dd5f74440735e5e959c9263ec073b9b0e9377144a794d159222a98b86ded47999de2ef6524a0eeb6cf31f5d6f7006a54490d3 Homepage: https://cran.r-project.org/package=taxa Description: CRAN Package 'taxa' (Classes for Storing and Manipulating Taxonomic Data) Provides classes for storing and manipulating taxonomic data. Most of the classes can be treated like base R vectors (e.g. can be used in tables as columns and can be named). Vectorized classes can store taxon names and authorities, taxon IDs from databases, taxon ranks, and other types of information. More complex classes are provided to store taxonomic trees and user-defined data associated with them. 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Package: r-cran-taxalight Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-thor, r-cran-contentid Suggests: r-cran-jsonlite, r-cran-spelling, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-progress, r-cran-utf8, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-taxalight_0.1.5-1.ca2604.1_all.deb Size: 53438 MD5sum: 1cbd7ca2f3bc05f18f88ccfc6230a345 SHA1: f39bbb252eaa42f9295bbe00e3c27e45ab471e4e SHA256: eda8c2e6c19172016de30fa94aa913770481e1bf77a60f96a2c113a078aab305 SHA512: aa56b016e2241c2b30807e033a315eb6e65a02ebfe3ccb321904768e914893ebaaac1e752f9a989921a5a253f3f933ffb11de63cc83672016aaa66db4afdd7fe Homepage: https://cran.r-project.org/package=taxalight Description: CRAN Package 'taxalight' (A Lightweight and Lightning-Fast Taxonomic Naming Interface) Creates a local Lightning Memory-Mapped Database ('LMDB') of many commonly used taxonomic authorities and provides functions that can quickly query this data. Supported taxonomic authorities include the Integrated Taxonomic Information System ('ITIS'), National Center for Biotechnology Information ('NCBI'), Global Biodiversity Information Facility ('GBIF'), Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). Name and identifier resolution using 'LMDB' can be hundreds of times faster than either relational databases or internet-based queries. Precise data provenance information for data derived from naming providers is also included. 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This method assumes that the effects of sequencing depth vary for each taxon on their mean and also incorporates a rational link of zero probability and taxon dispersion as a function of sequencing depth. Ziyue Wang, Dillon Lloyd, Shanshan Zhao, Alison Motsinger-Reif (2023) . Package: r-cran-taxdiv Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4094 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-taxdiv_0.1.0-1.ca2604.1_all.deb Size: 2523834 MD5sum: c04b3a0d594dc812c9e6da9b6cdfe060 SHA1: e00bcaf0b13264bcaf62b0dfc15176e4115119f2 SHA256: f37445695b6a2ed9556ac189a43867183d919c38434a43347daeb13e8c4211c7 SHA512: a9145e4a6e84900239f9e3ece954632cbe9982e76df1568536990bb045854dbdaea64c087ebbf46616ec73b10db4c49205eda42a69267774c2acfcec134cb20f Homepage: https://cran.r-project.org/package=taxdiv Description: CRAN Package 'taxdiv' (Taxonomic Diversity Indices Using Deng Entropy) Calculates taxonomic diversity indices for ecological community data using Deng entropy framework and classical approaches (Shannon, Simpson, Clarke & Warwick). Provides functions for computing taxonomic distinctness, average taxonomic distinctness (AvTD/Delta+), variation in taxonomic distinctness (VarTD/Lambda+), and Deng entropy-based measures that incorporate taxonomic hierarchy information. Includes tools for constructing taxonomic trees and computing pairwise taxonomic distances. Package: r-cran-taxicabca Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ga, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-taxicabca_0.1.1-1.ca2604.1_all.deb Size: 81900 MD5sum: 9e6b46d3bd5c8a1175584f67329c1be3 SHA1: f6cbece91c8a58a7b364256e5b92bff1f8fbcff6 SHA256: f82fea1f201463b46a025c57829a67d0d71840e181824902507be8f60a8dced6 SHA512: fba3587434fd5790bad95a76ec8a3bdd910643cc495554804bc7c9440746746da4fad1f9467437610b1cafca89c2d71d410b726b05c9335f918e96299534f742 Homepage: https://cran.r-project.org/package=TaxicabCA Description: CRAN Package 'TaxicabCA' (Taxicab Correspondence Analysis) Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) . Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod). Classical CA is based on the Euclidean distance. Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance. For some tables, Taxicab CA gives more informative results than classical CA. Package: r-cran-taxize Architecture: all Version: 0.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1750 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-xml2, r-cran-jsonlite, r-cran-ape, r-cran-zoo, r-cran-data.table, r-cran-tibble, r-cran-rredlist, r-cran-rotl, r-cran-ritis, r-cran-worrms, r-cran-natserv, r-cran-wikitaxa, r-cran-r6, r-cran-crayon, r-cran-cli, r-cran-phangorn, r-cran-lifecycle, r-cran-curl, r-cran-stringi Suggests: r-cran-testthat, r-cran-vegan, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-taxize_0.10.1-1.ca2604.1_all.deb Size: 1549528 MD5sum: 8775b285a732e8e3704584a6c8d58a6a SHA1: 4cd38949d304bdf26a953dde68e2fb40d2d05f26 SHA256: bbfdc837ab2757871b9215591176e74c44ee387a2dbcfc01508b20e9329af7bc SHA512: 493799e4dde15145c6480839ebf4fdd928276879d05db30c3c563349e54d57c7b58089dd0ddd9627a472254235328e9ff13ccd3ef3f57426afd1ebc5af0f2d05 Homepage: https://cran.r-project.org/package=taxize Description: CRAN Package 'taxize' (Taxonomic Information from Around the Web) Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include 'NCBI E-utilities' (), 'Encyclopedia of Life' (), 'Global Biodiversity Information Facility' (), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package. Package: r-cran-taxizedb Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-rsqlite, r-cran-dplyr, r-cran-tibble, r-cran-rlang, r-cran-readr, r-cran-dbplyr, r-cran-magrittr, r-cran-hoardr, r-cran-vroom Suggests: r-cran-taxize, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-taxizedb_0.3.2-1.ca2604.1_all.deb Size: 142456 MD5sum: b5e8bf123d005a1e695a3b4ead68b607 SHA1: c46d2827364a413229571775ad8364b50a47346d SHA256: bb8ba661537b60332263a98978eef57938db54124816fec19fdfb4dfdef64567 SHA512: 5ce51354e56ddefa39466f56df8d316bc85745a773cd146cc653001e9e0e56fca9a8abdbfdb5bead1996a4d6776a033d3e089ab12733b18256f8dc555b12e1e5 Homepage: https://cran.r-project.org/package=taxizedb Description: CRAN Package 'taxizedb' (Offline Access to Taxonomic Databases) Download taxonomic databases, convert them into 'SQLite' format, and query them locally for fast, reliable, and reproducible access to taxonomic data. Package: r-cran-taxlist Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biblio, r-cran-foreign, r-cran-stringdist, r-cran-stringi, r-cran-stringr Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-taxa, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-taxlist_0.3.5-1.ca2604.1_all.deb Size: 1719952 MD5sum: 56453731859b714c3260e6636b61e56c SHA1: 6573beaa55dafeae42b3432ae7cb6ba3795f2463 SHA256: 025f83b3b92ba42c911ac03295de01b990a9869aa188fb7c3117c609b697b60c SHA512: 3e3e8a972702484b2a29f2536c649550f67d92eb35c333c57ef3d4488405d6828f085085ad0277ab3825d910ccb35c52be0b5af65de54149379b25cb6949cc54 Homepage: https://cran.r-project.org/package=taxlist Description: CRAN Package 'taxlist' (Handling Taxonomic Lists) Handling taxonomic lists through objects of class 'taxlist'. This package provides functions to import species lists from 'Turboveg' () and the possibility to create backups from resulting R-objects. Also quick displays are implemented as summary-methods. Package: r-cran-taxnames Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-taxlist Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-taxnames_0.1.0-1.ca2604.1_all.deb Size: 27620 MD5sum: ae4ac1a79f7c418a5b3ac26d994af744 SHA1: 4585082dc54d7c6e70543f0cd91d98e6fcfa4107 SHA256: 65534b76487d7724ba691777ad8abb26be4b5935b09598df5ed0e501c5f5f5d0 SHA512: 51f4535c5a9c35683314aaefebdf999367a6e00b78b22368edfbaea749b45d35975f62e8268b12f8f888116ecd4367e93ec4ef2fe9e31317c1eb839d201e797e Homepage: https://cran.r-project.org/package=taxnames Description: CRAN Package 'taxnames' (Formatting Taxonomic Names in Markdown) A collection of functions used to format taxonomic names in Markdown documents. Those functions work with data structured according to Alvarez and Luebert (2018) . Package: r-cran-taxodist Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-rvest, r-cran-stringr, r-cran-purrr, r-cran-cli Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown, r-cran-xml2, r-cran-ape, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-taxodist_0.3.0-1.ca2604.1_all.deb Size: 125614 MD5sum: eefe7af29e9cd82c93bacdc3bbfcaa79 SHA1: 3002376aab3315f8017a37d043e07c294a4a3215 SHA256: a1ac0afc67fd2916ecb86a67fb6c020442b23a28396d31416de91a3227dd9de6 SHA512: cb38d6877b0c5dcc21ecda86df4fae824a52b77d38c5197b6e7d1ba2545535c81688177dd4052110f7c4c5e322ddbc8b1958447252b2c728c6b4077e9647cfc9 Homepage: https://cran.r-project.org/package=taxodist Description: CRAN Package 'taxodist' (Taxonomic Distance and Phylogenetic Lineage Computation) Computes phylogenetic distances between any two taxa using hierarchical lineage data retrieved from The Taxonomicon , a comprehensive curated classification of all life based on Systema Naturae 2000 (Brands, 1989 ). Given any two taxon names, retrieves their full lineages, identifies the most recent common ancestor (MRCA), and computes a dissimilarity index based on lineage depth. Outputs native dist objects, enabling direct integration with the R statistical ecosystem for hierarchical clustering, principal coordinate analysis (PCoA), and multivariate ecological analyses. Supports individual distance queries, pairwise distance matrices, clade filtering, and lineage utilities. Package: r-cran-taylor Architecture: all Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5008 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-askpass, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-httr2, r-cran-lifecycle, r-cran-rlang, r-cran-scales, r-cran-spotifyr, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-bookdown, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-taylor_4.0.0-1.ca2604.1_all.deb Size: 2058698 MD5sum: 1c810df65d461c2805fdd6e0a665f88a SHA1: 6d2ef72c4f6e5943a51c7c0fc2a3f78f727e107d SHA256: 247bcc0e61e2d1dbd9c4318a4db1cb8b0742235c547cbd516a64ca0bb0b7e59d SHA512: 7a8de13c8c3032c434ac98982db4966bacc580597d1e5ee8008c664e65d747e2a3360da1ca36b3d6326a9303c8ebf5397908df6cb0494f965e9309d6005072de Homepage: https://cran.r-project.org/package=taylor Description: CRAN Package 'taylor' (Lyrics and Song Data for Taylor Swift's Discography) A comprehensive resource for data on Taylor Swift songs. Data is included for all officially released studio albums, extended plays (EPs), and individual singles are included. Data comes from 'Genius' (lyrics) and 'SoundStat' (song characteristics). Additional functions are included for easily creating data visualizations with color palettes inspired by Taylor Swift's album covers. Package: r-cran-taylorrussell Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-shiny, r-cran-shinywidgets Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-taylorrussell_1.2.1-1.ca2604.1_all.deb Size: 38756 MD5sum: e3ec644d17ad862e95f7e24ccf5c528d SHA1: cf736f8b215280abbfc197cf5e9a3447240d0253 SHA256: 8a5b048dab894746179b73a01791591c46618e7d327b2a228f8ef9ec1f7042c2 SHA512: 814eb24c13d8856f5411f51b8a7fbc8f0ead69eb51f24da951793f1dff3b8f778de0b7d16728f9d50e8296c9d78bafedabc9bb05dc80cd5922a9048303086c11 Homepage: https://cran.r-project.org/package=TaylorRussell Description: CRAN Package 'TaylorRussell' (A Taylor-Russell Function for Multiple Predictors) The Taylor Russell model is a widely used method for assessing test validity in personnel selection tasks. The three functions in this package extend this model in a number of notable ways. TR() estimates test validity for a single selection test via the original Taylor Russell model. It extends this model by allowing users greater flexibility in argument choice. For example, users can specify any three of the four parameters (base rate, selection ratio, criterion validity, and positive predictive value) of the Taylor Russell model and estimate the remaining parameter (see the help file for examples). The TaylorRussell() function generalizes the original Taylor Russell model to allow for multiple selection tests (predictors). To our knowledge, this is the first generalization of the Taylor Russell model to allow for three or more selection tests (it is also the first to correctly handle models with two selection tests). TRDemo() is a 'shiny' program for illustrating the underlying logic of the Taylor Russell model. Taylor, HC and Russell, JT (1939) "The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables" . Package: r-cran-taylorswift Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-taylorswift_0.1.0-1.ca2604.1_all.deb Size: 301508 MD5sum: ef155176c186ac713c1bb9e54ac90a7c SHA1: 9fb7c3a94b2280f212c3933a60523aafb4b93920 SHA256: 71d21f68f738aa946c19500ddcec53eccb28202b02ea24dbc4c703292bc44d54 SHA512: d885b9e365b4dfbffc7d2a8356db9d18a34739467febd2ab6be0f27011bf97bbdf48398172997164f8540caa89a308a2fad559f345cda39927a5a5737dffef23 Homepage: https://cran.r-project.org/package=tayloRswift Description: CRAN Package 'tayloRswift' (Color Palettes Generated by Taylor Swift Albums) For when your colors absolutely should not be excluded from the narrative. Package: r-cran-tba Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-readxl, r-cran-reshape2, r-cran-shinybusy Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tba_0.1.0-1.ca2604.1_all.deb Size: 114110 MD5sum: e3602a561c1c6ebbd1bfdadb6dcadfef SHA1: fe441e87d69f07e281b2fab67d10fdbc65aada2d SHA256: 5a8739ac68b9ff431eb8587689d8871c88359e1ab9efdb6189cd1a2de5cfb2d6 SHA512: 1e419d7b44949e10218dd0570c6ea3c39e20c6b7f23e295c3329789ea97d22dbc2938ba1b18bf67e6cda15e8bec0f8f018143776b5e3633cab733c320755a51e Homepage: https://cran.r-project.org/package=TBA Description: CRAN Package 'TBA' (Collection of 'shiny' Apps for Tree Breeding Analysis) A collection of interactive 'shiny' applications for performing comprehensive analyses in the field of tree breeding and genetics. The package is designed to assist users in visualizing and interpreting experimental data through a user-friendly interface. Each application is launched via a simple function, and users can upload data in 'Excel' format for analysis. For more information, refer to Singh, R.K. and Chaudhary, B.D. (1977, ISBN:9788176633079). Package: r-cran-tbd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-tbd_0.1.0-1.ca2604.1_all.deb Size: 164686 MD5sum: bb984f0efcf75d3200394fb0fb603f16 SHA1: 300f56a7210d0d3b92e6d81275ccec3ea5c665b1 SHA256: f7512603924cfb9a8b04ed16516c40b7ae4433a4b3743d99889aa94b0e06cb4f SHA512: c058eff947e44d5cde9e25b3a5e811156a396b6efc72948b84823c53d20cbd8bef4d8b4fb839baaf3dfa7cf003a87aa7eb32d79bc7023b268c0199e0468b6a0a Homepage: https://cran.r-project.org/package=tbd Description: CRAN Package 'tbd' (Estimation of Causal Effects with Outcomes Truncated by Death) Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data. Package: r-cran-tbea Architecture: all Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 909 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rfit, r-cran-boot, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-kendall Filename: pool/dists/resolute/main/r-cran-tbea_1.7.0-1.ca2604.1_all.deb Size: 484454 MD5sum: 84574e49b5b0ef48c4213710dd3437d3 SHA1: e91a432178878aca5d6456520d121b4124669002 SHA256: 1b845171f9ea46ddc2381edd1ceda1b967ef71706a54216fa332314ab1be0048 SHA512: 348a33d3be60a6df7962136e99469cdc56200ebd8b7635eaf1f0d874eb04f06e653c5704ef379d00a31436fc6c5c7ba5b3414ce222d375ef70ae5952b7a84954 Homepage: https://cran.r-project.org/package=tbea Description: CRAN Package 'tbea' (Pre- And Post-Processing in Bayesian Evolutionary Analyses) Functions are provided for prior specification in divergence time estimation using fossils as well as other kinds of data. It provides tools for interacting with the input and output of Bayesian platforms in evolutionary biology such as 'BEAST2', 'MrBayes', 'RevBayes', or 'MCMCTree'. It Implements a simple measure similarity between probability density functions for comparing prior and posterior Bayesian densities, as well as code for calculating the combination of distributions using conflation of Hill (2008). Functions for estimating the origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and stratigraphic intervals (Marshall 2010) are also available. Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. , Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. , Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. . 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Package: r-cran-tbfmultinomial Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam, r-cran-nnet, r-cran-stringr, r-cran-plotrix Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-tbfmultinomial_0.1.3-1.ca2604.1_all.deb Size: 221000 MD5sum: 88ccc06a89f910904ea412d0e9c05dce SHA1: fda3924c8be5a13e1bdf0a5921ea1326f2e6f7a2 SHA256: 3cd88f046fea6a0632d67d25c76fbda94d434e91553ab10ca7a9b2dff9491d60 SHA512: 0b9f354c9c1d675487d761d5973bc446a92c47561208203d830c855b892c0bb2ab3386aada8ca94e1c82e1e8314c5d92b1880fc484dd073d7836781ff91af073 Homepage: https://cran.r-project.org/package=TBFmultinomial Description: CRAN Package 'TBFmultinomial' (TBF Methodology Extension for Multinomial Outcomes) Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) ] and for the Cox model [Held et al. (2016) ]. 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The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) . Package: r-cran-tcftt Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tcftt_0.1.0-1.ca2604.1_all.deb Size: 48766 MD5sum: 1b9136cec04dc2a51e1a64ccf7de32ef SHA1: e08f2cd56e049363ed623c8c6e4f96bc6e783ede SHA256: f06358d2a5e6f9ebc0989febef18bc891b36d16b985a1e37490e9dbbf289fbb4 SHA512: 0e8db2c580b960acc8fe9b0037609aefb87132472cc00265d1a23afa1bca831ced311bea80024a8ce0433e88662f283c5623276b577c8a5610be04de3fb6991f Homepage: https://cran.r-project.org/package=tcftt Description: CRAN Package 'tcftt' (Two-Sample Tests for Skewed Data) The classical two-sample t-test works well for the normally distributed data or data with large sample size. The tcfu() and tt() tests implemented in this package provide better type-I-error control with more accurate power when testing the equality of two-sample means for skewed populations having unequal variances. These tests are especially useful when the sample sizes are moderate. The tcfu() uses the Cornish-Fisher expansion to achieve a better approximation to the true percentiles. The tt() provides transformations of the Welch's t-statistic so that the sampling distribution become more symmetric. For more technical details, please refer to Zhang (2019) . Package: r-cran-tcgaretriever Architecture: all Version: 1.10.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-reshape2, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-tcgaretriever_1.10.3-1.ca2604.1_all.deb Size: 596612 MD5sum: 019efda4b9d168dc5f626eedaae692f3 SHA1: 3933974eff20a1d95fc2e2732cfa82b06f288cd5 SHA256: 4fe8b6b8aae3323b3c13a7fb9712172dbfacb3cb751459d62a25a3520557304c SHA512: 01de1a599cf00dfda0a86a03885467b04b8e89c058e8959290bb1a160f7b2188c2dda2713a6314bcbd3127a3b92415bcb1627c8609b15bcd9a68d61c20abe818 Homepage: https://cran.r-project.org/package=TCGAretriever Description: CRAN Package 'TCGAretriever' (Retrieve Genomic and Clinical Data from CBioPortal IncludingTCGA Data) The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. 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Method is detailed in: Hejblum, Skinner & Thiebaut (2015) . Package: r-cran-tciapathfinder Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-oro.dicom, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tciapathfinder_1.0.6-1.ca2604.1_all.deb Size: 86240 MD5sum: 5fa814054df620317350bf10ffea4863 SHA1: 50a5a9639914c8c2a1ecd99c1c9291e870b139ad SHA256: 1cbf1899c8d93980784b61788130cf61c9705cdf2c0176d3cdd7b2ae82a505cb SHA512: eeddfa283ae61ef0356d5781e6f43023c0fcc8df6de1a6f3825d32a4dc909f75d7a964f2dc933d4d0ca88eadadf38b95952622c6b8a4e26f7938d83c042cf214 Homepage: https://cran.r-project.org/package=TCIApathfinder Description: CRAN Package 'TCIApathfinder' (Client for the Cancer Imaging Archive REST API) A wrapper for The Cancer Imaging Archive's REST API. The Cancer Imaging Archive (TCIA) hosts de-identified medical images of cancer available for public download, as well as rich metadata for each image series. TCIA provides a REST API for programmatic access to the data. This package provides simple functions to access each API endpoint. For more information, see and TCIA's website. Package: r-cran-tcl Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-erm, r-cran-psychotools, r-cran-ltm, r-cran-numderiv, r-cran-mass, r-cran-matrix, r-cran-lattice, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tcl_1.0.1-1.ca2604.1_all.deb Size: 279002 MD5sum: 67a040b90c6c562baed457803839c163 SHA1: 523289af65127252a95e8abb9a66c1af2fa76bac SHA256: 5d3023fd3c541b555f4f75faa14abfd8906780790e9473c813ad39f78c9ce9ac SHA512: 14ce766814b06df9e9e48a995d73a7268b3752fb60ea76d75af7bcdc80df742734a32e55ef65d8fc9b4ac07687d7300de86699ac43bea6171af61fab67a44e6e Homepage: https://cran.r-project.org/package=tcl Description: CRAN Package 'tcl' (Testing in Conditional Likelihood Context) An implementation of hypothesis testing in an extended Rasch modeling framework, including sample size planning procedures and power computations. Provides 4 statistical tests, i.e., gradient test (GR), likelihood ratio test (LR), Rao score or Lagrange multiplier test (RS), and Wald test, for testing a number of hypotheses referring to the Rasch model (RM), linear logistic test model (LLTM), rating scale model (RSM), and partial credit model (PCM). Three types of functions for power and sample size computations are provided. Firstly, functions to compute the sample size given a user-specified (predetermined) deviation from the hypothesis to be tested, the level alpha, and the power of the test. Secondly, functions to evaluate the power of the tests given a user-specified (predetermined) deviation from the hypothesis to be tested, the level alpha of the test, and the sample size. Thirdly, functions to evaluate the so-called post hoc power of the tests. This is the power of the tests given the observed deviation of the data from the hypothesis to be tested and a user-specified level alpha of the test. Power and sample size computations are based on a Monte Carlo simulation approach. It is computationally very efficient. The variance of the random error in computing power and sample size arising from the simulation approach is analytically derived by using the delta method. Additionally, functions to compute the power of the tests as a function of an effect measure interpreted as explained variance are provided. Draxler, C., & Alexandrowicz, R. W. (2015), . Package: r-cran-tcltk2 Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5273 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tcltk2_1.6.1-1.ca2604.1_all.deb Size: 1068724 MD5sum: e594e36158a80e2776798b509b7ace00 SHA1: cecfbf2eb3c3e5405bdf1892847c4a36007f7c05 SHA256: 100235e939ce0dab84ab09242a259520e44f798214afa761d457e15eadc594d3 SHA512: 14a3fb2fb1019ad1c15eedfee250ae89d2fcdd6d2c32e655e2110829b9473603cae8421dfff80bbb42b5d183d35c80c00b7d4918c9398a5675c92524946c0f9c Homepage: https://cran.r-project.org/package=tcltk2 Description: CRAN Package 'tcltk2' (Tcl/Tk Additions) A series of additional Tcl commands and Tk widgets to supplement the tcltk package. 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The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but 'tcpl' can be used to support diverse chemical screening efforts. Package: r-cran-tcplfit2 Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-rcolorbrewer, r-cran-stringr, r-cran-reshape2, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-dt, r-cran-data.table, r-cran-tcpl, r-cran-prettydoc, r-cran-testthat, r-cran-here, r-cran-htmltable, r-cran-tidyr, r-cran-dplyr, r-cran-gridextra, r-cran-rmdformats Filename: pool/dists/resolute/main/r-cran-tcplfit2_0.1.9-1.ca2604.1_all.deb Size: 2328664 MD5sum: 39832bece696da450665e1a77b82a3e2 SHA1: 2daebda83baadba8e32d493750eec4432f974a1b SHA256: 50a322d1cd0c13747f8b090316657f5544ded73f5acde6a8302b647b00ab0e3e SHA512: 423d963537d85b452caea8976995dcb9624c1728b6a24d319ab423b7096b64a9d1c95a66fa39d846893c768323e68951497badf601fe6fc786342c700583cfbf Homepage: https://cran.r-project.org/package=tcplfit2 Description: CRAN Package 'tcplfit2' (A Concentration-Response Modeling Utility) The tcplfit2 R package performs basic concentration-response curve fitting. The original tcplFit() function in the tcpl R package performed basic concentration-response curvefitting to 3 models. With tcplfit2, the core tcpl concentration-response functionality has been expanded to process diverse high-throughput screen (HTS) data generated at the US Environmental Protection Agency, including targeted ToxCast, high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). tcplfit2 can be used independently to support analysis for diverse chemical screening efforts. 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Package: r-cran-tdstnn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-tdstnn_0.1.0-1.ca2604.1_all.deb Size: 13060 MD5sum: 3621d17e73e732cbf1032a2aeaeeafae SHA1: 6caf0bcacc727a50dde4b3853c96046f7667110a SHA256: 2d08f20a8bfc7ca0b21e25f456ba41ebb63212225d7401aeeaab2d5f2efb4643 SHA512: 42b13562042e7304a3ecd0b4c1f6903b10011d2651c1039144d6d0c775cb4a5dc06e4b6ca247e8785686a3968fc54ccf7db7210c1de663fbcc638ddefd01a4d1 Homepage: https://cran.r-project.org/package=TDSTNN Description: CRAN Package 'TDSTNN' (Time Delay Spatio Temporal Neural Network) STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. 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Package: r-cran-te Architecture: all Version: 0.3-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rainbow Filename: pool/dists/resolute/main/r-cran-te_0.3-0-1.ca2604.1_all.deb Size: 647292 MD5sum: 68c4ac6368efdbfa8d0c5215977a28c2 SHA1: 75494b503f826949d9cfbdb04868f16d768479b7 SHA256: b92cc187679788a8b6012da42b327e5431bba88a3aead41affc0bf6a0209358f SHA512: b66d030feb312763a63f15b1188beebcc6e8819b7f8b9fbc84256ded76a1c0285d3a7124d6f248be5fa0f43bb99e15def5c35de1672888584dd5080fac53c738 Homepage: https://cran.r-project.org/package=TE Description: CRAN Package 'TE' (Insertion/Deletion Dynamics for Transposable Elements) Provides functions to estimate the insertion and deletion rates of transposable element (TE) families. 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Package: r-cran-teal.modules.clinical Architecture: all Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-teal, r-cran-teal.transform, r-cran-tern, r-cran-broom, r-cran-bslib, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-dt, r-cran-formatters, r-cran-ggplot2, r-cran-ggrepel, r-cran-lifecycle, r-cran-rlistings, r-cran-rmarkdown, r-cran-rtables, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinyvalidate, r-cran-shinywidgets, r-cran-teal.code, r-cran-teal.data, r-cran-teal.logger, r-cran-teal.reporter, r-cran-teal.widgets, r-cran-tern.gee, r-cran-tern.mmrm, r-cran-vistime Suggests: r-cran-forcats, r-cran-knitr, r-cran-logger, r-cran-lubridate, r-cran-nestcolor, r-cran-pkgload, r-cran-roxy.shinylive, r-cran-rvest, r-cran-shinytest2, r-cran-styler, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-teal.modules.clinical_0.12.0-1.ca2604.1_all.deb Size: 2619782 MD5sum: 602a1d8373bb6e5cee5a2772faff4486 SHA1: 48d2e1adb82ca3a4e020d12cce386424b07ff46f SHA256: 7c88800d123a25e0b29421d90027851871c0fca9e4db7e44da73f5fbe8c7d984 SHA512: f1541af6b06d1a6d08270eddd910788e5ce740c6043f6f95e17ff5adfde46054b1cad859ff58cf02cb72319545ffd5f6cc6ec80601ce1f8d80db3089400848a3 Homepage: https://cran.r-project.org/package=teal.modules.clinical Description: CRAN Package 'teal.modules.clinical' ('teal' Modules for Standard Clinical Outputs) Provides user-friendly tools for creating and customizing clinical trial reports. By leveraging the 'teal' framework, this package provides 'teal' modules to easily create an interactive panel that allows for seamless adjustments to data presentation, thereby streamlining the creation of detailed and accurate reports. Package: r-cran-teal.modules.general Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny, r-cran-teal, r-cran-teal.transform, r-cran-bslib, r-cran-checkmate, r-cran-colourpicker, r-cran-dplyr, r-cran-dt, r-cran-forcats, r-cran-generics, r-cran-ggextra, r-cran-ggpmisc, r-cran-ggpp, r-cran-ggrepel, r-cran-goftest, r-cran-gridextra, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-lattice, r-cran-lifecycle, r-cran-mass, r-cran-rmarkdown, r-cran-rtables, r-cran-scales, r-cran-shinyjs, r-cran-shinytree, r-cran-shinyvalidate, r-cran-shinywidgets, r-cran-sparkline, r-cran-stringr, r-cran-teal.code, r-cran-teal.data, r-cran-teal.logger, r-cran-teal.reporter, r-cran-teal.widgets, r-cran-tern, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-logger, r-cran-nestcolor, r-cran-pkgload, r-cran-rlang, r-cran-roxy.shinylive, r-cran-rvest, r-cran-shinytest2, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-teal.modules.general_0.6.0-1.ca2604.1_all.deb Size: 843546 MD5sum: 9b136d8c4faa69a1eac73a9908bd0379 SHA1: e5b20549eb71e3f377dc56ca71d37fb607261673 SHA256: 139ba8866fc073f2646345e2d940a583835d5fe3cae0d0da6941251d1209595e SHA512: 9e981c4da231d641690b6ccb9c112f56b9b09714f3a2448945d7540a23843634585f7e80448512e8a9edee891b3cb3fc7c852656859f49580913e3496f258393 Homepage: https://cran.r-project.org/package=teal.modules.general Description: CRAN Package 'teal.modules.general' (General Modules for 'teal' Applications) Prebuilt 'shiny' modules containing tools for viewing data, visualizing data, understanding missing and outlier values within your data and performing simple data analysis. 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Package: r-cran-teal.picks Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bsicons, r-cran-checkmate, r-cran-dplyr, r-cran-htmltools, r-cran-logger, r-cran-rlang, r-cran-shiny, r-cran-shinywidgets, r-cran-teal, r-cran-teal.code, r-cran-teal.data, r-cran-teal.logger, r-cran-tidyselect, r-cran-yaml Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-rvest, r-cran-shinytest2, r-cran-teal.transform, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-teal.picks_0.1.0-1.ca2604.1_all.deb Size: 233438 MD5sum: 1db670be1d93effd199bf229930804b6 SHA1: 4355037e6408e62833a76be4bb4c214ec6051585 SHA256: 3d9254d056f962a0fdcf50af114ad46ef730473c950345a81cbccde839d06e4a SHA512: 7ae2366a782db338102f9ba67f97e8c1e825dbdbd89e21456674782b7d4a7b2acdd069d2e1fb14b10734ba098cefa94ede02952c65d2586fa6bfa027e390b3e4 Homepage: https://cran.r-project.org/package=teal.picks Description: CRAN Package 'teal.picks' (Dataset and Variable Picker and Merge Module for 'teal'Applications) Allows users to interactively select datasets, variables, and values within 'teal' applications using a 'tidyselect'-style interface. 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Package: r-cran-teal Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2434 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-teal.data, r-cran-teal.slice, r-cran-bsicons, r-cran-bslib, r-cran-checkmate, r-cran-cli, r-cran-htmltools, r-cran-jsonlite, r-cran-lifecycle, r-cran-logger, r-cran-rlang, r-cran-shinyjs, r-cran-teal.code, r-cran-teal.logger, r-cran-teal.reporter, r-cran-teal.widgets Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mirai, r-bioc-multiassayexperiment, r-cran-r6, r-cran-renv, r-cran-rmarkdown, r-cran-roxy.shinylive, r-cran-rvest, r-cran-shinytest2, r-cran-shinyvalidate, r-cran-testthat, r-cran-withr, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-teal_1.1.0-1.ca2604.1_all.deb Size: 1400780 MD5sum: 8c88bcf152888081a87377e93437dcbf SHA1: 6d1c59cf8e6740d9602e0811a9b46a06f62fe7c0 SHA256: 8067e0f93e45319935452eaaaaad355d58f40e4a0cbe6111544270a9acd8a003 SHA512: 9260583c20935e11124a0c658d5c6081046e5fe914d5bc5a8996b071be6335417f3a3acd50f4fc3c06c1c6617ee5dee29065ee469f5e1c52d5eb175deef07184 Homepage: https://cran.r-project.org/package=teal Description: CRAN Package 'teal' (Exploratory Web Apps for Analyzing Clinical Trials Data) A 'shiny' based interactive exploration framework for analyzing clinical trials data. 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It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately as in Foster and Smith (1986) . It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) . 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Package: r-cran-teda Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-teda_0.1.1-1.ca2604.1_all.deb Size: 33444 MD5sum: 527f8b2a0b5bea03f59cb28267115d15 SHA1: 6b7feebe0dff77f22e174eb6fd448e69ac6ba656 SHA256: a8c8caa922d8633b43a4b38eeb18454906e39343b5cd005204d434301698c9b6 SHA512: 43be0916eedbac6797c9ce8cc4c824ee916e45e3f37c9b143ac1fded649b3673f05cc9ecd2cba628753c58fa5631ef83d1dcb9e67544aac4b6550685080fdb82 Homepage: https://cran.r-project.org/package=teda Description: CRAN Package 'teda' (An Implementation of the Typicality and Eccentricity DataAnalysis Framework) The typicality and eccentricity data analysis (TEDA) framework was put forward by Angelov (2013) . 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Package: r-cran-tejapi Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-tejapi_1.0.1-1.ca2604.1_all.deb Size: 23610 MD5sum: d4dea671c50406991f3dde5101086bc6 SHA1: 5db158155dac2a9fa03cfcf5261da74a97f41991 SHA256: 2868544e4a223493925b9d7417d53d02f05096546a52dddae41da98038909ff6 SHA512: 4fabc2cc7836cfa4fa923947945b423a6a46d38550b2a9f187c25d42c17d9170e19db6d77790492bb1c924c4a0b5ed0d272871fd8b7a07d008376c98da92f5c3 Homepage: https://cran.r-project.org/package=Tejapi Description: CRAN Package 'Tejapi' (API Wrapper for Taiwan Economic Journal Data Service) Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to . Package: r-cran-telegram.bot Architecture: all Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httpuv, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-r6 Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-promises, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-telegram.bot_3.0.2-1.ca2604.1_all.deb Size: 540524 MD5sum: 63f1e738e04ca3910176a4c9ab760392 SHA1: dae57397cd5c2a034907a8d743869c6999dc86ce SHA256: 6fdac8c027cd9fd4b107eb8397d097746c4700490651c5015237347abf8695ba SHA512: 4438fa81268b425dbe047df1abb61f5403e37b8d36da1696c22cfd24fe822eebdad4fd362a04fe3cfe93dbb0309ccc62cdea3153890b210cbdd28a4c17388c83 Homepage: https://cran.r-project.org/package=telegram.bot Description: CRAN Package 'telegram.bot' (Develop a 'Telegram Bot' with R) Provides a pure interface for the 'Telegram Bot API' . 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Package: r-cran-telegram Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-curl Filename: pool/dists/resolute/main/r-cran-telegram_0.7.1-1.ca2604.1_all.deb Size: 183072 MD5sum: 46ecb1b9be1746c12e7dca2ed6a780a5 SHA1: f739e47c59427d6818761aea60f1c5f5cf0fb943 SHA256: f4b48afbb82e2c96dabeb8ded2c0b65d0c556b749f6744a22c732bedeffcf03c SHA512: 9ebbb01a7669111798511bfaa05899aa0dec155843b6d33b19f889f0efb79c259ba2bdf76dfaf3b2fe47b2932858d7861f5c9cd9d9388220475b2ef0de03b6b3 Homepage: https://cran.r-project.org/package=telegram Description: CRAN Package 'telegram' (R Wrapper Around the Telegram Bot API) A simple wrapper around the Telegram Bot API () to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone). Package: r-cran-telemetr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-zoo, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rerddap, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-telemetr_1.0-1.ca2604.1_all.deb Size: 1499962 MD5sum: 0f7537abe9df98a14305e846f7c5a05d SHA1: 53eb764b6d2c29fb018112e5b8de5d39152af2c8 SHA256: 6aebc13eb1ff48cc9ea1c45457fa6ef66706fc90c35cd4ea1aea6b95372b2413 SHA512: 2ec0c538e7fef18046c8af89ea4730ab88f0dbfa1716e87d960938e994428bc7c018e5181a377bb19661c79746b0ccb2f8c69068f78b00f1f9bba5a41dcf8e52 Homepage: https://cran.r-project.org/package=telemetR Description: CRAN Package 'telemetR' (Filter and Analyze Generalised Telemetry Data from Organisms) Analyze telemetry datasets generalized to allow any technology. 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For more details see Frühwirth-Schnatter et al. (2021) , Malsiner-Walli et al. (in press) and Malsiner-Walli et al. (2026) . Package: r-cran-telp Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-cran-tm, r-cran-wordcloud, r-cran-rcolorbrewer, r-cran-arules, r-cran-arulesviz, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-telp_1.0.3-1.ca2604.1_all.deb Size: 89158 MD5sum: 6c88a17f8dcd1df4f713323dd5d996aa SHA1: 21c2d22ddb1ab0444fe05d8ca025399086b88b8d SHA256: 8bd103d89c34ae2df9569c23778d66ebdaab2884bfcd4e9741c9693362e5d0df SHA512: dcea0ac27ea951b1613b48e37997e2151f6dcdb99172b5badcb55b864de686995079dcaa1dcd5470d829f2d62a500ba43a61413a0dd02fc75abbc4bf75a3599d Homepage: https://cran.r-project.org/package=TELP Description: CRAN Package 'TELP' (Social Representation Theory Application: The Free Evocation ofWords Technique) Using The Free Evocation of Words Technique method with some functions, this package will make a social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group. Package: r-cran-telraamstats Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-paletteer, r-cran-purrr, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-telraamstats_1.1.2-1.ca2604.1_all.deb Size: 2331550 MD5sum: 0fbfb2fc264167979f73496bfed96d20 SHA1: 90c84c741bea5495044014e4124c758d87ddc647 SHA256: d547ed4725c05939a91f611129e271b785cce7be336eadad56d2c7ed3c327387 SHA512: 9a0146e2b72de681d994d732cf6473ecc28ec16074318c406890e35e25556525a657f0b6f089155c3407787837faadf5b3d247549118c6008e51ebdfe9d7f3ec Homepage: https://cran.r-project.org/package=telraamStats Description: CRAN Package 'telraamStats' (Retrieval and Visualization of Mobility Data from 'Telraam'Sensors) Streamline the processing of 'Telraam' data, sourced from open data mobility sensors. These tools range from data retrieval (without the need for API knowledge) to data visualization, including data preprocessing. Package: r-cran-tempcont Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-tempcont_0.1.0-1.ca2604.1_all.deb Size: 49052 MD5sum: 2ffc0b1dd7cd6537c8b2c76501f955ad SHA1: be7685161606afbcef5af8089dedaded4d64933d SHA256: fa09e17f03da9bebbe7ee8137674898d418bfccd9782b861df3e632bb7b234c5 SHA512: 3e3175f2ee571cc30710157af23975945fb160bd5d85310807c4ff5066b3f4ff8794ba3f5f8c4470b4968476736aa7c09565b3171e45351c33c23e2c3b640f35 Homepage: https://cran.r-project.org/package=TempCont Description: CRAN Package 'TempCont' (Temporal Contributions on Trends using Mixed Models) Method to estimate the effect of the trend in predictor variables on the observed trend of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et al. (2017 and 2019) . Package: r-cran-tempdisagg Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tsbox, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-xts Filename: pool/dists/resolute/main/r-cran-tempdisagg_1.2.0-1.ca2604.1_all.deb Size: 387740 MD5sum: 4f4e211f79e7e3626b663f92febca7c0 SHA1: 8e5aae95f297497bae51f48dfeab28b6118082b2 SHA256: 77d94b1ad56be65891dce2299268ac3a8079add531724a55edb0f0c401fc22e8 SHA512: 143b050149dfe50cad26720bb11d18dbb0d1fdb0e9362e795ddd8d52d30f855be52cfe7e205813de516bf317212dfb34afa0d05a8e42daa1dc4323a862227d2d Homepage: https://cran.r-project.org/package=tempdisagg Description: CRAN Package 'tempdisagg' (Methods for Temporal Disaggregation and Interpolation of TimeSeries) Temporal disaggregation methods are used to disaggregate and interpolate a low frequency time series to a higher frequency series, where either the sum, the mean, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso, Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and Steiner (2013) . Supports most R time series classes. Package: r-cran-temper Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-purrr, r-cran-imputets, r-cran-lubridate, r-cran-ggplot2, r-cran-scales Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-temper_1.1.0-1.ca2604.1_all.deb Size: 119638 MD5sum: be0ac354fe4a0c6e791f5da7427cabcb SHA1: 13c72232e445e432bf9efce36ea987390ae46009 SHA256: 5412fbfcc08f71f005f5383b1876fe576536577ebb45c21e6afb9d3a7bf54967 SHA512: 14b24b5df98f1e5c959ca795ba8d20c288d88072ff0f9b71878490d357a379c234508e33fae7c1d3756d7fa485166a5c73832bf1a02e1af614257c7f0389f87b Homepage: https://cran.r-project.org/package=temper Description: CRAN Package 'temper' (Temporal Encoder-Masked Probabilistic Ensemble Regressor) Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with 'torch' tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing. Package: r-cran-temperatureresponse Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-rootsolve, r-cran-minpack.lm, r-cran-aiccmodavg, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-temperatureresponse_0.2-1.ca2604.1_all.deb Size: 69764 MD5sum: c4a20aac9e1f681cd3148f14c9d88d71 SHA1: f1bf0837293283163c4a2bd006d85b16a66ce93e SHA256: c91923a03288a80905102b57f7e065ce52c51860934dae490fc80aeac71752f6 SHA512: 5c3e82bc7a19351d203906fff05ce68de2b26ba1d45230d8b6eb324c08cae9f8deb36b87060d84ac34483dedf5785d9d662a1b87d57dd78e7870ffe36504efbb Homepage: https://cran.r-project.org/package=temperatureresponse Description: CRAN Package 'temperatureresponse' (Temperature Response) Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. Boatman, Noah Bennett,Will Passfield,Antonio Gavalas-Olea,Philipp Siegel, Richard J. Geider (2017) . Package: r-cran-templateicar Architecture: all Version: 0.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-fmritools, r-cran-fmriscrub, r-cran-foreach, r-cran-ica, r-cran-matrix, r-cran-matrixstats, r-cran-pesel, r-cran-squarem Suggests: r-cran-ciftitools, r-cran-excursions, r-cran-rnifti, r-cran-oro.nifti, r-cran-gifti, r-cran-covr, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-templateicar_0.10.0-1.ca2604.1_all.deb Size: 481262 MD5sum: 2e99113e379dd164f9a9b3bb5a6b61af SHA1: bbc69d6cdd307fbfb360fac96e63494e5d595906 SHA256: 77842fcb24c566ce320f111d5185438cbe0a3b3dabf38cdecccd3388d3d7cc66 SHA512: 633a713f1daed05fb35b4b64b6a5eb6b8412dc3b43af9c9787c42dae0cd41c2a160ea5003439967b54309940c77ced0a6bae6bfb2f6f6266d3b255aef53f844b Homepage: https://cran.r-project.org/package=templateICAr Description: CRAN Package 'templateICAr' (Estimate Brain Networks and Connectivity with ICA and EmpiricalPriors) Implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) and the spatial template ICA model proposed in proposed in Mejia et al. (2022) . Both models estimate subject-level brain as deviations from known population-level networks, which are estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats. Package: r-cran-templates Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-templates_0.4.0-1.ca2604.1_all.deb Size: 29488 MD5sum: 69eb2d2ccb2cb95621d5aea7ddd554f0 SHA1: 129fbc85626d612a30684aebb70321d4e6ae0a52 SHA256: aa46190fc322dd9032a018317b80facb580212550cf742062ec6d5b31a4842d8 SHA512: 607bb1157d0ba2a052ad01849230c6e26780114786ad176200ee3cb1a932830405f1c999c9f0505697fd64b8231c23ff00b046ba03db8c930b675f62205464bb Homepage: https://cran.r-project.org/package=templates Description: CRAN Package 'templates' (A System for Working with Templates) Provides tools to work with template code and text in R. It aims to provide a simple substitution mechanism for R-expressions inside these templates. Templates can be written in other languages like 'SQL', can simply be represented by characters in R, or can themselves be R-expressions or functions. Package: r-cran-templr Architecture: all Version: 0.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-remotes, r-cran-xml2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-future Filename: pool/dists/resolute/main/r-cran-templr_0.2-3-1.ca2604.1_all.deb Size: 82884 MD5sum: c28e0ff93047641925a8b140727ca5bd SHA1: 854c6a36650737279a819a6aa9811d4f287b49fe SHA256: e3e4f3350a69e81d929c2618005b487d568cb1236a9c4d8a45b5c0a219eda5f4 SHA512: c5932d8ea07fe6bd8e07b1178241fb85fd138f791ff76213e5b6aa9b54fc2f2ad1d47d186a048baa3e5bdf7c71bb4e380dcb140aefcde19b2d36de4dc0ee9426 Homepage: https://cran.r-project.org/package=templr Description: CRAN Package 'templr' (MASCOTNUM / RT-UQ Algorithms Template Tools) Helper functions for MASCOTNUM / RT-UQ algorithm template, for design of numerical experiments practice: algorithm template parser to support MASCOTNUM specification , 'ask & tell' decoupling injection (inspired by ) to use "crimped" algorithms (like uniroot(), optim(), ...) from outside R, basic template examples: Brent algorithm for 1 dim root finding and L-BFGS-B from base optim(). Package: r-cran-tempodisco Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rwiener Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tempodisco_2.1.0-1.ca2604.1_all.deb Size: 518064 MD5sum: f66ab33f7c5e47bfc29848a13c3aaf42 SHA1: 304b29154a7239d9e637845bee3570ee7189c792 SHA256: 9ecbc430368fff7289b3d7871b0b1d644559cfb9cc6ef006ae8d35a169efa419 SHA512: ea145991c6ca9c25249dc0759153474d610febf4724ea79d75abb7014fa550ca7ae1e88cd561081695245399431d56dcad0982ca1825e56321156a0bbe3f9df2 Homepage: https://cran.r-project.org/package=tempodisco Description: CRAN Package 'tempodisco' (Temporal Discounting Models) Tools for working with temporal discounting data, designed for behavioural researchers to simplify data cleaning/scoring and model fitting. The package implements widely used methods such as computing indifference points from adjusting amount task (Frye et al., 2016, ), testing for non-systematic discounting per the criteria of Johnson & Bickel (2008, ), scoring questionnaires according to the methods of Kirby et al. (1999, ) and Wileyto et al (2004, ), Bayesian model selection using a range of discount functions (Franck et al., 2015, ), drift diffusion models of discounting (Peters & D'Esposito, 2020, ), and model-agnostic measures of discounting such as area under the curve (Myerson et al., 2001, ) and ED50 (Yoon & Higgins, 2008, ). Package: r-cran-temporal Architecture: all Version: 0.3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-expint, r-cran-numderiv, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/resolute/main/r-cran-temporal_0.3.0.2-1.ca2604.1_all.deb Size: 594356 MD5sum: 6ebf73072aa2600cb7be7def2552435c SHA1: f6995567bb65c4acd66016f78190e8b45184509d SHA256: f754345ab1512bc78dc3b6803b544af6103fffd042da763af8b04bf718b46861 SHA512: 5fcea5c53ef8a1f3e8c6e54bb332b425b7b13cabdf1606c0b79544e9716785ced26d60344fc6cfcfa08a61e9b50851840f2acb27f8804c914e28cae4f37d8bee Homepage: https://cran.r-project.org/package=Temporal Description: CRAN Package 'Temporal' (Parametric Time to Event Analysis) Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull. Package: r-cran-temporalforest Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wgcna, r-cran-dynamictreecut, r-cran-flashclust, r-cran-glmertree, r-cran-partykit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-matrix, r-cran-mass Filename: pool/dists/resolute/main/r-cran-temporalforest_0.1.4-1.ca2604.1_all.deb Size: 69232 MD5sum: 5e52aa933b22ecc853c2a0205763b254 SHA1: 8ae263af90004f6f07baeb31f5370968e5a5b31d SHA256: d0b6f6b2219c0da7c86d4e1f1f21fd286237879777cf87014b9b17b50cca6bfb SHA512: bf1576f7840667e637621e3c593afd8c3659bfd8e76afbe5e6eaa08b124fff9602c2dc8fb4bed036ef55e3a55ca990b3fc36f4009b776ff3df2a833fb4a6228b Homepage: https://cran.r-project.org/package=TemporalForest Description: CRAN Package 'TemporalForest' (Network-Guided Temporal Forests for Feature Selection inHigh-Dimensional Longitudinal Data) Implements the Temporal Forest algorithm for feature selection in high-dimensional longitudinal data. The method combines time-aware network construction via weighted gene co-expression network analysis (WGCNA), module-based feature screening, and stability selection using tree-based models. This package provides tools for reproducible longitudinal analysis, closely following the methodology described in Shao, Moore, and Ramirez (2025) . Package: r-cran-temporalgssa Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-temporalgssa_1.0.1-1.ca2604.1_all.deb Size: 25432 MD5sum: e842b910ce8e7eca41b573a209ddf5b6 SHA1: 2c94254ae7b5f5e4bd7ffd25e513d759957c4358 SHA256: 45384c6708d0492207ab251825e98135f7021537ec5d053c1c6ca424a2d51a50 SHA512: b767516a647a38a41a9c48fe634b44d6866a318ce4bedc83afe9c23a26ffe96b9d28895d8adf072642aad40dbfd356ac63cf88a845755a59584c38cd36c8f02d Homepage: https://cran.r-project.org/package=TemporalGSSA Description: CRAN Package 'TemporalGSSA' (Outputs Temporal Profile of Molecules from Stochastic SimulationAlgorithm Generated Datasets) The data that is generated from independent and consecutive 'GillespieSSA' runs for a generic biochemical network is formatted as rows and constitutes an observation. The first column of each row is the computed timestep for each run. Subsequent columns are used for the number of molecules of each participating molecular species or "metabolite" of a generic biochemical network. In this way 'TemporalGSSA', is a wrapper for the R-package 'GillespieSSA'. The number of observations must be at least 30. This will generate data that is statistically significant. 'TemporalGSSA', transforms this raw data into a simulation time-dependent and metabolite-specific trial. Each such trial is defined as a set of linear models (n >= 30) between a timestep and number of molecules for a metabolite. Each linear model is characterized by coefficients such as the slope, arbitrary constant, etc. The user must enter an integer from 1-4. These specify the statistical modality utilized to compute a representative timestep (mean, median, random, all). These arguments are mandatory and will be checked. Whilst, the numeric indicator "0" indicates suitability, "1" prompts the user to revise and re-enter their data. An optional logical argument controls the output to the console with the default being "TRUE" (curtailed) whilst "FALSE" (verbose). The coefficients of each linear model are averaged (mean slope, mean constant) and are incorporated into a metabolite-specific linear regression model as the dependent variable. The independent variable is the representative timestep chosen previously. The generated data is the imputed molecule number for an in silico experiment with (n >=30) observations. These steps can be replicated with multiple set of observations. The generated "technical replicates" can be statistically evaluated (mean, standard deviation) and will constitute simulation time-dependent molecules for each metabolite. For SSA-generated datasets with varying simulation times 'TemporalGSSA' will generate a simulation time-dependent trajectory for each metabolite of the biochemical network under study. The relevant publication with the mathematical derivation of the algorithm is (2022, Journal of Bioinformatics and Computational Biology) . The algorithm has been deployed in the following publications (2021, Heliyon) and (2016, Journal of Theoretical Biology) . Package: r-cran-tempr Architecture: all Version: 0.10.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tempr_0.10.1.1-1.ca2604.1_all.deb Size: 253808 MD5sum: de68bdf318335070a5019d14a0c7c47d SHA1: 268de047305713ab440b7b1a8838547f684f77c9 SHA256: 77e20f11274fd008844c4daea52544509024134f614221a9058baf22807f2e7d SHA512: 913ef8e55a91c7ce574e8da8c26d1d16932ea558e021603409254a58a9cbee792294e52d13b645534602b6927508e0716b586ccd36ff7dd401e69512bbf31ad4 Homepage: https://cran.r-project.org/package=tempR Description: CRAN Package 'tempR' (Temporal Sensory Data Analysis) Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) , Castura, Baker, and Ross (2016) , and Pineau et al. (2009) . Package: r-cran-tempstable Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-doparallel, r-cran-foreach, r-cran-gsl, r-cran-hypergeo, r-cran-moments, r-cran-numderiv, r-cran-stabledist, r-cran-stableestim, r-cran-rootsolve, r-cran-vgam Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/resolute/main/r-cran-tempstable_0.2.2-1.ca2604.1_all.deb Size: 865788 MD5sum: c486b28251eba4038e8085e20fc74590 SHA1: 511e9270ca400344c74525e0d76d930d20a3bdf9 SHA256: de4eaafff32c66cb648772297de11a8d0c997af64e500593877a2a767b7a8d79 SHA512: 00a6e69483648b50baabfe250ab4423cdebeb2f6176c3a883574ebf7591bee7d150c8ce3f6b473e80706c6736711950a1742d81f393434186a55c560f3ad80ec Homepage: https://cran.r-project.org/package=TempStable Description: CRAN Package 'TempStable' (A Collection of Methods to Estimate Parameters of DifferentTempered Stable Distributions) A collection of methods to estimate parameters of different tempered stable distributions (TSD). Currently, there are seven different tempered stable distributions to choose from: Tempered stable subordinator distribution, classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid decreasing TSD, and Kim-Rachev TSD. The package also provides functions to compute density and probability functions and tools to run Monte Carlo simulations. This package has already been used for the estimation of tempered stable distributions (Massing (2023) ). The following references form the theoretical background for various functions in this package. References for each function are explicitly listed in its documentation: Bianchi et al. (2010) Bianchi et al. (2011) Carrasco (2017) Feuerverger (1981) Hansen et al. (1996) Hansen (1982) Hofert (2011) Kawai & Masuda (2011) Kim et al. (2008) Kim et al. (2009) Kim et al. (2010) Kuechler & Tappe (2013) Rachev et al. (2011) . Package: r-cran-tempted Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-np, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-tempted_0.1.1-1.ca2604.1_all.deb Size: 3093124 MD5sum: 01bc6161eccf0695900319f7555aca10 SHA1: b65f0e4df9027ce349b20199ef37829e3b0cea33 SHA256: 5301683f9f2b5549bd176829a9707e2b023bc67b81e19032403ba2f2a4fc2817 SHA512: 3c9e551ac3c3d1a9924805605ae34d60571a274637acca6a5fb07d2df1619f8d1c6a8b467cb9efffdff8fbc29517b6fbe0850d2990658c55fbdacb9e80f078a1 Homepage: https://cran.r-project.org/package=tempted Description: CRAN Package 'tempted' (Temporal Tensor Decomposition, a Dimensionality Reduction Toolfor Longitudinal Multivariate Data) TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed. Package: r-cran-tenispolar Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tenispolar_0.1.4-1.ca2604.1_all.deb Size: 21792 MD5sum: 288b37c6efade06ad98788edaed3043c SHA1: d2df1e95ed0c0cdd78b6ece677de1ca96fcd7671 SHA256: 8bb7af5b82a481e565affcb10115043446e2ce4fa20e8a8e38a8654f3efb9d00 SHA512: dda264e4ecfb803f28a932b0296406ac4ea57da6adc255500a37c8db473af434d9205e8cd5a4e774efa31fe66c799a1504b0a2bb6460fa27d1acbd531ae9eaa7 Homepage: https://cran.r-project.org/package=tenispolaR Description: CRAN Package 'tenispolaR' (Provides ZENIT-POLAR Substitution Cipher Method of Encryption) Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944). Package: r-cran-tenm Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2698 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-terra, r-cran-sf, r-cran-purrr, r-cran-dplyr, r-cran-stringr, r-cran-rgl, r-cran-future, r-cran-tidyr, r-cran-furrr, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tenm_0.5.1-1.ca2604.1_all.deb Size: 1006916 MD5sum: 523471a6e749112302d0c3cd33131823 SHA1: 3ebf1c62d2e635a352428e68ce67598e08288e68 SHA256: eb7bdd2da1f69c7c7aba758a91f6a73f1087750cafdee9655adaa7d242adf81c SHA512: 5705e1f0781ad29c3af9482a4715eaf2278d486fb740ff3680a61ca688b7b278dae2942556736be37999f27cf5dbcc1df0bd0d324a59dd03dd137251a4605bbc Homepage: https://cran.r-project.org/package=tenm Description: CRAN Package 'tenm' (Temporal Ecological Niche Models) Implements methods and functions to calibrate time-specific niche models (multi-temporal calibration), letting users execute a strict calibration and selection process of niche models based on ellipsoids, as well as functions to project the potential distribution in the present and in global change scenarios.The 'tenm' package has functions to recover information that may be lost or overlooked while applying a data curation protocol. This curation involves preserving occurrences that may appear spatially redundant (occurring in the same pixel) but originate from different time periods. A novel aspect of this package is that it might reconstruct the fundamental niche more accurately than mono-calibrated approaches. The theoretical background of the package can be found in Peterson et al. (2011). Package: r-cran-tensor Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tensor_1.5.1-1.ca2604.1_all.deb Size: 15396 MD5sum: 9e890f69bfec79c004396ed97ce39849 SHA1: 4baf0172519d7cd09cf238c54a3395b9771d253d SHA256: a9f0a5e07d31692de3881a6045aebb5d8ded2170b7ec46b53358c3ac7fd2078a SHA512: f753267324155ab071cd4eda0c5ce5088c3ed49664f9f1b41db396c4732c76588ad9c7cc114edb11b432498abd4517105d3a925c875ed0de48fe94fc74ba1f68 Homepage: https://cran.r-project.org/package=tensor Description: CRAN Package 'tensor' (Tensor Product of Arrays) The tensor product of two arrays is notionally an outer product of the arrays collapsed in specific extents by summing along the appropriate diagonals. 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The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling. Package: r-cran-tensorflow Architecture: all Version: 2.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-processx, r-cran-reticulate, r-cran-tfruns, r-cran-yaml, r-cran-tfautograph, r-cran-rstudioapi, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-keras3, r-cran-pillar, r-cran-withr, r-cran-callr Filename: pool/dists/resolute/main/r-cran-tensorflow_2.20.0-1.ca2604.1_all.deb Size: 214108 MD5sum: 6444b4249574acd53aa1d15be56c157a SHA1: 5e6193ee05306e42b2c00909b326c5f44378894e SHA256: e153ad446ebde403a1d1ba8036a79a5945f57382c3c142f6e3f9b7a4cc7ad8f9 SHA512: a3120711cfbc7329ba2b0c0e76fe07595477af799087ac3e71c8851f1189412e52034e602f3aa91e74acd043fbca8048c6546901476a323a75923fcb9949d466 Homepage: https://cran.r-project.org/package=tensorflow Description: CRAN Package 'tensorflow' (R Interface to 'TensorFlow') Interface to 'TensorFlow' , an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. Package: r-cran-tensorpreave Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtensor, r-cran-mass, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tensorpreave_1.1.0-1.ca2604.1_all.deb Size: 964844 MD5sum: 6312e836530fa3033cd927116704c57c SHA1: 19bbedf9b4e9c8f888358ee909bf624dc338c437 SHA256: dd5df8fdc6a18a69311552bbc1d60c498ac5f0352b533026dcb4f60c54f5b6d8 SHA512: 5e098409170aadf6f314b84e43862dd7cf58b918916d5020e884fa4d5c91dff2f410bd990c2042aff85f8484978d723387b293caf28cccb41114f2af7f7ee8c4 Homepage: https://cran.r-project.org/package=TensorPreAve Description: CRAN Package 'TensorPreAve' (Rank and Factor Loadings Estimation in Time Series Tensor FactorModels) A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. See Chen and Lam (2023) for more details. Package: r-cran-tensorregress Architecture: all Version: 5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-mass Filename: pool/dists/resolute/main/r-cran-tensorregress_5.1-1.ca2604.1_all.deb Size: 541450 MD5sum: b7e6746e5cc619526e7dd752d499cdbc SHA1: d68633374420df69c263778fd9497278266eb73a SHA256: 4e118bfe5fc72c54fc36243d9adefdccd4fb2bb3cdb0bd9a8bbb6a5fbdcb5d3f SHA512: 8772819cbff273731caa65c4273ca089b5a9c0784419c9777a381be480355631134510c6727c11886556cbcbdac691f9b39dc69dc8b7993cabdeba173915def3 Homepage: https://cran.r-project.org/package=tensorregress Description: CRAN Package 'tensorregress' (Supervised Tensor Decomposition with Side Information) Implement the alternating algorithm for supervised tensor decomposition with interactive side information. Details can be found in the publication Hu, Jiaxin, Chanwoo Lee, and Miaoyan Wang. "Generalized Tensor Decomposition with features on multiple modes." Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2022 . Package: r-cran-tensortest2d Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3373 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-abind, r-cran-glmnet, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tensortest2d_1.1.2-1.ca2604.1_all.deb Size: 3417528 MD5sum: 2f86902797465dc8c338ac41c263b49a SHA1: 73c90c24cfc22129d037c36469143d2cf9a5a703 SHA256: 67cb6bc632bb85cd5839deeb96953b749a6de9ade6326509bebc23fcaaf620b2 SHA512: 335637e9b94fa2212356a8458a1afc0f8249c6f81f30379f3f5f9d1aa832a8c640d15871926f2dd14f62c829a01873aa681ec7987adedaca9dee7d7490fc9953 Homepage: https://cran.r-project.org/package=TensorTest2D Description: CRAN Package 'TensorTest2D' (Fitting Second-Order Tensor Data) An implementation of fitting generalized linear models on second-order tensor type data. The functions within this package mainly focus on parameter estimation, including parameter coefficients and standard deviation. Package: r-cran-tensorts Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensor, r-cran-rtensor, r-cran-expm, r-cran-mass, r-cran-abind, r-cran-matrix, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-tensorts_1.0.2-1.ca2604.1_all.deb Size: 225930 MD5sum: 6ca851b174555aa624eb1ae5988237a6 SHA1: b7ce734b4d5008a4e942c724030aa289267f2236 SHA256: 37e393451f23938d6573d7052564dce1f269fb6431f1496f5f1b9cb4b8936da0 SHA512: b8b16e70ec9c0f555a40b7da32569ce020df9b59ec9a6f62298b9daf26764811c76fd47fa7f24bd34733ff2a89f4abb605082da92b7cb1c47727e50a097af92e Homepage: https://cran.r-project.org/package=tensorTS Description: CRAN Package 'tensorTS' (Factor and Autoregressive Models for Tensor Time Series) Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in Li et al (2021) , Chen et al (2020) , Chen et al (2020) , and Xiao et al (2020) . 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For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) and Gerard and Hoff (2016) . 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Package: r-cran-term Architecture: all Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chk, r-cran-extras, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-universals, r-cran-vctrs Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-term_0.3.7-1.ca2604.1_all.deb Size: 148842 MD5sum: 5abe7fdc1252d2afca29de68e6ec1e54 SHA1: fab2e98e2112be51e262d97ce2f1e8eba71e7821 SHA256: 4d77404bed2f237f797afbe06d21aa8f3c7b39c803663546b2cc0027b1fea499 SHA512: 406195ee10fba7f607d8c0e71f399e5db191f816c0b18c3ee6a7ccd18ceb59150d2b0c74d3eae8d8acb395434024c0ceb9cf20a0d54e8ee1215886afd8849ac9 Homepage: https://cran.r-project.org/package=term Description: CRAN Package 'term' (Create, Manipulate and Query Parameter Terms) Creates, manipulates, queries and repairs vectors of parameter terms. 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Package: r-cran-tern.gee Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tern, r-cran-checkmate, r-cran-emmeans, r-cran-formatters, r-cran-geeasy, r-cran-geepack, r-cran-nlme, r-cran-rtables Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tern.gee_0.1.5-1.ca2604.1_all.deb Size: 386420 MD5sum: 3366995282bae81cb926494e969be6c9 SHA1: bcd96d86fa3ac958bc29dea8b46adef34c117317 SHA256: cb551dc97802dcb57ebd8153285aee31d313990e21110de30e2232ab2d9155cd SHA512: 8e76930f1209fe2a3275de08b9e0046e46c20b36aceb9d48a7559a2016d8d18b8ab73c0dba68b10d072cf7d931ee1e2c386c83e14b3cba6ba420f666a8f79f4b Homepage: https://cran.r-project.org/package=tern.gee Description: CRAN Package 'tern.gee' (Tables and Graphs for Generalized Estimating Equations (GEE)Model Fits) Generalized estimating equations (GEE) are a popular choice for analyzing longitudinal binary outcomes. This package provides an interface for fitting GEE, currently for logistic regression, within the 'tern' framework (Zhu, Sabanés Bové et al., 2023) and tabulate results easily using 'rtables' (Becker, Waddell et al., 2023). It builds on 'geepack' (Højsgaard, Halekoh and Yan, 2006) for the actual GEE model fitting. Package: r-cran-tern.mmrm Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tern, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-emmeans, r-cran-formatters, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-mmrm, r-cran-parallelly, r-cran-rlang, r-cran-rtables, r-cran-tidyr Suggests: r-cran-broom, r-cran-knitr, r-cran-maditr, r-cran-matrix, r-cran-rmarkdown, r-cran-testthat, r-cran-tmb, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tern.mmrm_0.3.3-1.ca2604.1_all.deb Size: 514584 MD5sum: b75e7dcc93a5ab72675289450a29c626 SHA1: f15d00acaa130099cd2e0edaf87ec58521dbd3dd SHA256: acb30cf3537cac92620171d9661e5c44e925f5728461d28d6f365d804ec846c5 SHA512: 40b1d796d2b5c156065d916cca4e4f3285cd1d235994be493a1ab96c6e5b17b34ecf93df83bece767a3f8ff842a598844050fe65b079d3e55b3988db9e7d3639 Homepage: https://cran.r-project.org/package=tern.mmrm Description: CRAN Package 'tern.mmrm' (Tables and Graphs for Mixed Models for Repeated Measures (MMRM)) Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see for example Cnaan, Laird and Slasor (1997) . This package provides an interface for fitting MMRM within the 'tern' framework by Zhu et al. (2023) and tabulate results easily using 'rtables' by Becker et al. (2023). It builds on 'mmrm' by Sabanés Bové et al. (2023) for the actual MMRM computations. Package: r-cran-tern Architecture: all Version: 0.9.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtables, r-cran-broom, r-cran-car, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-emmeans, r-cran-forcats, r-cran-formatters, r-cran-ggplot2, r-cran-gridextra, r-cran-gtable, r-cran-labeling, r-cran-lifecycle, r-cran-magrittr, r-cran-mass, r-cran-nestcolor, r-cran-rdpack, r-cran-rlang, r-cran-scales, r-cran-survival, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lattice, r-cran-lubridate, r-cran-rmarkdown, r-cran-sass, r-cran-stringr, r-cran-svglite, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tern_0.9.10-1.ca2604.1_all.deb Size: 4521464 MD5sum: a54a18da563f28696e7a0d5093bab982 SHA1: 073fd0300f39081448a9c526ce06ac83fcd8734b SHA256: c8b443f2c780ed610288f639f55e10ed82af872dbd6f35634faad04bfb5365e6 SHA512: 242bf02287d003aff4a233aa61e00f7a79675cc63323922477a70ca77a353e002337892664bac6b32042d72ec0ec52e41dc111239c48c4ce663aa563bf68bc3a Homepage: https://cran.r-project.org/package=tern Description: CRAN Package 'tern' (Create Common TLGs Used in Clinical Trials) Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials. Package: r-cran-ternary Architecture: all Version: 2.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plottools, r-cran-shiny, r-cran-sp, r-cran-treedist Suggests: r-cran-colourpicker, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-shinyjs, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-ternary_2.3.6-1.ca2604.1_all.deb Size: 2852522 MD5sum: 133b8fa9038481b87998ea2fb80875cc SHA1: cf0c2896dcb45035eee73f41c8df05175332c9ad SHA256: 4e7158c90e3b17dfd570889a1126319c91fdd599eafa8c32415bcad39e85445c SHA512: f873dda719fdbc7923d01fc0f36c0e65e6462824359c09c6e6bc30d138409b66f09253f2bdb036151bdc37a3bbed84e3118c72b65fb0fe3f1709a22c938a3974 Homepage: https://cran.r-project.org/package=Ternary Description: CRAN Package 'Ternary' (Create Ternary and Holdridge Plots) Plots ternary diagrams (simplex plots / Gibbs triangles) and Holdridge life zone plots using the standard graphics functions. Allows custom annotation, interpolating, contouring and scaling of plotting region. Includes a 'Shiny' user interface for point-and-click ternary plotting. An alternative to 'ggtern', which uses the 'ggplot2' family of plotting functions. Package: r-cran-terntables Architecture: all Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1901 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-epitools, r-cran-flextable, r-cran-magrittr, r-cran-officer, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-withr, r-cran-writexl Suggests: r-cran-knitr, r-cran-multcompview, r-cran-rmarkdown, r-cran-rstatix, r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-terntables_1.6.4-1.ca2604.1_all.deb Size: 1289280 MD5sum: 03ace99f9f40c177a599951750cd8dc6 SHA1: d81c87ad81cee19eb4276c51322852d0133012a1 SHA256: c9253941eba5bda2cb7c6dfaa87b8deafd495d73a00c62e74f5b2b0320a4fa45 SHA512: a4298fcb628d3dc5ce298a378c492abf66041498f8572f2c3902ab02a69ccb3319bdc405e5957afaa92ed42ec0e80ef2710e4e91dbb46458d8f2e03760dba283 Homepage: https://cran.r-project.org/package=TernTables Description: CRAN Package 'TernTables' (Publication-Ready Summary Tables and Statistical Testing forClinical Research) Generates publication-ready summary tables for clinical research, supporting descriptive summaries and comparisons across two or three groups. The package streamlines the analytical workflow by detecting variable types and applying appropriate statistical tests (Welch t-test, Wilcoxon rank-sum, Welch ANOVA, Kruskal-Wallis, Chi-squared, or Fisher's exact test). Results are formatted as 'tibble' objects and can be exported to 'Word' or 'Excel' using the 'officer', 'flextable', and 'writexl' packages. Optional pairwise post-hoc testing for three-group comparisons (Games-Howell and Dunn's test) is available via the 'rstatix' package. Example data are derived from the landmark adjuvant colon cancer trial described in Moertel et al. (1990) . Package: r-cran-ternvis Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-maps, r-cran-dichromat Filename: pool/dists/resolute/main/r-cran-ternvis_1.3-1.ca2604.1_all.deb Size: 770592 MD5sum: 854e91583419dcdd62b6af5736b0e7c9 SHA1: d3d884e2dec3b7ea915abce8b4f9cf2887b536ab SHA256: c17271f4f75161a2f4d11e06a28452bf13490c296ae055bcc27da6dabc5b52d4 SHA512: 474e6addc22bd61ae28c2566a86a25e236e217554636516396519a7dc34d608c5b67523a16e7f2e9f1f21cac586c2ecf8bde96193f43b4e28f3052d3c2ab4061 Homepage: https://cran.r-project.org/package=ternvis Description: CRAN Package 'ternvis' (Visualisation, Verification and Calibration of TernaryProbabilistic Forecasts) A suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) . Package: r-cran-terrainr Architecture: all Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2942 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-ggplot2, r-cran-glue, r-cran-httr, r-cran-magick, r-cran-png, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-unifir, r-cran-units Suggests: r-cran-brio, r-cran-covr, r-cran-jpeg, r-cran-knitr, r-cran-progress, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat, r-cran-tiff Filename: pool/dists/resolute/main/r-cran-terrainr_0.7.6-1.ca2604.1_all.deb Size: 1990938 MD5sum: 4ee0189838dd0699a5a375a2a05ff0bc SHA1: 0e824a1ae631e04090170f48fa2ee6f236941a73 SHA256: c2cc85b15a4a25c98b440d16cf362a36f01df00d37ed0f1991170f4298420f3e SHA512: d26f03ddc0cebe6c673b0a7047494a77feb045266784b2a5b8663c9ed48549ae410308ad775082f2cd818bab462a2b4cb11077a2b53926283671aaaa30fe7fc3 Homepage: https://cran.r-project.org/package=terrainr Description: CRAN Package 'terrainr' (Landscape Visualizations in R and 'Unity') Functions for the retrieval, manipulation, and visualization of 'geospatial' data, with an aim towards producing '3D' landscape visualizations in the 'Unity' '3D' rendering engine. Functions are also provided for retrieving elevation data and base map tiles from the 'USGS' National Map . Package: r-cran-terralink Architecture: all Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1340 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-igraph, r-cran-r6, r-cran-sf, r-cran-stars, r-cran-terra Suggests: r-cran-gdistance, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-raster, r-cran-rmarkdown, r-cran-sp, r-cran-shiny, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-terralink_1.8.0-1.ca2604.1_all.deb Size: 872878 MD5sum: e23c06f4f1a7f8033a0fbca8822ff1c4 SHA1: dde2c477528388892f9768c6b4193f2dd3e12fea SHA256: 8d1597cc9a2904f9d6a74617d0ee23d0afe27da64b56b68b0de670783a8f0344 SHA512: 95d68f87f9f380075f0659b816ad5ea785fd1d341c4403e0f6e1a6f2704878e9433d8758417b28152d538b4cac1720a1e32368a5b4e36123585a4371dd5f51fe Homepage: https://cran.r-project.org/package=terralink Description: CRAN Package 'terralink' (Connectivity Corridor Optimization for Raster and Vector Data) Standalone R implementation of habitat connectivity corridor optimization for raster and vector workflows. Supports scenario-based planning with budget-constrained optimization, optional impassable areas, packaged parity fixtures, and comparative before-and-after connectivity metrics. The package exposes structural, movement-oriented, and species-oriented strategies in a reproducible workflow aligned with a companion GIS plugin while avoiding a desktop GIS dependency. Package: r-cran-tesiprov Architecture: all Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-digest, r-cran-nloptr, r-cran-ggplot2, r-cran-gridextra, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-evd, r-cran-brms Filename: pool/dists/resolute/main/r-cran-tesiprov_0.9.6-1.ca2604.1_all.deb Size: 510540 MD5sum: 539783cab1055f723a327a7b52280a15 SHA1: 7e79883be4ded83763bfa397564ea71a2144f7a0 SHA256: 1e00b15d395377ecfac6ba4d9066adcd60617f358567c0e5127eb71f454b32ba SHA512: 89391dadde6d871ed29ffbfff5a12dd6a67627326b6555d1794eb051c5e576f0c19e2d028b350b648f7c11e6a059a323f55b67ec6e371db507d52a7fc9ade858 Homepage: https://cran.r-project.org/package=TesiproV Description: CRAN Package 'TesiproV' (Reliability Analysis Methods for Structural Engineering) Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." , Breitung (1989) "Asymptotic approximations for probability integrals." . Package: r-cran-tesouror Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6889 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-stringi, r-cran-stringr, r-cran-tibble Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tesouror_0.2.2-1.ca2604.1_all.deb Size: 4813982 MD5sum: 9e6eb18e3f5ad8b63ca9dceffc4dcdb1 SHA1: a18ae34dd48d40bc05668f255ea47d3a5040d73d SHA256: 8560aac82a3860fc25c78d3e6efc1d5c5f79f4ef45efa6c92cd405938dddad0c SHA512: ff512c55869be1d84cb793765559b60e3501157815a53299f483c66c2a883ecf17785ff30657187e9592c0b164032e50d14546153c20467ac55aae59d43ddf6e Homepage: https://cran.r-project.org/package=tesouror Description: CRAN Package 'tesouror' (Access Brazilian National Treasury Open Data APIs) Provides a unified interface to access open data from the Brazilian National Treasury ('Tesouro Nacional') and related government APIs. Covers six data sources: 'SICONFI' for fiscal reports ('RREO', 'RGF', 'DCA', 'MSC') and entity information; 'CUSTOS' for federal government cost data; 'SADIPEM' for public debt and credit operations; 'Transferencias Constitucionais' for constitutional transfers to states and municipalities; 'SIORG' for federal organizational structure; and 'SIOPE' ('FNDE'/'MEC') for education spending data. Features automatic pagination, in-memory caching, retry logic, and tidy output. Package: r-cran-tesselle Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dimensio, r-cran-folio, r-cran-isopleuros, r-cran-kairos, r-cran-khroma, r-cran-nexus, r-cran-tabula Filename: pool/dists/resolute/main/r-cran-tesselle_1.6.0-1.ca2604.1_all.deb Size: 101380 MD5sum: ec52293b21c0164c4665e27f334f698a SHA1: 9869f13850152af0f0c040fbfb40d51527b9ef5b SHA256: 37bae1a342e003ced7998d57f724621f71b0f1be5161c69c136c1c63a99fb39a SHA512: 8bb742bc497e73a908bb8d9be6ff3b82a05386ffa90e27890f5e1df6511942a8033a76dd274e7bbbc390fb7fbb07cb74f2e7885bf0f307d6c241428f856ea2b4 Homepage: https://cran.r-project.org/package=tesselle Description: CRAN Package 'tesselle' (Easily Install and Load 'tesselle' Packages) Easy install and load key packages from the 'tesselle' suite in a single step. The 'tesselle' suite is a collection of packages for research and teaching in archaeology. These packages focus on quantitative analysis methods developed for archaeology. The 'tesselle' packages are designed to work seamlessly together and to complement general-purpose and other specialized statistical packages. These packages can be used to explore and analyze common data types in archaeology: count data, compositional data and chronological data. Learn more about 'tesselle' at . Package: r-cran-test.assessr Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-checkmate, r-cran-covr, r-cran-dplyr, r-cran-fs, r-cran-jsonlite, r-cran-pkgload, r-cran-remotes, r-cran-rlang, r-cran-rmarkdown, r-cran-runit, r-cran-stringr, r-cran-testthat, r-cran-tinytest, r-cran-tidyr, r-cran-withr Suggests: r-cran-devtools, r-cran-dt, r-cran-here, r-cran-kableextra, r-cran-knitr, r-cran-r6, r-cran-s7, r-cran-roxygen2, r-cran-tidyselect, r-cran-mockery Filename: pool/dists/resolute/main/r-cran-test.assessr_2.1.0-1.ca2604.1_all.deb Size: 1685626 MD5sum: 2c228d8be5c81e1cf2d78f895cdfc403 SHA1: d3eedceb60c07828f2bd6aaa0fd9afb0114ab5c5 SHA256: c6420a9259b3e3a1e854e0717504574c3f960ed048e4481509ef05f1121c4493 SHA512: fad9ce973a53b1137ff808b0b4b590557d40f4874900b8adc9dec00bc98a0027ce0edfc9baaf72b136757964e369462c883dc99161277eca53f65211dbbf5e4e Homepage: https://cran.r-project.org/package=test.assessr Description: CRAN Package 'test.assessr' (Assessing Package Test Reliability and Quality) A reliable and validated tool that calculates unit test coverage for R packages with standard testing frameworks and non-standard testing frameworks. Package: r-cran-test2norm Architecture: all Version: 0.3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mfp2 Filename: pool/dists/resolute/main/r-cran-test2norm_0.3.0.1-1.ca2604.1_all.deb Size: 58894 MD5sum: d0c3109d11398fd8aaa621019c55a13f SHA1: 67f5423707becf089127f1b2f3f1476b394919d8 SHA256: ee41077a0908e0898e47da767b2c6ea101a3157b3a2cb6698b193228e41413d7 SHA512: 46b29e950a14569f0bf92edc3c33ece70c18df89a8c0f5a1995867e85ce81a62a48cf89f454969ecd2f2eaab9d02ad10b92c48262d3ab4f290577c4ef3abe9ad Homepage: https://cran.r-project.org/package=test2norm Description: CRAN Package 'test2norm' (Normative Standards for Cognitive Tests) Package test2norm contains functions to generate formulas for normative standards applied to cognitive tests. It takes raw test scores (e.g., number of correct responses) and converts them to scaled scores and demographically adjusted scores, using methods described in Heaton et al. (2003) & Heaton et al. (2009, ISBN:9780199702800). The scaled scores are calculated as quantiles of the raw test scores, scaled to have the mean of 10 and standard deviation of 3, such that higher values always correspond to better performance on the test. The demographically adjusted scores are calculated from the residuals of a model that regresses scaled scores on demographic predictors (e.g., age). The norming procedure makes use of the mfp2() function from the 'mfp2' package to explore nonlinear associations between cognition and demographic variables. Package: r-cran-testanaapp Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 673 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mirt, r-cran-shinydashboard, r-cran-estcrm, r-cran-shiny, r-cran-dt, r-cran-golem, r-cran-officedown, r-cran-stringr, r-cran-dplyr, r-cran-brucer, r-cran-officer, r-cran-openxlsx, r-cran-rmarkdown, r-cran-plotrix, r-cran-cowplot, r-cran-flextable, r-cran-shinycssloaders, r-cran-tidyr, r-cran-difr, r-cran-lordif, r-cran-latticeextra, r-cran-semplot Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-testanaapp_1.1.2-1.ca2604.1_all.deb Size: 567582 MD5sum: 71622fd2816508388f79e9048c808a6a SHA1: 19a100c4b7f78ca4125393dc1c2307d190fcbac8 SHA256: 11c8fd83bfdff41ed58a1a1c80d09cbc401c094f85178a1092094f3a26b09598 SHA512: dac06a4e28bb1f8559efcce7478bbee09179fd450d5df0cfae0c3d9d17349de2ae2ab8c350634a3e74d44a540a811360b0f7f78b82ff90335948c2de7d3ef336 Homepage: https://cran.r-project.org/package=TestAnaAPP Description: CRAN Package 'TestAnaAPP' (A 'shiny' App for Test Analysis and Visualization) This application provides exploratory and confirmatory factor analysis, classical test theory, unidimensional and multidimensional item response theory, and continuous item response model analysis, through the 'shiny' interactive interface. In addition, it offers rich functionalities for visualizing and downloading results. Users can download figures, tables, and analysis reports via the interactive interface. Package: r-cran-testarguments Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-testarguments_0.0.1-1.ca2604.1_all.deb Size: 372252 MD5sum: b40a1faa95a08bb24f96f29869fd361b SHA1: 330fd722760dca217a9e6debbab0808f9ef8c5e9 SHA256: 9f5da2fb29ac3635efbfc33bf22665853555a5b5b87abd8991812f22795003c2 SHA512: 8575e4c28edb6e504cc08c4f1d9ff2c33c1008bf3c59bad93c2e6cc76e9fe7274aed2502e0995c7003d54bd373187ee65e683afe559d3ad4ba9a236c5afb32d7 Homepage: https://cran.r-project.org/package=testarguments Description: CRAN Package 'testarguments' (Test (Multiple) Arguments of a User-Defined Prediction Algorithm) Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic. Package: r-cran-testassay Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-testassay_0.1.1-1.ca2604.1_all.deb Size: 56198 MD5sum: 5d16a1312296270e7bd48409d20f2b78 SHA1: e97f53161d8834cecb4b0f7057f243b092091bf3 SHA256: 6a07338839c77b1a70d797b512bcae6b3b12559c5cbbe2746a1252716ff09a88 SHA512: 7e785811cb17347b30f6316091f69c31c25804e6bfa685a74c6f010614c160513299b3277a0864f32d74475d271066e05b7203f14a470349a82af4d9119acc79 Homepage: https://cran.r-project.org/package=testassay Description: CRAN Package 'testassay' (A Hypothesis Testing Framework for Validating an Assay forPrecision) A common way of validating a biological assay for is through a procedure, where m levels of an analyte are measured with n replicates at each level, and if all m estimates of the coefficient of variation (CV) are less than some prespecified level, then the assay is declared validated for precision within the range of the m analyte levels. Two limitations of this procedure are: there is no clear statistical statement of precision upon passing, and it is unclear how to modify the procedure for assays with constant standard deviation. We provide tools to convert such a procedure into a set of m hypothesis tests. This reframing motivates the m:n:q procedure, which upon completion delivers a 100q% upper confidence limit on the CV. Additionally, for a post-validation assay output of y, the method gives an ``effective standard deviation interval'' of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where mu is the expected value of the assay output for that sample. Further, the m:n:q procedure can be straightforwardly applied to constant standard deviation assays. We illustrate these tools by applying them to a growth inhibition assay. This is an implementation of the methods described in Fay, Sachs, and Miura (2018) . Package: r-cran-testcomparer Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-testcomparer_1.1.1-1.ca2604.1_all.deb Size: 131866 MD5sum: 52af2406b3306f52f71584a90e9f1ef4 SHA1: 61104c9d5e8578f82f2007b9a2c5046b903ef2b7 SHA256: 4f87d7b2d7ff7f72f68d52fe9279b1b962e2134f89bbd88711ed1032ea6ceb11 SHA512: 64c632b23319d5ea0ccee35a910a7ddc0355ec84e4762176152ae1019e10ffa2b2933899ce54921491e35742333f070217aecd91d9083cd7d19f3fc475191948 Homepage: https://cran.r-project.org/package=testCompareR Description: CRAN Package 'testCompareR' (Comparing Two Diagnostic Tests with Dichotomous Results usingPaired Data) Provides a method for comparing the results of two binary diagnostic tests using paired data. Users can rapidly perform descriptive and inferential statistics in a single function call. Options permit users to select which parameters they are interested in comparing and methods for correction for multiple comparisons. Confidence intervals are calculated using the methods with the best coverage. Hypothesis tests use the methods with the best asymptotic performance. A summary of the methods is available in Roldán-Nofuentes (2020) . This package is targeted at clinical researchers who want to rapidly and effectively compare results from binary diagnostic tests. Package: r-cran-testcorr Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-reshape2, r-cran-forcats, r-cran-knitr, r-cran-xts, r-cran-zoo Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-testcorr_0.4.0-1.ca2604.1_all.deb Size: 796302 MD5sum: 9886be7764471ac64330abe934a71adb SHA1: 27cf504e98aa9629cc9c7b41b81ba509695bc405 SHA256: d92e4cd15f457824c195a8e7e6109b79932d1450261bc0f7f965722edc71c2c1 SHA512: 543087dafc6c330486901178b50abff3ad542f5a4497b2121f8d1f86eb9f3e1c776c73b3f5329ad805ef2553c1155f77e601339da8f47f784244965a70933569 Homepage: https://cran.r-project.org/package=testcorr Description: CRAN Package 'testcorr' (Testing Zero Correlation) Computes the test statistics for examining the significance of autocorrelation in univariate time series, cross-correlation in bivariate time series, Pearson correlations in multivariate series and test statistics for i.i.d. property of univariate series given in Dalla, Giraitis and Phillips (2022), , . 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An extension of the 'testthat' unit testing framework with a family of functions and reporting tools for checking and validating data frames. 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This package enables missing imputation methods that are suitable for test and assessment data, including: listwise (LW) deletion (see De Ayala et al. 2001 ), treating as incorrect (IN, see Lord, 1974 ; Mislevy & Wu, 1996 ; Pohl et al., 2014 ), person mean imputation (PM), item mean imputation (IM), two-way (TW) and response function (RF) imputation, (see Sijtsma & van der Ark, 2003 ), logistic regression (LR) imputation, predictive mean matching (PMM), and expectation–maximization (EM) imputation (see Finch, 2008 ). 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H., & Hodges, D. C. (1985) , Greene, D. L. (1989) and Konigsberg, L. W. (1991) . Package: r-cran-testdriver Architecture: all Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-testdriver_0.5.3-1.ca2604.1_all.deb Size: 228258 MD5sum: fb6bba0ea3d47a7e35279d5d4813ee27 SHA1: dcb8baa99bfabd831d6b793dfbd3e317559da4a7 SHA256: 8e32805d05503a87cdddcefb689207109caae73203ebd4939e6c640188c77d55 SHA512: a1a73b2879c0482492b15f076ec241c947d902e20d261ea630cf0b24f545465c0ff4ec4d661230f4ab9a797a7cd2bb5e5f7556d59bb906383583e726f086aad0 Homepage: https://cran.r-project.org/package=testDriveR Description: CRAN Package 'testDriveR' (Teaching Data for Statistics and Data Science) Provides data sets for teaching statistics and data science courses. It includes a sample of data from John Edmund Kerrich's famous coinflip experiment. These are data that I used for statistics. The package also contains an R Markdown template with the required formatting for assignments in my former courses. Package: r-cran-testequavar Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-testequavar_0.1.5-1.ca2604.1_all.deb Size: 38840 MD5sum: 0e66d218d6c865c49811283ccb8cde86 SHA1: 0621d0a7e2ac0b0532a201f79f6171ffa701104a SHA256: bf6e2e1679e3b5c4e3af0910b89c60579a237ef0f705f2dd0acbb0f1ab30a0da SHA512: 3dd7474735724cb9b68791de5fcce66e56304036f265143689aacb450b3bc9c87fb176935acba4f22d1a23110bb040534d83bfdeb038c8b1fcef70860fff456f Homepage: https://cran.r-project.org/package=testequavar Description: CRAN Package 'testequavar' (Bootstrap Tests for Equality of 2, 3, or 4 Population Variances) Tests the hypothesis that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test. The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi{10.1016/j.csda.2010.04.012}. 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Package: r-cran-testex Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-withr, r-cran-callr, r-cran-roxygen2, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-testex_0.2.1-1.ca2604.1_all.deb Size: 129124 MD5sum: 43feaad4d031e6c474ff3acd58f90a90 SHA1: 0dac3abfc60c3d9a1789566ec3fc3e36dbba377d SHA256: f5c836c15f8e7578e90bfa6b9b0f5b0c8cb73518c2e9167d9409f9dc7ec98126 SHA512: bce07222c21e2b4adcaa10d6cf2dec61575e84715f0c7bffd7d487091c8c88c8f4285bc8701577bf436319033a2f1f709ca725194e132d51c940c88c3862264d Homepage: https://cran.r-project.org/package=testex Description: CRAN Package 'testex' (Add Tests to Examples) Add tests in-line in examples. Provides standalone functions for facilitating easier test writing in Rd files. However, a more familiar interface is provided using 'roxygen2' tags. Tools are also provided for facilitating package configuration and use with 'testthat'. Package: r-cran-testfunctions Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-contourfunctions, r-cran-numderiv, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-covr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-testfunctions_0.2.2-1.ca2604.1_all.deb Size: 218704 MD5sum: ed852c4aa61e22a9a0c8cd43f650923d SHA1: a317448a380d75d3d248e4250cc233f8f0e2ee4b SHA256: bcece10ff90d6c8d7a971ce75ddf33db96f70429167adca680efa6d2e25f2446 SHA512: c85c3774fc699d91d90059b612537dd4b9453e518859e5134c3d7edfef92cb43fd628a06a718a94d22f43cd895106bebf165f677b0066f66057315d983a8aab3 Homepage: https://cran.r-project.org/package=TestFunctions Description: CRAN Package 'TestFunctions' (Test Functions for Simulation Experiments and EvaluatingOptimization and Emulation Algorithms) Test functions are often used to test computer code. They are used in optimization to test algorithms and in metamodeling to evaluate model predictions. This package provides test functions that can be used for any purpose. Package: r-cran-testgardener Architecture: all Version: 3.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 937 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-ggplot2, r-cran-plotly, r-cran-dplyr, r-cran-ggpubr, r-cran-stringr, r-cran-tidyr, r-cran-pracma, r-cran-utf8, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-testgardener_3.3.6-1.ca2604.1_all.deb Size: 911674 MD5sum: aca63373561259e7eda25b720174b759 SHA1: ec09797ecb68ab41d3a11b5fc847a090f516a585 SHA256: bc54e8d5b2b5c5314ade6f649e8a328d4fbea775eacff9fce20665b19bd3cb73 SHA512: 014805c78f1de984099324b44f008de9bc7ea9ceda86c4c69a21239a6507023e73a7b0feb9e76e873dc2ae92876bb62a09817720fdd926bbb06fbceed59dd83d Homepage: https://cran.r-project.org/package=TestGardener Description: CRAN Package 'TestGardener' (Information Analysis for Test and Rating Scale Data) Develop, evaluate, and score multiple choice examinations, psychological scales, questionnaires, and similar types of data involving sequences of choices among one or more sets of answers. This version of the package should be considered as brand new. Almost all of the functions have been changed, including their argument list. See the file NEWS.Rd in the Inst folder for more information. Using the package does not require any formal statistical knowledge beyond what would be provided by a first course in statistics in a social science department. There the user would encounter the concept of probability and how it is used to model data and make decisions, and would become familiar with basic mathematical and statistical notation. Most of the output is in graphical form. 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Focusing on the text matrix as the primary object - represented either as a base R dense matrix or a 'Matrix' package sparse matrix - allows for a consistent and intuitive interface that stays close to the underlying mathematical foundation of computational text analysis. In particular, the package includes functions for working with word embeddings, text networks, and document-term matrices. Methods developed in Stoltz and Taylor (2019) , Taylor and Stoltz (2020) , Taylor and Stoltz (2020) , and Stoltz and Taylor (2021) . Package: r-cran-text2sdg Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-corpustools, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-ggplot2, r-cran-lifecycle, r-cran-ranger, r-cran-text2sdgdata Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-text2sdg_1.1.2-1.ca2604.1_all.deb Size: 1105054 MD5sum: ee592a720ec90f0468788d5a335337d2 SHA1: 7c28949f53c958b3d3cbff4da75c922caaadee44 SHA256: 213b46d4ea4db4a0d1d22511bc592ed467c834218dd2b626d7f0afa625304a36 SHA512: 3b62b18e40907bcb08b1f168a83b28bf160c56c84330b656d48ba7b9a288d1bad1d9dc583b41a7776a0c5acafe3a407abbae96fa9553a64ce4a60e592628b7ca Homepage: https://cran.r-project.org/package=text2sdg Description: CRAN Package 'text2sdg' (Detecting UN Sustainable Development Goals in Text) The United Nations' Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The 'text2sdg' package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications. For more information see Meier, Mata & Wulff (2025) and Wulff, Meier & Mata (2024) . 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Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards (2001) ) or extracting them into new variables. For example, emoticons are often used in text but not always easily handled by analysis algorithms. The replace_emoticon() function replaces emoticons with word equivalents. Package: r-cran-textdata Architecture: all Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs, r-cran-rappdirs, r-cran-readr, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-textdata_0.4.5-1.ca2604.1_all.deb Size: 500404 MD5sum: 0e5150ad0fd7ef357eff33b8a69ae856 SHA1: acb821aecc1edad2228b9b2dc89a72288036d748 SHA256: 8c546050f8bc23ab7da74dfe1680e4fa9056cc22e5ef8d1445bae88c241bad4d SHA512: f8f0fc6f5728525c2c4403520728737a3796f5c977c76ad28bcf83308740d53c10ba72453f0440045d0a4057bb5365b2da4534e34d3c06f2be2237766c8cf5ca Homepage: https://cran.r-project.org/package=textdata Description: CRAN Package 'textdata' (Download and Load Various Text Datasets) Provides a framework to download, parse, and store text datasets on the disk and load them when needed. 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Package: r-cran-texteffect Architecture: all Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-boot, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-texteffect_0.3-1.ca2604.1_all.deb Size: 69630 MD5sum: e16362aaf7e4af277d2085fc0c7598aa SHA1: 8b43218e497ca3a8b514f0f59136bfb650bd11c2 SHA256: 20d67f4cfec6743950635ba1bc499c871219a08b9908035216d0b2e5e4d2200c SHA512: e12571b144bb934aa785f20e778b6335836cb6449bc9a50ad31e25e13350a69d1f8bc0b6cdd84f5baf384fbd7aacb480a69776c089ebf1df22558323468fb777 Homepage: https://cran.r-project.org/package=texteffect Description: CRAN Package 'texteffect' (Discovering Latent Treatments in Text Corpora and EstimatingTheir Causal Effects) Implements the approach described in Fong and Grimmer (2016) for automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of each treatment. The data is divided into a training and test set. The supervised Indian Buffet Process (sibp) is used to discover latent treatments in the training set. The fitted model is then applied to the test set to infer the values of the latent treatments in the test set. Finally, Y is regressed on the latent treatments in the test set to estimate the causal effect of each treatment. Package: r-cran-texter Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plyr, r-cran-ggplot2, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-stopwords, r-cran-textdata, r-cran-tidytext, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-texter_0.1.9-1.ca2604.1_all.deb Size: 155238 MD5sum: 260b6f40c6bb95ff10acbe3109b01dc9 SHA1: 1b88a5da72894fa8d732982149c174b87e0890f8 SHA256: dbb53d7624186ab478c5b1ce5e0ac0d490428f8bf878235d7147135ff7788750 SHA512: 1dcf772d6ce6ab0b5439692c92e57a03efe267f07ab2ee0dd70839ed093abb41a03538dc3ac367692fdf58e8dcf08aff82f1360d5a2636bfafb0bf81fcf09116 Homepage: https://cran.r-project.org/package=texter Description: CRAN Package 'texter' (An Easy Text and Sentiment Analysis Library) Implement text and sentiment analysis with 'texter'. 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For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis and Taddy (2015, AoAS), Distributed Multinomial Regression, . A minimalist partial least squares routine is also included. Note that the topic modeling capability of earlier 'textir' is now a separate package, 'maptpx'. 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Package: r-cran-textometry Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-textometry_0.1.7-1.ca2604.1_all.deb Size: 50650 MD5sum: c6e00b39e13f9ef28319a1ebc7ae30e3 SHA1: 73508c6e97c01a91e6ca82efc949427c2fdc3483 SHA256: 9e8ad3a15b80a0a1137958d4a4af2f0aa219ec39721b1230738b432955ba4171 SHA512: c00f8eb04052d6fb0745ade53a2cd416dbed67db4cb854e6e6b03c0efa309b527b60f71b53836143e2307b2cd17891dac53fdee97ba41e67b5c9bb5be456fb18 Homepage: https://cran.r-project.org/package=textometry Description: CRAN Package 'textometry' (Textual Data Analysis Package Used by the TXM Software) Statistical exploration of textual corpora using several methods from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools. 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Please refer to Liu et al. (2013) for more details. 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The threshold regression methodology is well suited to applications involving survival and time-to-event data. Package: r-cran-thresher Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-classdiscovery, r-cran-pcdimension, r-cran-mass, r-cran-colorspace, r-cran-movmf, r-cran-ade4, r-cran-oompabase Suggests: r-cran-nbclust Filename: pool/dists/resolute/main/r-cran-thresher_1.1.5-1.ca2604.1_all.deb Size: 1390026 MD5sum: e6c20b0fd85f14570820a40d63749f1d SHA1: c441ae23761a592a0cd7d0128b60dbb91daeb477 SHA256: 2dc1721c69ba9ee35fb78bbf5973511f58be5583fdf722bc346a9392fb54d3b7 SHA512: a65873d6056e8f6552a23194c8a6d9ba7bc280f4c22d9f160ea14377226f6f9335dcd67e6bc1e95405659a2a19ec5fd668c1569c72cb40beb7317fb1b0b3d7af Homepage: https://cran.r-project.org/package=Thresher Description: CRAN Package 'Thresher' (Threshing and Reaping for Principal Components) Defines the classes used to identify outliers (threshing) and compute the number of significant principal components and number of clusters (reaping) in a joint application of PCA and hierarchical clustering. See Wang et al., 2018, . Package: r-cran-thresholdroc Architecture: all Version: 2.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-numderiv, r-cran-proc, r-cran-ks Filename: pool/dists/resolute/main/r-cran-thresholdroc_2.9.6-1.ca2604.1_all.deb Size: 236170 MD5sum: 39f484d8f286789df63d060a419c2bfe SHA1: a710d26fbdd6ae9f2dd0f6c09969cf4fddf77561 SHA256: ab07ab86630e5c45a3876ff391d40545c6b315a18d91ee284c07ad57f0489782 SHA512: 5a8c96c813fbe772559414ea98eda6f8ed7e7903d945ab530590ef21e819c0cd255686106e6f770ae74c6147f651ecc7babf1d452b5274d766766ab96a9eb604 Homepage: https://cran.r-project.org/package=ThresholdROC Description: CRAN Package 'ThresholdROC' (Optimum Threshold Estimation) Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) and in Skaltsa et al (2010, 2012) , . 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This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) and Heagerty et al. (2000) . 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It also performs predictive inferences about future extreme values, based either on a single threshold or on a weighted average of inferences from multiple thresholds, using the 'revdbayes' package . At the moment only the case where the data can be treated as independent identically distributed observations is considered. 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This package allows for the analysis of item response modeling (IRT) as well as confirmatory factor analysis (CFA) in the Thurstonian framework. Currently, estimation can be performed by 'Mplus' and 'lavaan'. References: Brown & Maydeu-Olivares (2011) ; Jansen, M. T., & Schulze, R. (in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.; Maydeu-Olivares & Böckenholt (2005) . 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The analysis should preferably cover an observation period of at least 19 years. For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion. The main objective of this package is to synthesize or predict a tidal time series. 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The package emphasizes clear actuarial notation consistent with standard curricula (e.g. SOA exams) and supports reproducible workflows using modern R. 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Package: r-cran-tidybayes Architecture: all Version: 3.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggdist, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-coda, r-cran-rlang, r-cran-arrayhelpers, r-cran-tidyselect, r-cran-tibble, r-cran-magrittr, r-cran-posterior, r-cran-withr, r-cran-cli, r-cran-vctrs Suggests: r-cran-knitr, r-cran-testthat, r-cran-purrr, r-cran-forcats, r-cran-vdiffr, r-cran-svglite, r-cran-rstan, r-cran-rstantools, r-cran-runjags, r-cran-rjags, r-cran-jagsui, r-cran-rstanarm, r-cran-emmeans, r-cran-broom, r-cran-mcmcglmm, r-cran-bayesplot, r-cran-modelr, r-cran-brms, r-cran-cowplot, r-cran-covr, r-cran-rmarkdown, r-cran-ggrepel, r-cran-bindrcpp, r-cran-rcolorbrewer, r-cran-gganimate, r-cran-gifski, r-cran-png, r-cran-ragg, r-cran-pkgdown, r-cran-distributional, r-cran-transformr Filename: pool/dists/resolute/main/r-cran-tidybayes_3.0.7-1.ca2604.1_all.deb Size: 1672074 MD5sum: ddf202da3e6135986aae0996c039d868 SHA1: e68741b15ca71b5e8f3b5456a550daa64add8b0a SHA256: 48368c8d3cd2f13a4837e5d417573ac6b366d4723f695fffebd12f8841638a4c SHA512: 2d0060d5b54e0ee62496f8361c90368ddc4b98f96004b18166678fe42ec33271e39117cad2a5098426d6a0a93c09e7ad4e67ee9dd85b5e5f26a76b825d2ed9e1 Homepage: https://cran.r-project.org/package=tidybayes Description: CRAN Package 'tidybayes' (Tidy Data and 'Geoms' for Bayesian Models) Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. 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Package: r-cran-tidybde Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-readr, r-cran-scales, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lifecycle, r-cran-quarto, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidybde_0.6.1-1.ca2604.1_all.deb Size: 280504 MD5sum: fb94a3e019ed10c2aed2af6bf3fc87b1 SHA1: b294aa9800bea80ea2a4c93b65f4448e698a87fb SHA256: a294f53d9a00f1f33d76d9d9bae187c4b9e791eb5e512cc24846640a462c764a SHA512: f58b2d0da559e34ebf40aa49693d5abd57b1e65a1d7960341c2dc62e6b99c61186d21455cbc6b81a9af9b84df6e1f159d02527d42156ac319d561eec5fa7a4eb Homepage: https://cran.r-project.org/package=tidyBdE Description: CRAN Package 'tidyBdE' (Retrieve Data from 'Banco de España') Tools for retrieving time series data from 'Banco de España' ('BdE') as 'tibble' objects. 'Banco de España' is the national central bank and, within the framework of the Single Supervisory Mechanism ('SSM'), the supervisor of the Spanish banking system alongside the European Central Bank. This package is not sponsored, endorsed or administered by 'Banco de España'. Package: r-cran-tidybins Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-purrr, r-cran-janitor, r-cran-tibble, r-cran-rlang, r-cran-lubridate, r-cran-scales, r-cran-ggplot2, r-cran-rlist, r-cran-oner, r-cran-strex, r-cran-clusterr, r-cran-framecleaner, r-cran-xgboost, r-cran-badger, r-cran-autostats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-arulescba, r-cran-embed, r-cran-woebinning, r-cran-recipes Filename: pool/dists/resolute/main/r-cran-tidybins_0.1.2-1.ca2604.1_all.deb Size: 71454 MD5sum: e8bbd3aa7c294b42c9da4bddb938f9eb SHA1: 46163ce5b7e83c07789d1ba1a88da3c26ed68e9c SHA256: 8ace661d77fa8c24b86eb0dbb2e84c4d9834b7605349930314c5d3cdb6f21efe SHA512: a4bd76d34dbcb20be57dd026ad1cfbcd695b458930d24ffa3d2a0fae88a64b1634b819166805b5b35e0da45ca01849c493fec8d6bd26af135ed293c6648939f8 Homepage: https://cran.r-project.org/package=tidybins Description: CRAN Package 'tidybins' (Make Tidy Bins) Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns. Package: r-cran-tidyboot Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-modelr, r-cran-purrr, r-cran-rlang, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-tidyboot_0.1.3-1.ca2604.1_all.deb Size: 28654 MD5sum: 828f5a613784e1d26cc36f89633335f1 SHA1: a0d6d9d5e6918074087ab5799dcbe65f93671045 SHA256: d02220fa85b29758ebb86dfe6e371a307544bf6fb2060a3ef1f8f7a5ecd8eb96 SHA512: 6f9c12ef9e48881258a815a4143222ac860ba511b0b2d6d5a50a791c6597bea6fea94ec117396173a22ee105011a3f8e1cd0be1ed085ac739c31e1a3d1334d59 Homepage: https://cran.r-project.org/package=tidyboot Description: CRAN Package 'tidyboot' (Tidyverse-Compatible Bootstrapping) Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames. Package: r-cran-tidycat Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-stringr, r-cran-forcats Suggests: r-cran-broom, r-cran-ggplot2, r-cran-ggforce, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-tidycat_0.1.2-1.ca2604.1_all.deb Size: 475966 MD5sum: 86ad73c5cdb662cb610ea68b9f572d73 SHA1: fa178d74d53fbc9bca43202c6678cf3d5b504611 SHA256: d19ca6c37091d7878dca9056a7ed327d6df9c35cc03cc518332629e4097aab1f SHA512: 4fb6103a5d844349b65ee9591122b6f54a4273b70d5614be270b773dfc6a349775dc52d0907c59157e4950d67d84ee67f5c7991858424cc1f267565c3fd7a792 Homepage: https://cran.r-project.org/package=tidycat Description: CRAN Package 'tidycat' (Expand Tidy Output for Categorical Parameter Estimates) Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates. Package: r-cran-tidycdisc Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-broom.helpers, r-cran-cicerone, r-cran-cli, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-fs, r-cran-ggally, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-ggsurvfit, r-cran-glue, r-cran-golem, r-cran-gt, r-cran-gtsummary, r-cran-haven, r-cran-ideafilter, r-cran-pkgload, r-cran-plotly, r-cran-purrr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-sjlabelled, r-cran-stringdist, r-cran-stringr, r-cran-survival, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-timevis, r-cran-tippy, r-cran-vroom Suggests: r-cran-devtools, r-cran-knitr, r-cran-shinyalert, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidycdisc_0.2.2-1.ca2604.1_all.deb Size: 2615422 MD5sum: 7ff90070e7ce0f65e9eb472b6515a995 SHA1: c93f474f884b572b382d2e5bff73e9a21c11aad0 SHA256: 6ea315c81e7a6dc326ba1ba732a2579b927baa1221f5041f717cf6def7a8ae65 SHA512: db9bc61fd425bfb9699ce5e14659673077a832aa8b188b93623ef64926137c1631619a7005d7fe314a3dfa7669979635975d20d65c2d2b0808ca92edc827155c Homepage: https://cran.r-project.org/package=tidyCDISC Description: CRAN Package 'tidyCDISC' (Quick Table Generation & Exploratory Analyses on ADaM-IshDatasets) Provides users a quick exploratory dive into common visualizations without writing a single line of code given the users data follows the Analysis Data Model (ADaM) standards put forth by the Clinical Data Interchange Standards Consortium (CDISC) . Prominent modules/ features of the application are the Table Generator, Population Explorer, and the Individual Explorer. The Table Generator allows users to drag and drop variables and desired statistics (frequencies, means, ANOVA, t-test, and other summary statistics) into bins that automagically create stunning tables with validated information. The Population Explorer offers various plots to visualize general trends in the population from various vantage points. Plot modules currently include scatter plot, spaghetti plot, box plot, histogram, means plot, and bar plot. Each plot type allows the user to plot uploaded variables against one another, and dissect the population by filtering out certain subjects. Last, the Individual Explorer establishes a cohesive patient narrative, allowing the user to interact with patient metrics (params) by visit or plotting important patient events on a timeline. All modules allow for concise filtering & downloading bulk outputs into html or pdf formats to save for later. Package: r-cran-tidycensus Architecture: all Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3684 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-dplyr, r-cran-tigris, r-cran-stringr, r-cran-jsonlite, r-cran-purrr, r-cran-rvest, r-cran-tidyr, r-cran-rappdirs, r-cran-readr, r-cran-xml2, r-cran-units, r-cran-rlang, r-cran-crayon, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-survey, r-cran-srvyr, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tidycensus_1.8.0-1.ca2604.1_all.deb Size: 3564738 MD5sum: 043d6e3c0696f8cf2976ec23bd05a583 SHA1: 7ffd5d6c87e96f987a63192b8bd1427cf565b712 SHA256: bf8cb4166bba4cfd28706c3e0d52d0f08af109c67403e582999ac29123611462 SHA512: 23953019e5c639fbedfd37413bd9f1f21f4ecbf794a1c343f0baf76804b71786f5b599a35ad43fdb84e46d5e2a5d39e56c13c52b0bd3076f0bd2457402252bc0 Homepage: https://cran.r-project.org/package=tidycensus Description: CRAN Package 'tidycensus' (Load US Census Boundary and Attribute Data as 'tidyverse' and'sf'-Ready Data Frames) An integrated R interface to several United States Census Bureau APIs () and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis. Package: r-cran-tidycensuskr Architecture: all Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7406 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-tidyr, r-cran-kosis, r-cran-rlang Suggests: r-cran-knitr, r-cran-withr, r-cran-purrr, r-cran-rmarkdown, r-cran-janitor, r-cran-geofacet, r-cran-ggplot2, r-cran-biscale, r-cran-cowplot, r-cran-tmap, r-cran-geodata, r-cran-testthat, r-cran-ggspatial, r-cran-spdep Filename: pool/dists/resolute/main/r-cran-tidycensuskr_0.2.8-1.ca2604.1_all.deb Size: 6656888 MD5sum: f1054f34b2769b1ea04b5b5eeda03028 SHA1: 36ce99ba4a257c37e635048cdc7808235af7019a SHA256: c7f5ff6f39a3a9d2c2f8d67fc84a7d57bd78d163fb3b9a07e9f780edd206d437 SHA512: f4ad57f2f25b034883ce7f0e1a677a8d803b994bd412273b671e6eb7a43b6a2eba2f06dceaa2210f44b456ad94a6f6c4d884858d00d71d9891aa883b4fd17228 Homepage: https://cran.r-project.org/package=tidycensuskr Description: CRAN Package 'tidycensuskr' (Easy Access for South Korea Census Data and Boundaries) Census and administrative data in South Korea are a basic source of quantitative and mixed-methods research for social and urban scientists. This package provides a 'sf' (Pebesma et al., 2024 ) based standardized workflow based on direct open API access to the major census and administrative data sources and pre-generated files in South Korea. Package: r-cran-tidychangepoint Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1080 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-changepoint, r-cran-changepointga, r-cran-cli, r-cran-dplyr, r-cran-ga, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-memoise, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-segmented, r-cran-stringr, r-cran-strucchange, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-vctrs, r-cran-wbs, r-cran-xts, r-cran-zoo Suggests: r-cran-bench, r-cran-broom, r-cran-knitr, r-cran-here, r-cran-multitaper, r-cran-patchwork, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidychangepoint_1.0.5-1.ca2604.1_all.deb Size: 668712 MD5sum: 1fc2cc84cc233ad3dc03423f48a1d835 SHA1: 705e817c3a76d122076057c4cb5093ff95f58d27 SHA256: f7a43dd225d11c8d25f7730c9eea89f54474049651f622498d108335adfdb94a SHA512: 22dffef09641143252e4c7f12eecdc93de6d2277f5add03db8dd1a1bb9aecbf6fe2457f3f5dc17b06a70cc9a352c3bad894154bbd7e148fe64a5341d20d3f97c Homepage: https://cran.r-project.org/package=tidychangepoint Description: CRAN Package 'tidychangepoint' (A Tidy Framework for Changepoint Detection Analysis) Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages. Package: r-cran-tidycharts Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-knitr, r-cran-magick, r-cran-rsvg, r-cran-rlang, r-cran-testthat, r-cran-htmlwidgets, r-cran-lubridate, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-palmerpenguins, r-cran-tidyverse, r-cran-dplyr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-tidycharts_0.1.3-1.ca2604.1_all.deb Size: 466308 MD5sum: 0ddb9c151a98c14b502b102a951f40df SHA1: 3b826d277b437b336fbda28ec008dc35094debe2 SHA256: 5fc8d2b59e3cda03f03e53fab7575e4b7dc88288e2d2d40180f9544b60427533 SHA512: d72bb7634e46161cac4b54195a8899fbfd674d212555a3b45c43637ed623d4aaf829d9e2df546e3f96fced909a64c37ec26a714a70763660a509fe5f9a2d5e4c Homepage: https://cran.r-project.org/package=tidycharts Description: CRAN Package 'tidycharts' (Generate Tidy Charts Inspired by 'IBCS') There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS'). It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments. We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible. All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'. Package: r-cran-tidyclust Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2056 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-dials, r-cran-dplyr, r-cran-flexclust, r-cran-generics, r-cran-glue, r-cran-hardhat, r-cran-mclust, r-cran-modelenv, r-cran-parsnip, r-cran-philentropy, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs Suggests: r-cran-butcher, r-cran-cluster, r-cran-clusterr, r-cran-clustmixtype, r-cran-covr, r-cran-dbscan, r-cran-future, r-cran-future.apply, r-cran-klar, r-cran-knitr, r-cran-lpcm, r-cran-meanshiftr, r-cran-mirai, r-cran-modeldata, r-cran-rcpphungarian, r-cran-recipes, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-workflows Filename: pool/dists/resolute/main/r-cran-tidyclust_0.3.0-1.ca2604.1_all.deb Size: 570640 MD5sum: aa86c82a32a28ffe4f096d62ca7572e9 SHA1: 1e9ceeaaa1e7870943032353695ccedb9962071c SHA256: 6ba8f8ecb4d3f0bfaa2d026c1d0d9ab05abdc3c50776ca8a9d29f959bfd95657 SHA512: 869f58a76cf244afeb97ec33d3af5098e83bbfdc6813d270656b86b39112479ebaf4dd1e02ae5ac16bfd61d85ad91c3fffdfa4a7ebddb89f55e10f2eb99fa8ee Homepage: https://cran.r-project.org/package=tidyclust Description: CRAN Package 'tidyclust' (A Common API to Clustering) A common interface to specifying clustering models, in the same style as 'parsnip'. Creates unified interface across different functions and computational engines. Package: r-cran-tidycmprsk Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-cmprsk, r-cran-dplyr, r-cran-ggplot2, r-cran-gtsummary, r-cran-hardhat, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-survival, r-cran-tibble, r-cran-tidyr Suggests: r-cran-aod, r-cran-broom.helpers, r-cran-cardx, r-cran-covr, r-cran-ggsurvfit, r-cran-knitr, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidycmprsk_1.1.2-1.ca2604.1_all.deb Size: 353214 MD5sum: 13fe8eb4015e9609fa09a5fabede5871 SHA1: 58e6fbac7f64b0a5a088330bf77294aebd9ccead SHA256: 0ba695d0749a7ce0928895b69d6a321761aef0e10fbf7eebc0dd04d3308e34ba SHA512: 3e6a68d68aab3f925a14a353e306a7ee49b565a9bd8212b472243524d5cf6efc31014b6ba7b4c6b80d208294da7ea5ea1c5a8aacbc4b10cbd3b82e171c17efbf Homepage: https://cran.r-project.org/package=tidycmprsk Description: CRAN Package 'tidycmprsk' (Competing Risks Estimation) Provides an intuitive interface for working with the competing risk endpoints. The package wraps the 'cmprsk' package, and exports functions for univariate cumulative incidence estimates and competing risk regression. Methods follow those introduced in Fine and Gray (1999) . 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This includes functions for univariate and bivariate data analysis, index generation and reliability computation, and intercoder reliability tests. All functions follow the style and syntax of the tidyverse, and are construed to perform their computations on multiple variables at once. Functions for univariate and bivariate data analysis comprise summary statistics for continuous and categorical variables, as well as several tests of bivariate association including effect sizes. Functions for data modification comprise index generation and automated reliability analysis of index variables. Functions for intercoder reliability comprise tests of several intercoder reliability estimates, including simple and mean pairwise percent agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN: 9780761915454), and various Kappa coefficients (Brennan & Prediger 1981 ; Cohen 1960 ; Fleiss 1971 ). 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This collection of packages is useful for anyone doing data science, data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting. 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At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes. Package: r-cran-tidyheatmaps Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-pheatmap, r-cran-rlang, r-cran-tidyr, r-cran-tibble, r-cran-rcolorbrewer Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tidyheatmaps_0.2.1-1.ca2604.1_all.deb Size: 503846 MD5sum: de5c392fe5beada8af724359db0865ab SHA1: 84bd2c0d3b35fb97f9866b4bc00bdbf9413d1c46 SHA256: 823730e8e55446da0e52840e1836e1602865244fec87859bc4ea73cf4a484930 SHA512: 57f56e11c7e90cd5e4a2892840a745cae00fe4c301db348a04e826f325fe6abc4f76d3aeaaf2e3e89cf35bf9c795a1c28907a6449796956d936a396d3d10f093 Homepage: https://cran.r-project.org/package=tidyheatmaps Description: CRAN Package 'tidyheatmaps' (Heatmaps from Tidy Data) The goal of 'tidyheatmaps' is to simplify the generation of publication-ready heatmaps from tidy data. 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Package: r-cran-tidyhte Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-progress, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-superlearner, r-cran-tibble Suggests: r-cran-covr, r-cran-devtools, r-cran-estimatr, r-cran-ggplot2, r-cran-glmnet, r-cran-knitr, r-cran-mockr, r-cran-nprobust, r-cran-palmerpenguins, r-cran-quadprog, r-cran-quickblock, r-cran-rmarkdown, r-cran-testthat, r-cran-vimp, r-cran-weightedroc Filename: pool/dists/resolute/main/r-cran-tidyhte_1.0.4-1.ca2604.1_all.deb Size: 1196074 MD5sum: 8e8562c130fc4ce4936f2bc553dd262f SHA1: d0bc422e55ebf6abffdfa3094da500abf08ba305 SHA256: 7418426a67a5be488b5a7908bd306cbb77714e9c090e8b648a2534be7e8e13b5 SHA512: 2eab4efaf7288a6b9db489d8686ad71db31dc4de85d9117f9715935ee2fdcd565e198715866369d84cdc9655887086ce490eee8ce623ec56a84ba9237f3ced3c Homepage: https://cran.r-project.org/package=tidyhte Description: CRAN Package 'tidyhte' (Tidy Estimation of Heterogeneous Treatment Effects) Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. 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Package: r-cran-tidyhydat Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3882 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-httr2, r-cran-lubridate, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-tidyhydat_1.0.0-1.ca2604.1_all.deb Size: 2099590 MD5sum: 9c3bb5a3633586f5614d0675ad56a6c9 SHA1: ddeb14cd9c39f7fcff27b6e90429e96d385f9931 SHA256: 6dd14933aaf24e7674834bd04ce2acf74fed53a7231e9ef01c71f04376f3fb42 SHA512: 986fad739a32381b69b4248407365fefdcf0dd41bbf085f22859c97c1933f1793fcae1dd6681175eaef79732fd316f59b1b6a7b6cb3f2b6ba09ee61102e9bfbd Homepage: https://cran.r-project.org/package=tidyhydat Description: CRAN Package 'tidyhydat' (Extract and Tidy Canadian 'Hydrometric' Data) Provides functions to access historical and real-time national 'hydrometric' data from Water Survey of Canada data sources and then applies tidy data principles. Package: r-cran-tidyild Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1999 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-lubridate, r-cran-rlang, r-cran-lme4, r-cran-nlme, r-cran-ggplot2, r-cran-mgcv Suggests: r-cran-bh, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-rcppeigen, r-cran-broom.mixed, r-cran-clubsandwich, r-cran-jsonlite, r-cran-yaml, r-cran-tsibble, r-cran-brms, r-cran-kfas, r-cran-ctsem Filename: pool/dists/resolute/main/r-cran-tidyild_0.4.0-1.ca2604.1_all.deb Size: 1255330 MD5sum: 6836ce66282e42692ee29fa119f9ccff SHA1: ebce13eb5f212c48a5a0a41160fd925f7c24499d SHA256: 28aa8cec6fe464d0c56057901a3416adfad915267ea520234dafbd388b3e6f57 SHA512: ab2423af3035023d8070297f2676d5473fca043337bec54f54c9ab6821d188d38f43637b27c7b43777cc608fd50dc1fbb65a758d5c06c02ee360ef40f40556e2 Homepage: https://cran.r-project.org/package=tidyILD Description: CRAN Package 'tidyILD' (Tidy Intensive Longitudinal Data Analysis) A reproducible, tidyverse-style framework for intensive longitudinal data analysis in R, with built-in methodological safeguards, provenance tracking, and reporting tools. Encodes time structure, enforces within-between decomposition, provides spacing-aware lags, and integrates diagnostics and visualization. Use ild_prepare(), ild_center(), ild_lag(), and related functions for a unified pipeline from raw EMA/diary data to interpretable models. 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Package: r-cran-tidyklips Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-readxl, r-cran-haven, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidyklips_0.3.0-1.ca2604.1_all.deb Size: 144378 MD5sum: 351e093082a1429679c52be983740c05 SHA1: dd524653e15078f2acce41695c02ee382b114785 SHA256: 5661458e1f992fa43eb8873accb282fb0359c66c90d367dd9be6cf2ce37b036b SHA512: 1f9c677f47f80c3a964a914c07d78315c7d7c75b2e9a11f8df5e578db3aab2a8687a8dea43bf77e1005178d5bab741e4f5a1a68cad4d1b56d081e3ab4d6fe42c Homepage: https://cran.r-project.org/package=tidyklips Description: CRAN Package 'tidyklips' (Load Korea Labor & Income Panel Study (KLIPS) Data as DataFrames) Loading the Korea Labor Institute's KLIPS (Korea Labor & Income Panel Study) panel data and returning data frames. Users must download 26 years of panel data from the Korea Labor Institute website and save it in a folder in an appropriate path. Afterwards, users can easily convert the data into a data frame using this package. Package: r-cran-tidylaslog Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-arrow, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidylaslog_0.1.2-1.ca2604.1_all.deb Size: 236892 MD5sum: 0c7b4e4ab19540c723768c1f244342b4 SHA1: a2a8a3ab85e1afdecc19e49ff89a7bc77a5397c4 SHA256: e901b8422d05034c085c28f1994d437eb25153832b7bc4cadd56a8dd7f7fc458 SHA512: 538ec877ed3f5650317bd9af5d20b7c4714adb2d72da2dce8d1a64d43c405a95212681d8079fea248e9c8aeee2420b19023a0864506e3dcef4266b85145dbeed Homepage: https://cran.r-project.org/package=tidylaslog Description: CRAN Package 'tidylaslog' (Tidy Import, Indexing, and Export of LAS Well Log Data) Provides tools for reading, parsing, indexing, and exporting LAS (Log ASCII Standard) well log files into tidy, analysis-ready tabular formats. The package separates LAS header information and log data into structured components, builds a searchable index across collections of LAS files, and enables reproducible subsetting of wells based on metadata or curve availability. Output tables can be written to CSV or Parquet formats to support large-scale statistical, machine learning, and earth science workflows. The tidy data structure follows Wickham (2014) . The LAS file structure follows the Canadian Well Logging Society LAS standard . Package: r-cran-tidylearn Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2855 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-rlang, r-cran-magrittr, r-cran-e1071, r-cran-gbm, r-cran-glmnet, r-cran-nnet, r-cran-randomforest, r-cran-rpart, r-cran-rsample, r-cran-rocr, r-cran-yardstick, r-cran-cluster, r-cran-dbscan, r-cran-mass, r-cran-smacof Suggests: r-cran-arules, r-cran-arulesviz, r-cran-bigrquery, r-cran-car, r-cran-dbi, r-cran-dt, r-cran-ggally, r-cran-ggforce, r-cran-gridextra, r-cran-gt, r-cran-jsonlite, r-cran-keras, r-cran-knitr, r-cran-lmtest, r-cran-moments, r-cran-nanoparquet, r-cran-neuralnettools, r-cran-paws.storage, r-cran-readr, r-cran-readxl, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rpart.plot, r-cran-rsqlite, r-cran-scales, r-cran-shiny, r-cran-shinydashboard, r-cran-tensorflow, r-cran-testthat, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-tidylearn_0.3.1-1.ca2604.1_all.deb Size: 2081074 MD5sum: 059d475515e0f273a5a16585b26d165b SHA1: ff088406021a770b4f3385462198d23c73f1afb6 SHA256: d6f96692b63f6751683ae7be9ea1e15d5b985a4b1d3929492316ba5fdcd2e152 SHA512: 644f330235791a8d48d7255f46889588d1d8cfb69f39d137e9e5111d9b5c16ebb9723bc79826e5506e47d64dc159917b45d7f1488e5ff0875b3e2acbc9115a64 Homepage: https://cran.r-project.org/package=tidylearn Description: CRAN Package 'tidylearn' (A Unified Tidy Interface to R's Machine Learning Ecosystem) Provides a unified tidyverse-compatible interface to R's machine learning ecosystem - from data ingestion to model publishing. The tl_read() family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub', 'Kaggle'). The tl_model() function wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function signatures and tidy tibble output. Results flow into unified 'ggplot2'-based visualization and optional formatted 'gt' tables via the tl_table() family. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) , LASSO regression Tibshirani (1996) , elastic net Zou and Hastie (2005) , support vector machines Cortes and Vapnik (1995) , and gradient boosting Friedman (2001) . Package: r-cran-tidyllm Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1762 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-s7, r-cran-base64enc, r-cran-glue, r-cran-jsonlite, r-cran-curl, r-cran-httr2, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-pdftools, r-cran-tibble, r-cran-cli, r-cran-png, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-httptest2, r-cran-httpuv, r-cran-ellmer Filename: pool/dists/resolute/main/r-cran-tidyllm_0.5.0-1.ca2604.1_all.deb Size: 1453188 MD5sum: 89e597911a42ea4e8d69fff08d40745a SHA1: 63347b0493c2b4e78f65328b958d9ca5bb1c4886 SHA256: 30dbbcbab4d326150491050de0f362d3ec7bd1fa0a95ca014720385356a3b48d SHA512: 6d6e76082461e2c702a268e985cb727f55934b16d750f50adfb5212e8bfd265344b3a9d8ea709b57ed34a0df6805d449877704382ac9696c4c2513541952e860 Homepage: https://cran.r-project.org/package=tidyllm Description: CRAN Package 'tidyllm' (Tidy Integration of Large Language Models) A tidy interface for integrating large language model (LLM) APIs such as 'Claude', 'OpenAI', 'Gemini', 'Mistral', and local models via 'Ollama' into R workflows. The package supports text, image, audio, video, and document interactions; a unified media interface for attaching inline files or uploading to provider file stores; batch request APIs for cost-efficient large-scale processing; and a pipeline-oriented interface for seamless integration into data workflows. Web services are available at , , , and . Package: r-cran-tidylo Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-covr, r-cran-ggplot2, r-cran-janeaustenr, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidytext Filename: pool/dists/resolute/main/r-cran-tidylo_0.2.0-1.ca2604.1_all.deb Size: 207744 MD5sum: 116658e0d7e64e24aafda7e9d1157a58 SHA1: 62ad1645837a1f8d8de203c3a4eb85bdd986fd68 SHA256: 636fc3a0182f686bf0168bad1cf4c551da0e9ca32e5b23475570b40cd01652c8 SHA512: a0eb86f89006a565b8d7164f4a37e547669940167cdf3a7fd5cedeba27eadf5a0420ed686aeb592fb180c92a20fdbbe920370b182ae4aa80cc0cb4c6f5e24681 Homepage: https://cran.r-project.org/package=tidylo Description: CRAN Package 'tidylo' (Weighted Tidy Log Odds Ratio) How can we measure how the usage or frequency of some feature, such as words, differs across some group or set, such as documents? One option is to use the log odds ratio, but the log odds ratio alone does not account for sampling variability; we haven't counted every feature the same number of times so how do we know which differences are meaningful? Enter the weighted log odds, which 'tidylo' provides an implementation for, using tidy data principles. In particular, here we use the method outlined in Monroe, Colaresi, and Quinn (2008) to weight the log odds ratio by a prior. By default, the prior is estimated from the data itself, an empirical Bayes approach, but an uninformative prior is also available. 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To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) . 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'tidyprompt' introduces the concept of prompt wraps, which are building blocks that you can use to quickly turn a simple prompt into a complex one. Prompt wraps do not just modify the prompt text, but also add extraction and validation functions that will be applied to the response of the LLM. This ensures that the user gets the desired output. 'tidyprompt' can add various features to prompts and their evaluation by LLMs, such as structured output, automatic feedback, retries, reasoning modes, autonomous R function calling, and R code generation and evaluation. It is designed to be compatible with any LLM provider that offers chat completion. Package: r-cran-tidyquant Architecture: all Version: 1.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-httr2, r-cran-curl, r-cran-jsonlite, r-cran-lazyeval, r-cran-lubridate, r-cran-magrittr, r-cran-performanceanalytics, r-cran-robstattm, r-cran-quantmod, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-timetk, r-cran-timedate, r-cran-ttr, r-cran-xts, r-cran-rlang, r-cran-zoo, r-cran-cli Suggests: r-cran-alphavantager, r-cran-riingo, r-cran-tibbletime, r-cran-broom, r-cran-knitr, r-cran-forcats, r-cran-rmarkdown, r-cran-testthat, r-cran-scales, r-cran-rblpapi, r-cran-janitor Filename: pool/dists/resolute/main/r-cran-tidyquant_1.0.12-1.ca2604.1_all.deb Size: 1170286 MD5sum: 0c6570329a2ff20388b4ac3db12ddeb9 SHA1: 7827ed2a58588b0c8012dcdeecb2a598ce44ba62 SHA256: 4a340cefdcbccee216f122a38cfef401722d57f27d7768031fb87fde6796c235 SHA512: 3fd0dbae8a48ba4463fd7736311135a5f518796b388f7d8b108a7792ef1b7c3cd5df6de10ee3fde62d7d94b6f76427090335b84f1b6dc439e67733268ebc1518 Homepage: https://cran.r-project.org/package=tidyquant Description: CRAN Package 'tidyquant' (Tidy Quantitative Financial Analysis) Bringing business and financial analysis to the 'tidyverse'. 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Package: r-cran-tidyrules Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-magrittr, r-cran-purrr, r-cran-partykit, r-cran-rlang, r-cran-generics, r-cran-checkmate, r-cran-tidytable, r-cran-data.table, r-cran-desctools, r-cran-metricsweighted, r-cran-cli, r-cran-glue, r-cran-pheatmap, r-cran-proxy, r-cran-tibble Suggests: r-cran-ameshousing, r-cran-dplyr, r-cran-c50, r-cran-cubist, r-cran-rpart, r-cran-rpart.plot, r-cran-modeldata, r-cran-testthat, r-cran-mass, r-cran-mlbench, r-cran-rmarkdown, r-cran-palmerpenguins Filename: pool/dists/resolute/main/r-cran-tidyrules_0.2.7-1.ca2604.1_all.deb Size: 622732 MD5sum: 668b48b9ac4c3215ad82d1fa5c004a80 SHA1: f8060f1861541e19a2ea3b5b13fbf386d12d01b0 SHA256: ccf664e40534367b7449c1c76e92edf20b5483915f116a4f567884cb4f05997e SHA512: ff1e0174378a6021abbec7633f79adf44c829eef830e2b7d6adab1021ac1f1e978818919a54875ca96b40fdc291c73a2ad34a6b3b43a4b95a73039d8905a3139 Homepage: https://cran.r-project.org/package=tidyrules Description: CRAN Package 'tidyrules' (Utilities to Retrieve Rulelists from Model Fits, Filter, Prune,Reorder and Predict on Unseen Data) Provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe. Package: r-cran-tidysdm Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidymodels, r-cran-spatialsample, r-cran-dalex, r-cran-dials, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-maxnet, r-cran-parsnip, r-cran-patchwork, r-cran-recipes, r-cran-rsample, r-cran-rlang, r-cran-stars, r-cran-sf, r-cran-terra, r-cran-tibble, r-cran-tune, r-cran-xgboost, r-cran-workflows, r-cran-workflowsets, r-cran-yardstick Suggests: r-cran-blockcv, r-cran-data.table, r-cran-dalextra, r-cran-doparallel, r-cran-earth, r-cran-kernlab, r-cran-knitr, r-cran-mgcv, r-cran-overlapping, r-cran-pastclim, r-cran-ranger, r-cran-rgbif, r-cran-rmarkdown, r-cran-spelling, r-cran-stacks, r-cran-testthat, r-cran-tidyterra, r-cran-vdiffr, r-cran-ggpattern, r-cran-rhpcblasctl Filename: pool/dists/resolute/main/r-cran-tidysdm_1.0.4-1.ca2604.1_all.deb Size: 4353800 MD5sum: 1068d832427994a53a9f2c65a6442f55 SHA1: cf8b37af264ea1ee07badc763d966d4bfcbadb63 SHA256: d2906d1e96780d0842dd24238d28afd35d34bb959e7109093d5bdee029654a5f SHA512: e6efbd46f836cbc23b2bebfc4896ceca27eccbcc754fe20948a5cde0ca79e3e53e67f4ee55525fd0e68bdf07c3899ccd426eff6675f85d1914aeac1958dcb4e1 Homepage: https://cran.r-project.org/package=tidysdm Description: CRAN Package 'tidysdm' (Species Distribution Models with Tidymodels) Fit species distribution models (SDMs) using the 'tidymodels' framework, which provides a standardised interface to define models and process their outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects, models and metrics specific to SDMs, as well as a number of specialised functions to process occurrences for contemporary and palaeo datasets. The full functionalities of the package are described in Leonardi et al. (2024) . Package: r-cran-tidysem Architecture: all Version: 0.2.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lavaan, r-cran-mplusautomation, r-cran-igraph, r-cran-psych, r-cran-gtable, r-cran-dbscan, r-cran-rann, r-cran-matrix, r-cran-car, r-cran-future.apply, r-cran-progressr, r-cran-progress, r-cran-nonnest2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-dplyr, r-cran-stringr, r-cran-covr, r-cran-tidylpa, r-cran-polca, r-cran-umx, r-cran-mclust, r-cran-mass, r-cran-scales, r-cran-yaml, r-cran-formatr, r-cran-dagitty, r-cran-mice, r-cran-ggraph, r-cran-openmx, r-cran-rmarkdown, r-cran-blavaan, r-cran-bain, r-cran-future Filename: pool/dists/resolute/main/r-cran-tidysem_0.2.10-1.ca2604.1_all.deb Size: 2560272 MD5sum: befff76263f558923047e4db5619bb10 SHA1: cf2d5462a6320b15d63be716b3c6736e687eecc3 SHA256: 1fcfe42c78805451940db1fd3d46d2a28814e3ff8ba624830256072e3f13a9cf SHA512: c0033329c301b5e6811b6da367bdb7ddb078cfe5648194c51c6143eaaf0eb464f314aae683e93d8a425042830ad4aa37c46644759de2e9199adf3b7ad023708c Homepage: https://cran.r-project.org/package=tidySEM Description: CRAN Package 'tidySEM' (Tidy Structural Equation Modeling) A tidy workflow for generating, estimating, reporting, and plotting structural equation models using 'lavaan', 'OpenMx', or 'Mplus'. 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The 'tidyspec' package provides functions for data transformation, normalization, baseline correction, smoothing, derivatives, and both interactive and static visualization. It promotes structured, reproducible workflows for spectral data exploration and preprocessing. Implemented methods include Savitzky and Golay (1964) "Smoothing and Differentiation of Data by Simplified Least Squares Procedures" , Sternberg (1983) "Biomedical Image Processing" , Zimmermann and Kohler (1996) "Baseline correction using the rolling ball algorithm" , Beattie and Esmonde-White (2021) "Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra" , Wickham et al. (2019) "Welcome to the tidyverse" , and Kuhn, Wickham and Hvitfeldt (2024) "recipes: Preprocessing and Feature Engineering Steps for Modeling" . 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Package: r-cran-tidysummary Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-cli, r-cran-dplyr, r-cran-fbasics, r-cran-glue, r-cran-qqplotr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyplots, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tidysummary_0.1.0-1.ca2604.1_all.deb Size: 133528 MD5sum: e81e91da5364fb57935841ce336bcf3a SHA1: d6aa60b528c9ef0774e37311b414ee31ea7581c5 SHA256: 1f0582f3d594ec8fb92b10d04c5c982a053b195f39745ad87fba22c2c337bcd1 SHA512: 3a4cd5e0f0e96fe30497281cc032fd09f58937d48d27f4eacc3d75e7530a789e14ab10716c87c1fd0a202df6466791a70b2f74e50375eb71f0e8e6b3dfceff9c Homepage: https://cran.r-project.org/package=tidysummary Description: CRAN Package 'tidysummary' (An Elegant Approach to Summarizing Clinical Data) Streamlines the analysis of clinical data by automatically selecting appropriate statistical descriptions and inference methods based on variable types. 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We provide tools for ordering a sequential synthesis, feature and target engineering, sampling, hyperparameter tuning, enforcing constraints, and adding extra noise during a synthesis. 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It includes also new 'geom_' functions that provide a convenient way of visualizing 'terra' objects with 'ggplot2'. Package: r-cran-tidytext Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-janeaustenr, r-cran-lifecycle, r-cran-matrix, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tokenizers, r-cran-vctrs Suggests: r-cran-broom, r-cran-covr, r-cran-data.table, r-cran-ggplot2, r-cran-hunspell, r-cran-knitr, r-cran-mallet, r-cran-nlp, r-cran-quanteda, r-cran-readr, r-cran-reshape2, r-cran-rmarkdown, r-cran-scales, r-cran-stm, r-cran-stopwords, r-cran-testthat, r-cran-textdata, r-cran-tidyr, r-cran-tm, r-cran-topicmodels, r-cran-vdiffr, r-cran-wordcloud Filename: pool/dists/resolute/main/r-cran-tidytext_0.4.3-1.ca2604.1_all.deb Size: 2636148 MD5sum: 30254403ac3e14d308bacfaf7c855938 SHA1: 0daba59136fd4e0eee8e23de003e6e94e24078a3 SHA256: 20797155b4a02e6d33738da6c6ee2b00b3caec7230ada9d4387554d49c3e2300 SHA512: 295739f3729c682b9ebcb80b6aeb97a89635e3658f53261da8b7fdfe07488a2adb3a7bf9fcefca2bf995c6ccade82cc441cc7f8f0a56a2f7ee97ae7471afdccb Homepage: https://cran.r-project.org/package=tidytext Description: CRAN Package 'tidytext' (Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools) Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like 'dplyr', 'broom', 'tidyr', and 'ggplot2'. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages. Package: r-cran-tidytitanic Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tidytitanic_0.0.1-1.ca2604.1_all.deb Size: 389780 MD5sum: 0a226b048b44d22194c9eb4affe440d1 SHA1: 3907f6f41e99822d14ff86c20fbedb71c51491f9 SHA256: b18322afaa5f08a681e33e192c131219c9df717c0cb719622dacd2f9f81c8bd5 SHA512: 26340258bdfb18580360ad68c51aa2b56067046f78e81de1f663a8b632486df9a2d896b859d72da1356d7ac092b24ab77215f5d7c91ac3e6afe3268b5ce2249f Homepage: https://cran.r-project.org/package=tidytitanic Description: CRAN Package 'tidytitanic' (Dataframes Based on Titanic Passengers and Crew) A version of the Titanic survival data tailored for people analytics demonstrations and practice. While another package, 'titanic', reproduces the Kaggle competition files with minimal preprocessing, 'tidytitanic' combines the train and test datasets into the single dataset, 'passengers', for exploration and summary across all passengers. It also extracts personal identifiers—such as first names, last names, and titles from the raw 'name' field, enabling demographic analysis. The 'passengers' data does not cover the crew, but this package also provides the more bare-bones, crew-containing datasets 'tidy_titanic' and 'flat_titanic' based on the 'Titanic' data set from 'datasets' for further exploration. This human-centered data package is designed to support exploratory data analysis, feature engineering, and pedagogical use cases. 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Tables can be created functionally, using a standard TLG process, or by specifying table and column metadata to create generic analysis summaries. The 'envsetup' package can also be leveraged to create environments for table creation. 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Functionality includes extracting tidy posterior summaries as in 'tidybayes' , estimating (average) treatment effects, common support calculations, and plotting useful summaries of these. 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'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data. Package: r-cran-tidyttmoment Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-tidyr, r-cran-fundiversity, r-cran-funrar, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidyttmoment_0.0.5-1.ca2604.1_all.deb Size: 43858 MD5sum: 3284cc8f6743f422714f4fba5011b9f8 SHA1: f999a3f73a35257fee6acaeec16aef8a97bc9da5 SHA256: 9520b338308ba92a3972bbf745b9448aaa932eb6691f6b12156f412cd1b1d2e7 SHA512: 588b3c81b5a09967f42eb6640c6aaa0e81e524353adb6674685605869143dd3209df52ea773c9733a7de800fab1d03cbfdd85d6cd14cda58f8db5f24c3ed4e1e Homepage: https://cran.r-project.org/package=tidyttmoment Description: CRAN Package 'tidyttmoment' (Functional Trait Moment Calculation) Calculates the community four 'moments' (mean, variance, skewness, and kurtosis) of a given trait based on the moments described in Wieczynski et al. (2019) . These functional metrics are extremely useful in characterizing the distribution of traits in a plant community. It also provides tidyverse-friendly wrappers to seamlessly calculate advanced functional diversity indices (e.g., FDis, Rao's Q) using 'fundiversity' (Grenie et al. 2023 ) and functional rarity indices using 'funrar' (Grenie et al. 2017 ). Evaluating these community-weighted moments and diversity metrics allows researchers to evaluate shifts in optimal phenotypes and understand ecological filtering with exactness. 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This package provides the tools to easily download this data and the description of the source. Package: r-cran-tidyusmacro Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringi, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-tidyusmacro_0.1.0-1.ca2604.1_all.deb Size: 63998 MD5sum: e1b958bcc0bf913e1c348d3043f0f225 SHA1: a3a15ccd70a48778dadbbbd909f08acf1e8dc5be SHA256: 51b706a41131ba99b3a60663f053158ff5854e2b0feab34eb8b57b9db4b94520 SHA512: 02a249652d9faa9ff144a8fb618f314a3d452380e21fd5dde3427351bd6f39aa09219122da8028028d1f3624bca693a6cbe68e9d939360fd29656a6d393cc6d7 Homepage: https://cran.r-project.org/package=tidyusmacro Description: CRAN Package 'tidyusmacro' (Downloading and Cleaning U.S. Macroeconomic Data) Utilities to retrieve and tidy U.S. macroeconomic data series from public government data providers. Functions streamline access to series from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED), the Bureau of Labor Statistics flat files, and the Bureau of Economic Analysis National Income and Product Accounts tables, then return consistent, tidy data frames ready for modeling and graphics. The package includes helpers for date alignment, log-linear projections, and common macro diagnostics, along with convenience plot builders for quick publication-quality charts. 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Using piping from 'magrittr', the intuitive syntax gives users a flexible and powerful method to generate VPCs using both traditional binning and a new binless approach Jamsen et al. (2018) with Additive Quantile Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) prediction correction. 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As many models have been included as possible, however, users should be aware that models have varying degrees of accuracy and applicability. To learn more, read the references provided below for the models implemented. Functions can be chained together to model a complete treatment process and are designed to work in a 'tidyverse' workflow. Models are primarily based on these sources: Benjamin, M. M. (2002, ISBN:147862308X), Crittenden, J. C., Trussell, R., Hand, D., Howe, J. K., & Tchobanoglous, G., Borchardt, J. H. (2012, ISBN:9781118131473), USEPA. (2001) . Package: r-cran-tidyweather Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-tibble, r-cran-dplyr, r-cran-optree Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tidyweather_0.2.0-1.ca2604.1_all.deb Size: 115362 MD5sum: c86af281760900465effa3550100bef5 SHA1: 1ae1c65237968ec3e3ff5985cbbd9f9314762e3a SHA256: ac3b83e54a8d7c9bcfedcfd030dbf0cfe2050c5318253671469ec926ad20eae4 SHA512: 54036967fa44216948b2bc6a4ac008181ab1074f4125cf8abc235e2fbc46ed52e24e26070f829e856207f4082b63a1559fd3b2c5324322d59f2524462e6e3685 Homepage: https://cran.r-project.org/package=tidyweather Description: CRAN Package 'tidyweather' (Analysis the Weather Data for Agriculture) Functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. 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Package: r-cran-tigerhitter Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-zoo, r-cran-hmisc, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-tigerhitter_1.1.0-1.ca2604.1_all.deb Size: 45938 MD5sum: a3f58846d5b469dffef307b842a0d5f4 SHA1: d1a1c1e5ebbbb4f56eeef42aa5049182f9f6bc78 SHA256: a4ed4a70cc58caa9792c44626c5386427663a205efb5c0db70594ad01245446b SHA512: 4d9cb0389899f0b6dacb0ab0b596eb7feb121a2d53fe027c526963b3328fe136e49a853a40e76edc02a65f149211b95b1da25546c552e09246a16b249db153c8 Homepage: https://cran.r-project.org/package=tigerhitteR Description: CRAN Package 'tigerhitteR' (Pre-Process of Time Series Data Set in R) Pre-process for discrete time series data set which is not continuous at the column of 'date'. Refilling records of missing 'date' and other columns to the hollow data set so that final data set is able to be dealt with time series analysis. Package: r-cran-tigerr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-ppcor, r-cran-randomforest Filename: pool/dists/resolute/main/r-cran-tigerr_1.0.0-1.ca2604.1_all.deb Size: 127634 MD5sum: c714a7f0f94c8ccaa424d4fb63b46e0e SHA1: f26a22bffc5cc895f86e174136dfde14203d989c SHA256: 2c3a6984b5eee45d8716396f138d3fdbb7b076cf78a99f74393255cb213ba2a0 SHA512: f8af190c013fb54453544cf780d2dbfbe092d6c20597de64f2db30f53f39af158f3f7aa1a39d723637d1ca9884e53e61fefc41d74fd044a0e488fb4c7e1c220e Homepage: https://cran.r-project.org/package=TIGERr Description: CRAN Package 'TIGERr' (Technical Variation Elimination with Ensemble LearningArchitecture) The R implementation of TIGER. 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Includes detection of any novel alleles. This information is then used to correct existing V allele calls from among the sample sequences. Citations: Gadala-Maria, et al (2015) , Gadala-Maria, et al (2019) . Package: r-cran-tightclust Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tightclust_1.1-1.ca2604.1_all.deb Size: 151020 MD5sum: d6feb51309058715a327c856244be693 SHA1: 82e3c61d2c9435eacc0d94e8fef2436a21f48e55 SHA256: ac6abecb3cb63d2ad5b3862a89e1995a75cedf3b05d31190d62b50b9e5f6cd68 SHA512: 07d7bf91d00d5f63ef66ceac17cf57087a5ffa742938c206d8799eb3cd0614f3a78121c76b6b40e4d98be1898f8bd24946ba2bf4dcd92c720fc5706f14baca15 Homepage: https://cran.r-project.org/package=tightClust Description: CRAN Package 'tightClust' (Tight Clustering) The functions needed to perform tight clustering Algorithm. 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The method uses fine balance, optimal subset matching (Rosenbaum, 2012 ) and two-criteria matching (Zhang et al 2023 ). The main function is tighten(). The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) , (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at , (iv) is not essential to use the package. Package: r-cran-tigrebrowserwriter Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite Filename: pool/dists/resolute/main/r-cran-tigrebrowserwriter_0.1.5-1.ca2604.1_all.deb Size: 49494 MD5sum: 5f7a86c99830a9ec8bcb2d660e58632d SHA1: f12aaf620f2b38ce1c4b7762b344c8e712943a0c SHA256: a8c2d18e2892ebf3515eab8ba7a9896589608cedcc55becbec0fe9ec269a3646 SHA512: 1310b5396a8307cdb425f86496bf23383ff888cb65f86d0be70954e483ebca0e023350c84d17bd12c6bb2b5d184c463937b74c7d81c1cad7c4083fb1969137d5 Homepage: https://cran.r-project.org/package=tigreBrowserWriter Description: CRAN Package 'tigreBrowserWriter' ('tigreBrowser' Database Writer) Write modelling results into a database for 'tigreBrowser', a web-based tool for browsing figures and summary data of independent model fits, such as Gaussian process models fitted for each gene or other genomic element. 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Package: r-cran-tikatuwq Architecture: all Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-tibble, r-cran-rlang, r-cran-ggplot2, r-cran-tidyr, r-cran-lubridate, r-cran-stringr, r-cran-glue, r-cran-scales, r-cran-broom, r-cran-purrr Suggests: r-cran-testthat, r-cran-spelling, r-cran-rmarkdown, r-cran-knitr, r-cran-pkgdown, r-cran-leaflet, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tikatuwq_0.8.2-1.ca2604.1_all.deb Size: 315208 MD5sum: 3668f7fb5f2ef38555c74b7b23433829 SHA1: 9625400f0d47b44ecd5504f7de9d521c00ad1c28 SHA256: a7687ef4b2df31e44d03193da0d496f1c0ca850c76b6aa9c18889385505517c6 SHA512: 1820039239f16d5659400916343be6dc4a9362baf8a0366589907bbd1a97edf094fb2b1e029527b0ad9885f77ca0c301a7338040966b8fb8ded761ef8121a91c Homepage: https://cran.r-project.org/package=tikatuwq Description: CRAN Package 'tikatuwq' (Water Quality Assessment and Environmental Compliance in Brazil) Tools to import, clean, validate, and analyze freshwater quality data in Brazil. Implements water quality indices including the Water Quality Index (WQI/IQA), the Trophic State Index (TSI/IET) after Carlson (1977) and Lamparelli (2004) , and the National Sanitation Foundation Water Quality Index (NSF WQI) . The package also checks compliance with Brazilian standard CONAMA Resolution 357/2005 and generates reproducible reports for routine monitoring workflows. The example dataset (`wq_demo`) is now a real subset from monitoring data (BURANHEM river, 2020-2024, 4 points, 20 rows, 14 columns including extra `rio`, `lat`, `lon`). All core examples and vignettes use this realistic sample, improving reproducibility and documentation value for users. Package: r-cran-tiktokadsr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/resolute/main/r-cran-tiktokadsr_0.1.0-1.ca2604.1_all.deb Size: 22798 MD5sum: c3a8f258e1b185e9852de6802dce920a SHA1: f8075bc0165e29c953ee7de2f502f9450f0f0f09 SHA256: 439113b3c21560722c0079daa0e50897e09672aa2b6fa708f0f077a4ce7ad7f0 SHA512: 9fe1d588ff97c906dccdcc9c05f30e3f0b37acc660ab8c2aa36c6fd4ab62805b19c77b1b7c78bf06f45a9b9db7efa55697572c06401c70f064145583a3b38d79 Homepage: https://cran.r-project.org/package=tiktokadsR Description: CRAN Package 'tiktokadsR' (Access to TikTok Ads via the 'Windsor.ai' API) Collect marketing data from TikTok Ads using the 'Windsor.ai' API . 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This package provides a tile generator function for creating map tile sets for use with packages such as 'leaflet'. In addition to generating map tiles based on a common raster layer source, it also handles the non-geographic edge case, producing map tiles from arbitrary images. These map tiles, which have a non-geographic, simple coordinate reference system (CRS), can also be used with 'leaflet' when applying the simple CRS option. Map tiles can be created from an input file with any of the following extensions: tif, grd and nc for spatial maps and png, jpg and bmp for basic images. This package requires 'Python' and the 'gdal' library for 'Python'. 'Windows' users are recommended to install 'OSGeo4W' () as an easy way to obtain the required 'gdal' support for 'Python'. 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You can create forest data structures from data frames and process them based on their hierarchies. Package: r-cran-timechecker Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-timechecker_1.1.5-1.ca2604.1_all.deb Size: 22656 MD5sum: b215b531c8ff957b2b2f1439bf18238c SHA1: 5bf881a009897cedd53bd4192d81607d1ff8ab35 SHA256: c546be331a9965e078635e3f5774e0059251644cf77b26d8ac1ceed8850d1047 SHA512: bc448e8102ba823b55cc79207f3ecba4199797ee833f283f085886dd6341ccb513cd56eaf1de7b462211974b65728c0b6808434edd534c5335d2c2348b5115c4 Homepage: https://cran.r-project.org/package=timechecker Description: CRAN Package 'timechecker' (Visualization of Processing Time with Standard Output) Displays processing time in a clear and structured way. One function supports iterative workflows by predicting and showing the total time required, while another reports the time taken for individual steps within a process. Package: r-cran-timedate Architecture: all Version: 4052.112-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1863 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/resolute/main/r-cran-timedate_4052.112-1.ca2604.1_all.deb Size: 1294252 MD5sum: 34a771f081484536da3354102d60cb32 SHA1: c2f979a5d17e1839ff7b21a1aa9f12f06c78cfc9 SHA256: 34035fa29d2263ac335285cbcc13f6dafd45bfc9652cafc788acf08eeac9ea6e SHA512: 46a57ff3b27ded9a23892c76200256870f39202f30705c6807034755d285d86b4b90db434d4d06264b6097cdf3a23c8ae95cffc7f562128e77cd10de2f5362f1 Homepage: https://cran.r-project.org/package=timeDate Description: CRAN Package 'timeDate' (Rmetrics - Chronological and Calendar Objects) The 'timeDate' class fulfils the conventions of the ISO 8601 standard as well as of the ANSI C and POSIX standards. Beyond these standards it provides the "Financial Center" concept which allows to handle data records collected in different time zones and mix them up to have always the proper time stamps with respect to your personal financial center, or alternatively to the GMT reference time. It can thus also handle time stamps from historical data records from the same time zone, even if the financial centers changed day light saving times at different calendar dates. Package: r-cran-timedelay Architecture: all Version: 1.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-timedelay_1.0.11-1.ca2604.1_all.deb Size: 122120 MD5sum: 102d10b167a42ba576d1ddf719c0edec SHA1: bbba623a97aea18dbad2b49f1000f00ab1b8050c SHA256: 65e0b9a67343f682343d932f6a6956bcf888b0726a5dc527d83e5f82db38ca83 SHA512: 2b2a359da699c2fe22f1326a856aba90c53d673d756e1ed41e99520060eae83732f467e118956df8b74355917c2dab957857c5ad238bd340d20e2ca0cf0938d7 Homepage: https://cran.r-project.org/package=timedelay Description: CRAN Package 'timedelay' (Time Delay Estimation for Stochastic Time Series ofGravitationally Lensed Quasars) We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) , Tak et al. (2018) , Hu and Tak (2020) . Package: r-cran-timedepfrail Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-timedepfrail_0.1.0-1.ca2604.1_all.deb Size: 260282 MD5sum: fe9f03b7dbd6361ec4fe947b276d4854 SHA1: 1a5fea3f9ef4624d9841369a9a88f23508f0a6cc SHA256: 1c91e36c97741176bfbdcbbeded5ac324da3e6a23208869afb136ef60c1e3ead SHA512: 9d4a5a841791e3b42262d6c9cb502debd79e62bdbacff537426af8281f3240389dc440826d9fc231118b5eeee524b4e361333254ec24d447999dba95403fef4d Homepage: https://cran.r-project.org/package=TimeDepFrail Description: CRAN Package 'TimeDepFrail' (Time Dependent Shared Frailty Cox Model) Fits time-dependent shared frailty Cox model (specifically the adapted Paik et al.'s Model) based on the paper "Centre-Effect on Survival After Bone Marrow Transplantation: Application of Time-Dependent Frailty Models", by C.M. Wintrebert, H. Putter, A.H. Zwinderman and J.C. van Houwelingen (2004) . Package: r-cran-timedeppar Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-timedeppar_1.0.3-1.ca2604.1_all.deb Size: 186934 MD5sum: ee7ac4f41834d11c4cd9802389515e6e SHA1: 64210f987f55bfc134da30be1d5dfb37dd2c1ae9 SHA256: 443220f17d7ae32ac3648e1df77d8d1d7be6d2fb34f745dfef75925054b6c72e SHA512: cf8321d8ee1450d07401433b70554f795ba61e24530d0bb66654c55c803270dd53818173393212908c25b2202160bf6f8e8032441ff36dbc70903810d439e80a Homepage: https://cran.r-project.org/package=timedeppar Description: CRAN Package 'timedeppar' (Infer Constant and Stochastic, Time-Dependent Model Parameters) Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits. The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes. The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes. References: Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009), A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model, Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, Reichert, P., and Mieleitner, J. (2009), Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45, W10402, Reichert, P., Ammann, L. and Fenicia, F. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation. Package: r-cran-timeel Architecture: all Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-km.ci, r-cran-prodlim, r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-timeel_0.9.1-1.ca2604.1_all.deb Size: 186422 MD5sum: 9e61f9c9eefdc644b27be063361b31f9 SHA1: c0bd892212cadfc09389206fbf35fd05345af982 SHA256: 2ec95af85a048bb392fe5709937e8910c308587f6e88100fa645165cc3bfd384 SHA512: afbc1413b8d76d60599228dd4a8c5d2c239c3b73de8901bdd980bb1920e03e93dfa020f85726c90242107c4c4b34ee8383c537693dfc10e3128b8d0ca5896417 Homepage: https://cran.r-project.org/package=timeEL Description: CRAN Package 'timeEL' (Time to Event Analysis via Empirical Likelihood Inference) Computation of t-year survival probabilities and t-year risks with right censored survival data. 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Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed. See Blanche et al. (2013) and references therein for the details of the methods implemented in the package. Package: r-cran-timeseries Architecture: all Version: 4052.112-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2691 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate Suggests: r-cran-runit, r-cran-robustbase, r-cran-xts, r-cran-zoo, r-cran-performanceanalytics, r-cran-ftrading Filename: pool/dists/resolute/main/r-cran-timeseries_4052.112-1.ca2604.1_all.deb Size: 1990942 MD5sum: fbb589dc8a65c747d127cc380ba59e93 SHA1: a460013688e33e985b6c34d7b742739026c10213 SHA256: 1e09d69af398c2b36c5fa3aed9d877b8c43e798f1a20a8e3c8ceb3b080c9bdeb SHA512: a7896e0204ee80d2178f1575e377fdbde8f9844691d8f3e59a52ccbb7429d8844a02cf9fa6ccac7641be3581249dd06597df4e04c088840e33843f357a5e252b Homepage: https://cran.r-project.org/package=timeSeries Description: CRAN Package 'timeSeries' (Financial Time Series Objects (Rmetrics)) 'S4' classes and various tools for financial time series: Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions. Package: r-cran-timeseriesdatasets Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1909 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-timeseriesdatasets_0.1.0-1.ca2604.1_all.deb Size: 842588 MD5sum: 5663c956daa9cb1b150b90730d099580 SHA1: ec4dc1aeee7ef722514aaeeb1a87b77f8455848a SHA256: 0987df135f569754f5bb74420b9fb5e47e0df6ea96deb66a9c49ac352fa74b95 SHA512: 0eefef62a9046d095c4f95cc64716a5f71d7b2a6655886eeeb76254387415bed5a4d3283d2bb9648789824b6ec996588f7b04c8efbbddcfa7a70ec8c815adfdd Homepage: https://cran.r-project.org/package=timeSeriesDataSets Description: CRAN Package 'timeSeriesDataSets' (Time Series Data Sets) Provides a diverse collection of time series datasets spanning various fields such as economics, finance, energy, healthcare, and more. Designed to support time series analysis in R by offering datasets from multiple disciplines, making it a valuable resource for researchers and analysts. Package: r-cran-timetk Architecture: all Version: 2.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3926 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-recipes, r-cran-rsample, r-cran-dplyr, r-cran-ggplot2, r-cran-forcats, r-cran-stringr, r-cran-plotly, r-cran-lubridate, r-cran-padr, r-cran-purrr, r-cran-readr, r-cran-stringi, r-cran-tibble, r-cran-tidyr, r-cran-xts, r-cran-zoo, r-cran-rlang, r-cran-tidyselect, r-cran-slider, r-cran-anytime, r-cran-timedate, r-cran-forecast, r-cran-tsfeatures, r-cran-hms, r-cran-generics Suggests: r-cran-modeltime, r-cran-glmnet, r-cran-workflows, r-cran-parsnip, r-cran-tune, r-cran-knitr, r-cran-rmarkdown, r-cran-broom, r-cran-scales, r-cran-testthat, r-cran-fracdiff, r-cran-timeseries, r-cran-tseries, r-cran-trelliscopejs Filename: pool/dists/resolute/main/r-cran-timetk_2.9.1-1.ca2604.1_all.deb Size: 2994860 MD5sum: 0a360d63d780c152d79afc37d070ae4a SHA1: 1398a24eb2a3160efd5df687b6c2bfa0be395734 SHA256: 52986e8efc562efaa483853e648bffaca9a3e2d5cabae17cdc484ac64d477996 SHA512: 0eeb1fcf4ee68aadb73670266d0fac1fcab521e445fbfe7d0084e622c0a0433e5e5ba5ef1999898615d43b9e2aea4b129ea46b40729b5bd6aba2ea2b39d85a22 Homepage: https://cran.r-project.org/package=timetk Description: CRAN Package 'timetk' (A Tool Kit for Working with Time Series) Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'. Package: r-cran-timetraits Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-lomb, r-cran-fda, r-cran-fdaoutlier Filename: pool/dists/resolute/main/r-cran-timetraits_1.1.0-1.ca2604.1_all.deb Size: 500078 MD5sum: ff2cc7ee4118649019314a547d83aacc SHA1: a4b870091f2b31b5fc301bd02978fd4f3b00a003 SHA256: 305b1c81d38ab1cec252520c1606d9241793983f550fb8dea87ac7fd2a51d875 SHA512: 087754c2e24f37b33e299db1db5815d734561ed07356f46dad6f584a185963f2a9c42094b6e7e1fa67c3a37c4192380e76bfca5535f7002298fdf8aa40f534f8 Homepage: https://cran.r-project.org/package=TimeTraits Description: CRAN Package 'TimeTraits' (Functional Data Analysis Pipeline, Extracting Functional Traitsfrom Biological Time-Series Data) Provides a pipeline of tools for analysing circadian time-series data using functional data analysis (FDA). The package supports smoothing of rhythmic time series, functional principle component analysis (FPCA), and extraction of group-level traits from functional representations. Analyses can incorporate multiple curve derivatives and optional temporal segmentation, enabling comparative analysis of circadian dynamics across experimental groups and time windows. Package: r-cran-timevarconcurrentmodel Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bolstad2, r-cran-fda Filename: pool/dists/resolute/main/r-cran-timevarconcurrentmodel_1.0-1.ca2604.1_all.deb Size: 31314 MD5sum: 6606db27942f079690a8b0bddb241f4c SHA1: 484cd9cd6e8882b42fb9c893824715212001ee7a SHA256: fafde110509ac250e9fa9dee246efc6e5bd0a3fc021740ffebe6a39e222f086b SHA512: 02161d9d3c3573e92d0e3f0842558a3e4f410dfdc4d7bb06eae516a4530385626da5be4b7025daf10aeab61213b41df96f46b20521eadd4f2fe9c6e65530ee16 Homepage: https://cran.r-project.org/package=TimeVarConcurrentModel Description: CRAN Package 'TimeVarConcurrentModel' (Concurrent Multivariate Models with Time-Varying Coefficients) Provides a hypothesis test and variable selection algorithm for use in time-varying, concurrent regression models. The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test. Package: r-cran-timevarcorr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpridge Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-timevarcorr_0.1.1-1.ca2604.1_all.deb Size: 365222 MD5sum: 547b4f30a0ddeca34115371b0bef6127 SHA1: 2e20b210569cdf313f9e362d2b2afcde07204f93 SHA256: 2744dd12bd8b318465d29342311bfd6270513dd5e32a00903388a4757300783c SHA512: 1da2673cfd39b0778aa549db95bef092f6066c0e2be19fef3394f638712a880e2c6c9c7c41ecce56844c3ee2e97be785d958859c026a7fbc09c04934e1bdf29c Homepage: https://cran.r-project.org/package=timevarcorr Description: CRAN Package 'timevarcorr' (Time Varying Correlation) Computes how the correlation between 2 time-series changes over time. To do so, the package follows the method from Choi & Shin (2021) . It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy). An automatic selection procedure for the bandwidth parameter is implemented. Alternative kernels can be used (Epanechnikov, box and normal). Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested. Package: r-cran-timevis Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-magrittr, r-cran-rmarkdown, r-cran-shiny Suggests: r-cran-lubridate, r-cran-testthat, r-cran-shinydisconnect Filename: pool/dists/resolute/main/r-cran-timevis_2.1.0-1.ca2604.1_all.deb Size: 534774 MD5sum: bf1b5c82d88d6e40ad1ccfe26c4acd23 SHA1: ca113b532b27c0863dcb5164fce543c222fabcd3 SHA256: ef35cac1608368ef34b419bcfc1f1a885597bb7d8b09a00bd20492bbfb680dbb SHA512: 41c968ebb1f5d700bc3a8e930e6969402b4189cc7bff3f595822f802a301d365f2def95beec30c56dd8298f607631a6cd625a243a172f076d6702c1d86dc1af3 Homepage: https://cran.r-project.org/package=timevis Description: CRAN Package 'timevis' (Create Interactive Timeline Visualizations in R) Create rich and fully interactive timeline visualizations. Timelines can be included in Shiny apps or R markdown documents. 'timevis' includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. Based on the 'vis.js' Timeline JavaScript library. Package: r-cran-timevizpro Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-countrycode, r-cran-sparkline, r-cran-ggplot2, r-cran-ggiraph, r-cran-dplyr Suggests: r-cran-openxlsx, r-cran-htmltools, r-cran-htmlwidgets, r-cran-dt Filename: pool/dists/resolute/main/r-cran-timevizpro_1.0.1-1.ca2604.1_all.deb Size: 39690 MD5sum: ff01c801fdce819667fbeb6be26ddad6 SHA1: 797d7eb93ff263ef9f590baf5711fd63ca9e951d SHA256: c83abee0e906f45334da385b960acfd0e10f1375a3069390e00d5192ed740b16 SHA512: 1d7abede13562eb2faec9d95bc9d8cedc7e7975e50e273a6f4338bfd11b40218f6ba13baf46415248a1bae99ae6b7fa741484835ecf231d12b87a61703937cbc Homepage: https://cran.r-project.org/package=TimeVizPro Description: CRAN Package 'TimeVizPro' (Dynamic Data Explorer: Visualize and Forecast with 'TimeVizPro') Unleash the power of time-series data visualization with ease using our package. Designed with simplicity in mind, it offers three key features through the 'shiny' package output. The first tab shows time- series charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays Averages per country presented in tables with accompanying sparklines, providing a quick and attractive overview of the data. The last tab presents A customizable world map colored based on user-defined variables for any chosen number of countries, offering an advanced visual approach to understanding geographical data distributions. This package operates with just a few simple arguments, enabling users to conduct sophisticated analyses without the need for complex programming skills. Transform your time-series data analysis experience with our user-friendly tool. Package: r-cran-timevtree Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-timevtree_0.3.1-1.ca2604.1_all.deb Size: 125158 MD5sum: 130432c447cef81b0af3894fe21c3cd7 SHA1: 3cc72ff218b977018e3a3292048c75c1adc0bcf8 SHA256: 917ace5ecf6388b9142d4f6752c1b0d7ef5ce6b5f09adb631e4ea8a541848903 SHA512: 8d75c93a0be77ee757f95ecf779cc685ba64a79eb8bb200290cf32be074149251c7f405d1560ceecf913fdab54d1aed08654cc1ae75b813eb730a1a3220ca5a1 Homepage: https://cran.r-project.org/package=TimeVTree Description: CRAN Package 'TimeVTree' (Survival Analysis of Time Varying Coefficients Using aTree-Based Approach) Estimates time varying regression effects under Cox type models in survival data using classification and regression tree. The codes in this package were originally written in S-Plus for the paper "Survival Analysis with Time-Varying Regression Effects Using a Tree-Based Approach," by Xu, R. and Adak, S. (2002) , Biometrics, 58: 305-315. Development of this package was supported by NIH grants AG053983 and AG057707, and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS). Package: r-cran-tinkr Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-commonmark, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-xml2, r-cran-xslt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tinkr_0.3.1-1.ca2604.1_all.deb Size: 211290 MD5sum: b1a3bddfd3f33145f41dcf9ebba6a8dd SHA1: 7af0524635f9dfc10f73166dc9a45094ebc2dd62 SHA256: 91512376fb8a98e2931ad7b3e6c53dba171e063ef65a680acbf6f21adda603be SHA512: f9bdc789ee7f65852759c7bd9fd589617d03b2ce6bb57d32c0da62f9f67d70fa8e80c933995d4e7bbd02a4e4658f072e7a374af5e96c15c46f664af87cd36d4e Homepage: https://cran.r-project.org/package=tinkr Description: CRAN Package 'tinkr' (Cast '(R)Markdown' Files to 'XML' and Back Again) Parsing '(R)Markdown' files with numerous regular expressions can be fraught with peril, but it does not have to be this way. 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Package: r-cran-tinytex Architecture: all Version: 0.59-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xfun Suggests: r-cran-testit, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-tinytex_0.59-1.ca2604.1_all.deb Size: 147494 MD5sum: 1baea59ecb3e2b0452d02991f7071199 SHA1: a42be65719ae36e8da78a5c6eba4b9c75e6fd829 SHA256: dc786383ed375e0058fed74b57484491d88db6108ece69c33f1cc1e0d36b323c SHA512: 4b046ce915a8d6599ded9eb5c8379de402412994c6acd08a93fb7a46de496c126eb819c403d28a60efacf28ed3bfebbea72bad446f5b8f3e633a0a5aae9c58db Homepage: https://cran.r-project.org/package=tinytex Description: CRAN Package 'tinytex' (Helper Functions to Install and Maintain TeX Live, and CompileLaTeX Documents) Helper functions to install and maintain the 'LaTeX' distribution named 'TinyTeX' (), a lightweight, cross-platform, portable, and easy-to-maintain version of 'TeX Live'. 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Harrison, Qing He, and Hsin-Hsiung Huang (2022) . TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors. Package: r-cran-tipa Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tipa_1.0.8-1.ca2604.1_all.deb Size: 27770 MD5sum: 54ad76ebdb329dec18e9a70d2919bc19 SHA1: 39f088c99999220031a47494238dcdc7cdfc3826 SHA256: 2b18d7b2366bbf024329738bf9c38fee55bb3d0643979b00e70748637d69d3ed SHA512: ce3bcdf5fb937728166cef22a2c9657f58a3bbb6b5023e2dfed9872d2629da8681a8c1e6fa37b07faab6ca11e885b040dfec67961010afc4e4724d582d2debb9 Homepage: https://cran.r-project.org/package=tipa Description: CRAN Package 'tipa' (Tau-Independent Phase Analysis for Circadian Time-Course Data) Accurately estimates phase shifts by accounting for period changes and for the point in the circadian cycle at which the stimulus occurs. See Tackenberg et al. (2018) . Package: r-cran-tipdatingbeast Architecture: all Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-teachingdemos, r-cran-desctools Filename: pool/dists/resolute/main/r-cran-tipdatingbeast_1.1-0-1.ca2604.1_all.deb Size: 1112368 MD5sum: 6ca3e7d7041d98d97bab778d8138400a SHA1: 3ac2a99b5e3f85362a54f423630124e36a6e85ef SHA256: 8baf888e1cb867ce47e3a1ceecdce63b4e06b96c21e3a3b21645e50715a6cb4e SHA512: 1dfdb3f96e031bf37781dc20cc5f13292d492db1037e0396df73b231e67d2611bebb2751ed1c2d843bea401338f4096739315a49e5d9295a5aa2e5ac4fd1e5c0 Homepage: https://cran.r-project.org/package=TipDatingBeast Description: CRAN Package 'TipDatingBeast' (Using Tip Dates with Phylogenetic Trees in BEAST) Assists performing tip-dating of phylogenetic trees with BEAST BEAST is a popular software for phylogenetic analysis. The package assists the implementation of various phylogenetic tip- dating tests using BEAST. It contains two main functions. The first one allows preparing date randomization analyses, which assess the temporal signal of a data set. The second function allows performing leave-one-out analyses, which test for the consistency between independent calibration sequences and allow pinpointing those leading to potential bias. The included tutorial provides detailed step-by-step instructions. An expanded description of the package can be found in article: Rieux, A. and Khatchikian, C.E. (2017), TIPDATINGBEAST: an R package to assist the implementation of phylogenetic tip-dating tests using BEAST. Molecular Ecology Resources, 17: 608-613. . Package: r-cran-tipmap Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-ggplot2, r-cran-rbest, r-cran-assertthat, r-cran-furrr, r-cran-future Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-tibble, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tipmap_0.5.2-1.ca2604.1_all.deb Size: 740850 MD5sum: 48ea5678ee4dce8a90769c8d7c74a258 SHA1: 2b74e1300dd36f2f82173e03b152040b748859c9 SHA256: 8f9d8e0db7b06f495064c7ab8e343135de87e83bbb1e5409c33396bd7d62ddd5 SHA512: b24429cd3935fb1b8b6ea4f466f00065c391739ebdc59112c96ccdd1fe8328193d2f046d2fe17c12f4efa5f2f5b405b9a3667827bf79b85bd50c2a3d4429c115 Homepage: https://cran.r-project.org/package=tipmap Description: CRAN Package 'tipmap' (Tipping Point Analysis for Bayesian Dynamic Borrowing) Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider. Package: r-cran-tippy Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-shiny, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-tippy_0.1.0-1.ca2604.1_all.deb Size: 45638 MD5sum: 76974f0f78f0abb367e243d5d388281c SHA1: 83b3ff687167e8e954b052d3734fd1901ffb6f25 SHA256: 2eddb6731ddb00bbcaff98243e701bcbd040af3f7141c066a8a6864763eb8a38 SHA512: e660c4180c3a44bccf126d0bba1f6737608541c5c93b2c97cda64ca50d723f3f6ee5ced9643c4d657149ab14ea26879f39c4828ac4eac171ee22f876402d3af4 Homepage: https://cran.r-project.org/package=tippy Description: CRAN Package 'tippy' (Add Tooltips to 'R markdown' Documents or 'Shiny' Apps) 'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents. Package: r-cran-tipr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-sensemakr, r-cran-tibble Suggests: r-cran-broom, r-cran-dplyr, r-cran-mass, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tipr_1.0.2-1.ca2604.1_all.deb Size: 307280 MD5sum: 11b8da827da3f478d46f1ee1c5c4b60b SHA1: 9ef74db0726d8b2c9ec1ecc352ddca2df097ee32 SHA256: bf8a6365d9c4e8e2eca5c679aaa6fdd6b9363926d596e58d197ac02f71c5e70a SHA512: 79154f65293a792c758a1f91947fde05bf97e5fb8e9ccf359d0e6ca3328a531b8da254d84317e8b4f2cfda0aa7adffa69059dbd99414f1ba1972bc5b29371b3f Homepage: https://cran.r-project.org/package=tipr Description: CRAN Package 'tipr' (Tipping Point Analyses) The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance. Package: r-cran-tipse Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-survival, r-cran-dplyr, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tipse_2.0-1.ca2604.1_all.deb Size: 335498 MD5sum: 88851ac39fde6b32b4077d100a6c0c54 SHA1: a2546ef276d4fcbc2a7d3b1dd4935f7650c0f19c SHA256: 3667724d835ebf40b56b85eaed37c6f42f5d670041ec10a120741e3ac92f3f9e SHA512: ec5bb61367de8868d8e5703ef29d86eec26fcf6ec5772fe52487b3ae2203008e216dedde41ff5a9e89621241b0dc084799157be2dd47e4c0c101ec8b5b147686 Homepage: https://cran.r-project.org/package=tipse Description: CRAN Package 'tipse' (Tipping Point Analysis for Survival Endpoints) Implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) . Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses. Package: r-cran-tirt Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-gtools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tirt_0.3.1-1.ca2604.1_all.deb Size: 2930338 MD5sum: 2a3e849ef55f6ae1381955c636a372fe SHA1: 6916b9982efdef3487b0edd159f85ecf31a1f626 SHA256: 1ee55f82d73e149aed884ebb4fd7ea21afe9e7c2d66ef3f17692f7c45077d38b SHA512: 568dc9f927cc72cd9b558da1503da1b8ba0dd6cad682d8a41f9102b4b7360c1ae7a7c18af3c94c60d4c68b2930219f9c184cb6042c1f30385594bd32da178940 Homepage: https://cran.r-project.org/package=tirt Description: CRAN Package 'tirt' (Testlet Item Response Theory) Implementation of Testlet and Item Response Theory. A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional and multidimensional Item Response Theory (IRT; Baker & Kim (2004) ) and Testlet Response Theory (TRT; Wainer et al., (2007) ). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration. Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint Maximum Likelihood, the package also offers Bayesian estimation using priors with maximum a posteriori (MAP) method for unidimensional item response theory models. It also provides functions for scale linking and equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability across mixed-format test forms. It also facilitates fixed-parameter calibration, enabling users to estimate person abilities with known item parameters or vice versa, which is essential for pre-equating studies and item bank maintenance. Comprehensive data simulation functions are included to generate synthetic datasets with complex structures, including mixed-model blocks and specific testlet effects, aiding in methodological research and study design validation. Researchers can try multiple simulation situations. Package: r-cran-tissot Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-gdalraster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-tissot_0.2.0-1.ca2604.1_all.deb Size: 2098018 MD5sum: 8646bbcffbb88833840f12d13dd05b7e SHA1: 651374397d0393361baa0cf7ae24510dca0a2988 SHA256: daa9dc678fec1865a4677404ef1fbc56522be6d920366d90445dde9539fd56da SHA512: 3bcde0f1842c8f5dc86b78ef5e4cdc2f8af7ae3d7a920dfb8b368e83791d78b5c1c87d453a8c2ea439ef10a1a349775f5d485ebb95d3d3184afe88a294d1d9c9 Homepage: https://cran.r-project.org/package=tissot Description: CRAN Package 'tissot' (Tissot Indicatrix for Map Projection Distortion) Compute and visualize the 'Tissot Indicatrix' for map projections. The indicatrix characterizes projection distortion by computing scale factors, angular deformation, areal distortion, and convergence at arbitrary points. Based on the calculations shared by Bill Huber on . Uses 'GDAL' for coordinate transformation. Developed using the method published in Snyder, JP (1987) . Package: r-cran-titan2 Architecture: all Version: 2.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-cowplot, r-cran-ggridges, r-cran-purrr, r-cran-tibble, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-titan2_2.4.4-1.ca2604.1_all.deb Size: 2225202 MD5sum: 118009c44922ff12b5ff86ae0612a36b SHA1: 872028cbfeba2cd5d238385dc23826a7b0b4fbfb SHA256: 1fa9b4f3e1ff72beb1d3757099fb2e84e3058b8af9420ffcd9d9dc88fd9d2d81 SHA512: 9d3445073dad7de746478600c4b018651a8a2ec1c6b2db9c8f3f08b0e77e2057b222f8d551c01d9a7fcc7221ec3632b8d75072db1245b05fb4d1695e243631df Homepage: https://cran.r-project.org/package=TITAN2 Description: CRAN Package 'TITAN2' (Threshold Indicator Taxa Analysis) Uses indicator species scores across binary partitions of a sample set to detect congruence in taxon-specific changes of abundance and occurrence frequency along an environmental gradient as evidence of an ecological community threshold. Relevant references include Baker and King (2010) , King and Baker (2010) , and Baker and King (2013) . Package: r-cran-titanic Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-titanic_0.1.0-1.ca2604.1_all.deb Size: 84252 MD5sum: d79988cd0cfe4b95459e3b0045cefe09 SHA1: a17d2d5b0467b7577d907291d65fa7408d9e7af2 SHA256: df88453cc24fd391262ee01f6288eac1a6247bb2ae75661f14950a109a18ecba SHA512: bfab318118add0f62a96962e3bfcf8f111fece68ba5d36bb214f94b42a5ff89a466bb47add96e719bc3d629009da7f1f4f6bb2ae8d1e31db6839ebd4e2044987 Homepage: https://cran.r-project.org/package=titanic Description: CRAN Package 'titanic' (Titanic Passenger Survival Data Set) This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner "Titanic", summarized according to economic status (class), sex, age and survival. Whereas the base R Titanic data found by calling data("Titanic") is an array resulting from cross-tabulating 2201 observations, these data sets are the individual non-aggregated observations and formatted in a machine learning context with a training sample, a testing sample, and two additional data sets that can be used for deeper machine learning analysis. These data sets are also the data sets downloaded from the Kaggle competition and thus lowers the barrier to entry for users new to R or machine learing. Package: r-cran-titegboin Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-titegboin_0.4.0-1.ca2604.1_all.deb Size: 59312 MD5sum: 6dd8ae38ee6e1776f6ee2cca64b67393 SHA1: fb4aa2e80937a344b95a95c7adbdb8e48adf6444 SHA256: 4f7f4d7b8986f900c8c44b1a91f76e5e4b0f744912b30bccb6a9b4fccbfc48ce SHA512: 2af49cdbab405438826ff6bb60c4b6d93b1fa728668d8f15f208ce2ee8aaab0cb28954df5436224fefa61f1ca1ba07ef30eb2ef329980e89c68ce5b51ac52a5f Homepage: https://cran.r-project.org/package=TITEgBOIN Description: CRAN Package 'TITEgBOIN' (Time-to-Event Dose-Finding Design for Multiple Toxicity Grades) In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) ), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) ), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) ) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) ) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules. Package: r-cran-titeir Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso Filename: pool/dists/resolute/main/r-cran-titeir_0.1.0-1.ca2604.1_all.deb Size: 27224 MD5sum: 3cd826cbdb023271333608293a8aba96 SHA1: c6dce91955d162c0bc85ecc775282a4e74ae2789 SHA256: ba8ea50f01065f62d7113c16d792f66508f10a8e3134713106ecf4f8eb71d9bb SHA512: ab1a085df36f191959a009b4cadac430416d65fa9a57ef42de2fac2aa3210ec5748e46c075a569623f60dd2db2e72b6f099b454fefa2b562127383fb12704fb9 Homepage: https://cran.r-project.org/package=titeIR Description: CRAN Package 'titeIR' (Isotonic Designs for Phase 1 Trials with Late-Onset Toxicities) Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability. Package: r-cran-titrationcurves Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-titrationcurves_0.1.0-1.ca2604.1_all.deb Size: 461814 MD5sum: b6b8ec0a5a628d09f64b17e06b07de55 SHA1: b15153fdc0174f920f61bb9b370e8ae62469899a SHA256: a789c7c21425668163c53e7abb8feb1714418349fb4b69ab949e8cc14a01494c SHA512: a44a5d966fdb73f21d1166206dd138562b029afa126d0fcebfdf3f0b76b7b5ef1b9f7797f057a24dcd25b65ec9a64cedab3bfdf841c1bc187890fd4fd467ef59 Homepage: https://cran.r-project.org/package=titrationCurves Description: CRAN Package 'titrationCurves' (Acid/Base, Complexation, Redox, and Precipitation TitrationCurves) A collection of functions to plot acid/base titration curves (pH vs. volume of titrant), complexation titration curves (pMetal vs. volume of EDTA), redox titration curves (potential vs.volume of titrant), and precipitation titration curves (either pAnalyte or pTitrant vs. volume of titrant). Options include the titration of mixtures, the ability to overlay two or more titration curves, and the ability to show equivalence points. Package: r-cran-tivy Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1779 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-stringr, r-cran-stringi, r-cran-ggplot2, r-cran-leaflet, r-cran-rcolorbrewer, r-cran-rlang, r-cran-httr, r-cran-rvest, r-cran-jsonlite, r-cran-pdftools, r-cran-future, r-cran-future.apply, r-cran-patchwork, r-cran-scales Suggests: r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tivy_0.1.1-1.ca2604.1_all.deb Size: 1625024 MD5sum: 637250dcecde522811f2ff439a1c206d SHA1: 6350d61b58b27bd0c29f7c2ce973c2eb17ffb566 SHA256: fdd4c38809db23bc702cc8fd33c1a288c2609ccad9e44d736f1e27f914f21828 SHA512: 3eb6f89f7df35d162d2f675b6cd06c5d9667d120432275f9562e5f95909d64426013d7f37c61e1366cc972beb982b26da794decd9b1ac12bdca819654472d776 Homepage: https://cran.r-project.org/package=Tivy Description: CRAN Package 'Tivy' (Toolkit for Investigation and Visualization of Young Anchovies) Specialized toolkit for processing biological and fisheries data from Peru's anchovy (Engraulis ringens) fishery. Provides functions to analyze fishing logbooks, calculate biological indicators (length-weight relationships, juvenile percentages), generate spatial fishing indicators, and visualize regulatory measures from Peru's Ministry of Production. Features automated data processing from multiple file formats, coordinate validation, spatial analysis of fishing zones, and tools for analyzing fishing closure announcements and regulatory compliance. Includes built-in datasets of Peruvian coastal coordinates and parallel lines for analyzing fishing activities within regulatory zones. Package: r-cran-tkcat Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4011 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-redamor, r-cran-dbi, r-cran-visnetwork, r-cran-dplyr, r-cran-clickhousehttp, r-cran-rlang, r-cran-tidyselect, r-cran-askpass, r-cran-shiny, r-cran-shinydashboard, r-cran-dt, r-cran-htmltools, r-cran-readr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-markdown, r-cran-promises, r-cran-future, r-cran-xml2, r-cran-matrix, r-cran-uuid, r-cran-crayon, r-cran-roxygen2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rclickhouse, r-cran-stringr, r-cran-data.tree, r-cran-bed Filename: pool/dists/resolute/main/r-cran-tkcat_1.2.1-1.ca2604.1_all.deb Size: 1551360 MD5sum: 60a4964dfadf575dd85dbe58186685dd SHA1: 8d29201166a39743cd2c5cb23c83b4c6cb7d2da2 SHA256: 0cd9830fa31b082e7ab289e6093cfb87b483939e899a4a5ba08729bbe8a0eb77 SHA512: c3fac04e91b546a228d92d584a5d6d52385fbfe2dacfae7ef612f24d6b91dcb42323bffcc79a3957c38a7bfa5e1ce2899e6dda6191f6ecaba86a5ee026cb5856 Homepage: https://cran.r-project.org/package=TKCat Description: CRAN Package 'TKCat' (Tailored Knowledge Catalog) Facilitate the management of data from knowledge resources that are frequently used alone or together in research environments. In 'TKCat', knowledge resources are manipulated as modeled database (MDB) objects. These objects provide access to the data tables along with a general description of the resource and a detailed data model documenting the tables, their fields and their relationships. These MDBs are then gathered in catalogs that can be easily explored and shared. Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and create new catalogs suited for specific needs. Package: r-cran-tkimgr Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tkrplotr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tkimgr_0.0.5-1.ca2604.1_all.deb Size: 78808 MD5sum: 4c22331c448f74bae7c1b2833bb2e44d SHA1: a6fc9c65a03c59fef5e5cebc784c4038c7a5cb9b SHA256: e3441a7f04969a5d48dedb858c56e5a003319b5b33ad7812a0bf666ff627b5ce SHA512: a808dd11e92ae8ab7de76a35cb9cb7685c4ea00339ef55b2aec5b9d3351d552143a40cc0f169b031ec2642ed82bafa2174d8e577f8d05a2ee7e4fa71a66ff031 Homepage: https://cran.r-project.org/package=tkImgR Description: CRAN Package 'tkImgR' (Simple Image Viewer for R Using the 'tcltk' Package) A 'Tcl/Tk' Graphical User Interface (GUI) to display images than can be zoomed and panned using the mouse and keyboard shortcuts. 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Package: r-cran-tlaginterim Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-r.utils Filename: pool/dists/resolute/main/r-cran-tlaginterim_1.1-1.ca2604.1_all.deb Size: 101800 MD5sum: ed0e5f708ceb16db99a62cc1820a9daa SHA1: c8f051516bda2a4366fd9dd192295af5d5ad71cb SHA256: d48b7ae269d7c5cdb75f3ea66890ac1de217ad837c9faa31bd61d489d034e710 SHA512: ed3736439557629855357849e586038c44b71c201573292fb9ff1393f711fd1064b60783c1abb3be5df332992e64e213bd7ea4014c2e40cfba5452672e43d1e8 Homepage: https://cran.r-project.org/package=tLagInterim Description: CRAN Package 'tLagInterim' (Interim Monitoring of Clinical Trials with Time-Lagged Outcome) Implements inverse and augmented inverse probability weighted estimators for common treatment effect parameters at an interim analysis with time-lagged outcome that may not be available for all enrolled subjects. Produces estimators, standard errors, and information that can be used to compute stopping boundaries using software that assumes that the estimators/test statistics have independent increments. Tsiatis, A. A. and Davidian, M., (2022) . Package: r-cran-tlagpropodds Architecture: all Version: 1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-r.utils Filename: pool/dists/resolute/main/r-cran-tlagpropodds_1.10-1.ca2604.1_all.deb Size: 80210 MD5sum: f16151aa9a622a42052e15e81e2a47de SHA1: 2370c40e828bc7586f97735f5c8dde6f89effc2c SHA256: 8045d7ae2ddd83f69de06e0861bf3d1bf992bc2bd24ac56529adb7243d088e31 SHA512: 8737e5a437c1ae80c7884658bfe7951fd78e25603fdc21296fdd2004ee0f3d0fa10cec326de2b5a1a7b246cd84206cdee7edd0d2fbe76fcb881578e946c9d52f Homepage: https://cran.r-project.org/package=tLagPropOdds Description: CRAN Package 'tLagPropOdds' (Proportional Odds Model with Censored, Time-Lagged CategoricalOutcome) Implements a semiparametric estimator for the odds ratio model with censored, time-lagged, ordered categorical outcome in a randomized clinical trial that incorporates baseline and time-dependent information. Tsiatis AA, Davidian M, Holloway ST (2023) . 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See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) for further insights. Package: r-cran-tlda Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 851 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tlda_0.1.0-1.ca2604.1_all.deb Size: 621762 MD5sum: d5d3c42a31270837af1151f5a51a17af SHA1: c3511e56c785897c0bf905f4ab5b831df893a0ec SHA256: 42fcbe466f160958c33bd5aa1d2058485be454c4617d2c3b4325d863cfc793f2 SHA512: f00044c1782262b489bdd70e2084a668df47e555a63b350de3a9dff277a8b96d487454bf5cb3dc25a70e9f543f5c3e3b1c42df0d8c6dbd8decb6750e2a86ce40 Homepage: https://cran.r-project.org/package=tlda Description: CRAN Package 'tlda' (Tools for Language Data Analysis) Support functions and datasets to facilitate the analysis of linguistic data. The current focus is on the calculation of corpus-linguistic dispersion measures as described in Gries (2021) and Soenning (2025) . The most commonly used parts-based indices are implemented, including different formulas and modifications that are found in the literature, with the additional option to obtain frequency-adjusted scores. Dispersion scores can be computed based on individual count variables or a term-document matrix. 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The philosophy of the package is described in Guo G. (2020) . Package: r-cran-tlm Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1751 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-tlm_0.2.0-1.ca2604.1_all.deb Size: 1640598 MD5sum: 5d1232c6ab483d500c623c80b27ce451 SHA1: b91f81387a7d7b35ad61408338afb218beda6159 SHA256: 5b80e6fa7022cbece2b6bc7f69b2b653b1d23a40ee21b9131da6f6b5db316eb2 SHA512: 6fff02be3eb5693aac502b656bdc76e348f548cd2f6fb24d81934df14e9c5075166aa8cccc7f087326b2ed069ced16ce93bcad9b8b62394f59cb9fe2ad250533 Homepage: https://cran.r-project.org/package=tlm Description: CRAN Package 'tlm' (Effects under Linear, Logistic and Poisson Regression Modelswith Transformed Variables) Computation of effects under linear, logistic and Poisson regression models with transformed variables. 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Package: r-cran-tm.plugin.korpus Architecture: all Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2080 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-korpus, r-cran-sylly, r-cran-tm, r-cran-nlp Suggests: r-cran-korpus.lang.en, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tm.plugin.korpus_0.4-2-1.ca2604.1_all.deb Size: 966566 MD5sum: 6ce48cd5b58f21612496eb231c6d3bfb SHA1: a0efeb7b529f031a39b0d9ca9a82adc4291f5a26 SHA256: f067893ea712df7cf8a0e39afdfeeeb3f522507385176567e2713aba4662bc4b SHA512: cf66288cb338c86ef2886a088f0a1e6b97e87ac5a4a68ae7ff97f26e6055f34c3c8405810d1672fc5c7a7775bf7c8a1c48a268ffd8aca7158f6cc1df9a830952 Homepage: https://cran.r-project.org/package=tm.plugin.koRpus Description: CRAN Package 'tm.plugin.koRpus' (Full Corpus Support for the 'koRpus' Package) Enhances 'koRpus' text object classes and methods to also support large corpora. 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Package: r-cran-tm.plugin.lexisnexis Architecture: all Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlp, r-cran-tm, r-cran-xml2, r-cran-isocodes Filename: pool/dists/resolute/main/r-cran-tm.plugin.lexisnexis_1.4.2-1.ca2604.1_all.deb Size: 35642 MD5sum: 4561cab73973f4f8d23c05626e73870e SHA1: 1169624d2e435031ba81b32120fd4d64a272bfb5 SHA256: ee3d8b0ab60e8878cd7065d708321b4248e7491fef11a6fd43a0ffda2f6987ac SHA512: 096582caf529d9370c5f7c59df0173401c5506993d83e0fe6e9af2b5be2e1f3f3b6829ab7d16ef6ebba1651af3575720d928651537367a36b97d08c23dd36ea3 Homepage: https://cran.r-project.org/package=tm.plugin.lexisnexis Description: CRAN Package 'tm.plugin.lexisnexis' (Import Articles from 'LexisNexis' Using the 'tm' Text MiningFramework) Provides a 'tm' Source to create corpora from articles exported from the 'LexisNexis' content provider as HTML files. It is able to read both text content and meta-data information (including source, date, title, author and pages). Note that the file format is highly unstable: there is no warranty that this package will work for your corpus, and you may have to adjust the code to adapt it to your particular format. Package: r-cran-tm1r Architecture: all Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Filename: pool/dists/resolute/main/r-cran-tm1r_1.1.8-1.ca2604.1_all.deb Size: 117932 MD5sum: 8565281941f9ada243c898721544405d SHA1: 8db766d4a05732ef1abaaef62aa95da20d0fbd0a SHA256: 01eb68b3d6496278bfa7763606b863c8b91778847fc979e7e02c12be90d6ff1a SHA512: 249ff65b8c21835bfac1c7e9d9d15230b457252e3fa9e7961879527b7854fd62d1102821ac39bb7c12abe2f2a81a1b0f187549f1210ffe0d50cafbb2836dd710 Homepage: https://cran.r-project.org/package=tm1r Description: CRAN Package 'tm1r' (The Integration Between 'IBM COGNOS TM1' and R) Useful functions to connect to 'TM1' instance from R via REST API. With the functions in the package, data can be imported from 'TM1' via mdx view or native view, data can be sent to 'TM1', processes and chores can be executed, and cube and dimension metadata information can be taken. Package: r-cran-tmap.cartogram Architecture: all Version: 0.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-sf, r-cran-cartogram Suggests: r-cran-knitr, r-cran-transformr, r-cran-gifski Filename: pool/dists/resolute/main/r-cran-tmap.cartogram_0.2-1-1.ca2604.1_all.deb Size: 80108 MD5sum: c56ef7f4070c79219395b55ca5561002 SHA1: 107320294ca63a9a36cb1fef11b292d6703056da SHA256: 9c48d2f0546043e1583636fd68a8181b5e89fa30e8b6719c8dc241ee8294532b SHA512: a70fda9a8455524c9c862878bfd2453576bbe8d211d0af1bb847e6471e5858ee767654c13f2d4ecae495a4675195be1277f77507e0ccfe3520659c572240cf3f Homepage: https://cran.r-project.org/package=tmap.cartogram Description: CRAN Package 'tmap.cartogram' (Extension to 'tmap' for Creating Cartograms) Provides new layer functions to 'tmap' for creating various types of cartograms. A cartogram is a type of thematic map in which geographic areas are resized or distorted based on a quantitative variable, such as population. The goal is to make the area sizes proportional to the selected variable while preserving geographic positions as much as possible. Package: r-cran-tmap.glyphs Architecture: all Version: 0.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-tmap.glyphs_0.1-1-1.ca2604.1_all.deb Size: 83740 MD5sum: 1801344a37ede993dcd96b4c51bed525 SHA1: 61e7d3d039602cdbdc24fbdcf66b1991949f218c SHA256: 9645b35c2cded40c85fd1cb4ad05d97b6fbcc5a7b386eec9df9c3ace0872355e SHA512: b6db3ff9327b8bd29c8321376cd35c7a32a025809b2abd9fabac94cce2dcf05c35d6e989f6af99bc312546fb43875b9d4972c7a5b881f261fc46e9e16d25d125 Homepage: https://cran.r-project.org/package=tmap.glyphs Description: CRAN Package 'tmap.glyphs' (Extension to 'tmap' for Creating Glyphs) Provides new layer functions to 'tmap' for drawing glyphs. A glyph is a small chart (e.g., donut chart) shown at specific map locations to visualize multivariate or time-series data. The functions work with the syntax of 'tmap' and allow flexible control over size, layout, and appearance. Package: r-cran-tmap.mapgl Architecture: all Version: 0.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-mapgl, r-cran-terra, r-cran-rlang, r-cran-cli, r-cran-stars, r-cran-data.table, r-cran-colorspace, r-cran-htmltools, r-cran-htmlwidgets, r-cran-sf, r-cran-tmaptools, r-cran-units Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-tmap.mapgl_0.2-1-1.ca2604.1_all.deb Size: 380616 MD5sum: f58f2abc16b008b402c1fd20902ea666 SHA1: 57eb5d6e43446e438a655a2f6716106681d0176d SHA256: 0c27804c0f41115d6206890e965e16385575551acf2c3128074bc9617b9122e8 SHA512: a53f8c028378d1005b1f2bffd17cf7821b9857e43d7f5368e6d1625d35f74dc7a5a04f3d29d4d0c11de6918bc152266b5c47eef8aabe792c57ea0a46450ca2f3 Homepage: https://cran.r-project.org/package=tmap.mapgl Description: CRAN Package 'tmap.mapgl' (Extensions to 'tmap' with Two New Modes: 'mapbox' and 'maplibre') The 'tmap' package provides two plotting modes for static and interactive thematic maps. This package extends 'tmap' with two additional modes based on 'Mapbox GL JS' and 'MapLibre GL JS'. These modes feature interactive vector tiles, globe views, and other modern web-mapping capabilities, while maintaining a consistent 'tmap' interface across all plotting modes. Package: r-cran-tmap.networks Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-sf, r-cran-sfnetworks, r-cran-lwgeom, r-cran-data.table, r-cran-igraph Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-tmap.networks_0.2-1.ca2604.1_all.deb Size: 102352 MD5sum: d032852940bef94ec0d62085fb045f46 SHA1: b2b67c0411d46cd7a3ff906548e68a75533f19c3 SHA256: a28887760b85498acce47618a52577e088bccda471918f9cce568286fe787e73 SHA512: f3457cd519e97b73e492193af057cdad79e867fb5d1953760faec7fe3496cbbb39c8aa036566155c68a9304b25e0808f2d3bc1ec9177f7eef039c75ef89244a1 Homepage: https://cran.r-project.org/package=tmap.networks Description: CRAN Package 'tmap.networks' (Extension to 'tmap' for Creating Network Visualizations) Provides functions for visualizing networks with 'tmap'. It supports 'sfnetworks' objects natively but is not limited to them. Useful for adding network layers such as edges and nodes to 'tmap' maps. More features may be added in future versions. Package: r-cran-tmap Architecture: all Version: 4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-cli, r-cran-cols4all, r-cran-data.table, r-cran-htmltools, r-cran-htmlwidgets, r-cran-base64enc, r-cran-leafem, r-cran-leafgl, r-cran-leaflegend, r-cran-leaflet, r-cran-leafsync, r-cran-maptiles, r-cran-rlang, r-cran-sf, r-cran-stars, r-cran-s2, r-cran-tmaptools, r-cran-units, r-cran-servr Suggests: r-cran-av, r-cran-transformr, r-cran-colorspace, r-cran-ggplot2, r-cran-gifski, r-cran-knitr, r-cran-shiny, r-cran-terra, r-cran-testthat, r-cran-widgetframe, r-cran-lobstr, r-cran-rsvg, r-cran-rstudioapi Filename: pool/dists/resolute/main/r-cran-tmap_4.3-1.ca2604.1_all.deb Size: 4201386 MD5sum: fe8aa3a2ee2c892b4af56791fdcfa65a SHA1: f935d932c66f45273ef4d1e3e5b650725aa957e6 SHA256: c010a6272ef93149a6749a4e2e597772b86a483fdbe87b462849a0a6deff8dd7 SHA512: 33580785395adb67255ee030a50f108852ede01aac5e966f6eaa7e3b0a11c4240daba35516a1c3bec5d1543c1076eb68ae20e5d5e7f9ebc2b8e48b5af3817509 Homepage: https://cran.r-project.org/package=tmap Description: CRAN Package 'tmap' (Thematic Maps) Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps. Package: r-cran-tmaptools Architecture: all Version: 3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-lwgeom, r-cran-stars, r-cran-units, r-cran-xml Suggests: r-cran-tmap, r-cran-cols4all, r-cran-rmapshaper, r-cran-osmdata, r-cran-openstreetmap, r-cran-raster Filename: pool/dists/resolute/main/r-cran-tmaptools_3.3-1.ca2604.1_all.deb Size: 124750 MD5sum: 0b4ba59ac7c032055012ea5bd9e6f4b0 SHA1: f1010bbb08963b289f063dc1bb9affe55121d9b6 SHA256: ae9d66bd8c942dcef60c9e6e7ee33b41f531f273cee90ea981b7ec37f23179de SHA512: d6afc0f4d11401e1d09e849bca7c05303df48d4d10847e4490a232f5bd69a50d9264146f97deac3c7ce2f1cca343203623d9988c5c9a5d31456841017c54fd10 Homepage: https://cran.r-project.org/package=tmaptools Description: CRAN Package 'tmaptools' (Thematic Map Tools) Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps. Package: r-cran-tmapverse Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tmap, r-cran-tmap.glyphs, r-cran-tmap.networks, r-cran-tmap.cartogram, r-cran-tmap.mapgl, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-cols4all, r-cran-cli, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-tmapverse_0.1.0-1.ca2604.1_all.deb Size: 20190 MD5sum: 343fe55efbaf4cf088b4227e9f457c90 SHA1: 6ce0248a891cd6d66ef1c7998b4b26ca85f48c79 SHA256: bbf8074e9c014464ab0e395273ccb898b378e9194207cd3c8369c2abcfe37a3a SHA512: 856a9b93e1d262a28eecb2f6cc86eeb1abb2b1f0ea62c71664db6a4909381620b4f12fcdbd9490a9b4fb6473ff8ec40d4ce1663ee25d6c796ac39899d04f5067 Homepage: https://cran.r-project.org/package=tmapverse Description: CRAN Package 'tmapverse' (Meta-Package for Thematic Mapping with 'tmap') Attaches a set of packages commonly used for spatial plotting with 'tmap'. It includes 'tmap' and its extensions ('tmap.glyphs', 'tmap.networks', 'tmap.cartogram', 'tmap.mapgl'), as well as supporting spatial data packages ('sf', 'stars', 'terra') and 'cols4all' for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem. Package: r-cran-tmcalculator Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seqinr, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-genomeinfodb, r-bioc-gviz, r-cran-dplyr, r-bioc-ggbio, r-cran-ggplot2, r-bioc-karyoploter, r-cran-viridis, r-cran-rlang, r-cran-plotly Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-bioc-bsgenome.hsapiens.ucsc.hg38 Filename: pool/dists/resolute/main/r-cran-tmcalculator_1.0.4-1.ca2604.1_all.deb Size: 1436416 MD5sum: bf0c5c2d83189147a60025af97d1933c SHA1: fecfbd153a052b0cf033af6a8f7b22cd176e6f40 SHA256: 67129538a9bcee062367f7b96f6b345bb2c1030b07571f60beb7693fd629fde4 SHA512: 8d8c5645a1f4b428dfc08705e09667feb8af814a13168102be1d50f83eac9fc0bbc058e4f8e90fb16ad6dff85cfac0aa9a039de15f2b2dffb22cda0c8dd4cf7e Homepage: https://cran.r-project.org/package=TmCalculator Description: CRAN Package 'TmCalculator' (A Calculator for Melting Temperature of Nucleic Acid Sequences) A comprehensive R package for calculating melting temperatures of nucleic acid sequences. Implements three calculation methods: 1. Wallace rule (Thein & Wallace, 1986) 2. Empirical formulas based on GC content (Marmur, 1962; Schildkraut, 2010; Wetmur, 1991; Untergasser, 2012; von Ahsen, 2001) 3. Nearest neighbor thermodynamics (Breslauer, 1986; Sugimoto, 1996; Allawi, 1998; SantaLucia, 2004; Freier, 1986; Xia, 1998; Chen, 2012; Bommarito, 2000; Turner, 2010; Sugimoto, 1995; Allawi, 1997; Santalucia, 2005) Includes corrections for: - Salt ions (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008) - Chemical compounds (dimethyl sulfoxide, formamide) Supports both direct sequence input and FASTA file input. Package: r-cran-tmdb Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-tmdb_1.1-1.ca2604.1_all.deb Size: 309748 MD5sum: f5c6052fbecaac2caf7bd37976180068 SHA1: e8b723344c483f9771962a87825cc6932e3a05f3 SHA256: b0f37bf4228252df96e7e6e98604fd7d609f4fa19324e405e690f83c227c3fe3 SHA512: 4793ed9dfb0579973b5554a32bf1f93f922bf5f3cd865d30f71cba2055892185d5e05e44c5c41c0c2cefa12e02e8adfa45eb6b627715d4a81f8bde82f771bf21 Homepage: https://cran.r-project.org/package=TMDb Description: CRAN Package 'TMDb' (Access to TMDb API) Provides an R-interface to the TMDb API (see TMDb API on ). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see ). Package: r-cran-tmisc Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-rstudioapi, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-tmisc_1.0.1-1.ca2604.1_all.deb Size: 90770 MD5sum: 84e6767f0786fe7d09d6f5029a57694e SHA1: 5b3a3d7f1352bb9d0759030d557b9ea30919cd6b SHA256: b76195d15a9498cc9671f859f3df4f94922b66fcd5eb46297f0c569cea5f3324 SHA512: 834728a2fcd925ea32886b31610c5a9723dcc5389b2c19615a2eb5288fb2868a2bb678614fcf7b13265ad5680c7c5d788920ef8ff90e19ed46b5c17256835d45 Homepage: https://cran.r-project.org/package=Tmisc Description: CRAN Package 'Tmisc' (Turner Miscellaneous) Miscellaneous utility functions for data manipulation, data tidying, and working with gene expression data. Package: r-cran-tml Architecture: all Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-rcppalgos, r-cran-rfast, r-cran-combinat, r-cran-gtools, r-cran-lpsolve, r-cran-lpsolveapi, r-cran-misctools, r-cran-phangorn, r-cran-rcdd, r-cran-rgl, r-cran-ape, r-cran-phytools, r-cran-maps, r-cran-cluster, r-cran-rocr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tml_2.3.0-1.ca2604.1_all.deb Size: 2096826 MD5sum: bfc2d39250afee6d8952e5e1b4a7b0a9 SHA1: a672a6966a3660c5de1a4ac007653b36f49ab89c SHA256: 1692baf85b3a4c603cf3fef33fa781b265528f6e81e8c79defb4d61539b9f17a SHA512: f081cd0818898264d003e6c983aaee8292a68af3f5485ed8591e999a4f7b9118029665c6271161dbb3fdc8e5eb7982b39bc61d86802fe9f05cf44f0d8abc5d35 Homepage: https://cran.r-project.org/package=TML Description: CRAN Package 'TML' (Tropical Geometry Tools for Machine Learning) Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) , Barnhill et al. (2023) , Barnhill and Yoshida (2023) , Aliatimis et al. (2023) , Yoshida et al. (2022) , and Yoshida et al. (2019) . Package: r-cran-tmle Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-superlearner Suggests: r-cran-dbarts, r-cran-gam, r-cran-rocr, r-cran-weightedroc Filename: pool/dists/resolute/main/r-cran-tmle_2.1.1-1.ca2604.1_all.deb Size: 211932 MD5sum: 9dd253e3f8a3740e582a2e816b35a0b5 SHA1: 0cd521d436bbf16b3a9b620a34ac13cc133258e9 SHA256: 0c4ca1525dad2a591d9a0b170f93e1986c0a209e141fd4a1cc7ab6f37be6d271 SHA512: 81f1449a69e25fccffc64b0ba802fcf4df018491b0091e029132524fa93c7910548e2db3e9b9f2aa37c6a92989161ef65adfcfca30beff57104c4bae4d68f12f Homepage: https://cran.r-project.org/package=tmle Description: CRAN Package 'tmle' (Targeted Maximum Likelihood Estimation) Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments. Package: r-cran-tmod Architecture: all Version: 0.50.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beeswarm, r-cran-tagcloud, r-cran-xml, r-cran-plotwidgets, r-cran-rcolorbrewer, r-cran-gplots, r-cran-tibble, r-cran-pheatmap, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect, r-cran-ggrepel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-pander, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-tmod_0.50.13-1.ca2604.1_all.deb Size: 3060132 MD5sum: 761ac4349faf04c926c0bafc25fbaccd SHA1: 250e6bac1832a77a93e0e6730520e5a27779e6aa SHA256: 4955cd9be866f27d66c01d2e53eca4e7f7a7d0c479c18252ed08524572693efd SHA512: 104fa737c3fc2020104b8ac2fef9849187138e8c7b0645d085532a4a466c542af9f16d8a8467ac6723af06218133c517af44ce0f83724c850cc3680bf9e737a0 Homepage: https://cran.r-project.org/package=tmod Description: CRAN Package 'tmod' (Feature Set Enrichment Analysis for Metabolomics andTranscriptomics) Methods and feature set definitions for feature or gene set enrichment analysis in transcriptional and metabolic profiling data. 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Package: r-cran-toolbox Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-toolbox_0.1.1-1.ca2604.1_all.deb Size: 43018 MD5sum: 6dfdadf4d17e59e00426cb6f772525e7 SHA1: 581470d4df3e6f02efc30ef9187d6ab3dbc4a0e8 SHA256: 31ee569fa8413161afd1d10ba280a082c55c6ba63b1f8f56285d75e16f713a47 SHA512: 1466b801a3fdb81e6837c938376004c441c6ec50e993d852f0e313d4792b40eb5a075288a0e838da214ee5342604295e6c6530a01d201f6bfd90bab43603849c Homepage: https://cran.r-project.org/package=toolbox Description: CRAN Package 'toolbox' (List, String, and Meta Programming Utility Functions) Includes functions for mapping named lists to function arguments, random strings, pasting and combining rows together across columns, etc. Package: r-cran-toolero Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 691 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-glue, r-cran-janitor, r-cran-purrr, r-cran-readr, r-cran-renv, r-cran-tibble, r-cran-usethis, r-cran-yaml, r-cran-rlang, r-cran-rvest, r-cran-xml2, r-cran-quarto, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-toolero_0.3.0-1.ca2604.1_all.deb Size: 555132 MD5sum: 1f984facbf5196fa8b3a1611a1bf894c SHA1: 63736dc17543d4eea1182f6844a0ad68ffc4c008 SHA256: 2220b6712846503cd9eec061bc8e6069f489152f16ba68472696c35572e5a26a SHA512: a882208e7858668dcef89fe0bde23c2f69f10c207ce45b121337d09484a8498943709b142c69e892552783badddcd7efecae4a7d35ef19fe7e758254ee0bda21 Homepage: https://cran.r-project.org/package=toolero Description: CRAN Package 'toolero' (A Toolkit for Research Workflows) Provides utility functions to help researchers implement best practices for their coding projects. Includes tools for reading and cleaning data files, initializing R projects with a standard folder structure, creating Quarto documents from a reproducible template, detecting the execution context across interactive, Quarto, and script-based workflows, and splitting data frames into group-level output files. Package: r-cran-toolmark Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-dplyr, r-cran-reshape2, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-toolmark_0.0.1-1.ca2604.1_all.deb Size: 573280 MD5sum: cc2164a8f282465b7ce1b9ebe51c7c5a SHA1: 755512bc91007f19d9935f5a914a040bce669204 SHA256: b4af92c67fbd7cb2829f4d57a00d5624d6e7528693d828abc30bbe00d64fe91b SHA512: 892f59a2d51d2836b21d07988d26de6aafb69d7714c6a31354983e8efaa5379899fef01e4aebc14a943b98acc3bd4518306b6a86fcecd37acb90d5efd772cf21 Homepage: https://cran.r-project.org/package=toolmaRk Description: CRAN Package 'toolmaRk' (Tests for Same-Source of Toolmarks) Implements two tests for same-source of toolmarks. The chumbley_non_random() test follows the paper "An Improved Version of a Tool Mark Comparison Algorithm" by Hadler and Morris (2017) . 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Package: r-cran-tools4uplift Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biasedurn, r-cran-dplyr, r-cran-glmnet, r-cran-latticeextra, r-cran-lhs Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-tools4uplift_1.0.0-1.ca2604.1_all.deb Size: 276922 MD5sum: 62aebb0bf8a4f13f04100baf6e21de2d SHA1: cf9dee279b8edce3c39bb5bf9a42d882967bafc7 SHA256: f187e1c3d0ca23df68ef05481e30bc7324a714b0aa5ace237c0166ef6f65faee SHA512: 4bd7cfbb7e818168ca49bf343b0593fbc8ac54c2a8cba35e321205459ce096432ac03fbc53f00c39a82ab92b76073046745c81e227c6d6df156b9f59933168b7 Homepage: https://cran.r-project.org/package=tools4uplift Description: CRAN Package 'tools4uplift' (Tools for Uplift Modeling) Uplift modeling aims at predicting the causal effect of an action such as a marketing campaign on a particular individual. 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Package: r-cran-toolsforcoda Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-calibrate, r-cran-correlplot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-toolsforcoda_1.1.0-1.ca2604.1_all.deb Size: 137400 MD5sum: 12cb8a668dc721b6fee8cfea0484fa74 SHA1: 88a6218f7c934fbf49a0a6bf74031c223d4ca5e4 SHA256: 9781f3c734e211161caf4f74c995ecc34ae60e1e96645c58aecf9f22115d6868 SHA512: 22a8af50f6c4f693a61398ab1cc1d8bdbf0e5c263274c9c26f0f6ac824c838047d111177bff7844ab4201ca2fa6f8b1ab7808bde158139fec3417be1c041926e Homepage: https://cran.r-project.org/package=ToolsForCoDa Description: CRAN Package 'ToolsForCoDa' (Multivariate Tools for Compositional Data Analysis) Provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis. This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) . Functions for log-ratio principal component analysis with condition number computations and log-ratio discriminant analysis have been added to the package. 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The basic data format requirement for 'toolStability' is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. Function "table_stability" offers the summary table of all stability indices in this package. This R package toolStability is part of the main publication: Wang, Casadebaig and Chen (2023) . Analysis pipeline for main publication can be found on github: . Sample dataset in this package is derived from another publication: Casadebaig P, Zheng B, Chapman S et al. (2016) . For detailed documentation of dataset, please see on Zenodo . Indices used in this package are from: Döring TF, Reckling M (2018) . Eberhart SA, Russell WA (1966) . Eskridge KM (1990) . Finlay KW, Wilkinson GN (1963) . Hanson WD (1970) Genotypic stability. . Lin CS, Binns MR (1988). Nassar R, Hühn M (1987). Pinthus MJ (1973) . Römer T (1917). Shukla GK (1972). Wricke G (1962). 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Package: r-cran-topdowntimeratio Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-geodist, r-cran-lubridate, r-cran-magrittr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-topdowntimeratio_0.1.0-1.ca2604.1_all.deb Size: 71138 MD5sum: c72f5259ee4962262e72da888cb2db60 SHA1: 4d398b53ebc375cfefa60d15926d0399f2487f33 SHA256: b3ddfc5f12de75496dbf7e9a08e89d58721d1f4dd605bc7b509ba538d727b7b7 SHA512: 88ed94985bbd40b0d5778da3d58bad4227cb94d598d1c78e74cda67b72b16e202ce9fa967aef39b6124a0cd9ae91f6a00f6e70d7a330e05fcd86e2d6b1f13a88 Homepage: https://cran.r-project.org/package=topdowntimeratio Description: CRAN Package 'topdowntimeratio' (Top-Down Time Ratio Segmentation for Coordinate Trajectories) Data collected on movement behavior is often in the form of time- stamped latitude/longitude coordinates sampled from the underlying movement behavior. These data can be compressed into a set of segments via the Top- Down Time Ratio Segmentation method described in Meratnia and de By (2004) which, with some loss of information, can both reduce the size of the data as well as provide corrective smoothing mechanisms to help reduce the impact of measurement error. This is an improvement on the well-known Douglas-Peucker algorithm for segmentation that operates not on the basis of perpendicular distances. Top-Down Time Ratio segmentation allows for disparate sampling time intervals by calculating the distance between locations and segments with respect to time. Provided a trajectory with timestamps, tdtr() returns a set of straight- line segments that can represent the full trajectory. McCool, Lugtig, and Schouten (2022) describe this method as implemented here in more detail. Package: r-cran-topicdoc Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-slam, r-cran-topicmodels Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stm, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-topicdoc_0.1.1-1.ca2604.1_all.deb Size: 120738 MD5sum: 9bc296532a5b0cc4690a16d7a9e1f62e SHA1: cf47678778e368e866195d86645dbce87838c844 SHA256: 717d82e085decb1d99d9888599ab1a1a4daa4e87d91fbe484d00f1e35236346e SHA512: 96b80dcd780ea96d473cc7d5a6a388501dd242ae57d7442342a1bcb4a24b3dd13513027f91e5cd0d0f54cb64baf40946d6c95d616dd9c62f0babf218429c2054 Homepage: https://cran.r-project.org/package=topicdoc Description: CRAN Package 'topicdoc' (Topic-Specific Diagnostics for LDA and CTM Topic Models) Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package. For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) . Package: r-cran-topiclabels Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-httr, r-cran-progress, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-topiclabels_0.4.0-1.ca2604.1_all.deb Size: 179092 MD5sum: 793ed3ee0e37ab5a844b65afe1b658cb SHA1: 64aedb5cf2812689807c9a1467baae18687ab62a SHA256: 9c9a0b0951199a579d296cd9008588822ad6992506ac655fd749744c40bd6738 SHA512: 54bde3c07e7a5c0ff80352fbbd39ac1741ed07309dc6d2756878db9055920c2f80845f27260b5fa8bdbb8c603c5689a5ff88a370ece05135e4b4dc2bc85b7f36 Homepage: https://cran.r-project.org/package=topiclabels Description: CRAN Package 'topiclabels' (Automated Topic Labeling with Language Models) Leveraging (large) language models for automatic topic labeling. The main function converts a list of top terms into a label for each topic. Hence, it is complementary to any topic modeling package that produces a list of top terms for each topic. While human judgement is indispensable for topic validation (i.e., inspecting top terms and most representative documents), automatic topic labeling can be a valuable tool for researchers in various scenarios. Package: r-cran-topicmodels.etm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-torch Suggests: r-cran-udpipe, r-cran-word2vec, r-cran-uwot, r-cran-tinytest, r-cran-textplot, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-topicmodels.etm_0.1.1-1.ca2604.1_all.deb Size: 4114720 MD5sum: 278f2601f01e7c3d5354aa167dd60193 SHA1: 1ebd182571580479fcf82ace8db77f52499c73d4 SHA256: 65aea3b3f70b852b28443732835b0c3c7ab6f11535348d99f59e08fa2de34e6e SHA512: 13ecae4b53497ee4a7e8826da6bd16bbc70c766da9f2b1d04417dd1b73cbc63559c2d1d66191e75004173f47000c6f18750408aaa54c284bb769736da1bd8c2d Homepage: https://cran.r-project.org/package=topicmodels.etm Description: CRAN Package 'topicmodels.etm' (Topic Modelling in Embedding Spaces) Find topics in texts which are semantically embedded using techniques like word2vec or Glove. This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at . Package: r-cran-topics Architecture: all Version: 0.70-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4991 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-textminer, r-cran-quanteda, r-cran-ggplot2, r-cran-dplyr, r-cran-ggwordcloud, r-cran-tibble, r-cran-readr, r-cran-stopwords, r-cran-matrix, r-cran-stringr, r-cran-stringi, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-data.table, r-cran-ggforce, r-cran-patchwork Suggests: r-cran-text, r-cran-rjava, r-cran-mallet, r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-vdiffr, r-cran-httr, r-cran-arrow Filename: pool/dists/resolute/main/r-cran-topics_0.70-1.ca2604.1_all.deb Size: 3045792 MD5sum: f2a86f17f2c243803e78db8a67c85f84 SHA1: 253aad9ebbe0d86cc45695b5c1b9c59ea82090f6 SHA256: e3fc64e549292dad90767a1110c8c98505a9f34fd699072619e9e632b7dc3e5e SHA512: 74536c96944655c11650412b85acdd2c7d326b7bf73ffc7777461a312e41bc75a3d69c4487bf47ed7aaff3714c4b496f74d2381e219466718ee5547a0a00969d Homepage: https://cran.r-project.org/package=topics Description: CRAN Package 'topics' (Creating and Significance Testing Language Features forVisualisation) Implements differential language analysis with statistical tests and offers various language visualization techniques for n-grams and topics. It also supports the 'text' package. For more information, visit and . Package: r-cran-topicscore Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra, r-cran-combinat, r-cran-quadprog, r-cran-matrix, r-cran-slam Filename: pool/dists/resolute/main/r-cran-topicscore_0.0.1-1.ca2604.1_all.deb Size: 701366 MD5sum: 22d4321d90986b8276a482ca2b5e0b41 SHA1: c348b8f9ef481403d4a6ddcad675fc91f31f6a0e SHA256: cba779e756bf6f5139bb9f98a236ebafec94ff3bc16dfe5fdf63cd65af2f9bca SHA512: 48a1ce855963212a4a139048d2788a5f969721b746420a886961162187a5ff893c6697f84c39be59b4be41fd3b1cc8d502011da9aecf202c13d3253f9c70babf Homepage: https://cran.r-project.org/package=TopicScore Description: CRAN Package 'TopicScore' (The Topic SCORE Algorithm to Fit Topic Models) Provides implementation of the "Topic SCORE" algorithm that is proposed by Tracy Ke and Minzhe Wang. The singular value decomposition step is optimized through the usage of svds() function in 'RSpectra' package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise error measure in the word-topic matrix A, and an algorithm for recovering the topic-document matrix W given A and D based on quadratic programming. The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) . Package: r-cran-topictestlet Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-topicmodels, r-cran-tm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-topictestlet_0.1.0-1.ca2604.1_all.deb Size: 85044 MD5sum: fb6887bdc0f5268881c876a67955dc67 SHA1: fd60e95603602af8ffb022f659673ef184a97989 SHA256: d7e02646a2f2c30ab56d90660703e1055efb48e8a184401a7d95ac203b878a9c SHA512: 7b4fb2742fe0a563f598c429e876ea4b2d4652aad976f6f0b57dfdaaa45ae1238c936f36ae8c780fce79c042085d9dc3bce2cb3eb89c493ce035a22be7177651 Homepage: https://cran.r-project.org/package=TopicTestlet Description: CRAN Package 'TopicTestlet' (A Topic Testlet Model for Calibrating Testlet ConstructedResponses) Implements the Topic Testlet Model (TTM) as described by Xiong et al. (2025) . The package integrates Latent Dirichlet Allocation (LDA) with the Partial Credit Model to account for local item dependence in testlets using latent topics from student textual responses. Package: r-cran-topksignal Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-nloptr, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-topksignal_1.0-1.ca2604.1_all.deb Size: 139784 MD5sum: 4224b72e6dd6f598c00b1a99bfc601e9 SHA1: bab8edf743ec596e844d9b7ac7eeee7adc93762f SHA256: 694d98ef891e26be1596f8dacb2c8a24e232f0f26a2ffd027c0a5533751dbb1f SHA512: b94df83b85fade8580841868ca1336b64d2c8668f6f83e5fd864392c751f1677cd72408657fce6ad9e82810554ecec3088f3336029ece2ebae768c2679998a60 Homepage: https://cran.r-project.org/package=TopKSignal Description: CRAN Package 'TopKSignal' (A Convex Optimization Tool for Signal Reconstruction fromMultiple Ranked Lists) A mathematical optimization procedure in combination with statistical bootstrap for the estimation of the latent signals (sometimes called scores) informing the global consensus ranking (often named aggregation ranking). To solve mid/large-scale problems, users should install the 'gurobi' optimiser (available from ). Package: r-cran-topodistance Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdistance, r-cran-plotly, r-cran-raster, r-cran-rcolorbrewer, r-cran-scales, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-topodistance_1.0.2-1.ca2604.1_all.deb Size: 885408 MD5sum: 087ec9a181fadde1ca29276ab6c74864 SHA1: ee32645e938077590c71bca19b127a753187b4a3 SHA256: 2319c1aafb64e67300415a3b04108be793840f8afe37896137102ad19cf992a8 SHA512: 855395d7c9463549f9794df7b560ec419a59138ff8422a67150c2c1d8ca0c688dbb3b4de0d16cf26f5e4af9f4596da3a5aee8d49c93a212924bc7bb4271b4df6 Homepage: https://cran.r-project.org/package=topoDistance Description: CRAN Package 'topoDistance' (Calculating Topographic Paths and Distances) A toolkit for calculating topographic distances and identifying and plotting topographic paths. Topographic distances can be calculated along shortest topographic paths (Wang (2009) ), weighted topographic paths (Zhan et al. (1993) ), and topographic least cost paths (Wang and Summers (2010) ). Functions can map topographic paths on colored or hill shade maps and plot topographic cross sections (elevation profiles) for the paths. Package: r-cran-topologygsa Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph, r-cran-grbase, r-bioc-qpgraph, r-cran-fields, r-cran-igraph Suggests: r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-cran-topologygsa_1.5.0-1.ca2604.1_all.deb Size: 74834 MD5sum: 04cc315ce64d3da1360c723de65fedb5 SHA1: 7bc6eeadda392c95eebac8a9305281cb5054d038 SHA256: ac45137f22312e45d4c171ee219524e55d5b0e59cf1ffda18543b94640d7deb0 SHA512: 273ca072f70a4e3ec5bc60917a05f8752c9611b8676fb60027334dbbddffe6db83a309318cff44712e03ec99649ea6450f68b7f6c6a8220922c095512dfad6b4 Homepage: https://cran.r-project.org/package=topologyGSA Description: CRAN Package 'topologyGSA' (Gene Set Analysis Exploiting Pathway Topology) Using Gaussian graphical models we propose a novel approach to perform pathway analysis using gene expression. Given the structure of a graph (a pathway) we introduce two statistical tests to compare the mean and the concentration matrices between two groups. Specifically, these tests can be performed on the graph and on its connected components (cliques). The package is based on the method described in Massa M.S., Chiogna M., Romualdi C. (2010) . Package: r-cran-topologyr Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-topologyr_0.1.2-1.ca2604.1_all.deb Size: 1116406 MD5sum: dc8dae242438e444ccbd5f915965ed0c SHA1: d6e6061faee6a65985ca0feccae66491afc8ce7e SHA256: 5a15070eae949e0fb1f536fdba92526fc7cb54417117ab76258c1d6e717a6909 SHA512: 48c411bfc8e22428bc15b755a8835cbfd7568fecde8a93889ed8c50d15f1a085fbb1043620e493ff63d17ba0e3e008b8ed4c2a0097ed0f0025c5f5ee3b11922c Homepage: https://cran.r-project.org/package=topologyR Description: CRAN Package 'topologyR' (Topological Connectivity Analysis for Numeric Data) Description: Implementation of topological data analysis methods based on graph-theoretic approaches for discovering topological structures in data. The core algorithm constructs topological spaces from graphs following Nada et al. (2018) "New types of topological structures via graphs". Package: r-cran-topolow Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-lifecycle, r-cran-ggplot2, r-cran-dplyr, r-cran-data.table, r-cran-reshape2, r-cran-filelock, r-cran-lhs, r-cran-rlang Suggests: r-cran-coda, r-cran-rtsne, r-cran-ape, r-cran-racmacs, r-cran-vegan, r-cran-umap, r-cran-igraph, r-cran-rgl, r-cran-scales, r-cran-ggrepel, r-cran-plotly, r-cran-gridextra, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-topolow_2.0.1-1.ca2604.1_all.deb Size: 1799116 MD5sum: 2cfc24fd42a138992e37e285010bbe1c SHA1: 58c00b81bd85bcd051cd4074aae8acbead8049d3 SHA256: 33137d0cc7cd732cfed90f1e63d6ddcf814672110b6c30381cc5a4d527df0e95 SHA512: 57d816a84a592118d056638e3bc6f2dcaf85e97194373daca79b9d8521f40b1494ad7b33457afc24adb713ae402ed4e0e1a1f41e951cd330dbb0be10aa74c5bc Homepage: https://cran.r-project.org/package=topolow Description: CRAN Package 'topolow' (Force-Directed Euclidean Embedding of Dissimilarity Data) A robust implementation of Topolow algorithm. It embeds objects into a low-dimensional Euclidean space from a matrix of pairwise dissimilarities, even when the data do not satisfy metric or Euclidean axioms. The package is particularly well-suited for sparse, incomplete, and censored (thresholded) datasets such as antigenic relationships. The core is a physics-inspired, gradient-free optimization framework that models objects as particles in a physical system, where observed dissimilarities define spring rest lengths and unobserved pairs exert repulsive forces. The package also provides functions specific to antigenic mapping to transform cross-reactivity and binding affinity measurements into accurate spatial representations in a phenotype space. Key features include: * Robust Embedding from Sparse Data: Effectively creates complete and consistent maps (in optimal dimensions) even with high proportions of missing data (e.g., >95%). * Physics-Inspired Optimization: Models objects (e.g., antigens, landmarks) as particles connected by springs (for measured dissimilarities) and subject to repulsive forces (for missing dissimilarities), and simulates the physical system using laws of mechanics, reducing the need for complex gradient computations. * Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal number of dimensions for the embedding/map, avoiding distortions common in methods with fixed low dimensions. * Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based, error-dampening mechanism. * Antigenic Velocity Calculation (for antigenic data): Introduces and quantifies "antigenic velocity," a vector that describes the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster transitions and potential lineage replacements. * Broad Applicability: Analyzes data from various objects that their dissimilarity may be of interest, ranging from complex biological measurements such as continuous and relational phenotypes, antibody-antigen interactions, and protein folding to abstract concepts, such as customer perception of different brands. Methods are described in the context of bioinformatics applications in Arhami and Rohani (2025a) , and mathematical proofs and Euclidean embedding details are in Arhami and Rohani (2025b) . Package: r-cran-toponym Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geodata, r-cran-ggplot2, r-cran-sf, r-cran-spatstat.geom, r-cran-spatstat.utils, r-cran-terra Filename: pool/dists/resolute/main/r-cran-toponym_2.0.1-1.ca2604.1_all.deb Size: 231746 MD5sum: 56d015ecc0cb657df07291d5ba890494 SHA1: 81c69dde8d925ed0ef3ed9e8bdfb60da4ba82f83 SHA256: 2b8e4028ec70a64e18cc32667caf1b6e90c658cce4dc9cee02fc624eb704bdb1 SHA512: 577cdff158d1b50b3c7c2be18bab57258c7d864590767d5edd3ec94b0b1579e6d1ceba93e7b8ee3199971e8bb8079de13211f6a4a8f4cc2348487c45de3d5292 Homepage: https://cran.r-project.org/package=toponym Description: CRAN Package 'toponym' (Analyze and Visualize Toponyms) A tool to analyze and visualize toponym distributions. This package is intended as an interface to the GeoNames data. A regular expression filters data and in a second step a map is created displaying all locations in the filtered data set. The functions make data and plots available for further analysis—either within R or in a chosen directory. Users can select regions within countries, provide coordinates to define regions, or specify a region within the package to restrict the data selection to that region or compare regions with the remainder of countries. This package relies on the R packages 'geodata' for map data and 'ggplot2' for plotting purposes. For more information on the study of toponyms, see Wichmann & Chevallier (2025) . Package: r-cran-toposort Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-rlang, r-cran-vctrs Suggests: r-cran-cli, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-toposort_1.0.0-1.ca2604.1_all.deb Size: 34310 MD5sum: e52bf16fe1059a89339dc505c5d87402 SHA1: 28886192d702dce6fc55c3ae5b65f8323f9b90e2 SHA256: a7f1fa5b335a5e7c7a87324baebe56f04e8d50f0a88a0a0cfd35bdd4f7db50bd SHA512: ff2e36491c3514de81084965d7dcc6d249d1e733f62673ce416b81eeed49c6e423994e495a82007dbafddf8d459e26a6393be1a0eb3610c940e58ff53a4cb6b9 Homepage: https://cran.r-project.org/package=toposort Description: CRAN Package 'toposort' (Topological Sorting Algorithms) Flexible and ergonomic topological sorting implementation for R. Supports a variety of input data encoding (lists of edges or adjacency matrices, graphs edge direction), stable sort variants as well as cycle detection with detailed diagnosis. 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Package: r-cran-torchmaum Architecture: all Version: 2025.7.30-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-torchmaum_2025.7.30-1.ca2604.1_all.deb Size: 37036 MD5sum: ffaa9b4844b2d661ada4c7e24afb437a SHA1: 2e528ef41445d3e81f7858b1457e57d4e3aa9a22 SHA256: be9a4665e7235fc7db1c0d302d1db8730657091fc2206cc5b935e0c03ee0fb6f SHA512: f348077a0ddf34269487ddc31943f334b2f6ac6c7b600f812fa1e0ac588d26b785b274ca7ef9d12f7781af077468d30b7ee6536d59bc7461a1391f9fb28a2176 Homepage: https://cran.r-project.org/package=torchMAUM Description: CRAN Package 'torchMAUM' (Multi-Class Area Under the Minimum in Torch) Torch code for computing multi-class Area Under The Minimum, , Generalization. Useful for optimizing Area under the curve. Package: r-cran-torchopt Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-torchopt_0.1.4-1.ca2604.1_all.deb Size: 172690 MD5sum: 8ba39ea9af2728ec8ae21fdedf525ac8 SHA1: 4a0dbb57283bfbd78e6499d33c2a25f9309f3b7a SHA256: 5954c16457d73e40a4da2d1c24a968e10503f3d7b38beb549708241b47d67fa2 SHA512: 37be233182b0acf6a89f203aeb1c7f8513012cf884af5954b9f8ecdfb8a1559ca59ab3b72186dbe540735a68a5242d65912f64f5129d94cdfe7ac5b35f163724 Homepage: https://cran.r-project.org/package=torchopt Description: CRAN Package 'torchopt' (Advanced Optimizers for Torch) Optimizers for 'torch' deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in 'torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) 'adabelief' by Zhuang et al (2020), ; (b) 'adabound' by Luo et al.(2019), ; (c) 'adahessian' by Yao et al.(2021) ; (d) 'adamw' by Loshchilov & Hutter (2019), ; (e) 'madgrad' by Defazio and Jelassi (2021), ; (f) 'nadam' by Dozat (2019), ; (g) 'qhadam' by Ma and Yarats(2019), ; (h) 'radam' by Liu et al. (2019), ; (i) 'swats' by Shekar and Sochee (2018), ; (j) 'yogi' by Zaheer et al.(2019), . Package: r-cran-torchvision Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1935 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-fs, r-cran-rlang, r-cran-rappdirs, r-cran-jpeg, r-cran-tiff, r-cran-magrittr, r-cran-png, r-cran-abind, r-cran-jsonlite, r-cran-withr, r-cran-cli, r-cran-glue, r-cran-zeallot Suggests: r-cran-arrow, r-cran-magick, r-cran-prettyunits, r-cran-testthat, r-cran-coro, r-cran-r.matlab, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-torchvisionlib Filename: pool/dists/resolute/main/r-cran-torchvision_0.9.0-1.ca2604.1_all.deb Size: 1563002 MD5sum: d2503747b608b60c63a0ef2a2d1be19c SHA1: f2f1c668f0b3d23cd5430d42cb1cc44300270f26 SHA256: 063665129f156fdee7b3f0d51606b0cf80e922a101065223ecf2ffb5ad139933 SHA512: 088057b1a5076fb8c841e1148a811ed9f5c6f6aefe75c486688d27b32e15adcecde976eeb088cec7ab14706217ee2c73af7b981e22c012b0ab14961952ee8e08 Homepage: https://cran.r-project.org/package=torchvision Description: CRAN Package 'torchvision' (Models, Datasets and Transformations for Images) Provides access to datasets, models and preprocessing facilities for deep learning with images. Integrates seamlessly with the 'torch' package and its API borrows heavily from the 'PyTorch' vision package. Package: r-cran-tords Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tords_1.0.0-1.ca2604.1_all.deb Size: 184500 MD5sum: 6f8025072118af8311c1736f8d9a16fa SHA1: 0870e411340fec98d1ac6478fa5987ca74b6190d SHA256: 6ef6e8c5bf13b8df2defb61f48ff183a563b22a9417067ed72c2383efa3e8dd4 SHA512: 59a9084e5a102d2a25ff7f87cc639cbd6347d1fcc55cf95060a5d1e33c7bfc5872f73f9d8b48a57728d21f233e0775a1e2cd084f63c4590fe4a136b5e5d036c9 Homepage: https://cran.r-project.org/package=TORDs Description: CRAN Package 'TORDs' (Third Order Rotatable Designs (TORDs)) Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) ."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). . "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model. 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Package: r-cran-tosi Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-hdi, r-cran-scalreg, r-cran-glmnet Filename: pool/dists/resolute/main/r-cran-tosi_0.3.0-1.ca2604.1_all.deb Size: 107182 MD5sum: 04344c9d6a0ce60283e867fafb71114b SHA1: d2d0ed11b8e54368bd0c0119d14bf4538e72e02d SHA256: 5a05a8ce0057449884c5a341f270d84366408787a4a910ac96d1ae51d8104a8b SHA512: 37b793dc63357c46d3339ca04c88fc19c85700204c37b63d9a73f1eb4e6cbd853879a3040a660f0e505d677398349650dfb53aa4287ff9f809caf4504ed8472c Homepage: https://cran.r-project.org/package=TOSI Description: CRAN Package 'TOSI' (Two-Directional Simultaneous Inference for High-DimensionalModels) A general framework of two directional simultaneous inference is provided for high-dimensional as well as the fixed dimensional models with manifest variable or latent variable structure, such as high-dimensional mean models, high- dimensional sparse regression models, and high-dimensional latent factors models. It is making the simultaneous inference on a set of parameters from two directions, one is testing whether the estimated zero parameters indeed are zero and the other is testing whether there exists zero in the parameter set of non-zero. More details can be referred to Wei Liu, et al. (2022) . Package: r-cran-tost.suite Architecture: all Version: 3.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat, r-cran-hmisc, r-cran-index0, r-cran-lm.beta, r-cran-mathjaxr, r-cran-rlang, r-cran-stringr, r-cran-webuse Filename: pool/dists/resolute/main/r-cran-tost.suite_3.1.9-1.ca2604.1_all.deb Size: 471318 MD5sum: d396d7741727ffc460124d0f034d152f SHA1: 3d4bec9089330e0b5e6047d87b979dad898d5f0c SHA256: 987f3c6af054000e322a7d61a15c9982d80e13abddecebb1571a9d0d85bceb96 SHA512: 43376910776594e0ab112e1c200f15ac8f5e08a5751cafd72458425e73889bfdc925b7c19226611ca0f3431c662635b16d614ced12fc2ffea29c742cc3e47380 Homepage: https://cran.r-project.org/package=tost.suite Description: CRAN Package 'tost.suite' (Two One-Sided Tests for Equivalence) Ports the 'Stata' ado package 'tost' which provides a suite of commands to perform two one-sided tests for equivalence following the approach by Schuirman (1987) . Commands are provided for t tests on means, z tests on proportions, McNemar's test (1947) on proportions and related tests, tests on the regression coefficients from OLS linear regression (not yet implementing all of the current regression options from the 'Stata' 'tostregress' command, e.g., survey regression options, estimation options, etc.), Wilcoxon's (1945) signed rank tests, Wilcoxon-Mann-Whitney (1947) rank sum tests, supporting inference about equivalence for a number of paired and unpaired, parametric and nonparametric study designs and data types. Each command tests a null hypothesis that samples were drawn from populations different by at least plus or minus some researcher-defined level of tolerance, which can be defined in terms of units of the data or rank units (Delta), or in units of the test statistic's distribution (epsilon) except for tost.rrp() and tost.rrpi(). Enough evidence rejects this null hypothesis in favor of equivalence within the tolerance. Equivalence intervals for all tests may be defined symmetrically or asymmetrically. 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Package: r-cran-tout Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-tout_1.0.3-1.ca2604.1_all.deb Size: 101248 MD5sum: cdb4d58fe2003bc51f040376c592a475 SHA1: f5ff6a57c4d247d313d0f87e4613d8d93937a2da SHA256: 67775cb516ba78c0ede862f92e2be69b951045272c70ec45ff6a34b7a5d0b7fc SHA512: a9c086aed94a9612b2b311c3260052ef642b2180c0e4a9bfcc3de6a10dbf278e095ea3e1fa7da2a18e10b13f20ff61ad5f17df897d15c5d2189146b2c04af645 Homepage: https://cran.r-project.org/package=tout Description: CRAN Package 'tout' (Optimal Sample Size and Progression Criteria for Three-OutcomeTrials) Find the optimal decision rules (AKA progression criteria) and sample size for clinical trials with three (stop/pause/go) outcomes. Both binary and continuous endpoints can be accommodated, as can cases where an adjustment is planned following a pause outcome. For more details see Wilson et al. (2024) . Package: r-cran-tower Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-purrr, r-cran-stringr, r-cran-curl, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-testthat, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-tower_0.2.0-1.ca2604.1_all.deb Size: 58750 MD5sum: 4a343e4909201e1f38a1db7a56938a48 SHA1: b341636e8d9f244222d8329bcaf6d8045015a9f4 SHA256: ef35f5e142dc6768bc078f4c4b387f218afaef62a7873554e69c368623f4343b SHA512: 0ce4b8ddf3f3b5943649a1c1be8460642f87b0a2b304954c8d12dba8b9c045eacb6a0cf94b762b54d6d9fe28639fe78679ab7c86b0b8508c75ab4eef21748785 Homepage: https://cran.r-project.org/package=tower Description: CRAN Package 'tower' (Easy Middle Ware Library for 'shiny') The best way to implement middle ware for 'shiny' Applications. 'tower' is designed to make implementing behavior on top of 'shiny' easy with a layering model for incoming HTTP requests and server sessions. 'tower' is a very minimal package with little overhead, it is mainly meant for other package developers to implement new behavior. 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Based on the notion of regression averaging (Matloff (2017, ISBN: 9781498710916)). Package: r-cran-toxcrit Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-toxcrit_1.1-1.ca2604.1_all.deb Size: 14332 MD5sum: 89c4a1a60758292b1d1ff8e60e6905fa SHA1: 0341bb4560404784dd4d8808159104b26ebc3d15 SHA256: 5cf1f5a937fefc6650888e706bec849b6d4ca5703def64fd9691657e090a6b54 SHA512: 193aef5bc8ce51c52b53038b0f0388584786e68c2f0e9a80faae3b629cad5a9419b14205b5f56ea75f84d71754f028e6864fe98817dc58523892dcbd5332d7f1 Homepage: https://cran.r-project.org/package=ToxCrit Description: CRAN Package 'ToxCrit' (Calculates Safety Stopping Boundaries for a Single-Arm Trialusing Bayes) Computation of stopping boundaries for a single-arm trial using a Bayesian criterion. For each m<=n (n=total patient number of the trial) the smallest number of observed toxicities is calculated leading to the termination of the trial/accrual according to the specified criteria. The probabilities of stopping the trial/accrual at and up until (resp.) the m-th patient (m<=n) is also calculated. This design is more conservative than the frequentist approach (using Clopper Pearson CIs) which might be preferred as it concerns safety. See also Aamot et al. (2010) "Continuous monitoring of toxicity in clinical Trials - simulating the risk of stopping prematurely" . Package: r-cran-toxdrc Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-outliers, r-cran-purrr, r-cran-rlang, r-cran-drc Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-toxdrc_1.0.1-1.ca2604.1_all.deb Size: 95098 MD5sum: b33abc4d98f4d296c6b4aa29206b2234 SHA1: f79137491f54701f3dfd9f5f34d357d8cf1a8748 SHA256: 80e756b6ee12377d077d0fef5f87619c93aed2a0ec2b5ebc6cb566d46713f23b SHA512: b6ecf2920811423d459439c288bf67b355e5f939fbe2644df5f7855d5cf08a59aaf9a7ab814de1c471732d1d3828ae91a997e626c9e236520181705aad8f3cd4 Homepage: https://cran.r-project.org/package=toxdrc Description: CRAN Package 'toxdrc' (Pipeline for Dose-Response Curve Analysis) Provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. 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Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell (2017) . 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'toxpiR' allows for more customization than the 'ToxPi GUI' () and integration into existing workflows for greater ease-of-use, reproducibility, and transparency. toxpiR package behaves nearly identically to the GUI; the package documentation includes notes about all differences. The vignettes download example files from . 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Package: r-cran-tpac Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-tpacdata Filename: pool/dists/resolute/main/r-cran-tpac_0.3.0-1.ca2604.1_all.deb Size: 309186 MD5sum: 9a234757c2a0748f1532d2cccddac4c6 SHA1: b08ec8f69ff2e8484525c3c0575dc12068e0a579 SHA256: c1537ad27c7edf7a12060107c69fcb9e22e26b0e110d323b473310290f9c84a4 SHA512: a2c56f805ceb13888bc4b5e789f3b6264669acdac1dd7fb0b5c746c3bae18034c35ac51ee626759659d0d2e161b6d1627eb0deec333863b07336ac29c20f9b8f Homepage: https://cran.r-project.org/package=TPAC Description: CRAN Package 'TPAC' (Tissue-Adjusted Pathway Analysis of Cancer (TPAC)) Contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" . 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Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" . 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References include: "Sen, S., Chandra, N. and Maiti, S. S. (2018). On properties and applications of a two-parameter XGamma distribution. Journal of Statistical Theory and Applications, 17(4): 674--685. ." "Wani, M. A., Ahmad, P. B., Para, B. A. and Elah, N. (2023). A new regression model for count data with applications to health care data. International Journal of Data Science and Analytics. ." 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Package: r-cran-trackreconstruction Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-rcolorbrewer Suggests: r-cran-lattice, r-cran-onion, r-cran-plotrix, r-cran-rgl, r-cran-scatterplot3d Filename: pool/dists/resolute/main/r-cran-trackreconstruction_1.3-1.ca2604.1_all.deb Size: 3283000 MD5sum: e3058b2c77b58b1940506abdc15c351d SHA1: 7e51f8d8ce5e8f33c74e07646b5abf3e3a134c49 SHA256: c310db079290b14203a756fd6267a6b30019e70b0b7737fff8131f2f38046e79 SHA512: 1520efd76b6ef32c1e2493b0344c0ad89bcb8514ce7bc2a0465d5d0ff94dd9f5338a4a11e071edd46bb943fcc96943199beab14418558d43ac46bf13a932427e Homepage: https://cran.r-project.org/package=TrackReconstruction Description: CRAN Package 'TrackReconstruction' (Reconstruct Animal Tracks from Magnetometer, Accelerometer,Depth and Optional Speed Data) Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data. Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals. Package: r-cran-tracktrap Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-daymetr, r-cran-degday, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-tracktrap_0.1.0-1.ca2604.1_all.deb Size: 20816 MD5sum: bfd09043f99b736c84dce87d62308e0d SHA1: 7511390bc753e67cb86b1aad5e872b0dcf4c6a21 SHA256: 05c16d405f1cd514cd210a5a371b25c426112656d4d71b7459f54b8aa51cc06e SHA512: 8603d9f0a0947be1fc6bc67b73e98aaff2b23bc58e3b5ff3cbbd9c89fba29c8963221dcb1113111e20dbde445b655925ea03520a6cea203bf22fe889bc841126 Homepage: https://cran.r-project.org/package=TrackTrap Description: CRAN Package 'TrackTrap' (Model Cumulative Growing Degree-Days for Pest Monitoring) Raw data from pest monitoring/traps can be correlated with environmental factors such as temperature, growing degree day etc. to get useful insights about the pest phenology. This package pulls temperature data from the California Irrigation Management Information System ('CIMIS', ) or the 'Daymet' application programming interface ('API', ) for a user-specified time period and calculates cumulative growing degree-days. Users provide pest development thresholds (lower and upper temperatures) and the geographic coordinates of the trap location to track emergence and phenology. Package: r-cran-tractor.base Architecture: all Version: 3.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ore, r-cran-rnifti, r-cran-reportr, r-cran-shades Suggests: r-cran-mmand, r-cran-loder, r-cran-divest, r-cran-yaml, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-tractor.base_3.5.0-1.ca2604.1_all.deb Size: 2899318 MD5sum: 426bfe8a4ee5a36d5cc7720683296893 SHA1: efb6c94fb30d7cdd25a23b2a6a65d5cfd85c80db SHA256: 204b6b55e1bcf583afb05743c368de5c7e851b36fd5cf6785d9a0108c5e43fc0 SHA512: 46229e0348298fca3865f7bfe74d2e17ef57fa22f223b78b6749e30a65ded5f274fdfd556343c8bf83c15e8baa0be2973d790635f146da954d857bbb45c15517 Homepage: https://cran.r-project.org/package=tractor.base Description: CRAN Package 'tractor.base' (Read, Manipulate and Visualise Magnetic Resonance Images) Functions for working with magnetic resonance images. Reading and writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH); interactive and non-interactive visualisation; flexible image manipulation; metadata and sparse image handling. Package: r-cran-tractortsbox Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate Suggests: r-cran-testthat, r-cran-renv, r-cran-fuzzr, r-cran-pkgdown, r-cran-devtools, r-cran-usethis, r-cran-covr, r-cran-withr, r-cran-altdoc Filename: pool/dists/resolute/main/r-cran-tractortsbox_0.1.1-1.ca2604.1_all.deb Size: 145266 MD5sum: e6167da6336b653a9a75fdfe7580b4e3 SHA1: 4aa2cb2b3d1d3b44941bc6848ca0ed7686b30d63 SHA256: 91cde90c1122523c6a60c539c37d99aca4f00ce8d0d1a3a0e73e97979134eefe SHA512: 746419c168c3dce0ce3e4f1dc134c6e706793e1b6d9725008ab5cf8a8dffcbc6a7d0a248d3a72ce3b0eab48d21d90ade1bb8b8c56142d56e01f79f2236107bf7 Homepage: https://cran.r-project.org/package=TractorTsbox Description: CRAN Package 'TractorTsbox' (Wrangle and Modify Ts Object with Classic Frequencies and ExactDates) The ts objects in R are managed using a very specific date format (in the form c(2022, 9) for September 2022 or c(2021, 2) for the second quarter of 2021, depending on the frequency, for example). We focus solely on monthly and quarterly series to manage the dates of ts objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic 'stats' functions for creating and managing time series (ts(), window()). Les objets ts en R sont gérés par un format de date très particulier (sous la forme c(2022, 9) pour septembre 2022 ou c(2021, 2) pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets ts. Lidée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. Cest un compromis entre simplicité, précision et utilisation des fonctions du package 'stats' de création et de gestion des séries temporelles (ts(), window()). Package: r-cran-trade Architecture: all Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2074 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-antitrust Suggests: r-cran-bb, r-cran-competitiontoolbox, r-cran-rmarkdown, r-cran-bookdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-trade_0.8.3-1.ca2604.1_all.deb Size: 887202 MD5sum: f25161e4501ede8b23afb78836b66927 SHA1: ff1c0f79135c320d919886a7d8d9a9c750608579 SHA256: 43bef6f15bb38005f8e44e577f1b7ce899269ac93fb008d417f1ad6600127339 SHA512: 2590d4ad6ede8378ed5067c53b562bf1fe9f9f3097ea3593b8711d229f84b6895e3e17b16f0afa608d43a194efc74286a3df4822b619fb8864598a1e0a1f7032 Homepage: https://cran.r-project.org/package=trade Description: CRAN Package 'trade' (Tools for Trade Practitioners) A collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) and Froeb et al. (2003) . 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This package aims at choosing the best model for a particular dataset, regarding its discriminant power and runtime. Package: r-cran-tradepolicy Architecture: all Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-fixest, r-cran-sandwich, r-cran-broom, r-cran-msm, r-cran-knitr, r-cran-formula Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-tradepolicy_0.7.0-1.ca2604.1_all.deb Size: 1325536 MD5sum: 76d5da1e5faba96ce135b224fcef236c SHA1: 1c0e70ee68eabce35001d720a0a3882807de0d8c SHA256: 98e683bbdacf5af85cf18df3a520fb48a413cbaf479430f543f14a3588f43f0d SHA512: 672154fc2e0f9a720b0c277b568852f4a9a74416a654967f965a1aaa76d2f579eb8be5e6adc4369abf95aada9f93efc837a1fb559f79c1238af0db06cf76caa1 Homepage: https://cran.r-project.org/package=tradepolicy Description: CRAN Package 'tradepolicy' (Replication of 'An Advanced Guide To Trade Policy Analysis') Datasets from Yotov, et al. (2016, ISBN:978-92-870-4367-2) "An Advanced Guide to Trade Policy Analysis" and functions to report regression summaries with clustered robust standard errors. Package: r-cran-trader Architecture: all Version: 1.2-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplr Filename: pool/dists/resolute/main/r-cran-trader_1.2-6-1.ca2604.1_all.deb Size: 219910 MD5sum: 33ce89b09dd84da73aebdbcc75176e64 SHA1: b7ca38edf263a6a13ceda49ba0de9f0656e7b1f7 SHA256: 9004324b2a3bfca4e4521c8fc113606b78773a26d806588726b7182e6ffcfe7d SHA512: 9f2828156a5c109e506b8733c489f35aec6a35a494e6025dbaffa8eb5bad1031ea42675474e0ec180f9a5b4a53c63ccec208badc0712baa336c2a2f3555a4f9a Homepage: https://cran.r-project.org/package=TRADER Description: CRAN Package 'TRADER' (Tree Ring Analysis of Disturbance Events in R) Tree Ring Analysis of Disturbance Events in R (TRADER) package provides functions for disturbance reconstruction from tree-ring data, e.g. boundary line, absolute increase, growth averaging methods. Package: r-cran-tradestatistics Architecture: all Version: 6.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-data.table, r-cran-digest, r-cran-jsonlite, r-cran-memoise Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-tradestatistics_6.0.0-1.ca2604.1_all.deb Size: 378904 MD5sum: 882f26932448746cb8e18048eb8597a6 SHA1: 4ea361a6db9adc25b0227971b4a68fac24a50b6c SHA256: 18d7def3946c15203af60625b2fb8b33a3b04e5462e6e18f138dc507d6fae4ba SHA512: edf385639483fa31877906d0125eb85a103f1fdbd100d99002f1a625fec5bb0fe29feb2413fcb5708576aebd8e7ae5a380dd174dbd22c9930787cb2e8362fa5f Homepage: https://cran.r-project.org/package=tradestatistics Description: CRAN Package 'tradestatistics' (Open Trade Statistics API Wrapper and Utility Program) Access 'Open Trade Statistics' API from R to download international trade data. Package: r-cran-trading Architecture: all Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-performanceanalytics, r-cran-data.table, r-cran-ggplot2, r-cran-readxl, r-cran-rcppalgos Filename: pool/dists/resolute/main/r-cran-trading_3.2-1.ca2604.1_all.deb Size: 661354 MD5sum: a1a666490871939da226ea3f63e30670 SHA1: d85a7b3022742e0f5da6c5d64e842728c52e995c SHA256: 96bcb521c2b359ad58d1b464192ade089bc01c0a432e8c3722058e355f4d263a SHA512: 1f8e10280d692e43d45ed7201996ca23aa52899d5b3e0cb65f96c86569295201bc43b2b88c7deae2d60c17e3ba0117f5b5368f090a1c6f6921d080fdf92028cc Homepage: https://cran.r-project.org/package=Trading Description: CRAN Package 'Trading' (Trade Objects, Advanced Correlation & Beta Estimates, BettingStrategies) Contains performance analysis metrics of track records including entropy-based correlation and dynamic beta based on a state/space algorithm. The normalized sample entropy method has been implemented which produces accurate entropy estimation even on smaller datasets. On a separate stream, trades from the five major assets classes and also functionality to use pricing curves, rating tables, Credit Support Annex and add-on tables. The implementation follows an object oriented logic whereby each trade inherits from more abstract classes while also the curves/tables are objects. Furthermore, odds calculators and P&L back-testing functionality has been implemented for the most widely used betting/trading strategies including martingale, 'DAlembert', 'Labouchere' and Fibonacci. Back testing has also been included for the 'EuroMillions', the 'EuroJackpot', the UK Lotto, the Set For Life and the UK 'ThunderBall' lotteries. Furthermore, some basic functionality about climate risk has been included. Package: r-cran-trafficbde Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-descriptivestats.obeu, r-cran-dplyr, r-cran-lubridate, r-cran-rcurl, r-cran-zoo Suggests: r-cran-devtools, r-cran-knitr, r-cran-neuralnet, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-trafficbde_0.1.2-1.ca2604.1_all.deb Size: 58642 MD5sum: a67300b78a3e88c6ae4a052b0baaea1a SHA1: 6306bc192427f3c990b0e5e69dabfe2035b75c31 SHA256: 3fa565f6500e8029b256a7aa9a46dbe8332013daf6e5d445005371fb71ba371c SHA512: 660dc470e231cefc2e864218eb253d53390bbb6c015f0a8b71f1787d51410dfe2185ff5bc169e5f579759e63d466c0693e8b23774a5e847e5c6fbadf4d80b660 Homepage: https://cran.r-project.org/package=TrafficBDE Description: CRAN Package 'TrafficBDE' (Traffic Predictions Using Neural Networks) Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot of the 'BigDataEurope' project. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date. Package: r-cran-trafficcar Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1966 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-igraph, r-cran-matrix, r-cran-units, r-cran-rlang, r-cran-posterior, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-osmdata, r-cran-lwgeom, r-cran-leaflet, r-cran-viridislite, r-cran-htmltools, r-cran-usethis, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-trafficcar_0.1.1-1.ca2604.1_all.deb Size: 621068 MD5sum: f31bafd87657b4bee008c505546867be SHA1: 275caa840cf13bb51214b14a21051cafb13480bc SHA256: 05f51db84e9aac2ad08855fa5fb67b949b1d7c113fda1d17e9712506bb8ac74e SHA512: f5e5cbe0cc7517126bdb6e8e3b0ed7e13331649281208273cb934cecc141ae52753304724df4ca1c2659627ec4cfc18cad9f48bf67439135349bb89dcbc33bd7 Homepage: https://cran.r-project.org/package=trafficCAR Description: CRAN Package 'trafficCAR' (Bayesian CAR Models for Road-Segment Traffic) Tools for simulating and modeling traffic flow on road networks using spatial conditional autoregressive (CAR) models. The package represents road systems as graphs derived from 'OpenStreetMap' data and supports network-based spatial dependence, basic preprocessing, and visualization for spatial traffic analysis. Package: r-cran-trainer Architecture: all Version: 2.2.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-neuralnet, r-cran-rpart, r-cran-xgboost, r-cran-randomforest, r-cran-e1071, r-cran-kknn, r-cran-dplyr, r-cran-mass, r-cran-nnet, r-cran-stringr, r-cran-rlang, r-cran-adabag, r-cran-glmnet, r-cran-rocr, r-cran-gbm, r-cran-ggplot2 Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-trainer_2.2.12-1.ca2604.1_all.deb Size: 199402 MD5sum: 8e68b1a486818d43eee20617b6102de6 SHA1: d8e44fadaaa1c2edfc461b25e2e76a17153575ec SHA256: 9ca14cde47c13626157c183a3d393748b3fe0b31cf4860877c3c3ce9816501ed SHA512: 7538a941c7e8db5253892b70e0c6752d0b41be80d87e38b2f6d79f118c97cdf6878846189def4489f7d6ae788124439175affdb69999236ee3fbd95d474b6d38 Homepage: https://cran.r-project.org/package=traineR Description: CRAN Package 'traineR' (Predictive (Classification and Regression) Models Homologator) Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes methods such as K-Nearest Neighbors Schliep, K. P. (2004) , Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) , ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) , Extreme Gradient Boosting Chen & Guestrin (2016) , Random Forest Breiman (2001) , Neural Networks Venables, W. N., & Ripley, B. D. (2002) , Support Vector Machines Bennett, K. P. & Campbell, C. (2000) , Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) , Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) , Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) , Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) . Package: r-cran-trainr Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcurl, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-usethis, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-trainr_0.0.1-1.ca2604.1_all.deb Size: 84970 MD5sum: a99b46fd7d56432350152466bbc83544 SHA1: da3d7f79b7a5fec445a1a8bbc4d373f8480bca5f SHA256: 09fe18a6f15e6cb577aeabc7fbaf55037aeb0baed40c6d245a8f75c836d86083 SHA512: 15819c0ec1c2ad9daf1232c07a01f6bb0d62c9832e768dbc688f4a996032ff9031c87d18c83090a5dfb860c1cbc3707539b0af74749cf938f394414e93357d3c Homepage: https://cran.r-project.org/package=trainR Description: CRAN Package 'trainR' (An Interface to the National Rail Enquiries Systems) The goal of 'trainR' is to provide a simple interface to the National Rail Enquiries (NRE) systems. There are few data feeds available, the simplest of them is Darwin, which provides real-time arrival and departure predictions, platform numbers, delay estimates, schedule changes and cancellations. Other data feeds provide historical data, Historic Service Performance (HSP), and much more. 'trainR' simplifies the data retrieval, so that the users can focus on their analyses. For more details visit . 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Package: r-cran-trajr Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal, r-cran-plotrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-readr, r-cran-sp, r-cran-mass, r-cran-covr Filename: pool/dists/resolute/main/r-cran-trajr_1.5.1-1.ca2604.1_all.deb Size: 691768 MD5sum: fe19b285e5a5e7a6b2bfe503cb3e700a SHA1: ffe01a3b06d0981878c6e381b9f964734e820287 SHA256: 45b91d6710ea5f6cad6bbf4ac0a7992d21b6d73b9130b1b08fff399372b17dd8 SHA512: 2eafd21b01e08264213c98a7a8a1dec1f3f96b4f838b22b34d05843f82240b9b41f84708e8a248c7f742d90b76e4d2518783e0123db08fb41e973ec11ff5afec Homepage: https://cran.r-project.org/package=trajr Description: CRAN Package 'trajr' (Animal Trajectory Analysis) A toolbox to assist with statistical analysis of animal trajectories. 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Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, ). The underlying theory is described in Hothorn et al. (2018) . An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, ) and a tutorial explains applications in survival analysis (Siegfried et al., 2025, ). Multivariate conditional transformation models (Klein et al, 2022, ) and shift-scale transformation models (Siegfried et al, 2023, ) can be fitted as well. The package contains an implementation of a doubly robust score test, described in Kook et al. (2024, ). Package: r-cran-tramicp Architecture: all Version: 0.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tram, r-cran-mlt, r-cran-coin, r-cran-multcomp, r-cran-survival, r-cran-variables, r-cran-basefun, r-cran-mass, r-cran-cotram, r-cran-dhsic, r-cran-ranger, r-cran-sandwich Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tramicp_0.1-0-1.ca2604.1_all.deb Size: 121514 MD5sum: 2a2015e3df26cc1ddd3b40bd4025f668 SHA1: a30389e0ff48182ce0d05708003264b170c8a28e SHA256: 4f821d2dade3bf2de427ea791560aa1d950146bfc5769c462ab2edc183edc62e SHA512: 134f01bfe3f677c08cb22528924d8ae5d03319d8bc5970df0ddb4df16739a7353891d31f6b62ea1618ee64c8edcd123a1879edd788860a04df55ca3bb220b8b2 Homepage: https://cran.r-project.org/package=tramicp Description: CRAN Package 'tramicp' (Model-Based Causal Feature Selection for General Response Types) Extends invariant causal prediction (Peters et al., 2016, ) to generalized linear and transformation models (Hothorn et al., 2018, ). 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Available models include a fully parametric version of the Cox model, other parametric survival models (Weibull, etc.), models for binary and ordered categorical variables, normal and transformed-normal (Box-Cox type) linear models, and continuous outcome logistic regression. Hyperparameter tuning is facilitated through model-based optimization functionalities from package 'mlrMBO'. The accompanying vignette describes the methodology used in 'tramnet' in detail. Transformation models and model-based optimization are described in Hothorn et al. (2019) and Bischl et al. (2016) , respectively. 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Applicable to models from packages 'tram' and 'cotram'. Application to shift-scale transformation models are described in Siegfried et al. (2024, ). Package: r-cran-transda Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-transda_1.0.2-1.ca2604.1_all.deb Size: 70246 MD5sum: cc5678929a6b24d64d92a59098673234 SHA1: 76e20c09765349e3d3a5959d7dd991ece5e59bc9 SHA256: 307dcf4f6a2a4b934286309416488519c23b44c984fab2dc76cb7de3cdf259ca SHA512: 5cbd9950800f7b495c0ff38e44ab186ee44a8eb8b15aa35c7daf380d82aafd5a26f4df4c4a3510fb284db0e04533450db2c24df80fff1307813f6b0a054382be Homepage: https://cran.r-project.org/package=transDA Description: CRAN Package 'transDA' (Transformation Discriminant Analysis) Performs transformation discrimination analysis and non-transformation discrimination analysis. 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The default text pipeline is Cross-Encoder's DistilRoBERTa and default image/video pipeline is Open AI's CLIP . All other zero-shot classification model pipelines can be implemented using their model name from . 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The package provides a method for estimating these parameters using an estimator defined by a corresponding Stochastic Differential Equation (SDE) system driven by cumulative hazard estimates. By providing cumulative hazard estimates as input, the package gives estimates of the parameter as output, along with pointwise (co)variances derived from an asymptotic expression. Examples of parameters that can be targeted in this way include the survival function, the restricted mean survival function, cumulative incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018) , and further applications in Stensrud, Røysland, and Ryalen (2019) and Ryalen et al. (2021) . Package: r-cran-transform Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-transform_1.0-1.ca2604.1_all.deb Size: 105272 MD5sum: 2ef8150b0e8454efa63267dbfc7574b2 SHA1: 4d43c75044fa965e66bbe136b5e73c5e327d0033 SHA256: 307863419bebe4bd4e1b06861d3ab3a6893f201be9db24dc5f64491983cb9d49 SHA512: e951c0d17058cd752685ffb071c29ee41a24d3dc61c9f8cecc6502446dfc07a0254df838beb915a56ea5e1d7ba574fcd01d725804e831356c42a0afc5f04876f Homepage: https://cran.r-project.org/package=Transform Description: CRAN Package 'Transform' (Statistical Transformations) Performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) , Glog (Durbin et al., 2002) , Neglog (Whittaker et al., 2005) , Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) , Bickel-Docksum (Bickel and Doksum, 1981) , Yeo-Johnson (Yeo and Johnson, 2000) , Square Root (Medina et al., 2019), Manly (Manly, 1976) , Modulus (John and Draper, 1980) , Dual (Yang, 2006) , Gpower (Kelmansky et al., 2013) . It also performs graphical approaches, assesses the success of the transformation via tests and plots. 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Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References: Nayak et al. (2024) and Nayak et al. (2024) . 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Transport profiles involving up to three species are produced as publication-ready plots and several membrane performance parameters (e.g. separation factors as defined in Koros et al. (1996) and non-linear regression parameters for the equations described in Rodriguez de San Miguel et al. (2014) ) can be obtained. Many widely used experimental setups (e.g. membrane physical aging) can be easily studied through the package's graphical representations. 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Package: r-cran-transplantr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-transplantr_0.2.0-1.ca2604.1_all.deb Size: 207726 MD5sum: 96b58ff0e3f12bee0359ceff5afcac4b SHA1: 09f30e297d2d88051dcb7839ca4cffce335e2a97 SHA256: 6fae45b9c37b81f460dfd89c9df0ca2524188789632144bcb34c7ef0bd56fdf4 SHA512: 4cf586de0c5759500febd2206b1088062ac5b645d20af0eb2327b30313b906b146fd67c2ee9c26b4d754cce098869693e965ab77e7bddab62839a44baf095c09 Homepage: https://cran.r-project.org/package=transplantr Description: CRAN Package 'transplantr' (Audit and Research Functions for Transplantation) A set of vectorised functions to calculate medical equations used in transplantation, focused mainly on transplantation of abdominal organs. These functions include donor and recipient risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. All functions are designed to work with either US or international units. References for the equations are provided in the vignettes and function documentation. 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'traumar' provides robust functions driven by the academic literature to automate the calculation of relevant metrics to individuals desiring to measure the performance of their trauma center or even a trauma system. 'traumar' also provides some helper functions for the data analysis journey. Users can refer to the following publications for descriptions of the methods used in 'traumar'. TRISS methodology, including probability of survival, and the W, M, and Z Scores - Flora (1978) , Boyd et al. (1987, PMID:3106646), Llullaku et al. (2009) , Singh et al. (2011) , Baker et al. (1974, PMID:4814394), and Champion et al. (1989) . For the Relative Mortality Metric, see Napoli et al. (2017) , Schroeder et al. (2019) , and Kassar et al. (2016) . For more information about methods to calculate over- and under-triage in trauma hospital populations and samples, please see the following publications - Peng & Xiang (2016) , Beam et al. (2022) , Roden-Foreman et al. (2017) . 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Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) for further details. Package: r-cran-tredesigns Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-tredesigns_1.0.1-1.ca2604.1_all.deb Size: 41968 MD5sum: 70968e55c95ea0862b901c251948402e SHA1: f605119fc4ea2c0f964e33a02d822e36dbecd1a7 SHA256: 68a8142ed2401dd3ae9f6b5e2a6e9480821550086a8bbdb1c8561f22ecf873e6 SHA512: 256b7e7e7153b0fa064f374275cc1869bb06a4cf7168a590a83e9ac64dcb84c3f67f780589d3930098e804cc004d4ec8950ef2261e40fc2b5b2299d6bf5b46df Homepage: https://cran.r-project.org/package=TREDesigns Description: CRAN Package 'TREDesigns' (Ternary Residual Effect Designs) There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design. Package: r-cran-tree3d Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rayvertex Filename: pool/dists/resolute/main/r-cran-tree3d_0.1.2-1.ca2604.1_all.deb Size: 383488 MD5sum: 4d17690fd7bdf4be66b15d973fe297c2 SHA1: 402075d8dd1bc1824b11b9d55c39bb0e082336c9 SHA256: 60c943d641afbf2278261f61f12880a458f1749d01b06d0d2c383c4fc07edd4d SHA512: d161cc3d70030005135a81783cc6c1dd895eccccd9656ae964ee9d372ab977393145a119ac84df11fcada195a3385214433481eb2e4c9e8606d808169baa9097 Homepage: https://cran.r-project.org/package=tree3d Description: CRAN Package 'tree3d' (3D Tree Models) Provides customizable 3D tree models (as 'OBJ' files) for use in data visualization. Includes both planar and solid tree models, various crown types (columnar, oval, palm, pyramidal, rounded, spreading, vase, weeping), and options to change the diameter, height, and color of the tree's crown and trunk. Package: r-cran-treebalance Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-memoise, r-cran-gmp Filename: pool/dists/resolute/main/r-cran-treebalance_1.2.0-1.ca2604.1_all.deb Size: 279102 MD5sum: 48816ba955f7701253c178b54e144730 SHA1: e0645be229557d5a0c2c89caa00b3b971fa50c06 SHA256: f1a75a7afea5c982492e821d631964fa022125d1498f5427023e06890a8db318 SHA512: 63b98b88a05bcd3239d945764d7d20338011f4ba15f8a345813e1ba2f55311f056c63a892da90ec9b5a2bd5c5131cfe0aca0346f233971ea0a257dd661db756d Homepage: https://cran.r-project.org/package=treebalance Description: CRAN Package 'treebalance' (Computation of Tree (Im)Balance Indices) The aim of the 'R' package 'treebalance' is to provide functions for the computation of a large variety of (im)balance indices for rooted trees. The package accompanies the book ''Tree balance indices: a comprehensive survey'' by M. Fischer, L. Herbst, S. Kersting, L. Kuehn and K. Wicke (2023) , , which gives a precise definition for the terms 'balance index' and 'imbalance index' (Chapter 4) and provides an overview of the terminology in this manual (Chapter 2). For further information on (im)balance indices, see also Fischer et al. (2021) . Considering both established and new (im)balance indices, 'treebalance' provides (among others) functions for calculating the following 18 established indices and index families: the average leaf depth, the B1 and B2 index, the Colijn-Plazzotta rank, the normal, corrected, quadratic and equal weights Colless index, the family of Colless-like indices, the family of I-based indices, the Rogers J index, the Furnas rank, the rooted quartet index, the s-shape statistic, the Sackin index, the symmetry nodes index, the total cophenetic index and the variance of leaf depths. Additionally, we include 9 tree shape statistics that satisfy the definition of an (im)balance index but have not been thoroughly analyzed in terms of tree balance in the literature yet. These are: the total internal path length, the total path length, the average vertex depth, the maximum width, the modified maximum difference in widths, the maximum depth, the maximum width over maximum depth, the stairs1 and the stairs2 index. As input, most functions of 'treebalance' require a rooted (phylogenetic) tree in 'phylo' format (as introduced in 'ape' 1.9 in November 2006). 'phylo' is used to store (phylogenetic) trees with no vertices of out-degree one. For further information on the format we kindly refer the reader to E. Paradis (2012) . Package: r-cran-treebase Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-xml, r-cran-rcurl, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-treebase_0.1.5-1.ca2604.1_all.deb Size: 4261238 MD5sum: 376531af3565ac4db50d3f7f50fa0a11 SHA1: 659cd13caf5d713122ad09db66b564214c7e685d SHA256: f4a8f4a3ee5acde808630915cc0dc3ce686da82ac54ba64b35409642e577a724 SHA512: bed379f245cadbbb95df419ff3c289f4fb46fcbbd1ab628ebad9d581c3590b120c363398daf1bdbc4d9e2d2784cc0466440782f93ac3f4e6b7e1c9b4bb90cd30 Homepage: https://cran.r-project.org/package=treebase Description: CRAN Package 'treebase' (Discovery, Access and Manipulation of 'TreeBASE' Phylogenies) Interface to the API for 'TreeBASE' from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic trees (of species, population, or genes) and the data used to create them. Package: r-cran-treeclust Architecture: all Version: 1.1-7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart, r-cran-cluster Filename: pool/dists/resolute/main/r-cran-treeclust_1.1-7.1-1.ca2604.1_all.deb Size: 76522 MD5sum: bca5b47d608b3976504f4c0e2bc0b348 SHA1: 7d20d48c160fec265ed5fcadc8443cc86bf79639 SHA256: ff5a72f84d03d13e869e95eed48b1612a5989cf15c2665deab048f11de476084 SHA512: 7534b7157bf78bfd5ce3d9a9b6c079052ab070458f371360480340419bfaa2de18e41ef56ff31a384ed36c4d860aff4555879af06634c90b3bed6b5069cdfceb Homepage: https://cran.r-project.org/package=treeClust Description: CRAN Package 'treeClust' (Cluster Distances Through Trees) Create a measure of inter-point dissimilarity useful for clustering mixed data, and, optionally, perform the clustering. Package: r-cran-treeda Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparselda, r-cran-matrix, r-cran-mvtnorm, r-cran-reshape2, r-cran-gtable, r-bioc-phyloseq, r-cran-ggplot2, r-cran-ape Suggests: r-cran-adaptivegpca, r-cran-knitr, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-treeda_0.0.5-1.ca2604.1_all.deb Size: 608900 MD5sum: 823651b813ad8bd851d268a986d366a8 SHA1: fd91b2e89670aaf530280e437ad6ab86bddb57b5 SHA256: 48207f7209cb57f2f135fa2ad600e85cadb6a6f69f750ea875dabe292b1eaac3 SHA512: ef72cfb6cc53f777ba2c94f64489999cbce24cace0914f7eead77f672945b8f26ee087ec72abbc2f270c04c5fd8bc7947a85b30ca9c950ff326c1bd8c9bac7fa Homepage: https://cran.r-project.org/package=treeDA Description: CRAN Package 'treeDA' (Tree-Based Discriminant Analysis) Performs sparse discriminant analysis on a combination of node and leaf predictors when the predictor variables are structured according to a tree, as described in Fukuyama et al. (2017) . Package: r-cran-treedata.table Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-lazyeval, r-cran-geiger, r-cran-data.table Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-utf8 Filename: pool/dists/resolute/main/r-cran-treedata.table_0.1.1-1.ca2604.1_all.deb Size: 193026 MD5sum: 524c1887c5aedd533fad3612e8bb0a6a SHA1: 66118d0d4a2d6aea14f92816ef3cc38c716b40a3 SHA256: eda1c46d95ea01446e83a8d319bd101eb8e79e7126a22ea196da065d6f28ebda SHA512: 21924bdfeebde3b5e6832ae4a76da03cb69f40b391cede4ca3469ac4e15645b443990b3ebd2b7a5d1faff0b21495d236a465e2c35303b00d25eafbca058373ee Homepage: https://cran.r-project.org/package=treedata.table Description: CRAN Package 'treedata.table' (Manipulation of Matched Phylogenies and Data using 'data.table') An implementation that combines trait data and a phylogenetic tree (or trees) into a single object of class 'treedata.table'. The resulting object can be easily manipulated to simultaneously change the trait- and tree-level sampling. Currently implemented functions allow users to use a 'data.table' syntax when performing operations on the trait dataset within the 'treedata.table' object. For more details see Roman-Palacios et al. (2021) . Package: r-cran-treedater Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-limsolve Suggests: r-cran-lubridate, r-cran-ggplot2, r-cran-foreach, r-cran-iterators, r-cran-mgcv, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-treedater_1.0.2-1.ca2604.1_all.deb Size: 289830 MD5sum: f187f32846034c7929ac45c49dfa5082 SHA1: c0fd3e6ce4367bd6b72a639cc50afc48e33240dc SHA256: 106d90b37751b8f9f0b4727ebd91237b0951cdde4d906cf6c1ed22c21fae8c94 SHA512: a4ee99c607a224e6ded76594310e85c7769526d9f2d0fd0632a5d62a59c7dcc5da12d4f1dcf58ca484d1d79ff7e465f292e9a3e606f72ffe769d7f06cd20de16 Homepage: https://cran.r-project.org/package=treedater Description: CRAN Package 'treedater' (Fast Molecular Clock Dating of Phylogenetic Trees with RateVariation) Functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models, parametric and nonparametric bootstrap confidence intervals, methods for detecting outlier lineages, root-to-tip regression, and a statistical test for selecting molecular clock models. For more details see Volz and Frost (2017) . Package: r-cran-treedbalance Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-r.matlab Filename: pool/dists/resolute/main/r-cran-treedbalance_1.2.0-1.ca2604.1_all.deb Size: 244756 MD5sum: c40bfe9bd8bad6010827b015a872840b SHA1: 5b04b7cc4922a2666733f9f799191d2fb6868f99 SHA256: 7f4961c6e60a18a37c6123d67cfff4e9195cc6db41b7d81103e00073a91bf421 SHA512: 9e6cfd72ed7646ed41664fa0ed486507cc49088f6f336c086f064dc23154cc3d14091dd4691a67a1f8d4e7dab5078b139e9f6899daf2bfdabcedea3f0c9cf2a9 Homepage: https://cran.r-project.org/package=treeDbalance Description: CRAN Package 'treeDbalance' (Computation of 3D Tree Imbalance) The main goal of the R package 'treeDbalance' is to provide functions for the computation of several measurements of 3D node imbalance and their respective 3D tree imbalance indices, as well as to introduce the new 'phylo3D' format for rooted 3D tree objects. Moreover, it encompasses an example dataset of 3D models of 63 beans in 'phylo3D' format. Please note that this R package was developed alongside the project described in the manuscript 'Measuring 3D tree imbalance of plant models using graph-theoretical approaches' by M. Fischer, S. Kersting, and L. Kühn (2023) , which provides precise mathematical definitions of the measurements. Furthermore, the package contains several helpful functions, for example, some auxiliary functions for computing the ancestors, descendants, and depths of the nodes, which ensures that the computations can be done in linear time, or functions that convert existing formats of 3D tree models of other software into the 'phylo3D' format. Moreover, it comprises functions to extract the graph-theoretical topology without vertices of in- and out-degree 1 of rooted 3D trees as well as to adapt node enumerations to the common 'phylo' format. Most functions of 'treeDbalance' require as input a rooted tree in the 'phylo3D' format, an extended 'phylo' format (as introduced in the R package 'ape' 1.9 in November 2006). Such a 'phylo3D' object must have at least two new attributes next to those required by the 'phylo' format: 'node.coord', the coordinates of the nodes, as well as 'edge.weight', the literal weight or volume of the edges. Optional attributes are 'edge.diam', the diameter of the edges, and 'edge.length', the length of the edges. For visualization purposes one can also specify 'edge.type', which ranges from normal cylinder to bud to leaf, as well as 'edge.color' to change the color of the edge depiction. This project was supported by the joint research project DIG-IT! funded by the European Social Fund (ESF), reference: ESF/14-BM-A55-0017/19, and the Ministry of Education, Science and Culture of Mecklenburg-Western Pomerania, Germany, as well as by the project ArtIGROW, which is a part of the WIR!-Alliance 'ArtIFARM – Artificial Intelligence in Farming' funded by the German Federal Ministry of Education and Research (FKZ: 03WIR4805). Package: r-cran-treedep Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-treedep_0.1.3-1.ca2604.1_all.deb Size: 282862 MD5sum: 5d128546d42d35b1359ebea43a0898dc SHA1: 2661178ca4aedc841152cc9d93f0fb9035ac3ead SHA256: 926d96f9078886d2cde680b12d235dac184bf989d2453070b43e5fc9b39d0ac1 SHA512: fbd8fd5f810ea0922fc341950d88c669a2fb242fc65f21dcf3367de4542266a723e3b9706a3c026fa0551df5a7afccb442c16ad35e27a09676e269869bbddced Homepage: https://cran.r-project.org/package=TreeDep Description: CRAN Package 'TreeDep' (Air Pollution Removal by Dry Deposition on Trees) The model estimates air pollution removal by dry deposition on trees. It also estimates or uses hourly values for aerodynamic resistance, boundary layer resistance, canopy resistance, stomatal resistance, cuticular resistance, mesophyll resistance, soil resistance, friction velocity and deposition velocity. It also allows plotting graphical results for a specific time period. The pollutants are nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide and particulate matter. Baldocchi D (1994) . Farquhar GD, von Caemmerer S, Berry JA (1980) Planta 149: 78-90. Hirabayashi S, Kroll CN, Nowak DJ (2015) i-Tree Eco Dry Deposition Model. Nowak DJ, Crane DE, Stevens JC (2006) . US EPA (1999) PCRAMMET User's Guide. EPA-454/B-96-001. Weiss A, Norman JM (1985) Agricultural and Forest Meteorology 34: 205—213. Package: r-cran-treediagram Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ape, r-cran-cowplot, r-cran-tree, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-treediagram_0.1.1-1.ca2604.1_all.deb Size: 52342 MD5sum: 1063e6ff5462d7db29d6ea612c3feaec SHA1: 2920468acd45ed70e0c21509716e263565b9a80e SHA256: 3e1ec476c814e4228bfc925ec7f3669483a36bdc46ef2d2361d454bb3366c126 SHA512: f42f8d097f925504b7e8fddbc6e3710aefd40d32c012404cf01656139be0c79430ba7ea6b8a18c15eebec33f7260c7063f78a4582bfad48448d74e332c96fc14 Homepage: https://cran.r-project.org/package=TreeDiagram Description: CRAN Package 'TreeDiagram' (Tree Diagram) Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process). Package: r-cran-treediff Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adjclust, r-bioc-biocgenerics, r-bioc-csaw, r-cran-data.table, r-cran-dplyr, r-bioc-interactionset, r-bioc-limma, r-bioc-summarizedexperiment, r-cran-reshape2, r-cran-testthat, r-cran-rlang, r-bioc-hicdoc, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-treediff_0.2.2-1.ca2604.1_all.deb Size: 94596 MD5sum: 96f36f07c8e2173bdebf6cea93fabae2 SHA1: 5e461a6759ebf03ab7e0c0ca1ee35b2d88b94f09 SHA256: 0fc1cfeddfb82d443a9946ce3f595c689ead1be2d75b32d2cb7fac09f73e4b55 SHA512: cab1b9ee115b6e075bac6cf2b5db2a3fa0b02867f781ec10c77900163c3fe0ff95ccd45d08c45030407cc309048240088b3822305f008921de0f2d60cfbee20e Homepage: https://cran.r-project.org/package=treediff Description: CRAN Package 'treediff' (Testing Differences Between Families of Trees) Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests. Package: r-cran-treefit Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-patchwork, r-cran-pracma Suggests: r-cran-seurat, r-cran-gridextra, r-cran-knitr, r-cran-plotly, r-cran-qpdf, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-treefit_1.0.3-1.ca2604.1_all.deb Size: 161090 MD5sum: ae46235c5a4a7a46dc7316fa4742bb31 SHA1: 20241d4e8936bf82bf3a83f371b27c0ad8dabed3 SHA256: a437748efb1396e4957cda284e993e20662350b1c0e94106ee18a5aab3f4210b SHA512: 4b19e1df40d6883003b4297cf21a5df970fea96c8317bde77cda8665b5a4a5cd7d11cb63aae1015488675d030777383c4229d38c8be491205a931e8a9e33d6a9 Homepage: https://cran.r-project.org/package=treefit Description: CRAN Package 'treefit' (The First Software for Quantitative Trajectory Inference) Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data. Package: r-cran-treeheatr Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-dplyr, r-cran-ggnewscale, r-cran-ggparty, r-cran-ggplot2, r-cran-gtable, r-cran-partykit, r-cran-seriation, r-cran-tidyr, r-cran-yardstick Suggests: r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-treeheatr_0.2.3-1.ca2604.1_all.deb Size: 1838070 MD5sum: 22262c286a4e75868cfe3561df4a54f1 SHA1: 438fddce40904a1db087fa8a027441c9e3367d27 SHA256: 378be4bfc5850bd6838d05d3364772fea544b892321f1984bea59e1981c7e2d8 SHA512: 8b23834f09180b6e6b3d0d1effe648090bd922a93bd9b8c8685d8db6e620a38b3f98ee7f54c2207b0d2d3d10b68b033f0d578f4bc4305cdb8abec765b2f35b75 Homepage: https://cran.r-project.org/package=treeheatr Description: CRAN Package 'treeheatr' (Heatmap-Integrated Decision Tree Visualizations) Creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. 'treeheatr' utilizes the customizable 'ggparty' package for drawing decision trees. 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Package: r-cran-treeminer Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-future, r-cran-future.apply, r-cran-cli Suggests: r-cran-testthat, r-cran-tidyr, r-cran-comorbidity, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-treeminer_1.0.4-1.ca2604.1_all.deb Size: 681348 MD5sum: 08d81fa373f83d3f6cf7096357a413f1 SHA1: 29bb1e10b2fe9105ad07e8f7d35d461eeafac181 SHA256: 1a9d91fd3db25223a062c7c075da4d320341743b090044fb271119a794a9dd82 SHA512: 39446d98ea3814cbdea09ede6f4c50adb57b614f54301e8283e8db920304bb271a080760ac8be163ef974131eaaad790cd34552e445df59c05643e289a1e5592 Homepage: https://cran.r-project.org/package=TreeMineR Description: CRAN Package 'TreeMineR' (Tree-Based Scan Statistics) Implementation of unconditional Bernoulli Scan Statistic developed by Kulldorff et al. (2003) for hierarchical tree structures. 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Package: r-cran-treeordertests Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-treeordertests_0.1.0-1.ca2604.1_all.deb Size: 27586 MD5sum: 26b40fae4749298ffef570f13c43198e SHA1: e318fe69968c505bb9c90f510008ab13939b25d5 SHA256: 062a21bc695dff1c300158dfe90af891c9667faca8d5cb2007cc74b872d395bb SHA512: 0ac87bfc0405325ad10a0bc099c757ebaa7755e604d37e6cd7947c7972a14060ba22484ac14fbce40237974d6040eb050480d1e76a30913111a2464b43c6abd1 Homepage: https://cran.r-project.org/package=TreeOrderTests Description: CRAN Package 'TreeOrderTests' (Tests for Tree Ordered Alternatives in One-Way ANOVA) Implements a likelihood ratio test and two pairwise standardized mean difference tests for testing equality of means against tree ordered alternatives in one-way ANOVA. The null hypothesis assumes all group means are equal, while the alternative assumes the control mean is less than or equal to each treatment mean with at least one strict inequality. Inputs are a list of numeric vectors (groups) and a significance level; outputs include the test statistic, critical value, and decision. Methods described in "Testing Against Tree Ordered Alternatives in One-way ANOVA" . Package: r-cran-treeplotarea Architecture: all Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fritools, r-cran-sf Suggests: r-cran-checkmate, r-cran-pkgload, r-cran-plotrix, r-cran-rmarkdown, r-cran-rprojroot, r-cran-runit, r-cran-testthat, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-treeplotarea_3.1.0-1.ca2604.1_all.deb Size: 1060640 MD5sum: 10931649a34779be594294746a1b9805 SHA1: f947920ca7d928deb89fa6ec20acaac57d8952a1 SHA256: 0681994395fb87c121227a83cfad79660b86ea57a4f2ca69e97f0e6706199dad SHA512: eeb92ebfd5d4284051db4804a9bb18a6faaedef703385ddfbe20d948654e994a34123fac4112a3559d0313ce669ac0b141295c355757aa1a21608fdbbbaea783 Homepage: https://cran.r-project.org/package=treePlotArea Description: CRAN Package 'treePlotArea' (Correction Factors for Tree Plot Areas Intersected by StandBoundaries) The German national forest inventory uses angle count sampling, a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung. Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 vom Juni 1947` and extended by Grosenbaugh () as probability proportional to size sampling. When plots are located near stand boundaries, their sizes and hence their probabilities need to be corrected. Package: r-cran-treeringshape Architecture: all Version: 3.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-waldo Filename: pool/dists/resolute/main/r-cran-treeringshape_3.0.5-1.ca2604.1_all.deb Size: 909674 MD5sum: 02b9846166ff097330bea59166de5ec6 SHA1: de64d91d31d9d2ef8d9d41a57d46634919af071a SHA256: bcbfe7831ea2e3514c1d8207989721887804ed15988fe1c1313ff8a5c160b94a SHA512: 85afae555c398e46b0b23caf90998b89095bdcc2cf7f2936e7a0f6bf19d70d01f0c7ab3718697fefb9fe87da24963ecfd9eb8913fb25a40fabfcbe118b84610d Homepage: https://cran.r-project.org/package=TreeRingShape Description: CRAN Package 'TreeRingShape' (Recording Tree-Ring Shapes of Tree Disks with Manual Digitizingand Interpolating Model) Record all tree-ring Shapefile of tree disk with GIS soft 'Qgis' and interpolating model from high resolution tree disk image. Package: r-cran-treesim Architecture: all Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-geiger Filename: pool/dists/resolute/main/r-cran-treesim_2.4-1.ca2604.1_all.deb Size: 172710 MD5sum: 2d708eed41c17fdad8d6104af6c6d821 SHA1: 4708f97c149f9706d5104f118dc6a1604c3c3d8e SHA256: 06ab5b6aaa3d420a20cf944cb39bad476ba8e2efdfaf3052cd9f8fc5ade94d51 SHA512: 4c728b2af67755f23b1c73606a44a3d190c9aca78370e14be5691fdd582c2d317c720262b9f7056cf005b81d3e5afecec11ef76052b3ec4d0efa7050753d7f04 Homepage: https://cran.r-project.org/package=TreeSim Description: CRAN Package 'TreeSim' (Simulating Phylogenetic Trees) Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees. Package: r-cran-treesimgm Architecture: all Version: 2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-treesim, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-treesimgm_2.5-1.ca2604.1_all.deb Size: 154824 MD5sum: 5cf03567598eb6e738c93074dca0757c SHA1: 7b240f9744f92cde9a2039d694271116482071ff SHA256: 2681bdcaca9fc89a46c563eb70439a2c31a5f1802ea71f0b6f84837ad30ec866 SHA512: 20ed6d2905e7a4246f6e236858f6f6a0d336eb5b9b5531bb377538c8ec1dfdc227b48cfe085f3b1d2df298e01686c27bb872290a1087750d3abc5b8d6eee1dfb Homepage: https://cran.r-project.org/package=TreeSimGM Description: CRAN Package 'TreeSimGM' (Simulating Phylogenetic Trees under General Bellman Harris andLineage Shift Model) Provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist. Reference: O. Hagen and T. Stadler (2017). TreeSimGM: Simulating phylogenetic trees under general Bellman Harris models with lineage-specific shifts of speciation and extinction in R. Methods in Ecology and Evolution. . Package: r-cran-treeslicer Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-sf Suggests: r-cran-devtools, r-cran-ggpubr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-treeslicer_1.1.0-1.ca2604.1_all.deb Size: 2626600 MD5sum: d6a243d46582621df152f9d317284412 SHA1: 091264eeb975820c8dadb15bee814389a2843735 SHA256: 78cd21d53d569ea962e231081222018400cf8a1b591b51c799742f1f964a972a SHA512: 9cc29f9c31852d0b7177b6203f39f39a796ff46cad220946e7afb02fd64da7dda9004fee97e4a1cc281655694d20ea77eba11f7ca5b698d12ac9c94ce792aad7 Homepage: https://cran.r-project.org/package=treesliceR Description: CRAN Package 'treesliceR' (To Slice Phylogenetic Trees and Infer Evolutionary Patterns OverTime) Provide a range of functions with multiple criteria for cutting phylogenetic trees at any evolutionary depth. 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This package provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS. High-level functions are provided for creating displays from within 'tidyverse' or 'ggplot2' workflows. Low-level functions are also provided for creating new interfaces. Package: r-cran-tremendousr Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-crul, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-clipr, r-cran-vcr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tremendousr_1.0.0-1.ca2604.1_all.deb Size: 188540 MD5sum: 2fa3e0b57174cced5f81daefa93d66c7 SHA1: c56c43a8f204a4ecffd490cb35af178ba904daa5 SHA256: d2b0dcbbec2de9c4c081da6c333135c72dcbe33806db5cf7f3510c8da71e66b8 SHA512: 507f867675bc447313379f6d0be625e338be93f54aa95ec52caf9136a3d7e66a03542b0d107c41e03a4c47a66d9f1eb1ea3cae906931b07adf8d2995b4961c91 Homepage: https://cran.r-project.org/package=tremendousr Description: CRAN Package 'tremendousr' (Easily Send Rewards and Incentives with 'Tremendous' from R) A slightly-opinionated R interface for the 'Tremendous' API (). In addition to supporting GET and POST requests, 'tremendousr' has, dare I say, tremendously intuitive functions for sending digital rewards and incentives directly from R. Package: r-cran-trenchr Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-msm, r-cran-rdpack, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-trenchr_1.2.1-1.ca2604.1_all.deb Size: 2642248 MD5sum: a33b337298beb0bfb1d7c41816a7a5bf SHA1: 4ee73311772d00b3de12f3e00c2eda9fd38e39f9 SHA256: 6ff56c6c2f60647d6a54dec3a82cc30a5116b145c6e23ffcfa70a32aa4872423 SHA512: 86a9714bac2c009671245aef55c6927365a8ba72cda989c9a5ef478e96b8fb26254b43eeab21e21045fbe2b29d0a8a9beaead36cfbf00fc1cd32935b2cac24db Homepage: https://cran.r-project.org/package=TrenchR Description: CRAN Package 'TrenchR' (Tools for Microclimate and Biophysical Ecology) Tools for translating environmental change into organismal response. Microclimate models to vertically scale weather station data to organismal heights. The biophysical modeling tools include both general models for heat flows and specific models to predict body temperatures for a variety of ectothermic taxa. Additional functions model and temporally partition air and soil temperatures and solar radiation. Utility functions estimate the organismal and environmental parameters needed for biophysical ecology. 'TrenchR' focuses on relatively simple and modular functions so users can create transparent and flexible biophysical models. Many functions are derived from Gates (1980) and Campbell and Norman (1988) . Package: r-cran-trendchange Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-trendchange_1.2-1.ca2604.1_all.deb Size: 30104 MD5sum: 93523cc038c6e3f4b4316bf8bc115f3e SHA1: fb2dbe3c7f1193e71e5a26211fe6499989b1b974 SHA256: cc75de832040d8400250f36582d4b4d01147b1fee07b67c97bedaeb05d165a5e SHA512: e81c37cd4537f12647a347c317cf36798f3ef9c015f1a122467f0a87adf3fc1c9cecf96bf636ebd2542f125de648330fa5cfd31c9124ec7d9056aeb5dd351a3b Homepage: https://cran.r-project.org/package=trendchange Description: CRAN Package 'trendchange' (Innovative Trend Analysis and Time-Series Change Point Analysis) Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series. Zekai, S. (2011). . Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp. Package: r-cran-trendeval Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trending, r-cran-yardstick, r-cran-rsample, r-cran-tibble Suggests: r-cran-testthat, r-cran-dplyr, r-cran-outbreaks Filename: pool/dists/resolute/main/r-cran-trendeval_0.1.1-1.ca2604.1_all.deb Size: 101740 MD5sum: 15df8c7c5e55a42c4df984a3bcf56983 SHA1: 8e82073369e6f3fe6d5be3acdbbcca60f6b4ff2f SHA256: 4834abd7d52131003fbfcbb9518667c16141e40a99d550fdb8a41d7e428f7991 SHA512: b39f0755ed5b4d7f06adbc4580f74ecc24c72f09bba3d7ade80127772a3f42ce79a25acc200694f3a407f7c3d99ce098b11ec35e375ddd2dfc2e8b94224bd0a4 Homepage: https://cran.r-project.org/package=trendeval Description: CRAN Package 'trendeval' (Evaluate Trending Models) Provides a coherent interface for evaluating models fit with the trending package. This package is part of the RECON () toolkit for outbreak analysis. Package: r-cran-trending Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-citools, r-cran-mass, r-cran-tibble, r-cran-vctrs, r-cran-pillar Suggests: r-cran-knitr, r-cran-brms, r-cran-bh, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-dplyr, r-cran-outbreaks, r-cran-testthat, r-cran-ggplot2, r-cran-patchwork, r-cran-covr Filename: pool/dists/resolute/main/r-cran-trending_0.1.0-1.ca2604.1_all.deb Size: 189852 MD5sum: 9805e3c680b0f82eb20b791d8d59c23c SHA1: be9fb8b3cfa0d40022551d3680eebb235292fafa SHA256: 02e6f3fcbc365157f84d0a1fe498f7eacda05cef1210736c960e97cd89204e91 SHA512: 9e0811668edaf8bf6d21d9c68fd468be97707f70909f11818f5ce12313ab86838cb2758af34cf3840a0472c44f2c66ce5eadc8fc89473bc85ca4fc920c54c826 Homepage: https://cran.r-project.org/package=trending Description: CRAN Package 'trending' (Model Temporal Trends) Provides a coherent interface to multiple modelling tools for fitting trends along with a standardised approach for generating confidence and prediction intervals. Package: r-cran-trendintrend Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-rms, r-cran-nleqslv, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-trendintrend_1.1.3-1.ca2604.1_all.deb Size: 63626 MD5sum: fa1658a00f51cac8a82aa0d81de5afa7 SHA1: 47e89adfee2b88dc155227978035529f60b43878 SHA256: 1bb36e760516d1160ecdd53a7d1eacb811c6a91ee3c1c6e4262d0cec38208f8e SHA512: f49c69e6f7fd72de8b865e8583457c836c3f812b377792a3056f9f97efa95c115e78e410ce17c8a111301c328a4c1062c80f3b85dc7606b0f178052968f754b2 Homepage: https://cran.r-project.org/package=TrendInTrend Description: CRAN Package 'TrendInTrend' (Odds Ratio Estimation and Power Calculation for the Trend inTrend Model) Estimation of causal odds ratio and power calculation given trends in exposure prevalence and outcome frequencies of stratified data. Package: r-cran-trendlsw Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh, r-cran-locits Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-trendlsw_1.0.6-1.ca2604.1_all.deb Size: 262380 MD5sum: 97fd4f53b4c3de073cda5c9373ba681f SHA1: 7dc2bb476eb816b36bf9381bdaeb8e3fde91e950 SHA256: c842a5ace1183b1fd1b29fbbcf478ee8e6a374831a1f9eb1d32005e87b089148 SHA512: 27b4858bb8f71976c6fbe9e039bcdf3ef605dc43127f27777cdb56fa2b84c7219485b1aa1c9f3c16f3011c3ba517e3f146be6413554bdc96f1e52784de79ac2b Homepage: https://cran.r-project.org/package=TrendLSW Description: CRAN Package 'TrendLSW' (Wavelet Methods for Analysing Locally Stationary Time Series) Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) , (2022b) . Further information regarding the use of the package, along with detailed examples, can be found in McGonigle, Killick and Nunes (2025) . New users will likely want to start with the TLSW function. Package: r-cran-trendsegmentr Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-trendsegmentr_1.3.2-1.ca2604.1_all.deb Size: 68968 MD5sum: cc0a2eeca3ac28f31a9a2fa96200093d SHA1: 86e98013fabe29143080d7d4cb57ec537f659c18 SHA256: a82d726480c9fdb00c7ed7482fc29e04ca4ac829d2c068171b397146e31ea1ae SHA512: 0c5be45303ef0c46cc062c0202e0bc4217cb656f3e259976f0d67bbacd346a36853eacc984affa7a7e9efde77fc59cfd0bb37de4bc60dc924c1cef25634179a6 Homepage: https://cran.r-project.org/package=trendsegmentR Description: CRAN Package 'trendsegmentR' (Linear Trend Segmentation) Performs the detection of linear trend changes for univariate time series by implementing the bottom-up unbalanced wavelet transformation proposed by H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the change-points are returned with the piecewise-linear estimator for signal. Package: r-cran-trendseries Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2587 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dlm, r-cran-glue, r-cran-hpfilter, r-cran-lubridate, r-cran-mfilter, r-cran-rcpproll, r-cran-tibble, r-cran-tsbox Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr, r-cran-xts Filename: pool/dists/resolute/main/r-cran-trendseries_1.2.0-1.ca2604.1_all.deb Size: 1906244 MD5sum: 59625c000f763a0c22014a600b699dba SHA1: 7a47fead61f97b0095aa74ac5cc7796737efe108 SHA256: f7c163308691d45fbe3c4080a6e989d46a7ba54c419dc6a201f40ceb653169bb SHA512: b78c6a275fb2246e1483fae7e171c5ae7b74e1422d9c9fdce47dc7c0118f09e6bbfb723dab3baa7c55d1b83c9ab4a94e48178a5136a30762635b098233cc6bfa Homepage: https://cran.r-project.org/package=trendseries Description: CRAN Package 'trendseries' (Extract Trends from Time Series) Extract trends from monthly and quarterly economic time series. Provides two main functions: augment_trends() for pipe-friendly 'tibble' workflows and extract_trends() for direct time series analysis. Includes established econometric filters such as Hodrick-Prescott (HP), Baxter-King, Christiano-Fitzgerald, and Hamilton, alongside moving averages and smoothing methods. Smart defaults are tuned for common economic frequencies following Ravn and Uhlig (2002) . Package: r-cran-trendtestr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-emmeans, r-cran-e1071, r-cran-forecast, r-cran-mass, r-cran-multcomp, r-cran-tidyselect, r-cran-tidyr, r-cran-tseries, r-cran-car, r-cran-fsa, r-cran-ggpubr, r-cran-rlang, r-cran-pscl, r-cran-mgcv Suggests: r-cran-testthat, r-cran-mockr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-trendtestr_1.0.1-1.ca2604.1_all.deb Size: 254094 MD5sum: a618521724e2a12f5c8f36d879d9280b SHA1: 7ebb223abd04fb32cbcc8aff999cd90dff6cd4ec SHA256: ce2db3e296f8f82704e69e5f9d7f2cebab7c8189bcc4ebceccdde9edb11ee4cf SHA512: e22ec5188e76fd486b3232dfa2526ae14747670f741c296c0a368fe06f1bbfc3cc44fc7c3e8149831ab45a3e792f8df2c15bc124dc178fd6be2a4fbbcd2d27d7 Homepage: https://cran.r-project.org/package=trendtestR Description: CRAN Package 'trendtestR' (Exploratory Trend Analysis and Visualization for Time-Series andGrouped Data) Provides a set of exploratory data analysis (EDA) tools for visualizing trends, diagnosing data types for beginner-friendly workflows, and automatically routing to suitable statistical tests or trend exploration models. Includes unified plotting functions for trend lines, grouped boxplots, and comparative scatterplots; automated statistical testing (e.g., t-test, Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size calculation; and model-based trend analysis using generalized additive models (GAM) for count data, generalized linear models (GLM) for continuous data, and zero-inflated models (ZIP/ZINB) for count data with potential zero-inflation. Also supports time-window continuity checks, cross-year handling in compare_monthly_cases(), and ARIMA-ready preparation with stationarity diagnostics, ensuring consistent parameter styles for reproducible research and user-friendly workflows.Methods are based on R Core Team (2024) , Wood, S.N.(2017, ISBN:978-1498728331), Hyndman RJ, Khandakar Y (2008) , Simon Jackman (2024) , Achim Zeileis, Christian Kleiber, Simon Jackman (2008) . Package: r-cran-trendtm Architecture: all Version: 2.0.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-softimpute, r-cran-capushe, r-cran-fda Filename: pool/dists/resolute/main/r-cran-trendtm_2.0.21-1.ca2604.1_all.deb Size: 51168 MD5sum: 6e68b98794f56b8dfb98642f5a35d4ba SHA1: 377033b5db951f466408c55ae06f31125acb53bb SHA256: 54a47a42cb8b887067b196335f53e96b3eef44a76be85c5c7ebc8246da0d2ca3 SHA512: 5a924a547983a5a5d3d0939cefff00706f9f581077e748a6c735b9efb31042fbf017602625d2b36497569855a16e4d7d87320ae37bd03044130a86d34a721c2a Homepage: https://cran.r-project.org/package=TrendTM Description: CRAN Package 'TrendTM' (Trend of High-Dimensional Time Series Matrix Estimation) Matrix factorization for multivariate time series with both low rank and temporal structures. The procedure is the one proposed by Alquier, P. and Marie, N. "Matrix factorization for multivariate time series analysis." Electronic Journal of Statistics, 13(2), 4346-4366 (2019). Package: r-cran-trendtwosub Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-trendtwosub_0.0.2-1.ca2604.1_all.deb Size: 77490 MD5sum: f28a6bd0f9862aca0fd0e797c0743fdb SHA1: bbe32aa69571d14e7eb84b0254d09d75ad5ba268 SHA256: 17ac5aae69059586e2aaf47473e5081898046247de2048592f31e1dfad744128 SHA512: be5b9fca231159b6f421f18dc40563ecde6c68320690bfa8a14ba42f598941ee7a207126ab49bd7a8137a77c545971476b2f50425d498d67d467c7b1c55b0a71 Homepage: https://cran.r-project.org/package=Trendtwosub Description: CRAN Package 'Trendtwosub' (Two Sample Order Free Trend Nonparametric Inference) Non-parametric trend comparison of two independent samples with sequential subsamples. For more details, please refer to Wang, Stapleton, and Chen (2018) . Package: r-cran-trendyy Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtrendsr, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-crayon, r-cran-stringr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-trendyy_0.1.1-1.ca2604.1_all.deb Size: 27042 MD5sum: 7d6bee5fb6ff557a50016a97fccfe1dc SHA1: 4d430ccb354cb1264a1f5ae86e5aa41fec197a5f SHA256: abf4f6453c95878f96de5779d71bca73efb2b13c3b7a1b6b11414a089474be9b SHA512: 1480b86bb29bc7b3e5e93656af72b397c589736897ba5fc3c605b27d6707892dcfaa528c474ff8384f34338c13b90a544d32af7ad62b7f622eff434615fe3e69 Homepage: https://cran.r-project.org/package=trendyy Description: CRAN Package 'trendyy' (A Tidy Wrapper Around 'gtrendsR') Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package. Use four spaces when indenting paragraphs within the Description. Package: r-cran-tres Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manifoldoptim, r-cran-mass, r-cran-pracma, r-cran-rtensor Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tres_1.1.5-1.ca2604.1_all.deb Size: 4287792 MD5sum: 561fdce8eeb6f79105f5e35423aebde1 SHA1: 613a7060f6ebffabeb19f4b8981c37403d78c54e SHA256: e00fa95bba3c98d2f897a9ebbd158a3af70c7cb7c1e0e96faf92114977d7416d SHA512: 815b48fae7e08e89caf47f644923cb5acddc454dae3e123425cc7ebd6545458f095cb2e5a8e3d43805ac7d16375ea4ec3d8a65a0bb32661b62438a86abd92b17 Homepage: https://cran.r-project.org/package=TRES Description: CRAN Package 'TRES' (Tensor Regression with Envelope Structure) Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) . For details of TPR, see Zhang X, Li L (2017) . For details of 1D algorithm, see Cook RD, Zhang X (2016) . For details of ECD algorithm, see Cook RD, Zhang X (2018) . For more details of the package, see Zeng J, Wang W, Zhang X (2021) . Package: r-cran-trexselector Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-tlars, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-glmnet, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-patchwork, r-cran-wgcna, r-cran-fastcluster, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-trexselector_1.0.0-1.ca2604.1_all.deb Size: 859872 MD5sum: 52bc43da7634ae108eaf5e0b8c6e8669 SHA1: 328944d383af6275cce570771807f6890a38ee0e SHA256: 4101fc9ac67cf5da04b9ccf7cc98cbca7807cb6d383be6504564e1f5c3149864 SHA512: 17515c5393294b1704988f6e50d56dee0176d0802433b5fd742d5d117dc4a74c91c154bf4d30da6f3f5736871d3f8cb1aa668f45de81108a926506be4056bda2 Homepage: https://cran.r-project.org/package=TRexSelector Description: CRAN Package 'TRexSelector' (T-Rex Selector: High-Dimensional Variable Selection & FDRControl) Performs fast variable selection in high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level. The package is based on the paper Machkour, Muma, and Palomar (2022) . Package: r-cran-tri.hierarchical.ibds Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tri.hierarchical.ibds_1.0.0-1.ca2604.1_all.deb Size: 75768 MD5sum: 9557939a797a6be72b92696a7c246fc0 SHA1: a64a67ce72f2ee716dd931c5d848fa9f73558526 SHA256: 360c818213be29cd743af4f589b54a01930457b9e7acd13f5bff2e6f2a9e8dad SHA512: dd5412f2ec38dd8f86a409bf9e6a2969b169fa5a002b7b37150392db21f461163bc0e6cdc649606ad6c7af1acd47eb5e88107bef1b1f72bac7354045ee8d5d72 Homepage: https://cran.r-project.org/package=Tri.Hierarchical.IBDs Description: CRAN Package 'Tri.Hierarchical.IBDs' (Tri-Hierarchical IBDs (Tri- Hierarchical Incomplete BlockDesigns)) Tri-hierarchical incomplete block design is defined as an arrangement of v treatments each replicated r times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) . This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions. Package: r-cran-triact Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17950 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-checkmate, r-cran-lubridate Suggests: r-cran-signal, r-cran-tibble, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-triact_0.3.1-1.ca2604.1_all.deb Size: 2343776 MD5sum: e1fc497606fbd7a80b6677850af74073 SHA1: 3871510f9a01ff3782ccb4ed76a4767e92c7ddc3 SHA256: f8e234a6b32cfaf25acd46e51584c56dcaf066adb89895883c1f9201ed8bba1d SHA512: 8b7309f3317f9fea239bddf457fdf5a33365b09a7b3c820345f152ad05706eb8115f428d185d27e6541b91468476dbb8cb711d1d370685ed18afacba534a180d Homepage: https://cran.r-project.org/package=triact Description: CRAN Package 'triact' (Analyzing the Lying Behavior of Cows from Accelerometer Data) Assists in analyzing the lying behavior of cows from raw data recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows the determination of common measures for lying behavior including total lying duration, the number of lying bouts, and the mean duration of lying bouts. Further capabilities are the description of lying laterality and the calculation of proxies for the level of physical activity of the cow. Reference: Simmler M., Brouwers S. P. (2024) . Package: r-cran-triadsim Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1627 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-snpstats, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-triadsim_0.3.1-1.ca2604.1_all.deb Size: 684606 MD5sum: b40c723816f7e81859373d32699c7673 SHA1: 29102a41de78bd1b2ab4e9c1bff18dd647fcbd2f SHA256: 4421dc3f21a768b699b827de3605ce797f77f0d88b40b59edbde83799398594f SHA512: bf599e0bf63814888ceeec22b7f91c6bb388d3fbd9b527987b97d32694d9fd8fe45dc0657165d89984f7d4d8cea14dc05226f8f8a6cc2ea6cbae5a33fbacffff Homepage: https://cran.r-project.org/package=TriadSim Description: CRAN Package 'TriadSim' (Simulating Triad Genomewide Genotypes) Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R. Package: r-cran-trialsimulator Architecture: all Version: 1.17.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4755 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-copula, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-gmcplite, r-cran-htmltools, r-cran-mvtnorm, r-cran-r6, r-cran-rlang, r-cran-rpact, r-cran-rstudioapi, r-cran-survival Suggests: r-cran-data.table, r-cran-dosefinding, r-cran-graphicalmcp, r-cran-kableextra, r-cran-knitr, r-cran-mirai, r-cran-pweall, r-cran-pwexp, r-cran-rmarkdown, r-cran-simdata, r-cran-survminer, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-trialsimulator_1.17.1-1.ca2604.1_all.deb Size: 2857320 MD5sum: 3a868aebdb26734667232e3b8a29ad04 SHA1: fe4a7bf1fa061f07e3a6706410cf8c8c4b0879de SHA256: f1a9e1511548bcdc45639dccdc78b8c731b89e18f32fc458f816f13679386cc8 SHA512: 3100ccca3e8b47c2b685e9ec605fcc9309953128dcfc5c11c033b2a3dbd4a04f1e160c08b54691cf742497de9521010bbea5b8bbbd07c9a13c8aaad7766348fb Homepage: https://cran.r-project.org/package=TrialSimulator Description: CRAN Package 'TrialSimulator' (Clinical Trial Simulator) Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided. Package: r-cran-triangle Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-rmpfr Filename: pool/dists/resolute/main/r-cran-triangle_1.1.0-1.ca2604.1_all.deb Size: 130754 MD5sum: c9ce2da083f9cbe1e8ec1be12794e3fe SHA1: ea3f5c76742d8d31d207ac5e7f9eadc8ccd9f4f5 SHA256: cb1553627f1716992a0b684daa539f6526bd982309e214e731dfa5c608cba014 SHA512: 876f082987c585e6d84224500fa4c6773d0f50918c0f905038eaa15eaeb9ee1187cc4970c7eea784490e844d50f4ee75a2ced8b8abfe6c3dc0cc47f176aef85a Homepage: https://cran.r-project.org/package=triangle Description: CRAN Package 'triangle' (Distribution Functions and Parameter Estimates for the TriangleDistribution) Provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters. Package: r-cran-triangulation Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-triangulation_0.5.0-1.ca2604.1_all.deb Size: 32694 MD5sum: e4103a33a1a9aa3dbbbc9ed08fda73f3 SHA1: db8d7edda73f2585de88f550849cfaa6dc42ec08 SHA256: b27304181353161f5bdc1f981772daa9c543cfc36a5440b1b679232d8f6a4a4f SHA512: 995e7032781becccac733f54cb56f3192bed98fab3362282079486ae48e6a4bdab532e45b7495e74e0775ee4ede1e288daf3a6f9718032e15e9e647889530216 Homepage: https://cran.r-project.org/package=triangulation Description: CRAN Package 'triangulation' (Determine Position of Observer) Measuring angles between points in a landscape is much easier than measuring distances. When the location of three points is known the position of the observer can be determined based solely on the angles between these points as seen by the observer. This task (known as triangulation) however requires onerous calculations - these calculations are automated by this package. Package: r-cran-tricolore Architecture: all Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggtern, r-cran-rlang, r-cran-shiny, r-cran-assertthat Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-leaflet, r-cran-httpuv, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-tricolore_1.2.6-1.ca2604.1_all.deb Size: 1294488 MD5sum: c6c2fa1e7f224894c548a27a63f248d3 SHA1: 19b003a3378a76417c7ba670ac4b8b9d8c6c4bad SHA256: 6ebe9c9fc8acd5448c0b403ee1c1845f97634a43f0fe703c254148b1e2016cf9 SHA512: 476f99258e6d25591798496ff098e9de69f0c0caa14bbd44a3d2d75ad6e715fe127ab7a03a45873de059fdf22a4278be7738a5d79ff4829c38ac24b7fcd3ae4c Homepage: https://cran.r-project.org/package=tricolore Description: CRAN Package 'tricolore' (A Flexible Color Scale for Ternary Compositions) Compositional data consisting of three-parts can be color mapped with a ternary color scale. Such a scale is provided by the tricolore packages with options for discrete and continuous colors, mean-centering and scaling. See Jonas Schöley (2021) "The centered ternary balance scheme. A technique to visualize surfaces of unbalanced three-part compositions" , Jonas Schöley, Frans Willekens (2017) "Visualizing compositional data on the Lexis surface" , and Ilya Kashnitsky, Jonas Schöley (2018) "Regional population structures at a glance" . Package: r-cran-triggerstrategy Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-ldbounds, r-cran-mvtnorm, r-cran-nleqslv Filename: pool/dists/resolute/main/r-cran-triggerstrategy_1.2.0-1.ca2604.1_all.deb Size: 96654 MD5sum: 94634f9dbea5107d7fa51eba4122fcbd SHA1: 1c846331f4499fdb4ade4ce0fde0549a98decb3b SHA256: 34ad4a5a3d95ea8046487417827d422fb843497a7ea229c1cfb161a16cc23e64 SHA512: 6611ffdfbcc5fa3706a66b208f4163ddeb1aad8e76409fb8a9b49105910a013f7a553a746f4320f171dda0bbec4227e17cfaaed8a53e52b6235ca41d5b3af4e3 Homepage: https://cran.r-project.org/package=triggerstrategy Description: CRAN Package 'triggerstrategy' (Trigger Strategy in Clinical Trials) The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy. See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311. Package: r-cran-trigon Architecture: all Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-caret, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-factoextra, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggridges, r-cran-markdown, r-cran-patchwork, r-cran-randomforest, r-cran-rcolorbrewer, r-cran-readxl, r-cran-sessioninfo, r-cran-shiny, r-cran-shinydashboardplus, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-simpleboot, r-cran-writexl Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-trigon_0.3.3-1.ca2604.1_all.deb Size: 1670428 MD5sum: c7a58d27e8e1db466edb1af0a02fda35 SHA1: 96ab68f81fc4b3282150a502c15cdc3cf012fd9a SHA256: 8e126acdea61524638aadf18e0b41a01042990e9b8c1da69108277a57db02a4f SHA512: 971782f955c059e6310b6648584209f23f839ad017f11ebdd136e7e830482abc2c288944a7d4f940bfce0f9147578f04e2670d0baa2e3eabdd2c9d9139e21b1b Homepage: https://cran.r-project.org/package=tRigon Description: CRAN Package 'tRigon' (Toolbox for Integrative Pathomics Analysis) Processing and analysis of pathomics, omics and other medical datasets. 'tRigon' serves as a toolbox for descriptive and statistical analysis, correlations, plotting and many other methods for exploratory analysis of high-dimensional datasets. Package: r-cran-trigpoints Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-trigpoints_1.0.0-1.ca2604.1_all.deb Size: 1043776 MD5sum: a3d6b02c300724d9a7be6bd1167d6b22 SHA1: b2d19e5860611b61402f7587d99e7599e9ebda62 SHA256: 631794f2e628b03f23a305a0fd3812c2d65c8e5be4fef69451a1053de3959ed3 SHA512: def670d5fdf06cd32ace7c47f38a88bf57063adacb7297b340bc0b62048fa6ba9f6f006b8956773c4c0ce2b7253ea23138f07fbf9ad56a46b816d4005b53c921 Homepage: https://cran.r-project.org/package=trigpoints Description: CRAN Package 'trigpoints' (Data Set of Trig Points in Great Britain in British NationalGrid Coordinates) A complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today. Package: r-cran-trilliem Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-trilliem_0.1.1-1.ca2604.1_all.deb Size: 130066 MD5sum: b2eca89e437923fe87da49e9aa196c54 SHA1: b57757307ccc7590109fa0f0d083d2e5147c7034 SHA256: 8658cb4c38e63b5fa9e479152b2888b95f788a30c48d195dfba91e4900403047 SHA512: c992103ec1277131cdc116b0b6c5cb6561de5f89281b365f52195083b153317f35bf86e0a41312a2d8be7dc068a4e2162ee004098c01da7495ef009cb7248875 Homepage: https://cran.r-project.org/package=TriLLIEM Description: CRAN Package 'TriLLIEM' (Log-Linear Modelling of Triad Genotype Data) Triad Log-Linear modelling of Imprinting Environmental interactions, and Maternal effects (TriLLIEM). This is an implementation of the log-linear model described in a series of papers, see for example Ainsworth et al. (2010) . 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Package: r-cran-trimr Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-trimr_1.1.1-1.ca2604.1_all.deb Size: 110580 MD5sum: 9e7c948a870634c347b1aad6721aab63 SHA1: 0b680997608154d877bdbae46722a953725c80c7 SHA256: e26af6fb184de12f8b22d5126538806b6814a5884f2df1884724552ed3a9373b SHA512: 82405e04c21d7a9e2358705843fb0101d0e21428d5db72ed36131246a30c00148b27329b65a4ba8eb758a8c102e0acc22e9fa08134f82535e159246e058848ba Homepage: https://cran.r-project.org/package=trimr Description: CRAN Package 'trimr' (An Implementation of Common Response Time Trimming Methods) Provides various commonly-used response time trimming methods, including the recursive / moving-criterion methods reported by Van Selst and Jolicoeur (1994). 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Package: r-cran-trinroc Architecture: all Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rgl, r-cran-gridextra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-reshape Filename: pool/dists/resolute/main/r-cran-trinroc_0.7-1.ca2604.1_all.deb Size: 305746 MD5sum: f6b4f9284f8e0b77f9cabc33be72f5dd SHA1: e759a942c3f469a42edcf0afb56554ff94b6a570 SHA256: d0bfe64d46cd81d0bbd80f4d0721be1dabe5ec75612167d794e0b1651af9d345 SHA512: 11f5d62f8f479028ab00cf7f4a529e3a71ac8fb8b8a01afb0b619ac83c76150b18bf010e6bae7392c78fbb064bc1385150b4059f96f5af6a60f3c340936a914b Homepage: https://cran.r-project.org/package=trinROC Description: CRAN Package 'trinROC' (Statistical Tests for Assessing Trinormal ROC Data) Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers. See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis and Gatsonis (2023), ISBN 9781482233704. Package: r-cran-trip Architecture: all Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geodist, r-cran-mass, r-cran-raster, r-cran-reproj, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-glue, r-cran-viridis, r-cran-traipse, r-cran-crsmeta, r-cran-dplyr, r-cran-rlang Suggests: r-cran-adehabitatlt, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-lubridate, r-cran-maps, r-cran-spelling, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-trip_1.10.0-1.ca2604.1_all.deb Size: 1726788 MD5sum: 326a5962dd2980f5a17e2b26b3e88b2f SHA1: 46a3607f0a60a72314d014f4fba9713d69b74d29 SHA256: 3d0b65c0764b200c97b322698c8c48cd848066fbabab3f126e90c1b5a00cfb1f SHA512: 0051ed1c04b76669a04ad6cb548be949397e017668ba1b38abe1b37179f2a2f0edb72298ce3299df4652878ae674d446b4785974657cab3047bea42e2a2bab4c Homepage: https://cran.r-project.org/package=trip Description: CRAN Package 'trip' (Tracking Data) Access and manipulate spatial tracking data, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from tracking data. There are coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects can be created from raw or grouped data frames, and from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, Sumner, MD (2011) . Package: r-cran-tripestimation Architecture: all Version: 0.0-46-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-mgcv, r-cran-reproj, r-cran-sp, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-tripestimation_0.0-46-1.ca2604.1_all.deb Size: 170572 MD5sum: 3a0a1bc74fe8ca873d211a4ac1405430 SHA1: 2ef715966bc97037ee444f22b63e8de95a9273ac SHA256: f5ddffa7d5bc4d10b59d6009216363f4e892be729a6b4e2c1546d0002d021738 SHA512: 2ef49683bf69061b0df1c68fbb33aef3e53caac777a402554eec6ccb79a9246c8acb6f3eeb925fcf9af3767c8bc32054b2c57fe37940f4170c23eb486165811a Homepage: https://cran.r-project.org/package=tripEstimation Description: CRAN Package 'tripEstimation' (Metropolis Sampler and Supporting Functions for EstimatingAnimal Movement from Archival Tags and Satellite Fixes) Data handling and estimation functions for animal movement estimation from archival or satellite tags. Helper functions are included for making image summaries binned by time interval from Markov Chain Monte Carlo simulations. Package: r-cran-tripler Architecture: all Version: 1.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr Suggests: r-cran-lme4, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-tripler_1.5.5-1.ca2604.1_all.deb Size: 591596 MD5sum: 79053d76f0428813f456c54d08cad1a1 SHA1: 4955d7b500247aca0f39753e4ac7d7b0faed9bb1 SHA256: dc213c5bfced95af4d448a247aa6eb91a1c7098ee3bcaed888df93f4ba7b60a0 SHA512: 03a469c577e2d8f5079d6f54a05a341a3e3c20c1236bc8c65a553e5701cb2ee9651a831769ae86aa688c73e6d78c1ad5b3a48eb2392bec84bf277ac7cf2f25e1 Homepage: https://cran.r-project.org/package=TripleR Description: CRAN Package 'TripleR' (Social Relation Model (SRM) Analyses for Single or MultipleGroups) Social Relation Model (SRM) analyses for single or multiple round-robin groups are performed. These analyses are either based on one manifest variable, one latent construct measured by two manifest variables, two manifest variables and their bivariate relations, or two latent constructs each measured by two manifest variables. Within-group t-tests for variance components and covariances are provided for single groups. For multiple groups two types of significance tests are provided: between-groups t-tests (as in SOREMO) and enhanced standard errors based on Lashley and Bond (1997) . Handling for missing values is provided. Package: r-cran-triplesmatch Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcbalance, r-cran-rlemon, r-cran-mass, r-cran-optmatch, r-cran-rlang Suggests: r-cran-testthat, r-cran-sensitivityfull, r-cran-informedsen, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-triplesmatch_1.1.0-1.ca2604.1_all.deb Size: 110094 MD5sum: dfb69f99eaa2fe7a9489826f43177f28 SHA1: 8244091b4ba9d6a6c162b222de1f36367308347a SHA256: 4da7c34b78cb6c1d3021bf27e8b562d523d59748aabf6671c2064fff28d2268e SHA512: 2d21703b232afd2c9a5d029772397a31c1d59525bda5d166ae501606710d0eebad2559292c423c1a6d11e255b979fde8d99418ebcedf0bd55b74ecd90fc76711 Homepage: https://cran.r-project.org/package=triplesmatch Description: CRAN Package 'triplesmatch' (Match Triples Consisting of Two Controls and a Treated Unit orVice Versa) Attain excellent covariate balance by matching two treated units and one control unit or vice versa within strata. Using such triples, as opposed to also allowing pairs of treated and control units, allows easier interpretation of the two possible weights of observations and better insensitivity to unmeasured bias in the test statistic. Using triples instead of matching in a fixed 1:2 or 2:1 ratio allows for the match to be feasible in more situations. The 'rrelaxiv' package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from 'GitHub' at . The 'Gurobi' commercial optimization software is required to use the two functions [infsentrip()] and [triplesIP()]. These functions are not essential to the main purpose of this package. A free academic license can be obtained at . The 'gurobi' R package can then be installed following the instructions at . Package: r-cran-tripsanddipr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tripsanddipr_0.1.0-1.ca2604.1_all.deb Size: 17384 MD5sum: 2deff1bfbeda8134d094e85a59872fdf SHA1: 200edd2868bd7c436c080cd9a1b53eeb22042876 SHA256: 0b4bd7ebcd3cc71cbcf8f091c227f6d0a4a6a9da55f0242fabb56ec6f4ab285f SHA512: a19896a256ccd78eed6c555b64849cb5faac76bee6b4ae9cdaf31aeeb22140ee22e57a008eb6c52451290fe102b32661f5e8dba31c6dd8a905ba53d290addc6b Homepage: https://cran.r-project.org/package=tripsAndDipR Description: CRAN Package 'tripsAndDipR' (Identification of 2n and 3n Samples from Amplicon SequencingData) Uses read counts for biallelic single nucleotide polymorphisms (SNPs) to compare the likelihoods for the observed read counts given that a sample is either diploid or triploid. It allows parameters to be specified to account for sequencing error rates and allelic bias. For details of the algorithm, please see Delomas (2019) . Package: r-cran-triversity Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-data.tree Filename: pool/dists/resolute/main/r-cran-triversity_1.0-1.ca2604.1_all.deb Size: 65890 MD5sum: 8e319554dfdcd6a0a98da8e3ae7bf879 SHA1: 52c0b522696e1b2528a41b817523e19b06fa3842 SHA256: 11997d0e73b829eae060f019517f78d2656deed214c6c305fa74f422659b9338 SHA512: e13f8db16e934c168756baba0b63aaf7e3dfd57070910f03f56ddd79da0acf8a9194739182fda903ba0011541b5907ea5ecd80d5f3e645bcd047a41649ca305d Homepage: https://cran.r-project.org/package=triversity Description: CRAN Package 'triversity' (Diversity Measures on Tripartite Graphs) Computing diversity measures on tripartite graphs. This package first implements a parametrized family of such diversity measures which apply on probability distributions. Sometimes called "True Diversity", this family contains famous measures such as the richness, the Shannon entropy, the Herfindahl-Hirschman index, and the Berger-Parker index. Second, the package allows to apply these measures on probability distributions resulting from random walks between the levels of tripartite graphs. By defining an initial distribution at a given level of the graph and a path to follow between the three levels, the probability of the walker's position within the final level is then computed, thus providing a particular instance of diversity to measure. Package: r-cran-trnslate Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-trnslate_0.0.3-1.ca2604.1_all.deb Size: 38520 MD5sum: c19b1e341a0192d25e101ca93a1467b2 SHA1: afc99ff49352bcb359fc1c1da9b9381904373770 SHA256: 12d0bbe5013590ac7c330c0ae95d284c77f4fc60b2a50178377e9f5d3dc5539c SHA512: 4b73198317baf850d96d6e129ef5d4de4a66fc479721ecf5427f295c9ead71c6dc02cf53fd74deecbe508c43a46cc46a1bfe1ee01cb856c5ba5f3cd15fa7505f Homepage: https://cran.r-project.org/package=tRnslate Description: CRAN Package 'tRnslate' (Translate R Code in Source Files) Evaluate inline or chunks of R code in template files and replace with their output modifying the resulting template. 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This package provides functions for pulling country-year troop deployment and basing data. Subsequent versions will hopefully include cross-national data on deploying countries. Package: r-cran-tropalgebra Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tropalgebra_0.1.1-1.ca2604.1_all.deb Size: 44574 MD5sum: fbfe94649edefb3ca5e6fbcdea739973 SHA1: 004b6a3f772caf05b95372e80ec8424f119dcf71 SHA256: ea1031815f839841aa2294dac39ba51807e1724d12c131cced9b44f655179b6b SHA512: 8f02c22b657ed930d78f3902e452cfe39e4ced340e28d5e26174b6f6859c6fdc0b8b656aefcf1e6dff6ced476e242c7a3f0e97e85882de5d03b2700f37c4a888 Homepage: https://cran.r-project.org/package=tropAlgebra Description: CRAN Package 'tropAlgebra' (Tropical Algebraic Functions) It includes functions like tropical addition, tropical multiplication for vectors and matrices. In tropical algebra, the tropical sum of two numbers is their minimum and the tropical product of two numbers is their ordinary sum. For more information see also I. Simon (1988) Recognizable sets with multiplicities in the tropical semi ring: Volume 324 Lecture Notes I Computer Science, pages 107-120 . Package: r-cran-tropfishr Architecture: all Version: 1.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-msm, r-cran-reshape2, r-cran-mass, r-cran-gensa, r-cran-ga, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-tropfishr_1.6.6-1.ca2604.1_all.deb Size: 1676508 MD5sum: 05818ea4f1cb0235d6a09410acee393d SHA1: 49f3c5f4d3a2c4a5238ae5578d93655c975bc8f0 SHA256: 69c276f1a16a62a200d2e82096e7fee8ef9df458f03d418b833ba8203bba6198 SHA512: b174817c73aa962c2479d1b58a32fa5457f86ad9792899bb3bb4e1f8877b9880c324341814024ff2a97077890dc4f39dfbc79b65a8b308d87bb77ad3daa1c645 Homepage: https://cran.r-project.org/package=TropFishR Description: CRAN Package 'TropFishR' (Tropical Fisheries Analysis) A compilation of fish stock assessment methods for the analysis of length-frequency data in the context of data-poor fisheries. Includes methods and examples included in the FAO Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish stock assessment" (), as well as other more recent methods. Package: r-cran-trotter Architecture: all Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-trotter_0.6-1.ca2604.1_all.deb Size: 162098 MD5sum: e86f65c4908078e05798fba50048a507 SHA1: d05d318e716e6613d9265c8c3ea8c45b8d9bb287 SHA256: 752981543437464171e2745bc887c5d79e78576d2484e44a1b7432a0b9aead5e SHA512: f921906d28d1f667f768b3df07986e7671f70ff05a2acf430aa5478aeaae582f6a4380310f4d40d314147593edef75e284f80b32670f0aaa76b01d70ad8a96ac Homepage: https://cran.r-project.org/package=trotter Description: CRAN Package 'trotter' (Pseudo-Vectors Containing All Permutations, Combinations andSubsets of Objects Taken from a Vector) Class definitions and constructors for pseudo-vectors containing all permutations, combinations and subsets of objects taken from a vector. Simplifies working with structures commonly encountered in combinatorics. 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Package: r-cran-tsallisqexp Architecture: all Version: 0.9-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tsallisqexp_0.9-5-1.ca2604.1_all.deb Size: 305866 MD5sum: 88b221d4fc1e52572f0d15e0c2bac217 SHA1: 1afb54183d60ccd74db44aacd2a2b3c90f661dd2 SHA256: edee535a0346f8cb5a8f9ff819f8ac0b076ca3b84dd6269dfd14d6f3ca6a8f8e SHA512: 4c8dcac44f9f30fa9cbbbcc5c912c4c2114ff5265b2d979fb989805d5cc894171ae4044b5f6d2983bfd6b6ab05296bedfa3eb24bacb71539e1840a5367c0e5f7 Homepage: https://cran.r-project.org/package=tsallisqexp Description: CRAN Package 'tsallisqexp' (Tsallis q-Exp Distribution) Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code. Package: r-cran-tsann Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-tsann_0.1.0-1.ca2604.1_all.deb Size: 20012 MD5sum: 784e27d19ba962e3a85208dd5cfa08f4 SHA1: 5f2c05ecadfca707040d1829b5fc0b44597a27db SHA256: 9988ce4bdb7a3f232e85e060855c520fcca6ed0653039f7a44a1f9e08a0a2141 SHA512: 1b5ec2c4e304c1050ec519f5553e69626de576de51801449dd5434202e47976a9c99183207f5f6c8f1954b5ef5172acc4a5fb4d0a1b6eb3a2f376274f63a1cf9 Homepage: https://cran.r-project.org/package=TSANN Description: CRAN Package 'TSANN' (Time Series Artificial Neural Network) The best ANN structure for time series data analysis is a demanding need in the present era. This package will find the best-fitted ANN model based on forecasting accuracy. The optimum size of the hidden layers was also determined after determining the number of lags to be included. This package has been developed using the algorithm of Paul and Garai (2021) . Package: r-cran-tsapp Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vars, r-cran-fftwtools, r-cran-hdm Filename: pool/dists/resolute/main/r-cran-tsapp_1.0.4-1.ca2604.1_all.deb Size: 682576 MD5sum: 63001a0d1fcdb1e92fa06a927ef93121 SHA1: cd84f6c3b3807207d09c14f73ef50c69754b2033 SHA256: 4157082cab0aa4b90970d56529eb5890971920d433175b84ad3c02a61d65cda6 SHA512: f68cd7c77871c0ebe1fabd16d96210a179b7011265d020ea5dde34b726e3f1fc2919ec784d5c1790174d6b0c30695b9e9c13b86726971112d0bddd83fc6b5be5 Homepage: https://cran.r-project.org/package=tsapp Description: CRAN Package 'tsapp' (Time Series, Analysis and Application) Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis. 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Also converts reliably between these classes. Package: r-cran-tsci Architecture: all Version: 3.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost, r-cran-rfast, r-cran-ranger, r-cran-fastdummies Suggests: r-cran-fda, r-cran-mass, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tsci_3.0.5-1.ca2604.1_all.deb Size: 181326 MD5sum: f7b4275fef2280f690944179f6347627 SHA1: c28321478b5bdf80874bc66d47260d6b07003198 SHA256: 267c9662a35a3317634a086f311211db7a0d77aa0942d5b70c6dcceda8d60b83 SHA512: e83fb36218dd92987ae3bf0bdfd32f5d79c0c74e121e663e72c8b478a9e8d37ed7b9c1b192c273a3a340855c8218a99d761ff0e9e281feb8253a83d45f3e58d2 Homepage: https://cran.r-project.org/package=TSCI Description: CRAN Package 'TSCI' (Tools for Causal Inference with Possibly Invalid InstrumentalVariables) Two stage curvature identification with machine learning for causal inference in settings when instrumental variable regression is not suitable because of potentially invalid instrumental variables. Based on Guo and Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables" . The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2025) "TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R" . 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The package is based on methodology described in the following references. McNeil, A.J. (2021) , Bladt, M., & McNeil, A.J. (2021) , Bladt, M., & McNeil, A.J. (2022) . 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Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial. Package: r-cran-tscs Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tseries, r-cran-rgl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-tscs_0.1.1-1.ca2604.1_all.deb Size: 1381814 MD5sum: fc2ffc5350cf40847c6f81e816f92fdc SHA1: 8f605b11c6542901de3eebde66aa76fed07929e3 SHA256: 686b03596db9fe0af3b669c65745baea0a028e51f420f29605028389e37d306c SHA512: 8f8af60817ab8ea4ee5cecc81a346b80f309a3b9678dcf6b688a30b3276fe9bddf0146cbd4ebe62efd605e696f370f06cb0c47e6641e6342c6499d6c6507d083 Homepage: https://cran.r-project.org/package=TSCS Description: CRAN Package 'TSCS' (Time Series Cointegrated System) A set of functions to implement Time Series Cointegrated System (TSCS) spatial interpolation and relevant data visualization. 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Package: r-cran-tsdataleaks Architecture: all Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-slider, r-cran-purrr, r-cran-cowplot, r-cran-plyr, r-cran-viridis Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tsdataleaks_2.1.1-1.ca2604.1_all.deb Size: 228110 MD5sum: 2d96847b371721b56ad73fa3be5503dc SHA1: 1d8c90f9270d79f0477f4f5d6bd0b148ddc73c40 SHA256: aaabb01d784245e43cb9e74b82ec6461f56b0c018a928653a4e33efc7e6b030c SHA512: 17f46b6fb20ee717688e3a2afe5c95a60cfd0bc2c20983a3f982704d0b286c56a5b01bc3284c5b86fa7e021e17cb84f5002d815004d3d0721bf0b0894cfed661 Homepage: https://cran.r-project.org/package=tsdataleaks Description: CRAN Package 'tsdataleaks' (Exploit Data Leakages in Time Series Forecasting Competitions) Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series, white noise is added in the original time series to form a new time series, etc. 'tsdataleaks' package can be used to detect data leakages in a collection of time series. Package: r-cran-tsdb Architecture: all Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-datetimeutils, r-cran-fastmatch, r-cran-zoo Suggests: r-cran-data.table, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-tsdb_1.1-0-1.ca2604.1_all.deb Size: 62662 MD5sum: 6cfef9e59e2fd82568b7629d9b38cb5f SHA1: 483f4d1de27aabe9ce3da7a019e670e700c10782 SHA256: 4871efee493b0c5ea95474caf4e5fff85becf80f28acd22658c959ad4eedb715 SHA512: 7e6e038616f5349e7025b988445aad2379c464fa357ffee5f07888e964095f3b479fe1bc5ac388b0fab755ead1d4b4eb4c3edbd3766b0c8e0c80c2d275582d69 Homepage: https://cran.r-project.org/package=tsdb Description: CRAN Package 'tsdb' (Terribly-Simple Data Base for Time Series) A terribly-simple data base for numeric time series, written purely in R, so no external database-software is needed. Series are stored in plain-text files (the most-portable and enduring file type) in CSV format. Timestamps are encoded using R's native numeric representation for 'Date'/'POSIXct', which makes them fast to parse, but keeps them accessible with other software. The package provides tools for saving and updating series in this standardised format, for retrieving and joining data, for summarising files and directories, and for coercing series from and to other data types (such as 'zoo' series). Package: r-cran-tsdecomp Architecture: all Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tsdecomp_0.2-1.ca2604.1_all.deb Size: 203680 MD5sum: 27751dfcaece8fa083db55aee52dbd22 SHA1: b503a9b5efc2fae31ce6a18acbc5453b9324c06f SHA256: 33bcdc63a80fc496e6c3dd48a952c05eae3241f50ce310454c27c9b516de7c91 SHA512: 90173ad0abb706a9f5abee5dccf8d72166673d3d833bebeecd86ed47ad89994ae6fcd0d9e047224eb6e6bd1afd0114829be0372e63fbd70096df2724ee6b8cf3 Homepage: https://cran.r-project.org/package=tsdecomp Description: CRAN Package 'tsdecomp' (Decomposition of Time Series Data) ARIMA-model-based decomposition of quarterly and monthly time series data. The methodology is developed and described, among others, in Burman (1980) and Hillmer and Tiao (1982) . Package: r-cran-tsdeeplearning Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-keras, r-cran-reticulate, r-cran-tsutils, r-bioc-biocgenerics, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-tsdeeplearning_1.0.1-1.ca2604.1_all.deb Size: 37760 MD5sum: 5c349db6b5e7d6b018af0a3da7d45fb9 SHA1: 2b795a4bc99b99af01fe95755258b9c63cbb442e SHA256: 8ae68ba1b7e8e466f5685c58e99d3e5ad17b17dc24901a60fdb87c2155c1f3ff SHA512: 973e388839bc6d1e251050ada2d04352f8109355bd7343917459586e797e51d65ab970f1d458160353c4e11ceeef900d5730a1c8fa73f04c3d97fd4b09f00fda Homepage: https://cran.r-project.org/package=TSdeeplearning Description: CRAN Package 'TSdeeplearning' (Deep Learning Model for Time Series Forecasting) Provides deep learning models for time series forecasting using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). These models capture temporal dependencies and address vanishing gradient issues in sequential data. The package enables efficient forecasting for univariate time series. For methodological details see Jaiswal and co-authors (2022). . Package: r-cran-tsdf Architecture: all Version: 1.1-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tsdf_1.1-9-1.ca2604.1_all.deb Size: 446286 MD5sum: 79b4836d7c289701098041dbdc6eb441 SHA1: 048205eb107cc897c488637ae67ccea5d4dc18e2 SHA256: e616b1a5bf951b8c049af9ea6130ff3a5e7c08d9bfb7a505d9e0b4d64ce6dc82 SHA512: 24b97b484b99f6eaacb6a38eba0424a724e5e9104031ad9bff5448bae1f9be951db76ad77f2c6e895ec2e13c978c575f4e4485c1f2364177e1ff81a6be4df437 Homepage: https://cran.r-project.org/package=tsdf Description: CRAN Package 'tsdf' (Two-/Three-Stage Designs for Phase 1&2 Clinical Trials) Calculates Zhong's optimal two-/three-stage Phase II designs for single-arm trials, generates target-toxicity decision tables for two-/three-stage Phase I dose-finding, and supports dose-finding simulations using custom decision tables. The Phase II design is based on Zhong (2012) . Package: r-cran-tsdisagg2 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-tsdisagg2_0.1.0-1.ca2604.1_all.deb Size: 341542 MD5sum: 49688015c7c5d917726cf27ecd4e4322 SHA1: 51e704b09f3a256fef9acb6b3c4ca5afe8d3f5a2 SHA256: a8699a1fccd9a465f643d2ee49a0a50371cb97d1c9073962f116807293b00dea SHA512: b80b51df70d569cf2910ac7ad18a7d8510c36f564ea0eef6ac1dda7655174d543acaf31d94a965dce8bece0f40189ab03583ae79405f19ba9e23c43930ab0755 Homepage: https://cran.r-project.org/package=tsdisagg2 Description: CRAN Package 'tsdisagg2' (Time Series Disaggregation) Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) , Chow and Lin (1971) , Fernandez (1981) and Litterman (1983) . Package: r-cran-tsdisaggregation Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-zoo, r-cran-lars, r-cran-matrix, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tsdisaggregation_2.0.0-1.ca2604.1_all.deb Size: 64780 MD5sum: 6d68001e8c5187ba25dbdd2dfc693657 SHA1: af99a35436e18aa261472b08facf0b891c8a19a1 SHA256: edb5a7293f7d5e0df5c0236aa8a7aebb610de5a58854d8e791a9ab4b48edbd33 SHA512: 8efa4ec937e209aa016be1c01d13b244e8b13b082377c867eaf414090b9c6dabb66a9de40ccd8cfbfceac7e7aee701b0b9155c125e8397af3039210f945b6f11 Homepage: https://cran.r-project.org/package=TSdisaggregation Description: CRAN Package 'TSdisaggregation' (High-Dimensional Temporal Disaggregation) First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) for disaggregating low-frequency series in the presence of high-dimensional indicator matrices. Package: r-cran-tsdt Architecture: all Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlbench, r-cran-hash, r-cran-party, r-cran-rpart, r-cran-survival, r-cran-survrm2, r-cran-modeltools Filename: pool/dists/resolute/main/r-cran-tsdt_1.0.8-1.ca2604.1_all.deb Size: 450330 MD5sum: 889e015063891414c8a09c34a4c2ff71 SHA1: 144cd9a774859df5c0a9edfd9360ce0b4715a488 SHA256: 828f42b515bcf0efe43df8a7107c63ab97701f677ca9d55a8f56f8dd52de9e5c SHA512: 0f2f20164f96931c7bedb832a66d17f26625e6bf76d8a8646f57ebd05924031b74fd962fdf244a8770f400d8629f67613c5c4cbcfff6ec09ab5a9e7b1b1a9b7c Homepage: https://cran.r-project.org/package=TSDT Description: CRAN Package 'TSDT' (Treatment-Specific Subgroup Detection Tool) Implements a method for identifying subgroups with superior response relative to the overall sample. Package: r-cran-tse Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tse_0.1.0-1.ca2604.1_all.deb Size: 61860 MD5sum: 4803228bb5005f1b0706e04231861915 SHA1: cb1121cd4251895b87225aa4cbc30f5b7d4635e1 SHA256: ae0b25faae2cc68b8f4e77e91b169a003fc365cf55707f47bb06280ee06461fd SHA512: d410d980535d060384f4dc41327d474bf2b1857e407331c1c3c0815dfd27aa3c2a604fff3f55e7486ba83410d3f1b01bbdcaf98c42b29f7fae9487c8b2e71787 Homepage: https://cran.r-project.org/package=TSE Description: CRAN Package 'TSE' (Total Survey Error) Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics. On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) . Package: r-cran-tseal Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-caret, r-cran-checkmate, r-cran-magrittr, r-cran-mass, r-cran-parallelly, r-cran-statcomp, r-cran-waveslim, r-cran-wdm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tseal_0.1.5-1.ca2604.1_all.deb Size: 4255208 MD5sum: edaa2af758f190b007c1a69041adba5f SHA1: d6cd10a6b63d995a11e5e1cf83c9821dc3e2f68a SHA256: 1246fb80d2a33f545815f3c346723647108b744afb8c60a4449862e3b199356b SHA512: f47aebde7b71317f60a31e139ecd6a1fe48c0ab3e76d9e73ea5e710e9f8138c38972edfb002fdda9be5b16eff5039c7b290734294603a6cbec8361a65962a780 Homepage: https://cran.r-project.org/package=TSEAL Description: CRAN Package 'TSEAL' (Time Series Analysis Library) The library allows to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. Note that all these steps can be done separately which allows to implement new steps. Velasco, I., Sipols, A., de Blas, C. S., Pastor, L., & Bayona, S. (2023) . Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) . Package: r-cran-tseffects Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mpoly, r-cran-car, r-cran-ggplot2, r-cran-sandwich Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tseffects_0.2.1-1.ca2604.1_all.deb Size: 380222 MD5sum: 47532bbcbce75d27e9fc12c04e211165 SHA1: ec7d334fdc888aee97900100ced82fade0cf2b50 SHA256: 949a32a12532171bcbd6d14fb512fb60e148098a096c00c4f7bbe6080c3f0f94 SHA512: 289f4f83ef9d52ad69cfe479d8d9629ccf51bb18e64d9e94abe00a702541361a302b777392d53fb6f21f411146b688fe1b0c9456665e4f26ce8de7a3101e2d77 Homepage: https://cran.r-project.org/package=tseffects Description: CRAN Package 'tseffects' (Dynamic Inferences from Time Series (with Interactions)) Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences. These tools apply to traditional time-series quantities of interest: especially instantaneous effects for any period and cumulative effects for any period (including the long-run effect). They also allow for a variety of shock histories to be applied to the independent variable (beyond just a one-time, one-unit increase) as well as the recovery of inferences in levels for shocks applies to (in)dependent variables in differences (what we call the Generalized Dynamic Response Function). These effects are also available for the general conditional dynamic model advocated by Warner, Vande Kamp, and Jordan (2026 ). We also provide the actual formulae for these effects. Package: r-cran-tseind Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tseind_0.1.0-1.ca2604.1_all.deb Size: 57096 MD5sum: 778ac04da5e618245abe2a9433206bc4 SHA1: 098c5a1c20cf7ee770e8d0607e05ebeb100752ad SHA256: 1cd269541c58a248db723fbd4361be13c0cae1643592da0271ac523842ed8fe8 SHA512: 6e749cf09ffc1baea17cad84dabc78dd2f0862c67a064b628cc35b47b3197cd9cc231cb11f87d3ec528f79c6510508d259c689f253065618ec8baa66e09052ce Homepage: https://cran.r-project.org/package=TSEind Description: CRAN Package 'TSEind' (Total Survey Error (Independent Samples)) Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) ; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc. Package: r-cran-tsensembler Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-rcpproll, r-cran-ranger, r-cran-glmnet, r-cran-earth, r-cran-kernlab, r-cran-cubist, r-cran-gbm, r-cran-pls, r-cran-monmlp, r-cran-doparallel, r-cran-foreach, r-cran-xgboost, r-cran-softimpute Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tsensembler_0.1.0-1.ca2604.1_all.deb Size: 508124 MD5sum: 30a39a105ce0164dbcff6bf4f1abec2c SHA1: 38ab34711fb22937e00c3f4a55f1dee1a6203452 SHA256: a5ee02d3f478ed4065f8f0cbb51ba8175774c6bac5b6ab3ae4ee7dd0dac98022 SHA512: 25d99d2355d2f3872580deb1b4142afacb63dccd031bd7d56c21a0c65ea371711c77716c27412d85684b0e9f454edc792f2cda7cb6fb11915482d72b8081eb1a Homepage: https://cran.r-project.org/package=tsensembler Description: CRAN Package 'tsensembler' (Dynamic Ensembles for Time Series Forecasting) A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 . Package: r-cran-tsentiment Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape2, r-cran-wordcloud, r-cran-ggplot2, r-cran-httr, r-cran-syuzhet, r-cran-tidytext, r-cran-dplyr, r-cran-tibble, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-tsentiment_1.0.5-1.ca2604.1_all.deb Size: 42200 MD5sum: 73b9c245d8a90895b3332316f14fda2f SHA1: 9df32a0f0cd192ab743f281bbd1fdf243ac606d1 SHA256: 42162fcf032de89ebbfa6d28d08ea3ef22e4eab53822799589ec5fce81078303 SHA512: c7b2545ec5d776b100462bcc92963bd5d26b09235a88d2fa66193c97b921e3421b49faf4f17dc40687c6c7da763ad93edffed815de6c70d3d49f7ae997bdd59a Homepage: https://cran.r-project.org/package=tsentiment Description: CRAN Package 'tsentiment' (Fetching Tweet Data for Sentiment Analysis) Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application. A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information ). In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information ). After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule. 'tsentiment' package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at . Package: r-cran-tseriesmma Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tseriesmma_0.1.1-1.ca2604.1_all.deb Size: 16224 MD5sum: 0cc2ae44790c216c7af41fc73a000ad5 SHA1: 5e613a59d0fa2734d9f5813ef8df3d6923ef645d SHA256: db2738f2aa4b2b51e3d2e2a55ffb910f6cc7e843d0db95789c46a30b2d9ab711 SHA512: ecbfe20533cbc5736168bec28814891c90ba1786f9a53300a615e12448638096a09075f2743a949d4b33199a0fc379ea4ecccda4e1c5c8b280adc1fb600009a5 Homepage: https://cran.r-project.org/package=TSeriesMMA Description: CRAN Package 'TSeriesMMA' (Multiscale Multifractal Analysis of Time Series Data) Multiscale multifractal analysis (MMA) (Gierałtowski et al., 2012) is a time series analysis method, designed to describe scaling properties of fluctuations within the signal analyzed. The main result of this procedure is the so called Hurst surface h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation scaling exponent) on the multifractal parameter q and the scale of observation s (data window width). Package: r-cran-tsetools Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-quantmod, r-cran-quadprog, r-cran-httr, r-cran-rjson Filename: pool/dists/resolute/main/r-cran-tsetools_1.0.0-1.ca2604.1_all.deb Size: 82708 MD5sum: 5ee8eb05a3d7f71eac185f1babf06bd8 SHA1: 7a8d3551d16e8f0b41a11825cd04adf738bb4a97 SHA256: fb40c69ac8203b4db01430825eeaffa7597ae7ed8e6b02ab769d7a31395ad24a SHA512: f9e41e3d7458b6be88547888f2a998f843d4ea2f92b44999afefd8773ff5d6e9eb6892e556ccb7ea18b06639dfed2ac1c925d169fb53afb6d7680a7983e31960 Homepage: https://cran.r-project.org/package=TSEtools Description: CRAN Package 'TSEtools' (Manage Data from the Finance Markets) A set of tools designed to perform descriptive data analysis on assets, manage asset portfolios and capital allocation, and download, organize, and maintain data from the "Tehran Stock Exchange" and "NOBITEX" platforms. Package: r-cran-tsewgt Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tsewgt_0.1.0-1.ca2604.1_all.deb Size: 60882 MD5sum: 788da7c914d29578ea571e72230f9261 SHA1: 9cf83ec063c4de84050fab470a401dfd4437acae SHA256: efcc97765cbb71e3d41c74658a588bd697da947153550d5fb27f03009a855af3 SHA512: 97f110bb9fe00db27deaa8b2d600ed2058ca7d4fcaa502588617954f50fa9ec77a0a0b93744e9317974a7889a84eadd63686a1ce7064e92450605af71e68a098 Homepage: https://cran.r-project.org/package=TSEwgt Description: CRAN Package 'TSEwgt' (Total Survey Error Under Multiple, Different Weighting Schemes) Calculates total survey error (TSE) for a survey under multiple, different weighting schemes, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTWGT and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) ; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc. Package: r-cran-tsf Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fracdiff, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-tsf_0.1.1-1.ca2604.1_all.deb Size: 21762 MD5sum: 9680952883ee10c7562cf43f42dae3c3 SHA1: 658fcd2d7d663382badd019060a21226a57797fc SHA256: 6575f7d5630f91ba09b57c6e778ca93e535802c243c420d19c1429aa68ae2af8 SHA512: 382925d493c1a4a36ec2e39b02b31de7619bb3c5eff277894c2b08a7888952b6a56932d36f6f994d01c8f5f2564fcb6d3ab0379599eb1a35f81e78f40cc70847 Homepage: https://cran.r-project.org/package=TSF Description: CRAN Package 'TSF' (Two Stage Forecasting (TSF) for Long Memory Time Series inPresence of Structural Break) Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) . Package: r-cran-tsfeatures Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fracdiff, r-cran-forecast, r-cran-purrr, r-cran-rcpproll, r-cran-tibble, r-cran-tseries, r-cran-urca, r-cran-future, r-cran-furrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-mcomp, r-cran-ggally Filename: pool/dists/resolute/main/r-cran-tsfeatures_1.1.1-1.ca2604.1_all.deb Size: 242716 MD5sum: 290b74095c28951c4b77ec03a59c7473 SHA1: 036730afc7da5dd74486c0e8adac82d6be6ed3ad SHA256: 7ed0968be2c43fafaff32735c6d593e064e196b040ad15e389ce57f1539ed2ba SHA512: 2c60ad47ef111ccbb661bc319841cd62e21094d6e36e7a6d63c712563160018ddb2169feecb3aa6c0f9b8eb695bf416908f8f9e8136f965b00be976a263f2c58 Homepage: https://cran.r-project.org/package=tsfeatures Description: CRAN Package 'tsfeatures' (Time Series Feature Extraction) Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) , Kang, Hyndman and Smith-Miles (2017) and from Fulcher, Little and Jones (2013) . Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions. Package: r-cran-tsfngm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tsfngm_0.1.0-1.ca2604.1_all.deb Size: 16098 MD5sum: 11197448052b1b4d6f72a63b657897b3 SHA1: c2baddd13ea7c2ac818fc6a992e6681aaadd472c SHA256: 7b638aa8de7a0fb285cdb8bc8d8952b9d93b3b1d0aa7cc315977db9f4a535ff3 SHA512: a4c5357f950d9712e9e56a1e230a15f7c86fe065f12aa388975005d3ec5801f205c0b8c4f455f6e157be67f3506d2c4b4407bc3efc9ce4ef949b4810871c9afc Homepage: https://cran.r-project.org/package=tsfngm Description: CRAN Package 'tsfngm' (Time Series Forecasting using Nonlinear Growth Models) Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally 'mechanistic,' with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models. Package: r-cran-tsforecast Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-forecast, r-cran-tseries, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tsforecast_1.3.0-1.ca2604.1_all.deb Size: 292094 MD5sum: fb1bcef0d0c76a9d26e94759c7d4f429 SHA1: a114352a7a1ca36403fe7007a75b5398afd97972 SHA256: 9a13a95359be8b1aaf699873279e2b8f5cadd182856a76e5cd951caac23ce86d SHA512: 50c25fe46c65ab25f97addf650a86cd5e08e7fe5c99fd22c613447a70a87bc3e83fd2abbfffeb2b9e774d6946b59b49987ea9be9d289da21ea45f081022335fd Homepage: https://cran.r-project.org/package=tsforecast Description: CRAN Package 'tsforecast' (Time Series Forecasting Functions) Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) . Package: r-cran-tsg Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-haven, r-cran-openxlsx, r-cran-fs, r-cran-forcats, r-cran-purrr, r-cran-tidyr, r-cran-glue, r-cran-jsonlite, r-cran-yaml, r-cran-dplyr, r-cran-stringr, r-cran-rlang, r-cran-gt, r-cran-lifecycle, r-cran-officer, r-cran-flextable, r-cran-qpdf Suggests: r-cran-testthat, r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-webshot2, r-cran-withr Filename: pool/dists/resolute/main/r-cran-tsg_0.1.4-1.ca2604.1_all.deb Size: 383636 MD5sum: 5082c48bfcfb93913d3deb183f4280b8 SHA1: fa9d084c17fb77d577891421b8f64ca23532abce SHA256: dc8ac69d90333b7f7b0c412a4a1d91d87c082457382b094780f2bf5baa7edf58 SHA512: c7df53228e8504410aebbc4bb62b74f10667d1ad73f40cbb6323a63ca1af467634cb18d277057d923bb6b7bb5ce88a09be61726bd4d4c24db6cb003220e6f921 Homepage: https://cran.r-project.org/package=tsg Description: CRAN Package 'tsg' (Generate Publication-Ready Statistical Tables) A collection of functions for generating frequency tables and cross-tabulations of categorical variables. 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Package: r-cran-tsgc Architecture: all Version: 0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kfas, r-cran-xts, r-cran-ggplot2, r-cran-ggthemes, r-cran-zoo, r-cran-magrittr, r-cran-scales, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-ggfortify, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-ggforce, r-cran-gridextra, r-cran-latex2exp, r-cran-here, r-cran-timetk, r-cran-testthat, r-cran-purrr, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-tsgc_0.0-1.ca2604.1_all.deb Size: 838394 MD5sum: 8532565ac49e2fcce3e5730725a19dbc SHA1: ded3f25dbb960c6dc76946485c53c361d4401646 SHA256: b81256841b2f54324c34f7c70bea4630763f163264e521364c7a065f48abc7d6 SHA512: 995783069684713d8d9ff5a6f3f2bc923eae134eeb862cdf9633898ea053d258a560b6d7c9b05ed5bedaabaf138d98d45f6c4f9ac60b7de39a87373b885952cb Homepage: https://cran.r-project.org/package=tsgc Description: CRAN Package 'tsgc' (Time Series Methods Based on Growth Curves) The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories. It is designed to model the progression of an epidemic over time while accounting for the various uncertainties inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach, using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves. The forecasts generated by the package are of value to public health officials and researchers who need to understand and predict the course of an epidemic to inform decision-making. Beyond its application in public health, the package is also a useful resource for researchers and practitioners in fields where the trajectories of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy. The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in Harvey and Kattuman (2020) , Harvey and Kattuman (2021) , and Ashby, Harvey, Kattuman, and Thamotheram (2024) . Package: r-cran-tsgs Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-bioc-edger, r-cran-fastmatch, r-cran-genalg, r-cran-kernlab, r-cran-e1071 Filename: pool/dists/resolute/main/r-cran-tsgs_1.0-1.ca2604.1_all.deb Size: 62224 MD5sum: 8434341ddfec92cc724f9acdaf890a96 SHA1: 35dbade0bd4ccc60286e0486c5b34b953e59076b SHA256: f3aeaaf82fc72e0c7bee8fb71a3446f8285511bb13296a3b791119a330a1608e SHA512: 29e4c71e3c80e68fc87a19052eefe65bfb06a3b44e1ac14daa1c6931be0811fa18b5044d853c55ec7782f93519bce998d3f546b4f6566fc3b754a324cf0996b9 Homepage: https://cran.r-project.org/package=TSGS Description: CRAN Package 'TSGS' (Trait Specific Gene Selection using SVM and GA) Obtaining relevant set of trait specific genes from gene expression data is important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package returns the trait specific gene set from the high dimensional RNA-seq count data by applying combination of two conventional machine learning algorithms, support vector machine (SVM) and genetic algorithm (GA). GA is used to control and optimize the subset of genes sent to the SVM for classification and evaluation. Genetic algorithm uses repeated learning steps and cross validation over number of possible solution and selects the best. The algorithm selects the set of genes based on a fitness function that is obtained via support vector machines. Using SVM as the classifier performance and the genetic algorithm for feature selection, a set of trait specific gene set is obtained. Package: r-cran-tsgsis Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass Filename: pool/dists/resolute/main/r-cran-tsgsis_0.1-1.ca2604.1_all.deb Size: 30176 MD5sum: 978766c9c202089a34470b5c85afdcda SHA1: 783acb93d66b9c32bf790b9fbe25ceb07c75a3c4 SHA256: 4316c7e1fba67798a29c5383dec437fa59385a5e4993e424bf4cb7a57be012cb SHA512: aaeb720effdad9c626e012a693a88008d8611511d6bb982e6b741fc7af585e43b8c9750e9540d23435759956af9ea46e09193eb4effa8d359d1d226f4120e2ff Homepage: https://cran.r-project.org/package=TSGSIS Description: CRAN Package 'TSGSIS' (Two Stage-Grouped Sure Independence Screening) To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review). 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These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted. Package: r-cran-tsibbletalk Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crosstalk, r-cran-dendextend, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-plotly, r-cran-r6, r-cran-rlang, r-cran-shiny, r-cran-tsibble, r-cran-vctrs Suggests: r-cran-fabletools, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-tsibbletalk_0.1.0-1.ca2604.1_all.deb Size: 642576 MD5sum: a861234e183bffedf455e2e8b0a964e5 SHA1: e42d8758257601c503bbab92336326c2ebd6c598 SHA256: a6280e2f966270b989710581e2ec7513513dc4f219038189ec3dc2fd59da9245 SHA512: 42edc7a58a5a99ffe816016e8886c3942ed2bbc1adc1322ec6c7de2e059e95da219d5879ec85e3db95c75f88aee086525c69ae866de1fe2058e2b86b231e1c18 Homepage: https://cran.r-project.org/package=tsibbletalk Description: CRAN Package 'tsibbletalk' (Interactive Graphics for Tsibble Objects) A shared tsibble data easily communicates between htmlwidgets on both client and server sides, powered by 'crosstalk'. A shiny module is provided to visually explore periodic/aperiodic temporal patterns. Package: r-cran-tsintermittent Architecture: all Version: 1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mapa Filename: pool/dists/resolute/main/r-cran-tsintermittent_1.10-1.ca2604.1_all.deb Size: 111508 MD5sum: c1d91a041a5009973271a4db4d22792e SHA1: 333878f3bf9c878845f235b201745d83d8daf062 SHA256: 66dcca4e8f2f6756d0c921920bc5cfc64f185e1340e6fd1e563110bd3dac705c SHA512: ed8213bd029084b64f645bb6910eed16f6e40f003b4ba0df362a674a61f0bb72f9c7f6a1966971c3967ce152481eb5ee7017eadb09448df53bbcc393268d2913 Homepage: https://cran.r-project.org/package=tsintermittent Description: CRAN Package 'tsintermittent' (Intermittent Time Series Forecasting) Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) ). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) ). Package: r-cran-tsir Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-kernlab, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-tsir_0.4.3-1.ca2604.1_all.deb Size: 341376 MD5sum: 5312722b74cfe3b8f351fd4d196f6279 SHA1: 1e1c9e85b68f7166c8d002c9ce44e9d1e10105dd SHA256: 6b85d565b4d52a53e59a9e0e524d624c72f0549bb2bdc6d7e3d6abb8886c67cb SHA512: 9a50689a1654c5749afe15187fbb2ba6deffc1ba78b8fb15fcb18e24af4c3f4df64bb854bf7d04db1bbd7469fd2f6cd164f472733dffbcf5b89ed39986035bca Homepage: https://cran.r-project.org/package=tsiR Description: CRAN Package 'tsiR' (An Implementation of the TSIR Model) An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model using a number of different fitting options for infectious disease time series data. The manuscript based on this package can be found here . The method implemented here is described by Finkenstadt and Grenfell (2000) . Package: r-cran-tslstm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-tsutils Filename: pool/dists/resolute/main/r-cran-tslstm_0.1.0-1.ca2604.1_all.deb Size: 24010 MD5sum: 08900cb6761c5b3808ec7e06e49dbe37 SHA1: fb86b7ae6c3864ea876edd08f85766bf301f80a8 SHA256: 53285a05283d1a671bcffac6df05f257090a76f1280275a8fd18605b71f98c17 SHA512: 186ce29f2183e4cc30e32a9158adcee8f18983137ef5fe8d888fa81dbcae6951e0d710e2c1d4ef37be04aaba01d0fba7bb76128a3dad702ad1fec0300c510f63 Homepage: https://cran.r-project.org/package=TSLSTM Description: CRAN Package 'TSLSTM' (Long Short Term Memory (LSTM) Model for Time Series Forecasting) The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) . Package: r-cran-tslstmplus Architecture: all Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-abind Filename: pool/dists/resolute/main/r-cran-tslstmplus_1.0.6-1.ca2604.1_all.deb Size: 55402 MD5sum: fd1b45a00460588762a78ebed1c70af3 SHA1: a9e645ce2b51ade37fa919bc2212f91333fb1f63 SHA256: 3968c24eb429d7ad779a394d35ee93f1ef52ed52fe08847e8280edaff8908330 SHA512: a96dbf179a84ab0ee5de4d4ac995cfa585110d20b7353f2fc85ca7fcfe346462ce35d11644ff519e635dedb13f574a15fe958e845a5485b5e2a3d73f0888df4e Homepage: https://cran.r-project.org/package=TSLSTMplus Description: CRAN Package 'TSLSTMplus' (Long-Short Term Memory for Time-Series Forecasting, Enhanced) The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) . Package: r-cran-tslstmx Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-allmetrics, r-cran-keras, r-cran-reticulate Filename: pool/dists/resolute/main/r-cran-tslstmx_0.1.0-1.ca2604.1_all.deb Size: 68270 MD5sum: 558171f020a08b6ae4a4443b2ddc1983 SHA1: e5839121576bc08768ad269d134fc05ec3e713c8 SHA256: 3c376b9610213daaae778e43f3d29c81350d8b0085f81a9fd0bd760346f02c72 SHA512: 3f9779516ae3d74a62125021f86b9a37a3b4ee0d27d14d8f8ee3d787b4539ff8fed3804aae8fde150a0df08f43b9a15e3df0ae1c6dbc55435e93a3fdd338392b Homepage: https://cran.r-project.org/package=tsLSTMx Description: CRAN Package 'tsLSTMx' (Predict Time Series Using LSTM Model Including ExogenousVariable to Denote Zero Values) It is a versatile tool for predicting time series data using Long Short-Term Memory (LSTM) models. 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This work was supported by grant R01HD68395 from the National Institute of Health. Package: r-cran-tsne Architecture: all Version: 0.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tsne_0.2-0-1.ca2604.1_all.deb Size: 22406 MD5sum: d52a49aff72a22d155d2957c44d741df SHA1: eada0413ad8c9d0d098ab616953509e15e9c97d7 SHA256: 3f836941a42a0544f2e954064255db7e6739ff8dd83fc7b1ac37bb2e492f4bde SHA512: fe70ec106bc0c119502bbb3c78f5d6cfc0962a142f129096d741fa3b19f0178ce05fc89ff4b3f492432aec4c521441ab31c9661293d7e7c799f9fa90a9e0e6a2 Homepage: https://cran.r-project.org/package=tsne Description: CRAN Package 'tsne' (T-Distributed Stochastic Neighbor Embedding for R (t-SNE)) A "pure R" implementation of the t-SNE algorithm. Package: r-cran-tsoutliers Architecture: all Version: 0.6-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-tsoutliers_0.6-10-1.ca2604.1_all.deb Size: 232808 MD5sum: 42178730410073b22bc76f89ed3addbc SHA1: 8d9a7e00f381bd6bc79c449a8509796758b73175 SHA256: e1339bba7b47fb5b6136a5d6c43aab114b0355a5dbee201b9c4c5d93ea5bdf41 SHA512: 627f055dbaaffb679276ecbd4cc07a29989e2f65a9048843776d57894020a6c9e1638d492dd9530328d758023ab8a7e2e9a71a2fab9fa261ae2d6871c94a2bab Homepage: https://cran.r-project.org/package=tsoutliers Description: CRAN Package 'tsoutliers' (Detection of Outliers in Time Series) Detection of outliers in time series following the Chen and Liu (1993) procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered. 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The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used. Package: r-cran-tspredit Architecture: all Version: 2.0.707-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-desctools, r-cran-e1071, r-cran-elmnnrcpp, r-cran-fnn, r-cran-forecast, r-cran-hht, r-cran-kfas, r-cran-mfilter, r-cran-nnet, r-cran-randomforest, r-cran-wavelets, r-cran-dplyr, r-cran-daltoolbox Filename: pool/dists/resolute/main/r-cran-tspredit_2.0.707-1.ca2604.1_all.deb Size: 535200 MD5sum: 0d95f315bd7aa3343a7dcd41a464cbac SHA1: 429871337d9a11373e315c6e894ce3362fa07f4c SHA256: 1b41ce8eed2de74a9a291d3cf29d94a39d7528f41378282182e4eaf87876f6f4 SHA512: ee98968cf858943b562cd330bd0f947530744086c1f12df4408dfd13a6f9c185e7062937a0cf4bf3d9ba1fc9030ed47d5b0d69d3b4167de986066c8b9bd71fd1 Homepage: https://cran.r-project.org/package=tspredit Description: CRAN Package 'tspredit' (Time Series Prediction with Integrated Tuning) Time series prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies. TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. More information is available in Salles et al. . Package: r-cran-tsqca Architecture: all Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qca Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tsqca_1.3.2-1.ca2604.1_all.deb Size: 313542 MD5sum: 41e2ab991c96d2db5b44410393a8b21a SHA1: bfbd8a94a619071586c89e50f7bf98933c99f676 SHA256: 91ecd3b3ea2d6840643ca892cfe04050e23e2c19cc018f594e3217ad45526fb7 SHA512: 0bde5daa59c576c6be5bdbffb150b61553442a9ffb1471219f40d2dce6e9c97f8c38828f9b12634bbba8151438fd28aeeed583cbbcad50fac139cb2e9b927199 Homepage: https://cran.r-project.org/package=TSQCA Description: CRAN Package 'TSQCA' (Threshold Sweep Extensions for Qualitative Comparative Analysis) Provides threshold sweep methods for Qualitative Comparative Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an exploratory tool for discovering causal structures. Also provides Fiss (2011) core/peripheral condition classification via compute_fiss_core() and generate_fiss_chart(), enabling four-symbol configuration charts that distinguish core conditions (present in both parsimonious and intermediate solutions) from peripheral conditions (intermediate only). Built on top of the 'QCA' package by Dusa (2019) , with function arguments following 'QCA' conventions. Based on set-theoretic methods by Ragin (2008) and established robustness protocols by Rubinson et al. (2019) . Package: r-cran-tsqn Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-mass, r-cran-fracdiff Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tsqn_1.2.0-1.ca2604.1_all.deb Size: 159248 MD5sum: d770c5a95d3d28f645f12a8d2d92f5dd SHA1: 3b142bdf8751731390bfb4f1c9a244ed4bab8dc0 SHA256: 1af86d9d7d7e68c79340fed25060362ef60b7edc62c2c07020fa5fb43eb2af82 SHA512: b4618f553ca51b92138dd1d827a07a0118eddc1c19aecb0ee5d19dd7bfff5dcca412cb26dddea24c2b53c429024b7527f89e930bc1c9ea41e9086847eb9285b5 Homepage: https://cran.r-project.org/package=tsqn Description: CRAN Package 'tsqn' (Applications of the Qn Estimator to Time Series (Univariate andMultivariate)) Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) , and Cotta (2017) are provided. The robust pseudo-periodogram of Molinares et. al. (2009) is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) . Package: r-cran-tsriadditive Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-tsriadditive_1.0.0-1.ca2604.1_all.deb Size: 68702 MD5sum: b95de3b840027b3d1f41b51d2c998d41 SHA1: 4d188747fad09ee374bcde1918af628ddb294fff SHA256: 523d817260d67e409f0bf36e5187ea4e10289d5c7ec7dd7b0f1439cae94e178a SHA512: eb992da559663e03fea3b379646f9f8871bc6d0a81faad17aa66874f091b3ce0bbafca69223676740446dc35e4daa84318f2a141a698d275c21df2b36808df05 Homepage: https://cran.r-project.org/package=tsriadditive Description: CRAN Package 'tsriadditive' (Two Stage Residual Inclusion Additive Hazards Estimator) Additive hazards models with two stage residual inclusion method are fitted under either survival data or competing risks data. The estimator incorporates an instrumental variable and therefore can recover causal estimand in the presence of unmeasured confounding under some assumptions. A.Ying, R. Xu and J. Murphy. (2019) . Package: r-cran-tsrobprep Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-matrix, r-cran-mclust, r-cran-quantreg, r-cran-rdpack, r-cran-texttinyr, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-tsrobprep_0.3.2-1.ca2604.1_all.deb Size: 187096 MD5sum: 3226733887d7aaa1d9aa8e05fd472bc5 SHA1: 00c8fa4a67979320328948f64443cfc7ed549aad SHA256: a5ac765d83978b81b6d6b9384d89849f779571538eb298557a17428aaec48662 SHA512: 24b8052dcf72a3e5aca287ce7aaf7922eca62f3c7fd68339f78b8e3f40f5e7b24a85076b55cdce0171f2fba91647b392c9919c0594b90b4dff868646719ae8af Homepage: https://cran.r-project.org/package=tsrobprep Description: CRAN Package 'tsrobprep' (Robust Preprocessing of Time Series Data) Methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data. For details, see Narajewski et al. (2021) . 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This data package includes a collection of 10 years (2006 - 2015) worth of data on materials used at U.S. breweries in pounds reported by the Brewer's Report of Operations and the Quarterly Brewer's Report of Operations forms, ready for data analysis. This package also includes historical tax rates on distilled spirits, wine, beer, champagne, and tobacco products as individual data sets. 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A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017). Package: r-cran-ttcg Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ttcg_1.0.1-1.ca2604.1_all.deb Size: 30948 MD5sum: 972564eee645a1919b70327d23982d43 SHA1: 0b29a15212b2db3bb146adabb769b9d84cc2426b SHA256: 5044102acd82a51a2604af6b9455c65ecde55482ed8c4fdae418a1c776d58cd1 SHA512: 1719c105e286677cc3da430222606867c00af9563505884dbe63dd150c9608b774e5ebb160ead679f2825ced62188a2a756f578136673511bb63ecb3b870fdbe Homepage: https://cran.r-project.org/package=ttcg Description: CRAN Package 'ttcg' (Three-Term Conjugate Gradient for Unconstrained Optimization) Some accelerated three-term conjugate gradient algorithms implemented purely in R with the same user interface as optim(). The search directions and acceleration scheme are described in Andrei, N. (2013) , Andrei, N. (2013) , and Andrei, N (2015) . Line search is done by a hybrid algorithm incorporating the ideas in Oliveia and Takahashi (2020) and More and Thuente (1994) . 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This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercurrent events (ICE) under the five strategies outlined in the ICH E9 (R1) addendum, see Deng (2025) . This package can be used for analyzing data from both randomized controlled trials and observational studies. In general, the data involve a primary outcome event and, potentially, an intercurrent event. Two data structures are allowed: competing risks, where only the time to the first event is recorded, and semicompeting risks, where the times to both the primary outcome event and intercurrent event (or censoring) are recorded. For estimation methods, users can choose nonparametric estimation (which does not use covariates) and semiparametrically efficient estimation. Package: r-cran-ttolr Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-mass, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-ttolr_0.2.3-1.ca2604.1_all.deb Size: 188590 MD5sum: 1f567269afab7128c82df3aeafa8e9a8 SHA1: d69873e29700d8e9c5ca6f93fdf956cf1b78f39d SHA256: 893b3c410c0fef356c34ab084b84210858c963557c5c9972ea05e3abbd4ed1c5 SHA512: b12851021a170ed103f1441ea24a657966d0de3b8607ba27dcb0eb8c805ee31100659ae3082d11dbd1e78cfd0426f600c5ad601bc7f292e964f429a2408f7dbe Homepage: https://cran.r-project.org/package=tTOlr Description: CRAN Package 'tTOlr' (Likelihood Ratio Statistics for One or Two Sample T-Tests) Likelihood ratio and maximum likelihood statistics are provided that can be used as alternatives to p-values Colquhoun (2017) . 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Package: r-cran-ttscreening Architecture: all Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-bioc-sva, r-bioc-limma, r-cran-corpcor, r-cran-simsalapar, r-cran-mass, r-cran-brglm2 Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-ttscreening_1.8-1.ca2604.1_all.deb Size: 69622 MD5sum: e81bb05e5ecfe3ea834232805477d9c4 SHA1: f182663d4664a6318e638769f8d53a7d70a5b403 SHA256: fe145f6d5256f5ca60d8ab0163f4517621dcfbde86ef19e9025c27e182a3c6f1 SHA512: a8bd0e885691ada2839a338abb71bb8662524bfbb46b883f1c5840cfd84c87def8918d5694e5e51e9b5a40b833b598ef297275ed5d101edecc69be4c60951bba Homepage: https://cran.r-project.org/package=ttScreening Description: CRAN Package 'ttScreening' (Genome-Wide DNA Methylation Sites Screening by Use of Trainingand Testing Samples) A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects. This package also provides two screening functions for screening high-dimensional predictors when the events are rare. The firth method is called 'Rare-Screening' which employs a repeated random sampling with replacement and using linear modeling with Bayes adjustment. The Second method is called 'Firth-ttScreening' which uses 'ttScreening' method with additional Firth correction term in the maximum likelihood for the logistic regression model. These methods handle the high-dimensionality and low event rates. 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Oseledets (2011) , Yuan Longao, et al (2017) , I. V. Oseledets (2010) . 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Package: r-cran-tuvalues Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-roi, r-cran-roi.plugin.glpk Filename: pool/dists/resolute/main/r-cran-tuvalues_1.1.1-1.ca2604.1_all.deb Size: 110190 MD5sum: c9f90b5acb9730837f1b84d59d22d263 SHA1: bb837fc9f4c662681d7806f9894d64fe953142a4 SHA256: d269cc10839054f81080a6fefd77cb3f5288ed0342f1c4f5bd7d9676f5d55bed SHA512: 2a50d7ee82224044a7e79e038ee82cb917dd0c9801b1a14fe3056b6a6c35db9faedc0ed648eae3308922b98daef45f701b042c1d6c53415da7ea982fcfcbd2e7 Homepage: https://cran.r-project.org/package=TUvalues Description: CRAN Package 'TUvalues' (Tools for Calculating Allocations in Game Theory using Exact andApproximated Methods) The main objective of cooperative Transferable-Utility games (TU-games) is to allocate a good among the agents involved. The package implements major solution concepts including the Shapley value, Banzhaf value, and egalitarian rules, alongside their extensions for structured games: the Owen value and Banzhaf-Owen value for games with a priori unions, and the Myerson value for communication games on networks. To address the inherent exponential computational complexity of exact evaluation, the package offers both exact algorithms and linear approximation methods based on sampling, enabling the analysis of large-scale games. Additionally, it supports core set-based solutions, allowing computation of the vertices and the centroid of the core. Package: r-cran-tv Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-tibble, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tv_2.0.2-1.ca2604.1_all.deb Size: 45668 MD5sum: 1ebc6342589491b8cd5037adae717f68 SHA1: 8c6f5c54b715a020ea2d24e28570d937e9b68020 SHA256: 2e4de2a3ede71fae1eac6511fc6346a989b6ae817b07e8a7fdec7e6cf92255e3 SHA512: b7de4e07ea93a9b21926d182fd9c251338617bfd8a7265397212584dcff149a8c8ebe7c8dab01b31c8741e200af4ae09dc4e98d23d1d777e84cdf293815cfcdf Homepage: https://cran.r-project.org/package=tv Description: CRAN Package 'tv' (Tools for Creating Time-Varying Datasets) Create a time-varying dataset using features, exposure, and look back specifications. Package: r-cran-tvcure Architecture: all Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubicbsplines, r-cran-rfast, r-cran-mass, r-cran-matrix, r-cran-mgcv Filename: pool/dists/resolute/main/r-cran-tvcure_0.6.6-1.ca2604.1_all.deb Size: 531420 MD5sum: 19f1a102a73e46dbaf35466e8225d609 SHA1: f83ec764b8365ae158f8b09dd8a9dad2f6304bbe SHA256: 63c3ca04b2fd65d0eb84b85ba6461a0ffc8c5b2bcfdea04f5028fe8662734ad5 SHA512: a266b3f6fdb68dc05c8e7d905ea264ee65b8d54fa1fd30f2eb88e7f30cf78e467104a262e73f676d8cf3643623a405e9a8fbbcf0a8921687b097f07d491bec64 Homepage: https://cran.r-project.org/package=tvcure Description: CRAN Package 'tvcure' (Additive Cure Survival Model with Time-Varying Covariates) Fit of a double additive cure survival model with time-varying covariates. The additive terms in the long- and short-term survival submodels, modelling the cure probability and the event timing for susceptible units, are estimated using Laplace P-splines. For more details, see Lambert and Kreyenfeld (2025) . Package: r-cran-tvem Architecture: all Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tvem_1.4.1-1.ca2604.1_all.deb Size: 123376 MD5sum: 28a8c9a0bb8a3732886638b2f7f93180 SHA1: ea3bb90df02d964b7ff9fc0f988fdbf69f395786 SHA256: cc4a6fa26b57e5faed76b48750feeb64e98521e81dedcb327110b6801f2ae929 SHA512: 8edea3882e950695460dd3e8e0ad650e13df98954db3cb098aba245ecc0c7750bf3f0f85bb69428d7e8b664622b3c9d91de688e5c740985f1dc9ca82c992eef6 Homepage: https://cran.r-project.org/package=tvem Description: CRAN Package 'tvem' (Time-Varying Effect Models) Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time. These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) . We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Package: r-cran-tvgarch Architecture: all Version: 2.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-garchx, r-cran-zoo, r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-tvgarch_2.4.3-1.ca2604.1_all.deb Size: 246598 MD5sum: 82bca2da0925a35a5c496327523b548e SHA1: 5919ed7b6eefbfd3b3e390368885534acc1e2361 SHA256: 3639df3ada59dea9135fd6dc636f84b0ecb1ec7b1a75e52ef881907be33ed4fd SHA512: e4589224ce6be219b826da5bcfddc67c21751e256b937aeb26ad712467fb3b3dfcfc79b063c5452e14d37d851058abc3f587e51480eb22234e4c7d1a97731168 Homepage: https://cran.r-project.org/package=tvgarch Description: CRAN Package 'tvgarch' (Time Varying GARCH Modelling) Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) . In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates. Package: r-cran-tvm Architecture: all Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tvm_0.5.2-1.ca2604.1_all.deb Size: 80890 MD5sum: 294f6a888b6d57dba5c55acd84b2e64e SHA1: ad5edda6261ed337f197b30c33e6dabd8bad6983 SHA256: 89a9a87422c2bac79e5249fd4654934ebba249748e3e4225b26723e1094ea573 SHA512: 43e854e3856c98a468c54dffa35690301588314b7ddf52d3d079fa37d9a81f9b8c6d57d41c982a853c27cabc4364bbfa99fcbfba87abfbc392afa0fac13b1a9a Homepage: https://cran.r-project.org/package=tvm Description: CRAN Package 'tvm' (Time Value of Money Functions) Functions for managing cashflows and interest rate curves. Package: r-cran-tvmcomp Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-tvmcomp_1.0.2-1.ca2604.1_all.deb Size: 126000 MD5sum: 288c70fb070913ec3e11d59bc5cec642 SHA1: fe2634ae3e220b76ace86de1123c0e2f4e573c84 SHA256: 12935a9f94bd0a6a11b910c9a2c740107328ca7600106dfcedea15a2f3c08a16 SHA512: bb9627673a8d03570f3c9efbaa7cbda89469b19a4bcf904e203145584dfdebbed8645ce6ac18dc8d3d36d413902da143a86e6db3850c8b0f06021bcb5d4dec84 Homepage: https://cran.r-project.org/package=tvmComp Description: CRAN Package 'tvmComp' (Discounting and Compounding Calculations for Various Scenarios) Functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money. Raymond M. Brooks (“Financial Management,” 2018, ISBN: 9780134730417). Sheridan Titman, Arthur J. Keown, John D. Martin (“Financial Management: Principles and Applications,” 2017, ISBN: 9780134417219). Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (“Fundamentals of Corporate Finance,” 2019, ISBN: 9780134735313). S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (“Contemporary Business Mathematics with Canadian Applications,” 2020, ISBN: 9780135285015). Package: r-cran-tvmediation Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-locpol Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tvmediation_1.1.1-1.ca2604.1_all.deb Size: 672900 MD5sum: 2a87699b3097a51abcecc03dd84d1d33 SHA1: 33c29f3a849fc286c34c3489421fe460b29c3983 SHA256: e2d8db9a7ccc3bd31e99beaf6395b206facce200859ad76eccc3c2e3d7ee8b14 SHA512: c71f976291671ab2de306d01bdebc04aa6c678b7cfdb0e870c0553a7b3e899b2f545129d13b2dd5e29e3e23d4a14ab79ead9c64e5590394c4c827252dcc0843a Homepage: https://cran.r-project.org/package=tvmediation Description: CRAN Package 'tvmediation' (Time Varying Mediation Analysis) Provides functions for estimating mediation effects that vary over time as described in Cai X, Coffman DL, Piper ME, Li R. Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med Res Methodol. 2022;22(1):1-12. 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P. Alves & Daniel F. Ferreira (2019) ). 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The package offers methods for determining the optimal number of factors to be used in the covariance estimation, a hypothesis test of time-varying covariance, and user-friendly functions for portfolio optimization and rolling window evaluation. The local PCA method, method for determining the number of factors, and associated hypothesis test are based on Su and Wang (2017) . The approach to time-varying portfolio optimization follows Fan et al. (2024) . The regularisation applied to the residual covariance matrix adopts the technique introduced by Chen et al. (2019) . Package: r-cran-tvreg Architecture: all Version: 0.5.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-systemfit, r-cran-mass, r-cran-vars, r-cran-bvarsv, r-cran-plm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tvreg_0.5.11-1.ca2604.1_all.deb Size: 1095030 MD5sum: 13b013b194b064469aa3f34cb630b2b2 SHA1: e0ad3b14f349ad27ac6416523ace4df84a052701 SHA256: 08aee5466830234b5d61b6277cd4af64f10a102fbeda128517be3e8cf9a4b5b4 SHA512: adddac4689d243ecf589295bdc0b2cf87910a694d01a1a39f53a80f975b94c459426c09a13cb8f04a0fa51ccf77327b587b8f9a0a4e54bd065b0a44bb73cf57c Homepage: https://cran.r-project.org/package=tvReg Description: CRAN Package 'tvReg' (Time-Varying Coefficient for Single and Multi-EquationRegressions) Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques. 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The package is model-agnostic and accepts only a time vector and survival matrices, returning RMST-based quantities and bootstrap summaries. For restricted mean survival time methodology, see Royston and Parmar (2013) . 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Package: r-cran-twangcontinuous Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lattice, r-cran-gbm, r-cran-survey, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-twangcontinuous_1.0.0-1.ca2604.1_all.deb Size: 372686 MD5sum: 6e6ba7daf9cce99de7c81c9847095628 SHA1: 1e580630b9cce4a18f78ea86b1c882c6b0eba94e SHA256: 3c955f5cbc9c3c82a3b213e73367247a5339ebee8ecd8d01a3eba0ec1c891bbb SHA512: 5701dd3955ccf883b6e73f407941384f23daaf3cccf8d52458013db9b8d4d7c243526d5fc03e07f79dda33bfa18351a1075af4f10b641aa58dcff912342d52d2 Homepage: https://cran.r-project.org/package=twangContinuous Description: CRAN Package 'twangContinuous' (Toolkit for Weighting and Analysis of Nonequivalent Groups -Continuous Exposures) Provides functions for propensity score estimation and weighting for continuous exposures as described in Zhu, Y., Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for estimating generalized propensity scores with continuous treatments. Journal of Causal Inference, 3(1), 25-40. . Package: r-cran-twangmediation Architecture: all Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-twang, r-cran-gbm, r-cran-gridextra, r-cran-lattice, r-cran-latticeextra, r-cran-survey Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-twangmediation_1.2.1-1.ca2604.1_all.deb Size: 1285326 MD5sum: be4c4aa1909acd5f6b2939d788cf01e4 SHA1: ab5e0e5baabe5ed0378d635e990a79879fda6efb SHA256: 05dae1819e1ff1d9a60dd59f0219897cc4c0de0326033ba472b8320c3d75b9f2 SHA512: d8c6e1eb68038ba700e8ffb72349664e26058ed68f9a505611edea0a2007e60d56f8c6322dff9491d94678a01ed1aa9be5ef8fc03e64d5a286d7c3fc9d1bb730 Homepage: https://cran.r-project.org/package=twangMediation Description: CRAN Package 'twangMediation' (Twang Causal Mediation Modeling via Weighting) Provides functions for estimating natural direct and indirect effects for mediation analysis. It uses weighting where the weights are functions of estimates of the probability of exposure or treatment assignment (Hong, G (2010). Huber, M. (2014). ). Estimation of probabilities can use generalized boosting or logistic regression. Additional functions provide diagnostics of the model fit and weights. The vignette provides details and examples. 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Package: r-cran-twfy Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-twfy_0.1.0-1.ca2604.1_all.deb Size: 80082 MD5sum: 72ba204e36604fd6708f5d83f15d3329 SHA1: ad634aef86849c11f0f11e7b40345a3272f5b405 SHA256: 0769fcc795ff16ae31ad9201e3c15a70c10e362b9812936e9df154a29e156f09 SHA512: df2e03e12e9d99de7083b76fc997411e8d2c9f4894d395a044f98a58c4cfbd7f24b4dae4dac75f2b62c70ac1106357025d13373db18f79d57e2bae872379b670 Homepage: https://cran.r-project.org/package=twfy Description: CRAN Package 'twfy' (Drive the API for TheyWorkForYou) An R wrapper around the API of TheyWorkForYou, a parliamentary monitoring site that scrapes and repackages Hansard (the UK's parliamentary record) and augments it with information from the Register of Members' Interests, election results, and voting records to provide a unified source of information about UK legislators and their activities. See for details. Package: r-cran-twig Architecture: all Version: 1.0.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2315 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-foreach, r-cran-reshape2, r-cran-abind, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-twig_1.0.0.0-1.ca2604.1_all.deb Size: 1923648 MD5sum: 77a21d45d218df07ec790b797218b5d2 SHA1: b31887f27daa1caebc78a0c065a9158b1990adbf SHA256: c7ad212b7caf02cf3b43d009383073eb8b76d2973ce2bff9c50a5215a981ecbb SHA512: d2a97a29745fc7661cf06d9b2bdeb835ce6e05eeb294dd7a05abb7a741c14030fcb65b093dac2df1d89bbe83fa4bde2019cc93c1bbc549570a60d5dedb36e160 Homepage: https://cran.r-project.org/package=twig Description: CRAN Package 'twig' (For Streamlining Decision and Economic Evaluation Models usingGrammar of Modeling) Provides tools for building decision and cost-effectiveness analysis models. 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Package: r-cran-twilio Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-twilio_0.1.0-1.ca2604.1_all.deb Size: 26156 MD5sum: 47040653df71049679e6ffba69e5ffd8 SHA1: 2c23c7b109373de9cc1a0d6ea872e0af8ab68400 SHA256: 7e1da11d54bc96ce8df54166c9be2620a92fc276e481c06173d1754cc103bf76 SHA512: 3d2ecd3cbeb96c86789f5a7b5997f4038147d5412146e5fe0103fdefe0993cce0de207a9ba050b11bacadf3ce61cee07c4dafe9967d38b1f0e7745d7fa69b968 Homepage: https://cran.r-project.org/package=twilio Description: CRAN Package 'twilio' (An Interface to the Twilio API for R) The Twilio web service provides an API for computer programs to interact with telephony. 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Package: r-cran-twitterwidget Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets Filename: pool/dists/resolute/main/r-cran-twitterwidget_0.1.1-1.ca2604.1_all.deb Size: 40034 MD5sum: 46448d10c13b5dbc8142b7f52ad7fc84 SHA1: f8b0cb790350820c8d41767c960fbf9e6e8ecea2 SHA256: e95621306dc0e547222c8d2a081aa261bc685a09e11730dec2b2f973384f3951 SHA512: 0cc148fe6e0e1aa473e589e1a1ccdac05e4d81ddf3046e45e0f47cc0d52372ad954ec2cfd22efffa5a2d24e931916c1ebd1d9c922a5866643028398e2ef49e0c Homepage: https://cran.r-project.org/package=twitterwidget Description: CRAN Package 'twitterwidget' (Render a Twitter Status in R Markdown Pages) Include the Twitter status widgets in HTML pages created using R markdown. The package uses the Twitter javascript APIs to embed in your document Twitter cards associated to specific statuses. The main targets are regular HTML pages or dashboards. Package: r-cran-twl Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mcmcpack, r-cran-corrplot, r-cran-rfast Filename: pool/dists/resolute/main/r-cran-twl_1.0-1.ca2604.1_all.deb Size: 1974922 MD5sum: 856b2dbedb3b2ebd9e3e8cc06d743f84 SHA1: 2356db632538f456ddb289a8f16c65a77b0231b5 SHA256: 8bec79c107c86853b1d3e7365b13874aa40230aea6fe5bb93e71ec3c75d888e3 SHA512: cdaaacc9a09ab748f5f62304aeec8416e7530685b44dae04cf5faff66038b7b5dd6a91f27556c932b21cafa2b13f5c99e8a1d17b68467985463cdc2e913db3e5 Homepage: https://cran.r-project.org/package=twl Description: CRAN Package 'twl' (Two-Way Latent Structure Clustering Model) Implementation of a Bayesian two-way latent structure model for integrative genomic clustering. The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation. A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler. The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density. Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources. Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU. See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, , Cold Spring Harbor Laboratory" at for model details. Package: r-cran-twn Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2690 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-crayon, r-cran-rlang, r-cran-stringr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-readr Filename: pool/dists/resolute/main/r-cran-twn_0.2.6-1.ca2604.1_all.deb Size: 2469712 MD5sum: e9f2662290415d6dfa0269fdadea3bb3 SHA1: 06a1e91f258b326bc7f9ae9327c726d1dfdbebaa SHA256: 175e36dc5606cda72c8a3681a6d51de031da3503b4cba78053174fb44bad4590 SHA512: 57f89e802dc9d3a97148617dba798247e3982ebc4ba52035ef9a58de9b750b4e2a04ab575f38d786874e6f7ef87b701f19edfa199713f4de38ed31fa96ec3413 Homepage: https://cran.r-project.org/package=twn Description: CRAN Package 'twn' (Taxa Waterbeheer Nederland voor R) The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming taxons in Dutch Watermanagement. This package makes it easier to use the TWN-list for ecological analyses. It consists of two parts. First it makes the TWN-list itself available in R. Second, it has a few functions that make it easy to perform some basic and often recurring tasks for checking and consulting taxonomic data from the TWN-list. Package: r-cran-twocoprimary Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-pbivnorm, r-cran-fpcompare Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-readr Filename: pool/dists/resolute/main/r-cran-twocoprimary_1.0.0-1.ca2604.1_all.deb Size: 294862 MD5sum: 96710b95d89890a9a653bfc8369e8e4f SHA1: 9dca15e7a0719f450cdb8ac50c33e95a8b7aa654 SHA256: b8be534b81f384127d2e14e046df266e78d1112ecc140ec82fb5e3596375b57c SHA512: 9c7843f60e0668954e6c89b4be2af446cc9a2a0ecfd112ba1ab4312923f3fc5f9829550c748a86bcf2fead6dc80d2d8deabdd89c44b8b3b7e201a3dd6c6df690 Homepage: https://cran.r-project.org/package=twoCoprimary Description: CRAN Package 'twoCoprimary' (Sample Size and Power Calculation for Two Co-Primary Endpoints) Comprehensive functions to calculate sample size and power for clinical trials with two co-primary endpoints. 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The package provides tools for fitting, visualization, and validation of two-mode cluster structures in data matrices. Package: r-cran-twopartm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 907 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mass Filename: pool/dists/resolute/main/r-cran-twopartm_0.1.0-1.ca2604.1_all.deb Size: 816396 MD5sum: 0a3d7ac9cd8d2e98d0f1cf2b8f4d871d SHA1: 2c5259533ad953e34ed1d38986b5de3908b4002a SHA256: 90188cbeeb1c3a0ef4e4daf159addba790979cd9a1e90cc56eac981c59faea8a SHA512: 8902eef0409c6faa23461bb4283eff2516f5794f9c119666713ba40ddb85f132f66a1d6c1198775e40313fbfaa250bef80a2e2b19e200f8c9e122f8e89c123b4 Homepage: https://cran.r-project.org/package=twopartm Description: CRAN Package 'twopartm' (Two-Part Model with Marginal Effects) Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) . 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MLE and PMLE are based on Mengjie Zheng (2013). QM is based on Entisar Elgmati and Nadia Gregni (2016). MEDRANK is based on Matthew Reid (2022). 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Version 1.1.1 includes improved efficiency factor classification with tolerance control, updated plot visualizations, and improved clarity of the results. The conceptual framework and the term two-phase were introduced by McIntyre (1955) ). 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The semiparametric results were developed in Yang et al. (2022 ), and the nonparametric results were developed in Yang (2025 ). For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 ), Pocock et al. 2012 , and Bebu and Lachin 2016 ) are included. The package also supports univariate survival analysis with a single event. In this package, effect size estimates and confidence intervals are obtained for each event type, and several testing procedures are implemented for the global null hypothesis of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazards assumptions, under which the event-specific win ratios converge to hazard ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment effect across all events, confidence intervals for linear combinations of the event-specific win ratios, RICH, or RITCH are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al. (2022 ) and Yang (2025 ). 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Package: r-cran-ubcrm Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ubcrm_1.0.3-1.ca2604.1_all.deb Size: 74722 MD5sum: 0174a6b25c4879993e6621d46467706e SHA1: 4d108ad5c67fb04a681bbca608d0a9f9212e8eb7 SHA256: 07869a90675037b00c98b7ca914b096cd70f511e8646ca8b4a95357e0c2451f0 SHA512: 83124ea14c100142543d10af7630d485a78569790881de237254ccce0952b627a1e920e7a67184cd6ec415ae71f46804b379d7c440fd43a85c3a0f3294e5ab86 Homepage: https://cran.r-project.org/package=UBCRM Description: CRAN Package 'UBCRM' (Simulate and Conduct Dose-Escalation Phase I Studies) Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method (). Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options. Package: r-cran-ubiquity Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-desolve, r-cran-dplyr, r-cran-digest, r-cran-doparallel, r-cran-flextable, r-cran-foreach, r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-onbrand, r-cran-optimx, r-cran-pknca, r-cran-pso, r-cran-readxl, r-cran-rmarkdown, r-cran-rhandsontable, r-cran-scales, r-cran-stringr, r-cran-shiny Suggests: r-cran-babelmixr2, r-cran-ga, r-cran-ggally, r-cran-gridgraphics, r-cran-gridextra, r-cran-officer, r-cran-rxode2, r-cran-webshot, r-cran-ggrepel, r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ubiquity_2.1.0-1.ca2604.1_all.deb Size: 2492116 MD5sum: a32cdc795c0cd8a18a094d7162f857e7 SHA1: 3b04735d5972cb5c6d56f129a89737b898b18a36 SHA256: facd57db27e052191698c9388dd64430d7ada1db078d153c57b3515241166e90 SHA512: 9819e121ff65acfa9a9d3c8331f1743987f05adcbacdd89b7f4ae28b882cf324891aafae5c2241694c1c41f4ce0c78bbe815f8ba663adb42ccefa6ded515de8e Homepage: https://cran.r-project.org/package=ubiquity Description: CRAN Package 'ubiquity' (PKPD, PBPK, and Systems Pharmacology Modeling Tools) Complete work flow for the analysis of pharmacokinetic pharmacodynamic (PKPD), physiologically-based pharmacokinetic (PBPK) and systems pharmacology models including: creation of ordinary differential equation-based models, pooled parameter estimation, individual/population based simulations, rule-based simulations for clinical trial design and modeling assays, deployment with a customizable 'Shiny' app, and non-compartmental analysis. System-specific analysis templates can be generated and each element includes integrated reporting with 'PowerPoint' and 'Word'. Package: r-cran-ubstats Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ubstats_0.3.0-1.ca2604.1_all.deb Size: 802902 MD5sum: 8ebcf501134e46cd528634ba8be4b281 SHA1: f1013220e7d1908d98237780ba5a61cf88c0c556 SHA256: d37513d70d8d2f2f04064a86cc1c3946fb331b2983aadc3687d5dd1df9e297d1 SHA512: 082c11e72acf2803312f6d06c7f8e86167e7f013aad40eeddb13f30d52daef6b36b0d79b77af9f6a4c2b51389a4a51d59deaa98c0234e576d7a8e1a99c2ec9b5 Homepage: https://cran.r-project.org/package=UBStats Description: CRAN Package 'UBStats' (Basic Statistics) Basic statistical analyses. The package has been developed to be used in statistics courses at Bocconi University (Milan, Italy). Currently, the package includes some exploratory and inferential analyses usually presented in introductory statistics courses. Package: r-cran-uci Architecture: all Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 729 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cpprouting, r-cran-data.table, r-cran-furrr, r-cran-future, r-cran-pbapply, r-cran-fields, r-cran-sf, r-cran-spdep Suggests: r-cran-covr, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-uci_0.3.1-1.ca2604.1_all.deb Size: 653040 MD5sum: 766c432cf2862da7f01f8aee6b5573b8 SHA1: 2240d3fbbe9e48e606fb47254252cebcf6678561 SHA256: d6f48f3132724799fd3f1d86d9eec94fbd85a992e4691419524202ce2c4e24fd SHA512: 109127ebdb913381f168f3eeca39dbd5e20308c9f08075aacb77a6f73c837f297c3eec489fc9eb08ca59caea0dd93a71cae14cddc7d4d8f701d7c2fb8badcb20 Homepage: https://cran.r-project.org/package=uci Description: CRAN Package 'uci' (Urban Centrality Index) Calculates the Urban Centrality Index (UCI) as in Pereira et al., (2013) . The UCI measures the extent to which the spatial organization of a city or region varies from extreme polycentric to extreme monocentric in a continuous scale from 0 to 1. Values closer to 0 indicate more polycentric patterns and values closer to 1 indicate a more monocentric urban form. Package: r-cran-ucie Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-dplyr, r-cran-geometry, r-cran-pracma, r-cran-ptinpoly, r-cran-rgl, r-cran-remotes Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ucie_1.0.2-1.ca2604.1_all.deb Size: 33888 MD5sum: e337a091306b0d77e9e597692663b797 SHA1: e86b08fde031cc098edb15efe0d909bca7ed3311 SHA256: 158e1929706f32353d1e324e0fbd76ec89ab29633bf8886f254c0b2d5405a44d SHA512: 78ca27edf7545a541aaa0136582a4dfbb364476e0c7a0b963a9b9ee28107b841331837aff29bae92090021111aca655ca0118707258c4efaa1f99e9d78a7f326 Homepage: https://cran.r-project.org/package=ucie Description: CRAN Package 'ucie' (Mapping 3D Data into CIELab Color Space) Returns a data frame with the names of the input data points and hex colors (or CIELab coordinates). Data can be mapped to colors for use in data visualization. It optimally maps data points into a polygon that represents the CIELab colour space. Since Euclidean distance approximates relative perceptual differences in CIELab color space, the result is a color encoding that aims to capture much of the structure of the original data. Package: r-cran-ucimlrepo Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ucimlrepo_0.0.2-1.ca2604.1_all.deb Size: 660392 MD5sum: 3f5b386a7e1a50736134213c8d2eebb0 SHA1: d8fd761850fa9aa0f627cb7c688dce3adf1010be SHA256: c5f37097d92b42b75de7f394703d8f324ef5f022374a4bcb52a507a89c96116a SHA512: 742c2dfa958a326ac7d2945bc208c59289ca88873b4d549f5a789959b2a2b13f7d78b1a6998fa0d8a5c9f8be925a066701696f557226318897f0a5176814a594 Homepage: https://cran.r-project.org/package=ucimlrepo Description: CRAN Package 'ucimlrepo' (Explore UCI ML Repository Datasets) Find and import datasets from the University of California Irvine Machine Learning (UCI ML) Repository into R. Supports working with data from UCI ML repository inside of R scripts, notebooks, and 'Quarto'/'RMarkdown' documents. Access the UCI ML repository directly at . Package: r-cran-uclust Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dendextend, r-cran-robcor Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-uclust_1.0.0-1.ca2604.1_all.deb Size: 160866 MD5sum: f566a33c5df630bacd9b4ae169677d33 SHA1: 943e31d1db48d658b086580aab65c04ef35638f1 SHA256: 6cecb95be53689ed2b35233b49460375237eba29bcafa83df07242df6aa3b34a SHA512: 58186271873792dcc48dffdb96b0751805d867e14e56f7b5ebc343409888cc84c5746f1d2bc078356c7aed573de0ab918e5ddc1acde088bf22847a1dc4082726 Homepage: https://cran.r-project.org/package=uclust Description: CRAN Package 'uclust' (Clustering and Classification Inference with U-Statistics) Clustering and classification inference for high dimension low sample size (HDLSS) data with U-statistics. The package contains implementations of nonparametric statistical tests for sample homogeneity, group separation, clustering, and classification of multivariate data. The methods have high statistical power and are tailored for data in which the dimension L is much larger than sample size n. See Gabriela B. Cybis, Marcio Valk and Sílvia RC Lopes (2018) , Marcio Valk and Gabriela B. Cybis (2020) , Debora Z. Bello, Marcio Valk and Gabriela B. Cybis (2021) . Package: r-cran-ucr.columnnames Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ucr.columnnames_0.1.0-1.ca2604.1_all.deb Size: 19390 MD5sum: 3a87e4e6c562da26cf267fd56b8f832c SHA1: 0be8de70e960a9f37c1eb4686503e16087d775a7 SHA256: a924e1909b0b36223d7c6231fe42c2dbc83c5ceda31c1df3181883f493f5391a SHA512: 19e6909f63f4fe335ef954dd42f19eb466f689483e9ebfe296d80415ef8ad96a5eac1e5994fb23aaf864e90e736aeff522cee38bb49681ae050940dee36d000d Homepage: https://cran.r-project.org/package=UCR.ColumnNames Description: CRAN Package 'UCR.ColumnNames' (Fixes Column Names for Uniform Crime Report "Offenses Known andClearance by Arrest" Datasets) Changes the column names of the inputted dataset to the correct names from the Uniform Crime Report codebook for the "Offenses Known and Clearance by Arrest" datasets from 1998-2014. Package: r-cran-ucscxenashiny Architecture: all Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8550 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-ezcox, r-cran-forcats, r-cran-ggplot2, r-cran-ggpubr, r-cran-httr, r-cran-magrittr, r-cran-ppcor, r-cran-psych, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-ucscxenatools Suggests: r-cran-covr, r-cran-cowplot, r-cran-dt, r-cran-funkyheatmap, r-cran-furrr, r-cran-future, r-cran-ggrepel, r-cran-ggstatsplot, r-cran-glue, r-cran-knitr, r-cran-pacman, r-cran-plotly, r-cran-plyr, r-cran-rcolorbrewer, r-cran-readr, r-cran-rmarkdown, r-cran-rtsne, r-cran-scales, r-cran-survival, r-cran-survminer, r-cran-testthat, r-cran-umap Filename: pool/dists/resolute/main/r-cran-ucscxenashiny_2.2.1-1.ca2604.1_all.deb Size: 3809910 MD5sum: 34d7113b96f174be2a733e3ca06c0a9c SHA1: f4efb767045fd46807b706f008ab22fbadb46034 SHA256: 9e31ae5556ff9905023a33a8d6fa92ffe8e47dfc3716986d5c3a38ed4737c384 SHA512: 02b73eb88bada25322ea41873f5785a497ff85ea2370736e614e2ba9e5db2fdc367b10e344e446715e992fe532273ba07207a42c2f391f2aee957983099fab6a Homepage: https://cran.r-project.org/package=UCSCXenaShiny Description: CRAN Package 'UCSCXenaShiny' (Interactive Analysis of UCSC Xena Data) Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Package: r-cran-ucscxenatools Architecture: all Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ucscxenatools_1.7.0-1.ca2604.1_all.deb Size: 699980 MD5sum: ab2668865b70a072bb8f340858a86d1b SHA1: 9a0a0a8e68f84bc53086dd7690abb6d55daedeac SHA256: 020fba06f4a61034267cc50a99f8d9f041817827ed25b916aa61377818a18594 SHA512: 60153010a45666815d32983cd0048587e877a5a6556620cfe49e11f4f4a0de4e90b226e5f53d19a97035bb037d20f4faf611addd4be0101d8fa756c3bfe1728e Homepage: https://cran.r-project.org/package=UCSCXenaTools Description: CRAN Package 'UCSCXenaTools' (Download and Explore Datasets from UCSC Xena Data Hubs) Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded. Package: r-cran-udderquarterinfectiondata Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-udderquarterinfectiondata_1.0.0-1.ca2604.1_all.deb Size: 39480 MD5sum: cb914e5b4c15bf17d0448795068b24c2 SHA1: b91246141ccffbe5017a32454bfb8b78bafe4851 SHA256: 29f875cc2989cca425493258c64403a7f44a7749a7c63806d3eb913587d06987 SHA512: caf0d4a2f7777866d46ffa51af8b32763644d65eb747bb9275bce81d3aebe236de806fb8541bb12285b142e6958ef1389aacb7b3f6b5db3ab870b12c1c2f73bd Homepage: https://cran.r-project.org/package=UdderQuarterInfectionData Description: CRAN Package 'UdderQuarterInfectionData' (Udder Quarter Infection Data) The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 ). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list. Package: r-cran-uei Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factominer, r-cran-factoextra, r-cran-metrics Filename: pool/dists/resolute/main/r-cran-uei_0.1.0-1.ca2604.1_all.deb Size: 14732 MD5sum: 414a7f3ac125a42be06cd42a9d6f829a SHA1: 751f33cc109f6616ce925fd5085e070ab3b419c7 SHA256: 93aa9da22116318c8fb36c510f1f1613a3e8b3286d842624464fa4ef073178a3 SHA512: ba17a979c3f4aaf672095cab6d230d1b7f0f6c3e2d4a6ec9325c792332b554c5db2132e828b166992e4430a7e246c543314be955d54c83994d77742213202263 Homepage: https://cran.r-project.org/package=UEI Description: CRAN Package 'UEI' (Compute Uniform Error Index) Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) . Package: r-cran-ufrisk Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 470 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-esemifar, r-cran-fracdiff, r-cran-rugarch, r-cran-smoots Filename: pool/dists/resolute/main/r-cran-ufrisk_1.0.7-1.ca2604.1_all.deb Size: 427502 MD5sum: e050a536a4259525a325ad5133ebacd0 SHA1: 4ee2ae3ce80eaae8499d80334d8187a48ab668bb SHA256: 4af18501c925caed482a90c0823fd202116f33c1448d925c3cd52399d53b2afa SHA512: 1ee380aed91d778faff56b529b428aba4c42fe3b065abe171fca2f530567c80d51fe859fe292cb5826aa55ef9e2fbbf818a2bfeca3bb631f72dbb0bebd34ab25 Homepage: https://cran.r-project.org/package=ufRisk Description: CRAN Package 'ufRisk' (Risk Measure Calculation in Financial TS) Enables the user to calculate Value at Risk (VaR) and Expected Shortfall (ES) by means of various parametric and semiparametric GARCH-type models. For the latter the estimation of the nonparametric scale function is carried out by means of a data-driven smoothing approach. Model quality, in terms of forecasting VaR and ES, can be assessed by means of various backtesting methods such as the traffic light test for VaR and a newly developed traffic light test for ES. The approaches implemented in this package are described in e.g. Feng Y., Beran J., Letmathe S. and Ghosh S. (2020) as well as Letmathe S., Feng Y. and Uhde A. (2021) . 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Therefore, distinct functionalities are being 'consciously uncoupled' into different packages. This package contains the general-purpose tools and utilities (see the 'behaviorchange' package, the 'rosetta' package, and the soon-to-be-released 'scd' package for other functionality), and is the most direct 'successor' of the original 'userfriendlyscience' package. For example, this package contains a number of basic functions to create higher level plots, such as diamond plots, to easily plot sampling distributions, to generate confidence intervals, to plan study sample sizes for confidence intervals, and to do some basic operations such as (dis)attenuate effect size estimates. 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Package: r-cran-ukc19 Architecture: all Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-ukc19_0.0.3-1.ca2604.1_all.deb Size: 3397584 MD5sum: 2606185145824b381d5ebaf80594611d SHA1: d8470bf45b6106503e821e60bd0a3562da5fef93 SHA256: cf1f5d52cb977aa6ed6592d2c86f48f7f8590f6e981233228cb85c4caf53c3ce SHA512: d0b8fd3d4bb05d781fc633adbb093e255af9d1314629c97646797132d0be23bfcc7f8eef228f91f355dc99f69b3ffcd2160be6ffcd38d8cb4de5e6cff25ee944 Homepage: https://cran.r-project.org/package=ukc19 Description: CRAN Package 'ukc19' (Datasets from the UK COVID-19 Outbreak) Provides easy access to a curated selection of pre-processed data sets relevant to the COVID-19 outbreak in the UK for teaching and demonstration purposes. 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Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, ). "The FEH 2025 statistical method update", UK Centre for Ecology and Hydrology (2025). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Data from the UK National River Flow Archive (, terms and conditions: ). 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Package: r-cran-ukhousing Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ukhousing_0.1.0-1.ca2604.1_all.deb Size: 140190 MD5sum: 9afe225f16abcf96fe7716e12d60152d SHA1: a7cc676ff61a6e8ae863097b5217da07f9fd3f3d SHA256: 35cb50c2af1b6ddb8db7170355b29f06804e1eddaff3ddd145934e2d28dbee41 SHA512: ec2f9fbe07a0012c70351483a2c9b72487ec367ae62959d6a24354ff9d47c02f32aefa645d62182a2855047a87a735fcb639d28ae219ff50676b1980cfb43f5e Homepage: https://cran.r-project.org/package=ukhousing Description: CRAN Package 'ukhousing' (Access UK Housing Data from Land Registry, EPC, and Planning) Fetch UK housing data from official sources. Access the UK House Price Index and Price Paid Data from 'HM Land Registry' , domestic and non-domestic Energy Performance Certificates from the 'MHCLG' Open Data service , and planning application, brownfield land, and local plan data from 'planning.data.gov.uk' . Data covers all 441 UK local authorities from 1995 to the present. Functions accept flexible filters (postcode, local authority, property type, date range) and return tidy data frames. Downloaded data is cached locally for subsequent calls. Package: r-cran-ukhsadatr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ukhsadatr_0.1.1-1.ca2604.1_all.deb Size: 25678 MD5sum: b9b8f800e62f0e9ae7cd688477ae8f12 SHA1: 0ad49d9486d58b34b4ff7b368387af340196d97a SHA256: c521fd87353dc19406613144864ce08c8214d3a9275c0a00f9a7157b27b42962 SHA512: d1519a0e54d8abec38f46da6e26f73e31ffb32acec3afffddab8f52bd15bf037ed328264e1274f53f65614b16bf8beaad866986484cad05f4d6b25180639150a Homepage: https://cran.r-project.org/package=ukhsadatR Description: CRAN Package 'ukhsadatR' (R Interface to Access UKHSA Dashboard Data) Programmatic interface to access data from the UK Health Security Agency (UKHSA) Data Dashboard API. The package was originally based on the 'ukcovid19' package by Pouria Hadjibagheri and has been substantially rewritten and extended. For more information on the API, see . Package: r-cran-ukpolice Architecture: all Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-tibble, r-cran-purrr, r-cran-httr, r-cran-snakecase Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-leaflet, r-cran-htmltools, r-cran-scales, r-cran-sf, r-cran-sp Filename: pool/dists/resolute/main/r-cran-ukpolice_0.2.2-1.ca2604.1_all.deb Size: 258164 MD5sum: 45e28ac7cb4631f53b5e82c8113f9115 SHA1: f5583052f7836e31ad0829c29cc3875163026d4d SHA256: 9bd50bddc1a65a81efedb3aafb1fe917e08362d1ff07239da4b0d4ccf608f7b4 SHA512: e4ac83ae3aa95802bfe3fe8c705e420a131681595831c90bc2056fc188788992472a4ab2bde65a457f2578808f17501325b978042958dafa39d2268cd91b1fe6 Homepage: https://cran.r-project.org/package=ukpolice Description: CRAN Package 'ukpolice' (Download Data on UK Police and Crime) Downloads data from the 'UK Police' public data API, the full docs of which are available at . 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Package: r-cran-ultimixt Architecture: all Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-ultimixt_2.1-1.ca2604.1_all.deb Size: 157242 MD5sum: 58ee2a74cd7dc5b641cc783b69f26faf SHA1: 1f6a243e33b15637e3ec2da2a355d845f6ea6b9a SHA256: 10f5b246ff57912a6035b5a184687b3cdfeb430cff9d72499d30cdbb701f2a60 SHA512: 884deeead43bc5397153311dc7058834ad38cad98ad8de10139da5f8267d4a64a575676a0fd00b472027ebd28c7a8fccc4ea1f9bfce1a7d0566bd6652c5be494 Homepage: https://cran.r-project.org/package=Ultimixt Description: CRAN Package 'Ultimixt' (Bayesian Analysis of Location-Scale Mixture Models using aWeakly Informative Prior) A generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. 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There is currently support for plotting means and standard deviations of each category's trace; Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well. The origin of the polar coordinates may be defined manually or automatically determined based on different algorithms. Points for each category can be split into two groups (anterior and posterior) at the point of maximum curvature of each trace. User can specify rays to intersect various parts of the tongue; intersections along these rays serve as input for a pairwise t-test to measure significant contrasts between segments. Currently 'ultrapolaRplot' supports ultrasound tongue imaging trace data from 'UltraTrace' (). 'UltraTrace' is capable of importing data from Articulate Instruments AAA. 'read_textgrid.R' is required for opening TextGrids to determine category and alignment information of ultrasound traces. 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See Zhang and Coombes (2012) . Version 2.0 adds the ability to simulate realistic mixed-typed clinical data. Package: r-cran-umweltapir Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-tidyr Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-umweltapir_0.1.0-1.ca2604.1_all.deb Size: 27324 MD5sum: 822b43d1f133d995dba94212da8728ef SHA1: 0032ca929bb6e946a1d9e02df3b673352a899d30 SHA256: 280ce0eb50a6b08a407ba3601f189a055db8a3e91c7622631eeff38e812ab491 SHA512: d2f05560ac5c17f3a0c0fd7197ddf565500c99d16cd502521df7574ed0c59a9f4010349776578616410bba4f1c1ee0035a16f13a8b623318667e1de1526f573a Homepage: https://cran.r-project.org/package=umweltapir Description: CRAN Package 'umweltapir' (Access Umwelt.Info API) Provides an R-based access to the datasets including their resources from the portal . The package allows for an easy integration of those datasets into your R-based workflows. The functionality of the package mirrors the web-based access as provided at . You can use the same queries and get the same datasets by accessing our API. 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(2015) . Package: r-cran-unifieddosefinding Architecture: all Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-unifieddosefinding_0.1.10-1.ca2604.1_all.deb Size: 140134 MD5sum: 79990a5cfd17f2b67b4358452ba9fc62 SHA1: b0c741b08ccb5b31347970029ffff987ada03963 SHA256: 6e910a0545ac708805f52cad34e56a3ddb6f723d623ac58f8d103f0d9c292e44 SHA512: abb120fdb735f7b59c7be0958c8c9b6fa0ce16dd12ff4442ad62d41e64efcbf937dee63feb13efa2841bafd7cf51317c9d0ffa68e447ebcc5693c1d834908bc1 Homepage: https://cran.r-project.org/package=UnifiedDoseFinding Description: CRAN Package 'UnifiedDoseFinding' (Dose-Finding Methods for Non-Binary Outcomes) In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. 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Gaussian mixture models with user-defined translation vectors identify clusters of records that differ in scale or unit. Core functionality includes cluster assignment via the EM algorithm, error correction based on posterior probabilities and pairwise scatterplot visualizations. For more details see Di Zio, Guarnera and Luzi (2005) . Package: r-cran-unitrootests Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-tseries, r-cran-urca, r-cran-strucchange Suggests: r-cran-testthat, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-unitrootests_1.1.0-1.ca2604.1_all.deb Size: 84384 MD5sum: 2955de104e45407da23ab6d6161cca96 SHA1: 21fd603aaa0335a83d1b148db84af471164b5962 SHA256: 87b52866f7c4819d276097403bb9c4b259b27b5f0d20e5fe049645287104d589 SHA512: 3171a0d17f8bc0ae7a80313911fcb77e3848166b085ad5dae121943f500aa154fe329179ea4acb14684a7fc6b62d6f9e847abeb5747ccf78bdc6258d019b1aa2 Homepage: https://cran.r-project.org/package=unitrootests Description: CRAN Package 'unitrootests' (Comprehensive Unit Root and Stationarity Tests) A unified framework for unit root and stationarity testing including quantile ADF tests (Koenker and Xiao, 2004) , GARCH-based unit root tests with endogenous structural breaks (Narayan and Liu, 2015) , and comprehensive Dickey-Fuller, Phillips-Perron, KPSS, ERS/DF-GLS, Zivot-Andrews, and Kobayashi-McAleer tests with an Elder-Kennedy decision strategy (Elder and Kennedy, 2001) . Package: r-cran-unitstat Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmtest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-unitstat_1.1.0-1.ca2604.1_all.deb Size: 47968 MD5sum: 84550cf58f02ce8acfbe57b3ee8f995e SHA1: a51ae8cc913601338b3e10f01fd71a632ed32252 SHA256: c80cdfaecef86ed87faefea87349d17db39860da044f9bc54fd5d275558db44c SHA512: 9cad3e8fc4db69b148a3695daffe699f8ca43d3635dcea46235f6e5d34c3b12ba23c9dfbf962f611298d1e7d75fc1a1f1a0b668d9cb48294086bc463ef465dd5 Homepage: https://cran.r-project.org/package=UnitStat Description: CRAN Package 'UnitStat' (Performs Unit Root Test Statistics) A test to understand the stability of the underlying stochastic data. Helps the user’s understand whether the random variable under consideration is stationary or non-stationary without any manual interpretation of the results. It further ensures to check all the prerequisites and assumptions which are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all the 4 lags the function diagnoses the input data and returns with an optimised solution on the same. 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Package: r-cran-unival Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych Filename: pool/dists/resolute/main/r-cran-unival_1.1.0-1.ca2604.1_all.deb Size: 54560 MD5sum: bcbda972fd4cc495b1d187547d126421 SHA1: dc6f90dabe4796e2943761eba300841476523e78 SHA256: ef14f6d5ee2d45688679404700656c1e239ef522033a18edb56480143759bfcc SHA512: 89e11ed673b1662dbcb22782b88e9c1a23d40a5049c595dbc0b989adf7c9c3abe5daf99cb050857e4463994cfea0de8d92158c5278c394b3aab19998bdf80d17 Homepage: https://cran.r-project.org/package=unival Description: CRAN Package 'unival' (Assessing Essential Unidimensionality Using External ValidityInformation) Assess essential unidimensionality using external validity information using the procedure proposed by Ferrando & Lorenzo-Seva (2019) . Provides two indices for assessing differential and incremental validity, both based on a second-order modelling schema for the general factor. Package: r-cran-univariateml Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-extradistr, r-cran-tibble, r-cran-logitnorm, r-cran-actuar, r-cran-nakagami, r-cran-fgarch, r-cran-rlang, r-cran-intervals, r-cran-rfast, r-cran-pracma, r-cran-sads Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-copula, r-cran-dplyr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-univariateml_1.5.0-1.ca2604.1_all.deb Size: 555388 MD5sum: 3fb7ae363fc9bdd3202ec4815eb8a90d SHA1: 544c957193c2529232563e58fa305a2dbf9accc7 SHA256: c2f03752499f8196b4fbae3232ae5d176af4da9e64af2c4ad11bcb5bcb49aae9 SHA512: 7643da0586460e861f1db814ce58dd8526f8e29a2ba14aad3474a80b1d433a7976e6d84eb85cec8c398c2189197d134aa2072e7e1b09f36063c18adf1e0bac3a Homepage: https://cran.r-project.org/package=univariateML Description: CRAN Package 'univariateML' (Maximum Likelihood Estimation for Univariate Densities) User-friendly maximum likelihood estimation (Fisher (1921) ) of univariate densities. Package: r-cran-universalcvi Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mclust Filename: pool/dists/resolute/main/r-cran-universalcvi_1.4.0-1.ca2604.1_all.deb Size: 641944 MD5sum: e5d80030ce4a91ebed2c1f8933da924e SHA1: 28da4e0f86b53ef2b96bf3bf9047df95cb4bc84e SHA256: 1929b5d172a93a908fe5fcf081c48bf3b7d9f9e0d5d2e330a092a7c1ae5117c1 SHA512: cdc5653709b9d10d6e5ef6fed06b344f90b1c403bb0dce9d1114c6364bbcd63ca6ada7738a83395271511c459deceec3c74c8941efb0140bf58f93bfff0e27be Homepage: https://cran.r-project.org/package=UniversalCVI Description: CRAN Package 'UniversalCVI' (Hard and Soft Cluster Validity Indices) Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). The details of the indices in this package can be found in: J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) , T. Calinski, J. Harabasz (1974) , C. H. Chou, M. C. Su, E. Lai (2004) , D. L. Davies, D. W. Bouldin (1979) , J. C. Dunn (1973) , F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) , M. Kim, R. S. Ramakrishna (2005) , S. H. Kwon (1998) , S. H. Kwon, J. Kim, S. H. Son (2021) , G. W. Miligan (1980) , M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) , M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) , S. Saitta, B. Raphael, I. Smith (2007) , A. Starczewski (2017) , Y. Tang, F. Sun, Z. Sun (2005) , N. Wiroonsri (2024) , N. Wiroonsri, O. Preedasawakul (2023) , C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) , X. Xie, G. Beni (1991) and P.J. Rousseeuw (1987) and L. Kaufman and P.J. Rousseeuw(2009) and C. Alok. (2010). Package: r-cran-universals Architecture: all Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-nlist, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-universals_0.0.5-1.ca2604.1_all.deb Size: 73022 MD5sum: c80ab09459974f28315e90f8beb7dcb6 SHA1: 5e8172e746a7fe7b57fa2bf3642efe956f1c1c9b SHA256: 1ccff51d140830c370fe2546afb858047ee9e4e6e0a74a20472ffbfd96004a03 SHA512: 01e4c4bbbcd2c4c7baadc8fa7aa2a6c88f288aedf5f5377dba2236fdbd401b3b2cd81a3771e17293aa50062771ae7656c090dadb4af4bef45b87d8b99a436e1f Homepage: https://cran.r-project.org/package=universals Description: CRAN Package 'universals' (S3 Generics for Bayesian Analyses) Provides S3 generic methods and some default implementations for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC) samples. The purpose of 'universals' is to reduce package dependencies and conflicts. 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Package: r-cran-uotm Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-forecast, r-cran-ggplot2, r-cran-hash Filename: pool/dists/resolute/main/r-cran-uotm_0.1.6-1.ca2604.1_all.deb Size: 34262 MD5sum: b15ac9b8270550c1bf11879cdd1342ee SHA1: f53003090764d3b175aeed70c57f4a38359e9bd9 SHA256: 8d7111a1cee67d01d8b7af90defeb754a890578dfd184fff043acc335afc4122 SHA512: 5b0121ee000ae3c4b9be2ac7176d6137efa598586fe4371ed28df4a84287c5db07d79af82e5bb7a94f2a007f63716a61ce3a0b108441365e2eb54b6fb935a61a Homepage: https://cran.r-project.org/package=uotm Description: CRAN Package 'uotm' (Uncertainty of Time Series Model Selection Methods) We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. for more information. 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Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) . 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Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association ". Package: r-cran-upndown Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cir, r-cran-expm, r-cran-mass, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-upndown_0.3.0-1.ca2604.1_all.deb Size: 240304 MD5sum: 73778d7889ee5197ebcd0a2468b226b7 SHA1: 0868004bd6e40bdf63359d1380612da77a033977 SHA256: 977d440daec59a592369bf2ee920c453b1390a4c6a39ed15d9b015a71e3f021d SHA512: f5f7e75879c9a31a086a82af291bfd2192343acf677e1c8982278474134d5cd4c97195ff8ba0a1f9c54a5a024ff416347a4b3ab02b33e18d8eecffd8b255b7dc Homepage: https://cran.r-project.org/package=upndown Description: CRAN Package 'upndown' (Utilities and Design Aids for Up-and-Down Dose-Finding Studies) Up-and-Down (UD) is the most popular design approach for dose-finding, but it has been severely under-served by the statistical and computing communities. This is the first package that comprehensively addresses UD's needs. Recent applied UD tutorial: Oron et al., 2022 . Recent methodological overview: Oron and Flournoy, 2024 . 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Package: r-cran-ura Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-irr, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-roxygen2, r-cran-stringr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ura_1.0.1-1.ca2604.1_all.deb Size: 31846 MD5sum: 05d6e3d85b8c58a4ddaf4e9dd84d1e72 SHA1: fba8651908798b0faec2b913f244d4a4738c181d SHA256: 2c5ea2be39b08f9c461aa7cdbf99172771fd4bfdebc1c172cf6d7f40503d3c18 SHA512: b83704eb9c34c5da348cdb713e29333ca4923c3f1ccea2a6829ecbfbd0d4868323a7bce99a983c5b296cb27090d2f589f9ec1a369d335c274fc1d94843b2e629 Homepage: https://cran.r-project.org/package=ura Description: CRAN Package 'ura' (Monitoring Rater Reliability) Provides researchers with a simple set of diagnostic tools for monitoring the progress and reliability of raters conducting content coding tasks. 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This can be particularly important if the estimation results are obtained with different models/estimators (e.g., linear probability model, logit, probit, ...) and/or with different transformations of the explanatory variable of interest (e.g., linear, quadratic, interval-coded, ...). The calculated unified measures are: (a) semi-elasticities of linear, quadratic, or interval-coded covariates and (b) effects of linear, quadratic, interval-coded, or categorical covariates when a linear or quadratic covariate changes between distinct intervals, the reference category of a categorical variable or the reference interval of an interval-coded variable needs to be changed, or some categories of a categorical covariate or some intervals of an interval-coded covariate need to be grouped together. Approximate standard errors of the unified measures are also calculated. 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Package: r-cran-us.census.geoheader Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-us.census.geoheader_1.0.2-1.ca2604.1_all.deb Size: 265684 MD5sum: 846ef57b2e368723dace205e78f0ea9a SHA1: 5eedbd7e8924af65cc1652aafa2f21703f13f7b7 SHA256: 259723ea0078cedc57259a924eec8de2597da8185f686c1e8be552be2352a051 SHA512: 0e9643ed0cf81caa0baf1a47d5d3e277af17b20ade66e06a458b0b16cf74498e38e4ba6cc869c0b0376edc47cbc23334d578e4bd415e38a9ccc2a18897335e4c Homepage: https://cran.r-project.org/package=us.census.geoheader Description: CRAN Package 'us.census.geoheader' (US 2010 Census SF2 Geographic Header Summary Levels 010-050) A simple interface to the Geographic Header information from the "2010 US Census Summary File 2". The entire Summary File 2 is described at , but note that this package only provides access to parts of the geographic header ('geoheader') of the file. In particular, only the first 101 columns of the geoheader are included and, more importantly, only rows with summary levels (SUMLEVs) 010 through 050 (nation down through county level) are included. In addition to access to (part of) the geoheader, the package also provides a decode function that takes a column name and value and, for certain columns, returns "the meaning" of that column (i.e., a "SUMLEV" value of 40 means "State"); without a value, the decode function attempts to describe the column itself. Package: r-cran-usa.state.boundaries Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6204 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-sf, r-cran-install.load, r-cran-drat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-usa.state.boundaries_1.0.1-1.ca2604.1_all.deb Size: 6313858 MD5sum: 85158b119b8fa749321595e73d299734 SHA1: 1730f78a922d5e70dc72553a487123dc27452356 SHA256: f68002a00f0957c8f4dade8e4d031dc93971a6c3d69bc225a0e4643d4e366a29 SHA512: 3209e38b95cbbec2a5f7302d6b447c4a00c8a8cceff57fec3580c0facbef577242f0e8eae44e1a2b4afef11bd3aaf4061ac59428485adc0f5829d090db10a8ba Homepage: https://cran.r-project.org/package=USA.state.boundaries Description: CRAN Package 'USA.state.boundaries' (WGS84 Datum Map of the USA, Including Puerto Rico and the U.S.Virgin Islands) Contains a WGS84 datum map of the USA, which includes all Commonwealth and State boundaries & also includes Puerto Rico and the U.S. Virgin Islands. This map is a reprojection of the NAD83 datum map from the USGS National Map. This package contains a subset of the data included in the 'USA.state.boundaries.data' package, which is available in a 'drat' repository. To install that data package, please follow the instructions at . Package: r-cran-usa Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2027 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble Suggests: r-cran-covr, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-usa_1.0.0-1.ca2604.1_all.deb Size: 1918090 MD5sum: 7a526a6bc9a7b7ed7119c4b4f5566df2 SHA1: 0a9be39c7d5358dee0f1625d9c1753acd31486df SHA256: 9cbf39b2b4cfac261df68a1279636defb5a76f1be7bb7bfab59d18a548e62d63 SHA512: a6d6d711f16397c26aec87085b41fdc14e3550fdb93d1a2d78177847c803baf1179d1da44bcf13728d6b2cb367a70c6331959a4fa0bcd63aae40abd7db1f38a7 Homepage: https://cran.r-project.org/package=usa Description: CRAN Package 'usa' (Updated US State Facts and Figures) Updated versions of the 1970s "US State Facts and Figures" objects from the 'datasets' package included with R. The new data is compiled from a number of sources, primarily from the United States Census Bureau or the relevant federal agency. Modern tidy tibbles provide richer state-level data including identifiers, geography, capitals, demographics, and socioeconomic statistics. Convenience vectors parallel the base 'datasets' state objects but extend coverage to all 51 jurisdictions: the 50 states and the District of Columbia. Package: r-cran-usaboundaries Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-leaflet, r-cran-pak, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-usaboundaries_0.5.1-1.ca2604.1_all.deb Size: 237354 MD5sum: d1ef73b4d117511bc2013228723fbf8d SHA1: 566d1da57ecae8c699d40584f7e76c0697c6beae SHA256: 32e51b6dfbda74dd7fed375cd797a9c5cf3a0449c497e134715bea369aec1484 SHA512: 67fcb0a7670e8a3793acd3c5a025ac104cd96552aa1adfae5ecc4ce68aa52c7c5ef0342e01984427d1bbfd5e6d372ea3267a5dadaee740c598182cca77ce854a Homepage: https://cran.r-project.org/package=USAboundaries Description: CRAN Package 'USAboundaries' (Historical and Contemporary Boundaries of the United States ofAmerica) The boundaries for geographical units in the United States of America contained in this package include state, county, congressional district, and zip code tabulation area. Contemporary boundaries are provided by the U.S. Census Bureau (public domain). Historical boundaries for the years from 1629 to 2000 are provided form the Newberry Library's Atlas of Historical County Boundaries (licensed CC BY-NC-SA). Additional data is provided in the USAboundariesData package; this package provides an interface to access that data. Package: r-cran-uscoauditlog Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-openxlsx, r-cran-readxl Filename: pool/dists/resolute/main/r-cran-uscoauditlog_1.0.3-1.ca2604.1_all.deb Size: 41046 MD5sum: bfc5c9f46a669019d37d64a1edac236c SHA1: f93c2407bf93aa555e223d7d92df5ca056d1bf70 SHA256: a5f3e70586e31577f115cfe30d6f93a5fa2bf43596995b29b86c32c8009981c7 SHA512: 6afd2017c4e62e3ae235667fc421b32cc8633f569386d6461e6307a5fcb51cad13785f91d6bda97228aba9ecf775ae16b0ca30f73c449f2f87e13434f93381ee Homepage: https://cran.r-project.org/package=uscoauditlog Description: CRAN Package 'uscoauditlog' (United States Copyright Office Product Management Division SRAudit Data Dataset Cleaning Algorithms) Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database. 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Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry. 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Package: r-cran-uwedragon Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-uwedragon_0.1.0-1.ca2604.1_all.deb Size: 20864 MD5sum: 3e35feb612c5ddd5b51ad6ffc8335a1e SHA1: 024aa62491b36c70aa17960bad864efd62d1bd1e SHA256: 269dfbe94214c3914b647d858fcf10e26207aee607f11b9262d612b344b2a053 SHA512: 8444c377737b43f2aac9188e424e7940a76c27f02c2f1b200bdb664cb74e36701ebcafc73c3a8e78e3eff20c94c3df9fa4977670d016ec229c7e72acff99a0a9 Homepage: https://cran.r-project.org/package=uwedragon Description: CRAN Package 'uwedragon' (Data Research, Access, Governance Network : StatisticalDisclosure Control) A tool for checking how much information is disclosed when reporting summary statistics. Package: r-cran-uwham Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust Filename: pool/dists/resolute/main/r-cran-uwham_1.1-1.ca2604.1_all.deb Size: 301734 MD5sum: f32ce07c4110da976aadeaf792995870 SHA1: 29191d3672cf38bafc6ea777af93beb86d7ebe81 SHA256: c346be7e77aa1bdc27fd06f2260aad73a62c731699b3d691177d164c8bdb492f SHA512: 6715ef46694b584b73a2e4e77a8c4586722712f409a87c7dd5b433c288e3f97581d8a5829293f840bb8b9d239cf9a86bb8de67cf846a61e6ed4a92246833a84e Homepage: https://cran.r-project.org/package=UWHAM Description: CRAN Package 'UWHAM' (Unbinned Weighted Histogram Analysis Method (UWHAM)) A method for estimating log-normalizing constants (or free energies) and expectations from multiple distributions (such as multiple generalized ensembles). Package: r-cran-uxr Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-huxtable, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-uxr_0.2.0-1.ca2604.1_all.deb Size: 100788 MD5sum: 16a3e4ef8dfaf50c731edfe5c5b1160a SHA1: 9db8da02fdb3f19d9f725e1b0815e829f0e07615 SHA256: 1969ff48707450778681e064b85a4ca979f28ad991a17b7c3fbd76ac8830f3ea SHA512: 1656a782de9306b94d81f3e0f6301d42d7412f76168b296d6a532d69d997c0c7ada6a573c2d3871405b0b96516024b817026ba297e92500d0a1abaf7cd2c6518 Homepage: https://cran.r-project.org/package=uxr Description: CRAN Package 'uxr' (User Experience Research) Provides convenience functions for user experience research with an emphasis on quantitative user experience testing and reporting. The functions are designed to translate statistical approaches to applied user experience research. Package: r-cran-vacalibration Architecture: all Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-openva, r-cran-ggplot2, r-cran-patchwork, r-cran-reshape2, r-cran-laplacesdemon, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-vacalibration_2.2-1.ca2604.1_all.deb Size: 2300604 MD5sum: 48466097169215c40fe5b6957b1a1f57 SHA1: 7359918142d3ae153023c378de4df6e0f8c52085 SHA256: be18e485b5437ce63c6e66195b72705ad408ce065a365a48977004b9fb721542 SHA512: 8efe72e86902259921b4f64932fc928c3766030279af78ba329a4c49b8a1ccd2289280e009be5306153f65cc529386f995dc3dd76b42c3b15028a95bea2c72d7 Homepage: https://cran.r-project.org/package=vacalibration Description: CRAN Package 'vacalibration' (Calibration of Computer-Coded Verbal Autopsy Algorithm) Calibrates population-level cause-specific mortality fractions (CSMFs) that are derived using computer-coded verbal autopsy (CCVA) algorithms. Leveraging the data collected in the Child Health and Mortality Prevention Surveillance (CHAMPS;) project, the package stores misclassification matrix estimates of three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. These estimates are obtained using the framework proposed in Pramanik et al. (2025;) and are analyzed in Pramanik et al. (2026;). Given VA-only data for an age group, CCVA algorithm, and country, the package utilizes the corresponding misclassification matrix estimate in the modular VA-Calibration framework (Pramanik et al.,2025;) and produces calibrated estimates of CSMFs. The package also supports ensemble calibration to accommodate multiple algorithms. More generally, this allows calibration of population-level prevalence derived from single-class predictions of discrete classifiers. For this, users need to provide fixed or uncertainty-quantified misclassification matrices. This work is supported by the Eunice Kennedy Shriver National Institute of Child Health K99 NIH Pathway to Independence Award (1K99HD114884-01A1), the Bill and Melinda Gates Foundation (INV-034842), and the Johns Hopkins Data Science and AI Institute. Package: r-cran-vaccinationimpact Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vaccinationimpact_0.1.0-1.ca2604.1_all.deb Size: 38762 MD5sum: 881f467d073693f3fb1f004201229d6b SHA1: bbef5f31fbc425cff01b512e08361d98c4842eda SHA256: 232ecf277b7853927ee0a67e3be529295694452379b7695055a2d60635e460c3 SHA512: 8df26638787bafa92abd6fd9d8c375d83f6d04cc14d6485753751295f797814a50ce8b4d6100272bbdccd1094dbeb1def4866e51ea7ba8fbcf6a09a531da107e Homepage: https://cran.r-project.org/package=vaccinationimpact Description: CRAN Package 'vaccinationimpact' (Impact Study of Vaccination Campaigns) Tools to estimate the impact of vaccination campaigns at population level (number of events averted, number of avertable events, number needed to vaccinate). Inspired by the methodology proposed by Foppa et al. (2015) and Machado et al. (2019) for influenza vaccination impact. Package: r-cran-vaccine Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fdrtool, r-cran-ggplot2, r-cran-ggpubr, r-cran-rlang, r-cran-rsolnp, r-cran-superlearner, r-cran-survival, r-cran-survml, r-cran-truncnorm, r-cran-e1071, r-cran-gam, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vaccine_1.3.1-1.ca2604.1_all.deb Size: 751806 MD5sum: 747e2b8e780b42f8b8f1cc1402a6f967 SHA1: 03358508aef895532b8c9c2dde496aa08cb8bcf4 SHA256: a14717598018f6c26bcbd69dd3d1952e282ee8e247c48ccc2397fbcca19993cd SHA512: 409a12c586c1389ebd46d29e566348884320d005d6d6ff57cb3e4eb7721b169ea5deb20c32123259765aac0fd0c9ac1f15b16a481db2daec8e17e3f8f1ee1a4c Homepage: https://cran.r-project.org/package=vaccine Description: CRAN Package 'vaccine' (Statistical Tools for Immune Correlates Analysis of VaccineClinical Trial Data) Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) and Fay MP and Follmann DA (2023) . Package: r-cran-vaccineff Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-ggplot2, r-cran-linelist, r-cran-matchit, r-cran-rlang, r-cran-scales, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vaccineff_1.0.1-1.ca2604.1_all.deb Size: 1187946 MD5sum: e49af7af6af1118e8f957497ef161c32 SHA1: f0e310e1b5381c09e64f1b32f00181149973d7a2 SHA256: 875f079fd470aaa88cc4324708f1c97719ee4fb436212b5950dc0ac56ee1db54 SHA512: 52b7dc9084c3209db3dc1de2fcd7ee2309ae026ff23a879ebb645de2ef54eda7c0e63eed27ea9a29a578d58ba68af1b6d306378609336931812ae32aa379b104 Homepage: https://cran.r-project.org/package=vaccineff Description: CRAN Package 'vaccineff' (Estimate Vaccine Effectiveness Based on Different Study Designs) Provides tools for estimating vaccine effectiveness and related metrics. The 'vaccineff_data' class manages key features for preparing, visualizing, and organizing cohort data, as well as estimating vaccine effectiveness. The results and model performance are assessed using the 'vaccineff' class. Package: r-cran-vachette Architecture: all Version: 0.40.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-prospectr, r-cran-hmisc, r-cran-purrr, r-cran-minpack.lm, r-cran-progress, r-cran-rlang, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mrgsolve, r-cran-tidyvpc, r-cran-testthat, r-cran-learnr Filename: pool/dists/resolute/main/r-cran-vachette_0.40.1-1.ca2604.1_all.deb Size: 618228 MD5sum: 583684da37326695a9b134b3d8b8684b SHA1: 7dfd0dfbac718275f0f713b0c3df0c283a0b5251 SHA256: 5c837930f933ae11b4278c6225d24e0396814b8ec7f55b4b8d5dfebd244699cb SHA512: 78dc0a3ed20130d32195fbaaaaf153fbfb76d9c87dbd51900c62cb3ac2f0ba2f0dfdd661357b702ed8ab05502b85ebd4d20b7abbd2e5737d26831f19b9753334 Homepage: https://cran.r-project.org/package=vachette Description: CRAN Package 'vachette' (A Method for Visualization of Pharmacometric Models) A method to visualize pharmacometric analyses which are impacted by covariate effects. Variability-aligned covariate harmonized-effects and time-transformation equivalent ('vachette') facilitates intuitive overlays of data and model predictions, allowing for comprehensive comparison without dilution effects. 'vachette' improves upon previous methods Lommerse et al. (2021) , enabling its application to all pharmacometric models and enhancing Visual Predictive Checks (VPC) by integrating data into cohesive plots that can highlight model misspecification. Package: r-cran-vacuum Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-tidyr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-vacuum_0.1.0-1.ca2604.1_all.deb Size: 79538 MD5sum: d214a0f71b9656df82a781f3a467c4b4 SHA1: 90dc1ee6a382c2beb388e07015e1c7de095099a3 SHA256: d34e05d29ae783fa4785db9ad8774c01fdf4f7559ac28727b5f89044cfc71e7f SHA512: 93fa70b5527d5460ce3b68d79657ab4bc2b4bfcb0eef3190b1b4b66850710beb7db23004426a4cddb086fd182dd9514a68f803688fe3bc581481c8a5c6374d8a Homepage: https://cran.r-project.org/package=vacuum Description: CRAN Package 'vacuum' (Tukey's Vacuum Cleaner) An implementation of three procedures developed by John Tukey: FUNOP (FUll NOrmal Plot), FUNOR-FUNOM (FUll NOrmal Rejection-FUll NOrmal Modification), and vacuum cleaner. Combined, they provide a way to identify, treat, and analyze outliers in two-way (i.e., contingency) tables, as described in his landmark paper "The Future of Data Analysis", Tukey, John W. (1962) . Package: r-cran-vader Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tm Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-vader_0.2.1-1.ca2604.1_all.deb Size: 145330 MD5sum: cd23a13ebd6dc1fd6ab7c27966c8dcef SHA1: 4ac40871353319d631d22995efe090df7b88c928 SHA256: 110fba13a2c9263e279dcac5136d9b0e84d5625bb4c150e26323b491300fade0 SHA512: 1ad1ba91c52e2ff59daa1c28ba61c34d389fa50cd2f3875e487f9a4d68fdd07ae07c3b37b9e8d6c76c2f02c724fb0ecee0143d613a4ab8b7e7189f48500c18dd Homepage: https://cran.r-project.org/package=vader Description: CRAN Package 'vader' (Valence Aware Dictionary and sEntiment Reasoner (VADER)) A lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. Hutto & Gilbert (2014) . Package: r-cran-vaersndvax Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vaersvax, r-cran-data.table, r-cran-dplyr, r-cran-rpivottable Filename: pool/dists/resolute/main/r-cran-vaersndvax_1.0.4-1.ca2604.1_all.deb Size: 588664 MD5sum: 61e926dd5b7bbe5fa668c4591832b0c3 SHA1: fc617967952eeb6d744628c0abc84e4c5a7f3d75 SHA256: c101c18f514b266dbb72cb1dd76f9529914eadd09056897b17a572656d2aa7e9 SHA512: caf9fcc30c8cf5c80805390b84a3c0fdef5c616da9453a3c0ac67525f5f6a4f6934ef51363eb80338005f8a9df05f0f58c04b61c056b2da6ea8ab7acb5995926 Homepage: https://cran.r-project.org/package=vaersNDvax Description: CRAN Package 'vaersNDvax' (Non-Domestic Vaccine Adverse Event Reporting System (VAERS)Vaccine Data for Present) Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the 'vaersND' package from the URL below. The URL and BugReports below correspond to the 'vaersND' package, of which 'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN due to the large size of the data set. To install the Suggested 'vaers' and 'vaersND' packages, use the following R code: 'devtools::install_git("https://gitlab.com/iembry/vaers.git", build_vignettes = TRUE)' and 'devtools::install_git("https://gitlab.com/iembry/vaersND.git", build_vignettes = TRUE)'. "VAERS is a national vaccine safety surveillance program co-sponsored by the US Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a post-marketing safety surveillance program, collecting information about adverse events (possible side effects) that occur after the administration of vaccines licensed for use in the United States." For more information about the data, visit . For information about vaccination/immunization hazards, visit . Package: r-cran-vaersvax Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vaersndvax, r-cran-dplyr, r-cran-data.table, r-cran-pivottabler, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-vaersvax_1.0.5-1.ca2604.1_all.deb Size: 148354 MD5sum: 27c4f1bf13159150b698400d186feafc SHA1: b40495d5b02c0d4b4a48e1a74f0a3b055537c3e6 SHA256: bc225f5b80791edb3e3f7b82f4507f0bf8999ce20257ec426393ee3d0334b42e SHA512: da3cad2b72d1eab94b89e76fd895577a5976b84651432f554785295b287498b45101ec7724f1c2010fcacb51aa977a848c6213df80cc3a7039f1e45b85e96e06 Homepage: https://cran.r-project.org/package=vaersvax Description: CRAN Package 'vaersvax' (US Vaccine Adverse Event Reporting System (VAERS) Vaccine Datafor Present) US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the 'vaers' package from the URL below. The URL and BugReports below correspond to the 'vaers' package, of which 'vaersvax' is a small subset (2018 only). 'vaers' is not hosted on CRAN due to the large size of the data set. To install the Suggested 'vaers' and 'vaersND' packages, use the following R code: 'devtools::install_git("", build_vignettes = TRUE)' and 'devtools::install_git("", build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS) is a national early warning system to detect possible safety problems in U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For more information about the data, visit . For information about vaccination/immunization hazards, visit . Package: r-cran-vagalumer Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-stringr, r-cran-dplyr, r-cran-purrr, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-tm Filename: pool/dists/resolute/main/r-cran-vagalumer_0.1.6-1.ca2604.1_all.deb Size: 30576 MD5sum: a1c917fa886ea1b1163ab23c2c2b666b SHA1: d2eb42f5275ec3bfc4a59e69c9947bec4d711a2e SHA256: 2c6272213596cb52275ae25c424fc8a7fb51ef301b9d4004295235c80afd66f2 SHA512: 7a734bd60ec53e459c219eaf1347f3d74be105a1170aba24d1f5e6dfe01a344adfb5e2e37de610a843905099034db7df3860a2d95abd24383a7a45c10fadf144 Homepage: https://cran.r-project.org/package=vagalumeR Description: CRAN Package 'vagalumeR' (Access to the 'Vagalume' API) Provides access to the 'Vagalume' API . The data extracted is basically lyrics of songs and information about artists/bands. Package: r-cran-vagam Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-gamm4, r-cran-matrix, r-cran-mvtnorm, r-cran-truncnorm Filename: pool/dists/resolute/main/r-cran-vagam_1.1-1.ca2604.1_all.deb Size: 132920 MD5sum: 5d270230ffcd44a658e61f3cd49c18d5 SHA1: 54fb02eb3b322320a5087758f4d764cf8b3cccd7 SHA256: 993af9398b6a75999fb93e2da24b0963272cf90e3b55b85fca1fcf5407a17bbd SHA512: c6e579585b0753731d344b26070317e251eb3f758a7d58da5125a8dccb63df4dca159ed4127ab215a0c8a57917175a3a8d33e991ddb018fb870665ef649ac55f Homepage: https://cran.r-project.org/package=vagam Description: CRAN Package 'vagam' (Variational Approximations for Generalized Additive Models) Fits generalized additive models (GAMs) using a variational approximations (VA) framework. In brief, the VA framework provides a fully or at least closed to fully tractable lower bound approximation to the marginal likelihood of a GAM when it is parameterized as a mixed model (using penalized splines, say). In doing so, the VA framework aims offers both the stability and natural inference tools available in the mixed model approach to GAMs, while achieving computation times comparable to that of using the penalized likelihood approach to GAMs. See Hui et al. (2018) . Package: r-cran-valaddin Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lazyeval Suggests: r-cran-magrittr, r-cran-testthat, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-valaddin_1.0.2-1.ca2604.1_all.deb Size: 183290 MD5sum: ebfae2b13f3b0c6e23a506a521db7e24 SHA1: 1de34371367cfa43a67a37d036705371cae4b8d3 SHA256: 2cbdb496b1d5060aa895f1eff110f2e8a03d5d5c70669f0cbefc7cd320fa033c SHA512: e96cd483e7024ebbad908d0686782b58d36610a22746db20f4f456c270c88176de220ddf2e4dcc9d3ac8b98ed611c397ec3f1d39015f1c962031d9d8046b59f9 Homepage: https://cran.r-project.org/package=valaddin Description: CRAN Package 'valaddin' (Functional Input Validation) A set of basic tools to transform functions into functions with input validation checks, in a manner suitable for both programmatic and interactive use. 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Package: r-cran-valdr Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-base64enc, r-cran-keyring, r-cran-jsonlite, r-cran-dplyr, r-cran-readr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-valdr_3.0.0-1.ca2604.1_all.deb Size: 238684 MD5sum: b4f4c0992594d3f7f070b3c40ab217fe SHA1: 91d3d37b31c3e1ee9a6d14e5b4fbc7e2d9f614b8 SHA256: 965ad1ae36b6440eb2b947038da04961f2f6e7c850c858ad261682a4bba1873d SHA512: babfecccbc7cdd1f85bc28db2aa6c2b91d2aa63f052c9b778ee41ec334d3943e6897da094911ccf617394cdfe107c3f960fc03f49e96437c7e5cf7b569d1ad53 Homepage: https://cran.r-project.org/package=valdr Description: CRAN Package 'valdr' (Access and Analyse 'VALD' Data via Our External 'APIs') Provides helper functions and wrappers to simplify authentication, data retrieval, and result processing from the 'VALD' 'APIs'. 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The 'valection' C library is required to use this package and can be downloaded from: . Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; . 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Methods are detailed in: Hivert B, Agniel D, Thiebaut R & Hejblum BP (2022). "Post-clustering difference testing: valid inference and practical considerations", . 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Based on vectors of observed and predicted values. Method: Kristin Piikki, Johanna Wetterlind, Mats Soderstrom and Bo Stenberg (2021). . Package: r-cran-valottery Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-valottery_0.0.1-1.ca2604.1_all.deb Size: 228384 MD5sum: 0cb636f214acb3597d6d9763d340f07c SHA1: f1d4ff4c989c89ef0f0f7a51a83c49ae0604067b SHA256: d2a989c39ac7236657ef9540f09c75d393b9f43e819dd22a7416f9fff7703360 SHA512: bfb22b08722e5e01fb282e889573ec6fd4319f4e44380be9cd59d9e8946881db9303296659bd40014e4a8f34179da54c097c9c51e8cb234bd65221550df29099 Homepage: https://cran.r-project.org/package=valottery Description: CRAN Package 'valottery' (Results from the Virginia Lottery Draw Games) Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/. 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Both adult versions of EQ-5D (EQ-5D-3L and EQ-5D-5L) contain a descriptive system and visual analog scale. The descriptive system measures the patient's health in 5 dimensions: the 5L versions has 5 levels and 3L version has 3 levels. The descriptive system scores are usually converted to index values using country specific values sets (that incorporates the country preferences). This package allows the calculation of both descriptive system scores to the index value scores. The value sets for EQ-5D-3L are from the references mentioned in the website The value sets for EQ-5D-3L for a total of 31 countries are used for the valuation (see the user guide for a complete list of references). The value sets for EQ-5D-5L are obtained from references mentioned in the and other sources. The value sets for EQ-5D-5L for a total of 17 countries are used for the valuation (see the user guide for a complete list of references). The package can also be used to map 5L scores to 3L index values for 10 countries: Denmark, France, Germany, Japan, Netherlands, Spain, Thailand, UK, USA, and Zimbabwe. The value set and method for mapping are obtained from Van Hout et al (2012) . 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This way, one may e.g. specify a VAR inducing a strong presence of long-term fluctuations in series 1 and in series 2, which are weakly correlated, but lagged by a number of time units to each other, while short-term fluctuations in series 1 and in series 2, are strongly present only in one of the two series, while they are strongly correlated to each other between the two series. Simulation from such models allows studying the behavior of data-analysis tools, such as estimation of the spectra, under different circumstances, as e.g. peaks in the spectra, generating bias, induced by leakage. Package: r-cran-varbin Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-varbin_0.2.1-1.ca2604.1_all.deb Size: 49302 MD5sum: 45b969530aa11b6663b04c438ebd2944 SHA1: 07d5ac7ff1c3943dd4f28771c5c9e8d6164574d8 SHA256: e6ee8d8990442614bf092778f2b5219741f4716a9b658c7b38edbf7c989c19c1 SHA512: ed94c79839c124a7d37dc1d503b37141ede298ef87c59821ca2790a89b314fde25430ec032b6f2fd090ea4939db7c9aef53465fc47f3844059ba2ec541e93040 Homepage: https://cran.r-project.org/package=varbin Description: CRAN Package 'varbin' (Optimal Binning of Continuous and Categorical Variables) Tool for easy and efficient discretization of continuous and categorical data. The package calculates the most optimal binning of a given explanatory variable with respect to a user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value to each of the calculated binpoints in order to recode the original variable. The package allows users to impose certain restrictions on the functional form on the resulting binning while maximizing the overall information value in the original data. The package is well suited for logistic scoring models where input variables may be subject to restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring is (Siddiqi 2006, ISBN: 978–0-471–75451–0). The package utilizes the discrete nature of decision trees and Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum information value. Package: r-cran-varcheck Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-varcheck_0.1.0-1.ca2604.1_all.deb Size: 494332 MD5sum: 7f82f4fb1366e430a1383089756d4bd8 SHA1: 47254e99ad1cbeb16afa877f64d979008ef61b7b SHA256: d0ebd52dffda47e82c3a3d82761d133d3844727b86a9d8e06e0f246bdeb41747 SHA512: c4fbefeb0ce8610ddf5a5e8cd941849c7ee198fe8c6ff09a8aa6fa6202a1303f1d5535dd31f8fe49a4c20774d347914c5657617d8c2c8c10d712775df7468b47 Homepage: https://cran.r-project.org/package=VARcheck Description: CRAN Package 'VARcheck' (Visual Diagnostic Checks for Vector Autoregressive Models) Provides model-agnostic visual diagnostics for vector autoregressive (VAR) models. 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Package: r-cran-varcpdetectonline Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-corpcor, r-cran-matrix, r-cran-glmnet, r-cran-doparallel Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-varcpdetectonline_0.2.0-1.ca2604.1_all.deb Size: 4352548 MD5sum: 0584a6de8e255b67897ecfcf3615fa03 SHA1: 0f7eb65bc44c598cb51dcf246eec27068ce2d025 SHA256: 011e5f1a06bf79adb7177d8c004be83483199d26243cede8cb48e6bcf766f04c SHA512: ef007aaf0dc077ba4bd6d45c8b40ffb458afe371c29fc3c2ca6305e201a354618cfd713ff2148c18176555f2a034bdeea0893cd44b643b359783968e4c55aec9 Homepage: https://cran.r-project.org/package=VARcpDetectOnline Description: CRAN Package 'VARcpDetectOnline' (Sequential Change Point Detection for High-Dimensional VARModels) Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024) , "Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models". This package provides tools for detecting change points in the transition matrices of VAR models, effectively identifying shifts in temporal and cross-correlations within high-dimensional time series data. 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Package: r-cran-vardpoor Architecture: all Version: 0.21.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-data.table, r-cran-mass, r-cran-stringr, r-cran-surveyplanning, r-cran-laeken Filename: pool/dists/resolute/main/r-cran-vardpoor_0.21.0-1.ca2604.1_all.deb Size: 586768 MD5sum: dba0ab5da4280d91b098076a0f0f1c47 SHA1: 12a5122066c4a060404fee0de53752f5d1c5fc55 SHA256: 350df5b9b3480dc625513286670bd77db4f3abf34494ffce2f0d30ea40911ffd SHA512: c5b82e273ffb82851615c8d20d46c2455e823d549e9b04397042e1fe36d4439fc81e34126efdea214539df29fbced028bb992847d6eb8f95908a949e6f06993e Homepage: https://cran.r-project.org/package=vardpoor Description: CRAN Package 'vardpoor' (Variance Estimation for Sample Surveys by the Ultimate ClusterMethod) Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. 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The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079). Package: r-cran-variables Architecture: all Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-variables_1.1-2-1.ca2604.1_all.deb Size: 43246 MD5sum: 430f2108543a20b43c320c12ebc701a9 SHA1: 683f58a5895114635f95b51e62ca77f7fcbe6ff1 SHA256: 0be8799353c6645bdad769e50193b41e96771038a3af255140b71a4e4b6507d2 SHA512: d3810dfc251a6e9cdf48c48ba5d801181d2471868b6d5fdf08b583eb102cc5189affb1adb68d97c127f13adc1772c168695a45ebf8f4fe75845bdd70fa6c581b Homepage: https://cran.r-project.org/package=variables Description: CRAN Package 'variables' (Variable Descriptions) Abstract descriptions of (yet) unobserved variables. 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Techniques for independent (iid) data, varying-coefficient models, and longitudinal data are implemented. The package currently contains three screen functions: screenIID(), screenLD() and screenVCM(), and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(), simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) , Runze LI, Wei ZHONG, & Liping ZHU (2012) , Jingyuan LIU, Runze LI, & Rongling WU (2014) Hengjian CUI, Runze LI, & Wei ZHONG (2015) , and Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) . 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It was tested on datasets with 3000 samples each one containing 80 million features in either unsupervised clustering approaches and supervised applications, like classification and regression. The genome datasets are usually writing in VCF, a specific text file format used in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great tool for genome research, because it is able to read VCF files, run analyses and return the output in a 'spark' data frame. Package: r-cran-variationaldcm Architecture: all Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-variationaldcm_2.0.1-1.ca2604.1_all.deb Size: 138822 MD5sum: 813b657fcc52769ecfca1a91e3e7f69e SHA1: 339dfe8e23fb4ca0998abef1e775dd636a93c4cd SHA256: 15920452a07fae924154476af28de8e2cba81c32cbc82ca96f5660da00cbbe41 SHA512: 3b5fe80b48315e311a867a1ecdc61b10c2b05916f986cf9ee9fce11c6188f9c2028cd56042b80cb49d4841e3038038bca505ff771fb0b64f9b1ea5bbb79f6d4d Homepage: https://cran.r-project.org/package=variationalDCM Description: CRAN Package 'variationalDCM' (Variational Bayesian Estimation for Diagnostic ClassificationModels) Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) , Yamaguchi and Okada (2020b) , Yamaguchi (2020) , Oka and Okada (2023) , and Yamaguchi and Martinez (2023) . To facilitate their applications, 'variationalDCM' is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs. Package: r-cran-varimp Architecture: all Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-measures, r-cran-party Suggests: r-cran-testthat, r-cran-ranger Filename: pool/dists/resolute/main/r-cran-varimp_0.4-1.ca2604.1_all.deb Size: 42426 MD5sum: 626aa35929e85cbc1b527e603169414b SHA1: a0693a8ab1d4ca4c2def7e6c4dc04e050fc12b52 SHA256: 0206bc45682c49f1e3fc50940b305b684e9782fd591ccf11962a76d9c9388c4b SHA512: 558016b60d524b6a9228ae1956cd319c264c7bb677d1af3827b928b39e54189cb66a8db746c7e6a19613ba430f3b1180c414703cadc2574402ac53505dcb58f3 Homepage: https://cran.r-project.org/package=varImp Description: CRAN Package 'varImp' (RF Variable Importance for Arbitrary Measures) Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC). Package: r-cran-variosig Architecture: all Version: 0.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-sp, r-cran-testthat Suggests: r-cran-geor Filename: pool/dists/resolute/main/r-cran-variosig_0.3-1-1.ca2604.1_all.deb Size: 39832 MD5sum: e9b5583f24b200c670681aa7fc431b7e SHA1: 38b78e975ff73d274cab40b180e0877f1cf788cd SHA256: c0e93f183added42e8eebaf5fe1734522610138d28aa433c1211047b1dfb1b76 SHA512: 6c23c70b7825b98a80a0988603b3444f62a733840e2218147f767daa4f9bc33259bd97a7d68a84addaa7b095680e0f931b991fff876669f4e777db9d1114d9a8 Homepage: https://cran.r-project.org/package=variosig Description: CRAN Package 'variosig' (Testing Spatial Dependence Using Empirical Variogram) Applying Monte Carlo permutation to generate pointwise variogram envelope and checking for spatial dependence at different scales using permutation test. Empirical Brown's method and Fisher's method are used to compute overall p-value for hypothesis test. Package: r-cran-variskscore Architecture: all Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-variskscore_2.0.0-1.ca2604.1_all.deb Size: 22516 MD5sum: 7ad96b82214ac6bc7cbef97c889c09a6 SHA1: 0ee5cd9c025118967ad4f06bed2bb8555b2bbcf1 SHA256: 22a8f84b3528e73401dccd0147f01a4919e4e73455013df65cd2cceca6aa986d SHA512: b312023b54f23437e2601ab397dc13dc9c751fd36ef9909e88ddec2cb47c2d82c3f7b60c28e8477aecb2fb8575304a4fd27f4742cc34b9de597492df9cb8ff62 Homepage: https://cran.r-project.org/package=vaRiskScore Description: CRAN Package 'vaRiskScore' (VA CVD Risk Score) Estimates the predicted 10-year cardiovascular (CVD) risk score (in probability) for civilian women, women military service members and veterans by inputting patient profiles. The proposed women CVD risk score improves the accuracy of the existing American College of Cardiology/American Heart Association CVD risk assessment tool in predicting long‐term CVD risk for VA women, particularly in young and racial/ethnic minority women. See the reference: Jeon‐Slaughter, H., Chen, X., Tsai, S., Ramanan, B., & Ebrahimi, R. (2021) . 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Main applications in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications). 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Package: r-cran-vartest Architecture: all Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-suppdists, r-cran-psych, r-cran-moments, r-cran-pearsonds Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vartest_1.5-1.ca2604.1_all.deb Size: 161580 MD5sum: 48702b02af173888a37baa2fd46c491c SHA1: c8d37eafaeea4f6eaaa21e8f9443bc394c73e7a4 SHA256: 60d9ce9b1155423ed23d0ead0f61c56ebe0c03a8b15539575e33dbfec0b8adcd SHA512: b543441f4fbe49c05f64dade64de43090eecaa6f7658986a6179c2e8585591a01c72c86aee2b5c40bb6d183f6b601f2a20d26a515e025932648bdc37b7495f17 Homepage: https://cran.r-project.org/package=vartest Description: CRAN Package 'vartest' (Tests for Variance Homogeneity) Performs 18 omnibus tests yielding a total of 28 distinct methodological variations for testing the composite hypothesis of variance homogeneity. 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(2021a) ). Covariance tapering (Furrer et al. (2006) ) can be applied such that the method scales to large data. Further, it implements a joint variable selection of the fixed and random effects (Dambon et al. (2021b) ). The package and its capabilities are described in (Dambon et al. (2021c) ). 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A VCD file captures the register values at discrete timepoints from a simulated trace of execution of a hardware design in Verilog or VHDL. The returned dataframe contains a row for each register, by name, and a column for each time point, specified VCD-style using octothorpe-prefixed multiples of the timescale as strings. The only non-trivial implementation details are that (1) VCD 'x' and 'z' non-numerical values are encoded as negative value -1 (as otherwise all bit values are positive) and (2) registers with repeated names in distinct modules are ignored, rather than duplicated, as we anticipate these registers to have the same values. Read more in arXiv preprint: 'vcd2df' -- Leveraging Data Science Insights for Hardware Security Research . 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The package was package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015). Package: r-cran-vcdextra Architecture: all Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vcd, r-cran-gnm, r-cran-mass, r-cran-dplyr, r-cran-ca, r-cran-igraph, r-cran-colorspace, r-cran-gt, r-cran-scales, r-cran-knitr, r-cran-htmlwidgets, r-cran-webshot2 Suggests: r-cran-gmodels, r-cran-fahrmeir, r-cran-effects, r-cran-vgam, r-cran-plyr, r-cran-lmtest, r-cran-nnet, r-cran-ggplot2, r-cran-sleuth2, r-cran-car, r-cran-lattice, r-cran-aer, r-cran-coin, r-cran-hmisc, r-cran-rmarkdown, r-cran-seriation, r-cran-testthat, r-cran-tibble, r-cran-glue, r-cran-here, r-cran-purrr, r-cran-readxl, r-cran-stringr, r-cran-tidyr, r-cran-rgl Filename: pool/dists/resolute/main/r-cran-vcdextra_0.9.3-1.ca2604.1_all.deb Size: 2998056 MD5sum: faa57d928ff15baaa811b82ab7fc5c82 SHA1: 01a30b7306f00725a5a2fc2536efcb90b8d2bb5b SHA256: e71eb1f95ed03be49340e384cbfc7b0e2d1d89d232709a8994bf750d90d3f3fa SHA512: 4d8f84eeed2910d933aca91821619fd5a2f7fac718129d522bf3cb5dba8df2f9b039a3f52f167e1ee46c85b60c0b0cf0d19913a22202fc0b585eadc772da83a9 Homepage: https://cran.r-project.org/package=vcdExtra Description: CRAN Package 'vcdExtra' ('vcd' Extensions and Additions) Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer. Package: r-cran-vcfheader Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-vcfheader_0.1.0-1.ca2604.1_all.deb Size: 922228 MD5sum: eb1db0ce663f7f19d7b99a72e7dca2f2 SHA1: b736c3612c27b80123fe5c546e5d3b843d1ee3ab SHA256: 7c1a7ea55cae55390a5aac26a507f8d37be011a154c36b37b4cf0bc329f875b5 SHA512: fabe688a3707efafb63022dfa93dda5fbeb832fc3a200247aeda1bb6f6823bc6b9de42dcda44b4166850037a388bc7294cb5dc9f95da5581a32d812534e6b317 Homepage: https://cran.r-project.org/package=vcfheader Description: CRAN Package 'vcfheader' (Fast Genetic Variant Call Format File Header Intelligence andAudit) Streams and parses variant call format file headers without reading full files. Provides structured metadata, validation, inference, and HTML reporting. For details on the specifications used see Danecek et al. (2021) . 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Subgroup effect size comparisons, general linear effect size contrasts, and linear models of effect sizes based on varying coefficient methods can be used to describe effect size heterogeneity. Varying coefficient meta-analysis methods do not require the unrealistic assumptions of the traditional fixed-effect and random-effects meta-analysis methods. For details see: Statistical Methods for Psychologists, Volume 5, . 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Our model is the first work on the longitudinal data, and also can set a varying variable to find the complicated association between other variables and the varying variable. Our work is an extension of the Peters-Belson method which was originally published in Peters (1941) and Belson (1956). 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'vDiveR' allows visualization of the diversity motifs (index and its variants – major, minor and unique) for elucidation of the underlying inherent dynamics. Please refer for more information. 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Sources include IMGT from MP Lefranc (2009) and Vgenerepertoire from publication DN Olivieri (2014) . 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Package: r-cran-vdsm Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-dplyr, r-cran-viridis, r-cran-gridextra, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vdsm_0.1.1-1.ca2604.1_all.deb Size: 84658 MD5sum: 4042dbf6448f1018e982d14c9ac0ff4c SHA1: cb311b67fd5c20f3efd449bad75bacb05ae21220 SHA256: 3157f58c998519282542a2b2b4a6f769ef64a1d6437a8a349ac2ead8a50d5ccc SHA512: f519f722f600da45e7db0791bf052e5c58939e8b957795d327c0e6315e94baf0630196a7650da238e6879b80af97d716236535c75d4a14ff8602eefaf2bf66d3 Homepage: https://cran.r-project.org/package=VDSM Description: CRAN Package 'VDSM' (Visualization of Distribution of Selected Model) Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. This R package offers several graphical tools to visualize the distribution of the selected model. For example, Gplot(), Hplot(), VDSM_scatterplot() and VDSM_heatmap(). To the best of our knowledge, this is the first attempt to visualize such a distribution. About what distribution of selected model is and how it work please see Qin,Y.and Wang,L. (2021) "Visualization of Model Selection Uncertainty" . Package: r-cran-vdspcalibration Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-vdspcalibration_1.0-1.ca2604.1_all.deb Size: 35916 MD5sum: 612ed4e89b291c65a0817d46e022c3a3 SHA1: 4f3e20150031c0d25acca3c5ceb59d584da92003 SHA256: 1800a76f8f0f151adcbd50bf5f8e0b66c1fbc88335334fb702913e8ae8a87dc4 SHA512: bcf59cbecd6d9a85636729191fc73e7bdb461a2f4cd04d8a48498c638e636479d614695bd90f34330d323956e701224c9a6e2324263338b7564eee6ebe431f93 Homepage: https://cran.r-project.org/package=VDSPCalibration Description: CRAN Package 'VDSPCalibration' (Statistical Methods for Designing and Analyzing a CalibrationStudy) Provides statistical methods for the design and analysis of a calibration study, which aims for calibrating measurements using two different methods. The package includes sample size calculation, sample selection, regression analysis with error-in measurements and change-point regression. The method is described in Tian, Durazo-Arvizu, Myers, et al. (2014) . Package: r-cran-vec2dtransf Architecture: all Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/resolute/main/r-cran-vec2dtransf_1.1.5-1.ca2604.1_all.deb Size: 179818 MD5sum: aafffa10f29806773968e3056ef68cf8 SHA1: 129e638dc9ed26209f4fe17890d2f88718f68c97 SHA256: 932dc1a9b60afe4b6868bb35bdcb420da173d3a3e7e4d0f8324815dfc3d10bbc SHA512: 30a3edb7e2b9e653dad214a9fe206c9aaf9bc7d49c837e7a2acdae5725f409dbf4f99ee53b26000fe9f65b4709812f644b0d6777b468c469d3b00764cd3d8514 Homepage: https://cran.r-project.org/package=vec2dtransf Description: CRAN Package 'vec2dtransf' (2D Cartesian Coordinate Transformation) Applies affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. 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Package: r-cran-veccompare Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-gtools, r-cran-pander, r-cran-purrr, r-cran-reshape2, r-cran-qgraph, r-cran-venndiagram Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-veccompare_0.1.0-1.ca2604.1_all.deb Size: 71390 MD5sum: c82259403a69f759a0d757f213f626e8 SHA1: eb12caa4a0957ada1cf1f32f2996179f5efea93b SHA256: c4d262a5f911c948b62d358725f362545c19d2ba35f5a8f012d1cb085627f3af SHA512: 97b2a0ab63061a51fc30a395b49b68ced3ae9504e8ae32a028edb4bca17b3c99e6ac1d9f3dfad0fe99673e32419eb219330eb04f29d9a8a9f10ee965379bcb7f Homepage: https://cran.r-project.org/package=veccompare Description: CRAN Package 'veccompare' (Perform Set Operations on Vectors, Automatically Generating Alln-Wise Comparisons, and Create Markdown Output) Automates set operations (i.e., comparisons of overlap) between multiple vectors. 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Package: r-cran-vecdep Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-elliptcopulas, r-cran-hac, r-cran-hash, r-cran-sets, r-cran-covglasso, r-cran-expm, r-cran-magic, r-cran-pbapply, r-cran-rmpfr, r-cran-reticulate, r-cran-gtools Suggests: r-cran-mvtnorm, r-cran-ggplot2, r-cran-extradistr, r-cran-fossil, r-cran-dendextend, r-cran-copula, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vecdep_0.1.3-1.ca2604.1_all.deb Size: 287776 MD5sum: f5e9326a77c8e1ff4f4df072d2911e3c SHA1: 618b28a6cb38470981ad6b30f6d51506153ce3e3 SHA256: 37543bb24bf1adfac69f457fada8a7fea67cb8c23fa00fb5103c5be32e71bc6e SHA512: c7a2bd08ab4914d1b67728159e1f7940703fbe5ac0d30cca22f04d288878ce6f93bae8a6a4555c5e7716d9feec9e52f5bd2cb119561c26f2a554d364b1ad9674 Homepage: https://cran.r-project.org/package=VecDep Description: CRAN Package 'VecDep' (Measuring Copula-Based Dependence Between Random Vectors) Provides functions for estimation (parametric, semi-parametric and non-parametric) of copula-based dependence coefficients between a finite collection of random vectors, including phi-dependence measures and Bures-Wasserstein dependence measures. An algorithm for agglomerative hierarchical variable clustering is also implemented. Following the articles De Keyser & Gijbels (2024) , De Keyser & Gijbels (2024) , and De Keyser & Gijbels (2024) . 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The package includes tools for visualizing initial confounder imbalances, estimating treatment assignment probabilities using various methods, defining the common support region, performing matching across multiple groups, and evaluating matching quality. For more details, see Lopez and Gutman (2017) . Package: r-cran-vecsets Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-vecsets_1.4-1.ca2604.1_all.deb Size: 29358 MD5sum: d8278b77dde0cc5154a419d43ee3a19a SHA1: e3a3c955e18d59fbd05f16e5cddef92d81ceb0a6 SHA256: 152fa9172f12f65151b05b0e2f942e04afa26152a59b43c6a8c9c7c441f9a43a SHA512: e6c73a1b6533fce7c61c218df1b9a907b891e8d9bc13eb12e99afa0b400aa30bdbfbad7f1a03e27c0abaa42f629f2d1a7b4c0419138595d4c9715a8a3d9c4787 Homepage: https://cran.r-project.org/package=vecsets Description: CRAN Package 'vecsets' (Like Set Tools in 'Base' Package but Keeps Duplicate Elements) The 'base' tools union() intersect(), etc., follow the algebraic definition that each element of a set must be unique. Since it's often helpful to compare all elements of two vectors, this toolset treats every element as unique for counting purposes. For ease of use, all functions in vecsets have an argument 'multiple' which, when set to FALSE, reverts them to the base::sets (alias for all the items) tools functionality. Package: r-cran-vectorcoder Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-tidyverse, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vectorcoder_0.2.0-1.ca2604.1_all.deb Size: 37946 MD5sum: feb44f0fd830f8748dea5234f49f81c4 SHA1: da5a08ff9e1f6d6051ea149c62bcf5058fe8636a SHA256: 9565cda36afdc5d66da16cd0c65a0da51fe1f195eef9643e0cfac417524836ef SHA512: c495b7bf0ab6d498f869ede0d73da38e9fedeca77efd85666ac5567c6f1f6cf2ecfee3fbc91d11e1e694f205817dd128d20365ba18b8cd3daf1d3a993c0090a0 Homepage: https://cran.r-project.org/package=VectorCodeR Description: CRAN Package 'VectorCodeR' (Easily Analyze Your Gait Patterns Using Vector Coding Technique) Facilitate the analysis of inter-limb and intra-limb coordination in human movement. It provides functions for calculating the phase angle between two segments, enabling researchers and practitioners to quantify the coordination patterns within and between limbs during various motor tasks. Needham, R., Naemi, R., & Chockalingam, N. (2014) . Needham, R., Naemi, R., & Chockalingam, N. (2015) . Tepavac, D., & Field-Fote, E. C. (2001) . Park, J.H., Lee, H., Cho, Js. et al. (2021) . Package: r-cran-vectorialcalculus Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-plotly, r-cran-pracma, r-cran-ggplot2, r-cran-purrr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vectorialcalculus_1.0.5-1.ca2604.1_all.deb Size: 641476 MD5sum: afd21c2762b56ad70ce7a5fbba0dab01 SHA1: 92c42c2014e2e2ca9896a05b0b57f1b45059397a SHA256: 9225548c7e949d12d5e3ccf3aba2972c66286830e81c096e39fc3565e5867134 SHA512: 4afbd2f500f364fcdd0135fe481aff7bd260f6e903371cc9f14a338924164ffd17b42db0bcf0e1addd377f36e4be6936759d58f047a485421dc729c787b85447 Homepage: https://cran.r-project.org/package=vectorialcalculus Description: CRAN Package 'vectorialcalculus' (Vector Calculus Tools for Visualization and Analysis) Provides pedagogical tools for visualization and numerical computation in vector calculus. Includes functions for parametric curves, scalar and vector fields, gradients, divergences, curls, line and surface integrals, and dynamic 2D/3D graphical analysis to support teaching and learning. The implemented methods follow standard treatments in vector calculus and multivariable analysis as presented in Marsden and Tromba (2011) , Stewart (2015) , Thomas, Weir and Hass (2018) , Larson and Edwards (2016) , Apostol (1969) , Spivak (1971) , Schey (2005) , Colley (2019) , Lizarazo Osorio (2020) , Sievert (2020) , and Borowko (2013) . Package: r-cran-vectorsurvr Architecture: all Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3995 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rstudioapi, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-lubridate, r-cran-stringr, r-cran-httr2, r-cran-tidyr, r-cran-magrittr, r-cran-dt, r-cran-sf, r-cran-scales, r-cran-ggplot2, r-cran-rlang, r-cran-leaflet, r-cran-base64enc, r-cran-viridis, r-cran-leaflet.minicharts Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-devtools, r-cran-mockery, r-cran-webshot2, r-cran-htmlwidgets Filename: pool/dists/resolute/main/r-cran-vectorsurvr_1.6.3-1.ca2604.1_all.deb Size: 3591214 MD5sum: 4a81be3b40b98079439655b7a77bbfef SHA1: 6a03709dd99fb05d14db03178ae816575dc9849e SHA256: 066e75d54804c247b80d28edf1975cd8b929ac6c8b83894affc5d95cdd772a28 SHA512: 8e7d72fd2b66378fb3065040a3cdbc91db6bbf9b25769b399f51a31f2a8bcd1baf3156b60a1b2e124c9c062976c864ba93805d4ff1b492967a9b3f074f583066 Homepage: https://cran.r-project.org/package=vectorsurvR Description: CRAN Package 'vectorsurvR' (Data Access and Analytical Tools for 'VectorSurv' Users) Allows registered 'VectorSurv' users access to data through the 'VectorSurv API' . Additionally provides functions for analysis and visualization. Package: r-cran-vectorwavelet Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biwavelet, r-cran-iterators, r-cran-spam, r-cran-maps, r-cran-fields, r-cran-foreach, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-vectorwavelet_0.1.0-1.ca2604.1_all.deb Size: 69288 MD5sum: 35e71f588d9f948ae0f257a353262875 SHA1: 13d76e53c976562e2f7c6b25be3ac2786cec1c67 SHA256: 5afb118057ebdeb79055e2582bf03fb88d7e3129f057a2b3220ab447a2c093d5 SHA512: 7e59ce64b4acb326294e108e4bfd74cd4ad2ee1260dbaa5cd3bc807f45c9a9fe23304cd7852176fcf2b4f55d4e65a7de2551e5981cb747110df8a19b52bd25a4 Homepage: https://cran.r-project.org/package=vectorwavelet Description: CRAN Package 'vectorwavelet' (Vector Wavelet Coherence for Multiple Time Series) New wavelet methodology (vector wavelet coherence) (Oygur, T., Unal, G, 2020 ) to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. This package can be used to perform multiple wavelet coherence, quadruple wavelet coherence, and n-dimensional vector wavelet coherence analyses. Package: r-cran-vectrixdb Architecture: all Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2357 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-jsonlite, r-cran-digest, r-cran-matrix, r-cran-text2vec, r-cran-stopwords, r-cran-rsqlite, r-cran-dbi Suggests: r-cran-testthat, r-cran-shiny, r-cran-plumber, r-cran-pkgdown, r-cran-rcppannoy, r-cran-reticulate, r-cran-rappdirs Filename: pool/dists/resolute/main/r-cran-vectrixdb_1.1.2-1.ca2604.1_all.deb Size: 1577974 MD5sum: 6c6e795b1a7b6a9e217f5b17342015d1 SHA1: 9c950f3d1c71f65ad5949a968f1827101176c0b8 SHA256: e807c4ab13b0ea09bc2cc55ebe8c134a583ad1e6850ef5031642b509318140f7 SHA512: e9036a49476bad2c0e9fe023663b2679455421115d6edee5e8e6fe2cdb3cf11bd2abd266e5f278a21fa2f88855c69c8cc5adfc9ae136d60c93dcb072f09be635 Homepage: https://cran.r-project.org/package=VectrixDB Description: CRAN Package 'VectrixDB' (Lightweight Vector Database with Embedded Machine LearningModels) A lightweight vector database for text retrieval in R with embedded machine learning models and no external API (Application Programming Interface) keys. Supports dense and hybrid search, optional HNSW (Hierarchical Navigable Small World) approximate nearest-neighbor indexing, faceted filters with ACL (Access Control List) metadata, command-line tools, and a local dashboard built with 'shiny'. The HNSW method is described by Malkov and Yashunin (2018) . Package: r-cran-vecvec Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-s7, r-cran-vctrs Suggests: r-cran-lifecycle, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vecvec_1.0.0-1.ca2604.1_all.deb Size: 148136 MD5sum: 19fd9678d9d39b077e78a2ec2ff2f25e SHA1: 89d4f93ffd3b979ded0f894e1261838b2a46a908 SHA256: f432e100ff2506a9de2f2cf858e4e0f0896545ea0f6ee26195bba34e74e4bfe1 SHA512: 461116b735b5b281858795ce72660bec29ccdb4220e0e3f302c4fcfc03065a149f648a809c2b7db22575944647f50d185c1ab87de1d4357ef56d58c67069a00d Homepage: https://cran.r-project.org/package=vecvec Description: CRAN Package 'vecvec' (Construct Mixed Type Data Structures with Vectors of Vectors) Mixed type vectors are useful for combining semantically similar classes. Some examples of semantically related classes include time across different granularities (e.g. daily, monthly, annual) and probability distributions (e.g. Normal, Uniform, Poisson). These groups of vector types typically share common statistical operations which vary in results with the attributes of each vector. The 'vecvec' data structure facilitates efficient storage and computation across multiple vectors within the same object. Package: r-cran-vedicdatetime Architecture: all Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-swephr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-qpdf, r-cran-formatr, r-cran-spelling, r-cran-tinytex Filename: pool/dists/resolute/main/r-cran-vedicdatetime_0.1.9-1.ca2604.1_all.deb Size: 368730 MD5sum: 3eb3916881f3f720342b1bf7acf3bf5b SHA1: 29ba7848bf02f96c80c0177334c143ab2916a261 SHA256: 5d4be8a340a148c0856873401a99e8aa7df931a033c6e48106b1b014365e9ad2 SHA512: 03e53afd3babd1e0a6b0c97c0d3eba5fbdbbe5f84ae4760d7953dd8a8bb256f3b7e09884b67fe0e613af63e1f447ebfb6e6caaeb55563a316c440312f74e8a65 Homepage: https://cran.r-project.org/package=VedicDateTime Description: CRAN Package 'VedicDateTime' (Vedic Calendar System) Provides platform for Vedic calendar system having several functionalities to facilitate conversion between Gregorian and Vedic calendar systems, and helpful in examining its impact in the time series analysis domain. Package: r-cran-veesa Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fdasrvf, r-cran-forcats, r-cran-ggplot2, r-cran-purrr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-randomforest, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-veesa_0.1.7-1.ca2604.1_all.deb Size: 663364 MD5sum: 538c53c65fcd43c182464b46b9c4575a SHA1: 0a709f4bd35640c219ec7f545a829dce0ab2e80b SHA256: 851e819c9d2bdc6a288bf6025cc1df42babe51711d35164666828a61228f8726 SHA512: fecdf6435d073438f9255f360233b1e1f41107793b430672af266f80788304685d8e2355b5be20da855874dfe65afb0464e9a614fc5870457dc1d4426a7690e6 Homepage: https://cran.r-project.org/package=veesa Description: CRAN Package 'veesa' (Pipeline for Explainable Machine Learning with Functional Data) Implements the Variable importance Explainable Elastic Shape Analysis pipeline for explainable machine learning with functional data inputs. Converts training and testing data functional inputs to elastic shape analysis principal components that account for vertical and/or horizontal variability. Computes feature importance to identify important principal components and visualizes variability captured by functional principal components. See Goode et al. (2025) for technical details about the methodology. Package: r-cran-vegalite Architecture: all Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3846 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-htmlwidgets, r-cran-htmltools, r-cran-magrittr, r-cran-digest, r-cran-clipr, r-cran-webshot, r-cran-base64 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-vegalite_0.6.1-1.ca2604.1_all.deb Size: 974808 MD5sum: a8b8dbbd696c23a67a7dbd1b801e8395 SHA1: 78b5e10f431f72137981ec852b4d7f047f657184 SHA256: b3ce2d67ac2403d3e5ec6c0fd5ce62fcc5fc867770851089bae4f56cb41208c5 SHA512: 64942b0f70da1e15ac3238f2c58d3dd53e18b07cf1fdfd08bbfda7be8f5a71b18c3c02a9bf5fe873e81d3a5383f6eda592142dd621b7ccaca0b1b8d975fbb0e7 Homepage: https://cran.r-project.org/package=vegalite Description: CRAN Package 'vegalite' (Tools to Encode Visualizations with the 'Grammar ofGraphics'-Like 'Vega-Lite' 'Spec') The 'Vega-Lite' 'JavaScript' framework provides a higher-level grammar for visual analysis, akin to 'ggplot' or 'Tableau', that generates complete 'Vega' specifications. Functions exist which enable building a valid 'spec' from scratch or importing a previously created 'spec' file. Functions also exist to export 'spec' files and to generate code which will enable plots to be embedded in properly configured web pages. The default behavior is to generate an 'htmlwidget'. Package: r-cran-vegan3d Architecture: all Version: 1.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-cluster, r-cran-lattice, r-cran-rgl, r-cran-scatterplot3d Filename: pool/dists/resolute/main/r-cran-vegan3d_1.4-1-1.ca2604.1_all.deb Size: 147370 MD5sum: 43bf046160913e5c1db09e1174a998f2 SHA1: 067e94c6cf93a91e219da04d0e909c15190f7a67 SHA256: 771441ad6dad0543cb483f0d22c41b40fed16052fea1e8e05b233e21c82fe5cd SHA512: 73753c4e30688a96ddede94de559905ae535e477eba96b467f359d6d5577876b43785fb77dc4fcfd067378ce219e8aa320177287dc98650d6b2f8bf1280b3c5e Homepage: https://cran.r-project.org/package=vegan3d Description: CRAN Package 'vegan3d' (Static and Dynamic 3D and Editable Interactive Plots for the'vegan' Package) Static and dynamic 3D plots to be used with ordination results and in diversity analysis, especially with the vegan package. Package: r-cran-vegawidget Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4516 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-htmlwidgets, r-cran-assertthat, r-cran-rlang, r-cran-glue, r-cran-magrittr, r-cran-htmltools, r-cran-digest Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-listviewer, r-cran-testthat, r-cran-yaml, r-cran-fs, r-cran-usethis, r-cran-readr, r-cran-tibble, r-cran-lubridate, r-cran-v8, r-cran-withr, r-cran-learnr, r-cran-rsvg, r-cran-dplyr, r-cran-png, r-cran-conflicted, r-cran-here, r-cran-shiny, r-cran-purrr, r-cran-rsconnect Filename: pool/dists/resolute/main/r-cran-vegawidget_0.5.0-1.ca2604.1_all.deb Size: 826396 MD5sum: 9af2e73b484b8833fb97106cee7f3a99 SHA1: 95e161de494b0018a5389f7130476c689de696ac SHA256: 1cb84f8abdb6bf016db3795b98d00887e0ca00b607d53eac48a4e87549572a86 SHA512: 749cf3927b87035b2dd8bc378948f34e3f17595be22955e8d1f06d6f9a3ba2fcd14830121d88eeb3cfe788122f5d2dcad4032f33194b5eca3ec6812ff935cb2c Homepage: https://cran.r-project.org/package=vegawidget Description: CRAN Package 'vegawidget' ('Htmlwidget' for 'Vega' and 'Vega-Lite') 'Vega' and 'Vega-Lite' parse text in 'JSON' notation to render chart-specifications into 'HTML'. This package is used to facilitate the rendering. It also provides a means to interact with signals, events, and datasets in a 'Vega' chart using 'JavaScript' or 'Shiny'. Package: r-cran-vegclust Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vegclust_2.0.3-1.ca2604.1_all.deb Size: 711662 MD5sum: 42a3a2a7aa8ad30ffb706a97a445d451 SHA1: bbd94451815c09c79004ee49906132df7a02a8b6 SHA256: e30eb71470aac7d3e040417b34ee56ee61f44461777620b11be2ab25b9129e15 SHA512: 59c243329d90e614dac1332a470210a012d05afa04a5a248cf2334f6b409763669c1c98aa7a084b99c4ec2e77eb3f6368b8f9a59fa6b1d77ec6a1c2d615635b7 Homepage: https://cran.r-project.org/package=vegclust Description: CRAN Package 'vegclust' (Fuzzy Clustering of Vegetation Data) A set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) ; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) . Package: r-cran-vegetablessrilanka Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tsibble, r-cran-dplyr, r-cran-naniar, r-cran-visdat, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-vegetablessrilanka_1.1.0-1.ca2604.1_all.deb Size: 156368 MD5sum: c901b379d29d48bc8a4ef74e029ed62f SHA1: ee1775496e28020b2e862aecf91c8426caf085f7 SHA256: 678faa048041f33cb83b2810d24d7959d9bddc7a25c3f34db4063d423a726842 SHA512: 046061da052505ded353ea3b4f207b1a3a32affea53b3a484f6c488fd3ead4cc6ffb68863eaea52e6d96d95830294c7deb0989300ba8bc57c54e9cfedd17ef09 Homepage: https://cran.r-project.org/package=vegetablesSriLanka Description: CRAN Package 'vegetablesSriLanka' (Daily Vegetable Prices of Sri Lanka) Provides retail and wholesale vegetable price data from two major market hubs in Sri Lanka, Dambulla and Pettah. Includes tools for analyzing, visualizing, and comparing vegetable prices across markets. Package: r-cran-vegindexcalc Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-vegindexcalc_0.1.0-1.ca2604.1_all.deb Size: 21504 MD5sum: ae21874871f26637b0712bad922ca22e SHA1: c58067fb4d690261c372872eef61d6a38f25a454 SHA256: d3ba2c021a71a02a058603d6206de04c1efab3ccde80cf5bd3420fda440255dc SHA512: f655c8c527d07e3516bbc937aa6d73e43ad9898799d747a9e4961661ad5453cb3b6a830ec0c9ea545224026516972dedfd9e5f510da9eb57cbf6593f2411bcd6 Homepage: https://cran.r-project.org/package=vegIndexCalc Description: CRAN Package 'vegIndexCalc' (Vegetation Indices (VIs) Calculation for Remote Sensing Analysis) It provides a comprehensive toolkit for calculating a suite of common vegetation indices (VIs) derived from remote sensing imagery. VIs are essential tools used to quantify vegetation characteristics, such as biomass, leaf area index (LAI) and photosynthetic activity, which are essential parameters in various ecological, agricultural, and environmental studies. Applications of this package include biomass estimation, crop monitoring, forest management, land use and land cover change analysis and climate change studies. For method details see, Deb,D.,Deb,S.,Chakraborty,D.,Singh,J.P.,Singh,A.K.,Dutta,P.and Choudhury,A.(2020). Utilizing this R package, users can effectively extract and analyze critical information from remote sensing imagery, enhancing their comprehension of vegetation dynamics and their importance in global ecosystems. The package includes the function vegetation_indices(). Package: r-cran-vegperiod Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-vegperiod_0.4.0-1.ca2604.1_all.deb Size: 125932 MD5sum: 8cc054a87eb84c5f94d874e0cf63b48e SHA1: da4462bb4d58cb598909946ce8af130c363e34dd SHA256: 73fa2f8d6347744b32125ece7f97eeaa8c2706c2a0d958ad61bb4f6a627d8499 SHA512: 75461405f8b315e528965a2e67043b7470a0c410b7ecdd88c70dc0fed461917a053c956d58208ea757a22e2d9b5a47ea0c1c734ee230afef4958eeb821be84c1 Homepage: https://cran.r-project.org/package=vegperiod Description: CRAN Package 'vegperiod' (Determine Thermal Vegetation Periods) Collection of common methods to determine growing season length in a simple manner. Start and end dates of the vegetation periods are calculated solely based on daily mean temperatures and the day of the year. Package: r-cran-vegspecindex Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-vegspecindex_0.1.0-1.ca2604.1_all.deb Size: 43682 MD5sum: 33e3b706e074eb90ea5bd9584326c019 SHA1: 44eb53ff9e658b083bcdeaca4cf37fe8dfa68ce7 SHA256: 47ee49985e024b859a3f0ac752bdc56299ce1525dc42133c1358174976e1c7cd SHA512: 6a679eee01537e5f797557bdcdaf9175844a476a0168092cab2bc0f825b555306ab040d9d3b001cddeae87f782b100b4f201558553c40e14b0cd0e8331304289 Homepage: https://cran.r-project.org/package=VegSpecIndex Description: CRAN Package 'VegSpecIndex' (Vegetation and Spectral Indices for Environmental Assessment) Earth system dynamics, such as plant dynamics, water bodies, and fire regimes, are widely monitored using spectral indicators obtained from multispectral remote sensing products. There is a great need for spectral index catalogues and computing tools as a result of the quick rise of suggested spectral indices. Unfortunately, the majority of these resources lack a standard Application Programming Interface, are out-of-date, closed-source, or are not linked to a catalogue. We now introduce 'VegSpecIndex', a standardised list of spectral indices for studies of the earth system. A thorough inventory of spectral indices is offered by 'VegSpecIndex' and is connected to an R library. For every spectral index, 'VegSpecIndex' provides a comprehensive collection of information, such as names, formulae, and source references. The user community may add more items to the catalogue, which will keep 'VegSpecIndex' up to date and allow for further scientific uses. Additionally, the R library makes it possible to apply the catalogue to actual data, which makes it easier to employ remote sensing resources effectively across a variety of Earth system domains. 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Build, render, and statistically analyze Venn / 'UpSet' diagrams from 'CSV' / 'TSV' / 'GMT' / 'GMX' inputs. Provides the same 44 SVG models, intersection / 'Jaccard' / hypergeometric statistics, and PDF report layout as the web tool, with byte-equivalent 'TSV' exports (parity-tested against the published Python package). Integrates with 'ggplot2', 'tidygraph', and 'broom'. 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The original data set may be downloaded from . This data set is for a real-time quantitative polymerase chain reaction (PCR) experiment that comprises the raw fluorescence data of 24,576 amplification curves. This data set comprises 59 genes of interest and 5 reference genes. Each gene was assessed on 366 neuroblastoma complementary DNA (cDNA) samples and on 18 standard dilution series samples (10-fold 5-point dilution series x 3 replicates + no template controls (NTC) x 3 replicates). 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Package: r-cran-vertexwiser Architecture: all Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dosnow, r-cran-foreach, r-cran-freesurferformats, r-cran-fs, r-cran-gifti, r-cran-igraph, r-cran-plotly, r-cran-png, r-cran-rappdirs, r-cran-rcolorbrewer, r-cran-reticulate, r-cran-stringr Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-vertexwiser_1.5.1-1.ca2604.1_all.deb Size: 2143204 MD5sum: eccfef17adc893b30da9a7f39ae8054d SHA1: 0818807c8739f4b200ebd342196c40da265e5af8 SHA256: c9457b5afb455f5b68604b61ad96d80095f0c3325e0e6d1845bd744c72ebb0a9 SHA512: 64a07758dc845f998506184855cbcae82a0c934681553e4f7de990eb4383c4065f9b5607f7cf52ee687731a68f51f22cac5cc1bc1f4823024e5182d342cbab3d Homepage: https://cran.r-project.org/package=VertexWiseR Description: CRAN Package 'VertexWiseR' (Simplified Vertex-Wise Analyses of Whole-Brain and SubcorticalSurface) Provides functions to run statistical analyses on surface-based neuroimaging data, computing measures including cortical thickness and surface area of the whole-brain and of the hippocampi. It can make use of 'FreeSurfer', 'fMRIprep', 'XCP-D', 'HCP' and 'CAT12' preprocessed datasets, 'HippUnfold' hippocampal outputs and 'SubCortexMesh' subcortical outputs for a given sample by restructuring the data values into a single file. The single file can then be used by the package for analyses independently from its base dataset and without need for its access. Package: r-cran-vetiver Architecture: all Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bundle, r-cran-butcher, r-cran-cereal, r-cran-cli, r-cran-fs, r-cran-generics, r-cran-glue, r-cran-hardhat, r-cran-lifecycle, r-cran-pins, r-cran-purrr, r-cran-rapidoc, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-withr Suggests: r-cran-arrow, r-cran-callr, r-cran-caret, r-cran-clustmixtype, r-cran-covr, r-cran-curl, r-cran-dplyr, r-cran-flexdashboard, r-cran-ggplot2, r-cran-httpuv, r-cran-httr, r-cran-jsonlite, r-cran-keras, r-cran-knitr, r-cran-liblinear, r-cran-luz, r-cran-mgcv, r-cran-mlr3, r-cran-mlr3data, r-cran-mlr3learners, r-cran-mockery, r-cran-modeldata, r-cran-parsnip, r-cran-paws.machine.learning, r-cran-pingr, r-cran-plotly, r-cran-plumber, r-cran-probably, r-cran-quantregforest, r-cran-ranger, r-cran-recipes, r-cran-reticulate, r-cran-rmarkdown, r-cran-rpart, r-cran-rsample, r-cran-rsconnect, r-cran-slider, r-cran-smdocker, r-cran-stacks, r-cran-tensorflow, r-cran-testthat, r-cran-tidyselect, r-cran-torch, r-cran-tune, r-cran-vdiffr, r-cran-workflows, r-cran-xgboost, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-vetiver_0.2.7-1.ca2604.1_all.deb Size: 537896 MD5sum: 642e265083941d0d057dbe151b31c99c SHA1: 1bb4e6c481ec86bf623f03f381207947ed216c14 SHA256: cb64a4cfcb279abb8b5e310253db96d2f65230ae42a342b124a337c8b59ae242 SHA512: 0c11615db7d33e1b2e0d2702594e422dec3a51330f68820b51e6ebedf821693e8b1686f77a36cd1e0e21a0f2e32415c4190dda552e8c498834ffc1310e3a1337 Homepage: https://cran.r-project.org/package=vetiver Description: CRAN Package 'vetiver' (Version, Share, Deploy, and Monitor Models) The goal of 'vetiver' is to provide fluent tooling to version, share, deploy, and monitor a trained model. Functions handle both recording and checking the model's input data prototype, and predicting from a remote API endpoint. The 'vetiver' package is extensible, with generics that can support many kinds of models. Package: r-cran-vetresearchlmm Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-collapse, r-cran-emmeans, r-cran-ggplot2, r-cran-lme4, r-cran-lmertest, r-cran-multcomp, r-cran-nlme, r-cran-quarto, r-cran-report, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vetresearchlmm_1.1.0-1.ca2604.1_all.deb Size: 181726 MD5sum: 61ab62e3c6c11306bca27ff53c92ce68 SHA1: 71d1e5a45b790508ef121e06cd99cab852fcbc4a SHA256: 5d067f63e088a638018608439da514d6d19753fde9b68ce90b75ecf48054f3a2 SHA512: c4bf869da364d60142c05b8cf98b27a6d9d94e24ef9b972c1f49cbd1c4aa3e5758d266e2a19f9290def52cfaced678c8b54ce1b87ef269d9f280d316ce4b4830 Homepage: https://cran.r-project.org/package=VetResearchLMM Description: CRAN Package 'VetResearchLMM' (Linear Mixed Models - An Introduction with Applications inVeterinary Research) R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute. Package: r-cran-vewaning Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-vewaning_1.4-1.ca2604.1_all.deb Size: 649724 MD5sum: ee11390f1ae7ada0c8da9d5a5ca88135 SHA1: 3562bd5c1626d120d594fcd19ac6fa19440cb29e SHA256: 443e87f8abd97b92bf3bdcde4624b8c8a286b2ea97b246795801e50b27a21bf9 SHA512: 1717e9db5351f8d5c66506b5bee71226c56b8bbeabaec44ac765d362bc1a344b3036a73c1f25c85eb562d1b358807bdc36300af0394beb77dd313cbba53fce9c Homepage: https://cran.r-project.org/package=VEwaning Description: CRAN Package 'VEwaning' (Vaccine Efficacy Over Time) Implements methods for inference on potential waning of vaccine efficacy and for estimation of vaccine efficacy at a user-specified time after vaccination based on data from a randomized, double-blind, placebo-controlled vaccine trial in which participants may be unblinded and placebo subjects may be crossed over to the study vaccine. The methods also allow adjustment for possible confounding via inverse probability weighting through specification of models for the trial entry process, unblinding mechanisms, and the probability an unblinded placebo participant accepts study vaccine: Tsiatis, A. A. and Davidian, M. (2022) . 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Here, the idea is to fit multiple variance functions to a data set and consequently assess which function reflects the relationship 'Var ~ Mean' best. For 'in-vitro diagnostic' ('IVD') assays modeling this relationship is of great importance when individual test-results are used for defining follow-up treatment of patients. Package: r-cran-vfprogression Architecture: all Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-vfprogression_0.7.1-1.ca2604.1_all.deb Size: 85128 MD5sum: 5050c849a3935db135aa068a5ca0e83d SHA1: f5584d53e4c88e6387e5876ee474b60a6b6ba0d9 SHA256: 581489b69b67700dbe60eb5b5d185a2d4d070346793b1742218e0aad576a1dc2 SHA512: b632d557f2a8eae775c23ded2f4fb29d3cb1b280dd4c364d0d498d42712df45e95edc6cc032fd71afb95a1eb810293c9a936f848e0b847ae51c800cdf2356228 Homepage: https://cran.r-project.org/package=vfprogression Description: CRAN Package 'vfprogression' (Visual Field (VF) Progression Analysis and Plotting Methods) Realization of published methods to analyze visual field (VF) progression. Introduction to the plotting methods (designed by author TE) for VF output visualization. A sample dataset for two eyes, each with 10 follow-ups is included. The VF analysis methods could be found in -- Musch et al. (1999) , Nouri-Mahdavi et at. (2012) , Schell et at. (2014) , Aptel et al. (2015) . 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The other arithmetic operators are similarly implemented. A wide class of coding bugs is eliminated. Package: r-cran-vgamdata Architecture: all Version: 1.1-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2573 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam Filename: pool/dists/resolute/main/r-cran-vgamdata_1.1-13-1.ca2604.1_all.deb Size: 2445240 MD5sum: 9fe84605e3296955704ebaa4b2d5e6d9 SHA1: 99fb2b57ae9b420ed2be4eba6182b23d6e26fb12 SHA256: 739f700a275e0081e94835ade7b3e680723765cb8e017c02cbb51a875a0737d2 SHA512: a9206552619dd4ebf92db5dadec12faffabeeded9fe05dad1f8ad886a2d4e1ebe2e716fcaafe58c3ce46f57c089a765441db0421afbdc20323f62bc37bb0ad72 Homepage: https://cran.r-project.org/package=VGAMdata Description: CRAN Package 'VGAMdata' (Data Supporting the 'VGAM' Package) Mainly data sets to accompany the VGAM package and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) . These are used to illustrate vector generalized linear and additive models (VGLMs/VGAMs), and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column Interaction Models, and constrained and unconstrained ordination models in ecology). This package now contains some old VGAM family functions which have been replaced by newer ones (often because they are now special cases). Package: r-cran-vhcub Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2039 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-cran-ggplot2, r-cran-seqinr, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vhcub_1.0.0-1.ca2604.1_all.deb Size: 2044660 MD5sum: 0b28258afb68dc78584c0d64bf3ca40e SHA1: ec0d1be104af0857eb1a1834c40d5d60033ec5a8 SHA256: 1565e4a81df2bd9a09658db163ff3149a667dc426043ecb9a59e5ff9648f2e2e SHA512: 0a69d1e321956a6d1cb43d2092825b6a5a8b2fabf4c683d27adb2a3c08fc73b92f0d6a296f58f3c292da8d946efcc6b6fba3a8035693e7556189cdd574ff0304 Homepage: https://cran.r-project.org/package=vhcub Description: CRAN Package 'vhcub' (Virus-Host Codon Usage Co-Adaptation Analysis) Analyze the co-adaptation of codon usage between a virus and its host, calculate various codon usage bias measurements as: effective number of codons (ENc) Novembre (2002) , codon adaptation index (CAI) Sharp and Li (1987) , relative codon deoptimization index (RCDI) Puigbò et al (2010) , similarity index (SiD) Zhou et al (2013) , synonymous codon usage orderliness (SCUO) Wan et al (2004) and, relative synonymous codon usage (RSCU) Sharp et al (1986) . Also, it provides a statistical dinucleotide over- and underrepresentation with three different models. Implement several methods for visualization of codon usage as ENc.GC3plot() and PR2.plot(). Package: r-cran-vhica Architecture: all Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ape, r-cran-plotrix, r-cran-seqinr, r-cran-gtools Filename: pool/dists/resolute/main/r-cran-vhica_0.2.9-1.ca2604.1_all.deb Size: 111912 MD5sum: b9e3b1ceba3f70729e84ee383cb60f4c SHA1: 279741abb8592eb969ed1fadbf00596cf73c38b2 SHA256: 153b96ca07246664aca8889d7c57cc9ffb253d39233cf15f6eebe53d11b930d7 SHA512: 1ddf4f664ce1938980ca821193098e674344427434823cbaf7a652b61176f8317337ff3725151e8ea04b43a094beb5d8d7fcfc6d51e9d78151e3891058142d0c Homepage: https://cran.r-project.org/package=vhica Description: CRAN Package 'vhica' (Vertical and Horizontal Inheritance Consistence Analysis) The "Vertical and Horizontal Inheritance Consistence Analysis" method is described in the following publication: "VHICA: a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila" by G. Wallau. et al. (2016) . The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes. Package: r-cran-via Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1573 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-terra, r-cran-sp, r-cran-sf Filename: pool/dists/resolute/main/r-cran-via_0.2.0-1.ca2604.1_all.deb Size: 1394690 MD5sum: 51c6d5ba63351d272aa1cb668b2a3e17 SHA1: fe38a7723a4f6e96bdaf1eacdd3fe542998f1ad4 SHA256: 9f0c2614689ec60eca715610a21d90513b1556a38fcecd5e1445f2f9f83d72de SHA512: a25c137736078c52f0c880e3af15d40ae71373ade0c163f96102883c9a89b23544ecca957d13617ed818388667c9ed92dd2a4854944e918b21744c1ad9ab1bfd Homepage: https://cran.r-project.org/package=via Description: CRAN Package 'via' (Virtual Arrays) The base class 'VirtualArray' is defined, which acts as a wrapper around lists allowing users to fold arbitrary sequential data into n-dimensional, R-style virtual arrays. The derived 'XArray' class is defined to be used for homogeneous lists that contain a single class of objects. The 'RasterArray' and 'SfArray' classes enable the use of stacked spatial data instead of lists. Package: r-cran-viafoundry Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-askpass, r-cran-stringr, r-cran-mime Filename: pool/dists/resolute/main/r-cran-viafoundry_1.0.1-1.ca2604.1_all.deb Size: 146230 MD5sum: bb80273a5661cc050abc14b935b8ac2d SHA1: 1bfb1c43e284f37dbe35983c7b6730f4ed3424b2 SHA256: e3bc4f9a8fbafa8fec9944cf62c267c59c891284c79adf782ffc75a4725c3fa3 SHA512: 6fdfb0f830046a1182e1cb2308a5f4841547c8ea5eb7541b04d3e00ad0308f30c288dd4512e15fe2a7412b62ff472b8c8f41cf2fdccba469259ed80c44367b7c Homepage: https://cran.r-project.org/package=viafoundry Description: CRAN Package 'viafoundry' (R Client for 'Via Foundry' API) 'Via Foundry' API provides streamlined tools for interacting with and extracting data from structured responses, particularly for use cases involving hierarchical data from Foundry's API. It includes functions to fetch and parse process-level and file-level metadata, allowing users to efficiently query and manipulate nested data structures. Key features include the ability to list all unique process names, retrieve file metadata for specific or all processes, and dynamically load or download files based on their type. With built-in support for handling various file formats (e.g., tabular and non-tabular files) and seamless integration with API through authentication, this package is designed to enhance workflows involving large-scale data management and analysis. Robust error handling and flexible configuration ensure reliable performance across diverse data environments. Please consult the documentation for the API endpoint for your installation. 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Key features include: a searchable, paginated data table with drag-and-drop column reordering and variable-label 'tooltips'; multi-condition filters (AND/OR) with live preview; multi-column sorting; column visibility management with search; an Excel-like cell editor powered by 'rhandsontable'; find-and-replace across one or all columns (literal or regex) with automatic live preview; a Plots tab with auto-detected histograms and bar charts for every column; automatic 'dplyr' code generation reflecting every operation performed in the 'UI'; one-click CSV export; and a Variable Info tab with type, missing values, and summary statistics. The entire interface is launched with a single call to ViewR() and works as a popup dialog, in the 'RStudio' Viewer pane, or in the system browser. Package: r-cran-vigicaen Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-glue, r-cran-gridextra, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tzdb, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-vigicaen_1.0.0-1.ca2604.1_all.deb Size: 1089888 MD5sum: e9b431c4fd621672853192222e647b90 SHA1: ccbfff3df68b927eae7abc85207bf7f34f35fe04 SHA256: 93e4076f0cecbee9d9034f3488dd5318f6820bf1a7c884ee8dfb24e539c84026 SHA512: 80f34aed18f7c860b907dbac97a0e2784d662603b023e983f1c87955dff5b2a262c1788b966b0bd55b8dc3b67d7b4d4243633fd64b8890a32a814ddc7c202453 Homepage: https://cran.r-project.org/package=vigicaen Description: CRAN Package 'vigicaen' ('VigiBase' Pharmacovigilance Database Toolbox) Perform the analysis of the World Health Organization (WHO) Pharmacovigilance database 'VigiBase' (Extract Case Level version), e.g., load data, perform data management, disproportionality analysis, and descriptive statistics. Intended for pharmacovigilance routine use or studies. This package is NOT supported nor reflect the opinion of the WHO, or the Uppsala Monitoring Centre. Disproportionality methods are described by Norén et al (2013) . 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For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020). 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The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020). 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Report generation tools for short-term forecasts, and validation metrics for an arbitrary number of customizable respiratory viruses. Estimation of the effective reproduction number is based on the 'EpiEstim' framework described in work by 'Cori' and colleagues. (2013) . 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Package: r-cran-virtualspecies Architecture: all Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-ade4, r-cran-rnaturalearth, r-cran-raster, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-virtualspecies_1.6.1-1.ca2604.1_all.deb Size: 247572 MD5sum: c73cca9d7237ea507d268d872c7729f0 SHA1: 70ee5131cec7d4d2bfdfe0ef53d4ab7384ef7910 SHA256: 58fefe02f626497fac981e6531f1a1179a368761344b0af9039df4700c87df05 SHA512: f5e9264c34df26cd2dc1f950e8505585a3c4659128cfe197b47befb22874f8868ae42181693909d69ff22dcfc424961dd64c54eb7ea11315357b4e2f076dcffe Homepage: https://cran.r-project.org/package=virtualspecies Description: CRAN Package 'virtualspecies' (Generation of Virtual Species Distributions) Provides a framework for generating virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. 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'Virtuoso' is a high-performance "universal server," which can act as both a relational database, supporting standard Structured Query Language ('SQL') queries, while also supporting data following the Resource Description Framework ('RDF') model for Linked Data. 'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language) queries, a graph-based query that supports semantic reasoning. This allows users to leverage the performance of local or remote 'Virtuoso' servers using popular 'R' packages such as 'DBI' and 'dplyr', while also providing a high-performance solution for working with large 'RDF' 'triplestores' from 'R.' The package also provides helper routines to install, launch, and manage a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using the standard interactive installers from the 'R' command-line. By automatically handling these setup steps, the package can make using 'Virtuoso' considerably faster and easier for a most users to deploy in a local environment. Managing the bulk import of triples from common serializations with a single intuitive command is another key feature of this package. Bulk import performance can be tens to hundreds of times faster than the comparable imports using existing 'R' tools, including 'rdflib' and 'redland' packages. Package: r-cran-virusparies Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chromote, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-virusparies_1.1.0-1.ca2604.1_all.deb Size: 448894 MD5sum: 82180ac3dbdefcf9efc9bfb626c9ed42 SHA1: 0edafd8435fe1f12846a137e1c427a884f4144cb SHA256: 673fad98e3fac24f733ddfb0f246bfbd0bda32ae5ccf75763cee019340e78fdf SHA512: 7eb885b92fbdee91e281d1dd93ed4ca7d3d100f8cd4277ef3d206ffd5a36e232a304ce9ff1b06532cdbb2c407e2e53acc8f913eed16d98e860bc6522cc598233 Homepage: https://cran.r-project.org/package=Virusparies Description: CRAN Package 'Virusparies' (Visualize and Process Output from 'VirusHunterGatherer') A collection of tools for downstream analysis of 'VirusHunterGatherer' output. Processing of hittables and plotting of results, enabling better interpretation, is made easier with the provided functions. Package: r-cran-virustotal Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 897 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-base64enc, r-cran-jsonlite, r-cran-checkmate, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lintr, r-cran-httptest, r-cran-covr, r-cran-pkgdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-virustotal_0.6.0-1.ca2604.1_all.deb Size: 427722 MD5sum: f933ed0f1a6c3e2e8d3731ade907bd16 SHA1: c46bf0bfe869d5b07daaae0ec1615545ad61a108 SHA256: 3ab0fd863bce071482615c761c16acb75f37988241c495fa1121a3fe6d638388 SHA512: 203e8da06c3a9b3720125a8adc45be9fe85392fca453ad67800541b8da8b10e239d3bfb0dcd24a64c045f37e53f3983a875236b5577d524c315d4c33cfa27f00 Homepage: https://cran.r-project.org/package=virustotal Description: CRAN Package 'virustotal' (R Client for the 'VirusTotal' API) Provides a comprehensive R interface to the 'VirusTotal' API v3.0 , a Google service that analyzes files and URLs for viruses, worms, trojans and other malware. Features include file/URL scanning, domain categorization, passive DNS information, IP reputation analysis, IoC relationships, sandbox analysis, and comment/voting systems. Implements rate limiting, error handling, and response validation for robust security analysis workflows. Package: r-cran-visa Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpmisc, r-cran-magrittr, r-cran-matrix, r-cran-plot3d, r-cran-plotly, r-cran-reshape2, r-cran-rcolorbrewer Suggests: r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-visa_1.0.0-1.ca2604.1_all.deb Size: 373504 MD5sum: feba940d17a477529867b97136b59c14 SHA1: 6e4b64096187a7ef4b817a40405be4c4836ba002 SHA256: 478e5b1bc6fa122e7579d2c0e8015c1f9db124de64dafce63a6659925c4ce7ed SHA512: 9ae692e9b28d788933cacfbf4a30c73bb1494c234479c7c241b0fe59df0a3ce30e204c47e1ae2445e1d4f42ddfe4c01726b105947cc8a359b7a69c6278e447e1 Homepage: https://cran.r-project.org/package=visa Description: CRAN Package 'visa' (Vegetation Imaging Spectroscopy Analyzer) Provides easy-to-use tools for data analysis and visualization for hyperspectral remote sensing (also known as imaging spectroscopy), with a particular focus on vegetation hyperspectral data analysis. It consists of a set of functions, ranging from the organization of hyperspectral data in the proper data structure for spectral feature selection, calculation of vegetation index, multivariate analysis, as well as to the visualization of spectra and results of analysis in the 'ggplot2' style. Package: r-cran-visachartr Architecture: all Version: 4.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6611 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-visachartr_4.0.1-1.ca2604.1_all.deb Size: 1562898 MD5sum: a61c1a165ecca311f39bbd2d16c8207f SHA1: 341cbb903b13239f375de626a4d198a345e92b1c SHA256: ebc7574957fd7aa36e902780deab0310dd4d533dc9ee635237b2d935e28465d8 SHA512: c4333c456d048b4dd9c5d8f6a125478e8adb51ec79544bb3d5a849ee9251c5e3e8faf3ada5f41fdd7484867ab909d8a5f162bfbf648d57e852f4323ffb4f12cf Homepage: https://cran.r-project.org/package=visachartR Description: CRAN Package 'visachartR' (Wrapper for 'Visa Chart Components') Provides a set of wrapper functions for 'Visa Chart Components'. 'Visa Chart Components' is an accessibility focused, framework agnostic set of data experience design systems components for the web. Package: r-cran-visae Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dplyr, r-cran-ggplot2, r-cran-shinyjs, r-cran-ca, r-cran-tidyr, r-cran-ggrepel, r-cran-rlang, r-cran-dt Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-visae_0.2.1-1.ca2604.1_all.deb Size: 451810 MD5sum: 09098553d3b1b1549be7bce190e00201 SHA1: 150687fc458d843fe1baf263f1470ba7a5150d5f SHA256: 872fafdce25332711e90f4047903affda9b3eb0b81d64e3a9a5b7423bc573466 SHA512: 4c84c508c45806d893702dd9af3d490389bcc4a16ab2f29c0d1de132e9113470c14f055feb3f65615d431abd17f6e8a08b791b1a58c9d83b16a292d1e7ab639c Homepage: https://cran.r-project.org/package=visae Description: CRAN Package 'visae' (Visualization of Adverse Events) Implementation of 'shiny' app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021). Package: r-cran-viscollin Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-car, r-cran-corrgram, r-cran-corrplot, r-cran-dplyr, r-cran-lmtest, r-cran-knitr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-viscollin_0.1.2-1.ca2604.1_all.deb Size: 189998 MD5sum: 96dc80ed17789be450694a51606d95dd SHA1: 33792f4cf98b2c446e125c7e9a9ea138c6102dfb SHA256: 23075d73030b3dc5f830bde90f715a7f78bba3359027ce4692d72e4ed2db7684 SHA512: 92714f2b12cbc740b31ad8c8e0d78eec3cdc51dca6a801898ac982f60feee13a810ff26b504f69401946b0e89018968aeaccab7434a8c9dfec72266098c52e4c Homepage: https://cran.r-project.org/package=VisCollin Description: CRAN Package 'VisCollin' (Visualizing Collinearity Diagnostics) Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Where’s Waldo: Visualizing Collinearity Diagnostics", . These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent. Package: r-cran-viscomp Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-cran-dplyr, r-cran-ggextra, r-cran-ggnewscale, r-cran-ggplot2, r-cran-hmisc, r-cran-mass, r-cran-netmeta, r-cran-plyr, r-cran-qgraph, r-cran-reshape2, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-viscomp_1.0.0-1.ca2604.1_all.deb Size: 459802 MD5sum: 95204c1e64c31f560461baf9ee7603f8 SHA1: 2eda7188a1b237fc5f40b398814fd289ec946b3e SHA256: d95b259d44661fddd1c8da8cbccc21f2fbeceb7134c9814f2f078f7754991595 SHA512: 5bb3de9d8fa4e37126030f6ff42c1f2e9e0914864f1de457bb0aa0e89442a905bbfb0f05bad9d04a1a1df56389ba0d388cde41b3172cde36d8106e47599a37f1 Homepage: https://cran.r-project.org/package=viscomp Description: CRAN Package 'viscomp' (Visualize Multi-Component Interventions in Network Meta-Analysis) A set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: - components descriptive analysis - heat plot of the two-by-two component combinations - leaving one component combination out scatter plot - violin plot for specific component combinations' effects - density plot for components' effects - waterfall plot for the interventions' effects that differ by a certain component combination - network graph of components - rank heat plot of components for multiple outcomes. The implemented tools are described by Seitidis et al. (2023) . Package: r-cran-viscov Architecture: all Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesm, r-cran-clustergeneration, r-cran-scatterplot3d, r-cran-kernsmooth, r-cran-trialr Filename: pool/dists/resolute/main/r-cran-viscov_1.6.0-1.ca2604.1_all.deb Size: 86702 MD5sum: 0db92ee68ac530488357f3b11af8f7ed SHA1: dd029f271c60b08eefcc2c07aedb2ec421e7b55a SHA256: 2ef05fa6bad06cf265de2fc1f0a7387d249744a051fba72182a5d3f185ba1f57 SHA512: 6e2693848c56df6e3737cb1e3ae8530894ae364da01458624116a54e5afaac5307cddc286a227700ecec31c131972ca66655a844184b6fa6b7a919d8a3ea11d9 Homepage: https://cran.r-project.org/package=VisCov Description: CRAN Package 'VisCov' (Visualizing of Distributions of Covariance Matrices) Visualizing of distributions of covariance matrices. The package implements the methodology described in Tokuda, T., Goodrich, B., Van Mechelen, I., Gelman, A., & Tuerlinckx, F. (2012) . Package: r-cran-visdat Architecture: all Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-magrittr, r-cran-tibble, r-cran-glue, r-cran-forcats, r-cran-cli, r-cran-scales Suggests: r-cran-testthat, r-cran-plotly, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-spelling, r-cran-covr, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-visdat_0.6.0-1.ca2604.1_all.deb Size: 1032882 MD5sum: 93e11033d3e76bf7cf1b2bce33eb3f07 SHA1: 64ed5603d486d251c3702ab7d51804749c721e56 SHA256: fda573afc3a4963824581ef12874ec7d28ee0c314ae3757b0432e21d89d021bd SHA512: 68c1b1557243480ba3dffd78aa83b2e99f66d178c8e73c8fa25af475fced626f07c028b4da490ffe28ce013af7c7d2c1552a1c17831f4a1feeea28e9fc89cf6c Homepage: https://cran.r-project.org/package=visdat Description: CRAN Package 'visdat' (Preliminary Visualisation of Data) Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'. Package: r-cran-vise Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 936 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-scales, r-cran-cowplot, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-vise_0.1.3-1.ca2604.1_all.deb Size: 604718 MD5sum: 919bb72c45ad1a7c2526a2db197385b2 SHA1: 6841c1c74fd204fa38175249478d31b40fb81e7b SHA256: 5df1548fdf636aa60462264bd745a1494d51f73de97874e6af2b5e1bc1c6b03b SHA512: a02e9b65160a5f8f40c4afcd32e2864ac219c0cce47e636160ecb13eacae151b17ccc61aeb7c07175f6ea9f8308a1bb2065e011ed9f3d45a0e1eeadcf4910dc6 Homepage: https://cran.r-project.org/package=ViSe Description: CRAN Package 'ViSe' (Visualizing Sensitivity) Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) . Package: r-cran-visitorcounts Architecture: all Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rssa, r-cran-ggplot2, r-cran-zoo, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-visitorcounts_2.0.3-1.ca2604.1_all.deb Size: 819962 MD5sum: 60a4fdf725afdb7c1c3cbaa2290bdce5 SHA1: 5800a34ad00eab2c7e62d7c2c0a37bd2ae7d2472 SHA256: d3cd7c0fd0e4ba7b5144a7eaaee4200fb9ecc1eb4cfefabfca0350b8bfebddbd SHA512: 9f774f59779ae386e6146d9819ee734a06859f9124b0d42d6aca1070d21e9ca32ff3a6f82b876c8eb627192c325c7d7c403acd0de6979ff0740c9d62838febc1 Homepage: https://cran.r-project.org/package=VisitorCounts Description: CRAN Package 'VisitorCounts' (Modeling and Forecasting Visitor Counts Using Social Media) Performs modeling and forecasting of park visitor counts using social media data and (partial) on-site visitor counts. Specifically, the model is built based on an automatic decomposition of the trend and seasonal components of the social media-based park visitor counts, from which short-term forecasts of the visitor counts and percent changes in the visitor counts can be made. A reference for the underlying model that 'VisitorCounts' uses can be found at Russell Goebel, Austin Schmaltz, Beth Ann Brackett, Spencer A. Wood, Kimihiro Noguchi (2023) . Package: r-cran-vismeteor Architecture: all Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1618 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-testthat, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-vismeteor_3.0.1-1.ca2604.1_all.deb Size: 1130562 MD5sum: 0c422632fc2cc5bafa63368760173f2c SHA1: 9c583ea6481425e3b6e91bcf95e321c6463dacd0 SHA256: 0be9f036901443a61958c81ae99d273e881a703f8ca2d138ff9aa72e5ab2bea5 SHA512: 7cce1d5d985988f86322b32a25350051572fbd3517c0a8b5e9744c9dd9561195a733117f7c8aa11f2c4adef81d3458e1b65c8893ecfcc3b6d6e8510a761cd32a Homepage: https://cran.r-project.org/package=vismeteor Description: CRAN Package 'vismeteor' (Analysis of Visual Meteor Data) Provides a suite of analytical functionalities to process and analyze visual meteor observations from the Visual Meteor Database of the International Meteor Organization . Package: r-cran-vismi Architecture: all Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-ggally, r-cran-ggplot2, r-cran-ggtext, r-cran-gridextra, r-cran-ggridges, r-cran-mixgb, r-cran-patchwork, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-trelliscopejs Filename: pool/dists/resolute/main/r-cran-vismi_0.9.5-1.ca2604.1_all.deb Size: 1919904 MD5sum: fd724adcc854e37c1cd971249868598d SHA1: 0c7ff812072c23a1af05805538353f63bbeeaf55 SHA256: 806857543287d2729376aec903b47cf9f5e0f54fd9615b558820a74531c78efc SHA512: 5f0631cec3df4b7af21eccb234852bff569418a38c76d6def9b8fe491edb1f6b996352c7828dfff5a89b42761a8b9a75fc37ecbc669957cee7ac82c2a404debd Homepage: https://cran.r-project.org/package=vismi Description: CRAN Package 'vismi' (Visual Diagnostics for Multiple Imputation) A comprehensive suite of static and interactive visual diagnostics for assessing the quality of multiply-imputed data obtained from packages such as 'mixgb' and 'mice'. The package supports inspection of distributional characteristics, diagnostics based on masking observed values and comparing them with re-imputed values, and convergence diagnostics. Package: r-cran-visnetwork Architecture: all Version: 2.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10700 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-igraph, r-cran-rpart, r-cran-shiny, r-cran-shinywidgets, r-cran-colourpicker, r-cran-sparkline, r-cran-ggraph, r-cran-tidygraph, r-cran-flashclust Filename: pool/dists/resolute/main/r-cran-visnetwork_2.1.4-1.ca2604.1_all.deb Size: 3759794 MD5sum: ab373ada05629fcca57213f865b376d3 SHA1: 2f5482edc4bd4af612cd8b2cda733d1541600c37 SHA256: 5b84fe5f8e162dc48231cf0136754c924f7f9a39c1836258ed6a6fdf2d85a2d0 SHA512: 900e9b2ed06a115ffad64d1eca6bd41efb1b6e300bee6df0994e9750669e3ed8e0731558540232e3553e6949bf22736d681928169c753211979e58a5872f477f Homepage: https://cran.r-project.org/package=visNetwork Description: CRAN Package 'visNetwork' (Network Visualization using 'vis.js' Library) Provides an R interface to the 'vis.js' JavaScript charting library. It allows an interactive visualization of networks. Package: r-cran-visomopresults Architecture: all Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4819 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brand.yml, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-systemfonts, r-cran-tidyr Suggests: r-cran-bslib, r-cran-cohortcharacteristics, r-cran-covr, r-cran-dt, r-cran-flextable, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggsankeyfier, r-cran-gt, r-cran-here, r-cran-htmltools, r-cran-incidenceprevalence, r-cran-knitr, r-cran-lifecycle, r-cran-officer, r-cran-palmerpenguins, r-cran-patientprofiles, r-cran-plotly, r-cran-reactable, r-cran-rmarkdown, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-sortable, r-cran-testthat, r-cran-tinytable, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-visomopresults_1.5.0-1.ca2604.1_all.deb Size: 2452792 MD5sum: 51f05c37902cb81cbae500787b7d54af SHA1: 70ff1e930e910dc2290fa39e6eec6bc50b5dd925 SHA256: 8dd8099e93f761cd00d8e926f0966286e23ec64400d7069bb502b7466bfe7f99 SHA512: 954ed9c4db404fe57c4399e06c084917e9ae55bb1512f03b0487815265c2b3cb331686ee09f12684147e4f277e329a64155deb97cc3c47aa558c7f67a835dbae Homepage: https://cran.r-project.org/package=visOmopResults Description: CRAN Package 'visOmopResults' (Graphs and Tables for OMOP Results) Provides methods to transform omop_result objects into formatted tables and figures, facilitating the visualisation of study results working with the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Package: r-cran-visor Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sfheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-visor_0.1.1-1.ca2604.1_all.deb Size: 87560 MD5sum: 1b758ecb080a3761bf6fe8939f508a8c SHA1: 5fa3c3371493b97a8646f7618ffd2a5be72da7e0 SHA256: 8c9ab87b2a4f1c992b2520d3c2f4350f95a69485c6845edf3935621b31603fe3 SHA512: e6dae8d27e98dc5284b0e5a8a95a489d4726c57b1ffb214ceb594c630b04017370af899778fbcc8dae2365f1b87748f4ec087605715cc9de6a5a2fa06888e4ac Homepage: https://cran.r-project.org/package=visor Description: CRAN Package 'visor' (Geospatial Tools for Visibility Analysis) Provides tools for visibility analysis in geospatial data. It offers functionality to perform isovist calculations, using arbitrary geometries as both viewpoints and occluders. Package: r-cran-visreg Architecture: all Version: 2.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-ggplot2, r-cran-glmmtmb, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-rgl, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-visreg_2.8.0-1.ca2604.1_all.deb Size: 283972 MD5sum: fe07c9bb4b6cdc76950e7aa97f0dfeb0 SHA1: 00115e0824bdb3450198fa9e2cd49d7f2603574a SHA256: 43858810d771af6eddd7448958decbad4535b241a871276942a90a0b6a7e83ed SHA512: b54e46ff4ce6d9a9cd6cf208840a38fce574a9117a0ff91d6d12068acd6a7294b2f82d6894ca41dc3a63fd067f800a7329f97555241060273cc74e6d7f448363 Homepage: https://cran.r-project.org/package=visreg Description: CRAN Package 'visreg' (Visualization of Regression Models) Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.) Package: r-cran-visstatistics Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4809 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cairo, r-cran-multcompview, r-cran-nortest, r-cran-vcd Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-visstatistics_0.2.0-1.ca2604.1_all.deb Size: 3282298 MD5sum: 7aeca53e35e68b32a8abe96528f851c7 SHA1: 06f92d47745a9844d607fa181141c610d6d432d9 SHA256: 0b449717c8c6e40be1dd6852b2af3edcc359b2b162996b6e023c6db716935845 SHA512: 49964533dfb2ee876634b19498c53b896d8f16b64a40af9e50f4f6cdc0d63728da8d9bfa853c83c17aa7c4ff52c65b679878528002f7d1aaad6dc4e6933954e9 Homepage: https://cran.r-project.org/package=visStatistics Description: CRAN Package 'visStatistics' (Automated Selection and Visualisation of Statistical HypothesisTests) The right test, visualised. 'visStatistics' automatically selects and visualises statistical hypothesis tests comparing two vectors, based on their class, distribution, and sample size. Visual outputs, including box plots, bar charts, regression lines with confidence bands, mosaic plots, residual plots, and Q-Q plots, are annotated with relevant test statistics, assumption checks, and post-hoc analyses where applicable. The algorithmic workflow shifts attention from ad-hoc test selection to visual diagnostic assessment and statistical interpretation. It is particularly suited for server-side R applications, where end users interact solely through a web interface to select data groups and receive a complete visual statistical analysis automatically. The same automation makes it useful in time-constrained contexts such as statistical consulting, where it reduces effort spent on test selection and leaves more room for interpretation. The implemented tests cover the most frequently applied inferential methods in biomedical research (Hayat et al. (2017) ). The test selection algorithm proceeds as follows: Input vectors of class numeric or integer are considered numerical; those of class factor are considered categorical; those of class ordered are considered ordinal. Assumptions of residual normality and homogeneity of variances are considered met if the corresponding test yields a p-value greater than the significance level alpha = 1 - conf.level. (1) When the response is numerical and the predictor is categorical, a test comparing central tendencies is selected. If every group contains more than 50 observations, the sampling distribution of the group means is assumed approximately normal by the central limit theorem (Lumley et al. (2002) ); otherwise, residual normality is assessed using shapiro.test() applied to the standardised residuals of lm(). If normality is not met, wilcox.test() is used when the predictor has two levels and kruskal.test() followed by pairwise.wilcox.test() otherwise. If normality is met, levene.test() assesses variance homogeneity. For two-level predictors, Student's t.test(var.equal = TRUE) is applied if variances are homogeneous and Welch's t.test() otherwise. For predictors with more than two levels, aov() followed by TukeyHSD() is applied if variances are homogeneous, and oneway.test() followed by games.howell() otherwise. (2) When both vectors are numerical, lm() is fitted by default (correlation = FALSE). If correlation = TRUE, Spearman rank correlation is performed. (3) When the response is ordinal, it is converted to numeric ranks and the non-parametric path from (1) is followed (Wilcoxon or Kruskal-Wallis). When both variables are ordinal and correlation = TRUE, Kendall's tau_b is used instead. (4) When both vectors are categorical, Cochran's rule (Cochran (1954) ) is applied to test independence either by chisq.test() or fisher.test(). Package: r-cran-vistime Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-assertthat, r-cran-plotly, r-cran-ggplot2, r-cran-ggrepel, r-cran-rcolorbrewer Suggests: r-cran-prettydoc, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-highcharter Filename: pool/dists/resolute/main/r-cran-vistime_1.3.0-1.ca2604.1_all.deb Size: 1547196 MD5sum: f62faf63dafb3796879ba0ef4ac463fb SHA1: 1c09adfb18d994bb85b83e12086b554c7facbd26 SHA256: 73f148742ddbb3b1f74f303fbe2f98b71a7cf5ea84d2e265f7c7e181696cdaa3 SHA512: ffab4e8e12b37b69b11b196b3ee6d7151a5bfd6d3b03f61375f45349b3107a129ae5ff8532fdd279a07f765dcfaafc6c35ad47eeb6df3b5e220679a07d72e667 Homepage: https://cran.r-project.org/package=vistime Description: CRAN Package 'vistime' (Pretty Timelines in R) A library for creating time based charts, like Gantt or timelines. Possible outputs include 'ggplot2' diagrams, 'plotly.js' graphs, 'Highcharts.js' widgets and data.frames. Results can be used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in Shiny apps. In the interactive outputs created by vistime() and hc_vistime(), you can interact with the plot using mouse hover or zoom. Package: r-cran-vistree Architecture: all Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-rpart, r-cran-colorspace Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vistree_0.8.1-1.ca2604.1_all.deb Size: 76618 MD5sum: 8425b6fbd3a2627516e7ceb871d915c6 SHA1: 53c78e8e2e1b80628d147f5bd2b71ee98a681b63 SHA256: e3685bb2fb133f35519cf1104249c97d27a00341f6292677b14d3486096909c0 SHA512: 5fed352a50e9fbd16a2569720c318704737c1160fbfd0447b85d36ee4ae9bd09e1f8935d895716a68b9a33a0b5554bba9b888f0258425fcfb10a1cd06e16d62d Homepage: https://cran.r-project.org/package=visTree Description: CRAN Package 'visTree' (Visualization of Subgroups for Decision Trees) Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes. Package: r-cran-vistributions Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-xplorerr Filename: pool/dists/resolute/main/r-cran-vistributions_0.2.0-1.ca2604.1_all.deb Size: 388232 MD5sum: 4126ceef4b1ec4ddbd497a90d787bae2 SHA1: c936ffb3093356cd3a8b60170e2ff43cd1c35bbe SHA256: 460af0e4cce9355ec146e77db70ff0fe38f0f063bbf63292359cc008255c8591 SHA512: d8357f42950a5d93cde8cc8a3ece54e58486d7c86ce78ad06f53c1bc6493d0f8dc00c813a1bad38cbb255c18ade0f0771db215a95623a57df6a621595fcc7a4d Homepage: https://cran.r-project.org/package=vistributions Description: CRAN Package 'vistributions' (Visualize Probability Distributions) Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions. Package: r-cran-visualdom Architecture: all Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-waveslim, r-cran-wavemulcor, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-visualdom_0.8.0-1.ca2604.1_all.deb Size: 97820 MD5sum: 194935ac20df7aa70a461577edf4e327 SHA1: c152523e2faa4e72d6594eda1049b1d4ac02c095 SHA256: f344a30fad9141657b8e887b091e350416bab5428d91f6b273a1d3d0872b9516 SHA512: 8cf67ec891276e76ebcdf054762241894847e535b4e8232370f8f6b8303623415ea0c9ba7e0dcdc61729a58e354cee9d3f99272ccf2f945f6f19305819185f15 Homepage: https://cran.r-project.org/package=VisualDom Description: CRAN Package 'VisualDom' (Visualize Dominant Variables in Wavelet Multiple Correlation) Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation 'WLMC' (Fernández-Macho 2018) and the 'dominant' variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martínez et al. 2020, Polanco-Martínez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the 'dominant' variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in 'VisualDom' are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named 'rdata_climate' and 'rdata_Lorenz') to exemplify the use of the functions contained in 'VisualDom'. Methods derived from Fernández-Macho (2018) , Polanco-Martínez et al. (2020) and Polanco-Martínez (2023, in press). Package: r-cran-visualfields Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-dplyr, r-cran-polyclip, r-cran-deldir, r-cran-plotrix, r-cran-gtools, r-cran-combinat, r-cran-xml, r-cran-oro.dicom, r-cran-rlang, r-cran-shiny, r-cran-shinyjs, r-cran-dt, r-cran-htmltable, r-cran-boot, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-visualfields_1.0.7-1.ca2604.1_all.deb Size: 6289418 MD5sum: bcadfe68aa64e4a12a621e4df5dbd8f3 SHA1: f782e40356fa997b9e9dffd598879158cb479d66 SHA256: 406b1c27496c489491068e2ed88d3d38c743b99e27828a0fc8dc73339ccc6514 SHA512: c67b0afe24efb3be851e8b8931ef2d08e7cffddcac5b08b6b48d47be24876db8832b7f1ffe657e48af7ac9408f4ba1bc802f5314ce8cc0c6ed78150ed97d14ba Homepage: https://cran.r-project.org/package=visualFields Description: CRAN Package 'visualFields' (Statistical Methods for Visual Fields) A collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research. The package is described in Marin-Franch and Swanson (2013) and is part of the Open Perimetry Initiative (OPI) [Turpin, Artes, and McKendrick (2012) ]. 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Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities. Also provided on the graph is the mean and variance of the distribution. 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To evaluate and visualize the operating characteristics of Simon's two-stage design. Package: r-cran-visualpred Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbm, r-cran-randomforest, r-cran-nnet, r-cran-e1071, r-cran-mass, r-cran-magrittr, r-cran-factominer, r-cran-ggplot2, r-cran-mltools, r-cran-dplyr, r-cran-data.table, r-cran-mba, r-cran-proc, r-cran-ggrepel Suggests: r-cran-knitr, r-cran-markdown, r-cran-egg Filename: pool/dists/resolute/main/r-cran-visualpred_0.1.2-1.ca2604.1_all.deb Size: 1279308 MD5sum: d6f25bbf324483d6a2d4d48c318b37e4 SHA1: 16b57798d323c3d2a3568aabe5aaf932c58e1064 SHA256: d2092eb859b3f347b8353056c634d5f178750df648058a995e164109aaae190d SHA512: 2b8bff8a12321ba3b90e14c710f7d2a3642c744780388be9071fe5d26a0e95b7f1158ac0ed7f8b48ff969bd4e3387fa1011a29db7d0d70a328bdad12367257fe Homepage: https://cran.r-project.org/package=visualpred Description: CRAN Package 'visualpred' (Visualization 2D of Binary Classification Models) Visual contour and 2D point and contour plots for binary classification modeling under algorithms such as 'glm', 'rf', 'gbm', 'nnet' and 'svm', presented over two dimensions generated by 'famd' and 'mca' methods. Package 'FactoMineR' for multivariate reduction functions and package 'MBA' for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses under the 'XAI' paradigm. 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Users can easily upload their datasets, perform analyses, and download the results as a well-formatted document, streamlining the process of data analysis and reporting in agricultural research.The experimental design methods are based on classical work by Fisher (1925) and Scheffe (1959). The correlation visualization approaches follow methods developed by Wei & Simko (2021) and Friendly (2002) . 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VK is the largest European online social networking service, based in Russia. Package: r-cran-vlf Architecture: all Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-vlf_1.1-3-1.ca2604.1_all.deb Size: 630412 MD5sum: 4ea4dc5db80edaff85ee0bdaf0adf8d8 SHA1: 768a695ac3fee4d889b642ffad7cd63e0feb363a SHA256: ec4c079ae0dc563bcd06b40cf727f5aad1a23f13cb50e9b2903107194407dff9 SHA512: 5d3a517771dc5c163f802acdcdaebfd40813a936eff15d3b67713dc007970a2825e6fc5e6e7fa0228aa10a2bb2c7116da16ba6e39e40f8b8f5847f8e596285bf Homepage: https://cran.r-project.org/package=VLF Description: CRAN Package 'VLF' (Frequency Matrix Approach for Assessing Very Low FrequencyVariants in Sequence Records) Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences. The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs. The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another. Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures. Based on Stoeckle and Kerr (2012) and Phillips et al. (2023) . 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The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) . 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Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) when referring to this package in publications. 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Package: r-cran-voice Architecture: all Version: 0.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1876 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrangements, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-httr, r-cran-httr2, r-cran-r.utils, r-cran-reticulate, r-cran-seewave, r-cran-tabr, r-cran-tibble, r-cran-tidyselect, r-cran-tuner, r-cran-wrassp, r-cran-zoo Suggests: r-cran-gm, r-cran-knitr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-voice_0.5.6-1.ca2604.1_all.deb Size: 1513254 MD5sum: 3e55361fabda0d4bb836ca03e9b24d9c SHA1: cce700a39710e8b22b642c3e5ea0a5f92e4af948 SHA256: 9ba092b55ec35ba63241722523c35606bbd2c9f2fd0ef6029c86f700896caa10 SHA512: 914bc83ca03a6c6f36884a9517d442cc04cdcc535c4e54c83f2baee8c9d02812e2ce763811781ab210919e4a1c620d6ef4a06bc2197a59e7b2034ea212c7bedd Homepage: https://cran.r-project.org/package=voice Description: CRAN Package 'voice' (Speaker Recognition, Voice Analysis and Mood Inference via MusicTheory) Provides tools for audio data analysis, including feature extraction, pitch detection, and speaker identification. Designed for voice research and signal processing applications. 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This package offers an accessible and easy-to-use interface, including an interactive Shiny app, that simplifies the processing, extraction, analysis, and reporting of voice recording data in the behavioral and social sciences. The package includes batch processing capabilities to read and analyze multiple voice files in parallel, automates the extraction of key vocal features for further analysis, and automatically generates APA formatted reports for typical between-group comparisons in experimental social science research. A more extensive methodological introduction that inspired the development of the 'voiceR' package is provided in Hildebrand et al. 2020 . Package: r-cran-voigt Architecture: all Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-invgamma, r-cran-coda, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-voigt_2.0-1.ca2604.1_all.deb Size: 50060 MD5sum: d1845795d8af47dda8f2a8b2db3569fc SHA1: 90cb229ae15cf48fd21e922c7caa3c17f6108ad8 SHA256: cb08a7ee8170a4f34951b534767bde63019cbffa86df9c202b788a0eb83180e7 SHA512: 47788d603f0b7b7500d74483d8ed4bc5421913f8e2ad4902029cacc176c51287dd240bcb73143b57a23ec4d118c355557d3940337e2aaa7444ba1cf96010cbde Homepage: https://cran.r-project.org/package=voigt Description: CRAN Package 'voigt' (The Voigt Distribution) Random generation, density function and parameter estimation for the Voigt distribution. The main objective of this package is to provide R users with efficient estimation of Voigt parameters using classic iid data in a Bayesian framework. 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). Package: r-cran-volcano3d Architecture: all Version: 2.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4901 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-ggplot2, r-cran-ggpubr, r-cran-htmlwidgets, r-cran-magrittr, r-cran-rfast, r-cran-rlang, r-cran-matrixtests Suggests: r-bioc-deseq2, r-bioc-edger, r-bioc-limma, r-bioc-qvalue, r-bioc-summarizedexperiment, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-easylabel Filename: pool/dists/resolute/main/r-cran-volcano3d_2.0.11-1.ca2604.1_all.deb Size: 2028612 MD5sum: 4bfbc1886982c5c504614435d859e56d SHA1: 255236fac06a4c1c4dd8e530ed0b256de43ddbd6 SHA256: d00135c5d376a487be2e394df32118aed538c85a3588de76b2642d3640d7be34 SHA512: cbfe006daf4aea2a2451592521acdea9bde5b7c8b6e94c05f41be29ebc806b831920a6707c80b0d406608f43bfd0d6a9ecbad8b60aa3c2ac5544ba0411b5ee5f Homepage: https://cran.r-project.org/package=volcano3D Description: CRAN Package 'volcano3D' (3D Volcano Plots and Polar Plots for Three-Class Data) Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data. Package: r-cran-volcanoplot Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmsb, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-tidyr, r-cran-shiny, r-cran-purrr Suggests: r-cran-safetydata Filename: pool/dists/resolute/main/r-cran-volcanoplot_1.0.0-1.ca2604.1_all.deb Size: 40100 MD5sum: 7a0c877d10f78f846d978b60388feb2f SHA1: fe1db0378a9cc618b10a4f101caee54adfffa2fd SHA256: 7f56161377882792837ed9c85e7a6cabc3c1c731343607b3a9f0be27daf9a435 SHA512: 3ac1f9495384097f0175b0d3d0dc9e96808ca5423f76b4606a04ffd2298023278b3a4bc5be9c5e8159ebd3c1d66da68ede5019dab1952a904331a11b1ede0b26 Homepage: https://cran.r-project.org/package=volcanoPlot Description: CRAN Package 'volcanoPlot' (Volcano Plot for Clinical Trial Adverse Events) Interactive adverse event (AE) volcano plot for monitoring clinical trial safety. 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Package: r-cran-voronoibiomedplot Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-deldir, r-cran-mass Filename: pool/dists/resolute/main/r-cran-voronoibiomedplot_0.3.2-1.ca2604.1_all.deb Size: 49930 MD5sum: 155cf8e7119a3b1037673673c8cbc556 SHA1: 287d1573da9efc6ff537a81200f169b090e9b5a9 SHA256: 313f183df79eb4003bffbf692057ee1778dbbb697579f82d8f4f74f89af0f51a SHA512: eb7b3eaaf21605ab1231e2289664d35c757832501e9a7964fcfae3357538982785a4921c93e786a7bcb8ad7976a26f6f38a50b9a635545500add11f1c30dd8eb Homepage: https://cran.r-project.org/package=VoronoiBiomedPlot Description: CRAN Package 'VoronoiBiomedPlot' (Tesselation Visualization Plots for 2D Data) Creates visualization plots for 2D data including ellipse plots, Voronoi tesselation plots, and combined ellipse-Voronoi plots. 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The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. . This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'simulatedNMF' package is approximately 8 MB. 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A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the 'walkboutr' package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) . 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A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events. Package: r-cran-walmartapi Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-purrr, r-cran-httr, r-cran-tibble, r-cran-magrittr, r-cran-stringr, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-walmartapi_0.1.5-1.ca2604.1_all.deb Size: 44378 MD5sum: 2475b7698d044d637e0cdc3a3d4dc72c SHA1: 9f922d16082317f1e14d41f1ae752b6d2741261b SHA256: 952782ddc084c53ff7a05fb63a9fa474677a6cbcdea339b210e8a4674c61fa67 SHA512: 9c1f2773851e3780e6a643e02915450a6b5101d66e3129de04bf12b464d748480b64d57b63bcd4abe670c5ee11667d630fef175d8f65c9b7db06cd9a695e99c8 Homepage: https://cran.r-project.org/package=walmartAPI Description: CRAN Package 'walmartAPI' (Walmart Open API Wrapper) Provides API access to the Walmart Open API , that contains data about stores, Value of the day and products which includes names, sale prices, shipping rates and taxonomies. 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Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book 'Introduction to Robust Estimation & Hypothesis Testing'. Package: r-cran-wals Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-mass, r-cran-rdpack Suggests: r-cran-aer, r-cran-bayesvarsel, r-cran-bms, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wals_0.2.6-1.ca2604.1_all.deb Size: 347926 MD5sum: 7701e2ac3b6298d2b4aca17766f909ac SHA1: da2c9cd911d272b06d1751b884e6f37b0a8d4b3b SHA256: 2623d1865cdfec77f7b3a406340e832419e56b62a7d05a75902d09d0daf53759 SHA512: bffd4c37e2c3eb97b1c2664973543433550d44dfed39167368f773c8dd56b6d170175b83b9a1e6d36dab83f08ee165d6256027f83e1d5bbaba3502af8c559071 Homepage: https://cran.r-project.org/package=WALS Description: CRAN Package 'WALS' (Weighted-Average Least Squares Model Averaging) Implements Weighted-Average Least Squares model averaging for negative binomial regression models of Huynh (2024) , generalized linear models of De Luca, Magnus, Peracchi (2018) and linear regression models of Magnus, Powell, Pruefer (2010) , see also Magnus, De Luca (2016) . Weighted-Average Least Squares for the linear regression model is based on the original 'MATLAB' code by Magnus and De Luca , see also Kumar, Magnus (2013) and De Luca, Magnus (2011) . Package: r-cran-wamasim Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wamasim_1.0.0-1.ca2604.1_all.deb Size: 69960 MD5sum: 837e40c9c24f33d3b6af75c7c28940d2 SHA1: 0108a32eee9026c97ed88faccd1a9f3248214b0a SHA256: 10a1c4784bea3d9c61e149bd4e157ad63a9429d4f7931edd63aae6c3083e9cf7 SHA512: 7a145babf49de160a44123aeab490a958ca60f77f38fe078e306ce36ac491fb5bf653e874635fcfceaacf56fb6d44555d2f1b8e7e18d906baccce2423f2a4c3d Homepage: https://cran.r-project.org/package=WaMaSim Description: CRAN Package 'WaMaSim' (Simulate Rehabilitation Strategies for Water DistributionSystems) The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland. Package: r-cran-wand Architecture: all Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-wand_0.5.0-1.ca2604.1_all.deb Size: 190272 MD5sum: a532c0d0f45a15e63d2634d20a20cd07 SHA1: 934707a5e74b1c5182c5945128cb3b4a03e24cbe SHA256: 242656c3454723ac1f7be449b9708a4e487794f6e687ca4c44a032a757fd8815 SHA512: 8e745498c4b24171a515a72eb18d22ce7c74639b783892f3d35179c7e661d9bf268c59f5682c18f9a3e498dd146be7af001a98123d8d21b8a3f2a672b93c66c9 Homepage: https://cran.r-project.org/package=wand Description: CRAN Package 'wand' (Retrieve 'Magic' Attributes from Files and Directories) 'MIME' types are shorthand descriptors for file contents and can be determined from "magic" bytes in file headers, file contents or intuited from file extensions. 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Package: r-cran-wanova Architecture: all Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-suppdists Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wanova_0.4.0-1.ca2604.1_all.deb Size: 40436 MD5sum: 4d8def6eabc19e2a5108a6688b982a5e SHA1: d84a5a358c2e8429776c1464e168e4a72473783b SHA256: b03c9d6fa3fe32a2755b1106b00de83cd33c22d2995c9eacc187a71642ba12c3 SHA512: 29b4661dcb34dee77bf29cdca36bc6fca5e851db0ed86317dbec90a4a774c5ea076fc0faff27e078cd6824fe604e88298b54bed3f49e54cbfdb7bf8c6d195327 Homepage: https://cran.r-project.org/package=WAnova Description: CRAN Package 'WAnova' (Welch's Anova from Summary Statistics) Provides the functions to perform a Welch's one-way Anova with fixed effects based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test for multiple comparisons and provides the effect size estimator adjusted omega squared. In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo simulation is included to bootstrap residual normality and homoscedasticity Welch, B. L. (1951) Kirk, R. E. (1996) Carroll, R. M., & Nordholm, L. A. (1975) Albers, C., & Lakens, D. (2018) Games, P. A., & Howell, J. F. (1976) Levy, K. J. (1978a) Show-Li, J., & Gwowen, S. (2014) . Package: r-cran-waou Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-dplyr, r-cran-stringr, r-cran-glue, r-cran-mice, r-cran-nonprobsvy, r-cran-survey, r-cran-ggplot2, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-waou_0.1.0-1.ca2604.1_all.deb Size: 4458756 MD5sum: 871ded16ca3743c548f5f0ff0eb4f618 SHA1: d47d4a6c0e47a2439f8563d80865fc5379b0a52e SHA256: 9d0dcc65aeb088a006c778c32f194d5447247d748394a37fbebaded9f446eef6 SHA512: a9be78957f5d31ba5a83e5474f2412d6a2464541f2bef77494a03588e0dde83fd1ed18b4cd63c342e2a12e9ab693560706f5c259a3b074e9a735b15f61128b9b Homepage: https://cran.r-project.org/package=waou Description: CRAN Package 'waou' (Weighting All of Us) Utilities for using a probability sample to reweight prevalence estimates calculated from the All of Us research program. Weighted estimates will still not be representative of the general U.S. population. However, they will provide an early indication for how unweighted estimates may be biased by the sampling bias in the All of Us sample. Package: r-cran-warabandi Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-readtext, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-warabandi_0.1.0-1.ca2604.1_all.deb Size: 61926 MD5sum: c01c410183e464e167d92a43fe109273 SHA1: 8939d21fe2da663e7637b907a0b3c79c821f6baa SHA256: afdd30879064404d4f2f502f583f39c20e6c1c0421ea03035e51c464c057d412 SHA512: e0dc4f325d903e20fbca9fd0bf371db81e4e648f70da26dc83b35949ff2a8664d367458a10709ebf4e4078ce51df746359e160aaf6aa13c0879a90bacc017e7d Homepage: https://cran.r-project.org/package=warabandi Description: CRAN Package 'warabandi' (Roster Generation of Turn for Weekdays:'warabandi') It generates the roster of turn for an outlet which is flowing (water) 24X7 or 168 hours towards the area under command or agricutural area (to be irrigated). The area under command is differentially owned by different individual farmers. The Outlet runs for free of cost to irrigate the area under command 24X7. So, flow time of the outlet has to be divided based on an area owned by an individual farmer and the location of his land or farm. This roster is known as 'warabandi' and its generation in agriculture practices is a very tedious task. Calculations of time in microseconds are more error-prone, especially whenever it is performed by hands. That division of flow time for an individual farmer can be calculated by 'warabandi'. However, it generates a full publishable report for an outlet and all the farmers who have farms subjected to be irrigated. It reduces error risk and makes a more reproducible roster. For more details about warabandi system you can found elsewhere in Bandaragoda DJ(1995) . Package: r-cran-wareg Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-survival, r-cran-nleqslv, r-cran-mass, r-cran-magrittr, r-cran-rlang Suggests: r-cran-splines2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wareg_0.1.0-1.ca2604.1_all.deb Size: 305492 MD5sum: 8118077cdf1317721ea09534e60825e4 SHA1: aa3106c3864a1f97d52decda163d67d72b916aca SHA256: d5b3040e25a5396c7cf3130e15d57f939e4c774494a46d3b891f9a5928179e7b SHA512: 29fea334f705eb414c617a2f32d98864653842487b0ba21923f4e6a9c7f5c17ca5b95a531f569c022c75d08a30c9439a60b27762efefae17c698b15beab585cf Homepage: https://cran.r-project.org/package=WAreg Description: CRAN Package 'WAreg' (While-Alive Regression for Composite Endpoints withCluster-Robust Inference) Provides estimation and inference for while-alive regression models targeting the while-alive loss rate for composite endpoints that include recurrent events and a terminal event. The implementation supports flexible time-varying covariate effects through user-selected time bases, including B-splines, natural splines, M-splines, step functions, truncated linear bases, interval-local bases, and piecewise polynomials. Inference can be performed using cluster-robust variance estimators for cluster-randomized trials, with subject-level (IID) variance as a special case. The package includes prediction and plotting utilities and K-fold cross-validation for selecting basis and tuning parameters. Methodology is based on Fang et al. (2025) . Package: r-cran-warehousetools Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-clustersim Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-warehousetools_0.1.4-1.ca2604.1_all.deb Size: 104864 MD5sum: 2de48797f3993e1d3fc8634d3853cc4d SHA1: 8c432d1c093063a055689cf1c4b434820d92ad62 SHA256: 8e4532c477e67f2aa59990c6eec293518aafebaaad307ebd8924138a38181f41 SHA512: 25d54b35a3e02689bc83bf8b75cdcb7fc0684e0549228201f1e2723cdde287f62ea317ba7e7af721a433427c81948358458b7865117ac58bdf47a1921b9fc534 Homepage: https://cran.r-project.org/package=warehouseTools Description: CRAN Package 'warehouseTools' (Heuristics for Solving the Traveling Salesman Problem inWarehouse Layouts) Heuristic methods to solve the routing problems in a warehouse management. Package includes several heuristics such as the Midpoint, Return, S-Shape and Semi-Optimal Heuristics for designation of the picker’s route in order picking. The heuristics aim to provide the acceptable travel distances while considering warehouse layout constraints such as aisles and shelves. It also includes implementation of the COPRAS (COmplex PRoportional ASsessment) method for supporting selection of locations to be visited by the picker in shared storage systems. The package is designed to facilitate more efficient warehouse routing and logistics operations. see: Bartholdi, J. J., Hackman, S. T. (2019). "WAREHOUSE & DISTRIBUTION SCIENCE. Release 0.98.1." The Supply Chain & Logistics Institute. H. Milton Stewart School of Industrial and Systems Engineering. Georgia Institute of Technology. . Package: r-cran-warmthcompetence Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3176 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-spacyr, r-cran-caret, r-cran-dplyr, r-cran-lexicon, r-cran-ngram, r-cran-qdap, r-cran-politeness, r-cran-qdapdictionaries, r-cran-quanteda, r-cran-sentimentr, r-cran-tidyr, r-cran-tidytext, r-cran-tm, r-cran-quanteda.textstats Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-warmthcompetence_0.1.5-1.ca2604.1_all.deb Size: 3176654 MD5sum: 6a5612b81d1c60a0c300f82e63cfd3cd SHA1: 24d68565d97cbce16847d18287a39f2e31f2f03b SHA256: d22521ae0ccd3f39abe1fa92c89531791e2a45aeb740c4408e10b283ea08f19c SHA512: 3538fb2a9213245cc59ae83a8549052b8def5f7864839a4324cec1e3bcfe1a520c4a89913685a3d4bf08f13714cc7fb38d7362b991585b6ae769fd09b12a0960 Homepage: https://cran.r-project.org/package=warmthcompetence Description: CRAN Package 'warmthcompetence' (Warmth and Competence Detectors) Detects perceptions of warmth and competence in American English self-presentation language. Using trained elastic net regression models, this package provides a numerical representation of warmth and competence perceptions. Methods are described here:. 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Package: r-cran-warnepi Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-warnepi_1.0.1-1.ca2604.1_all.deb Size: 37168 MD5sum: 08db8a2e64909b36e05ced4d86c83465 SHA1: ec7e7ed18d834d9f8fc8c9e9d8851147541f8aa5 SHA256: cc9ab7c3979594bd59770ce18ad865fa6436ecad1cae6981ef7bc77a2e5d536d SHA512: fbf69e27e8c53736f45be5a767f093201ed79510461c1a18a424f5aebcce1864c55d17cbfa6322940a6aa0fe7b4ecd5942da48f1384bb29755b770f036908c0a Homepage: https://cran.r-project.org/package=WarnEpi Description: CRAN Package 'WarnEpi' (A Comprehensive Tool for Early Warning in Infectious Disease) Infectious disease surveillance requires early outbreak detection. This package provides statistical tools for analyzing time-series monitoring data through three core methods: a) EWMA (Exponentially Weighted Moving Average) b) Modified-CUSUM (Modified Cumulative Sum) c) Adjusted-Serfling models Methodologies are based on: - Wang et al. (2010) - Wang et al. (2015) Designed for epidemiologists and public health researchers working with disease surveillance systems. Package: r-cran-warpmix Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-fields, r-cran-mass, r-cran-reshape2, r-cran-nlme, r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-warpmix_0.1.0-1.ca2604.1_all.deb Size: 46172 MD5sum: e1c36c7f63ebba12c3714f46839e4b1f SHA1: 717c05e91fae64d852fc28888b93db50cf6bf11a SHA256: a0f8f7e4db84bbaff2d5c4a4c4e8f5f2dfd080458b77d817652e060783cd07e5 SHA512: 7d0229d0af3d95dead064f0b820fab4bfdb596d4b487854dfd3b73cdf3b3b0f5b21d765c486fc01a8afe7516dd8f3ba54c284e4d0ea65b9843241b219c9a6987 Homepage: https://cran.r-project.org/package=warpMix Description: CRAN Package 'warpMix' (Mixed Effects Modeling with Warping for Functional Data UsingB-Spline) Mixed effects modeling with warping for functional data using B- spline. Warping coefficients are considered as random effects, and warping functions are general functions, parameters representing the projection onto B- spline basis of a part of the warping functions. Warped data are modelled by a linear mixed effect functional model, the noise is Gaussian and independent from the warping functions. Package: r-cran-washdata Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 879 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-washdata_0.1.5-1.ca2604.1_all.deb Size: 759818 MD5sum: 50ded24d514afd33939906a9265170c3 SHA1: dae2c3f7016e0a249ffea10116a42e65bd81312d SHA256: 6fc34397435a5eee4a5aea29e8e2a6d218176b9252fe155ff4b0b5442221d715 SHA512: d2c102a129f2b86e885a3a2a4466e8704ff36e3be82b55f6abc531d7627b023dce3caa37926d6cf67b7e5703011ccb5c4baa4ece94c0af6172cea90a3eb3fb8f Homepage: https://cran.r-project.org/package=washdata Description: CRAN Package 'washdata' (Urban Water and Sanitation Survey Dataset) Urban water and sanitation survey dataset collected by Water and Sanitation for the Urban Poor (WSUP) with technical support from Valid International. These citywide surveys have been collecting data allowing water and sanitation service levels across the entire city to be characterised, while also allowing more detailed data to be collected in areas of the city of particular interest. These surveys are intended to generate useful information for others working in the water and sanitation sector. Current release version includes datasets collected from a survey conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of a series of surveys to be conducted by WSUP in various cities in which they operate including Accra, Ghana; Nakuru, Kenya; Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This package will be updated once the surveys in other cities are completed and datasets have been made available. Package: r-cran-washer Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gplots Filename: pool/dists/resolute/main/r-cran-washer_0.1.3-1.ca2604.1_all.deb Size: 40652 MD5sum: 16e635dc67cca496324e9228d156e70d SHA1: b9cc3dcc0c3751e2788286cea9cdea26a8624756 SHA256: 3dbefcd12a26452ed7f089e9efb2f18b46ebd4021914821e039d68fc39149ae5 SHA512: bf903a08817a603f304e56e853834014de3c120288617a26d12ab19678f9f3fa9a3de125945c7c3ed0f53c3b9cbfbfb9e386e2cf233b06c3068784d21c2682a0 Homepage: https://cran.r-project.org/package=washeR Description: CRAN Package 'washeR' (Time Series Outlier Detection) Time series outlier detection with non parametric test. This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web. Package: r-cran-washi Architecture: all Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-flextable, r-cran-ggplot2, r-cran-officer, r-cran-scales, r-cran-systemfonts Suggests: r-cran-covr, r-cran-forcats, r-cran-ragg, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-washi_0.2.1-1.ca2604.1_all.deb Size: 4631012 MD5sum: 8fe16d0e3ba116ae2823a1729125cf34 SHA1: 7473278a1d656d9ab2c9ab08b1967a7db00f3e43 SHA256: a8efefa205db443bf91b620ea05aba80402f8337c6ab77a4ebaac216da4b4eaf SHA512: f22eb89fe913d39380ddd39253d3f28261002266711e934f9d6aec667448a8ada84e683bad019f9828f2cc8cfe177b43ede114487ea75d768ead0e21483c8937 Homepage: https://cran.r-project.org/package=washi Description: CRAN Package 'washi' (Washington Soil Health Initiative Branding) Create plots and tables in a consistent style with WaSHI (Washington Soil Health Initiative) branding. Use 'washi' to easily style your 'ggplot2' plots and 'flextable' tables. Package: r-cran-washr Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desc, r-cran-devtools, r-cran-cffr, r-cran-pkgdown, r-cran-rlang, r-cran-usethis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-washr_1.0.1-1.ca2604.1_all.deb Size: 50508 MD5sum: b7ce287360a9389fdba07b44e363f7be SHA1: ae18c4308decd173dc9fbe9cadf3c12260a15ee7 SHA256: cd5b300989f9a39460499778c351e09bb56f02d659a79efee7e1b76828861051 SHA512: 1e2b4a1513516da0f21eb9c4a344d3f33e4c2a55c66e548be60c904fae1ab49b24d9536adf4133613ec7cb74c06666852841964e5cc3a67bdf878e61f22e6843 Homepage: https://cran.r-project.org/package=washr Description: CRAN Package 'washr' (Publication Toolkit for Water, Sanitation and Hygiene (WASH)Data) A toolkit to set up an R data package in a consistent structure. Automates tasks like tidy data export, data dictionary documentation, README and website creation, and citation management. Package: r-cran-wasp Architecture: all Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4503 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-waveslim, r-cran-tidyr, r-cran-readr, r-cran-ggplot2, r-cran-sp, r-cran-zoo, r-cran-fitdistrplus Suggests: r-cran-npred, r-cran-fnn, r-cran-spei, r-cran-knitr, r-cran-dplyr, r-cran-cowplot, r-cran-gridgraphics, r-cran-bookdown, r-cran-rmarkdown, r-cran-synthesis, r-cran-kableextra, r-cran-devtools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wasp_1.4.5-1.ca2604.1_all.deb Size: 3816004 MD5sum: 279a06f75b44a052081b23e3cb33445e SHA1: 9468928d88280ad5d8f77fb1d9cd57b6fcc441b9 SHA256: 8a54dcac8b1284d965d93156bbdba586ba499ccda06b75133fe1480dbe832b80 SHA512: 4f565e2341910acedc700d5236fe0bef20d899e2d75f5a6cb812c4dc398831e4f7bab46ebb93aacd8d8c98f3bfacb9dab3299671aef26868e62cde8ba1cf25b1 Homepage: https://cran.r-project.org/package=WASP Description: CRAN Package 'WASP' (Wavelet System Prediction) The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. Details of methodologies used in the package can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) , Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) , and Jiang, Z., Sharma, A., & Johnson, F. (2021) . Package: r-cran-waspasr Architecture: all Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-waspasr_0.1.5-1.ca2604.1_all.deb Size: 111120 MD5sum: d9ed466c83ade3690228cba595093ee9 SHA1: 2960620131e4e527af87c9e6505618d7d03b4a60 SHA256: 9bdf1df301d4c8bb49a880e6920573743fb889d8ba20c1e246f4b9b1f9fc7e94 SHA512: 87daffa87c0df0e63206ae6e42b83c9bb10270b15bb9c9e1b76ebe666fc649e612554e33c8cec47087eba55a5bbd96783be37f1ed82f968b9c9938a3f1f96a8e Homepage: https://cran.r-project.org/package=waspasR Description: CRAN Package 'waspasR' (Tool Kit to Implement a W.A.S.P.A.S. Based Multi-CriteriaDecision Analysis Solution) Provides a set of functions to implement decision-making systems based on the W.A.S.P.A.S. method (Weighted Aggregated Sum Product Assessment), Chakraborty and Zavadskas (2012) . So this package offers functions that analyze and validate the raw data, which must be entered in a determined format; extract specific vectors and matrices from this raw database; normalize the input data; calculate rankings by intermediate methods; apply the lambda parameter for the main method; and a function that does everything at once. The package has an example database called choppers, with which the user can see how the input data should be organized so that everything works as recommended by the decision methods based on multiple criteria that this package solves. Basically, the data are composed of a set of alternatives, which will be ranked, a set of choice criteria, a matrix of values for each Alternative-Criterion relationship, a vector of weights associated with the criteria, since certain criteria are considered more important than others, as well as a vector that defines each criterion as cost or benefit, this determines the calculation formula, as there are those criteria that we want the highest possible value (e.g. durability) and others that we want the lowest possible value (e.g. price). Package: r-cran-waterbalancer Architecture: all Version: 0.1.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 28936 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sf, r-cran-janitor, r-cran-zoo, r-cran-lubridate, r-cran-stars, r-cran-ggplot2, r-cran-tidyr, r-cran-rdwd, r-cran-terra, r-cran-gridextra, r-cran-httr, r-cran-rselenium, r-cran-stringr, r-cran-rvest, r-cran-sp, r-cran-geosphere, r-cran-rcurl, r-cran-tidyselect, r-cran-openeo, r-cran-magrittr, r-cran-jsonlite, r-cran-dplyr, r-cran-readxl, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-waterbalancer_0.1.21-1.ca2604.1_all.deb Size: 8161352 MD5sum: 45973cb9f18768c5d6c71a0875ad5c47 SHA1: f14d26970184f6e6a27123025f86a44f49b3cc38 SHA256: f21fb8e4f4f6979cb3e2842a58262f4db7449830d0d3ef8e639238963b404f09 SHA512: 4bae80a2bfbba4cb9ed5d8cbc29164c3a1c7362f594a1a6de5c72109f26f6df17a7a935c909bf0e0faf249a06b8559613e46d9b9796decf101a29dd22131ab52 Homepage: https://cran.r-project.org/package=WaterBalanceR Description: CRAN Package 'WaterBalanceR' (Calculate High Resolution Water Balance of Starch Potatoes) Calculates the water balance of starch potatoes from Normalized Distance Vegetation Index (NDVI) images, German Weather Service (DWD) reference evapotranspiration, German Weather Service RADOLAN precipitation data and irrigation information. For more details see Piernicke et al. (2025) . Package: r-cran-waterfalls Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-waterfalls_1.1.4-1.ca2604.1_all.deb Size: 24734 MD5sum: 91053f8976498b8758fc187ab9601ffb SHA1: 910ec0185432ab6e670e948da7d042a5b73f84ea SHA256: bdc454cd4ac6ff86dd374a3c33752fe1e351436988b6a253657a2e16d71430d7 SHA512: 09e09c059cfee49802248804078ca67776f581c09c987c6cf8279e41afa477105c9588f44d4d81c2567ef1ed54e01184856b13fe1fe9c78255ba0143d6545914 Homepage: https://cran.r-project.org/package=waterfalls Description: CRAN Package 'waterfalls' (Create Waterfall Charts using 'ggplot2' Simply) A not uncommon task for quants is to create 'waterfall charts'. There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme. Package: r-cran-waterquality Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4022 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-purrr, r-cran-caret, r-cran-magrittr, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-tibble, r-cran-rmarkdown, r-cran-covr, r-cran-tmap, r-cran-tmaptools, r-cran-sf Filename: pool/dists/resolute/main/r-cran-waterquality_1.0.0-1.ca2604.1_all.deb Size: 3010020 MD5sum: e4e685eb335d9ab3307360fb9b3e542d SHA1: 8ad767a10f1643fbb53e4d537c013107524401ae SHA256: 6f1c9259136bd667da52f531229abe7a15f88288e0dd066f6945e71f01e4b627 SHA512: f27e69aafcde3525920795f5cf863342a39f637dd3bf13f4dfd37a53cc8916ae1a68d7886747d0fb49142ab27ea25faa49cd583bb9a4e68e1a54bbc08282455e Homepage: https://cran.r-project.org/package=waterquality Description: CRAN Package 'waterquality' (Satellite Derived Water Quality Detection Algorithms) The main purpose of waterquality is to quickly and easily convert satellite-based reflectance imagery into one or many well-known water quality algorithms designed for the detection of harmful algal blooms or the following pigment proxies: chlorophyll-a, blue-green algae (phycocyanin), and turbidity. Johansen et al. (2019) . Package: r-cran-wateryeartype Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-wateryeartype_1.0.1-1.ca2604.1_all.deb Size: 15870 MD5sum: 685f987519119182a1c2bebc6f4b301f SHA1: d62400aaf11003cee88bda7f52e56328d67e9177 SHA256: d2d66ba34f13d698bb14e19222a229b67ecda2ab9de260d0b720ecf2c25f0aa5 SHA512: da23e59237fde1fc921fef74fd8d989a48589b46ac57aef8e8999f7996c46549f3f122de542248e461c9f0de44b143f09557feb3c0094d363b9cd1abdf730063 Homepage: https://cran.r-project.org/package=waterYearType Description: CRAN Package 'waterYearType' (Sacramento and San Joaquin Valley Water Year Types) Provides Water Year Hydrologic Classification Indices based on measured unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources and subject to revision. Package: r-cran-wats Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-rcolorbrewer, r-cran-rlang, r-cran-testit, r-cran-tibble, r-cran-zoo Suggests: r-cran-boot, r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-scales, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wats_1.0.1-1.ca2604.1_all.deb Size: 1609774 MD5sum: 9771427494eeaf74fb8076dd3927958f SHA1: cf18d08debc825caca4ccca9e20f861c60faacad SHA256: d2168246d329a4a342cf146d5001b2ee22bd651451d5410cf1c4b9d3c4b4cde4 SHA512: 713cbad8e7cf597bd946eb65a2d858489f3dd708f7b3b3f725fe5c1cacf8e035cdcd0bf72dc48aab746d05bcc35f5199a436d72353328bf0edbdf334d4951fef Homepage: https://cran.r-project.org/package=Wats Description: CRAN Package 'Wats' (Wrap Around Time Series Graphics) Wrap-around Time Series (WATS) plots for interrupted time series designs with seasonal patterns. Longitudinal trajectories are shown in both Cartesian and polar coordinates. In many scenarios, a WATS plot more clearly shows the existence and effect size of of an intervention. This package accompanies "Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs" by Rodgers, Beasley, & Schuelke (2014) ; see 'citation("Wats")' for details. Package: r-cran-waved Architecture: all Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-waved_1.3-1.ca2604.1_all.deb Size: 138046 MD5sum: 1b9684101fbfe420f79065e415783f19 SHA1: d426fd02b6c75023cd2a0c19ee89fdc49f7bc771 SHA256: 4b9d5231a1f0e71a6d28a12f1fe1c1633046eb6431f4fe9a31a0a2d1aaae371f SHA512: 5adc4dc866487a3f2dd54419bd1a38aff9022f299745e5edaaf4e4f2b5a56cba696cf7ab26314ea91f35a26037729dbbb186228be5ca5a4239c6c1a83813c678 Homepage: https://cran.r-project.org/package=waved Description: CRAN Package 'waved' (Wavelet Deconvolution) Makes available code necessary to reproduce figures and tables in papers on the WaveD method for wavelet deconvolution of noisy signals as presented in The WaveD Transform in R, Journal of Statistical Software Volume 21, No. 3, 2007. Package: r-cran-waveletann Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast, r-cran-metrics Filename: pool/dists/resolute/main/r-cran-waveletann_0.1.2-1.ca2604.1_all.deb Size: 19608 MD5sum: b05e366bbef75b601977d2d5fd10f4b7 SHA1: 7e0aedb203c10585492bc1fd6686c1fc0179dfb5 SHA256: 9748a6063bc0155140edbbacad8eb5ab4555e35a5c80b66dab71132bc23231bd SHA512: 7d3edc92cd317454d738c70cf39a4ba6af659f858b30b5dce1b1a83cffadb836b2b8542fb0b4de221c76ae90b5e8fe5d0487f1e6ab67e78913bfc45001b446c8 Homepage: https://cran.r-project.org/package=WaveletANN Description: CRAN Package 'WaveletANN' (Wavelet ANN Model) The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) . Package: r-cran-waveletarima Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast Filename: pool/dists/resolute/main/r-cran-waveletarima_0.1.2-1.ca2604.1_all.deb Size: 19778 MD5sum: 94fb1afbc35310e7664d6cfe4a095be2 SHA1: 270ee658f07738e9a12cf283295a602ec6e0d063 SHA256: b024aa610c252e3cc05bc705aba9f7e7a02cfa6f160088eab4c209d6e8ff2899 SHA512: a6e056a29290263f1fe77af6f133ddc938544414a91f9c6a0a3d1cbff7481d1c05739acf88d5ceb66c4a536b9f99e9a80ddacef1ed53c41c2e8482247f015e60 Homepage: https://cran.r-project.org/package=WaveletArima Description: CRAN Package 'WaveletArima' (Wavelet-ARIMA Model for Time Series Forecasting) Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) . Package: r-cran-waveletcomp Architecture: all Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-waveletcomp_1.2-1.ca2604.1_all.deb Size: 554930 MD5sum: 5a041d2fc697b63335c1c4a61592ca29 SHA1: dd2dfea43aea7125e25a2c9d8e80823df80cc0ee SHA256: ff58b7b3b2f1ae06f0a2107c7ca7382fb14028a363aeee40f950acb9a2d570e2 SHA512: 26c55b05d261c6dbb7ff95cd974c06e981b64fbe441642b01672f05e11a04a31d37f017e5269b8200e58d6f948e7f3fe6fe5b81b7ea621f9be0faf457043c194 Homepage: https://cran.r-project.org/package=WaveletComp Description: CRAN Package 'WaveletComp' (Computational Wavelet Analysis) Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms. Package: r-cran-waveletets Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-metrics, r-cran-tseries, r-cran-wavelets, r-cran-forecast, r-cran-caretforecast Filename: pool/dists/resolute/main/r-cran-waveletets_0.1.0-1.ca2604.1_all.deb Size: 19212 MD5sum: aedd11257b24755d8f2dc9ffa466b408 SHA1: cc22a4aa2d62843609df2312698050c93b8ca233 SHA256: 4242d954a1c0c1502f2877f7a40645cac37b0d4d5c1452e920d7911db0230884 SHA512: 338f8420ccda3f2a54277e568d66b641c11dd17c6a914dce29fa66c5c8f1244c687e6cba92eb3373ca635df15bd127babec820a1a3bc85c9b79de8397bda2f7f Homepage: https://cran.r-project.org/package=WaveletETS Description: CRAN Package 'WaveletETS' (Wavelet Based Error Trend Seasonality Model) ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) . Package: r-cran-waveletgarch Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavelets, r-cran-fints, r-cran-forecast, r-cran-rugarch, r-cran-fracdiff Filename: pool/dists/resolute/main/r-cran-waveletgarch_0.1.1-1.ca2604.1_all.deb Size: 37640 MD5sum: e04ec9df23667c295e18ce6f164deb52 SHA1: 4c082c5ce8f51d3304a89f4c2b1d99060b4c3df2 SHA256: 691489078f118e5d5a10ad3cd4330455b7598204296063fb75793566cd671dce SHA512: 793b826dbf97c5bee6288c85dd1099f855e2a1188aecc85b878546adfbf414eeb5801a52bf9a1a9669c8e94ef19312922570bf8e424636a01b8370404a605690 Homepage: https://cran.r-project.org/package=WaveletGARCH Description: CRAN Package 'WaveletGARCH' (Fit the Wavelet-GARCH Model to Volatile Time Series Data) Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) . Package: r-cran-waveletgbm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-metrics, r-cran-tseries, r-cran-wavelets, r-cran-gbm Filename: pool/dists/resolute/main/r-cran-waveletgbm_0.1.0-1.ca2604.1_all.deb Size: 22944 MD5sum: 1f67084bf1e0a617e8a0465d720b02df SHA1: 7d184e25fd6f339f71334dcc6033c75faa6242c4 SHA256: a2d5745959db660b90e9dab31798b7296c2acc188d882825009f04578272528b SHA512: 5329955492b97a9649ad26b65ef0289c696be762cf7baeb10091b90e5286b017db2f07cef1356096963298e5879b6fae440137ffff747448188e55c6286771aa Homepage: https://cran.r-project.org/package=WaveletGBM Description: CRAN Package 'WaveletGBM' (Wavelet Based Gradient Boosting Method) Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) . Package: r-cran-waveletknn Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-metrics, r-cran-tseries, r-cran-wavelets Filename: pool/dists/resolute/main/r-cran-waveletknn_0.1.0-1.ca2604.1_all.deb Size: 22734 MD5sum: f3f0db7e8cbb281e21c829f211a1a4bd SHA1: c98e7dff3f16c9ffccc723d405c406a546d5e4a0 SHA256: 8db8ea882f2c5b9dd783092cb7932f0428bc4fdd5996a8b5a022f85c1a03fb60 SHA512: 23237f08e3a25966737b981f2eaf2c0619e799844f106a07be90730e4af702ae3e7c3b08d8f88a0e55abc6306d395668d2a294d39c5c5715f7f565788409efb3 Homepage: https://cran.r-project.org/package=WaveletKNN Description: CRAN Package 'WaveletKNN' (Wavelet Based K-Nearest Neighbor Model) The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) . Package: r-cran-waveletlstm Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-tseries, r-cran-wavelets, r-cran-tslstm Filename: pool/dists/resolute/main/r-cran-waveletlstm_0.1.0-1.ca2604.1_all.deb Size: 20752 MD5sum: ae701f1ce3bf37e150fdbbb7e3de2b0e SHA1: 68472072818789b04c7ca9172627307870e7661b SHA256: c2660b9f6bd6a7139c7c18d98d254a64b1e64b6a1de860075544f6c0e3fcd7bd SHA512: 7f24d5eb553c10cf1beb9df9a6bcd1c7cd1f583e2c94c2f59c6aaea80102f98fbf3f0c8aa2304f916459855e040251a2377227e7a370f2c5e30ad17800d86a18 Homepage: https://cran.r-project.org/package=WaveletLSTM Description: CRAN Package 'WaveletLSTM' (Wavelet Based LSTM Model) A wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) . Package: r-cran-waveletml Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavelets, r-cran-tseries, r-cran-forecast, r-cran-fgarch, r-cran-atsa, r-cran-fints, r-cran-lsts, r-cran-earth, r-cran-caret, r-cran-neuralnet, r-cran-e1071, r-cran-pso Filename: pool/dists/resolute/main/r-cran-waveletml_0.1.0-1.ca2604.1_all.deb Size: 50056 MD5sum: 18a848b38c3bbfcb18f6fe5355d3b9ea SHA1: d6d9c4133f938c40651129c4fc302bd577769e70 SHA256: f3e8bc52855733cb4d83b882ff12e4edcca323922727bcec7d893486c06d34d1 SHA512: 488a6e078d2be8730f5f6640cd33365fc804b8e494643f3d96ad38e651721002dc0812806980079ceb2c58dfc3aa5bc7064c083818e0d00b8afcad46a1798ece Homepage: https://cran.r-project.org/package=WaveletML Description: CRAN Package 'WaveletML' (Wavelet Decomposition Based Hybrid Machine Learning Models) Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021) . Package: r-cran-waveletmlbestfl Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-waveletml, r-cran-ceemdanml, r-cran-describedf Filename: pool/dists/resolute/main/r-cran-waveletmlbestfl_0.1.0-1.ca2604.1_all.deb Size: 33372 MD5sum: f661f56afa25a73df9a3891b21ad324b SHA1: 5d1da86a7c64cc9400f244fb4876d33674d9748a SHA256: 10fe9e8c684c77a3328696b22b35b4e4597ad86a566e586c7e5576bb26599f6f SHA512: d984452050083d45da34a450c8174b49ac5ce82bbe4c1b3cfa9e0bf83faca1543a82d5cd2be1ae989c243c858e20fb932588e399fd73a68d0b22ea919b17f7be Homepage: https://cran.r-project.org/package=WaveletMLbestFL Description: CRAN Package 'WaveletMLbestFL' (The Best Wavelet Filter-Level for Prepared Wavelet-Based Models) Four filters have been chosen namely 'haar', 'c6', 'la8', and 'bl14' (Kindly refer to 'wavelets' in 'CRAN' repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in 'input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as 'MIN' and other values are denoted as 'NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as 'MAX' and other values are denoted as 'NA'. 'output' contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of 'input'. Values in 'output' are corresponding 'NA', 'MIN' or 'MAX'. Finally, the column containing minimum number of 'NA' values is denoted as the best ('FL'). In special case, if two columns having equal 'NA', it has been checked among these two columns which one is having least 'NA' in first five rows and has been inferred as the best. 'FL_metrics_values' are the corresponding metrics values. 'WARIGAANbest' is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination. More details can be found in Garai and others (2023) . Package: r-cran-waveletrf Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast, r-cran-randomforest, r-cran-tsutils Filename: pool/dists/resolute/main/r-cran-waveletrf_0.1.0-1.ca2604.1_all.deb Size: 22382 MD5sum: 4b9843d7614aeadb67ddf39625d49e39 SHA1: 8ac8faacfde1f32557eafbb9102a7ee98bbe016b SHA256: b9a8cdd1d22e66db1c7f4e8f2281b64907f1f3ae3c091038a8cdc0e7ef36bad2 SHA512: 25eaac56f09cf1b7adf550c7f8d68df18bd820d949f5b84744a1ae9c6f6d668afe4989615f4f09b8c6954fc2a0d3de2e2bab418fb2f0a69ac598dee43851ccea Homepage: https://cran.r-project.org/package=WaveletRF Description: CRAN Package 'WaveletRF' (Wavelet-RF Hybrid Model for Time Series Forecasting) The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) . Package: r-cran-waveletsvr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast, r-cran-e1071, r-cran-tsutils Filename: pool/dists/resolute/main/r-cran-waveletsvr_0.1.0-1.ca2604.1_all.deb Size: 22214 MD5sum: 4ecc6c597510416bc4af9a79fdb458d1 SHA1: 27d2232287e78da4237e95fee44140c8a8064f31 SHA256: 93c071b86f33e061b703f55d34ce17f1407205ea47df656573bc125520f9c163 SHA512: 53cffbc906f32e43a9dfbc0c0775635cdc68d7c6a2379f56e54336566dfc348a19be9d83018e4aa3ecf812cde5270d78b44eebba5b93792e4885580129a4e89d Homepage: https://cran.r-project.org/package=WaveletSVR Description: CRAN Package 'WaveletSVR' (Wavelet-SVR Hybrid Model for Time Series Forecasting) The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) . Package: r-cran-wavemulcor Architecture: all Version: 3.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2646 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-waveslim, r-cran-plot3d, r-cran-rcolorbrewer Suggests: r-cran-covr, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wavemulcor_3.1.2-1.ca2604.1_all.deb Size: 1685080 MD5sum: 8ee8e7a3dd4fd6ed5811e7b421e87692 SHA1: e6fa95ef706c1a4698d22837ff944f08d95bc870 SHA256: 4613dedfc90dc31b36d600de088db397a9d3d484734c7030d8af77bf6c2d6fcc SHA512: 2f956e3e203cafd3c72a594fc72331d81d735574c74f3a191f4af8f418482c173598bdc1a0de02a317127ef3d2b1839886bcd119c96c1a839563f254b921e92f Homepage: https://cran.r-project.org/package=wavemulcor Description: CRAN Package 'wavemulcor' (Wavelet Routines for Global and Local Multiple Regression andCorrelation) Wavelet routines that calculate single sets of wavelet multiple regressions and correlations, and cross-regressions and cross-correlations from a multivariate time series. Dynamic versions of the routines allow the wavelet local multiple (cross-)regressions and (cross-)correlations to evolve over time. Package: r-cran-waver Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-geosphere Suggests: r-cran-lwgeom, r-cran-sp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-waver_0.3.0-1.ca2604.1_all.deb Size: 33938 MD5sum: a4e02b0cdf551afebf5a8233458de72a SHA1: c9a297042a940f27b8a24746b65da590cad64e55 SHA256: 3e7d6c458d47e9b2f1a4aa11f87d3ff6d7dbe2aeaa2b879e564a546aeb4b6453 SHA512: ca154152ce28723cd48f40318a6436641766be7f58722b9208019d8d5356871283abedbc7dc1258ccdee54845aea8f47cf3e26436d18597ccecb5c65f0d25e96 Homepage: https://cran.r-project.org/package=waver Description: CRAN Package 'waver' (Calculate Fetch and Wave Energy) Functions for calculating the fetch (length of open water distance along given directions) and estimating wave energy from wind and wave monitoring data. Package: r-cran-waverider Architecture: all Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-hmisc, r-cran-matrix, r-cran-colorednoise, r-cran-dosnow, r-cran-foreach, r-cran-matrixstats, r-cran-reshape2, r-cran-truncnorm, r-cran-astrochron, r-cran-rcolorbrewer, r-cran-colorramps, r-cran-viridis, r-cran-magick, r-cran-rlist, r-cran-trapezoid, r-cran-fancova, r-cran-decomposer, r-cran-scico Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-waverider_0.5.1-1.ca2604.1_all.deb Size: 1147536 MD5sum: bd931cd49d249ec23bde8bf41238e0a8 SHA1: 284dbc280f589294e9bd954c7382927d8d8efdd7 SHA256: 5b883b23370a0e3405846b78c8e24e7cf600325dbfebaff2055d567fe4056aed SHA512: 9fb89e9d6383ef44497e2e4380236795de988f8c5b82c4c77e7a5d887b6771d10dcd483ddb32a187bc2f7323feab60ace2d31d0d2ec375a3bd9d306573384b39 Homepage: https://cran.r-project.org/package=WaverideR Description: CRAN Package 'WaverideR' (Extracting Signals from Wavelet Spectra) Tools for extracting and analyzing cyclic signals from time series. Package: r-cran-waverr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-kimisc Filename: pool/dists/resolute/main/r-cran-waverr_1.0-1.ca2604.1_all.deb Size: 18892 MD5sum: 5ffe180133fab26d6c3617b9c257ea0e SHA1: 08391053c645a67d73e41da3aedce8f9e1b9ebfe SHA256: 9f6d413bac4de56ae424987d7ad9485dc60c393735e0f9b033b3dab459a2630e SHA512: f320a07986d8b8030c03547b8a5d337e414e573df7db3654964f8f67824494e8c9c8e27ed8502f9e26935e29f9d28c15dbf9817edbdb2cf1e01abbbbcd905ab5 Homepage: https://cran.r-project.org/package=WaverR Description: CRAN Package 'WaverR' (Data Estimation using Weighted Averages of Multiple Regressions) For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available. Package: r-cran-waves Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-pls, r-cran-prospectr, r-cran-randomforest, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-spectacles, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-waves_0.2.6-1.ca2604.1_all.deb Size: 3743520 MD5sum: e99ffb2c8ddccf523573d00e18e31448 SHA1: 5f62bd707cd66271957232d4f0f2de0fc4f91bc5 SHA256: 9a7c686660b31c8239eb5f773fa37a3178432232ce93f831e65d70e8bdcb2e62 SHA512: 36526be30c1439259843dd24e86a20b63a1c85fa6029464a942b54fd87d8282023deefb8fb3c7762b8a9e3504531b13df434cb0aa89ad806fa85386510cd6d7f Homepage: https://cran.r-project.org/package=waves Description: CRAN Package 'waves' (Vis-NIR Spectral Analysis Wrapper) Originally designed application in the context of resource-limited plant research and breeding programs, 'waves' provides an open-source solution to spectral data processing and model development by bringing useful packages together into a streamlined pipeline. This package is wrapper for functions related to the analysis of point visible and near-infrared reflectance measurements. It includes visualization, filtering, aggregation, preprocessing, cross-validation set formation, model training, and prediction functions to enable open-source association of spectral and reference data. This package is documented in a peer-reviewed manuscript in the Plant Phenome Journal . Specialized cross-validation schemes are described in detail in Jarquín et al. (2017) . Example data is from Ikeogu et al. (2017) . Package: r-cran-wavest Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-neuralnet, r-cran-tsutils, r-cran-wavelets Suggests: r-cran-devtools, r-cran-roxygen2, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-wavest_0.1.0-1.ca2604.1_all.deb Size: 41780 MD5sum: 449812c7e868d47a845d6e573e69f892 SHA1: 175be0face20dbb21724c60da44fcc490372a86e SHA256: baa9f5213159525a1f4de9c231b2f34ce612c2ab76a7a7bd0af6937acf2af6c8 SHA512: aadc3f54a6581fa45ad13fac963777c0f0da214a0f8554ff49eeaea12f63fff6b77f40b1da5ee1595449c7647abdf2674adf468d8ac11fd566f833677bdb38ac Homepage: https://cran.r-project.org/package=WaveST Description: CRAN Package 'WaveST' (Wavelet-Based Spatial Time Series Models) An integrated wavelet-based spatial time series modelling framework designed to enhance predictive accuracy under noisy and nonstationary conditions by jointly exploiting multi-resolution (wavelet) information and spatial dependence. The package implements WaveSARIMA() (Wavelet Based Spatial AutoRegressive Integrated Moving Average model using regression features with forecast::auto.arima()) and WaveSNN() (Wavelet Based Spatial Neural Network model using neuralnet with hyperparameter search). Both functions support spatial transformation via a user-supplied spatial matrix, lag feature construction, MODWT-based wavelet sub-series feature generation, time-ordered train/test splitting, and performance evaluation (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE)), returning fitted models and actual vs predicted values for train and test sets. The package has been developed using the algorithm of Paul et al. (2023) . Package: r-cran-wavscalogram Architecture: all Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-fields Filename: pool/dists/resolute/main/r-cran-wavscalogram_1.1.3-1.ca2604.1_all.deb Size: 131792 MD5sum: 52ddf51600a1eab8c87493c99efb441d SHA1: 40ec53a863f0d2c786225958ab6cb4ca89932d32 SHA256: f0d3d9bde75ce32e5aeb90ab7ff0d480045cfcb74d8b543c6edc02b6074bf18e SHA512: e09a2724d09576e54dbad155c5e1f02ac19609a50807da983bfe4e077b93f7746bfd7d4df3f68dd38ede9dbb10cdda9be021cc52f99d6afe8ee9937c7f415656 Homepage: https://cran.r-project.org/package=wavScalogram Description: CRAN Package 'wavScalogram' (Wavelet Scalogram Tools for Time Series Analysis) Provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) ), scale index and windowed scale index (Benitez et al. (2010) ). 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It is known from physics that only gradient fields, also known as conservative, have a well defined potential function. Here we present an algorithm, based on the classical Helmholtz decomposition, to obtain an approximate potential function for non gradient fields. More information in Rodríguez-Sánchez (2020) . 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The WCM algorithm attributed to Pervot et al.(1993) . The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work. 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Package: r-cran-wconf Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret Filename: pool/dists/resolute/main/r-cran-wconf_1.2.0-1.ca2604.1_all.deb Size: 66766 MD5sum: b05ba9d2e603c2560b0abe1fb81fd870 SHA1: 50fe93b634c109fff8505306cbca5975951bca81 SHA256: ba064a52f8f74b66a92c5551692962f8cd4ac77953332136f311aa355b3ea7e4 SHA512: 092e6a9fa1c6421067ad3d11eab4348ebe6176ed4b25309795131ba1c1a15c34c3d82fd3fdcd2d385d2c8e62385af6bd0d94a247ee65eaff125d9549add8fb6f Homepage: https://cran.r-project.org/package=wconf Description: CRAN Package 'wconf' (Weighted Confusion Matrix) Allows users to create weighted confusion matrices and accuracy metrics that help with the model selection process for classification problems, where distance from the correct category is important. The package includes several weighting schemes which can be parameterized, as well as custom configuration options. Furthermore, users can decide whether they wish to positively or negatively affect the accuracy score as a result of applying weights to the confusion matrix. Functions are included to calculate accuracy metrics for imbalanced data. Finally, 'wconf' integrates well with the 'caret' package, but it can also work standalone when provided data in matrix form. References: Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package" Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization (and the mewto R package)" Monahov, A. (2024) "Improved Accuracy Metrics for Classification with Imbalanced Data and Where Distance from the Truth Matters, with the wconf R Package" Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced Data Classification Results. Pattern Recognition and Image Analysis, 295–302 Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023) "A general framework for implementing distances for categorical variables" . 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Interim monitoring is anchored to a locked interim cohort and a pre-specified follow-up requirement, so analysis timing remains predictable while preserving follow-up maturity. The package searches feasible interim rules, optimizes final sample size and decision thresholds, evaluates operating characteristics by Monte Carlo simulation, and supports exponential, Weibull, log-normal, log-logistic, and user-defined baseline survival models. Related published foundations include Simon (1989) and Cotterill and Whitehead (2015) . Package: r-cran-wcvpmatch Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-fozziejoin, r-cran-lifecycle, r-cran-magrittr, r-cran-memoise, r-cran-purrr, r-cran-stringdist, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rlang, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wcvpmatch_0.0.1-1.ca2604.1_all.deb Size: 224848 MD5sum: f414d425a6de3ce55dd13841f2a0cd1e SHA1: 7340ca80ace3a0e1b70f158c879281b40e685a27 SHA256: 9d831bd345c701ffdf8cc89a7a12abb14fc1116d016614331a6fcb412f5a8266 SHA512: 4742cfc67d70702adbf3780509c6cef8d87e86bf7f018ecba5c1cd6e17d85f23817ab74d61ea6275544437e08e8015c195259a33cab44aa51f83f5896fdebbf4 Homepage: https://cran.r-project.org/package=wcvpmatch Description: CRAN Package 'wcvpmatch' (Taxonomic Name Reconciliation Against the 'WCVP' Backbone) Standardizes and reconciles scientific plant names against a World Checklist of Vascular Plants ('WCVP')-style taxonomic backbone. The package parses names into taxonomic components and applies staged exact and fuzzy matching for binomial and trinomial inputs, including infraspecific rank-aware checks. It also returns accepted-name context and row-level matching flags to support reproducible, auditable preprocessing for downstream biodiversity, spatial, and trait analyses. A user-supplied backbone can be passed through 'target_df'; when the optional companion package 'wcvpdata' is installed, its default checklist can also be used. Package: r-cran-wdata Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesmeta, r-cran-evmix, r-cran-kscorrect, r-cran-progress, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-wdata_0.1.1-1.ca2604.1_all.deb Size: 869948 MD5sum: a1a1e83d071363db31892dbb8532a09c SHA1: fb71792d36b5833cffecc7fb79df4ea3125756f4 SHA256: 14da44c84069049bf73b31dfe34ec0e8a39c608cc686b5b958f2ceec1fe7b115 SHA512: 793e2618d1afed6f905ab06b017e37abb988ac0c1b2b4556b54d121b97d4fbc4ccf5cb7200830415a280008de3b2e34448baf6eb5fd27d66d92e4bfe96801b97 Homepage: https://cran.r-project.org/package=WData Description: CRAN Package 'WData' (Statistical Inference for Weighted Data) Analyzes and models data subject to sampling biases. Provides functions to estimate the density and cumulative distribution functions from biased samples of continuous distributions. Includes the estimators proposed by Bhattacharyya et al. (1988) and Jones (1991) for density, and by Cox (2005, ISBN:052184939X) and Bose and Dutta (2022) for distribution, with different bandwidth selectors. Also includes a real length-biased dataset on shrub width from Muttlak (1988) . Package: r-cran-wdi Architecture: all Version: 2.7.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-altdoc, r-cran-curl, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-wdi_2.7.10-1.ca2604.1_all.deb Size: 806856 MD5sum: 0bdebb9e75969059ce3f6b34052e3608 SHA1: 8fd39e668e6a95967c2dd46d0ae4d56a8aa5bb73 SHA256: ef88d7d125f7d573aa76960c754ada9094ea07ec92cbc11038c222b7e48183aa SHA512: 1958f366bb975298ba82efbdecda5cf3b5d95321d4fecaa41808970787b5ed2ed17374c5cb287962fc5119a087841e236e0ffd2093747de21114f6ea0fce24c7 Homepage: https://cran.r-project.org/package=WDI Description: CRAN Package 'WDI' (World Development Indicators and Other World Bank Data) Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators. Package: r-cran-wdief Architecture: all Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-terra Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wdief_1.0.4-1.ca2604.1_all.deb Size: 1657290 MD5sum: 527ab1c4b5c47affa5cca194ffe6b7de SHA1: 57daeb8d20c51c13241f962b913013086f97d8ab SHA256: faf323475ec35207306e899bd72be31628ea71a4cbe544f4c03b23025513fcfb SHA512: 056a0a9ef2732965d2f77c2a030db5db4562fb314a49a4a618fef4c84d2e4d2dadbf500c2ba2b630ffea55c24854aa7bbbda157609c308e8c38fdd15b9497dce Homepage: https://cran.r-project.org/package=wdiEF Description: CRAN Package 'wdiEF' (Calculation of the Water Deficit Index (WDI) and the EvaporativeFraction (EF) on Rasters) Provides functions to calculate the Water Deficit Index (WDI) and the Evaporative Fraction (EF) using geospatial raster data such as fractional vegetation cover (FVC) and surface-air temperature difference (TS-TA). 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Package: r-cran-wdiexplorer Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7097 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-tibble, r-cran-tsibble, r-cran-rlang, r-cran-wdi, r-cran-cluster, r-cran-fabletools, r-cran-feasts, r-cran-forcats, r-cran-ggplot2, r-cran-ggiraph, r-cran-ggtext, r-cran-ggdist, r-cran-scales, r-cran-patchwork, r-cran-ggnewscale Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-naniar, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wdiexplorer_0.1.2-1.ca2604.1_all.deb Size: 2578712 MD5sum: ba7f5c7d98bb07fd9fb2856ab168f8de SHA1: 0a839dd8225ca51664e7249a82fd06a65076f705 SHA256: 7831baac85c79c203c47cb93f35a4e9356019dce913232158786797a9ab051bc SHA512: c740e5b4b449c29c07ce76eee222ef9c85db13f3754f668fc90ed20938165529b88ddcccd102e606cb8dd14367d5c749890431c217e71fe5a15233de9442fbc0 Homepage: https://cran.r-project.org/package=wdiexplorer Description: CRAN Package 'wdiexplorer' (Explore World Development Indicators Data) Provides a workflow for exploring World Development Indicators (WDI) country-level panel data. It downloads WDI data using the 'WDI' package and computes diagnostic indices that capture the temporal behaviour of the data by incorporating the grouping structure of the data. The set of diagnostic indices implemented includes variation features, trend and shape features, and sequential temporal features. This method is described in Akinfenwa, Cahill, and Hurley (2025) "'wdiexplorer': An R package Designed for Exploratory Analysis of World Development Indicators (WDI) Data" . We adapt the clustering diagnostics and visualisation methodology described in Rousseeuw (1987) and selected time series features from Hyndman and Athanasopoulos (2021) "Forecasting: Principles and Practice" . Package: r-cran-wdman Architecture: all Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-binman, r-cran-assertthat, r-cran-processx, r-cran-yaml, r-cran-semver Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wdman_0.2.6-1.ca2604.1_all.deb Size: 82106 MD5sum: f2bc504c4df69827fae22cfd9d9da200 SHA1: d349a7b5829dc163fbf4b925e3a36bef7223e44a SHA256: 2f05fb991a9306259ab517206603f80f7205e4be769fe8c5b30204d971e09501 SHA512: e545a32d8b646c64d2ca380a009fbef38958ee219fd629f28e0b8f6bb1ff775bdb3926b3bbf1f0246d6178b4739f7f60a5d2b54635491aa10b731cc13b14e298 Homepage: https://cran.r-project.org/package=wdman Description: CRAN Package 'wdman' ('Webdriver'/'Selenium' Binary Manager) There are a number of binary files associated with the 'Webdriver'/'Selenium' project. This package provides functions to download these binaries and to manage processes involving them. Package: r-cran-wdnr.gis Architecture: all Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcpullr, r-cran-sf, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wdnr.gis_0.1.7-1.ca2604.1_all.deb Size: 214526 MD5sum: 384d9363100c80142e21343c3f1160cb SHA1: 88c2b923f3a858b6133b32f0ddf50edf4505d966 SHA256: 8d2a3daee8f6b6397f7920be5e22e9485f663929015923d70b88199ee59c5100 SHA512: 21485a417c0bbdedc731587091c7e46592f3877478582452e25099b247af66434fa599ee99e2917a05728fee8b12df701d4cfdba5ca332eb428d05232cde6041 Homepage: https://cran.r-project.org/package=wdnr.gis Description: CRAN Package 'wdnr.gis' (Pull Spatial Layers from 'WDNR ArcGIS REST API') Functions for finding and pulling data from the 'Wisconsin Department of Natural Resources ArcGIS REST APIs' and . 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Data is obtained from Protected Planet . To augment data cleaning procedures, users can install the 'prepr' R package (available at ). For more information on this package, see Hanson (2022) . Package: r-cran-wdsmatch Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wdsmatch_0.1.1-1.ca2604.1_all.deb Size: 63364 MD5sum: 0c040701ca27afdc8031004c49893f75 SHA1: 03e895c4f0f51182a567bcdeb43fdde44c3e8116 SHA256: 110548a0f3b6b6f839ca30d36e1c40c568fd349d0c8130196afe6b0076170693 SHA512: 084400a5d758acd64dc7015fce627988e0385dfa861b7d6b884a745d59afed4f43c7349d112cf7586eae251157fc01d355e5b5016c2fda8186a7b937d3983a0b Homepage: https://cran.r-project.org/package=wdsmatch Description: CRAN Package 'wdsmatch' (Weighted Double Score Matching for Survey-Weighted CausalInference) Implements weighted double score matching (WDSM) for estimating population-level causal effects from complex survey data. Combines propensity scores and prognostic scores with survey design weights for matching, survey-weighted imputation within match sets, and Hajek normalization to target the population average treatment effect (PATE) and the population average treatment effect on the treated (PATT). Supports both retrospective (treatment-dependent) and prospective (treatment-independent) sampling designs. Achieves double robustness: consistent estimation when either the propensity score or prognostic score model is correctly specified. Provides polynomial sieve bias correction and linearization-based multinomial bootstrap variance estimation that preserves the survey-weighted matching structure without re-matching. Methods are described in Zeng, Tong, Tong, Lu, Mukherjee, and Li (2026, under review) "Where to weight? Estimating population causal effects with weighted double score matching in complex surveys". Package: r-cran-weaana Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1516 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-settings, r-cran-reshape2, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-weaana_0.3.0-1.ca2604.1_all.deb Size: 1058210 MD5sum: 3bf23374ce23dd3a734723a27e89bdf7 SHA1: 6a06daa1c03dd145b3d392e9f93d6733c19d4a64 SHA256: 48ec34ab24d8238a7aae0109bae17f1d910dcb25ec7089cf91a83842adb2196a SHA512: 0f2adeeaf03294dbe875b891803b6e1ed6ca99c0d4c995d26bec6331a5d3a017e03e8744ffcfc116e1734e0f6cddf6ff3ba1d4a1945893592264d98118ba7579 Homepage: https://cran.r-project.org/package=weaana Description: CRAN Package 'weaana' (Analysis the Weather Data) Functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. 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These models are called weak ARMA by opposition to the standard ARMA models, also called strong ARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This package allows the study of nonlinear time series models through weak ARMA representations. It determines identification, estimation and validation for ARMA models and for AR and MA models in particular. Functions can also be used in the strong case. This package also works on white noises by omitting arguments 'p', 'q', 'ar' and 'ma'. See Francq, C. and Zakoïan, J. (1998) and Boubacar Maïnassara, Y. and Saussereau, B. (2018) for more details. Package: r-cran-wearables Architecture: all Version: 0.11.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2434 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-kernlab, r-cran-lubridate, r-cran-magrittr, r-cran-padr, r-cran-r.utils, r-cran-rhrv, r-cran-rlang, r-cran-signal, r-cran-sparklyr, r-cran-varian, r-cran-waveslim, r-cran-xts, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-futile.logger Filename: pool/dists/resolute/main/r-cran-wearables_0.11.3-1.ca2604.1_all.deb Size: 1926276 MD5sum: ebffdf3fcb5e953d479ea062dc4fc379 SHA1: 4f42510c0d8326b551a08f90a026335430075af2 SHA256: 83ee2da8fc1557a2583c66c772825c7697635916409f2d7fb27af78a04cf54c9 SHA512: 38e04379eb82765ac3de53abfb502f3e6039682491a9df8cda73d2317312b258155fe6230b6b9c4a9b6c9fb60f369c9738d74077fd4e32d3e5ba9e66f9e8b69f Homepage: https://cran.r-project.org/package=wearables Description: CRAN Package 'wearables' (Tools to Read and Convert Wearables Data) Package to read Empatica E4, Embrace Plus, and Nowatch data, perform several transformations, perform signal processing and analyses, including batch analyses. Package: r-cran-weatherindices Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-weatherindices_0.1.0-1.ca2604.1_all.deb Size: 42960 MD5sum: 0a9b88d7aeee9df87a3f117f5583a5d3 SHA1: aa527cca7582f3c2ecd2e6ca05bed54895c71167 SHA256: 0331b5630557112c7b634aca9a0ac1a24e86f9d81aaeadfa2a9c8667a1a1b8ef SHA512: c749d1d23bc6dce5739e3949f94e84ded96525c73006012bf2edae080063e7f63b2b8ee818314ef7d1b1063e0b21d725ac0810582689a6eaefd0519737afb791 Homepage: https://cran.r-project.org/package=weatherindices Description: CRAN Package 'weatherindices' (Calculate Weather Indices) Weather indices represent the overall weekly effect of a weather variable on crop yield throughout the cropping season. This package contains functions that can convert the weekly weather data into yearly weighted Weather indices with weights being the correlation coefficient between weekly weather data over the years and crop yield over the years. This can be done for an individual weather variable and for two weather variables at a time as the interaction effect. This method was first devised by Jain, RC, Agrawal R, and Jha, MP (1980), "Effect of climatic variables on rice yield and its forecast",MAUSAM, 31(4), 591–596, . Later, the method have been used by various researchers and the latest can found in Gupta, AK, Sarkar, KA, Dhakre, DS, & Bhattacharya, D (2022), "Weather Based Potato Yield Modelling using Statistical and Machine Learning Technique",Environment and Ecology, 40(3B), 1444–1449,. Package: r-cran-weatherjoin Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite Suggests: r-cran-nasapower, r-cran-digest, r-cran-fst, r-cran-anytime, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/resolute/main/r-cran-weatherjoin_0.2.3-1.ca2604.1_all.deb Size: 134224 MD5sum: 5f90ff485a1842bdd8e6a93336f2ecea SHA1: e9be93a8acf7a522af604964625a22b0a104cf34 SHA256: 9af48fdaa8a0fb8360dfc075b47a42b73b6314cc6501b77828499dbba233a209 SHA512: 2639227aa6099f2a9b50a4c14dd2f3504da8b1e86beb6061d0f23ddf36f497ee2bed2d97300b71d8c5f94dd56325333a3ee013705003d660315e7fa860aa268e Homepage: https://cran.r-project.org/package=weatherjoin Description: CRAN Package 'weatherjoin' (Join Gridded Weather Data to Event Tables) High-level tools to attach gridded weather data from the NASA POWER Project to event-based datasets. The package plans efficient spatio-temporal API calls via the 'nasapower' R package, caches downloaded segments locally, and joins weather variables back to the input table using exact or rolling joins. This package is not affiliated with or endorsed by NASA. Package: r-cran-weathermetrics Architecture: all Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-weathermetrics_1.2.2-1.ca2604.1_all.deb Size: 100974 MD5sum: 06cc49b93510e0366e5292a0bd946325 SHA1: ec49491d9f720634989016a026424d51a04c4283 SHA256: bafee605d88188e0c223080ac1361d935796b8b7ea6e37f5b9feed026436ebfa SHA512: 306e2ae8366b60458786ffd53def5b05d1cf8cf506eb9b668ef77f0571a41730b5f12dff1569e75c64e0410d14dabaf35783f959122653549f59ccab09b3c172 Homepage: https://cran.r-project.org/package=weathermetrics Description: CRAN Package 'weathermetrics' (Functions to Convert Between Weather Metrics) Functions to convert between weather metrics, including conversions for metrics of temperature, air moisture, wind speed, and precipitation. This package also includes functions to calculate the heat index from air temperature and air moisture. Package: r-cran-weatheroz Architecture: all Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1752 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-apsimx, r-cran-clock, r-cran-crayon, r-cran-curl, r-cran-crul, r-cran-data.table, r-cran-foreign, r-cran-jsonlite, r-cran-knitr, r-cran-lubridate, r-cran-magick, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-xml2 Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridextra, r-cran-mailr, r-cran-mapproj, r-cran-maps, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-vcr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/resolute/main/r-cran-weatheroz_3.0.0-1.ca2604.1_all.deb Size: 818690 MD5sum: f7f87b5b67f89a323fdb701885225589 SHA1: 8db7ae2ec235ef634cfdceb32d5447c56d967db0 SHA256: a16fb8a43a64294792a2c68a911ab1199cad7562052d1db48d3eaf08b1456246 SHA512: ea230526cde3a0a5cc8bb1f86e6262876716f6740128783322781e0315a733e77b9dc88b1861345ac2408150bae67b8295fc4b8c24f32b8c1a7ac62d5e45d183 Homepage: https://cran.r-project.org/package=weatherOz Description: CRAN Package 'weatherOz' (An API Client for Australian Weather and Climate Data Resources) Provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development (DPIRD) of Western Australia and by the Science and Technology Division of the Queensland Government's Department of Environment and Science (DES). As well as the Bureau of Meteorology (BOM) of the Australian government precis and coastal forecasts, and downloading and importing radar and satellite imagery files. DPIRD weather data are accessed through public APIs provided by DPIRD, , providing access to weather station data from the DPIRD weather station network. Australia-wide weather data are based on data from the Australian Bureau of Meteorology (BOM) data and accessed through SILO (Scientific Information for Land Owners) Jeffrey et al. (2001) . DPIRD data are made available under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU) license . SILO data are released under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) . BOM data are (c) Australian Government Bureau of Meteorology and released under a Creative Commons (CC) Attribution 3.0 licence or Public Access Licence (PAL) as appropriate, see for further details. Package: r-cran-weathersentiment Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-wordcloud, r-cran-sentimentr, r-cran-tidytext, r-cran-ggplot2, r-cran-stringr, r-cran-data.table, r-cran-rcolorbrewer, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-syuzhet Filename: pool/dists/resolute/main/r-cran-weathersentiment_1.0-1.ca2604.1_all.deb Size: 34678 MD5sum: e4aaa0da610976be97ea8f6317fb2fdb SHA1: 6a474c3987bb89c665684896e04ea1c95a790332 SHA256: 48705385fd11b6f97abd061c5ace7cff245608f1ffcafa2878e3b12d1b305491 SHA512: 68e8877e109ee06d858ff125236dad3b8a1cbaf1e2bd76d0f9fb171001b8ef358ebe51c73ce80a0b0c74faf8fdbfc8fcb41b1cc4ea8206f408b28da349b9f290 Homepage: https://cran.r-project.org/package=WeatherSentiment Description: CRAN Package 'WeatherSentiment' (Comprehensive Analysis of Tweet Sentiments and Weather Data) A comprehensive suite of functions for processing, analyzing, and visualizing textual data from tweets is offered. Users can clean tweets, analyze their sentiments, visualize data, and examine the correlation between sentiments and environmental data such as weather conditions. Main features include text processing, sentiment analysis, data visualization, correlation analysis, and synthetic data generation. Text processing involves cleaning and preparing tweets by removing textual noise and irrelevant words. Sentiment analysis extracts and accurately analyzes sentiments from tweet texts using advanced algorithms. Data visualization creates various charts like word clouds and sentiment polarity graphs for visual representation of data. Correlation analysis examines and calculates the correlation between tweet sentiments and environmental variables such as weather conditions. Additionally, random tweets can be generated for testing and evaluating the performance of analyses, empowering users to effectively analyze and interpret 'Twitter' data for research and commercial purposes. Package: r-cran-weatherstats Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-weatherstats_0.1-1.ca2604.1_all.deb Size: 148820 MD5sum: fba9826a195acf2f16595f6d8999164d SHA1: 5de8d1afe0489d9f06455fa7083eaa8ed2b02e44 SHA256: 0afd64615d38076ce72cc292f0d42c130c0d5684f185a0dc3ced2344880bd8e1 SHA512: 4cac2f068b2470c50e6d57f8597a84b8b465d566f6bf7123a56baf751178d8d316dbcef286b0381d8e48d013e2c97b658d0e5ca1429c49eb0ed838fb072a3a74 Homepage: https://cran.r-project.org/package=weatherStats Description: CRAN Package 'weatherStats' (Airport Weather Station Statistics) Download daily weather data recorded at airport weather stations using the National Centers for Environmental Information (NCEI) API . Package: r-cran-weathr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-lutz, r-cran-magrittr, r-cran-purrr, r-cran-sf, r-cran-tibble Suggests: r-cran-gt, r-cran-snakecase, r-cran-tmap Filename: pool/dists/resolute/main/r-cran-weathr_0.1.0-1.ca2604.1_all.deb Size: 67084 MD5sum: 4818d2166d0c1118557ff59cb0cea06b SHA1: 3a6770165ba47a868e384efe01412ca532523a7f SHA256: a8f19de2be82a4c32671bd45ffd4d2cc0496fcbd5e61f14a38ceb26514a99e97 SHA512: b09e31acc0537590ef4b794c582c5af7b31b38d20c553c135eb38f107aa76bb50b90cf104a6f25f1f05df4876d517ba753016d85cfafb4a053cccad7f7dba3f6 Homepage: https://cran.r-project.org/package=weathR Description: CRAN Package 'weathR' (Interact with the U.S. National Weather Service API) Enables interaction with the National Weather Service application programming web-interface for fetching of real-time and forecast meteorological data. Users can provide latitude and longitude, Automated Surface Observing System identifier, or Automated Weather Observing System identifier to fetch recent weather observations and recent forecasts for the given location or station. Additionally, auxiliary functions exist to identify stations nearest to a point, convert wind direction from character to degrees, and fetch active warnings. Results are returned as simple feature objects whenever possible. Package: r-cran-webanalytics Architecture: all Version: 0.9.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3784 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xtable, r-cran-scales, r-cran-brew, r-cran-fs, r-cran-reshape2, r-cran-digest, r-cran-uaparserjs Suggests: r-cran-whoami, r-cran-testthat, r-cran-tinytex, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-webanalytics_0.9.15-1.ca2604.1_all.deb Size: 1751006 MD5sum: fd75514ed988e44efbc67ec5f344f9b1 SHA1: c58cdc0390f9a274a72b133027b2c02adefa1f15 SHA256: 599c5763bdb36a13b426fff3d271b208ff4a17ad536e7f85385897633d5599a6 SHA512: 93c9f558ee3f10a5f69575e2bf8d6ffa20a005e2dd726f40c1bf6d3a468217d91d98aa78fd678859923d347f7702b724f1d1c3be40c971524e02b3fc25441d51 Homepage: https://cran.r-project.org/package=WebAnalytics Description: CRAN Package 'WebAnalytics' (Web Server Log Analysis) Provides Apache and IIS log analytics for transaction performance, client populations and workload definitions. Package: r-cran-webchem Architecture: all Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-rvest, r-cran-jsonlite, r-cran-stringr, r-cran-dplyr, r-cran-purrr, r-cran-data.tree, r-cran-tibble, r-cran-base64enc, r-cran-rlang Suggests: r-cran-testthat, r-cran-rcdk, r-cran-covr, r-cran-robotstxt, r-cran-knitr, r-cran-rmarkdown, r-cran-plot.matrix, r-cran-usethis, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-webchem_1.3.1-1.ca2604.1_all.deb Size: 360964 MD5sum: de4ee415fc8fc95335606b8b7a1d2a74 SHA1: f291b2620c9f9b5b7263f0c0a573eb478693307b SHA256: c63236012411dfa45a1cf518efa7386ae6cc8b7a6ac7eadcfd89372720d18dcc SHA512: 37454eda91b9eb9a4b12481bf4ca141d929506306ed1ed0c95ef94222ce59239d3a7d536c542219c29f68eb898fdbee563286bb124e1ea4e065c9136e9eb256a Homepage: https://cran.r-project.org/package=webchem Description: CRAN Package 'webchem' (Chemical Information from the Web) Chemical information from around the web. This package interacts with a suite of web services for chemical information. Sources include: Alan Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver, ChEBI, Chemical Translation Service, ChemSpider, ETOX, Flavornet, NIST Chemistry WebBook, OPSIN, PubChem, SRS, Wikidata. 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Package: r-cran-webdeveloper Architecture: all Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httpuv, r-cran-html5, r-cran-future, r-cran-promises, r-cran-readr, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-webdeveloper_1.0.5-1.ca2604.1_all.deb Size: 51964 MD5sum: 3ec3f7edbfeed5aeb96a8f9617a77905 SHA1: 627b7cf6c4799b3658c8b24efffa62229974330e SHA256: a5e33233870eb8277f37c89a2f3f8311d989d139bdbb35b46df95305302d8556 SHA512: 1f7a869956c886c35449be60ea07bb92b3f74f22037ffc15b3f75a1ad290bc1faac111ca891d7d66e046ca592ba092add61ef040cccd50cde8787f77de443ea0 Homepage: https://cran.r-project.org/package=webdeveloper Description: CRAN Package 'webdeveloper' (Functions for Web Development) Organizational framework for web development in R including functions to serve static and dynamic content via HTTP methods, includes the html5 package to create HTML pages, and offers other utility functions for common tasks related to web development. 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Package: r-cran-webexercises Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-knitr, r-cran-yaml, r-cran-rstudioapi, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-bookdown, r-cran-quarto, r-cran-xfun Filename: pool/dists/resolute/main/r-cran-webexercises_1.1.0-1.ca2604.1_all.deb Size: 507386 MD5sum: c0b42379c69f4407cf50323a6385fb42 SHA1: cb5e4d8bbb1fb1a221c9a6e9818c84b64defec57 SHA256: fbe0c2ca1fe514c8b20c8a7025899284cf64073a9c785ad115bf858a94cdf410 SHA512: 066f97a5b4f4f05fbd05e3ee35e35e8be9dea487b3b34af7dfc8bf684795726c29b3b644d66b5d73853ad03090fe0b747026a845e21dec2f5056d26a9a297f95 Homepage: https://cran.r-project.org/package=webexercises Description: CRAN Package 'webexercises' (Create Interactive Web Exercises in 'R Markdown' (Formerly'webex')) Functions for easily creating interactive web pages using 'R Markdown' that students can use in self-guided learning. Package: r-cran-webglobe Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9883 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonio, r-cran-jsonlite, r-cran-httpuv Suggests: r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-webglobe_1.0.3-1.ca2604.1_all.deb Size: 2843108 MD5sum: 43da7bddd3f4640c8dff6566ac5ee7cc SHA1: 10563d0095dc8f46864d4e19439a1368e50b0967 SHA256: 948be5653b97092ed990bba6ed067866c1963d4a0b6594aab467de8009c9ff22 SHA512: f141e7cd3713eba8062f972a6e419043bcc03e864fdce6421a5ce720f473365ec622ab143ab36a2a1c42e05bca61f4f257405e2487e3d26f2e647b053c0ffb80 Homepage: https://cran.r-project.org/package=webglobe Description: CRAN Package 'webglobe' (3D Interactive Globes) Displays geospatial data on an interactive 3D globe in the web browser. 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Package: r-cran-wec Architecture: all Version: 0.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-wec_0.4-1-1.ca2604.1_all.deb Size: 118434 MD5sum: 7d637255d6ba4f8d3839faab8babdd18 SHA1: 084e30c8c70550e2968e7ec1ff1cb24009e1f8dc SHA256: 882069d29c5c437ff306a14fd72878aa5d6fb29d30ebb4aa8a99ac34adb30459 SHA512: f47bcd3ba1989ae615fe5d6504a215aad0a093b95241e605588fd02a3afca10069ff2fb2058b9135f38ff11bd97e067a4e5a340600dec068c28781b6c38978ab Homepage: https://cran.r-project.org/package=wec Description: CRAN Package 'wec' (Weighted Effect Coding) Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. 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These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions. Package: r-cran-wefnexus Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wefnexus_1.0.0-1.ca2604.1_all.deb Size: 339118 MD5sum: 4019136d35b00f770f96b674555d6e14 SHA1: b147f2b86d5b1dc9b301c4e311d3e6884592f2e2 SHA256: 4c87eb5a034ed5d0c07292b6d294bcbb6ac8b9ca0f0427c13878fbcfa397d38a SHA512: 4aa30a0f644fa861b51db029e662ad71d3e3cef3cdde9bf728b76fc711a33557c770f2b4eb71f157e11825e3353a26ec1b2f8dfe1b0bfd44aaaad0b1cc533553 Homepage: https://cran.r-project.org/package=wefnexus Description: CRAN Package 'wefnexus' (Water-Energy-Food-Nutrient-Carbon Nexus Analysis for AgronomicSystems) Provides functions for analysing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural production systems. Includes functions for computing water use efficiency (WUE), water productivity (WP), and water footprint (WF) including green, blue, and grey components following the methodology of Hoekstra et al. (2011, ISBN:9781849712798). Includes energy budgeting tools for energy use efficiency (EUE), energy return on investment (EROI), net energy (NE), and energy productivity (EP). Computes nutrient use efficiency (NUE) metrics including agronomic efficiency (AE), physiological efficiency (PE), recovery efficiency (RE), and partial factor productivity (PFP) as defined by Dobermann (2007) and Congreves et al. (2021) . Estimates carbon footprint (CF), greenhouse gas (GHG) emissions, soil organic carbon (SOC) stocks, and global warming potential (GWP) using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) default values (CH4 = 27, N2O = 273) as reported in Forster et al. (2021) . Computes composite Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus indices, trade-off correlation matrices, and generates radar and heatmap visualizations for comparing agricultural treatments. Supports conservation agriculture (CA), irrigated and rain-fed systems, and arid and semi-arid production environments. Methods follow Lal (2004) for carbon emissions from farm operations, and Hoover et al. (2023) for water use efficiency indicators. Package: r-cran-wege Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-raster Filename: pool/dists/resolute/main/r-cran-wege_0.1.0-1.ca2604.1_all.deb Size: 52192 MD5sum: e527e685b0d799f70a53ceaafadb6f50 SHA1: 204334c8bc75f020adb713cae90c5bff74da0193 SHA256: af9d1eee3e04d489df3b9e4f2474510f6164c52a06d12657ce940d6a530ae863 SHA512: 50e2cfca7590ee8a92b67614e9b2478a1eda329a03db241ec84166a6cf603957f656da7a2ad497226f964794f6deb5dff116b353e43a2155604df2eff8884d9e Homepage: https://cran.r-project.org/package=WEGE Description: CRAN Package 'WEGE' (A Metric to Rank Locations for Biodiversity Conservation) Calculates the WEGE (Weighted Endemism including Global Endangerment index) index for a particular area. Additionally it also calculates rasters of KBA's (Key Biodiversity Area) criteria (A1a, A1b, A1e, and B1), Weighted endemism (WE), the EDGE (Evolutionarily Distinct and Globally Endangered) score, Evolutionary Distinctiveness (ED) and Extinction risk (ER). Farooq, H., Azevedo, J., Belluardo F., Nanvonamuquitxo, C., Bennett, D., Moat, J., Soares, A., Faurby, S. & Antonelli, A. (2020) . 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A scraping and aggregating interface for the WNBA Stats API and ESPN's women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves. Package: r-cran-weibullfit Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-xtable, r-cran-sqldf, r-cran-r.oo, r-cran-fadist, r-cran-mixdist, r-cran-optimx, r-cran-ksamples, r-cran-e1071, r-cran-r.methodss3 Filename: pool/dists/resolute/main/r-cran-weibullfit_0.1.0-1.ca2604.1_all.deb Size: 201394 MD5sum: 5a8bef61ded0dfa4ef16eb5ec95f8d41 SHA1: f8962dace88365a68294588c320b892e6b85aa82 SHA256: 79fb74242dff4b1e051bdd7900544d7808d3748b521552f01df7d9eb714ae2b0 SHA512: fefd974d500f08722a4a22bd99f21a8d36afb7bd6fdefb9bce06cb3f12a00b3fe93edfa3c367ba5cfbc1253c485012e2f3d4d23637ac2daf0939fe2b74a3e852 Homepage: https://cran.r-project.org/package=WeibullFit Description: CRAN Package 'WeibullFit' (Fits and Plots a Dataset to the Weibull Probability DistributionFunction) Provides a single function to fit data of an input data frame into one of the selected Weibull functions (w2, w3 and it's truncated versions), calculating the scale, location and shape parameters accordingly. The resulting plots and files are saved into the 'folder' parameter provided by the user. References: a) John C. Nash, Ravi Varadhan (2011). "Unifying Optimization Algorithms to Aid Software System Users: optimx for R" . Package: r-cran-weibullmodiamr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-weibullmodiamr_0.1.0-1.ca2604.1_all.deb Size: 35404 MD5sum: 4a2049da95cacc3bf6e24ac043ae15e4 SHA1: 3d3f2fa8eec05a12334039dfa254a593cad027d8 SHA256: 62796305a6c6717bdd8f1cfe71dc1b4d553e13c0d88c8866fbf58390871300f5 SHA512: 359297a9e9efc1f625e092676752b685e8d4f1d51faa40793d723cfb3159daf24427ef8da26ec8556bfdce79a19824db89bd4a8b0b13b2c867c5ea74694f04c6 Homepage: https://cran.r-project.org/package=WeibullModiAMR Description: CRAN Package 'WeibullModiAMR' (Fit Modified Weibull-Type Distributions) Provides maximum likelihood estimation methods for eight modified Weibull-type distributions. It returns parameter estimates, log-likelihood, AIC, and BIC, and also supports model fitting, validation, and comparison across different distributional forms. These methods can be applied to reliability, survival, and lifetime data analysis, making the package useful for researchers and practitioners in statistics, engineering, and medicine. The following distributions are included: Rangoli2023, Peng2014, Lai2003, Xie1996, Sarhan2009, Rangoli2025, Mustafa2012, and Alwasel2009. Package: r-cran-weibullness Architecture: all Version: 1.24.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4400 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bsgof Filename: pool/dists/resolute/main/r-cran-weibullness_1.24.1-1.ca2604.1_all.deb Size: 3553586 MD5sum: 6e598dcd201712ed780c8155c5ae581b SHA1: 1e3a57ed8af8df034d02bab8e9a79cb5d4166dba SHA256: 15133ea9a5a7a0fb15a4f4d00a32c0d5dbf696ea764ec5cfc100d97009310631 SHA512: 30709621fcc60d1b729b00b2341726b23776dd5d6f22368a22b159aeafa6d0795d83459c65a83964986c6ca2a4564e7de23adea223f7a552ef6b320a113f9f9a Homepage: https://cran.r-project.org/package=weibullness Description: CRAN Package 'weibullness' (Goodness-of-Fit Test for Weibull Distribution (Weibullness)) Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations. For more details, see Park (2017) , Park (2018) , and Park (2023) . This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528). 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Package: r-cran-weightedporttest Architecture: all Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-weightedporttest_1.1-1.ca2604.1_all.deb Size: 28032 MD5sum: cabd5c56106b33af5a71ef74506e92e6 SHA1: 6b778f80113924424afb606a13426042152e24a1 SHA256: 93263af2915c1ce30b0e830aee88222ad98e92a268107743a7b8d5ee7e18d761 SHA512: ec3f9348546afc16b2eb291602f2ebf6d8a4f4204a9a1ef94076197b61d76593cd5cc8b80e8add9b1b96ccd0bdc4273382cd706876ce085356e9a9f892db4cf9 Homepage: https://cran.r-project.org/package=WeightedPortTest Description: CRAN Package 'WeightedPortTest' (Weighted Portmanteau Tests for Time Series Goodness-of-Fit) An implementation of the Weighted Portmanteau Tests described in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing" published by the Journal of the American Statistical Association, Volume 107, Issue 498, pages 777-787, 2012. 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Package: r-cran-wget Architecture: all Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yulab.utils Filename: pool/dists/resolute/main/r-cran-wget_0.0.4-1.ca2604.1_all.deb Size: 13820 MD5sum: 17492bd70449ee56ee569fe8811a9dcc SHA1: 5ae704b3917439c20dd752183bca46831d94d2ae SHA256: 58edf2341afc7398e1bf9e207bc6ad5129a11af4af01a2d32aeca8f0f4b9605f SHA512: 36afa203681d544756b8a3e98de0db10554a29a867fcfbe01540e8d68b3cf29bbaecee676e42cc99126d0f9295539294ea6be783b30563268be1586b739cd85b Homepage: https://cran.r-project.org/package=wget Description: CRAN Package 'wget' (Setting Download Method to 'wget') Provides function, wget_set(), to change the method (default to 'wget -c') using in download.file(). Using 'wget -c' allowing continued downloading, which is especially useful for slow internet connection and for downloading large files. User can run wget_unset() to restore previous setting. Package: r-cran-wgteff Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wgteff_0.1.2-1.ca2604.1_all.deb Size: 30158 MD5sum: ea23e978453216bcca2ce684c7c59cab SHA1: e47821d5566755dcec5fa94c25cf7413b76e131f SHA256: 863d9f2762be104484c18016bccef1c6e7d276e0766b3a9596c27146dfee1749 SHA512: 35e344b60ba85fcb81c4d2e6abf84b01d71a51acbf5668e29f237b4c2d39e41ec9cb0fc6a1d02cdb0ff2a7b780505b70fa1aab3e56d4dcbff6769642a0726b95 Homepage: https://cran.r-project.org/package=WgtEff Description: CRAN Package 'WgtEff' (Functions for Weighting Effects) Functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error. Package: r-cran-whalestrike Architecture: all Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1096 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-desolve, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-whalestrike_0.6.2-1.ca2604.1_all.deb Size: 712210 MD5sum: ce27b29812593cdc62bbdfe26aaf5546 SHA1: 9b79b9d9bace42c91e4b583f8bd76dd3ac7e4ca3 SHA256: 87e1384a1e3943a655d709c0553138844fe96e15164102976a04324b721df15b SHA512: d12d724a44a51c17b7435f4f3eec421a869fb7499f5080e4e8860d5d408d94ca2800e6d5d6647a1639858794a14809e41ee9ed464127c54f8479c7101d697dfd Homepage: https://cran.r-project.org/package=whalestrike Description: CRAN Package 'whalestrike' (Simulate Whale Ship Strikes) Provides tools for simulating the biophysical effects of vessel-strikes on whales. The aim is to support the evaluation of marine policies limiting ship speeds through regions in which whales reside. This is important because ship strikes are a major source of lethality for several whale species, including the critically endangered North Atlantic right whale. In this analysis, whales are modelled with a four-layer system comprising skin, blubber, sub-layer (muscle or organ) and bone. Reasonable values for the material properties of these layers, along with other factors such as whale surface area and mass, are provided for a variety of whale species. Similarly, key values are provided for several ship types. The collision is modelled according to Newtonian dynamics, with stresses and strains within the whale layers being simulated over time. The simulation results are analyzed in the context of whale-strike data, to develop a Lethality Index for the whale in the modelled collision. For the underlying science, see Kelley and other "Assessing the Lethality of Ship Strikes on Whales Using Simple Biophysical Models." (2021) . For more on the R code, see Kelley "`whalestrike`: An R package for simulating ship strikes on whales" (2024) . Package: r-cran-whapi Architecture: all Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-cli, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-stringi, r-cran-dplyr, r-cran-readr, r-cran-lubridate, r-cran-jsonlite, r-cran-mime, r-cran-openssl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-whapi_0.0.2-1.ca2604.1_all.deb Size: 244854 MD5sum: c9477f2dac70c63712bbcf75c31e2c05 SHA1: b1598c771440c56ed43c38a74c63554ed99254a9 SHA256: 1fea990d7ee33fca60b6ff84ca0574a0390f6817dfd9db53b989d59555dffa19 SHA512: 26a55874c517e88a67997e6a7a4a828183db8aaaef1cb850dc3472c78c13f0591c4ced81f2eccdb0d2f68311711cb9653f941aeb327ed28eb2ba2d45cc5a4f11 Homepage: https://cran.r-project.org/package=whapi Description: CRAN Package 'whapi' (R Client for 'whapi.cloud') Provides an 'R' interface to the 'Whapi' 'API' , enabling sending and receiving 'WhatsApp' messages directly from 'R'. Functions include sending text, images, documents, stickers, geographic locations, and interactive messages (buttons and lists). Also includes 'webhook' parsing utilities and channel health checks. Package: r-cran-whatif Architecture: all Version: 1.5-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-pbmcapply Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-whatif_1.5-11-1.ca2604.1_all.deb Size: 82212 MD5sum: e9a0d0ebed24c72173a38926d6baeb33 SHA1: 79d270b3efaf190f5e633bc987f62da7e2a7209e SHA256: 8b0d162df785fd3d7f819a683a53829f709827d582460c419948a501aec44449 SHA512: 51a2a279455b688c84e3bebe98c8870e37b907404a57788755170a6f619b7ee1377479b7a780071b993618c2ba2944e80786084fb29ace69ad8abd5910bc8dcb Homepage: https://cran.r-project.org/package=WhatIf Description: CRAN Package 'WhatIf' (Software for Evaluating Counterfactuals) Inferences about counterfactuals are essential for prediction, answering what if questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, which makes this problem hard to detect. 'WhatIf' offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests offered here, then we know that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use. 'WhatIf' implements the methods for evaluating counterfactuals discussed in Gary King and Langche Zeng, 2006, "The Dangers of Extreme Counterfactuals," Political Analysis 14 (2) ; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) . Package: r-cran-whatifbandit Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bandit, r-cran-data.table, r-cran-dplyr, r-cran-furrr, r-cran-ggplot2, r-cran-lubridate, r-cran-purrr, r-cran-randomizr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-whatifbandit_0.3.0-1.ca2604.1_all.deb Size: 473630 MD5sum: 5119736340e79c594facc44cb1592fb3 SHA1: 8df9b5712601685f58414e9f390fedef06e52378 SHA256: 02114d19b8e4219f830e7b0f3845c195f5d14b4483b0e1d573080d1683c445b4 SHA512: fed0182d3aba4fb3f4dbf901dbe68786465d75ec67ca0b3db11e05b340de3320bdce8b63c992f0f994ad30c1672ad807953c00b88f4f890eae4e21b3f3a6a34f Homepage: https://cran.r-project.org/package=whatifbandit Description: CRAN Package 'whatifbandit' (Analyzing Randomized Experiments as Multi-Arm Bandits) Simulates the results of completed randomized controlled trials, as if they had been conducted as adaptive Multi-Arm Bandit (MAB) trials instead. Augmented inverse probability weighted estimation (AIPW), outlined by Hadad et al. (2021) , is used to robustly estimate the probability of success for each treatment arm under the adaptive design. Provides customization options to simulate perfect/imperfect information, stationary/non-stationary bandits, blocked treatment assignments, along with control augmentation, and other hybrid strategies for assigning treatment arms. The methods used in simulation were inspired by Offer-Westort et al. (2021) . 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The parser works on chats exported from Android or iOS phones and on Linux, macOS and Windows. The parser has multiple options for extracting smileys and emojis from the messages, extracting URLs and domains from the messages, extracting names and types of sent media files from the messages, extracting timestamps from messages, extracting and anonymizing author names from messages. Can be used to create anonymized versions of data. Package: r-cran-whatthreewords Architecture: all Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-whatthreewords_0.1.3-1.ca2604.1_all.deb Size: 27090 MD5sum: ff5630418889356cc86b4d869e5bf97b SHA1: 41ad2d373dc41de8c75b3d2e788ffcb5653c280d SHA256: 82c94da17cba97470e0b57c6f03b6cf65e1f7c02d36f312a5992ef456fda508f SHA512: b471003324fe374f8df45da3284212ec5f161e6a1e7bb03d30714925791de5b41c03e3a7b11635683936f2232f6ac383a7e7373c48a22a3fbf0ae4de56315d01 Homepage: https://cran.r-project.org/package=whatthreewords Description: CRAN Package 'whatthreewords' (Work with the 'what3words' API for Easy Location Referencing) Use the 'what3words' API to return three words which uniquely identify every 3m x 3m square on Earth. It is also possible to return coordinates from any valid three words location. Supports multiple languages. Package: r-cran-wheatmap Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2682 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wheatmap_0.2.0-1.ca2604.1_all.deb Size: 986710 MD5sum: e2a5efa346470f5081ef0967cba373d8 SHA1: 353981f235a349b9dc69258d3c2d741e30110dc4 SHA256: 6166692ed7d9c03fec5b4e93ab3c186122a18e7ae84c1ef13c4aed9244189417 SHA512: cd7ad187365d679988566bcb0953547043b068435750929b8b483cb7d690a7e84527dec1062411db197f489d5bb094a80b98b5ce7a7cfefc7a55360a73b9d02d Homepage: https://cran.r-project.org/package=wheatmap Description: CRAN Package 'wheatmap' (Incrementally Build Complex Plots using Natural Semantics) Builds complex plots, heatmaps in particular, using natural semantics. 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The purpose of this package is to provide support for implementing this dimension in the form of date and time tables for Relational On-Line Analytical Processing star database systems. 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For more details on multi-regional input–output model "Food and Agriculture Biomass Input–Output" (FABIO) see Bruckner et al. (2019) . Package: r-cran-where Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-where_1.0.0-1.ca2604.1_all.deb Size: 27452 MD5sum: 400b67a56dff04a32bb810a5715811ff SHA1: 2d31bd73dada8fb4692590398921fa091de4be1e SHA256: 4f51d5f234ff572937898547d02f2ff2e6b2d687192dca458b6e7663f7ea74a3 SHA512: 1aa7d9c0733513b1aa7f818cb35fb500676a418ba53f1c6f0bf02102554fdf5ed5cdf4298dba57b21f73711ea9c7dee3ab086ef69c2024c108032afbcdd23a0a Homepage: https://cran.r-project.org/package=where Description: CRAN Package 'where' (Vectorised Substitution and Evaluation) Provides a clean syntax for vectorising the use of Non-Standard Evaluation (NSE), for example in 'ggplot2', 'dplyr', or 'data.table'. 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This can be from the 'RScript'/R terminal commands or 'RStudio' console, source editor, 'Rmarkdown' document and a Shiny application. Package: r-cran-whereport Architecture: all Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-whereport_0.1-1.ca2604.1_all.deb Size: 235996 MD5sum: 119fd02132d26d4d8852d66a64781738 SHA1: 37a82c13a8abd8cb14bdd6de1bd243ac65a1a791 SHA256: 68146d6ad093c68fe8162c3c067f29ac7b16e0b0baa3b33bb3f59761fac6d231 SHA512: 52e655c1918eab1656ca455de62e3351742b2dc9a0453c632bd57a56fea2abe2ac872fa50b22b1c38e9eb7c7388b3677e5137b2bdbaa249e23a69c37e163709c Homepage: https://cran.r-project.org/package=whereport Description: CRAN Package 'whereport' (Geolocalization of IATA Codes) Retrieve geographical information for airports using their IATA or ICAO codes. 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Package: r-cran-whirl Architecture: all Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3019 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-dplyr, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-purrr, r-cran-quarto, r-cran-r6, r-cran-renv, r-cran-reticulate, r-cran-rlang, r-cran-sessioninfo, r-cran-stringr, r-cran-tibble, r-cran-unglue, r-cran-withr, r-cran-yaml, r-cran-zephyr Suggests: r-cran-ggplot2, r-cran-rstudioapi, r-cran-testthat, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-whirl_0.3.2-1.ca2604.1_all.deb Size: 1020580 MD5sum: 6711cb4136a61276c9d87ee6b5241785 SHA1: 886def71dacaff9e4a441b7bcb4ab87b35070360 SHA256: 76a7815f24c8075e6cf03eea9de9b4f63b1eafa82eda6636f294322ed82f3950 SHA512: d05a56ab39bc74d70af11ff191fecbfe875ac8b3a911cd4c9c92a6719ef21f6bdc3a4fc926f45d93b5ed7863823fc3a7a1ebe1632117139e64ceb0e0b0b3882f Homepage: https://cran.r-project.org/package=whirl Description: CRAN Package 'whirl' (Log Execution of Scripts) Logging of scripts suitable for clinical trials using 'Quarto' to create nice human readable logs. 'whirl' enables execution of scripts in batch, while simultaneously creating logs for the execution of each script, and providing an overview summary log of the entire batch execution. Package: r-cran-whisker Architecture: all Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-markdown Filename: pool/dists/resolute/main/r-cran-whisker_0.4.1-1.ca2604.1_all.deb Size: 62746 MD5sum: 94244bef89dba61d5bd5c767ab1aa663 SHA1: 4016a7c1f1eaeb8449cc9e68949cb643e0a6403f SHA256: a30f0fcf1dcfb65f8d9ab36d7450bfc8412491ae47cebf94e677425c291fcad9 SHA512: dcfb4fb817b8c998d530fd738625a88448c587c08b5593779093ea35735ccb5d04cd3d381504bc1ea649b8f17cca7a7cf054bf02c8e18c2fc40677a3e366d486 Homepage: https://cran.r-project.org/package=whisker Description: CRAN Package 'whisker' ({{mustache}} for R, Logicless Templating) Implements 'Mustache' logicless templating. Package: r-cran-whisper Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-av, r-cran-jsonlite, r-cran-hfhub, r-cran-safetensors Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-whisper_0.3.0-1.ca2604.1_all.deb Size: 581980 MD5sum: 6b5173b2e14ed1540e49f70813b1a1ed SHA1: e28475be519595c30204c62431d73f93719015f3 SHA256: 11db40a3243d1c3e59f9a7320d99c672550b0f662777bfd6315028065a40e144 SHA512: f76581ea4ae6d9aa31c9414e402b59979cb265f2238f8917d5019a47840dba08f3f96434c7270bd26016ea74e932a63a550fe5cc7b98b73c682f7f6c9abafce7 Homepage: https://cran.r-project.org/package=whisper Description: CRAN Package 'whisper' (Native R 'torch' Implementation of 'OpenAI' 'Whisper') Speech-to-text transcription using a native R 'torch' implementation of 'OpenAI' 'Whisper' model . Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from 'HuggingFace' via the 'hfhub' package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the 'torch' package. Package: r-cran-whitebox Architecture: all Version: 2.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3627 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-terra, r-cran-sf, r-cran-raster Filename: pool/dists/resolute/main/r-cran-whitebox_2.4.3-1.ca2604.1_all.deb Size: 2704550 MD5sum: 2a39dd0749634fa68ddced56c758fe92 SHA1: 9ac1c49ddafe403a7f3d5c2abc84c2e79a98ff7d SHA256: 65d158e50059c78f3a3588e2792af1927d401df5e17b22d62802075a451299ca SHA512: 547bf6905e7a701701dc2043daf69dfea91d37719906ec612ac87c1d794d96858615551224cee02c273855a84efdc37b0d8acdbd1dd933a4337fa0a920158847 Homepage: https://cran.r-project.org/package=whitebox Description: CRAN Package 'whitebox' ('WhiteboxTools' R Frontend) An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) . Package: r-cran-whitechapelr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-igraph Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-whitechapelr_0.3.0-1.ca2604.1_all.deb Size: 37682 MD5sum: 63b5a62e037419a8064b6c2678d80f15 SHA1: 50f677b5c7816158e40e5a832ed04676d4249358 SHA256: 7421d3eb209a94cccd84bf87ad72d405c84959f8409b69c0f17d99663274d942 SHA512: 3a09671ef810c76eaf2a40c783975c239ebdaa95b15ed264ce50bb660b41eeb8dc7dc6dc5be932fdd84bd51fde5c3b917a3e858b2fa0095b30f79ff508e475e8 Homepage: https://cran.r-project.org/package=whitechapelR Description: CRAN Package 'whitechapelR' (Advanced Policing Techniques for the Board Game "Letters fromWhitechapel") Provides a set of functions to make tracking the hidden movements of the 'Jack' player easier. By tracking every possible path Jack might have traveled from the point of the initial murder including special movement such as through alleyways and via carriages, the police can more accurately narrow the field of their search. Additionally, by tracking all possible hideouts from round to round, rounds 3 and 4 should have a vastly reduced field of search. Package: r-cran-whitening Architecture: all Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor Filename: pool/dists/resolute/main/r-cran-whitening_1.4.0-1.ca2604.1_all.deb Size: 526978 MD5sum: c9000d02db129d098ae435c7978b3942 SHA1: c997638d825626ec3f00b8cf54615913e258df05 SHA256: 338cb326c28e3f6a6713c7a70e5d45fb1938ba6a7b2b17932b45564e9b1c0c2e SHA512: 98e55c69d47e5228a81f2e6c4586ffa8eed2df404067ab9eef87df86484cd5eef04ca09d91d6188ca97c370015b173a4192515ed800c1d907b2a955564e592fc Homepage: https://cran.r-project.org/package=whitening Description: CRAN Package 'whitening' (Whitening and High-Dimensional Canonical Correlation Analysis) Implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) "Optimal whitening and decorrelation", , as well as the whitening approach to canonical correlation analysis allowing negative canonical correlations described in Jendoubi and Strimmer (2019) "A whitening approach to probabilistic canonical correlation analysis for omics data integration", . The package also offers functions to simulate random orthogonal matrices, compute (correlation) loadings and explained variation. It also contains four example data sets (extended UCI wine data, TCGA LUSC data, nutrimouse data, extended pitprops data). Package: r-cran-whitestrap Architecture: all Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-whitestrap_0.0.1-1.ca2604.1_all.deb Size: 202884 MD5sum: dafde99fafc9b8f0651c1dc52f54f8d1 SHA1: e7b45b5ef723500dd6bd7a2057f4a6b5753cb7a6 SHA256: ca2b10c631e38254f480566672b0a775433e5ccb44cfc98151e90aad0d16ca41 SHA512: 6afa0fcf590c438b2a18ba8a4aefd496c6f8418bb6877e90108ef81db76873c19978918eea6a04be4924634841e6e8a8197f79253e0e79ebd6ce7550dee2a699 Homepage: https://cran.r-project.org/package=whitestrap Description: CRAN Package 'whitestrap' (White Test and Bootstrapped White Test for Heteroskedasticity) Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) . Package: r-cran-whitestripe Architecture: all Version: 2.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1733 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oro.nifti, r-cran-mgcv, r-cran-neurobase Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-whitestripe_2.5.0-1.ca2604.1_all.deb Size: 1670784 MD5sum: 285e693676260547bf63b03f8ad0ad14 SHA1: 8a324e69d1740eccb3c9b46be832142bf023b379 SHA256: 507da3cbf1f8b7628d28ef1a59dcd3ce0b50647abf743d81651d1df6ee6102dd SHA512: 508c10d58fbd3b954314eb7804583bf871f3b71bfc0b95ba9d66426c811ed1136565968045e69a594e8fc6c352267bc4fdf6e492be576a49abdb8de033057f21 Homepage: https://cran.r-project.org/package=WhiteStripe Description: CRAN Package 'WhiteStripe' (White Matter Normalization for Magnetic Resonance Images) Shinohara (2014) introduced 'WhiteStripe', an intensity-based normalization of T1 and T2 images, where normal appearing white matter performs well, but requires segmentation. 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Package: r-cran-whoami Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-covr, r-cran-mockery, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-whoami_1.3.0-1.ca2604.1_all.deb Size: 30868 MD5sum: c7e9c793d76afb4a1e5217df30bd8ba2 SHA1: f6fafd3af08c80dd845104673ab0d61b0a613c75 SHA256: 854b7aa8a5dcd3f772ea9c0f037b5710e91ffe859d6ffd4bd443a259ced9464f SHA512: 1a6ae12ad10901e1ab364f1f3a1051f63c319a86a3320770038d59dc561ae2ce23bcf93644ef1c993b3a62266fde8d3eb5d0562b3098a6ae114a00ca88c22d66 Homepage: https://cran.r-project.org/package=whoami Description: CRAN Package 'whoami' (Username, Full Name, Email Address, 'GitHub' Username of theCurrent User) Look up the username and full name of the current user, the current user's email address and 'GitHub' username, using various sources of system and configuration information. Package: r-cran-whomds Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4007 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-colorspace, r-cran-dplyr, r-cran-erm, r-cran-ggraph, r-cran-ggplot2, r-cran-gparotation, r-cran-igraph, r-cran-nfactors, r-cran-plyr, r-cran-polycor, r-cran-purrr, r-cran-rcolorbrewer, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-srvyr, r-cran-stringr, r-cran-tam, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-wrightmap Suggests: r-cran-covr, r-cran-httr, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-whomds_1.1.1-1.ca2604.1_all.deb Size: 1654822 MD5sum: 68671385b11a6475619c9d1a6fa927f2 SHA1: 6f8082562855a4a136f57f9569111ec26a8d0690 SHA256: 36912b5d3522b69f7576a3ec754e153319505c9314a3327faeda6fa36a5dcbd9 SHA512: 897cf738753aef62d869aaa7bef3950ad24cba8c18784752aa611b773f6df000c1db7c991f8752fe030226437429d4ad798fd6f182271fbc2153989a763205ef Homepage: https://cran.r-project.org/package=whomds Description: CRAN Package 'whomds' (Calculate Results from WHO Model Disability Survey Data) The Model Disability Survey (MDS) is a World Health Organization (WHO) general population survey instrument to assess the distribution of disability within a country or region, grounded in the International Classification of Functioning, Disability and Health . This package provides fit-for-purpose functions for calculating and presenting the results from this survey, as used by the WHO. The package primarily provides functions for implementing Rasch Analysis (see Andrich (2011) ) to calculate a metric scale for disability. Package: r-cran-whoriskcalculator Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-renv, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-whoriskcalculator_1.0.0-1.ca2604.1_all.deb Size: 50580 MD5sum: df789cf3049914cd087ee184f1d20a61 SHA1: 42fb9ba434c034a03ae07307bba32c6a1ab2760d SHA256: e066bdff6421d0943336daab50cdf3c5bc90f271fda57c70f649147a29aa4ec0 SHA512: da7aeb2066b01182a0d114ae5a15da00c6688c77d0447213566465227a188f4dc4e2039dc8eda01b60db6f99b71c3b5b34eae01222e898ad27f8956d9024b9db Homepage: https://cran.r-project.org/package=WHORiskCalculator Description: CRAN Package 'WHORiskCalculator' (WHO Cardiovascular Disease Risk Calculator) Implements the 2019 World Health Organization (WHO) cardiovascular disease (CVD) risk prediction models, as described in Kaptoge et al. (2019) . Provides two validated models for estimating 10-year risk of fatal and non-fatal cardiovascular events (myocardial infarction and stroke): a laboratory-based model using age, sex, systolic blood pressure, total cholesterol, smoking status, and diabetes history; and a non-laboratory-based model substituting body mass index (BMI) for cholesterol and diabetes, suitable for resource-limited settings. Risk estimates are recalibrated to 21 Global Burden of Disease regions using region-specific incidence rates and risk factor distributions derived from the Emerging Risk Factors Collaboration. Functions are fully vectorized for efficient batch calculations and support automatic country-to-region mapping via ISO 3166-1 alpha-3 country codes. 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Users select an .xlsx, .csv, or delimited .txt file with population data and are walked through selecting the sample type (Simple Random Sample or Stratified), the number of backups desired, and a "stratify_on" value (if desired). The sample size is determined using a normal approximation to the hypergeometric distribution based on Nicholson (1956) . An .xlsx file is created with the sample and key metadata for reference. It is menu-driven and lets users pick an output directory. See vignettes for a detailed walk-through. 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The World Inequality Database is an extensive source on the historical evolution of the distribution of income and wealth both within and between countries. It relies on the combined effort of an international network of over a hundred researchers covering more than seventy countries from all continents. 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Baseline characteristics may be strongly associated with the outcome, and adjustment for them can improve power. The win ratio (ignores ties) and the win odds (accounts for ties) can be useful when analyzing these types of data from randomized controlled trials. This package provides straightforward functions for adjustment of the win ratio and win odds for stratification and baseline covariates, facilitating the comparison of test and control treatments in multi-visit clinical trials. For additional information concerning the methodologies and applied examples within this package, please refer to the following publications: 1. Weideman, A.M.K., Kowalewski, E.K., & Koch, G.G. (2024). “Randomization-based covariance adjustment of win ratios and win odds for randomized multi-visit studies with ordinal outcomes.” Journal of Statistical Research, 58(1), 33–48. . 2. Kowalewski, E.K., Weideman, A.M.K., & Koch, G.G. (2023). “SAS macro for randomization-based methods for covariance and stratified adjustment of win ratios and win odds for ordinal outcomes.” SESUG 2023 Proceedings, Paper 139-2023. 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Acknowledgements: The author wish to thank Conselleria de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031), Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research. 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Package: r-cran-woodvaluationde Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-woodvaluationde_1.0.2-1.ca2604.1_all.deb Size: 97174 MD5sum: 5f3effe47ce2442ec54a526025dc671a SHA1: 0a0e9c11706dbc5123590c6a6aae9da229ee641a SHA256: 70af6a7c9566e8d207c014d0464a440e7efc022d1a33cfeb5e7dcb5ff26c1557 SHA512: 11553e0d881cdb108e9034b760fd21bd9ad02073b960b2ff72e3dcf18138575cec3ea91b98ce49a5f6947704ada7a6120289621477fb8db968f221e5c20ffd37 Homepage: https://cran.r-project.org/package=woodValuationDE Description: CRAN Package 'woodValuationDE' (Wood Valuation Germany) Monetary valuation of wood in German forests (stumpage values), including estimations of harvest quantities, wood revenues, and harvest costs. The functions are sensitive to tree species, mean diameter of the harvested trees, stand quality, and logging method. The functions include estimations for the consequences of disturbances on revenues and costs. The underlying assortment tables are taken from Offer and Staupendahl (2018) with corresponding functions for salable and skidded volume derived in Fuchs et al. (2023). Wood revenue and harvest cost functions were taken from v. Bodelschwingh (2018). The consequences of disturbances refer to Dieter (2001), Moellmann and Moehring (2017), and Fuchs et al. (2022a, 2022b). For the full references see documentation of the functions, package README, and Fuchs et al. (2023). Apart from Dieter (2001) and Moellmann and Moehring (2017), all functions and factors are based on data from HessenForst, the forest administration of the Federal State of Hesse in Germany. 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Package: r-cran-wordlistsanalytics Architecture: all Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-readr, r-cran-dplyr, r-cran-reshape2, r-cran-lsa Filename: pool/dists/resolute/main/r-cran-wordlistsanalytics_0.2.4-1.ca2604.1_all.deb Size: 558322 MD5sum: c35db7908a93588dbbda6e0f1056c726 SHA1: bebf5a87144a3994cea52ea85a9a3117b68bfed7 SHA256: 4b29655be84370eb8109d706daa00e96e851efc242e060c2eea55b14eb61b790 SHA512: 9a3c5c07e9fd2925bd7aa047e7d737a6d6f2bf1a8f2c637dfbada90df9ce1f2ba851cb7335550915294ff7cac61dc17a4c3660e352d14f3e8d7ea9d85e8522c4 Homepage: https://cran.r-project.org/package=WordListsAnalytics Description: CRAN Package 'WordListsAnalytics' (Multiple Data Analysis Tools for Property Listing Tasks) Application to estimate statistical values using properties provided by a group of individuals to describe concepts using 'shiny'. 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Package: r-cran-wordmap Architecture: all Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-quanteda, r-cran-stringi, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wordmap_0.9.5-1.ca2604.1_all.deb Size: 914956 MD5sum: 7fd58cb4f22ad1e2d9f53525c930c6a8 SHA1: 235c68cd84b341f6aedf6dc8d2fc4e7e6d5b2639 SHA256: 6f057a2cc9dc466b10eb2c3f9dda56071605d9b4e34b196c924c7ee5ae913dc1 SHA512: 79f13f36ba66cf0d6a02f0cf7f0c09e78e97685e7ea890cf86028ca3bcffc1954af52ac900950f7b30b805829201b0fff06c998e4e100bc794de32476844a79d Homepage: https://cran.r-project.org/package=wordmap Description: CRAN Package 'wordmap' (Feature Extraction and Document Classification with Noisy Labels) Extract features and classify documents with noisy labels given by document-meta data or keyword matching Watanabe & Zhou (2020) . Package: r-cran-wordnet Architecture: all Version: 0.1-18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Filename: pool/dists/resolute/main/r-cran-wordnet_0.1-18-1.ca2604.1_all.deb Size: 126324 MD5sum: 1bb95f696bcabd23ac07af22c3b0d1a1 SHA1: edd17b6c8dec5b631826a974f28535fdf85a954a SHA256: cd809ba1c99f046795fa5f2c144797fea493b5c1b4191692ed4310907e327659 SHA512: 9a2bfeca1193aef534252fd82885c327148283df945aba4c286dbb385646c74eca92f4b6e4fd009f0f43bd35b45c6b98972c9c93ae567b6f35934887d2b35215 Homepage: https://cran.r-project.org/package=wordnet Description: CRAN Package 'wordnet' (WordNet Interface) An interface to WordNet using the Jawbone Java API to WordNet. WordNet () is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. 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Package: r-cran-wordofmouth Architecture: all Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lambertw Filename: pool/dists/resolute/main/r-cran-wordofmouth_1.2.0-1.ca2604.1_all.deb Size: 161550 MD5sum: 6ad5729542b73b45fbc8c03d0d4d56fa SHA1: 96b9f3359a0b37957d5f50f4c6519403ae375270 SHA256: dd6e60bdd537d259ca46c1d486644b570a1481d53a8d48b66f7d53383df2b9da SHA512: ef282f0700b264b0a1d523261830924d7b0b7709941916e9d79e341679f0cdbd40621227e4baab25fdc6dd389c29fd706cac5815515844b47c8ecca9f6d9aaf8 Homepage: https://cran.r-project.org/package=WordOfMouth Description: CRAN Package 'WordOfMouth' (Estimates Economic Variables for Word-of-Mouth-Campaigns) Methods for estimating profit, profit-maximizing price, demand and consumer surplus of Word-of-Mouth-campaigns on mean-field networks. 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Package: r-cran-words Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 684 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-words_1.0.1-1.ca2604.1_all.deb Size: 660884 MD5sum: d35a8e19c24f5dcd2bc4973e789ab2da SHA1: 728bc18bc289dc5cf3ca3c0938f211e9f19d65e5 SHA256: 61bc5923bd90f2cebc190311db3711d84acd7ac5b947087a1ca6f53866fa3e27 SHA512: 915d330ced41d3248bb782e73602c58af1eb8608a36f8dca344ab5be39b3210f636cb6681ccce90463a28194aafc2840849ddd9afacf3ea076801577a826dcb4 Homepage: https://cran.r-project.org/package=words Description: CRAN Package 'words' (List of English Words from the Scrabble Dictionary) List of english scrabble words as listed in the OTCWL2014 . Words are collated from the 'Word Game Dictionary' . Package: r-cran-wordsalad Architecture: all Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-text2vec, r-cran-word2vec, r-cran-fasttextr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wordsalad_0.2.0-1.ca2604.1_all.deb Size: 53864 MD5sum: 6858113682a4689a430c6b7a375954c1 SHA1: 35d2b43783c2d9f761e148e253c6d4c0083281db SHA256: 6856e25c0fa2c306826ef18ffab3c2a439b92ae1ec1edf3ece05b0b7bde4f7a6 SHA512: 7f8e6a91c25d04f813f769290a3bc94e0e826c251cd331e8e4cc287f510342f92c5227a0916ea7769cd18a8be113e8252ba108bd863025668228fb19906fa3c9 Homepage: https://cran.r-project.org/package=wordsalad Description: CRAN Package 'wordsalad' (Provide Tools to Extract and Analyze Word Vectors) Provides access to various word embedding methods (GloVe, fasttext and word2vec) to extract word vectors using a unified framework to increase reproducibility and correctness. 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Package: r-cran-workflowsets Architecture: all Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-hardhat, r-cran-lifecycle, r-cran-parsnip, r-cran-pillar, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs, r-cran-withr, r-cran-workflows Suggests: r-cran-covr, r-cran-dials, r-cran-finetune, r-cran-kknn, r-cran-knitr, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyclust, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-workflowsets_1.1.1-1.ca2604.1_all.deb Size: 1794682 MD5sum: d864ae8508a705b6b70cb70078f2891d SHA1: 289f401a1c93b7163891f71bf24b25d35adc871d SHA256: a216bc8cd474ba92c5c655f14df16db0918d7536cb82f2a0c5b61fd4c2400baf SHA512: 8523747c6d257c06f9248e6c243765f411ef25cbdf18dc44f4afd4610afda8c407bafe209f2245a6a307f8013077ffcef80b3376cb4980d034ece102e2bcb6f6 Homepage: https://cran.r-project.org/package=workflowsets Description: CRAN Package 'workflowsets' (Create a Collection of 'tidymodels' Workflows) A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021) ). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results. Package: r-cran-workloopr Architecture: all Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3796 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-signal Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-workloopr_1.1.4-1.ca2604.1_all.deb Size: 771572 MD5sum: 77c9c74f898848dab9be7c64426ce18a SHA1: f5518ce763ea73cb476dc296d1ca5440ea3e6c14 SHA256: 437a43de0b105b092e1bda331e7e178691945e15b2130257a93e366f8ac34dbd SHA512: 28b6535bdc35c5575f62ae2fed9eb57a7b1bbb3d50949c2e14c2ecc7db8f824b47cb1ba11196546fb468e0967beac497d6535a2f38a12c5202965a082116ab01 Homepage: https://cran.r-project.org/package=workloopR Description: CRAN Package 'workloopR' (Analysis of Work Loops and Other Data from Muscle PhysiologyExperiments) Functions for the import, transformation, and analysis of data from muscle physiology experiments. The work loop technique is used to evaluate the mechanical work and power output of muscle. Josephson (1985) modernized the technique for application in comparative biomechanics. Although our initial motivation was to provide functions to analyze work loop experiment data, as we developed the package we incorporated the ability to analyze data from experiments that are often complementary to work loops. There are currently three supported experiment types: work loops, simple twitches, and tetanus trials. Data can be imported directly from .ddf files or via an object constructor function. Through either method, data can then be cleaned or transformed via methods typically used in studies of muscle physiology. Data can then be analyzed to determine the timing and magnitude of force development and relaxation (for isometric trials) or the magnitude of work, net power, and instantaneous power among other things (for work loops). Although we do not provide plotting functions, all resultant objects are designed to be friendly to visualization via either base-R plotting or 'tidyverse' functions. This package has been peer-reviewed by rOpenSci (v. 1.1.0). Package: r-cran-workspace Architecture: all Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-yaml, r-cran-zip Suggests: r-cran-sf, r-cran-terra, r-cran-testthat, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-workspace_0.1.6-1.ca2604.1_all.deb Size: 104724 MD5sum: 61a73d07f889d5f8938566b841da45bf SHA1: 4f8c6523442513fb7a4fec53ca7673420d963790 SHA256: a7a14e6fee8afc36ea9ced0dc0d03f97acaac07663bc9a72804c384128263c5b SHA512: 6d1dbec2080136f1e37b84d56df913c71cb1dee5d26a39d3322289c40a7cb14fd59bd6a47b051d89606c72c6c998018b9fe1b26b72d1952c9ba09c595473a36b Homepage: https://cran.r-project.org/package=workspace Description: CRAN Package 'workspace' (Manage Collections of Datasets and Objects) Create, store, read and manage structured collections of datasets and other objects using a 'workspace', then bundle it into a compressed archive. Using open and interoperable formats makes it possible to exchange bundled data from 'R' to other languages such as 'Python' or 'Julia'. Multiple formats are supported 'Parquet', 'JSON', 'yaml', spatial data and raster data are supported. Package: r-cran-worldbank Architecture: all Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-ggplot2, r-cran-scales, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-worldbank_0.9.0-1.ca2604.1_all.deb Size: 405050 MD5sum: f54a006ae84eb57e76d7657c0fd61912 SHA1: b154fd519de1ce7f7feff65bc6c34b104177bc21 SHA256: 814fc0a4d013b76fd9f66bb333301339041fca4ae3977d2b622e07420604a823 SHA512: 3f8e996434c09417beb6bffbd3be6e3f8ba71fe06fc10c884b2bdd1b3aee7aa6958db8d27a6025447042552740952aaac8ef302731f4ec5ccb9708fa78c4999c Homepage: https://cran.r-project.org/package=worldbank Description: CRAN Package 'worldbank' (Client for the 'World Bank' APIs) Download and search data from the 'World Bank' APIs, including the 'Indicators' API, the 'Poverty and Inequality Platform (PIP)' API, the 'Finances One' API, and the 'Projects' API. See for further details. Package: r-cran-worldflora Architecture: all Version: 1.14-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-stringr, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-stringdist Filename: pool/dists/resolute/main/r-cran-worldflora_1.14-5-1.ca2604.1_all.deb Size: 156860 MD5sum: 71ba9f83224c24eafb81c1cffe583001 SHA1: 27660b7161d6081c2443ee3d3a7584ada6a157fa SHA256: 030e3291957dcc592d4dbce086991bd112591ccadab80e8277935f4cf3d46a92 SHA512: 549388e88400ecb064a4b8f8934e5f2032de0c50579bafe81f45d97fce4629a047222327fc397a1a38245019cfa64101b136bae613900b82ba4242ea1c78041b Homepage: https://cran.r-project.org/package=WorldFlora Description: CRAN Package 'WorldFlora' (Standardize Plant Names According to World Flora OnlineTaxonomic Backbone) World Flora Online is an online flora of all known plants, available from . Methods are provided of matching a list of plant names (scientific names, taxonomic names, botanical names) against a static copy of the World Flora Online Taxonomic Backbone data that can be downloaded from the World Flora Online website. The World Flora Online Taxonomic Backbone is an updated version of The Plant List (), a working list of plant names that has become static since 2013. 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Package: r-cran-worldmapr Architecture: all Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2031 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sf, r-cran-countrycode, r-cran-ggfx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-worldmapr_1.3.0-1.ca2604.1_all.deb Size: 1691248 MD5sum: 4d2006d320a2523cac294a36cdc7090e SHA1: 6a22c296709de38c545cf851672bd0865fbd1866 SHA256: b6fe9e8eccc436bbc7d1af667900fd13d62dfeb2f4e0f20d2a625dafe3c350e5 SHA512: bc34a42371992e4817b65ecf3cfeaf8e005f9d1e344a564db8b029ba2c44f9997215a86c29d25b3a09c740a591136f0107549267ccc33ec4de7e4943b775e30a Homepage: https://cran.r-project.org/package=WorldMapR Description: CRAN Package 'WorldMapR' (Worldwide or Coordinates-Based Heat Maps) Easily plot heat maps of the world, based on continuous or categorical data. Country labels can also be added to the map. Package: r-cran-worldmet Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-carrier, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-mirai, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-arrow, r-cran-knitr, r-cran-leaflet, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-worldmet_1.1.0-1.ca2604.1_all.deb Size: 363608 MD5sum: 48425c15227888588b4650a29c4a8300 SHA1: 490f8678717e787f800a95c5d47d787e8301dcfc SHA256: cbfb19e37e336ededcf227b5f6e575698b9b15bcfefce4569dcb88c8f279adaa SHA512: 7d7beb8b723c2ef61cada7aab9801c39410e1dfe45d22d13125194f71a35458ad8b54baf41073c47c3d7b0a01f3de02b8afa0dd311c6f0d7ee18d30c59e5af16 Homepage: https://cran.r-project.org/package=worldmet Description: CRAN Package 'worldmet' (Import Surface Meteorological Data from NOAA) Functions to import data from more than 30,000 surface meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Global Historical Climate Network (GHCN) and Integrated Surface Database (ISD). 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The distance matrices are assumed to be calculated between the cells of multiple animals ('Caenorhabditis elegans') from input time-series matrices. Some functions for generating distance matrices, performing clustering, evaluating the clustering, and visualizing the results of clustering and evaluation are available. We're also providing the download function to retrieve the calculated distance matrices from 'figshare' . Package: r-cran-worrms Architecture: all Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-tibble, r-cran-jsonlite, r-cran-data.table Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr Filename: pool/dists/resolute/main/r-cran-worrms_0.4.3-1.ca2604.1_all.deb Size: 99554 MD5sum: a1109166c220bd12e6ebdb0bbfd4f6c2 SHA1: cd9d3528632534229a601d8c62f614b957e0065c SHA256: 94b22d66e19c1b2815defc6b8d1b65f68c7a48560bf5fba99466fe6c67a6072c SHA512: f7eba5d86b869b1fb3ff0c0f7d30d17979a990c9ee9e635c2ab05acbc19dc74c13f7cdd647cd39e69eba9010e3f8696c3582703527450abab2e0ab65446feda8 Homepage: https://cran.r-project.org/package=worrms Description: CRAN Package 'worrms' (World Register of Marine Species (WoRMS) Client) Client for World Register of Marine Species (). Includes functions for each of the API methods, including searching for names by name, date and common names, searching using external identifiers, fetching synonyms, as well as fetching taxonomic children and taxonomic classification. Package: r-cran-worrrd Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-yaml, r-cran-glue, r-cran-ggtext, r-cran-ggfittext, r-cran-cowplot Suggests: r-cran-magick, r-cran-emoji, r-cran-rvest, r-cran-english, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-worrrd_0.1.0-1.ca2604.1_all.deb Size: 409338 MD5sum: b06c32146ddd7a15874d37ca8093deda SHA1: a5b2937efd846d8dc60b9357546ce5619567efcc SHA256: 6d64f1de7b6af209c30f6aa8490dc2937ba990da407096ea38a210e56568fe43 SHA512: 58ee33ce2cc58ed49a735164867b9a68735b7b18d5008e4c4f4751f558693e2a86e39dd71510b82e7fb9407320584bdecbd9ddac7c9a5e34a4e8545b63790356 Homepage: https://cran.r-project.org/package=worrrd Description: CRAN Package 'worrrd' (Generate Wordsearch and Crossword Puzzles) Generate wordsearch and crossword puzzles using custom lists of words (and clues). Make them easy or hard, and print them to solve offline with paper and pencil! Package: r-cran-wosr Architecture: all Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-xml2, r-cran-jsonlite, r-cran-pbapply Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-wosr_0.3.0-1.ca2604.1_all.deb Size: 103764 MD5sum: 372d4074ed873536c697bee90cf79a1d SHA1: b81546adcd6da5495cf1e37511547849338d7b5e SHA256: 3f5eb389ff7662ced64362c8a7ebad1aa1afacd88503e06aa5341055b686e058 SHA512: c560dee49b548efb25a601ff9ba04162633420c0c275a38238249532e57a4790001c9f3bbb07a6b1e37185608dead7d8d8f63bbdec33e712430c83fe6a9ad0d7 Homepage: https://cran.r-project.org/package=wosr Description: CRAN Package 'wosr' (Clients to the 'Web of Science' and 'InCites' APIs) R clients to the 'Web of Science' and 'InCites' APIs, which allow you to programmatically download publication and citation data indexed in the 'Web of Science' and 'InCites' databases. Package: r-cran-wotply Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-ggally, r-cran-sna Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-wotply_0.1.0-1.ca2604.1_all.deb Size: 127466 MD5sum: 075f01c0e075d859c55fef3dea8ab702 SHA1: 5a8ef4ec25e57fd8413df495044877844a356ee6 SHA256: 0f4bd0cb6461381feb9194c8fda79fce1801d55f7bb063582fa6da34aa21124b SHA512: 86e3c4f0152ca9ace8506a554ed922ae4ffeda6afc6265134f9d7a4837ec23b5c7940d7dbfbe4b8eba7d896900792b0cf8b950fe1b47749c807cdbed0755942e Homepage: https://cran.r-project.org/package=WOTPLY Description: CRAN Package 'WOTPLY' (Plot Connectivity Between Cells from Different Time Points) It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis . Package: r-cran-woylier Architecture: all Version: 0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tourr, r-cran-geozoo, r-cran-dplyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-ggplot2, r-cran-ash, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-woylier_0.0.9-1.ca2604.1_all.deb Size: 394118 MD5sum: 2e987e057132c2ab5f2c1b7604c1db77 SHA1: 4b5695a7b9967cf3e1de9adca787e63f193da0e1 SHA256: e780761525eaefc78491a2fa405c617840f842eb1e93d3ed7029c8119615825e SHA512: 529cdded10643805dc1641b83d8e237a97beaafff5b8065998ff49422e3a7a32e8affa5e2c16f746aa0fd2e39a3f9a0f141735f76ad30a0d3d0c1e0bb87d398c Homepage: https://cran.r-project.org/package=woylier Description: CRAN Package 'woylier' (Alternative Tour Frame Interpolation Method) This method generates a tour path by interpolating between d-D frames in p-D using Givens rotations. The algorithm arises from the problem of zeroing elements of a matrix. This interpolation method is useful for showing specific d-D frames in the tour, as opposed to d-D planes, as done by the geodesic interpolation. It is useful for projection pursuit indexes which are not s invariant. See more details in Buj, Cook, Asimov and Hurley (2005) and Batsaikhan, Cook and Laa (2023) . Package: r-cran-wpa Architecture: all Version: 1.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2652 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-magrittr, r-cran-purrr, r-cran-reshape2, r-cran-ggplot2, r-cran-ggrepel, r-cran-scales, r-cran-htmltools, r-cran-markdown, r-cran-rmarkdown, r-cran-networkd3, r-cran-dt, r-cran-tidytext, r-cran-ggraph, r-cran-igraph, r-cran-proxy, r-cran-ggwordcloud, r-cran-data.table Suggests: r-cran-knitr, r-cran-extrafont, r-cran-lifecycle, r-cran-fst, r-cran-glue, r-cran-flexdashboard, r-cran-lmtest, r-cran-sandwich, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wpa_1.10.1-1.ca2604.1_all.deb Size: 2454366 MD5sum: e9c64ebfad323e39600d60105497b865 SHA1: 2f348e2eeda07bdcfae45df7bbb53149d88249b3 SHA256: 892180d2a759179015319a96b209168761f6651eba7ad1a66b3f45478b1c6f72 SHA512: 829eac64e963a6e9cb80ce734aa20cbc91cb82d750ec46397d0b13bbe0bb6deccfa885f176b2028fb9cb38eaf759fb47d529a696453b7ece1b276e8ab54a7dd9 Homepage: https://cran.r-project.org/package=wpa Description: CRAN Package 'wpa' (Tools for Analysing and Visualising Viva Insights Data) Opinionated functions that enable easier and faster analysis of Viva Insights data. There are three main types of functions in 'wpa': (i) Standard functions create a 'ggplot' visual or a summary table based on a specific Viva Insights metric; (2) Report Generation functions generate HTML reports on a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover more specific applications (e.g. Subject Line text mining) of Viva Insights data. This package adheres to 'tidyverse' principles and works well with the pipe syntax. 'wpa' is built with the beginner-to-intermediate R users in mind, and is optimised for simplicity. Package: r-cran-wper Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-sf Suggests: r-cran-basemaps, r-cran-ggforce, r-cran-ggrepel, r-cran-gridextra, r-cran-kinship2, r-cran-knitr, r-cran-leaflet, r-cran-leaflet.providers, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wper_0.1.0-1.ca2604.1_all.deb Size: 2917288 MD5sum: 526ddf620b36ce89671fd1b47f49538c SHA1: 79852dca3437e39ec54a156a60a5813f2185ced7 SHA256: c16279df4453fde04d01a7e4710d0e77c13bcb2fa9143c67050bf645c375340a SHA512: 813261933826af403f1e5786d794f48f05dd7dec31fc0bf98cd8b04bbd2927ba940073491c518211e5d53a05fb86c52f70d0a5f40df19344b81983937b8d7d00 Homepage: https://cran.r-project.org/package=wpeR Description: CRAN Package 'wpeR' (Streamlined Analysis of Wild Pedigree Data) Analyzing pedigree data of wild populations. 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Package: r-cran-wqc Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-waveslim, r-cran-qcsis, r-cran-lattice, r-cran-viridislite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wqc_0.1.2-1.ca2604.1_all.deb Size: 28002 MD5sum: bf4d6843ddf8ab23d31c9e8f515ad8fb SHA1: 0e06c687e9ef25b1e46875960f09bc5f9a3f5a3a SHA256: 124446339dfedb9473445541c3a392446bb06c1cdd46c4374dc4716ebf27c46d SHA512: 18cea182433f9f899cb85cb146457860eead9b66d410aa1765f0266e8f99c2927c8fb71ce60ba990e0d90b03874f50bf38ac791b15ca10f547a1aefc33ad6a61 Homepage: https://cran.r-project.org/package=wqc Description: CRAN Package 'wqc' (Wavelet Quantile Correlation Analysis) Estimate and plot wavelet quantile correlations(Kumar and Padakandla,2022) between two time series. Wavelet quantile correlation is used to capture the dependency between two time series across quantiles and different frequencies. This method is useful in identifying potential hedges and safe-haven instruments for investment purposes. See Kumar and Padakandla(2022) for further details. Package: r-cran-wql Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wql_1.0.3-1.ca2604.1_all.deb Size: 2028518 MD5sum: 6e8b142beb51107ea12b7c854bc12728 SHA1: 0cfdc642f311f2fbb8f69e262fb619b2fae8f632 SHA256: f0b6939575a6491b93f3f9373175a39f8d74c0a884b9990eb45b75dacaffd85d SHA512: 5774ad9c09f29b9ef43298470a4d4ac8daabf34f6d56edca040cee68b700d00a072d944be99b82f8e8f2a72bccbee2da523359f8ee71a6a6ff5c35fd38cbe15d Homepage: https://cran.r-project.org/package=wql Description: CRAN Package 'wql' (Exploring Water Quality Monitoring Data) Functions to assist in the processing and exploration of data from environmental monitoring programs. The package name stands for "water quality" and reflects the original focus on time series data for physical and chemical properties of water, as well as the biota. Intended for programs that sample approximately monthly, quarterly or annually at discrete stations, a feature of many legacy data sets. Most of the functions should be useful for analysis of similar-frequency time series regardless of the subject matter. Package: r-cran-wqm Architecture: all Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1720 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mbc, r-cran-waveletcomp, r-cran-matrixstats, r-cran-ggplot2 Suggests: r-cran-tidyr, r-cran-dplyr, r-cran-scales, r-cran-data.table, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-wqm_0.1.4-1.ca2604.1_all.deb Size: 1220072 MD5sum: 717ffadb2481562d65a3910047cb51d0 SHA1: 06cfaffbc84912163291c1f675b20039635b5c4a SHA256: bc4065f4fc3a464105702f8e84a6538bb74a641e49fb0c4f463052b812c41118 SHA512: 52d1a3d9fd59c1c396b31c180b034abcfee59697019b605c083f53b093a0e02f1791b57fd6f30c5efccd2b45daa664cb4a6d09357bb3e1b044b520017a7df167 Homepage: https://cran.r-project.org/package=WQM Description: CRAN Package 'WQM' (Wavelet-Based Quantile Mapping for Postprocessing NumericalWeather Predictions) The wavelet-based quantile mapping (WQM) technique is designed to correct biases in spatio-temporal precipitation forecasts across multiple time scales. 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Package: r-cran-wqspt Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-gwqs, r-cran-pbapply, r-cran-ggplot2, r-cran-mvtnorm, r-cran-viridis, r-cran-extradistr, r-cran-cowplot, r-cran-mass, r-cran-car, r-cran-future, r-cran-future.apply, r-cran-pscl, r-cran-reshape2, r-cran-nnet Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wqspt_1.0.2-1.ca2604.1_all.deb Size: 209798 MD5sum: 03d3b271679f586df09b559c69763ef0 SHA1: 076dd066cb059d8c2fafea2d25a83a3bf86ca16a SHA256: 07ce51eb210694634b6e73946ef39fc829be46debe4f4d4b91738e81cd3e2597 SHA512: f191977c72cdb092e7d0f85a5b2da227863d5a0131118e44f1588f0cb1204cf9ea4863cf0f4bec2ef5931c408f0ebab8c276c85693af69e92a79ca92c2cac772 Homepage: https://cran.r-project.org/package=wqspt Description: CRAN Package 'wqspt' (Permutation Test for Weighted Quantile Sum Regression) Implements a permutation test method for the weighted quantile sum (WQS) regression, building off the 'gWQS' package (Renzetti et al. ). Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. 2015 ). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. 2022 ). 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(2019) . Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and visualization of results. Package: r-cran-wr Architecture: all Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-cubature, r-cran-gumbel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wr_1.0-1.ca2604.1_all.deb Size: 207966 MD5sum: 26f2410c12979a83da028fb47cc22244 SHA1: 1abcb5005fb9198aeefa10396febdaeaae06ce35 SHA256: 759e36a9966a31335e4288d7270c7a9649fdd95fe1e84af818d58ba3d553ff33 SHA512: 3d1adff8226d64481592315f14c3ccccdac4914dbac58522a3c42799f2205c7789edc609a454e1bcf586626d944794386a2241d190137cb1fac5f28476d31c17 Homepage: https://cran.r-project.org/package=WR Description: CRAN Package 'WR' (Win Ratio Analysis of Composite Time-to-Event Outcomes) Implements various win ratio methodologies for composite endpoints of death and non-fatal events, including the (stratified) proportional win-fractions (PW) regression models (Mao and Wang, 2020 ), (stratified) two-sample tests with possibly recurrent nonfatal event, and sample size calculation for standard win ratio test (Mao et al., 2021 ). Package: r-cran-wrangle Architecture: all Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-wrangle_0.6.4-1.ca2604.1_all.deb Size: 69208 MD5sum: b969a8c632f65b81c58df8e08fc889c5 SHA1: e4bc5418de672e1d569438711f7adbddd9daa675 SHA256: a6f96dc71d62e99d18b9473ebc85cd6f54b0ee0f7abc3eec9a55327c042a4bb7 SHA512: 7ef50392ef02994bf101d539c103a9ae837c50188ab56fb703c79d56b016629af4e429bcbee67847cae36ae2c5b918e274c92c528f0bc9da443a1561ff32858e Homepage: https://cran.r-project.org/package=wrangle Description: CRAN Package 'wrangle' (A Systematic Data Wrangling Idiom) Supports systematic scrutiny, modification, and integration of data. The function status() counts rows that have missing values in grouping columns (returned by na() ), have non-unique combinations of grouping columns (returned by dup() ), and that are not locally sorted (returned by unsorted() ). Functions enumerate() and itemize() give sorted unique combinations of columns, with or without occurrence counts, respectively. Function ignore() drops columns in x that are present in y, and informative() drops columns in x that are entirely NA; constant() returns values that are constant, given a key. Data that have defined unique combinations of grouping values behave more predictably during merge operations. Package: r-cran-wrappedtools Architecture: all Version: 0.9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-knitr, r-cran-coin, r-cran-dplyr, r-cran-tidyr, r-cran-forcats, r-cran-purrr, r-cran-glue, r-cran-rlang, r-cran-stringr, r-cran-ggplot2, r-cran-tibble, r-cran-kableextra, r-cran-lifecycle, r-cran-broom, r-cran-rlist, r-cran-desctools, r-cran-flextable, r-cran-nortest Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-ggrepel Filename: pool/dists/resolute/main/r-cran-wrappedtools_0.9.9-1.ca2604.1_all.deb Size: 340482 MD5sum: 0c5bc7b3a8e775115ef812a751ba64bc SHA1: ea6f30d96066e7e7ebde233176998e734e0eb1b9 SHA256: c2b71d36541e105a2b1b6a2f1b694b6c92d99acd45a7d1a50696c1d96d15e36c SHA512: d66aa14218a3739cd5d13435eadcec559a57d5eb6c4aba518171101b9cdfaad00c17f337f17431fdc488d4c038dc294938cc580c67b19b4b1a7c69bdd374303c Homepage: https://cran.r-project.org/package=wrappedtools Description: CRAN Package 'wrappedtools' (Useful Wrappers Around Commonly Used Functions) The main functionalities of 'wrappedtools' are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well. Package: r-cran-wrappr Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wrappr_0.1.0-1.ca2604.1_all.deb Size: 82014 MD5sum: c41c87d1501c7eab86c2c0d4787724ef SHA1: 686a07e28cff51434898c659ccfaa0f05b1a9446 SHA256: 6637713c08aeb44041ea83a0d13b37923298da17a4a6ef31fe87ee6eebf2a8d0 SHA512: 0de26c41aacc893ee447cd1120c126cd670e20db89b107dc502d918d7d108c7dda7ded5f8d6bc49379743789e642212bea43b29cc3b5f5ab769a1618e12b4050 Homepage: https://cran.r-project.org/package=wrappr Description: CRAN Package 'wrappr' (A Collection of Helper and Wrapper Functions) Helper functions to easily add functionality to functions. The package can assign functions to have an lazy evaluation allowing you to save and update the arguments before and after each function call. You can set a temporary working directory within functions and wrap console messages around other functions. Package: r-cran-wrapr Architecture: all Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-wrapr_2.1.0-1.ca2604.1_all.deb Size: 675102 MD5sum: 848ee67c6b6f5846eb0a07776318e887 SHA1: 93c8abbecfac41a1f8a21a4c88479bf0652078b3 SHA256: b5f2ae3a882f198349dd62d193eb9976fd084192c5608eb0dc87b99208e790b6 SHA512: ea66be6dd5e47323f1562b506bc73337da83c91976a79c269e5f3f6c20bc5970edf4be31e64e7b67958394e530a728816a350985559248dbc4142ccaed614d3e Homepage: https://cran.r-project.org/package=wrapr Description: CRAN Package 'wrapr' (Wrap R Tools for Debugging and Parametric Programming) Tools for writing and debugging R code. Provides: '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return), 'build_frame()'/'draw_frame()' ('data.frame' example tools), 'qc()' (quoting concatenate), ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more. Package: r-cran-wrds Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-keyring, r-cran-rlang, r-cran-rpostgres, r-cran-tidylog Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-wrds_0.1.1-1.ca2604.1_all.deb Size: 87642 MD5sum: 66a6704a9b356fa762fd6bc5955229f0 SHA1: 14e40e2c567f41b63a9bf1f7f000e064c5e5f6d3 SHA256: 0aec26e25fc9adfed4eb4faae9b4602acd83bacce2b17294f45ff910968a0d1d SHA512: b76ab88d79e654c02131265422e5325ee84f7059c4a705d4946319e51fab196ab9aeab31159f0ed28c5d067fe3b72f75ebf88d7ad2bcd1138c53588b6be7722e Homepage: https://cran.r-project.org/package=wrds Description: CRAN Package 'wrds' (Access 'Wharton Research Data Services' ('WRDS')) Provides simple functions for accessing data from 'Wharton Research Data Services' ('WRDS'), a widely used financial database in academic research. Includes credential management via the system keyring, database tools, and functions for downloading generic tables, 'Compustat' fundamentals, and linking tables. Package: r-cran-wrensbookshelf Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-wrensbookshelf_0.1.0-1.ca2604.1_all.deb Size: 44340 MD5sum: 72e1debbcbe353c805c4a3dd5425f13c SHA1: 0878b8303f9b9471b1dfb5c5aac0eb933ccd73b0 SHA256: 48ead06a8fd54d078b880cc4df2ed9ce2b18c4a37240daa8e895ecbf4bf5058d SHA512: a4ce8123c97b76ca63a97bf09e12fbc28bb8b8c7f4e470e9a221f23b2c9e2717180af5f7e9a0cf548d75b030be46183652fc05b43b8709e78b6d98e865323d42 Homepage: https://cran.r-project.org/package=WrensBookshelf Description: CRAN Package 'WrensBookshelf' (A Collection of Palettes and Some Functions to Help Use Them) A collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors. Package: r-cran-wrestimates Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wrestimates_0.1.0-1.ca2604.1_all.deb Size: 35042 MD5sum: f3b4d60cdfb431f91bfdb3e1e75cc139 SHA1: 2137ea7a17ff191fcafe4506cbc756732ecac5b1 SHA256: 175891c339792123c32213c3db125532aedaa0cca7f58f5b2c4c0c7a656b055a SHA512: 3431d898e7dd41d30b2cbdba0168419b393f6f5ae09e316f5d02bace7da0d7818f4dcc5333aad09ed9c0717171a3c2ae65f7716724dc4aa45e463288c67f3a81 Homepage: https://cran.r-project.org/package=WRestimates Description: CRAN Package 'WRestimates' (Sample Size, Power and CI for the Win Ratio) Calculates non-parametric estimates of the sample size, power and confidence intervals for the win-ratio. For more detail on the theory behind the methodologies implemented see Yu, R. X. and Ganju, J. (2022) . Package: r-cran-wrgraph Architecture: all Version: 1.3.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rcolorbrewer, r-cran-wrmisc Suggests: r-cran-dplyr, r-cran-factoextra, r-cran-factominer, r-cran-ggpubr, r-cran-knitr, r-bioc-limma, r-cran-rmarkdown, r-cran-sm Filename: pool/dists/resolute/main/r-cran-wrgraph_1.3.15-1.ca2604.1_all.deb Size: 1145428 MD5sum: c8bbeab4fd1233ca246a95b054762739 SHA1: b1bd10211daa3cd2599f2ab38d540ed00d46d056 SHA256: 17204765920876c95c4d97602220b1e69c2f00366c41515ac6612852e3b0ee52 SHA512: 51e18be9706d164acf2459ee9fc5b2041a19485827b5331fdd7ff4c8bf0d7941b254c9f107581ba4e688dec8c58ccf59d496164f28992a0ec6d0d957630817bc Homepage: https://cran.r-project.org/package=wrGraph Description: CRAN Package 'wrGraph' (Graphics in the Context of Analyzing High-Throughput Data) Additional options for making graphics in the context of analyzing high-throughput data are available here. This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots, automatic checking for optimal location of legends in plots, small histograms to insert as legends, histograms re-transforming axis labels to linear when plotting log2-transformed data, a violin-plot function for a wide variety of input-formats, principal components analysis (PCA) with bag-plots to highlight and compare the center areas for groups of samples, generic MA-plots (differential- versus average-value plots) , staggered count plots and generation of mouse-over interactive html pages. Package: r-cran-wrictools Architecture: all Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1396 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcurl, r-cran-readr, r-cran-rlang, r-cran-tidyr, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-wrictools_1.0.1-1.ca2604.1_all.deb Size: 541748 MD5sum: ba688d4c20940f7335314640d76cb4c2 SHA1: 4459b9aec14f9340f0eb2c0892cfe2ba870f636f SHA256: babd3c48ec12254278c471e690a43383c8fc3f4e34141c43d747f2ffc365e563 SHA512: 27ea7d74fce42ba2148439afbc97032f82e739c6387f37729fc1199333d1e7ec6e9698be2ea13bedad84598b434cf63f53b84104c3a614765ae0ed947c61e5c0 Homepage: https://cran.r-project.org/package=wrictools Description: CRAN Package 'wrictools' (Analyze Whole Room Indirect Calorimetry (WRIC) Data) Provides functions, tutorials, and examples to preprocess, analyze, and visualize data from whole room indirect calorimeters (WRIC) by Maastricht Instruments, using the 'OmniCal' software. Some functions may also work with WRICs from other manufacturers, though full functionality has only been validated for Maastricht Instruments devices. Package: r-cran-wrightmap Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-wrightmap_1.4-1.ca2604.1_all.deb Size: 238464 MD5sum: 06696351992311693fe766630dd3926a SHA1: c37093486f92ba1cba3ebacf81508343c4d54b69 SHA256: 0b5a0eecccdd06ae74f6fa27784322d82a0a05cb1485add44f7af742f4a6b1a1 SHA512: f906875fbbddff17b49e4b7c498ab82909ba6e9559095d56eee7640a7314256861e38cf15a51e7fc05a47d5b42f74fcfdac244be1412e2712a34f88a9fa12d0f Homepage: https://cran.r-project.org/package=WrightMap Description: CRAN Package 'WrightMap' (IRT Item-Person Map with 'ConQuest' Integration) A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.), 'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters. Package: r-cran-writealizer Architecture: all Version: 1.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1684 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-caretensemble, r-cran-cubist, r-cran-curl, r-cran-earth, r-cran-gbm, r-cran-glmnet, r-cran-kernlab, r-cran-knitr, r-cran-pls, r-cran-randomforest, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-writealizer_1.7.3-1.ca2604.1_all.deb Size: 451690 MD5sum: 4f4edebbe2eb666b28711f4de6610d9a SHA1: 156a3210dc1cba40c9a75f5140cbd5c1759179aa SHA256: 82156662a32c6cc95c12f421fa2f58248825204803ebc0e43dfc8682d2b42785 SHA512: 025a6f5f8e539ab484704e22ccee248b9b0337e62fe3a06aae2cf8e974e146354c4ee17d4415a6bb467e41796cf4456a862cf7e4f6e1b9a246ba8c6bd868d730 Homepage: https://cran.r-project.org/package=writeAlizer Description: CRAN Package 'writeAlizer' (Generate Predicted Writing Quality Scores) Imports variables from 'ReaderBench' (Dascalu et al., 2018), 'Coh-Metrix' (McNamara et al., 2014), and/or 'GAMET' (Crossley et al., 2019) output files; downloads predictive scoring models described in Mercer & Cannon (2022) and Mercer et al.(2021); and generates predicted writing quality and curriculum-based measurement (McMaster & Espin, 2007) scores. Package: r-cran-writer Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-glue, r-cran-cli, r-cran-rlang Suggests: r-cran-rsqlite, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-writer_0.1.0-1.ca2604.1_all.deb Size: 345260 MD5sum: ca8afc4badfa4ec8074c4250f335e683 SHA1: 521d4bbc525bd567823f15d58eba721a9cfac5e1 SHA256: e1202391cbd5a1bbc45cdc598b64f94705409b5867bdae7439ff6649845a7fa0 SHA512: 45f74a7a2dce6fbff8304c188ffbe528c1b728912e59f5c97daf198abac402d636c21b91d9cfc72b5d32608330030ed58abffbc0e9bb5fea8a0b48c9c1887f67 Homepage: https://cran.r-project.org/package=writer Description: CRAN Package 'writer' (Write from Multiple Sources to a Database Table) Provides unified syntax to write data from lazy 'dplyr' 'tbl' or 'dplyr' 'sql' query or a dataframe to a database table with modes such as create, append, insert, update, upsert, patch, delete, overwrite, overwrite_schema. Package: r-cran-writexls Architecture: all Version: 6.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3822 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-writexls_6.8.0-1.ca2604.1_all.deb Size: 561672 MD5sum: cd61f49f4df5367e04746e6f75870d78 SHA1: e345cda8d533826c06917790f3a28444872a0db0 SHA256: 682a85709bfa98f6cc11f71b24067ac69064b2ad9f75caa5dda8592c60a32347 SHA512: a09644251f11b5a056aa7a6a30a75b2e1739f0a6c0953b6f031e34688aa72b11977f410038ff60035c63125080949aef4238c19fd82df754dc8e5cdaed53a4ef Homepage: https://cran.r-project.org/package=WriteXLS Description: CRAN Package 'WriteXLS' (Cross-Platform Perl Based R Function to Create Excel 2003 (XLS)and Excel 2007 (XLSX) Files) Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX) files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains or can be specified by the user. Package: r-cran-wrmisc Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-bbmisc, r-cran-boot, r-cran-coin, r-cran-data.table, r-cran-data.tree, r-cran-fdrtool, r-cran-flexclust, r-cran-knitr, r-bioc-limma, r-cran-markdown, r-cran-mixdist, r-cran-nbclust, r-bioc-preprocesscore, r-bioc-qvalue, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-readxl, r-cran-rmarkdown, r-cran-som, r-cran-stringi, r-cran-vgam, r-bioc-vsn, r-cran-wrgraph Filename: pool/dists/resolute/main/r-cran-wrmisc_2.0.2-1.ca2604.1_all.deb Size: 1982766 MD5sum: 8c4a3186b8d7e96b82b79bfcd250f73b SHA1: 881343ebe02080b0001a466c0819add6e25fe0e8 SHA256: 503b611af22ba1e7eab7d4d1ca5d73b382afb27c7f1f3537155ea8907f9b8c9f SHA512: 55f4b0ff43b3c4e66030aa0a2e0a761adf3a4cb2f40f70d56e6c9e14f4eb417231fe5e167413e0f6562a531390325f0189e6dd6d8d09f09baf731eac41723283 Homepage: https://cran.r-project.org/package=wrMisc Description: CRAN Package 'wrMisc' (Analyze Experimental High-Throughput (Omics) Data) The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too. Package: r-cran-wrproteo Architecture: all Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7073 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-bioc-limma, r-cran-wrmisc Suggests: r-cran-data.table, r-cran-fdrtool, r-cran-kableextra, r-cran-mass, r-cran-rcolorbrewer, r-cran-readxl, r-bioc-rots, r-cran-rmarkdown, r-cran-r.utils, r-cran-sm, r-cran-wrgraph Filename: pool/dists/resolute/main/r-cran-wrproteo_2.0.2-1.ca2604.1_all.deb Size: 5239190 MD5sum: 4bfd5cd8a7a30a8ab1154193939ff2fa SHA1: 4333afd800a784924862ec78dacbd9068ac9a278 SHA256: 4d8d4423a53e0993472fc0d1c1363d217d23709539c1e3d455786f5a37ac9bff SHA512: 5d1423c13bfd85855c93be99d14ea50248a9f710501396e5aa17ac577debe06a5fa280f539fa60e9d7bd580eea46d08ae9d0c453c147b47ae731fa5e2ad2e21c Homepage: https://cran.r-project.org/package=wrProteo Description: CRAN Package 'wrProteo' (Proteomics Data Analysis Functions) Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium ) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 ), Dia-NN (Demichev et al 2020 ), Fragpipe (da Veiga et al 2020 ), ionbot (Degroeve et al 2021 ), MassChroq (Valot et al 2011 ), OpenMS (Strauss et al 2021 ), ProteomeDiscoverer (Orsburn 2021 ), Proline (Bouyssie et al 2020 ), AlphaPept (preprint Strauss et al ) and Wombat-P (Bouyssie et al 2023 . Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 ) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. Dedicated filtering and statistical testing using the framework of package 'limma' can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 , Ramus et al 2016 ), can be run with special options considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 ) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 ) quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins. 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It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures. 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Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, conditioned scatter plot with marginal densities, box and stem with matching theoretical distribution, and density with matching theoretical distribution. 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Package: r-cran-wwntests Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sde, r-cran-ftsa, r-cran-rainbow, r-cran-mass, r-cran-fda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-compquadform, r-cran-tensora Filename: pool/dists/resolute/main/r-cran-wwntests_1.1.0-1.ca2604.1_all.deb Size: 148072 MD5sum: c0701beb7c368918bbc3e6f8f88e3685 SHA1: cd9c12e2c7ad63bb701b1d165749bd3bbf007d1a SHA256: 2f5086c5e0570520266411553e3795bccc2ef58e1e5d0ad74b828ea226348700 SHA512: 5577f153561232111296978c2894cdc76ffd2969a44ed8cc3e0ade648129efea4219233a1c86d652289f7ec61f90c703571db1bdb516159d6d7d90dc86f6cd42 Homepage: https://cran.r-project.org/package=wwntests Description: CRAN Package 'wwntests' (Hypothesis Tests for Functional Time Series) Provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) , Characiejus and Rice (2019) , Gabrys and Kokoszka (2007) , and Kim et al. (2023) respectively. Package: r-cran-wxgenr Architecture: all Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2627 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-msm, r-cran-sm, r-cran-dplyr, r-cran-plyr, r-cran-foreach, r-cran-doparallel, r-cran-magrittr, r-cran-dorng, r-cran-mc2d, r-cran-qmap, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-ggpubr, r-cran-moments, r-cran-seas, r-cran-padr, r-cran-hydrogof, r-cran-specsverification, r-cran-ggridges, r-cran-tigris, r-cran-terra, r-cran-stringr, r-cran-mapview, r-cran-xts, r-cran-sf, r-cran-lfstat, r-cran-feddata, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-wxgenr_1.4.5-1.ca2604.1_all.deb Size: 2061606 MD5sum: df7ecb4305364c7d6a54602c7bd20452 SHA1: 580865b5f61ba7287d83a81a08b08d9b683bdba0 SHA256: 66e7aa554e57c2a089aa15d4a881b4ea6f642a7cde01e716838169a03669ea5f SHA512: 40a06927d3192716debfdbecf6ba8cbe7ec04b6b6be8a043a7ef2edaf2285d3bf12eca096f096c8f73141bf326f075fe0b38d28dd471a5d89694ef9598b05c2b Homepage: https://cran.r-project.org/package=wxgenR Description: CRAN Package 'wxgenR' (A Stochastic Weather Generator with Seasonality) A weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc. Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. . Package: r-cran-wyz.code.metatesting Architecture: all Version: 1.1.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-wyz.code.offensiveprogramming, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wyz.code.metatesting_1.1.22-1.ca2604.1_all.deb Size: 222842 MD5sum: bbcacb968fb9ad9896d395c3e3c08630 SHA1: fcbd95f7d0f21d57d69d633ff37f73867a0e9bf0 SHA256: 928cbd0d8a4822627b641d3fb5e9419c38dbfbc9d0a3a9545869f68b6ff2aa91 SHA512: 47fbc21852e007f1b7e02a55ca56937605cec51854a618e37e7ccc0320c4b8eade5e00122a776c04a25da9ce0b426d23494475dc9f629e58dd0752bbb4f05b81 Homepage: https://cran.r-project.org/package=wyz.code.metaTesting Description: CRAN Package 'wyz.code.metaTesting' (Wizardry Code Meta Testing) Meta testing is the ability to test a function without having to provide its parameter values. Those values will be generated, based on semantic naming of parameters, as introduced by package 'wyz.code.offensiveProgramming'. Value generation logic can be completed with your own data types and generation schemes. This to meet your most specific requirements and to answer to a wide variety of usages, from general use case to very specific ones. While using meta testing, it becomes easier to generate stress test campaigns, non-regression test campaigns and robustness test campaigns, as generated tests can be saved and reused from session to session. Main benefits of using 'wyz.code.metaTesting' is ability to discover valid and invalid function parameter combinations, ability to infer valid parameter values, and to provide smart summaries that allows you to focus on dysfunctional cases. Package: r-cran-wyz.code.offensiveprogramming Architecture: all Version: 1.1.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-stringr, r-cran-r6, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-wyz.code.offensiveprogramming_1.1.24-1.ca2604.1_all.deb Size: 297102 MD5sum: 87012530a9741df25e8279e17910b898 SHA1: a8639671cb26d8fa3ce86f925f576dd12ea5d355 SHA256: e12e9613055556f87bd08013979b7e72d9764ef051056f41fc9f76a84dcfdca6 SHA512: 69a648106cb74d7c782f8083d0fc9f65f3288e44f1bddf72b06e0424a8db4fb6b84cd2720aa742b87e69a89ff80f1764aae5cf082fadeafb212e0fc6ff2fad32 Homepage: https://cran.r-project.org/package=wyz.code.offensiveProgramming Description: CRAN Package 'wyz.code.offensiveProgramming' (Wizardry Code Offensive Programming) Allows to turn standard R code into offensive programming code. Provides code instrumentation to ease this change and tools to assist and accelerate code production and tuning while using offensive programming code technics. Should improve code robustness and quality. Function calls can be easily verified on-demand or in batch mode to assess parameter types and length conformities. Should improve coders productivity as offensive programming reduces the code size due to reduced number of controls all along the call chain. Should speed up processing as many checks will be reduced to one single check. 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The Bayesian model is described in "Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones" (Bierlich et al., 2021, ). 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Package: r-cran-xega Architecture: all Version: 0.9.0.23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parallelly, r-cran-filelock, r-cran-xegaselectgene, r-cran-xegabnf, r-cran-xegaderivationtrees, r-cran-xegagagene, r-cran-xegagpgene, r-cran-xegagegene, r-cran-xegadfgene, r-cran-xegapermgene, r-cran-xegapopulation Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-xega_0.9.0.23-1.ca2604.1_all.deb Size: 207386 MD5sum: 3ad775476994808f3e997308647482b0 SHA1: bc91ba53fcb3f5b49201fa6d86be0826da9260d8 SHA256: b7912c473447bbbe8c345bdc14e8fc77953fdc6d5a7df90437878abbc892b271 SHA512: b189f6f3bb11c8d50e3b2f442ebac4f8f7a068b380552a937e1ac44c625be7a461cdae3b2b6365b702dae449e3439150eff47dd37a709de1d0d32b6ebc3092ef Homepage: https://cran.r-project.org/package=xega Description: CRAN Package 'xega' (Extended Evolutionary and Genetic Algorithms) Implementation of a scalable, highly configurable, and e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms. Multiple representations (binary, real-coded, permutation, and derivation-tree), a rich collection of genetic operators, as well as an extended processing pipeline are provided for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)), differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) ), simulated annealing (Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7)), grammar-based genetic programming (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), grammatical evolution (Ryan, C., O'Neill, M., and Collins, J. J. (2018) ), and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in Arabinia, H. (2006, ISBN:978-193-241596-3). All algorithms reuse basic adaptive mechanisms for performance optimization. For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Sequential or parallel execution (on multi-core machines, local clusters, and high-performance computing environments) is available for all algorithms. See . Package: r-cran-xegabnf Architecture: all Version: 1.0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-xegabnf_1.0.0.5-1.ca2604.1_all.deb Size: 120230 MD5sum: ff6966fcf561c9767f290f1addef9238 SHA1: 09eae098d1217dc778fd2db6a0c7cf7474ec0d88 SHA256: c7d0cfc5c644b758922d7c654467994de905c673509d397fe74840f28fdc87ae SHA512: 9596e9acc0a0481ed2ec6f7a85499b0523a20fd761920224c5f7e4403846de88261735aab8f85b3d4dc1754501c48c6f2f2d83f25746936d9de6fba31fa7fa52 Homepage: https://cran.r-project.org/package=xegaBNF Description: CRAN Package 'xegaBNF' (Compile a Backus-Naur Form Specification into an R GrammarObject) Translates a BNF (Backus-Naur Form) specification of a context-free language into an R grammar object which consists of the start symbol, the symbol table, the production table, and a short production table. The short production table is non-recursive. The grammar object contains the file name from which it was generated (without a path). In addition, it provides functions to determine the type of a symbol (isTerminal() and isNonterminal()) and functions to access the production table (rules() and derives()). For the BNF specification, see Backus, John et al. (1962) "Revised Report on the Algorithmic Language ALGOL 60". (ALGOL60 standards page , html-edition ) A preprocessor for macros which expand to standard BNF is included. The grammar compiler is an extension of the APL2 implementation in Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X). 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These operations are necessary for the initialization and for decoders of a random population of programs, as well as for implementing crossover and mutation operators. Depth-bounds are guaranteed by switching to a grammar without recursive production rules. For executing the examples, the package 'BNF' is needed. The basic tree operations for generating, extracting, and inserting derivation trees as well as the conditions for guaranteeing complete derivation trees have been presented in Geyer-Schulz (1997, ISBN:978-3-7908-0830-X). The use of random integer vectors for the generation of derivation trees has been introduced in Ryan, C., Collins, J. J., and O'Neill, M. (1998) for grammatical evolution. Package: r-cran-xegadfgene Architecture: all Version: 1.0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-xegadfgene_1.0.0.9-1.ca2604.1_all.deb Size: 108838 MD5sum: 069fc0a862f53c114e314dc6599f7eb1 SHA1: d4dee8d72455eccf78b8fcaf47ad043ded4a6891 SHA256: f66aedca6446766892911695451ec387492d4bd0738419888865860bd7a3afa7 SHA512: 3f62d443c64b5ee109a3e97d2e04e419a94f27cd983361decdc0a183e14b2b2c98125bafe645426fa670a50371d63afbcb2cd452c389010550cd0541bb7cca93 Homepage: https://cran.r-project.org/package=xegaDfGene Description: CRAN Package 'xegaDfGene' (Gene Operations for Real-Coded Genes) Representation-dependent gene-level operations for genetic and evolutionary algorithms with real-coded genes used in the R-package 'xega' are collected in this package. The common feature of the gene operations is that all of them are useful for derivation-free optimization algorithms. At the moment the package implements initialization, mutation, crossover, and replication operations for differential evolution as described in Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) . In addition, several (more recent) methods for determining the scale factor are provided. For 'xega''s architecture, see Geyer-Schulz, A. (2025) . 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See Goldberg, D. E. (1989, ISBN:0-201-15767-5). For crossover operators, see Syswerda, G. (1989, ISBN:1-55860-066-3), Spears, W. and De Jong, K. (1991, ISBN:1-55860-208-9). For mutation operators, see Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Package: r-cran-xegagegene Architecture: all Version: 1.0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numbers, r-cran-xegaselectgene, r-cran-xegabnf, r-cran-xegaderivationtrees Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-xegagegene_1.0.0.3-1.ca2604.1_all.deb Size: 80880 MD5sum: 9282ca760e19f8491267b7331c1e2318 SHA1: fea88a07362ca2b09474ffbcec88825ecc07dc1f SHA256: df649dcac20139bb21ab5dcca76fa7da450aaeff9e033db33f8b2cd5a5d98532 SHA512: 65e41be84158b24c201e8fe48ca4a86daeb0e9e215bab0b599bd8769ba850d212a2ee259f82294ac0d78a915c0d829c983fac661ed5dd15be0890acf55925657 Homepage: https://cran.r-project.org/package=xegaGeGene Description: CRAN Package 'xegaGeGene' (Grammatical Evolution) Grammatical evolution (see O'Neil, M. and Ryan, C. (2003,ISBN:1-4020-7444-1)) uses decoders to convert linear (binary or integer genes) into programs. In addition, automatic determination of codon precision with a limited rule choice bias is provided. For a recent survey of grammatical evolution, see Ryan, C., O'Neill, M., and Collins, J. J. (2018) . Package: r-cran-xegagpgene Architecture: all Version: 1.0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xegabnf, r-cran-xegaderivationtrees, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-xegagpgene_1.0.0.3-1.ca2604.1_all.deb Size: 76780 MD5sum: ba5c774b9df3731c2b71561df4c0c7e2 SHA1: b2f7b3c57d67802990f69c04b1b4e51b25df1c0d SHA256: a1ffade701e024de673e455d44d501d84990ab28ff6264c86a0d37a6babf7adc SHA512: 852d5be9a20c53c609b57cd5049bb4c1fdb081b701c97bcaae03dbbbc606b0fd94406234e771232edc08a1aeee313132c13367b373a66c4dc0eb9e34bca61dec Homepage: https://cran.r-project.org/package=xegaGpGene Description: CRAN Package 'xegaGpGene' (Genetic Operations for Grammar-Based Genetic Programming) An implementation of the representation-dependent gene level operations of grammar-based genetic programming with genes which are derivation trees of a context-free grammar: Initialization of a gene with a complete random derivation tree, decoding of a derivation tree. Crossover is implemented by exchanging subtrees. Depth-bounds for the minimal and the maximal depth of the roots of the subtrees exchanged by crossover can be set. Mutation is implemented by replacing a subtree by a random subtree. The depth of the random subtree and the insertion node are configurable. For details, see Geyer-Schulz (1997, ISBN:978-3-7908-0830-X). Package: r-cran-xegapermgene Architecture: all Version: 1.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-xegapermgene_1.0.0.1-1.ca2604.1_all.deb Size: 85406 MD5sum: 7a704f0d8ce13c9a0ad2815ef0cd1218 SHA1: db006ace23f2b7904819b404ec4ab92f0c51c4e5 SHA256: 242cf027b8264c16ffa07b0cfd500f0eea81e8c12791c4077f7d94ff0a69dab3 SHA512: b39b2dfc8b26d473ca65cfed0cc6209929f970ee002e8631949e546f4201028ddd9df2789ac5aea4d59a61790a72a94fe083d1d25ab2c31875fe307da6294685 Homepage: https://cran.r-project.org/package=xegaPermGene Description: CRAN Package 'xegaPermGene' (Operations on Permutation Genes) An implementation of representation-dependent gene level operations for genetic algorithms with genes representing permutations: Initialization of genes, mutation, and crossover. The crossover operation provided is position-based crossover (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8). For mutation, several variants are included: Order-based mutation (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8), randomized Lin-Kernighan heuristics (Croes, G. A. (1958) and Lin, S. and Kernighan. B. W. (1973) ), and randomized greedy operators. A random mix operator for mutation selects a mutation variant randomly. Package: r-cran-xegapopulation Architecture: all Version: 1.0.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-xegagagene, r-cran-xegaselectgene Suggests: r-cran-testthat, r-cran-future, r-cran-parallelly Filename: pool/dists/resolute/main/r-cran-xegapopulation_1.0.0.12-1.ca2604.1_all.deb Size: 223994 MD5sum: 356b6bb9997ba263c76bf87412ae1ba1 SHA1: 09d6cd7a151aefaea1465e86301fb8554b228114 SHA256: ad37bf0a3b86312d59db613740c6cb22fba2065b21e92a74d61e73d5dc479491 SHA512: aabd416671d33b5c043de0783943b6132abe25ce8be74edba38d120c4077472018c9ed1279ad9207956930625785a785a8493004df7e9e7c30f2441644616f37 Homepage: https://cran.r-project.org/package=xegaPopulation Description: CRAN Package 'xegaPopulation' (Genetic Population Level Functions) This collection of gene representation-independent functions implements the population layer of extended evolutionary and genetic algorithms and its support for the R-package 'xega' . The population layer consists of functions for initializing, logging, observing, evaluating a population of genes, as well as of computing the next population. For parallel evaluation of a population of genes 4 execution models - named Sequential, MultiCore, FutureApply, and Cluster - are provided. They are implemented by configuring the lapply() function. The execution model FutureApply can be externally configured as recommended by Bengtsson (2021) . Configurable acceptance rules and cooling schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) , and Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized approximate search procedure elements. Adaptive crossover and mutation rates depending on population statistics generalize the approach of Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). For 'xega''s architecture, see Geyer-Schulz, A. (2025) . 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Second, evaluation functions for deterministic functions avoid recomputation. Evaluation of stochastic functions incrementally improve the estimation of the mean and variance of fitness values at almost no additional cost. Evaluation functions for gene repair handle error-correcting decoders. Third, timing and counting functions for profiling the algorithm pipeline are provided to assess bottlenecks in the algorithms. Fourth, a small collection of problem environments for function optimization, combinatorial optimization, and grammar-based genetic programming and grammatical evolution is provided for tutorial examples. For xega's architecture, see Geyer-Schulz, A. (2025) . The methods in the package are described by the following references: Baker, James E. (1987, ISBN:978-08058-0158-8), De Jong, Kenneth A. (1975) , Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X), Grefenstette, John J. (1987, ISBN:978-08058-0158-8), Grefenstette, John J. and Baker, James E. (1989, ISBN:1-55860-066-3), Holland, John (1975, ISBN:0-472-08460-7), Lau, H. T. (1986) , Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) , Reynolds, J. C. (1993) , Schaffer, J. David (1989, ISBN:1-55860-066-3), Wenstop, Fred (1980) , Whitley, Darrell (1989, ISBN:1-55860-066-3), Wickham, Hadley (2019, ISBN:978-815384571). 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Package: r-cran-ypmodel Architecture: all Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ypmodel_1.4-1.ca2604.1_all.deb Size: 377900 MD5sum: 45a83b3b127d2d156578b5c36773588e SHA1: d2a90846eaa4d9100c5df053239c5995ff6b320f SHA256: 36bdb4d2079e18e9faa95cfad101c83325e299ae61e30e04d6b1d360dd972f94 SHA512: 2b1c81bfe47796350d47f5ffb29eef1318115a6af65a42f5a0359a983841846fe27e6e23f8fb3fa21d1448abb1c8895b484ee47190e96ed48972ad966b66224d Homepage: https://cran.r-project.org/package=YPmodel Description: CRAN Package 'YPmodel' (The Short-Term and Long-Term Hazard Ratio Model for SurvivalData) Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. 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The model was studied in Yang and Prentice (2015) and it extends the two sample version of the short-term and long-term hazard ratio model proposed in Yang and Prentice (2005) . The model extends the usual Cox proportional hazards model to allow more flexible hazard ratio patterns, such as gradual onset of effect, diminishing effect, and crossing hazard or survival functions. This package provides the following: 1) point estimates and confidence intervals for model parameters; 2) point estimate and confidence interval of the average hazard ratio; and 3) plots of estimated hazard ratio function with point-wise and simultaneous confidence bands. 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Implements the bias-corrected and accelerated bootstrap method as described in "An Introduction to the Bootstrap" Efron (1983) <0-412-04231-2>. Package: r-cran-zeroonedists Architecture: all Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist Filename: pool/dists/resolute/main/r-cran-zeroonedists_1.0.0-1.ca2604.1_all.deb Size: 163324 MD5sum: 30e7e186ac6a7882bdf8f66cb3aa6f08 SHA1: 0e32533f83b7804fa3c9a5c12100a2a72c327be9 SHA256: c8500c2fcd49b0f60e5d305c904ed429d30c92b44ec2abc21eb6fd6b1c8d3cf7 SHA512: 55dd38e63ba5449ca4c8febaeee041ce847120f36ce533bcf3d3eaaf3fe911cc7cc2a1a72ae0cea559d51be8596ed3f2e0547e87e9bfd792a3e5c14cb57dac70 Homepage: https://cran.r-project.org/package=ZeroOneDists Description: CRAN Package 'ZeroOneDists' (One Zero Statistical Distributions) Implementation of new statistical distributions in (0, 1) interval. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters using Generalized Additive Models for Location, Scale and Shape, GAMLSS by Rigby & Stasinopoulos (2005) . 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The package includes functions to compute zeta-diversity for a specific number of assemblages and to compute zeta-diversity for a range of numbers of assemblages. It also includes functions to explain how zeta-diversity varies with distance and with differences in environmental variables between assemblages, using generalised linear models, linear models with negative constraints, generalised additive models,shape constrained additive models, and I-splines. Package: r-cran-zetasuite Architecture: all Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4809 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-e1071, r-cran-ggplot2, r-cran-reshape2, r-cran-gridextra, r-cran-mixtools, r-cran-shinyjs, r-cran-shinydashboard, r-cran-shiny, r-cran-plotly, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-zetasuite_1.0.3-1.ca2604.1_all.deb Size: 4491452 MD5sum: 2ebdf8cbe29390b594eea5d49c2655a0 SHA1: 04fde159f89432468629717c3c4086135ed2f62e SHA256: 44b7e9dda2755474c63e1a3906047daa2d04e17370aada89c08dcccc85721306 SHA512: 59fe026bbcd896126bee7d894c56ab9e90e8c43c86690f0856f32161ef533cd8f9f4d85f54765897c780b38ba5bf98b2f7da1f818841787af29e36b1b4d2634c Homepage: https://cran.r-project.org/package=ZetaSuite Description: CRAN Package 'ZetaSuite' (Analyze High-Dimensional High-Throughput Dataset and QualityControl Single-Cell RNA-Seq) The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) and Tsherniak A (2017) ) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) ). In 'ZetaSuite', we have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength. 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Eric Z. Chen and Hongzhe Li (2016) . 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For more details, see Kalktawi (2017) , Taconeli and Rodrigues de Lara (2022) , and Yeh and Young (2025) . 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It allows users to simulate time series data for these models, estimate parameters, and evaluate model fit using various criteria. Functions include model estimation, simulation, and likelihood-based metrics. 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This method was originally described by Fan, Sun, and Li in Genetic Epidemiology 46(1):73-86 . Package: r-cran-zim Architecture: all Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-pscl, r-cran-tsa Filename: pool/dists/resolute/main/r-cran-zim_1.1.0-1.ca2604.1_all.deb Size: 147214 MD5sum: ad5b97e02c30f038d6bc068f0c7acfff SHA1: dfa7571579c0c3b78418ddd78c0cced06be1b52c SHA256: c2aa4f2bd8b9329c819e923ec22b04ea2988514c45ba209eb246c55b26afd2d2 SHA512: 6faa66ce09977b81f63d98e22c7bbc6024b101c8e29c5b419f4ad6abf90829ba88a9bf720ced7f4359346ab4d47f217bcbb70b39b8922767e7d88fdd14dfbe1e Homepage: https://cran.r-project.org/package=ZIM Description: CRAN Package 'ZIM' (Zero-Inflated Models (ZIM) for Count Time Series with ExcessZeros) Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) and state-space models by Yang et al. (2015) . They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions. Package: r-cran-zinar1 Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss.dist, r-cran-vgam, r-cran-mass, r-cran-statmod, r-cran-gtools, r-cran-scales Suggests: r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-zinar1_0.1.0-1.ca2604.1_all.deb Size: 44022 MD5sum: a7767abb8e5c282bf43c73295f5d03fa SHA1: 974062e2bfd6aeab50377225ea4f086a77b3bcaf SHA256: 53ad854c962dd263b914865e4fca876a006e2d885c76f64ce3aedf5933a619ff SHA512: fed773dc74f8556bbc7044ce3f6afcb4edc0c2df8023349cf3ab38c128b48802895692f4b39db4b1eb1297d3a66a973c60dad91ab282a41319704e4a6e3a40c5 Homepage: https://cran.r-project.org/package=ZINAR1 Description: CRAN Package 'ZINAR1' (Simulates ZINAR(1) Model and Estimates Its Parameters UnderFrequentist Approach) Generates Realizations of First-Order Integer Valued Autoregressive Processes with Zero-Inflated Innovations (ZINAR(1)) and Estimates its Parameters as described in Garay et al. (2021) . Package: r-cran-zinarp Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-progress Filename: pool/dists/resolute/main/r-cran-zinarp_0.1.0-1.ca2604.1_all.deb Size: 40094 MD5sum: 7f12db9aec7fef89d7efaf16462533bb SHA1: 185d199584ce3cb3080160f791b5526222f2f87f SHA256: a8656af085f55e685d836f0eb3dac932a641387a61610afcd855e6093b68679b SHA512: d903d1be152b430d5a32ffc06802a3a8304cd4068a059d05747fd7ce762e19d7bf7a26fcb2048861f2da55715f1d9bdb54cdd2dc8a76a8a19303e01844309f3c Homepage: https://cran.r-project.org/package=ZINARp Description: CRAN Package 'ZINARp' (Simulate INAR/ZINAR(p) Models and Estimate Its Parameters) Simulation, exploratory data analysis and Bayesian analysis of the p-order Integer-valued Autoregressive (INAR(p)) and Zero-inflated p-order Integer-valued Autoregressive (ZINAR(p)) processes, as described in Garay et al. (2020) . Package: r-cran-zipcoder Architecture: all Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-stringr, r-cran-raster, r-cran-tidycensus, r-cran-tidyr, r-cran-dplyr, r-cran-jsonlite, r-cran-httr, r-cran-curl, r-cran-rsqlite, r-cran-dbi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-readr, r-cran-testthat, r-cran-covr, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-zipcoder_0.3.5-1.ca2604.1_all.deb Size: 3580532 MD5sum: 9ad3c800fb97805f1ea0e0623fbd051f SHA1: 8280cfd493d94ec85a3c4ac78743555ab5bbeacf SHA256: 98e890c6f53ad421aa90d3cf5a0c903d0561a17d347e3bbea61e8f83ad39e275 SHA512: 06f599c2b3f8f24896de1131a785e04426b1ae64c1865f0b1971602db81d1b9faa9d286cb7f8b814bcad9e432a7ff8a490977c7b9a13346f954e52d95fb20e08 Homepage: https://cran.r-project.org/package=zipcodeR Description: CRAN Package 'zipcodeR' (Data & Functions for Working with US ZIP Codes) Make working with ZIP codes in R painless with an integrated dataset of U.S. ZIP codes and functions for working with them. Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating ZIP codes to Census data, geocoding & distance calculations. Package: r-cran-zipfa Architecture: all Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-optimx, r-cran-trustoptim Filename: pool/dists/resolute/main/r-cran-zipfa_0.8.1-1.ca2604.1_all.deb Size: 72124 MD5sum: 13f98eb2ac16c2a37f6daa5dd906a513 SHA1: 37a92499331e969e20a198faa9b0bc1c503b0ea0 SHA256: 6c10c95a2684037eb06025cc2bee711c083ec3e804d6d42e27bf4afabd661551 SHA512: d5197b03c843bbc3830bb1f0dccd92ed8c48e5a8f67bd71907ba0f5b7896c75a6ca58e2b94e6f06d82a989e4bbfe892820271a7e6d1d81f98a03d509026ad843 Homepage: https://cran.r-project.org/package=ZIPFA Description: CRAN Package 'ZIPFA' (Zero Inflated Poisson Factor Analysis) Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. It provides estimates of coefficients in a new type of zero-inflated regression. It provides a cross-validation method to determine the potential rank of the data in the ZIPFA and conducts zero-inflated Poisson factor analysis based on the determined rank. Package: r-cran-zipfextr Architecture: all Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam, r-cran-tolerance, r-cran-copula Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-zipfextr_1.0.2-1.ca2604.1_all.deb Size: 170330 MD5sum: a1f61af867a4a39ca88ece24dc5a77d3 SHA1: 2aa8e605df148d39e814fdc0b7b5ca1e47080fb6 SHA256: f856557a1923703748f3089e4251b928b103eb6969ee0c09b5385e9492bd9772 SHA512: 325027e8268fc452b43c55e1bf458efb4d525c2d5606fa7e62d2a55161ec0a0e9051c67f02e4cc3c5e1f47433a33948646fb15b5b8b4f86dee535b5c62691bf6 Homepage: https://cran.r-project.org/package=zipfextR Description: CRAN Package 'zipfextR' (Zipf Extended Distributions) Implementation of four extensions of the Zipf distribution: the Marshall-Olkin Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) , the Zipf-Poisson Extreme (Zipf-PE), the Zipf-Poisson Stopped Sum (Zipf-PSS) and the Zipf-Polylog distributions. In log-log scale, the two first extensions allow for top-concavity and top-convexity while the third one only allows for top-concavity. All the extensions maintain the linearity associated with the Zipf model in the tail. Package: r-cran-zipfr Architecture: all Version: 0.6-70-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2665 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-zipfr_0.6-70-1.ca2604.1_all.deb Size: 2478080 MD5sum: 6d3a0a5656cba880f0021850b1995d62 SHA1: 81ecbb51cbece336dcc4b45dde0cd82ab2f0a091 SHA256: 7a77b20162810a8902e583e69b9560668bdc68b6ddcea9d99db4c74d39889e9b SHA512: 55434e0eb5c9b1a9ac1d16070272c87458fc7921c44ee8374142735cdc045392b3374d5814050790b61191797d71c39527cfcbbe6b149020ff9a08d7d4efdb6a Homepage: https://cran.r-project.org/package=zipfR Description: CRAN Package 'zipfR' (Statistical Models for Word Frequency Distributions) Statistical models and utilities for the analysis of word frequency distributions. The utilities include functions for loading, manipulating and visualizing word frequency data and vocabulary growth curves. 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These functions can be used to analyze microbiome count data with zero-inflation and overdispersion. The model is discussed in Jiang et al (2023) . 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These include accessing ZIP Code to ZIP Code Tabulation Area (ZCTA) crosswalks, retrieving demographic data for ZCTAs, and tabulating demographic data for three-digit ZCTAs. Package: r-cran-zipr Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-zipr_0.1.1-1.ca2604.1_all.deb Size: 20130 MD5sum: 08ba21e05be668054e76a2fad8d6d1ae SHA1: 8bd4653979606107ea615dd2cd28250e6c9b7f13 SHA256: baa9acbc2a2327f05e0085c01cbf7337216c10fab2ee810f84985914c7b2febf SHA512: 7515836e38e7b8de540403837ad6de6d854ca4c9c185acd66a6dcdb90dfdc5e899ff2e5ca673f5e3ea4eead12d47af94536ec4d16b7fd4e0eaeab0ec7ff7965c Homepage: https://cran.r-project.org/package=zipR Description: CRAN Package 'zipR' (Pythonic Zip() for R) Implements Python-style zip for R. Is a more flexible version of cbind. Package: r-cran-ziprop Architecture: all Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgenoud, r-cran-purrr, r-cran-data.table Suggests: r-cran-markdown, r-cran-knitr, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggthemes, r-cran-kableextra, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-ziprop_0.1.1-1.ca2604.1_all.deb Size: 202078 MD5sum: 08baafeed409b9b6968582aa4b5c5574 SHA1: a3722161f39e38d9619565c00cef21082c491d89 SHA256: 38c4e31d6b2686ba271ae583758ae72df1e6287b13ce97d1d573a2806d076bd1 SHA512: e284c330ffb62ac34dfde82d4597db9e1bf9971baa2d34a893ad3ff278c0d38b97823f6496af5bc3857a9b6e2f9d04ce23903c6ef322695606c29ffdbd1f4578 Homepage: https://cran.r-project.org/package=ZIprop Description: CRAN Package 'ZIprop' (Permutations Tests and Performance Indicator for Zero-InflatedProportions Response) Permutations tests to identify factor correlated to zero-inflated proportions response. Provide a performance indicator based on Spearman correlation to quantify the part of correlation explained by the selected set of factors. See details for the method at the following preprint e.g.: . 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To handle this kind of situation, this model is created. The model in this package is based on Small Area Estimation with Zero-Inflated Poisson model proposed by Dian Christien Arisona (2018). For the data sample itself, we use combination method between Roberto Benavent and Domingo Morales (2015) and Sabine Krieg, Harm Jan Boonstra and Marc Smeets (2016). 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The package addresses limitations of existing implementations when the parameter b approaches zero. It provides distribution functions, maximum likelihood estimation, and diagnostic tools for modeling count data with excess zeros. The methodology is based on 'Rodriguez-Avi' and coauthors (2003) . 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For interested readers, see Sloane and Plouffe (1995, ISBN:978-0125586306). Package: r-cran-ztable Architecture: all Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-flextable, r-cran-officer, r-cran-rstudioapi, r-cran-scales Suggests: r-cran-mass, r-cran-moonbook, r-cran-survival, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ztable_0.2.3-1.ca2604.1_all.deb Size: 415810 MD5sum: 980cf6060360995ba4145eb3ac184079 SHA1: 8ad7e2cd0e8e8fefdd35025fa44d5ca0835f1f86 SHA256: 0d8938b0b3beccc049e635afc0e2567bbabddd03a69ce5bad35eaa5b82d9e57b SHA512: 205e234e56b75e2ea4bd44f7f6b8222c1319923eb78eda0ae527073e92d073f4f5fb454521f456f65a7377e446dfd1163602c12b4764dc8f81ccc3bb149a69e0 Homepage: https://cran.r-project.org/package=ztable Description: CRAN Package 'ztable' (Zebra-Striped Tables in LaTeX and HTML Formats) Makes zebra-striped tables (tables with alternating row colors) in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova, glm, coxph, nls, fitdistr, mytable and cbind.mytable objects. 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Package: r-cran-ztree Architecture: all Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-ztree_1.0.7-1.ca2604.1_all.deb Size: 47470 MD5sum: 92961c6932915b32fc0d6e100f5f69e0 SHA1: 4437202666a6d9e9c1e9187fe3f495666a6ab0c2 SHA256: 2b83f700b9020286d6177dece34251e95fe46be0676f71f1a0c5d9e657a0fd2d SHA512: 11d5a692bb8344f6ee77dd7b4f5b85bb327ea6f3ef791dbe2e4270185cede9bc32aef1242db183914141cd8bfdf7952dca04f81d3d95e28c919504a01de6a084 Homepage: https://cran.r-project.org/package=zTree Description: CRAN Package 'zTree' (Functions to Import Data from 'z-Tree' into R) Read '.xls' and '.sbj' files which are written by the Microsoft Windows program 'z-Tree'. The latter is a software for developing and carrying out economic experiments (see for more information). Package: r-cran-ztype Architecture: all Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rvest, r-cran-stringr, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-assertthat Filename: pool/dists/resolute/main/r-cran-ztype_0.1.0-1.ca2604.1_all.deb Size: 20106 MD5sum: 6f39cafeecdd21d7352a1cd5ab4f2ef4 SHA1: 2d82e7ff5d34936803091634ed4a035b22c85522 SHA256: dc55058876bceaf7b5c747a5d69ce232f08e444ff1ffe67fe46394973f306d93 SHA512: 036d2da1f47f852e682505e8d59649d0189e6ce1cb44d1fe0030aa7ea3afdab4875a763820e41e7a4e22f0da6f5800b82d7ffa61ff77100c7cd3afcbcfe667d6 Homepage: https://cran.r-project.org/package=ztype Description: CRAN Package 'ztype' (Run a Ztype Game Loaded with R Functions) How fast can you type R functions on your keyboard? Find out by running a 'zty.pe' game: export R functions as instructions to type to destroy opponents vessels. Package: r-cran-zyp Architecture: all Version: 0.11-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kendall Filename: pool/dists/resolute/main/r-cran-zyp_0.11-1-1.ca2604.1_all.deb Size: 39730 MD5sum: a51d4c50e7b318fcadea9b8e2bd861bf SHA1: acf485b7f6b68284ea81de2856c32e179cd6b442 SHA256: 231feee61b5a8121acab26f0fe5a421b2474c7a893c192d2c474b63ed8385644 SHA512: 5051e731ee0c766f174d19593172d3d838eadccac1cb808226a89ac8f45cb6735c5bb1d86614a8c83665d30e9e5cdea083e3fe09d51d59e3e38347f5c21c0777 Homepage: https://cran.r-project.org/package=zyp Description: CRAN Package 'zyp' (Zhang + Yue-Pilon Trends Package) An efficient implementation of the slope method described by Sen (1968) plus implementation of prewhitening approaches to determining trends in climate data described by Zhang, Vincent, Hogg, and Niitsoo (2000) and Yue, Pilon, Phinney, and Cavadias (2002) . Package: r-cran-zzlite Architecture: all Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-httptest, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-zzlite_0.1.2-1.ca2604.1_all.deb Size: 49866 MD5sum: ed00526040945732cdbc01afcc40e312 SHA1: 44c7d475e87854afd8aa4fc65f9ece936b46e617 SHA256: d6b6eb8194720f87f864fa3e353c2763a258a950f50da1e26c93b6537071c956 SHA512: 0b83fa025206f2159874c5a5601b8ba3863fdd232dfbb508c29eb1ec349865fd4ab9c259134e61411b33658efb6fa2b69618a8d45bca366f7f7776dc8135898d Homepage: https://cran.r-project.org/package=zzlite Description: CRAN Package 'zzlite' (Lite Wrapper for the 'Zamzar File Conversion' API) A minor collection of HTTP wrappers for the 'Zamzar File Conversion' API. The wrappers makes it easy to utilize the API and thus convert between more than 100 different file formats (ranging from audio files, images, movie formats, etc., etc.) through an R session. For specifics regarding the API, please see .